diff --git a/demos/OracleAutoMLx_AnomalyDetection.html b/demos/OracleAutoMLx_AnomalyDetection.html
index 45d26a7..39f7438 100644
--- a/demos/OracleAutoMLx_AnomalyDetection.html
+++ b/demos/OracleAutoMLx_AnomalyDetection.html
@@ -14622,7 +14622,7 @@ <h1><font color=red>Building and Explaining an Anomaly Detector using AutoMLx -
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
 </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
 <p>Anomaly Detection Demo Notebook.</p>
-<p>Copyright © 2024, Oracle and/or its affiliates.</p>
+<p>Copyright © 2025, Oracle and/or its affiliates.</p>
 <p>Licensed under the Universal Permissive License v 1.0 as shown at <a href="https://oss.oracle.com/licenses/upl/">https://oss.oracle.com/licenses/upl/</a></p>
 
 </div>
@@ -14736,14 +14736,14 @@ <h2 id="Setup">Setup<a class="anchor-link" href="#Setup">&#182;</a></h2><p>Basic
 <div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Collecting rdata==0.9
   Using cached rdata-0.9-py3-none-any.whl.metadata (1.1 kB)
-Requirement already satisfied: numpy in /scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-forewardportAMLX2203todev/lib/python3.9/site-packages (from rdata==0.9) (1.26.4)
-Requirement already satisfied: xarray in /scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-forewardportAMLX2203todev/lib/python3.9/site-packages (from rdata==0.9) (2024.7.0)
-Requirement already satisfied: pandas in /scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-forewardportAMLX2203todev/lib/python3.9/site-packages (from rdata==0.9) (2.2.2)
-Requirement already satisfied: python-dateutil&gt;=2.8.2 in /scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-forewardportAMLX2203todev/lib/python3.9/site-packages (from pandas-&gt;rdata==0.9) (2.9.0.post0)
-Requirement already satisfied: pytz&gt;=2020.1 in /scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-forewardportAMLX2203todev/lib/python3.9/site-packages (from pandas-&gt;rdata==0.9) (2024.2)
-Requirement already satisfied: tzdata&gt;=2022.7 in /scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-forewardportAMLX2203todev/lib/python3.9/site-packages (from pandas-&gt;rdata==0.9) (2024.2)
-Requirement already satisfied: packaging&gt;=23.1 in /scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-forewardportAMLX2203todev/lib/python3.9/site-packages (from xarray-&gt;rdata==0.9) (24.2)
-Requirement already satisfied: six&gt;=1.5 in /scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-forewardportAMLX2203todev/lib/python3.9/site-packages (from python-dateutil&gt;=2.8.2-&gt;pandas-&gt;rdata==0.9) (1.17.0)
+Requirement already satisfied: numpy in /scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages (from rdata==0.9) (1.26.4)
+Requirement already satisfied: xarray in /scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages (from rdata==0.9) (2024.7.0)
+Requirement already satisfied: pandas in /scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages (from rdata==0.9) (2.2.2)
+Requirement already satisfied: python-dateutil&gt;=2.8.2 in /scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages (from pandas-&gt;rdata==0.9) (2.9.0.post0)
+Requirement already satisfied: pytz&gt;=2020.1 in /scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages (from pandas-&gt;rdata==0.9) (2025.2)
+Requirement already satisfied: tzdata&gt;=2022.7 in /scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages (from pandas-&gt;rdata==0.9) (2025.2)
+Requirement already satisfied: packaging&gt;=23.1 in /scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages (from xarray-&gt;rdata==0.9) (25.0)
+Requirement already satisfied: six&gt;=1.5 in /scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages (from python-dateutil&gt;=2.8.2-&gt;pandas-&gt;rdata==0.9) (1.17.0)
 Using cached rdata-0.9-py3-none-any.whl (19 kB)
 Installing collected packages: rdata
   Attempting uninstall: rdata
@@ -14779,25 +14779,25 @@ <h2 id="Setup">Setup<a class="anchor-link" href="#Setup">&#182;</a></h2><p>Basic
 <div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[2]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">urllib</span>
-<span class="kn">import</span> <span class="nn">rdata</span>
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-
-<span class="kn">import</span> <span class="nn">plotly.figure_factory</span> <span class="k">as</span> <span class="nn">ff</span>
-<span class="kn">import</span> <span class="nn">plotly.express</span> <span class="k">as</span> <span class="nn">px</span>
-<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">f1_score</span><span class="p">,</span> <span class="n">confusion_matrix</span>
-<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
-<span class="kn">from</span> <span class="nn">pyod.models.iforest</span> <span class="kn">import</span> <span class="n">IForest</span>
-<span class="kn">import</span> <span class="nn">time</span>
-<span class="kn">import</span> <span class="nn">datetime</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">urllib</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">rdata</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pd</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
+
+<span class="kn">import</span><span class="w"> </span><span class="nn">plotly.figure_factory</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">ff</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">plotly.express</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">px</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.metrics</span><span class="w"> </span><span class="kn">import</span> <span class="n">f1_score</span><span class="p">,</span> <span class="n">confusion_matrix</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <span class="n">train_test_split</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">pyod.models.iforest</span><span class="w"> </span><span class="kn">import</span> <span class="n">IForest</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">time</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">datetime</span>
 
 <span class="c1"># Settings for plots</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">&#39;figure.figsize&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">7</span><span class="p">]</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">&#39;font.size&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">15</span>
-<span class="kn">import</span> <span class="nn">automlx</span>
-<span class="kn">from</span> <span class="nn">automlx</span> <span class="kn">import</span> <span class="n">init</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">automlx</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">automlx</span><span class="w"> </span><span class="kn">import</span> <span class="n">init</span>
 </pre></div>
 
      </div>
@@ -15565,9 +15565,9 @@ <h2 id="Load-the-Credit-Card-Fraud-Dataset">Load the Credit Card Fraud Dataset<a
 
 
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                       {"responsive": true}                    ).then(function(){
                             
-var gd = document.getElementById('5006226c-9709-4ae6-957c-3f777255b8b6');
+var gd = document.getElementById('8f7caf7c-6f0e-4318-b6aa-baece2e84b5f');
 var x = new MutationObserver(function (mutations, observer) {{
         var display = window.getComputedStyle(gd).display;
         if (!display || display === 'none') {{
@@ -15653,7 +15653,7 @@ <h3 id="Setting-the-execution-engine">Setting the execution engine<a class="anch
 
 
 <div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
-<pre>[2024-12-26 05:49:13,605] [automlx.backend] Overwriting ray session directory to /tmp/4kx0ukqw/ray, which will be deleted at engine shutdown. If you wish to retain ray logs, provide _temp_dir in ray_setup dict of engine_opts when initializing the AutoMLx engine.
+<pre>[2025-04-25 03:01:14,603] [automlx.backend] Overwriting ray session directory to /tmp/1odefte7/ray, which will be deleted at engine shutdown. If you wish to retain ray logs, provide _temp_dir in ray_setup dict of engine_opts when initializing the AutoMLx engine.
 </pre>
 </div>
 </div>
@@ -15744,21 +15744,21 @@ <h3 id="Train-a-model-using-Oracle-AutoMLx">Train a model using Oracle AutoMLx<a
 
 
 <div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
-<pre>[2024-12-26 05:49:24,355] [automlx.interface] Dataset shape: (65505,19)
-[2024-12-26 05:50:15,810] [sanerec.autotuning.parameter] Hyperparameter epsilon autotune range is set to its validation range. This could lead to long training times
-[2024-12-26 05:50:23,982] [sanerec.autotuning.parameter] Hyperparameter repeat_quality_threshold autotune range is set to its validation range. This could lead to long training times
-[2024-12-26 05:50:24,142] [sanerec.autotuning.parameter] Hyperparameter scope autotune range is set to its validation range. This could lead to long training times
-[2024-12-26 05:50:24,502] [automlx.data_transform] Running preprocessing. Number of features: 20
-[2024-12-26 05:50:24,824] [automlx.data_transform] Preprocessing completed. Took 0.323 secs
-[2024-12-26 05:50:24,850] [automlx.process] Running Model Generation
-[2024-12-26 05:50:24,898] [automlx.process] Model Generation completed.
-[2024-12-26 05:50:24,963] [automlx.model_selection] Running Model Selection
-[2024-12-26 05:51:07,368] [automlx.model_selection] Model Selection completed - Took 42.405 sec - Selected models: [[&#39;IsolationForestOD&#39;]]
-[2024-12-26 05:51:07,418] [automlx.trials] Running Model Tuning for [&#39;IsolationForestOD&#39;]
-[2024-12-26 05:51:13,805] [automlx.trials] Best parameters for IsolationForestOD: {&#39;contamination&#39;: 0.1, &#39;n_estimators&#39;: 5, &#39;max_samples&#39;: 6, &#39;max_features&#39;: 0.05, &#39;bootstrap&#39;: False, &#39;behaviour&#39;: &#39;old&#39;}
-[2024-12-26 05:51:13,806] [automlx.trials] Model Tuning completed. Took: 6.387 secs
-[2024-12-26 05:51:14,372] [automlx.interface] Re-fitting pipeline
-[2024-12-26 05:51:14,387] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_fe93eeae-9
+<pre>[2025-04-25 03:01:54,867] [automlx.interface] Dataset shape: (65505,19)
+[2025-04-25 03:02:52,433] [sanerec.autotuning.parameter] Hyperparameter epsilon autotune range is set to its validation range. This could lead to long training times
+[2025-04-25 03:03:01,882] [sanerec.autotuning.parameter] Hyperparameter repeat_quality_threshold autotune range is set to its validation range. This could lead to long training times
+[2025-04-25 03:03:02,032] [sanerec.autotuning.parameter] Hyperparameter scope autotune range is set to its validation range. This could lead to long training times
+[2025-04-25 03:03:02,367] [automlx.data_transform] Running preprocessing. Number of features: 20
+[2025-04-25 03:03:02,684] [automlx.data_transform] Preprocessing completed. Took 0.317 secs
+[2025-04-25 03:03:02,728] [automlx.process] Running Model Generation
+[2025-04-25 03:03:02,773] [automlx.process] Model Generation completed.
+[2025-04-25 03:03:02,835] [automlx.model_selection] Running Model Selection
+[2025-04-25 03:03:49,626] [automlx.model_selection] Model Selection completed - Took 46.791 sec - Selected models: [[&#39;IsolationForestOD&#39;]]
+[2025-04-25 03:03:49,674] [automlx.trials] Running Model Tuning for [&#39;IsolationForestOD&#39;]
+[2025-04-25 03:03:56,558] [automlx.trials] Best parameters for IsolationForestOD: {&#39;contamination&#39;: 0.1, &#39;n_estimators&#39;: 5, &#39;max_samples&#39;: 6, &#39;max_features&#39;: 0.05, &#39;bootstrap&#39;: False, &#39;behaviour&#39;: &#39;old&#39;}
+[2025-04-25 03:03:56,559] [automlx.trials] Model Tuning completed. Took: 6.885 secs
+[2025-04-25 03:03:57,138] [automlx.interface] Re-fitting pipeline
+[2025-04-25 03:03:57,153] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_85a27f35-1
 F1-Score on test data : 0.026709401709401708
 </pre>
 </div>
@@ -15871,7 +15871,7 @@ <h3 id="Analyze-the-AutoML-optimization-process">Analyze the AutoML optimization
       <td>{'contamination': 0.1, 'n_estimators': 5, 'max_samples': 6, 'max_features': 0.05, 'bootstrap': False, 'behaviour': 'old'}</td>
     </tr>
     <tr>
-      <td>25.1.0</td>
+      <td>25.2.1</td>
     </tr>
     <tr>
       <td>3.9.21 (main, Dec 11 2024, 16:24:11) \n[GCC 11.2.0]</td>
@@ -15927,9 +15927,9 @@ <h3 id="Analyze-the-AutoML-optimization-process">Analyze the AutoML optimization
       <td>{'contamination': 0.1, 'n_estimators': 100, 'max_samples': 5, 'max_features': 1.0, 'bootstrap': False, 'behaviour': 'old'}</td>
       <td>0.031</td>
       <td>{'f1': 0.031007751937984496}</td>
-      <td>1.2960</td>
-      <td>0.3333</td>
-      <td>Thu Dec 26 05:50:33 2024</td>
+      <td>1.6463</td>
+      <td>0.3198</td>
+      <td>Fri Apr 25 03:03:13 2025</td>
     </tr>
     <tr>
       <td>Model Selection</td>
@@ -15939,33 +15939,33 @@ <h3 id="Analyze-the-AutoML-optimization-process">Analyze the AutoML optimization
       <td>{'contamination': 0.1, 'n_bins': 10, 'alpha': 0.1, 'tol': 0.5}</td>
       <td>0.0299</td>
       <td>{'f1': 0.029925187032418952}</td>
-      <td>5.9000</td>
-      <td>0.3698</td>
-      <td>Thu Dec 26 05:50:38 2024</td>
+      <td>6.5625</td>
+      <td>0.3786</td>
+      <td>Fri Apr 25 03:03:18 2025</td>
     </tr>
     <tr>
       <td>Model Selection</td>
       <td>8544</td>
       <td>20</td>
-      <td>AutoEncoder</td>
-      <td>{'contamination': 0.1, 'middle_layer_size': 2, 'encoder_length': 2, 'layer_size_growth': 'exponential', 'hidden_activation': 'relu', 'batch_norm': True, 'learning_rate': 0.001, 'epochs': 100, 'batch_size': 256, 'dropout_rate': 0.05, 'weight_decay': 1e-05, 'preprocessing': False, 'input_dim': 20}</td>
-      <td>0.0296</td>
-      <td>{'f1': 0.029561504352110364}</td>
-      <td>34.5267</td>
-      <td>0.6290</td>
-      <td>Thu Dec 26 05:51:07 2024</td>
+      <td>PrincipalCompOD</td>
+      <td>{'contamination': 0.1, 'whiten': False, 'n_components': 0.9999, 'weighted': True, 'svd_solver': 'full', 'n_selected_components': None, 'copy': True, 'tol': 0.0, 'iterated_power': 'auto', 'standardization': True}</td>
+      <td>0.0293</td>
+      <td>{'f1': 0.029301644147810516}</td>
+      <td>1.3347</td>
+      <td>0.3462</td>
+      <td>Fri Apr 25 03:03:13 2025</td>
     </tr>
     <tr>
       <td>Model Selection</td>
       <td>8544</td>
       <td>20</td>
-      <td>PrincipalCompOD</td>
-      <td>{'contamination': 0.1, 'whiten': False, 'n_components': 0.9999, 'weighted': True, 'svd_solver': 'full', 'n_selected_components': None, 'copy': True, 'tol': 0.0, 'iterated_power': 'auto', 'standardization': True}</td>
-      <td>0.0293</td>
-      <td>{'f1': 0.029301644147810516}</td>
-      <td>0.9450</td>
-      <td>0.2594</td>
-      <td>Thu Dec 26 05:50:33 2024</td>
+      <td>AutoEncoder</td>
+      <td>{'contamination': 0.1, 'middle_layer_size': 2, 'encoder_length': 2, 'layer_size_growth': 'exponential', 'hidden_activation': 'relu', 'batch_norm': True, 'learning_rate': 0.001, 'epochs': 100, 'batch_size': 256, 'dropout_rate': 0.05, 'weight_decay': 1e-05, 'preprocessing': False, 'input_dim': 20}</td>
+      <td>0.029</td>
+      <td>{'f1': 0.029035821366577244}</td>
+      <td>37.3068</td>
+      <td>0.6494</td>
+      <td>Fri Apr 25 03:03:49 2025</td>
     </tr>
     <tr>
       <td>Model Selection</td>
@@ -15975,9 +15975,9 @@ <h3 id="Analyze-the-AutoML-optimization-process">Analyze the AutoML optimization
       <td>{'contamination': 0.1, 'n_clusters': 9, 'alpha': 0.8, 'beta': 5, 'use_weights': False, 'clustering_estimator': None, 'check_estimator': False}</td>
       <td>0.0284</td>
       <td>{'f1': 0.02841530054644809}</td>
-      <td>6.0984</td>
-      <td>0.4128</td>
-      <td>Thu Dec 26 05:50:38 2024</td>
+      <td>6.7511</td>
+      <td>0.4133</td>
+      <td>Fri Apr 25 03:03:18 2025</td>
     </tr>
     <tr>
       <td>Model Selection</td>
@@ -15987,9 +15987,9 @@ <h3 id="Analyze-the-AutoML-optimization-process">Analyze the AutoML optimization
       <td>{'contamination': 0.1, 'assume_centered': False, 'support_fraction': 0.5012289325842697, 'store_precision': True}</td>
       <td>0.0283</td>
       <td>{'f1': 0.0282780676653762}</td>
-      <td>3.9195</td>
-      <td>0.3351</td>
-      <td>Thu Dec 26 05:50:36 2024</td>
+      <td>4.0895</td>
+      <td>0.3637</td>
+      <td>Fri Apr 25 03:03:16 2025</td>
     </tr>
     <tr>
       <td>Model Selection</td>
@@ -15999,9 +15999,9 @@ <h3 id="Analyze-the-AutoML-optimization-process">Analyze the AutoML optimization
       <td>{'contamination': 0.1, 'n_neighbors': 5, 'method': 'largest', 'radius': 1.0, 'algorithm': 'ball_tree', 'leaf_size': 30, 'metric': 'minkowski', 'p': 2, 'metric_params': None}</td>
       <td>0.0262</td>
       <td>{'f1': 0.026224783861671472}</td>
-      <td>19.7189</td>
-      <td>0.3409</td>
-      <td>Thu Dec 26 05:50:52 2024</td>
+      <td>20.9862</td>
+      <td>0.3448</td>
+      <td>Fri Apr 25 03:03:33 2025</td>
     </tr>
     <tr>
       <td>Model Selection</td>
@@ -16011,9 +16011,9 @@ <h3 id="Analyze-the-AutoML-optimization-process">Analyze the AutoML optimization
       <td>{'contamination': 0.1, 'gamma': 0.5, 'kernel': 'rbf', 'nu': 0.5, 'coef0': 0, 'degree': 3, 'tol': 0.001, 'shrinking': True, 'cache_size': 200, 'max_iter': -1}</td>
       <td>0.0136</td>
       <td>{'f1': 0.013550135501355014}</td>
-      <td>30.1431</td>
-      <td>0.5187</td>
-      <td>Thu Dec 26 05:51:02 2024</td>
+      <td>23.1880</td>
+      <td>0.5145</td>
+      <td>Fri Apr 25 03:03:35 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
@@ -16023,9 +16023,9 @@ <h3 id="Analyze-the-AutoML-optimization-process">Analyze the AutoML optimization
       <td>{'contamination': 0.1, 'n_estimators': 5, 'max_samples': 6, 'max_features': 0.05, 'bootstrap': False, 'behaviour': 'old'}</td>
       <td>0.032</td>
       <td>{'f1': 0.03197925669835782}</td>
-      <td>0.1381</td>
-      <td>0.6547</td>
-      <td>Thu Dec 26 05:51:08 2024</td>
+      <td>0.1362</td>
+      <td>0.3567</td>
+      <td>Fri Apr 25 03:03:51 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
@@ -16035,9 +16035,9 @@ <h3 id="Analyze-the-AutoML-optimization-process">Analyze the AutoML optimization
       <td>{'contamination': 0.1, 'n_estimators': 101, 'max_samples': 5, 'max_features': 1.0, 'bootstrap': False, 'behaviour': 'old'}</td>
       <td>0.0315</td>
       <td>{'f1': 0.03149606299212599}</td>
-      <td>0.4364</td>
-      <td>0.3549</td>
-      <td>Thu Dec 26 05:51:08 2024</td>
+      <td>0.4225</td>
+      <td>0.6598</td>
+      <td>Fri Apr 25 03:03:50 2025</td>
     </tr>
     <tr>
       <td>...</td>
@@ -16059,9 +16059,9 @@ <h3 id="Analyze-the-AutoML-optimization-process">Analyze the AutoML optimization
       <td>{'contamination': 0.1, 'n_estimators': 5, 'max_samples': 37, 'max_features': 0.05, 'bootstrap': False, 'behaviour': 'old'}</td>
       <td>0.0237</td>
       <td>{'f1': 0.02370163820146393}</td>
-      <td>0.1270</td>
-      <td>0.4141</td>
-      <td>Thu Dec 26 05:51:08 2024</td>
+      <td>0.1536</td>
+      <td>0.3488</td>
+      <td>Fri Apr 25 03:03:51 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
@@ -16071,9 +16071,9 @@ <h3 id="Analyze-the-AutoML-optimization-process">Analyze the AutoML optimization
       <td>{'contamination': 0.1, 'n_estimators': 5, 'max_samples': 36, 'max_features': 0.05, 'bootstrap': False, 'behaviour': 'old'}</td>
       <td>0.0236</td>
       <td>{'f1': 0.023570190641247834}</td>
-      <td>0.1362</td>
-      <td>0.3471</td>
-      <td>Thu Dec 26 05:51:08 2024</td>
+      <td>0.1406</td>
+      <td>0.6632</td>
+      <td>Fri Apr 25 03:03:51 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
@@ -16083,9 +16083,9 @@ <h3 id="Analyze-the-AutoML-optimization-process">Analyze the AutoML optimization
       <td>{'contamination': 0.1, 'n_estimators': 5, 'max_samples': 5, 'max_features': 1.0, 'bootstrap': False, 'behaviour': 'old'}</td>
       <td>0.0038</td>
       <td>{'f1': 0.0037695207323640285}</td>
-      <td>0.1206</td>
-      <td>0.3476</td>
-      <td>Thu Dec 26 05:51:08 2024</td>
+      <td>0.1230</td>
+      <td>0.3496</td>
+      <td>Fri Apr 25 03:03:51 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
@@ -16095,9 +16095,9 @@ <h3 id="Analyze-the-AutoML-optimization-process">Analyze the AutoML optimization
       <td>{'contamination': 0.1, 'n_estimators': 5, 'max_samples': 5, 'max_features': 0.9999905, 'bootstrap': False, 'behaviour': 'old'}</td>
       <td>0.0012</td>
       <td>{'f1': 0.0011851851851851852}</td>
-      <td>0.1324</td>
-      <td>0.3425</td>
-      <td>Thu Dec 26 05:51:08 2024</td>
+      <td>0.1246</td>
+      <td>0.6598</td>
+      <td>Fri Apr 25 03:03:51 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
@@ -16107,9 +16107,9 @@ <h3 id="Analyze-the-AutoML-optimization-process">Analyze the AutoML optimization
       <td>{'contamination': 0.1, 'n_estimators': 5, 'max_samples': 5, 'max_features': 0.05, 'bootstrap': False, 'behaviour': 'old'}</td>
       <td>0.0012</td>
       <td>{'f1': 0.0011813349084465446}</td>
-      <td>0.1030</td>
-      <td>0.3369</td>
-      <td>Thu Dec 26 05:51:13 2024</td>
+      <td>0.0994</td>
+      <td>0.3379</td>
+      <td>Fri Apr 25 03:03:55 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
@@ -16119,9 +16119,9 @@ <h3 id="Analyze-the-AutoML-optimization-process">Analyze the AutoML optimization
       <td>{'contamination': 0.1, 'n_estimators': 5, 'max_samples': 5, 'max_features': 0.05, 'bootstrap': False, 'behaviour': 'old'}</td>
       <td>0.0012</td>
       <td>{'f1': 0.0011813349084465446}</td>
-      <td>0.1280</td>
-      <td>0.4141</td>
-      <td>Thu Dec 26 05:51:08 2024</td>
+      <td>0.1292</td>
+      <td>0.3519</td>
+      <td>Fri Apr 25 03:03:51 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
@@ -16131,9 +16131,9 @@ <h3 id="Analyze-the-AutoML-optimization-process">Analyze the AutoML optimization
       <td>{'contamination': 0.1, 'n_estimators': 5, 'max_samples': 5, 'max_features': 0.050009500000000005, 'bootstrap': False, 'behaviour': 'old'}</td>
       <td>0.0012</td>
       <td>{'f1': 0.0011813349084465446}</td>
-      <td>0.1468</td>
-      <td>0.4024</td>
-      <td>Thu Dec 26 05:51:08 2024</td>
+      <td>0.1274</td>
+      <td>0.3477</td>
+      <td>Fri Apr 25 03:03:50 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
@@ -16143,9 +16143,9 @@ <h3 id="Analyze-the-AutoML-optimization-process">Analyze the AutoML optimization
       <td>{'contamination': 0.1, 'n_estimators': 5, 'max_samples': 5, 'max_features': 0.05001900000000001, 'bootstrap': False, 'behaviour': 'old'}</td>
       <td>0.0012</td>
       <td>{'f1': 0.0011813349084465446}</td>
-      <td>0.1284</td>
-      <td>0.3424</td>
-      <td>Thu Dec 26 05:51:08 2024</td>
+      <td>0.1151</td>
+      <td>0.3501</td>
+      <td>Fri Apr 25 03:03:50 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
@@ -16155,9 +16155,9 @@ <h3 id="Analyze-the-AutoML-optimization-process">Analyze the AutoML optimization
       <td>{'contamination': 0.1, 'n_estimators': 5, 'max_samples': 5, 'max_features': 0.472893264264607, 'bootstrap': False, 'behaviour': 'old'}</td>
       <td>0.0005</td>
       <td>{'f1': 0.00046838407494145194}</td>
-      <td>0.1373</td>
-      <td>0.4123</td>
-      <td>Thu Dec 26 05:51:08 2024</td>
+      <td>0.1258</td>
+      <td>0.3477</td>
+      <td>Fri Apr 25 03:03:50 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
@@ -16167,9 +16167,9 @@ <h3 id="Analyze-the-AutoML-optimization-process">Analyze the AutoML optimization
       <td>{'contamination': 0.1, 'n_estimators': 5, 'max_samples': 5, 'max_features': 0.472902764264607, 'bootstrap': False, 'behaviour': 'old'}</td>
       <td>0.0005</td>
       <td>{'f1': 0.00046838407494145194}</td>
-      <td>0.1214</td>
-      <td>0.6527</td>
-      <td>Thu Dec 26 05:51:08 2024</td>
+      <td>0.1166</td>
+      <td>0.3585</td>
+      <td>Fri Apr 25 03:03:50 2025</td>
     </tr>
   </tbody>
 </table>
@@ -16265,7 +16265,7 @@ <h4 id="Algorithm-Selection">Algorithm Selection<a class="anchor-link" href="#Al
 
 
 <div class="jp-RenderedImage jp-OutputArea-output ">
-<img 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ABukPXr11v/pf7pp58u8th+k8mkLl26SJIyMzP1xRdflHodxZ3lulHc3Nycuv6bXfXq1fXss89qx44d1nAwb9486+Wlvz9L89VXX8m4+uyAa36Ku0z4jwrn7t27t0Pz/v41E87+vrsVlfVn6ddff1V0dLRycnLUsWNHxcfH6/Lly0pPT9e5c+f0yy+/aPHixWWau2nTpjp8+LCWLl2qZ555RnfffbeysrL09ddfa/DgwYqIiND3339fprkBuAbCIQDcIKW9VLQ0/YOCgiRdvacqNze32H7FvVS9MIiUdJlZYfuNPEN4OwoODrbeN5mammpzZrjwLJajl4s6qnr16mWet3BsWcdfD753ba1du1YZGRmqWrWqVq1apfbt2xd5X+bvz/SWloeHh6KiovTBBx/o+++/14ULF/T+++8rICBAp06d0oABA653EwA4EeEQAG6ACxcuaOXKlZKkJUuW6OLFi8V+du7cKUnatm2b9b6xkjRv3lzS1cveinuYjWEY+ve//223LTIyUtLVP6CPHj1qt09+fr714Sb33nuvQ3WVxu/vFzOu4wXut4rfX1Ls6ekp6eo9pS1btpQkrVq1qlzXV3iP2+7du3X27NlSjY2MjLRezlzaugqPe1mPeeH37qZNm+w+wEeSDh8+bP2HkYr43nUlp06dkiQ1aNCg2Es8v/7663Jb3x133KFnn31Wb731liRpz549PLAGuIkRDgHgBvjkk0905coVWSwWdevWTb6+vsV+7r33XkVEREhy/Oxhs2bNrA9nefPNN+3+ob1w4cJiz+o8+OCDuuOOOyQV/7TSDz74wHpfWuETK8uTv7+/9f+npaWV+/yuYsuWLSXeB5iZmally5ZJuvrwmsInRUrSM888I+nqGaK1a9dec57SPEDliSeeUJUqVXTlyhWNGjXqmmGtoKDA5hj5+PjoqaeeknT1+68woDii8LiX9ZgXrvfnn3/Whx9+aLfP+PHjJV19UmynTp3KtJ6bReETbI8ePWr33tG9e/cW+7Tha/n9/Yv2/P7sZHEPBgLg+vjpBYAboDDkPfbYY9d8YEyhJ554QpL08ccfKy8vr8T+JpNJEydOlHT13sYBAwbYPGDkX//6l5599llVrVrV7nhvb29rKPzss8/03HPP6dy5c5KuPnhk1qxZ1tc+9O7dWy1atCixptKqUqWK9aEi8+fPd2i7b0YzZ85UrVq19MILL+jrr79WRkaGtS0jI0NffPGF2rRpYw3yo0ePthnfv39/derUSYZhqGfPnpoyZYr1WEtXXyuxadMmDRkyROHh4Q7XVaVKFc2YMUOS9Pnnn+uRRx7Rjh07rGfjCgoK9MMPP2j69Olq1KiRVq9ebTP+jTfeUGBgoH799Ve1bdtWX3zxhbKysiRdPSt44MABjRkzRp988onNuLvvvlvS1bBb3GXP19KyZUs9/vjjkqQXXnhBs2fPtobvX375RX/729+s99hNnjxZXl5epV7HzeShhx6S2WzWb7/9pn79+ln3aW5urr744gs99NBD13z4TnE+//xztW3bVh988IFOnDhhXZ6fn6/169dr7NixkqTWrVsX+3sGgOsjHAJABUtISNChQ4ck/Tf0laSw37lz57RmzRqHxvTt29ca4D755BPVrFlTAQEB8vf319NPP63WrVvrueeekyS7fyAPHTpUI0eOlHT1LGFISIgCAgJksVg0fPhwXblyRffff7/mzp3rUD1lUVjfu+++K19fX9WqVUu1a9e2nh26FVSqVEm//vqrZs+erQcffFAWi0X+/v7y8/OTxWJR79699f3331vfGTlkyBCb8W5ublq6dKkeffRR5ebm6vXXX9edd94pi8WiqlWrys/PTx07dtScOXNK9TRK6erTcv/5z3/Kw8NDX331le677z75+PgoMDBQXl5e+tOf/qQXX3xRhw8fLvIQmpo1a2r9+vW68847derUKfXu3Vt+fn4KDAyUj4+PGjdurH/84x9FLjkcMGCAvLy8dOzYMdWqVUvVq1dX7dq1Vbt2bYdftfCvf/1L7du3V25url544QVZLBYFBASoRo0a1rOJL774ovX761ZWr149jRkzRpK0bNky1axZU1WqVJGvr6969+4tX19fzZo1q9TzGoahbdu26bnnnlPdunXl5eWlwMBAeXh4qEuXLjp9+rRq1KihefPmlfcmAbiBCIcAUMEKzxpaLBY99NBDDo1p3Lix9RUHpXkwzTvvvKNly5apQ4cO8vPzU05Ojho2bKhp06Zp/fr11rDw+8sUfy82NlbffvutHn/8cQUHByszM1N+fn66//77NW/ePG3cuLFMZx0c9corr2jmzJmKjIxUpUqVdPr0aZ08efK6HqDhaj755BN9++23GjdunB544AHVrFlTubm5ys7OVtWqVXXvvfdq5MiR2rNnj9588027c/j7+2vVqlVau3atevfurVq1aiknJ0eXL1/WnXfeqYceekhTp051+J7V33vuued05MgRvfjii2ratKk8PT2VlpYmX19fRUZG6oUXXtDGjRvtXlrcvHlz/fDDD3rzzTd13333yc/PTxcvXlRQUJA6dOig2NhY9e3b12ZMvXr1tGnTJnXv3l1BQUH69ddfdfLkSZ08edLhs8cWi0XffPON/vWvf1m/9zMzM1W9enU9/vjj2rRpk6ZNm1bqfXGzevPNN/Xxxx+rZcuW8vb21pUrV3TXXXfplVde0Z49e2zeA+qo7t276+OPP9bAgQPVtGlTWSwWpaeny8/PTy1bttTkyZN18OBB6yXxAG5OJoO7/gHgttG2bVtt27ZNkyZN0uuvv+7scgAAgAvhzCEA3CY2b95sfZJp4fsUAQAAChEOAeAWMmTIEC1YsEC//PKL9WmTaWlp+uCDD6zvzevYseMt/zh/AABQelxWCgC3kGbNmmnfvn2Srr4bz8fHR2lpadag+Kc//UkbNmywPhUUAACgEOEQAG4hK1eu1IoVK7Rjxw6dO3dO6enp8vf3V6NGjRQVFaVnnnmm2BdjAwCA2xvhEAAAAADAPYcAAAAAAMnd2QVcS1ZWlqZOnarPP/9cycnJCggIUJcuXTR58uRS3y+TmpqqmJgYrVixQr/88ouqV6+unj17KiYmxu77vhYsWKB169Zp3759OnfunDIzMxUYGKg2bdpo5MiRatu2bZExu3fv1qpVq7RhwwYdOnRIly9fVnBwsNq3b6+XXnpJTZo0KTImKSlJderUKbbu4ODgUr/fq6CgQGfOnJGfn1+RlxQDAAAAuH0YhqGLFy+qRo0aMpuvfW7QZS8rzc7O1v3336+EhASFhISoXbt2SkpK0s6dOxUUFKSEhASFh4c7NFdKSopat26tY8eOKTw8XJGRkTp48KAOHjyo+vXra/v27QoICLAZExkZqX379qlx48aqWbOmvLy8dOTIEe3fv18mk0lz5szRc889Z+2fl5enSpUqSZICAgLUqlUrVa5cWXv27NHx48fl4eGhRYsWqVevXjbrKQyHwcHBdh8tb7FYNHPmzFLtu9OnTys0NLRUYwAAAADcuk6dOqWaNWtes4/LhsPXXntNb7zxhlq3bq0NGzbI19dXkhQbG6vRo0erffv2io+Pd2iu/v37a9GiRYqKilJcXJzc3a+eMB02bJjeffddDRgwQAsWLLAZs2PHDv3pT3+Sn5+fzfKVK1cqKipKlSpV0qlTpxQYGCjpajhs06aNXn31VT366KNyc3OTdPUs3vjx4/XGG2/Iz89PJ06csI6R/hsOS7M9JUlPT1eVKlV06tQp+fv7l8ucAAAAAG4+GRkZCg0NVVpamiwWyzX7umQ4zM3NVbVq1ZSenq7ExETdc889Nu1NmzbV/v37tWvXLrVo0eKac509e1Y1a9aUu7u7kpOTFRwcbG3LyclRaGiofvvtN505c0bVqlVzqL5OnTrpm2++0Zdffqnu3buX2N8wDDVs2FBHjhzRggULNGDAAGtbRYTDjIwMWSwW61MKAQAAANyeSpMNXPKBNFu3blV6errq1q1bJBhKsl6auWrVqhLnWrdunQoKCtSuXTubYChdfQdYt27dlJ+fr7Vr1zpcX+Hlox4eHg71N5lM1vsNz5w54/B6AAAAAOBGcckH0hS+wLl58+Z22wuX79+/v1zmmjdvnkNzSdI333yjb7/9VlWrVtV9993n0BhJOnHihCSpevXqdtvPnTunCRMm6OzZs7JYLGrVqpW6d+/ucAAFAAAAgOvhkuEwOTlZkoq9YbJw+cmTJyt8rvnz52vz5s3Kzs7W8ePHtWvXLlksFn322Wd2n3Jqz3/+8x/t3r1bHh4edh86I0mHDx/WpEmTbJbVqlVLixcvVsuWLa85f05OjnJycqxfZ2RkOFQXAAAAABRyyctKMzMzJUk+Pj522ytXrixJunjxYoXPtXXrVn300UeKi4vTrl27FBAQoHnz5qlz584lrlu6GtQGDRokSRo5cqRCQkJs2j09PfX8888rPj5e586dU0ZGhrZv366uXbsqOTlZnTt3LjEET506VRaLxfrhSaUAAAAASsslw6Er+fDDD63vBtm1a5c6deqkxx9/XM8880yJY/Pz89WvXz/9+OOPatmyZZEzg5IUEhKiOXPmqH379qpWrZr8/Px03333ac2aNerbt6/S0tL097///ZrrGTdunNLT062fU6dOlXl7AQAAANyeXDIcFr624vLly3bbL126JElFXjNRkXP5+vqqRYsWiouLU/fu3TV37lwtXbr0mmOef/55rV69Wg0aNNCaNWtKff/gK6+8Iklav379Nft5enrK39/f5gMAAAAApeGS4bBWrVqSrr7M3Z7C5WFhYTd0rkL9+/eXJH355ZfF9hk7dqzmzp2r0NBQbdy40ebdho6qV6+epKuv4wAAAACAiuSS4bBp06aSpMTERLvthcsLXw9xo+YqVBj0Lly4YLf97bff1ltvvaVq1app48aNZb4HMDU1VdJ/74sEAAAAgIrikuGwbdu2slgsOn78uPbu3VukfcmSJZKkbt26lThXly5dZDabtWXLFp0/f96mLScnR6tWrZKbm5u6du3qcH2bN2+WJNWtW7dI29y5c/Xyyy+rSpUqWr9+vRo0aODwvH9UeNlqca/hAAAAAIDy4pLh0MPDQ0OHDpUkDRkyxHpfoCTFxsZq//79at++vVq0aGFdPnv2bEVERGjcuHE2c4WEhKhPnz7Kzc3V4MGDlZeXZ2176aWXdOHCBfXv31/VqlWzLv/hhx/0xRdfKDc312YuwzD0+eef6+2335bJZNKAAQNs2pcsWaLnnntOvr6+Wrt2rZo1a1bits6dO1eHDx8usnzZsmUaO3asdR8AAAAAQEUyGYZhOLsIe7Kzs9WhQwft2LFDISEhateunU6ePKkdO3YoKChICQkJCg8Pt/aPiYnRxIkTNWDAAC1YsMBmrpSUFN133306fvy46tatq8jISB08eFAHDhxQvXr1lJCQoICAAGv/+Ph43X///bJYLGrRooWqV6+utLQ0HTp0SElJSTKbzZo+fbpGjBhhHXP+/HmFhoYqNzdXjRs3LvZsX48ePdSjRw/r1x06dNDmzZvVpEkT1a9fXwUFBTp06JA1MI4ZM0Zvv/12qfZdRkaGLBaL0tPTeTgNAAAAcBsrTTZwv0E1lZqXl5c2bdqkqVOn6tNPP9WKFSsUEBCg6OhoTZ48udiX2tsTGBionTt3KiYmRitWrNDy5csVHBysYcOGaeLEiUVeZt+oUSNNmjRJ8fHxOnr0qLZu3Sqz2ayaNWtq0KBBGjJkSJHwd/nyZeuZxu+//17ff/+93Vpq165tEw7/9re/KSgoSHv37tWGDRuUlZWloKAgRUVF6fnnn1enTp0c3k4AAAAAKCuXPXOIsuPMIQAAAACpdNnAJe85BAAAAADcWIRDAAAAAADhEAAAAABAOAQAAAAAiHAIAAAAABDhEAAAAAAgwiEAAAAAQIRDAAAAAIAIhwAAAAAAEQ4BAAAAACIcAgAAAABEOAQAAAAAiHAIAAAAABDhEAAAAAAgwiEAAAAAQIRDAAAAAIAIhwAAAAAAEQ4BAAAAACIcAgAAAABEOAQAAAAAiHAIAAAAABDhEAAAAAAgwiEAAAAAQIRDAAAAAIAIhwAAAAAAEQ4BAAAAACIcAgAAAABEOAQAAAAASHJ3dgG4OSUnJyslJcXZZVyXwMBA1apVy9llAAAAAC6BcIhSS05OVsOIBrqcle3sUq6Lj7eXfjh8hIAIAAAAiHCIMkhJSdHlrGwtHCw1rOHsasrmhzNS/znZSklJIRwCAAAAIhziOjSsITWv4+wqAAAAAJQHHkgDAAAAACAcAgAAAAAIhwAAAAAAEQ4BAAAAACIcAgAAAABEOAQAAAAAiHAIAAAAABDhEAAAAAAgwiEAAAAAQIRDAAAAAIAIhwAAAAAAEQ4BAAAAACIcAgAAAABEOAQAAAAAiHAIAAAAAJCLh8OsrCyNHz9e9evXl5eXl2rUqKFBgwbp559/LvVcqampGj58uMLCwuTp6amwsDCNGDFCaWlpdvsvWLBATz31lBo2bKiAgAB5eHioRo0a6tWrl7Zu3XrNda1atUrt27eXv7+//P391aFDB61Zs+aaYw4ePKgnnnhCQUFB8vb2VuPGjTVjxgwVFBSUelsBAAAAoLRcNhxmZ2erY8eOmjx5sjIzM/XYY48pNDRU8+fP1z333KMTJ044PFdKSopatmypWbNmyd3dXT169JCfn59mzpypVq1a6bfffisyZvbs2Vq6dKm8vb315z//WT169FBQUJCWLl2qdu3a6f3337e7rhkzZqh79+7atm2b2rZtq44dO2rnzp169NFHNXv2bLtjtm/frnvvvVdLlixReHi4unfvrpSUFI0cOVJPPfWUDMNweFsBAAAAoCxcNhxOmTJFCQkJat26tY4ePaq4uDjt2LFD06dP14ULFzRo0CCH5xoxYoSOHTumqKgoHTlyRHFxcTpw4IBeeOEFHT16VKNGjSoy5r333tNvv/2mxMRErVy5Ul988YX27dunL7/8UmazWSNHjlRKSorNmCNHjujFF1+Up6en/v3vf+urr77SihUrtHfvXt1xxx0aOXKkjh07ZjPmypUr6tevn7KyshQbG6sdO3YoLi5OP/74o1q3bq3Fixfro48+KttOBAAAAAAHuWQ4zM3NtZ5le++99+Tr62ttGzVqlJo0aaLNmzdr9+7dJc519uxZffbZZ/Lw8NCcOXPk7u5ubZs2bZqCgoK0cOFCnT9/3mZcq1at5OfnV2S+7t27q0OHDsrOzta2bdts2mbOnKn8/Hw999xzat26tXV5/fr19eqrryovL08zZ860GbN8+XL99NNPatq0qUaOHGld7uvra90H06dPL3E7AQAAAOB6uGQ43Lp1q9LT01W3bl3dc889Rdp79eol6eq9fSVZt26dCgoK1K5dOwUHB9u0eXp6qlu3bsrPz9fatWsdrq9SpUqSJA8PD5vlhfcVFtbnSM3XGtO8eXOFh4frwIEDSkpKcrg+AAAAACgtlwyH+/btk3Q1HNlTuHz//v03dC5J+uabb/Ttt9+qatWquu+++6zL09LSlJycLEl2A21oaKgCAwN18uRJZWRkVFh9AAAAAFAW7iV3ufEKQ1bNmjXtthcuP3nyZIXPNX/+fG3evFnZ2dk6fvy4du3aJYvFos8++0xVqlQpsp6qVauqcuXKxa4rJSVFJ0+eVOPGjculPknKyclRTk6O9evfh08AAAAAcIRLhsPMzExJko+Pj932wvB18eLFCp9r69atNg+ECQgI0Ny5c9W5c+dSrae4dZXHtk6dOlUTJ04sth0AAAAASuKS4dCVfPjhh/rwww+VmZmpI0eO6O2339bjjz+uv/3tb/q///s/Z5cnSRo3bpzNE1czMjIUGhrqxIpwIyQnJxd5Yu7NJjAwULVq1XJ2GQAAAJCLhsPCp5NevnzZbvulS5ckye7TRCtqLl9fX7Vo0UJxcXHKzs62nj18/PHHHVpPcevy9fVVamrqddXn6ekpT0/Pa9aPW0tycrIaREQoOyvL2aVcFy9vbx05fJiACAAA4AJcMhwW/qF4+vRpu+2Fy8PCwm7oXIX69++vlStX6ssvv7SGw8L1pKam6tKlS3bvO7S3rlq1aik1NVWnT59WkyZNyqU+3PpSUlKuBsOoKCkw0NnllE1KirKXLVNKSgrhEAAAwAW4ZDhs2rSpJCkxMdFue+Fye2GqIucqFPj//xi/cOGCdVmVKlVUq1YtJScna8+ePfrzn/9sM+bUqVNKSUlRWFiY/P39berbt2+fEhMT1bVr13KpD7eRwECpRg1nVwEAAIBbgEu+yqJt27ayWCw6fvy49u7dW6R9yZIlkqRu3bqVOFeXLl1kNpu1ZcuWIi+6z8nJ0apVq+Tm5mY3mBVn8+bNkqS6devaLH/kkUds6nOk5muN2bNnj06cOKG7775btWvXdrg+AAAAACgtlwyHHh4eGjp0qCRpyJAh1vvuJCk2Nlb79+9X+/bt1aJFC+vy2bNnKyIiQuPGjbOZKyQkRH369FFubq4GDx6svLw8a9tLL72kCxcuqH///qpWrZp1+Q8//KAvvvhCubm5NnMZhqHPP/9cb7/9tkwmkwYMGGDTPnz4cLm5uen9999XQkKCdfmPP/6oN954Q+7u7ho+fLjNmJ49e6pOnTrat2+f3nnnHevyS5cuaciQIZKk0aNHO7bjAAAAAKCMXPKyUkl67bXX9PXXX2vbtm2qV6+e2rVrp5MnT2rHjh0KCgrSvHnzbPqnpKToyJEjOnv2bJG5ZsyYoYSEBC1dulQRERGKjIzUwYMHdeDAAdWrV0+xsbE2/c+dO6fevXvLYrGoRYsWql69utLS0nTo0CElJSXJbDYrNjZW9957r824Bg0aaNq0aRo1apTatWunBx98UB4eHtqwYYOysrI0a9Ys3XXXXTZjKlWqpIULF6pTp04aNWqU4uLiFBYWpi1btujs2bPq1atXkRAKAAAAAOXNJc8cSpKXl5c2bdqk119/XT4+PlqxYoVOnjyp6OhoJSYmKjw83OG5AgMDtXPnTr3wwgvKzc3V8uXLlZ6ermHDhmnnzp0KCAiw6d+oUSNNmjRJLVq00NGjR7V06VJt2rRJlSpV0qBBg/Tdd99pxIgRdtc1cuRIrVy5Uq1bt9aWLVv0zTffKDIyUqtWrdILL7xgd0ybNm303Xff6fHHH9exY8e0cuVKBQQEKDY2VnFxcTKZTA5vKwAAAACUhckwDMPZRaB8ZWRkyGKxKD093ebhN+UlMTFRLVq00O4pUvM65T79DZH4k9TiNWn37t1q3ry5s8sptcJjoGeeuXkfSHPmjPR//3fTHgMAAICbQWmygcueOQQAAAAA3Dgue88hALi65ORkpaSkOLuM6xIYGMh7JgEAgCTCIQCUSXJyshpERCg7K8vZpVwXL29vHTl8mIAIAAAIhwBQFikpKVeDYVSUFBjo7HLKJiVF2cuWKSUlhXAIAAAIhwBwXQIDb96HAgEAAPwOD6QBAAAAABAOAQAAAABcVgoAuInd7E+M5WmxAABXQjgEANyUboUnxvK0WACAKyEcAgBuSjf9E2N5WiwAwMUQDgEANzeeGAsAQLnggTQAAAAAAMIhAAAAAIBwCAAAAAAQ4RAAAAAAIMIhAAAAAECEQwAAAACACIcAAAAAABEOAQAAAAAiHAIAAAAARDgEAAAAAIhwCAAAAAAQ4RAAAAAAIMIhAAAAAECEQwAAAACACIcAAAAAABEOAQAAAAAiHAIAAAAARDgEAAAAAIhwCAAAAACQ5O7sAgAAwM0rOTlZKSkpzi6jzAIDA1WrVi1nlwEALoFwCAAAyiQ5OVkNIiKUnZXl7FLKzMvbW0cOHyYgAoAIhwAAoIxSUlKuBsOoKCkw0NnllF5KirKXLVNKSgrhEABEOAQAANcrMFCqUcPZVQAArhPhEAAA4CbGfZ8AygvhEAAA4CbFfZ8AyhPhEAAA4CbFfZ8AyhPhEAAA4GbHfZ8AyoHZ2QUAAAAAAJyPcAgAAAAAIBwCAAAAAAiHAAAAAAARDgEAAAAAIhwCAAAAAEQ4BAAAAACIcAgAAAAAEOEQAAAAACDCIQAAAABALh4Os7KyNH78eNWvX19eXl6qUaOGBg0apJ9//rnUc6Wmpmr48OEKCwuTp6enwsLCNGLECKWlpRXpe+XKFW3YsEFDhw7V3XffLR8fH3l7e6thw4Z68cUXdeHCBbvrqF27tkwm0zU/4eHhNmOSkpKu2b969eql3lYAAAAAKC13ZxdQnOzsbHXs2FEJCQkKCQnRY489pqSkJM2fP1+rV69WQkJCkaBVnJSUFLVu3VrHjh1TeHi4evTooYMHD2rmzJn66quvtH37dgUEBFj7b968WZ07d5Z0NfA9/PDDunLlirZv367p06dr0aJFio+PV4MGDWzW06tXL6WkpNitYfPmzUpKSlK7du3stgcHB6tLly5FllssFoe2EQAAAACuh8uGwylTpighIUGtW7fWhg0b5OvrK0mKjY3V6NGjNWjQIMXHxzs014gRI3Ts2DFFRUUpLi5O7u5XN3vYsGF69913NWrUKC1YsMDa32w268knn9To0aPVsmVL6/L09HT17t1b69ev18CBA7Vt2zab9fzjH/+wu/6CggLVrFlTkvQ///M/dvtERETY1AAAAAAAN5JLXlaam5ur2bNnS5Lee+89azCUpFGjRqlJkybavHmzdu/eXeJcZ8+e1WeffSYPDw/NmTPHGgwladq0aQoKCtLChQt1/vx56/KOHTsqLi7OJhhKV8/izZs3T5K0fft2nTx50qHt+eabb3T27Fndeeed6tixo0NjAAAAAOBGcslwuHXrVqWnp6tu3bq65557irT36tVLkrRq1aoS51q3bp0KCgrUrl07BQcH27R5enqqW7duys/P19q1ax2qrUaNGgoKCpIknTlzxqExCxculCT17dtXZrNL7nIAAAAAtzmXvKx03759kqTmzZvbbS9cvn///nKZa968eQ7NJUlpaWlKTU2VJIceFpOVlaXly5dLkvr3719sv3PnzmnChAk6e/asLBaLWrVqpe7du8vDw8OhugAAAADgerhkOExOTpYk6316f1S43JHLOstzLunqZa55eXlq3Lix6tSpU2L/FStW6OLFi2rSpImaNGlSbL/Dhw9r0qRJNstq1aqlxYsXF7m89Y9ycnKUk5Nj/TojI6PEugAAAADg91zyGsfMzExJko+Pj932ypUrS5IuXrx4Q+fas2ePpkyZIkl66623SuwvSZ988omk4h9E4+npqeeff17x8fE6d+6cMjIytH37dnXt2lXJycnq3LlzicF16tSpslgs1k9oaKhDtQEAAABAIZcMh67o3LlzioqKUnZ2tkaMGKGHH364xDHnz5/Xxo0bZTab1bdvX7t9QkJCNGfOHLVv317VqlWTn5+f7rvvPq1Zs0Z9+/ZVWlqa/v73v19zPePGjVN6err1c+rUqTJtIwAAAIDbl0uGw8Knk16+fNlu+6VLlyRJfn5+N2SuixcvqmvXrkpKStITTzyh6dOnl7heSfr888+Vl5enBx54QDVq1HBozO+98sorkqT169dfs5+np6f8/f1tPgAAAABQGi4ZDmvVqiVJOn36tN32wuVhYWEVPld2dra6d++uxMREPfTQQ1q4cKHDTxwtfErptR5Ecy316tWTdPV1HAAAAABQkVwyHDZt2lSSlJiYaLe9cPm1HvBSHnPl5eWpd+/eio+PV5s2bbRs2TKHnx569OhRfffdd/Lx8VFUVJRDY/6o8KmohfdFAgAAAEBFcclw2LZtW1ksFh0/flx79+4t0r5kyRJJUrdu3Uqcq0uXLjKbzdqyZYvNi+6lq0/5XLVqldzc3NS1a1ebNsMwNHDgQK1cuVLNmjXTmjVrShXSCs8a9uzZ03ppa2ktXbpUUvGv4QAAAACA8uKS4dDDw0NDhw6VJA0ZMsR6X6AkxcbGav/+/Wrfvr1atGhhXT579mxFRERo3LhxNnOFhISoT58+ys3N1eDBg5WXl2dte+mll3ThwgX1799f1apVsxk3YsQILVy4UBEREdqwYYOqVKlSqm1YtGiRpOKfUlpo7ty5Onz4cJHly5Yt09ixYyVd3QcAAAAAUJFc8j2HkvTaa6/p66+/1rZt21SvXj21a9dOJ0+e1I4dOxQUFKR58+bZ9E9JSdGRI0fs3p83Y8YMJSQkaOnSpYqIiFBkZKQOHjyoAwcOqF69eoqNjbXp/+WXX2rWrFmSpNDQUI0ZM8ZujWPHjlVERESR5du2bdOJEydUvXp1derU6ZrbuWjRIj3zzDNq0qSJ6tevr4KCAh06dMgaGMeMGaOePXtecw4AAAAAuF4uGw69vLy0adMmTZ06VZ9++qlWrFihgIAARUdHa/LkycW+1N6ewMBA7dy5UzExMVqxYoWWL1+u4OBgDRs2TBMnTixyVrDwXj9J2rhxY7HzRkdH2w2HhZeU9unTR25ubtes7W9/+5uCgoK0d+9ebdiwQVlZWQoKClJUVJSef/75EsMlAAAAAJQHlw2HkuTt7a1JkyZp0qRJJfaNiYlRTExMse0BAQGaNWuW9YzgtURHRys6OroUldqaM2eO5syZ41Dffv36qV+/fmVeFwAAAACUB5e85xAAAAAAcGMRDgEAAAAArn1ZKa5P7969ValSpXKfNz09XZI0cqFk8Sn36W+I9MtX/3fkyJGyWCzOLaYMCo+B1q2TvLycW0xZZWdL4hg4FcfAuW7y/S9xDFwBxwBASa5cueJwX5NhGEYF1gInyMjIkMViUXp6uvz9/ct9/sTERLVo0UK7p0jN65T79DdE4k9Si9ek3bt335TvkSw8BnrmGalGDWeXUzZnzkj/938cA2fiGDjXTb7/JY6BK+AYAChJabIBl5UCAAAAAAiHAAAAAADCIQAAAABAhEMAAAAAgAiHAAAAAAARDgEAAAAAIhwCAAAAAEQ4BAAAAACIcAgAAAAAEOEQAAAAACDCIQAAAABAhEMAAAAAgAiHAAAAAAARDgEAAAAAIhwCAAAAAEQ4BAAAAACIcAgAAAAAEOEQAAAAACDJ3dkFAAAAADez5ORkpaSkOLuMMgsMDFStWrWcXQZcAOEQAAAAKKPk5GQ1aNhQ2ZcvO7uUMvPy8dGRH34gIIJwCAAAAJRVSkqKsi9fVsMZb8jnrnBnl1Nql4+d0A8jXlVKSgrhEIRDAAAA4Hr53BUuv7sbOrsM4LrwQBoAAAAAAOEQAAAAAEA4BAAAAACIcAgAAAAAEOEQAAAAACDCIQAAAABAhEMAAAAAgAiHAAAAAAARDgEAAAAAIhwCAAAAAEQ4BAAAAACIcAgAAAAAEOEQAAAAACDCIQAAAABAhEMAAAAAgAiHAAAAAAARDgEAAAAAIhwCAAAAAEQ4BAAAAACIcAgAAAAAEOEQAAAAACAXD4dZWVkaP3686tevLy8vL9WoUUODBg3Szz//XOq5UlNTNXz4cIWFhcnT01NhYWEaMWKE0tLSivS9cuWKNmzYoKFDh+ruu++Wj4+PvL291bBhQ7344ou6cOGC3XUsWLBAJpOp2M9TTz1VbH0HDx7UE088oaCgIHl7e6tx48aaMWOGCgoKSr2tAAAAAFBa7s4uoDjZ2dnq2LGjEhISFBISoscee0xJSUmaP3++Vq9erYSEBIWHhzs0V0pKilq3bq1jx44pPDxcPXr00MGDBzVz5kx99dVX2r59uwICAqz9N2/erM6dO0uSateurYcfflhXrlzR9u3bNX36dC1atEjx8fFq0KCB3fU1bdpUzZo1K7K8VatWdvtv375dDzzwgLKystSyZUvVrl1b//73vzVy5Eht27ZNcXFxMplMDm0rAAAAAJSFy4bDKVOmKCEhQa1bt9aGDRvk6+srSYqNjdXo0aM1aNAgxcfHOzTXiBEjdOzYMUVFRSkuLk7u7lc3e9iwYXr33Xc1atQoLViwwNrfbDbrySef1OjRo9WyZUvr8vT0dPXu3Vvr16/XwIEDtW3bNrvr69Gjh2JiYhyq7cqVK+rXr5+ysrIUGxurkSNHSpIyMzP10EMPafHixeratauio6Mdmg8AAAAAysIlLyvNzc3V7NmzJUnvvfeeNRhK0qhRo9SkSRNt3rxZu3fvLnGus2fP6rPPPpOHh4fmzJljDYaSNG3aNAUFBWnhwoU6f/68dXnHjh0VFxdnEwwlyWKxaN68eZKunu07efLkdW2nJC1fvlw//fSTmjZtag2GkuTr62vdB9OnT7/u9QAAAADAtbhkONy6davS09NVt25d3XPPPUXae/XqJUlatWpViXOtW7dOBQUFateunYKDg23aPD091a1bN+Xn52vt2rUO1VajRg0FBQVJks6cOePQmGtZs2aNpP9u0+81b95c4eHhOnDggJKSkq57XQAAAABQHJcMh/v27ZN0NRzZU7h8//79N3QuSUpLS1NqaqokqXr16nb77N69W2PGjNGzzz6rCRMmaPPmzTesPgAAAAAoC5e85zA5OVmSVLNmTbvthcsduayzPOeSrl7mmpeXp8aNG6tOnTp2+6xevVqrV6+2fj1p0iS1b99ecXFxRc5elkd9OTk5ysnJsX6dkZHh0LYAAAAAQCGXPHOYmZkpSfLx8bHbXrlyZUnSxYsXb+hce/bs0ZQpUyRJb731VpH2kJAQxcTEaM+ePUpPT9cvv/yilStXKiIiQps3b9ajjz6q/Pz8cq9v6tSpslgs1k9oaGiJ2wIAAAAAv+eS4dAVnTt3TlFRUcrOztaIESP08MMPF+nTuXNnTZgwQc2aNZO/v7+Cg4PVrVs3fffdd6pfv7527dqlL774otxrGzdunNLT062fU6dOlfs6AAAAANzaXDIcFj6d9PLly3bbL126JEny8/O7IXNdvHhRXbt2VVJSkp544olSPz3U19dXw4YNkyStX7++3Ovz9PSUv7+/zQcAAAAASsMlw2GtWrUkSadPn7bbXrg8LCyswufKzs5W9+7dlZiYqIceekgLFy6U2Vz63VavXj1JV1+tUZ71AQAAAEB5cMlw2LRpU0lSYmKi3fbC5U2aNKnQufLy8tS7d2/Fx8erTZs2WrZsmTw8PEreADsKn3BaeA9hedQHAAAAAOXFJcNh27ZtZbFYdPz4ce3du7dI+5IlSyRJ3bp1K3GuLl26yGw2a8uWLTYvupeuPuVz1apVcnNzU9euXW3aDMPQwIEDtXLlSjVr1kxr1qwpEuxKY+nSpZKKvrLikUcesdmm39uzZ49OnDihu+++W7Vr1y7zugEAAACgJC4ZDj08PDR06FBJ0pAhQ6z33UlSbGys9u/fr/bt26tFixbW5bNnz1ZERITGjRtnM1dISIj69Omj3NxcDR48WHl5eda2l156SRcuXFD//v1VrVo1m3EjRozQwoULFRERoQ0bNqhKlSol1j116lSlpKTYLLty5YomTpyoxYsXy9vbWwMHDrRp79mzp+rUqaN9+/bpnXfesS6/dOmShgwZIkkaPXp0iesGAAAAgOvhku85lKTXXntNX3/9tbZt26Z69eqpXbt2OnnypHbs2KGgoCDNmzfPpn9KSoqOHDlS5J4+SZoxY4YSEhK0dOlSRUREKDIyUgcPHtSBAwdUr149xcbG2vT/8ssvNWvWLElSaGioxowZY7fGsWPHKiIiwvr1K6+8ookTJyoyMlKhoaHKyMjQ3r17debMGXl5eWnhwoW68847beaoVKmSFi5cqE6dOmnUqFGKi4tTWFiYtmzZorNnz6pXr14aMGBAmfYhAAAAADjKZcOhl5eXNm3apKlTp+rTTz/VihUrFBAQoOjoaE2ePLnYl8bbExgYqJ07dyomJkYrVqzQ8uXLFRwcrGHDhmnixIlFzgoW3h8oSRs3bix23ujoaJtwOH78eG3fvl1HjhxRYmKiDMNQzZo19eyzz2rkyJFq0KCB3XnatGmj7777ThMmTFB8fLz27dununXrasyYMRo+fLhMJpPD2woAAAAAZeGy4VCSvL29NWnSJE2aNKnEvjExMYqJiSm2PSAgQLNmzbKeEbyW6OhoRUdHl6LSqyZOnFjqMYUaNWpk975DAAAAALgRXPKeQwAAAADAjUU4BAAAAAAQDgEAAAAAhEMAAAAAgAiHAAAAAAARDgEAAAAAIhwCAAAAAEQ4BAAAAACIcAgAAAAAEOEQAAAAACDCIQAAAABAhEMAAAAAgAiHAAAAAAARDgEAAAAAIhwCAAAAAEQ4BAAAAACIcAgAAAAAEOEQAAAAACDCIQAAAABAhEMAAAAAgAiHAAAAAAARDgEAAAAAIhwCAAAAAEQ4BAAAAACIcAgAAAAAEOEQAAAAACDCIQAAAABAhEMAAAAAgAiHAAAAAAARDgEAAAAAIhwCAAAAAEQ4BAAAAACIcAgAAAAAEOEQAAAAACDCIQAAAABAhEMAAAAAgAiHAAAAAAARDgEAAAAAIhwCAAAAAEQ4BAAAAACIcAgAAAAAEOEQAAAAACDCIQAAAABAhEMAAAAAgAiHAAAAAAARDgEAAAAAIhwCAAAAAOTi4TArK0vjx49X/fr15eXlpRo1amjQoEH6+eefSz1Xamqqhg8frrCwMHl6eiosLEwjRoxQWlpakb5XrlzRhg0bNHToUN19993y8fGRt7e3GjZsqBdffFEXLlywu47du3crJiZGbdq0UZUqVeTh4aHQ0FD1799f+/fvtzsmKSlJJpOp2E/16tVLva0AAAAAUFruzi6gONnZ2erYsaMSEhIUEhKixx57TElJSZo/f75Wr16thIQEhYeHOzRXSkqKWrdurWPHjik8PFw9evTQwYMHNXPmTH311Vfavn27AgICrP03b96szp07S5Jq166thx9+WFeuXNH27ds1ffp0LVq0SPHx8WrQoIF1TF5eniIjIyVJAQEBatOmjSpXrqw9e/Zo0aJFWrx4sRYtWqRevXrZrTE4OFhdunQpstxisTi8zwAAAACgrFw2HE6ZMkUJCQlq3bq1NmzYIF9fX0lSbGysRo8erUGDBik+Pt6huUaMGKFjx44pKipKcXFxcne/utnDhg3Tu+++q1GjRmnBggXW/mazWU8++aRGjx6tli1bWpenp6erd+/eWr9+vQYOHKht27bZrOfee+/Vq6++qkcffVRubm6SpIKCAo0fP15vvPGGBg0apA4dOigwMLBIjRERETY1AAAAAMCN5JKXlebm5mr27NmSpPfee88aDCVp1KhRatKkiTZv3qzdu3eXONfZs2f12WefycPDQ3PmzLEGQ0maNm2agoKCtHDhQp0/f966vGPHjoqLi7MJhtLVs3jz5s2TJG3fvl0nT560trm7u2vnzp167LHHrMFQuho0J0+erAYNGujixYtas2ZNKfcGAAAAAFQ8lwyHW7duVXp6uurWrat77rmnSHvhpZmrVq0qca5169apoKBA7dq1U3BwsE2bp6enunXrpvz8fK1du9ah2mrUqKGgoCBJ0pkzZxwaYzKZ1KRJk1KNAQAAAIAbySUvK923b58kqXnz5nbbC5cX95CX0s41b948h+aSpLS0NKWmpkpSqR4Wc+LEiWuOOXfunCZMmKCzZ8/KYrGoVatW6t69uzw8PBxeBwAAAACUlUuGw+TkZElSzZo17bYXLv/9ZZ03Yi7p6mWueXl5aty4serUqePQmP/85z/avXu3PDw87D50RpIOHz6sSZMm2SyrVauWFi9eXOTy1j/KyclRTk6O9euMjAyH6gIAAACAQhV6WemQIUP0wAMPlHpcZmamJMnHx8due+XKlSVJFy9evKFz7dmzR1OmTJEkvfXWWyX2l64GtUGDBkmSRo4cqZCQEJt2T09PPf/884qPj9e5c+eUkZGh7du3q2vXrkpOTlbnzp1LDK5Tp06VxWKxfkJDQx2qDQAAAAAKVWg4TExMdPiJoq7u3LlzioqKUnZ2tkaMGKGHH364xDH5+fnq16+ffvzxR7Vs2bLImUFJCgkJ0Zw5c9S+fXtVq1ZNfn5+uu+++7RmzRr17dtXaWlp+vvf/37N9YwbN07p6enWz6lTp8q8nQAAAABuTy75QJrCp5NevnzZbvulS5ckSX5+fjdkrosXL6pr165KSkrSE088oenTp5e4Xkl6/vnntXr1ajVo0EBr1qwp9f2Dr7zyiiRp/fr11+zn6ekpf39/mw8AAAAAlIZD9xx+/PHHZZr8woULZRpXq1YtSdLp06ftthcuDwsLq/C5srOz1b17dyUmJuqhhx7SwoULZTaXnKnHjh2ruXPnKjQ0VBs3brT7bsOS1KtXT9LV13EAAAAAQEVyKBxGR0fLZDKVenLDMMo0rmnTppKuXpZqT+HywtdDVNRceXl56t27t+Lj49WmTRstW7bMobN/b7/9tt566y1Vq1ZNGzduLPM9gIVPRS28LxIAAAAAKopD4dDNzU0FBQV6+umnHTprVmj58uVlOnvYtm1bWSwWHT9+XHv37lWzZs1s2pcsWSJJ6tatW4lzdenSRWazWVu2bNH58+dVrVo1a1tOTo5WrVolNzc3de3a1WacYRgaOHCgVq5cqWbNmmnNmjUOhbS5c+fq5ZdfVpUqVbR+/Xo1aNDAgS22b+nSpZKKfw0HAAAAAJQXh8Lh3Xffrf3792v48OH605/+5PDke/fuLVM49PDw0NChQ/XGG29oyJAh2rBhgzWYxcbGav/+/Wrfvr1atGhhHTN79mzNnj1bPXv21NSpU63LQ0JC1KdPHy1atEiDBw/W559/Lnf3q5v90ksv6cKFCxowYIBNaJSkESNGaOHChYqIiNCGDRtUpUqVEutesmSJnnvuOfn6+mrt2rVFQq09c+fOVbt27RQREWGzfNmyZRo7dqykq099BQAAAICK5FA4bNmypfbv369du3aVKhxej9dee01ff/21tm3bpnr16qldu3Y6efKkduzYoaCgIM2bN8+mf0pKio4cOWL3/rwZM2YoISFBS5cuVUREhCIjI3Xw4EEdOHBA9erVU2xsrE3/L7/8UrNmzZIkhYaGasyYMXZrHDt2rDXUnT9/Xv369VNBQYHq1KmjDz74QB988EGRMT169FCPHj2sXy9atEjPPPOMmjRpovr166ugoECHDh3S4cOHJUljxoxRz549Hd9xAAAAAFAGDofDuXPn6rvvvtP//u//Ojy5YRgyDKNMhXl5eWnTpk2aOnWqPv30U61YsUIBAQGKjo7W5MmTi32pvT2BgYHauXOnYmJitGLFCi1fvlzBwcEaNmyYJk6cWOSsYOG9fpK0cePGYueNjo62hsPLly8rNzdXkvT999/r+++/tzumdu3aNuHwb3/7m4KCgrR3715t2LBBWVlZCgoKUlRUlJ5//nl16tTJ4e0EAAAAgLJyKBz27NlTgYGBCg4OLtXkCQkJZSqqkLe3tyZNmmT3/YB/FBMTo5iYmGLbAwICNGvWLOsZwWuJjo5WdHR0KSq9GvrKEoT79eunfv36lXocAAAAAJQnh54u06tXLx05ckT33XefJOnf//63jh49WqGFAQAAAABuHIfCYXx8vPUeOEnq0KGD3nrrrQorCgAAAABwYzkUDj08PHTp0iWbZWW9lxAAAAAA4Hocuufwrrvu0jfffKPNmzerTp06kqTMzEwlJyc7tJJatWqVvUIAAAAAQIVzKBw+88wzGjFihDp27GhdtnTpUutL2q/FZDIpLy+v7BUCAAAAACqcQ+Fw2LBhqlmzpr788kudPn1amzZtUrVq1Yq8uB0AAAAAcHNyKBxKUlRUlKKioiRJZrNZDz/8cJEX0QMAAAAAbk4Oh8PfmzBhgu65557yrgUAAAAA4CRlDocAAAAAgFuHQ6+yAAAAAADc2giHAAAAAADCIQAAAACAcAgAAAAAEOEQAAAAACDCIQAAAABAhEMAAAAAgAiHAAAAAAARDgEAAAAAIhwCAAAAAEQ4BAAAAACIcAgAAAAAEOEQAAAAACDCIQAAAABAhEMAAAAAgAiHAAAAAAARDgEAAAAAIhwCAAAAAEQ4BAAAAACIcAgAAAAAEOEQAAAAACDCIQAAAABAhEMAAAAAgAiHAAAAAAARDgEAAAAAIhwCAAAAAEQ4BAAAAACIcAgAAAAAEOEQAAAAACDCIQAAAABAhEMAAAAAgAiHAAAAAAARDgEAAAAAIhwCAAAAAEQ4BAAAAACIcAgAAAAAEOEQAAAAACDCIQAAAABALh4Os7KyNH78eNWvX19eXl6qUaOGBg0apJ9//rnUc6Wmpmr48OEKCwuTp6enwsLCNGLECKWlpRXpe+XKFW3YsEFDhw7V3XffLR8fH3l7e6thw4Z68cUXdeHChWuua9WqVWrfvr38/f3l7++vDh06aM2aNdccc/DgQT3xxBMKCgqSt7e3GjdurBkzZqigoKDU2woAAAAApeWy4TA7O1sdO3bU5MmTlZmZqccee0yhoaGaP3++7rnnHp04ccLhuVJSUtSyZUvNmjVL7u7u6tGjh/z8/DRz5ky1atVKv/32m03/zZs3q3Pnznrvvfd06dIlPfzww3rwwQeVkpKi6dOnq0mTJjpy5Ijddc2YMUPdu3fXtm3b1LZtW3Xs2FE7d+7Uo48+qtmzZ9sds337dt17771asmSJwsPD1b17d6WkpGjkyJF66qmnZBiG4zsOAAAAAMrAZcPhlClTlJCQoNatW+vo0aOKi4vTjh07NH36dF24cEGDBg1yeK4RI0bo2LFjioqK0pEjRxQXF6cDBw7ohRde0NGjRzVq1Cib/mazWU8++aR27Nihn376SUuXLtXKlSt17Ngxde7cWb/88osGDhxYZD1HjhzRiy++KE9PT/373//WV199pRUrVmjv3r264447NHLkSB07dsxmzJUrV9SvXz9lZWUpNjZWO3bsUFxcnH788Ue1bt1aixcv1kcffVS2nQgAAAAADnLJcJibm2s9y/bee+/J19fX2jZq1Cg1adJEmzdv1u7du0uc6+zZs/rss8/k4eGhOXPmyN3d3do2bdo0BQUFaeHChTp//rx1eceOHRUXF6eWLVvazGWxWDRv3jxJV8/2nTx50qZ95syZys/P13PPPafWrVtbl9evX1+vvvqq8vLyNHPmTJsxy5cv108//aSmTZtq5MiR1uW+vr7WfTB9+vQStxMAAAAArodLhsOtW7cqPT1ddevW1T333FOkvVevXpKu3ttXknXr1qmgoEDt2rVTcHCwTZunp6e6deum/Px8rV271qHaatSooaCgIEnSmTNnbNoK7yssrM+Rmq81pnnz5goPD9eBAweUlJTkUH0AAAAAUBYuGQ737dsn6Wo4sqdw+f79+2/oXJKUlpam1NRUSVL16tVtlicnJ0uS3UAbGhqqwMBAnTx5UhkZGRVWHwAAAACUhUuGw8KQVbNmTbvthcv/eFlnRc8lXb3MNS8vT40bN1adOnWKrKdq1aqqXLmyw+sq7/oAAAAAoCzcS+5y42VmZkqSfHx87LYXhq+LFy/e0Ln27NmjKVOmSJLeeuutUq2nuHWVR305OTnKycmxfv37M5MAAAAA4AiXPHPois6dO6eoqChlZ2drxIgRevjhh51dktXUqVNlsVisn9DQUGeXBAAAAOAm45LhsPDppJcvX7bbfunSJUmSn5/fDZnr4sWL6tq1q5KSkvTEE0/YfXpoSespbl3lUd+4ceOUnp5u/Zw6darYvgAAAABgj0uGw1q1akmSTp8+bbe9cHlYWFiFz5Wdna3u3bsrMTFRDz30kBYuXCizuehuK1xPamqqNdA5sq7y2FZPT0/5+/vbfAAAAACgNFwyHDZt2lSSlJiYaLe9cHmTJk0qdK68vDz17t1b8fHxatOmjZYtWyYPDw+781SpUsUa9Pbs2VOk/dSpU0pJSVFYWJhNeCvPbQUAAACAsnLJcNi2bVtZLBYdP35ce/fuLdK+ZMkSSVK3bt1KnKtLly4ym83asmWLzYvupasPclm1apXc3NzUtWtXmzbDMDRw4ECtXLlSzZo105o1a4p9CmmhRx55xKY+R2q+1pg9e/boxIkTuvvuu1W7du1rbygAAAAAXAeXDIceHh4aOnSoJGnIkCE2l2nGxsZq//79at++vVq0aGFdPnv2bEVERGjcuHE2c4WEhKhPnz7Kzc3V4MGDlZeXZ2176aWXdOHCBfXv31/VqlWzGTdixAgtXLhQERER2rBhg6pUqVJi3cOHD5ebm5vef/99JSQkWJf/+OOPeuONN+Tu7q7hw4fbjOnZs6fq1Kmjffv26Z133rEuv3TpkoYMGSJJGj16dInrBgAAAIDr4ZKvspCk1157TV9//bW2bdumevXqqV27djp58qR27NihoKAgzZs3z6Z/SkqKjhw5orNnzxaZa8aMGUpISNDSpUsVERGhyMhIHTx4UAcOHFC9evUUGxtr0//LL7/UrFmzJF19ef2YMWPs1jh27FhFRERYv27QoIGmTZumUaNGqV27dnrwwQfl4eGhDRs2KCsrS7NmzdJdd91lM0elSpW0cOFCderUSaNGjVJcXJzCwsK0ZcsWnT17Vr169dKAAQPKtA8BAAAAwFEuGw69vLy0adMmTZ06VZ9++qlWrFihgIAARUdHa/LkycW+NN6ewMBA7dy5UzExMVqxYoWWL1+u4OBgDRs2TBMnTixyVjA1NdX6/zdu3FjsvNHR0TbhUJJGjhypu+66S9OmTdOWLVskSZGRkXrppZf06KOP2p2nTZs2+u677zRhwgTFx8dr3759qlu3rsaMGaPhw4fLZDI5vK0AAAAAUBYuGw4lydvbW5MmTdKkSZNK7BsTE6OYmJhi2wMCAjRr1izrGcFriY6OVnR0dCkqtdWtWzeH7of8vUaNGtm97xAAAAAAbgSXvOcQAAAAAHBjEQ4BAAAAAIRDAAAAAADhEAAAAAAgwiEAAAAAQIRDAAAAAIAIhwAAAAAAEQ4BAAAAACIcAgAAAABEOAQAAAAAiHAIAAAAABDhEAAAAAAgwiEAAAAAQIRDAAAAAIAIhwAAAAAAEQ4BAAAAACIcAgAAAABEOAQAAAAAiHAIAAAAABDhEAAAAAAgwiEAAAAAQIRDAAAAAIAIhwAAAAAAEQ4BAAAAACIcAgAAAABEOAQAAAAAiHAIAAAAABDhEAAAAAAgwiEAAAAAQIRDAAAAAIAIhwAAAAAAEQ4BAAAAACIcAgAAAABEOAQAAAAAiHAIAAAAABDhEAAAAAAgwiEAAAAAQIRDAAAAAIAIhwAAAAAAEQ4BAAAAACIcAgAAAABEOAQAAAAAiHAIAAAAABDhEAAAAAAgwiEAAAAAQIRDAAAAAIAIhwAAAAAAEQ4BAAAAAHLxcJiVlaXx48erfv368vLyUo0aNTRo0CD9/PPPpZ4rNTVVw4cPV1hYmDw9PRUWFqYRI0YoLS3Nbv8jR47onXfeUZ8+fVS3bl2ZTCaZTCYlJSUVu47atWtb+xX3CQ8PtxmTlJR0zf7Vq1cv9bYCAAAAQGm5O7uA4mRnZ6tjx45KSEhQSEiIHnvsMSUlJWn+/PlavXq1EhISigSt4qSkpKh169Y6duyYwsPD1aNHDx08eFAzZ87UV199pe3btysgIMBmzD//+U/NnDmzVDX36tVLKSkpdts2b96spKQktWvXzm57cHCwunTpUmS5xWIpVQ0AAAAAUBYuGw6nTJmihIQEtW7dWhs2bJCvr68kKTY2VqNHj9agQYMUHx/v0FwjRozQsWPHFBUVpbi4OLm7X93sYcOG6d1339WoUaO0YMECmzGNGzfWyy+/rHvvvVeRkZHq3Lmzjhw5cs31/OMf/7C7vKCgQDVr1pQk/c///I/dPhEREUVqAAAAAIAbxSUvK83NzdXs2bMlSe+99541GErSqFGj1KRJE23evFm7d+8uca6zZ8/qs88+k4eHh+bMmWMNhpI0bdo0BQUFaeHChTp//rzNuL/+9a9688039fjjjyssLOy6tuebb77R2bNndeedd6pjx47XNRcAAAAAVASXDIdbt25Venq66tatq3vuuadIe69evSRJq1atKnGudevWqaCgQO3atVNwcLBNm6enp7p166b8/HytXbu2fIq3Y+HChZKkvn37ymx2yV0OAAAA4DbnkpeV7tu3T5LUvHlzu+2Fy/fv318uc82bN8+hucoiKytLy5cvlyT179+/2H7nzp3ThAkTdPbsWVksFrVq1Urdu3eXh4dHhdQFAAAAAL/nkuEwOTlZkqz36f1R4fKTJ0/e0LnKYsWKFbp48aKaNGmiJk2aFNvv8OHDmjRpks2yWrVqafHixWrZsuU115GTk6OcnBzr1xkZGddXNAAAAIDbjkte45iZmSlJ8vHxsdteuXJlSdLFixdv6Fxl8cknn0gq/kE0np6eev755xUfH69z584pIyND27dvV9euXZWcnKzOnTuXGFynTp0qi8Vi/YSGhpb7dgAAAAC4tblkOLxVnD9/Xhs3bpTZbFbfvn3t9gkJCdGcOXPUvn17VatWTX5+frrvvvu0Zs0a9e3bV2lpafr73/9+zfWMGzdO6enp1s+pU6cqYnMAAAAA3MJcMhwWPp308uXLdtsvXbokSfLz87uhc5XW559/rry8PD3wwAOqUaNGqce/8sorkqT169dfs5+np6f8/f1tPgAAAABQGi4ZDmvVqiVJOn36tN32wuWOvGKiPOcqrcKnlF7rQTTXUq9ePUlXX8cBAAAAABXJJcNh06ZNJUmJiYl22wuXX+sBLxUxV2kcPXpU3333nXx8fBQVFVWmOVJTUyX9975IAAAAAKgoLhkO27ZtK4vFouPHj2vv3r1F2pcsWSJJ6tatW4lzdenSRWazWVu2bCnyovucnBytWrVKbm5u6tq1a7nUXqjwrGHPnj2tl7aW1tKlSyUV/xoOAAAAACgvLhkOPTw8NHToUEnSkCFDrPcFSlJsbKz279+v9u3bq0WLFtbls2fPVkREhMaNG2czV0hIiPr06aPc3FwNHjxYeXl51raXXnpJFy5cUP/+/VWtWrVy3YZFixZJKv4ppYXmzp2rw4cPF1m+bNkyjR07VtLVfQAAAAAAFckl33MoSa+99pq+/vprbdu2TfXq1VO7du108uRJ7dixQ0FBQZo3b55N/5SUFB05csTu/XkzZsxQQkKCli5dqoiICEVGRurgwYM6cOCA6tWrp9jY2CJjEhMTNXjwYOvXha+T6Nmzpzw9PSVJTz/9tJ5++ukiY7dt26YTJ06oevXq6tSp0zW3c9GiRXrmmWfUpEkT1a9fXwUFBTp06JA1MI4ZM0Y9e/YsYW8BAAAAwPVxyTOHkuTl5aVNmzbp9ddfl4+Pj1asWKGTJ08qOjpaiYmJCg8Pd3iuwMBA7dy5Uy+88IJyc3O1fPlypaena9iwYdq5c6cCAgKKjMnIyNCOHTusn+zsbEnS3r17rcuKe8hN4SWlffr0kZub2zVr+9vf/qZevXrp8uXL2rBhg1atWqWMjAxFRUVp48aNevvttx3eTgAAAAAoK5c9cyhJ3t7emjRpkiZNmlRi35iYGMXExBTbHhAQoFmzZmnWrFkOrbtDhw4yDMPRUm3MmTNHc+bMcahvv3791K9fvzKtBwAAAADKi8ueOQQAAAAA3DiEQwAAAAAA4RAAAAAAQDgEAAAAAIhwCAAAAAAQ4RAAAAAAIMIhAAAAAECEQwAAAACACIcAAAAAABEOAQAAAAAiHAIAAAAARDgEAAAAAIhwCAAAAAAQ4RAAAAAAIMIhAAAAAECEQwAAAACACIcAAAAAABEOAQAAAAAiHAIAAAAARDgEAAAAAIhwCAAAAAAQ4RAAAAAAIMIhAAAAAECEQwAAAACACIcAAAAAABEOAQAAAAAiHAIAAAAARDgEAAAAAIhwCAAAAAAQ4RAAAAAAIMIhAAAAAECEQwAAAACACIcAAAAAABEOAQAAAAAiHAIAAAAARDgEAAAAAIhwCAAAAAAQ4RAAAAAAIMIhAAAAAECEQwAAAACACIcAAAAAABEOAQAAAAAiHAIAAAAARDgEAAAAAIhwCAAAAAAQ4RAAAAAAIMIhAAAAAEAuHg6zsrI0fvx41a9fX15eXqpRo4YGDRqkn3/+udRzpaamavjw4QoLC5Onp6fCwsI0YsQIpaWl2e1/5MgRvfPOO+rTp4/q1q0rk8kkk8mkpKSkYtexYMECaz97n6eeeqrYsQcPHtQTTzyhoKAgeXt7q3HjxpoxY4YKCgpKva0AAAAAUFruzi6gONnZ2erYsaMSEhIUEhKixx57TElJSZo/f75Wr16thIQEhYeHOzRXSkqKWrdurWPHjik8PFw9evTQwYMHNXPmTH311Vfavn27AgICbMb885//1MyZM8tUe9OmTdWsWbMiy1u1amW3//bt2/XAAw8oKytLLVu2VO3atfXvf/9bI0eO1LZt2xQXFyeTyVSmWgAAAADAES4bDqdMmaKEhAS1bt1aGzZskK+vryQpNjZWo0eP1qBBgxQfH+/QXCNGjNCxY8cUFRWluLg4ubtf3exhw4bp3Xff1ahRo7RgwQKbMY0bN9bLL7+se++9V5GRkercubOOHDni0Pp69OihmJgYh/peuXJF/fr1U1ZWlmJjYzVy5EhJUmZmph566CEtXrxYXbt2VXR0tEPzAQAAAEBZuORlpbm5uZo9e7Yk6b333rMGQ0kaNWqUmjRpos2bN2v37t0lznX27Fl99tln8vDw0Jw5c6zBUJKmTZumoKAgLVy4UOfPn7cZ99e//lVvvvmmHn/8cYWFhZXTlhW1fPly/fTTT2ratKk1GEqSr6+vdR9Mnz69wtYPAAAAAJKLhsOtW7cqPT1ddevW1T333FOkvVevXpKkVatWlTjXunXrVFBQoHbt2ik4ONimzdPTU926dVN+fr7Wrl1bPsWX0po1ayT9d5t+r3nz5goPD9eBAweuea8jAAAAAFwvlwyH+/btk3Q1HNlTuHz//v03dC5H7d69W2PGjNGzzz6rCRMmaPPmzS5VHwAAAAD8kUvec5icnCxJqlmzpt32wuUnT568oXM5avXq1Vq9erX160mTJql9+/aKi4srcvayPOrLyclRTk6O9euMjIwy1w4AAADg9uSSZw4zMzMlST4+PnbbK1euLEm6ePHiDZ2rJCEhIYqJidGePXuUnp6uX375RStXrlRERIQ2b96sRx99VPn5+eVe39SpU2WxWKyf0NDQ694WAAAAALcXlwyHN6vOnTtrwoQJatasmfz9/RUcHKxu3brpu+++U/369bVr1y598cUX5b7ecePGKT093fo5depUua8DAAAAwK3NJcNh4dNJL1++bLf90qVLkiQ/P78bOldZ+fr6atiwYZKk9evXl3t9np6e8vf3t/kAAAAAQGm4ZDisVauWJOn06dN22wuXO/KKifKc63rUq1dP0tVXa/yeq9QHAAAA4PbmkuGwadOmkqTExES77YXLmzRpckPnuh6pqamS/nsPYSFXqQ8AAADA7c0lw2Hbtm1lsVh0/Phx7d27t0j7kiVLJEndunUrca4uXbrIbDZry5YtRV50n5OTo1WrVsnNzU1du3Ytl9qLs3TpUklFX1nxyCOPSPrvNv3enj17dOLECd19992qXbt2hdYHAAAA4PbmkuHQw8NDQ4cOlSQNGTLEet+dJMXGxmr//v1q3769WrRoYV0+e/ZsRUREaNy4cTZzhYSEqE+fPsrNzdXgwYOVl5dnbXvppZd04cIF9e/fX9WqVbvuuqdOnaqUlBSbZVeuXNHEiRO1ePFieXt7a+DAgTbtPXv2VJ06dbRv3z6988471uWXLl3SkCFDJEmjR4++7toAAAAA4Fpc8j2HkvTaa6/p66+/1rZt21SvXj21a9dOJ0+e1I4dOxQUFKR58+bZ9E9JSdGRI0eK3NMnSTNmzFBCQoKWLl2qiIgIRUZG6uDBgzpw4IDq1aun2NjYImMSExM1ePBg69eF7xns2bOnPD09JUlPP/20nn76aWufV155RRMnTlRkZKRCQ0OVkZGhvXv36syZM/Ly8tLChQt155132qynUqVKWrhwoTp16qRRo0YpLi5OYWFh2rJli86ePatevXppwIABZd+RAAAAAOAAlzxzKEleXl7atGmTXn/9dfn4+GjFihU6efKkoqOjlZiYqPDwcIfnCgwM1M6dO/XCCy8oNzdXy5cvV3p6uoYNG6adO3cqICCgyJiMjAzt2LHD+snOzpYk7d2717rsjw+RGT9+vP7yl7/o1KlT+vLLL/Xtt9/Kx8dHzz77rPbu3auoqCi79bVp00bfffedHn/8cR07dkwrV65UQECAYmNjFRcXJ5PJVIo9BwAAAACl57JnDiXJ29tbkyZN0qRJk0rsGxMTo5iYmGLbAwICNGvWLM2aNcuhdXfo0EGGYThaqiRp4sSJper/e40aNbJ73yEAAAAA3Ague+YQAAAAAHDjEA4BAAAAAIRDAAAAAADhEAAAAAAgwiEAAAAAQIRDAAAAAIAIhwAAAAAAEQ4BAAAAACIcAgAAAABEOAQAAAAAiHAIAAAAABDhEAAAAAAgwiEAAAAAQIRDAAAAAIAIhwAAAAAAEQ4BAAAAACIcAgAAAABEOAQAAAAAiHAIAAAAABDhEAAAAAAgwiEAAAAAQIRDAAAAAIAIhwAAAAAAEQ4BAAAAACIcAgAAAABEOAQAAAAAiHAIAAAAAJDk7uwCAAAAAKCskpOTlZKS4uwyrktgYKBq1arl7DIIhwAAAABuTsnJyWrQsKGyL192dinXxcvHR0d++MHpAZFwCAAAAOCmlJKSouzLl9VwxhvyuSvc2eWUyeVjJ/TDiFeVkpJCOAQAAACA6+FzV7j87m7o7DJuejyQBgAAAABAOAQAAAAAEA4BAAAAACIcAgAAAABEOAQAAAAAiHAIAAAAABDhEAAAAAAgwiEAAAAAQIRDAAAAAIAIhwAAAAAAEQ4BAAAAACIcAgAAAABEOAQAAAAAiHAIAAAAABDhEAAAAAAgwiEAAAAAQC4eDrOysjR+/HjVr19fXl5eqlGjhgYNGqSff/651HOlpqZq+PDhCgsLk6enp8LCwjRixAilpaXZ7X/kyBG988476tOnj+rWrSuTySSTyaSkpKRi17F7927FxMSoTZs2qlKlijw8PBQaGqr+/ftr//79dsckJSVZ57b3qV69eqm3FQAAAABKy93ZBRQnOztbHTt2VEJCgkJCQvTYY48pKSlJ8+fP1+rVq5WQkKDw8HCH5kpJSVHr1q117NgxhYeHq0ePHjp48KBmzpypr776Stu3b1dAQIDNmH/+85+aOXOmw/Xm5eUpMjJSkhQQEKA2bdqocuXK2rNnjxYtWqTFixdr0aJF6tWrl93xwcHB6tKlS5HlFovF4RoAAAAAoKxcNhxOmTJFCQkJat26tTZs2CBfX19JUmxsrEaPHq1BgwYpPj7eoblGjBihY8eOKSoqSnFxcXJ3v7rZw4YN07vvvqtRo0ZpwYIFNmMaN26sl19+Wffee68iIyPVuXNnHTly5Jrruffee/Xqq6/q0UcflZubmySpoKBA48eP1xtvvKFBgwapQ4cOCgwMLDI2IiKiSA0AAAAAcKO45GWlubm5mj17tiTpvffeswZDSRo1apSaNGmizZs3a/fu3SXOdfbsWX322Wfy8PDQnDlzrMFQkqZNm6agoCAtXLhQ58+ftxn317/+VW+++aYef/xxhYWFlbged3d37dy5U4899pg1GEqS2WzW5MmT1aBBA128eFFr1qwpcS4AAAAAuNFcMhxu3bpV6enpqlu3ru65554i7YWXZq5atarEudatW6eCggK1a9dOwcHBNm2enp7q1q2b8vPztXbt2vIp3g6TyaQmTZpIks6cOVNh6wEAAACAsnLJy0r37dsnSWrevLnd9sLlxT3kpbRzzZs3z6G5rseJEyckqdgHzJw7d04TJkzQ2bNnZbFY1KpVK3Xv3l0eHh4VWhcAAAAASC4aDpOTkyVJNWvWtNteuPzkyZM3dK6y+s9//qPdu3fLw8PD7kNnJOnw4cOaNGmSzbJatWpp8eLFatmyZYXVBgAAAACSi15WmpmZKUny8fGx2165cmVJ0sWLF2/oXGWRkZGhQYMGSZJGjhypkJAQm3ZPT089//zzio+P17lz55SRkaHt27era9euSk5OVufOnUsMrjk5OcrIyLD5AAAAAEBpuGQ4vFXk5+erX79++vHHH9WyZcsiZwYlKSQkRHPmzFH79u1VrVo1+fn56b777tOaNWvUt29fpaWl6e9///s11zN16lRZLBbrJzQ0tKI2CQAAAMAtyiXDYeHTSS9fvmy3/dKlS5IkPz+/GzpXaT3//PNavXq1GjRooDVr1pT6/sFXXnlFkrR+/fpr9hs3bpzS09Otn1OnTpW5ZgAAAAC3J5e857BWrVqSpNOnT9ttL1zuyCsmynOu0hg7dqzmzp2r0NBQbdy40e67DUtSr149SVdfx3Etnp6e8vT0LFOdAAAAACC56JnDpk2bSpISExPtthcuL3w9xI2ay1Fvv/223nrrLVWrVk0bN24s82Weqampkv57XyQAAAAAVBSXDIdt27aVxWLR8ePHtXfv3iLtS5YskSR169atxLm6dOkis9msLVu2FHnRfU5OjlatWiU3Nzd17dq1XGqfO3euXn75ZVWpUkXr169XgwYNyjzX0qVLJRX/Gg4AAAAAKC8uGQ49PDw0dOhQSdKQIUOs9wVKUmxsrPbv36/27durRYsW1uWzZ89WRESExo0bZzNXSEiI+vTpo9zcXA0ePFh5eXnWtpdeekkXLlxQ//79Va1ateuue8mSJXruuefk6+urtWvXqlmzZiWOmTt3rg4fPlxk+bJlyzR27FhJV/cBAAAAAFQkl7znUJJee+01ff3119q2bZvq1aundu3a6eTJk9qxY4eCgoI0b948m/4pKSk6cuSI3fvzZsyYoYSEBC1dulQRERGKjIzUwYMHdeDAAdWrV0+xsbFFxiQmJmrw4MHWrwtfJ9GzZ0/r/X1PP/20nn76aUnS+fPn1a9fPxUUFKhOnTr64IMP9MEHHxSZt0ePHurRo4f160WLFumZZ55RkyZNVL9+fRUUFOjQoUPWwDhmzBj17NmzlHsPAAAAAErHZcOhl5eXNm3apKlTp+rTTz/VihUrFBAQoOjoaE2ePLnYl9rbExgYqJ07dyomJkYrVqzQ8uXLFRwcrGHDhmnixImqUqVKkTEZGRnasWNHkeW/v8z19y+0v3z5snJzcyVJ33//vb7//nu7tdSuXdsmHP7tb39TUFCQ9u7dqw0bNigrK0tBQUGKiorS888/r06dOjm8nQAAAABQVi4bDiXJ29tbkyZNsvt+wD+KiYlRTExMse0BAQGaNWuWZs2a5dC6O3ToIMMwHC1VtWvXLlX/Qv369VO/fv1KPQ4AAAAAypNL3nMIAAAAALixCIcAAAAAAMIhAAAAAIBwCAAAAAAQ4RAAAAAAIMIhAAAAAECEQwAAAACACIcAAAAAAEnuzi4A5c8wDElSRkZGhcyfmZl59X+zpYzLFbKKCpeZ/f//NzOzwvZTRSo8BsrNlbKznVtMWeXmSuIYOBXHwLlu8v0vcQxcAcfA+QqPQf7ly8q7mOnkakov//LVP+Zu1mNws+9/qeKPQeGchRnhWkyGI71wUzl9+rRCQ0OdXQYAAAAAF3Hq1CnVrFnzmn0Ih7eggoICnTlzRn5+fjKZTM4up0wyMjIUGhqqU6dOyd/f39nl3HbY/87HMXA+joHzcQycj2PgfBwD57vZj4FhGLp48aJq1Kghs/nadxVyWektyGw2l/ivAjcLf3//m/KH8FbB/nc+joHzcQycj2PgfBwD5+MYON/NfAwsFotD/XggDQAAAACAcAgAAAAAIBzCRXl6emrChAny9PR0dim3Jfa/83EMnI9j4HwcA+fjGDgfx8D5bqdjwANpAAAAAACcOQQAAAAAEA4BAAAAACIcAgAAAABEOAQAAAAAiHAIAAAAABDhEACcbtasWfrwww+dXQYAALjN8SoLAHCySpUqqWvXrvryyy+dXQoAALiNuTu7AKBQXl6ekpKSlJqaKpPJpICAANWuXVtmMye4bwT2v/NUr15dXl5ezi7jtnblyhUtXrxYmzZt0g8//GDzc/CnP/1J999/vx5//HG5u/OfzYp05swZHTp0qMj+r169urNLu22cPn1amzdvtvtz8Je//EU1atRwdom3tAsXLmju3LnF/i7q2LGj/vrXv+qOO+5wdqm3rG3btl3zvwWtWrVydokVywCcbNWqVcaDDz5oVK5c2TCbzTYfX19f4+GHHzbWrVvn7DJvWex/5/vrX/9q1KhRw8jJyXF2Kbel9evXGzVq1DDMZrNhMpnsfsxms1GrVi3jm2++cXa5t5y8vDzj3XffNerVq1fkd1Dhp2HDhsb7779v5OfnO7vcW9bp06eNxx9/3HBzc7Pu999//5vNZsPd3d146qmnjLNnzzq73FvShx9+aPj4+JT4u8jX19f46KOPnF3uLWffvn1GixYtinz///Hn4L777jMOHjzo7HIrDJeVwmmuXLmiQYMG6dNPP1Xht6Gnp6eqVq0qwzCUmpqq3NxcSZLJZNKAAQM0d+5cubm5ObPsWwb733WcO3dO9957r1q1aqVZs2YpJCTE2SXdNtatW6du3bopPz9fYWFheuKJJ9SsWTMFBgbKMAylpKRoz549WrJkiZKTk+Xu7q61a9eqU6dOzi79lnDx4kU99thj2rx5swzDkNlsVnh4uM3+P3HihAzDkMlk0gMPPKDly5ercuXKzi79lvLjjz+qffv2OnfunCSpRYsWdn8O9uzZI8MwdOedd2rz5s0KDw93cuW3jo8++kiDBg2SYRj685//rKeeesruMfj888+1bds2mUwmffLJJ+rbt6+zS78lfPfdd+rYsaMuXbqkypUr6+GHH7a7/9evX69Lly7Jz89P8fHxuueee5xdevlzQiAFDMMwjFGjRhkmk8nw8/MzXn/9dePgwYNGQUGBtb2goMD4/vvvjVdffdXw9fU1zGaz8dJLLzmx4lsL+991DBw40Hj00UcNs9lseHt7G23btjX69OljDBw4sMhn0KBBzi73lpGdnW1Ur17dMJlMxhtvvHHNs1L5+fnGpEmTDJPJZNx5552c5S0n/fv3t+7Tf/3rX8bFixeL9MnIyDDmzp1rhISEGGaz2YiOjnZCpbe2yMhIw2QyGZ07dzZ+/PHHYvsdPXrUeOihhwyTyWTcd999N7DCW1t6errh7+9vVKpUyVi0aFGJ/T/++GPD3d3dqFq1qt2fGZROXl6eUa9ePcNkMhlPP/20kZ6eXmzf9PR0469//athMpmMiIgIm7+bbhWEQzhFUlKS4ebmZgQFBRmHDx8usf8PP/xgBAYGGu7u7kZycvINqPDWxv53LcVdPlTcJUUoHx999JFhMpmMF154weExQ4cONcxms7Fw4cIKrOz2cPDgQcNkMhnh4eHG+fPnS+x/7tw5o3bt2obZbDYOHTp0Ayq8PWzcuNEwmUxGp06dHPpDNz8/3+jYsaNhNpuNr7/++gZUeOt77733DJPJZEycONHhMTExMYbZbDbef//9Cqzs9rB06VLDZDIZTz31lMNjnnzyScNsNhtffvllBVbmHDxpAk6xYMECGYahGTNmqEGDBiX2j4iI0IwZM5Sfn6+PPvroBlR4a2P/u5ZNmzY5/Pn222+dXe4tY/369XJ3d9drr73m8JjXXntNZrNZa9eurcDKbg8ff/yxTCaT3n33XQUFBZXYv1q1anrvvfdkGIY++eSTG1Dh7WHJkiUymUyKjY2VyWQqsb/ZbNY777wjwzC0dOnSG1Dhre/bb7+Vh4eHRo4c6fCYkSNHqlKlSvr6668rsLLbw6pVq2Q2m/X3v//d4TFTp06VJK1YsaKCqnIe7jmEU3Tu3Fk7d+7Ur7/+6vDTMPPz83XHHXfovvvu07p16yq4wlsb+x+QGjZsKG9vbyUmJpZqXPPmzZWdna1Dhw5VUGW3h/vvv1+HDh2y3ufmqGrVqqlRo0batGlTBVV2e4mMjFRqaqqOHz9eqnF33XWXqlatqu+++66CKrt91KtXT3fccYcSEhJKNa5169ZKSUnRjz/+WEGV3R6aNGmi/Px8HTx4sFTjGjVqJHd3d+3bt6+CKnMOzhzCKX744Qc1b968VK9JcHNzU2RkJH+QlQP2PyCdP39ederUKfW4OnXqlDrQoKijR4+W6WEOzZs354/hcnTq1CndfffdpR7XqFEjJScnV0BFt59ff/1VoaGhpR5Xs2ZN/frrrxVQ0e3l7Nmzql+/fqnH1a9fX2fOnKmAipyLcAinSEtLU3BwcKnHBQUFKS0trfwLus2w/13Tr7/+qpkzZ6pfv37q3Lmz3n77bWvbwYMHtXLlSl2+fNmJFd5aMjIy5O/vX+pxfn5+ysjIqICKbi/p6ekKDAws9bg77riD30PlKD09XVWqVCn1OIvFovT09PIv6DaUkZEhPz+/Uo/z9fXVxYsXK6Ci20t6erosFkupx/n7+9+S/y3gbb5wiszMzDK99NvLy0uXLl2qgIpuL+x/17N48WI9/fTTyszMtD62/84777S2//zzz+rZs6c++ugj9e/f34mV3jry8/Mdusfqj0wmkwoKCiqgotvL5cuX5eHhUepxHh4eysrKqoCKbk+5ubllekWRm5ubrly5UgEV3X6u5/cJv4uuX15eXqmupCpkNpuVl5dXARU5F+EQAJxs+/bt6tu3r/z9/TV9+nT9+c9/VsuWLW36PPDAA7JYLFq2bBnhsBxlZmaW+tK4zMzMCqoGwO3ql19+0b///e9SjwHKGw+kgVOYzWY1a9ZMPXr0KNW45cuXa//+/crPz6+Ywm4T7H/X0q1bN23YsEHbt29X8+bNJV09RtHR0Zo3b561X6dOnZScnKyjR486q9RbitlsLtOZw0L8HFwfs9msu+66S3/+859LNW7Lli06ceIE+7+cmM1m+fr6lvoS35SUFF26dInjUA7K+ruo8CoTjsH14b8FtjhzCKfZu3ev9u7d63B/k8lk/UWI68f+dx3btm1T69atrcGwONWrV9eOHTtuUFW3vlq1avH97GTHjh3TsWPHSj2O41a+MjMzy3RGnONQPv7yl7+wL52srOfKbsXjRjiEU0yYMMHZJdzW2P+u5fLlyw695y01NfUGVHP7SEpKcnYJt7X58+c7uwRI+umnn5xdwm0vPj7e2SXc1rhv0xaXlQKAk911113y8PCweU3IHy8rNQxDtWvXlsVi0f79+51VKgAAuIXxKgsAcLIuXbroyJEj+vzzz4vt8+GHH+rUqVN65JFHbmBlAABnyc/P14ULF3ThwoVb7r42uC7OHMJl/Pbbb9q3b59SUlIkSYGBgWratKkCAgKcXNntgf3vPKdPn1aTJk2UmZmpkSNHqmfPnmrTpo2eeOIJjR07VsuXL9fbb78ti8Wi77//XtWqVXN2ybecrKwsffTRR/rqq6+0d+9em5+DZs2aqWvXrvqf//kf+fj4OLnSW5dhGNZ7of+4/5s1a3ZL3tvjig4fPlzsz8HDDz+siIgIJ1d4azt16pTee+89ffXVVzp48KD1XjiTyaRGjRqpa9euGjx4sEJDQ51c6a1r3bp11/wZ6NKli5MrrGAG4GRffPGF0apVK8PNzc0wm81FPvfdd5+xePFiZ5d5y2L/u4Zt27YZISEhhslkKnIMTCaTERwcbCQkJDi7zFvSypUrjWrVqln3tb2P2Ww2goODjVWrVjm73FtOfn6+8fbbbxt33nmn3d9BZrPZuPPOO41p06YZeXl5zi73lnXq1Cmja9euNr93/vgzYDabjUceecRITk52drm3pNmzZxve3t4l/i7y9vY2Zs+e7exybzm7d+827r777mJ/Bgr3f+PGjY3du3c7u9wKw5lDOE1ubq769eunZcuWWZ+CWbt2betZkfPnzyspKcnaFhUVpUWLFpXppckoiv3vei5evKh//etf2rhxo5KSklRQUKCaNWvqwQcf1LPPPiuLxeLsEm85n376qf73f/9XBQUFioyMVN++fdWiRQubn4PExEQtWrRIu3btktls1ieffKI+ffo4ufJbQ1pamrp27aodO3bIMAxVrVpVzZo1s9n/+/bt02+//SaTyaRWrVrpq6++4mehnO3fv18dO3bUb7/9Jk9PT3Xu3Nnuz8H69euVnZ2tO+64Q5s2bdLdd9/t5MpvHdOmTdPYsWMlSVFRUerXr1+xv4uWLl0qSXrrrbf04osvOq3mW8m3336rRx55RDk5OQoJCdETTzxhd/8vXrxYZ86ckaenp9auXav777/fyZVXACcGU9zmnnzySesZkdmzZxu//fZbkT6//fab8d577xnBwcGG2Ww2evfu7YRKb03sf9zuTp06Zfj4+Bienp7GggULSuz/8ccfG56enoaPj49x6tSpG1Dhra9Dhw6GyWQyGjVqZKxevdrIz88v0qegoMBYs2aN0ahRI8NkMhn333+/Eyq9dV2+fNkIDQ01TCaTER0dbaSkpBTb99dffzUGDRpkmEwmIywszLh8+fINrPTWdejQIcPd3d2oWrWqsWnTphL7x8fHG1WqVDEqVapkHDp0qOILvMX9+uuvRkBAgGE2m42YmBgjNze32L5XrlwxJk2aZJjNZuOOO+6w+7fTzY5wCKf46quvjP/X3n1HRXW1bwO+zxA6CKgQQEBBBbuIoICIiAKKvaCIoogaa2Jv0dhirK/dWBK7EMCGFQWVXjSAYiwIiDSxgEpRQer5/eHHfCECgmHmTM4811qsNzNnn7XuFxXmOXvvZzMMw3bp0oXNycn54vicnBy2S5curEAgYAMDA8WQkN/o+08Iyy5cuJAVCATs4cOH633PkSNHWIZh2EWLFokwmXTw9fVlGYZh7ezs6lVkFBcXs3Z2dqxAIGD9/PzEkFA6bN26lWUYhv3+++/rfc/333/PCgQC9n//+58Ik0mPmTNnsgKBoEHL1i9dusQyDMPOmjVLhMmkw+rVq1mGYdhffvml3vds2LCBZRiGXbNmjQiTcYOWlRJOjBkzBufOncNff/2FDh061OueR48eoXPnzhg9ejT8/PxEnJDf6PvPrczMzH91v4GBQSMlkW6dO3dGYWEhMjIyGnRfy5YtoaqqigcPHogomXQYMmQIgoKCkJqaCj09vXrdk5WVhdatW2PAgAG4ePGiiBNKh169euHhw4d4+fIlFBQU6nVPcXExdHR00LFjR0RFRYk4If+ZmJiAYRg8fvy4Qfe1a9cOlZWVSE5OFlEy6dC9e3c8e/YML168gEBQv4McKioqoKurCz09PcTHx4s4oXh9w3UAIp1iYmJgYWFR78IEADp06IAePXogOjpahMmkA33/udWqVauv7rzIMAzKy8sbOZF0yszMRP/+/Rt8n4WFBYKCgkSQSLrEx8fDysqq3oUhAOjr66NXr16Ii4sTYTLpkpSUBBsbm3oXhgCgqKgIGxsb3Lp1S4TJpEd2dvZXHVPUtWtXXLlyRQSJpMvTp0/Rp0+feheGACAjIwMrKyuEhYWJMBk3qDgknMjNzYWtrW2D7zMyMkJCQkLjB5Iy9P3nlq2t7WfFYUlJifCDloaGBlq2bAngUwHz92Yc8vLyYs/LVxUVFfjmm4b/GpSRkQEtuvn33r5926DCsIquri5iYmJEkEg6ffjw4asa/DRp0gQfPnwQQSLpIyMj81UP/SoqKhpU0JCalZSUQFFRscH3KSoqoqSkRASJuEXFIeGEqqoq8vPzG3xffn4+lJWVGz+QlKHvP7dCQ0OrvX737h3s7e3RqVMnbN26FU5OTtWuBwUFYcmSJSgrK0NgYKAYk/Kbnp7eVz3sSEhIgK6ubuMHkjLq6urIyclp8H25ubnUrbQRaWpqIjU1tcH3PX36FJqamiJIJH1atmyJ2NjYBt/3559/Ch8kkq+nra2NR48eNfi+xMREaGtriyARt+hxA+GEiYkJoqKiUFxcXO97iouLERkZSQfwNgL6/kuWn376CampqQgODv6sMAQAR0dH3LhxA6mpqVixYgUHCfnJ3t4eT548waVLl+p9z6VLl5CSkvJVy1FJdZ06dUJkZCTevn1b73vevn2LyMhIdOnSRYTJpIuVlRXi4uIa9OH40aNHiI2NhbW1tQiTSQ9HR0dkZ2fj8OHD9b7nyJEjePbsGQYOHCjCZNLB1tYWDx48aND+2ejoaPz111/o06ePCJNxg4pDwokRI0agsLAQS5Ysqfc9S5cuxfv37zFy5EgRJpMO9P2XLOfOnYO9vT2aN29e65jmzZvD3t4e/v7+YkzGbz/88ANkZGQwceJEhIeHf3F8ZGQkJk2ahG+++Qbff/+9GBLy25gxY/Dx40fMmDGj3st0Z8yYgZKSEowdO1bE6aSHp6cnKisrMW7cOLx58+aL49+8eSM859PT01PU8aTC/PnzoaCggDlz5sDLy+uL4729vTFr1iwoKSlh7ty5YkjIbzNnzgTLsnBzc0NKSsoXxz958gTjxo0DwzCYOXOmGBKKGae9UonUqjpXSSAQsFOmTKnznJi8vDx26tSprEAgYA0MDNji4mIxJuUn+v5LFgUFBXbYsGFfHDds2DBWQUFB9IGkyObNm1mGYVgZGRl27Nix7IULF9js7Gy2tLSULS0tZbOzs9kLFy6wrq6urIyMDMswDLtlyxauY/NCeXk526lTJ1YgELCOjo5sYmJirWMTExNZJycnViAQsJ07d2bLy8vFmJT/qs691dbWZvfv38/m5eV9NiY/P589cOAAq6urS+feioCXlxcrEAhYgUDAWlpasrt27WKjoqLY5ORkNjk5mY2KimJ37drFWlpaCsd5e3tzHZs35s6dyzIMwyopKbFLly5l7927x1ZWVgqvV1ZWsvfu3WOXLVvGKisrswzDsPPmzeMwsejQURaEM/fu3UPfvn1RUFAABQUFODk5wdzcXLiHITc3F3FxcQgMDERxcTHU1dURGhpKy4kaCX3/JUf79u3x7NkzPHr0CPr6+jWOycrKQocOHaCnp4fExEQxJ+S33bt3Y+nSpSgpKam1iyzLspCXl8fmzZvxww8/iDkhf2VmZqJPnz7IyMgAwzDo2rVrjT+H7t27B5Zl0apVK4SHh39VIxtSu5KSEowbNw7nz58HwzBgGAZGRkbV/hyePn0K9tP52Bg+fDh8fX0hJyfHcXJ+OX/+PKZNm4Y3b97U+bOoWbNmOHToEIYNGybmhPzFsiwWLlyIXbt2Cd+Tk5ND06ZNAQB5eXnC5jMsy2LevHnYtm3bV3cel2iclaWEsCybkZHBOjk5sQzDsAzDCJ+GVX1Vve/k5MSmp6dzHZd36PsvGaoOoTY0NGSPHz9ebXb248eP7IkTJ1hDQ0NWIBCwW7du5TApf2VkZLCLFy9mO3bsWO3vvkAgYDt27MguXryY/g2ISEFBATt9+nRWUVHxs59FVa8VFBTY6dOns/n5+VzH5TUfHx/WwsJC+H3/55eFhQXr4+PDdUxee//+Pfvrr7+ygwYNYvX09FhFRUVWUVGR1dPTYwcNGsT++uuv7Pv377mOyVsxMTGsi4sLq6am9tnf/yZNmrAuLi5sdHQ01zFFimYOiUR49OgRAgICcO/ePeGeh2bNmqFr165wdnZu0Hl8pOHo+8+tyspKjB8/Hn5+fsKnkH9/Yg98elLp4uICHx8fal0uYmVlZcjLywPw6VgRWVlZjhNJh7dv3yI4OLjGn0P29vbCJ/hE9F6/fl3jn0Nd+6IJ4ZPKykqkpaVV+zdgaGgoFb9/qTgkhBAJ4efnh3379uH27dsoLS0F8GlZS8+ePTFz5ky4urpynJAQ0fnrr78gEAjQqVMnrqMQQojUouKQSARPT0/Y2Nh8sfPZsWPHEB4ejiNHjogpGSHiV15eXu1p5dcc1E7+nejoaEREROD58+cAPh28bmNjg169enGcjL8EAgHs7OwQHBzMdRSpNnr0aLi7u8PZ2ZlmzTny6tUrJCUlwcTEBN9++63w/arjjB48eAADAwOsWrUKlpaWHCYlfETFIZEIAoEAHh4eXyz6pk2bhiNHjqCiokJMyaRLUVER4uLi8OLFC+HG65pMnDhRjKkIEZ/79+/Dw8MDCQkJACA8YqFquW/Xrl1x7NgxaswkAs2bN4eTkxO8vb25jiLVBAIBGIaBmpoaXFxcMH78eNja2nIdS6rMnz8fu3fvRmJiIoyNjQEAhYWFMDExQU5OjvDnkqKiIhISEtC2bVsu4/7n/ZsjWRiGadD5lP8FVBwSiVDf4nDSpEnw8fERLrkjjYNlWaxatQo7d+5EUVFRneMYhqHiXIRiYmIQERGB7OxsAECLFi3Qu3dvWFlZcZyM/5KSkmBlZYX8/Hzo6elh9OjRaNWqFQAgIyMDZ8+eRWZmJtTV1REdHY127dpxG5hnBg8ejMzMTPz1119cR5Fqly5dgre3Ny5duoTi4mIwDAM9PT24ublh/PjxtOxXDLp164by8nLcv39f+N7u3bsxb948uLm5YfXq1bhy5QoWLFiA7777DgcOHOAw7X9f1QORrymJ+PiZiIpDIhHqUxyyLIsuXbrg7du3wg/OpHGsXbsWa9euhZycHAYNGgQjIyOoqKjUOn716tViTCcdkpOT4e7ujri4OACfz1iZm5vDy8uLnhCL0KhRo+Dv749ly5Zh3bp1ny3nraiowKpVq7Bx40aMGDECZ8+e5SgpP8XGxsLGxgYbNmzAwoULuY4j9d6/f49z587B29sbwcHBqKioAMMw6Ny5M8aPH49x48bRkSIioqWlBSsrK1y4cEH4npOTE0JCQvD8+XNhY6Bu3brh48ePdLzRv1RVHJqZmcHNzQ26uroNun/s2LEiSsYNKg4JZ+zt7YX/HRoaCm1t7VqfxJeXlyM1NRUvX76Eu7s7jh07JqaU0sHAwACFhYWIiYlB+/btuY4jdV68eAEzMzO8evUKurq6cHFxQatWrcAwDNLT03H69GlkZ2dDR0cHcXFx0NHR4ToyLzVt2hQtWrSo9rS+Jp07d0Z2djbevn0rpmTS4cSJEwgLCxMu2x00aBAMDAygoKBQ43ha3i4+OTk58PHxgbe3t/ABlkAggK2tLe0RFQFFRUUMHz4cPj4+AD49mNLQ0EDHjh0RExMjHDdu3DhcvnwZ79694yoqL8yaNQunT5/GmzdvICMjAzs7O4wfPx6jR4+u80E5b4nz3AxC/u7vZ8f8/Tyr2r7k5OTYoUOHsrm5uVxH5x1FRUV26NChXMeQWrNmzWIZhmEXLFjAlpSUfHa9tLSUXbhwIcswDDtnzhwOEkoHFRUV1s3N7Yvj3NzcWBUVFTEkki41/S7459mrVdcFAgHXcaVWSkoKO336dPpzECEjIyPW1NRU+Do0NJRlGIZdtmxZtXGjR49mmzZtKu54vFRWVsZeuHCBdXFxYRUVFVmBQMAqKSmxrq6u7OXLl9ny8nKuI4oNzRwSzmRkZAD4tHzOyMgIo0ePxtatW2scKycnh+bNm1PnNBHp2rUrDAwMcOnSJa6jSCVDQ0MoKCjUuTSIZVl06NABHz9+RFpamhjTSQ8bGxuUl5fj1q1bdY6ztLTEN998g8jISDElkw5r1qwRLqOuD1reLl7v3r3D2bNn8ccffyAkJES4zJRv+60kwYQJE+Dj44Nt27ahX79+mDVrlrCDsrW1tXBcp06dIBAIaJ9uI3v37h3OnDkDLy8vhIWFgWVZNGvWDGPHjsX48eP53yGW09KUkP9nzZo17Pnz57mOIbX279/PKisrs2lpaVxHkUry8vL1nrGSl5cXQyLpFBAQwAoEAvbw4cO1jjly5AgrEAjYK1euiDEZIdwoLS1l/f392dGjRwtnUxiGYdu0acOuXr2aTU5O5joiLz148ED4/a76ntvb21cbk5aWxjIMw06dOpWjlNIhOzub3bJlC9u1a1fhbHn37t25jiVSdHgWkQj0BJhbM2bMQGJiInr37o2ff/4ZDg4OaNGiBdexpEaTJk2QlZX1xXFZWVlo0qSJGBJJJ2VlZcycORPTpk3DsWPHMHbsWLRs2RLAp5UOp06dQmRkJGbOnAkVFRWEh4dXu5/a/RO+CAkJwR9//IGzZ8+ioKAALMtCU1MTU6dOxfjx49GzZ0+uI/Jax44dERERgd27d+P169fo3r07Fi9eXG1MYGAgunbtiuHDh3MTUkro6upi8eLFcHR0xPLly3Ht2jVkZmZyHUukaFkpkQgpKSmIiYlB7969YWhoKHz/1q1bmDdvnvDA1/Xr12PkyJEcJuWvR48eYcyYMV/sesYwDMrLy8WUSjoMGzYMly9fxsWLFzFo0KAaxwQEBGDIkCEYMmQIzp8/L96AUuKf7cz/ucSxtver0PK6xpOdnY2oqKhqR7r06tWLHlqJgZ6eHl68eAGWZaGsrIxhw4Zh/PjxcHR0hIyMDNfxCBGbrKws/PHHH/D29sbDhw/Bsizatm0LT09PLF26lOt4IkPFIZEIM2bMwKFDh5Ceni5sjf3q1SsYGxvj3bt3wg9sMjIyuH37NszMzDhOzC8xMTFwdHTEhw8fwDAMmjZtWmeHLtrz1rhiYmJga2sLhmEwduxYuLm5VTtfz8fHB76+vqisrERERAT/9ztwxMPDo0F73v7p6NGjjZhGOuXm5mL27Nnw9/dHZWVltWsMw2DUqFHYu3cvNDU1OUrIf9988w0cHR0xfvx4jBgxAkpKSlxHkjpNmzZF586dERYWxnUUqVNQUIBTp07B29sbkZGRqKyshJaWFlxdXTF+/HhYWFhwHVHkqDgkEqFTp05QUFAQtsgGgI0bN2LFihVYsGABNmzYgICAAIwaNQrjxo2Dl5cXh2n5p3fv3oiKisLq1asxf/58WrrIAS8vL0yfPl146PTfsSwLRUVFHDx4EBMmTOAoISGiVVBQAEtLSyQlJUFRURGOjo7VjnQJDAxEcXExTExMcOvWLaipqXEdmZdyc3Op+OZYkyZNMGTIEHh7e3MdRSqUlpbi8uXL8PLywtWrV1FSUgJlZWUMHz4c48ePh4ODg1TNmlNxSCRCs2bNYGdnV+1Q6T59+uDPP/9Ebm6ucBbL2toaubm5SElJ4SoqL6moqKBLly6Ijo7mOopUe/bsGX7//XdERkbi+fPnAD7td+jduzemTJkCfX19jhMSIjrLly/H5s2b4eLiUuPs4OvXrzFnzhycOnUKy5Ytw4YNGzhKSohoWVlZQV5eHqGhoVxHkQoaGhooLCyEjIwMHBwcMGHCBAwbNkxqZ82pOCQSQVlZGYMGDcKpU6cAACUlJdDQ0ECPHj2q/XAcP348zp8/jw8fPnCUlJ90dHTQt29f/PHHH1xHIYRIqXbt2qG4uBhPnjyp9diisrIytGnTBgoKCkhKShJzQuny119/4ddff0VERES1vZ+2traYNWsWunTpwnFC/vrjjz8wceJEhIaGwsbGhus4vFe137xz587Q0tJq0L0MwyAwMFBEybhB3UqJRNDT06t2Ts+NGzfw8eNH2NvbVxtXXFwMZWVlccfjPWdnZ+G5VdK0dIKQfyopKYGvry/CwsLw4sULlJSU1DiOYRjcvHlTzOn4LSMjAyNGjKjzPFtZWVn06tUL/v7+YkwmfXbt2oXFixejoqICf59DePz4MR4/fowjR45g69atmDt3Locp+cvGxgZTp06Fk5MTpk6diiFDhsDAwAAKCgo1jjcwMBBzQv5hWfarzov8N/vUJRXNHBKJMHPmTPz222/4/vvv0a9fPyxfvhyJiYm4c+cOunbtKhzXtm1bNGnSBPHx8Rym5Z/c3FxYWVnBxsYGu3btor08YhYcHIy9e/di/vz56N27d41jwsPDsXPnTsybN4+OTBCRzMxM9O/fH6mpqfjSr0Y6/LvxNW3aFObm5ggKCqpznKOjI+Li4vD27VsxJZMu169fh5OTE5SUlDBjxgy4u7tX2/t58uRJHDhwAEVFRQgKCkK/fv24jsw7f++c/KXigzqI/3v/tvFPnz59GimJZKDikEiEzMxMdOvWDfn5+QA+PcEZO3YsfHx8hGMePnyIzp07Y86cOdi9ezdHSfnJ09MTeXl5uHjxIpo0aQJzc3O0aNECAoHgs7EMw+Dw4cMcpOSvsWPHIiAgAM+fP4eqqmqNYwoLC6Grq4uhQ4fS8l8RGT58OC5evAh7e3vMnTsXRkZGdXbtrToDkTQOBwcHhIeHIyoqCubm5jWOiY+Ph5WVFezs7L5YRJKvM3DgQNy8eROhoaGwtraucUxVh2UHBwcEBASIOSH/2dnZNWhGKiQkRIRpiLSh4pBIjGfPnuHQoUPIzc1F9+7d4eHhUa048fLywtmzZ7Fw4UJag9/IaioCa0MzJo3P0NAQ+vr6nx2q/k+2trbIzs5GamqqmJJJF1VVVejq6uLhw4f45hvadSFuAQEBGDx4MFRVVfHDDz/UeKTL7t27UVhYiMuXL2PgwIHcBuapZs2awczMDNevX69znIODA+7cuYM3b96IKRkhovHrr7/C1dUVzZo14zqKRKDikBDS4CUVfFtCwTVFRUWMHDnyi23Lx48fD39/fxQVFYkpmXRp1qwZHB0dq61YIOK1ceNG/PTTT7Uu62UYBj///DOWL18u5mTSQ1FRESNGjPjiCgU3Nzf4+/ujuLhYTMkIEQ2BQABZWVkMHDgQEyZMwJAhQyAvL891LM5QcUgIIRxr3rw5TE1NcePGjTrHOTg4ID4+nvZaiciQIUPw8uVLxMbGch1FqsXFxWHPnj01Hukye/ZsqTiEmkvGxsYoKytDSkpKrTPo5eXlaNu2LWRlZZGcnCzmhNInJyenWsfYhnbUJHVzdnbGjRs3UF5eDoZh0KRJE7i4uGDChAlSuce//mvJCBGDoKAgjBgxAi1atIC8vDymTJkivBYYGIgFCxYIPywQwhfdunVDVFQUMjMzax2TmZmJiIiIag2aSONau3YtEhMT8fvvv3MdRaqZm5vj+PHjSE1NRXFxMYqLi5Gamopjx45RYSgGw4YNQ0ZGBjw9PYV9AP6usLAQ06ZNQ2ZmJoYPHy72fNJk3759MDExgY6ODszNzWFubg4dHR20a9cO+/fv5zoebwQEBCA7Oxs7d+5E9+7dUVBQgEOHDqFv375o1aoVVq5cicePH3MdU2xo5pBIjLlz52Lv3r1gWRYqKip4//49PDw8cOTIEQCfzlwyNTXFtm3bMH/+fI7TEtJ4fHx8MH78eHTq1AlHjhz5rBlHXFwcpkyZggcPHuDYsWNwd3fnKCn/RUdHY8KECdDX14ejo2OtjZkAYOLEiWJOx28nTpxAmzZtam2CUuXWrVtITk6m77+IvH37FhYWFkhPT4eKigoGDBhQbe/ntWvXUFhYCCMjI8TGxkJDQ4PbwDxUWVmJMWPGwN/fHyzLQl1dHS1btgTDMMjIyEBeXh4YhsGIESNw+vRpXh6nwKUnT57g5MmT+OOPP5Camir8/nbv3h0TJkyAq6srv2dvWUIkwPHjx1mGYVgLCwv27t27LMuyLMMw7OTJk6uNMzAwYPv27ctBQv778OED+/PPP7Pm5uasmpoaKxAIavySkZHhOiovubi4sAzDsAKBgO3WrRs7evRodvTo0Wy3bt1YgUDAMgzDjhw5kuuYvLdmzRpWWVlZ+GdR01fVNdK4avqZX5OpU6fS91/EsrOz2cGDB7MMw9T4NXjwYDY7O5vrmLy1f/9+lmEYtl27duylS5c+u3758mW2ffv2rEAgYA8cOMBBQukRExPDzp49m9XU1BT+7JeVlWWdnZ1ZHx8ftri4mOuIjY5mDolEsLKyQlJSEpKSkqCpqQng0wbhv88cAp/2BN2/fx/p6ekcJeWngoIC9O7dGw8fPoSMjAzk5ORQVFQEHR0dvHz5Utgcoqp1f1paGpdxeamyshIbNmzA9u3bP1vKpa6ujvnz5+PHH3+EjIwMNwGlwNatW7F06VLIy8vD2dn5i0dZrF69Wozp+K+mn/k1mTJlCo4fP05nu4lBWlraZ3s/bWxsYGhoyHEyfuvZs6fwM9G3335b45iXL1/CxMQE7dq1w+3bt8WcUPpUVFTg6tWr8PLywqVLl/Dx40cAn7pc17T8+r+MenUTifDgwQP06dNHWBjWRk1NDa9evRJTKumxadMmPHjwANOnT8eOHTswY8YMnDx5EtnZ2fj48SNOnTqFZcuWoWfPntTJUUQEAgFWrlyJpUuXIi4uDllZWQAAfX19dO/eHXJychwn5L/9+/ejSZMmuH37NkxMTLiOQ2rx9OlTNGnShOsYUsHQ0JAKQQ48evQIDg4OtRaGAKCtrY1+/fp98cgR0jhkZGQwePBgODs748yZM5gzZw5ev36Nd+/ecR2t0VFxSCRGfdbMP3/+HIqKimJII13Onz8PXV1d7N69G7KystX+LBQUFDBx4kSYm5ujW7du2LZtGxYtWsRhWn6TlZWFlZUVrKysuI4idV6+fAkHBwcqDMVo3bp11V4nJCR89l6V8vJyJCUlITw8HA4ODuKIJzUSExORm5uLVq1awcDAoM6xGRkZyMjIgJaWFtq1ayemhNKnPp+JaK+h+MTGxsLLywt+fn7Izc0Fy7KQkZGBk5MT19EaHRWHRCK0bdsWd+7cQVlZGWRlZWsc8+7dOyQkJKBjx45iTsd/GRkZ6N+/v/B7X9WA4+9/Hh06dECfPn1w7NgxKg4JL7Vu3RqVlZVcx5Aqa9asAcMwYFkWDMMgISEBCQkJdd6jpaWFDRs2iCegFHj9+jWsrKygpKSE+Pj4L46Xk5ODq6srSktL8eTJE6irq4s+pJQxMTFBcHAwXr9+jebNm9c45vXr1wgODqaHWSL09OlTeHl5wdvbG0+ePBFusTEzM4O7uzvGjRvHy8Y0VBwSieDi4oIVK1Zg2bJl2LZtW41jli9fjoKCAri6uoo5Hf8pKChAQUFB+LpqydbLly+hr68vfL9p06aIiooSez5pUFJSAh8fH4SHh+PFixcoKSmpcRzDMLh586aY00mHGTNmYOnSpUhPTxd2ZySidfToUQAAy7Lw9PSEjY1NtSOM/k5OTg66urqwtLSU6gOqG9vRo0dRWFiInTt3QkdH54vjdXR0sGnTJnh4eODo0aPUPVwEJk2ahLlz56Jfv37Yvn07+vXrV+16SEgIFixYgMLCQnh4eHATkqfevHkDPz8/eHl5CfdysiwLAwMDjB8/Hu7u7ryfMaeGNEQiFBcXw9LSEg8ePECPHj0wbNgw/Pjjj+jduzeGDx8Of39/REZGwszMDNHR0bT/qpF17doVSkpKiImJAQAcPHgQs2bNwvHjxzFhwgQAn5Z0mZiYoKysrM7z+EjDZWdno1+/fkhJScGXfiQzDIOKigoxJZM+CxYswOnTp/Hzzz/DwcEBLVq04DqS1Ojbty8GDhyIJUuWcB1FqtjZ2eHevXvIzc2t9dD7f6qoqICmpia6du2KkJAQESeUPhUVFRg6dCiuXr0KhmGgqakpbAiXkZEhXNbo7OyMixcv1nrcDqmfjx8/4sKFC/Dy8kJQUBDKy8vBsizU1NQwevRoTJgwAX369OE6pthQcUgkRm5uLjw8PIQ/DP/5V9PBwQFeXl5fbFpDGm7OnDk4evQoXr58CVVVVTx//hyGhoZQVlbGhg0b0KJFCxw+fBiXLl2Cm5sbTp48yXVkXnFzc4Ovry+sra2xYMECGBsbQ1VVtdbxVR8SSOOq6gRbtcSxLgzDULdMwguampowNzfH1atXG3TfwIEDER8fj5ycHBElk26VlZXYsWMHdu/eLWxQVsXAwADff/895s+fT4VhI1BTU8P79+/BsixkZWXh5OQEd3d3DB06VCpXKVBxSCTOvXv3EBQUhPT0dFRWVkJPTw8ODg7o0aMH19F4KzIyEosXL8aaNWuEm6u3b9+ORYsWCT8ksywLbW1txMXFQVdXl8u4vNOsWTOoqqri8ePH1Zb3EvFq1apVgxo80JEujevJkycICAiAvb09OnXqVOOYBw8eIDg4GIMHD4aRkZGYE/KTnJwcxowZAy8vrwbdN2HCBJw+fbrWJfCk8WRlZVU7TuTv2z3IvycQCNCjRw+4u7vD1dUVzZo14zoSp6g4JBJh5MiR0NHRwa+//sp1FPI3t2/fhr+/P/Ly8mBsbIzJkyejadOmXMfiHRUVFQwaNAh+fn5cRyGEM3PmzMHBgweRmppaa8fMjIwMtG7dGnPmzMHOnTvFG5CntLS0YG5ujoCAgAbd5+zsjNjYWOTm5oooGSHikZKSgrZt23IdQ2JQcUgkgoKCAoYPHw5fX1+uo0ilwsJCMAxT51JGIjpVnQKp0QyRZu3bt4eKigpiY2PrHGdubo7i4mI8fPhQTMn4zdraGklJScjJyREurf6S8vJy4VEW0dHRIk4o3bKzsxEZGVlt5rBXr17Q09PjOJl0KSoqQnR0NF6/fo0WLVrAysqq3nt0/2tooTKRCIaGhvjw4QPXMaSWuro6HB0duY4htZYuXYrw8HD8+eefXEch/5CXl4e8vDyuY0iFrKysei0Vbd26NZ49eyaGRNJhwIAByM/Px969e+t9z969e1FQUICBAweKMJl0e/78OUaOHImWLVvCzc0NixYtwqJFi+Dm5oZWrVphxIgR9O+gkaSmpmLLli24dOlSjdfPnDkDfX19ODk5Yfz48bCzs4ORkRHCwsLEnFQ8qDgkEmHcuHEICwvDy5cvuY4ildTU1Gj/DofMzMywYMEC9OvXD6tXr0ZkZCTS09ORmZlZ4xcRrYCAADg5OUFFRQXNmzdH8+bNoaKiggEDBjR46R2pPxkZmXrtXyspKaGOvY1ozpw5UFFRwZIlS+q17/DkyZNYsmQJVFVVMXv2bDEklD7Pnz+HlZUVzp8/DwUFBQwbNgxz587F3LlzMXz4cCgqKuLChQuwtrYWziiSr+fr64vly5fXOEkRHx8PNzc35OXlQVFREWZmZtDQ0MCzZ88wZMiQz5oF8QJLiAQoLS1lnZ2dWWNjY/bcuXNsaWkp15GkSt++fdmePXtyHUNqMQzDCgQC4f/W9SUjI8N1XF6bN2+e8M+CYRhWXV2d1dDQEL4WCATs/PnzuY7JS+bm5myzZs3YDx8+1Drmw4cPbLNmzVhTU1MxJuM/f39/VkZGhhUIBKy1tTW7Z88eNioqik1OTmaTk5PZqKgods+ePay1tbXw59D58+e5js1bkyZNYhmGYSdMmMC+efPms+tv375lJ06cyDIMw3p4eHCQkF8cHR1ZZWVltqio6LNrQ4cOZRmGYTt16sS+fPmSZVmWraioYKdNm8YyDMMuXrxY3HFFjvYcEolgZGSEyspK4RMYhmGgpaVVY+dGhmGQmpoq7oi8FhgYCGdnZ/j5+WH06NFcx5E6dnZ2DeqSSeeKiYafnx/GjRsHLS0trFy5Eu7u7lBTUwPwaV/uyZMnsX79euTk5MDHxwdjxozhODG/bN68GcuXL4erqyt+//13KCsrV7teVFSEadOmwdfXF+vWrcOKFSs4SspPV69exaRJk/D69etafx6xLAtNTU0cO3aMlpSK0LfffgsVFRUkJyfXug+0oqICxsbGePfuHR0n8i+1bt0a+vr6CA0NrfZ+cXEx1NXVUV5ejsuXL1f7O19UVAR9fX0YGBjg7t27Yk4sWlQcEonQ0HN6KisrRZREOoWHh8Pb2xuHDh3C4MGDMWTIEBgYGNR6rIKtra2YExIien369EFsbCwSEhJgbGxc45jk5GSYmpqiR48en32QIP9OUVERevTogcTERGhpaWHcuHFo3bo1gE97gnx8fJCTkwMTExPExsZ+VjySf6+oqAjHjx9HQEAAEhIS8ObNGwCfjtsxNTXFoEGDMHHiRCgpKXGclN+UlZUxfPhweHt71znOzc0NFy5coJ4N/5KqqiqGDRv22bLqiIgI9OnTB6qqqnjz5s1nDWicnJxw+/Zt5OfnizGt6PGzzQ75z6Fij1tVM1csy+LSpUu4fPlyneNpvw/ho3v37sHe3r7WwhAAjI2NYW9vj8jISDEmkw5KSkq4ceMGJkyYgODgYOzcubPaOasA0LdvX5w8eZIKQxFRUlLCzJkzMXPmTK6jSLX27dvXay/hixcv0K5dOzEk4rfy8vIaC7z4+HgAQPfu3WvsTKqpqYni4mJRxxM7Kg4JIZg4cWKDljUSbnz8+BGlpaVo0qQJ11F4qbS0tF5Fh7KyMkpLS8WQSPpoa2vjxo0biI2NxY0bN4RbDfT19dG/f39YWFhwnJAQ0Zs3bx4mTZqEwMBAODk51TgmKCgI4eHhOHr0qJjT8Y++vj4SEhJQUVFRbRnvzZs3wTAMLC0ta7wvLy8Pmpqa4oopNrSslBBCxMzIyAguLi7YvHnzZ9cWLFiAHj16wNXV9bNrkydPxsmTJ1FeXi6OmFKnc+fOyM3NRWpqaq1FYlFREYyMjKCpqYn79++LOSEh4lVeXo43b97U2UXWwMBAjIn4qaYu1Fu3bsVvv/2GsWPHYuzYsWjZsiUAICMjA6dOnYKfnx++++47LFq0iP4M/qUZM2bg999/x4oVK7Bu3ToAQFRUFOzs7FBZWYmoqKgaC0RdXV0YGBjg1q1b4o4sWpy1wiGkBvfu3WO/++47tn379myTJk3YJk2asO3bt2enT5/O3rt3j+t4hDQKhmHYyZMnN/iah4cHKxAIRBlNqq1bt45lGIbt378/m5yc/Nn1J0+esE5OTqxAIGB//vlnDhISIh7Xr19n+/Tpw8rLy1P3ZDGorVN1XR2sq67Rn8G/l5KSwiopKbECgYDV19dnzczMWFlZWZZhGNbGxqbGe6Kjo1mGYdh58+aJOa3o0bJSIjF27dqFxYsXo6KiQri/BAAeP36Mx48f48iRI9i6dSvmzp3LYUpCCF8tWrQIFy5cwM2bN9GhQweYmZmhVatWAD49rY+Pj0dFRQXMzc2xcOFCbsPy3MOHD5GSkoJ3795V+33wdxMnThRzKulw+fJljBgxAhUVFdDQ0IChoSFUVVW5jsVrtra2tLWDQ23atIG/vz/c3d3x7NkzPHv2DADQoUOHWpsC7dy5EwBqXfb7X0bLSolEuH79OpycnKCkpIQZM2bA3d0drVq1AsMwSE9Px8mTJ3HgwAEUFRUhKCgI/fr14zoyr3h6etZrnJycnLBr3eDBg6GoqCjiZPwkEAjg4eGBI0eONOja5MmTceLECWoIJELv37/H8uXLceTIkc8aDSgqKsLT0xMbN26EiooKRwn57caNG5g1a1adxxWxLAuGYejfgYhYWFjgzp072L59O+bMmVPrUQqE8M3Hjx8RGRmJ3Nxc6Ovrw9rautZu+pcuXUJlZSUGDhwIOTk5MScVLSoOiUQYOHAgbt68idDQUFhbW9c4JiYmBra2tnBwcEBAQICYE/Jb1Q+/f3YGrPLP9xmGgYaGBg4ePIhRo0aJMSk/UHEo+YqKihAfHy/sGKirq4vu3btTC38RiouLQ69evcAwDFxcXHD//n3cv38fy5YtQ2pqKm7cuIG8vDx4eHjAwMAAq1ev5joyLykpKcHMzIw68hIipWhZKZEIf/75J/r06VNrYQgAVlZWsLOzw+3bt8WYTDqEhITg3Llz2LNnD3r16oWxY8cKN7hnZWXBz88PkZGRmDNnDiwtLREeHo7Dhw9j3LhxiIiIQM+ePTn+f0BI41JSUkLv3r25jiFVNm7ciPLycly7dg0ODg6YPHky7t+/j19++QUAkJ+fj+nTp+Py5cuIi4vjOC1/qaioUIMTQmpRUFCAlJQU6OnpQVtbm+s4IkHFIZEIRUVF9WoHrKmpiaKiIjEkki5lZWXYt28fDh8+jMmTJ392ffbs2Th27BimTp2KIUOG4MCBA+jbty/GjRuHbdu24dSpUxykJuTfCw4OxrNnz2Bubo4OHTrUOfbRo0eIi4uDvr4++vbtK6aE0iM6OhrdunWDg4NDjdfV1dVx4sQJtGrVCitXrsTx48fFnFA69O/fn4pvCREaGorw8HC8ePGi1o6xDMPg8OHDYk7Gb0FBQfD19cX333+Pbt26Cd/fs2cPlixZgtLSUjAMg++//x47duzgMKlo0LJSIhGMjY1RVlaGlJSUGg8aBT611G7bti1kZWWRnJws5oT81qdPH5SUlHyxHbOlpSXk5OQQHh4O4NNBvYWFhcjOzhZHTN4QCAQYMGAAli1b9tk1Ozu7Wq9t3LgRQUFBtKy0kWRlZcHY2Bj6+vqIj4//YtONd+/ewdzcHM+ePUNKSgp0dXXFlFQ6yMvLY+TIkfDx8QEATJ8+HYcOHUJhYWG1o0XGjBmDyMjIeh0SThouKysLFhYWmDRpEn755ZdafycT0SkoKMCwYcMQERFRa0OmKrT/tvGNGTMGV69exYsXL4T7y+/fvw9TU1PIyMjAwsICiYmJKCgowLlz5zBs2DCOEzcu+hdPJMKwYcOwbds2eHp6Yvfu3VBXV692vbCwEHPnzkVmZiZ1CRSBu3fvYsiQIV8c17p1a1y6dEn4un379rhy5Yooo/FWYGAgAgMDG3StqhEHaRyHDh1CaWkptmzZUq9ujKqqqti6dSuGDx+Ow4cP46effhJDSumhqamJwsLCaq8B4OnTp+jcubPw/eLiYhQUFIg9n7Q4evQoBg4ciP/97384e/Ys7OzsoKenV2NjDoZh6N+BCCxduhTh4eFo06YNZsyYAWNjY+oYK0Z37tyBqalptcZjx44dE/6vm5sb0tLS0KFDB+zbt493xSHNHBKJ8PbtW1hYWCA9PR0qKioYMGBAtRby165dQ2FhIYyMjBAbGwsNDQ1uA/OMhoYGWrVqhbt379Y5rlu3bkhPT0deXh4AYOTIkQgNDcXbt2/FEZM37Ozs/lWRFxIS0ohppJe1tTWePn2Kly9fNug+HR0dGBoaIjo6WkTJpJOdnR1ycnLw6NEjAMDFixcxfPhwzJo1C3v37gUAPHnyBF27doWRkRHu37/PZVzeEggEYBjmizNWAM1aiYqOjg6AT0e6NG3alOM00kdVVRWDBw8WrmIAAFNTU2RlZSE3N1f4oGTAgAF49OgRMjMzuYoqEjRzSCRC06ZNERERgenTp+PKlSs4ffr0Z2MGDRqEgwcPUmEoAjY2NggICMDmzZuxdOnSGsds2bIF9+7dqzbD+PTpU1pa9xVCQ0O5jkDw6QzVXr16Nfg+c3NzKgxFwNnZGcuXL0diYiLat2+PAQMGoGXLlti/fz9iY2Ohp6eH4OBgfPz4EVOmTOE6Lm8dPXqU6whSr6CgAM7OzlQYckRGRgYfP34Uvn779i0ePHiAwYMHV5tB19TURG5uLhcRRYqKQyIxdHV1cenSJaSlpVXbT6KrqwsbGxsYGhpynJC/NmzYgJCQEPz44484evQoRo0aBX19fQCf9p/4+/sjKSkJysrKWL9+PYBPM7p//fUX5syZw2V0Qr7ahw8foKam1uD71NTU8P79exEkkm4TJ06EmpoaKisrAXw6V/XixYsYM2YMYmNjERsbC4FAgKlTp2Lu3Lkcp+WvSZMmcR1B6rVt2xYfPnzgOobUatWqFaKjo1FWVgZZWVmcO3cOLMt+1izrzZs3aNasGUcpRYeWlRJCAHw6R9LDwwMpKSkAPj/bsG3btjh+/DgsLS0BAHl5eUhOToaRkVG9Os2Shqlauksz5aKjra2Nzp074/r16w26z8HBAffv32/wclRSXWZmJlRUVOo1O/L48WPk5eWhTZs29POG8N6hQ4fw/fff4/79+2jTpg3XcaTOxo0bsWLFCvTs2RPW1tY4evQoioqKkJqaihYtWgD49NlIW1sbJiYmwiZ9fEHFIeGEkZERXFxcsHnz5s+uhYeHQ1tbG8bGxhwkk24syyI4OBhRUVF48eIFgE97H3r16gV7e3tqhiJiAQEB2LVrF6KiolBcXAwAUFRUhI2NDX744Qc4OztznJBf7O3tERsbi5ycHCgqKtbrnqKiImhpaaFHjx4IDg4WcUJ+k5GRgYeHR41t+NetWwdTU1MMHTqUg2SEcG/p0qXw8vLC+vXr4eDgAD09Pa4jSY0PHz6gX79++PPPPwF82oe7detWzJ8/Xzjm5s2bcHBwwE8//YS1a9dyFVUkaFkp4UR6enqt67Tt7OwwefJkOreHAwzDoF+/fujXrx/XUaTO/PnzsXv3buFMrZqaGhiGQX5+PoKCgnD9+nXMnTsX27dv5zgpfwwePBihoaFYv3698KD1L1m/fj2Ki4vr1d2X1I1l2VqbnqxZswYeHh5UHIpB1YO/48ePQ09PD/b29vW+l2EY3Lx5U4TppIOMjEyN77Msi6lTp9Z5L8MwKC8vF0UsqaWsrIzo6GiEh4cjNzcXpqamaNu2bbUxMjIy2LFjBy9/F1BxSCQSTWhzj5Y1io+fnx927doFLS0trFy5Eu7u7sK9cIWFhTh58iTWr1+PXbt2wdLSEmPGjOE4MT/MmDEDW7duxaZNm6CoqIgff/yxxnb9AFBZWYlffvkFmzZtgra2NqZPny7mtISIRmhoKBiGQVFRkfB1fdFqksahr69P30sJIxAIYGdnV+t1Ozu7Oq//l1FxSAgRomWN3Ni3bx8UFBQQHh7+2XLqJk2aYPbs2XBwcICpqSn27dtHxWEjUVJSwtmzZ9G/f3+sXr0av//+O1xcXGBmZibc15abm4s7d+7g9OnTePbsGRQUFHD27FkoKSlxnJ6QxpGWlgYAwr1UVa+J+KSnp3MdgdRTYmIiHj58CH19ffTs2ZPrOCJBxSEhBAAta+TSvXv3YG9vX+c+W2NjY9jb2yMyMlKMyfjP2toa0dHRcHd3x8OHD7Fjx47PxlT9m+jYsSO8vLzQtWtXccckRGRatmxZ52tCpI2fnx8OHjyIjRs3VisAFy9eXO0z0NChQ3HmzJlalwX/V9W8foYQIlWqljVqampi9+7dyMvLQ15eHt6+fYv8/Hzs2bMHWlpa2LVrF06dOsV1XN4pLS2FsrLyF8cpKyujtLRUDImki6mpKe7fv4+AgADMmjUL1tbWMDExgYmJCaytrTF79mwEBATg/v37VBgSQgjPeXl5ISEhAd26dRO+Fx0djW3btkFVVRWurq5o1aoVLl68CG9vbw6TigbNHBLOvH//HpmZmQ2+BgAGBgaiiiWVaFkjt1q3bo2wsDB8+PCh1iKxqKgIYWFhaN26tZjTSY8BAwZgwIABXMeQKk+ePMGJEycafA34dC4iIXxU19/7v5OTk0OzZs3QtWtXaGlpiTiV9Hjw4AG6dOkCOTk54XsnT54EwzA4deoUHB0d8fbtWxgaGuLQoUO8+1lER1kQTggEgq/efE2duRqfuro6bGxscPny5TrHDR48GJGRkcjPzxdPMCnx888/Y/Xq1ejXrx/27dv3WVe01NRUzJ49G9evX8fatWuxcuVKjpIS0njq+j1Q9dGkrt8TFRUVIsklbYyMjL76XoZhkJqa2ohpCNDwz0gMw6B///7Ys2fPZ78/SMMpKytj+PDh1WYF27Vrh4KCAuExXwAwZMgQxMfH4/nz51zEFBmaOSScMDAwoM5cEoSWNXJr0aJFuHDhAm7evIkOHTrAzMwMrVq1AgBkZGQgPj4eFRUVMDc3x8KFC7kNS0gjmTRpEtcRCD41Q2EY5qu6hNPvcdFYtWoV0tPTceLECaioqMDR0VG4YiorKwtBQUF49+4d3N3dIS8vj+joaAQFBaF3796Ij48XNhciX0dRURGFhYXC1y9evEBycvJnq6bU1dWFnd35hIpDwgnqzCVZaFkjtxQVFREaGorly5fjyJEjiI2NRWxsbLXrnp6e2LhxY70Paydf9m+aCNAKhn/v6NGjXEcgf9O9e3dMmDABw4YNo58zHHN3d0ePHj3g6emJbdu2CY82qlJYWIgFCxbA398ft2/fhpGRERYvXowdO3Zg06ZN2LNnD0fJ+cHIyAgRERHIz8+Huro6vL29wTAMHB0dq417+fIlL5fz0rJSQggta5QgRUVF1Zap6Orqonv37nR0ggi0atXqX818UMt/wgenTp2Ct7c3rl27hvLycqioqGDkyJGYMGEC7O3taXaQA2PGjMGdO3eQnJxc59mrxsbGMDMzw6lTp1BaWgpDQ0MoKSkhJSVFzIn5Zd++fZgzZw4MDQ1hamqKK1euQF5eHk+fPkWzZs0AAGVlZdDS0oK5uTmuX7/OceLGRcUhIQTFxcXo3bs37ty5AxkZmTqXNYaFhdFTZUII4Zm3b9/C19cXXl5euHXrFhiGgY6ODsaNG4fx48fD1NSU64hSQ1NTE46Ojl/shOnm5oagoCC8fv0aAODs7IzQ0FAUFRWJIyZvlZWVwc3NDWfPngXwaUvN77//DldXV+EYf39/jBo1Cr/88guWL1/OVVSRoOKQSJSioiLExcXhxYsXKCkpqXUc3zpDSYL3798LlzUWFxdXu/b3ZY0qKiocJeQvGRkZeHh44PDhw3WOmzZtGo4ePUrLGQkv1LcjY23o94DopKWlwcvLC3/88QeSkpLAMAzat28Pd3d3uLm5QV9fn+uIvKasrAxLS0vcvHmzznH9+vXD7du38f79ewCAq6srAgICqu2XI18vPT0dubm5aNeuHVRVVatdS0hIQEZGBiwtLfHtt99ylFA0qDgkEmPVqlXYsWNHnU+8WJYFwzDUpU6EaFmj+AkEAnh4eODIkSN1jps2bRqOHDlCf/8JL3xt12r6PSBesbGx8Pb2hp+fH3JycqCpqYmXL19yHYvXLC0tER8fj5s3b8LW1rbGMREREejbty8sLCwQExMDAOjVqxdevnxJHWTJv0INaYhE2LJlC9avXw8ZGRkMGjQIxsbGnz2lIeKhpKSE3r17cx2D1KCgoADy8vJcx5AK7969Q2pqKt69e1drF8faPrSR+lm1ahXtZ/sPaNmyJYyMjKCrq4tXr16hsrKS60i8t2TJEowePRpOTk6YOHEiRo8eLZytzcrKwtmzZ3HixAmwLIslS5YA+PT7IT4+HqNGjeIyOuEBmjkkEqFt27Z4/vw5IiIiYGZmxnUcqUPLGsUvMzNT+N+tWrXC6NGj8b///a/GseXl5UhKSsK4ceOgr6+P+/fviyum1Hnw4AHmzZuH0NDQL7b2p5krwldFRUU4d+4cvL29cfPmTVRUVEBNTQ0uLi5wd3eHjY0N1xF5b9euXVi6dClKS0s/e4jCsizk5OSwZcsW/PDDDwCAp0+f4vTp0+jXrx/Mzc25iMw7mZmZuHTpElJSUmp9UMgwzBc/O/3X0MwhkQhZWVmwt7enwpAjLMvW+4wrep7UOP7ZKfPs2bPCze+1YVkW06ZNE3U0qZWSkgIbGxsUFhaiV69eePHiBdLS0uDq6oqnT5/izp07KC8vx9ChQ6Gurs51XEIaVWVlJQIDA+Hl5YWLFy+iqKgIcnJyGDp0KCZMmABnZ2fIyclxHVNqzJ07F0OHDsXhw4cRHR0tPHxdR0cHvXr1wuTJk2FkZCQcb2RkhKVLl3IVl3fWrVuHn3/+udpMedXnn6rf3VVL3Kk4JEQEtLW163UIO+EWLWtsPLa2tsJfMGFhYdDS0kK7du1qHCsnJwddXV0MHToUI0aMEGdMqbJ+/Xq8e/cOR48exaRJkzB58mSkpaUJOwY+efIEU6ZMwaNHj3Dr1i2O0xLSOG7fvi3cU5ibmwuGYWBra4sJEyZg9OjRn52xR8TH0NAQ69ev5zqG1PHz88OaNWvQqlUrrFixAqdPn8b169cRGBiIp0+fws/PD6GhoViwYAGGDBnCddxGR8UhkQiurq44fPhwnYewk8b192WNwKdupf98r0rVssagoCC0bt1aHPF4LzQ0VPjfAoEAAwcO/GJDGiJawcHBaN++PSZNmlTj9TZt2uDChQswMjLCTz/9hL1794o5oXSQ1qVcXLGysgLDMOjcuTMWLVoENzc3tGjRgutYhHBm3759kJOTQ0hICFq2bInIyEgAgIODAwBg+vTp2LFjB5YsWYLhw4dzmFQ0aM8hkQgfP36Eo6MjZGVlcfDgQbRp04brSLz3906BVUsjvoRlWezcuVO4x4E0joyMDKioqAgP1yXckJeXx7Bhw3Dq1CkAwHfffSd8aKWgoCAcN3LkSNy5cwfp6ekcJeWvhizloj2fjaPqd4GMjEyD72UYps5jpwj5L9LQ0EC3bt0QHBwMAPD09MTx48dRXl5e7bNSp06doKenh2vXrnEVVSRo5pBIBGdnZ1RWViI0NBTt27dHy5YtoaenB4FA8NlYhmG+ePYP+TJa1ig5WrZsyXUEAqBp06bVPug2bdoUwKfi3cTEpNrYnJwcsWaTBtK+lItLLMtSozEOCQQCCAQCPHr0CMbGxg0q1BmGoT+7RlZSUgJtbW3h66qHg/n5+dDQ0BC+37VrV94VhgAVh0RC/H2JXUVFBZ4+fYqnT5/WOJZanzcOWtYoOdatW1fvsQzD4KeffhJhGullaGiIjIwM4WtTU1OwLAs/Pz+sWrUKAPD69WuEhobCwMCAq5i8Je1LubhCR1Nwz8DAAAzDQFZWFgCgr69Pn3U4pKOjU+0BYNUy64cPH1br1Pvs2TNermCgZaVEIvz9A1l90ExL46JljdyqWtZV249jWk4nHmvWrMH69euRmpqKli1b4sOHDzA0NMTbt28xevRotGjRAufOnUNmZiZWrFjRoKKefJm0L+UihEiG4cOHIz4+HllZWQCAqKgo9O7dG3379sXFixehrKyMU6dOwdXVFVZWVoiKiuI4ceOimUMiEajY41Zd3//ExEQ8fPgQ+vr66NmzpxhTSY+jR4/W+H5lZSWysrJw/fp1REVFYfbs2XR+lQi5u7ujpKQEr169QsuWLaGsrAxfX1+MGTNGuA8R+DSTtWLFCg6T8pO0L+UihEiGIUOG4OLFiwgODoa9vT169eqFvn37IiQkBBoaGmjSpAny8vJ4u5KHZg4JIfDz88PBgwexcePGagXg4sWLsX37duHroUOH4syZM1/VuID8O1u2bMG6desQExODzp07cx1Hqnz48AERERHIy8uDsbExunfvznUkXmrdujUMDQ1x48YNAMAvv/yCVatWISwsrNpSrj59+uDevXvIz8/nKCkhhM9KSkqQnp4OTU1N4d7zwsJCLFmyBOfPnxf+Lli+fDnc3Nw4Ttv4qDgkEuXVq1c4cuQIIiIikJ2dDeDTWm9bW1tMnjwZ3377LccJ+WnIkCGIiorCy5cvhYccR0dHw8bGBk2aNMGgQYNw69YtpKen4+jRo5g4cSLHiaVTu3bt0LZtW1y6dInrKIQ0OmlfykXIPwUFBWH//v34888/8fr1a0yYMEF4hEtgYCACAwOxaNEi6OrqcpyU8AktKyUS4+zZs/D09MT79++r7b26f/8+AgMDsWnTJhw+fBijRo3iMCU/PXjwAF26dBEWhgBw8uRJMAyDU6dOwdHREW/fvoWhoSEOHTpExSFHOnfuLJxVIaL36tUrPH/+HACgq6tLD6dETNqXchHyd3PnzsXevXvBsixUVFRQVlZW7bORjo4Odu7cCX19fcyfP5/DpIRvPj8ngBAOxMXFYdy4cfjw4QNGjBgBf39/3L17FwkJCTh//jxGjhyJ9+/fw83NDXFxcVzH5Z2cnJzPDj0OCQmBlpYWHB0dAXxq629ra4snT55wEZEASE1NpZblIsayLHbv3g1jY2Po6urC3Nwc5ubm0NXVRdu2bbFr1y7q7igiEyZMQGJiIkxNTYXv+fv747vvvkPTpk3x7t07dOjQASdPnsSAAQO4C0qIiJ04cQJ79uxB9+7dcefOHRQWFn42pkuXLtDX16eVJKTR0cwhkQgbN25ERUUFzpw589k5el26dMHQoUPh7++PUaNGYdOmTThz5gxHSflJUVGx2i+fFy9eIDk5GWPGjKk2Tl1dHXl5eeKOJ/Xy8vKwfv16JCQkoG/fvlzH4a2SkhIMGTIEN2/eBMuy0NDQEDZryszMRGpqKhYsWIDLly/j8uXLkJeX5zgxv8jLy392nmSTJk1w4MABHDhwgKNUhIjf/v37oa6ujitXrkBTU7PWcV26dMH9+/fFmIyf7O3tv/pePp69TcUhkQiRkZGwtrau84D1ESNGoFevXoiIiBBjMulgZGSEiIgI5OfnQ11dHd7e3mAYRjhrWOXly5fQ0tLiKCV/GRkZ1Xrt/fv3ePPmDViWhaKiIjZu3CjGZNJlw4YNuHHjBjp16oStW7fCycmp2vWgoCAsXrwYwcHB2LBhA9auXctRUkIInz148AB9+vSpszAEADU1Nbx69UpMqfjr7+c+NxQfz6Ok4pBIhIKCgnodKm1gYIDY2FgxJJIuHh4emDNnDrp37w5TU1NcuXIFKioqGDZsmHBMWVkZ4uLi6CgFEUhPT6/1mqysLPT19dGnTx8sXboUHTp0EF8wKePl5QV1dXWEhITUeOano6MjzMzMYGxsjJMnT1Jx2MiePHmCgIAA2Nvbo1OnTjWOefDgAYKDgzF48OA6H6oQ8l9Xn6Lj+fPnUFRUFEMafktLS+M6gkSh4pBIBG1tbdy9e/eL4xISEqqdg0Uax7Rp0xASEoKzZ88iLS0NysrKOHjwYLUPyJcvX0ZBQcG/Wn5BataQPWyVlZUQCGi7uCg8f/4cgwcPrrEwrNK8eXPY29vjypUrYkwmHXbu3ImDBw8iNTW11jGqqqpYsGABnj59ip07d4ovHCFi1LZtW9y5cwdlZWWQlZWtccy7d++QkJCAjh07ijkd/9BZ29XRJwwiEZycnJCUlIQff/wRFRUVn11nWRYrV67E48ePqRGBCMjKyuL06dN4+vQpbt++jefPn8PV1bXaGENDQ/j7+8PT05OjlNLt7t27WLBgAfT09LiOwlstWrRAaWnpF8eVlZVR63gRuHnzJkxNTetcRdKyZUuYmpri+vXrYkxGiHi5uLjgxYsXWLZsWa1jli9fjoKCgs9+VxPyb9E5h0QiPHv2DN26dcPbt29hYGCAMWPGoFWrVgCAjIwMnD59Gunp6WjWrBnu3LlDH5CJVMjKyoK3tze8vLyQmJgIlmXBMEyND1DIv7d69Wps27YNDx8+rPVJckZGBjp27Ij58+fj559/FnNCflNRUcGgQYPg5+dX57ixY8fi2rVrKCgoEFMyQsSruLgYlpaWePDgAXr06IFhw4bhxx9/RO/evTF8+HD4+/sjMjISZmZmiI6OrnYMFfk606ZNQ2pqKtavXw9ra+s6x8bExGDFihUwMTHB/v37xZRQjFhCJMRff/3Fdu7cmWUYhmUYhhUIBKxAIBC+7tKlC3v//n2uYxIiUoWFhezhw4fZvn37sjIyMsJ/A3p6euzChQvZuLg4riPyVmlpKTtkyBDWwMCAPXz4MPv+/Xvhtffv37NHjhxhW7ZsyQ4dOpQtLS3lMCk/NWnShB02bNgXxw0bNoxVVlYWfSBCOJSTk8M6OzsLPw9VfRaq+nJ0dGRzcnK4jskLN27cYBmGYceMGVPve8aOHcsKBAI2LCxMhMm4QTOHROKEhoYiIiKi2uHTvXv3hp2dHbfBeGTdunVgGAazZ89G06ZNsW7dunrfSwdQN76Kigpcu3YNJ0+exKVLl/Dx40fhYccMwyAkJAS9e/fmZVc0SWJkZASWZZGZmSl8T0NDAwCqHeFiYGDw2Z8FwzB17pUjX2ZhYYG0tDRkZmZCSUmpxjFFRUUwMDCAvr5+vfapE/Jfd+/ePQQFBSE9PR2VlZXQ09ODg4MDevTowXU03vDw8MDJkyeRlJSENm3a1Ouep0+fok2bNpg8eTIOHz4s4oTiRcUhIVJIIBCAYRgkJibC2NhY+Lo+Pw5oWWPjiY2NxcmTJ+Hn54fXr1+DZVnIysrC2dkZEyZMwJYtWxAXF0ffbzH5t41+GtJYiHxu8+bNWL58OVxdXfH7779DWVm52vWioiJMmzYNvr6+WLduHVasWMFRUkIIn5iYmEBRUREJCQkNuq9bt24oLi7G48ePRROMI9StlBApdPToUQCAjo5OtddEPNavXw9vb28kJycLC3Jra2tMmDABY8aMQdOmTQGAujGKGRV33Pr++++FD0tCQkIwbtw4tG7dGgCQmpoKHx8f5OTkwMTEBPPmzeM2LCGEN549e4ZBgwY1+L62bdsiICBABIm4RcUh4UR4eDgAoEePHlBQUBC+ri9bW1tRxJIakyZNqvM1Ea1Vq1aBYRhoa2tj1qxZGD9+vLABEyHSSklJCTdu3MCECRMQHByMnTt3CpfvVj1E6du3L06ePPnZrCIh/2UN/Qz0T/SZiBv1XXH1X0PLSgknalvWWF+0zI78l1UtX2QYBr169YK7uztcXFygrq5ebVzv3r0RHR1Nf9+J1ImNjcWNGzeQlZUFANDX10f//v1hYWHBcTJCGl9DPwP9HcMwKC8vb+RE0sXIyAjq6uq4c+dOg+4zMzNDfn4+nj59KqJk3KCZQ8KJiRMngmEYqKmpVXtNiDS4ffs2Tpw4AT8/P0RGRiIqKgo//PADnJ2dMX78eAwZMqTWg49J46lqPNOiRQvIyMhUa0RTH3Wdx0f+HQsLCyoEidRwdHRs0GcglmURERGB4uJiEaaSHlZWVvDz88Pjx4/Rrl27et2TmJiIe/fuYdy4cSJOJ340c0iIFPo3B9kzDMO7zlxcKS8vx9WrV+Hl5SXsUsowDDQ0NDBq1CiEhIQgNTWVZg5FRCAQQCAQ4NGjRw1ewUBP68WjqktsVddYQqSdv78/1qxZg/v37wMA+vTpg5CQEI5T/bddu3YNzs7OsLKyws2bN6GgoFDn+I8fP8Le3h63b99GQEAAnJycxJRUPKg4JEQK1dWV8Z97fGq6TsVK4yssLMTp06dx8uRJRERECA+8B4DFixfD1dUVpqam3IbkGTs7OzAMg5MnT0JPT0/4ur7oA5loBAQEYNeuXYiKihLOjCgqKsLGxkY4w06ItLl06RLWrFmDhIQEsCwLGxsbrF27Fn379uU6Gi8MHjwYAQEB6Ny5M/73v//BwcGhxnFBQUFYtGgRHj58CGdnZ1y6dEnMSUWPikMiEYyMjODi4oLNmzfXOW758uU4deoUnSf2Lx0/frzG91mWhaenJ2xsbDBlypRa76cGNqKVlZUFLy8veHl5ITExUViwGBsbw83Njc6ZJLw1f/587N69W/hwSk1NDQzDID8/H8Cnh1Nz587F9u3bOUxJiPgEBARgzZo1iI+PB8uysLKywtq1a9G/f3+uo/HK+/fvMXDgQERFRYFhGDRr1gympqbQ1NQEAOTm5iIhIQFv3rwRFucBAQFQUVHhOLkIsIRIAIZh2MmTJ39x3NSpU1mBQCCGRNKrvn8WRDzi4+PZefPmsdra2izDMPT3n/CWr68vyzAM++2337J79uxh8/PzhdcKCgrYvXv3stra2qxAIGD9/Pw4TEqI6AUGBrKWlpasQCBgGYZhe/bsyV67do3rWLxWXl7ObtmyhdXV1WUZhqnxS1dXl926dStbXl7OdVyRoZlDIhEEAgE8PDxw5MiROse5ubnh3Llz+Pjxo5iSSZ/6/lkQ8aqsrERgYCC8vLzg7e3NdRxCGl2fPn0QGxuLhIQEGBsb1zgmOTkZpqam6NGjB0JDQ8UbkBAxuHnzJlavXo2YmBiwLAtzc3OsXbsWAwcO5Dqa1KisrMTdu3eFM4UAhDOJ3bp1q3NrDh9Qt1Lyn1BZWYmkpCSEhIRQh0AilQQCAQYOHEgfEETo0KFDWLJkCf744w8MGDCgxjFXr17F+PHjsX37dnh4eIg3IM/du3cP9vb2tRaGwKel1fb29oiMjBRjMkJELzQ0FKtXr0ZkZCRYloWZmRnWrFmDwYMHcx1N6ggEAnTv3h3du3fnOgon+F36EokmIyMj/AI+7YP7+3t//5KVlUWnTp3w6tUrXrYNJoRwz9fXF/Ly8nB0dKx1jKOjI+Tk5PDHH3+IMZl0KC0trdfh9srKyigtLRVDIkLEw97eHv369UNkZCS6du2K8+fPIy4ujgpDCZGSkoKYmBgkJydzHUUsaOaQcEZfX1/YaCMzMxNKSkpo3rx5jWPl5OSgq6uLoUOH4ocffhBnTEKIlHj06BG6dOlS55IhGRkZdO3aFQ8fPhRjMunQunVrhIWF4cOHD7UWiUVFRQgLC0Pr1q3FnI4Q0QkNDQXDMFBUVESzZs2wd+9e7N27t173MgyDwMBAESeUPiUlJVi7di1+++034ZE6kyZNEm658fLywvbt23HkyBHedRKn4pBwJj09XfjfAoEALi4utM+NEMKZt2/f1vqA6u+aN2+O169fiyGRdBkzZgxWr16N4cOHY9++fWjbtm2166mpqZg9ezZyc3MxZ84cjlISIhosy6KoqAg3b95s0H0NOX6H1E9xcTHs7e3x559/4ttvv4WzszOuXLlSbYy9vT0mTZqEU6dOUXFIiCiEhIRAW1ub6xiEECnWvHlzpKSkfHFcSkoKHcouAosWLcKFCxdw8+ZNdOjQAWZmZmjVqhUAICMjA/Hx8aioqIC5uTkWLlzIbVhCGhGdmSpZtmzZgtu3b2PKlCnYs2cPFBQUPltRoquriw4dOuDGjRvYsGEDR0lFg7qVEiKFqvZ5fg2GYVBeXt6IaQiRDK6urjh9+jRu3rwJOzu7GseEhobC3t4eI0eOxJkzZ8QbUAq8f/8ey5cvx5EjR1BcXFztmqKiIjw9PbFx40Z+ni1GCJEIHTp0QFFREZ48eYJvvvk0j1ZTJ/fRo0cjKioKL1684CqqSNDMIZE4Dx8+REpKCt69e4fanl1MnDhRzKn45d88E6LnSYSvFixYgDNnzmD48OFYuXIlpk2bBjU1NQBAYWEhfvvtN/zyyy8QCASYP38+x2n5SUVFBXv27MHmzZsRHx+P58+fA/j0lL579+5QUlLiOCEhohceHg5tbe06O/cCn1YxvHjxAra2tmJKJh3S0tIwaNAgYWFYGzk5OeF+RD6hmUMiMW7cuIFZs2YhNTW11jEsy4JhGFRUVIgxGSFEWuzatQsLFiwQvm7atCmAT/sRq2zdurXaGCJes2fPxuPHjxu8N4uQ/wqBQIDJkyfj8OHDdY6bNm0ajhw5Qp+JGlnTpk3RrVu3aj9japo5tLKyQnp6Ou9mDukoCyIR4uLiMGjQIGRmZsLNzQ2dO3cGACxbtgwuLi7C/T2TJ0/GqlWruIxKCOGxuXPnIiQkBE5OTlBQUMCbN2/w5s0bKCgoYMCAAQgJCaHCkGN37txBaGgo1zEIEan6zN3Q/I5omJqaIi4uDrm5ubWOSUtLw927d2FhYSHGZOJBy0qJRNi4cSPKy8tx7do1ODg4YPLkybh//z5++eUXAEB+fj6mT5+Oy5cvIy4ujuO0hBA+s7W1ha2tLSoqKvDmzRsAn5rV1HXEBSGEiNvz589p/60ITJs2DaGhoRg3bhx8fX0/62Kdn58PT09PlJWV4bvvvuMopehQcUgkQnR0NLp16wYHB4car6urq+PEiRNo1aoVVq5ciePHj4s5ISFE2sjIyEBLS4vrGIQQKXDixIlqr588efLZe1XKy8uRlJSEGzduwNLSUhzxpMq4ceNw6dIl+Pr6wsjICNbW1gCAqKgoDBs2DGFhYSgsLMTEiRMxePBgjtM2PioOiUR4+/Ztte6AcnJyAFDtMGR5eXn07t0b169f5yIiIYQQQohIeHh4CM8sZBgGUVFRiIqKqnU8y7JQUFCgrTYi4u3tjW7dumHr1q0ICgoC8KkBUEpKCtTU1PDLL79g2bJlHKcUDSoOiUTQ1NREYWFhtdcA8PTpU+H+Q+DTwaQFBQViz0cIkQ6PHj3Cli1bEB4ejhcvXqC0tLTGcXSkCyGkMa1atQoMw4BlWaxbtw6mpqYYNmxYjWPl5OSgq6sLR0dH6OjoiDmpdGAYBosXL8aCBQtw584dpKeno7KyEnp6erCwsBBOYvARFYdEIrRp0wZpaWnC1z169ADLsjh48CD27t0L4NMSi+DgYBgZGXEVkxDCYzExMejfv7/wfL2mTZtCW1ub41SEEGmwZs0a4X8fO3YM/fv3x+rVq7kLRAB82l5gYWHBy8YztaHikEgEZ2dnLF++HImJiWjfvj0GDBiAli1bYv/+/YiNjYWenh6Cg4Px8eNHTJkyheu4hBAeWr58OYqLizFv3jysXLlSeIwFEY3w8PCvuu/vq0wI4aP09HSuIxApRuccEonw8uVLXLhwATY2NujYsSMA4P79+xgzZgySkpIAfDpjZsqUKThw4IBwXT4hhDQWFRUVGBsb486dO1xHkQoCgeCrfpbTebdEWpWXl+PQoUN48OAB9PX18d133wmP+iKNq6SkBD4+PsItBiUlJTWOYxiGd2euUnFIJN7jx4+Rl5eHNm3aCPciEkJIY2vatCkGDBiAP/74g+soUsHOzu5fPegLCQlpxDSESI5169Zh7dq1CAkJga2tLQCgsrIStra2iImJET4gMTQ0RFxcHNTV1bkNzDPZ2dno168fUlJSvniWJB8fVNGyUiLx2rVrx3UEQogUsLGxwaNHj7iOITXoIHtCanb9+nXo6ekJC0MAOHPmDKKjo9GlSxfMnj0bV69exfnz5/Hrr79ixYoVHKbln8WLFyM5ORnW1tZYsGABjI2NoaqqynUssaGZQ0IIIQTAgwcPYGlpic2bN2P27NlcxyGESKkWLVqgc+fOuHbtmvA9V1dXnD59Gg8ePED79u3BsixatWqFpk2b4u7duxym5Z9mzZpBVVUVjx8/hoKCAtdxxI5mDgkn1q1b99X3MgyDn376qRHTEEIIcOfOHUyePBk//PADTp06BQcHB+jp6UEgENQ4fuLEiWJOSAiRBm/evEHz5s2rvRcWFoa2bduiffv2AD59FrKwsKDl1SJQUlKC/v37S2VhCNDMIeFIVSOCr/nrx8f13YQQ7tX0c6mmPXHUEEU0Tpw4Ua9xcnJyaNasGbp27QotLS0RpyJE/PT09NC2bVth4ffo0SN06tQJ3333HQ4cOCAc5+bmhkuXLuHdu3dcReUlKysrKCkp8a7RTH3RzCHhxNGjR7mOQAgh1VQdQk244eHh0aDvP8Mw6N+/P/bs2YO2bduKMBkh4tW+fXuEhYXh7t276NatG7Zv3w6GYeDs7FxtXHp6OnR0dDhKyV9Lly6Fi4sL/vzzT/To0YPrOGJHM4eEEEII4dyaNWuQnp6OEydOQEVFBY6OjjAwMAAAZGVlISgoCO/evYO7uzvk5eURHR2NR48eQUtLC/Hx8WjRogXH/w8IaRwBAQEYPHgwBAIB1NXVkZeXB0NDQzx69AhycnIAgIKCAmhpaWHo0KE4ffo0x4n5JTMzE7/++iv27duHBQsWfHGLQdXPKb6g4pAQQgghnEtNTUWPHj0wYsQIbNu2DWpqatWuFxYWYsGCBfD398ft27dhZGSExYsXY8eOHZg9ezb27NnDUXJCGt+vv/6KLVu24PXr1+jevTt+/fVXdO7cWXh9z549mDt3Lvbt24cZM2ZwmJR//r7F4EurGRiGQXl5uZiSiQcVh0TiZGdnIyoqCtnZ2QA+de3q1asXPRUmhBAeGzNmDO7cuYPk5ORan9BXVlbC2NgYZmZmOHXqFEpLS2FoaAglJSWkpKSIOTEh3CkuLkZpaSlUVFQgIyPDdRxeaegZrHxrCkR7DonEyM3NxezZs+Hv74/Kyspq1xiGwahRo7B3715oampylJAQwieenp5gGAYbNmzAt99+C09Pz3rfyzAMDh8+LMJ00ickJASOjo61FobApyf6PXr0QFBQEIBPzWm6du1KZyYSqaOoqAhFRUWuY/CStP88oZlDIhEKCgpgaWmJpKQkKCoqwtHREa1atQLDMEhPT0dgYCCKi4thYmKCW7dufbbciBBCGqpq6VBiYiKMjY3rLEr+ibqVNj5lZWVYWlp+sUNgv379cPv2bbx//x7Ap/PfAgICUFhYKI6YhIhNeXk5rly5gj///BOvX79Gz549hQ+xnj9/jtevX6NDhw745hua6xGFtLQ0vH79GgDQvHlzGBoacpxIPOhvE5EImzZtQlJSElxcXGqcHXz9+jXmzJmDU6dOYfPmzdiwYQNHSQkhfFG1FKiqmQDflgb913Tu3Bnh4eEIDw+Hra1tjWMiIiIQFhYGCwsL4XtZWVm0ooTwTmRkJCZMmICsrCzh3reysjJhcRgTE4MxY8bg9OnTGDlyJMdp+ePPP//E9u3bERQUhIKCgmrX1NTU4OTkhPnz5/O6iynNHBKJ0K5dOxQXF+PJkyeQlZWtcUxZWRnatGkDBQUFJCUliTkhIYQQUTp37hxGjx4NeXl5TJw4EaNHj4a+vj6ATwXg2bNnceLECZSUlODMmTMYMWIECgoK8O2332LUqFHw9vbm+P8BIY3j0aNH6NGjB8rKyjBz5kzY2NhgzJgx8PDwwJEjRwAApaWlaNasGYYOHUp/9xtJVYOrqq1NcnJy0NDQAADk5eWhtLQUwKdVJ/Pnz8fWrVs5yypKNHNIJEJGRgZGjBhRa2EIALKysujVqxf8/f3FmIwQIi1GjhwJHR0d/Prrr1xHkUojR47Ejh07sHTpUvz+++84dOhQtessy0JOTg47duzAiBEjAABv3rzB2rVr0a9fPy4iEyISP//8Mz5+/IiAgAA4OjrWOEZOTg5mZma4e/eumNPx04IFC7Bz504oKChg9uzZGD9+PDp37ixs9lNRUYEHDx7A29sbv/76K7Zv347y8nLs2LGD4+SNj2YOiURo2rQpzM3NhU0GauPo6Ii4uDi8fftWTMkIIdJCQUEBw4cPh6+vL9dRpFpaWhoOHz6M6OhovHjxAgCgo6ODXr16YfLkyTAyMuI4ISGipa2tDSMjI0RHRwvfEwgE1WYOAcDNzQ0BAQHIz8/nICV/3Lp1C9bW1tDX10dgYCDatWtX5/ikpCQ4ODggOzsb0dHR6Nmzp5iSigfNHBKJ0L17d4SFhSEuLg7m5uY1jomPj0doaCjs7OzEG44QIhUMDQ3x4cMHrmNIPUNDQ6xfv57rGIRwJj8/X7ikui4fPnxAWVmZGBLx24EDB8AwDHx8fL5YGAKAiYkJfH19YWNjgwMHDvCuOKx/azZCRGj+/PkoKytDv3798NNPPyExMRHFxcUoLi7G48ePsXr1avTv3x8VFRWYP38+13EJITw0btw4hIWF4eXLl1xHIYRIMS0tLTx58uSL4xITE+tVRJK6hYeHo0uXLrC2tq73PdbW1rw9RodmDolEcHZ2xi+//IKffvoJGzZsqLEbKcMwWL9+PQYOHMhBQkII3y1fvhy3b99Gnz59sGnTJgwePLjOfdBEdDIzM/HixQuUlJTUOqa2jqaE/NfZ29vj5MmTCAkJQd++fWsc4+/vjydPnmD27NliTsc/L1++hKWlZYPva9euHS5cuCCCRNyi4pBIjOXLl8PBwQF79uxBZGQknj9/DgDQ1dVF7969MXv27GrtywkhpDGZmJigsrISWVlZGD16NBiGgZaWFhQUFD4byzAMUlNTOUjJb0eOHMHPP/+MzMzML46lcyYJXy1btgx+fn4YPnw4Nm3aJGzABHzqmunv749FixZBWVkZCxYs4DApPygoKKCoqKjB9xUXF0NeXl4EibhFDWkIIYQQfGr40BBV7c5J4zh69CimTJkCAOjUqROMjY2hqqpa53hC+Or8+fNwd3evtWhRUFCAj48Phg4dKuZk/GNhYYGMjAy8ePFC2J30SyoqKqCjowMDAwPExcWJOKF40cwhIYQQAir2uLZ9+3Z88803OHPmDH3gJVJv+PDhePDgAXbs2IHr168jPT0dlZWV0NPTg4ODAxYuXIjWrVtzHZMXBg0ahJ9//hmbN2/Gjz/+WK97tmzZgjdv3vByWS/NHBKJ8OrVKyQlJcHExATffvut8P3U1FSsWLECDx48gIGBAVatWvVV68IJIYRINgUFBdjY2ODGjRtcRyGEUxcvXoSsrCz1WBCTvLw8tG7dGoWFhVizZg2WL19e6wxiRUUFNm3ahFWrVkFDQwMpKSnQ0NAQc2LRouKQSIT58+dj9+7dSExMhLGxMQCgsLAQJiYmyMnJQdVfU0VFRSQkJKBt27ZcxiWE8EhAQADOnz+PrKwsyMvLo0uXLpg8eTIMDQ25jiZVdHV1YWtrS+dMEqknIyMDR0dHXL16lesoUuPmzZsYNGgQysrKoKurCxcXF5ibm0NTUxMAkJubi7i4OJw5cwbZ2dmQlZVFQEAA7O3tOU7e+Kg4JBKhW7duKC8vx/3794Xv7d69G/PmzYObmxtWr16NK1euYMGCBfjuu+9w4MABDtMSQvhi/PjxwmKk6tchwzCQl5eHr68vLW8Uo5kzZ+Ly5ct4+vQpdYklUk1bWxv29vb4448/uI4iVWJjYzFp0iQ8fvwYDMN8dr3qd0S7du1w4sSJWs/l/q+j4pBIBC0tLVhZWVVrCezk5ISQkBA8f/4czZs3B/CpiPz48SMSExO5ikoI4YnDhw9j2rRp+Oabb+Du7o5u3brh3bt3uHz5MmJiYtCkSRNkZGRATU2N66hSIS8vD1ZWVjA3N8eePXt4t1SLkPpydXVFXFwcUlJSaixSiGhdvXoVV65cwb179/DmzRsAQLNmzdC1a1c4Oztj4MCBvP5zoeKQSARFRUUMHz4cPj4+AD6t6dbQ0EDHjh0RExMjHDdu3DhcvnwZ79694yoqIYQnbG1tER0djcDAQPTr16/atcmTJ+PEiRM4dOgQJk+ezFFC6eLp6Yn8/HxcuHABTZo0Qffu3aGnp1djF1mGYXD48GEOUhIieikpKbCwsICHhwc2bdpU43E6hIgKdSslEkFXVxePHz8Wvo6MjMT79+9hZ2dXbVx5eTnk5OTEnI4Qwkf379+HpaXlZ4UhAPz44484fvx4taXuRLSOHTsm/O+CggIEBwfXOpaKQ8JnPj4+cHZ2xp49e+Dr64v+/fvDwMCg1jNXf/rpJw5SEr6i4pBIBCsrK/j4+GDnzp3o168fVq5cCYZhMGTIkGrjEhMT0aJFC45SEkL4pLCwsNZW8FXvFxYWijOSVAsJCeE6AiESYc2aNWAYBizLIicnp869h1QcksZGxSGRCMuXL8e5c+ewcOFCAJ82/fbt2xfW1tbCMenp6Xj06JHwkGRCCPk3WJattV151VJGOvtQfPr06cN1BEIkwtGjR7mOQKQYFYdEInTs2BGRkZHYtWsXXr9+je7du2Px4sXVxgQGBqJr164YPnw4NyEJIYQQQkRs0qRJXEcgUowa0hBCCJFKAoHgqzvOMQyD8vLyRk5ECCGEcItmDgkhhEitr30+Ss9V/z0jIyMwDIMbN27A0NAQRkZG9b6XYRikpqaKMB0hhEgnKg4JIYRIJdpPyK309HQAQFlZWbXXhJBPD6C8vb1x4cIFpKSk4N27dzU+lKIHJaSx0bJSwonamkDUBy3nIoQQQghflZaWYtCgQQgODq51lUJVN1OAHnSRxvX5ybKEiAHLsl/9RT8ECSGEEMJX27Ztw82bNzF48GCkpKTA3d0dDMOgpKQEiYmJWLNmDZSVlbF48WL6TEQaHc0cEkIIIYRzTZs2RefOnREWFsZ1FEI4ZWpqimfPniEjIwPKysqYPHkyTpw4gYqKCuGYiIgI9O3bF7/99hs8PT05TEv4hmYOCSGEEMK58vJy6OnpcR2DEM49efIEPXr0gLKyMoD/f+7q34vD3r17o1evXti3bx8nGQl/UXFICCGEEM517NgR2dnZXMcghHMyMjJQU1MTvq4qEnNzc6uNa9GiBZKSksSajfAfFYeEEEII4dz333+PyMhIREZGch2FEE61aNECz549E75u06YNAODWrVvVxv31119QUVERazbCf3SUBSGEEEI4Z2Njg6lTp8LJyQlTp07FkCFDYGBgAAUFhRrHGxgYiDkhIeJhaWkJf39/lJSUQF5eHs7Ozpg/fz7mzZsHBQUFtGjRAr/99hsSExMxZMgQruMSnqGGNIQQQgjhnEAgELbnZximzrF0pBHhsytXrmDq1Kn47bffhMXfwoULsWPHDuG/DZZloaysjPj4eBgbG3MZl/AMFYeEEEII4Zydnd0Xi8K/CwkJEWEaQiSPr68vzp8/j7y8PBgbG+OHH35A27ZtuY5FeIaKQ0IIIYQQQggh1JCGEEIIIYQQQggVh4QQQgiRQCkpKYiJiUFycjLXUQjhxKNHjzB//nz06tULJiYmWLJkifBadHQ0du/ejbdv33KYkPARFYeEEEIIkQglJSX48ccf0bx5c7Rr1w42NjbYtGmT8LqXlxfMzMyQkJDAXUhCxGD79u0wNTXFrl27EBMTgydPnuD169fVxsyfPx+nT5/mKCHhKyoOCSGEEMK54uJi2NnZYfPmzZCTk4OzszP+2RbB3t4e9+7dw6lTpzhKSYjoXblyBYsWLYK+vj7OnTuHnJycz/4tWFtbQ1NTExcuXOAoJeErKg4JIYQQwrktW7bg9u3b8PT0xNOnT3Hp0qXPxujq6qJDhw64ceMGBwkJEY/t27dDWVkZ169fx/Dhw9G8efMax5mamiIpKUnM6QjfUXFICCGEEM75+fnBwMAA+/fvr/XgewAwMTFBVlaWGJMRIl7x8fGwtLSEkZFRneOaN2+Oly9fiikVkRZUHBJCCCGEc2lpaTA3N8c333xT5zg5OTnk5eWJKRUh4ldaWgpVVdUvjsvJyfnivxdCGoqKQ0IIIYRwTlFRsV5FX1paGjQ0NMSQiBBuGBoa4t69e3WOKS0txV9//QVjY2MxpSLSgopDQgghhHDO1NQUcXFxyM3NrXVMWloa7t69CwsLCzEmI0S8hg4divT0dGzfvr3WMVu2bEFubi5GjhwpxmREGlBxSAghhBDOTZs2De/evcO4ceM+a9kPAPn5+fD09ERZWRm+++47DhISIh5LlixBixYtsHjxYowdOxa+vr4AgFevXsHf3x8TJ07E6tWrYWhoiDlz5nCclvANw/6zNy4hhBBCCAfc3Nzg6+sLFRUVWFtbIygoCG3btkW7du0QFhaGwsJCTJw4EceOHeM6KiEilZycjNGjR+PBgwdgGAYsy4JhGAAAy7Lo0KEDzp8/jzZt2nCclPANFYeEEEIIkQgsy+J///sftm7d+tnsoZqaGpYsWYJly5YJPyQTwmeVlZW4dOkSgoKCkJ6ejsrKSujp6cHBwQGjRo2CjIwM1xEJD1FxSAghhBCJUlFRgTt37lT7QGxhYQE5OTmuoxFCCK9RcUgIIYQQQgghBHQ4CiGEEEI4JyMjAw8PDxw+fLjOcdOmTcPRo0dRXl4upmSEiFZmZua/ut/AwKCRkhBCxSEhhBBCJADLsqjvYiZa9ET4pFWrVl+9j5ZhGHpQQhoVFYeEEEII+c8oKCiAvLw81zEIaTTGxsYNLg4zMzNRXFwsokREmlFxSAghhBBO/HM53fv372tdYldeXo6kpCQEBQWhdevW4ohHiFg8fvy43mPv3LmD1atXIykpCQDo3wJpdNSQhhBCCCGcEAgE1c5uq8/sCcuy2LlzJ3744QdRxyNEYvz1119YvXo1Ll68CJZlYWhoiJUrV2LixIl0pAVpVDRzSAghhBBO2NraCgvCsLAwaGlpoV27djWOlZOTg66uLoYOHYoRI0aIMyYhnHn48CHWrFkDf39/VFZWomXLllixYgU8PDzwzTf0MZ40Ppo5JIQQQgjnBAIBPDw8cOTIEa6jEMK5x48fY82aNThz5gwqKyuhr6+PH3/8EVOmTKGikIgU/e0ihBBCCOfS0tKgoqLCdQxCOJWSkoI1a9bg1KlTqKioQIsWLfDjjz9i6tSpkJWV5ToekQI0c0gIIYQQiZaYmIiHDx9CX18fPXv25DoOIY0uNTUV69atg4+PD8rLy6Gjo4Ply5fju+++g5ycHNfxiBQRcB2AEEIIIcTPzw/29va4fft2tfcXL16MTp06YezYsbC2tsaIESNQUVHBUUpCGp+npyfat28PLy8vNG/eHDt37sTTp08xZ84cKgyJ2NHMISGEEEI4N2TIEERFReHly5fCD8TR0dGwsbFBkyZNMGjQINy6dQvp6ek4evQoJk6cyHFiQhpHVddeRUVFjBgxAoqKivW+l2EYHDx4UITpiLSh4pAQQgghnDM0NETLli0RGhoqfG/mzJn47bffcPXqVTg6OuLt27cwNDRE165dER4ezl1YQhqRQPD1C/kYhqGZdNKoqCENIYQQQjiXk5MDa2vrau+FhIRAS0sLjo6OAICmTZvC1tYW8fHxXEQkRCSOHj3KdQRChKg4JIQQQgjnFBUVUVhYKHz94sULJCcnY8yYMdXGqaurIy8vT9zxCBGZSZMmcR2BECFqSEMIIYQQzhkZGSEiIgL5+fkAAG9vbzAMI5w1rPLy5UtoaWlxkJAQ0cjOzv7qe5ctW9aISQih4pAQQgghEsDDwwOFhYXo3r07Ro0ahZUrV0JFRQXDhg0TjikrK0NcXByMjY05TEpI4+rbty9evnzZ4Ptmz56NrVu3iiARkWZUHBJCCCGEc9OmTcOoUaOQlpYGf39/yMrK4uDBg2jWrJlwzOXLl1FQUAB7e3sOkxLSuJ48eYK+ffvi1atX9RrPsiwmTZqE/fv3Q0VFRcTpiLShbqWEEEIIkRjp6enIzc1Fu3btoKqqWu1aQkICMjIyYGlpiW+//ZajhIQ0rrFjx+L06dNo3769sAlTbcrLy+Hq6opz585BQ0MDV69eRY8ePcSYlvAdFYeEEEIIIYRwpKKiAq6urjh79iw6duyIkJAQNG/e/LNxHz9+xIgRIxAYGAgtLS0EBQWhS5cuHCQmfEbFISGEEEIIIRyqqKjAmDFj4O/vj86dOyM4OLjakur3799j0KBBiIiIgJ6eHm7cuEF7b4lIUHFICCGEEM55enrWeyzDMDh8+LAI0xAifuXl5XBxccGFCxdgamqKmzdvQkNDA2/fvsWAAQMQFxeH1q1b48aNG2jZsiXXcQlPUXFICCGEEM4JBF/ukccwDFiWBcMwqKioEEMqQsSrvLwco0ePxsWLF2FmZoYTJ05g7NixePjwITp27Ijr169DW1ub65iEx6g4JIQQQgjnwsLCany/srISWVlZCAoKgq+vL+bNm4chQ4agT58+Yk5IiHiUlZVh1KhRuHz5MgQCASorK2FmZobAwMBqS00JEQUqDgkhhBDyn+Dj44NJkybhxo0bsLW15ToOISJTVlaGkSNH4sqVK7CxscGVK1c+695LiChQcUgIIYSQ/4xu3bpBQ0MDwcHBXEchpFHIyMh89b0Mw6C8vLwR0xBp9+UF/oQQQgghEqJt27aIi4vjOgYhjYZl2a/+qqys5Do+4ZlvuA5ACCGEEFIflZWV+Ouvv+rVvIaQ/woq8IgkoZ+uhBBCCJFoRUVFSEhIwLhx45CSkkLNaAghRERozyEhhBBCOFeffVcsy0JTUxMRERF0ADghhIgALSslhBBCCOf09fXBMEyN1+Tk5KCjo4M+ffpg9uzZ0NLSEnM6QgiRDjRzSAghhBBCCCGE9hwSQgghhBBCCKHikBBCCCGEEEIIaM8hIYQQQjhw4sSJf3X/xIkTGykJIYSQKrTnkBBCCCFiJxAIam1AUxeWZcEwDCoqKkSQihBCpBvNHBJCCCFE7FatWvVVxSEhhBDRoZlDQgghhBBCCCE0c0gIIYQQbgQHB+PZs2cwNzdHhw4d6hz76NEjxMXFQV9fH3379hVTQkIIkS40c0gIIYQQscvKyoKxsTH09fURHx8PVVXVOse/e/cO5ubmePbsGVJSUqCrqyumpIQQIj3oKAtCCCGEiN2hQ4dQWlqKLVu2fLEwBABVVVVs3boVxcXFOHz4sBgSEkKI9KGZQ0IIIYSInbW1NZ4+fYqXL1826D4dHR0YGhoiOjpaRMkIIUR60cwhIYQQQsTu8ePHsLCwaPB95ubmSEpKEkEiQgghVBwSQgghROw+fPgANTW1Bt+npqaG9+/fiyARIYQQKg4JIYQQInYaGhp49epVg+979eoVNDQ0RJCIEEIIFYeEEEIIEbsOHTrg1q1bKC4urvc9RUVFiImJ+eKxF4QQQr4OFYeEEEIIEbvBgwfjw4cPWL9+fb3vWb9+PYqLizFkyBARJiOEEOlF3UoJIYQQInZFRUVo3bo1cnJysHbtWvz4448QCGp+Zl1ZWYlffvkFq1evhra2Np48eQIlJSUxJyaEEP6j4pAQQgghnIiOjkb//v1RUlICPT09uLi4wMzMDJqamgCA3Nxc3LlzB6dPn8azZ88gLy+PmzdvwsrKiuPkhBDCT1QcEkIIIYQzCQkJcHd3x8OHD8EwzGfXqz6mdOzYEV5eXujatau4IxJCiNSg4pAQQgghnLt27RquXLmChIQEvHnzBgDQrFkzmJqaYtCgQRgwYADHCQkhhP+oOCSEEEIIIYQQQt1KCSGEEEIIIYRQcUgIIYQQQgghBFQcEkIIIYQQQggBFYeEEEIIIYQQQkDFISGEEEIIIYQQUHFICCGEEEIIIQRUHBJCCCGEEEIIAfB/OENn9ErZ9+IAAAAASUVORK5CYII="
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"
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@@ -16348,7 +16348,7 @@ <h4 id="Hyperparameter-Tuning">Hyperparameter Tuning<a class="anchor-link" href=
 
 
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"
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"
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@@ -16506,16 +16506,16 @@ <h3 id="Specify-a-time-budget-to-Oracle-AutoMLx">Specify a time budget to Oracle
 
 
 <div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
-<pre>[2024-12-26 05:51:20,029] [automlx.interface] Dataset shape: (17088,19)
-[2024-12-26 05:51:20,130] [automlx.data_transform] Running preprocessing. Number of features: 20
-[2024-12-26 05:51:20,307] [automlx.data_transform] Preprocessing completed. Took 0.177 secs
-[2024-12-26 05:51:20,357] [automlx.process] Running Model Generation
-[2024-12-26 05:51:20,404] [automlx.process] Model Generation completed.
-[2024-12-26 05:51:20,473] [automlx.model_selection] Running Model Selection
-[2024-12-26 05:51:30,409] [automlx.model_selection] Model Selection completed - Took 9.936 sec - Selected models: [[&#39;MinCovOD&#39;]]
-[2024-12-26 05:51:30,419] [automlx.process] Timebudget exceeded for steps [&#39;HyperparameterOptimization&#39;], skipping processing of [MinCovOD (InputTargetDataTransformer_MinCovOD)]
-[2024-12-26 05:51:30,503] [automlx.interface] Re-fitting pipeline
-[2024-12-26 05:51:30,514] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_0acb6af2-a
+<pre>[2025-04-25 03:04:02,809] [automlx.interface] Dataset shape: (17088,19)
+[2025-04-25 03:04:02,906] [automlx.data_transform] Running preprocessing. Number of features: 20
+[2025-04-25 03:04:03,080] [automlx.data_transform] Preprocessing completed. Took 0.174 secs
+[2025-04-25 03:04:03,122] [automlx.process] Running Model Generation
+[2025-04-25 03:04:03,169] [automlx.process] Model Generation completed.
+[2025-04-25 03:04:03,235] [automlx.model_selection] Running Model Selection
+[2025-04-25 03:04:13,204] [automlx.model_selection] Model Selection completed - Took 9.969 sec - Selected models: [[&#39;MinCovOD&#39;]]
+[2025-04-25 03:04:13,215] [automlx.process] Timebudget exceeded for steps [&#39;HyperparameterOptimization&#39;], skipping processing of [MinCovOD (InputTargetDataTransformer_MinCovOD)]
+[2025-04-25 03:04:13,296] [automlx.interface] Re-fitting pipeline
+[2025-04-25 03:04:13,307] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_c8b84ec6-f
 F1-Score on test data : 0.3114754098360656
 </pre>
 </div>
@@ -16722,37 +16722,37 @@ <h4 id="Visualization">Visualization<a class="anchor-link" href="#Visualization"
     <tr>
       <th>0</th>
       <td>V19</td>
-      <td>0.062620</td>
-      <td>0.057794</td>
-      <td>0.067446</td>
+      <td>0.062594</td>
+      <td>0.058448</td>
+      <td>0.066740</td>
     </tr>
     <tr>
       <th>1</th>
       <td>V8</td>
-      <td>0.051431</td>
-      <td>0.044974</td>
-      <td>0.057888</td>
+      <td>0.047495</td>
+      <td>0.042024</td>
+      <td>0.052967</td>
     </tr>
     <tr>
       <th>2</th>
       <td>V4</td>
-      <td>0.034813</td>
-      <td>0.028819</td>
-      <td>0.040807</td>
+      <td>0.035441</td>
+      <td>0.030222</td>
+      <td>0.040659</td>
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     <tr>
       <th>3</th>
       <td>V3</td>
-      <td>0.030684</td>
-      <td>0.026032</td>
-      <td>0.035337</td>
+      <td>0.030964</td>
+      <td>0.028117</td>
+      <td>0.033810</td>
     </tr>
     <tr>
       <th>4</th>
       <td>V14</td>
-      <td>0.015264</td>
-      <td>0.011293</td>
-      <td>0.019234</td>
+      <td>0.013733</td>
+      <td>0.011846</td>
+      <td>0.015620</td>
     </tr>
     <tr>
       <th>12</th>
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     <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_6b8c9_level0_col0\" class=\"col_heading level0 col0\" >V1</th>\n      <th id=\"T_6b8c9_level0_col1\" class=\"col_heading level0 col1\" >V2</th>\n      <th id=\"T_6b8c9_level0_col2\" class=\"col_heading level0 col2\" >V3</th>\n      <th id=\"T_6b8c9_level0_col3\" class=\"col_heading level0 col3\" >V4</th>\n      <th id=\"T_6b8c9_level0_col4\" class=\"col_heading level0 col4\" >V5</th>\n      <th id=\"T_6b8c9_level0_col5\" class=\"col_heading level0 col5\" >V6</th>\n      <th id=\"T_6b8c9_level0_col6\" class=\"col_heading level0 col6\" >V7</th>\n      <th id=\"T_6b8c9_level0_col7\" class=\"col_heading level0 col7\" >V8</th>\n      <th id=\"T_6b8c9_level0_col8\" class=\"col_heading level0 col8\" >V9</th>\n      <th id=\"T_6b8c9_level0_col9\" class=\"col_heading level0 col9\" >V10</th>\n      <th id=\"T_6b8c9_level0_col10\" class=\"col_heading level0 col10\" >V11</th>\n      <th id=\"T_6b8c9_level0_col11\" class=\"col_heading level0 col11\" >V12</th>\n      <th id=\"T_6b8c9_level0_col12\" class=\"col_heading level0 col12\" >V13</th>\n      <th id=\"T_6b8c9_level0_col13\" class=\"col_heading level0 col13\" >V14</th>\n      <th id=\"T_6b8c9_level0_col14\" class=\"col_heading level0 col14\" >V15</th>\n      <th id=\"T_6b8c9_level0_col15\" class=\"col_heading level0 col15\" >V16</th>\n      <th id=\"T_6b8c9_level0_col16\" class=\"col_heading level0 col16\" >V17</th>\n      <th id=\"T_6b8c9_level0_col17\" class=\"col_heading level0 col17\" >V18</th>\n      <th id=\"T_6b8c9_level0_col18\" class=\"col_heading level0 col18\" >V19</th>\n      <th id=\"T_6b8c9_level0_col19\" class=\"col_heading level0 col19\" >V20</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_6b8c9_level0_row0\" class=\"row_heading level0 row0\" >Original Sample</th>\n      <td id=\"T_6b8c9_row0_col0\" class=\"data row0 col0\" >-0.814601292682208</td>\n      <td id=\"T_6b8c9_row0_col1\" class=\"data row0 col1\" >0.8527854763983154</td>\n      <td id=\"T_6b8c9_row0_col2\" class=\"data row0 col2\" >1.0423288727328561</td>\n      <td id=\"T_6b8c9_row0_col3\" class=\"data row0 col3\" >-2.2223952125512327</td>\n      <td id=\"T_6b8c9_row0_col4\" class=\"data row0 col4\" >0.28871233525783424</td>\n      <td id=\"T_6b8c9_row0_col5\" class=\"data row0 col5\" >-0.8395058532520351</td>\n      <td id=\"T_6b8c9_row0_col6\" class=\"data row0 col6\" >0.9928911115798961</td>\n      <td id=\"T_6b8c9_row0_col7\" class=\"data row0 col7\" >-0.20909651374437896</td>\n      <td id=\"T_6b8c9_row0_col8\" class=\"data row0 col8\" >0.7806740947971558</td>\n      <td id=\"T_6b8c9_row0_col9\" class=\"data row0 col9\" >-0.6137903575618552</td>\n      <td id=\"T_6b8c9_row0_col10\" class=\"data row0 col10\" >1.8301436434282867</td>\n      <td id=\"T_6b8c9_row0_col11\" class=\"data row0 col11\" >1.1944318873587512</td>\n      <td id=\"T_6b8c9_row0_col12\" class=\"data row0 col12\" >-0.12699362950573215</td>\n      <td id=\"T_6b8c9_row0_col13\" class=\"data row0 col13\" >0.16516219940938834</td>\n      <td id=\"T_6b8c9_row0_col14\" class=\"data row0 col14\" >0.4184581901658031</td>\n      <td id=\"T_6b8c9_row0_col15\" class=\"data row0 col15\" >-0.7801235968866925</td>\n      <td id=\"T_6b8c9_row0_col16\" class=\"data row0 col16\" >-0.4978313765414018</td>\n      <td id=\"T_6b8c9_row0_col17\" class=\"data row0 col17\" >-0.028500870424174533</td>\n      <td id=\"T_6b8c9_row0_col18\" class=\"data row0 col18\" >-0.27366085753472397</td>\n      <td id=\"T_6b8c9_row0_col19\" class=\"data row0 col19\" >0.12669591871187177</td>\n    </tr>\n    <tr>\n      <th id=\"T_6b8c9_level0_row1\" class=\"row_heading level0 row1\" >Modified Sample</th>\n      <td id=\"T_6b8c9_row1_col0\" class=\"data row1 col0\" >-0.814601292682208</td>\n      <td id=\"T_6b8c9_row1_col1\" class=\"data row1 col1\" >0.8527854763983154</td>\n      <td id=\"T_6b8c9_row1_col2\" class=\"data row1 col2\" >1.0423288727328561</td>\n      <td id=\"T_6b8c9_row1_col3\" class=\"data row1 col3\" >-2.2223952125512327</td>\n      <td id=\"T_6b8c9_row1_col4\" class=\"data row1 col4\" >0.28871233525783424</td>\n      <td id=\"T_6b8c9_row1_col5\" class=\"data row1 col5\" >-0.8395058532520351</td>\n      <td id=\"T_6b8c9_row1_col6\" class=\"data row1 col6\" >0.9928911115798961</td>\n      <td id=\"T_6b8c9_row1_col7\" class=\"data row1 col7\" >-0.20909651374437896</td>\n      <td id=\"T_6b8c9_row1_col8\" class=\"data row1 col8\" >0.7806740947971558</td>\n      <td id=\"T_6b8c9_row1_col9\" class=\"data row1 col9\" >-0.6137903575618552</td>\n      <td id=\"T_6b8c9_row1_col10\" class=\"data row1 col10\" >1.8301436434282867</td>\n      <td id=\"T_6b8c9_row1_col11\" class=\"data row1 col11\" >1.1944318873587512</td>\n      <td id=\"T_6b8c9_row1_col12\" class=\"data row1 col12\" >-0.12699362950573215</td>\n      <td id=\"T_6b8c9_row1_col13\" class=\"data row1 col13\" >0.16516219940938834</td>\n      <td id=\"T_6b8c9_row1_col14\" class=\"data row1 col14\" >0.4184581901658031</td>\n      <td id=\"T_6b8c9_row1_col15\" class=\"data row1 col15\" >-0.7801235968866925</td>\n      <td id=\"T_6b8c9_row1_col16\" class=\"data row1 col16\" >-0.4978313765414018</td>\n      <td id=\"T_6b8c9_row1_col17\" class=\"data row1 col17\" >-0.028500870424174533</td>\n      <td id=\"T_6b8c9_row1_col18\" class=\"data row1 col18\" >-0.27366085753472397</td>\n      <td id=\"T_6b8c9_row1_col19\" class=\"data row1 col19\" >0.12669591871187177</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "0f1a02b9f4fb44fba495ec27ef458a14": {"model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": 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id=\"T_0606d_level0_col5\" class=\"col_heading level0 col5\" >V6</th>\n      <th id=\"T_0606d_level0_col6\" class=\"col_heading level0 col6\" >V7</th>\n      <th id=\"T_0606d_level0_col7\" class=\"col_heading level0 col7\" >V8</th>\n      <th id=\"T_0606d_level0_col8\" class=\"col_heading level0 col8\" >V9</th>\n      <th id=\"T_0606d_level0_col9\" class=\"col_heading level0 col9\" >V10</th>\n      <th id=\"T_0606d_level0_col10\" class=\"col_heading level0 col10\" >V11</th>\n      <th id=\"T_0606d_level0_col11\" class=\"col_heading level0 col11\" >V12</th>\n      <th id=\"T_0606d_level0_col12\" class=\"col_heading level0 col12\" >V13</th>\n      <th id=\"T_0606d_level0_col13\" class=\"col_heading level0 col13\" >V14</th>\n      <th id=\"T_0606d_level0_col14\" class=\"col_heading level0 col14\" >V15</th>\n      <th id=\"T_0606d_level0_col15\" class=\"col_heading level0 col15\" >V16</th>\n      <th id=\"T_0606d_level0_col16\" class=\"col_heading level0 col16\" >V17</th>\n      <th id=\"T_0606d_level0_col17\" class=\"col_heading level0 col17\" >V18</th>\n      <th id=\"T_0606d_level0_col18\" class=\"col_heading level0 col18\" >V19</th>\n      <th id=\"T_0606d_level0_col19\" class=\"col_heading level0 col19\" >V20</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_0606d_level0_row0\" class=\"row_heading level0 row0\" >Original Sample</th>\n      <td id=\"T_0606d_row0_col0\" class=\"data row0 col0\" >-0.814601292682208</td>\n      <td id=\"T_0606d_row0_col1\" class=\"data row0 col1\" >0.8527854763983154</td>\n      <td id=\"T_0606d_row0_col2\" class=\"data row0 col2\" >1.0423288727328561</td>\n      <td id=\"T_0606d_row0_col3\" class=\"data row0 col3\" >-2.2223952125512327</td>\n      <td id=\"T_0606d_row0_col4\" class=\"data row0 col4\" >0.28871233525783424</td>\n      <td id=\"T_0606d_row0_col5\" class=\"data row0 col5\" >-0.8395058532520351</td>\n      <td id=\"T_0606d_row0_col6\" class=\"data row0 col6\" >0.9928911115798961</td>\n      <td id=\"T_0606d_row0_col7\" class=\"data row0 col7\" >-0.20909651374437896</td>\n      <td id=\"T_0606d_row0_col8\" class=\"data row0 col8\" >0.7806740947971558</td>\n      <td id=\"T_0606d_row0_col9\" class=\"data row0 col9\" >-0.6137903575618552</td>\n      <td id=\"T_0606d_row0_col10\" class=\"data row0 col10\" >1.8301436434282867</td>\n      <td id=\"T_0606d_row0_col11\" class=\"data row0 col11\" >1.1944318873587512</td>\n      <td id=\"T_0606d_row0_col12\" class=\"data row0 col12\" >-0.12699362950573215</td>\n      <td id=\"T_0606d_row0_col13\" class=\"data row0 col13\" >0.16516219940938834</td>\n      <td id=\"T_0606d_row0_col14\" class=\"data row0 col14\" >0.4184581901658031</td>\n      <td id=\"T_0606d_row0_col15\" class=\"data row0 col15\" >-0.7801235968866925</td>\n      <td id=\"T_0606d_row0_col16\" class=\"data row0 col16\" >-0.4978313765414018</td>\n      <td id=\"T_0606d_row0_col17\" class=\"data row0 col17\" >-0.028500870424174533</td>\n      <td id=\"T_0606d_row0_col18\" 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background-color: #F5F5F5;\n  min-width: 115px;\n  padding: 0 10px;\n  text-align: right;\n}\n#T_fc400 td {\n  text-align: right;\n  padding: 0 10px;\n}\n#T_fc400 tr:nth-child(even) {\n  background-color: #F9F9F9;\n}\n#T_fc400  {\n  border-collapse: collapse;\n  border: 1px solid #DDD;\n}\n</style>\n<table id=\"T_fc400\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_fc400_level0_col0\" class=\"col_heading level0 col0\" >V1</th>\n      <th id=\"T_fc400_level0_col1\" class=\"col_heading level0 col1\" >V2</th>\n      <th id=\"T_fc400_level0_col2\" class=\"col_heading level0 col2\" >V3</th>\n      <th id=\"T_fc400_level0_col3\" class=\"col_heading level0 col3\" >V4</th>\n      <th id=\"T_fc400_level0_col4\" class=\"col_heading level0 col4\" >V5</th>\n      <th id=\"T_fc400_level0_col5\" class=\"col_heading level0 col5\" >V6</th>\n      <th id=\"T_fc400_level0_col6\" class=\"col_heading level0 col6\" >V7</th>\n      <th id=\"T_fc400_level0_col7\" class=\"col_heading level0 col7\" >V8</th>\n      <th id=\"T_fc400_level0_col8\" class=\"col_heading level0 col8\" >V9</th>\n      <th id=\"T_fc400_level0_col9\" class=\"col_heading level0 col9\" >V10</th>\n      <th id=\"T_fc400_level0_col10\" class=\"col_heading level0 col10\" >V11</th>\n      <th id=\"T_fc400_level0_col11\" class=\"col_heading level0 col11\" >V12</th>\n      <th id=\"T_fc400_level0_col12\" class=\"col_heading level0 col12\" >V13</th>\n      <th id=\"T_fc400_level0_col13\" class=\"col_heading level0 col13\" >V14</th>\n      <th id=\"T_fc400_level0_col14\" class=\"col_heading level0 col14\" >V15</th>\n      <th id=\"T_fc400_level0_col15\" class=\"col_heading level0 col15\" >V16</th>\n      <th id=\"T_fc400_level0_col16\" class=\"col_heading level0 col16\" >V17</th>\n      <th id=\"T_fc400_level0_col17\" class=\"col_heading level0 col17\" >V18</th>\n      <th id=\"T_fc400_level0_col18\" class=\"col_heading level0 col18\" >V19</th>\n      <th id=\"T_fc400_level0_col19\" class=\"col_heading level0 col19\" >V20</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_fc400_level0_row0\" class=\"row_heading level0 row0\" >Original Sample</th>\n      <td id=\"T_fc400_row0_col0\" class=\"data row0 col0\" >-0.814601292682208</td>\n      <td id=\"T_fc400_row0_col1\" class=\"data row0 col1\" >0.8527854763983154</td>\n      <td id=\"T_fc400_row0_col2\" class=\"data row0 col2\" >1.0423288727328561</td>\n      <td id=\"T_fc400_row0_col3\" class=\"data row0 col3\" >-2.2223952125512327</td>\n      <td id=\"T_fc400_row0_col4\" class=\"data row0 col4\" >0.28871233525783424</td>\n      <td id=\"T_fc400_row0_col5\" class=\"data row0 col5\" >-0.8395058532520351</td>\n      <td id=\"T_fc400_row0_col6\" class=\"data row0 col6\" >0.9928911115798961</td>\n      <td id=\"T_fc400_row0_col7\" class=\"data row0 col7\" >-0.20909651374437896</td>\n      <td id=\"T_fc400_row0_col8\" class=\"data row0 col8\" >0.7806740947971558</td>\n      <td id=\"T_fc400_row0_col9\" class=\"data row0 col9\" >-0.6137903575618552</td>\n      <td id=\"T_fc400_row0_col10\" class=\"data row0 col10\" >1.8301436434282867</td>\n      <td id=\"T_fc400_row0_col11\" class=\"data row0 col11\" >1.1944318873587512</td>\n      <td id=\"T_fc400_row0_col12\" class=\"data row0 col12\" >-0.12699362950573215</td>\n      <td id=\"T_fc400_row0_col13\" class=\"data row0 col13\" >0.16516219940938834</td>\n      <td id=\"T_fc400_row0_col14\" class=\"data row0 col14\" >0.4184581901658031</td>\n      <td id=\"T_fc400_row0_col15\" class=\"data row0 col15\" >-0.7801235968866925</td>\n      <td id=\"T_fc400_row0_col16\" class=\"data row0 col16\" >-0.4978313765414018</td>\n      <td id=\"T_fc400_row0_col17\" class=\"data row0 col17\" >-0.028500870424174533</td>\n      <td id=\"T_fc400_row0_col18\" class=\"data row0 col18\" >-0.27366085753472397</td>\n      <td id=\"T_fc400_row0_col19\" class=\"data row0 col19\" >0.12669591871187177</td>\n    </tr>\n    <tr>\n      <th id=\"T_fc400_level0_row1\" class=\"row_heading level0 row1\" >Modified Sample</th>\n      <td id=\"T_fc400_row1_col0\" class=\"data row1 col0\" >-0.814601292682208</td>\n      <td id=\"T_fc400_row1_col1\" class=\"data row1 col1\" >0.8527854763983154</td>\n      <td id=\"T_fc400_row1_col2\" class=\"data row1 col2\" >1.0423288727328561</td>\n      <td id=\"T_fc400_row1_col3\" class=\"data row1 col3\" >-2.2223952125512327</td>\n      <td id=\"T_fc400_row1_col4\" class=\"data row1 col4\" >0.28871233525783424</td>\n      <td id=\"T_fc400_row1_col5\" class=\"data row1 col5\" >-0.8395058532520351</td>\n      <td id=\"T_fc400_row1_col6\" class=\"data row1 col6\" >0.9928911115798961</td>\n      <td id=\"T_fc400_row1_col7\" class=\"data row1 col7\" >-0.20909651374437896</td>\n      <td id=\"T_fc400_row1_col8\" class=\"data row1 col8\" >0.7806740947971558</td>\n      <td id=\"T_fc400_row1_col9\" class=\"data row1 col9\" >-0.6137903575618552</td>\n      <td id=\"T_fc400_row1_col10\" class=\"data row1 col10\" 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<th id=\"T_e77a3_level0_col3\" class=\"col_heading level0 col3\" >V4</th>\n      <th id=\"T_e77a3_level0_col4\" class=\"col_heading level0 col4\" >V5</th>\n      <th id=\"T_e77a3_level0_col5\" class=\"col_heading level0 col5\" >V6</th>\n      <th id=\"T_e77a3_level0_col6\" class=\"col_heading level0 col6\" >V7</th>\n      <th id=\"T_e77a3_level0_col7\" class=\"col_heading level0 col7\" >V8</th>\n      <th id=\"T_e77a3_level0_col8\" class=\"col_heading level0 col8\" >V9</th>\n      <th id=\"T_e77a3_level0_col9\" class=\"col_heading level0 col9\" >V10</th>\n      <th id=\"T_e77a3_level0_col10\" class=\"col_heading level0 col10\" >V11</th>\n      <th id=\"T_e77a3_level0_col11\" class=\"col_heading level0 col11\" >V12</th>\n      <th id=\"T_e77a3_level0_col12\" class=\"col_heading level0 col12\" >V13</th>\n      <th id=\"T_e77a3_level0_col13\" class=\"col_heading level0 col13\" >V14</th>\n      <th id=\"T_e77a3_level0_col14\" class=\"col_heading level0 col14\" >V15</th>\n      <th id=\"T_e77a3_level0_col15\" class=\"col_heading level0 col15\" >V16</th>\n      <th id=\"T_e77a3_level0_col16\" class=\"col_heading level0 col16\" >V17</th>\n      <th id=\"T_e77a3_level0_col17\" class=\"col_heading level0 col17\" >V18</th>\n      <th id=\"T_e77a3_level0_col18\" class=\"col_heading level0 col18\" >V19</th>\n      <th id=\"T_e77a3_level0_col19\" class=\"col_heading level0 col19\" >V20</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_e77a3_level0_row0\" class=\"row_heading level0 row0\" >Original Sample</th>\n      <td id=\"T_e77a3_row0_col0\" class=\"data row0 col0\" >-0.814601292682208</td>\n      <td id=\"T_e77a3_row0_col1\" class=\"data row0 col1\" >0.8527854763983154</td>\n      <td id=\"T_e77a3_row0_col2\" class=\"data row0 col2\" >1.0423288727328561</td>\n      <td id=\"T_e77a3_row0_col3\" class=\"data row0 col3\" >-2.2223952125512327</td>\n      <td id=\"T_e77a3_row0_col4\" class=\"data row0 col4\" >0.28871233525783424</td>\n      <td id=\"T_e77a3_row0_col5\" class=\"data row0 col5\" >-0.8395058532520351</td>\n      <td id=\"T_e77a3_row0_col6\" class=\"data row0 col6\" >0.9928911115798961</td>\n      <td id=\"T_e77a3_row0_col7\" class=\"data row0 col7\" >-0.20909651374437896</td>\n      <td id=\"T_e77a3_row0_col8\" class=\"data row0 col8\" >0.7806740947971558</td>\n      <td id=\"T_e77a3_row0_col9\" class=\"data row0 col9\" >-0.6137903575618552</td>\n      <td id=\"T_e77a3_row0_col10\" class=\"data row0 col10\" >1.8301436434282867</td>\n      <td id=\"T_e77a3_row0_col11\" class=\"data row0 col11\" >1.1944318873587512</td>\n      <td id=\"T_e77a3_row0_col12\" class=\"data row0 col12\" >-0.12699362950573215</td>\n      <td id=\"T_e77a3_row0_col13\" class=\"data row0 col13\" >0.16516219940938834</td>\n      <td id=\"T_e77a3_row0_col14\" class=\"data row0 col14\" >0.4184581901658031</td>\n      <td id=\"T_e77a3_row0_col15\" class=\"data row0 col15\" >-0.7801235968866925</td>\n      <td id=\"T_e77a3_row0_col16\" class=\"data row0 col16\" >-0.4978313765414018</td>\n      <td id=\"T_e77a3_row0_col17\" class=\"data row0 col17\" >-0.028500870424174533</td>\n      <td id=\"T_e77a3_row0_col18\" class=\"data row0 col18\" >-0.27366085753472397</td>\n      <td id=\"T_e77a3_row0_col19\" class=\"data row0 col19\" >0.12669591871187177</td>\n    </tr>\n    <tr>\n      <th id=\"T_e77a3_level0_row1\" class=\"row_heading level0 row1\" >Modified Sample</th>\n      <td id=\"T_e77a3_row1_col0\" class=\"data row1 col0\" >-0.8195973075521539</td>\n      <td id=\"T_e77a3_row1_col1\" class=\"data row1 col1\" >0.8258281811402914</td>\n      <td id=\"T_e77a3_row1_col2\" class=\"data row1 col2\" >1.1492178989013921</td>\n      <td id=\"T_e77a3_row1_col3\" class=\"data row1 col3\" >-2.11301493636727</td>\n      <td id=\"T_e77a3_row1_col4\" class=\"data row1 col4\" >0.28096492348525604</td>\n      <td id=\"T_e77a3_row1_col5\" class=\"data row1 col5\" >-0.8289637892279351</td>\n      <td id=\"T_e77a3_row1_col6\" class=\"data row1 col6\" >0.9239569002372758</td>\n      <td id=\"T_e77a3_row1_col7\" class=\"data row1 col7\" >0.6267308703871985</td>\n      <td id=\"T_e77a3_row1_col8\" class=\"data row1 col8\" >0.5965079515639202</td>\n      <td id=\"T_e77a3_row1_col9\" class=\"data row1 col9\" >-0.6253149438613781</td>\n      <td id=\"T_e77a3_row1_col10\" class=\"data row1 col10\" >1.828519947646691</td>\n      <td id=\"T_e77a3_row1_col11\" class=\"data row1 col11\" >1.1650589176757253</td>\n      <td id=\"T_e77a3_row1_col12\" class=\"data row1 col12\" >-0.12427833398317614</td>\n      <td id=\"T_e77a3_row1_col13\" class=\"data row1 col13\" >0.2479554103817461</td>\n      <td id=\"T_e77a3_row1_col14\" class=\"data row1 col14\" >0.38310734245838746</td>\n      <td id=\"T_e77a3_row1_col15\" class=\"data row1 col15\" >-0.6167199744600596</td>\n      <td id=\"T_e77a3_row1_col16\" class=\"data row1 col16\" >-0.48996835884172363</td>\n      <td id=\"T_e77a3_row1_col17\" class=\"data row1 col17\" >-0.0312665834514463</td>\n      <td id=\"T_e77a3_row1_col18\" class=\"data row1 col18\" >-0.5662567785776434</td>\n      <td id=\"T_e77a3_row1_col19\" class=\"data row1 col19\" >0.10517011223668252</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "f1404cea0c1845549ac859e5812b723b": {"model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "f256e1ce34bc407db4eee248da57996a": {"model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatTextModel", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": 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"IPY_MODEL_7fa2134d1f1b4de9b963e0c798f86d50", "tabbable": null, "tooltip": null, "value": "<style type=\"text/css\">\n#T_b10ba thead th {\n  border-bottom: 1px solid #DDD;\n  background-color: #F5F5F5;\n  min-width: 20px;\n  padding: 0 10px;\n  text-align: right;\n}\n#T_b10ba tbody th {\n  border-bottom: 1px solid #DDD;\n  background-color: #F5F5F5;\n  min-width: 115px;\n  padding: 0 10px;\n  text-align: right;\n}\n#T_b10ba td {\n  text-align: right;\n  padding: 0 10px;\n}\n#T_b10ba tr:nth-child(even) {\n  background-color: #F9F9F9;\n}\n#T_b10ba  {\n  border-collapse: collapse;\n  border: 1px solid #DDD;\n}\n#T_b10ba_row1_col0 {\n  background-color: #BAFFBA;\n}\n</style>\n<table id=\"T_b10ba\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_b10ba_level0_col0\" class=\"col_heading level0 col0\" >Prediction (True value: Normal)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_b10ba_level0_row0\" class=\"row_heading level0 row0\" >Original Sample</th>\n      <td id=\"T_b10ba_row0_col0\" class=\"data row0 col0\" >Normal</td>\n    </tr>\n    <tr>\n      <th id=\"T_b10ba_level0_row1\" class=\"row_heading level0 row1\" >Modified Sample</th>\n      <td id=\"T_b10ba_row1_col0\" class=\"data row1 col0\" >Anomaly</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "f5b642acc7024460adbf2f068594fa36": {"model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, 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diff --git a/demos/OracleAutoMLx_AnomalyDetection.ipynb b/demos/OracleAutoMLx_AnomalyDetection.ipynb
index c1ef178..6e5e562 100644
--- a/demos/OracleAutoMLx_AnomalyDetection.ipynb
+++ b/demos/OracleAutoMLx_AnomalyDetection.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "ae2ab720",
+   "id": "00f3548a",
    "metadata": {},
    "source": [
     "***\n",
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8a24a53d",
+   "id": "b1ba63d9",
    "metadata": {},
    "source": [
     "Anomaly Detection Demo Notebook.\n",
@@ -26,7 +26,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6215ca79",
+   "id": "1b54cad5",
    "metadata": {},
    "source": [
     "## Overview of this Notebook\n",
@@ -80,7 +80,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bc10323c",
+   "id": "ed5d41ec",
    "metadata": {},
    "source": [
     "<a id='setup'></a>\n",
@@ -92,13 +92,13 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "8c70652f",
+   "id": "124c551f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:47:47.931154Z",
-     "iopub.status.busy": "2024-12-26T13:47:47.930954Z",
-     "iopub.status.idle": "2024-12-26T13:47:57.172521Z",
-     "shell.execute_reply": "2024-12-26T13:47:57.171934Z"
+     "iopub.execute_input": "2025-04-25T09:59:34.026432Z",
+     "iopub.status.busy": "2025-04-25T09:59:34.026094Z",
+     "iopub.status.idle": "2025-04-25T09:59:44.508269Z",
+     "shell.execute_reply": "2025-04-25T09:59:44.507603Z"
     }
    },
    "outputs": [
@@ -114,20 +114,20 @@
      "output_type": "stream",
      "text": [
       "  Using cached rdata-0.9-py3-none-any.whl.metadata (1.1 kB)\r\n",
-      "Requirement already satisfied: numpy in /scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-forewardportAMLX2203todev/lib/python3.9/site-packages (from rdata==0.9) (1.26.4)\r\n",
-      "Requirement already satisfied: xarray in /scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-forewardportAMLX2203todev/lib/python3.9/site-packages (from rdata==0.9) (2024.7.0)\r\n",
-      "Requirement already satisfied: pandas in /scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-forewardportAMLX2203todev/lib/python3.9/site-packages (from rdata==0.9) (2.2.2)\r\n",
-      "Requirement already satisfied: python-dateutil>=2.8.2 in /scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-forewardportAMLX2203todev/lib/python3.9/site-packages (from pandas->rdata==0.9) (2.9.0.post0)\r\n",
-      "Requirement already satisfied: pytz>=2020.1 in /scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-forewardportAMLX2203todev/lib/python3.9/site-packages (from pandas->rdata==0.9) (2024.2)\r\n",
-      "Requirement already satisfied: tzdata>=2022.7 in /scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-forewardportAMLX2203todev/lib/python3.9/site-packages (from pandas->rdata==0.9) (2024.2)\r\n"
+      "Requirement already satisfied: numpy in /scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages (from rdata==0.9) (1.26.4)\r\n",
+      "Requirement already satisfied: xarray in /scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages (from rdata==0.9) (2024.7.0)\r\n",
+      "Requirement already satisfied: pandas in /scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages (from rdata==0.9) (2.2.2)\r\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Requirement already satisfied: packaging>=23.1 in /scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-forewardportAMLX2203todev/lib/python3.9/site-packages (from xarray->rdata==0.9) (24.2)\r\n",
-      "Requirement already satisfied: six>=1.5 in /scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-forewardportAMLX2203todev/lib/python3.9/site-packages (from python-dateutil>=2.8.2->pandas->rdata==0.9) (1.17.0)\r\n",
+      "Requirement already satisfied: python-dateutil>=2.8.2 in /scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages (from pandas->rdata==0.9) (2.9.0.post0)\r\n",
+      "Requirement already satisfied: pytz>=2020.1 in /scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages (from pandas->rdata==0.9) (2025.2)\r\n",
+      "Requirement already satisfied: tzdata>=2022.7 in /scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages (from pandas->rdata==0.9) (2025.2)\r\n",
+      "Requirement already satisfied: packaging>=23.1 in /scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages (from xarray->rdata==0.9) (25.0)\r\n",
+      "Requirement already satisfied: six>=1.5 in /scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages (from python-dateutil>=2.8.2->pandas->rdata==0.9) (1.17.0)\r\n",
       "Using cached rdata-0.9-py3-none-any.whl (19 kB)\r\n"
      ]
     },
@@ -137,7 +137,13 @@
      "text": [
       "Installing collected packages: rdata\r\n",
       "  Attempting uninstall: rdata\r\n",
-      "    Found existing installation: rdata 0.11.2\r\n",
+      "    Found existing installation: rdata 0.11.2\r\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "    Uninstalling rdata-0.11.2:\r\n"
      ]
     },
@@ -166,7 +172,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2bf0b6b9",
+   "id": "67e80ff5",
    "metadata": {},
    "source": [
     "Load the required modules."
@@ -175,13 +181,13 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "d20eb108",
+   "id": "09821a36",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:47:57.174977Z",
-     "iopub.status.busy": "2024-12-26T13:47:57.174669Z",
-     "iopub.status.idle": "2024-12-26T13:48:35.705958Z",
-     "shell.execute_reply": "2024-12-26T13:48:35.705344Z"
+     "iopub.execute_input": "2025-04-25T09:59:44.510808Z",
+     "iopub.status.busy": "2025-04-25T09:59:44.510323Z",
+     "iopub.status.idle": "2025-04-25T10:00:26.554123Z",
+     "shell.execute_reply": "2025-04-25T10:00:26.553486Z"
     }
    },
    "outputs": [],
@@ -209,7 +215,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0b61d493",
+   "id": "cfe2a223",
    "metadata": {},
    "source": [
     "<a id='load-data'></a>\n",
@@ -220,13 +226,13 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "d1de2c33",
+   "id": "0f03efa0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:48:35.708636Z",
-     "iopub.status.busy": "2024-12-26T13:48:35.708143Z",
-     "iopub.status.idle": "2024-12-26T13:48:56.470642Z",
-     "shell.execute_reply": "2024-12-26T13:48:56.470025Z"
+     "iopub.execute_input": "2025-04-25T10:00:26.557270Z",
+     "iopub.status.busy": "2025-04-25T10:00:26.556221Z",
+     "iopub.status.idle": "2025-04-25T10:00:49.388257Z",
+     "shell.execute_reply": "2025-04-25T10:00:49.387666Z"
     }
    },
    "outputs": [],
@@ -243,7 +249,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "626c18b5",
+   "id": "52d15861",
    "metadata": {},
    "source": [
     "In this case, the target is identified by the `Class` column."
@@ -252,13 +258,13 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "f85f9eaa",
+   "id": "3f3dc853",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:48:56.473191Z",
-     "iopub.status.busy": "2024-12-26T13:48:56.472806Z",
-     "iopub.status.idle": "2024-12-26T13:48:56.926486Z",
-     "shell.execute_reply": "2024-12-26T13:48:56.926011Z"
+     "iopub.execute_input": "2025-04-25T10:00:49.390778Z",
+     "iopub.status.busy": "2025-04-25T10:00:49.390321Z",
+     "iopub.status.idle": "2025-04-25T10:00:49.978757Z",
+     "shell.execute_reply": "2025-04-25T10:00:49.978272Z"
     }
    },
    "outputs": [],
@@ -268,7 +274,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5d423bc0",
+   "id": "34f93770",
    "metadata": {},
    "source": [
     "We reduce the total number of features to 20 to have a reasonable training time for this demonstration."
@@ -277,13 +283,13 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "3c76e9d2",
+   "id": "a461a8de",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:48:56.928452Z",
-     "iopub.status.busy": "2024-12-26T13:48:56.928128Z",
-     "iopub.status.idle": "2024-12-26T13:48:56.961924Z",
-     "shell.execute_reply": "2024-12-26T13:48:56.961324Z"
+     "iopub.execute_input": "2025-04-25T10:00:49.980843Z",
+     "iopub.status.busy": "2025-04-25T10:00:49.980499Z",
+     "iopub.status.idle": "2025-04-25T10:00:50.026498Z",
+     "shell.execute_reply": "2025-04-25T10:00:50.025899Z"
     }
    },
    "outputs": [],
@@ -293,7 +299,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6ec86525",
+   "id": "cc0825a0",
    "metadata": {},
    "source": [
     "Since the dataset is not split into training and validation sets by default, we now split it into training (60%) and test (40%) datasets. The training set will be used to create a Machine Learning model using AutoML, and the test set will be used to evaluate the model's performance on unseen data."
@@ -302,13 +308,13 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "1e03cbfd",
+   "id": "a5e4d1ce",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:48:56.964225Z",
-     "iopub.status.busy": "2024-12-26T13:48:56.963867Z",
-     "iopub.status.idle": "2024-12-26T13:48:57.289796Z",
-     "shell.execute_reply": "2024-12-26T13:48:57.289267Z"
+     "iopub.execute_input": "2025-04-25T10:00:50.028702Z",
+     "iopub.status.busy": "2025-04-25T10:00:50.028386Z",
+     "iopub.status.idle": "2025-04-25T10:00:50.356653Z",
+     "shell.execute_reply": "2025-04-25T10:00:50.356037Z"
     }
    },
    "outputs": [
@@ -332,7 +338,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a001be54",
+   "id": "0a1599c9",
    "metadata": {},
    "source": [
     "Again to keep the training time reasonable, we also downsample to use 5% of the total training set."
@@ -341,13 +347,13 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "cc404d7b",
+   "id": "039a183b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:48:57.291569Z",
-     "iopub.status.busy": "2024-12-26T13:48:57.291300Z",
-     "iopub.status.idle": "2024-12-26T13:48:57.440948Z",
-     "shell.execute_reply": "2024-12-26T13:48:57.440419Z"
+     "iopub.execute_input": "2025-04-25T10:00:50.358715Z",
+     "iopub.status.busy": "2025-04-25T10:00:50.358271Z",
+     "iopub.status.idle": "2025-04-25T10:00:50.713308Z",
+     "shell.execute_reply": "2025-04-25T10:00:50.712736Z"
     }
    },
    "outputs": [
@@ -370,7 +376,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3eaf3886",
+   "id": "85655da0",
    "metadata": {},
    "source": [
     "We also need to reset the indexes after our downsampling."
@@ -379,13 +385,13 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "2813078c",
+   "id": "0503cae9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:48:57.442707Z",
-     "iopub.status.busy": "2024-12-26T13:48:57.442445Z",
-     "iopub.status.idle": "2024-12-26T13:48:57.469620Z",
-     "shell.execute_reply": "2024-12-26T13:48:57.469142Z"
+     "iopub.execute_input": "2025-04-25T10:00:50.715240Z",
+     "iopub.status.busy": "2025-04-25T10:00:50.714914Z",
+     "iopub.status.idle": "2025-04-25T10:00:50.805439Z",
+     "shell.execute_reply": "2025-04-25T10:00:50.804947Z"
     }
    },
    "outputs": [],
@@ -396,7 +402,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "44d5e8cb",
+   "id": "98732c51",
    "metadata": {},
    "source": [
     "Lets look at a few of the samples in the training dataset."
@@ -405,13 +411,13 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "8734493e",
+   "id": "7ae1ab1b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:48:57.471504Z",
-     "iopub.status.busy": "2024-12-26T13:48:57.471176Z",
-     "iopub.status.idle": "2024-12-26T13:48:57.538715Z",
-     "shell.execute_reply": "2024-12-26T13:48:57.538222Z"
+     "iopub.execute_input": "2025-04-25T10:00:50.807262Z",
+     "iopub.status.busy": "2025-04-25T10:00:50.806947Z",
+     "iopub.status.idle": "2025-04-25T10:00:50.973830Z",
+     "shell.execute_reply": "2025-04-25T10:00:50.973303Z"
     }
    },
    "outputs": [
@@ -612,7 +618,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0d6bbb63",
+   "id": "4881ab23",
    "metadata": {},
    "source": [
     "The Credit card fraud dataset contains numerical data."
@@ -621,13 +627,13 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "ef99e3d1",
+   "id": "d902ba26",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:48:57.540440Z",
-     "iopub.status.busy": "2024-12-26T13:48:57.540115Z",
-     "iopub.status.idle": "2024-12-26T13:48:57.572100Z",
-     "shell.execute_reply": "2024-12-26T13:48:57.571612Z"
+     "iopub.execute_input": "2025-04-25T10:00:50.975651Z",
+     "iopub.status.busy": "2025-04-25T10:00:50.975228Z",
+     "iopub.status.idle": "2025-04-25T10:00:51.008740Z",
+     "shell.execute_reply": "2025-04-25T10:00:51.008219Z"
     }
    },
    "outputs": [
@@ -724,7 +730,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "310ac8fa",
+   "id": "b7f4cf7c",
    "metadata": {},
    "source": [
     "The Oracle AutoMLx solution automatically handles missing values by dropping features with too many missing values, and filling in the remaining missing values based on the feature type.\n",
@@ -735,13 +741,13 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "7e75ff1a",
+   "id": "26fc0d22",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:48:57.573872Z",
-     "iopub.status.busy": "2024-12-26T13:48:57.573557Z",
-     "iopub.status.idle": "2024-12-26T13:48:57.607333Z",
-     "shell.execute_reply": "2024-12-26T13:48:57.606864Z"
+     "iopub.execute_input": "2025-04-25T10:00:51.010442Z",
+     "iopub.status.busy": "2025-04-25T10:00:51.010121Z",
+     "iopub.status.idle": "2025-04-25T10:00:51.044585Z",
+     "shell.execute_reply": "2025-04-25T10:00:51.044102Z"
     }
    },
    "outputs": [
@@ -835,7 +841,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "29c7e0f5",
+   "id": "53565863",
    "metadata": {},
    "source": [
     "We visualize the distribution of the target variable in the data. The dataset is heavily unbalanced, as is often the case in the anomaly detection use-case."
@@ -844,13 +850,13 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "071982cf",
+   "id": "747e19d4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:48:57.609063Z",
-     "iopub.status.busy": "2024-12-26T13:48:57.608757Z",
-     "iopub.status.idle": "2024-12-26T13:49:10.073564Z",
-     "shell.execute_reply": "2024-12-26T13:49:10.073003Z"
+     "iopub.execute_input": "2025-04-25T10:00:51.046308Z",
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+     "shell.execute_reply": "2025-04-25T10:01:10.363186Z"
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    "outputs": [
@@ -10303,9 +10309,9 @@
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        "                            \n",
-       "var gd = document.getElementById('5006226c-9709-4ae6-957c-3f777255b8b6');\n",
+       "var gd = document.getElementById('8f7caf7c-6f0e-4318-b6aa-baece2e84b5f');\n",
        "var x = new MutationObserver(function (mutations, observer) {{\n",
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@@ -10345,7 +10351,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6642c80b",
+   "id": "a1deb8bf",
    "metadata": {},
    "source": [
     "<a id='AutoML'></a>\n",
@@ -10354,7 +10360,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "eb7e0b48",
+   "id": "7d8fbc64",
    "metadata": {},
    "source": [
     "<a id='Engine'></a>\n",
@@ -10365,13 +10371,13 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "9364b571",
+   "id": "4cce0222",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:49:10.075650Z",
-     "iopub.status.busy": "2024-12-26T13:49:10.075383Z",
-     "iopub.status.idle": "2024-12-26T13:49:23.721724Z",
-     "shell.execute_reply": "2024-12-26T13:49:23.720841Z"
+     "iopub.execute_input": "2025-04-25T10:01:10.366463Z",
+     "iopub.status.busy": "2025-04-25T10:01:10.365818Z",
+     "iopub.status.idle": "2025-04-25T10:01:53.296227Z",
+     "shell.execute_reply": "2025-04-25T10:01:53.295467Z"
     }
    },
    "outputs": [
@@ -10379,7 +10385,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:49:13,605] [automlx.backend] Overwriting ray session directory to /tmp/4kx0ukqw/ray, which will be deleted at engine shutdown. If you wish to retain ray logs, provide _temp_dir in ray_setup dict of engine_opts when initializing the AutoMLx engine.\n"
+      "[2025-04-25 03:01:14,603] [automlx.backend] Overwriting ray session directory to /tmp/1odefte7/ray, which will be deleted at engine shutdown. If you wish to retain ray logs, provide _temp_dir in ray_setup dict of engine_opts when initializing the AutoMLx engine.\n"
      ]
     }
    ],
@@ -10389,7 +10395,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f7f3ea6e",
+   "id": "b783a49c",
    "metadata": {},
    "source": [
     "<a id='provider'></a>\n",
@@ -10416,7 +10422,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2196d186",
+   "id": "59b2b333",
    "metadata": {},
    "source": [
     "<a id='default'></a>\n",
@@ -10430,13 +10436,13 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "4dcfad1d",
+   "id": "2291de89",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:49:23.724669Z",
-     "iopub.status.busy": "2024-12-26T13:49:23.724046Z",
-     "iopub.status.idle": "2024-12-26T13:51:17.980823Z",
-     "shell.execute_reply": "2024-12-26T13:51:17.979942Z"
+     "iopub.execute_input": "2025-04-25T10:01:53.299137Z",
+     "iopub.status.busy": "2025-04-25T10:01:53.298755Z",
+     "iopub.status.idle": "2025-04-25T10:04:00.683938Z",
+     "shell.execute_reply": "2025-04-25T10:04:00.683146Z"
     }
    },
    "outputs": [
@@ -10444,105 +10450,105 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:49:24,355] [automlx.interface] Dataset shape: (65505,19)\n"
+      "[2025-04-25 03:01:54,867] [automlx.interface] Dataset shape: (65505,19)\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:50:15,810] [sanerec.autotuning.parameter] Hyperparameter epsilon autotune range is set to its validation range. This could lead to long training times\n"
+      "[2025-04-25 03:02:52,433] [sanerec.autotuning.parameter] Hyperparameter epsilon autotune range is set to its validation range. This could lead to long training times\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:50:23,982] [sanerec.autotuning.parameter] Hyperparameter repeat_quality_threshold autotune range is set to its validation range. This could lead to long training times\n"
+      "[2025-04-25 03:03:01,882] [sanerec.autotuning.parameter] Hyperparameter repeat_quality_threshold autotune range is set to its validation range. This could lead to long training times\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:50:24,142] [sanerec.autotuning.parameter] Hyperparameter scope autotune range is set to its validation range. This could lead to long training times\n"
+      "[2025-04-25 03:03:02,032] [sanerec.autotuning.parameter] Hyperparameter scope autotune range is set to its validation range. This could lead to long training times\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:50:24,502] [automlx.data_transform] Running preprocessing. Number of features: 20\n"
+      "[2025-04-25 03:03:02,367] [automlx.data_transform] Running preprocessing. Number of features: 20\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:50:24,824] [automlx.data_transform] Preprocessing completed. Took 0.323 secs\n"
+      "[2025-04-25 03:03:02,684] [automlx.data_transform] Preprocessing completed. Took 0.317 secs\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:50:24,850] [automlx.process] Running Model Generation\n"
+      "[2025-04-25 03:03:02,728] [automlx.process] Running Model Generation\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:50:24,898] [automlx.process] Model Generation completed.\n"
+      "[2025-04-25 03:03:02,773] [automlx.process] Model Generation completed.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:50:24,963] [automlx.model_selection] Running Model Selection\n"
+      "[2025-04-25 03:03:02,835] [automlx.model_selection] Running Model Selection\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:51:07,368] [automlx.model_selection] Model Selection completed - Took 42.405 sec - Selected models: [['IsolationForestOD']]\n"
+      "[2025-04-25 03:03:49,626] [automlx.model_selection] Model Selection completed - Took 46.791 sec - Selected models: [['IsolationForestOD']]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:51:07,418] [automlx.trials] Running Model Tuning for ['IsolationForestOD']\n"
+      "[2025-04-25 03:03:49,674] [automlx.trials] Running Model Tuning for ['IsolationForestOD']\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:51:13,805] [automlx.trials] Best parameters for IsolationForestOD: {'contamination': 0.1, 'n_estimators': 5, 'max_samples': 6, 'max_features': 0.05, 'bootstrap': False, 'behaviour': 'old'}\n"
+      "[2025-04-25 03:03:56,558] [automlx.trials] Best parameters for IsolationForestOD: {'contamination': 0.1, 'n_estimators': 5, 'max_samples': 6, 'max_features': 0.05, 'bootstrap': False, 'behaviour': 'old'}\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:51:13,806] [automlx.trials] Model Tuning completed. Took: 6.387 secs\n"
+      "[2025-04-25 03:03:56,559] [automlx.trials] Model Tuning completed. Took: 6.885 secs\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:51:14,372] [automlx.interface] Re-fitting pipeline\n"
+      "[2025-04-25 03:03:57,138] [automlx.interface] Re-fitting pipeline\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:51:14,387] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_fe93eeae-9\n"
+      "[2025-04-25 03:03:57,153] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_85a27f35-1\n"
      ]
     },
     {
@@ -10566,7 +10572,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f5e4f1a5",
+   "id": "22fbb022",
    "metadata": {},
    "source": [
     "<a id='analyze'></a>\n",
@@ -10581,7 +10587,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7207c9be",
+   "id": "214edb28",
    "metadata": {},
    "source": [
     "AutoML provides a `print_summary()` API to output all the different trials performed."
@@ -10590,13 +10596,13 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "8c57f908",
+   "id": "b0986699",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:51:17.984647Z",
-     "iopub.status.busy": "2024-12-26T13:51:17.983165Z",
-     "iopub.status.idle": "2024-12-26T13:51:18.616779Z",
-     "shell.execute_reply": "2024-12-26T13:51:18.616130Z"
+     "iopub.execute_input": "2025-04-25T10:04:00.687290Z",
+     "iopub.status.busy": "2025-04-25T10:04:00.685975Z",
+     "iopub.status.idle": "2025-04-25T10:04:01.444137Z",
+     "shell.execute_reply": "2025-04-25T10:04:01.443546Z"
     }
    },
    "outputs": [
@@ -10641,7 +10647,7 @@
        "      <td>{'contamination': 0.1, 'n_estimators': 5, 'max_samples': 6, 'max_features': 0.05, 'bootstrap': False, 'behaviour': 'old'}</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>25.1.0</td>\n",
+       "      <td>25.2.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>3.9.21 (main, Dec 11 2024, 16:24:11) \\n[GCC 11.2.0]</td>\n",
@@ -10695,9 +10701,9 @@
        "      <td>{'contamination': 0.1, 'n_estimators': 100, 'max_samples': 5, 'max_features': 1.0, 'bootstrap': False, 'behaviour': 'old'}</td>\n",
        "      <td>0.031</td>\n",
        "      <td>{'f1': 0.031007751937984496}</td>\n",
-       "      <td>1.2960</td>\n",
-       "      <td>0.3333</td>\n",
-       "      <td>Thu Dec 26 05:50:33 2024</td>\n",
+       "      <td>1.6463</td>\n",
+       "      <td>0.3198</td>\n",
+       "      <td>Fri Apr 25 03:03:13 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Selection</td>\n",
@@ -10707,33 +10713,33 @@
        "      <td>{'contamination': 0.1, 'n_bins': 10, 'alpha': 0.1, 'tol': 0.5}</td>\n",
        "      <td>0.0299</td>\n",
        "      <td>{'f1': 0.029925187032418952}</td>\n",
-       "      <td>5.9000</td>\n",
-       "      <td>0.3698</td>\n",
-       "      <td>Thu Dec 26 05:50:38 2024</td>\n",
+       "      <td>6.5625</td>\n",
+       "      <td>0.3786</td>\n",
+       "      <td>Fri Apr 25 03:03:18 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Selection</td>\n",
        "      <td>8544</td>\n",
        "      <td>20</td>\n",
-       "      <td>AutoEncoder</td>\n",
-       "      <td>{'contamination': 0.1, 'middle_layer_size': 2, 'encoder_length': 2, 'layer_size_growth': 'exponential', 'hidden_activation': 'relu', 'batch_norm': True, 'learning_rate': 0.001, 'epochs': 100, 'batch_size': 256, 'dropout_rate': 0.05, 'weight_decay': 1e-05, 'preprocessing': False, 'input_dim': 20}</td>\n",
-       "      <td>0.0296</td>\n",
-       "      <td>{'f1': 0.029561504352110364}</td>\n",
-       "      <td>34.5267</td>\n",
-       "      <td>0.6290</td>\n",
-       "      <td>Thu Dec 26 05:51:07 2024</td>\n",
+       "      <td>PrincipalCompOD</td>\n",
+       "      <td>{'contamination': 0.1, 'whiten': False, 'n_components': 0.9999, 'weighted': True, 'svd_solver': 'full', 'n_selected_components': None, 'copy': True, 'tol': 0.0, 'iterated_power': 'auto', 'standardization': True}</td>\n",
+       "      <td>0.0293</td>\n",
+       "      <td>{'f1': 0.029301644147810516}</td>\n",
+       "      <td>1.3347</td>\n",
+       "      <td>0.3462</td>\n",
+       "      <td>Fri Apr 25 03:03:13 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Selection</td>\n",
        "      <td>8544</td>\n",
        "      <td>20</td>\n",
-       "      <td>PrincipalCompOD</td>\n",
-       "      <td>{'contamination': 0.1, 'whiten': False, 'n_components': 0.9999, 'weighted': True, 'svd_solver': 'full', 'n_selected_components': None, 'copy': True, 'tol': 0.0, 'iterated_power': 'auto', 'standardization': True}</td>\n",
-       "      <td>0.0293</td>\n",
-       "      <td>{'f1': 0.029301644147810516}</td>\n",
-       "      <td>0.9450</td>\n",
-       "      <td>0.2594</td>\n",
-       "      <td>Thu Dec 26 05:50:33 2024</td>\n",
+       "      <td>AutoEncoder</td>\n",
+       "      <td>{'contamination': 0.1, 'middle_layer_size': 2, 'encoder_length': 2, 'layer_size_growth': 'exponential', 'hidden_activation': 'relu', 'batch_norm': True, 'learning_rate': 0.001, 'epochs': 100, 'batch_size': 256, 'dropout_rate': 0.05, 'weight_decay': 1e-05, 'preprocessing': False, 'input_dim': 20}</td>\n",
+       "      <td>0.029</td>\n",
+       "      <td>{'f1': 0.029035821366577244}</td>\n",
+       "      <td>37.3068</td>\n",
+       "      <td>0.6494</td>\n",
+       "      <td>Fri Apr 25 03:03:49 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Selection</td>\n",
@@ -10743,9 +10749,9 @@
        "      <td>{'contamination': 0.1, 'n_clusters': 9, 'alpha': 0.8, 'beta': 5, 'use_weights': False, 'clustering_estimator': None, 'check_estimator': False}</td>\n",
        "      <td>0.0284</td>\n",
        "      <td>{'f1': 0.02841530054644809}</td>\n",
-       "      <td>6.0984</td>\n",
-       "      <td>0.4128</td>\n",
-       "      <td>Thu Dec 26 05:50:38 2024</td>\n",
+       "      <td>6.7511</td>\n",
+       "      <td>0.4133</td>\n",
+       "      <td>Fri Apr 25 03:03:18 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Selection</td>\n",
@@ -10755,9 +10761,9 @@
        "      <td>{'contamination': 0.1, 'assume_centered': False, 'support_fraction': 0.5012289325842697, 'store_precision': True}</td>\n",
        "      <td>0.0283</td>\n",
        "      <td>{'f1': 0.0282780676653762}</td>\n",
-       "      <td>3.9195</td>\n",
-       "      <td>0.3351</td>\n",
-       "      <td>Thu Dec 26 05:50:36 2024</td>\n",
+       "      <td>4.0895</td>\n",
+       "      <td>0.3637</td>\n",
+       "      <td>Fri Apr 25 03:03:16 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Selection</td>\n",
@@ -10767,9 +10773,9 @@
        "      <td>{'contamination': 0.1, 'n_neighbors': 5, 'method': 'largest', 'radius': 1.0, 'algorithm': 'ball_tree', 'leaf_size': 30, 'metric': 'minkowski', 'p': 2, 'metric_params': None}</td>\n",
        "      <td>0.0262</td>\n",
        "      <td>{'f1': 0.026224783861671472}</td>\n",
-       "      <td>19.7189</td>\n",
-       "      <td>0.3409</td>\n",
-       "      <td>Thu Dec 26 05:50:52 2024</td>\n",
+       "      <td>20.9862</td>\n",
+       "      <td>0.3448</td>\n",
+       "      <td>Fri Apr 25 03:03:33 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Selection</td>\n",
@@ -10779,9 +10785,9 @@
        "      <td>{'contamination': 0.1, 'gamma': 0.5, 'kernel': 'rbf', 'nu': 0.5, 'coef0': 0, 'degree': 3, 'tol': 0.001, 'shrinking': True, 'cache_size': 200, 'max_iter': -1}</td>\n",
        "      <td>0.0136</td>\n",
        "      <td>{'f1': 0.013550135501355014}</td>\n",
-       "      <td>30.1431</td>\n",
-       "      <td>0.5187</td>\n",
-       "      <td>Thu Dec 26 05:51:02 2024</td>\n",
+       "      <td>23.1880</td>\n",
+       "      <td>0.5145</td>\n",
+       "      <td>Fri Apr 25 03:03:35 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
@@ -10791,9 +10797,9 @@
        "      <td>{'contamination': 0.1, 'n_estimators': 5, 'max_samples': 6, 'max_features': 0.05, 'bootstrap': False, 'behaviour': 'old'}</td>\n",
        "      <td>0.032</td>\n",
        "      <td>{'f1': 0.03197925669835782}</td>\n",
-       "      <td>0.1381</td>\n",
-       "      <td>0.6547</td>\n",
-       "      <td>Thu Dec 26 05:51:08 2024</td>\n",
+       "      <td>0.1362</td>\n",
+       "      <td>0.3567</td>\n",
+       "      <td>Fri Apr 25 03:03:51 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
@@ -10803,9 +10809,9 @@
        "      <td>{'contamination': 0.1, 'n_estimators': 101, 'max_samples': 5, 'max_features': 1.0, 'bootstrap': False, 'behaviour': 'old'}</td>\n",
        "      <td>0.0315</td>\n",
        "      <td>{'f1': 0.03149606299212599}</td>\n",
-       "      <td>0.4364</td>\n",
-       "      <td>0.3549</td>\n",
-       "      <td>Thu Dec 26 05:51:08 2024</td>\n",
+       "      <td>0.4225</td>\n",
+       "      <td>0.6598</td>\n",
+       "      <td>Fri Apr 25 03:03:50 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>...</td>\n",
@@ -10827,9 +10833,9 @@
        "      <td>{'contamination': 0.1, 'n_estimators': 5, 'max_samples': 37, 'max_features': 0.05, 'bootstrap': False, 'behaviour': 'old'}</td>\n",
        "      <td>0.0237</td>\n",
        "      <td>{'f1': 0.02370163820146393}</td>\n",
-       "      <td>0.1270</td>\n",
-       "      <td>0.4141</td>\n",
-       "      <td>Thu Dec 26 05:51:08 2024</td>\n",
+       "      <td>0.1536</td>\n",
+       "      <td>0.3488</td>\n",
+       "      <td>Fri Apr 25 03:03:51 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
@@ -10839,9 +10845,9 @@
        "      <td>{'contamination': 0.1, 'n_estimators': 5, 'max_samples': 36, 'max_features': 0.05, 'bootstrap': False, 'behaviour': 'old'}</td>\n",
        "      <td>0.0236</td>\n",
        "      <td>{'f1': 0.023570190641247834}</td>\n",
-       "      <td>0.1362</td>\n",
-       "      <td>0.3471</td>\n",
-       "      <td>Thu Dec 26 05:51:08 2024</td>\n",
+       "      <td>0.1406</td>\n",
+       "      <td>0.6632</td>\n",
+       "      <td>Fri Apr 25 03:03:51 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
@@ -10851,9 +10857,9 @@
        "      <td>{'contamination': 0.1, 'n_estimators': 5, 'max_samples': 5, 'max_features': 1.0, 'bootstrap': False, 'behaviour': 'old'}</td>\n",
        "      <td>0.0038</td>\n",
        "      <td>{'f1': 0.0037695207323640285}</td>\n",
-       "      <td>0.1206</td>\n",
-       "      <td>0.3476</td>\n",
-       "      <td>Thu Dec 26 05:51:08 2024</td>\n",
+       "      <td>0.1230</td>\n",
+       "      <td>0.3496</td>\n",
+       "      <td>Fri Apr 25 03:03:51 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
@@ -10863,9 +10869,9 @@
        "      <td>{'contamination': 0.1, 'n_estimators': 5, 'max_samples': 5, 'max_features': 0.9999905, 'bootstrap': False, 'behaviour': 'old'}</td>\n",
        "      <td>0.0012</td>\n",
        "      <td>{'f1': 0.0011851851851851852}</td>\n",
-       "      <td>0.1324</td>\n",
-       "      <td>0.3425</td>\n",
-       "      <td>Thu Dec 26 05:51:08 2024</td>\n",
+       "      <td>0.1246</td>\n",
+       "      <td>0.6598</td>\n",
+       "      <td>Fri Apr 25 03:03:51 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
@@ -10875,9 +10881,9 @@
        "      <td>{'contamination': 0.1, 'n_estimators': 5, 'max_samples': 5, 'max_features': 0.05, 'bootstrap': False, 'behaviour': 'old'}</td>\n",
        "      <td>0.0012</td>\n",
        "      <td>{'f1': 0.0011813349084465446}</td>\n",
-       "      <td>0.1030</td>\n",
-       "      <td>0.3369</td>\n",
-       "      <td>Thu Dec 26 05:51:13 2024</td>\n",
+       "      <td>0.0994</td>\n",
+       "      <td>0.3379</td>\n",
+       "      <td>Fri Apr 25 03:03:55 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
@@ -10887,9 +10893,9 @@
        "      <td>{'contamination': 0.1, 'n_estimators': 5, 'max_samples': 5, 'max_features': 0.05, 'bootstrap': False, 'behaviour': 'old'}</td>\n",
        "      <td>0.0012</td>\n",
        "      <td>{'f1': 0.0011813349084465446}</td>\n",
-       "      <td>0.1280</td>\n",
-       "      <td>0.4141</td>\n",
-       "      <td>Thu Dec 26 05:51:08 2024</td>\n",
+       "      <td>0.1292</td>\n",
+       "      <td>0.3519</td>\n",
+       "      <td>Fri Apr 25 03:03:51 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
@@ -10899,9 +10905,9 @@
        "      <td>{'contamination': 0.1, 'n_estimators': 5, 'max_samples': 5, 'max_features': 0.050009500000000005, 'bootstrap': False, 'behaviour': 'old'}</td>\n",
        "      <td>0.0012</td>\n",
        "      <td>{'f1': 0.0011813349084465446}</td>\n",
-       "      <td>0.1468</td>\n",
-       "      <td>0.4024</td>\n",
-       "      <td>Thu Dec 26 05:51:08 2024</td>\n",
+       "      <td>0.1274</td>\n",
+       "      <td>0.3477</td>\n",
+       "      <td>Fri Apr 25 03:03:50 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
@@ -10911,9 +10917,9 @@
        "      <td>{'contamination': 0.1, 'n_estimators': 5, 'max_samples': 5, 'max_features': 0.05001900000000001, 'bootstrap': False, 'behaviour': 'old'}</td>\n",
        "      <td>0.0012</td>\n",
        "      <td>{'f1': 0.0011813349084465446}</td>\n",
-       "      <td>0.1284</td>\n",
-       "      <td>0.3424</td>\n",
-       "      <td>Thu Dec 26 05:51:08 2024</td>\n",
+       "      <td>0.1151</td>\n",
+       "      <td>0.3501</td>\n",
+       "      <td>Fri Apr 25 03:03:50 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
@@ -10923,9 +10929,9 @@
        "      <td>{'contamination': 0.1, 'n_estimators': 5, 'max_samples': 5, 'max_features': 0.472893264264607, 'bootstrap': False, 'behaviour': 'old'}</td>\n",
        "      <td>0.0005</td>\n",
        "      <td>{'f1': 0.00046838407494145194}</td>\n",
-       "      <td>0.1373</td>\n",
-       "      <td>0.4123</td>\n",
-       "      <td>Thu Dec 26 05:51:08 2024</td>\n",
+       "      <td>0.1258</td>\n",
+       "      <td>0.3477</td>\n",
+       "      <td>Fri Apr 25 03:03:50 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
@@ -10935,9 +10941,9 @@
        "      <td>{'contamination': 0.1, 'n_estimators': 5, 'max_samples': 5, 'max_features': 0.472902764264607, 'bootstrap': False, 'behaviour': 'old'}</td>\n",
        "      <td>0.0005</td>\n",
        "      <td>{'f1': 0.00046838407494145194}</td>\n",
-       "      <td>0.1214</td>\n",
-       "      <td>0.6527</td>\n",
-       "      <td>Thu Dec 26 05:51:08 2024</td>\n",
+       "      <td>0.1166</td>\n",
+       "      <td>0.3585</td>\n",
+       "      <td>Fri Apr 25 03:03:50 2025</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>"
@@ -10956,7 +10962,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f0ff3c79",
+   "id": "e94cb207",
    "metadata": {},
    "source": [
     "We also provide the capability to visualize the results of each stage of the AutoML pipeline."
@@ -10964,7 +10970,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "63e6ffe6",
+   "id": "399ae6a7",
    "metadata": {},
    "source": [
     "<a id='algorithm-selection'></a>\n",
@@ -10976,19 +10982,19 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "dbff4ed3",
+   "id": "87aa015f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:51:18.619342Z",
-     "iopub.status.busy": "2024-12-26T13:51:18.618687Z",
-     "iopub.status.idle": "2024-12-26T13:51:19.234938Z",
-     "shell.execute_reply": "2024-12-26T13:51:19.234266Z"
+     "iopub.execute_input": "2025-04-25T10:04:01.446371Z",
+     "iopub.status.busy": "2025-04-25T10:04:01.445865Z",
+     "iopub.status.idle": "2025-04-25T10:04:02.034548Z",
+     "shell.execute_reply": "2025-04-25T10:04:02.033899Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x700 with 1 Axes>"
       ]
@@ -11030,7 +11036,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8b280917",
+   "id": "39201562",
    "metadata": {},
    "source": [
     "<a id='hyperparameter-tuning'></a>\n",
@@ -11042,19 +11048,19 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "2b531a69",
+   "id": "d400ee3a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:51:19.237775Z",
-     "iopub.status.busy": "2024-12-26T13:51:19.237187Z",
-     "iopub.status.idle": "2024-12-26T13:51:19.484212Z",
-     "shell.execute_reply": "2024-12-26T13:51:19.483578Z"
+     "iopub.execute_input": "2025-04-25T10:04:02.036740Z",
+     "iopub.status.busy": "2025-04-25T10:04:02.036261Z",
+     "iopub.status.idle": "2025-04-25T10:04:02.283905Z",
+     "shell.execute_reply": "2025-04-25T10:04:02.283305Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x700 with 1 Axes>"
       ]
@@ -11095,7 +11101,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3ff3f5d8",
+   "id": "b8eabd7c",
    "metadata": {},
    "source": [
     "#### Confusion Matrix\n",
@@ -11105,13 +11111,13 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "8bf509ac",
+   "id": "0b47cd30",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:51:19.486209Z",
-     "iopub.status.busy": "2024-12-26T13:51:19.486007Z",
-     "iopub.status.idle": "2024-12-26T13:51:19.918775Z",
-     "shell.execute_reply": "2024-12-26T13:51:19.918168Z"
+     "iopub.execute_input": "2025-04-25T10:04:02.285941Z",
+     "iopub.status.busy": "2025-04-25T10:04:02.285479Z",
+     "iopub.status.idle": "2025-04-25T10:04:02.713134Z",
+     "shell.execute_reply": "2025-04-25T10:04:02.712574Z"
     }
    },
    "outputs": [
@@ -12094,9 +12100,9 @@
        }
       },
       "text/html": [
-       "<div>                            <div id=\"5470dec8-434f-4c1f-9324-c2a54a5226ae\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"5470dec8-434f-4c1f-9324-c2a54a5226ae\")) {                    Plotly.newPlot(                        \"5470dec8-434f-4c1f-9324-c2a54a5226ae\",                        [{\"colorscale\":[[0.0,\"#440154\"],[0.1111111111111111,\"#482878\"],[0.2222222222222222,\"#3e4989\"],[0.3333333333333333,\"#31688e\"],[0.4444444444444444,\"#26828e\"],[0.5555555555555556,\"#1f9e89\"],[0.6666666666666666,\"#35b779\"],[0.7777777777777778,\"#6ece58\"],[0.8888888888888888,\"#b5de2b\"],[1.0,\"#fde725\"]],\"reversescale\":false,\"showscale\":false,\"x\":[\"Normal\",\"Fraud\"],\"y\":[\"Normal\",\"Fraud\"],\"z\":[[0.9367778696164466,0.06322213038355345],[0.494949494949495,0.5050505050505051]],\"type\":\"heatmap\"}],                        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    "cell_type": "markdown",
-   "id": "c9735fbb",
+   "id": "d6b59afa",
    "metadata": {},
    "source": [
     "<a id='timebudget'></a>\n",
@@ -12164,13 +12170,13 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "dec2669d",
+   "id": "34681372",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:51:19.920900Z",
-     "iopub.status.busy": "2024-12-26T13:51:19.920380Z",
-     "iopub.status.idle": "2024-12-26T13:51:36.707914Z",
-     "shell.execute_reply": "2024-12-26T13:51:36.707370Z"
+     "iopub.execute_input": "2025-04-25T10:04:02.715088Z",
+     "iopub.status.busy": "2025-04-25T10:04:02.714896Z",
+     "iopub.status.idle": "2025-04-25T10:04:19.625334Z",
+     "shell.execute_reply": "2025-04-25T10:04:19.624707Z"
     }
    },
    "outputs": [
@@ -12178,70 +12184,70 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:51:20,029] [automlx.interface] Dataset shape: (17088,19)\n"
+      "[2025-04-25 03:04:02,809] [automlx.interface] Dataset shape: (17088,19)\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:51:20,130] [automlx.data_transform] Running preprocessing. Number of features: 20\n"
+      "[2025-04-25 03:04:02,906] [automlx.data_transform] Running preprocessing. Number of features: 20\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:51:20,307] [automlx.data_transform] Preprocessing completed. Took 0.177 secs\n"
+      "[2025-04-25 03:04:03,080] [automlx.data_transform] Preprocessing completed. Took 0.174 secs\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:51:20,357] [automlx.process] Running Model Generation\n"
+      "[2025-04-25 03:04:03,122] [automlx.process] Running Model Generation\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:51:20,404] [automlx.process] Model Generation completed.\n"
+      "[2025-04-25 03:04:03,169] [automlx.process] Model Generation completed.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:51:20,473] [automlx.model_selection] Running Model Selection\n"
+      "[2025-04-25 03:04:03,235] [automlx.model_selection] Running Model Selection\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:51:30,409] [automlx.model_selection] Model Selection completed - Took 9.936 sec - Selected models: [['MinCovOD']]\n"
+      "[2025-04-25 03:04:13,204] [automlx.model_selection] Model Selection completed - Took 9.969 sec - Selected models: [['MinCovOD']]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:51:30,419] [automlx.process] Timebudget exceeded for steps ['HyperparameterOptimization'], skipping processing of [MinCovOD (InputTargetDataTransformer_MinCovOD)]\n"
+      "[2025-04-25 03:04:13,215] [automlx.process] Timebudget exceeded for steps ['HyperparameterOptimization'], skipping processing of [MinCovOD (InputTargetDataTransformer_MinCovOD)]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:51:30,503] [automlx.interface] Re-fitting pipeline\n"
+      "[2025-04-25 03:04:13,296] [automlx.interface] Re-fitting pipeline\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:51:30,514] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_0acb6af2-a\n"
+      "[2025-04-25 03:04:13,307] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_c8b84ec6-f\n"
      ]
     },
     {
@@ -12263,7 +12269,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5ea2f7eb",
+   "id": "16dc3cc3",
    "metadata": {},
    "source": [
     "<a id='MLX'></a>\n",
@@ -12272,7 +12278,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a131170c",
+   "id": "722d3c7a",
    "metadata": {},
    "source": [
     "For a variety of decision-making tasks, getting only a prediction as model output is not sufficient. A user may wish to know why the model outputs that prediction, or which data features are relevant for that prediction. For that purpose the Oracle AutoMLx solution defines the `MLExplainer` object, which allows to compute a variety of model explanations.\n",
@@ -12286,13 +12292,13 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "a3fddc11",
+   "id": "3fb287bc",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:51:36.710002Z",
-     "iopub.status.busy": "2024-12-26T13:51:36.709589Z",
-     "iopub.status.idle": "2024-12-26T13:51:40.807010Z",
-     "shell.execute_reply": "2024-12-26T13:51:40.806317Z"
+     "iopub.execute_input": "2025-04-25T10:04:19.627597Z",
+     "iopub.status.busy": "2025-04-25T10:04:19.627122Z",
+     "iopub.status.idle": "2025-04-25T10:04:25.314927Z",
+     "shell.execute_reply": "2025-04-25T10:04:25.314270Z"
     }
    },
    "outputs": [],
@@ -12305,7 +12311,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a591eb65",
+   "id": "d947d61e",
    "metadata": {},
    "source": [
     "<a id='MLX-global'></a>\n",
@@ -12314,7 +12320,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8aef96e9",
+   "id": "d24b65ec",
    "metadata": {},
    "source": [
     "The notion of Global Feature Importance intuitively measures how much the model's performance (relative to the model's original predictions or the provided train labels, if available) would change if a given feature were dropped from the dataset, ***and the model was retrained***. (Note that this is unlike the default explainers for classification and regression tasks, which explain the model as if it were not retrained. Also unlike these supervised explainers, the anomaly detection explainer does not support interventional explanations.) Note that this notion of feature importance still considers each feature independently from all other features."
@@ -12322,7 +12328,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ae921814",
+   "id": "47267091",
    "metadata": {},
    "source": [
     "#### Compute the importance"
@@ -12330,7 +12336,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e2cce518",
+   "id": "f474f126",
    "metadata": {},
    "source": [
     "By default we use a permutation method to successively measure the importance of each feature. Such a method therefore runs in linear time with respect to the\n",
@@ -12342,13 +12348,13 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "a2c92dca",
+   "id": "643fc84a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:51:40.810827Z",
-     "iopub.status.busy": "2024-12-26T13:51:40.809386Z",
-     "iopub.status.idle": "2024-12-26T13:51:48.338090Z",
-     "shell.execute_reply": "2024-12-26T13:51:48.337418Z"
+     "iopub.execute_input": "2025-04-25T10:04:25.318061Z",
+     "iopub.status.busy": "2025-04-25T10:04:25.316997Z",
+     "iopub.status.idle": "2025-04-25T10:04:32.918643Z",
+     "shell.execute_reply": "2025-04-25T10:04:32.917937Z"
     }
    },
    "outputs": [],
@@ -12358,7 +12364,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4bdf629a",
+   "id": "d8d09f86",
    "metadata": {},
    "source": [
     "#### Visualization"
@@ -12366,7 +12372,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "421e4c44",
+   "id": "27f1897b",
    "metadata": {},
    "source": [
     "There are two options to show the explanation's results:\n",
@@ -12379,13 +12385,13 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "12ee7f45",
+   "id": "cd98fb4d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:51:48.340605Z",
-     "iopub.status.busy": "2024-12-26T13:51:48.340232Z",
-     "iopub.status.idle": "2024-12-26T13:51:48.771357Z",
-     "shell.execute_reply": "2024-12-26T13:51:48.770658Z"
+     "iopub.execute_input": "2025-04-25T10:04:32.921426Z",
+     "iopub.status.busy": "2025-04-25T10:04:32.920832Z",
+     "iopub.status.idle": "2025-04-25T10:04:33.025837Z",
+     "shell.execute_reply": "2025-04-25T10:04:33.025232Z"
     }
    },
    "outputs": [
@@ -12420,37 +12426,37 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>V19</td>\n",
-       "      <td>0.062620</td>\n",
-       "      <td>0.057794</td>\n",
-       "      <td>0.067446</td>\n",
+       "      <td>0.062594</td>\n",
+       "      <td>0.058448</td>\n",
+       "      <td>0.066740</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>V8</td>\n",
-       "      <td>0.051431</td>\n",
-       "      <td>0.044974</td>\n",
-       "      <td>0.057888</td>\n",
+       "      <td>0.047495</td>\n",
+       "      <td>0.042024</td>\n",
+       "      <td>0.052967</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>V4</td>\n",
-       "      <td>0.034813</td>\n",
-       "      <td>0.028819</td>\n",
-       "      <td>0.040807</td>\n",
+       "      <td>0.035441</td>\n",
+       "      <td>0.030222</td>\n",
+       "      <td>0.040659</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>V3</td>\n",
-       "      <td>0.030684</td>\n",
-       "      <td>0.026032</td>\n",
-       "      <td>0.035337</td>\n",
+       "      <td>0.030964</td>\n",
+       "      <td>0.028117</td>\n",
+       "      <td>0.033810</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>V14</td>\n",
-       "      <td>0.015264</td>\n",
-       "      <td>0.011293</td>\n",
-       "      <td>0.019234</td>\n",
+       "      <td>0.013733</td>\n",
+       "      <td>0.011846</td>\n",
+       "      <td>0.015620</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
@@ -12563,11 +12569,11 @@
       ],
       "text/plain": [
        "   Feature  Attribution  Lower Bound  Upper Bound\n",
-       "0      V19     0.062620     0.057794     0.067446\n",
-       "1       V8     0.051431     0.044974     0.057888\n",
-       "2       V4     0.034813     0.028819     0.040807\n",
-       "3       V3     0.030684     0.026032     0.035337\n",
-       "4      V14     0.015264     0.011293     0.019234\n",
+       "0      V19     0.062594     0.058448     0.066740\n",
+       "1       V8     0.047495     0.042024     0.052967\n",
+       "2       V4     0.035441     0.030222     0.040659\n",
+       "3       V3     0.030964     0.028117     0.033810\n",
+       "4      V14     0.013733     0.011846     0.015620\n",
        "12     V11     0.000000     0.000000     0.000000\n",
        "18     V18     0.000000     0.000000     0.000000\n",
        "17     V17     0.000000     0.000000     0.000000\n",
@@ -12597,13 +12603,13 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "e1139d4e",
+   "id": "592321d8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:51:48.773619Z",
-     "iopub.status.busy": "2024-12-26T13:51:48.773038Z",
-     "iopub.status.idle": "2024-12-26T13:51:49.513902Z",
-     "shell.execute_reply": "2024-12-26T13:51:49.513361Z"
+     "iopub.execute_input": "2025-04-25T10:04:33.027991Z",
+     "iopub.status.busy": "2025-04-25T10:04:33.027518Z",
+     "iopub.status.idle": "2025-04-25T10:04:34.180109Z",
+     "shell.execute_reply": "2025-04-25T10:04:34.179503Z"
     }
    },
    "outputs": [
@@ -12617,11 +12623,11 @@
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@@ -13576,9 +13582,9 @@
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   },
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    "cell_type": "markdown",
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     "###  Feature Dependence Explanations (Partial Dependence Plots)\n",
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@@ -14940,7 +14946,7 @@
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   {
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@@ -14949,7 +14955,7 @@
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   {
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-   "id": "65ba4bc0",
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     "In addition to the Model's behavior, a user might be curious about decision-logic behind the specific predictions made by the model or the impact of specific feature values on the prediction. The Oracle AutoMLx offers prediction explanations to address such concerns."
@@ -14957,7 +14963,7 @@
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   {
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-   "id": "1fddc9fe",
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@@ -14966,7 +14972,7 @@
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-   "id": "acc659be",
+   "id": "53362cd2",
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    "source": [
     "Given a data sample, one can also obtain the local importance, which is the importance of the features for the model's prediction on that sample.\n",
@@ -14978,13 +14984,13 @@
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-   "id": "d7492b0c",
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-     "iopub.status.idle": "2024-12-26T13:51:51.242808Z",
-     "shell.execute_reply": "2024-12-26T13:51:51.242208Z"
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+     "shell.execute_reply": "2025-04-25T10:04:35.781712Z"
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@@ -14995,13 +15001,13 @@
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-     "shell.execute_reply": "2024-12-26T13:51:51.660300Z"
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+     "iopub.status.busy": "2025-04-25T10:04:35.783777Z",
+     "iopub.status.idle": "2025-04-25T10:04:36.200218Z",
+     "shell.execute_reply": "2025-04-25T10:04:36.199643Z"
     }
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    "outputs": [
@@ -15029,7 +15035,7 @@
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@@ -15094,7 +15100,7 @@
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@@ -16298,9 +16304,9 @@
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   {
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-     "iopub.status.idle": "2024-12-26T13:51:52.594831Z",
-     "shell.execute_reply": "2024-12-26T13:51:52.594210Z"
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@@ -40177,7 +40123,7 @@
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-          "V19",
+          "V18",
           "V10",
-          "V9",
+          "V8",
+          "V17",
           "V20",
-          "V14",
-          "V4",
+          "V11",
           "V16",
-          "V18",
+          "V7",
+          "V1",
+          "V19",
           "V12",
-          "V11",
-          "V8",
+          "V14",
+          "V9",
+          "V5",
           "V2",
-          "V6",
-          "V7",
           "V15",
-          "V5"
+          "V3",
+          "V4",
+          "V6"
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@@ -41242,25 +41188,25 @@
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           "V13",
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-          "V3",
-          "V1",
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+          "V18",
           "V10",
-          "V9",
+          "V8",
+          "V17",
           "V20",
-          "V14",
-          "V4",
+          "V11",
           "V16",
-          "V18",
+          "V7",
+          "V1",
+          "V19",
           "V12",
-          "V11",
-          "V8",
+          "V14",
+          "V9",
+          "V5",
           "V2",
-          "V6",
-          "V7",
           "V15",
-          "V5"
+          "V3",
+          "V4",
+          "V6"
          ],
          "categoryorder": "array",
          "gridcolor": "white",
@@ -41279,9 +41225,9 @@
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        "                            \n",
-       "var gd = document.getElementById('8f6c2e6f-5378-42ca-a915-b93611d1f066');\n",
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@@ -41319,7 +41265,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "199beb3e",
+   "id": "4463eb81",
    "metadata": {},
    "source": [
     "<a id='MLX-advanced-ale'></a>\n",
@@ -41332,7 +41278,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3f8f36e4",
+   "id": "5e4ff7ea",
    "metadata": {},
    "source": [
     "The X-axis is the value of the `V17` feature and the y-axis is the corresponding computed ALE (price unit).\n",
@@ -41343,13 +41289,13 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "id": "5589f68c",
+   "id": "4fde81dd",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:52:04.403429Z",
-     "iopub.status.busy": "2024-12-26T13:52:04.403042Z",
-     "iopub.status.idle": "2024-12-26T13:52:05.478794Z",
-     "shell.execute_reply": "2024-12-26T13:52:05.478214Z"
+     "iopub.execute_input": "2025-04-25T10:04:49.292042Z",
+     "iopub.status.busy": "2025-04-25T10:04:49.291584Z",
+     "iopub.status.idle": "2025-04-25T10:04:50.535509Z",
+     "shell.execute_reply": "2025-04-25T10:04:50.534969Z"
     }
    },
    "outputs": [
@@ -41417,7 +41363,7 @@
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@@ -41458,7 +41404,7 @@
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@@ -41496,27 +41442,27 @@
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@@ -42598,8 +42544,8 @@
          "matches": "y2",
          "mirror": true,
          "range": [
-          -0.08339393490366324,
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@@ -42616,9 +42562,9 @@
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id=\"T_6b8c9_level0_col4\" class=\"col_heading level0 col4\" >V5</th>\n      <th id=\"T_6b8c9_level0_col5\" class=\"col_heading level0 col5\" >V6</th>\n      <th id=\"T_6b8c9_level0_col6\" class=\"col_heading level0 col6\" >V7</th>\n      <th id=\"T_6b8c9_level0_col7\" class=\"col_heading level0 col7\" >V8</th>\n      <th id=\"T_6b8c9_level0_col8\" class=\"col_heading level0 col8\" >V9</th>\n      <th id=\"T_6b8c9_level0_col9\" class=\"col_heading level0 col9\" >V10</th>\n      <th id=\"T_6b8c9_level0_col10\" class=\"col_heading level0 col10\" >V11</th>\n      <th id=\"T_6b8c9_level0_col11\" class=\"col_heading level0 col11\" >V12</th>\n      <th id=\"T_6b8c9_level0_col12\" class=\"col_heading level0 col12\" >V13</th>\n      <th id=\"T_6b8c9_level0_col13\" class=\"col_heading level0 col13\" >V14</th>\n      <th id=\"T_6b8c9_level0_col14\" class=\"col_heading level0 col14\" >V15</th>\n      <th id=\"T_6b8c9_level0_col15\" class=\"col_heading level0 col15\" >V16</th>\n      <th 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",
         "encoding": "base64",
         "path": [
          "_data",
@@ -51062,7 +44267,7 @@
         ]
        },
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         "encoding": "base64",
         "path": [
          "_data",
@@ -51072,7 +44277,7 @@
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        },
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",
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",
         "encoding": "base64",
         "path": [
          "_data",
@@ -51082,7 +44287,7 @@
         ]
        },
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class=\"col_heading level0 col10\" >V11</th>\n      <th id=\"T_0606d_level0_col11\" class=\"col_heading level0 col11\" >V12</th>\n      <th id=\"T_0606d_level0_col12\" class=\"col_heading level0 col12\" >V13</th>\n      <th id=\"T_0606d_level0_col13\" class=\"col_heading level0 col13\" >V14</th>\n      <th id=\"T_0606d_level0_col14\" class=\"col_heading level0 col14\" >V15</th>\n      <th id=\"T_0606d_level0_col15\" class=\"col_heading level0 col15\" >V16</th>\n      <th id=\"T_0606d_level0_col16\" class=\"col_heading level0 col16\" >V17</th>\n      <th id=\"T_0606d_level0_col17\" class=\"col_heading level0 col17\" >V18</th>\n      <th id=\"T_0606d_level0_col18\" class=\"col_heading level0 col18\" >V19</th>\n      <th id=\"T_0606d_level0_col19\" class=\"col_heading level0 col19\" >V20</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_0606d_level0_row0\" class=\"row_heading level0 row0\" >Original Sample</th>\n      <td id=\"T_0606d_row0_col0\" class=\"data row0 col0\" >-0.814601292682208</td>\n      <td id=\"T_0606d_row0_col1\" class=\"data row0 col1\" >0.8527854763983154</td>\n      <td id=\"T_0606d_row0_col2\" class=\"data row0 col2\" >1.0423288727328561</td>\n      <td id=\"T_0606d_row0_col3\" class=\"data row0 col3\" >-2.2223952125512327</td>\n      <td id=\"T_0606d_row0_col4\" class=\"data row0 col4\" >0.28871233525783424</td>\n      <td id=\"T_0606d_row0_col5\" class=\"data row0 col5\" >-0.8395058532520351</td>\n      <td id=\"T_0606d_row0_col6\" class=\"data row0 col6\" >0.9928911115798961</td>\n      <td id=\"T_0606d_row0_col7\" class=\"data row0 col7\" >-0.20909651374437896</td>\n      <td id=\"T_0606d_row0_col8\" class=\"data row0 col8\" >0.7806740947971558</td>\n      <td id=\"T_0606d_row0_col9\" class=\"data row0 col9\" >-0.6137903575618552</td>\n      <td id=\"T_0606d_row0_col10\" class=\"data row0 col10\" >1.8301436434282867</td>\n      <td id=\"T_0606d_row0_col11\" class=\"data row0 col11\" >1.1944318873587512</td>\n      <td id=\"T_0606d_row0_col12\" class=\"data row0 col12\" >-0.12699362950573215</td>\n      <td id=\"T_0606d_row0_col13\" class=\"data row0 col13\" >0.16516219940938834</td>\n      <td id=\"T_0606d_row0_col14\" class=\"data row0 col14\" >0.4184581901658031</td>\n      <td id=\"T_0606d_row0_col15\" class=\"data row0 col15\" >-0.7801235968866925</td>\n      <td id=\"T_0606d_row0_col16\" class=\"data row0 col16\" >-0.4978313765414018</td>\n      <td id=\"T_0606d_row0_col17\" class=\"data row0 col17\" >-0.028500870424174533</td>\n      <td id=\"T_0606d_row0_col18\" class=\"data row0 col18\" >-0.27366085753472397</td>\n      <td id=\"T_0606d_row0_col19\" class=\"data row0 col19\" >0.12669591871187177</td>\n    </tr>\n    <tr>\n      <th id=\"T_0606d_level0_row1\" class=\"row_heading level0 row1\" >Modified Sample</th>\n      <td id=\"T_0606d_row1_col0\" class=\"data row1 col0\" >-0.8206489258385204</td>\n      <td id=\"T_0606d_row1_col1\" class=\"data row1 col1\" >0.8018711456873213</td>\n      <td id=\"T_0606d_row1_col2\" class=\"data row1 col2\" >1.169181690064734</td>\n      <td id=\"T_0606d_row1_col3\" class=\"data row1 col3\" >-2.2068027294887687</td>\n      <td id=\"T_0606d_row1_col4\" class=\"data row1 col4\" >0.24901913676340676</td>\n      <td id=\"T_0606d_row1_col5\" class=\"data row1 col5\" >-0.8326174476937197</td>\n      <td id=\"T_0606d_row1_col6\" class=\"data row1 col6\" >0.9857848028130605</td>\n      <td id=\"T_0606d_row1_col7\" class=\"data row1 col7\" >0.6195219088418539</td>\n      <td id=\"T_0606d_row1_col8\" class=\"data row1 col8\" >0.7679270334228689</td>\n      <td id=\"T_0606d_row1_col9\" class=\"data row1 col9\" >-0.6166745641378114</td>\n      <td id=\"T_0606d_row1_col10\" class=\"data row1 col10\" >1.6668020759695792</td>\n      <td id=\"T_0606d_row1_col11\" class=\"data row1 col11\" >1.1929639695086505</td>\n      <td id=\"T_0606d_row1_col12\" class=\"data row1 col12\" >-0.12383260175036157</td>\n      <td id=\"T_0606d_row1_col13\" class=\"data row1 col13\" >0.23360247082342925</td>\n      <td id=\"T_0606d_row1_col14\" class=\"data row1 col14\" >0.4143558685262748</td>\n      <td id=\"T_0606d_row1_col15\" class=\"data row1 col15\" >-0.7020214594660359</td>\n      <td id=\"T_0606d_row1_col16\" class=\"data row1 col16\" >-0.49515471316881243</td>\n      <td id=\"T_0606d_row1_col17\" class=\"data row1 col17\" >-0.08633893974487566</td>\n      <td id=\"T_0606d_row1_col18\" class=\"data row1 col18\" >-0.47404568046687656</td>\n      <td id=\"T_0606d_row1_col19\" class=\"data row1 col19\" >0.11621956966714478</td>\n    </tr>\n  </tbody>\n</table>\n"
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@@ -53763,29 +49066,7 @@
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-       "_view_module": "@jupyter-widgets/base",
-       "_view_module_version": "2.0.0",
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-       "text_decoration": null
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-     "a6c9b740ff9344e3a3185a639386726e": {
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@@ -53838,7 +49119,7 @@
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-     "a6d4825a173a43b7b7c2c49857b24f9c": {
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@@ -53888,58 +49169,10 @@
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-       "_view_module_version": "2.0.0",
-       "_view_name": "HTMLView",
-       "description": "",
-       "description_allow_html": false,
-       "layout": "IPY_MODEL_5a889f2ecdc9482fbae13b16a18e1c05",
-       "placeholder": "​",
-       "style": "IPY_MODEL_875e9f4175c84564b9ca40f360deb6b6",
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-       "tooltip": null,
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-       "_view_module": "@jupyter-widgets/controls",
-       "_view_module_version": "2.0.0",
-       "_view_name": "FloatTextView",
-       "continuous_update": false,
-       "description": "V8",
-       "description_allow_html": false,
-       "disabled": false,
-       "layout": "IPY_MODEL_ff51c17f335f4bd7af0a4d8566acbbe0",
-       "step": null,
-       "style": "IPY_MODEL_c6ab843505244574adf33624f71ebf3f",
-       "tabbable": null,
-       "tooltip": null,
-       "value": 0.6195219088418539
+       "width": "initial"
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-     "aa7c0855870b48d5835929ffbf98f957": {
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@@ -53992,7 +49225,7 @@
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-     "aacfd55b2db64a46920c2d3d223626d8": {
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@@ -54045,7 +49278,7 @@
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-     "ab1aed3e2d6c42f599ffbe54505b9e0a": {
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@@ -54095,32 +49328,35 @@
        "right": null,
        "top": null,
        "visibility": null,
-       "width": "1100px"
+       "width": null
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-     "ab8fe509344d456ba01c8a263698e21b": {
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-       "_view_module": "@jupyter-widgets/base",
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-       "font_style": null,
-       "font_variant": null,
-       "font_weight": null,
-       "text_color": null,
-       "text_decoration": null
+       "_view_name": "FloatTextView",
+       "continuous_update": false,
+       "description": "V14",
+       "description_allow_html": false,
+       "disabled": false,
+       "layout": "IPY_MODEL_8824a361252043609f82acedc541fe4c",
+       "step": null,
+       "style": "IPY_MODEL_eef6984126574a49b9783ff9f5596294",
+       "tabbable": null,
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@@ -54138,7 +49374,30 @@
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-     "ad242bb152fd42d59fa82c44f8537aaf": {
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+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
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+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "HTMLView",
+       "description": "",
+       "description_allow_html": false,
+       "layout": "IPY_MODEL_44c3758a8c064ca0803b22d77ed530cd",
+       "placeholder": "​",
+       "style": "IPY_MODEL_759306603aa84627975c74ae5eda46f1",
+       "tabbable": null,
+       "tooltip": null,
+       "value": "<h2>Sample Values</h2>"
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@@ -54188,10 +49447,10 @@
        "right": null,
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-       "width": "initial"
+       "width": "1100px"
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-     "ad8ba133dc584f85bab50549013fa3ae": {
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-     "b2033954e77549a593728e3e90aad52c": {
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@@ -54340,7 +49540,7 @@
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@@ -54349,110 +49549,97 @@
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id=\"T_fc400_level0_col4\" class=\"col_heading level0 col4\" >V5</th>\n      <th id=\"T_fc400_level0_col5\" class=\"col_heading level0 col5\" >V6</th>\n      <th id=\"T_fc400_level0_col6\" class=\"col_heading level0 col6\" >V7</th>\n      <th id=\"T_fc400_level0_col7\" class=\"col_heading level0 col7\" >V8</th>\n      <th id=\"T_fc400_level0_col8\" class=\"col_heading level0 col8\" >V9</th>\n      <th id=\"T_fc400_level0_col9\" class=\"col_heading level0 col9\" >V10</th>\n      <th id=\"T_fc400_level0_col10\" class=\"col_heading level0 col10\" >V11</th>\n      <th id=\"T_fc400_level0_col11\" class=\"col_heading level0 col11\" >V12</th>\n      <th id=\"T_fc400_level0_col12\" class=\"col_heading level0 col12\" >V13</th>\n      <th id=\"T_fc400_level0_col13\" class=\"col_heading level0 col13\" >V14</th>\n      <th id=\"T_fc400_level0_col14\" class=\"col_heading level0 col14\" >V15</th>\n      <th id=\"T_fc400_level0_col15\" class=\"col_heading level0 col15\" >V16</th>\n      <th 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background-color: #BAFFBA;\n}\n</style>\n<table id=\"T_e77a3\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_e77a3_level0_col0\" class=\"col_heading level0 col0\" >V1</th>\n      <th id=\"T_e77a3_level0_col1\" class=\"col_heading level0 col1\" >V2</th>\n      <th id=\"T_e77a3_level0_col2\" class=\"col_heading level0 col2\" >V3</th>\n      <th id=\"T_e77a3_level0_col3\" class=\"col_heading level0 col3\" >V4</th>\n      <th id=\"T_e77a3_level0_col4\" class=\"col_heading level0 col4\" >V5</th>\n      <th id=\"T_e77a3_level0_col5\" class=\"col_heading level0 col5\" >V6</th>\n      <th id=\"T_e77a3_level0_col6\" class=\"col_heading level0 col6\" >V7</th>\n      <th id=\"T_e77a3_level0_col7\" class=\"col_heading level0 col7\" >V8</th>\n      <th id=\"T_e77a3_level0_col8\" class=\"col_heading level0 col8\" >V9</th>\n      <th id=\"T_e77a3_level0_col9\" class=\"col_heading level0 col9\" >V10</th>\n      <th id=\"T_e77a3_level0_col10\" class=\"col_heading level0 col10\" >V11</th>\n      <th id=\"T_e77a3_level0_col11\" class=\"col_heading level0 col11\" >V12</th>\n      <th id=\"T_e77a3_level0_col12\" class=\"col_heading level0 col12\" >V13</th>\n      <th id=\"T_e77a3_level0_col13\" class=\"col_heading level0 col13\" >V14</th>\n      <th id=\"T_e77a3_level0_col14\" class=\"col_heading level0 col14\" >V15</th>\n      <th id=\"T_e77a3_level0_col15\" class=\"col_heading level0 col15\" >V16</th>\n      <th id=\"T_e77a3_level0_col16\" class=\"col_heading level0 col16\" >V17</th>\n      <th id=\"T_e77a3_level0_col17\" class=\"col_heading level0 col17\" >V18</th>\n      <th id=\"T_e77a3_level0_col18\" class=\"col_heading level0 col18\" >V19</th>\n      <th id=\"T_e77a3_level0_col19\" class=\"col_heading level0 col19\" >V20</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_e77a3_level0_row0\" class=\"row_heading level0 row0\" >Original Sample</th>\n      <td id=\"T_e77a3_row0_col0\" class=\"data row0 col0\" >-0.814601292682208</td>\n      <td id=\"T_e77a3_row0_col1\" class=\"data row0 col1\" >0.8527854763983154</td>\n      <td id=\"T_e77a3_row0_col2\" class=\"data row0 col2\" >1.0423288727328561</td>\n      <td id=\"T_e77a3_row0_col3\" class=\"data row0 col3\" >-2.2223952125512327</td>\n      <td id=\"T_e77a3_row0_col4\" class=\"data row0 col4\" >0.28871233525783424</td>\n      <td id=\"T_e77a3_row0_col5\" class=\"data row0 col5\" >-0.8395058532520351</td>\n      <td id=\"T_e77a3_row0_col6\" class=\"data row0 col6\" >0.9928911115798961</td>\n      <td id=\"T_e77a3_row0_col7\" class=\"data row0 col7\" >-0.20909651374437896</td>\n      <td id=\"T_e77a3_row0_col8\" class=\"data row0 col8\" >0.7806740947971558</td>\n      <td id=\"T_e77a3_row0_col9\" class=\"data row0 col9\" >-0.6137903575618552</td>\n      <td id=\"T_e77a3_row0_col10\" class=\"data row0 col10\" >1.8301436434282867</td>\n      <td id=\"T_e77a3_row0_col11\" class=\"data row0 col11\" >1.1944318873587512</td>\n      <td id=\"T_e77a3_row0_col12\" class=\"data row0 col12\" >-0.12699362950573215</td>\n      <td id=\"T_e77a3_row0_col13\" class=\"data row0 col13\" >0.16516219940938834</td>\n      <td id=\"T_e77a3_row0_col14\" class=\"data row0 col14\" >0.4184581901658031</td>\n      <td id=\"T_e77a3_row0_col15\" class=\"data row0 col15\" >-0.7801235968866925</td>\n      <td id=\"T_e77a3_row0_col16\" class=\"data row0 col16\" >-0.4978313765414018</td>\n      <td id=\"T_e77a3_row0_col17\" class=\"data row0 col17\" >-0.028500870424174533</td>\n      <td id=\"T_e77a3_row0_col18\" class=\"data row0 col18\" >-0.27366085753472397</td>\n      <td id=\"T_e77a3_row0_col19\" class=\"data row0 col19\" >0.12669591871187177</td>\n    </tr>\n    <tr>\n      <th id=\"T_e77a3_level0_row1\" class=\"row_heading level0 row1\" >Modified Sample</th>\n      <td id=\"T_e77a3_row1_col0\" class=\"data row1 col0\" >-0.8195973075521539</td>\n      <td id=\"T_e77a3_row1_col1\" class=\"data row1 col1\" >0.8258281811402914</td>\n      <td id=\"T_e77a3_row1_col2\" class=\"data row1 col2\" >1.1492178989013921</td>\n      <td id=\"T_e77a3_row1_col3\" class=\"data row1 col3\" >-2.11301493636727</td>\n      <td id=\"T_e77a3_row1_col4\" class=\"data row1 col4\" >0.28096492348525604</td>\n      <td id=\"T_e77a3_row1_col5\" class=\"data row1 col5\" >-0.8289637892279351</td>\n      <td id=\"T_e77a3_row1_col6\" class=\"data row1 col6\" >0.9239569002372758</td>\n      <td id=\"T_e77a3_row1_col7\" class=\"data row1 col7\" >0.6267308703871985</td>\n      <td id=\"T_e77a3_row1_col8\" class=\"data row1 col8\" >0.5965079515639202</td>\n      <td id=\"T_e77a3_row1_col9\" class=\"data row1 col9\" >-0.6253149438613781</td>\n      <td id=\"T_e77a3_row1_col10\" class=\"data row1 col10\" >1.828519947646691</td>\n      <td id=\"T_e77a3_row1_col11\" class=\"data row1 col11\" >1.1650589176757253</td>\n      <td id=\"T_e77a3_row1_col12\" class=\"data row1 col12\" >-0.12427833398317614</td>\n      <td id=\"T_e77a3_row1_col13\" class=\"data row1 col13\" >0.2479554103817461</td>\n      <td id=\"T_e77a3_row1_col14\" class=\"data row1 col14\" >0.38310734245838746</td>\n      <td id=\"T_e77a3_row1_col15\" class=\"data row1 col15\" >-0.6167199744600596</td>\n      <td id=\"T_e77a3_row1_col16\" class=\"data row1 col16\" >-0.48996835884172363</td>\n      <td id=\"T_e77a3_row1_col17\" class=\"data row1 col17\" >-0.0312665834514463</td>\n      <td id=\"T_e77a3_row1_col18\" class=\"data row1 col18\" >-0.5662567785776434</td>\n      <td id=\"T_e77a3_row1_col19\" class=\"data row1 col19\" >0.10517011223668252</td>\n    </tr>\n  </tbody>\n</table>\n"
       }
      },
-     "f9c865d4fa704686a2516a366d95871c": {
+     "f1404cea0c1845549ac859e5812b723b": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
-      "model_name": "FloatTextModel",
+      "model_name": "HTMLStyleModel",
       "state": {
-       "_dom_classes": [],
        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "2.0.0",
-       "_model_name": "FloatTextModel",
+       "_model_name": "HTMLStyleModel",
        "_view_count": null,
-       "_view_module": "@jupyter-widgets/controls",
+       "_view_module": "@jupyter-widgets/base",
        "_view_module_version": "2.0.0",
-       "_view_name": "FloatTextView",
-       "continuous_update": false,
-       "description": "V19",
-       "description_allow_html": false,
-       "disabled": false,
-       "layout": "IPY_MODEL_a6d4825a173a43b7b7c2c49857b24f9c",
-       "step": null,
-       "style": "IPY_MODEL_1cd710b3c0b94cb3bdbc739021a8a354",
-       "tabbable": null,
-       "tooltip": null,
-       "value": -0.27366085753472397
+       "_view_name": "StyleView",
+       "background": null,
+       "description_width": "",
+       "font_size": null,
+       "text_color": null
       }
      },
-     "fa558ce3f4204f19bfcc7fa4a1475db6": {
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       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
       "model_name": "FloatTextModel",
@@ -57794,58 +57623,94 @@
        "_view_module_version": "2.0.0",
        "_view_name": "FloatTextView",
        "continuous_update": false,
-       "description": "V6",
+       "description": "V3",
        "description_allow_html": false,
        "disabled": false,
-       "layout": "IPY_MODEL_2cbf4113170749cc889b7a9d5eca22c9",
+       "layout": "IPY_MODEL_85c86709ed894eb294f2ebde78533bf5",
        "step": null,
-       "style": "IPY_MODEL_8923c65dcd194a55bee7797dda0ee7be",
+       "style": "IPY_MODEL_6992a7816a7d42fabd3d0192d40cb34c",
        "tabbable": null,
        "tooltip": null,
-       "value": -0.8326174476937197
+       "value": 1.0423288727328561
       }
      },
-     "fc706236760d42d69fb2f13dd7749c2b": {
+     "f25e2c652dfd40c9901d2dbe4af56330": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
-      "model_name": "FloatTextModel",
+      "model_name": "HTMLModel",
       "state": {
        "_dom_classes": [],
        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "2.0.0",
-       "_model_name": "FloatTextModel",
+       "_model_name": "HTMLModel",
        "_view_count": null,
        "_view_module": "@jupyter-widgets/controls",
        "_view_module_version": "2.0.0",
-       "_view_name": "FloatTextView",
-       "continuous_update": false,
-       "description": "V15",
+       "_view_name": "HTMLView",
+       "description": "",
        "description_allow_html": false,
-       "disabled": false,
-       "layout": "IPY_MODEL_c3065ee9b4404786be85f863a33f1e58",
-       "step": null,
-       "style": "IPY_MODEL_959efc2fcea94470b1cf60086659ea2c",
+       "layout": "IPY_MODEL_50f588fdbde74013bcf1b2e1c08b7540",
+       "placeholder": "​",
+       "style": "IPY_MODEL_7fa2134d1f1b4de9b963e0c798f86d50",
        "tabbable": null,
        "tooltip": null,
-       "value": 0.4184581901658031
+       "value": "<style type=\"text/css\">\n#T_b10ba thead th {\n  border-bottom: 1px solid #DDD;\n  background-color: #F5F5F5;\n  min-width: 20px;\n  padding: 0 10px;\n  text-align: right;\n}\n#T_b10ba tbody th {\n  border-bottom: 1px solid #DDD;\n  background-color: #F5F5F5;\n  min-width: 115px;\n  padding: 0 10px;\n  text-align: right;\n}\n#T_b10ba td {\n  text-align: right;\n  padding: 0 10px;\n}\n#T_b10ba tr:nth-child(even) {\n  background-color: #F9F9F9;\n}\n#T_b10ba  {\n  border-collapse: collapse;\n  border: 1px solid #DDD;\n}\n#T_b10ba_row1_col0 {\n  background-color: #BAFFBA;\n}\n</style>\n<table id=\"T_b10ba\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_b10ba_level0_col0\" class=\"col_heading level0 col0\" >Prediction (True value: Normal)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_b10ba_level0_row0\" class=\"row_heading level0 row0\" >Original Sample</th>\n      <td id=\"T_b10ba_row0_col0\" class=\"data row0 col0\" >Normal</td>\n    </tr>\n    <tr>\n      <th id=\"T_b10ba_level0_row1\" class=\"row_heading level0 row1\" >Modified Sample</th>\n      <td id=\"T_b10ba_row1_col0\" class=\"data row1 col0\" >Anomaly</td>\n    </tr>\n  </tbody>\n</table>\n"
       }
      },
-     "fc858fd0556a41cea54acf4e3465355e": {
-      "model_module": "@jupyter-widgets/controls",
+     "f5b642acc7024460adbf2f068594fa36": {
+      "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
-      "model_name": "DescriptionStyleModel",
+      "model_name": "LayoutModel",
       "state": {
-       "_model_module": "@jupyter-widgets/controls",
+       "_model_module": "@jupyter-widgets/base",
        "_model_module_version": "2.0.0",
-       "_model_name": "DescriptionStyleModel",
+       "_model_name": "LayoutModel",
        "_view_count": null,
        "_view_module": "@jupyter-widgets/base",
        "_view_module_version": "2.0.0",
-       "_view_name": "StyleView",
-       "description_width": ""
+       "_view_name": "LayoutView",
+       "align_content": null,
+       "align_items": null,
+       "align_self": null,
+       "border_bottom": null,
+       "border_left": null,
+       "border_right": null,
+       "border_top": null,
+       "bottom": null,
+       "display": null,
+       "flex": null,
+       "flex_flow": null,
+       "grid_area": null,
+       "grid_auto_columns": null,
+       "grid_auto_flow": null,
+       "grid_auto_rows": null,
+       "grid_column": null,
+       "grid_gap": null,
+       "grid_row": null,
+       "grid_template_areas": null,
+       "grid_template_columns": null,
+       "grid_template_rows": null,
+       "height": null,
+       "justify_content": null,
+       "justify_items": null,
+       "left": null,
+       "margin": null,
+       "max_height": null,
+       "max_width": null,
+       "min_height": null,
+       "min_width": null,
+       "object_fit": null,
+       "object_position": null,
+       "order": null,
+       "overflow": null,
+       "padding": null,
+       "right": null,
+       "top": null,
+       "visibility": null,
+       "width": null
       }
      },
-     "fdc2e8839d78402d9b7865eef7c7a31f": {
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       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
       "model_name": "FloatTextModel",
@@ -57859,33 +57724,124 @@
        "_view_module_version": "2.0.0",
        "_view_name": "FloatTextView",
        "continuous_update": false,
-       "description": "V7",
+       "description": "V10",
        "description_allow_html": false,
        "disabled": false,
-       "layout": "IPY_MODEL_0777315c472b4baeb5b19bd31b52a1ed",
+       "layout": "IPY_MODEL_2078a61fd9a54b91928e87f344580cd6",
        "step": null,
-       "style": "IPY_MODEL_1b2dcb5d1b3d49328404a54addae7bc7",
+       "style": "IPY_MODEL_7a102fab811c4baeb37f0a67a803eaba",
        "tabbable": null,
        "tooltip": null,
-       "value": 0.9928911115798961
+       "value": -0.6137903575618552
       }
      },
-     "fdca118a74f142ffb897b78ca8a4c286": {
-      "model_module": "@jupyter-widgets/controls",
+     "f8a76daeca1b444987ff76e6174f3cee": {
+      "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
-      "model_name": "DescriptionStyleModel",
+      "model_name": "LayoutModel",
       "state": {
-       "_model_module": "@jupyter-widgets/controls",
+       "_model_module": "@jupyter-widgets/base",
        "_model_module_version": "2.0.0",
-       "_model_name": "DescriptionStyleModel",
+       "_model_name": "LayoutModel",
        "_view_count": null,
        "_view_module": "@jupyter-widgets/base",
        "_view_module_version": "2.0.0",
-       "_view_name": "StyleView",
-       "description_width": ""
+       "_view_name": "LayoutView",
+       "align_content": null,
+       "align_items": null,
+       "align_self": null,
+       "border_bottom": null,
+       "border_left": null,
+       "border_right": null,
+       "border_top": null,
+       "bottom": null,
+       "display": null,
+       "flex": null,
+       "flex_flow": null,
+       "grid_area": null,
+       "grid_auto_columns": null,
+       "grid_auto_flow": null,
+       "grid_auto_rows": null,
+       "grid_column": null,
+       "grid_gap": null,
+       "grid_row": null,
+       "grid_template_areas": null,
+       "grid_template_columns": null,
+       "grid_template_rows": null,
+       "height": null,
+       "justify_content": null,
+       "justify_items": null,
+       "left": null,
+       "margin": null,
+       "max_height": null,
+       "max_width": null,
+       "min_height": null,
+       "min_width": null,
+       "object_fit": null,
+       "object_position": null,
+       "order": null,
+       "overflow": null,
+       "padding": null,
+       "right": null,
+       "top": null,
+       "visibility": null,
+       "width": "initial"
+      }
+     },
+     "f9dc02a1ab77440883c8b52b2b4f2a84": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/base",
+       "_model_module_version": "2.0.0",
+       "_model_name": "LayoutModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "LayoutView",
+       "align_content": null,
+       "align_items": null,
+       "align_self": null,
+       "border_bottom": null,
+       "border_left": null,
+       "border_right": null,
+       "border_top": null,
+       "bottom": null,
+       "display": null,
+       "flex": null,
+       "flex_flow": null,
+       "grid_area": null,
+       "grid_auto_columns": null,
+       "grid_auto_flow": null,
+       "grid_auto_rows": null,
+       "grid_column": null,
+       "grid_gap": null,
+       "grid_row": null,
+       "grid_template_areas": null,
+       "grid_template_columns": null,
+       "grid_template_rows": null,
+       "height": null,
+       "justify_content": null,
+       "justify_items": null,
+       "left": null,
+       "margin": "20px 0 0 0",
+       "max_height": null,
+       "max_width": null,
+       "min_height": null,
+       "min_width": null,
+       "object_fit": null,
+       "object_position": null,
+       "order": null,
+       "overflow": null,
+       "padding": null,
+       "right": null,
+       "top": null,
+       "visibility": null,
+       "width": null
       }
      },
-     "ff51c17f335f4bd7af0a4d8566acbbe0": {
+     "f9f43c6881534f4ebe83ed42d92ee54c": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
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@@ -57938,7 +57894,7 @@
        "width": null
       }
      },
-     "ff9c02a43104457bb957275c11467353": {
+     "fa6480e81d454456a4306a9ce9919b42": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -57990,6 +57946,125 @@
        "visibility": null,
        "width": null
       }
+     },
+     "fb34ab7b6eb5469a810922c44f912a08": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "FloatTextModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "FloatTextModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "FloatTextView",
+       "continuous_update": false,
+       "description": "V6",
+       "description_allow_html": false,
+       "disabled": false,
+       "layout": "IPY_MODEL_65392f13225d4ecabf10c61dc1be6018",
+       "step": null,
+       "style": "IPY_MODEL_bbebb1ee69aa41efb084bd274f510ee2",
+       "tabbable": null,
+       "tooltip": null,
+       "value": -0.8289637892279351
+      }
+     },
+     "fbc4f6b925b843d0b2b2eb693b4f4ea5": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "HTMLView",
+       "description": "",
+       "description_allow_html": false,
+       "layout": "IPY_MODEL_32eb6172997e422cae3cce06b2f6b9c7",
+       "placeholder": "​",
+       "style": "IPY_MODEL_a4a2afa54c89431c83f575a9ae7b760c",
+       "tabbable": null,
+       "tooltip": null,
+       "value": "<h2>Sample Values</h2>"
+      }
+     },
+     "fc29a31d02e04c578d06d76627294640": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "HTMLStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "StyleView",
+       "background": null,
+       "description_width": "",
+       "font_size": null,
+       "text_color": null
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diff --git a/demos/OracleAutoMLx_Classification.html b/demos/OracleAutoMLx_Classification.html
index e9420dd..b241547 100644
--- a/demos/OracleAutoMLx_Classification.html
+++ b/demos/OracleAutoMLx_Classification.html
@@ -14622,7 +14622,7 @@ <h1><font color=red>Building and Explaining a Classifier using AutoMLx</font></h
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
 </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
 <p>Classification Demo Notebook.</p>
-<p>Copyright © 2024, Oracle and/or its affiliates.</p>
+<p>Copyright © 2025, Oracle and/or its affiliates.</p>
 <p>Licensed under the Universal Permissive License v 1.0 as shown at <a href="https://oss.oracle.com/licenses/upl/">https://oss.oracle.com/licenses/upl/</a></p>
 
 </div>
@@ -14743,34 +14743,34 @@ <h2 id="Setup">Setup<a class="anchor-link" href="#Setup">&#182;</a></h2><p>Basic
 <div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[2]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">plotly.express</span> <span class="k">as</span> <span class="nn">px</span>
-<span class="kn">import</span> <span class="nn">plotly.figure_factory</span> <span class="k">as</span> <span class="nn">ff</span>
-<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">roc_auc_score</span><span class="p">,</span> <span class="n">confusion_matrix</span>
-<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
-<span class="kn">from</span> <span class="nn">sklearn.compose</span> <span class="kn">import</span> <span class="n">make_column_selector</span> <span class="k">as</span> <span class="n">selector</span>
-<span class="kn">from</span> <span class="nn">sklearn.impute</span> <span class="kn">import</span> <span class="n">SimpleImputer</span>
-<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">StandardScaler</span><span class="p">,</span> <span class="n">OneHotEncoder</span>
-<span class="kn">from</span> <span class="nn">sklearn.compose</span> <span class="kn">import</span> <span class="n">ColumnTransformer</span>
-<span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <span class="n">Pipeline</span>
-<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">fetch_openml</span>
-<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
-<span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <span class="n">Pipeline</span>
-<span class="kn">from</span> <span class="nn">sklearn.impute</span> <span class="kn">import</span> <span class="n">SimpleImputer</span>
-<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">StandardScaler</span><span class="p">,</span> <span class="n">OneHotEncoder</span>
-<span class="kn">from</span> <span class="nn">sklearn.compose</span> <span class="kn">import</span> <span class="n">ColumnTransformer</span>
-<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
-<span class="kn">from</span> <span class="nn">sklearn.compose</span> <span class="kn">import</span> <span class="n">make_column_selector</span> <span class="k">as</span> <span class="n">selector</span>
-<span class="kn">import</span> <span class="nn">time</span>
-<span class="kn">import</span> <span class="nn">datetime</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pd</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">plotly.express</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">px</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">plotly.figure_factory</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">ff</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.metrics</span><span class="w"> </span><span class="kn">import</span> <span class="n">roc_auc_score</span><span class="p">,</span> <span class="n">confusion_matrix</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.linear_model</span><span class="w"> </span><span class="kn">import</span> <span class="n">LogisticRegression</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.compose</span><span class="w"> </span><span class="kn">import</span> <span class="n">make_column_selector</span> <span class="k">as</span> <span class="n">selector</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.impute</span><span class="w"> </span><span class="kn">import</span> <span class="n">SimpleImputer</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.preprocessing</span><span class="w"> </span><span class="kn">import</span> <span class="n">StandardScaler</span><span class="p">,</span> <span class="n">OneHotEncoder</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.compose</span><span class="w"> </span><span class="kn">import</span> <span class="n">ColumnTransformer</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.pipeline</span><span class="w"> </span><span class="kn">import</span> <span class="n">Pipeline</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.datasets</span><span class="w"> </span><span class="kn">import</span> <span class="n">fetch_openml</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <span class="n">train_test_split</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.pipeline</span><span class="w"> </span><span class="kn">import</span> <span class="n">Pipeline</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.impute</span><span class="w"> </span><span class="kn">import</span> <span class="n">SimpleImputer</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.preprocessing</span><span class="w"> </span><span class="kn">import</span> <span class="n">StandardScaler</span><span class="p">,</span> <span class="n">OneHotEncoder</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.compose</span><span class="w"> </span><span class="kn">import</span> <span class="n">ColumnTransformer</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.linear_model</span><span class="w"> </span><span class="kn">import</span> <span class="n">LogisticRegression</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.compose</span><span class="w"> </span><span class="kn">import</span> <span class="n">make_column_selector</span> <span class="k">as</span> <span class="n">selector</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">time</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">datetime</span>
 <span class="c1"># Settings for plots</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">&#39;figure.figsize&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">7</span><span class="p">]</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">&#39;font.size&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">15</span>
 
-<span class="kn">import</span> <span class="nn">automlx</span>
-<span class="kn">from</span> <span class="nn">automlx</span> <span class="kn">import</span> <span class="n">init</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">automlx</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">automlx</span><span class="w"> </span><span class="kn">import</span> <span class="n">init</span>
 </pre></div>
 
      </div>
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@@ -15427,7 +15427,7 @@ <h3 id="Setting-the-execution-engine">Setting the execution engine<a class="anch
 
 
 <div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
-<pre>[2024-12-26 05:52:20,597] [automlx.backend] Overwriting ray session directory to /tmp/pq3gt0yj/ray, which will be deleted at engine shutdown. If you wish to retain ray logs, provide _temp_dir in ray_setup dict of engine_opts when initializing the AutoMLx engine.
+<pre>[2025-04-25 03:05:06,718] [automlx.backend] Overwriting ray session directory to /tmp/n4cg234n/ray, which will be deleted at engine shutdown. If you wish to retain ray logs, provide _temp_dir in ray_setup dict of engine_opts when initializing the AutoMLx engine.
 </pre>
 </div>
 </div>
@@ -15518,28 +15518,28 @@ <h3 id="Train-a-model-using-AutoMLx">Train a model using AutoMLx<a class="anchor
 
 
 <div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
-<pre>[2024-12-26 05:52:25,988] [automlx.interface] Dataset shape: (34189,14)
-[2024-12-26 05:52:31,802] [sanerec.autotuning.parameter] Hyperparameter epsilon autotune range is set to its validation range. This could lead to long training times
-[2024-12-26 05:52:32,859] [sanerec.autotuning.parameter] Hyperparameter repeat_quality_threshold autotune range is set to its validation range. This could lead to long training times
-[2024-12-26 05:52:32,876] [sanerec.autotuning.parameter] Hyperparameter scope autotune range is set to its validation range. This could lead to long training times
-[2024-12-26 05:52:32,960] [automlx.data_transform] Running preprocessing. Number of features: 15
-[2024-12-26 05:52:33,696] [automlx.data_transform] Preprocessing completed. Took 0.736 secs
-[2024-12-26 05:52:33,741] [automlx.process] Running Model Generation
-[2024-12-26 05:52:33,795] [automlx.process] KNeighborsClassifier is disabled. The KNeighborsClassifier model is only recommended for datasets with less than 10000 samples and 1000 features.
-[2024-12-26 05:52:33,796] [automlx.process] SVC is disabled. The SVC model is only recommended for datasets with less than 10000 samples and 1000 features.
-[2024-12-26 05:52:33,798] [automlx.process] Model Generation completed.
-[2024-12-26 05:52:33,873] [automlx.model_selection] Running Model Selection
-[2024-12-26 05:52:54,133] [automlx.model_selection] Model Selection completed - Took 20.261 sec - Selected models: [[&#39;XGBClassifier&#39;]]
-[2024-12-26 05:52:54,162] [automlx.adaptive_sampling] Running Adaptive Sampling. Dataset shape: (34189,16).
-[2024-12-26 05:52:56,231] [automlx.trials] Adaptive Sampling completed - Took 2.0696 sec.
-[2024-12-26 05:52:56,324] [automlx.feature_selection] Starting feature ranking for XGBClassifier
-[2024-12-26 05:53:05,244] [automlx.feature_selection] Feature Selection completed. Took 8.935 secs.
-[2024-12-26 05:53:05,301] [automlx.trials] Running Model Tuning for [&#39;XGBClassifier&#39;]
-[2024-12-26 05:53:52,094] [automlx.trials] Best parameters for XGBClassifier: {&#39;learning_rate&#39;: 0.10242113515453982, &#39;min_child_weight&#39;: 2, &#39;max_depth&#39;: 4, &#39;reg_alpha&#39;: 0.0007113117640155693, &#39;booster&#39;: &#39;gbtree&#39;, &#39;reg_lambda&#39;: 1.001, &#39;n_estimators&#39;: 141, &#39;use_label_encoder&#39;: False}
-[2024-12-26 05:53:52,095] [automlx.trials] Model Tuning completed. Took: 46.795 secs
-[2024-12-26 05:53:58,742] [automlx.interface] Re-fitting pipeline
-[2024-12-26 05:53:58,757] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_565e433d-1
-[2024-12-26 05:54:01,148] [automlx.interface] AutoMLx completed.
+<pre>[2025-04-25 03:05:12,061] [automlx.interface] Dataset shape: (34189,14)
+[2025-04-25 03:05:19,855] [sanerec.autotuning.parameter] Hyperparameter epsilon autotune range is set to its validation range. This could lead to long training times
+[2025-04-25 03:05:21,017] [sanerec.autotuning.parameter] Hyperparameter repeat_quality_threshold autotune range is set to its validation range. This could lead to long training times
+[2025-04-25 03:05:21,041] [sanerec.autotuning.parameter] Hyperparameter scope autotune range is set to its validation range. This could lead to long training times
+[2025-04-25 03:05:21,155] [automlx.data_transform] Running preprocessing. Number of features: 15
+[2025-04-25 03:05:21,812] [automlx.data_transform] Preprocessing completed. Took 0.657 secs
+[2025-04-25 03:05:21,854] [automlx.process] Running Model Generation
+[2025-04-25 03:05:21,909] [automlx.process] KNeighborsClassifier is disabled. The KNeighborsClassifier model is only recommended for datasets with less than 10000 samples and 1000 features.
+[2025-04-25 03:05:21,909] [automlx.process] SVC is disabled. The SVC model is only recommended for datasets with less than 10000 samples and 1000 features.
+[2025-04-25 03:05:21,911] [automlx.process] Model Generation completed.
+[2025-04-25 03:05:21,985] [automlx.model_selection] Running Model Selection
+[2025-04-25 03:05:53,108] [automlx.model_selection] Model Selection completed - Took 31.123 sec - Selected models: [[&#39;XGBClassifier&#39;]]
+[2025-04-25 03:05:53,134] [automlx.adaptive_sampling] Running Adaptive Sampling. Dataset shape: (34189,16).
+[2025-04-25 03:05:55,173] [automlx.trials] Adaptive Sampling completed - Took 2.0383 sec.
+[2025-04-25 03:05:55,267] [automlx.feature_selection] Starting feature ranking for XGBClassifier
+[2025-04-25 03:06:03,981] [automlx.feature_selection] Feature Selection completed. Took 8.730 secs.
+[2025-04-25 03:06:04,028] [automlx.trials] Running Model Tuning for [&#39;XGBClassifier&#39;]
+[2025-04-25 03:06:47,769] [automlx.trials] Best parameters for XGBClassifier: {&#39;learning_rate&#39;: 0.10242113515453982, &#39;min_child_weight&#39;: 2, &#39;max_depth&#39;: 4, &#39;reg_alpha&#39;: 0.0007113117640155693, &#39;booster&#39;: &#39;gbtree&#39;, &#39;reg_lambda&#39;: 1.001, &#39;n_estimators&#39;: 141, &#39;use_label_encoder&#39;: False}
+[2025-04-25 03:06:47,770] [automlx.trials] Model Tuning completed. Took: 43.742 secs
+[2025-04-25 03:06:54,215] [automlx.interface] Re-fitting pipeline
+[2025-04-25 03:06:54,230] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_806b0073-0
+[2025-04-25 03:06:56,578] [automlx.interface] AutoMLx completed.
 </pre>
 </div>
 </div>
@@ -15553,7 +15553,7 @@ <h3 id="Train-a-model-using-AutoMLx">Train a model using AutoMLx<a class="anchor
 
 
 <div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
-<pre>&lt;automlx._interface.classifier.AutoClassifier at 0x153f16a2b2e0&gt;</pre>
+<pre>&lt;automlx._interface.classifier.AutoClassifier at 0x151aab3c6d00&gt;</pre>
 </div>
 
 </div>
@@ -15720,7 +15720,7 @@ <h3 id="Analyze-the-AutoMLx-optimization-process">Analyze the AutoMLx optimizati
       <td>{'learning_rate': 0.10242113515453982, 'min_child_weight': 2, 'max_depth': 4, 'reg_alpha': 0.0007113117640155693, 'booster': 'gbtree', 'reg_lambda': 1.001, 'n_estimators': 141, 'use_label_encoder': False}</td>
     </tr>
     <tr>
-      <td>25.1.0</td>
+      <td>25.2.1</td>
     </tr>
     <tr>
       <td>3.9.21 (main, Dec 11 2024, 16:24:11) \n[GCC 11.2.0]</td>
@@ -15770,51 +15770,51 @@ <h3 id="Analyze-the-AutoMLx-optimization-process">Analyze the AutoMLx optimizati
   <tbody>
     <tr>
       <td>Model Selection</td>
-      <td>{1: 4000, 2: 4000, 3: 4000, 4: 4000, 5: 4000}</td>
+      <td>{1: 4000, 2: 4000, 3: 4000, 5: 4000, 4: 4000}</td>
       <td>15</td>
       <td>XGBClassifier</td>
       <td>{'learning_rate': 0.1, 'min_child_weight': 1, 'max_depth': 3, 'reg_alpha': 0, 'booster': 'gbtree', 'reg_lambda': 1, 'n_estimators': 100, 'use_label_encoder': False}</td>
       <td>-0.3813</td>
       <td>{'neg_log_loss': -0.38129734602336923}</td>
-      <td>1.3873</td>
-      <td>0.3182</td>
-      <td>Thu Dec 26 05:52:47 2024</td>
+      <td>1.1177</td>
+      <td>0.3214</td>
+      <td>Fri Apr 25 03:05:43 2025</td>
     </tr>
     <tr>
       <td>Model Selection</td>
-      <td>{1: 4000, 2: 4000, 5: 4000, 4: 4000, 3: 4000}</td>
+      <td>{2: 4000, 5: 4000, 1: 4000, 3: 4000, 4: 4000}</td>
       <td>15</td>
       <td>CatBoostClassifier</td>
       <td>{'iterations': 235, 'learning_rate': 0.787168, 'leaf_estimation_method': 'Newton', 'colsample_bylevel': 0.096865, 'depth': 3, 'l2_leaf_reg': 2.567326, 'feature_border_type': 'UniformAndQuantiles', 'model_size_reg': 3.85132, 'leaf_estimation_iterations': 1, 'boosting_type': 'Plain', 'bootstrap_type': 'MVS', 'auto_class_weights': 'SqrtBalanced', 'allow_writing_files': False, 'allow_const_label': True}</td>
       <td>-0.3881</td>
       <td>{'neg_log_loss': -0.38808832474759763}</td>
-      <td>11.7876</td>
-      <td>0.3163</td>
-      <td>Thu Dec 26 05:52:46 2024</td>
+      <td>10.8342</td>
+      <td>0.3193</td>
+      <td>Fri Apr 25 03:05:42 2025</td>
     </tr>
     <tr>
       <td>Model Selection</td>
-      <td>{1: 4000, 4: 4000, 5: 4000, 2: 4000, 3: 4000}</td>
+      <td>{3: 4000, 1: 4000, 5: 4000, 4: 4000, 2: 4000}</td>
       <td>15</td>
       <td>LGBMClassifier</td>
       <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
       <td>-0.3898</td>
-      <td>{'neg_log_loss': -0.3898051926295403}</td>
-      <td>12.4180</td>
-      <td>0.3071</td>
-      <td>Thu Dec 26 05:52:47 2024</td>
+      <td>{'neg_log_loss': -0.38980519262954033}</td>
+      <td>12.8814</td>
+      <td>0.3083</td>
+      <td>Fri Apr 25 03:05:43 2025</td>
     </tr>
     <tr>
       <td>Model Selection</td>
-      <td>{3: 4000, 4: 4000, 1: 4000, 2: 4000, 5: 4000}</td>
+      <td>{1: 4000, 5: 4000, 3: 4000, 4: 4000, 2: 4000}</td>
       <td>15</td>
       <td>LogisticRegressionClassifier</td>
       <td>{'C': 1.0, 'solver': 'liblinear', 'class_weight': 'balanced'}</td>
       <td>-0.3908</td>
       <td>{'neg_log_loss': -0.3907935186356912}</td>
-      <td>0.4044</td>
-      <td>0.3092</td>
-      <td>Thu Dec 26 05:52:47 2024</td>
+      <td>0.6874</td>
+      <td>0.3279</td>
+      <td>Fri Apr 25 03:05:43 2025</td>
     </tr>
     <tr>
       <td>Model Selection</td>
@@ -15824,57 +15824,57 @@ <h3 id="Analyze-the-AutoMLx-optimization-process">Analyze the AutoMLx optimizati
       <td>{'n_estimators': 100, 'min_samples_split': 0.00125, 'min_samples_leaf': 0.000625, 'max_features': 0.777777778, 'class_weight': 'balanced'}</td>
       <td>-0.4093</td>
       <td>{'neg_log_loss': -0.40930045927988684}</td>
-      <td>2.8873</td>
-      <td>0.2929</td>
-      <td>Thu Dec 26 05:52:46 2024</td>
+      <td>2.7773</td>
+      <td>0.2950</td>
+      <td>Fri Apr 25 03:05:43 2025</td>
     </tr>
     <tr>
       <td>Model Selection</td>
-      <td>{2: 4000, 1: 4000, 3: 4000, 5: 4000, 4: 4000}</td>
+      <td>{2: 4000, 1: 4000, 3: 4000, 4: 4000, 5: 4000}</td>
       <td>15</td>
       <td>ExtraTreesClassifier</td>
       <td>{'n_estimators': 100, 'min_samples_split': 0.00125, 'min_samples_leaf': 0.000625, 'max_features': 0.777777778, 'class_weight': 'balanced', 'criterion': 'gini'}</td>
       <td>-0.4196</td>
       <td>{'neg_log_loss': -0.4196315514455088}</td>
-      <td>2.9559</td>
-      <td>0.2837</td>
-      <td>Thu Dec 26 05:52:44 2024</td>
+      <td>3.1310</td>
+      <td>0.2772</td>
+      <td>Fri Apr 25 03:05:41 2025</td>
     </tr>
     <tr>
       <td>Model Selection</td>
-      <td>{1: 4000, 4: 4000, 2: 4000, 3: 4000, 5: 4000}</td>
+      <td>{3: 4000, 5: 4000, 4: 4000, 2: 4000, 1: 4000}</td>
       <td>15</td>
       <td>TorchMLPClassifier</td>
       <td>{'optimizer_class': 'Adam', 'shuffle_dataset_each_epoch': True, 'optimizer_params': {}, 'criterion_class': None, 'criterion_params': {}, 'scheduler_class': None, 'scheduler_params': {}, 'batch_size': 128, 'lr': 0.001, 'epochs': 18, 'input_transform': 'auto', 'tensorboard_dir': None, 'use_tqdm': None, 'prediction_batch_size': 128, 'prediction_input_transform': 'auto', 'shuffling_buffer_size': None, 'depth': 4, 'num_logits': 1000, 'div_factor': 2, 'activation': 'ReLU', 'dropout': 0.1}</td>
-      <td>-0.7243</td>
-      <td>{'neg_log_loss': -0.7242573803956669}</td>
-      <td>38.6758</td>
-      <td>0.6001</td>
-      <td>Thu Dec 26 05:52:53 2024</td>
+      <td>-0.8294</td>
+      <td>{'neg_log_loss': -0.8293741212177753}</td>
+      <td>49.1100</td>
+      <td>0.6041</td>
+      <td>Fri Apr 25 03:05:52 2025</td>
     </tr>
     <tr>
       <td>Model Selection</td>
-      <td>{1: 4000, 2: 4000, 4: 4000, 5: 4000, 3: 4000}</td>
+      <td>{1: 4000, 2: 4000, 3: 4000, 4: 4000, 5: 4000}</td>
       <td>15</td>
       <td>GaussianNB</td>
       <td>{}</td>
       <td>-1.0622</td>
       <td>{'neg_log_loss': -1.0622002049203993}</td>
-      <td>0.4157</td>
-      <td>0.2737</td>
-      <td>Thu Dec 26 05:52:44 2024</td>
+      <td>0.3964</td>
+      <td>0.2871</td>
+      <td>Fri Apr 25 03:05:41 2025</td>
     </tr>
     <tr>
       <td>Model Selection</td>
-      <td>{1: 4000, 3: 4000, 5: 4000, 2: 4000, 4: 4000}</td>
+      <td>{4: 4000, 3: 4000, 1: 4000, 2: 4000, 5: 4000}</td>
       <td>15</td>
       <td>DecisionTreeClassifier</td>
       <td>{'min_samples_split': 0.00125, 'min_samples_leaf': 0.000625, 'max_features': 1.0, 'class_weight': None}</td>
       <td>-4.6323</td>
-      <td>{'neg_log_loss': -4.632288461805367}</td>
-      <td>1.3414</td>
-      <td>0.2706</td>
-      <td>Thu Dec 26 05:52:44 2024</td>
+      <td>{'neg_log_loss': -4.632288461805368}</td>
+      <td>1.3565</td>
+      <td>0.2737</td>
+      <td>Fri Apr 25 03:05:41 2025</td>
     </tr>
     <tr>
       <td>Adaptive Sampling</td>
@@ -15884,9 +15884,9 @@ <h3 id="Analyze-the-AutoMLx-optimization-process">Analyze the AutoMLx optimizati
       <td>{'learning_rate': 0.1, 'min_child_weight': 1, 'max_depth': 3, 'reg_alpha': 0, 'booster': 'gbtree', 'reg_lambda': 1, 'n_estimators': 100, 'use_label_encoder': False}</td>
       <td>-0.349</td>
       <td>{'neg_log_loss': -0.3490238812123456}</td>
-      <td>3.1201</td>
-      <td>0.6042</td>
-      <td>Thu Dec 26 05:52:55 2024</td>
+      <td>3.3692</td>
+      <td>0.6036</td>
+      <td>Fri Apr 25 03:05:54 2025</td>
     </tr>
     <tr>
       <td>...</td>
@@ -15908,9 +15908,9 @@ <h3 id="Analyze-the-AutoMLx-optimization-process">Analyze the AutoMLx optimizati
       <td>{'learning_rate': 0.0001, 'min_child_weight': 0, 'max_depth': 2, 'reg_alpha': 0.0007113117640155693, 'booster': 'gbtree', 'reg_lambda': 0, 'n_estimators': 50, 'use_label_encoder': False}</td>
       <td>-0.6912</td>
       <td>{'neg_log_loss': -0.6911964137047958}</td>
-      <td>1.2032</td>
-      <td>0.6172</td>
-      <td>Thu Dec 26 05:53:21 2024</td>
+      <td>1.2147</td>
+      <td>0.6153</td>
+      <td>Fri Apr 25 03:06:19 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
@@ -15920,9 +15920,9 @@ <h3 id="Analyze-the-AutoMLx-optimization-process">Analyze the AutoMLx optimizati
       <td>{'learning_rate': 0.0001, 'min_child_weight': 0, 'max_depth': 2, 'reg_alpha': 0.0007513117640155693, 'booster': 'gbtree', 'reg_lambda': 0, 'n_estimators': 50, 'use_label_encoder': False}</td>
       <td>-0.6912</td>
       <td>{'neg_log_loss': -0.6911964137047958}</td>
-      <td>1.2971</td>
-      <td>0.6044</td>
-      <td>Thu Dec 26 05:53:22 2024</td>
+      <td>1.2364</td>
+      <td>0.6092</td>
+      <td>Fri Apr 25 03:06:20 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
@@ -15931,10 +15931,10 @@ <h3 id="Analyze-the-AutoMLx-optimization-process">Analyze the AutoMLx optimizati
       <td>XGBClassifier</td>
       <td>{'learning_rate': 0.0001, 'min_child_weight': 0, 'max_depth': 2, 'reg_alpha': 0, 'booster': 'gbtree', 'reg_lambda': 0.01778279410038923, 'n_estimators': 50, 'use_label_encoder': False}</td>
       <td>-0.6912</td>
-      <td>{'neg_log_loss': -0.6911964480070907}</td>
-      <td>1.1361</td>
-      <td>0.6171</td>
-      <td>Thu Dec 26 05:53:22 2024</td>
+      <td>{'neg_log_loss': -0.6911964480070908}</td>
+      <td>1.1856</td>
+      <td>0.6153</td>
+      <td>Fri Apr 25 03:06:20 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
@@ -15944,9 +15944,9 @@ <h3 id="Analyze-the-AutoMLx-optimization-process">Analyze the AutoMLx optimizati
       <td>{'learning_rate': 0.0001, 'min_child_weight': 0, 'max_depth': 2, 'reg_alpha': 0, 'booster': 'gbtree', 'reg_lambda': 0.01878279410038923, 'n_estimators': 50, 'use_label_encoder': False}</td>
       <td>-0.6912</td>
       <td>{'neg_log_loss': -0.6911964560751593}</td>
-      <td>1.2639</td>
-      <td>0.6044</td>
-      <td>Thu Dec 26 05:53:22 2024</td>
+      <td>1.1809</td>
+      <td>0.6098</td>
+      <td>Fri Apr 25 03:06:20 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
@@ -15956,9 +15956,9 @@ <h3 id="Analyze-the-AutoMLx-optimization-process">Analyze the AutoMLx optimizati
       <td>{'learning_rate': 0.0001, 'min_child_weight': 0, 'max_depth': 2, 'reg_alpha': 0.2249365300761397, 'booster': 'gbtree', 'reg_lambda': 0, 'n_estimators': 50, 'use_label_encoder': False}</td>
       <td>-0.6912</td>
       <td>{'neg_log_loss': -0.6911968446197632}</td>
-      <td>1.2609</td>
+      <td>1.2241</td>
       <td>0.6153</td>
-      <td>Thu Dec 26 05:53:22 2024</td>
+      <td>Fri Apr 25 03:06:20 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
@@ -15968,9 +15968,9 @@ <h3 id="Analyze-the-AutoMLx-optimization-process">Analyze the AutoMLx optimizati
       <td>{'learning_rate': 0.0001, 'min_child_weight': 0, 'max_depth': 2, 'reg_alpha': 0.2249765300761397, 'booster': 'gbtree', 'reg_lambda': 0, 'n_estimators': 50, 'use_label_encoder': False}</td>
       <td>-0.6912</td>
       <td>{'neg_log_loss': -0.6911968446197632}</td>
-      <td>1.1930</td>
-      <td>0.6171</td>
-      <td>Thu Dec 26 05:53:22 2024</td>
+      <td>1.1658</td>
+      <td>0.6098</td>
+      <td>Fri Apr 25 03:06:20 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
@@ -15980,9 +15980,9 @@ <h3 id="Analyze-the-AutoMLx-optimization-process">Analyze the AutoMLx optimizati
       <td>{'learning_rate': 0.0001, 'min_child_weight': 0, 'max_depth': 2, 'reg_alpha': 0, 'booster': 'gbtree', 'reg_lambda': 1, 'n_estimators': 50, 'use_label_encoder': False}</td>
       <td>-0.6912</td>
       <td>{'neg_log_loss': -0.6911999162246485}</td>
-      <td>1.2578</td>
-      <td>0.6171</td>
-      <td>Thu Dec 26 05:53:22 2024</td>
+      <td>1.2465</td>
+      <td>0.6134</td>
+      <td>Fri Apr 25 03:06:20 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
@@ -15992,9 +15992,9 @@ <h3 id="Analyze-the-AutoMLx-optimization-process">Analyze the AutoMLx optimizati
       <td>{'learning_rate': 0.0001, 'min_child_weight': 0, 'max_depth': 2, 'reg_alpha': 0, 'booster': 'gbtree', 'reg_lambda': 1.001, 'n_estimators': 50, 'use_label_encoder': False}</td>
       <td>-0.6912</td>
       <td>{'neg_log_loss': -0.6911999174202924}</td>
-      <td>1.1765</td>
+      <td>1.2102</td>
       <td>0.6153</td>
-      <td>Thu Dec 26 05:53:22 2024</td>
+      <td>Fri Apr 25 03:06:20 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
@@ -16004,9 +16004,9 @@ <h3 id="Analyze-the-AutoMLx-optimization-process">Analyze the AutoMLx optimizati
       <td>{'learning_rate': 0.0001, 'min_child_weight': 0, 'max_depth': 2, 'reg_alpha': 0, 'booster': 'gbtree', 'reg_lambda': 5.623413251903491, 'n_estimators': 50, 'use_label_encoder': False}</td>
       <td>-0.6912</td>
       <td>{'neg_log_loss': -0.6912154271501312}</td>
-      <td>1.1926</td>
-      <td>0.6171</td>
-      <td>Thu Dec 26 05:53:23 2024</td>
+      <td>1.1138</td>
+      <td>0.6098</td>
+      <td>Fri Apr 25 03:06:21 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
@@ -16016,9 +16016,9 @@ <h3 id="Analyze-the-AutoMLx-optimization-process">Analyze the AutoMLx optimizati
       <td>{'learning_rate': 0.0001, 'min_child_weight': 0, 'max_depth': 2, 'reg_alpha': 0, 'booster': 'gbtree', 'reg_lambda': 5.6244132519034915, 'n_estimators': 50, 'use_label_encoder': False}</td>
       <td>-0.6912</td>
       <td>{'neg_log_loss': -0.691215428004673}</td>
-      <td>1.2962</td>
-      <td>0.6073</td>
-      <td>Thu Dec 26 05:53:23 2024</td>
+      <td>1.2120</td>
+      <td>0.6153</td>
+      <td>Fri Apr 25 03:06:21 2025</td>
     </tr>
   </tbody>
 </table>
@@ -16114,7 +16114,7 @@ <h4 id="Algorithm-Selection">Algorithm Selection<a class="anchor-link" href="#Al
 
 
 <div class="jp-RenderedImage jp-OutputArea-output ">
-<img src="data:image/png;base64,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"
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"
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@@ -16363,7 +16363,7 @@ <h4 id="Hyperparameter-Tuning">Hyperparameter Tuning<a class="anchor-link" href=
 
 
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"
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-<pre>[2024-12-26 05:54:05,708] [automlx.interface] Dataset shape: (34189,14)
-[2024-12-26 05:54:05,770] [automlx.interface] Adaptive Sampling disabled.
-[2024-12-26 05:54:05,821] [automlx.data_transform] Running preprocessing. Number of features: 15
-[2024-12-26 05:54:06,443] [automlx.data_transform] Preprocessing completed. Took 0.622 secs
-[2024-12-26 05:54:06,465] [automlx.process] Running Model Generation
-[2024-12-26 05:54:06,524] [automlx.process] Model Generation completed.
-[2024-12-26 05:54:06,557] [automlx.model_selection] Running Model Selection
-[2024-12-26 05:54:08,021] [automlx.model_selection] Model Selection completed - Took 1.463 sec - Selected models: [[&#39;LGBMClassifier&#39;, &#39;LogisticRegressionClassifier&#39;]]
-[2024-12-26 05:54:08,125] [automlx.feature_selection] Starting feature ranking for LGBMClassifier
-[2024-12-26 05:54:11,862] [automlx.feature_selection] Feature Selection completed. Took 3.737 secs.
-[2024-12-26 05:54:12,016] [automlx.trials] Running Model Tuning for [&#39;LGBMClassifier&#39;]
-[2024-12-26 05:54:13,247] [automlx.trials] Best parameters for LGBMClassifier: {&#39;num_leaves&#39;: 31, &#39;boosting_type&#39;: &#39;gbdt&#39;, &#39;learning_rate&#39;: 0.01, &#39;min_child_weight&#39;: 0.001, &#39;max_depth&#39;: -1, &#39;reg_alpha&#39;: 0, &#39;reg_lambda&#39;: 1, &#39;n_estimators&#39;: 100, &#39;class_weight&#39;: None}
-[2024-12-26 05:54:13,248] [automlx.trials] Model Tuning completed. Took: 1.232 secs
-[2024-12-26 05:54:13,352] [automlx.feature_selection] Starting feature ranking for LogisticRegressionClassifier
-[2024-12-26 05:54:19,664] [automlx.feature_selection] Feature Selection completed. Took 6.313 secs.
-[2024-12-26 05:54:19,739] [automlx.trials] Running Model Tuning for [&#39;LogisticRegressionClassifier&#39;]
-[2024-12-26 05:54:20,872] [automlx.trials] Best parameters for LogisticRegressionClassifier: {&#39;C&#39;: 0.0363696875, &#39;solver&#39;: &#39;liblinear&#39;, &#39;class_weight&#39;: &#39;balanced&#39;}
-[2024-12-26 05:54:20,873] [automlx.trials] Model Tuning completed. Took: 1.133 secs
-[2024-12-26 05:54:21,249] [automlx.interface] Re-fitting pipeline
-[2024-12-26 05:54:21,274] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_dc1eb50b-7
-[2024-12-26 05:54:23,379] [automlx.interface] AutoMLx completed.
+<pre>[2025-04-25 03:07:01,457] [automlx.interface] Dataset shape: (34189,14)
+[2025-04-25 03:07:01,518] [automlx.interface] Adaptive Sampling disabled.
+[2025-04-25 03:07:01,563] [automlx.data_transform] Running preprocessing. Number of features: 15
+[2025-04-25 03:07:02,094] [automlx.data_transform] Preprocessing completed. Took 0.531 secs
+[2025-04-25 03:07:02,127] [automlx.process] Running Model Generation
+[2025-04-25 03:07:02,185] [automlx.process] Model Generation completed.
+[2025-04-25 03:07:02,217] [automlx.model_selection] Running Model Selection
+[2025-04-25 03:07:03,661] [automlx.model_selection] Model Selection completed - Took 1.444 sec - Selected models: [[&#39;LGBMClassifier&#39;, &#39;LogisticRegressionClassifier&#39;]]
+[2025-04-25 03:07:03,746] [automlx.feature_selection] Starting feature ranking for LGBMClassifier
+[2025-04-25 03:07:07,088] [automlx.feature_selection] Feature Selection completed. Took 3.342 secs.
+[2025-04-25 03:07:07,215] [automlx.trials] Running Model Tuning for [&#39;LGBMClassifier&#39;]
+[2025-04-25 03:07:08,436] [automlx.trials] Best parameters for LGBMClassifier: {&#39;num_leaves&#39;: 31, &#39;boosting_type&#39;: &#39;gbdt&#39;, &#39;learning_rate&#39;: 0.01, &#39;min_child_weight&#39;: 0.001, &#39;max_depth&#39;: -1, &#39;reg_alpha&#39;: 0, &#39;reg_lambda&#39;: 1, &#39;n_estimators&#39;: 100, &#39;class_weight&#39;: None}
+[2025-04-25 03:07:08,437] [automlx.trials] Model Tuning completed. Took: 1.222 secs
+[2025-04-25 03:07:08,521] [automlx.feature_selection] Starting feature ranking for LogisticRegressionClassifier
+[2025-04-25 03:07:15,245] [automlx.feature_selection] Feature Selection completed. Took 6.724 secs.
+[2025-04-25 03:07:15,272] [automlx.trials] Running Model Tuning for [&#39;LogisticRegressionClassifier&#39;]
+[2025-04-25 03:07:16,400] [automlx.trials] Best parameters for LogisticRegressionClassifier: {&#39;C&#39;: 0.0363696875, &#39;solver&#39;: &#39;liblinear&#39;, &#39;class_weight&#39;: &#39;balanced&#39;}
+[2025-04-25 03:07:16,401] [automlx.trials] Model Tuning completed. Took: 1.130 secs
+[2025-04-25 03:07:16,754] [automlx.interface] Re-fitting pipeline
+[2025-04-25 03:07:16,780] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_33c32b58-6
+[2025-04-25 03:07:18,836] [automlx.interface] AutoMLx completed.
 ROC AUC Score on test data : 0.901457699826238
 </pre>
 </div>
@@ -16844,37 +16844,37 @@ <h4 id="Visualization">Visualization<a class="anchor-link" href="#Visualization"
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       <th>0</th>
       <td>capitalgain</td>
-      <td>0.071287</td>
-      <td>0.057273</td>
-      <td>0.085301</td>
+      <td>0.071744</td>
+      <td>0.062587</td>
+      <td>0.080901</td>
     </tr>
     <tr>
       <th>1</th>
       <td>capitalloss</td>
-      <td>0.001714</td>
-      <td>-0.000482</td>
-      <td>0.003910</td>
+      <td>0.001740</td>
+      <td>-0.000836</td>
+      <td>0.004317</td>
     </tr>
     <tr>
       <th>2</th>
       <td>age</td>
-      <td>0.001237</td>
-      <td>-0.000383</td>
-      <td>0.002856</td>
+      <td>0.001172</td>
+      <td>-0.000865</td>
+      <td>0.003210</td>
     </tr>
     <tr>
       <th>3</th>
       <td>fnlwgt</td>
-      <td>0.000300</td>
-      <td>-0.001479</td>
-      <td>0.002079</td>
+      <td>0.000606</td>
+      <td>-0.001714</td>
+      <td>0.002927</td>
     </tr>
     <tr>
       <th>4</th>
       <td>hoursperweek</td>
       <td>0.000125</td>
-      <td>-0.000928</td>
-      <td>0.001177</td>
+      <td>-0.001286</td>
+      <td>0.001536</td>
     </tr>
     <tr>
       <th>5</th>
@@ -16885,56 +16885,56 @@ <h4 id="Visualization">Visualization<a class="anchor-link" href="#Visualization"
     </tr>
     <tr>
       <th>6</th>
-      <td>education</td>
+      <td>native-country</td>
       <td>0.000000</td>
       <td>0.000000</td>
       <td>0.000000</td>
     </tr>
     <tr>
       <th>7</th>
-      <td>sex</td>
+      <td>race</td>
       <td>0.000000</td>
       <td>0.000000</td>
       <td>0.000000</td>
     </tr>
     <tr>
       <th>8</th>
-      <td>marital-status</td>
+      <td>education</td>
       <td>0.000000</td>
       <td>0.000000</td>
       <td>0.000000</td>
     </tr>
     <tr>
       <th>9</th>
-      <td>relationship</td>
+      <td>sex</td>
       <td>0.000000</td>
       <td>0.000000</td>
       <td>0.000000</td>
     </tr>
     <tr>
       <th>10</th>
-      <td>race</td>
+      <td>relationship</td>
       <td>0.000000</td>
       <td>0.000000</td>
       <td>0.000000</td>
     </tr>
     <tr>
       <th>11</th>
-      <td>occupation</td>
+      <td>workclass</td>
       <td>0.000000</td>
       <td>0.000000</td>
       <td>0.000000</td>
     </tr>
     <tr>
       <th>12</th>
-      <td>native-country</td>
+      <td>marital-status</td>
       <td>0.000000</td>
       <td>0.000000</td>
       <td>0.000000</td>
     </tr>
     <tr>
       <th>13</th>
-      <td>workclass</td>
+      <td>occupation</td>
       <td>0.000000</td>
       <td>0.000000</td>
       <td>0.000000</td>
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", 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 <th id=\"T_7f2ec_level0_col6\" class=\"col_heading level0 col6\" >occupation</th>\n      <th id=\"T_7f2ec_level0_col7\" class=\"col_heading level0 col7\" >relationship</th>\n      <th id=\"T_7f2ec_level0_col8\" class=\"col_heading level0 col8\" >race</th>\n      <th id=\"T_7f2ec_level0_col9\" class=\"col_heading level0 col9\" >sex</th>\n      <th id=\"T_7f2ec_level0_col10\" class=\"col_heading level0 col10\" >capitalgain</th>\n      <th id=\"T_7f2ec_level0_col11\" class=\"col_heading level0 col11\" >capitalloss</th>\n      <th id=\"T_7f2ec_level0_col12\" class=\"col_heading level0 col12\" >hoursperweek</th>\n      <th id=\"T_7f2ec_level0_col13\" class=\"col_heading level0 col13\" >native-country</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_7f2ec_level0_row0\" class=\"row_heading level0 row0\" >Original Sample</th>\n      <td id=\"T_7f2ec_row0_col0\" class=\"data row0 col0\" >2</td>\n      <td id=\"T_7f2ec_row0_col1\" class=\"data row0 col1\" >Private</td>\n      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<td>10.0</td>\n      <td>4</td>\n      <td>9</td>\n      <td>3</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>38</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>1</td>\n      <td>3</td>\n      <td>162358.0</td>\n      <td>9</td>\n      <td>13.0</td>\n      <td>4</td>\n      <td>3</td>\n      <td>1</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3</td>\n      <td>38</td>\n    </tr>\n  </tbody>\n</table>\n</div>", "text/plain": "   age  workclass    fnlwgt  education  education-num  marital-status  \\\n0    1          3   84119.0         11            9.0               2   \n1    0          3   50397.0         15           10.0               2   \n2    4          3  115890.0          9           13.0               2   \n3    3          4  134793.0         15           10.0               2   \n4    1          3  282944.0         10           16.0               2   \n5    1          3  158592.0          8           11.0               2   \n6    2          3  351576.0          9           13.0               4   \n7    2          3  237993.0         15           10.0               2   \n8    3          5  102346.0         15           10.0               4   \n9    1          3  162358.0          9           13.0               4   \n\n   occupation  relationship  race  sex  capitalgain  capitalloss  \\\n0           3             0     4    1            0            0   \n1          11             0     2    1            0            0   \n2          13             0     4    1            0            0   \n3           3             0     4    1            4            0   \n4           9             0     4    1            0            0   \n5           3             0     4    1            1            0   \n6          11             1     4    1            0            0   \n7          12             0     4    1            0            0   \n8           9             3     4    0            0            0   \n9           3             1     4    0            0            0   \n\n   hoursperweek  native-country  \n0             4              38  \n1             1              38  \n2             0              38  \n3             3              38  \n4             3              38  \n5             3              38  \n6             3              38  \n7             2              38  \n8             1              38  \n9             3              38  "}, "metadata": {}, "output_type": "display_data"}], "tabbable": null, "tooltip": null}}, "2135a97390c84e56a0ac3b53f8601685": {"model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "VBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "VBoxView", 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", 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id=\"T_f9fbe_row0_col6\" class=\"data row0 col6\" >Protective-serv</td>\n      <td id=\"T_f9fbe_row0_col7\" class=\"data row0 col7\" >Husband</td>\n      <td id=\"T_f9fbe_row0_col8\" class=\"data row0 col8\" >White</td>\n      <td id=\"T_f9fbe_row0_col9\" class=\"data row0 col9\" >Male</td>\n      <td id=\"T_f9fbe_row0_col10\" class=\"data row0 col10\" >0</td>\n      <td id=\"T_f9fbe_row0_col11\" class=\"data row0 col11\" >0</td>\n      <td id=\"T_f9fbe_row0_col12\" class=\"data row0 col12\" >2</td>\n      <td id=\"T_f9fbe_row0_col13\" class=\"data row0 col13\" >United-States</td>\n    </tr>\n    <tr>\n      <th id=\"T_f9fbe_level0_row1\" class=\"row_heading level0 row1\" >Modified Sample</th>\n      <td id=\"T_f9fbe_row1_col0\" class=\"data row1 col0\" >2</td>\n      <td id=\"T_f9fbe_row1_col1\" class=\"data row1 col1\" >Private</td>\n      <td id=\"T_f9fbe_row1_col2\" class=\"data row1 col2\" >151856.0</td>\n      <td id=\"T_f9fbe_row1_col3\" class=\"data row1 col3\" >HS-grad</td>\n  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col1\" >Private</td>\n      <td id=\"T_ce365_row0_col2\" class=\"data row0 col2\" >151856.0</td>\n      <td id=\"T_ce365_row0_col3\" class=\"data row0 col3\" >HS-grad</td>\n      <td id=\"T_ce365_row0_col4\" class=\"data row0 col4\" >9.0</td>\n      <td id=\"T_ce365_row0_col5\" class=\"data row0 col5\" >Married-civ-spouse</td>\n      <td id=\"T_ce365_row0_col6\" class=\"data row0 col6\" >Protective-serv</td>\n      <td id=\"T_ce365_row0_col7\" class=\"data row0 col7\" >Husband</td>\n      <td id=\"T_ce365_row0_col8\" class=\"data row0 col8\" >White</td>\n      <td id=\"T_ce365_row0_col9\" class=\"data row0 col9\" >Male</td>\n      <td id=\"T_ce365_row0_col10\" class=\"data row0 col10\" >0</td>\n      <td id=\"T_ce365_row0_col11\" class=\"data row0 col11\" >0</td>\n      <td id=\"T_ce365_row0_col12\" class=\"data row0 col12\" >2</td>\n      <td id=\"T_ce365_row0_col13\" class=\"data row0 col13\" >United-States</td>\n    </tr>\n    <tr>\n      <th id=\"T_ce365_level0_row1\" 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diff --git a/demos/OracleAutoMLx_Classification.ipynb b/demos/OracleAutoMLx_Classification.ipynb
index 4e82451..1d22cdf 100644
--- a/demos/OracleAutoMLx_Classification.ipynb
+++ b/demos/OracleAutoMLx_Classification.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "93e287de",
+   "id": "20ec0f8d",
    "metadata": {},
    "source": [
     "***\n",
@@ -14,19 +14,19 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6770a091",
+   "id": "9b985fc1",
    "metadata": {},
    "source": [
     "Classification Demo Notebook.\n",
     "\n",
-    "Copyright © 2024, Oracle and/or its affiliates.\n",
+    "Copyright © 2025, Oracle and/or its affiliates.\n",
     "\n",
     "Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "f1fe9177",
+   "id": "57d4d21b",
    "metadata": {},
    "source": [
     "## Overview of this Notebook\n",
@@ -82,7 +82,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1822224f",
+   "id": "99388a84",
    "metadata": {},
    "source": [
     "<a id='setup'></a>\n",
@@ -94,13 +94,13 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "7909ef70",
+   "id": "2cb7a457",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:52:14.025297Z",
-     "iopub.status.busy": "2024-12-26T13:52:14.024918Z",
-     "iopub.status.idle": "2024-12-26T13:52:14.737909Z",
-     "shell.execute_reply": "2024-12-26T13:52:14.737212Z"
+     "iopub.execute_input": "2025-04-25T10:04:59.198961Z",
+     "iopub.status.busy": "2025-04-25T10:04:59.198523Z",
+     "iopub.status.idle": "2025-04-25T10:05:00.002181Z",
+     "shell.execute_reply": "2025-04-25T10:05:00.001648Z"
     }
    },
    "outputs": [],
@@ -113,7 +113,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b7399ce1",
+   "id": "ad83cf51",
    "metadata": {},
    "source": [
     "Load the required modules."
@@ -122,13 +122,13 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "7869a27a",
+   "id": "6fcc1e56",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:52:14.740842Z",
-     "iopub.status.busy": "2024-12-26T13:52:14.740347Z",
-     "iopub.status.idle": "2024-12-26T13:52:17.669135Z",
-     "shell.execute_reply": "2024-12-26T13:52:17.668414Z"
+     "iopub.execute_input": "2025-04-25T10:05:00.004524Z",
+     "iopub.status.busy": "2025-04-25T10:05:00.003985Z",
+     "iopub.status.idle": "2025-04-25T10:05:03.344639Z",
+     "shell.execute_reply": "2025-04-25T10:05:03.344044Z"
     }
    },
    "outputs": [],
@@ -165,7 +165,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ac2dcc74",
+   "id": "7796b3e4",
    "metadata": {},
    "source": [
     "<a id='load-data'></a>\n",
@@ -176,13 +176,13 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "710d8f5a",
+   "id": "9a06420f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:52:17.671935Z",
-     "iopub.status.busy": "2024-12-26T13:52:17.671304Z",
-     "iopub.status.idle": "2024-12-26T13:52:18.495202Z",
-     "shell.execute_reply": "2024-12-26T13:52:18.494399Z"
+     "iopub.execute_input": "2025-04-25T10:05:03.347224Z",
+     "iopub.status.busy": "2025-04-25T10:05:03.346530Z",
+     "iopub.status.idle": "2025-04-25T10:05:04.284214Z",
+     "shell.execute_reply": "2025-04-25T10:05:04.283606Z"
     }
    },
    "outputs": [],
@@ -193,7 +193,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7500453c",
+   "id": "abceeeac",
    "metadata": {},
    "source": [
     "Lets look at a few of the values in the data"
@@ -202,13 +202,13 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "d8beca57",
+   "id": "41d71a78",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:52:18.498077Z",
-     "iopub.status.busy": "2024-12-26T13:52:18.497367Z",
-     "iopub.status.idle": "2024-12-26T13:52:18.694127Z",
-     "shell.execute_reply": "2024-12-26T13:52:18.693516Z"
+     "iopub.execute_input": "2025-04-25T10:05:04.286478Z",
+     "iopub.status.busy": "2025-04-25T10:05:04.285972Z",
+     "iopub.status.idle": "2025-04-25T10:05:04.328608Z",
+     "shell.execute_reply": "2025-04-25T10:05:04.328101Z"
     }
    },
    "outputs": [
@@ -373,7 +373,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a565d8ea",
+   "id": "cef2b492",
    "metadata": {},
    "source": [
     "The Adult dataset contains a mix of numerical and string data, making it a challenging problem to train machine learning models on."
@@ -382,13 +382,13 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "a2e738bd",
+   "id": "dd70d8ae",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:52:18.696261Z",
-     "iopub.status.busy": "2024-12-26T13:52:18.695747Z",
-     "iopub.status.idle": "2024-12-26T13:52:18.727538Z",
-     "shell.execute_reply": "2024-12-26T13:52:18.726931Z"
+     "iopub.execute_input": "2025-04-25T10:05:04.330541Z",
+     "iopub.status.busy": "2025-04-25T10:05:04.330052Z",
+     "iopub.status.idle": "2025-04-25T10:05:04.361416Z",
+     "shell.execute_reply": "2025-04-25T10:05:04.360918Z"
     }
    },
    "outputs": [
@@ -473,7 +473,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e53c06a1",
+   "id": "cab51aea",
    "metadata": {},
    "source": [
     "The dataset also contains a lot of missing values. The Oracle AutoMLx solution automatically handles missing values by dropping features with too many missing values, and filling in the remaining missing values based on the feature type."
@@ -482,13 +482,13 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "435d5234",
+   "id": "6295574c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:52:18.729568Z",
-     "iopub.status.busy": "2024-12-26T13:52:18.729076Z",
-     "iopub.status.idle": "2024-12-26T13:52:18.762250Z",
-     "shell.execute_reply": "2024-12-26T13:52:18.761653Z"
+     "iopub.execute_input": "2025-04-25T10:05:04.363288Z",
+     "iopub.status.busy": "2025-04-25T10:05:04.362814Z",
+     "iopub.status.idle": "2025-04-25T10:05:04.395592Z",
+     "shell.execute_reply": "2025-04-25T10:05:04.395094Z"
     }
    },
    "outputs": [
@@ -573,7 +573,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f06ac880",
+   "id": "76bf62f4",
    "metadata": {},
    "source": [
     "We visualize the distribution of the target variable in the training data."
@@ -582,13 +582,13 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "5901e20f",
+   "id": "3fe2f853",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:52:18.764258Z",
-     "iopub.status.busy": "2024-12-26T13:52:18.763764Z",
-     "iopub.status.idle": "2024-12-26T13:52:20.291727Z",
-     "shell.execute_reply": "2024-12-26T13:52:20.291060Z"
+     "iopub.execute_input": "2025-04-25T10:05:04.397463Z",
+     "iopub.status.busy": "2025-04-25T10:05:04.396980Z",
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+     "shell.execute_reply": "2025-04-25T10:05:06.329511Z"
     }
    },
    "outputs": [
@@ -50339,9 +50339,9 @@
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        "                            \n",
-       "var gd = document.getElementById('f9519b4c-7168-4117-a7f1-fa9571449457');\n",
+       "var gd = document.getElementById('f8331207-f4db-4465-b8bf-410e0ddd9f3a');\n",
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        "        var display = window.getComputedStyle(gd).display;\n",
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@@ -50380,7 +50380,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8a8b6cb5",
+   "id": "422fcd88",
    "metadata": {},
    "source": [
     "We now separate the predictions (`y`) from the training data (`X`) for both the training (70%) and test (30%) datasets. The training set will be used to create a Machine Learning model using AutoMLx, and the test set will be used to evaluate the model's performance on unseen data."
@@ -50389,13 +50389,13 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "45f718cf",
+   "id": "23545948",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:52:20.327275Z",
-     "iopub.status.busy": "2024-12-26T13:52:20.326867Z",
-     "iopub.status.idle": "2024-12-26T13:52:20.378004Z",
-     "shell.execute_reply": "2024-12-26T13:52:20.377424Z"
+     "iopub.execute_input": "2025-04-25T10:05:06.366123Z",
+     "iopub.status.busy": "2025-04-25T10:05:06.365499Z",
+     "iopub.status.idle": "2025-04-25T10:05:06.405558Z",
+     "shell.execute_reply": "2025-04-25T10:05:06.405090Z"
     }
    },
    "outputs": [
@@ -50428,7 +50428,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ff6e92a3",
+   "id": "76f8fa50",
    "metadata": {},
    "source": [
     "<a id='AutoML'></a>\n",
@@ -50437,7 +50437,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "05e9506e",
+   "id": "fd63fc6f",
    "metadata": {},
    "source": [
     "<a id='Engine'></a>\n",
@@ -50448,13 +50448,13 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "781d3af2",
+   "id": "7db30224",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:52:20.379862Z",
-     "iopub.status.busy": "2024-12-26T13:52:20.379521Z",
-     "iopub.status.idle": "2024-12-26T13:52:25.878278Z",
-     "shell.execute_reply": "2024-12-26T13:52:25.877445Z"
+     "iopub.execute_input": "2025-04-25T10:05:06.407580Z",
+     "iopub.status.busy": "2025-04-25T10:05:06.407034Z",
+     "iopub.status.idle": "2025-04-25T10:05:11.949363Z",
+     "shell.execute_reply": "2025-04-25T10:05:11.948598Z"
     }
    },
    "outputs": [
@@ -50462,7 +50462,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:52:20,597] [automlx.backend] Overwriting ray session directory to /tmp/pq3gt0yj/ray, which will be deleted at engine shutdown. If you wish to retain ray logs, provide _temp_dir in ray_setup dict of engine_opts when initializing the AutoMLx engine.\n"
+      "[2025-04-25 03:05:06,718] [automlx.backend] Overwriting ray session directory to /tmp/n4cg234n/ray, which will be deleted at engine shutdown. If you wish to retain ray logs, provide _temp_dir in ray_setup dict of engine_opts when initializing the AutoMLx engine.\n"
      ]
     }
    ],
@@ -50472,7 +50472,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d53c9d51",
+   "id": "38f44a48",
    "metadata": {},
    "source": [
     "<a id='provider'></a>\n",
@@ -50506,7 +50506,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2b1fa1d3",
+   "id": "2ef797fc",
    "metadata": {},
    "source": [
     "<a id='default'></a>\n",
@@ -50518,13 +50518,13 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "26576267",
+   "id": "d46a13cd",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:52:25.880973Z",
-     "iopub.status.busy": "2024-12-26T13:52:25.880479Z",
-     "iopub.status.idle": "2024-12-26T13:54:02.558819Z",
-     "shell.execute_reply": "2024-12-26T13:54:02.558181Z"
+     "iopub.execute_input": "2025-04-25T10:05:11.952277Z",
+     "iopub.status.busy": "2025-04-25T10:05:11.951871Z",
+     "iopub.status.idle": "2025-04-25T10:06:58.126309Z",
+     "shell.execute_reply": "2025-04-25T10:06:58.125606Z"
     }
    },
    "outputs": [
@@ -50532,160 +50532,160 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:52:25,988] [automlx.interface] Dataset shape: (34189,14)\n"
+      "[2025-04-25 03:05:12,061] [automlx.interface] Dataset shape: (34189,14)\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:52:31,802] [sanerec.autotuning.parameter] Hyperparameter epsilon autotune range is set to its validation range. This could lead to long training times\n"
+      "[2025-04-25 03:05:19,855] [sanerec.autotuning.parameter] Hyperparameter epsilon autotune range is set to its validation range. This could lead to long training times\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:52:32,859] [sanerec.autotuning.parameter] Hyperparameter repeat_quality_threshold autotune range is set to its validation range. This could lead to long training times\n"
+      "[2025-04-25 03:05:21,017] [sanerec.autotuning.parameter] Hyperparameter repeat_quality_threshold autotune range is set to its validation range. This could lead to long training times\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:52:32,876] [sanerec.autotuning.parameter] Hyperparameter scope autotune range is set to its validation range. This could lead to long training times\n"
+      "[2025-04-25 03:05:21,041] [sanerec.autotuning.parameter] Hyperparameter scope autotune range is set to its validation range. This could lead to long training times\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:52:32,960] [automlx.data_transform] Running preprocessing. Number of features: 15\n"
+      "[2025-04-25 03:05:21,155] [automlx.data_transform] Running preprocessing. Number of features: 15\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:52:33,696] [automlx.data_transform] Preprocessing completed. Took 0.736 secs\n"
+      "[2025-04-25 03:05:21,812] [automlx.data_transform] Preprocessing completed. Took 0.657 secs\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:52:33,741] [automlx.process] Running Model Generation\n"
+      "[2025-04-25 03:05:21,854] [automlx.process] Running Model Generation\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:52:33,795] [automlx.process] KNeighborsClassifier is disabled. The KNeighborsClassifier model is only recommended for datasets with less than 10000 samples and 1000 features.\n"
+      "[2025-04-25 03:05:21,909] [automlx.process] KNeighborsClassifier is disabled. The KNeighborsClassifier model is only recommended for datasets with less than 10000 samples and 1000 features.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:52:33,796] [automlx.process] SVC is disabled. The SVC model is only recommended for datasets with less than 10000 samples and 1000 features.\n"
+      "[2025-04-25 03:05:21,909] [automlx.process] SVC is disabled. The SVC model is only recommended for datasets with less than 10000 samples and 1000 features.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:52:33,798] [automlx.process] Model Generation completed.\n"
+      "[2025-04-25 03:05:21,911] [automlx.process] Model Generation completed.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:52:33,873] [automlx.model_selection] Running Model Selection\n"
+      "[2025-04-25 03:05:21,985] [automlx.model_selection] Running Model Selection\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:52:54,133] [automlx.model_selection] Model Selection completed - Took 20.261 sec - Selected models: [['XGBClassifier']]\n"
+      "[2025-04-25 03:05:53,108] [automlx.model_selection] Model Selection completed - Took 31.123 sec - Selected models: [['XGBClassifier']]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:52:54,162] [automlx.adaptive_sampling] Running Adaptive Sampling. Dataset shape: (34189,16).\n"
+      "[2025-04-25 03:05:53,134] [automlx.adaptive_sampling] Running Adaptive Sampling. Dataset shape: (34189,16).\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:52:56,231] [automlx.trials] Adaptive Sampling completed - Took 2.0696 sec.\n"
+      "[2025-04-25 03:05:55,173] [automlx.trials] Adaptive Sampling completed - Took 2.0383 sec.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:52:56,324] [automlx.feature_selection] Starting feature ranking for XGBClassifier\n"
+      "[2025-04-25 03:05:55,267] [automlx.feature_selection] Starting feature ranking for XGBClassifier\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:53:05,244] [automlx.feature_selection] Feature Selection completed. Took 8.935 secs.\n"
+      "[2025-04-25 03:06:03,981] [automlx.feature_selection] Feature Selection completed. Took 8.730 secs.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:53:05,301] [automlx.trials] Running Model Tuning for ['XGBClassifier']\n"
+      "[2025-04-25 03:06:04,028] [automlx.trials] Running Model Tuning for ['XGBClassifier']\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:53:52,094] [automlx.trials] Best parameters for XGBClassifier: {'learning_rate': 0.10242113515453982, 'min_child_weight': 2, 'max_depth': 4, 'reg_alpha': 0.0007113117640155693, 'booster': 'gbtree', 'reg_lambda': 1.001, 'n_estimators': 141, 'use_label_encoder': False}\n"
+      "[2025-04-25 03:06:47,769] [automlx.trials] Best parameters for XGBClassifier: {'learning_rate': 0.10242113515453982, 'min_child_weight': 2, 'max_depth': 4, 'reg_alpha': 0.0007113117640155693, 'booster': 'gbtree', 'reg_lambda': 1.001, 'n_estimators': 141, 'use_label_encoder': False}\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:53:52,095] [automlx.trials] Model Tuning completed. Took: 46.795 secs\n"
+      "[2025-04-25 03:06:47,770] [automlx.trials] Model Tuning completed. Took: 43.742 secs\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:53:58,742] [automlx.interface] Re-fitting pipeline\n"
+      "[2025-04-25 03:06:54,215] [automlx.interface] Re-fitting pipeline\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:53:58,757] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_565e433d-1\n"
+      "[2025-04-25 03:06:54,230] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_806b0073-0\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:54:01,148] [automlx.interface] AutoMLx completed.\n"
+      "[2025-04-25 03:06:56,578] [automlx.interface] AutoMLx completed.\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<automlx._interface.classifier.AutoClassifier at 0x153f16a2b2e0>"
+       "<automlx._interface.classifier.AutoClassifier at 0x151aab3c6d00>"
       ]
      },
      "execution_count": 10,
@@ -50700,7 +50700,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "51d80d2a",
+   "id": "a6d0d90c",
    "metadata": {},
    "source": [
     "A model is then generated (`est1`) and can be used for prediction tasks. We use the `roc_auc_score` scoring metric to evaluate the performance of this model on unseen data (`X_test`)."
@@ -50709,13 +50709,13 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "886a225f",
+   "id": "a0e9c5d9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:54:02.561270Z",
-     "iopub.status.busy": "2024-12-26T13:54:02.560909Z",
-     "iopub.status.idle": "2024-12-26T13:54:04.274953Z",
-     "shell.execute_reply": "2024-12-26T13:54:04.274257Z"
+     "iopub.execute_input": "2025-04-25T10:06:58.128575Z",
+     "iopub.status.busy": "2025-04-25T10:06:58.128284Z",
+     "iopub.status.idle": "2025-04-25T10:07:00.026193Z",
+     "shell.execute_reply": "2025-04-25T10:07:00.025538Z"
     }
    },
    "outputs": [
@@ -50736,7 +50736,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "175f1e85",
+   "id": "845fead6",
    "metadata": {},
    "source": [
     "<a id='analyze'></a>\n",
@@ -50752,7 +50752,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3cd4da55",
+   "id": "0d49e4b4",
    "metadata": {},
    "source": [
     "AutoMLx provides a `print_summary` API to output all the different trials performed."
@@ -50761,13 +50761,13 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "b7849b6d",
+   "id": "561d66f4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:54:04.277684Z",
-     "iopub.status.busy": "2024-12-26T13:54:04.277145Z",
-     "iopub.status.idle": "2024-12-26T13:54:04.347271Z",
-     "shell.execute_reply": "2024-12-26T13:54:04.346732Z"
+     "iopub.execute_input": "2025-04-25T10:07:00.028168Z",
+     "iopub.status.busy": "2025-04-25T10:07:00.027826Z",
+     "iopub.status.idle": "2025-04-25T10:07:00.097517Z",
+     "shell.execute_reply": "2025-04-25T10:07:00.096936Z"
     }
    },
    "outputs": [
@@ -50812,7 +50812,7 @@
        "      <td>{'learning_rate': 0.10242113515453982, 'min_child_weight': 2, 'max_depth': 4, 'reg_alpha': 0.0007113117640155693, 'booster': 'gbtree', 'reg_lambda': 1.001, 'n_estimators': 141, 'use_label_encoder': False}</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>25.1.0</td>\n",
+       "      <td>25.2.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>3.9.21 (main, Dec 11 2024, 16:24:11) \\n[GCC 11.2.0]</td>\n",
@@ -50860,51 +50860,51 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <td>Model Selection</td>\n",
-       "      <td>{1: 4000, 2: 4000, 3: 4000, 4: 4000, 5: 4000}</td>\n",
+       "      <td>{1: 4000, 2: 4000, 3: 4000, 5: 4000, 4: 4000}</td>\n",
        "      <td>15</td>\n",
        "      <td>XGBClassifier</td>\n",
        "      <td>{'learning_rate': 0.1, 'min_child_weight': 1, 'max_depth': 3, 'reg_alpha': 0, 'booster': 'gbtree', 'reg_lambda': 1, 'n_estimators': 100, 'use_label_encoder': False}</td>\n",
        "      <td>-0.3813</td>\n",
        "      <td>{'neg_log_loss': -0.38129734602336923}</td>\n",
-       "      <td>1.3873</td>\n",
-       "      <td>0.3182</td>\n",
-       "      <td>Thu Dec 26 05:52:47 2024</td>\n",
+       "      <td>1.1177</td>\n",
+       "      <td>0.3214</td>\n",
+       "      <td>Fri Apr 25 03:05:43 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Selection</td>\n",
-       "      <td>{1: 4000, 2: 4000, 5: 4000, 4: 4000, 3: 4000}</td>\n",
+       "      <td>{2: 4000, 5: 4000, 1: 4000, 3: 4000, 4: 4000}</td>\n",
        "      <td>15</td>\n",
        "      <td>CatBoostClassifier</td>\n",
        "      <td>{'iterations': 235, 'learning_rate': 0.787168, 'leaf_estimation_method': 'Newton', 'colsample_bylevel': 0.096865, 'depth': 3, 'l2_leaf_reg': 2.567326, 'feature_border_type': 'UniformAndQuantiles', 'model_size_reg': 3.85132, 'leaf_estimation_iterations': 1, 'boosting_type': 'Plain', 'bootstrap_type': 'MVS', 'auto_class_weights': 'SqrtBalanced', 'allow_writing_files': False, 'allow_const_label': True}</td>\n",
        "      <td>-0.3881</td>\n",
        "      <td>{'neg_log_loss': -0.38808832474759763}</td>\n",
-       "      <td>11.7876</td>\n",
-       "      <td>0.3163</td>\n",
-       "      <td>Thu Dec 26 05:52:46 2024</td>\n",
+       "      <td>10.8342</td>\n",
+       "      <td>0.3193</td>\n",
+       "      <td>Fri Apr 25 03:05:42 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Selection</td>\n",
-       "      <td>{1: 4000, 4: 4000, 5: 4000, 2: 4000, 3: 4000}</td>\n",
+       "      <td>{3: 4000, 1: 4000, 5: 4000, 4: 4000, 2: 4000}</td>\n",
        "      <td>15</td>\n",
        "      <td>LGBMClassifier</td>\n",
        "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
        "      <td>-0.3898</td>\n",
-       "      <td>{'neg_log_loss': -0.3898051926295403}</td>\n",
-       "      <td>12.4180</td>\n",
-       "      <td>0.3071</td>\n",
-       "      <td>Thu Dec 26 05:52:47 2024</td>\n",
+       "      <td>{'neg_log_loss': -0.38980519262954033}</td>\n",
+       "      <td>12.8814</td>\n",
+       "      <td>0.3083</td>\n",
+       "      <td>Fri Apr 25 03:05:43 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Selection</td>\n",
-       "      <td>{3: 4000, 4: 4000, 1: 4000, 2: 4000, 5: 4000}</td>\n",
+       "      <td>{1: 4000, 5: 4000, 3: 4000, 4: 4000, 2: 4000}</td>\n",
        "      <td>15</td>\n",
        "      <td>LogisticRegressionClassifier</td>\n",
        "      <td>{'C': 1.0, 'solver': 'liblinear', 'class_weight': 'balanced'}</td>\n",
        "      <td>-0.3908</td>\n",
        "      <td>{'neg_log_loss': -0.3907935186356912}</td>\n",
-       "      <td>0.4044</td>\n",
-       "      <td>0.3092</td>\n",
-       "      <td>Thu Dec 26 05:52:47 2024</td>\n",
+       "      <td>0.6874</td>\n",
+       "      <td>0.3279</td>\n",
+       "      <td>Fri Apr 25 03:05:43 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Selection</td>\n",
@@ -50914,57 +50914,57 @@
        "      <td>{'n_estimators': 100, 'min_samples_split': 0.00125, 'min_samples_leaf': 0.000625, 'max_features': 0.777777778, 'class_weight': 'balanced'}</td>\n",
        "      <td>-0.4093</td>\n",
        "      <td>{'neg_log_loss': -0.40930045927988684}</td>\n",
-       "      <td>2.8873</td>\n",
-       "      <td>0.2929</td>\n",
-       "      <td>Thu Dec 26 05:52:46 2024</td>\n",
+       "      <td>2.7773</td>\n",
+       "      <td>0.2950</td>\n",
+       "      <td>Fri Apr 25 03:05:43 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Selection</td>\n",
-       "      <td>{2: 4000, 1: 4000, 3: 4000, 5: 4000, 4: 4000}</td>\n",
+       "      <td>{2: 4000, 1: 4000, 3: 4000, 4: 4000, 5: 4000}</td>\n",
        "      <td>15</td>\n",
        "      <td>ExtraTreesClassifier</td>\n",
        "      <td>{'n_estimators': 100, 'min_samples_split': 0.00125, 'min_samples_leaf': 0.000625, 'max_features': 0.777777778, 'class_weight': 'balanced', 'criterion': 'gini'}</td>\n",
        "      <td>-0.4196</td>\n",
        "      <td>{'neg_log_loss': -0.4196315514455088}</td>\n",
-       "      <td>2.9559</td>\n",
-       "      <td>0.2837</td>\n",
-       "      <td>Thu Dec 26 05:52:44 2024</td>\n",
+       "      <td>3.1310</td>\n",
+       "      <td>0.2772</td>\n",
+       "      <td>Fri Apr 25 03:05:41 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Selection</td>\n",
-       "      <td>{1: 4000, 4: 4000, 2: 4000, 3: 4000, 5: 4000}</td>\n",
+       "      <td>{3: 4000, 5: 4000, 4: 4000, 2: 4000, 1: 4000}</td>\n",
        "      <td>15</td>\n",
        "      <td>TorchMLPClassifier</td>\n",
        "      <td>{'optimizer_class': 'Adam', 'shuffle_dataset_each_epoch': True, 'optimizer_params': {}, 'criterion_class': None, 'criterion_params': {}, 'scheduler_class': None, 'scheduler_params': {}, 'batch_size': 128, 'lr': 0.001, 'epochs': 18, 'input_transform': 'auto', 'tensorboard_dir': None, 'use_tqdm': None, 'prediction_batch_size': 128, 'prediction_input_transform': 'auto', 'shuffling_buffer_size': None, 'depth': 4, 'num_logits': 1000, 'div_factor': 2, 'activation': 'ReLU', 'dropout': 0.1}</td>\n",
-       "      <td>-0.7243</td>\n",
-       "      <td>{'neg_log_loss': -0.7242573803956669}</td>\n",
-       "      <td>38.6758</td>\n",
-       "      <td>0.6001</td>\n",
-       "      <td>Thu Dec 26 05:52:53 2024</td>\n",
+       "      <td>-0.8294</td>\n",
+       "      <td>{'neg_log_loss': -0.8293741212177753}</td>\n",
+       "      <td>49.1100</td>\n",
+       "      <td>0.6041</td>\n",
+       "      <td>Fri Apr 25 03:05:52 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Selection</td>\n",
-       "      <td>{1: 4000, 2: 4000, 4: 4000, 5: 4000, 3: 4000}</td>\n",
+       "      <td>{1: 4000, 2: 4000, 3: 4000, 4: 4000, 5: 4000}</td>\n",
        "      <td>15</td>\n",
        "      <td>GaussianNB</td>\n",
        "      <td>{}</td>\n",
        "      <td>-1.0622</td>\n",
        "      <td>{'neg_log_loss': -1.0622002049203993}</td>\n",
-       "      <td>0.4157</td>\n",
-       "      <td>0.2737</td>\n",
-       "      <td>Thu Dec 26 05:52:44 2024</td>\n",
+       "      <td>0.3964</td>\n",
+       "      <td>0.2871</td>\n",
+       "      <td>Fri Apr 25 03:05:41 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Selection</td>\n",
-       "      <td>{1: 4000, 3: 4000, 5: 4000, 2: 4000, 4: 4000}</td>\n",
+       "      <td>{4: 4000, 3: 4000, 1: 4000, 2: 4000, 5: 4000}</td>\n",
        "      <td>15</td>\n",
        "      <td>DecisionTreeClassifier</td>\n",
        "      <td>{'min_samples_split': 0.00125, 'min_samples_leaf': 0.000625, 'max_features': 1.0, 'class_weight': None}</td>\n",
        "      <td>-4.6323</td>\n",
-       "      <td>{'neg_log_loss': -4.632288461805367}</td>\n",
-       "      <td>1.3414</td>\n",
-       "      <td>0.2706</td>\n",
-       "      <td>Thu Dec 26 05:52:44 2024</td>\n",
+       "      <td>{'neg_log_loss': -4.632288461805368}</td>\n",
+       "      <td>1.3565</td>\n",
+       "      <td>0.2737</td>\n",
+       "      <td>Fri Apr 25 03:05:41 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Adaptive Sampling</td>\n",
@@ -50974,9 +50974,9 @@
        "      <td>{'learning_rate': 0.1, 'min_child_weight': 1, 'max_depth': 3, 'reg_alpha': 0, 'booster': 'gbtree', 'reg_lambda': 1, 'n_estimators': 100, 'use_label_encoder': False}</td>\n",
        "      <td>-0.349</td>\n",
        "      <td>{'neg_log_loss': -0.3490238812123456}</td>\n",
-       "      <td>3.1201</td>\n",
-       "      <td>0.6042</td>\n",
-       "      <td>Thu Dec 26 05:52:55 2024</td>\n",
+       "      <td>3.3692</td>\n",
+       "      <td>0.6036</td>\n",
+       "      <td>Fri Apr 25 03:05:54 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>...</td>\n",
@@ -50998,9 +50998,9 @@
        "      <td>{'learning_rate': 0.0001, 'min_child_weight': 0, 'max_depth': 2, 'reg_alpha': 0.0007113117640155693, 'booster': 'gbtree', 'reg_lambda': 0, 'n_estimators': 50, 'use_label_encoder': False}</td>\n",
        "      <td>-0.6912</td>\n",
        "      <td>{'neg_log_loss': -0.6911964137047958}</td>\n",
-       "      <td>1.2032</td>\n",
-       "      <td>0.6172</td>\n",
-       "      <td>Thu Dec 26 05:53:21 2024</td>\n",
+       "      <td>1.2147</td>\n",
+       "      <td>0.6153</td>\n",
+       "      <td>Fri Apr 25 03:06:19 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
@@ -51010,9 +51010,9 @@
        "      <td>{'learning_rate': 0.0001, 'min_child_weight': 0, 'max_depth': 2, 'reg_alpha': 0.0007513117640155693, 'booster': 'gbtree', 'reg_lambda': 0, 'n_estimators': 50, 'use_label_encoder': False}</td>\n",
        "      <td>-0.6912</td>\n",
        "      <td>{'neg_log_loss': -0.6911964137047958}</td>\n",
-       "      <td>1.2971</td>\n",
-       "      <td>0.6044</td>\n",
-       "      <td>Thu Dec 26 05:53:22 2024</td>\n",
+       "      <td>1.2364</td>\n",
+       "      <td>0.6092</td>\n",
+       "      <td>Fri Apr 25 03:06:20 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
@@ -51021,10 +51021,10 @@
        "      <td>XGBClassifier</td>\n",
        "      <td>{'learning_rate': 0.0001, 'min_child_weight': 0, 'max_depth': 2, 'reg_alpha': 0, 'booster': 'gbtree', 'reg_lambda': 0.01778279410038923, 'n_estimators': 50, 'use_label_encoder': False}</td>\n",
        "      <td>-0.6912</td>\n",
-       "      <td>{'neg_log_loss': -0.6911964480070907}</td>\n",
-       "      <td>1.1361</td>\n",
-       "      <td>0.6171</td>\n",
-       "      <td>Thu Dec 26 05:53:22 2024</td>\n",
+       "      <td>{'neg_log_loss': -0.6911964480070908}</td>\n",
+       "      <td>1.1856</td>\n",
+       "      <td>0.6153</td>\n",
+       "      <td>Fri Apr 25 03:06:20 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
@@ -51034,9 +51034,9 @@
        "      <td>{'learning_rate': 0.0001, 'min_child_weight': 0, 'max_depth': 2, 'reg_alpha': 0, 'booster': 'gbtree', 'reg_lambda': 0.01878279410038923, 'n_estimators': 50, 'use_label_encoder': False}</td>\n",
        "      <td>-0.6912</td>\n",
        "      <td>{'neg_log_loss': -0.6911964560751593}</td>\n",
-       "      <td>1.2639</td>\n",
-       "      <td>0.6044</td>\n",
-       "      <td>Thu Dec 26 05:53:22 2024</td>\n",
+       "      <td>1.1809</td>\n",
+       "      <td>0.6098</td>\n",
+       "      <td>Fri Apr 25 03:06:20 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
@@ -51046,9 +51046,9 @@
        "      <td>{'learning_rate': 0.0001, 'min_child_weight': 0, 'max_depth': 2, 'reg_alpha': 0.2249365300761397, 'booster': 'gbtree', 'reg_lambda': 0, 'n_estimators': 50, 'use_label_encoder': False}</td>\n",
        "      <td>-0.6912</td>\n",
        "      <td>{'neg_log_loss': -0.6911968446197632}</td>\n",
-       "      <td>1.2609</td>\n",
+       "      <td>1.2241</td>\n",
        "      <td>0.6153</td>\n",
-       "      <td>Thu Dec 26 05:53:22 2024</td>\n",
+       "      <td>Fri Apr 25 03:06:20 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
@@ -51058,9 +51058,9 @@
        "      <td>{'learning_rate': 0.0001, 'min_child_weight': 0, 'max_depth': 2, 'reg_alpha': 0.2249765300761397, 'booster': 'gbtree', 'reg_lambda': 0, 'n_estimators': 50, 'use_label_encoder': False}</td>\n",
        "      <td>-0.6912</td>\n",
        "      <td>{'neg_log_loss': -0.6911968446197632}</td>\n",
-       "      <td>1.1930</td>\n",
-       "      <td>0.6171</td>\n",
-       "      <td>Thu Dec 26 05:53:22 2024</td>\n",
+       "      <td>1.1658</td>\n",
+       "      <td>0.6098</td>\n",
+       "      <td>Fri Apr 25 03:06:20 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
@@ -51070,9 +51070,9 @@
        "      <td>{'learning_rate': 0.0001, 'min_child_weight': 0, 'max_depth': 2, 'reg_alpha': 0, 'booster': 'gbtree', 'reg_lambda': 1, 'n_estimators': 50, 'use_label_encoder': False}</td>\n",
        "      <td>-0.6912</td>\n",
        "      <td>{'neg_log_loss': -0.6911999162246485}</td>\n",
-       "      <td>1.2578</td>\n",
-       "      <td>0.6171</td>\n",
-       "      <td>Thu Dec 26 05:53:22 2024</td>\n",
+       "      <td>1.2465</td>\n",
+       "      <td>0.6134</td>\n",
+       "      <td>Fri Apr 25 03:06:20 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
@@ -51082,9 +51082,9 @@
        "      <td>{'learning_rate': 0.0001, 'min_child_weight': 0, 'max_depth': 2, 'reg_alpha': 0, 'booster': 'gbtree', 'reg_lambda': 1.001, 'n_estimators': 50, 'use_label_encoder': False}</td>\n",
        "      <td>-0.6912</td>\n",
        "      <td>{'neg_log_loss': -0.6911999174202924}</td>\n",
-       "      <td>1.1765</td>\n",
+       "      <td>1.2102</td>\n",
        "      <td>0.6153</td>\n",
-       "      <td>Thu Dec 26 05:53:22 2024</td>\n",
+       "      <td>Fri Apr 25 03:06:20 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
@@ -51094,9 +51094,9 @@
        "      <td>{'learning_rate': 0.0001, 'min_child_weight': 0, 'max_depth': 2, 'reg_alpha': 0, 'booster': 'gbtree', 'reg_lambda': 5.623413251903491, 'n_estimators': 50, 'use_label_encoder': False}</td>\n",
        "      <td>-0.6912</td>\n",
        "      <td>{'neg_log_loss': -0.6912154271501312}</td>\n",
-       "      <td>1.1926</td>\n",
-       "      <td>0.6171</td>\n",
-       "      <td>Thu Dec 26 05:53:23 2024</td>\n",
+       "      <td>1.1138</td>\n",
+       "      <td>0.6098</td>\n",
+       "      <td>Fri Apr 25 03:06:21 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
@@ -51106,9 +51106,9 @@
        "      <td>{'learning_rate': 0.0001, 'min_child_weight': 0, 'max_depth': 2, 'reg_alpha': 0, 'booster': 'gbtree', 'reg_lambda': 5.6244132519034915, 'n_estimators': 50, 'use_label_encoder': False}</td>\n",
        "      <td>-0.6912</td>\n",
        "      <td>{'neg_log_loss': -0.691215428004673}</td>\n",
-       "      <td>1.2962</td>\n",
-       "      <td>0.6073</td>\n",
-       "      <td>Thu Dec 26 05:53:23 2024</td>\n",
+       "      <td>1.2120</td>\n",
+       "      <td>0.6153</td>\n",
+       "      <td>Fri Apr 25 03:06:21 2025</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>"
@@ -51127,7 +51127,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4cdf857d",
+   "id": "8c69a9b2",
    "metadata": {},
    "source": [
     "We also provide the capability to visualize the results of each stage of the AutoMLx pipeline."
@@ -51135,7 +51135,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6efec4e7",
+   "id": "ea101c26",
    "metadata": {},
    "source": [
     "<a id='algorithm-selection'></a>\n",
@@ -51147,19 +51147,19 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "fe0dd95c",
+   "id": "1080e2dd",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:54:04.349295Z",
-     "iopub.status.busy": "2024-12-26T13:54:04.348864Z",
-     "iopub.status.idle": "2024-12-26T13:54:04.632955Z",
-     "shell.execute_reply": "2024-12-26T13:54:04.632407Z"
+     "iopub.execute_input": "2025-04-25T10:07:00.099566Z",
+     "iopub.status.busy": "2025-04-25T10:07:00.099068Z",
+     "iopub.status.idle": "2025-04-25T10:07:00.406866Z",
+     "shell.execute_reply": "2025-04-25T10:07:00.406253Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 1000x700 with 1 Axes>"
       ]
@@ -51201,7 +51201,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "40a5fcf5",
+   "id": "c8856dfb",
    "metadata": {},
    "source": [
     "<a id='adaptive-sampling'></a>\n",
@@ -51213,13 +51213,13 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "4f6f54e0",
+   "id": "fd72b252",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:54:04.635083Z",
-     "iopub.status.busy": "2024-12-26T13:54:04.634623Z",
-     "iopub.status.idle": "2024-12-26T13:54:04.834230Z",
-     "shell.execute_reply": "2024-12-26T13:54:04.833661Z"
+     "iopub.execute_input": "2025-04-25T10:07:00.408708Z",
+     "iopub.status.busy": "2025-04-25T10:07:00.408363Z",
+     "iopub.status.idle": "2025-04-25T10:07:00.596906Z",
+     "shell.execute_reply": "2025-04-25T10:07:00.596275Z"
     }
    },
    "outputs": [
@@ -51257,7 +51257,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ad65b9af",
+   "id": "c4382074",
    "metadata": {},
    "source": [
     "<a id='feature-selection'></a>\n",
@@ -51269,13 +51269,13 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "c6a4906d",
+   "id": "0cbce094",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:54:04.836378Z",
-     "iopub.status.busy": "2024-12-26T13:54:04.835856Z",
-     "iopub.status.idle": "2024-12-26T13:54:05.031311Z",
-     "shell.execute_reply": "2024-12-26T13:54:05.030712Z"
+     "iopub.execute_input": "2025-04-25T10:07:00.598809Z",
+     "iopub.status.busy": "2025-04-25T10:07:00.598424Z",
+     "iopub.status.idle": "2025-04-25T10:07:00.785104Z",
+     "shell.execute_reply": "2025-04-25T10:07:00.784524Z"
     }
    },
    "outputs": [
@@ -51326,7 +51326,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "52194beb",
+   "id": "bafd5362",
    "metadata": {},
    "source": [
     "<a id='hyperparameter-tuning'></a>\n",
@@ -51338,19 +51338,19 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "f535d2b7",
+   "id": "d24a6513",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:54:05.033537Z",
-     "iopub.status.busy": "2024-12-26T13:54:05.032965Z",
-     "iopub.status.idle": "2024-12-26T13:54:05.285504Z",
-     "shell.execute_reply": "2024-12-26T13:54:05.284891Z"
+     "iopub.execute_input": "2025-04-25T10:07:00.787034Z",
+     "iopub.status.busy": "2025-04-25T10:07:00.786644Z",
+     "iopub.status.idle": "2025-04-25T10:07:01.029328Z",
+     "shell.execute_reply": "2025-04-25T10:07:01.028725Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x700 with 1 Axes>"
       ]
@@ -51391,7 +51391,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fb6a9cba",
+   "id": "af9781b2",
    "metadata": {},
    "source": [
     "<a id='confusion-matrix'></a>\n",
@@ -51403,13 +51403,13 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "db5c2247",
+   "id": "ee9da09a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:54:05.287721Z",
-     "iopub.status.busy": "2024-12-26T13:54:05.287189Z",
-     "iopub.status.idle": "2024-12-26T13:54:05.474478Z",
-     "shell.execute_reply": "2024-12-26T13:54:05.473907Z"
+     "iopub.execute_input": "2025-04-25T10:07:01.031355Z",
+     "iopub.status.busy": "2025-04-25T10:07:01.030867Z",
+     "iopub.status.idle": "2025-04-25T10:07:01.229887Z",
+     "shell.execute_reply": "2025-04-25T10:07:01.229277Z"
     }
    },
    "outputs": [
@@ -52392,9 +52392,9 @@
        }
       },
       "text/html": [
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        "                            \n",
-       "var gd = document.getElementById('a6d0a665-c8b2-4c3b-86e7-0d3dab3f37ea');\n",
+       "var gd = document.getElementById('232c08bb-8cd1-4d88-a808-628e168b4cd2');\n",
        "var x = new MutationObserver(function (mutations, observer) {{\n",
        "        var display = window.getComputedStyle(gd).display;\n",
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@@ -52452,7 +52452,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "14032e9b",
+   "id": "c3596896",
    "metadata": {},
    "source": [
     "<a id='modellist'></a>\n",
@@ -52464,13 +52464,13 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "5e8deb01",
+   "id": "cfcca7f7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:54:05.476654Z",
-     "iopub.status.busy": "2024-12-26T13:54:05.476193Z",
-     "iopub.status.idle": "2024-12-26T13:54:05.535676Z",
-     "shell.execute_reply": "2024-12-26T13:54:05.535140Z"
+     "iopub.execute_input": "2025-04-25T10:07:01.231676Z",
+     "iopub.status.busy": "2025-04-25T10:07:01.231423Z",
+     "iopub.status.idle": "2025-04-25T10:07:01.288638Z",
+     "shell.execute_reply": "2025-04-25T10:07:01.288056Z"
     }
    },
    "outputs": [],
@@ -52503,7 +52503,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1dc0aac7",
+   "id": "51d63967",
    "metadata": {},
    "source": [
     "<a id='custom-validation'></a>\n",
@@ -52514,13 +52514,13 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "3aa0e3fd",
+   "id": "fff902f2",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:54:05.537695Z",
-     "iopub.status.busy": "2024-12-26T13:54:05.537262Z",
-     "iopub.status.idle": "2024-12-26T13:54:05.595851Z",
-     "shell.execute_reply": "2024-12-26T13:54:05.595323Z"
+     "iopub.execute_input": "2025-04-25T10:07:01.290633Z",
+     "iopub.status.busy": "2025-04-25T10:07:01.290261Z",
+     "iopub.status.idle": "2025-04-25T10:07:01.348892Z",
+     "shell.execute_reply": "2025-04-25T10:07:01.348307Z"
     }
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    "outputs": [],
@@ -52530,7 +52530,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9a356f39",
+   "id": "889f0970",
    "metadata": {},
    "source": [
     "A few of the advanced settings can be passed directly to the pipeline's fit method, instead of its constructor."
@@ -52539,13 +52539,13 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "003e504a",
+   "id": "72e12a1c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:54:05.598116Z",
-     "iopub.status.busy": "2024-12-26T13:54:05.597625Z",
-     "iopub.status.idle": "2024-12-26T13:54:23.845221Z",
-     "shell.execute_reply": "2024-12-26T13:54:23.844373Z"
+     "iopub.execute_input": "2025-04-25T10:07:01.350639Z",
+     "iopub.status.busy": "2025-04-25T10:07:01.350324Z",
+     "iopub.status.idle": "2025-04-25T10:07:19.276060Z",
+     "shell.execute_reply": "2025-04-25T10:07:19.275368Z"
     }
    },
    "outputs": [
@@ -52553,147 +52553,147 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:54:05,708] [automlx.interface] Dataset shape: (34189,14)\n"
+      "[2025-04-25 03:07:01,457] [automlx.interface] Dataset shape: (34189,14)\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:54:05,770] [automlx.interface] Adaptive Sampling disabled.\n"
+      "[2025-04-25 03:07:01,518] [automlx.interface] Adaptive Sampling disabled.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:54:05,821] [automlx.data_transform] Running preprocessing. Number of features: 15\n"
+      "[2025-04-25 03:07:01,563] [automlx.data_transform] Running preprocessing. Number of features: 15\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:54:06,443] [automlx.data_transform] Preprocessing completed. Took 0.622 secs\n"
+      "[2025-04-25 03:07:02,094] [automlx.data_transform] Preprocessing completed. Took 0.531 secs\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:54:06,465] [automlx.process] Running Model Generation\n"
+      "[2025-04-25 03:07:02,127] [automlx.process] Running Model Generation\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:54:06,524] [automlx.process] Model Generation completed.\n"
+      "[2025-04-25 03:07:02,185] [automlx.process] Model Generation completed.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:54:06,557] [automlx.model_selection] Running Model Selection\n"
+      "[2025-04-25 03:07:02,217] [automlx.model_selection] Running Model Selection\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:54:08,021] [automlx.model_selection] Model Selection completed - Took 1.463 sec - Selected models: [['LGBMClassifier', 'LogisticRegressionClassifier']]\n"
+      "[2025-04-25 03:07:03,661] [automlx.model_selection] Model Selection completed - Took 1.444 sec - Selected models: [['LGBMClassifier', 'LogisticRegressionClassifier']]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:54:08,125] [automlx.feature_selection] Starting feature ranking for LGBMClassifier\n"
+      "[2025-04-25 03:07:03,746] [automlx.feature_selection] Starting feature ranking for LGBMClassifier\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:54:11,862] [automlx.feature_selection] Feature Selection completed. Took 3.737 secs.\n"
+      "[2025-04-25 03:07:07,088] [automlx.feature_selection] Feature Selection completed. Took 3.342 secs.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:54:12,016] [automlx.trials] Running Model Tuning for ['LGBMClassifier']\n"
+      "[2025-04-25 03:07:07,215] [automlx.trials] Running Model Tuning for ['LGBMClassifier']\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:54:13,247] [automlx.trials] Best parameters for LGBMClassifier: {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.01, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': None}\n"
+      "[2025-04-25 03:07:08,436] [automlx.trials] Best parameters for LGBMClassifier: {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.01, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': None}\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:54:13,248] [automlx.trials] Model Tuning completed. Took: 1.232 secs\n"
+      "[2025-04-25 03:07:08,437] [automlx.trials] Model Tuning completed. Took: 1.222 secs\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:54:13,352] [automlx.feature_selection] Starting feature ranking for LogisticRegressionClassifier\n"
+      "[2025-04-25 03:07:08,521] [automlx.feature_selection] Starting feature ranking for LogisticRegressionClassifier\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:54:19,664] [automlx.feature_selection] Feature Selection completed. Took 6.313 secs.\n"
+      "[2025-04-25 03:07:15,245] [automlx.feature_selection] Feature Selection completed. Took 6.724 secs.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:54:19,739] [automlx.trials] Running Model Tuning for ['LogisticRegressionClassifier']\n"
+      "[2025-04-25 03:07:15,272] [automlx.trials] Running Model Tuning for ['LogisticRegressionClassifier']\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:54:20,872] [automlx.trials] Best parameters for LogisticRegressionClassifier: {'C': 0.0363696875, 'solver': 'liblinear', 'class_weight': 'balanced'}\n"
+      "[2025-04-25 03:07:16,400] [automlx.trials] Best parameters for LogisticRegressionClassifier: {'C': 0.0363696875, 'solver': 'liblinear', 'class_weight': 'balanced'}\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:54:20,873] [automlx.trials] Model Tuning completed. Took: 1.133 secs\n"
+      "[2025-04-25 03:07:16,401] [automlx.trials] Model Tuning completed. Took: 1.130 secs\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:54:21,249] [automlx.interface] Re-fitting pipeline\n"
+      "[2025-04-25 03:07:16,754] [automlx.interface] Re-fitting pipeline\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:54:21,274] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_dc1eb50b-7\n"
+      "[2025-04-25 03:07:16,780] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_33c32b58-6\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:54:23,379] [automlx.interface] AutoMLx completed.\n"
+      "[2025-04-25 03:07:18,836] [automlx.interface] AutoMLx completed.\n"
      ]
     },
     {
@@ -52725,7 +52725,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a680ed21",
+   "id": "1e61e464",
    "metadata": {},
    "source": [
     "<a id='MLX'></a>\n",
@@ -52734,7 +52734,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5e0f0602",
+   "id": "9c58d5b6",
    "metadata": {},
    "source": [
     "For a variety of decision-making tasks, getting only a prediction as model output is not sufficient. A user may wish to know why the model outputs that prediction, or which data features are relevant for that prediction. For that purpose the Oracle AutoMLx solution defines the `MLExplainer` object, which allows to compute a variety of model explanations\n",
@@ -52748,13 +52748,13 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "42911635",
+   "id": "84d374f7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:54:23.847915Z",
-     "iopub.status.busy": "2024-12-26T13:54:23.847425Z",
-     "iopub.status.idle": "2024-12-26T13:54:23.946090Z",
-     "shell.execute_reply": "2024-12-26T13:54:23.945428Z"
+     "iopub.execute_input": "2025-04-25T10:07:19.278475Z",
+     "iopub.status.busy": "2025-04-25T10:07:19.278079Z",
+     "iopub.status.idle": "2025-04-25T10:07:19.372035Z",
+     "shell.execute_reply": "2025-04-25T10:07:19.371420Z"
     }
    },
    "outputs": [],
@@ -52766,7 +52766,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dc8da39b",
+   "id": "48148767",
    "metadata": {},
    "source": [
     "<a id='MLX-global'></a>\n",
@@ -52775,7 +52775,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "28e19438",
+   "id": "4d3979d7",
    "metadata": {},
    "source": [
     "The notion of Global Feature Importance intuitively measures how much the model's performance (relative to the provided train labels) would change if a given feature were dropped from the dataset, <b><i>without</i></b> retraining the model. This notion of feature importance considers each feature independently from all other features."
@@ -52783,7 +52783,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4b916c74",
+   "id": "e447554c",
    "metadata": {},
    "source": [
     "#### Computing the importance"
@@ -52791,7 +52791,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "021a38a3",
+   "id": "1677784a",
    "metadata": {},
    "source": [
     "By default we use a permutation method to successively measure the importance of each feature. Such a method therefore runs in linear time with respect to the\n",
@@ -52803,13 +52803,13 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "0e13778f",
+   "id": "91136fe4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:54:23.948284Z",
-     "iopub.status.busy": "2024-12-26T13:54:23.947864Z",
-     "iopub.status.idle": "2024-12-26T13:54:31.944345Z",
-     "shell.execute_reply": "2024-12-26T13:54:31.943608Z"
+     "iopub.execute_input": "2025-04-25T10:07:19.374180Z",
+     "iopub.status.busy": "2025-04-25T10:07:19.373988Z",
+     "iopub.status.idle": "2025-04-25T10:07:27.741076Z",
+     "shell.execute_reply": "2025-04-25T10:07:27.740477Z"
     }
    },
    "outputs": [],
@@ -52824,7 +52824,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3b2eb688",
+   "id": "25083d11",
    "metadata": {},
    "source": [
     "#### Visualization"
@@ -52832,7 +52832,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "72048474",
+   "id": "074bc7e3",
    "metadata": {},
    "source": [
     "There are two options to show the explanation's results:\n",
@@ -52845,13 +52845,13 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "739405b2",
+   "id": "675936c8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:54:31.946937Z",
-     "iopub.status.busy": "2024-12-26T13:54:31.946408Z",
-     "iopub.status.idle": "2024-12-26T13:54:32.026259Z",
-     "shell.execute_reply": "2024-12-26T13:54:32.025639Z"
+     "iopub.execute_input": "2025-04-25T10:07:27.743567Z",
+     "iopub.status.busy": "2025-04-25T10:07:27.743113Z",
+     "iopub.status.idle": "2025-04-25T10:07:27.823293Z",
+     "shell.execute_reply": "2025-04-25T10:07:27.822782Z"
     }
    },
    "outputs": [
@@ -52886,37 +52886,37 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>capitalgain</td>\n",
-       "      <td>0.071287</td>\n",
-       "      <td>0.057273</td>\n",
-       "      <td>0.085301</td>\n",
+       "      <td>0.071744</td>\n",
+       "      <td>0.062587</td>\n",
+       "      <td>0.080901</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>capitalloss</td>\n",
-       "      <td>0.001714</td>\n",
-       "      <td>-0.000482</td>\n",
-       "      <td>0.003910</td>\n",
+       "      <td>0.001740</td>\n",
+       "      <td>-0.000836</td>\n",
+       "      <td>0.004317</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>age</td>\n",
-       "      <td>0.001237</td>\n",
-       "      <td>-0.000383</td>\n",
-       "      <td>0.002856</td>\n",
+       "      <td>0.001172</td>\n",
+       "      <td>-0.000865</td>\n",
+       "      <td>0.003210</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>fnlwgt</td>\n",
-       "      <td>0.000300</td>\n",
-       "      <td>-0.001479</td>\n",
-       "      <td>0.002079</td>\n",
+       "      <td>0.000606</td>\n",
+       "      <td>-0.001714</td>\n",
+       "      <td>0.002927</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>hoursperweek</td>\n",
        "      <td>0.000125</td>\n",
-       "      <td>-0.000928</td>\n",
-       "      <td>0.001177</td>\n",
+       "      <td>-0.001286</td>\n",
+       "      <td>0.001536</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
@@ -52927,56 +52927,56 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
-       "      <td>education</td>\n",
+       "      <td>native-country</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
-       "      <td>sex</td>\n",
+       "      <td>race</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
-       "      <td>marital-status</td>\n",
+       "      <td>education</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
-       "      <td>relationship</td>\n",
+       "      <td>sex</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
-       "      <td>race</td>\n",
+       "      <td>relationship</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
-       "      <td>occupation</td>\n",
+       "      <td>workclass</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
-       "      <td>native-country</td>\n",
+       "      <td>marital-status</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
-       "      <td>workclass</td>\n",
+       "      <td>occupation</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
@@ -52987,20 +52987,20 @@
       ],
       "text/plain": [
        "           Feature  Attribution  Lower Bound  Upper Bound\n",
-       "0      capitalgain     0.071287     0.057273     0.085301\n",
-       "1      capitalloss     0.001714    -0.000482     0.003910\n",
-       "2              age     0.001237    -0.000383     0.002856\n",
-       "3           fnlwgt     0.000300    -0.001479     0.002079\n",
-       "4     hoursperweek     0.000125    -0.000928     0.001177\n",
+       "0      capitalgain     0.071744     0.062587     0.080901\n",
+       "1      capitalloss     0.001740    -0.000836     0.004317\n",
+       "2              age     0.001172    -0.000865     0.003210\n",
+       "3           fnlwgt     0.000606    -0.001714     0.002927\n",
+       "4     hoursperweek     0.000125    -0.001286     0.001536\n",
        "5    education-num     0.000085    -0.000435     0.000604\n",
-       "6        education     0.000000     0.000000     0.000000\n",
-       "7              sex     0.000000     0.000000     0.000000\n",
-       "8   marital-status     0.000000     0.000000     0.000000\n",
-       "9     relationship     0.000000     0.000000     0.000000\n",
-       "10            race     0.000000     0.000000     0.000000\n",
-       "11      occupation     0.000000     0.000000     0.000000\n",
-       "12  native-country     0.000000     0.000000     0.000000\n",
-       "13       workclass     0.000000     0.000000     0.000000"
+       "6   native-country     0.000000     0.000000     0.000000\n",
+       "7             race     0.000000     0.000000     0.000000\n",
+       "8        education     0.000000     0.000000     0.000000\n",
+       "9              sex     0.000000     0.000000     0.000000\n",
+       "10    relationship     0.000000     0.000000     0.000000\n",
+       "11       workclass     0.000000     0.000000     0.000000\n",
+       "12  marital-status     0.000000     0.000000     0.000000\n",
+       "13      occupation     0.000000     0.000000     0.000000"
       ]
      },
      "execution_count": 23,
@@ -53015,13 +53015,13 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "ee0175e1",
+   "id": "8698c81a",
    "metadata": {
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-     "iopub.execute_input": "2024-12-26T13:54:32.028315Z",
-     "iopub.status.busy": "2024-12-26T13:54:32.027769Z",
-     "iopub.status.idle": "2024-12-26T13:54:32.187472Z",
-     "shell.execute_reply": "2024-12-26T13:54:32.186883Z"
+     "iopub.execute_input": "2025-04-25T10:07:27.825149Z",
+     "iopub.status.busy": "2025-04-25T10:07:27.824724Z",
+     "iopub.status.idle": "2025-04-25T10:07:28.013816Z",
+     "shell.execute_reply": "2025-04-25T10:07:28.013276Z"
     }
    },
    "outputs": [
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@@ -53970,9 +53970,9 @@
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        "                            \n",
-       "var gd = document.getElementById('8967626a-d2d9-4fce-8236-fb7dff047174');\n",
+       "var gd = document.getElementById('f4c9878c-92a9-4ebe-8ab0-da78b7381123');\n",
        "var x = new MutationObserver(function (mutations, observer) {{\n",
        "        var display = window.getComputedStyle(gd).display;\n",
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@@ -58797,7 +58797,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "916e36f2",
+   "id": "cecc0689",
    "metadata": {},
    "source": [
     "PDPs can be computed for an arbitrary number of variables; however, they can only be visualized with up to 4. We show an example with 3 below."
@@ -58806,13 +58806,13 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "id": "53283b1f",
+   "id": "e9c61a90",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:54:35.668286Z",
-     "iopub.status.busy": "2024-12-26T13:54:35.667721Z",
-     "iopub.status.idle": "2024-12-26T13:54:40.287416Z",
-     "shell.execute_reply": "2024-12-26T13:54:40.286797Z"
+     "iopub.execute_input": "2025-04-25T10:07:31.344208Z",
+     "iopub.status.busy": "2025-04-25T10:07:31.343680Z",
+     "iopub.status.idle": "2025-04-25T10:07:35.867985Z",
+     "shell.execute_reply": "2025-04-25T10:07:35.867350Z"
     }
    },
    "outputs": [
@@ -61159,9 +61159,9 @@
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       },
       "text/html": [
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@@ -61197,7 +61197,7 @@
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@@ -61206,7 +61206,7 @@
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@@ -61218,13 +61218,13 @@
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@@ -61365,7 +61365,7 @@
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@@ -61375,7 +61375,7 @@
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@@ -61385,7 +61385,7 @@
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@@ -61405,7 +61405,7 @@
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@@ -61415,7 +61415,7 @@
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@@ -61425,7 +61425,7 @@
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@@ -61435,7 +61435,7 @@
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@@ -61455,7 +61455,7 @@
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          {
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-          "text": "fnlwgt = 185384.0",
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@@ -61465,7 +61465,7 @@
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@@ -61475,7 +61475,7 @@
          },
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-          "text": "age = 1.0",
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@@ -61485,7 +61485,7 @@
          },
          {
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-          "text": "hoursperweek = 3.0",
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@@ -62368,9 +62368,9 @@
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+          "fnlwgt",
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@@ -62402,9 +62402,9 @@
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+          "fnlwgt",
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@@ -62425,9 +62425,9 @@
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@@ -85012,7 +84634,7 @@
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@@ -85030,7 +84652,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f7304988",
+   "id": "28cd75be",
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    "source": [
     "<a id='Counterfactuals'></a>\n",
@@ -85039,7 +84661,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "43a29b6b",
+   "id": "a2dfd497",
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     "Counterfactual explainers are another set of advanced features that Oracle AutoMLx supports, which help to explain a trained ML model's predictions by identifying the minimal set of changes necessary to flip the model's decision, resulting in a different outcome. To achieve this, the solution frames the explanation process as an optimization problem, similar to adversarial discoveries, while ensuring that the counterfactual perturbations used are feasible and diverse.\n",
@@ -85057,20 +84679,20 @@
   {
    "cell_type": "code",
    "execution_count": 31,
-   "id": "8f37e11f",
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-     "iopub.status.idle": "2024-12-26T13:54:46.062350Z",
-     "shell.execute_reply": "2024-12-26T13:54:46.061659Z"
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     {
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@@ -85094,7 +84716,7 @@
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-   "id": "ce8a4119",
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     "<a id='MLX-advanced'></a>\n",
@@ -85108,13 +84730,13 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "id": "e5f1cfa2",
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-     "shell.execute_reply": "2024-12-26T13:54:46.368357Z"
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@@ -85154,7 +84776,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "26fa003a",
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@@ -85179,7 +84801,7 @@
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-   "id": "b73fe015",
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     "<a id='MLX-advance-tabulator-prediction'></a>\n",
@@ -85191,13 +84813,13 @@
   {
    "cell_type": "code",
    "execution_count": 33,
-   "id": "04b30e50",
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    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:54:46.371183Z",
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-     "shell.execute_reply": "2024-12-26T13:54:46.435745Z"
+     "iopub.execute_input": "2025-04-25T10:07:41.836672Z",
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@@ -85209,7 +84831,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "951f984e",
+   "id": "8e4ccd7e",
    "metadata": {},
    "source": [
     "#### Local feature importance using surrogate models (LIME+)\n",
@@ -85228,13 +84850,13 @@
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    "cell_type": "code",
    "execution_count": 34,
-   "id": "f81ec328",
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-     "shell.execute_reply": "2024-12-26T13:54:47.177160Z"
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@@ -85271,7 +84893,7 @@
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@@ -85309,7 +84931,7 @@
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@@ -86413,9 +86035,9 @@
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        "                            \n",
-       "var gd = document.getElementById('db8f402b-f27a-4393-b31f-29b33fa676a0');\n",
+       "var gd = document.getElementById('caf1eabd-a10d-45ab-9807-7a1646831551');\n",
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@@ -87509,7 +87131,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0c375caa",
+   "id": "d70585b8",
    "metadata": {},
    "source": [
     "<a id='MLX-advanced-ale'></a>\n",
@@ -87522,7 +87144,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8b4874ad",
+   "id": "70b297d7",
    "metadata": {},
    "source": [
     "The plot below is the ALE plot for the `education-num` and `sex` features. The X-axis shows the values of `education-num` however, there are now multiple lines, one for each value of the feature `sex`.\n",
@@ -87533,13 +87155,13 @@
   {
    "cell_type": "code",
    "execution_count": 38,
-   "id": "39a8076a",
+   "id": "3b1c672d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:54:51.795423Z",
-     "iopub.status.busy": "2024-12-26T13:54:51.794839Z",
-     "iopub.status.idle": "2024-12-26T13:54:55.018037Z",
-     "shell.execute_reply": "2024-12-26T13:54:55.017369Z"
+     "iopub.execute_input": "2025-04-25T10:07:47.216829Z",
+     "iopub.status.busy": "2025-04-25T10:07:47.216382Z",
+     "iopub.status.idle": "2025-04-25T10:07:50.311759Z",
+     "shell.execute_reply": "2025-04-25T10:07:50.311203Z"
     }
    },
    "outputs": [
@@ -88857,9 +88479,9 @@
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       },
       "text/html": [
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",
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",
         "encoding": "base64",
         "path": [
          "_data",
@@ -90666,7 +90960,7 @@
         ]
        },
        {
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+        "Adm-clerical",
+        "Armed-Forces",
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+        "Handlers-cleaners",
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+        "Other-service",
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+       "_view_name": "DropdownView",
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+       "description_allow_html": false,
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-        "scatter",
-        "bar",
-        "box"
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+        "Federal-gov",
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-       "description": "Plot Type",
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-       "layout": "IPY_MODEL_49e79276ab4f400c948f873f11ba1892",
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+       "description": "education-num",
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        "_view_module": "@jupyter-widgets/controls",
        "_view_module_version": "2.0.0",
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+       "layout": "IPY_MODEL_6543afc53fd0431b953f1e0d430f739f",
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         "None",
-        "Husband",
-        "Not-in-family",
-        "Other-relative",
-        "Own-child",
-        "Unmarried",
-        "Wife"
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+        "11th",
+        "12th",
+        "1st-4th",
+        "5th-6th",
+        "7th-8th",
+        "9th",
+        "Assoc-acdm",
+        "Assoc-voc",
+        "Bachelors",
+        "Doctorate",
+        "HS-grad",
+        "Masters",
+        "Preschool",
+        "Prof-school",
+        "Some-college"
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-       "description": "relationship",
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        "description_allow_html": false,
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-       "index": 1,
-       "layout": "IPY_MODEL_39b5c53eb3f64355be5044a03f51704a",
-       "style": "IPY_MODEL_838f133124724345affc84adc8d23b9a",
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        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "2.0.0",
-       "_model_name": "HBoxModel",
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-       "layout": "IPY_MODEL_2e319a9634f94becb3cabe7efad769a1",
+       "_view_name": "IntTextView",
+       "continuous_update": false,
+       "description": "hoursperweek",
+       "description_allow_html": false,
+       "disabled": false,
+       "layout": "IPY_MODEL_12fada066a0c467aad4b3368656add7c",
+       "step": 1,
+       "style": "IPY_MODEL_eac2687abc9548cdb7bea76cb54fb64d",
        "tabbable": null,
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@@ -108236,58 +107647,30 @@
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        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "2.0.0",
-       "_model_name": "HBoxModel",
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        "_view_module": "@jupyter-widgets/controls",
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-        "IPY_MODEL_0c6cd8b7e8cb444f86954a41ef49a72d",
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-        "IPY_MODEL_e6ff3a16b5474fabaa288e16a325b5cd",
-        "IPY_MODEL_cfdd9115eac24bd19b290cc4e24503a7",
-        "IPY_MODEL_f466d04aa022493d97b5ce92f06a5673",
-        "IPY_MODEL_fcb4d9beb0764648aa0e1088f2c5a205",
-        "IPY_MODEL_e9a8f8ff70d94485896867c586f7a21e",
-        "IPY_MODEL_87e652da23394afa81773d1f55840b72"
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-       "layout": "IPY_MODEL_0d6f020bb0b7480d945c3b8a90724af6",
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+       "description": "",
+       "description_allow_html": false,
+       "layout": "IPY_MODEL_d6f80a5de2b848209b383b350b0ad002",
+       "placeholder": "​",
+       "style": "IPY_MODEL_6f6bb75ea0254032b1fddda2d29da625",
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-       "_model_module_version": "2.0.0",
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-       "_view_module": "@jupyter-widgets/base",
-       "_view_module_version": "2.0.0",
-       "_view_name": "StyleView",
-       "background": null,
-       "description_width": ""
+       "tooltip": null,
+       "value": "<h2>Sample (Row: 0)</h2>"
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-     "9ad2fb7325044f59a1fa7c6b3dc73d7c": {
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@@ -108340,7 +107723,7 @@
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-     "9c24186bc9024b74b4d1ab3e6ba2546b": {
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@@ -108352,10 +107735,10 @@
        "_view_module": "@jupyter-widgets/base",
        "_view_module_version": "2.0.0",
        "_view_name": "StyleView",
-       "description_width": ""
+       "description_width": "initial"
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@@ -108408,33 +107791,109 @@
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-       "_model_name": "HBoxModel",
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-<h1><font color=red>Building and Explaining a Text Classifier using AutoMLx</font></h1>
-<p style="margin-left:10%; margin-right:10%;">by the <font color=teal> Oracle AutoMLx Team </font></p><hr>
+<h1><font color="red">Building and Explaining a Text Classifier using AutoMLx</font></h1>
+<p style="margin-left:10%; margin-right:10%;">by the <font color="teal"> Oracle AutoMLx Team </font></p><hr>
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-<p>AutoMLx Text Classification Demo version 23.2.0.</p>
+<p>AutoMLx Text Classification</p>
 <p>Copyright © 2023, Oracle and/or its affiliates.</p>
 <p>Licensed under the Universal Permissive License v 1.0 as shown at <a href="https://oss.oracle.com/licenses/upl/">https://oss.oracle.com/licenses/upl/</a></p>
 
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-<h2 id="Overview-of-this-Notebook">Overview of this Notebook<a class="anchor-link" href="#Overview-of-this-Notebook">&#182;</a></h2><p>In this notebook we will build a classifier using the Oracle AutoMLx tool for the public 20newsgroup dataset. The dataset is a binary classification dataset, and more details about the dataset can be found at <a href="http://qwone.com/~jason/20Newsgroups/">http://qwone.com/~jason/20Newsgroups/</a>.
-We explore the various options provided by the Oracle AutoMLx tool, allowing the user to exercise control over the AutoML training process. We then evaluate the different models trained by AutoML. Finally we provide an overview of the possibilites that Oracle AutoMLx offers for explaining the predictions of the tuned model.</p>
+<h2 id="Overview-of-this-Notebook">Overview of this Notebook<a class="anchor-link" href="http://automl.oraclecorp.com/multiversion/v24.2.0/_static/notebooks/OracleAutoMLx_Classification_Text.html#Overview-of-this-Notebook">¶</a></h2><p>In this notebook we will build a classifier using the Oracle AutoMLx tool for the public 20newsgroup dataset. The dataset is a binary classification dataset, and more details about the dataset can be found at <a href="http://qwone.com/~jason/20Newsgroups/">http://qwone.com/~jason/20Newsgroups/</a>.
+We explore the various options provided by the Oracle AutoMLx tool, allowing the user to exercise control over the AutoML training process. We then evaluate the different models trained by AutoML. Finally we provide an overview of the possibilities that Oracle AutoMLx offers for explaining the predictions of the tuned model.</p>
 <hr>
-<h2 id="Prerequisites">Prerequisites<a class="anchor-link" href="#Prerequisites">&#182;</a></h2><ul>
+<h2 id="Prerequisites">Prerequisites<a class="anchor-link" href="http://automl.oraclecorp.com/multiversion/v24.2.0/_static/notebooks/OracleAutoMLx_Classification_Text.html#Prerequisites">¶</a></h2><ul>
 <li>Experience level: Novice (Python and Machine Learning)</li>
 <li>Professional experience: Some industry experience</li>
 </ul>
 <hr>
-<h2 id="Business-Use">Business Use<a class="anchor-link" href="#Business-Use">&#182;</a></h2><p>Data analytics and modeling problems using Machine Learning (ML) are becoming popular and often rely on data science expertise to build accurate ML models. Such modeling tasks primarily involve the following steps:</p>
+<h2 id="Business-Use">Business Use<a class="anchor-link" href="http://automl.oraclecorp.com/multiversion/v24.2.0/_static/notebooks/OracleAutoMLx_Classification_Text.html#Business-Use">¶</a></h2><p>Data analytics and modeling problems using Machine Learning (ML) are becoming popular and often rely on data science expertise to build accurate ML models. Such modeling tasks differs according to type of problem to be solved, eg. Text classification primarily involve the following steps:</p>
 <ul>
-<li>Preprocess dataset (clean, impute, engineer features, normalize).</li>
+<li>Preprocess dataset (vectorize).</li>
 <li>Pick an appropriate model for the given dataset and prediction task at hand.</li>
 <li>Tune the chosen model’s hyperparameters for the given dataset.</li>
 </ul>
-<p>All of these steps are significantly time consuming and heavily rely on data scientist expertise. Unfortunately, to make this problem harder, the best feature subset, model, and hyperparameter choice widely varies with the dataset and the prediction task. Hence, there is no one-size-fits-all solution to achieve reasonably good model performance. Using a simple Python API, AutoML can quickly (faster) jump-start the datascience process with an accurately-tuned model and appropriate features for a given prediction task.</p>
-<h2 id="Table-of-Contents">Table of Contents<a class="anchor-link" href="#Table-of-Contents">&#182;</a></h2><ul>
-<li><a href='#setup'>0. Setup</a></li>
-<li><a href='#load-data'>1. Load the 20newsgroup Income dataset</a></li>
-<li><a href='#AutoML'>2. AutoML</a><ul>
-<li><a href='#Engine'>2.0. Set the engine and deprecation warnings</a></li>
-<li><a href='#provider'>2.1. Create an Instance of Oracle AutoMLx</a></li>
-<li><a href='#default'>2.2. Train a Model using AutoML</a></li>
-<li><a href='#analyze'>2.3. Analyze the AutoML optimization process </a><ul>
-<li><a href='#algorithm-selection'>2.3.1. Algorithm Selection</a></li>
-<li><a href='#adaptive-sampling'>2.3.2. Adaptive Sampling</a></li>
-<li><a href='#feature-selection'>2.3.3. Feature Selection</a></li>
-<li><a href='#hyperparameter-tuning'>2.3.4. Hyperparameter Tuning</a></li>
+<p>All of these steps are significantly time consuming and heavily rely on data scientist expertise. Unfortunately, to make this problem harder, the best feature subset, model, and hyperparameter choice widely varies with the dataset and the prediction task. Hence, there is no one-size-fits-all solution to achieve reasonably good model performance. Using a simple Python API, AutoML can quickly jump-start the datascience process with an accurately-tuned model and appropriate features for a given prediction task.</p>
+<h2 id="Table-of-Contents">Table of Contents<a class="anchor-link" href="http://automl.oraclecorp.com/multiversion/v24.2.0/_static/notebooks/OracleAutoMLx_Classification_Text.html#Table-of-Contents">¶</a></h2><ul>
+<li><a href="http://automl.oraclecorp.com/multiversion/v24.2.0/_static/notebooks/OracleAutoMLx_Classification_Text.html#setup">Setup</a></li>
+<li><a href="http://automl.oraclecorp.com/multiversion/v24.2.0/_static/notebooks/OracleAutoMLx_Classification_Text.html#load-data">Load the 20newsgroup Income dataset</a></li>
+<li><a href="http://automl.oraclecorp.com/multiversion/v24.2.0/_static/notebooks/OracleAutoMLx_Classification_Text.html#AutoML">AutoML</a><ul>
+<li><a href="http://automl.oraclecorp.com/multiversion/v24.2.0/_static/notebooks/OracleAutoMLx_Classification_Text.html#Engine">Set the engine</a></li>
+<li><a href="http://automl.oraclecorp.com/multiversion/v24.2.0/_static/notebooks/OracleAutoMLx_Classification_Text.html#provider">Create an Instance of Oracle AutoMLx</a></li>
+<li><a href="http://automl.oraclecorp.com/multiversion/v24.2.0/_static/notebooks/OracleAutoMLx_Classification_Text.html#default">Train a Model using AutoMLx</a></li>
+<li><a href="http://automl.oraclecorp.com/multiversion/v24.2.0/_static/notebooks/OracleAutoMLx_Classification_Text.html#analyze">Analyze the AutoMLX optimization process </a><ul>
+<li><a href="http://automl.oraclecorp.com/multiversion/v24.2.0/_static/notebooks/OracleAutoMLx_Classification_Text.html#algorithm-selection">Algorithm Selection</a></li>
+<li><a href="http://automl.oraclecorp.com/multiversion/v24.2.0/_static/notebooks/OracleAutoMLx_Classification_Text.html#adaptive-sampling">Adaptive Sampling</a></li>
+<li><a href="http://automl.oraclecorp.com/multiversion/v24.2.0/_static/notebooks/OracleAutoMLx_Classification_Text.html#feature-selection">Feature Selection</a></li>
+<li><a href="http://automl.oraclecorp.com/multiversion/v24.2.0/_static/notebooks/OracleAutoMLx_Classification_Text.html#hyperparameter-tuning">Hyperparameter Tuning</a></li>
 </ul>
 </li>
-<li><a href='#timebudget'>2.4. Specify a time budget to AutoML</a></li>
-<li><a href='#scoringfn'>2.5. Specify a different scoring metric to AutoML</a></li>
+<li><a href="http://automl.oraclecorp.com/multiversion/v24.2.0/_static/notebooks/OracleAutoMLx_Classification_Text.html#advanced">Advanced AutoML Configuration</a></li>
 </ul>
 </li>
-<li><a href='#MLX'>3. Machine Learning Explainability (MLX)</a><ul>
-<li><a href='#MLX-initialization'> 3.1. Initialize an MLExplainer</a></li>
-<li><a href='#MLX-global'> 3.2. Global Token Importance</a></li>
-<li><a href='#MLX-local'> 3.3. Local Token Importance</a></li>
+<li><a href="http://automl.oraclecorp.com/multiversion/v24.2.0/_static/notebooks/OracleAutoMLx_Classification_Text.html#MLX">Machine Learning Explainability (MLX)</a><ul>
+<li><a href="http://automl.oraclecorp.com/multiversion/v24.2.0/_static/notebooks/OracleAutoMLx_Classification_Text.html#MLX-initialization">Initialize an MLExplainer</a></li>
+<li><a href="http://automl.oraclecorp.com/multiversion/v24.2.0/_static/notebooks/OracleAutoMLx_Classification_Text.html#MLX-global">Global Token Importance</a></li>
+<li><a href="http://automl.oraclecorp.com/multiversion/v24.2.0/_static/notebooks/OracleAutoMLx_Classification_Text.html#MLX-local">Local Token Importance</a></li>
 </ul>
 </li>
-<li><a href='#ref'>References</a></li>
+<li><a href="http://automl.oraclecorp.com/multiversion/v24.2.0/_static/notebooks/OracleAutoMLx_Classification_Text.html#ref">References</a></li>
 </ul>
 
 </div>
@@ -14659,13 +14711,13 @@ <h2 id="Table-of-Contents">Table of Contents<a class="anchor-link" href="#Table-
 </div>
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 </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
-<p><a id='setup'></a></p>
-<h2 id="Setup">Setup<a class="anchor-link" href="#Setup">&#182;</a></h2><p>Basic setup for the Notebook.</p>
+<p><a id="setup"></a></p>
+<h2 id="Setup">Setup<a class="anchor-link" href="http://automl.oraclecorp.com/multiversion/v24.2.0/_static/notebooks/OracleAutoMLx_Classification_Text.html#Setup">¶</a></h2><p>Basic setup for the Notebook.</p>
 
 </div>
 </div>
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
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@@ -14673,9 +14725,7 @@ <h2 id="Setup">Setup<a class="anchor-link" href="#Setup">&#182;</a></h2><p>Basic
 <div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[1]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="o">!</span> pip install <span class="nv">seaborn</span><span class="o">==</span><span class="m">0</span>.12.1
-
-<span class="o">%</span><span class="k">matplotlib</span> inline
+<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
 <span class="o">%</span><span class="k">load_ext</span> autoreload
 <span class="o">%</span><span class="k">autoreload</span> 2
 </pre></div>
@@ -14685,45 +14735,6 @@ <h2 id="Setup">Setup<a class="anchor-link" href="#Setup">&#182;</a></h2><p>Basic
 </div>
 </div>
 
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-
-
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-    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
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-<pre>Requirement already satisfied: seaborn==0.12.1 in /scratch_user/ypushak/automl-3/automl/py_3.8.7/lib/python3.8/site-packages (0.12.1)
-Requirement already satisfied: pandas&gt;=0.25 in /scratch_user/ypushak/automl-3/automl/py_3.8.7/lib/python3.8/site-packages (from seaborn==0.12.1) (1.4.1)
-Requirement already satisfied: matplotlib!=3.6.1,&gt;=3.1 in /scratch_user/ypushak/automl-3/automl/py_3.8.7/lib/python3.8/site-packages (from seaborn==0.12.1) (3.5.1)
-Requirement already satisfied: numpy&gt;=1.17 in /scratch_user/ypushak/automl-3/automl/py_3.8.7/lib/python3.8/site-packages (from seaborn==0.12.1) (1.22.2)
-Requirement already satisfied: kiwisolver&gt;=1.0.1 in /scratch_user/ypushak/automl-3/automl/py_3.8.7/lib/python3.8/site-packages (from matplotlib!=3.6.1,&gt;=3.1-&gt;seaborn==0.12.1) (1.1.0)
-Requirement already satisfied: pyparsing&gt;=2.2.1 in /scratch_user/ypushak/automl-3/automl/py_3.8.7/lib/python3.8/site-packages (from matplotlib!=3.6.1,&gt;=3.1-&gt;seaborn==0.12.1) (2.4.7)
-Requirement already satisfied: python-dateutil&gt;=2.7 in /scratch_user/ypushak/automl-3/automl/py_3.8.7/lib/python3.8/site-packages (from matplotlib!=3.6.1,&gt;=3.1-&gt;seaborn==0.12.1) (2.8.2)
-Requirement already satisfied: packaging&gt;=20.0 in /scratch_user/ypushak/automl-3/automl/py_3.8.7/lib/python3.8/site-packages (from matplotlib!=3.6.1,&gt;=3.1-&gt;seaborn==0.12.1) (21.3)
-Requirement already satisfied: pillow&gt;=6.2.0 in /scratch_user/ypushak/automl-3/automl/py_3.8.7/lib/python3.8/site-packages (from matplotlib!=3.6.1,&gt;=3.1-&gt;seaborn==0.12.1) (9.4.0)
-Requirement already satisfied: fonttools&gt;=4.22.0 in /scratch_user/ypushak/automl-3/automl/py_3.8.7/lib/python3.8/site-packages (from matplotlib!=3.6.1,&gt;=3.1-&gt;seaborn==0.12.1) (4.29.1)
-Requirement already satisfied: cycler&gt;=0.10 in /scratch_user/ypushak/automl-3/automl/py_3.8.7/lib/python3.8/site-packages (from matplotlib!=3.6.1,&gt;=3.1-&gt;seaborn==0.12.1) (0.10.0)
-Requirement already satisfied: pytz&gt;=2020.1 in /scratch_user/ypushak/automl-3/automl/py_3.8.7/lib/python3.8/site-packages (from pandas&gt;=0.25-&gt;seaborn==0.12.1) (2022.1)
-Requirement already satisfied: six in /scratch_user/ypushak/automl-3/automl/py_3.8.7/lib/python3.8/site-packages (from cycler&gt;=0.10-&gt;matplotlib!=3.6.1,&gt;=3.1-&gt;seaborn==0.12.1) (1.16.0)
-Requirement already satisfied: setuptools in /scratch_user/ypushak/automl-3/automl/py_3.8.7/lib/python3.8/site-packages (from kiwisolver&gt;=1.0.1-&gt;matplotlib!=3.6.1,&gt;=3.1-&gt;seaborn==0.12.1) (62.3.4)
-
-<span class="ansi-bold">[</span><span class="ansi-blue-fg">notice</span><span class="ansi-bold">]</span> A new release of pip available: <span class="ansi-red-fg">22.2.2</span> -&gt; <span class="ansi-green-fg">23.0.1</span>
-<span class="ansi-bold">[</span><span class="ansi-blue-fg">notice</span><span class="ansi-bold">]</span> To update, run: <span class="ansi-green-fg">pip install --upgrade pip</span>
-</pre>
-</div>
-</div>
-
-</div>
-
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-
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 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
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@@ -14744,24 +14755,21 @@ <h2 id="Setup">Setup<a class="anchor-link" href="#Setup">&#182;</a></h2><p>Basic
 <div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[2]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">gzip</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
 <span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">f1_score</span>
 <span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
 <span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">fetch_20newsgroups</span>
+<span class="kn">import</span> <span class="nn">time</span>
+<span class="kn">import</span> <span class="nn">datetime</span>
 
 <span class="c1"># Settings for plots</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">&#39;figure.figsize&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">7</span><span class="p">]</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">&#39;font.size&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">15</span>
-<span class="n">sns</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">color_codes</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="n">sns</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">font_scale</span><span class="o">=</span><span class="mf">1.5</span><span class="p">)</span>
-<span class="n">sns</span><span class="o">.</span><span class="n">set_palette</span><span class="p">(</span><span class="s2">&quot;bright&quot;</span><span class="p">)</span>
-<span class="n">sns</span><span class="o">.</span><span class="n">set_style</span><span class="p">(</span><span class="s2">&quot;whitegrid&quot;</span><span class="p">)</span>
-
-<span class="kn">import</span> <span class="nn">automl</span>
-<span class="kn">from</span> <span class="nn">automl</span> <span class="kn">import</span> <span class="n">init</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">'figure.figsize'</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">7</span><span class="p">]</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">'font.size'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">15</span>
+
+<span class="kn">import</span> <span class="nn">automlx</span>
+<span class="kn">from</span> <span class="nn">automlx</span> <span class="kn">import</span> <span class="n">init</span>
 </pre></div>
 
      </div>
@@ -14776,8 +14784,8 @@ <h2 id="Setup">Setup<a class="anchor-link" href="#Setup">&#182;</a></h2><p>Basic
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
 </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
-<p><a id='load-data'></a></p>
-<h2 id="Load-the-20-News-Group-dataset">Load the 20 News Group dataset<a class="anchor-link" href="#Load-the-20-News-Group-dataset">&#182;</a></h2><p>We start by reading in the dataset from sklearn. The dataset has already been pre-split into training and test sets. The training set will be used to create a Machine Learning model using AutoML, and the test set will be used to evaluate the model's performance on unseen data.</p>
+<p><a id="load-data"></a></p>
+<h2 id="Load-the-20-News-Group-dataset">Load the 20 News Group dataset<a class="anchor-link" href="http://automl.oraclecorp.com/multiversion/v24.2.0/_static/notebooks/OracleAutoMLx_Classification_Text.html#Load-the-20-News-Group-dataset">¶</a></h2><p>We start by reading in the dataset from sklearn. The dataset has already been pre-split into training and test sets. The training set will be used to create a Machine Learning model using AutoMLx, and the test set will be used to evaluate the model's performance on unseen data.</p>
 
 </div>
 </div>
@@ -14790,15 +14798,15 @@ <h2 id="Load-the-20-News-Group-dataset">Load the 20 News Group dataset<a class="
 <div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[3]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">train</span> <span class="o">=</span> <span class="n">fetch_20newsgroups</span><span class="p">(</span><span class="n">subset</span><span class="o">=</span><span class="s1">&#39;train&#39;</span><span class="p">)</span>
-<span class="n">test</span> <span class="o">=</span> <span class="n">fetch_20newsgroups</span><span class="p">(</span><span class="n">subset</span><span class="o">=</span><span class="s1">&#39;test&#39;</span><span class="p">)</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">train</span> <span class="o">=</span> <span class="n">fetch_20newsgroups</span><span class="p">(</span><span class="n">subset</span><span class="o">=</span><span class="s1">'train'</span><span class="p">)</span>
+<span class="n">test</span> <span class="o">=</span> <span class="n">fetch_20newsgroups</span><span class="p">(</span><span class="n">subset</span><span class="o">=</span><span class="s1">'test'</span><span class="p">)</span>
 
 <span class="n">target_names</span> <span class="o">=</span> <span class="n">train</span><span class="o">.</span><span class="n">target_names</span>
 
 <span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">train</span><span class="o">.</span><span class="n">data</span><span class="p">),</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">train</span><span class="o">.</span><span class="n">target</span><span class="p">)</span>
 <span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">test</span><span class="o">.</span><span class="n">data</span><span class="p">),</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">test</span><span class="o">.</span><span class="n">target</span><span class="p">)</span>
 
-<span class="n">column_names</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;Message&quot;</span><span class="p">]</span>
+<span class="n">column_names</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"Message"</span><span class="p">]</span>
 <span class="n">X_train</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">column_names</span>
 <span class="n">X_test</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">column_names</span>
 </pre></div>
@@ -14852,26 +14860,26 @@ <h2 id="Load-the-20-News-Group-dataset">Load the 20 News Group dataset<a class="
 
 
 <div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
-<pre>[&#39;alt.atheism&#39;,
- &#39;comp.graphics&#39;,
- &#39;comp.os.ms-windows.misc&#39;,
- &#39;comp.sys.ibm.pc.hardware&#39;,
- &#39;comp.sys.mac.hardware&#39;,
- &#39;comp.windows.x&#39;,
- &#39;misc.forsale&#39;,
- &#39;rec.autos&#39;,
- &#39;rec.motorcycles&#39;,
- &#39;rec.sport.baseball&#39;,
- &#39;rec.sport.hockey&#39;,
- &#39;sci.crypt&#39;,
- &#39;sci.electronics&#39;,
- &#39;sci.med&#39;,
- &#39;sci.space&#39;,
- &#39;soc.religion.christian&#39;,
- &#39;talk.politics.guns&#39;,
- &#39;talk.politics.mideast&#39;,
- &#39;talk.politics.misc&#39;,
- &#39;talk.religion.misc&#39;]</pre>
+<pre>['alt.atheism',
+ 'comp.graphics',
+ 'comp.os.ms-windows.misc',
+ 'comp.sys.ibm.pc.hardware',
+ 'comp.sys.mac.hardware',
+ 'comp.windows.x',
+ 'misc.forsale',
+ 'rec.autos',
+ 'rec.motorcycles',
+ 'rec.sport.baseball',
+ 'rec.sport.hockey',
+ 'sci.crypt',
+ 'sci.electronics',
+ 'sci.med',
+ 'sci.space',
+ 'soc.religion.christian',
+ 'talk.politics.guns',
+ 'talk.politics.mideast',
+ 'talk.politics.misc',
+ 'talk.religion.misc']</pre>
 </div>
 
 </div>
@@ -14924,7 +14932,7 @@ <h2 id="Load-the-20-News-Group-dataset">Load the 20 News Group dataset<a class="
 
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 <div>
-<style scoped>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -14976,47 +14984,12 @@ <h2 id="Load-the-20-News-Group-dataset">Load the 20 News Group dataset<a class="
 
 </div>
 
-</div>
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-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
-<p>We restrict the data to the 4 topics that are related to science.</p>
-
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[6]:</div>
-<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-     <div class="CodeMirror cm-s-jupyter">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">science_labels</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">target_names</span><span class="p">))</span> <span class="k">if</span> <span class="s1">&#39;sci&#39;</span> <span class="ow">in</span> <span class="n">target_names</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span>
-<span class="n">train_science_indices</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">y_train</span><span class="p">))</span> <span class="k">if</span> <span class="n">y_train</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="ow">in</span> <span class="n">science_labels</span><span class="p">]</span>
-<span class="n">test_science_indices</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">y_test</span><span class="p">))</span> <span class="k">if</span> <span class="n">y_test</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="ow">in</span> <span class="n">science_labels</span><span class="p">]</span>
-
-<span class="n">X_train</span> <span class="o">=</span> <span class="n">X_train</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">train_science_indices</span><span class="p">]</span>
-<span class="n">y_train</span> <span class="o">=</span> <span class="n">y_train</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">train_science_indices</span><span class="p">]</span>
-<span class="n">X_test</span> <span class="o">=</span> <span class="n">X_test</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">test_science_indices</span><span class="p">]</span>
-<span class="n">y_test</span> <span class="o">=</span> <span class="n">y_test</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">test_science_indices</span><span class="p">]</span>
-</pre></div>
-
-     </div>
-</div>
-</div>
-</div>
-
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[7]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[6]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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 <div class=" highlight hl-ipython3"><pre><span></span><span class="nb">len</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>
@@ -15037,13 +15010,13 @@ <h2 id="Load-the-20-News-Group-dataset">Load the 20 News Group dataset<a class="
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-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[7]:</div>
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[6]:</div>
 
 
 
 
 <div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
-<pre>2373</pre>
+<pre>11314</pre>
 </div>
 
 </div>
@@ -15069,11 +15042,11 @@ <h2 id="Load-the-20-News-Group-dataset">Load the 20 News Group dataset<a class="
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+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[7]:</div>
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 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">X_train</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span>
-                                          <span class="n">test_size</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
+                                          <span class="n">test_size</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span>
                                           <span class="n">stratify</span><span class="o">=</span><span class="n">y_train</span><span class="p">,</span>
                                           <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
 </pre></div>
@@ -15100,7 +15073,7 @@ <h2 id="Load-the-20-News-Group-dataset">Load the 20 News Group dataset<a class="
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[9]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[8]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">X_train</span><span class="p">,</span> <span class="n">X_valid</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_valid</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span>
@@ -15121,78 +15094,11 @@ <h2 id="Load-the-20-News-Group-dataset">Load the 20 News Group dataset<a class="
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-<p><a id='AutoML'></a></p>
-<h2 id="AutoML">AutoML<a class="anchor-link" href="#AutoML">&#182;</a></h2>
-</div>
-</div>
-</div>
-</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
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-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
-<p><a id='Engine'></a></p>
-<h3 id="Setting-the-engine-and-deprecation-warnings">Setting the engine and deprecation warnings<a class="anchor-link" href="#Setting-the-engine-and-deprecation-warnings">&#182;</a></h3><p>The AutoML pipeline offers the function <code>init</code>, which allows to initialize the parallelization engine.</p>
-
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[10]:</div>
-<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-     <div class="CodeMirror cm-s-jupyter">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">init</span><span class="p">(</span><span class="n">engine</span><span class="o">=</span><span class="s1">&#39;dask&#39;</span><span class="p">)</span>
-</pre></div>
-
-     </div>
-</div>
+<p><a id="AutoML"></a></p>
+<h2 id="AutoML">AutoML<a class="anchor-link" href="http://automl.oraclecorp.com/multiversion/v24.2.0/_static/notebooks/OracleAutoMLx_Classification_Text.html#AutoML">¶</a></h2>
 </div>
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-<pre>[2023-03-22 09:11:28,653] [automl.xengine] Using Dask Execution
-</pre>
 </div>
-</div>
-
-<div class="jp-OutputArea-child">
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-<pre>/scratch_user/ypushak/automl-3/automl/package/automl/interface/default.py:79: AutoMLxDeprecationWarning: engine dask is deprecated and will be removed in version 23.3.0.
-  warn(f&#39;engine {engine} is deprecated and will be removed in version 23.3.0.&#39;, AutoMLxDeprecationWarning)
-/scratch_user/ypushak/automl-3/automl/package/automl/interface/default.py:82: AutoMLxDeprecationWarning: engine option dask_scheduler is deprecated and will be removed in version 23.3.0
-  warn(f&#39;engine option {engine_opt} is deprecated and will be removed in version 23.3.0&#39;, AutoMLxDeprecationWarning)
-</pre>
-</div>
-</div>
-
-</div>
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-
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@@ -15200,8 +15106,8 @@ <h3 id="Setting-the-engine-and-deprecation-warnings">Setting the engine and depr
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-<p>As you can see from the above deprecation warning, dask and it's related configuration options are deprecated, and will be removed in AutoMLx version 23.3.0. This, along with a few other API changes, are coming in 23.3.0. By default, the AutoMLx package is configured to display deprecation warnings for all such changes. However, they can be disabled for any newly-created AutoMLx objects via <code>init</code>.</p>
-<p>We will also switch to the <code>'local'</code> parallelization engine, which uses python's multiprocessing library for parallelism instead.</p>
+<p><a id="Engine"></a></p>
+<h3 id="Setting-the-execution-engine">Setting the execution engine<a class="anchor-link" href="http://automl.oraclecorp.com/multiversion/v24.2.0/_static/notebooks/OracleAutoMLx_Classification_Text.html#Setting-the-execution-engine">¶</a></h3><p>The AutoML package offers the function <code>init</code>, which allows to initialize the parallelization engine.</p>
 
 </div>
 </div>
@@ -15211,10 +15117,10 @@ <h3 id="Setting-the-engine-and-deprecation-warnings">Setting the engine and depr
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[11]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[9]:</div>
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-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">init</span><span class="p">(</span><span class="n">engine</span><span class="o">=</span><span class="s1">&#39;local&#39;</span><span class="p">,</span> <span class="n">check_deprecation_warnings</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">init</span><span class="p">(</span><span class="n">engine</span><span class="o">=</span><span class="s1">'ray'</span><span class="p">)</span>
 </pre></div>
 
      </div>
@@ -15236,8 +15142,7 @@ <h3 id="Setting-the-engine-and-deprecation-warnings">Setting the engine and depr
 
 
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-<pre>[2023-03-22 09:11:30,484] [automl.interface] Execution engine (local) has already been initialized. Reinitializing!
-[2023-03-22 09:11:30,925] [automl.xengine] Local ProcessPool execution (n_jobs=40)
+<pre>[2024-05-23 09:56:52,788] [automlx.backend] Overwriting ray session directory to /tmp/zdvafa74/ray, which will be deleted at engine shutdown. If you wish to retain ray logs, provide _temp_dir in ray_setup dict of engine_opts when initializing the AutoMLx engine.
 </pre>
 </div>
 </div>
@@ -15253,13 +15158,14 @@ <h3 id="Setting-the-engine-and-deprecation-warnings">Setting the engine and depr
 </div>
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 </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
-<p><a id='provider'></a></p>
-<h3 id="Create-an-instance-of-Oracle-AutoMLx">Create an instance of Oracle AutoMLx<a class="anchor-link" href="#Create-an-instance-of-Oracle-AutoMLx">&#182;</a></h3><p>The Oracle AutoMLx solution provides a pipeline that automatically finds a tuned model given a prediction task and a training dataset. In particular it allows to find a tuned model for any supervised prediction task, e.g. classification or regression where the target can be binary, categorical or real-valued.</p>
-<p>AutoML consists of five main modules:</p>
+<p><a id="provider"></a></p>
+<h3 id="Create-an-instance-of-Oracle-AutoMLx">Create an instance of Oracle AutoMLx<a class="anchor-link" href="http://automl.oraclecorp.com/multiversion/v24.2.0/_static/notebooks/OracleAutoMLx_Classification_Text.html#Create-an-instance-of-Oracle-AutoMLx">¶</a></h3><p>The Oracle AutoMLx solution provides a pipeline that automatically finds a tuned model given a prediction task and a training dataset. In particular it allows to find a tuned model for any supervised prediction task, for example: classification or regression where the target can be binary, categorical or real-valued.</p>
+<p>AutoML consists of five main steps:</p>
 <ul>
 <li><strong>Preprocessing</strong> (Feature extraction and selection) : The pipeline extracts tabular features using <a href="https://en.wikipedia.org/wiki/Tf%E2%80%93idf">TFIDF</a> that it then selects between.</li>
-<li><strong>Algorithm Selection</strong> : Identify the right classification algorithm -in this notebook- for a given dataset, choosing from amongst:<ul>
+<li><strong>Algorithm Selection</strong> : Identify the right classification algorithm for a given dataset, choosing from amongst:<ul>
 <li>AdaBoostClassifier</li>
+<li>CatBoostClassifier</li>
 <li>DecisionTreeClassifier</li>
 <li>ExtraTreesClassifier</li>
 <li>TorchMLPClassifier</li>
@@ -15271,13 +15177,14 @@ <h3 id="Create-an-instance-of-Oracle-AutoMLx">Create an instance of Oracle AutoM
 <li>SVC</li>
 <li>XGBClassifier</li>
 <li>GaussianNB</li>
+<li>TabNetClassifier</li>
 </ul>
 </li>
 <li><strong>Adaptive Sampling</strong> : Select a subset of the data samples for the model to be trained on.</li>
 <li><strong>Feature Selection</strong> : Select a subset of the features (extracted with TFIDF), based on the previously selected model. In the rest of this notebook, we will implicitly refer to the extracted features as data features.</li>
 <li><strong>Hyperparameter Tuning</strong> : Find the right model parameters that maximize score for the given dataset.</li>
 </ul>
-<p>All these pieces are readily combined into a simple AutoML pipeline which automates the entire Machine Learning process with minimal user input/interaction.</p>
+<p>All these pieces are readily combined into a simple AutoMLx pipeline which automates the entire Machine Learning process with minimal user input/interaction.</p>
 
 </div>
 </div>
@@ -15289,8 +15196,8 @@ <h3 id="Create-an-instance-of-Oracle-AutoMLx">Create an instance of Oracle AutoM
 </div>
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 </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
-<p><a id='default'></a></p>
-<h3 id="Train-a-model-using-AutoML">Train a model using AutoML<a class="anchor-link" href="#Train-a-model-using-AutoML">&#182;</a></h3><p>The AutoML API is quite simple to work with. We create an instance of the pipeline. Next, the training data is passed to the <code>fit()</code> function which executes the three previously mentioned steps.</p>
+<p><a id="default"></a></p>
+<h3 id="Train-a-model-using-AutoMLx">Train a model using AutoMLx<a class="anchor-link" href="http://automl.oraclecorp.com/multiversion/v24.2.0/_static/notebooks/OracleAutoMLx_Classification_Text.html#Train-a-model-using-AutoMLx">¶</a></h3><p>The AutoMLx API is quite simple to work with. We create an instance of the pipeline. Next, the training data is passed to the <code>fit()</code> function which executes the four previously mentioned steps.</p>
 <p>A model is then generated and can be used for prediction tasks. We use the roc_auc scoring metric to evaluate the performance of this model on unseen data (<code>X_test</code>).</p>
 
 </div>
@@ -15301,16 +15208,16 @@ <h3 id="Train-a-model-using-AutoML">Train a model using AutoML<a class="anchor-l
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[12]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[10]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">est1</span> <span class="o">=</span> <span class="n">automl</span><span class="o">.</span><span class="n">Pipeline</span><span class="p">(</span><span class="n">task</span><span class="o">=</span><span class="s1">&#39;classification&#39;</span><span class="p">)</span>
-<span class="n">est1</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_valid</span><span class="p">,</span> <span class="n">y_valid</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">col_types</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;text&#39;</span><span class="p">])</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">est1</span> <span class="o">=</span> <span class="n">automlx</span><span class="o">.</span><span class="n">Pipeline</span><span class="p">(</span><span class="n">task</span><span class="o">=</span><span class="s1">'classification'</span><span class="p">)</span>
+<span class="n">est1</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_valid</span><span class="p">,</span> <span class="n">y_valid</span><span class="p">,</span> <span class="n">col_types</span><span class="o">=</span><span class="p">[</span><span class="s1">'text'</span><span class="p">])</span>
 
 <span class="n">y_predict</span> <span class="o">=</span> <span class="n">est1</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
-<span class="n">score_default</span> <span class="o">=</span> <span class="n">f1_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_predict</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="s2">&quot;micro&quot;</span><span class="p">)</span>
+<span class="n">score_default</span> <span class="o">=</span> <span class="n">f1_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_predict</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="s2">"micro"</span><span class="p">)</span>
 
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;F1 Micro Score on test data: </span><span class="si">{:3.3f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">score_default</span><span class="p">))</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">'F1 Micro Score on test data: </span><span class="si">{:3.3f}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">score_default</span><span class="p">))</span>
 </pre></div>
 
      </div>
@@ -15332,23 +15239,36 @@ <h3 id="Train-a-model-using-AutoML">Train a model using AutoML<a class="anchor-l
 
 
 <div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
-<pre>[2023-03-22 09:11:31,698] [automl.pipeline] Random state (7) is used for model builds
-[2023-03-22 09:11:31,965] [automl.pipeline] Dataset shape: (948, 1)
-[2023-03-22 09:11:32,011] [automl.pipeline] Running Auto-Preprocessing
-[2023-03-22 09:11:34,034] [automl.pipeline] Preprocessing completed. Updated Dataset shape: (948, 11026), cv: None
-[2023-03-22 09:11:38,804] [automl.pipeline] SVC, KNeighborsClassifier are disabled for datasets with &gt; 10K samples or &gt; 1K features
-[2023-03-22 09:11:38,805] [automl.pipeline] Running Model Selection
-[2023-03-22 09:12:22,758] [automl.pipeline] Model Selection completed. Selected model: [&#39;TorchMLPClassifier&#39;]
-[2023-03-22 09:12:22,760] [automl.pipeline] Running Adaptive Sampling. Dataset Shape: (948, 11026), Valid Shape: (238, 11026), CV: None, Class counts: [237 237 237 237]
-[2023-03-22 09:13:40,825] [automl.pipeline] Adaptive Sampling Completed. Updated Dataset Shape: (948, 11026), Valid Shape: (238, 11026), CV: None, Class counts: [237 237 237 237]
-[2023-03-22 09:13:40,826] [automl.pipeline] Starting Feature Selection 0. Dataset Shape: (948, 11026)
-[2023-03-22 09:16:13,915] [automl.pipeline] Feature Selection 0 completed. Updated Dataset shape: (948, 3972)
-[2023-03-22 09:16:13,987] [automl.pipeline] Tuning TorchMLPClassifier
-[2023-03-22 09:17:36,774] [automl.pipeline] Tuning completed. Best params: {&#39;activation&#39;: &#39;relu&#39;, &#39;class_weight&#39;: None, &#39;dropout&#39;: 0.1, &#39;l2_reg&#39;: 0.0, &#39;nr_layers&#39;: 3, &#39;nr_neurons&#39;: 100, &#39;optimizer&#39;: &#39;adagrad&#39;}
-[2023-03-22 09:17:42,692] [automl.pipeline] (Re)fitting Pipeline
-[2023-03-22 09:18:05,836] [automl.xengine] Local ProcessPool execution (n_jobs=40)
-[2023-03-22 09:18:06,041] [automl.pipeline] AutoML completed. Time taken - 387.478 sec
-F1 Micro Score on test data: 0.913
+<pre>[2024-05-23 09:56:57,946] [automlx.interface] Dataset shape: (3394,1)
+[2024-05-23 09:57:01,123] [sanerec.autotuning.parameter] Hyperparameter epsilon autotune range is set to its validation range. This could lead to long training times
+[2024-05-23 09:57:01,147] [sanerec.autotuning.parameter] Hyperparameter repeat_quality_threshold autotune range is set to its validation range. This could lead to long training times
+[2024-05-23 09:57:01,154] [sanerec.autotuning.parameter] Hyperparameter scope autotune range is set to its validation range. This could lead to long training times
+[2024-05-23 09:57:01,487] [automlx.data_transform] Running preprocessing. Number of features: 2
+[2024-05-23 09:57:16,738] [automlx.data_transform] Preprocessing completed. Took 15.251 secs
+[2024-05-23 09:57:50,052] [automlx.process] Running Model Generation
+[2024-05-23 09:57:50,345] [automlx.process] CatBoostClassifier is disabled. The CatBoostClassifier model is only recommended for datasets with less than 10000 features.
+[2024-05-23 09:57:50,349] [automlx.process] ExtraTreesClassifier is disabled. The ExtraTreesClassifier model is only recommended for datasets with less than 10000 features.
+[2024-05-23 09:57:50,353] [automlx.process] KNeighborsClassifier is disabled. The KNeighborsClassifier model is only recommended for datasets with less than 10000 samples and 1000 features.
+[2024-05-23 09:57:50,358] [automlx.process] RandomForestClassifier is disabled. The RandomForestClassifier model is only recommended for datasets with less than 10000 features.
+[2024-05-23 09:57:50,360] [automlx.process] SVC is disabled. The SVC model is only recommended for datasets with less than 10000 samples and 1000 features.
+[2024-05-23 09:57:50,364] [automlx.process] XGBClassifier is disabled. The XGBClassifier model is only recommended for datasets with less than 5000 features.
+[2024-05-23 09:57:50,366] [automlx.process] LogisticRegressionClassifier is disabled. The LogisticRegressionClassifier model is only recommended for datasets with less than 5000 features.
+[2024-05-23 09:57:50,368] [automlx.process] Model Generation completed.
+[2024-05-23 09:57:50,472] [automlx.model_selection] Running Model Selection
+[2024-05-23 10:13:18,091] [automlx.model_selection] Model Selection completed - Took 927.619 sec - Selected models: ['LGBMClassifier']
+[2024-05-23 10:13:18,704] [automlx.adaptive_sampling] Running Adaptive Sampling. Dataset shape: (3394,25239).
+[2024-05-23 10:13:21,982] [automlx.adaptive_sampling] Adaptive Sampling: top_limit: 144 &lt; bottom_limit: 1000,
+sampling process will be skipped
+[2024-05-23 10:13:21,995] [automlx.adaptive_sampling] Adaptive Sampling: top_limit: 36 &lt; bottom_limit: 1000,
+sampling process will be skipped
+[2024-05-23 10:13:22,295] [automlx.feature_selection] Starting feature ranking for LGBMClassifier
+[2024-05-23 10:37:23,506] [automlx.feature_selection] Feature Selection completed. Took 1441.215 secs.
+[2024-05-23 10:37:27,037] [automlx.trials] Running Model Tuning for ['LGBMClassifier']
+[2024-05-23 10:40:58,453] [automlx.trials] Best parameters for LGBMClassifier: {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.00119999, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}
+[2024-05-23 10:40:58,455] [automlx.trials] Model Tuning completed. Took: 211.419 secs
+[2024-05-23 10:41:25,079] [automlx.interface] Re-fitting pipeline
+[2024-05-23 10:42:06,170] [automlx.interface] AutoMLx completed.
+F1 Micro Score on test data: 0.699
 </pre>
 </div>
 </div>
@@ -15364,12 +15284,12 @@ <h3 id="Train-a-model-using-AutoML">Train a model using AutoML<a class="anchor-l
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
 </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
-<p><a id='analyze'></a></p>
-<h3 id="Analyze-the-AutoML-optimization-process">Analyze the AutoML optimization process<a class="anchor-link" href="#Analyze-the-AutoML-optimization-process">&#182;</a></h3><p>During the AutoML process, a summary of the optimization process is logged. It consists of:</p>
+<p><a id="analyze"></a></p>
+<h3 id="Analyze-the-AutoMLx-optimization-process">Analyze the AutoMLx optimization process<a class="anchor-link" href="http://automl.oraclecorp.com/multiversion/v24.2.0/_static/notebooks/OracleAutoMLx_Classification_Text.html#Analyze-the-AutoMLx-optimization-process">¶</a></h3><p>During the AutoML process, a summary of the optimization process is logged. It consists of:</p>
 <ul>
 <li>Information about the training data</li>
-<li>Information about the AutoML Pipeline, such as:<ul>
-<li>selected features that AutoML found to be most predictive in the training data;</li>
+<li>Information about the AutoMLx Pipeline, such as:<ul>
+<li>selected features that AutoMLx found to be most predictive in the training data;</li>
 <li>selected algorithm that was the best choice for this data;</li>
 <li>hyperparameters for the selected algorithm.</li>
 </ul>
@@ -15386,7 +15306,7 @@ <h3 id="Analyze-the-AutoML-optimization-process">Analyze the AutoML optimization
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
 </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
-<p>AutoML provides a print_summary API to output all the different trials performed.</p>
+<p>AutoMLx provides a print_summary API to output all the different trials performed.</p>
 
 </div>
 </div>
@@ -15396,7 +15316,7 @@ <h3 id="Analyze-the-AutoML-optimization-process">Analyze the AutoML optimization
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[13]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[11]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">est1</span><span class="o">.</span><span class="n">print_summary</span><span class="p">()</span>
@@ -15422,55 +15342,7 @@ <h3 id="Analyze-the-AutoML-optimization-process">Analyze the AutoML optimization
 
 
 <div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output " data-mime-type="text/html">
-<table border="1" class="dataframe">
-  <tbody>
-    <tr>
-      <td>Training Dataset size</td>
-      <td>(948, 1)</td>
-    </tr>
-    <tr>
-      <td>Validation Dataset size</td>
-      <td>(238, 1)</td>
-    </tr>
-    <tr>
-      <td>CV</td>
-      <td>None</td>
-    </tr>
-    <tr>
-      <td>Optimization Metric</td>
-      <td>neg_log_loss</td>
-    </tr>
-    <tr>
-      <td>Selected Features</td>
-      <td>Index(['001230', '004418', '00r', '01', '01wb', '02', '022922', '032623',
-       '0358', '0366',
-       ...
-       'za', 'zabriskie', 'zcomm', 'zenier', 'zikzak', 'zisfein', 'zmodem',
-       'zoo', 'zoology', 'zrepachol'],
-      dtype='object', length=3972)</td>
-    </tr>
-    <tr>
-      <td>Selected Algorithm</td>
-      <td>TorchMLPClassifier</td>
-    </tr>
-    <tr>
-      <td>Time taken</td>
-      <td>364.896</td>
-    </tr>
-    <tr>
-      <td>Selected Hyperparameters</td>
-      <td>{'hidden_layer_sizes': [100, 100, 100], 'dropout': 0.1, 'activation': 'relu', 'loss': 'default', 'optimizer': 'adagrad', 'n_epochs': 200, 'batch_size': 256, 'l2_reg': 0.0, 'class_weight': None, 'random_state': 7}</td>
-    </tr>
-    <tr>
-      <td>AutoML version</td>
-      <td>23.1.1</td>
-    </tr>
-    <tr>
-      <td>Python version</td>
-      <td>3.8.7 (default, Aug 25 2022, 13:59:56) \n[GCC 8.5.0 20210514 (Red Hat 8.5.0-10.1.0.1)]</td>
-    </tr>
-  </tbody>
-</table>
+<br><div style="text-align: center; font-weight: bold; font-size: 16px;">General Summary</div>
 </div>
 
 </div>
@@ -15483,201 +15355,76 @@ <h3 id="Analyze-the-AutoML-optimization-process">Analyze the AutoML optimization
 
 
 <div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output " data-mime-type="text/html">
+<div>
+<style scoped="">
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
 <table border="1" class="dataframe">
   <thead>
     <tr style="text-align: right;">
-      <th>Algorithm</th>
-      <th>#Samples</th>
-      <th>#Features</th>
-      <th>Mean Validation Score</th>
-      <th>Hyperparameters</th>
-      <th>CPU Time</th>
-      <th>Memory Usage (GB)</th>
+      <th></th>
+      <th></th>
     </tr>
   </thead>
   <tbody>
     <tr>
-      <td>TorchMLPClassifier_HT</td>
-      <td>948</td>
-      <td>3972</td>
-      <td>-0.0589</td>
-      <td>{'activation': 'relu', 'class_weight': None, 'dropout': 0.1, 'l2_reg': 0.0, 'nr_layers': 3, 'nr_neurons': 100, 'optimizer': 'adagrad'}</td>
-      <td>15.5464</td>
-      <td>(0.007289886474609375, None)</td>
-    </tr>
-    <tr>
-      <td>TorchMLPClassifier_HT</td>
-      <td>948</td>
-      <td>3972</td>
-      <td>-0.0740</td>
-      <td>{'activation': 'relu', 'class_weight': None, 'dropout': 0.0012425681319433632, 'l2_reg': 0.0, 'nr_layers': 3, 'nr_neurons': 100, 'optimizer': 'adam'}</td>
-      <td>14.4130</td>
-      <td>(6.103515625e-05, None)</td>
-    </tr>
-    <tr>
-      <td>TorchMLPClassifier_HT</td>
-      <td>948</td>
-      <td>3972</td>
-      <td>-0.0740</td>
-      <td>{'activation': 'relu', 'class_weight': None, 'dropout': 1e-05, 'l2_reg': 0.0, 'nr_layers': 3, 'nr_neurons': 100, 'optimizer': 'adam'}</td>
-      <td>15.2612</td>
-      <td>(0.000148773193359375, None)</td>
-    </tr>
-    <tr>
-      <td>TorchMLPClassifier_HT</td>
-      <td>948</td>
-      <td>3972</td>
-      <td>-0.0740</td>
-      <td>{'activation': 'relu', 'class_weight': 'balanced', 'dropout': 0.1, 'l2_reg': 0.0, 'nr_layers': 3, 'nr_neurons': 100, 'optimizer': 'adam'}</td>
-      <td>15.6056</td>
-      <td>(0.0, None)</td>
-    </tr>
-    <tr>
-      <td>TorchMLPClassifier_HT</td>
-      <td>948</td>
-      <td>3972</td>
-      <td>-0.0740</td>
-      <td>{'activation': 'relu', 'class_weight': None, 'dropout': 0.1, 'l2_reg': 0.0, 'nr_layers': 3, 'nr_neurons': 100, 'optimizer': 'adam'}</td>
-      <td>17.4901</td>
-      <td>(3.0517578125e-05, None)</td>
-    </tr>
-    <tr>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-    </tr>
-    <tr>
-      <td>TorchMLPClassifier_AdaBoostClassifier_FS</td>
-      <td>948</td>
-      <td>1</td>
-      <td>-1.4784</td>
-      <td>{'activation': 'relu', 'class_weight': None, 'dropout': 0.1, 'l2_reg': 0.0, 'nr_layers': 1, 'nr_neurons': 100, 'optimizer': 'adam'}</td>
-      <td>0.5320</td>
-      <td>0.0</td>
+      <th>Training Dataset size</th>
+      <td>(2715, 1)</td>
     </tr>
     <tr>
-      <td>GaussianNB_AS</td>
-      <td>948</td>
-      <td>11026</td>
-      <td>-2.6122</td>
-      <td>{}</td>
-      <td>0.2168</td>
-      <td>0.0</td>
+      <th>Validation Dataset size</th>
+      <td>(679, 1)</td>
+    </tr>
+    <tr>
+      <th>CV</th>
+      <td>ManualSplit(Shuffle=False, Seed=7)</td>
+    </tr>
+    <tr>
+      <th>Optimization Metric</th>
+      <td>neg_log_loss</td>
     </tr>
     <tr>
-      <td>TorchMLPClassifier_HT</td>
-      <td>948</td>
-      <td>3972</td>
-      <td>-8.2554</td>
-      <td>{'activation': 'tanh', 'class_weight': 'balanced', 'dropout': 5e-06, 'l2_reg': 0.0, 'nr_layers': 3, 'nr_neurons': 133, 'optimizer': 'rmsprop'}</td>
-      <td>20.4976</td>
-      <td>(0.0020904541015625, None)</td>
+      <th>Selected Algorithm</th>
+      <td>LGBMClassifier</td>
     </tr>
     <tr>
-      <td>DecisionTreeClassifier_AS</td>
-      <td>948</td>
-      <td>11026</td>
-      <td>-10.0133</td>
-      <td>{'class_weight': None, 'max_features': 1.0, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125}</td>
-      <td>0.5121</td>
-      <td>0.0</td>
+      <th>Selected Hyperparameters</th>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.00119999, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
     </tr>
     <tr>
-      <td>TorchMLPClassifier_HT</td>
-      <td>948</td>
-      <td>3972</td>
-      <td>-12.1224</td>
-      <td>{'activation': 'relu', 'class_weight': None, 'dropout': 0.1, 'l2_reg': 0.0, 'nr_layers': 3, 'nr_neurons': 100, 'optimizer': 'rmsprop'}</td>
-      <td>16.5498</td>
-      <td>(0.0, None)</td>
+      <th>Python version</th>
+      <td>3.8.7 (default, Aug 25 2022, 13:59:56) \n[GCC 8.5.0 20210514 (Red Hat 8.5.0-10.1.0.1)]</td>
     </tr>
   </tbody>
 </table>
 </div>
-
-</div>
-
-</div>
-
 </div>
 
 </div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
-<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
-</div>
-<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
-<p>We also provide the capability to visualize the results of each stage of the AutoML pipeline.</p>
 
-</div>
-</div>
-</div>
-</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
-<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
-</div>
-<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
-<p><a id='algorithm-selection'></a></p>
-<h4 id="Algorithm-Selection">Algorithm Selection<a class="anchor-link" href="#Algorithm-Selection">&#182;</a></h4><p>The plot below shows the scores predicted by Algorithm Selection for each algorithm. The horizontal line shows the average score across all algorithms. Algorithms below the line are colored turquoise, whereas those with a score higher than the mean are colored teal. Here we can see that the <code>TorchMLPClassifier</code> achieved the highest predicted score (orange bar), and is chosen for subsequent stages of the Pipeline.</p>
+<div class="jp-OutputArea-child">
 
-</div>
-</div>
-</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
-<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
-</div>
-<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[14]:</div>
-<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-     <div class="CodeMirror cm-s-jupyter">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Each trial is a tuple of</span>
-<span class="c1"># (algorithm, no. samples, no. features, mean CV score, hyperparameters, </span>
-<span class="c1"># all CV scores, total CV time (s), memory usage (Gb))</span>
-<span class="n">trials</span> <span class="o">=</span> <span class="n">est1</span><span class="o">.</span><span class="n">model_selection_trials_</span>
-<span class="n">colors</span> <span class="o">=</span> <span class="p">[]</span>
-<span class="n">scores</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">trials</span><span class="p">]</span>
-<span class="n">models</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">trials</span><span class="p">]</span>
-<span class="n">y_margin</span> <span class="o">=</span> <span class="mf">0.10</span> <span class="o">*</span> <span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="n">scores</span><span class="p">)</span> <span class="o">-</span> <span class="nb">min</span><span class="p">(</span><span class="n">scores</span><span class="p">))</span>
-<span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">scores</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="n">models</span><span class="p">)</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
 
-<span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">s</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
-    <span class="k">if</span> <span class="n">f</span> <span class="o">==</span> <span class="s1">&#39;</span><span class="si">{}</span><span class="s1">_AS&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">est1</span><span class="o">.</span><span class="n">selected_model_</span><span class="p">):</span>
-        <span class="n">colors</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;orange&#39;</span><span class="p">)</span>
-    <span class="k">elif</span> <span class="n">s</span><span class="p">[</span><span class="n">f</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">s</span><span class="o">.</span><span class="n">mean</span><span class="p">():</span>
-        <span class="n">colors</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;teal&#39;</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">colors</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;turquoise&#39;</span><span class="p">)</span>
-        
 
-<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Algorithm Selection Trials&quot;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="nb">min</span><span class="p">(</span><span class="n">scores</span><span class="p">)</span> <span class="o">-</span> <span class="n">y_margin</span><span class="p">,</span> <span class="nb">max</span><span class="p">(</span><span class="n">scores</span><span class="p">)</span> <span class="o">+</span> <span class="n">y_margin</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="n">est1</span><span class="o">.</span><span class="n">inferred_score_metric</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-<span class="n">s</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">colors</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">axhline</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="n">s</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
 
-     </div>
-</div>
-</div>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output " data-mime-type="text/html">
+<br><div style="text-align: center; font-weight: bold; font-size: 16px;">Trials Summary</div>
 </div>
 
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
 
-
-<div class="jp-OutputArea jp-Cell-outputArea">
-
 <div class="jp-OutputArea-child">
 
     
@@ -15685,234 +15432,4084 @@ <h4 id="Algorithm-Selection">Algorithm Selection<a class="anchor-link" href="#Al
 
 
 
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output " data-mime-type="text/html">
+<div>
+<style scoped="">
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
 
-<div class="jp-RenderedImage jp-OutputArea-output ">
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
-"
-class="
-"
->
-</div>
-
-</div>
-
-</div>
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
 
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Step</th>
+      <th># Samples</th>
+      <th># Features</th>
+      <th>Algorithm</th>
+      <th>Hyperparameters</th>
+      <th>Score (neg_log_loss)</th>
+      <th>Runtime (Seconds)</th>
+      <th>Memory Usage (GB)</th>
+      <th>Finished</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>Model Selection</td>
+      <td>2715</td>
+      <td>25238</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8177</td>
+      <td>13.8053</td>
+      <td>1.2231</td>
+      <td>Thu May 23 09:58:12 2024</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>Model Selection</td>
+      <td>2715</td>
+      <td>25238</td>
+      <td>TorchMLPClassifier</td>
+      <td>{'optimizer_class': 'Adam', 'shuffle_dataset_each_epoch': True, 'optimizer_params': {}, 'criterion_class': None, 'criterion_params': {}, 'scheduler_class': None, 'scheduler_params': {}, 'batch_size': 128, 'lr': 0.001, 'epochs': 18, 'input_transform': 'auto', 'tensorboard_dir': None, 'use_tqdm': None, 'prediction_batch_size': 128, 'prediction_input_transform': 'auto', 'shuffling_buffer_size': None, 'depth': 4, 'num_logits': 1000, 'div_factor': 2, 'activation': 'ReLU', 'dropout': 0.1}</td>
+      <td>-2.9938</td>
+      <td>915.1965</td>
+      <td>1.5376</td>
+      <td>Thu May 23 10:13:14 2024</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>Model Selection</td>
+      <td>2715</td>
+      <td>25238</td>
+      <td>GaussianNB</td>
+      <td>{}</td>
+      <td>-7.644</td>
+      <td>11.5540</td>
+      <td>1.4871</td>
+      <td>Thu May 23 09:58:09 2024</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>Model Selection</td>
+      <td>2715</td>
+      <td>25238</td>
+      <td>DecisionTreeClassifier</td>
+      <td>{'min_samples_split': 0.00125, 'min_samples_leaf': 0.000625, 'max_features': 1.0, 'class_weight': None}</td>
+      <td>-15.4821</td>
+      <td>12.6717</td>
+      <td>1.2525</td>
+      <td>Thu May 23 09:58:10 2024</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8081</td>
+      <td>25.3911</td>
+      <td>1.8933</td>
+      <td>Thu May 23 10:29:20 2024</td>
+    </tr>
+    <tr>
+      <th>5</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>9885</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8153</td>
+      <td>20.3971</td>
+      <td>2.6275</td>
+      <td>Thu May 23 10:31:25 2024</td>
+    </tr>
+    <tr>
+      <th>6</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>11862</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8173</td>
+      <td>22.7057</td>
+      <td>1.8509</td>
+      <td>Thu May 23 10:31:16 2024</td>
+    </tr>
+    <tr>
+      <th>7</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>20499</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8188</td>
+      <td>27.9225</td>
+      <td>2.7320</td>
+      <td>Thu May 23 10:28:37 2024</td>
+    </tr>
+    <tr>
+      <th>8</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.819</td>
+      <td>23.8457</td>
+      <td>1.8576</td>
+      <td>Thu May 23 10:31:04 2024</td>
+    </tr>
+    <tr>
+      <th>9</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>17082</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8193</td>
+      <td>28.2084</td>
+      <td>2.8665</td>
+      <td>Thu May 23 10:29:08 2024</td>
+    </tr>
+    <tr>
+      <th>10</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>5720</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8235</td>
+      <td>16.3118</td>
+      <td>1.7139</td>
+      <td>Thu May 23 10:30:16 2024</td>
+    </tr>
+    <tr>
+      <th>11</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>2800</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8242</td>
+      <td>13.4597</td>
+      <td>2.5051</td>
+      <td>Thu May 23 10:30:25 2024</td>
+    </tr>
+    <tr>
+      <th>12</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>24599</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8251</td>
+      <td>31.1346</td>
+      <td>2.2462</td>
+      <td>Thu May 23 10:28:11 2024</td>
+    </tr>
+    <tr>
+      <th>13</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>11862</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8253</td>
+      <td>23.1930</td>
+      <td>1.8396</td>
+      <td>Thu May 23 10:29:30 2024</td>
+    </tr>
+    <tr>
+      <th>14</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>20499</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8255</td>
+      <td>28.9252</td>
+      <td>2.0524</td>
+      <td>Thu May 23 10:28:54 2024</td>
+    </tr>
+    <tr>
+      <th>15</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>17082</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8259</td>
+      <td>27.1348</td>
+      <td>2.7111</td>
+      <td>Thu May 23 10:30:54 2024</td>
+    </tr>
+    <tr>
+      <th>16</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>24599</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.826</td>
+      <td>32.5141</td>
+      <td>3.0002</td>
+      <td>Thu May 23 10:27:55 2024</td>
+    </tr>
+    <tr>
+      <th>17</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>2757</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8305</td>
+      <td>13.5560</td>
+      <td>1.7122</td>
+      <td>Thu May 23 10:32:43 2024</td>
+    </tr>
+    <tr>
+      <th>18</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>8237</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8315</td>
+      <td>19.6689</td>
+      <td>1.8418</td>
+      <td>Thu May 23 10:31:34 2024</td>
+    </tr>
+    <tr>
+      <th>19</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>6864</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8315</td>
+      <td>18.0505</td>
+      <td>2.5070</td>
+      <td>Thu May 23 10:30:09 2024</td>
+    </tr>
+    <tr>
+      <th>20</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>4766</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8318</td>
+      <td>12.5170</td>
+      <td>1.7119</td>
+      <td>Thu May 23 10:30:19 2024</td>
+    </tr>
+    <tr>
+      <th>21</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>9885</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8326</td>
+      <td>21.3303</td>
+      <td>1.7615</td>
+      <td>Thu May 23 10:29:52 2024</td>
+    </tr>
+    <tr>
+      <th>22</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>8237</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.834</td>
+      <td>20.3790</td>
+      <td>1.8199</td>
+      <td>Thu May 23 10:30:02 2024</td>
+    </tr>
+    <tr>
+      <th>23</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>3309</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8374</td>
+      <td>12.2541</td>
+      <td>1.7265</td>
+      <td>Thu May 23 10:32:19 2024</td>
+    </tr>
+    <tr>
+      <th>24</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>6864</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8377</td>
+      <td>17.6653</td>
+      <td>1.7352</td>
+      <td>Thu May 23 10:31:42 2024</td>
+    </tr>
+    <tr>
+      <th>25</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>1914</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8378</td>
+      <td>10.8578</td>
+      <td>1.7405</td>
+      <td>Thu May 23 10:32:56 2024</td>
+    </tr>
+    <tr>
+      <th>26</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>5720</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8383</td>
+      <td>15.5746</td>
+      <td>1.7591</td>
+      <td>Thu May 23 10:31:55 2024</td>
+    </tr>
+    <tr>
+      <th>27</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>1595</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8403</td>
+      <td>11.4344</td>
+      <td>1.7144</td>
+      <td>Thu May 23 10:33:00 2024</td>
+    </tr>
+    <tr>
+      <th>28</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>2297</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8418</td>
+      <td>11.2165</td>
+      <td>1.1772</td>
+      <td>Thu May 23 10:32:53 2024</td>
+    </tr>
+    <tr>
+      <th>29</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>4766</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8441</td>
+      <td>14.2588</td>
+      <td>2.5272</td>
+      <td>Thu May 23 10:32:03 2024</td>
+    </tr>
+    <tr>
+      <th>30</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>3309</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.845</td>
+      <td>11.1807</td>
+      <td>1.7050</td>
+      <td>Thu May 23 10:32:12 2024</td>
+    </tr>
+    <tr>
+      <th>31</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>2757</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8463</td>
+      <td>11.5175</td>
+      <td>2.5268</td>
+      <td>Thu May 23 10:32:23 2024</td>
+    </tr>
+    <tr>
+      <th>32</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>1329</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8471</td>
+      <td>10.7158</td>
+      <td>2.5423</td>
+      <td>Thu May 23 10:33:19 2024</td>
+    </tr>
+    <tr>
+      <th>33</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>3971</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8474</td>
+      <td>10.0837</td>
+      <td>1.7367</td>
+      <td>Thu May 23 10:32:05 2024</td>
+    </tr>
+    <tr>
+      <th>34</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>3971</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8509</td>
+      <td>14.6562</td>
+      <td>2.5236</td>
+      <td>Thu May 23 10:31:46 2024</td>
+    </tr>
+    <tr>
+      <th>35</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>2757</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8575</td>
+      <td>9.8742</td>
+      <td>1.7469</td>
+      <td>Thu May 23 10:32:44 2024</td>
+    </tr>
+    <tr>
+      <th>36</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>1329</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8596</td>
+      <td>9.6186</td>
+      <td>2.5307</td>
+      <td>Thu May 23 10:32:35 2024</td>
+    </tr>
+    <tr>
+      <th>37</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>1595</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8598</td>
+      <td>10.0047</td>
+      <td>1.7409</td>
+      <td>Thu May 23 10:32:32 2024</td>
+    </tr>
+    <tr>
+      <th>38</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>1914</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8646</td>
+      <td>10.9595</td>
+      <td>1.1232</td>
+      <td>Thu May 23 10:32:30 2024</td>
+    </tr>
+    <tr>
+      <th>39</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>11862</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8662</td>
+      <td>21.0217</td>
+      <td>2.5132</td>
+      <td>Thu May 23 10:29:40 2024</td>
+    </tr>
+    <tr>
+      <th>40</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>2297</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8669</td>
+      <td>9.3428</td>
+      <td>2.5347</td>
+      <td>Thu May 23 10:32:48 2024</td>
+    </tr>
+    <tr>
+      <th>41</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>2297</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8679</td>
+      <td>10.5597</td>
+      <td>1.7073</td>
+      <td>Thu May 23 10:32:26 2024</td>
+    </tr>
+    <tr>
+      <th>42</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>13407</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8725</td>
+      <td>22.8446</td>
+      <td>1.7808</td>
+      <td>Thu May 23 10:28:16 2024</td>
+    </tr>
+    <tr>
+      <th>43</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>1107</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8795</td>
+      <td>9.4064</td>
+      <td>1.7200</td>
+      <td>Thu May 23 10:33:25 2024</td>
+    </tr>
+    <tr>
+      <th>44</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>1107</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8817</td>
+      <td>10.1807</td>
+      <td>1.2341</td>
+      <td>Thu May 23 10:33:21 2024</td>
+    </tr>
+    <tr>
+      <th>45</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>922</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8867</td>
+      <td>9.4246</td>
+      <td>1.7571</td>
+      <td>Thu May 23 10:33:22 2024</td>
+    </tr>
+    <tr>
+      <th>46</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>922</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8874</td>
+      <td>8.5782</td>
+      <td>0.5493</td>
+      <td>Thu May 23 10:33:26 2024</td>
+    </tr>
+    <tr>
+      <th>47</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>1914</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8974</td>
+      <td>8.5941</td>
+      <td>2.5387</td>
+      <td>Thu May 23 10:33:00 2024</td>
+    </tr>
+    <tr>
+      <th>48</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>640</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.9003</td>
+      <td>7.8711</td>
+      <td>0.5570</td>
+      <td>Thu May 23 10:33:36 2024</td>
+    </tr>
+    <tr>
+      <th>49</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>768</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.9037</td>
+      <td>8.7857</td>
+      <td>1.7596</td>
+      <td>Thu May 23 10:33:32 2024</td>
+    </tr>
+    <tr>
+      <th>50</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>9885</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.9057</td>
+      <td>19.1298</td>
+      <td>1.0824</td>
+      <td>Thu May 23 10:34:23 2024</td>
+    </tr>
+    <tr>
+      <th>51</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>768</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.9157</td>
+      <td>8.8506</td>
+      <td>0.5383</td>
+      <td>Thu May 23 10:33:28 2024</td>
+    </tr>
+    <tr>
+      <th>52</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>1595</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.9177</td>
+      <td>6.8742</td>
+      <td>1.2233</td>
+      <td>Thu May 23 10:33:02 2024</td>
+    </tr>
+    <tr>
+      <th>53</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>1329</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.9346</td>
+      <td>7.1818</td>
+      <td>1.7467</td>
+      <td>Thu May 23 10:33:05 2024</td>
+    </tr>
+    <tr>
+      <th>54</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>640</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.9355</td>
+      <td>8.0854</td>
+      <td>2.5462</td>
+      <td>Thu May 23 10:33:30 2024</td>
+    </tr>
+    <tr>
+      <th>55</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>6864</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.9442</td>
+      <td>15.6325</td>
+      <td>1.8140</td>
+      <td>Thu May 23 10:34:38 2024</td>
+    </tr>
+    <tr>
+      <th>56</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>8237</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.9559</td>
+      <td>19.4176</td>
+      <td>2.5739</td>
+      <td>Thu May 23 10:34:33 2024</td>
+    </tr>
+    <tr>
+      <th>57</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>533</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.9581</td>
+      <td>7.4298</td>
+      <td>0.5434</td>
+      <td>Thu May 23 10:33:38 2024</td>
+    </tr>
+    <tr>
+      <th>58</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>1107</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.9609</td>
+      <td>6.4102</td>
+      <td>0.5082</td>
+      <td>Thu May 23 10:33:16 2024</td>
+    </tr>
+    <tr>
+      <th>59</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>533</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.9718</td>
+      <td>7.9872</td>
+      <td>1.2181</td>
+      <td>Thu May 23 10:33:33 2024</td>
+    </tr>
+    <tr>
+      <th>60</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>922</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.9788</td>
+      <td>6.2687</td>
+      <td>0.4773</td>
+      <td>Thu May 23 10:33:16 2024</td>
+    </tr>
+    <tr>
+      <th>61</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>5720</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.9839</td>
+      <td>12.0948</td>
+      <td>0.9443</td>
+      <td>Thu May 23 10:34:42 2024</td>
+    </tr>
+    <tr>
+      <th>62</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>444</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.9901</td>
+      <td>6.8942</td>
+      <td>2.5539</td>
+      <td>Thu May 23 10:33:39 2024</td>
+    </tr>
+    <tr>
+      <th>63</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>768</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.015</td>
+      <td>4.8162</td>
+      <td>1.2038</td>
+      <td>Thu May 23 10:33:09 2024</td>
+    </tr>
+    <tr>
+      <th>64</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>4766</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.0206</td>
+      <td>12.8944</td>
+      <td>2.5746</td>
+      <td>Thu May 23 10:34:50 2024</td>
+    </tr>
+    <tr>
+      <th>65</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>370</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.0231</td>
+      <td>6.4792</td>
+      <td>1.7241</td>
+      <td>Thu May 23 10:33:43 2024</td>
+    </tr>
+    <tr>
+      <th>66</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>444</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.026</td>
+      <td>7.5108</td>
+      <td>1.7203</td>
+      <td>Thu May 23 10:33:34 2024</td>
+    </tr>
+    <tr>
+      <th>67</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>3971</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.0487</td>
+      <td>11.7980</td>
+      <td>0.9098</td>
+      <td>Thu May 23 10:34:55 2024</td>
+    </tr>
+    <tr>
+      <th>68</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>2757</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.0631</td>
+      <td>9.5604</td>
+      <td>2.5782</td>
+      <td>Thu May 23 10:35:03 2024</td>
+    </tr>
+    <tr>
+      <th>69</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>370</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.0693</td>
+      <td>6.8228</td>
+      <td>0.4255</td>
+      <td>Thu May 23 10:33:43 2024</td>
+    </tr>
+    <tr>
+      <th>70</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>3309</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.0706</td>
+      <td>10.1032</td>
+      <td>1.7449</td>
+      <td>Thu May 23 10:34:58 2024</td>
+    </tr>
+    <tr>
+      <th>71</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>308</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.0747</td>
+      <td>4.8309</td>
+      <td>0.5516</td>
+      <td>Thu May 23 10:33:43 2024</td>
+    </tr>
+    <tr>
+      <th>72</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>640</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.0868</td>
+      <td>5.4962</td>
+      <td>1.7518</td>
+      <td>Thu May 23 10:33:11 2024</td>
+    </tr>
+    <tr>
+      <th>73</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>256</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.0918</td>
+      <td>5.6216</td>
+      <td>2.5555</td>
+      <td>Thu May 23 10:33:46 2024</td>
+    </tr>
+    <tr>
+      <th>74</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>2297</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.1067</td>
+      <td>8.5754</td>
+      <td>0.8990</td>
+      <td>Thu May 23 10:35:06 2024</td>
+    </tr>
+    <tr>
+      <th>75</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>1914</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.1302</td>
+      <td>7.5573</td>
+      <td>1.7469</td>
+      <td>Thu May 23 10:35:09 2024</td>
+    </tr>
+    <tr>
+      <th>76</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>308</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.133</td>
+      <td>6.4522</td>
+      <td>1.2210</td>
+      <td>Thu May 23 10:33:41 2024</td>
+    </tr>
+    <tr>
+      <th>77</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>256</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.147</td>
+      <td>6.0581</td>
+      <td>0.5699</td>
+      <td>Thu May 23 10:33:43 2024</td>
+    </tr>
+    <tr>
+      <th>78</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>533</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.1487</td>
+      <td>4.9926</td>
+      <td>0.5538</td>
+      <td>Thu May 23 10:33:51 2024</td>
+    </tr>
+    <tr>
+      <th>79</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>213</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.1505</td>
+      <td>5.7629</td>
+      <td>1.2248</td>
+      <td>Thu May 23 10:33:48 2024</td>
+    </tr>
+    <tr>
+      <th>80</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>444</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.1718</td>
+      <td>4.7018</td>
+      <td>1.2313</td>
+      <td>Thu May 23 10:33:54 2024</td>
+    </tr>
+    <tr>
+      <th>81</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>1329</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.1741</td>
+      <td>5.8695</td>
+      <td>1.2676</td>
+      <td>Thu May 23 10:35:13 2024</td>
+    </tr>
+    <tr>
+      <th>82</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>213</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.1905</td>
+      <td>4.7250</td>
+      <td>0.3950</td>
+      <td>Thu May 23 10:33:49 2024</td>
+    </tr>
+    <tr>
+      <th>83</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>1595</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.2008</td>
+      <td>7.2515</td>
+      <td>2.5813</td>
+      <td>Thu May 23 10:35:12 2024</td>
+    </tr>
+    <tr>
+      <th>84</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>177</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.2023</td>
+      <td>4.5543</td>
+      <td>2.5595</td>
+      <td>Thu May 23 10:33:51 2024</td>
+    </tr>
+    <tr>
+      <th>85</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>177</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.2085</td>
+      <td>5.4495</td>
+      <td>1.7271</td>
+      <td>Thu May 23 10:33:49 2024</td>
+    </tr>
+    <tr>
+      <th>86</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>1107</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.2119</td>
+      <td>6.8125</td>
+      <td>0.9248</td>
+      <td>Thu May 23 10:35:16 2024</td>
+    </tr>
+    <tr>
+      <th>87</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>370</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.2418</td>
+      <td>4.5516</td>
+      <td>1.7326</td>
+      <td>Thu May 23 10:33:56 2024</td>
+    </tr>
+    <tr>
+      <th>88</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>768</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.2567</td>
+      <td>6.0074</td>
+      <td>2.5853</td>
+      <td>Thu May 23 10:35:19 2024</td>
+    </tr>
+    <tr>
+      <th>89</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>922</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.2606</td>
+      <td>6.2707</td>
+      <td>1.7489</td>
+      <td>Thu May 23 10:35:18 2024</td>
+    </tr>
+    <tr>
+      <th>90</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>147</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.2657</td>
+      <td>4.1396</td>
+      <td>0.6062</td>
+      <td>Thu May 23 10:35:34 2024</td>
+    </tr>
+    <tr>
+      <th>91</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>147</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.274</td>
+      <td>5.1502</td>
+      <td>0.5799</td>
+      <td>Thu May 23 10:33:53 2024</td>
+    </tr>
+    <tr>
+      <th>92</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>308</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.2995</td>
+      <td>3.9022</td>
+      <td>2.5635</td>
+      <td>Thu May 23 10:33:56 2024</td>
+    </tr>
+    <tr>
+      <th>93</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>256</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.3103</td>
+      <td>4.0943</td>
+      <td>0.6180</td>
+      <td>Thu May 23 10:35:40 2024</td>
+    </tr>
+    <tr>
+      <th>94</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>213</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.3271</td>
+      <td>4.7975</td>
+      <td>0.9252</td>
+      <td>Thu May 23 10:35:42 2024</td>
+    </tr>
+    <tr>
+      <th>95</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>640</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.3282</td>
+      <td>5.6817</td>
+      <td>1.2557</td>
+      <td>Thu May 23 10:35:21 2024</td>
+    </tr>
+    <tr>
+      <th>96</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>122</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.3316</td>
+      <td>4.0702</td>
+      <td>1.7594</td>
+      <td>Thu May 23 10:35:36 2024</td>
+    </tr>
+    <tr>
+      <th>97</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>122</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.3586</td>
+      <td>4.8518</td>
+      <td>0.9120</td>
+      <td>Thu May 23 10:35:36 2024</td>
+    </tr>
+    <tr>
+      <th>98</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>444</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.3692</td>
+      <td>5.0455</td>
+      <td>0.9041</td>
+      <td>Thu May 23 10:35:23 2024</td>
+    </tr>
+    <tr>
+      <th>99</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>533</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.3852</td>
+      <td>5.3884</td>
+      <td>0.6020</td>
+      <td>Thu May 23 10:35:22 2024</td>
+    </tr>
+    <tr>
+      <th>100</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>370</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.421</td>
+      <td>4.4831</td>
+      <td>1.7529</td>
+      <td>Thu May 23 10:35:24 2024</td>
+    </tr>
+    <tr>
+      <th>101</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>147</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.4254</td>
+      <td>3.8612</td>
+      <td>0.6319</td>
+      <td>Thu May 23 10:35:50 2024</td>
+    </tr>
+    <tr>
+      <th>102</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>101</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.4297</td>
+      <td>4.7787</td>
+      <td>0.4363</td>
+      <td>Thu May 23 10:35:56 2024</td>
+    </tr>
+    <tr>
+      <th>103</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>177</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.4351</td>
+      <td>3.5339</td>
+      <td>2.6053</td>
+      <td>Thu May 23 10:35:48 2024</td>
+    </tr>
+    <tr>
+      <th>104</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>101</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.4378</td>
+      <td>4.1025</td>
+      <td>2.5972</td>
+      <td>Thu May 23 10:35:37 2024</td>
+    </tr>
+    <tr>
+      <th>105</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>308</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.4792</td>
+      <td>5.1696</td>
+      <td>2.5894</td>
+      <td>Thu May 23 10:35:26 2024</td>
+    </tr>
+    <tr>
+      <th>106</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>122</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.5403</td>
+      <td>3.0611</td>
+      <td>0.4371</td>
+      <td>Thu May 23 10:35:50 2024</td>
+    </tr>
+    <tr>
+      <th>107</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>256</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.5427</td>
+      <td>4.6979</td>
+      <td>1.2626</td>
+      <td>Thu May 23 10:35:27 2024</td>
+    </tr>
+    <tr>
+      <th>108</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>84</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.5441</td>
+      <td>4.5536</td>
+      <td>1.2702</td>
+      <td>Thu May 23 10:35:39 2024</td>
+    </tr>
+    <tr>
+      <th>109</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>84</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.5765</td>
+      <td>4.3546</td>
+      <td>0.6318</td>
+      <td>Thu May 23 10:35:57 2024</td>
+    </tr>
+    <tr>
+      <th>110</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>101</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.5962</td>
+      <td>3.7936</td>
+      <td>0.9268</td>
+      <td>Thu May 23 10:35:52 2024</td>
+    </tr>
+    <tr>
+      <th>111</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>213</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.6081</td>
+      <td>4.5165</td>
+      <td>0.6061</td>
+      <td>Thu May 23 10:35:28 2024</td>
+    </tr>
+    <tr>
+      <th>112</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>70</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.6246</td>
+      <td>4.7872</td>
+      <td>2.6133</td>
+      <td>Thu May 23 10:35:59 2024</td>
+    </tr>
+    <tr>
+      <th>113</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>84</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.6468</td>
+      <td>3.3117</td>
+      <td>2.6093</td>
+      <td>Thu May 23 10:35:53 2024</td>
+    </tr>
+    <tr>
+      <th>114</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>177</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.6859</td>
+      <td>4.4852</td>
+      <td>0.9052</td>
+      <td>Thu May 23 10:35:30 2024</td>
+    </tr>
+    <tr>
+      <th>115</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>70</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.7001</td>
+      <td>3.3399</td>
+      <td>0.6615</td>
+      <td>Thu May 23 10:36:25 2024</td>
+    </tr>
+    <tr>
+      <th>116</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>58</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.762</td>
+      <td>3.0358</td>
+      <td>0.9763</td>
+      <td>Thu May 23 10:36:26 2024</td>
+    </tr>
+    <tr>
+      <th>117</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>70</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.7848</td>
+      <td>4.0995</td>
+      <td>1.2841</td>
+      <td>Thu May 23 10:35:59 2024</td>
+    </tr>
+    <tr>
+      <th>118</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>58</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.793</td>
+      <td>4.2778</td>
+      <td>0.4462</td>
+      <td>Thu May 23 10:36:01 2024</td>
+    </tr>
+    <tr>
+      <th>119</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>147</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.8009</td>
+      <td>4.4626</td>
+      <td>1.7556</td>
+      <td>Thu May 23 10:35:31 2024</td>
+    </tr>
+    <tr>
+      <th>120</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>48</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.801</td>
+      <td>3.1682</td>
+      <td>0.4796</td>
+      <td>Thu May 23 10:36:20 2024</td>
+    </tr>
+    <tr>
+      <th>121</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>51</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.8163</td>
+      <td>2.9546</td>
+      <td>0.5748</td>
+      <td>Thu May 23 10:33:56 2024</td>
+    </tr>
+    <tr>
+      <th>122</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>40</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.8358</td>
+      <td>2.6926</td>
+      <td>0.6556</td>
+      <td>Thu May 23 10:36:20 2024</td>
+    </tr>
+    <tr>
+      <th>123</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>48</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.8889</td>
+      <td>3.0477</td>
+      <td>1.3058</td>
+      <td>Thu May 23 10:36:19 2024</td>
+    </tr>
+    <tr>
+      <th>124</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>122</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.8891</td>
+      <td>4.5973</td>
+      <td>2.5934</td>
+      <td>Thu May 23 10:35:32 2024</td>
+    </tr>
+    <tr>
+      <th>125</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>48</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.8955</td>
+      <td>3.0244</td>
+      <td>0.6552</td>
+      <td>Thu May 23 10:36:01 2024</td>
+    </tr>
+    <tr>
+      <th>126</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>33</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.913</td>
+      <td>2.9698</td>
+      <td>0.9724</td>
+      <td>Thu May 23 10:36:22 2024</td>
+    </tr>
+    <tr>
+      <th>127</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>58</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.9231</td>
+      <td>4.1955</td>
+      <td>0.9366</td>
+      <td>Thu May 23 10:36:00 2024</td>
+    </tr>
+    <tr>
+      <th>128</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>101</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.9252</td>
+      <td>4.4641</td>
+      <td>1.2652</td>
+      <td>Thu May 23 10:35:33 2024</td>
+    </tr>
+    <tr>
+      <th>129</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>48</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.9401</td>
+      <td>3.0640</td>
+      <td>1.3055</td>
+      <td>Thu May 23 10:36:27 2024</td>
+    </tr>
+    <tr>
+      <th>130</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>84</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.9515</td>
+      <td>4.5635</td>
+      <td>1.7627</td>
+      <td>Thu May 23 10:35:43 2024</td>
+    </tr>
+    <tr>
+      <th>131</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>27</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.9782</td>
+      <td>2.8900</td>
+      <td>1.3096</td>
+      <td>Thu May 23 10:36:22 2024</td>
+    </tr>
+    <tr>
+      <th>132</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>40</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.9929</td>
+      <td>3.6222</td>
+      <td>1.7660</td>
+      <td>Thu May 23 10:36:03 2024</td>
+    </tr>
+    <tr>
+      <th>133</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>70</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.0381</td>
+      <td>4.4439</td>
+      <td>2.6012</td>
+      <td>Thu May 23 10:35:44 2024</td>
+    </tr>
+    <tr>
+      <th>134</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>40</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.0503</td>
+      <td>3.0116</td>
+      <td>1.7699</td>
+      <td>Thu May 23 10:36:28 2024</td>
+    </tr>
+    <tr>
+      <th>135</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>33</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.0699</td>
+      <td>4.1546</td>
+      <td>1.2980</td>
+      <td>Thu May 23 10:36:04 2024</td>
+    </tr>
+    <tr>
+      <th>136</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>22</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.0727</td>
+      <td>2.8416</td>
+      <td>0.6702</td>
+      <td>Thu May 23 10:36:32 2024</td>
+    </tr>
+    <tr>
+      <th>137</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>40</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.0826</td>
+      <td>2.7600</td>
+      <td>0.9562</td>
+      <td>Thu May 23 10:36:17 2024</td>
+    </tr>
+    <tr>
+      <th>138</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>33</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.1023</td>
+      <td>2.7728</td>
+      <td>0.4761</td>
+      <td>Thu May 23 10:36:29 2024</td>
+    </tr>
+    <tr>
+      <th>139</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>58</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.1095</td>
+      <td>4.4697</td>
+      <td>1.2739</td>
+      <td>Thu May 23 10:35:45 2024</td>
+    </tr>
+    <tr>
+      <th>140</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>48</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.1411</td>
+      <td>3.6205</td>
+      <td>0.6216</td>
+      <td>Thu May 23 10:35:45 2024</td>
+    </tr>
+    <tr>
+      <th>141</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>27</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.1411</td>
+      <td>2.5351</td>
+      <td>0.9802</td>
+      <td>Thu May 23 10:36:29 2024</td>
+    </tr>
+    <tr>
+      <th>142</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>22</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.1458</td>
+      <td>2.9758</td>
+      <td>0.4945</td>
+      <td>Thu May 23 10:36:37 2024</td>
+    </tr>
+    <tr>
+      <th>143</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>33</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.1561</td>
+      <td>2.7518</td>
+      <td>0.6710</td>
+      <td>Thu May 23 10:36:16 2024</td>
+    </tr>
+    <tr>
+      <th>144</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>18</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.1817</td>
+      <td>2.8651</td>
+      <td>0.4852</td>
+      <td>Thu May 23 10:36:33 2024</td>
+    </tr>
+    <tr>
+      <th>145</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>40</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.1938</td>
+      <td>3.8338</td>
+      <td>0.4333</td>
+      <td>Thu May 23 10:35:46 2024</td>
+    </tr>
+    <tr>
+      <th>146</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>18</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.215</td>
+      <td>2.3860</td>
+      <td>0.9673</td>
+      <td>Thu May 23 10:36:38 2024</td>
+    </tr>
+    <tr>
+      <th>147</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>15</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.2183</td>
+      <td>2.7508</td>
+      <td>0.6656</td>
+      <td>Thu May 23 10:36:36 2024</td>
+    </tr>
+    <tr>
+      <th>148</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>27</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.2236</td>
+      <td>3.6306</td>
+      <td>0.4883</td>
+      <td>Thu May 23 10:36:24 2024</td>
+    </tr>
+    <tr>
+      <th>149</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>15</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.2565</td>
+      <td>2.4850</td>
+      <td>0.5074</td>
+      <td>Thu May 23 10:36:44 2024</td>
+    </tr>
+    <tr>
+      <th>150</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>22</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.3029</td>
+      <td>2.9781</td>
+      <td>1.3064</td>
+      <td>Thu May 23 10:36:31 2024</td>
+    </tr>
+    <tr>
+      <th>151</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>12</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.3109</td>
+      <td>2.5892</td>
+      <td>1.7721</td>
+      <td>Thu May 23 10:36:39 2024</td>
+    </tr>
+    <tr>
+      <th>152</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>27</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.3112</td>
+      <td>2.6588</td>
+      <td>0.4688</td>
+      <td>Thu May 23 10:36:15 2024</td>
+    </tr>
+    <tr>
+      <th>153</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>12</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.3525</td>
+      <td>2.6103</td>
+      <td>0.6733</td>
+      <td>Thu May 23 10:36:40 2024</td>
+    </tr>
+    <tr>
+      <th>154</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>12</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.3792</td>
+      <td>2.7457</td>
+      <td>0.6697</td>
+      <td>Thu May 23 10:36:45 2024</td>
+    </tr>
+    <tr>
+      <th>155</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>18</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.4022</td>
+      <td>3.2052</td>
+      <td>0.9690</td>
+      <td>Thu May 23 10:36:34 2024</td>
+    </tr>
+    <tr>
+      <th>156</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>33</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.4071</td>
+      <td>3.6131</td>
+      <td>0.9412</td>
+      <td>Thu May 23 10:35:47 2024</td>
+    </tr>
+    <tr>
+      <th>157</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>10</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.431</td>
+      <td>2.2560</td>
+      <td>0.5033</td>
+      <td>Thu May 23 10:36:40 2024</td>
+    </tr>
+    <tr>
+      <th>158</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>22</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.4472</td>
+      <td>2.5898</td>
+      <td>1.3019</td>
+      <td>Thu May 23 10:36:14 2024</td>
+    </tr>
+    <tr>
+      <th>159</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>15</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.4729</td>
+      <td>3.4469</td>
+      <td>1.3117</td>
+      <td>Thu May 23 10:36:35 2024</td>
+    </tr>
+    <tr>
+      <th>160</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>27</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.4812</td>
+      <td>3.7719</td>
+      <td>0.9786</td>
+      <td>Thu May 23 10:36:43 2024</td>
+    </tr>
+    <tr>
+      <th>161</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>10</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.5027</td>
+      <td>2.8314</td>
+      <td>0.4960</td>
+      <td>Thu May 23 10:36:48 2024</td>
+    </tr>
+    <tr>
+      <th>162</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>8</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.5188</td>
+      <td>2.1882</td>
+      <td>0.6821</td>
+      <td>Thu May 23 10:36:49 2024</td>
+    </tr>
+    <tr>
+      <th>163</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>8</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.5313</td>
+      <td>2.6851</td>
+      <td>0.5192</td>
+      <td>Thu May 23 10:36:56 2024</td>
+    </tr>
+    <tr>
+      <th>164</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>18</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.5424</td>
+      <td>2.3904</td>
+      <td>0.9496</td>
+      <td>Thu May 23 10:36:13 2024</td>
+    </tr>
+    <tr>
+      <th>165</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>8</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.5501</td>
+      <td>2.5698</td>
+      <td>0.5057</td>
+      <td>Thu May 23 10:36:52 2024</td>
+    </tr>
+    <tr>
+      <th>166</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>15</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.5725</td>
+      <td>2.3022</td>
+      <td>0.6671</td>
+      <td>Thu May 23 10:36:12 2024</td>
+    </tr>
+    <tr>
+      <th>167</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>22</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.6016</td>
+      <td>3.2742</td>
+      <td>1.7780</td>
+      <td>Thu May 23 10:36:44 2024</td>
+    </tr>
+    <tr>
+      <th>168</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>6</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.6036</td>
+      <td>2.3810</td>
+      <td>0.6845</td>
+      <td>Thu May 23 10:36:53 2024</td>
+    </tr>
+    <tr>
+      <th>169</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>5</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.6066</td>
+      <td>2.2897</td>
+      <td>1.7814</td>
+      <td>Thu May 23 10:36:54 2024</td>
+    </tr>
+    <tr>
+      <th>170</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>6</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.6299</td>
+      <td>2.2368</td>
+      <td>0.9856</td>
+      <td>Thu May 23 10:36:55 2024</td>
+    </tr>
+    <tr>
+      <th>171</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>5</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.6518</td>
+      <td>2.3437</td>
+      <td>1.7899</td>
+      <td>Thu May 23 10:36:58 2024</td>
+    </tr>
+    <tr>
+      <th>172</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>6</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.6546</td>
+      <td>2.5006</td>
+      <td>0.6908</td>
+      <td>Thu May 23 10:36:57 2024</td>
+    </tr>
+    <tr>
+      <th>173</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>12</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.6615</td>
+      <td>2.3334</td>
+      <td>0.4765</td>
+      <td>Thu May 23 10:36:10 2024</td>
+    </tr>
+    <tr>
+      <th>174</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>4</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.6717</td>
+      <td>2.3237</td>
+      <td>0.5135</td>
+      <td>Thu May 23 10:37:01 2024</td>
+    </tr>
+    <tr>
+      <th>175</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>18</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.6718</td>
+      <td>2.8963</td>
+      <td>1.3165</td>
+      <td>Thu May 23 10:36:46 2024</td>
+    </tr>
+    <tr>
+      <th>176</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>4</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.6834</td>
+      <td>2.3249</td>
+      <td>1.7896</td>
+      <td>Thu May 23 10:37:02 2024</td>
+    </tr>
+    <tr>
+      <th>177</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>10</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.692</td>
+      <td>3.3026</td>
+      <td>0.9810</td>
+      <td>Thu May 23 10:36:52 2024</td>
+    </tr>
+    <tr>
+      <th>178</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>4</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.7024</td>
+      <td>2.1647</td>
+      <td>0.6942</td>
+      <td>Thu May 23 10:37:01 2024</td>
+    </tr>
+    <tr>
+      <th>179</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>10</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.7094</td>
+      <td>2.0194</td>
+      <td>0.9606</td>
+      <td>Thu May 23 10:36:09 2024</td>
+    </tr>
+    <tr>
+      <th>180</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>15</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.7318</td>
+      <td>3.1588</td>
+      <td>1.7826</td>
+      <td>Thu May 23 10:36:48 2024</td>
+    </tr>
+    <tr>
+      <th>181</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>3</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.7357</td>
+      <td>2.1384</td>
+      <td>1.3207</td>
+      <td>Thu May 23 10:37:03 2024</td>
+    </tr>
+    <tr>
+      <th>182</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>8</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.758</td>
+      <td>1.9295</td>
+      <td>0.6628</td>
+      <td>Thu May 23 10:36:08 2024</td>
+    </tr>
+    <tr>
+      <th>183</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>3</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.7777</td>
+      <td>2.1884</td>
+      <td>0.9740</td>
+      <td>Thu May 23 10:37:02 2024</td>
+    </tr>
+    <tr>
+      <th>184</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>12</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.7778</td>
+      <td>3.6926</td>
+      <td>1.3224</td>
+      <td>Thu May 23 10:36:51 2024</td>
+    </tr>
+    <tr>
+      <th>185</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>3</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.7811</td>
+      <td>2.1009</td>
+      <td>1.0076</td>
+      <td>Thu May 23 10:37:05 2024</td>
+    </tr>
+    <tr>
+      <th>186</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>2</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.8153</td>
+      <td>2.4933</td>
+      <td>2.6172</td>
+      <td>Thu May 23 10:37:09 2024</td>
+    </tr>
+    <tr>
+      <th>187</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>6</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.8435</td>
+      <td>2.0641</td>
+      <td>0.4708</td>
+      <td>Thu May 23 10:36:07 2024</td>
+    </tr>
+    <tr>
+      <th>188</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>2</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.8592</td>
+      <td>2.1753</td>
+      <td>1.3369</td>
+      <td>Thu May 23 10:37:06 2024</td>
+    </tr>
+    <tr>
+      <th>189</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>2</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.8717</td>
+      <td>2.0603</td>
+      <td>2.6133</td>
+      <td>Thu May 23 10:37:04 2024</td>
+    </tr>
+    <tr>
+      <th>190</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>5</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.8946</td>
+      <td>1.6382</td>
+      <td>0.9568</td>
+      <td>Thu May 23 10:36:06 2024</td>
+    </tr>
+    <tr>
+      <th>191</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>1</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.9047</td>
+      <td>1.7968</td>
+      <td>1.7984</td>
+      <td>Thu May 23 10:37:07 2024</td>
+    </tr>
+    <tr>
+      <th>192</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>1</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.922</td>
+      <td>1.8707</td>
+      <td>1.0115</td>
+      <td>Thu May 23 10:37:09 2024</td>
+    </tr>
+    <tr>
+      <th>193</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>4</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.9311</td>
+      <td>1.9900</td>
+      <td>0.6433</td>
+      <td>Thu May 23 10:36:05 2024</td>
+    </tr>
+    <tr>
+      <th>194</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>3</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.946</td>
+      <td>1.9046</td>
+      <td>0.4669</td>
+      <td>Thu May 23 10:36:04 2024</td>
+    </tr>
+    <tr>
+      <th>195</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>2</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.97</td>
+      <td>1.9467</td>
+      <td>0.9528</td>
+      <td>Thu May 23 10:36:03 2024</td>
+    </tr>
+    <tr>
+      <th>196</th>
+      <td>Feature Selection</td>
+      <td>2715</td>
+      <td>1</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.9959</td>
+      <td>1.9442</td>
+      <td>1.2903</td>
+      <td>Thu May 23 10:35:52 2024</td>
+    </tr>
+    <tr>
+      <th>197</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8081</td>
+      <td>20.2350</td>
+      <td>1.0069</td>
+      <td>Thu May 23 10:37:52 2024</td>
+    </tr>
+    <tr>
+      <th>198</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.00119999, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8081</td>
+      <td>20.3789</td>
+      <td>0.9145</td>
+      <td>Thu May 23 10:39:42 2024</td>
+    </tr>
+    <tr>
+      <th>199</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': 0, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8081</td>
+      <td>25.8773</td>
+      <td>0.9123</td>
+      <td>Thu May 23 10:38:05 2024</td>
+    </tr>
+    <tr>
+      <th>200</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8081</td>
+      <td>26.2765</td>
+      <td>0.8674</td>
+      <td>Thu May 23 10:38:06 2024</td>
+    </tr>
+    <tr>
+      <th>201</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.00119999, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8081</td>
+      <td>26.6993</td>
+      <td>0.8137</td>
+      <td>Thu May 23 10:38:09 2024</td>
+    </tr>
+    <tr>
+      <th>202</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8081</td>
+      <td>28.0649</td>
+      <td>0.9741</td>
+      <td>Thu May 23 10:38:31 2024</td>
+    </tr>
+    <tr>
+      <th>203</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8081</td>
+      <td>32.8766</td>
+      <td>0.7946</td>
+      <td>Thu May 23 10:38:24 2024</td>
+    </tr>
+    <tr>
+      <th>204</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8081</td>
+      <td>32.9077</td>
+      <td>0.8929</td>
+      <td>Thu May 23 10:38:25 2024</td>
+    </tr>
+    <tr>
+      <th>205</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8081</td>
+      <td>33.4196</td>
+      <td>0.8874</td>
+      <td>Thu May 23 10:38:15 2024</td>
+    </tr>
+    <tr>
+      <th>206</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8081</td>
+      <td>37.2014</td>
+      <td>0.8310</td>
+      <td>Thu May 23 10:38:25 2024</td>
+    </tr>
+    <tr>
+      <th>207</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 0.999990000000001, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8081</td>
+      <td>28.7169</td>
+      <td>2.0712</td>
+      <td>Thu May 23 10:38:30 2024</td>
+    </tr>
+    <tr>
+      <th>208</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 101, 'class_weight': 'balanced'}</td>
+      <td>-0.8084</td>
+      <td>33.3972</td>
+      <td>2.8085</td>
+      <td>Thu May 23 10:38:17 2024</td>
+    </tr>
+    <tr>
+      <th>209</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 125, 'class_weight': 'balanced'}</td>
+      <td>-0.8091</td>
+      <td>27.4936</td>
+      <td>1.3298</td>
+      <td>Thu May 23 10:39:37 2024</td>
+    </tr>
+    <tr>
+      <th>210</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 127, 'class_weight': 'balanced'}</td>
+      <td>-0.8094</td>
+      <td>28.5509</td>
+      <td>0.8472</td>
+      <td>Thu May 23 10:39:40 2024</td>
+    </tr>
+    <tr>
+      <th>211</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 128, 'class_weight': 'balanced'}</td>
+      <td>-0.8096</td>
+      <td>28.6981</td>
+      <td>1.7672</td>
+      <td>Thu May 23 10:39:42 2024</td>
+    </tr>
+    <tr>
+      <th>212</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 126, 'class_weight': 'balanced'}</td>
+      <td>-0.8096</td>
+      <td>27.2710</td>
+      <td>1.3253</td>
+      <td>Thu May 23 10:39:38 2024</td>
+    </tr>
+    <tr>
+      <th>213</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 9.999999999e-06, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8113</td>
+      <td>30.9955</td>
+      <td>0.8170</td>
+      <td>Thu May 23 10:38:30 2024</td>
+    </tr>
+    <tr>
+      <th>214</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 32, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8128</td>
+      <td>15.1494</td>
+      <td>1.1774</td>
+      <td>Thu May 23 10:39:45 2024</td>
+    </tr>
+    <tr>
+      <th>215</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 32, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8128</td>
+      <td>33.5865</td>
+      <td>0.8409</td>
+      <td>Thu May 23 10:38:26 2024</td>
+    </tr>
+    <tr>
+      <th>216</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 177, 'class_weight': 'balanced'}</td>
+      <td>-0.8131</td>
+      <td>36.7752</td>
+      <td>0.8498</td>
+      <td>Thu May 23 10:39:40 2024</td>
+    </tr>
+    <tr>
+      <th>217</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 178, 'class_weight': 'balanced'}</td>
+      <td>-0.8131</td>
+      <td>21.8527</td>
+      <td>0.8002</td>
+      <td>Thu May 23 10:40:05 2024</td>
+    </tr>
+    <tr>
+      <th>218</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 178, 'class_weight': 'balanced'}</td>
+      <td>-0.8131</td>
+      <td>37.2306</td>
+      <td>0.9073</td>
+      <td>Thu May 23 10:39:42 2024</td>
+    </tr>
+    <tr>
+      <th>219</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 174, 'class_weight': 'balanced'}</td>
+      <td>-0.8133</td>
+      <td>36.2606</td>
+      <td>0.8474</td>
+      <td>Thu May 23 10:39:39 2024</td>
+    </tr>
+    <tr>
+      <th>220</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 173, 'class_weight': 'balanced'}</td>
+      <td>-0.8135</td>
+      <td>36.2719</td>
+      <td>0.8471</td>
+      <td>Thu May 23 10:39:38 2024</td>
+    </tr>
+    <tr>
+      <th>221</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.10241113615453983, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8149</td>
+      <td>19.9035</td>
+      <td>1.9263</td>
+      <td>Thu May 23 10:37:53 2024</td>
+    </tr>
+    <tr>
+      <th>222</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 252, 'class_weight': 'balanced'}</td>
+      <td>-0.8157</td>
+      <td>51.4688</td>
+      <td>0.9162</td>
+      <td>Thu May 23 10:38:41 2024</td>
+    </tr>
+    <tr>
+      <th>223</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 253, 'class_weight': 'balanced'}</td>
+      <td>-0.8157</td>
+      <td>52.1458</td>
+      <td>0.8702</td>
+      <td>Thu May 23 10:38:43 2024</td>
+    </tr>
+    <tr>
+      <th>224</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.09939959374943852, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8174</td>
+      <td>21.5247</td>
+      <td>0.8437</td>
+      <td>Thu May 23 10:39:07 2024</td>
+    </tr>
+    <tr>
+      <th>225</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.09926466710275127, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8185</td>
+      <td>19.0557</td>
+      <td>0.8452</td>
+      <td>Thu May 23 10:39:02 2024</td>
+    </tr>
+    <tr>
+      <th>226</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.09922325977512633, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8186</td>
+      <td>19.7455</td>
+      <td>0.8507</td>
+      <td>Thu May 23 10:39:02 2024</td>
+    </tr>
+    <tr>
+      <th>227</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.09914044511987648, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8186</td>
+      <td>21.6535</td>
+      <td>1.3283</td>
+      <td>Thu May 23 10:39:07 2024</td>
+    </tr>
+    <tr>
+      <th>228</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0.05623427310243317, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8187</td>
+      <td>31.1631</td>
+      <td>0.8408</td>
+      <td>Thu May 23 10:38:27 2024</td>
+    </tr>
+    <tr>
+      <th>229</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': None}</td>
+      <td>-0.8189</td>
+      <td>24.7093</td>
+      <td>2.2353</td>
+      <td>Thu May 23 10:37:55 2024</td>
+    </tr>
+    <tr>
+      <th>230</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.10242113515453982, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8195</td>
+      <td>30.5136</td>
+      <td>0.8853</td>
+      <td>Thu May 23 10:38:10 2024</td>
+    </tr>
+    <tr>
+      <th>231</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.09927537176656373, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8204</td>
+      <td>23.9768</td>
+      <td>0.8466</td>
+      <td>Thu May 23 10:39:11 2024</td>
+    </tr>
+    <tr>
+      <th>232</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.09921326077512634, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8214</td>
+      <td>19.5727</td>
+      <td>0.8485</td>
+      <td>Thu May 23 10:39:01 2024</td>
+    </tr>
+    <tr>
+      <th>233</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 511, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8229</td>
+      <td>25.5383</td>
+      <td>1.2588</td>
+      <td>Thu May 23 10:39:24 2024</td>
+    </tr>
+    <tr>
+      <th>234</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 523, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8229</td>
+      <td>26.0735</td>
+      <td>1.2678</td>
+      <td>Thu May 23 10:39:26 2024</td>
+    </tr>
+    <tr>
+      <th>235</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 510, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8229</td>
+      <td>27.2237</td>
+      <td>1.2769</td>
+      <td>Thu May 23 10:39:24 2024</td>
+    </tr>
+    <tr>
+      <th>236</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 522, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8229</td>
+      <td>28.2897</td>
+      <td>1.2703</td>
+      <td>Thu May 23 10:39:28 2024</td>
+    </tr>
+    <tr>
+      <th>237</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 564, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8229</td>
+      <td>29.8900</td>
+      <td>1.2834</td>
+      <td>Thu May 23 10:38:25 2024</td>
+    </tr>
+    <tr>
+      <th>238</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 190, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8229</td>
+      <td>31.0321</td>
+      <td>0.8975</td>
+      <td>Thu May 23 10:38:24 2024</td>
+    </tr>
+    <tr>
+      <th>239</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 563, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8229</td>
+      <td>32.3063</td>
+      <td>1.3149</td>
+      <td>Thu May 23 10:38:27 2024</td>
+    </tr>
+    <tr>
+      <th>240</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 189, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8229</td>
+      <td>33.7906</td>
+      <td>1.7289</td>
+      <td>Thu May 23 10:38:22 2024</td>
+    </tr>
+    <tr>
+      <th>241</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.07370106425810359, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.824</td>
+      <td>23.1302</td>
+      <td>0.8315</td>
+      <td>Thu May 23 10:39:42 2024</td>
+    </tr>
+    <tr>
+      <th>242</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0.056244273102432164, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8241</td>
+      <td>32.7792</td>
+      <td>0.8992</td>
+      <td>Thu May 23 10:38:29 2024</td>
+    </tr>
+    <tr>
+      <th>243</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.09925466810275127, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8243</td>
+      <td>21.6087</td>
+      <td>0.8517</td>
+      <td>Thu May 23 10:39:04 2024</td>
+    </tr>
+    <tr>
+      <th>244</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0.13554964757452176, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8264</td>
+      <td>22.2450</td>
+      <td>0.8670</td>
+      <td>Thu May 23 10:39:28 2024</td>
+    </tr>
+    <tr>
+      <th>245</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0.0001778292746984456, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8272</td>
+      <td>33.6113</td>
+      <td>0.8963</td>
+      <td>Thu May 23 10:38:27 2024</td>
+    </tr>
+    <tr>
+      <th>246</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0.00018782927469744562, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8272</td>
+      <td>34.7928</td>
+      <td>0.8317</td>
+      <td>Thu May 23 10:38:30 2024</td>
+    </tr>
+    <tr>
+      <th>247</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.100009999, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8274</td>
+      <td>20.5852</td>
+      <td>0.8955</td>
+      <td>Thu May 23 10:37:53 2024</td>
+    </tr>
+    <tr>
+      <th>248</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.09928537076656373, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8276</td>
+      <td>24.9883</td>
+      <td>1.7325</td>
+      <td>Thu May 23 10:39:12 2024</td>
+    </tr>
+    <tr>
+      <th>249</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0.13555964757452077, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8284</td>
+      <td>24.3892</td>
+      <td>0.8488</td>
+      <td>Thu May 23 10:39:32 2024</td>
+    </tr>
+    <tr>
+      <th>250</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0.13727546589824888, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8331</td>
+      <td>24.0493</td>
+      <td>0.8336</td>
+      <td>Thu May 23 10:39:14 2024</td>
+    </tr>
+    <tr>
+      <th>251</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0.14413873919316136, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8334</td>
+      <td>23.6209</td>
+      <td>0.8506</td>
+      <td>Thu May 23 10:39:32 2024</td>
+    </tr>
+    <tr>
+      <th>252</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0.14412873919316235, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8335</td>
+      <td>23.4433</td>
+      <td>0.9310</td>
+      <td>Thu May 23 10:39:31 2024</td>
+    </tr>
+    <tr>
+      <th>253</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0.13726546589824987, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8347</td>
+      <td>23.0090</td>
+      <td>0.9776</td>
+      <td>Thu May 23 10:39:12 2024</td>
+    </tr>
+    <tr>
+      <th>254</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.14397169870175522, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8358</td>
+      <td>15.7275</td>
+      <td>1.1751</td>
+      <td>Thu May 23 10:38:59 2024</td>
+    </tr>
+    <tr>
+      <th>255</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.1323499720056148, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8373</td>
+      <td>24.0774</td>
+      <td>1.0237</td>
+      <td>Thu May 23 10:39:17 2024</td>
+    </tr>
+    <tr>
+      <th>256</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0.1321280109270645, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8384</td>
+      <td>21.7320</td>
+      <td>1.3201</td>
+      <td>Thu May 23 10:39:10 2024</td>
+    </tr>
+    <tr>
+      <th>257</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0.1321180109270655, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8404</td>
+      <td>22.4910</td>
+      <td>2.1311</td>
+      <td>Thu May 23 10:39:10 2024</td>
+    </tr>
+    <tr>
+      <th>258</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.1325499620056148, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8409</td>
+      <td>22.4901</td>
+      <td>0.8413</td>
+      <td>Thu May 23 10:39:17 2024</td>
+    </tr>
+    <tr>
+      <th>259</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.15920068096327575, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8428</td>
+      <td>23.0640</td>
+      <td>0.8597</td>
+      <td>Thu May 23 10:39:19 2024</td>
+    </tr>
+    <tr>
+      <th>260</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.15328982261520555, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8445</td>
+      <td>17.4436</td>
+      <td>0.8408</td>
+      <td>Thu May 23 10:39:02 2024</td>
+    </tr>
+    <tr>
+      <th>261</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.15491868834972752, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8454</td>
+      <td>21.1098</td>
+      <td>0.8806</td>
+      <td>Thu May 23 10:38:04 2024</td>
+    </tr>
+    <tr>
+      <th>262</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.15348981261520556, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8455</td>
+      <td>18.8633</td>
+      <td>0.8435</td>
+      <td>Thu May 23 10:39:04 2024</td>
+    </tr>
+    <tr>
+      <th>263</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.1437717087017552, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8463</td>
+      <td>16.0854</td>
+      <td>0.8449</td>
+      <td>Thu May 23 10:38:59 2024</td>
+    </tr>
+    <tr>
+      <th>264</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.15511867834972753, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8499</td>
+      <td>24.6760</td>
+      <td>0.9141</td>
+      <td>Thu May 23 10:38:09 2024</td>
+    </tr>
+    <tr>
+      <th>265</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.15900069096327574, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.85</td>
+      <td>23.7525</td>
+      <td>2.8674</td>
+      <td>Thu May 23 10:39:20 2024</td>
+    </tr>
+    <tr>
+      <th>266</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 0.068760000093124, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8703</td>
+      <td>23.4299</td>
+      <td>0.8458</td>
+      <td>Thu May 23 10:39:41 2024</td>
+    </tr>
+    <tr>
+      <th>267</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 0.018760000098124004, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8783</td>
+      <td>24.7752</td>
+      <td>0.8998</td>
+      <td>Thu May 23 10:39:40 2024</td>
+    </tr>
+    <tr>
+      <th>268</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 0.068750000093125, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8844</td>
+      <td>24.4053</td>
+      <td>2.1321</td>
+      <td>Thu May 23 10:39:41 2024</td>
+    </tr>
+    <tr>
+      <th>269</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 0.01250000009875, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8845</td>
+      <td>31.2005</td>
+      <td>1.9282</td>
+      <td>Thu May 23 10:39:21 2024</td>
+    </tr>
+    <tr>
+      <th>270</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 0.012510000098749, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8861</td>
+      <td>30.5409</td>
+      <td>0.8413</td>
+      <td>Thu May 23 10:39:22 2024</td>
+    </tr>
+    <tr>
+      <th>271</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 0.018750000098125004, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.887</td>
+      <td>26.0495</td>
+      <td>0.9392</td>
+      <td>Thu May 23 10:39:40 2024</td>
+    </tr>
+    <tr>
+      <th>272</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'goss', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.8906</td>
+      <td>11.5748</td>
+      <td>2.8070</td>
+      <td>Thu May 23 10:37:41 2024</td>
+    </tr>
+    <tr>
+      <th>273</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'goss', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 177, 'class_weight': 'balanced'}</td>
+      <td>-0.9051</td>
+      <td>12.0602</td>
+      <td>0.8516</td>
+      <td>Thu May 23 10:39:55 2024</td>
+    </tr>
+    <tr>
+      <th>274</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 0.025010000097499003, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.9125</td>
+      <td>26.9627</td>
+      <td>0.9389</td>
+      <td>Thu May 23 10:39:20 2024</td>
+    </tr>
+    <tr>
+      <th>275</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'goss', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 253, 'class_weight': 'balanced'}</td>
+      <td>-0.9154</td>
+      <td>27.2352</td>
+      <td>1.1787</td>
+      <td>Thu May 23 10:39:29 2024</td>
+    </tr>
+    <tr>
+      <th>276</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 0.025000000097500003, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.9154</td>
+      <td>27.6526</td>
+      <td>0.8441</td>
+      <td>Thu May 23 10:39:19 2024</td>
+    </tr>
+    <tr>
+      <th>277</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': 6, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.9358</td>
+      <td>16.5386</td>
+      <td>0.8969</td>
+      <td>Thu May 23 10:37:57 2024</td>
+    </tr>
+    <tr>
+      <th>278</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': 5, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.9575</td>
+      <td>12.7152</td>
+      <td>0.9137</td>
+      <td>Thu May 23 10:37:53 2024</td>
+    </tr>
+    <tr>
+      <th>279</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1.0000099999e-05, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.9639</td>
+      <td>34.5683</td>
+      <td>2.2728</td>
+      <td>Thu May 23 10:38:32 2024</td>
+    </tr>
+    <tr>
+      <th>280</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 2.0000099998e-05, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.9664</td>
+      <td>32.5344</td>
+      <td>0.8386</td>
+      <td>Thu May 23 10:38:31 2024</td>
+    </tr>
+    <tr>
+      <th>281</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 0.0001778292746984456, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.9674</td>
+      <td>32.2133</td>
+      <td>0.8406</td>
+      <td>Thu May 23 10:38:31 2024</td>
+    </tr>
+    <tr>
+      <th>282</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 1.7602198064430905, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.9702</td>
+      <td>16.4763</td>
+      <td>0.9145</td>
+      <td>Thu May 23 10:38:01 2024</td>
+    </tr>
+    <tr>
+      <th>283</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 0.00018782927469744562, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.9718</td>
+      <td>30.9401</td>
+      <td>1.3249</td>
+      <td>Thu May 23 10:38:31 2024</td>
+    </tr>
+    <tr>
+      <th>284</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': 4, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.9725</td>
+      <td>12.0292</td>
+      <td>0.9135</td>
+      <td>Thu May 23 10:37:52 2024</td>
+    </tr>
+    <tr>
+      <th>285</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'dart', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.9785</td>
+      <td>25.2995</td>
+      <td>1.2987</td>
+      <td>Thu May 23 10:37:55 2024</td>
+    </tr>
+    <tr>
+      <th>286</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 1.7604197964430905, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-0.9788</td>
+      <td>15.2991</td>
+      <td>0.9009</td>
+      <td>Thu May 23 10:38:01 2024</td>
+    </tr>
+    <tr>
+      <th>287</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': 3, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-1.0059</td>
+      <td>10.9781</td>
+      <td>0.8135</td>
+      <td>Thu May 23 10:37:51 2024</td>
+    </tr>
+    <tr>
+      <th>288</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 6, 'class_weight': 'balanced'}</td>
+      <td>-1.6527</td>
+      <td>8.1246</td>
+      <td>0.8817</td>
+      <td>Thu May 23 10:37:55 2024</td>
+    </tr>
+    <tr>
+      <th>289</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 5, 'class_weight': 'balanced'}</td>
+      <td>-1.7479</td>
+      <td>7.9676</td>
+      <td>0.8152</td>
+      <td>Thu May 23 10:37:53 2024</td>
+    </tr>
+    <tr>
+      <th>290</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.10242113515453982, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>
+      <td>-2.1368</td>
+      <td>9.2758</td>
+      <td>0.8781</td>
+      <td>Thu May 23 10:38:24 2024</td>
+    </tr>
+    <tr>
+      <th>291</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.10241113615453983, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>
+      <td>-2.1368</td>
+      <td>10.3302</td>
+      <td>0.9744</td>
+      <td>Thu May 23 10:38:23 2024</td>
+    </tr>
+    <tr>
+      <th>292</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.100009999, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>
+      <td>-2.1502</td>
+      <td>10.7863</td>
+      <td>0.8417</td>
+      <td>Thu May 23 10:38:23 2024</td>
+    </tr>
+    <tr>
+      <th>293</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>
+      <td>-2.1502</td>
+      <td>10.2856</td>
+      <td>1.1617</td>
+      <td>Thu May 23 10:38:19 2024</td>
+    </tr>
+    <tr>
+      <th>294</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 253, 'class_weight': 'balanced'}</td>
+      <td>-2.9114</td>
+      <td>8.2499</td>
+      <td>0.8578</td>
+      <td>Thu May 23 10:38:39 2024</td>
+    </tr>
+    <tr>
+      <th>295</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 252, 'class_weight': 'balanced'}</td>
+      <td>-2.9117</td>
+      <td>7.0266</td>
+      <td>0.8430</td>
+      <td>Thu May 23 10:38:37 2024</td>
+    </tr>
+    <tr>
+      <th>296</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.000119998, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.9481</td>
+      <td>20.8551</td>
+      <td>0.9050</td>
+      <td>Thu May 23 10:37:52 2024</td>
+    </tr>
+    <tr>
+      <th>297</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.000109999, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.9534</td>
+      <td>15.2459</td>
+      <td>1.6288</td>
+      <td>Thu May 23 10:37:46 2024</td>
+    </tr>
+    <tr>
+      <th>298</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 101, 'class_weight': 'balanced'}</td>
+      <td>-2.9693</td>
+      <td>5.7732</td>
+      <td>1.7309</td>
+      <td>Thu May 23 10:38:36 2024</td>
+    </tr>
+    <tr>
+      <th>299</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 100, 'class_weight': 'balanced'}</td>
+      <td>-2.9697</td>
+      <td>5.6526</td>
+      <td>0.8149</td>
+      <td>Thu May 23 10:38:35 2024</td>
+    </tr>
+    <tr>
+      <th>300</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': None}</td>
+      <td>-2.9877</td>
+      <td>10.6908</td>
+      <td>0.9739</td>
+      <td>Thu May 23 10:38:17 2024</td>
+    </tr>
+    <tr>
+      <th>301</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 0, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>
+      <td>-3.0112</td>
+      <td>6.4505</td>
+      <td>0.7836</td>
+      <td>Thu May 23 10:38:29 2024</td>
+    </tr>
+    <tr>
+      <th>302</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 6, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>
+      <td>-3.0112</td>
+      <td>6.9859</td>
+      <td>1.1621</td>
+      <td>Thu May 23 10:38:29 2024</td>
+    </tr>
+    <tr>
+      <th>303</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>
+      <td>-3.0112</td>
+      <td>7.3460</td>
+      <td>0.8478</td>
+      <td>Thu May 23 10:38:28 2024</td>
+    </tr>
+    <tr>
+      <th>304</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 4, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>
+      <td>-3.0112</td>
+      <td>8.2352</td>
+      <td>0.8335</td>
+      <td>Thu May 23 10:38:27 2024</td>
+    </tr>
+    <tr>
+      <th>305</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 3, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>
+      <td>-3.0112</td>
+      <td>8.4559</td>
+      <td>0.8229</td>
+      <td>Thu May 23 10:38:27 2024</td>
+    </tr>
+    <tr>
+      <th>306</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 5, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>
+      <td>-3.0112</td>
+      <td>8.4749</td>
+      <td>2.8363</td>
+      <td>Thu May 23 10:38:29 2024</td>
+    </tr>
+    <tr>
+      <th>307</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.000119998, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>
+      <td>-3.0116</td>
+      <td>10.9574</td>
+      <td>0.9758</td>
+      <td>Thu May 23 10:38:20 2024</td>
+    </tr>
+    <tr>
+      <th>308</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 6, 'class_weight': 'balanced'}</td>
+      <td>-3.0116</td>
+      <td>4.3542</td>
+      <td>1.1986</td>
+      <td>Thu May 23 10:38:33 2024</td>
+    </tr>
+    <tr>
+      <th>309</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.000109999, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>
+      <td>-3.0118</td>
+      <td>11.2326</td>
+      <td>0.9755</td>
+      <td>Thu May 23 10:38:19 2024</td>
+    </tr>
+    <tr>
+      <th>310</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 0, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>
+      <td>-3.0121</td>
+      <td>4.5213</td>
+      <td>0.8368</td>
+      <td>Thu May 23 10:38:39 2024</td>
+    </tr>
+    <tr>
+      <th>311</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 564, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>
+      <td>-3.0121</td>
+      <td>4.2517</td>
+      <td>0.8345</td>
+      <td>Thu May 23 10:38:38 2024</td>
+    </tr>
+    <tr>
+      <th>312</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 189, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>
+      <td>-3.0121</td>
+      <td>4.3668</td>
+      <td>0.8418</td>
+      <td>Thu May 23 10:38:37 2024</td>
+    </tr>
+    <tr>
+      <th>313</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 32, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>
+      <td>-3.0121</td>
+      <td>4.3696</td>
+      <td>1.3160</td>
+      <td>Thu May 23 10:38:36 2024</td>
+    </tr>
+    <tr>
+      <th>314</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>
+      <td>-3.0121</td>
+      <td>4.3753</td>
+      <td>0.8418</td>
+      <td>Thu May 23 10:38:36 2024</td>
+    </tr>
+    <tr>
+      <th>315</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 563, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>
+      <td>-3.0121</td>
+      <td>4.3863</td>
+      <td>1.1756</td>
+      <td>Thu May 23 10:38:38 2024</td>
+    </tr>
+    <tr>
+      <th>316</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 190, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>
+      <td>-3.0121</td>
+      <td>4.4110</td>
+      <td>2.1299</td>
+      <td>Thu May 23 10:38:37 2024</td>
+    </tr>
+    <tr>
+      <th>317</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>
+      <td>-3.0121</td>
+      <td>4.8695</td>
+      <td>0.8391</td>
+      <td>Thu May 23 10:38:33 2024</td>
+    </tr>
+    <tr>
+      <th>318</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.15511867834972753, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>
+      <td>-3.0121</td>
+      <td>5.5249</td>
+      <td>0.8365</td>
+      <td>Thu May 23 10:38:31 2024</td>
+    </tr>
+    <tr>
+      <th>319</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.15491868834972752, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>
+      <td>-3.0121</td>
+      <td>6.0223</td>
+      <td>0.8328</td>
+      <td>Thu May 23 10:38:30 2024</td>
+    </tr>
+    <tr>
+      <th>320</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>
+      <td>-3.0121</td>
+      <td>6.2898</td>
+      <td>1.7299</td>
+      <td>Thu May 23 10:38:29 2024</td>
+    </tr>
+    <tr>
+      <th>321</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.00119999, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>
+      <td>-3.0121</td>
+      <td>6.4380</td>
+      <td>0.8360</td>
+      <td>Thu May 23 10:38:29 2024</td>
+    </tr>
+    <tr>
+      <th>322</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 3, 'boosting_type': 'dart', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>
+      <td>-3.0121</td>
+      <td>10.9715</td>
+      <td>0.9731</td>
+      <td>Thu May 23 10:38:14 2024</td>
+    </tr>
+    <tr>
+      <th>323</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 3, 'boosting_type': 'goss', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>
+      <td>-3.0121</td>
+      <td>11.4807</td>
+      <td>0.8321</td>
+      <td>Thu May 23 10:38:16 2024</td>
+    </tr>
+    <tr>
+      <th>324</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 9.999999999e-06, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>
+      <td>-3.0121</td>
+      <td>4.2741</td>
+      <td>1.3259</td>
+      <td>Thu May 23 10:38:40 2024</td>
+    </tr>
+    <tr>
+      <th>325</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1.0000099999e-05, 'n_estimators': 5, 'class_weight': 'balanced'}</td>
+      <td>-3.0121</td>
+      <td>4.4722</td>
+      <td>0.9776</td>
+      <td>Thu May 23 10:38:42 2024</td>
+    </tr>
+    <tr>
+      <th>326</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 0.0001778292746984456, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>
+      <td>-3.0121</td>
+      <td>4.4987</td>
+      <td>0.8284</td>
+      <td>Thu May 23 10:38:40 2024</td>
+    </tr>
+    <tr>
+      <th>327</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 0.00018782927469744562, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>
+      <td>-3.0121</td>
+      <td>4.5715</td>
+      <td>1.7346</td>
+      <td>Thu May 23 10:38:41 2024</td>
+    </tr>
+    <tr>
+      <th>328</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 2.0000099998e-05, 'n_estimators': 5, 'class_weight': 'balanced'}</td>
+      <td>-3.0121</td>
+      <td>4.2560</td>
+      <td>1.1777</td>
+      <td>Thu May 23 10:38:43 2024</td>
+    </tr>
+    <tr>
+      <th>329</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 0.0001778292746984456, 'n_estimators': 5, 'class_weight': 'balanced'}</td>
+      <td>-3.0121</td>
+      <td>4.1203</td>
+      <td>0.8478</td>
+      <td>Thu May 23 10:38:43 2024</td>
+    </tr>
+    <tr>
+      <th>330</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 0.00018782927469744562, 'n_estimators': 5, 'class_weight': 'balanced'}</td>
+      <td>-3.0121</td>
+      <td>4.3616</td>
+      <td>0.8424</td>
+      <td>Thu May 23 10:38:44 2024</td>
+    </tr>
+    <tr>
+      <th>331</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 0.05623427310243317, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>
+      <td>-3.0121</td>
+      <td>4.4524</td>
+      <td>0.8340</td>
+      <td>Thu May 23 10:38:41 2024</td>
+    </tr>
+    <tr>
+      <th>332</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 0.056244273102432164, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>
+      <td>-3.0121</td>
+      <td>4.4193</td>
+      <td>0.8345</td>
+      <td>Thu May 23 10:38:42 2024</td>
+    </tr>
+    <tr>
+      <th>333</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 1.7604197964430905, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>
+      <td>-3.0121</td>
+      <td>4.7082</td>
+      <td>0.8322</td>
+      <td>Thu May 23 10:38:32 2024</td>
+    </tr>
+    <tr>
+      <th>334</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 1.7602198064430905, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>
+      <td>-3.0121</td>
+      <td>5.0464</td>
+      <td>1.3080</td>
+      <td>Thu May 23 10:38:31 2024</td>
+    </tr>
+    <tr>
+      <th>335</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 0.999990000000001, 'n_estimators': 5, 'class_weight': 'balanced'}</td>
+      <td>-3.0126</td>
+      <td>4.3677</td>
+      <td>1.3344</td>
+      <td>Thu May 23 10:38:45 2024</td>
+    </tr>
+    <tr>
+      <th>336</th>
+      <td>Model Tuning</td>
+      <td>2715</td>
+      <td>14235</td>
+      <td>LGBMClassifier</td>
+      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1, 'n_estimators': 5, 'class_weight': 'balanced'}</td>
+      <td>-3.0126</td>
+      <td>4.4191</td>
+      <td>0.7945</td>
+      <td>Thu May 23 10:38:45 2024</td>
+    </tr>
+  </tbody>
+</table>
 </div>
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-<p><a id='adaptive-sampling'></a></p>
-<h4 id="Adaptive-Sampling">Adaptive Sampling<a class="anchor-link" href="#Adaptive-Sampling">&#182;</a></h4><p>Following Algorithm Selection, Adaptive Sampling aims to find the smallest dataset sample that can be created without compromising validation set score for the chosen model. Given the small size of the training data (948 samples), Adaptive Sampling is not relevant here.</p>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[15]:</div>
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-<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Each trial is a tuple of</span>
-<span class="c1"># (algorithm, no. samples, no. features, mean CV score, hyperparameters, </span>
-<span class="c1"># all CV scores, total CV time (s), memory usage (Gb))</span>
-<span class="n">trials</span> <span class="o">=</span> <span class="n">est1</span><span class="o">.</span><span class="n">adaptive_sampling_trials_</span>
-<span class="n">scores</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">trials</span><span class="p">]</span>
-<span class="n">n_samples</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">trials</span><span class="p">]</span>
-<span class="n">y_margin</span> <span class="o">=</span> <span class="mf">0.10</span> <span class="o">*</span> <span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="n">scores</span><span class="p">)</span> <span class="o">-</span> <span class="nb">min</span><span class="p">(</span><span class="n">scores</span><span class="p">))</span>
-
-<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Adaptive Sampling (</span><span class="si">{}</span><span class="s2">)&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">trials</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]))</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Dataset sample size&#39;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="n">est1</span><span class="o">.</span><span class="n">inferred_score_metric</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s1">&#39;g&#39;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;-&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="nb">min</span><span class="p">(</span><span class="n">scores</span><span class="p">)</span> <span class="o">-</span> <span class="n">y_margin</span><span class="p">,</span> <span class="nb">max</span><span class="p">(</span><span class="n">scores</span><span class="p">)</span> <span class="o">+</span> <span class="n">y_margin</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">n_samples</span><span class="p">,</span> <span class="n">scores</span><span class="p">,</span> <span class="s1">&#39;k:&#39;</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;s&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;teal&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
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-<h4 id="Feature-Selection">Feature Selection<a class="anchor-link" href="#Feature-Selection">&#182;</a></h4><p>After finding a sample subset, the next step is to find a relevant feature subset to maximize score for the chosen algorithm. The Feature Selection step identifies the smallest feature subset that does not compromise on the score of the chosen algorithm. The orange line shows the optimal number of features chosen by Feature Selection (in this case, retaining only about 4 000 of the more than 11 000 available words).</p>
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-<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Each trial is a tuple of</span>
-<span class="c1"># (algorithm, no. samples, no. features, mean CV score, hyperparameters, </span>
-<span class="c1"># all CV scores, total CV time (s), memory usage (Gb))</span>
-<span class="n">trials</span> <span class="o">=</span> <span class="n">est1</span><span class="o">.</span><span class="n">feature_selection_trials_</span>
-<span class="n">scores</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">trials</span><span class="p">]</span>
-<span class="n">n_features</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">trials</span><span class="p">]</span>
-<span class="n">y_margin</span> <span class="o">=</span> <span class="mf">0.10</span> <span class="o">*</span> <span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="n">scores</span><span class="p">)</span> <span class="o">-</span> <span class="nb">min</span><span class="p">(</span><span class="n">scores</span><span class="p">))</span>
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-<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Feature Selection Trials&quot;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Number of Features&quot;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="n">est1</span><span class="o">.</span><span class="n">inferred_score_metric</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s1">&#39;g&#39;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;-&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="nb">min</span><span class="p">(</span><span class="n">scores</span><span class="p">)</span> <span class="o">-</span> <span class="n">y_margin</span><span class="p">,</span> <span class="nb">max</span><span class="p">(</span><span class="n">scores</span><span class="p">)</span> <span class="o">+</span> <span class="n">y_margin</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">n_features</span><span class="p">,</span> <span class="n">scores</span><span class="p">,</span> <span class="s1">&#39;k:&#39;</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;s&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;teal&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">est1</span><span class="o">.</span><span class="n">selected_features_names_</span><span class="p">),</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;orange&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">2.0</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
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1srlDMhLCFjY2MAuRVbVpudp2dnAADMjEzfciWpA+u7HO3J3Xot/b27rO9ywu93+WJ9lz/WedEyMzNx//59MX8piAlhCcm6iU1MTGBqWjZfPqlB7hKRpsb8cpcH1nc5yn4JAJCamLC+ywm/3+WL9V3+WOfFU9gwN04qISIiItJzTAiJiIiI9BwTQiIiIiI9p5UJYWZmJhYtWoT27dvDx8cHAQEBOHfuXLHujYqKwuTJk9G8eXM0bdoUn3zyCZ4/f17GERMRERFVXFqZEH755ZdYv349evfujVmzZsHAwAAff/wxrl27VuR9KSkpCAwMxJUrVzBu3Dh8+umnuHPnDgIDA5GQkFBO0RMRERFVLFo3yzg0NBQHDhzAzJkzMWrUKABA37590bNnTwQFBWHjxo2F3rtp0yY8ffoUO3fuRP369QEAHTp0QK9evbBu3TpMnjy5PN4CERERUYWidS2EISEhMDY2xqBBg8QyU1NTDBw4EFeuXEF0dHSh9x46dAiNGzcWk0EAcHd3R5s2bXDw4MEyjZuIiIiootK6FsKwsDDUqlULlpaWcuU+Pj4QBAFhYWFwcnJSuE8qleLevXsYPHiwwrmGDRvizJkzSEtLg7m5uUrxpGdniGsfqVtaVnqZPJeUY32XH9mfMtZ5+WFdly/Wd/ljnRctMzuzyPNa10IYExOjNOFzdHQEgEJbCOPj45GZmSleV/BeQRAQExOj3mCJiIiItIDWtRCmp6cr3XZFtltIRkaG0vtk5cq2mZPdm56u+r8uzIxMy3xVdHNjszJ9PsljfZcfc2Mz1nc5Y32XL9Z3+WOdK2cgVb5DiXi+nOJQGzMzM2RlZSmUyxK+wraRk5VnZio2mcruNTPjl4iIiIj0j9YlhI6Ojkq7hWXdvcq6kwHA1tYWJiYmSruFY2JiIJFIlHYnExEREek6rUsI69ati8ePHyMlJUWu/MaNG+J5ZQwMDODp6Ylbt24pnAsNDYWbm5vKE0qIiIiIdIHWJYTdu3dHVlYWtm/fLpZlZmZi586daNq0KZydnQEAkZGRCA8Pl7vXz88P169fx507d8SyR48e4fz58+jevXv5vAEiIiKiCkbrJpU0atQI3bt3R1BQEGJiYlCjRg3s2rULkZGRmD9/vnjdjBkzcPHiRdy7d08sGzZsGLZv344xY8bggw8+gKGhIdatWwdHR0dxkWsiIiIifaN1CSEALFy4EL/99hv27NmDhIQEeHl5YeXKlWjWrFmR91lZWSE4OBg//vgjfv/9d0ilUrRq1QqzZs2CnZ1dOUVPREREVLFIBEEom1WVdVxGRgZu3boFb2/vQmc2l5ZskU1OoS8frO9ytCl3+YO0QWms73LC73f5Yn2XP9Z50d6Wt2jdGEIiIiIiUi8mhERERER6jgkhERERkZ5jQkhERESk55gQEhEREek5JoREREREeo4JIREREZGeY0JIREREpOeYEBIRERHpOSaERERERHqOCSERaYzFjwtQJShI02EQEek9JoREeiwjO1vTISAqJQX3X7/Gm7Q0TYdCFUBmTg6ycnI0HQaR3jHSdABEmpKSmQnnoCCkZGUBAJwsLfFk8kQNR5WrSlAQolJSAADOlpZ4NXWq2l8jKycHZj/8IB6X1esUx7j9+/EmPR3Xxo4FALxITISLtTUkEolG4qnokjIyUOmnn8RjTX526pL/Ow8AkZ9/jqrW1th08yaWXbyIg8OHw8bMDMcePcKRR4/wXZcuMDE0RFhMDJ7Ex8OvTh0YSCRIzsyEgUQCC2NjDb4bIu3DFkLSW2GxsWIyCADR+f4y0rT8fzFGlVFchgbyf/zL6nXextnSEj927YoF3boBAARBQMvVq/HR3r3iNan5PqeyIAgCHBctgmTuXEjmzoXZvHn45dw58fzKK1dw5tkz8TgiMRFpZRxTUQomO5r67EpDKgji55qVk6PwHuzNzQEAJoaGsDQxgXnee74cGYlfz5+HYd4/Fv4ODUWvzZsh+6fDdydPwmHhQvE580+fRqd168TjTTdvYvbx4+Lx+YgIhDx8KB6/Tk1FfHq62t4nkbZgQkh6q7mLi1qeYz1/vphIlOV4uPTsbPx89ixi1PSXv4GS1rf0cu5CTv1qBl5NnYrWrq54190dAJAjCPjR1xcjfHwAAPHp6XBYuBArr1wBkJu8CYKg1jgEALGpqeJxRk4OpPle47OQEOy6e1d8/TpLluDbEyfE41arV2PttWsAchOdn/77D1ciI8XjB69fqzWpLZjMa4OQhw9x7NEj8bjGr79i2uHDAABjQ0OF602NcjuwBtavjyMjR8Ik75oZ7dsjfdYssQ4mtmyJcx9+KLYm9/T0xMK8f1wAgIOFBdxsbMTjCxER2H3vnni8+MIFfHrwoHg8dv9+tP3rL/F4zL59GLhtm3gcdPYsFp45Ix7/++CB3Pt6npCA1/m+S8VRJShI/B1SOS+ZDYuJwdh9+/Dg9WsAwK3oaHxy4ACexMcDAG68eoVJ//6LF4mJAIBrL19iSkgIXiUnA8hNnKcePizGcvHFC8w4ckRMds9HROCrY8eQnJkJADj7/DlmHz8u/g7479kzfHvihNh9f+rpU3x/8qT45+Lkkyf46b//xPdw4skT/Hz2rHh8/PFjLL1wQTw+9ugR/rx8WTw++uiR+GcGAI6Eh+Pv0FC54223b8sd7woLU6gvbR6DXNHeh/b9ViEqoDR/qJwtLeWOt9++o/Lry36hArktNYfDwxGV90u5pKxNTMSfZTGeePIEU48cwc3oaABAWlYWcqTSEr9GVk4OKpmaypV9cehQiZ+nLkYGBni/cWN0qVULQG6cn7dujZbVqgEArr58ieq//opzz5+r7TWVdUxPbdtW/PnlF1/g644dAeQmj7+/9x7616sHIDeJdjA3FxOYN2lpmHnsGM7kxRebmgrPZcvEv/yikpPRaMUK7M1LSuLS0vDdyZO4ExMDIPdzvR0dXWQC+SYtTa6VsOD3WBOycnLwMilJPJ559CimhISIx7OOH8eifAnDF23awN/Do0SvlX8oQVVra7TI+24AQEc3N0xq1Uo8HtOsGTb06yceL/b3x83x48XjX959F/uGDpW7/ptOncTjOvb2qFu5snh88cULnIuIEI/nnjwp9776bt2K93fvFo9916/HJwcOiMdTQkKwIl9iFHzjhlzr6Ou8sbQxqanYe/8+4vKOXyUnY/udO+JxRGIiNt68iTd5Cd7j+HisuX4dCXnH91+/xorLl5GYkQEAuB0djSUXL4q/r669fImgs2fF79nFFy/w43//ieOKzzx7hrknTyI773fMiSdP8E3eP4IA4MijR/g6X0vrgfv3Mft//xOP99y9K3f9jjt35I433byJOfmO19+4If4jCwBWXr0qd7zs0iV8d+oUAMUelPC4OFjPn48dd3ITxruxsXBYuBB78v4RdzMqCi4//4xDeS3B116+RK3Fi3HyyRMAwKUXL+C1bBnO532u554/h88ff+Day5cAcpPjFqtWiX9GTz55gnZr1uBhXByA3OTXd/16PEtIAJCb7Hb/+2/xz8Ph8HD02bJFTM7tFyyAZO5chfeh6eSQYwhJ65W0e/X+69foVrs2ZrRrB08HB3x38gS613EvdTxD//kH79SujS0DB5b4Gf+NHo2zz5/jg8aNxUSje506uDthAjwcHAAAv54/j5VXruDm+PGwLpDYFUdmTg4SMzKwsFs3TGvXDv/cuYP2NWqUOOay4mhpiR+6dhWPDQ0M0KZ6dbjb2wMAtt++jeWXLmHrwIFwtrIq0WtIJBI4W1rKjdvMz8bMTPzZQCLB6CZNxGNzY2P8O3y4eOxgYYGUr74Sjy2MjbGhb18xoc2WSlHT1lZM+p8nJGDOiRNo6OSE+o6OuBUdjZarV2PPkCHo7eWFqy9fYvjOnVjTuzfaVK+O8Lg4/HD6dJl3o7/NqadPcScmBuOaNwcABOzYgQevX+PWJ58AAFKysuRanLcNHIjKFhbi8ZQ2beSeV1T9l6Wq1taoam0tHstaqmWmt2snd7xt0CC54z1DhohJEwB826kTzIz+/6/WNq6uqFapknh87dUrGOVr4f0ir5W0oI5ubnj5xRficbfatREzbZp4/J6nJ+JmzBCP+9erJ/4jBQCGNWyIYQ0biscfNGmCD/J9b8e3aIHxLVqIx5+1bo3PWrcWj2e0b48Z7duLrfFfd+yIWR06iP94+rZzZ8zO+0cSAPzYtSvmdukiHi985x25P7e/+Pnhp3wtt0v8/eUmD/3+3ntyxyt79kRWvnr9q3dvuXrOr5KpKcY0bYradnbi8VBvb9TIaxmuZGqKnp6e4u8Ha1NTdHRzg13esARLExM0qVJF/DNpbmyMOvb24jAFE0NDOFlaip+bgUQCcyMjsZdFEARkS6ViXWVkZ+NNejpkfQxJGRl4Gh+PnLzzb94yJEFTQ0Akgrr7XvRERkYGbt26BW9vb5iW4C/j4kjLyv3SmBubveVK/SaZO1fueFP//nidloZPWrRQ2i0qcyUyEoN37MC6vn3RvkYNsb6NDIwx4+hRTGvbVu4visI4Llokdjc6W1ri1AcfwEAiQR17e7xMSsLXx49jTufO4i8ndQl5+BDHHz/GwnfeAQD8fukS3Gxs8J6nZ7HulwoCIpOSYGNqKpdQCoKABWfO4KOmTeX+AlerTbmfS9qgtFJ/v7fdvo3fL13CscBAGBoYYNnFi7gdHY3l771X5OefnyAIuBsbi8oWFnDUQGub7C9CY0NDxKam4uijR+jk5oaq1ta4GRWFuSdP4vsuXVDP0REHHzxAz82b5bq0AUCYM6fI11D190laVhYexMXBx9kZQO44ylVXr+LiRx9BIpFgSkgI/rp2DQlffgmJRIKQhw8Rl5Yml4Tos+LWd0xKCpwKtAi97bPUdwV/58vqS1v+ziwYvzJl8R14W97CLmPSevm/xM6Wlth3/z623Lr11vuaubjg4aefKrSK3YqOxsorV/C/vO6Et+nj5YVq1tYQ5szBq6lT4enggDp5rVcXX7zAtjt3xL/wC/sXbkGJGRl4lpCg8Jd+ft3r1BGTQakgYMmFC9iRN8YGgFz3nTIGEglcK1VSaF28ExODuSdPYtPNm8WKVdMCGjTAiVGjxDFlL5OS8Dg+XkwG558+jVV54w8LIwCo//vv+CNfV155MjY0FMfRVbawwBBvb/EfIw2dnbEjIAD1HB0BAP4eHsj8+mu1x3AzKgozjx4VuxR/v3QJjVasELu5rE1MUMPGBml5rX7fdOqEqKlTxe7b7nXqMBksAUdLS7kW0YrQ/V/RaXt9FYxfmDOnQrwnthCWEFsIK47jjx/D2dISDZycAOS29iRkZMDWzAxJGRkIOnsW09q1g1W+cXnK5K/vl0lJ4l/Ib9LSxK4FZUKjohCbmgrfvDFvBSVnZoqv/eGePYjPyMCOQYOKXFLl13Pn8Pnhw4ifMUOuu7Io2VIpEjMyYG9ujifx8XBfsgSre/WS6ybKLysnByuvXEHb6tXRpGpVuXP3X7+Gh709qv78s8LyN0suXICngwO616kDAPjr6lV4Va4sJtZbb92Cp4OD+MwD9++jjr09vPLGYZ1++hQdztQEoJ4WwrfpvG4datraYl3fvgCAeadOoXPNmnL/EBAEAdtu34a3k5P4ParIwuPiUGfpUvG4OMvOxKUm4WZ0NHycXWBnbo6TT57g/d27sXfoUPg4O2P33bsI2L4dl8eMgY+zM+6/fo3rr17hPQ8PWL7lzw4p4u/v8sc6LxpbCEnn+daqJfeXuEQigW1eEnUoPBzzTp/G7byJGPndjY1FwPbtCI2KUjgnSwbD4+LgsXSp3Gy4gnycnQtNBgHIJaL1HB3R0MlJTAYvR0YqnTH7jrs7/urdWxzDUhxGBgbiUh3WJib4rnNndK1dG0DuzMopISFysx8zc3Iw8eBBHM03Q1LG08EBEolE6fjMH06fxu68wdoAMPXIEWzPNxvwgz175FoX+2zZguC82YNSQUDHfEuAlMdOJSdGjcKqXr0A5La8LjxzBqefPgWQmxT/dv48IhITMdjbWyuSQQDixIJ33d3RtGpVfJpvEoWsVTkqORmfhYTg4osXAIDbMbHovP5vnMhr+a5qbY12NWqIy7f08PBAyldfiV3Eng4OCGjQgMkgkZ7gpBKqcFRdlPnc8+eobGEhTrbIb2D9+ng4aRJq5Q023nb7Ntq4uqK6jQ1Ss7JwKzpabpZwQS7W1hhYvz461axZ6DUPXr/Gm/R0cdJAUfLPXL0VHY0Wq1Zhmb8/JrRsKXedt5MTvEuRnDhYWGBWvgHfV16+RHBoKOb5+gIAHr15A2dLSzhaWGD60aOYfvRosRc3fj5litzx/YkTxYkvABA6frzc7OXzH32EKvkmexwZORI4/614XB4DqGXdsZVMTRE7fToy87rwr758iSmHDqGGjQ2a/PmnOMOzoi/03KJaNThbWuJweDiA3Pcx6/hxSJA7WH9iy5YwMjDAqqtX0bRqVbSsVg0NHCtj+6AB6ODmBiA34dvYv7/4TBMlS78Qkf5gCyFVOAVbpVKzsvDTf/8hspAxcf22bsXP+RYRLkiWDCZnZmLc/v3i0ghNq1bFnQkT0LZ69ULvNTc2xoqePcXZa79fuiS3Xh0ALDhzBv23bi3em8vH08EBa3r3xhBvbwC54w3XX7+OrJwcvElLw4PXr0u1rEx+n7RogedTpoitPaP37EHHdesQk++9KEvMlI1rMTE0lEseHC0t5RLAOvb2cMp3X3MXF7jmzbI0kEjQLa/VUlNMDA3FVttWrq54Mnky3nV3F5NBQDsWelYWowCIS6Q4WFggaeZMBDZqBCD3u/yeR52ymyhERFqNLYRUoSTlrZmV35lnzzDz2DG0qlYNLtbWiElJQUpWFmra2gIAdgQEFOsvOSsTE1wdOxbmea1ZEYmJeJGYiFaursWK7dGbN/gib6HX2fnWKfu8TRu8n/eXripMDA3lxvdtuHED2+/cQUCDBlh3/brKYwjfJn/38/dduiA2NRX98y24q0x5tJJpelC4m62t2hLviiB/wl3cWdZEREwIqUJ5WWBBZ2dLS7zj7o4nkyeLrUyrr17FrOPHEfnFF6hiZYU2rq7F3rlBlkQCuS2Ll/N2kwDevpdxbTs7XP74Y7EFJisnB8aGhqifN/uztJb6++OLNm1gbmwsrk1mu2ABDADkqHkJgg5ubkqT7/KW+tWMCjEAvGDipA1dJ/nX7ctfRkRUEkwIqUJxs7FB6LhxqG5jI04MAXJbcWSGNmyIqtbW4rg0k3nzxIH0qoz9WtitG3w3bBCPi7OXsWzSQUJ6OjqsXYtJLVuifY0aeJ2WVupFnSUSidi9nX+aSVm1XZVkMWtdVXDGtza0F1bkMY5EpH204R/CpEdMjYzQ0NlZLhksqKatLUY1biwe51+rT5WxX12KmBn8NoYGBuJ6gz+fO4chO3aU+Fn6rDxmGRMR0duxhZCKTdXZv8okZ2bicmQk6js65nbRxsfj90uX8FHTpvB0cMCR8HC8+/ff4vVlOdtTtim8jCqjraxMTLAjIAAA0HXDBgjIXX2+LLp3y5KmtgvLr6JM4KgIdUFEpClMCKnYSrpncHJmJirNny/XDbpj0CAMqF8fcWlpWHLhArrUrAlPBwc8zdscXJXXKelf5AcePJA7LukK7eXRvVtW2O34/1gXRKTPmBBSmfv2xAmFZEu2rl+TKlWQnm8brsBGjfDxvn0qPT81KwsfNG6MNX36qHRfcxcXla6nssHWOCIizWNCqOeUdQNb/vADUvP2K83fZWthbIzUrCyxPCsnBx/t24epbdqgYd7uBjLJmZlIzsxEFSsrzOrQQWGdQNkyMQUH85sYGqrc4peUmYm116+rnBA2rVpV7rWc1JCYqGtQrr50X1aUWcZERPpOKxPCxMRELFq0CEeOHEF6ejp8fHwwc+ZM1KtXr8j7pFIpdu3ahSNHjiAsLAwJCQlwdXVFz549MXr0aJjo4RZNyrqBZclgwfMpX30ld29YTAwOPXyI3p6ecglhjlSKdmvWoIqVFQ6NGAE7c3NI8P9dq0UlTVciIxGVkoL5Xbviy/bti/Uero4ZU+Rew4V5npCAT1u1wlBvb9SysxP3wVRVZQsLcbFqRzUlb+y+JCKi8qR1CaFUKsWYMWNw//59jB49GnZ2dti0aRNGjhyJnTt3okYRS3+kpaXhq6++QuPGjTFkyBA4ODjg2rVrWLx4Mc6fP491+fZYpber5+iIB5MmwdrUVKGlcXH37qiWt24gACR/9RXepKXB2coKRkWsGZiel4y+LrAbSFGaVK1aovj/ffAAs44fh4FEUuzkU5nYt+z2QUREVNFpXUIYEhKCa9euYfny5ejWrRsAwN/fH35+fli2bBkWLlxY6L3GxsbYvHkzmjZtKpYFBASgWrVqWLp0KS5cuIBW+TaJ1wdOlpbi+ntOed24jhYW4pZmJoaGePzmDWrZ2aHywoUY26wZfujaVbxftpZdwZbGwXnbsclEp6Tg4osX6OHhIW4bpkzLatXwePJklbbXksydK/6syqzk1q6uaFmtGroUsU8xERGRPtC6dQgPHToEJycndM2XlNjb28Pf3x9Hjx5FVt4YN2VMTEzkkkGZd955BwAQnrdRvD6JmjoV0VOnIvzTTxE1bRoAwM7cHEO8vXF/4kQ4WVri/uvXAIAxzZoVue9vUVZduYLBO3aIzyqMsaEhatraFpk0FkWVFrpGVargwkcfFXvrusIo2++XiIhIm2hdC2FYWBgaNGigMBmhYcOG2Lp1K549ewZ3d3eVnhkbGwsAsMvbJUKfbL55E8aGhhhYvz6A3PF/87p0gYOFBTwcHPBw0iSY5u39+2O+JFxVsl1FZPsIF+bSixdouXo1vuvcWW6/4LLwLCEBa65dw0gfH7jb25f4ORzvR0RE2k7rEsKYmBi0bt1aodwpb0ux6OholRPC1atXw9raGu1LMI4sPTsDUoOSrmBXtJJOcihKzcXL/r+L2NIStWxtYGVigvc8amPakaN4mpCIbQP7y71+WlY29ty7j1vR0ZjVQXkd2ZqZIT49XXxuwdhH+DRA37oesDMzK/J9JWemAQDi09OK/f7lur2VvHZhzjx7grknT2LuyZO591pYIGzCx8W6l0pHNgWoLL7jpBzrunyxvssf67xomdmZRZ7XaEIolUqL7OLNzzRvrFp6errS2cCysvR01b4QK1aswNmzZ/Hdd9/B2tpapXu1Uf79eqNTUnB7/BixtbWGjQ2MDAzwND4BZkZGcLb6/+7P44+fYNXVa/jh9BkAuYnXk8kTxfOyZDD1qxlKX/dlUjLOv3iBnh51itxDt7mLC8I/nQBbFfbZLc4exMr08fKUf44KE1mIiIh0iUYTwkuXLiEwMLBY1547dw729vYwMzNDZqZilisrMytiD9yC/v33X/z2228YPHgwBg8eXOz78jMzMoWpcfGTl5Ioy3XaKlv+/0zg6e06AAC8li1D06pVsXnAAPHc8h7vYdXVa+JxdEqKXFzNXVzQ1tW10FiDQ2/hpzNncPnjj9HMxabQeARBgMfS30u8RV7BuFRlbmzGdfHKEeu7/LG+yxfru/yxzpUzkBa9QatGE8LatWtj/vz5xbrWKm8MmqOjI6KjoxXOy8pkXcdvc+bMGUyfPh1dunTBHC3ae7a08nevOltaYsONGzAxNMSQfLOCXyQm4v7r19hy65aYkBkbGhb53EsfF93VuvzSJQBA81WrikzyLr54UeIt8kpC3QtTExERaSONJoSOjo7o37+/SvfUrVsX165dgyAIchNLQkNDYWFhUeQ6hDI3btzAxIkT0bBhQ/z6668wfEuyo0uipk5FVk6OmOC1/esvWJmYiAlhjlSKlHzd+PkTMkOJBDlC7njJgrNpQx4+RGULi0K3g0vK16pbVJInFVQfj1maXT3yJ6Ycf0JERPpK6yaVdO/eHYcOHcKxY8fEdQjj4uIQEhKCrl27wtjYWLz22bNnACCXJIaHh2PMmDGoVq0aVqxYoVIXszZyDgqSaxH8sn171LCxQf969SAIAk6MGiV3vWERi0bnCAJcK1XC8ylTFM75b9wIABBK2draslo1uXUQi5PgcZYvERFR6WhdQujn54fGjRtj+vTp4k4lmzdvhlQqxaRJk+SuHZWX7Bw/fhwAkJycjA8//BCJiYn48MMPceLECbnrvby8ULdu3fJ4G+UmukD365ILF9C+Rg3suXcP2VIpNippoS2sJfAHX1+0rFZN6esYGxggSyqFZO5cpV3CBfdBLoxEIsGzKVNgbGBQZHJKRERE6qN1CaGhoSFWrlyJhQsXIjg4GBkZGWjYsCEWLFgANze3Iu+Nj4/Hy5cvAQA///yzwvmJEyfqXEJY0KPJkwEA806dQo5Uir+uXoWJoSFGNmokXvNJixaoZWuLKW3ayN37VYcOhT43SyoVf1bWJfzzu+9i/IEDuDthArwqVy70ORdfvECbv/4Sj1WdVEJERESq07qEEABsbGzwww8/4IcffijyOlnLoIyrqyvu3btXlqFVOPnH19mbm4vlX3fsCOD/xxDmTwgPhYejqZL9gd1++w2jGzfGnM6dVY7j27zW2LrLlxeZ5DUsMCmIewMTERGVPa1MCKn4HkyahFvR0TA0MEALFxcsPHMGbjY2GOztDUEQsKFfP9QpsEtHdEoKtty6JTfLGMjd2ePbkyfx7cmTRSZ1yrqEiztz2LKEW9YRERFRyXGQlo47FxGBtmvWIFsqhUQiwZpr13A4b8/mBWfOoN7y5YgtsCBzfL7FvQtL3gqW9/DwwDcdO0KYM6fUXbzcG5iIiKh8sYVQB2Xm5MD2p5+Qlp0NALAxNYWdmRk+P3QImwYMELuD+9Wti+137mDzzZuY1KpVqV7zSmQk/n3wAN+dOqW09VCVpWE4ZpCIiKh8MSHUQZFJSWIyCAAJGRmo//vvkABo4OgoJoRelSvjZlQUPg0JwachIWIiV9gsYwkA2SqBBZO6t3UJM8kjIiKquJgQ6iAzI+UfqwDgZXIyNt28iWENGwJQPjt45+DBsDc3R/sCi3wLANzt7PDw00/LJG4iIiLSDI4h1EFP4uMLPbft9m2cePKkyPunHTkibjWX35Lu3bG2Tx+l93DcHxERkfZiC6EOalKlitJyZ0tL3H/9Gjejo7Hq6lU4W1rCzMgI6Xndy7JEzs/dHbVsbRXuL2qcIbuEiYiItBcTQh3z6M0bJKTL78nrYG6OZ1OmwMLYGJK5c8XyqJQUdK1VC5YmJtgzZIhYXtg6hKbz5mFSy5YIevfdsnsDREREVO6YEOqYSQcP4t8HDwAAy3v0QGULCwzesQOnnj5F9zp1FK5f+M47MJRI5MoWvfOO3CLWMpk5Odh3/z4TQiIiIh3DhFDH/Ojri35162LvvXvwcnBA/23bAAD+GzcqjO0zANB69WpxYolslvG0I0fQtGpVhUkllUxMcP/160L3KyYiIiLtxIRQhxjOnQvZnGEJgFW9eiExI0M8X3A5GCkAqZJZxt3d3VHLzk7h+YmZmYU+i4iIiLQXE0IdIs33swDg4MOHJXpOSCFjCImIiEg3MSHUEa8LbD8HAKlZWbAxNUVCXiuhs6UlkjMzkZKVJR5nSaWIS0sTj4HCxxCqstsIERERaQ8mhDrCysREoaxe5cpY1qMHRu7ahX1Dh6Knpyfa/PUXWrq4YLG/PwCg7V9/wcrEBIdHjhTvK2wMIccMEhER6SYmhDrC1MgIwpw5AIC0rCycj4hA3cqV0ahKFfzv/ffFtQmtTUxgbmws3lfbzg4W+Y6BwtchJCIiIt3EnUp0QI5UihWXL+PggwfYGBqKqJQU+G7YgEPh4TCQSGBmZASDvKVlPB0c4FqpknhvQkaG3MQTIHcdwouRkeX6HoiIiEhz2EKoA668fInxBw6Ixw8mTQIAfLBnDz7YswcAcGDYMPTw8ICZkRFMDA3Fa79s1w6GBvL/LggqZAwhERER6SYmhDqg9+bNcsceS5cqXCMIAgDg4osXkOb9DOSOFyw4hnBq3hjCdgXGEBIREZFuYkKoA4qzJmAHNzcAgKGBgdh9DOSOITQ3kv8acAwhERGRfmFCqOUqL1z41mvszMzEFsLGzs6oY28vnkvNyoKkwNZ1he1lTERERLqJCaEWe5aQgNd5awgqs7xHD1QyNcXIXbtw+tkz9PT0RI4gICff7iSTWraEUYExhIWtQ0hERES6iQmhFiuYyAGAmZERnk+ZgmlHjsDH2RnPExIAAIZ5rYB3YmLkuohnHT+uMIaw/9atyMlrUeSexURERLqPCaEWc7G2lts9BADSs7NhYWyMfnXrooaNDXycnXH544/hVbkyACAtOxsZOTni9crWIczJN+mEexYTERHpPq5DqOWUtd5Fp6Sgz5YtOP74MbJychCXlobsvG7i9tWrw9vJSbw2SypFllSq8AwiIiLSH0wItVh4XBxq/vabQrks+UvNysK/Dx7g3b//xumnTwEAiRkZSMnMFK/9qEkTBPr4yN2ff59i7llMRESk+9hlrMUsjI3RuWZNrL9xQ67czswMY5s1Q5MqVfAsbwyhcd5i1E8SEuQmjMw5cQJWJiboUquWWMYxg0RERPqFCaGWcly4ELFKZhg7W1rCzMgI3WrXRlVra3g7OSF03DjUtrMDAMSmpiIh31Z1ytYhJCIiIv3CLmMtpSwZBICpbdsiJjUVg7Zvx4knT5CenY1nCQnIzJtI0t3dHc1dXOTuKbgOIREREekXJoQ6ZtqRIwpjCHtu3oxTeWMII5OSEJcvmRzq7Y2B9etrJFYiIiKqGNhXqKUKLjcD5I4d/LhpU9iZmWFSy5ZoVrWqOIbQLK9bODYtDfHp6eI9P5w+DSsTE7zr7l5+wRMREVGFwoRQSx0LDMSWW7fw++XLYoufiaEhFrzzDpIzM9GqWjVUtrCAt5MT7k2ciOqVKgEAnsTHo6qVlfgcZesQEhERkX5hl7GWCg4NxaKzZ+W6f6NSUjDnf/9DbGoqRuRtV5ecmYnb0dFIzcoCAAysVw/ta9QQ7zE2NISxkh1PiIiISH9oZSaQmJiI2bNno3Xr1mjcuDECAwMRFham8nNycnLQq1cveHl5Yd26deoPtAx92qoV/uzZU6H8u1OnFMYQ9t+2DaefPQMA3I+Lw4vERPH6vl5e6OHhUT5BExERUYWkdQmhVCrFmDFjcODAAYwYMQLTpk3D69evMXLkSDzLS3qKa8uWLYiIiCijSMuWi7U1auUtJSNjZ2aG77t0gb25Oaa2aYMWLi6oZGoKALDM6xZOz84WZxwDwIIzZ7D4woXyC5yIiIgqHK0bQxgSEoJr165h+fLl6NatGwDA398ffn5+WLZsGRYuXFis58THx2PJkiX48MMPsXTp0rIMuUyERkXhWUICLn/8MSyMjZEtlcLbyQkSiQRJGRmo7+gIWzMzeDs54fHkyaiSN27wdnQ0bPKSRIBjCImIiEgLWwgPHToEJycndO3aVSyzt7eHv78/jh49iqy8sXJvs3jxYri6uqJPnz5lFWqZ2nTzJj7auxfNXFwwZv9++KxYgQMPHuCLQ4fwOi0No/fuxZnnzxGfno7zERFIztuuLrBRI3SrXVt8zs6wMKy6ehWSuXNRJShIU2+HiIiINEjrEsKwsDA0aNBAYTHlhg0bIiUlpVjdxvfu3cPWrVsxc+ZMrV2UeUrr1vird29I5s7Ff3nvudfmzfjl/Hm5MYQHHz7E0H/+EdchvP7qFcLj4sTnpGVniz8XXMaGiIiI9IPWdRnHxMSgdevWCuVOTk4AgOjoaLi/ZU29efPmoVu3bmjevHmpxxCmZ2dAaiCU6hmFSctKL/RcJVMjOFvm7kn8WauW+LFrF5x7HoErL1/CwsgA09q2QeMqjniWkDuBxNRQgrSsdAiCFDlCTqHPLuo1dZ0+v/fyJttNm3VefljX5Yv1Xf5Y50XLzM4s8rxGE0KpVFrsLl7TvHFv6enpMDExUTgvK0tPL/oLIRuDePDgQRWjrViuvXqF8DdvcPGj0TCUSHDl5Uu0dq2GNtVdkZSRgeqVKsHS2Bh+7rXxcNInqGxhAQC48vIVTAwNxec4WVoiOq9l0MnSUiPvhYiIiDRLownhpUuXEBgYWKxrz507B3t7e5iZmSEzUzHLlZWZmZkV+oyMjAwsXLgQgYGBqF69esmCLsDMyBSmxqZvv7AUzI0V39Peew/x2/nzSP/6a7T96y+ci4jAPwEBCHn4EDPbt8enIYewpndvOFpWwpnnkehepw5szMwwvnlzuNnais+Mmjq1TGPXRsrqm8qGubEZ67ucsb7LF+u7/LHOlTOQFj1ETqMJYe3atTF//vxiXWuVN0vW0dER0dHRCudlZbKuY2U2bdqEN2/eoHfv3mJX8atXrwAACQkJiIiIgLOzM4wr+KzbKkFB4ng/ydy5YvmAbdsAANPbtQOQOz7w3wcP8PG+fdgxaBAG1K+PM8+fIzkzk/sXExERkUijCaGjoyP69++v0j1169bFtWvXIAiC3ISQ0NBQWFhYoEa+XTgKioyMRGpqqtKZxb///jt+//13/Pvvv28dg6hpBSd/WBobI/mrr3DpxQtcf/UKlS0s8G2nTmhbvTqexMcDAOzMc0dtHXz4ELvu3sW3J0/C2dISr9hCSEREpPe0blJJ9+7dcejQIRw7dkxchzAuLg4hISHo2rWrXOuebMaxLEkcOHAgWrVqJfe8169f45tvvsGAAQPg6+uLKlWqlNM7UZ+UrCycff4cbVxd0aJaNSRmZMDO3Bwmhobo4eGB2GnTxAWq0zmrmIiIiArQuoTQz88PjRs3xvTp0zF69GjY2dlh8+bNkEqlmDRpkty1o0aNAgAcP34cAODl5QUvLy+5a2Rdx56enmKCWdGZGhoiI99uIwDQbs0abOrfH3vv38ePvr6YHBKC1b16oZKpKQ4+eIDeXl5wzut2JyIiIspP69YhNDQ0xMqVK+Hv74/g4GAsWrQIdnZ22LBhA9zc3DQdXrnYP2wYlA0NHbZzJ7bcugWpkLsMTrZUin8fPMCY/fvFdQid880kduasYiIiIoIWthACgI2NDX744Qf88MMPRV4naxksiqurK+7du6eu0MpFt9q1UXDlQ2HOHFx9+RI3o6JQ2cIC87t2Rdvq1fE4bwyhbNkZjhkkIiKigrSuhZCAl0lJCmUnnjxBkypV8H7jxgAAIwMDSCQSjNm3DwDgu2EDt6YjIiIipZgQaqFP/v1X7tjIwABd1q/Hhhs30GvzZrxOS8O0I0dw9vlzuYkjnERCREREyjAh1EKftWqFLQMGYN/QoQAg7l08as8e7L9/X7xOO3dpJiIiovKmlWMI9V2nmjWx+upVjDtwAAAwq0MHzPP1xY1Xr3AnJgYO5ub4+d130bZ6dThbWootg5xEQkRERMowIdRCTosWISY1FQDgaGGBwQ0a4NDDh3jX3R2NqlRBfHo60rKykC2VchIJERERvRW7jLWQLBmU/Tz0n3/QfeNGrLxyBZ3XrcPr1FR8/b//4VzeGotERERERWELoQ64HRMDABh/4AAEAAZ5W/qZGBpqMCoiIiLSFkwIdcA3HTtibpcuuB0djXuvX8PBwgLL/P3RxtVV06ERERGRFmCXsQ4YWL8+9t27h/qOjuhfrx5ypFJEp6TI7VtMREREVBgmhFrO0cICA7dvR+8tW7Ds4kU0X7kSr9PS8N2pUxxDSERERMXCLmMt9HuPHohMSkLnmjXRLThYnGTy+eHDyJZKYWSQm+dbGBtrMkwiIiLSEkwItUx4XByA3Akj/hs3AgC+7dQJczp3xt3YWDyMi4ODuTkqmZjggz178MGePXC2tOTyM0RERFQoJoRa5vSzZ3Jb1yV++SUevXmDHXfuYEC9eqhbuXJueWameA23rCMiIqKicAyhlgls1Ej82VAiQXJmJvpu3YpB27fj53Pn4LVsmQajIyIiIm3EFkItY/zdd+LPUkGAyy+/AMjdt/jr48eRkZMDANyyjoiIiIqNCaGWkeb7Wcj7/3edO2N2p0548Po1nsTHAwDHDBIREVGxMSHUcumzZiEsNhabbt7EUG9veDg4aDokIiIi0jJMCLWMAeRbCV8mJ6Pnpk14kZSEJ/HxWHbxIiK/+EJT4REREZEWYkKoZXLmzMHWW7fwJj0dDZ2cUGvxYkgAVDI1xY+nTyMlK0vTIRIREZGWYUKoZXbfvYu4tDSkZWej/dq1AIDvu3TBrI4d8fjNGzxLSNBwhERERKRtmBBqmW23b2PzrVvisTBnDq6/eoV116/j/UaNUMvOToPRERERkTbiOoRaZkO/fuLPFsbGuP/6Nfz+/hsf7NmDb0+cQKX58zUYHREREWkjthBqGSMDA+wZMgQJ6emoZWcHr2XLYGxgABdra/x24QKS8u1QQkRERFQcTAi1SJWgIHGxaSsTEyTnJX/fd+mCGe3b41lCAiISEzUZIhEREWkhdhlrkfx7EsuSQWHOHHStXRt/Xr6M6pUqoW316poKj4iIiLQUWwi1mI2pKW5GReGd4GDEp6fjcXw8Fpw5A2HOHE2HRkRERFpE5YQwJycHmZmZMDc3F8sSExOxY8cOJCQkoEePHvDy8lJrkKTcvqFD4bNiBcyNjODl4IBVV69qOiQiIiLSQip3GX/zzTcYNGiQeJyVlYVhw4Zh4cKF+PPPPzFo0CCEhYWpNUjK5WhhIf5sZWKCjuvWAQDm+fri7sSJMJRIAACSuXNRJShIEyESERGRFlI5Ibxy5Qp8fX3F40OHDuHhw4f45ptvsGXLFlSuXBkrV65Ua5CU69LHH8PJ0hKA/BjCdtWrY+mFC4hJTRWvzT/ekIiIiKgoKncZx8TEwNXVVTw+ceIEPDw8MGzYMABAQEAAtm7dqr4ISbTn3j1E50v0KltY4EpkJLpu2MAt64iIiKjEVG4hFAQBOTk54vHFixfRqlUr8djR0RGvX79WT3QkZ1D9+rAxNUW9ypXx77Bh+CcgAM1XrYKhgQGau7jAOa/1EIDcz0RERERFUbmF0NXVFf/99x+GDh2KK1euICYmRi4hjI6OhrW1tVqDpFxVra1hbWqK+PR0XHn5ErP/9z8AuesQfprvMyAiIiJShcothP3798exY8fQs2dPjBs3Dg4ODmjfvr14/saNG6hdu7ZagywoMTERs2fPRuvWrdG4cWMEBgaqNJFFKpXi77//Rq9eveDj44PWrVvjww8/xLNnz8ow6tJ79OYNXiQm4mVyspgMCnPmoLmLC4LOnoUgCBqOkIiIiLSRyi2E77//PlJSUnDs2DHUq1cPn3/+ubgEzZs3b3Djxg2MHj1a7YHKSKVSjBkzBvfv38fo0aNhZ2eHTZs2YeTIkdi5cydq1Kjx1mdMnz4dR48excCBAxEYGIjk5GSEhoYiPj6+WPdryv7791Ew5Tv3/Dk6r1uHLKkUn7dpA4lGIiMiIiJtpnJCKJFIMGHCBEyYMEHhnJ2dHc6dO6eWwAoTEhKCa9euYfny5ejWrRsAwN/fH35+fli2bBkWLlxY5P379+9HSEgINm7ciEaNGpVprOo2xNsbk0NC5MrarlkDe3NzNHJ2ZjJIREREJaK2revi4uLw5MkTdT2uUIcOHYKTkxO6du0qltnb28Pf3x9Hjx5F1ltm265fvx7dunVDo0aNkJ2djbS0tLIOWW2cLC1hbKD4kc3r0gXH338fEglTQiIiIlKdyi2Eu3fvxpUrV/D999+LZT///DNWr14NAGjUqBFWr14NKysr9UWZT1hYGBo0aKCQ/DRs2BBbt27Fs2fP4O7urvTe5ORk3Lx5E76+vvjmm2+wa9cuZGZmwsPDA19++aXcWMjiSs/OgNSgbMbupWWlyx0/jIuDlYkJ3qT/f3nqVzNw5tlzzD3xP0xr25pJYSkUrG8qO7J9jljn5Yd1Xb5Y3+WPdV60zOzMIs+r3EK4ZcsWZGdni8c3b97EqlWr0Lx5cwwaNAg3b97EurwdNMpCTEwMnJycFMplZdHR0YXe++zZMwiCgHXr1uH8+fP49ttvsWDBAgDA2LFjERoaWjZBq4nPilVyyWANm0o49fQZ3vl7E749eUphfCERERFRcajcQvjs2TN0795dPA4JCYGNjQ3++usvmJiYQCKR4ODBg5g4ceJbnyWVSt/axStjamoKAEhPT4eJiYnCeVlZenrh/0JIzdvJIyUlBbt370bVqlUBAB06dEC3bt3w559/Yvny5cWKR8bMyBSmxqYq3aMqc2MzvFHStb2+bz90Wb8eTpaWaO3qCgtjM7YQqoG5sZmmQ9Ab5sZmrO9yxvouX6zv8sc6V85AWnR+oHJCmJSUJLfO4Llz59C2bVsxIfP29sbevXuL9axLly4hMDCwWNeeO3cO9vb2MDMzQ2amYrOnrMzMrPAvgiypbNq0qZgMAoCDgwPatm2Lq1evFiuWiqLL+vUAcscQftysmYajISIiIm2lckLo6OiIp0+fAsidSHL37l0MGDBAPJ+amgpDQ8NiPat27dqYP39+sa6VjUl0dHRU2i0sK1PWnSwjO1e5cmWFcw4ODkhMTCxWLJqQmW93mPyEOXNw4skTfHviBOZ06sQWQiIiIlKZyglhq1atsHHjRtjY2ODChQuQSCTo1KmTeP7x48dwdnYu1rMcHR3Rv39/lV6/bt26uHbtGgRBkEt+QkNDYWFhUeQ6gs7OzqhcuTKioqIUzkVFRcHOzk6lWMpTjpJFpz3s7XE4PBzd//4bAoC5J0/C2dISr6ZOLf8AiYiISGupPKlk8uTJcHR0xKJFi3Dq1CmMGTMGrq6uAIDs7GwcPnwYLVq0UHugMt27d0d0dDSOHTsmlsXFxSEkJARdu3aFsbGxWP7s2TOF3Ue6d++Oa9euITw8XCyLiIjAmTNn0LZt2zKLu7SMCiw3IwGwrm9f+OUlgzJRKSnlGhcRERFpP5VbCKtUqYIDBw7g4cOHsLa2houLi3guPT0d3333HerWravWIPPz8/ND48aNMX36dHGnks2bN0MqlWLSpEly144aNQoAcPz4cbFs7NixCAkJwfvvv4+RI0fC0NAQf//9N0xNTZUutl1RGBToChYAvBscrJlgiIiISKeonBACgKGhIby8vBTKraysxN1DyoqhoSFWrlyJhQsXIjg4GBkZGWjYsCEWLFgANze3t97v5OSEjRs34qeffsKff/4JQRDQtGlTTJ8+vVj3a4qyWcYpSmZoO1talkc4REREpEMkgqBkcFoxnD9/HkePHsXz588BANWrV8c777yDVq1aqTXAiiojIwO3bt2Ct7e3OHtZ3WSLbJobm+FZQgLcfvtN7nwLFxc8S0gQu4k5frB08tc3lbFNuS3eaYPSWN/lhN/v8sX6Ln+s86K9LW9RuYVQKpVixowZ2L9/PwRBgEHe2DapVIqNGzeiV69eWLBgAWe7qpmZ0f9/VKaGhrg7cSJq2NgodCUTERERqUrlSSVr1qzBvn374Ofnh927d+PGjRu4ceMGdu/eDX9/f+zbtw9r164ti1j1Wv5lZzJycrDi8mVY/PCDBiMiIiIiXaFyQrhr1y60a9cOv/32G+rWrQtjY2MYGxujbt26+OWXX9C2bVv8888/ZRGrXrsTEyN3vODMGWQUsjYhERERkSpUTgifP38OX1/fQs/7+vqK4wpJfWrZ2iqUtatevfwDISIiIp2j8hhCc3NzxMbGFno+JiYG5ubmpQqKFNnk25LPwtgYDydNgh3rmYiIiNRA5RbC5s2bY+PGjXjw4IHCuYcPH2LTpk1lujC1PkrLyoJzUJB4nJqVhcUXLsD2p580GBURERHpCpVbCD/99FMMHjwY/fr1g6+vL+rUqQMgNxk8fvw4jI2NFRaIptJJysxUKFtw5owGIiEiIiJdpHJC6OXlheDgYPzwww84fPgwDh8+LJ5r0qQJZs2apXTRaio5m0LWOfStVaucIyEiIiJdVKKdSho2bIgtW7YgLi4OERERAABXV1fY29urNTjKlZ6drVAWN306DA1U7vEnIiIiUlCihFDG3t6eSWA5SFOSEC44cwa/nT+P9K+/1kBEREREpEtKlRBS+VC2GwnHEBIREZG6vDUhrFu3rsrb0EkkEty5c6fEQZE8WzPl+zL6ubuXcyRERESki96aEPbt25f7EmuY/YIFcsdVrKxwb+JEZCjpSiYiIiJS1VsTwp+41p3GpWRlyR2/Sk7Gj6dPcwwhERERqUWZT1NNSEhAYGAgu5DVjHsZExERkbqUeUKYlZWFixcvIiEhoaxfSmc5FLFFnWTuXFTJt4sJERERkaq4kJ0WaFmtWpHno1JSyikSIiIi0kVMCLWAo6WlpkMgIiIiHcaEUAvM7dxZoSz/B+fMhJGIiIhKgQtTa4GE9HSFMikAYc6c8g+GiIiIdA5bCLXAtCNH5I5drK2ZDBIREZHaMCHUAq6VKskdRyYlaSgSIiIi0kXsMtYC33XpgrXXr8uVSebOBZA7fvDV1KkaiIqIiIh0RZm3EJqZmaFfv35wcnIq65fSWdFFLCvDJWeIiIiotMq8hdDKygrz588v65fRaQXHEBIRERGpk8oJYdeuXYs8L5FIYGZmhqpVq6Jdu3YICAiAhYVFiQMkoLatLY4Xco5LzhAREVFpqZwQuri4IDo6Gk+fPoW1tTVcXV0BABEREUhKSoKbmxtMTU1x48YNnD59Glu2bMGmTZtgb2+v9uD1xfR27bD62jXx2MzICGmzZmkwIiIiItIlKo8h/OqrrxAfH485c+bg7Nmz2LVrF3bt2oVz585h9uzZSEhIwA8//IDz589j9uzZePr0KRYvXlwWseuNJ/Hxcsfp2dmaCYSIiIh0ksoJ4YIFC9CjRw8MHToUxsbGYrmRkRGGDx+O7t27Y8GCBTAwMMDw4cPx3nvv4eTJk2oNWt/MO31a0yEQERGRDlM5Ibxx4wa8vLwKPe/l5YXr+ZZIadKkCWJjY0sUHOXyZHc7ERERlSGVE0ITExPcvHmz0POhoaEwMTERj7OysmDJiQ+lMrl1a7nj0Y0bayYQIiIi0kkqJ4S+vr7YuXMnVq5cibS0NLE8LS0Nf/75J3bv3g1fX1+x/OrVq6hZs6ZagpVJTEzE7Nmz0bp1azRu3BiBgYEICwsr9v3//vsvBg0ahGbNmqF169YIDAzE2bNn1RqjOnVcu1bueGzz5hqKhIiIiHSRygnhjBkz4OXlhV9++QUtWrSAr68vfH190aJFC/z666/w8PDA9OnTAQAZGRkwNTXFsGHD1BawVCrFmDFjcODAAYwYMQLTpk3D69evMXLkSDx79uyt92/cuBFTpkyBvb09pk6dinHjxuHNmzcYPXo0zpw5o7Y41elNerrccZvVqzUUCREREekilZedsbW1xbZt27B9+3acOHECERERAIDWrVujS5cuGDRokNhlbGpqikWLFqk14JCQEFy7dg3Lly9Ht27dAAD+/v7w8/PDsmXLsHDhwiLv//vvv9GwYUOsWLECEokEANC3b1+0b98ee/fuRbt27dQab1mQajoAIiIi0ikl2qnExMQEw4cPx/Dhw9Udz1sdOnQITk5Ocgtk29vbw9/fH/v370dWVpbc7OeCkpOTUaNGDTEZBIBKlSrB1NQUpqamZRo7ERERUUVU6r2M4+LiEBcXp45YiiUsLAwNGjSQS+gAoGHDhkhJSXlrt3HLli1x+vRpBAcHIyIiAuHh4fjmm28gCIJGEtzisDMzkzu2L3BMREREVBolaiGMiorCL7/8gmPHjiElJQVA7p7FXbt2xZQpU+Ds7KzWIPOLiYlB6wKzbgHAyckJABAdHQ13d/dC7//qq6/w+vVrzJs3D/PmzQMAVK5cGRs2bChyOZ3CpGdnQGogqHxfcaRl5Y4dLDiG0NjQUDxH6sM6LT/mef9nnZcf1nX5Yn2XP9Z50TKzM4s8r3JCGBkZiYCAAMTGxqJevXqoU6cOACA8PBy7d+/GmTNnsG3bNlStWvWtz5JKpcjKyirW68q6c9PT0+WWtZGRlaWnF/2FMDc3R+3atVG1alV06tQJKSkpWLduHcaPH49NmzahevXqxYpHk6LyknAiIiIidVA5IVy8eDESExPx559/olOnTnLnTp48iUmTJmHx4sX46aef3vqsS5cuITAwsFive+7cOdjb28PMzAyZmYpZrqzM7C3dqZ9++ilMTU2xfPlysaxr167w8/PDb7/9hp9//rlY8ciYGZnC1Lhsxx7WsbfHw3zd8pNatoS5MbuNywrrtvyYG5uxvssZ67t8sb7LH+tcOQOppMjzKieEZ86cwbBhwxSSQQDo1KkThg4div379xfrWbVr18b8+fOLda2VlRUAwNHREdHR0QrnZWWyrmNlnj9/jtOnT+PHH3+UK7e1tUXTpk1x7dq1YsVS3pb36AG/v/8Wj/vVravBaIiIiEjXqJwQJiQkwM3NrdDzbm5uSExMLNazHB0d0b9/f5Vev27durh27RoEQZCbWBIaGgoLCwvUqFGj0HtlW+hJpYoLt2RnZyM7O1ulWMpL/mQQAIbt3ImXX3yhoWiIiIhI16g8y7hKlSq4ePFioecvX76MKlWqlCqoonTv3h3R0dE4duyYWBYXF4eQkBB07dpVbsmZZ8+eyc06dnNzg4GBAf7991+5Z7569QqXL19G/fr1yyxudXqVnKzpEIiIiEiHqNxC2L17d6xevRqurq4YM2YMrK2tAeSu77dy5UocPHgQY8aMUXugMn5+fmjcuDGmT5+O0aNHw87ODps3b4ZUKsWkSZPkrh01ahQA4Pjx4wBy1yscMGAAtm/fjvfffx/vvvsukpOTsWnTJmRmZuLjjz8us7hLo76jI+7ExIjHU5TMsiYiIiIqKZUTwk8++QSXL1/GqlWrsGbNGrnlXnJyctC0aVOMHz9e7YHKGBoaYuXKlVi4cCGCg4ORkZGBhg0bYsGCBUV2Zct8++23qFu3Lnbs2IGgoCAAgI+PDxYtWoRmzZqVWdylEfTOO+ixaZN47J83s5uIiIhIHSSCIKi8iF52djZ27tyJo0ePilvXVa9eHd26dUO/fv1gZFSi5Q21SkZGBm7dugVvb+8y2+FEtqaSxY8L5MqdLS3xaurUMnlNfSarb85QKwebcsf/pg1KY32XE36/yxfru/yxzov2trylRJmbkZERAgICEBAQUOoASXVch5CIiIjUqdRb11HZa6xkko5k7lxUyevyJiIiIiqNt7YQ7t69u0QP7tu3b4nuI0XzunRBz82bFcrZUkhERETq8NaE8Msvv4REIoEqQw0lEgkTQjVSlgwSERERqctbE8INGzaURxxUAs6WlpoOgYiIiHTAWxPCli1bluoFpFIpXr16hcqVK8PExKRUz9JXLatVw8UXLxTKOdOYiIiI1KHMJ5XExcWha9euuHLlSlm/lM6a3bGjpkMgIiIiHVYus4xLsNQh5dNLyRhCdhcTERGRuuj+CtI6SJgzR9MhEBERkQ7hOoQVXGJGhkIZ1x8kIiIidWJCWMGlZ2crlHH9QSIiIlInJoQVXKUy2ieZiIiISIYJYQVnZqR8mCe7jYmIiEhdmBBWcAnpimMIAXYbExERkfowIazgMnIUxxASERERqVOZLztjY2ODDRs2oF69emX9UjrJppAxhFyHkIiIiNRF5YTw0qVLRZ6XSCQwNTWFi4sLHBwcYGxsXOrt7/SZiaGh3LGzpSW3rCMiIiK1UjkhHDlyJCQSSbGu9fT0xBdffIGO3HqtxBIzMuWOmQwSERGRuqmcEP7444/YuHEjnj59il69eqFWrVoAgEePHmH//v2oVasW+vTpg8ePH2PPnj0YP348/vrrL7Ru3VrtweuDhitWij9zwCcRERGVBZUTwrS0NLx58waHDh2Cg4OD3LkJEyZg8ODBMDAwwOzZszF27Fj07dsXK1euZEJYQrGpqeLPUg3GQURERLpL5UanDRs2ICAgQCEZBABHR0cMGjQI69evBwA4OTlh0KBBuHnzZukjJSIiIqIyoXJCGBkZCTMzs0LPm5ub4+XLl+Kxq6srMpTsx0vFw51KiIiIqKypnBBWq1YN+/fvR2ZmpsK5zMxM7N27Fy4uLmLZq1evYGtrW6og9dmNsR+LP9ewsdFgJERERKSrVB5DGBgYiO+++w4BAQEYOnQoatasCQB4/PgxNm/ejPv372P27Nni9YcPH4aPj4/aAtY3d2JixJ9HNGyowUiIiIhIV6mcEA4bNgzJyclYvnw55syZIy5BIwgCTExMMGXKFAwbNgxAbovh9OnT4ebmpt6o9UhcWpr4809nzuCHrl01GA0RERHpohLtVDJmzBgMHjwYZ86cQUREBIDcsYJt27aV6x42MTFBhw4d1BKovsoRBPFnqSCgSlAQ1yIkIiIitSrx1nU2Njbo0aOHOmMhJbrkdcnLRKWkaCYQIiIi0lklTggjIiJw7tw5xMbGolevXnB1dUVmZiZiY2NRuXJlmJiYqDNOvRUaHaXpEIiIiEjHlSghXLRoEdatW4ecnBxIJBI0btxYTAjfe+89TJ48GaNGjVJzqPopPi1d7tjZ0lJDkRAREZGuUnnZmS1btuCvv/7CsGHDsGbNGgj5xrhZWVnB19cX//vf/9QapD6T5qtfgHsZExERkfqp3EK4adMmvPPOO5g1axbevHmjcN7LywuXLl1SS3AEdKlVU/y5buXKmguEiIiIdJbKLYRPnjxB27ZtCz1vZ2enNFGkkrn+6v/HEN6NjYVk7lxUCQrSYERERESka1RuITQ1NUVavrXxCoqMjESlSpVKFVRRHj16hC1btiA0NBR37txBRkYGjh07BldX12I/4+rVq1i0aBHu3LkDKysr+Pv744svvoC5uXmZxV1SfbduVyjjTGMiIiJSJ5VbCH18fHDkyBGl5zIyMrBnzx40bdq01IEV5vr16wgODkZycjLc3d1Vvj8sLAyjRo1CRkYGvvzySwwcOBBbt27FlClTyiDa0is4hpCIiIhI3VRuIfzwww/x4YcfYtq0aRgwYAAAIDY2FqdPn8bSpUsRFRWFn3/+We2Byvj6+uLSpUuwsrLCunXrcOfOHZXu/+WXX2Bra4vg4GBY5s3YdXV1xddff41z586hTZs2ZRE2ERERUYWlcgth27Zt8e233+LQoUP44IMPAADTp0/HmDFjcPfuXXz//fdo0qSJ2gOVsbW1hZWVVYnuTU5OxtmzZ9G3b18xGQSAPn36wMLCAgcPHlRXmGpjxfUciYiIqIyVaB3CwYMHw9fXFyEhIXj06BEEQUDNmjXh7+8PZ2dndceoNvfu3UN2dja8vb3lyk1MTFCvXj2EhYWp/Mz07AxIDcqmWzctKx3JmZkK5U6WlkjLSldyB5UG67T8yEbrss7LD+u6fLG+yx/rvGiZ2Yr5RH4l3qnE0dERI0eOLOntGhETEwMgN/aCHB0dcf369XKO6O0MJIA0X77pZGmJJ5Mnai4gIiIi0jklSgivXbuGv//+G0+fPkV8fLzc4tQAIJFIcPTo0bc+RyqVIisrq1ivaWpqWpJQ5aSn5/7rQdm2eqampuJ5VZgZmcLUuPSxFUZaoPExigtTlzlzYzNNh6A3zI3NWN/ljPVdvljf5Y91rpyBVFLkeZUTwt27d2PmzJkwMjJCzZo1UbVq1RIHd+nSJQQGBhbr2nPnzsHe3r7ErwUAZma5X5JMJd2wGRkZ4nkiIiIifaJyQvjHH3+gVq1aWLt2banHC9auXRvz588v1rUlnUiSn6yrWNZ1nF9MTAycnJxK/RplifsYExERUVlQOSGMjIzE9OnT1TJ5xNHREf379y/1c4rL09MTRkZGuHXrFt59912xPDMzE2FhYejVq1e5xVJcHvb2eBAXB4D7GBMREVHZUHnZmSpVqijtcq2IwsPDERkZKR5bW1ujTZs22LNnD1Ly7faxZ88epKamonv37poIs0jbBuYmzH4lWISbiIiIqDhUbiEcMmQI9u3bh1GjRsHQ0LAsYipSUlISgoODAUCcFbxx40ZYW1vDxcUFffv2Fa/t0aMHWrZsKV4PAFOmTMGQIUMwcuRIDBo0CK9evcLatWvRsWPHIvdo1pRO6zcAAA6Fh2s4EiIiItJVKieEDRo0wOHDhzFo0CAMGzYMrq6uShPDFi1aqCXAghISErB48WK5sjVr1gAAWrZsKZcQKtOgQQOsXbsWQUFBmD9/PqysrBAQEIDPP/+8TOItrcSM/2+NrRIUxG5jIiIiUjuVE8JRo0aJP3/99deQSOSnMQuCAIlEUqJFnovD1dUV9+7dK9a1hV3XvHlzbNmyRZ1hlYuofN3cREREROqickJY3FnBRERERKQdVE4I+/XrVxZxEBEREZGGqDzLmDSH6xASERFRWWBCqEWiUlJQJShI02EQERGRjmFCWMEV3HmQE0uIiIhI3ZgQVnCCpgMgIiIinceEkIiIiEjPMSEkIiIi0nNMCImIiIj0HBNCIiIiIj3HhFDLcC1CIiIiUjcmhFrm1dSpmg6BiIiIdAwTQi3DhamJiIhI3ZgQahkuTE1ERETqxoSQiIiISM8xIdQynFRCRERE6saEUMtwUgkRERGpGxNCIiIiIj3HhJCIiIhIzzEh1DJcdoaIiIjUjQmhluGyM0RERKRuTAiJiIiI9BwTQi3DZWeIiIhI3ZgQahkuO0NERETqxoRQi/DDIiIiorLAHEOLSDUdABEREekkJoREREREeo4JIREREZGeY0JYwUny/cwZxkRERFQWjDQdABVNyPv/0ZEj0bV2bY3GQkRERLqJLYRaYuHZs5oOgYiIiHSU1rUQPnr0CFu2bEFoaCju3LmDjIwMHDt2DK6urm+9Ny0tDTt37sTRo0fx4MEDpKSkoGbNmggICEBAQAAMDQ3L4R2UjJ2ZmaZDICIiIh2ldS2E169fR3BwMJKTk+Hu7q7Svc+fP8f3338PABg1ahRmzJiBatWq4dtvv8Xs2bPLItxSk31AW2/fRpWgII3GQkRERLpJ61oIfX19cenSJVhZWWHdunW4c+dOse+tXLky9u3bBw8PD7FsyJAhmDlzJv755x+MHz8e1atXL4uwSyz/2oNRKSkai4OIiIh0l9a1ENra2sLKyqpE99rb28slgzLvvPMOgNzuaCIiIiJ9o3UJYVmIjY0FANjZ2Wk4EiIiIqLyp3VdxuqWmZmJ9evXo0aNGvD29lb5/vTsDEgNhLdfWAJpWekwkEggFXKf72RpibSs9DJ5LQLrthyZ5/2fdV5+WNfli/Vd/ljnRcvMzizyvEYTQqlUiqysrGJda2pqWiYxfP/99wgPD8eqVatgYFDxGkxlyWDI8KHo6FZDw9EQERGRLtJoQnjp0iUEBgYW69pz587B3t5era+/evVqbNu2DV988QU6dOhQomeYGZnC1LhsktX8fjpzDn51PMv8dQgwN+YSP+XF3NiM9V3OWN/li/Vd/ljnyhlIJUWe12hCWLt2bcyfP79Y15Z0Iklhdu7ciaCgIAwfPhxjxoxR67PLwoknTyCZOxfOlpZ4NXWqpsMhIiIiHaLRhNDR0RH9+/cv99c9evQovv76a7z77rv4+uuvy/31S4NLzxAREZG6VbxBc2oUHh6OyMhIubJLly7h888/R/PmzREUFFQhxw0SERERlSetm2WclJSE4OBgALm7lgDAxo0bYW1tDRcXF/Tt21e8tkePHmjZsqV4/YsXLzB+/HhIJBL4+fnh4MGDcs9u2rRphVuYuiBnS0tNh0BEREQ6RusSwoSEBCxevFiubM2aNQCAli1byiWEBUVERCApKQkA8N133ymcnz9/foVPCDl+kIiIiNRN6xJCV1dX3Lt3r1jXFryuVatWxb6XiIiISF9wAJ0W4YdFREREZYE5hhaRajoAIiIi0klMCImIiIj0HBNCIiIiIj3HhJCIiIhIzzEhJCIiItJzTAiJiIiI9BwTwgpMEARNh0BERER6gAlhBRaZnKzpEIiIiEgPMCGswFysrDQdAhEREekBJoQVmEQi0XQIREREpAeYEFZgr9hlTEREROWACWEFlsNJJURERFQOmBBWYNWsrTUdAhEREekBJoREREREeo4JYQX2kmMIiYiIqBwwIdQi/LCIiIioLBhpOgAqXBVLS/w7bAi8Kjuhtp2dpsMhIiIiHcVGpwpMANBj0xYE37ih6VCIiIhIhzEhrMBkYwi/PXkSkrlzUSUoSMMRERERkS5iQliBGRbYqSQqJUVDkRAREZEuY0JYgTlZWmo6BCIiItIDTAgrsLi0NE2HQERERHqACWEFdjs6RtMhEBERkR5gQliB1XOsrOkQiIiISA8wIazA7M3NNR0CERER6QEmhBXY2ecRmg6BiIiI9AATwgrMgS2EREREVA6YEFZgDZwcNR0CERER6QEmhBVYYkaGpkMgIiIiPcCEsAILOnte0yEQERGRHmBCWIF91LSxpkMgIiIiPaB1CeGjR4/w448/YsiQIfDx8YGXlxciIko2Gzc5ORnt2rWDl5cXjh49quZIS6+GjY2mQyAiIiI9oHUJ4fXr1xEcHIzk5GS4u7uX6lnLly9HamqqmiJTvzXXrms6BCIiItIDWpcQ+vr64tKlS9i/fz/69OlT4uc8fvwYwcHB+PDDD9UYnXo9jo+XO3a2tNRMIERERKTTjDQdgKpsbW3V8pz58+ejS5cuaNGihVqeVxY+atIELxKTMLVtOzRzcdF0OERERKSjtC4hVIeTJ0/i7Nmz+Pfff/Hy5UtNh1MoN1sbrOnTC+bGZpoOhYiIiHSY3iWEWVlZ+PHHHzFy5EjUqFGj1AlhenYGpAaCmqKTt+ziBUw/egLLe3THB40blclr0P9Ly0rXdAh6Q7YHD+u8/LCuyxfru/yxzouWmZ1Z5HmNjiGUSqXIyMgo1n/qsmHDBiQkJGD8+PFqe2ZZeZ6QBAC4FBmp4UiIiIhIl2m0hfDSpUsIDAws1rXnzp2Dvb19qV4vNjYWv//+Oz7//HNUqlSpVM+SMTMyhamxqVqeVdD4Fs0Rn5GJya3asNu4HLGuy4+5sRnru5yxvssX67v8sc6VM5BKijyv0YSwdu3amD9/frGutbKyKvXrrVixAtbW1mjfvr24dmFsbCwA4PXr14iIiEC1atUgkRRdaeWlpq0tDoU/QnDoTQC5s4xfTZ2q4aiIiIhI12g0IXR0dET//v3L7fUiIyPx8uVLvPvuuwrnvvnmGwBAaGgoTE3LpsVPVcsvXkZ0Sop4HJXvZyIiIiJ10elJJeHh4TA3N4dL3pItY8eOVUhA79+/j8WLF2PMmDFo1KgRjI2NNRGqUhFJiZoOgYiIiPSA1iWESUlJCA4OBpC7awkAbNy4EdbW1nBxcUHfvn3Fa3v06IGWLVuK1zdqpDhT19raWjzXrVu3sg1eRWObNcXKK9eQlp0NgAtTExERUdnQuoQwISEBixcvlitbs2YNAKBly5ZyCaG267QuWC4Z5PhBIiIiKgtalxC6urri3r17xbq2ONe1atWq2M8rbzH59lnm+EEiIiIqK1q3lzERERERqRcTQiIiIiI9x4SQiIiISM8xISQiIiLSc0wIKzAzI0PxZy45Q0RERGVF62YZ65MrH3+EVVevYULLVqhTyn2ciYiIiArDFsIKzNzYCG2ru6KyhYWmQyEiIiIdxhbCCmzg9n9w9eUr8ZiLUxMREVFZYAthBTakQX25Yy5OTURERGWBCWEFFnTugqZDICIiIj3AhLACiy7QIsiZxkRERFQWmBBqEY4fJCIiorLAhLACc8rXIsjWQSIiIiornGVcgT2ZPBEAYG5spuFIiIiISJexhZCIiIhIzzEhJCIiItJzTAiJiIiI9BwTQiIiIiI9x4SQiIiISM8xISQiIiLSc0wIiYiIiPQcE0IiIiIiPceEkIiIiEjPMSEkIiIi0nNMCImIiIj0HBNCIiIiIj3HhJCIiIhIzzEhJCIiItJzTAiJiIiI9BwTQiIiIiI9x4SQiIiISM8xISQiIiLSc0aaDkBVjx49wpYtWxAaGoo7d+4gIyMDx44dg6ura7GfkZmZiVWrVmHv3r148eIFbG1t0ahRI/z444+wsbEpw+iJiIiIKh6tSwivX7+O4OBguLu7w93dHXfu3FHp/szMTHz00Ue4d+8eAgIC4Obmhjdv3uDq1atIT09nQkhERER6R+sSQl9fX1y6dAlWVlZYt26dygnh2rVrERYWhp07d6J69eplFCURERGR9tC6hNDW1rbE90qlUgQHByMgIADVq1dHZmYmBEGAqamp+gIkIiIi0jJalxCWxoMHDxATEwM3Nzd8+umnOHr0KHJyctCoUSN888038Pb2VvmZ6dkZkBoIZRAtkJaVXibPJeVY3+XHPO//rPPyw7ouX6zv8sc6L1pmdmaR5/VqlvGzZ88AAD///DMiIyPx008/4bvvvsOrV6/w/vvv48WLFxqOkIiIiKj8abSFUCqVIisrq1jXqqNbNyUlBQAgkUiwfv16WFpaAgCaNGmC3r17Y/369fjqq69UeqaZkSlMjcu2y9nc2KxMn0/yWN/lx9zYjPVdzljf5Yv1Xf5Y58oZSCVFntdoQnjp0iUEBgYW69pz587B3t6+VK9nZpb7JenSpYuYDAKAp6cn6tati6tXr5bq+URERETaSKMJYe3atTF//vxiXWtlZVXq13N0dAQAVK5cWeGcg4MDnj9/XuxnCULuuMHMzKL75EtD1t//tqye1IP1XY6MqgLI/fPD+i4f/H6XL9Z3+WOdF02Wr8jyl4I0mhA6Ojqif//+5fZ6Xl5eMDY2RlRUlMK5qKgolVogZV3d9+/fV1t8RHrDfV/u/+8+0GwcRER6JisrS+wxzU+nZxmHh4fD3NwcLi4uAHJbGdu3b49jx44hLi5OTACvXbuGBw8e4JNPPin2sy0tLeHp6QljY2NIJPzXCBEREVVcgiAgKytLbshcfhKhsLbDCiopKQnBwcEAcnctOXnyJEaPHg1ra2u4uLigb9++4rVeXl5o2bKleD2Q26IXEBAAZ2dnDBkyBKmpqVi/fj0sLCywe/fuUq1zSERERKSNtC4hjIiIQNeuXZWeK5j8KUsIASA0NBSLFi3CzZs3YWhoiHbt2mHGjBmoVq1amcZOREREVBFpXUJIREREROqlVwtTExEREZEiJoREREREeo4JIREREZGeY0JIREREpOeYEBIRERHpOSaERERERHqOCWEFlJmZiUWLFqF9+/bw8fFBQEAAzp07p+mwKqzQ0FDMnTsXPXr0QOPGjdG5c2dMmTIFT58+Vbj26tWrGDp0KBo1aoR27dph3rx5SEtLU7hOlc+guM/UVatWrYKXlxf69OmjcI71rT6hoaEYM2YMWrRogSZNmqB3797YuXOn3DXHjh1Dv3790LBhQ3Tu3BnLli1Ddna2wrMSExMxe/ZstG7dGo0bN0ZgYCDCwsKUvm5xn6lLnjx5gs8++wwdO3ZE48aN0aNHD6xcuVJh73p+v1UTHR2NoKAgjBw5Ek2aNIGXlxcuXLig9FpNfpdVeaYu4TqEFdDnn3+Ow4cPIzAwEG5ubti1axdu3bqF4OBgNGnSRNPhVTiffvoprl69iu7du8PLywsxMTHYuHEjUlNTsWPHDri7uwMAwsLCMHjwYNSpUweDBg3Cq1evsGbNGrRr1w4rVqyQe2ZxPwNVnqmLYmJi4OfnB0EQUKNGDezZs0c8x/pWn5MnT2LChAlo2bIlfH19YWRkhCdPnsDa2hoTJkwQrxk7dixat26NHj164P79+9i4cSOGDRuG2bNni8+SSqUYNmwY7t+/j9GjR8POzg6bNm1CVFQUdu7ciRo1asi9bnGeqUuioqLQs2dPWFtbY8iQIbCxscHly5exd+9e9O7dG4sWLQLA73dJXLhwQawDe3t7XLt2DRs2bECrVq3krtPkd1mVZ+ocgSqUGzduCJ6ensLatWvFsvT0dKFbt27CsGHDNBdYBXblyhUhIyNDruzx48eCt7e3MGPGDLHso48+Ejp06CAkJyeLZdu2bRM8PT2Fs2fPimWqfAbFfaaumjFjhjBy5EhhxIgRQu/eveXOsb7VIzExUWjTpo3w/fffF3ldjx49hH79+gnZ2dli2S+//CLUrVtXePz4sVh24MABwdPTUzhy5IhY9vr1a6F58+bCtGnTSvRMXfLnn38Knp6ewv379+XKJ02aJNSvX1/IzMwUBIHf75JISkoS4uLiBEEQhCNHjgienp7C+fPnFa7T5HdZlWfqGnYZVzAhISEwNjbGoEGDxDJTU1MMHDgQV65cQXR0tAajq5iaNm0KExMTubKaNWvCw8MD4eHhAIDk5GScPXsWffv2ldvYu0+fPrCwsMDBgwfFsuJ+Bqo8UxeFhoZi7969mDlzpsI51rf67Nu3D4mJiZg8eTKA3HoQCnTsPHz4EA8fPsTgwYNhaGgolg8bNgxSqRSHDx8Wyw4dOgQnJye5LUDt7e3h7++Po0ePIisrS+Vn6pKUlBQAgIODg1x55cqVYWRkBENDQ36/S8jKygp2dnZFXqPp73Jxn6mLmBBWMGFhYahVq5bcLwQA8PHxgSAIejGOQR0EQUBsbKz4y+fevXvIzs6Gt7e33HUmJiaoV6+eXL0W9zNQ5Zm6RhAEfP/99+jbty/q1auncJ71rT7nzp1D7dq1cfLkSXTq1AnNmjVDy5YtERQUhJycHADAnTt3AEChbpydnVGlShXxPJBb3w0aNIBEIpG7tmHDhkhJScGzZ89UfqYuadGiBQBg1qxZuHv3Ll6+fIm9e/di165d+Pjjj2FgYMDvdxnS9He5uM/URUwIK5iYmBg4OTkplDs6OgIAWwiLae/evYiKioK/vz+A3HoF/r8e83N0dJSr1+J+Bqo8U9fs3r0bDx8+xGeffab0POtbfZ4+fYpXr17hyy+/RL9+/bB06VJ069YNq1atwk8//QRAPfUtK9P3+m7fvj0mT56Ms2fPok+fPujcuTOmTZuGjz76CBMnTgTA73dZ0vR3ubjP1EVGmg6A5KWnp8PY2Fih3NTUFACQkZFR3iFpnfDwcHz33Xdo1qyZOPM1PT0dABS6loHcupWdl11bnM9AlWfqkuTkZPz8888YM2aM0l+cAOtbnVJTU5GQkIAvvvgCY8aMAQC8++67SE1NxebNmzF+/Pi31k3+Warp6elKr5OVyZ6lyjN1jaurK1q2bIl33nkHtra2OHHiBJYuXQp7e3sMHTqU3+8ypOnvcnGfqYuYEFYwZmZmSscoyH5pyH6JkHIxMTEYO3YsbGxssHjxYhgY5DaCm5mZAYDCshFAbt3KzsuuLc5noMozdckff/wBY2NjfPDBB4Vew/pWH9n76tmzp1x5r169EBISgps3b6pc38quk5XJrtXX+j5w4ADmzJmDkJAQODs7A8hNwAVBwMKFC9GjRw9+v8uQpr/LxX2mLmKXcQVTWNeArMm7sBYZApKSkvDxxx8jKSkJq1evlusekP0sq8f8CnYRFPczUOWZuiI6Ohrr16/HsGHDEBsbi4iICERERCAjIwNZWVmIiIhAQkIC61uNZO+7cuXKcuWyY3XVt6xM3+t706ZNaNCggZgMyvj6+iI1NRV3797l97sMafq7XNxn6iImhBVM3bp18fjxY3Gmm8yNGzfE86QoIyMD48aNw5MnT/Dnn3+idu3acuc9PT1hZGSEW7duyZVnZmYiLCxMbmJEcT8DVZ6pK16/fo2srCwEBQWha9eu4n83btxAeHg4unbtilWrVrG+1ahBgwYActfHy+/Vq1cAcmdAyt57wbqJiorCq1evFOr79u3bCjOVQ0NDYWFhIa6zpsozdUlsbKw4WSc/WStfTk4Ov99lSNPf5eI+UxcxIaxgunfvjqysLGzfvl0sy8zMxM6dO9G0aVOFf7VS7i/ozz77DNevX8fixYvRuHFjhWusra3Rpk0b7NmzR+4X8549e5Camoru3buLZcX9DFR5pq5wdXXF8uXLFf7z8PBAtWrVsHz5cvTt25f1rUay97Vjxw6xTBAEbN++HRYWFmjcuDE8PDxQu3ZtbN26VS6Z2bx5MwwMDPDuu+/KPS86OhrHjh0Ty+Li4hASEoKuXbuK491UeaYuqVWrFm7duqUwm/TAgQMwNDSEl5cXv99lSNPf5eI+Uxdxp5IKaPLkyTh27Bjef/991KhRQ1zVfv369WjWrJmmw6twfvjhB2zYsAFdunQRZxXLWFpaolu3bgCA27dvY8iQIfDw8BB3AVi7di1atWqFVatWyd1X3M9AlWfqspEjRyIxMVFupxLWt/rMmDEDe/bswcCBA1G/fn2cPHkSJ06cEGe/AsD//vc/jB8/XmEnhsGDB+Pbb78Vn5WTk4Nhw4bhwYMH4k4MmzdvxsuXL7Fz5064ubmJ1xb3mbrk0qVLeP/992FnZ4fhw4fDxsYGJ06cwKlTpzBkyBDMnTsXAL/fJfX7778DyJ38t3//fgwYMACurq6oVKkSRowYAUCz32VVnqlrmBBWQBkZGfjtt9+wb98+JCQkwMvLC59//jnatm2r6dAqpJEjR+LixYtKz1WrVg3Hjx8Xjy9fvoygoCDcuXMHVlZW6NGjBz7//HNYWFjI3afKZ1DcZ+oyZQkhwPpWl8zMTPz+++/YvXs3YmNj4erqilGjRmHIkCFy1x09ehTLli1DeHg47O3tMWDAAHzyyScwMpKfP5iQkICFCxfi6NGjyMjIQMOGDfHll1+K3dMleaYuCQ0NxdKlSxEWFob4+HhUq1YNAwYMwIcffii3sDG/36rz8vJSWl7wd7Umv8uqPFOXMCEkIiIi0nMcQ0hERESk55gQEhEREek5JoREREREeo4JIREREZGeY0JIREREpOeYEBIRERHpOSaERERERHqOCSER6TQvLy98+eWXmg6jRNLS0jBv3jx07twZ9erVg6+vr6ZDIiIdpbtLzRNRmblw4QICAwMBAN9//z0CAgIUrvHy8kLnzp3x559/lnd4OmPVqlUIDg7G6NGj4eXlBSsrqyKvL2wXCAD44osvMGbMGHWHKDp69CjCwsIwadKkMnsNIio7TAiJqFSWLl2K3r17w8zMTNOh6JyzZ8/C09MTM2bMKPY99erVwwcffKBQXr9+fXWGpuDo0aPYtWsXE0IiLcWEkIhKzNvbG7du3cL69esxduxYTYejcTk5OcjMzIS5ublanhcTEwMXFxeV7nF2dkafPn3U8voVhSAISE1NhaWlpaZDIdJZHENIRCXm7++PBg0aYNWqVXjz5s1bry9sPN/OnTvh5eWFCxcuiGVLly6Fl5cXHj58iB9++AHt27dHo0aN8P777+PRo0cAgMOHD6Nfv37w8fGBr68vtm7dWuhrnz17FgEBAWjUqBHatWuHefPmISUlReG6pKQkLFq0CO+88w68vb3RunVrfP7553j+/LnSmM+ePYvly5ejW7du8PHxwcGDB4usg+zsbKxcuRI9evRAw4YN0apVK0yYMAH37t1TeHZERAQuXrwILy8veHl5YenSpUU+u7hu3ryJCRMmoFWrVvD29oafnx/++OMPZGdny10XGhqKL7/8En5+fmjUqBGaNGmCIUOG4MiRI3LXjRw5Ert27QIAMVYvLy/s3LlTPK9s/GNERITC+7pw4YJ478aNG8V6WrNmjXjNv//+i6FDh6JJkyZo1KgRBg0ahJCQEIXnnzhxAiNGjECrVq3g4+ODzp07Y+LEiXj8+HHJK49IR7GFkIhKTCKRYOrUqfjggw+wYsUKzJw5U+2vMWPGDFhYWGDs2LGIi4vD2rVr8dFHH+HTTz9FUFAQhgwZggEDBmDHjh345ptv4O7ujubNm8s94/bt2zh06BAGDRqEPn364MKFCwgODsaDBw+wdu1aGBjk/ts4KSkJQ4YMQWRkJAYMGAAPDw/ExMRg06ZNGDRoEP755x9Uq1ZN7tkLFixAdnY2AgICYGlpiVq1ahX5fqZOnYqDBw+iXbt2GDp0KGJjY7Fx40YMGTIEGzduRP369dGiRQssXLgQ8+fPh52dHcaNGweg6DGCMtnZ2YiLi5MrMzAwgK2tLYDcJGnixIlwc3PD6NGjYWNjg+vXr2PJkiUICwvDkiVLxPuOHDmCR48eoXv37qhWrRri4+Oxa9cuTJw4EUFBQejVqxcAYNy4cZBKpbh8+TIWLlwo3t+0adO3xluY9evXIz4+HoMGDYKjoyOqVKkCAPj111+xYsUKdOjQAZMnT4aBgQGOHDmCyZMn45tvvsHw4cMBABcvXsT48ePh4eGBsWPHwtraGtHR0Th37hyePXv21s+JSO8IREQqOn/+vODp6SmsXr1aEARB+OCDDwRvb28hIiJCvMbT01MYM2aM3H2enp7CjBkzFJ73zz//CJ6ensL58+fFsiVLlgienp7C2LFjBalUKpavX79e8PT0FJo0aSJERkaK5a9fvxa8vb2FKVOmKLymp6encOTIEbny77//XvD09BT2798vV9awYUMhLCxM7tqIiAihSZMmcrHLYn733XeF1NTUwisrn//++0/w9PQUJk+eLPeewsLChHr16glDhw6Vu75Lly7CiBEjivXs/O+14H9t27YVBEEQ0tPThbZt2wrDhg0TsrKy5O5du3atwmeQkpKi8BqpqanCu+++K/j7+8uVz5gxQ/D09FQa14gRI4QuXboolD9//lzw9PQUlixZIpbJvlstWrQQYmNj5a6/deuW4OnpKfz8888Kzxo/frzQpEkTISkpSRAEQfjxxx8FT09PhWcQkXLsMiaiUps6dSqysrKwePFitT975MiRkEgk4rGs9c/X1xdVq1YVy+3t7VGrVi08efJE4Rm1atVCt27d5MpkM25l3Z+CIGDfvn1o0aIFnJycEBcXJ/5nbm6Oxo0b47///lN49tChQ4s9ZlD2WuPGjZN7T3Xr1kWXLl1w5coVhdY9VTVq1Ahr166V+0/2uZw5cwaxsbHo378/EhMT5d5jx44dxWtkLCwsxJ/T0tLw5s0bpKWloXXr1ggPD0dycnKpYi1Knz594ODgIFe2b98+SCQS9O3bVy72uLg4+Pr6IiUlBdevXwcAWFtbAwAOHTqk0BVORIrYZUxEpVa/fn2899572LdvH0aPHo26deuq7dnVq1eXO65UqRIAwNXVVeFaGxsbvHjxQqHc3d1doczJyQmVKlUSxwbGxcUhPj4e//33H9q0aaM0FlnXcn6qdD1GRETAwMBAaTx16tTB0aNHERERAXt7+2I/syA7Ozu0bdtW6bnw8HAAwFdffVXo/bGxseLPr1+/xm+//YZjx47h9evXCtcmJia+dSmckqpZs6ZCWXh4OARBgL+/f6H3yeIfPnw4jh07hrlz5yIoKAjNmjVDhw4d0LNnz1LVL5GuYkJIRGrx2Wef4dChQwgKCsLq1atVujcnJ6fQc8qSMAAwNDRU6TXeRhAEAEDbtm3x8ccfF/s+bVpuR/Yep0+fjnr16im9xsnJSbx29OjRCA8PR2BgILy9vWFtbQ1DQ0P8888/2L9/P6RSaaniKepzV9bqKggCJBIJVq1aVejnX6dOHQC5ifGOHTtw+fJlnD17FpcuXcL8+fOxdOlSrFy5Ek2aNClV7ES6hgkhEalF9erVMXToUGzYsEFutnB+tra2iI+PVygvOINX3WQtY/lFR0cjMTFRbIG0t7dHpUqVkJycXGgLW2lVr14dUqkU4eHhCq2oshiVtXyqi6zVzdzc/K3v8d69e7h79y4mTJiATz/9VO7c9u3bFa7P3wVekK2tLW7fvq1QrurnXrNmTZw+fRouLi5KW1kLMjQ0RKtWrdCqVSsAwN27dzFgwAD88ccfWLlypUqvTaTrOIaQiNRm/PjxsLKywqJFi5Ser1mzJq5fv460tDSxLCEhQVyepKw8fvwYR48elStbtWoVAIhjCw0MDNCrVy+EhoYqXcIEgNJuU1XIXmvlypViax0A3L9/H8ePH0ezZs3KtDuzffv2cHBwwKpVq5Qm5unp6eK4QFnLbP44ZbEWXHYG+P/xhsqeW7NmTaSkpCA0NFQsk0qlWLdunUrx9+7dGwDwyy+/KG1dzN/drWwsZu3atWFqaoqEhASVXpdIH7CFkIjUxt7eHh9++GGhk0uGDx+OadOm4f3330efPn2QmJiI7du3w8XFBTExMWUWl6enJ6ZNm4ZBgwbBzc0NFy5cwKFDh9CyZUv06NFDvG7KlCm4evUqPvvsM/j7+6NRo0YwNjZGZGQkTp06hQYNGuCnn34qcRzt2rWDv78/Dhw4gISEBHTp0kVc1sbU1BRff/21Ot5uoSwsLLBgwQJMmDAB3bt3x4ABA+Dm5obExEQ8evQIR44cwbJly9CqVSu4u7vDw8MDq1evRnp6OmrVqoXHjx9j69at8PT0VGjxa9SoEf7++2/MnTsXnTp1grGxMXx8fFC9enUEBARg7dq1mDBhAgIDA2FsbIxDhw4V2WWsjI+PDyZNmoSlS5eib9++8PPzg7OzM6Kjo3H79m2cOnUKt27dAgDMnj0br169Qvv27eHi4oL09HQcPHgQKSkpOrdwN5E6MCEkIrX64IMPsGnTJqUJXu/evREdHY2NGzdi/vz5qF69Oj755BMYGBjgxo0bZRZTgwYNMHPmTPz666/YsmULrKysMGLECEyZMkVujKK1tTU2b96MNWvWICQkBMeOHYOhoSGqVKmCZs2aYdCgQaWOJSgoCPXr18euXbvw008/wcLCAi1atMDkyZOLtc5gaXXo0AE7duzAypUrsXfvXrx58waVKlVCjRo1MGrUKDEGQ0ND/Pnnn1iwYAF27dqFtLQ0eHh4YMGCBbh7965CQtizZ0+EhYXhwIEDCAkJgVQqFT/j6tWrY/ny5fjll1+wePFi2Nraok+fPhgwYECRE0SUmThxIry9vREcHIwNGzYgNTUVDg4O8PDwwKxZs8Tr+vTpg507d2LXrl2Ii4uDlZUV6tSpgyVLlsDPz6/0FUmkYyRCwf4AIiIiItIrHENIREREpOeYEBIRERHpOSaERERERHqOCSERERGRnmNCSERERKTnmBASERER6TkmhERERER6jgkhERERkZ5jQkhERESk55gQEhEREem5/wMNvorYuJS3oAAAAABJRU5ErkJggg==
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-<p><a id='hyperparameter-tuning'></a></p>
-<h4 id="Hyperparameter-Tuning">Hyperparameter Tuning<a class="anchor-link" href="#Hyperparameter-Tuning">&#182;</a></h4><p>Hyperparameter Tuning is the last stage of the AutoML pipeline, and focuses on improving the chosen algorithm's score on the reduced dataset (after Adaptive Sampling and Feature Selection). We use a novel algorithm to search across many hyperparameters dimensions, and converge automatically when optimal hyperparameters are identified. Each trial in the graph below represents a particular hyperparameters configuration for the selected model.</p>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[17]:</div>
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-<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Each trial is a tuple of</span>
-<span class="c1"># (algorithm, no. samples, no. features, mean CV score, hyperparameters, </span>
-<span class="c1"># all CV scores, total CV time (s), memory usage (Gb))</span>
-<span class="n">trials</span> <span class="o">=</span> <span class="n">est1</span><span class="o">.</span><span class="n">tuning_trials_</span>
-<span class="n">scores</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">reversed</span><span class="p">(</span><span class="n">trials</span><span class="p">)]</span>
-<span class="n">y_margin</span> <span class="o">=</span> <span class="mf">0.10</span> <span class="o">*</span> <span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="n">scores</span><span class="p">)</span> <span class="o">-</span> <span class="nb">min</span><span class="p">(</span><span class="n">scores</span><span class="p">))</span>
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-<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Hyperparameter Tuning Trials&quot;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Iteration $n$&quot;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="n">est1</span><span class="o">.</span><span class="n">inferred_score_metric</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s1">&#39;g&#39;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;-&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="nb">min</span><span class="p">(</span><span class="n">scores</span><span class="p">)</span> <span class="o">-</span> <span class="n">y_margin</span><span class="p">,</span> <span class="nb">max</span><span class="p">(</span><span class="n">scores</span><span class="p">)</span> <span class="o">+</span> <span class="n">y_margin</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">trials</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">),</span> <span class="n">scores</span><span class="p">,</span> <span class="s1">&#39;k:&#39;</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;s&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;teal&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
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
-"
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->
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@@ -15923,109 +19520,16 @@ <h4 id="Hyperparameter-Tuning">Hyperparameter Tuning<a class="anchor-link" href=
 
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-<p><a id='timebudget'></a></p>
-<h3 id="Specify-a-time-budget-to-AutoML">Specify a time budget to AutoML<a class="anchor-link" href="#Specify-a-time-budget-to-AutoML">&#182;</a></h3><p>The Oracle AutoMLx tool also supports a user given time budget in seconds. Given the small size of this dataset, we give a small <code>time budget</code> of 10 seconds using the time_budget argument.</p>
-
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[18]:</div>
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-     <div class="CodeMirror cm-s-jupyter">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">est2</span> <span class="o">=</span> <span class="n">automl</span><span class="o">.</span><span class="n">Pipeline</span><span class="p">(</span><span class="n">task</span><span class="o">=</span><span class="s2">&quot;classification&quot;</span><span class="p">)</span>
-<span class="n">est2</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_valid</span><span class="p">,</span> <span class="n">y_valid</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">col_types</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;text&#39;</span><span class="p">],</span> <span class="n">time_budget</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
-
-<span class="n">y_predict</span> <span class="o">=</span> <span class="n">est2</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
-<span class="n">score_default</span> <span class="o">=</span> <span class="n">f1_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_predict</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="s2">&quot;micro&quot;</span><span class="p">)</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;F1 micro Score on test data: </span><span class="si">{:3.3f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">score_default</span><span class="p">))</span>
-</pre></div>
-
-     </div>
-</div>
-</div>
-</div>
-
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
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-<pre>[2023-03-22 09:18:12,531] [automl.pipeline] Random state (7) is used for model builds
-[2023-03-22 09:18:12,794] [automl.pipeline] Dataset shape: (948, 1)
-[2023-03-22 09:18:12,841] [automl.pipeline] Running Auto-Preprocessing
-[2023-03-22 09:18:14,757] [automl.pipeline] Preprocessing completed. Updated Dataset shape: (948, 11026), cv: None
-[2023-03-22 09:18:19,069] [automl.pipeline] SVC, KNeighborsClassifier are disabled for datasets with &gt; 10K samples or &gt; 1K features
-[2023-03-22 09:18:19,070] [automl.pipeline] Running Model Selection
-</pre>
-</div>
-</div>
-
-<div class="jp-OutputArea-child">
-
-    
-    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-
-
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
-<pre>Time budget exceeded by 0.05s, resetting XEngine
-Timebudget Exceeded or Timedout completed 1/9, 0 tasks timedout
-</pre>
-</div>
-</div>
-
-<div class="jp-OutputArea-child">
-
-    
-    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
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-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
-<pre>[2023-03-22 09:18:22,915] [automl.pipeline] Model Selection completed. Selected model: [&#39;DecisionTreeClassifier&#39;]
-[2023-03-22 09:18:22,917] [automl.pipeline] Running Adaptive Sampling. Dataset Shape: (948, 11026), Valid Shape: (238, 11026), CV: None, Class counts: [237 237 237 237]
-[2023-03-22 09:18:22,969] [automl.pipeline] Timebudget exhausted. Skipping Adaptive Sampling
-[2023-03-22 09:18:22,987] [automl.pipeline] Adaptive Sampling Completed. Updated Dataset Shape: (948, 11026), Valid Shape: (238, 11026), CV: None, Class counts: [237 237 237 237]
-[2023-03-22 09:18:22,987] [automl.pipeline] Starting Feature Selection 0. Dataset Shape: (948, 11026)
-[2023-03-22 09:18:23,036] [automl.pipeline] Timebudget exhausted. Skipping Feature Selection
-[2023-03-22 09:18:23,037] [automl.pipeline] Using all features: Index([&#39;00&#39;, &#39;000&#39;, &#39;0000&#39;, &#39;00000&#39;, &#39;00041032&#39;, &#39;0004422&#39;, &#39;001230&#39;, &#39;001321&#39;,
-       &#39;0029&#39;, &#39;004418&#39;,
-       ...
-       &#39;zinc&#39;, &#39;zion&#39;, &#39;zip&#39;, &#39;zipping&#39;, &#39;zisfein&#39;, &#39;zmodem&#39;, &#39;zone&#39;, &#39;zoo&#39;,
-       &#39;zoology&#39;, &#39;zrepachol&#39;],
-      dtype=&#39;object&#39;, length=11026)
-[2023-03-22 09:18:23,058] [automl.pipeline] Feature Selection 0 completed. Updated Dataset shape: (948, 11026)
-[2023-03-22 09:18:23,107] [automl.pipeline] Timebudget exhausted. Skipping Hyperparameter Optimization for DecisionTreeClassifier
-[2023-03-22 09:18:23,564] [automl.pipeline] (Re)fitting Pipeline
-[2023-03-22 09:18:29,310] [automl.xengine] Local ProcessPool execution (n_jobs=40)
-[2023-03-22 09:18:29,512] [automl.pipeline] AutoML completed. Time taken - 15.504 sec
-F1 micro Score on test data: 0.716
-</pre>
-</div>
+<div class="jp-Cell-inputWrapper">
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+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>We also provide the capability to visualize the results of each stage of the AutoMLx pipeline.</p>
 
 </div>
-
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-
+</div>
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@@ -16033,15 +19537,8 @@ <h3 id="Specify-a-time-budget-to-AutoML">Specify a time budget to AutoML<a class
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-<p><a id='scoringstr'></a></p>
-<h3 id="Specify-a-different-scoring-metric-to-AutoML">Specify a different scoring metric to AutoML<a class="anchor-link" href="#Specify-a-different-scoring-metric-to-AutoML">&#182;</a></h3><p>By default, the score metric is set to <code>neg_log_loss</code> for classifcation and <code>neg_mean_squared_error</code> for regression.</p>
-<p>The user can also choose another scoring metric. The list of possible metrics is given by:</p>
-<ul>
-<li>For binary classification, one of 'roc_auc', 'accuracy', 'f1', 'precision', 'recall', 'f1_micro', 'f1_macro', 'f1_weighted', 'f1_samples', 'recall_micro', 'recall_macro', 'recall_weighted', 'recall_samples', 'precision_micro', 'precision_macro', 'precision_weighted', 'precision_samples'</li>
-<li>For multiclass classification , one of  'neg_log_loss', 'recall_macro', 'accuracy','f1_micro', 'f1_macro', 'f1_weighted', 'f1_samples', 'recall_micro', 'recall_weighted', 'recall_samples', 'precision_micro', 'precision_macro', 'precision_weighted', 'precision_samples'</li>
-<li>For regression, one of 'neg_mean_squared_error', 'r2', 'neg_mean_absolute_error', 'neg_mean_squared_log_error', 'neg_median_absolute_error'</li>
-</ul>
-<p>Here, we ask AutoML to optimize for the 'f1_micro' scoring metric.</p>
+<p><a id="algorithm-selection"></a></p>
+<h4 id="Algorithm-Selection">Algorithm Selection<a class="anchor-link" href="http://automl.oraclecorp.com/multiversion/v24.2.0/_static/notebooks/OracleAutoMLx_Classification_Text.html#Algorithm-Selection">¶</a></h4><p>The plot below shows the scores predicted by Algorithm Selection for each algorithm. The horizontal line shows the average score across all algorithms. Algorithms below the line are colored turquoise, whereas those with a score higher than the mean are colored teal.</p>
 
 </div>
 </div>
@@ -16051,16 +19548,37 @@ <h3 id="Specify-a-different-scoring-metric-to-AutoML">Specify a different scorin
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[19]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[12]:</div>
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-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">est3</span> <span class="o">=</span> <span class="n">automl</span><span class="o">.</span><span class="n">Pipeline</span><span class="p">(</span><span class="n">task</span><span class="o">=</span><span class="s2">&quot;classification&quot;</span><span class="p">,</span> <span class="n">score_metric</span><span class="o">=</span><span class="s1">&#39;f1_micro&#39;</span><span class="p">)</span>
-<span class="n">est3</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_valid</span><span class="p">,</span> <span class="n">y_valid</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">col_types</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;text&#39;</span><span class="p">],</span> <span class="n">time_budget</span><span class="o">=</span><span class="mi">60</span><span class="p">)</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Each trial is a row in a dataframe that contains</span>
+<span class="c1"># Algorithm, Number of Samples, Number of Features, Hyperparameters, Score, Runtime, Memory Usage, Step as features</span>
+<span class="n">trials</span> <span class="o">=</span> <span class="n">est1</span><span class="o">.</span><span class="n">completed_trials_summary_</span><span class="p">[</span><span class="n">est1</span><span class="o">.</span><span class="n">completed_trials_summary_</span><span class="p">[</span><span class="s2">"Step"</span><span class="p">]</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">contains</span><span class="p">(</span><span class="s1">'Model Selection'</span><span class="p">)]</span>
+<span class="n">name_of_score_column</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"Score (</span><span class="si">{</span><span class="n">est1</span><span class="o">.</span><span class="n">_inferred_score_metric</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s2">)"</span>
+<span class="n">trials</span><span class="o">.</span><span class="n">replace</span><span class="p">([</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">,</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">trials</span><span class="o">.</span><span class="n">dropna</span><span class="p">(</span><span class="n">subset</span><span class="o">=</span><span class="p">[</span><span class="n">name_of_score_column</span><span class="p">],</span> <span class="n">inplace</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span>
+<span class="n">scores</span> <span class="o">=</span> <span class="n">trials</span><span class="p">[</span><span class="n">name_of_score_column</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+<span class="n">models</span> <span class="o">=</span> <span class="n">trials</span><span class="p">[</span><span class="s1">'Algorithm'</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+<span class="n">colors</span> <span class="o">=</span> <span class="p">[]</span>
 
-<span class="n">y_predict</span> <span class="o">=</span> <span class="n">est3</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
-<span class="n">score_default</span> <span class="o">=</span> <span class="n">f1_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_predict</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="s2">&quot;micro&quot;</span><span class="p">)</span>
+<span class="n">y_margin</span> <span class="o">=</span> <span class="mf">0.10</span> <span class="o">*</span> <span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="n">scores</span><span class="p">)</span> <span class="o">-</span> <span class="nb">min</span><span class="p">(</span><span class="n">scores</span><span class="p">))</span>
+<span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">scores</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="n">models</span><span class="p">)</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="n">s</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">dropna</span><span class="p">()</span>
+<span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">s</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+    <span class="k">if</span> <span class="n">f</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span>  <span class="o">==</span>  <span class="n">est1</span><span class="o">.</span><span class="n">selected_model_</span><span class="o">.</span><span class="n">strip</span><span class="p">():</span>
+        <span class="n">colors</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">'orange'</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="n">s</span><span class="p">[</span><span class="n">f</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">s</span><span class="o">.</span><span class="n">mean</span><span class="p">():</span>
+        <span class="n">colors</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">'teal'</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">colors</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">'turquoise'</span><span class="p">)</span>
 
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;F1 micro Score on test data: </span><span class="si">{:3.3f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">score_default</span><span class="p">))</span>
+<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"Algorithm Selection Trials"</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="nb">min</span><span class="p">(</span><span class="n">scores</span><span class="p">)</span> <span class="o">-</span> <span class="n">y_margin</span><span class="p">,</span> <span class="nb">max</span><span class="p">(</span><span class="n">scores</span><span class="p">)</span> <span class="o">+</span> <span class="n">y_margin</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="n">est1</span><span class="o">.</span><span class="n">_inferred_score_metric</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
+<span class="n">s</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">colors</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">'black'</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">axhline</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="n">s</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span> <span class="n">color</span><span class="o">=</span><span class="s1">'black'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 </pre></div>
 
      </div>
@@ -16081,172 +19599,17 @@ <h3 id="Specify-a-different-scoring-metric-to-AutoML">Specify a different scorin
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-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
-<pre>[2023-03-22 09:18:32,696] [automl.pipeline] Random state (7) is used for model builds
-[2023-03-22 09:18:32,931] [automl.pipeline] Dataset shape: (948, 1)
-[2023-03-22 09:18:32,976] [automl.pipeline] Running Auto-Preprocessing
-[2023-03-22 09:18:34,815] [automl.pipeline] Preprocessing completed. Updated Dataset shape: (948, 11026), cv: None
-[2023-03-22 09:18:39,467] [automl.pipeline] SVC, KNeighborsClassifier are disabled for datasets with &gt; 10K samples or &gt; 1K features
-[2023-03-22 09:18:39,468] [automl.pipeline] Running Model Selection
-[2023-03-22 09:19:22,252] [automl.pipeline] Model Selection completed. Selected model: [&#39;TorchMLPClassifier&#39;]
-[2023-03-22 09:19:22,253] [automl.pipeline] Running Adaptive Sampling. Dataset Shape: (948, 11026), Valid Shape: (238, 11026), CV: None, Class counts: [237 237 237 237]
-</pre>
-</div>
-</div>
-
-<div class="jp-OutputArea-child">
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-    
-    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-
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-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
-<pre>Time budget exceeded by 4.87s, resetting XEngine
-Timebudget Exceeded or Timedout completed 0/1, 0 tasks timedout
-</pre>
-</div>
-</div>
-
-<div class="jp-OutputArea-child">
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-    
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-<pre>[2023-03-22 09:19:37,891] [automl.pipeline] Adaptive Sampling Completed. Updated Dataset Shape: (948, 11026), Valid Shape: (238, 11026), CV: None, Class counts: [237 237 237 237]
-[2023-03-22 09:19:37,891] [automl.pipeline] Starting Feature Selection 0. Dataset Shape: (948, 11026)
-[2023-03-22 09:19:37,935] [automl.pipeline] Timebudget exhausted. Skipping Feature Selection
-[2023-03-22 09:19:37,936] [automl.pipeline] Using all features: Index([&#39;00&#39;, &#39;000&#39;, &#39;0000&#39;, &#39;00000&#39;, &#39;00041032&#39;, &#39;0004422&#39;, &#39;001230&#39;, &#39;001321&#39;,
-       &#39;0029&#39;, &#39;004418&#39;,
-       ...
-       &#39;zinc&#39;, &#39;zion&#39;, &#39;zip&#39;, &#39;zipping&#39;, &#39;zisfein&#39;, &#39;zmodem&#39;, &#39;zone&#39;, &#39;zoo&#39;,
-       &#39;zoology&#39;, &#39;zrepachol&#39;],
-      dtype=&#39;object&#39;, length=11026)
-[2023-03-22 09:19:37,955] [automl.pipeline] Feature Selection 0 completed. Updated Dataset shape: (948, 11026)
-[2023-03-22 09:19:37,999] [automl.pipeline] Timebudget exhausted. Skipping Hyperparameter Optimization for TorchMLPClassifier
-[2023-03-22 09:19:38,594] [automl.pipeline] (Re)fitting Pipeline
-[2023-03-22 09:20:31,302] [automl.xengine] Local ProcessPool execution (n_jobs=40)
-[2023-03-22 09:20:31,487] [automl.pipeline] AutoML completed. Time taken - 117.251 sec
-F1 micro Score on test data: 0.908
-</pre>
-</div>
-</div>
-
-</div>
-
-</div>
-
-</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
-<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
-</div>
-<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
-<p><a id='MLX'></a></p>
-<h2 id="Machine-Learning-Explainability">Machine Learning Explainability<a class="anchor-link" href="#Machine-Learning-Explainability">&#182;</a></h2>
-</div>
-</div>
-</div>
-</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
-<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
-</div>
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-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
-<p>For a variety of decision-making tasks, getting only a prediction as model output is not sufficient. A user may wish to know why the model outputs that prediction, or which data features are relevant for that prediction. For that purpose the Oracle AutoMLx solution defines the MLExplainer object, which allows to compute a variety of model explanations</p>
-<p><a id='MLX-initializing'></a></p>
-<h3 id="Initializing-an-MLExplainer">Initializing an MLExplainer<a class="anchor-link" href="#Initializing-an-MLExplainer">&#182;</a></h3><p>The MLExplainer object takes as argument the trained model, the training data and labels, as well as the task.</p>
-
-</div>
-</div>
-</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
-<div class="jp-Cell-inputWrapper">
-<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[20]:</div>
-<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-     <div class="CodeMirror cm-s-jupyter">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">explainer</span> <span class="o">=</span> <span class="n">automl</span><span class="o">.</span><span class="n">MLExplainer</span><span class="p">(</span><span class="n">est1</span><span class="p">,</span>
-                               <span class="n">X_train</span><span class="p">,</span>
-                               <span class="n">y_train</span><span class="p">,</span>
-                               <span class="n">target_names</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;sci.crypt&#39;</span><span class="p">,</span> <span class="s1">&#39;sci.electronics&#39;</span><span class="p">,</span> <span class="s1">&#39;sci.med&#39;</span><span class="p">,</span> <span class="s1">&#39;sci.space&#39;</span><span class="p">],</span>
-                               <span class="n">task</span><span class="o">=</span><span class="s2">&quot;classification&quot;</span><span class="p">,</span>
-                               <span class="n">col_types</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;text&quot;</span><span class="p">])</span>
-</pre></div>
 
-     </div>
-</div>
-</div>
-</div>
 
+<div class="jp-RenderedImage jp-OutputArea-output ">
+<img 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" class="
+">
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-<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
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-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
-<p><a id='MLX-global'></a></p>
-<h3 id="Model-Explanations-(Global-Token-Importance)">Model Explanations (Global Token Importance)<a class="anchor-link" href="#Model-Explanations-(Global-Token-Importance)">&#182;</a></h3>
-</div>
-</div>
-</div>
-</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
-<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
-</div>
-<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
-<p>For text classification tasks, since we first extract tokens (features), the Oracle AutoMLx solution offers a single way to compute a notion of token importance: Global Token Importance. The notion of Global Token Importance intuitively measures how much a token impacts the model's predictions (relative to the provided train labels). This notion of token importance considers each token independently from all other tokens. Tokens are the most fine-grained building blocks of the NLP model, such as sentences, words, or characters.</p>
 
 </div>
-</div>
-</div>
-</div>
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-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
-<h4 id="Computing-the-importance">Computing the importance<a class="anchor-link" href="#Computing-the-importance">&#182;</a></h4>
-</div>
-</div>
-</div>
-</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
-<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
-</div>
-<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
-<p>We use a permutation-based method to successively measure the importance of each token (feature). Such a method therefore runs in linear time with respect to the
-number of features (tokens) in the dataset.</p>
-<p>The method <code>explain_model()</code> allows to compute such feature importances. It also provides 95% confidence intervals for each token importance attribution.</p>
 
 </div>
-</div>
-</div>
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-</div>
-<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[21]:</div>
-<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-     <div class="CodeMirror cm-s-jupyter">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">result_explain_model_default</span> <span class="o">=</span> <span class="n">explainer</span><span class="o">.</span><span class="n">explain_model</span><span class="p">()</span>
-</pre></div>
 
-     </div>
-</div>
-</div>
 </div>
 
 </div>
@@ -16256,23 +19619,8 @@ <h4 id="Computing-the-importance">Computing the importance<a class="anchor-link"
 </div>
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 </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
-<h4 id="Visualization">Visualization<a class="anchor-link" href="#Visualization">&#182;</a></h4>
-</div>
-</div>
-</div>
-</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
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-</div>
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-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
-<p>There are two options to show the explanation's results:</p>
-<ul>
-<li><code>to_dataframe()</code> will return a dataframe of the results.</li>
-<li><code>show_in_notebook()</code> will show the results as a bar plot.</li>
-</ul>
-<p>The features are returned in decreasing order of importance.</p>
+<p><a id="feature-selection"></a></p>
+<h4 id="Feature-Selection">Feature Selection<a class="anchor-link" href="http://automl.oraclecorp.com/multiversion/v24.2.0/_static/notebooks/OracleAutoMLx_Classification_Text.html#Feature-Selection">¶</a></h4><p>After finding a sample subset, the next step is to find a relevant feature subset to maximize score for the chosen algorithm. The feature selection step identifies the smallest feature subset that does not compromise on the score of the chosen algorithm. The orange line shows the optimal number of features chosen by Feature Selection.</p>
 
 </div>
 </div>
@@ -16282,10 +19630,28 @@ <h4 id="Visualization">Visualization<a class="anchor-link" href="#Visualization"
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[22]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[13]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">result_explain_model_default</span><span class="o">.</span><span class="n">to_dataframe</span><span class="p">(</span><span class="n">n_tokens</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Each trial is a row in a dataframe that contains</span>
+<span class="c1"># Algorithm, Number of Samples, Number of Features, Hyperparameters, Score, Runtime, Memory Usage, Step as features</span>
+<span class="n">trials</span> <span class="o">=</span> <span class="n">est1</span><span class="o">.</span><span class="n">completed_trials_summary_</span><span class="p">[</span><span class="n">est1</span><span class="o">.</span><span class="n">completed_trials_summary_</span><span class="p">[</span><span class="s2">"Step"</span><span class="p">]</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">contains</span><span class="p">(</span><span class="s1">'Feature Selection'</span><span class="p">)]</span>
+<span class="n">trials</span><span class="o">.</span><span class="n">replace</span><span class="p">([</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">,</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">trials</span><span class="o">.</span><span class="n">dropna</span><span class="p">(</span><span class="n">subset</span><span class="o">=</span><span class="p">[</span><span class="n">name_of_score_column</span><span class="p">],</span> <span class="n">inplace</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span>
+<span class="n">trials</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="p">[</span><span class="s1">'# Features'</span><span class="p">],</span><span class="n">ascending</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">inplace</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span>
+<span class="n">scores</span> <span class="o">=</span> <span class="n">trials</span><span class="p">[</span><span class="n">name_of_score_column</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+<span class="n">n_features</span> <span class="o">=</span> <span class="n">trials</span><span class="p">[</span><span class="s1">'# Features'</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+
+<span class="n">y_margin</span> <span class="o">=</span> <span class="mf">0.10</span> <span class="o">*</span> <span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="n">scores</span><span class="p">)</span> <span class="o">-</span> <span class="nb">min</span><span class="p">(</span><span class="n">scores</span><span class="p">))</span>
+<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"Feature Selection Trials"</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">"Number of Features"</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="n">est1</span><span class="o">.</span><span class="n">_inferred_score_metric</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s1">'g'</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">'-'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="nb">min</span><span class="p">(</span><span class="n">scores</span><span class="p">)</span> <span class="o">-</span> <span class="n">y_margin</span><span class="p">,</span> <span class="nb">max</span><span class="p">(</span><span class="n">scores</span><span class="p">)</span> <span class="o">+</span> <span class="n">y_margin</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">n_features</span><span class="p">,</span> <span class="n">scores</span><span class="p">,</span> <span class="s1">'k:'</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s2">"s"</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'teal'</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">est1</span><span class="o">.</span><span class="n">selected_features_names_</span><span class="p">),</span> <span class="n">color</span><span class="o">=</span><span class="s1">'orange'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">2.0</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 </pre></div>
 
      </div>
@@ -16303,179 +19669,16 @@ <h4 id="Visualization">Visualization<a class="anchor-link" href="#Visualization"
 <div class="jp-OutputArea-child">
 
     
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[22]:</div>
-
-
-
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
-<div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
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-    .dataframe thead th {
-        text-align: right;
-    }
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-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>token</th>
-      <th>attribution</th>
-      <th>upper_bound</th>
-      <th>lower_bound</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>security</td>
-      <td>0.000048</td>
-      <td>0.000049</td>
-      <td>0.000048</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>orbit</td>
-      <td>0.000026</td>
-      <td>0.000046</td>
-      <td>0.000006</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>space</td>
-      <td>0.000025</td>
-      <td>0.000036</td>
-      <td>0.000013</td>
-    </tr>
-    <tr>
-      <th>3</th>
-      <td>prb</td>
-      <td>0.000021</td>
-      <td>0.000025</td>
-      <td>0.000017</td>
-    </tr>
-    <tr>
-      <th>4</th>
-      <td>gtoal</td>
-      <td>0.000017</td>
-      <td>0.000028</td>
-      <td>0.000006</td>
-    </tr>
-    <tr>
-      <th>5</th>
-      <td>moon</td>
-      <td>0.000016</td>
-      <td>0.000016</td>
-      <td>0.000016</td>
-    </tr>
-    <tr>
-      <th>6</th>
-      <td>encryption</td>
-      <td>0.000015</td>
-      <td>0.000018</td>
-      <td>0.000013</td>
-    </tr>
-    <tr>
-      <th>7</th>
-      <td>aurora</td>
-      <td>0.000012</td>
-      <td>0.000013</td>
-      <td>0.000010</td>
-    </tr>
-    <tr>
-      <th>8</th>
-      <td>tapped</td>
-      <td>0.000011</td>
-      <td>0.000012</td>
-      <td>0.000010</td>
-    </tr>
-    <tr>
-      <th>9</th>
-      <td>circuit</td>
-      <td>0.000009</td>
-      <td>0.000012</td>
-      <td>0.000006</td>
-    </tr>
-    <tr>
-      <th>10</th>
-      <td>alaska</td>
-      <td>0.000007</td>
-      <td>0.000009</td>
-      <td>0.000005</td>
-    </tr>
-    <tr>
-      <th>11</th>
-      <td>disease</td>
-      <td>0.000006</td>
-      <td>0.000006</td>
-      <td>0.000006</td>
-    </tr>
-    <tr>
-      <th>12</th>
-      <td>launch</td>
-      <td>0.000005</td>
-      <td>0.000009</td>
-      <td>0.000002</td>
-    </tr>
-    <tr>
-      <th>13</th>
-      <td>shuttle</td>
-      <td>0.000005</td>
-      <td>0.000006</td>
-      <td>0.000004</td>
-    </tr>
-    <tr>
-      <th>14</th>
-      <td>crypto</td>
-      <td>0.000005</td>
-      <td>0.000005</td>
-      <td>0.000005</td>
-    </tr>
-    <tr>
-      <th>15</th>
-      <td>power</td>
-      <td>0.000005</td>
-      <td>0.000005</td>
-      <td>0.000004</td>
-    </tr>
-    <tr>
-      <th>16</th>
-      <td>pitt</td>
-      <td>0.000005</td>
-      <td>0.000006</td>
-      <td>0.000003</td>
-    </tr>
-    <tr>
-      <th>17</th>
-      <td>encrypted</td>
-      <td>0.000004</td>
-      <td>0.000004</td>
-      <td>0.000003</td>
-    </tr>
-    <tr>
-      <th>18</th>
-      <td>spacecraft</td>
-      <td>0.000004</td>
-      <td>0.000004</td>
-      <td>0.000003</td>
-    </tr>
-    <tr>
-      <th>19</th>
-      <td>clipper</td>
-      <td>0.000004</td>
-      <td>0.000004</td>
-      <td>0.000003</td>
-    </tr>
-  </tbody>
-</table>
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
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+<div class="jp-RenderedImage jp-OutputArea-output ">
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" class="
+">
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+
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@@ -16483,16 +19686,53 @@ <h4 id="Visualization">Visualization<a class="anchor-link" href="#Visualization"
 </div>
 
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+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p><a id="hyperparameter-tuning"></a></p>
+<h4 id="Hyperparameter-Tuning">Hyperparameter Tuning<a class="anchor-link" href="http://automl.oraclecorp.com/multiversion/v24.2.0/_static/notebooks/OracleAutoMLx_Classification_Text.html#Hyperparameter-Tuning">¶</a></h4><p>Hyperparameter Tuning is the last stage of the AutoMLx pipeline, and focuses on improving the chosen algorithm's score on the reduced dataset (after Adaptive Sampling and Feature Selection). We use a novel algorithm to search across many hyperparameters dimensions, and converge automatically when optimal hyperparameters are identified. Each trial in the graph below represents a particular hyperparameters configuration for the selected model.</p>
 
+</div>
+</div>
+</div>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[23]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[14]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">result_explain_model_default</span><span class="o">.</span><span class="n">show_in_notebook</span><span class="p">(</span><span class="n">n_tokens</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Each trial is a row in a dataframe that contains</span>
+<span class="c1"># Algorithm, Number of Samples, Number of Features, Hyperparameters, Score, Runtime, Memory Usage, Step as features</span>
+<span class="n">trials</span> <span class="o">=</span> <span class="n">est1</span><span class="o">.</span><span class="n">completed_trials_summary_</span><span class="p">[</span><span class="n">est1</span><span class="o">.</span><span class="n">completed_trials_summary_</span><span class="p">[</span><span class="s2">"Step"</span><span class="p">]</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">contains</span><span class="p">(</span><span class="s1">'Model Tuning'</span><span class="p">)]</span>
+<span class="n">trials</span><span class="o">.</span><span class="n">replace</span><span class="p">([</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">,</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">trials</span><span class="o">.</span><span class="n">dropna</span><span class="p">(</span><span class="n">subset</span><span class="o">=</span><span class="p">[</span><span class="n">name_of_score_column</span><span class="p">],</span> <span class="n">inplace</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span>
+<span class="n">trials</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">trials</span><span class="p">[</span><span class="n">trials</span><span class="p">[</span><span class="s1">'Finished'</span><span class="p">]</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">inplace</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span>
+<span class="n">trials</span><span class="p">[</span><span class="s1">'Finished'</span><span class="p">]</span><span class="o">=</span> <span class="n">trials</span><span class="p">[</span><span class="s1">'Finished'</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">time</span><span class="o">.</span><span class="n">mktime</span><span class="p">(</span><span class="n">datetime</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">strptime</span><span class="p">(</span><span class="n">x</span><span class="p">,</span>
+                                             <span class="s2">"</span><span class="si">%a</span><span class="s2"> %b </span><span class="si">%d</span><span class="s2"> %H:%M:%S %Y"</span><span class="p">)</span><span class="o">.</span><span class="n">timetuple</span><span class="p">()))</span>
+<span class="n">trials</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="p">[</span><span class="s1">'Finished'</span><span class="p">],</span><span class="n">ascending</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">inplace</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span>
+<span class="n">scores</span> <span class="o">=</span> <span class="n">trials</span><span class="p">[</span><span class="n">name_of_score_column</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+<span class="n">score</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="n">score</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">scores</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="nb">len</span><span class="p">(</span><span class="n">scores</span><span class="p">)):</span>
+    <span class="k">if</span> <span class="n">scores</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">&gt;=</span> <span class="n">score</span><span class="p">[</span><span class="n">i</span><span class="o">-</span><span class="mi">1</span><span class="p">]:</span>
+        <span class="n">score</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">scores</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">score</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">score</span><span class="p">[</span><span class="n">i</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+<span class="n">y_margin</span> <span class="o">=</span> <span class="mf">0.10</span> <span class="o">*</span> <span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="n">score</span><span class="p">)</span> <span class="o">-</span> <span class="nb">min</span><span class="p">(</span><span class="n">score</span><span class="p">))</span>
+
+<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"Hyperparameter Tuning Trials"</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">"Iteration $n$"</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="n">est1</span><span class="o">.</span><span class="n">_inferred_score_metric</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s1">'g'</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">'-'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="nb">min</span><span class="p">(</span><span class="n">score</span><span class="p">)</span> <span class="o">-</span> <span class="n">y_margin</span><span class="p">,</span> <span class="nb">max</span><span class="p">(</span><span class="n">score</span><span class="p">)</span> <span class="o">+</span> <span class="n">y_margin</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">trials</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">),</span> <span class="n">score</span><span class="p">,</span> <span class="s1">'k:'</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s2">"s"</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'teal'</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 </pre></div>
 
      </div>
@@ -16514,128 +19754,186 @@ <h4 id="Visualization">Visualization<a class="anchor-link" href="#Visualization"
 
 
 
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i=e.geoCentroid({type:"MultiPoint",coordinates:[t,r,n]}),a=[-i[0],-i[1]],o=e.geoRotation(a),s=at(ot(o(t)),ot(o(r)),ot(o(n)));s.invert=H(s);var l=e.geoProjection(s).rotate(a),c=l.center;return delete l.rotate,l.center=function(t){return arguments.length?c(o(t)):o.invert(c())},l.clipAngle(90)}function lt(t,e){var r=L(1-g(e));return[2/T*t*r,T*(1-r)]}function ct(t){var e=m(t);function r(t,r){return[t,(t?t/g(t):1)*(g(r)*o(t)-e*o(r))]}return r.invert=e?function(t,r){t&&(r*=g(t)/t);var n=o(t);return[t,2*a(L(n*n+e*e-r*r)-n,e-r)]}:function(t,e){return[t,S(t?e*m(t)/t:e)]},r}lt.invert=function(t,e){var r=(r=e/T-1)*r;return[r>0?t*L(y/r)/2:0,S(1-r)]};var ut=L(3);function ft(t,e){return[ut*t*(2*o(2*e/3)-1)/T,ut*T*g(e/3)]}function ht(t){var e=o(t);function r(t,r){return[t*e,g(r)/e]}return r.invert=function(t,r){return[t/e,S(r*e)]},r}function pt(t){var e=o(t);function r(t,r){return[t*e,(1+e)*m(r/2)]}return r.invert=function(t,r){return[t/e,2*i(r/(1+e))]},r}function dt(t,e){var r=L(8/(3*y));return[r*t*(1-n(e)/y),r*e]}function gt(t,e){var r=L(4-3*g(n(e)));return[2/L(6*y)*t*r,d(e)*L(2*y/3)*(2-r)]}function mt(t,e){var r=L(y*(4+y));return[2/r*t*(1+L(1-4*e*e/(y*y))),4/r*e]}function vt(t,e){var r=(2+x)*g(e);e/=2;for(var i=0,a=1/0;i<10&&n(a)>v;i++){var s=o(e);e-=a=(e+g(e)*(s+2)-r)/(2*s*(1+s))}return[2/L(y*(4+y))*t*(1+o(e)),2*L(y/(4+y))*g(e)]}function yt(t,e){return[t*(1+o(e))/L(2+y),2*e/L(2+y)]}function xt(t,e){for(var r=(1+x)*g(e),i=0,a=1/0;i<10&&n(a)>v;i++)e-=a=(e+g(e)-r)/(1+o(e));return r=L(2+y),[t*(1+o(e))/r,2*e/r]}ft.invert=function(t,e){var r=3*S(e/(ut*T));return[T*t/(ut*(2*o(2*r/3)-1)),r]},dt.invert=function(t,e){var r=L(8/(3*y)),i=e/r;return[t/(r*(1-n(i)/y)),i]},gt.invert=function(t,e){var r=2-n(e)/L(2*y/3);return[t*L(6*y)/(2*r),d(e)*S((4-r*r)/3)]},mt.invert=function(t,e){var r=L(y*(4+y))/2;return[t*r/(1+L(1-e*e*(4+y)/(4*y))),e*r/2]},vt.invert=function(t,e){var r=e*L((4+y)/y)/2,n=S(r),i=o(n);return[t/(2/L(y*(4+y))*(1+i)),S((n+r*(i+2))/(2+x))]},yt.invert=function(t,e){var r=L(2+y),n=e*r/2;return[r*t/(1+o(n)),n]},xt.invert=function(t,e){var r=1+x,n=L(r/2);return[2*t*n/(1+o(e*=n)),S((e+g(e))/r)]};var bt=3+2*w;function _t(t,e){var r=g(t/=2),n=o(t),a=L(o(e)),s=o(e/=2),l=g(e)/(s+w*n*a),u=L(2/(1+l*l)),f=L((w*s+(n+r)*a)/(w*s+(n-r)*a));return[bt*(u*(f-1/f)-2*c(f)),bt*(u*l*(f+1/f)-2*i(l))]}_t.invert=function(t,e){if(!(r=D.invert(t/1.2,1.065*e)))return null;var r,a=r[0],s=r[1],l=20;t/=bt,e/=bt;do{var h=a/2,p=s/2,d=g(h),m=o(h),y=g(p),b=o(p),T=o(s),k=L(T),A=y/(b+w*m*k),M=A*A,S=L(2/(1+M)),E=(w*b+(m+d)*k)/(w*b+(m-d)*k),C=L(E),P=C-1/C,I=C+1/C,O=S*P-2*c(C)-t,z=S*A*I-2*i(A)-e,R=y&&_*k*d*M/y,F=(w*m*b+k)/(2*(b+w*m*k)*(b+w*m*k)*k),B=-.5*A*S*S*S,N=B*R,j=B*F,U=(U=2*b+w*k*(m-d))*U*C,V=(w*m*b*k+T)/U,H=-w*d*y/(k*U),q=P*N-2*V/C+S*(V+V/E),G=P*j-2*H/C+S*(H+H/E),Y=A*I*N-2*R/(1+M)+S*I*R+S*A*(V-V/E),W=A*I*j-2*F/(1+M)+S*I*F+S*A*(H-H/E),X=G*Y-W*q;if(!X)break;var 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b=m*m,_=t+b*(e+b*(r+b*i)),w=m*(a-1+b*(s-c+b*l)),T=_*_+w*w,k=2*w,A=T/k,M=A*A,E=S(_/A)/y,C=d*E,P=_*_,I=(2*e+b*(4*r+6*b*i))*m,O=a+b*(3*s+5*b*l),z=(2*(_*I+w*(O-1))*k-T*(2*(O-1)))/(k*k),D=o(C),R=g(C),F=A*D,B=A*R,N=d/y*(1/L(1-P/M))*(I*A-_*z)/M,j=B-u,U=m*(1+b*c)+A-F-f,V=z*R+F*N,H=F*E,q=1+z-(z*D-B*N),G=B*E,Y=V*G-q*H;if(!Y)break;d-=h=(U*V-j*q)/Y,m-=p=(j*G-U*H)/Y}while((n(h)>v||n(p)>v)&&--x>0);return[d,m]},u}Tt.invert=function(t,e){var r=e/(1+wt);return[t&&t/(wt*L(1-r*r)),2*i(r)]},kt.invert=function(t,e){var r=i(e/T),n=o(r),a=2*r;return[t*T/2/(o(a)*n*n),a]};var It=Pt(2.8284,-1.6988,.75432,-.18071,1.76003,-.38914,.042555);var Ot=Pt(2.583819,-.835827,.170354,-.038094,1.543313,-.411435,.082742);var zt=Pt(5/6*y,-.62636,-.0344,0,1.3493,-.05524,0,.045);function Dt(t,e){var r=t*t,n=e*e;return[t*(1-.162388*n)*(.87-952426e-9*r*r),e*(1+n/12)]}Dt.invert=function(t,e){var r,i=t,a=e,o=50;do{var s=a*a;a-=r=(a*(1+s/12)-e)/(1+s/4)}while(n(r)>v&&--o>0);o=50,t/=1-.162388*s;do{var 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n(t);f=g-_,h=m-w,p=v-T,d=1/Math.sqrt(f*f+h*h+p*p),a=x*(p*=d)-b*(h*=d),o=b*(f*=d)-y*p,s=y*h-x*f,(d=Math.sqrt(a*a+o*o+s*s))?(a*=d=1/d,o*=d,s*=d):(a=0,o=0,s=0);l=h*s-p*o,c=p*a-f*s,u=f*o-h*a,(d=Math.sqrt(l*l+c*c+u*u))?(l*=d=1/d,c*=d,u*=d):(l=0,c=0,u=0);return t[0]=a,t[1]=l,t[2]=f,t[3]=0,t[4]=o,t[5]=c,t[6]=h,t[7]=0,t[8]=s,t[9]=u,t[10]=p,t[11]=0,t[12]=-(a*g+o*m+s*v),t[13]=-(l*g+c*m+u*v),t[14]=-(f*g+h*m+p*v),t[15]=1,t}},{"./identity":209}],213:[function(t,e,r){e.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],g=e[12],m=e[13],v=e[14],y=e[15],x=r[0],b=r[1],_=r[2],w=r[3];return t[0]=x*n+b*s+_*f+w*g,t[1]=x*i+b*l+_*h+w*m,t[2]=x*a+b*c+_*p+w*v,t[3]=x*o+b*u+_*d+w*y,x=r[4],b=r[5],_=r[6],w=r[7],t[4]=x*n+b*s+_*f+w*g,t[5]=x*i+b*l+_*h+w*m,t[6]=x*a+b*c+_*p+w*v,t[7]=x*o+b*u+_*d+w*y,x=r[8],b=r[9],_=r[10],w=r[11],t[8]=x*n+b*s+_*f+w*g,t[9]=x*i+b*l+_*h+w*m,t[10]=x*a+b*c+_*p+w*v,t[11]=x*o+b*u+_*d+w*y,x=r[12],b=r[13],_=r[14],w=r[15],t[12]=x*n+b*s+_*f+w*g,t[13]=x*i+b*l+_*h+w*m,t[14]=x*a+b*c+_*p+w*v,t[15]=x*o+b*u+_*d+w*y,t}},{}],214:[function(t,e,r){e.exports=function(t,e,r,n,i,a,o){var s=1/(e-r),l=1/(n-i),c=1/(a-o);return t[0]=-2*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*l,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*c,t[11]=0,t[12]=(e+r)*s,t[13]=(i+n)*l,t[14]=(o+a)*c,t[15]=1,t}},{}],215:[function(t,e,r){e.exports=function(t,e,r,n,i){var a=1/Math.tan(e/2),o=1/(n-i);return t[0]=a/r,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=a,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=(i+n)*o,t[11]=-1,t[12]=0,t[13]=0,t[14]=2*i*n*o,t[15]=0,t}},{}],216:[function(t,e,r){e.exports=function(t,e,r,n){var i=Math.tan(e.upDegrees*Math.PI/180),a=Math.tan(e.downDegrees*Math.PI/180),o=Math.tan(e.leftDegrees*Math.PI/180),s=Math.tan(e.rightDegrees*Math.PI/180),l=2/(o+s),c=2/(i+a);return t[0]=l,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=c,t[6]=0,t[7]=0,t[8]=-(o-s)*l*.5,t[9]=(i-a)*c*.5,t[10]=n/(r-n),t[11]=-1,t[12]=0,t[13]=0,t[14]=n*r/(r-n),t[15]=0,t}},{}],217:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c,u,f,h,p,d,g,m,v,y,x,b,_,w,T,k,A,M,S,E=n[0],L=n[1],C=n[2],P=Math.sqrt(E*E+L*L+C*C);if(Math.abs(P)<1e-6)return null;E*=P=1/P,L*=P,C*=P,i=Math.sin(r),a=Math.cos(r),o=1-a,s=e[0],l=e[1],c=e[2],u=e[3],f=e[4],h=e[5],p=e[6],d=e[7],g=e[8],m=e[9],v=e[10],y=e[11],x=E*E*o+a,b=L*E*o+C*i,_=C*E*o-L*i,w=E*L*o-C*i,T=L*L*o+a,k=C*L*o+E*i,A=E*C*o+L*i,M=L*C*o-E*i,S=C*C*o+a,t[0]=s*x+f*b+g*_,t[1]=l*x+h*b+m*_,t[2]=c*x+p*b+v*_,t[3]=u*x+d*b+y*_,t[4]=s*w+f*T+g*k,t[5]=l*w+h*T+m*k,t[6]=c*w+p*T+v*k,t[7]=u*w+d*T+y*k,t[8]=s*A+f*M+g*S,t[9]=l*A+h*M+m*S,t[10]=c*A+p*M+v*S,t[11]=u*A+d*M+y*S,e!==t&&(t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t}},{}],218:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[4],o=e[5],s=e[6],l=e[7],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[4]=a*i+c*n,t[5]=o*i+u*n,t[6]=s*i+f*n,t[7]=l*i+h*n,t[8]=c*i-a*n,t[9]=u*i-o*n,t[10]=f*i-s*n,t[11]=h*i-l*n,t}},{}],219:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i-c*n,t[1]=o*i-u*n,t[2]=s*i-f*n,t[3]=l*i-h*n,t[8]=a*n+c*i,t[9]=o*n+u*i,t[10]=s*n+f*i,t[11]=l*n+h*i,t}},{}],220:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[4],u=e[5],f=e[6],h=e[7];e!==t&&(t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i+c*n,t[1]=o*i+u*n,t[2]=s*i+f*n,t[3]=l*i+h*n,t[4]=c*i-a*n,t[5]=u*i-o*n,t[6]=f*i-s*n,t[7]=h*i-l*n,t}},{}],221:[function(t,e,r){e.exports=function(t,e,r){var n=r[0],i=r[1],a=r[2];return t[0]=e[0]*n,t[1]=e[1]*n,t[2]=e[2]*n,t[3]=e[3]*n,t[4]=e[4]*i,t[5]=e[5]*i,t[6]=e[6]*i,t[7]=e[7]*i,t[8]=e[8]*a,t[9]=e[9]*a,t[10]=e[10]*a,t[11]=e[11]*a,t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t}},{}],222:[function(t,e,r){e.exports=function(t){return"mat4("+t[0]+", "+t[1]+", "+t[2]+", "+t[3]+", "+t[4]+", "+t[5]+", "+t[6]+", "+t[7]+", "+t[8]+", "+t[9]+", "+t[10]+", "+t[11]+", "+t[12]+", "+t[13]+", "+t[14]+", "+t[15]+")"}},{}],223:[function(t,e,r){e.exports=function(t,e,r){var n,i,a,o,s,l,c,u,f,h,p,d,g=r[0],m=r[1],v=r[2];e===t?(t[12]=e[0]*g+e[4]*m+e[8]*v+e[12],t[13]=e[1]*g+e[5]*m+e[9]*v+e[13],t[14]=e[2]*g+e[6]*m+e[10]*v+e[14],t[15]=e[3]*g+e[7]*m+e[11]*v+e[15]):(n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],t[0]=n,t[1]=i,t[2]=a,t[3]=o,t[4]=s,t[5]=l,t[6]=c,t[7]=u,t[8]=f,t[9]=h,t[10]=p,t[11]=d,t[12]=n*g+s*m+f*v+e[12],t[13]=i*g+l*m+h*v+e[13],t[14]=a*g+c*m+p*v+e[14],t[15]=o*g+u*m+d*v+e[15]);return t}},{}],224:[function(t,e,r){e.exports=function(t,e){if(t===e){var r=e[1],n=e[2],i=e[3],a=e[6],o=e[7],s=e[11];t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=r,t[6]=e[9],t[7]=e[13],t[8]=n,t[9]=a,t[11]=e[14],t[12]=i,t[13]=o,t[14]=s}else t[0]=e[0],t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=e[1],t[5]=e[5],t[6]=e[9],t[7]=e[13],t[8]=e[2],t[9]=e[6],t[10]=e[10],t[11]=e[14],t[12]=e[3],t[13]=e[7],t[14]=e[11],t[15]=e[15];return t}},{}],225:[function(t,e,r){"use strict";var n=t("css-font"),i=t("pick-by-alias"),a=t("regl"),o=t("gl-util/context"),s=t("es6-weak-map"),l=t("color-normalize"),c=t("font-atlas"),u=t("typedarray-pool"),f=t("parse-rect"),h=t("is-plain-obj"),p=t("parse-unit"),d=t("to-px"),g=t("detect-kerning"),m=t("object-assign"),v=t("font-measure"),y=t("flatten-vertex-data"),x=t("bit-twiddle").nextPow2,b=new s,_=!1;if(document.body){var w=document.body.appendChild(document.createElement("div"));w.style.font="italic small-caps bold condensed 16px/2 cursive",getComputedStyle(w).fontStretch&&(_=!0),document.body.removeChild(w)}var T=function(t){!function(t){return"function"==typeof t&&t._gl&&t.prop&&t.texture&&t.buffer}(t)?this.gl=o(t):(t={regl:t},this.gl=t.regl._gl),this.shader=b.get(this.gl),this.shader?this.regl=this.shader.regl:this.regl=t.regl||a({gl:this.gl}),this.charBuffer=this.regl.buffer({type:"uint8",usage:"stream"}),this.sizeBuffer=this.regl.buffer({type:"float",usage:"stream"}),this.shader||(this.shader=this.createShader(),b.set(this.gl,this.shader)),this.batch=[],this.fontSize=[],this.font=[],this.fontAtlas=[],this.draw=this.shader.draw.bind(this),this.render=function(){this.regl._refresh(),this.draw(this.batch)},this.canvas=this.gl.canvas,this.update(h(t)?t:{})};T.prototype.createShader=function(){var t=this.regl,e=t({blend:{enable:!0,color:[0,0,0,1],func:{srcRGB:"src alpha",dstRGB:"one minus src alpha",srcAlpha:"one minus dst 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maskY;\n\t\t\t\tcolor.a *= opacity;\n\n\t\t\t\t// color.a += .1;\n\n\t\t\t\t// antialiasing, see yiq color space y-channel formula\n\t\t\t\t// color.rgb += (1. - color.rgb) * (1. - mask.rgb);\n\n\t\t\t\tgl_FragColor = color;\n\t\t\t}"});return{regl:t,draw:e,atlas:{}}},T.prototype.update=function(t){var e=this;if("string"==typeof t)t={text:t};else if(!t)return;null!=(t=i(t,{position:"position positions coord coords coordinates",font:"font fontFace fontface typeface cssFont css-font family fontFamily",fontSize:"fontSize fontsize size font-size",text:"text texts chars characters value values symbols",align:"align alignment textAlign textbaseline",baseline:"baseline textBaseline textbaseline",direction:"dir direction textDirection",color:"color colour fill fill-color fillColor textColor textcolor",kerning:"kerning kern",range:"range dataBox",viewport:"vp viewport viewBox viewbox viewPort",opacity:"opacity alpha transparency visible visibility opaque",offset:"offset positionOffset padding 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cr=function(t,e){this.type=e.type,this.bindings=[].concat(t),this.result=e};cr.prototype.evaluate=function(t){return this.result.evaluate(t)},cr.prototype.eachChild=function(t){for(var e=0,r=this.bindings;e<r.length;e+=1){t(r[e][1])}t(this.result)},cr.parse=function(t,e){if(t.length<4)return e.error("Expected at least 3 arguments, but found "+(t.length-1)+" instead.");for(var r=[],n=1;n<t.length-1;n+=2){var i=t[n];if("string"!=typeof i)return e.error("Expected string, but found "+typeof i+" instead.",n);if(/[^a-zA-Z0-9_]/.test(i))return e.error("Variable names must contain only alphanumeric characters or '_'.",n);var a=e.parse(t[n+1],n+1);if(!a)return null;r.push([i,a])}var o=e.parse(t[t.length-1],t.length-1,e.expectedType,r);return o?new cr(r,o):null},cr.prototype.outputDefined=function(){return this.result.outputDefined()},cr.prototype.serialize=function(){for(var t=["let"],e=0,r=this.bindings;e<r.length;e+=1){var n=r[e],i=n[0],a=n[1];t.push(i,a.serialize())}return 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sn(t){if(null==t)return{filter:function(){return!0},needGeometry:!1};an(t)||(t=cn(t));var e=Wr(t,on);if("error"===e.result)throw new Error(e.value.map((function(t){return t.key+": "+t.message})).join(", "));return{filter:function(t,r,n){return e.value.evaluate(t,r,{},n)},needGeometry:function t(e){if(!Array.isArray(e))return!1;if("within"===e[0])return!0;for(var r=1;r<e.length;r++)if(t(e[r]))return!0;return!1}(t)}}function ln(t,e){return t<e?-1:t>e?1:0}function cn(t){if(!t)return!0;var e,r=t[0];return t.length<=1?"any"!==r:"=="===r?un(t[1],t[2],"=="):"!="===r?pn(un(t[1],t[2],"==")):"<"===r||">"===r||"<="===r||">="===r?un(t[1],t[2],r):"any"===r?(e=t.slice(1),["any"].concat(e.map(cn))):"all"===r?["all"].concat(t.slice(1).map(cn)):"none"===r?["all"].concat(t.slice(1).map(cn).map(pn)):"in"===r?fn(t[1],t.slice(2)):"!in"===r?pn(fn(t[1],t.slice(2))):"has"===r?hn(t[1]):"!has"===r?pn(hn(t[1])):"within"!==r||t}function 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Ct(n,r,'filter array for "'+r[0]+'" operator must have 2 elements')):"string"!==i&&o.push(new Ct(n+"[1]",r[1],"string expected, "+i+" found"));break;case"within":i=Fr(r[1]),2!==r.length?o.push(new Ct(n,r,'filter array for "'+r[0]+'" operator must have 2 elements')):"object"!==i&&o.push(new Ct(n+"[1]",r[1],"object expected, "+i+" found"))}return o}(t)}function gn(t,e){var r=t.key,n=t.style,i=t.styleSpec,a=t.value,o=t.objectKey,s=i[e+"_"+t.layerType];if(!s)return[];var l=o.match(/^(.*)-transition$/);if("paint"===e&&l&&s[l[1]]&&s[l[1]].transition)return kn({key:r,value:a,valueSpec:i.transition,style:n,styleSpec:i});var c,u=t.valueSpec||s[o];if(!u)return[new Ct(r,a,'unknown property "'+o+'"')];if("string"===Fr(a)&&zr(u)&&!u.tokens&&(c=/^{([^}]+)}$/.exec(a)))return[new Ct(r,a,'"'+o+'" does not support interpolation syntax\nUse an identity property function instead: `{ "type": "identity", "property": '+JSON.stringify(c[1])+" }`.")];var 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e.push(new Ct(n,r,'missing required property "source"'));return e=e.concat(Qr({key:n,value:r,valueSpec:a.layer,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{"*":function(){return[]},type:function(){return kn({key:n+".type",value:r.type,valueSpec:a.layer.type,style:t.style,styleSpec:t.styleSpec,object:r,objectKey:"type"})},filter:dn,layout:function(t){return Qr({layer:r,key:t.key,value:t.value,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{"*":function(t){return vn(It({layerType:s},t))}}})},paint:function(t){return Qr({layer:r,key:t.key,value:t.value,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{"*":function(t){return mn(It({layerType:s},t))}}})}}}))}function xn(t){var e=t.value,r=t.key,n=Fr(e);return"string"!==n?[new Ct(r,e,"string expected, "+n+" found")]:[]}var bn={promoteId:function(t){var e=t.key,r=t.value;if("string"===Fr(r))return xn({key:e,value:r});var n=[];for(var i in r)n.push.apply(n,xn({key:e+"."+i,value:r[i]}));return n}};function _n(t){var e=t.value,r=t.key,n=t.styleSpec,i=t.style;if(!e.type)return[new Ct(r,e,'"type" is required')];var a,o=Ot(e.type);switch(o){case"vector":case"raster":case"raster-dem":return a=Qr({key:r,value:e,valueSpec:n["source_"+o.replace("-","_")],style:t.style,styleSpec:n,objectElementValidators:bn});case"geojson":if(a=Qr({key:r,value:e,valueSpec:n.source_geojson,style:i,styleSpec:n,objectElementValidators:bn}),e.cluster)for(var s in e.clusterProperties){var l=e.clusterProperties[s],c=l[0],u=l[1],f="string"==typeof c?[c,["accumulated"],["get",s]]:c;a.push.apply(a,rn({key:r+"."+s+".map",value:u,expressionContext:"cluster-map"})),a.push.apply(a,rn({key:r+"."+s+".reduce",value:f,expressionContext:"cluster-reduce"}))}return a;case"video":return Qr({key:r,value:e,valueSpec:n.source_video,style:i,styleSpec:n});case"image":return Qr({key:r,value:e,valueSpec:n.source_image,style:i,styleSpec:n});case"canvas":return[new Ct(r,null,"Please use runtime APIs to add canvas sources, rather 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u=0;u<this.d*this.d;u++)n.push([]);this.keys=[],this.bboxes=[]}this.n=e,this.extent=t,this.padding=r,this.scale=e/t,this.uid=0;var f=r/e*t;this.min=-f,this.max=t+f}Dn.prototype.insert=function(t,e,r,n,i){this._forEachCell(e,r,n,i,this._insertCell,this.uid++),this.keys.push(t),this.bboxes.push(e),this.bboxes.push(r),this.bboxes.push(n),this.bboxes.push(i)},Dn.prototype._insertReadonly=function(){throw"Cannot insert into a GridIndex created from an ArrayBuffer."},Dn.prototype._insertCell=function(t,e,r,n,i,a){this.cells[i].push(a)},Dn.prototype.query=function(t,e,r,n,i){var a=this.min,o=this.max;if(t<=a&&e<=a&&o<=r&&o<=n&&!i)return Array.prototype.slice.call(this.keys);var s=[];return this._forEachCell(t,e,r,n,this._queryCell,s,{},i),s},Dn.prototype._queryCell=function(t,e,r,n,i,a,o,s){var l=this.cells[i];if(null!==l)for(var c=this.keys,u=this.bboxes,f=0;f<l.length;f++){var h=l[f];if(void 0===o[h]){var p=4*h;(s?s(u[p+0],u[p+1],u[p+2],u[p+3]):t<=u[p+2]&&e<=u[p+3]&&r>=u[p+0]&&n>=u[p+1])?(o[h]=!0,a.push(c[h])):o[h]=!1}}},Dn.prototype._forEachCell=function(t,e,r,n,i,a,o,s){for(var l=this._convertToCellCoord(t),c=this._convertToCellCoord(e),u=this._convertToCellCoord(r),f=this._convertToCellCoord(n),h=l;h<=u;h++)for(var p=c;p<=f;p++){var d=this.d*p+h;if((!s||s(this._convertFromCellCoord(h),this._convertFromCellCoord(p),this._convertFromCellCoord(h+1),this._convertFromCellCoord(p+1)))&&i.call(this,t,e,r,n,d,a,o,s))return}},Dn.prototype._convertFromCellCoord=function(t){return(t-this.padding)/this.scale},Dn.prototype._convertToCellCoord=function(t){return Math.max(0,Math.min(this.d-1,Math.floor(t*this.scale)+this.padding))},Dn.prototype.toArrayBuffer=function(){if(this.arrayBuffer)return this.arrayBuffer;for(var t=this.cells,e=3+this.cells.length+1+1,r=0,n=0;n<this.cells.length;n++)r+=this.cells[n].length;var i=new Int32Array(e+r+this.keys.length+this.bboxes.length);i[0]=this.extent,i[1]=this.n,i[2]=this.padding;for(var a=e,o=0;o<t.length;o++){var s=t[o];i[3+o]=a,i.set(s,a),a+=s.length}return i[3+t.length]=a,i.set(this.keys,a),a+=this.keys.length,i[3+t.length+1]=a,i.set(this.bboxes,a),a+=this.bboxes.length,i.buffer};var Rn=self.ImageData,Fn=self.ImageBitmap,Bn={};function Nn(t,e,r){void 0===r&&(r={}),Object.defineProperty(e,"_classRegistryKey",{value:t,writeable:!1}),Bn[t]={klass:e,omit:r.omit||[],shallow:r.shallow||[]}}for(var jn in Nn("Object",Object),zn.serialize=function(t,e){var r=t.toArrayBuffer();return e&&e.push(r),{buffer:r}},zn.deserialize=function(t){return new 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i=this._transitionablePaint._values[t],a="cross-faded-data-driven"===i.property.specification["property-type"],o=i.value.isDataDriven(),s=i.value;this._transitionablePaint.setValue(t,e),this._handleSpecialPaintPropertyUpdate(t);var l=this._transitionablePaint._values[t].value;return l.isDataDriven()||o||a||this._handleOverridablePaintPropertyUpdate(t,s,l)},e.prototype._handleSpecialPaintPropertyUpdate=function(t){},e.prototype._handleOverridablePaintPropertyUpdate=function(t,e,r){return!1},e.prototype.isHidden=function(t){return!!(this.minzoom&&t<this.minzoom)||(!!(this.maxzoom&&t>=this.maxzoom)||"none"===this.visibility)},e.prototype.updateTransitions=function(t){this._transitioningPaint=this._transitionablePaint.transitioned(t,this._transitioningPaint)},e.prototype.hasTransition=function(){return this._transitioningPaint.hasTransition()},e.prototype.recalculate=function(t,e){t.getCrossfadeParameters&&(this._crossfadeParameters=t.getCrossfadeParameters()),this._unevaluatedLayout&&(this.layout=this._unevaluatedLayout.possiblyEvaluate(t,void 0,e)),this.paint=this._transitioningPaint.possiblyEvaluate(t,void 0,e)},e.prototype.serialize=function(){var t={id:this.id,type:this.type,source:this.source,"source-layer":this.sourceLayer,metadata:this.metadata,minzoom:this.minzoom,maxzoom:this.maxzoom,filter:this.filter,layout:this._unevaluatedLayout&&this._unevaluatedLayout.serialize(),paint:this._transitionablePaint&&this._transitionablePaint.serialize()};return this.visibility&&(t.layout=t.layout||{},t.layout.visibility=this.visibility),y(t,(function(t,e){return!(void 0===t||"layout"===e&&!Object.keys(t).length||"paint"===e&&!Object.keys(t).length)}))},e.prototype._validate=function(t,e,r,n,i){return void 0===i&&(i={}),(!i||!1!==i.validate)&&On(this,t.call(Ln,{key:e,layerType:this.type,objectKey:r,value:n,styleSpec:Lt,style:{glyphs:!0,sprite:!0}}))},e.prototype.is3D=function(){return!1},e.prototype.isTileClipped=function(){return!1},e.prototype.hasOffscreenPass=function(){return!1},e.prototype.resize=function(){},e.prototype.isStateDependent=function(){for(var t in this.paint._values){var e=this.paint.get(t);if(e instanceof bi&&zr(e.property.specification)&&(("source"===e.value.kind||"composite"===e.value.kind)&&e.value.isStateDependent))return!0}return!1},e}(Et),Li={Int8:Int8Array,Uint8:Uint8Array,Int16:Int16Array,Uint16:Uint16Array,Int32:Int32Array,Uint32:Uint32Array,Float32:Float32Array},Ci=function(t,e){this._structArray=t,this._pos1=e*this.size,this._pos2=this._pos1/2,this._pos4=this._pos1/4,this._pos8=this._pos1/8},Pi=function(){this.isTransferred=!1,this.capacity=-1,this.resize(0)};function Ii(t,e){void 0===e&&(e=1);var r=0,n=0;return{members:t.map((function(t){var i,a=(i=t.type,Li[i].BYTES_PER_ELEMENT),o=r=Oi(r,Math.max(e,a)),s=t.components||1;return n=Math.max(n,a),r+=a*s,{name:t.name,type:t.type,components:s,offset:o}})),size:Oi(r,Math.max(n,e)),alignment:e}}function Oi(t,e){return Math.ceil(t/e)*e}Pi.serialize=function(t,e){return t._trim(),e&&(t.isTransferred=!0,e.push(t.arrayBuffer)),{length:t.length,arrayBuffer:t.arrayBuffer}},Pi.deserialize=function(t){var e=Object.create(this.prototype);return e.arrayBuffer=t.arrayBuffer,e.length=t.length,e.capacity=t.arrayBuffer.byteLength/e.bytesPerElement,e._refreshViews(),e},Pi.prototype._trim=function(){this.length!==this.capacity&&(this.capacity=this.length,this.arrayBuffer=this.arrayBuffer.slice(0,this.length*this.bytesPerElement),this._refreshViews())},Pi.prototype.clear=function(){this.length=0},Pi.prototype.resize=function(t){this.reserve(t),this.length=t},Pi.prototype.reserve=function(t){if(t>this.capacity){this.capacity=Math.max(t,Math.floor(5*this.capacity),128),this.arrayBuffer=new ArrayBuffer(this.capacity*this.bytesPerElement);var e=this.uint8;this._refreshViews(),e&&this.uint8.set(e)}},Pi.prototype._refreshViews=function(){throw new Error("_refreshViews() must be implemented by each concrete StructArray layout")};var zi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.int16[n+0]=e,this.int16[n+1]=r,t},e}(Pi);zi.prototype.bytesPerElement=4,Nn("StructArrayLayout2i4",zi);var Di=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=4*t;return this.int16[a+0]=e,this.int16[a+1]=r,this.int16[a+2]=n,this.int16[a+3]=i,t},e}(Pi);Di.prototype.bytesPerElement=8,Nn("StructArrayLayout4i8",Di);var Ri=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=6*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t},e}(Pi);Ri.prototype.bytesPerElement=12,Nn("StructArrayLayout2i4i12",Ri);var Fi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=4*t,l=8*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.uint8[l+4]=n,this.uint8[l+5]=i,this.uint8[l+6]=a,this.uint8[l+7]=o,t},e}(Pi);Fi.prototype.bytesPerElement=8,Nn("StructArrayLayout2i4ub8",Fi);var Bi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c){var u=this.length;return this.resize(u+1),this.emplace(u,t,e,r,n,i,a,o,s,l,c)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u){var f=9*t,h=18*t;return this.uint16[f+0]=e,this.uint16[f+1]=r,this.uint16[f+2]=n,this.uint16[f+3]=i,this.uint16[f+4]=a,this.uint16[f+5]=o,this.uint16[f+6]=s,this.uint16[f+7]=l,this.uint8[h+16]=c,this.uint8[h+17]=u,t},e}(Pi);Bi.prototype.bytesPerElement=18,Nn("StructArrayLayout8ui2ub18",Bi);var Ni=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f){var h=this.length;return this.resize(h+1),this.emplace(h,t,e,r,n,i,a,o,s,l,c,u,f)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h){var p=12*t;return this.int16[p+0]=e,this.int16[p+1]=r,this.int16[p+2]=n,this.int16[p+3]=i,this.uint16[p+4]=a,this.uint16[p+5]=o,this.uint16[p+6]=s,this.uint16[p+7]=l,this.int16[p+8]=c,this.int16[p+9]=u,this.int16[p+10]=f,this.int16[p+11]=h,t},e}(Pi);Ni.prototype.bytesPerElement=24,Nn("StructArrayLayout4i4ui4i24",Ni);var ji=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.float32[i+0]=e,this.float32[i+1]=r,this.float32[i+2]=n,t},e}(Pi);ji.prototype.bytesPerElement=12,Nn("StructArrayLayout3f12",ji);var Ui=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.uint32[r+0]=e,t},e}(Pi);Ui.prototype.bytesPerElement=4,Nn("StructArrayLayout1ul4",Ui);var Vi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l){var c=this.length;return this.resize(c+1),this.emplace(c,t,e,r,n,i,a,o,s,l)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c){var u=10*t,f=5*t;return this.int16[u+0]=e,this.int16[u+1]=r,this.int16[u+2]=n,this.int16[u+3]=i,this.int16[u+4]=a,this.int16[u+5]=o,this.uint32[f+3]=s,this.uint16[u+8]=l,this.uint16[u+9]=c,t},e}(Pi);Vi.prototype.bytesPerElement=20,Nn("StructArrayLayout6i1ul2ui20",Vi);var Hi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=6*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t},e}(Pi);Hi.prototype.bytesPerElement=12,Nn("StructArrayLayout2i2i2i12",Hi);var qi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i){var a=this.length;return this.resize(a+1),this.emplace(a,t,e,r,n,i)},e.prototype.emplace=function(t,e,r,n,i,a){var o=4*t,s=8*t;return this.float32[o+0]=e,this.float32[o+1]=r,this.float32[o+2]=n,this.int16[s+6]=i,this.int16[s+7]=a,t},e}(Pi);qi.prototype.bytesPerElement=16,Nn("StructArrayLayout2f1f2i16",qi);var Gi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=12*t,o=3*t;return this.uint8[a+0]=e,this.uint8[a+1]=r,this.float32[o+1]=n,this.float32[o+2]=i,t},e}(Pi);Gi.prototype.bytesPerElement=12,Nn("StructArrayLayout2ub2f12",Gi);var Yi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.uint16[i+0]=e,this.uint16[i+1]=r,this.uint16[i+2]=n,t},e}(Pi);Yi.prototype.bytesPerElement=6,Nn("StructArrayLayout3ui6",Yi);var Wi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g,m){var v=this.length;return this.resize(v+1),this.emplace(v,t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g,m)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g,m,v){var y=24*t,x=12*t,b=48*t;return this.int16[y+0]=e,this.int16[y+1]=r,this.uint16[y+2]=n,this.uint16[y+3]=i,this.uint32[x+2]=a,this.uint32[x+3]=o,this.uint32[x+4]=s,this.uint16[y+10]=l,this.uint16[y+11]=c,this.uint16[y+12]=u,this.float32[x+7]=f,this.float32[x+8]=h,this.uint8[b+36]=p,this.uint8[b+37]=d,this.uint8[b+38]=g,this.uint32[x+10]=m,this.int16[y+22]=v,t},e}(Pi);Wi.prototype.bytesPerElement=48,Nn("StructArrayLayout2i2ui3ul3ui2f3ub1ul1i48",Wi);var Xi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g,m,v,y,x,b,_,w,T,k,A,M,S){var E=this.length;return this.resize(E+1),this.emplace(E,t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g,m,v,y,x,b,_,w,T,k,A,M,S)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g,m,v,y,x,b,_,w,T,k,A,M,S,E){var L=34*t,C=17*t;return this.int16[L+0]=e,this.int16[L+1]=r,this.int16[L+2]=n,this.int16[L+3]=i,this.int16[L+4]=a,this.int16[L+5]=o,this.int16[L+6]=s,this.int16[L+7]=l,this.uint16[L+8]=c,this.uint16[L+9]=u,this.uint16[L+10]=f,this.uint16[L+11]=h,this.uint16[L+12]=p,this.uint16[L+13]=d,this.uint16[L+14]=g,this.uint16[L+15]=m,this.uint16[L+16]=v,this.uint16[L+17]=y,this.uint16[L+18]=x,this.uint16[L+19]=b,this.uint16[L+20]=_,this.uint16[L+21]=w,this.uint16[L+22]=T,this.uint32[C+12]=k,this.float32[C+13]=A,this.float32[C+14]=M,this.float32[C+15]=S,this.float32[C+16]=E,t},e}(Pi);Xi.prototype.bytesPerElement=68,Nn("StructArrayLayout8i15ui1ul4f68",Xi);var Zi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.float32[r+0]=e,t},e}(Pi);Zi.prototype.bytesPerElement=4,Nn("StructArrayLayout1f4",Zi);var Ji=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.int16[i+0]=e,this.int16[i+1]=r,this.int16[i+2]=n,t},e}(Pi);Ji.prototype.bytesPerElement=6,Nn("StructArrayLayout3i6",Ji);var Ki=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=2*t,a=4*t;return this.uint32[i+0]=e,this.uint16[a+2]=r,this.uint16[a+3]=n,t},e}(Pi);Ki.prototype.bytesPerElement=8,Nn("StructArrayLayout1ul2ui8",Ki);var Qi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.uint16[n+0]=e,this.uint16[n+1]=r,t},e}(Pi);Qi.prototype.bytesPerElement=4,Nn("StructArrayLayout2ui4",Qi);var $i=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.uint16[r+0]=e,t},e}(Pi);$i.prototype.bytesPerElement=2,Nn("StructArrayLayout1ui2",$i);var ta=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.float32[n+0]=e,this.float32[n+1]=r,t},e}(Pi);ta.prototype.bytesPerElement=8,Nn("StructArrayLayout2f8",ta);var ea=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=4*t;return this.float32[a+0]=e,this.float32[a+1]=r,this.float32[a+2]=n,this.float32[a+3]=i,t},e}(Pi);ea.prototype.bytesPerElement=16,Nn("StructArrayLayout4f16",ea);var ra=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorPointX:{configurable:!0},anchorPointY:{configurable:!0},x1:{configurable:!0},y1:{configurable:!0},x2:{configurable:!0},y2:{configurable:!0},featureIndex:{configurable:!0},sourceLayerIndex:{configurable:!0},bucketIndex:{configurable:!0},anchorPoint:{configurable:!0}};return r.anchorPointX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorPointY.get=function(){return this._structArray.int16[this._pos2+1]},r.x1.get=function(){return this._structArray.int16[this._pos2+2]},r.y1.get=function(){return this._structArray.int16[this._pos2+3]},r.x2.get=function(){return this._structArray.int16[this._pos2+4]},r.y2.get=function(){return this._structArray.int16[this._pos2+5]},r.featureIndex.get=function(){return this._structArray.uint32[this._pos4+3]},r.sourceLayerIndex.get=function(){return this._structArray.uint16[this._pos2+8]},r.bucketIndex.get=function(){return this._structArray.uint16[this._pos2+9]},r.anchorPoint.get=function(){return new i(this.anchorPointX,this.anchorPointY)},Object.defineProperties(e.prototype,r),e}(Ci);ra.prototype.size=20;var na=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new ra(this,t)},e}(Vi);Nn("CollisionBoxArray",na);var ia=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorX:{configurable:!0},anchorY:{configurable:!0},glyphStartIndex:{configurable:!0},numGlyphs:{configurable:!0},vertexStartIndex:{configurable:!0},lineStartIndex:{configurable:!0},lineLength:{configurable:!0},segment:{configurable:!0},lowerSize:{configurable:!0},upperSize:{configurable:!0},lineOffsetX:{configurable:!0},lineOffsetY:{configurable:!0},writingMode:{configurable:!0},placedOrientation:{configurable:!0},hidden:{configurable:!0},crossTileID:{configurable:!0},associatedIconIndex:{configurable:!0}};return r.anchorX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorY.get=function(){return this._structArray.int16[this._pos2+1]},r.glyphStartIndex.get=function(){return this._structArray.uint16[this._pos2+2]},r.numGlyphs.get=function(){return this._structArray.uint16[this._pos2+3]},r.vertexStartIndex.get=function(){return this._structArray.uint32[this._pos4+2]},r.lineStartIndex.get=function(){return this._structArray.uint32[this._pos4+3]},r.lineLength.get=function(){return this._structArray.uint32[this._pos4+4]},r.segment.get=function(){return this._structArray.uint16[this._pos2+10]},r.lowerSize.get=function(){return this._structArray.uint16[this._pos2+11]},r.upperSize.get=function(){return this._structArray.uint16[this._pos2+12]},r.lineOffsetX.get=function(){return this._structArray.float32[this._pos4+7]},r.lineOffsetY.get=function(){return this._structArray.float32[this._pos4+8]},r.writingMode.get=function(){return this._structArray.uint8[this._pos1+36]},r.placedOrientation.get=function(){return this._structArray.uint8[this._pos1+37]},r.placedOrientation.set=function(t){this._structArray.uint8[this._pos1+37]=t},r.hidden.get=function(){return this._structArray.uint8[this._pos1+38]},r.hidden.set=function(t){this._structArray.uint8[this._pos1+38]=t},r.crossTileID.get=function(){return this._structArray.uint32[this._pos4+10]},r.crossTileID.set=function(t){this._structArray.uint32[this._pos4+10]=t},r.associatedIconIndex.get=function(){return this._structArray.int16[this._pos2+22]},Object.defineProperties(e.prototype,r),e}(Ci);ia.prototype.size=48;var aa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new ia(this,t)},e}(Wi);Nn("PlacedSymbolArray",aa);var oa=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorX:{configurable:!0},anchorY:{configurable:!0},rightJustifiedTextSymbolIndex:{configurable:!0},centerJustifiedTextSymbolIndex:{configurable:!0},leftJustifiedTextSymbolIndex:{configurable:!0},verticalPlacedTextSymbolIndex:{configurable:!0},placedIconSymbolIndex:{configurable:!0},verticalPlacedIconSymbolIndex:{configurable:!0},key:{configurable:!0},textBoxStartIndex:{configurable:!0},textBoxEndIndex:{configurable:!0},verticalTextBoxStartIndex:{configurable:!0},verticalTextBoxEndIndex:{configurable:!0},iconBoxStartIndex:{configurable:!0},iconBoxEndIndex:{configurable:!0},verticalIconBoxStartIndex:{configurable:!0},verticalIconBoxEndIndex:{configurable:!0},featureIndex:{configurable:!0},numHorizontalGlyphVertices:{configurable:!0},numVerticalGlyphVertices:{configurable:!0},numIconVertices:{configurable:!0},numVerticalIconVertices:{configurable:!0},useRuntimeCollisionCircles:{configurable:!0},crossTileID:{configurable:!0},textBoxScale:{configurable:!0},textOffset0:{configurable:!0},textOffset1:{configurable:!0},collisionCircleDiameter:{configurable:!0}};return r.anchorX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorY.get=function(){return this._structArray.int16[this._pos2+1]},r.rightJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+2]},r.centerJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+3]},r.leftJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+4]},r.verticalPlacedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+5]},r.placedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+6]},r.verticalPlacedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+7]},r.key.get=function(){return this._structArray.uint16[this._pos2+8]},r.textBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+9]},r.textBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+10]},r.verticalTextBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+11]},r.verticalTextBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+12]},r.iconBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+13]},r.iconBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+14]},r.verticalIconBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+15]},r.verticalIconBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+16]},r.featureIndex.get=function(){return this._structArray.uint16[this._pos2+17]},r.numHorizontalGlyphVertices.get=function(){return this._structArray.uint16[this._pos2+18]},r.numVerticalGlyphVertices.get=function(){return this._structArray.uint16[this._pos2+19]},r.numIconVertices.get=function(){return this._structArray.uint16[this._pos2+20]},r.numVerticalIconVertices.get=function(){return this._structArray.uint16[this._pos2+21]},r.useRuntimeCollisionCircles.get=function(){return this._structArray.uint16[this._pos2+22]},r.crossTileID.get=function(){return this._structArray.uint32[this._pos4+12]},r.crossTileID.set=function(t){this._structArray.uint32[this._pos4+12]=t},r.textBoxScale.get=function(){return this._structArray.float32[this._pos4+13]},r.textOffset0.get=function(){return this._structArray.float32[this._pos4+14]},r.textOffset1.get=function(){return this._structArray.float32[this._pos4+15]},r.collisionCircleDiameter.get=function(){return this._structArray.float32[this._pos4+16]},Object.defineProperties(e.prototype,r),e}(Ci);oa.prototype.size=68;var sa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new oa(this,t)},e}(Xi);Nn("SymbolInstanceArray",sa);var la=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getoffsetX=function(t){return this.float32[1*t+0]},e}(Zi);Nn("GlyphOffsetArray",la);var ca=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getx=function(t){return this.int16[3*t+0]},e.prototype.gety=function(t){return this.int16[3*t+1]},e.prototype.gettileUnitDistanceFromAnchor=function(t){return this.int16[3*t+2]},e}(Ji);Nn("SymbolLineVertexArray",ca);var ua=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={featureIndex:{configurable:!0},sourceLayerIndex:{configurable:!0},bucketIndex:{configurable:!0}};return r.featureIndex.get=function(){return this._structArray.uint32[this._pos4+0]},r.sourceLayerIndex.get=function(){return this._structArray.uint16[this._pos2+2]},r.bucketIndex.get=function(){return this._structArray.uint16[this._pos2+3]},Object.defineProperties(e.prototype,r),e}(Ci);ua.prototype.size=8;var fa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new ua(this,t)},e}(Ki);Nn("FeatureIndexArray",fa);var ha=Ii([{name:"a_pos",components:2,type:"Int16"}],4).members,pa=function(t){void 0===t&&(t=[]),this.segments=t};function da(t,e){return 256*(t=l(Math.floor(t),0,255))+(e=l(Math.floor(e),0,255))}pa.prototype.prepareSegment=function(t,e,r,n){var i=this.segments[this.segments.length-1];return t>pa.MAX_VERTEX_ARRAY_LENGTH&&_("Max vertices per segment is "+pa.MAX_VERTEX_ARRAY_LENGTH+": bucket requested "+t),(!i||i.vertexLength+t>pa.MAX_VERTEX_ARRAY_LENGTH||i.sortKey!==n)&&(i={vertexOffset:e.length,primitiveOffset:r.length,vertexLength:0,primitiveLength:0},void 0!==n&&(i.sortKey=n),this.segments.push(i)),i},pa.prototype.get=function(){return this.segments},pa.prototype.destroy=function(){for(var t=0,e=this.segments;t<e.length;t+=1){var r=e[t];for(var n in r.vaos)r.vaos[n].destroy()}},pa.simpleSegment=function(t,e,r,n){return new pa([{vertexOffset:t,primitiveOffset:e,vertexLength:r,primitiveLength:n,vaos:{},sortKey:0}])},pa.MAX_VERTEX_ARRAY_LENGTH=Math.pow(2,16)-1,Nn("SegmentVector",pa);var ga=Ii([{name:"a_pattern_from",components:4,type:"Uint16"},{name:"a_pattern_to",components:4,type:"Uint16"},{name:"a_pixel_ratio_from",components:1,type:"Uint8"},{name:"a_pixel_ratio_to",components:1,type:"Uint8"}]),ma=e((function(t){t.exports=function(t,e){var r,n,i,a,o,s,l,c;for(r=3&t.length,n=t.length-r,i=e,o=3432918353,s=461845907,c=0;c<n;)l=255&t.charCodeAt(c)|(255&t.charCodeAt(++c))<<8|(255&t.charCodeAt(++c))<<16|(255&t.charCodeAt(++c))<<24,++c,i=27492+(65535&(a=5*(65535&(i=(i^=l=(65535&(l=(l=(65535&l)*o+(((l>>>16)*o&65535)<<16)&4294967295)<<15|l>>>17))*s+(((l>>>16)*s&65535)<<16)&4294967295)<<13|i>>>19))+((5*(i>>>16)&65535)<<16)&4294967295))+((58964+(a>>>16)&65535)<<16);switch(l=0,r){case 3:l^=(255&t.charCodeAt(c+2))<<16;case 2:l^=(255&t.charCodeAt(c+1))<<8;case 1:i^=l=(65535&(l=(l=(65535&(l^=255&t.charCodeAt(c)))*o+(((l>>>16)*o&65535)<<16)&4294967295)<<15|l>>>17))*s+(((l>>>16)*s&65535)<<16)&4294967295}return i^=t.length,i=2246822507*(65535&(i^=i>>>16))+((2246822507*(i>>>16)&65535)<<16)&4294967295,i=3266489909*(65535&(i^=i>>>13))+((3266489909*(i>>>16)&65535)<<16)&4294967295,(i^=i>>>16)>>>0}})),va=e((function(t){t.exports=function(t,e){for(var r,n=t.length,i=e^n,a=0;n>=4;)r=1540483477*(65535&(r=255&t.charCodeAt(a)|(255&t.charCodeAt(++a))<<8|(255&t.charCodeAt(++a))<<16|(255&t.charCodeAt(++a))<<24))+((1540483477*(r>>>16)&65535)<<16),i=1540483477*(65535&i)+((1540483477*(i>>>16)&65535)<<16)^(r=1540483477*(65535&(r^=r>>>24))+((1540483477*(r>>>16)&65535)<<16)),n-=4,++a;switch(n){case 3:i^=(255&t.charCodeAt(a+2))<<16;case 2:i^=(255&t.charCodeAt(a+1))<<8;case 1:i=1540483477*(65535&(i^=255&t.charCodeAt(a)))+((1540483477*(i>>>16)&65535)<<16)}return i=1540483477*(65535&(i^=i>>>13))+((1540483477*(i>>>16)&65535)<<16),(i^=i>>>15)>>>0}})),ya=ma,xa=ma,ba=va;ya.murmur3=xa,ya.murmur2=ba;var _a=function(){this.ids=[],this.positions=[],this.indexed=!1};_a.prototype.add=function(t,e,r,n){this.ids.push(Ta(t)),this.positions.push(e,r,n)},_a.prototype.getPositions=function(t){for(var e=Ta(t),r=0,n=this.ids.length-1;r<n;){var i=r+n>>1;this.ids[i]>=e?n=i:r=i+1}for(var a=[];this.ids[r]===e;){var o=this.positions[3*r],s=this.positions[3*r+1],l=this.positions[3*r+2];a.push({index:o,start:s,end:l}),r++}return a},_a.serialize=function(t,e){var r=new Float64Array(t.ids),n=new Uint32Array(t.positions);return function t(e,r,n,i){for(;n<i;){for(var a=e[n+i>>1],o=n-1,s=i+1;;){do{o++}while(e[o]<a);do{s--}while(e[s]>a);if(o>=s)break;ka(e,o,s),ka(r,3*o,3*s),ka(r,3*o+1,3*s+1),ka(r,3*o+2,3*s+2)}s-n<i-s?(t(e,r,n,s),n=s+1):(t(e,r,s+1,i),i=s)}}(r,n,0,r.length-1),e&&e.push(r.buffer,n.buffer),{ids:r,positions:n}},_a.deserialize=function(t){var e=new _a;return e.ids=t.ids,e.positions=t.positions,e.indexed=!0,e};var wa=Math.pow(2,53)-1;function Ta(t){var e=+t;return!isNaN(e)&&e<=wa?e:ya(String(t))}function ka(t,e,r){var n=t[e];t[e]=t[r],t[r]=n}Nn("FeaturePositionMap",_a);var Aa=function(t,e){this.gl=t.gl,this.location=e},Ma=function(t){function e(e,r){t.call(this,e,r),this.current=0}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.set=function(t){this.current!==t&&(this.current=t,this.gl.uniform1i(this.location,t))},e}(Aa),Sa=function(t){function e(e,r){t.call(this,e,r),this.current=0}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.set=function(t){this.current!==t&&(this.current=t,this.gl.uniform1f(this.location,t))},e}(Aa),Ea=function(t){function e(e,r){t.call(this,e,r),this.current=[0,0]}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.set=function(t){t[0]===this.current[0]&&t[1]===this.current[1]||(this.current=t,this.gl.uniform2f(this.location,t[0],t[1]))},e}(Aa),La=function(t){function e(e,r){t.call(this,e,r),this.current=[0,0,0]}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.set=function(t){t[0]===this.current[0]&&t[1]===this.current[1]&&t[2]===this.current[2]||(this.current=t,this.gl.uniform3f(this.location,t[0],t[1],t[2]))},e}(Aa),Ca=function(t){function e(e,r){t.call(this,e,r),this.current=[0,0,0,0]}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.set=function(t){t[0]===this.current[0]&&t[1]===this.current[1]&&t[2]===this.current[2]&&t[3]===this.current[3]||(this.current=t,this.gl.uniform4f(this.location,t[0],t[1],t[2],t[3]))},e}(Aa),Pa=function(t){function e(e,r){t.call(this,e,r),this.current=te.transparent}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.set=function(t){t.r===this.current.r&&t.g===this.current.g&&t.b===this.current.b&&t.a===this.current.a||(this.current=t,this.gl.uniform4f(this.location,t.r,t.g,t.b,t.a))},e}(Aa),Ia=new Float32Array(16),Oa=function(t){function e(e,r){t.call(this,e,r),this.current=Ia}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.set=function(t){if(t[12]!==this.current[12]||t[0]!==this.current[0])return this.current=t,void this.gl.uniformMatrix4fv(this.location,!1,t);for(var e=1;e<16;e++)if(t[e]!==this.current[e]){this.current=t,this.gl.uniformMatrix4fv(this.location,!1,t);break}},e}(Aa);function za(t){return[da(255*t.r,255*t.g),da(255*t.b,255*t.a)]}var Da=function(t,e,r){this.value=t,this.uniformNames=e.map((function(t){return"u_"+t})),this.type=r};Da.prototype.setUniform=function(t,e,r){t.set(r.constantOr(this.value))},Da.prototype.getBinding=function(t,e,r){return"color"===this.type?new Pa(t,e):new Sa(t,e)};var Ra=function(t,e){this.uniformNames=e.map((function(t){return"u_"+t})),this.patternFrom=null,this.patternTo=null,this.pixelRatioFrom=1,this.pixelRatioTo=1};Ra.prototype.setConstantPatternPositions=function(t,e){this.pixelRatioFrom=e.pixelRatio,this.pixelRatioTo=t.pixelRatio,this.patternFrom=e.tlbr,this.patternTo=t.tlbr},Ra.prototype.setUniform=function(t,e,r,n){var i="u_pattern_to"===n?this.patternTo:"u_pattern_from"===n?this.patternFrom:"u_pixel_ratio_to"===n?this.pixelRatioTo:"u_pixel_ratio_from"===n?this.pixelRatioFrom:null;i&&t.set(i)},Ra.prototype.getBinding=function(t,e,r){return"u_pattern"===r.substr(0,9)?new Ca(t,e):new Sa(t,e)};var Fa=function(t,e,r,n){this.expression=t,this.type=r,this.maxValue=0,this.paintVertexAttributes=e.map((function(t){return{name:"a_"+t,type:"Float32",components:"color"===r?2:1,offset:0}})),this.paintVertexArray=new n};Fa.prototype.populatePaintArray=function(t,e,r,n,i){var a=this.paintVertexArray.length,o=this.expression.evaluate(new pi(0),e,{},n,[],i);this.paintVertexArray.resize(t),this._setPaintValue(a,t,o)},Fa.prototype.updatePaintArray=function(t,e,r,n){var i=this.expression.evaluate({zoom:0},r,n);this._setPaintValue(t,e,i)},Fa.prototype._setPaintValue=function(t,e,r){if("color"===this.type)for(var n=za(r),i=t;i<e;i++)this.paintVertexArray.emplace(i,n[0],n[1]);else{for(var a=t;a<e;a++)this.paintVertexArray.emplace(a,r);this.maxValue=Math.max(this.maxValue,Math.abs(r))}},Fa.prototype.upload=function(t){this.paintVertexArray&&this.paintVertexArray.arrayBuffer&&(this.paintVertexBuffer&&this.paintVertexBuffer.buffer?this.paintVertexBuffer.updateData(this.paintVertexArray):this.paintVertexBuffer=t.createVertexBuffer(this.paintVertexArray,this.paintVertexAttributes,this.expression.isStateDependent))},Fa.prototype.destroy=function(){this.paintVertexBuffer&&this.paintVertexBuffer.destroy()};var Ba=function(t,e,r,n,i,a){this.expression=t,this.uniformNames=e.map((function(t){return"u_"+t+"_t"})),this.type=r,this.useIntegerZoom=n,this.zoom=i,this.maxValue=0,this.paintVertexAttributes=e.map((function(t){return{name:"a_"+t,type:"Float32",components:"color"===r?4:2,offset:0}})),this.paintVertexArray=new a};Ba.prototype.populatePaintArray=function(t,e,r,n,i){var a=this.expression.evaluate(new pi(this.zoom),e,{},n,[],i),o=this.expression.evaluate(new pi(this.zoom+1),e,{},n,[],i),s=this.paintVertexArray.length;this.paintVertexArray.resize(t),this._setPaintValue(s,t,a,o)},Ba.prototype.updatePaintArray=function(t,e,r,n){var i=this.expression.evaluate({zoom:this.zoom},r,n),a=this.expression.evaluate({zoom:this.zoom+1},r,n);this._setPaintValue(t,e,i,a)},Ba.prototype._setPaintValue=function(t,e,r,n){if("color"===this.type)for(var i=za(r),a=za(n),o=t;o<e;o++)this.paintVertexArray.emplace(o,i[0],i[1],a[0],a[1]);else{for(var s=t;s<e;s++)this.paintVertexArray.emplace(s,r,n);this.maxValue=Math.max(this.maxValue,Math.abs(r),Math.abs(n))}},Ba.prototype.upload=function(t){this.paintVertexArray&&this.paintVertexArray.arrayBuffer&&(this.paintVertexBuffer&&this.paintVertexBuffer.buffer?this.paintVertexBuffer.updateData(this.paintVertexArray):this.paintVertexBuffer=t.createVertexBuffer(this.paintVertexArray,this.paintVertexAttributes,this.expression.isStateDependent))},Ba.prototype.destroy=function(){this.paintVertexBuffer&&this.paintVertexBuffer.destroy()},Ba.prototype.setUniform=function(t,e){var r=this.useIntegerZoom?Math.floor(e.zoom):e.zoom,n=l(this.expression.interpolationFactor(r,this.zoom,this.zoom+1),0,1);t.set(n)},Ba.prototype.getBinding=function(t,e,r){return new Sa(t,e)};var Na=function(t,e,r,n,i,a){this.expression=t,this.type=e,this.useIntegerZoom=r,this.zoom=n,this.layerId=a,this.zoomInPaintVertexArray=new i,this.zoomOutPaintVertexArray=new i};Na.prototype.populatePaintArray=function(t,e,r){var n=this.zoomInPaintVertexArray.length;this.zoomInPaintVertexArray.resize(t),this.zoomOutPaintVertexArray.resize(t),this._setPaintValues(n,t,e.patterns&&e.patterns[this.layerId],r)},Na.prototype.updatePaintArray=function(t,e,r,n,i){this._setPaintValues(t,e,r.patterns&&r.patterns[this.layerId],i)},Na.prototype._setPaintValues=function(t,e,r,n){if(n&&r){var i=r.min,a=r.mid,o=r.max,s=n[i],l=n[a],c=n[o];if(s&&l&&c)for(var u=t;u<e;u++)this.zoomInPaintVertexArray.emplace(u,l.tl[0],l.tl[1],l.br[0],l.br[1],s.tl[0],s.tl[1],s.br[0],s.br[1],l.pixelRatio,s.pixelRatio),this.zoomOutPaintVertexArray.emplace(u,l.tl[0],l.tl[1],l.br[0],l.br[1],c.tl[0],c.tl[1],c.br[0],c.br[1],l.pixelRatio,c.pixelRatio)}},Na.prototype.upload=function(t){this.zoomInPaintVertexArray&&this.zoomInPaintVertexArray.arrayBuffer&&this.zoomOutPaintVertexArray&&this.zoomOutPaintVertexArray.arrayBuffer&&(this.zoomInPaintVertexBuffer=t.createVertexBuffer(this.zoomInPaintVertexArray,ga.members,this.expression.isStateDependent),this.zoomOutPaintVertexBuffer=t.createVertexBuffer(this.zoomOutPaintVertexArray,ga.members,this.expression.isStateDependent))},Na.prototype.destroy=function(){this.zoomOutPaintVertexBuffer&&this.zoomOutPaintVertexBuffer.destroy(),this.zoomInPaintVertexBuffer&&this.zoomInPaintVertexBuffer.destroy()};var ja=function(t,e,r,n){this.binders={},this.layoutAttributes=n,this._buffers=[];var i=[];for(var a in t.paint._values)if(r(a)){var o=t.paint.get(a);if(o instanceof bi&&zr(o.property.specification)){var s=Va(a,t.type),l=o.value,c=o.property.specification.type,u=o.property.useIntegerZoom,f=o.property.specification["property-type"],h="cross-faded"===f||"cross-faded-data-driven"===f;if("constant"===l.kind)this.binders[a]=h?new Ra(l.value,s):new Da(l.value,s,c),i.push("/u_"+a);else if("source"===l.kind||h){var p=Ha(a,c,"source");this.binders[a]=h?new Na(l,c,u,e,p,t.id):new Fa(l,s,c,p),i.push("/a_"+a)}else{var d=Ha(a,c,"composite");this.binders[a]=new Ba(l,s,c,u,e,d),i.push("/z_"+a)}}}this.cacheKey=i.sort().join("")};ja.prototype.getMaxValue=function(t){var e=this.binders[t];return e instanceof Fa||e instanceof Ba?e.maxValue:0},ja.prototype.populatePaintArrays=function(t,e,r,n,i){for(var a in this.binders){var o=this.binders[a];(o instanceof Fa||o instanceof Ba||o instanceof Na)&&o.populatePaintArray(t,e,r,n,i)}},ja.prototype.setConstantPatternPositions=function(t,e){for(var r in this.binders){var n=this.binders[r];n instanceof Ra&&n.setConstantPatternPositions(t,e)}},ja.prototype.updatePaintArrays=function(t,e,r,n,i){var a=!1;for(var o in t)for(var s=0,l=e.getPositions(o);s<l.length;s+=1){var c=l[s],u=r.feature(c.index);for(var f in this.binders){var h=this.binders[f];if((h instanceof Fa||h instanceof Ba||h instanceof Na)&&!0===h.expression.isStateDependent){var p=n.paint.get(f);h.expression=p.value,h.updatePaintArray(c.start,c.end,u,t[o],i),a=!0}}}return a},ja.prototype.defines=function(){var t=[];for(var e in this.binders){var r=this.binders[e];(r instanceof Da||r instanceof Ra)&&t.push.apply(t,r.uniformNames.map((function(t){return"#define HAS_UNIFORM_"+t})))}return t},ja.prototype.getPaintVertexBuffers=function(){return this._buffers},ja.prototype.getUniforms=function(t,e){var r=[];for(var n in this.binders){var i=this.binders[n];if(i instanceof Da||i instanceof Ra||i instanceof Ba)for(var a=0,o=i.uniformNames;a<o.length;a+=1){var s=o[a];if(e[s]){var l=i.getBinding(t,e[s],s);r.push({name:s,property:n,binding:l})}}}return r},ja.prototype.setUniforms=function(t,e,r,n){for(var i=0,a=e;i<a.length;i+=1){var o=a[i],s=o.name,l=o.property,c=o.binding;this.binders[l].setUniform(c,n,r.get(l),s)}},ja.prototype.updatePaintBuffers=function(t){for(var e in this._buffers=[],this.binders){var r=this.binders[e];if(t&&r instanceof Na){var n=2===t.fromScale?r.zoomInPaintVertexBuffer:r.zoomOutPaintVertexBuffer;n&&this._buffers.push(n)}else(r instanceof Fa||r instanceof Ba)&&r.paintVertexBuffer&&this._buffers.push(r.paintVertexBuffer)}},ja.prototype.upload=function(t){for(var e in this.binders){var r=this.binders[e];(r instanceof Fa||r instanceof Ba||r instanceof Na)&&r.upload(t)}this.updatePaintBuffers()},ja.prototype.destroy=function(){for(var t in this.binders){var e=this.binders[t];(e instanceof Fa||e instanceof Ba||e instanceof Na)&&e.destroy()}};var Ua=function(t,e,r,n){void 0===n&&(n=function(){return!0}),this.programConfigurations={};for(var i=0,a=e;i<a.length;i+=1){var o=a[i];this.programConfigurations[o.id]=new ja(o,r,n,t)}this.needsUpload=!1,this._featureMap=new _a,this._bufferOffset=0};function Va(t,e){return{"text-opacity":["opacity"],"icon-opacity":["opacity"],"text-color":["fill_color"],"icon-color":["fill_color"],"text-halo-color":["halo_color"],"icon-halo-color":["halo_color"],"text-halo-blur":["halo_blur"],"icon-halo-blur":["halo_blur"],"text-halo-width":["halo_width"],"icon-halo-width":["halo_width"],"line-gap-width":["gapwidth"],"line-pattern":["pattern_to","pattern_from","pixel_ratio_to","pixel_ratio_from"],"fill-pattern":["pattern_to","pattern_from","pixel_ratio_to","pixel_ratio_from"],"fill-extrusion-pattern":["pattern_to","pattern_from","pixel_ratio_to","pixel_ratio_from"]}[t]||[t.replace(e+"-","").replace(/-/g,"_")]}function Ha(t,e,r){var n={color:{source:ta,composite:ea},number:{source:Zi,composite:ta}},i=function(t){return{"line-pattern":{source:Bi,composite:Bi},"fill-pattern":{source:Bi,composite:Bi},"fill-extrusion-pattern":{source:Bi,composite:Bi}}[t]}(t);return i&&i[r]||n[e][r]}Ua.prototype.populatePaintArrays=function(t,e,r,n,i,a){for(var o in this.programConfigurations)this.programConfigurations[o].populatePaintArrays(t,e,n,i,a);void 0!==e.id&&this._featureMap.add(e.id,r,this._bufferOffset,t),this._bufferOffset=t,this.needsUpload=!0},Ua.prototype.updatePaintArrays=function(t,e,r,n){for(var i=0,a=r;i<a.length;i+=1){var o=a[i];this.needsUpload=this.programConfigurations[o.id].updatePaintArrays(t,this._featureMap,e,o,n)||this.needsUpload}},Ua.prototype.get=function(t){return this.programConfigurations[t]},Ua.prototype.upload=function(t){if(this.needsUpload){for(var e in this.programConfigurations)this.programConfigurations[e].upload(t);this.needsUpload=!1}},Ua.prototype.destroy=function(){for(var t in this.programConfigurations)this.programConfigurations[t].destroy()},Nn("ConstantBinder",Da),Nn("CrossFadedConstantBinder",Ra),Nn("SourceExpressionBinder",Fa),Nn("CrossFadedCompositeBinder",Na),Nn("CompositeExpressionBinder",Ba),Nn("ProgramConfiguration",ja,{omit:["_buffers"]}),Nn("ProgramConfigurationSet",Ua);var qa,Ga=(qa=15,{min:-1*Math.pow(2,qa-1),max:Math.pow(2,qa-1)-1});function Ya(t){for(var e=8192/t.extent,r=t.loadGeometry(),n=0;n<r.length;n++)for(var i=r[n],a=0;a<i.length;a++){var o=i[a];o.x=Math.round(o.x*e),o.y=Math.round(o.y*e),(o.x<Ga.min||o.x>Ga.max||o.y<Ga.min||o.y>Ga.max)&&(_("Geometry exceeds allowed extent, reduce your vector tile buffer size"),o.x=l(o.x,Ga.min,Ga.max),o.y=l(o.y,Ga.min,Ga.max))}return r}function Wa(t,e,r,n,i){t.emplaceBack(2*e+(n+1)/2,2*r+(i+1)/2)}var Xa=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.layoutVertexArray=new zi,this.indexArray=new Yi,this.segments=new pa,this.programConfigurations=new Ua(ha,t.layers,t.zoom),this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};function Za(t,e){for(var r=0;r<t.length;r++)if(io(e,t[r]))return!0;for(var n=0;n<e.length;n++)if(io(t,e[n]))return!0;return!!$a(t,e)}function Ja(t,e,r){return!!io(t,e)||!!eo(e,t,r)}function Ka(t,e){if(1===t.length)return no(e,t[0]);for(var r=0;r<e.length;r++)for(var n=e[r],i=0;i<n.length;i++)if(io(t,n[i]))return!0;for(var a=0;a<t.length;a++)if(no(e,t[a]))return!0;for(var o=0;o<e.length;o++)if($a(t,e[o]))return!0;return!1}function Qa(t,e,r){if(t.length>1){if($a(t,e))return!0;for(var n=0;n<e.length;n++)if(eo(e[n],t,r))return!0}for(var i=0;i<t.length;i++)if(eo(t[i],e,r))return!0;return!1}function $a(t,e){if(0===t.length||0===e.length)return!1;for(var r=0;r<t.length-1;r++)for(var n=t[r],i=t[r+1],a=0;a<e.length-1;a++){if(to(n,i,e[a],e[a+1]))return!0}return!1}function to(t,e,r,n){return w(t,r,n)!==w(e,r,n)&&w(t,e,r)!==w(t,e,n)}function eo(t,e,r){var n=r*r;if(1===e.length)return t.distSqr(e[0])<n;for(var i=1;i<e.length;i++){if(ro(t,e[i-1],e[i])<n)return!0}return!1}function ro(t,e,r){var n=e.distSqr(r);if(0===n)return t.distSqr(e);var i=((t.x-e.x)*(r.x-e.x)+(t.y-e.y)*(r.y-e.y))/n;return i<0?t.distSqr(e):i>1?t.distSqr(r):t.distSqr(r.sub(e)._mult(i)._add(e))}function no(t,e){for(var r,n,i,a=!1,o=0;o<t.length;o++)for(var s=0,l=(r=t[o]).length-1;s<r.length;l=s++)n=r[s],i=r[l],n.y>e.y!=i.y>e.y&&e.x<(i.x-n.x)*(e.y-n.y)/(i.y-n.y)+n.x&&(a=!a);return a}function io(t,e){for(var r=!1,n=0,i=t.length-1;n<t.length;i=n++){var a=t[n],o=t[i];a.y>e.y!=o.y>e.y&&e.x<(o.x-a.x)*(e.y-a.y)/(o.y-a.y)+a.x&&(r=!r)}return r}function ao(t,e,r){var n=r[0],i=r[2];if(t.x<n.x&&e.x<n.x||t.x>i.x&&e.x>i.x||t.y<n.y&&e.y<n.y||t.y>i.y&&e.y>i.y)return!1;var a=w(t,e,r[0]);return a!==w(t,e,r[1])||a!==w(t,e,r[2])||a!==w(t,e,r[3])}function oo(t,e,r){var n=e.paint.get(t).value;return"constant"===n.kind?n.value:r.programConfigurations.get(e.id).getMaxValue(t)}function so(t){return Math.sqrt(t[0]*t[0]+t[1]*t[1])}function lo(t,e,r,n,a){if(!e[0]&&!e[1])return t;var o=i.convert(e)._mult(a);"viewport"===r&&o._rotate(-n);for(var s=[],l=0;l<t.length;l++){var c=t[l];s.push(c.sub(o))}return s}Xa.prototype.populate=function(t,e,r){var n=this.layers[0],i=[],a=null;"circle"===n.type&&(a=n.layout.get("circle-sort-key"));for(var o=0,s=t;o<s.length;o+=1){var l=s[o],c=l.feature,u=l.id,f=l.index,h=l.sourceLayerIndex,p=this.layers[0]._featureFilter.needGeometry,d={type:c.type,id:u,properties:c.properties,geometry:p?Ya(c):[]};if(this.layers[0]._featureFilter.filter(new pi(this.zoom),d,r)){p||(d.geometry=Ya(c));var g=a?a.evaluate(d,{},r):void 0,m={id:u,properties:c.properties,type:c.type,sourceLayerIndex:h,index:f,geometry:d.geometry,patterns:{},sortKey:g};i.push(m)}}a&&i.sort((function(t,e){return t.sortKey-e.sortKey}));for(var v=0,y=i;v<y.length;v+=1){var x=y[v],b=x,_=b.geometry,w=b.index,T=b.sourceLayerIndex,k=t[w].feature;this.addFeature(x,_,w,r),e.featureIndex.insert(k,_,w,T,this.index)}},Xa.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},Xa.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},Xa.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},Xa.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,ha),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},Xa.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},Xa.prototype.addFeature=function(t,e,r,n){for(var i=0,a=e;i<a.length;i+=1)for(var o=0,s=a[i];o<s.length;o+=1){var l=s[o],c=l.x,u=l.y;if(!(c<0||c>=8192||u<0||u>=8192)){var f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray,t.sortKey),h=f.vertexLength;Wa(this.layoutVertexArray,c,u,-1,-1),Wa(this.layoutVertexArray,c,u,1,-1),Wa(this.layoutVertexArray,c,u,1,1),Wa(this.layoutVertexArray,c,u,-1,1),this.indexArray.emplaceBack(h,h+1,h+2),this.indexArray.emplaceBack(h,h+3,h+2),f.vertexLength+=4,f.primitiveLength+=2}}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,{},n)},Nn("CircleBucket",Xa,{omit:["layers"]});var co=new Si({"circle-sort-key":new Ti(Lt.layout_circle["circle-sort-key"])}),uo={paint:new Si({"circle-radius":new Ti(Lt.paint_circle["circle-radius"]),"circle-color":new Ti(Lt.paint_circle["circle-color"]),"circle-blur":new Ti(Lt.paint_circle["circle-blur"]),"circle-opacity":new Ti(Lt.paint_circle["circle-opacity"]),"circle-translate":new wi(Lt.paint_circle["circle-translate"]),"circle-translate-anchor":new wi(Lt.paint_circle["circle-translate-anchor"]),"circle-pitch-scale":new 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c=e[s]*r,u=s<l-1?e[s+1]*r:t.length;o-=Math.abs(ls(t,c,u,r))}var f=0;for(s=0;s<n.length;s+=3){var h=n[s]*r,p=n[s+1]*r,d=n[s+2]*r;f+=Math.abs((t[h]-t[d])*(t[p+1]-t[h+1])-(t[h]-t[p])*(t[d+1]-t[h+1]))}return 0===o&&0===f?0:Math.abs((f-o)/o)},Fo.flatten=function(t){for(var e=t[0][0].length,r={vertices:[],holes:[],dimensions:e},n=0,i=0;i<t.length;i++){for(var a=0;a<t[i].length;a++)for(var o=0;o<e;o++)r.vertices.push(t[i][a][o]);i>0&&(n+=t[i-1].length,r.holes.push(n))}return r},Do.default=Ro;var ms=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.patternFeatures=[],this.layoutVertexArray=new zi,this.indexArray=new Yi,this.indexArray2=new Qi,this.programConfigurations=new Ua(zo,t.layers,t.zoom),this.segments=new pa,this.segments2=new pa,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};ms.prototype.populate=function(t,e,r){this.hasPattern=ds("fill",this.layers,e);for(var n=this.layers[0].layout.get("fill-sort-key"),i=[],a=0,o=t;a<o.length;a+=1){var s=o[a],l=s.feature,c=s.id,u=s.index,f=s.sourceLayerIndex,h=this.layers[0]._featureFilter.needGeometry,p={type:l.type,id:c,properties:l.properties,geometry:h?Ya(l):[]};if(this.layers[0]._featureFilter.filter(new pi(this.zoom),p,r)){h||(p.geometry=Ya(l));var d=n?n.evaluate(p,{},r,e.availableImages):void 0,g={id:c,properties:l.properties,type:l.type,sourceLayerIndex:f,index:u,geometry:p.geometry,patterns:{},sortKey:d};i.push(g)}}n&&i.sort((function(t,e){return t.sortKey-e.sortKey}));for(var m=0,v=i;m<v.length;m+=1){var y=v[m],x=y,b=x.geometry,_=x.index,w=x.sourceLayerIndex;if(this.hasPattern){var T=gs("fill",this.layers,y,this.zoom,e);this.patternFeatures.push(T)}else this.addFeature(y,b,_,r,{});var k=t[_].feature;e.featureIndex.insert(k,b,_,w,this.index)}},ms.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},ms.prototype.addFeatures=function(t,e,r){for(var n=0,i=this.patternFeatures;n<i.length;n+=1){var a=i[n];this.addFeature(a,a.geometry,a.index,e,r)}},ms.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},ms.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},ms.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,zo),this.indexBuffer=t.createIndexBuffer(this.indexArray),this.indexBuffer2=t.createIndexBuffer(this.indexArray2)),this.programConfigurations.upload(t),this.uploaded=!0},ms.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.indexBuffer2.destroy(),this.programConfigurations.destroy(),this.segments.destroy(),this.segments2.destroy())},ms.prototype.addFeature=function(t,e,r,n,i){for(var a=0,o=hs(e,500);a<o.length;a+=1){for(var s=o[a],l=0,c=0,u=s;c<u.length;c+=1){l+=u[c].length}for(var f=this.segments.prepareSegment(l,this.layoutVertexArray,this.indexArray),h=f.vertexLength,p=[],d=[],g=0,m=s;g<m.length;g+=1){var v=m[g];if(0!==v.length){v!==s[0]&&d.push(p.length/2);var y=this.segments2.prepareSegment(v.length,this.layoutVertexArray,this.indexArray2),x=y.vertexLength;this.layoutVertexArray.emplaceBack(v[0].x,v[0].y),this.indexArray2.emplaceBack(x+v.length-1,x),p.push(v[0].x),p.push(v[0].y);for(var b=1;b<v.length;b++)this.layoutVertexArray.emplaceBack(v[b].x,v[b].y),this.indexArray2.emplaceBack(x+b-1,x+b),p.push(v[b].x),p.push(v[b].y);y.vertexLength+=v.length,y.primitiveLength+=v.length}}for(var _=Do(p,d),w=0;w<_.length;w+=3)this.indexArray.emplaceBack(h+_[w],h+_[w+1],h+_[w+2]);f.vertexLength+=l,f.primitiveLength+=_.length/3}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,i,n)},Nn("FillBucket",ms,{omit:["layers","patternFeatures"]});var vs=new Si({"fill-sort-key":new Ti(Lt.layout_fill["fill-sort-key"])}),ys={paint:new Si({"fill-antialias":new wi(Lt.paint_fill["fill-antialias"]),"fill-opacity":new Ti(Lt.paint_fill["fill-opacity"]),"fill-color":new Ti(Lt.paint_fill["fill-color"]),"fill-outline-color":new Ti(Lt.paint_fill["fill-outline-color"]),"fill-translate":new wi(Lt.paint_fill["fill-translate"]),"fill-translate-anchor":new wi(Lt.paint_fill["fill-translate-anchor"]),"fill-pattern":new ki(Lt.paint_fill["fill-pattern"])}),layout:vs},xs=function(t){function e(e){t.call(this,e,ys)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.recalculate=function(e,r){t.prototype.recalculate.call(this,e,r);var n=this.paint._values["fill-outline-color"];"constant"===n.value.kind&&void 0===n.value.value&&(this.paint._values["fill-outline-color"]=this.paint._values["fill-color"])},e.prototype.createBucket=function(t){return new ms(t)},e.prototype.queryRadius=function(){return so(this.paint.get("fill-translate"))},e.prototype.queryIntersectsFeature=function(t,e,r,n,i,a,o){return 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c=t.readVarint();n=7&c,a=c>>3}if(a--,1===n||2===n)o+=t.readSVarint(),s+=t.readSVarint(),1===n&&(e&&l.push(e),e=[]),e.push(new i(o,s));else{if(7!==n)throw new Error("unknown command "+n);e&&e.push(e[0].clone())}}return e&&l.push(e),l},ws.prototype.bbox=function(){var t=this._pbf;t.pos=this._geometry;for(var e=t.readVarint()+t.pos,r=1,n=0,i=0,a=0,o=1/0,s=-1/0,l=1/0,c=-1/0;t.pos<e;){if(n<=0){var u=t.readVarint();r=7&u,n=u>>3}if(n--,1===r||2===r)(i+=t.readSVarint())<o&&(o=i),i>s&&(s=i),(a+=t.readSVarint())<l&&(l=a),a>c&&(c=a);else if(7!==r)throw new Error("unknown command "+r)}return[o,l,s,c]},ws.prototype.toGeoJSON=function(t,e,r){var n,i,a=this.extent*Math.pow(2,r),o=this.extent*t,s=this.extent*e,l=this.loadGeometry(),c=ws.types[this.type];function u(t){for(var e=0;e<t.length;e++){var r=t[e],n=180-360*(r.y+s)/a;t[e]=[360*(r.x+o)/a-180,360/Math.PI*Math.atan(Math.exp(n*Math.PI/180))-90]}}switch(this.type){case 1:var f=[];for(n=0;n<l.length;n++)f[n]=l[n][0];u(l=f);break;case 2:for(n=0;n<l.length;n++)u(l[n]);break;case 3:for(l=function(t){var e=t.length;if(e<=1)return[t];for(var r,n,i=[],a=0;a<e;a++){var o=ks(t[a]);0!==o&&(void 0===n&&(n=o<0),n===o<0?(r&&i.push(r),r=[t[a]]):r.push(t[a]))}r&&i.push(r);return i}(l),n=0;n<l.length;n++)for(i=0;i<l[n].length;i++)u(l[n][i])}1===l.length?l=l[0]:c="Multi"+c;var h={type:"Feature",geometry:{type:c,coordinates:l},properties:this.properties};return"id"in this&&(h.id=this.id),h};var As=Ms;function Ms(t,e){this.version=1,this.name=null,this.extent=4096,this.length=0,this._pbf=t,this._keys=[],this._values=[],this._features=[],t.readFields(Ss,this,e),this.length=this._features.length}function Ss(t,e,r){15===t?e.version=r.readVarint():1===t?e.name=r.readString():5===t?e.extent=r.readVarint():2===t?e._features.push(r.pos):3===t?e._keys.push(r.readString()):4===t&&e._values.push(function(t){var e=null,r=t.readVarint()+t.pos;for(;t.pos<r;){var n=t.readVarint()>>3;e=1===n?t.readString():2===n?t.readFloat():3===n?t.readDouble():4===n?t.readVarint64():5===n?t.readVarint():6===n?t.readSVarint():7===n?t.readBoolean():null}return e}(r))}function Es(t,e,r){if(3===t){var n=new As(r,r.readVarint()+r.pos);n.length&&(e[n.name]=n)}}Ms.prototype.feature=function(t){if(t<0||t>=this._features.length)throw new Error("feature index out of bounds");this._pbf.pos=this._features[t];var e=this._pbf.readVarint()+this._pbf.pos;return new _s(this._pbf,e,this.extent,this._keys,this._values)};var Ls={VectorTile:function(t,e){this.layers=t.readFields(Es,{},e)},VectorTileFeature:_s,VectorTileLayer:As},Cs=Ls.VectorTileFeature.types,Ps=Math.pow(2,13);function Is(t,e,r,n,i,a,o,s){t.emplaceBack(e,r,2*Math.floor(n*Ps)+o,i*Ps*2,a*Ps*2,Math.round(s))}var Os=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.layoutVertexArray=new Ri,this.indexArray=new Yi,this.programConfigurations=new Ua(bs,t.layers,t.zoom),this.segments=new pa,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};function zs(t,e){return t.x===e.x&&(t.x<0||t.x>8192)||t.y===e.y&&(t.y<0||t.y>8192)}function Ds(t){return t.every((function(t){return t.x<0}))||t.every((function(t){return t.x>8192}))||t.every((function(t){return t.y<0}))||t.every((function(t){return t.y>8192}))}Os.prototype.populate=function(t,e,r){this.features=[],this.hasPattern=ds("fill-extrusion",this.layers,e);for(var n=0,i=t;n<i.length;n+=1){var a=i[n],o=a.feature,s=a.id,l=a.index,c=a.sourceLayerIndex,u=this.layers[0]._featureFilter.needGeometry,f={type:o.type,id:s,properties:o.properties,geometry:u?Ya(o):[]};if(this.layers[0]._featureFilter.filter(new pi(this.zoom),f,r)){var h={id:s,sourceLayerIndex:c,index:l,geometry:u?f.geometry:Ya(o),properties:o.properties,type:o.type,patterns:{}};void 0!==o.id&&(h.id=o.id),this.hasPattern?this.features.push(gs("fill-extrusion",this.layers,h,this.zoom,e)):this.addFeature(h,h.geometry,l,r,{}),e.featureIndex.insert(o,h.geometry,l,c,this.index,!0)}}},Os.prototype.addFeatures=function(t,e,r){for(var n=0,i=this.features;n<i.length;n+=1){var a=i[n],o=a.geometry;this.addFeature(a,o,a.index,e,r)}},Os.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},Os.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},Os.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},Os.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,bs),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},Os.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},Os.prototype.addFeature=function(t,e,r,n,i){for(var a=0,o=hs(e,500);a<o.length;a+=1){for(var s=o[a],l=0,c=0,u=s;c<u.length;c+=1){l+=u[c].length}for(var f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray),h=0,p=s;h<p.length;h+=1){var d=p[h];if(0!==d.length&&!Ds(d))for(var g=0,m=0;m<d.length;m++){var v=d[m];if(m>=1){var y=d[m-1];if(!zs(v,y)){f.vertexLength+4>pa.MAX_VERTEX_ARRAY_LENGTH&&(f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray));var x=v.sub(y)._perp()._unit(),b=y.dist(v);g+b>32768&&(g=0),Is(this.layoutVertexArray,v.x,v.y,x.x,x.y,0,0,g),Is(this.layoutVertexArray,v.x,v.y,x.x,x.y,0,1,g),g+=b,Is(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,0,g),Is(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,1,g);var _=f.vertexLength;this.indexArray.emplaceBack(_,_+2,_+1),this.indexArray.emplaceBack(_+1,_+2,_+3),f.vertexLength+=4,f.primitiveLength+=2}}}}if(f.vertexLength+l>pa.MAX_VERTEX_ARRAY_LENGTH&&(f=this.segments.prepareSegment(l,this.layoutVertexArray,this.indexArray)),"Polygon"===Cs[t.type]){for(var w=[],T=[],k=f.vertexLength,A=0,M=s;A<M.length;A+=1){var S=M[A];if(0!==S.length){S!==s[0]&&T.push(w.length/2);for(var E=0;E<S.length;E++){var L=S[E];Is(this.layoutVertexArray,L.x,L.y,0,0,1,1,0),w.push(L.x),w.push(L.y)}}}for(var 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wi(Lt["paint_fill-extrusion"]["fill-extrusion-vertical-gradient"])})},Fs=function(t){function e(e){t.call(this,e,Rs)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.createBucket=function(t){return new Os(t)},e.prototype.queryRadius=function(){return so(this.paint.get("fill-extrusion-translate"))},e.prototype.is3D=function(){return!0},e.prototype.queryIntersectsFeature=function(t,e,r,n,a,o,s,l){var c=lo(t,this.paint.get("fill-extrusion-translate"),this.paint.get("fill-extrusion-translate-anchor"),o.angle,s),u=this.paint.get("fill-extrusion-height").evaluate(e,r),f=this.paint.get("fill-extrusion-base").evaluate(e,r),h=function(t,e,r,n){for(var a=[],o=0,s=t;o<s.length;o+=1){var l=s[o],c=[l.x,l.y,n,1];xo(c,c,e),a.push(new i(c[0]/c[3],c[1]/c[3]))}return a}(c,l,0,0),p=function(t,e,r,n){for(var a=[],o=[],s=n[8]*e,l=n[9]*e,c=n[10]*e,u=n[11]*e,f=n[8]*r,h=n[9]*r,p=n[10]*r,d=n[11]*r,g=0,m=t;g<m.length;g+=1){for(var 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a=i[n];this.addFeature(a,a.geometry,a.index,e,r)}},qs.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},qs.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},qs.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,js),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},qs.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},qs.prototype.addFeature=function(t,e,r,n,i){for(var a=this.layers[0].layout,o=a.get("line-join").evaluate(t,{}),s=a.get("line-cap"),l=a.get("line-miter-limit"),c=a.get("line-round-limit"),u=0,f=e;u<f.length;u+=1){var h=f[u];this.addLine(h,t,o,s,l,c)}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,i,n)},qs.prototype.addLine=function(t,e,r,n,i,a){if(this.distance=0,this.scaledDistance=0,this.totalDistance=0,e.properties&&e.properties.hasOwnProperty("mapbox_clip_start")&&e.properties.hasOwnProperty("mapbox_clip_end")){this.clipStart=+e.properties.mapbox_clip_start,this.clipEnd=+e.properties.mapbox_clip_end;for(var o=0;o<t.length-1;o++)this.totalDistance+=t[o].dist(t[o+1]);this.updateScaledDistance()}for(var s="Polygon"===Us[e.type],l=t.length;l>=2&&t[l-1].equals(t[l-2]);)l--;for(var c=0;c<l-1&&t[c].equals(t[c+1]);)c++;if(!(l<(s?3:2))){"bevel"===r&&(i=1.05);var u,f=this.overscaling<=16?122880/(512*this.overscaling):0,h=this.segments.prepareSegment(10*l,this.layoutVertexArray,this.indexArray),p=void 0,d=void 0,g=void 0,m=void 0;this.e1=this.e2=-1,s&&(u=t[l-2],m=t[c].sub(u)._unit()._perp());for(var v=c;v<l;v++)if(!(d=v===l-1?s?t[c+1]:void 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O=Math.round(180*w/Math.PI/20),z=1;z<O;z++){var D=z/O;if(.5!==D){var R=D-.5;D+=D*R*(D-1)*((1.0904+x*(x*(3.55645-1.43519*x)-3.2452))*R*R+(.848013+x*(.215638*x-1.06021)))}var F=m.sub(g)._mult(D)._add(g)._unit()._mult(k?-1:1);this.addHalfVertex(u,F.x,F.y,!1,k,0,h)}d&&this.addCurrentVertex(u,m,-P,-I,h)}else if("butt"===E)this.addCurrentVertex(u,y,0,0,h);else if("square"===E){var B=p?1:-1;this.addCurrentVertex(u,y,B,B,h)}else"round"===E&&(p&&(this.addCurrentVertex(u,g,0,0,h),this.addCurrentVertex(u,g,1,1,h,!0)),d&&(this.addCurrentVertex(u,m,-1,-1,h,!0),this.addCurrentVertex(u,m,0,0,h)));if(T&&v<l-1){var N=u.dist(d);if(N>2*f){var j=u.add(d.sub(u)._mult(f/N)._round());this.updateDistance(u,j),this.addCurrentVertex(j,m,0,0,h),u=j}}}}},qs.prototype.addCurrentVertex=function(t,e,r,n,i,a){void 0===a&&(a=!1);var 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e=ll(this);for(t=t||[];this.pos<e;)t.push(this.readSFixed32());return t},readPackedFixed64:function(t){if(this.type!==ol.Bytes)return t.push(this.readFixed64());var e=ll(this);for(t=t||[];this.pos<e;)t.push(this.readFixed64());return t},readPackedSFixed64:function(t){if(this.type!==ol.Bytes)return t.push(this.readSFixed64());var e=ll(this);for(t=t||[];this.pos<e;)t.push(this.readSFixed64());return t},skip:function(t){var e=7&t;if(e===ol.Varint)for(;this.buf[this.pos++]>127;);else if(e===ol.Bytes)this.pos=this.readVarint()+this.pos;else if(e===ol.Fixed32)this.pos+=4;else{if(e!==ol.Fixed64)throw new Error("Unimplemented type: "+e);this.pos+=8}},writeTag:function(t,e){this.writeVarint(t<<3|e)},realloc:function(t){for(var e=this.length||16;e<this.pos+t;)e*=2;if(e!==this.length){var r=new Uint8Array(e);r.set(this.buf),this.buf=r,this.length=e}},finish:function(){return 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r=(7&t)<<4;if(e.buf[e.pos++]|=r|((t>>>=3)?128:0),!t)return;if(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),!t)return;if(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),!t)return;if(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),!t)return;if(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),!t)return;e.buf[e.pos++]=127&t}(n,e)}(t,this):(this.realloc(4),this.buf[this.pos++]=127&t|(t>127?128:0),t<=127||(this.buf[this.pos++]=127&(t>>>=7)|(t>127?128:0),t<=127||(this.buf[this.pos++]=127&(t>>>=7)|(t>127?128:0),t<=127||(this.buf[this.pos++]=t>>>7&127))))},writeSVarint:function(t){this.writeVarint(t<0?2*-t-1:2*t)},writeBoolean:function(t){this.writeVarint(Boolean(t))},writeString:function(t){t=String(t),this.realloc(4*t.length),this.pos++;var e=this.pos;this.pos=function(t,e,r){for(var n,i,a=0;a<e.length;a++){if((n=e.charCodeAt(a))>55295&&n<57344){if(!i){n>56319||a+1===e.length?(t[r++]=239,t[r++]=191,t[r++]=189):i=n;continue}if(n<56320){t[r++]=239,t[r++]=191,t[r++]=189,i=n;continue}n=i-55296<<10|n-56320|65536,i=null}else 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n=this.pos-r;n>=128&&ul(r,n,this),this.pos=r-1,this.writeVarint(n),this.pos+=n},writeMessage:function(t,e,r){this.writeTag(t,ol.Bytes),this.writeRawMessage(e,r)},writePackedVarint:function(t,e){e.length&&this.writeMessage(t,fl,e)},writePackedSVarint:function(t,e){e.length&&this.writeMessage(t,hl,e)},writePackedBoolean:function(t,e){e.length&&this.writeMessage(t,gl,e)},writePackedFloat:function(t,e){e.length&&this.writeMessage(t,pl,e)},writePackedDouble:function(t,e){e.length&&this.writeMessage(t,dl,e)},writePackedFixed32:function(t,e){e.length&&this.writeMessage(t,ml,e)},writePackedSFixed32:function(t,e){e.length&&this.writeMessage(t,vl,e)},writePackedFixed64:function(t,e){e.length&&this.writeMessage(t,yl,e)},writePackedSFixed64:function(t,e){e.length&&this.writeMessage(t,xl,e)},writeBytesField:function(t,e){this.writeTag(t,ol.Bytes),this.writeBytes(e)},writeFixed32Field:function(t,e){this.writeTag(t,ol.Fixed32),this.writeFixed32(e)},writeSFixed32Field:function(t,e){this.writeTag(t,ol.Fixed32),this.writeSFixed32(e)},writeFixed64Field:function(t,e){this.writeTag(t,ol.Fixed64),this.writeFixed64(e)},writeSFixed64Field:function(t,e){this.writeTag(t,ol.Fixed64),this.writeSFixed64(e)},writeVarintField:function(t,e){this.writeTag(t,ol.Varint),this.writeVarint(e)},writeSVarintField:function(t,e){this.writeTag(t,ol.Varint),this.writeSVarint(e)},writeStringField:function(t,e){this.writeTag(t,ol.Bytes),this.writeString(e)},writeFloatField:function(t,e){this.writeTag(t,ol.Fixed32),this.writeFloat(e)},writeDoubleField:function(t,e){this.writeTag(t,ol.Fixed64),this.writeDouble(e)},writeBooleanField:function(t,e){this.writeVarintField(t,Boolean(e))}};function Tl(t,e,r){1===t&&r.readMessage(kl,e)}function kl(t,e,r){if(3===t){var n=r.readMessage(Al,{}),i=n.id,a=n.bitmap,o=n.width,s=n.height,l=n.left,c=n.top,u=n.advance;e.push({id:i,bitmap:new So({width:o+6,height:s+6},a),metrics:{width:o,height:s,left:l,top:c,advance:u}})}}function Al(t,e,r){1===t?e.id=r.readVarint():2===t?e.bitmap=r.readBytes():3===t?e.width=r.readVarint():4===t?e.height=r.readVarint():5===t?e.left=r.readSVarint():6===t?e.top=r.readSVarint():7===t&&(e.advance=r.readVarint())}function Ml(t){for(var e=0,r=0,n=0,i=t;n<i.length;n+=1){var a=i[n];e+=a.w*a.h,r=Math.max(r,a.w)}t.sort((function(t,e){return e.h-t.h}));for(var o=[{x:0,y:0,w:Math.max(Math.ceil(Math.sqrt(e/.95)),r),h:1/0}],s=0,l=0,c=0,u=t;c<u.length;c+=1)for(var f=u[c],h=o.length-1;h>=0;h--){var p=o[h];if(!(f.w>p.w||f.h>p.h)){if(f.x=p.x,f.y=p.y,l=Math.max(l,f.y+f.h),s=Math.max(s,f.x+f.w),f.w===p.w&&f.h===p.h){var d=o.pop();h<o.length&&(o[h]=d)}else 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Eo({width:o||1,height:s||1});for(var c in t){var u=t[c],f=r[c].paddedRect;Eo.copy(u.data,l,{x:0,y:0},{x:f.x+1,y:f.y+1},u.data)}for(var h in e){var p=e[h],d=n[h].paddedRect,g=d.x+1,m=d.y+1,v=p.data.width,y=p.data.height;Eo.copy(p.data,l,{x:0,y:0},{x:g,y:m},p.data),Eo.copy(p.data,l,{x:0,y:y-1},{x:g,y:m-1},{width:v,height:1}),Eo.copy(p.data,l,{x:0,y:0},{x:g,y:m+y},{width:v,height:1}),Eo.copy(p.data,l,{x:v-1,y:0},{x:g-1,y:m},{width:1,height:y}),Eo.copy(p.data,l,{x:0,y:0},{x:g+v,y:m},{width:1,height:y})}this.image=l,this.iconPositions=r,this.patternPositions=n};Ll.prototype.addImages=function(t,e,r){for(var n in t){var i=t[n],a={x:0,y:0,w:i.data.width+2,h:i.data.height+2};r.push(a),e[n]=new Sl(a,i),i.hasRenderCallback&&this.haveRenderCallbacks.push(n)}},Ll.prototype.patchUpdatedImages=function(t,e){for(var r in t.dispatchRenderCallbacks(this.haveRenderCallbacks),t.updatedImages)this.patchUpdatedImage(this.iconPositions[r],t.getImage(r),e),this.patchUpdatedImage(this.patternPositions[r],t.getImage(r),e)},Ll.prototype.patchUpdatedImage=function(t,e,r){if(t&&e&&t.version!==e.version){t.version=e.version;var n=t.tl,i=n[0],a=n[1];r.update(e.data,void 0,{x:i,y:a})}},Nn("ImagePosition",Sl),Nn("ImageAtlas",Ll);var Cl={horizontal:1,vertical:2,horizontalOnly:3};var Pl=function(){this.scale=1,this.fontStack="",this.imageName=null};Pl.forText=function(t,e){var r=new Pl;return r.scale=t||1,r.fontStack=e,r},Pl.forImage=function(t){var e=new Pl;return e.imageName=t,e};var Il=function(){this.text="",this.sectionIndex=[],this.sections=[],this.imageSectionID=null};function Ol(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g){var m,v=Il.fromFeature(t,i);f===Cl.vertical&&v.verticalizePunctuation();var y=hi.processBidirectionalText,x=hi.processStyledBidirectionalText;if(y&&1===v.sections.length){m=[];for(var b=0,_=y(v.toString(),jl(v,c,a,e,n,p,d));b<_.length;b+=1){var w=_[b],T=new Il;T.text=w,T.sections=v.sections;for(var k=0;k<w.length;k++)T.sectionIndex.push(0);m.push(T)}}else if(x){m=[];for(var A=0,M=x(v.text,v.sectionIndex,jl(v,c,a,e,n,p,d));A<M.length;A+=1){var S=M[A],E=new Il;E.text=S[0],E.sectionIndex=S[1],E.sections=v.sections,m.push(E)}}else m=function(t,e){for(var r=[],n=t.text,i=0,a=0,o=e;a<o.length;a+=1){var s=o[a];r.push(t.substring(i,s)),i=s}return i<n.length&&r.push(t.substring(i,n.length)),r}(v,jl(v,c,a,e,n,p,d));var L=[],C={positionedLines:L,text:v.toString(),top:u[1],bottom:u[1],left:u[0],right:u[0],writingMode:f,iconsInText:!1,verticalizable:!1};return function(t,e,r,n,i,a,o,s,l,c,u,f){for(var h=0,p=-17,d=0,g=0,m="right"===s?1:"left"===s?0:.5,v=0,y=0,x=i;y<x.length;y+=1){var b=x[y];b.trim();var _=b.getMaxScale(),w=24*(_-1),T={positionedGlyphs:[],lineOffset:0};t.positionedLines[v]=T;var k=T.positionedGlyphs,A=0;if(b.length()){for(var M=0;M<b.length();M++){var S=b.getSection(M),E=b.getSectionIndex(M),L=b.getCharCode(M),C=0,P=null,I=null,O=null,z=24,D=!(l===Cl.horizontal||!u&&!Zn(L)||u&&(zl[L]||(Y=L,Yn.Arabic(Y)||Yn["Arabic Supplement"](Y)||Yn["Arabic Extended-A"](Y)||Yn["Arabic Presentation Forms-A"](Y)||Yn["Arabic Presentation Forms-B"](Y))));if(S.imageName){var R=n[S.imageName];if(!R)continue;O=S.imageName,t.iconsInText=t.iconsInText||!0,I=R.paddedRect;var F=R.displaySize;S.scale=24*S.scale/f,P={width:F[0],height:F[1],left:1,top:-3,advance:D?F[1]:F[0]};var B=24-F[1]*S.scale;C=w+B,z=P.advance;var N=D?F[0]*S.scale-24*_:F[1]*S.scale-24*_;N>0&&N>A&&(A=N)}else{var j=r[S.fontStack],U=j&&j[L];if(U&&U.rect)I=U.rect,P=U.metrics;else{var V=e[S.fontStack],H=V&&V[L];if(!H)continue;P=H.metrics}C=24*(_-S.scale)}D?(t.verticalizable=!0,k.push({glyph:L,imageName:O,x:h,y:p+C,vertical:D,scale:S.scale,fontStack:S.fontStack,sectionIndex:E,metrics:P,rect:I}),h+=z*S.scale+c):(k.push({glyph:L,imageName:O,x:h,y:p+C,vertical:D,scale:S.scale,fontStack:S.fontStack,sectionIndex:E,metrics:P,rect:I}),h+=P.advance*S.scale+c)}if(0!==k.length){var q=h-c;d=Math.max(q,d),Vl(k,0,k.length-1,m,A)}h=0;var G=a*_+A;T.lineOffset=Math.max(A,w),p+=G,g=Math.max(G,g),++v}else p+=a,++v}var Y;var W=p- -17,X=Ul(o),Z=X.horizontalAlign,J=X.verticalAlign;(function(t,e,r,n,i,a,o,s,l){var c=(e-r)*i,u=0;u=a!==o?-s*n- -17:(-n*l+.5)*o;for(var f=0,h=t;f<h.length;f+=1)for(var p=h[f],d=0,g=p.positionedGlyphs;d<g.length;d+=1){var m=g[d];m.x+=c,m.y+=u}})(t.positionedLines,m,Z,J,d,g,a,W,i.length),t.top+=-J*W,t.bottom=t.top+W,t.left+=-Z*d,t.right=t.left+d}(C,e,r,n,m,o,s,l,f,c,h,g),!function(t){for(var e=0,r=t;e<r.length;e+=1){if(0!==r[e].positionedGlyphs.length)return!1}return!0}(L)&&C}Il.fromFeature=function(t,e){for(var r=new Il,n=0;n<t.sections.length;n++){var i=t.sections[n];i.image?r.addImageSection(i):r.addTextSection(i,e)}return r},Il.prototype.length=function(){return this.text.length},Il.prototype.getSection=function(t){return this.sections[this.sectionIndex[t]]},Il.prototype.getSectionIndex=function(t){return this.sectionIndex[t]},Il.prototype.getCharCode=function(t){return this.text.charCodeAt(t)},Il.prototype.verticalizePunctuation=function(){this.text=function(t){for(var e="",r=0;r<t.length;r++){var n=t.charCodeAt(r+1)||null,i=t.charCodeAt(r-1)||null;(!n||!Jn(n)||rl[t[r+1]])&&(!i||!Jn(i)||rl[t[r-1]])&&rl[t[r]]?e+=rl[t[r]]:e+=t[r]}return e}(this.text)},Il.prototype.trim=function(){for(var t=0,e=0;e<this.text.length&&zl[this.text.charCodeAt(e)];e++)t++;for(var r=this.text.length,n=this.text.length-1;n>=0&&n>=t&&zl[this.text.charCodeAt(n)];n--)r--;this.text=this.text.substring(t,r),this.sectionIndex=this.sectionIndex.slice(t,r)},Il.prototype.substring=function(t,e){var r=new Il;return r.text=this.text.substring(t,e),r.sectionIndex=this.sectionIndex.slice(t,e),r.sections=this.sections,r},Il.prototype.toString=function(){return this.text},Il.prototype.getMaxScale=function(){var t=this;return this.sectionIndex.reduce((function(e,r){return Math.max(e,t.sections[r].scale)}),0)},Il.prototype.addTextSection=function(t,e){this.text+=t.text,this.sections.push(Pl.forText(t.scale,t.fontStack||e));for(var r=this.sections.length-1,n=0;n<t.text.length;++n)this.sectionIndex.push(r)},Il.prototype.addImageSection=function(t){var e=t.image?t.image.name:"";if(0!==e.length){var r=this.getNextImageSectionCharCode();r?(this.text+=String.fromCharCode(r),this.sections.push(Pl.forImage(e)),this.sectionIndex.push(this.sections.length-1)):_("Reached maximum number of images 6401")}else _("Can't add FormattedSection with an empty image.")},Il.prototype.getNextImageSectionCharCode=function(){return this.imageSectionID?this.imageSectionID>=63743?null:++this.imageSectionID:(this.imageSectionID=57344,this.imageSectionID)};var zl={9:!0,10:!0,11:!0,12:!0,13:!0,32:!0},Dl={};function Rl(t,e,r,n,i,a){if(e.imageName){var o=n[e.imageName];return o?o.displaySize[0]*e.scale*24/a+i:0}var s=r[e.fontStack],l=s&&s[t];return l?l.metrics.advance*e.scale+i:0}function Fl(t,e,r,n){var i=Math.pow(t-e,2);return n?t<e?i/2:2*i:i+Math.abs(r)*r}function Bl(t,e,r){var n=0;return 10===t&&(n-=1e4),r&&(n+=150),40!==t&&65288!==t||(n+=50),41!==e&&65289!==e||(n+=50),n}function Nl(t,e,r,n,i,a){for(var o=null,s=Fl(e,r,i,a),l=0,c=n;l<c.length;l+=1){var u=c[l],f=Fl(e-u.x,r,i,a)+u.badness;f<=s&&(o=u,s=f)}return{index:t,x:e,priorBreak:o,badness:s}}function jl(t,e,r,n,i,a,o){if("point"!==a)return[];if(!t)return[];for(var s,l=[],c=function(t,e,r,n,i,a){for(var o=0,s=0;s<t.length();s++){var l=t.getSection(s);o+=Rl(t.getCharCode(s),l,n,i,e,a)}return o/Math.max(1,Math.ceil(o/r))}(t,e,r,n,i,o),u=t.text.indexOf("\u200b")>=0,f=0,h=0;h<t.length();h++){var p=t.getSection(h),d=t.getCharCode(h);if(zl[d]||(f+=Rl(d,p,n,i,e,o)),h<t.length()-1){var g=!!(!((s=d)<11904)&&(Yn["Bopomofo Extended"](s)||Yn.Bopomofo(s)||Yn["CJK Compatibility Forms"](s)||Yn["CJK Compatibility Ideographs"](s)||Yn["CJK Compatibility"](s)||Yn["CJK Radicals Supplement"](s)||Yn["CJK Strokes"](s)||Yn["CJK Symbols and Punctuation"](s)||Yn["CJK Unified Ideographs Extension A"](s)||Yn["CJK Unified Ideographs"](s)||Yn["Enclosed CJK Letters and Months"](s)||Yn["Halfwidth and Fullwidth Forms"](s)||Yn.Hiragana(s)||Yn["Ideographic Description Characters"](s)||Yn["Kangxi Radicals"](s)||Yn["Katakana Phonetic Extensions"](s)||Yn.Katakana(s)||Yn["Vertical Forms"](s)||Yn["Yi Radicals"](s)||Yn["Yi Syllables"](s)));(Dl[d]||g||p.imageName)&&l.push(Nl(h+1,f,c,l,Bl(d,t.getCharCode(h+1),g&&u),!1))}}return function t(e){return e?t(e.priorBreak).concat(e.index):[]}(Nl(t.length(),f,c,l,0,!0))}function Ul(t){var e=.5,r=.5;switch(t){case"right":case"top-right":case"bottom-right":e=1;break;case"left":case"top-left":case"bottom-left":e=0}switch(t){case"bottom":case"bottom-right":case"bottom-left":r=1;break;case"top":case"top-right":case"top-left":r=0}return{horizontalAlign:e,verticalAlign:r}}function Vl(t,e,r,n,i){if(n||i)for(var a=t[r],o=a.metrics.advance*a.scale,s=(t[r].x+o)*n,l=e;l<=r;l++)t[l].x-=s,t[l].y+=i}function Hl(t,e,r,n,i,a){var o,s=t.image;if(s.content){var l=s.content,c=s.pixelRatio||1;o=[l[0]/c,l[1]/c,s.displaySize[0]-l[2]/c,s.displaySize[1]-l[3]/c]}var u,f,h,p,d=e.left*a,g=e.right*a;"width"===r||"both"===r?(p=i[0]+d-n[3],f=i[0]+g+n[1]):f=(p=i[0]+(d+g-s.displaySize[0])/2)+s.displaySize[0];var m=e.top*a,v=e.bottom*a;return"height"===r||"both"===r?(u=i[1]+m-n[0],h=i[1]+v+n[2]):h=(u=i[1]+(m+v-s.displaySize[1])/2)+s.displaySize[1],{image:s,top:u,right:f,bottom:h,left:p,collisionPadding:o}}Dl[10]=!0,Dl[32]=!0,Dl[38]=!0,Dl[40]=!0,Dl[41]=!0,Dl[43]=!0,Dl[45]=!0,Dl[47]=!0,Dl[173]=!0,Dl[183]=!0,Dl[8203]=!0,Dl[8208]=!0,Dl[8211]=!0,Dl[8231]=!0;var ql=function(t){function e(e,r,n,i){t.call(this,e,r),this.angle=n,void 0!==i&&(this.segment=i)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.clone=function(){return new e(this.x,this.y,this.angle,this.segment)},e}(i);Nn("Anchor",ql);function Gl(t,e){var r=e.expression;if("constant"===r.kind)return{kind:"constant",layoutSize:r.evaluate(new pi(t+1))};if("source"===r.kind)return{kind:"source"};for(var n=r.zoomStops,i=r.interpolationType,a=0;a<n.length&&n[a]<=t;)a++;for(var o=a=Math.max(0,a-1);o<n.length&&n[o]<t+1;)o++;o=Math.min(n.length-1,o);var s=n[a],l=n[o];return"composite"===r.kind?{kind:"composite",minZoom:s,maxZoom:l,interpolationType:i}:{kind:"camera",minZoom:s,maxZoom:l,minSize:r.evaluate(new pi(s)),maxSize:r.evaluate(new pi(l)),interpolationType:i}}function Yl(t,e,r){var n=e.uSize,i=e.uSizeT,a=r.lowerSize,o=r.upperSize;return"source"===t.kind?a/128:"composite"===t.kind?qe(a/128,o/128,i):n}function Wl(t,e){var r=0,n=0;if("constant"===t.kind)n=t.layoutSize;else if("source"!==t.kind){var i=t.interpolationType,a=t.minZoom,o=t.maxZoom,s=i?l(or.interpolationFactor(i,e,a,o),0,1):0;"camera"===t.kind?n=qe(t.minSize,t.maxSize,s):r=s}return{uSizeT:r,uSize:n}}var Xl=Object.freeze({__proto__:null,getSizeData:Gl,evaluateSizeForFeature:Yl,evaluateSizeForZoom:Wl,SIZE_PACK_FACTOR:128});function Zl(t,e,r,n,i){if(void 0===e.segment)return!0;for(var a=e,o=e.segment+1,s=0;s>-r/2;){if(--o<0)return!1;s-=t[o].dist(a),a=t[o]}s+=t[o].dist(t[o+1]),o++;for(var l=[],c=0;s<r/2;){var u=t[o-1],f=t[o],h=t[o+1];if(!h)return!1;var p=u.angleTo(f)-f.angleTo(h);for(p=Math.abs((p+3*Math.PI)%(2*Math.PI)-Math.PI),l.push({distance:s,angleDelta:p}),c+=p;s-l[0].distance>n;)c-=l.shift().angleDelta;if(c>i)return!1;o++,s+=f.dist(h)}return!0}function Jl(t){for(var e=0,r=0;r<t.length-1;r++)e+=t[r].dist(t[r+1]);return e}function Kl(t,e,r){return t?.6*e*r:0}function Ql(t,e){return Math.max(t?t.right-t.left:0,e?e.right-e.left:0)}function $l(t,e,r,n,i,a){for(var o=Kl(r,i,a),s=Ql(r,n)*a,l=0,c=Jl(t)/2,u=0;u<t.length-1;u++){var f=t[u],h=t[u+1],p=f.dist(h);if(l+p>c){var d=(c-l)/p,g=qe(f.x,h.x,d),m=qe(f.y,h.y,d),v=new ql(g,m,h.angleTo(f),u);return v._round(),!o||Zl(t,v,s,o,e)?v:void 0}l+=p}}function tc(t,e,r,n,i,a,o,s,l){var c=Kl(n,a,o),u=Ql(n,i),f=u*o,h=0===t[0].x||t[0].x===l||0===t[0].y||t[0].y===l;return e-f<e/4&&(e=f+e/4),function t(e,r,n,i,a,o,s,l,c){for(var u=o/2,f=Jl(e),h=0,p=r-n,d=[],g=0;g<e.length-1;g++){for(var m=e[g],v=e[g+1],y=m.dist(v),x=v.angleTo(m);p+n<h+y;){var b=((p+=n)-h)/y,_=qe(m.x,v.x,b),w=qe(m.y,v.y,b);if(_>=0&&_<c&&w>=0&&w<c&&p-u>=0&&p+u<=f){var T=new ql(_,w,x,g);T._round(),i&&!Zl(e,T,o,i,a)||d.push(T)}}h+=y}l||d.length||s||(d=t(e,h/2,n,i,a,o,s,!0,c));return d}(t,h?e/2*s%e:(u/2+2*a)*o*s%e,e,c,r,f,h,!1,l)}function ec(t,e,r,n,a){for(var o=[],s=0;s<t.length;s++)for(var l=t[s],c=void 0,u=0;u<l.length-1;u++){var f=l[u],h=l[u+1];f.x<e&&h.x<e||(f.x<e?f=new i(e,f.y+(h.y-f.y)*((e-f.x)/(h.x-f.x)))._round():h.x<e&&(h=new i(e,f.y+(h.y-f.y)*((e-f.x)/(h.x-f.x)))._round()),f.y<r&&h.y<r||(f.y<r?f=new i(f.x+(h.x-f.x)*((r-f.y)/(h.y-f.y)),r)._round():h.y<r&&(h=new i(f.x+(h.x-f.x)*((r-f.y)/(h.y-f.y)),r)._round()),f.x>=n&&h.x>=n||(f.x>=n?f=new i(n,f.y+(h.y-f.y)*((n-f.x)/(h.x-f.x)))._round():h.x>=n&&(h=new i(n,f.y+(h.y-f.y)*((n-f.x)/(h.x-f.x)))._round()),f.y>=a&&h.y>=a||(f.y>=a?f=new i(f.x+(h.x-f.x)*((a-f.y)/(h.y-f.y)),a)._round():h.y>=a&&(h=new i(f.x+(h.x-f.x)*((a-f.y)/(h.y-f.y)),a)._round()),c&&f.equals(c[c.length-1])||(c=[f],o.push(c)),c.push(h)))))}return o}function rc(t,e,r,n){var a=[],o=t.image,s=o.pixelRatio,l=o.paddedRect.w-2,c=o.paddedRect.h-2,u=t.right-t.left,f=t.bottom-t.top,h=o.stretchX||[[0,l]],p=o.stretchY||[[0,c]],d=function(t,e){return t+e[1]-e[0]},g=h.reduce(d,0),m=p.reduce(d,0),v=l-g,y=c-m,x=0,b=g,_=0,w=m,T=0,k=v,A=0,M=y;if(o.content&&n){var S=o.content;x=nc(h,0,S[0]),_=nc(p,0,S[1]),b=nc(h,S[0],S[2]),w=nc(p,S[1],S[3]),T=S[0]-x,A=S[1]-_,k=S[2]-S[0]-b,M=S[3]-S[1]-w}var E=function(n,a,l,c){var h=ac(n.stretch-x,b,u,t.left),p=oc(n.fixed-T,k,n.stretch,g),d=ac(a.stretch-_,w,f,t.top),v=oc(a.fixed-A,M,a.stretch,m),y=ac(l.stretch-x,b,u,t.left),S=oc(l.fixed-T,k,l.stretch,g),E=ac(c.stretch-_,w,f,t.top),L=oc(c.fixed-A,M,c.stretch,m),C=new i(h,d),P=new i(y,d),I=new i(y,E),O=new i(h,E),z=new i(p/s,v/s),D=new i(S/s,L/s),R=e*Math.PI/180;if(R){var F=Math.sin(R),B=Math.cos(R),N=[B,-F,F,B];C._matMult(N),P._matMult(N),O._matMult(N),I._matMult(N)}var j=n.stretch+n.fixed,U=l.stretch+l.fixed,V=a.stretch+a.fixed,H=c.stretch+c.fixed;return{tl:C,tr:P,bl:O,br:I,tex:{x:o.paddedRect.x+1+j,y:o.paddedRect.y+1+V,w:U-j,h:H-V},writingMode:void 0,glyphOffset:[0,0],sectionIndex:0,pixelOffsetTL:z,pixelOffsetBR:D,minFontScaleX:k/s/u,minFontScaleY:M/s/f,isSDF:r}};if(n&&(o.stretchX||o.stretchY))for(var L=ic(h,v,g),C=ic(p,y,m),P=0;P<L.length-1;P++)for(var I=L[P],O=L[P+1],z=0;z<C.length-1;z++){var D=C[z],R=C[z+1];a.push(E(I,D,O,R))}else a.push(E({fixed:0,stretch:-1},{fixed:0,stretch:-1},{fixed:0,stretch:l+1},{fixed:0,stretch:c+1}));return a}function nc(t,e,r){for(var n=0,i=0,a=t;i<a.length;i+=1){var o=a[i];n+=Math.max(e,Math.min(r,o[1]))-Math.max(e,Math.min(r,o[0]))}return n}function ic(t,e,r){for(var n=[{fixed:-1,stretch:0}],i=0,a=t;i<a.length;i+=1){var o=a[i],s=o[0],l=o[1],c=n[n.length-1];n.push({fixed:s-c.stretch,stretch:c.stretch}),n.push({fixed:s-c.stretch,stretch:c.stretch+(l-s)})}return n.push({fixed:e+1,stretch:r}),n}function ac(t,e,r,n){return t/e*r+n}function oc(t,e,r,n){return t-e*r/n}var sc=function(t,e,r,n,a,o,s,l,c,u){if(this.boxStartIndex=t.length,c){var f=o.top,h=o.bottom,p=o.collisionPadding;p&&(f-=p[1],h+=p[3]);var d=h-f;d>0&&(d=Math.max(10,d),this.circleDiameter=d)}else{var g=o.top*s-l,m=o.bottom*s+l,v=o.left*s-l,y=o.right*s+l,x=o.collisionPadding;if(x&&(v-=x[0]*s,g-=x[1]*s,y+=x[2]*s,m+=x[3]*s),u){var b=new i(v,g),_=new i(y,g),w=new i(v,m),T=new i(y,m),k=u*Math.PI/180;b._rotate(k),_._rotate(k),w._rotate(k),T._rotate(k),v=Math.min(b.x,_.x,w.x,T.x),y=Math.max(b.x,_.x,w.x,T.x),g=Math.min(b.y,_.y,w.y,T.y),m=Math.max(b.y,_.y,w.y,T.y)}t.emplaceBack(e.x,e.y,v,g,y,m,r,n,a)}this.boxEndIndex=t.length},lc=function(t,e){if(void 0===t&&(t=[]),void 0===e&&(e=cc),this.data=t,this.length=this.data.length,this.compare=e,this.length>0)for(var r=(this.length>>1)-1;r>=0;r--)this._down(r)};function cc(t,e){return t<e?-1:t>e?1:0}function uc(t,e,r){void 0===e&&(e=1),void 0===r&&(r=!1);for(var n=1/0,a=1/0,o=-1/0,s=-1/0,l=t[0],c=0;c<l.length;c++){var u=l[c];(!c||u.x<n)&&(n=u.x),(!c||u.y<a)&&(a=u.y),(!c||u.x>o)&&(o=u.x),(!c||u.y>s)&&(s=u.y)}var f=o-n,h=s-a,p=Math.min(f,h),d=p/2,g=new lc([],fc);if(0===p)return new i(n,a);for(var m=n;m<o;m+=p)for(var v=a;v<s;v+=p)g.push(new hc(m+d,v+d,d,t));for(var y=function(t){for(var e=0,r=0,n=0,i=t[0],a=0,o=i.length,s=o-1;a<o;s=a++){var l=i[a],c=i[s],u=l.x*c.y-c.x*l.y;r+=(l.x+c.x)*u,n+=(l.y+c.y)*u,e+=3*u}return new hc(r/e,n/e,0,t)}(t),x=g.length;g.length;){var b=g.pop();(b.d>y.d||!y.d)&&(y=b,r&&console.log("found best %d after %d probes",Math.round(1e4*b.d)/1e4,x)),b.max-y.d<=e||(d=b.h/2,g.push(new hc(b.p.x-d,b.p.y-d,d,t)),g.push(new hc(b.p.x+d,b.p.y-d,d,t)),g.push(new hc(b.p.x-d,b.p.y+d,d,t)),g.push(new hc(b.p.x+d,b.p.y+d,d,t)),x+=4)}return r&&(console.log("num probes: "+x),console.log("best distance: "+y.d)),y.p}function fc(t,e){return e.max-t.max}function hc(t,e,r,n){this.p=new i(t,e),this.h=r,this.d=function(t,e){for(var r=!1,n=1/0,i=0;i<e.length;i++)for(var a=e[i],o=0,s=a.length,l=s-1;o<s;l=o++){var c=a[o],u=a[l];c.y>t.y!=u.y>t.y&&t.x<(u.x-c.x)*(t.y-c.y)/(u.y-c.y)+c.x&&(r=!r),n=Math.min(n,ro(t,c,u))}return(r?1:-1)*Math.sqrt(n)}(this.p,n),this.max=this.d+this.h*Math.SQRT2}lc.prototype.push=function(t){this.data.push(t),this.length++,this._up(this.length-1)},lc.prototype.pop=function(){if(0!==this.length){var t=this.data[0],e=this.data.pop();return this.length--,this.length>0&&(this.data[0]=e,this._down(0)),t}},lc.prototype.peek=function(){return this.data[0]},lc.prototype._up=function(t){for(var e=this.data,r=this.compare,n=e[t];t>0;){var i=t-1>>1,a=e[i];if(r(n,a)>=0)break;e[t]=a,t=i}e[t]=n},lc.prototype._down=function(t){for(var e=this.data,r=this.compare,n=this.length>>1,i=e[t];t<n;){var a=1+(t<<1),o=e[a],s=a+1;if(s<this.length&&r(e[s],o)<0&&(a=s,o=e[s]),r(o,i)>=0)break;e[t]=o,t=a}e[t]=i};var pc=Number.POSITIVE_INFINITY;function dc(t,e){return e[1]!==pc?function(t,e,r){var n=0,i=0;switch(e=Math.abs(e),r=Math.abs(r),t){case"top-right":case"top-left":case"top":i=r-7;break;case"bottom-right":case"bottom-left":case"bottom":i=7-r}switch(t){case"top-right":case"bottom-right":case"right":n=-e;break;case"top-left":case"bottom-left":case"left":n=e}return[n,i]}(t,e[0],e[1]):function(t,e){var r=0,n=0;e<0&&(e=0);var i=e/Math.sqrt(2);switch(t){case"top-right":case"top-left":n=i-7;break;case"bottom-right":case"bottom-left":n=7-i;break;case"bottom":n=7-e;break;case"top":n=e-7}switch(t){case"top-right":case"bottom-right":r=-i;break;case"top-left":case"bottom-left":r=i;break;case"left":r=e;break;case"right":r=-e}return[r,n]}(t,e[0])}function gc(t){switch(t){case"right":case"top-right":case"bottom-right":return"right";case"left":case"top-left":case"bottom-left":return"left"}return"center"}function mc(t,e,r,n,a,o,s,l,c,u,f,h,p,d,g){var m=function(t,e,r,n,a,o,s,l){for(var c=n.layout.get("text-rotate").evaluate(o,{})*Math.PI/180,u=[],f=0,h=e.positionedLines;f<h.length;f+=1)for(var p=h[f],d=0,g=p.positionedGlyphs;d<g.length;d+=1){var m=g[d];if(m.rect){var v=m.rect||{},y=4,x=!0,b=1,_=0,w=(a||l)&&m.vertical,T=m.metrics.advance*m.scale/2;if(l&&e.verticalizable){var k=24*(m.scale-1),A=(24-m.metrics.width*m.scale)/2;_=p.lineOffset/2-(m.imageName?-A:k)}if(m.imageName){var M=s[m.imageName];x=M.sdf,y=1/(b=M.pixelRatio)}var S=a?[m.x+T,m.y]:[0,0],E=a?[0,0]:[m.x+T+r[0],m.y+r[1]-_],L=[0,0];w&&(L=E,E=[0,0]);var C=(m.metrics.left-y)*m.scale-T+E[0],P=(-m.metrics.top-y)*m.scale+E[1],I=C+v.w*m.scale/b,O=P+v.h*m.scale/b,z=new i(C,P),D=new i(I,P),R=new i(C,O),F=new i(I,O);if(w){var B=new i(-T,T- -17),N=-Math.PI/2,j=12-T,U=m.imageName?j:0,V=new i(22-j,-U),H=new(Function.prototype.bind.apply(i,[null].concat(L)));z._rotateAround(N,B)._add(V)._add(H),D._rotateAround(N,B)._add(V)._add(H),R._rotateAround(N,B)._add(V)._add(H),F._rotateAround(N,B)._add(V)._add(H)}if(c){var q=Math.sin(c),G=Math.cos(c),Y=[G,-q,q,G];z._matMult(Y),D._matMult(Y),R._matMult(Y),F._matMult(Y)}var W=new i(0,0),X=new i(0,0);u.push({tl:z,tr:D,bl:R,br:F,tex:v,writingMode:e.writingMode,glyphOffset:S,sectionIndex:m.sectionIndex,isSDF:x,pixelOffsetTL:W,pixelOffsetBR:X,minFontScaleX:0,minFontScaleY:0})}}return u}(0,r,l,a,o,s,n,t.allowVerticalPlacement),v=t.textSizeData,y=null;"source"===v.kind?(y=[128*a.layout.get("text-size").evaluate(s,{})])[0]>32640&&_(t.layerIds[0]+': Value for "text-size" is >= 255. Reduce your "text-size".'):"composite"===v.kind&&((y=[128*d.compositeTextSizes[0].evaluate(s,{},g),128*d.compositeTextSizes[1].evaluate(s,{},g)])[0]>32640||y[1]>32640)&&_(t.layerIds[0]+': Value for "text-size" is >= 255. Reduce your "text-size".'),t.addSymbols(t.text,m,y,l,o,s,u,e,c.lineStartIndex,c.lineLength,p,g);for(var x=0,b=f;x<b.length;x+=1){h[b[x]]=t.text.placedSymbolArray.length-1}return 4*m.length}function vc(t){for(var e in t)return t[e];return null}function yc(t,e,r,n){var i=t.compareText;if(e in i){for(var a=i[e],o=a.length-1;o>=0;o--)if(n.dist(a[o])<r)return!0}else i[e]=[];return i[e].push(n),!1}var xc=Ls.VectorTileFeature.types,bc=[{name:"a_fade_opacity",components:1,type:"Uint8",offset:0}];function _c(t,e,r,n,i,a,o,s,l,c,u,f,h){var p=s?Math.min(32640,Math.round(s[0])):0,d=s?Math.min(32640,Math.round(s[1])):0;t.emplaceBack(e,r,Math.round(32*n),Math.round(32*i),a,o,(p<<1)+(l?1:0),d,16*c,16*u,256*f,256*h)}function wc(t,e,r){t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r)}function Tc(t){for(var e=0,r=t.sections;e<r.length;e+=1){if($n(r[e].text))return!0}return!1}var kc=function(t){this.layoutVertexArray=new Ni,this.indexArray=new Yi,this.programConfigurations=t,this.segments=new pa,this.dynamicLayoutVertexArray=new ji,this.opacityVertexArray=new Ui,this.placedSymbolArray=new aa};kc.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length&&0===this.indexArray.length&&0===this.dynamicLayoutVertexArray.length&&0===this.opacityVertexArray.length},kc.prototype.upload=function(t,e,r,n){this.isEmpty()||(r&&(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,Js.members),this.indexBuffer=t.createIndexBuffer(this.indexArray,e),this.dynamicLayoutVertexBuffer=t.createVertexBuffer(this.dynamicLayoutVertexArray,Ks.members,!0),this.opacityVertexBuffer=t.createVertexBuffer(this.opacityVertexArray,bc,!0),this.opacityVertexBuffer.itemSize=1),(r||n)&&this.programConfigurations.upload(t))},kc.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy(),this.dynamicLayoutVertexBuffer.destroy(),this.opacityVertexBuffer.destroy())},Nn("SymbolBuffers",kc);var Ac=function(t,e,r){this.layoutVertexArray=new t,this.layoutAttributes=e,this.indexArray=new r,this.segments=new pa,this.collisionVertexArray=new Gi};Ac.prototype.upload=function(t){this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,this.layoutAttributes),this.indexBuffer=t.createIndexBuffer(this.indexArray),this.collisionVertexBuffer=t.createVertexBuffer(this.collisionVertexArray,Qs.members,!0)},Ac.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.segments.destroy(),this.collisionVertexBuffer.destroy())},Nn("CollisionBuffers",Ac);var Mc=function(t){this.collisionBoxArray=t.collisionBoxArray,this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.pixelRatio=t.pixelRatio,this.sourceLayerIndex=t.sourceLayerIndex,this.hasPattern=!1,this.hasRTLText=!1,this.sortKeyRanges=[],this.collisionCircleArray=[],this.placementInvProjMatrix=ho([]),this.placementViewportMatrix=ho([]);var e=this.layers[0]._unevaluatedLayout._values;this.textSizeData=Gl(this.zoom,e["text-size"]),this.iconSizeData=Gl(this.zoom,e["icon-size"]);var r=this.layers[0].layout,n=r.get("symbol-sort-key"),i=r.get("symbol-z-order");this.sortFeaturesByKey="viewport-y"!==i&&void 0!==n.constantOr(1);var a="viewport-y"===i||"auto"===i&&!this.sortFeaturesByKey;this.sortFeaturesByY=a&&(r.get("text-allow-overlap")||r.get("icon-allow-overlap")||r.get("text-ignore-placement")||r.get("icon-ignore-placement")),"point"===r.get("symbol-placement")&&(this.writingModes=r.get("text-writing-mode").map((function(t){return Cl[t]}))),this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id})),this.sourceID=t.sourceID};Mc.prototype.createArrays=function(){this.text=new kc(new Ua(Js.members,this.layers,this.zoom,(function(t){return/^text/.test(t)}))),this.icon=new kc(new Ua(Js.members,this.layers,this.zoom,(function(t){return/^icon/.test(t)}))),this.glyphOffsetArray=new la,this.lineVertexArray=new ca,this.symbolInstances=new sa},Mc.prototype.calculateGlyphDependencies=function(t,e,r,n,i){for(var a=0;a<t.length;a++)if(e[t.charCodeAt(a)]=!0,(r||n)&&i){var o=rl[t.charAt(a)];o&&(e[o.charCodeAt(0)]=!0)}},Mc.prototype.populate=function(t,e,r){var n=this.layers[0],i=n.layout,a=i.get("text-font"),o=i.get("text-field"),s=i.get("icon-image"),l=("constant"!==o.value.kind||o.value.value instanceof ne&&!o.value.value.isEmpty()||o.value.value.toString().length>0)&&("constant"!==a.value.kind||a.value.value.length>0),c="constant"!==s.value.kind||!!s.value.value||Object.keys(s.parameters).length>0,u=i.get("symbol-sort-key");if(this.features=[],l||c){for(var f=e.iconDependencies,h=e.glyphDependencies,p=e.availableImages,d=new pi(this.zoom),g=0,m=t;g<m.length;g+=1){var v=m[g],y=v.feature,x=v.id,b=v.index,_=v.sourceLayerIndex,w=n._featureFilter.needGeometry,T={type:y.type,id:x,properties:y.properties,geometry:w?Ya(y):[]};if(n._featureFilter.filter(d,T,r)){w||(T.geometry=Ya(y));var k=void 0;if(l){var A=n.getValueAndResolveTokens("text-field",T,r,p),M=ne.factory(A);Tc(M)&&(this.hasRTLText=!0),(!this.hasRTLText||"unavailable"===ui()||this.hasRTLText&&hi.isParsed())&&(k=el(M,n,T))}var S=void 0;if(c){var E=n.getValueAndResolveTokens("icon-image",T,r,p);S=E instanceof ie?E:ie.fromString(E)}if(k||S){var L=this.sortFeaturesByKey?u.evaluate(T,{},r):void 0,C={id:x,text:k,icon:S,index:b,sourceLayerIndex:_,geometry:Ya(y),properties:y.properties,type:xc[y.type],sortKey:L};if(this.features.push(C),S&&(f[S.name]=!0),k){var P=a.evaluate(T,{},r).join(","),I="map"===i.get("text-rotation-alignment")&&"point"!==i.get("symbol-placement");this.allowVerticalPlacement=this.writingModes&&this.writingModes.indexOf(Cl.vertical)>=0;for(var O=0,z=k.sections;O<z.length;O+=1){var D=z[O];if(D.image)f[D.image.name]=!0;else{var R=Wn(k.toString()),F=D.fontStack||P,B=h[F]=h[F]||{};this.calculateGlyphDependencies(D.text,B,I,this.allowVerticalPlacement,R)}}}}}}"line"===i.get("symbol-placement")&&(this.features=function(t){var e={},r={},n=[],i=0;function a(e){n.push(t[e]),i++}function o(t,e,i){var a=r[t];return delete r[t],r[e]=a,n[a].geometry[0].pop(),n[a].geometry[0]=n[a].geometry[0].concat(i[0]),a}function s(t,r,i){var a=e[r];return delete e[r],e[t]=a,n[a].geometry[0].shift(),n[a].geometry[0]=i[0].concat(n[a].geometry[0]),a}function l(t,e,r){var n=r?e[0][e[0].length-1]:e[0][0];return t+":"+n.x+":"+n.y}for(var c=0;c<t.length;c++){var u=t[c],f=u.geometry,h=u.text?u.text.toString():null;if(h){var p=l(h,f),d=l(h,f,!0);if(p in r&&d in e&&r[p]!==e[d]){var g=s(p,d,f),m=o(p,d,n[g].geometry);delete e[p],delete r[d],r[l(h,n[m].geometry,!0)]=m,n[g].geometry=null}else p in r?o(p,d,f):d in e?s(p,d,f):(a(c),e[p]=i-1,r[d]=i-1)}else a(c)}return n.filter((function(t){return t.geometry}))}(this.features)),this.sortFeaturesByKey&&this.features.sort((function(t,e){return t.sortKey-e.sortKey}))}},Mc.prototype.update=function(t,e,r){this.stateDependentLayers.length&&(this.text.programConfigurations.updatePaintArrays(t,e,this.layers,r),this.icon.programConfigurations.updatePaintArrays(t,e,this.layers,r))},Mc.prototype.isEmpty=function(){return 0===this.symbolInstances.length&&!this.hasRTLText},Mc.prototype.uploadPending=function(){return!this.uploaded||this.text.programConfigurations.needsUpload||this.icon.programConfigurations.needsUpload},Mc.prototype.upload=function(t){!this.uploaded&&this.hasDebugData()&&(this.textCollisionBox.upload(t),this.iconCollisionBox.upload(t)),this.text.upload(t,this.sortFeaturesByY,!this.uploaded,this.text.programConfigurations.needsUpload),this.icon.upload(t,this.sortFeaturesByY,!this.uploaded,this.icon.programConfigurations.needsUpload),this.uploaded=!0},Mc.prototype.destroyDebugData=function(){this.textCollisionBox.destroy(),this.iconCollisionBox.destroy()},Mc.prototype.destroy=function(){this.text.destroy(),this.icon.destroy(),this.hasDebugData()&&this.destroyDebugData()},Mc.prototype.addToLineVertexArray=function(t,e){var r=this.lineVertexArray.length;if(void 0!==t.segment){for(var n=t.dist(e[t.segment+1]),i=t.dist(e[t.segment]),a={},o=t.segment+1;o<e.length;o++)a[o]={x:e[o].x,y:e[o].y,tileUnitDistanceFromAnchor:n},o<e.length-1&&(n+=e[o+1].dist(e[o]));for(var s=t.segment||0;s>=0;s--)a[s]={x:e[s].x,y:e[s].y,tileUnitDistanceFromAnchor:i},s>0&&(i+=e[s-1].dist(e[s]));for(var l=0;l<e.length;l++){var c=a[l];this.lineVertexArray.emplaceBack(c.x,c.y,c.tileUnitDistanceFromAnchor)}}return{lineStartIndex:r,lineLength:this.lineVertexArray.length-r}},Mc.prototype.addSymbols=function(t,e,r,n,i,a,o,s,l,c,u,f){for(var h=t.indexArray,p=t.layoutVertexArray,d=t.segments.prepareSegment(4*e.length,p,h,a.sortKey),g=this.glyphOffsetArray.length,m=d.vertexLength,v=this.allowVerticalPlacement&&o===Cl.vertical?Math.PI/2:0,y=a.text&&a.text.sections,x=0;x<e.length;x++){var b=e[x],_=b.tl,w=b.tr,T=b.bl,k=b.br,A=b.tex,M=b.pixelOffsetTL,S=b.pixelOffsetBR,E=b.minFontScaleX,L=b.minFontScaleY,C=b.glyphOffset,P=b.isSDF,I=b.sectionIndex,O=d.vertexLength,z=C[1];_c(p,s.x,s.y,_.x,z+_.y,A.x,A.y,r,P,M.x,M.y,E,L),_c(p,s.x,s.y,w.x,z+w.y,A.x+A.w,A.y,r,P,S.x,M.y,E,L),_c(p,s.x,s.y,T.x,z+T.y,A.x,A.y+A.h,r,P,M.x,S.y,E,L),_c(p,s.x,s.y,k.x,z+k.y,A.x+A.w,A.y+A.h,r,P,S.x,S.y,E,L),wc(t.dynamicLayoutVertexArray,s,v),h.emplaceBack(O,O+1,O+2),h.emplaceBack(O+1,O+2,O+3),d.vertexLength+=4,d.primitiveLength+=2,this.glyphOffsetArray.emplaceBack(C[0]),x!==e.length-1&&I===e[x+1].sectionIndex||t.programConfigurations.populatePaintArrays(p.length,a,a.index,{},f,y&&y[I])}t.placedSymbolArray.emplaceBack(s.x,s.y,g,this.glyphOffsetArray.length-g,m,l,c,s.segment,r?r[0]:0,r?r[1]:0,n[0],n[1],o,0,!1,0,u)},Mc.prototype._addCollisionDebugVertex=function(t,e,r,n,i,a){return e.emplaceBack(0,0),t.emplaceBack(r.x,r.y,n,i,Math.round(a.x),Math.round(a.y))},Mc.prototype.addCollisionDebugVertices=function(t,e,r,n,a,o,s){var l=a.segments.prepareSegment(4,a.layoutVertexArray,a.indexArray),c=l.vertexLength,u=a.layoutVertexArray,f=a.collisionVertexArray,h=s.anchorX,p=s.anchorY;this._addCollisionDebugVertex(u,f,o,h,p,new i(t,e)),this._addCollisionDebugVertex(u,f,o,h,p,new i(r,e)),this._addCollisionDebugVertex(u,f,o,h,p,new i(r,n)),this._addCollisionDebugVertex(u,f,o,h,p,new i(t,n)),l.vertexLength+=4;var d=a.indexArray;d.emplaceBack(c,c+1),d.emplaceBack(c+1,c+2),d.emplaceBack(c+2,c+3),d.emplaceBack(c+3,c),l.primitiveLength+=4},Mc.prototype.addDebugCollisionBoxes=function(t,e,r,n){for(var i=t;i<e;i++){var a=this.collisionBoxArray.get(i),o=a.x1,s=a.y1,l=a.x2,c=a.y2;this.addCollisionDebugVertices(o,s,l,c,n?this.textCollisionBox:this.iconCollisionBox,a.anchorPoint,r)}},Mc.prototype.generateCollisionDebugBuffers=function(){this.hasDebugData()&&this.destroyDebugData(),this.textCollisionBox=new Ac(Hi,$s.members,Qi),this.iconCollisionBox=new Ac(Hi,$s.members,Qi);for(var t=0;t<this.symbolInstances.length;t++){var e=this.symbolInstances.get(t);this.addDebugCollisionBoxes(e.textBoxStartIndex,e.textBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.verticalTextBoxStartIndex,e.verticalTextBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.iconBoxStartIndex,e.iconBoxEndIndex,e,!1),this.addDebugCollisionBoxes(e.verticalIconBoxStartIndex,e.verticalIconBoxEndIndex,e,!1)}},Mc.prototype._deserializeCollisionBoxesForSymbol=function(t,e,r,n,i,a,o,s,l){for(var c={},u=e;u<r;u++){var f=t.get(u);c.textBox={x1:f.x1,y1:f.y1,x2:f.x2,y2:f.y2,anchorPointX:f.anchorPointX,anchorPointY:f.anchorPointY},c.textFeatureIndex=f.featureIndex;break}for(var h=n;h<i;h++){var p=t.get(h);c.verticalTextBox={x1:p.x1,y1:p.y1,x2:p.x2,y2:p.y2,anchorPointX:p.anchorPointX,anchorPointY:p.anchorPointY},c.verticalTextFeatureIndex=p.featureIndex;break}for(var d=a;d<o;d++){var g=t.get(d);c.iconBox={x1:g.x1,y1:g.y1,x2:g.x2,y2:g.y2,anchorPointX:g.anchorPointX,anchorPointY:g.anchorPointY},c.iconFeatureIndex=g.featureIndex;break}for(var m=s;m<l;m++){var v=t.get(m);c.verticalIconBox={x1:v.x1,y1:v.y1,x2:v.x2,y2:v.y2,anchorPointX:v.anchorPointX,anchorPointY:v.anchorPointY},c.verticalIconFeatureIndex=v.featureIndex;break}return c},Mc.prototype.deserializeCollisionBoxes=function(t){this.collisionArrays=[];for(var e=0;e<this.symbolInstances.length;e++){var r=this.symbolInstances.get(e);this.collisionArrays.push(this._deserializeCollisionBoxesForSymbol(t,r.textBoxStartIndex,r.textBoxEndIndex,r.verticalTextBoxStartIndex,r.verticalTextBoxEndIndex,r.iconBoxStartIndex,r.iconBoxEndIndex,r.verticalIconBoxStartIndex,r.verticalIconBoxEndIndex))}},Mc.prototype.hasTextData=function(){return this.text.segments.get().length>0},Mc.prototype.hasIconData=function(){return this.icon.segments.get().length>0},Mc.prototype.hasDebugData=function(){return this.textCollisionBox&&this.iconCollisionBox},Mc.prototype.hasTextCollisionBoxData=function(){return this.hasDebugData()&&this.textCollisionBox.segments.get().length>0},Mc.prototype.hasIconCollisionBoxData=function(){return this.hasDebugData()&&this.iconCollisionBox.segments.get().length>0},Mc.prototype.addIndicesForPlacedSymbol=function(t,e){for(var r=t.placedSymbolArray.get(e),n=r.vertexStartIndex+4*r.numGlyphs,i=r.vertexStartIndex;i<n;i+=4)t.indexArray.emplaceBack(i,i+1,i+2),t.indexArray.emplaceBack(i+1,i+2,i+3)},Mc.prototype.getSortedSymbolIndexes=function(t){if(this.sortedAngle===t&&void 0!==this.symbolInstanceIndexes)return this.symbolInstanceIndexes;for(var e=Math.sin(t),r=Math.cos(t),n=[],i=[],a=[],o=0;o<this.symbolInstances.length;++o){a.push(o);var s=this.symbolInstances.get(o);n.push(0|Math.round(e*s.anchorX+r*s.anchorY)),i.push(s.featureIndex)}return a.sort((function(t,e){return n[t]-n[e]||i[e]-i[t]})),a},Mc.prototype.addToSortKeyRanges=function(t,e){var r=this.sortKeyRanges[this.sortKeyRanges.length-1];r&&r.sortKey===e?r.symbolInstanceEnd=t+1:this.sortKeyRanges.push({sortKey:e,symbolInstanceStart:t,symbolInstanceEnd:t+1})},Mc.prototype.sortFeatures=function(t){var e=this;if(this.sortFeaturesByY&&this.sortedAngle!==t&&!(this.text.segments.get().length>1||this.icon.segments.get().length>1)){this.symbolInstanceIndexes=this.getSortedSymbolIndexes(t),this.sortedAngle=t,this.text.indexArray.clear(),this.icon.indexArray.clear(),this.featureSortOrder=[];for(var r=0,n=this.symbolInstanceIndexes;r<n.length;r+=1){var i=n[r],a=this.symbolInstances.get(i);this.featureSortOrder.push(a.featureIndex),[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t,r,n){t>=0&&n.indexOf(t)===r&&e.addIndicesForPlacedSymbol(e.text,t)})),a.verticalPlacedTextSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.text,a.verticalPlacedTextSymbolIndex),a.placedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,a.placedIconSymbolIndex),a.verticalPlacedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,a.verticalPlacedIconSymbolIndex)}this.text.indexBuffer&&this.text.indexBuffer.updateData(this.text.indexArray),this.icon.indexBuffer&&this.icon.indexBuffer.updateData(this.icon.indexArray)}},Nn("SymbolBucket",Mc,{omit:["layers","collisionBoxArray","features","compareText"]}),Mc.MAX_GLYPHS=65535,Mc.addDynamicAttributes=wc;var Sc=new Si({"symbol-placement":new wi(Lt.layout_symbol["symbol-placement"]),"symbol-spacing":new wi(Lt.layout_symbol["symbol-spacing"]),"symbol-avoid-edges":new wi(Lt.layout_symbol["symbol-avoid-edges"]),"symbol-sort-key":new Ti(Lt.layout_symbol["symbol-sort-key"]),"symbol-z-order":new wi(Lt.layout_symbol["symbol-z-order"]),"icon-allow-overlap":new wi(Lt.layout_symbol["icon-allow-overlap"]),"icon-ignore-placement":new wi(Lt.layout_symbol["icon-ignore-placement"]),"icon-optional":new wi(Lt.layout_symbol["icon-optional"]),"icon-rotation-alignment":new wi(Lt.layout_symbol["icon-rotation-alignment"]),"icon-size":new Ti(Lt.layout_symbol["icon-size"]),"icon-text-fit":new wi(Lt.layout_symbol["icon-text-fit"]),"icon-text-fit-padding":new wi(Lt.layout_symbol["icon-text-fit-padding"]),"icon-image":new Ti(Lt.layout_symbol["icon-image"]),"icon-rotate":new Ti(Lt.layout_symbol["icon-rotate"]),"icon-padding":new wi(Lt.layout_symbol["icon-padding"]),"icon-keep-upright":new wi(Lt.layout_symbol["icon-keep-upright"]),"icon-offset":new Ti(Lt.layout_symbol["icon-offset"]),"icon-anchor":new Ti(Lt.layout_symbol["icon-anchor"]),"icon-pitch-alignment":new wi(Lt.layout_symbol["icon-pitch-alignment"]),"text-pitch-alignment":new wi(Lt.layout_symbol["text-pitch-alignment"]),"text-rotation-alignment":new wi(Lt.layout_symbol["text-rotation-alignment"]),"text-field":new Ti(Lt.layout_symbol["text-field"]),"text-font":new Ti(Lt.layout_symbol["text-font"]),"text-size":new Ti(Lt.layout_symbol["text-size"]),"text-max-width":new Ti(Lt.layout_symbol["text-max-width"]),"text-line-height":new wi(Lt.layout_symbol["text-line-height"]),"text-letter-spacing":new Ti(Lt.layout_symbol["text-letter-spacing"]),"text-justify":new Ti(Lt.layout_symbol["text-justify"]),"text-radial-offset":new Ti(Lt.layout_symbol["text-radial-offset"]),"text-variable-anchor":new wi(Lt.layout_symbol["text-variable-anchor"]),"text-anchor":new Ti(Lt.layout_symbol["text-anchor"]),"text-max-angle":new wi(Lt.layout_symbol["text-max-angle"]),"text-writing-mode":new wi(Lt.layout_symbol["text-writing-mode"]),"text-rotate":new Ti(Lt.layout_symbol["text-rotate"]),"text-padding":new wi(Lt.layout_symbol["text-padding"]),"text-keep-upright":new wi(Lt.layout_symbol["text-keep-upright"]),"text-transform":new Ti(Lt.layout_symbol["text-transform"]),"text-offset":new Ti(Lt.layout_symbol["text-offset"]),"text-allow-overlap":new wi(Lt.layout_symbol["text-allow-overlap"]),"text-ignore-placement":new wi(Lt.layout_symbol["text-ignore-placement"]),"text-optional":new wi(Lt.layout_symbol["text-optional"])}),Ec={paint:new Si({"icon-opacity":new Ti(Lt.paint_symbol["icon-opacity"]),"icon-color":new Ti(Lt.paint_symbol["icon-color"]),"icon-halo-color":new Ti(Lt.paint_symbol["icon-halo-color"]),"icon-halo-width":new Ti(Lt.paint_symbol["icon-halo-width"]),"icon-halo-blur":new Ti(Lt.paint_symbol["icon-halo-blur"]),"icon-translate":new wi(Lt.paint_symbol["icon-translate"]),"icon-translate-anchor":new wi(Lt.paint_symbol["icon-translate-anchor"]),"text-opacity":new Ti(Lt.paint_symbol["text-opacity"]),"text-color":new Ti(Lt.paint_symbol["text-color"],{runtimeType:Ut,getOverride:function(t){return t.textColor},hasOverride:function(t){return!!t.textColor}}),"text-halo-color":new Ti(Lt.paint_symbol["text-halo-color"]),"text-halo-width":new Ti(Lt.paint_symbol["text-halo-width"]),"text-halo-blur":new Ti(Lt.paint_symbol["text-halo-blur"]),"text-translate":new wi(Lt.paint_symbol["text-translate"]),"text-translate-anchor":new wi(Lt.paint_symbol["text-translate-anchor"])}),layout:Sc},Lc=function(t){this.type=t.property.overrides?t.property.overrides.runtimeType:Ft,this.defaultValue=t};Lc.prototype.evaluate=function(t){if(t.formattedSection){var e=this.defaultValue.property.overrides;if(e&&e.hasOverride(t.formattedSection))return e.getOverride(t.formattedSection)}return t.feature&&t.featureState?this.defaultValue.evaluate(t.feature,t.featureState):this.defaultValue.property.specification.default},Lc.prototype.eachChild=function(t){this.defaultValue.isConstant()||t(this.defaultValue.value._styleExpression.expression)},Lc.prototype.outputDefined=function(){return!1},Lc.prototype.serialize=function(){return null},Nn("FormatSectionOverride",Lc,{omit:["defaultValue"]});var Cc=function(t){function e(e){t.call(this,e,Ec)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.recalculate=function(e,r){if(t.prototype.recalculate.call(this,e,r),"auto"===this.layout.get("icon-rotation-alignment")&&("point"!==this.layout.get("symbol-placement")?this.layout._values["icon-rotation-alignment"]="map":this.layout._values["icon-rotation-alignment"]="viewport"),"auto"===this.layout.get("text-rotation-alignment")&&("point"!==this.layout.get("symbol-placement")?this.layout._values["text-rotation-alignment"]="map":this.layout._values["text-rotation-alignment"]="viewport"),"auto"===this.layout.get("text-pitch-alignment")&&(this.layout._values["text-pitch-alignment"]=this.layout.get("text-rotation-alignment")),"auto"===this.layout.get("icon-pitch-alignment")&&(this.layout._values["icon-pitch-alignment"]=this.layout.get("icon-rotation-alignment")),"point"===this.layout.get("symbol-placement")){var n=this.layout.get("text-writing-mode");if(n){for(var i=[],a=0,o=n;a<o.length;a+=1){var s=o[a];i.indexOf(s)<0&&i.push(s)}this.layout._values["text-writing-mode"]=i}else this.layout._values["text-writing-mode"]=["horizontal"]}this._setPaintOverrides()},e.prototype.getValueAndResolveTokens=function(t,e,r,n){var i=this.layout.get(t).evaluate(e,{},r,n),a=this._unevaluatedLayout._values[t];return a.isDataDriven()||Yr(a.value)||!i?i:function(t,e){return e.replace(/{([^{}]+)}/g,(function(e,r){return r in t?String(t[r]):""}))}(e.properties,i)},e.prototype.createBucket=function(t){return new Mc(t)},e.prototype.queryRadius=function(){return 0},e.prototype.queryIntersectsFeature=function(){return!1},e.prototype._setPaintOverrides=function(){for(var t=0,r=Ec.paint.overridableProperties;t<r.length;t+=1){var n=r[t];if(e.hasPaintOverride(this.layout,n)){var i=this.paint.get(n),a=new Lc(i),o=new Gr(a,i.property.specification),s=null;s="constant"===i.value.kind||"source"===i.value.kind?new Xr("source",o):new Zr("composite",o,i.value.zoomStops,i.value._interpolationType),this.paint._values[n]=new bi(i.property,s,i.parameters)}}},e.prototype._handleOverridablePaintPropertyUpdate=function(t,r,n){return!(!this.layout||r.isDataDriven()||n.isDataDriven())&&e.hasPaintOverride(this.layout,t)},e.hasPaintOverride=function(t,e){var r=t.get("text-field"),n=Ec.paint.properties[e],i=!1,a=function(t){for(var e=0,r=t;e<r.length;e+=1){var a=r[e];if(n.overrides&&n.overrides.hasOverride(a))return void(i=!0)}};if("constant"===r.value.kind&&r.value.value instanceof ne)a(r.value.value.sections);else if("source"===r.value.kind){var o=function(t){if(!i)if(t instanceof ce&&se(t.value)===Gt){var e=t.value;a(e.sections)}else t instanceof pe?a(t.sections):t.eachChild(o)},s=r.value;s._styleExpression&&o(s._styleExpression.expression)}return i},e}(Ei),Pc={paint:new Si({"background-color":new wi(Lt.paint_background["background-color"]),"background-pattern":new Ai(Lt.paint_background["background-pattern"]),"background-opacity":new wi(Lt.paint_background["background-opacity"])})},Ic=function(t){function e(e){t.call(this,e,Pc)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(Ei),Oc={paint:new Si({"raster-opacity":new wi(Lt.paint_raster["raster-opacity"]),"raster-hue-rotate":new wi(Lt.paint_raster["raster-hue-rotate"]),"raster-brightness-min":new wi(Lt.paint_raster["raster-brightness-min"]),"raster-brightness-max":new wi(Lt.paint_raster["raster-brightness-max"]),"raster-saturation":new wi(Lt.paint_raster["raster-saturation"]),"raster-contrast":new wi(Lt.paint_raster["raster-contrast"]),"raster-resampling":new wi(Lt.paint_raster["raster-resampling"]),"raster-fade-duration":new wi(Lt.paint_raster["raster-fade-duration"])})},zc=function(t){function e(e){t.call(this,e,Oc)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(Ei);var Dc=function(t){function e(e){t.call(this,e,{}),this.implementation=e}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.is3D=function(){return"3d"===this.implementation.renderingMode},e.prototype.hasOffscreenPass=function(){return void 0!==this.implementation.prerender},e.prototype.recalculate=function(){},e.prototype.updateTransitions=function(){},e.prototype.hasTransition=function(){},e.prototype.serialize=function(){},e.prototype.onAdd=function(t){this.implementation.onAdd&&this.implementation.onAdd(t,t.painter.context.gl)},e.prototype.onRemove=function(t){this.implementation.onRemove&&this.implementation.onRemove(t,t.painter.context.gl)},e}(Ei),Rc={circle:_o,heatmap:Po,hillshade:Oo,fill:xs,"fill-extrusion":Fs,line:Xs,symbol:Cc,background:Ic,raster:zc};var Fc=self.HTMLImageElement,Bc=self.HTMLCanvasElement,Nc=self.HTMLVideoElement,jc=self.ImageData,Uc=self.ImageBitmap,Vc=function(t,e,r,n){this.context=t,this.format=r,this.texture=t.gl.createTexture(),this.update(e,n)};Vc.prototype.update=function(t,e,r){var n=t.width,i=t.height,a=!(this.size&&this.size[0]===n&&this.size[1]===i||r),o=this.context,s=o.gl;if(this.useMipmap=Boolean(e&&e.useMipmap),s.bindTexture(s.TEXTURE_2D,this.texture),o.pixelStoreUnpackFlipY.set(!1),o.pixelStoreUnpack.set(1),o.pixelStoreUnpackPremultiplyAlpha.set(this.format===s.RGBA&&(!e||!1!==e.premultiply)),a)this.size=[n,i],t instanceof Fc||t instanceof Bc||t instanceof Nc||t instanceof jc||Uc&&t instanceof Uc?s.texImage2D(s.TEXTURE_2D,0,this.format,this.format,s.UNSIGNED_BYTE,t):s.texImage2D(s.TEXTURE_2D,0,this.format,n,i,0,this.format,s.UNSIGNED_BYTE,t.data);else{var l=r||{x:0,y:0},c=l.x,u=l.y;t instanceof Fc||t instanceof Bc||t instanceof Nc||t instanceof jc||Uc&&t instanceof Uc?s.texSubImage2D(s.TEXTURE_2D,0,c,u,s.RGBA,s.UNSIGNED_BYTE,t):s.texSubImage2D(s.TEXTURE_2D,0,c,u,n,i,s.RGBA,s.UNSIGNED_BYTE,t.data)}this.useMipmap&&this.isSizePowerOfTwo()&&s.generateMipmap(s.TEXTURE_2D)},Vc.prototype.bind=function(t,e,r){var n=this.context.gl;n.bindTexture(n.TEXTURE_2D,this.texture),r!==n.LINEAR_MIPMAP_NEAREST||this.isSizePowerOfTwo()||(r=n.LINEAR),t!==this.filter&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,t),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,r||t),this.filter=t),e!==this.wrap&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,e),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,e),this.wrap=e)},Vc.prototype.isSizePowerOfTwo=function(){return this.size[0]===this.size[1]&&Math.log(this.size[0])/Math.LN2%1==0},Vc.prototype.destroy=function(){this.context.gl.deleteTexture(this.texture),this.texture=null};var Hc=function(t){var e=this;this._callback=t,this._triggered=!1,"undefined"!=typeof MessageChannel&&(this._channel=new MessageChannel,this._channel.port2.onmessage=function(){e._triggered=!1,e._callback()})};Hc.prototype.trigger=function(){var t=this;this._triggered||(this._triggered=!0,this._channel?this._channel.port1.postMessage(!0):setTimeout((function(){t._triggered=!1,t._callback()}),0))},Hc.prototype.remove=function(){delete this._channel,this._callback=function(){}};var qc=function(t,e,r){this.target=t,this.parent=e,this.mapId=r,this.callbacks={},this.tasks={},this.taskQueue=[],this.cancelCallbacks={},g(["receive","process"],this),this.invoker=new Hc(this.process),this.target.addEventListener("message",this.receive,!1),this.globalScope=k()?t:self};function Gc(t,e,r){var n=2*Math.PI*6378137/256/Math.pow(2,r);return[t*n-2*Math.PI*6378137/2,e*n-2*Math.PI*6378137/2]}qc.prototype.send=function(t,e,r,n,i){var a=this;void 0===i&&(i=!1);var o=Math.round(1e18*Math.random()).toString(36).substring(0,10);r&&(this.callbacks[o]=r);var s=S(this.globalScope)?void 0:[];return this.target.postMessage({id:o,type:t,hasCallback:!!r,targetMapId:n,mustQueue:i,sourceMapId:this.mapId,data:Hn(e,s)},s),{cancel:function(){r&&delete a.callbacks[o],a.target.postMessage({id:o,type:"<cancel>",targetMapId:n,sourceMapId:a.mapId})}}},qc.prototype.receive=function(t){var e=t.data,r=e.id;if(r&&(!e.targetMapId||this.mapId===e.targetMapId))if("<cancel>"===e.type){delete this.tasks[r];var n=this.cancelCallbacks[r];delete this.cancelCallbacks[r],n&&n()}else k()||e.mustQueue?(this.tasks[r]=e,this.taskQueue.push(r),this.invoker.trigger()):this.processTask(r,e)},qc.prototype.process=function(){if(this.taskQueue.length){var t=this.taskQueue.shift(),e=this.tasks[t];delete this.tasks[t],this.taskQueue.length&&this.invoker.trigger(),e&&this.processTask(t,e)}},qc.prototype.processTask=function(t,e){var r=this;if("<response>"===e.type){var n=this.callbacks[t];delete this.callbacks[t],n&&(e.error?n(qn(e.error)):n(null,qn(e.data)))}else{var i=!1,a=S(this.globalScope)?void 0:[],o=e.hasCallback?function(e,n){i=!0,delete r.cancelCallbacks[t],r.target.postMessage({id:t,type:"<response>",sourceMapId:r.mapId,error:e?Hn(e):null,data:Hn(n,a)},a)}:function(t){i=!0},s=null,l=qn(e.data);if(this.parent[e.type])s=this.parent[e.type](e.sourceMapId,l,o);else if(this.parent.getWorkerSource){var c=e.type.split(".");s=this.parent.getWorkerSource(e.sourceMapId,c[0],l.source)[c[1]](l,o)}else o(new Error("Could not find function "+e.type));!i&&s&&s.cancel&&(this.cancelCallbacks[t]=s.cancel)}},qc.prototype.remove=function(){this.invoker.remove(),this.target.removeEventListener("message",this.receive,!1)};var Yc=function(t,e){t&&(e?this.setSouthWest(t).setNorthEast(e):4===t.length?this.setSouthWest([t[0],t[1]]).setNorthEast([t[2],t[3]]):this.setSouthWest(t[0]).setNorthEast(t[1]))};Yc.prototype.setNorthEast=function(t){return this._ne=t instanceof Wc?new Wc(t.lng,t.lat):Wc.convert(t),this},Yc.prototype.setSouthWest=function(t){return this._sw=t instanceof Wc?new Wc(t.lng,t.lat):Wc.convert(t),this},Yc.prototype.extend=function(t){var e,r,n=this._sw,i=this._ne;if(t instanceof Wc)e=t,r=t;else{if(!(t instanceof Yc)){if(Array.isArray(t)){if(4===t.length||t.every(Array.isArray)){var a=t;return this.extend(Yc.convert(a))}var o=t;return this.extend(Wc.convert(o))}return this}if(e=t._sw,r=t._ne,!e||!r)return this}return n||i?(n.lng=Math.min(e.lng,n.lng),n.lat=Math.min(e.lat,n.lat),i.lng=Math.max(r.lng,i.lng),i.lat=Math.max(r.lat,i.lat)):(this._sw=new Wc(e.lng,e.lat),this._ne=new Wc(r.lng,r.lat)),this},Yc.prototype.getCenter=function(){return new Wc((this._sw.lng+this._ne.lng)/2,(this._sw.lat+this._ne.lat)/2)},Yc.prototype.getSouthWest=function(){return this._sw},Yc.prototype.getNorthEast=function(){return this._ne},Yc.prototype.getNorthWest=function(){return new Wc(this.getWest(),this.getNorth())},Yc.prototype.getSouthEast=function(){return new Wc(this.getEast(),this.getSouth())},Yc.prototype.getWest=function(){return this._sw.lng},Yc.prototype.getSouth=function(){return this._sw.lat},Yc.prototype.getEast=function(){return this._ne.lng},Yc.prototype.getNorth=function(){return this._ne.lat},Yc.prototype.toArray=function(){return[this._sw.toArray(),this._ne.toArray()]},Yc.prototype.toString=function(){return"LngLatBounds("+this._sw.toString()+", "+this._ne.toString()+")"},Yc.prototype.isEmpty=function(){return!(this._sw&&this._ne)},Yc.prototype.contains=function(t){var e=Wc.convert(t),r=e.lng,n=e.lat,i=this._sw.lat<=n&&n<=this._ne.lat,a=this._sw.lng<=r&&r<=this._ne.lng;return this._sw.lng>this._ne.lng&&(a=this._sw.lng>=r&&r>=this._ne.lng),i&&a},Yc.convert=function(t){return!t||t instanceof Yc?t:new Yc(t)};var Wc=function(t,e){if(isNaN(t)||isNaN(e))throw new Error("Invalid LngLat object: ("+t+", "+e+")");if(this.lng=+t,this.lat=+e,this.lat>90||this.lat<-90)throw new Error("Invalid LngLat latitude value: must be between -90 and 90")};Wc.prototype.wrap=function(){return new Wc(c(this.lng,-180,180),this.lat)},Wc.prototype.toArray=function(){return[this.lng,this.lat]},Wc.prototype.toString=function(){return"LngLat("+this.lng+", "+this.lat+")"},Wc.prototype.distanceTo=function(t){var e=Math.PI/180,r=this.lat*e,n=t.lat*e,i=Math.sin(r)*Math.sin(n)+Math.cos(r)*Math.cos(n)*Math.cos((t.lng-this.lng)*e);return 6371008.8*Math.acos(Math.min(i,1))},Wc.prototype.toBounds=function(t){void 0===t&&(t=0);var e=360*t/40075017,r=e/Math.cos(Math.PI/180*this.lat);return new Yc(new Wc(this.lng-r,this.lat-e),new Wc(this.lng+r,this.lat+e))},Wc.convert=function(t){if(t instanceof Wc)return t;if(Array.isArray(t)&&(2===t.length||3===t.length))return new Wc(Number(t[0]),Number(t[1]));if(!Array.isArray(t)&&"object"==typeof t&&null!==t)return new Wc(Number("lng"in t?t.lng:t.lon),Number(t.lat));throw new Error("`LngLatLike` argument must be specified as a LngLat instance, an object {lng: <lng>, lat: <lat>}, an object {lon: <lng>, lat: <lat>}, or an array of [<lng>, <lat>]")};var Xc=2*Math.PI*6371008.8;function Zc(t){return Xc*Math.cos(t*Math.PI/180)}function Jc(t){return(180+t)/360}function Kc(t){return(180-180/Math.PI*Math.log(Math.tan(Math.PI/4+t*Math.PI/360)))/360}function Qc(t,e){return t/Zc(e)}function $c(t){var e=180-360*t;return 360/Math.PI*Math.atan(Math.exp(e*Math.PI/180))-90}var tu=function(t,e,r){void 0===r&&(r=0),this.x=+t,this.y=+e,this.z=+r};tu.fromLngLat=function(t,e){void 0===e&&(e=0);var r=Wc.convert(t);return new tu(Jc(r.lng),Kc(r.lat),Qc(e,r.lat))},tu.prototype.toLngLat=function(){return new Wc(360*this.x-180,$c(this.y))},tu.prototype.toAltitude=function(){return t=this.z,e=this.y,t*Zc($c(e));var t,e},tu.prototype.meterInMercatorCoordinateUnits=function(){return 1/Xc*(t=$c(this.y),1/Math.cos(t*Math.PI/180));var t};var eu=function(t,e,r){this.z=t,this.x=e,this.y=r,this.key=iu(0,t,t,e,r)};eu.prototype.equals=function(t){return this.z===t.z&&this.x===t.x&&this.y===t.y},eu.prototype.url=function(t,e){var r,n,i,a,o,s=(r=this.x,n=this.y,i=this.z,a=Gc(256*r,256*(n=Math.pow(2,i)-n-1),i),o=Gc(256*(r+1),256*(n+1),i),a[0]+","+a[1]+","+o[0]+","+o[1]),l=function(t,e,r){for(var n,i="",a=t;a>0;a--)i+=(e&(n=1<<a-1)?1:0)+(r&n?2:0);return i}(this.z,this.x,this.y);return t[(this.x+this.y)%t.length].replace("{prefix}",(this.x%16).toString(16)+(this.y%16).toString(16)).replace("{z}",String(this.z)).replace("{x}",String(this.x)).replace("{y}",String("tms"===e?Math.pow(2,this.z)-this.y-1:this.y)).replace("{quadkey}",l).replace("{bbox-epsg-3857}",s)},eu.prototype.getTilePoint=function(t){var e=Math.pow(2,this.z);return new i(8192*(t.x*e-this.x),8192*(t.y*e-this.y))},eu.prototype.toString=function(){return this.z+"/"+this.x+"/"+this.y};var ru=function(t,e){this.wrap=t,this.canonical=e,this.key=iu(t,e.z,e.z,e.x,e.y)},nu=function(t,e,r,n,i){this.overscaledZ=t,this.wrap=e,this.canonical=new eu(r,+n,+i),this.key=iu(e,t,r,n,i)};function iu(t,e,r,n,i){(t*=2)<0&&(t=-1*t-1);var a=1<<r;return(a*a*t+a*i+n).toString(36)+r.toString(36)+e.toString(36)}nu.prototype.equals=function(t){return this.overscaledZ===t.overscaledZ&&this.wrap===t.wrap&&this.canonical.equals(t.canonical)},nu.prototype.scaledTo=function(t){var e=this.canonical.z-t;return t>this.canonical.z?new nu(t,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y):new nu(t,this.wrap,t,this.canonical.x>>e,this.canonical.y>>e)},nu.prototype.calculateScaledKey=function(t,e){var r=this.canonical.z-t;return t>this.canonical.z?iu(this.wrap*+e,t,this.canonical.z,this.canonical.x,this.canonical.y):iu(this.wrap*+e,t,t,this.canonical.x>>r,this.canonical.y>>r)},nu.prototype.isChildOf=function(t){if(t.wrap!==this.wrap)return!1;var e=this.canonical.z-t.canonical.z;return 0===t.overscaledZ||t.overscaledZ<this.overscaledZ&&t.canonical.x===this.canonical.x>>e&&t.canonical.y===this.canonical.y>>e},nu.prototype.children=function(t){if(this.overscaledZ>=t)return[new nu(this.overscaledZ+1,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y)];var e=this.canonical.z+1,r=2*this.canonical.x,n=2*this.canonical.y;return[new nu(e,this.wrap,e,r,n),new nu(e,this.wrap,e,r+1,n),new nu(e,this.wrap,e,r,n+1),new nu(e,this.wrap,e,r+1,n+1)]},nu.prototype.isLessThan=function(t){return this.wrap<t.wrap||!(this.wrap>t.wrap)&&(this.overscaledZ<t.overscaledZ||!(this.overscaledZ>t.overscaledZ)&&(this.canonical.x<t.canonical.x||!(this.canonical.x>t.canonical.x)&&this.canonical.y<t.canonical.y))},nu.prototype.wrapped=function(){return new nu(this.overscaledZ,0,this.canonical.z,this.canonical.x,this.canonical.y)},nu.prototype.unwrapTo=function(t){return new nu(this.overscaledZ,t,this.canonical.z,this.canonical.x,this.canonical.y)},nu.prototype.overscaleFactor=function(){return Math.pow(2,this.overscaledZ-this.canonical.z)},nu.prototype.toUnwrapped=function(){return new ru(this.wrap,this.canonical)},nu.prototype.toString=function(){return this.overscaledZ+"/"+this.canonical.x+"/"+this.canonical.y},nu.prototype.getTilePoint=function(t){return this.canonical.getTilePoint(new tu(t.x-this.wrap,t.y))},Nn("CanonicalTileID",eu),Nn("OverscaledTileID",nu,{omit:["posMatrix"]});var au=function(t,e,r){if(this.uid=t,e.height!==e.width)throw new RangeError("DEM tiles must be square");if(r&&"mapbox"!==r&&"terrarium"!==r)return _('"'+r+'" is not a valid encoding type. Valid types include "mapbox" and "terrarium".');this.stride=e.height;var n=this.dim=e.height-2;this.data=new Uint32Array(e.data.buffer),this.encoding=r||"mapbox";for(var i=0;i<n;i++)this.data[this._idx(-1,i)]=this.data[this._idx(0,i)],this.data[this._idx(n,i)]=this.data[this._idx(n-1,i)],this.data[this._idx(i,-1)]=this.data[this._idx(i,0)],this.data[this._idx(i,n)]=this.data[this._idx(i,n-1)];this.data[this._idx(-1,-1)]=this.data[this._idx(0,0)],this.data[this._idx(n,-1)]=this.data[this._idx(n-1,0)],this.data[this._idx(-1,n)]=this.data[this._idx(0,n-1)],this.data[this._idx(n,n)]=this.data[this._idx(n-1,n-1)]};au.prototype.get=function(t,e){var r=new Uint8Array(this.data.buffer),n=4*this._idx(t,e);return("terrarium"===this.encoding?this._unpackTerrarium:this._unpackMapbox)(r[n],r[n+1],r[n+2])},au.prototype.getUnpackVector=function(){return"terrarium"===this.encoding?[256,1,1/256,32768]:[6553.6,25.6,.1,1e4]},au.prototype._idx=function(t,e){if(t<-1||t>=this.dim+1||e<-1||e>=this.dim+1)throw new RangeError("out of range source coordinates for DEM data");return(e+1)*this.stride+(t+1)},au.prototype._unpackMapbox=function(t,e,r){return(256*t*256+256*e+r)/10-1e4},au.prototype._unpackTerrarium=function(t,e,r){return 256*t+e+r/256-32768},au.prototype.getPixels=function(){return new Eo({width:this.stride,height:this.stride},new Uint8Array(this.data.buffer))},au.prototype.backfillBorder=function(t,e,r){if(this.dim!==t.dim)throw new Error("dem dimension mismatch");var n=e*this.dim,i=e*this.dim+this.dim,a=r*this.dim,o=r*this.dim+this.dim;switch(e){case-1:n=i-1;break;case 1:i=n+1}switch(r){case-1:a=o-1;break;case 1:o=a+1}for(var s=-e*this.dim,l=-r*this.dim,c=a;c<o;c++)for(var u=n;u<i;u++)this.data[this._idx(u,c)]=t.data[this._idx(u+s,c+l)]},Nn("DEMData",au);var ou=function(t){this._stringToNumber={},this._numberToString=[];for(var e=0;e<t.length;e++){var r=t[e];this._stringToNumber[r]=e,this._numberToString[e]=r}};ou.prototype.encode=function(t){return this._stringToNumber[t]},ou.prototype.decode=function(t){return this._numberToString[t]};var su=function(t,e,r,n,i){this.type="Feature",this._vectorTileFeature=t,t._z=e,t._x=r,t._y=n,this.properties=t.properties,this.id=i},lu={geometry:{configurable:!0}};lu.geometry.get=function(){return void 0===this._geometry&&(this._geometry=this._vectorTileFeature.toGeoJSON(this._vectorTileFeature._x,this._vectorTileFeature._y,this._vectorTileFeature._z).geometry),this._geometry},lu.geometry.set=function(t){this._geometry=t},su.prototype.toJSON=function(){var t={geometry:this.geometry};for(var e in this)"_geometry"!==e&&"_vectorTileFeature"!==e&&(t[e]=this[e]);return t},Object.defineProperties(su.prototype,lu);var cu=function(){this.state={},this.stateChanges={},this.deletedStates={}};cu.prototype.updateState=function(t,e,r){var n=String(e);if(this.stateChanges[t]=this.stateChanges[t]||{},this.stateChanges[t][n]=this.stateChanges[t][n]||{},u(this.stateChanges[t][n],r),null===this.deletedStates[t])for(var i in this.deletedStates[t]={},this.state[t])i!==n&&(this.deletedStates[t][i]=null);else if(this.deletedStates[t]&&null===this.deletedStates[t][n])for(var a in this.deletedStates[t][n]={},this.state[t][n])r[a]||(this.deletedStates[t][n][a]=null);else for(var o in r){this.deletedStates[t]&&this.deletedStates[t][n]&&null===this.deletedStates[t][n][o]&&delete this.deletedStates[t][n][o]}},cu.prototype.removeFeatureState=function(t,e,r){if(!(null===this.deletedStates[t])){var n=String(e);if(this.deletedStates[t]=this.deletedStates[t]||{},r&&void 0!==e)null!==this.deletedStates[t][n]&&(this.deletedStates[t][n]=this.deletedStates[t][n]||{},this.deletedStates[t][n][r]=null);else if(void 0!==e){if(this.stateChanges[t]&&this.stateChanges[t][n])for(r in this.deletedStates[t][n]={},this.stateChanges[t][n])this.deletedStates[t][n][r]=null;else this.deletedStates[t][n]=null}else this.deletedStates[t]=null}},cu.prototype.getState=function(t,e){var r=String(e),n=this.state[t]||{},i=this.stateChanges[t]||{},a=u({},n[r],i[r]);if(null===this.deletedStates[t])return{};if(this.deletedStates[t]){var o=this.deletedStates[t][e];if(null===o)return{};for(var s in o)delete a[s]}return a},cu.prototype.initializeTileState=function(t,e){t.setFeatureState(this.state,e)},cu.prototype.coalesceChanges=function(t,e){var r={};for(var n in this.stateChanges){this.state[n]=this.state[n]||{};var i={};for(var a in this.stateChanges[n])this.state[n][a]||(this.state[n][a]={}),u(this.state[n][a],this.stateChanges[n][a]),i[a]=this.state[n][a];r[n]=i}for(var o in this.deletedStates){this.state[o]=this.state[o]||{};var s={};if(null===this.deletedStates[o])for(var l in this.state[o])s[l]={},this.state[o][l]={};else for(var c in this.deletedStates[o]){if(null===this.deletedStates[o][c])this.state[o][c]={};else for(var f=0,h=Object.keys(this.deletedStates[o][c]);f<h.length;f+=1){var p=h[f];delete this.state[o][c][p]}s[c]=this.state[o][c]}r[o]=r[o]||{},u(r[o],s)}if(this.stateChanges={},this.deletedStates={},0!==Object.keys(r).length)for(var d in t){t[d].setFeatureState(r,e)}};var uu=function(t,e){this.tileID=t,this.x=t.canonical.x,this.y=t.canonical.y,this.z=t.canonical.z,this.grid=new zn(8192,16,0),this.grid3D=new zn(8192,16,0),this.featureIndexArray=new fa,this.promoteId=e};function fu(t,e,r,n,i){return v(t,(function(t,a){var o=e instanceof _i?e.get(a):null;return o&&o.evaluate?o.evaluate(r,n,i):o}))}function hu(t){for(var e=1/0,r=1/0,n=-1/0,i=-1/0,a=0,o=t;a<o.length;a+=1){var s=o[a];e=Math.min(e,s.x),r=Math.min(r,s.y),n=Math.max(n,s.x),i=Math.max(i,s.y)}return{minX:e,minY:r,maxX:n,maxY:i}}function pu(t,e){return e-t}uu.prototype.insert=function(t,e,r,n,i,a){var o=this.featureIndexArray.length;this.featureIndexArray.emplaceBack(r,n,i);for(var s=a?this.grid3D:this.grid,l=0;l<e.length;l++){for(var c=e[l],u=[1/0,1/0,-1/0,-1/0],f=0;f<c.length;f++){var h=c[f];u[0]=Math.min(u[0],h.x),u[1]=Math.min(u[1],h.y),u[2]=Math.max(u[2],h.x),u[3]=Math.max(u[3],h.y)}u[0]<8192&&u[1]<8192&&u[2]>=0&&u[3]>=0&&s.insert(o,u[0],u[1],u[2],u[3])}},uu.prototype.loadVTLayers=function(){return this.vtLayers||(this.vtLayers=new Ls.VectorTile(new al(this.rawTileData)).layers,this.sourceLayerCoder=new ou(this.vtLayers?Object.keys(this.vtLayers).sort():["_geojsonTileLayer"])),this.vtLayers},uu.prototype.query=function(t,e,r,n){var a=this;this.loadVTLayers();for(var o=t.params||{},s=8192/t.tileSize/t.scale,l=sn(o.filter),c=t.queryGeometry,u=t.queryPadding*s,f=hu(c),h=this.grid.query(f.minX-u,f.minY-u,f.maxX+u,f.maxY+u),p=hu(t.cameraQueryGeometry),d=this.grid3D.query(p.minX-u,p.minY-u,p.maxX+u,p.maxY+u,(function(e,r,n,a){return function(t,e,r,n,a){for(var o=0,s=t;o<s.length;o+=1){var l=s[o];if(e<=l.x&&r<=l.y&&n>=l.x&&a>=l.y)return!0}var c=[new i(e,r),new i(e,a),new i(n,a),new i(n,r)];if(t.length>2)for(var u=0,f=c;u<f.length;u+=1){if(io(t,f[u]))return!0}for(var h=0;h<t.length-1;h++){if(ao(t[h],t[h+1],c))return!0}return!1}(t.cameraQueryGeometry,e-u,r-u,n+u,a+u)})),g=0,m=d;g<m.length;g+=1){var v=m[g];h.push(v)}h.sort(pu);for(var y,x={},b=function(i){var u=h[i];if(u!==y){y=u;var f=a.featureIndexArray.get(u),p=null;a.loadMatchingFeature(x,f.bucketIndex,f.sourceLayerIndex,f.featureIndex,l,o.layers,o.availableImages,e,r,n,(function(e,r,n){return p||(p=Ya(e)),r.queryIntersectsFeature(c,e,n,p,a.z,t.transform,s,t.pixelPosMatrix)}))}},_=0;_<h.length;_++)b(_);return x},uu.prototype.loadMatchingFeature=function(t,e,r,n,i,a,o,s,l,c,u){var f=this.bucketLayerIDs[e];if(!a||function(t,e){for(var r=0;r<t.length;r++)if(e.indexOf(t[r])>=0)return!0;return!1}(a,f)){var h=this.sourceLayerCoder.decode(r),p=this.vtLayers[h].feature(n);if(i.filter(new pi(this.tileID.overscaledZ),p))for(var d=this.getId(p,h),g=0;g<f.length;g++){var m=f[g];if(!(a&&a.indexOf(m)<0)){var v=s[m];if(v){var y={};void 0!==d&&c&&(y=c.getState(v.sourceLayer||"_geojsonTileLayer",d));var x=l[m];x.paint=fu(x.paint,v.paint,p,y,o),x.layout=fu(x.layout,v.layout,p,y,o);var b=!u||u(p,v,y);if(b){var _=new su(p,this.z,this.x,this.y,d);_.layer=x;var w=t[m];void 0===w&&(w=t[m]=[]),w.push({featureIndex:n,feature:_,intersectionZ:b})}}}}}},uu.prototype.lookupSymbolFeatures=function(t,e,r,n,i,a,o,s){var l={};this.loadVTLayers();for(var c=sn(i),u=0,f=t;u<f.length;u+=1){var h=f[u];this.loadMatchingFeature(l,r,n,h,c,a,o,s,e)}return l},uu.prototype.hasLayer=function(t){for(var e=0,r=this.bucketLayerIDs;e<r.length;e+=1)for(var n=0,i=r[e];n<i.length;n+=1){if(t===i[n])return!0}return!1},uu.prototype.getId=function(t,e){var r=t.id;if(this.promoteId){var n="string"==typeof this.promoteId?this.promoteId:this.promoteId[e];"boolean"==typeof(r=t.properties[n])&&(r=Number(r))}return r},Nn("FeatureIndex",uu,{omit:["rawTileData","sourceLayerCoder"]});var du=function(t,e){this.tileID=t,this.uid=h(),this.uses=0,this.tileSize=e,this.buckets={},this.expirationTime=null,this.queryPadding=0,this.hasSymbolBuckets=!1,this.hasRTLText=!1,this.dependencies={},this.expiredRequestCount=0,this.state="loading"};du.prototype.registerFadeDuration=function(t){var e=t+this.timeAdded;e<R.now()||this.fadeEndTime&&e<this.fadeEndTime||(this.fadeEndTime=e)},du.prototype.wasRequested=function(){return"errored"===this.state||"loaded"===this.state||"reloading"===this.state},du.prototype.loadVectorData=function(t,e,r){if(this.hasData()&&this.unloadVectorData(),this.state="loaded",t){for(var n in t.featureIndex&&(this.latestFeatureIndex=t.featureIndex,t.rawTileData?(this.latestRawTileData=t.rawTileData,this.latestFeatureIndex.rawTileData=t.rawTileData):this.latestRawTileData&&(this.latestFeatureIndex.rawTileData=this.latestRawTileData)),this.collisionBoxArray=t.collisionBoxArray,this.buckets=function(t,e){var r={};if(!e)return r;for(var n=function(){var t=a[i],n=t.layerIds.map((function(t){return e.getLayer(t)})).filter(Boolean);if(0!==n.length){t.layers=n,t.stateDependentLayerIds&&(t.stateDependentLayers=t.stateDependentLayerIds.map((function(t){return n.filter((function(e){return e.id===t}))[0]})));for(var o=0,s=n;o<s.length;o+=1){var l=s[o];r[l.id]=t}}},i=0,a=t;i<a.length;i+=1)n();return r}(t.buckets,e.style),this.hasSymbolBuckets=!1,this.buckets){var i=this.buckets[n];if(i instanceof Mc){if(this.hasSymbolBuckets=!0,!r)break;i.justReloaded=!0}}if(this.hasRTLText=!1,this.hasSymbolBuckets)for(var a in this.buckets){var o=this.buckets[a];if(o instanceof Mc&&o.hasRTLText){this.hasRTLText=!0,hi.isLoading()||hi.isLoaded()||"deferred"!==ui()||fi();break}}for(var s in this.queryPadding=0,this.buckets){var l=this.buckets[s];this.queryPadding=Math.max(this.queryPadding,e.style.getLayer(s).queryRadius(l))}t.imageAtlas&&(this.imageAtlas=t.imageAtlas),t.glyphAtlasImage&&(this.glyphAtlasImage=t.glyphAtlasImage)}else this.collisionBoxArray=new na},du.prototype.unloadVectorData=function(){for(var t in this.buckets)this.buckets[t].destroy();this.buckets={},this.imageAtlasTexture&&this.imageAtlasTexture.destroy(),this.imageAtlas&&(this.imageAtlas=null),this.glyphAtlasTexture&&this.glyphAtlasTexture.destroy(),this.latestFeatureIndex=null,this.state="unloaded"},du.prototype.getBucket=function(t){return this.buckets[t.id]},du.prototype.upload=function(t){for(var e in this.buckets){var r=this.buckets[e];r.uploadPending()&&r.upload(t)}var n=t.gl;this.imageAtlas&&!this.imageAtlas.uploaded&&(this.imageAtlasTexture=new Vc(t,this.imageAtlas.image,n.RGBA),this.imageAtlas.uploaded=!0),this.glyphAtlasImage&&(this.glyphAtlasTexture=new Vc(t,this.glyphAtlasImage,n.ALPHA),this.glyphAtlasImage=null)},du.prototype.prepare=function(t){this.imageAtlas&&this.imageAtlas.patchUpdatedImages(t,this.imageAtlasTexture)},du.prototype.queryRenderedFeatures=function(t,e,r,n,i,a,o,s,l,c){return this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData?this.latestFeatureIndex.query({queryGeometry:n,cameraQueryGeometry:i,scale:a,tileSize:this.tileSize,pixelPosMatrix:c,transform:s,params:o,queryPadding:this.queryPadding*l},t,e,r):{}},du.prototype.querySourceFeatures=function(t,e){var r=this.latestFeatureIndex;if(r&&r.rawTileData){var n=r.loadVTLayers(),i=e?e.sourceLayer:"",a=n._geojsonTileLayer||n[i];if(a)for(var o=sn(e&&e.filter),s=this.tileID.canonical,l=s.z,c=s.x,u=s.y,f={z:l,x:c,y:u},h=0;h<a.length;h++){var p=a.feature(h);if(o.filter(new pi(this.tileID.overscaledZ),p)){var d=r.getId(p,i),g=new su(p,l,c,u,d);g.tile=f,t.push(g)}}}},du.prototype.hasData=function(){return"loaded"===this.state||"reloading"===this.state||"expired"===this.state},du.prototype.patternsLoaded=function(){return this.imageAtlas&&!!Object.keys(this.imageAtlas.patternPositions).length},du.prototype.setExpiryData=function(t){var e=this.expirationTime;if(t.cacheControl){var r=A(t.cacheControl);r["max-age"]&&(this.expirationTime=Date.now()+1e3*r["max-age"])}else t.expires&&(this.expirationTime=new Date(t.expires).getTime());if(this.expirationTime){var n=Date.now(),i=!1;if(this.expirationTime>n)i=!1;else if(e)if(this.expirationTime<e)i=!0;else{var a=this.expirationTime-e;a?this.expirationTime=n+Math.max(a,3e4):i=!0}else i=!0;i?(this.expiredRequestCount++,this.state="expired"):this.expiredRequestCount=0}},du.prototype.getExpiryTimeout=function(){if(this.expirationTime)return this.expiredRequestCount?1e3*(1<<Math.min(this.expiredRequestCount-1,31)):Math.min(this.expirationTime-(new Date).getTime(),Math.pow(2,31)-1)},du.prototype.setFeatureState=function(t,e){if(this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData&&0!==Object.keys(t).length){var r=this.latestFeatureIndex.loadVTLayers();for(var n in this.buckets)if(e.style.hasLayer(n)){var i=this.buckets[n],a=i.layers[0].sourceLayer||"_geojsonTileLayer",o=r[a],s=t[a];if(o&&s&&0!==Object.keys(s).length){i.update(s,o,this.imageAtlas&&this.imageAtlas.patternPositions||{});var l=e&&e.style&&e.style.getLayer(n);l&&(this.queryPadding=Math.max(this.queryPadding,l.queryRadius(i)))}}}},du.prototype.holdingForFade=function(){return void 0!==this.symbolFadeHoldUntil},du.prototype.symbolFadeFinished=function(){return!this.symbolFadeHoldUntil||this.symbolFadeHoldUntil<R.now()},du.prototype.clearFadeHold=function(){this.symbolFadeHoldUntil=void 0},du.prototype.setHoldDuration=function(t){this.symbolFadeHoldUntil=R.now()+t},du.prototype.setDependencies=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1){r[i[n]]=!0}this.dependencies[t]=r},du.prototype.hasDependency=function(t,e){for(var r=0,n=t;r<n.length;r+=1){var i=n[r],a=this.dependencies[i];if(a)for(var o=0,s=e;o<s.length;o+=1){if(a[s[o]])return!0}}return!1};var gu=self.performance,mu=function(t){this._marks={start:[t.url,"start"].join("#"),end:[t.url,"end"].join("#"),measure:t.url.toString()},gu.mark(this._marks.start)};mu.prototype.finish=function(){gu.mark(this._marks.end);var t=gu.getEntriesByName(this._marks.measure);return 0===t.length&&(gu.measure(this._marks.measure,this._marks.start,this._marks.end),t=gu.getEntriesByName(this._marks.measure),gu.clearMarks(this._marks.start),gu.clearMarks(this._marks.end),gu.clearMeasures(this._marks.measure)),t},t.Actor=qc,t.AlphaImage=So,t.CanonicalTileID=eu,t.CollisionBoxArray=na,t.Color=te,t.DEMData=au,t.DataConstantProperty=wi,t.DictionaryCoder=ou,t.EXTENT=8192,t.ErrorEvent=St,t.EvaluationParameters=pi,t.Event=Mt,t.Evented=Et,t.FeatureIndex=uu,t.FillBucket=ms,t.FillExtrusionBucket=Os,t.ImageAtlas=Ll,t.ImagePosition=Sl,t.LineBucket=qs,t.LngLat=Wc,t.LngLatBounds=Yc,t.MercatorCoordinate=tu,t.ONE_EM=24,t.OverscaledTileID=nu,t.Point=i,t.Point$1=i,t.Properties=Si,t.Protobuf=al,t.RGBAImage=Eo,t.RequestManager=H,t.RequestPerformance=mu,t.ResourceType=dt,t.SegmentVector=pa,t.SourceFeatureState=cu,t.StructArrayLayout1ui2=$i,t.StructArrayLayout2f1f2i16=qi,t.StructArrayLayout2i4=zi,t.StructArrayLayout3ui6=Yi,t.StructArrayLayout4i8=Di,t.SymbolBucket=Mc,t.Texture=Vc,t.Tile=du,t.Transitionable=mi,t.Uniform1f=Sa,t.Uniform1i=Ma,t.Uniform2f=Ea,t.Uniform3f=La,t.Uniform4f=Ca,t.UniformColor=Pa,t.UniformMatrix4f=Oa,t.UnwrappedTileID=ru,t.ValidationError=Ct,t.WritingMode=Cl,t.ZoomHistory=Gn,t.add=function(t,e,r){return t[0]=e[0]+r[0],t[1]=e[1]+r[1],t[2]=e[2]+r[2],t},t.addDynamicAttributes=wc,t.asyncAll=function(t,e,r){if(!t.length)return r(null,[]);var n=t.length,i=new Array(t.length),a=null;t.forEach((function(t,o){e(t,(function(t,e){t&&(a=t),i[o]=e,0==--n&&r(a,i)}))}))},t.bezier=o,t.bindAll=g,t.browser=R,t.cacheEntryPossiblyAdded=function(t){++ht>ot&&(t.getActor().send("enforceCacheSizeLimit",at),ht=0)},t.clamp=l,t.clearTileCache=function(t){var e=self.caches.delete("mapbox-tiles");t&&e.catch(t).then((function(){return t()}))},t.clipLine=ec,t.clone=function(t){var e=new fo(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e},t.clone$1=x,t.clone$2=function(t){var e=new fo(3);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e},t.collisionCircleLayout=tl,t.config=F,t.create=function(){var t=new fo(16);return fo!=Float32Array&&(t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[11]=0,t[12]=0,t[13]=0,t[14]=0),t[0]=1,t[5]=1,t[10]=1,t[15]=1,t},t.create$1=function(){var t=new fo(9);return fo!=Float32Array&&(t[1]=0,t[2]=0,t[3]=0,t[5]=0,t[6]=0,t[7]=0),t[0]=1,t[4]=1,t[8]=1,t},t.create$2=function(){var t=new fo(4);return fo!=Float32Array&&(t[1]=0,t[2]=0),t[0]=1,t[3]=1,t},t.createCommonjsModule=e,t.createExpression=Wr,t.createLayout=Ii,t.createStyleLayer=function(t){return"custom"===t.type?new Dc(t):new Rc[t.type](t)},t.cross=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=r[0],s=r[1],l=r[2];return t[0]=i*l-a*s,t[1]=a*o-n*l,t[2]=n*s-i*o,t},t.deepEqual=function t(e,r){if(Array.isArray(e)){if(!Array.isArray(r)||e.length!==r.length)return!1;for(var n=0;n<e.length;n++)if(!t(e[n],r[n]))return!1;return!0}if("object"==typeof e&&null!==e&&null!==r){if("object"!=typeof r)return!1;if(Object.keys(e).length!==Object.keys(r).length)return!1;for(var i in e)if(!t(e[i],r[i]))return!1;return!0}return e===r},t.dot=function(t,e){return t[0]*e[0]+t[1]*e[1]+t[2]*e[2]},t.dot$1=function(t,e){return t[0]*e[0]+t[1]*e[1]+t[2]*e[2]+t[3]*e[3]},t.ease=s,t.emitValidationErrors=On,t.endsWith=m,t.enforceCacheSizeLimit=function(t){st(),Q&&Q.then((function(e){e.keys().then((function(r){for(var n=0;n<r.length-t;n++)e.delete(r[n])}))}))},t.evaluateSizeForFeature=Yl,t.evaluateSizeForZoom=Wl,t.evaluateVariableOffset=dc,t.evented=ci,t.extend=u,t.featureFilter=sn,t.filterObject=y,t.fromRotation=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=n,t[1]=r,t[2]=0,t[3]=-r,t[4]=n,t[5]=0,t[6]=0,t[7]=0,t[8]=1,t},t.getAnchorAlignment=Ul,t.getAnchorJustification=gc,t.getArrayBuffer=xt,t.getImage=Tt,t.getJSON=function(t,e){return yt(u(t,{type:"json"}),e)},t.getRTLTextPluginStatus=ui,t.getReferrer=mt,t.getVideo=function(t,e){var r,n,i=self.document.createElement("video");i.muted=!0,i.onloadstart=function(){e(null,i)};for(var a=0;a<t.length;a++){var o=self.document.createElement("source");r=t[a],n=void 0,(n=self.document.createElement("a")).href=r,(n.protocol!==self.document.location.protocol||n.host!==self.document.location.host)&&(i.crossOrigin="Anonymous"),o.src=t[a],i.appendChild(o)}return{cancel:function(){}}},t.identity=ho,t.invert=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],c=e[7],u=e[8],f=e[9],h=e[10],p=e[11],d=e[12],g=e[13],m=e[14],v=e[15],y=r*s-n*o,x=r*l-i*o,b=r*c-a*o,_=n*l-i*s,w=n*c-a*s,T=i*c-a*l,k=u*g-f*d,A=u*m-h*d,M=u*v-p*d,S=f*m-h*g,E=f*v-p*g,L=h*v-p*m,C=y*L-x*E+b*S+_*M-w*A+T*k;return C?(C=1/C,t[0]=(s*L-l*E+c*S)*C,t[1]=(i*E-n*L-a*S)*C,t[2]=(g*T-m*w+v*_)*C,t[3]=(h*w-f*T-p*_)*C,t[4]=(l*M-o*L-c*A)*C,t[5]=(r*L-i*M+a*A)*C,t[6]=(m*b-d*T-v*x)*C,t[7]=(u*T-h*b+p*x)*C,t[8]=(o*E-s*M+c*k)*C,t[9]=(n*M-r*E-a*k)*C,t[10]=(d*w-g*b+v*y)*C,t[11]=(f*b-u*w-p*y)*C,t[12]=(s*A-o*S-l*k)*C,t[13]=(r*S-n*A+i*k)*C,t[14]=(g*x-d*_-m*y)*C,t[15]=(u*_-f*x+h*y)*C,t):null},t.isChar=Yn,t.isMapboxURL=q,t.keysDifference=function(t,e){var r=[];for(var n in t)n in e||r.push(n);return r},t.makeRequest=yt,t.mapObject=v,t.mercatorXfromLng=Jc,t.mercatorYfromLat=Kc,t.mercatorZfromAltitude=Qc,t.mul=go,t.multiply=po,t.mvt=Ls,t.normalize=function(t,e){var r=e[0],n=e[1],i=e[2],a=r*r+n*n+i*i;return a>0&&(a=1/Math.sqrt(a)),t[0]=e[0]*a,t[1]=e[1]*a,t[2]=e[2]*a,t},t.number=qe,t.offscreenCanvasSupported=pt,t.ortho=function(t,e,r,n,i,a,o){var s=1/(e-r),l=1/(n-i),c=1/(a-o);return t[0]=-2*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*l,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*c,t[11]=0,t[12]=(e+r)*s,t[13]=(i+n)*l,t[14]=(o+a)*c,t[15]=1,t},t.parseGlyphPBF=function(t){return new al(t).readFields(Tl,[])},t.pbf=al,t.performSymbolLayout=function(t,e,r,n,i,a,o){t.createArrays();var s=512*t.overscaling;t.tilePixelRatio=8192/s,t.compareText={},t.iconsNeedLinear=!1;var l=t.layers[0].layout,c=t.layers[0]._unevaluatedLayout._values,u={};if("composite"===t.textSizeData.kind){var f=t.textSizeData,h=f.minZoom,p=f.maxZoom;u.compositeTextSizes=[c["text-size"].possiblyEvaluate(new 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i,a=t[n],o=a.type;if("Point"===o||"MultiPoint"===o||"LineString"===o)i=gt(a.geometry,e);else if("MultiLineString"===o||"Polygon"===o){i=[];for(var s=0;s<a.geometry.length;s++)i.push(gt(a.geometry[s],e))}else if("MultiPolygon"===o)for(i=[],s=0;s<a.geometry.length;s++){for(var l=[],c=0;c<a.geometry[s].length;c++)l.push(gt(a.geometry[s][c],e));i.push(l)}r.push(Q(a.id,o,i,a.tags))}return r}function gt(t,e){var r=[];r.size=t.size,void 0!==t.start&&(r.start=t.start,r.end=t.end);for(var n=0;n<t.length;n+=3)r.push(t[n]+e,t[n+1],t[n+2]);return r}function mt(t,e){if(t.transformed)return t;var r,n,i,a=1<<t.z,o=t.x,s=t.y;for(r=0;r<t.features.length;r++){var l=t.features[r],c=l.geometry,u=l.type;if(l.geometry=[],1===u)for(n=0;n<c.length;n+=2)l.geometry.push(vt(c[n],c[n+1],e,a,o,s));else for(n=0;n<c.length;n++){var f=[];for(i=0;i<c[n].length;i+=2)f.push(vt(c[n][i],c[n][i+1],e,a,o,s));l.geometry.push(f)}}return t.transformed=!0,t}function vt(t,e,r,n,i,a){return[Math.round(r*(t*n-i)),Math.round(r*(e*n-a))]}function yt(t,e,r,n,i){for(var a=e===i.maxZoom?0:i.tolerance/((1<<e)*i.extent),o={features:[],numPoints:0,numSimplified:0,numFeatures:0,source:null,x:r,y:n,z:e,transformed:!1,minX:2,minY:1,maxX:-1,maxY:0},s=0;s<t.length;s++){o.numFeatures++,xt(o,t[s],a,i);var l=t[s].minX,c=t[s].minY,u=t[s].maxX,f=t[s].maxY;l<o.minX&&(o.minX=l),c<o.minY&&(o.minY=c),u>o.maxX&&(o.maxX=u),f>o.maxY&&(o.maxY=f)}return o}function xt(t,e,r,n){var i=e.geometry,a=e.type,o=[];if("Point"===a||"MultiPoint"===a)for(var s=0;s<i.length;s+=3)o.push(i[s]),o.push(i[s+1]),t.numPoints++,t.numSimplified++;else if("LineString"===a)bt(o,i,t,r,!1,!1);else if("MultiLineString"===a||"Polygon"===a)for(s=0;s<i.length;s++)bt(o,i[s],t,r,"Polygon"===a,0===s);else if("MultiPolygon"===a)for(var l=0;l<i.length;l++){var c=i[l];for(s=0;s<c.length;s++)bt(o,c[s],t,r,!0,0===s)}if(o.length){var u=e.tags||null;if("LineString"===a&&n.lineMetrics){for(var f in u={},e.tags)u[f]=e.tags[f];u.mapbox_clip_start=i.start/i.size,u.mapbox_clip_end=i.end/i.size}var h={geometry:o,type:"Polygon"===a||"MultiPolygon"===a?3:"LineString"===a||"MultiLineString"===a?2:1,tags:u};null!==e.id&&(h.id=e.id),t.features.push(h)}}function bt(t,e,r,n,i,a){var o=n*n;if(n>0&&e.size<(i?o:n))r.numPoints+=e.length/3;else{for(var s=[],l=0;l<e.length;l+=3)(0===n||e[l+2]>o)&&(r.numSimplified++,s.push(e[l]),s.push(e[l+1])),r.numPoints++;i&&function(t,e){for(var r=0,n=0,i=t.length,a=i-2;n<i;a=n,n+=2)r+=(t[n]-t[a])*(t[n+1]+t[a+1]);if(r>0===e)for(n=0,i=t.length;n<i/2;n+=2){var o=t[n],s=t[n+1];t[n]=t[i-2-n],t[n+1]=t[i-1-n],t[i-2-n]=o,t[i-1-n]=s}}(s,a),t.push(s)}}function _t(t,e){var r=(e=this.options=function(t,e){for(var r in e)t[r]=e[r];return t}(Object.create(this.options),e)).debug;if(r&&console.time("preprocess data"),e.maxZoom<0||e.maxZoom>24)throw new Error("maxZoom should be in the 0-24 range");if(e.promoteId&&e.generateId)throw new Error("promoteId and generateId cannot be used together.");var n=function(t,e){var r=[];if("FeatureCollection"===t.type)for(var n=0;n<t.features.length;n++)tt(r,t.features[n],e,n);else"Feature"===t.type?tt(r,t,e):tt(r,{geometry:t},e);return r}(t,e);this.tiles={},this.tileCoords=[],r&&(console.timeEnd("preprocess data"),console.log("index: maxZoom: %d, maxPoints: %d",e.indexMaxZoom,e.indexMaxPoints),console.time("generate tiles"),this.stats={},this.total=0),(n=function(t,e){var r=e.buffer/e.extent,n=t,i=ot(t,1,-1-r,r,0,-1,2,e),a=ot(t,1,1-r,2+r,0,-1,2,e);return(i||a)&&(n=ot(t,1,-r,1+r,0,-1,2,e)||[],i&&(n=dt(i,1).concat(n)),a&&(n=n.concat(dt(a,-1)))),n}(n,e)).length&&this.splitTile(n,0,0,0),r&&(n.length&&console.log("features: %d, points: %d",this.tiles[0].numFeatures,this.tiles[0].numPoints),console.timeEnd("generate tiles"),console.log("tiles generated:",this.total,JSON.stringify(this.stats)))}function wt(t,e,r){return 32*((1<<t)*r+e)+t}function Tt(t,e){var r=t.tileID.canonical;if(!this._geoJSONIndex)return e(null,null);var n=this._geoJSONIndex.getTile(r.z,r.x,r.y);if(!n)return e(null,null);var i=new m(n.features),a=_(i);0===a.byteOffset&&a.byteLength===a.buffer.byteLength||(a=new Uint8Array(a)),e(null,{vectorTile:i,rawData:a.buffer})}U.prototype.load=function(t){var e=this.options,r=e.log,n=e.minZoom,i=e.maxZoom,a=e.nodeSize;r&&console.time("total time");var o="prepare "+t.length+" points";r&&console.time(o),this.points=t;for(var s=[],l=0;l<t.length;l++)t[l].geometry&&s.push(H(t[l],l));this.trees[i+1]=new N(s,Z,J,a,Float32Array),r&&console.timeEnd(o);for(var c=i;c>=n;c--){var u=+Date.now();s=this._cluster(s,c),this.trees[c]=new N(s,Z,J,a,Float32Array),r&&console.log("z%d: %d clusters in %dms",c,s.length,+Date.now()-u)}return r&&console.timeEnd("total time"),this},U.prototype.getClusters=function(t,e){var r=((t[0]+180)%360+360)%360-180,n=Math.max(-90,Math.min(90,t[1])),i=180===t[2]?180:((t[2]+180)%360+360)%360-180,a=Math.max(-90,Math.min(90,t[3]));if(t[2]-t[0]>=360)r=-180,i=180;else if(r>i){var o=this.getClusters([r,n,180,a],e),s=this.getClusters([-180,n,i,a],e);return o.concat(s)}for(var l=this.trees[this._limitZoom(e)],c=[],u=0,f=l.range(Y(r),W(a),Y(i),W(n));u<f.length;u+=1){var h=f[u],p=l.points[h];c.push(p.numPoints?q(p):this.points[p.index])}return c},U.prototype.getChildren=function(t){var e=this._getOriginId(t),r=this._getOriginZoom(t),n="No cluster with the specified id.",i=this.trees[r];if(!i)throw new Error(n);var a=i.points[e];if(!a)throw new Error(n);for(var o=this.options.radius/(this.options.extent*Math.pow(2,r-1)),s=[],l=0,c=i.within(a.x,a.y,o);l<c.length;l+=1){var u=c[l],f=i.points[u];f.parentId===t&&s.push(f.numPoints?q(f):this.points[f.index])}if(0===s.length)throw new Error(n);return s},U.prototype.getLeaves=function(t,e,r){e=e||10,r=r||0;var n=[];return this._appendLeaves(n,t,e,r,0),n},U.prototype.getTile=function(t,e,r){var n=this.trees[this._limitZoom(t)],i=Math.pow(2,t),a=this.options,o=a.extent,s=a.radius/o,l=(r-s)/i,c=(r+1+s)/i,u={features:[]};return this._addTileFeatures(n.range((e-s)/i,l,(e+1+s)/i,c),n.points,e,r,i,u),0===e&&this._addTileFeatures(n.range(1-s/i,l,1,c),n.points,i,r,i,u),e===i-1&&this._addTileFeatures(n.range(0,l,s/i,c),n.points,-1,r,i,u),u.features.length?u:null},U.prototype.getClusterExpansionZoom=function(t){for(var e=this._getOriginZoom(t)-1;e<=this.options.maxZoom;){var r=this.getChildren(t);if(e++,1!==r.length)break;t=r[0].properties.cluster_id}return e},U.prototype._appendLeaves=function(t,e,r,n,i){for(var a=0,o=this.getChildren(e);a<o.length;a+=1){var s=o[a],l=s.properties;if(l&&l.cluster?i+l.point_count<=n?i+=l.point_count:i=this._appendLeaves(t,l.cluster_id,r,n,i):i<n?i++:t.push(s),t.length===r)break}return i},U.prototype._addTileFeatures=function(t,e,r,n,i,a){for(var o=0,s=t;o<s.length;o+=1){var l=e[s[o]],c=l.numPoints,u={type:1,geometry:[[Math.round(this.options.extent*(l.x*i-r)),Math.round(this.options.extent*(l.y*i-n))]],tags:c?G(l):this.points[l.index].properties},f=void 0;c?f=l.id:this.options.generateId?f=l.index:this.points[l.index].id&&(f=this.points[l.index].id),void 0!==f&&(u.id=f),a.features.push(u)}},U.prototype._limitZoom=function(t){return Math.max(this.options.minZoom,Math.min(t,this.options.maxZoom+1))},U.prototype._cluster=function(t,e){for(var r=[],n=this.options,i=n.radius,a=n.extent,o=n.reduce,s=i/(a*Math.pow(2,e)),l=0;l<t.length;l++){var c=t[l];if(!(c.zoom<=e)){c.zoom=e;for(var u=this.trees[e+1],f=u.within(c.x,c.y,s),h=c.numPoints||1,p=c.x*h,d=c.y*h,g=o&&h>1?this._map(c,!0):null,m=(l<<5)+(e+1)+this.points.length,v=0,y=f;v<y.length;v+=1){var x=y[v],b=u.points[x];if(!(b.zoom<=e)){b.zoom=e;var _=b.numPoints||1;p+=b.x*_,d+=b.y*_,h+=_,b.parentId=m,o&&(g||(g=this._map(c,!0)),o(g,this._map(b)))}}1===h?r.push(c):(c.parentId=m,r.push(V(p/h,d/h,m,h,g)))}}return r},U.prototype._getOriginId=function(t){return t-this.points.length>>5},U.prototype._getOriginZoom=function(t){return(t-this.points.length)%32},U.prototype._map=function(t,e){if(t.numPoints)return e?X({},t.properties):t.properties;var r=this.points[t.index].properties,n=this.options.map(r);return e&&n===r?X({},n):n},_t.prototype.options={maxZoom:14,indexMaxZoom:5,indexMaxPoints:1e5,tolerance:3,extent:4096,buffer:64,lineMetrics:!1,promoteId:null,generateId:!1,debug:0},_t.prototype.splitTile=function(t,e,r,n,i,a,o){for(var s=[t,e,r,n],l=this.options,c=l.debug;s.length;){n=s.pop(),r=s.pop(),e=s.pop(),t=s.pop();var u=1<<e,f=wt(e,r,n),h=this.tiles[f];if(!h&&(c>1&&console.time("creation"),h=this.tiles[f]=yt(t,e,r,n,l),this.tileCoords.push({z:e,x:r,y:n}),c)){c>1&&(console.log("tile z%d-%d-%d (features: %d, points: %d, simplified: %d)",e,r,n,h.numFeatures,h.numPoints,h.numSimplified),console.timeEnd("creation"));var p="z"+e;this.stats[p]=(this.stats[p]||0)+1,this.total++}if(h.source=t,i){if(e===l.maxZoom||e===i)continue;var d=1<<i-e;if(r!==Math.floor(a/d)||n!==Math.floor(o/d))continue}else if(e===l.indexMaxZoom||h.numPoints<=l.indexMaxPoints)continue;if(h.source=null,0!==t.length){c>1&&console.time("clipping");var g,m,v,y,x,b,_=.5*l.buffer/l.extent,w=.5-_,T=.5+_,k=1+_;g=m=v=y=null,x=ot(t,u,r-_,r+T,0,h.minX,h.maxX,l),b=ot(t,u,r+w,r+k,0,h.minX,h.maxX,l),t=null,x&&(g=ot(x,u,n-_,n+T,1,h.minY,h.maxY,l),m=ot(x,u,n+w,n+k,1,h.minY,h.maxY,l),x=null),b&&(v=ot(b,u,n-_,n+T,1,h.minY,h.maxY,l),y=ot(b,u,n+w,n+k,1,h.minY,h.maxY,l),b=null),c>1&&console.timeEnd("clipping"),s.push(g||[],e+1,2*r,2*n),s.push(m||[],e+1,2*r,2*n+1),s.push(v||[],e+1,2*r+1,2*n),s.push(y||[],e+1,2*r+1,2*n+1)}}},_t.prototype.getTile=function(t,e,r){var n=this.options,i=n.extent,a=n.debug;if(t<0||t>24)return null;var o=1<<t,s=wt(t,e=(e%o+o)%o,r);if(this.tiles[s])return mt(this.tiles[s],i);a>1&&console.log("drilling down to z%d-%d-%d",t,e,r);for(var l,c=t,u=e,f=r;!l&&c>0;)c--,u=Math.floor(u/2),f=Math.floor(f/2),l=this.tiles[wt(c,u,f)];return l&&l.source?(a>1&&console.log("found parent tile z%d-%d-%d",c,u,f),a>1&&console.time("drilling down"),this.splitTile(l.source,c,u,f,t,e,r),a>1&&console.timeEnd("drilling down"),this.tiles[s]?mt(this.tiles[s],i):null):null};var kt=function(e){function r(t,r,n,i){e.call(this,t,r,n,Tt),i&&(this.loadGeoJSON=i)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadData=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),this._pendingCallback=e,this._pendingLoadDataParams=t,this._state&&"Idle"!==this._state?this._state="NeedsLoadData":(this._state="Coalescing",this._loadData())},r.prototype._loadData=function(){var e=this;if(this._pendingCallback&&this._pendingLoadDataParams){var r=this._pendingCallback,n=this._pendingLoadDataParams;delete this._pendingCallback,delete this._pendingLoadDataParams;var i=!!(n&&n.request&&n.request.collectResourceTiming)&&new t.RequestPerformance(n.request);this.loadGeoJSON(n,(function(a,o){if(a||!o)return r(a);if("object"!=typeof o)return r(new Error("Input data given to '"+n.source+"' is not a valid GeoJSON object."));f(o,!0);try{e._geoJSONIndex=n.cluster?new U(function(e){var r=e.superclusterOptions,n=e.clusterProperties;if(!n||!r)return r;for(var i={},a={},o={accumulated:null,zoom:0},s={properties:null},l=Object.keys(n),c=0,u=l;c<u.length;c+=1){var f=u[c],h=n[f],p=h[0],d=h[1],g=t.createExpression(d),m=t.createExpression("string"==typeof p?[p,["accumulated"],["get",f]]:p);i[f]=g.value,a[f]=m.value}return r.map=function(t){s.properties=t;for(var e={},r=0,n=l;r<n.length;r+=1){var a=n[r];e[a]=i[a].evaluate(o,s)}return e},r.reduce=function(t,e){s.properties=e;for(var r=0,n=l;r<n.length;r+=1){var i=n[r];o.accumulated=t[i],t[i]=a[i].evaluate(o,s)}},r}(n)).load(o.features):function(t,e){return new _t(t,e)}(o,n.geojsonVtOptions)}catch(a){return r(a)}e.loaded={};var s={};if(i){var l=i.finish();l&&(s.resourceTiming={},s.resourceTiming[n.source]=JSON.parse(JSON.stringify(l)))}r(null,s)}))}},r.prototype.coalesce=function(){"Coalescing"===this._state?this._state="Idle":"NeedsLoadData"===this._state&&(this._state="Coalescing",this._loadData())},r.prototype.reloadTile=function(t,r){var n=this.loaded,i=t.uid;return n&&n[i]?e.prototype.reloadTile.call(this,t,r):this.loadTile(t,r)},r.prototype.loadGeoJSON=function(e,r){if(e.request)t.getJSON(e.request,r);else{if("string"!=typeof e.data)return r(new Error("Input data given to '"+e.source+"' is not a valid GeoJSON object."));try{return r(null,JSON.parse(e.data))}catch(t){return r(new Error("Input data given to '"+e.source+"' is not a valid GeoJSON object."))}}},r.prototype.removeSource=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),e()},r.prototype.getClusterExpansionZoom=function(t,e){try{e(null,this._geoJSONIndex.getClusterExpansionZoom(t.clusterId))}catch(t){e(t)}},r.prototype.getClusterChildren=function(t,e){try{e(null,this._geoJSONIndex.getChildren(t.clusterId))}catch(t){e(t)}},r.prototype.getClusterLeaves=function(t,e){try{e(null,this._geoJSONIndex.getLeaves(t.clusterId,t.limit,t.offset))}catch(t){e(t)}},r}(l);var At=function(e){var r=this;this.self=e,this.actor=new t.Actor(e,this),this.layerIndexes={},this.availableImages={},this.workerSourceTypes={vector:l,geojson:kt},this.workerSources={},this.demWorkerSources={},this.self.registerWorkerSource=function(t,e){if(r.workerSourceTypes[t])throw new Error('Worker source with name "'+t+'" already registered.');r.workerSourceTypes[t]=e},this.self.registerRTLTextPlugin=function(e){if(t.plugin.isParsed())throw new Error("RTL text plugin already registered.");t.plugin.applyArabicShaping=e.applyArabicShaping,t.plugin.processBidirectionalText=e.processBidirectionalText,t.plugin.processStyledBidirectionalText=e.processStyledBidirectionalText}};return At.prototype.setReferrer=function(t,e){this.referrer=e},At.prototype.setImages=function(t,e,r){for(var n in this.availableImages[t]=e,this.workerSources[t]){var i=this.workerSources[t][n];for(var a in i)i[a].availableImages=e}r()},At.prototype.setLayers=function(t,e,r){this.getLayerIndex(t).replace(e),r()},At.prototype.updateLayers=function(t,e,r){this.getLayerIndex(t).update(e.layers,e.removedIds),r()},At.prototype.loadTile=function(t,e,r){this.getWorkerSource(t,e.type,e.source).loadTile(e,r)},At.prototype.loadDEMTile=function(t,e,r){this.getDEMWorkerSource(t,e.source).loadTile(e,r)},At.prototype.reloadTile=function(t,e,r){this.getWorkerSource(t,e.type,e.source).reloadTile(e,r)},At.prototype.abortTile=function(t,e,r){this.getWorkerSource(t,e.type,e.source).abortTile(e,r)},At.prototype.removeTile=function(t,e,r){this.getWorkerSource(t,e.type,e.source).removeTile(e,r)},At.prototype.removeDEMTile=function(t,e){this.getDEMWorkerSource(t,e.source).removeTile(e)},At.prototype.removeSource=function(t,e,r){if(this.workerSources[t]&&this.workerSources[t][e.type]&&this.workerSources[t][e.type][e.source]){var n=this.workerSources[t][e.type][e.source];delete this.workerSources[t][e.type][e.source],void 0!==n.removeSource?n.removeSource(e,r):r()}},At.prototype.loadWorkerSource=function(t,e,r){try{this.self.importScripts(e.url),r()}catch(t){r(t.toString())}},At.prototype.syncRTLPluginState=function(e,r,n){try{t.plugin.setState(r);var i=t.plugin.getPluginURL();if(t.plugin.isLoaded()&&!t.plugin.isParsed()&&null!=i){this.self.importScripts(i);var a=t.plugin.isParsed();n(a?void 0:new Error("RTL Text Plugin failed to import scripts from "+i),a)}}catch(t){n(t.toString())}},At.prototype.getAvailableImages=function(t){var e=this.availableImages[t];return e||(e=[]),e},At.prototype.getLayerIndex=function(t){var e=this.layerIndexes[t];return e||(e=this.layerIndexes[t]=new n),e},At.prototype.getWorkerSource=function(t,e,r){var n=this;if(this.workerSources[t]||(this.workerSources[t]={}),this.workerSources[t][e]||(this.workerSources[t][e]={}),!this.workerSources[t][e][r]){var i={send:function(e,r,i){n.actor.send(e,r,i,t)}};this.workerSources[t][e][r]=new this.workerSourceTypes[e](i,this.getLayerIndex(t),this.getAvailableImages(t))}return this.workerSources[t][e][r]},At.prototype.getDEMWorkerSource=function(t,e){return this.demWorkerSources[t]||(this.demWorkerSources[t]={}),this.demWorkerSources[t][e]||(this.demWorkerSources[t][e]=new u),this.demWorkerSources[t][e]},At.prototype.enforceCacheSizeLimit=function(e,r){t.enforceCacheSizeLimit(r)},"undefined"!=typeof WorkerGlobalScope&&void 0!==t.window&&t.window instanceof WorkerGlobalScope&&(t.window.worker=new At(t.window)),At})),n(0,(function(t){var e=t.createCommonjsModule((function(t){function e(t){return!r(t)}function r(t){return"undefined"==typeof window||"undefined"==typeof document?"not a 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e=t.getContext("2d");if(!e)return!1;var r=e.getImageData(0,0,1,1);return r&&r.width===t.width}()?function(t){void 0===n[t]&&(n[t]=function(t){var r=function(t){var r=document.createElement("canvas"),n=Object.create(e.webGLContextAttributes);return n.failIfMajorPerformanceCaveat=t,r.probablySupportsContext?r.probablySupportsContext("webgl",n)||r.probablySupportsContext("experimental-webgl",n):r.supportsContext?r.supportsContext("webgl",n)||r.supportsContext("experimental-webgl",n):r.getContext("webgl",n)||r.getContext("experimental-webgl",n)}(t);if(!r)return!1;var n=r.createShader(r.VERTEX_SHADER);if(!n||r.isContextLost())return!1;return r.shaderSource(n,"void main() {}"),r.compileShader(n),!0===r.getShaderParameter(n,r.COMPILE_STATUS)}(t));return n[t]}(t&&t.failIfMajorPerformanceCaveat)?void 0:"insufficient WebGL support":"insufficient Canvas/getImageData support":"insufficient ArrayBuffer support":"insufficient Uint8ClampedArray support":"insufficient worker support":"insufficient JSON support":"insufficient Object support":"insufficient Function support":"insufficent Array support"}t.exports?t.exports=e:window&&(window.mapboxgl=window.mapboxgl||{},window.mapboxgl.supported=e,window.mapboxgl.notSupportedReason=r);var n={};e.webGLContextAttributes={antialias:!1,alpha:!0,stencil:!0,depth:!0}})),r={create:function(e,r,n){var i=t.window.document.createElement(e);return void 0!==r&&(i.className=r),n&&n.appendChild(i),i},createNS:function(e,r){return t.window.document.createElementNS(e,r)}},n=t.window.document.documentElement.style;function i(t){if(!n)return t[0];for(var e=0;e<t.length;e++)if(t[e]in n)return t[e];return t[0]}var a,o=i(["userSelect","MozUserSelect","WebkitUserSelect","msUserSelect"]);r.disableDrag=function(){n&&o&&(a=n[o],n[o]="none")},r.enableDrag=function(){n&&o&&(n[o]=a)};var s=i(["transform","WebkitTransform"]);r.setTransform=function(t,e){t.style[s]=e};var l=!1;try{var c=Object.defineProperty({},"passive",{get:function(){l=!0}});t.window.addEventListener("test",c,c),t.window.removeEventListener("test",c,c)}catch(t){l=!1}r.addEventListener=function(t,e,r,n){void 0===n&&(n={}),"passive"in n&&l?t.addEventListener(e,r,n):t.addEventListener(e,r,n.capture)},r.removeEventListener=function(t,e,r,n){void 0===n&&(n={}),"passive"in n&&l?t.removeEventListener(e,r,n):t.removeEventListener(e,r,n.capture)};var u=function(e){e.preventDefault(),e.stopPropagation(),t.window.removeEventListener("click",u,!0)};function f(t){var e=t.userImage;if(e&&e.render&&e.render())return t.data.replace(new Uint8Array(e.data.buffer)),!0;return!1}r.suppressClick=function(){t.window.addEventListener("click",u,!0),t.window.setTimeout((function(){t.window.removeEventListener("click",u,!0)}),0)},r.mousePos=function(e,r){var n=e.getBoundingClientRect();return new t.Point(r.clientX-n.left-e.clientLeft,r.clientY-n.top-e.clientTop)},r.touchPos=function(e,r){for(var 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this.images[t]},r.prototype.addImage=function(t,e){this._validate(t,e)&&(this.images[t]=e)},r.prototype._validate=function(e,r){var n=!0;return this._validateStretch(r.stretchX,r.data&&r.data.width)||(this.fire(new t.ErrorEvent(new Error('Image "'+e+'" has invalid "stretchX" value'))),n=!1),this._validateStretch(r.stretchY,r.data&&r.data.height)||(this.fire(new t.ErrorEvent(new Error('Image "'+e+'" has invalid "stretchY" value'))),n=!1),this._validateContent(r.content,r)||(this.fire(new t.ErrorEvent(new Error('Image "'+e+'" has invalid "content" value'))),n=!1),n},r.prototype._validateStretch=function(t,e){if(!t)return!0;for(var r=0,n=0,i=t;n<i.length;n+=1){var a=i[n];if(a[0]<r||a[1]<a[0]||e<a[1])return!1;r=a[1]}return!0},r.prototype._validateContent=function(t,e){return!t||4===t.length&&(!(t[0]<0||e.data.width<t[0])&&(!(t[1]<0||e.data.height<t[1])&&(!(t[2]<0||e.data.width<t[2])&&(!(t[3]<0||e.data.height<t[3])&&(!(t[2]<t[0])&&!(t[3]<t[1]))))))},r.prototype.updateImage=function(t,e){var r=this.images[t];e.version=r.version+1,this.images[t]=e,this.updatedImages[t]=!0},r.prototype.removeImage=function(t){var e=this.images[t];delete this.images[t],delete this.patterns[t],e.userImage&&e.userImage.onRemove&&e.userImage.onRemove()},r.prototype.listImages=function(){return Object.keys(this.images)},r.prototype.getImages=function(t,e){var r=!0;if(!this.isLoaded())for(var n=0,i=t;n<i.length;n+=1){var a=i[n];this.images[a]||(r=!1)}this.isLoaded()||r?this._notify(t,e):this.requestors.push({ids:t,callback:e})},r.prototype._notify=function(e,r){for(var n={},i=0,a=e;i<a.length;i+=1){var o=a[i];this.images[o]||this.fire(new t.Event("styleimagemissing",{id:o}));var s=this.images[o];s?n[o]={data:s.data.clone(),pixelRatio:s.pixelRatio,sdf:s.sdf,version:s.version,stretchX:s.stretchX,stretchY:s.stretchY,content:s.content,hasRenderCallback:Boolean(s.userImage&&s.userImage.render)}:t.warnOnce('Image "'+o+'" could not be loaded. Please make sure you have added the image with map.addImage() or a "sprite" property in your style. 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o.resize({width:i||1,height:a||1}),this.patterns){var l=this.patterns[s].bin,c=l.x+1,u=l.y+1,f=this.images[s].data,h=f.width,p=f.height;t.RGBAImage.copy(f,o,{x:0,y:0},{x:c,y:u},{width:h,height:p}),t.RGBAImage.copy(f,o,{x:0,y:p-1},{x:c,y:u-1},{width:h,height:1}),t.RGBAImage.copy(f,o,{x:0,y:0},{x:c,y:u+p},{width:h,height:1}),t.RGBAImage.copy(f,o,{x:h-1,y:0},{x:c-1,y:u},{width:1,height:p}),t.RGBAImage.copy(f,o,{x:0,y:0},{x:c+h,y:u},{width:1,height:p})}this.dirty=!0},r.prototype.beginFrame=function(){this.callbackDispatchedThisFrame={}},r.prototype.dispatchRenderCallbacks=function(t){for(var e=0,r=t;e<r.length;e+=1){var n=r[e];if(!this.callbackDispatchedThisFrame[n]){this.callbackDispatchedThisFrame[n]=!0;var i=this.images[n];f(i)&&this.updateImage(n,i)}}},r}(t.Evented);var p=m,d=m,g=1e20;function m(t,e,r,n,i,a){this.fontSize=t||24,this.buffer=void 0===e?3:e,this.cutoff=n||.25,this.fontFamily=i||"sans-serif",this.fontWeight=a||"normal",this.radius=r||8;var o=this.size=this.fontSize+2*this.buffer;this.canvas=document.createElement("canvas"),this.canvas.width=this.canvas.height=o,this.ctx=this.canvas.getContext("2d"),this.ctx.font=this.fontWeight+" "+this.fontSize+"px "+this.fontFamily,this.ctx.textBaseline="middle",this.ctx.fillStyle="black",this.gridOuter=new Float64Array(o*o),this.gridInner=new Float64Array(o*o),this.f=new Float64Array(o),this.d=new Float64Array(o),this.z=new Float64Array(o+1),this.v=new Int16Array(o),this.middle=Math.round(o/2*(navigator.userAgent.indexOf("Gecko/")>=0?1.2:1))}function v(t,e,r,n,i,a,o){for(var s=0;s<e;s++){for(var l=0;l<r;l++)n[l]=t[l*e+s];for(y(n,i,a,o,r),l=0;l<r;l++)t[l*e+s]=i[l]}for(l=0;l<r;l++){for(s=0;s<e;s++)n[s]=t[l*e+s];for(y(n,i,a,o,e),s=0;s<e;s++)t[l*e+s]=Math.sqrt(i[s])}}function y(t,e,r,n,i){r[0]=0,n[0]=-g,n[1]=+g;for(var a=1,o=0;a<i;a++){for(var 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x=function(t,e){this.requestManager=t,this.localIdeographFontFamily=e,this.entries={}};x.prototype.setURL=function(t){this.url=t},x.prototype.getGlyphs=function(e,r){var n=this,i=[];for(var a in e)for(var o=0,s=e[a];o<s.length;o+=1){var l=s[o];i.push({stack:a,id:l})}t.asyncAll(i,(function(t,e){var r=t.stack,i=t.id,a=n.entries[r];a||(a=n.entries[r]={glyphs:{},requests:{},ranges:{}});var o=a.glyphs[i];if(void 0===o){if(o=n._tinySDF(a,r,i))return a.glyphs[i]=o,void e(null,{stack:r,id:i,glyph:o});var s=Math.floor(i/256);if(256*s>65535)e(new Error("glyphs > 65535 not supported"));else if(a.ranges[s])e(null,{stack:r,id:i,glyph:o});else{var l=a.requests[s];l||(l=a.requests[s]=[],x.loadGlyphRange(r,s,n.url,n.requestManager,(function(t,e){if(e){for(var r in e)n._doesCharSupportLocalGlyph(+r)||(a.glyphs[+r]=e[+r]);a.ranges[s]=!0}for(var i=0,o=l;i<o.length;i+=1){(0,o[i])(t,e)}delete a.requests[s]}))),l.push((function(t,n){t?e(t):n&&e(null,{stack:r,id:i,glyph:n[i]||null})}))}}else 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o=256*r,s=o+255,l=i.transformRequest(i.normalizeGlyphsURL(n).replace("{fontstack}",e).replace("{range}",o+"-"+s),t.ResourceType.Glyphs);t.getArrayBuffer(l,(function(e,r){if(e)a(e);else if(r){for(var n={},i=0,o=t.parseGlyphPBF(r);i<o.length;i+=1){var s=o[i];n[s.id]=s}a(null,n)}}))},x.TinySDF=p;var b=function(){this.specification=t.styleSpec.light.position};b.prototype.possiblyEvaluate=function(e,r){return t.sphericalToCartesian(e.expression.evaluate(r))},b.prototype.interpolate=function(e,r,n){return{x:t.number(e.x,r.x,n),y:t.number(e.y,r.y,n),z:t.number(e.z,r.z,n)}};var _=new t.Properties({anchor:new t.DataConstantProperty(t.styleSpec.light.anchor),position:new b,color:new t.DataConstantProperty(t.styleSpec.light.color),intensity:new t.DataConstantProperty(t.styleSpec.light.intensity)}),w=function(e){function r(r){e.call(this),this._transitionable=new t.Transitionable(_),this.setLight(r),this._transitioning=this._transitionable.untransitioned()}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getLight=function(){return this._transitionable.serialize()},r.prototype.setLight=function(e,r){if(void 0===r&&(r={}),!this._validate(t.validateLight,e,r))for(var n in e){var i=e[n];t.endsWith(n,"-transition")?this._transitionable.setTransition(n.slice(0,-"-transition".length),i):this._transitionable.setValue(n,i)}},r.prototype.updateTransitions=function(t){this._transitioning=this._transitionable.transitioned(t,this._transitioning)},r.prototype.hasTransition=function(){return this._transitioning.hasTransition()},r.prototype.recalculate=function(t){this.properties=this._transitioning.possiblyEvaluate(t)},r.prototype._validate=function(e,r,n){return(!n||!1!==n.validate)&&t.emitValidationErrors(this,e.call(t.validateStyle,t.extend({value:r,style:{glyphs:!0,sprite:!0},styleSpec:t.styleSpec})))},r}(t.Evented),T=function(t,e){this.width=t,this.height=e,this.nextRow=0,this.data=new Uint8Array(this.width*this.height),this.dashEntry={}};T.prototype.getDash=function(t,e){var r=t.join(",")+String(e);return this.dashEntry[r]||(this.dashEntry[r]=this.addDash(t,e)),this.dashEntry[r]},T.prototype.getDashRanges=function(t,e,r){var n=[],i=t.length%2==1?-t[t.length-1]*r:0,a=t[0]*r,o=!0;n.push({left:i,right:a,isDash:o,zeroLength:0===t[0]});for(var s=t[0],l=1;l<t.length;l++){o=!o;var c=t[l];i=s*r,a=(s+=c)*r,n.push({left:i,right:a,isDash:o,zeroLength:0===c})}return n},T.prototype.addRoundDash=function(t,e,r){for(var n=e/2,i=-r;i<=r;i++)for(var a=this.nextRow+r+i,o=this.width*a,s=0,l=t[s],c=0;c<this.width;c++){c/l.right>1&&(l=t[++s]);var u=Math.abs(c-l.left),f=Math.abs(c-l.right),h=Math.min(u,f),p=void 0,d=i/r*(n+1);if(l.isDash){var g=n-Math.abs(d);p=Math.sqrt(h*h+g*g)}else p=n-Math.sqrt(h*h+d*d);this.data[o+c]=Math.max(0,Math.min(255,p+128))}},T.prototype.addRegularDash=function(t){for(var e=t.length-1;e>=0;--e){var r=t[e],n=t[e+1];r.zeroLength?t.splice(e,1):n&&n.isDash===r.isDash&&(n.left=r.left,t.splice(e,1))}var i=t[0],a=t[t.length-1];i.isDash===a.isDash&&(i.left=a.left-this.width,a.right=i.right+this.width);for(var o=this.width*this.nextRow,s=0,l=t[s],c=0;c<this.width;c++){c/l.right>1&&(l=t[++s]);var u=Math.abs(c-l.left),f=Math.abs(c-l.right),h=Math.min(u,f),p=l.isDash?h:-h;this.data[o+c]=Math.max(0,Math.min(255,p+128))}},T.prototype.addDash=function(e,r){var n=r?7:0,i=2*n+1;if(this.nextRow+i>this.height)return t.warnOnce("LineAtlas out of space"),null;for(var a=0,o=0;o<e.length;o++)a+=e[o];if(0!==a){var s=this.width/a,l=this.getDashRanges(e,this.width,s);r?this.addRoundDash(l,s,n):this.addRegularDash(l)}var c={y:(this.nextRow+n+.5)/this.height,height:2*n/this.height,width:a};return this.nextRow+=i,this.dirty=!0,c},T.prototype.bind=function(t){var e=t.gl;this.texture?(e.bindTexture(e.TEXTURE_2D,this.texture),this.dirty&&(this.dirty=!1,e.texSubImage2D(e.TEXTURE_2D,0,0,0,this.width,this.height,e.ALPHA,e.UNSIGNED_BYTE,this.data))):(this.texture=e.createTexture(),e.bindTexture(e.TEXTURE_2D,this.texture),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_S,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_T,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,e.LINEAR),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,e.LINEAR),e.texImage2D(e.TEXTURE_2D,0,e.ALPHA,this.width,this.height,0,e.ALPHA,e.UNSIGNED_BYTE,this.data))};var k=function e(r,n){this.workerPool=r,this.actors=[],this.currentActor=0,this.id=t.uniqueId();for(var i=this.workerPool.acquire(this.id),a=0;a<i.length;a++){var o=i[a],s=new e.Actor(o,n,this.id);s.name="Worker "+a,this.actors.push(s)}};function A(e,r,n){var i=function(i,a){if(i)return n(i);if(a){var o=t.pick(t.extend(a,e),["tiles","minzoom","maxzoom","attribution","mapbox_logo","bounds","scheme","tileSize","encoding"]);a.vector_layers&&(o.vectorLayers=a.vector_layers,o.vectorLayerIds=o.vectorLayers.map((function(t){return t.id}))),o.tiles=r.canonicalizeTileset(o,e.url),n(null,o)}};return e.url?t.getJSON(r.transformRequest(r.normalizeSourceURL(e.url),t.ResourceType.Source),i):t.browser.frame((function(){return i(null,e)}))}k.prototype.broadcast=function(e,r,n){n=n||function(){},t.asyncAll(this.actors,(function(t,n){t.send(e,r,n)}),n)},k.prototype.getActor=function(){return this.currentActor=(this.currentActor+1)%this.actors.length,this.actors[this.currentActor]},k.prototype.remove=function(){this.actors.forEach((function(t){t.remove()})),this.actors=[],this.workerPool.release(this.id)},k.Actor=t.Actor;var M=function(e,r,n){this.bounds=t.LngLatBounds.convert(this.validateBounds(e)),this.minzoom=r||0,this.maxzoom=n||24};M.prototype.validateBounds=function(t){return Array.isArray(t)&&4===t.length?[Math.max(-180,t[0]),Math.max(-90,t[1]),Math.min(180,t[2]),Math.min(90,t[3])]:[-180,-90,180,90]},M.prototype.contains=function(e){var r=Math.pow(2,e.z),n=Math.floor(t.mercatorXfromLng(this.bounds.getWest())*r),i=Math.floor(t.mercatorYfromLat(this.bounds.getNorth())*r),a=Math.ceil(t.mercatorXfromLng(this.bounds.getEast())*r),o=Math.ceil(t.mercatorYfromLat(this.bounds.getSouth())*r);return e.x>=n&&e.x<a&&e.y>=i&&e.y<o};var S=function(e){function r(r,n,i,a){if(e.call(this),this.id=r,this.dispatcher=i,this.type="vector",this.minzoom=0,this.maxzoom=22,this.scheme="xyz",this.tileSize=512,this.reparseOverscaled=!0,this.isTileClipped=!0,this._loaded=!1,t.extend(this,t.pick(n,["url","scheme","tileSize","promoteId"])),this._options=t.extend({type:"vector"},n),this._collectResourceTiming=n.collectResourceTiming,512!==this.tileSize)throw new Error("vector tile sources must have a tileSize of 512");this.setEventedParent(a)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1,this.fire(new t.Event("dataloading",{dataType:"source"})),this._tileJSONRequest=A(this._options,this.map._requestManager,(function(r,n){e._tileJSONRequest=null,e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new M(n.bounds,e.minzoom,e.maxzoom)),t.postTurnstileEvent(n.tiles,e.map._requestManager._customAccessToken),t.postMapLoadEvent(n.tiles,e.map._getMapId(),e.map._requestManager._skuToken,e.map._requestManager._customAccessToken),e.fire(new t.Event("data",{dataType:"source",sourceDataType:"metadata"})),e.fire(new t.Event("data",{dataType:"source",sourceDataType:"content"})))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.loadTile=function(e,r){var n=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme)),i={request:this.map._requestManager.transformRequest(n,t.ResourceType.Tile),uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,tileSize:this.tileSize*e.tileID.overscaleFactor(),type:this.type,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};function a(n,i){return delete e.request,e.aborted?r(null):n&&404!==n.status?r(n):(i&&i.resourceTiming&&(e.resourceTiming=i.resourceTiming),this.map._refreshExpiredTiles&&i&&e.setExpiryData(i),e.loadVectorData(i,this.map.painter),t.cacheEntryPossiblyAdded(this.dispatcher),r(null),void(e.reloadCallback&&(this.loadTile(e,e.reloadCallback),e.reloadCallback=null)))}i.request.collectResourceTiming=this._collectResourceTiming,e.actor&&"expired"!==e.state?"loading"===e.state?e.reloadCallback=r:e.request=e.actor.send("reloadTile",i,a.bind(this)):(e.actor=this.dispatcher.getActor(),e.request=e.actor.send("loadTile",i,a.bind(this)))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.actor&&t.actor.send("abortTile",{uid:t.uid,type:this.type,source:this.id},void 0)},r.prototype.unloadTile=function(t){t.unloadVectorData(),t.actor&&t.actor.send("removeTile",{uid:t.uid,type:this.type,source:this.id},void 0)},r.prototype.hasTransition=function(){return!1},r}(t.Evented),E=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.dispatcher=i,this.setEventedParent(a),this.type="raster",this.minzoom=0,this.maxzoom=22,this.roundZoom=!0,this.scheme="xyz",this.tileSize=512,this._loaded=!1,this._options=t.extend({type:"raster"},n),t.extend(this,t.pick(n,["url","scheme","tileSize"]))}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1,this.fire(new t.Event("dataloading",{dataType:"source"})),this._tileJSONRequest=A(this._options,this.map._requestManager,(function(r,n){e._tileJSONRequest=null,e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new M(n.bounds,e.minzoom,e.maxzoom)),t.postTurnstileEvent(n.tiles),t.postMapLoadEvent(n.tiles,e.map._getMapId(),e.map._requestManager._skuToken),e.fire(new t.Event("data",{dataType:"source",sourceDataType:"metadata"})),e.fire(new t.Event("data",{dataType:"source",sourceDataType:"content"})))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.loadTile=function(e,r){var n=this,i=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);e.request=t.getImage(this.map._requestManager.transformRequest(i,t.ResourceType.Tile),(function(i,a){if(delete e.request,e.aborted)e.state="unloaded",r(null);else if(i)e.state="errored",r(i);else if(a){n.map._refreshExpiredTiles&&e.setExpiryData(a),delete a.cacheControl,delete a.expires;var o=n.map.painter.context,s=o.gl;e.texture=n.map.painter.getTileTexture(a.width),e.texture?e.texture.update(a,{useMipmap:!0}):(e.texture=new t.Texture(o,a,s.RGBA,{useMipmap:!0}),e.texture.bind(s.LINEAR,s.CLAMP_TO_EDGE,s.LINEAR_MIPMAP_NEAREST),o.extTextureFilterAnisotropic&&s.texParameterf(s.TEXTURE_2D,o.extTextureFilterAnisotropic.TEXTURE_MAX_ANISOTROPY_EXT,o.extTextureFilterAnisotropicMax)),e.state="loaded",t.cacheEntryPossiblyAdded(n.dispatcher),r(null)}}))},r.prototype.abortTile=function(t,e){t.request&&(t.request.cancel(),delete t.request),e()},r.prototype.unloadTile=function(t,e){t.texture&&this.map.painter.saveTileTexture(t.texture),e()},r.prototype.hasTransition=function(){return!1},r}(t.Evented),L=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),this.type="raster-dem",this.maxzoom=22,this._options=t.extend({type:"raster-dem"},n),this.encoding=n.encoding||"mapbox"}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.serialize=function(){return{type:"raster-dem",url:this.url,tileSize:this.tileSize,tiles:this.tiles,bounds:this.bounds,encoding:this.encoding}},r.prototype.loadTile=function(e,r){var n=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);function i(t,n){t&&(e.state="errored",r(t)),n&&(e.dem=n,e.needsHillshadePrepare=!0,e.state="loaded",r(null))}e.request=t.getImage(this.map._requestManager.transformRequest(n,t.ResourceType.Tile),function(n,a){if(delete e.request,e.aborted)e.state="unloaded",r(null);else if(n)e.state="errored",r(n);else if(a){this.map._refreshExpiredTiles&&e.setExpiryData(a),delete a.cacheControl,delete a.expires;var o=t.window.ImageBitmap&&a instanceof t.window.ImageBitmap&&t.offscreenCanvasSupported()?a:t.browser.getImageData(a,1),s={uid:e.uid,coord:e.tileID,source:this.id,rawImageData:o,encoding:this.encoding};e.actor&&"expired"!==e.state||(e.actor=this.dispatcher.getActor(),e.actor.send("loadDEMTile",s,i.bind(this)))}}.bind(this)),e.neighboringTiles=this._getNeighboringTiles(e.tileID)},r.prototype._getNeighboringTiles=function(e){var r=e.canonical,n=Math.pow(2,r.z),i=(r.x-1+n)%n,a=0===r.x?e.wrap-1:e.wrap,o=(r.x+1+n)%n,s=r.x+1===n?e.wrap+1:e.wrap,l={};return l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y).key]={backfilled:!1},r.y>0&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y-1).key]={backfilled:!1}),r.y+1<n&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y+1).key]={backfilled:!1}),l},r.prototype.unloadTile=function(t){t.demTexture&&this.map.painter.saveTileTexture(t.demTexture),t.fbo&&(t.fbo.destroy(),delete t.fbo),t.dem&&delete t.dem,delete t.neighboringTiles,t.state="unloaded",t.actor&&t.actor.send("removeDEMTile",{uid:t.uid,source:this.id})},r}(E),C=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.type="geojson",this.minzoom=0,this.maxzoom=18,this.tileSize=512,this.isTileClipped=!0,this.reparseOverscaled=!0,this._removed=!1,this._loaded=!1,this.actor=i.getActor(),this.setEventedParent(a),this._data=n.data,this._options=t.extend({},n),this._collectResourceTiming=n.collectResourceTiming,this._resourceTiming=[],void 0!==n.maxzoom&&(this.maxzoom=n.maxzoom),n.type&&(this.type=n.type),n.attribution&&(this.attribution=n.attribution),this.promoteId=n.promoteId;var o=t.EXTENT/this.tileSize;this.workerOptions=t.extend({source:this.id,cluster:n.cluster||!1,geojsonVtOptions:{buffer:(void 0!==n.buffer?n.buffer:128)*o,tolerance:(void 0!==n.tolerance?n.tolerance:.375)*o,extent:t.EXTENT,maxZoom:this.maxzoom,lineMetrics:n.lineMetrics||!1,generateId:n.generateId||!1},superclusterOptions:{maxZoom:void 0!==n.clusterMaxZoom?Math.min(n.clusterMaxZoom,this.maxzoom-1):this.maxzoom-1,extent:t.EXTENT,radius:(n.clusterRadius||50)*o,log:!1,generateId:n.generateId||!1},clusterProperties:n.clusterProperties},n.workerOptions)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this.fire(new t.Event("dataloading",{dataType:"source"})),this._updateWorkerData((function(r){if(r)e.fire(new t.ErrorEvent(r));else{var n={dataType:"source",sourceDataType:"metadata"};e._collectResourceTiming&&e._resourceTiming&&e._resourceTiming.length>0&&(n.resourceTiming=e._resourceTiming,e._resourceTiming=[]),e.fire(new t.Event("data",n))}}))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setData=function(e){var r=this;return this._data=e,this.fire(new t.Event("dataloading",{dataType:"source"})),this._updateWorkerData((function(e){if(e)r.fire(new t.ErrorEvent(e));else{var n={dataType:"source",sourceDataType:"content"};r._collectResourceTiming&&r._resourceTiming&&r._resourceTiming.length>0&&(n.resourceTiming=r._resourceTiming,r._resourceTiming=[]),r.fire(new t.Event("data",n))}})),this},r.prototype.getClusterExpansionZoom=function(t,e){return this.actor.send("geojson.getClusterExpansionZoom",{clusterId:t,source:this.id},e),this},r.prototype.getClusterChildren=function(t,e){return this.actor.send("geojson.getClusterChildren",{clusterId:t,source:this.id},e),this},r.prototype.getClusterLeaves=function(t,e,r,n){return this.actor.send("geojson.getClusterLeaves",{source:this.id,clusterId:t,limit:e,offset:r},n),this},r.prototype._updateWorkerData=function(e){var r=this;this._loaded=!1;var n=t.extend({},this.workerOptions),i=this._data;"string"==typeof i?(n.request=this.map._requestManager.transformRequest(t.browser.resolveURL(i),t.ResourceType.Source),n.request.collectResourceTiming=this._collectResourceTiming):n.data=JSON.stringify(i),this.actor.send(this.type+".loadData",n,(function(t,i){r._removed||i&&i.abandoned||(r._loaded=!0,i&&i.resourceTiming&&i.resourceTiming[r.id]&&(r._resourceTiming=i.resourceTiming[r.id].slice(0)),r.actor.send(r.type+".coalesce",{source:n.source},null),e(t))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.loadTile=function(e,r){var n=this,i=e.actor?"reloadTile":"loadTile";e.actor=this.actor;var a={type:this.type,uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,maxZoom:this.maxzoom,tileSize:this.tileSize,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};e.request=this.actor.send(i,a,(function(t,a){return delete e.request,e.unloadVectorData(),e.aborted?r(null):t?r(t):(e.loadVectorData(a,n.map.painter,"reloadTile"===i),r(null))}))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.aborted=!0},r.prototype.unloadTile=function(t){t.unloadVectorData(),this.actor.send("removeTile",{uid:t.uid,type:this.type,source:this.id})},r.prototype.onRemove=function(){this._removed=!0,this.actor.send("removeSource",{type:this.type,source:this.id})},r.prototype.serialize=function(){return t.extend({},this._options,{type:this.type,data:this._data})},r.prototype.hasTransition=function(){return!1},r}(t.Evented),P=t.createLayout([{name:"a_pos",type:"Int16",components:2},{name:"a_texture_pos",type:"Int16",components:2}]),I=function(e){function r(t,r,n,i){e.call(this),this.id=t,this.dispatcher=n,this.coordinates=r.coordinates,this.type="image",this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.tiles={},this._loaded=!1,this.setEventedParent(i),this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(e,r){var n=this;this._loaded=!1,this.fire(new t.Event("dataloading",{dataType:"source"})),this.url=this.options.url,t.getImage(this.map._requestManager.transformRequest(this.url,t.ResourceType.Image),(function(i,a){n._loaded=!0,i?n.fire(new t.ErrorEvent(i)):a&&(n.image=a,e&&(n.coordinates=e),r&&r(),n._finishLoading())}))},r.prototype.loaded=function(){return this._loaded},r.prototype.updateImage=function(t){var e=this;return this.image&&t.url?(this.options.url=t.url,this.load(t.coordinates,(function(){e.texture=null})),this):this},r.prototype._finishLoading=function(){this.map&&(this.setCoordinates(this.coordinates),this.fire(new t.Event("data",{dataType:"source",sourceDataType:"metadata"})))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setCoordinates=function(e){var r=this;this.coordinates=e;var n=e.map(t.MercatorCoordinate.fromLngLat);this.tileID=function(e){for(var r=1/0,n=1/0,i=-1/0,a=-1/0,o=0,s=e;o<s.length;o+=1){var l=s[o];r=Math.min(r,l.x),n=Math.min(n,l.y),i=Math.max(i,l.x),a=Math.max(a,l.y)}var c=i-r,u=a-n,f=Math.max(c,u),h=Math.max(0,Math.floor(-Math.log(f)/Math.LN2)),p=Math.pow(2,h);return new t.CanonicalTileID(h,Math.floor((r+i)/2*p),Math.floor((n+a)/2*p))}(n),this.minzoom=this.maxzoom=this.tileID.z;var i=n.map((function(t){return r.tileID.getTilePoint(t)._round()}));return this._boundsArray=new t.StructArrayLayout4i8,this._boundsArray.emplaceBack(i[0].x,i[0].y,0,0),this._boundsArray.emplaceBack(i[1].x,i[1].y,t.EXTENT,0),this._boundsArray.emplaceBack(i[3].x,i[3].y,0,t.EXTENT),this._boundsArray.emplaceBack(i[2].x,i[2].y,t.EXTENT,t.EXTENT),this.boundsBuffer&&(this.boundsBuffer.destroy(),delete this.boundsBuffer),this.fire(new t.Event("data",{dataType:"source",sourceDataType:"content"})),this},r.prototype.prepare=function(){if(0!==Object.keys(this.tiles).length&&this.image){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture||(this.texture=new t.Texture(e,this.image,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];"loaded"!==i.state&&(i.state="loaded",i.texture=this.texture)}}},r.prototype.loadTile=function(t,e){this.tileID&&this.tileID.equals(t.tileID.canonical)?(this.tiles[String(t.tileID.wrap)]=t,t.buckets={},e(null)):(t.state="errored",e(null))},r.prototype.serialize=function(){return{type:"image",url:this.options.url,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return!1},r}(t.Evented);var O=function(e){function r(t,r,n,i){e.call(this,t,r,n,i),this.roundZoom=!0,this.type="video",this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1;var r=this.options;this.urls=[];for(var n=0,i=r.urls;n<i.length;n+=1){var a=i[n];this.urls.push(this.map._requestManager.transformRequest(a,t.ResourceType.Source).url)}t.getVideo(this.urls,(function(r,n){e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(e.video=n,e.video.loop=!0,e.video.addEventListener("playing",(function(){e.map.triggerRepaint()})),e.map&&e.video.play(),e._finishLoading())}))},r.prototype.pause=function(){this.video&&this.video.pause()},r.prototype.play=function(){this.video&&this.video.play()},r.prototype.seek=function(e){if(this.video){var r=this.video.seekable;e<r.start(0)||e>r.end(0)?this.fire(new t.ErrorEvent(new t.ValidationError("sources."+this.id,null,"Playback for this video can be set only between the "+r.start(0)+" and "+r.end(0)+"-second mark."))):this.video.currentTime=e}},r.prototype.getVideo=function(){return this.video},r.prototype.onAdd=function(t){this.map||(this.map=t,this.load(),this.video&&(this.video.play(),this.setCoordinates(this.coordinates)))},r.prototype.prepare=function(){if(!(0===Object.keys(this.tiles).length||this.video.readyState<2)){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?this.video.paused||(this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE),r.texSubImage2D(r.TEXTURE_2D,0,0,0,r.RGBA,r.UNSIGNED_BYTE,this.video)):(this.texture=new t.Texture(e,this.video,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];"loaded"!==i.state&&(i.state="loaded",i.texture=this.texture)}}},r.prototype.serialize=function(){return{type:"video",urls:this.urls,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this.video&&!this.video.paused},r}(I),z=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),n.coordinates?Array.isArray(n.coordinates)&&4===n.coordinates.length&&!n.coordinates.some((function(t){return!Array.isArray(t)||2!==t.length||t.some((function(t){return"number"!=typeof t}))}))||this.fire(new t.ErrorEvent(new t.ValidationError("sources."+r,null,'"coordinates" property must be an array of 4 longitude/latitude array pairs'))):this.fire(new t.ErrorEvent(new t.ValidationError("sources."+r,null,'missing required property "coordinates"'))),n.animate&&"boolean"!=typeof n.animate&&this.fire(new t.ErrorEvent(new t.ValidationError("sources."+r,null,'optional "animate" property must be a boolean value'))),n.canvas?"string"==typeof n.canvas||n.canvas instanceof t.window.HTMLCanvasElement||this.fire(new t.ErrorEvent(new t.ValidationError("sources."+r,null,'"canvas" must be either a string representing the ID of the canvas element from which to read, or an HTMLCanvasElement instance'))):this.fire(new t.ErrorEvent(new t.ValidationError("sources."+r,null,'missing required property "canvas"'))),this.options=n,this.animate=void 0===n.animate||n.animate}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){this._loaded=!0,this.canvas||(this.canvas=this.options.canvas instanceof t.window.HTMLCanvasElement?this.options.canvas:t.window.document.getElementById(this.options.canvas)),this.width=this.canvas.width,this.height=this.canvas.height,this._hasInvalidDimensions()?this.fire(new t.ErrorEvent(new Error("Canvas dimensions cannot be less than or equal to zero."))):(this.play=function(){this._playing=!0,this.map.triggerRepaint()},this.pause=function(){this._playing&&(this.prepare(),this._playing=!1)},this._finishLoading())},r.prototype.getCanvas=function(){return this.canvas},r.prototype.onAdd=function(t){this.map=t,this.load(),this.canvas&&this.animate&&this.play()},r.prototype.onRemove=function(){this.pause()},r.prototype.prepare=function(){var e=!1;if(this.canvas.width!==this.width&&(this.width=this.canvas.width,e=!0),this.canvas.height!==this.height&&(this.height=this.canvas.height,e=!0),!this._hasInvalidDimensions()&&0!==Object.keys(this.tiles).length){var r=this.map.painter.context,n=r.gl;for(var i in this.boundsBuffer||(this.boundsBuffer=r.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?(e||this._playing)&&this.texture.update(this.canvas,{premultiply:!0}):this.texture=new t.Texture(r,this.canvas,n.RGBA,{premultiply:!0}),this.tiles){var a=this.tiles[i];"loaded"!==a.state&&(a.state="loaded",a.texture=this.texture)}}},r.prototype.serialize=function(){return{type:"canvas",coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this._playing},r.prototype._hasInvalidDimensions=function(){for(var t=0,e=[this.canvas.width,this.canvas.height];t<e.length;t+=1){var r=e[t];if(isNaN(r)||r<=0)return!0}return!1},r}(I),D={vector:S,raster:E,"raster-dem":L,geojson:C,video:O,image:I,canvas:z};function R(e,r){var n=t.identity([]);return t.translate(n,n,[1,1,0]),t.scale(n,n,[.5*e.width,.5*e.height,1]),t.multiply(n,n,e.calculatePosMatrix(r.toUnwrapped()))}function F(t,e,r,n,i,a){var o=function(t,e,r){if(t)for(var n=0,i=t;n<i.length;n+=1){var a=e[i[n]];if(a&&a.source===r&&"fill-extrusion"===a.type)return!0}else for(var o in e){var s=e[o];if(s.source===r&&"fill-extrusion"===s.type)return!0}return!1}(i&&i.layers,e,t.id),s=a.maxPitchScaleFactor(),l=t.tilesIn(n,s,o);l.sort(B);for(var c=[],u=0,f=l;u<f.length;u+=1){var h=f[u];c.push({wrappedTileID:h.tileID.wrapped().key,queryResults:h.tile.queryRenderedFeatures(e,r,t._state,h.queryGeometry,h.cameraQueryGeometry,h.scale,i,a,s,R(t.transform,h.tileID))})}var p=function(t){for(var e={},r={},n=0,i=t;n<i.length;n+=1){var a=i[n],o=a.queryResults,s=a.wrappedTileID,l=r[s]=r[s]||{};for(var c in o)for(var u=o[c],f=l[c]=l[c]||{},h=e[c]=e[c]||[],p=0,d=u;p<d.length;p+=1){var g=d[p];f[g.featureIndex]||(f[g.featureIndex]=!0,h.push(g))}}return e}(c);for(var d in p)p[d].forEach((function(e){var r=e.feature,n=t.getFeatureState(r.layer["source-layer"],r.id);r.source=r.layer.source,r.layer["source-layer"]&&(r.sourceLayer=r.layer["source-layer"]),r.state=n}));return p}function B(t,e){var r=t.tileID,n=e.tileID;return r.overscaledZ-n.overscaledZ||r.canonical.y-n.canonical.y||r.wrap-n.wrap||r.canonical.x-n.canonical.x}var N=function(t,e){this.max=t,this.onRemove=e,this.reset()};N.prototype.reset=function(){for(var t in this.data)for(var e=0,r=this.data[t];e<r.length;e+=1){var n=r[e];n.timeout&&clearTimeout(n.timeout),this.onRemove(n.value)}return this.data={},this.order=[],this},N.prototype.add=function(t,e,r){var n=this,i=t.wrapped().key;void 0===this.data[i]&&(this.data[i]=[]);var a={value:e,timeout:void 0};if(void 0!==r&&(a.timeout=setTimeout((function(){n.remove(t,a)}),r)),this.data[i].push(a),this.order.push(i),this.order.length>this.max){var o=this._getAndRemoveByKey(this.order[0]);o&&this.onRemove(o)}return this},N.prototype.has=function(t){return t.wrapped().key in this.data},N.prototype.getAndRemove=function(t){return this.has(t)?this._getAndRemoveByKey(t.wrapped().key):null},N.prototype._getAndRemoveByKey=function(t){var e=this.data[t].shift();return e.timeout&&clearTimeout(e.timeout),0===this.data[t].length&&delete this.data[t],this.order.splice(this.order.indexOf(t),1),e.value},N.prototype.getByKey=function(t){var e=this.data[t];return e?e[0].value:null},N.prototype.get=function(t){return this.has(t)?this.data[t.wrapped().key][0].value:null},N.prototype.remove=function(t,e){if(!this.has(t))return this;var r=t.wrapped().key,n=void 0===e?0:this.data[r].indexOf(e),i=this.data[r][n];return this.data[r].splice(n,1),i.timeout&&clearTimeout(i.timeout),0===this.data[r].length&&delete this.data[r],this.onRemove(i.value),this.order.splice(this.order.indexOf(r),1),this},N.prototype.setMaxSize=function(t){for(this.max=t;this.order.length>this.max;){var e=this._getAndRemoveByKey(this.order[0]);e&&this.onRemove(e)}return this},N.prototype.filter=function(t){var e=[];for(var r in this.data)for(var n=0,i=this.data[r];n<i.length;n+=1){var a=i[n];t(a.value)||e.push(a)}for(var o=0,s=e;o<s.length;o+=1){var l=s[o];this.remove(l.value.tileID,l)}};var j=function(t,e,r){this.context=t;var n=t.gl;this.buffer=n.createBuffer(),this.dynamicDraw=Boolean(r),this.context.unbindVAO(),t.bindElementBuffer.set(this.buffer),n.bufferData(n.ELEMENT_ARRAY_BUFFER,e.arrayBuffer,this.dynamicDraw?n.DYNAMIC_DRAW:n.STATIC_DRAW),this.dynamicDraw||delete e.arrayBuffer};j.prototype.bind=function(){this.context.bindElementBuffer.set(this.buffer)},j.prototype.updateData=function(t){var e=this.context.gl;this.context.unbindVAO(),this.bind(),e.bufferSubData(e.ELEMENT_ARRAY_BUFFER,0,t.arrayBuffer)},j.prototype.destroy=function(){var t=this.context.gl;this.buffer&&(t.deleteBuffer(this.buffer),delete this.buffer)};var U={Int8:"BYTE",Uint8:"UNSIGNED_BYTE",Int16:"SHORT",Uint16:"UNSIGNED_SHORT",Int32:"INT",Uint32:"UNSIGNED_INT",Float32:"FLOAT"},V=function(t,e,r,n){this.length=e.length,this.attributes=r,this.itemSize=e.bytesPerElement,this.dynamicDraw=n,this.context=t;var i=t.gl;this.buffer=i.createBuffer(),t.bindVertexBuffer.set(this.buffer),i.bufferData(i.ARRAY_BUFFER,e.arrayBuffer,this.dynamicDraw?i.DYNAMIC_DRAW:i.STATIC_DRAW),this.dynamicDraw||delete e.arrayBuffer};V.prototype.bind=function(){this.context.bindVertexBuffer.set(this.buffer)},V.prototype.updateData=function(t){var e=this.context.gl;this.bind(),e.bufferSubData(e.ARRAY_BUFFER,0,t.arrayBuffer)},V.prototype.enableAttributes=function(t,e){for(var r=0;r<this.attributes.length;r++){var n=this.attributes[r],i=e.attributes[n.name];void 0!==i&&t.enableVertexAttribArray(i)}},V.prototype.setVertexAttribPointers=function(t,e,r){for(var n=0;n<this.attributes.length;n++){var i=this.attributes[n],a=e.attributes[i.name];void 0!==a&&t.vertexAttribPointer(a,i.components,t[U[i.type]],!1,this.itemSize,i.offset+this.itemSize*(r||0))}},V.prototype.destroy=function(){var t=this.context.gl;this.buffer&&(t.deleteBuffer(this.buffer),delete this.buffer)};var H=function(t){this.gl=t.gl,this.default=this.getDefault(),this.current=this.default,this.dirty=!1};H.prototype.get=function(){return this.current},H.prototype.set=function(t){},H.prototype.getDefault=function(){return this.default},H.prototype.setDefault=function(){this.set(this.default)};var q=function(e){function r(){e.apply(this,arguments)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getDefault=function(){return t.Color.transparent},r.prototype.set=function(t){var e=this.current;(t.r!==e.r||t.g!==e.g||t.b!==e.b||t.a!==e.a||this.dirty)&&(this.gl.clearColor(t.r,t.g,t.b,t.a),this.current=t,this.dirty=!1)},r}(H),G=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return 1},e.prototype.set=function(t){(t!==this.current||this.dirty)&&(this.gl.clearDepth(t),this.current=t,this.dirty=!1)},e}(H),Y=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return 0},e.prototype.set=function(t){(t!==this.current||this.dirty)&&(this.gl.clearStencil(t),this.current=t,this.dirty=!1)},e}(H),W=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return[!0,!0,!0,!0]},e.prototype.set=function(t){var e=this.current;(t[0]!==e[0]||t[1]!==e[1]||t[2]!==e[2]||t[3]!==e[3]||this.dirty)&&(this.gl.colorMask(t[0],t[1],t[2],t[3]),this.current=t,this.dirty=!1)},e}(H),X=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return!0},e.prototype.set=function(t){(t!==this.current||this.dirty)&&(this.gl.depthMask(t),this.current=t,this.dirty=!1)},e}(H),Z=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return 255},e.prototype.set=function(t){(t!==this.current||this.dirty)&&(this.gl.stencilMask(t),this.current=t,this.dirty=!1)},e}(H),J=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return{func:this.gl.ALWAYS,ref:0,mask:255}},e.prototype.set=function(t){var e=this.current;(t.func!==e.func||t.ref!==e.ref||t.mask!==e.mask||this.dirty)&&(this.gl.stencilFunc(t.func,t.ref,t.mask),this.current=t,this.dirty=!1)},e}(H),K=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){var t=this.gl;return[t.KEEP,t.KEEP,t.KEEP]},e.prototype.set=function(t){var e=this.current;(t[0]!==e[0]||t[1]!==e[1]||t[2]!==e[2]||this.dirty)&&(this.gl.stencilOp(t[0],t[1],t[2]),this.current=t,this.dirty=!1)},e}(H),Q=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return!1},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;t?e.enable(e.STENCIL_TEST):e.disable(e.STENCIL_TEST),this.current=t,this.dirty=!1}},e}(H),$=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return[0,1]},e.prototype.set=function(t){var e=this.current;(t[0]!==e[0]||t[1]!==e[1]||this.dirty)&&(this.gl.depthRange(t[0],t[1]),this.current=t,this.dirty=!1)},e}(H),tt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return!1},e.prototype.set=function(t){if(t!==this.current||this.dirty){var 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t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return null},e.prototype.set=function(t){(t!==this.current||this.dirty)&&(this.gl.useProgram(t),this.current=t,this.dirty=!1)},e}(H),ut=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return this.gl.TEXTURE0},e.prototype.set=function(t){(t!==this.current||this.dirty)&&(this.gl.activeTexture(t),this.current=t,this.dirty=!1)},e}(H),ft=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){var t=this.gl;return[0,0,t.drawingBufferWidth,t.drawingBufferHeight]},e.prototype.set=function(t){var e=this.current;(t[0]!==e[0]||t[1]!==e[1]||t[2]!==e[2]||t[3]!==e[3]||this.dirty)&&(this.gl.viewport(t[0],t[1],t[2],t[3]),this.current=t,this.dirty=!1)},e}(H),ht=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return null},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.bindFramebuffer(e.FRAMEBUFFER,t),this.current=t,this.dirty=!1}},e}(H),pt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return null},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.bindRenderbuffer(e.RENDERBUFFER,t),this.current=t,this.dirty=!1}},e}(H),dt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return null},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.bindTexture(e.TEXTURE_2D,t),this.current=t,this.dirty=!1}},e}(H),gt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return null},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.bindBuffer(e.ARRAY_BUFFER,t),this.current=t,this.dirty=!1}},e}(H),mt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return null},e.prototype.set=function(t){var e=this.gl;e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,t),this.current=t,this.dirty=!1},e}(H),vt=function(t){function e(e){t.call(this,e),this.vao=e.extVertexArrayObject}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return null},e.prototype.set=function(t){this.vao&&(t!==this.current||this.dirty)&&(this.vao.bindVertexArrayOES(t),this.current=t,this.dirty=!1)},e}(H),yt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return 4},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.pixelStorei(e.UNPACK_ALIGNMENT,t),this.current=t,this.dirty=!1}},e}(H),xt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return!1},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.pixelStorei(e.UNPACK_PREMULTIPLY_ALPHA_WEBGL,t),this.current=t,this.dirty=!1}},e}(H),bt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return!1},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.pixelStorei(e.UNPACK_FLIP_Y_WEBGL,t),this.current=t,this.dirty=!1}},e}(H),_t=function(t){function e(e,r){t.call(this,e),this.context=e,this.parent=r}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return null},e}(H),wt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.setDirty=function(){this.dirty=!0},e.prototype.set=function(t){if(t!==this.current||this.dirty){this.context.bindFramebuffer.set(this.parent);var e=this.gl;e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,t,0),this.current=t,this.dirty=!1}},e}(_t),Tt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.set=function(t){if(t!==this.current||this.dirty){this.context.bindFramebuffer.set(this.parent);var e=this.gl;e.framebufferRenderbuffer(e.FRAMEBUFFER,e.DEPTH_ATTACHMENT,e.RENDERBUFFER,t),this.current=t,this.dirty=!1}},e}(_t),kt=function(t,e,r,n){this.context=t,this.width=e,this.height=r;var i=t.gl,a=this.framebuffer=i.createFramebuffer();this.colorAttachment=new wt(t,a),n&&(this.depthAttachment=new Tt(t,a))};kt.prototype.destroy=function(){var t=this.context.gl,e=this.colorAttachment.get();if(e&&t.deleteTexture(e),this.depthAttachment){var r=this.depthAttachment.get();r&&t.deleteRenderbuffer(r)}t.deleteFramebuffer(this.framebuffer)};var At=function(t,e,r){this.func=t,this.mask=e,this.range=r};At.ReadOnly=!1,At.ReadWrite=!0,At.disabled=new At(519,At.ReadOnly,[0,1]);var Mt=function(t,e,r,n,i,a){this.test=t,this.ref=e,this.mask=r,this.fail=n,this.depthFail=i,this.pass=a};Mt.disabled=new Mt({func:519,mask:0},0,0,7680,7680,7680);var St=function(t,e,r){this.blendFunction=t,this.blendColor=e,this.mask=r};St.disabled=new St(St.Replace=[1,0],t.Color.transparent,[!1,!1,!1,!1]),St.unblended=new St(St.Replace,t.Color.transparent,[!0,!0,!0,!0]),St.alphaBlended=new St([1,771],t.Color.transparent,[!0,!0,!0,!0]);var Et=function(t,e,r){this.enable=t,this.mode=e,this.frontFace=r};Et.disabled=new Et(!1,1029,2305),Et.backCCW=new Et(!0,1029,2305);var Lt=function(t){this.gl=t,this.extVertexArrayObject=this.gl.getExtension("OES_vertex_array_object"),this.clearColor=new q(this),this.clearDepth=new G(this),this.clearStencil=new Y(this),this.colorMask=new W(this),this.depthMask=new X(this),this.stencilMask=new Z(this),this.stencilFunc=new J(this),this.stencilOp=new K(this),this.stencilTest=new Q(this),this.depthRange=new $(this),this.depthTest=new tt(this),this.depthFunc=new et(this),this.blend=new rt(this),this.blendFunc=new nt(this),this.blendColor=new it(this),this.blendEquation=new at(this),this.cullFace=new ot(this),this.cullFaceSide=new st(this),this.frontFace=new lt(this),this.program=new ct(this),this.activeTexture=new ut(this),this.viewport=new ft(this),this.bindFramebuffer=new ht(this),this.bindRenderbuffer=new pt(this),this.bindTexture=new dt(this),this.bindVertexBuffer=new gt(this),this.bindElementBuffer=new mt(this),this.bindVertexArrayOES=this.extVertexArrayObject&&new vt(this),this.pixelStoreUnpack=new yt(this),this.pixelStoreUnpackPremultiplyAlpha=new xt(this),this.pixelStoreUnpackFlipY=new bt(this),this.extTextureFilterAnisotropic=t.getExtension("EXT_texture_filter_anisotropic")||t.getExtension("MOZ_EXT_texture_filter_anisotropic")||t.getExtension("WEBKIT_EXT_texture_filter_anisotropic"),this.extTextureFilterAnisotropic&&(this.extTextureFilterAnisotropicMax=t.getParameter(this.extTextureFilterAnisotropic.MAX_TEXTURE_MAX_ANISOTROPY_EXT)),this.extTextureHalfFloat=t.getExtension("OES_texture_half_float"),this.extTextureHalfFloat&&(t.getExtension("OES_texture_half_float_linear"),this.extRenderToTextureHalfFloat=t.getExtension("EXT_color_buffer_half_float")),this.extTimerQuery=t.getExtension("EXT_disjoint_timer_query")};Lt.prototype.setDefault=function(){this.unbindVAO(),this.clearColor.setDefault(),this.clearDepth.setDefault(),this.clearStencil.setDefault(),this.colorMask.setDefault(),this.depthMask.setDefault(),this.stencilMask.setDefault(),this.stencilFunc.setDefault(),this.stencilOp.setDefault(),this.stencilTest.setDefault(),this.depthRange.setDefault(),this.depthTest.setDefault(),this.depthFunc.setDefault(),this.blend.setDefault(),this.blendFunc.setDefault(),this.blendColor.setDefault(),this.blendEquation.setDefault(),this.cullFace.setDefault(),this.cullFaceSide.setDefault(),this.frontFace.setDefault(),this.program.setDefault(),this.activeTexture.setDefault(),this.bindFramebuffer.setDefault(),this.pixelStoreUnpack.setDefault(),this.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.pixelStoreUnpackFlipY.setDefault()},Lt.prototype.setDirty=function(){this.clearColor.dirty=!0,this.clearDepth.dirty=!0,this.clearStencil.dirty=!0,this.colorMask.dirty=!0,this.depthMask.dirty=!0,this.stencilMask.dirty=!0,this.stencilFunc.dirty=!0,this.stencilOp.dirty=!0,this.stencilTest.dirty=!0,this.depthRange.dirty=!0,this.depthTest.dirty=!0,this.depthFunc.dirty=!0,this.blend.dirty=!0,this.blendFunc.dirty=!0,this.blendColor.dirty=!0,this.blendEquation.dirty=!0,this.cullFace.dirty=!0,this.cullFaceSide.dirty=!0,this.frontFace.dirty=!0,this.program.dirty=!0,this.activeTexture.dirty=!0,this.viewport.dirty=!0,this.bindFramebuffer.dirty=!0,this.bindRenderbuffer.dirty=!0,this.bindTexture.dirty=!0,this.bindVertexBuffer.dirty=!0,this.bindElementBuffer.dirty=!0,this.extVertexArrayObject&&(this.bindVertexArrayOES.dirty=!0),this.pixelStoreUnpack.dirty=!0,this.pixelStoreUnpackPremultiplyAlpha.dirty=!0,this.pixelStoreUnpackFlipY.dirty=!0},Lt.prototype.createIndexBuffer=function(t,e){return new j(this,t,e)},Lt.prototype.createVertexBuffer=function(t,e,r){return new V(this,t,e,r)},Lt.prototype.createRenderbuffer=function(t,e,r){var n=this.gl,i=n.createRenderbuffer();return this.bindRenderbuffer.set(i),n.renderbufferStorage(n.RENDERBUFFER,t,e,r),this.bindRenderbuffer.set(null),i},Lt.prototype.createFramebuffer=function(t,e,r){return new kt(this,t,e,r)},Lt.prototype.clear=function(t){var e=t.color,r=t.depth,n=this.gl,i=0;e&&(i|=n.COLOR_BUFFER_BIT,this.clearColor.set(e),this.colorMask.set([!0,!0,!0,!0])),void 0!==r&&(i|=n.DEPTH_BUFFER_BIT,this.depthRange.set([0,1]),this.clearDepth.set(r),this.depthMask.set(!0)),n.clear(i)},Lt.prototype.setCullFace=function(t){!1===t.enable?this.cullFace.set(!1):(this.cullFace.set(!0),this.cullFaceSide.set(t.mode),this.frontFace.set(t.frontFace))},Lt.prototype.setDepthMode=function(t){t.func!==this.gl.ALWAYS||t.mask?(this.depthTest.set(!0),this.depthFunc.set(t.func),this.depthMask.set(t.mask),this.depthRange.set(t.range)):this.depthTest.set(!1)},Lt.prototype.setStencilMode=function(t){t.test.func!==this.gl.ALWAYS||t.mask?(this.stencilTest.set(!0),this.stencilMask.set(t.mask),this.stencilOp.set([t.fail,t.depthFail,t.pass]),this.stencilFunc.set({func:t.test.func,ref:t.ref,mask:t.test.mask})):this.stencilTest.set(!1)},Lt.prototype.setColorMode=function(e){t.deepEqual(e.blendFunction,St.Replace)?this.blend.set(!1):(this.blend.set(!0),this.blendFunc.set(e.blendFunction),this.blendColor.set(e.blendColor)),this.colorMask.set(e.mask)},Lt.prototype.unbindVAO=function(){this.extVertexArrayObject&&this.bindVertexArrayOES.set(null)};var Ct=function(e){function r(r,n,i){var a=this;e.call(this),this.id=r,this.dispatcher=i,this.on("data",(function(t){"source"===t.dataType&&"metadata"===t.sourceDataType&&(a._sourceLoaded=!0),a._sourceLoaded&&!a._paused&&"source"===t.dataType&&"content"===t.sourceDataType&&(a.reload(),a.transform&&a.update(a.transform))})),this.on("error",(function(){a._sourceErrored=!0})),this._source=function(e,r,n,i){var a=new D[r.type](e,r,n,i);if(a.id!==e)throw new Error("Expected Source id to be "+e+" instead of "+a.id);return t.bindAll(["load","abort","unload","serialize","prepare"],a),a}(r,n,i,this),this._tiles={},this._cache=new N(0,this._unloadTile.bind(this)),this._timers={},this._cacheTimers={},this._maxTileCacheSize=null,this._loadedParentTiles={},this._coveredTiles={},this._state=new t.SourceFeatureState}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.onAdd=function(t){this.map=t,this._maxTileCacheSize=t?t._maxTileCacheSize:null,this._source&&this._source.onAdd&&this._source.onAdd(t)},r.prototype.onRemove=function(t){this._source&&this._source.onRemove&&this._source.onRemove(t)},r.prototype.loaded=function(){if(this._sourceErrored)return!0;if(!this._sourceLoaded)return!1;if(!this._source.loaded())return!1;for(var t in this._tiles){var e=this._tiles[t];if("loaded"!==e.state&&"errored"!==e.state)return!1}return!0},r.prototype.getSource=function(){return this._source},r.prototype.pause=function(){this._paused=!0},r.prototype.resume=function(){if(this._paused){var t=this._shouldReloadOnResume;this._paused=!1,this._shouldReloadOnResume=!1,t&&this.reload(),this.transform&&this.update(this.transform)}},r.prototype._loadTile=function(t,e){return this._source.loadTile(t,e)},r.prototype._unloadTile=function(t){if(this._source.unloadTile)return this._source.unloadTile(t,(function(){}))},r.prototype._abortTile=function(t){if(this._source.abortTile)return this._source.abortTile(t,(function(){}))},r.prototype.serialize=function(){return this._source.serialize()},r.prototype.prepare=function(t){for(var e in this._source.prepare&&this._source.prepare(),this._state.coalesceChanges(this._tiles,this.map?this.map.painter:null),this._tiles){var r=this._tiles[e];r.upload(t),r.prepare(this.map.style.imageManager)}},r.prototype.getIds=function(){return t.values(this._tiles).map((function(t){return t.tileID})).sort(Pt).map((function(t){return t.key}))},r.prototype.getRenderableIds=function(e){var r=this,n=[];for(var i in this._tiles)this._isIdRenderable(i,e)&&n.push(this._tiles[i]);return e?n.sort((function(e,n){var i=e.tileID,a=n.tileID,o=new t.Point(i.canonical.x,i.canonical.y)._rotate(r.transform.angle),s=new t.Point(a.canonical.x,a.canonical.y)._rotate(r.transform.angle);return i.overscaledZ-a.overscaledZ||s.y-o.y||s.x-o.x})).map((function(t){return t.tileID.key})):n.map((function(t){return t.tileID})).sort(Pt).map((function(t){return t.key}))},r.prototype.hasRenderableParent=function(t){var e=this.findLoadedParent(t,0);return!!e&&this._isIdRenderable(e.tileID.key)},r.prototype._isIdRenderable=function(t,e){return this._tiles[t]&&this._tiles[t].hasData()&&!this._coveredTiles[t]&&(e||!this._tiles[t].holdingForFade())},r.prototype.reload=function(){if(this._paused)this._shouldReloadOnResume=!0;else for(var t in this._cache.reset(),this._tiles)"errored"!==this._tiles[t].state&&this._reloadTile(t,"reloading")},r.prototype._reloadTile=function(t,e){var r=this._tiles[t];r&&("loading"!==r.state&&(r.state=e),this._loadTile(r,this._tileLoaded.bind(this,r,t,e)))},r.prototype._tileLoaded=function(e,r,n,i){if(i)return e.state="errored",void(404!==i.status?this._source.fire(new t.ErrorEvent(i,{tile:e})):this.update(this.transform));e.timeAdded=t.browser.now(),"expired"===n&&(e.refreshedUponExpiration=!0),this._setTileReloadTimer(r,e),"raster-dem"===this.getSource().type&&e.dem&&this._backfillDEM(e),this._state.initializeTileState(e,this.map?this.map.painter:null),this._source.fire(new t.Event("data",{dataType:"source",tile:e,coord:e.tileID}))},r.prototype._backfillDEM=function(t){for(var e=this.getRenderableIds(),r=0;r<e.length;r++){var n=e[r];if(t.neighboringTiles&&t.neighboringTiles[n]){var i=this.getTileByID(n);a(t,i),a(i,t)}}function a(t,e){t.needsHillshadePrepare=!0;var r=e.tileID.canonical.x-t.tileID.canonical.x,n=e.tileID.canonical.y-t.tileID.canonical.y,i=Math.pow(2,t.tileID.canonical.z),a=e.tileID.key;0===r&&0===n||Math.abs(n)>1||(Math.abs(r)>1&&(1===Math.abs(r+i)?r+=i:1===Math.abs(r-i)&&(r-=i)),e.dem&&t.dem&&(t.dem.backfillBorder(e.dem,r,n),t.neighboringTiles&&t.neighboringTiles[a]&&(t.neighboringTiles[a].backfilled=!0)))}},r.prototype.getTile=function(t){return this.getTileByID(t.key)},r.prototype.getTileByID=function(t){return this._tiles[t]},r.prototype._retainLoadedChildren=function(t,e,r,n){for(var i in this._tiles){var a=this._tiles[i];if(!(n[i]||!a.hasData()||a.tileID.overscaledZ<=e||a.tileID.overscaledZ>r)){for(var o=a.tileID;a&&a.tileID.overscaledZ>e+1;){var s=a.tileID.scaledTo(a.tileID.overscaledZ-1);(a=this._tiles[s.key])&&a.hasData()&&(o=s)}for(var l=o;l.overscaledZ>e;)if(t[(l=l.scaledTo(l.overscaledZ-1)).key]){n[o.key]=o;break}}}},r.prototype.findLoadedParent=function(t,e){if(t.key in this._loadedParentTiles){var r=this._loadedParentTiles[t.key];return r&&r.tileID.overscaledZ>=e?r:null}for(var n=t.overscaledZ-1;n>=e;n--){var i=t.scaledTo(n),a=this._getLoadedTile(i);if(a)return a}},r.prototype._getLoadedTile=function(t){var e=this._tiles[t.key];return e&&e.hasData()?e:this._cache.getByKey(t.wrapped().key)},r.prototype.updateCacheSize=function(t){var e=(Math.ceil(t.width/this._source.tileSize)+1)*(Math.ceil(t.height/this._source.tileSize)+1),r=Math.floor(5*e),n="number"==typeof this._maxTileCacheSize?Math.min(this._maxTileCacheSize,r):r;this._cache.setMaxSize(n)},r.prototype.handleWrapJump=function(t){var e=(t-(void 0===this._prevLng?t:this._prevLng))/360,r=Math.round(e);if(this._prevLng=t,r){var n={};for(var i in this._tiles){var a=this._tiles[i];a.tileID=a.tileID.unwrapTo(a.tileID.wrap+r),n[a.tileID.key]=a}for(var o in this._tiles=n,this._timers)clearTimeout(this._timers[o]),delete this._timers[o];for(var s in this._tiles){var l=this._tiles[s];this._setTileReloadTimer(s,l)}}},r.prototype.update=function(e){var n=this;if(this.transform=e,this._sourceLoaded&&!this._paused){var i;this.updateCacheSize(e),this.handleWrapJump(this.transform.center.lng),this._coveredTiles={},this.used?this._source.tileID?i=e.getVisibleUnwrappedCoordinates(this._source.tileID).map((function(e){return new t.OverscaledTileID(e.canonical.z,e.wrap,e.canonical.z,e.canonical.x,e.canonical.y)})):(i=e.coveringTiles({tileSize:this._source.tileSize,minzoom:this._source.minzoom,maxzoom:this._source.maxzoom,roundZoom:this._source.roundZoom,reparseOverscaled:this._source.reparseOverscaled}),this._source.hasTile&&(i=i.filter((function(t){return n._source.hasTile(t)})))):i=[];var a=e.coveringZoomLevel(this._source),o=Math.max(a-r.maxOverzooming,this._source.minzoom),s=Math.max(a+r.maxUnderzooming,this._source.minzoom),l=this._updateRetainedTiles(i,a);if(It(this._source.type)){for(var c={},u={},f=0,h=Object.keys(l);f<h.length;f+=1){var p=h[f],d=l[p],g=this._tiles[p];if(g&&!(g.fadeEndTime&&g.fadeEndTime<=t.browser.now())){var m=this.findLoadedParent(d,o);m&&(this._addTile(m.tileID),c[m.tileID.key]=m.tileID),u[p]=d}}for(var v in this._retainLoadedChildren(u,a,s,l),c)l[v]||(this._coveredTiles[v]=!0,l[v]=c[v])}for(var y in l)this._tiles[y].clearFadeHold();for(var x=0,b=t.keysDifference(this._tiles,l);x<b.length;x+=1){var _=b[x],w=this._tiles[_];w.hasSymbolBuckets&&!w.holdingForFade()?w.setHoldDuration(this.map._fadeDuration):w.hasSymbolBuckets&&!w.symbolFadeFinished()||this._removeTile(_)}this._updateLoadedParentTileCache()}},r.prototype.releaseSymbolFadeTiles=function(){for(var t in this._tiles)this._tiles[t].holdingForFade()&&this._removeTile(t)},r.prototype._updateRetainedTiles=function(t,e){for(var n={},i={},a=Math.max(e-r.maxOverzooming,this._source.minzoom),o=Math.max(e+r.maxUnderzooming,this._source.minzoom),s={},l=0,c=t;l<c.length;l+=1){var u=c[l],f=this._addTile(u);n[u.key]=u,f.hasData()||e<this._source.maxzoom&&(s[u.key]=u)}this._retainLoadedChildren(s,e,o,n);for(var h=0,p=t;h<p.length;h+=1){var d=p[h],g=this._tiles[d.key];if(!g.hasData()){if(e+1>this._source.maxzoom){var m=d.children(this._source.maxzoom)[0],v=this.getTile(m);if(v&&v.hasData()){n[m.key]=m;continue}}else{var y=d.children(this._source.maxzoom);if(n[y[0].key]&&n[y[1].key]&&n[y[2].key]&&n[y[3].key])continue}for(var x=g.wasRequested(),b=d.overscaledZ-1;b>=a;--b){var _=d.scaledTo(b);if(i[_.key])break;if(i[_.key]=!0,!(g=this.getTile(_))&&x&&(g=this._addTile(_)),g&&(n[_.key]=_,x=g.wasRequested(),g.hasData()))break}}}return n},r.prototype._updateLoadedParentTileCache=function(){for(var t in this._loadedParentTiles={},this._tiles){for(var e=[],r=void 0,n=this._tiles[t].tileID;n.overscaledZ>0;){if(n.key in this._loadedParentTiles){r=this._loadedParentTiles[n.key];break}e.push(n.key);var i=n.scaledTo(n.overscaledZ-1);if(r=this._getLoadedTile(i))break;n=i}for(var a=0,o=e;a<o.length;a+=1){var s=o[a];this._loadedParentTiles[s]=r}}},r.prototype._addTile=function(e){var r=this._tiles[e.key];if(r)return r;(r=this._cache.getAndRemove(e))&&(this._setTileReloadTimer(e.key,r),r.tileID=e,this._state.initializeTileState(r,this.map?this.map.painter:null),this._cacheTimers[e.key]&&(clearTimeout(this._cacheTimers[e.key]),delete this._cacheTimers[e.key],this._setTileReloadTimer(e.key,r)));var n=Boolean(r);return n||(r=new t.Tile(e,this._source.tileSize*e.overscaleFactor()),this._loadTile(r,this._tileLoaded.bind(this,r,e.key,r.state))),r?(r.uses++,this._tiles[e.key]=r,n||this._source.fire(new t.Event("dataloading",{tile:r,coord:r.tileID,dataType:"source"})),r):null},r.prototype._setTileReloadTimer=function(t,e){var r=this;t in this._timers&&(clearTimeout(this._timers[t]),delete this._timers[t]);var n=e.getExpiryTimeout();n&&(this._timers[t]=setTimeout((function(){r._reloadTile(t,"expired"),delete r._timers[t]}),n))},r.prototype._removeTile=function(t){var e=this._tiles[t];e&&(e.uses--,delete this._tiles[t],this._timers[t]&&(clearTimeout(this._timers[t]),delete this._timers[t]),e.uses>0||(e.hasData()&&"reloading"!==e.state?this._cache.add(e.tileID,e,e.getExpiryTimeout()):(e.aborted=!0,this._abortTile(e),this._unloadTile(e))))},r.prototype.clearTiles=function(){for(var t in this._shouldReloadOnResume=!1,this._paused=!1,this._tiles)this._removeTile(t);this._cache.reset()},r.prototype.tilesIn=function(e,r,n){var i=this,a=[],o=this.transform;if(!o)return a;for(var s=n?o.getCameraQueryGeometry(e):e,l=e.map((function(t){return o.pointCoordinate(t)})),c=s.map((function(t){return o.pointCoordinate(t)})),u=this.getIds(),f=1/0,h=1/0,p=-1/0,d=-1/0,g=0,m=c;g<m.length;g+=1){var v=m[g];f=Math.min(f,v.x),h=Math.min(h,v.y),p=Math.max(p,v.x),d=Math.max(d,v.y)}for(var y=function(e){var n=i._tiles[u[e]];if(!n.holdingForFade()){var s=n.tileID,g=Math.pow(2,o.zoom-n.tileID.overscaledZ),m=r*n.queryPadding*t.EXTENT/n.tileSize/g,v=[s.getTilePoint(new t.MercatorCoordinate(f,h)),s.getTilePoint(new t.MercatorCoordinate(p,d))];if(v[0].x-m<t.EXTENT&&v[0].y-m<t.EXTENT&&v[1].x+m>=0&&v[1].y+m>=0){var y=l.map((function(t){return s.getTilePoint(t)})),x=c.map((function(t){return s.getTilePoint(t)}));a.push({tile:n,tileID:s,queryGeometry:y,cameraQueryGeometry:x,scale:g})}}},x=0;x<u.length;x++)y(x);return a},r.prototype.getVisibleCoordinates=function(t){for(var e=this,r=this.getRenderableIds(t).map((function(t){return e._tiles[t].tileID})),n=0,i=r;n<i.length;n+=1){var a=i[n];a.posMatrix=this.transform.calculatePosMatrix(a.toUnwrapped())}return r},r.prototype.hasTransition=function(){if(this._source.hasTransition())return!0;if(It(this._source.type))for(var e in this._tiles){var r=this._tiles[e];if(void 0!==r.fadeEndTime&&r.fadeEndTime>=t.browser.now())return!0}return!1},r.prototype.setFeatureState=function(t,e,r){t=t||"_geojsonTileLayer",this._state.updateState(t,e,r)},r.prototype.removeFeatureState=function(t,e,r){t=t||"_geojsonTileLayer",this._state.removeFeatureState(t,e,r)},r.prototype.getFeatureState=function(t,e){return t=t||"_geojsonTileLayer",this._state.getState(t,e)},r.prototype.setDependencies=function(t,e,r){var n=this._tiles[t];n&&n.setDependencies(e,r)},r.prototype.reloadTilesForDependencies=function(t,e){for(var r in this._tiles){this._tiles[r].hasDependency(t,e)&&this._reloadTile(r,"reloading")}this._cache.filter((function(r){return!r.hasDependency(t,e)}))},r}(t.Evented);function Pt(t,e){var r=Math.abs(2*t.wrap)-+(t.wrap<0),n=Math.abs(2*e.wrap)-+(e.wrap<0);return t.overscaledZ-e.overscaledZ||n-r||e.canonical.y-t.canonical.y||e.canonical.x-t.canonical.x}function It(t){return"raster"===t||"image"===t||"video"===t}function Ot(){return new t.window.Worker(Zi.workerUrl)}Ct.maxOverzooming=10,Ct.maxUnderzooming=3;var zt="mapboxgl_preloaded_worker_pool",Dt=function(){this.active={}};Dt.prototype.acquire=function(t){if(!this.workers)for(this.workers=[];this.workers.length<Dt.workerCount;)this.workers.push(new Ot);return this.active[t]=!0,this.workers.slice()},Dt.prototype.release=function(t){delete this.active[t],0===this.numActive()&&(this.workers.forEach((function(t){t.terminate()})),this.workers=null)},Dt.prototype.isPreloaded=function(){return!!this.active[zt]},Dt.prototype.numActive=function(){return Object.keys(this.active).length};var Rt,Ft=Math.floor(t.browser.hardwareConcurrency/2);function Bt(){return Rt||(Rt=new Dt),Rt}function Nt(e,r){var n={};for(var i in e)"ref"!==i&&(n[i]=e[i]);return t.refProperties.forEach((function(t){t in r&&(n[t]=r[t])})),n}function jt(t){t=t.slice();for(var e=Object.create(null),r=0;r<t.length;r++)e[t[r].id]=t[r];for(var n=0;n<t.length;n++)"ref"in t[n]&&(t[n]=Nt(t[n],e[t[n].ref]));return t}Dt.workerCount=Math.max(Math.min(Ft,6),1);var Ut={setStyle:"setStyle",addLayer:"addLayer",removeLayer:"removeLayer",setPaintProperty:"setPaintProperty",setLayoutProperty:"setLayoutProperty",setFilter:"setFilter",addSource:"addSource",removeSource:"removeSource",setGeoJSONSourceData:"setGeoJSONSourceData",setLayerZoomRange:"setLayerZoomRange",setLayerProperty:"setLayerProperty",setCenter:"setCenter",setZoom:"setZoom",setBearing:"setBearing",setPitch:"setPitch",setSprite:"setSprite",setGlyphs:"setGlyphs",setTransition:"setTransition",setLight:"setLight"};function Vt(t,e,r){r.push({command:Ut.addSource,args:[t,e[t]]})}function Ht(t,e,r){e.push({command:Ut.removeSource,args:[t]}),r[t]=!0}function qt(t,e,r,n){Ht(t,r,n),Vt(t,e,r)}function Gt(e,r,n){var i;for(i in e[n])if(e[n].hasOwnProperty(i)&&"data"!==i&&!t.deepEqual(e[n][i],r[n][i]))return!1;for(i in r[n])if(r[n].hasOwnProperty(i)&&"data"!==i&&!t.deepEqual(e[n][i],r[n][i]))return!1;return!0}function Yt(e,r,n,i,a,o){var s;for(s in r=r||{},e=e||{})e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}));for(s in r)r.hasOwnProperty(s)&&!e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}))}function Wt(t){return t.id}function Xt(t,e){return t[e.id]=e,t}function Zt(e,r){if(!e)return[{command:Ut.setStyle,args:[r]}];var n=[];try{if(!t.deepEqual(e.version,r.version))return[{command:Ut.setStyle,args:[r]}];t.deepEqual(e.center,r.center)||n.push({command:Ut.setCenter,args:[r.center]}),t.deepEqual(e.zoom,r.zoom)||n.push({command:Ut.setZoom,args:[r.zoom]}),t.deepEqual(e.bearing,r.bearing)||n.push({command:Ut.setBearing,args:[r.bearing]}),t.deepEqual(e.pitch,r.pitch)||n.push({command:Ut.setPitch,args:[r.pitch]}),t.deepEqual(e.sprite,r.sprite)||n.push({command:Ut.setSprite,args:[r.sprite]}),t.deepEqual(e.glyphs,r.glyphs)||n.push({command:Ut.setGlyphs,args:[r.glyphs]}),t.deepEqual(e.transition,r.transition)||n.push({command:Ut.setTransition,args:[r.transition]}),t.deepEqual(e.light,r.light)||n.push({command:Ut.setLight,args:[r.light]});var i={},a=[];!function(e,r,n,i){var a;for(a in r=r||{},e=e||{})e.hasOwnProperty(a)&&(r.hasOwnProperty(a)||Ht(a,n,i));for(a in r)r.hasOwnProperty(a)&&(e.hasOwnProperty(a)?t.deepEqual(e[a],r[a])||("geojson"===e[a].type&&"geojson"===r[a].type&&Gt(e,r,a)?n.push({command:Ut.setGeoJSONSourceData,args:[a,r[a].data]}):qt(a,r,n,i)):Vt(a,r,n))}(e.sources,r.sources,a,i);var o=[];e.layers&&e.layers.forEach((function(t){i[t.source]?n.push({command:Ut.removeLayer,args:[t.id]}):o.push(t)})),n=n.concat(a),function(e,r,n){r=r||[];var i,a,o,s,l,c,u,f=(e=e||[]).map(Wt),h=r.map(Wt),p=e.reduce(Xt,{}),d=r.reduce(Xt,{}),g=f.slice(),m=Object.create(null);for(i=0,a=0;i<f.length;i++)o=f[i],d.hasOwnProperty(o)?a++:(n.push({command:Ut.removeLayer,args:[o]}),g.splice(g.indexOf(o,a),1));for(i=0,a=0;i<h.length;i++)o=h[h.length-1-i],g[g.length-1-i]!==o&&(p.hasOwnProperty(o)?(n.push({command:Ut.removeLayer,args:[o]}),g.splice(g.lastIndexOf(o,g.length-a),1)):a++,c=g[g.length-i],n.push({command:Ut.addLayer,args:[d[o],c]}),g.splice(g.length-i,0,o),m[o]=!0);for(i=0;i<h.length;i++)if(s=p[o=h[i]],l=d[o],!m[o]&&!t.deepEqual(s,l))if(t.deepEqual(s.source,l.source)&&t.deepEqual(s["source-layer"],l["source-layer"])&&t.deepEqual(s.type,l.type)){for(u in Yt(s.layout,l.layout,n,o,null,Ut.setLayoutProperty),Yt(s.paint,l.paint,n,o,null,Ut.setPaintProperty),t.deepEqual(s.filter,l.filter)||n.push({command:Ut.setFilter,args:[o,l.filter]}),t.deepEqual(s.minzoom,l.minzoom)&&t.deepEqual(s.maxzoom,l.maxzoom)||n.push({command:Ut.setLayerZoomRange,args:[o,l.minzoom,l.maxzoom]}),s)s.hasOwnProperty(u)&&"layout"!==u&&"paint"!==u&&"filter"!==u&&"metadata"!==u&&"minzoom"!==u&&"maxzoom"!==u&&(0===u.indexOf("paint.")?Yt(s[u],l[u],n,o,u.slice(6),Ut.setPaintProperty):t.deepEqual(s[u],l[u])||n.push({command:Ut.setLayerProperty,args:[o,u,l[u]]}));for(u in l)l.hasOwnProperty(u)&&!s.hasOwnProperty(u)&&"layout"!==u&&"paint"!==u&&"filter"!==u&&"metadata"!==u&&"minzoom"!==u&&"maxzoom"!==u&&(0===u.indexOf("paint.")?Yt(s[u],l[u],n,o,u.slice(6),Ut.setPaintProperty):t.deepEqual(s[u],l[u])||n.push({command:Ut.setLayerProperty,args:[o,u,l[u]]}))}else n.push({command:Ut.removeLayer,args:[o]}),c=g[g.lastIndexOf(o)+1],n.push({command:Ut.addLayer,args:[l,c]})}(o,r.layers,n)}catch(t){console.warn("Unable to compute style diff:",t),n=[{command:Ut.setStyle,args:[r]}]}return n}var Jt=function(t,e){this.reset(t,e)};Jt.prototype.reset=function(t,e){this.points=t||[],this._distances=[0];for(var r=1;r<this.points.length;r++)this._distances[r]=this._distances[r-1]+this.points[r].dist(this.points[r-1]);this.length=this._distances[this._distances.length-1],this.padding=Math.min(e||0,.5*this.length),this.paddedLength=this.length-2*this.padding},Jt.prototype.lerp=function(e){if(1===this.points.length)return this.points[0];e=t.clamp(e,0,1);for(var r=1,n=this._distances[r],i=e*this.paddedLength+this.padding;n<i&&r<this._distances.length;)n=this._distances[++r];var a=r-1,o=this._distances[a],s=n-o,l=s>0?(i-o)/s:0;return this.points[a].mult(1-l).add(this.points[r].mult(l))};var Kt=function(t,e,r){var n=this.boxCells=[],i=this.circleCells=[];this.xCellCount=Math.ceil(t/r),this.yCellCount=Math.ceil(e/r);for(var a=0;a<this.xCellCount*this.yCellCount;a++)n.push([]),i.push([]);this.circleKeys=[],this.boxKeys=[],this.bboxes=[],this.circles=[],this.width=t,this.height=e,this.xScale=this.xCellCount/t,this.yScale=this.yCellCount/e,this.boxUid=0,this.circleUid=0};function Qt(e,r,n,i,a){var o=t.create();return r?(t.scale(o,o,[1/a,1/a,1]),n||t.rotateZ(o,o,i.angle)):t.multiply(o,i.labelPlaneMatrix,e),o}function $t(e,r,n,i,a){if(r){var o=t.clone(e);return t.scale(o,o,[a,a,1]),n||t.rotateZ(o,o,-i.angle),o}return i.glCoordMatrix}function te(e,r){var n=[e.x,e.y,0,1];fe(n,n,r);var i=n[3];return{point:new t.Point(n[0]/i,n[1]/i),signedDistanceFromCamera:i}}function ee(t,e){return.5+t/e*.5}function re(t,e){var r=t[0]/t[3],n=t[1]/t[3];return r>=-e[0]&&r<=e[0]&&n>=-e[1]&&n<=e[1]}function ne(e,r,n,i,a,o,s,l){var c=i?e.textSizeData:e.iconSizeData,u=t.evaluateSizeForZoom(c,n.transform.zoom),f=[256/n.width*2+1,256/n.height*2+1],h=i?e.text.dynamicLayoutVertexArray:e.icon.dynamicLayoutVertexArray;h.clear();for(var p=e.lineVertexArray,d=i?e.text.placedSymbolArray:e.icon.placedSymbolArray,g=n.transform.width/n.transform.height,m=!1,v=0;v<d.length;v++){var y=d.get(v);if(y.hidden||y.writingMode===t.WritingMode.vertical&&!m)ue(y.numGlyphs,h);else{m=!1;var x=[y.anchorX,y.anchorY,0,1];if(t.transformMat4(x,x,r),re(x,f)){var b=x[3],_=ee(n.transform.cameraToCenterDistance,b),w=t.evaluateSizeForFeature(c,u,y),T=s?w/_:w*_,k=new t.Point(y.anchorX,y.anchorY),A=te(k,a).point,M={},S=oe(y,T,!1,l,r,a,o,e.glyphOffsetArray,p,h,A,k,M,g);m=S.useVertical,(S.notEnoughRoom||m||S.needsFlipping&&oe(y,T,!0,l,r,a,o,e.glyphOffsetArray,p,h,A,k,M,g).notEnoughRoom)&&ue(y.numGlyphs,h)}else ue(y.numGlyphs,h)}}i?e.text.dynamicLayoutVertexBuffer.updateData(h):e.icon.dynamicLayoutVertexBuffer.updateData(h)}function ie(t,e,r,n,i,a,o,s,l,c,u){var f=s.glyphStartIndex+s.numGlyphs,h=s.lineStartIndex,p=s.lineStartIndex+s.lineLength,d=e.getoffsetX(s.glyphStartIndex),g=e.getoffsetX(f-1),m=le(t*d,r,n,i,a,o,s.segment,h,p,l,c,u);if(!m)return null;var v=le(t*g,r,n,i,a,o,s.segment,h,p,l,c,u);return v?{first:m,last:v}:null}function ae(e,r,n,i){if(e===t.WritingMode.horizontal&&Math.abs(n.y-r.y)>Math.abs(n.x-r.x)*i)return{useVertical:!0};return(e===t.WritingMode.vertical?r.y<n.y:r.x>n.x)?{needsFlipping:!0}:null}function oe(e,r,n,i,a,o,s,l,c,u,f,h,p,d){var g,m=r/24,v=e.lineOffsetX*m,y=e.lineOffsetY*m;if(e.numGlyphs>1){var x=e.glyphStartIndex+e.numGlyphs,b=e.lineStartIndex,_=e.lineStartIndex+e.lineLength,w=ie(m,l,v,y,n,f,h,e,c,o,p);if(!w)return{notEnoughRoom:!0};var T=te(w.first.point,s).point,k=te(w.last.point,s).point;if(i&&!n){var A=ae(e.writingMode,T,k,d);if(A)return A}g=[w.first];for(var M=e.glyphStartIndex+1;M<x-1;M++)g.push(le(m*l.getoffsetX(M),v,y,n,f,h,e.segment,b,_,c,o,p));g.push(w.last)}else{if(i&&!n){var S=te(h,a).point,E=e.lineStartIndex+e.segment+1,L=new t.Point(c.getx(E),c.gety(E)),C=te(L,a),P=C.signedDistanceFromCamera>0?C.point:se(h,L,S,1,a),I=ae(e.writingMode,S,P,d);if(I)return I}var O=le(m*l.getoffsetX(e.glyphStartIndex),v,y,n,f,h,e.segment,e.lineStartIndex,e.lineStartIndex+e.lineLength,c,o,p);if(!O)return{notEnoughRoom:!0};g=[O]}for(var z=0,D=g;z<D.length;z+=1){var R=D[z];t.addDynamicAttributes(u,R.point,R.angle)}return{}}function se(t,e,r,n,i){var a=te(t.add(t.sub(e)._unit()),i).point,o=r.sub(a);return r.add(o._mult(n/o.mag()))}function le(e,r,n,i,a,o,s,l,c,u,f,h){var p=i?e-r:e+r,d=p>0?1:-1,g=0;i&&(d*=-1,g=Math.PI),d<0&&(g+=Math.PI);for(var m=d>0?l+s:l+s+1,v=a,y=a,x=0,b=0,_=Math.abs(p),w=[];x+b<=_;){if((m+=d)<l||m>=c)return null;if(y=v,w.push(v),void 0===(v=h[m])){var T=new t.Point(u.getx(m),u.gety(m)),k=te(T,f);if(k.signedDistanceFromCamera>0)v=h[m]=k.point;else{var A=m-d;v=se(0===x?o:new t.Point(u.getx(A),u.gety(A)),T,y,_-x+1,f)}}x+=b,b=y.dist(v)}var M=(_-x)/b,S=v.sub(y),E=S.mult(M)._add(y);E._add(S._unit()._perp()._mult(n*d));var L=g+Math.atan2(v.y-y.y,v.x-y.x);return w.push(E),{point:E,angle:L,path:w}}Kt.prototype.keysLength=function(){return this.boxKeys.length+this.circleKeys.length},Kt.prototype.insert=function(t,e,r,n,i){this._forEachCell(e,r,n,i,this._insertBoxCell,this.boxUid++),this.boxKeys.push(t),this.bboxes.push(e),this.bboxes.push(r),this.bboxes.push(n),this.bboxes.push(i)},Kt.prototype.insertCircle=function(t,e,r,n){this._forEachCell(e-n,r-n,e+n,r+n,this._insertCircleCell,this.circleUid++),this.circleKeys.push(t),this.circles.push(e),this.circles.push(r),this.circles.push(n)},Kt.prototype._insertBoxCell=function(t,e,r,n,i,a){this.boxCells[i].push(a)},Kt.prototype._insertCircleCell=function(t,e,r,n,i,a){this.circleCells[i].push(a)},Kt.prototype._query=function(t,e,r,n,i,a){if(r<0||t>this.width||n<0||e>this.height)return!i&&[];var o=[];if(t<=0&&e<=0&&this.width<=r&&this.height<=n){if(i)return!0;for(var s=0;s<this.boxKeys.length;s++)o.push({key:this.boxKeys[s],x1:this.bboxes[4*s],y1:this.bboxes[4*s+1],x2:this.bboxes[4*s+2],y2:this.bboxes[4*s+3]});for(var l=0;l<this.circleKeys.length;l++){var c=this.circles[3*l],u=this.circles[3*l+1],f=this.circles[3*l+2];o.push({key:this.circleKeys[l],x1:c-f,y1:u-f,x2:c+f,y2:u+f})}return a?o.filter(a):o}var h={hitTest:i,seenUids:{box:{},circle:{}}};return this._forEachCell(t,e,r,n,this._queryCell,o,h,a),i?o.length>0:o},Kt.prototype._queryCircle=function(t,e,r,n,i){var a=t-r,o=t+r,s=e-r,l=e+r;if(o<0||a>this.width||l<0||s>this.height)return!n&&[];var c=[],u={hitTest:n,circle:{x:t,y:e,radius:r},seenUids:{box:{},circle:{}}};return this._forEachCell(a,s,o,l,this._queryCellCircle,c,u,i),n?c.length>0:c},Kt.prototype.query=function(t,e,r,n,i){return this._query(t,e,r,n,!1,i)},Kt.prototype.hitTest=function(t,e,r,n,i){return this._query(t,e,r,n,!0,i)},Kt.prototype.hitTestCircle=function(t,e,r,n){return this._queryCircle(t,e,r,!0,n)},Kt.prototype._queryCell=function(t,e,r,n,i,a,o,s){var l=o.seenUids,c=this.boxCells[i];if(null!==c)for(var u=this.bboxes,f=0,h=c;f<h.length;f+=1){var p=h[f];if(!l.box[p]){l.box[p]=!0;var d=4*p;if(t<=u[d+2]&&e<=u[d+3]&&r>=u[d+0]&&n>=u[d+1]&&(!s||s(this.boxKeys[p]))){if(o.hitTest)return a.push(!0),!0;a.push({key:this.boxKeys[p],x1:u[d],y1:u[d+1],x2:u[d+2],y2:u[d+3]})}}}var g=this.circleCells[i];if(null!==g)for(var m=this.circles,v=0,y=g;v<y.length;v+=1){var x=y[v];if(!l.circle[x]){l.circle[x]=!0;var b=3*x;if(this._circleAndRectCollide(m[b],m[b+1],m[b+2],t,e,r,n)&&(!s||s(this.circleKeys[x]))){if(o.hitTest)return a.push(!0),!0;var _=m[b],w=m[b+1],T=m[b+2];a.push({key:this.circleKeys[x],x1:_-T,y1:w-T,x2:_+T,y2:w+T})}}}},Kt.prototype._queryCellCircle=function(t,e,r,n,i,a,o,s){var l=o.circle,c=o.seenUids,u=this.boxCells[i];if(null!==u)for(var f=this.bboxes,h=0,p=u;h<p.length;h+=1){var d=p[h];if(!c.box[d]){c.box[d]=!0;var g=4*d;if(this._circleAndRectCollide(l.x,l.y,l.radius,f[g+0],f[g+1],f[g+2],f[g+3])&&(!s||s(this.boxKeys[d])))return a.push(!0),!0}}var m=this.circleCells[i];if(null!==m)for(var v=this.circles,y=0,x=m;y<x.length;y+=1){var b=x[y];if(!c.circle[b]){c.circle[b]=!0;var _=3*b;if(this._circlesCollide(v[_],v[_+1],v[_+2],l.x,l.y,l.radius)&&(!s||s(this.circleKeys[b])))return a.push(!0),!0}}},Kt.prototype._forEachCell=function(t,e,r,n,i,a,o,s){for(var l=this._convertToXCellCoord(t),c=this._convertToYCellCoord(e),u=this._convertToXCellCoord(r),f=this._convertToYCellCoord(n),h=l;h<=u;h++)for(var p=c;p<=f;p++){var d=this.xCellCount*p+h;if(i.call(this,t,e,r,n,d,a,o,s))return}},Kt.prototype._convertToXCellCoord=function(t){return Math.max(0,Math.min(this.xCellCount-1,Math.floor(t*this.xScale)))},Kt.prototype._convertToYCellCoord=function(t){return Math.max(0,Math.min(this.yCellCount-1,Math.floor(t*this.yScale)))},Kt.prototype._circlesCollide=function(t,e,r,n,i,a){var o=n-t,s=i-e,l=r+a;return l*l>o*o+s*s},Kt.prototype._circleAndRectCollide=function(t,e,r,n,i,a,o){var s=(a-n)/2,l=Math.abs(t-(n+s));if(l>s+r)return!1;var c=(o-i)/2,u=Math.abs(e-(i+c));if(u>c+r)return!1;if(l<=s||u<=c)return!0;var f=l-s,h=u-c;return f*f+h*h<=r*r};var ce=new Float32Array([-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0]);function ue(t,e){for(var r=0;r<t;r++){var n=e.length;e.resize(n+4),e.float32.set(ce,3*n)}}function fe(t,e,r){var n=e[0],i=e[1];return t[0]=r[0]*n+r[4]*i+r[12],t[1]=r[1]*n+r[5]*i+r[13],t[3]=r[3]*n+r[7]*i+r[15],t}var he=function(t,e,r){void 0===e&&(e=new Kt(t.width+200,t.height+200,25)),void 0===r&&(r=new Kt(t.width+200,t.height+200,25)),this.transform=t,this.grid=e,this.ignoredGrid=r,this.pitchfactor=Math.cos(t._pitch)*t.cameraToCenterDistance,this.screenRightBoundary=t.width+100,this.screenBottomBoundary=t.height+100,this.gridRightBoundary=t.width+200,this.gridBottomBoundary=t.height+200};function pe(e,r,n){return r*(t.EXTENT/(e.tileSize*Math.pow(2,n-e.tileID.overscaledZ)))}he.prototype.placeCollisionBox=function(t,e,r,n,i){var a=this.projectAndGetPerspectiveRatio(n,t.anchorPointX,t.anchorPointY),o=r*a.perspectiveRatio,s=t.x1*o+a.point.x,l=t.y1*o+a.point.y,c=t.x2*o+a.point.x,u=t.y2*o+a.point.y;return!this.isInsideGrid(s,l,c,u)||!e&&this.grid.hitTest(s,l,c,u,i)?{box:[],offscreen:!1}:{box:[s,l,c,u],offscreen:this.isOffscreen(s,l,c,u)}},he.prototype.placeCollisionCircles=function(e,r,n,i,a,o,s,l,c,u,f,h,p){var d=[],g=new t.Point(r.anchorX,r.anchorY),m=te(g,o),v=ee(this.transform.cameraToCenterDistance,m.signedDistanceFromCamera),y=(u?a/v:a*v)/t.ONE_EM,x=te(g,s).point,b=ie(y,i,r.lineOffsetX*y,r.lineOffsetY*y,!1,x,g,r,n,s,{}),_=!1,w=!1,T=!0;if(b){for(var k=.5*h*v+p,A=new t.Point(-100,-100),M=new t.Point(this.screenRightBoundary,this.screenBottomBoundary),S=new Jt,E=b.first,L=b.last,C=[],P=E.path.length-1;P>=1;P--)C.push(E.path[P]);for(var I=1;I<L.path.length;I++)C.push(L.path[I]);var O=2.5*k;if(l){var z=C.map((function(t){return te(t,l)}));C=z.some((function(t){return t.signedDistanceFromCamera<=0}))?[]:z.map((function(t){return t.point}))}var D=[];if(C.length>0){for(var R=C[0].clone(),F=C[0].clone(),B=1;B<C.length;B++)R.x=Math.min(R.x,C[B].x),R.y=Math.min(R.y,C[B].y),F.x=Math.max(F.x,C[B].x),F.y=Math.max(F.y,C[B].y);D=R.x>=A.x&&F.x<=M.x&&R.y>=A.y&&F.y<=M.y?[C]:F.x<A.x||R.x>M.x||F.y<A.y||R.y>M.y?[]:t.clipLine([C],A.x,A.y,M.x,M.y)}for(var N=0,j=D;N<j.length;N+=1){var U=j[N];S.reset(U,.25*k);var V=0;V=S.length<=.5*k?1:Math.ceil(S.paddedLength/O)+1;for(var H=0;H<V;H++){var q=H/Math.max(V-1,1),G=S.lerp(q),Y=G.x+100,W=G.y+100;d.push(Y,W,k,0);var X=Y-k,Z=W-k,J=Y+k,K=W+k;if(T=T&&this.isOffscreen(X,Z,J,K),w=w||this.isInsideGrid(X,Z,J,K),!e&&this.grid.hitTestCircle(Y,W,k,f)&&(_=!0,!c))return{circles:[],offscreen:!1,collisionDetected:_}}}}return{circles:!c&&_||!w?[]:d,offscreen:T,collisionDetected:_}},he.prototype.queryRenderedSymbols=function(e){if(0===e.length||0===this.grid.keysLength()&&0===this.ignoredGrid.keysLength())return{};for(var r=[],n=1/0,i=1/0,a=-1/0,o=-1/0,s=0,l=e;s<l.length;s+=1){var c=l[s],u=new t.Point(c.x+100,c.y+100);n=Math.min(n,u.x),i=Math.min(i,u.y),a=Math.max(a,u.x),o=Math.max(o,u.y),r.push(u)}for(var f={},h={},p=0,d=this.grid.query(n,i,a,o).concat(this.ignoredGrid.query(n,i,a,o));p<d.length;p+=1){var g=d[p],m=g.key;if(void 0===f[m.bucketInstanceId]&&(f[m.bucketInstanceId]={}),!f[m.bucketInstanceId][m.featureIndex]){var v=[new t.Point(g.x1,g.y1),new t.Point(g.x2,g.y1),new t.Point(g.x2,g.y2),new t.Point(g.x1,g.y2)];t.polygonIntersectsPolygon(r,v)&&(f[m.bucketInstanceId][m.featureIndex]=!0,void 0===h[m.bucketInstanceId]&&(h[m.bucketInstanceId]=[]),h[m.bucketInstanceId].push(m.featureIndex))}}return h},he.prototype.insertCollisionBox=function(t,e,r,n,i){var a={bucketInstanceId:r,featureIndex:n,collisionGroupID:i};(e?this.ignoredGrid:this.grid).insert(a,t[0],t[1],t[2],t[3])},he.prototype.insertCollisionCircles=function(t,e,r,n,i){for(var a=e?this.ignoredGrid:this.grid,o={bucketInstanceId:r,featureIndex:n,collisionGroupID:i},s=0;s<t.length;s+=4)a.insertCircle(o,t[s],t[s+1],t[s+2])},he.prototype.projectAndGetPerspectiveRatio=function(e,r,n){var i=[r,n,0,1];return fe(i,i,e),{point:new t.Point((i[0]/i[3]+1)/2*this.transform.width+100,(-i[1]/i[3]+1)/2*this.transform.height+100),perspectiveRatio:.5+this.transform.cameraToCenterDistance/i[3]*.5}},he.prototype.isOffscreen=function(t,e,r,n){return r<100||t>=this.screenRightBoundary||n<100||e>this.screenBottomBoundary},he.prototype.isInsideGrid=function(t,e,r,n){return r>=0&&t<this.gridRightBoundary&&n>=0&&e<this.gridBottomBoundary},he.prototype.getViewportMatrix=function(){var e=t.identity([]);return t.translate(e,e,[-100,-100,0]),e};var de=function(t,e,r,n){this.opacity=t?Math.max(0,Math.min(1,t.opacity+(t.placed?e:-e))):n&&r?1:0,this.placed=r};de.prototype.isHidden=function(){return 0===this.opacity&&!this.placed};var ge=function(t,e,r,n,i){this.text=new de(t?t.text:null,e,r,i),this.icon=new de(t?t.icon:null,e,n,i)};ge.prototype.isHidden=function(){return this.text.isHidden()&&this.icon.isHidden()};var me=function(t,e,r){this.text=t,this.icon=e,this.skipFade=r},ve=function(){this.invProjMatrix=t.create(),this.viewportMatrix=t.create(),this.circles=[]},ye=function(t,e,r,n,i){this.bucketInstanceId=t,this.featureIndex=e,this.sourceLayerIndex=r,this.bucketIndex=n,this.tileID=i},xe=function(t){this.crossSourceCollisions=t,this.maxGroupID=0,this.collisionGroups={}};function be(e,r,n,i,a){var o=t.getAnchorAlignment(e),s=-(o.horizontalAlign-.5)*r,l=-(o.verticalAlign-.5)*n,c=t.evaluateVariableOffset(e,i);return new t.Point(s+c[0]*a,l+c[1]*a)}function _e(e,r,n,i,a,o){var s=e.x1,l=e.x2,c=e.y1,u=e.y2,f=e.anchorPointX,h=e.anchorPointY,p=new t.Point(r,n);return i&&p._rotate(a?o:-o),{x1:s+p.x,y1:c+p.y,x2:l+p.x,y2:u+p.y,anchorPointX:f,anchorPointY:h}}xe.prototype.get=function(t){if(this.crossSourceCollisions)return{ID:0,predicate:null};if(!this.collisionGroups[t]){var e=++this.maxGroupID;this.collisionGroups[t]={ID:e,predicate:function(t){return t.collisionGroupID===e}}}return this.collisionGroups[t]};var we=function(t,e,r,n){this.transform=t.clone(),this.collisionIndex=new he(this.transform),this.placements={},this.opacities={},this.variableOffsets={},this.stale=!1,this.commitTime=0,this.fadeDuration=e,this.retainedQueryData={},this.collisionGroups=new xe(r),this.collisionCircleArrays={},this.prevPlacement=n,n&&(n.prevPlacement=void 0),this.placedOrientations={}};function Te(t,e,r,n,i){t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0)}we.prototype.getBucketParts=function(e,r,n,i){var a=n.getBucket(r),o=n.latestFeatureIndex;if(a&&o&&r.id===a.layerIds[0]){var s=n.collisionBoxArray,l=a.layers[0].layout,c=Math.pow(2,this.transform.zoom-n.tileID.overscaledZ),u=n.tileSize/t.EXTENT,f=this.transform.calculatePosMatrix(n.tileID.toUnwrapped()),h="map"===l.get("text-pitch-alignment"),p="map"===l.get("text-rotation-alignment"),d=pe(n,1,this.transform.zoom),g=Qt(f,h,p,this.transform,d),m=null;if(h){var v=$t(f,h,p,this.transform,d);m=t.multiply([],this.transform.labelPlaneMatrix,v)}this.retainedQueryData[a.bucketInstanceId]=new ye(a.bucketInstanceId,o,a.sourceLayerIndex,a.index,n.tileID);var y={bucket:a,layout:l,posMatrix:f,textLabelPlaneMatrix:g,labelToScreenMatrix:m,scale:c,textPixelRatio:u,holdingForFade:n.holdingForFade(),collisionBoxArray:s,partiallyEvaluatedTextSize:t.evaluateSizeForZoom(a.textSizeData,this.transform.zoom),collisionGroup:this.collisionGroups.get(a.sourceID)};if(i)for(var x=0,b=a.sortKeyRanges;x<b.length;x+=1){var _=b[x],w=_.sortKey,T=_.symbolInstanceStart,k=_.symbolInstanceEnd;e.push({sortKey:w,symbolInstanceStart:T,symbolInstanceEnd:k,parameters:y})}else e.push({symbolInstanceStart:0,symbolInstanceEnd:a.symbolInstances.length,parameters:y})}},we.prototype.attemptAnchorPlacement=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d){var g,m=[f.textOffset0,f.textOffset1],v=be(t,r,n,m,i),y=this.collisionIndex.placeCollisionBox(_e(e,v.x,v.y,a,o,this.transform.angle),u,s,l,c.predicate);if(d&&0===this.collisionIndex.placeCollisionBox(_e(d,v.x,v.y,a,o,this.transform.angle),u,s,l,c.predicate).box.length)return;if(y.box.length>0)return this.prevPlacement&&this.prevPlacement.variableOffsets[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID].text&&(g=this.prevPlacement.variableOffsets[f.crossTileID].anchor),this.variableOffsets[f.crossTileID]={textOffset:m,width:r,height:n,anchor:t,textBoxScale:i,prevAnchor:g},this.markUsedJustification(h,t,f,p),h.allowVerticalPlacement&&(this.markUsedOrientation(h,p,f),this.placedOrientations[f.crossTileID]=p),{shift:v,placedGlyphBoxes:y}},we.prototype.placeLayerBucketPart=function(e,r,n){var i=this,a=e.parameters,o=a.bucket,s=a.layout,l=a.posMatrix,c=a.textLabelPlaneMatrix,u=a.labelToScreenMatrix,f=a.textPixelRatio,h=a.holdingForFade,p=a.collisionBoxArray,d=a.partiallyEvaluatedTextSize,g=a.collisionGroup,m=s.get("text-optional"),v=s.get("icon-optional"),y=s.get("text-allow-overlap"),x=s.get("icon-allow-overlap"),b="map"===s.get("text-rotation-alignment"),_="map"===s.get("text-pitch-alignment"),w="none"!==s.get("icon-text-fit"),T="viewport-y"===s.get("symbol-z-order"),k=y&&(x||!o.hasIconData()||v),A=x&&(y||!o.hasTextData()||m);!o.collisionArrays&&p&&o.deserializeCollisionBoxes(p);var M=function(e,a){if(!r[e.crossTileID])if(h)i.placements[e.crossTileID]=new me(!1,!1,!1);else{var p,T=!1,M=!1,S=!0,E=null,L={box:null,offscreen:null},C={box:null,offscreen:null},P=null,I=null,O=0,z=0,D=0;a.textFeatureIndex?O=a.textFeatureIndex:e.useRuntimeCollisionCircles&&(O=e.featureIndex),a.verticalTextFeatureIndex&&(z=a.verticalTextFeatureIndex);var R=a.textBox;if(R){var F=function(r){var n=t.WritingMode.horizontal;if(o.allowVerticalPlacement&&!r&&i.prevPlacement){var a=i.prevPlacement.placedOrientations[e.crossTileID];a&&(i.placedOrientations[e.crossTileID]=a,n=a,i.markUsedOrientation(o,n,e))}return n},B=function(r,n){if(o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&a.verticalTextBox)for(var i=0,s=o.writingModes;i<s.length;i+=1){if(s[i]===t.WritingMode.vertical?(L=n(),C=L):L=r(),L&&L.box&&L.box.length)break}else L=r()};if(s.get("text-variable-anchor")){var N=s.get("text-variable-anchor");if(i.prevPlacement&&i.prevPlacement.variableOffsets[e.crossTileID]){var j=i.prevPlacement.variableOffsets[e.crossTileID];N.indexOf(j.anchor)>0&&(N=N.filter((function(t){return t!==j.anchor}))).unshift(j.anchor)}var U=function(t,r,n){for(var a=t.x2-t.x1,s=t.y2-t.y1,c=e.textBoxScale,u=w&&!x?r:null,h={box:[],offscreen:!1},p=y?2*N.length:N.length,d=0;d<p;++d){var m=N[d%N.length],v=d>=N.length,k=i.attemptAnchorPlacement(m,t,a,s,c,b,_,f,l,g,v,e,o,n,u);if(k&&(h=k.placedGlyphBoxes)&&h.box&&h.box.length){T=!0,E=k.shift;break}}return h};B((function(){return U(R,a.iconBox,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox,n=L&&L.box&&L.box.length;return o.allowVerticalPlacement&&!n&&e.numVerticalGlyphVertices>0&&r?U(r,a.verticalIconBox,t.WritingMode.vertical):{box:null,offscreen:null}})),L&&(T=L.box,S=L.offscreen);var V=F(L&&L.box);if(!T&&i.prevPlacement){var H=i.prevPlacement.variableOffsets[e.crossTileID];H&&(i.variableOffsets[e.crossTileID]=H,i.markUsedJustification(o,H.anchor,e,V))}}else{var q=function(t,r){var n=i.collisionIndex.placeCollisionBox(t,y,f,l,g.predicate);return n&&n.box&&n.box.length&&(i.markUsedOrientation(o,r,e),i.placedOrientations[e.crossTileID]=r),n};B((function(){return q(R,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox;return o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&r?q(r,t.WritingMode.vertical):{box:null,offscreen:null}})),F(L&&L.box&&L.box.length)}}if(T=(p=L)&&p.box&&p.box.length>0,S=p&&p.offscreen,e.useRuntimeCollisionCircles){var G=o.text.placedSymbolArray.get(e.centerJustifiedTextSymbolIndex),Y=t.evaluateSizeForFeature(o.textSizeData,d,G),W=s.get("text-padding"),X=e.collisionCircleDiameter;P=i.collisionIndex.placeCollisionCircles(y,G,o.lineVertexArray,o.glyphOffsetArray,Y,l,c,u,n,_,g.predicate,X,W),T=y||P.circles.length>0&&!P.collisionDetected,S=S&&P.offscreen}if(a.iconFeatureIndex&&(D=a.iconFeatureIndex),a.iconBox){var Z=function(t){var e=w&&E?_e(t,E.x,E.y,b,_,i.transform.angle):t;return i.collisionIndex.placeCollisionBox(e,x,f,l,g.predicate)};M=C&&C.box&&C.box.length&&a.verticalIconBox?(I=Z(a.verticalIconBox)).box.length>0:(I=Z(a.iconBox)).box.length>0,S=S&&I.offscreen}var J=m||0===e.numHorizontalGlyphVertices&&0===e.numVerticalGlyphVertices,K=v||0===e.numIconVertices;if(J||K?K?J||(M=M&&T):T=M&&T:M=T=M&&T,T&&p&&p.box&&(C&&C.box&&z?i.collisionIndex.insertCollisionBox(p.box,s.get("text-ignore-placement"),o.bucketInstanceId,z,g.ID):i.collisionIndex.insertCollisionBox(p.box,s.get("text-ignore-placement"),o.bucketInstanceId,O,g.ID)),M&&I&&i.collisionIndex.insertCollisionBox(I.box,s.get("icon-ignore-placement"),o.bucketInstanceId,D,g.ID),P&&(T&&i.collisionIndex.insertCollisionCircles(P.circles,s.get("text-ignore-placement"),o.bucketInstanceId,O,g.ID),n)){var Q=o.bucketInstanceId,$=i.collisionCircleArrays[Q];void 0===$&&($=i.collisionCircleArrays[Q]=new ve);for(var tt=0;tt<P.circles.length;tt+=4)$.circles.push(P.circles[tt+0]),$.circles.push(P.circles[tt+1]),$.circles.push(P.circles[tt+2]),$.circles.push(P.collisionDetected?1:0)}i.placements[e.crossTileID]=new me(T||k,M||A,S||o.justReloaded),r[e.crossTileID]=!0}};if(T)for(var S=o.getSortedSymbolIndexes(this.transform.angle),E=S.length-1;E>=0;--E){var L=S[E];M(o.symbolInstances.get(L),o.collisionArrays[L])}else for(var C=e.symbolInstanceStart;C<e.symbolInstanceEnd;C++)M(o.symbolInstances.get(C),o.collisionArrays[C]);if(n&&o.bucketInstanceId in this.collisionCircleArrays){var P=this.collisionCircleArrays[o.bucketInstanceId];t.invert(P.invProjMatrix,l),P.viewportMatrix=this.collisionIndex.getViewportMatrix()}o.justReloaded=!1},we.prototype.markUsedJustification=function(e,r,n,i){var a,o={left:n.leftJustifiedTextSymbolIndex,center:n.centerJustifiedTextSymbolIndex,right:n.rightJustifiedTextSymbolIndex};a=i===t.WritingMode.vertical?n.verticalPlacedTextSymbolIndex:o[t.getAnchorJustification(r)];for(var s=0,l=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex,n.verticalPlacedTextSymbolIndex];s<l.length;s+=1){var c=l[s];c>=0&&(e.text.placedSymbolArray.get(c).crossTileID=a>=0&&c!==a?0:n.crossTileID)}},we.prototype.markUsedOrientation=function(e,r,n){for(var i=r===t.WritingMode.horizontal||r===t.WritingMode.horizontalOnly?r:0,a=r===t.WritingMode.vertical?r:0,o=0,s=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex];o<s.length;o+=1){var l=s[o];e.text.placedSymbolArray.get(l).placedOrientation=i}n.verticalPlacedTextSymbolIndex&&(e.text.placedSymbolArray.get(n.verticalPlacedTextSymbolIndex).placedOrientation=a)},we.prototype.commit=function(t){this.commitTime=t,this.zoomAtLastRecencyCheck=this.transform.zoom;var e=this.prevPlacement,r=!1;this.prevZoomAdjustment=e?e.zoomAdjustment(this.transform.zoom):0;var n=e?e.symbolFadeChange(t):1,i=e?e.opacities:{},a=e?e.variableOffsets:{},o=e?e.placedOrientations:{};for(var s in this.placements){var l=this.placements[s],c=i[s];c?(this.opacities[s]=new ge(c,n,l.text,l.icon),r=r||l.text!==c.text.placed||l.icon!==c.icon.placed):(this.opacities[s]=new ge(null,n,l.text,l.icon,l.skipFade),r=r||l.text||l.icon)}for(var u in i){var f=i[u];if(!this.opacities[u]){var h=new ge(f,n,!1,!1);h.isHidden()||(this.opacities[u]=h,r=r||f.text.placed||f.icon.placed)}}for(var p in a)this.variableOffsets[p]||!this.opacities[p]||this.opacities[p].isHidden()||(this.variableOffsets[p]=a[p]);for(var d in o)this.placedOrientations[d]||!this.opacities[d]||this.opacities[d].isHidden()||(this.placedOrientations[d]=o[d]);r?this.lastPlacementChangeTime=t:"number"!=typeof this.lastPlacementChangeTime&&(this.lastPlacementChangeTime=e?e.lastPlacementChangeTime:t)},we.prototype.updateLayerOpacities=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1){var a=i[n],o=a.getBucket(t);o&&a.latestFeatureIndex&&t.id===o.layerIds[0]&&this.updateBucketOpacities(o,r,a.collisionBoxArray)}},we.prototype.updateBucketOpacities=function(e,r,n){var i=this;e.hasTextData()&&e.text.opacityVertexArray.clear(),e.hasIconData()&&e.icon.opacityVertexArray.clear(),e.hasIconCollisionBoxData()&&e.iconCollisionBox.collisionVertexArray.clear(),e.hasTextCollisionBoxData()&&e.textCollisionBox.collisionVertexArray.clear();var a=e.layers[0].layout,o=new ge(null,0,!1,!1,!0),s=a.get("text-allow-overlap"),l=a.get("icon-allow-overlap"),c=a.get("text-variable-anchor"),u="map"===a.get("text-rotation-alignment"),f="map"===a.get("text-pitch-alignment"),h="none"!==a.get("icon-text-fit"),p=new ge(null,0,s&&(l||!e.hasIconData()||a.get("icon-optional")),l&&(s||!e.hasTextData()||a.get("text-optional")),!0);!e.collisionArrays&&n&&(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData())&&e.deserializeCollisionBoxes(n);for(var d=function(t,e,r){for(var n=0;n<e/4;n++)t.opacityVertexArray.emplaceBack(r)},g=function(n){var a=e.symbolInstances.get(n),s=a.numHorizontalGlyphVertices,l=a.numVerticalGlyphVertices,g=a.crossTileID,m=r[g],v=i.opacities[g];m?v=o:v||(v=p,i.opacities[g]=v),r[g]=!0;var y=s>0||l>0,x=a.numIconVertices>0,b=i.placedOrientations[a.crossTileID],_=b===t.WritingMode.vertical,w=b===t.WritingMode.horizontal||b===t.WritingMode.horizontalOnly;if(y){var T=Pe(v.text),k=_?Ie:T;d(e.text,s,k);var A=w?Ie:T;d(e.text,l,A);var M=v.text.isHidden();[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t){t>=0&&(e.text.placedSymbolArray.get(t).hidden=M||_?1:0)})),a.verticalPlacedTextSymbolIndex>=0&&(e.text.placedSymbolArray.get(a.verticalPlacedTextSymbolIndex).hidden=M||w?1:0);var S=i.variableOffsets[a.crossTileID];S&&i.markUsedJustification(e,S.anchor,a,b);var E=i.placedOrientations[a.crossTileID];E&&(i.markUsedJustification(e,"left",a,E),i.markUsedOrientation(e,E,a))}if(x){var L=Pe(v.icon),C=!(h&&a.verticalPlacedIconSymbolIndex&&_);if(a.placedIconSymbolIndex>=0){var P=C?L:Ie;d(e.icon,a.numIconVertices,P),e.icon.placedSymbolArray.get(a.placedIconSymbolIndex).hidden=v.icon.isHidden()}if(a.verticalPlacedIconSymbolIndex>=0){var I=C?Ie:L;d(e.icon,a.numVerticalIconVertices,I),e.icon.placedSymbolArray.get(a.verticalPlacedIconSymbolIndex).hidden=v.icon.isHidden()}}if(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData()){var O=e.collisionArrays[n];if(O){var z=new t.Point(0,0);if(O.textBox||O.verticalTextBox){var D=!0;if(c){var R=i.variableOffsets[g];R?(z=be(R.anchor,R.width,R.height,R.textOffset,R.textBoxScale),u&&z._rotate(f?i.transform.angle:-i.transform.angle)):D=!1}O.textBox&&Te(e.textCollisionBox.collisionVertexArray,v.text.placed,!D||_,z.x,z.y),O.verticalTextBox&&Te(e.textCollisionBox.collisionVertexArray,v.text.placed,!D||w,z.x,z.y)}var F=Boolean(!w&&O.verticalIconBox);O.iconBox&&Te(e.iconCollisionBox.collisionVertexArray,v.icon.placed,F,h?z.x:0,h?z.y:0),O.verticalIconBox&&Te(e.iconCollisionBox.collisionVertexArray,v.icon.placed,!F,h?z.x:0,h?z.y:0)}}},m=0;m<e.symbolInstances.length;m++)g(m);if(e.sortFeatures(this.transform.angle),this.retainedQueryData[e.bucketInstanceId]&&(this.retainedQueryData[e.bucketInstanceId].featureSortOrder=e.featureSortOrder),e.hasTextData()&&e.text.opacityVertexBuffer&&e.text.opacityVertexBuffer.updateData(e.text.opacityVertexArray),e.hasIconData()&&e.icon.opacityVertexBuffer&&e.icon.opacityVertexBuffer.updateData(e.icon.opacityVertexArray),e.hasIconCollisionBoxData()&&e.iconCollisionBox.collisionVertexBuffer&&e.iconCollisionBox.collisionVertexBuffer.updateData(e.iconCollisionBox.collisionVertexArray),e.hasTextCollisionBoxData()&&e.textCollisionBox.collisionVertexBuffer&&e.textCollisionBox.collisionVertexBuffer.updateData(e.textCollisionBox.collisionVertexArray),e.bucketInstanceId in this.collisionCircleArrays){var v=this.collisionCircleArrays[e.bucketInstanceId];e.placementInvProjMatrix=v.invProjMatrix,e.placementViewportMatrix=v.viewportMatrix,e.collisionCircleArray=v.circles,delete this.collisionCircleArrays[e.bucketInstanceId]}},we.prototype.symbolFadeChange=function(t){return 0===this.fadeDuration?1:(t-this.commitTime)/this.fadeDuration+this.prevZoomAdjustment},we.prototype.zoomAdjustment=function(t){return Math.max(0,(this.transform.zoom-t)/1.5)},we.prototype.hasTransitions=function(t){return this.stale||t-this.lastPlacementChangeTime<this.fadeDuration},we.prototype.stillRecent=function(t,e){var r=this.zoomAtLastRecencyCheck===e?1-this.zoomAdjustment(e):1;return this.zoomAtLastRecencyCheck=e,this.commitTime+this.fadeDuration*r>t},we.prototype.setStale=function(){this.stale=!0};var ke=Math.pow(2,25),Ae=Math.pow(2,24),Me=Math.pow(2,17),Se=Math.pow(2,16),Ee=Math.pow(2,9),Le=Math.pow(2,8),Ce=Math.pow(2,1);function Pe(t){if(0===t.opacity&&!t.placed)return 0;if(1===t.opacity&&t.placed)return 4294967295;var e=t.placed?1:0,r=Math.floor(127*t.opacity);return r*ke+e*Ae+r*Me+e*Se+r*Ee+e*Le+r*Ce+e}var Ie=0,Oe=function(t){this._sortAcrossTiles="viewport-y"!==t.layout.get("symbol-z-order")&&void 0!==t.layout.get("symbol-sort-key").constantOr(1),this._currentTileIndex=0,this._currentPartIndex=0,this._seenCrossTileIDs={},this._bucketParts=[]};Oe.prototype.continuePlacement=function(t,e,r,n,i){for(var a=this._bucketParts;this._currentTileIndex<t.length;){var o=t[this._currentTileIndex];if(e.getBucketParts(a,n,o,this._sortAcrossTiles),this._currentTileIndex++,i())return!0}for(this._sortAcrossTiles&&(this._sortAcrossTiles=!1,a.sort((function(t,e){return t.sortKey-e.sortKey})));this._currentPartIndex<a.length;){var s=a[this._currentPartIndex];if(e.placeLayerBucketPart(s,this._seenCrossTileIDs,r),this._currentPartIndex++,i())return!0}return!1};var ze=function(t,e,r,n,i,a,o){this.placement=new we(t,i,a,o),this._currentPlacementIndex=e.length-1,this._forceFullPlacement=r,this._showCollisionBoxes=n,this._done=!1};ze.prototype.isDone=function(){return this._done},ze.prototype.continuePlacement=function(e,r,n){for(var i=this,a=t.browser.now(),o=function(){var e=t.browser.now()-a;return!i._forceFullPlacement&&e>2};this._currentPlacementIndex>=0;){var s=r[e[this._currentPlacementIndex]],l=this.placement.collisionIndex.transform.zoom;if("symbol"===s.type&&(!s.minzoom||s.minzoom<=l)&&(!s.maxzoom||s.maxzoom>l)){if(this._inProgressLayer||(this._inProgressLayer=new Oe(s)),this._inProgressLayer.continuePlacement(n[s.source],this.placement,this._showCollisionBoxes,s,o))return;delete this._inProgressLayer}this._currentPlacementIndex--}this._done=!0},ze.prototype.commit=function(t){return this.placement.commit(t),this.placement};var De=512/t.EXTENT/2,Re=function(t,e,r){this.tileID=t,this.indexedSymbolInstances={},this.bucketInstanceId=r;for(var n=0;n<e.length;n++){var i=e.get(n),a=i.key;this.indexedSymbolInstances[a]||(this.indexedSymbolInstances[a]=[]),this.indexedSymbolInstances[a].push({crossTileID:i.crossTileID,coord:this.getScaledCoordinates(i,t)})}};Re.prototype.getScaledCoordinates=function(e,r){var n=r.canonical.z-this.tileID.canonical.z,i=De/Math.pow(2,n);return{x:Math.floor((r.canonical.x*t.EXTENT+e.anchorX)*i),y:Math.floor((r.canonical.y*t.EXTENT+e.anchorY)*i)}},Re.prototype.findMatches=function(t,e,r){for(var n=this.tileID.canonical.z<e.canonical.z?1:Math.pow(2,this.tileID.canonical.z-e.canonical.z),i=0;i<t.length;i++){var a=t.get(i);if(!a.crossTileID){var o=this.indexedSymbolInstances[a.key];if(o)for(var s=this.getScaledCoordinates(a,e),l=0,c=o;l<c.length;l+=1){var u=c[l];if(Math.abs(u.coord.x-s.x)<=n&&Math.abs(u.coord.y-s.y)<=n&&!r[u.crossTileID]){r[u.crossTileID]=!0,a.crossTileID=u.crossTileID;break}}}}};var Fe=function(){this.maxCrossTileID=0};Fe.prototype.generate=function(){return++this.maxCrossTileID};var Be=function(){this.indexes={},this.usedCrossTileIDs={},this.lng=0};Be.prototype.handleWrapJump=function(t){var e=Math.round((t-this.lng)/360);if(0!==e)for(var r in this.indexes){var n=this.indexes[r],i={};for(var a in n){var o=n[a];o.tileID=o.tileID.unwrapTo(o.tileID.wrap+e),i[o.tileID.key]=o}this.indexes[r]=i}this.lng=t},Be.prototype.addBucket=function(t,e,r){if(this.indexes[t.overscaledZ]&&this.indexes[t.overscaledZ][t.key]){if(this.indexes[t.overscaledZ][t.key].bucketInstanceId===e.bucketInstanceId)return!1;this.removeBucketCrossTileIDs(t.overscaledZ,this.indexes[t.overscaledZ][t.key])}for(var n=0;n<e.symbolInstances.length;n++){e.symbolInstances.get(n).crossTileID=0}this.usedCrossTileIDs[t.overscaledZ]||(this.usedCrossTileIDs[t.overscaledZ]={});var i=this.usedCrossTileIDs[t.overscaledZ];for(var a in this.indexes){var o=this.indexes[a];if(Number(a)>t.overscaledZ)for(var s in o){var l=o[s];l.tileID.isChildOf(t)&&l.findMatches(e.symbolInstances,t,i)}else{var c=o[t.scaledTo(Number(a)).key];c&&c.findMatches(e.symbolInstances,t,i)}}for(var u=0;u<e.symbolInstances.length;u++){var f=e.symbolInstances.get(u);f.crossTileID||(f.crossTileID=r.generate(),i[f.crossTileID]=!0)}return void 0===this.indexes[t.overscaledZ]&&(this.indexes[t.overscaledZ]={}),this.indexes[t.overscaledZ][t.key]=new Re(t,e.symbolInstances,e.bucketInstanceId),!0},Be.prototype.removeBucketCrossTileIDs=function(t,e){for(var r in e.indexedSymbolInstances)for(var n=0,i=e.indexedSymbolInstances[r];n<i.length;n+=1){var a=i[n];delete this.usedCrossTileIDs[t][a.crossTileID]}},Be.prototype.removeStaleBuckets=function(t){var e=!1;for(var r in this.indexes){var n=this.indexes[r];for(var i in n)t[n[i].bucketInstanceId]||(this.removeBucketCrossTileIDs(r,n[i]),delete n[i],e=!0)}return e};var Ne=function(){this.layerIndexes={},this.crossTileIDs=new Fe,this.maxBucketInstanceId=0,this.bucketsInCurrentPlacement={}};Ne.prototype.addLayer=function(t,e,r){var n=this.layerIndexes[t.id];void 0===n&&(n=this.layerIndexes[t.id]=new Be);var i=!1,a={};n.handleWrapJump(r);for(var o=0,s=e;o<s.length;o+=1){var l=s[o],c=l.getBucket(t);c&&t.id===c.layerIds[0]&&(c.bucketInstanceId||(c.bucketInstanceId=++this.maxBucketInstanceId),n.addBucket(l.tileID,c,this.crossTileIDs)&&(i=!0),a[c.bucketInstanceId]=!0)}return n.removeStaleBuckets(a)&&(i=!0),i},Ne.prototype.pruneUnusedLayers=function(t){var e={};for(var r in t.forEach((function(t){e[t]=!0})),this.layerIndexes)e[r]||delete this.layerIndexes[r]};var je=function(e,r){return t.emitValidationErrors(e,r&&r.filter((function(t){return"source.canvas"!==t.identifier})))},Ue=t.pick(Ut,["addLayer","removeLayer","setPaintProperty","setLayoutProperty","setFilter","addSource","removeSource","setLayerZoomRange","setLight","setTransition","setGeoJSONSourceData"]),Ve=t.pick(Ut,["setCenter","setZoom","setBearing","setPitch"]),He=function(){var e={},r=t.styleSpec.$version;for(var n in t.styleSpec.$root){var i=t.styleSpec.$root[n];if(i.required){var a=null;null!=(a="version"===n?r:"array"===i.type?[]:{})&&(e[n]=a)}}return e}(),qe=function(e){function r(n,i){var a=this;void 0===i&&(i={}),e.call(this),this.map=n,this.dispatcher=new k(Bt(),this),this.imageManager=new h,this.imageManager.setEventedParent(this),this.glyphManager=new x(n._requestManager,i.localIdeographFontFamily),this.lineAtlas=new T(256,512),this.crossTileSymbolIndex=new Ne,this._layers={},this._serializedLayers={},this._order=[],this.sourceCaches={},this.zoomHistory=new t.ZoomHistory,this._loaded=!1,this._availableImages=[],this._resetUpdates(),this.dispatcher.broadcast("setReferrer",t.getReferrer());var o=this;this._rtlTextPluginCallback=r.registerForPluginStateChange((function(e){var r={pluginStatus:e.pluginStatus,pluginURL:e.pluginURL};o.dispatcher.broadcast("syncRTLPluginState",r,(function(e,r){if((t.triggerPluginCompletionEvent(e),r)&&r.every((function(t){return t})))for(var n in o.sourceCaches)o.sourceCaches[n].reload()}))})),this.on("data",(function(t){if("source"===t.dataType&&"metadata"===t.sourceDataType){var e=a.sourceCaches[t.sourceId];if(e){var r=e.getSource();if(r&&r.vectorLayerIds)for(var n in a._layers){var i=a._layers[n];i.source===r.id&&a._validateLayer(i)}}}}))}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadURL=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event("dataloading",{dataType:"style"}));var i="boolean"==typeof r.validate?r.validate:!t.isMapboxURL(e);e=this.map._requestManager.normalizeStyleURL(e,r.accessToken);var a=this.map._requestManager.transformRequest(e,t.ResourceType.Style);this._request=t.getJSON(a,(function(e,r){n._request=null,e?n.fire(new t.ErrorEvent(e)):r&&n._load(r,i)}))},r.prototype.loadJSON=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event("dataloading",{dataType:"style"})),this._request=t.browser.frame((function(){n._request=null,n._load(e,!1!==r.validate)}))},r.prototype.loadEmpty=function(){this.fire(new t.Event("dataloading",{dataType:"style"})),this._load(He,!1)},r.prototype._load=function(e,r){if(!r||!je(this,t.validateStyle(e))){for(var n in this._loaded=!0,this.stylesheet=e,e.sources)this.addSource(n,e.sources[n],{validate:!1});e.sprite?this._loadSprite(e.sprite):this.imageManager.setLoaded(!0),this.glyphManager.setURL(e.glyphs);var i=jt(this.stylesheet.layers);this._order=i.map((function(t){return t.id})),this._layers={},this._serializedLayers={};for(var a=0,o=i;a<o.length;a+=1){var s=o[a];(s=t.createStyleLayer(s)).setEventedParent(this,{layer:{id:s.id}}),this._layers[s.id]=s,this._serializedLayers[s.id]=s.serialize()}this.dispatcher.broadcast("setLayers",this._serializeLayers(this._order)),this.light=new w(this.stylesheet.light),this.fire(new t.Event("data",{dataType:"style"})),this.fire(new t.Event("style.load"))}},r.prototype._loadSprite=function(e){var r=this;this._spriteRequest=function(e,r,n){var i,a,o,s=t.browser.devicePixelRatio>1?"@2x":"",l=t.getJSON(r.transformRequest(r.normalizeSpriteURL(e,s,".json"),t.ResourceType.SpriteJSON),(function(t,e){l=null,o||(o=t,i=e,u())})),c=t.getImage(r.transformRequest(r.normalizeSpriteURL(e,s,".png"),t.ResourceType.SpriteImage),(function(t,e){c=null,o||(o=t,a=e,u())}));function u(){if(o)n(o);else if(i&&a){var e=t.browser.getImageData(a),r={};for(var s in i){var l=i[s],c=l.width,u=l.height,f=l.x,h=l.y,p=l.sdf,d=l.pixelRatio,g=l.stretchX,m=l.stretchY,v=l.content,y=new t.RGBAImage({width:c,height:u});t.RGBAImage.copy(e,y,{x:f,y:h},{x:0,y:0},{width:c,height:u}),r[s]={data:y,pixelRatio:d,sdf:p,stretchX:g,stretchY:m,content:v}}n(null,r)}}return{cancel:function(){l&&(l.cancel(),l=null),c&&(c.cancel(),c=null)}}}(e,this.map._requestManager,(function(e,n){if(r._spriteRequest=null,e)r.fire(new t.ErrorEvent(e));else if(n)for(var i in n)r.imageManager.addImage(i,n[i]);r.imageManager.setLoaded(!0),r._availableImages=r.imageManager.listImages(),r.dispatcher.broadcast("setImages",r._availableImages),r.fire(new t.Event("data",{dataType:"style"}))}))},r.prototype._validateLayer=function(e){var r=this.sourceCaches[e.source];if(r){var n=e.sourceLayer;if(n){var i=r.getSource();("geojson"===i.type||i.vectorLayerIds&&-1===i.vectorLayerIds.indexOf(n))&&this.fire(new t.ErrorEvent(new Error('Source layer "'+n+'" does not exist on source "'+i.id+'" as specified by style layer "'+e.id+'"')))}}},r.prototype.loaded=function(){if(!this._loaded)return!1;if(Object.keys(this._updatedSources).length)return!1;for(var t in this.sourceCaches)if(!this.sourceCaches[t].loaded())return!1;return!!this.imageManager.isLoaded()},r.prototype._serializeLayers=function(t){for(var e=[],r=0,n=t;r<n.length;r+=1){var i=n[r],a=this._layers[i];"custom"!==a.type&&e.push(a.serialize())}return e},r.prototype.hasTransitions=function(){if(this.light&&this.light.hasTransition())return!0;for(var t in this.sourceCaches)if(this.sourceCaches[t].hasTransition())return!0;for(var e in this._layers)if(this._layers[e].hasTransition())return!0;return!1},r.prototype._checkLoaded=function(){if(!this._loaded)throw new Error("Style is not done loading")},r.prototype.update=function(e){if(this._loaded){var r=this._changed;if(this._changed){var n=Object.keys(this._updatedLayers),i=Object.keys(this._removedLayers);for(var a in(n.length||i.length)&&this._updateWorkerLayers(n,i),this._updatedSources){var o=this._updatedSources[a];"reload"===o?this._reloadSource(a):"clear"===o&&this._clearSource(a)}for(var s in this._updateTilesForChangedImages(),this._updatedPaintProps)this._layers[s].updateTransitions(e);this.light.updateTransitions(e),this._resetUpdates()}for(var l in this.sourceCaches)this.sourceCaches[l].used=!1;for(var c=0,u=this._order;c<u.length;c+=1){var f=u[c],h=this._layers[f];h.recalculate(e,this._availableImages),!h.isHidden(e.zoom)&&h.source&&(this.sourceCaches[h.source].used=!0)}this.light.recalculate(e),this.z=e.zoom,r&&this.fire(new t.Event("data",{dataType:"style"}))}},r.prototype._updateTilesForChangedImages=function(){var t=Object.keys(this._changedImages);if(t.length){for(var e in this.sourceCaches)this.sourceCaches[e].reloadTilesForDependencies(["icons","patterns"],t);this._changedImages={}}},r.prototype._updateWorkerLayers=function(t,e){this.dispatcher.broadcast("updateLayers",{layers:this._serializeLayers(t),removedIds:e})},r.prototype._resetUpdates=function(){this._changed=!1,this._updatedLayers={},this._removedLayers={},this._updatedSources={},this._updatedPaintProps={},this._changedImages={}},r.prototype.setState=function(e){var r=this;if(this._checkLoaded(),je(this,t.validateStyle(e)))return!1;(e=t.clone$1(e)).layers=jt(e.layers);var n=Zt(this.serialize(),e).filter((function(t){return!(t.command in Ve)}));if(0===n.length)return!1;var i=n.filter((function(t){return!(t.command in Ue)}));if(i.length>0)throw new Error("Unimplemented: "+i.map((function(t){return t.command})).join(", ")+".");return n.forEach((function(t){"setTransition"!==t.command&&r[t.command].apply(r,t.args)})),this.stylesheet=e,!0},r.prototype.addImage=function(e,r){if(this.getImage(e))return this.fire(new t.ErrorEvent(new Error("An image with this name already exists.")));this.imageManager.addImage(e,r),this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.fire(new t.Event("data",{dataType:"style"}))},r.prototype.updateImage=function(t,e){this.imageManager.updateImage(t,e)},r.prototype.getImage=function(t){return this.imageManager.getImage(t)},r.prototype.removeImage=function(e){if(!this.getImage(e))return this.fire(new t.ErrorEvent(new Error("No image with this name exists.")));this.imageManager.removeImage(e),this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.fire(new t.Event("data",{dataType:"style"}))},r.prototype.listImages=function(){return this._checkLoaded(),this.imageManager.listImages()},r.prototype.addSource=function(e,r,n){var i=this;if(void 0===n&&(n={}),this._checkLoaded(),void 0!==this.sourceCaches[e])throw new Error("There is already a source with this ID");if(!r.type)throw new Error("The type property must be defined, but the only the following properties were given: "+Object.keys(r).join(", ")+".");if(!(["vector","raster","geojson","video","image"].indexOf(r.type)>=0)||!this._validate(t.validateStyle.source,"sources."+e,r,null,n)){this.map&&this.map._collectResourceTiming&&(r.collectResourceTiming=!0);var a=this.sourceCaches[e]=new Ct(e,r,this.dispatcher);a.style=this,a.setEventedParent(this,(function(){return{isSourceLoaded:i.loaded(),source:a.serialize(),sourceId:e}})),a.onAdd(this.map),this._changed=!0}},r.prototype.removeSource=function(e){if(this._checkLoaded(),void 0===this.sourceCaches[e])throw new Error("There is no source with this ID");for(var r in this._layers)if(this._layers[r].source===e)return this.fire(new t.ErrorEvent(new Error('Source "'+e+'" cannot be removed while layer "'+r+'" is using it.')));var n=this.sourceCaches[e];delete this.sourceCaches[e],delete this._updatedSources[e],n.fire(new t.Event("data",{sourceDataType:"metadata",dataType:"source",sourceId:e})),n.setEventedParent(null),n.clearTiles(),n.onRemove&&n.onRemove(this.map),this._changed=!0},r.prototype.setGeoJSONSourceData=function(t,e){this._checkLoaded(),this.sourceCaches[t].getSource().setData(e),this._changed=!0},r.prototype.getSource=function(t){return this.sourceCaches[t]&&this.sourceCaches[t].getSource()},r.prototype.addLayer=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=e.id;if(this.getLayer(i))this.fire(new t.ErrorEvent(new Error('Layer with id "'+i+'" already exists on this map')));else{var a;if("custom"===e.type){if(je(this,t.validateCustomStyleLayer(e)))return;a=t.createStyleLayer(e)}else{if("object"==typeof e.source&&(this.addSource(i,e.source),e=t.clone$1(e),e=t.extend(e,{source:i})),this._validate(t.validateStyle.layer,"layers."+i,e,{arrayIndex:-1},n))return;a=t.createStyleLayer(e),this._validateLayer(a),a.setEventedParent(this,{layer:{id:i}}),this._serializedLayers[a.id]=a.serialize()}var o=r?this._order.indexOf(r):this._order.length;if(r&&-1===o)this.fire(new t.ErrorEvent(new Error('Layer with id "'+r+'" does not exist on this map.')));else{if(this._order.splice(o,0,i),this._layerOrderChanged=!0,this._layers[i]=a,this._removedLayers[i]&&a.source&&"custom"!==a.type){var s=this._removedLayers[i];delete this._removedLayers[i],s.type!==a.type?this._updatedSources[a.source]="clear":(this._updatedSources[a.source]="reload",this.sourceCaches[a.source].pause())}this._updateLayer(a),a.onAdd&&a.onAdd(this.map)}}},r.prototype.moveLayer=function(e,r){if(this._checkLoaded(),this._changed=!0,this._layers[e]){if(e!==r){var n=this._order.indexOf(e);this._order.splice(n,1);var i=r?this._order.indexOf(r):this._order.length;r&&-1===i?this.fire(new t.ErrorEvent(new Error('Layer with id "'+r+'" does not exist on this map.'))):(this._order.splice(i,0,e),this._layerOrderChanged=!0)}}else this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be moved.")))},r.prototype.removeLayer=function(e){this._checkLoaded();var r=this._layers[e];if(r){r.setEventedParent(null);var n=this._order.indexOf(e);this._order.splice(n,1),this._layerOrderChanged=!0,this._changed=!0,this._removedLayers[e]=r,delete this._layers[e],delete this._serializedLayers[e],delete this._updatedLayers[e],delete this._updatedPaintProps[e],r.onRemove&&r.onRemove(this.map)}else this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be removed.")))},r.prototype.getLayer=function(t){return this._layers[t]},r.prototype.hasLayer=function(t){return t in this._layers},r.prototype.setLayerZoomRange=function(e,r,n){this._checkLoaded();var i=this.getLayer(e);i?i.minzoom===r&&i.maxzoom===n||(null!=r&&(i.minzoom=r),null!=n&&(i.maxzoom=n),this._updateLayer(i)):this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot have zoom extent.")))},r.prototype.setFilter=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=this.getLayer(e);if(i){if(!t.deepEqual(i.filter,r))return null==r?(i.filter=void 0,void this._updateLayer(i)):void(this._validate(t.validateStyle.filter,"layers."+i.id+".filter",r,null,n)||(i.filter=t.clone$1(r),this._updateLayer(i)))}else this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be filtered.")))},r.prototype.getFilter=function(e){return t.clone$1(this.getLayer(e).filter)},r.prototype.setLayoutProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getLayoutProperty(r),n)||(a.setLayoutProperty(r,n,i),this._updateLayer(a)):this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be styled.")))},r.prototype.getLayoutProperty=function(e,r){var n=this.getLayer(e);if(n)return n.getLayoutProperty(r);this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style.")))},r.prototype.setPaintProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getPaintProperty(r),n)||(a.setPaintProperty(r,n,i)&&this._updateLayer(a),this._changed=!0,this._updatedPaintProps[e]=!0):this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be styled.")))},r.prototype.getPaintProperty=function(t,e){return this.getLayer(t).getPaintProperty(e)},r.prototype.setFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=e.sourceLayer,a=this.sourceCaches[n];if(void 0!==a){var o=a.getSource().type;"geojson"===o&&i?this.fire(new t.ErrorEvent(new Error("GeoJSON sources cannot have a sourceLayer parameter."))):"vector"!==o||i?(void 0===e.id&&this.fire(new t.ErrorEvent(new Error("The feature id parameter must be provided."))),a.setFeatureState(i,e.id,r)):this.fire(new t.ErrorEvent(new Error("The sourceLayer parameter must be provided for vector source types.")))}else this.fire(new t.ErrorEvent(new Error("The source '"+n+"' does not exist in the map's style.")))},r.prototype.removeFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=this.sourceCaches[n];if(void 0!==i){var a=i.getSource().type,o="vector"===a?e.sourceLayer:void 0;"vector"!==a||o?r&&"string"!=typeof e.id&&"number"!=typeof e.id?this.fire(new t.ErrorEvent(new Error("A feature id is requred to remove its specific state property."))):i.removeFeatureState(o,e.id,r):this.fire(new t.ErrorEvent(new Error("The sourceLayer parameter must be provided for vector source types.")))}else this.fire(new t.ErrorEvent(new Error("The source '"+n+"' does not exist in the map's style.")))},r.prototype.getFeatureState=function(e){this._checkLoaded();var r=e.source,n=e.sourceLayer,i=this.sourceCaches[r];if(void 0!==i){if("vector"!==i.getSource().type||n)return void 0===e.id&&this.fire(new t.ErrorEvent(new Error("The feature id parameter must be provided."))),i.getFeatureState(n,e.id);this.fire(new t.ErrorEvent(new Error("The sourceLayer parameter must be provided for vector source types.")))}else this.fire(new t.ErrorEvent(new Error("The source '"+r+"' does not exist in the map's style.")))},r.prototype.getTransition=function(){return t.extend({duration:300,delay:0},this.stylesheet&&this.stylesheet.transition)},r.prototype.serialize=function(){return t.filterObject({version:this.stylesheet.version,name:this.stylesheet.name,metadata:this.stylesheet.metadata,light:this.stylesheet.light,center:this.stylesheet.center,zoom:this.stylesheet.zoom,bearing:this.stylesheet.bearing,pitch:this.stylesheet.pitch,sprite:this.stylesheet.sprite,glyphs:this.stylesheet.glyphs,transition:this.stylesheet.transition,sources:t.mapObject(this.sourceCaches,(function(t){return t.serialize()})),layers:this._serializeLayers(this._order)},(function(t){return void 0!==t}))},r.prototype._updateLayer=function(t){this._updatedLayers[t.id]=!0,t.source&&!this._updatedSources[t.source]&&"raster"!==this.sourceCaches[t.source].getSource().type&&(this._updatedSources[t.source]="reload",this.sourceCaches[t.source].pause()),this._changed=!0},r.prototype._flattenAndSortRenderedFeatures=function(t){for(var e=this,r=function(t){return"fill-extrusion"===e._layers[t].type},n={},i=[],a=this._order.length-1;a>=0;a--){var o=this._order[a];if(r(o)){n[o]=a;for(var s=0,l=t;s<l.length;s+=1){var c=l[s][o];if(c)for(var u=0,f=c;u<f.length;u+=1){var h=f[u];i.push(h)}}}}i.sort((function(t,e){return e.intersectionZ-t.intersectionZ}));for(var p=[],d=this._order.length-1;d>=0;d--){var g=this._order[d];if(r(g))for(var m=i.length-1;m>=0;m--){var v=i[m].feature;if(n[v.layer.id]<d)break;p.push(v),i.pop()}else for(var y=0,x=t;y<x.length;y+=1){var b=x[y][g];if(b)for(var _=0,w=b;_<w.length;_+=1){var T=w[_];p.push(T.feature)}}}return p},r.prototype.queryRenderedFeatures=function(e,r,n){r&&r.filter&&this._validate(t.validateStyle.filter,"queryRenderedFeatures.filter",r.filter,null,r);var i={};if(r&&r.layers){if(!Array.isArray(r.layers))return this.fire(new t.ErrorEvent(new Error("parameters.layers must be an Array."))),[];for(var a=0,o=r.layers;a<o.length;a+=1){var s=o[a],l=this._layers[s];if(!l)return this.fire(new t.ErrorEvent(new Error("The layer '"+s+"' does not exist in the map's style and cannot be queried for features."))),[];i[l.source]=!0}}var c=[];for(var u in r.availableImages=this._availableImages,this.sourceCaches)r.layers&&!i[u]||c.push(F(this.sourceCaches[u],this._layers,this._serializedLayers,e,r,n));return this.placement&&c.push(function(t,e,r,n,i,a,o){for(var s={},l=a.queryRenderedSymbols(n),c=[],u=0,f=Object.keys(l).map(Number);u<f.length;u+=1){var h=f[u];c.push(o[h])}c.sort(B);for(var p=function(){var r=g[d],n=r.featureIndex.lookupSymbolFeatures(l[r.bucketInstanceId],e,r.bucketIndex,r.sourceLayerIndex,i.filter,i.layers,i.availableImages,t);for(var a in n){var o=s[a]=s[a]||[],c=n[a];c.sort((function(t,e){var n=r.featureSortOrder;if(n){var i=n.indexOf(t.featureIndex);return n.indexOf(e.featureIndex)-i}return e.featureIndex-t.featureIndex}));for(var u=0,f=c;u<f.length;u+=1){var h=f[u];o.push(h)}}},d=0,g=c;d<g.length;d+=1)p();var m=function(e){s[e].forEach((function(n){var i=n.feature,a=t[e],o=r[a.source].getFeatureState(i.layer["source-layer"],i.id);i.source=i.layer.source,i.layer["source-layer"]&&(i.sourceLayer=i.layer["source-layer"]),i.state=o}))};for(var v in s)m(v);return s}(this._layers,this._serializedLayers,this.sourceCaches,e,r,this.placement.collisionIndex,this.placement.retainedQueryData)),this._flattenAndSortRenderedFeatures(c)},r.prototype.querySourceFeatures=function(e,r){r&&r.filter&&this._validate(t.validateStyle.filter,"querySourceFeatures.filter",r.filter,null,r);var n=this.sourceCaches[e];return n?function(t,e){for(var r=t.getRenderableIds().map((function(e){return t.getTileByID(e)})),n=[],i={},a=0;a<r.length;a++){var o=r[a],s=o.tileID.canonical.key;i[s]||(i[s]=!0,o.querySourceFeatures(n,e))}return n}(n,r):[]},r.prototype.addSourceType=function(t,e,n){return r.getSourceType(t)?n(new Error('A source type called "'+t+'" already exists.')):(r.setSourceType(t,e),e.workerSourceURL?void this.dispatcher.broadcast("loadWorkerSource",{name:t,url:e.workerSourceURL},n):n(null,null))},r.prototype.getLight=function(){return this.light.getLight()},r.prototype.setLight=function(e,r){void 0===r&&(r={}),this._checkLoaded();var n=this.light.getLight(),i=!1;for(var a in e)if(!t.deepEqual(e[a],n[a])){i=!0;break}if(i){var o={now:t.browser.now(),transition:t.extend({duration:300,delay:0},this.stylesheet.transition)};this.light.setLight(e,r),this.light.updateTransitions(o)}},r.prototype._validate=function(e,r,n,i,a){return void 0===a&&(a={}),(!a||!1!==a.validate)&&je(this,e.call(t.validateStyle,t.extend({key:r,style:this.serialize(),value:n,styleSpec:t.styleSpec},i)))},r.prototype._remove=function(){for(var e in this._request&&(this._request.cancel(),this._request=null),this._spriteRequest&&(this._spriteRequest.cancel(),this._spriteRequest=null),t.evented.off("pluginStateChange",this._rtlTextPluginCallback),this._layers){this._layers[e].setEventedParent(null)}for(var r in this.sourceCaches)this.sourceCaches[r].clearTiles(),this.sourceCaches[r].setEventedParent(null);this.imageManager.setEventedParent(null),this.setEventedParent(null),this.dispatcher.remove()},r.prototype._clearSource=function(t){this.sourceCaches[t].clearTiles()},r.prototype._reloadSource=function(t){this.sourceCaches[t].resume(),this.sourceCaches[t].reload()},r.prototype._updateSources=function(t){for(var e in this.sourceCaches)this.sourceCaches[e].update(t)},r.prototype._generateCollisionBoxes=function(){for(var t in this.sourceCaches)this._reloadSource(t)},r.prototype._updatePlacement=function(e,r,n,i,a){void 0===a&&(a=!1);for(var o=!1,s=!1,l={},c=0,u=this._order;c<u.length;c+=1){var f=u[c],h=this._layers[f];if("symbol"===h.type){if(!l[h.source]){var p=this.sourceCaches[h.source];l[h.source]=p.getRenderableIds(!0).map((function(t){return p.getTileByID(t)})).sort((function(t,e){return e.tileID.overscaledZ-t.tileID.overscaledZ||(t.tileID.isLessThan(e.tileID)?-1:1)}))}var d=this.crossTileSymbolIndex.addLayer(h,l[h.source],e.center.lng);o=o||d}}if(this.crossTileSymbolIndex.pruneUnusedLayers(this._order),((a=a||this._layerOrderChanged||0===n)||!this.pauseablePlacement||this.pauseablePlacement.isDone()&&!this.placement.stillRecent(t.browser.now(),e.zoom))&&(this.pauseablePlacement=new ze(e,this._order,a,r,n,i,this.placement),this._layerOrderChanged=!1),this.pauseablePlacement.isDone()?this.placement.setStale():(this.pauseablePlacement.continuePlacement(this._order,this._layers,l),this.pauseablePlacement.isDone()&&(this.placement=this.pauseablePlacement.commit(t.browser.now()),s=!0),o&&this.pauseablePlacement.placement.setStale()),s||o)for(var g=0,m=this._order;g<m.length;g+=1){var v=m[g],y=this._layers[v];"symbol"===y.type&&this.placement.updateLayerOpacities(y,l[y.source])}return!this.pauseablePlacement.isDone()||this.placement.hasTransitions(t.browser.now())},r.prototype._releaseSymbolFadeTiles=function(){for(var t in this.sourceCaches)this.sourceCaches[t].releaseSymbolFadeTiles()},r.prototype.getImages=function(t,e,r){this.imageManager.getImages(e.icons,r),this._updateTilesForChangedImages();var n=this.sourceCaches[e.source];n&&n.setDependencies(e.tileID.key,e.type,e.icons)},r.prototype.getGlyphs=function(t,e,r){this.glyphManager.getGlyphs(e.stacks,r)},r.prototype.getResource=function(e,r,n){return t.makeRequest(r,n)},r}(t.Evented);qe.getSourceType=function(t){return D[t]},qe.setSourceType=function(t,e){D[t]=e},qe.registerForPluginStateChange=t.registerForPluginStateChange;var Ge=t.createLayout([{name:"a_pos",type:"Int16",components:2}]),Ye=yr("#ifdef GL_ES\nprecision mediump float;\n#else\n#if !defined(lowp)\n#define lowp\n#endif\n#if !defined(mediump)\n#define mediump\n#endif\n#if !defined(highp)\n#define highp\n#endif\n#endif","#ifdef GL_ES\nprecision highp float;\n#else\n#if !defined(lowp)\n#define lowp\n#endif\n#if !defined(mediump)\n#define mediump\n#endif\n#if !defined(highp)\n#define highp\n#endif\n#endif\nvec2 unpack_float(const float packedValue) {int packedIntValue=int(packedValue);int v0=packedIntValue/256;return vec2(v0,packedIntValue-v0*256);}vec2 unpack_opacity(const float packedOpacity) {int intOpacity=int(packedOpacity)/2;return vec2(float(intOpacity)/127.0,mod(packedOpacity,2.0));}vec4 decode_color(const vec2 encodedColor) {return vec4(unpack_float(encodedColor[0])/255.0,unpack_float(encodedColor[1])/255.0\n);}float unpack_mix_vec2(const vec2 packedValue,const float t) {return mix(packedValue[0],packedValue[1],t);}vec4 unpack_mix_color(const vec4 packedColors,const float t) {vec4 minColor=decode_color(vec2(packedColors[0],packedColors[1]));vec4 maxColor=decode_color(vec2(packedColors[2],packedColors[3]));return mix(minColor,maxColor,t);}vec2 get_pattern_pos(const vec2 pixel_coord_upper,const vec2 pixel_coord_lower,const vec2 pattern_size,const float tile_units_to_pixels,const vec2 pos) {vec2 offset=mod(mod(mod(pixel_coord_upper,pattern_size)*256.0,pattern_size)*256.0+pixel_coord_lower,pattern_size);return (tile_units_to_pixels*pos+offset)/pattern_size;}"),We=yr("uniform vec4 u_color;uniform float u_opacity;void main() {gl_FragColor=u_color*u_opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}"),Xe=yr("uniform vec2 u_pattern_tl_a;uniform vec2 u_pattern_br_a;uniform vec2 u_pattern_tl_b;uniform vec2 u_pattern_br_b;uniform vec2 u_texsize;uniform float u_mix;uniform float u_opacity;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(u_pattern_tl_a/u_texsize,u_pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(u_pattern_tl_b/u_texsize,u_pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_mix)*u_opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_pattern_size_a;uniform vec2 u_pattern_size_b;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_scale_a;uniform float u_scale_b;uniform float u_tile_units_to_pixels;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_a*u_pattern_size_a,u_tile_units_to_pixels,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_b*u_pattern_size_b,u_tile_units_to_pixels,a_pos);}"),Ze=yr("varying vec3 v_data;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define mediump float radius\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define highp vec4 stroke_color\n#pragma mapbox: define mediump float stroke_width\n#pragma mapbox: define lowp float stroke_opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize mediump float radius\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize highp vec4 stroke_color\n#pragma mapbox: initialize mediump float stroke_width\n#pragma mapbox: initialize lowp float stroke_opacity\nvec2 extrude=v_data.xy;float extrude_length=length(extrude);lowp float antialiasblur=v_data.z;float antialiased_blur=-max(blur,antialiasblur);float opacity_t=smoothstep(0.0,antialiased_blur,extrude_length-1.0);float color_t=stroke_width < 0.01 ? 0.0 : smoothstep(antialiased_blur,0.0,extrude_length-radius/(radius+stroke_width));gl_FragColor=opacity_t*mix(color*opacity,stroke_color*stroke_opacity,color_t);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform bool u_scale_with_map;uniform bool u_pitch_with_map;uniform vec2 u_extrude_scale;uniform lowp float u_device_pixel_ratio;uniform highp float u_camera_to_center_distance;attribute vec2 a_pos;varying vec3 v_data;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define mediump float radius\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define highp vec4 stroke_color\n#pragma mapbox: define mediump float stroke_width\n#pragma mapbox: define lowp float stroke_opacity\nvoid main(void) {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize mediump float radius\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize highp vec4 stroke_color\n#pragma mapbox: initialize mediump float stroke_width\n#pragma mapbox: initialize lowp float stroke_opacity\nvec2 extrude=vec2(mod(a_pos,2.0)*2.0-1.0);vec2 circle_center=floor(a_pos*0.5);if (u_pitch_with_map) {vec2 corner_position=circle_center;if (u_scale_with_map) {corner_position+=extrude*(radius+stroke_width)*u_extrude_scale;} else {vec4 projected_center=u_matrix*vec4(circle_center,0,1);corner_position+=extrude*(radius+stroke_width)*u_extrude_scale*(projected_center.w/u_camera_to_center_distance);}gl_Position=u_matrix*vec4(corner_position,0,1);} else {gl_Position=u_matrix*vec4(circle_center,0,1);if (u_scale_with_map) {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*u_camera_to_center_distance;} else {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*gl_Position.w;}}lowp float antialiasblur=1.0/u_device_pixel_ratio/(radius+stroke_width);v_data=vec3(extrude.x,extrude.y,antialiasblur);}"),Je=yr("void main() {gl_FragColor=vec4(1.0);}","attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}"),Ke=yr("uniform highp float u_intensity;varying vec2 v_extrude;\n#pragma mapbox: define highp float weight\n#define GAUSS_COEF 0.3989422804014327\nvoid main() {\n#pragma mapbox: initialize highp float weight\nfloat d=-0.5*3.0*3.0*dot(v_extrude,v_extrude);float val=weight*u_intensity*GAUSS_COEF*exp(d);gl_FragColor=vec4(val,1.0,1.0,1.0);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform float u_extrude_scale;uniform float u_opacity;uniform float u_intensity;attribute vec2 a_pos;varying vec2 v_extrude;\n#pragma mapbox: define highp float weight\n#pragma mapbox: define mediump float radius\nconst highp float ZERO=1.0/255.0/16.0;\n#define GAUSS_COEF 0.3989422804014327\nvoid main(void) {\n#pragma mapbox: initialize highp float weight\n#pragma mapbox: initialize mediump float radius\nvec2 unscaled_extrude=vec2(mod(a_pos,2.0)*2.0-1.0);float S=sqrt(-2.0*log(ZERO/weight/u_intensity/GAUSS_COEF))/3.0;v_extrude=S*unscaled_extrude;vec2 extrude=v_extrude*radius*u_extrude_scale;vec4 pos=vec4(floor(a_pos*0.5)+extrude,0,1);gl_Position=u_matrix*pos;}"),Qe=yr("uniform sampler2D u_image;uniform sampler2D u_color_ramp;uniform float u_opacity;varying vec2 v_pos;void main() {float t=texture2D(u_image,v_pos).r;vec4 color=texture2D(u_color_ramp,vec2(t,0.5));gl_FragColor=color*u_opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(0.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_world;attribute vec2 a_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos*u_world,0,1);v_pos.x=a_pos.x;v_pos.y=1.0-a_pos.y;}"),$e=yr("varying float v_placed;varying float v_notUsed;void main() {float alpha=0.5;gl_FragColor=vec4(1.0,0.0,0.0,1.0)*alpha;if (v_placed > 0.5) {gl_FragColor=vec4(0.0,0.0,1.0,0.5)*alpha;}if (v_notUsed > 0.5) {gl_FragColor*=.1;}}","attribute vec2 a_pos;attribute vec2 a_anchor_pos;attribute vec2 a_extrude;attribute vec2 a_placed;attribute vec2 a_shift;uniform mat4 u_matrix;uniform vec2 u_extrude_scale;uniform float u_camera_to_center_distance;varying float v_placed;varying float v_notUsed;void main() {vec4 projectedPoint=u_matrix*vec4(a_anchor_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);gl_Position=u_matrix*vec4(a_pos,0.0,1.0);gl_Position.xy+=(a_extrude+a_shift)*u_extrude_scale*gl_Position.w*collision_perspective_ratio;v_placed=a_placed.x;v_notUsed=a_placed.y;}"),tr=yr("varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;void main() {float alpha=0.5*min(v_perspective_ratio,1.0);float stroke_radius=0.9*max(v_perspective_ratio,1.0);float distance_to_center=length(v_extrude);float distance_to_edge=abs(distance_to_center-v_radius);float opacity_t=smoothstep(-stroke_radius,0.0,-distance_to_edge);vec4 color=mix(vec4(0.0,0.0,1.0,0.5),vec4(1.0,0.0,0.0,1.0),v_collision);gl_FragColor=color*alpha*opacity_t;}","attribute vec2 a_pos;attribute float a_radius;attribute vec2 a_flags;uniform mat4 u_matrix;uniform mat4 u_inv_matrix;uniform vec2 u_viewport_size;uniform float u_camera_to_center_distance;varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;vec3 toTilePosition(vec2 screenPos) {vec4 rayStart=u_inv_matrix*vec4(screenPos,-1.0,1.0);vec4 rayEnd  =u_inv_matrix*vec4(screenPos, 1.0,1.0);rayStart.xyz/=rayStart.w;rayEnd.xyz  /=rayEnd.w;highp float t=(0.0-rayStart.z)/(rayEnd.z-rayStart.z);return mix(rayStart.xyz,rayEnd.xyz,t);}void main() {vec2 quadCenterPos=a_pos;float radius=a_radius;float collision=a_flags.x;float vertexIdx=a_flags.y;vec2 quadVertexOffset=vec2(mix(-1.0,1.0,float(vertexIdx >=2.0)),mix(-1.0,1.0,float(vertexIdx >=1.0 && vertexIdx <=2.0)));vec2 quadVertexExtent=quadVertexOffset*radius;vec3 tilePos=toTilePosition(quadCenterPos);vec4 clipPos=u_matrix*vec4(tilePos,1.0);highp float camera_to_anchor_distance=clipPos.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);float padding_factor=1.2;v_radius=radius;v_extrude=quadVertexExtent*padding_factor;v_perspective_ratio=collision_perspective_ratio;v_collision=collision;gl_Position=vec4(clipPos.xyz/clipPos.w,1.0)+vec4(quadVertexExtent*padding_factor/u_viewport_size*2.0,0.0,0.0);}"),er=yr("uniform highp vec4 u_color;uniform sampler2D u_overlay;varying vec2 v_uv;void main() {vec4 overlay_color=texture2D(u_overlay,v_uv);gl_FragColor=mix(u_color,overlay_color,overlay_color.a);}","attribute vec2 a_pos;varying vec2 v_uv;uniform mat4 u_matrix;uniform float u_overlay_scale;void main() {v_uv=a_pos/8192.0;gl_Position=u_matrix*vec4(a_pos*u_overlay_scale,0,1);}"),rr=yr("#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float opacity\ngl_FragColor=color*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","attribute vec2 a_pos;uniform mat4 u_matrix;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float opacity\ngl_Position=u_matrix*vec4(a_pos,0,1);}"),nr=yr("varying vec2 v_pos;\n#pragma mapbox: define highp vec4 outline_color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 outline_color\n#pragma mapbox: initialize lowp float opacity\nfloat dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=outline_color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","attribute vec2 a_pos;uniform mat4 u_matrix;uniform vec2 u_world;varying vec2 v_pos;\n#pragma mapbox: define highp vec4 outline_color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 outline_color\n#pragma mapbox: initialize lowp float opacity\ngl_Position=u_matrix*vec4(a_pos,0,1);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}"),ir=yr("uniform vec2 u_texsize;uniform sampler2D u_image;uniform float u_fade;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);float dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=mix(color1,color2,u_fade)*alpha*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_world;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;gl_Position=u_matrix*vec4(a_pos,0,1);vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,a_pos);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}"),ar=yr("uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_fade)*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileZoomRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileZoomRatio,a_pos);}"),or=yr("varying vec4 v_color;void main() {gl_FragColor=v_color;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;uniform float u_vertical_gradient;uniform lowp float u_opacity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec4 v_color;\n#pragma mapbox: define highp float base\n#pragma mapbox: define highp float height\n#pragma mapbox: define highp vec4 color\nvoid main() {\n#pragma mapbox: initialize highp float base\n#pragma mapbox: initialize highp float height\n#pragma mapbox: initialize highp vec4 color\nvec3 normal=a_normal_ed.xyz;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);gl_Position=u_matrix*vec4(a_pos,t > 0.0 ? height : base,1);float colorvalue=color.r*0.2126+color.g*0.7152+color.b*0.0722;v_color=vec4(0.0,0.0,0.0,1.0);vec4 ambientlight=vec4(0.03,0.03,0.03,1.0);color+=ambientlight;float directional=clamp(dot(normal/16384.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((1.0-colorvalue+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_color.r+=clamp(color.r*directional*u_lightcolor.r,mix(0.0,0.3,1.0-u_lightcolor.r),1.0);v_color.g+=clamp(color.g*directional*u_lightcolor.g,mix(0.0,0.3,1.0-u_lightcolor.g),1.0);v_color.b+=clamp(color.b*directional*u_lightcolor.b,mix(0.0,0.3,1.0-u_lightcolor.b),1.0);v_color*=u_opacity;}"),sr=yr("uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\n#pragma mapbox: define lowp float base\n#pragma mapbox: define lowp float height\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float base\n#pragma mapbox: initialize lowp float height\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);vec4 mixedColor=mix(color1,color2,u_fade);gl_FragColor=mixedColor*v_lighting;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_height_factor;uniform vec3 u_scale;uniform float u_vertical_gradient;uniform lowp float u_opacity;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\n#pragma mapbox: define lowp float base\n#pragma mapbox: define lowp float height\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float base\n#pragma mapbox: initialize lowp float height\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec3 normal=a_normal_ed.xyz;float edgedistance=a_normal_ed.w;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);float z=t > 0.0 ? height : base;gl_Position=u_matrix*vec4(a_pos,z,1);vec2 pos=normal.x==1.0 && normal.y==0.0 && normal.z==16384.0\n? a_pos\n: vec2(edgedistance,z*u_height_factor);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,pos);v_lighting=vec4(0.0,0.0,0.0,1.0);float directional=clamp(dot(normal/16383.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((0.5+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_lighting.rgb+=clamp(directional*u_lightcolor,mix(vec3(0.0),vec3(0.3),1.0-u_lightcolor),vec3(1.0));v_lighting*=u_opacity;}"),lr=yr("#ifdef GL_ES\nprecision highp float;\n#endif\nuniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_dimension;uniform float u_zoom;uniform float u_maxzoom;uniform vec4 u_unpack;float getElevation(vec2 coord,float bias) {vec4 data=texture2D(u_image,coord)*255.0;data.a=-1.0;return dot(data,u_unpack)/4.0;}void main() {vec2 epsilon=1.0/u_dimension;float a=getElevation(v_pos+vec2(-epsilon.x,-epsilon.y),0.0);float b=getElevation(v_pos+vec2(0,-epsilon.y),0.0);float c=getElevation(v_pos+vec2(epsilon.x,-epsilon.y),0.0);float d=getElevation(v_pos+vec2(-epsilon.x,0),0.0);float e=getElevation(v_pos,0.0);float f=getElevation(v_pos+vec2(epsilon.x,0),0.0);float g=getElevation(v_pos+vec2(-epsilon.x,epsilon.y),0.0);float h=getElevation(v_pos+vec2(0,epsilon.y),0.0);float i=getElevation(v_pos+vec2(epsilon.x,epsilon.y),0.0);float exaggeration=u_zoom < 2.0 ? 0.4 : u_zoom < 4.5 ? 0.35 : 0.3;vec2 deriv=vec2((c+f+f+i)-(a+d+d+g),(g+h+h+i)-(a+b+b+c))/ pow(2.0,(u_zoom-u_maxzoom)*exaggeration+19.2562-u_zoom);gl_FragColor=clamp(vec4(deriv.x/2.0+0.5,deriv.y/2.0+0.5,1.0,1.0),0.0,1.0);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_dimension;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);highp vec2 epsilon=1.0/u_dimension;float scale=(u_dimension.x-2.0)/u_dimension.x;v_pos=(a_texture_pos/8192.0)*scale+epsilon;}"),cr=yr("uniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_latrange;uniform vec2 u_light;uniform vec4 u_shadow;uniform vec4 u_highlight;uniform vec4 u_accent;\n#define PI 3.141592653589793\nvoid main() {vec4 pixel=texture2D(u_image,v_pos);vec2 deriv=((pixel.rg*2.0)-1.0);float scaleFactor=cos(radians((u_latrange[0]-u_latrange[1])*(1.0-v_pos.y)+u_latrange[1]));float slope=atan(1.25*length(deriv)/scaleFactor);float aspect=deriv.x !=0.0 ? atan(deriv.y,-deriv.x) : PI/2.0*(deriv.y > 0.0 ? 1.0 :-1.0);float intensity=u_light.x;float azimuth=u_light.y+PI;float base=1.875-intensity*1.75;float maxValue=0.5*PI;float scaledSlope=intensity !=0.5 ? ((pow(base,slope)-1.0)/(pow(base,maxValue)-1.0))*maxValue : slope;float accent=cos(scaledSlope);vec4 accent_color=(1.0-accent)*u_accent*clamp(intensity*2.0,0.0,1.0);float shade=abs(mod((aspect+azimuth)/PI+0.5,2.0)-1.0);vec4 shade_color=mix(u_shadow,u_highlight,shade)*sin(scaledSlope)*clamp(intensity*2.0,0.0,1.0);gl_FragColor=accent_color*(1.0-shade_color.a)+shade_color;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos=a_texture_pos/8192.0;}"),ur=yr("uniform lowp float u_device_pixel_ratio;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);gl_FragColor=color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","\n#define scale 0.015873016\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform vec2 u_units_to_pixels;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_linesofar;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float width\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float width\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}"),fr=yr("uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;varying highp float v_lineprogress;\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);vec4 color=texture2D(u_image,vec2(v_lineprogress,0.5));gl_FragColor=color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","\n#define MAX_LINE_DISTANCE 32767.0\n#define scale 0.015873016\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_lineprogress;\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float width\nvoid main() {\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float width\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_lineprogress=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0/MAX_LINE_DISTANCE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}"),hr=yr("uniform lowp float u_device_pixel_ratio;uniform vec2 u_texsize;uniform float u_fade;uniform mediump vec3 u_scale;uniform sampler2D u_image;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;vec2 pattern_size_a=vec2(display_size_a.x*fromScale/tileZoomRatio,display_size_a.y);vec2 pattern_size_b=vec2(display_size_b.x*toScale/tileZoomRatio,display_size_b.y);float aspect_a=display_size_a.y/v_width;float aspect_b=display_size_b.y/v_width;float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float x_a=mod(v_linesofar/pattern_size_a.x*aspect_a,1.0);float x_b=mod(v_linesofar/pattern_size_b.x*aspect_b,1.0);float y=0.5*v_normal.y+0.5;vec2 texel_size=1.0/u_texsize;vec2 pos_a=mix(pattern_tl_a*texel_size-texel_size,pattern_br_a*texel_size+texel_size,vec2(x_a,y));vec2 pos_b=mix(pattern_tl_b*texel_size-texel_size,pattern_br_b*texel_size+texel_size,vec2(x_b,y));vec4 color=mix(texture2D(u_image,pos_a),texture2D(u_image,pos_b),u_fade);gl_FragColor=color*alpha*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","\n#define scale 0.015873016\n#define LINE_DISTANCE_SCALE 2.0\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform vec2 u_units_to_pixels;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define mediump float width\n#pragma mapbox: define lowp float floorwidth\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize mediump float width\n#pragma mapbox: initialize lowp float floorwidth\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_linesofar=a_linesofar;v_width2=vec2(outset,inset);v_width=floorwidth;}"),pr=yr("uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;uniform float u_sdfgamma;uniform float u_mix;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float width\n#pragma mapbox: define lowp float floorwidth\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float width\n#pragma mapbox: initialize lowp float floorwidth\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float sdfdist_a=texture2D(u_image,v_tex_a).a;float sdfdist_b=texture2D(u_image,v_tex_b).a;float sdfdist=mix(sdfdist_a,sdfdist_b,u_mix);alpha*=smoothstep(0.5-u_sdfgamma/floorwidth,0.5+u_sdfgamma/floorwidth,sdfdist);gl_FragColor=color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","\n#define scale 0.015873016\n#define LINE_DISTANCE_SCALE 2.0\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_patternscale_a;uniform float u_tex_y_a;uniform vec2 u_patternscale_b;uniform float u_tex_y_b;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float width\n#pragma mapbox: define lowp float floorwidth\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float width\n#pragma mapbox: initialize lowp float floorwidth\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_tex_a=vec2(a_linesofar*u_patternscale_a.x/floorwidth,normal.y*u_patternscale_a.y+u_tex_y_a);v_tex_b=vec2(a_linesofar*u_patternscale_b.x/floorwidth,normal.y*u_patternscale_b.y+u_tex_y_b);v_width2=vec2(outset,inset);}"),dr=yr("uniform float u_fade_t;uniform float u_opacity;uniform sampler2D u_image0;uniform sampler2D u_image1;varying vec2 v_pos0;varying vec2 v_pos1;uniform float u_brightness_low;uniform float u_brightness_high;uniform float u_saturation_factor;uniform float u_contrast_factor;uniform vec3 u_spin_weights;void main() {vec4 color0=texture2D(u_image0,v_pos0);vec4 color1=texture2D(u_image1,v_pos1);if (color0.a > 0.0) {color0.rgb=color0.rgb/color0.a;}if (color1.a > 0.0) {color1.rgb=color1.rgb/color1.a;}vec4 color=mix(color0,color1,u_fade_t);color.a*=u_opacity;vec3 rgb=color.rgb;rgb=vec3(dot(rgb,u_spin_weights.xyz),dot(rgb,u_spin_weights.zxy),dot(rgb,u_spin_weights.yzx));float average=(color.r+color.g+color.b)/3.0;rgb+=(average-rgb)*u_saturation_factor;rgb=(rgb-0.5)*u_contrast_factor+0.5;vec3 u_high_vec=vec3(u_brightness_low,u_brightness_low,u_brightness_low);vec3 u_low_vec=vec3(u_brightness_high,u_brightness_high,u_brightness_high);gl_FragColor=vec4(mix(u_high_vec,u_low_vec,rgb)*color.a,color.a);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_tl_parent;uniform float u_scale_parent;uniform float u_buffer_scale;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos0;varying vec2 v_pos1;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos0=(((a_texture_pos/8192.0)-0.5)/u_buffer_scale )+0.5;v_pos1=(v_pos0*u_scale_parent)+u_tl_parent;}"),gr=yr("uniform sampler2D u_texture;varying vec2 v_tex;varying float v_fade_opacity;\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\nlowp float alpha=opacity*v_fade_opacity;gl_FragColor=texture2D(u_texture,v_tex)*alpha;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform highp float u_camera_to_center_distance;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform float u_fade_change;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform vec2 u_texsize;varying vec2 v_tex;varying float v_fade_opacity;\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;vec2 a_minFontScale=a_pixeloffset.zw/256.0;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\ncamera_to_anchor_distance/u_camera_to_center_distance :\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*max(a_minFontScale,fontScale)+a_pxoffset/16.0),0.0,1.0);v_tex=a_tex/u_texsize;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;v_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));}"),mr=yr("#define SDF_PX 8.0\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;uniform bool u_is_text;varying vec2 v_data0;varying vec3 v_data1;\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nfloat EDGE_GAMMA=0.105/u_device_pixel_ratio;vec2 tex=v_data0.xy;float gamma_scale=v_data1.x;float size=v_data1.y;float fade_opacity=v_data1[2];float fontScale=u_is_text ? size/24.0 : size;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;varying vec2 v_data0;varying vec3 v_data1;\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\ncamera_to_anchor_distance/u_camera_to_center_distance :\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale+a_pxoffset),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0=a_tex/u_texsize;v_data1=vec3(gamma_scale,size,interpolated_fade_opacity);}"),vr=yr("#define SDF_PX 8.0\n#define SDF 1.0\n#define ICON 0.0\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform sampler2D u_texture_icon;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;varying vec4 v_data0;varying vec4 v_data1;\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nfloat fade_opacity=v_data1[2];if (v_data1.w==ICON) {vec2 tex_icon=v_data0.zw;lowp float alpha=opacity*fade_opacity;gl_FragColor=texture2D(u_texture_icon,tex_icon)*alpha;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\nreturn;}vec2 tex=v_data0.xy;float EDGE_GAMMA=0.105/u_device_pixel_ratio;float gamma_scale=v_data1.x;float size=v_data1.y;float fontScale=size/24.0;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;uniform vec2 u_texsize_icon;varying vec4 v_data0;varying vec4 v_data1;\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);float is_sdf=a_size[0]-2.0*a_size_min;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\ncamera_to_anchor_distance/u_camera_to_center_distance :\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=size/24.0;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0.xy=a_tex/u_texsize;v_data0.zw=a_tex/u_texsize_icon;v_data1=vec4(gamma_scale,size,interpolated_fade_opacity,is_sdf);}");function yr(t,e){var r=/#pragma mapbox: ([\w]+) ([\w]+) ([\w]+) ([\w]+)/g,n={};return{fragmentSource:t=t.replace(r,(function(t,e,r,i,a){return n[a]=!0,"define"===e?"\n#ifndef HAS_UNIFORM_u_"+a+"\nvarying "+r+" "+i+" "+a+";\n#else\nuniform "+r+" "+i+" u_"+a+";\n#endif\n":"\n#ifdef HAS_UNIFORM_u_"+a+"\n    "+r+" "+i+" "+a+" = u_"+a+";\n#endif\n"})),vertexSource:e=e.replace(r,(function(t,e,r,i,a){var o="float"===i?"vec2":"vec4",s=a.match(/color/)?"color":o;return n[a]?"define"===e?"\n#ifndef HAS_UNIFORM_u_"+a+"\nuniform lowp float u_"+a+"_t;\nattribute "+r+" "+o+" a_"+a+";\nvarying "+r+" "+i+" "+a+";\n#else\nuniform "+r+" "+i+" u_"+a+";\n#endif\n":"vec4"===s?"\n#ifndef HAS_UNIFORM_u_"+a+"\n    "+a+" = a_"+a+";\n#else\n    "+r+" "+i+" "+a+" = u_"+a+";\n#endif\n":"\n#ifndef HAS_UNIFORM_u_"+a+"\n    "+a+" = unpack_mix_"+s+"(a_"+a+", u_"+a+"_t);\n#else\n    "+r+" "+i+" "+a+" = u_"+a+";\n#endif\n":"define"===e?"\n#ifndef HAS_UNIFORM_u_"+a+"\nuniform lowp float u_"+a+"_t;\nattribute "+r+" "+o+" a_"+a+";\n#else\nuniform "+r+" "+i+" u_"+a+";\n#endif\n":"vec4"===s?"\n#ifndef HAS_UNIFORM_u_"+a+"\n    "+r+" "+i+" "+a+" = a_"+a+";\n#else\n    "+r+" "+i+" "+a+" = u_"+a+";\n#endif\n":"\n#ifndef HAS_UNIFORM_u_"+a+"\n    "+r+" "+i+" "+a+" = unpack_mix_"+s+"(a_"+a+", u_"+a+"_t);\n#else\n    "+r+" "+i+" "+a+" = u_"+a+";\n#endif\n"}))}}var xr=Object.freeze({__proto__:null,prelude:Ye,background:We,backgroundPattern:Xe,circle:Ze,clippingMask:Je,heatmap:Ke,heatmapTexture:Qe,collisionBox:$e,collisionCircle:tr,debug:er,fill:rr,fillOutline:nr,fillOutlinePattern:ir,fillPattern:ar,fillExtrusion:or,fillExtrusionPattern:sr,hillshadePrepare:lr,hillshade:cr,line:ur,lineGradient:fr,linePattern:hr,lineSDF:pr,raster:dr,symbolIcon:gr,symbolSDF:mr,symbolTextAndIcon:vr}),br=function(){this.boundProgram=null,this.boundLayoutVertexBuffer=null,this.boundPaintVertexBuffers=[],this.boundIndexBuffer=null,this.boundVertexOffset=null,this.boundDynamicVertexBuffer=null,this.vao=null};br.prototype.bind=function(t,e,r,n,i,a,o,s){this.context=t;for(var l=this.boundPaintVertexBuffers.length!==n.length,c=0;!l&&c<n.length;c++)this.boundPaintVertexBuffers[c]!==n[c]&&(l=!0);var u=!this.vao||this.boundProgram!==e||this.boundLayoutVertexBuffer!==r||l||this.boundIndexBuffer!==i||this.boundVertexOffset!==a||this.boundDynamicVertexBuffer!==o||this.boundDynamicVertexBuffer2!==s;!t.extVertexArrayObject||u?this.freshBind(e,r,n,i,a,o,s):(t.bindVertexArrayOES.set(this.vao),o&&o.bind(),i&&i.dynamicDraw&&i.bind(),s&&s.bind())},br.prototype.freshBind=function(t,e,r,n,i,a,o){var s,l=t.numAttributes,c=this.context,u=c.gl;if(c.extVertexArrayObject)this.vao&&this.destroy(),this.vao=c.extVertexArrayObject.createVertexArrayOES(),c.bindVertexArrayOES.set(this.vao),s=0,this.boundProgram=t,this.boundLayoutVertexBuffer=e,this.boundPaintVertexBuffers=r,this.boundIndexBuffer=n,this.boundVertexOffset=i,this.boundDynamicVertexBuffer=a,this.boundDynamicVertexBuffer2=o;else{s=c.currentNumAttributes||0;for(var f=l;f<s;f++)u.disableVertexAttribArray(f)}e.enableAttributes(u,t);for(var h=0,p=r;h<p.length;h+=1){p[h].enableAttributes(u,t)}a&&a.enableAttributes(u,t),o&&o.enableAttributes(u,t),e.bind(),e.setVertexAttribPointers(u,t,i);for(var d=0,g=r;d<g.length;d+=1){var m=g[d];m.bind(),m.setVertexAttribPointers(u,t,i)}a&&(a.bind(),a.setVertexAttribPointers(u,t,i)),n&&n.bind(),o&&(o.bind(),o.setVertexAttribPointers(u,t,i)),c.currentNumAttributes=l},br.prototype.destroy=function(){this.vao&&(this.context.extVertexArrayObject.deleteVertexArrayOES(this.vao),this.vao=null)};var _r=function(t,e,r,n,i){var a=t.gl;this.program=a.createProgram();var o=r?r.defines():[];i&&o.push("#define OVERDRAW_INSPECTOR;");var s=o.concat(Ye.fragmentSource,e.fragmentSource).join("\n"),l=o.concat(Ye.vertexSource,e.vertexSource).join("\n"),c=a.createShader(a.FRAGMENT_SHADER);if(a.isContextLost())this.failedToCreate=!0;else{a.shaderSource(c,s),a.compileShader(c),a.attachShader(this.program,c);var u=a.createShader(a.VERTEX_SHADER);if(a.isContextLost())this.failedToCreate=!0;else{a.shaderSource(u,l),a.compileShader(u),a.attachShader(this.program,u);for(var f=r?r.layoutAttributes:[],h=0;h<f.length;h++)a.bindAttribLocation(this.program,h,f[h].name);a.linkProgram(this.program),a.deleteShader(u),a.deleteShader(c),this.numAttributes=a.getProgramParameter(this.program,a.ACTIVE_ATTRIBUTES),this.attributes={};for(var p={},d=0;d<this.numAttributes;d++){var g=a.getActiveAttrib(this.program,d);g&&(this.attributes[g.name]=a.getAttribLocation(this.program,g.name))}for(var m=a.getProgramParameter(this.program,a.ACTIVE_UNIFORMS),v=0;v<m;v++){var y=a.getActiveUniform(this.program,v);y&&(p[y.name]=a.getUniformLocation(this.program,y.name))}this.fixedUniforms=n(t,p),this.binderUniforms=r?r.getUniforms(t,p):[]}}};function wr(t,e,r){var n=1/pe(r,1,e.transform.tileZoom),i=Math.pow(2,r.tileID.overscaledZ),a=r.tileSize*Math.pow(2,e.transform.tileZoom)/i,o=a*(r.tileID.canonical.x+r.tileID.wrap*i),s=a*r.tileID.canonical.y;return{u_image:0,u_texsize:r.imageAtlasTexture.size,u_scale:[n,t.fromScale,t.toScale],u_fade:t.t,u_pixel_coord_upper:[o>>16,s>>16],u_pixel_coord_lower:[65535&o,65535&s]}}_r.prototype.draw=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g){var m,v=t.gl;if(!this.failedToCreate){for(var y in t.program.set(this.program),t.setDepthMode(r),t.setStencilMode(n),t.setColorMode(i),t.setCullFace(a),this.fixedUniforms)this.fixedUniforms[y].set(o[y]);p&&p.setUniforms(t,this.binderUniforms,f,{zoom:h});for(var x=(m={},m[v.LINES]=2,m[v.TRIANGLES]=3,m[v.LINE_STRIP]=1,m)[e],b=0,_=u.get();b<_.length;b+=1){var w=_[b],T=w.vaos||(w.vaos={});(T[s]||(T[s]=new br)).bind(t,this,l,p?p.getPaintVertexBuffers():[],c,w.vertexOffset,d,g),v.drawElements(e,w.primitiveLength*x,v.UNSIGNED_SHORT,w.primitiveOffset*x*2)}}};var Tr=function(e,r,n,i){var a=r.style.light,o=a.properties.get("position"),s=[o.x,o.y,o.z],l=t.create$1();"viewport"===a.properties.get("anchor")&&t.fromRotation(l,-r.transform.angle),t.transformMat3(s,s,l);var c=a.properties.get("color");return{u_matrix:e,u_lightpos:s,u_lightintensity:a.properties.get("intensity"),u_lightcolor:[c.r,c.g,c.b],u_vertical_gradient:+n,u_opacity:i}},kr=function(e,r,n,i,a,o,s){return t.extend(Tr(e,r,n,i),wr(o,r,s),{u_height_factor:-Math.pow(2,a.overscaledZ)/s.tileSize/8})},Ar=function(t){return{u_matrix:t}},Mr=function(e,r,n,i){return t.extend(Ar(e),wr(n,r,i))},Sr=function(t,e){return{u_matrix:t,u_world:e}},Er=function(e,r,n,i,a){return t.extend(Mr(e,r,n,i),{u_world:a})},Lr=function(e,r,n,i){var a,o,s=e.transform;if("map"===i.paint.get("circle-pitch-alignment")){var l=pe(n,1,s.zoom);a=!0,o=[l,l]}else a=!1,o=s.pixelsToGLUnits;return{u_camera_to_center_distance:s.cameraToCenterDistance,u_scale_with_map:+("map"===i.paint.get("circle-pitch-scale")),u_matrix:e.translatePosMatrix(r.posMatrix,n,i.paint.get("circle-translate"),i.paint.get("circle-translate-anchor")),u_pitch_with_map:+a,u_device_pixel_ratio:t.browser.devicePixelRatio,u_extrude_scale:o}},Cr=function(t,e,r){var n=pe(r,1,e.zoom),i=Math.pow(2,e.zoom-r.tileID.overscaledZ),a=r.tileID.overscaleFactor();return{u_matrix:t,u_camera_to_center_distance:e.cameraToCenterDistance,u_pixels_to_tile_units:n,u_extrude_scale:[e.pixelsToGLUnits[0]/(n*i),e.pixelsToGLUnits[1]/(n*i)],u_overscale_factor:a}},Pr=function(t,e,r){return{u_matrix:t,u_inv_matrix:e,u_camera_to_center_distance:r.cameraToCenterDistance,u_viewport_size:[r.width,r.height]}},Ir=function(t,e,r){return void 0===r&&(r=1),{u_matrix:t,u_color:e,u_overlay:0,u_overlay_scale:r}},Or=function(t){return{u_matrix:t}},zr=function(t,e,r,n){return{u_matrix:t,u_extrude_scale:pe(e,1,r),u_intensity:n}};function Dr(e,r){var n=Math.pow(2,r.canonical.z),i=r.canonical.y;return[new t.MercatorCoordinate(0,i/n).toLngLat().lat,new t.MercatorCoordinate(0,(i+1)/n).toLngLat().lat]}var Rr=function(e,r,n){var i=e.transform;return{u_matrix:Ur(e,r,n),u_ratio:1/pe(r,1,i.zoom),u_device_pixel_ratio:t.browser.devicePixelRatio,u_units_to_pixels:[1/i.pixelsToGLUnits[0],1/i.pixelsToGLUnits[1]]}},Fr=function(e,r,n){return t.extend(Rr(e,r,n),{u_image:0})},Br=function(e,r,n,i){var a=e.transform,o=jr(r,a);return{u_matrix:Ur(e,r,n),u_texsize:r.imageAtlasTexture.size,u_ratio:1/pe(r,1,a.zoom),u_device_pixel_ratio:t.browser.devicePixelRatio,u_image:0,u_scale:[o,i.fromScale,i.toScale],u_fade:i.t,u_units_to_pixels:[1/a.pixelsToGLUnits[0],1/a.pixelsToGLUnits[1]]}},Nr=function(e,r,n,i,a){var o=e.transform,s=e.lineAtlas,l=jr(r,o),c="round"===n.layout.get("line-cap"),u=s.getDash(i.from,c),f=s.getDash(i.to,c),h=u.width*a.fromScale,p=f.width*a.toScale;return t.extend(Rr(e,r,n),{u_patternscale_a:[l/h,-u.height/2],u_patternscale_b:[l/p,-f.height/2],u_sdfgamma:s.width/(256*Math.min(h,p)*t.browser.devicePixelRatio)/2,u_image:0,u_tex_y_a:u.y,u_tex_y_b:f.y,u_mix:a.t})};function jr(t,e){return 1/pe(t,1,e.tileZoom)}function Ur(t,e,r){return t.translatePosMatrix(e.tileID.posMatrix,e,r.paint.get("line-translate"),r.paint.get("line-translate-anchor"))}var Vr=function(t,e,r,n,i){return{u_matrix:t,u_tl_parent:e,u_scale_parent:r,u_buffer_scale:1,u_fade_t:n.mix,u_opacity:n.opacity*i.paint.get("raster-opacity"),u_image0:0,u_image1:1,u_brightness_low:i.paint.get("raster-brightness-min"),u_brightness_high:i.paint.get("raster-brightness-max"),u_saturation_factor:(o=i.paint.get("raster-saturation"),o>0?1-1/(1.001-o):-o),u_contrast_factor:(a=i.paint.get("raster-contrast"),a>0?1/(1-a):1+a),u_spin_weights:Hr(i.paint.get("raster-hue-rotate"))};var a,o};function Hr(t){t*=Math.PI/180;var e=Math.sin(t),r=Math.cos(t);return[(2*r+1)/3,(-Math.sqrt(3)*e-r+1)/3,(Math.sqrt(3)*e-r+1)/3]}var qr,Gr=function(t,e,r,n,i,a,o,s,l,c){var u=i.transform;return{u_is_size_zoom_constant:+("constant"===t||"source"===t),u_is_size_feature_constant:+("constant"===t||"camera"===t),u_size_t:e?e.uSizeT:0,u_size:e?e.uSize:0,u_camera_to_center_distance:u.cameraToCenterDistance,u_pitch:u.pitch/360*2*Math.PI,u_rotate_symbol:+r,u_aspect_ratio:u.width/u.height,u_fade_change:i.options.fadeDuration?i.symbolFadeChange:1,u_matrix:a,u_label_plane_matrix:o,u_coord_matrix:s,u_is_text:+l,u_pitch_with_map:+n,u_texsize:c,u_texture:0}},Yr=function(e,r,n,i,a,o,s,l,c,u,f){var h=a.transform;return t.extend(Gr(e,r,n,i,a,o,s,l,c,u),{u_gamma_scale:i?Math.cos(h._pitch)*h.cameraToCenterDistance:1,u_device_pixel_ratio:t.browser.devicePixelRatio,u_is_halo:+f})},Wr=function(e,r,n,i,a,o,s,l,c,u){return t.extend(Yr(e,r,n,i,a,o,s,l,!0,c,!0),{u_texsize_icon:u,u_texture_icon:1})},Xr=function(t,e,r){return{u_matrix:t,u_opacity:e,u_color:r}},Zr=function(e,r,n,i,a,o){return t.extend(function(t,e,r,n){var i=r.imageManager.getPattern(t.from.toString()),a=r.imageManager.getPattern(t.to.toString()),o=r.imageManager.getPixelSize(),s=o.width,l=o.height,c=Math.pow(2,n.tileID.overscaledZ),u=n.tileSize*Math.pow(2,r.transform.tileZoom)/c,f=u*(n.tileID.canonical.x+n.tileID.wrap*c),h=u*n.tileID.canonical.y;return{u_image:0,u_pattern_tl_a:i.tl,u_pattern_br_a:i.br,u_pattern_tl_b:a.tl,u_pattern_br_b:a.br,u_texsize:[s,l],u_mix:e.t,u_pattern_size_a:i.displaySize,u_pattern_size_b:a.displaySize,u_scale_a:e.fromScale,u_scale_b:e.toScale,u_tile_units_to_pixels:1/pe(n,1,r.transform.tileZoom),u_pixel_coord_upper:[f>>16,h>>16],u_pixel_coord_lower:[65535&f,65535&h]}}(i,o,n,a),{u_matrix:e,u_opacity:r})},Jr={fillExtrusion:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fillExtrusionPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_height_factor:new t.Uniform1f(e,r.u_height_factor),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fill:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},fillPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},fillOutline:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world)}},fillOutlinePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},circle:function(e,r){return{u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_scale_with_map:new t.Uniform1i(e,r.u_scale_with_map),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},collisionBox:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pixels_to_tile_units:new t.Uniform1f(e,r.u_pixels_to_tile_units),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_overscale_factor:new t.Uniform1f(e,r.u_overscale_factor)}},collisionCircle:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_inv_matrix:new t.UniformMatrix4f(e,r.u_inv_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_viewport_size:new t.Uniform2f(e,r.u_viewport_size)}},debug:function(e,r){return{u_color:new t.UniformColor(e,r.u_color),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_overlay:new t.Uniform1i(e,r.u_overlay),u_overlay_scale:new t.Uniform1f(e,r.u_overlay_scale)}},clippingMask:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmap:function(e,r){return{u_extrude_scale:new t.Uniform1f(e,r.u_extrude_scale),u_intensity:new t.Uniform1f(e,r.u_intensity),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmapTexture:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_color_ramp:new t.Uniform1i(e,r.u_color_ramp),u_opacity:new t.Uniform1f(e,r.u_opacity)}},hillshade:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_latrange:new t.Uniform2f(e,r.u_latrange),u_light:new t.Uniform2f(e,r.u_light),u_shadow:new t.UniformColor(e,r.u_shadow),u_highlight:new t.UniformColor(e,r.u_highlight),u_accent:new t.UniformColor(e,r.u_accent)}},hillshadePrepare:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_dimension:new t.Uniform2f(e,r.u_dimension),u_zoom:new t.Uniform1f(e,r.u_zoom),u_maxzoom:new t.Uniform1f(e,r.u_maxzoom),u_unpack:new t.Uniform4f(e,r.u_unpack)}},line:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels)}},lineGradient:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_image:new t.Uniform1i(e,r.u_image)}},linePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_texsize:new t.Uniform2f(e,r.u_texsize),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_image:new t.Uniform1i(e,r.u_image),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},lineSDF:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_patternscale_a:new t.Uniform2f(e,r.u_patternscale_a),u_patternscale_b:new t.Uniform2f(e,r.u_patternscale_b),u_sdfgamma:new t.Uniform1f(e,r.u_sdfgamma),u_image:new t.Uniform1i(e,r.u_image),u_tex_y_a:new t.Uniform1f(e,r.u_tex_y_a),u_tex_y_b:new t.Uniform1f(e,r.u_tex_y_b),u_mix:new t.Uniform1f(e,r.u_mix)}},raster:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_tl_parent:new t.Uniform2f(e,r.u_tl_parent),u_scale_parent:new t.Uniform1f(e,r.u_scale_parent),u_buffer_scale:new t.Uniform1f(e,r.u_buffer_scale),u_fade_t:new t.Uniform1f(e,r.u_fade_t),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image0:new t.Uniform1i(e,r.u_image0),u_image1:new t.Uniform1i(e,r.u_image1),u_brightness_low:new t.Uniform1f(e,r.u_brightness_low),u_brightness_high:new t.Uniform1f(e,r.u_brightness_high),u_saturation_factor:new t.Uniform1f(e,r.u_saturation_factor),u_contrast_factor:new t.Uniform1f(e,r.u_contrast_factor),u_spin_weights:new t.Uniform3f(e,r.u_spin_weights)}},symbolIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture)}},symbolSDF:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},symbolTextAndIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texsize_icon:new t.Uniform2f(e,r.u_texsize_icon),u_texture:new t.Uniform1i(e,r.u_texture),u_texture_icon:new t.Uniform1i(e,r.u_texture_icon),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},background:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_color:new t.UniformColor(e,r.u_color)}},backgroundPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image:new t.Uniform1i(e,r.u_image),u_pattern_tl_a:new t.Uniform2f(e,r.u_pattern_tl_a),u_pattern_br_a:new t.Uniform2f(e,r.u_pattern_br_a),u_pattern_tl_b:new t.Uniform2f(e,r.u_pattern_tl_b),u_pattern_br_b:new t.Uniform2f(e,r.u_pattern_br_b),u_texsize:new t.Uniform2f(e,r.u_texsize),u_mix:new t.Uniform1f(e,r.u_mix),u_pattern_size_a:new t.Uniform2f(e,r.u_pattern_size_a),u_pattern_size_b:new t.Uniform2f(e,r.u_pattern_size_b),u_scale_a:new t.Uniform1f(e,r.u_scale_a),u_scale_b:new t.Uniform1f(e,r.u_scale_b),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_tile_units_to_pixels:new t.Uniform1f(e,r.u_tile_units_to_pixels)}}};function Kr(e,r,n,i,a,o,s){for(var l=e.context,c=l.gl,u=e.useProgram("collisionBox"),f=[],h=0,p=0,d=0;d<i.length;d++){var g=i[d],m=r.getTile(g),v=m.getBucket(n);if(v){var y=g.posMatrix;0===a[0]&&0===a[1]||(y=e.translatePosMatrix(g.posMatrix,m,a,o));var x=s?v.textCollisionBox:v.iconCollisionBox,b=v.collisionCircleArray;if(b.length>0){var _=t.create(),w=y;t.mul(_,v.placementInvProjMatrix,e.transform.glCoordMatrix),t.mul(_,_,v.placementViewportMatrix),f.push({circleArray:b,circleOffset:p,transform:w,invTransform:_}),p=h+=b.length/4}x&&u.draw(l,c.LINES,At.disabled,Mt.disabled,e.colorModeForRenderPass(),Et.disabled,Cr(y,e.transform,m),n.id,x.layoutVertexBuffer,x.indexBuffer,x.segments,null,e.transform.zoom,null,null,x.collisionVertexBuffer)}}if(s&&f.length){var T=e.useProgram("collisionCircle"),k=new t.StructArrayLayout2f1f2i16;k.resize(4*h),k._trim();for(var A=0,M=0,S=f;M<S.length;M+=1)for(var E=S[M],L=0;L<E.circleArray.length/4;L++){var C=4*L,P=E.circleArray[C+0],I=E.circleArray[C+1],O=E.circleArray[C+2],z=E.circleArray[C+3];k.emplace(A++,P,I,O,z,0),k.emplace(A++,P,I,O,z,1),k.emplace(A++,P,I,O,z,2),k.emplace(A++,P,I,O,z,3)}(!qr||qr.length<2*h)&&(qr=function(e){var r=2*e,n=new t.StructArrayLayout3ui6;n.resize(r),n._trim();for(var i=0;i<r;i++){var a=6*i;n.uint16[a+0]=4*i+0,n.uint16[a+1]=4*i+1,n.uint16[a+2]=4*i+2,n.uint16[a+3]=4*i+2,n.uint16[a+4]=4*i+3,n.uint16[a+5]=4*i+0}return n}(h));for(var D=l.createIndexBuffer(qr,!0),R=l.createVertexBuffer(k,t.collisionCircleLayout.members,!0),F=0,B=f;F<B.length;F+=1){var N=B[F],j=Pr(N.transform,N.invTransform,e.transform);T.draw(l,c.TRIANGLES,At.disabled,Mt.disabled,e.colorModeForRenderPass(),Et.disabled,j,n.id,R,D,t.SegmentVector.simpleSegment(0,2*N.circleOffset,N.circleArray.length,N.circleArray.length/2),null,e.transform.zoom,null,null,null)}R.destroy(),D.destroy()}}var Qr=t.identity(new Float32Array(16));function $r(e,r,n,i,a,o){var s=t.getAnchorAlignment(e),l=-(s.horizontalAlign-.5)*r,c=-(s.verticalAlign-.5)*n,u=t.evaluateVariableOffset(e,i);return new t.Point((l/a+u[0])*o,(c/a+u[1])*o)}function tn(e,r,n,i,a,o,s,l,c,u,f){var h=e.text.placedSymbolArray,p=e.text.dynamicLayoutVertexArray,d=e.icon.dynamicLayoutVertexArray,g={};p.clear();for(var m=0;m<h.length;m++){var v=h.get(m),y=e.allowVerticalPlacement&&!v.placedOrientation,x=v.hidden||!v.crossTileID||y?null:i[v.crossTileID];if(x){var b=new t.Point(v.anchorX,v.anchorY),_=te(b,n?l:s),w=ee(o.cameraToCenterDistance,_.signedDistanceFromCamera),T=a.evaluateSizeForFeature(e.textSizeData,u,v)*w/t.ONE_EM;n&&(T*=e.tilePixelRatio/c);for(var k=x.width,A=x.height,M=$r(x.anchor,k,A,x.textOffset,x.textBoxScale,T),S=n?te(b.add(M),s).point:_.point.add(r?M.rotate(-o.angle):M),E=e.allowVerticalPlacement&&v.placedOrientation===t.WritingMode.vertical?Math.PI/2:0,L=0;L<v.numGlyphs;L++)t.addDynamicAttributes(p,S,E);f&&v.associatedIconIndex>=0&&(g[v.associatedIconIndex]={shiftedAnchor:S,angle:E})}else ue(v.numGlyphs,p)}if(f){d.clear();for(var C=e.icon.placedSymbolArray,P=0;P<C.length;P++){var I=C.get(P);if(I.hidden)ue(I.numGlyphs,d);else{var O=g[P];if(O)for(var z=0;z<I.numGlyphs;z++)t.addDynamicAttributes(d,O.shiftedAnchor,O.angle);else ue(I.numGlyphs,d)}}e.icon.dynamicLayoutVertexBuffer.updateData(d)}e.text.dynamicLayoutVertexBuffer.updateData(p)}function en(t,e,r){return r.iconsInText&&e?"symbolTextAndIcon":t?"symbolSDF":"symbolIcon"}function rn(e,r,n,i,a,o,s,l,c,u,f,h){for(var p=e.context,d=p.gl,g=e.transform,m="map"===l,v="map"===c,y=m&&"point"!==n.layout.get("symbol-placement"),x=m&&!v&&!y,b=void 0!==n.layout.get("symbol-sort-key").constantOr(1),_=e.depthModeForSublayer(0,At.ReadOnly),w=n.layout.get("text-variable-anchor"),T=[],k=0,A=i;k<A.length;k+=1){var M=A[k],S=r.getTile(M),E=S.getBucket(n);if(E){var L=a?E.text:E.icon;if(L&&L.segments.get().length){var C=L.programConfigurations.get(n.id),P=a||E.sdfIcons,I=a?E.textSizeData:E.iconSizeData,O=v||0!==g.pitch,z=e.useProgram(en(P,a,E),C),D=t.evaluateSizeForZoom(I,g.zoom),R=void 0,F=[0,0],B=void 0,N=void 0,j=null,U=void 0;if(a){if(B=S.glyphAtlasTexture,N=d.LINEAR,R=S.glyphAtlasTexture.size,E.iconsInText){F=S.imageAtlasTexture.size,j=S.imageAtlasTexture;var V="composite"===I.kind||"camera"===I.kind;U=O||e.options.rotating||e.options.zooming||V?d.LINEAR:d.NEAREST}}else{var H=1!==n.layout.get("icon-size").constantOr(0)||E.iconsNeedLinear;B=S.imageAtlasTexture,N=P||e.options.rotating||e.options.zooming||H||O?d.LINEAR:d.NEAREST,R=S.imageAtlasTexture.size}var q=pe(S,1,e.transform.zoom),G=Qt(M.posMatrix,v,m,e.transform,q),Y=$t(M.posMatrix,v,m,e.transform,q),W=w&&E.hasTextData(),X="none"!==n.layout.get("icon-text-fit")&&W&&E.hasIconData();y&&ne(E,M.posMatrix,e,a,G,Y,v,u);var Z=e.translatePosMatrix(M.posMatrix,S,o,s),J=y||a&&w||X?Qr:G,K=e.translatePosMatrix(Y,S,o,s,!0),Q=P&&0!==n.paint.get(a?"text-halo-width":"icon-halo-width").constantOr(1),$={program:z,buffers:L,uniformValues:P?E.iconsInText?Wr(I.kind,D,x,v,e,Z,J,K,R,F):Yr(I.kind,D,x,v,e,Z,J,K,a,R,!0):Gr(I.kind,D,x,v,e,Z,J,K,a,R),atlasTexture:B,atlasTextureIcon:j,atlasInterpolation:N,atlasInterpolationIcon:U,isSDF:P,hasHalo:Q};if(b)for(var tt=0,et=L.segments.get();tt<et.length;tt+=1){var rt=et[tt];T.push({segments:new t.SegmentVector([rt]),sortKey:rt.sortKey,state:$})}else T.push({segments:L.segments,sortKey:0,state:$})}}}b&&T.sort((function(t,e){return t.sortKey-e.sortKey}));for(var nt=0,it=T;nt<it.length;nt+=1){var at=it[nt],ot=at.state;if(p.activeTexture.set(d.TEXTURE0),ot.atlasTexture.bind(ot.atlasInterpolation,d.CLAMP_TO_EDGE),ot.atlasTextureIcon&&(p.activeTexture.set(d.TEXTURE1),ot.atlasTextureIcon&&ot.atlasTextureIcon.bind(ot.atlasInterpolationIcon,d.CLAMP_TO_EDGE)),ot.isSDF){var st=ot.uniformValues;ot.hasHalo&&(st.u_is_halo=1,nn(ot.buffers,at.segments,n,e,ot.program,_,f,h,st)),st.u_is_halo=0}nn(ot.buffers,at.segments,n,e,ot.program,_,f,h,ot.uniformValues)}}function nn(t,e,r,n,i,a,o,s,l){var c=n.context,u=c.gl;i.draw(c,u.TRIANGLES,a,o,s,Et.disabled,l,r.id,t.layoutVertexBuffer,t.indexBuffer,e,r.paint,n.transform.zoom,t.programConfigurations.get(r.id),t.dynamicLayoutVertexBuffer,t.opacityVertexBuffer)}function an(t,e,r,n,i,a,o){var s,l,c,u,f,h=t.context.gl,p=r.paint.get("fill-pattern"),d=p&&p.constantOr(1),g=r.getCrossfadeParameters();o?(l=d&&!r.getPaintProperty("fill-outline-color")?"fillOutlinePattern":"fillOutline",s=h.LINES):(l=d?"fillPattern":"fill",s=h.TRIANGLES);for(var m=0,v=n;m<v.length;m+=1){var y=v[m],x=e.getTile(y);if(!d||x.patternsLoaded()){var b=x.getBucket(r);if(b){var _=b.programConfigurations.get(r.id),w=t.useProgram(l,_);d&&(t.context.activeTexture.set(h.TEXTURE0),x.imageAtlasTexture.bind(h.LINEAR,h.CLAMP_TO_EDGE),_.updatePaintBuffers(g));var T=p.constantOr(null);if(T&&x.imageAtlas){var k=x.imageAtlas,A=k.patternPositions[T.to.toString()],M=k.patternPositions[T.from.toString()];A&&M&&_.setConstantPatternPositions(A,M)}var S=t.translatePosMatrix(y.posMatrix,x,r.paint.get("fill-translate"),r.paint.get("fill-translate-anchor"));if(o){u=b.indexBuffer2,f=b.segments2;var E=[h.drawingBufferWidth,h.drawingBufferHeight];c="fillOutlinePattern"===l&&d?Er(S,t,g,x,E):Sr(S,E)}else u=b.indexBuffer,f=b.segments,c=d?Mr(S,t,g,x):Ar(S);w.draw(t.context,s,i,t.stencilModeForClipping(y),a,Et.disabled,c,r.id,b.layoutVertexBuffer,u,f,r.paint,t.transform.zoom,_)}}}}function on(t,e,r,n,i,a,o){for(var s=t.context,l=s.gl,c=r.paint.get("fill-extrusion-pattern"),u=c.constantOr(1),f=r.getCrossfadeParameters(),h=r.paint.get("fill-extrusion-opacity"),p=0,d=n;p<d.length;p+=1){var g=d[p],m=e.getTile(g),v=m.getBucket(r);if(v){var y=v.programConfigurations.get(r.id),x=t.useProgram(u?"fillExtrusionPattern":"fillExtrusion",y);u&&(t.context.activeTexture.set(l.TEXTURE0),m.imageAtlasTexture.bind(l.LINEAR,l.CLAMP_TO_EDGE),y.updatePaintBuffers(f));var b=c.constantOr(null);if(b&&m.imageAtlas){var _=m.imageAtlas,w=_.patternPositions[b.to.toString()],T=_.patternPositions[b.from.toString()];w&&T&&y.setConstantPatternPositions(w,T)}var k=t.translatePosMatrix(g.posMatrix,m,r.paint.get("fill-extrusion-translate"),r.paint.get("fill-extrusion-translate-anchor")),A=r.paint.get("fill-extrusion-vertical-gradient"),M=u?kr(k,t,A,h,g,f,m):Tr(k,t,A,h);x.draw(s,s.gl.TRIANGLES,i,a,o,Et.backCCW,M,r.id,v.layoutVertexBuffer,v.indexBuffer,v.segments,r.paint,t.transform.zoom,y)}}}function sn(t,e,r,n,i,a){var o=t.context,s=o.gl,l=e.fbo;if(l){var c=t.useProgram("hillshade");o.activeTexture.set(s.TEXTURE0),s.bindTexture(s.TEXTURE_2D,l.colorAttachment.get());var u=function(t,e,r){var n=r.paint.get("hillshade-shadow-color"),i=r.paint.get("hillshade-highlight-color"),a=r.paint.get("hillshade-accent-color"),o=r.paint.get("hillshade-illumination-direction")*(Math.PI/180);"viewport"===r.paint.get("hillshade-illumination-anchor")&&(o-=t.transform.angle);var s=!t.options.moving;return{u_matrix:t.transform.calculatePosMatrix(e.tileID.toUnwrapped(),s),u_image:0,u_latrange:Dr(t,e.tileID),u_light:[r.paint.get("hillshade-exaggeration"),o],u_shadow:n,u_highlight:i,u_accent:a}}(t,e,r);c.draw(o,s.TRIANGLES,n,i,a,Et.disabled,u,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}}function ln(e,r,n,i,a,o,s){var l=e.context,c=l.gl,u=r.dem;if(u&&u.data){var f=u.dim,h=u.stride,p=u.getPixels();if(l.activeTexture.set(c.TEXTURE1),l.pixelStoreUnpackPremultiplyAlpha.set(!1),r.demTexture=r.demTexture||e.getTileTexture(h),r.demTexture){var d=r.demTexture;d.update(p,{premultiply:!1}),d.bind(c.NEAREST,c.CLAMP_TO_EDGE)}else r.demTexture=new t.Texture(l,p,c.RGBA,{premultiply:!1}),r.demTexture.bind(c.NEAREST,c.CLAMP_TO_EDGE);l.activeTexture.set(c.TEXTURE0);var g=r.fbo;if(!g){var m=new t.Texture(l,{width:f,height:f,data:null},c.RGBA);m.bind(c.LINEAR,c.CLAMP_TO_EDGE),(g=r.fbo=l.createFramebuffer(f,f,!0)).colorAttachment.set(m.texture)}l.bindFramebuffer.set(g.framebuffer),l.viewport.set([0,0,f,f]),e.useProgram("hillshadePrepare").draw(l,c.TRIANGLES,a,o,s,Et.disabled,function(e,r,n){var i=r.stride,a=t.create();return t.ortho(a,0,t.EXTENT,-t.EXTENT,0,0,1),t.translate(a,a,[0,-t.EXTENT,0]),{u_matrix:a,u_image:1,u_dimension:[i,i],u_zoom:e.overscaledZ,u_maxzoom:n,u_unpack:r.getUnpackVector()}}(r.tileID,u,i),n.id,e.rasterBoundsBuffer,e.quadTriangleIndexBuffer,e.rasterBoundsSegments),r.needsHillshadePrepare=!1}}function cn(e,r,n,i,a){var o=i.paint.get("raster-fade-duration");if(o>0){var s=t.browser.now(),l=(s-e.timeAdded)/o,c=r?(s-r.timeAdded)/o:-1,u=n.getSource(),f=a.coveringZoomLevel({tileSize:u.tileSize,roundZoom:u.roundZoom}),h=!r||Math.abs(r.tileID.overscaledZ-f)>Math.abs(e.tileID.overscaledZ-f),p=h&&e.refreshedUponExpiration?1:t.clamp(h?l:1-c,0,1);return e.refreshedUponExpiration&&l>=1&&(e.refreshedUponExpiration=!1),r?{opacity:1,mix:1-p}:{opacity:p,mix:0}}return{opacity:1,mix:0}}var un=new t.Color(1,0,0,1),fn=new t.Color(0,1,0,1),hn=new t.Color(0,0,1,1),pn=new t.Color(1,0,1,1),dn=new t.Color(0,1,1,1);function gn(t){var e=t.transform.padding;mn(t,t.transform.height-(e.top||0),3,un),mn(t,e.bottom||0,3,fn),vn(t,e.left||0,3,hn),vn(t,t.transform.width-(e.right||0),3,pn);var r=t.transform.centerPoint;!function(t,e,r,n){yn(t,e-1,r-10,2,20,n),yn(t,e-10,r-1,20,2,n)}(t,r.x,t.transform.height-r.y,dn)}function mn(t,e,r,n){yn(t,0,e+r/2,t.transform.width,r,n)}function vn(t,e,r,n){yn(t,e-r/2,0,r,t.transform.height,n)}function yn(e,r,n,i,a,o){var s=e.context,l=s.gl;l.enable(l.SCISSOR_TEST),l.scissor(r*t.browser.devicePixelRatio,n*t.browser.devicePixelRatio,i*t.browser.devicePixelRatio,a*t.browser.devicePixelRatio),s.clear({color:o}),l.disable(l.SCISSOR_TEST)}function xn(e,r,n){var i=e.context,a=i.gl,o=n.posMatrix,s=e.useProgram("debug"),l=At.disabled,c=Mt.disabled,u=e.colorModeForRenderPass();i.activeTexture.set(a.TEXTURE0),e.emptyTexture.bind(a.LINEAR,a.CLAMP_TO_EDGE),s.draw(i,a.LINE_STRIP,l,c,u,Et.disabled,Ir(o,t.Color.red),"$debug",e.debugBuffer,e.tileBorderIndexBuffer,e.debugSegments);var f=r.getTileByID(n.key).latestRawTileData,h=f&&f.byteLength||0,p=Math.floor(h/1024),d=r.getTile(n).tileSize,g=512/Math.min(d,512)*(n.overscaledZ/e.transform.zoom)*.5,m=n.canonical.toString();n.overscaledZ!==n.canonical.z&&(m+=" => "+n.overscaledZ),function(t,e){t.initDebugOverlayCanvas();var r=t.debugOverlayCanvas,n=t.context.gl,i=t.debugOverlayCanvas.getContext("2d");i.clearRect(0,0,r.width,r.height),i.shadowColor="white",i.shadowBlur=2,i.lineWidth=1.5,i.strokeStyle="white",i.textBaseline="top",i.font="bold 36px Open Sans, sans-serif",i.fillText(e,5,5),i.strokeText(e,5,5),t.debugOverlayTexture.update(r),t.debugOverlayTexture.bind(n.LINEAR,n.CLAMP_TO_EDGE)}(e,m+" "+p+"kb"),s.draw(i,a.TRIANGLES,l,c,St.alphaBlended,Et.disabled,Ir(o,t.Color.transparent,g),"$debug",e.debugBuffer,e.quadTriangleIndexBuffer,e.debugSegments)}var bn={symbol:function(e,r,n,i,a){if("translucent"===e.renderPass){var o=Mt.disabled,s=e.colorModeForRenderPass();n.layout.get("text-variable-anchor")&&function(e,r,n,i,a,o,s){for(var l=r.transform,c="map"===a,u="map"===o,f=0,h=e;f<h.length;f+=1){var p=h[f],d=i.getTile(p),g=d.getBucket(n);if(g&&g.text&&g.text.segments.get().length){var m=g.textSizeData,v=t.evaluateSizeForZoom(m,l.zoom),y=pe(d,1,r.transform.zoom),x=Qt(p.posMatrix,u,c,r.transform,y),b="none"!==n.layout.get("icon-text-fit")&&g.hasIconData();if(v){var _=Math.pow(2,l.zoom-d.tileID.overscaledZ);tn(g,c,u,s,t.symbolSize,l,x,p.posMatrix,_,v,b)}}}}(i,e,n,r,n.layout.get("text-rotation-alignment"),n.layout.get("text-pitch-alignment"),a),0!==n.paint.get("icon-opacity").constantOr(1)&&rn(e,r,n,i,!1,n.paint.get("icon-translate"),n.paint.get("icon-translate-anchor"),n.layout.get("icon-rotation-alignment"),n.layout.get("icon-pitch-alignment"),n.layout.get("icon-keep-upright"),o,s),0!==n.paint.get("text-opacity").constantOr(1)&&rn(e,r,n,i,!0,n.paint.get("text-translate"),n.paint.get("text-translate-anchor"),n.layout.get("text-rotation-alignment"),n.layout.get("text-pitch-alignment"),n.layout.get("text-keep-upright"),o,s),r.map.showCollisionBoxes&&(Kr(e,r,n,i,n.paint.get("text-translate"),n.paint.get("text-translate-anchor"),!0),Kr(e,r,n,i,n.paint.get("icon-translate"),n.paint.get("icon-translate-anchor"),!1))}},circle:function(e,r,n,i){if("translucent"===e.renderPass){var a=n.paint.get("circle-opacity"),o=n.paint.get("circle-stroke-width"),s=n.paint.get("circle-stroke-opacity"),l=void 0!==n.layout.get("circle-sort-key").constantOr(1);if(0!==a.constantOr(1)||0!==o.constantOr(1)&&0!==s.constantOr(1)){for(var c=e.context,u=c.gl,f=e.depthModeForSublayer(0,At.ReadOnly),h=Mt.disabled,p=e.colorModeForRenderPass(),d=[],g=0;g<i.length;g++){var m=i[g],v=r.getTile(m),y=v.getBucket(n);if(y){var x=y.programConfigurations.get(n.id),b={programConfiguration:x,program:e.useProgram("circle",x),layoutVertexBuffer:y.layoutVertexBuffer,indexBuffer:y.indexBuffer,uniformValues:Lr(e,m,v,n)};if(l)for(var _=0,w=y.segments.get();_<w.length;_+=1){var T=w[_];d.push({segments:new t.SegmentVector([T]),sortKey:T.sortKey,state:b})}else d.push({segments:y.segments,sortKey:0,state:b})}}l&&d.sort((function(t,e){return t.sortKey-e.sortKey}));for(var k=0,A=d;k<A.length;k+=1){var M=A[k],S=M.state,E=S.programConfiguration,L=S.program,C=S.layoutVertexBuffer,P=S.indexBuffer,I=S.uniformValues,O=M.segments;L.draw(c,u.TRIANGLES,f,h,p,Et.disabled,I,n.id,C,P,O,n.paint,e.transform.zoom,E)}}}},heatmap:function(e,r,n,i){if(0!==n.paint.get("heatmap-opacity"))if("offscreen"===e.renderPass){var a=e.context,o=a.gl,s=Mt.disabled,l=new St([o.ONE,o.ONE],t.Color.transparent,[!0,!0,!0,!0]);!function(t,e,r){var n=t.gl;t.activeTexture.set(n.TEXTURE1),t.viewport.set([0,0,e.width/4,e.height/4]);var i=r.heatmapFbo;if(i)n.bindTexture(n.TEXTURE_2D,i.colorAttachment.get()),t.bindFramebuffer.set(i.framebuffer);else{var a=n.createTexture();n.bindTexture(n.TEXTURE_2D,a),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,n.LINEAR),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,n.LINEAR),i=r.heatmapFbo=t.createFramebuffer(e.width/4,e.height/4,!1),function(t,e,r,n){var i=t.gl,a=t.extRenderToTextureHalfFloat?t.extTextureHalfFloat.HALF_FLOAT_OES:i.UNSIGNED_BYTE;i.texImage2D(i.TEXTURE_2D,0,i.RGBA,e.width/4,e.height/4,0,i.RGBA,a,null),n.colorAttachment.set(r)}(t,e,a,i)}}(a,e,n),a.clear({color:t.Color.transparent});for(var c=0;c<i.length;c++){var u=i[c];if(!r.hasRenderableParent(u)){var f=r.getTile(u),h=f.getBucket(n);if(h){var p=h.programConfigurations.get(n.id),d=e.useProgram("heatmap",p),g=e.transform.zoom;d.draw(a,o.TRIANGLES,At.disabled,s,l,Et.disabled,zr(u.posMatrix,f,g,n.paint.get("heatmap-intensity")),n.id,h.layoutVertexBuffer,h.indexBuffer,h.segments,n.paint,e.transform.zoom,p)}}}a.viewport.set([0,0,e.width,e.height])}else"translucent"===e.renderPass&&(e.context.setColorMode(e.colorModeForRenderPass()),function(e,r){var n=e.context,i=n.gl,a=r.heatmapFbo;if(!a)return;n.activeTexture.set(i.TEXTURE0),i.bindTexture(i.TEXTURE_2D,a.colorAttachment.get()),n.activeTexture.set(i.TEXTURE1);var o=r.colorRampTexture;o||(o=r.colorRampTexture=new t.Texture(n,r.colorRamp,i.RGBA));o.bind(i.LINEAR,i.CLAMP_TO_EDGE),e.useProgram("heatmapTexture").draw(n,i.TRIANGLES,At.disabled,Mt.disabled,e.colorModeForRenderPass(),Et.disabled,function(e,r,n,i){var a=t.create();t.ortho(a,0,e.width,e.height,0,0,1);var o=e.context.gl;return{u_matrix:a,u_world:[o.drawingBufferWidth,o.drawingBufferHeight],u_image:n,u_color_ramp:i,u_opacity:r.paint.get("heatmap-opacity")}}(e,r,0,1),r.id,e.viewportBuffer,e.quadTriangleIndexBuffer,e.viewportSegments,r.paint,e.transform.zoom)}(e,n))},line:function(e,r,n,i){if("translucent"===e.renderPass){var a=n.paint.get("line-opacity"),o=n.paint.get("line-width");if(0!==a.constantOr(1)&&0!==o.constantOr(1)){var s=e.depthModeForSublayer(0,At.ReadOnly),l=e.colorModeForRenderPass(),c=n.paint.get("line-dasharray"),u=n.paint.get("line-pattern"),f=u.constantOr(1),h=n.paint.get("line-gradient"),p=n.getCrossfadeParameters(),d=f?"linePattern":c?"lineSDF":h?"lineGradient":"line",g=e.context,m=g.gl,v=!0;if(h){g.activeTexture.set(m.TEXTURE0);var y=n.gradientTexture;if(!n.gradient)return;y||(y=n.gradientTexture=new t.Texture(g,n.gradient,m.RGBA)),y.bind(m.LINEAR,m.CLAMP_TO_EDGE)}for(var x=0,b=i;x<b.length;x+=1){var _=b[x],w=r.getTile(_);if(!f||w.patternsLoaded()){var T=w.getBucket(n);if(T){var k=T.programConfigurations.get(n.id),A=e.context.program.get(),M=e.useProgram(d,k),S=v||M.program!==A,E=u.constantOr(null);if(E&&w.imageAtlas){var L=w.imageAtlas,C=L.patternPositions[E.to.toString()],P=L.patternPositions[E.from.toString()];C&&P&&k.setConstantPatternPositions(C,P)}var I=f?Br(e,w,n,p):c?Nr(e,w,n,c,p):h?Fr(e,w,n):Rr(e,w,n);f?(g.activeTexture.set(m.TEXTURE0),w.imageAtlasTexture.bind(m.LINEAR,m.CLAMP_TO_EDGE),k.updatePaintBuffers(p)):c&&(S||e.lineAtlas.dirty)&&(g.activeTexture.set(m.TEXTURE0),e.lineAtlas.bind(g)),M.draw(g,m.TRIANGLES,s,e.stencilModeForClipping(_),l,Et.disabled,I,n.id,T.layoutVertexBuffer,T.indexBuffer,T.segments,n.paint,e.transform.zoom,k),v=!1}}}}}},fill:function(e,r,n,i){var a=n.paint.get("fill-color"),o=n.paint.get("fill-opacity");if(0!==o.constantOr(1)){var s=e.colorModeForRenderPass(),l=n.paint.get("fill-pattern"),c=e.opaquePassEnabledForLayer()&&!l.constantOr(1)&&1===a.constantOr(t.Color.transparent).a&&1===o.constantOr(0)?"opaque":"translucent";if(e.renderPass===c){var u=e.depthModeForSublayer(1,"opaque"===e.renderPass?At.ReadWrite:At.ReadOnly);an(e,r,n,i,u,s,!1)}if("translucent"===e.renderPass&&n.paint.get("fill-antialias")){var f=e.depthModeForSublayer(n.getPaintProperty("fill-outline-color")?2:0,At.ReadOnly);an(e,r,n,i,f,s,!0)}}},"fill-extrusion":function(t,e,r,n){var i=r.paint.get("fill-extrusion-opacity");if(0!==i&&"translucent"===t.renderPass){var a=new At(t.context.gl.LEQUAL,At.ReadWrite,t.depthRangeFor3D);if(1!==i||r.paint.get("fill-extrusion-pattern").constantOr(1))on(t,e,r,n,a,Mt.disabled,St.disabled),on(t,e,r,n,a,t.stencilModeFor3D(),t.colorModeForRenderPass());else{var o=t.colorModeForRenderPass();on(t,e,r,n,a,Mt.disabled,o)}}},hillshade:function(t,e,r,n){if("offscreen"===t.renderPass||"translucent"===t.renderPass){for(var i=t.context,a=e.getSource().maxzoom,o=t.depthModeForSublayer(0,At.ReadOnly),s=t.colorModeForRenderPass(),l="translucent"===t.renderPass?t.stencilConfigForOverlap(n):[{},n],c=l[0],u=0,f=l[1];u<f.length;u+=1){var h=f[u],p=e.getTile(h);p.needsHillshadePrepare&&"offscreen"===t.renderPass?ln(t,p,r,a,o,Mt.disabled,s):"translucent"===t.renderPass&&sn(t,p,r,o,c[h.overscaledZ],s)}i.viewport.set([0,0,t.width,t.height])}},raster:function(t,e,r,n){if("translucent"===t.renderPass&&0!==r.paint.get("raster-opacity")&&n.length)for(var i=t.context,a=i.gl,o=e.getSource(),s=t.useProgram("raster"),l=t.colorModeForRenderPass(),c=o instanceof I?[{},n]:t.stencilConfigForOverlap(n),u=c[0],f=c[1],h=f[f.length-1].overscaledZ,p=!t.options.moving,d=0,g=f;d<g.length;d+=1){var m=g[d],v=t.depthModeForSublayer(m.overscaledZ-h,1===r.paint.get("raster-opacity")?At.ReadWrite:At.ReadOnly,a.LESS),y=e.getTile(m),x=t.transform.calculatePosMatrix(m.toUnwrapped(),p);y.registerFadeDuration(r.paint.get("raster-fade-duration"));var b=e.findLoadedParent(m,0),_=cn(y,b,e,r,t.transform),w=void 0,T=void 0,k="nearest"===r.paint.get("raster-resampling")?a.NEAREST:a.LINEAR;i.activeTexture.set(a.TEXTURE0),y.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),i.activeTexture.set(a.TEXTURE1),b?(b.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),w=Math.pow(2,b.tileID.overscaledZ-y.tileID.overscaledZ),T=[y.tileID.canonical.x*w%1,y.tileID.canonical.y*w%1]):y.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST);var A=Vr(x,T||[0,0],w||1,_,r);o instanceof I?s.draw(i,a.TRIANGLES,v,Mt.disabled,l,Et.disabled,A,r.id,o.boundsBuffer,t.quadTriangleIndexBuffer,o.boundsSegments):s.draw(i,a.TRIANGLES,v,u[m.overscaledZ],l,Et.disabled,A,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}},background:function(t,e,r){var n=r.paint.get("background-color"),i=r.paint.get("background-opacity");if(0!==i){var a=t.context,o=a.gl,s=t.transform,l=s.tileSize,c=r.paint.get("background-pattern");if(!t.isPatternMissing(c)){var u=!c&&1===n.a&&1===i&&t.opaquePassEnabledForLayer()?"opaque":"translucent";if(t.renderPass===u){var f=Mt.disabled,h=t.depthModeForSublayer(0,"opaque"===u?At.ReadWrite:At.ReadOnly),p=t.colorModeForRenderPass(),d=t.useProgram(c?"backgroundPattern":"background"),g=s.coveringTiles({tileSize:l});c&&(a.activeTexture.set(o.TEXTURE0),t.imageManager.bind(t.context));for(var m=r.getCrossfadeParameters(),v=0,y=g;v<y.length;v+=1){var x=y[v],b=t.transform.calculatePosMatrix(x.toUnwrapped()),_=c?Zr(b,i,t,c,{tileID:x,tileSize:l},m):Xr(b,i,n);d.draw(a,o.TRIANGLES,h,f,p,Et.disabled,_,r.id,t.tileExtentBuffer,t.quadTriangleIndexBuffer,t.tileExtentSegments)}}}}},debug:function(t,e,r){for(var n=0;n<r.length;n++)xn(t,e,r[n])},custom:function(t,e,r){var n=t.context,i=r.implementation;if("offscreen"===t.renderPass){var a=i.prerender;a&&(t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),a.call(i,n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState())}else if("translucent"===t.renderPass){t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),n.setStencilMode(Mt.disabled);var o="3d"===i.renderingMode?new At(t.context.gl.LEQUAL,At.ReadWrite,t.depthRangeFor3D):t.depthModeForSublayer(0,At.ReadOnly);n.setDepthMode(o),i.render(n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState(),n.bindFramebuffer.set(null)}}},_n=function(t,e){this.context=new Lt(t),this.transform=e,this._tileTextures={},this.setup(),this.numSublayers=Ct.maxUnderzooming+Ct.maxOverzooming+1,this.depthEpsilon=1/Math.pow(2,16),this.crossTileSymbolIndex=new Ne,this.gpuTimers={}};_n.prototype.resize=function(e,r){if(this.width=e*t.browser.devicePixelRatio,this.height=r*t.browser.devicePixelRatio,this.context.viewport.set([0,0,this.width,this.height]),this.style)for(var n=0,i=this.style._order;n<i.length;n+=1){var a=i[n];this.style._layers[a].resize()}},_n.prototype.setup=function(){var e=this.context,r=new t.StructArrayLayout2i4;r.emplaceBack(0,0),r.emplaceBack(t.EXTENT,0),r.emplaceBack(0,t.EXTENT),r.emplaceBack(t.EXTENT,t.EXTENT),this.tileExtentBuffer=e.createVertexBuffer(r,Ge.members),this.tileExtentSegments=t.SegmentVector.simpleSegment(0,0,4,2);var n=new t.StructArrayLayout2i4;n.emplaceBack(0,0),n.emplaceBack(t.EXTENT,0),n.emplaceBack(0,t.EXTENT),n.emplaceBack(t.EXTENT,t.EXTENT),this.debugBuffer=e.createVertexBuffer(n,Ge.members),this.debugSegments=t.SegmentVector.simpleSegment(0,0,4,5);var i=new t.StructArrayLayout4i8;i.emplaceBack(0,0,0,0),i.emplaceBack(t.EXTENT,0,t.EXTENT,0),i.emplaceBack(0,t.EXTENT,0,t.EXTENT),i.emplaceBack(t.EXTENT,t.EXTENT,t.EXTENT,t.EXTENT),this.rasterBoundsBuffer=e.createVertexBuffer(i,P.members),this.rasterBoundsSegments=t.SegmentVector.simpleSegment(0,0,4,2);var a=new t.StructArrayLayout2i4;a.emplaceBack(0,0),a.emplaceBack(1,0),a.emplaceBack(0,1),a.emplaceBack(1,1),this.viewportBuffer=e.createVertexBuffer(a,Ge.members),this.viewportSegments=t.SegmentVector.simpleSegment(0,0,4,2);var o=new t.StructArrayLayout1ui2;o.emplaceBack(0),o.emplaceBack(1),o.emplaceBack(3),o.emplaceBack(2),o.emplaceBack(0),this.tileBorderIndexBuffer=e.createIndexBuffer(o);var s=new t.StructArrayLayout3ui6;s.emplaceBack(0,1,2),s.emplaceBack(2,1,3),this.quadTriangleIndexBuffer=e.createIndexBuffer(s),this.emptyTexture=new t.Texture(e,{width:1,height:1,data:new Uint8Array([0,0,0,0])},e.gl.RGBA);var l=this.context.gl;this.stencilClearMode=new Mt({func:l.ALWAYS,mask:0},0,255,l.ZERO,l.ZERO,l.ZERO)},_n.prototype.clearStencil=function(){var e=this.context,r=e.gl;this.nextStencilID=1,this.currentStencilSource=void 0;var n=t.create();t.ortho(n,0,this.width,this.height,0,0,1),t.scale(n,n,[r.drawingBufferWidth,r.drawingBufferHeight,0]),this.useProgram("clippingMask").draw(e,r.TRIANGLES,At.disabled,this.stencilClearMode,St.disabled,Et.disabled,Or(n),"$clipping",this.viewportBuffer,this.quadTriangleIndexBuffer,this.viewportSegments)},_n.prototype._renderTileClippingMasks=function(t,e){if(this.currentStencilSource!==t.source&&t.isTileClipped()&&e&&e.length){this.currentStencilSource=t.source;var r=this.context,n=r.gl;this.nextStencilID+e.length>256&&this.clearStencil(),r.setColorMode(St.disabled),r.setDepthMode(At.disabled);var i=this.useProgram("clippingMask");this._tileClippingMaskIDs={};for(var a=0,o=e;a<o.length;a+=1){var s=o[a],l=this._tileClippingMaskIDs[s.key]=this.nextStencilID++;i.draw(r,n.TRIANGLES,At.disabled,new Mt({func:n.ALWAYS,mask:0},l,255,n.KEEP,n.KEEP,n.REPLACE),St.disabled,Et.disabled,Or(s.posMatrix),"$clipping",this.tileExtentBuffer,this.quadTriangleIndexBuffer,this.tileExtentSegments)}}},_n.prototype.stencilModeFor3D=function(){this.currentStencilSource=void 0,this.nextStencilID+1>256&&this.clearStencil();var t=this.nextStencilID++,e=this.context.gl;return new Mt({func:e.NOTEQUAL,mask:255},t,255,e.KEEP,e.KEEP,e.REPLACE)},_n.prototype.stencilModeForClipping=function(t){var e=this.context.gl;return new Mt({func:e.EQUAL,mask:255},this._tileClippingMaskIDs[t.key],0,e.KEEP,e.KEEP,e.REPLACE)},_n.prototype.stencilConfigForOverlap=function(t){var e,r=this.context.gl,n=t.sort((function(t,e){return e.overscaledZ-t.overscaledZ})),i=n[n.length-1].overscaledZ,a=n[0].overscaledZ-i+1;if(a>1){this.currentStencilSource=void 0,this.nextStencilID+a>256&&this.clearStencil();for(var o={},s=0;s<a;s++)o[s+i]=new Mt({func:r.GEQUAL,mask:255},s+this.nextStencilID,255,r.KEEP,r.KEEP,r.REPLACE);return this.nextStencilID+=a,[o,n]}return[(e={},e[i]=Mt.disabled,e),n]},_n.prototype.colorModeForRenderPass=function(){var e=this.context.gl;if(this._showOverdrawInspector){return new St([e.CONSTANT_COLOR,e.ONE],new t.Color(1/8,1/8,1/8,0),[!0,!0,!0,!0])}return"opaque"===this.renderPass?St.unblended:St.alphaBlended},_n.prototype.depthModeForSublayer=function(t,e,r){if(!this.opaquePassEnabledForLayer())return At.disabled;var n=1-((1+this.currentLayer)*this.numSublayers+t)*this.depthEpsilon;return new At(r||this.context.gl.LEQUAL,e,[n,n])},_n.prototype.opaquePassEnabledForLayer=function(){return this.currentLayer<this.opaquePassCutoff},_n.prototype.render=function(e,r){var n=this;this.style=e,this.options=r,this.lineAtlas=e.lineAtlas,this.imageManager=e.imageManager,this.glyphManager=e.glyphManager,this.symbolFadeChange=e.placement.symbolFadeChange(t.browser.now()),this.imageManager.beginFrame();var i=this.style._order,a=this.style.sourceCaches;for(var o in a){var s=a[o];s.used&&s.prepare(this.context)}var l,c,u={},f={},h={};for(var p in a){var d=a[p];u[p]=d.getVisibleCoordinates(),f[p]=u[p].slice().reverse(),h[p]=d.getVisibleCoordinates(!0).reverse()}this.opaquePassCutoff=1/0;for(var g=0;g<i.length;g++){var m=i[g];if(this.style._layers[m].is3D()){this.opaquePassCutoff=g;break}}this.renderPass="offscreen";for(var v=0,y=i;v<y.length;v+=1){var x=y[v],b=this.style._layers[x];if(b.hasOffscreenPass()&&!b.isHidden(this.transform.zoom)){var _=f[b.source];("custom"===b.type||_.length)&&this.renderLayer(this,a[b.source],b,_)}}for(this.context.bindFramebuffer.set(null),this.context.clear({color:r.showOverdrawInspector?t.Color.black:t.Color.transparent,depth:1}),this.clearStencil(),this._showOverdrawInspector=r.showOverdrawInspector,this.depthRangeFor3D=[0,1-(e._order.length+2)*this.numSublayers*this.depthEpsilon],this.renderPass="opaque",this.currentLayer=i.length-1;this.currentLayer>=0;this.currentLayer--){var w=this.style._layers[i[this.currentLayer]],T=a[w.source],k=u[w.source];this._renderTileClippingMasks(w,k),this.renderLayer(this,T,w,k)}for(this.renderPass="translucent",this.currentLayer=0;this.currentLayer<i.length;this.currentLayer++){var A=this.style._layers[i[this.currentLayer]],M=a[A.source],S=("symbol"===A.type?h:f)[A.source];this._renderTileClippingMasks(A,u[A.source]),this.renderLayer(this,M,A,S)}this.options.showTileBoundaries&&(t.values(this.style._layers).forEach((function(t){t.source&&!t.isHidden(n.transform.zoom)&&(t.source!==(c&&c.id)&&(c=n.style.sourceCaches[t.source]),(!l||l.getSource().maxzoom<c.getSource().maxzoom)&&(l=c))})),l&&bn.debug(this,l,l.getVisibleCoordinates()));this.options.showPadding&&gn(this),this.context.setDefault()},_n.prototype.renderLayer=function(t,e,r,n){r.isHidden(this.transform.zoom)||("background"===r.type||"custom"===r.type||n.length)&&(this.id=r.id,this.gpuTimingStart(r),bn[r.type](t,e,r,n,this.style.placement.variableOffsets),this.gpuTimingEnd())},_n.prototype.gpuTimingStart=function(t){if(this.options.gpuTiming){var e=this.context.extTimerQuery,r=this.gpuTimers[t.id];r||(r=this.gpuTimers[t.id]={calls:0,cpuTime:0,query:e.createQueryEXT()}),r.calls++,e.beginQueryEXT(e.TIME_ELAPSED_EXT,r.query)}},_n.prototype.gpuTimingEnd=function(){if(this.options.gpuTiming){var t=this.context.extTimerQuery;t.endQueryEXT(t.TIME_ELAPSED_EXT)}},_n.prototype.collectGpuTimers=function(){var t=this.gpuTimers;return this.gpuTimers={},t},_n.prototype.queryGpuTimers=function(t){var e={};for(var r in t){var n=t[r],i=this.context.extTimerQuery,a=i.getQueryObjectEXT(n.query,i.QUERY_RESULT_EXT)/1e6;i.deleteQueryEXT(n.query),e[r]=a}return e},_n.prototype.translatePosMatrix=function(e,r,n,i,a){if(!n[0]&&!n[1])return e;var o=a?"map"===i?this.transform.angle:0:"viewport"===i?-this.transform.angle:0;if(o){var s=Math.sin(o),l=Math.cos(o);n=[n[0]*l-n[1]*s,n[0]*s+n[1]*l]}var c=[a?n[0]:pe(r,n[0],this.transform.zoom),a?n[1]:pe(r,n[1],this.transform.zoom),0],u=new Float32Array(16);return t.translate(u,e,c),u},_n.prototype.saveTileTexture=function(t){var e=this._tileTextures[t.size[0]];e?e.push(t):this._tileTextures[t.size[0]]=[t]},_n.prototype.getTileTexture=function(t){var e=this._tileTextures[t];return e&&e.length>0?e.pop():null},_n.prototype.isPatternMissing=function(t){if(!t)return!1;if(!t.from||!t.to)return!0;var e=this.imageManager.getPattern(t.from.toString()),r=this.imageManager.getPattern(t.to.toString());return!e||!r},_n.prototype.useProgram=function(t,e){this.cache=this.cache||{};var r=""+t+(e?e.cacheKey:"")+(this._showOverdrawInspector?"/overdraw":"");return this.cache[r]||(this.cache[r]=new _r(this.context,xr[t],e,Jr[t],this._showOverdrawInspector)),this.cache[r]},_n.prototype.setCustomLayerDefaults=function(){this.context.unbindVAO(),this.context.cullFace.setDefault(),this.context.activeTexture.setDefault(),this.context.pixelStoreUnpack.setDefault(),this.context.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.context.pixelStoreUnpackFlipY.setDefault()},_n.prototype.setBaseState=function(){var t=this.context.gl;this.context.cullFace.set(!1),this.context.viewport.set([0,0,this.width,this.height]),this.context.blendEquation.set(t.FUNC_ADD)},_n.prototype.initDebugOverlayCanvas=function(){if(null==this.debugOverlayCanvas){this.debugOverlayCanvas=t.window.document.createElement("canvas"),this.debugOverlayCanvas.width=512,this.debugOverlayCanvas.height=512;var e=this.context.gl;this.debugOverlayTexture=new t.Texture(this.context,this.debugOverlayCanvas,e.RGBA)}},_n.prototype.destroy=function(){this.emptyTexture.destroy(),this.debugOverlayTexture&&this.debugOverlayTexture.destroy()};var wn=function(t,e){this.points=t,this.planes=e};wn.fromInvProjectionMatrix=function(e,r,n){var i=Math.pow(2,n),a=[[-1,1,-1,1],[1,1,-1,1],[1,-1,-1,1],[-1,-1,-1,1],[-1,1,1,1],[1,1,1,1],[1,-1,1,1],[-1,-1,1,1]].map((function(r){return t.transformMat4([],r,e)})).map((function(e){return t.scale$1([],e,1/e[3]/r*i)})),o=[[0,1,2],[6,5,4],[0,3,7],[2,1,5],[3,2,6],[0,4,5]].map((function(e){var r=t.sub([],a[e[0]],a[e[1]]),n=t.sub([],a[e[2]],a[e[1]]),i=t.normalize([],t.cross([],r,n)),o=-t.dot(i,a[e[1]]);return i.concat(o)}));return new wn(a,o)};var Tn=function(e,r){this.min=e,this.max=r,this.center=t.scale$2([],t.add([],this.min,this.max),.5)};Tn.prototype.quadrant=function(e){for(var r=[e%2==0,e<2],n=t.clone$2(this.min),i=t.clone$2(this.max),a=0;a<r.length;a++)n[a]=r[a]?this.min[a]:this.center[a],i[a]=r[a]?this.center[a]:this.max[a];return i[2]=this.max[2],new Tn(n,i)},Tn.prototype.distanceX=function(t){return Math.max(Math.min(this.max[0],t[0]),this.min[0])-t[0]},Tn.prototype.distanceY=function(t){return Math.max(Math.min(this.max[1],t[1]),this.min[1])-t[1]},Tn.prototype.intersects=function(e){for(var r=[[this.min[0],this.min[1],0,1],[this.max[0],this.min[1],0,1],[this.max[0],this.max[1],0,1],[this.min[0],this.max[1],0,1]],n=!0,i=0;i<e.planes.length;i++){for(var a=e.planes[i],o=0,s=0;s<r.length;s++)o+=t.dot$1(a,r[s])>=0;if(0===o)return 0;o!==r.length&&(n=!1)}if(n)return 2;for(var l=0;l<3;l++){for(var c=Number.MAX_VALUE,u=-Number.MAX_VALUE,f=0;f<e.points.length;f++){var h=e.points[f][l]-this.min[l];c=Math.min(c,h),u=Math.max(u,h)}if(u<0||c>this.max[l]-this.min[l])return 0}return 1};var kn=function(t,e,r,n){if(void 0===t&&(t=0),void 0===e&&(e=0),void 0===r&&(r=0),void 0===n&&(n=0),isNaN(t)||t<0||isNaN(e)||e<0||isNaN(r)||r<0||isNaN(n)||n<0)throw new Error("Invalid value for edge-insets, top, bottom, left and right must all be numbers");this.top=t,this.bottom=e,this.left=r,this.right=n};kn.prototype.interpolate=function(e,r,n){return null!=r.top&&null!=e.top&&(this.top=t.number(e.top,r.top,n)),null!=r.bottom&&null!=e.bottom&&(this.bottom=t.number(e.bottom,r.bottom,n)),null!=r.left&&null!=e.left&&(this.left=t.number(e.left,r.left,n)),null!=r.right&&null!=e.right&&(this.right=t.number(e.right,r.right,n)),this},kn.prototype.getCenter=function(e,r){var n=t.clamp((this.left+e-this.right)/2,0,e),i=t.clamp((this.top+r-this.bottom)/2,0,r);return new t.Point(n,i)},kn.prototype.equals=function(t){return this.top===t.top&&this.bottom===t.bottom&&this.left===t.left&&this.right===t.right},kn.prototype.clone=function(){return new kn(this.top,this.bottom,this.left,this.right)},kn.prototype.toJSON=function(){return{top:this.top,bottom:this.bottom,left:this.left,right:this.right}};var An=function(e,r,n,i,a){this.tileSize=512,this.maxValidLatitude=85.051129,this._renderWorldCopies=void 0===a||a,this._minZoom=e||0,this._maxZoom=r||22,this._minPitch=null==n?0:n,this._maxPitch=null==i?60:i,this.setMaxBounds(),this.width=0,this.height=0,this._center=new t.LngLat(0,0),this.zoom=0,this.angle=0,this._fov=.6435011087932844,this._pitch=0,this._unmodified=!0,this._edgeInsets=new kn,this._posMatrixCache={},this._alignedPosMatrixCache={}},Mn={minZoom:{configurable:!0},maxZoom:{configurable:!0},minPitch:{configurable:!0},maxPitch:{configurable:!0},renderWorldCopies:{configurable:!0},worldSize:{configurable:!0},centerOffset:{configurable:!0},size:{configurable:!0},bearing:{configurable:!0},pitch:{configurable:!0},fov:{configurable:!0},zoom:{configurable:!0},center:{configurable:!0},padding:{configurable:!0},centerPoint:{configurable:!0},unmodified:{configurable:!0},point:{configurable:!0}};An.prototype.clone=function(){var t=new An(this._minZoom,this._maxZoom,this._minPitch,this.maxPitch,this._renderWorldCopies);return t.tileSize=this.tileSize,t.latRange=this.latRange,t.width=this.width,t.height=this.height,t._center=this._center,t.zoom=this.zoom,t.angle=this.angle,t._fov=this._fov,t._pitch=this._pitch,t._unmodified=this._unmodified,t._edgeInsets=this._edgeInsets.clone(),t._calcMatrices(),t},Mn.minZoom.get=function(){return this._minZoom},Mn.minZoom.set=function(t){this._minZoom!==t&&(this._minZoom=t,this.zoom=Math.max(this.zoom,t))},Mn.maxZoom.get=function(){return this._maxZoom},Mn.maxZoom.set=function(t){this._maxZoom!==t&&(this._maxZoom=t,this.zoom=Math.min(this.zoom,t))},Mn.minPitch.get=function(){return this._minPitch},Mn.minPitch.set=function(t){this._minPitch!==t&&(this._minPitch=t,this.pitch=Math.max(this.pitch,t))},Mn.maxPitch.get=function(){return this._maxPitch},Mn.maxPitch.set=function(t){this._maxPitch!==t&&(this._maxPitch=t,this.pitch=Math.min(this.pitch,t))},Mn.renderWorldCopies.get=function(){return this._renderWorldCopies},Mn.renderWorldCopies.set=function(t){void 0===t?t=!0:null===t&&(t=!1),this._renderWorldCopies=t},Mn.worldSize.get=function(){return this.tileSize*this.scale},Mn.centerOffset.get=function(){return this.centerPoint._sub(this.size._div(2))},Mn.size.get=function(){return new t.Point(this.width,this.height)},Mn.bearing.get=function(){return-this.angle/Math.PI*180},Mn.bearing.set=function(e){var r=-t.wrap(e,-180,180)*Math.PI/180;this.angle!==r&&(this._unmodified=!1,this.angle=r,this._calcMatrices(),this.rotationMatrix=t.create$2(),t.rotate(this.rotationMatrix,this.rotationMatrix,this.angle))},Mn.pitch.get=function(){return this._pitch/Math.PI*180},Mn.pitch.set=function(e){var r=t.clamp(e,this.minPitch,this.maxPitch)/180*Math.PI;this._pitch!==r&&(this._unmodified=!1,this._pitch=r,this._calcMatrices())},Mn.fov.get=function(){return this._fov/Math.PI*180},Mn.fov.set=function(t){t=Math.max(.01,Math.min(60,t)),this._fov!==t&&(this._unmodified=!1,this._fov=t/180*Math.PI,this._calcMatrices())},Mn.zoom.get=function(){return this._zoom},Mn.zoom.set=function(t){var e=Math.min(Math.max(t,this.minZoom),this.maxZoom);this._zoom!==e&&(this._unmodified=!1,this._zoom=e,this.scale=this.zoomScale(e),this.tileZoom=Math.floor(e),this.zoomFraction=e-this.tileZoom,this._constrain(),this._calcMatrices())},Mn.center.get=function(){return this._center},Mn.center.set=function(t){t.lat===this._center.lat&&t.lng===this._center.lng||(this._unmodified=!1,this._center=t,this._constrain(),this._calcMatrices())},Mn.padding.get=function(){return this._edgeInsets.toJSON()},Mn.padding.set=function(t){this._edgeInsets.equals(t)||(this._unmodified=!1,this._edgeInsets.interpolate(this._edgeInsets,t,1),this._calcMatrices())},Mn.centerPoint.get=function(){return this._edgeInsets.getCenter(this.width,this.height)},An.prototype.isPaddingEqual=function(t){return this._edgeInsets.equals(t)},An.prototype.interpolatePadding=function(t,e,r){this._unmodified=!1,this._edgeInsets.interpolate(t,e,r),this._constrain(),this._calcMatrices()},An.prototype.coveringZoomLevel=function(t){var e=(t.roundZoom?Math.round:Math.floor)(this.zoom+this.scaleZoom(this.tileSize/t.tileSize));return Math.max(0,e)},An.prototype.getVisibleUnwrappedCoordinates=function(e){var r=[new t.UnwrappedTileID(0,e)];if(this._renderWorldCopies)for(var n=this.pointCoordinate(new t.Point(0,0)),i=this.pointCoordinate(new t.Point(this.width,0)),a=this.pointCoordinate(new t.Point(this.width,this.height)),o=this.pointCoordinate(new t.Point(0,this.height)),s=Math.floor(Math.min(n.x,i.x,a.x,o.x)),l=Math.floor(Math.max(n.x,i.x,a.x,o.x)),c=s-1;c<=l+1;c++)0!==c&&r.push(new t.UnwrappedTileID(c,e));return r},An.prototype.coveringTiles=function(e){var r=this.coveringZoomLevel(e),n=r;if(void 0!==e.minzoom&&r<e.minzoom)return[];void 0!==e.maxzoom&&r>e.maxzoom&&(r=e.maxzoom);var i=t.MercatorCoordinate.fromLngLat(this.center),a=Math.pow(2,r),o=[a*i.x,a*i.y,0],s=wn.fromInvProjectionMatrix(this.invProjMatrix,this.worldSize,r),l=e.minzoom||0;this.pitch<=60&&this._edgeInsets.top<.1&&(l=r);var c=function(t){return{aabb:new Tn([t*a,0,0],[(t+1)*a,a,0]),zoom:0,x:0,y:0,wrap:t,fullyVisible:!1}},u=[],f=[],h=r,p=e.reparseOverscaled?n:r;if(this._renderWorldCopies)for(var d=1;d<=3;d++)u.push(c(-d)),u.push(c(d));for(u.push(c(0));u.length>0;){var g=u.pop(),m=g.x,v=g.y,y=g.fullyVisible;if(!y){var x=g.aabb.intersects(s);if(0===x)continue;y=2===x}var b=g.aabb.distanceX(o),_=g.aabb.distanceY(o),w=Math.max(Math.abs(b),Math.abs(_)),T=3+(1<<h-g.zoom)-2;if(g.zoom===h||w>T&&g.zoom>=l)f.push({tileID:new t.OverscaledTileID(g.zoom===h?p:g.zoom,g.wrap,g.zoom,m,v),distanceSq:t.sqrLen([o[0]-.5-m,o[1]-.5-v])});else for(var k=0;k<4;k++){var A=(m<<1)+k%2,M=(v<<1)+(k>>1);u.push({aabb:g.aabb.quadrant(k),zoom:g.zoom+1,x:A,y:M,wrap:g.wrap,fullyVisible:y})}}return f.sort((function(t,e){return t.distanceSq-e.distanceSq})).map((function(t){return t.tileID}))},An.prototype.resize=function(t,e){this.width=t,this.height=e,this.pixelsToGLUnits=[2/t,-2/e],this._constrain(),this._calcMatrices()},Mn.unmodified.get=function(){return this._unmodified},An.prototype.zoomScale=function(t){return Math.pow(2,t)},An.prototype.scaleZoom=function(t){return Math.log(t)/Math.LN2},An.prototype.project=function(e){var r=t.clamp(e.lat,-this.maxValidLatitude,this.maxValidLatitude);return new t.Point(t.mercatorXfromLng(e.lng)*this.worldSize,t.mercatorYfromLat(r)*this.worldSize)},An.prototype.unproject=function(e){return new t.MercatorCoordinate(e.x/this.worldSize,e.y/this.worldSize).toLngLat()},Mn.point.get=function(){return this.project(this.center)},An.prototype.setLocationAtPoint=function(e,r){var n=this.pointCoordinate(r),i=this.pointCoordinate(this.centerPoint),a=this.locationCoordinate(e),o=new t.MercatorCoordinate(a.x-(n.x-i.x),a.y-(n.y-i.y));this.center=this.coordinateLocation(o),this._renderWorldCopies&&(this.center=this.center.wrap())},An.prototype.locationPoint=function(t){return this.coordinatePoint(this.locationCoordinate(t))},An.prototype.pointLocation=function(t){return this.coordinateLocation(this.pointCoordinate(t))},An.prototype.locationCoordinate=function(e){return t.MercatorCoordinate.fromLngLat(e)},An.prototype.coordinateLocation=function(t){return t.toLngLat()},An.prototype.pointCoordinate=function(e){var r=[e.x,e.y,0,1],n=[e.x,e.y,1,1];t.transformMat4(r,r,this.pixelMatrixInverse),t.transformMat4(n,n,this.pixelMatrixInverse);var i=r[3],a=n[3],o=r[0]/i,s=n[0]/a,l=r[1]/i,c=n[1]/a,u=r[2]/i,f=n[2]/a,h=u===f?0:(0-u)/(f-u);return new t.MercatorCoordinate(t.number(o,s,h)/this.worldSize,t.number(l,c,h)/this.worldSize)},An.prototype.coordinatePoint=function(e){var r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix),new t.Point(r[0]/r[3],r[1]/r[3])},An.prototype.getBounds=function(){return(new t.LngLatBounds).extend(this.pointLocation(new t.Point(0,0))).extend(this.pointLocation(new t.Point(this.width,0))).extend(this.pointLocation(new t.Point(this.width,this.height))).extend(this.pointLocation(new t.Point(0,this.height)))},An.prototype.getMaxBounds=function(){return this.latRange&&2===this.latRange.length&&this.lngRange&&2===this.lngRange.length?new t.LngLatBounds([this.lngRange[0],this.latRange[0]],[this.lngRange[1],this.latRange[1]]):null},An.prototype.setMaxBounds=function(t){t?(this.lngRange=[t.getWest(),t.getEast()],this.latRange=[t.getSouth(),t.getNorth()],this._constrain()):(this.lngRange=null,this.latRange=[-this.maxValidLatitude,this.maxValidLatitude])},An.prototype.calculatePosMatrix=function(e,r){void 0===r&&(r=!1);var n=e.key,i=r?this._alignedPosMatrixCache:this._posMatrixCache;if(i[n])return i[n];var a=e.canonical,o=this.worldSize/this.zoomScale(a.z),s=a.x+Math.pow(2,a.z)*e.wrap,l=t.identity(new Float64Array(16));return t.translate(l,l,[s*o,a.y*o,0]),t.scale(l,l,[o/t.EXTENT,o/t.EXTENT,1]),t.multiply(l,r?this.alignedProjMatrix:this.projMatrix,l),i[n]=new Float32Array(l),i[n]},An.prototype.customLayerMatrix=function(){return this.mercatorMatrix.slice()},An.prototype._constrain=function(){if(this.center&&this.width&&this.height&&!this._constraining){this._constraining=!0;var e,r,n,i,a=-90,o=90,s=-180,l=180,c=this.size,u=this._unmodified;if(this.latRange){var f=this.latRange;a=t.mercatorYfromLat(f[1])*this.worldSize,e=(o=t.mercatorYfromLat(f[0])*this.worldSize)-a<c.y?c.y/(o-a):0}if(this.lngRange){var h=this.lngRange;s=t.mercatorXfromLng(h[0])*this.worldSize,r=(l=t.mercatorXfromLng(h[1])*this.worldSize)-s<c.x?c.x/(l-s):0}var p=this.point,d=Math.max(r||0,e||0);if(d)return this.center=this.unproject(new t.Point(r?(l+s)/2:p.x,e?(o+a)/2:p.y)),this.zoom+=this.scaleZoom(d),this._unmodified=u,void(this._constraining=!1);if(this.latRange){var g=p.y,m=c.y/2;g-m<a&&(i=a+m),g+m>o&&(i=o-m)}if(this.lngRange){var v=p.x,y=c.x/2;v-y<s&&(n=s+y),v+y>l&&(n=l-y)}void 0===n&&void 0===i||(this.center=this.unproject(new t.Point(void 0!==n?n:p.x,void 0!==i?i:p.y))),this._unmodified=u,this._constraining=!1}},An.prototype._calcMatrices=function(){if(this.height){var e=this._fov/2,r=this.centerOffset;this.cameraToCenterDistance=.5/Math.tan(e)*this.height;var n=Math.PI/2+this._pitch,i=this._fov*(.5+r.y/this.height),a=Math.sin(i)*this.cameraToCenterDistance/Math.sin(t.clamp(Math.PI-n-i,.01,Math.PI-.01)),o=this.point,s=o.x,l=o.y,c=1.01*(Math.cos(Math.PI/2-this._pitch)*a+this.cameraToCenterDistance),u=this.height/50,f=new Float64Array(16);t.perspective(f,this._fov,this.width/this.height,u,c),f[8]=2*-r.x/this.width,f[9]=2*r.y/this.height,t.scale(f,f,[1,-1,1]),t.translate(f,f,[0,0,-this.cameraToCenterDistance]),t.rotateX(f,f,this._pitch),t.rotateZ(f,f,this.angle),t.translate(f,f,[-s,-l,0]),this.mercatorMatrix=t.scale([],f,[this.worldSize,this.worldSize,this.worldSize]),t.scale(f,f,[1,1,t.mercatorZfromAltitude(1,this.center.lat)*this.worldSize,1]),this.projMatrix=f,this.invProjMatrix=t.invert([],this.projMatrix);var h=this.width%2/2,p=this.height%2/2,d=Math.cos(this.angle),g=Math.sin(this.angle),m=s-Math.round(s)+d*h+g*p,v=l-Math.round(l)+d*p+g*h,y=new Float64Array(f);if(t.translate(y,y,[m>.5?m-1:m,v>.5?v-1:v,0]),this.alignedProjMatrix=y,f=t.create(),t.scale(f,f,[this.width/2,-this.height/2,1]),t.translate(f,f,[1,-1,0]),this.labelPlaneMatrix=f,f=t.create(),t.scale(f,f,[1,-1,1]),t.translate(f,f,[-1,-1,0]),t.scale(f,f,[2/this.width,2/this.height,1]),this.glCoordMatrix=f,this.pixelMatrix=t.multiply(new Float64Array(16),this.labelPlaneMatrix,this.projMatrix),!(f=t.invert(new Float64Array(16),this.pixelMatrix)))throw new Error("failed to invert matrix");this.pixelMatrixInverse=f,this._posMatrixCache={},this._alignedPosMatrixCache={}}},An.prototype.maxPitchScaleFactor=function(){if(!this.pixelMatrixInverse)return 1;var e=this.pointCoordinate(new t.Point(0,0)),r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix)[3]/this.cameraToCenterDistance},An.prototype.getCameraPoint=function(){var e=this._pitch,r=Math.tan(e)*(this.cameraToCenterDistance||1);return this.centerPoint.add(new t.Point(0,r))},An.prototype.getCameraQueryGeometry=function(e){var r=this.getCameraPoint();if(1===e.length)return[e[0],r];for(var n=r.x,i=r.y,a=r.x,o=r.y,s=0,l=e;s<l.length;s+=1){var c=l[s];n=Math.min(n,c.x),i=Math.min(i,c.y),a=Math.max(a,c.x),o=Math.max(o,c.y)}return[new t.Point(n,i),new t.Point(a,i),new t.Point(a,o),new t.Point(n,o),new t.Point(n,i)]},Object.defineProperties(An.prototype,Mn);var Sn=function(e){var r,n,i,a,o;this._hashName=e&&encodeURIComponent(e),t.bindAll(["_getCurrentHash","_onHashChange","_updateHash"],this),this._updateHash=(r=this._updateHashUnthrottled.bind(this),n=300,i=!1,a=null,o=function(){a=null,i&&(r(),a=setTimeout(o,n),i=!1)},function(){return i=!0,a||o(),a})};Sn.prototype.addTo=function(e){return this._map=e,t.window.addEventListener("hashchange",this._onHashChange,!1),this._map.on("moveend",this._updateHash),this},Sn.prototype.remove=function(){return t.window.removeEventListener("hashchange",this._onHashChange,!1),this._map.off("moveend",this._updateHash),clearTimeout(this._updateHash()),delete this._map,this},Sn.prototype.getHashString=function(e){var r=this._map.getCenter(),n=Math.round(100*this._map.getZoom())/100,i=Math.ceil((n*Math.LN2+Math.log(512/360/.5))/Math.LN10),a=Math.pow(10,i),o=Math.round(r.lng*a)/a,s=Math.round(r.lat*a)/a,l=this._map.getBearing(),c=this._map.getPitch(),u="";if(u+=e?"/"+o+"/"+s+"/"+n:n+"/"+s+"/"+o,(l||c)&&(u+="/"+Math.round(10*l)/10),c&&(u+="/"+Math.round(c)),this._hashName){var f=this._hashName,h=!1,p=t.window.location.hash.slice(1).split("&").map((function(t){var e=t.split("=")[0];return e===f?(h=!0,e+"="+u):t})).filter((function(t){return t}));return h||p.push(f+"="+u),"#"+p.join("&")}return"#"+u},Sn.prototype._getCurrentHash=function(){var e,r=this,n=t.window.location.hash.replace("#","");return this._hashName?(n.split("&").map((function(t){return t.split("=")})).forEach((function(t){t[0]===r._hashName&&(e=t)})),(e&&e[1]||"").split("/")):n.split("/")},Sn.prototype._onHashChange=function(){var t=this._getCurrentHash();if(t.length>=3&&!t.some((function(t){return isNaN(t)}))){var e=this._map.dragRotate.isEnabled()&&this._map.touchZoomRotate.isEnabled()?+(t[3]||0):this._map.getBearing();return this._map.jumpTo({center:[+t[2],+t[1]],zoom:+t[0],bearing:e,pitch:+(t[4]||0)}),!0}return!1},Sn.prototype._updateHashUnthrottled=function(){var e=this.getHashString();try{t.window.history.replaceState(t.window.history.state,"",e)}catch(t){}};var En={linearity:.3,easing:t.bezier(0,0,.3,1)},Ln=t.extend({deceleration:2500,maxSpeed:1400},En),Cn=t.extend({deceleration:20,maxSpeed:1400},En),Pn=t.extend({deceleration:1e3,maxSpeed:360},En),In=t.extend({deceleration:1e3,maxSpeed:90},En),On=function(t){this._map=t,this.clear()};function zn(t,e){(!t.duration||t.duration<e.duration)&&(t.duration=e.duration,t.easing=e.easing)}function Dn(e,r,n){var i=n.maxSpeed,a=n.linearity,o=n.deceleration,s=t.clamp(e*a/(r/1e3),-i,i),l=Math.abs(s)/(o*a);return{easing:n.easing,duration:1e3*l,amount:s*(l/2)}}On.prototype.clear=function(){this._inertiaBuffer=[]},On.prototype.record=function(e){this._drainInertiaBuffer(),this._inertiaBuffer.push({time:t.browser.now(),settings:e})},On.prototype._drainInertiaBuffer=function(){for(var e=this._inertiaBuffer,r=t.browser.now();e.length>0&&r-e[0].time>160;)e.shift()},On.prototype._onMoveEnd=function(e){if(this._drainInertiaBuffer(),!(this._inertiaBuffer.length<2)){for(var r={zoom:0,bearing:0,pitch:0,pan:new t.Point(0,0),pinchAround:void 0,around:void 0},n=0,i=this._inertiaBuffer;n<i.length;n+=1){var a=i[n].settings;r.zoom+=a.zoomDelta||0,r.bearing+=a.bearingDelta||0,r.pitch+=a.pitchDelta||0,a.panDelta&&r.pan._add(a.panDelta),a.around&&(r.around=a.around),a.pinchAround&&(r.pinchAround=a.pinchAround)}var o=this._inertiaBuffer[this._inertiaBuffer.length-1].time-this._inertiaBuffer[0].time,s={};if(r.pan.mag()){var l=Dn(r.pan.mag(),o,t.extend({},Ln,e||{}));s.offset=r.pan.mult(l.amount/r.pan.mag()),s.center=this._map.transform.center,zn(s,l)}if(r.zoom){var c=Dn(r.zoom,o,Cn);s.zoom=this._map.transform.zoom+c.amount,zn(s,c)}if(r.bearing){var u=Dn(r.bearing,o,Pn);s.bearing=this._map.transform.bearing+t.clamp(u.amount,-179,179),zn(s,u)}if(r.pitch){var f=Dn(r.pitch,o,In);s.pitch=this._map.transform.pitch+f.amount,zn(s,f)}if(s.zoom||s.bearing){var h=void 0===r.pinchAround?r.around:r.pinchAround;s.around=h?this._map.unproject(h):this._map.getCenter()}return this.clear(),t.extend(s,{noMoveStart:!0})}};var Rn=function(e){function n(n,i,a,o){void 0===o&&(o={});var s=r.mousePos(i.getCanvasContainer(),a),l=i.unproject(s);e.call(this,n,t.extend({point:s,lngLat:l,originalEvent:a},o)),this._defaultPrevented=!1,this.target=i}e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n;var i={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},i.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,i),n}(t.Event),Fn=function(e){function n(n,i,a){var o="touchend"===n?a.changedTouches:a.touches,s=r.touchPos(i.getCanvasContainer(),o),l=s.map((function(t){return i.unproject(t)})),c=s.reduce((function(t,e,r,n){return t.add(e.div(n.length))}),new t.Point(0,0)),u=i.unproject(c);e.call(this,n,{points:s,point:c,lngLats:l,lngLat:u,originalEvent:a}),this._defaultPrevented=!1}e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n;var i={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},i.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,i),n}(t.Event),Bn=function(t){function e(e,r,n){t.call(this,e,{originalEvent:n}),this._defaultPrevented=!1}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={defaultPrevented:{configurable:!0}};return e.prototype.preventDefault=function(){this._defaultPrevented=!0},r.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(e.prototype,r),e}(t.Event),Nn=function(t,e){this._map=t,this._clickTolerance=e.clickTolerance};Nn.prototype.reset=function(){delete this._mousedownPos},Nn.prototype.wheel=function(t){return this._firePreventable(new Bn(t.type,this._map,t))},Nn.prototype.mousedown=function(t,e){return this._mousedownPos=e,this._firePreventable(new Rn(t.type,this._map,t))},Nn.prototype.mouseup=function(t){this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.click=function(t,e){this._mousedownPos&&this._mousedownPos.dist(e)>=this._clickTolerance||this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.dblclick=function(t){return this._firePreventable(new Rn(t.type,this._map,t))},Nn.prototype.mouseover=function(t){this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.mouseout=function(t){this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.touchstart=function(t){return this._firePreventable(new Fn(t.type,this._map,t))},Nn.prototype.touchmove=function(t){this._map.fire(new Fn(t.type,this._map,t))},Nn.prototype.touchend=function(t){this._map.fire(new Fn(t.type,this._map,t))},Nn.prototype.touchcancel=function(t){this._map.fire(new Fn(t.type,this._map,t))},Nn.prototype._firePreventable=function(t){if(this._map.fire(t),t.defaultPrevented)return{}},Nn.prototype.isEnabled=function(){return!0},Nn.prototype.isActive=function(){return!1},Nn.prototype.enable=function(){},Nn.prototype.disable=function(){};var jn=function(t){this._map=t};jn.prototype.reset=function(){this._delayContextMenu=!1,delete this._contextMenuEvent},jn.prototype.mousemove=function(t){this._map.fire(new Rn(t.type,this._map,t))},jn.prototype.mousedown=function(){this._delayContextMenu=!0},jn.prototype.mouseup=function(){this._delayContextMenu=!1,this._contextMenuEvent&&(this._map.fire(new Rn("contextmenu",this._map,this._contextMenuEvent)),delete this._contextMenuEvent)},jn.prototype.contextmenu=function(t){this._delayContextMenu?this._contextMenuEvent=t:this._map.fire(new Rn(t.type,this._map,t)),this._map.listens("contextmenu")&&t.preventDefault()},jn.prototype.isEnabled=function(){return!0},jn.prototype.isActive=function(){return!1},jn.prototype.enable=function(){},jn.prototype.disable=function(){};var Un=function(t,e){this._map=t,this._el=t.getCanvasContainer(),this._container=t.getContainer(),this._clickTolerance=e.clickTolerance||1};function Vn(t,e){for(var r={},n=0;n<t.length;n++)r[t[n].identifier]=e[n];return r}Un.prototype.isEnabled=function(){return!!this._enabled},Un.prototype.isActive=function(){return!!this._active},Un.prototype.enable=function(){this.isEnabled()||(this._enabled=!0)},Un.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},Un.prototype.mousedown=function(t,e){this.isEnabled()&&t.shiftKey&&0===t.button&&(r.disableDrag(),this._startPos=this._lastPos=e,this._active=!0)},Un.prototype.mousemoveWindow=function(t,e){if(this._active){var n=e;if(!(this._lastPos.equals(n)||!this._box&&n.dist(this._startPos)<this._clickTolerance)){var i=this._startPos;this._lastPos=n,this._box||(this._box=r.create("div","mapboxgl-boxzoom",this._container),this._container.classList.add("mapboxgl-crosshair"),this._fireEvent("boxzoomstart",t));var a=Math.min(i.x,n.x),o=Math.max(i.x,n.x),s=Math.min(i.y,n.y),l=Math.max(i.y,n.y);r.setTransform(this._box,"translate("+a+"px,"+s+"px)"),this._box.style.width=o-a+"px",this._box.style.height=l-s+"px"}}},Un.prototype.mouseupWindow=function(e,n){var i=this;if(this._active&&0===e.button){var a=this._startPos,o=n;if(this.reset(),r.suppressClick(),a.x!==o.x||a.y!==o.y)return this._map.fire(new t.Event("boxzoomend",{originalEvent:e})),{cameraAnimation:function(t){return t.fitScreenCoordinates(a,o,i._map.getBearing(),{linear:!0})}};this._fireEvent("boxzoomcancel",e)}},Un.prototype.keydown=function(t){this._active&&27===t.keyCode&&(this.reset(),this._fireEvent("boxzoomcancel",t))},Un.prototype.reset=function(){this._active=!1,this._container.classList.remove("mapboxgl-crosshair"),this._box&&(r.remove(this._box),this._box=null),r.enableDrag(),delete this._startPos,delete this._lastPos},Un.prototype._fireEvent=function(e,r){return this._map.fire(new t.Event(e,{originalEvent:r}))};var Hn=function(t){this.reset(),this.numTouches=t.numTouches};Hn.prototype.reset=function(){delete this.centroid,delete this.startTime,delete this.touches,this.aborted=!1},Hn.prototype.touchstart=function(e,r,n){(this.centroid||n.length>this.numTouches)&&(this.aborted=!0),this.aborted||(void 0===this.startTime&&(this.startTime=e.timeStamp),n.length===this.numTouches&&(this.centroid=function(e){for(var r=new t.Point(0,0),n=0,i=e;n<i.length;n+=1){var a=i[n];r._add(a)}return r.div(e.length)}(r),this.touches=Vn(n,r)))},Hn.prototype.touchmove=function(t,e,r){if(!this.aborted&&this.centroid){var n=Vn(r,e);for(var i in this.touches){var a=this.touches[i],o=n[i];(!o||o.dist(a)>30)&&(this.aborted=!0)}}},Hn.prototype.touchend=function(t,e,r){if((!this.centroid||t.timeStamp-this.startTime>500)&&(this.aborted=!0),0===r.length){var n=!this.aborted&&this.centroid;if(this.reset(),n)return n}};var qn=function(t){this.singleTap=new Hn(t),this.numTaps=t.numTaps,this.reset()};qn.prototype.reset=function(){this.lastTime=1/0,delete this.lastTap,this.count=0,this.singleTap.reset()},qn.prototype.touchstart=function(t,e,r){this.singleTap.touchstart(t,e,r)},qn.prototype.touchmove=function(t,e,r){this.singleTap.touchmove(t,e,r)},qn.prototype.touchend=function(t,e,r){var n=this.singleTap.touchend(t,e,r);if(n){var i=t.timeStamp-this.lastTime<500,a=!this.lastTap||this.lastTap.dist(n)<30;if(i&&a||this.reset(),this.count++,this.lastTime=t.timeStamp,this.lastTap=n,this.count===this.numTaps)return this.reset(),n}};var Gn=function(){this._zoomIn=new qn({numTouches:1,numTaps:2}),this._zoomOut=new qn({numTouches:2,numTaps:1}),this.reset()};Gn.prototype.reset=function(){this._active=!1,this._zoomIn.reset(),this._zoomOut.reset()},Gn.prototype.touchstart=function(t,e,r){this._zoomIn.touchstart(t,e,r),this._zoomOut.touchstart(t,e,r)},Gn.prototype.touchmove=function(t,e,r){this._zoomIn.touchmove(t,e,r),this._zoomOut.touchmove(t,e,r)},Gn.prototype.touchend=function(t,e,r){var n=this,i=this._zoomIn.touchend(t,e,r),a=this._zoomOut.touchend(t,e,r);return i?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()+1,around:e.unproject(i)},{originalEvent:t})}}):a?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()-1,around:e.unproject(a)},{originalEvent:t})}}):void 0},Gn.prototype.touchcancel=function(){this.reset()},Gn.prototype.enable=function(){this._enabled=!0},Gn.prototype.disable=function(){this._enabled=!1,this.reset()},Gn.prototype.isEnabled=function(){return this._enabled},Gn.prototype.isActive=function(){return this._active};var Yn=function(t){this.reset(),this._clickTolerance=t.clickTolerance||1};Yn.prototype.reset=function(){this._active=!1,this._moved=!1,delete this._lastPoint,delete this._eventButton},Yn.prototype._correctButton=function(t,e){return!1},Yn.prototype._move=function(t,e){return{}},Yn.prototype.mousedown=function(t,e){if(!this._lastPoint){var n=r.mouseButton(t);this._correctButton(t,n)&&(this._lastPoint=e,this._eventButton=n)}},Yn.prototype.mousemoveWindow=function(t,e){var r=this._lastPoint;if(r&&(t.preventDefault(),this._moved||!(e.dist(r)<this._clickTolerance)))return this._moved=!0,this._lastPoint=e,this._move(r,e)},Yn.prototype.mouseupWindow=function(t){r.mouseButton(t)===this._eventButton&&(this._moved&&r.suppressClick(),this.reset())},Yn.prototype.enable=function(){this._enabled=!0},Yn.prototype.disable=function(){this._enabled=!1,this.reset()},Yn.prototype.isEnabled=function(){return this._enabled},Yn.prototype.isActive=function(){return this._active};var Wn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.mousedown=function(e,r){t.prototype.mousedown.call(this,e,r),this._lastPoint&&(this._active=!0)},e.prototype._correctButton=function(t,e){return 0===e&&!t.ctrlKey},e.prototype._move=function(t,e){return{around:e,panDelta:e.sub(t)}},e}(Yn),Xn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._correctButton=function(t,e){return 0===e&&t.ctrlKey||2===e},e.prototype._move=function(t,e){var r=.8*(e.x-t.x);if(r)return this._active=!0,{bearingDelta:r}},e.prototype.contextmenu=function(t){t.preventDefault()},e}(Yn),Zn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._correctButton=function(t,e){return 0===e&&t.ctrlKey||2===e},e.prototype._move=function(t,e){var r=-.5*(e.y-t.y);if(r)return this._active=!0,{pitchDelta:r}},e.prototype.contextmenu=function(t){t.preventDefault()},e}(Yn),Jn=function(t){this._minTouches=1,this._clickTolerance=t.clickTolerance||1,this.reset()};Jn.prototype.reset=function(){this._active=!1,this._touches={},this._sum=new t.Point(0,0)},Jn.prototype.touchstart=function(t,e,r){return this._calculateTransform(t,e,r)},Jn.prototype.touchmove=function(t,e,r){if(this._active)return t.preventDefault(),this._calculateTransform(t,e,r)},Jn.prototype.touchend=function(t,e,r){this._calculateTransform(t,e,r),this._active&&r.length<this._minTouches&&this.reset()},Jn.prototype.touchcancel=function(){this.reset()},Jn.prototype._calculateTransform=function(e,r,n){n.length>0&&(this._active=!0);var i=Vn(n,r),a=new t.Point(0,0),o=new t.Point(0,0),s=0;for(var l in i){var c=i[l],u=this._touches[l];u&&(a._add(c),o._add(c.sub(u)),s++,i[l]=c)}if(this._touches=i,!(s<this._minTouches)&&o.mag()){var f=o.div(s);if(this._sum._add(f),!(this._sum.mag()<this._clickTolerance))return{around:a.div(s),panDelta:f}}},Jn.prototype.enable=function(){this._enabled=!0},Jn.prototype.disable=function(){this._enabled=!1,this.reset()},Jn.prototype.isEnabled=function(){return this._enabled},Jn.prototype.isActive=function(){return this._active};var Kn=function(){this.reset()};function Qn(t,e,r){for(var n=0;n<t.length;n++)if(t[n].identifier===r)return e[n]}Kn.prototype.reset=function(){this._active=!1,delete this._firstTwoTouches},Kn.prototype._start=function(t){},Kn.prototype._move=function(t,e,r){return{}},Kn.prototype.touchstart=function(t,e,r){this._firstTwoTouches||r.length<2||(this._firstTwoTouches=[r[0].identifier,r[1].identifier],this._start([e[0],e[1]]))},Kn.prototype.touchmove=function(t,e,r){if(this._firstTwoTouches){t.preventDefault();var n=this._firstTwoTouches,i=n[0],a=n[1],o=Qn(r,e,i),s=Qn(r,e,a);if(o&&s){var l=this._aroundCenter?null:o.add(s).div(2);return this._move([o,s],l,t)}}},Kn.prototype.touchend=function(t,e,n){if(this._firstTwoTouches){var i=this._firstTwoTouches,a=i[0],o=i[1],s=Qn(n,e,a),l=Qn(n,e,o);s&&l||(this._active&&r.suppressClick(),this.reset())}},Kn.prototype.touchcancel=function(){this.reset()},Kn.prototype.enable=function(t){this._enabled=!0,this._aroundCenter=!!t&&"center"===t.around},Kn.prototype.disable=function(){this._enabled=!1,this.reset()},Kn.prototype.isEnabled=function(){return this._enabled},Kn.prototype.isActive=function(){return this._active};function $n(t,e){return Math.log(t/e)/Math.LN2}var ti=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),delete this._distance,delete this._startDistance},e.prototype._start=function(t){this._startDistance=this._distance=t[0].dist(t[1])},e.prototype._move=function(t,e){var r=this._distance;if(this._distance=t[0].dist(t[1]),this._active||!(Math.abs($n(this._distance,this._startDistance))<.1))return this._active=!0,{zoomDelta:$n(this._distance,r),pinchAround:e}},e}(Kn);function ei(t,e){return 180*t.angleWith(e)/Math.PI}var ri=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),delete this._minDiameter,delete this._startVector,delete this._vector},e.prototype._start=function(t){this._startVector=this._vector=t[0].sub(t[1]),this._minDiameter=t[0].dist(t[1])},e.prototype._move=function(t,e){var r=this._vector;if(this._vector=t[0].sub(t[1]),this._active||!this._isBelowThreshold(this._vector))return this._active=!0,{bearingDelta:ei(this._vector,r),pinchAround:e}},e.prototype._isBelowThreshold=function(t){this._minDiameter=Math.min(this._minDiameter,t.mag());var e=25/(Math.PI*this._minDiameter)*360,r=ei(t,this._startVector);return Math.abs(r)<e},e}(Kn);function ni(t){return Math.abs(t.y)>Math.abs(t.x)}var ii=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),this._valid=void 0,delete this._firstMove,delete this._lastPoints},e.prototype._start=function(t){this._lastPoints=t,ni(t[0].sub(t[1]))&&(this._valid=!1)},e.prototype._move=function(t,e,r){var n=t[0].sub(this._lastPoints[0]),i=t[1].sub(this._lastPoints[1]);if(this._valid=this.gestureBeginsVertically(n,i,r.timeStamp),this._valid){this._lastPoints=t,this._active=!0;return{pitchDelta:-.5*((n.y+i.y)/2)}}},e.prototype.gestureBeginsVertically=function(t,e,r){if(void 0!==this._valid)return this._valid;var n=t.mag()>=2,i=e.mag()>=2;if(n||i){if(!n||!i)return void 0===this._firstMove&&(this._firstMove=r),r-this._firstMove<100&&void 0;var a=t.y>0==e.y>0;return ni(t)&&ni(e)&&a}},e}(Kn),ai={panStep:100,bearingStep:15,pitchStep:10},oi=function(){var t=ai;this._panStep=t.panStep,this._bearingStep=t.bearingStep,this._pitchStep=t.pitchStep};function si(t){return t*(2-t)}oi.prototype.reset=function(){this._active=!1},oi.prototype.keydown=function(t){var e=this;if(!(t.altKey||t.ctrlKey||t.metaKey)){var r=0,n=0,i=0,a=0,o=0;switch(t.keyCode){case 61:case 107:case 171:case 187:r=1;break;case 189:case 109:case 173:r=-1;break;case 37:t.shiftKey?n=-1:(t.preventDefault(),a=-1);break;case 39:t.shiftKey?n=1:(t.preventDefault(),a=1);break;case 38:t.shiftKey?i=1:(t.preventDefault(),o=-1);break;case 40:t.shiftKey?i=-1:(t.preventDefault(),o=1);break;default:return}return{cameraAnimation:function(s){var l=s.getZoom();s.easeTo({duration:300,easeId:"keyboardHandler",easing:si,zoom:r?Math.round(l)+r*(t.shiftKey?2:1):l,bearing:s.getBearing()+n*e._bearingStep,pitch:s.getPitch()+i*e._pitchStep,offset:[-a*e._panStep,-o*e._panStep],center:s.getCenter()},{originalEvent:t})}}}},oi.prototype.enable=function(){this._enabled=!0},oi.prototype.disable=function(){this._enabled=!1,this.reset()},oi.prototype.isEnabled=function(){return this._enabled},oi.prototype.isActive=function(){return this._active};var li=function(e,r){this._map=e,this._el=e.getCanvasContainer(),this._handler=r,this._delta=0,this._defaultZoomRate=.01,this._wheelZoomRate=1/450,t.bindAll(["_onWheel","_onTimeout","_onScrollFrame","_onScrollFinished"],this)};li.prototype.setZoomRate=function(t){this._defaultZoomRate=t},li.prototype.setWheelZoomRate=function(t){this._wheelZoomRate=t},li.prototype.isEnabled=function(){return!!this._enabled},li.prototype.isActive=function(){return!!this._active||void 0!==this._finishTimeout},li.prototype.isZooming=function(){return!!this._zooming},li.prototype.enable=function(t){this.isEnabled()||(this._enabled=!0,this._aroundCenter=t&&"center"===t.around)},li.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},li.prototype.wheel=function(e){if(this.isEnabled()){var r=e.deltaMode===t.window.WheelEvent.DOM_DELTA_LINE?40*e.deltaY:e.deltaY,n=t.browser.now(),i=n-(this._lastWheelEventTime||0);this._lastWheelEventTime=n,0!==r&&r%4.000244140625==0?this._type="wheel":0!==r&&Math.abs(r)<4?this._type="trackpad":i>400?(this._type=null,this._lastValue=r,this._timeout=setTimeout(this._onTimeout,40,e)):this._type||(this._type=Math.abs(i*r)<200?"trackpad":"wheel",this._timeout&&(clearTimeout(this._timeout),this._timeout=null,r+=this._lastValue)),e.shiftKey&&r&&(r/=4),this._type&&(this._lastWheelEvent=e,this._delta-=r,this._active||this._start(e)),e.preventDefault()}},li.prototype._onTimeout=function(t){this._type="wheel",this._delta-=this._lastValue,this._active||this._start(t)},li.prototype._start=function(e){if(this._delta){this._frameId&&(this._frameId=null),this._active=!0,this.isZooming()||(this._zooming=!0),this._finishTimeout&&(clearTimeout(this._finishTimeout),delete this._finishTimeout);var n=r.mousePos(this._el,e);this._around=t.LngLat.convert(this._aroundCenter?this._map.getCenter():this._map.unproject(n)),this._aroundPoint=this._map.transform.locationPoint(this._around),this._frameId||(this._frameId=!0,this._handler._triggerRenderFrame())}},li.prototype.renderFrame=function(){return this._onScrollFrame()},li.prototype._onScrollFrame=function(){var e=this;if(this._frameId&&(this._frameId=null,this.isActive())){var r=this._map.transform;if(0!==this._delta){var n="wheel"===this._type&&Math.abs(this._delta)>4.000244140625?this._wheelZoomRate:this._defaultZoomRate,i=2/(1+Math.exp(-Math.abs(this._delta*n)));this._delta<0&&0!==i&&(i=1/i);var a="number"==typeof this._targetZoom?r.zoomScale(this._targetZoom):r.scale;this._targetZoom=Math.min(r.maxZoom,Math.max(r.minZoom,r.scaleZoom(a*i))),"wheel"===this._type&&(this._startZoom=r.zoom,this._easing=this._smoothOutEasing(200)),this._delta=0}var o,s="number"==typeof this._targetZoom?this._targetZoom:r.zoom,l=this._startZoom,c=this._easing,u=!1;if("wheel"===this._type&&l&&c){var f=Math.min((t.browser.now()-this._lastWheelEventTime)/200,1),h=c(f);o=t.number(l,s,h),f<1?this._frameId||(this._frameId=!0):u=!0}else o=s,u=!0;return this._active=!0,u&&(this._active=!1,this._finishTimeout=setTimeout((function(){e._zooming=!1,e._handler._triggerRenderFrame(),delete e._targetZoom,delete e._finishTimeout}),200)),{noInertia:!0,needsRenderFrame:!u,zoomDelta:o-r.zoom,around:this._aroundPoint,originalEvent:this._lastWheelEvent}}},li.prototype._smoothOutEasing=function(e){var r=t.ease;if(this._prevEase){var n=this._prevEase,i=(t.browser.now()-n.start)/n.duration,a=n.easing(i+.01)-n.easing(i),o=.27/Math.sqrt(a*a+1e-4)*.01,s=Math.sqrt(.0729-o*o);r=t.bezier(o,s,.25,1)}return this._prevEase={start:t.browser.now(),duration:e,easing:r},r},li.prototype.reset=function(){this._active=!1};var ci=function(t,e){this._clickZoom=t,this._tapZoom=e};ci.prototype.enable=function(){this._clickZoom.enable(),this._tapZoom.enable()},ci.prototype.disable=function(){this._clickZoom.disable(),this._tapZoom.disable()},ci.prototype.isEnabled=function(){return this._clickZoom.isEnabled()&&this._tapZoom.isEnabled()},ci.prototype.isActive=function(){return this._clickZoom.isActive()||this._tapZoom.isActive()};var ui=function(){this.reset()};ui.prototype.reset=function(){this._active=!1},ui.prototype.dblclick=function(t,e){return t.preventDefault(),{cameraAnimation:function(r){r.easeTo({duration:300,zoom:r.getZoom()+(t.shiftKey?-1:1),around:r.unproject(e)},{originalEvent:t})}}},ui.prototype.enable=function(){this._enabled=!0},ui.prototype.disable=function(){this._enabled=!1,this.reset()},ui.prototype.isEnabled=function(){return this._enabled},ui.prototype.isActive=function(){return this._active};var fi=function(){this._tap=new qn({numTouches:1,numTaps:1}),this.reset()};fi.prototype.reset=function(){this._active=!1,delete this._swipePoint,delete this._swipeTouch,delete this._tapTime,this._tap.reset()},fi.prototype.touchstart=function(t,e,r){this._swipePoint||(this._tapTime&&t.timeStamp-this._tapTime>500&&this.reset(),this._tapTime?r.length>0&&(this._swipePoint=e[0],this._swipeTouch=r[0].identifier):this._tap.touchstart(t,e,r))},fi.prototype.touchmove=function(t,e,r){if(this._tapTime){if(this._swipePoint){if(r[0].identifier!==this._swipeTouch)return;var n=e[0],i=n.y-this._swipePoint.y;return this._swipePoint=n,t.preventDefault(),this._active=!0,{zoomDelta:i/128}}}else this._tap.touchmove(t,e,r)},fi.prototype.touchend=function(t,e,r){this._tapTime?this._swipePoint&&0===r.length&&this.reset():this._tap.touchend(t,e,r)&&(this._tapTime=t.timeStamp)},fi.prototype.touchcancel=function(){this.reset()},fi.prototype.enable=function(){this._enabled=!0},fi.prototype.disable=function(){this._enabled=!1,this.reset()},fi.prototype.isEnabled=function(){return this._enabled},fi.prototype.isActive=function(){return this._active};var hi=function(t,e,r){this._el=t,this._mousePan=e,this._touchPan=r};hi.prototype.enable=function(t){this._inertiaOptions=t||{},this._mousePan.enable(),this._touchPan.enable(),this._el.classList.add("mapboxgl-touch-drag-pan")},hi.prototype.disable=function(){this._mousePan.disable(),this._touchPan.disable(),this._el.classList.remove("mapboxgl-touch-drag-pan")},hi.prototype.isEnabled=function(){return this._mousePan.isEnabled()&&this._touchPan.isEnabled()},hi.prototype.isActive=function(){return this._mousePan.isActive()||this._touchPan.isActive()};var pi=function(t,e,r){this._pitchWithRotate=t.pitchWithRotate,this._mouseRotate=e,this._mousePitch=r};pi.prototype.enable=function(){this._mouseRotate.enable(),this._pitchWithRotate&&this._mousePitch.enable()},pi.prototype.disable=function(){this._mouseRotate.disable(),this._mousePitch.disable()},pi.prototype.isEnabled=function(){return this._mouseRotate.isEnabled()&&(!this._pitchWithRotate||this._mousePitch.isEnabled())},pi.prototype.isActive=function(){return this._mouseRotate.isActive()||this._mousePitch.isActive()};var di=function(t,e,r,n){this._el=t,this._touchZoom=e,this._touchRotate=r,this._tapDragZoom=n,this._rotationDisabled=!1,this._enabled=!0};di.prototype.enable=function(t){this._touchZoom.enable(t),this._rotationDisabled||this._touchRotate.enable(t),this._tapDragZoom.enable(),this._el.classList.add("mapboxgl-touch-zoom-rotate")},di.prototype.disable=function(){this._touchZoom.disable(),this._touchRotate.disable(),this._tapDragZoom.disable(),this._el.classList.remove("mapboxgl-touch-zoom-rotate")},di.prototype.isEnabled=function(){return this._touchZoom.isEnabled()&&(this._rotationDisabled||this._touchRotate.isEnabled())&&this._tapDragZoom.isEnabled()},di.prototype.isActive=function(){return this._touchZoom.isActive()||this._touchRotate.isActive()||this._tapDragZoom.isActive()},di.prototype.disableRotation=function(){this._rotationDisabled=!0,this._touchRotate.disable()},di.prototype.enableRotation=function(){this._rotationDisabled=!1,this._touchZoom.isEnabled()&&this._touchRotate.enable()};var gi=function(t){return t.zoom||t.drag||t.pitch||t.rotate},mi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(t.Event);function vi(t){return t.panDelta&&t.panDelta.mag()||t.zoomDelta||t.bearingDelta||t.pitchDelta}var yi=function(e,n){this._map=e,this._el=this._map.getCanvasContainer(),this._handlers=[],this._handlersById={},this._changes=[],this._inertia=new On(e),this._bearingSnap=n.bearingSnap,this._previousActiveHandlers={},this._eventsInProgress={},this._addDefaultHandlers(n),t.bindAll(["handleEvent","handleWindowEvent"],this);var i=this._el;this._listeners=[[i,"touchstart",{passive:!1}],[i,"touchmove",{passive:!1}],[i,"touchend",void 0],[i,"touchcancel",void 0],[i,"mousedown",void 0],[i,"mousemove",void 0],[i,"mouseup",void 0],[t.window.document,"mousemove",{capture:!0}],[t.window.document,"mouseup",void 0],[i,"mouseover",void 0],[i,"mouseout",void 0],[i,"dblclick",void 0],[i,"click",void 0],[i,"keydown",{capture:!1}],[i,"keyup",void 0],[i,"wheel",{passive:!1}],[i,"contextmenu",void 0],[t.window,"blur",void 0]];for(var a=0,o=this._listeners;a<o.length;a+=1){var s=o[a],l=s[0],c=s[1],u=s[2];r.addEventListener(l,c,l===t.window.document?this.handleWindowEvent:this.handleEvent,u)}};yi.prototype.destroy=function(){for(var e=0,n=this._listeners;e<n.length;e+=1){var i=n[e],a=i[0],o=i[1],s=i[2];r.removeEventListener(a,o,a===t.window.document?this.handleWindowEvent:this.handleEvent,s)}},yi.prototype._addDefaultHandlers=function(t){var e=this._map,r=e.getCanvasContainer();this._add("mapEvent",new Nn(e,t));var n=e.boxZoom=new Un(e,t);this._add("boxZoom",n);var i=new Gn,a=new ui;e.doubleClickZoom=new ci(a,i),this._add("tapZoom",i),this._add("clickZoom",a);var o=new fi;this._add("tapDragZoom",o);var s=e.touchPitch=new ii;this._add("touchPitch",s);var l=new Xn(t),c=new Zn(t);e.dragRotate=new pi(t,l,c),this._add("mouseRotate",l,["mousePitch"]),this._add("mousePitch",c,["mouseRotate"]);var u=new Wn(t),f=new Jn(t);e.dragPan=new hi(r,u,f),this._add("mousePan",u),this._add("touchPan",f,["touchZoom","touchRotate"]);var h=new ri,p=new ti;e.touchZoomRotate=new di(r,p,h,o),this._add("touchRotate",h,["touchPan","touchZoom"]),this._add("touchZoom",p,["touchPan","touchRotate"]);var d=e.scrollZoom=new li(e,this);this._add("scrollZoom",d,["mousePan"]);var g=e.keyboard=new oi;this._add("keyboard",g),this._add("blockableMapEvent",new jn(e));for(var m=0,v=["boxZoom","doubleClickZoom","tapDragZoom","touchPitch","dragRotate","dragPan","touchZoomRotate","scrollZoom","keyboard"];m<v.length;m+=1){var y=v[m];t.interactive&&t[y]&&e[y].enable(t[y])}},yi.prototype._add=function(t,e,r){this._handlers.push({handlerName:t,handler:e,allowed:r}),this._handlersById[t]=e},yi.prototype.stop=function(){if(!this._updatingCamera){for(var t=0,e=this._handlers;t<e.length;t+=1){e[t].handler.reset()}this._inertia.clear(),this._fireEvents({},{}),this._changes=[]}},yi.prototype.isActive=function(){for(var t=0,e=this._handlers;t<e.length;t+=1){if(e[t].handler.isActive())return!0}return!1},yi.prototype.isZooming=function(){return!!this._eventsInProgress.zoom||this._map.scrollZoom.isZooming()},yi.prototype.isRotating=function(){return!!this._eventsInProgress.rotate},yi.prototype.isMoving=function(){return Boolean(gi(this._eventsInProgress))||this.isZooming()},yi.prototype._blockedByActive=function(t,e,r){for(var n in t)if(n!==r&&(!e||e.indexOf(n)<0))return!0;return!1},yi.prototype.handleWindowEvent=function(t){this.handleEvent(t,t.type+"Window")},yi.prototype._getMapTouches=function(t){for(var e=[],r=0,n=t;r<n.length;r+=1){var i=n[r],a=i.target;this._el.contains(a)&&e.push(i)}return e},yi.prototype.handleEvent=function(t,e){if("blur"!==t.type){this._updatingCamera=!0;for(var n="renderFrame"===t.type?void 0:t,i={needsRenderFrame:!1},a={},o={},s=t.touches?this._getMapTouches(t.touches):void 0,l=s?r.touchPos(this._el,s):r.mousePos(this._el,t),c=0,u=this._handlers;c<u.length;c+=1){var f=u[c],h=f.handlerName,p=f.handler,d=f.allowed;if(p.isEnabled()){var g=void 0;this._blockedByActive(o,d,h)?p.reset():p[e||t.type]&&(g=p[e||t.type](t,l,s),this.mergeHandlerResult(i,a,g,h,n),g&&g.needsRenderFrame&&this._triggerRenderFrame()),(g||p.isActive())&&(o[h]=p)}}var m={};for(var v in this._previousActiveHandlers)o[v]||(m[v]=n);this._previousActiveHandlers=o,(Object.keys(m).length||vi(i))&&(this._changes.push([i,a,m]),this._triggerRenderFrame()),(Object.keys(o).length||vi(i))&&this._map._stop(!0),this._updatingCamera=!1;var y=i.cameraAnimation;y&&(this._inertia.clear(),this._fireEvents({},{}),this._changes=[],y(this._map))}else this.stop()},yi.prototype.mergeHandlerResult=function(e,r,n,i,a){if(n){t.extend(e,n);var o={handlerName:i,originalEvent:n.originalEvent||a};void 0!==n.zoomDelta&&(r.zoom=o),void 0!==n.panDelta&&(r.drag=o),void 0!==n.pitchDelta&&(r.pitch=o),void 0!==n.bearingDelta&&(r.rotate=o)}},yi.prototype._applyChanges=function(){for(var e={},r={},n={},i=0,a=this._changes;i<a.length;i+=1){var o=a[i],s=o[0],l=o[1],c=o[2];s.panDelta&&(e.panDelta=(e.panDelta||new t.Point(0,0))._add(s.panDelta)),s.zoomDelta&&(e.zoomDelta=(e.zoomDelta||0)+s.zoomDelta),s.bearingDelta&&(e.bearingDelta=(e.bearingDelta||0)+s.bearingDelta),s.pitchDelta&&(e.pitchDelta=(e.pitchDelta||0)+s.pitchDelta),void 0!==s.around&&(e.around=s.around),void 0!==s.pinchAround&&(e.pinchAround=s.pinchAround),s.noInertia&&(e.noInertia=s.noInertia),t.extend(r,l),t.extend(n,c)}this._updateMapTransform(e,r,n),this._changes=[]},yi.prototype._updateMapTransform=function(t,e,r){var n=this._map,i=n.transform;if(!vi(t))return this._fireEvents(e,r);var a=t.panDelta,o=t.zoomDelta,s=t.bearingDelta,l=t.pitchDelta,c=t.around,u=t.pinchAround;void 0!==u&&(c=u),n._stop(!0),c=c||n.transform.centerPoint;var f=i.pointLocation(a?c.sub(a):c);s&&(i.bearing+=s),l&&(i.pitch+=l),o&&(i.zoom+=o),i.setLocationAtPoint(f,c),this._map._update(),t.noInertia||this._inertia.record(t),this._fireEvents(e,r)},yi.prototype._fireEvents=function(e,r){var n=this,i=gi(this._eventsInProgress),a=gi(e),o={};for(var s in e){var l=e[s].originalEvent;this._eventsInProgress[s]||(o[s+"start"]=l),this._eventsInProgress[s]=e[s]}for(var c in!i&&a&&this._fireEvent("movestart",a.originalEvent),o)this._fireEvent(c,o[c]);for(var u in e.rotate&&(this._bearingChanged=!0),a&&this._fireEvent("move",a.originalEvent),e){var f=e[u].originalEvent;this._fireEvent(u,f)}var h,p={};for(var d in this._eventsInProgress){var g=this._eventsInProgress[d],m=g.handlerName,v=g.originalEvent;this._handlersById[m].isActive()||(delete this._eventsInProgress[d],h=r[m]||v,p[d+"end"]=h)}for(var y in p)this._fireEvent(y,p[y]);var x=gi(this._eventsInProgress);if((i||a)&&!x){this._updatingCamera=!0;var b=this._inertia._onMoveEnd(this._map.dragPan._inertiaOptions),_=function(t){return 0!==t&&-n._bearingSnap<t&&t<n._bearingSnap};b?(_(b.bearing||this._map.getBearing())&&(b.bearing=0),this._map.easeTo(b,{originalEvent:h})):(this._map.fire(new t.Event("moveend",{originalEvent:h})),_(this._map.getBearing())&&this._map.resetNorth()),this._bearingChanged=!1,this._updatingCamera=!1}},yi.prototype._fireEvent=function(e,r){this._map.fire(new t.Event(e,r?{originalEvent:r}:{}))},yi.prototype._triggerRenderFrame=function(){var t=this;void 0===this._frameId&&(this._frameId=this._map._requestRenderFrame((function(e){delete t._frameId,t.handleEvent(new mi("renderFrame",{timeStamp:e})),t._applyChanges()})))};var xi=function(e){function r(r,n){e.call(this),this._moving=!1,this._zooming=!1,this.transform=r,this._bearingSnap=n.bearingSnap,t.bindAll(["_renderFrameCallback"],this)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getCenter=function(){return new t.LngLat(this.transform.center.lng,this.transform.center.lat)},r.prototype.setCenter=function(t,e){return this.jumpTo({center:t},e)},r.prototype.panBy=function(e,r,n){return e=t.Point.convert(e).mult(-1),this.panTo(this.transform.center,t.extend({offset:e},r),n)},r.prototype.panTo=function(e,r,n){return this.easeTo(t.extend({center:e},r),n)},r.prototype.getZoom=function(){return this.transform.zoom},r.prototype.setZoom=function(t,e){return this.jumpTo({zoom:t},e),this},r.prototype.zoomTo=function(e,r,n){return this.easeTo(t.extend({zoom:e},r),n)},r.prototype.zoomIn=function(t,e){return this.zoomTo(this.getZoom()+1,t,e),this},r.prototype.zoomOut=function(t,e){return this.zoomTo(this.getZoom()-1,t,e),this},r.prototype.getBearing=function(){return this.transform.bearing},r.prototype.setBearing=function(t,e){return this.jumpTo({bearing:t},e),this},r.prototype.getPadding=function(){return this.transform.padding},r.prototype.setPadding=function(t,e){return this.jumpTo({padding:t},e),this},r.prototype.rotateTo=function(e,r,n){return this.easeTo(t.extend({bearing:e},r),n)},r.prototype.resetNorth=function(e,r){return this.rotateTo(0,t.extend({duration:1e3},e),r),this},r.prototype.resetNorthPitch=function(e,r){return this.easeTo(t.extend({bearing:0,pitch:0,duration:1e3},e),r),this},r.prototype.snapToNorth=function(t,e){return Math.abs(this.getBearing())<this._bearingSnap?this.resetNorth(t,e):this},r.prototype.getPitch=function(){return this.transform.pitch},r.prototype.setPitch=function(t,e){return this.jumpTo({pitch:t},e),this},r.prototype.cameraForBounds=function(e,r){return e=t.LngLatBounds.convert(e),this._cameraForBoxAndBearing(e.getNorthWest(),e.getSouthEast(),0,r)},r.prototype._cameraForBoxAndBearing=function(e,r,n,i){var a={top:0,bottom:0,right:0,left:0};if("number"==typeof(i=t.extend({padding:a,offset:[0,0],maxZoom:this.transform.maxZoom},i)).padding){var o=i.padding;i.padding={top:o,bottom:o,right:o,left:o}}i.padding=t.extend(a,i.padding);var s=this.transform,l=s.padding,c=s.project(t.LngLat.convert(e)),u=s.project(t.LngLat.convert(r)),f=c.rotate(-n*Math.PI/180),h=u.rotate(-n*Math.PI/180),p=new t.Point(Math.max(f.x,h.x),Math.max(f.y,h.y)),d=new t.Point(Math.min(f.x,h.x),Math.min(f.y,h.y)),g=p.sub(d),m=(s.width-(l.left+l.right+i.padding.left+i.padding.right))/g.x,v=(s.height-(l.top+l.bottom+i.padding.top+i.padding.bottom))/g.y;if(!(v<0||m<0)){var y=Math.min(s.scaleZoom(s.scale*Math.min(m,v)),i.maxZoom),x=t.Point.convert(i.offset),b=(i.padding.left-i.padding.right)/2,_=(i.padding.top-i.padding.bottom)/2,w=new t.Point(x.x+b,x.y+_).mult(s.scale/s.zoomScale(y));return{center:s.unproject(c.add(u).div(2).sub(w)),zoom:y,bearing:n}}t.warnOnce("Map cannot fit within canvas with the given bounds, padding, and/or offset.")},r.prototype.fitBounds=function(t,e,r){return this._fitInternal(this.cameraForBounds(t,e),e,r)},r.prototype.fitScreenCoordinates=function(e,r,n,i,a){return this._fitInternal(this._cameraForBoxAndBearing(this.transform.pointLocation(t.Point.convert(e)),this.transform.pointLocation(t.Point.convert(r)),n,i),i,a)},r.prototype._fitInternal=function(e,r,n){return e?(delete(r=t.extend(e,r)).padding,r.linear?this.easeTo(r,n):this.flyTo(r,n)):this},r.prototype.jumpTo=function(e,r){this.stop();var n=this.transform,i=!1,a=!1,o=!1;return"zoom"in e&&n.zoom!==+e.zoom&&(i=!0,n.zoom=+e.zoom),void 0!==e.center&&(n.center=t.LngLat.convert(e.center)),"bearing"in e&&n.bearing!==+e.bearing&&(a=!0,n.bearing=+e.bearing),"pitch"in e&&n.pitch!==+e.pitch&&(o=!0,n.pitch=+e.pitch),null==e.padding||n.isPaddingEqual(e.padding)||(n.padding=e.padding),this.fire(new t.Event("movestart",r)).fire(new t.Event("move",r)),i&&this.fire(new t.Event("zoomstart",r)).fire(new t.Event("zoom",r)).fire(new t.Event("zoomend",r)),a&&this.fire(new t.Event("rotatestart",r)).fire(new t.Event("rotate",r)).fire(new t.Event("rotateend",r)),o&&this.fire(new t.Event("pitchstart",r)).fire(new t.Event("pitch",r)).fire(new t.Event("pitchend",r)),this.fire(new t.Event("moveend",r))},r.prototype.easeTo=function(e,r){var n=this;this._stop(!1,e.easeId),(!1===(e=t.extend({offset:[0,0],duration:500,easing:t.ease},e)).animate||!e.essential&&t.browser.prefersReducedMotion)&&(e.duration=0);var i=this.transform,a=this.getZoom(),o=this.getBearing(),s=this.getPitch(),l=this.getPadding(),c="zoom"in e?+e.zoom:a,u="bearing"in e?this._normalizeBearing(e.bearing,o):o,f="pitch"in e?+e.pitch:s,h="padding"in e?e.padding:i.padding,p=t.Point.convert(e.offset),d=i.centerPoint.add(p),g=i.pointLocation(d),m=t.LngLat.convert(e.center||g);this._normalizeCenter(m);var v,y,x=i.project(g),b=i.project(m).sub(x),_=i.zoomScale(c-a);e.around&&(v=t.LngLat.convert(e.around),y=i.locationPoint(v));var w={moving:this._moving,zooming:this._zooming,rotating:this._rotating,pitching:this._pitching};return this._zooming=this._zooming||c!==a,this._rotating=this._rotating||o!==u,this._pitching=this._pitching||f!==s,this._padding=!i.isPaddingEqual(h),this._easeId=e.easeId,this._prepareEase(r,e.noMoveStart,w),clearTimeout(this._easeEndTimeoutID),this._ease((function(e){if(n._zooming&&(i.zoom=t.number(a,c,e)),n._rotating&&(i.bearing=t.number(o,u,e)),n._pitching&&(i.pitch=t.number(s,f,e)),n._padding&&(i.interpolatePadding(l,h,e),d=i.centerPoint.add(p)),v)i.setLocationAtPoint(v,y);else{var g=i.zoomScale(i.zoom-a),m=c>a?Math.min(2,_):Math.max(.5,_),w=Math.pow(m,1-e),T=i.unproject(x.add(b.mult(e*w)).mult(g));i.setLocationAtPoint(i.renderWorldCopies?T.wrap():T,d)}n._fireMoveEvents(r)}),(function(t){n._afterEase(r,t)}),e),this},r.prototype._prepareEase=function(e,r,n){void 0===n&&(n={}),this._moving=!0,r||n.moving||this.fire(new t.Event("movestart",e)),this._zooming&&!n.zooming&&this.fire(new t.Event("zoomstart",e)),this._rotating&&!n.rotating&&this.fire(new t.Event("rotatestart",e)),this._pitching&&!n.pitching&&this.fire(new t.Event("pitchstart",e))},r.prototype._fireMoveEvents=function(e){this.fire(new t.Event("move",e)),this._zooming&&this.fire(new t.Event("zoom",e)),this._rotating&&this.fire(new t.Event("rotate",e)),this._pitching&&this.fire(new t.Event("pitch",e))},r.prototype._afterEase=function(e,r){if(!this._easeId||!r||this._easeId!==r){delete this._easeId;var n=this._zooming,i=this._rotating,a=this._pitching;this._moving=!1,this._zooming=!1,this._rotating=!1,this._pitching=!1,this._padding=!1,n&&this.fire(new t.Event("zoomend",e)),i&&this.fire(new t.Event("rotateend",e)),a&&this.fire(new t.Event("pitchend",e)),this.fire(new t.Event("moveend",e))}},r.prototype.flyTo=function(e,r){var n=this;if(!e.essential&&t.browser.prefersReducedMotion){var i=t.pick(e,["center","zoom","bearing","pitch","around"]);return this.jumpTo(i,r)}this.stop(),e=t.extend({offset:[0,0],speed:1.2,curve:1.42,easing:t.ease},e);var a=this.transform,o=this.getZoom(),s=this.getBearing(),l=this.getPitch(),c=this.getPadding(),u="zoom"in e?t.clamp(+e.zoom,a.minZoom,a.maxZoom):o,f="bearing"in e?this._normalizeBearing(e.bearing,s):s,h="pitch"in e?+e.pitch:l,p="padding"in e?e.padding:a.padding,d=a.zoomScale(u-o),g=t.Point.convert(e.offset),m=a.centerPoint.add(g),v=a.pointLocation(m),y=t.LngLat.convert(e.center||v);this._normalizeCenter(y);var x=a.project(v),b=a.project(y).sub(x),_=e.curve,w=Math.max(a.width,a.height),T=w/d,k=b.mag();if("minZoom"in e){var A=t.clamp(Math.min(e.minZoom,o,u),a.minZoom,a.maxZoom),M=w/a.zoomScale(A-o);_=Math.sqrt(M/k*2)}var S=_*_;function E(t){var e=(T*T-w*w+(t?-1:1)*S*S*k*k)/(2*(t?T:w)*S*k);return Math.log(Math.sqrt(e*e+1)-e)}function L(t){return(Math.exp(t)-Math.exp(-t))/2}function C(t){return(Math.exp(t)+Math.exp(-t))/2}var P=E(0),I=function(t){return C(P)/C(P+_*t)},O=function(t){return w*((C(P)*(L(e=P+_*t)/C(e))-L(P))/S)/k;var e},z=(E(1)-P)/_;if(Math.abs(k)<1e-6||!isFinite(z)){if(Math.abs(w-T)<1e-6)return this.easeTo(e,r);var D=T<w?-1:1;z=Math.abs(Math.log(T/w))/_,O=function(){return 0},I=function(t){return Math.exp(D*_*t)}}if("duration"in e)e.duration=+e.duration;else{var R="screenSpeed"in e?+e.screenSpeed/_:+e.speed;e.duration=1e3*z/R}return e.maxDuration&&e.duration>e.maxDuration&&(e.duration=0),this._zooming=!0,this._rotating=s!==f,this._pitching=h!==l,this._padding=!a.isPaddingEqual(p),this._prepareEase(r,!1),this._ease((function(e){var i=e*z,d=1/I(i);a.zoom=1===e?u:o+a.scaleZoom(d),n._rotating&&(a.bearing=t.number(s,f,e)),n._pitching&&(a.pitch=t.number(l,h,e)),n._padding&&(a.interpolatePadding(c,p,e),m=a.centerPoint.add(g));var v=1===e?y:a.unproject(x.add(b.mult(O(i))).mult(d));a.setLocationAtPoint(a.renderWorldCopies?v.wrap():v,m),n._fireMoveEvents(r)}),(function(){return n._afterEase(r)}),e),this},r.prototype.isEasing=function(){return!!this._easeFrameId},r.prototype.stop=function(){return this._stop()},r.prototype._stop=function(t,e){if(this._easeFrameId&&(this._cancelRenderFrame(this._easeFrameId),delete this._easeFrameId,delete this._onEaseFrame),this._onEaseEnd){var r=this._onEaseEnd;delete this._onEaseEnd,r.call(this,e)}if(!t){var n=this.handlers;n&&n.stop()}return this},r.prototype._ease=function(e,r,n){!1===n.animate||0===n.duration?(e(1),r()):(this._easeStart=t.browser.now(),this._easeOptions=n,this._onEaseFrame=e,this._onEaseEnd=r,this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback))},r.prototype._renderFrameCallback=function(){var e=Math.min((t.browser.now()-this._easeStart)/this._easeOptions.duration,1);this._onEaseFrame(this._easeOptions.easing(e)),e<1?this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback):this.stop()},r.prototype._normalizeBearing=function(e,r){e=t.wrap(e,-180,180);var n=Math.abs(e-r);return Math.abs(e-360-r)<n&&(e-=360),Math.abs(e+360-r)<n&&(e+=360),e},r.prototype._normalizeCenter=function(t){var e=this.transform;if(e.renderWorldCopies&&!e.lngRange){var r=t.lng-e.center.lng;t.lng+=r>180?-360:r<-180?360:0}},r}(t.Evented),bi=function(e){void 0===e&&(e={}),this.options=e,t.bindAll(["_updateEditLink","_updateData","_updateCompact"],this)};bi.prototype.getDefaultPosition=function(){return"bottom-right"},bi.prototype.onAdd=function(t){var e=this.options&&this.options.compact;return this._map=t,this._container=r.create("div","mapboxgl-ctrl mapboxgl-ctrl-attrib"),this._innerContainer=r.create("div","mapboxgl-ctrl-attrib-inner",this._container),e&&this._container.classList.add("mapboxgl-compact"),this._updateAttributions(),this._updateEditLink(),this._map.on("styledata",this._updateData),this._map.on("sourcedata",this._updateData),this._map.on("moveend",this._updateEditLink),void 0===e&&(this._map.on("resize",this._updateCompact),this._updateCompact()),this._container},bi.prototype.onRemove=function(){r.remove(this._container),this._map.off("styledata",this._updateData),this._map.off("sourcedata",this._updateData),this._map.off("moveend",this._updateEditLink),this._map.off("resize",this._updateCompact),this._map=void 0,this._attribHTML=void 0},bi.prototype._updateEditLink=function(){var e=this._editLink;e||(e=this._editLink=this._container.querySelector(".mapbox-improve-map"));var r=[{key:"owner",value:this.styleOwner},{key:"id",value:this.styleId},{key:"access_token",value:this._map._requestManager._customAccessToken||t.config.ACCESS_TOKEN}];if(e){var n=r.reduce((function(t,e,n){return e.value&&(t+=e.key+"="+e.value+(n<r.length-1?"&":"")),t}),"?");e.href=t.config.FEEDBACK_URL+"/"+n+(this._map._hash?this._map._hash.getHashString(!0):""),e.rel="noopener nofollow"}},bi.prototype._updateData=function(t){!t||"metadata"!==t.sourceDataType&&"style"!==t.dataType||(this._updateAttributions(),this._updateEditLink())},bi.prototype._updateAttributions=function(){if(this._map.style){var t=[];if(this.options.customAttribution&&(Array.isArray(this.options.customAttribution)?t=t.concat(this.options.customAttribution.map((function(t){return"string"!=typeof t?"":t}))):"string"==typeof this.options.customAttribution&&t.push(this.options.customAttribution)),this._map.style.stylesheet){var e=this._map.style.stylesheet;this.styleOwner=e.owner,this.styleId=e.id}var r=this._map.style.sourceCaches;for(var n in r){var i=r[n];if(i.used){var a=i.getSource();a.attribution&&t.indexOf(a.attribution)<0&&t.push(a.attribution)}}t.sort((function(t,e){return t.length-e.length}));var o=(t=t.filter((function(e,r){for(var n=r+1;n<t.length;n++)if(t[n].indexOf(e)>=0)return!1;return!0}))).join(" | ");o!==this._attribHTML&&(this._attribHTML=o,t.length?(this._innerContainer.innerHTML=o,this._container.classList.remove("mapboxgl-attrib-empty")):this._container.classList.add("mapboxgl-attrib-empty"),this._editLink=null)}},bi.prototype._updateCompact=function(){this._map.getCanvasContainer().offsetWidth<=640?this._container.classList.add("mapboxgl-compact"):this._container.classList.remove("mapboxgl-compact")};var _i=function(){t.bindAll(["_updateLogo"],this),t.bindAll(["_updateCompact"],this)};_i.prototype.onAdd=function(t){this._map=t,this._container=r.create("div","mapboxgl-ctrl");var e=r.create("a","mapboxgl-ctrl-logo");return e.target="_blank",e.rel="noopener nofollow",e.href="https://www.mapbox.com/",e.setAttribute("aria-label",this._map._getUIString("LogoControl.Title")),e.setAttribute("rel","noopener nofollow"),this._container.appendChild(e),this._container.style.display="none",this._map.on("sourcedata",this._updateLogo),this._updateLogo(),this._map.on("resize",this._updateCompact),this._updateCompact(),this._container},_i.prototype.onRemove=function(){r.remove(this._container),this._map.off("sourcedata",this._updateLogo),this._map.off("resize",this._updateCompact)},_i.prototype.getDefaultPosition=function(){return"bottom-left"},_i.prototype._updateLogo=function(t){t&&"metadata"!==t.sourceDataType||(this._container.style.display=this._logoRequired()?"block":"none")},_i.prototype._logoRequired=function(){if(this._map.style){var t=this._map.style.sourceCaches;for(var e in t){if(t[e].getSource().mapbox_logo)return!0}return!1}},_i.prototype._updateCompact=function(){var t=this._container.children;if(t.length){var e=t[0];this._map.getCanvasContainer().offsetWidth<250?e.classList.add("mapboxgl-compact"):e.classList.remove("mapboxgl-compact")}};var wi=function(){this._queue=[],this._id=0,this._cleared=!1,this._currentlyRunning=!1};wi.prototype.add=function(t){var e=++this._id;return this._queue.push({callback:t,id:e,cancelled:!1}),e},wi.prototype.remove=function(t){for(var e=this._currentlyRunning,r=0,n=e?this._queue.concat(e):this._queue;r<n.length;r+=1){var i=n[r];if(i.id===t)return void(i.cancelled=!0)}},wi.prototype.run=function(t){void 0===t&&(t=0);var e=this._currentlyRunning=this._queue;this._queue=[];for(var r=0,n=e;r<n.length;r+=1){var i=n[r];if(!i.cancelled&&(i.callback(t),this._cleared))break}this._cleared=!1,this._currentlyRunning=!1},wi.prototype.clear=function(){this._currentlyRunning&&(this._cleared=!0),this._queue=[]};var Ti={"FullscreenControl.Enter":"Enter fullscreen","FullscreenControl.Exit":"Exit fullscreen","GeolocateControl.FindMyLocation":"Find my location","GeolocateControl.LocationNotAvailable":"Location not available","LogoControl.Title":"Mapbox logo","NavigationControl.ResetBearing":"Reset bearing to north","NavigationControl.ZoomIn":"Zoom in","NavigationControl.ZoomOut":"Zoom out","ScaleControl.Feet":"ft","ScaleControl.Meters":"m","ScaleControl.Kilometers":"km","ScaleControl.Miles":"mi","ScaleControl.NauticalMiles":"nm"},ki=t.window.HTMLImageElement,Ai=t.window.HTMLElement,Mi=t.window.ImageBitmap,Si={center:[0,0],zoom:0,bearing:0,pitch:0,minZoom:-2,maxZoom:22,minPitch:0,maxPitch:60,interactive:!0,scrollZoom:!0,boxZoom:!0,dragRotate:!0,dragPan:!0,keyboard:!0,doubleClickZoom:!0,touchZoomRotate:!0,touchPitch:!0,bearingSnap:7,clickTolerance:3,pitchWithRotate:!0,hash:!1,attributionControl:!0,failIfMajorPerformanceCaveat:!1,preserveDrawingBuffer:!1,trackResize:!0,renderWorldCopies:!0,refreshExpiredTiles:!0,maxTileCacheSize:null,localIdeographFontFamily:"sans-serif",transformRequest:null,accessToken:null,fadeDuration:300,crossSourceCollisions:!0},Ei=function(n){function i(e){var r=this;if(null!=(e=t.extend({},Si,e)).minZoom&&null!=e.maxZoom&&e.minZoom>e.maxZoom)throw new Error("maxZoom must be greater than or equal to minZoom");if(null!=e.minPitch&&null!=e.maxPitch&&e.minPitch>e.maxPitch)throw new Error("maxPitch must be greater than or equal to minPitch");if(null!=e.minPitch&&e.minPitch<0)throw new Error("minPitch must be greater than or equal to 0");if(null!=e.maxPitch&&e.maxPitch>60)throw new Error("maxPitch must be less than or equal to 60");var i=new An(e.minZoom,e.maxZoom,e.minPitch,e.maxPitch,e.renderWorldCopies);if(n.call(this,i,e),this._interactive=e.interactive,this._maxTileCacheSize=e.maxTileCacheSize,this._failIfMajorPerformanceCaveat=e.failIfMajorPerformanceCaveat,this._preserveDrawingBuffer=e.preserveDrawingBuffer,this._antialias=e.antialias,this._trackResize=e.trackResize,this._bearingSnap=e.bearingSnap,this._refreshExpiredTiles=e.refreshExpiredTiles,this._fadeDuration=e.fadeDuration,this._crossSourceCollisions=e.crossSourceCollisions,this._crossFadingFactor=1,this._collectResourceTiming=e.collectResourceTiming,this._renderTaskQueue=new wi,this._controls=[],this._mapId=t.uniqueId(),this._locale=t.extend({},Ti,e.locale),this._requestManager=new t.RequestManager(e.transformRequest,e.accessToken),"string"==typeof e.container){if(this._container=t.window.document.getElementById(e.container),!this._container)throw new Error("Container '"+e.container+"' not found.")}else{if(!(e.container instanceof Ai))throw new Error("Invalid type: 'container' must be a String or HTMLElement.");this._container=e.container}if(e.maxBounds&&this.setMaxBounds(e.maxBounds),t.bindAll(["_onWindowOnline","_onWindowResize","_contextLost","_contextRestored"],this),this._setupContainer(),this._setupPainter(),void 0===this.painter)throw new Error("Failed to initialize WebGL.");this.on("move",(function(){return r._update(!1)})),this.on("moveend",(function(){return r._update(!1)})),this.on("zoom",(function(){return r._update(!0)})),void 0!==t.window&&(t.window.addEventListener("online",this._onWindowOnline,!1),t.window.addEventListener("resize",this._onWindowResize,!1)),this.handlers=new yi(this,e);var a="string"==typeof e.hash&&e.hash||void 0;this._hash=e.hash&&new Sn(a).addTo(this),this._hash&&this._hash._onHashChange()||(this.jumpTo({center:e.center,zoom:e.zoom,bearing:e.bearing,pitch:e.pitch}),e.bounds&&(this.resize(),this.fitBounds(e.bounds,t.extend({},e.fitBoundsOptions,{duration:0})))),this.resize(),this._localIdeographFontFamily=e.localIdeographFontFamily,e.style&&this.setStyle(e.style,{localIdeographFontFamily:e.localIdeographFontFamily}),e.attributionControl&&this.addControl(new bi({customAttribution:e.customAttribution})),this.addControl(new _i,e.logoPosition),this.on("style.load",(function(){r.transform.unmodified&&r.jumpTo(r.style.stylesheet)})),this.on("data",(function(e){r._update("style"===e.dataType),r.fire(new t.Event(e.dataType+"data",e))})),this.on("dataloading",(function(e){r.fire(new t.Event(e.dataType+"dataloading",e))}))}n&&(i.__proto__=n),i.prototype=Object.create(n&&n.prototype),i.prototype.constructor=i;var a={showTileBoundaries:{configurable:!0},showPadding:{configurable:!0},showCollisionBoxes:{configurable:!0},showOverdrawInspector:{configurable:!0},repaint:{configurable:!0},vertices:{configurable:!0},version:{configurable:!0}};return i.prototype._getMapId=function(){return this._mapId},i.prototype.addControl=function(e,r){if(void 0===r&&e.getDefaultPosition&&(r=e.getDefaultPosition()),void 0===r&&(r="top-right"),!e||!e.onAdd)return this.fire(new t.ErrorEvent(new Error("Invalid argument to map.addControl(). Argument must be a control with onAdd and onRemove methods.")));var n=e.onAdd(this);this._controls.push(e);var i=this._controlPositions[r];return-1!==r.indexOf("bottom")?i.insertBefore(n,i.firstChild):i.appendChild(n),this},i.prototype.removeControl=function(e){if(!e||!e.onRemove)return this.fire(new t.ErrorEvent(new Error("Invalid argument to map.removeControl(). Argument must be a control with onAdd and onRemove methods.")));var r=this._controls.indexOf(e);return r>-1&&this._controls.splice(r,1),e.onRemove(this),this},i.prototype.resize=function(e){var r=this._containerDimensions(),n=r[0],i=r[1];this._resizeCanvas(n,i),this.transform.resize(n,i),this.painter.resize(n,i);var a=!this._moving;return a&&(this.stop(),this.fire(new t.Event("movestart",e)).fire(new t.Event("move",e))),this.fire(new t.Event("resize",e)),a&&this.fire(new t.Event("moveend",e)),this},i.prototype.getBounds=function(){return this.transform.getBounds()},i.prototype.getMaxBounds=function(){return this.transform.getMaxBounds()},i.prototype.setMaxBounds=function(e){return this.transform.setMaxBounds(t.LngLatBounds.convert(e)),this._update()},i.prototype.setMinZoom=function(t){if((t=null==t?-2:t)>=-2&&t<=this.transform.maxZoom)return this.transform.minZoom=t,this._update(),this.getZoom()<t&&this.setZoom(t),this;throw new Error("minZoom must be between -2 and the current maxZoom, inclusive")},i.prototype.getMinZoom=function(){return this.transform.minZoom},i.prototype.setMaxZoom=function(t){if((t=null==t?22:t)>=this.transform.minZoom)return this.transform.maxZoom=t,this._update(),this.getZoom()>t&&this.setZoom(t),this;throw new Error("maxZoom must be greater than the current minZoom")},i.prototype.getMaxZoom=function(){return this.transform.maxZoom},i.prototype.setMinPitch=function(t){if((t=null==t?0:t)<0)throw new Error("minPitch must be greater than or equal to 0");if(t>=0&&t<=this.transform.maxPitch)return this.transform.minPitch=t,this._update(),this.getPitch()<t&&this.setPitch(t),this;throw new Error("minPitch must be between 0 and the current maxPitch, inclusive")},i.prototype.getMinPitch=function(){return this.transform.minPitch},i.prototype.setMaxPitch=function(t){if((t=null==t?60:t)>60)throw new Error("maxPitch must be less than or equal to 60");if(t>=this.transform.minPitch)return this.transform.maxPitch=t,this._update(),this.getPitch()>t&&this.setPitch(t),this;throw new Error("maxPitch must be greater than the current minPitch")},i.prototype.getMaxPitch=function(){return this.transform.maxPitch},i.prototype.getRenderWorldCopies=function(){return this.transform.renderWorldCopies},i.prototype.setRenderWorldCopies=function(t){return this.transform.renderWorldCopies=t,this._update()},i.prototype.project=function(e){return this.transform.locationPoint(t.LngLat.convert(e))},i.prototype.unproject=function(e){return this.transform.pointLocation(t.Point.convert(e))},i.prototype.isMoving=function(){return this._moving||this.handlers.isMoving()},i.prototype.isZooming=function(){return this._zooming||this.handlers.isZooming()},i.prototype.isRotating=function(){return this._rotating||this.handlers.isRotating()},i.prototype._createDelegatedListener=function(t,e,r){var n,i=this;if("mouseenter"===t||"mouseover"===t){var a=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){var o=i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[];o.length?a||(a=!0,r.call(i,new Rn(t,i,n.originalEvent,{features:o}))):a=!1},mouseout:function(){a=!1}}}}if("mouseleave"===t||"mouseout"===t){var o=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){(i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[]).length?o=!0:o&&(o=!1,r.call(i,new Rn(t,i,n.originalEvent)))},mouseout:function(e){o&&(o=!1,r.call(i,new Rn(t,i,e.originalEvent)))}}}}return{layer:e,listener:r,delegates:(n={},n[t]=function(t){var n=i.getLayer(e)?i.queryRenderedFeatures(t.point,{layers:[e]}):[];n.length&&(t.features=n,r.call(i,t),delete t.features)},n)}},i.prototype.on=function(t,e,r){if(void 0===r)return n.prototype.on.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in this._delegatedListeners=this._delegatedListeners||{},this._delegatedListeners[t]=this._delegatedListeners[t]||[],this._delegatedListeners[t].push(i),i.delegates)this.on(a,i.delegates[a]);return this},i.prototype.once=function(t,e,r){if(void 0===r)return n.prototype.once.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in i.delegates)this.once(a,i.delegates[a]);return this},i.prototype.off=function(t,e,r){var i=this;if(void 0===r)return n.prototype.off.call(this,t,e);return this._delegatedListeners&&this._delegatedListeners[t]&&function(n){for(var a=n[t],o=0;o<a.length;o++){var s=a[o];if(s.layer===e&&s.listener===r){for(var l in s.delegates)i.off(l,s.delegates[l]);return a.splice(o,1),i}}}(this._delegatedListeners),this},i.prototype.queryRenderedFeatures=function(e,r){if(!this.style)return[];var n;if(void 0!==r||void 0===e||e instanceof t.Point||Array.isArray(e)||(r=e,e=void 0),r=r||{},(e=e||[[0,0],[this.transform.width,this.transform.height]])instanceof t.Point||"number"==typeof e[0])n=[t.Point.convert(e)];else{var i=t.Point.convert(e[0]),a=t.Point.convert(e[1]);n=[i,new t.Point(a.x,i.y),a,new t.Point(i.x,a.y),i]}return this.style.queryRenderedFeatures(n,r,this.transform)},i.prototype.querySourceFeatures=function(t,e){return this.style.querySourceFeatures(t,e)},i.prototype.setStyle=function(e,r){return!1!==(r=t.extend({},{localIdeographFontFamily:this._localIdeographFontFamily},r)).diff&&r.localIdeographFontFamily===this._localIdeographFontFamily&&this.style&&e?(this._diffStyle(e,r),this):(this._localIdeographFontFamily=r.localIdeographFontFamily,this._updateStyle(e,r))},i.prototype._getUIString=function(t){var e=this._locale[t];if(null==e)throw new Error("Missing UI string '"+t+"'");return e},i.prototype._updateStyle=function(t,e){return this.style&&(this.style.setEventedParent(null),this.style._remove()),t?(this.style=new qe(this,e||{}),this.style.setEventedParent(this,{style:this.style}),"string"==typeof t?this.style.loadURL(t):this.style.loadJSON(t),this):(delete this.style,this)},i.prototype._lazyInitEmptyStyle=function(){this.style||(this.style=new qe(this,{}),this.style.setEventedParent(this,{style:this.style}),this.style.loadEmpty())},i.prototype._diffStyle=function(e,r){var n=this;if("string"==typeof e){var i=this._requestManager.normalizeStyleURL(e),a=this._requestManager.transformRequest(i,t.ResourceType.Style);t.getJSON(a,(function(e,i){e?n.fire(new t.ErrorEvent(e)):i&&n._updateDiff(i,r)}))}else"object"==typeof e&&this._updateDiff(e,r)},i.prototype._updateDiff=function(e,r){try{this.style.setState(e)&&this._update(!0)}catch(n){t.warnOnce("Unable to perform style diff: "+(n.message||n.error||n)+".  Rebuilding the style from scratch."),this._updateStyle(e,r)}},i.prototype.getStyle=function(){if(this.style)return this.style.serialize()},i.prototype.isStyleLoaded=function(){return this.style?this.style.loaded():t.warnOnce("There is no style added to the map.")},i.prototype.addSource=function(t,e){return this._lazyInitEmptyStyle(),this.style.addSource(t,e),this._update(!0)},i.prototype.isSourceLoaded=function(e){var r=this.style&&this.style.sourceCaches[e];if(void 0!==r)return r.loaded();this.fire(new t.ErrorEvent(new Error("There is no source with ID '"+e+"'")))},i.prototype.areTilesLoaded=function(){var t=this.style&&this.style.sourceCaches;for(var e in t){var r=t[e]._tiles;for(var n in r){var i=r[n];if("loaded"!==i.state&&"errored"!==i.state)return!1}}return!0},i.prototype.addSourceType=function(t,e,r){return this._lazyInitEmptyStyle(),this.style.addSourceType(t,e,r)},i.prototype.removeSource=function(t){return this.style.removeSource(t),this._update(!0)},i.prototype.getSource=function(t){return this.style.getSource(t)},i.prototype.addImage=function(e,r,n){void 0===n&&(n={});var i=n.pixelRatio;void 0===i&&(i=1);var a=n.sdf;void 0===a&&(a=!1);var o=n.stretchX,s=n.stretchY,l=n.content;this._lazyInitEmptyStyle();if(r instanceof ki||Mi&&r instanceof Mi){var c=t.browser.getImageData(r),u=c.width,f=c.height,h=c.data;this.style.addImage(e,{data:new t.RGBAImage({width:u,height:f},h),pixelRatio:i,stretchX:o,stretchY:s,content:l,sdf:a,version:0})}else{if(void 0===r.width||void 0===r.height)return this.fire(new t.ErrorEvent(new Error("Invalid arguments to map.addImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`")));var p=r.width,d=r.height,g=r.data,m=r;this.style.addImage(e,{data:new t.RGBAImage({width:p,height:d},new Uint8Array(g)),pixelRatio:i,stretchX:o,stretchY:s,content:l,sdf:a,version:0,userImage:m}),m.onAdd&&m.onAdd(this,e)}},i.prototype.updateImage=function(e,r){var n=this.style.getImage(e);if(!n)return this.fire(new t.ErrorEvent(new Error("The map has no image with that id. If you are adding a new image use `map.addImage(...)` instead.")));var i=r instanceof ki||Mi&&r instanceof Mi?t.browser.getImageData(r):r,a=i.width,o=i.height,s=i.data;if(void 0===a||void 0===o)return this.fire(new t.ErrorEvent(new Error("Invalid arguments to map.updateImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`")));if(a!==n.data.width||o!==n.data.height)return this.fire(new t.ErrorEvent(new Error("The width and height of the updated image must be that same as the previous version of the image")));var l=!(r instanceof ki||Mi&&r instanceof Mi);n.data.replace(s,l),this.style.updateImage(e,n)},i.prototype.hasImage=function(e){return e?!!this.style.getImage(e):(this.fire(new t.ErrorEvent(new Error("Missing required image id"))),!1)},i.prototype.removeImage=function(t){this.style.removeImage(t)},i.prototype.loadImage=function(e,r){t.getImage(this._requestManager.transformRequest(e,t.ResourceType.Image),r)},i.prototype.listImages=function(){return this.style.listImages()},i.prototype.addLayer=function(t,e){return this._lazyInitEmptyStyle(),this.style.addLayer(t,e),this._update(!0)},i.prototype.moveLayer=function(t,e){return this.style.moveLayer(t,e),this._update(!0)},i.prototype.removeLayer=function(t){return this.style.removeLayer(t),this._update(!0)},i.prototype.getLayer=function(t){return this.style.getLayer(t)},i.prototype.setLayerZoomRange=function(t,e,r){return this.style.setLayerZoomRange(t,e,r),this._update(!0)},i.prototype.setFilter=function(t,e,r){return void 0===r&&(r={}),this.style.setFilter(t,e,r),this._update(!0)},i.prototype.getFilter=function(t){return this.style.getFilter(t)},i.prototype.setPaintProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setPaintProperty(t,e,r,n),this._update(!0)},i.prototype.getPaintProperty=function(t,e){return this.style.getPaintProperty(t,e)},i.prototype.setLayoutProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setLayoutProperty(t,e,r,n),this._update(!0)},i.prototype.getLayoutProperty=function(t,e){return this.style.getLayoutProperty(t,e)},i.prototype.setLight=function(t,e){return void 0===e&&(e={}),this._lazyInitEmptyStyle(),this.style.setLight(t,e),this._update(!0)},i.prototype.getLight=function(){return this.style.getLight()},i.prototype.setFeatureState=function(t,e){return this.style.setFeatureState(t,e),this._update()},i.prototype.removeFeatureState=function(t,e){return this.style.removeFeatureState(t,e),this._update()},i.prototype.getFeatureState=function(t){return this.style.getFeatureState(t)},i.prototype.getContainer=function(){return this._container},i.prototype.getCanvasContainer=function(){return this._canvasContainer},i.prototype.getCanvas=function(){return this._canvas},i.prototype._containerDimensions=function(){var t=0,e=0;return this._container&&(t=this._container.clientWidth||400,e=this._container.clientHeight||300),[t,e]},i.prototype._detectMissingCSS=function(){"rgb(250, 128, 114)"!==t.window.getComputedStyle(this._missingCSSCanary).getPropertyValue("background-color")&&t.warnOnce("This page appears to be missing CSS declarations for Mapbox GL JS, which may cause the map to display incorrectly. Please ensure your page includes mapbox-gl.css, as described in https://www.mapbox.com/mapbox-gl-js/api/.")},i.prototype._setupContainer=function(){var t=this._container;t.classList.add("mapboxgl-map"),(this._missingCSSCanary=r.create("div","mapboxgl-canary",t)).style.visibility="hidden",this._detectMissingCSS();var e=this._canvasContainer=r.create("div","mapboxgl-canvas-container",t);this._interactive&&e.classList.add("mapboxgl-interactive"),this._canvas=r.create("canvas","mapboxgl-canvas",e),this._canvas.addEventListener("webglcontextlost",this._contextLost,!1),this._canvas.addEventListener("webglcontextrestored",this._contextRestored,!1),this._canvas.setAttribute("tabindex","0"),this._canvas.setAttribute("aria-label","Map");var n=this._containerDimensions();this._resizeCanvas(n[0],n[1]);var i=this._controlContainer=r.create("div","mapboxgl-control-container",t),a=this._controlPositions={};["top-left","top-right","bottom-left","bottom-right"].forEach((function(t){a[t]=r.create("div","mapboxgl-ctrl-"+t,i)}))},i.prototype._resizeCanvas=function(e,r){var n=t.browser.devicePixelRatio||1;this._canvas.width=n*e,this._canvas.height=n*r,this._canvas.style.width=e+"px",this._canvas.style.height=r+"px"},i.prototype._setupPainter=function(){var r=t.extend({},e.webGLContextAttributes,{failIfMajorPerformanceCaveat:this._failIfMajorPerformanceCaveat,preserveDrawingBuffer:this._preserveDrawingBuffer,antialias:this._antialias||!1}),n=this._canvas.getContext("webgl",r)||this._canvas.getContext("experimental-webgl",r);n?(this.painter=new _n(n,this.transform),t.webpSupported.testSupport(n)):this.fire(new t.ErrorEvent(new Error("Failed to initialize WebGL")))},i.prototype._contextLost=function(e){e.preventDefault(),this._frame&&(this._frame.cancel(),this._frame=null),this.fire(new t.Event("webglcontextlost",{originalEvent:e}))},i.prototype._contextRestored=function(e){this._setupPainter(),this.resize(),this._update(),this.fire(new t.Event("webglcontextrestored",{originalEvent:e}))},i.prototype.loaded=function(){return!this._styleDirty&&!this._sourcesDirty&&!!this.style&&this.style.loaded()},i.prototype._update=function(t){return this.style?(this._styleDirty=this._styleDirty||t,this._sourcesDirty=!0,this.triggerRepaint(),this):this},i.prototype._requestRenderFrame=function(t){return this._update(),this._renderTaskQueue.add(t)},i.prototype._cancelRenderFrame=function(t){this._renderTaskQueue.remove(t)},i.prototype._render=function(e){var r,n=this,i=0,a=this.painter.context.extTimerQuery;if(this.listens("gpu-timing-frame")&&(r=a.createQueryEXT(),a.beginQueryEXT(a.TIME_ELAPSED_EXT,r),i=t.browser.now()),this.painter.context.setDirty(),this.painter.setBaseState(),this._renderTaskQueue.run(e),!this._removed){var o=!1;if(this.style&&this._styleDirty){this._styleDirty=!1;var s=this.transform.zoom,l=t.browser.now();this.style.zoomHistory.update(s,l);var c=new t.EvaluationParameters(s,{now:l,fadeDuration:this._fadeDuration,zoomHistory:this.style.zoomHistory,transition:this.style.getTransition()}),u=c.crossFadingFactor();1===u&&u===this._crossFadingFactor||(o=!0,this._crossFadingFactor=u),this.style.update(c)}if(this.style&&this._sourcesDirty&&(this._sourcesDirty=!1,this.style._updateSources(this.transform)),this._placementDirty=this.style&&this.style._updatePlacement(this.painter.transform,this.showCollisionBoxes,this._fadeDuration,this._crossSourceCollisions),this.painter.render(this.style,{showTileBoundaries:this.showTileBoundaries,showOverdrawInspector:this._showOverdrawInspector,rotating:this.isRotating(),zooming:this.isZooming(),moving:this.isMoving(),fadeDuration:this._fadeDuration,showPadding:this.showPadding,gpuTiming:!!this.listens("gpu-timing-layer")}),this.fire(new t.Event("render")),this.loaded()&&!this._loaded&&(this._loaded=!0,this.fire(new t.Event("load"))),this.style&&(this.style.hasTransitions()||o)&&(this._styleDirty=!0),this.style&&!this._placementDirty&&this.style._releaseSymbolFadeTiles(),this.listens("gpu-timing-frame")){var f=t.browser.now()-i;a.endQueryEXT(a.TIME_ELAPSED_EXT,r),setTimeout((function(){var e=a.getQueryObjectEXT(r,a.QUERY_RESULT_EXT)/1e6;a.deleteQueryEXT(r),n.fire(new t.Event("gpu-timing-frame",{cpuTime:f,gpuTime:e}))}),50)}if(this.listens("gpu-timing-layer")){var h=this.painter.collectGpuTimers();setTimeout((function(){var e=n.painter.queryGpuTimers(h);n.fire(new t.Event("gpu-timing-layer",{layerTimes:e}))}),50)}return this._sourcesDirty||this._styleDirty||this._placementDirty||this._repaint?this.triggerRepaint():!this.isMoving()&&this.loaded()&&(this._fullyLoaded||(this._fullyLoaded=!0),this.fire(new t.Event("idle"))),this}},i.prototype.remove=function(){this._hash&&this._hash.remove();for(var e=0,r=this._controls;e<r.length;e+=1){r[e].onRemove(this)}this._controls=[],this._frame&&(this._frame.cancel(),this._frame=null),this._renderTaskQueue.clear(),this.painter.destroy(),this.handlers.destroy(),delete this.handlers,this.setStyle(null),void 0!==t.window&&(t.window.removeEventListener("resize",this._onWindowResize,!1),t.window.removeEventListener("online",this._onWindowOnline,!1));var n=this.painter.context.gl.getExtension("WEBGL_lose_context");n&&n.loseContext(),Li(this._canvasContainer),Li(this._controlContainer),Li(this._missingCSSCanary),this._container.classList.remove("mapboxgl-map"),this._removed=!0,this.fire(new t.Event("remove"))},i.prototype.triggerRepaint=function(){var e=this;this.style&&!this._frame&&(this._frame=t.browser.frame((function(t){e._frame=null,e._render(t)})))},i.prototype._onWindowOnline=function(){this._update()},i.prototype._onWindowResize=function(t){this._trackResize&&this.resize({originalEvent:t})._update()},a.showTileBoundaries.get=function(){return!!this._showTileBoundaries},a.showTileBoundaries.set=function(t){this._showTileBoundaries!==t&&(this._showTileBoundaries=t,this._update())},a.showPadding.get=function(){return!!this._showPadding},a.showPadding.set=function(t){this._showPadding!==t&&(this._showPadding=t,this._update())},a.showCollisionBoxes.get=function(){return!!this._showCollisionBoxes},a.showCollisionBoxes.set=function(t){this._showCollisionBoxes!==t&&(this._showCollisionBoxes=t,t?this.style._generateCollisionBoxes():this._update())},a.showOverdrawInspector.get=function(){return!!this._showOverdrawInspector},a.showOverdrawInspector.set=function(t){this._showOverdrawInspector!==t&&(this._showOverdrawInspector=t,this._update())},a.repaint.get=function(){return!!this._repaint},a.repaint.set=function(t){this._repaint!==t&&(this._repaint=t,this.triggerRepaint())},a.vertices.get=function(){return!!this._vertices},a.vertices.set=function(t){this._vertices=t,this._update()},i.prototype._setCacheLimits=function(e,r){t.setCacheLimits(e,r)},a.version.get=function(){return t.version},Object.defineProperties(i.prototype,a),i}(xi);function Li(t){t.parentNode&&t.parentNode.removeChild(t)}var Ci={showCompass:!0,showZoom:!0,visualizePitch:!1},Pi=function(e){var n=this;this.options=t.extend({},Ci,e),this._container=r.create("div","mapboxgl-ctrl mapboxgl-ctrl-group"),this._container.addEventListener("contextmenu",(function(t){return t.preventDefault()})),this.options.showZoom&&(t.bindAll(["_setButtonTitle","_updateZoomButtons"],this),this._zoomInButton=this._createButton("mapboxgl-ctrl-zoom-in",(function(t){return n._map.zoomIn({},{originalEvent:t})})),r.create("span","mapboxgl-ctrl-icon",this._zoomInButton).setAttribute("aria-hidden",!0),this._zoomOutButton=this._createButton("mapboxgl-ctrl-zoom-out",(function(t){return n._map.zoomOut({},{originalEvent:t})})),r.create("span","mapboxgl-ctrl-icon",this._zoomOutButton).setAttribute("aria-hidden",!0)),this.options.showCompass&&(t.bindAll(["_rotateCompassArrow"],this),this._compass=this._createButton("mapboxgl-ctrl-compass",(function(t){n.options.visualizePitch?n._map.resetNorthPitch({},{originalEvent:t}):n._map.resetNorth({},{originalEvent:t})})),this._compassIcon=r.create("span","mapboxgl-ctrl-icon",this._compass),this._compassIcon.setAttribute("aria-hidden",!0))};Pi.prototype._updateZoomButtons=function(){var t=this._map.getZoom();this._zoomInButton.disabled=t===this._map.getMaxZoom(),this._zoomOutButton.disabled=t===this._map.getMinZoom()},Pi.prototype._rotateCompassArrow=function(){var t=this.options.visualizePitch?"scale("+1/Math.pow(Math.cos(this._map.transform.pitch*(Math.PI/180)),.5)+") rotateX("+this._map.transform.pitch+"deg) rotateZ("+this._map.transform.angle*(180/Math.PI)+"deg)":"rotate("+this._map.transform.angle*(180/Math.PI)+"deg)";this._compassIcon.style.transform=t},Pi.prototype.onAdd=function(t){return this._map=t,this.options.showZoom&&(this._setButtonTitle(this._zoomInButton,"ZoomIn"),this._setButtonTitle(this._zoomOutButton,"ZoomOut"),this._map.on("zoom",this._updateZoomButtons),this._updateZoomButtons()),this.options.showCompass&&(this._setButtonTitle(this._compass,"ResetBearing"),this.options.visualizePitch&&this._map.on("pitch",this._rotateCompassArrow),this._map.on("rotate",this._rotateCompassArrow),this._rotateCompassArrow(),this._handler=new Ii(this._map,this._compass,this.options.visualizePitch)),this._container},Pi.prototype.onRemove=function(){r.remove(this._container),this.options.showZoom&&this._map.off("zoom",this._updateZoomButtons),this.options.showCompass&&(this.options.visualizePitch&&this._map.off("pitch",this._rotateCompassArrow),this._map.off("rotate",this._rotateCompassArrow),this._handler.off(),delete this._handler),delete this._map},Pi.prototype._createButton=function(t,e){var n=r.create("button",t,this._container);return n.type="button",n.addEventListener("click",e),n},Pi.prototype._setButtonTitle=function(t,e){var r=this._map._getUIString("NavigationControl."+e);t.title=r,t.setAttribute("aria-label",r)};var Ii=function(e,n,i){void 0===i&&(i=!1),this._clickTolerance=10,this.element=n,this.mouseRotate=new Xn({clickTolerance:e.dragRotate._mouseRotate._clickTolerance}),this.map=e,i&&(this.mousePitch=new Zn({clickTolerance:e.dragRotate._mousePitch._clickTolerance})),t.bindAll(["mousedown","mousemove","mouseup","touchstart","touchmove","touchend","reset"],this),r.addEventListener(n,"mousedown",this.mousedown),r.addEventListener(n,"touchstart",this.touchstart,{passive:!1}),r.addEventListener(n,"touchmove",this.touchmove),r.addEventListener(n,"touchend",this.touchend),r.addEventListener(n,"touchcancel",this.reset)};function Oi(e,r,n){if(e=new t.LngLat(e.lng,e.lat),r){var i=new t.LngLat(e.lng-360,e.lat),a=new t.LngLat(e.lng+360,e.lat),o=n.locationPoint(e).distSqr(r);n.locationPoint(i).distSqr(r)<o?e=i:n.locationPoint(a).distSqr(r)<o&&(e=a)}for(;Math.abs(e.lng-n.center.lng)>180;){var s=n.locationPoint(e);if(s.x>=0&&s.y>=0&&s.x<=n.width&&s.y<=n.height)break;e.lng>n.center.lng?e.lng-=360:e.lng+=360}return e}Ii.prototype.down=function(t,e){this.mouseRotate.mousedown(t,e),this.mousePitch&&this.mousePitch.mousedown(t,e),r.disableDrag()},Ii.prototype.move=function(t,e){var r=this.map,n=this.mouseRotate.mousemoveWindow(t,e);if(n&&n.bearingDelta&&r.setBearing(r.getBearing()+n.bearingDelta),this.mousePitch){var i=this.mousePitch.mousemoveWindow(t,e);i&&i.pitchDelta&&r.setPitch(r.getPitch()+i.pitchDelta)}},Ii.prototype.off=function(){var t=this.element;r.removeEventListener(t,"mousedown",this.mousedown),r.removeEventListener(t,"touchstart",this.touchstart,{passive:!1}),r.removeEventListener(t,"touchmove",this.touchmove),r.removeEventListener(t,"touchend",this.touchend),r.removeEventListener(t,"touchcancel",this.reset),this.offTemp()},Ii.prototype.offTemp=function(){r.enableDrag(),r.removeEventListener(t.window,"mousemove",this.mousemove),r.removeEventListener(t.window,"mouseup",this.mouseup)},Ii.prototype.mousedown=function(e){this.down(t.extend({},e,{ctrlKey:!0,preventDefault:function(){return e.preventDefault()}}),r.mousePos(this.element,e)),r.addEventListener(t.window,"mousemove",this.mousemove),r.addEventListener(t.window,"mouseup",this.mouseup)},Ii.prototype.mousemove=function(t){this.move(t,r.mousePos(this.element,t))},Ii.prototype.mouseup=function(t){this.mouseRotate.mouseupWindow(t),this.mousePitch&&this.mousePitch.mouseupWindow(t),this.offTemp()},Ii.prototype.touchstart=function(t){1!==t.targetTouches.length?this.reset():(this._startPos=this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.down({type:"mousedown",button:0,ctrlKey:!0,preventDefault:function(){return t.preventDefault()}},this._startPos))},Ii.prototype.touchmove=function(t){1!==t.targetTouches.length?this.reset():(this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.move({preventDefault:function(){return t.preventDefault()}},this._lastPos))},Ii.prototype.touchend=function(t){0===t.targetTouches.length&&this._startPos&&this._lastPos&&this._startPos.dist(this._lastPos)<this._clickTolerance&&this.element.click(),this.reset()},Ii.prototype.reset=function(){this.mouseRotate.reset(),this.mousePitch&&this.mousePitch.reset(),delete this._startPos,delete this._lastPos,this.offTemp()};var zi={center:"translate(-50%,-50%)",top:"translate(-50%,0)","top-left":"translate(0,0)","top-right":"translate(-100%,0)",bottom:"translate(-50%,-100%)","bottom-left":"translate(0,-100%)","bottom-right":"translate(-100%,-100%)",left:"translate(0,-50%)",right:"translate(-100%,-50%)"};function Di(t,e,r){var n=t.classList;for(var i in zi)n.remove("mapboxgl-"+r+"-anchor-"+i);n.add("mapboxgl-"+r+"-anchor-"+e)}var Ri,Fi=function(e){function n(n,i){var a=this;if(e.call(this),(n instanceof t.window.HTMLElement||i)&&(n=t.extend({element:n},i)),t.bindAll(["_update","_onMove","_onUp","_addDragHandler","_onMapClick","_onKeyPress"],this),this._anchor=n&&n.anchor||"center",this._color=n&&n.color||"#3FB1CE",this._draggable=n&&n.draggable||!1,this._state="inactive",this._rotation=n&&n.rotation||0,this._rotationAlignment=n&&n.rotationAlignment||"auto",this._pitchAlignment=n&&n.pitchAlignment&&"auto"!==n.pitchAlignment?n.pitchAlignment:this._rotationAlignment,n&&n.element)this._element=n.element,this._offset=t.Point.convert(n&&n.offset||[0,0]);else{this._defaultMarker=!0,this._element=r.create("div"),this._element.setAttribute("aria-label","Map marker");var o=r.createNS("http://www.w3.org/2000/svg","svg");o.setAttributeNS(null,"display","block"),o.setAttributeNS(null,"height","41px"),o.setAttributeNS(null,"width","27px"),o.setAttributeNS(null,"viewBox","0 0 27 41");var s=r.createNS("http://www.w3.org/2000/svg","g");s.setAttributeNS(null,"stroke","none"),s.setAttributeNS(null,"stroke-width","1"),s.setAttributeNS(null,"fill","none"),s.setAttributeNS(null,"fill-rule","evenodd");var l=r.createNS("http://www.w3.org/2000/svg","g");l.setAttributeNS(null,"fill-rule","nonzero");var c=r.createNS("http://www.w3.org/2000/svg","g");c.setAttributeNS(null,"transform","translate(3.0, 29.0)"),c.setAttributeNS(null,"fill","#000000");for(var u=0,f=[{rx:"10.5",ry:"5.25002273"},{rx:"10.5",ry:"5.25002273"},{rx:"9.5",ry:"4.77275007"},{rx:"8.5",ry:"4.29549936"},{rx:"7.5",ry:"3.81822308"},{rx:"6.5",ry:"3.34094679"},{rx:"5.5",ry:"2.86367051"},{rx:"4.5",ry:"2.38636864"}];u<f.length;u+=1){var h=f[u],p=r.createNS("http://www.w3.org/2000/svg","ellipse");p.setAttributeNS(null,"opacity","0.04"),p.setAttributeNS(null,"cx","10.5"),p.setAttributeNS(null,"cy","5.80029008"),p.setAttributeNS(null,"rx",h.rx),p.setAttributeNS(null,"ry",h.ry),c.appendChild(p)}var d=r.createNS("http://www.w3.org/2000/svg","g");d.setAttributeNS(null,"fill",this._color);var g=r.createNS("http://www.w3.org/2000/svg","path");g.setAttributeNS(null,"d","M27,13.5 C27,19.074644 20.250001,27.000002 14.75,34.500002 C14.016665,35.500004 12.983335,35.500004 12.25,34.500002 C6.7499993,27.000002 0,19.222562 0,13.5 C0,6.0441559 6.0441559,0 13.5,0 C20.955844,0 27,6.0441559 27,13.5 Z"),d.appendChild(g);var m=r.createNS("http://www.w3.org/2000/svg","g");m.setAttributeNS(null,"opacity","0.25"),m.setAttributeNS(null,"fill","#000000");var v=r.createNS("http://www.w3.org/2000/svg","path");v.setAttributeNS(null,"d","M13.5,0 C6.0441559,0 0,6.0441559 0,13.5 C0,19.222562 6.7499993,27 12.25,34.5 C13,35.522727 14.016664,35.500004 14.75,34.5 C20.250001,27 27,19.074644 27,13.5 C27,6.0441559 20.955844,0 13.5,0 Z M13.5,1 C20.415404,1 26,6.584596 26,13.5 C26,15.898657 24.495584,19.181431 22.220703,22.738281 C19.945823,26.295132 16.705119,30.142167 13.943359,33.908203 C13.743445,34.180814 13.612715,34.322738 13.5,34.441406 C13.387285,34.322738 13.256555,34.180814 13.056641,33.908203 C10.284481,30.127985 7.4148684,26.314159 5.015625,22.773438 C2.6163816,19.232715 1,15.953538 1,13.5 C1,6.584596 6.584596,1 13.5,1 Z"),m.appendChild(v);var y=r.createNS("http://www.w3.org/2000/svg","g");y.setAttributeNS(null,"transform","translate(6.0, 7.0)"),y.setAttributeNS(null,"fill","#FFFFFF");var x=r.createNS("http://www.w3.org/2000/svg","g");x.setAttributeNS(null,"transform","translate(8.0, 8.0)");var b=r.createNS("http://www.w3.org/2000/svg","circle");b.setAttributeNS(null,"fill","#000000"),b.setAttributeNS(null,"opacity","0.25"),b.setAttributeNS(null,"cx","5.5"),b.setAttributeNS(null,"cy","5.5"),b.setAttributeNS(null,"r","5.4999962");var _=r.createNS("http://www.w3.org/2000/svg","circle");_.setAttributeNS(null,"fill","#FFFFFF"),_.setAttributeNS(null,"cx","5.5"),_.setAttributeNS(null,"cy","5.5"),_.setAttributeNS(null,"r","5.4999962"),x.appendChild(b),x.appendChild(_),l.appendChild(c),l.appendChild(d),l.appendChild(m),l.appendChild(y),l.appendChild(x),o.appendChild(l),this._element.appendChild(o),this._offset=t.Point.convert(n&&n.offset||[0,-14])}this._element.classList.add("mapboxgl-marker"),this._element.addEventListener("dragstart",(function(t){t.preventDefault()})),this._element.addEventListener("mousedown",(function(t){t.preventDefault()})),this._element.addEventListener("focus",(function(){var t=a._map.getContainer();t.scrollTop=0,t.scrollLeft=0})),Di(this._element,this._anchor,"marker"),this._popup=null}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.addTo=function(t){return this.remove(),this._map=t,t.getCanvasContainer().appendChild(this._element),t.on("move",this._update),t.on("moveend",this._update),this.setDraggable(this._draggable),this._update(),this._map.on("click",this._onMapClick),this},n.prototype.remove=function(){return this._map&&(this._map.off("click",this._onMapClick),this._map.off("move",this._update),this._map.off("moveend",this._update),this._map.off("mousedown",this._addDragHandler),this._map.off("touchstart",this._addDragHandler),this._map.off("mouseup",this._onUp),this._map.off("touchend",this._onUp),this._map.off("mousemove",this._onMove),this._map.off("touchmove",this._onMove),delete this._map),r.remove(this._element),this._popup&&this._popup.remove(),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._popup&&this._popup.setLngLat(this._lngLat),this._update(),this},n.prototype.getElement=function(){return this._element},n.prototype.setPopup=function(t){if(this._popup&&(this._popup.remove(),this._popup=null,this._element.removeEventListener("keypress",this._onKeyPress),this._originalTabIndex||this._element.removeAttribute("tabindex")),t){if(!("offset"in t.options)){var e=Math.sqrt(Math.pow(13.5,2)/2);t.options.offset=this._defaultMarker?{top:[0,0],"top-left":[0,0],"top-right":[0,0],bottom:[0,-38.1],"bottom-left":[e,-1*(24.6+e)],"bottom-right":[-e,-1*(24.6+e)],left:[13.5,-24.6],right:[-13.5,-24.6]}:this._offset}this._popup=t,this._lngLat&&this._popup.setLngLat(this._lngLat),this._originalTabIndex=this._element.getAttribute("tabindex"),this._originalTabIndex||this._element.setAttribute("tabindex","0"),this._element.addEventListener("keypress",this._onKeyPress)}return this},n.prototype._onKeyPress=function(t){var e=t.code,r=t.charCode||t.keyCode;"Space"!==e&&"Enter"!==e&&32!==r&&13!==r||this.togglePopup()},n.prototype._onMapClick=function(t){var e=t.originalEvent.target,r=this._element;this._popup&&(e===r||r.contains(e))&&this.togglePopup()},n.prototype.getPopup=function(){return this._popup},n.prototype.togglePopup=function(){var t=this._popup;return t?(t.isOpen()?t.remove():t.addTo(this._map),this):this},n.prototype._update=function(t){if(this._map){this._map.transform.renderWorldCopies&&(this._lngLat=Oi(this._lngLat,this._pos,this._map.transform)),this._pos=this._map.project(this._lngLat)._add(this._offset);var e="";"viewport"===this._rotationAlignment||"auto"===this._rotationAlignment?e="rotateZ("+this._rotation+"deg)":"map"===this._rotationAlignment&&(e="rotateZ("+(this._rotation-this._map.getBearing())+"deg)");var n="";"viewport"===this._pitchAlignment||"auto"===this._pitchAlignment?n="rotateX(0deg)":"map"===this._pitchAlignment&&(n="rotateX("+this._map.getPitch()+"deg)"),t&&"moveend"!==t.type||(this._pos=this._pos.round()),r.setTransform(this._element,zi[this._anchor]+" translate("+this._pos.x+"px, "+this._pos.y+"px) "+n+" "+e)}},n.prototype.getOffset=function(){return this._offset},n.prototype.setOffset=function(e){return this._offset=t.Point.convert(e),this._update(),this},n.prototype._onMove=function(e){this._pos=e.point.sub(this._positionDelta),this._lngLat=this._map.unproject(this._pos),this.setLngLat(this._lngLat),this._element.style.pointerEvents="none","pending"===this._state&&(this._state="active",this.fire(new t.Event("dragstart"))),this.fire(new t.Event("drag"))},n.prototype._onUp=function(){this._element.style.pointerEvents="auto",this._positionDelta=null,this._map.off("mousemove",this._onMove),this._map.off("touchmove",this._onMove),"active"===this._state&&this.fire(new t.Event("dragend")),this._state="inactive"},n.prototype._addDragHandler=function(t){this._element.contains(t.originalEvent.target)&&(t.preventDefault(),this._positionDelta=t.point.sub(this._pos).add(this._offset),this._state="pending",this._map.on("mousemove",this._onMove),this._map.on("touchmove",this._onMove),this._map.once("mouseup",this._onUp),this._map.once("touchend",this._onUp))},n.prototype.setDraggable=function(t){return this._draggable=!!t,this._map&&(t?(this._map.on("mousedown",this._addDragHandler),this._map.on("touchstart",this._addDragHandler)):(this._map.off("mousedown",this._addDragHandler),this._map.off("touchstart",this._addDragHandler))),this},n.prototype.isDraggable=function(){return this._draggable},n.prototype.setRotation=function(t){return this._rotation=t||0,this._update(),this},n.prototype.getRotation=function(){return this._rotation},n.prototype.setRotationAlignment=function(t){return this._rotationAlignment=t||"auto",this._update(),this},n.prototype.getRotationAlignment=function(){return this._rotationAlignment},n.prototype.setPitchAlignment=function(t){return this._pitchAlignment=t&&"auto"!==t?t:this._rotationAlignment,this._update(),this},n.prototype.getPitchAlignment=function(){return this._pitchAlignment},n}(t.Evented),Bi={positionOptions:{enableHighAccuracy:!1,maximumAge:0,timeout:6e3},fitBoundsOptions:{maxZoom:15},trackUserLocation:!1,showAccuracyCircle:!0,showUserLocation:!0};var Ni=0,ji=!1,Ui=function(e){function n(r){e.call(this),this.options=t.extend({},Bi,r),t.bindAll(["_onSuccess","_onError","_onZoom","_finish","_setupUI","_updateCamera","_updateMarker"],this)}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.onAdd=function(e){var n;return this._map=e,this._container=r.create("div","mapboxgl-ctrl mapboxgl-ctrl-group"),n=this._setupUI,void 0!==Ri?n(Ri):void 0!==t.window.navigator.permissions?t.window.navigator.permissions.query({name:"geolocation"}).then((function(t){Ri="denied"!==t.state,n(Ri)})):(Ri=!!t.window.navigator.geolocation,n(Ri)),this._container},n.prototype.onRemove=function(){void 0!==this._geolocationWatchID&&(t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0),this.options.showUserLocation&&this._userLocationDotMarker&&this._userLocationDotMarker.remove(),this.options.showAccuracyCircle&&this._accuracyCircleMarker&&this._accuracyCircleMarker.remove(),r.remove(this._container),this._map.off("zoom",this._onZoom),this._map=void 0,Ni=0,ji=!1},n.prototype._isOutOfMapMaxBounds=function(t){var e=this._map.getMaxBounds(),r=t.coords;return e&&(r.longitude<e.getWest()||r.longitude>e.getEast()||r.latitude<e.getSouth()||r.latitude>e.getNorth())},n.prototype._setErrorState=function(){switch(this._watchState){case"WAITING_ACTIVE":this._watchState="ACTIVE_ERROR",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active-error");break;case"ACTIVE_LOCK":this._watchState="ACTIVE_ERROR",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting");break;case"BACKGROUND":this._watchState="BACKGROUND_ERROR",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting")}},n.prototype._onSuccess=function(e){if(this._map){if(this._isOutOfMapMaxBounds(e))return this._setErrorState(),this.fire(new t.Event("outofmaxbounds",e)),this._updateMarker(),void this._finish();if(this.options.trackUserLocation)switch(this._lastKnownPosition=e,this._watchState){case"WAITING_ACTIVE":case"ACTIVE_LOCK":case"ACTIVE_ERROR":this._watchState="ACTIVE_LOCK",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active");break;case"BACKGROUND":case"BACKGROUND_ERROR":this._watchState="BACKGROUND",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background")}this.options.showUserLocation&&"OFF"!==this._watchState&&this._updateMarker(e),this.options.trackUserLocation&&"ACTIVE_LOCK"!==this._watchState||this._updateCamera(e),this.options.showUserLocation&&this._dotElement.classList.remove("mapboxgl-user-location-dot-stale"),this.fire(new t.Event("geolocate",e)),this._finish()}},n.prototype._updateCamera=function(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude),n=e.coords.accuracy,i=this._map.getBearing(),a=t.extend({bearing:i},this.options.fitBoundsOptions);this._map.fitBounds(r.toBounds(n),a,{geolocateSource:!0})},n.prototype._updateMarker=function(e){if(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude);this._accuracyCircleMarker.setLngLat(r).addTo(this._map),this._userLocationDotMarker.setLngLat(r).addTo(this._map),this._accuracy=e.coords.accuracy,this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()}else this._userLocationDotMarker.remove(),this._accuracyCircleMarker.remove()},n.prototype._updateCircleRadius=function(){var t=this._map._container.clientHeight/2,e=this._map.unproject([0,t]),r=this._map.unproject([1,t]),n=e.distanceTo(r),i=Math.ceil(2*this._accuracy/n);this._circleElement.style.width=i+"px",this._circleElement.style.height=i+"px"},n.prototype._onZoom=function(){this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()},n.prototype._onError=function(e){if(this._map){if(this.options.trackUserLocation)if(1===e.code){this._watchState="OFF",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background-error"),this._geolocateButton.disabled=!0;var r=this._map._getUIString("GeolocateControl.LocationNotAvailable");this._geolocateButton.title=r,this._geolocateButton.setAttribute("aria-label",r),void 0!==this._geolocationWatchID&&this._clearWatch()}else{if(3===e.code&&ji)return;this._setErrorState()}"OFF"!==this._watchState&&this.options.showUserLocation&&this._dotElement.classList.add("mapboxgl-user-location-dot-stale"),this.fire(new t.Event("error",e)),this._finish()}},n.prototype._finish=function(){this._timeoutId&&clearTimeout(this._timeoutId),this._timeoutId=void 0},n.prototype._setupUI=function(e){var n=this;if(this._container.addEventListener("contextmenu",(function(t){return t.preventDefault()})),this._geolocateButton=r.create("button","mapboxgl-ctrl-geolocate",this._container),r.create("span","mapboxgl-ctrl-icon",this._geolocateButton).setAttribute("aria-hidden",!0),this._geolocateButton.type="button",!1===e){t.warnOnce("Geolocation support is not available so the GeolocateControl will be disabled.");var i=this._map._getUIString("GeolocateControl.LocationNotAvailable");this._geolocateButton.disabled=!0,this._geolocateButton.title=i,this._geolocateButton.setAttribute("aria-label",i)}else{var a=this._map._getUIString("GeolocateControl.FindMyLocation");this._geolocateButton.title=a,this._geolocateButton.setAttribute("aria-label",a)}this.options.trackUserLocation&&(this._geolocateButton.setAttribute("aria-pressed","false"),this._watchState="OFF"),this.options.showUserLocation&&(this._dotElement=r.create("div","mapboxgl-user-location-dot"),this._userLocationDotMarker=new Fi(this._dotElement),this._circleElement=r.create("div","mapboxgl-user-location-accuracy-circle"),this._accuracyCircleMarker=new Fi({element:this._circleElement,pitchAlignment:"map"}),this.options.trackUserLocation&&(this._watchState="OFF"),this._map.on("zoom",this._onZoom)),this._geolocateButton.addEventListener("click",this.trigger.bind(this)),this._setup=!0,this.options.trackUserLocation&&this._map.on("movestart",(function(e){var r=e.originalEvent&&"resize"===e.originalEvent.type;e.geolocateSource||"ACTIVE_LOCK"!==n._watchState||r||(n._watchState="BACKGROUND",n._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background"),n._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),n.fire(new t.Event("trackuserlocationend")))}))},n.prototype.trigger=function(){if(!this._setup)return t.warnOnce("Geolocate control triggered before added to a map"),!1;if(this.options.trackUserLocation){switch(this._watchState){case"OFF":this._watchState="WAITING_ACTIVE",this.fire(new t.Event("trackuserlocationstart"));break;case"WAITING_ACTIVE":case"ACTIVE_LOCK":case"ACTIVE_ERROR":case"BACKGROUND_ERROR":Ni--,ji=!1,this._watchState="OFF",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background-error"),this.fire(new t.Event("trackuserlocationend"));break;case"BACKGROUND":this._watchState="ACTIVE_LOCK",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._lastKnownPosition&&this._updateCamera(this._lastKnownPosition),this.fire(new t.Event("trackuserlocationstart"))}switch(this._watchState){case"WAITING_ACTIVE":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active");break;case"ACTIVE_LOCK":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active");break;case"ACTIVE_ERROR":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active-error");break;case"BACKGROUND":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background");break;case"BACKGROUND_ERROR":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background-error")}if("OFF"===this._watchState&&void 0!==this._geolocationWatchID)this._clearWatch();else if(void 0===this._geolocationWatchID){var e;this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.setAttribute("aria-pressed","true"),++Ni>1?(e={maximumAge:6e5,timeout:0},ji=!0):(e=this.options.positionOptions,ji=!1),this._geolocationWatchID=t.window.navigator.geolocation.watchPosition(this._onSuccess,this._onError,e)}}else t.window.navigator.geolocation.getCurrentPosition(this._onSuccess,this._onError,this.options.positionOptions),this._timeoutId=setTimeout(this._finish,1e4);return!0},n.prototype._clearWatch=function(){t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0,this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.setAttribute("aria-pressed","false"),this.options.showUserLocation&&this._updateMarker(null)},n}(t.Evented),Vi={maxWidth:100,unit:"metric"},Hi=function(e){this.options=t.extend({},Vi,e),t.bindAll(["_onMove","setUnit"],this)};function qi(t,e,r){var n=r&&r.maxWidth||100,i=t._container.clientHeight/2,a=t.unproject([0,i]),o=t.unproject([n,i]),s=a.distanceTo(o);if(r&&"imperial"===r.unit){var l=3.2808*s;if(l>5280)Gi(e,n,l/5280,t._getUIString("ScaleControl.Miles"));else Gi(e,n,l,t._getUIString("ScaleControl.Feet"))}else if(r&&"nautical"===r.unit){Gi(e,n,s/1852,t._getUIString("ScaleControl.NauticalMiles"))}else s>=1e3?Gi(e,n,s/1e3,t._getUIString("ScaleControl.Kilometers")):Gi(e,n,s,t._getUIString("ScaleControl.Meters"))}function Gi(t,e,r,n){var i,a,o,s=(i=r,a=Math.pow(10,(""+Math.floor(i)).length-1),o=(o=i/a)>=10?10:o>=5?5:o>=3?3:o>=2?2:o>=1?1:function(t){var e=Math.pow(10,Math.ceil(-Math.log(t)/Math.LN10));return Math.round(t*e)/e}(o),a*o),l=s/r;t.style.width=e*l+"px",t.innerHTML=s+"&nbsp;"+n}Hi.prototype.getDefaultPosition=function(){return"bottom-left"},Hi.prototype._onMove=function(){qi(this._map,this._container,this.options)},Hi.prototype.onAdd=function(t){return this._map=t,this._container=r.create("div","mapboxgl-ctrl mapboxgl-ctrl-scale",t.getContainer()),this._map.on("move",this._onMove),this._onMove(),this._container},Hi.prototype.onRemove=function(){r.remove(this._container),this._map.off("move",this._onMove),this._map=void 0},Hi.prototype.setUnit=function(t){this.options.unit=t,qi(this._map,this._container,this.options)};var Yi=function(e){this._fullscreen=!1,e&&e.container&&(e.container instanceof t.window.HTMLElement?this._container=e.container:t.warnOnce("Full screen control 'container' must be a DOM element.")),t.bindAll(["_onClickFullscreen","_changeIcon"],this),"onfullscreenchange"in t.window.document?this._fullscreenchange="fullscreenchange":"onmozfullscreenchange"in t.window.document?this._fullscreenchange="mozfullscreenchange":"onwebkitfullscreenchange"in t.window.document?this._fullscreenchange="webkitfullscreenchange":"onmsfullscreenchange"in t.window.document&&(this._fullscreenchange="MSFullscreenChange")};Yi.prototype.onAdd=function(e){return this._map=e,this._container||(this._container=this._map.getContainer()),this._controlContainer=r.create("div","mapboxgl-ctrl mapboxgl-ctrl-group"),this._checkFullscreenSupport()?this._setupUI():(this._controlContainer.style.display="none",t.warnOnce("This device does not support fullscreen mode.")),this._controlContainer},Yi.prototype.onRemove=function(){r.remove(this._controlContainer),this._map=null,t.window.document.removeEventListener(this._fullscreenchange,this._changeIcon)},Yi.prototype._checkFullscreenSupport=function(){return!!(t.window.document.fullscreenEnabled||t.window.document.mozFullScreenEnabled||t.window.document.msFullscreenEnabled||t.window.document.webkitFullscreenEnabled)},Yi.prototype._setupUI=function(){var e=this._fullscreenButton=r.create("button","mapboxgl-ctrl-fullscreen",this._controlContainer);r.create("span","mapboxgl-ctrl-icon",e).setAttribute("aria-hidden",!0),e.type="button",this._updateTitle(),this._fullscreenButton.addEventListener("click",this._onClickFullscreen),t.window.document.addEventListener(this._fullscreenchange,this._changeIcon)},Yi.prototype._updateTitle=function(){var t=this._getTitle();this._fullscreenButton.setAttribute("aria-label",t),this._fullscreenButton.title=t},Yi.prototype._getTitle=function(){return this._map._getUIString(this._isFullscreen()?"FullscreenControl.Exit":"FullscreenControl.Enter")},Yi.prototype._isFullscreen=function(){return this._fullscreen},Yi.prototype._changeIcon=function(){(t.window.document.fullscreenElement||t.window.document.mozFullScreenElement||t.window.document.webkitFullscreenElement||t.window.document.msFullscreenElement)===this._container!==this._fullscreen&&(this._fullscreen=!this._fullscreen,this._fullscreenButton.classList.toggle("mapboxgl-ctrl-shrink"),this._fullscreenButton.classList.toggle("mapboxgl-ctrl-fullscreen"),this._updateTitle())},Yi.prototype._onClickFullscreen=function(){this._isFullscreen()?t.window.document.exitFullscreen?t.window.document.exitFullscreen():t.window.document.mozCancelFullScreen?t.window.document.mozCancelFullScreen():t.window.document.msExitFullscreen?t.window.document.msExitFullscreen():t.window.document.webkitCancelFullScreen&&t.window.document.webkitCancelFullScreen():this._container.requestFullscreen?this._container.requestFullscreen():this._container.mozRequestFullScreen?this._container.mozRequestFullScreen():this._container.msRequestFullscreen?this._container.msRequestFullscreen():this._container.webkitRequestFullscreen&&this._container.webkitRequestFullscreen()};var Wi={closeButton:!0,closeOnClick:!0,className:"",maxWidth:"240px"},Xi=function(e){function n(r){e.call(this),this.options=t.extend(Object.create(Wi),r),t.bindAll(["_update","_onClose","remove","_onMouseMove","_onMouseUp","_onDrag"],this)}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.addTo=function(e){return this._map&&this.remove(),this._map=e,this.options.closeOnClick&&this._map.on("click",this._onClose),this.options.closeOnMove&&this._map.on("move",this._onClose),this._map.on("remove",this.remove),this._update(),this._trackPointer?(this._map.on("mousemove",this._onMouseMove),this._map.on("mouseup",this._onMouseUp),this._container&&this._container.classList.add("mapboxgl-popup-track-pointer"),this._map._canvasContainer.classList.add("mapboxgl-track-pointer")):this._map.on("move",this._update),this.fire(new t.Event("open")),this},n.prototype.isOpen=function(){return!!this._map},n.prototype.remove=function(){return this._content&&r.remove(this._content),this._container&&(r.remove(this._container),delete this._container),this._map&&(this._map.off("move",this._update),this._map.off("move",this._onClose),this._map.off("click",this._onClose),this._map.off("remove",this.remove),this._map.off("mousemove",this._onMouseMove),this._map.off("mouseup",this._onMouseUp),this._map.off("drag",this._onDrag),delete this._map),this.fire(new t.Event("close")),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._trackPointer=!1,this._update(),this._map&&(this._map.on("move",this._update),this._map.off("mousemove",this._onMouseMove),this._container&&this._container.classList.remove("mapboxgl-popup-track-pointer"),this._map._canvasContainer.classList.remove("mapboxgl-track-pointer")),this},n.prototype.trackPointer=function(){return this._trackPointer=!0,this._pos=null,this._update(),this._map&&(this._map.off("move",this._update),this._map.on("mousemove",this._onMouseMove),this._map.on("drag",this._onDrag),this._container&&this._container.classList.add("mapboxgl-popup-track-pointer"),this._map._canvasContainer.classList.add("mapboxgl-track-pointer")),this},n.prototype.getElement=function(){return this._container},n.prototype.setText=function(e){return this.setDOMContent(t.window.document.createTextNode(e))},n.prototype.setHTML=function(e){var r,n=t.window.document.createDocumentFragment(),i=t.window.document.createElement("body");for(i.innerHTML=e;r=i.firstChild;)n.appendChild(r);return this.setDOMContent(n)},n.prototype.getMaxWidth=function(){return this._container&&this._container.style.maxWidth},n.prototype.setMaxWidth=function(t){return this.options.maxWidth=t,this._update(),this},n.prototype.setDOMContent=function(t){return this._createContent(),this._content.appendChild(t),this._update(),this},n.prototype.addClassName=function(t){this._container&&this._container.classList.add(t)},n.prototype.removeClassName=function(t){this._container&&this._container.classList.remove(t)},n.prototype.toggleClassName=function(t){if(this._container)return this._container.classList.toggle(t)},n.prototype._createContent=function(){this._content&&r.remove(this._content),this._content=r.create("div","mapboxgl-popup-content",this._container),this.options.closeButton&&(this._closeButton=r.create("button","mapboxgl-popup-close-button",this._content),this._closeButton.type="button",this._closeButton.setAttribute("aria-label","Close popup"),this._closeButton.innerHTML="&#215;",this._closeButton.addEventListener("click",this._onClose))},n.prototype._onMouseUp=function(t){this._update(t.point)},n.prototype._onMouseMove=function(t){this._update(t.point)},n.prototype._onDrag=function(t){this._update(t.point)},n.prototype._update=function(e){var n=this,i=this._lngLat||this._trackPointer;if(this._map&&i&&this._content&&(this._container||(this._container=r.create("div","mapboxgl-popup",this._map.getContainer()),this._tip=r.create("div","mapboxgl-popup-tip",this._container),this._container.appendChild(this._content),this.options.className&&this.options.className.split(" ").forEach((function(t){return n._container.classList.add(t)})),this._trackPointer&&this._container.classList.add("mapboxgl-popup-track-pointer")),this.options.maxWidth&&this._container.style.maxWidth!==this.options.maxWidth&&(this._container.style.maxWidth=this.options.maxWidth),this._map.transform.renderWorldCopies&&!this._trackPointer&&(this._lngLat=Oi(this._lngLat,this._pos,this._map.transform)),!this._trackPointer||e)){var a=this._pos=this._trackPointer&&e?e:this._map.project(this._lngLat),o=this.options.anchor,s=function e(r){if(r){if("number"==typeof r){var n=Math.round(Math.sqrt(.5*Math.pow(r,2)));return{center:new t.Point(0,0),top:new t.Point(0,r),"top-left":new t.Point(n,n),"top-right":new t.Point(-n,n),bottom:new t.Point(0,-r),"bottom-left":new t.Point(n,-n),"bottom-right":new t.Point(-n,-n),left:new t.Point(r,0),right:new t.Point(-r,0)}}if(r instanceof t.Point||Array.isArray(r)){var i=t.Point.convert(r);return{center:i,top:i,"top-left":i,"top-right":i,bottom:i,"bottom-left":i,"bottom-right":i,left:i,right:i}}return{center:t.Point.convert(r.center||[0,0]),top:t.Point.convert(r.top||[0,0]),"top-left":t.Point.convert(r["top-left"]||[0,0]),"top-right":t.Point.convert(r["top-right"]||[0,0]),bottom:t.Point.convert(r.bottom||[0,0]),"bottom-left":t.Point.convert(r["bottom-left"]||[0,0]),"bottom-right":t.Point.convert(r["bottom-right"]||[0,0]),left:t.Point.convert(r.left||[0,0]),right:t.Point.convert(r.right||[0,0])}}return e(new t.Point(0,0))}(this.options.offset);if(!o){var l,c=this._container.offsetWidth,u=this._container.offsetHeight;l=a.y+s.bottom.y<u?["top"]:a.y>this._map.transform.height-u?["bottom"]:[],a.x<c/2?l.push("left"):a.x>this._map.transform.width-c/2&&l.push("right"),o=0===l.length?"bottom":l.join("-")}var f=a.add(s[o]).round();r.setTransform(this._container,zi[o]+" 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t.regions.forEach(e.addRegion),{segments:e.calculate(t.inverted),inverted:t.inverted}},combine:function(t,e){return{combined:o(!1,f,u).calculate(t.segments,t.inverted,e.segments,e.inverted),inverted1:t.inverted,inverted2:e.inverted}},selectUnion:function(t){return{segments:l.union(t.combined,u),inverted:t.inverted1||t.inverted2}},selectIntersect:function(t){return{segments:l.intersect(t.combined,u),inverted:t.inverted1&&t.inverted2}},selectDifference:function(t){return{segments:l.difference(t.combined,u),inverted:t.inverted1&&!t.inverted2}},selectDifferenceRev:function(t){return{segments:l.differenceRev(t.combined,u),inverted:!t.inverted1&&t.inverted2}},selectXor:function(t){return{segments:l.xor(t.combined,u),inverted:t.inverted1!==t.inverted2}},polygon:function(t){return{regions:s(t.segments,f,u),inverted:t.inverted}},polygonFromGeoJSON:function(t){return c.toPolygon(n,t)},polygonToGeoJSON:function(t){return c.fromPolygon(n,f,t)},union:function(t,e){return h(t,e,n.selectUnion)},intersect:function(t,e){return h(t,e,n.selectIntersect)},difference:function(t,e){return h(t,e,n.selectDifference)},differenceRev:function(t,e){return h(t,e,n.selectDifferenceRev)},xor:function(t,e){return h(t,e,n.selectXor)}},"object"==typeof window&&(window.PolyBool=n),e.exports=n},{"./lib/build-log":255,"./lib/epsilon":256,"./lib/geojson":257,"./lib/intersecter":258,"./lib/segment-chainer":260,"./lib/segment-selector":261}],255:[function(t,e,r){e.exports=function(){var t,e=0,r=!1;function n(e,r){return t.list.push({type:e,data:r?JSON.parse(JSON.stringify(r)):void 0}),t}return t={list:[],segmentId:function(){return e++},checkIntersection:function(t,e){return n("check",{seg1:t,seg2:e})},segmentChop:function(t,e){return n("div_seg",{seg:t,pt:e}),n("chop",{seg:t,pt:e})},statusRemove:function(t){return n("pop_seg",{seg:t})},segmentUpdate:function(t){return n("seg_update",{seg:t})},segmentNew:function(t,e){return n("new_seg",{seg:t,primary:e})},segmentRemove:function(t){return n("rem_seg",{seg:t})},tempStatus:function(t,e,r){return n("temp_status",{seg:t,above:e,below:r})},rewind:function(t){return n("rewind",{seg:t})},status:function(t,e,r){return n("status",{seg:t,above:e,below:r})},vert:function(e){return e===r?t:(r=e,n("vert",{x:e}))},log:function(t){return"string"!=typeof t&&(t=JSON.stringify(t,!1,"  ")),n("log",{txt:t})},reset:function(){return n("reset")},selected:function(t){return n("selected",{segs:t})},chainStart:function(t){return n("chain_start",{seg:t})},chainRemoveHead:function(t,e){return n("chain_rem_head",{index:t,pt:e})},chainRemoveTail:function(t,e){return n("chain_rem_tail",{index:t,pt:e})},chainNew:function(t,e){return n("chain_new",{pt1:t,pt2:e})},chainMatch:function(t){return n("chain_match",{index:t})},chainClose:function(t){return n("chain_close",{index:t})},chainAddHead:function(t,e){return n("chain_add_head",{index:t,pt:e})},chainAddTail:function(t,e){return n("chain_add_tail",{index:t,pt:e})},chainConnect:function(t,e){return n("chain_con",{index1:t,index2:e})},chainReverse:function(t){return n("chain_rev",{index:t})},chainJoin:function(t,e){return n("chain_join",{index1:t,index2:e})},done:function(){return n("done")}}}},{}],256:[function(t,e,r){e.exports=function(t){"number"!=typeof t&&(t=1e-10);var e={epsilon:function(e){return"number"==typeof e&&(t=e),t},pointAboveOrOnLine:function(e,r,n){var i=r[0],a=r[1],o=n[0],s=n[1],l=e[0];return(o-i)*(e[1]-a)-(s-a)*(l-i)>=-t},pointBetween:function(e,r,n){var i=e[1]-r[1],a=n[0]-r[0],o=e[0]-r[0],s=n[1]-r[1],l=o*a+i*s;return!(l<t)&&!(l-(a*a+s*s)>-t)},pointsSameX:function(e,r){return Math.abs(e[0]-r[0])<t},pointsSameY:function(e,r){return Math.abs(e[1]-r[1])<t},pointsSame:function(t,r){return e.pointsSameX(t,r)&&e.pointsSameY(t,r)},pointsCompare:function(t,r){return e.pointsSameX(t,r)?e.pointsSameY(t,r)?0:t[1]<r[1]?-1:1:t[0]<r[0]?-1:1},pointsCollinear:function(e,r,n){var i=e[0]-r[0],a=e[1]-r[1],o=r[0]-n[0],s=r[1]-n[1];return Math.abs(i*s-o*a)<t},linesIntersect:function(e,r,n,i){var a=r[0]-e[0],o=r[1]-e[1],s=i[0]-n[0],l=i[1]-n[1],c=a*l-o*s;if(Math.abs(c)<t)return!1;var u=e[0]-n[0],f=e[1]-n[1],h=(s*f-l*u)/c,p=(a*f-o*u)/c,d={alongA:0,alongB:0,pt:[e[0]+h*a,e[1]+h*o]};return d.alongA=h<=-t?-2:h<t?-1:h-1<=-t?0:h-1<t?1:2,d.alongB=p<=-t?-2:p<t?-1:p-1<=-t?0:p-1<t?1:2,d},pointInsideRegion:function(e,r){for(var n=e[0],i=e[1],a=r[r.length-1][0],o=r[r.length-1][1],s=!1,l=0;l<r.length;l++){var c=r[l][0],u=r[l][1];u-i>t!=o-i>t&&(a-c)*(i-u)/(o-u)+c-n>t&&(s=!s),a=c,o=u}return s}};return e}},{}],257:[function(t,e,r){var n={toPolygon:function(t,e){function r(e){if(e.length<=0)return t.segments({inverted:!1,regions:[]});function r(e){var r=e.slice(0,e.length-1);return t.segments({inverted:!1,regions:[r]})}for(var n=r(e[0]),i=1;i<e.length;i++)n=t.selectDifference(t.combine(n,r(e[i])));return n}if("Polygon"===e.type)return t.polygon(r(e.coordinates));if("MultiPolygon"===e.type){for(var n=t.segments({inverted:!1,regions:[]}),i=0;i<e.coordinates.length;i++)n=t.selectUnion(t.combine(n,r(e.coordinates[i])));return t.polygon(n)}throw new Error("PolyBool: Cannot convert GeoJSON object to PolyBool polygon")},fromPolygon:function(t,e,r){function n(t,r){return e.pointInsideRegion([.5*(t[0][0]+t[1][0]),.5*(t[0][1]+t[1][1])],r)}function i(t){return{region:t,children:[]}}r=t.polygon(t.segments(r));var a=i(null);function o(t,e){for(var r=0;r<t.children.length;r++){if(n(e,(s=t.children[r]).region))return void o(s,e)}var a=i(e);for(r=0;r<t.children.length;r++){var s;n((s=t.children[r]).region,e)&&(a.children.push(s),t.children.splice(r,1),r--)}t.children.push(a)}for(var s=0;s<r.regions.length;s++){var l=r.regions[s];l.length<3||o(a,l)}function c(t,e){for(var r=0,n=t[t.length-1][0],i=t[t.length-1][1],a=[],o=0;o<t.length;o++){var s=t[o][0],l=t[o][1];a.push([s,l]),r+=l*n-s*i,n=s,i=l}return r<0!==e&&a.reverse(),a.push([a[0][0],a[0][1]]),a}var u=[];function f(t){var e=[c(t.region,!1)];u.push(e);for(var r=0;r<t.children.length;r++)e.push(h(t.children[r]))}function h(t){for(var e=0;e<t.children.length;e++)f(t.children[e]);return c(t.region,!0)}for(s=0;s<a.children.length;s++)f(a.children[s]);return u.length<=0?{type:"Polygon",coordinates:[]}:1==u.length?{type:"Polygon",coordinates:u[0]}:{type:"MultiPolygon",coordinates:u}}};e.exports=n},{}],258:[function(t,e,r){var n=t("./linked-list");e.exports=function(t,e,r){function i(t,e,n){return{id:r?r.segmentId():-1,start:t,end:e,myFill:{above:n.myFill.above,below:n.myFill.below},otherFill:null}}var a=n.create();function o(t,r){a.insertBefore(t,(function(n){return function(t,r,n,i,a,o){var s=e.pointsCompare(r,a);return 0!==s?s:e.pointsSame(n,o)?0:t!==i?t?1:-1:e.pointAboveOrOnLine(n,i?a:o,i?o:a)?1:-1}(t.isStart,t.pt,r,n.isStart,n.pt,n.other.pt)<0}))}function s(t,e){var r=function(t,e){var r=n.node({isStart:!0,pt:t.start,seg:t,primary:e,other:null,status:null});return o(r,t.end),r}(t,e);return function(t,e,r){var i=n.node({isStart:!1,pt:e.end,seg:e,primary:r,other:t,status:null});t.other=i,o(i,t.pt)}(r,t,e),r}function l(t,e){var n=i(e,t.seg.end,t.seg);return function(t,e){r&&r.segmentChop(t.seg,e),t.other.remove(),t.seg.end=e,t.other.pt=e,o(t.other,t.pt)}(t,e),s(n,t.primary)}function c(i,o){var s=n.create();function c(t){return s.findTransition((function(r){var n,i,a,o,s,l;return(n=t,i=r.ev,a=n.seg.start,o=n.seg.end,s=i.seg.start,l=i.seg.end,e.pointsCollinear(a,s,l)?e.pointsCollinear(o,s,l)||e.pointAboveOrOnLine(o,s,l)?1:-1:e.pointAboveOrOnLine(a,s,l)?1:-1)>0}))}function u(t,n){var i=t.seg,a=n.seg,o=i.start,s=i.end,c=a.start,u=a.end;r&&r.checkIntersection(i,a);var f=e.linesIntersect(o,s,c,u);if(!1===f){if(!e.pointsCollinear(o,s,c))return!1;if(e.pointsSame(o,u)||e.pointsSame(s,c))return!1;var h=e.pointsSame(o,c),p=e.pointsSame(s,u);if(h&&p)return n;var d=!h&&e.pointBetween(o,c,u),g=!p&&e.pointBetween(s,c,u);if(h)return g?l(n,s):l(t,u),n;d&&(p||(g?l(n,s):l(t,u)),l(n,o))}else 0===f.alongA&&(-1===f.alongB?l(t,c):0===f.alongB?l(t,f.pt):1===f.alongB&&l(t,u)),0===f.alongB&&(-1===f.alongA?l(n,o):0===f.alongA?l(n,f.pt):1===f.alongA&&l(n,s));return!1}for(var f=[];!a.isEmpty();){var h=a.getHead();if(r&&r.vert(h.pt[0]),h.isStart){r&&r.segmentNew(h.seg,h.primary);var p=c(h),d=p.before?p.before.ev:null,g=p.after?p.after.ev:null;function m(){if(d){var t=u(h,d);if(t)return t}return!!g&&u(h,g)}r&&r.tempStatus(h.seg,!!d&&d.seg,!!g&&g.seg);var v,y=m();if(y){var x;if(t)(x=null===h.seg.myFill.below||h.seg.myFill.above!==h.seg.myFill.below)&&(y.seg.myFill.above=!y.seg.myFill.above);else y.seg.otherFill=h.seg.myFill;r&&r.segmentUpdate(y.seg),h.other.remove(),h.remove()}if(a.getHead()!==h){r&&r.rewind(h.seg);continue}if(t)x=null===h.seg.myFill.below||h.seg.myFill.above!==h.seg.myFill.below,h.seg.myFill.below=g?g.seg.myFill.above:i,h.seg.myFill.above=x?!h.seg.myFill.below:h.seg.myFill.below;else if(null===h.seg.otherFill)v=g?h.primary===g.primary?g.seg.otherFill.above:g.seg.myFill.above:h.primary?o:i,h.seg.otherFill={above:v,below:v};r&&r.status(h.seg,!!d&&d.seg,!!g&&g.seg),h.other.status=p.insert(n.node({ev:h}))}else{var b=h.status;if(null===b)throw new Error("PolyBool: Zero-length segment detected; your epsilon is probably too small or too large");if(s.exists(b.prev)&&s.exists(b.next)&&u(b.prev.ev,b.next.ev),r&&r.statusRemove(b.ev.seg),b.remove(),!h.primary){var _=h.seg.myFill;h.seg.myFill=h.seg.otherFill,h.seg.otherFill=_}f.push(h.seg)}a.getHead().remove()}return r&&r.done(),f}return t?{addRegion:function(t){for(var n,i,a,o=t[t.length-1],l=0;l<t.length;l++){n=o,o=t[l];var c=e.pointsCompare(n,o);0!==c&&s((i=c<0?n:o,a=c<0?o:n,{id:r?r.segmentId():-1,start:i,end:a,myFill:{above:null,below:null},otherFill:null}),!0)}},calculate:function(t){return c(t,!1)}}:{calculate:function(t,e,r,n){return t.forEach((function(t){s(i(t.start,t.end,t),!0)})),r.forEach((function(t){s(i(t.start,t.end,t),!1)})),c(e,n)}}}},{"./linked-list":259}],259:[function(t,e,r){e.exports={create:function(){var t={root:{root:!0,next:null},exists:function(e){return null!==e&&e!==t.root},isEmpty:function(){return null===t.root.next},getHead:function(){return t.root.next},insertBefore:function(e,r){for(var n=t.root,i=t.root.next;null!==i;){if(r(i))return e.prev=i.prev,e.next=i,i.prev.next=e,void(i.prev=e);n=i,i=i.next}n.next=e,e.prev=n,e.next=null},findTransition:function(e){for(var r=t.root,n=t.root.next;null!==n&&!e(n);)r=n,n=n.next;return{before:r===t.root?null:r,after:n,insert:function(t){return t.prev=r,t.next=n,r.next=t,null!==n&&(n.prev=t),t}}}};return t},node:function(t){return t.prev=null,t.next=null,t.remove=function(){t.prev.next=t.next,t.next&&(t.next.prev=t.prev),t.prev=null,t.next=null},t}}},{}],260:[function(t,e,r){e.exports=function(t,e,r){var n=[],i=[];return t.forEach((function(t){var a=t.start,o=t.end;if(e.pointsSame(a,o))console.warn("PolyBool: Warning: Zero-length segment detected; your epsilon is probably too small or too large");else{r&&r.chainStart(t);for(var s={index:0,matches_head:!1,matches_pt1:!1},l={index:0,matches_head:!1,matches_pt1:!1},c=s,u=0;u<n.length;u++){var f=(m=n[u])[0],h=(m[1],m[m.length-1]);m[m.length-2];if(e.pointsSame(f,a)){if(k(u,!0,!0))break}else if(e.pointsSame(f,o)){if(k(u,!0,!1))break}else if(e.pointsSame(h,a)){if(k(u,!1,!0))break}else if(e.pointsSame(h,o)&&k(u,!1,!1))break}if(c===s)return n.push([a,o]),void(r&&r.chainNew(a,o));if(c===l){r&&r.chainMatch(s.index);var p=s.index,d=s.matches_pt1?o:a,g=s.matches_head,m=n[p],v=g?m[0]:m[m.length-1],y=g?m[1]:m[m.length-2],x=g?m[m.length-1]:m[0],b=g?m[m.length-2]:m[1];return e.pointsCollinear(y,v,d)&&(g?(r&&r.chainRemoveHead(s.index,d),m.shift()):(r&&r.chainRemoveTail(s.index,d),m.pop()),v=y),e.pointsSame(x,d)?(n.splice(p,1),e.pointsCollinear(b,x,v)&&(g?(r&&r.chainRemoveTail(s.index,v),m.pop()):(r&&r.chainRemoveHead(s.index,v),m.shift())),r&&r.chainClose(s.index),void i.push(m)):void(g?(r&&r.chainAddHead(s.index,d),m.unshift(d)):(r&&r.chainAddTail(s.index,d),m.push(d)))}var _=s.index,w=l.index;r&&r.chainConnect(_,w);var T=n[_].length<n[w].length;s.matches_head?l.matches_head?T?(A(_),M(_,w)):(A(w),M(w,_)):M(w,_):l.matches_head?M(_,w):T?(A(_),M(w,_)):(A(w),M(_,w))}function k(t,e,r){return c.index=t,c.matches_head=e,c.matches_pt1=r,c===s?(c=l,!1):(c=null,!0)}function A(t){r&&r.chainReverse(t),n[t].reverse()}function M(t,i){var a=n[t],o=n[i],s=a[a.length-1],l=a[a.length-2],c=o[0],u=o[1];e.pointsCollinear(l,s,c)&&(r&&r.chainRemoveTail(t,s),a.pop(),s=l),e.pointsCollinear(s,c,u)&&(r&&r.chainRemoveHead(i,c),o.shift()),r&&r.chainJoin(t,i),n[t]=a.concat(o),n.splice(i,1)}})),i}},{}],261:[function(t,e,r){function n(t,e,r){var n=[];return t.forEach((function(t){var i=(t.myFill.above?8:0)+(t.myFill.below?4:0)+(t.otherFill&&t.otherFill.above?2:0)+(t.otherFill&&t.otherFill.below?1:0);0!==e[i]&&n.push({id:r?r.segmentId():-1,start:t.start,end:t.end,myFill:{above:1===e[i],below:2===e[i]},otherFill:null})})),r&&r.selected(n),n}var i={union:function(t,e){return n(t,[0,2,1,0,2,2,0,0,1,0,1,0,0,0,0,0],e)},intersect:function(t,e){return n(t,[0,0,0,0,0,2,0,2,0,0,1,1,0,2,1,0],e)},difference:function(t,e){return n(t,[0,0,0,0,2,0,2,0,1,1,0,0,0,1,2,0],e)},differenceRev:function(t,e){return n(t,[0,2,1,0,0,0,1,1,0,2,0,2,0,0,0,0],e)},xor:function(t,e){return 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t[e+3]|t[e+2]<<8|t[e+1]<<16|16777216*t[e]},o.prototype=Object.create(Error.prototype),o.prototype.constructor=o,r.ProbeError=o},{stream:285,"stream-parser":301}],263:[function(t,e,r){"use strict";function n(t,e){var r=new Error(t);return r.code=e,r}function i(t){try{return decodeURIComponent(escape(t))}catch(e){return t}}function a(t,e,r){this.input=t.subarray(e,r),this.start=e;var i=String.fromCharCode.apply(null,this.input.subarray(0,4));if("II*\0"!==i&&"MM\0*"!==i)throw n("invalid TIFF signature","EBADDATA");this.big_endian="M"===i[0]}a.prototype.each=function(t){this.aborted=!1;var e=this.read_uint32(4);for(this.ifds_to_read=[{id:0,offset:e}];this.ifds_to_read.length>0&&!this.aborted;){var r=this.ifds_to_read.shift();r.offset&&this.scan_ifd(r.id,r.offset,t)}},a.prototype.read_uint16=function(t){var e=this.input;if(t+2>e.length)throw n("unexpected EOF","EBADDATA");return this.big_endian?256*e[t]+e[t+1]:e[t]+256*e[t+1]},a.prototype.read_uint32=function(t){var e=this.input;if(t+4>e.length)throw n("unexpected EOF","EBADDATA");return this.big_endian?16777216*e[t]+65536*e[t+1]+256*e[t+2]+e[t+3]:e[t]+256*e[t+1]+65536*e[t+2]+16777216*e[t+3]},a.prototype.is_subifd_link=function(t,e){return 0===t&&34665===e||0===t&&34853===e||34665===t&&40965===e},a.prototype.exif_format_length=function(t){switch(t){case 1:case 2:case 6:case 7:return 1;case 3:case 8:return 2;case 4:case 9:case 11:return 4;case 5:case 10:case 12:return 8;default:return 0}},a.prototype.exif_format_read=function(t,e){var r;switch(t){case 1:case 2:return r=this.input[e];case 6:return(r=this.input[e])|33554430*(128&r);case 3:return r=this.read_uint16(e);case 8:return(r=this.read_uint16(e))|131070*(32768&r);case 4:return r=this.read_uint32(e);case 9:return 0|(r=this.read_uint32(e));case 5:case 10:case 11:case 12:case 7:default:return null}},a.prototype.scan_ifd=function(t,e,r){var a=this.read_uint16(e);e+=2;for(var o=0;o<a;o++){var s=this.read_uint16(e),l=this.read_uint16(e+2),c=this.read_uint32(e+4),u=this.exif_format_length(l),f=c*u,h=f<=4?e+8:this.read_uint32(e+8),p=!1;if(h+f>this.input.length)throw n("unexpected EOF","EBADDATA");for(var d=[],g=h,m=0;m<c;m++,g+=u){var v=this.exif_format_read(l,g);if(null===v){d=null;break}d.push(v)}if(Array.isArray(d)&&2===l&&(d=i(String.fromCharCode.apply(null,d)))&&"\0"===d[d.length-1]&&(d=d.slice(0,-1)),this.is_subifd_link(t,s)&&Array.isArray(d)&&Number.isInteger(d[0])&&d[0]>0&&(this.ifds_to_read.push({id:s,offset:d[0]}),p=!0),!1===r({is_big_endian:this.big_endian,ifd:t,tag:s,format:l,count:c,entry_offset:e+this.start,data_length:f,data_offset:h+this.start,value:d,is_subifd_link:p}))return void(this.aborted=!0);e+=12}0===t&&this.ifds_to_read.push({id:1,offset:this.read_uint32(e)})},e.exports.ExifParser=a,e.exports.get_orientation=function(t){var e=0;try{return new a(t,0,t.length).each((function(t){if(0===t.ifd&&274===t.tag&&Array.isArray(t.value))return e=t.value[0],!1})),e}catch(t){return-1}}},{}],264:[function(t,e,r){"use strict";var n=t("./common").readUInt16BE,i=t("./common").readUInt32BE;function a(t,e){if(t.length<4+e)return null;var r=i(t,e);return t.length<r+e||r<8?null:{boxtype:String.fromCharCode.apply(null,t.slice(e+4,e+8)),data:t.slice(e+8,e+r),end:e+r}}function o(t,e){for(var r=0;;){var n=a(t,r);if(!n)break;switch(n.boxtype){case"ispe":e.sizes.push({width:i(n.data,4),height:i(n.data,8)});break;case"irot":e.transforms.push({type:"irot",value:3&n.data[0]});break;case"imir":e.transforms.push({type:"imir",value:1&n.data[0]})}r=n.end}}function s(t,e,r){for(var n=0,i=0;i<r;i++)n=256*n+(t[e+i]||0);return n}function l(t,e){for(var r=t[4]>>4&15,i=15&t[4],a=t[5]>>4&15,o=n(t,6),l=8,c=0;c<o;c++){var u=n(t,l),f=n(t,l+=2),h=s(t,l+=2,a),p=n(t,l+=a);if(l+=2,0===f&&1===p){var d=s(t,l,r),g=s(t,l+r,i);e.item_loc[u]={length:g,offset:d+h}}l+=p*(r+i)}}function c(t,e){for(var r=n(t,4),i=6,o=0;o<r;o++){var s=a(t,i);if(!s)break;if("infe"===s.boxtype){for(var l=n(s.data,4),c="",u=8;u<s.data.length&&s.data[u];u++)c+=String.fromCharCode(s.data[u]);e.item_inf[c]=l}i=s.end}}function u(t,e){for(var r=0;;){var n=a(t,r);if(!n)break;"ipco"===n.boxtype&&o(n.data,e),r=n.end}}e.exports.unbox=a,e.exports.readSizeFromMeta=function(t){var e={sizes:[],transforms:[],item_inf:{},item_loc:{}};if(function(t,e){for(var r=4;;){var n=a(t,r);if(!n)break;"iprp"===n.boxtype&&u(n.data,e),"iloc"===n.boxtype&&l(n.data,e),"iinf"===n.boxtype&&c(n.data,e),r=n.end}}(t,e),e.sizes.length){var r,n,i,o=(r=e.sizes,n=r.reduce((function(t,e){return t.width>e.width||t.width===e.width&&t.height>e.height?t:e})),i=r.reduce((function(t,e){return t.height>e.height||t.height===e.height&&t.width>e.width?t:e})),n.width>i.height||n.width===i.height&&n.height>i.width?n:i),s=1;e.transforms.forEach((function(t){var e={1:6,2:5,3:8,4:7,5:4,6:3,7:2,8:1},r={1:4,2:3,3:2,4:1,5:6,6:5,7:8,8:7};if("imir"===t.type&&(s=0===t.value?r[s]:e[s=e[s=r[s]]]),"irot"===t.type)for(var n=0;n<t.value;n++)s=e[s]}));var f=null;return e.item_inf.Exif&&(f=e.item_loc[e.item_inf.Exif]),{width:o.width,height:o.height,orientation:e.transforms.length?s:null,variants:e.sizes,exif_location:f}}},e.exports.getMimeType=function(t){var e=String.fromCharCode.apply(null,t.slice(0,4)),r={};r[e]=!0;for(var n=8;n<t.length;n+=4)r[String.fromCharCode.apply(null,t.slice(n,n+4))]=!0;if(r.mif1||r.msf1||r.miaf)return"avif"===e||"avis"===e||"avio"===e?{type:"avif",mime:"image/avif"}:"heic"===e||"heix"===e?{type:"heic",mime:"image/heic"}:"hevc"===e||"hevx"===e?{type:"heic",mime:"image/heic-sequence"}:r.avif||r.avis?{type:"avif",mime:"image/avif"}:r.heic||r.heix||r.hevc||r.hevx||r.heis?r.msf1?{type:"heif",mime:"image/heif-sequence"}:{type:"heif",mime:"image/heif"}:{type:"avif",mime:"image/avif"}}},{"./common":262}],265:[function(t,e,r){"use 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n=t("../common").str2arr,i=t("../common").sliceEq,a=t("../common").readUInt16LE,o=n("BM");e.exports=function(t){if(!(t.length<26)&&i(t,0,o))return{width:a(t,18),height:a(t,22),type:"bmp",mime:"image/bmp",wUnits:"px",hUnits:"px"}}},{"../common":262}],267:[function(t,e,r){"use strict";var n=t("../common").str2arr,i=t("../common").sliceEq,a=t("../common").readUInt16LE,o=n("GIF87a"),s=n("GIF89a");e.exports=function(t){if(!(t.length<10)&&(i(t,0,o)||i(t,0,s)))return{width:a(t,6),height:a(t,8),type:"gif",mime:"image/gif",wUnits:"px",hUnits:"px"}}},{"../common":262}],268:[function(t,e,r){"use strict";var n=t("../common").readUInt16LE;e.exports=function(t){var e=n(t,0),r=n(t,2),i=n(t,4);if(0===e&&1===r&&i){for(var a=[],o={width:0,height:0},s=0;s<i;s++){var l=t[6+16*s]||256,c=t[6+16*s+1]||256,u={width:l,height:c};a.push(u),(l>o.width||c>o.height)&&(o=u)}return{width:o.width,height:o.height,variants:a,type:"ico",mime:"image/x-icon",wUnits:"px",hUnits:"px"}}}},{"../common":262}],269:[function(t,e,r){"use strict";var n=t("../common").readUInt16BE,i=t("../common").str2arr,a=t("../common").sliceEq,o=t("../exif_utils"),s=i("Exif\0\0");e.exports=function(t){if(!(t.length<2)&&255===t[0]&&216===t[1])for(var e=2;;){if(t.length-e<2)return;if(255!==t[e++])return;for(var r,i,l=t[e++];255===l;)l=t[e++];if(208<=l&&l<=217||1===l)r=0;else{if(!(192<=l&&l<=254))return;if(t.length-e<2)return;r=n(t,e)-2,e+=2}if(217===l||218===l)return;if(225===l&&r>=10&&a(t,e,s)&&(i=o.get_orientation(t.slice(e+6,e+r))),r>=5&&192<=l&&l<=207&&196!==l&&200!==l&&204!==l){if(t.length-e<r)return;var c={width:n(t,e+3),height:n(t,e+1),type:"jpg",mime:"image/jpeg",wUnits:"px",hUnits:"px"};return i>0&&(c.orientation=i),c}e+=r}}},{"../common":262,"../exif_utils":263}],270:[function(t,e,r){"use strict";var n=t("../common").str2arr,i=t("../common").sliceEq,a=t("../common").readUInt32BE,o=n("\x89PNG\r\n\x1a\n"),s=n("IHDR");e.exports=function(t){if(!(t.length<24)&&i(t,0,o)&&i(t,12,s))return{width:a(t,16),height:a(t,20),type:"png",mime:"image/png",wUnits:"px",hUnits:"px"}}},{"../common":262}],271:[function(t,e,r){"use strict";var n=t("../common").str2arr,i=t("../common").sliceEq,a=t("../common").readUInt32BE,o=n("8BPS\0\x01");e.exports=function(t){if(!(t.length<22)&&i(t,0,o))return{width:a(t,18),height:a(t,14),type:"psd",mime:"image/vnd.adobe.photoshop",wUnits:"px",hUnits:"px"}}},{"../common":262}],272:[function(t,e,r){"use strict";function n(t){return"number"==typeof t&&isFinite(t)&&t>0}var i=/<[-_.:a-zA-Z0-9][^>]*>/,a=/^<([-_.:a-zA-Z0-9]+:)?svg\s/,o=/[^-]\bwidth="([^%]+?)"|[^-]\bwidth='([^%]+?)'/,s=/\bheight="([^%]+?)"|\bheight='([^%]+?)'/,l=/\bview[bB]ox="(.+?)"|\bview[bB]ox='(.+?)'/,c=/in$|mm$|cm$|pt$|pc$|px$|em$|ex$/;function u(t){return c.test(t)?t.match(c)[0]:"px"}e.exports=function(t){if(function(t){var e,r=0,n=t.length;for(239===t[0]&&187===t[1]&&191===t[2]&&(r=3);r<n&&(32===(e=t[r])||9===e||13===e||10===e);)r++;return r!==n&&60===t[r]}(t)){for(var e="",r=0;r<t.length;r++)e+=String.fromCharCode(t[r]);var c=(e.match(i)||[""])[0];if(a.test(c)){var f=function(t){var e=t.match(o),r=t.match(s),n=t.match(l);return{width:e&&(e[1]||e[2]),height:r&&(r[1]||r[2]),viewbox:n&&(n[1]||n[2])}}(c),h=parseFloat(f.width),p=parseFloat(f.height);if(f.width&&f.height){if(!n(h)||!n(p))return;return{width:h,height:p,type:"svg",mime:"image/svg+xml",wUnits:u(f.width),hUnits:u(f.height)}}var d=(f.viewbox||"").split(" 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e=77===t[0],r=h(t,4,e)-8;if(!(r<0)){var n=r+8;if(!(t.length-n<2)){var a=12*f(t,n+0,e);if(!(a<=0||(n+=2,t.length-n<a))){var o,s,l,d;for(o=0;o<a;o+=12)256===(d=f(t,n+o,e))?s=p(t,n+o,e):257===d&&(l=p(t,n+o,e));return s&&l?{width:s,height:l,type:"tiff",mime:"image/tiff",wUnits:"px",hUnits:"px"}:void 0}}}}}},{"../common":262}],274:[function(t,e,r){"use strict";var n=t("../common").str2arr,i=t("../common").sliceEq,a=t("../common").readUInt16LE,o=t("../common").readUInt32LE,s=t("../exif_utils"),l=n("RIFF"),c=n("WEBP");function u(t,e){if(157===t[e+3]&&1===t[e+4]&&42===t[e+5])return{width:16383&a(t,e+6),height:16383&a(t,e+8),type:"webp",mime:"image/webp",wUnits:"px",hUnits:"px"}}function f(t,e){if(47===t[e]){var r=o(t,e+1);return{width:1+(16383&r),height:1+(r>>14&16383),type:"webp",mime:"image/webp",wUnits:"px",hUnits:"px"}}}function h(t,e){return{width:1+(t[e+6]<<16|t[e+5]<<8|t[e+4]),height:1+(t[e+9]<<e|t[e+8]<<8|t[e+7]),type:"webp",mime:"image/webp",wUnits:"px",hUnits:"px"}}e.exports=function(t){if(!(t.length<16)&&(i(t,0,l)||i(t,8,c))){var e=12,r=null,n=0,a=o(t,4)+8;if(!(a>t.length)){for(;e+8<a;)if(0!==t[e]){var p=String.fromCharCode.apply(null,t.slice(e,e+4)),d=o(t,e+4);"VP8 "===p&&d>=10?r=r||u(t,e+8):"VP8L"===p&&d>=9?r=r||f(t,e+8):"VP8X"===p&&d>=10?r=r||h(t,e+8):"EXIF"===p&&(n=s.get_orientation(t.slice(e+8,e+8+d)),e=1/0),e+=8+d}else e++;if(r)return n>0&&(r.orientation=n),r}}}},{"../common":262,"../exif_utils":263}],275:[function(t,e,r){"use 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opacity;\n\t\t}\n\t\t",uniforms:{range:t.prop("range"),lineWidth:t.prop("lineWidth"),capSize:t.prop("capSize"),opacity:t.prop("opacity"),scale:t.prop("scale"),translate:t.prop("translate"),scaleFract:t.prop("scaleFract"),translateFract:t.prop("translateFract"),viewport:function(t,e){return[e.viewport.x,e.viewport.y,t.viewportWidth,t.viewportHeight]}},attributes:{color:{buffer:d,offset:function(t,e){return 4*e.offset},divisor:1},position:{buffer:c,offset:function(t,e){return 8*e.offset},divisor:1},positionFract:{buffer:p,offset:function(t,e){return 8*e.offset},divisor:1},error:{buffer:g,offset:function(t,e){return 16*e.offset},divisor:1},direction:{buffer:m,stride:24,offset:0},lineOffset:{buffer:m,stride:24,offset:8},capOffset:{buffer:m,stride:24,offset:16}},primitive:"triangles",blend:{enable:!0,color:[0,0,0,0],equation:{rgb:"add",alpha:"add"},func:{srcRGB:"src alpha",dstRGB:"one minus src alpha",srcAlpha:"one minus dst 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n=t("color-normalize"),i=t("array-bounds"),a=t("object-assign"),o=t("glslify"),s=t("pick-by-alias"),l=t("flatten-vertex-data"),c=t("earcut"),u=t("array-normalize"),f=t("to-float32"),h=f.float32,p=f.fract32,d=t("es6-weak-map"),g=t("parse-rect"),m=t("array-find-index");function v(t,e){if(!(this instanceof v))return new v(t,e);if("function"==typeof t?(e||(e={}),e.regl=t):e=t,e.length&&(e.positions=e),!(t=e.regl).hasExtension("ANGLE_instanced_arrays"))throw Error("regl-error2d: `ANGLE_instanced_arrays` extension should be enabled");this.gl=t._gl,this.regl=t,this.passes=[],this.shaders=v.shaders.has(t)?v.shaders.get(t):v.shaders.set(t,v.createShaders(t)).get(t),this.update(e)}e.exports=v,v.dashMult=2,v.maxPatternLength=256,v.precisionThreshold=3e6,v.maxPoints=1e4,v.maxLines=2048,v.shaders=new d,v.createShaders=function(t){var e,r=t.buffer({usage:"static",type:"float",data:[0,1,0,0,1,1,1,0]}),n={primitive:"triangle 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color;\nattribute float lineEnd, lineTop;\n\nuniform vec2 scale, scaleFract, translate, translateFract;\nuniform float thickness, pixelRatio, id, depth;\nuniform vec4 viewport;\n\nvarying vec4 fragColor;\nvarying vec2 tangent;\n\nvec2 project(vec2 position, vec2 positionFract, vec2 scale, vec2 scaleFract, vec2 translate, vec2 translateFract) {\n\t// the order is important\n\treturn position * scale + translate\n       + positionFract * scale + translateFract\n       + position * scaleFract\n       + positionFract * scaleFract;\n}\n\nvoid main() {\n\tfloat lineStart = 1. - lineEnd;\n\tfloat lineOffset = lineTop * 2. - 1.;\n\n\tvec2 diff = (bCoord + bCoordFract - aCoord - aCoordFract);\n\ttangent = normalize(diff * scale * viewport.zw);\n\tvec2 normal = vec2(-tangent.y, tangent.x);\n\n\tvec2 position = project(aCoord, aCoordFract, scale, scaleFract, translate, translateFract) * lineStart\n\t\t+ project(bCoord, bCoordFract, scale, scaleFract, translate, translateFract) * lineEnd\n\n\t\t+ thickness * normal * .5 * lineOffset / viewport.zw;\n\n\tgl_Position = vec4(position * 2.0 - 1.0, depth, 1);\n\n\tfragColor = color / 255.;\n}\n"]),frag:o(["precision highp float;\n#define GLSLIFY 1\n\nuniform float dashLength, pixelRatio, thickness, opacity, id;\nuniform sampler2D dashTexture;\n\nvarying vec4 fragColor;\nvarying vec2 tangent;\n\nvoid main() {\n\tfloat alpha = 1.;\n\n\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashLength) * .5 + .25;\n\tfloat dash = texture2D(dashTexture, vec2(t, .5)).r;\n\n\tgl_FragColor = fragColor;\n\tgl_FragColor.a *= alpha * opacity * dash;\n}\n"]),attributes:{lineEnd:{buffer:r,divisor:0,stride:8,offset:0},lineTop:{buffer:r,divisor:0,stride:8,offset:4},aCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:8,divisor:1},bCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:16,divisor:1},aCoordFract:{buffer:t.prop("positionFractBuffer"),stride:8,offset:8,divisor:1},bCoordFract:{buffer:t.prop("positionFractBuffer"),stride:8,offset:16,divisor:1},color:{buffer:t.prop("colorBuffer"),stride:4,offset:0,divisor:1}}},n));try{e=t(a({cull:{enable:!0,face:"back"},vert:o(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec2 aCoord, bCoord, nextCoord, prevCoord;\nattribute vec4 aColor, bColor;\nattribute float lineEnd, lineTop;\n\nuniform vec2 scale, translate;\nuniform float thickness, pixelRatio, id, depth;\nuniform vec4 viewport;\nuniform float miterLimit, miterMode;\n\nvarying vec4 fragColor;\nvarying vec4 startCutoff, endCutoff;\nvarying vec2 tangent;\nvarying vec2 startCoord, endCoord;\nvarying float enableStartMiter, enableEndMiter;\n\nconst float REVERSE_THRESHOLD = -.875;\nconst float MIN_DIFF = 1e-6;\n\n// TODO: possible optimizations: avoid overcalculating all for vertices and calc just one instead\n// TODO: precalculate dot products, normalize things beforehead etc.\n// TODO: refactor to rectangular algorithm\n\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\n\tvec2 diff = b - a;\n\tvec2 perp = normalize(vec2(-diff.y, diff.x));\n\treturn dot(p - a, perp);\n}\n\nbool isNaN( float val ){\n  return ( val < 0.0 || 0.0 < val || val == 0.0 ) ? false : true;\n}\n\nvoid main() {\n\tvec2 aCoord = aCoord, bCoord = bCoord, prevCoord = prevCoord, nextCoord = nextCoord;\n\n  vec2 adjustedScale;\n  adjustedScale.x = (abs(scale.x) < MIN_DIFF) ? MIN_DIFF : scale.x;\n  adjustedScale.y = (abs(scale.y) < MIN_DIFF) ? MIN_DIFF : scale.y;\n\n  vec2 scaleRatio = adjustedScale * viewport.zw;\n\tvec2 normalWidth = thickness / scaleRatio;\n\n\tfloat lineStart = 1. - lineEnd;\n\tfloat lineBot = 1. - lineTop;\n\n\tfragColor = (lineStart * aColor + lineEnd * bColor) / 255.;\n\n\tif (isNaN(aCoord.x) || isNaN(aCoord.y) || isNaN(bCoord.x) || isNaN(bCoord.y)) return;\n\n\tif (aCoord == prevCoord) prevCoord = aCoord + normalize(bCoord - aCoord);\n\tif (bCoord == nextCoord) nextCoord = bCoord - normalize(bCoord - aCoord);\n\n\tvec2 prevDiff = aCoord - prevCoord;\n\tvec2 currDiff = bCoord - aCoord;\n\tvec2 nextDiff = nextCoord - bCoord;\n\n\tvec2 prevTangent = normalize(prevDiff * scaleRatio);\n\tvec2 currTangent = normalize(currDiff * scaleRatio);\n\tvec2 nextTangent = normalize(nextDiff * scaleRatio);\n\n\tvec2 prevNormal = vec2(-prevTangent.y, prevTangent.x);\n\tvec2 currNormal = vec2(-currTangent.y, currTangent.x);\n\tvec2 nextNormal = vec2(-nextTangent.y, nextTangent.x);\n\n\tvec2 startJoinDirection = normalize(prevTangent - currTangent);\n\tvec2 endJoinDirection = normalize(currTangent - nextTangent);\n\n\t// collapsed/unidirectional segment cases\n\t// FIXME: there should be more elegant solution\n\tvec2 prevTanDiff = abs(prevTangent - currTangent);\n\tvec2 nextTanDiff = abs(nextTangent - currTangent);\n\tif (max(prevTanDiff.x, prevTanDiff.y) < MIN_DIFF) {\n\t\tstartJoinDirection = currNormal;\n\t}\n\tif (max(nextTanDiff.x, nextTanDiff.y) < MIN_DIFF) {\n\t\tendJoinDirection = currNormal;\n\t}\n\tif (aCoord == bCoord) {\n\t\tendJoinDirection = startJoinDirection;\n\t\tcurrNormal = prevNormal;\n\t\tcurrTangent = prevTangent;\n\t}\n\n\ttangent = currTangent;\n\n\t//calculate join shifts relative to normals\n\tfloat startJoinShift = dot(currNormal, startJoinDirection);\n\tfloat endJoinShift = dot(currNormal, endJoinDirection);\n\n\tfloat startMiterRatio = abs(1. / startJoinShift);\n\tfloat endMiterRatio = abs(1. / endJoinShift);\n\n\tvec2 startJoin = startJoinDirection * startMiterRatio;\n\tvec2 endJoin = endJoinDirection * endMiterRatio;\n\n\tvec2 startTopJoin, startBotJoin, endTopJoin, endBotJoin;\n\tstartTopJoin = sign(startJoinShift) * startJoin * .5;\n\tstartBotJoin = -startTopJoin;\n\n\tendTopJoin = sign(endJoinShift) * endJoin * .5;\n\tendBotJoin = -endTopJoin;\n\n\tvec2 aTopCoord = aCoord + normalWidth * startTopJoin;\n\tvec2 bTopCoord = bCoord + normalWidth * endTopJoin;\n\tvec2 aBotCoord = aCoord + normalWidth * startBotJoin;\n\tvec2 bBotCoord = bCoord + normalWidth * endBotJoin;\n\n\t//miter anti-clipping\n\tfloat baClipping = distToLine(bCoord, aCoord, aBotCoord) / dot(normalize(normalWidth * endBotJoin), normalize(normalWidth.yx * vec2(-startBotJoin.y, startBotJoin.x)));\n\tfloat abClipping = distToLine(aCoord, bCoord, bTopCoord) / dot(normalize(normalWidth * startBotJoin), normalize(normalWidth.yx * vec2(-endBotJoin.y, endBotJoin.x)));\n\n\t//prevent close to reverse direction switch\n\tbool prevReverse = dot(currTangent, prevTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, prevNormal)) * min(length(prevDiff), length(currDiff)) <  length(normalWidth * currNormal);\n\tbool nextReverse = dot(currTangent, nextTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, nextNormal)) * min(length(nextDiff), length(currDiff)) <  length(normalWidth * currNormal);\n\n\tif (prevReverse) {\n\t\t//make join rectangular\n\t\tvec2 miterShift = normalWidth * startJoinDirection * miterLimit * .5;\n\t\tfloat normalAdjust = 1. - min(miterLimit / startMiterRatio, 1.);\n\t\taBotCoord = aCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\n\t\taTopCoord = aCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\n\t}\n\telse if (!nextReverse && baClipping > 0. && baClipping < length(normalWidth * endBotJoin)) {\n\t\t//handle miter clipping\n\t\tbTopCoord -= normalWidth * endTopJoin;\n\t\tbTopCoord += normalize(endTopJoin * normalWidth) * baClipping;\n\t}\n\n\tif (nextReverse) {\n\t\t//make join rectangular\n\t\tvec2 miterShift = normalWidth * endJoinDirection * miterLimit * .5;\n\t\tfloat normalAdjust = 1. - min(miterLimit / endMiterRatio, 1.);\n\t\tbBotCoord = bCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\n\t\tbTopCoord = bCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\n\t}\n\telse if (!prevReverse && abClipping > 0. && abClipping < length(normalWidth * startBotJoin)) {\n\t\t//handle miter clipping\n\t\taBotCoord -= normalWidth * startBotJoin;\n\t\taBotCoord += normalize(startBotJoin * normalWidth) * abClipping;\n\t}\n\n\tvec2 aTopPosition = (aTopCoord) * adjustedScale + translate;\n\tvec2 aBotPosition = (aBotCoord) * adjustedScale + translate;\n\n\tvec2 bTopPosition = (bTopCoord) * adjustedScale + translate;\n\tvec2 bBotPosition = (bBotCoord) * adjustedScale + translate;\n\n\t//position is normalized 0..1 coord on the screen\n\tvec2 position = (aTopPosition * lineTop + aBotPosition * lineBot) * lineStart + (bTopPosition * lineTop + bBotPosition * lineBot) * lineEnd;\n\n\tstartCoord = aCoord * scaleRatio + translate * viewport.zw + viewport.xy;\n\tendCoord = bCoord * scaleRatio + translate * viewport.zw + viewport.xy;\n\n\tgl_Position = vec4(position  * 2.0 - 1.0, depth, 1);\n\n\tenableStartMiter = step(dot(currTangent, prevTangent), .5);\n\tenableEndMiter = step(dot(currTangent, nextTangent), .5);\n\n\t//bevel miter cutoffs\n\tif (miterMode == 1.) {\n\t\tif (enableStartMiter == 1.) {\n\t\t\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * miterLimit * .5;\n\t\t\tstartCutoff = vec4(aCoord, aCoord);\n\t\t\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\n\t\t\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\n\t\t\tstartCutoff += viewport.xyxy;\n\t\t\tstartCutoff += startMiterWidth.xyxy;\n\t\t}\n\n\t\tif (enableEndMiter == 1.) {\n\t\t\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * miterLimit * .5;\n\t\t\tendCutoff = vec4(bCoord, bCoord);\n\t\t\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\n\t\t\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\n\t\t\tendCutoff += viewport.xyxy;\n\t\t\tendCutoff += endMiterWidth.xyxy;\n\t\t}\n\t}\n\n\t//round miter cutoffs\n\telse if (miterMode == 2.) {\n\t\tif (enableStartMiter == 1.) {\n\t\t\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * abs(dot(startJoinDirection, currNormal)) * .5;\n\t\t\tstartCutoff = vec4(aCoord, aCoord);\n\t\t\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\n\t\t\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\n\t\t\tstartCutoff += viewport.xyxy;\n\t\t\tstartCutoff += startMiterWidth.xyxy;\n\t\t}\n\n\t\tif (enableEndMiter == 1.) {\n\t\t\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * abs(dot(endJoinDirection, currNormal)) * .5;\n\t\t\tendCutoff = vec4(bCoord, bCoord);\n\t\t\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\n\t\t\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\n\t\t\tendCutoff += viewport.xyxy;\n\t\t\tendCutoff += endMiterWidth.xyxy;\n\t\t}\n\t}\n}\n"]),frag:o(["precision highp float;\n#define GLSLIFY 1\n\nuniform float dashLength, pixelRatio, thickness, opacity, id, miterMode;\nuniform sampler2D dashTexture;\n\nvarying vec4 fragColor;\nvarying vec2 tangent;\nvarying vec4 startCutoff, endCutoff;\nvarying vec2 startCoord, endCoord;\nvarying float enableStartMiter, enableEndMiter;\n\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\n\tvec2 diff = b - a;\n\tvec2 perp = normalize(vec2(-diff.y, diff.x));\n\treturn dot(p - a, perp);\n}\n\nvoid main() {\n\tfloat alpha = 1., distToStart, distToEnd;\n\tfloat cutoff = thickness * .5;\n\n\t//bevel miter\n\tif (miterMode == 1.) {\n\t\tif (enableStartMiter == 1.) {\n\t\t\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\n\t\t\tif (distToStart < -1.) {\n\t\t\t\tdiscard;\n\t\t\t\treturn;\n\t\t\t}\n\t\t\talpha *= min(max(distToStart + 1., 0.), 1.);\n\t\t}\n\n\t\tif (enableEndMiter == 1.) {\n\t\t\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\n\t\t\tif (distToEnd < -1.) {\n\t\t\t\tdiscard;\n\t\t\t\treturn;\n\t\t\t}\n\t\t\talpha *= min(max(distToEnd + 1., 0.), 1.);\n\t\t}\n\t}\n\n\t// round miter\n\telse if (miterMode == 2.) {\n\t\tif (enableStartMiter == 1.) {\n\t\t\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\n\t\t\tif (distToStart < 0.) {\n\t\t\t\tfloat radius = length(gl_FragCoord.xy - startCoord);\n\n\t\t\t\tif(radius > cutoff + .5) {\n\t\t\t\t\tdiscard;\n\t\t\t\t\treturn;\n\t\t\t\t}\n\n\t\t\t\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\n\t\t\t}\n\t\t}\n\n\t\tif (enableEndMiter == 1.) {\n\t\t\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\n\t\t\tif (distToEnd < 0.) {\n\t\t\t\tfloat radius = length(gl_FragCoord.xy - endCoord);\n\n\t\t\t\tif(radius > cutoff + .5) {\n\t\t\t\t\tdiscard;\n\t\t\t\t\treturn;\n\t\t\t\t}\n\n\t\t\t\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\n\t\t\t}\n\t\t}\n\t}\n\n\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashLength) * .5 + .25;\n\tfloat dash = texture2D(dashTexture, vec2(t, .5)).r;\n\n\tgl_FragColor = fragColor;\n\tgl_FragColor.a *= alpha * opacity * dash;\n}\n"]),attributes:{lineEnd:{buffer:r,divisor:0,stride:8,offset:0},lineTop:{buffer:r,divisor:0,stride:8,offset:4},aColor:{buffer:t.prop("colorBuffer"),stride:4,offset:0,divisor:1},bColor:{buffer:t.prop("colorBuffer"),stride:4,offset:4,divisor:1},prevCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:0,divisor:1},aCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:8,divisor:1},bCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:16,divisor:1},nextCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:24,divisor:1}}},n))}catch(t){e=i}return{fill:t({primitive:"triangle",elements:function(t,e){return e.triangles},offset:0,vert:o(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec2 position, positionFract;\n\nuniform vec4 color;\nuniform vec2 scale, scaleFract, translate, translateFract;\nuniform float pixelRatio, id;\nuniform vec4 viewport;\nuniform float opacity;\n\nvarying vec4 fragColor;\n\nconst float MAX_LINES = 256.;\n\nvoid main() {\n\tfloat depth = (MAX_LINES - 4. - id) / (MAX_LINES);\n\n\tvec2 position = position * scale + translate\n       + positionFract * scale + translateFract\n       + position * scaleFract\n       + positionFract * scaleFract;\n\n\tgl_Position = vec4(position * 2.0 - 1.0, depth, 1);\n\n\tfragColor = color / 255.;\n\tfragColor.a *= opacity;\n}\n"]),frag:o(["precision highp float;\n#define GLSLIFY 1\n\nvarying vec4 fragColor;\n\nvoid main() {\n\tgl_FragColor = fragColor;\n}\n"]),uniforms:{scale:t.prop("scale"),color:t.prop("fill"),scaleFract:t.prop("scaleFract"),translateFract:t.prop("translateFract"),translate:t.prop("translate"),opacity:t.prop("opacity"),pixelRatio:t.context("pixelRatio"),id:t.prop("id"),viewport:function(t,e){return[e.viewport.x,e.viewport.y,t.viewportWidth,t.viewportHeight]}},attributes:{position:{buffer:t.prop("positionBuffer"),stride:8,offset:8},positionFract:{buffer:t.prop("positionFractBuffer"),stride:8,offset:8}},blend:n.blend,depth:{enable:!1},scissor:n.scissor,stencil:n.stencil,viewport:n.viewport}),rect:i,miter:e}},v.defaults={dashes:null,join:"miter",miterLimit:1,thickness:10,cap:"square",color:"black",opacity:1,overlay:!1,viewport:null,range:null,close:!1,fill:null},v.prototype.render=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];e.length&&(t=this).update.apply(t,e),this.draw()},v.prototype.draw=function(){for(var t=this,e=[],r=arguments.length;r--;)e[r]=arguments[r];return(e.length?e:this.passes).forEach((function(e,r){var n;if(e&&Array.isArray(e))return(n=t).draw.apply(n,e);"number"==typeof e&&(e=t.passes[e]),e&&e.count>1&&e.opacity&&(t.regl._refresh(),e.fill&&e.triangles&&e.triangles.length>2&&t.shaders.fill(e),e.thickness&&(e.scale[0]*e.viewport.width>v.precisionThreshold||e.scale[1]*e.viewport.height>v.precisionThreshold||"rect"===e.join||!e.join&&(e.thickness<=2||e.count>=v.maxPoints)?t.shaders.rect(e):t.shaders.miter(e)))})),this},v.prototype.update=function(t){var e=this;if(t){null!=t.length?"number"==typeof t[0]&&(t=[{positions:t}]):Array.isArray(t)||(t=[t]);var r=this.regl,o=this.gl;if(t.forEach((function(t,f){var d=e.passes[f];if(void 0!==t)if(null!==t){if("number"==typeof t[0]&&(t={positions:t}),t=s(t,{positions:"positions points data coords",thickness:"thickness lineWidth lineWidths line-width linewidth width stroke-width strokewidth strokeWidth",join:"lineJoin linejoin join type mode",miterLimit:"miterlimit miterLimit",dashes:"dash dashes dasharray dash-array dashArray",color:"color colour stroke colors colours stroke-color strokeColor",fill:"fill fill-color fillColor",opacity:"alpha opacity",overlay:"overlay crease overlap intersect",close:"closed close closed-path closePath",range:"range dataBox",viewport:"viewport viewBox",hole:"holes hole hollow",splitNull:"splitNull"}),d||(e.passes[f]=d={id:f,scale:null,scaleFract:null,translate:null,translateFract:null,count:0,hole:[],depth:0,dashLength:1,dashTexture:r.texture({channels:1,data:new Uint8Array([255]),width:1,height:1,mag:"linear",min:"linear"}),colorBuffer:r.buffer({usage:"dynamic",type:"uint8",data:new Uint8Array}),positionBuffer:r.buffer({usage:"dynamic",type:"float",data:new Uint8Array}),positionFractBuffer:r.buffer({usage:"dynamic",type:"float",data:new Uint8Array})},t=a({},v.defaults,t)),null!=t.thickness&&(d.thickness=parseFloat(t.thickness)),null!=t.opacity&&(d.opacity=parseFloat(t.opacity)),null!=t.miterLimit&&(d.miterLimit=parseFloat(t.miterLimit)),null!=t.overlay&&(d.overlay=!!t.overlay,f<v.maxLines&&(d.depth=2*(v.maxLines-1-f%v.maxLines)/v.maxLines-1)),null!=t.join&&(d.join=t.join),null!=t.hole&&(d.hole=t.hole),null!=t.fill&&(d.fill=t.fill?n(t.fill,"uint8"):null),null!=t.viewport&&(d.viewport=g(t.viewport)),d.viewport||(d.viewport=g([o.drawingBufferWidth,o.drawingBufferHeight])),null!=t.close&&(d.close=t.close),null===t.positions&&(t.positions=[]),t.positions){var y,x;if(t.positions.x&&t.positions.y){var b=t.positions.x,_=t.positions.y;x=d.count=Math.max(b.length,_.length),y=new Float64Array(2*x);for(var w=0;w<x;w++)y[2*w]=b[w],y[2*w+1]=_[w]}else y=l(t.positions,"float64"),x=d.count=Math.floor(y.length/2);var T=d.bounds=i(y,2);if(d.fill){for(var k=[],A={},M=0,S=0,E=0,L=d.count;S<L;S++){var C=y[2*S],P=y[2*S+1];isNaN(C)||isNaN(P)||null==C||null==P?(C=y[2*M],P=y[2*M+1],A[S]=M):M=S,k[E++]=C,k[E++]=P}if(t.splitNull){d.count-1 in A||(A[d.count]=d.count-1);var I=Object.keys(A).map(Number).sort((function(t,e){return t-e})),O=[],z=0,D=null!=d.hole?d.hole[0]:null;if(null!=D){var R=m(I,(function(t){return t>=D}));(I=I.slice(0,R)).push(D)}for(var F=function(t){var e=k.slice(2*z,2*I[t]).concat(D?k.slice(2*D):[]),r=(d.hole||[]).map((function(e){return e-D+(I[t]-z)})),n=c(e,r);n=n.map((function(e){return e+z+(e+z<I[t]?0:D-I[t])})),O.push.apply(O,n),z=I[t]+1},B=0;B<I.length;B++)F(B);for(var N=0,j=O.length;N<j;N++)null!=A[O[N]]&&(O[N]=A[O[N]]);d.triangles=O}else{for(var U=c(k,d.hole||[]),V=0,H=U.length;V<H;V++)null!=A[U[V]]&&(U[V]=A[U[V]]);d.triangles=U}}var q=new Float64Array(y);u(q,2,T);var G=new Float64Array(2*x+6);d.close?y[0]===y[2*x-2]&&y[1]===y[2*x-1]?(G[0]=q[2*x-4],G[1]=q[2*x-3]):(G[0]=q[2*x-2],G[1]=q[2*x-1]):(G[0]=q[0],G[1]=q[1]),G.set(q,2),d.close?y[0]===y[2*x-2]&&y[1]===y[2*x-1]?(G[2*x+2]=q[2],G[2*x+3]=q[3],d.count-=1):(G[2*x+2]=q[0],G[2*x+3]=q[1],G[2*x+4]=q[2],G[2*x+5]=q[3]):(G[2*x+2]=q[2*x-2],G[2*x+3]=q[2*x-1],G[2*x+4]=q[2*x-2],G[2*x+5]=q[2*x-1]);var Y=h(G);d.positionBuffer(Y);var W=p(G,Y);d.positionFractBuffer(W)}if(t.range?d.range=t.range:d.range||(d.range=d.bounds),(t.range||t.positions)&&d.count){var X=d.bounds,Z=X[2]-X[0],J=X[3]-X[1],K=d.range[2]-d.range[0],Q=d.range[3]-d.range[1];d.scale=[Z/K,J/Q],d.translate=[-d.range[0]/K+X[0]/K||0,-d.range[1]/Q+X[1]/Q||0],d.scaleFract=p(d.scale),d.translateFract=p(d.translate)}if(t.dashes){var $,tt=0;if(!t.dashes||t.dashes.length<2)tt=1,$=new Uint8Array([255,255,255,255,255,255,255,255]);else{tt=0;for(var et=0;et<t.dashes.length;++et)tt+=t.dashes[et];$=new Uint8Array(tt*v.dashMult);for(var rt=0,nt=255,it=0;it<2;it++)for(var at=0;at<t.dashes.length;++at){for(var ot=0,st=t.dashes[at]*v.dashMult*.5;ot<st;++ot)$[rt++]=nt;nt^=255}}d.dashLength=tt,d.dashTexture({channels:1,data:$,width:$.length,height:1,mag:"linear",min:"linear"},0,0)}if(t.color){var lt=d.count,ct=t.color;ct||(ct="transparent");var ut=new Uint8Array(4*lt+4);if(Array.isArray(ct)&&"number"!=typeof ct[0]){for(var ft=0;ft<lt;ft++){var ht=n(ct[ft],"uint8");ut.set(ht,4*ft)}ut.set(n(ct[0],"uint8"),4*lt)}else for(var pt=n(ct,"uint8"),dt=0;dt<lt+1;dt++)ut.set(pt,4*dt);d.colorBuffer({usage:"dynamic",type:"uint8",data:ut})}}else e.passes[f]=null})),t.length<this.passes.length){for(var f=t.length;f<this.passes.length;f++){var d=this.passes[f];d&&(d.colorBuffer.destroy(),d.positionBuffer.destroy(),d.dashTexture.destroy())}this.passes.length=t.length}for(var y=[],x=0;x<this.passes.length;x++)null!==this.passes[x]&&y.push(this.passes[x]);return this.passes=y,this}},v.prototype.destroy=function(){return 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e.yAttr||{buffer:e.positionBuffer,stride:8,offset:4}},xFract:function(t,e){return e.xAttr?{constant:[0,0]}:{buffer:e.positionFractBuffer,stride:8,offset:0}},yFract:function(t,e){return e.yAttr?{constant:[0,0]}:{buffer:e.positionFractBuffer,stride:8,offset:4}},size:function(t,e){return e.size.length?{buffer:e.sizeBuffer,stride:2,offset:0}:{constant:[Math.round(255*e.size/r.maxSize)]}},borderSize:function(t,e){return e.borderSize.length?{buffer:e.sizeBuffer,stride:2,offset:1}:{constant:[Math.round(255*e.borderSize/r.maxSize)]}},colorId:function(t,e){return e.color.length?{buffer:e.colorBuffer,stride:r.tooManyColors?8:4,offset:0}:{constant:r.tooManyColors?a.slice(4*e.color,4*e.color+4):[e.color]}},borderColorId:function(t,e){return e.borderColor.length?{buffer:e.colorBuffer,stride:r.tooManyColors?8:4,offset:r.tooManyColors?4:2}:{constant:r.tooManyColors?a.slice(4*e.borderColor,4*e.borderColor+4):[e.borderColor]}},isActive:function(t,e){return!0===e.activation?{constant:[1]}:e.activation?e.activation:{constant:[0]}}},blend:{enable:!0,color:[0,0,0,1],func:{srcRGB:"src alpha",dstRGB:"one minus src alpha",srcAlpha:"one minus dst alpha",dstAlpha:"one"}},scissor:{enable:!0,box:t.prop("viewport")},viewport:t.prop("viewport"),stencil:{enable:!1},depth:{enable:!1},elements:t.prop("elements"),count:t.prop("count"),offset:t.prop("offset"),primitive:"points"},s=f({},o);s.frag=h(["precision highp float;\n#define GLSLIFY 1\n\nuniform float opacity;\nuniform sampler2D markerTexture;\n\nvarying vec4 fragColor, fragBorderColor;\nvarying float fragWidth, fragBorderColorLevel, fragColorLevel;\n\nfloat smoothStep(float x, float y) {\n  return 1.0 / (1.0 + exp(50.0*(x - y)));\n}\n\nvoid main() {\n  float dist = texture2D(markerTexture, gl_PointCoord).r, delta = fragWidth;\n\n  // max-distance alpha\n  if (dist < 0.003) discard;\n\n  // null-border case\n  if (fragBorderColorLevel == fragColorLevel || fragBorderColor.a == 0.) {\n    float colorAmt = smoothstep(.5 - delta, .5 + delta, dist);\n    gl_FragColor = vec4(fragColor.rgb, colorAmt * fragColor.a * opacity);\n  }\n  else {\n    float borderColorAmt = smoothstep(fragBorderColorLevel - delta, fragBorderColorLevel + delta, dist);\n    float colorAmt = smoothstep(fragColorLevel - delta, fragColorLevel + delta, dist);\n\n    vec4 color = fragBorderColor;\n    color.a *= borderColorAmt;\n    color = mix(color, fragColor, colorAmt);\n    color.a *= opacity;\n\n    gl_FragColor = color;\n  }\n\n}\n"]),s.vert=h(["precision highp float;\n#define GLSLIFY 1\n\nattribute float x, y, xFract, yFract;\nattribute float size, borderSize;\nattribute vec4 colorId, borderColorId;\nattribute float isActive;\n\nuniform bool constPointSize;\nuniform float pixelRatio;\nuniform vec2 scale, scaleFract, translate, translateFract, paletteSize;\nuniform sampler2D paletteTexture;\n\nconst float maxSize = 100.;\nconst float borderLevel = .5;\n\nvarying vec4 fragColor, fragBorderColor;\nvarying float fragPointSize, fragBorderRadius, fragWidth, fragBorderColorLevel, fragColorLevel;\n\nfloat pointSizeScale = (constPointSize) ? 2. : pixelRatio;\n\nbool isDirect = (paletteSize.x < 1.);\n\nvec4 getColor(vec4 id) {\n  return isDirect ? id / 255. : texture2D(paletteTexture,\n    vec2(\n      (id.x + .5) / paletteSize.x,\n      (id.y + .5) / paletteSize.y\n    )\n  );\n}\n\nvoid main() {\n  // ignore inactive points\n  if (isActive == 0.) return;\n\n  vec2 position = vec2(x, y);\n  vec2 positionFract = vec2(xFract, yFract);\n\n  vec4 color = getColor(colorId);\n  vec4 borderColor = getColor(borderColorId);\n\n  float size = size * maxSize / 255.;\n  float borderSize = borderSize * maxSize / 255.;\n\n  gl_PointSize = 2. * size * pointSizeScale;\n  fragPointSize = size * pixelRatio;\n\n  vec2 pos = (position + translate) * scale\n      + (positionFract + translateFract) * scale\n      + (position + translate) * scaleFract\n      + (positionFract + translateFract) * scaleFract;\n\n  gl_Position = vec4(pos * 2. - 1., 0., 1.);\n\n  fragColor = color;\n  fragBorderColor = borderColor;\n  fragWidth = 1. / gl_PointSize;\n\n  fragBorderColorLevel = clamp(borderLevel - borderLevel * borderSize / size, 0., 1.);\n  fragColorLevel = clamp(borderLevel + (1. - borderLevel) * borderSize / size, 0., 1.);\n}"]),this.drawMarker=t(s);var l=f({},o);l.frag=h(["precision highp float;\n#define GLSLIFY 1\n\nvarying vec4 fragColor, fragBorderColor;\nvarying float fragBorderRadius, fragWidth;\n\nuniform float opacity;\n\nfloat smoothStep(float edge0, float edge1, float x) {\n\tfloat t;\n\tt = clamp((x - edge0) / (edge1 - edge0), 0.0, 1.0);\n\treturn t * t * (3.0 - 2.0 * t);\n}\n\nvoid main() {\n\tfloat radius, alpha = 1.0, delta = fragWidth;\n\n\tradius = length(2.0 * gl_PointCoord.xy - 1.0);\n\n\tif (radius > 1.0 + delta) {\n\t\tdiscard;\n\t}\n\n\talpha -= smoothstep(1.0 - delta, 1.0 + delta, radius);\n\n\tfloat borderRadius = fragBorderRadius;\n\tfloat ratio = smoothstep(borderRadius - delta, borderRadius + delta, radius);\n\tvec4 color = mix(fragColor, fragBorderColor, ratio);\n\tcolor.a *= alpha * opacity;\n\tgl_FragColor = color;\n}\n"]),l.vert=h(["precision highp float;\n#define GLSLIFY 1\n\nattribute float x, y, xFract, yFract;\nattribute float size, borderSize;\nattribute vec4 colorId, borderColorId;\nattribute float isActive;\n\nuniform bool constPointSize;\nuniform float pixelRatio;\nuniform vec2 paletteSize, scale, scaleFract, translate, translateFract;\nuniform sampler2D paletteTexture;\n\nconst float maxSize = 100.;\n\nvarying vec4 fragColor, fragBorderColor;\nvarying float fragBorderRadius, fragWidth;\n\nfloat pointSizeScale = (constPointSize) ? 2. : pixelRatio;\n\nbool isDirect = (paletteSize.x < 1.);\n\nvec4 getColor(vec4 id) {\n  return isDirect ? id / 255. : texture2D(paletteTexture,\n    vec2(\n      (id.x + .5) / paletteSize.x,\n      (id.y + .5) / paletteSize.y\n    )\n  );\n}\n\nvoid main() {\n  // ignore inactive points\n  if (isActive == 0.) return;\n\n  vec2 position = vec2(x, y);\n  vec2 positionFract = vec2(xFract, yFract);\n\n  vec4 color = getColor(colorId);\n  vec4 borderColor = getColor(borderColorId);\n\n  float size = size * maxSize / 255.;\n  float borderSize = borderSize * maxSize / 255.;\n\n  gl_PointSize = (size + borderSize) * pointSizeScale;\n\n  vec2 pos = (position + translate) * scale\n      + (positionFract + translateFract) * scale\n      + (position + translate) * scaleFract\n      + (positionFract + translateFract) * scaleFract;\n\n  gl_Position = vec4(pos * 2. - 1., 0., 1.);\n\n  fragBorderRadius = 1. - 2. * borderSize / (size + borderSize);\n  fragColor = color;\n  fragBorderColor = borderColor.a == 0. || borderSize == 0. ? vec4(color.rgb, 0.) : borderColor;\n  fragWidth = 1. / gl_PointSize;\n}\n"]),m&&(l.frag=l.frag.replace("smoothstep","smoothStep"),s.frag=s.frag.replace("smoothstep","smoothStep")),this.drawCircle=t(l)}b.defaults={color:"black",borderColor:"transparent",borderSize:0,size:12,opacity:1,marker:void 0,viewport:null,range:null,pixelSize:null,count:0,offset:0,bounds:null,positions:[],snap:1e4},b.prototype.render=function(){return arguments.length&&this.update.apply(this,arguments),this.draw(),this},b.prototype.draw=function(){for(var t=this,e=arguments.length,r=new Array(e),n=0;n<e;n++)r[n]=arguments[n];var i=this.groups;if(1===r.length&&Array.isArray(r[0])&&(null===r[0][0]||Array.isArray(r[0][0]))&&(r=r[0]),this.regl._refresh(),r.length)for(var a=0;a<r.length;a++)this.drawItem(a,r[a]);else i.forEach((function(e,r){t.drawItem(r)}));return this},b.prototype.drawItem=function(t,e){var r=this.groups,n=r[t];if("number"==typeof e&&(t=e,n=r[e],e=null),n&&n.count&&n.opacity){n.activation[0]&&this.drawCircle(this.getMarkerDrawOptions(0,n,e));for(var a=[],o=1;o<n.activation.length;o++)n.activation[o]&&(!0===n.activation[o]||n.activation[o].data.length)&&a.push.apply(a,i(this.getMarkerDrawOptions(o,n,e)));a.length&&this.drawMarker(a)}},b.prototype.getMarkerDrawOptions=function(t,e,r){var i=e.range,a=e.tree,o=e.viewport,s=e.activation,l=e.selectionBuffer,c=e.count;this.regl;if(!a)return r?[f({},e,{markerTexture:this.markerTextures[t],activation:s[t],count:r.length,elements:r,offset:0})]:[f({},e,{markerTexture:this.markerTextures[t],activation:s[t],offset:0})];var u=[],h=a.range(i,{lod:!0,px:[(i[2]-i[0])/o.width,(i[3]-i[1])/o.height]});if(r){for(var p=s[t].data,d=new Uint8Array(c),g=0;g<r.length;g++){var m=r[g];d[m]=p?p[m]:1}l.subdata(d)}for(var v=h.length;v--;){var y=n(h[v],2),x=y[0],b=y[1];u.push(f({},e,{markerTexture:this.markerTextures[t],activation:r?l:s[t],offset:x,count:b-x}))}return u},b.prototype.update=function(){for(var t=this,e=arguments.length,r=new Array(e),n=0;n<e;n++)r[n]=arguments[n];if(r.length){1===r.length&&Array.isArray(r[0])&&(r=r[0]);var i=this.groups,a=this.gl,o=this.regl,s=this.maxSize,c=this.maxColors,h=this.palette;this.groups=i=r.map((function(e,r){var n=i[r];if(void 0===e)return n;null===e?e={positions:null}:"function"==typeof e?e={ondraw:e}:"number"==typeof e[0]&&(e={positions:e}),null===(e=p(e,{positions:"positions data points",snap:"snap cluster lod tree",size:"sizes size radius",borderSize:"borderSizes borderSize border-size bordersize borderWidth borderWidths border-width borderwidth stroke-width strokeWidth strokewidth outline",color:"colors color fill fill-color fillColor",borderColor:"borderColors borderColor stroke stroke-color strokeColor",marker:"markers marker shape",range:"range dataBox databox",viewport:"viewport viewPort viewBox viewbox",opacity:"opacity alpha transparency",bounds:"bound bounds boundaries limits",tooManyColors:"tooManyColors palette paletteMode optimizePalette enablePalette"})).positions&&(e.positions=[]),null!=e.tooManyColors&&(t.tooManyColors=e.tooManyColors),n||(i[r]=n={id:r,scale:null,translate:null,scaleFract:null,translateFract:null,activation:[],selectionBuffer:o.buffer({data:new Uint8Array(0),usage:"stream",type:"uint8"}),sizeBuffer:o.buffer({data:new Uint8Array(0),usage:"dynamic",type:"uint8"}),colorBuffer:o.buffer({data:new Uint8Array(0),usage:"dynamic",type:"uint8"}),positionBuffer:o.buffer({data:new Uint8Array(0),usage:"dynamic",type:"float"}),positionFractBuffer:o.buffer({data:new Uint8Array(0),usage:"dynamic",type:"float"})},e=f({},b.defaults,e)),e.positions&&!("marker"in e)&&(e.marker=n.marker,delete n.marker),e.marker&&!("positions"in e)&&(e.positions=n.positions,delete n.positions);var m=0,x=0;if(d(n,e,[{snap:!0,size:function(t,e){return null==t&&(t=b.defaults.size),m+=t&&t.length?1:0,t},borderSize:function(t,e){return null==t&&(t=b.defaults.borderSize),m+=t&&t.length?1:0,t},opacity:parseFloat,color:function(e,r){return null==e&&(e=b.defaults.color),e=t.updateColor(e),x++,e},borderColor:function(e,r){return null==e&&(e=b.defaults.borderColor),e=t.updateColor(e),x++,e},bounds:function(t,e,r){return"range"in r||(r.range=null),t},positions:function(t,e,r){var n=e.snap,i=e.positionBuffer,a=e.positionFractBuffer,s=e.selectionBuffer;if(t.x||t.y)return t.x.length?e.xAttr={buffer:o.buffer(t.x),offset:0,stride:4,count:t.x.length}:e.xAttr={buffer:t.x.buffer,offset:4*t.x.offset||0,stride:4*(t.x.stride||1),count:t.x.count},t.y.length?e.yAttr={buffer:o.buffer(t.y),offset:0,stride:4,count:t.y.length}:e.yAttr={buffer:t.y.buffer,offset:4*t.y.offset||0,stride:4*(t.y.stride||1),count:t.y.count},e.count=Math.max(e.xAttr.count,e.yAttr.count),t;t=g(t,"float64");var c=e.count=Math.floor(t.length/2),f=e.bounds=c?l(t,2):null;if(r.range||e.range||(delete e.range,r.range=f),r.marker||e.marker||(delete e.marker,r.marker=null),n&&(!0===n||c>n)?e.tree=u(t,{bounds:f}):n&&n.length&&(e.tree=n),e.tree){var h={primitive:"points",usage:"static",data:e.tree,type:"uint32"};e.elements?e.elements(h):e.elements=o.elements(h)}var p=v.float32(t);return i({data:p,usage:"dynamic"}),a({data:v.fract32(t,p),usage:"dynamic"}),s({data:new Uint8Array(c),type:"uint8",usage:"stream"}),t}},{marker:function(e,r,n){var i=r.activation;if(i.forEach((function(t){return t&&t.destroy&&t.destroy()})),i.length=0,e&&"number"!=typeof e[0]){for(var a=[],s=0,l=Math.min(e.length,r.count);s<l;s++){var c=t.addMarker(e[s]);a[c]||(a[c]=new Uint8Array(r.count)),a[c][s]=1}for(var u=0;u<a.length;u++)if(a[u]){var f={data:a[u],type:"uint8",usage:"static"};i[u]?i[u](f):i[u]=o.buffer(f),i[u].data=a[u]}}else{i[t.addMarker(e)]=!0}return e},range:function(t,e,r){var n=e.bounds;if(n)return t||(t=n),e.scale=[1/(t[2]-t[0]),1/(t[3]-t[1])],e.translate=[-t[0],-t[1]],e.scaleFract=v.fract(e.scale),e.translateFract=v.fract(e.translate),t},viewport:function(t){return y(t||[a.drawingBufferWidth,a.drawingBufferHeight])}}]),m){var _=n,w=_.count,T=_.size,k=_.borderSize,A=_.sizeBuffer,M=new Uint8Array(2*w);if(T.length||k.length)for(var S=0;S<w;S++)M[2*S]=Math.round(255*(null==T[S]?T:T[S])/s),M[2*S+1]=Math.round(255*(null==k[S]?k:k[S])/s);A({data:M,usage:"dynamic"})}if(x){var E,L=n,C=L.count,P=L.color,I=L.borderColor,O=L.colorBuffer;if(t.tooManyColors){if(P.length||I.length){E=new Uint8Array(8*C);for(var z=0;z<C;z++){var D=P[z];E[8*z]=h[4*D],E[8*z+1]=h[4*D+1],E[8*z+2]=h[4*D+2],E[8*z+3]=h[4*D+3];var R=I[z];E[8*z+4]=h[4*R],E[8*z+5]=h[4*R+1],E[8*z+6]=h[4*R+2],E[8*z+7]=h[4*R+3]}}}else if(P.length||I.length){E=new Uint8Array(4*C+2);for(var F=0;F<C;F++)null!=P[F]&&(E[4*F]=P[F]%c,E[4*F+1]=Math.floor(P[F]/c)),null!=I[F]&&(E[4*F+2]=I[F]%c,E[4*F+3]=Math.floor(I[F]/c))}O({data:E||new Uint8Array(0),type:"uint8",usage:"dynamic"})}return n}))}},b.prototype.addMarker=function(t){var e,r=this.markerTextures,n=this.regl,i=this.markerCache,a=null==t?0:i.indexOf(t);if(a>=0)return a;if(t instanceof Uint8Array||t instanceof Uint8ClampedArray)e=t;else{e=new Uint8Array(t.length);for(var o=0,s=t.length;o<s;o++)e[o]=255*t[o]}var l=Math.floor(Math.sqrt(e.length));return a=r.length,i.push(t),r.push(n.texture({channels:1,data:e,radius:l,mag:"linear",min:"linear"})),a},b.prototype.updateColor=function(t){var e=this.paletteIds,r=this.palette,n=this.maxColors;Array.isArray(t)||(t=[t]);var i=[];if("number"==typeof t[0]){var a=[];if(Array.isArray(t))for(var o=0;o<t.length;o+=4)a.push(t.slice(o,o+4));else for(var l=0;l<t.length;l+=4)a.push(t.subarray(l,l+4));t=a}for(var u=0;u<t.length;u++){var f=t[u];f=s(f,"uint8");var h=c(f,!1);if(null==e[h]){var p=r.length;e[h]=Math.floor(p/4),r[p]=f[0],r[p+1]=f[1],r[p+2]=f[2],r[p+3]=f[3]}i[u]=e[h]}return!this.tooManyColors&&r.length>4*n&&(this.tooManyColors=!0),this.updatePalette(r),1===i.length?i[0]:i},b.prototype.updatePalette=function(t){if(!this.tooManyColors){var e=this.maxColors,r=this.paletteTexture,n=Math.ceil(.25*t.length/e);if(n>1)for(var i=.25*(t=t.slice()).length%e;i<n*e;i++)t.push(0,0,0,0);r.height<n&&r.resize(e,n),r.subimage({width:Math.min(.25*t.length,e),height:n,data:t},0,0)}},b.prototype.destroy=function(){return this.groups.forEach((function(t){t.sizeBuffer.destroy(),t.positionBuffer.destroy(),t.positionFractBuffer.destroy(),t.colorBuffer.destroy(),t.activation.forEach((function(t){return t&&t.destroy&&t.destroy()})),t.selectionBuffer.destroy(),t.elements&&t.elements.destroy()})),this.groups.length=0,this.paletteTexture.destroy(),this.markerTextures.forEach((function(t){return t&&t.destroy&&t.destroy()})),this};var _=t("object-assign");e.exports=function(t,e){var r=new x(t,e),n=r.render.bind(r);return _(n,{render:n,update:r.update.bind(r),draw:r.draw.bind(r),destroy:r.destroy.bind(r),regl:r.regl,gl:r.gl,canvas:r.gl.canvas,groups:r.groups,markers:r.markerCache,palette:r.palette}),n}},{"@plotly/point-cluster":59,"array-bounds":71,"color-id":87,"color-normalize":89,"flatten-vertex-data":191,glslify:227,"is-iexplorer":233,"object-assign":247,"parse-rect":249,"pick-by-alias":253,"to-float32":313,"update-diff":329}],282:[function(t,e,r){"use strict";var n=t("regl-scatter2d"),i=t("pick-by-alias"),a=t("array-bounds"),o=t("raf"),s=t("array-range"),l=t("parse-rect"),c=t("flatten-vertex-data");function u(t,e){if(!(this instanceof u))return new u(t,e);this.traces=[],this.passes={},this.regl=t,this.scatter=n(t),this.canvas=this.scatter.canvas}function f(t,e,r){return(null!=t.id?t.id:t)<<16|(255&e)<<8|255&r}function h(t,e,r){var n,i,a,o,s=t[e],l=t[r];return s.length>2?(s[0],s[2],n=s[1],i=s[3]):s.length?(n=s[0],i=s[1]):(s.x,n=s.y,s.x+s.width,i=s.y+s.height),l.length>2?(a=l[0],o=l[2],l[1],l[3]):l.length?(a=l[0],o=l[1]):(a=l.x,l.y,o=l.x+l.width,l.y+l.height),[a,n,o,i]}function p(t){if("number"==typeof t)return[t,t,t,t];if(2===t.length)return[t[0],t[1],t[0],t[1]];var e=l(t);return[e.x,e.y,e.x+e.width,e.y+e.height]}e.exports=u,u.prototype.render=function(){for(var t,e=this,r=[],n=arguments.length;n--;)r[n]=arguments[n];return r.length&&(t=this).update.apply(t,r),this.regl.attributes.preserveDrawingBuffer?this.draw():(this.dirty?null==this.planned&&(this.planned=o((function(){e.draw(),e.dirty=!0,e.planned=null}))):(this.draw(),this.dirty=!0,o((function(){e.dirty=!1}))),this)},u.prototype.update=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];if(e.length){for(var n=0;n<e.length;n++)this.updateItem(n,e[n]);this.traces=this.traces.filter(Boolean);for(var i=[],a=0,o=0;o<this.traces.length;o++){for(var s=this.traces[o],l=this.traces[o].passes,c=0;c<l.length;c++)i.push(this.passes[l[c]]);s.passOffset=a,a+=s.passes.length}return(t=this.scatter).update.apply(t,i),this}},u.prototype.updateItem=function(t,e){var r=this.regl;if(null===e)return this.traces[t]=null,this;if(!e)return this;var n,o=i(e,{data:"data items columns rows values dimensions samples x",snap:"snap cluster",size:"sizes size radius",color:"colors color fill fill-color fillColor",opacity:"opacity alpha transparency opaque",borderSize:"borderSizes borderSize border-size bordersize borderWidth borderWidths border-width borderwidth stroke-width strokeWidth strokewidth outline",borderColor:"borderColors borderColor bordercolor stroke stroke-color strokeColor",marker:"markers marker shape",range:"range ranges databox dataBox",viewport:"viewport viewBox viewbox",domain:"domain domains area areas",padding:"pad padding paddings pads margin margins",transpose:"transpose transposed",diagonal:"diagonal diag showDiagonal",upper:"upper 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this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),!1},formatYear:function(t){t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year();var e=Math.floor(t/400);return t%=400,t+=t<0?400:0,e+"."+Math.floor(t/20)+"."+t%20},forYear:function(t){if((t=t.split(".")).length<3)throw"Invalid Mayan year";for(var e=0,r=0;r<t.length;r++){var n=parseInt(t[r],10);if(Math.abs(n)>19||r>0&&n<0)throw"Invalid Mayan year";e=20*e+n}return e},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),18},weekOfYear:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate),0},daysInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),360},daysInMonth:function(t,e){return this._validate(t,e,this.minDay,n.local.invalidMonth),20},daysInWeek:function(){return 5},dayOfWeek:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate).day()},weekDay:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate),!0},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate).toJD(),a=this._toHaab(i),o=this._toTzolkin(i);return{haabMonthName:this.local.haabMonths[a[0]-1],haabMonth:a[0],haabDay:a[1],tzolkinDayName:this.local.tzolkinMonths[o[0]-1],tzolkinDay:o[0],tzolkinTrecena:o[1]}},_toHaab:function(t){var e=o((t-=this.jdEpoch)+8+340,365);return[Math.floor(e/20)+1,o(e,20)]},_toTzolkin:function(t){return[s((t-=this.jdEpoch)+20,20),s(t+4,13)]},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return i.day()+20*i.month()+360*i.year()+this.jdEpoch},fromJD:function(t){t=Math.floor(t)+.5-this.jdEpoch;var e=Math.floor(t/360);t%=360,t+=t<0?360:0;var r=Math.floor(t/20),n=t%20;return this.newDate(e,r,n)}}),n.calendars.mayan=a},{"../main":346,"object-assign":247}],340:[function(t,e,r){var n=t("../main"),i=t("object-assign");function a(t){this.local=this.regionalOptions[t||""]||this.regionalOptions[""]}a.prototype=new n.baseCalendar;var o=n.instance("gregorian");i(a.prototype,{name:"Nanakshahi",jdEpoch:2257673.5,daysPerMonth:[31,31,31,31,31,30,30,30,30,30,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{"":{name:"Nanakshahi",epochs:["BN","AN"],monthNames:["Chet","Vaisakh","Jeth","Harh","Sawan","Bhadon","Assu","Katak","Maghar","Poh","Magh","Phagun"],monthNamesShort:["Che","Vai","Jet","Har","Saw","Bha","Ass","Kat","Mgr","Poh","Mgh","Pha"],dayNames:["Somvaar","Mangalvar","Budhvaar","Veervaar","Shukarvaar","Sanicharvaar","Etvaar"],dayNamesShort:["Som","Mangal","Budh","Veer","Shukar","Sanichar","Et"],dayNamesMin:["So","Ma","Bu","Ve","Sh","Sa","Et"],digits:null,dateFormat:"dd-mm-yyyy",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear||n.regionalOptions[""].invalidYear);return o.leapYear(e.year()+(e.year()<1?1:0)+1469)},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(1-(n.dayOfWeek()||7),"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidMonth);(t=i.year())<0&&t++;for(var a=i.day(),s=1;s<i.month();s++)a+=this.daysPerMonth[s-1];return a+o.toJD(t+1468,3,13)},fromJD:function(t){t=Math.floor(t+.5);for(var e=Math.floor((t-(this.jdEpoch-1))/366);t>=this.toJD(e+1,1,1);)e++;for(var r=t-Math.floor(this.toJD(e,1,1)+.5)+1,n=1;r>this.daysInMonth(e,n);)r-=this.daysInMonth(e,n),n++;return this.newDate(e,n,r)}}),n.calendars.nanakshahi=a},{"../main":346,"object-assign":247}],341:[function(t,e,r){var n=t("../main"),i=t("object-assign");function a(t){this.local=this.regionalOptions[t||""]||this.regionalOptions[""]}a.prototype=new n.baseCalendar,i(a.prototype,{name:"Nepali",jdEpoch:1700709.5,daysPerMonth:[31,31,32,32,31,30,30,29,30,29,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,daysPerYear:365,regionalOptions:{"":{name:"Nepali",epochs:["BBS","ABS"],monthNames:["Baisakh","Jestha","Ashadh","Shrawan","Bhadra","Ashwin","Kartik","Mangsir","Paush","Mangh","Falgun","Chaitra"],monthNamesShort:["Bai","Je","As","Shra","Bha","Ash","Kar","Mang","Pau","Ma","Fal","Chai"],dayNames:["Aaitabaar","Sombaar","Manglbaar","Budhabaar","Bihibaar","Shukrabaar","Shanibaar"],dayNamesShort:["Aaita","Som","Mangl","Budha","Bihi","Shukra","Shani"],dayNamesMin:["Aai","So","Man","Bu","Bi","Shu","Sha"],digits:null,dateFormat:"dd/mm/yyyy",firstDay:1,isRTL:!1}},leapYear:function(t){return this.daysInYear(t)!==this.daysPerYear},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){if(t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year(),void 0===this.NEPALI_CALENDAR_DATA[t])return this.daysPerYear;for(var e=0,r=this.minMonth;r<=12;r++)e+=this.NEPALI_CALENDAR_DATA[t][r];return e},daysInMonth:function(t,e){return t.year&&(e=t.month(),t=t.year()),this._validate(t,e,this.minDay,n.local.invalidMonth),void 0===this.NEPALI_CALENDAR_DATA[t]?this.daysPerMonth[e-1]:this.NEPALI_CALENDAR_DATA[t][e]},weekDay:function(t,e,r){return 6!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=n.instance(),o=0,s=e,l=t;this._createMissingCalendarData(t);var c=t-(s>9||9===s&&r>=this.NEPALI_CALENDAR_DATA[l][0]?56:57);for(9!==e&&(o=r,s--);9!==s;)s<=0&&(s=12,l--),o+=this.NEPALI_CALENDAR_DATA[l][s],s--;return 9===e?(o+=r-this.NEPALI_CALENDAR_DATA[l][0])<0&&(o+=a.daysInYear(c)):o+=this.NEPALI_CALENDAR_DATA[l][9]-this.NEPALI_CALENDAR_DATA[l][0],a.newDate(c,1,1).add(o,"d").toJD()},fromJD:function(t){var e=n.instance().fromJD(t),r=e.year(),i=e.dayOfYear(),a=r+56;this._createMissingCalendarData(a);for(var o=9,s=this.NEPALI_CALENDAR_DATA[a][0],l=this.NEPALI_CALENDAR_DATA[a][o]-s+1;i>l;)++o>12&&(o=1,a++),l+=this.NEPALI_CALENDAR_DATA[a][o];var c=this.NEPALI_CALENDAR_DATA[a][o]-(l-i);return this.newDate(a,o,c)},_createMissingCalendarData:function(t){var e=this.daysPerMonth.slice(0);e.unshift(17);for(var r=t-1;r<t+2;r++)void 0===this.NEPALI_CALENDAR_DATA[r]&&(this.NEPALI_CALENDAR_DATA[r]=e)},NEPALI_CALENDAR_DATA:{1970:[18,31,31,32,31,31,31,30,29,30,29,30,30],1971:[18,31,31,32,31,32,30,30,29,30,29,30,30],1972:[17,31,32,31,32,31,30,30,30,29,29,30,30],1973:[19,30,32,31,32,31,30,30,30,29,30,29,31],1974:[19,31,31,32,30,31,31,30,29,30,29,30,30],1975:[18,31,31,32,32,30,31,30,29,30,29,30,30],1976:[17,31,32,31,32,31,30,30,30,29,29,30,31],1977:[18,31,32,31,32,31,31,29,30,29,30,29,31],1978:[18,31,31,32,31,31,31,30,29,30,29,30,30],1979:[18,31,31,32,32,31,30,30,29,30,29,30,30],1980:[17,31,32,31,32,31,30,30,30,29,29,30,31],1981:[18,31,31,31,32,31,31,29,30,30,29,30,30],1982:[18,31,31,32,31,31,31,30,29,30,29,30,30],1983:[18,31,31,32,32,31,30,30,29,30,29,30,30],1984:[17,31,32,31,32,31,30,30,30,29,29,30,31],1985:[18,31,31,31,32,31,31,29,30,30,29,30,30],1986:[18,31,31,32,31,31,31,30,29,30,29,30,30],1987:[18,31,32,31,32,31,30,30,29,30,29,30,30],1988:[17,31,32,31,32,31,30,30,30,29,29,30,31],1989:[18,31,31,31,32,31,31,30,29,30,29,30,30],1990:[18,31,31,32,31,31,31,30,29,30,29,30,30],1991:[18,31,32,31,32,31,30,30,29,30,29,30,30],1992:[17,31,32,31,32,31,30,30,30,29,30,29,31],1993:[18,31,31,31,32,31,31,30,29,30,29,30,30],1994:[18,31,31,32,31,31,31,30,29,30,29,30,30],1995:[17,31,32,31,32,31,30,30,30,29,29,30,30],1996:[17,31,32,31,32,31,30,30,30,29,30,29,31],1997:[18,31,31,32,31,31,31,30,29,30,29,30,30],1998:[18,31,31,32,31,31,31,30,29,30,29,30,30],1999:[17,31,32,31,32,31,30,30,30,29,29,30,31],2e3:[17,30,32,31,32,31,30,30,30,29,30,29,31],2001:[18,31,31,32,31,31,31,30,29,30,29,30,30],2002:[18,31,31,32,32,31,30,30,29,30,29,30,30],2003:[17,31,32,31,32,31,30,30,30,29,29,30,31],2004:[17,30,32,31,32,31,30,30,30,29,30,29,31],2005:[18,31,31,32,31,31,31,30,29,30,29,30,30],2006:[18,31,31,32,32,31,30,30,29,30,29,30,30],2007:[17,31,32,31,32,31,30,30,30,29,29,30,31],2008:[17,31,31,31,32,31,31,29,30,30,29,29,31],2009:[18,31,31,32,31,31,31,30,29,30,29,30,30],2010:[18,31,31,32,32,31,30,30,29,30,29,30,30],2011:[17,31,32,31,32,31,30,30,30,29,29,30,31],2012:[17,31,31,31,32,31,31,29,30,30,29,30,30],2013:[18,31,31,32,31,31,31,30,29,30,29,30,30],2014:[18,31,31,32,32,31,30,30,29,30,29,30,30],2015:[17,31,32,31,32,31,30,30,30,29,29,30,31],2016:[17,31,31,31,32,31,31,29,30,30,29,30,30],2017:[18,31,31,32,31,31,31,30,29,30,29,30,30],2018:[18,31,32,31,32,31,30,30,29,30,29,30,30],2019:[17,31,32,31,32,31,30,30,30,29,30,29,31],2020:[17,31,31,31,32,31,31,30,29,30,29,30,30],2021:[18,31,31,32,31,31,31,30,29,30,29,30,30],2022:[17,31,32,31,32,31,30,30,30,29,29,30,30],2023:[17,31,32,31,32,31,30,30,30,29,30,29,31],2024:[17,31,31,31,32,31,31,30,29,30,29,30,30],2025:[18,31,31,32,31,31,31,30,29,30,29,30,30],2026:[17,31,32,31,32,31,30,30,30,29,29,30,31],2027:[17,30,32,31,32,31,30,30,30,29,30,29,31],2028:[17,31,31,32,31,31,31,30,29,30,29,30,30],2029:[18,31,31,32,31,32,30,30,29,30,29,30,30],2030:[17,31,32,31,32,31,30,30,30,30,30,30,31],2031:[17,31,32,31,32,31,31,31,31,31,31,31,31],2032:[17,32,32,32,32,32,32,32,32,32,32,32,32],2033:[18,31,31,32,32,31,30,30,29,30,29,30,30],2034:[17,31,32,31,32,31,30,30,30,29,29,30,31],2035:[17,30,32,31,32,31,31,29,30,30,29,29,31],2036:[17,31,31,32,31,31,31,30,29,30,29,30,30],2037:[18,31,31,32,32,31,30,30,29,30,29,30,30],2038:[17,31,32,31,32,31,30,30,30,29,29,30,31],2039:[17,31,31,31,32,31,31,29,30,30,29,30,30],2040:[17,31,31,32,31,31,31,30,29,30,29,30,30],2041:[18,31,31,32,32,31,30,30,29,30,29,30,30],2042:[17,31,32,31,32,31,30,30,30,29,29,30,31],2043:[17,31,31,31,32,31,31,29,30,30,29,30,30],2044:[17,31,31,32,31,31,31,30,29,30,29,30,30],2045:[18,31,32,31,32,31,30,30,29,30,29,30,30],2046:[17,31,32,31,32,31,30,30,30,29,29,30,31],2047:[17,31,31,31,32,31,31,30,29,30,29,30,30],2048:[17,31,31,32,31,31,31,30,29,30,29,30,30],2049:[17,31,32,31,32,31,30,30,30,29,29,30,30],2050:[17,31,32,31,32,31,30,30,30,29,30,29,31],2051:[17,31,31,31,32,31,31,30,29,30,29,30,30],2052:[17,31,31,32,31,31,31,30,29,30,29,30,30],2053:[17,31,32,31,32,31,30,30,30,29,29,30,30],2054:[17,31,32,31,32,31,30,30,30,29,30,29,31],2055:[17,31,31,32,31,31,31,30,29,30,30,29,30],2056:[17,31,31,32,31,32,30,30,29,30,29,30,30],2057:[17,31,32,31,32,31,30,30,30,29,29,30,31],2058:[17,30,32,31,32,31,30,30,30,29,30,29,31],2059:[17,31,31,32,31,31,31,30,29,30,29,30,30],2060:[17,31,31,32,32,31,30,30,29,30,29,30,30],2061:[17,31,32,31,32,31,30,30,30,29,29,30,31],2062:[17,30,32,31,32,31,31,29,30,29,30,29,31],2063:[17,31,31,32,31,31,31,30,29,30,29,30,30],2064:[17,31,31,32,32,31,30,30,29,30,29,30,30],2065:[17,31,32,31,32,31,30,30,30,29,29,30,31],2066:[17,31,31,31,32,31,31,29,30,30,29,29,31],2067:[17,31,31,32,31,31,31,30,29,30,29,30,30],2068:[17,31,31,32,32,31,30,30,29,30,29,30,30],2069:[17,31,32,31,32,31,30,30,30,29,29,30,31],2070:[17,31,31,31,32,31,31,29,30,30,29,30,30],2071:[17,31,31,32,31,31,31,30,29,30,29,30,30],2072:[17,31,32,31,32,31,30,30,29,30,29,30,30],2073:[17,31,32,31,32,31,30,30,30,29,29,30,31],2074:[17,31,31,31,32,31,31,30,29,30,29,30,30],2075:[17,31,31,32,31,31,31,30,29,30,29,30,30],2076:[16,31,32,31,32,31,30,30,30,29,29,30,30],2077:[17,31,32,31,32,31,30,30,30,29,30,29,31],2078:[17,31,31,31,32,31,31,30,29,30,29,30,30],2079:[17,31,31,32,31,31,31,30,29,30,29,30,30],2080:[16,31,32,31,32,31,30,30,30,29,29,30,30],2081:[17,31,31,32,32,31,30,30,30,29,30,30,30],2082:[17,31,32,31,32,31,30,30,30,29,30,30,30],2083:[17,31,31,32,31,31,30,30,30,29,30,30,30],2084:[17,31,31,32,31,31,30,30,30,29,30,30,30],2085:[17,31,32,31,32,31,31,30,30,29,30,30,30],2086:[17,31,32,31,32,31,30,30,30,29,30,30,30],2087:[16,31,31,32,31,31,31,30,30,29,30,30,30],2088:[16,30,31,32,32,30,31,30,30,29,30,30,30],2089:[17,31,32,31,32,31,30,30,30,29,30,30,30],2090:[17,31,32,31,32,31,30,30,30,29,30,30,30],2091:[16,31,31,32,31,31,31,30,30,29,30,30,30],2092:[16,31,31,32,32,31,30,30,30,29,30,30,30],2093:[17,31,32,31,32,31,30,30,30,29,30,30,30],2094:[17,31,31,32,31,31,30,30,30,29,30,30,30],2095:[17,31,31,32,31,31,31,30,29,30,30,30,30],2096:[17,30,31,32,32,31,30,30,29,30,29,30,30],2097:[17,31,32,31,32,31,30,30,30,29,30,30,30],2098:[17,31,31,32,31,31,31,29,30,29,30,30,31],2099:[17,31,31,32,31,31,31,30,29,29,30,30,30],2100:[17,31,32,31,32,30,31,30,29,30,29,30,30]}}),n.calendars.nepali=a},{"../main":346,"object-assign":247}],342:[function(t,e,r){var n=t("../main"),i=t("object-assign");function a(t){this.local=this.regionalOptions[t||""]||this.regionalOptions[""]}function o(t,e){return t-e*Math.floor(t/e)}a.prototype=new n.baseCalendar,i(a.prototype,{name:"Persian",jdEpoch:1948320.5,daysPerMonth:[31,31,31,31,31,31,30,30,30,30,30,29],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{"":{name:"Persian",epochs:["BP","AP"],monthNames:["Farvardin","Ordibehesht","Khordad","Tir","Mordad","Shahrivar","Mehr","Aban","Azar","Day","Bahman","Esfand"],monthNamesShort:["Far","Ord","Kho","Tir","Mor","Sha","Meh","Aba","Aza","Day","Bah","Esf"],dayNames:["Yekshambe","Doshambe","Seshambe","Ch\xe6harshambe","Panjshambe","Jom'e","Shambe"],dayNamesShort:["Yek","Do","Se","Ch\xe6","Panj","Jom","Sha"],dayNamesMin:["Ye","Do","Se","Ch","Pa","Jo","Sh"],digits:null,dateFormat:"yyyy/mm/dd",firstDay:6,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return 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n=t("object-assign");function i(){this.regionalOptions=[],this.regionalOptions[""]={invalidCalendar:"Calendar {0} not found",invalidDate:"Invalid {0} date",invalidMonth:"Invalid {0} month",invalidYear:"Invalid {0} year",differentCalendars:"Cannot mix {0} and {1} dates"},this.local=this.regionalOptions[""],this.calendars={},this._localCals={}}function a(t,e,r,n){if(this._calendar=t,this._year=e,this._month=r,this._day=n,0===this._calendar._validateLevel&&!this._calendar.isValid(this._year,this._month,this._day))throw(c.local.invalidDate||c.regionalOptions[""].invalidDate).replace(/\{0\}/,this._calendar.local.name)}function o(t,e){return"000000".substring(0,e-(t=""+t).length)+t}function s(){this.shortYearCutoff="+10"}function l(t){this.local=this.regionalOptions[t]||this.regionalOptions[""]}n(i.prototype,{instance:function(t,e){t=(t||"gregorian").toLowerCase(),e=e||"";var r=this._localCals[t+"-"+e];if(!r&&this.calendars[t]&&(r=new this.calendars[t](e),this._localCals[t+"-"+e]=r),!r)throw(this.local.invalidCalendar||this.regionalOptions[""].invalidCalendar).replace(/\{0\}/,t);return r},newDate:function(t,e,r,n,i){return(n=(null!=t&&t.year?t.calendar():"string"==typeof n?this.instance(n,i):n)||this.instance()).newDate(t,e,r)},substituteDigits:function(t){return function(e){return(e+"").replace(/[0-9]/g,(function(e){return t[e]}))}},substituteChineseDigits:function(t,e){return function(r){for(var n="",i=0;r>0;){var a=r%10;n=(0===a?"":t[a]+e[i])+n,i++,r=Math.floor(r/10)}return 0===n.indexOf(t[1]+e[1])&&(n=n.substr(1)),n||t[0]}}}),n(a.prototype,{newDate:function(t,e,r){return this._calendar.newDate(null==t?this:t,e,r)},year:function(t){return 0===arguments.length?this._year:this.set(t,"y")},month:function(t){return 0===arguments.length?this._month:this.set(t,"m")},day:function(t){return 0===arguments.length?this._day:this.set(t,"d")},date:function(t,e,r){if(!this._calendar.isValid(t,e,r))throw(c.local.invalidDate||c.regionalOptions[""].invalidDate).replace(/\{0\}/,this._calendar.local.name);return this._year=t,this._month=e,this._day=r,this},leapYear:function(){return this._calendar.leapYear(this)},epoch:function(){return this._calendar.epoch(this)},formatYear:function(){return this._calendar.formatYear(this)},monthOfYear:function(){return this._calendar.monthOfYear(this)},weekOfYear:function(){return this._calendar.weekOfYear(this)},daysInYear:function(){return this._calendar.daysInYear(this)},dayOfYear:function(){return this._calendar.dayOfYear(this)},daysInMonth:function(){return this._calendar.daysInMonth(this)},dayOfWeek:function(){return this._calendar.dayOfWeek(this)},weekDay:function(){return this._calendar.weekDay(this)},extraInfo:function(){return this._calendar.extraInfo(this)},add:function(t,e){return this._calendar.add(this,t,e)},set:function(t,e){return this._calendar.set(this,t,e)},compareTo:function(t){if(this._calendar.name!==t._calendar.name)throw(c.local.differentCalendars||c.regionalOptions[""].differentCalendars).replace(/\{0\}/,this._calendar.local.name).replace(/\{1\}/,t._calendar.local.name);var e=this._year!==t._year?this._year-t._year:this._month!==t._month?this.monthOfYear()-t.monthOfYear():this._day-t._day;return 0===e?0:e<0?-1:1},calendar:function(){return this._calendar},toJD:function(){return this._calendar.toJD(this)},fromJD:function(t){return this._calendar.fromJD(t)},toJSDate:function(){return this._calendar.toJSDate(this)},fromJSDate:function(t){return this._calendar.fromJSDate(t)},toString:function(){return(this.year()<0?"-":"")+o(Math.abs(this.year()),4)+"-"+o(this.month(),2)+"-"+o(this.day(),2)}}),n(s.prototype,{_validateLevel:0,newDate:function(t,e,r){return null==t?this.today():(t.year&&(this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[""].invalidDate),r=t.day(),e=t.month(),t=t.year()),new a(this,t,e,r))},today:function(){return this.fromJSDate(new Date)},epoch:function(t){return this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[""].invalidYear).year()<0?this.local.epochs[0]:this.local.epochs[1]},formatYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[""].invalidYear);return(e.year()<0?"-":"")+o(Math.abs(e.year()),4)},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[""].invalidYear),12},monthOfYear:function(t,e){var r=this._validate(t,e,this.minDay,c.local.invalidMonth||c.regionalOptions[""].invalidMonth);return(r.month()+this.monthsInYear(r)-this.firstMonth)%this.monthsInYear(r)+this.minMonth},fromMonthOfYear:function(t,e){var r=(e+this.firstMonth-2*this.minMonth)%this.monthsInYear(t)+this.minMonth;return this._validate(t,r,this.minDay,c.local.invalidMonth||c.regionalOptions[""].invalidMonth),r},daysInYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[""].invalidYear);return this.leapYear(e)?366:365},dayOfYear:function(t,e,r){var n=this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[""].invalidDate);return n.toJD()-this.newDate(n.year(),this.fromMonthOfYear(n.year(),this.minMonth),this.minDay).toJD()+1},daysInWeek:function(){return 7},dayOfWeek:function(t,e,r){var n=this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[""].invalidDate);return(Math.floor(this.toJD(n))+2)%this.daysInWeek()},extraInfo:function(t,e,r){return this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[""].invalidDate),{}},add:function(t,e,r){return this._validate(t,this.minMonth,this.minDay,c.local.invalidDate||c.regionalOptions[""].invalidDate),this._correctAdd(t,this._add(t,e,r),e,r)},_add:function(t,e,r){if(this._validateLevel++,"d"===r||"w"===r){var n=t.toJD()+e*("w"===r?this.daysInWeek():1),i=t.calendar().fromJD(n);return this._validateLevel--,[i.year(),i.month(),i.day()]}try{var a=t.year()+("y"===r?e:0),o=t.monthOfYear()+("m"===r?e:0);i=t.day();"y"===r?(t.month()!==this.fromMonthOfYear(a,o)&&(o=this.newDate(a,t.month(),this.minDay).monthOfYear()),o=Math.min(o,this.monthsInYear(a)),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o)))):"m"===r&&(!function(t){for(;o<t.minMonth;)a--,o+=t.monthsInYear(a);for(var e=t.monthsInYear(a);o>e-1+t.minMonth;)a++,o-=e,e=t.monthsInYear(a)}(this),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o))));var s=[a,this.fromMonthOfYear(a,o),i];return this._validateLevel--,s}catch(t){throw 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":r._dfltTitle.annotation),a("textangle"),n.coerceFont(a,"font",r.font),a("width"),a("align"),a("height")&&a("valign"),u){var f,h,p=a("arrowside");-1!==p.indexOf("end")&&(f=a("arrowhead"),h=a("arrowsize")),-1!==p.indexOf("start")&&(a("startarrowhead",f),a("startarrowsize",h)),a("arrowcolor",l?e.bordercolor:i.defaultLine),a("arrowwidth",2*(l&&c||1)),a("standoff"),a("startstandoff")}var d=a("hovertext"),g=r.hoverlabel||{};if(d){var m=a("hoverlabel.bgcolor",g.bgcolor||(i.opacity(o)?i.rgb(o):i.defaultLine)),v=a("hoverlabel.bordercolor",g.bordercolor||i.contrast(m));n.coerceFont(a,"hoverlabel.font",{family:g.font.family,size:g.font.size,color:g.font.color||v})}a("captureevents",!!d)}},{"../../lib":503,"../color":366}],353:[function(t,e,r){"use strict";var n=t("fast-isnumeric"),i=t("../../lib/to_log_range");e.exports=function(t,e,r,a){e=e||{};var o="log"===r&&"linear"===e.type,s="linear"===r&&"log"===e.type;if(o||s)for(var l,c,u=t._fullLayout.annotations,f=e._id.charAt(0),h=0;h<u.length;h++)l=u[h],c="annotations["+h+"].",l[f+"ref"]===e._id&&p(f),l["a"+f+"ref"]===e._id&&p("a"+f);function p(t){var r=l[t],s=null;s=o?i(r,e.range):Math.pow(10,r),n(s)||(s=null),a(c+t,s)}}},{"../../lib/to_log_range":531,"fast-isnumeric":190}],354:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../../plots/cartesian/axes"),a=t("../../plots/array_container_defaults"),o=t("./common_defaults"),s=t("./attributes");function l(t,e,r){function a(r,i){return n.coerce(t,e,s,r,i)}var l=a("visible"),c=a("clicktoshow");if(l||c){o(t,e,r,a);for(var u=e.showarrow,f=["x","y"],h=[-10,-30],p={_fullLayout:r},d=0;d<2;d++){var g=f[d],m=i.coerceRef(t,e,p,g,"","paper");if("paper"!==m)i.getFromId(p,m)._annIndices.push(e._index);if(i.coercePosition(e,p,a,m,g,.5),u){var v="a"+g,y=i.coerceRef(t,e,p,v,"pixel",["pixel","paper"]);"pixel"!==y&&y!==m&&(y=e[v]="pixel");var x="pixel"===y?h[d]:.4;i.coercePosition(e,p,a,y,v,x)}a(g+"anchor"),a(g+"shift")}if(n.noneOrAll(t,e,["x","y"]),u&&n.noneOrAll(t,e,["ax","ay"]),c){var b=a("xclick"),_=a("yclick");e._xclick=void 0===b?e.x:i.cleanPosition(b,p,e.xref),e._yclick=void 0===_?e.y:i.cleanPosition(_,p,e.yref)}}}e.exports=function(t,e){a(t,e,{name:"annotations",handleItemDefaults:l})}},{"../../lib":503,"../../plots/array_container_defaults":549,"../../plots/cartesian/axes":554,"./attributes":349,"./common_defaults":352}],355:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("../../registry"),a=t("../../plots/plots"),o=t("../../lib"),s=o.strTranslate,l=t("../../plots/cartesian/axes"),c=t("../color"),u=t("../drawing"),f=t("../fx"),h=t("../../lib/svg_text_utils"),p=t("../../lib/setcursor"),d=t("../dragelement"),g=t("../../plot_api/plot_template").arrayEditor,m=t("./draw_arrow_head");function v(t,e){var r=t._fullLayout.annotations[e]||{},n=l.getFromId(t,r.xref),i=l.getFromId(t,r.yref);n&&n.setScale(),i&&i.setScale(),x(t,r,e,!1,n,i)}function y(t,e,r,n,i){var a=i[r],o=i[r+"ref"],s=-1!==r.indexOf("y"),c="domain"===l.getRefType(o),u=s?n.h:n.w;return t?c?a+(s?-e:e)/t._length:t.p2r(t.r2p(a)+e):a+(s?-e:e)/u}function x(t,e,r,a,v,x){var b,_,w=t._fullLayout,T=t._fullLayout._size,k=t._context.edits;a?(b="annotation-"+a,_=a+".annotations"):(b="annotation",_="annotations");var A=g(t.layout,_,e),M=A.modifyBase,S=A.modifyItem,E=A.getUpdateObj;w._infolayer.selectAll("."+b+'[data-index="'+r+'"]').remove();var L="clip"+w._uid+"_ann"+r;if(e._input&&!1!==e.visible){var C={x:{},y:{}},P=+e.textangle||0,I=w._infolayer.append("g").classed(b,!0).attr("data-index",String(r)).style("opacity",e.opacity),O=I.append("g").classed("annotation-text-g",!0),z=k[e.showarrow?"annotationTail":"annotationPosition"],D=e.captureevents||k.annotationText||z,R=O.append("g").style("pointer-events",D?"all":null).call(p,"pointer").on("click",(function(){t._dragging=!1,t.emit("plotly_clickannotation",Y(n.event))}));e.hovertext&&R.on("mouseover",(function(){var r=e.hoverlabel,n=r.font,i=this.getBoundingClientRect(),a=t.getBoundingClientRect();f.loneHover({x0:i.left-a.left,x1:i.right-a.left,y:(i.top+i.bottom)/2-a.top,text:e.hovertext,color:r.bgcolor,borderColor:r.bordercolor,fontFamily:n.family,fontSize:n.size,fontColor:n.color},{container:w._hoverlayer.node(),outerContainer:w._paper.node(),gd:t})})).on("mouseout",(function(){f.loneUnhover(w._hoverlayer.node())}));var F=e.borderwidth,B=e.borderpad,N=F+B,j=R.append("rect").attr("class","bg").style("stroke-width",F+"px").call(c.stroke,e.bordercolor).call(c.fill,e.bgcolor),U=e.width||e.height,V=w._topclips.selectAll("#"+L).data(U?[0]:[]);V.enter().append("clipPath").classed("annclip",!0).attr("id",L).append("rect"),V.exit().remove();var H=e.font,q=w._meta?o.templateString(e.text,w._meta):e.text,G=R.append("text").classed("annotation-text",!0).text(q);k.annotationText?G.call(h.makeEditable,{delegate:R,gd:t}).call(W).on("edit",(function(r){e.text=r,this.call(W),S("text",r),v&&v.autorange&&M(v._name+".autorange",!0),x&&x.autorange&&M(x._name+".autorange",!0),i.call("_guiRelayout",t,E())})):G.call(W)}else n.selectAll("#"+L).remove();function Y(t){var n={index:r,annotation:e._input,fullAnnotation:e,event:t};return a&&(n.subplotId=a),n}function W(r){return r.call(u.font,H).attr({"text-anchor":{left:"start",right:"end"}[e.align]||"middle"}),h.convertToTspans(r,t,X),r}function X(){var r=G.selectAll("a");1===r.size()&&r.text()===G.text()&&R.insert("a",":first-child").attr({"xlink:xlink:href":r.attr("xlink:href"),"xlink:xlink:show":r.attr("xlink:show")}).style({cursor:"pointer"}).node().appendChild(j.node());var n=R.select(".annotation-text-math-group"),f=!n.empty(),g=u.bBox((f?n:G).node()),b=g.width,_=g.height,A=e.width||b,D=e.height||_,B=Math.round(A+2*N),H=Math.round(D+2*N);function q(t,e){return"auto"===e&&(e=t<1/3?"left":t>2/3?"right":"center"),{center:0,middle:0,left:.5,bottom:-.5,right:-.5,top:.5}[e]}for(var W=!1,X=["x","y"],Z=0;Z<X.length;Z++){var J,K,Q,$,tt,et=X[Z],rt=e[et+"ref"]||et,nt=e["a"+et+"ref"],it={x:v,y:x}[et],at=(P+("x"===et?0:-90))*Math.PI/180,ot=B*Math.cos(at),st=H*Math.sin(at),lt=Math.abs(ot)+Math.abs(st),ct=e[et+"anchor"],ut=e[et+"shift"]*("x"===et?1:-1),ft=C[et],ht=l.getRefType(rt);if(it&&"domain"!==ht){var pt=it.r2fraction(e[et]);(pt<0||pt>1)&&(nt===rt?((pt=it.r2fraction(e["a"+et]))<0||pt>1)&&(W=!0):W=!0),J=it._offset+it.r2p(e[et]),$=.5}else{var dt="domain"===ht;"x"===et?(Q=e[et],J=dt?it._offset+it._length*Q:J=T.l+T.w*Q):(Q=1-e[et],J=dt?it._offset+it._length*Q:J=T.t+T.h*Q),$=e.showarrow?.5:Q}if(e.showarrow){ft.head=J;var gt=e["a"+et];if(tt=ot*q(.5,e.xanchor)-st*q(.5,e.yanchor),nt===rt){var mt=l.getRefType(nt);"domain"===mt?("y"===et&&(gt=1-gt),ft.tail=it._offset+it._length*gt):"paper"===mt?"y"===et?(gt=1-gt,ft.tail=T.t+T.h*gt):ft.tail=T.l+T.w*gt:ft.tail=it._offset+it.r2p(gt),K=tt}else ft.tail=J+gt,K=tt+gt;ft.text=ft.tail+tt;var vt=w["x"===et?"width":"height"];if("paper"===rt&&(ft.head=o.constrain(ft.head,1,vt-1)),"pixel"===nt){var yt=-Math.max(ft.tail-3,ft.text),xt=Math.min(ft.tail+3,ft.text)-vt;yt>0?(ft.tail+=yt,ft.text+=yt):xt>0&&(ft.tail-=xt,ft.text-=xt)}ft.tail+=ut,ft.head+=ut}else K=tt=lt*q($,ct),ft.text=J+tt;ft.text+=ut,tt+=ut,K+=ut,e["_"+et+"padplus"]=lt/2+K,e["_"+et+"padminus"]=lt/2-K,e["_"+et+"size"]=lt,e["_"+et+"shift"]=tt}if(W)R.remove();else{var bt=0,_t=0;if("left"!==e.align&&(bt=(A-b)*("center"===e.align?.5:1)),"top"!==e.valign&&(_t=(D-_)*("middle"===e.valign?.5:1)),f)n.select("svg").attr({x:N+bt-1,y:N+_t}).call(u.setClipUrl,U?L:null,t);else{var wt=N+_t-g.top,Tt=N+bt-g.left;G.call(h.positionText,Tt,wt).call(u.setClipUrl,U?L:null,t)}V.select("rect").call(u.setRect,N,N,A,D),j.call(u.setRect,F/2,F/2,B-F,H-F),R.call(u.setTranslate,Math.round(C.x.text-B/2),Math.round(C.y.text-H/2)),O.attr({transform:"rotate("+P+","+C.x.text+","+C.y.text+")"});var kt,At=function(r,n){I.selectAll(".annotation-arrow-g").remove();var l=C.x.head,f=C.y.head,h=C.x.tail+r,p=C.y.tail+n,g=C.x.text+r,b=C.y.text+n,_=o.rotationXYMatrix(P,g,b),w=o.apply2DTransform(_),A=o.apply2DTransform2(_),L=+j.attr("width"),z=+j.attr("height"),D=g-.5*L,F=D+L,B=b-.5*z,N=B+z,U=[[D,B,D,N],[D,N,F,N],[F,N,F,B],[F,B,D,B]].map(A);if(!U.reduce((function(t,e){return t^!!o.segmentsIntersect(l,f,l+1e6,f+1e6,e[0],e[1],e[2],e[3])}),!1)){U.forEach((function(t){var e=o.segmentsIntersect(h,p,l,f,t[0],t[1],t[2],t[3]);e&&(h=e.x,p=e.y)}));var V=e.arrowwidth,H=e.arrowcolor,q=e.arrowside,G=I.append("g").style({opacity:c.opacity(H)}).classed("annotation-arrow-g",!0),Y=G.append("path").attr("d","M"+h+","+p+"L"+l+","+f).style("stroke-width",V+"px").call(c.stroke,c.rgb(H));if(m(Y,q,e),k.annotationPosition&&Y.node().parentNode&&!a){var W=l,X=f;if(e.standoff){var Z=Math.sqrt(Math.pow(l-h,2)+Math.pow(f-p,2));W+=e.standoff*(h-l)/Z,X+=e.standoff*(p-f)/Z}var J,K,Q=G.append("path").classed("annotation-arrow",!0).classed("anndrag",!0).classed("cursor-move",!0).attr({d:"M3,3H-3V-3H3ZM0,0L"+(h-W)+","+(p-X),transform:s(W,X)}).style("stroke-width",V+6+"px").call(c.stroke,"rgba(0,0,0,0)").call(c.fill,"rgba(0,0,0,0)");d.init({element:Q.node(),gd:t,prepFn:function(){var t=u.getTranslate(R);J=t.x,K=t.y,v&&v.autorange&&M(v._name+".autorange",!0),x&&x.autorange&&M(x._name+".autorange",!0)},moveFn:function(t,r){var n=w(J,K),i=n[0]+t,a=n[1]+r;R.call(u.setTranslate,i,a),S("x",y(v,t,"x",T,e)),S("y",y(x,r,"y",T,e)),e.axref===e.xref&&S("ax",y(v,t,"ax",T,e)),e.ayref===e.yref&&S("ay",y(x,r,"ay",T,e)),G.attr("transform",s(t,r)),O.attr({transform:"rotate("+P+","+i+","+a+")"})},doneFn:function(){i.call("_guiRelayout",t,E());var e=document.querySelector(".js-notes-box-panel");e&&e.redraw(e.selectedObj)}})}}};if(e.showarrow&&At(0,0),z)d.init({element:R.node(),gd:t,prepFn:function(){kt=O.attr("transform")},moveFn:function(t,r){var n="pointer";if(e.showarrow)e.axref===e.xref?S("ax",y(v,t,"ax",T,e)):S("ax",e.ax+t),e.ayref===e.yref?S("ay",y(x,r,"ay",T.w,e)):S("ay",e.ay+r),At(t,r);else{if(a)return;var i,o;if(v)i=y(v,t,"x",T,e);else{var l=e._xsize/T.w,c=e.x+(e._xshift-e.xshift)/T.w-l/2;i=d.align(c+t/T.w,l,0,1,e.xanchor)}if(x)o=y(x,r,"y",T,e);else{var u=e._ysize/T.h,f=e.y-(e._yshift+e.yshift)/T.h-u/2;o=d.align(f-r/T.h,u,0,1,e.yanchor)}S("x",i),S("y",o),v&&x||(n=d.getCursor(v?.5:i,x?.5:o,e.xanchor,e.yanchor))}O.attr({transform:s(t,r)+kt}),p(R,n)},clickFn:function(r,n){e.captureevents&&t.emit("plotly_clickannotation",Y(n))},doneFn:function(){p(R),i.call("_guiRelayout",t,E());var e=document.querySelector(".js-notes-box-panel");e&&e.redraw(e.selectedObj)}})}}}e.exports={draw:function(t){var e=t._fullLayout;e._infolayer.selectAll(".annotation").remove();for(var r=0;r<e.annotations.length;r++)e.annotations[r].visible&&v(t,r);return a.previousPromises(t)},drawOne:v,drawRaw:x}},{"../../lib":503,"../../lib/setcursor":524,"../../lib/svg_text_utils":529,"../../plot_api/plot_template":543,"../../plots/cartesian/axes":554,"../../plots/plots":619,"../../registry":638,"../color":366,"../dragelement":385,"../drawing":388,"../fx":406,"./draw_arrow_head":356,"@plotly/d3":58}],356:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("../color"),a=t("./arrow_paths"),o=t("../../lib"),s=o.strScale,l=o.strRotate,c=o.strTranslate;e.exports=function(t,e,r){var o,u,f,h,p=t.node(),d=a[r.arrowhead||0],g=a[r.startarrowhead||0],m=(r.arrowwidth||1)*(r.arrowsize||1),v=(r.arrowwidth||1)*(r.startarrowsize||1),y=e.indexOf("start")>=0,x=e.indexOf("end")>=0,b=d.backoff*m+r.standoff,_=g.backoff*v+r.startstandoff;if("line"===p.nodeName){o={x:+t.attr("x1"),y:+t.attr("y1")},u={x:+t.attr("x2"),y:+t.attr("y2")};var w=o.x-u.x,T=o.y-u.y;if(h=(f=Math.atan2(T,w))+Math.PI,b&&_&&b+_>Math.sqrt(w*w+T*T))return void z();if(b){if(b*b>w*w+T*T)return void z();var k=b*Math.cos(f),A=b*Math.sin(f);u.x+=k,u.y+=A,t.attr({x2:u.x,y2:u.y})}if(_){if(_*_>w*w+T*T)return void z();var M=_*Math.cos(f),S=_*Math.sin(f);o.x-=M,o.y-=S,t.attr({x1:o.x,y1:o.y})}}else if("path"===p.nodeName){var E=p.getTotalLength(),L="";if(E<b+_)return void z();var C=p.getPointAtLength(0),P=p.getPointAtLength(.1);f=Math.atan2(C.y-P.y,C.x-P.x),o=p.getPointAtLength(Math.min(_,E)),L="0px,"+_+"px,";var I=p.getPointAtLength(E),O=p.getPointAtLength(E-.1);h=Math.atan2(I.y-O.y,I.x-O.x),u=p.getPointAtLength(Math.max(0,E-b)),L+=E-(L?_+b:b)+"px,"+E+"px",t.style("stroke-dasharray",L)}function z(){t.style("stroke-dasharray","0px,100px")}function D(e,a,o,u){e.path&&(e.noRotate&&(o=0),n.select(p.parentNode).append("path").attr({class:t.attr("class"),d:e.path,transform:c(a.x,a.y)+l(180*o/Math.PI)+s(u)}).style({fill:i.rgb(r.arrowcolor),"stroke-width":0}))}y&&D(g,o,f,v),x&&D(d,u,h,m)}},{"../../lib":503,"../color":366,"./arrow_paths":348,"@plotly/d3":58}],357:[function(t,e,r){"use strict";var n=t("./draw"),i=t("./click");e.exports={moduleType:"component",name:"annotations",layoutAttributes:t("./attributes"),supplyLayoutDefaults:t("./defaults"),includeBasePlot:t("../../plots/cartesian/include_components")("annotations"),calcAutorange:t("./calc_autorange"),draw:n.draw,drawOne:n.drawOne,drawRaw:n.drawRaw,hasClickToShow:i.hasClickToShow,onClick:i.onClick,convertCoords:t("./convert_coords")}},{"../../plots/cartesian/include_components":567,"./attributes":349,"./calc_autorange":350,"./click":351,"./convert_coords":353,"./defaults":354,"./draw":355}],358:[function(t,e,r){"use strict";var n=t("../annotations/attributes"),i=t("../../plot_api/edit_types").overrideAll,a=t("../../plot_api/plot_template").templatedArray;e.exports=i(a("annotation",{visible:n.visible,x:{valType:"any"},y:{valType:"any"},z:{valType:"any"},ax:{valType:"number"},ay:{valType:"number"},xanchor:n.xanchor,xshift:n.xshift,yanchor:n.yanchor,yshift:n.yshift,text:n.text,textangle:n.textangle,font:n.font,width:n.width,height:n.height,opacity:n.opacity,align:n.align,valign:n.valign,bgcolor:n.bgcolor,bordercolor:n.bordercolor,borderpad:n.borderpad,borderwidth:n.borderwidth,showarrow:n.showarrow,arrowcolor:n.arrowcolor,arrowhead:n.arrowhead,startarrowhead:n.startarrowhead,arrowside:n.arrowside,arrowsize:n.arrowsize,startarrowsize:n.startarrowsize,arrowwidth:n.arrowwidth,standoff:n.standoff,startstandoff:n.startstandoff,hovertext:n.hovertext,hoverlabel:n.hoverlabel,captureevents:n.captureevents}),"calc","from-root")},{"../../plot_api/edit_types":536,"../../plot_api/plot_template":543,"../annotations/attributes":349}],359:[function(t,e,r){"use 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function(e){return n.coerceHoverinfo({hoverinfo:e},{_module:t._module},r)}}for(var s=0;s<e.length;s++){var l=e[s],c=l[0].trace;if(!i.traceIs(c,"pie-like")){var u=i.traceIs(c,"2dMap")?a:n.fillArray;u(c.hoverinfo,l,"hi",o(c)),c.hovertemplate&&u(c.hovertemplate,l,"ht"),c.hoverlabel&&(u(c.hoverlabel.bgcolor,l,"hbg"),u(c.hoverlabel.bordercolor,l,"hbc"),u(c.hoverlabel.font.size,l,"hts"),u(c.hoverlabel.font.color,l,"htc"),u(c.hoverlabel.font.family,l,"htf"),u(c.hoverlabel.namelength,l,"hnl"),u(c.hoverlabel.align,l,"hta"))}}}},{"../../lib":503,"../../registry":638}],399:[function(t,e,r){"use strict";var n=t("../../registry"),i=t("./hover").hover;e.exports=function(t,e,r){var a=n.getComponentMethod("annotations","onClick")(t,t._hoverdata);function o(){t.emit("plotly_click",{points:t._hoverdata,event:e})}void 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b=y.width/2+S;m<b?(m=b,e.attr("d","M-"+(b-M)+",0L-"+(b-2*M)+","+x+M+"H"+(S+y.width/2)+"v"+x+(2*S+y.height)+"H-"+b+"V"+x+M+"Z")):m>i.width-b?(m=i.width-b,e.attr("d","M"+(b-M)+",0L"+b+","+x+M+"v"+x+(2*S+y.height)+"H-"+b+"V"+x+M+"H"+(b-2*M)+"Z")):e.attr("d","M0,0L"+M+","+x+M+"H"+(S+y.width/2)+"v"+x+(2*S+y.height)+"H-"+(S+y.width/2)+"V"+x+M+"H-"+M+"Z")}else{var _,C,P;"right"===L.side?(_="start",C=1,P="",m=E._offset+E._length):(_="end",C=-1,P="-",m=E._offset),v=L._offset+(A.y0+A.y1)/2,l.attr("text-anchor",_),e.attr("d","M0,0L"+P+M+","+M+"V"+(S+y.height/2)+"h"+P+(2*S+y.width)+"V-"+(S+y.height/2)+"H"+P+M+"V-"+M+"Z");var O,z=y.height/2,R=D-y.top-z,F="clip"+i._uid+"commonlabel"+L._id;if(m<y.width+2*S+M){O="M-"+(M+S)+"-"+z+"h-"+(y.width-S)+"V"+z+"h"+(y.width-S)+"Z";var B=y.width-m+S;u.positionText(l,B,R),"end"===_&&l.selectAll("tspan").each((function(){var t=n.select(this),e=h.tester.append("text").text(t.text()).call(h.font,g),i=V(r,e.node());Math.round(i.width)<Math.round(y.width)&&t.attr("x",B-i.width),e.remove()}))}else u.positionText(l,C*(S+M),R),O=null;var N=i._topclips.selectAll("#"+F).data(O?[0]:[]);N.enter().append("clipPath").attr("id",F).append("path"),N.exit().remove(),N.select("path").attr("d",O),h.setClipUrl(l,O?F:null,r)}t.attr("transform",s(m,v))})),v.isUnifiedHover(a)){d.selectAll("g.hovertext").remove();var G=t.filter((function(t){return"none"!==t.hoverinfo}));if(0===G.length)return;var Y=i.hoverlabel,W=Y.font,X={showlegend:!0,legend:{title:{text:I,font:W},font:W,bgcolor:Y.bgcolor,bordercolor:Y.bordercolor,borderwidth:1,tracegroupgap:7,traceorder:i.legend?i.legend.traceorder:void 0,orientation:"v"}},Z={};x(X,Z,r._fullData);var J=Z.legend;J.entries=[];for(var K=0;K<G.length;K++){var Q=G[K];if("none"!==Q.hoverinfo){var $=O(Q,!0,a,i,I),tt=$[0],et=$[1];Q.name=et,Q.text=""!==et?et+" : "+tt:tt;var 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G,Y,W=(H+M+S+E)*i._invScaleX;if(c)t.pos=P,G=z+j/2+W<=F,Y=z-j/2-W>=0,"top"!==t.idealAlign&&G||!Y?G?(z+=j/2,t.anchor="start"):t.anchor="middle":(z-=j/2,t.anchor="end");else if(t.pos=z,G=P+N/2+W<=R,Y=P-N/2-W>=0,"left"!==t.idealAlign&&G||!Y)if(G)P+=N/2,t.anchor="start";else{t.anchor="middle";var X=W/2,Z=P+X-R,J=P-X;Z>0&&(P-=Z),J<0&&(P+=-J)}else P-=N/2,t.anchor="end";w.attr("text-anchor",t.anchor),E&&A.attr("text-anchor",t.anchor),e.attr("transform",s(P,z)+(c?l(_):""))})),xt}function O(t,e,r,n,i,a){var s="",l="";void 0!==t.nameOverride&&(t.name=t.nameOverride),t.name&&(t.trace._meta&&(t.name=o.templateString(t.name,t.trace._meta)),s=B(t.name,t.nameLength));var c=r.charAt(0),u="x"===c?"y":"x";void 0!==t.zLabel?(void 0!==t.xLabel&&(l+="x: "+t.xLabel+"<br>"),void 0!==t.yLabel&&(l+="y: "+t.yLabel+"<br>"),"choropleth"!==t.trace.type&&"choroplethmapbox"!==t.trace.type&&(l+=(l?"z: ":"")+t.zLabel)):e&&t[c+"Label"]===i?l=t[u+"Label"]||"":void 0===t.xLabel?void 0!==t.yLabel&&"scattercarpet"!==t.trace.type&&(l=t.yLabel):l=void 0===t.yLabel?t.xLabel:"("+t.xLabel+", "+t.yLabel+")",!t.text&&0!==t.text||Array.isArray(t.text)||(l+=(l?"<br>":"")+t.text),void 0!==t.extraText&&(l+=(l?"<br>":"")+t.extraText),a&&""===l&&!t.hovertemplate&&(""===s&&a.remove(),l=s);var f=t.hovertemplate||!1;if(f){var h=t.hovertemplateLabels||t;t[c+"Label"]!==i&&(h[c+"other"]=h[c+"Val"],h[c+"otherLabel"]=h[c+"Label"]),l=(l=o.hovertemplateString(f,h,n._d3locale,t.eventData[0]||{},t.trace._meta)).replace(P,(function(e,r){return s=B(r,t.nameLength),""}))}return[l,s]}function z(t,e,r,i){var a=function(t){return t*r},o=function(t){return t*i};t.each((function(t){var r=n.select(this);if(t.del)return r.remove();var 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c=Array.isArray(r)?function(t,e){var i=o.castOption(a,r,t);return l(i)?i:o.extractOption({},n,"",e)}:function(t,e){return o.extractOption(s,n,t,e)};function u(e,r,n){var i=c(r,n);l(i)&&(t[e]=i)}if(u("hoverinfo","hi","hoverinfo"),u("bgcolor","hbg","hoverlabel.bgcolor"),u("borderColor","hbc","hoverlabel.bordercolor"),u("fontFamily","htf","hoverlabel.font.family"),u("fontSize","hts","hoverlabel.font.size"),u("fontColor","htc","hoverlabel.font.color"),u("nameLength","hnl","hoverlabel.namelength"),u("textAlign","hta","hoverlabel.align"),t.posref="y"===e||"closest"===e&&"h"===n.orientation?t.xa._offset+(t.x0+t.x1)/2:t.ya._offset+(t.y0+t.y1)/2,t.x0=o.constrain(t.x0,0,t.xa._length),t.x1=o.constrain(t.x1,0,t.xa._length),t.y0=o.constrain(t.y0,0,t.ya._length),t.y1=o.constrain(t.y1,0,t.ya._length),void 0!==t.xLabelVal&&(t.xLabel="xLabel"in t?t.xLabel:g.hoverLabelText(t.xa,t.xLabelVal,n.xhoverformat),t.xVal=t.xa.c2d(t.xLabelVal)),void 0!==t.yLabelVal&&(t.yLabel="yLabel"in t?t.yLabel:g.hoverLabelText(t.ya,t.yLabelVal,n.yhoverformat),t.yVal=t.ya.c2d(t.yLabelVal)),void 0!==t.zLabelVal&&void 0===t.zLabel&&(t.zLabel=String(t.zLabelVal)),!(isNaN(t.xerr)||"log"===t.xa.type&&t.xerr<=0)){var f=g.tickText(t.xa,t.xa.c2l(t.xerr),"hover").text;void 0!==t.xerrneg?t.xLabel+=" +"+f+" / -"+g.tickText(t.xa,t.xa.c2l(t.xerrneg),"hover").text:t.xLabel+=" \xb1 "+f,"x"===e&&(t.distance+=1)}if(!(isNaN(t.yerr)||"log"===t.ya.type&&t.yerr<=0)){var h=g.tickText(t.ya,t.ya.c2l(t.yerr),"hover").text;void 0!==t.yerrneg?t.yLabel+=" +"+h+" / -"+g.tickText(t.ya,t.ya.c2l(t.yerrneg),"hover").text:t.yLabel+=" \xb1 "+h,"y"===e&&(t.distance+=1)}var p=t.hoverinfo||t.trace.hoverinfo;return p&&"all"!==p&&(-1===(p=Array.isArray(p)?p:p.split("+")).indexOf("x")&&(t.xLabel=void 0),-1===p.indexOf("y")&&(t.yLabel=void 0),-1===p.indexOf("z")&&(t.zLabel=void 0),-1===p.indexOf("text")&&(t.text=void 0),-1===p.indexOf("name")&&(t.name=void 0)),t}function R(t,e,r){var n,i,o=r.container,s=r.fullLayout,l=s._size,c=r.event,u=!!e.hLinePoint,f=!!e.vLinePoint;if(o.selectAll(".spikeline").remove(),f||u){var d=p.combine(s.plot_bgcolor,s.paper_bgcolor);if(u){var m,v,y=e.hLinePoint;n=y&&y.xa,"cursor"===(i=y&&y.ya).spikesnap?(m=c.pointerX,v=c.pointerY):(m=n._offset+y.x,v=i._offset+y.y);var x,b,_=a.readability(y.color,d)<1.5?p.contrast(d):y.color,w=i.spikemode,T=i.spikethickness,k=i.spikecolor||_,A=g.getPxPosition(t,i);if(-1!==w.indexOf("toaxis")||-1!==w.indexOf("across")){if(-1!==w.indexOf("toaxis")&&(x=A,b=m),-1!==w.indexOf("across")){var M=i._counterDomainMin,S=i._counterDomainMax;"free"===i.anchor&&(M=Math.min(M,i.position),S=Math.max(S,i.position)),x=l.l+M*l.w,b=l.l+S*l.w}o.insert("line",":first-child").attr({x1:x,x2:b,y1:v,y2:v,"stroke-width":T,stroke:k,"stroke-dasharray":h.dashStyle(i.spikedash,T)}).classed("spikeline",!0).classed("crisp",!0),o.insert("line",":first-child").attr({x1:x,x2:b,y1:v,y2:v,"stroke-width":T+2,stroke:d}).classed("spikeline",!0).classed("crisp",!0)}-1!==w.indexOf("marker")&&o.insert("circle",":first-child").attr({cx:A+("right"!==i.side?T:-T),cy:v,r:T,fill:k}).classed("spikeline",!0)}if(f){var E,L,C=e.vLinePoint;n=C&&C.xa,i=C&&C.ya,"cursor"===n.spikesnap?(E=c.pointerX,L=c.pointerY):(E=n._offset+C.x,L=i._offset+C.y);var P,I,O=a.readability(C.color,d)<1.5?p.contrast(d):C.color,z=n.spikemode,D=n.spikethickness,R=n.spikecolor||O,F=g.getPxPosition(t,n);if(-1!==z.indexOf("toaxis")||-1!==z.indexOf("across")){if(-1!==z.indexOf("toaxis")&&(P=F,I=L),-1!==z.indexOf("across")){var B=n._counterDomainMin,N=n._counterDomainMax;"free"===n.anchor&&(B=Math.min(B,n.position),N=Math.max(N,n.position)),P=l.t+(1-N)*l.h,I=l.t+(1-B)*l.h}o.insert("line",":first-child").attr({x1:E,x2:E,y1:P,y2:I,"stroke-width":D,stroke:R,"stroke-dasharray":h.dashStyle(n.spikedash,D)}).classed("spikeline",!0).classed("crisp",!0),o.insert("line",":first-child").attr({x1:E,x2:E,y1:P,y2:I,"stroke-width":D+2,stroke:d}).classed("spikeline",!0).classed("crisp",!0)}-1!==z.indexOf("marker")&&o.insert("circle",":first-child").attr({cx:E,cy:F-("top"!==n.side?D:-D),r:D,fill:R}).classed("spikeline",!0)}}}function F(t,e){return!e||(e.vLinePoint!==t._spikepoints.vLinePoint||e.hLinePoint!==t._spikepoints.hLinePoint)}function B(t,e){return u.plainText(t||"",{len:e,allowedTags:["br","sub","sup","b","i","em"]})}function N(t,e,r){var n=e[t+"a"],i=e[t+"Val"],a=e.cd[0];if("category"===n.type)i=n._categoriesMap[i];else if("date"===n.type){var o=e.trace[t+"periodalignment"];if(o){var 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r=t._fullLayout,n=e.getBoundingClientRect(),i=n.x,a=n.y,s=i+n.width,l=a+n.height,c=o.apply3DTransform(r._invTransform)(i,a),u=o.apply3DTransform(r._invTransform)(s,l),f=c[0],h=c[1],p=u[0],d=u[1];return{x:f,y:h,width:p-f,height:d-h,top:Math.min(h,d),left:Math.min(f,p),right:Math.max(f,p),bottom:Math.max(h,d)}}},{"../../lib":503,"../../lib/events":492,"../../lib/override_cursor":514,"../../lib/svg_text_utils":529,"../../plots/cartesian/axes":554,"../../registry":638,"../color":366,"../dragelement":385,"../drawing":388,"../legend/defaults":418,"../legend/draw":419,"./constants":400,"./helpers":402,"@plotly/d3":58,"fast-isnumeric":190,tinycolor2:312}],404:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../color"),a=t("./helpers").isUnifiedHover;e.exports=function(t,e,r,o){function 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n=t("@plotly/d3"),i=t("../../lib"),a=t("../dragelement"),o=t("./helpers"),s=t("./layout_attributes"),l=t("./hover");e.exports={moduleType:"component",name:"fx",constants:t("./constants"),schema:{layout:s},attributes:t("./attributes"),layoutAttributes:s,supplyLayoutGlobalDefaults:t("./layout_global_defaults"),supplyDefaults:t("./defaults"),supplyLayoutDefaults:t("./layout_defaults"),calc:t("./calc"),getDistanceFunction:o.getDistanceFunction,getClosest:o.getClosest,inbox:o.inbox,quadrature:o.quadrature,appendArrayPointValue:o.appendArrayPointValue,castHoverOption:function(t,e,r){return i.castOption(t,e,"hoverlabel."+r)},castHoverinfo:function(t,e,r){return i.castOption(t,r,"hoverinfo",(function(r){return i.coerceHoverinfo({hoverinfo:r},{_module:t._module},e)}))},hover:l.hover,unhover:a.unhover,loneHover:l.loneHover,loneUnhover:function(t){var 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n=t("../../plots/cartesian/constants"),i=t("../../plot_api/plot_template").templatedArray;t("../../constants/axis_placeable_objects");e.exports=i("image",{visible:{valType:"boolean",dflt:!0,editType:"arraydraw"},source:{valType:"string",editType:"arraydraw"},layer:{valType:"enumerated",values:["below","above"],dflt:"above",editType:"arraydraw"},sizex:{valType:"number",dflt:0,editType:"arraydraw"},sizey:{valType:"number",dflt:0,editType:"arraydraw"},sizing:{valType:"enumerated",values:["fill","contain","stretch"],dflt:"contain",editType:"arraydraw"},opacity:{valType:"number",min:0,max:1,dflt:1,editType:"arraydraw"},x:{valType:"any",dflt:0,editType:"arraydraw"},y:{valType:"any",dflt:0,editType:"arraydraw"},xanchor:{valType:"enumerated",values:["left","center","right"],dflt:"left",editType:"arraydraw"},yanchor:{valType:"enumerated",values:["top","middle","bottom"],dflt:"top",editType:"arraydraw"},xref:{valType:"enumerated",values:["paper",n.idRegex.x.toString()],dflt:"paper",editType:"arraydraw"},yref:{valType:"enumerated",values:["paper",n.idRegex.y.toString()],dflt:"paper",editType:"arraydraw"},editType:"arraydraw"})},{"../../constants/axis_placeable_objects":472,"../../plot_api/plot_template":543,"../../plots/cartesian/constants":561}],412:[function(t,e,r){"use 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l={_fullLayout:r},c=["x","y"],u=0;u<2;u++){var f=c[u],h=i.coerceRef(t,e,l,f,"paper",void 0);if("paper"!==h)i.getFromId(l,h)._imgIndices.push(e._index);i.coercePosition(e,l,a,h,f,0)}return e}e.exports=function(t,e){a(t,e,{name:"images",handleItemDefaults:s})}},{"../../lib":503,"../../plots/array_container_defaults":549,"../../plots/cartesian/axes":554,"./attributes":411}],414:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("../drawing"),a=t("../../plots/cartesian/axes"),o=t("../../plots/cartesian/axis_ids"),s=t("../../constants/xmlns_namespaces");e.exports=function(t){var e,r,l=t._fullLayout,c=[],u={},f=[];for(r=0;r<l.images.length;r++){var h=l.images[r];if(h.visible)if("below"===h.layer&&"paper"!==h.xref&&"paper"!==h.yref){e=o.ref2id(h.xref)+o.ref2id(h.yref);var p=l._plots[e];if(!p){f.push(h);continue}p.mainplot&&(e=p.mainplot.id),u[e]||(u[e]=[]),u[e].push(h)}else"above"===h.layer?c.push(h):f.push(h)}var 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y=l._imageLowerLayer.selectAll("image").data(f),x=l._imageUpperLayer.selectAll("image").data(c);y.enter().append("image"),x.enter().append("image"),y.exit().remove(),x.exit().remove(),y.each((function(t){m.bind(this)(t),v.bind(this)(t)})),x.each((function(t){m.bind(this)(t),v.bind(this)(t)}));var b=Object.keys(l._plots);for(r=0;r<b.length;r++){e=b[r];var _=l._plots[e];if(_.imagelayer){var w=_.imagelayer.selectAll("image").data(u[e]||[]);w.enter().append("image"),w.exit().remove(),w.each((function(t){m.bind(this)(t),v.bind(this)(t)}))}}}},{"../../constants/xmlns_namespaces":480,"../../plots/cartesian/axes":554,"../../plots/cartesian/axis_ids":558,"../drawing":388,"@plotly/d3":58}],415:[function(t,e,r){"use 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n=t("../../plots/font_attributes"),i=t("../color/attributes");e.exports={bgcolor:{valType:"color",editType:"legend"},bordercolor:{valType:"color",dflt:i.defaultLine,editType:"legend"},borderwidth:{valType:"number",min:0,dflt:0,editType:"legend"},font:n({editType:"legend"}),orientation:{valType:"enumerated",values:["v","h"],dflt:"v",editType:"legend"},traceorder:{valType:"flaglist",flags:["reversed","grouped"],extras:["normal"],editType:"legend"},tracegroupgap:{valType:"number",min:0,dflt:10,editType:"legend"},itemsizing:{valType:"enumerated",values:["trace","constant"],dflt:"trace",editType:"legend"},itemwidth:{valType:"number",min:30,dflt:30,editType:"legend"},itemclick:{valType:"enumerated",values:["toggle","toggleothers",!1],dflt:"toggle",editType:"legend"},itemdoubleclick:{valType:"enumerated",values:["toggle","toggleothers",!1],dflt:"toggleothers",editType:"legend"},groupclick:{valType:"enumerated",values:["toggleitem","togglegroup"],dflt:"togglegroup",editType:"legend"},x:{valType:"number",min:-2,max:3,editType:"legend"},xanchor:{valType:"enumerated",values:["auto","left","center","right"],dflt:"left",editType:"legend"},y:{valType:"number",min:-2,max:3,editType:"legend"},yanchor:{valType:"enumerated",values:["auto","top","middle","bottom"],editType:"legend"},uirevision:{valType:"any",editType:"none"},valign:{valType:"enumerated",values:["top","middle","bottom"],dflt:"middle",editType:"legend"},title:{text:{valType:"string",dflt:"",editType:"legend"},font:n({editType:"legend"}),side:{valType:"enumerated",values:["top","left","top 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b=m[a].label;h[x][b]||(g(x,{label:b,color:m[a].color,i:m[a].i,trace:y,pts:m[a].pts}),h[x][b]=!0,d=Math.max(d,(b||"").length))}else g(x,v),d=Math.max(d,(y.name||"").length)}if(!u.length)return[];var _=!f||!s,w=[];for(r=0;r<u.length;r++){var T=c[u[r]];_?w.push(T[0]):w.push(T)}for(_&&(w=[w]),r=0;r<w.length;r++){var k=1/0;for(a=0;a<w[r].length;a++){var A=w[r][a].trace.legendrank;k>A&&(k=A)}w[r][0]._groupMinRank=k,w[r][0]._preGroupSort=r}var M=function(t,e){return t.trace.legendrank-e.trace.legendrank||t._preSort-e._preSort};for(w.forEach((function(t,e){t[0]._preGroupSort=e})),w.sort((function(t,e){return t[0]._groupMinRank-e[0]._groupMinRank||t[0]._preGroupSort-e[0]._preGroupSort})),r=0;r<w.length;r++){w[r].forEach((function(t,e){t._preSort=e})),w[r].sort(M);var S=w[r][0].trace,E=null;for(a=0;a<w[r].length;a++){var L=w[r][a].trace.legendgrouptitle;if(L&&L.text){E=L,o&&(L.font=e._groupTitleFont);break}}if(l&&w[r].reverse(),E){var 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e=(t._context.toImageButtonOptions||{}).format||"png";return c(t,"png"===e?"Download plot as a png":"Download plot")},icon:o.camera,click:function(t){var e=t._context.toImageButtonOptions,r={format:e.format||"png"};l.notifier(c(t,"Taking snapshot - this may take a few seconds"),"long"),"svg"!==r.format&&l.isIE()&&(l.notifier(c(t,"IE only supports svg.  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Select")},attr:"dragmode",val:"select",icon:o.selectbox,click:f},u.lasso2d={name:"lasso2d",_cat:"lasso",title:function(t){return c(t,"Lasso Select")},attr:"dragmode",val:"lasso",icon:o.lasso,click:f},u.drawclosedpath={name:"drawclosedpath",title:function(t){return c(t,"Draw closed freeform")},attr:"dragmode",val:"drawclosedpath",icon:o.drawclosedpath,click:f},u.drawopenpath={name:"drawopenpath",title:function(t){return c(t,"Draw open freeform")},attr:"dragmode",val:"drawopenpath",icon:o.drawopenpath,click:f},u.drawline={name:"drawline",title:function(t){return c(t,"Draw line")},attr:"dragmode",val:"drawline",icon:o.drawline,click:f},u.drawrect={name:"drawrect",title:function(t){return c(t,"Draw rectangle")},attr:"dragmode",val:"drawrect",icon:o.drawrect,click:f},u.drawcircle={name:"drawcircle",title:function(t){return c(t,"Draw circle")},attr:"dragmode",val:"drawcircle",icon:o.drawcircle,click:f},u.eraseshape={name:"eraseshape",title:function(t){return c(t,"Erase active shape")},icon:o.eraseshape,click:s},u.zoomIn2d={name:"zoomIn2d",_cat:"zoomin",title:function(t){return c(t,"Zoom in")},attr:"zoom",val:"in",icon:o.zoom_plus,click:f},u.zoomOut2d={name:"zoomOut2d",_cat:"zoomout",title:function(t){return c(t,"Zoom out")},attr:"zoom",val:"out",icon:o.zoom_minus,click:f},u.autoScale2d={name:"autoScale2d",_cat:"autoscale",title:function(t){return c(t,"Autoscale")},attr:"zoom",val:"auto",icon:o.autoscale,click:f},u.resetScale2d={name:"resetScale2d",_cat:"resetscale",title:function(t){return c(t,"Reset axes")},attr:"zoom",val:"reset",icon:o.home,click:f},u.hoverClosestCartesian={name:"hoverClosestCartesian",_cat:"hoverclosest",title:function(t){return c(t,"Show closest data on hover")},attr:"hovermode",val:"closest",icon:o.tooltip_basic,gravity:"ne",click:f},u.hoverCompareCartesian={name:"hoverCompareCartesian",_cat:"hoverCompare",title:function(t){return c(t,"Compare data on hover")},attr:"hovermode",val:function(t){return t._fullLayout._isHoriz?"y":"x"},icon:o.tooltip_compare,gravity:"ne",click:f},u.zoom3d={name:"zoom3d",_cat:"zoom",title:function(t){return c(t,"Zoom")},attr:"scene.dragmode",val:"zoom",icon:o.zoombox,click:h},u.pan3d={name:"pan3d",_cat:"pan",title:function(t){return c(t,"Pan")},attr:"scene.dragmode",val:"pan",icon:o.pan,click:h},u.orbitRotation={name:"orbitRotation",title:function(t){return c(t,"Orbital rotation")},attr:"scene.dragmode",val:"orbit",icon:o["3d_rotate"],click:h},u.tableRotation={name:"tableRotation",title:function(t){return c(t,"Turntable rotation")},attr:"scene.dragmode",val:"turntable",icon:o["z-axis"],click:h},u.resetCameraDefault3d={name:"resetCameraDefault3d",_cat:"resetCameraDefault",title:function(t){return c(t,"Reset camera to default")},attr:"resetDefault",icon:o.home,click:p},u.resetCameraLastSave3d={name:"resetCameraLastSave3d",_cat:"resetCameraLastSave",title:function(t){return c(t,"Reset camera to last save")},attr:"resetLastSave",icon:o.movie,click:p},u.hoverClosest3d={name:"hoverClosest3d",_cat:"hoverclosest",title:function(t){return c(t,"Toggle show closest data on hover")},attr:"hovermode",val:null,toggle:!0,icon:o.tooltip_basic,gravity:"ne",click:function(t,e){var r=d(t,e);n.call("_guiRelayout",t,r)}},u.zoomInGeo={name:"zoomInGeo",_cat:"zoomin",title:function(t){return c(t,"Zoom in")},attr:"zoom",val:"in",icon:o.zoom_plus,click:g},u.zoomOutGeo={name:"zoomOutGeo",_cat:"zoomout",title:function(t){return c(t,"Zoom out")},attr:"zoom",val:"out",icon:o.zoom_minus,click:g},u.resetGeo={name:"resetGeo",_cat:"reset",title:function(t){return c(t,"Reset")},attr:"reset",val:null,icon:o.autoscale,click:g},u.hoverClosestGeo={name:"hoverClosestGeo",_cat:"hoverclosest",title:function(t){return c(t,"Toggle show closest data on hover")},attr:"hovermode",val:null,toggle:!0,icon:o.tooltip_basic,gravity:"ne",click:v},u.hoverClosestGl2d={name:"hoverClosestGl2d",_cat:"hoverclosest",title:function(t){return c(t,"Toggle show closest data on hover")},attr:"hovermode",val:null,toggle:!0,icon:o.tooltip_basic,gravity:"ne",click:v},u.hoverClosestPie={name:"hoverClosestPie",_cat:"hoverclosest",title:function(t){return c(t,"Toggle show closest data on hover")},attr:"hovermode",val:"closest",icon:o.tooltip_basic,gravity:"ne",click:v},u.resetViewSankey={name:"resetSankeyGroup",title:function(t){return c(t,"Reset view")},icon:o.home,click:function(t){for(var e={"node.groups":[],"node.x":[],"node.y":[]},r=0;r<t._fullData.length;r++){var i=t._fullData[r]._viewInitial;e["node.groups"].push(i.node.groups.slice()),e["node.x"].push(i.node.x.slice()),e["node.y"].push(i.node.y.slice())}n.call("restyle",t,e)}},u.toggleHover={name:"toggleHover",title:function(t){return c(t,"Toggle show closest data on hover")},attr:"hovermode",val:null,toggle:!0,icon:o.tooltip_basic,gravity:"ne",click:function(t,e){var r=d(t,e);r.hovermode=m(t),n.call("_guiRelayout",t,r)}},u.resetViews={name:"resetViews",title:function(t){return c(t,"Reset views")},icon:o.home,click:function(t,e){var r=e.currentTarget;r.setAttribute("data-attr","zoom"),r.setAttribute("data-val","reset"),f(t,e),r.setAttribute("data-attr","resetLastSave"),p(t,e),x(t,"geo"),x(t,"mapbox")}},u.toggleSpikelines={name:"toggleSpikelines",title:function(t){return c(t,"Toggle Spike Lines")},icon:o.spikeline,attr:"_cartesianSpikesEnabled",val:"on",click:function(t){var e=t._fullLayout,r=e._cartesianSpikesEnabled;e._cartesianSpikesEnabled="on"===r?"off":"on",n.call("_guiRelayout",t,function(t){for(var e="on"===t._fullLayout._cartesianSpikesEnabled,r=a.list(t,null,!0),n={},i=0;i<r.length;i++){var o=r[i];n[o._name+".showspikes"]=!!e||o._showSpikeInitial}return n}(t))}},u.resetViewMapbox={name:"resetViewMapbox",_cat:"resetView",title:function(t){return c(t,"Reset view")},attr:"reset",icon:o.home,click:function(t){x(t,"mapbox")}},u.zoomInMapbox={name:"zoomInMapbox",_cat:"zoomin",title:function(t){return c(t,"Zoom in")},attr:"zoom",val:"in",icon:o.zoom_plus,click:y},u.zoomOutMapbox={name:"zoomOutMapbox",_cat:"zoomout",title:function(t){return c(t,"Zoom out")},attr:"zoom",val:"out",icon:o.zoom_minus,click:y}},{"../../fonts/ploticon":482,"../../lib":503,"../../plots/cartesian/axis_ids":558,"../../plots/plots":619,"../../registry":638,"../shapes/draw":450}],427:[function(t,e,r){"use strict";var n=t("./buttons"),i=Object.keys(n),a=["drawline","drawopenpath","drawclosedpath","drawcircle","drawrect","eraseshape"],o=["v1hovermode","hoverclosest","hovercompare","togglehover","togglespikelines"].concat(a),s=[];i.forEach((function(t){!function(t){if(-1===o.indexOf(t._cat||t.name)){var e=t.name,r=(t._cat||t.name).toLowerCase();-1===s.indexOf(e)&&s.push(e),-1===s.indexOf(r)&&s.push(r)}}(n[t])})),s.sort(),e.exports={DRAW_MODES:a,backButtons:o,foreButtons:s}},{"./buttons":426}],428:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../color"),a=t("../../plot_api/plot_template"),o=t("./attributes");e.exports=function(t,e){var r=t.modebar||{},s=a.newContainer(e,"modebar");function l(t,e){return n.coerce(r,s,o,t,e)}l("orientation"),l("bgcolor",i.addOpacity(e.paper_bgcolor,.5));var c=i.contrast(i.rgb(e.modebar.bgcolor));l("color",i.addOpacity(c,.3)),l("activecolor",i.addOpacity(c,.7)),l("uirevision",e.uirevision),l("add"),l("remove")}},{"../../lib":503,"../../plot_api/plot_template":543,"../color":366,"./attributes":425}],429:[function(t,e,r){"use strict";e.exports={moduleType:"component",name:"modebar",layoutAttributes:t("./attributes"),supplyLayoutDefaults:t("./defaults"),manage:t("./manage")}},{"./attributes":425,"./defaults":428,"./manage":430}],430:[function(t,e,r){"use strict";var n=t("../../plots/cartesian/axis_ids"),i=t("../../traces/scatter/subtypes"),a=t("../../registry"),o=t("../fx/helpers").isUnifiedHover,s=t("./modebar"),l=t("./buttons"),c=t("./constants").DRAW_MODES;e.exports=function(t){var e=t._fullLayout,r=t._context,u=e._modeBar;if(r.displayModeBar||r.watermark){if(!Array.isArray(r.modeBarButtonsToRemove))throw new Error(["*modeBarButtonsToRemove* configuration options","must be an array."].join(" "));if(!Array.isArray(r.modeBarButtonsToAdd))throw new Error(["*modeBarButtonsToAdd* configuration options","must be an array."].join(" "));var f,h=r.modeBarButtons;f=Array.isArray(h)&&h.length?function(t){for(var e=0;e<t.length;e++)for(var r=t[e],n=0;n<r.length;n++){var i=r[n];if("string"==typeof i){if(void 0===l[i])throw new Error(["*modeBarButtons* configuration options","invalid button name"].join(" "));t[e][n]=l[i]}}return t}(h):!r.displayModeBar&&r.watermark?[]:function(t){var e=t._fullLayout,r=t._fullData,s=t._context;function u(t,e){if("string"==typeof e){if(e.toLowerCase()===t.toLowerCase())return!0}else{var r=e.name,n=e._cat||e.name;if(r===t||n===t.toLowerCase())return!0}return!1}var f=e.modebar.add;"string"==typeof f&&(f=[f]);var h=e.modebar.remove;"string"==typeof h&&(h=[h]);var p=s.modeBarButtonsToAdd.concat(f.filter((function(t){for(var e=0;e<s.modeBarButtonsToRemove.length;e++)if(u(t,s.modeBarButtonsToRemove[e]))return!1;return!0}))),d=s.modeBarButtonsToRemove.concat(h.filter((function(t){for(var e=0;e<s.modeBarButtonsToAdd.length;e++)if(u(t,s.modeBarButtonsToAdd[e]))return!1;return!0}))),g=e._has("cartesian"),m=e._has("gl3d"),v=e._has("geo"),y=e._has("pie"),x=e._has("funnelarea"),b=e._has("gl2d"),_=e._has("ternary"),w=e._has("mapbox"),T=e._has("polar"),k=e._has("smith"),A=e._has("sankey"),M=function(t){for(var e=n.list({_fullLayout:t},null,!0),r=0;r<e.length;r++)if(!e[r].fixedrange)return!1;return!0}(e),S=o(e.hovermode),E=[];function L(t){if(t.length){for(var e=[],r=0;r<t.length;r++){for(var n=t[r],i=l[n],a=i.name.toLowerCase(),o=(i._cat||i.name).toLowerCase(),s=!1,c=0;c<d.length;c++){var u=d[c].toLowerCase();if(u===a||u===o){s=!0;break}}s||e.push(l[n])}E.push(e)}}var C=["toImage"];s.showEditInChartStudio?C.push("editInChartStudio"):s.showSendToCloud&&C.push("sendDataToCloud");L(C);var 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n=t[r];n._module&&n._module.selectPoints&&(a.traceIs(n,"scatter-like")?(i.hasMarkers(n)||i.hasText(n))&&(e=!0):a.traceIs(n,"box-violin")&&"all"!==n.boxpoints&&"all"!==n.points||(e=!0))}return e})(r)&&z.push("select2d","lasso2d");var D=[],R=function(t){-1===D.indexOf(t)&&-1!==I.indexOf(t)&&D.push(t)};if(Array.isArray(p)){for(var F=[],B=0;B<p.length;B++){var N=p[B];"string"==typeof N?(N=N.toLowerCase(),-1!==c.indexOf(N)?(e._has("mapbox")||e._has("cartesian"))&&z.push(N):"togglespikelines"===N?R("toggleSpikelines"):"togglehover"===N?R("toggleHover"):"hovercompare"===N?R("hoverCompareCartesian"):"hoverclosest"===N?(R("hoverClosestCartesian"),R("hoverClosestGeo"),R("hoverClosest3d"),R("hoverClosestGl2d"),R("hoverClosestPie")):"v1hovermode"===N&&(R("toggleHover"),R("hoverClosestCartesian"),R("hoverCompareCartesian"),R("hoverClosestGeo"),R("hoverClosest3d"),R("hoverClosestGl2d"),R("hoverClosestPie"))):F.push(N)}p=F}return L(z),L(P.concat(O)),L(D),function(t,e){if(e.length)if(Array.isArray(e[0]))for(var r=0;r<e.length;r++)t.push(e[r]);else t.push(e);return t}(E,p)}(t),u?u.update(t,f):e._modeBar=s(t,f)}else u&&(u.destroy(),delete e._modeBar)}},{"../../plots/cartesian/axis_ids":558,"../../registry":638,"../../traces/scatter/subtypes":950,"../fx/helpers":402,"./buttons":426,"./constants":427,"./modebar":431}],431:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("fast-isnumeric"),a=t("../../lib"),o=t("../../fonts/ploticon"),s=new DOMParser;function l(t){this.container=t.container,this.element=document.createElement("div"),this.update(t.graphInfo,t.buttons),this.container.appendChild(this.element)}var c=l.prototype;c.update=function(t,e){this.graphInfo=t;var r=this.graphInfo._context,n=this.graphInfo._fullLayout,i="modebar-"+n._uid;this.element.setAttribute("id",i),this._uid=i,this.element.className="modebar","hover"===r.displayModeBar&&(this.element.className+=" modebar--hover 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e=this;this.buttons=t,this.buttonElements=[],this.buttonsNames=[],this.buttons.forEach((function(t){var r=e.createGroup();t.forEach((function(t){var n=t.name;if(!n)throw new Error("must provide button 'name' in button config");if(-1!==e.buttonsNames.indexOf(n))throw new Error("button name '"+n+"' is taken");e.buttonsNames.push(n);var i=e.createButton(t);e.buttonElements.push(i),r.appendChild(i)})),e.element.appendChild(r)}))},c.createGroup=function(){var t=document.createElement("div");return t.className="modebar-group",t},c.createButton=function(t){var e=this,r=document.createElement("a");r.setAttribute("rel","tooltip"),r.className="modebar-btn";var i=t.title;void 0===i?i=t.name:"function"==typeof i&&(i=i(this.graphInfo)),(i||0===i)&&r.setAttribute("data-title",i),void 0!==t.attr&&r.setAttribute("data-attr",t.attr);var a=t.val;if(void 0!==a&&("function"==typeof a&&(a=a(this.graphInfo)),r.setAttribute("data-val",a)),"function"!=typeof t.click)throw new Error("must provide button 'click' function in button config");r.addEventListener("click",(function(r){t.click(e.graphInfo,r),e.updateActiveButton(r.currentTarget)})),r.setAttribute("data-toggle",t.toggle||!1),t.toggle&&n.select(r).classed("active",!0);var s=t.icon;return"function"==typeof s?r.appendChild(s()):r.appendChild(this.createIcon(s||o.question)),r.setAttribute("data-gravity",t.gravity||"n"),r},c.createIcon=function(t){var e,r=i(t.height)?Number(t.height):t.ascent-t.descent,n="http://www.w3.org/2000/svg";if(t.path){(e=document.createElementNS(n,"svg")).setAttribute("viewBox",[0,0,t.width,r].join(" ")),e.setAttribute("class","icon");var a=document.createElementNS(n,"path");a.setAttribute("d",t.path),t.transform?a.setAttribute("transform",t.transform):void 0!==t.ascent&&a.setAttribute("transform","matrix(1 0 0 -1 0 "+t.ascent+")"),e.appendChild(a)}t.svg&&(e=s.parseFromString(t.svg,"application/xml").childNodes[0]);return e.setAttribute("height","1em"),e.setAttribute("width","1em"),e},c.updateActiveButton=function(t){var e=this.graphInfo._fullLayout,r=void 0!==t?t.getAttribute("data-attr"):null;this.buttonElements.forEach((function(t){var i=t.getAttribute("data-val")||!0,o=t.getAttribute("data-attr"),s="true"===t.getAttribute("data-toggle"),l=n.select(t);if(s)o===r&&l.classed("active",!l.classed("active"));else{var c=null===o?o:a.nestedProperty(e,o).get();l.classed("active",c===i)}}))},c.hasButtons=function(t){var e=this.buttons;if(!e)return!1;if(t.length!==e.length)return!1;for(var r=0;r<t.length;++r){if(t[r].length!==e[r].length)return!1;for(var n=0;n<t[r].length;n++)if(t[r][n].name!==e[r][n].name)return!1}return!0},c.getLogo=function(){var t=this.createGroup(),e=document.createElement("a");return e.href="https://plotly.com/",e.target="_blank",e.setAttribute("data-title",a._(this.graphInfo,"Produced with Plotly")),e.className="modebar-btn plotlyjsicon 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n=t("../../plots/font_attributes"),i=t("../color/attributes"),a=(0,t("../../plot_api/plot_template").templatedArray)("button",{visible:{valType:"boolean",dflt:!0,editType:"plot"},step:{valType:"enumerated",values:["month","year","day","hour","minute","second","all"],dflt:"month",editType:"plot"},stepmode:{valType:"enumerated",values:["backward","todate"],dflt:"backward",editType:"plot"},count:{valType:"number",min:0,dflt:1,editType:"plot"},label:{valType:"string",editType:"plot"},editType:"plot"});e.exports={visible:{valType:"boolean",editType:"plot"},buttons:a,x:{valType:"number",min:-2,max:3,editType:"plot"},xanchor:{valType:"enumerated",values:["auto","left","center","right"],dflt:"left",editType:"plot"},y:{valType:"number",min:-2,max:3,editType:"plot"},yanchor:{valType:"enumerated",values:["auto","top","middle","bottom"],dflt:"bottom",editType:"plot"},font:n({editType:"plot"}),bgcolor:{valType:"color",dflt:i.lightLine,editType:"plot"},activecolor:{valType:"color",editType:"plot"},bordercolor:{valType:"color",dflt:i.defaultLine,editType:"plot"},borderwidth:{valType:"number",min:0,dflt:0,editType:"plot"},editType:"plot"}},{"../../plot_api/plot_template":543,"../../plots/font_attributes":585,"../color/attributes":365}],433:[function(t,e,r){"use 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strict";e.exports={name:"sliders",containerClassName:"slider-container",groupClassName:"slider-group",inputAreaClass:"slider-input-area",railRectClass:"slider-rail-rect",railTouchRectClass:"slider-rail-touch-rect",gripRectClass:"slider-grip-rect",tickRectClass:"slider-tick-rect",inputProxyClass:"slider-input-proxy",labelsClass:"slider-labels",labelGroupClass:"slider-label-group",labelClass:"slider-label",currentValueClass:"slider-current-value",railHeight:5,menuIndexAttrName:"slider-active-index",autoMarginIdRoot:"slider-",minWidth:30,minHeight:30,textPadX:40,arrowOffsetX:4,railRadius:2,railWidth:5,railBorder:4,railBorderWidth:1,railBorderColor:"#bec8d9",railBgColor:"#f8fafc",railInset:8,stepInset:10,gripRadius:10,gripWidth:20,gripHeight:20,gripBorder:20,gripBorderWidth:1,gripBorderColor:"#bec8d9",gripBgColor:"#f6f8fa",gripBgActiveColor:"#dbdde0",labelPadding:8,labelOffset:0,tickWidth:1,tickColor:"#333",tickOffset:25,tickLength:7,minorTickOffset:25,minorTickColor:"#333",minorTickLength:4,currentValuePadding:8,currentValueInset:0}},{}],461:[function(t,e,r){"use 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T={y:e.y,b:u.height*g[w],t:u.height*d[w]};"fraction"===e.lenmode?(T.l=0,T.xl=e.x-e.len*d[b],T.r=0,T.xr=e.x+e.len*g[b]):(T.x=e.x,T.l=u.outerLength*d[b],T.r=u.outerLength*g[b]),i.autoMargin(t,m(e),T)}function x(t,e,r){if(e.currentvalue.visible){var n,i,a=e._dims;switch(e.currentvalue.xanchor){case"right":n=a.inputAreaLength-f.currentValueInset-a.currentValueMaxWidth,i="left";break;case"center":n=.5*a.inputAreaLength,i="middle";break;default:n=f.currentValueInset,i="left"}var l=s.ensureSingle(t,"text",f.labelClass,(function(t){t.attr({"text-anchor":i,"data-notex":1})})),u=e.currentvalue.prefix?e.currentvalue.prefix:"";if("string"==typeof r)u+=r;else{var h=e.steps[e.active].label,d=e._gd._fullLayout._meta;d&&(h=s.templateString(h,d)),u+=h}e.currentvalue.suffix&&(u+=e.currentvalue.suffix),l.call(o.font,e.currentvalue.font).text(u).call(c.convertToTspans,e._gd);var g=c.lineCount(l),m=(a.currentValueMaxLines+1-g)*e.currentvalue.font.size*p;return c.positionText(l,n,m),l}}function 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n=t("../../plots/font_attributes"),i=t("../color/attributes"),a=t("../../lib/extend").extendFlat,o=t("../../plot_api/edit_types").overrideAll,s=t("../../plots/pad_attributes"),l=t("../../plot_api/plot_template").templatedArray,c=l("button",{visible:{valType:"boolean"},method:{valType:"enumerated",values:["restyle","relayout","animate","update","skip"],dflt:"restyle"},args:{valType:"info_array",freeLength:!0,items:[{valType:"any"},{valType:"any"},{valType:"any"}]},args2:{valType:"info_array",freeLength:!0,items:[{valType:"any"},{valType:"any"},{valType:"any"}]},label:{valType:"string",dflt:""},execute:{valType:"boolean",dflt:!0}});e.exports=o(l("updatemenu",{_arrayAttrRegexps:[/^updatemenus\[(0|[1-9][0-9]+)\]\.buttons/],visible:{valType:"boolean"},type:{valType:"enumerated",values:["dropdown","buttons"],dflt:"dropdown"},direction:{valType:"enumerated",values:["left","right","up","down"],dflt:"down"},active:{valType:"integer",min:-1,dflt:0},showactive:{valType:"boolean",dflt:!0},buttons:c,x:{valType:"number",min:-2,max:3,dflt:-.05},xanchor:{valType:"enumerated",values:["auto","left","center","right"],dflt:"right"},y:{valType:"number",min:-2,max:3,dflt:1},yanchor:{valType:"enumerated",values:["auto","top","middle","bottom"],dflt:"top"},pad:a(s({editType:"arraydraw"}),{}),font:n({}),bgcolor:{valType:"color"},bordercolor:{valType:"color",dflt:i.borderLine},borderwidth:{valType:"number",min:0,dflt:1,editType:"arraydraw"}}),"arraydraw","from-root")},{"../../lib/extend":493,"../../plot_api/edit_types":536,"../../plot_api/plot_template":543,"../../plots/font_attributes":585,"../../plots/pad_attributes":618,"../color/attributes":365}],466:[function(t,e,r){"use 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k=g>w,A=s.barLength+2*s.barPad,M=s.barWidth+2*s.barPad,S=d,E=m+v;E+M>c&&(E=c-M);var L=this.container.selectAll("rect.scrollbar-horizontal").data(k?[0]:[]);L.exit().on(".drag",null).remove(),L.enter().append("rect").classed("scrollbar-horizontal",!0).call(i.fill,s.barColor),k?(this.hbar=L.attr({rx:s.barRadius,ry:s.barRadius,x:S,y:E,width:A,height:M}),this._hbarXMin=S+A/2,this._hbarTranslateMax=w-A):(delete this.hbar,delete this._hbarXMin,delete this._hbarTranslateMax);var C=v>T,P=s.barWidth+2*s.barPad,I=s.barLength+2*s.barPad,O=d+g,z=m;O+P>l&&(O=l-P);var D=this.container.selectAll("rect.scrollbar-vertical").data(C?[0]:[]);D.exit().on(".drag",null).remove(),D.enter().append("rect").classed("scrollbar-vertical",!0).call(i.fill,s.barColor),C?(this.vbar=D.attr({rx:s.barRadius,ry:s.barRadius,x:O,y:z,width:P,height:I}),this._vbarYMin=z+I/2,this._vbarTranslateMax=T-I):(delete this.vbar,delete this._vbarYMin,delete this._vbarTranslateMax);var 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H=n.behavior.drag().on("dragstart",(function(){n.event.sourceEvent.preventDefault(),n.event.sourceEvent.stopPropagation()})).on("drag",this._onBarDrag.bind(this));k&&this.hbar.on(".drag",null).call(H),C&&this.vbar.on(".drag",null).call(H)}this.setTranslate(e,r)},s.prototype.disable=function(){(this.hbar||this.vbar)&&(this.bg.attr({width:0,height:0}),this.container.on("wheel",null).on(".drag",null).call(a.setClipUrl,null),delete this._clipRect),this.hbar&&(this.hbar.on(".drag",null),this.hbar.remove(),delete this.hbar,delete this._hbarXMin,delete this._hbarTranslateMax),this.vbar&&(this.vbar.on(".drag",null),this.vbar.remove(),delete this.vbar,delete this._vbarYMin,delete this._vbarTranslateMax)},s.prototype._onBoxDrag=function(){var t=this.translateX,e=this.translateY;this.hbar&&(t-=n.event.dx),this.vbar&&(e-=n.event.dy),this.setTranslate(t,e)},s.prototype._onBoxWheel=function(){var 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s=e/n;this.vbar.call(a.setTranslate,t,e+s*this._vbarTranslateMax)}}},{"../../lib":503,"../color":366,"../drawing":388,"@plotly/d3":58}],471:[function(t,e,r){"use strict";e.exports={FROM_BL:{left:0,center:.5,right:1,bottom:0,middle:.5,top:1},FROM_TL:{left:0,center:.5,right:1,bottom:1,middle:.5,top:0},FROM_BR:{left:1,center:.5,right:0,bottom:0,middle:.5,top:1},LINE_SPACING:1.3,CAP_SHIFT:.7,MID_SHIFT:.35,OPPOSITE_SIDE:{left:"right",right:"left",top:"bottom",bottom:"top"}}},{}],472:[function(t,e,r){"use strict";e.exports={axisRefDescription:function(t,e,r){return["If set to a",t,"axis id (e.g. *"+t+"* or","*"+t+"2*), the `"+t+"` position refers to a",t,"coordinate. 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d=!1;for(n=0;n<e.length;n++){s=(o=(a=e[n]).cd)[0].trace,i.traceIs(s,"regl")&&(d=!0);var g=a._module,m=g.styleOnSelect||g.style;m&&(m(t,o,o[0].node3),o[0].nodeRangePlot3&&m(t,o,o[0].nodeRangePlot3))}d&&(w(t),T(t))}function V(t,e){if(Array.isArray(t))for(var r=e.cd,n=e.cd[0].trace,i=0;i<t.length;i++)t[i]=l(t[i],n,r);return t}function H(t,e){for(var r=[],n=0;n<t.length;n++){r[n]=[];for(var i=0;i<t[n].length;i++){r[n][i]=[],r[n][i][0]=i?"L":"M";for(var a=0;a<t[n][i].length;a++)r[n][i].push(t[n][i][a])}e||r[n].push(["Z",r[n][0][1],r[n][0][2]])}return r}e.exports={prepSelect:function(t,e,r,i,l){var c=u(l),v=f(l),x=p(l),_=h(l),w=d(l),T="drawcircle"===l,E="drawline"===l||T,L=i.gd,z=L._fullLayout,D=z._zoomlayer,j=i.element.getBoundingClientRect(),q=i.plotinfo,G=I(q),Y=e-j.left,W=r-j.top;z._calcInverseTransform(L);var X=y.apply3DTransform(z._invTransform)(Y,W);Y=X[0],W=X[1];var 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a.cleanDate(e,g,t.calendar)}):"category"===t.type?(t.d2c=t.d2l=I,t.r2d=t.c2d=t.l2d=P,t.d2r=t.d2l_noadd=z,t.r2c=function(e){var r=D(e);return void 0!==r?r:t.fraction2r(.5)},t.l2r=t.c2r=f,t.r2l=D,t.d2p=function(e){return t.l2p(t.r2c(e))},t.p2d=function(t){return P(N(t))},t.r2p=t.d2p,t.p2r=N,t.cleanPos=function(t){return"string"==typeof t&&""!==t?t:f(t)}):"multicategory"===t.type&&(t.r2d=t.c2d=t.l2d=P,t.d2r=t.d2l_noadd=z,t.r2c=function(e){var r=z(e);return void 0!==r?r:t.fraction2r(.5)},t.r2c_just_indices=O,t.l2r=t.c2r=f,t.r2l=z,t.d2p=function(e){return t.l2p(t.r2c(e))},t.p2d=function(t){return P(N(t))},t.r2p=t.d2p,t.p2r=N,t.cleanPos=function(t){return Array.isArray(t)||"string"==typeof t&&""!==t?t:f(t)},t.setupMultiCategory=function(n){var i,o,s=t._traceIndices,l=t._matchGroup;if(l&&0===t._categories.length)for(var c in l)if(c!==r){var u=e[w.id2name(c)];s=s.concat(u._traceIndices)}var f=[[0,{}],[0,{}]],d=[];for(i=0;i<s.length;i++){var g=n[s[i]];if(p in g){var 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n,i,o=a.nestedProperty(t,e).get();if(i=(i="date"===t.type?a.dfltRange(t.calendar):"y"===p?T.DFLTRANGEY:"realaxis"===t._name?[0,1]:r.dfltRange||T.DFLTRANGEX).slice(),"tozero"!==t.rangemode&&"nonnegative"!==t.rangemode||(i[0]=0),o&&2===o.length)for("date"!==t.type||t.autorange||(o[0]=a.cleanDate(o[0],g,t.calendar),o[1]=a.cleanDate(o[1],g,t.calendar)),n=0;n<2;n++)if("date"===t.type){if(!a.isDateTime(o[n],t.calendar)){t[e]=i;break}if(t.r2l(o[0])===t.r2l(o[1])){var l=a.constrain(t.r2l(o[0]),a.MIN_MS+1e3,a.MAX_MS-1e3);o[0]=t.l2r(l-1e3),o[1]=t.l2r(l+1e3);break}}else{if(!s(o[n])){if(!s(o[1-n])){t[e]=i;break}o[n]=o[1-n]*(n?10:.1)}if(o[n]<-d?o[n]=-d:o[n]>d&&(o[n]=d),o[0]===o[1]){var c=Math.max(1,Math.abs(1e-6*o[0]));o[0]-=c,o[1]+=c}}else a.nestedProperty(t,e).set(i)},t.setScale=function(r){var n=e._size;if(t.overlaying){var i=w.getFromId({_fullLayout:e},t.overlaying);t.domain=i.domain}var a=r&&t._r?"_r":"range",o=t.calendar;t.cleanRange(a);var s,l,c=t.r2l(t[a][0],o),u=t.r2l(t[a][1],o),f="y"===p;if((f?(t._offset=n.t+(1-t.domain[1])*n.h,t._length=n.h*(t.domain[1]-t.domain[0]),t._m=t._length/(c-u),t._b=-t._m*u):(t._offset=n.l+t.domain[0]*n.w,t._length=n.w*(t.domain[1]-t.domain[0]),t._m=t._length/(u-c),t._b=-t._m*c),t._rangebreaks=[],t._lBreaks=0,t._m2=0,t._B=[],t.rangebreaks)&&(t._rangebreaks=t.locateBreaks(Math.min(c,u),Math.max(c,u)),t._rangebreaks.length)){for(s=0;s<t._rangebreaks.length;s++)l=t._rangebreaks[s],t._lBreaks+=Math.abs(l.max-l.min);var h=f;c>u&&(h=!h),h&&t._rangebreaks.reverse();var d=h?-1:1;for(t._m2=d*t._length/(Math.abs(u-c)-t._lBreaks),t._B.push(-t._m2*(f?u:c)),s=0;s<t._rangebreaks.length;s++)l=t._rangebreaks[s],t._B.push(t._B[t._B.length-1]-d*t._m2*(l.max-l.min));for(s=0;s<t._rangebreaks.length;s++)(l=t._rangebreaks[s]).pmin=B(l.min),l.pmax=B(l.max)}if(!isFinite(t._m)||!isFinite(t._b)||t._length<0)throw e._replotting=!1,new Error("Something went wrong with axis scaling")},t.maskBreaks=function(e){var 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n=t("@plotly/d3"),i=t("d3-geo"),a=i.geoPath,o=i.geoDistance,s=t("d3-geo-projection"),l=t("../../registry"),c=t("../../lib"),u=c.strTranslate,f=t("../../components/color"),h=t("../../components/drawing"),p=t("../../components/fx"),d=t("../plots"),g=t("../cartesian/axes"),m=t("../cartesian/autorange").getAutoRange,v=t("../../components/dragelement"),y=t("../cartesian/select").prepSelect,x=t("../cartesian/select").clearSelect,b=t("../cartesian/select").selectOnClick,_=t("./zoom"),w=t("./constants"),T=t("../../lib/geo_location_utils"),k=t("../../lib/topojson_utils"),A=t("topojson-client").feature;function 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r=this.bounds,n=(e.framewidth||0)/2,i=r[0][0]-n,a=r[0][1]-n,o=r[1][0]-i+n,s=r[1][1]-a+n;h.setRect(this.clipRect,i,a,o,s),this.bgRect.call(h.setRect,i,a,o,s).call(f.fill,e.bgcolor),this.xaxis._offset=i,this.xaxis._length=o,this.yaxis._offset=a,this.yaxis._length=s},S.updateFx=function(t,e){var r=this,i=r.graphDiv,a=r.bgRect,o=t.dragmode,s=t.clickmode;if(!r.isStatic){var u;"select"===o?u=function(t,e){(t.range={})[r.id]=[h([e.xmin,e.ymin]),h([e.xmax,e.ymax])]}:"lasso"===o&&(u=function(t,e,n){(t.lassoPoints={})[r.id]=n.filtered.map(h)});var f={element:r.bgRect.node(),gd:i,plotinfo:{id:r.id,xaxis:r.xaxis,yaxis:r.yaxis,fillRangeItems:u},xaxes:[r.xaxis],yaxes:[r.yaxis],subplot:r.id,clickFn:function(t){2===t&&x(i)}};"pan"===o?(a.node().onmousedown=null,a.call(_(r,e)),a.on("dblclick.zoom",(function(){var t=r.viewInitial,e={};for(var n in 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t=this,e=t.graphDiv,r=e._fullLayout,i="clip"+r._uid+t.id;t.clipDef=r._clips.append("clipPath").attr("id",i),t.clipRect=t.clipDef.append("rect"),t.framework=n.select(t.container).append("g").attr("class","geo "+t.id).call(h.setClipUrl,i,e),t.project=function(e){var r=t.projection(e);return r?[r[0]-t.xaxis._offset,r[1]-t.yaxis._offset]:[null,null]},t.xaxis={_id:"x",c2p:function(e){return t.project(e)[0]}},t.yaxis={_id:"y",c2p:function(e){return t.project(e)[1]}},t.mockAxis={type:"linear",showexponent:"all",exponentformat:"B"},g.setConvert(t.mockAxis,r)},S.saveViewInitial=function(t){var e,r=t.center||{},n=t.projection,i=n.rotation||{};this.viewInitial={fitbounds:t.fitbounds,"projection.scale":n.scale},e=t._isScoped?{"center.lon":r.lon,"center.lat":r.lat}:t._isClipped?{"projection.rotation.lon":i.lon,"projection.rotation.lat":i.lat}:{"center.lon":r.lon,"center.lat":r.lat,"projection.rotation.lon":i.lon},c.extendFlat(this.viewInitial,e)},S.render=function(){var 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n=t("../../components/color/attributes"),i=t("../domain").attributes,a=t("./constants"),o=t("../../plot_api/edit_types").overrideAll,s=t("../../lib/sort_object_keys"),l={range:{valType:"info_array",items:[{valType:"number"},{valType:"number"}]},showgrid:{valType:"boolean",dflt:!1},tick0:{valType:"number",dflt:0},dtick:{valType:"number"},gridcolor:{valType:"color",dflt:n.lightLine},gridwidth:{valType:"number",min:0,dflt:1}};(e.exports=o({domain:i({name:"geo"},{}),fitbounds:{valType:"enumerated",values:[!1,"locations","geojson"],dflt:!1,editType:"plot"},resolution:{valType:"enumerated",values:[110,50],dflt:110,coerceNumber:!0},scope:{valType:"enumerated",values:s(a.scopeDefaults),dflt:"world"},projection:{type:{valType:"enumerated",values:s(a.projNames)},rotation:{lon:{valType:"number"},lat:{valType:"number"},roll:{valType:"number"}},tilt:{valType:"number",dflt:0},distance:{valType:"number",min:1.001,dflt:2},parallels:{valType:"info_array",items:[{valType:"number"},{valType:"number"}]},scale:{valType:"number",min:0,dflt:1}},center:{lon:{valType:"number"},lat:{valType:"number"}},visible:{valType:"boolean",dflt:!0},showcoastlines:{valType:"boolean"},coastlinecolor:{valType:"color",dflt:n.defaultLine},coastlinewidth:{valType:"number",min:0,dflt:1},showland:{valType:"boolean",dflt:!1},landcolor:{valType:"color",dflt:a.landColor},showocean:{valType:"boolean",dflt:!1},oceancolor:{valType:"color",dflt:a.waterColor},showlakes:{valType:"boolean",dflt:!1},lakecolor:{valType:"color",dflt:a.waterColor},showrivers:{valType:"boolean",dflt:!1},rivercolor:{valType:"color",dflt:a.waterColor},riverwidth:{valType:"number",min:0,dflt:1},showcountries:{valType:"boolean"},countrycolor:{valType:"color",dflt:n.defaultLine},countrywidth:{valType:"number",min:0,dflt:1},showsubunits:{valType:"boolean"},subunitcolor:{valType:"color",dflt:n.defaultLine},subunitwidth:{valType:"number",min:0,dflt:1},showframe:{valType:"boolean"},framecolor:{valType:"color",dflt:n.defaultLine},framewidth:{valType:"number",min:0,dflt:1},bgcolor:{valType:"color",dflt:n.background},lonaxis:l,lataxis:l},"plot","from-root")).uirevision={valType:"any",editType:"none"}},{"../../components/color/attributes":365,"../../lib/sort_object_keys":526,"../../plot_api/edit_types":536,"../domain":584,"./constants":587}],591:[function(t,e,r){"use 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n=t("@plotly/d3"),i=t("../../lib"),a=t("../../registry"),o=Math.PI/180,s=180/Math.PI,l={cursor:"pointer"},c={cursor:"auto"};function u(t,e){return n.behavior.zoom().translate(e.translate()).scale(e.scale())}function f(t,e,r){var n=t.id,o=t.graphDiv,s=o.layout,l=s[n],c=o._fullLayout,u=c[n],f={},h={};function p(t,e){f[n+"."+t]=i.nestedProperty(l,t).get(),a.call("_storeDirectGUIEdit",s,c._preGUI,f);var r=i.nestedProperty(u,t);r.get()!==e&&(r.set(e),i.nestedProperty(l,t).set(e),h[n+"."+t]=e)}r(p),p("projection.scale",e.scale()/t.fitScale),p("fitbounds",!1),o.emit("plotly_relayout",h)}function h(t,e){var r=u(0,e);function i(r){var n=e.invert(t.midPt);r("center.lon",n[0]),r("center.lat",n[1])}return r.on("zoomstart",(function(){n.select(this).style(l)})).on("zoom",(function(){e.scale(n.event.scale).translate(n.event.translate),t.render();var r=e.invert(t.midPt);t.graphDiv.emit("plotly_relayouting",{"geo.projection.scale":e.scale()/t.fitScale,"geo.center.lon":r[0],"geo.center.lat":r[1]})})).on("zoomend",(function(){n.select(this).style(c),f(t,e,i)})),r}function p(t,e){var r,i,a,o,s,h,p,d,g,m=u(0,e);function v(t){return e.invert(t)}function y(r){var n=e.rotate(),i=e.invert(t.midPt);r("projection.rotation.lon",-n[0]),r("center.lon",i[0]),r("center.lat",i[1])}return m.on("zoomstart",(function(){n.select(this).style(l),r=n.mouse(this),i=e.rotate(),a=e.translate(),o=i,s=v(r)})).on("zoom",(function(){if(h=n.mouse(this),function(t){var r=v(t);if(!r)return!0;var n=e(r);return Math.abs(n[0]-t[0])>2||Math.abs(n[1]-t[1])>2}(r))return m.scale(e.scale()),void m.translate(e.translate());e.scale(n.event.scale),e.translate([a[0],n.event.translate[1]]),s?v(h)&&(d=v(h),p=[o[0]+(d[0]-s[0]),i[1],i[2]],e.rotate(p),o=p):s=v(r=h),g=!0,t.render();var l=e.rotate(),c=e.invert(t.midPt);t.graphDiv.emit("plotly_relayouting",{"geo.projection.scale":e.scale()/t.fitScale,"geo.center.lon":c[0],"geo.center.lat":c[1],"geo.projection.rotation.lon":-l[0]})})).on("zoomend",(function(){n.select(this).style(c),g&&f(t,e,y)})),m}function d(t,e){var r,i={r:e.rotate(),k:e.scale()},a=u(0,e),o=function(t){var e=0,r=arguments.length,i=[];for(;++e<r;)i.push(arguments[e]);var a=n.dispatch.apply(null,i);return a.of=function(e,r){return function(i){var o;try{o=i.sourceEvent=n.event,i.target=t,n.event=i,a[i.type].apply(e,r)}finally{n.event=o}}},a}(a,"zoomstart","zoom","zoomend"),s=0,h=a.on;function p(t){s++||t({type:"zoomstart"})}function d(t){t({type:"zoom"})}function b(t){--s||t({type:"zoomend"})}function _(t){var r=e.rotate();t("projection.rotation.lon",-r[0]),t("projection.rotation.lat",-r[1])}return a.on("zoomstart",(function(){n.select(this).style(l);var t=n.mouse(this),s=e.rotate(),c=s,u=e.translate(),f=m(s);r=g(e,t),h.call(a,"zoom",(function(){var 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a(t){this.scene=t,this.gl=t.gl,this.pixelRatio=t.pixelRatio,this.screenBox=[0,0,1,1],this.viewBox=[0,0,1,1],this.dataBox=[-1,-1,1,1],this.borderLineEnable=[!1,!1,!1,!1],this.borderLineWidth=[1,1,1,1],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.ticks=[[],[]],this.tickEnable=[!0,!0,!1,!1],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labels=["x","y"],this.labelEnable=[!0,!0,!1,!1],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelPad=[15,15,15,15],this.labelSize=[12,12],this.labelFont=["sans-serif","sans-serif"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.title="",this.titleEnable=!0,this.titleCenter=[0,0,0,0],this.titleAngle=0,this.titleColor=[0,0,0,1],this.titleFont="sans-serif",this.titleSize=18,this.gridLineEnable=[!0,!0],this.gridLineColor=[[0,0,0,.5],[0,0,0,.5]],this.gridLineWidth=[1,1],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[1,1],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.static=this.scene.staticPlot}var o=a.prototype,s=["xaxis","yaxis"];o.merge=function(t){var e,r,n,a,o,l,c,u,f,h,p;for(this.titleEnable=!1,this.backgroundColor=i(t.plot_bgcolor),h=0;h<2;++h){var d=(e=s[h]).charAt(0);for(n=(r=t[this.scene[e]._name]).title.text===this.scene.fullLayout._dfltTitle[d]?"":r.title.text,p=0;p<=2;p+=2)this.labelEnable[h+p]=!1,this.labels[h+p]=n,this.labelColor[h+p]=i(r.title.font.color),this.labelFont[h+p]=r.title.font.family,this.labelSize[h+p]=r.title.font.size,this.labelPad[h+p]=this.getLabelPad(e,r),this.tickEnable[h+p]=!1,this.tickColor[h+p]=i((r.tickfont||{}).color),this.tickAngle[h+p]="auto"===r.tickangle?0:Math.PI*-r.tickangle/180,this.tickPad[h+p]=this.getTickPad(r),this.tickMarkLength[h+p]=0,this.tickMarkWidth[h+p]=r.tickwidth||0,this.tickMarkColor[h+p]=i(r.tickcolor),this.borderLineEnable[h+p]=!1,this.borderLineColor[h+p]=i(r.linecolor),this.borderLineWidth[h+p]=r.linewidth||0;c=this.hasSharedAxis(r),o=this.hasAxisInDfltPos(e,r)&&!c,l=this.hasAxisInAltrPos(e,r)&&!c,a=r.mirror||!1,u=c?-1!==String(a).indexOf("all"):!!a,f=c?"allticks"===a:-1!==String(a).indexOf("ticks"),o?this.labelEnable[h]=!0:l&&(this.labelEnable[h+2]=!0),o?this.tickEnable[h]=r.showticklabels:l&&(this.tickEnable[h+2]=r.showticklabels),(o||u)&&(this.borderLineEnable[h]=r.showline),(l||u)&&(this.borderLineEnable[h+2]=r.showline),(o||f)&&(this.tickMarkLength[h]=this.getTickMarkLength(r)),(l||f)&&(this.tickMarkLength[h+2]=this.getTickMarkLength(r)),this.gridLineEnable[h]=r.showgrid,this.gridLineColor[h]=i(r.gridcolor),this.gridLineWidth[h]=r.gridwidth,this.zeroLineEnable[h]=r.zeroline,this.zeroLineColor[h]=i(r.zerolinecolor),this.zeroLineWidth[h]=r.zerolinewidth}},o.hasSharedAxis=function(t){var e=this.scene,r=e.fullLayout._subplots.gl2d;return 0!==n.findSubplotsWithAxis(r,t).indexOf(e.id)},o.hasAxisInDfltPos=function(t,e){var r=e.side;return"xaxis"===t?"bottom"===r:"yaxis"===t?"left"===r:void 0},o.hasAxisInAltrPos=function(t,e){var r=e.side;return"xaxis"===t?"top"===r:"yaxis"===t?"right"===r:void 0},o.getLabelPad=function(t,e){var r=e.title.font.size,n=e.showticklabels;return"xaxis"===t?"top"===e.side?r*(1.5+(n?1:0))-10:r*(1.5+(n?.5:0))-10:"yaxis"===t?"right"===e.side?10+r*(1.5+(n?1:.5)):10+r*(1.5+(n?.5:0)):void 0},o.getTickPad=function(t){return"outside"===t.ticks?10+t.ticklen:15},o.getTickMarkLength=function(t){if(!t.ticks)return 0;var e=t.ticklen;return"inside"===t.ticks?-e:e},e.exports=function(t){return new a(t)}},{"../../lib/str2rgbarray":528,"../cartesian/axes":554}],596:[function(t,e,r){"use strict";var n=t("../../plot_api/edit_types").overrideAll,i=t("./scene2d"),a=t("../layout_attributes"),o=t("../../constants/xmlns_namespaces"),s=t("../cartesian/constants"),l=t("../cartesian"),c=t("../../components/fx/layout_attributes"),u=t("../get_data").getSubplotData;r.name="gl2d",r.attr=["xaxis","yaxis"],r.idRoot=["x","y"],r.idRegex=s.idRegex,r.attrRegex=s.attrRegex,r.attributes=t("../cartesian/attributes"),r.supplyLayoutDefaults=function(t,e,r){e._has("cartesian")||l.supplyLayoutDefaults(t,e,r)},r.layoutAttrOverrides=n(l.layoutAttributes,"plot","from-root"),r.baseLayoutAttrOverrides=n({plot_bgcolor:a.plot_bgcolor,hoverlabel:c.hoverlabel},"plot","nested"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots.gl2d,a=0;a<n.length;a++){var o=n[a],s=e._plots[o],l=u(r,"gl2d",o),c=s._scene2d;void 0===c&&(c=new i({id:o,graphDiv:t,container:t.querySelector(".gl-container"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio},e),s._scene2d=c),c.plot(l,t.calcdata,e,t.layout)}},r.clean=function(t,e,r,n){for(var i=n._subplots.gl2d||[],a=0;a<i.length;a++){var o=i[a],s=n._plots[o];if(s._scene2d){var c=u(t,"gl2d",o);0===c.length&&(s._scene2d.destroy(),delete n._plots[o])}}l.clean.apply(this,arguments)},r.drawFramework=function(t){t._context.staticPlot||l.drawFramework(t)},r.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++){var i=e._plots[r[n]]._scene2d,a=i.toImage("png");e._glimages.append("svg:image").attr({xmlns:o.svg,"xlink:href":a,x:0,y:0,width:"100%",height:"100%",preserveAspectRatio:"none"}),i.destroy()}},r.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++){e._plots[r[n]]._scene2d.updateFx(e.dragmode)}}},{"../../components/fx/layout_attributes":407,"../../constants/xmlns_namespaces":480,"../../plot_api/edit_types":536,"../cartesian":568,"../cartesian/attributes":552,"../cartesian/constants":561,"../get_data":593,"../layout_attributes":610,"./scene2d":597}],597:[function(t,e,r){"use strict";var n,i,a=t("../../registry"),o=t("../../plots/cartesian/axes"),s=t("../../components/fx"),l=t("../../../stackgl_modules").gl_plot2d,c=t("../../../stackgl_modules").gl_spikes2d,u=t("../../../stackgl_modules").gl_select_box,f=t("webgl-context"),h=t("./convert"),p=t("./camera"),d=t("../../lib/show_no_webgl_msg"),g=t("../cartesian/constraints"),m=g.enforce,v=g.clean,y=t("../cartesian/autorange").doAutoRange,x=t("../../components/dragelement/helpers"),b=x.drawMode,_=x.selectMode,w=["xaxis","yaxis"],T=t("../cartesian/constants").SUBPLOT_PATTERN;function k(t,e){this.container=t.container,this.graphDiv=t.graphDiv,this.pixelRatio=t.plotGlPixelRatio||window.devicePixelRatio,this.id=t.id,this.staticPlot=!!t.staticPlot,this.scrollZoom=this.graphDiv._context._scrollZoom.cartesian,this.fullData=null,this.updateRefs(e),this.makeFramework(),this.stopped||(this.glplotOptions=h(this),this.glplotOptions.merge(e),this.glplot=l(this.glplotOptions),this.camera=p(this),this.traces={},this.spikes=c(this.glplot),this.selectBox=u(this.glplot,{innerFill:!1,outerFill:!0}),this.lastButtonState=0,this.pickResult=null,this.isMouseOver=!0,this.stopped=!1,this.redraw=this.draw.bind(this),this.redraw())}e.exports=k;var A=k.prototype;A.makeFramework=function(){if(this.staticPlot){if(!(i||(n=document.createElement("canvas"),i=f({canvas:n,preserveDrawingBuffer:!1,premultipliedAlpha:!0,antialias:!0}))))throw new Error("Error creating static canvas/context for image server");this.canvas=n,this.gl=i}else{var t=this.container.querySelector(".gl-canvas-focus"),e=f({canvas:t,preserveDrawingBuffer:!0,premultipliedAlpha:!0});if(!e)return d(this),void(this.stopped=!0);this.canvas=t,this.gl=e}var r=this.canvas;r.style.width="100%",r.style.height="100%",r.style.position="absolute",r.style.top="0px",r.style.left="0px",r.style["pointer-events"]="none",this.updateSize(r);var a=this.svgContainer=document.createElementNS("http://www.w3.org/2000/svg","svg");a.style.position="absolute",a.style.top=a.style.left="0px",a.style.width=a.style.height="100%",a.style["z-index"]=20,a.style["pointer-events"]="none";var o=this.mouseContainer=document.createElement("div");o.style.position="absolute",o.style["pointer-events"]="auto",this.pickCanvas=this.container.querySelector(".gl-canvas-pick");var s=this.container;s.appendChild(a),s.appendChild(o);var l=this;o.addEventListener("mouseout",(function(){l.isMouseOver=!1,l.unhover()})),o.addEventListener("mouseover",(function(){l.isMouseOver=!0}))},A.toImage=function(t){t||(t="png"),this.stopped=!0,this.staticPlot&&this.container.appendChild(n),this.updateSize(this.canvas);var e=this.glplot.gl,r=e.drawingBufferWidth,i=e.drawingBufferHeight;e.clearColor(1,1,1,0),e.clear(e.COLOR_BUFFER_BIT|e.DEPTH_BUFFER_BIT),this.glplot.setDirty(),this.glplot.draw(),e.bindFramebuffer(e.FRAMEBUFFER,null);var a=new Uint8Array(r*i*4);e.readPixels(0,0,r,i,e.RGBA,e.UNSIGNED_BYTE,a);for(var o=0,s=i-1;o<s;++o,--s)for(var l=0;l<r;++l)for(var c=0;c<4;++c){var u=a[4*(r*o+l)+c];a[4*(r*o+l)+c]=a[4*(r*s+l)+c],a[4*(r*s+l)+c]=u}var f=document.createElement("canvas");f.width=r,f.height=i;var h,p=f.getContext("2d"),d=p.createImageData(r,i);switch(d.data.set(a),p.putImageData(d,0,0),t){case"jpeg":h=f.toDataURL("image/jpeg");break;case"webp":h=f.toDataURL("image/webp");break;default:h=f.toDataURL("image/png")}return this.staticPlot&&this.container.removeChild(n),h},A.updateSize=function(t){t||(t=this.canvas);var e=this.pixelRatio,r=this.fullLayout,n=r.width,i=r.height,a=0|Math.ceil(e*n),o=0|Math.ceil(e*i);return t.width===a&&t.height===o||(t.width=a,t.height=o),t},A.computeTickMarks=function(){this.xaxis.setScale(),this.yaxis.setScale();for(var t=[o.calcTicks(this.xaxis),o.calcTicks(this.yaxis)],e=0;e<2;++e)for(var r=0;r<t[e].length;++r)t[e][r].text=t[e][r].text+"";return t},A.updateRefs=function(t){this.fullLayout=t;var e=this.id.match(T),r="xaxis"+e[1],n="yaxis"+e[2];this.xaxis=this.fullLayout[r],this.yaxis=this.fullLayout[n]},A.relayoutCallback=function(){var t=this.graphDiv,e=this.xaxis,r=this.yaxis,n=t.layout,i={},o=i[e._name+".range"]=e.range.slice(),s=i[r._name+".range"]=r.range.slice();i[e._name+".autorange"]=e.autorange,i[r._name+".autorange"]=r.autorange,a.call("_storeDirectGUIEdit",t.layout,t._fullLayout._preGUI,i);var l=n[e._name];l.range=o,l.autorange=e.autorange;var c=n[r._name];c.range=s,c.autorange=r.autorange,i.lastInputTime=this.camera.lastInputTime,t.emit("plotly_relayout",i)},A.cameraChanged=function(){var t=this.camera;this.glplot.setDataBox(this.calcDataBox());var e=this.computeTickMarks();(function(t,e){for(var r=0;r<2;++r){var n=t[r],i=e[r];if(n.length!==i.length)return!0;for(var a=0;a<n.length;++a)if(n[a].x!==i[a].x)return!0}return!1})(e,this.glplotOptions.ticks)&&(this.glplotOptions.ticks=e,this.glplotOptions.dataBox=t.dataBox,this.glplot.update(this.glplotOptions),this.handleAnnotations())},A.handleAnnotations=function(){for(var t=this.graphDiv,e=this.fullLayout.annotations,r=0;r<e.length;r++){var n=e[r];n.xref===this.xaxis._id&&n.yref===this.yaxis._id&&a.getComponentMethod("annotations","drawOne")(t,r)}},A.destroy=function(){if(this.glplot){var t=this.traces;t&&Object.keys(t).map((function(e){t[e].dispose(),delete t[e]})),this.glplot.dispose(),this.container.removeChild(this.svgContainer),this.container.removeChild(this.mouseContainer),this.fullData=null,this.glplot=null,this.stopped=!0,this.camera.mouseListener.enabled=!1,this.mouseContainer.removeEventListener("wheel",this.camera.wheelListener),this.camera=null}},A.plot=function(t,e,r){var n=this.glplot;this.updateRefs(r),this.xaxis.clearCalc(),this.yaxis.clearCalc(),this.updateTraces(t,e),this.updateFx(r.dragmode);var i=r.width,a=r.height;this.updateSize(this.canvas);var o=this.glplotOptions;o.merge(r),o.screenBox=[0,0,i,a];var s={_fullLayout:{_axisConstraintGroups:r._axisConstraintGroups,xaxis:this.xaxis,yaxis:this.yaxis,_size:r._size}};v(s,this.xaxis),v(s,this.yaxis);var l,c,u=r._size,f=this.xaxis.domain,h=this.yaxis.domain;for(o.viewBox=[u.l+f[0]*u.w,u.b+h[0]*u.h,i-u.r-(1-f[1])*u.w,a-u.t-(1-h[1])*u.h],this.mouseContainer.style.width=u.w*(f[1]-f[0])+"px",this.mouseContainer.style.height=u.h*(h[1]-h[0])+"px",this.mouseContainer.height=u.h*(h[1]-h[0]),this.mouseContainer.style.left=u.l+f[0]*u.w+"px",this.mouseContainer.style.top=u.t+(1-h[1])*u.h+"px",c=0;c<2;++c)(l=this[w[c]])._length=o.viewBox[c+2]-o.viewBox[c],y(this.graphDiv,l),l.setScale();m(s),o.ticks=this.computeTickMarks(),o.dataBox=this.calcDataBox(),o.merge(r),n.update(o),this.glplot.draw()},A.calcDataBox=function(){var t=this.xaxis,e=this.yaxis,r=t.range,n=e.range,i=t.r2l,a=e.r2l;return[i(r[0]),a(n[0]),i(r[1]),a(n[1])]},A.setRanges=function(t){var e=this.xaxis,r=this.yaxis,n=e.l2r,i=r.l2r;e.range=[n(t[0]),n(t[2])],r.range=[i(t[1]),i(t[3])]},A.updateTraces=function(t,e){var r,n,i,a=Object.keys(this.traces);this.fullData=t;t:for(r=0;r<a.length;r++){var o=a[r],s=this.traces[o];for(n=0;n<t.length;n++)if((i=t[n]).uid===o&&i.type===s.type)continue t;s.dispose(),delete this.traces[o]}for(r=0;r<t.length;r++){i=t[r];var l=e[r],c=this.traces[i.uid];c?c.update(i,l):(c=i._module.plot(this,i,l),this.traces[i.uid]=c)}this.glplot.objects.sort((function(t,e){return t._trace.index-e._trace.index}))},A.updateFx=function(t){_(t)||b(t)?(this.pickCanvas.style["pointer-events"]="none",this.mouseContainer.style["pointer-events"]="none"):(this.pickCanvas.style["pointer-events"]="auto",this.mouseContainer.style["pointer-events"]="auto"),this.mouseContainer.style.cursor="pan"===t?"move":"zoom"===t?"crosshair":null},A.emitPointAction=function(t,e){for(var r,n=t.trace.uid,i=t.pointIndex,a=0;a<this.fullData.length;a++)this.fullData[a].uid===n&&(r=this.fullData[a]);var o={x:t.traceCoord[0],y:t.traceCoord[1],curveNumber:r.index,pointNumber:i,data:r._input,fullData:this.fullData,xaxis:this.xaxis,yaxis:this.yaxis};s.appendArrayPointValue(o,r,i),this.graphDiv.emit(e,{points:[o]})},A.draw=function(){if(!this.stopped){requestAnimationFrame(this.redraw);var t=this.glplot,e=this.camera,r=e.mouseListener,n=1===this.lastButtonState&&0===r.buttons,i=this.fullLayout;this.lastButtonState=r.buttons,this.cameraChanged();var a,o=r.x*t.pixelRatio,l=this.canvas.height-t.pixelRatio*r.y;if(e.boxEnabled&&"zoom"===i.dragmode){this.selectBox.enabled=!0;for(var c=this.selectBox.selectBox=[Math.min(e.boxStart[0],e.boxEnd[0]),Math.min(e.boxStart[1],e.boxEnd[1]),Math.max(e.boxStart[0],e.boxEnd[0]),Math.max(e.boxStart[1],e.boxEnd[1])],u=0;u<2;u++)e.boxStart[u]===e.boxEnd[u]&&(c[u]=t.dataBox[u],c[u+2]=t.dataBox[u+2]);t.setDirty()}else if(!e.panning&&this.isMouseOver){this.selectBox.enabled=!1;var f=i._size,h=this.xaxis.domain,p=this.yaxis.domain,d=(a=t.pick(o/t.pixelRatio+f.l+h[0]*f.w,l/t.pixelRatio-(f.t+(1-p[1])*f.h)))&&a.object._trace.handlePick(a);if(d&&n&&this.emitPointAction(d,"plotly_click"),a&&"skip"!==a.object._trace.hoverinfo&&i.hovermode&&d&&(!this.lastPickResult||this.lastPickResult.traceUid!==d.trace.uid||this.lastPickResult.dataCoord[0]!==d.dataCoord[0]||this.lastPickResult.dataCoord[1]!==d.dataCoord[1])){var g=d;this.lastPickResult={traceUid:d.trace?d.trace.uid:null,dataCoord:d.dataCoord.slice()},this.spikes.update({center:a.dataCoord}),g.screenCoord=[((t.viewBox[2]-t.viewBox[0])*(a.dataCoord[0]-t.dataBox[0])/(t.dataBox[2]-t.dataBox[0])+t.viewBox[0])/t.pixelRatio,(this.canvas.height-(t.viewBox[3]-t.viewBox[1])*(a.dataCoord[1]-t.dataBox[1])/(t.dataBox[3]-t.dataBox[1])-t.viewBox[1])/t.pixelRatio],this.emitPointAction(d,"plotly_hover");var m=this.fullData[g.trace.index]||{},v=g.pointIndex,y=s.castHoverinfo(m,i,v);if(y&&"all"!==y){var x=y.split("+");-1===x.indexOf("x")&&(g.traceCoord[0]=void 0),-1===x.indexOf("y")&&(g.traceCoord[1]=void 0),-1===x.indexOf("z")&&(g.traceCoord[2]=void 0),-1===x.indexOf("text")&&(g.textLabel=void 0),-1===x.indexOf("name")&&(g.name=void 0)}s.loneHover({x:g.screenCoord[0],y:g.screenCoord[1],xLabel:this.hoverFormatter("xaxis",g.traceCoord[0]),yLabel:this.hoverFormatter("yaxis",g.traceCoord[1]),zLabel:g.traceCoord[2],text:g.textLabel,name:g.name,color:s.castHoverOption(m,v,"bgcolor")||g.color,borderColor:s.castHoverOption(m,v,"bordercolor"),fontFamily:s.castHoverOption(m,v,"font.family"),fontSize:s.castHoverOption(m,v,"font.size"),fontColor:s.castHoverOption(m,v,"font.color"),nameLength:s.castHoverOption(m,v,"namelength"),textAlign:s.castHoverOption(m,v,"align")},{container:this.svgContainer,gd:this.graphDiv})}}a||this.unhover(),t.draw()}},A.unhover=function(){this.lastPickResult&&(this.spikes.update({}),this.lastPickResult=null,this.graphDiv.emit("plotly_unhover"),s.loneUnhover(this.svgContainer))},A.hoverFormatter=function(t,e){if(void 0!==e){var r=this[t];return o.tickText(r,r.c2l(e),"hover").text}}},{"../../../stackgl_modules":1119,"../../components/dragelement/helpers":384,"../../components/fx":406,"../../lib/show_no_webgl_msg":525,"../../plots/cartesian/axes":554,"../../registry":638,"../cartesian/autorange":553,"../cartesian/constants":561,"../cartesian/constraints":562,"./camera":594,"./convert":595,"webgl-context":331}],598:[function(t,e,r){"use strict";var n=t("../../plot_api/edit_types").overrideAll,i=t("../../components/fx/layout_attributes"),a=t("./scene"),o=t("../get_data").getSubplotData,s=t("../../lib"),l=t("../../constants/xmlns_namespaces");r.name="gl3d",r.attr="scene",r.idRoot="scene",r.idRegex=r.attrRegex=s.counterRegex("scene"),r.attributes=t("./layout/attributes"),r.layoutAttributes=t("./layout/layout_attributes"),r.baseLayoutAttrOverrides=n({hoverlabel:i.hoverlabel},"plot","nested"),r.supplyLayoutDefaults=t("./layout/defaults"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots.gl3d,i=0;i<n.length;i++){var s=n[i],l=o(r,"gl3d",s),c=e[s],u=c.camera,f=c._scene;f||(f=new a({id:s,graphDiv:t,container:t.querySelector(".gl-container"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio,camera:u},e),c._scene=f),f.viewInitial||(f.viewInitial={up:{x:u.up.x,y:u.up.y,z:u.up.z},eye:{x:u.eye.x,y:u.eye.y,z:u.eye.z},center:{x:u.center.x,y:u.center.y,z:u.center.z}}),f.plot(l,e,t.layout)}},r.clean=function(t,e,r,n){for(var i=n._subplots.gl3d||[],a=0;a<i.length;a++){var o=i[a];!e[o]&&n[o]._scene&&(n[o]._scene.destroy(),n._infolayer&&n._infolayer.selectAll(".annotation-"+o).remove())}},r.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots.gl3d,n=e._size,i=0;i<r.length;i++){var a=e[r[i]],o=a.domain,s=a._scene,c=s.toImage("png");e._glimages.append("svg:image").attr({xmlns:l.svg,"xlink:href":c,x:n.l+n.w*o.x[0],y:n.t+n.h*(1-o.y[1]),width:n.w*(o.x[1]-o.x[0]),height:n.h*(o.y[1]-o.y[0]),preserveAspectRatio:"none"}),s.destroy()}},r.cleanId=function(t){if(t.match(/^scene[0-9]*$/)){var e=t.substr(5);return"1"===e&&(e=""),"scene"+e}},r.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots.gl3d,n=0;n<r.length;n++){e[r[n]]._scene.updateFx(e.dragmode,e.hovermode)}}},{"../../components/fx/layout_attributes":407,"../../constants/xmlns_namespaces":480,"../../lib":503,"../../plot_api/edit_types":536,"../get_data":593,"./layout/attributes":599,"./layout/defaults":603,"./layout/layout_attributes":604,"./scene":608}],599:[function(t,e,r){"use strict";e.exports={scene:{valType:"subplotid",dflt:"scene",editType:"calc+clearAxisTypes"}}},{}],600:[function(t,e,r){"use strict";var n=t("../../../components/color"),i=t("../../cartesian/layout_attributes"),a=t("../../../lib/extend").extendFlat,o=t("../../../plot_api/edit_types").overrideAll;e.exports=o({visible:i.visible,showspikes:{valType:"boolean",dflt:!0},spikesides:{valType:"boolean",dflt:!0},spikethickness:{valType:"number",min:0,dflt:2},spikecolor:{valType:"color",dflt:n.defaultLine},showbackground:{valType:"boolean",dflt:!1},backgroundcolor:{valType:"color",dflt:"rgba(204, 204, 204, 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204, 204)"}),gridwidth:i.gridwidth,zeroline:i.zeroline,zerolinecolor:i.zerolinecolor,zerolinewidth:i.zerolinewidth,_deprecated:{title:i._deprecated.title,titlefont:i._deprecated.titlefont}},"plot","from-root")},{"../../../components/color":366,"../../../lib/extend":493,"../../../plot_api/edit_types":536,"../../cartesian/layout_attributes":569}],601:[function(t,e,r){"use strict";var n=t("tinycolor2").mix,i=t("../../../lib"),a=t("../../../plot_api/plot_template"),o=t("./axis_attributes"),s=t("../../cartesian/type_defaults"),l=t("../../cartesian/axis_defaults"),c=["xaxis","yaxis","zaxis"];e.exports=function(t,e,r){var u,f;function h(t,e){return i.coerce(u,f,o,t,e)}for(var p=0;p<c.length;p++){var d=c[p];u=t[d]||{},(f=a.newContainer(e,d))._id=d[0]+r.scene,f._name=d,s(u,f,h,r),l(u,f,h,{font:r.font,letter:d[0],data:r.data,showGrid:!0,noTickson:!0,noTicklabelmode:!0,noTicklabelposition:!0,noTicklabeloverflow:!0,bgColor:r.bgColor,calendar:r.calendar},r.fullLayout),h("gridcolor",n(f.color,r.bgColor,13600/187).toRgbString()),h("title.text",d[0]),f.setScale=i.noop,h("showspikes")&&(h("spikesides"),h("spikethickness"),h("spikecolor",f.color)),h("showaxeslabels"),h("showbackground")&&h("backgroundcolor")}}},{"../../../lib":503,"../../../plot_api/plot_template":543,"../../cartesian/axis_defaults":556,"../../cartesian/type_defaults":582,"./axis_attributes":600,tinycolor2:312}],602:[function(t,e,r){"use strict";var n=t("../../../lib/str2rgbarray"),i=t("../../../lib"),a=["xaxis","yaxis","zaxis"];function o(){this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.tickEnable=[!0,!0,!0],this.tickFont=["sans-serif","sans-serif","sans-serif"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[18,18,18],this.labels=["x","y","z"],this.labelEnable=[!0,!0,!0],this.labelFont=["Open Sans","Open Sans","Open 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o=e[a[r]];o.visible?(this.labels[r]=t._meta?i.templateString(o.title.text,t._meta):o.title.text,"font"in o.title&&(o.title.font.color&&(this.labelColor[r]=n(o.title.font.color)),o.title.font.family&&(this.labelFont[r]=o.title.font.family),o.title.font.size&&(this.labelSize[r]=o.title.font.size)),"showline"in o&&(this.lineEnable[r]=o.showline),"linecolor"in o&&(this.lineColor[r]=n(o.linecolor)),"linewidth"in o&&(this.lineWidth[r]=o.linewidth),"showgrid"in o&&(this.gridEnable[r]=o.showgrid),"gridcolor"in o&&(this.gridColor[r]=n(o.gridcolor)),"gridwidth"in o&&(this.gridWidth[r]=o.gridwidth),"log"===o.type?this.zeroEnable[r]=!1:"zeroline"in o&&(this.zeroEnable[r]=o.zeroline),"zerolinecolor"in o&&(this.zeroLineColor[r]=n(o.zerolinecolor)),"zerolinewidth"in o&&(this.zeroLineWidth[r]=o.zerolinewidth),"ticks"in o&&o.ticks?this.lineTickEnable[r]=!0:this.lineTickEnable[r]=!1,"ticklen"in o&&(this.lineTickLength[r]=this._defaultLineTickLength[r]=o.ticklen),"tickcolor"in o&&(this.lineTickColor[r]=n(o.tickcolor)),"tickwidth"in o&&(this.lineTickWidth[r]=o.tickwidth),"tickangle"in o&&(this.tickAngle[r]="auto"===o.tickangle?-3600:Math.PI*-o.tickangle/180),"showticklabels"in o&&(this.tickEnable[r]=o.showticklabels),"tickfont"in o&&(o.tickfont.color&&(this.tickColor[r]=n(o.tickfont.color)),o.tickfont.family&&(this.tickFont[r]=o.tickfont.family),o.tickfont.size&&(this.tickSize[r]=o.tickfont.size)),"mirror"in o?-1!==["ticks","all","allticks"].indexOf(o.mirror)?(this.lineTickMirror[r]=!0,this.lineMirror[r]=!0):!0===o.mirror?(this.lineTickMirror[r]=!1,this.lineMirror[r]=!0):(this.lineTickMirror[r]=!1,this.lineMirror[r]=!1):this.lineMirror[r]=!1,"showbackground"in o&&!1!==o.showbackground?(this.backgroundEnable[r]=!0,this.backgroundColor[r]=n(o.backgroundcolor)):this.backgroundEnable[r]=!1):(this.tickEnable[r]=!1,this.labelEnable[r]=!1,this.lineEnable[r]=!1,this.lineTickEnable[r]=!1,this.gridEnable[r]=!1,this.zeroEnable[r]=!1,this.backgroundEnable[r]=!1)}},e.exports=function(t,e){var r=new o;return r.merge(t,e),r}},{"../../../lib":503,"../../../lib/str2rgbarray":528}],603:[function(t,e,r){"use strict";var n=t("../../../lib"),i=t("../../../components/color"),a=t("../../../registry"),o=t("../../subplot_defaults"),s=t("./axis_defaults"),l=t("./layout_attributes"),c=t("../../get_data").getSubplotData;function u(t,e,r,n){for(var o=r("bgcolor"),l=i.combine(o,n.paper_bgcolor),u=["up","center","eye"],f=0;f<u.length;f++)r("camera."+u[f]+".x"),r("camera."+u[f]+".y"),r("camera."+u[f]+".z");r("camera.projection.type");var h=!!r("aspectratio.x")&&!!r("aspectratio.y")&&!!r("aspectratio.z"),p=r("aspectmode",h?"manual":"auto");h||(t.aspectratio=e.aspectratio={x:1,y:1,z:1},"manual"===p&&(e.aspectmode="auto"),t.aspectmode=e.aspectmode);var d=c(n.fullData,"gl3d",n.id);s(t,e,{font:n.font,scene:n.id,data:d,bgColor:l,calendar:n.calendar,autotypenumbersDflt:n.autotypenumbersDflt,fullLayout:n.fullLayout}),a.getComponentMethod("annotations3d","handleDefaults")(t,e,n);var g=n.getDfltFromLayout("dragmode");if(!1!==g&&!g)if(g="orbit",t.camera&&t.camera.up){var m=t.camera.up.x,v=t.camera.up.y,y=t.camera.up.z;0!==y&&(m&&v&&y?y/Math.sqrt(m*m+v*v+y*y)>.999&&(g="turntable"):g="turntable")}else g="turntable";r("dragmode",g),r("hovermode",n.getDfltFromLayout("hovermode"))}e.exports=function(t,e,r){var i=e._basePlotModules.length>1;o(t,e,r,{type:"gl3d",attributes:l,handleDefaults:u,fullLayout:e,font:e.font,fullData:r,getDfltFromLayout:function(e){if(!i)return n.validate(t[e],l[e])?t[e]:void 0},autotypenumbersDflt:e.autotypenumbers,paper_bgcolor:e.paper_bgcolor,calendar:e.calendar})}},{"../../../components/color":366,"../../../lib":503,"../../../registry":638,"../../get_data":593,"../../subplot_defaults":632,"./axis_defaults":601,"./layout_attributes":604}],604:[function(t,e,r){"use strict";var n=t("./axis_attributes"),i=t("../../domain").attributes,a=t("../../../lib/extend").extendFlat,o=t("../../../lib").counterRegex;function s(t,e,r){return{x:{valType:"number",dflt:t,editType:"camera"},y:{valType:"number",dflt:e,editType:"camera"},z:{valType:"number",dflt:r,editType:"camera"},editType:"camera"}}e.exports={_arrayAttrRegexps:[o("scene",".annotations",!0)],bgcolor:{valType:"color",dflt:"rgba(0,0,0,0)",editType:"plot"},camera:{up:a(s(0,0,1),{}),center:a(s(0,0,0),{}),eye:a(s(1.25,1.25,1.25),{}),projection:{type:{valType:"enumerated",values:["perspective","orthographic"],dflt:"perspective",editType:"calc"},editType:"calc"},editType:"camera"},domain:i({name:"scene",editType:"plot"}),aspectmode:{valType:"enumerated",values:["auto","cube","data","manual"],dflt:"auto",editType:"plot",impliedEdits:{"aspectratio.x":void 0,"aspectratio.y":void 0,"aspectratio.z":void 0}},aspectratio:{x:{valType:"number",min:0,editType:"plot",impliedEdits:{"^aspectmode":"manual"}},y:{valType:"number",min:0,editType:"plot",impliedEdits:{"^aspectmode":"manual"}},z:{valType:"number",min:0,editType:"plot",impliedEdits:{"^aspectmode":"manual"}},editType:"plot",impliedEdits:{aspectmode:"manual"}},xaxis:n,yaxis:n,zaxis:n,dragmode:{valType:"enumerated",values:["orbit","turntable","zoom","pan",!1],editType:"plot"},hovermode:{valType:"enumerated",values:["closest",!1],dflt:"closest",editType:"modebar"},uirevision:{valType:"any",editType:"none"},editType:"plot",_deprecated:{cameraposition:{valType:"info_array",editType:"camera"}}}},{"../../../lib":503,"../../../lib/extend":493,"../../domain":584,"./axis_attributes":600}],605:[function(t,e,r){"use strict";var n=t("../../../lib/str2rgbarray"),i=["xaxis","yaxis","zaxis"];function a(){this.enabled=[!0,!0,!0],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.drawSides=[!0,!0,!0],this.lineWidth=[1,1,1]}a.prototype.merge=function(t){for(var e=0;e<3;++e){var r=t[i[e]];r.visible?(this.enabled[e]=r.showspikes,this.colors[e]=n(r.spikecolor),this.drawSides[e]=r.spikesides,this.lineWidth[e]=r.spikethickness):(this.enabled[e]=!1,this.drawSides[e]=!1)}},e.exports=function(t){var e=new a;return e.merge(t),e}},{"../../../lib/str2rgbarray":528}],606:[function(t,e,r){"use strict";e.exports=function(t){for(var e=t.axesOptions,r=t.glplot.axesPixels,s=t.fullSceneLayout,l=[[],[],[]],c=0;c<3;++c){var u=s[a[c]];if(u._length=(r[c].hi-r[c].lo)*r[c].pixelsPerDataUnit/t.dataScale[c],Math.abs(u._length)===1/0||isNaN(u._length))l[c]=[];else{u._input_range=u.range.slice(),u.range[0]=r[c].lo/t.dataScale[c],u.range[1]=r[c].hi/t.dataScale[c],u._m=1/(t.dataScale[c]*r[c].pixelsPerDataUnit),u.range[0]===u.range[1]&&(u.range[0]-=1,u.range[1]+=1);var f=u.tickmode;if("auto"===u.tickmode){u.tickmode="linear";var h=u.nticks||i.constrain(u._length/40,4,9);n.autoTicks(u,Math.abs(u.range[1]-u.range[0])/h)}for(var p=n.calcTicks(u,{msUTC:!0}),d=0;d<p.length;++d)p[d].x=p[d].x*t.dataScale[c],"date"===u.type&&(p[d].text=p[d].text.replace(/\<br\>/g," "));l[c]=p,u.tickmode=f}}e.ticks=l;for(c=0;c<3;++c){o[c]=.5*(t.glplot.bounds[0][c]+t.glplot.bounds[1][c]);for(d=0;d<2;++d)e.bounds[d][c]=t.glplot.bounds[d][c]}t.contourLevels=function(t){for(var e=new Array(3),r=0;r<3;++r){for(var n=t[r],i=new Array(n.length),a=0;a<n.length;++a)i[a]=n[a].x;e[r]=i}return e}(l)};var n=t("../../cartesian/axes"),i=t("../../../lib"),a=["xaxis","yaxis","zaxis"],o=[0,0,0]},{"../../../lib":503,"../../cartesian/axes":554}],607:[function(t,e,r){"use strict";function n(t,e){var r,n,i=[0,0,0,0];for(r=0;r<4;++r)for(n=0;n<4;++n)i[n]+=t[4*r+n]*e[r];return i}e.exports=function(t,e){return n(t.projection,n(t.view,n(t.model,[e[0],e[1],e[2],1])))}},{}],608:[function(t,e,r){"use strict";var n,i,a=t("../../../stackgl_modules").gl_plot3d,o=a.createCamera,s=a.createScene,l=t("webgl-context"),c=t("has-passive-events"),u=t("../../registry"),f=t("../../lib"),h=f.preserveDrawingBuffer(),p=t("../../plots/cartesian/axes"),d=t("../../components/fx"),g=t("../../lib/str2rgbarray"),m=t("../../lib/show_no_webgl_msg"),v=t("./project"),y=t("./layout/convert"),x=t("./layout/spikes"),b=t("./layout/tick_marks");function _(t,e){var r=document.createElement("div"),n=t.container;this.graphDiv=t.graphDiv;var i=document.createElementNS("http://www.w3.org/2000/svg","svg");i.style.position="absolute",i.style.top=i.style.left="0px",i.style.width=i.style.height="100%",i.style["z-index"]=20,i.style["pointer-events"]="none",r.appendChild(i),this.svgContainer=i,r.id=t.id,r.style.position="absolute",r.style.top=r.style.left="0px",r.style.width=r.style.height="100%",n.appendChild(r),this.fullLayout=e,this.id=t.id||"scene",this.fullSceneLayout=e[this.id],this.plotArgs=[[],{},{}],this.axesOptions=y(e,e[this.id]),this.spikeOptions=x(e[this.id]),this.container=r,this.staticMode=!!t.staticPlot,this.pixelRatio=this.pixelRatio||t.plotGlPixelRatio||2,this.dataScale=[1,1,1],this.contourLevels=[[],[],[]],this.convertAnnotations=u.getComponentMethod("annotations3d","convert"),this.drawAnnotations=u.getComponentMethod("annotations3d","draw"),this.initializeGLPlot()}var w=_.prototype;w.prepareOptions=function(){var t={canvas:this.canvas,gl:this.gl,glOptions:{preserveDrawingBuffer:h,premultipliedAlpha:!0,antialias:!0},container:this.container,axes:this.axesOptions,spikes:this.spikeOptions,pickRadius:10,snapToData:!0,autoScale:!0,autoBounds:!1,cameraObject:this.camera,pixelRatio:this.pixelRatio};if(this.staticMode){if(!(i||(n=document.createElement("canvas"),i=l({canvas:n,preserveDrawingBuffer:!0,premultipliedAlpha:!0,antialias:!0}))))throw new Error("error creating static canvas/context for image server");t.gl=i,t.canvas=n}return t};var T=!0;w.tryCreatePlot=function(){var t=this.prepareOptions(),e=!0;try{this.glplot=s(t)}catch(r){if(this.staticMode||!T||h)e=!1;else{f.warn(["webgl setup failed possibly due to","false preserveDrawingBuffer config.","The mobile/tablet device may not be detected by is-mobile module.","Enabling preserveDrawingBuffer in second attempt to create webgl scene..."].join(" "));try{h=t.glOptions.preserveDrawingBuffer=!0,this.glplot=s(t)}catch(r){h=t.glOptions.preserveDrawingBuffer=!1,e=!1}}}return T=!1,e},w.initializeGLCamera=function(){var t=this.fullSceneLayout.camera,e="orthographic"===t.projection.type;this.camera=o(this.container,{center:[t.center.x,t.center.y,t.center.z],eye:[t.eye.x,t.eye.y,t.eye.z],up:[t.up.x,t.up.y,t.up.z],_ortho:e,zoomMin:.01,zoomMax:100,mode:"orbit"})},w.initializeGLPlot=function(){var t=this;if(t.initializeGLCamera(),!t.tryCreatePlot())return m(t);t.traces={},t.make4thDimension();var e=t.graphDiv,r=e.layout,n=function(){var e={};return t.isCameraChanged(r)&&(e[t.id+".camera"]=t.getCamera()),t.isAspectChanged(r)&&(e[t.id+".aspectratio"]=t.glplot.getAspectratio(),"manual"!==r[t.id].aspectmode&&(t.fullSceneLayout.aspectmode=r[t.id].aspectmode=e[t.id+".aspectmode"]="manual")),e},i=function(t){if(!1!==t.fullSceneLayout.dragmode){var e=n();t.saveLayout(r),t.graphDiv.emit("plotly_relayout",e)}};return t.glplot.canvas&&(t.glplot.canvas.addEventListener("mouseup",(function(){i(t)})),t.glplot.canvas.addEventListener("wheel",(function(r){if(e._context._scrollZoom.gl3d){if(t.camera._ortho){var n=r.deltaX>r.deltaY?1.1:1/1.1,a=t.glplot.getAspectratio();t.glplot.setAspectratio({x:n*a.x,y:n*a.y,z:n*a.z})}i(t)}}),!!c&&{passive:!1}),t.glplot.canvas.addEventListener("mousemove",(function(){if(!1!==t.fullSceneLayout.dragmode&&0!==t.camera.mouseListener.buttons){var e=n();t.graphDiv.emit("plotly_relayouting",e)}})),t.staticMode||t.glplot.canvas.addEventListener("webglcontextlost",(function(r){e&&e.emit&&e.emit("plotly_webglcontextlost",{event:r,layer:t.id})}),!1)),t.glplot.oncontextloss=function(){t.recoverContext()},t.glplot.onrender=function(){t.render()},!0},w.render=function(){var t,e=this,r=e.graphDiv,n=e.svgContainer,i=e.container.getBoundingClientRect();r._fullLayout._calcInverseTransform(r);var a=r._fullLayout._invScaleX,o=r._fullLayout._invScaleY,s=i.width*a,l=i.height*o;n.setAttributeNS(null,"viewBox","0 0 "+s+" "+l),n.setAttributeNS(null,"width",s),n.setAttributeNS(null,"height",l),b(e),e.glplot.axes.update(e.axesOptions);for(var c=Object.keys(e.traces),u=null,h=e.glplot.selection,g=0;g<c.length;++g)"skip"!==(t=e.traces[c[g]]).data.hoverinfo&&t.handlePick(h)&&(u=t),t.setContourLevels&&t.setContourLevels();function m(t,r,n){var i=e.fullSceneLayout[t+"axis"];return"log"!==i.type&&(r=i.d2l(r)),p.hoverLabelText(i,r,n)}if(null!==u){var y=v(e.glplot.cameraParams,h.dataCoordinate);t=u.data;var x,_=r._fullData[t.index],w=h.index,T={xLabel:m("x",h.traceCoordinate[0],t.xhoverformat),yLabel:m("y",h.traceCoordinate[1],t.yhoverformat),zLabel:m("z",h.traceCoordinate[2],t.zhoverformat)},k=d.castHoverinfo(_,e.fullLayout,w),A=(k||"").split("+"),M=k&&"all"===k;_.hovertemplate||M||(-1===A.indexOf("x")&&(T.xLabel=void 0),-1===A.indexOf("y")&&(T.yLabel=void 0),-1===A.indexOf("z")&&(T.zLabel=void 0),-1===A.indexOf("text")&&(h.textLabel=void 0),-1===A.indexOf("name")&&(u.name=void 0));var S=[];"cone"===t.type||"streamtube"===t.type?(T.uLabel=m("x",h.traceCoordinate[3],t.uhoverformat),(M||-1!==A.indexOf("u"))&&S.push("u: "+T.uLabel),T.vLabel=m("y",h.traceCoordinate[4],t.vhoverformat),(M||-1!==A.indexOf("v"))&&S.push("v: "+T.vLabel),T.wLabel=m("z",h.traceCoordinate[5],t.whoverformat),(M||-1!==A.indexOf("w"))&&S.push("w: "+T.wLabel),T.normLabel=h.traceCoordinate[6].toPrecision(3),(M||-1!==A.indexOf("norm"))&&S.push("norm: "+T.normLabel),"streamtube"===t.type&&(T.divergenceLabel=h.traceCoordinate[7].toPrecision(3),(M||-1!==A.indexOf("divergence"))&&S.push("divergence: "+T.divergenceLabel)),h.textLabel&&S.push(h.textLabel),x=S.join("<br>")):"isosurface"===t.type||"volume"===t.type?(T.valueLabel=p.hoverLabelText(e._mockAxis,e._mockAxis.d2l(h.traceCoordinate[3]),t.valuehoverformat),S.push("value: "+T.valueLabel),h.textLabel&&S.push(h.textLabel),x=S.join("<br>")):x=h.textLabel;var E={x:h.traceCoordinate[0],y:h.traceCoordinate[1],z:h.traceCoordinate[2],data:_._input,fullData:_,curveNumber:_.index,pointNumber:w};d.appendArrayPointValue(E,_,w),t._module.eventData&&(E=_._module.eventData(E,h,_,{},w));var L={points:[E]};if(e.fullSceneLayout.hovermode){var C=[];d.loneHover({trace:_,x:(.5+.5*y[0]/y[3])*s,y:(.5-.5*y[1]/y[3])*l,xLabel:T.xLabel,yLabel:T.yLabel,zLabel:T.zLabel,text:x,name:u.name,color:d.castHoverOption(_,w,"bgcolor")||u.color,borderColor:d.castHoverOption(_,w,"bordercolor"),fontFamily:d.castHoverOption(_,w,"font.family"),fontSize:d.castHoverOption(_,w,"font.size"),fontColor:d.castHoverOption(_,w,"font.color"),nameLength:d.castHoverOption(_,w,"namelength"),textAlign:d.castHoverOption(_,w,"align"),hovertemplate:f.castOption(_,w,"hovertemplate"),hovertemplateLabels:f.extendFlat({},E,T),eventData:[E]},{container:n,gd:r,inOut_bbox:C}),E.bbox=C[0]}h.buttons&&h.distance<5?r.emit("plotly_click",L):r.emit("plotly_hover",L),this.oldEventData=L}else d.loneUnhover(n),this.oldEventData&&r.emit("plotly_unhover",this.oldEventData),this.oldEventData=void 0;e.drawAnnotations(e)},w.recoverContext=function(){var t=this;t.glplot.dispose();var e=function(){t.glplot.gl.isContextLost()?requestAnimationFrame(e):t.initializeGLPlot()?t.plot.apply(t,t.plotArgs):f.error("Catastrophic and unrecoverable WebGL error. Context lost.")};requestAnimationFrame(e)};var k=["xaxis","yaxis","zaxis"];function A(t,e,r){for(var n=t.fullSceneLayout,i=0;i<3;i++){var a=k[i],o=a.charAt(0),s=n[a],l=e[o],c=e[o+"calendar"],u=e["_"+o+"length"];if(f.isArrayOrTypedArray(l))for(var h,p=0;p<(u||l.length);p++)if(f.isArrayOrTypedArray(l[p]))for(var d=0;d<l[p].length;++d)h=s.d2l(l[p][d],0,c),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else h=s.d2l(l[p],0,c),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else r[0][i]=Math.min(r[0][i],0),r[1][i]=Math.max(r[1][i],u-1)}}w.plot=function(t,e,r){if(this.plotArgs=[t,e,r],!this.glplot.contextLost){var n,i,a,o,s,l,c=e[this.id],u=r[this.id];this.fullLayout=e,this.fullSceneLayout=c,this.axesOptions.merge(e,c),this.spikeOptions.merge(c),this.setViewport(c),this.updateFx(c.dragmode,c.hovermode),this.camera.enableWheel=this.graphDiv._context._scrollZoom.gl3d,this.glplot.setClearColor(g(c.bgcolor)),this.setConvert(s),t?Array.isArray(t)||(t=[t]):t=[];var f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(a=0;a<t.length;++a)!0===(n=t[a]).visible&&0!==n._length&&A(this,n,f);!function(t,e){for(var r=t.fullSceneLayout,n=r.annotations||[],i=0;i<3;i++)for(var a=k[i],o=a.charAt(0),s=r[a],l=0;l<n.length;l++){var c=n[l];if(c.visible){var u=s.r2l(c[o]);!isNaN(u)&&isFinite(u)&&(e[0][i]=Math.min(e[0][i],u),e[1][i]=Math.max(e[1][i],u))}}}(this,f);var h=[1,1,1];for(o=0;o<3;++o)f[1][o]===f[0][o]?h[o]=1:h[o]=1/(f[1][o]-f[0][o]);for(this.dataScale=h,this.convertAnnotations(this),a=0;a<t.length;++a)!0===(n=t[a]).visible&&0!==n._length&&((i=this.traces[n.uid])?i.data.type===n.type?i.update(n):(i.dispose(),i=n._module.plot(this,n),this.traces[n.uid]=i):(i=n._module.plot(this,n),this.traces[n.uid]=i),i.name=n.name);var p=Object.keys(this.traces);t:for(a=0;a<p.length;++a){for(o=0;o<t.length;++o)if(t[o].uid===p[a]&&!0===t[o].visible&&0!==t[o]._length)continue t;(i=this.traces[p[a]]).dispose(),delete this.traces[p[a]]}this.glplot.objects.sort((function(t,e){return t._trace.data.index-e._trace.data.index}));var d,m=[[0,0,0],[0,0,0]],v=[],y={};for(a=0;a<3;++a){if((l=(s=c[k[a]]).type)in y?(y[l].acc*=h[a],y[l].count+=1):y[l]={acc:h[a],count:1},s.autorange){m[0][a]=1/0,m[1][a]=-1/0;var x=this.glplot.objects,b=this.fullSceneLayout.annotations||[],_=s._name.charAt(0);for(o=0;o<x.length;o++){var w=x[o],T=w.bounds,M=w._trace.data._pad||0;"ErrorBars"===w.constructor.name&&s._lowerLogErrorBound?m[0][a]=Math.min(m[0][a],s._lowerLogErrorBound):m[0][a]=Math.min(m[0][a],T[0][a]/h[a]-M),m[1][a]=Math.max(m[1][a],T[1][a]/h[a]+M)}for(o=0;o<b.length;o++){var S=b[o];if(S.visible){var E=s.r2l(S[_]);m[0][a]=Math.min(m[0][a],E),m[1][a]=Math.max(m[1][a],E)}}if("rangemode"in s&&"tozero"===s.rangemode&&(m[0][a]=Math.min(m[0][a],0),m[1][a]=Math.max(m[1][a],0)),m[0][a]>m[1][a])m[0][a]=-1,m[1][a]=1;else{var L=m[1][a]-m[0][a];m[0][a]-=L/32,m[1][a]+=L/32}if("reversed"===s.autorange){var C=m[0][a];m[0][a]=m[1][a],m[1][a]=C}}else{var P=s.range;m[0][a]=s.r2l(P[0]),m[1][a]=s.r2l(P[1])}m[0][a]===m[1][a]&&(m[0][a]-=1,m[1][a]+=1),v[a]=m[1][a]-m[0][a],this.glplot.setBounds(a,{min:m[0][a]*h[a],max:m[1][a]*h[a]})}var I=c.aspectmode;if("cube"===I)d=[1,1,1];else if("manual"===I){var O=c.aspectratio;d=[O.x,O.y,O.z]}else{if("auto"!==I&&"data"!==I)throw new Error("scene.js aspectRatio was not one of the enumerated types");var z=[1,1,1];for(a=0;a<3;++a){var D=y[l=(s=c[k[a]]).type];z[a]=Math.pow(D.acc,1/D.count)/h[a]}d="data"===I||Math.max.apply(null,z)/Math.min.apply(null,z)<=4?z:[1,1,1]}c.aspectratio.x=u.aspectratio.x=d[0],c.aspectratio.y=u.aspectratio.y=d[1],c.aspectratio.z=u.aspectratio.z=d[2],this.glplot.setAspectratio(c.aspectratio),this.viewInitial.aspectratio||(this.viewInitial.aspectratio={x:c.aspectratio.x,y:c.aspectratio.y,z:c.aspectratio.z}),this.viewInitial.aspectmode||(this.viewInitial.aspectmode=c.aspectmode);var R=c.domain||null,F=e._size||null;if(R&&F){var B=this.container.style;B.position="absolute",B.left=F.l+R.x[0]*F.w+"px",B.top=F.t+(1-R.y[1])*F.h+"px",B.width=F.w*(R.x[1]-R.x[0])+"px",B.height=F.h*(R.y[1]-R.y[0])+"px"}this.glplot.redraw()}},w.destroy=function(){this.glplot&&(this.camera.mouseListener.enabled=!1,this.container.removeEventListener("wheel",this.camera.wheelListener),this.camera=null,this.glplot.dispose(),this.container.parentNode.removeChild(this.container),this.glplot=null)},w.getCamera=function(){var t;return this.camera.view.recalcMatrix(this.camera.view.lastT()),{up:{x:(t=this.camera).up[0],y:t.up[1],z:t.up[2]},center:{x:t.center[0],y:t.center[1],z:t.center[2]},eye:{x:t.eye[0],y:t.eye[1],z:t.eye[2]},projection:{type:!0===t._ortho?"orthographic":"perspective"}}},w.setViewport=function(t){var e,r=t.camera;this.camera.lookAt.apply(this,[[(e=r).eye.x,e.eye.y,e.eye.z],[e.center.x,e.center.y,e.center.z],[e.up.x,e.up.y,e.up.z]]),this.glplot.setAspectratio(t.aspectratio),"orthographic"===r.projection.type!==this.camera._ortho&&(this.glplot.redraw(),this.glplot.clearRGBA(),this.glplot.dispose(),this.initializeGLPlot())},w.isCameraChanged=function(t){var e=this.getCamera(),r=f.nestedProperty(t,this.id+".camera").get();function n(t,e,r,n){var i=["up","center","eye"],a=["x","y","z"];return e[i[r]]&&t[i[r]][a[n]]===e[i[r]][a[n]]}var i=!1;if(void 0===r)i=!0;else{for(var a=0;a<3;a++)for(var o=0;o<3;o++)if(!n(e,r,a,o)){i=!0;break}(!r.projection||e.projection&&e.projection.type!==r.projection.type)&&(i=!0)}return i},w.isAspectChanged=function(t){var e=this.glplot.getAspectratio(),r=f.nestedProperty(t,this.id+".aspectratio").get();return void 0===r||r.x!==e.x||r.y!==e.y||r.z!==e.z},w.saveLayout=function(t){var e,r,n,i,a,o,s=this.fullLayout,l=this.isCameraChanged(t),c=this.isAspectChanged(t),h=l||c;if(h){var p={};if(l&&(e=this.getCamera(),n=(r=f.nestedProperty(t,this.id+".camera")).get(),p[this.id+".camera"]=n),c&&(i=this.glplot.getAspectratio(),o=(a=f.nestedProperty(t,this.id+".aspectratio")).get(),p[this.id+".aspectratio"]=o),u.call("_storeDirectGUIEdit",t,s._preGUI,p),l)r.set(e),f.nestedProperty(s,this.id+".camera").set(e);if(c)a.set(i),f.nestedProperty(s,this.id+".aspectratio").set(i),this.glplot.redraw()}return h},w.updateFx=function(t,e){var r=this.camera;if(r)if("orbit"===t)r.mode="orbit",r.keyBindingMode="rotate";else if("turntable"===t){r.up=[0,0,1],r.mode="turntable",r.keyBindingMode="rotate";var n=this.graphDiv,i=n._fullLayout,a=this.fullSceneLayout.camera,o=a.up.x,s=a.up.y,l=a.up.z;if(l/Math.sqrt(o*o+s*s+l*l)<.999){var c=this.id+".camera.up",h={x:0,y:0,z:1},p={};p[c]=h;var d=n.layout;u.call("_storeDirectGUIEdit",d,i._preGUI,p),a.up=h,f.nestedProperty(d,c).set(h)}}else r.keyBindingMode=t;this.fullSceneLayout.hovermode=e},w.toImage=function(t){t||(t="png"),this.staticMode&&this.container.appendChild(n),this.glplot.redraw();var e=this.glplot.gl,r=e.drawingBufferWidth,i=e.drawingBufferHeight;e.bindFramebuffer(e.FRAMEBUFFER,null);var a=new Uint8Array(r*i*4);e.readPixels(0,0,r,i,e.RGBA,e.UNSIGNED_BYTE,a),function(t,e,r){for(var n=0,i=r-1;n<i;++n,--i)for(var a=0;a<e;++a)for(var o=0;o<4;++o){var s=4*(e*n+a)+o,l=4*(e*i+a)+o,c=t[s];t[s]=t[l],t[l]=c}}(a,r,i),function(t,e,r){for(var n=0;n<r;++n)for(var i=0;i<e;++i){var a=4*(e*n+i),o=t[a+3];if(o>0)for(var s=255/o,l=0;l<3;++l)t[a+l]=Math.min(s*t[a+l],255)}}(a,r,i);var o=document.createElement("canvas");o.width=r,o.height=i;var s,l=o.getContext("2d"),c=l.createImageData(r,i);switch(c.data.set(a),l.putImageData(c,0,0),t){case"jpeg":s=o.toDataURL("image/jpeg");break;case"webp":s=o.toDataURL("image/webp");break;default:s=o.toDataURL("image/png")}return this.staticMode&&this.container.removeChild(n),s},w.setConvert=function(){for(var t=0;t<3;t++){var e=this.fullSceneLayout[k[t]];p.setConvert(e,this.fullLayout),e.setScale=f.noop}},w.make4thDimension=function(){var t=this.graphDiv._fullLayout;this._mockAxis={type:"linear",showexponent:"all",exponentformat:"B"},p.setConvert(this._mockAxis,t)},e.exports=_},{"../../../stackgl_modules":1119,"../../components/fx":406,"../../lib":503,"../../lib/show_no_webgl_msg":525,"../../lib/str2rgbarray":528,"../../plots/cartesian/axes":554,"../../registry":638,"./layout/convert":602,"./layout/spikes":605,"./layout/tick_marks":606,"./project":607,"has-passive-events":229,"webgl-context":331}],609:[function(t,e,r){"use strict";e.exports=function(t,e,r,n){n=n||t.length;for(var i=new Array(n),a=0;a<n;a++)i[a]=[t[a],e[a],r[a]];return i}},{}],610:[function(t,e,r){"use strict";var n=t("./font_attributes"),i=t("./animation_attributes"),a=t("../components/color/attributes"),o=t("../components/shapes/draw_newshape/attributes"),s=t("./pad_attributes"),l=t("../lib/extend").extendFlat,c=n({editType:"calc"});c.family.dflt='"Open Sans", verdana, arial, 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n=t("../../lib/sort_object_keys"),i='\xa9 <a target="_blank" href="https://www.openstreetmap.org/copyright">OpenStreetMap</a> contributors',a=['\xa9 <a target="_blank" href="https://carto.com/">Carto</a>',i].join(" "),o=['Map tiles by <a target="_blank" href="https://stamen.com">Stamen Design</a>','under <a target="_blank" href="https://creativecommons.org/licenses/by/3.0">CC BY 3.0</a>',"|",'Data by <a target="_blank" href="https://openstreetmap.org">OpenStreetMap</a> contributors','under <a target="_blank" href="https://www.openstreetmap.org/copyright">ODbL</a>'].join(" "),s={"open-street-map":{id:"osm",version:8,sources:{"plotly-osm-tiles":{type:"raster",attribution:i,tiles:["https://a.tile.openstreetmap.org/{z}/{x}/{y}.png","https://b.tile.openstreetmap.org/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-osm-tiles",type:"raster",source:"plotly-osm-tiles",minzoom:0,maxzoom:22}]},"white-bg":{id:"white-bg",version:8,sources:{},layers:[{id:"white-bg",type:"background",paint:{"background-color":"#FFFFFF"},minzoom:0,maxzoom:22}]},"carto-positron":{id:"carto-positron",version:8,sources:{"plotly-carto-positron":{type:"raster",attribution:a,tiles:["https://cartodb-basemaps-c.global.ssl.fastly.net/light_all/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-carto-positron",type:"raster",source:"plotly-carto-positron",minzoom:0,maxzoom:22}]},"carto-darkmatter":{id:"carto-darkmatter",version:8,sources:{"plotly-carto-darkmatter":{type:"raster",attribution:a,tiles:["https://cartodb-basemaps-c.global.ssl.fastly.net/dark_all/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-carto-darkmatter",type:"raster",source:"plotly-carto-darkmatter",minzoom:0,maxzoom:22}]},"stamen-terrain":{id:"stamen-terrain",version:8,sources:{"plotly-stamen-terrain":{type:"raster",attribution:o,tiles:["https://stamen-tiles.a.ssl.fastly.net/terrain/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-stamen-terrain",type:"raster",source:"plotly-stamen-terrain",minzoom:0,maxzoom:22}]},"stamen-toner":{id:"stamen-toner",version:8,sources:{"plotly-stamen-toner":{type:"raster",attribution:o,tiles:["https://stamen-tiles.a.ssl.fastly.net/toner/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-stamen-toner",type:"raster",source:"plotly-stamen-toner",minzoom:0,maxzoom:22}]},"stamen-watercolor":{id:"stamen-watercolor",version:8,sources:{"plotly-stamen-watercolor":{type:"raster",attribution:['Map tiles by <a target="_blank" href="https://stamen.com">Stamen Design</a>','under <a target="_blank" href="https://creativecommons.org/licenses/by/3.0">CC BY 3.0</a>',"|",'Data by <a target="_blank" href="https://openstreetmap.org">OpenStreetMap</a> contributors','under <a target="_blank" href="https://creativecommons.org/licenses/by-sa/3.0">CC BY SA</a>'].join(" "),tiles:["https://stamen-tiles.a.ssl.fastly.net/watercolor/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-stamen-watercolor",type:"raster",source:"plotly-stamen-watercolor",minzoom:0,maxzoom:22}]}},l=n(s);e.exports={requiredVersion:"1.10.1",styleUrlPrefix:"mapbox://styles/mapbox/",styleUrlSuffix:"v9",styleValuesMapbox:["basic","streets","outdoors","light","dark","satellite","satellite-streets"],styleValueDflt:"basic",stylesNonMapbox:s,styleValuesNonMapbox:l,traceLayerPrefix:"plotly-trace-layer-",layoutLayerPrefix:"plotly-layout-layer-",wrongVersionErrorMsg:["Your custom plotly.js bundle is not using the correct mapbox-gl 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16.59,8.09 14.74,6.25 Z m -2.27,4.09 -0.91,1.87 -0.9,-1.87 -1.86,-0.91 1.86,-0.9 0.9,-1.87 0.91,1.87 1.86,0.9 z" style="opacity:0.35;enable-background:new" class="st1"/%3E%3Cpolygon points="11.56,12.21 10.66,10.34 8.8,9.43 10.66,8.53 11.56,6.66 12.47,8.53 14.33,9.43 12.47,10.34 " style="opacity:0.9;fill:%23ffffff;enable-background:new" class="st0"/%3E%3C/g%3E%3C/svg%3E\')'}}},{"../../lib/sort_object_keys":526}],612:[function(t,e,r){"use strict";var n=t("../../lib");e.exports=function(t,e){var r=t.split(" "),i=r[0],a=r[1],o=n.isArrayOrTypedArray(e)?n.mean(e):e,s=.5+o/100,l=1.5+o/100,c=["",""],u=[0,0];switch(i){case"top":c[0]="top",u[1]=-l;break;case"bottom":c[0]="bottom",u[1]=l}switch(a){case"left":c[1]="right",u[0]=-s;break;case"right":c[1]="left",u[0]=s}return{anchor:c[0]&&c[1]?c.join("-"):c[0]?c[0]:c[1]?c[1]:"center",offset:u}}},{"../../lib":503}],613:[function(t,e,r){"use strict";var n=t("mapbox-gl/dist/mapbox-gl-unminified"),i=t("../../lib"),a=i.strTranslate,o=i.strScale,s=t("../../plots/get_data").getSubplotCalcData,l=t("../../constants/xmlns_namespaces"),c=t("@plotly/d3"),u=t("../../components/drawing"),f=t("../../lib/svg_text_utils"),h=t("./mapbox"),p=r.constants=t("./constants");function d(t){return"string"==typeof t&&(-1!==p.styleValuesMapbox.indexOf(t)||0===t.indexOf("mapbox://"))}r.name="mapbox",r.attr="subplot",r.idRoot="mapbox",r.idRegex=r.attrRegex=i.counterRegex("mapbox"),r.attributes={subplot:{valType:"subplotid",dflt:"mapbox",editType:"calc"}},r.layoutAttributes=t("./layout_attributes"),r.supplyLayoutDefaults=t("./layout_defaults"),r.plot=function(t){var e=t._fullLayout,r=t.calcdata,a=e._subplots.mapbox;if(n.version!==p.requiredVersion)throw new Error(p.wrongVersionErrorMsg);var o=function(t,e){var r=t._fullLayout;if(""===t._context.mapboxAccessToken)return"";for(var n=[],a=[],o=!1,s=!1,l=0;l<e.length;l++){var 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s=e[r[i]],h=s.domain,d=s._subplot.toImage("png");e._glimages.append("svg:image").attr({xmlns:l.svg,"xlink:href":d,x:n.l+n.w*h.x[0],y:n.t+n.h*(1-h.y[1]),width:n.w*(h.x[1]-h.x[0]),height:n.h*(h.y[1]-h.y[0]),preserveAspectRatio:"none"});var g=c.select(s._subplot.div);if(!(null===g.select(".mapboxgl-ctrl-logo").node().offsetParent)){var m=e._glimages.append("g");m.attr("transform",a(n.l+n.w*h.x[0]+10,n.t+n.h*(1-h.y[0])-31)),m.append("path").attr("d",p.mapboxLogo.path0).style({opacity:.9,fill:"#ffffff","enable-background":"new"}),m.append("path").attr("d",p.mapboxLogo.path1).style("opacity",.35).style("enable-background","new"),m.append("path").attr("d",p.mapboxLogo.path2).style("opacity",.35).style("enable-background","new"),m.append("polygon").attr("points",p.mapboxLogo.polygon).style({opacity:.9,fill:"#ffffff","enable-background":"new"})}var v=g.select(".mapboxgl-ctrl-attrib").text().replace("Improve this 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i=t.symbol,o=a(i.textposition,i.iconsize);n.extendFlat(e,{"icon-image":i.icon+"-15","icon-size":i.iconsize/10,"text-field":i.text,"text-size":i.textfont.size,"text-anchor":o.anchor,"text-offset":o.offset,"symbol-placement":i.placement}),n.extendFlat(r,{"icon-color":t.color,"text-color":i.textfont.color,"text-opacity":t.opacity});break;case"raster":n.extendFlat(r,{"raster-fade-duration":0,"raster-opacity":t.opacity})}return{layout:e,paint:r}}l.update=function(t){this.visible?this.needsNewImage(t)?this.updateImage(t):this.needsNewSource(t)?(this.removeLayer(),this.updateSource(t),this.updateLayer(t)):this.needsNewLayer(t)?this.updateLayer(t):this.updateStyle(t):(this.updateSource(t),this.updateLayer(t)),this.visible=c(t)},l.needsNewImage=function(t){return this.subplot.map.getSource(this.idSource)&&"image"===this.sourceType&&"image"===t.sourcetype&&(this.source!==t.source||JSON.stringify(this.coordinates)!==JSON.stringify(t.coordinates))},l.needsNewSource=function(t){return this.sourceType!==t.sourcetype||JSON.stringify(this.source)!==JSON.stringify(t.source)||this.layerType!==t.type},l.needsNewLayer=function(t){return this.layerType!==t.type||this.below!==this.subplot.belowLookup["layout-"+this.index]},l.lookupBelow=function(){return this.subplot.belowLookup["layout-"+this.index]},l.updateImage=function(t){this.subplot.map.getSource(this.idSource).updateImage({url:t.source,coordinates:t.coordinates});var e=this.findFollowingMapboxLayerId(this.lookupBelow());null!==e&&this.subplot.map.moveLayer(this.idLayer,e)},l.updateSource=function(t){var e=this.subplot.map;if(e.getSource(this.idSource)&&e.removeSource(this.idSource),this.sourceType=t.sourcetype,this.source=t.source,c(t)){var r=function(t){var e,r=t.sourcetype,n=t.source,a={type:r};"geojson"===r?e="data":"vector"===r?e="string"==typeof 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n=t("../../lib").extendFlat,i=t("../../plots/cartesian/axis_format_attributes").axisHoverFormat,a=t("../ohlc/attributes"),o=t("../box/attributes");function s(t){return{line:{color:n({},o.line.color,{dflt:t}),width:o.line.width,editType:"style"},fillcolor:o.fillcolor,editType:"style"}}e.exports={xperiod:a.xperiod,xperiod0:a.xperiod0,xperiodalignment:a.xperiodalignment,xhoverformat:i("x"),yhoverformat:i("y"),x:a.x,open:a.open,high:a.high,low:a.low,close:a.close,line:{width:n({},o.line.width,{}),editType:"style"},increasing:s(a.increasing.line.color.dflt),decreasing:s(a.decreasing.line.color.dflt),text:a.text,hovertext:a.hovertext,whiskerwidth:n({},o.whiskerwidth,{dflt:0}),hoverlabel:a.hoverlabel}},{"../../lib":503,"../../plots/cartesian/axis_format_attributes":557,"../box/attributes":673,"../ohlc/attributes":871}],686:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../../plots/cartesian/axes"),a=t("../../plots/cartesian/align_period"),o=t("../ohlc/calc").calcCommon;function 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n=t("../../lib/extend").extendFlat,i=t("../../lib/extend").extendDeep,a=t("../../plot_api/edit_types").overrideAll,o=t("../../plots/font_attributes"),s=t("../../components/color/attributes"),l=t("../../plots/domain").attributes,c=t("../../plots/cartesian/layout_attributes"),u=t("../../plot_api/plot_template").templatedArray,f=t("../../constants/delta.js"),h=t("../../plots/cartesian/axis_format_attributes").descriptionOnlyNumbers,p=o({editType:"plot",colorEditType:"plot"}),d={color:{valType:"color",editType:"plot"},line:{color:{valType:"color",dflt:s.defaultLine,editType:"plot"},width:{valType:"number",min:0,dflt:0,editType:"plot"},editType:"calc"},thickness:{valType:"number",min:0,max:1,dflt:1,editType:"plot"},editType:"calc"},g={valType:"info_array",items:[{valType:"number",editType:"plot"},{valType:"number",editType:"plot"}],editType:"plot"},m=u("step",i({},d,{range:g}));e.exports={mode:{valType:"flaglist",editType:"calc",flags:["number","delta","gauge"],dflt:"number"},value:{valType:"number",editType:"calc",anim:!0},align:{valType:"enumerated",values:["left","center","right"],editType:"plot"},domain:l({name:"indicator",trace:!0,editType:"calc"}),title:{text:{valType:"string",editType:"plot"},align:{valType:"enumerated",values:["left","center","right"],editType:"plot"},font:n({},p,{}),editType:"plot"},number:{valueformat:{valType:"string",dflt:"",editType:"plot",description:h("value")},font:n({},p,{}),prefix:{valType:"string",dflt:"",editType:"plot"},suffix:{valType:"string",dflt:"",editType:"plot"},editType:"plot"},delta:{reference:{valType:"number",editType:"calc"},position:{valType:"enumerated",values:["top","bottom","left","right"],dflt:"bottom",editType:"plot"},relative:{valType:"boolean",editType:"plot",dflt:!1},valueformat:{valType:"string",editType:"plot",description:h("value")},increasing:{symbol:{valType:"string",dflt:f.INCREASING.SYMBOL,editType:"plot"},color:{valType:"color",dflt:f.INCREASING.COLOR,editType:"plot"},editType:"plot"},decreasing:{symbol:{valType:"string",dflt:f.DECREASING.SYMBOL,editType:"plot"},color:{valType:"color",dflt:f.DECREASING.COLOR,editType:"plot"},editType:"plot"},font:n({},p,{}),editType:"calc"},gauge:{shape:{valType:"enumerated",editType:"plot",dflt:"angular",values:["angular","bullet"]},bar:i({},d,{color:{dflt:"green"}}),bgcolor:{valType:"color",editType:"plot"},bordercolor:{valType:"color",dflt:s.defaultLine,editType:"plot"},borderwidth:{valType:"number",min:0,dflt:1,editType:"plot"},axis:a({range:g,visible:n({},c.visible,{dflt:!0}),tickmode:c.tickmode,nticks:c.nticks,tick0:c.tick0,dtick:c.dtick,tickvals:c.tickvals,ticktext:c.ticktext,ticks:n({},c.ticks,{dflt:"outside"}),ticklen:c.ticklen,tickwidth:c.tickwidth,tickcolor:c.tickcolor,showticklabels:c.showticklabels,tickfont:o({}),tickangle:c.tickangle,tickformat:c.tickformat,tickformatstops:c.tickformatstops,tickprefix:c.tickprefix,showtickprefix:c.showtickprefix,ticksuffix:c.ticksuffix,showticksuffix:c.showticksuffix,separatethousands:c.separatethousands,exponentformat:c.exponentformat,minexponent:c.minexponent,showexponent:c.showexponent,editType:"plot"},"plot"),steps:m,threshold:{line:{color:n({},d.line.color,{}),width:n({},d.line.width,{dflt:1}),editType:"plot"},thickness:n({},d.thickness,{dflt:.85}),value:{valType:"number",editType:"calc",dflt:!1},editType:"plot"},editType:"plot"}}},{"../../components/color/attributes":365,"../../constants/delta.js":473,"../../lib/extend":493,"../../plot_api/edit_types":536,"../../plot_api/plot_template":543,"../../plots/cartesian/axis_format_attributes":557,"../../plots/cartesian/layout_attributes":569,"../../plots/domain":584,"../../plots/font_attributes":585}],855:[function(t,e,r){"use 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n=t("../../lib"),i=t("./attributes"),a=t("../../plots/domain").defaults,o=t("../../plot_api/plot_template"),s=t("../../plots/array_container_defaults"),l=t("./constants.js"),c=t("../../plots/cartesian/tick_value_defaults"),u=t("../../plots/cartesian/tick_mark_defaults"),f=t("../../plots/cartesian/tick_label_defaults"),h=t("../../plots/cartesian/prefix_suffix_defaults");function p(t,e){function r(r,a){return n.coerce(t,e,i.gauge.steps,r,a)}r("color"),r("line.color"),r("line.width"),r("range"),r("thickness")}e.exports={supplyDefaults:function(t,e,r,d){function g(r,a){return n.coerce(t,e,i,r,a)}a(e,d,g),g("mode"),e._hasNumber=-1!==e.mode.indexOf("number"),e._hasDelta=-1!==e.mode.indexOf("delta"),e._hasGauge=-1!==e.mode.indexOf("gauge");var m=g("value");e._range=[0,"number"==typeof m?1.5*m:1];var v,y,x,b,_,w,T=new Array(2);function k(t,e){return n.coerce(x,b,i.gauge,t,e)}function A(t,e){return 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l(t,e){return o.coerce(i,a,v,t,e)}return g(i,a,l,s,n),m(i,a,l,s),a}function A(t,e,r){return[Math.min(e/t.width,r/t.height),t,e+"x"+r]}function M(t,e,r,i){var a=document.createElementNS("http://www.w3.org/2000/svg","text"),o=n.select(a);return o.text(t).attr("x",0).attr("y",0).attr("text-anchor",r).attr("data-unformatted",t).call(p.convertToTspans,i).call(f.font,e),f.bBox(o.node())}function S(t,e,r,n,i,a){var s="_cache"+e;t[s]&&t[s].key===i||(t[s]={key:i,value:r});var l=o.aggNums(a,null,[t[s].value,n],2);return t[s].value=l,l}e.exports=function(t,e,r,g){var m,v=t._fullLayout;w(r)&&g&&(m=g()),o.makeTraceGroups(v._indicatorlayer,e,"trace").each((function(e){var g,E,L,C,P,I=e[0].trace,O=n.select(this),z=I._hasGauge,D=I._isAngular,R=I._isBullet,F=I.domain,B={w:v._size.w*(F.x[1]-F.x[0]),h:v._size.h*(F.y[1]-F.y[0]),l:v._size.l+v._size.w*F.x[0],r:v._size.r+v._size.w*(1-F.x[1]),t:v._size.t+v._size.h*(1-F.y[1]),b:v._size.b+v._size.h*F.y[0]},N=B.l+B.w/2,j=B.t+B.h/2,U=Math.min(B.w/2,B.h),V=h.innerRadius*U,H=I.align||"center";if(E=j,z){if(D&&(g=N,E=j+U/2,L=function(t){return function(t,e){var r=Math.sqrt(t.width/2*(t.width/2)+t.height*t.height);return[e/r,t,e]}(t,.9*V)}),R){var q=h.bulletPadding,G=1-h.bulletNumberDomainSize+q;g=B.l+(G+(1-G)*b[H])*B.w,L=function(t){return A(t,(h.bulletNumberDomainSize-q)*B.w,B.h)}}}else g=B.l+b[H]*B.w,L=function(t){return A(t,B.w,B.h)};!function(t,e,r,i){var c,u,h,g=r[0].trace,m=i.numbersX,v=i.numbersY,T=g.align||"center",A=x[T],E=i.transitionOpts,L=i.onComplete,C=o.ensureSingle(e,"g","numbers"),P=[];g._hasNumber&&P.push("number");g._hasDelta&&(P.push("delta"),"left"===g.delta.position&&P.reverse());var I=C.selectAll("text").data(P);function O(e,r,n,i){if(!e.match("s")||n>=0==i>=0||r(n).slice(-1).match(_)||r(i).slice(-1).match(_))return r;var a=e.slice().replace("s","f").replace(/\d+/,(function(t){return parseInt(t)-1})),o=k(t,{tickformat:a});return function(t){return Math.abs(t)<1?d.tickText(o,t).text:r(t)}}I.enter().append("text"),I.attr("text-anchor",(function(){return A})).attr("class",(function(t){return t})).attr("x",null).attr("y",null).attr("dx",null).attr("dy",null),I.exit().remove();var z,D=g.mode+g.align;g._hasDelta&&(z=function(){var e=k(t,{tickformat:g.delta.valueformat},g._range);e.setScale(),d.prepTicks(e);var i=function(t){return d.tickText(e,t).text},o=function(t){return g.delta.relative?t.relativeDelta:t.delta},s=function(t,e){return 0===t||"number"!=typeof t||isNaN(t)?"-":(t>0?g.delta.increasing.symbol:g.delta.decreasing.symbol)+e(t)},l=function(t){return t.delta>=0?g.delta.increasing.color:g.delta.decreasing.color};void 0===g._deltaLastValue&&(g._deltaLastValue=o(r[0]));var c=C.select("text.delta");function h(){c.text(s(o(r[0]),i)).call(y.fill,l(r[0])).call(p.convertToTspans,t)}return c.call(f.font,g.delta.font).call(y.fill,l({delta:g._deltaLastValue})),w(E)?c.transition().duration(E.duration).ease(E.easing).tween("text",(function(){var t=n.select(this),e=o(r[0]),c=g._deltaLastValue,u=O(g.delta.valueformat,i,c,e),f=a(c,e);return g._deltaLastValue=e,function(e){t.text(s(f(e),u)),t.call(y.fill,l({delta:f(e)}))}})).each("end",(function(){h(),L&&L()})).each("interrupt",(function(){h(),L&&L()})):h(),u=M(s(o(r[0]),i),g.delta.font,A,t),c}(),D+=g.delta.position+g.delta.font.size+g.delta.font.family+g.delta.valueformat,D+=g.delta.increasing.symbol+g.delta.decreasing.symbol,h=u);g._hasNumber&&(!function(){var e=k(t,{tickformat:g.number.valueformat},g._range);e.setScale(),d.prepTicks(e);var i=function(t){return d.tickText(e,t).text},o=g.number.suffix,s=g.number.prefix,l=C.select("text.number");function u(){var 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R,F,B=[(c.left+c.right)/2,(c.top+c.bottom)/2],N=[(u.left+u.right)/2,(u.top+u.bottom)/2],j=.75*g.delta.font.size;"left"===g.delta.position&&(R=S(g,"deltaPos",0,-1*(c.width*b[g.align]+u.width*(1-b[g.align])+j),D,Math.min),F=B[1]-N[1],h={width:c.width+u.width+j,height:Math.max(c.height,u.height),left:u.left+R,right:c.right,top:Math.min(c.top,u.top+F),bottom:Math.max(c.bottom,u.bottom+F)}),"right"===g.delta.position&&(R=S(g,"deltaPos",0,c.width*(1-b[g.align])+u.width*b[g.align]+j,D,Math.max),F=B[1]-N[1],h={width:c.width+u.width+j,height:Math.max(c.height,u.height),left:c.left,right:u.right+R,top:Math.min(c.top,u.top+F),bottom:Math.max(c.bottom,u.bottom+F)}),"bottom"===g.delta.position&&(R=null,F=u.height,h={width:Math.max(c.width,u.width),height:c.height+u.height,left:Math.min(c.left,u.left),right:Math.max(c.right,u.right),top:c.bottom-c.height,bottom:c.bottom+u.height}),"top"===g.delta.position&&(R=null,F=c.top,h={width:Math.max(c.width,u.width),height:c.height+u.height,left:Math.min(c.left,u.left),right:Math.max(c.right,u.right),top:c.bottom-c.height-u.height,bottom:c.bottom}),z.attr({dx:R,dy:F})}(g._hasNumber||g._hasDelta)&&C.attr("transform",(function(){var t=i.numbersScaler(h);D+=t[2];var e,r=S(g,"numbersScale",1,t[0],D,Math.min);g._scaleNumbers||(r=1),e=g._isAngular?v-r*h.bottom:v-r*(h.top+h.bottom)/2,g._numbersTop=r*h.top+e;var n=h[T];"center"===T&&(n=(h.left+h.right)/2);var a=m-r*n;return a=S(g,"numbersTranslate",0,a,D,Math.max),l(a,e)+s(r)}))}(t,O,e,{numbersX:g,numbersY:E,numbersScaler:L,transitionOpts:r,onComplete:m}),z&&(C={range:I.gauge.axis.range,color:I.gauge.bgcolor,line:{color:I.gauge.bordercolor,width:0},thickness:1},P={range:I.gauge.axis.range,color:"rgba(0, 0, 0, 0)",line:{color:I.gauge.bordercolor,width:I.gauge.borderwidth},thickness:1});var Y=O.selectAll("g.angular").data(D?e:[]);Y.exit().remove();var W=O.selectAll("g.angularaxis").data(D?e:[]);W.exit().remove(),D&&function(t,e,r,a){var o,s,f,h,p=r[0].trace,g=a.size,m=a.radius,v=a.innerRadius,y=a.gaugeBg,x=a.gaugeOutline,b=[g.l+g.w/2,g.t+g.h/2+m/2],_=a.gauge,A=a.layer,M=a.transitionOpts,S=a.onComplete,E=Math.PI/2;function L(t){var e=p.gauge.axis.range[0],r=(t-e)/(p.gauge.axis.range[1]-e)*Math.PI-E;return r<-E?-E:r>E?E:r}function C(t){return n.svg.arc().innerRadius((v+m)/2-t/2*(m-v)).outerRadius((v+m)/2+t/2*(m-v)).startAngle(-E)}function P(t){t.attr("d",(function(t){return C(t.thickness).startAngle(L(t.range[0])).endAngle(L(t.range[1]))()}))}_.enter().append("g").classed("angular",!0),_.attr("transform",l(b[0],b[1])),A.enter().append("g").classed("angularaxis",!0).classed("crisp",!0),A.selectAll("g.xangularaxistick,path,text").remove(),(o=k(t,p.gauge.axis)).type="linear",o.range=p.gauge.axis.range,o._id="xangularaxis",o.ticklabeloverflow="allow",o.setScale();var I=function(t){return(o.range[0]-t.x)/(o.range[1]-o.range[0])*Math.PI+Math.PI},O={},z=d.makeLabelFns(o,0).labelStandoff;O.xFn=function(t){var e=I(t);return Math.cos(e)*z},O.yFn=function(t){var e=I(t),r=Math.sin(e)>0?.2:1;return-Math.sin(e)*(z+t.fontSize*r)+Math.abs(Math.cos(e))*(t.fontSize*u)},O.anchorFn=function(t){var e=I(t),r=Math.cos(e);return 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U=j.select("path");w(M)?(U.transition().duration(M.duration).ease(M.easing).each("end",(function(){S&&S()})).each("interrupt",(function(){S&&S()})).attrTween("d",(V=N,H=L(r[0].lastY),q=L(r[0].y),function(){var t=i(H,q);return function(e){return V.endAngle(t(e))()}})),p._lastValue=r[0].y):U.attr("d","number"==typeof r[0].y?N.endAngle(L(r[0].y)):"M0,0Z");var V,H,q;U.call(T),j.exit().remove(),F=[];var G=p.gauge.threshold.value;(G||0===G)&&F.push({range:[G,G],color:p.gauge.threshold.color,line:{color:p.gauge.threshold.line.color,width:p.gauge.threshold.line.width},thickness:p.gauge.threshold.thickness});var Y=_.selectAll("g.threshold-arc").data(F);Y.enter().append("g").classed("threshold-arc",!0).append("path"),Y.select("path").call(P).call(T),Y.exit().remove();var W=_.selectAll("g.gauge-outline").data([x]);W.enter().append("g").classed("gauge-outline",!0).append("path"),W.select("path").call(P).call(T),W.exit().remove()}(t,0,e,{radius:U,innerRadius:V,gauge:Y,layer:W,size:B,gaugeBg:C,gaugeOutline:P,transitionOpts:r,onComplete:m});var X=O.selectAll("g.bullet").data(R?e:[]);X.exit().remove();var Z=O.selectAll("g.bulletaxis").data(R?e:[]);Z.exit().remove(),R&&function(t,e,r,n){var i,a,o,s,c,u=r[0].trace,f=n.gauge,p=n.layer,g=n.gaugeBg,m=n.gaugeOutline,v=n.size,x=u.domain,b=n.transitionOpts,_=n.onComplete;f.enter().append("g").classed("bullet",!0),f.attr("transform",l(v.l,v.t)),p.enter().append("g").classed("bulletaxis",!0).classed("crisp",!0),p.selectAll("g.xbulletaxistick,path,text").remove();var A=v.h,M=u.gauge.bar.thickness*A,S=x.x[0],E=x.x[0]+(x.x[1]-x.x[0])*(u._hasNumber||u._hasDelta?1-h.bulletNumberDomainSize:1);(i=k(t,u.gauge.axis))._id="xbulletaxis",i.domain=[S,E],i.setScale(),a=d.calcTicks(i),o=d.makeTransTickFn(i),s=d.getTickSigns(i)[2],c=v.t+v.h,i.visible&&(d.drawTicks(t,i,{vals:"inside"===i.ticks?d.clipEnds(i,a):a,layer:p,path:d.makeTickPath(i,c,s),transFn:o}),d.drawLabels(t,i,{vals:a,layer:p,transFn:o,labelFns:d.makeLabelFns(i,c)}));function L(t){t.attr("width",(function(t){return Math.max(0,i.c2p(t.range[1])-i.c2p(t.range[0]))})).attr("x",(function(t){return i.c2p(t.range[0])})).attr("y",(function(t){return.5*(1-t.thickness)*A})).attr("height",(function(t){return t.thickness*A}))}var C=[g].concat(u.gauge.steps),P=f.selectAll("g.bg-bullet").data(C);P.enter().append("g").classed("bg-bullet",!0).append("rect"),P.select("rect").call(L).call(T),P.exit().remove();var I=f.selectAll("g.value-bullet").data([u.gauge.bar]);I.enter().append("g").classed("value-bullet",!0).append("rect"),I.select("rect").attr("height",M).attr("y",(A-M)/2).call(T),w(b)?I.select("rect").transition().duration(b.duration).ease(b.easing).each("end",(function(){_&&_()})).each("interrupt",(function(){_&&_()})).attr("width",Math.max(0,i.c2p(Math.min(u.gauge.axis.range[1],r[0].y)))):I.select("rect").attr("width","number"==typeof r[0].y?Math.max(0,i.c2p(Math.min(u.gauge.axis.range[1],r[0].y))):0);I.exit().remove();var O=r.filter((function(){return u.gauge.threshold.value||0===u.gauge.threshold.value})),z=f.selectAll("g.threshold-bullet").data(O);z.enter().append("g").classed("threshold-bullet",!0).append("line"),z.select("line").attr("x1",i.c2p(u.gauge.threshold.value)).attr("x2",i.c2p(u.gauge.threshold.value)).attr("y1",(1-u.gauge.threshold.thickness)/2*A).attr("y2",(1-(1-u.gauge.threshold.thickness)/2)*A).call(y.stroke,u.gauge.threshold.line.color).style("stroke-width",u.gauge.threshold.line.width),z.exit().remove();var D=f.selectAll("g.gauge-outline").data([m]);D.enter().append("g").classed("gauge-outline",!0).append("rect"),D.select("rect").call(L).call(T),D.exit().remove()}(t,0,e,{gauge:X,layer:Z,size:B,gaugeBg:C,gaugeOutline:P,transitionOpts:r,onComplete:m});var J=O.selectAll("text.title").data(e);J.exit().remove(),J.enter().append("text").classed("title",!0),J.attr("text-anchor",(function(){return R?x.right:x[I.title.align]})).text(I.title.text).call(f.font,I.title.font).call(p.convertToTspans,t),J.attr("transform",(function(){var t,e=B.l+B.w*b[I.title.align],r=h.titlePadding,n=f.bBox(J.node());if(z){if(D)if(I.gauge.axis.visible)t=f.bBox(W.node()).top-r-n.bottom;else t=B.t+B.h/2-U/2-n.bottom-r;R&&(t=E-(n.top+n.bottom)/2,e=B.l-h.bulletPadding*B.w)}else t=I._numbersTop-r-n.bottom;return l(e,t)}))}))}},{"../../components/color":366,"../../components/drawing":388,"../../constants/alignment":471,"../../lib":503,"../../lib/svg_text_utils":529,"../../plots/cartesian/axes":554,"../../plots/cartesian/axis_defaults":556,"../../plots/cartesian/layout_attributes":569,"../../plots/cartesian/position_defaults":572,"./constants":857,"@plotly/d3":58,"d3-interpolate":116}],861:[function(t,e,r){"use strict";var n=t("../../components/colorscale/attributes"),i=t("../../plots/cartesian/axis_format_attributes").axisHoverFormat,a=t("../../plots/template_attributes").hovertemplateAttrs,o=t("../mesh3d/attributes"),s=t("../../plots/attributes"),l=t("../../lib/extend").extendFlat,c=t("../../plot_api/edit_types").overrideAll;var u=e.exports=c(l({x:{valType:"data_array"},y:{valType:"data_array"},z:{valType:"data_array"},value:{valType:"data_array"},isomin:{valType:"number"},isomax:{valType:"number"},surface:{show:{valType:"boolean",dflt:!0},count:{valType:"integer",dflt:2,min:1},fill:{valType:"number",min:0,max:1,dflt:1},pattern:{valType:"flaglist",flags:["A","B","C","D","E"],extras:["all","odd","even"],dflt:"all"}},spaceframe:{show:{valType:"boolean",dflt:!1},fill:{valType:"number",min:0,max:1,dflt:.15}},slices:{x:{show:{valType:"boolean",dflt:!1},locations:{valType:"data_array",dflt:[]},fill:{valType:"number",min:0,max:1,dflt:1}},y:{show:{valType:"boolean",dflt:!1},locations:{valType:"data_array",dflt:[]},fill:{valType:"number",min:0,max:1,dflt:1}},z:{show:{valType:"boolean",dflt:!1},locations:{valType:"data_array",dflt:[]},fill:{valType:"number",min:0,max:1,dflt:1}}},caps:{x:{show:{valType:"boolean",dflt:!0},fill:{valType:"number",min:0,max:1,dflt:1}},y:{show:{valType:"boolean",dflt:!0},fill:{valType:"number",min:0,max:1,dflt:1}},z:{show:{valType:"boolean",dflt:!0},fill:{valType:"number",min:0,max:1,dflt:1}}},text:{valType:"string",dflt:"",arrayOk:!0},hovertext:{valType:"string",dflt:"",arrayOk:!0},hovertemplate:a(),xhoverformat:i("x"),yhoverformat:i("y"),zhoverformat:i("z"),valuehoverformat:i("value",1),showlegend:l({},s.showlegend,{dflt:!1})},n("",{colorAttr:"`value`",showScaleDflt:!0,editTypeOverride:"calc"}),{opacity:o.opacity,lightposition:o.lightposition,lighting:o.lighting,flatshading:o.flatshading,contour:o.contour,hoverinfo:l({},s.hoverinfo)}),"calc","nested");u.flatshading.dflt=!0,u.lighting.facenormalsepsilon.dflt=0,u.x.editType=u.y.editType=u.z.editType=u.value.editType="calc+clearAxisTypes",u.transforms=void 0},{"../../components/colorscale/attributes":373,"../../lib/extend":493,"../../plot_api/edit_types":536,"../../plots/attributes":550,"../../plots/cartesian/axis_format_attributes":557,"../../plots/template_attributes":633,"../mesh3d/attributes":866}],862:[function(t,e,r){"use strict";var n=t("../../components/colorscale/calc"),i=t("../streamtube/calc").processGrid,a=t("../streamtube/calc").filter;e.exports=function(t,e){e._len=Math.min(e.x.length,e.y.length,e.z.length,e.value.length),e._x=a(e.x,e._len),e._y=a(e.y,e._len),e._z=a(e.z,e._len),e._value=a(e.value,e._len);var r=i(e);e._gridFill=r.fill,e._Xs=r.Xs,e._Ys=r.Ys,e._Zs=r.Zs,e._len=r.len;for(var o=1/0,s=-1/0,l=0;l<e._len;l++){var c=e._value[l];o=Math.min(o,c),s=Math.max(s,c)}e._minValues=o,e._maxValues=s,e._vMin=void 0===e.isomin||null===e.isomin?o:e.isomin,e._vMax=void 0===e.isomax||null===e.isomin?s:e.isomax,n(t,e,{vals:[e._vMin,e._vMax],containerStr:"",cLetter:"c"})}},{"../../components/colorscale/calc":374,"../streamtube/calc":1040}],863:[function(t,e,r){"use strict";var n=t("../../../stackgl_modules").gl_mesh3d,i=t("../../lib/gl_format_color").parseColorScale,a=t("../../lib/str2rgbarray"),o=t("../../components/colorscale").extractOpts,s=t("../../plots/gl3d/zip3"),l=function(t,e){for(var r=e.length-1;r>0;r--){var n=Math.min(e[r],e[r-1]),i=Math.max(e[r],e[r-1]);if(i>n&&n<t&&t<=i)return{id:r,distRatio:(i-t)/(i-n)}}return{id:0,distRatio:0}};function c(t,e,r){this.scene=t,this.uid=r,this.mesh=e,this.name="",this.data=null,this.showContour=!1}var u=c.prototype;u.handlePick=function(t){if(t.object===this.mesh){var e=t.data.index,r=this.data._meshX[e],n=this.data._meshY[e],i=this.data._meshZ[e],a=this.data._Ys.length,o=this.data._Zs.length,s=l(r,this.data._Xs).id,c=l(n,this.data._Ys).id,u=l(i,this.data._Zs).id,f=t.index=u+o*c+o*a*s;t.traceCoordinate=[this.data._meshX[f],this.data._meshY[f],this.data._meshZ[f],this.data._value[f]];var h=this.data.hovertext||this.data.text;return Array.isArray(h)&&void 0!==h[f]?t.textLabel=h[f]:h&&(t.textLabel=h),!0}},u.update=function(t){var e=this.scene,r=e.fullSceneLayout;function n(t,e,r,n){return e.map((function(e){return t.d2l(e,0,n)*r}))}this.data=h(t);var l={positions:s(n(r.xaxis,t._meshX,e.dataScale[0],t.xcalendar),n(r.yaxis,t._meshY,e.dataScale[1],t.ycalendar),n(r.zaxis,t._meshZ,e.dataScale[2],t.zcalendar)),cells:s(t._meshI,t._meshJ,t._meshK),lightPosition:[t.lightposition.x,t.lightposition.y,t.lightposition.z],ambient:t.lighting.ambient,diffuse:t.lighting.diffuse,specular:t.lighting.specular,roughness:t.lighting.roughness,fresnel:t.lighting.fresnel,vertexNormalsEpsilon:t.lighting.vertexnormalsepsilon,faceNormalsEpsilon:t.lighting.facenormalsepsilon,opacity:t.opacity,contourEnable:t.contour.show,contourColor:a(t.contour.color).slice(0,3),contourWidth:t.contour.width,useFacetNormals:t.flatshading},c=o(t);l.vertexIntensity=t._meshIntensity,l.vertexIntensityBounds=[c.min,c.max],l.colormap=i(t),this.mesh.update(l)},u.dispose=function(){this.scene.glplot.remove(this.mesh),this.mesh.dispose()};var f=["xyz","xzy","yxz","yzx","zxy","zyx"];function h(t){t._meshI=[],t._meshJ=[],t._meshK=[];var e,r,n,i,a,o,s,c=t.surface.show,u=t.spaceframe.show,h=t.surface.fill,p=t.spaceframe.fill,d=!1,g=!1,m=0,v=t._Xs,y=t._Ys,x=t._Zs,b=v.length,_=y.length,w=x.length,T=f.indexOf(t._gridFill.replace(/-/g,"").replace(/\+/g,"")),k=function(t,e,r){switch(T){case 5:return r+w*e+w*_*t;case 4:return r+w*t+w*b*e;case 3:return e+_*r+_*w*t;case 2:return e+_*t+_*b*r;case 1:return t+b*r+b*w*e;default:return t+b*e+b*_*r}},A=t._minValues,M=t._maxValues,S=t._vMin,E=t._vMax;function L(t,e,s){for(var l=o.length,c=r;c<l;c++)if(t===n[c]&&e===i[c]&&s===a[c])return c;return-1}function C(){r=e}function P(){n=[],i=[],a=[],o=[],e=0,C()}function I(t,r,s,l){return n.push(t),i.push(r),a.push(s),o.push(l),++e-1}function O(t,e,r){for(var n=[],i=0;i<t.length;i++)n[i]=t[i]*(1-r)+r*e[i];return n}function z(t){s=t}function D(t,e){return"all"===t||null===t||t.indexOf(e)>-1}function R(t,e){return null===t?e:t}function F(e,r,n){C();var i,a,o,l=[r],c=[n];if(s>=1)l=[r],c=[n];else if(s>0){var u=function(t,e){var r=t[0],n=t[1],i=t[2],a=function(t,e,r){for(var n=[],i=0;i<t.length;i++)n[i]=(t[i]+e[i]+r[i])/3;return n}(r,n,i),o=Math.sqrt(1-s),l=O(a,r,o),c=O(a,n,o),u=O(a,i,o),f=e[0],h=e[1],p=e[2];return{xyzv:[[r,n,c],[c,l,r],[n,i,u],[u,c,n],[i,r,l],[l,u,i]],abc:[[f,h,-1],[-1,-1,f],[h,p,-1],[-1,-1,h],[p,f,-1],[-1,-1,p]]}}(r,n);l=u.xyzv,c=u.abc}for(var f=0;f<l.length;f++){r=l[f],n=c[f];for(var h=[],p=0;p<3;p++){var d=r[p][0],g=r[p][1],v=r[p][2],y=r[p][3],x=n[p]>-1?n[p]:L(d,g,v);h[p]=x>-1?x:I(d,g,v,R(e,y))}i=h[0],a=h[1],o=h[2],t._meshI.push(i),t._meshJ.push(a),t._meshK.push(o),++m}}function B(t,e,r,n){var i=t[3];i<r&&(i=r),i>n&&(i=n);for(var a=(t[3]-i)/(t[3]-e[3]+1e-9),o=[],s=0;s<4;s++)o[s]=(1-a)*t[s]+a*e[s];return o}function N(t,e,r){return t>=e&&t<=r}function j(t){var e=.001*(E-S);return t>=S-e&&t<=E+e}function U(e){for(var r=[],n=0;n<4;n++){var i=e[n];r.push([t._x[i],t._y[i],t._z[i],t._value[i]])}return r}function V(t,e,r,n,i,a){a||(a=1),r=[-1,-1,-1];var 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v={positions:h,cells:n,lightPosition:[t.lightposition.x,t.lightposition.y,t.lightposition.z],ambient:t.lighting.ambient,diffuse:t.lighting.diffuse,specular:t.lighting.specular,roughness:t.lighting.roughness,fresnel:t.lighting.fresnel,vertexNormalsEpsilon:t.lighting.vertexnormalsepsilon,faceNormalsEpsilon:t.lighting.facenormalsepsilon,opacity:t.opacity,contourEnable:t.contour.show,contourColor:l(t.contour.color).slice(0,3),contourWidth:t.contour.width,useFacetNormals:t.flatshading};if(t.intensity){var y=c(t);this.color="#fff";var x=t.intensitymode;v[x+"Intensity"]=t.intensity,v[x+"IntensityBounds"]=[y.min,y.max],v.colormap=s(t)}else t.vertexcolor?(this.color=t.vertexcolor[0],v.vertexColors=p(t.vertexcolor)):t.facecolor?(this.color=t.facecolor[0],v.cellColors=p(t.facecolor)):(this.color=t.color,v.meshColor=l(t.color));this.mesh.update(v)},h.dispose=function(){this.scene.glplot.remove(this.mesh),this.mesh.dispose()},e.exports=function(t,e){var r=t.glplot.gl,i=n({gl:r}),a=new 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a=t.map(F.bind(0,e,r)),o=i.selectAll("g.parcatslayer").data([null]);o.enter().append("g").attr("class","parcatslayer").style("pointer-events","all");var u=o.selectAll("g.trace.parcats").data(a,p),h=u.enter().append("g").attr("class","trace parcats");u.attr("transform",(function(t){return l(t.x,t.y)})),h.append("g").attr("class","paths");var y=u.select("g.paths").selectAll("path.path").data((function(t){return t.paths}),p);y.attr("fill",(function(t){return t.model.color}));var x=y.enter().append("path").attr("class","path").attr("stroke-opacity",0).attr("fill",(function(t){return t.model.color})).attr("fill-opacity",0);_(x),y.attr("d",(function(t){return t.svgD})),x.empty()||y.sort(g),y.exit().remove(),y.on("mouseover",m).on("mouseout",v).on("click",b),h.append("g").attr("class","dimensions");var w=u.select("g.dimensions").selectAll("g.dimension").data((function(t){return t.dimensions}),p);w.enter().append("g").attr("class","dimension"),w.attr("transform",(function(t){return l(t.x,0)})),w.exit().remove();var A=w.selectAll("g.category").data((function(t){return t.categories}),p),M=A.enter().append("g").attr("class","category");A.attr("transform",(function(t){return l(0,t.y)})),M.append("rect").attr("class","catrect").attr("pointer-events","none"),A.select("rect.catrect").attr("fill","none").attr("width",(function(t){return t.width})).attr("height",(function(t){return t.height})),T(M);var S=A.selectAll("rect.bandrect").data((function(t){return t.bands}),p);S.each((function(){s.raiseToTop(this)})),S.attr("fill",(function(t){return t.color}));var E=S.enter().append("rect").attr("class","bandrect").attr("stroke-opacity",0).attr("fill",(function(t){return t.color})).attr("fill-opacity",0);S.attr("fill",(function(t){return t.color})).attr("width",(function(t){return t.width})).attr("height",(function(t){return t.height})).attr("y",(function(t){return t.y})).attr("cursor",(function(t){return"fixed"===t.parcatsViewModel.arrangement?"default":"perpendicular"===t.parcatsViewModel.arrangement?"ns-resize":"move"})),k(E),S.exit().remove(),M.append("text").attr("class","catlabel").attr("pointer-events","none");var z=e._fullLayout.paper_bgcolor;A.select("text.catlabel").attr("text-anchor",(function(t){return d(t)?"start":"end"})).attr("alignment-baseline","middle").style("text-shadow",f.makeTextShadow(z)).style("fill","rgb(0, 0, 0)").attr("x",(function(t){return d(t)?t.width+5:-5})).attr("y",(function(t){return t.height/2})).text((function(t){return t.model.categoryLabel})).each((function(t){c.font(n.select(this),t.parcatsViewModel.categorylabelfont),f.convertToTspans(n.select(this),e)})),M.append("text").attr("class","dimlabel"),A.select("text.dimlabel").attr("text-anchor","middle").attr("alignment-baseline","baseline").attr("cursor",(function(t){return"fixed"===t.parcatsViewModel.arrangement?"default":"ew-resize"})).attr("x",(function(t){return t.width/2})).attr("y",-5).text((function(t,e){return 0===e?t.parcatsViewModel.model.dimensions[t.model.dimensionInd].dimensionLabel:null})).each((function(t){c.font(n.select(this),t.parcatsViewModel.labelfont)})),A.selectAll("rect.bandrect").on("mouseover",L).on("mouseout",C),A.exit().remove(),w.call(n.behavior.drag().origin((function(t){return{x:t.x,y:0}})).on("dragstart",P).on("drag",I).on("dragend",O)),u.each((function(t){t.traceSelection=n.select(this),t.pathSelection=n.select(this).selectAll("g.paths").selectAll("path.path"),t.dimensionSelection=n.select(this).selectAll("g.dimensions").selectAll("g.dimension")})),u.exit().remove()}function p(t){return t.key}function d(t){var e=t.parcatsViewModel.dimensions.length,r=t.parcatsViewModel.dimensions[e-1].model.dimensionInd;return t.model.dimensionInd===r}function g(t,e){return t.model.rawColor>e.model.rawColor?1:t.model.rawColor<e.model.rawColor?-1:0}function m(t){if(!t.parcatsViewModel.dragDimension&&-1===t.parcatsViewModel.hoverinfoItems.indexOf("skip")){s.raiseToTop(this),w(n.select(this));var e=y(t),r=x(t);if(t.parcatsViewModel.graphDiv.emit("plotly_hover",{points:e,event:n.event,constraints:r}),-1===t.parcatsViewModel.hoverinfoItems.indexOf("none")){var i,a,l,c=n.mouse(this)[0],f=t.parcatsViewModel.graphDiv,h=t.parcatsViewModel.trace,p=f._fullLayout,d=p._paperdiv.node().getBoundingClientRect(),g=t.parcatsViewModel.graphDiv.getBoundingClientRect();for(l=0;l<t.leftXs.length-1;l++)if(t.leftXs[l]+t.dimWidths[l]-2<=c&&c<=t.leftXs[l+1]+2){var m=t.parcatsViewModel.dimensions[l],v=t.parcatsViewModel.dimensions[l+1];i=(m.x+m.width+v.x)/2,a=(t.topYs[l]+t.topYs[l+1]+t.height)/2;break}var b=t.parcatsViewModel.x+i,_=t.parcatsViewModel.y+a,T=u.mostReadable(t.model.color,["black","white"]),k=t.model.count,A=k/t.parcatsViewModel.model.count,M={countLabel:k,probabilityLabel:A.toFixed(3)},S=[];-1!==t.parcatsViewModel.hoverinfoItems.indexOf("count")&&S.push(["Count:",M.countLabel].join(" ")),-1!==t.parcatsViewModel.hoverinfoItems.indexOf("probability")&&S.push(["P:",M.probabilityLabel].join(" "));var E=S.join("<br>"),L=n.mouse(f)[0];o.loneHover({trace:h,x:b-d.left+g.left,y:_-d.top+g.top,text:E,color:t.model.color,borderColor:"black",fontFamily:'Monaco, "Courier New", monospace',fontSize:10,fontColor:T,idealAlign:L<b?"right":"left",hovertemplate:(h.line||{}).hovertemplate,hovertemplateLabels:M,eventData:[{data:h._input,fullData:h,count:k,probability:A}]},{container:p._hoverlayer.node(),outerContainer:p._paper.node(),gd:f})}}}function v(t){if(!t.parcatsViewModel.dragDimension&&(_(n.select(this)),o.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()),t.parcatsViewModel.pathSelection.sort(g),-1===t.parcatsViewModel.hoverinfoItems.indexOf("skip"))){var e=y(t),r=x(t);t.parcatsViewModel.graphDiv.emit("plotly_unhover",{points:e,event:n.event,constraints:r})}}function y(t){for(var e=[],r=z(t.parcatsViewModel),n=0;n<t.model.valueInds.length;n++){var i=t.model.valueInds[n];e.push({curveNumber:r,pointNumber:i})}return e}function x(t){for(var e={},r=t.parcatsViewModel.model.dimensions,n=0;n<r.length;n++){var i=r[n],a=i.categories[t.model.categoryInds[n]];e[i.containerInd]=a.categoryValue}return void 0!==t.model.rawColor&&(e.color=t.model.rawColor),e}function b(t){if(-1===t.parcatsViewModel.hoverinfoItems.indexOf("skip")){var e=y(t),r=x(t);t.parcatsViewModel.graphDiv.emit("plotly_click",{points:e,event:n.event,constraints:r})}}function _(t){t.attr("fill",(function(t){return t.model.color})).attr("fill-opacity",.6).attr("stroke","lightgray").attr("stroke-width",.2).attr("stroke-opacity",1)}function w(t){t.attr("fill-opacity",.8).attr("stroke",(function(t){return u.mostReadable(t.model.color,["black","white"])})).attr("stroke-width",.3)}function T(t){t.select("rect.catrect").attr("stroke","black").attr("stroke-width",1).attr("stroke-opacity",1)}function k(t){t.attr("stroke","black").attr("stroke-width",.2).attr("stroke-opacity",1).attr("fill-opacity",1)}function A(t){var e=t.parcatsViewModel.pathSelection,r=t.categoryViewModel.model.dimensionInd,n=t.categoryViewModel.model.categoryInd;return e.filter((function(e){return e.model.categoryInds[r]===n&&e.model.color===t.color}))}function M(t,e,r){var i=n.select(t).datum(),a=i.categoryViewModel.model,o=i.parcatsViewModel.graphDiv,s=n.select(t.parentNode).selectAll("rect.bandrect"),l=[];s.each((function(t){A(t).each((function(t){Array.prototype.push.apply(l,y(t))}))}));var c={};c[a.dimensionInd]=a.categoryValue,o.emit(e,{points:l,event:r,constraints:c})}function S(t,e,r){var i=n.select(t).datum(),a=i.categoryViewModel.model,o=i.parcatsViewModel.graphDiv,s=A(i),l=[];s.each((function(t){Array.prototype.push.apply(l,y(t))}));var c={};c[a.dimensionInd]=a.categoryValue,void 0!==i.rawColor&&(c.color=i.rawColor),o.emit(e,{points:l,event:r,constraints:c})}function E(t,e,r){t._fullLayout._calcInverseTransform(t);var i,a,o=t._fullLayout._invScaleX,s=t._fullLayout._invScaleY,l=n.select(r.parentNode).select("rect.catrect"),c=l.node().getBoundingClientRect(),u=l.datum(),f=u.parcatsViewModel,h=f.model.dimensions[u.model.dimensionInd],p=f.trace,d=c.top+c.height/2;f.dimensions.length>1&&h.displayInd===f.dimensions.length-1?(i=c.left,a="left"):(i=c.left+c.width,a="right");var g=u.model.count,m=u.model.categoryLabel,v=g/u.parcatsViewModel.model.count,y={countLabel:g,categoryLabel:m,probabilityLabel:v.toFixed(3)},x=[];-1!==u.parcatsViewModel.hoverinfoItems.indexOf("count")&&x.push(["Count:",y.countLabel].join(" ")),-1!==u.parcatsViewModel.hoverinfoItems.indexOf("probability")&&x.push(["P("+y.categoryLabel+"):",y.probabilityLabel].join(" "));var b=x.join("<br>");return{trace:p,x:o*(i-e.left),y:s*(d-e.top),text:b,color:"lightgray",borderColor:"black",fontFamily:'Monaco, "Courier New", monospace',fontSize:12,fontColor:"black",idealAlign:a,hovertemplate:p.hovertemplate,hovertemplateLabels:y,eventData:[{data:p._input,fullData:p,count:g,category:m,probability:v}]}}function L(t){if(!t.parcatsViewModel.dragDimension&&-1===t.parcatsViewModel.hoverinfoItems.indexOf("skip")){if(n.mouse(this)[1]<-1)return;var e,r=t.parcatsViewModel.graphDiv,i=r._fullLayout,a=i._paperdiv.node().getBoundingClientRect(),l=t.parcatsViewModel.hoveron;if("color"===l?(!function(t){var e=n.select(t).datum(),r=A(e);w(r),r.each((function(){s.raiseToTop(this)})),n.select(t.parentNode).selectAll("rect.bandrect").filter((function(t){return t.color===e.color})).each((function(){s.raiseToTop(this),n.select(this).attr("stroke","black").attr("stroke-width",1.5)}))}(this),S(this,"plotly_hover",n.event)):(!function(t){n.select(t.parentNode).selectAll("rect.bandrect").each((function(t){var e=A(t);w(e),e.each((function(){s.raiseToTop(this)}))})),n.select(t.parentNode).select("rect.catrect").attr("stroke","black").attr("stroke-width",2.5)}(this),M(this,"plotly_hover",n.event)),-1===t.parcatsViewModel.hoverinfoItems.indexOf("none"))"category"===l?e=E(r,a,this):"color"===l?e=function(t,e,r){t._fullLayout._calcInverseTransform(t);var i,a,o=t._fullLayout._invScaleX,s=t._fullLayout._invScaleY,l=r.getBoundingClientRect(),c=n.select(r).datum(),f=c.categoryViewModel,h=f.parcatsViewModel,p=h.model.dimensions[f.model.dimensionInd],d=h.trace,g=l.y+l.height/2;h.dimensions.length>1&&p.displayInd===h.dimensions.length-1?(i=l.left,a="left"):(i=l.left+l.width,a="right");var m=f.model.categoryLabel,v=c.parcatsViewModel.model.count,y=0;c.categoryViewModel.bands.forEach((function(t){t.color===c.color&&(y+=t.count)}));var x=f.model.count,b=0;h.pathSelection.each((function(t){t.model.color===c.color&&(b+=t.model.count)}));var _=y/v,w=y/b,T=y/x,k={countLabel:v,categoryLabel:m,probabilityLabel:_.toFixed(3)},A=[];-1!==f.parcatsViewModel.hoverinfoItems.indexOf("count")&&A.push(["Count:",k.countLabel].join(" ")),-1!==f.parcatsViewModel.hoverinfoItems.indexOf("probability")&&(A.push("P(color \u2229 "+m+"): "+k.probabilityLabel),A.push("P("+m+" | color): "+w.toFixed(3)),A.push("P(color | "+m+"): "+T.toFixed(3)));var M=A.join("<br>"),S=u.mostReadable(c.color,["black","white"]);return{trace:d,x:o*(i-e.left),y:s*(g-e.top),text:M,color:c.color,borderColor:"black",fontFamily:'Monaco, "Courier New", monospace',fontColor:S,fontSize:10,idealAlign:a,hovertemplate:d.hovertemplate,hovertemplateLabels:k,eventData:[{data:d._input,fullData:d,category:m,count:v,probability:_,categorycount:x,colorcount:b,bandcolorcount:y}]}}(r,a,this):"dimension"===l&&(e=function(t,e,r){var i=[];return n.select(r.parentNode.parentNode).selectAll("g.category").select("rect.catrect").each((function(){i.push(E(t,e,this))})),i}(r,a,this)),e&&o.loneHover(e,{container:i._hoverlayer.node(),outerContainer:i._paper.node(),gd:r})}}function C(t){var e=t.parcatsViewModel;if(!e.dragDimension&&(_(e.pathSelection),T(e.dimensionSelection.selectAll("g.category")),k(e.dimensionSelection.selectAll("g.category").selectAll("rect.bandrect")),o.loneUnhover(e.graphDiv._fullLayout._hoverlayer.node()),e.pathSelection.sort(g),-1===e.hoverinfoItems.indexOf("skip"))){"color"===t.parcatsViewModel.hoveron?S(this,"plotly_unhover",n.event):M(this,"plotly_unhover",n.event)}}function P(t){"fixed"!==t.parcatsViewModel.arrangement&&(t.dragDimensionDisplayInd=t.model.displayInd,t.initialDragDimensionDisplayInds=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),t.dragHasMoved=!1,t.dragCategoryDisplayInd=null,n.select(this).selectAll("g.category").select("rect.catrect").each((function(e){var r=n.mouse(this)[0],i=n.mouse(this)[1];-2<=r&&r<=e.width+2&&-2<=i&&i<=e.height+2&&(t.dragCategoryDisplayInd=e.model.displayInd,t.initialDragCategoryDisplayInds=t.model.categories.map((function(t){return t.displayInd})),e.model.dragY=e.y,s.raiseToTop(this.parentNode),n.select(this.parentNode).selectAll("rect.bandrect").each((function(e){e.y<i&&i<=e.y+e.height&&(t.potentialClickBand=this)})))})),t.parcatsViewModel.dragDimension=t,o.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()))}function I(t){if("fixed"!==t.parcatsViewModel.arrangement&&(t.dragHasMoved=!0,null!==t.dragDimensionDisplayInd)){var e=t.dragDimensionDisplayInd,r=e-1,i=e+1,a=t.parcatsViewModel.dimensions[e];if(null!==t.dragCategoryDisplayInd){var o=a.categories[t.dragCategoryDisplayInd];o.model.dragY+=n.event.dy;var s=o.model.dragY,l=o.model.displayInd,c=a.categories,u=c[l-1],f=c[l+1];void 0!==u&&s<u.y+u.height/2&&(o.model.displayInd=u.model.displayInd,u.model.displayInd=l),void 0!==f&&s+o.height>f.y+f.height/2&&(o.model.displayInd=f.model.displayInd,f.model.displayInd=l),t.dragCategoryDisplayInd=o.model.displayInd}if(null===t.dragCategoryDisplayInd||"freeform"===t.parcatsViewModel.arrangement){a.model.dragX=n.event.x;var h=t.parcatsViewModel.dimensions[r],p=t.parcatsViewModel.dimensions[i];void 0!==h&&a.model.dragX<h.x+h.width&&(a.model.displayInd=h.model.displayInd,h.model.displayInd=e),void 0!==p&&a.model.dragX+a.width>p.x&&(a.model.displayInd=p.model.displayInd,p.model.displayInd=t.dragDimensionDisplayInd),t.dragDimensionDisplayInd=a.model.displayInd}j(t.parcatsViewModel),N(t.parcatsViewModel),R(t.parcatsViewModel),D(t.parcatsViewModel)}}function O(t){if("fixed"!==t.parcatsViewModel.arrangement&&null!==t.dragDimensionDisplayInd){n.select(this).selectAll("text").attr("font-weight","normal");var e={},r=z(t.parcatsViewModel),i=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),o=t.initialDragDimensionDisplayInds.some((function(t,e){return t!==i[e]}));o&&i.forEach((function(r,n){var i=t.parcatsViewModel.model.dimensions[n].containerInd;e["dimensions["+i+"].displayindex"]=r}));var s=!1;if(null!==t.dragCategoryDisplayInd){var l=t.model.categories.map((function(t){return t.displayInd}));if(s=t.initialDragCategoryDisplayInds.some((function(t,e){return t!==l[e]}))){var c=t.model.categories.slice().sort((function(t,e){return t.displayInd-e.displayInd})),u=c.map((function(t){return t.categoryValue})),f=c.map((function(t){return t.categoryLabel}));e["dimensions["+t.model.containerInd+"].categoryarray"]=[u],e["dimensions["+t.model.containerInd+"].ticktext"]=[f],e["dimensions["+t.model.containerInd+"].categoryorder"]="array"}}if(-1===t.parcatsViewModel.hoverinfoItems.indexOf("skip")&&!t.dragHasMoved&&t.potentialClickBand&&("color"===t.parcatsViewModel.hoveron?S(t.potentialClickBand,"plotly_click",n.event.sourceEvent):M(t.potentialClickBand,"plotly_click",n.event.sourceEvent)),t.model.dragX=null,null!==t.dragCategoryDisplayInd)t.parcatsViewModel.dimensions[t.dragDimensionDisplayInd].categories[t.dragCategoryDisplayInd].model.dragY=null,t.dragCategoryDisplayInd=null;t.dragDimensionDisplayInd=null,t.parcatsViewModel.dragDimension=null,t.dragHasMoved=null,t.potentialClickBand=null,j(t.parcatsViewModel),N(t.parcatsViewModel),n.transition().duration(300).ease("cubic-in-out").each((function(){R(t.parcatsViewModel,!0),D(t.parcatsViewModel,!0)})).each("end",(function(){(o||s)&&a.restyle(t.parcatsViewModel.graphDiv,e,[r])}))}}function z(t){for(var e,r=t.graphDiv._fullData,n=0;n<r.length;n++)if(t.key===r[n].uid){e=n;break}return e}function D(t,e){var r;void 0===e&&(e=!1),t.pathSelection.data((function(t){return t.paths}),p),(r=t.pathSelection,e?r.transition():r).attr("d",(function(t){return t.svgD}))}function R(t,e){function r(t){return e?t.transition():t}void 0===e&&(e=!1),t.dimensionSelection.data((function(t){return t.dimensions}),p);var i=t.dimensionSelection.selectAll("g.category").data((function(t){return t.categories}),p);r(t.dimensionSelection).attr("transform",(function(t){return l(t.x,0)})),r(i).attr("transform",(function(t){return l(0,t.y)})),i.select(".dimlabel").text((function(t,e){return 0===e?t.parcatsViewModel.model.dimensions[t.model.dimensionInd].dimensionLabel:null})),i.select(".catlabel").attr("text-anchor",(function(t){return d(t)?"start":"end"})).attr("x",(function(t){return d(t)?t.width+5:-5})).each((function(t){var e,r;d(t)?(e=t.width+5,r="start"):(e=-5,r="end"),n.select(this).selectAll("tspan").attr("x",e).attr("text-anchor",r)}));var a=i.selectAll("rect.bandrect").data((function(t){return t.bands}),p),o=a.enter().append("rect").attr("class","bandrect").attr("cursor","move").attr("stroke-opacity",0).attr("fill",(function(t){return t.color})).attr("fill-opacity",0);a.attr("fill",(function(t){return t.color})).attr("width",(function(t){return t.width})).attr("height",(function(t){return t.height})).attr("y",(function(t){return t.y})),k(o),a.each((function(){s.raiseToTop(this)})),a.exit().remove()}function F(t,e,r){var n,i=r[0],a=e.margin||{l:80,r:80,t:100,b:80},o=i.trace,s=o.domain,l=e.width,c=e.height,u=Math.floor(l*(s.x[1]-s.x[0])),f=Math.floor(c*(s.y[1]-s.y[0])),h=s.x[0]*l+a.l,p=e.height-s.y[1]*e.height+a.t,d=o.line.shape;n="all"===o.hoverinfo?["count","probability"]:(o.hoverinfo||"").split("+");var g={trace:o,key:o.uid,model:i,x:h,y:p,width:u,height:f,hoveron:o.hoveron,hoverinfoItems:n,arrangement:o.arrangement,bundlecolors:o.bundlecolors,sortpaths:o.sortpaths,labelfont:o.labelfont,categorylabelfont:o.tickfont,pathShape:d,dragDimension:null,margin:a,paths:[],dimensions:[],graphDiv:t,traceSelection:null,pathSelection:null,dimensionSelection:null};return i.dimensions&&(j(g),N(g)),g}function B(t,e,r,n,a){var o,s,l=[],c=[];for(s=0;s<r.length-1;s++)o=i(r[s]+t[s],t[s+1]),l.push(o(a)),c.push(o(1-a));var u="M "+t[0]+","+e[0];for(u+="l"+r[0]+",0 ",s=1;s<r.length;s++)u+="C"+l[s-1]+","+e[s-1]+" "+c[s-1]+","+e[s]+" "+t[s]+","+e[s],u+="l"+r[s]+",0 ";for(u+="l0,"+n+" ",u+="l -"+r[r.length-1]+",0 ",s=r.length-2;s>=0;s--)u+="C"+c[s]+","+(e[s+1]+n)+" "+l[s]+","+(e[s]+n)+" "+(t[s]+r[s])+","+(e[s]+n),u+="l-"+r[s]+",0 ";return u+="Z"}function N(t){var e=t.dimensions,r=t.model,n=e.map((function(t){return t.categories.map((function(t){return t.y}))})),i=t.model.dimensions.map((function(t){return 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n=t("./parcats");e.exports=function(t,e,r,i){var a=t._fullLayout,o=a._paper,s=a._size;n(t,o,e,{width:s.w,height:s.h,margin:{t:s.t,r:s.r,b:s.b,l:s.l}},r,i)}},{"./parcats":885}],887:[function(t,e,r){"use strict";var n=t("../../components/colorscale/attributes"),i=t("../../plots/cartesian/layout_attributes"),a=t("../../plots/font_attributes"),o=t("../../plots/domain").attributes,s=t("../../lib/extend").extendFlat,l=t("../../plot_api/plot_template").templatedArray;e.exports={domain:o({name:"parcoords",trace:!0,editType:"plot"}),labelangle:{valType:"angle",dflt:0,editType:"plot"},labelside:{valType:"enumerated",values:["top","bottom"],dflt:"top",editType:"plot"},labelfont:a({editType:"plot"}),tickfont:a({editType:"plot"}),rangefont:a({editType:"plot"}),dimensions:l("dimension",{label:{valType:"string",editType:"plot"},tickvals:s({},i.tickvals,{editType:"plot"}),ticktext:s({},i.ticktext,{editType:"plot"}),tickformat:s({},i.tickformat,{editType:"plot"}),visible:{valType:"boolean",dflt:!0,editType:"plot"},range:{valType:"info_array",items:[{valType:"number",editType:"plot"},{valType:"number",editType:"plot"}],editType:"plot"},constraintrange:{valType:"info_array",freeLength:!0,dimensions:"1-2",items:[{valType:"any",editType:"plot"},{valType:"any",editType:"plot"}],editType:"plot"},multiselect:{valType:"boolean",dflt:!0,editType:"plot"},values:{valType:"data_array",editType:"calc"},editType:"calc"}),line:s({editType:"calc"},n("line",{colorscaleDflt:"Viridis",autoColorDflt:!1,editTypeOverride:"calc"}))}},{"../../components/colorscale/attributes":373,"../../lib/extend":493,"../../plot_api/plot_template":543,"../../plots/cartesian/layout_attributes":569,"../../plots/domain":584,"../../plots/font_attributes":585}],888:[function(t,e,r){"use 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r=e.brush,n=r.filter,a=r.svgBrush;a._dragging||(T(t,e),w(t,e),e.brush.svgBrush.wasDragged=!1),a._dragging=!1,i.event.sourceEvent.stopPropagation();var o=a.grabbingBar;if(a.grabbingBar=!1,a.grabLocation=void 0,e.parent.inBrushDrag=!1,y(),!a.wasDragged)return a.wasDragged=void 0,a.clickableOrdinalRange?r.filterSpecified&&e.multiselect?a.extent.push(a.clickableOrdinalRange):(a.extent=[a.clickableOrdinalRange],r.filterSpecified=!0):o?(a.extent=a.stayingIntervals,0===a.extent.length&&M(r)):M(r),a.brushCallback(e),b(t.parentNode),void a.brushEndCallback(r.filterSpecified?n.getConsolidated():[]);var s=function(){n.set(n.getConsolidated())};if(e.ordinal){var l=e.unitTickvals;l[l.length-1]<l[0]&&l.reverse(),a.newExtent=[p(0,l,a.newExtent[0],a.stayingIntervals),p(1,l,a.newExtent[1],a.stayingIntervals)];var c=a.newExtent[1]>a.newExtent[0];a.extent=a.stayingIntervals.concat(c?[a.newExtent]:[]),a.extent.length||M(r),a.brushCallback(e),c?b(t.parentNode,s):(s(),b(t.parentNode))}else s();a.brushEndCallback(r.filterSpecified?n.getConsolidated():[])}(this,t)})))}function A(t,e){return t[0]-e[0]}function M(t){t.filterSpecified=!1,t.svgBrush.extent=[[-1/0,1/0]]}function S(t){for(var e,r=t.slice(),n=[],i=r.shift();i;){for(e=i.slice();(i=r.shift())&&i[0]<=e[1];)e[1]=Math.max(e[1],i[1]);n.push(e)}return 1===n.length&&n[0][0]>n[0][1]&&(n=[]),n}e.exports={makeBrush:function(t,e,r,n,i,a){var o,l=function(){var t,e,r=[];return{set:function(n){1===(r=n.map((function(t){return t.slice().sort(s)})).sort(A)).length&&r[0][0]===-1/0&&r[0][1]===1/0&&(r=[[0,-1]]),t=S(r),e=r.reduce((function(t,e){return[Math.min(t[0],e[0]),Math.max(t[1],e[1])]}),[1/0,-1/0])},get:function(){return r.slice()},getConsolidated:function(){return t},getBounds:function(){return e}}}();return l.set(r),{filter:l,filterSpecified:e,svgBrush:{extent:[],brushStartCallback:n,brushCallback:(o=i,function(t){var e=t.brush,r=function(t){return t.svgBrush.extent.map((function(t){return t.slice()}))}(e).slice();e.filter.set(r),o()}),brushEndCallback:a}}},ensureAxisBrush:function(t,e){var r=t.selectAll("."+n.cn.axisBrush).data(o,a);r.enter().append("g").classed(n.cn.axisBrush,!0),function(t,e){var r=t.selectAll(".background").data(o);r.enter().append("rect").classed("background",!0).call(d).call(g).style("pointer-events","auto").attr("transform",l(0,n.verticalPadding)),r.call(k).attr("height",(function(t){return t.height-n.verticalPadding}));var i=t.selectAll(".highlight-shadow").data(o);i.enter().append("line").classed("highlight-shadow",!0).attr("x",-n.bar.width/2).attr("stroke-width",n.bar.width+n.bar.strokeWidth).attr("stroke",e).attr("opacity",n.bar.strokeOpacity).attr("stroke-linecap","butt"),i.attr("y1",(function(t){return t.height})).call(x);var a=t.selectAll(".highlight").data(o);a.enter().append("line").classed("highlight",!0).attr("x",-n.bar.width/2).attr("stroke-width",n.bar.width-n.bar.strokeWidth).attr("stroke",n.bar.fillColor).attr("opacity",n.bar.fillOpacity).attr("stroke-linecap","butt"),a.attr("y1",(function(t){return t.height})).call(x)}(r,e)},cleanRanges:function(t,e){if(Array.isArray(t[0])?(t=t.map((function(t){return t.sort(s)})),t=e.multiselect?S(t.sort(A)):[t[0]]):t=[t.sort(s)],e.tickvals){var r=e.tickvals.slice().sort(s);if(!(t=t.map((function(t){var e=[p(0,r,t[0],[]),p(1,r,t[1],[])];if(e[1]>e[0])return e})).filter((function(t){return t}))).length)return}return t.length>1?t:t[0]}}},{"../../lib":503,"../../lib/gup":500,"./constants":891,"@plotly/d3":58}],889:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("../../plots/get_data").getModuleCalcData,a=t("./plot"),o=t("../../constants/xmlns_namespaces");r.name="parcoords",r.plot=function(t){var e=i(t.calcdata,"parcoords")[0];e.length&&a(t,e)},r.clean=function(t,e,r,n){var i=n._has&&n._has("parcoords"),a=e._has&&e._has("parcoords");i&&!a&&(n._paperdiv.selectAll(".parcoords").remove(),n._glimages.selectAll("*").remove())},r.toSVG=function(t){var e=t._fullLayout._glimages,r=n.select(t).selectAll(".svg-container");r.filter((function(t,e){return e===r.size()-1})).selectAll(".gl-canvas-context, .gl-canvas-focus").each((function(){var t=this.toDataURL("image/png");e.append("svg:image").attr({xmlns:o.svg,"xlink:href":t,preserveAspectRatio:"none",x:0,y:0,width:this.style.width,height:this.style.height})})),window.setTimeout((function(){n.selectAll("#filterBarPattern").attr("id","filterBarPattern")}),60)}},{"../../constants/xmlns_namespaces":480,"../../plots/get_data":593,"./plot":898,"@plotly/d3":58}],890:[function(t,e,r){"use strict";var n=t("../../lib").isArrayOrTypedArray,i=t("../../components/colorscale"),a=t("../../lib/gup").wrap;e.exports=function(t,e){var r,o;return i.hasColorscale(e,"line")&&n(e.line.color)?(r=e.line.color,o=i.extractOpts(e.line).colorscale,i.calc(t,e,{vals:r,containerStr:"line",cLetter:"c"})):(r=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=.5;return e}(e._length),o=[[0,e.line.color],[1,e.line.color]]),a({lineColor:r,cscale:o})}},{"../../components/colorscale":378,"../../lib":503,"../../lib/gup":500}],891:[function(t,e,r){"use 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strict";var n=t("../../lib"),i=t("../../components/colorscale/helpers").hasColorscale,a=t("../../components/colorscale/defaults"),o=t("../../plots/domain").defaults,s=t("../../plots/array_container_defaults"),l=t("../../plots/cartesian/axes"),c=t("./attributes"),u=t("./axisbrush"),f=t("./constants").maxDimensionCount,h=t("./merge_length");function p(t,e,r,i){function a(r,i){return n.coerce(t,e,c.dimensions,r,i)}var o=a("values"),s=a("visible");if(o&&o.length||(s=e.visible=!1),s){a("label"),a("tickvals"),a("ticktext"),a("tickformat");var f=a("range");e._ax={_id:"y",type:"linear",showexponent:"all",exponentformat:"B",range:f},l.setConvert(e._ax,i.layout),a("multiselect");var h=a("constraintrange");h&&(e.constraintrange=u.cleanRanges(h,e))}}e.exports=function(t,e,r,l){function u(r,i){return n.coerce(t,e,c,r,i)}var d=t.dimensions;Array.isArray(d)&&d.length>f&&(n.log("parcoords traces support up to "+f+" dimensions at the moment"),d.splice(f));var g=s(t,e,{name:"dimensions",layout:l,handleItemDefaults:p}),m=function(t,e,r,o,s){var l=s("line.color",r);if(i(t,"line")&&n.isArrayOrTypedArray(l)){if(l.length)return s("line.colorscale"),a(t,e,o,s,{prefix:"line.",cLetter:"c"}),l.length;e.line.color=r}return 1/0}(t,e,r,l,u);o(e,l,u),Array.isArray(g)&&g.length||(e.visible=!1),h(e,g,"values",m);var v={family:l.font.family,size:Math.round(l.font.size/1.2),color:l.font.color};n.coerceFont(u,"labelfont",v),n.coerceFont(u,"tickfont",v),n.coerceFont(u,"rangefont",v),u("labelangle"),u("labelside")}},{"../../components/colorscale/defaults":376,"../../components/colorscale/helpers":377,"../../lib":503,"../../plots/array_container_defaults":549,"../../plots/cartesian/axes":554,"../../plots/domain":584,"./attributes":887,"./axisbrush":888,"./constants":891,"./merge_length":896}],893:[function(t,e,r){"use strict";var n=t("../../lib").isTypedArray;r.convertTypedArray=function(t){return n(t)?Array.prototype.slice.call(t):t},r.isOrdinal=function(t){return!!t.tickvals},r.isVisible=function(t){return t.visible||!("visible"in t)}},{"../../lib":503}],894:[function(t,e,r){"use strict";e.exports={attributes:t("./attributes"),supplyDefaults:t("./defaults"),calc:t("./calc"),plot:t("./plot"),colorbar:{container:"line",min:"cmin",max:"cmax"},moduleType:"trace",name:"parcoords",basePlotModule:t("./base_plot"),categories:["gl","regl","noOpacity","noHover"],meta:{}}},{"./attributes":887,"./base_plot":889,"./calc":890,"./defaults":892,"./plot":898}],895:[function(t,e,r){"use strict";var n=t("glslify"),i=n(["precision highp float;\n#define GLSLIFY 1\n\nvarying vec4 fragColor;\n\nattribute vec4 p01_04, p05_08, p09_12, p13_16,\n               p17_20, p21_24, p25_28, p29_32,\n               p33_36, p37_40, p41_44, p45_48,\n               p49_52, p53_56, p57_60, colors;\n\nuniform mat4 dim0A, dim1A, dim0B, dim1B, dim0C, dim1C, dim0D, dim1D,\n             loA, hiA, loB, hiB, loC, hiC, 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colors[3];\n\n    gl_Position = position(isContext, v, A, B, C, D);\n\n    fragColor =\n        isContext ? vec4(contextColor) :\n        isPick ? vec4(colors.rgb, 1.0) : texture2D(palette, vec2(abs(v), 0.5));\n}\n"]),a=n(["precision highp float;\n#define GLSLIFY 1\n\nvarying vec4 fragColor;\n\nvoid main() {\n    gl_FragColor = fragColor;\n}\n"]),o=t("./constants").maxDimensionCount,s=t("../../lib"),l=new Uint8Array(4),c=new Uint8Array(4),u={shape:[256,1],format:"rgba",type:"uint8",mag:"nearest",min:"nearest"};function f(t,e,r,n,i){var a=t._gl;a.enable(a.SCISSOR_TEST),a.scissor(e,r,n,i),t.clear({color:[0,0,0,0],depth:1})}function h(t,e,r,n,i,a){var o=a.key;r.drawCompleted||(!function(t){t.read({x:0,y:0,width:1,height:1,data:l})}(t),r.drawCompleted=!0),function s(l){var c=Math.min(n,i-l*n);0===l&&(window.cancelAnimationFrame(r.currentRafs[o]),delete 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s.extendFlat({key:f,resolution:[x,b],viewBoxPos:[o+v,l],viewBoxSize:[c,u],i0:i,i1:a,dim0A:g[0].slice(0,16),dim0B:g[0].slice(16,32),dim0C:g[0].slice(32,48),dim0D:g[0].slice(48,64),dim1A:g[1].slice(0,16),dim1B:g[1].slice(16,32),dim1C:g[1].slice(32,48),dim1D:g[1].slice(48,64),drwLayer:h,contextColor:[A[0]/255,A[1]/255,A[2]/255,A[3]<1?A[3]:Math.max(1/255,Math.pow(1/t.lines.color.length,1/3))],scissorX:(n===e?0:o+v)+(_-v)+k*y.x[0],scissorWidth:(n===r?x-o+v:c+.5)+(n===e?o+v:0),scissorY:l+w+T*y.y[0],scissorHeight:u,viewportX:_-v+k*y.x[0],viewportY:w+T*y.y[0],viewportWidth:x,viewportHeight:b},p)}function x(t){var e=Math.max(0,Math.floor(2047*t[0]),0),r=Math.min(2047,Math.ceil(2047*t[1]),2047);return[Math.min(e,r),Math.max(e,r)]}e.exports=function(t,e){var r,n,l,m,b,_=e.context,w=e.pick,T=e.regl,k=T._gl,A=k.getParameter(k.ALIASED_LINE_WIDTH_RANGE),M=Math.max(A[0],Math.min(A[1],e.viewModel.plotGlPixelRatio)),S={currentRafs:{},drawCompleted:!0,clearOnly:!1},E=function(t){for(var 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I(t){r=t.model,n=t.viewModel,l=n.dimensions.slice(),m=l[0]?l[0].values.length:0;var e=r.lines,i=w?e.color.map((function(t,r){return r/e.color.length})):e.color,a=function(t,e,r){for(var n,i=new Array(t*(o+4)),a=0,s=0;s<t;s++){for(var l=0;l<o;l++)i[a++]=l<e.length?e[l].paddedUnitValues[s]:.5;i[a++]=d(s,2),i[a++]=d(s,1),i[a++]=d(s,0),i[a++]=(n=r[s],Math.max(1e-6,Math.min(.999999,n)))}return i}(m,l,i);!function(t,e,r){for(var n=0;n<=o;n+=4)t[v(n)](g(n/4,e,r))}(E,m,a),_||w||(L=T.texture(s.extendFlat({data:p(r.unitToColor,255)},u)))}return{render:function(t,e,n){var i,a,o,s=t.length,c=1/0,u=-1/0;for(i=0;i<s;i++)t[i].dim0.canvasX<c&&(c=t[i].dim0.canvasX,a=i),t[i].dim1.canvasX>u&&(u=t[i].dim1.canvasX,o=i);0===s&&f(T,0,0,r.canvasWidth,r.canvasHeight);var p=function(t){var e,r,n,i=[[],[]];for(n=0;n<64;n++){var a=!t&&n<l.length?l[n].brush.filter.getBounds():[-1/0,1/0];i[0][n]=a[0],i[1][n]=a[1]}var o=new Array(16384);for(e=0;e<16384;e++)o[e]=255;if(!t)for(e=0;e<l.length;e++){var s=e%8,c=(e-s)/8,u=Math.pow(2,s),f=l[e].brush.filter.get();if(!(f.length<2)){var h=x(f[0])[1];for(r=1;r<f.length;r++){var p=x(f[r]);for(n=h+1;n<p[0];n++)o[8*n+c]&=~u;h=Math.max(h,p[1])}}}var d={shape:[8,2048],format:"alpha",type:"uint8",mag:"nearest",min:"nearest",data:o};return b?b(d):b=T.texture(d),{maskTexture:b,maskHeight:2048,loA:i[0].slice(0,16),loB:i[0].slice(16,32),loC:i[0].slice(32,48),loD:i[0].slice(48,64),hiA:i[1].slice(0,16),hiB:i[1].slice(16,32),hiC:i[1].slice(32,48),hiD:i[1].slice(48,64)}}(_);for(i=0;i<s;i++){var d=t[i],g=d.dim0.crossfilterDimensionIndex,v=d.dim1.crossfilterDimensionIndex,k=d.canvasX,A=d.canvasY,M=k+d.panelSizeX,E=d.plotGlPixelRatio;if(e||!C[g]||C[g][0]!==k||C[g][1]!==M){C[g]=[k,M];var L=y(r,a,o,i,g,v,k,A,d.panelSizeX,d.panelSizeY,d.dim0.crossfilterDimensionIndex,_?0:w?2:1,p,E);S.clearOnly=n;var I=e?r.lines.blockLineCount:m;h(T,P,S,I,m,L)}}},readPixel:function(t,e){return T.read({x:t,y:e,width:1,height:1,data:c}),c},readPixels:function(t,e,r,n){var i=new Uint8Array(4*r*n);return T.read({x:t,y:e,width:r,height:n,data:i}),i},destroy:function(){for(var e in t.style["pointer-events"]="none",L.destroy(),b&&b.destroy(),E)E[e].destroy()},update:I}}},{"../../lib":503,"./constants":891,glslify:227}],896:[function(t,e,r){"use strict";e.exports=function(t,e,r,n){var i,a;for(n||(n=1/0),i=0;i<e.length;i++)(a=e[i]).visible&&(n=Math.min(n,a[r].length));for(n===1/0&&(n=0),t._length=n,i=0;i<e.length;i++)(a=e[i]).visible&&(a._length=n);return n}},{}],897:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("../../lib"),a=i.numberFormat,o=t("color-rgba"),s=t("../../plots/cartesian/axes"),l=i.strRotate,c=i.strTranslate,u=t("../../lib/svg_text_utils"),f=t("../../components/drawing"),h=t("../../components/colorscale"),p=t("../../lib/gup"),d=p.keyFun,g=p.repeat,m=p.unwrap,v=t("./helpers"),y=t("./constants"),x=t("./axisbrush"),b=t("./lines");function _(t,e,r){return i.aggNums(t,null,e,r)}function w(t,e){return k(_(Math.min,t,e),_(Math.max,t,e))}function T(t){var e=t.range;return e?k(e[0],e[1]):w(t.values,t._length)}function k(t,e){return!isNaN(t)&&isFinite(t)||(t=0),!isNaN(e)&&isFinite(e)||(e=0),t===e&&(0===t?(t-=1,e+=1):(t*=.9,e*=1.1)),[t,e]}function A(t,e,r,i,o){var s,l,c=T(r);return i?n.scale.ordinal().domain(i.map((s=a(r.tickformat),l=o,l?function(t,e){var r=l[e];return null==r?s(t):r}:s))).range(i.map((function(r){var n=(r-c[0])/(c[1]-c[0]);return t-e+n*(2*e-t)}))):n.scale.linear().domain(c).range([t-e,e])}function M(t){if(t.tickvals){var e=T(t);return n.scale.ordinal().domain(t.tickvals).range(t.tickvals.map((function(t){return(t-e[0])/(e[1]-e[0])})))}}function S(t){var e=t.map((function(t){return t[0]})),r=t.map((function(t){var e=o(t[1]);return n.rgb("rgb("+e[0]+","+e[1]+","+e[2]+")")})),i="rgb".split("").map((function(t){return n.scale.linear().clamp(!0).domain(e).range(r.map((i=t,function(t){return t[i]})));var i}));return function(t){return i.map((function(e){return e(t)}))}}function E(t){return t.dimensions.some((function(t){return t.brush.filterSpecified}))}function L(t,e,r){var a=m(e),s=a.trace,l=v.convertTypedArray(a.lineColor),c=s.line,u={color:o(y.deselectedLineColor)},f=h.extractOpts(c),p=f.reversescale?h.flipScale(a.cscale):a.cscale,d=s.domain,g=s.dimensions,x=t.width,b=s.labelangle,_=s.labelside,w=s.labelfont,k=s.tickfont,A=s.rangefont,M=i.extendDeepNoArrays({},c,{color:l.map(n.scale.linear().domain(T({values:l,range:[f.min,f.max],_length:s._length}))),blockLineCount:y.blockLineCount,canvasOverdrag:y.overdrag*y.canvasPixelRatio}),E=Math.floor(x*(d.x[1]-d.x[0])),L=Math.floor(t.height*(d.y[1]-d.y[0])),C=t.margin||{l:80,r:80,t:100,b:80},P=E,I=L;return{key:r,colCount:g.filter(v.isVisible).length,dimensions:g,tickDistance:y.tickDistance,unitToColor:S(p),lines:M,deselectedLines:u,labelAngle:b,labelSide:_,labelFont:w,tickFont:k,rangeFont:A,layoutWidth:x,layoutHeight:t.height,domain:d,translateX:d.x[0]*x,translateY:t.height-d.y[1]*t.height,pad:C,canvasWidth:P*y.canvasPixelRatio+2*M.canvasOverdrag,canvasHeight:I*y.canvasPixelRatio,width:P,height:I,canvasPixelRatio:y.canvasPixelRatio}}function 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n=t("fast-isnumeric"),i=t("../../lib"),a=t("./attributes"),o=t("../../plots/domain").defaults,s=t("../bar/defaults").handleText;function l(t,e){var r=Array.isArray(t),a=i.isArrayOrTypedArray(e),o=Math.min(r?t.length:1/0,a?e.length:1/0);if(isFinite(o)||(o=0),o&&a){for(var s,l=0;l<o;l++){var c=e[l];if(n(c)&&c>0){s=!0;break}}s||(o=0)}return{hasLabels:r,hasValues:a,len:o}}e.exports={handleLabelsAndValues:l,supplyDefaults:function(t,e,r,n){function c(r,n){return i.coerce(t,e,a,r,n)}var u=l(c("labels"),c("values")),f=u.len;if(e._hasLabels=u.hasLabels,e._hasValues=u.hasValues,!e._hasLabels&&e._hasValues&&(c("label0"),c("dlabel")),f){e._length=f,c("marker.line.width")&&c("marker.line.color"),c("marker.colors"),c("scalegroup");var h,p=c("text"),d=c("texttemplate");if(d||(h=c("textinfo",Array.isArray(p)?"text+percent":"percent")),c("hovertext"),c("hovertemplate"),d||h&&"none"!==h){var 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1===t.pts.length&&(r.pointNumber=r.i=t.pts[0]),n(r,e,t.pts),"funnelarea"===e.type&&(delete r.v,delete r.i),r}},{"../../components/fx/helpers":402}],904:[function(t,e,r){"use strict";var n=t("../../lib");function i(t){return-1!==t.indexOf("e")?t.replace(/[.]?0+e/,"e"):-1!==t.indexOf(".")?t.replace(/[.]?0+$/,""):t}r.formatPiePercent=function(t,e){var r=i((100*t).toPrecision(3));return n.numSeparate(r,e)+"%"},r.formatPieValue=function(t,e){var r=i(t.toPrecision(10));return n.numSeparate(r,e)},r.getFirstFilled=function(t,e){if(Array.isArray(t))for(var r=0;r<e.length;r++){var n=t[e[r]];if(n||0===n||""===n)return n}},r.castOption=function(t,e){return Array.isArray(t)?r.getFirstFilled(t,e):t||void 0},r.getRotationAngle=function(t){return("auto"===t?0:t)*Math.PI/180}},{"../../lib":503}],905:[function(t,e,r){"use strict";e.exports={attributes:t("./attributes"),supplyDefaults:t("./defaults").supplyDefaults,supplyLayoutDefaults:t("./layout_defaults"),layoutAttributes:t("./layout_attributes"),calc:t("./calc").calc,crossTraceCalc:t("./calc").crossTraceCalc,plot:t("./plot").plot,style:t("./style"),styleOne:t("./style_one"),moduleType:"trace",name:"pie",basePlotModule:t("./base_plot"),categories:["pie-like","pie","showLegend"],meta:{}}},{"./attributes":899,"./base_plot":900,"./calc":901,"./defaults":902,"./layout_attributes":906,"./layout_defaults":907,"./plot":908,"./style":909,"./style_one":910}],906:[function(t,e,r){"use strict";e.exports={hiddenlabels:{valType:"data_array",editType:"calc"},piecolorway:{valType:"colorlist",editType:"calc"},extendpiecolors:{valType:"boolean",dflt:!0,editType:"calc"}}},{}],907:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("./layout_attributes");e.exports=function(t,e){function r(r,a){return n.coerce(t,e,i,r,a)}r("hiddenlabels"),r("piecolorway",e.colorway),r("extendpiecolors")}},{"../../lib":503,"./layout_attributes":906}],908:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("../../plots/plots"),a=t("../../components/fx"),o=t("../../components/color"),s=t("../../components/drawing"),l=t("../../lib"),c=l.strScale,u=l.strTranslate,f=t("../../lib/svg_text_utils"),h=t("../bar/uniform_text"),p=h.recordMinTextSize,d=h.clearMinTextSize,g=t("../bar/constants").TEXTPAD,m=t("./helpers"),v=t("./event_data"),y=t("../../lib").isValidTextValue;function x(t,e,r){var i=r[0],o=i.cx,s=i.cy,c=i.trace,u="funnelarea"===c.type;"_hasHoverLabel"in c||(c._hasHoverLabel=!1),"_hasHoverEvent"in c||(c._hasHoverEvent=!1),t.on("mouseover",(function(t){var r=e._fullLayout,f=e._fullData[c.index];if(!e._dragging&&!1!==r.hovermode){var h=f.hoverinfo;if(Array.isArray(h)&&(h=a.castHoverinfo({hoverinfo:[m.castOption(h,t.pts)],_module:c._module},r,0)),"all"===h&&(h="label+text+value+percent+name"),f.hovertemplate||"none"!==h&&"skip"!==h&&h){var p=t.rInscribed||0,d=o+t.pxmid[0]*(1-p),g=s+t.pxmid[1]*(1-p),y=r.separators,x=[];if(h&&-1!==h.indexOf("label")&&x.push(t.label),t.text=m.castOption(f.hovertext||f.text,t.pts),h&&-1!==h.indexOf("text")){var b=t.text;l.isValidTextValue(b)&&x.push(b)}t.value=t.v,t.valueLabel=m.formatPieValue(t.v,y),h&&-1!==h.indexOf("value")&&x.push(t.valueLabel),t.percent=t.v/i.vTotal,t.percentLabel=m.formatPiePercent(t.percent,y),h&&-1!==h.indexOf("percent")&&x.push(t.percentLabel);var _=f.hoverlabel,w=_.font,T=[];a.loneHover({trace:c,x0:d-p*i.r,x1:d+p*i.r,y:g,_x0:u?o+t.TL[0]:d-p*i.r,_x1:u?o+t.TR[0]:d+p*i.r,_y0:u?s+t.TL[1]:g-p*i.r,_y1:u?s+t.BL[1]:g+p*i.r,text:x.join("<br>"),name:f.hovertemplate||-1!==h.indexOf("name")?f.name:void 0,idealAlign:t.pxmid[0]<0?"left":"right",color:m.castOption(_.bgcolor,t.pts)||t.color,borderColor:m.castOption(_.bordercolor,t.pts),fontFamily:m.castOption(w.family,t.pts),fontSize:m.castOption(w.size,t.pts),fontColor:m.castOption(w.color,t.pts),nameLength:m.castOption(_.namelength,t.pts),textAlign:m.castOption(_.align,t.pts),hovertemplate:m.castOption(f.hovertemplate,t.pts),hovertemplateLabels:t,eventData:[v(t,f)]},{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:e,inOut_bbox:T}),t.bbox=T[0],c._hasHoverLabel=!0}c._hasHoverEvent=!0,e.emit("plotly_hover",{points:[v(t,f)],event:n.event})}})),t.on("mouseout",(function(t){var r=e._fullLayout,i=e._fullData[c.index],o=n.select(this).datum();c._hasHoverEvent&&(t.originalEvent=n.event,e.emit("plotly_unhover",{points:[v(o,i)],event:n.event}),c._hasHoverEvent=!1),c._hasHoverLabel&&(a.loneUnhover(r._hoverlayer.node()),c._hasHoverLabel=!1)})),t.on("click",(function(t){var r=e._fullLayout,i=e._fullData[c.index];e._dragging||!1===r.hovermode||(e._hoverdata=[v(t,i)],a.click(e,n.event))}))}function b(t,e,r){var n=m.castOption(t.insidetextfont.color,e.pts);!n&&t._input.textfont&&(n=m.castOption(t._input.textfont.color,e.pts));var i=m.castOption(t.insidetextfont.family,e.pts)||m.castOption(t.textfont.family,e.pts)||r.family,a=m.castOption(t.insidetextfont.size,e.pts)||m.castOption(t.textfont.size,e.pts)||r.size;return{color:n||o.contrast(e.color),family:i,size:a}}function _(t,e){for(var r,n,i=0;i<t.length;i++)if((n=(r=t[i][0]).trace).title.text){var a=n.title.text;n._meta&&(a=l.templateString(a,n._meta));var o=s.tester.append("text").attr("data-notex",1).text(a).call(s.font,n.title.font).call(f.convertToTspans,e),c=s.bBox(o.node(),!0);r.titleBox={width:c.width,height:c.height},o.remove()}}function w(t,e,r){var n=r.r||e.rpx1,i=e.rInscribed;if(e.startangle===e.stopangle)return{rCenter:1-i,scale:0,rotate:0,textPosAngle:0};var a,o=e.ring,s=1===o&&Math.abs(e.startangle-e.stopangle)===2*Math.PI,l=e.halfangle,c=e.midangle,u=r.trace.insidetextorientation,f="horizontal"===u,h="tangential"===u,p="radial"===u,d="auto"===u,g=[];if(!d){var m,v=function(r,i){if(function(t,e){var r=t.startangle,n=t.stopangle;return r>e&&e>n||r<e&&e<n}(e,r)){var s=Math.abs(r-e.startangle),l=Math.abs(r-e.stopangle),c=s<l?s:l;(a="tan"===i?k(t,n,o,c,0):T(t,n,o,c,Math.PI/2)).textPosAngle=r,g.push(a)}};if(f||h){for(m=4;m>=-4;m-=2)v(Math.PI*m,"tan");for(m=4;m>=-4;m-=2)v(Math.PI*(m+1),"tan")}if(f||p){for(m=4;m>=-4;m-=2)v(Math.PI*(m+1.5),"rad");for(m=4;m>=-4;m-=2)v(Math.PI*(m+.5),"rad")}}if(s||d||f){var y=Math.sqrt(t.width*t.width+t.height*t.height);if((a={scale:i*n*2/y,rCenter:1-i,rotate:0}).textPosAngle=(e.startangle+e.stopangle)/2,a.scale>=1)return a;g.push(a)}(d||p)&&((a=T(t,n,o,l,c)).textPosAngle=(e.startangle+e.stopangle)/2,g.push(a)),(d||h)&&((a=k(t,n,o,l,c)).textPosAngle=(e.startangle+e.stopangle)/2,g.push(a));for(var 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r,n,i,a=t.trace,o={x:t.cx,y:t.cy},s={tx:0,ty:0};s.ty+=a.title.font.size,i=I(a),-1!==a.title.position.indexOf("top")?(o.y-=(1+i)*t.r,s.ty-=t.titleBox.height):-1!==a.title.position.indexOf("bottom")&&(o.y+=(1+i)*t.r);var l,c,u=(l=t.r,c=t.trace.aspectratio,l/(void 0===c?1:c)),f=e.w*(a.domain.x[1]-a.domain.x[0])/2;return-1!==a.title.position.indexOf("left")?(f+=u,o.x-=(1+i)*u,s.tx+=t.titleBox.width/2):-1!==a.title.position.indexOf("center")?f*=2:-1!==a.title.position.indexOf("right")&&(f+=u,o.x+=(1+i)*u,s.tx-=t.titleBox.width/2),r=f/t.titleBox.width,n=P(t,e)/t.titleBox.height,{x:o.x,y:o.y,scale:Math.min(r,n),tx:s.tx,ty:s.ty}}function P(t,e){var r=t.trace,n=e.h*(r.domain.y[1]-r.domain.y[0]);return Math.min(t.titleBox.height,n/2)}function I(t){var e,r=t.pull;if(!r)return 0;if(Array.isArray(r))for(r=0,e=0;e<t.pull.length;e++)t.pull[e]>r&&(r=t.pull[e]);return r}function O(t,e){for(var r=[],n=0;n<t.length;n++){var i=t[n][0],a=i.trace,o=a.domain,s=e.w*(o.x[1]-o.x[0]),l=e.h*(o.y[1]-o.y[0]);a.title.text&&"middle center"!==a.title.position&&(l-=P(i,e));var c=s/2,u=l/2;"funnelarea"!==a.type||a.scalegroup||(u/=a.aspectratio),i.r=Math.min(c,u)/(1+I(a)),i.cx=e.l+e.w*(a.domain.x[1]+a.domain.x[0])/2,i.cy=e.t+e.h*(1-a.domain.y[0])-l/2,a.title.text&&-1!==a.title.position.indexOf("bottom")&&(i.cy-=P(i,e)),a.scalegroup&&-1===r.indexOf(a.scalegroup)&&r.push(a.scalegroup)}!function(t,e){for(var r,n,i,a=0;a<e.length;a++){var o=1/0,s=e[a];for(n=0;n<t.length;n++)if(r=t[n][0],(i=r.trace).scalegroup===s){var l;if("pie"===i.type)l=r.r*r.r;else if("funnelarea"===i.type){var c,u;i.aspectratio>1?(c=r.r,u=c/i.aspectratio):(u=r.r,c=u*i.aspectratio),c*=(1+i.baseratio)/2,l=c*u}o=Math.min(o,l/r.vTotal)}for(n=0;n<t.length;n++)if(r=t[n][0],(i=r.trace).scalegroup===s){var f=o*r.vTotal;"funnelarea"===i.type&&(f/=(1+i.baseratio)/2,f/=i.aspectratio),r.r=Math.sqrt(f)}}}(t,r)}function z(t,e){return[t*Math.sin(e),-t*Math.cos(e)]}function D(t,e,r){var n=t._fullLayout,i=r.trace,a=i.texttemplate,o=i.textinfo;if(!a&&o&&"none"!==o){var s,c=o.split("+"),u=function(t){return-1!==c.indexOf(t)},f=u("label"),h=u("text"),p=u("value"),d=u("percent"),g=n.separators;if(s=f?[e.label]:[],h){var v=m.getFirstFilled(i.text,e.pts);y(v)&&s.push(v)}p&&s.push(m.formatPieValue(e.v,g)),d&&s.push(m.formatPiePercent(e.v/r.vTotal,g)),e.text=s.join("<br>")}if(a){var x=l.castOption(i,e.i,"texttemplate");if(x){var b=function(t){return{label:t.label,value:t.v,valueLabel:m.formatPieValue(t.v,n.separators),percent:t.v/r.vTotal,percentLabel:m.formatPiePercent(t.v/r.vTotal,n.separators),color:t.color,text:t.text,customdata:l.castOption(i,t.i,"customdata")}}(e),_=m.getFirstFilled(i.text,e.pts);(y(_)||""===_)&&(b.text=_),e.text=l.texttemplateString(x,b,t._fullLayout._d3locale,b,i._meta||{})}else e.text=""}}function R(t,e){var r=t.rotate*Math.PI/180,n=Math.cos(r),i=Math.sin(r),a=(e.left+e.right)/2,o=(e.top+e.bottom)/2;t.textX=a*n-o*i,t.textY=a*i+o*n,t.noCenter=!0}e.exports={plot:function(t,e){var r=t._fullLayout,a=r._size;d("pie",r),_(e,t),O(e,a);var h=l.makeTraceGroups(r._pielayer,e,"trace").each((function(e){var h=n.select(this),d=e[0],g=d.trace;!function(t){var e,r,n,i=t[0],a=i.r,o=i.trace,s=m.getRotationAngle(o.rotation),l=2*Math.PI/i.vTotal,c="px0",u="px1";if("counterclockwise"===o.direction){for(e=0;e<t.length&&t[e].hidden;e++);if(e===t.length)return;s+=l*t[e].v,l*=-1,c="px1",u="px0"}for(n=z(a,s),e=0;e<t.length;e++)(r=t[e]).hidden||(r[c]=n,r.startangle=s,s+=l*r.v/2,r.pxmid=z(a,s),r.midangle=s,s+=l*r.v/2,n=z(a,s),r.stopangle=s,r[u]=n,r.largeArc=r.v>i.vTotal/2?1:0,r.halfangle=Math.PI*Math.min(r.v/i.vTotal,.5),r.ring=1-o.hole,r.rInscribed=E(r,i))}(e),h.attr("stroke-linejoin","round"),h.each((function(){var v=n.select(this).selectAll("g.slice").data(e);v.enter().append("g").classed("slice",!0),v.exit().remove();var y=[[[],[]],[[],[]]],_=!1;v.each((function(i,a){if(i.hidden)n.select(this).selectAll("path,g").remove();else{i.pointNumber=i.i,i.curveNumber=g.index,y[i.pxmid[1]<0?0:1][i.pxmid[0]<0?0:1].push(i);var o=d.cx,c=d.cy,u=n.select(this),h=u.selectAll("path.surface").data([i]);if(h.enter().append("path").classed("surface",!0).style({"pointer-events":"all"}),u.call(x,t,e),g.pull){var v=+m.castOption(g.pull,i.pts)||0;v>0&&(o+=v*i.pxmid[0],c+=v*i.pxmid[1])}i.cxFinal=o,i.cyFinal=c;var T=g.hole;if(i.v===d.vTotal){var k="M"+(o+i.px0[0])+","+(c+i.px0[1])+C(i.px0,i.pxmid,!0,1)+C(i.pxmid,i.px0,!0,1)+"Z";T?h.attr("d","M"+(o+T*i.px0[0])+","+(c+T*i.px0[1])+C(i.px0,i.pxmid,!1,T)+C(i.pxmid,i.px0,!1,T)+"Z"+k):h.attr("d",k)}else{var A=C(i.px0,i.px1,!0,1);if(T){var M=1-T;h.attr("d","M"+(o+T*i.px1[0])+","+(c+T*i.px1[1])+C(i.px1,i.px0,!1,T)+"l"+M*i.px0[0]+","+M*i.px0[1]+A+"Z")}else h.attr("d","M"+o+","+c+"l"+i.px0[0]+","+i.px0[1]+A+"Z")}D(t,i,d);var S=m.castOption(g.textposition,i.pts),E=u.selectAll("g.slicetext").data(i.text&&"none"!==S?[0]:[]);E.enter().append("g").classed("slicetext",!0),E.exit().remove(),E.each((function(){var u=l.ensureSingle(n.select(this),"text","",(function(t){t.attr("data-notex",1)})),h=l.ensureUniformFontSize(t,"outside"===S?function(t,e,r){var n=m.castOption(t.outsidetextfont.color,e.pts)||m.castOption(t.textfont.color,e.pts)||r.color,i=m.castOption(t.outsidetextfont.family,e.pts)||m.castOption(t.textfont.family,e.pts)||r.family,a=m.castOption(t.outsidetextfont.size,e.pts)||m.castOption(t.textfont.size,e.pts)||r.size;return{color:n,family:i,size:a}}(g,i,r.font):b(g,i,r.font));u.text(i.text).attr({class:"slicetext",transform:"","text-anchor":"middle"}).call(s.font,h).call(f.convertToTspans,t);var v,y=s.bBox(u.node());if("outside"===S)v=L(y,i);else if(v=w(y,i,d),"auto"===S&&v.scale<1){var x=l.ensureUniformFontSize(t,g.outsidetextfont);u.call(s.font,x),v=L(y=s.bBox(u.node()),i)}var T=v.textPosAngle,k=void 0===T?i.pxmid:z(d.r,T);if(v.targetX=o+k[0]*v.rCenter+(v.x||0),v.targetY=c+k[1]*v.rCenter+(v.y||0),R(v,y),v.outside){var A=v.targetY;i.yLabelMin=A-y.height/2,i.yLabelMid=A,i.yLabelMax=A+y.height/2,i.labelExtraX=0,i.labelExtraY=0,_=!0}v.fontSize=h.size,p(g.type,v,r),e[a].transform=v,u.attr("transform",l.getTextTransform(v))}))}function C(t,e,r,n){var a=n*(e[0]-t[0]),o=n*(e[1]-t[1]);return"a"+n*d.r+","+n*d.r+" 0 "+i.largeArc+(r?" 1 ":" 0 ")+a+","+o}}));var T=n.select(this).selectAll("g.titletext").data(g.title.text?[0]:[]);if(T.enter().append("g").classed("titletext",!0),T.exit().remove(),T.each((function(){var e,r=l.ensureSingle(n.select(this),"text","",(function(t){t.attr("data-notex",1)})),i=g.title.text;g._meta&&(i=l.templateString(i,g._meta)),r.text(i).attr({class:"titletext",transform:"","text-anchor":"middle"}).call(s.font,g.title.font).call(f.convertToTspans,t),e="middle center"===g.title.position?function(t){var e=Math.sqrt(t.titleBox.width*t.titleBox.width+t.titleBox.height*t.titleBox.height);return{x:t.cx,y:t.cy,scale:t.trace.hole*t.r*2/e,tx:0,ty:-t.titleBox.height/2+t.trace.title.font.size}}(d):C(d,a),r.attr("transform",u(e.x,e.y)+c(Math.min(1,e.scale))+u(e.tx,e.ty))})),_&&function(t,e){var r,n,i,a,o,s,l,c,u,f,h,p,d;function g(t,e){return t.pxmid[1]-e.pxmid[1]}function v(t,e){return e.pxmid[1]-t.pxmid[1]}function y(t,r){r||(r={});var 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r=t.hoverlabel||{},n=i.nestedProperty(r,e).get();return!Array.isArray(n)&&n}e.exports=function(t,e){for(var r=t._fullLayout,i=r._paper,h=r._size,g=0;g<t._fullData.length;g++)if(t._fullData[g].visible&&t._fullData[g].type===c.sankey&&!t._fullData[g]._viewInitial){var m=t._fullData[g].node;t._fullData[g]._viewInitial={node:{groups:m.groups.slice(),x:m.x.slice(),y:m.y.slice()}}}var w=u(t,"source:")+" ",T=u(t,"target:")+" ",k=u(t,"concentration:")+" ",A=u(t,"incoming flow count:")+" ",M=u(t,"outgoing flow count:")+" ";o(t,i,e,{width:h.w,height:h.h,margin:{t:h.t,r:h.r,b:h.b,l:h.l}},{linkEvents:{hover:function(e,r,i){!1!==t._fullLayout.hovermode&&(n.select(e).call(x.bind(0,r,i,!0)),"skip"!==r.link.trace.link.hoverinfo&&(r.link.fullData=r.link.trace,t.emit("plotly_hover",{event:n.event,points:[r.link]})))},follow:function(e,i){if(!1!==t._fullLayout.hovermode){var o=i.link.trace.link;if("none"!==o.hoverinfo&&"skip"!==o.hoverinfo){for(var c=[],u=0,h=0;h<i.flow.links.length;h++){var g=i.flow.links[h];if("closest"!==t._fullLayout.hovermode||i.link.pointNumber===g.pointNumber){i.link.pointNumber===g.pointNumber&&(u=h),g.fullData=g.trace,o=i.link.trace.link;var m=y(g),v={valueLabel:a(i.valueFormat)(g.value)+i.valueSuffix};c.push({x:m[0],y:m[1],name:v.valueLabel,text:[g.label||"",w+g.source.label,T+g.target.label,g.concentrationscale?k+a("%0.2f")(g.flow.labelConcentration):""].filter(f).join("<br>"),color:_(o,"bgcolor")||l.addOpacity(g.color,1),borderColor:_(o,"bordercolor"),fontFamily:_(o,"font.family"),fontSize:_(o,"font.size"),fontColor:_(o,"font.color"),nameLength:_(o,"namelength"),textAlign:_(o,"align"),idealAlign:n.event.x<m[0]?"right":"left",hovertemplate:o.hovertemplate,hovertemplateLabels:v,eventData:[g]})}}s.loneHover(c,{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:t,anchorIndex:u}).each((function(){i.link.concentrationscale||p(this,.65),d(this)}))}}function y(t){var e,r;t.circular?(e=(t.circularPathData.leftInnerExtent+t.circularPathData.rightInnerExtent)/2,r=t.circularPathData.verticalFullExtent):(e=(t.source.x1+t.target.x0)/2,r=(t.y0+t.y1)/2);var n=[e,r];return"v"===t.trace.orientation&&n.reverse(),n[0]+=i.parent.translateX,n[1]+=i.parent.translateY,n}},unhover:function(e,i,a){!1!==t._fullLayout.hovermode&&(n.select(e).call(b.bind(0,i,a,!0)),"skip"!==i.link.trace.link.hoverinfo&&(i.link.fullData=i.link.trace,t.emit("plotly_unhover",{event:n.event,points:[i.link]})),s.loneUnhover(r._hoverlayer.node()))},select:function(e,r){var i=r.link;i.originalEvent=n.event,t._hoverdata=[i],s.click(t,{target:!0})}},nodeEvents:{hover:function(e,r,i){!1!==t._fullLayout.hovermode&&(n.select(e).call(v,r,i),"skip"!==r.node.trace.node.hoverinfo&&(r.node.fullData=r.node.trace,t.emit("plotly_hover",{event:n.event,points:[r.node]})))},follow:function(e,i){if(!1!==t._fullLayout.hovermode){var o=i.node.trace.node;if("none"!==o.hoverinfo&&"skip"!==o.hoverinfo){var l=n.select(e).select("."+c.nodeRect),u=t._fullLayout._paperdiv.node().getBoundingClientRect(),h=l.node().getBoundingClientRect(),g=h.left-2-u.left,m=h.right+2-u.left,v=h.top+h.height/4-u.top,y={valueLabel:a(i.valueFormat)(i.node.value)+i.valueSuffix};i.node.fullData=i.node.trace,t._fullLayout._calcInverseTransform(t);var x=t._fullLayout._invScaleX,b=t._fullLayout._invScaleY,w=s.loneHover({x0:x*g,x1:x*m,y:b*v,name:a(i.valueFormat)(i.node.value)+i.valueSuffix,text:[i.node.label,A+i.node.targetLinks.length,M+i.node.sourceLinks.length].filter(f).join("<br>"),color:_(o,"bgcolor")||i.tinyColorHue,borderColor:_(o,"bordercolor"),fontFamily:_(o,"font.family"),fontSize:_(o,"font.size"),fontColor:_(o,"font.color"),nameLength:_(o,"namelength"),textAlign:_(o,"align"),idealAlign:"left",hovertemplate:o.hovertemplate,hovertemplateLabels:y,eventData:[i.node]},{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:t});p(w,.85),d(w)}}},unhover:function(e,i,a){!1!==t._fullLayout.hovermode&&(n.select(e).call(y,i,a),"skip"!==i.node.trace.node.hoverinfo&&(i.node.fullData=i.node.trace,t.emit("plotly_unhover",{event:n.event,points:[i.node]})),s.loneUnhover(r._hoverlayer.node()))},select:function(e,r,i){var a=r.node;a.originalEvent=n.event,t._hoverdata=[a],n.select(e).call(y,r,i),s.click(t,{target:!0})}}})}},{"../../components/color":366,"../../components/fx":406,"../../lib":503,"./constants":918,"./render":922,"@plotly/d3":58}],922:[function(t,e,r){"use strict";var n=t("d3-force"),i=t("d3-interpolate").interpolateNumber,a=t("@plotly/d3"),o=t("@plotly/d3-sankey"),s=t("@plotly/d3-sankey-circular"),l=t("./constants"),c=t("tinycolor2"),u=t("../../components/color"),f=t("../../components/drawing"),h=t("../../lib"),p=h.strTranslate,d=h.strRotate,g=t("../../lib/gup"),m=g.keyFun,v=g.repeat,y=g.unwrap,x=t("../../lib/svg_text_utils"),b=t("../../registry"),_=t("../../constants/alignment"),w=_.CAP_SHIFT,T=_.LINE_SPACING;function k(t,e,r){var n,i=y(e),a=i.trace,u=a.domain,f="h"===a.orientation,p=a.node.pad,d=a.node.thickness,g=t.width*(u.x[1]-u.x[0]),m=t.height*(u.y[1]-u.y[0]),v=i._nodes,x=i._links,b=i.circular;(n=b?s.sankeyCircular().circularLinkGap(0):o.sankey()).iterations(l.sankeyIterations).size(f?[g,m]:[m,g]).nodeWidth(d).nodePadding(p).nodeId((function(t){return t.pointNumber})).nodes(v).links(x);var _,w,T,k=n();for(var A in n.nodePadding()<p&&h.warn("node.pad was reduced to ",n.nodePadding()," to fit within the figure."),i._groupLookup){var M,S=parseInt(i._groupLookup[A]);for(_=0;_<k.nodes.length;_++)if(k.nodes[_].pointNumber===S){M=k.nodes[_];break}if(M){var E={pointNumber:parseInt(A),x0:M.x0,x1:M.x1,y0:M.y0,y1:M.y1,partOfGroup:!0,sourceLinks:[],targetLinks:[]};k.nodes.unshift(E),M.childrenNodes.unshift(E)}}if(function(){for(_=0;_<k.nodes.length;_++){var t,e,r=k.nodes[_],n={};for(w=0;w<r.targetLinks.length;w++)t=(e=r.targetLinks[w]).source.pointNumber+":"+e.target.pointNumber,n.hasOwnProperty(t)||(n[t]=[]),n[t].push(e);var i=Object.keys(n);for(w=0;w<i.length;w++){var a=n[t=i[w]],o=0,s={};for(T=0;T<a.length;T++)s[(e=a[T]).label]||(s[e.label]=0),s[e.label]+=e.value,o+=e.value;for(T=0;T<a.length;T++)(e=a[T]).flow={value:o,labelConcentration:s[e.label]/o,concentration:e.value/o,links:a},e.concentrationscale&&(e.color=c(e.concentrationscale(e.flow.labelConcentration)))}var l=0;for(w=0;w<r.sourceLinks.length;w++)l+=r.sourceLinks[w].value;for(w=0;w<r.sourceLinks.length;w++)(e=r.sourceLinks[w]).concentrationOut=e.value/l;var u=0;for(w=0;w<r.targetLinks.length;w++)u+=r.targetLinks[w].value;for(w=0;w<r.targetLinks.length;w++)(e=r.targetLinks[w]).concenrationIn=e.value/u}}(),a.node.x.length&&a.node.y.length){for(_=0;_<Math.min(a.node.x.length,a.node.y.length,k.nodes.length);_++)if(a.node.x[_]&&a.node.y[_]){var L=[a.node.x[_]*g,a.node.y[_]*m];k.nodes[_].x0=L[0]-d/2,k.nodes[_].x1=L[0]+d/2;var C=k.nodes[_].y1-k.nodes[_].y0;k.nodes[_].y0=L[1]-C/2,k.nodes[_].y1=L[1]+C/2}if("snap"===a.arrangement)!function(t){t.forEach((function(t){var e,r,n,i=0,a=t.length;for(t.sort((function(t,e){return t.y0-e.y0})),n=0;n<a;++n)(e=t[n]).y0>=i||(r=i-e.y0)>1e-6&&(e.y0+=r,e.y1+=r),i=e.y1+p}))}(function(t){var e,r,n=t.map((function(t,e){return{x0:t.x0,index:e}})).sort((function(t,e){return t.x0-e.x0})),i=[],a=-1,o=-1/0;for(_=0;_<n.length;_++){var s=t[n[_].index];s.x0>o+d&&(a+=1,e=s.x0),o=s.x0,i[a]||(i[a]=[]),i[a].push(s),r=e-s.x0,s.x0+=r,s.x1+=r}return i}(v=k.nodes));n.update(k)}return{circular:b,key:r,trace:a,guid:h.randstr(),horizontal:f,width:g,height:m,nodePad:a.node.pad,nodeLineColor:a.node.line.color,nodeLineWidth:a.node.line.width,linkLineColor:a.link.line.color,linkLineWidth:a.link.line.width,valueFormat:a.valueformat,valueSuffix:a.valuesuffix,textFont:a.textfont,translateX:u.x[0]*t.width+t.margin.l,translateY:t.height-u.y[1]*t.height+t.margin.t,dragParallel:f?m:g,dragPerpendicular:f?g:m,arrangement:a.arrangement,sankey:n,graph:k,forceLayouts:{},interactionState:{dragInProgress:!1,hovered:!1}}}function A(t,e,r){var n=c(e.color),i=e.source.label+"|"+e.target.label+"__"+r;return e.trace=t.trace,e.curveNumber=t.trace.index,{circular:t.circular,key:i,traceId:t.key,pointNumber:e.pointNumber,link:e,tinyColorHue:u.tinyRGB(n),tinyColorAlpha:n.getAlpha(),linkPath:M,linkLineColor:t.linkLineColor,linkLineWidth:t.linkLineWidth,valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,parent:t,interactionState:t.interactionState,flow:e.flow}}function M(){return function(t){if(t.link.circular)return e=t.link,r=e.width/2,n=e.circularPathData,"top"===e.circularLinkType?"M "+n.targetX+" "+(n.targetY+r)+" L"+n.rightInnerExtent+" "+(n.targetY+r)+"A"+(n.rightLargeArcRadius+r)+" "+(n.rightSmallArcRadius+r)+" 0 0 1 "+(n.rightFullExtent-r)+" "+(n.targetY-n.rightSmallArcRadius)+"L"+(n.rightFullExtent-r)+" "+n.verticalRightInnerExtent+"A"+(n.rightLargeArcRadius+r)+" "+(n.rightLargeArcRadius+r)+" 0 0 1 "+n.rightInnerExtent+" "+(n.verticalFullExtent-r)+"L"+n.leftInnerExtent+" "+(n.verticalFullExtent-r)+"A"+(n.leftLargeArcRadius+r)+" "+(n.leftLargeArcRadius+r)+" 0 0 1 "+(n.leftFullExtent+r)+" "+n.verticalLeftInnerExtent+"L"+(n.leftFullExtent+r)+" "+(n.sourceY-n.leftSmallArcRadius)+"A"+(n.leftLargeArcRadius+r)+" "+(n.leftSmallArcRadius+r)+" 0 0 1 "+n.leftInnerExtent+" "+(n.sourceY+r)+"L"+n.sourceX+" "+(n.sourceY+r)+"L"+n.sourceX+" "+(n.sourceY-r)+"L"+n.leftInnerExtent+" "+(n.sourceY-r)+"A"+(n.leftLargeArcRadius-r)+" "+(n.leftSmallArcRadius-r)+" 0 0 0 "+(n.leftFullExtent-r)+" "+(n.sourceY-n.leftSmallArcRadius)+"L"+(n.leftFullExtent-r)+" "+n.verticalLeftInnerExtent+"A"+(n.leftLargeArcRadius-r)+" "+(n.leftLargeArcRadius-r)+" 0 0 0 "+n.leftInnerExtent+" "+(n.verticalFullExtent+r)+"L"+n.rightInnerExtent+" "+(n.verticalFullExtent+r)+"A"+(n.rightLargeArcRadius-r)+" "+(n.rightLargeArcRadius-r)+" 0 0 0 "+(n.rightFullExtent+r)+" "+n.verticalRightInnerExtent+"L"+(n.rightFullExtent+r)+" "+(n.targetY-n.rightSmallArcRadius)+"A"+(n.rightLargeArcRadius-r)+" "+(n.rightSmallArcRadius-r)+" 0 0 0 "+n.rightInnerExtent+" "+(n.targetY-r)+"L"+n.targetX+" "+(n.targetY-r)+"Z":"M "+n.targetX+" "+(n.targetY-r)+" L"+n.rightInnerExtent+" "+(n.targetY-r)+"A"+(n.rightLargeArcRadius+r)+" "+(n.rightSmallArcRadius+r)+" 0 0 0 "+(n.rightFullExtent-r)+" "+(n.targetY+n.rightSmallArcRadius)+"L"+(n.rightFullExtent-r)+" "+n.verticalRightInnerExtent+"A"+(n.rightLargeArcRadius+r)+" "+(n.rightLargeArcRadius+r)+" 0 0 0 "+n.rightInnerExtent+" "+(n.verticalFullExtent+r)+"L"+n.leftInnerExtent+" "+(n.verticalFullExtent+r)+"A"+(n.leftLargeArcRadius+r)+" "+(n.leftLargeArcRadius+r)+" 0 0 0 "+(n.leftFullExtent+r)+" "+n.verticalLeftInnerExtent+"L"+(n.leftFullExtent+r)+" "+(n.sourceY+n.leftSmallArcRadius)+"A"+(n.leftLargeArcRadius+r)+" "+(n.leftSmallArcRadius+r)+" 0 0 0 "+n.leftInnerExtent+" "+(n.sourceY-r)+"L"+n.sourceX+" "+(n.sourceY-r)+"L"+n.sourceX+" "+(n.sourceY+r)+"L"+n.leftInnerExtent+" "+(n.sourceY+r)+"A"+(n.leftLargeArcRadius-r)+" "+(n.leftSmallArcRadius-r)+" 0 0 1 "+(n.leftFullExtent-r)+" "+(n.sourceY+n.leftSmallArcRadius)+"L"+(n.leftFullExtent-r)+" "+n.verticalLeftInnerExtent+"A"+(n.leftLargeArcRadius-r)+" "+(n.leftLargeArcRadius-r)+" 0 0 1 "+n.leftInnerExtent+" "+(n.verticalFullExtent-r)+"L"+n.rightInnerExtent+" "+(n.verticalFullExtent-r)+"A"+(n.rightLargeArcRadius-r)+" "+(n.rightLargeArcRadius-r)+" 0 0 1 "+(n.rightFullExtent+r)+" "+n.verticalRightInnerExtent+"L"+(n.rightFullExtent+r)+" "+(n.targetY+n.rightSmallArcRadius)+"A"+(n.rightLargeArcRadius-r)+" "+(n.rightSmallArcRadius-r)+" 0 0 1 "+n.rightInnerExtent+" "+(n.targetY+r)+"L"+n.targetX+" "+(n.targetY+r)+"Z";var e,r,n,a=t.link.source.x1,o=t.link.target.x0,s=i(a,o),l=s(.5),c=s(.5),u=t.link.y0-t.link.width/2,f=t.link.y0+t.link.width/2,h=t.link.y1-t.link.width/2,p=t.link.y1+t.link.width/2;return"M"+a+","+u+"C"+l+","+u+" "+c+","+h+" "+o+","+h+"L"+o+","+p+"C"+c+","+p+" "+l+","+f+" "+a+","+f+"Z"}}function S(t,e){var r=c(e.color),n=l.nodePadAcross,i=t.nodePad/2;e.dx=e.x1-e.x0,e.dy=e.y1-e.y0;var a=e.dx,o=Math.max(.5,e.dy),s="node_"+e.pointNumber;return e.group&&(s=h.randstr()),e.trace=t.trace,e.curveNumber=t.trace.index,{index:e.pointNumber,key:s,partOfGroup:e.partOfGroup||!1,group:e.group,traceId:t.key,trace:t.trace,node:e,nodePad:t.nodePad,nodeLineColor:t.nodeLineColor,nodeLineWidth:t.nodeLineWidth,textFont:t.textFont,size:t.horizontal?t.height:t.width,visibleWidth:Math.ceil(a),visibleHeight:o,zoneX:-n,zoneY:-i,zoneWidth:a+2*n,zoneHeight:o+2*i,labelY:t.horizontal?e.dy/2+1:e.dx/2+1,left:1===e.originalLayer,sizeAcross:t.width,forceLayouts:t.forceLayouts,horizontal:t.horizontal,darkBackground:r.getBrightness()<=128,tinyColorHue:u.tinyRGB(r),tinyColorAlpha:r.getAlpha(),valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,graph:t.graph,arrangement:t.arrangement,uniqueNodeLabelPathId:[t.guid,t.key,s].join("_"),interactionState:t.interactionState,figure:t}}function E(t){t.attr("transform",(function(t){return p(t.node.x0.toFixed(3),t.node.y0.toFixed(3))}))}function L(t){t.call(E)}function C(t,e){t.call(L),e.attr("d",M())}function P(t){t.attr("width",(function(t){return t.node.x1-t.node.x0})).attr("height",(function(t){return t.visibleHeight}))}function I(t){return t.link.width>1||t.linkLineWidth>0}function O(t){return p(t.translateX,t.translateY)+(t.horizontal?"matrix(1 0 0 1 0 0)":"matrix(0 1 1 0 0 0)")}function z(t,e,r){t.on(".basic",null).on("mouseover.basic",(function(t){t.interactionState.dragInProgress||t.partOfGroup||(r.hover(this,t,e),t.interactionState.hovered=[this,t])})).on("mousemove.basic",(function(t){t.interactionState.dragInProgress||t.partOfGroup||(r.follow(this,t),t.interactionState.hovered=[this,t])})).on("mouseout.basic",(function(t){t.interactionState.dragInProgress||t.partOfGroup||(r.unhover(this,t,e),t.interactionState.hovered=!1)})).on("click.basic",(function(t){t.interactionState.hovered&&(r.unhover(this,t,e),t.interactionState.hovered=!1),t.interactionState.dragInProgress||t.partOfGroup||r.select(this,t,e)}))}function D(t,e,r,i){var 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y={padded:!0},x={padded:!0};s&&(y.ppad=x.ppad=s);var b=c<2||i[0]!==i[c-1]||o[0]!==o[c-1];b&&("tozerox"===v||"tonextx"===v&&(p||"h"===d))?y.tozero=!0:(e.error_y||{}).visible||"tonexty"!==v&&"tozeroy"!==v&&(l.hasMarkers(e)||l.hasText(e))||(y.padded=!1,y.ppad=0),b&&("tozeroy"===v||"tonexty"===v&&(p||"v"===d))?x.tozero=!0:"tonextx"!==v&&"tozerox"!==v||(x.padded=!1),f&&(e._extremes[f]=a.findExtremes(r,i,y)),h&&(e._extremes[h]=a.findExtremes(n,o,x))}function p(t,e){if(l.hasMarkers(t)){var r,n=t.marker,o=1.6*(t.marker.sizeref||1);if(r="area"===t.marker.sizemode?function(t){return Math.max(Math.sqrt((t||0)/o),3)}:function(t){return Math.max((t||0)/o,3)},i.isArrayOrTypedArray(n.size)){var s={type:"linear"};a.setConvert(s);for(var c=s.makeCalcdata(t.marker,"size"),u=new Array(e),f=0;f<e;f++)u[f]=r(c[f]);return u}return r(n.size)}}function d(t,e){var r=g(e),n=t._firstScatter;n[r]||(n[r]=e.uid)}function g(t){var e=t.stackgroup;return t.xaxis+t.yaxis+t.type+(e?"-"+e:"")}function m(t,e,r,n){var 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n=t("../../components/colorscale/helpers").hasColorscale,i=t("../../components/colorscale/calc"),a=t("./subtypes");e.exports=function(t,e){a.hasLines(e)&&n(e,"line")&&i(t,e,{vals:e.line.color,containerStr:"line",cLetter:"c"}),a.hasMarkers(e)&&(n(e,"marker")&&i(t,e,{vals:e.marker.color,containerStr:"marker",cLetter:"c"}),n(e,"marker.line")&&i(t,e,{vals:e.marker.line.color,containerStr:"marker.line",cLetter:"c"}))}},{"../../components/colorscale/calc":374,"../../components/colorscale/helpers":377,"./subtypes":950}],929:[function(t,e,r){"use strict";e.exports={PTS_LINESONLY:20,minTolerance:.2,toleranceGrowth:10,maxScreensAway:20,eventDataKeys:[]}},{}],930:[function(t,e,r){"use strict";var n=t("./calc");function i(t,e,r,n,i,a,o){i[n]=!0;var s={i:null,gap:!0,s:0};if(s[o]=r,t.splice(e,0,s),e&&r===t[e-1][o]){var l=t[e-1];s.s=l.s,s.i=l.i,s.gap=l.gap}else a&&(s.s=function(t,e,r,n){var i=t[e-1],a=t[e+1];return 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n=t("../../plots/cartesian/axes");e.exports=function(t,e,r){var i={},a={_fullLayout:r},o=n.getFromTrace(a,e,"x"),s=n.getFromTrace(a,e,"y");return i.xLabel=n.tickText(o,o.c2l(t.x),!0).text,i.yLabel=n.tickText(s,s.c2l(t.y),!0).text,i}},{"../../plots/cartesian/axes":554}],935:[function(t,e,r){"use strict";var n=t("../../components/color"),i=t("./subtypes");e.exports=function(t,e){var r,a;if("lines"===t.mode)return(r=t.line.color)&&n.opacity(r)?r:t.fillcolor;if("none"===t.mode)return t.fill?t.fillcolor:"";var o=e.mcc||(t.marker||{}).color,s=e.mlcc||((t.marker||{}).line||{}).color;return(a=o&&n.opacity(o)?o:s&&n.opacity(s)&&(e.mlw||((t.marker||{}).line||{}).width)?s:"")?n.opacity(a)<.3?n.addOpacity(a,.3):a:(r=(t.line||{}).color)&&n.opacity(r)&&i.hasLines(t)&&t.line.width?r:t.fillcolor}},{"../../components/color":366,"./subtypes":950}],936:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../../components/fx"),a=t("../../registry"),o=t("./get_trace_color"),s=t("../../components/color"),l=n.fillText;e.exports=function(t,e,r,c){var u=t.cd,f=u[0].trace,h=t.xa,p=t.ya,d=h.c2p(e),g=p.c2p(r),m=[d,g],v=f.hoveron||"",y=-1!==f.mode.indexOf("markers")?3:.5,x=!!f.xperiodalignment,b=!!f.yperiodalignment;if(-1!==v.indexOf("points")){var _=function(t){var e=Math.max(y,t.mrc||0),r=h.c2p(t.x)-d,n=p.c2p(t.y)-g;return Math.max(Math.sqrt(r*r+n*n)-e,1-y/e)},w=i.getDistanceFunction(c,(function(t){if(x){var e=h.c2p(t.xStart),r=h.c2p(t.xEnd);return d>=Math.min(e,r)&&d<=Math.max(e,r)?0:1/0}var n=Math.max(3,t.mrc||0),i=1-1/n,a=Math.abs(h.c2p(t.x)-d);return a<n?i*a/n:a-n+i}),(function(t){if(b){var e=p.c2p(t.yStart),r=p.c2p(t.yEnd);return g>=Math.min(e,r)&&g<=Math.max(e,r)?0:1/0}var n=Math.max(3,t.mrc||0),i=1-1/n,a=Math.abs(p.c2p(t.y)-g);return a<n?i*a/n:a-n+i}),_);if(i.getClosest(u,w,t),!1!==t.index){var T=u[t.index],k=h.c2p(T.x,!0),A=p.c2p(T.y,!0),M=T.mrc||1;t.index=T.i;var 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n=t("./subtypes");e.exports={hasLines:n.hasLines,hasMarkers:n.hasMarkers,hasText:n.hasText,isBubble:n.isBubble,attributes:t("./attributes"),supplyDefaults:t("./defaults"),crossTraceDefaults:t("./cross_trace_defaults"),calc:t("./calc").calc,crossTraceCalc:t("./cross_trace_calc"),arraysToCalcdata:t("./arrays_to_calcdata"),plot:t("./plot"),colorbar:t("./marker_colorbar"),formatLabels:t("./format_labels"),style:t("./style").style,styleOnSelect:t("./style").styleOnSelect,hoverPoints:t("./hover"),selectPoints:t("./select"),animatable:!0,moduleType:"trace",name:"scatter",basePlotModule:t("../../plots/cartesian"),categories:["cartesian","svg","symbols","errorBarsOK","showLegend","scatter-like","zoomScale"],meta:{}}},{"../../plots/cartesian":568,"./arrays_to_calcdata":924,"./attributes":925,"./calc":926,"./cross_trace_calc":930,"./cross_trace_defaults":931,"./defaults":932,"./format_labels":934,"./hover":936,"./marker_colorbar":943,"./plot":946,"./select":947,"./style":949,"./subtypes":950}],938:[function(t,e,r){"use 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n=t("../../registry"),i=t("../../lib"),a=t("../scatter/subtypes"),o=t("../scatter/marker_defaults"),s=t("../scatter/line_defaults"),l=t("../scatter/text_defaults"),c=t("./attributes");e.exports=function(t,e,r,u){function f(r,n){return i.coerce(t,e,c,r,n)}if(function(t,e,r,i){var a=0,o=r("x"),s=r("y"),l=r("z");n.getComponentMethod("calendars","handleTraceDefaults")(t,e,["x","y","z"],i),o&&s&&l&&(a=Math.min(o.length,s.length,l.length),e._length=e._xlength=e._ylength=e._zlength=a);return a}(t,e,f,u)){f("text"),f("hovertext"),f("hovertemplate"),f("xhoverformat"),f("yhoverformat"),f("zhoverformat"),f("mode"),a.hasLines(e)&&(f("connectgaps"),s(t,e,r,u,f)),a.hasMarkers(e)&&o(t,e,r,u,f,{noSelect:!0}),a.hasText(e)&&(f("texttemplate"),l(t,e,u,f,{noSelect:!0}));var h=(e.line||{}).color,p=(e.marker||{}).color;f("surfaceaxis")>=0&&f("surfacecolor",h||p);for(var d=["x","y","z"],g=0;g<3;++g){var m="projection."+d[g];f(m+".show")&&(f(m+".opacity"),f(m+".scale"))}var 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n=t("../../lib"),i=t("../scatter/constants"),a=t("../scatter/subtypes"),o=t("../scatter/marker_defaults"),s=t("../scatter/line_defaults"),l=t("../scatter/line_shape_defaults"),c=t("../scatter/text_defaults"),u=t("../scatter/fillcolor_defaults"),f=t("./attributes");e.exports=function(t,e,r,h){function p(r,i){return n.coerce(t,e,f,r,i)}p("carpet"),e.xaxis="x",e.yaxis="y";var d=p("a"),g=p("b"),m=Math.min(d.length,g.length);if(m){e._length=m,p("text"),p("texttemplate"),p("hovertext"),p("mode",m<i.PTS_LINESONLY?"lines+markers":"lines"),a.hasLines(e)&&(s(t,e,r,h,p),l(t,e,p),p("connectgaps")),a.hasMarkers(e)&&o(t,e,r,h,p,{gradient:!0}),a.hasText(e)&&c(t,e,h,p);var v=[];(a.hasMarkers(e)||a.hasText(e))&&(p("marker.maxdisplayed"),v.push("points")),p("fill"),"none"!==e.fill&&(u(t,e,r,p),a.hasLines(e)||l(t,e,p)),"tonext"!==e.fill&&"toself"!==e.fill||v.push("fills"),"fills"!==p("hoveron",v.join("+")||"points")&&p("hovertemplate"),n.coerceSelectionMarkerOpacity(e,p)}else 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0;var p=s.trace,d=p._carpet,g=p._module.formatLabels(h,p);s.yLabel=g.yLabel,delete s.text;var m=[];if(!p.hovertemplate){var v=(h.hi||p.hoverinfo).split("+");-1!==v.indexOf("all")&&(v=["a","b","text"]),-1!==v.indexOf("a")&&y(d.aaxis,h.a),-1!==v.indexOf("b")&&y(d.baxis,h.b),m.push("y: "+s.yLabel),-1!==v.indexOf("text")&&i(h,p,m),s.extraText=m.join("<br>")}return o}function y(t,e){var r;r=t.labelprefix&&t.labelprefix.length>0?t.labelprefix.replace(/ = $/,""):t._hovertitle,m.push(r+": "+e.toFixed(3)+t.labelsuffix)}}},{"../../lib":503,"../scatter/hover":936}],965:[function(t,e,r){"use 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s,l,c,u=r[0][0].carpet,f={xaxis:i.getFromId(t,u.xaxis||"x"),yaxis:i.getFromId(t,u.yaxis||"y"),plot:e.plot};for(n(t,f,r,o),s=0;s<r.length;s++)l=r[s][0].trace,c=o.selectAll("g.trace"+l.uid+" .js-line"),a.setClipUrl(c,r[s][0].carpet._clipPathId,t)}},{"../../components/drawing":388,"../../plots/cartesian/axes":554,"../scatter/plot":946}],967:[function(t,e,r){"use strict";var n=t("../../plots/template_attributes").hovertemplateAttrs,i=t("../../plots/template_attributes").texttemplateAttrs,a=t("../scatter/attributes"),o=t("../../plots/attributes"),s=t("../../components/colorscale/attributes"),l=t("../../components/drawing/attributes").dash,c=t("../../lib/extend").extendFlat,u=t("../../plot_api/edit_types").overrideAll,f=a.marker,h=a.line,p=f.line;e.exports=u({lon:{valType:"data_array"},lat:{valType:"data_array"},locations:{valType:"data_array"},locationmode:{valType:"enumerated",values:["ISO-3","USA-states","country 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i={opacity:1,text:e.text,texttemplate:e.texttemplate,textposition:e.textposition,textfont:s.extendFlat({},e.textfont)};r.textfont&&s.extendFlat(i.textfont,r.textfont),n=y(t,i)}return n}function w(t,e,r){var n={capSize:2*e.width*r,lineWidth:e.thickness*r,color:e.color};return e.copy_ystyle&&(n=t.error_y),n}var T=d.SYMBOL_SDF_SIZE,k=d.SYMBOL_SIZE,A=d.SYMBOL_STROKE,M={},S=l.symbolFuncs[0](.05*k);function E(t){if("circle"===t)return null;var e,r,n=l.symbolNumber(t),a=l.symbolFuncs[n%100],o=!!l.symbolNoDot[n%100],s=!!l.symbolNoFill[n%100],c=p.isDotSymbol(t);return M[t]?M[t]:(e=c&&!o?a(1.1*k)+S:a(k),r=i(e,{w:T,h:T,viewBox:[-k,-k,k,k],stroke:s?A:-A}),M[t]=r,r||null)}e.exports={style:function(t,e){var r,n={marker:void 0,markerSel:void 0,markerUnsel:void 0,line:void 0,fill:void 0,errorX:void 0,errorY:void 0,text:void 0,textSel:void 0,textUnsel:void 0},i=t._context.plotGlPixelRatio;if(!0!==e.visible)return n;if(f.hasText(e)&&(n.text=y(t,e),n.textSel=_(t,e,e.selected),n.textUnsel=_(t,e,e.unselected)),f.hasMarkers(e)&&(n.marker=x(e),n.markerSel=b(e,e.selected),n.markerUnsel=b(e,e.unselected),!e.unselected&&s.isArrayOrTypedArray(e.marker.opacity))){var a=e.marker.opacity;for(n.markerUnsel.opacity=new Array(a.length),r=0;r<a.length;r++)n.markerUnsel.opacity[r]=g*a[r]}if(f.hasLines(e)){n.line={overlay:!0,thickness:e.line.width*i,color:e.line.color,opacity:e.opacity};var o=(d.DASHES[e.line.dash]||[1]).slice();for(r=0;r<o.length;++r)o[r]*=e.line.width*i;n.line.dashes=o}return e.error_x&&e.error_x.visible&&(n.errorX=w(e,e.error_x,i)),e.error_y&&e.error_y.visible&&(n.errorY=w(e,e.error_y,i)),e.fill&&"none"!==e.fill&&(n.fill={closed:!0,fill:e.fillcolor,thickness:0}),n},markerStyle:x,markerSelection:b,linePositions:function(t,e,r){var n,i,a=r.length,o=a/2;if(f.hasLines(e)&&o)if("hv"===e.line.shape){for(n=[],i=0;i<o-1;i++)isNaN(r[2*i])||isNaN(r[2*i+1])?n.push(NaN,NaN,NaN,NaN):(n.push(r[2*i],r[2*i+1]),isNaN(r[2*i+2])||isNaN(r[2*i+3])?n.push(NaN,NaN):n.push(r[2*i+2],r[2*i+1]));n.push(r[a-2],r[a-1])}else if("hvh"===e.line.shape){for(n=[],i=0;i<o-1;i++)if(isNaN(r[2*i])||isNaN(r[2*i+1])||isNaN(r[2*i+2])||isNaN(r[2*i+3]))isNaN(r[2*i])||isNaN(r[2*i+1])?n.push(NaN,NaN):n.push(r[2*i],r[2*i+1]),n.push(NaN,NaN);else{var s=(r[2*i]+r[2*i+2])/2;n.push(r[2*i],r[2*i+1],s,r[2*i+1],s,r[2*i+3])}n.push(r[a-2],r[a-1])}else if("vhv"===e.line.shape){for(n=[],i=0;i<o-1;i++)if(isNaN(r[2*i])||isNaN(r[2*i+1])||isNaN(r[2*i+2])||isNaN(r[2*i+3]))isNaN(r[2*i])||isNaN(r[2*i+1])?n.push(NaN,NaN):n.push(r[2*i],r[2*i+1]),n.push(NaN,NaN);else{var l=(r[2*i+1]+r[2*i+3])/2;n.push(r[2*i],r[2*i+1],r[2*i],l,r[2*i+2],l)}n.push(r[a-2],r[a-1])}else if("vh"===e.line.shape){for(n=[],i=0;i<o-1;i++)isNaN(r[2*i])||isNaN(r[2*i+1])?n.push(NaN,NaN,NaN,NaN):(n.push(r[2*i],r[2*i+1]),isNaN(r[2*i+2])||isNaN(r[2*i+3])?n.push(NaN,NaN):n.push(r[2*i],r[2*i+3]));n.push(r[a-2],r[a-1])}else n=r;var c=!1;for(i=0;i<n.length;i++)if(isNaN(n[i])){c=!0;break}var u=c||n.length>d.TOO_MANY_POINTS||f.hasMarkers(e)?"rect":"round";if(c&&e.connectgaps){var h=n[0],p=n[1];for(i=0;i<n.length;i+=2)isNaN(n[i])||isNaN(n[i+1])?(n[i]=h,n[i+1]=p):(h=n[i],p=n[i+1])}return{join:u,positions:n}},errorBarPositions:function(t,e,r,i,a){var s=o.getComponentMethod("errorbars","makeComputeError"),l=c.getFromId(t,e.xaxis),u=c.getFromId(t,e.yaxis),f=r.length/2,h={};function p(t,i){var a=i._id.charAt(0),o=e["error_"+a];if(o&&o.visible&&("linear"===i.type||"log"===i.type)){for(var l=s(o),c={x:0,y:1}[a],u={x:[0,1,2,3],y:[2,3,0,1]}[a],p=new Float64Array(4*f),d=1/0,g=-1/0,m=0,v=0;m<f;m++,v+=4){var y=t[m];if(n(y)){var x=r[2*m+c],b=l(y,m),_=b[0],w=b[1];if(n(_)&&n(w)){var T=y-_,k=y+w;p[v+u[0]]=x-i.c2l(T),p[v+u[1]]=i.c2l(k)-x,p[v+u[2]]=0,p[v+u[3]]=0,d=Math.min(d,y-_),g=Math.max(g,y+w)}}}h[a]={positions:r,errors:p,_bnds:[d,g]}}}return p(i,l),p(a,u),h},textPosition:function(t,e,r,n){var i,a=e._length,o={};if(f.hasMarkers(e)){var s=r.font,l=r.align,c=r.baseline;for(o.offset=new Array(a),i=0;i<a;i++){var u=n.sizes?n.sizes[i]:n.size,h=Array.isArray(s)?s[i].size:s.size,p=Array.isArray(l)?l.length>1?l[i]:l[0]:l,d=Array.isArray(c)?c.length>1?c[i]:c[0]:c,g=m[p],v=m[d],y=u?u/.8+1:0,x=-v*y-.5*v;o.offset[i]=[g*y/h,x/h]}}return o}}},{"../../components/drawing":388,"../../components/fx/helpers":402,"../../constants/interactions":478,"../../lib":503,"../../lib/gl_format_color":499,"../../plots/cartesian/axis_ids":558,"../../registry":638,"../scatter/make_bubble_size_func":942,"../scatter/subtypes":950,"./constants":979,"./helpers":984,"color-normalize":89,"fast-isnumeric":190,"svg-path-sdf":310}],981:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../../registry"),a=t("./helpers"),o=t("./attributes"),s=t("../scatter/constants"),l=t("../scatter/subtypes"),c=t("../scatter/xy_defaults"),u=t("../scatter/period_defaults"),f=t("../scatter/marker_defaults"),h=t("../scatter/line_defaults"),p=t("../scatter/fillcolor_defaults"),d=t("../scatter/text_defaults");e.exports=function(t,e,r,g){function m(r,i){return n.coerce(t,e,o,r,i)}var v=!!t.marker&&a.isOpenSymbol(t.marker.symbol),y=l.isBubble(t),x=c(t,e,g,m);if(x){u(t,e,g,m),m("xhoverformat"),m("yhoverformat");var b=x<s.PTS_LINESONLY?"lines+markers":"lines";m("text"),m("hovertext"),m("hovertemplate"),m("mode",b),l.hasLines(e)&&(m("connectgaps"),h(t,e,r,g,m),m("line.shape")),l.hasMarkers(e)&&(f(t,e,r,g,m),m("marker.line.width",v||y?1:0)),l.hasText(e)&&(m("texttemplate"),d(t,e,g,m));var _=(e.line||{}).color,w=(e.marker||{}).color;m("fill"),"none"!==e.fill&&p(t,e,r,m);var T=i.getComponentMethod("errorbars","supplyDefaults");T(t,e,_||w||r,{axis:"y"}),T(t,e,_||w||r,{axis:"x",inherit:"y"}),n.coerceSelectionMarkerOpacity(e,m)}else e.visible=!1}},{"../../lib":503,"../../registry":638,"../scatter/constants":929,"../scatter/fillcolor_defaults":933,"../scatter/line_defaults":938,"../scatter/marker_defaults":944,"../scatter/period_defaults":945,"../scatter/subtypes":950,"../scatter/text_defaults":951,"../scatter/xy_defaults":952,"./attributes":977,"./helpers":984}],982:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../../components/color"),a=t("../../constants/interactions").DESELECTDIM;e.exports={styleTextSelection:function(t){var e,r,o=t[0],s=o.trace,l=o.t,c=l._scene,u=l.index,f=c.selectBatch[u],h=c.unselectBatch[u],p=c.textOptions[u],d=c.textSelectedOptions[u]||{},g=c.textUnselectedOptions[u]||{},m=n.extendFlat({},p);if(f.length||h.length){var v=d.color,y=g.color,x=p.color,b=Array.isArray(x);for(m.color=new Array(s._length),e=0;e<f.length;e++)r=f[e],m.color[r]=v||(b?x[r]:x);for(e=0;e<h.length;e++){r=h[e];var _=b?x[r]:x;m.color[r]=y||(v?_:i.addOpacity(_,a))}}c.glText[u].update(m)}}},{"../../components/color":366,"../../constants/interactions":478,"../../lib":503}],983:[function(t,e,r){"use strict";var n=t("../scatter/format_labels");e.exports=function(t,e,r){var i=t.i;return"x"in t||(t.x=e._x[i]),"y"in t||(t.y=e._y[i]),n(t,e,r)}},{"../scatter/format_labels":934}],984:[function(t,e,r){"use strict";var n=t("./constants");r.isOpenSymbol=function(t){return"string"==typeof t?n.OPEN_RE.test(t):t%200>100},r.isDotSymbol=function(t){return"string"==typeof t?n.DOT_RE.test(t):t>200}},{"./constants":979}],985:[function(t,e,r){"use strict";var n=t("../../registry"),i=t("../../lib"),a=t("../scatter/get_trace_color");function o(t,e,r,o){var s=t.xa,l=t.ya,c=t.distance,u=t.dxy,f=t.index,h={pointNumber:f,x:e[f],y:r[f]};h.tx=Array.isArray(o.text)?o.text[f]:o.text,h.htx=Array.isArray(o.hovertext)?o.hovertext[f]:o.hovertext,h.data=Array.isArray(o.customdata)?o.customdata[f]:o.customdata,h.tp=Array.isArray(o.textposition)?o.textposition[f]:o.textposition;var p=o.textfont;p&&(h.ts=i.isArrayOrTypedArray(p.size)?p.size[f]:p.size,h.tc=Array.isArray(p.color)?p.color[f]:p.color,h.tf=Array.isArray(p.family)?p.family[f]:p.family);var d=o.marker;d&&(h.ms=i.isArrayOrTypedArray(d.size)?d.size[f]:d.size,h.mo=i.isArrayOrTypedArray(d.opacity)?d.opacity[f]:d.opacity,h.mx=i.isArrayOrTypedArray(d.symbol)?d.symbol[f]:d.symbol,h.mc=i.isArrayOrTypedArray(d.color)?d.color[f]:d.color);var g=d&&d.line;g&&(h.mlc=Array.isArray(g.color)?g.color[f]:g.color,h.mlw=i.isArrayOrTypedArray(g.width)?g.width[f]:g.width);var m=d&&d.gradient;m&&"none"!==m.type&&(h.mgt=Array.isArray(m.type)?m.type[f]:m.type,h.mgc=Array.isArray(m.color)?m.color[f]:m.color);var v=s.c2p(h.x,!0),y=l.c2p(h.y,!0),x=h.mrc||1,b=o.hoverlabel;b&&(h.hbg=Array.isArray(b.bgcolor)?b.bgcolor[f]:b.bgcolor,h.hbc=Array.isArray(b.bordercolor)?b.bordercolor[f]:b.bordercolor,h.hts=i.isArrayOrTypedArray(b.font.size)?b.font.size[f]:b.font.size,h.htc=Array.isArray(b.font.color)?b.font.color[f]:b.font.color,h.htf=Array.isArray(b.font.family)?b.font.family[f]:b.font.family,h.hnl=i.isArrayOrTypedArray(b.namelength)?b.namelength[f]:b.namelength);var _=o.hoverinfo;_&&(h.hi=Array.isArray(_)?_[f]:_);var w=o.hovertemplate;w&&(h.ht=Array.isArray(w)?w[f]:w);var T={};T[t.index]=h;var k=o._origX,A=o._origY,M=i.extendFlat({},t,{color:a(o,h),x0:v-x,x1:v+x,xLabelVal:k?k[f]:h.x,y0:y-x,y1:y+x,yLabelVal:A?A[f]:h.y,cd:T,distance:c,spikeDistance:u,hovertemplate:h.ht});return h.htx?M.text=h.htx:h.tx?M.text=h.tx:o.text&&(M.text=o.text),i.fillText(h,o,M),n.getComponentMethod("errorbars","hoverInfo")(h,o,M),M}e.exports={hoverPoints:function(t,e,r,n){var i,a,s,l,c,u,f,h,p,d,g=t.cd,m=g[0].t,v=g[0].trace,y=t.xa,x=t.ya,b=m.x,_=m.y,w=y.c2p(e),T=x.c2p(r),k=t.distance;if(m.tree){var A=y.p2c(w-k),M=y.p2c(w+k),S=x.p2c(T-k),E=x.p2c(T+k);i="x"===n?m.tree.range(Math.min(A,M),Math.min(x._rl[0],x._rl[1]),Math.max(A,M),Math.max(x._rl[0],x._rl[1])):m.tree.range(Math.min(A,M),Math.min(S,E),Math.max(A,M),Math.max(S,E))}else i=m.ids;var L=k;if("x"===n){var C=!!v.xperiodalignment,P=!!v.yperiodalignment;for(u=0;u<i.length;u++){if(l=b[a=i[u]],f=Math.abs(y.c2p(l)-w),C){var I=y.c2p(v._xStarts[a]),O=y.c2p(v._xEnds[a]);f=w>=Math.min(I,O)&&w<=Math.max(I,O)?0:1/0}if(f<L){if(L=f,c=_[a],h=x.c2p(c)-T,P){var z=x.c2p(v._yStarts[a]),D=x.c2p(v._yEnds[a]);h=T>=Math.min(z,D)&&T<=Math.max(z,D)?0:1/0}d=Math.sqrt(f*f+h*h),s=i[u]}}}else for(u=i.length-1;u>-1;u--)l=b[a=i[u]],c=_[a],f=y.c2p(l)-w,h=x.c2p(c)-T,(p=Math.sqrt(f*f+h*h))<L&&(L=d=p,s=a);return t.index=s,t.distance=L,t.dxy=d,void 0===s?[t]:[o(t,b,_,v)]},calcHover:o}},{"../../lib":503,"../../registry":638,"../scatter/get_trace_color":935}],986:[function(t,e,r){"use strict";var n=t("./hover");e.exports={moduleType:"trace",name:"scattergl",basePlotModule:t("../../plots/cartesian"),categories:["gl","regl","cartesian","symbols","errorBarsOK","showLegend","scatter-like"],attributes:t("./attributes"),supplyDefaults:t("./defaults"),crossTraceDefaults:t("../scatter/cross_trace_defaults"),colorbar:t("../scatter/marker_colorbar"),formatLabels:t("./format_labels"),calc:t("./calc"),plot:t("./plot"),hoverPoints:n.hoverPoints,selectPoints:t("./select"),meta:{}}},{"../../plots/cartesian":568,"../scatter/cross_trace_defaults":931,"../scatter/marker_colorbar":943,"./attributes":977,"./calc":978,"./defaults":981,"./format_labels":983,"./hover":985,"./plot":987,"./select":989}],987:[function(t,e,r){"use strict";var n=t("regl-scatter2d"),i=t("regl-line2d"),a=t("regl-error2d"),o=t("gl-text"),s=t("../../lib"),l=t("../../components/dragelement/helpers").selectMode,c=t("../../lib/prepare_regl"),u=t("../scatter/subtypes"),f=t("../scatter/link_traces"),h=t("./edit_style").styleTextSelection;function p(t,e,r,n){var i=t._size,a=t.width*n,o=t.height*n,s=i.l*n,l=i.b*n,c=i.r*n,u=i.t*n,f=i.w*n,h=i.h*n;return[s+e.domain[0]*f,l+r.domain[0]*h,a-c-(1-e.domain[1])*f,o-u-(1-r.domain[1])*h]}e.exports=function(t,e,r){if(r.length){var d,g,m=t._fullLayout,v=e._scene,y=e.xaxis,x=e.yaxis;if(v)if(c(t,["ANGLE_instanced_arrays","OES_element_index_uint"])){var b=v.count,_=m._glcanvas.data()[0].regl;if(f(t,e,r),v.dirty){if(!0===v.error2d&&(v.error2d=a(_)),!0===v.line2d&&(v.line2d=i(_)),!0===v.scatter2d&&(v.scatter2d=n(_)),!0===v.fill2d&&(v.fill2d=i(_)),!0===v.glText)for(v.glText=new Array(b),d=0;d<b;d++)v.glText[d]=new o(_);if(v.glText){if(b>v.glText.length){var w=b-v.glText.length;for(d=0;d<w;d++)v.glText.push(new o(_))}else if(b<v.glText.length){var T=v.glText.length-b;v.glText.splice(b,T).forEach((function(t){t.destroy()}))}for(d=0;d<b;d++)v.glText[d].update(v.textOptions[d])}if(v.line2d&&(v.line2d.update(v.lineOptions),v.lineOptions=v.lineOptions.map((function(t){if(t&&t.positions){for(var e=t.positions,r=0;r<e.length&&(isNaN(e[r])||isNaN(e[r+1]));)r+=2;for(var n=e.length-2;n>r&&(isNaN(e[n])||isNaN(e[n+1]));)n-=2;t.positions=e.slice(r,n+2)}return t})),v.line2d.update(v.lineOptions)),v.error2d){var k=(v.errorXOptions||[]).concat(v.errorYOptions||[]);v.error2d.update(k)}v.scatter2d&&v.scatter2d.update(v.markerOptions),v.fillOrder=s.repeat(null,b),v.fill2d&&(v.fillOptions=v.fillOptions.map((function(t,e){var n=r[e];if(t&&n&&n[0]&&n[0].trace){var i,a,o=n[0],s=o.trace,l=o.t,c=v.lineOptions[e],u=[];s._ownfill&&u.push(e),s._nexttrace&&u.push(e+1),u.length&&(v.fillOrder[e]=u);var 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n=t("../../components/fx"),i=t("../../lib"),a=t("../scatter/get_trace_color"),o=i.fillText,s=t("../../constants/numerical").BADNUM;function l(t,e,r){if(!t.hovertemplate){var n=(e.hi||t.hoverinfo).split("+"),i=-1!==n.indexOf("all"),a=-1!==n.indexOf("lon"),s=-1!==n.indexOf("lat"),l=e.lonlat,c=[];return i||a&&s?c.push("("+u(l[1])+", "+u(l[0])+")"):a?c.push(r.lon+u(l[0])):s&&c.push(r.lat+u(l[1])),(i||-1!==n.indexOf("text"))&&o(e,t,c),c.join("<br>")}function u(t){return t+"\xb0"}}e.exports={hoverPoints:function(t,e,r){var o=t.cd,c=o[0].trace,u=t.xa,f=t.ya,h=t.subplot,p=360*(e>=0?Math.floor((e+180)/360):Math.ceil((e-180)/360)),d=e-p;if(n.getClosest(o,(function(t){var e=t.lonlat;if(e[0]===s)return 1/0;var n=i.modHalf(e[0],360),a=e[1],o=h.project([n,a]),l=o.x-u.c2p([d,a]),c=o.y-f.c2p([n,r]),p=Math.max(3,t.mrc||0);return Math.max(Math.sqrt(l*l+c*c)-p,1-3/p)}),t),!1!==t.index){var 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T=l.matrixOptions;return b.length||_.length?l.selectBatch.length||l.unselectBatch.length||l.matrix.update(l.unselectedOptions,n.extendFlat({},T,l.selectedOptions,l.viewOpts)):l.matrix.update(T,null),l.selectBatch=b,l.unselectBatch=_,h}},{"../../lib":503,"../scatter/subtypes":950,"./helpers":1033}],1039:[function(t,e,r){"use strict";var n=t("../../components/colorscale/attributes"),i=t("../../plots/cartesian/axis_format_attributes").axisHoverFormat,a=t("../../plots/template_attributes").hovertemplateAttrs,o=t("../mesh3d/attributes"),s=t("../../plots/attributes"),l=t("../../lib/extend").extendFlat,c={x:{valType:"data_array",editType:"calc+clearAxisTypes"},y:{valType:"data_array",editType:"calc+clearAxisTypes"},z:{valType:"data_array",editType:"calc+clearAxisTypes"},u:{valType:"data_array",editType:"calc"},v:{valType:"data_array",editType:"calc"},w:{valType:"data_array",editType:"calc"},starts:{x:{valType:"data_array",editType:"calc"},y:{valType:"data_array",editType:"calc"},z:{valType:"data_array",editType:"calc"},editType:"calc"},maxdisplayed:{valType:"integer",min:0,dflt:1e3,editType:"calc"},sizeref:{valType:"number",editType:"calc",min:0,dflt:1},text:{valType:"string",dflt:"",editType:"calc"},hovertext:{valType:"string",dflt:"",editType:"calc"},hovertemplate:a({editType:"calc"},{keys:["tubex","tubey","tubez","tubeu","tubev","tubew","norm","divergence"]}),uhoverformat:i("u",1),vhoverformat:i("v",1),whoverformat:i("w",1),xhoverformat:i("x"),yhoverformat:i("y"),zhoverformat:i("z"),showlegend:l({},s.showlegend,{dflt:!1})};l(c,n("",{colorAttr:"u/v/w 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e,r,i,a,s,l,c,u,f,h,p,d,g=t._x,m=t._y,v=t._z,y=t._len,x=-1/0,b=1/0,_=-1/0,w=1/0,T=-1/0,k=1/0,A="";for(y&&(c=g[0],f=m[0],p=v[0]),y>1&&(u=g[y-1],h=m[y-1],d=v[y-1]),e=0;e<y;e++)x=Math.max(x,g[e]),b=Math.min(b,g[e]),_=Math.max(_,m[e]),w=Math.min(w,m[e]),T=Math.max(T,v[e]),k=Math.min(k,v[e]),a||g[e]===c||(a=!0,A+="x"),s||m[e]===f||(s=!0,A+="y"),l||v[e]===p||(l=!0,A+="z");a||(A+="x"),s||(A+="y"),l||(A+="z");var M=o(t._x),S=o(t._y),E=o(t._z);A=(A=(A=A.replace("x",(c>u?"-":"+")+"x")).replace("y",(f>h?"-":"+")+"y")).replace("z",(p>d?"-":"+")+"z");var L=function(){y=0,M=[],S=[],E=[]};(!y||y<M.length*S.length*E.length)&&L();var C=function(t){return"x"===t?g:"y"===t?m:v},P=function(t){return"x"===t?M:"y"===t?S:E},I=function(t){return t[y-1]<t[0]?-1:1},O=C(A[1]),z=C(A[3]),D=C(A[5]),R=P(A[1]).length,F=P(A[3]).length,B=P(A[5]).length,N=!1,j=function(t,e,r){return R*(F*t+e)+r},U=I(C(A[1])),V=I(C(A[3])),H=I(C(A[5]));for(e=0;e<B-1;e++){for(r=0;r<F-1;r++){for(i=0;i<R-1;i++){var 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v=n[u];r.xMax=Math.max(r.xMax,v),r.xMin=Math.min(r.xMin,v);var y=o[u];r.yMax=Math.max(r.yMax,y),r.yMin=Math.min(r.yMin,y);var x=l[u];r.zMax=Math.max(r.zMax,x),r.zMin=Math.min(r.zMin,x)}e._slen=c,e._normMax=f,e._xbnds=[r.xMin,r.xMax],e._ybnds=[r.yMin,r.yMax],e._zbnds=[r.zMin,r.zMax]},filter:s,processGrid:a}},{"../../components/colorscale/calc":374,"../../lib":503}],1041:[function(t,e,r){"use strict";var n=t("../../../stackgl_modules").gl_streamtube3d,i=n.createTubeMesh,a=t("../../lib"),o=t("../../lib/gl_format_color").parseColorScale,s=t("../../components/colorscale").extractOpts,l=t("../../plots/gl3d/zip3"),c={xaxis:0,yaxis:1,zaxis:2};function u(t,e){this.scene=t,this.uid=e,this.mesh=null,this.data=null}var f=u.prototype;function h(t){var e=t.length;return e>2?t.slice(1,e-1):2===e?[(t[0]+t[1])/2]:t}function p(t){var e=t.length;return 1===e?[.5,.5]:[t[1]-t[0],t[e-1]-t[e-2]]}function d(t,e){var r=t.fullSceneLayout,i=t.dataScale,u=e._len,f={};function d(t,e){var n=r[e],o=i[c[e]];return a.simpleMap(t,(function(t){return n.d2l(t)*o}))}if(f.vectors=l(d(e._u,"xaxis"),d(e._v,"yaxis"),d(e._w,"zaxis"),u),!u)return{positions:[],cells:[]};var g=d(e._Xs,"xaxis"),m=d(e._Ys,"yaxis"),v=d(e._Zs,"zaxis");if(f.meshgrid=[g,m,v],f.gridFill=e._gridFill,e._slen)f.startingPositions=l(d(e._startsX,"xaxis"),d(e._startsY,"yaxis"),d(e._startsZ,"zaxis"));else{for(var y=m[0],x=h(g),b=h(v),_=new Array(x.length*b.length),w=0,T=0;T<x.length;T++)for(var k=0;k<b.length;k++)_[w++]=[x[T],y,b[k]];f.startingPositions=_}f.colormap=o(e),f.tubeSize=e.sizeref,f.maxLength=e.maxdisplayed;var A=d(e._xbnds,"xaxis"),M=d(e._ybnds,"yaxis"),S=d(e._zbnds,"zaxis"),E=p(g),L=p(m),C=p(v),P=[[A[0]-E[0],M[0]-L[0],S[0]-C[0]],[A[1]+E[1],M[1]+L[1],S[1]+C[1]]],I=n(f,P),O=s(e);I.vertexIntensityBounds=[O.min/e._normMax,O.max/e._normMax];var z=e.lightposition;return I.lightPosition=[z.x,z.y,z.z],I.ambient=e.lighting.ambient,I.diffuse=e.lighting.diffuse,I.specular=e.lighting.specular,I.roughness=e.lighting.roughness,I.fresnel=e.lighting.fresnel,I.opacity=e.opacity,e._pad=I.tubeScale*e.sizeref*2,I}f.handlePick=function(t){var e=this.scene.fullSceneLayout,r=this.scene.dataScale;function n(t,n){var i=e[n],a=r[c[n]];return i.l2c(t)/a}if(t.object===this.mesh){var i=t.data.position,a=t.data.velocity;return t.traceCoordinate=[n(i[0],"xaxis"),n(i[1],"yaxis"),n(i[2],"zaxis"),n(a[0],"xaxis"),n(a[1],"yaxis"),n(a[2],"zaxis"),t.data.intensity*this.data._normMax,t.data.divergence],t.textLabel=this.data.hovertext||this.data.text,!0}},f.update=function(t){this.data=t;var e=d(this.scene,t);this.mesh.update(e)},f.dispose=function(){this.scene.glplot.remove(this.mesh),this.mesh.dispose()},e.exports=function(t,e){var r=t.glplot.gl,n=d(t,e),a=i(r,n),o=new u(t,e.uid);return o.mesh=a,o.data=e,a._trace=o,t.glplot.add(a),o}},{"../../../stackgl_modules":1119,"../../components/colorscale":378,"../../lib":503,"../../lib/gl_format_color":499,"../../plots/gl3d/zip3":609}],1042:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../../components/colorscale/defaults"),a=t("./attributes");e.exports=function(t,e,r,o){function s(r,i){return n.coerce(t,e,a,r,i)}var l=s("u"),c=s("v"),u=s("w"),f=s("x"),h=s("y"),p=s("z");l&&l.length&&c&&c.length&&u&&u.length&&f&&f.length&&h&&h.length&&p&&p.length?(s("starts.x"),s("starts.y"),s("starts.z"),s("maxdisplayed"),s("sizeref"),s("lighting.ambient"),s("lighting.diffuse"),s("lighting.specular"),s("lighting.roughness"),s("lighting.fresnel"),s("lightposition.x"),s("lightposition.y"),s("lightposition.z"),i(t,e,o,s,{prefix:"",cLetter:"c"}),s("text"),s("hovertext"),s("hovertemplate"),s("uhoverformat"),s("vhoverformat"),s("whoverformat"),s("xhoverformat"),s("yhoverformat"),s("zhoverformat"),e._length=null):e.visible=!1}},{"../../components/colorscale/defaults":376,"../../lib":503,"./attributes":1039}],1043:[function(t,e,r){"use strict";e.exports={moduleType:"trace",name:"streamtube",basePlotModule:t("../../plots/gl3d"),categories:["gl3d","showLegend"],attributes:t("./attributes"),supplyDefaults:t("./defaults"),colorbar:{min:"cmin",max:"cmax"},calc:t("./calc").calc,plot:t("./convert"),eventData:function(t,e){return t.tubex=t.x,t.tubey=t.y,t.tubez=t.z,t.tubeu=e.traceCoordinate[3],t.tubev=e.traceCoordinate[4],t.tubew=e.traceCoordinate[5],t.norm=e.traceCoordinate[6],t.divergence=e.traceCoordinate[7],delete t.x,delete t.y,delete t.z,t},meta:{}}},{"../../plots/gl3d":598,"./attributes":1039,"./calc":1040,"./convert":1041,"./defaults":1042}],1044:[function(t,e,r){"use strict";var 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c=n.select(this),f=s.ensureSingle(c,"path","surface",(function(t){t.style("pointer-events","all")}));i.rpx0=H(i.y0),i.rpx1=H(i.y1),i.xmid=(i.x0+i.x1)/2,i.pxmid=q(i.rpx1,i.xmid),i.midangle=-(i.xmid-Math.PI/2),i.startangle=-(i.x0-Math.PI/2),i.stopangle=-(i.x1-Math.PI/2),i.halfangle=.5*Math.min(s.angleDelta(i.x0,i.x1)||Math.PI,Math.PI),i.ring=1-i.rpx0/i.rpx1,i.rInscribed=function(t){return 0===t.rpx0&&s.isFullCircle([t.x0,t.x1])?1:Math.max(0,Math.min(1/(1+1/Math.sin(t.halfangle)),t.ring/2))}(i),v?f.transition().attrTween("d",(function(t){var e=function(t){var e,r=R[b.getPtId(t)],n={x0:t.x0,x1:t.x1,rpx0:t.rpx0,rpx1:t.rpx1};if(r)e=r;else if(D)if(t.parent)if(X){var i=(t.x1>X?2*Math.PI:0)+U;e={x0:i,x1:i}}else e={rpx0:I,rpx1:I},s.extendFlat(e,K(t));else e={rpx0:0,rpx1:0};else e={x0:U,x1:U};return a(e,n)}(t);return function(t){return G(e(t))}})):f.attr("d",G),c.call(y,M,t,e,{eventDataKeys:x.eventDataKeys,transitionTime:x.CLICK_TRANSITION_TIME,transitionEasing:x.CLICK_TRANSITION_EASING}).call(b.setSliceCursor,t,{hideOnRoot:!0,hideOnLeaves:!0,isTransitioning:t._transitioning}),f.call(m,i,k);var p=s.ensureSingle(c,"g","slicetext"),_=s.ensureSingle(p,"text","",(function(t){t.attr("data-notex",1)})),w=s.ensureUniformFontSize(t,b.determineTextFont(k,i,h.font));_.text(r.formatSliceLabel(i,M,k,e,h)).classed("slicetext",!0).attr("text-anchor","middle").call(o.font,w).call(l.convertToTspans,t);var A=o.bBox(_.node());i.transform=g(A,i,T),i.transform.targetX=Y(i),i.transform.targetY=W(i);var S=function(t,e){var r=t.transform;return d(r,e),r.fontSize=w.size,u(k.type,r,h),s.getTextTransform(r)};v?_.transition().attrTween("transform",(function(t){var e=function(t){var e,r=R[b.getPtId(t)],n=t.transform;if(r)e=r;else if(e={rpx1:t.rpx1,transform:{textPosAngle:n.textPosAngle,scale:0,rotate:n.rotate,rCenter:n.rCenter,x:n.x,y:n.y}},D)if(t.parent)if(X){var i=t.x1>X?2*Math.PI:0;e.x0=e.x1=i}else s.extendFlat(e,K(t));else e.x0=e.x1=U;else e.x0=e.x1=U;var o=a(e.transform.textPosAngle,t.transform.textPosAngle),l=a(e.rpx1,t.rpx1),c=a(e.x0,t.x0),f=a(e.x1,t.x1),p=a(e.transform.scale,n.scale),d=a(e.transform.rotate,n.rotate),g=0===n.rCenter?3:0===e.transform.rCenter?1/3:1,m=a(e.transform.rCenter,n.rCenter);return function(t){var e=l(t),r=c(t),i=f(t),a=function(t){return m(Math.pow(t,g))}(t),s={pxmid:q(e,(r+i)/2),rpx1:e,transform:{textPosAngle:o(t),rCenter:a,x:n.x,y:n.y}};return u(k.type,n,h),{transform:{targetX:Y(s),targetY:W(s),scale:p(t),rotate:d(t),rCenter:a}}}}(t);return function(t){return S(e(t),A)}})):_.attr("transform",S(i,A))}))}function w(t){return e=t.rpx1,r=t.transform.textPosAngle,[e*Math.sin(r),-e*Math.cos(r)];var e,r}r.plot=function(t,e,r,i){var a,o,s=t._fullLayout,l=s._sunburstlayer,c=!r,u=!s.uniformtext.mode&&b.hasTransition(r);(f("sunburst",s),(a=l.selectAll("g.trace.sunburst").data(e,(function(t){return t[0].trace.uid}))).enter().append("g").classed("trace",!0).classed("sunburst",!0).attr("stroke-linejoin","round"),a.order(),u)?(i&&(o=i()),n.transition().duration(r.duration).ease(r.easing).each("end",(function(){o&&o()})).each("interrupt",(function(){o&&o()})).each((function(){l.selectAll("g.trace").each((function(e){_(t,e,this,r)}))}))):(a.each((function(e){_(t,e,this,r)})),s.uniformtext.mode&&v(t,s._sunburstlayer.selectAll(".trace"),"sunburst"));c&&a.exit().remove()},r.formatSliceLabel=function(t,e,r,n,i){var a=r.texttemplate,o=r.textinfo;if(!(a||o&&"none"!==o))return"";var l=i.separators,c=n[0],u=t.data.data,f=c.hierarchy,h=b.isHierarchyRoot(t),p=b.getParent(f,t),d=b.getValue(t);if(!a){var g,m=o.split("+"),v=function(t){return-1!==m.indexOf(t)},y=[];if(v("label")&&u.label&&y.push(u.label),u.hasOwnProperty("v")&&v("value")&&y.push(b.formatValue(u.v,l)),!h){v("current path")&&y.push(b.getPath(t.data));var x=0;v("percent parent")&&x++,v("percent entry")&&x++,v("percent root")&&x++;var _=x>1;if(x){var w,T=function(t){g=b.formatPercent(w,l),_&&(g+=" of "+t),y.push(g)};v("percent parent")&&!h&&(w=d/b.getValue(p),T("parent")),v("percent entry")&&(w=d/b.getValue(e),T("entry")),v("percent root")&&(w=d/b.getValue(f),T("root"))}}return v("text")&&(g=s.castOption(r,u.i,"text"),s.isValidTextValue(g)&&y.push(g)),y.join("<br>")}var k=s.castOption(r,u.i,"texttemplate");if(!k)return"";var A={};u.label&&(A.label=u.label),u.hasOwnProperty("v")&&(A.value=u.v,A.valueLabel=b.formatValue(u.v,l)),A.currentPath=b.getPath(t.data),h||(A.percentParent=d/b.getValue(p),A.percentParentLabel=b.formatPercent(A.percentParent,l),A.parent=b.getPtLabel(p)),A.percentEntry=d/b.getValue(e),A.percentEntryLabel=b.formatPercent(A.percentEntry,l),A.entry=b.getPtLabel(e),A.percentRoot=d/b.getValue(f),A.percentRootLabel=b.formatPercent(A.percentRoot,l),A.root=b.getPtLabel(f),u.hasOwnProperty("color")&&(A.color=u.color);var M=s.castOption(r,u.i,"text");return(s.isValidTextValue(M)||""===M)&&(A.text=M),A.customdata=s.castOption(r,u.i,"customdata"),s.texttemplateString(k,A,i._d3locale,A,r._meta||{})}},{"../../components/drawing":388,"../../lib":503,"../../lib/svg_text_utils":529,"../bar/style":662,"../bar/uniform_text":664,"../pie/helpers":904,"../pie/plot":908,"./constants":1047,"./fx":1049,"./helpers":1050,"./style":1055,"@plotly/d3":58,"d3-hierarchy":115,"d3-interpolate":116}],1055:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("../../components/color"),a=t("../../lib"),o=t("../bar/uniform_text").resizeText;function s(t,e,r){var n=e.data.data,o=!e.children,s=n.i,l=a.castOption(r,s,"marker.line.color")||i.defaultLine,c=a.castOption(r,s,"marker.line.width")||0;t.style("stroke-width",c).call(i.fill,n.color).call(i.stroke,l).style("opacity",o?r.leaf.opacity:null)}e.exports={style:function(t){var e=t._fullLayout._sunburstlayer.selectAll(".trace");o(t,e,"sunburst"),e.each((function(t){var e=n.select(this),r=t[0].trace;e.style("opacity",r.opacity),e.selectAll("path.surface").each((function(t){n.select(this).call(s,t,r)}))}))},styleOne:s}},{"../../components/color":366,"../../lib":503,"../bar/uniform_text":664,"@plotly/d3":58}],1056:[function(t,e,r){"use strict";var n=t("../../components/color"),i=t("../../components/colorscale/attributes"),a=t("../../plots/cartesian/axis_format_attributes").axisHoverFormat,o=t("../../plots/template_attributes").hovertemplateAttrs,s=t("../../plots/attributes"),l=t("../../lib/extend").extendFlat,c=t("../../plot_api/edit_types").overrideAll;function u(t){return{show:{valType:"boolean",dflt:!1},start:{valType:"number",dflt:null,editType:"plot"},end:{valType:"number",dflt:null,editType:"plot"},size:{valType:"number",dflt:null,min:0,editType:"plot"},project:{x:{valType:"boolean",dflt:!1},y:{valType:"boolean",dflt:!1},z:{valType:"boolean",dflt:!1}},color:{valType:"color",dflt:n.defaultLine},usecolormap:{valType:"boolean",dflt:!1},width:{valType:"number",min:1,max:16,dflt:2},highlight:{valType:"boolean",dflt:!0},highlightcolor:{valType:"color",dflt:n.defaultLine},highlightwidth:{valType:"number",min:1,max:16,dflt:2}}}var f=e.exports=c(l({z:{valType:"data_array"},x:{valType:"data_array"},y:{valType:"data_array"},text:{valType:"string",dflt:"",arrayOk:!0},hovertext:{valType:"string",dflt:"",arrayOk:!0},hovertemplate:o(),xhoverformat:a("x"),yhoverformat:a("y"),zhoverformat:a("z"),connectgaps:{valType:"boolean",dflt:!1,editType:"calc"},surfacecolor:{valType:"data_array"}},i("",{colorAttr:"z or surfacecolor",showScaleDflt:!0,autoColorDflt:!1,editTypeOverride:"calc"}),{contours:{x:u(),y:u(),z:u()},hidesurface:{valType:"boolean",dflt:!1},lightposition:{x:{valType:"number",min:-1e5,max:1e5,dflt:10},y:{valType:"number",min:-1e5,max:1e5,dflt:1e4},z:{valType:"number",min:-1e5,max:1e5,dflt:0}},lighting:{ambient:{valType:"number",min:0,max:1,dflt:.8},diffuse:{valType:"number",min:0,max:1,dflt:.8},specular:{valType:"number",min:0,max:2,dflt:.05},roughness:{valType:"number",min:0,max:1,dflt:.5},fresnel:{valType:"number",min:0,max:5,dflt:.2}},opacity:{valType:"number",min:0,max:1,dflt:1},opacityscale:{valType:"any",editType:"calc"},_deprecated:{zauto:l({},i.zauto,{}),zmin:l({},i.zmin,{}),zmax:l({},i.zmax,{})},hoverinfo:l({},s.hoverinfo),showlegend:l({},s.showlegend,{dflt:!1})}),"calc","nested");f.x.editType=f.y.editType=f.z.editType="calc+clearAxisTypes",f.transforms=void 0},{"../../components/color":366,"../../components/colorscale/attributes":373,"../../lib/extend":493,"../../plot_api/edit_types":536,"../../plots/attributes":550,"../../plots/cartesian/axis_format_attributes":557,"../../plots/template_attributes":633}],1057:[function(t,e,r){"use strict";var n=t("../../components/colorscale/calc");e.exports=function(t,e){e.surfacecolor?n(t,e,{vals:e.surfacecolor,containerStr:"",cLetter:"c"}):n(t,e,{vals:e.z,containerStr:"",cLetter:"c"})}},{"../../components/colorscale/calc":374}],1058:[function(t,e,r){"use strict";var n=t("../../../stackgl_modules").gl_surface3d,i=t("../../../stackgl_modules").ndarray,a=t("../../../stackgl_modules").ndarray_linear_interpolate.d2,o=t("../heatmap/interp2d"),s=t("../heatmap/find_empties"),l=t("../../lib").isArrayOrTypedArray,c=t("../../lib/gl_format_color").parseColorScale,u=t("../../lib/str2rgbarray"),f=t("../../components/colorscale").extractOpts;function h(t,e,r){this.scene=t,this.uid=r,this.surface=e,this.data=null,this.showContour=[!1,!1,!1],this.contourStart=[null,null,null],this.contourEnd=[null,null,null],this.contourSize=[0,0,0],this.minValues=[1/0,1/0,1/0],this.maxValues=[-1/0,-1/0,-1/0],this.dataScaleX=1,this.dataScaleY=1,this.refineData=!0,this.objectOffset=[0,0,0]}var p=h.prototype;p.getXat=function(t,e,r,n){var i=l(this.data.x)?l(this.data.x[0])?this.data.x[e][t]:this.data.x[t]:t;return void 0===r?i:n.d2l(i,0,r)},p.getYat=function(t,e,r,n){var i=l(this.data.y)?l(this.data.y[0])?this.data.y[e][t]:this.data.y[e]:e;return void 0===r?i:n.d2l(i,0,r)},p.getZat=function(t,e,r,n){var i=this.data.z[e][t];return null===i&&this.data.connectgaps&&this.data._interpolatedZ&&(i=this.data._interpolatedZ[e][t]),void 0===r?i:n.d2l(i,0,r)},p.handlePick=function(t){if(t.object===this.surface){var e=(t.data.index[0]-1)/this.dataScaleX-1,r=(t.data.index[1]-1)/this.dataScaleY-1,n=Math.max(Math.min(Math.round(e),this.data.z[0].length-1),0),i=Math.max(Math.min(Math.round(r),this.data._ylength-1),0);t.index=[n,i],t.traceCoordinate=[this.getXat(n,i),this.getYat(n,i),this.getZat(n,i)],t.dataCoordinate=[this.getXat(n,i,this.data.xcalendar,this.scene.fullSceneLayout.xaxis),this.getYat(n,i,this.data.ycalendar,this.scene.fullSceneLayout.yaxis),this.getZat(n,i,this.data.zcalendar,this.scene.fullSceneLayout.zaxis)];for(var a=0;a<3;a++){var o=t.dataCoordinate[a];null!=o&&(t.dataCoordinate[a]*=this.scene.dataScale[a])}var s=this.data.hovertext||this.data.text;return Array.isArray(s)&&s[i]&&void 0!==s[i][n]?t.textLabel=s[i][n]:t.textLabel=s||"",t.data.dataCoordinate=t.dataCoordinate.slice(),this.surface.highlight(t.data),this.scene.glplot.spikes.position=t.dataCoordinate,!0}};var 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n=t("../../components/annotations/attributes"),i=t("../../lib/extend").extendFlat,a=t("../../plot_api/edit_types").overrideAll,o=t("../../plots/font_attributes"),s=t("../../plots/domain").attributes,l=t("../../plots/cartesian/axis_format_attributes").descriptionOnlyNumbers;(e.exports=a({domain:s({name:"table",trace:!0}),columnwidth:{valType:"number",arrayOk:!0,dflt:null},columnorder:{valType:"data_array"},header:{values:{valType:"data_array",dflt:[]},format:{valType:"data_array",dflt:[],description:l("cell value")},prefix:{valType:"string",arrayOk:!0,dflt:null},suffix:{valType:"string",arrayOk:!0,dflt:null},height:{valType:"number",dflt:28},align:i({},n.align,{arrayOk:!0}),line:{width:{valType:"number",arrayOk:!0,dflt:1},color:{valType:"color",arrayOk:!0,dflt:"grey"}},fill:{color:{valType:"color",arrayOk:!0,dflt:"white"}},font:i({},o({arrayOk:!0}))},cells:{values:{valType:"data_array",dflt:[]},format:{valType:"data_array",dflt:[],description:l("cell 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L=Math.max(o(e.header.line.width),o(e.cells.line.width)),C={key:e.uid+t._context.staticPlot,translateX:m.x[0]*t._fullLayout._size.w,translateY:t._fullLayout._size.h*(1-m.y[1]),size:t._fullLayout._size,width:v,maxLineWidth:L,height:y,columnOrder:M,groupHeight:y,rowBlocks:k,headerRowBlocks:T,scrollY:0,cells:i({},e.cells,{values:r}),headerCells:i({},e.header,{values:g}),gdColumns:g.map((function(t){return t[0]})),gdColumnsOriginalOrder:g.map((function(t){return t[0]})),prevPages:[0,0],scrollbarState:{scrollbarScrollInProgress:!1},columns:g.map((function(t,e){var r=A[t];return A[t]=(r||0)+1,{key:t+"__"+A[t],label:t,specIndex:e,xIndex:M[e],xScale:u,x:void 0,calcdata:void 0,columnWidth:S[e]}}))};return C.columns.forEach((function(t){t.calcdata=C,t.x=u(t)})),C}},{"../../lib/extend":493,"./constants":1064,"fast-isnumeric":190}],1066:[function(t,e,r){"use strict";var n=t("../../lib/extend").extendFlat;r.splitToPanels=function(t){var 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0!==t.negative&&0===this.negative?(t.negative=0,e=this.sub(t),t.negative^=1,e):0===t.negative&&0!==this.negative?(this.negative=0,e=t.sub(this),this.negative=1,e):this.length>t.length?this.clone().iadd(t):t.clone().iadd(this)},a.prototype.isub=function(t){if(0!==t.negative){t.negative=0;var e=this.iadd(t);return t.negative=1,e._normSign()}if(0!==this.negative)return this.negative=0,this.iadd(t),this.negative=1,this._normSign();var r,n,i=this.cmp(t);if(0===i)return this.negative=0,this.length=1,this.words[0]=0,this;i>0?(r=this,n=t):(r=t,n=this);for(var a=0,o=0;o<n.length;o++)a=(e=(0|r.words[o])-(0|n.words[o])+a)>>26,this.words[o]=67108863&e;for(;0!==a&&o<r.length;o++)a=(e=(0|r.words[o])+a)>>26,this.words[o]=67108863&e;if(0===a&&o<r.length&&r!==this)for(;o<r.length;o++)this.words[o]=r.words[o];return this.length=Math.max(this.length,o),r!==this&&(this.negative=1),this.strip()},a.prototype.sub=function(t){return this.clone().isub(t)};var d=function(t,e,r){var n,i,a,o=t.words,s=e.words,l=r.words,c=0,u=0|o[0],f=8191&u,h=u>>>13,p=0|o[1],d=8191&p,g=p>>>13,m=0|o[2],v=8191&m,y=m>>>13,x=0|o[3],b=8191&x,_=x>>>13,w=0|o[4],T=8191&w,k=w>>>13,A=0|o[5],M=8191&A,S=A>>>13,E=0|o[6],L=8191&E,C=E>>>13,P=0|o[7],I=8191&P,O=P>>>13,z=0|o[8],D=8191&z,R=z>>>13,F=0|o[9],B=8191&F,N=F>>>13,j=0|s[0],U=8191&j,V=j>>>13,H=0|s[1],q=8191&H,G=H>>>13,Y=0|s[2],W=8191&Y,X=Y>>>13,Z=0|s[3],J=8191&Z,K=Z>>>13,Q=0|s[4],$=8191&Q,tt=Q>>>13,et=0|s[5],rt=8191&et,nt=et>>>13,it=0|s[6],at=8191&it,ot=it>>>13,st=0|s[7],lt=8191&st,ct=st>>>13,ut=0|s[8],ft=8191&ut,ht=ut>>>13,pt=0|s[9],dt=8191&pt,gt=pt>>>13;r.negative=t.negative^e.negative,r.length=19;var mt=(c+(n=Math.imul(f,U))|0)+((8191&(i=(i=Math.imul(f,V))+Math.imul(h,U)|0))<<13)|0;c=((a=Math.imul(h,V))+(i>>>13)|0)+(mt>>>26)|0,mt&=67108863,n=Math.imul(d,U),i=(i=Math.imul(d,V))+Math.imul(g,U)|0,a=Math.imul(g,V);var vt=(c+(n=n+Math.imul(f,q)|0)|0)+((8191&(i=(i=i+Math.imul(f,G)|0)+Math.imul(h,q)|0))<<13)|0;c=((a=a+Math.imul(h,G)|0)+(i>>>13)|0)+(vt>>>26)|0,vt&=67108863,n=Math.imul(v,U),i=(i=Math.imul(v,V))+Math.imul(y,U)|0,a=Math.imul(y,V),n=n+Math.imul(d,q)|0,i=(i=i+Math.imul(d,G)|0)+Math.imul(g,q)|0,a=a+Math.imul(g,G)|0;var yt=(c+(n=n+Math.imul(f,W)|0)|0)+((8191&(i=(i=i+Math.imul(f,X)|0)+Math.imul(h,W)|0))<<13)|0;c=((a=a+Math.imul(h,X)|0)+(i>>>13)|0)+(yt>>>26)|0,yt&=67108863,n=Math.imul(b,U),i=(i=Math.imul(b,V))+Math.imul(_,U)|0,a=Math.imul(_,V),n=n+Math.imul(v,q)|0,i=(i=i+Math.imul(v,G)|0)+Math.imul(y,q)|0,a=a+Math.imul(y,G)|0,n=n+Math.imul(d,W)|0,i=(i=i+Math.imul(d,X)|0)+Math.imul(g,W)|0,a=a+Math.imul(g,X)|0;var xt=(c+(n=n+Math.imul(f,J)|0)|0)+((8191&(i=(i=i+Math.imul(f,K)|0)+Math.imul(h,J)|0))<<13)|0;c=((a=a+Math.imul(h,K)|0)+(i>>>13)|0)+(xt>>>26)|0,xt&=67108863,n=Math.imul(T,U),i=(i=Math.imul(T,V))+Math.imul(k,U)|0,a=Math.imul(k,V),n=n+Math.imul(b,q)|0,i=(i=i+Math.imul(b,G)|0)+Math.imul(_,q)|0,a=a+Math.imul(_,G)|0,n=n+Math.imul(v,W)|0,i=(i=i+Math.imul(v,X)|0)+Math.imul(y,W)|0,a=a+Math.imul(y,X)|0,n=n+Math.imul(d,J)|0,i=(i=i+Math.imul(d,K)|0)+Math.imul(g,J)|0,a=a+Math.imul(g,K)|0;var bt=(c+(n=n+Math.imul(f,$)|0)|0)+((8191&(i=(i=i+Math.imul(f,tt)|0)+Math.imul(h,$)|0))<<13)|0;c=((a=a+Math.imul(h,tt)|0)+(i>>>13)|0)+(bt>>>26)|0,bt&=67108863,n=Math.imul(M,U),i=(i=Math.imul(M,V))+Math.imul(S,U)|0,a=Math.imul(S,V),n=n+Math.imul(T,q)|0,i=(i=i+Math.imul(T,G)|0)+Math.imul(k,q)|0,a=a+Math.imul(k,G)|0,n=n+Math.imul(b,W)|0,i=(i=i+Math.imul(b,X)|0)+Math.imul(_,W)|0,a=a+Math.imul(_,X)|0,n=n+Math.imul(v,J)|0,i=(i=i+Math.imul(v,K)|0)+Math.imul(y,J)|0,a=a+Math.imul(y,K)|0,n=n+Math.imul(d,$)|0,i=(i=i+Math.imul(d,tt)|0)+Math.imul(g,$)|0,a=a+Math.imul(g,tt)|0;var _t=(c+(n=n+Math.imul(f,rt)|0)|0)+((8191&(i=(i=i+Math.imul(f,nt)|0)+Math.imul(h,rt)|0))<<13)|0;c=((a=a+Math.imul(h,nt)|0)+(i>>>13)|0)+(_t>>>26)|0,_t&=67108863,n=Math.imul(L,U),i=(i=Math.imul(L,V))+Math.imul(C,U)|0,a=Math.imul(C,V),n=n+Math.imul(M,q)|0,i=(i=i+Math.imul(M,G)|0)+Math.imul(S,q)|0,a=a+Math.imul(S,G)|0,n=n+Math.imul(T,W)|0,i=(i=i+Math.imul(T,X)|0)+Math.imul(k,W)|0,a=a+Math.imul(k,X)|0,n=n+Math.imul(b,J)|0,i=(i=i+Math.imul(b,K)|0)+Math.imul(_,J)|0,a=a+Math.imul(_,K)|0,n=n+Math.imul(v,$)|0,i=(i=i+Math.imul(v,tt)|0)+Math.imul(y,$)|0,a=a+Math.imul(y,tt)|0,n=n+Math.imul(d,rt)|0,i=(i=i+Math.imul(d,nt)|0)+Math.imul(g,rt)|0,a=a+Math.imul(g,nt)|0;var wt=(c+(n=n+Math.imul(f,at)|0)|0)+((8191&(i=(i=i+Math.imul(f,ot)|0)+Math.imul(h,at)|0))<<13)|0;c=((a=a+Math.imul(h,ot)|0)+(i>>>13)|0)+(wt>>>26)|0,wt&=67108863,n=Math.imul(I,U),i=(i=Math.imul(I,V))+Math.imul(O,U)|0,a=Math.imul(O,V),n=n+Math.imul(L,q)|0,i=(i=i+Math.imul(L,G)|0)+Math.imul(C,q)|0,a=a+Math.imul(C,G)|0,n=n+Math.imul(M,W)|0,i=(i=i+Math.imul(M,X)|0)+Math.imul(S,W)|0,a=a+Math.imul(S,X)|0,n=n+Math.imul(T,J)|0,i=(i=i+Math.imul(T,K)|0)+Math.imul(k,J)|0,a=a+Math.imul(k,K)|0,n=n+Math.imul(b,$)|0,i=(i=i+Math.imul(b,tt)|0)+Math.imul(_,$)|0,a=a+Math.imul(_,tt)|0,n=n+Math.imul(v,rt)|0,i=(i=i+Math.imul(v,nt)|0)+Math.imul(y,rt)|0,a=a+Math.imul(y,nt)|0,n=n+Math.imul(d,at)|0,i=(i=i+Math.imul(d,ot)|0)+Math.imul(g,at)|0,a=a+Math.imul(g,ot)|0;var Tt=(c+(n=n+Math.imul(f,lt)|0)|0)+((8191&(i=(i=i+Math.imul(f,ct)|0)+Math.imul(h,lt)|0))<<13)|0;c=((a=a+Math.imul(h,ct)|0)+(i>>>13)|0)+(Tt>>>26)|0,Tt&=67108863,n=Math.imul(D,U),i=(i=Math.imul(D,V))+Math.imul(R,U)|0,a=Math.imul(R,V),n=n+Math.imul(I,q)|0,i=(i=i+Math.imul(I,G)|0)+Math.imul(O,q)|0,a=a+Math.imul(O,G)|0,n=n+Math.imul(L,W)|0,i=(i=i+Math.imul(L,X)|0)+Math.imul(C,W)|0,a=a+Math.imul(C,X)|0,n=n+Math.imul(M,J)|0,i=(i=i+Math.imul(M,K)|0)+Math.imul(S,J)|0,a=a+Math.imul(S,K)|0,n=n+Math.imul(T,$)|0,i=(i=i+Math.imul(T,tt)|0)+Math.imul(k,$)|0,a=a+Math.imul(k,tt)|0,n=n+Math.imul(b,rt)|0,i=(i=i+Math.imul(b,nt)|0)+Math.imul(_,rt)|0,a=a+Math.imul(_,nt)|0,n=n+Math.imul(v,at)|0,i=(i=i+Math.imul(v,ot)|0)+Math.imul(y,at)|0,a=a+Math.imul(y,ot)|0,n=n+Math.imul(d,lt)|0,i=(i=i+Math.imul(d,ct)|0)+Math.imul(g,lt)|0,a=a+Math.imul(g,ct)|0;var kt=(c+(n=n+Math.imul(f,ft)|0)|0)+((8191&(i=(i=i+Math.imul(f,ht)|0)+Math.imul(h,ft)|0))<<13)|0;c=((a=a+Math.imul(h,ht)|0)+(i>>>13)|0)+(kt>>>26)|0,kt&=67108863,n=Math.imul(B,U),i=(i=Math.imul(B,V))+Math.imul(N,U)|0,a=Math.imul(N,V),n=n+Math.imul(D,q)|0,i=(i=i+Math.imul(D,G)|0)+Math.imul(R,q)|0,a=a+Math.imul(R,G)|0,n=n+Math.imul(I,W)|0,i=(i=i+Math.imul(I,X)|0)+Math.imul(O,W)|0,a=a+Math.imul(O,X)|0,n=n+Math.imul(L,J)|0,i=(i=i+Math.imul(L,K)|0)+Math.imul(C,J)|0,a=a+Math.imul(C,K)|0,n=n+Math.imul(M,$)|0,i=(i=i+Math.imul(M,tt)|0)+Math.imul(S,$)|0,a=a+Math.imul(S,tt)|0,n=n+Math.imul(T,rt)|0,i=(i=i+Math.imul(T,nt)|0)+Math.imul(k,rt)|0,a=a+Math.imul(k,nt)|0,n=n+Math.imul(b,at)|0,i=(i=i+Math.imul(b,ot)|0)+Math.imul(_,at)|0,a=a+Math.imul(_,ot)|0,n=n+Math.imul(v,lt)|0,i=(i=i+Math.imul(v,ct)|0)+Math.imul(y,lt)|0,a=a+Math.imul(y,ct)|0,n=n+Math.imul(d,ft)|0,i=(i=i+Math.imul(d,ht)|0)+Math.imul(g,ft)|0,a=a+Math.imul(g,ht)|0;var At=(c+(n=n+Math.imul(f,dt)|0)|0)+((8191&(i=(i=i+Math.imul(f,gt)|0)+Math.imul(h,dt)|0))<<13)|0;c=((a=a+Math.imul(h,gt)|0)+(i>>>13)|0)+(At>>>26)|0,At&=67108863,n=Math.imul(B,q),i=(i=Math.imul(B,G))+Math.imul(N,q)|0,a=Math.imul(N,G),n=n+Math.imul(D,W)|0,i=(i=i+Math.imul(D,X)|0)+Math.imul(R,W)|0,a=a+Math.imul(R,X)|0,n=n+Math.imul(I,J)|0,i=(i=i+Math.imul(I,K)|0)+Math.imul(O,J)|0,a=a+Math.imul(O,K)|0,n=n+Math.imul(L,$)|0,i=(i=i+Math.imul(L,tt)|0)+Math.imul(C,$)|0,a=a+Math.imul(C,tt)|0,n=n+Math.imul(M,rt)|0,i=(i=i+Math.imul(M,nt)|0)+Math.imul(S,rt)|0,a=a+Math.imul(S,nt)|0,n=n+Math.imul(T,at)|0,i=(i=i+Math.imul(T,ot)|0)+Math.imul(k,at)|0,a=a+Math.imul(k,ot)|0,n=n+Math.imul(b,lt)|0,i=(i=i+Math.imul(b,ct)|0)+Math.imul(_,lt)|0,a=a+Math.imul(_,ct)|0,n=n+Math.imul(v,ft)|0,i=(i=i+Math.imul(v,ht)|0)+Math.imul(y,ft)|0,a=a+Math.imul(y,ht)|0;var Mt=(c+(n=n+Math.imul(d,dt)|0)|0)+((8191&(i=(i=i+Math.imul(d,gt)|0)+Math.imul(g,dt)|0))<<13)|0;c=((a=a+Math.imul(g,gt)|0)+(i>>>13)|0)+(Mt>>>26)|0,Mt&=67108863,n=Math.imul(B,W),i=(i=Math.imul(B,X))+Math.imul(N,W)|0,a=Math.imul(N,X),n=n+Math.imul(D,J)|0,i=(i=i+Math.imul(D,K)|0)+Math.imul(R,J)|0,a=a+Math.imul(R,K)|0,n=n+Math.imul(I,$)|0,i=(i=i+Math.imul(I,tt)|0)+Math.imul(O,$)|0,a=a+Math.imul(O,tt)|0,n=n+Math.imul(L,rt)|0,i=(i=i+Math.imul(L,nt)|0)+Math.imul(C,rt)|0,a=a+Math.imul(C,nt)|0,n=n+Math.imul(M,at)|0,i=(i=i+Math.imul(M,ot)|0)+Math.imul(S,at)|0,a=a+Math.imul(S,ot)|0,n=n+Math.imul(T,lt)|0,i=(i=i+Math.imul(T,ct)|0)+Math.imul(k,lt)|0,a=a+Math.imul(k,ct)|0,n=n+Math.imul(b,ft)|0,i=(i=i+Math.imul(b,ht)|0)+Math.imul(_,ft)|0,a=a+Math.imul(_,ht)|0;var St=(c+(n=n+Math.imul(v,dt)|0)|0)+((8191&(i=(i=i+Math.imul(v,gt)|0)+Math.imul(y,dt)|0))<<13)|0;c=((a=a+Math.imul(y,gt)|0)+(i>>>13)|0)+(St>>>26)|0,St&=67108863,n=Math.imul(B,J),i=(i=Math.imul(B,K))+Math.imul(N,J)|0,a=Math.imul(N,K),n=n+Math.imul(D,$)|0,i=(i=i+Math.imul(D,tt)|0)+Math.imul(R,$)|0,a=a+Math.imul(R,tt)|0,n=n+Math.imul(I,rt)|0,i=(i=i+Math.imul(I,nt)|0)+Math.imul(O,rt)|0,a=a+Math.imul(O,nt)|0,n=n+Math.imul(L,at)|0,i=(i=i+Math.imul(L,ot)|0)+Math.imul(C,at)|0,a=a+Math.imul(C,ot)|0,n=n+Math.imul(M,lt)|0,i=(i=i+Math.imul(M,ct)|0)+Math.imul(S,lt)|0,a=a+Math.imul(S,ct)|0,n=n+Math.imul(T,ft)|0,i=(i=i+Math.imul(T,ht)|0)+Math.imul(k,ft)|0,a=a+Math.imul(k,ht)|0;var Et=(c+(n=n+Math.imul(b,dt)|0)|0)+((8191&(i=(i=i+Math.imul(b,gt)|0)+Math.imul(_,dt)|0))<<13)|0;c=((a=a+Math.imul(_,gt)|0)+(i>>>13)|0)+(Et>>>26)|0,Et&=67108863,n=Math.imul(B,$),i=(i=Math.imul(B,tt))+Math.imul(N,$)|0,a=Math.imul(N,tt),n=n+Math.imul(D,rt)|0,i=(i=i+Math.imul(D,nt)|0)+Math.imul(R,rt)|0,a=a+Math.imul(R,nt)|0,n=n+Math.imul(I,at)|0,i=(i=i+Math.imul(I,ot)|0)+Math.imul(O,at)|0,a=a+Math.imul(O,ot)|0,n=n+Math.imul(L,lt)|0,i=(i=i+Math.imul(L,ct)|0)+Math.imul(C,lt)|0,a=a+Math.imul(C,ct)|0,n=n+Math.imul(M,ft)|0,i=(i=i+Math.imul(M,ht)|0)+Math.imul(S,ft)|0,a=a+Math.imul(S,ht)|0;var Lt=(c+(n=n+Math.imul(T,dt)|0)|0)+((8191&(i=(i=i+Math.imul(T,gt)|0)+Math.imul(k,dt)|0))<<13)|0;c=((a=a+Math.imul(k,gt)|0)+(i>>>13)|0)+(Lt>>>26)|0,Lt&=67108863,n=Math.imul(B,rt),i=(i=Math.imul(B,nt))+Math.imul(N,rt)|0,a=Math.imul(N,nt),n=n+Math.imul(D,at)|0,i=(i=i+Math.imul(D,ot)|0)+Math.imul(R,at)|0,a=a+Math.imul(R,ot)|0,n=n+Math.imul(I,lt)|0,i=(i=i+Math.imul(I,ct)|0)+Math.imul(O,lt)|0,a=a+Math.imul(O,ct)|0,n=n+Math.imul(L,ft)|0,i=(i=i+Math.imul(L,ht)|0)+Math.imul(C,ft)|0,a=a+Math.imul(C,ht)|0;var Ct=(c+(n=n+Math.imul(M,dt)|0)|0)+((8191&(i=(i=i+Math.imul(M,gt)|0)+Math.imul(S,dt)|0))<<13)|0;c=((a=a+Math.imul(S,gt)|0)+(i>>>13)|0)+(Ct>>>26)|0,Ct&=67108863,n=Math.imul(B,at),i=(i=Math.imul(B,ot))+Math.imul(N,at)|0,a=Math.imul(N,ot),n=n+Math.imul(D,lt)|0,i=(i=i+Math.imul(D,ct)|0)+Math.imul(R,lt)|0,a=a+Math.imul(R,ct)|0,n=n+Math.imul(I,ft)|0,i=(i=i+Math.imul(I,ht)|0)+Math.imul(O,ft)|0,a=a+Math.imul(O,ht)|0;var Pt=(c+(n=n+Math.imul(L,dt)|0)|0)+((8191&(i=(i=i+Math.imul(L,gt)|0)+Math.imul(C,dt)|0))<<13)|0;c=((a=a+Math.imul(C,gt)|0)+(i>>>13)|0)+(Pt>>>26)|0,Pt&=67108863,n=Math.imul(B,lt),i=(i=Math.imul(B,ct))+Math.imul(N,lt)|0,a=Math.imul(N,ct),n=n+Math.imul(D,ft)|0,i=(i=i+Math.imul(D,ht)|0)+Math.imul(R,ft)|0,a=a+Math.imul(R,ht)|0;var It=(c+(n=n+Math.imul(I,dt)|0)|0)+((8191&(i=(i=i+Math.imul(I,gt)|0)+Math.imul(O,dt)|0))<<13)|0;c=((a=a+Math.imul(O,gt)|0)+(i>>>13)|0)+(It>>>26)|0,It&=67108863,n=Math.imul(B,ft),i=(i=Math.imul(B,ht))+Math.imul(N,ft)|0,a=Math.imul(N,ht);var Ot=(c+(n=n+Math.imul(D,dt)|0)|0)+((8191&(i=(i=i+Math.imul(D,gt)|0)+Math.imul(R,dt)|0))<<13)|0;c=((a=a+Math.imul(R,gt)|0)+(i>>>13)|0)+(Ot>>>26)|0,Ot&=67108863;var zt=(c+(n=Math.imul(B,dt))|0)+((8191&(i=(i=Math.imul(B,gt))+Math.imul(N,dt)|0))<<13)|0;return c=((a=Math.imul(N,gt))+(i>>>13)|0)+(zt>>>26)|0,zt&=67108863,l[0]=mt,l[1]=vt,l[2]=yt,l[3]=xt,l[4]=bt,l[5]=_t,l[6]=wt,l[7]=Tt,l[8]=kt,l[9]=At,l[10]=Mt,l[11]=St,l[12]=Et,l[13]=Lt,l[14]=Ct,l[15]=Pt,l[16]=It,l[17]=Ot,l[18]=zt,0!==c&&(l[19]=c,r.length++),r};function g(t,e,r){return(new m).mulp(t,e,r)}function m(t,e){this.x=t,this.y=e}Math.imul||(d=p),a.prototype.mulTo=function(t,e){var r=this.length+t.length;return 10===this.length&&10===t.length?d(this,t,e):r<63?p(this,t,e):r<1024?function(t,e,r){r.negative=e.negative^t.negative,r.length=t.length+e.length;for(var n=0,i=0,a=0;a<r.length-1;a++){var o=i;i=0;for(var s=67108863&n,l=Math.min(a,e.length-1),c=Math.max(0,a-t.length+1);c<=l;c++){var u=a-c,f=(0|t.words[u])*(0|e.words[c]),h=67108863&f;s=67108863&(h=h+s|0),i+=(o=(o=o+(f/67108864|0)|0)+(h>>>26)|0)>>>26,o&=67108863}r.words[a]=s,n=o,o=i}return 0!==n?r.words[a]=n:r.length--,r.strip()}(this,t,e):g(this,t,e)},m.prototype.makeRBT=function(t){for(var e=new Array(t),r=a.prototype._countBits(t)-1,n=0;n<t;n++)e[n]=this.revBin(n,r,t);return e},m.prototype.revBin=function(t,e,r){if(0===t||t===r-1)return t;for(var n=0,i=0;i<e;i++)n|=(1&t)<<e-i-1,t>>=1;return n},m.prototype.permute=function(t,e,r,n,i,a){for(var o=0;o<a;o++)n[o]=e[t[o]],i[o]=r[t[o]]},m.prototype.transform=function(t,e,r,n,i,a){this.permute(a,t,e,r,n,i);for(var o=1;o<i;o<<=1)for(var s=o<<1,l=Math.cos(2*Math.PI/s),c=Math.sin(2*Math.PI/s),u=0;u<i;u+=s)for(var f=l,h=c,p=0;p<o;p++){var d=r[u+p],g=n[u+p],m=r[u+p+o],v=n[u+p+o],y=f*m-h*v;v=f*v+h*m,m=y,r[u+p]=d+m,n[u+p]=g+v,r[u+p+o]=d-m,n[u+p+o]=g-v,p!==s&&(y=l*f-c*h,h=l*h+c*f,f=y)}},m.prototype.guessLen13b=function(t,e){var r=1|Math.max(e,t),n=1&r,i=0;for(r=r/2|0;r;r>>>=1)i++;return 1<<i+1+n},m.prototype.conjugate=function(t,e,r){if(!(r<=1))for(var n=0;n<r/2;n++){var i=t[n];t[n]=t[r-n-1],t[r-n-1]=i,i=e[n],e[n]=-e[r-n-1],e[r-n-1]=-i}},m.prototype.normalize13b=function(t,e){for(var r=0,n=0;n<e/2;n++){var i=8192*Math.round(t[2*n+1]/e)+Math.round(t[2*n]/e)+r;t[n]=67108863&i,r=i<67108864?0:i/67108864|0}return t},m.prototype.convert13b=function(t,e,r,i){for(var a=0,o=0;o<e;o++)a+=0|t[o],r[2*o]=8191&a,a>>>=13,r[2*o+1]=8191&a,a>>>=13;for(o=2*e;o<i;++o)r[o]=0;n(0===a),n(0==(-8192&a))},m.prototype.stub=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=0;return e},m.prototype.mulp=function(t,e,r){var n=2*this.guessLen13b(t.length,e.length),i=this.makeRBT(n),a=this.stub(n),o=new Array(n),s=new Array(n),l=new Array(n),c=new Array(n),u=new Array(n),f=new Array(n),h=r.words;h.length=n,this.convert13b(t.words,t.length,o,n),this.convert13b(e.words,e.length,c,n),this.transform(o,a,s,l,n,i),this.transform(c,a,u,f,n,i);for(var p=0;p<n;p++){var d=s[p]*u[p]-l[p]*f[p];l[p]=s[p]*f[p]+l[p]*u[p],s[p]=d}return this.conjugate(s,l,n),this.transform(s,l,h,a,n,i),this.conjugate(h,a,n),this.normalize13b(h,n),r.negative=t.negative^e.negative,r.length=t.length+e.length,r.strip()},a.prototype.mul=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),this.mulTo(t,e)},a.prototype.mulf=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),g(this,t,e)},a.prototype.imul=function(t){return this.clone().mulTo(t,this)},a.prototype.imuln=function(t){n("number"==typeof t),n(t<67108864);for(var e=0,r=0;r<this.length;r++){var i=(0|this.words[r])*t,a=(67108863&i)+(67108863&e);e>>=26,e+=i/67108864|0,e+=a>>>26,this.words[r]=67108863&a}return 0!==e&&(this.words[r]=e,this.length++),this},a.prototype.muln=function(t){return this.clone().imuln(t)},a.prototype.sqr=function(){return this.mul(this)},a.prototype.isqr=function(){return this.imul(this.clone())},a.prototype.pow=function(t){var e=function(t){for(var e=new Array(t.bitLength()),r=0;r<e.length;r++){var n=r/26|0,i=r%26;e[r]=(t.words[n]&1<<i)>>>i}return e}(t);if(0===e.length)return new a(1);for(var r=this,n=0;n<e.length&&0===e[n];n++,r=r.sqr());if(++n<e.length)for(var i=r.sqr();n<e.length;n++,i=i.sqr())0!==e[n]&&(r=r.mul(i));return r},a.prototype.iushln=function(t){n("number"==typeof t&&t>=0);var e,r=t%26,i=(t-r)/26,a=67108863>>>26-r<<26-r;if(0!==r){var o=0;for(e=0;e<this.length;e++){var s=this.words[e]&a,l=(0|this.words[e])-s<<r;this.words[e]=l|o,o=s>>>26-r}o&&(this.words[e]=o,this.length++)}if(0!==i){for(e=this.length-1;e>=0;e--)this.words[e+i]=this.words[e];for(e=0;e<i;e++)this.words[e]=0;this.length+=i}return this.strip()},a.prototype.ishln=function(t){return n(0===this.negative),this.iushln(t)},a.prototype.iushrn=function(t,e,r){var i;n("number"==typeof t&&t>=0),i=e?(e-e%26)/26:0;var a=t%26,o=Math.min((t-a)/26,this.length),s=67108863^67108863>>>a<<a,l=r;if(i-=o,i=Math.max(0,i),l){for(var c=0;c<o;c++)l.words[c]=this.words[c];l.length=o}if(0===o);else if(this.length>o)for(this.length-=o,c=0;c<this.length;c++)this.words[c]=this.words[c+o];else this.words[0]=0,this.length=1;var u=0;for(c=this.length-1;c>=0&&(0!==u||c>=i);c--){var f=0|this.words[c];this.words[c]=u<<26-a|f>>>a,u=f&s}return l&&0!==u&&(l.words[l.length++]=u),0===this.length&&(this.words[0]=0,this.length=1),this.strip()},a.prototype.ishrn=function(t,e,r){return n(0===this.negative),this.iushrn(t,e,r)},a.prototype.shln=function(t){return this.clone().ishln(t)},a.prototype.ushln=function(t){return this.clone().iushln(t)},a.prototype.shrn=function(t){return this.clone().ishrn(t)},a.prototype.ushrn=function(t){return this.clone().iushrn(t)},a.prototype.testn=function(t){n("number"==typeof t&&t>=0);var e=t%26,r=(t-e)/26,i=1<<e;return!(this.length<=r)&&!!(this.words[r]&i)},a.prototype.imaskn=function(t){n("number"==typeof t&&t>=0);var e=t%26,r=(t-e)/26;if(n(0===this.negative,"imaskn works only with positive numbers"),this.length<=r)return this;if(0!==e&&r++,this.length=Math.min(r,this.length),0!==e){var i=67108863^67108863>>>e<<e;this.words[this.length-1]&=i}return this.strip()},a.prototype.maskn=function(t){return this.clone().imaskn(t)},a.prototype.iaddn=function(t){return n("number"==typeof t),n(t<67108864),t<0?this.isubn(-t):0!==this.negative?1===this.length&&(0|this.words[0])<t?(this.words[0]=t-(0|this.words[0]),this.negative=0,this):(this.negative=0,this.isubn(t),this.negative=1,this):this._iaddn(t)},a.prototype._iaddn=function(t){this.words[0]+=t;for(var e=0;e<this.length&&this.words[e]>=67108864;e++)this.words[e]-=67108864,e===this.length-1?this.words[e+1]=1:this.words[e+1]++;return this.length=Math.max(this.length,e+1),this},a.prototype.isubn=function(t){if(n("number"==typeof t),n(t<67108864),t<0)return this.iaddn(-t);if(0!==this.negative)return this.negative=0,this.iaddn(t),this.negative=1,this;if(this.words[0]-=t,1===this.length&&this.words[0]<0)this.words[0]=-this.words[0],this.negative=1;else for(var e=0;e<this.length&&this.words[e]<0;e++)this.words[e]+=67108864,this.words[e+1]-=1;return this.strip()},a.prototype.addn=function(t){return this.clone().iaddn(t)},a.prototype.subn=function(t){return this.clone().isubn(t)},a.prototype.iabs=function(){return this.negative=0,this},a.prototype.abs=function(){return this.clone().iabs()},a.prototype._ishlnsubmul=function(t,e,r){var i,a,o=t.length+r;this._expand(o);var s=0;for(i=0;i<t.length;i++){a=(0|this.words[i+r])+s;var l=(0|t.words[i])*e;s=((a-=67108863&l)>>26)-(l/67108864|0),this.words[i+r]=67108863&a}for(;i<this.length-r;i++)s=(a=(0|this.words[i+r])+s)>>26,this.words[i+r]=67108863&a;if(0===s)return this.strip();for(n(-1===s),s=0,i=0;i<this.length;i++)s=(a=-(0|this.words[i])+s)>>26,this.words[i]=67108863&a;return this.negative=1,this.strip()},a.prototype._wordDiv=function(t,e){var r=(this.length,t.length),n=this.clone(),i=t,o=0|i.words[i.length-1];0!==(r=26-this._countBits(o))&&(i=i.ushln(r),n.iushln(r),o=0|i.words[i.length-1]);var s,l=n.length-i.length;if("mod"!==e){(s=new a(null)).length=l+1,s.words=new Array(s.length);for(var c=0;c<s.length;c++)s.words[c]=0}var u=n.clone()._ishlnsubmul(i,1,l);0===u.negative&&(n=u,s&&(s.words[l]=1));for(var f=l-1;f>=0;f--){var h=67108864*(0|n.words[i.length+f])+(0|n.words[i.length+f-1]);for(h=Math.min(h/o|0,67108863),n._ishlnsubmul(i,h,f);0!==n.negative;)h--,n.negative=0,n._ishlnsubmul(i,1,f),n.isZero()||(n.negative^=1);s&&(s.words[f]=h)}return s&&s.strip(),n.strip(),"div"!==e&&0!==r&&n.iushrn(r),{div:s||null,mod:n}},a.prototype.divmod=function(t,e,r){return n(!t.isZero()),this.isZero()?{div:new a(0),mod:new a(0)}:0!==this.negative&&0===t.negative?(s=this.neg().divmod(t,e),"mod"!==e&&(i=s.div.neg()),"div"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.iadd(t)),{div:i,mod:o}):0===this.negative&&0!==t.negative?(s=this.divmod(t.neg(),e),"mod"!==e&&(i=s.div.neg()),{div:i,mod:s.mod}):0!=(this.negative&t.negative)?(s=this.neg().divmod(t.neg(),e),"div"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.isub(t)),{div:s.div,mod:o}):t.length>this.length||this.cmp(t)<0?{div:new a(0),mod:this}:1===t.length?"div"===e?{div:this.divn(t.words[0]),mod:null}:"mod"===e?{div:null,mod:new a(this.modn(t.words[0]))}:{div:this.divn(t.words[0]),mod:new a(this.modn(t.words[0]))}:this._wordDiv(t,e);var i,o,s},a.prototype.div=function(t){return this.divmod(t,"div",!1).div},a.prototype.mod=function(t){return this.divmod(t,"mod",!1).mod},a.prototype.umod=function(t){return this.divmod(t,"mod",!0).mod},a.prototype.divRound=function(t){var e=this.divmod(t);if(e.mod.isZero())return e.div;var r=0!==e.div.negative?e.mod.isub(t):e.mod,n=t.ushrn(1),i=t.andln(1),a=r.cmp(n);return a<0||1===i&&0===a?e.div:0!==e.div.negative?e.div.isubn(1):e.div.iaddn(1)},a.prototype.modn=function(t){n(t<=67108863);for(var e=(1<<26)%t,r=0,i=this.length-1;i>=0;i--)r=(e*r+(0|this.words[i]))%t;return r},a.prototype.idivn=function(t){n(t<=67108863);for(var e=0,r=this.length-1;r>=0;r--){var i=(0|this.words[r])+67108864*e;this.words[r]=i/t|0,e=i%t}return this.strip()},a.prototype.divn=function(t){return this.clone().idivn(t)},a.prototype.egcd=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i=new a(1),o=new a(0),s=new a(0),l=new a(1),c=0;e.isEven()&&r.isEven();)e.iushrn(1),r.iushrn(1),++c;for(var u=r.clone(),f=e.clone();!e.isZero();){for(var h=0,p=1;0==(e.words[0]&p)&&h<26;++h,p<<=1);if(h>0)for(e.iushrn(h);h-- >0;)(i.isOdd()||o.isOdd())&&(i.iadd(u),o.isub(f)),i.iushrn(1),o.iushrn(1);for(var d=0,g=1;0==(r.words[0]&g)&&d<26;++d,g<<=1);if(d>0)for(r.iushrn(d);d-- >0;)(s.isOdd()||l.isOdd())&&(s.iadd(u),l.isub(f)),s.iushrn(1),l.iushrn(1);e.cmp(r)>=0?(e.isub(r),i.isub(s),o.isub(l)):(r.isub(e),s.isub(i),l.isub(o))}return{a:s,b:l,gcd:r.iushln(c)}},a.prototype._invmp=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i,o=new a(1),s=new a(0),l=r.clone();e.cmpn(1)>0&&r.cmpn(1)>0;){for(var c=0,u=1;0==(e.words[0]&u)&&c<26;++c,u<<=1);if(c>0)for(e.iushrn(c);c-- 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this._expand(r+1),this.words[r]|=i,this;for(var a=i,o=r;0!==a&&o<this.length;o++){var s=0|this.words[o];a=(s+=a)>>>26,s&=67108863,this.words[o]=s}return 0!==a&&(this.words[o]=a,this.length++),this},a.prototype.isZero=function(){return 1===this.length&&0===this.words[0]},a.prototype.cmpn=function(t){var e,r=t<0;if(0!==this.negative&&!r)return-1;if(0===this.negative&&r)return 1;if(this.strip(),this.length>1)e=1;else{r&&(t=-t),n(t<=67108863,"Number is too big");var i=0|this.words[0];e=i===t?0:i<t?-1:1}return 0!==this.negative?0|-e:e},a.prototype.cmp=function(t){if(0!==this.negative&&0===t.negative)return-1;if(0===this.negative&&0!==t.negative)return 1;var e=this.ucmp(t);return 0!==this.negative?0|-e:e},a.prototype.ucmp=function(t){if(this.length>t.length)return 1;if(this.length<t.length)return-1;for(var e=0,r=this.length-1;r>=0;r--){var n=0|this.words[r],i=0|t.words[r];if(n!==i){n<i?e=-1:n>i&&(e=1);break}}return e},a.prototype.gtn=function(t){return 1===this.cmpn(t)},a.prototype.gt=function(t){return 1===this.cmp(t)},a.prototype.gten=function(t){return this.cmpn(t)>=0},a.prototype.gte=function(t){return this.cmp(t)>=0},a.prototype.ltn=function(t){return-1===this.cmpn(t)},a.prototype.lt=function(t){return-1===this.cmp(t)},a.prototype.lten=function(t){return this.cmpn(t)<=0},a.prototype.lte=function(t){return this.cmp(t)<=0},a.prototype.eqn=function(t){return 0===this.cmpn(t)},a.prototype.eq=function(t){return 0===this.cmp(t)},a.red=function(t){return new T(t)},a.prototype.toRed=function(t){return n(!this.red,"Already a number in reduction context"),n(0===this.negative,"red works only with positives"),t.convertTo(this)._forceRed(t)},a.prototype.fromRed=function(){return n(this.red,"fromRed works only with numbers in reduction context"),this.red.convertFrom(this)},a.prototype._forceRed=function(t){return this.red=t,this},a.prototype.forceRed=function(t){return n(!this.red,"Already a number in reduction 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0===t.words[t.length-1]&&(t.length--,0===t.words[t.length-1]&&t.length--),t},i(b,y),i(_,y),i(w,y),w.prototype.imulK=function(t){for(var e=0,r=0;r<t.length;r++){var n=19*(0|t.words[r])+e,i=67108863&n;n>>>=26,t.words[r]=i,e=n}return 0!==e&&(t.words[t.length++]=e),t},a._prime=function(t){if(v[t])return v[t];var e;if("k256"===t)e=new x;else if("p224"===t)e=new b;else if("p192"===t)e=new _;else{if("p25519"!==t)throw new Error("Unknown prime "+t);e=new w}return v[t]=e,e},T.prototype._verify1=function(t){n(0===t.negative,"red works only with positives"),n(t.red,"red works only with red numbers")},T.prototype._verify2=function(t,e){n(0==(t.negative|e.negative),"red works only with positives"),n(t.red&&t.red===e.red,"red works only with red numbers")},T.prototype.imod=function(t){return this.prime?this.prime.ireduce(t)._forceRed(this):t.umod(this.m)._forceRed(this)},T.prototype.neg=function(t){return t.isZero()?t.clone():this.m.sub(t)._forceRed(this)},T.prototype.add=function(t,e){this._verify2(t,e);var r=t.add(e);return r.cmp(this.m)>=0&&r.isub(this.m),r._forceRed(this)},T.prototype.iadd=function(t,e){this._verify2(t,e);var r=t.iadd(e);return r.cmp(this.m)>=0&&r.isub(this.m),r},T.prototype.sub=function(t,e){this._verify2(t,e);var r=t.sub(e);return r.cmpn(0)<0&&r.iadd(this.m),r._forceRed(this)},T.prototype.isub=function(t,e){this._verify2(t,e);var r=t.isub(e);return r.cmpn(0)<0&&r.iadd(this.m),r},T.prototype.shl=function(t,e){return this._verify1(t),this.imod(t.ushln(e))},T.prototype.imul=function(t,e){return this._verify2(t,e),this.imod(t.imul(e))},T.prototype.mul=function(t,e){return this._verify2(t,e),this.imod(t.mul(e))},T.prototype.isqr=function(t){return this.imul(t,t.clone())},T.prototype.sqr=function(t){return this.mul(t,t)},T.prototype.sqrt=function(t){if(t.isZero())return t.clone();var e=this.m.andln(3);if(n(e%2==1),3===e){var r=this.m.add(new a(1)).iushrn(2);return this.pow(t,r)}for(var i=this.m.subn(1),o=0;!i.isZero()&&0===i.andln(1);)o++,i.iushrn(1);n(!i.isZero());var s=new a(1).toRed(this),l=s.redNeg(),c=this.m.subn(1).iushrn(1),u=this.m.bitLength();for(u=new a(2*u*u).toRed(this);0!==this.pow(u,c).cmp(l);)u.redIAdd(l);for(var f=this.pow(u,i),h=this.pow(t,i.addn(1).iushrn(1)),p=this.pow(t,i),d=o;0!==p.cmp(s);){for(var g=p,m=0;0!==g.cmp(s);m++)g=g.redSqr();n(m<d);var v=this.pow(f,new a(1).iushln(d-m-1));h=h.redMul(v),f=v.redSqr(),p=p.redMul(f),d=m}return h},T.prototype.invm=function(t){var e=t._invmp(this.m);return 0!==e.negative?(e.negative=0,this.imod(e).redNeg()):this.imod(e)},T.prototype.pow=function(t,e){if(e.isZero())return new a(1).toRed(this);if(0===e.cmpn(1))return t.clone();var r=new Array(16);r[0]=new a(1).toRed(this),r[1]=t;for(var n=2;n<r.length;n++)r[n]=this.mul(r[n-1],t);var i=r[0],o=0,s=0,l=e.bitLength()%26;for(0===l&&(l=26),n=e.length-1;n>=0;n--){for(var c=e.words[n],u=l-1;u>=0;u--){var f=c>>u&1;i!==r[0]&&(i=this.sqr(i)),0!==f||0!==o?(o<<=1,o|=f,(4===++s||0===n&&0===u)&&(i=this.mul(i,r[o]),s=0,o=0)):s=0}l=26}return i},T.prototype.convertTo=function(t){var e=t.umod(this.m);return e===t?e.clone():e},T.prototype.convertFrom=function(t){var e=t.clone();return e.red=null,e},a.mont=function(t){return new k(t)},i(k,T),k.prototype.convertTo=function(t){return this.imod(t.ushln(this.shift))},k.prototype.convertFrom=function(t){var e=this.imod(t.mul(this.rinv));return e.red=null,e},k.prototype.imul=function(t,e){if(t.isZero()||e.isZero())return t.words[0]=0,t.length=1,t;var r=t.imul(e),n=r.maskn(this.shift).mul(this.minv).imaskn(this.shift).mul(this.m),i=r.isub(n).iushrn(this.shift),a=i;return i.cmp(this.m)>=0?a=i.isub(this.m):i.cmpn(0)<0&&(a=i.iadd(this.m)),a._forceRed(this)},k.prototype.mul=function(t,e){if(t.isZero()||e.isZero())return new a(0)._forceRed(this);var 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r=0;r<t;++r)if(!(e[r]<=e[r+t]))return!0;return!1}function l(t,e,r,n){for(var i=0,a=0,o=0,l=t.length;o<l;++o){var c=t[o];if(!s(e,c)){for(var u=0;u<2*e;++u)r[i++]=c[u];n[a++]=o}}return a}function c(t,e,r,n){var s=t.length,c=e.length;if(!(s<=0||c<=0)){var u=t[0].length>>>1;if(!(u<=0)){var f,h=i.mallocDouble(2*u*s),p=i.mallocInt32(s);if((s=l(t,u,h,p))>0){if(1===u&&n)a.init(s),f=a.sweepComplete(u,r,0,s,h,p,0,s,h,p);else{var d=i.mallocDouble(2*u*c),g=i.mallocInt32(c);(c=l(e,u,d,g))>0&&(a.init(s+c),f=1===u?a.sweepBipartite(u,r,0,s,h,p,0,c,d,g):o(u,r,n,s,h,p,c,d,g),i.free(d),i.free(g))}i.free(h),i.free(p)}return f}}}function u(t,e){n.push([t,e])}function f(t){return n=[],c(t,t,u,!0),n}function h(t,e){return n=[],c(t,e,u,!1),n}},{"./lib/intersect":37,"./lib/sweep":41,"typedarray-pool":308}],36:[function(t,e,r){"use strict";function n(t){return t?function(t,e,r,n,i,a,o,s,l,c,u){return i-n>l-s?function(t,e,r,n,i,a,o,s,l,c,u){for(var f=2*t,h=n,p=f*n;h<i;++h,p+=f){var 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f=e[e.length-2];f.left===r?f.left=null:f.right===r&&(f.right=null),e.pop();for(l=0;l<e.length;++l)e[l]._count--;return new s(this.tree._compare,e[0])}if(r.left||r.right){r.left?d(r,r.left):r.right&&d(r,r.right),r._color=1;for(l=0;l<e.length-1;++l)e[l]._count--;return new s(this.tree._compare,e[0])}if(1===e.length)return new s(this.tree._compare,null);for(l=0;l<e.length;++l)e[l]._count--;var h=e[e.length-2];return function(t){for(var e,r,n,s,l=t.length-1;l>=0;--l){if(e=t[l],0===l)return void(e._color=1);if((r=t[l-1]).left===e){if((n=r.right).right&&0===n.right._color){if(s=(n=r.right=i(n)).right=i(n.right),r.right=n.left,n.left=r,n.right=s,n._color=r._color,e._color=1,r._color=1,s._color=1,o(r),o(n),l>1)(c=t[l-2]).left===r?c.left=n:c.right=n;return void(t[l-1]=n)}if(n.left&&0===n.left._color){if(s=(n=r.right=i(n)).left=i(n.left),r.right=s.left,n.left=s.right,s.left=r,s.right=n,s._color=r._color,r._color=1,n._color=1,e._color=1,o(r),o(n),o(s),l>1)(c=t[l-2]).left===r?c.left=s:c.right=s;return void(t[l-1]=s)}if(1===n._color){if(0===r._color)return r._color=1,void(r.right=a(0,n));r.right=a(0,n);continue}n=i(n),r.right=n.left,n.left=r,n._color=r._color,r._color=0,o(r),o(n),l>1&&((c=t[l-2]).left===r?c.left=n:c.right=n),t[l-1]=n,t[l]=r,l+1<t.length?t[l+1]=e:t.push(e),l+=2}else{if((n=r.left).left&&0===n.left._color){if(s=(n=r.left=i(n)).left=i(n.left),r.left=n.right,n.right=r,n.left=s,n._color=r._color,e._color=1,r._color=1,s._color=1,o(r),o(n),l>1)(c=t[l-2]).right===r?c.right=n:c.left=n;return void(t[l-1]=n)}if(n.right&&0===n.right._color){if(s=(n=r.left=i(n)).right=i(n.right),r.left=s.right,n.right=s.left,s.right=r,s.left=n,s._color=r._color,r._color=1,n._color=1,e._color=1,o(r),o(n),o(s),l>1)(c=t[l-2]).right===r?c.right=s:c.left=s;return void(t[l-1]=s)}if(1===n._color){if(0===r._color)return r._color=1,void(r.left=a(0,n));r.left=a(0,n);continue}var c;n=i(n),r.left=n.right,n.right=r,n._color=r._color,r._color=0,o(r),o(n),l>1&&((c=t[l-2]).right===r?c.right=n:c.left=n),t[l-1]=n,t[l]=r,l+1<t.length?t[l+1]=e:t.push(e),l+=2}}}(e),h.left===r?h.left=null:h.right=null,new s(this.tree._compare,e[0])},Object.defineProperty(p,"key",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].key},enumerable:!0}),Object.defineProperty(p,"value",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].value},enumerable:!0}),Object.defineProperty(p,"index",{get:function(){var t=0,e=this._stack;if(0===e.length){var r=this.tree.root;return r?r._count:0}e[e.length-1].left&&(t=e[e.length-1].left._count);for(var n=e.length-2;n>=0;--n)e[n+1]===e[n].right&&(++t,e[n].left&&(t+=e[n].left._count));return t},enumerable:!0}),p.next=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.right)for(e=e.right;e;)t.push(e),e=e.left;else for(t.pop();t.length>0&&t[t.length-1].right===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(p,"hasNext",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].right)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].left===t[e])return!0;return!1}}),p.update=function(t){var e=this._stack;if(0===e.length)throw new Error("Can't update empty node!");var r=new Array(e.length),i=e[e.length-1];r[r.length-1]=new n(i._color,i.key,t,i.left,i.right,i._count);for(var a=e.length-2;a>=0;--a)(i=e[a]).left===e[a+1]?r[a]=new n(i._color,i.key,i.value,r[a+1],i.right,i._count):r[a]=new n(i._color,i.key,i.value,i.left,r[a+1],i._count);return new s(this.tree._compare,r[0])},p.prev=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.left)for(e=e.left;e;)t.push(e),e=e.right;else for(t.pop();t.length>0&&t[t.length-1].left===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(p,"hasPrev",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].left)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].right===t[e])return!0;return!1}})},{}],70:[function(t,e,r){"use strict";e.exports=function(t,e){var r=new u(t);return r.update(e),r};var n=t("./lib/text.js"),i=t("./lib/lines.js"),a=t("./lib/background.js"),o=t("./lib/cube.js"),s=t("./lib/ticks.js"),l=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]);function c(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function u(t){this.gl=t,this.pixelRatio=1,this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.autoTicks=!0,this.tickSpacing=[1,1,1],this.tickEnable=[!0,!0,!0],this.tickFont=["sans-serif","sans-serif","sans-serif"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickAlign=["auto","auto","auto"],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[10,10,10],this.lastCubeProps={cubeEdges:[0,0,0],axis:[0,0,0]},this.labels=["x","y","z"],this.labelEnable=[!0,!0,!0],this.labelFont="sans-serif",this.labelSize=[20,20,20],this.labelAngle=[0,0,0],this.labelAlign=["auto","auto","auto"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[10,10,10],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[0,0,0],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!1,!1,!1],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._firstInit=!0,this._text=null,this._lines=null,this._background=a(t)}var f=u.prototype;function h(){this.primalOffset=[0,0,0],this.primalMinor=[0,0,0],this.mirrorOffset=[0,0,0],this.mirrorMinor=[0,0,0]}f.update=function(t){function e(e,r,n){if(n in t){var i,a=t[n],o=this[n];(e?Array.isArray(a)&&Array.isArray(a[0]):Array.isArray(a))?this[n]=i=[r(a[0]),r(a[1]),r(a[2])]:this[n]=i=[r(a),r(a),r(a)];for(var s=0;s<3;++s)if(i[s]!==o[s])return!0}return!1}t=t||{};var r,a=e.bind(this,!1,Number),o=e.bind(this,!1,Boolean),l=e.bind(this,!1,String),c=e.bind(this,!0,(function(t){if(Array.isArray(t)){if(3===t.length)return[+t[0],+t[1],+t[2],1];if(4===t.length)return[+t[0],+t[1],+t[2],+t[3]]}return[0,0,0,1]})),u=!1,f=!1;if("bounds"in t)for(var h=t.bounds,p=0;p<2;++p)for(var d=0;d<3;++d)h[p][d]!==this.bounds[p][d]&&(f=!0),this.bounds[p][d]=h[p][d];if("ticks"in t){r=t.ticks,u=!0,this.autoTicks=!1;for(p=0;p<3;++p)this.tickSpacing[p]=0}else a("tickSpacing")&&(this.autoTicks=!0,f=!0);if(this._firstInit&&("ticks"in t||"tickSpacing"in t||(this.autoTicks=!0),f=!0,u=!0,this._firstInit=!1),f&&this.autoTicks&&(r=s.create(this.bounds,this.tickSpacing),u=!0),u){for(p=0;p<3;++p)r[p].sort((function(t,e){return t.x-e.x}));s.equal(r,this.ticks)?u=!1:this.ticks=r}o("tickEnable"),l("tickFont")&&(u=!0),a("tickSize"),a("tickAngle"),a("tickPad"),c("tickColor");var g=l("labels");l("labelFont")&&(g=!0),o("labelEnable"),a("labelSize"),a("labelPad"),c("labelColor"),o("lineEnable"),o("lineMirror"),a("lineWidth"),c("lineColor"),o("lineTickEnable"),o("lineTickMirror"),a("lineTickLength"),a("lineTickWidth"),c("lineTickColor"),o("gridEnable"),a("gridWidth"),c("gridColor"),o("zeroEnable"),c("zeroLineColor"),a("zeroLineWidth"),o("backgroundEnable"),c("backgroundColor"),this._text?this._text&&(g||u)&&this._text.update(this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont):this._text=n(this.gl,this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont),this._lines&&u&&(this._lines.dispose(),this._lines=null),this._lines||(this._lines=i(this.gl,this.bounds,this.ticks))};var p=[new h,new h,new h];function d(t,e,r,n,i){for(var a=t.primalOffset,o=t.primalMinor,s=t.mirrorOffset,l=t.mirrorMinor,c=n[e],u=0;u<3;++u)if(e!==u){var f=a,h=s,p=o,d=l;c&1<<u&&(f=s,h=a,p=l,d=o),f[u]=r[0][u],h[u]=r[1][u],i[u]>0?(p[u]=-1,d[u]=0):(p[u]=0,d[u]=1)}}var g=[0,0,0],m={model:l,view:l,projection:l,_ortho:!1};f.isOpaque=function(){return!0},f.isTransparent=function(){return!1},f.drawTransparent=function(t){};var v=[0,0,0],y=[0,0,0],x=[0,0,0];f.draw=function(t){t=t||m;for(var e=this.gl,r=t.model||l,n=t.view||l,i=t.projection||l,a=this.bounds,s=t._ortho||!1,u=o(r,n,i,a,s),f=u.cubeEdges,h=u.axis,b=n[12],_=n[13],w=n[14],T=n[15],k=(s?2:1)*this.pixelRatio*(i[3]*b+i[7]*_+i[11]*w+i[15]*T)/e.drawingBufferHeight,A=0;A<3;++A)this.lastCubeProps.cubeEdges[A]=f[A],this.lastCubeProps.axis[A]=h[A];var M=p;for(A=0;A<3;++A)d(p[A],A,this.bounds,f,h);e=this.gl;var S,E=g;for(A=0;A<3;++A)this.backgroundEnable[A]?E[A]=h[A]:E[A]=0;this._background.draw(r,n,i,a,E,this.backgroundColor),this._lines.bind(r,n,i,this);for(A=0;A<3;++A){var L=[0,0,0];h[A]>0?L[A]=a[1][A]:L[A]=a[0][A];for(var C=0;C<2;++C){var P=(A+1+C)%3,I=(A+1+(1^C))%3;this.gridEnable[P]&&this._lines.drawGrid(P,I,this.bounds,L,this.gridColor[P],this.gridWidth[P]*this.pixelRatio)}for(C=0;C<2;++C){P=(A+1+C)%3,I=(A+1+(1^C))%3;this.zeroEnable[I]&&Math.min(a[0][I],a[1][I])<=0&&Math.max(a[0][I],a[1][I])>=0&&this._lines.drawZero(P,I,this.bounds,L,this.zeroLineColor[I],this.zeroLineWidth[I]*this.pixelRatio)}}for(A=0;A<3;++A){this.lineEnable[A]&&this._lines.drawAxisLine(A,this.bounds,M[A].primalOffset,this.lineColor[A],this.lineWidth[A]*this.pixelRatio),this.lineMirror[A]&&this._lines.drawAxisLine(A,this.bounds,M[A].mirrorOffset,this.lineColor[A],this.lineWidth[A]*this.pixelRatio);var O=c(v,M[A].primalMinor),z=c(y,M[A].mirrorMinor),D=this.lineTickLength;for(C=0;C<3;++C){var R=k/r[5*C];O[C]*=D[C]*R,z[C]*=D[C]*R}this.lineTickEnable[A]&&this._lines.drawAxisTicks(A,M[A].primalOffset,O,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio),this.lineTickMirror[A]&&this._lines.drawAxisTicks(A,M[A].mirrorOffset,z,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio)}this._lines.unbind(),this._text.bind(r,n,i,this.pixelRatio);var F,B;function N(t){(B=[0,0,0])[t]=1}function j(t,e,r){var n=(t+1)%3,i=(t+2)%3,a=e[n],o=e[i],s=r[n],l=r[i];a>0&&l>0||a>0&&l<0||a<0&&l>0||a<0&&l<0?N(n):(o>0&&s>0||o>0&&s<0||o<0&&s>0||o<0&&s<0)&&N(i)}for(A=0;A<3;++A){var U=M[A].primalMinor,V=M[A].mirrorMinor,H=c(x,M[A].primalOffset);for(C=0;C<3;++C)this.lineTickEnable[A]&&(H[C]+=k*U[C]*Math.max(this.lineTickLength[C],0)/r[5*C]);var q=[0,0,0];if(q[A]=1,this.tickEnable[A]){-3600===this.tickAngle[A]?(this.tickAngle[A]=0,this.tickAlign[A]="auto"):this.tickAlign[A]=-1,F=1,"auto"===(S=[this.tickAlign[A],.5,F])[0]?S[0]=0:S[0]=parseInt(""+S[0]),B=[0,0,0],j(A,U,V);for(C=0;C<3;++C)H[C]+=k*U[C]*this.tickPad[C]/r[5*C];this._text.drawTicks(A,this.tickSize[A],this.tickAngle[A],H,this.tickColor[A],q,B,S)}if(this.labelEnable[A]){F=0,B=[0,0,0],this.labels[A].length>4&&(N(A),F=1),"auto"===(S=[this.labelAlign[A],.5,F])[0]?S[0]=0:S[0]=parseInt(""+S[0]);for(C=0;C<3;++C)H[C]+=k*U[C]*this.labelPad[C]/r[5*C];H[A]+=.5*(a[0][A]+a[1][A]),this._text.drawLabel(A,this.labelSize[A],this.labelAngle[A],H,this.labelColor[A],[0,0,0],B,S)}}this._text.unbind()},f.dispose=function(){this._text.dispose(),this._lines.dispose(),this._background.dispose(),this._lines=null,this._text=null,this._background=null,this.gl=null}},{"./lib/background.js":71,"./lib/cube.js":72,"./lib/lines.js":73,"./lib/text.js":75,"./lib/ticks.js":76}],71:[function(t,e,r){"use strict";e.exports=function(t){for(var e=[],r=[],s=0,l=0;l<3;++l)for(var c=(l+1)%3,u=(l+2)%3,f=[0,0,0],h=[0,0,0],p=-1;p<=1;p+=2){r.push(s,s+2,s+1,s+1,s+2,s+3),f[l]=p,h[l]=p;for(var d=-1;d<=1;d+=2){f[c]=d;for(var g=-1;g<=1;g+=2)f[u]=g,e.push(f[0],f[1],f[2],h[0],h[1],h[2]),s+=1}var m=c;c=u,u=m}var v=n(t,new Float32Array(e)),y=n(t,new Uint16Array(r),t.ELEMENT_ARRAY_BUFFER),x=i(t,[{buffer:v,type:t.FLOAT,size:3,offset:0,stride:24},{buffer:v,type:t.FLOAT,size:3,offset:12,stride:24}],y),b=a(t);return b.attributes.position.location=0,b.attributes.normal.location=1,new o(t,v,x,b)};var n=t("gl-buffer"),i=t("gl-vao"),a=t("./shaders").bg;function o(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n}var s=o.prototype;s.draw=function(t,e,r,n,i,a){for(var o=!1,s=0;s<3;++s)o=o||i[s];if(o){var l=this.gl;l.enable(l.POLYGON_OFFSET_FILL),l.polygonOffset(1,2),this.shader.bind(),this.shader.uniforms={model:t,view:e,projection:r,bounds:n,enable:i,colors:a},this.vao.bind(),this.vao.draw(this.gl.TRIANGLES,36),this.vao.unbind(),l.disable(l.POLYGON_OFFSET_FILL)}},s.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},{"./shaders":74,"gl-buffer":78,"gl-vao":150}],72:[function(t,e,r){"use strict";e.exports=function(t,e,r,a,p){i(s,e,t),i(s,r,s);for(var y=0,x=0;x<2;++x){u[2]=a[x][2];for(var b=0;b<2;++b){u[1]=a[b][1];for(var _=0;_<2;++_)u[0]=a[_][0],h(l[y],u,s),y+=1}}var w=-1;for(x=0;x<8;++x){for(var T=l[x][3],k=0;k<3;++k)c[x][k]=l[x][k]/T;p&&(c[x][2]*=-1),T<0&&(w<0||c[x][2]<c[w][2])&&(w=x)}if(w<0){w=0;for(var A=0;A<3;++A){for(var M=(A+2)%3,S=(A+1)%3,E=-1,L=-1,C=0;C<2;++C){var P=(O=C<<A)+(C<<M)+(1-C<<S),I=O+(1-C<<M)+(C<<S);o(c[O],c[P],c[I],f)<0||(C?E=1:L=1)}if(E<0||L<0)L>E&&(w|=1<<A);else{for(C=0;C<2;++C){P=(O=C<<A)+(C<<M)+(1-C<<S),I=O+(1-C<<M)+(C<<S);var 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e=0;e<p.length;++e)if((t=a.positive(t,p[e])).length<3)return 0;var r=t[0],n=r[0]/r[3],i=r[1]/r[3],o=0;for(e=1;e+1<t.length;++e){var s=t[e],l=t[e+1],c=s[0]/s[3]-n,u=s[1]/s[3]-i,f=l[0]/l[3]-n,h=l[1]/l[3]-i;o+=Math.abs(c*h-u*f)}return o}var g=[1,1,1],m=[0,0,0],v={cubeEdges:g,axis:m}},{"bit-twiddle":32,"gl-mat4/multiply":100,"robust-orientation":284,"split-polygon":300}],73:[function(t,e,r){"use strict";e.exports=function(t,e,r){var o=[],s=[0,0,0],l=[0,0,0],c=[0,0,0],u=[0,0,0];o.push(0,0,1,0,1,1,0,0,-1,0,0,-1,0,1,1,0,1,-1);for(var f=0;f<3;++f){for(var h=o.length/3|0,d=0;d<r[f].length;++d){var g=+r[f][d].x;o.push(g,0,1,g,1,1,g,0,-1,g,0,-1,g,1,1,g,1,-1)}var m=o.length/3|0;s[f]=h,l[f]=m-h;h=o.length/3|0;for(var v=0;v<r[f].length;++v){g=+r[f][v].x;o.push(g,0,1,g,1,1,g,0,-1,g,0,-1,g,1,1,g,1,-1)}m=o.length/3|0;c[f]=h,u[f]=m-h}var y=n(t,new Float32Array(o)),x=i(t,[{buffer:y,type:t.FLOAT,size:3,stride:0,offset:0}]),b=a(t);return b.attributes.position.location=0,new p(t,y,x,b,l,s,u,c)};var n=t("gl-buffer"),i=t("gl-vao"),a=t("./shaders").line,o=[0,0,0],s=[0,0,0],l=[0,0,0],c=[0,0,0],u=[1,1];function f(t){return t[0]=t[1]=t[2]=0,t}function h(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function p(t,e,r,n,i,a,o,s){this.gl=t,this.vertBuffer=e,this.vao=r,this.shader=n,this.tickCount=i,this.tickOffset=a,this.gridCount=o,this.gridOffset=s}var d=p.prototype;d.bind=function(t,e,r){this.shader.bind(),this.shader.uniforms.model=t,this.shader.uniforms.view=e,this.shader.uniforms.projection=r,u[0]=this.gl.drawingBufferWidth,u[1]=this.gl.drawingBufferHeight,this.shader.uniforms.screenShape=u,this.vao.bind()},d.unbind=function(){this.vao.unbind()},d.drawAxisLine=function(t,e,r,n,i){var a=f(s);this.shader.uniforms.majorAxis=s,a[t]=e[1][t]-e[0][t],this.shader.uniforms.minorAxis=a;var o,u=h(c,r);u[t]+=e[0][t],this.shader.uniforms.offset=u,this.shader.uniforms.lineWidth=i,this.shader.uniforms.color=n,(o=f(l))[(t+2)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6),(o=f(l))[(t+1)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6)},d.drawAxisTicks=function(t,e,r,n,i){if(this.tickCount[t]){var a=f(o);a[t]=1,this.shader.uniforms.majorAxis=a,this.shader.uniforms.offset=e,this.shader.uniforms.minorAxis=r,this.shader.uniforms.color=n,this.shader.uniforms.lineWidth=i;var s=f(l);s[t]=1,this.shader.uniforms.screenAxis=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t])}},d.drawGrid=function(t,e,r,n,i,a){if(this.gridCount[t]){var u=f(s);u[e]=r[1][e]-r[0][e],this.shader.uniforms.minorAxis=u;var p=h(c,n);p[e]+=r[0][e],this.shader.uniforms.offset=p;var d=f(o);d[t]=1,this.shader.uniforms.majorAxis=d;var g=f(l);g[t]=1,this.shader.uniforms.screenAxis=g,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,this.gridCount[t],this.gridOffset[t])}},d.drawZero=function(t,e,r,n,i,a){var o=f(s);this.shader.uniforms.majorAxis=o,o[t]=r[1][t]-r[0][t],this.shader.uniforms.minorAxis=o;var u=h(c,n);u[t]+=r[0][t],this.shader.uniforms.offset=u;var p=f(l);p[e]=1,this.shader.uniforms.screenAxis=p,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,6)},d.dispose=function(){this.vao.dispose(),this.vertBuffer.dispose(),this.shader.dispose()}},{"./shaders":74,"gl-buffer":78,"gl-vao":150}],74:[function(t,e,r){"use strict";var n=t("glslify"),i=t("gl-shader"),a=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\n\nuniform mat4 model, view, projection;\nuniform vec3 offset, majorAxis, minorAxis, screenAxis;\nuniform float lineWidth;\nuniform vec2 screenShape;\n\nvec3 project(vec3 p) {\n  vec4 pp = projection * view * model * vec4(p, 1.0);\n  return pp.xyz / max(pp.w, 0.0001);\n}\n\nvoid main() {\n  vec3 major = position.x * majorAxis;\n  vec3 minor = position.y * minorAxis;\n\n  vec3 vPosition = major + minor + offset;\n  vec3 pPosition = project(vPosition);\n  vec3 offset = project(vPosition + screenAxis * position.z);\n\n  vec2 screen = normalize((offset - pPosition).xy * screenShape) / screenShape;\n\n  gl_Position = vec4(pPosition + vec3(0.5 * screen * lineWidth, 0), 1.0);\n}\n"]),o=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform vec4 color;\nvoid main() {\n  gl_FragColor = color;\n}"]);r.line=function(t){return i(t,a,o,null,[{name:"position",type:"vec3"}])};var s=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\n\nuniform mat4 model, view, projection;\nuniform vec3 offset, axis, alignDir, alignOpt;\nuniform float scale, angle, pixelScale;\nuniform vec2 resolution;\n\nvec3 project(vec3 p) {\n  vec4 pp = projection * view * model * vec4(p, 1.0);\n  return pp.xyz / max(pp.w, 0.0001);\n}\n\nfloat computeViewAngle(vec3 a, vec3 b) {\n  vec3 A = project(a);\n  vec3 B = project(b);\n\n  return atan(\n    (B.y - A.y) * resolution.y,\n    (B.x - A.x) * resolution.x\n  );\n}\n\nconst float PI = 3.141592;\nconst float TWO_PI = 2.0 * PI;\nconst float HALF_PI = 0.5 * PI;\nconst float ONE_AND_HALF_PI = 1.5 * PI;\n\nint option = int(floor(alignOpt.x + 0.001));\nfloat hv_ratio =       alignOpt.y;\nbool enableAlign =    (alignOpt.z != 0.0);\n\nfloat mod_angle(float a) {\n  return mod(a, PI);\n}\n\nfloat positive_angle(float a) {\n  return mod_angle((a < 0.0) ?\n    a + TWO_PI :\n    a\n  );\n}\n\nfloat look_upwards(float a) {\n  float b = positive_angle(a);\n  return ((b > HALF_PI) && (b <= ONE_AND_HALF_PI)) ?\n    b - PI :\n    b;\n}\n\nfloat look_horizontal_or_vertical(float a, float ratio) {\n  // ratio controls the ratio between being horizontal to (vertical + horizontal)\n  // if ratio is set to 0.5 then it is 50%, 50%.\n  // when using a higher ratio e.g. 0.75 the result would\n  // likely be more horizontal than vertical.\n\n  float b = positive_angle(a);\n\n  return\n    (b < (      ratio) * HALF_PI) ? 0.0 :\n    (b < (2.0 - ratio) * HALF_PI) ? -HALF_PI :\n    (b < (2.0 + ratio) * HALF_PI) ? 0.0 :\n    (b < (4.0 - ratio) * HALF_PI) ? 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HALF_PI :\n                      computeViewAngle(dataPosition, dataPosition + axis);\n    clipAngle = computeViewAngle(dataPosition, dataPosition + alignDir);\n\n    axisAngle += (sin(axisAngle) < 0.0) ? PI : 0.0;\n    clipAngle += (sin(clipAngle) < 0.0) ? PI : 0.0;\n\n    flip = (dot(vec2(cos(axisAngle), sin(axisAngle)),\n                vec2(sin(clipAngle),-cos(clipAngle))) > 0.0) ? 1.0 : 0.0;\n\n    beta += applyAlignOption(clipAngle, flip * PI);\n  }\n\n  //Compute plane offset\n  vec2 planeCoord = position.xy * pixelScale;\n\n  mat2 planeXform = scale * mat2(\n     cos(beta), sin(beta),\n    -sin(beta), cos(beta)\n  );\n\n  vec2 viewOffset = 2.0 * planeXform * planeCoord / resolution;\n\n  //Compute clip position\n  vec3 clipPosition = project(dataPosition);\n\n  //Apply text offset in clip coordinates\n  clipPosition += vec3(viewOffset, 0.0);\n\n  //Done\n  gl_Position = vec4(clipPosition, 1.0);\n}"]),l=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform vec4 color;\nvoid main() {\n  gl_FragColor = color;\n}"]);r.text=function(t){return i(t,s,l,null,[{name:"position",type:"vec3"}])};var c=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\nattribute vec3 normal;\n\nuniform mat4 model, view, projection;\nuniform vec3 enable;\nuniform vec3 bounds[2];\n\nvarying vec3 colorChannel;\n\nvoid main() {\n\n  vec3 signAxis = sign(bounds[1] - bounds[0]);\n\n  vec3 realNormal = signAxis * normal;\n\n  if(dot(realNormal, enable) > 0.0) {\n    vec3 minRange = min(bounds[0], bounds[1]);\n    vec3 maxRange = max(bounds[0], bounds[1]);\n    vec3 nPosition = mix(minRange, maxRange, 0.5 * (position + 1.0));\n    gl_Position = projection * view * model * vec4(nPosition, 1.0);\n  } else {\n    gl_Position = vec4(0,0,0,0);\n  }\n\n  colorChannel = abs(realNormal);\n}"]),u=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform vec4 colors[3];\n\nvarying vec3 colorChannel;\n\nvoid main() {\n  gl_FragColor = colorChannel.x * colors[0] +\n                 colorChannel.y * colors[1] +\n                 colorChannel.z * colors[2];\n}"]);r.bg=function(t){return i(t,c,u,null,[{name:"position",type:"vec3"},{name:"normal",type:"vec3"}])}},{"gl-shader":132,glslify:231}],75:[function(t,e,r){(function(r){(function(){"use strict";e.exports=function(t,e,r,a,s,l){var u=n(t),f=i(t,[{buffer:u,size:3}]),h=o(t);h.attributes.position.location=0;var p=new c(t,h,u,f);return p.update(e,r,a,s,l),p};var n=t("gl-buffer"),i=t("gl-vao"),a=t("vectorize-text"),o=t("./shaders").text,s=window||r.global||{},l=s.__TEXT_CACHE||{};s.__TEXT_CACHE={};function c(t,e,r,n){this.gl=t,this.shader=e,this.buffer=r,this.vao=n,this.tickOffset=this.tickCount=this.labelOffset=this.labelCount=null}var u=c.prototype,f=[0,0];u.bind=function(t,e,r,n){this.vao.bind(),this.shader.bind();var i=this.shader.uniforms;i.model=t,i.view=e,i.projection=r,i.pixelScale=n,f[0]=this.gl.drawingBufferWidth,f[1]=this.gl.drawingBufferHeight,this.shader.uniforms.resolution=f},u.unbind=function(){this.vao.unbind()},u.update=function(t,e,r,n,i){var o=[];function s(t,e,r,n,i,s){var c=l[r];c||(c=l[r]={});var u=c[e];u||(u=c[e]=function(t,e){try{return a(t,e)}catch(e){return console.warn('error vectorizing text:"'+t+'" error:',e),{cells:[],positions:[]}}}(e,{triangles:!0,font:r,textAlign:"center",textBaseline:"middle",lineSpacing:i,styletags:s}));for(var f=(n||12)/12,h=u.positions,p=u.cells,d=0,g=p.length;d<g;++d)for(var m=p[d],v=2;v>=0;--v){var y=h[m[v]];o.push(f*y[0],-f*y[1],t)}}for(var c=[0,0,0],u=[0,0,0],f=[0,0,0],h=[0,0,0],p={breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},d=0;d<3;++d){f[d]=o.length/3|0,s(.5*(t[0][d]+t[1][d]),e[d],r[d],12,1.25,p),h[d]=(o.length/3|0)-f[d],c[d]=o.length/3|0;for(var g=0;g<n[d].length;++g)n[d][g].text&&s(n[d][g].x,n[d][g].text,n[d][g].font||i,n[d][g].fontSize||12,1.25,p);u[d]=(o.length/3|0)-c[d]}this.buffer.update(o),this.tickOffset=c,this.tickCount=u,this.labelOffset=f,this.labelCount=h},u.drawTicks=function(t,e,r,n,i,a,o,s){this.tickCount[t]&&(this.shader.uniforms.axis=a,this.shader.uniforms.color=i,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.shader.uniforms.alignDir=o,this.shader.uniforms.alignOpt=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t]))},u.drawLabel=function(t,e,r,n,i,a,o,s){this.labelCount[t]&&(this.shader.uniforms.axis=a,this.shader.uniforms.color=i,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.shader.uniforms.alignDir=o,this.shader.uniforms.alignOpt=s,this.vao.draw(this.gl.TRIANGLES,this.labelCount[t],this.labelOffset[t]))},u.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()}}).call(this)}).call(this,t("_process"))},{"./shaders":74,_process:5,"gl-buffer":78,"gl-vao":150,"vectorize-text":311}],76:[function(t,e,r){"use strict";function n(t,e){var r=t+"",n=r.indexOf("."),i=0;n>=0&&(i=r.length-n-1);var a=Math.pow(10,i),o=Math.round(t*e*a),s=o+"";if(s.indexOf("e")>=0)return s;var l=o/a,c=o%a;o<0?(l=0|-Math.ceil(l),c=0|-c):(l=0|Math.floor(l),c|=0);var u=""+l;if(o<0&&(u="-"+u),i){for(var f=""+c;f.length<i;)f="0"+f;return u+"."+f}return u}r.create=function(t,e){for(var r=[],i=0;i<3;++i){for(var a=[],o=(t[0][i],t[1][i],0);o*e[i]<=t[1][i];++o)a.push({x:o*e[i],text:n(e[i],o)});for(o=-1;o*e[i]>=t[0][i];--o)a.push({x:o*e[i],text:n(e[i],o)});r.push(a)}return r},r.equal=function(t,e){for(var r=0;r<3;++r){if(t[r].length!==e[r].length)return!1;for(var n=0;n<t[r].length;++n){var i=t[r][n],a=e[r][n];if(i.x!==a.x||i.text!==a.text||i.font!==a.font||i.fontColor!==a.fontColor||i.fontSize!==a.fontSize||i.dx!==a.dx||i.dy!==a.dy)return!1}}return!0}},{}],77:[function(t,e,r){"use strict";e.exports=function(t,e,r,l,f){var h=e.model||c,p=e.view||c,v=e.projection||c,y=e._ortho||!1,x=t.bounds,b=(f=f||a(h,p,v,x,y)).axis;o(u,p,h),o(u,v,u);for(var _=g,w=0;w<3;++w)_[w].lo=1/0,_[w].hi=-1/0,_[w].pixelsPerDataUnit=1/0;var T=n(s(u,u));s(u,u);for(var k=0;k<3;++k){var A=(k+1)%3,M=(k+2)%3,S=m;t:for(w=0;w<2;++w){var E=[];if(b[k]<0!=!!w){S[k]=x[w][k];for(var L=0;L<2;++L){S[A]=x[L^w][A];for(var C=0;C<2;++C)S[M]=x[C^L^w][M],E.push(S.slice())}var P=y?5:4;for(L=P;L===P;++L){if(0===E.length)continue t;E=i.positive(E,T[L])}for(L=0;L<E.length;++L){M=E[L];var I=d(m,u,M,r,l);for(C=0;C<3;++C)_[C].lo=Math.min(_[C].lo,M[C]),_[C].hi=Math.max(_[C].hi,M[C]),C!==k&&(_[C].pixelsPerDataUnit=Math.min(_[C].pixelsPerDataUnit,Math.abs(I[C])))}}}}return _};var n=t("extract-frustum-planes"),i=t("split-polygon"),a=t("./lib/cube.js"),o=t("gl-mat4/multiply"),s=t("gl-mat4/transpose"),l=t("gl-vec4/transformMat4"),c=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]),u=new Float32Array(16);function f(t,e,r){this.lo=t,this.hi=e,this.pixelsPerDataUnit=r}var h=[0,0,0,1],p=[0,0,0,1];function d(t,e,r,n,i){for(var a=0;a<3;++a){for(var o=h,s=p,c=0;c<3;++c)s[c]=o[c]=r[c];s[3]=o[3]=1,s[a]+=1,l(s,s,e),s[3]<0&&(t[a]=1/0),o[a]-=1,l(o,o,e),o[3]<0&&(t[a]=1/0);var u=(o[0]/o[3]-s[0]/s[3])*n,f=(o[1]/o[3]-s[1]/s[3])*i;t[a]=.25*Math.sqrt(u*u+f*f)}return t}var g=[new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0)],m=[0,0,0]},{"./lib/cube.js":72,"extract-frustum-planes":67,"gl-mat4/multiply":100,"gl-mat4/transpose":109,"gl-vec4/transformMat4":221,"split-polygon":300}],78:[function(t,e,r){"use strict";var n=t("typedarray-pool"),i=t("ndarray-ops"),a=t("ndarray"),o=["uint8","uint8_clamped","uint16","uint32","int8","int16","int32","float32"];function s(t,e,r,n,i){this.gl=t,this.type=e,this.handle=r,this.length=n,this.usage=i}var l=s.prototype;function c(t,e,r,n,i,a){var o=i.length*i.BYTES_PER_ELEMENT;if(a<0)return t.bufferData(e,i,n),o;if(o+a>r)throw new Error("gl-buffer: If resizing buffer, must not specify offset");return t.bufferSubData(e,a,i),r}function u(t,e){for(var r=n.malloc(t.length,e),i=t.length,a=0;a<i;++a)r[a]=t[a];return r}l.bind=function(){this.gl.bindBuffer(this.type,this.handle)},l.unbind=function(){this.gl.bindBuffer(this.type,null)},l.dispose=function(){this.gl.deleteBuffer(this.handle)},l.update=function(t,e){if("number"!=typeof e&&(e=-1),this.bind(),"object"==typeof t&&void 0!==t.shape){var r=t.dtype;if(o.indexOf(r)<0&&(r="float32"),this.type===this.gl.ELEMENT_ARRAY_BUFFER)r=gl.getExtension("OES_element_index_uint")&&"uint16"!==r?"uint32":"uint16";if(r===t.dtype&&function(t,e){for(var r=1,n=e.length-1;n>=0;--n){if(e[n]!==r)return!1;r*=t[n]}return!0}(t.shape,t.stride))0===t.offset&&t.data.length===t.shape[0]?this.length=c(this.gl,this.type,this.length,this.usage,t.data,e):this.length=c(this.gl,this.type,this.length,this.usage,t.data.subarray(t.offset,t.shape[0]),e);else{var s=n.malloc(t.size,r),l=a(s,t.shape);i.assign(l,t),this.length=c(this.gl,this.type,this.length,this.usage,e<0?s:s.subarray(0,t.size),e),n.free(s)}}else if(Array.isArray(t)){var f;f=this.type===this.gl.ELEMENT_ARRAY_BUFFER?u(t,"uint16"):u(t,"float32"),this.length=c(this.gl,this.type,this.length,this.usage,e<0?f:f.subarray(0,t.length),e),n.free(f)}else if("object"==typeof t&&"number"==typeof t.length)this.length=c(this.gl,this.type,this.length,this.usage,t,e);else{if("number"!=typeof t&&void 0!==t)throw new Error("gl-buffer: Invalid data type");if(e>=0)throw new Error("gl-buffer: Cannot specify offset when resizing buffer");(t|=0)<=0&&(t=1),this.gl.bufferData(this.type,0|t,this.usage),this.length=t}},e.exports=function(t,e,r,n){if(r=r||t.ARRAY_BUFFER,n=n||t.DYNAMIC_DRAW,r!==t.ARRAY_BUFFER&&r!==t.ELEMENT_ARRAY_BUFFER)throw new Error("gl-buffer: Invalid type for webgl buffer, must be either gl.ARRAY_BUFFER or gl.ELEMENT_ARRAY_BUFFER");if(n!==t.DYNAMIC_DRAW&&n!==t.STATIC_DRAW&&n!==t.STREAM_DRAW)throw new Error("gl-buffer: Invalid usage for buffer, must be either gl.DYNAMIC_DRAW, gl.STATIC_DRAW or gl.STREAM_DRAW");var i=t.createBuffer(),a=new s(t,r,i,0,n);return a.update(e),a}},{ndarray:259,"ndarray-ops":254,"typedarray-pool":308}],79:[function(t,e,r){"use strict";var n=t("gl-vec3");e.exports=function(t,e){var r=t.positions,i=t.vectors,a={positions:[],vertexIntensity:[],vertexIntensityBounds:t.vertexIntensityBounds,vectors:[],cells:[],coneOffset:t.coneOffset,colormap:t.colormap};if(0===t.positions.length)return e&&(e[0]=[0,0,0],e[1]=[0,0,0]),a;for(var o=0,s=1/0,l=-1/0,c=1/0,u=-1/0,f=1/0,h=-1/0,p=null,d=null,g=[],m=1/0,v=!1,y=0;y<r.length;y++){var x=r[y];s=Math.min(x[0],s),l=Math.max(x[0],l),c=Math.min(x[1],c),u=Math.max(x[1],u),f=Math.min(x[2],f),h=Math.max(x[2],h);var b=i[y];if(n.length(b)>o&&(o=n.length(b)),y){var _=2*n.distance(p,x)/(n.length(d)+n.length(b));_?(m=Math.min(m,_),v=!1):v=!0}v||(p=x,d=b),g.push(b)}var w=[s,c,f],T=[l,u,h];e&&(e[0]=w,e[1]=T),0===o&&(o=1);var k=1/o;isFinite(m)||(m=1),a.vectorScale=m;var A=t.coneSize||.5;t.absoluteConeSize&&(A=t.absoluteConeSize*k),a.coneScale=A;y=0;for(var M=0;y<r.length;y++)for(var S=(x=r[y])[0],E=x[1],L=x[2],C=g[y],P=n.length(C)*k,I=0;I<8;I++){a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vertexIntensity.push(P,P,P),a.vertexIntensity.push(P,P,P);var O=a.positions.length;a.cells.push([O-6,O-5,O-4],[O-3,O-2,O-1])}return a};var i=t("./lib/shaders");e.exports.createMesh=t("./create_mesh"),e.exports.createConeMesh=function(t,r){return e.exports.createMesh(t,r,{shaders:i,traceType:"cone"})}},{"./create_mesh":80,"./lib/shaders":81,"gl-vec3":169}],80:[function(t,e,r){"use strict";var n=t("gl-shader"),i=t("gl-buffer"),a=t("gl-vao"),o=t("gl-texture2d"),s=t("gl-mat4/multiply"),l=t("gl-mat4/invert"),c=t("ndarray"),u=t("colormap"),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e,r,n,i,a,o,s,l,c,u){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.pickShader=n,this.trianglePositions=i,this.triangleVectors=a,this.triangleColors=s,this.triangleUVs=l,this.triangleIds=o,this.triangleVAO=c,this.triangleCount=0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.traceType=u,this.tubeScale=1,this.coneScale=2,this.vectorScale=1,this.coneOffset=.25,this._model=f,this._view=f,this._projection=f,this._resolution=[1,1]}var p=h.prototype;function d(t,e){var r=n(t,e.meshShader.vertex,e.meshShader.fragment,null,e.meshShader.attributes);return r.attributes.position.location=0,r.attributes.color.location=2,r.attributes.uv.location=3,r.attributes.vector.location=4,r}function g(t,e){var r=n(t,e.pickShader.vertex,e.pickShader.fragment,null,e.pickShader.attributes);return r.attributes.position.location=0,r.attributes.id.location=1,r.attributes.vector.location=4,r}p.isOpaque=function(){return this.opacity>=1},p.isTransparent=function(){return this.opacity<1},p.pickSlots=1,p.setPickBase=function(t){this.pickId=t},p.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,"lightPosition"in t&&(this.lightPosition=t.lightPosition),"opacity"in t&&(this.opacity=t.opacity),"ambient"in t&&(this.ambientLight=t.ambient),"diffuse"in t&&(this.diffuseLight=t.diffuse),"specular"in t&&(this.specularLight=t.specular),"roughness"in t&&(this.roughness=t.roughness),"fresnel"in t&&(this.fresnel=t.fresnel),void 0!==t.tubeScale&&(this.tubeScale=t.tubeScale),void 0!==t.vectorScale&&(this.vectorScale=t.vectorScale),void 0!==t.coneScale&&(this.coneScale=t.coneScale),void 0!==t.coneOffset&&(this.coneOffset=t.coneOffset),t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t){for(var e=u({colormap:t,nshades:256,format:"rgba"}),r=new Uint8Array(1024),n=0;n<256;++n){for(var i=e[n],a=0;a<3;++a)r[4*n+a]=i[a];r[4*n+3]=255*i[3]}return c(r,[256,256,4],[4,0,1])}(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions,i=t.vectors;if(n&&r&&i){var a=[],o=[],s=[],l=[],f=[];this.cells=r,this.positions=n,this.vectors=i;var h=t.meshColor||[1,1,1,1],p=t.vertexIntensity,d=1/0,g=-1/0;if(p)if(t.vertexIntensityBounds)d=+t.vertexIntensityBounds[0],g=+t.vertexIntensityBounds[1];else for(var m=0;m<p.length;++m){var v=p[m];d=Math.min(d,v),g=Math.max(g,v)}else for(m=0;m<n.length;++m){v=n[m][2];d=Math.min(d,v),g=Math.max(g,v)}this.intensity=p||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n),this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(m=0;m<n.length;++m)for(var y=n[m],x=0;x<3;++x)!isNaN(y[x])&&isFinite(y[x])&&(this.bounds[0][x]=Math.min(this.bounds[0][x],y[x]),this.bounds[1][x]=Math.max(this.bounds[1][x],y[x]));var b=0;t:for(m=0;m<r.length;++m){var _=r[m];switch(_.length){case 3:for(x=0;x<3;++x){y=n[T=_[x]];for(var w=0;w<3;++w)if(isNaN(y[w])||!isFinite(y[w]))continue t}for(x=0;x<3;++x){var T;y=n[T=_[2-x]];a.push(y[0],y[1],y[2],y[3]);var k=i[T];o.push(k[0],k[1],k[2],k[3]||0);var A,M=h;3===M.length?s.push(M[0],M[1],M[2],1):s.push(M[0],M[1],M[2],M[3]),A=p?[(p[T]-d)/(g-d),0]:[(y[2]-d)/(g-d),0],l.push(A[0],A[1]),f.push(m)}b+=1}}this.triangleCount=b,this.trianglePositions.update(a),this.triangleVectors.update(o),this.triangleColors.update(s),this.triangleUVs.update(l),this.triangleIds.update(new Uint32Array(f))}},p.drawTransparent=p.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var c={model:r,view:n,projection:i,inverseModel:f.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,texture:0};c.inverseModel=l(c.inverseModel,c.model),e.disable(e.CULL_FACE),this.texture.bind(0);var u=new Array(16);s(u,c.view,c.model),s(u,c.projection,u),l(u,u);for(o=0;o<3;++o)c.eyePosition[o]=u[12+o]/u[15];var h=u[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*u[4*o+3];for(o=0;o<3;++o){for(var p=u[12+o],d=0;d<3;++d)p+=u[4*d+o]*this.lightPosition[d];c.lightPosition[o]=p/h}if(this.triangleCount>0){var g=this.triShader;g.bind(),g.uniforms=c,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()}},p.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s={model:r,view:n,projection:i,clipBounds:a,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,pickId:this.pickId/255},l=this.pickShader;l.bind(),l.uniforms=s,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind())},p.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions[r[1]].slice(0,3),i={position:n,dataCoordinate:n,index:Math.floor(r[1]/48)};return"cone"===this.traceType?i.index=Math.floor(r[1]/48):"streamtube"===this.traceType&&(i.intensity=this.intensity[r[1]],i.velocity=this.vectors[r[1]].slice(0,3),i.divergence=this.vectors[r[1]][3],i.index=e),i},p.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.pickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleVectors.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleIds.dispose()},e.exports=function(t,e,r){var n=r.shaders;1===arguments.length&&(t=(e=t).gl);var s=d(t,n),l=g(t,n),u=o(t,c(new Uint8Array([255,255,255,255]),[1,1,4]));u.generateMipmap(),u.minFilter=t.LINEAR_MIPMAP_LINEAR,u.magFilter=t.LINEAR;var f=i(t),p=i(t),m=i(t),v=i(t),y=i(t),x=a(t,[{buffer:f,type:t.FLOAT,size:4},{buffer:y,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:m,type:t.FLOAT,size:4},{buffer:v,type:t.FLOAT,size:2},{buffer:p,type:t.FLOAT,size:4}]),b=new h(t,u,s,l,f,p,y,m,v,x,r.traceType||"cone");return b.update(e),b}},{colormap:53,"gl-buffer":78,"gl-mat4/invert":98,"gl-mat4/multiply":100,"gl-shader":132,"gl-texture2d":146,"gl-vao":150,ndarray:259}],81:[function(t,e,r){var n=t("glslify"),i=n(["precision highp float;\n\nprecision highp float;\n#define GLSLIFY 1\n\nvec3 getOrthogonalVector(vec3 v) {\n  // Return up-vector for only-z vector.\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n  // Assign z = 0, x = -b, y = a:\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n    return normalize(vec3(-v.y, v.x, 0.0));\n  } else {\n    return normalize(vec3(0.0, v.z, -v.y));\n  }\n}\n\n// Calculate the cone vertex and normal at the given index.\n//\n// The returned vertex is for a cone with its top at origin and height of 1.0,\n// pointing in the direction of the vector attribute.\n//\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\n// These vertices are used to make up the triangles of the cone by the following:\n//   segment + 0 top vertex\n//   segment + 1 perimeter vertex a+1\n//   segment + 2 perimeter vertex a\n//   segment + 3 center base vertex\n//   segment + 4 perimeter vertex a\n//   segment + 5 perimeter vertex a+1\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\n// To go from index to segment, floor(index / 6)\n// To go from segment to angle, 2*pi * (segment/segmentCount)\n// To go from index to segment index, index - (segment*6)\n//\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\n\n  const float segmentCount = 8.0;\n\n  float index = rawIndex - floor(rawIndex /\n    (segmentCount * 6.0)) *\n    (segmentCount * 6.0);\n\n  float segment = floor(0.001 + index/6.0);\n  float segmentIndex = index - (segment*6.0);\n\n  normal = -normalize(d);\n\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\n    return mix(vec3(0.0), -d, coneOffset);\n  }\n\n  float nextAngle = (\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\n  ) ? 1.0 : 0.0;\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\n\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\n  vec3 v2 = v1 - d;\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d)*0.25;\n  vec3 y = v * sin(angle) * length(d)*0.25;\n  vec3 v3 = v2 + x + y;\n  if (segmentIndex < 3.0) {\n    vec3 tx = u * sin(angle);\n    vec3 ty = v * -cos(angle);\n    vec3 tangent = tx + ty;\n    normal = normalize(cross(v3 - v1, tangent));\n  }\n\n  if (segmentIndex == 0.0) {\n    return mix(d, vec3(0.0), coneOffset);\n  }\n  return v3;\n}\n\nattribute vec3 vector;\nattribute vec4 color, position;\nattribute vec2 uv;\n\nuniform float vectorScale, coneScale, coneOffset;\nuniform mat4 model, view, projection, inverseModel;\nuniform vec3 eyePosition, lightPosition;\n\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  // Scale the vector magnitude to stay constant with\n  // model & view changes.\n  vec3 normal;\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector), position.w, coneOffset, normal);\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n\n  //Lighting geometry parameters\n  vec4 cameraCoordinate = view * conePosition;\n  cameraCoordinate.xyz /= cameraCoordinate.w;\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\n  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\n\n  // vec4 m_position  = model * vec4(conePosition, 1.0);\n  vec4 t_position  = view * conePosition;\n  gl_Position      = projection * t_position;\n\n  f_color          = color;\n  f_data           = conePosition.xyz;\n  f_position       = position.xyz;\n  f_uv             = uv;\n}\n"]),a=n(["#extension GL_OES_standard_derivatives : enable\n\nprecision highp float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat cookTorranceSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness,\n  float fresnel) {\n\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\n\n  //Half angle vector\n  vec3 H = normalize(lightDirection + viewDirection);\n\n  //Geometric term\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\n  float VdotH = max(dot(viewDirection, H), 0.000001);\n  float LdotH = max(dot(lightDirection, H), 0.000001);\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\n  float G = min(1.0, min(G1, G2));\n  \n  //Distribution term\n  float D = beckmannDistribution(NdotH, roughness);\n\n  //Fresnel term\n  float F = pow(1.0 - VdotN, fresnel);\n\n  //Multiply terms and done\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\n}\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\nuniform sampler2D texture;\n\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n  vec3 N = normalize(f_normal);\n  vec3 L = normalize(f_lightDirection);\n  vec3 V = normalize(f_eyeDirection);\n\n  if(gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = litColor * opacity;\n}\n"]),o=n(["precision highp float;\n\nprecision highp float;\n#define GLSLIFY 1\n\nvec3 getOrthogonalVector(vec3 v) {\n  // Return up-vector for only-z vector.\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n  // Assign z = 0, x = -b, y = a:\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n    return normalize(vec3(-v.y, v.x, 0.0));\n  } else {\n    return normalize(vec3(0.0, v.z, -v.y));\n  }\n}\n\n// Calculate the cone vertex and normal at the given index.\n//\n// The returned vertex is for a cone with its top at origin and height of 1.0,\n// pointing in the direction of the vector attribute.\n//\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\n// These vertices are used to make up the triangles of the cone by the following:\n//   segment + 0 top vertex\n//   segment + 1 perimeter vertex a+1\n//   segment + 2 perimeter vertex a\n//   segment + 3 center base vertex\n//   segment + 4 perimeter vertex a\n//   segment + 5 perimeter vertex a+1\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\n// To go from index to segment, floor(index / 6)\n// To go from segment to angle, 2*pi * (segment/segmentCount)\n// To go from index to segment index, index - (segment*6)\n//\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\n\n  const float segmentCount = 8.0;\n\n  float index = rawIndex - floor(rawIndex /\n    (segmentCount * 6.0)) *\n    (segmentCount * 6.0);\n\n  float segment = floor(0.001 + index/6.0);\n  float segmentIndex = index - (segment*6.0);\n\n  normal = -normalize(d);\n\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\n    return mix(vec3(0.0), -d, coneOffset);\n  }\n\n  float nextAngle = (\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\n  ) ? 1.0 : 0.0;\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\n\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\n  vec3 v2 = v1 - d;\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d)*0.25;\n  vec3 y = v * sin(angle) * length(d)*0.25;\n  vec3 v3 = v2 + x + y;\n  if (segmentIndex < 3.0) {\n    vec3 tx = u * sin(angle);\n    vec3 ty = v * -cos(angle);\n    vec3 tangent = tx + ty;\n    normal = normalize(cross(v3 - v1, tangent));\n  }\n\n  if (segmentIndex == 0.0) {\n    return mix(d, vec3(0.0), coneOffset);\n  }\n  return v3;\n}\n\nattribute vec4 vector;\nattribute vec4 position;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\nuniform float vectorScale, coneScale, coneOffset;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  vec3 normal;\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector.xyz), position.w, coneOffset, normal);\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n  gl_Position = projection * view * conePosition;\n  f_id        = id;\n  f_position  = position.xyz;\n}\n"]),s=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3  clipBounds[2];\nuniform float pickId;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n\n  gl_FragColor = vec4(pickId, f_id.xyz);\n}"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:"position",type:"vec4"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"},{name:"vector",type:"vec3"}]},r.pickShader={vertex:o,fragment:s,attributes:[{name:"position",type:"vec4"},{name:"id",type:"vec4"},{name:"vector",type:"vec3"}]}},{glslify:231}],82:[function(t,e,r){e.exports={0:"NONE",1:"ONE",2:"LINE_LOOP",3:"LINE_STRIP",4:"TRIANGLES",5:"TRIANGLE_STRIP",6:"TRIANGLE_FAN",256:"DEPTH_BUFFER_BIT",512:"NEVER",513:"LESS",514:"EQUAL",515:"LEQUAL",516:"GREATER",517:"NOTEQUAL",518:"GEQUAL",519:"ALWAYS",768:"SRC_COLOR",769:"ONE_MINUS_SRC_COLOR",770:"SRC_ALPHA",771:"ONE_MINUS_SRC_ALPHA",772:"DST_ALPHA",773:"ONE_MINUS_DST_ALPHA",774:"DST_COLOR",775:"ONE_MINUS_DST_COLOR",776:"SRC_ALPHA_SATURATE",1024:"STENCIL_BUFFER_BIT",1028:"FRONT",1029:"BACK",1032:"FRONT_AND_BACK",1280:"INVALID_ENUM",1281:"INVALID_VALUE",1282:"INVALID_OPERATION",1285:"OUT_OF_MEMORY",1286:"INVALID_FRAMEBUFFER_OPERATION",2304:"CW",2305:"CCW",2849:"LINE_WIDTH",2884:"CULL_FACE",2885:"CULL_FACE_MODE",2886:"FRONT_FACE",2928:"DEPTH_RANGE",2929:"DEPTH_TEST",2930:"DEPTH_WRITEMASK",2931:"DEPTH_CLEAR_VALUE",2932:"DEPTH_FUNC",2960:"STENCIL_TEST",2961:"STENCIL_CLEAR_VALUE",2962:"STENCIL_FUNC",2963:"STENCIL_VALUE_MASK",2964:"STENCIL_FAIL",2965:"STENCIL_PASS_DEPTH_FAIL",2966:"STENCIL_PASS_DEPTH_PASS",2967:"STENCIL_REF",2968:"STENCIL_WRITEMASK",2978:"VIEWPORT",3024:"DITHER",3042:"BLEND",3088:"SCISSOR_BOX",3089:"SCISSOR_TEST",3106:"COLOR_CLEAR_VALUE",3107:"COLOR_WRITEMASK",3317:"UNPACK_ALIGNMENT",3333:"PACK_ALIGNMENT",3379:"MAX_TEXTURE_SIZE",3386:"MAX_VIEWPORT_DIMS",3408:"SUBPIXEL_BITS",3410:"RED_BITS",3411:"GREEN_BITS",3412:"BLUE_BITS",3413:"ALPHA_BITS",3414:"DEPTH_BITS",3415:"STENCIL_BITS",3553:"TEXTURE_2D",4352:"DONT_CARE",4353:"FASTEST",4354:"NICEST",5120:"BYTE",5121:"UNSIGNED_BYTE",5122:"SHORT",5123:"UNSIGNED_SHORT",5124:"INT",5125:"UNSIGNED_INT",5126:"FLOAT",5386:"INVERT",5890:"TEXTURE",6401:"STENCIL_INDEX",6402:"DEPTH_COMPONENT",6406:"ALPHA",6407:"RGB",6408:"RGBA",6409:"LUMINANCE",6410:"LUMINANCE_ALPHA",7680:"KEEP",7681:"REPLACE",7682:"INCR",7683:"DECR",7936:"VENDOR",7937:"RENDERER",7938:"VERSION",9728:"NEAREST",9729:"LINEAR",9984:"NEAREST_MIPMAP_NEAREST",9985:"LINEAR_MIPMAP_NEAREST",9986:"NEAREST_MIPMAP_LINEAR",9987:"LINEAR_MIPMAP_LINEAR",10240:"TEXTURE_MAG_FILTER",10241:"TEXTURE_MIN_FILTER",10242:"TEXTURE_WRAP_S",10243:"TEXTURE_WRAP_T",10497:"REPEAT",10752:"POLYGON_OFFSET_UNITS",16384:"COLOR_BUFFER_BIT",32769:"CONSTANT_COLOR",32770:"ONE_MINUS_CONSTANT_COLOR",32771:"CONSTANT_ALPHA",32772:"ONE_MINUS_CONSTANT_ALPHA",32773:"BLEND_COLOR",32774:"FUNC_ADD",32777:"BLEND_EQUATION_RGB",32778:"FUNC_SUBTRACT",32779:"FUNC_REVERSE_SUBTRACT",32819:"UNSIGNED_SHORT_4_4_4_4",32820:"UNSIGNED_SHORT_5_5_5_1",32823:"POLYGON_OFFSET_FILL",32824:"POLYGON_OFFSET_FACTOR",32854:"RGBA4",32855:"RGB5_A1",32873:"TEXTURE_BINDING_2D",32926:"SAMPLE_ALPHA_TO_COVERAGE",32928:"SAMPLE_COVERAGE",32936:"SAMPLE_BUFFERS",32937:"SAMPLES",32938:"SAMPLE_COVERAGE_VALUE",32939:"SAMPLE_COVERAGE_INVERT",32968:"BLEND_DST_RGB",32969:"BLEND_SRC_RGB",32970:"BLEND_DST_ALPHA",32971:"BLEND_SRC_ALPHA",33071:"CLAMP_TO_EDGE",33170:"GENERATE_MIPMAP_HINT",33189:"DEPTH_COMPONENT16",33306:"DEPTH_STENCIL_ATTACHMENT",33635:"UNSIGNED_SHORT_5_6_5",33648:"MIRRORED_REPEAT",33901:"ALIASED_POINT_SIZE_RANGE",33902:"ALIASED_LINE_WIDTH_RANGE",33984:"TEXTURE0",33985:"TEXTURE1",33986:"TEXTURE2",33987:"TEXTURE3",33988:"TEXTURE4",33989:"TEXTURE5",33990:"TEXTURE6",33991:"TEXTURE7",33992:"TEXTURE8",33993:"TEXTURE9",33994:"TEXTURE10",33995:"TEXTURE11",33996:"TEXTURE12",33997:"TEXTURE13",33998:"TEXTURE14",33999:"TEXTURE15",34e3:"TEXTURE16",34001:"TEXTURE17",34002:"TEXTURE18",34003:"TEXTURE19",34004:"TEXTURE20",34005:"TEXTURE21",34006:"TEXTURE22",34007:"TEXTURE23",34008:"TEXTURE24",34009:"TEXTURE25",34010:"TEXTURE26",34011:"TEXTURE27",34012:"TEXTURE28",34013:"TEXTURE29",34014:"TEXTURE30",34015:"TEXTURE31",34016:"ACTIVE_TEXTURE",34024:"MAX_RENDERBUFFER_SIZE",34041:"DEPTH_STENCIL",34055:"INCR_WRAP",34056:"DECR_WRAP",34067:"TEXTURE_CUBE_MAP",34068:"TEXTURE_BINDING_CUBE_MAP",34069:"TEXTURE_CUBE_MAP_POSITIVE_X",34070:"TEXTURE_CUBE_MAP_NEGATIVE_X",34071:"TEXTURE_CUBE_MAP_POSITIVE_Y",34072:"TEXTURE_CUBE_MAP_NEGATIVE_Y",34073:"TEXTURE_CUBE_MAP_POSITIVE_Z",34074:"TEXTURE_CUBE_MAP_NEGATIVE_Z",34076:"MAX_CUBE_MAP_TEXTURE_SIZE",34338:"VERTEX_ATTRIB_ARRAY_ENABLED",34339:"VERTEX_ATTRIB_ARRAY_SIZE",34340:"VERTEX_ATTRIB_ARRAY_STRIDE",34341:"VERTEX_ATTRIB_ARRAY_TYPE",34342:"CURRENT_VERTEX_ATTRIB",34373:"VERTEX_ATTRIB_ARRAY_POINTER",34466:"NUM_COMPRESSED_TEXTURE_FORMATS",34467:"COMPRESSED_TEXTURE_FORMATS",34660:"BUFFER_SIZE",34661:"BUFFER_USAGE",34816:"STENCIL_BACK_FUNC",34817:"STENCIL_BACK_FAIL",34818:"STENCIL_BACK_PASS_DEPTH_FAIL",34819:"STENCIL_BACK_PASS_DEPTH_PASS",34877:"BLEND_EQUATION_ALPHA",34921:"MAX_VERTEX_ATTRIBS",34922:"VERTEX_ATTRIB_ARRAY_NORMALIZED",34930:"MAX_TEXTURE_IMAGE_UNITS",34962:"ARRAY_BUFFER",34963:"ELEMENT_ARRAY_BUFFER",34964:"ARRAY_BUFFER_BINDING",34965:"ELEMENT_ARRAY_BUFFER_BINDING",34975:"VERTEX_ATTRIB_ARRAY_BUFFER_BINDING",35040:"STREAM_DRAW",35044:"STATIC_DRAW",35048:"DYNAMIC_DRAW",35632:"FRAGMENT_SHADER",35633:"VERTEX_SHADER",35660:"MAX_VERTEX_TEXTURE_IMAGE_UNITS",35661:"MAX_COMBINED_TEXTURE_IMAGE_UNITS",35663:"SHADER_TYPE",35664:"FLOAT_VEC2",35665:"FLOAT_VEC3",35666:"FLOAT_VEC4",35667:"INT_VEC2",35668:"INT_VEC3",35669:"INT_VEC4",35670:"BOOL",35671:"BOOL_VEC2",35672:"BOOL_VEC3",35673:"BOOL_VEC4",35674:"FLOAT_MAT2",35675:"FLOAT_MAT3",35676:"FLOAT_MAT4",35678:"SAMPLER_2D",35680:"SAMPLER_CUBE",35712:"DELETE_STATUS",35713:"COMPILE_STATUS",35714:"LINK_STATUS",35715:"VALIDATE_STATUS",35716:"INFO_LOG_LENGTH",35717:"ATTACHED_SHADERS",35718:"ACTIVE_UNIFORMS",35719:"ACTIVE_UNIFORM_MAX_LENGTH",35720:"SHADER_SOURCE_LENGTH",35721:"ACTIVE_ATTRIBUTES",35722:"ACTIVE_ATTRIBUTE_MAX_LENGTH",35724:"SHADING_LANGUAGE_VERSION",35725:"CURRENT_PROGRAM",36003:"STENCIL_BACK_REF",36004:"STENCIL_BACK_VALUE_MASK",36005:"STENCIL_BACK_WRITEMASK",36006:"FRAMEBUFFER_BINDING",36007:"RENDERBUFFER_BINDING",36048:"FRAMEBUFFER_ATTACHMENT_OBJECT_TYPE",36049:"FRAMEBUFFER_ATTACHMENT_OBJECT_NAME",36050:"FRAMEBUFFER_ATTACHMENT_TEXTURE_LEVEL",36051:"FRAMEBUFFER_ATTACHMENT_TEXTURE_CUBE_MAP_FACE",36053:"FRAMEBUFFER_COMPLETE",36054:"FRAMEBUFFER_INCOMPLETE_ATTACHMENT",36055:"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT",36057:"FRAMEBUFFER_INCOMPLETE_DIMENSIONS",36061:"FRAMEBUFFER_UNSUPPORTED",36064:"COLOR_ATTACHMENT0",36096:"DEPTH_ATTACHMENT",36128:"STENCIL_ATTACHMENT",36160:"FRAMEBUFFER",36161:"RENDERBUFFER",36162:"RENDERBUFFER_WIDTH",36163:"RENDERBUFFER_HEIGHT",36164:"RENDERBUFFER_INTERNAL_FORMAT",36168:"STENCIL_INDEX8",36176:"RENDERBUFFER_RED_SIZE",36177:"RENDERBUFFER_GREEN_SIZE",36178:"RENDERBUFFER_BLUE_SIZE",36179:"RENDERBUFFER_ALPHA_SIZE",36180:"RENDERBUFFER_DEPTH_SIZE",36181:"RENDERBUFFER_STENCIL_SIZE",36194:"RGB565",36336:"LOW_FLOAT",36337:"MEDIUM_FLOAT",36338:"HIGH_FLOAT",36339:"LOW_INT",36340:"MEDIUM_INT",36341:"HIGH_INT",36346:"SHADER_COMPILER",36347:"MAX_VERTEX_UNIFORM_VECTORS",36348:"MAX_VARYING_VECTORS",36349:"MAX_FRAGMENT_UNIFORM_VECTORS",37440:"UNPACK_FLIP_Y_WEBGL",37441:"UNPACK_PREMULTIPLY_ALPHA_WEBGL",37442:"CONTEXT_LOST_WEBGL",37443:"UNPACK_COLORSPACE_CONVERSION_WEBGL",37444:"BROWSER_DEFAULT_WEBGL"}},{}],83:[function(t,e,r){var n=t("./1.0/numbers");e.exports=function(t){return n[t]}},{"./1.0/numbers":82}],84:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e),o=i(e,[{buffer:r,type:e.FLOAT,size:3,offset:0,stride:40},{buffer:r,type:e.FLOAT,size:4,offset:12,stride:40},{buffer:r,type:e.FLOAT,size:3,offset:28,stride:40}]),l=a(e);l.attributes.position.location=0,l.attributes.color.location=1,l.attributes.offset.location=2;var c=new s(e,r,o,l);return c.update(t),c};var n=t("gl-buffer"),i=t("gl-vao"),a=t("./shaders/index"),o=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function s(t,e,r,n){this.gl=t,this.shader=n,this.buffer=e,this.vao=r,this.pixelRatio=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lineWidth=[1,1,1],this.capSize=[10,10,10],this.lineCount=[0,0,0],this.lineOffset=[0,0,0],this.opacity=1,this.hasAlpha=!1}var l=s.prototype;function c(t,e){for(var r=0;r<3;++r)t[0][r]=Math.min(t[0][r],e[r]),t[1][r]=Math.max(t[1][r],e[r])}l.isOpaque=function(){return!this.hasAlpha},l.isTransparent=function(){return this.hasAlpha},l.drawTransparent=l.draw=function(t){var e=this.gl,r=this.shader.uniforms;this.shader.bind();var n=r.view=t.view||o,i=r.projection=t.projection||o;r.model=t.model||o,r.clipBounds=this.clipBounds,r.opacity=this.opacity;var a=n[12],s=n[13],l=n[14],c=n[15],u=(t._ortho||!1?2:1)*this.pixelRatio*(i[3]*a+i[7]*s+i[11]*l+i[15]*c)/e.drawingBufferHeight;this.vao.bind();for(var f=0;f<3;++f)e.lineWidth(this.lineWidth[f]*this.pixelRatio),r.capSize=this.capSize[f]*u,this.lineCount[f]&&e.drawArrays(e.LINES,this.lineOffset[f],this.lineCount[f]);this.vao.unbind()};var u=function(){for(var t=new Array(3),e=0;e<3;++e){for(var r=[],n=1;n<=2;++n)for(var i=-1;i<=1;i+=2){var a=[0,0,0];a[(n+e)%3]=i,r.push(a)}t[e]=r}return t}();function f(t,e,r,n){for(var i=u[n],a=0;a<i.length;++a){var o=i[a];t.push(e[0],e[1],e[2],r[0],r[1],r[2],r[3],o[0],o[1],o[2])}return i.length}l.update=function(t){"lineWidth"in(t=t||{})&&(this.lineWidth=t.lineWidth,Array.isArray(this.lineWidth)||(this.lineWidth=[this.lineWidth,this.lineWidth,this.lineWidth])),"capSize"in t&&(this.capSize=t.capSize,Array.isArray(this.capSize)||(this.capSize=[this.capSize,this.capSize,this.capSize])),this.hasAlpha=!1,"opacity"in t&&(this.opacity=+t.opacity,this.opacity<1&&(this.hasAlpha=!0));var e=t.color||[[0,0,0],[0,0,0],[0,0,0]],r=t.position,n=t.error;if(Array.isArray(e[0])||(e=[e,e,e]),r&&n){var i=[],a=r.length,o=0;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.lineCount=[0,0,0];for(var s=0;s<3;++s){this.lineOffset[s]=o;t:for(var l=0;l<a;++l){for(var u=r[l],h=0;h<3;++h)if(isNaN(u[h])||!isFinite(u[h]))continue t;var p=n[l],d=e[s];if(Array.isArray(d[0])&&(d=e[l]),3===d.length?d=[d[0],d[1],d[2],1]:4===d.length&&(d=[d[0],d[1],d[2],d[3]],!this.hasAlpha&&d[3]<1&&(this.hasAlpha=!0)),!isNaN(p[0][s])&&!isNaN(p[1][s])){var g;if(p[0][s]<0)(g=u.slice())[s]+=p[0][s],i.push(u[0],u[1],u[2],d[0],d[1],d[2],d[3],0,0,0,g[0],g[1],g[2],d[0],d[1],d[2],d[3],0,0,0),c(this.bounds,g),o+=2+f(i,g,d,s);if(p[1][s]>0)(g=u.slice())[s]+=p[1][s],i.push(u[0],u[1],u[2],d[0],d[1],d[2],d[3],0,0,0,g[0],g[1],g[2],d[0],d[1],d[2],d[3],0,0,0),c(this.bounds,g),o+=2+f(i,g,d,s)}}this.lineCount[s]=o-this.lineOffset[s]}this.buffer.update(i)}},l.dispose=function(){this.shader.dispose(),this.buffer.dispose(),this.vao.dispose()}},{"./shaders/index":85,"gl-buffer":78,"gl-vao":150}],85:[function(t,e,r){"use strict";var n=t("glslify"),i=t("gl-shader"),a=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position, offset;\nattribute vec4 color;\nuniform mat4 model, view, projection;\nuniform float capSize;\nvarying vec4 fragColor;\nvarying vec3 fragPosition;\n\nvoid main() {\n  vec4 worldPosition  = model * vec4(position, 1.0);\n  worldPosition       = (worldPosition / worldPosition.w) + vec4(capSize * offset, 0.0);\n  gl_Position         = projection * view * worldPosition;\n  fragColor           = color;\n  fragPosition        = position;\n}"]),o=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform float opacity;\nvarying vec3 fragPosition;\nvarying vec4 fragColor;\n\nvoid main() {\n  if (\n    outOfRange(clipBounds[0], clipBounds[1], fragPosition) ||\n    fragColor.a * opacity == 0.\n  ) discard;\n\n  gl_FragColor = opacity * fragColor;\n}"]);e.exports=function(t){return i(t,a,o,null,[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"offset",type:"vec3"}])}},{"gl-shader":132,glslify:231}],86:[function(t,e,r){"use strict";var n=t("gl-texture2d");e.exports=function(t,e,r,n){i||(i=t.FRAMEBUFFER_UNSUPPORTED,a=t.FRAMEBUFFER_INCOMPLETE_ATTACHMENT,o=t.FRAMEBUFFER_INCOMPLETE_DIMENSIONS,s=t.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT);var c=t.getExtension("WEBGL_draw_buffers");!l&&c&&function(t,e){var r=t.getParameter(e.MAX_COLOR_ATTACHMENTS_WEBGL);l=new Array(r+1);for(var n=0;n<=r;++n){for(var i=new Array(r),a=0;a<n;++a)i[a]=t.COLOR_ATTACHMENT0+a;for(a=n;a<r;++a)i[a]=t.NONE;l[n]=i}}(t,c);Array.isArray(e)&&(n=r,r=0|e[1],e=0|e[0]);if("number"!=typeof e)throw new Error("gl-fbo: Missing shape parameter");var u=t.getParameter(t.MAX_RENDERBUFFER_SIZE);if(e<0||e>u||r<0||r>u)throw new Error("gl-fbo: Parameters are too large for FBO");var f=1;if("color"in(n=n||{})){if((f=Math.max(0|n.color,0))<0)throw new Error("gl-fbo: Must specify a nonnegative number of colors");if(f>1){if(!c)throw new Error("gl-fbo: Multiple draw buffer extension not supported");if(f>t.getParameter(c.MAX_COLOR_ATTACHMENTS_WEBGL))throw new Error("gl-fbo: Context does not support "+f+" draw buffers")}}var h=t.UNSIGNED_BYTE,p=t.getExtension("OES_texture_float");if(n.float&&f>0){if(!p)throw new Error("gl-fbo: Context does not support floating point textures");h=t.FLOAT}else n.preferFloat&&f>0&&p&&(h=t.FLOAT);var g=!0;"depth"in n&&(g=!!n.depth);var m=!1;"stencil"in n&&(m=!!n.stencil);return new d(t,e,r,h,f,g,m,c)};var i,a,o,s,l=null;function c(t){return[t.getParameter(t.FRAMEBUFFER_BINDING),t.getParameter(t.RENDERBUFFER_BINDING),t.getParameter(t.TEXTURE_BINDING_2D)]}function u(t,e){t.bindFramebuffer(t.FRAMEBUFFER,e[0]),t.bindRenderbuffer(t.RENDERBUFFER,e[1]),t.bindTexture(t.TEXTURE_2D,e[2])}function f(t){switch(t){case i:throw new Error("gl-fbo: Framebuffer unsupported");case a:throw new Error("gl-fbo: Framebuffer incomplete attachment");case o:throw new Error("gl-fbo: Framebuffer incomplete dimensions");case s:throw new Error("gl-fbo: Framebuffer incomplete missing attachment");default:throw new Error("gl-fbo: Framebuffer failed for unspecified reason")}}function h(t,e,r,i,a,o){if(!i)return null;var s=n(t,e,r,a,i);return s.magFilter=t.NEAREST,s.minFilter=t.NEAREST,s.mipSamples=1,s.bind(),t.framebufferTexture2D(t.FRAMEBUFFER,o,t.TEXTURE_2D,s.handle,0),s}function p(t,e,r,n,i){var a=t.createRenderbuffer();return t.bindRenderbuffer(t.RENDERBUFFER,a),t.renderbufferStorage(t.RENDERBUFFER,n,e,r),t.framebufferRenderbuffer(t.FRAMEBUFFER,i,t.RENDERBUFFER,a),a}function d(t,e,r,n,i,a,o,s){this.gl=t,this._shape=[0|e,0|r],this._destroyed=!1,this._ext=s,this.color=new Array(i);for(var d=0;d<i;++d)this.color[d]=null;this._color_rb=null,this.depth=null,this._depth_rb=null,this._colorType=n,this._useDepth=a,this._useStencil=o;var g=this,m=[0|e,0|r];Object.defineProperties(m,{0:{get:function(){return g._shape[0]},set:function(t){return g.width=t}},1:{get:function(){return g._shape[1]},set:function(t){return g.height=t}}}),this._shapeVector=m,function(t){var e=c(t.gl),r=t.gl,n=t.handle=r.createFramebuffer(),i=t._shape[0],a=t._shape[1],o=t.color.length,s=t._ext,d=t._useStencil,g=t._useDepth,m=t._colorType;r.bindFramebuffer(r.FRAMEBUFFER,n);for(var v=0;v<o;++v)t.color[v]=h(r,i,a,m,r.RGBA,r.COLOR_ATTACHMENT0+v);0===o?(t._color_rb=p(r,i,a,r.RGBA4,r.COLOR_ATTACHMENT0),s&&s.drawBuffersWEBGL(l[0])):o>1&&s.drawBuffersWEBGL(l[o]);var y=r.getExtension("WEBGL_depth_texture");y?d?t.depth=h(r,i,a,y.UNSIGNED_INT_24_8_WEBGL,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):g&&(t.depth=h(r,i,a,r.UNSIGNED_SHORT,r.DEPTH_COMPONENT,r.DEPTH_ATTACHMENT)):g&&d?t._depth_rb=p(r,i,a,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):g?t._depth_rb=p(r,i,a,r.DEPTH_COMPONENT16,r.DEPTH_ATTACHMENT):d&&(t._depth_rb=p(r,i,a,r.STENCIL_INDEX,r.STENCIL_ATTACHMENT));var x=r.checkFramebufferStatus(r.FRAMEBUFFER);if(x!==r.FRAMEBUFFER_COMPLETE){t._destroyed=!0,r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteFramebuffer(t.handle),t.handle=null,t.depth&&(t.depth.dispose(),t.depth=null),t._depth_rb&&(r.deleteRenderbuffer(t._depth_rb),t._depth_rb=null);for(v=0;v<t.color.length;++v)t.color[v].dispose(),t.color[v]=null;t._color_rb&&(r.deleteRenderbuffer(t._color_rb),t._color_rb=null),u(r,e),f(x)}u(r,e)}(this)}var g=d.prototype;function m(t,e,r){if(t._destroyed)throw new Error("gl-fbo: Can't resize destroyed FBO");if(t._shape[0]!==e||t._shape[1]!==r){var n=t.gl,i=n.getParameter(n.MAX_RENDERBUFFER_SIZE);if(e<0||e>i||r<0||r>i)throw new Error("gl-fbo: Can't resize FBO, invalid dimensions");t._shape[0]=e,t._shape[1]=r;for(var a=c(n),o=0;o<t.color.length;++o)t.color[o].shape=t._shape;t._color_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._color_rb),n.renderbufferStorage(n.RENDERBUFFER,n.RGBA4,t._shape[0],t._shape[1])),t.depth&&(t.depth.shape=t._shape),t._depth_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._depth_rb),t._useDepth&&t._useStencil?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_STENCIL,t._shape[0],t._shape[1]):t._useDepth?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_COMPONENT16,t._shape[0],t._shape[1]):t._useStencil&&n.renderbufferStorage(n.RENDERBUFFER,n.STENCIL_INDEX,t._shape[0],t._shape[1])),n.bindFramebuffer(n.FRAMEBUFFER,t.handle);var s=n.checkFramebufferStatus(n.FRAMEBUFFER);s!==n.FRAMEBUFFER_COMPLETE&&(t.dispose(),u(n,a),f(s)),u(n,a)}}Object.defineProperties(g,{shape:{get:function(){return this._destroyed?[0,0]:this._shapeVector},set:function(t){if(Array.isArray(t)||(t=[0|t,0|t]),2!==t.length)throw new Error("gl-fbo: Shape vector must be length 2");var e=0|t[0],r=0|t[1];return m(this,e,r),[e,r]},enumerable:!1},width:{get:function(){return this._destroyed?0:this._shape[0]},set:function(t){return m(this,t|=0,this._shape[1]),t},enumerable:!1},height:{get:function(){return this._destroyed?0:this._shape[1]},set:function(t){return t|=0,m(this,this._shape[0],t),t},enumerable:!1}}),g.bind=function(){if(!this._destroyed){var t=this.gl;t.bindFramebuffer(t.FRAMEBUFFER,this.handle),t.viewport(0,0,this._shape[0],this._shape[1])}},g.dispose=function(){if(!this._destroyed){this._destroyed=!0;var t=this.gl;t.deleteFramebuffer(this.handle),this.handle=null,this.depth&&(this.depth.dispose(),this.depth=null),this._depth_rb&&(t.deleteRenderbuffer(this._depth_rb),this._depth_rb=null);for(var e=0;e<this.color.length;++e)this.color[e].dispose(),this.color[e]=null;this._color_rb&&(t.deleteRenderbuffer(this._color_rb),this._color_rb=null)}}},{"gl-texture2d":146}],87:[function(t,e,r){var n=t("sprintf-js").sprintf,i=t("gl-constants/lookup"),a=t("glsl-shader-name"),o=t("add-line-numbers");e.exports=function(t,e,r){"use strict";var s=a(e)||"of unknown name (see npm glsl-shader-name)",l="unknown type";void 0!==r&&(l=r===i.FRAGMENT_SHADER?"fragment":"vertex");for(var c=n("Error compiling %s shader %s:\n",l,s),u=n("%s%s",c,t),f=t.split("\n"),h={},p=0;p<f.length;p++){var d=f[p];if(""!==d&&"\0"!==d){var g=parseInt(d.split(":")[2]);if(isNaN(g))throw new Error(n("Could not parse error: %s",d));h[g]=d}}var m=o(e).split("\n");for(p=0;p<m.length;p++)if(h[p+3]||h[p+2]||h[p+1]){var v=m[p];if(c+=v+"\n",h[p+1]){var y=h[p+1];y=y.substr(y.split(":",3).join(":").length+1).trim(),c+=n("^^^ %s\n\n",y)}}return{long:c.trim(),short:u.trim()}}},{"add-line-numbers":9,"gl-constants/lookup":83,"glsl-shader-name":223,"sprintf-js":301}],88:[function(t,e,r){"use strict";e.exports=function(t,e){var r=t.gl,n=o(r,l.vertex,l.fragment),i=o(r,l.pickVertex,l.pickFragment),a=s(r),u=s(r),f=s(r),h=s(r),p=new c(t,n,i,a,u,f,h);return p.update(e),t.addObject(p),p};var n=t("binary-search-bounds"),i=t("iota-array"),a=t("typedarray-pool"),o=t("gl-shader"),s=t("gl-buffer"),l=t("./lib/shaders");function c(t,e,r,n,i,a,o){this.plot=t,this.shader=e,this.pickShader=r,this.positionBuffer=n,this.weightBuffer=i,this.colorBuffer=a,this.idBuffer=o,this.xData=[],this.yData=[],this.shape=[0,0],this.bounds=[1/0,1/0,-1/0,-1/0],this.pickOffset=0}var u,f=c.prototype,h=[0,0,1,0,0,1,1,0,1,1,0,1];f.draw=(u=[1,0,0,0,1,0,0,0,1],function(){var t=this.plot,e=this.shader,r=this.bounds,n=this.numVertices;if(!(n<=0)){var i=t.gl,a=t.dataBox,o=r[2]-r[0],s=r[3]-r[1],l=a[2]-a[0],c=a[3]-a[1];u[0]=2*o/l,u[4]=2*s/c,u[6]=2*(r[0]-a[0])/l-1,u[7]=2*(r[1]-a[1])/c-1,e.bind();var f=e.uniforms;f.viewTransform=u,f.shape=this.shape;var h=e.attributes;this.positionBuffer.bind(),h.position.pointer(),this.weightBuffer.bind(),h.weight.pointer(i.UNSIGNED_BYTE,!1),this.colorBuffer.bind(),h.color.pointer(i.UNSIGNED_BYTE,!0),i.drawArrays(i.TRIANGLES,0,n)}}),f.drawPick=function(){var t=[1,0,0,0,1,0,0,0,1],e=[0,0,0,0];return function(r){var n=this.plot,i=this.pickShader,a=this.bounds,o=this.numVertices;if(!(o<=0)){var s=n.gl,l=n.dataBox,c=a[2]-a[0],u=a[3]-a[1],f=l[2]-l[0],h=l[3]-l[1];t[0]=2*c/f,t[4]=2*u/h,t[6]=2*(a[0]-l[0])/f-1,t[7]=2*(a[1]-l[1])/h-1;for(var p=0;p<4;++p)e[p]=r>>8*p&255;this.pickOffset=r,i.bind();var d=i.uniforms;d.viewTransform=t,d.pickOffset=e,d.shape=this.shape;var g=i.attributes;return this.positionBuffer.bind(),g.position.pointer(),this.weightBuffer.bind(),g.weight.pointer(s.UNSIGNED_BYTE,!1),this.idBuffer.bind(),g.pickId.pointer(s.UNSIGNED_BYTE,!1),s.drawArrays(s.TRIANGLES,0,o),r+this.shape[0]*this.shape[1]}}}(),f.pick=function(t,e,r){var n=this.pickOffset,i=this.shape[0]*this.shape[1];if(r<n||r>=n+i)return null;var a=r-n,o=this.xData,s=this.yData;return{object:this,pointId:a,dataCoord:[o[a%this.shape[0]],s[a/this.shape[0]|0]]}},f.update=function(t){var e=(t=t||{}).shape||[0,0],r=t.x||i(e[0]),o=t.y||i(e[1]),s=t.z||new Float32Array(e[0]*e[1]),l=!1!==t.zsmooth;this.xData=r,this.yData=o;var c,u,f,p,d=t.colorLevels||[0],g=t.colorValues||[0,0,0,1],m=d.length,v=this.bounds;l?(c=v[0]=r[0],u=v[1]=o[0],f=v[2]=r[r.length-1],p=v[3]=o[o.length-1]):(c=v[0]=r[0]+(r[1]-r[0])/2,u=v[1]=o[0]+(o[1]-o[0])/2,f=v[2]=r[r.length-1]+(r[r.length-1]-r[r.length-2])/2,p=v[3]=o[o.length-1]+(o[o.length-1]-o[o.length-2])/2);var y=1/(f-c),x=1/(p-u),b=e[0],_=e[1];this.shape=[b,_];var w=(l?(b-1)*(_-1):b*_)*(h.length>>>1);this.numVertices=w;for(var T=a.mallocUint8(4*w),k=a.mallocFloat32(2*w),A=a.mallocUint8(2*w),M=a.mallocUint32(w),S=0,E=l?b-1:b,L=l?_-1:_,C=0;C<L;++C){var P,I;l?(P=x*(o[C]-u),I=x*(o[C+1]-u)):(P=C<_-1?x*(o[C]-(o[C+1]-o[C])/2-u):x*(o[C]-(o[C]-o[C-1])/2-u),I=C<_-1?x*(o[C]+(o[C+1]-o[C])/2-u):x*(o[C]+(o[C]-o[C-1])/2-u));for(var O=0;O<E;++O){var z,D;l?(z=y*(r[O]-c),D=y*(r[O+1]-c)):(z=O<b-1?y*(r[O]-(r[O+1]-r[O])/2-c):y*(r[O]-(r[O]-r[O-1])/2-c),D=O<b-1?y*(r[O]+(r[O+1]-r[O])/2-c):y*(r[O]+(r[O]-r[O-1])/2-c));for(var R=0;R<h.length;R+=2){var F,B,N,j,U=h[R],V=h[R+1],H=s[l?(C+V)*b+(O+U):C*b+O],q=n.le(d,H);if(q<0)F=g[0],B=g[1],N=g[2],j=g[3];else if(q===m-1)F=g[4*m-4],B=g[4*m-3],N=g[4*m-2],j=g[4*m-1];else{var G=(H-d[q])/(d[q+1]-d[q]),Y=1-G,W=4*q,X=4*(q+1);F=Y*g[W]+G*g[X],B=Y*g[W+1]+G*g[X+1],N=Y*g[W+2]+G*g[X+2],j=Y*g[W+3]+G*g[X+3]}T[4*S]=255*F,T[4*S+1]=255*B,T[4*S+2]=255*N,T[4*S+3]=255*j,k[2*S]=.5*z+.5*D,k[2*S+1]=.5*P+.5*I,A[2*S]=U,A[2*S+1]=V,M[S]=C*b+O,S+=1}}}this.positionBuffer.update(k),this.weightBuffer.update(A),this.colorBuffer.update(T),this.idBuffer.update(M),a.free(k),a.free(T),a.free(A),a.free(M)},f.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.positionBuffer.dispose(),this.weightBuffer.dispose(),this.colorBuffer.dispose(),this.idBuffer.dispose(),this.plot.removeObject(this)}},{"./lib/shaders":89,"binary-search-bounds":31,"gl-buffer":78,"gl-shader":132,"iota-array":235,"typedarray-pool":308}],89:[function(t,e,r){"use strict";var n=t("glslify");e.exports={fragment:n(["precision lowp float;\n#define GLSLIFY 1\nvarying vec4 fragColor;\nvoid main() {\n  gl_FragColor = vec4(fragColor.rgb * fragColor.a, fragColor.a);\n}\n"]),vertex:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 position;\nattribute vec4 color;\nattribute vec2 weight;\n\nuniform vec2 shape;\nuniform mat3 viewTransform;\n\nvarying vec4 fragColor;\n\nvoid main() {\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\n  fragColor = color;\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\n}\n"]),pickFragment:n(["precision mediump float;\n#define GLSLIFY 1\n\nvarying vec4 fragId;\nvarying vec2 vWeight;\n\nuniform vec2 shape;\nuniform vec4 pickOffset;\n\nvoid main() {\n  vec2 d = step(.5, vWeight);\n  vec4 id = fragId + pickOffset;\n  id.x += d.x + d.y*shape.x;\n\n  id.y += floor(id.x / 256.0);\n  id.x -= floor(id.x / 256.0) * 256.0;\n\n  id.z += floor(id.y / 256.0);\n  id.y -= floor(id.y / 256.0) * 256.0;\n\n  id.w += floor(id.z / 256.0);\n  id.z -= floor(id.z / 256.0) * 256.0;\n\n  gl_FragColor = id/255.;\n}\n"]),pickVertex:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 position;\nattribute vec4 pickId;\nattribute vec2 weight;\n\nuniform vec2 shape;\nuniform mat3 viewTransform;\n\nvarying vec4 fragId;\nvarying vec2 vWeight;\n\nvoid main() {\n  vWeight = weight;\n\n  fragId = pickId;\n\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\n}\n"])}},{glslify:231}],90:[function(t,e,r){var n=t("glslify"),i=t("gl-shader"),a=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position, nextPosition;\nattribute float arcLength, lineWidth;\nattribute vec4 color;\n\nuniform vec2 screenShape;\nuniform float pixelRatio;\nuniform mat4 model, view, projection;\n\nvarying vec4 fragColor;\nvarying vec3 worldPosition;\nvarying float pixelArcLength;\n\nvec4 project(vec3 p) {\n  return projection * view * model * vec4(p, 1.0);\n}\n\nvoid main() {\n  vec4 startPoint = project(position);\n  vec4 endPoint   = project(nextPosition);\n\n  vec2 A = startPoint.xy / startPoint.w;\n  vec2 B =   endPoint.xy /   endPoint.w;\n\n  float clipAngle = atan(\n    (B.y - A.y) * screenShape.y,\n    (B.x - A.x) * screenShape.x\n  );\n\n  vec2 offset = 0.5 * pixelRatio * lineWidth * vec2(\n    sin(clipAngle),\n    -cos(clipAngle)\n  ) / screenShape;\n\n  gl_Position = vec4(startPoint.xy + startPoint.w * offset, startPoint.zw);\n\n  worldPosition = position;\n  pixelArcLength = arcLength;\n  fragColor = color;\n}\n"]),o=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3      clipBounds[2];\nuniform sampler2D dashTexture;\nuniform float     dashScale;\nuniform float     opacity;\n\nvarying vec3    worldPosition;\nvarying float   pixelArcLength;\nvarying vec4    fragColor;\n\nvoid main() {\n  if (\n    outOfRange(clipBounds[0], clipBounds[1], worldPosition) ||\n    fragColor.a * opacity == 0.\n  ) discard;\n\n  float dashWeight = texture2D(dashTexture, vec2(dashScale * pixelArcLength, 0)).r;\n  if(dashWeight < 0.5) {\n    discard;\n  }\n  gl_FragColor = fragColor * opacity;\n}\n"]),s=n(["precision highp float;\n#define GLSLIFY 1\n\n#define FLOAT_MAX  1.70141184e38\n#define FLOAT_MIN  1.17549435e-38\n\n// https://github.com/mikolalysenko/glsl-read-float/blob/master/index.glsl\nvec4 packFloat(float v) {\n  float av = abs(v);\n\n  //Handle special cases\n  if(av < FLOAT_MIN) {\n    return vec4(0.0, 0.0, 0.0, 0.0);\n  } else if(v > FLOAT_MAX) {\n    return vec4(127.0, 128.0, 0.0, 0.0) / 255.0;\n  } else if(v < -FLOAT_MAX) {\n    return vec4(255.0, 128.0, 0.0, 0.0) / 255.0;\n  }\n\n  vec4 c = vec4(0,0,0,0);\n\n  //Compute exponent and mantissa\n  float e = floor(log2(av));\n  float m = av * pow(2.0, -e) - 1.0;\n\n  //Unpack mantissa\n  c[1] = floor(128.0 * m);\n  m -= c[1] / 128.0;\n  c[2] = floor(32768.0 * m);\n  m -= c[2] / 32768.0;\n  c[3] = floor(8388608.0 * m);\n\n  //Unpack exponent\n  float ebias = e + 127.0;\n  c[0] = floor(ebias / 2.0);\n  ebias -= c[0] * 2.0;\n  c[1] += floor(ebias) * 128.0;\n\n  //Unpack sign bit\n  c[0] += 128.0 * step(0.0, -v);\n\n  //Scale back to range\n  return c / 255.0;\n}\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform float pickId;\nuniform vec3 clipBounds[2];\n\nvarying vec3 worldPosition;\nvarying float pixelArcLength;\nvarying vec4 fragColor;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], worldPosition)) discard;\n\n  gl_FragColor = vec4(pickId/255.0, packFloat(pixelArcLength).xyz);\n}"]),l=[{name:"position",type:"vec3"},{name:"nextPosition",type:"vec3"},{name:"arcLength",type:"float"},{name:"lineWidth",type:"float"},{name:"color",type:"vec4"}];r.createShader=function(t){return i(t,a,o,null,l)},r.createPickShader=function(t){return i(t,a,s,null,l)}},{"gl-shader":132,glslify:231}],91:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl||t.scene&&t.scene.gl,r=f(e);r.attributes.position.location=0,r.attributes.nextPosition.location=1,r.attributes.arcLength.location=2,r.attributes.lineWidth.location=3,r.attributes.color.location=4;var o=h(e);o.attributes.position.location=0,o.attributes.nextPosition.location=1,o.attributes.arcLength.location=2,o.attributes.lineWidth.location=3,o.attributes.color.location=4;for(var s=n(e),l=i(e,[{buffer:s,size:3,offset:0,stride:48},{buffer:s,size:3,offset:12,stride:48},{buffer:s,size:1,offset:24,stride:48},{buffer:s,size:1,offset:28,stride:48},{buffer:s,size:4,offset:32,stride:48}]),u=c(new Array(1024),[256,1,4]),p=0;p<1024;++p)u.data[p]=255;var d=a(e,u);d.wrap=e.REPEAT;var g=new v(e,r,o,s,l,d);return g.update(t),g};var n=t("gl-buffer"),i=t("gl-vao"),a=t("gl-texture2d"),o=new Uint8Array(4),s=new Float32Array(o.buffer);var l=t("binary-search-bounds"),c=t("ndarray"),u=t("./lib/shaders"),f=u.createShader,h=u.createPickShader,p=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function d(t,e){for(var r=0,n=0;n<3;++n){var i=t[n]-e[n];r+=i*i}return Math.sqrt(r)}function g(t){for(var e=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],r=0;r<3;++r)e[0][r]=Math.max(t[0][r],e[0][r]),e[1][r]=Math.min(t[1][r],e[1][r]);return e}function m(t,e,r,n){this.arcLength=t,this.position=e,this.index=r,this.dataCoordinate=n}function v(t,e,r,n,i,a){this.gl=t,this.shader=e,this.pickShader=r,this.buffer=n,this.vao=i,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.points=[],this.arcLength=[],this.vertexCount=0,this.bounds=[[0,0,0],[0,0,0]],this.pickId=0,this.lineWidth=1,this.texture=a,this.dashScale=1,this.opacity=1,this.hasAlpha=!1,this.dirty=!0,this.pixelRatio=1}var y=v.prototype;y.isTransparent=function(){return this.hasAlpha},y.isOpaque=function(){return!this.hasAlpha},y.pickSlots=1,y.setPickBase=function(t){this.pickId=t},y.drawTransparent=y.draw=function(t){if(this.vertexCount){var e=this.gl,r=this.shader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,clipBounds:g(this.clipBounds),dashTexture:this.texture.bind(),dashScale:this.dashScale/this.arcLength[this.arcLength.length-1],opacity:this.opacity,screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},y.drawPick=function(t){if(this.vertexCount){var e=this.gl,r=this.pickShader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,pickId:this.pickId,clipBounds:g(this.clipBounds),screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},y.update=function(t){var e,r;this.dirty=!0;var n=!!t.connectGaps;"dashScale"in t&&(this.dashScale=t.dashScale),this.hasAlpha=!1,"opacity"in t&&(this.opacity=+t.opacity,this.opacity<1&&(this.hasAlpha=!0));var i=[],a=[],o=[],s=0,u=0,f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],h=t.position||t.positions;if(h){var p=t.color||t.colors||[0,0,0,1],g=t.lineWidth||1,m=!1;t:for(e=1;e<h.length;++e){var v,y,x,b=h[e-1],_=h[e];for(a.push(s),o.push(b.slice()),r=0;r<3;++r){if(isNaN(b[r])||isNaN(_[r])||!isFinite(b[r])||!isFinite(_[r])){if(!n&&i.length>0){for(var w=0;w<24;++w)i.push(i[i.length-12]);u+=2,m=!0}continue t}f[0][r]=Math.min(f[0][r],b[r],_[r]),f[1][r]=Math.max(f[1][r],b[r],_[r])}Array.isArray(p[0])?(v=p.length>e-1?p[e-1]:p.length>0?p[p.length-1]:[0,0,0,1],y=p.length>e?p[e]:p.length>0?p[p.length-1]:[0,0,0,1]):v=y=p,3===v.length&&(v=[v[0],v[1],v[2],1]),3===y.length&&(y=[y[0],y[1],y[2],1]),!this.hasAlpha&&v[3]<1&&(this.hasAlpha=!0),x=Array.isArray(g)?g.length>e-1?g[e-1]:g.length>0?g[g.length-1]:[0,0,0,1]:g;var T=s;if(s+=d(b,_),m){for(r=0;r<2;++r)i.push(b[0],b[1],b[2],_[0],_[1],_[2],T,x,v[0],v[1],v[2],v[3]);u+=2,m=!1}i.push(b[0],b[1],b[2],_[0],_[1],_[2],T,x,v[0],v[1],v[2],v[3],b[0],b[1],b[2],_[0],_[1],_[2],T,-x,v[0],v[1],v[2],v[3],_[0],_[1],_[2],b[0],b[1],b[2],s,-x,y[0],y[1],y[2],y[3],_[0],_[1],_[2],b[0],b[1],b[2],s,x,y[0],y[1],y[2],y[3]),u+=4}}if(this.buffer.update(i),a.push(s),o.push(h[h.length-1].slice()),this.bounds=f,this.vertexCount=u,this.points=o,this.arcLength=a,"dashes"in t){var k=t.dashes.slice();for(k.unshift(0),e=1;e<k.length;++e)k[e]=k[e-1]+k[e];var A=c(new Array(1024),[256,1,4]);for(e=0;e<256;++e){for(r=0;r<4;++r)A.set(e,0,r,0);1&l.le(k,k[k.length-1]*e/255)?A.set(e,0,0,0):A.set(e,0,0,255)}this.texture.setPixels(A)}},y.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()},y.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=function(t,e,r,n){return o[0]=n,o[1]=r,o[2]=e,o[3]=t,s[0]}(t.value[0],t.value[1],t.value[2],0),r=l.le(this.arcLength,e);if(r<0)return null;if(r===this.arcLength.length-1)return new m(this.arcLength[this.arcLength.length-1],this.points[this.points.length-1].slice(),r);for(var n=this.points[r],i=this.points[Math.min(r+1,this.points.length-1)],a=(e-this.arcLength[r])/(this.arcLength[r+1]-this.arcLength[r]),c=1-a,u=[0,0,0],f=0;f<3;++f)u[f]=c*n[f]+a*i[f];var h=Math.min(a<.5?r:r+1,this.points.length-1);return new m(e,u,h,this.points[h])}},{"./lib/shaders":90,"binary-search-bounds":31,"gl-buffer":78,"gl-texture2d":146,"gl-vao":150,ndarray:259}],92:[function(t,e,r){e.exports=function(t){var e=new Float32Array(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e}},{}],93:[function(t,e,r){e.exports=function(){var t=new Float32Array(16);return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],94:[function(t,e,r){e.exports=function(t){var e=t[0],r=t[1],n=t[2],i=t[3],a=t[4],o=t[5],s=t[6],l=t[7],c=t[8],u=t[9],f=t[10],h=t[11],p=t[12],d=t[13],g=t[14],m=t[15];return(e*o-r*a)*(f*m-h*g)-(e*s-n*a)*(u*m-h*d)+(e*l-i*a)*(u*g-f*d)+(r*s-n*o)*(c*m-h*p)-(r*l-i*o)*(c*g-f*p)+(n*l-i*s)*(c*d-u*p)}},{}],95:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=r+r,s=n+n,l=i+i,c=r*o,u=n*o,f=n*s,h=i*o,p=i*s,d=i*l,g=a*o,m=a*s,v=a*l;return t[0]=1-f-d,t[1]=u+v,t[2]=h-m,t[3]=0,t[4]=u-v,t[5]=1-c-d,t[6]=p+g,t[7]=0,t[8]=h+m,t[9]=p-g,t[10]=1-c-f,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],96:[function(t,e,r){e.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=n+n,l=i+i,c=a+a,u=n*s,f=n*l,h=n*c,p=i*l,d=i*c,g=a*c,m=o*s,v=o*l,y=o*c;return t[0]=1-(p+g),t[1]=f+y,t[2]=h-v,t[3]=0,t[4]=f-y,t[5]=1-(u+g),t[6]=d+m,t[7]=0,t[8]=h+v,t[9]=d-m,t[10]=1-(u+p),t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t}},{}],97:[function(t,e,r){e.exports=function(t){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],98:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],c=e[7],u=e[8],f=e[9],h=e[10],p=e[11],d=e[12],g=e[13],m=e[14],v=e[15],y=r*s-n*o,x=r*l-i*o,b=r*c-a*o,_=n*l-i*s,w=n*c-a*s,T=i*c-a*l,k=u*g-f*d,A=u*m-h*d,M=u*v-p*d,S=f*m-h*g,E=f*v-p*g,L=h*v-p*m,C=y*L-x*E+b*S+_*M-w*A+T*k;if(!C)return null;return C=1/C,t[0]=(s*L-l*E+c*S)*C,t[1]=(i*E-n*L-a*S)*C,t[2]=(g*T-m*w+v*_)*C,t[3]=(h*w-f*T-p*_)*C,t[4]=(l*M-o*L-c*A)*C,t[5]=(r*L-i*M+a*A)*C,t[6]=(m*b-d*T-v*x)*C,t[7]=(u*T-h*b+p*x)*C,t[8]=(o*E-s*M+c*k)*C,t[9]=(n*M-r*E-a*k)*C,t[10]=(d*w-g*b+v*y)*C,t[11]=(f*b-u*w-p*y)*C,t[12]=(s*A-o*S-l*k)*C,t[13]=(r*S-n*A+i*k)*C,t[14]=(g*x-d*_-m*y)*C,t[15]=(u*_-f*x+h*y)*C,t}},{}],99:[function(t,e,r){var n=t("./identity");e.exports=function(t,e,r,i){var a,o,s,l,c,u,f,h,p,d,g=e[0],m=e[1],v=e[2],y=i[0],x=i[1],b=i[2],_=r[0],w=r[1],T=r[2];if(Math.abs(g-_)<1e-6&&Math.abs(m-w)<1e-6&&Math.abs(v-T)<1e-6)return n(t);f=g-_,h=m-w,p=v-T,d=1/Math.sqrt(f*f+h*h+p*p),a=x*(p*=d)-b*(h*=d),o=b*(f*=d)-y*p,s=y*h-x*f,(d=Math.sqrt(a*a+o*o+s*s))?(a*=d=1/d,o*=d,s*=d):(a=0,o=0,s=0);l=h*s-p*o,c=p*a-f*s,u=f*o-h*a,(d=Math.sqrt(l*l+c*c+u*u))?(l*=d=1/d,c*=d,u*=d):(l=0,c=0,u=0);return t[0]=a,t[1]=l,t[2]=f,t[3]=0,t[4]=o,t[5]=c,t[6]=h,t[7]=0,t[8]=s,t[9]=u,t[10]=p,t[11]=0,t[12]=-(a*g+o*m+s*v),t[13]=-(l*g+c*m+u*v),t[14]=-(f*g+h*m+p*v),t[15]=1,t}},{"./identity":97}],100:[function(t,e,r){e.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],g=e[12],m=e[13],v=e[14],y=e[15],x=r[0],b=r[1],_=r[2],w=r[3];return t[0]=x*n+b*s+_*f+w*g,t[1]=x*i+b*l+_*h+w*m,t[2]=x*a+b*c+_*p+w*v,t[3]=x*o+b*u+_*d+w*y,x=r[4],b=r[5],_=r[6],w=r[7],t[4]=x*n+b*s+_*f+w*g,t[5]=x*i+b*l+_*h+w*m,t[6]=x*a+b*c+_*p+w*v,t[7]=x*o+b*u+_*d+w*y,x=r[8],b=r[9],_=r[10],w=r[11],t[8]=x*n+b*s+_*f+w*g,t[9]=x*i+b*l+_*h+w*m,t[10]=x*a+b*c+_*p+w*v,t[11]=x*o+b*u+_*d+w*y,x=r[12],b=r[13],_=r[14],w=r[15],t[12]=x*n+b*s+_*f+w*g,t[13]=x*i+b*l+_*h+w*m,t[14]=x*a+b*c+_*p+w*v,t[15]=x*o+b*u+_*d+w*y,t}},{}],101:[function(t,e,r){e.exports=function(t,e,r,n,i,a,o){var s=1/(e-r),l=1/(n-i),c=1/(a-o);return t[0]=-2*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*l,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*c,t[11]=0,t[12]=(e+r)*s,t[13]=(i+n)*l,t[14]=(o+a)*c,t[15]=1,t}},{}],102:[function(t,e,r){e.exports=function(t,e,r,n,i){var a=1/Math.tan(e/2),o=1/(n-i);return t[0]=a/r,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=a,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=(i+n)*o,t[11]=-1,t[12]=0,t[13]=0,t[14]=2*i*n*o,t[15]=0,t}},{}],103:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c,u,f,h,p,d,g,m,v,y,x,b,_,w,T,k,A,M,S,E=n[0],L=n[1],C=n[2],P=Math.sqrt(E*E+L*L+C*C);if(Math.abs(P)<1e-6)return null;E*=P=1/P,L*=P,C*=P,i=Math.sin(r),a=Math.cos(r),o=1-a,s=e[0],l=e[1],c=e[2],u=e[3],f=e[4],h=e[5],p=e[6],d=e[7],g=e[8],m=e[9],v=e[10],y=e[11],x=E*E*o+a,b=L*E*o+C*i,_=C*E*o-L*i,w=E*L*o-C*i,T=L*L*o+a,k=C*L*o+E*i,A=E*C*o+L*i,M=L*C*o-E*i,S=C*C*o+a,t[0]=s*x+f*b+g*_,t[1]=l*x+h*b+m*_,t[2]=c*x+p*b+v*_,t[3]=u*x+d*b+y*_,t[4]=s*w+f*T+g*k,t[5]=l*w+h*T+m*k,t[6]=c*w+p*T+v*k,t[7]=u*w+d*T+y*k,t[8]=s*A+f*M+g*S,t[9]=l*A+h*M+m*S,t[10]=c*A+p*M+v*S,t[11]=u*A+d*M+y*S,e!==t&&(t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t}},{}],104:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[4],o=e[5],s=e[6],l=e[7],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[4]=a*i+c*n,t[5]=o*i+u*n,t[6]=s*i+f*n,t[7]=l*i+h*n,t[8]=c*i-a*n,t[9]=u*i-o*n,t[10]=f*i-s*n,t[11]=h*i-l*n,t}},{}],105:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i-c*n,t[1]=o*i-u*n,t[2]=s*i-f*n,t[3]=l*i-h*n,t[8]=a*n+c*i,t[9]=o*n+u*i,t[10]=s*n+f*i,t[11]=l*n+h*i,t}},{}],106:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[4],u=e[5],f=e[6],h=e[7];e!==t&&(t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i+c*n,t[1]=o*i+u*n,t[2]=s*i+f*n,t[3]=l*i+h*n,t[4]=c*i-a*n,t[5]=u*i-o*n,t[6]=f*i-s*n,t[7]=h*i-l*n,t}},{}],107:[function(t,e,r){e.exports=function(t,e,r){var n=r[0],i=r[1],a=r[2];return t[0]=e[0]*n,t[1]=e[1]*n,t[2]=e[2]*n,t[3]=e[3]*n,t[4]=e[4]*i,t[5]=e[5]*i,t[6]=e[6]*i,t[7]=e[7]*i,t[8]=e[8]*a,t[9]=e[9]*a,t[10]=e[10]*a,t[11]=e[11]*a,t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t}},{}],108:[function(t,e,r){e.exports=function(t,e,r){var n,i,a,o,s,l,c,u,f,h,p,d,g=r[0],m=r[1],v=r[2];e===t?(t[12]=e[0]*g+e[4]*m+e[8]*v+e[12],t[13]=e[1]*g+e[5]*m+e[9]*v+e[13],t[14]=e[2]*g+e[6]*m+e[10]*v+e[14],t[15]=e[3]*g+e[7]*m+e[11]*v+e[15]):(n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],t[0]=n,t[1]=i,t[2]=a,t[3]=o,t[4]=s,t[5]=l,t[6]=c,t[7]=u,t[8]=f,t[9]=h,t[10]=p,t[11]=d,t[12]=n*g+s*m+f*v+e[12],t[13]=i*g+l*m+h*v+e[13],t[14]=a*g+c*m+p*v+e[14],t[15]=o*g+u*m+d*v+e[15]);return t}},{}],109:[function(t,e,r){e.exports=function(t,e){if(t===e){var r=e[1],n=e[2],i=e[3],a=e[6],o=e[7],s=e[11];t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=r,t[6]=e[9],t[7]=e[13],t[8]=n,t[9]=a,t[11]=e[14],t[12]=i,t[13]=o,t[14]=s}else t[0]=e[0],t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=e[1],t[5]=e[5],t[6]=e[9],t[7]=e[13],t[8]=e[2],t[9]=e[6],t[10]=e[10],t[11]=e[14],t[12]=e[3],t[13]=e[7],t[14]=e[11],t[15]=e[15];return t}},{}],110:[function(t,e,r){"use strict";var n=t("barycentric"),i=t("polytope-closest-point/lib/closest_point_2d.js");function a(t,e){for(var r=[0,0,0,0],n=0;n<4;++n)for(var i=0;i<4;++i)r[i]+=t[4*n+i]*e[n];return r}function o(t,e,r,n,i){for(var o=a(n,a(r,a(e,[t[0],t[1],t[2],1]))),s=0;s<3;++s)o[s]/=o[3];return[.5*i[0]*(1+o[0]),.5*i[1]*(1-o[1])]}function s(t,e){for(var r=[0,0,0],n=0;n<t.length;++n)for(var i=t[n],a=e[n],o=0;o<3;++o)r[o]+=a*i[o];return r}e.exports=function(t,e,r,a,l,c){if(1===t.length)return[0,t[0].slice()];for(var u=new Array(t.length),f=0;f<t.length;++f)u[f]=o(t[f],r,a,l,c);var h=0,p=1/0;for(f=0;f<u.length;++f){for(var d=0,g=0;g<2;++g)d+=Math.pow(u[f][g]-e[g],2);d<p&&(p=d,h=f)}var m=function(t,e){if(2===t.length){for(var r=0,a=0,o=0;o<2;++o)r+=Math.pow(e[o]-t[0][o],2),a+=Math.pow(e[o]-t[1][o],2);return r=Math.sqrt(r),a=Math.sqrt(a),r+a<1e-6?[1,0]:[a/(r+a),r/(a+r)]}if(3===t.length){var s=[0,0];return i(t[0],t[1],t[2],e,s),n(t,s)}return[]}(u,e),v=0;for(f=0;f<3;++f){if(m[f]<-.001||m[f]>1.0001)return null;v+=m[f]}if(Math.abs(v-1)>.001)return null;return[h,s(t,m),m]}},{barycentric:14,"polytope-closest-point/lib/closest_point_2d.js":270}],111:[function(t,e,r){var n=t("glslify"),i=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position, normal;\nattribute vec4 color;\nattribute vec2 uv;\n\nuniform mat4 model\n           , view\n           , projection\n           , inverseModel;\nuniform vec3 eyePosition\n           , lightPosition;\n\nvarying vec3 f_normal\n           , f_lightDirection\n           , f_eyeDirection\n           , f_data;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvec4 project(vec3 p) {\n  return projection * view * model * vec4(p, 1.0);\n}\n\nvoid main() {\n  gl_Position      = project(position);\n\n  //Lighting geometry parameters\n  vec4 cameraCoordinate = view * vec4(position , 1.0);\n  cameraCoordinate.xyz /= cameraCoordinate.w;\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\n  f_normal  = normalize((vec4(normal, 0.0) * inverseModel).xyz);\n\n  f_color          = color;\n  f_data           = position;\n  f_uv             = uv;\n}\n"]),a=n(["#extension GL_OES_standard_derivatives : enable\n\nprecision highp float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat cookTorranceSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness,\n  float fresnel) {\n\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\n\n  //Half angle vector\n  vec3 H = normalize(lightDirection + viewDirection);\n\n  //Geometric term\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\n  float VdotH = max(dot(viewDirection, H), 0.000001);\n  float LdotH = max(dot(lightDirection, H), 0.000001);\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\n  float G = min(1.0, min(G1, G2));\n  \n  //Distribution term\n  float D = beckmannDistribution(NdotH, roughness);\n\n  //Fresnel term\n  float F = pow(1.0 - VdotN, fresnel);\n\n  //Multiply terms and done\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\n}\n\n//#pragma glslify: beckmann = require(glsl-specular-beckmann) // used in gl-surface3d\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform float roughness\n            , fresnel\n            , kambient\n            , kdiffuse\n            , kspecular;\nuniform sampler2D texture;\n\nvarying vec3 f_normal\n           , f_lightDirection\n           , f_eyeDirection\n           , f_data;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (f_color.a == 0.0 ||\n    outOfRange(clipBounds[0], clipBounds[1], f_data)\n  ) discard;\n\n  vec3 N = normalize(f_normal);\n  vec3 L = normalize(f_lightDirection);\n  vec3 V = normalize(f_eyeDirection);\n\n  if(gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\n  //float specular = max(0.0, beckmann(L, V, N, roughness)); // used in gl-surface3d\n\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  vec4 surfaceColor = vec4(f_color.rgb, 1.0) * texture2D(texture, f_uv);\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = litColor * f_color.a;\n}\n"]),o=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 uv;\n\nuniform mat4 model, view, projection;\n\nvarying vec4 f_color;\nvarying vec3 f_data;\nvarying vec2 f_uv;\n\nvoid main() {\n  gl_Position = projection * view * model * vec4(position, 1.0);\n  f_color = color;\n  f_data  = position;\n  f_uv    = uv;\n}"]),s=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform sampler2D texture;\nuniform float opacity;\n\nvarying vec4 f_color;\nvarying vec3 f_data;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_data)) discard;\n\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\n}"]),l=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 uv;\nattribute float pointSize;\n\nuniform mat4 model, view, projection;\nuniform vec3 clipBounds[2];\n\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0.0, 0.0 ,0.0 ,0.0);\n  } else {\n    gl_Position = projection * view * model * vec4(position, 1.0);\n  }\n  gl_PointSize = pointSize;\n  f_color = color;\n  f_uv = uv;\n}"]),c=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform sampler2D texture;\nuniform float opacity;\n\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  vec2 pointR = gl_PointCoord.xy - vec2(0.5, 0.5);\n  if(dot(pointR, pointR) > 0.25) {\n    discard;\n  }\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\n}"]),u=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  gl_Position = projection * view * model * vec4(position, 1.0);\n  f_id        = id;\n  f_position  = position;\n}"]),f=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3  clipBounds[2];\nuniform float pickId;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n\n  gl_FragColor = vec4(pickId, f_id.xyz);\n}"]),h=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3  position;\nattribute float pointSize;\nattribute vec4  id;\n\nuniform mat4 model, view, projection;\nuniform vec3 clipBounds[2];\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0.0, 0.0, 0.0, 0.0);\n  } else {\n    gl_Position  = projection * view * model * vec4(position, 1.0);\n    gl_PointSize = pointSize;\n  }\n  f_id         = id;\n  f_position   = position;\n}"]),p=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\n\nuniform mat4 model, view, projection;\n\nvoid main() {\n  gl_Position = projection * view * model * vec4(position, 1.0);\n}"]),d=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform vec3 contourColor;\n\nvoid main() {\n  gl_FragColor = vec4(contourColor, 1.0);\n}\n"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:"position",type:"vec3"},{name:"normal",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"}]},r.wireShader={vertex:o,fragment:s,attributes:[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"}]},r.pointShader={vertex:l,fragment:c,attributes:[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"},{name:"pointSize",type:"float"}]},r.pickShader={vertex:u,fragment:f,attributes:[{name:"position",type:"vec3"},{name:"id",type:"vec4"}]},r.pointPickShader={vertex:h,fragment:f,attributes:[{name:"position",type:"vec3"},{name:"pointSize",type:"float"},{name:"id",type:"vec4"}]},r.contourShader={vertex:p,fragment:d,attributes:[{name:"position",type:"vec3"}]}},{glslify:231}],112:[function(t,e,r){"use strict";var n=t("gl-shader"),i=t("gl-buffer"),a=t("gl-vao"),o=t("gl-texture2d"),s=t("normals"),l=t("gl-mat4/multiply"),c=t("gl-mat4/invert"),u=t("ndarray"),f=t("colormap"),h=t("simplicial-complex-contour"),p=t("typedarray-pool"),d=t("./lib/shaders"),g=t("./lib/closest-point"),m=d.meshShader,v=d.wireShader,y=d.pointShader,x=d.pickShader,b=d.pointPickShader,_=d.contourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function T(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g,m,v,y,x,b,_,T,k,A,M,S){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.lineShader=n,this.pointShader=i,this.pickShader=a,this.pointPickShader=o,this.contourShader=s,this.trianglePositions=l,this.triangleColors=u,this.triangleNormals=h,this.triangleUVs=f,this.triangleIds=c,this.triangleVAO=p,this.triangleCount=0,this.lineWidth=1,this.edgePositions=d,this.edgeColors=m,this.edgeUVs=v,this.edgeIds=g,this.edgeVAO=y,this.edgeCount=0,this.pointPositions=x,this.pointColors=_,this.pointUVs=T,this.pointSizes=k,this.pointIds=b,this.pointVAO=A,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=M,this.contourVAO=S,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!0,this.pickVertex=!0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.hasAlpha=!1,this.opacityscale=!1,this._model=w,this._view=w,this._projection=w,this._resolution=[1,1]}var k=T.prototype;function A(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;r<e.length;++r){if(e.length<2)return 1;if(e[r][0]===t)return e[r][1];if(e[r][0]>t&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}function M(t){var e=n(t,m.vertex,m.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.normal.location=4,e}function S(t){var e=n(t,v.vertex,v.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e}function E(t){var e=n(t,y.vertex,y.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.pointSize.location=4,e}function L(t){var e=n(t,x.vertex,x.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e}function C(t){var e=n(t,b.vertex,b.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e.attributes.pointSize.location=4,e}function P(t){var e=n(t,_.vertex,_.fragment);return e.attributes.position.location=0,e}k.isOpaque=function(){return!this.hasAlpha},k.isTransparent=function(){return this.hasAlpha},k.pickSlots=1,k.setPickBase=function(t){this.pickId=t},k.highlight=function(t){if(t&&this.contourEnable){for(var e=h(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,a=r.length,o=p.mallocFloat32(6*a),s=0,l=0;l<a;++l)for(var c=r[l],u=0;u<2;++u){var f=c[0];2===c.length&&(f=c[u]);for(var d=n[f][0],g=n[f][1],m=i[f],v=1-m,y=this.positions[d],x=this.positions[g],b=0;b<3;++b)o[s++]=m*y[b]+v*x[b]}this.contourCount=s/3|0,this.contourPositions.update(o.subarray(0,s)),p.free(o)}else this.contourCount=0},k.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,"contourEnable"in t&&(this.contourEnable=t.contourEnable),"contourColor"in t&&(this.contourColor=t.contourColor),"lineWidth"in t&&(this.lineWidth=t.lineWidth),"lightPosition"in t&&(this.lightPosition=t.lightPosition),this.hasAlpha=!1,"opacity"in t&&(this.opacity=t.opacity,this.opacity<1&&(this.hasAlpha=!0)),"opacityscale"in t&&(this.opacityscale=t.opacityscale,this.hasAlpha=!0),"ambient"in t&&(this.ambientLight=t.ambient),"diffuse"in t&&(this.diffuseLight=t.diffuse),"specular"in t&&(this.specularLight=t.specular),"roughness"in t&&(this.roughness=t.roughness),"fresnel"in t&&(this.fresnel=t.fresnel),t.texture?(this.texture.dispose(),this.texture=o(e,t.texture)):t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t,e){for(var r=f({colormap:t,nshades:256,format:"rgba"}),n=new Uint8Array(1024),i=0;i<256;++i){for(var a=r[i],o=0;o<3;++o)n[4*i+o]=a[o];n[4*i+3]=e?255*A(i/255,e):255*a[3]}return u(n,[256,256,4],[4,0,1])}(t.colormap,this.opacityscale)),this.texture.generateMipmap());var r=t.cells,n=t.positions;if(n&&r){var i=[],a=[],l=[],c=[],h=[],p=[],d=[],g=[],m=[],v=[],y=[],x=[],b=[],_=[];this.cells=r,this.positions=n;var w=t.vertexNormals,T=t.cellNormals,k=void 0===t.vertexNormalsEpsilon?1e-6:t.vertexNormalsEpsilon,M=void 0===t.faceNormalsEpsilon?1e-6:t.faceNormalsEpsilon;t.useFacetNormals&&!T&&(T=s.faceNormals(r,n,M)),T||w||(w=s.vertexNormals(r,n,k));var S=t.vertexColors,E=t.cellColors,L=t.meshColor||[1,1,1,1],C=t.vertexUVs,P=t.vertexIntensity,I=t.cellUVs,O=t.cellIntensity,z=1/0,D=-1/0;if(!C&&!I)if(P)if(t.vertexIntensityBounds)z=+t.vertexIntensityBounds[0],D=+t.vertexIntensityBounds[1];else for(var R=0;R<P.length;++R){var F=P[R];z=Math.min(z,F),D=Math.max(D,F)}else if(O)if(t.cellIntensityBounds)z=+t.cellIntensityBounds[0],D=+t.cellIntensityBounds[1];else for(R=0;R<O.length;++R){F=O[R];z=Math.min(z,F),D=Math.max(D,F)}else for(R=0;R<n.length;++R){F=n[R][2];z=Math.min(z,F),D=Math.max(D,F)}this.intensity=P||(O||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n)),this.pickVertex=!(O||E);var B=t.pointSizes,N=t.pointSize||1;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(R=0;R<n.length;++R)for(var j=n[R],U=0;U<3;++U)!isNaN(j[U])&&isFinite(j[U])&&(this.bounds[0][U]=Math.min(this.bounds[0][U],j[U]),this.bounds[1][U]=Math.max(this.bounds[1][U],j[U]));var V=0,H=0,q=0;t:for(R=0;R<r.length;++R){var G=r[R];switch(G.length){case 1:for(j=n[W=G[0]],U=0;U<3;++U)if(isNaN(j[U])||!isFinite(j[U]))continue t;v.push(j[0],j[1],j[2]),X=S?S[W]:E?E[R]:L,this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[W]-z)/(D-z),this.opacityscale)):3===X.length?y.push(X[0],X[1],X[2],this.opacity):(y.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)),Z=C?C[W]:P?[(P[W]-z)/(D-z),0]:I?I[R]:O?[(O[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],x.push(Z[0],Z[1]),B?b.push(B[W]):b.push(N),_.push(R),q+=1;break;case 2:for(U=0;U<2;++U){j=n[W=G[U]];for(var Y=0;Y<3;++Y)if(isNaN(j[Y])||!isFinite(j[Y]))continue t}for(U=0;U<2;++U){j=n[W=G[U]];p.push(j[0],j[1],j[2]),X=S?S[W]:E?E[R]:L,this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[W]-z)/(D-z),this.opacityscale)):3===X.length?d.push(X[0],X[1],X[2],this.opacity):(d.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)),Z=C?C[W]:P?[(P[W]-z)/(D-z),0]:I?I[R]:O?[(O[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],g.push(Z[0],Z[1]),m.push(R)}H+=1;break;case 3:for(U=0;U<3;++U)for(j=n[W=G[U]],Y=0;Y<3;++Y)if(isNaN(j[Y])||!isFinite(j[Y]))continue t;for(U=0;U<3;++U){var W,X,Z,J;j=n[W=G[2-U]];i.push(j[0],j[1],j[2]),(X=S?S[W]:E?E[R]:L)?this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[W]-z)/(D-z),this.opacityscale)):3===X.length?a.push(X[0],X[1],X[2],this.opacity):(a.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)):a.push(.5,.5,.5,1),Z=C?C[W]:P?[(P[W]-z)/(D-z),0]:I?I[R]:O?[(O[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],c.push(Z[0],Z[1]),J=w?w[W]:T[R],l.push(J[0],J[1],J[2]),h.push(R)}V+=1}}this.pointCount=q,this.edgeCount=H,this.triangleCount=V,this.pointPositions.update(v),this.pointColors.update(y),this.pointUVs.update(x),this.pointSizes.update(b),this.pointIds.update(new Uint32Array(_)),this.edgePositions.update(p),this.edgeColors.update(d),this.edgeUVs.update(g),this.edgeIds.update(new Uint32Array(m)),this.trianglePositions.update(i),this.triangleColors.update(a),this.triangleUVs.update(c),this.triangleNormals.update(l),this.triangleIds.update(new Uint32Array(h))}},k.drawTransparent=k.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var s={model:r,view:n,projection:i,inverseModel:w.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],contourColor:this.contourColor,texture:0};s.inverseModel=c(s.inverseModel,s.model),e.disable(e.CULL_FACE),this.texture.bind(0);var u=new Array(16);l(u,s.view,s.model),l(u,s.projection,u),c(u,u);for(o=0;o<3;++o)s.eyePosition[o]=u[12+o]/u[15];var f,h=u[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*u[4*o+3];for(o=0;o<3;++o){for(var p=u[12+o],d=0;d<3;++d)p+=u[4*d+o]*this.lightPosition[d];s.lightPosition[o]=p/h}this.triangleCount>0&&((f=this.triShader).bind(),f.uniforms=s,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind());this.edgeCount>0&&this.lineWidth>0&&((f=this.lineShader).bind(),f.uniforms=s,this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind());this.pointCount>0&&((f=this.pointShader).bind(),f.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind());this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0&&((f=this.contourShader).bind(),f.uniforms=s,this.contourVAO.bind(),e.drawArrays(e.LINES,0,this.contourCount),this.contourVAO.unbind())},k.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s,l={model:r,view:n,projection:i,clipBounds:a,pickId:this.pickId/255};((s=this.pickShader).bind(),s.uniforms=l,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0)&&((s=this.pointPickShader).bind(),s.uniforms=l,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind())},k.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;for(var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions,i=new Array(r.length),a=0;a<r.length;++a)i[a]=n[r[a]];var o=t.coord[0],s=t.coord[1];if(!this.pickVertex){var l=this.positions[r[0]],c=this.positions[r[1]],u=this.positions[r[2]],f=[(l[0]+c[0]+u[0])/3,(l[1]+c[1]+u[1])/3,(l[2]+c[2]+u[2])/3];return{_cellCenter:!0,position:[o,s],index:e,cell:r,cellId:e,intensity:this.intensity[e],dataCoordinate:f}}var h=g(i,[o*this.pixelRatio,this._resolution[1]-s*this.pixelRatio],this._model,this._view,this._projection,this._resolution);if(!h)return null;var p=h[2],d=0;for(a=0;a<r.length;++a)d+=p[a]*this.intensity[r[a]];return{position:h[1],index:r[h[0]],cell:r,cellId:e,intensity:d,dataCoordinate:this.positions[r[h[0]]]}},k.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.lineShader.dispose(),this.pointShader.dispose(),this.pickShader.dispose(),this.pointPickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleNormals.dispose(),this.triangleIds.dispose(),this.edgeVAO.dispose(),this.edgePositions.dispose(),this.edgeColors.dispose(),this.edgeUVs.dispose(),this.edgeIds.dispose(),this.pointVAO.dispose(),this.pointPositions.dispose(),this.pointColors.dispose(),this.pointUVs.dispose(),this.pointSizes.dispose(),this.pointIds.dispose(),this.contourVAO.dispose(),this.contourPositions.dispose(),this.contourShader.dispose()},e.exports=function(t,e){1===arguments.length&&(t=(e=t).gl);var r=t.getExtension("OES_standard_derivatives")||t.getExtension("MOZ_OES_standard_derivatives")||t.getExtension("WEBKIT_OES_standard_derivatives");if(!r)throw new Error("derivatives not supported");var n=M(t),s=S(t),l=E(t),c=L(t),f=C(t),h=P(t),p=o(t,u(new Uint8Array([255,255,255,255]),[1,1,4]));p.generateMipmap(),p.minFilter=t.LINEAR_MIPMAP_LINEAR,p.magFilter=t.LINEAR;var d=i(t),g=i(t),m=i(t),v=i(t),y=i(t),x=a(t,[{buffer:d,type:t.FLOAT,size:3},{buffer:y,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:g,type:t.FLOAT,size:4},{buffer:m,type:t.FLOAT,size:2},{buffer:v,type:t.FLOAT,size:3}]),b=i(t),_=i(t),w=i(t),k=i(t),A=a(t,[{buffer:b,type:t.FLOAT,size:3},{buffer:k,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:_,type:t.FLOAT,size:4},{buffer:w,type:t.FLOAT,size:2}]),I=i(t),O=i(t),z=i(t),D=i(t),R=i(t),F=a(t,[{buffer:I,type:t.FLOAT,size:3},{buffer:R,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:O,type:t.FLOAT,size:4},{buffer:z,type:t.FLOAT,size:2},{buffer:D,type:t.FLOAT,size:1}]),B=i(t),N=a(t,[{buffer:B,type:t.FLOAT,size:3}]),j=new T(t,p,n,s,l,c,f,h,d,y,g,m,v,x,b,k,_,w,A,I,R,O,z,D,F,B,N);return j.update(e),j}},{"./lib/closest-point":110,"./lib/shaders":111,colormap:53,"gl-buffer":78,"gl-mat4/invert":98,"gl-mat4/multiply":100,"gl-shader":132,"gl-texture2d":146,"gl-vao":150,ndarray:259,normals:261,"simplicial-complex-contour":291,"typedarray-pool":308}],113:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e,[0,0,0,1,1,0,1,1]),s=i(e,a.boxVert,a.lineFrag);return new o(t,r,s)};var n=t("gl-buffer"),i=t("gl-shader"),a=t("./shaders");function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,c=o.prototype;c.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},c.drawBox=(s=[0,0],l=[0,0],function(t,e,r,n,i){var a=this.plot,o=this.shader,c=a.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,o.uniforms.lo=s,o.uniforms.hi=l,o.uniforms.color=i,c.drawArrays(c.TRIANGLE_STRIP,0,4)}),c.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{"./shaders":116,"gl-buffer":78,"gl-shader":132}],114:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e),a=i(e,o.gridVert,o.gridFrag),l=i(e,o.tickVert,o.gridFrag);return new s(t,r,a,l)};var n=t("gl-buffer"),i=t("gl-shader"),a=t("binary-search-bounds"),o=t("./shaders");function s(t,e,r,n){this.plot=t,this.vbo=e,this.shader=r,this.tickShader=n,this.ticks=[[],[]]}function l(t,e){return t-e}var c,u,f,h,p,d=s.prototype;d.draw=(c=[0,0],u=[0,0],f=[0,0],function(){for(var t=this.plot,e=this.vbo,r=this.shader,n=this.ticks,i=t.gl,a=t._tickBounds,o=t.dataBox,s=t.viewBox,l=t.gridLineWidth,h=t.gridLineColor,p=t.gridLineEnable,d=t.pixelRatio,g=0;g<2;++g){var m=a[g],v=a[g+2]-m,y=.5*(o[g+2]+o[g]),x=o[g+2]-o[g];u[g]=2*v/x,c[g]=2*(m-y)/x}r.bind(),e.bind(),r.attributes.dataCoord.pointer(),r.uniforms.dataShift=c,r.uniforms.dataScale=u;var b=0;for(g=0;g<2;++g){f[0]=f[1]=0,f[g]=1,r.uniforms.dataAxis=f,r.uniforms.lineWidth=l[g]/(s[g+2]-s[g])*d,r.uniforms.color=h[g];var _=6*n[g].length;p[g]&&_&&i.drawArrays(i.TRIANGLES,b,_),b+=_}}),d.drawTickMarks=function(){var t=[0,0],e=[0,0],r=[1,0],n=[0,1],i=[0,0],o=[0,0];return function(){for(var s=this.plot,c=this.vbo,u=this.tickShader,f=this.ticks,h=s.gl,p=s._tickBounds,d=s.dataBox,g=s.viewBox,m=s.pixelRatio,v=s.screenBox,y=v[2]-v[0],x=v[3]-v[1],b=g[2]-g[0],_=g[3]-g[1],w=0;w<2;++w){var T=p[w],k=p[w+2]-T,A=.5*(d[w+2]+d[w]),M=d[w+2]-d[w];e[w]=2*k/M,t[w]=2*(T-A)/M}e[0]*=b/y,t[0]*=b/y,e[1]*=_/x,t[1]*=_/x,u.bind(),c.bind(),u.attributes.dataCoord.pointer();var S=u.uniforms;S.dataShift=t,S.dataScale=e;var E=s.tickMarkLength,L=s.tickMarkWidth,C=s.tickMarkColor,P=6*f[0].length,I=Math.min(a.ge(f[0],(d[0]-p[0])/(p[2]-p[0]),l),f[0].length),O=Math.min(a.gt(f[0],(d[2]-p[0])/(p[2]-p[0]),l),f[0].length),z=0+6*I,D=6*Math.max(0,O-I),R=Math.min(a.ge(f[1],(d[1]-p[1])/(p[3]-p[1]),l),f[1].length),F=Math.min(a.gt(f[1],(d[3]-p[1])/(p[3]-p[1]),l),f[1].length),B=P+6*R,N=6*Math.max(0,F-R);i[0]=2*(g[0]-E[1])/y-1,i[1]=(g[3]+g[1])/x-1,o[0]=E[1]*m/y,o[1]=L[1]*m/x,N&&(S.color=C[1],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,B,N)),i[0]=(g[2]+g[0])/y-1,i[1]=2*(g[1]-E[0])/x-1,o[0]=L[0]*m/y,o[1]=E[0]*m/x,D&&(S.color=C[0],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,z,D)),i[0]=2*(g[2]+E[3])/y-1,i[1]=(g[3]+g[1])/x-1,o[0]=E[3]*m/y,o[1]=L[3]*m/x,N&&(S.color=C[3],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,B,N)),i[0]=(g[2]+g[0])/y-1,i[1]=2*(g[3]+E[2])/x-1,o[0]=L[2]*m/y,o[1]=E[2]*m/x,D&&(S.color=C[2],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,z,D))}}(),d.update=(h=[1,1,-1,-1,1,-1],p=[1,-1,1,1,-1,-1],function(t){for(var e=t.ticks,r=t.bounds,n=new Float32Array(18*(e[0].length+e[1].length)),i=(this.plot.zeroLineEnable,0),a=[[],[]],o=0;o<2;++o)for(var s=a[o],l=e[o],c=r[o],u=r[o+2],f=0;f<l.length;++f){var d=(l[f].x-c)/(u-c);s.push(d);for(var g=0;g<6;++g)n[i++]=d,n[i++]=h[g],n[i++]=p[g]}this.ticks=a,this.vbo.update(n)}),d.dispose=function(){this.vbo.dispose(),this.shader.dispose(),this.tickShader.dispose()}},{"./shaders":116,"binary-search-bounds":31,"gl-buffer":78,"gl-shader":132}],115:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e,[-1,-1,-1,1,1,-1,1,1]),s=i(e,a.lineVert,a.lineFrag);return new o(t,r,s)};var n=t("gl-buffer"),i=t("gl-shader"),a=t("./shaders");function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,c=o.prototype;c.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},c.drawLine=(s=[0,0],l=[0,0],function(t,e,r,n,i,a){var o=this.plot,c=this.shader,u=o.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,c.uniforms.start=s,c.uniforms.end=l,c.uniforms.width=i*o.pixelRatio,c.uniforms.color=a,u.drawArrays(u.TRIANGLE_STRIP,0,4)}),c.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{"./shaders":116,"gl-buffer":78,"gl-shader":132}],116:[function(t,e,r){"use strict";var n=t("glslify"),i=n(["precision lowp float;\n#define GLSLIFY 1\nuniform vec4 color;\nvoid main() {\n  gl_FragColor = vec4(color.xyz * color.w, color.w);\n}\n"]);e.exports={lineVert:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 coord;\n\nuniform vec4 screenBox;\nuniform vec2 start, end;\nuniform float width;\n\nvec2 perp(vec2 v) {\n  return vec2(v.y, -v.x);\n}\n\nvec2 screen(vec2 v) {\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\n}\n\nvoid main() {\n  vec2 delta = normalize(perp(start - end));\n  vec2 offset = mix(start, end, 0.5 * (coord.y+1.0));\n  gl_Position = vec4(screen(offset + 0.5 * width * delta * coord.x), 0, 1);\n}\n"]),lineFrag:i,textVert:n(["#define GLSLIFY 1\nattribute vec3 textCoordinate;\n\nuniform vec2 dataScale, dataShift, dataAxis, screenOffset, textScale;\nuniform float angle;\n\nvoid main() {\n  float dataOffset  = textCoordinate.z;\n  vec2 glyphOffset  = textCoordinate.xy;\n  mat2 glyphMatrix = mat2(cos(angle), sin(angle), -sin(angle), cos(angle));\n  vec2 screenCoordinate = dataAxis * (dataScale * dataOffset + dataShift) +\n    glyphMatrix * glyphOffset * textScale + screenOffset;\n  gl_Position = vec4(screenCoordinate, 0, 1);\n}\n"]),textFrag:i,gridVert:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 dataCoord;\n\nuniform vec2 dataAxis, dataShift, dataScale;\nuniform float lineWidth;\n\nvoid main() {\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\n  pos += 10.0 * dataCoord.y * vec2(dataAxis.y, -dataAxis.x) + dataCoord.z * lineWidth;\n  gl_Position = vec4(pos, 0, 1);\n}\n"]),gridFrag:i,boxVert:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 coord;\n\nuniform vec4 screenBox;\nuniform vec2 lo, hi;\n\nvec2 screen(vec2 v) {\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\n}\n\nvoid main() {\n  gl_Position = vec4(screen(mix(lo, hi, coord)), 0, 1);\n}\n"]),tickVert:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 dataCoord;\n\nuniform vec2 dataAxis, dataShift, dataScale, screenOffset, tickScale;\n\nvoid main() {\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\n  gl_Position = vec4(pos + tickScale*dataCoord.yz + screenOffset, 0, 1);\n}\n"])}},{glslify:231}],117:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e),a=i(e,s.textVert,s.textFrag);return new l(t,r,a)};var n=t("gl-buffer"),i=t("gl-shader"),a=t("text-cache"),o=t("binary-search-bounds"),s=t("./shaders");function l(t,e,r){this.plot=t,this.vbo=e,this.shader=r,this.tickOffset=[[],[]],this.tickX=[[],[]],this.labelOffset=[0,0],this.labelCount=[0,0]}var c,u,f,h,p,d,g=l.prototype;g.drawTicks=(c=[0,0],u=[0,0],f=[0,0],function(t){var e=this.plot,r=this.shader,n=this.tickX[t],i=this.tickOffset[t],a=e.gl,s=e.viewBox,l=e.dataBox,h=e.screenBox,p=e.pixelRatio,d=e.tickEnable,g=e.tickPad,m=e.tickColor,v=e.tickAngle,y=e.labelEnable,x=e.labelPad,b=e.labelColor,_=e.labelAngle,w=this.labelOffset[t],T=this.labelCount[t],k=o.lt(n,l[t]),A=o.le(n,l[t+2]);c[0]=c[1]=0,c[t]=1,u[t]=(s[2+t]+s[t])/(h[2+t]-h[t])-1;var M=2/h[2+(1^t)]-h[1^t];u[1^t]=M*s[1^t]-1,d[t]&&(u[1^t]-=M*p*g[t],k<A&&i[A]>i[k]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=m[t],r.uniforms.angle=v[t],a.drawArrays(a.TRIANGLES,i[k],i[A]-i[k]))),y[t]&&T&&(u[1^t]-=M*p*x[t],r.uniforms.dataAxis=f,r.uniforms.screenOffset=u,r.uniforms.color=b[t],r.uniforms.angle=_[t],a.drawArrays(a.TRIANGLES,w,T)),u[1^t]=M*s[2+(1^t)]-1,d[t+2]&&(u[1^t]+=M*p*g[t+2],k<A&&i[A]>i[k]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=m[t+2],r.uniforms.angle=v[t+2],a.drawArrays(a.TRIANGLES,i[k],i[A]-i[k]))),y[t+2]&&T&&(u[1^t]+=M*p*x[t+2],r.uniforms.dataAxis=f,r.uniforms.screenOffset=u,r.uniforms.color=b[t+2],r.uniforms.angle=_[t+2],a.drawArrays(a.TRIANGLES,w,T))}),g.drawTitle=function(){var t=[0,0],e=[0,0];return function(){var r=this.plot,n=this.shader,i=r.gl,a=r.screenBox,o=r.titleCenter,s=r.titleAngle,l=r.titleColor,c=r.pixelRatio;if(this.titleCount){for(var u=0;u<2;++u)e[u]=2*(o[u]*c-a[u])/(a[2+u]-a[u])-1;n.bind(),n.uniforms.dataAxis=t,n.uniforms.screenOffset=e,n.uniforms.angle=s,n.uniforms.color=l,i.drawArrays(i.TRIANGLES,this.titleOffset,this.titleCount)}}}(),g.bind=(h=[0,0],p=[0,0],d=[0,0],function(){var t=this.plot,e=this.shader,r=t._tickBounds,n=t.dataBox,i=t.screenBox,a=t.viewBox;e.bind();for(var o=0;o<2;++o){var s=r[o],l=r[o+2]-s,c=.5*(n[o+2]+n[o]),u=n[o+2]-n[o],f=a[o],g=a[o+2]-f,m=i[o],v=i[o+2]-m;p[o]=2*l/u*g/v,h[o]=2*(s-c)/u*g/v}d[1]=2*t.pixelRatio/(i[3]-i[1]),d[0]=d[1]*(i[3]-i[1])/(i[2]-i[0]),e.uniforms.dataScale=p,e.uniforms.dataShift=h,e.uniforms.textScale=d,this.vbo.bind(),e.attributes.textCoordinate.pointer()}),g.update=function(t){var e,r,n,i,o,s=[],l=t.ticks,c=t.bounds;for(o=0;o<2;++o){var u=[Math.floor(s.length/3)],f=[-1/0],h=l[o];for(e=0;e<h.length;++e){var p=h[e],d=p.x,g=p.text,m=p.font||"sans-serif";i=p.fontSize||12;for(var v=1/(c[o+2]-c[o]),y=c[o],x=g.split("\n"),b=0;b<x.length;b++)for(n=a(m,x[b]).data,r=0;r<n.length;r+=2)s.push(n[r]*i,-n[r+1]*i-b*i*1.2,(d-y)*v);u.push(Math.floor(s.length/3)),f.push(d)}this.tickOffset[o]=u,this.tickX[o]=f}for(o=0;o<2;++o){for(this.labelOffset[o]=Math.floor(s.length/3),n=a(t.labelFont[o],t.labels[o],{textAlign:"center"}).data,i=t.labelSize[o],e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.labelCount[o]=Math.floor(s.length/3)-this.labelOffset[o]}for(this.titleOffset=Math.floor(s.length/3),n=a(t.titleFont,t.title).data,i=t.titleSize,e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.titleCount=Math.floor(s.length/3)-this.titleOffset,this.vbo.update(s)},g.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{"./shaders":116,"binary-search-bounds":31,"gl-buffer":78,"gl-shader":132,"text-cache":303}],118:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e,[e.drawingBufferWidth,e.drawingBufferHeight]),c=new l(e,r);return c.grid=i(c),c.text=a(c),c.line=o(c),c.box=s(c),c.update(t),c};var n=t("gl-select-static"),i=t("./lib/grid"),a=t("./lib/text"),o=t("./lib/line"),s=t("./lib/box");function l(t,e){this.gl=t,this.pickBuffer=e,this.screenBox=[0,0,t.drawingBufferWidth,t.drawingBufferHeight],this.viewBox=[0,0,0,0],this.dataBox=[-10,-10,10,10],this.gridLineEnable=[!0,!0],this.gridLineWidth=[1,1],this.gridLineColor=[[0,0,0,1],[0,0,0,1]],this.pixelRatio=1,this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickEnable=[!0,!0,!0,!0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[15,15,15,15],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelEnable=[!0,!0,!0,!0],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.titleCenter=[0,0],this.titleEnable=!0,this.titleAngle=0,this.titleColor=[0,0,0,1],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[4,4],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderLineEnable=[!0,!0,!0,!0],this.borderLineWidth=[2,2,2,2],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.grid=null,this.text=null,this.line=null,this.box=null,this.objects=[],this.overlays=[],this._tickBounds=[1/0,1/0,-1/0,-1/0],this.static=!1,this.dirty=!1,this.pickDirty=!1,this.pickDelay=120,this.pickRadius=10,this._pickTimeout=null,this._drawPick=this.drawPick.bind(this),this._depthCounter=0}var c=l.prototype;function u(t){for(var e=t.slice(),r=0;r<e.length;++r)e[r]=e[r].slice();return e}function f(t,e){return t.x-e.x}c.setDirty=function(){this.dirty=this.pickDirty=!0},c.setOverlayDirty=function(){this.dirty=!0},c.nextDepthValue=function(){return this._depthCounter++/65536},c.draw=function(){var t=this.gl,e=this.screenBox,r=this.viewBox,n=this.dataBox,i=this.pixelRatio,a=this.grid,o=this.line,s=this.text,l=this.objects;if(this._depthCounter=0,this.pickDirty&&(this._pickTimeout&&clearTimeout(this._pickTimeout),this.pickDirty=!1,this._pickTimeout=setTimeout(this._drawPick,this.pickDelay)),this.dirty){if(this.dirty=!1,t.bindFramebuffer(t.FRAMEBUFFER,null),t.enable(t.SCISSOR_TEST),t.disable(t.DEPTH_TEST),t.depthFunc(t.LESS),t.depthMask(!1),t.enable(t.BLEND),t.blendEquation(t.FUNC_ADD,t.FUNC_ADD),t.blendFunc(t.ONE,t.ONE_MINUS_SRC_ALPHA),this.borderColor){t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]);var c=this.borderColor;t.clearColor(c[0]*c[3],c[1]*c[3],c[2]*c[3],c[3]),t.clear(t.COLOR_BUFFER_BIT|t.DEPTH_BUFFER_BIT)}t.scissor(r[0],r[1],r[2]-r[0],r[3]-r[1]),t.viewport(r[0],r[1],r[2]-r[0],r[3]-r[1]);var u=this.backgroundColor;t.clearColor(u[0]*u[3],u[1]*u[3],u[2]*u[3],u[3]),t.clear(t.COLOR_BUFFER_BIT),a.draw();var f=this.zeroLineEnable,h=this.zeroLineColor,p=this.zeroLineWidth;if(f[0]||f[1]){o.bind();for(var d=0;d<2;++d)if(f[d]&&n[d]<=0&&n[d+2]>=0){var g=e[d]-n[d]*(e[d+2]-e[d])/(n[d+2]-n[d]);0===d?o.drawLine(g,e[1],g,e[3],p[d],h[d]):o.drawLine(e[0],g,e[2],g,p[d],h[d])}}for(d=0;d<l.length;++d)l[d].draw();t.viewport(e[0],e[1],e[2]-e[0],e[3]-e[1]),t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]),this.grid.drawTickMarks(),o.bind();var m=this.borderLineEnable,v=this.borderLineWidth,y=this.borderLineColor;for(m[1]&&o.drawLine(r[0],r[1]-.5*v[1]*i,r[0],r[3]+.5*v[3]*i,v[1],y[1]),m[0]&&o.drawLine(r[0]-.5*v[0]*i,r[1],r[2]+.5*v[2]*i,r[1],v[0],y[0]),m[3]&&o.drawLine(r[2],r[1]-.5*v[1]*i,r[2],r[3]+.5*v[3]*i,v[3],y[3]),m[2]&&o.drawLine(r[0]-.5*v[0]*i,r[3],r[2]+.5*v[2]*i,r[3],v[2],y[2]),s.bind(),d=0;d<2;++d)s.drawTicks(d);this.titleEnable&&s.drawTitle();var x=this.overlays;for(d=0;d<x.length;++d)x[d].draw();t.disable(t.SCISSOR_TEST),t.disable(t.BLEND),t.depthMask(!0)}},c.drawPick=function(){if(!this.static){var t=this.pickBuffer;this.gl,this._pickTimeout=null,t.begin();for(var e=1,r=this.objects,n=0;n<r.length;++n)e=r[n].drawPick(e);t.end()}},c.pick=function(t,e){if(!this.static){var r=this.pixelRatio,n=this.pickPixelRatio,i=this.viewBox,a=0|Math.round((t-i[0]/r)*n),o=0|Math.round((e-i[1]/r)*n),s=this.pickBuffer.query(a,o,this.pickRadius);if(!s)return null;for(var l=s.id+(s.value[0]<<8)+(s.value[1]<<16)+(s.value[2]<<24),c=this.objects,u=0;u<c.length;++u){var f=c[u].pick(a,o,l);if(f)return f}return null}},c.setScreenBox=function(t){var e=this.screenBox,r=this.pixelRatio;e[0]=0|Math.round(t[0]*r),e[1]=0|Math.round(t[1]*r),e[2]=0|Math.round(t[2]*r),e[3]=0|Math.round(t[3]*r),this.setDirty()},c.setDataBox=function(t){var e=this.dataBox;(e[0]!==t[0]||e[1]!==t[1]||e[2]!==t[2]||e[3]!==t[3])&&(e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],this.setDirty())},c.setViewBox=function(t){var e=this.pixelRatio,r=this.viewBox;r[0]=0|Math.round(t[0]*e),r[1]=0|Math.round(t[1]*e),r[2]=0|Math.round(t[2]*e),r[3]=0|Math.round(t[3]*e);var n=this.pickPixelRatio;this.pickBuffer.shape=[0|Math.round((t[2]-t[0])*n),0|Math.round((t[3]-t[1])*n)],this.setDirty()},c.update=function(t){t=t||{};var e=this.gl;this.pixelRatio=t.pixelRatio||1;var r=this.pixelRatio;this.pickPixelRatio=Math.max(r,1),this.setScreenBox(t.screenBox||[0,0,e.drawingBufferWidth/r,e.drawingBufferHeight/r]);this.screenBox;this.setViewBox(t.viewBox||[.125*(this.screenBox[2]-this.screenBox[0])/r,.125*(this.screenBox[3]-this.screenBox[1])/r,.875*(this.screenBox[2]-this.screenBox[0])/r,.875*(this.screenBox[3]-this.screenBox[1])/r]);var n=this.viewBox,i=(n[2]-n[0])/(n[3]-n[1]);this.setDataBox(t.dataBox||[-10,-10/i,10,10/i]),this.borderColor=!1!==t.borderColor&&(t.borderColor||[0,0,0,0]).slice(),this.backgroundColor=(t.backgroundColor||[0,0,0,0]).slice(),this.gridLineEnable=(t.gridLineEnable||[!0,!0]).slice(),this.gridLineWidth=(t.gridLineWidth||[1,1]).slice(),this.gridLineColor=u(t.gridLineColor||[[.5,.5,.5,1],[.5,.5,.5,1]]),this.zeroLineEnable=(t.zeroLineEnable||[!0,!0]).slice(),this.zeroLineWidth=(t.zeroLineWidth||[4,4]).slice(),this.zeroLineColor=u(t.zeroLineColor||[[0,0,0,1],[0,0,0,1]]),this.tickMarkLength=(t.tickMarkLength||[0,0,0,0]).slice(),this.tickMarkWidth=(t.tickMarkWidth||[0,0,0,0]).slice(),this.tickMarkColor=u(t.tickMarkColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.titleCenter=(t.titleCenter||[.5*(n[0]+n[2])/r,(n[3]+120)/r]).slice(),this.titleEnable=!("titleEnable"in t)||!!t.titleEnable,this.titleAngle=t.titleAngle||0,this.titleColor=(t.titleColor||[0,0,0,1]).slice(),this.labelPad=(t.labelPad||[15,15,15,15]).slice(),this.labelAngle=(t.labelAngle||[0,Math.PI/2,0,3*Math.PI/2]).slice(),this.labelEnable=(t.labelEnable||[!0,!0,!0,!0]).slice(),this.labelColor=u(t.labelColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.tickPad=(t.tickPad||[15,15,15,15]).slice(),this.tickAngle=(t.tickAngle||[0,0,0,0]).slice(),this.tickEnable=(t.tickEnable||[!0,!0,!0,!0]).slice(),this.tickColor=u(t.tickColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.borderLineEnable=(t.borderLineEnable||[!0,!0,!0,!0]).slice(),this.borderLineWidth=(t.borderLineWidth||[2,2,2,2]).slice(),this.borderLineColor=u(t.borderLineColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]);var a=t.ticks||[[],[]],o=this._tickBounds;o[0]=o[1]=1/0,o[2]=o[3]=-1/0;for(var s=0;s<2;++s){var l=a[s].slice(0);0!==l.length&&(l.sort(f),o[s]=Math.min(o[s],l[0].x),o[s+2]=Math.max(o[s+2],l[l.length-1].x))}this.grid.update({bounds:o,ticks:a}),this.text.update({bounds:o,ticks:a,labels:t.labels||["x","y"],labelSize:t.labelSize||[12,12],labelFont:t.labelFont||["sans-serif","sans-serif"],title:t.title||"",titleSize:t.titleSize||18,titleFont:t.titleFont||"sans-serif"}),this.static=!!t.static,this.setDirty()},c.dispose=function(){this.box.dispose(),this.grid.dispose(),this.text.dispose(),this.line.dispose();for(var t=this.objects.length-1;t>=0;--t)this.objects[t].dispose();this.objects.length=0;for(t=this.overlays.length-1;t>=0;--t)this.overlays[t].dispose();this.overlays.length=0,this.gl=null},c.addObject=function(t){this.objects.indexOf(t)<0&&(this.objects.push(t),this.setDirty())},c.removeObject=function(t){for(var e=this.objects,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setDirty();break}},c.addOverlay=function(t){this.overlays.indexOf(t)<0&&(this.overlays.push(t),this.setOverlayDirty())},c.removeOverlay=function(t){for(var e=this.overlays,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setOverlayDirty();break}}},{"./lib/box":113,"./lib/grid":114,"./lib/line":115,"./lib/text":117,"gl-select-static":131}],119:[function(t,e,r){"use strict";e.exports=function(t,e){t=t||document.body,e=e||{};var r=[.01,1/0];"distanceLimits"in e&&(r[0]=e.distanceLimits[0],r[1]=e.distanceLimits[1]);"zoomMin"in e&&(r[0]=e.zoomMin);"zoomMax"in e&&(r[1]=e.zoomMax);var c=i({center:e.center||[0,0,0],up:e.up||[0,1,0],eye:e.eye||[0,0,10],mode:e.mode||"orbit",distanceLimits:r}),u=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],f=0,h=t.clientWidth,p=t.clientHeight,d={keyBindingMode:"rotate",enableWheel:!0,view:c,element:t,delay:e.delay||16,rotateSpeed:e.rotateSpeed||1,zoomSpeed:e.zoomSpeed||1,translateSpeed:e.translateSpeed||1,flipX:!!e.flipX,flipY:!!e.flipY,modes:c.modes,_ortho:e._ortho||e.projection&&"orthographic"===e.projection.type||!1,tick:function(){var e=n(),r=this.delay,i=e-2*r;c.idle(e-r),c.recalcMatrix(i),c.flush(e-(100+2*r));for(var a=!0,o=c.computedMatrix,s=0;s<16;++s)a=a&&u[s]===o[s],u[s]=o[s];var l=t.clientWidth===h&&t.clientHeight===p;return h=t.clientWidth,p=t.clientHeight,a?!l:(f=Math.exp(c.computedRadius[0]),!0)},lookAt:function(t,e,r){c.lookAt(c.lastT(),t,e,r)},rotate:function(t,e,r){c.rotate(c.lastT(),t,e,r)},pan:function(t,e,r){c.pan(c.lastT(),t,e,r)},translate:function(t,e,r){c.translate(c.lastT(),t,e,r)}};return Object.defineProperties(d,{matrix:{get:function(){return c.computedMatrix},set:function(t){return c.setMatrix(c.lastT(),t),c.computedMatrix},enumerable:!0},mode:{get:function(){return c.getMode()},set:function(t){var e=c.computedUp.slice(),r=c.computedEye.slice(),i=c.computedCenter.slice();if(c.setMode(t),"turntable"===t){var a=n();c._active.lookAt(a,r,i,e),c._active.lookAt(a+500,r,i,[0,0,1]),c._active.flush(a)}return c.getMode()},enumerable:!0},center:{get:function(){return c.computedCenter},set:function(t){return c.lookAt(c.lastT(),null,t),c.computedCenter},enumerable:!0},eye:{get:function(){return c.computedEye},set:function(t){return c.lookAt(c.lastT(),t),c.computedEye},enumerable:!0},up:{get:function(){return c.computedUp},set:function(t){return c.lookAt(c.lastT(),null,null,t),c.computedUp},enumerable:!0},distance:{get:function(){return f},set:function(t){return c.setDistance(c.lastT(),t),t},enumerable:!0},distanceLimits:{get:function(){return c.getDistanceLimits(r)},set:function(t){return c.setDistanceLimits(t),t},enumerable:!0}}),t.addEventListener("contextmenu",(function(t){return t.preventDefault(),!1})),d._lastX=-1,d._lastY=-1,d._lastMods={shift:!1,control:!1,alt:!1,meta:!1},d.enableMouseListeners=function(){function e(e,r,i,a){var o=d.keyBindingMode;if(!1!==o){var s="rotate"===o,l="pan"===o,u="zoom"===o,h=!!a.control,p=!!a.alt,g=!!a.shift,m=!!(1&e),v=!!(2&e),y=!!(4&e),x=1/t.clientHeight,b=x*(r-d._lastX),_=x*(i-d._lastY),w=d.flipX?1:-1,T=d.flipY?1:-1,k=Math.PI*d.rotateSpeed,A=n();if(-1!==d._lastX&&-1!==d._lastY&&((s&&m&&!h&&!p&&!g||m&&!h&&!p&&g)&&c.rotate(A,w*k*b,-T*k*_,0),(l&&m&&!h&&!p&&!g||v||m&&h&&!p&&!g)&&c.pan(A,-d.translateSpeed*b*f,d.translateSpeed*_*f,0),u&&m&&!h&&!p&&!g||y||m&&!h&&p&&!g)){var M=-d.zoomSpeed*_/window.innerHeight*(A-c.lastT())*100;c.pan(A,0,0,f*(Math.exp(M)-1))}return d._lastX=r,d._lastY=i,d._lastMods=a,!0}}d.mouseListener=a(t,e),t.addEventListener("touchstart",(function(r){var n=s(r.changedTouches[0],t);e(0,n[0],n[1],d._lastMods),e(1,n[0],n[1],d._lastMods)}),!!l&&{passive:!0}),t.addEventListener("touchmove",(function(r){var n=s(r.changedTouches[0],t);e(1,n[0],n[1],d._lastMods),r.preventDefault()}),!!l&&{passive:!1}),t.addEventListener("touchend",(function(t){e(0,d._lastX,d._lastY,d._lastMods)}),!!l&&{passive:!0}),d.wheelListener=o(t,(function(t,e){if(!1!==d.keyBindingMode&&d.enableWheel){var r=d.flipX?1:-1,i=d.flipY?1:-1,a=n();if(Math.abs(t)>Math.abs(e))c.rotate(a,0,0,-t*r*Math.PI*d.rotateSpeed/window.innerWidth);else if(!d._ortho){var o=-d.zoomSpeed*i*e/window.innerHeight*(a-c.lastT())/20;c.pan(a,0,0,f*(Math.exp(o)-1))}}}),!0)},d.enableMouseListeners(),d};var n=t("right-now"),i=t("3d-view"),a=t("mouse-change"),o=t("mouse-wheel"),s=t("mouse-event-offset"),l=t("has-passive-events")},{"3d-view":7,"has-passive-events":232,"mouse-change":247,"mouse-event-offset":248,"mouse-wheel":250,"right-now":278}],120:[function(t,e,r){var n=t("glslify"),i=t("gl-shader"),a=n(["precision mediump float;\n#define GLSLIFY 1\nattribute vec2 position;\nvarying vec2 uv;\nvoid main() {\n  uv = position;\n  gl_Position = vec4(position, 0, 1);\n}"]),o=n(["precision mediump float;\n#define GLSLIFY 1\n\nuniform sampler2D accumBuffer;\nvarying vec2 uv;\n\nvoid main() {\n  vec4 accum = texture2D(accumBuffer, 0.5 * (uv + 1.0));\n  gl_FragColor = min(vec4(1,1,1,1), accum);\n}"]);e.exports=function(t){return i(t,a,o,null,[{name:"position",type:"vec2"}])}},{"gl-shader":132,glslify:231}],121:[function(t,e,r){"use strict";var n=t("./camera.js"),i=t("gl-axes3d"),a=t("gl-axes3d/properties"),o=t("gl-spikes3d"),s=t("gl-select-static"),l=t("gl-fbo"),c=t("a-big-triangle"),u=t("mouse-change"),f=t("gl-mat4/perspective"),h=t("gl-mat4/ortho"),p=t("./lib/shader"),d=t("is-mobile")({tablet:!0,featureDetect:!0});function g(){this.mouse=[-1,-1],this.screen=null,this.distance=1/0,this.index=null,this.dataCoordinate=null,this.dataPosition=null,this.object=null,this.data=null}function m(t){var e=Math.round(Math.log(Math.abs(t))/Math.log(10));if(e<0){var r=Math.round(Math.pow(10,-e));return Math.ceil(t*r)/r}if(e>0){r=Math.round(Math.pow(10,e));return Math.ceil(t/r)*r}return Math.ceil(t)}function v(t){return"boolean"!=typeof t||t}e.exports={createScene:function(t){(t=t||{}).camera=t.camera||{};var e=t.canvas;if(!e){if(e=document.createElement("canvas"),t.container)t.container.appendChild(e);else document.body.appendChild(e)}var r=t.gl;r||(t.glOptions&&(d=!!t.glOptions.preserveDrawingBuffer),r=function(t,e){var r=null;try{(r=t.getContext("webgl",e))||(r=t.getContext("experimental-webgl",e))}catch(t){return null}return r}(e,t.glOptions||{premultipliedAlpha:!0,antialias:!0,preserveDrawingBuffer:d}));if(!r)throw new Error("webgl not supported");var y=t.bounds||[[-10,-10,-10],[10,10,10]],x=new g,b=l(r,r.drawingBufferWidth,r.drawingBufferHeight,{preferFloat:!d}),_=p(r),w=t.cameraObject&&!0===t.cameraObject._ortho||t.camera.projection&&"orthographic"===t.camera.projection.type||!1,T={eye:t.camera.eye||[2,0,0],center:t.camera.center||[0,0,0],up:t.camera.up||[0,1,0],zoomMin:t.camera.zoomMax||.1,zoomMax:t.camera.zoomMin||100,mode:t.camera.mode||"turntable",_ortho:w},k=t.axes||{},A=i(r,k);A.enable=!k.disable;var M=t.spikes||{},S=o(r,M),E=[],L=[],C=[],P=[],I=!0,O=!0,z=new Array(16),D=new Array(16),R={view:null,projection:z,model:D,_ortho:!1},F=(O=!0,[r.drawingBufferWidth,r.drawingBufferHeight]),B=t.cameraObject||n(e,T),N={gl:r,contextLost:!1,pixelRatio:t.pixelRatio||1,canvas:e,selection:x,camera:B,axes:A,axesPixels:null,spikes:S,bounds:y,objects:E,shape:F,aspect:t.aspectRatio||[1,1,1],pickRadius:t.pickRadius||10,zNear:t.zNear||.01,zFar:t.zFar||1e3,fovy:t.fovy||Math.PI/4,clearColor:t.clearColor||[0,0,0,0],autoResize:v(t.autoResize),autoBounds:v(t.autoBounds),autoScale:!!t.autoScale,autoCenter:v(t.autoCenter),clipToBounds:v(t.clipToBounds),snapToData:!!t.snapToData,onselect:t.onselect||null,onrender:t.onrender||null,onclick:t.onclick||null,cameraParams:R,oncontextloss:null,mouseListener:null,_stopped:!1,getAspectratio:function(){return{x:this.aspect[0],y:this.aspect[1],z:this.aspect[2]}},setAspectratio:function(t){this.aspect[0]=t.x,this.aspect[1]=t.y,this.aspect[2]=t.z,O=!0},setBounds:function(t,e){this.bounds[0][t]=e.min,this.bounds[1][t]=e.max},setClearColor:function(t){this.clearColor=t},clearRGBA:function(){this.gl.clearColor(this.clearColor[0],this.clearColor[1],this.clearColor[2],this.clearColor[3]),this.gl.clear(this.gl.COLOR_BUFFER_BIT|this.gl.DEPTH_BUFFER_BIT)}},j=[r.drawingBufferWidth/N.pixelRatio|0,r.drawingBufferHeight/N.pixelRatio|0];function U(){if(!N._stopped&&N.autoResize){var t=e.parentNode,r=1,n=1;t&&t!==document.body?(r=t.clientWidth,n=t.clientHeight):(r=window.innerWidth,n=window.innerHeight);var i=0|Math.ceil(r*N.pixelRatio),a=0|Math.ceil(n*N.pixelRatio);if(i!==e.width||a!==e.height){e.width=i,e.height=a;var o=e.style;o.position=o.position||"absolute",o.left="0px",o.top="0px",o.width=r+"px",o.height=n+"px",I=!0}}}N.autoResize&&U();function V(){for(var t=E.length,e=P.length,n=0;n<e;++n)C[n]=0;t:for(n=0;n<t;++n){var i=E[n],a=i.pickSlots;if(a){for(var o=0;o<e;++o)if(C[o]+a<255){L[n]=o,i.setPickBase(C[o]+1),C[o]+=a;continue t}var l=s(r,F);L[n]=e,P.push(l),C.push(a),i.setPickBase(1),e+=1}else L[n]=-1}for(;e>0&&0===C[e-1];)C.pop(),P.pop().dispose()}function H(){if(N.contextLost)return!0;r.isContextLost()&&(N.contextLost=!0,N.mouseListener.enabled=!1,N.selection.object=null,N.oncontextloss&&N.oncontextloss())}window.addEventListener("resize",U),N.update=function(t){N._stopped||(t=t||{},I=!0,O=!0)},N.add=function(t){N._stopped||(t.axes=A,E.push(t),L.push(-1),I=!0,O=!0,V())},N.remove=function(t){if(!N._stopped){var e=E.indexOf(t);e<0||(E.splice(e,1),L.pop(),I=!0,O=!0,V())}},N.dispose=function(){if(!N._stopped&&(N._stopped=!0,window.removeEventListener("resize",U),e.removeEventListener("webglcontextlost",H),N.mouseListener.enabled=!1,!N.contextLost)){A.dispose(),S.dispose();for(var t=0;t<E.length;++t)E[t].dispose();b.dispose();for(t=0;t<P.length;++t)P[t].dispose();_.dispose(),r=null,A=null,S=null,E=[]}},N._mouseRotating=!1,N._prevButtons=0,N.enableMouseListeners=function(){N.mouseListener=u(e,(function(t,e,r){if(!N._stopped){var n=P.length,i=E.length,a=x.object;x.distance=1/0,x.mouse[0]=e,x.mouse[1]=r,x.object=null,x.screen=null,x.dataCoordinate=x.dataPosition=null;var o=!1;if(t&&N._prevButtons)N._mouseRotating=!0;else{N._mouseRotating&&(O=!0),N._mouseRotating=!1;for(var s=0;s<n;++s){var l=P[s].query(e,j[1]-r-1,N.pickRadius);if(l){if(l.distance>x.distance)continue;for(var c=0;c<i;++c){var u=E[c];if(L[c]===s){var f=u.pick(l);f&&(x.buttons=t,x.screen=l.coord,x.distance=l.distance,x.object=u,x.index=f.distance,x.dataPosition=f.position,x.dataCoordinate=f.dataCoordinate,x.data=f,o=!0)}}}}}a&&a!==x.object&&(a.highlight&&a.highlight(null),I=!0),x.object&&(x.object.highlight&&x.object.highlight(x.data),I=!0),(o=o||x.object!==a)&&N.onselect&&N.onselect(x),1&t&&!(1&N._prevButtons)&&N.onclick&&N.onclick(x),N._prevButtons=t}}))},e.addEventListener("webglcontextlost",H);var q=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],G=[q[0].slice(),q[1].slice()];function Y(){if(!H()){U();var t=N.camera.tick();R.view=N.camera.matrix,I=I||t,O=O||t,A.pixelRatio=N.pixelRatio,S.pixelRatio=N.pixelRatio;var e=E.length,n=q[0],i=q[1];n[0]=n[1]=n[2]=1/0,i[0]=i[1]=i[2]=-1/0;for(var o=0;o<e;++o){(C=E[o]).pixelRatio=N.pixelRatio,C.axes=N.axes,I=I||!!C.dirty,O=O||!!C.dirty;var s=C.bounds;if(s)for(var l=s[0],u=s[1],p=0;p<3;++p)n[p]=Math.min(n[p],l[p]),i[p]=Math.max(i[p],u[p])}var d=N.bounds;if(N.autoBounds)for(p=0;p<3;++p){if(i[p]<n[p])n[p]=-1,i[p]=1;else{n[p]===i[p]&&(n[p]-=1,i[p]+=1);var g=.05*(i[p]-n[p]);n[p]=n[p]-g,i[p]=i[p]+g}d[0][p]=n[p],d[1][p]=i[p]}var v=!1;for(p=0;p<3;++p)v=v||G[0][p]!==d[0][p]||G[1][p]!==d[1][p],G[0][p]=d[0][p],G[1][p]=d[1][p];if(O=O||v,I=I||v){if(v){var y=[0,0,0];for(o=0;o<3;++o)y[o]=m((d[1][o]-d[0][o])/10);A.autoTicks?A.update({bounds:d,tickSpacing:y}):A.update({bounds:d})}var T=r.drawingBufferWidth,k=r.drawingBufferHeight;F[0]=T,F[1]=k,j[0]=0|Math.max(T/N.pixelRatio,1),j[1]=0|Math.max(k/N.pixelRatio,1),function(t,e){var r=t.bounds,n=t.cameraParams,i=n.projection,a=n.model,o=t.gl.drawingBufferWidth,s=t.gl.drawingBufferHeight,l=t.zNear,c=t.zFar,u=t.fovy,p=o/s;e?(h(i,-p,p,-1,1,l,c),n._ortho=!0):(f(i,u,p,l,c),n._ortho=!1);for(var d=0;d<16;++d)a[d]=0;a[15]=1;var g=0;for(d=0;d<3;++d)g=Math.max(g,r[1][d]-r[0][d]);for(d=0;d<3;++d)t.autoScale?a[5*d]=t.aspect[d]/(r[1][d]-r[0][d]):a[5*d]=1/g,t.autoCenter&&(a[12+d]=.5*-a[5*d]*(r[0][d]+r[1][d]))}(N,w);for(o=0;o<e;++o){(C=E[o]).axesBounds=d,N.clipToBounds&&(C.clipBounds=d)}x.object&&(N.snapToData?S.position=x.dataCoordinate:S.position=x.dataPosition,S.bounds=d),O&&(O=!1,function(){if(!H()){r.colorMask(!0,!0,!0,!0),r.depthMask(!0),r.disable(r.BLEND),r.enable(r.DEPTH_TEST),r.depthFunc(r.LEQUAL);for(var t=E.length,e=P.length,n=0;n<e;++n){var i=P[n];i.shape=j,i.begin();for(var a=0;a<t;++a)if(L[a]===n){var o=E[a];o.drawPick&&(o.pixelRatio=1,o.drawPick(R))}i.end()}}}()),N.axesPixels=a(N.axes,R,T,k),N.onrender&&N.onrender(),r.bindFramebuffer(r.FRAMEBUFFER,null),r.viewport(0,0,T,k),N.clearRGBA(),r.depthMask(!0),r.colorMask(!0,!0,!0,!0),r.enable(r.DEPTH_TEST),r.depthFunc(r.LEQUAL),r.disable(r.BLEND),r.disable(r.CULL_FACE);var M=!1;A.enable&&(M=M||A.isTransparent(),A.draw(R)),S.axes=A,x.object&&S.draw(R),r.disable(r.CULL_FACE);for(o=0;o<e;++o){(C=E[o]).axes=A,C.pixelRatio=N.pixelRatio,C.isOpaque&&C.isOpaque()&&C.draw(R),C.isTransparent&&C.isTransparent()&&(M=!0)}if(M){b.shape=F,b.bind(),r.clear(r.DEPTH_BUFFER_BIT),r.colorMask(!1,!1,!1,!1),r.depthMask(!0),r.depthFunc(r.LESS),A.enable&&A.isTransparent()&&A.drawTransparent(R);for(o=0;o<e;++o){(C=E[o]).isOpaque&&C.isOpaque()&&C.draw(R)}r.enable(r.BLEND),r.blendEquation(r.FUNC_ADD),r.blendFunc(r.ONE,r.ONE_MINUS_SRC_ALPHA),r.colorMask(!0,!0,!0,!0),r.depthMask(!1),r.clearColor(0,0,0,0),r.clear(r.COLOR_BUFFER_BIT),A.isTransparent()&&A.drawTransparent(R);for(o=0;o<e;++o){var C;(C=E[o]).isTransparent&&C.isTransparent()&&C.drawTransparent(R)}r.bindFramebuffer(r.FRAMEBUFFER,null),r.blendFunc(r.ONE,r.ONE_MINUS_SRC_ALPHA),r.disable(r.DEPTH_TEST),_.bind(),b.color[0].bind(0),_.uniforms.accumBuffer=0,c(r),r.disable(r.BLEND)}I=!1;for(o=0;o<e;++o)E[o].dirty=!1}}}return N.enableMouseListeners(),function t(){if(N._stopped||N.contextLost)return;Y(),requestAnimationFrame(t)}(),N.redraw=function(){N._stopped||(I=!0,Y())},N},createCamera:n}},{"./camera.js":119,"./lib/shader":120,"a-big-triangle":8,"gl-axes3d":70,"gl-axes3d/properties":77,"gl-fbo":86,"gl-mat4/ortho":101,"gl-mat4/perspective":102,"gl-select-static":131,"gl-spikes3d":141,"is-mobile":238,"mouse-change":247}],122:[function(t,e,r){var n=t("glslify");r.pointVertex=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 position;\n\nuniform mat3 matrix;\nuniform float pointSize;\nuniform float pointCloud;\n\nhighp float rand(vec2 co) {\n  highp float a = 12.9898;\n  highp float b = 78.233;\n  highp float c = 43758.5453;\n  highp float d = dot(co.xy, vec2(a, b));\n  highp float e = mod(d, 3.14);\n  return fract(sin(e) * c);\n}\n\nvoid main() {\n  vec3 hgPosition = matrix * vec3(position, 1);\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\n    // if we don't jitter the point size a bit, overall point cloud\n    // saturation 'jumps' on zooming, which is disturbing and confusing\n  gl_PointSize = pointSize * ((19.5 + rand(position)) / 20.0);\n  if(pointCloud != 0.0) { // pointCloud is truthy\n    // get the same square surface as circle would be\n    gl_PointSize *= 0.886;\n  }\n}"]),r.pointFragment=n(["precision mediump float;\n#define GLSLIFY 1\n\nuniform vec4 color, borderColor;\nuniform float centerFraction;\nuniform float pointCloud;\n\nvoid main() {\n  float radius;\n  vec4 baseColor;\n  if(pointCloud != 0.0) { // pointCloud is truthy\n    if(centerFraction == 1.0) {\n      gl_FragColor = color;\n    } else {\n      gl_FragColor = mix(borderColor, color, centerFraction);\n    }\n  } else {\n    radius = length(2.0 * gl_PointCoord.xy - 1.0);\n    if(radius > 1.0) {\n      discard;\n    }\n    baseColor = mix(borderColor, color, step(radius, centerFraction));\n    gl_FragColor = vec4(baseColor.rgb * baseColor.a, baseColor.a);\n  }\n}\n"]),r.pickVertex=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 position;\nattribute vec4 pickId;\n\nuniform mat3 matrix;\nuniform float pointSize;\nuniform vec4 pickOffset;\n\nvarying vec4 fragId;\n\nvoid main() {\n  vec3 hgPosition = matrix * vec3(position, 1);\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\n  gl_PointSize = pointSize;\n\n  vec4 id = pickId + pickOffset;\n  id.y += floor(id.x / 256.0);\n  id.x -= floor(id.x / 256.0) * 256.0;\n\n  id.z += floor(id.y / 256.0);\n  id.y -= floor(id.y / 256.0) * 256.0;\n\n  id.w += floor(id.z / 256.0);\n  id.z -= floor(id.z / 256.0) * 256.0;\n\n  fragId = id;\n}\n"]),r.pickFragment=n(["precision mediump float;\n#define GLSLIFY 1\n\nvarying vec4 fragId;\n\nvoid main() {\n  float radius = length(2.0 * gl_PointCoord.xy - 1.0);\n  if(radius > 1.0) {\n    discard;\n  }\n  gl_FragColor = fragId / 255.0;\n}\n"])},{glslify:231}],123:[function(t,e,r){"use strict";var n=t("gl-shader"),i=t("gl-buffer"),a=t("typedarray-pool"),o=t("./lib/shader");function s(t,e,r,n,i){this.plot=t,this.offsetBuffer=e,this.pickBuffer=r,this.shader=n,this.pickShader=i,this.sizeMin=.5,this.sizeMinCap=2,this.sizeMax=20,this.areaRatio=1,this.pointCount=0,this.color=[1,0,0,1],this.borderColor=[0,0,0,1],this.blend=!1,this.pickOffset=0,this.points=null}e.exports=function(t,e){var r=t.gl,a=i(r),l=i(r),c=n(r,o.pointVertex,o.pointFragment),u=n(r,o.pickVertex,o.pickFragment),f=new s(t,a,l,c,u);return f.update(e),t.addObject(f),f};var l,c,u=s.prototype;u.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.offsetBuffer.dispose(),this.pickBuffer.dispose(),this.plot.removeObject(this)},u.update=function(t){var e;function r(e,r){return e in t?t[e]:r}t=t||{},this.sizeMin=r("sizeMin",.5),this.sizeMax=r("sizeMax",20),this.color=r("color",[1,0,0,1]).slice(),this.areaRatio=r("areaRatio",1),this.borderColor=r("borderColor",[0,0,0,1]).slice(),this.blend=r("blend",!1);var n=t.positions.length>>>1,i=t.positions instanceof Float32Array,o=t.idToIndex instanceof Int32Array&&t.idToIndex.length>=n,s=t.positions,l=i?s:a.mallocFloat32(s.length),c=o?t.idToIndex:a.mallocInt32(n);if(i||l.set(s),!o)for(l.set(s),e=0;e<n;e++)c[e]=e;this.points=s,this.offsetBuffer.update(l),this.pickBuffer.update(c),i||a.free(l),o||a.free(c),this.pointCount=n,this.pickOffset=0},u.unifiedDraw=(l=[1,0,0,0,1,0,0,0,1],c=[0,0,0,0],function(t){var e=void 0!==t,r=e?this.pickShader:this.shader,n=this.plot.gl,i=this.plot.dataBox;if(0===this.pointCount)return t;var a=i[2]-i[0],o=i[3]-i[1],s=function(t,e){var r,n=0,i=t.length>>>1;for(r=0;r<i;r++){var a=t[2*r],o=t[2*r+1];a>=e[0]&&a<=e[2]&&o>=e[1]&&o<=e[3]&&n++}return n}(this.points,i),u=this.plot.pickPixelRatio*Math.max(Math.min(this.sizeMinCap,this.sizeMin),Math.min(this.sizeMax,this.sizeMax/Math.pow(s,.33333)));l[0]=2/a,l[4]=2/o,l[6]=-2*i[0]/a-1,l[7]=-2*i[1]/o-1,this.offsetBuffer.bind(),r.bind(),r.attributes.position.pointer(),r.uniforms.matrix=l,r.uniforms.color=this.color,r.uniforms.borderColor=this.borderColor,r.uniforms.pointCloud=u<5,r.uniforms.pointSize=u,r.uniforms.centerFraction=Math.min(1,Math.max(0,Math.sqrt(1-this.areaRatio))),e&&(c[0]=255&t,c[1]=t>>8&255,c[2]=t>>16&255,c[3]=t>>24&255,this.pickBuffer.bind(),r.attributes.pickId.pointer(n.UNSIGNED_BYTE),r.uniforms.pickOffset=c,this.pickOffset=t);var f=n.getParameter(n.BLEND),h=n.getParameter(n.DITHER);return f&&!this.blend&&n.disable(n.BLEND),h&&n.disable(n.DITHER),n.drawArrays(n.POINTS,0,this.pointCount),f&&!this.blend&&n.enable(n.BLEND),h&&n.enable(n.DITHER),t+this.pointCount}),u.draw=u.unifiedDraw,u.drawPick=u.unifiedDraw,u.pick=function(t,e,r){var n=this.pickOffset,i=this.pointCount;if(r<n||r>=n+i)return null;var a=r-n,o=this.points;return{object:this,pointId:a,dataCoord:[o[2*a],o[2*a+1]]}}},{"./lib/shader":122,"gl-buffer":78,"gl-shader":132,"typedarray-pool":308}],124:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c=e[0],u=e[1],f=e[2],h=e[3],p=r[0],d=r[1],g=r[2],m=r[3];(a=c*p+u*d+f*g+h*m)<0&&(a=-a,p=-p,d=-d,g=-g,m=-m);1-a>1e-6?(i=Math.acos(a),o=Math.sin(i),s=Math.sin((1-n)*i)/o,l=Math.sin(n*i)/o):(s=1-n,l=n);return t[0]=s*c+l*p,t[1]=s*u+l*d,t[2]=s*f+l*g,t[3]=s*h+l*m,t}},{}],125:[function(t,e,r){"use strict";e.exports=function(t){return t||0===t?t.toString():""}},{}],126:[function(t,e,r){"use strict";var n=t("vectorize-text");e.exports=function(t,e,r){var a=i[e];a||(a=i[e]={});if(t in a)return a[t];var o={textAlign:"center",textBaseline:"middle",lineHeight:1,font:e,lineSpacing:1.25,styletags:{breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},triangles:!0},s=n(t,o);o.triangles=!1;var l,c,u=n(t,o);if(r&&1!==r){for(l=0;l<s.positions.length;++l)for(c=0;c<s.positions[l].length;++c)s.positions[l][c]/=r;for(l=0;l<u.positions.length;++l)for(c=0;c<u.positions[l].length;++c)u.positions[l][c]/=r}var f=[[1/0,1/0],[-1/0,-1/0]],h=u.positions.length;for(l=0;l<h;++l){var p=u.positions[l];for(c=0;c<2;++c)f[0][c]=Math.min(f[0][c],p[c]),f[1][c]=Math.max(f[1][c],p[c])}return a[t]=[s,u,f]};var i={}},{"vectorize-text":311}],127:[function(t,e,r){var n=t("gl-shader"),i=t("glslify"),a=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 glyph;\nattribute vec4 id;\n\nuniform vec4 highlightId;\nuniform float highlightScale;\nuniform mat4 model, view, projection;\nuniform vec3 clipBounds[2];\n\nvarying vec4 interpColor;\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0,0,0,0);\n  } else {\n    float scale = 1.0;\n    if(distance(highlightId, id) < 0.0001) {\n      scale = highlightScale;\n    }\n\n    vec4 worldPosition = model * vec4(position, 1);\n    vec4 viewPosition = view * worldPosition;\n    viewPosition = viewPosition / viewPosition.w;\n    vec4 clipPosition = projection * (viewPosition + scale * vec4(glyph.x, -glyph.y, 0, 0));\n\n    gl_Position = clipPosition;\n    interpColor = color;\n    pickId = id;\n    dataCoordinate = position;\n  }\n}"]),o=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 glyph;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\nuniform vec2 screenSize;\nuniform vec3 clipBounds[2];\nuniform float highlightScale, pixelRatio;\nuniform vec4 highlightId;\n\nvarying vec4 interpColor;\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0,0,0,0);\n  } else {\n    float scale = pixelRatio;\n    if(distance(highlightId.bgr, id.bgr) < 0.001) {\n      scale *= highlightScale;\n    }\n\n    vec4 worldPosition = model * vec4(position, 1.0);\n    vec4 viewPosition = view * worldPosition;\n    vec4 clipPosition = projection * viewPosition;\n    clipPosition /= clipPosition.w;\n\n    gl_Position = clipPosition + vec4(screenSize * scale * vec2(glyph.x, -glyph.y), 0.0, 0.0);\n    interpColor = color;\n    pickId = id;\n    dataCoordinate = position;\n  }\n}"]),s=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 glyph;\nattribute vec4 id;\n\nuniform float highlightScale;\nuniform vec4 highlightId;\nuniform vec3 axes[2];\nuniform mat4 model, view, projection;\nuniform vec2 screenSize;\nuniform vec3 clipBounds[2];\nuniform float scale, pixelRatio;\n\nvarying vec4 interpColor;\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0,0,0,0);\n  } else {\n    float lscale = pixelRatio * scale;\n    if(distance(highlightId, id) < 0.0001) {\n      lscale *= highlightScale;\n    }\n\n    vec4 clipCenter   = projection * view * model * vec4(position, 1);\n    vec3 dataPosition = position + 0.5*lscale*(axes[0] * glyph.x + axes[1] * glyph.y) * clipCenter.w * screenSize.y;\n    vec4 clipPosition = projection * view * model * vec4(dataPosition, 1);\n\n    gl_Position = clipPosition;\n    interpColor = color;\n    pickId = id;\n    dataCoordinate = dataPosition;\n  }\n}\n"]),l=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 fragClipBounds[2];\nuniform float opacity;\n\nvarying vec4 interpColor;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (\n    outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate) ||\n    interpColor.a * opacity == 0.\n  ) discard;\n  gl_FragColor = interpColor * opacity;\n}\n"]),c=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 fragClipBounds[2];\nuniform float pickGroup;\n\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate)) discard;\n\n  gl_FragColor = vec4(pickGroup, pickId.bgr);\n}"]),u=[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"glyph",type:"vec2"},{name:"id",type:"vec4"}],f={vertex:a,fragment:l,attributes:u},h={vertex:o,fragment:l,attributes:u},p={vertex:s,fragment:l,attributes:u},d={vertex:a,fragment:c,attributes:u},g={vertex:o,fragment:c,attributes:u},m={vertex:s,fragment:c,attributes:u};function v(t,e){var r=n(t,e),i=r.attributes;return i.position.location=0,i.color.location=1,i.glyph.location=2,i.id.location=3,r}r.createPerspective=function(t){return v(t,f)},r.createOrtho=function(t){return v(t,h)},r.createProject=function(t){return v(t,p)},r.createPickPerspective=function(t){return v(t,d)},r.createPickOrtho=function(t){return v(t,g)},r.createPickProject=function(t){return v(t,m)}},{"gl-shader":132,glslify:231}],128:[function(t,e,r){"use strict";var n=t("is-string-blank"),i=t("gl-buffer"),a=t("gl-vao"),o=t("typedarray-pool"),s=t("gl-mat4/multiply"),l=t("./lib/shaders"),c=t("./lib/glyphs"),u=t("./lib/get-simple-string"),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e){var r=t[0],n=t[1],i=t[2],a=t[3];return t[0]=e[0]*r+e[4]*n+e[8]*i+e[12]*a,t[1]=e[1]*r+e[5]*n+e[9]*i+e[13]*a,t[2]=e[2]*r+e[6]*n+e[10]*i+e[14]*a,t[3]=e[3]*r+e[7]*n+e[11]*i+e[15]*a,t}function p(t,e,r,n){return h(n,n),h(n,n),h(n,n)}function d(t,e){this.index=t,this.dataCoordinate=this.position=e}function g(t){return!0===t||t>1?1:t}function m(t,e,r,n,i,a,o,s,l,c,u,f){this.gl=t,this.pixelRatio=1,this.shader=e,this.orthoShader=r,this.projectShader=n,this.pointBuffer=i,this.colorBuffer=a,this.glyphBuffer=o,this.idBuffer=s,this.vao=l,this.vertexCount=0,this.lineVertexCount=0,this.opacity=1,this.hasAlpha=!1,this.lineWidth=0,this.projectScale=[2/3,2/3,2/3],this.projectOpacity=[1,1,1],this.projectHasAlpha=!1,this.pickId=0,this.pickPerspectiveShader=c,this.pickOrthoShader=u,this.pickProjectShader=f,this.points=[],this._selectResult=new d(0,[0,0,0]),this.useOrtho=!0,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.axesProject=[!0,!0,!0],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.highlightId=[1,1,1,1],this.highlightScale=2,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.dirty=!0}e.exports=function(t){var e=t.gl,r=l.createPerspective(e),n=l.createOrtho(e),o=l.createProject(e),s=l.createPickPerspective(e),c=l.createPickOrtho(e),u=l.createPickProject(e),f=i(e),h=i(e),p=i(e),d=i(e),g=a(e,[{buffer:f,size:3,type:e.FLOAT},{buffer:h,size:4,type:e.FLOAT},{buffer:p,size:2,type:e.FLOAT},{buffer:d,size:4,type:e.UNSIGNED_BYTE,normalized:!0}]),v=new m(e,r,n,o,f,h,p,d,g,s,c,u);return v.update(t),v};var v=m.prototype;v.pickSlots=1,v.setPickBase=function(t){this.pickId=t},v.isTransparent=function(){if(this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&this.projectHasAlpha)return!0;return!1},v.isOpaque=function(){if(!this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&!this.projectHasAlpha)return!0;return!1};var y=[0,0],x=[0,0,0],b=[0,0,0],_=[0,0,0,1],w=[0,0,0,1],T=f.slice(),k=[0,0,0],A=[[0,0,0],[0,0,0]];function M(t){return t[0]=t[1]=t[2]=0,t}function S(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=1,t}function E(t,e,r,n){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[r]=n,t}function L(t,e,r,n){var i,a=e.axesProject,o=e.gl,l=t.uniforms,c=r.model||f,u=r.view||f,h=r.projection||f,d=e.axesBounds,g=function(t){for(var e=A,r=0;r<2;++r)for(var n=0;n<3;++n)e[r][n]=Math.max(Math.min(t[r][n],1e8),-1e8);return e}(e.clipBounds);i=e.axes&&e.axes.lastCubeProps?e.axes.lastCubeProps.axis:[1,1,1],y[0]=2/o.drawingBufferWidth,y[1]=2/o.drawingBufferHeight,t.bind(),l.view=u,l.projection=h,l.screenSize=y,l.highlightId=e.highlightId,l.highlightScale=e.highlightScale,l.clipBounds=g,l.pickGroup=e.pickId/255,l.pixelRatio=n;for(var m=0;m<3;++m)if(a[m]){l.scale=e.projectScale[m],l.opacity=e.projectOpacity[m];for(var v=T,L=0;L<16;++L)v[L]=0;for(L=0;L<4;++L)v[5*L]=1;v[5*m]=0,i[m]<0?v[12+m]=d[0][m]:v[12+m]=d[1][m],s(v,c,v),l.model=v;var C=(m+1)%3,P=(m+2)%3,I=M(x),O=M(b);I[C]=1,O[P]=1;var z=p(0,0,0,S(_,I)),D=p(0,0,0,S(w,O));if(Math.abs(z[1])>Math.abs(D[1])){var R=z;z=D,D=R,R=I,I=O,O=R;var F=C;C=P,P=F}z[0]<0&&(I[C]=-1),D[1]>0&&(O[P]=-1);var B=0,N=0;for(L=0;L<4;++L)B+=Math.pow(c[4*C+L],2),N+=Math.pow(c[4*P+L],2);I[C]/=Math.sqrt(B),O[P]/=Math.sqrt(N),l.axes[0]=I,l.axes[1]=O,l.fragClipBounds[0]=E(k,g[0],m,-1e8),l.fragClipBounds[1]=E(k,g[1],m,1e8),e.vao.bind(),e.vao.draw(o.TRIANGLES,e.vertexCount),e.lineWidth>0&&(o.lineWidth(e.lineWidth*n),e.vao.draw(o.LINES,e.lineVertexCount,e.vertexCount)),e.vao.unbind()}}var C=[[-1e8,-1e8,-1e8],[1e8,1e8,1e8]];function P(t,e,r,n,i,a,o){var s=r.gl;if((a===r.projectHasAlpha||o)&&L(e,r,n,i),a===r.hasAlpha||o){t.bind();var l=t.uniforms;l.model=n.model||f,l.view=n.view||f,l.projection=n.projection||f,y[0]=2/s.drawingBufferWidth,y[1]=2/s.drawingBufferHeight,l.screenSize=y,l.highlightId=r.highlightId,l.highlightScale=r.highlightScale,l.fragClipBounds=C,l.clipBounds=r.axes.bounds,l.opacity=r.opacity,l.pickGroup=r.pickId/255,l.pixelRatio=i,r.vao.bind(),r.vao.draw(s.TRIANGLES,r.vertexCount),r.lineWidth>0&&(s.lineWidth(r.lineWidth*i),r.vao.draw(s.LINES,r.lineVertexCount,r.vertexCount)),r.vao.unbind()}}function I(t,e,r,i){var a;a=Array.isArray(t)?e<t.length?t[e]:void 0:t,a=u(a);var o=!0;n(a)&&(a="\u25bc",o=!1);var s=c(a,r,i);return{mesh:s[0],lines:s[1],bounds:s[2],visible:o}}v.draw=function(t){P(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!1,!1)},v.drawTransparent=function(t){P(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!0,!1)},v.drawPick=function(t){P(this.useOrtho?this.pickOrthoShader:this.pickPerspectiveShader,this.pickProjectShader,this,t,1,!0,!0)},v.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[2]+(t.value[1]<<8)+(t.value[0]<<16);if(e>=this.pointCount||e<0)return null;var r=this.points[e],n=this._selectResult;n.index=e;for(var i=0;i<3;++i)n.position[i]=n.dataCoordinate[i]=r[i];return n},v.highlight=function(t){if(t){var e=t.index,r=255&e,n=e>>8&255,i=e>>16&255;this.highlightId=[r/255,n/255,i/255,0]}else this.highlightId=[1,1,1,1]},v.update=function(t){if("perspective"in(t=t||{})&&(this.useOrtho=!t.perspective),"orthographic"in t&&(this.useOrtho=!!t.orthographic),"lineWidth"in t&&(this.lineWidth=t.lineWidth),"project"in t)if(Array.isArray(t.project))this.axesProject=t.project;else{var e=!!t.project;this.axesProject=[e,e,e]}if("projectScale"in t)if(Array.isArray(t.projectScale))this.projectScale=t.projectScale.slice();else{var r=+t.projectScale;this.projectScale=[r,r,r]}if(this.projectHasAlpha=!1,"projectOpacity"in t){if(Array.isArray(t.projectOpacity))this.projectOpacity=t.projectOpacity.slice();else{r=+t.projectOpacity;this.projectOpacity=[r,r,r]}for(var n=0;n<3;++n)this.projectOpacity[n]=g(this.projectOpacity[n]),this.projectOpacity[n]<1&&(this.projectHasAlpha=!0)}this.hasAlpha=!1,"opacity"in t&&(this.opacity=g(t.opacity),this.opacity<1&&(this.hasAlpha=!0)),this.dirty=!0;var i,a,s=t.position,l=t.font||"normal",c=t.alignment||[0,0];if(2===c.length)i=c[0],a=c[1];else{i=[],a=[];for(n=0;n<c.length;++n)i[n]=c[n][0],a[n]=c[n][1]}var u=[1/0,1/0,1/0],f=[-1/0,-1/0,-1/0],h=t.glyph,p=t.color,d=t.size,m=t.angle,v=t.lineColor,y=-1,x=0,b=0,_=0;if(s.length){_=s.length;t:for(n=0;n<_;++n){for(var w=s[n],T=0;T<3;++T)if(isNaN(w[T])||!isFinite(w[T]))continue t;var k=(N=I(h,n,l,this.pixelRatio)).mesh,A=N.lines,M=N.bounds;x+=3*k.cells.length,b+=2*A.edges.length}}var S=x+b,E=o.mallocFloat(3*S),L=o.mallocFloat(4*S),C=o.mallocFloat(2*S),P=o.mallocUint32(S);if(S>0){var O=0,z=x,D=[0,0,0,1],R=[0,0,0,1],F=Array.isArray(p)&&Array.isArray(p[0]),B=Array.isArray(v)&&Array.isArray(v[0]);t:for(n=0;n<_;++n){y+=1;for(w=s[n],T=0;T<3;++T){if(isNaN(w[T])||!isFinite(w[T]))continue t;f[T]=Math.max(f[T],w[T]),u[T]=Math.min(u[T],w[T])}k=(N=I(h,n,l,this.pixelRatio)).mesh,A=N.lines,M=N.bounds;var N,j=N.visible;if(j)if(Array.isArray(p)){if(3===(U=F?n<p.length?p[n]:[0,0,0,0]:p).length){for(T=0;T<3;++T)D[T]=U[T];D[3]=1}else if(4===U.length){for(T=0;T<4;++T)D[T]=U[T];!this.hasAlpha&&U[3]<1&&(this.hasAlpha=!0)}}else D[0]=D[1]=D[2]=0,D[3]=1;else D=[1,1,1,0];if(j)if(Array.isArray(v)){var U;if(3===(U=B?n<v.length?v[n]:[0,0,0,0]:v).length){for(T=0;T<3;++T)R[T]=U[T];R[T]=1}else if(4===U.length){for(T=0;T<4;++T)R[T]=U[T];!this.hasAlpha&&U[3]<1&&(this.hasAlpha=!0)}}else R[0]=R[1]=R[2]=0,R[3]=1;else R=[1,1,1,0];var V=.5;j?Array.isArray(d)?V=n<d.length?+d[n]:12:d?V=+d:this.useOrtho&&(V=12):V=0;var H=0;Array.isArray(m)?H=n<m.length?+m[n]:0:m&&(H=+m);var q=Math.cos(H),G=Math.sin(H);for(w=s[n],T=0;T<3;++T)f[T]=Math.max(f[T],w[T]),u[T]=Math.min(u[T],w[T]);var Y=i,W=a;Y=0;Array.isArray(i)?Y=n<i.length?i[n]:0:i&&(Y=i);W=0;Array.isArray(a)?W=n<a.length?a[n]:0:a&&(W=a);var X=[Y*=Y>0?1-M[0][0]:Y<0?1+M[1][0]:1,W*=W>0?1-M[0][1]:W<0?1+M[1][1]:1],Z=k.cells||[],J=k.positions||[];for(T=0;T<Z.length;++T)for(var K=Z[T],Q=0;Q<3;++Q){for(var $=0;$<3;++$)E[3*O+$]=w[$];for($=0;$<4;++$)L[4*O+$]=D[$];P[O]=y;var tt=J[K[Q]];C[2*O]=V*(q*tt[0]-G*tt[1]+X[0]),C[2*O+1]=V*(G*tt[0]+q*tt[1]+X[1]),O+=1}for(Z=A.edges,J=A.positions,T=0;T<Z.length;++T)for(K=Z[T],Q=0;Q<2;++Q){for($=0;$<3;++$)E[3*z+$]=w[$];for($=0;$<4;++$)L[4*z+$]=R[$];P[z]=y;tt=J[K[Q]];C[2*z]=V*(q*tt[0]-G*tt[1]+X[0]),C[2*z+1]=V*(G*tt[0]+q*tt[1]+X[1]),z+=1}}}this.bounds=[u,f],this.points=s,this.pointCount=s.length,this.vertexCount=x,this.lineVertexCount=b,this.pointBuffer.update(E),this.colorBuffer.update(L),this.glyphBuffer.update(C),this.idBuffer.update(P),o.free(E),o.free(L),o.free(C),o.free(P)},v.dispose=function(){this.shader.dispose(),this.orthoShader.dispose(),this.pickPerspectiveShader.dispose(),this.pickOrthoShader.dispose(),this.vao.dispose(),this.pointBuffer.dispose(),this.colorBuffer.dispose(),this.glyphBuffer.dispose(),this.idBuffer.dispose()}},{"./lib/get-simple-string":125,"./lib/glyphs":126,"./lib/shaders":127,"gl-buffer":78,"gl-mat4/multiply":100,"gl-vao":150,"is-string-blank":239,"typedarray-pool":308}],129:[function(t,e,r){"use strict";var n=t("glslify");r.boxVertex=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 vertex;\n\nuniform vec2 cornerA, cornerB;\n\nvoid main() {\n  gl_Position = vec4(mix(cornerA, cornerB, vertex), 0, 1);\n}\n"]),r.boxFragment=n(["precision mediump float;\n#define GLSLIFY 1\n\nuniform vec4 color;\n\nvoid main() {\n  gl_FragColor = color;\n}\n"])},{glslify:231}],130:[function(t,e,r){"use strict";var n=t("gl-shader"),i=t("gl-buffer"),a=t("./lib/shaders");function o(t,e,r){this.plot=t,this.boxBuffer=e,this.boxShader=r,this.enabled=!0,this.selectBox=[1/0,1/0,-1/0,-1/0],this.borderColor=[0,0,0,1],this.innerFill=!1,this.innerColor=[0,0,0,.25],this.outerFill=!0,this.outerColor=[0,0,0,.5],this.borderWidth=10}e.exports=function(t,e){var r=t.gl,s=i(r,[0,0,0,1,1,0,1,1]),l=n(r,a.boxVertex,a.boxFragment),c=new o(t,s,l);return c.update(e),t.addOverlay(c),c};var s=o.prototype;s.draw=function(){if(this.enabled){var t=this.plot,e=this.selectBox,r=this.borderWidth,n=(this.innerFill,this.innerColor),i=(this.outerFill,this.outerColor),a=this.borderColor,o=t.box,s=t.screenBox,l=t.dataBox,c=t.viewBox,u=t.pixelRatio,f=(e[0]-l[0])*(c[2]-c[0])/(l[2]-l[0])+c[0],h=(e[1]-l[1])*(c[3]-c[1])/(l[3]-l[1])+c[1],p=(e[2]-l[0])*(c[2]-c[0])/(l[2]-l[0])+c[0],d=(e[3]-l[1])*(c[3]-c[1])/(l[3]-l[1])+c[1];if(f=Math.max(f,c[0]),h=Math.max(h,c[1]),p=Math.min(p,c[2]),d=Math.min(d,c[3]),!(p<f||d<h)){o.bind();var g=s[2]-s[0],m=s[3]-s[1];if(this.outerFill&&(o.drawBox(0,0,g,h,i),o.drawBox(0,h,f,d,i),o.drawBox(0,d,g,m,i),o.drawBox(p,h,g,d,i)),this.innerFill&&o.drawBox(f,h,p,d,n),r>0){var v=r*u;o.drawBox(f-v,h-v,p+v,h+v,a),o.drawBox(f-v,d-v,p+v,d+v,a),o.drawBox(f-v,h-v,f+v,d+v,a),o.drawBox(p-v,h-v,p+v,d+v,a)}}}},s.update=function(t){t=t||{},this.innerFill=!!t.innerFill,this.outerFill=!!t.outerFill,this.innerColor=(t.innerColor||[0,0,0,.5]).slice(),this.outerColor=(t.outerColor||[0,0,0,.5]).slice(),this.borderColor=(t.borderColor||[0,0,0,1]).slice(),this.borderWidth=t.borderWidth||0,this.selectBox=(t.selectBox||this.selectBox).slice()},s.dispose=function(){this.boxBuffer.dispose(),this.boxShader.dispose(),this.plot.removeOverlay(this)}},{"./lib/shaders":129,"gl-buffer":78,"gl-shader":132}],131:[function(t,e,r){"use strict";e.exports=function(t,e){var r=e[0],a=e[1],o=n(t,r,a,{}),s=i.mallocUint8(r*a*4);return new l(t,o,s)};var n=t("gl-fbo"),i=t("typedarray-pool"),a=t("ndarray"),o=t("bit-twiddle").nextPow2;function s(t,e,r,n,i){this.coord=[t,e],this.id=r,this.value=n,this.distance=i}function l(t,e,r){this.gl=t,this.fbo=e,this.buffer=r,this._readTimeout=null;var n=this;this._readCallback=function(){n.gl&&(e.bind(),t.readPixels(0,0,e.shape[0],e.shape[1],t.RGBA,t.UNSIGNED_BYTE,n.buffer),n._readTimeout=null)}}var c=l.prototype;Object.defineProperty(c,"shape",{get:function(){return this.gl?this.fbo.shape.slice():[0,0]},set:function(t){if(this.gl){this.fbo.shape=t;var e=this.fbo.shape[0],r=this.fbo.shape[1];if(r*e*4>this.buffer.length){i.free(this.buffer);for(var n=this.buffer=i.mallocUint8(o(r*e*4)),a=0;a<r*e*4;++a)n[a]=255}return t}}}),c.begin=function(){var t=this.gl;this.shape;t&&(this.fbo.bind(),t.clearColor(1,1,1,1),t.clear(t.COLOR_BUFFER_BIT|t.DEPTH_BUFFER_BIT))},c.end=function(){var t=this.gl;t&&(t.bindFramebuffer(t.FRAMEBUFFER,null),this._readTimeout||clearTimeout(this._readTimeout),this._readTimeout=setTimeout(this._readCallback,1))},c.query=function(t,e,r){if(!this.gl)return null;var n=this.fbo.shape.slice();t|=0,e|=0,"number"!=typeof r&&(r=1);var 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t=this.enable,e=this.width,r=this.color,n=this.center,i=this.plot,a=i.line,o=i.dataBox,s=i.viewBox;if(a.bind(),o[0]<=n[0]&&n[0]<=o[2]&&o[1]<=n[1]&&n[1]<=o[3]){var l=s[0]+(n[0]-o[0])/(o[2]-o[0])*(s[2]-s[0]),c=s[1]+(n[1]-o[1])/(o[3]-o[1])*(s[3]-s[1]);t[0]&&a.drawLine(l,c,s[0],c,e[0],r[0]),t[1]&&a.drawLine(l,c,l,s[1],e[1],r[1]),t[2]&&a.drawLine(l,c,s[2],c,e[2],r[2]),t[3]&&a.drawLine(l,c,l,s[3],e[3],r[3])}},i.dispose=function(){this.plot.removeOverlay(this)}},{}],140:[function(t,e,r){"use strict";var n=t("glslify"),i=t("gl-shader"),a=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 position, color;\nattribute float weight;\n\nuniform mat4 model, view, projection;\nuniform vec3 coordinates[3];\nuniform vec4 colors[3];\nuniform vec2 screenShape;\nuniform float lineWidth;\n\nvarying vec4 fragColor;\n\nvoid main() {\n  vec3 vertexPosition = mix(coordinates[0],\n    mix(coordinates[2], coordinates[1], 0.5 * (position + 1.0)), abs(position));\n\n  vec4 clipPos = projection * view * model * vec4(vertexPosition, 1.0);\n  vec2 clipOffset = (projection * view * model * vec4(color, 0.0)).xy;\n  vec2 delta = weight * clipOffset * screenShape;\n  vec2 lineOffset = normalize(vec2(delta.y, -delta.x)) / screenShape;\n\n  gl_Position   = vec4(clipPos.xy + clipPos.w * 0.5 * lineWidth * lineOffset, clipPos.z, clipPos.w);\n  fragColor     = color.x * colors[0] + color.y * colors[1] + color.z * colors[2];\n}\n"]),o=n(["precision mediump float;\n#define GLSLIFY 1\n\nvarying vec4 fragColor;\n\nvoid main() {\n  gl_FragColor = fragColor;\n}"]);e.exports=function(t){return i(t,a,o,null,[{name:"position",type:"vec3"},{name:"color",type:"vec3"},{name:"weight",type:"float"}])}},{"gl-shader":132,glslify:231}],141:[function(t,e,r){"use strict";var n=t("gl-buffer"),i=t("gl-vao"),a=t("./shaders/index");e.exports=function(t,e){var r=[];function o(t,e,n,i,a,o){var s=[t,e,n,0,0,0,1];s[i+3]=1,s[i]=a,r.push.apply(r,s),s[6]=-1,r.push.apply(r,s),s[i]=o,r.push.apply(r,s),r.push.apply(r,s),s[6]=1,r.push.apply(r,s),s[i]=a,r.push.apply(r,s)}o(0,0,0,0,0,1),o(0,0,0,1,0,1),o(0,0,0,2,0,1),o(1,0,0,1,-1,1),o(1,0,0,2,-1,1),o(0,1,0,0,-1,1),o(0,1,0,2,-1,1),o(0,0,1,0,-1,1),o(0,0,1,1,-1,1);var l=n(t,r),c=i(t,[{type:t.FLOAT,buffer:l,size:3,offset:0,stride:28},{type:t.FLOAT,buffer:l,size:3,offset:12,stride:28},{type:t.FLOAT,buffer:l,size:1,offset:24,stride:28}]),u=a(t);u.attributes.position.location=0,u.attributes.color.location=1,u.attributes.weight.location=2;var f=new s(t,l,c,u);return f.update(e),f};var o=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function s(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n,this.pixelRatio=1,this.bounds=[[-1e3,-1e3,-1e3],[1e3,1e3,1e3]],this.position=[0,0,0],this.lineWidth=[2,2,2],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.enabled=[!0,!0,!0],this.drawSides=[!0,!0,!0],this.axes=null}var l=s.prototype,c=[0,0,0],u=[0,0,0],f=[0,0];l.isTransparent=function(){return!1},l.drawTransparent=function(t){},l.draw=function(t){var e=this.gl,r=this.vao,n=this.shader;r.bind(),n.bind();var i,a=t.model||o,s=t.view||o,l=t.projection||o;this.axes&&(i=this.axes.lastCubeProps.axis);for(var h=c,p=u,d=0;d<3;++d)i&&i[d]<0?(h[d]=this.bounds[0][d],p[d]=this.bounds[1][d]):(h[d]=this.bounds[1][d],p[d]=this.bounds[0][d]);f[0]=e.drawingBufferWidth,f[1]=e.drawingBufferHeight,n.uniforms.model=a,n.uniforms.view=s,n.uniforms.projection=l,n.uniforms.coordinates=[this.position,h,p],n.uniforms.colors=this.colors,n.uniforms.screenShape=f;for(d=0;d<3;++d)n.uniforms.lineWidth=this.lineWidth[d]*this.pixelRatio,this.enabled[d]&&(r.draw(e.TRIANGLES,6,6*d),this.drawSides[d]&&r.draw(e.TRIANGLES,12,18+12*d));r.unbind()},l.update=function(t){t&&("bounds"in t&&(this.bounds=t.bounds),"position"in t&&(this.position=t.position),"lineWidth"in t&&(this.lineWidth=t.lineWidth),"colors"in t&&(this.colors=t.colors),"enabled"in t&&(this.enabled=t.enabled),"drawSides"in t&&(this.drawSides=t.drawSides))},l.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},{"./shaders/index":140,"gl-buffer":78,"gl-vao":150}],142:[function(t,e,r){var n=t("glslify"),i=n(["precision highp float;\n\nprecision highp float;\n#define GLSLIFY 1\n\nvec3 getOrthogonalVector(vec3 v) {\n  // Return up-vector for only-z vector.\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n  // Assign z = 0, x = -b, y = a:\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n    return normalize(vec3(-v.y, v.x, 0.0));\n  } else {\n    return normalize(vec3(0.0, v.z, -v.y));\n  }\n}\n\n// Calculate the tube vertex and normal at the given index.\n//\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\n//\n// Each tube segment is made up of a ring of vertices.\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\n// The indexes of tube segments run from 0 to 8.\n//\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\n  float segmentCount = 8.0;\n\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d);\n  vec3 y = v * sin(angle) * length(d);\n  vec3 v3 = x + y;\n\n  normal = normalize(v3);\n\n  return v3;\n}\n\nattribute vec4 vector;\nattribute vec4 color, position;\nattribute vec2 uv;\n\nuniform float vectorScale, tubeScale;\nuniform mat4 model, view, projection, inverseModel;\nuniform vec3 eyePosition, lightPosition;\n\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  // Scale the vector magnitude to stay constant with\n  // model & view changes.\n  vec3 normal;\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n\n  //Lighting geometry parameters\n  vec4 cameraCoordinate = view * tubePosition;\n  cameraCoordinate.xyz /= cameraCoordinate.w;\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\n  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\n\n  // vec4 m_position  = model * vec4(tubePosition, 1.0);\n  vec4 t_position  = view * tubePosition;\n  gl_Position      = projection * t_position;\n\n  f_color          = color;\n  f_data           = tubePosition.xyz;\n  f_position       = position.xyz;\n  f_uv             = uv;\n}\n"]),a=n(["#extension GL_OES_standard_derivatives : enable\n\nprecision highp float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat cookTorranceSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness,\n  float fresnel) {\n\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\n\n  //Half angle vector\n  vec3 H = normalize(lightDirection + viewDirection);\n\n  //Geometric term\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\n  float VdotH = max(dot(viewDirection, H), 0.000001);\n  float LdotH = max(dot(lightDirection, H), 0.000001);\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\n  float G = min(1.0, min(G1, G2));\n  \n  //Distribution term\n  float D = beckmannDistribution(NdotH, roughness);\n\n  //Fresnel term\n  float F = pow(1.0 - VdotN, fresnel);\n\n  //Multiply terms and done\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\n}\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\nuniform sampler2D texture;\n\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n  vec3 N = normalize(f_normal);\n  vec3 L = normalize(f_lightDirection);\n  vec3 V = normalize(f_eyeDirection);\n\n  if(gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = litColor * opacity;\n}\n"]),o=n(["precision highp float;\n\nprecision highp float;\n#define GLSLIFY 1\n\nvec3 getOrthogonalVector(vec3 v) {\n  // Return up-vector for only-z vector.\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n  // Assign z = 0, x = -b, y = a:\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n    return normalize(vec3(-v.y, v.x, 0.0));\n  } else {\n    return normalize(vec3(0.0, v.z, -v.y));\n  }\n}\n\n// Calculate the tube vertex and normal at the given index.\n//\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\n//\n// Each tube segment is made up of a ring of vertices.\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\n// The indexes of tube segments run from 0 to 8.\n//\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\n  float segmentCount = 8.0;\n\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d);\n  vec3 y = v * sin(angle) * length(d);\n  vec3 v3 = x + y;\n\n  normal = normalize(v3);\n\n  return v3;\n}\n\nattribute vec4 vector;\nattribute vec4 position;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\nuniform float tubeScale;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  vec3 normal;\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n\n  gl_Position = projection * view * tubePosition;\n  f_id        = id;\n  f_position  = position.xyz;\n}\n"]),s=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3  clipBounds[2];\nuniform float pickId;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n\n  gl_FragColor = vec4(pickId, f_id.xyz);\n}"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:"position",type:"vec4"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"},{name:"vector",type:"vec4"}]},r.pickShader={vertex:o,fragment:s,attributes:[{name:"position",type:"vec4"},{name:"id",type:"vec4"},{name:"vector",type:"vec4"}]}},{glslify:231}],143:[function(t,e,r){"use strict";var n=t("gl-vec3"),i=t("gl-vec4"),a=["xyz","xzy","yxz","yzx","zxy","zyx"],o=function(t,e,r,a){for(var o=0,s=0;s<t.length;s++)for(var l=t[s].velocities,c=0;c<l.length;c++)o=Math.max(o,n.length(l[c]));var u=t.map((function(t){return function(t,e,r,a){for(var o=t.points,s=t.velocities,l=t.divergences,c=[],u=[],f=[],h=[],p=[],d=[],g=0,m=0,v=i.create(),y=i.create(),x=0;x<o.length;x++){var b=o[x],_=s[x],w=l[x];0===e&&(w=.05*r),m=n.length(_)/a,v=i.create(),n.copy(v,_),v[3]=w;for(var T=0;T<8;T++)p[T]=[b[0],b[1],b[2],T];if(h.length>0)for(T=0;T<8;T++){var k=(T+1)%8;c.push(h[T],p[T],p[k],p[k],h[k],h[T]),f.push(y,v,v,v,y,y),d.push(g,m,m,m,g,g);var A=c.length;u.push([A-6,A-5,A-4],[A-3,A-2,A-1])}var M=h;h=p,p=M;var S=y;y=v,v=S;var E=g;g=m,m=E}return{positions:c,cells:u,vectors:f,vertexIntensity:d}}(t,r,a,o)})),f=[],h=[],p=[],d=[];for(s=0;s<u.length;s++){var g=u[s],m=f.length;f=f.concat(g.positions),p=p.concat(g.vectors),d=d.concat(g.vertexIntensity);for(c=0;c<g.cells.length;c++){var v=g.cells[c],y=[];h.push(y);for(var x=0;x<v.length;x++)y.push(v[x]+m)}}return{positions:f,cells:h,vectors:p,vertexIntensity:d,colormap:e}},s=function(t,e){var r,n=t.length;for(r=0;r<n;r++){var i=t[r];if(i===e)return r;if(i>e)return r-1}return r},l=function(t,e,r){return t<e?e:t>r?r:t},c=function(t){var e=1/0;t.sort((function(t,e){return t-e}));for(var r=t.length,n=1;n<r;n++){var i=Math.abs(t[n]-t[n-1]);i<e&&(e=i)}return e};e.exports=function(t,e){var r=t.startingPositions,i=t.maxLength||1e3,u=t.tubeSize||1,f=t.absoluteTubeSize,h=t.gridFill||"+x+y+z",p={};-1!==h.indexOf("-x")&&(p.reversedX=!0),-1!==h.indexOf("-y")&&(p.reversedY=!0),-1!==h.indexOf("-z")&&(p.reversedZ=!0),p.filled=a.indexOf(h.replace(/-/g,"").replace(/\+/g,""));var d=t.getVelocity||function(e){return function(t,e,r){var i=e.vectors,a=e.meshgrid,o=t[0],c=t[1],u=t[2],f=a[0].length,h=a[1].length,p=a[2].length,d=s(a[0],o),g=s(a[1],c),m=s(a[2],u),v=d+1,y=g+1,x=m+1;if(d=l(d,0,f-1),v=l(v,0,f-1),g=l(g,0,h-1),y=l(y,0,h-1),m=l(m,0,p-1),x=l(x,0,p-1),d<0||g<0||m<0||v>f-1||y>h-1||x>p-1)return n.create();var b,_,w,T,k,A,M=a[0][d],S=a[0][v],E=a[1][g],L=a[1][y],C=a[2][m],P=(o-M)/(S-M),I=(c-E)/(L-E),O=(u-C)/(a[2][x]-C);switch(isFinite(P)||(P=.5),isFinite(I)||(I=.5),isFinite(O)||(O=.5),r.reversedX&&(d=f-1-d,v=f-1-v),r.reversedY&&(g=h-1-g,y=h-1-y),r.reversedZ&&(m=p-1-m,x=p-1-x),r.filled){case 5:k=m,A=x,w=g*p,T=y*p,b=d*p*h,_=v*p*h;break;case 4:k=m,A=x,b=d*p,_=v*p,w=g*p*f,T=y*p*f;break;case 3:w=g,T=y,k=m*h,A=x*h,b=d*h*p,_=v*h*p;break;case 2:w=g,T=y,b=d*h,_=v*h,k=m*h*f,A=x*h*f;break;case 1:b=d,_=v,k=m*f,A=x*f,w=g*f*p,T=y*f*p;break;default:b=d,_=v,w=g*f,T=y*f,k=m*f*h,A=x*f*h}var z=i[b+w+k],D=i[b+w+A],R=i[b+T+k],F=i[b+T+A],B=i[_+w+k],N=i[_+w+A],j=i[_+T+k],U=i[_+T+A],V=n.create(),H=n.create(),q=n.create(),G=n.create();n.lerp(V,z,B,P),n.lerp(H,D,N,P),n.lerp(q,R,j,P),n.lerp(G,F,U,P);var Y=n.create(),W=n.create();n.lerp(Y,V,q,I),n.lerp(W,H,G,I);var X=n.create();return n.lerp(X,Y,W,O),X}(e,t,p)},g=t.getDivergence||function(t,e){var r=n.create(),i=1e-4;n.add(r,t,[i,0,0]);var a=d(r);n.subtract(a,a,e),n.scale(a,a,1/i),n.add(r,t,[0,i,0]);var o=d(r);n.subtract(o,o,e),n.scale(o,o,1/i),n.add(r,t,[0,0,i]);var s=d(r);return n.subtract(s,s,e),n.scale(s,s,1/i),n.add(r,a,o),n.add(r,r,s),r},m=[],v=e[0][0],y=e[0][1],x=e[0][2],b=e[1][0],_=e[1][1],w=e[1][2],T=function(t){var e=t[0],r=t[1],n=t[2];return!(e<v||e>b||r<y||r>_||n<x||n>w)},k=10*n.distance(e[0],e[1])/i,A=k*k,M=1,S=0,E=r.length;E>1&&(M=function(t){for(var e=[],r=[],n=[],i={},a={},o={},s=t.length,l=0;l<s;l++){var u=t[l],f=u[0],h=u[1],p=u[2];i[f]||(e.push(f),i[f]=!0),a[h]||(r.push(h),a[h]=!0),o[p]||(n.push(p),o[p]=!0)}var d=c(e),g=c(r),m=c(n),v=Math.min(d,g,m);return isFinite(v)?v:1}(r));for(var L=0;L<E;L++){var C=n.create();n.copy(C,r[L]);var P=[C],I=[],O=d(C),z=C;I.push(O);var D=[],R=g(C,O),F=n.length(R);isFinite(F)&&F>S&&(S=F),D.push(F),m.push({points:P,velocities:I,divergences:D});for(var B=0;B<100*i&&P.length<i&&T(C);){B++;var N=n.clone(O),j=n.squaredLength(N);if(0===j)break;if(j>A&&n.scale(N,N,k/Math.sqrt(j)),n.add(N,N,C),O=d(N),n.squaredDistance(z,N)-A>-1e-4*A){P.push(N),z=N,I.push(O);R=g(N,O),F=n.length(R);isFinite(F)&&F>S&&(S=F),D.push(F)}C=N}}var U=o(m,t.colormap,S,M);return f?U.tubeScale=f:(0===S&&(S=1),U.tubeScale=.5*u*M/S),U};var u=t("./lib/shaders"),f=t("gl-cone3d").createMesh;e.exports.createTubeMesh=function(t,e){return f(t,e,{shaders:u,traceType:"streamtube"})}},{"./lib/shaders":142,"gl-cone3d":79,"gl-vec3":169,"gl-vec4":205}],144:[function(t,e,r){var n=t("gl-shader"),i=t("glslify"),a=i(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec4 uv;\nattribute vec3 f;\nattribute vec3 normal;\n\nuniform vec3 objectOffset;\nuniform mat4 model, view, projection, inverseModel;\nuniform vec3 lightPosition, eyePosition;\nuniform sampler2D colormap;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec2 planeCoordinate;\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\nvarying vec4 vColor;\n\nvoid main() {\n  vec3 localCoordinate = vec3(uv.zw, f.x);\n  worldCoordinate = objectOffset + localCoordinate;\n  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\n  vec4 clipPosition = projection * view * worldPosition;\n  gl_Position = clipPosition;\n  kill = f.y;\n  value = f.z;\n  planeCoordinate = uv.xy;\n\n  vColor = texture2D(colormap, vec2(value, value));\n\n  //Lighting geometry parameters\n  vec4 cameraCoordinate = view * worldPosition;\n  cameraCoordinate.xyz /= cameraCoordinate.w;\n  lightDirection = lightPosition - cameraCoordinate.xyz;\n  eyeDirection   = eyePosition - cameraCoordinate.xyz;\n  surfaceNormal  = normalize((vec4(normal,0) * inverseModel).xyz);\n}\n"]),o=i(["precision highp float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat beckmannSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness) {\n  return beckmannDistribution(dot(surfaceNormal, normalize(lightDirection + viewDirection)), roughness);\n}\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 lowerBound, upperBound;\nuniform float contourTint;\nuniform vec4 contourColor;\nuniform sampler2D colormap;\nuniform vec3 clipBounds[2];\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\nuniform float vertexColor;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\nvarying vec4 vColor;\n\nvoid main() {\n  if (\n    kill > 0.0 ||\n    vColor.a == 0.0 ||\n    outOfRange(clipBounds[0], clipBounds[1], worldCoordinate)\n  ) discard;\n\n  vec3 N = normalize(surfaceNormal);\n  vec3 V = normalize(eyeDirection);\n  vec3 L = normalize(lightDirection);\n\n  if(gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = max(beckmannSpecular(L, V, N, roughness), 0.);\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  //decide how to interpolate color \u2014 in vertex or in fragment\n  vec4 surfaceColor =\n    step(vertexColor, .5) * texture2D(colormap, vec2(value, value)) +\n    step(.5, vertexColor) * vColor;\n\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = mix(litColor, contourColor, contourTint) * opacity;\n}\n"]),s=i(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec4 uv;\nattribute float f;\n\nuniform vec3 objectOffset;\nuniform mat3 permutation;\nuniform mat4 model, view, projection;\nuniform float height, zOffset;\nuniform sampler2D colormap;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec2 planeCoordinate;\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\nvarying vec4 vColor;\n\nvoid main() {\n  vec3 dataCoordinate = permutation * vec3(uv.xy, height);\n  worldCoordinate = objectOffset + dataCoordinate;\n  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\n\n  vec4 clipPosition = projection * view * worldPosition;\n  clipPosition.z += zOffset;\n\n  gl_Position = clipPosition;\n  value = f + objectOffset.z;\n  kill = -1.0;\n  planeCoordinate = uv.zw;\n\n  vColor = texture2D(colormap, vec2(value, value));\n\n  //Don't do lighting for contours\n  surfaceNormal   = vec3(1,0,0);\n  eyeDirection    = vec3(0,1,0);\n  lightDirection  = vec3(0,0,1);\n}\n"]),l=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec2 shape;\nuniform vec3 clipBounds[2];\nuniform float pickId;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec2 planeCoordinate;\nvarying vec3 surfaceNormal;\n\nvec2 splitFloat(float v) {\n  float vh = 255.0 * v;\n  float upper = floor(vh);\n  float lower = fract(vh);\n  return vec2(upper / 255.0, floor(lower * 16.0) / 16.0);\n}\n\nvoid main() {\n  if ((kill > 0.0) ||\n      (outOfRange(clipBounds[0], clipBounds[1], worldCoordinate))) discard;\n\n  vec2 ux = splitFloat(planeCoordinate.x / shape.x);\n  vec2 uy = splitFloat(planeCoordinate.y / shape.y);\n  gl_FragColor = vec4(pickId, ux.x, uy.x, ux.y + (uy.y/16.0));\n}\n"]);r.createShader=function(t){var e=n(t,a,o,null,[{name:"uv",type:"vec4"},{name:"f",type:"vec3"},{name:"normal",type:"vec3"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createPickShader=function(t){var e=n(t,a,l,null,[{name:"uv",type:"vec4"},{name:"f",type:"vec3"},{name:"normal",type:"vec3"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createContourShader=function(t){var e=n(t,s,o,null,[{name:"uv",type:"vec4"},{name:"f",type:"float"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e},r.createPickContourShader=function(t){var e=n(t,s,l,null,[{name:"uv",type:"vec4"},{name:"f",type:"float"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e}},{"gl-shader":132,glslify:231}],145:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=y(e),n=b(e),s=x(e),l=_(e),c=i(e),u=a(e,[{buffer:c,size:4,stride:40,offset:0},{buffer:c,size:3,stride:40,offset:16},{buffer:c,size:3,stride:40,offset:28}]),f=i(e),h=a(e,[{buffer:f,size:4,stride:20,offset:0},{buffer:f,size:1,stride:20,offset:16}]),p=i(e),d=a(e,[{buffer:p,size:2,type:e.FLOAT}]),g=o(e,1,256,e.RGBA,e.UNSIGNED_BYTE);g.minFilter=e.LINEAR,g.magFilter=e.LINEAR;var m=new M(e,[0,0],[[0,0,0],[0,0,0]],r,n,c,u,g,s,l,f,h,p,d,[0,0,0]),v={levels:[[],[],[]]};for(var w in t)v[w]=t[w];return v.colormap=v.colormap||"jet",m.update(v),m};var n=t("bit-twiddle"),i=t("gl-buffer"),a=t("gl-vao"),o=t("gl-texture2d"),s=t("typedarray-pool"),l=t("colormap"),c=t("ndarray-ops"),u=t("ndarray-pack"),f=t("ndarray"),h=t("surface-nets"),p=t("gl-mat4/multiply"),d=t("gl-mat4/invert"),g=t("binary-search-bounds"),m=t("ndarray-gradient"),v=t("./lib/shaders"),y=v.createShader,x=v.createContourShader,b=v.createPickShader,_=v.createPickContourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],T=[[0,0],[0,1],[1,0],[1,1],[1,0],[0,1]],k=[[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0]];function A(t,e,r,n,i){this.position=t,this.index=e,this.uv=r,this.level=n,this.dataCoordinate=i}!function(){for(var t=0;t<3;++t){var e=k[t],r=(t+2)%3;e[(t+1)%3+0]=1,e[r+3]=1,e[t+6]=1}}();function M(t,e,r,n,i,a,o,l,c,u,h,p,d,g,m){this.gl=t,this.shape=e,this.bounds=r,this.objectOffset=m,this.intensityBounds=[],this._shader=n,this._pickShader=i,this._coordinateBuffer=a,this._vao=o,this._colorMap=l,this._contourShader=c,this._contourPickShader=u,this._contourBuffer=h,this._contourVAO=p,this._contourOffsets=[[],[],[]],this._contourCounts=[[],[],[]],this._vertexCount=0,this._pickResult=new A([0,0,0],[0,0],[0,0],[0,0,0],[0,0,0]),this._dynamicBuffer=d,this._dynamicVAO=g,this._dynamicOffsets=[0,0,0],this._dynamicCounts=[0,0,0],this.contourWidth=[1,1,1],this.contourLevels=[[1],[1],[1]],this.contourTint=[0,0,0],this.contourColor=[[.5,.5,.5,1],[.5,.5,.5,1],[.5,.5,.5,1]],this.showContour=!0,this.showSurface=!0,this.enableHighlight=[!0,!0,!0],this.highlightColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.highlightTint=[1,1,1],this.highlightLevel=[-1,-1,-1],this.enableDynamic=[!0,!0,!0],this.dynamicLevel=[NaN,NaN,NaN],this.dynamicColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.dynamicTint=[1,1,1],this.dynamicWidth=[1,1,1],this.axesBounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.surfaceProject=[!1,!1,!1],this.contourProject=[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],this.colorBounds=[!1,!1],this._field=[f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0])],this.pickId=1,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.snapToData=!1,this.pixelRatio=1,this.opacity=1,this.lightPosition=[10,1e4,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.vertexColor=0,this.dirty=!0}var S=M.prototype;S.genColormap=function(t,e){var r=!1,n=u([l({colormap:t,nshades:256,format:"rgba"}).map((function(t,n){var i=e?function(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;r<e.length;++r){if(e.length<2)return 1;if(e[r][0]===t)return e[r][1];if(e[r][0]>t&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}(n/255,e):t[3];return i<1&&(r=!0),[t[0],t[1],t[2],255*i]}))]);return c.divseq(n,255),this.hasAlphaScale=r,n},S.isTransparent=function(){return this.opacity<1||this.hasAlphaScale},S.isOpaque=function(){return!this.isTransparent()},S.pickSlots=1,S.setPickBase=function(t){this.pickId=t};var E=[0,0,0],L={showSurface:!1,showContour:!1,projections:[w.slice(),w.slice(),w.slice()],clipBounds:[[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]]]};function C(t,e){var r,n,i,a=e.axes&&e.axes.lastCubeProps.axis||E,o=e.showSurface,s=e.showContour;for(r=0;r<3;++r)for(o=o||e.surfaceProject[r],n=0;n<3;++n)s=s||e.contourProject[r][n];for(r=0;r<3;++r){var l=L.projections[r];for(n=0;n<16;++n)l[n]=0;for(n=0;n<4;++n)l[5*n]=1;l[5*r]=0,l[12+r]=e.axesBounds[+(a[r]>0)][r],p(l,t.model,l);var c=L.clipBounds[r];for(i=0;i<2;++i)for(n=0;n<3;++n)c[i][n]=t.clipBounds[i][n];c[0][r]=-1e8,c[1][r]=1e8}return L.showSurface=o,L.showContour=s,L}var P={model:w,view:w,projection:w,inverseModel:w.slice(),lowerBound:[0,0,0],upperBound:[0,0,0],colorMap:0,clipBounds:[[0,0,0],[0,0,0]],height:0,contourTint:0,contourColor:[0,0,0,1],permutation:[1,0,0,0,1,0,0,0,1],zOffset:-1e-4,objectOffset:[0,0,0],kambient:1,kdiffuse:1,kspecular:1,lightPosition:[1e3,1e3,1e3],eyePosition:[0,0,0],roughness:1,fresnel:1,opacity:1,vertexColor:0},I=w.slice(),O=[1,0,0,0,1,0,0,0,1];function z(t,e){t=t||{};var r=this.gl;r.disable(r.CULL_FACE),this._colorMap.bind(0);var n=P;n.model=t.model||w,n.view=t.view||w,n.projection=t.projection||w,n.lowerBound=[this.bounds[0][0],this.bounds[0][1],this.colorBounds[0]||this.bounds[0][2]],n.upperBound=[this.bounds[1][0],this.bounds[1][1],this.colorBounds[1]||this.bounds[1][2]],n.objectOffset=this.objectOffset,n.contourColor=this.contourColor[0],n.inverseModel=d(n.inverseModel,n.model);for(var i=0;i<2;++i)for(var a=n.clipBounds[i],o=0;o<3;++o)a[o]=Math.min(Math.max(this.clipBounds[i][o],-1e8),1e8);n.kambient=this.ambientLight,n.kdiffuse=this.diffuseLight,n.kspecular=this.specularLight,n.roughness=this.roughness,n.fresnel=this.fresnel,n.opacity=this.opacity,n.height=0,n.permutation=O,n.vertexColor=this.vertexColor;var s=I;for(p(s,n.view,n.model),p(s,n.projection,s),d(s,s),i=0;i<3;++i)n.eyePosition[i]=s[12+i]/s[15];var l=s[15];for(i=0;i<3;++i)l+=this.lightPosition[i]*s[4*i+3];for(i=0;i<3;++i){var c=s[12+i];for(o=0;o<3;++o)c+=s[4*o+i]*this.lightPosition[o];n.lightPosition[i]=c/l}var u=C(n,this);if(u.showSurface){for(this._shader.bind(),this._shader.uniforms=n,this._vao.bind(),this.showSurface&&this._vertexCount&&this._vao.draw(r.TRIANGLES,this._vertexCount),i=0;i<3;++i)this.surfaceProject[i]&&this.vertexCount&&(this._shader.uniforms.model=u.projections[i],this._shader.uniforms.clipBounds=u.clipBounds[i],this._vao.draw(r.TRIANGLES,this._vertexCount));this._vao.unbind()}if(u.showContour){var f=this._contourShader;n.kambient=1,n.kdiffuse=0,n.kspecular=0,n.opacity=1,f.bind(),f.uniforms=n;var h=this._contourVAO;for(h.bind(),i=0;i<3;++i)for(f.uniforms.permutation=k[i],r.lineWidth(this.contourWidth[i]*this.pixelRatio),o=0;o<this.contourLevels[i].length;++o)o===this.highlightLevel[i]?(f.uniforms.contourColor=this.highlightColor[i],f.uniforms.contourTint=this.highlightTint[i]):0!==o&&o-1!==this.highlightLevel[i]||(f.uniforms.contourColor=this.contourColor[i],f.uniforms.contourTint=this.contourTint[i]),this._contourCounts[i][o]&&(f.uniforms.height=this.contourLevels[i][o],h.draw(r.LINES,this._contourCounts[i][o],this._contourOffsets[i][o]));for(i=0;i<3;++i)for(f.uniforms.model=u.projections[i],f.uniforms.clipBounds=u.clipBounds[i],o=0;o<3;++o)if(this.contourProject[i][o]){f.uniforms.permutation=k[o],r.lineWidth(this.contourWidth[o]*this.pixelRatio);for(var g=0;g<this.contourLevels[o].length;++g)g===this.highlightLevel[o]?(f.uniforms.contourColor=this.highlightColor[o],f.uniforms.contourTint=this.highlightTint[o]):0!==g&&g-1!==this.highlightLevel[o]||(f.uniforms.contourColor=this.contourColor[o],f.uniforms.contourTint=this.contourTint[o]),this._contourCounts[o][g]&&(f.uniforms.height=this.contourLevels[o][g],h.draw(r.LINES,this._contourCounts[o][g],this._contourOffsets[o][g]))}for(h.unbind(),(h=this._dynamicVAO).bind(),i=0;i<3;++i)if(0!==this._dynamicCounts[i])for(f.uniforms.model=n.model,f.uniforms.clipBounds=n.clipBounds,f.uniforms.permutation=k[i],r.lineWidth(this.dynamicWidth[i]*this.pixelRatio),f.uniforms.contourColor=this.dynamicColor[i],f.uniforms.contourTint=this.dynamicTint[i],f.uniforms.height=this.dynamicLevel[i],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]),o=0;o<3;++o)this.contourProject[o][i]&&(f.uniforms.model=u.projections[o],f.uniforms.clipBounds=u.clipBounds[o],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]));h.unbind()}}S.draw=function(t){return z.call(this,t,!1)},S.drawTransparent=function(t){return z.call(this,t,!0)};var D={model:w,view:w,projection:w,inverseModel:w,clipBounds:[[0,0,0],[0,0,0]],height:0,shape:[0,0],pickId:0,lowerBound:[0,0,0],upperBound:[0,0,0],zOffset:0,objectOffset:[0,0,0],permutation:[1,0,0,0,1,0,0,0,1],lightPosition:[0,0,0],eyePosition:[0,0,0]};function R(t,e){return Array.isArray(t)?[e(t[0]),e(t[1]),e(t[2])]:[e(t),e(t),e(t)]}function F(t){return Array.isArray(t)?3===t.length?[t[0],t[1],t[2],1]:[t[0],t[1],t[2],t[3]]:[0,0,0,1]}function B(t){if(Array.isArray(t)){if(Array.isArray(t))return[F(t[0]),F(t[1]),F(t[2])];var e=F(t);return[e.slice(),e.slice(),e.slice()]}}S.drawPick=function(t){t=t||{};var e=this.gl;e.disable(e.CULL_FACE);var r=D;r.model=t.model||w,r.view=t.view||w,r.projection=t.projection||w,r.shape=this._field[2].shape,r.pickId=this.pickId/255,r.lowerBound=this.bounds[0],r.upperBound=this.bounds[1],r.objectOffset=this.objectOffset,r.permutation=O;for(var n=0;n<2;++n)for(var i=r.clipBounds[n],a=0;a<3;++a)i[a]=Math.min(Math.max(this.clipBounds[n][a],-1e8),1e8);var o=C(r,this);if(o.showSurface){for(this._pickShader.bind(),this._pickShader.uniforms=r,this._vao.bind(),this._vao.draw(e.TRIANGLES,this._vertexCount),n=0;n<3;++n)this.surfaceProject[n]&&(this._pickShader.uniforms.model=o.projections[n],this._pickShader.uniforms.clipBounds=o.clipBounds[n],this._vao.draw(e.TRIANGLES,this._vertexCount));this._vao.unbind()}if(o.showContour){var s=this._contourPickShader;s.bind(),s.uniforms=r;var l=this._contourVAO;for(l.bind(),a=0;a<3;++a)for(e.lineWidth(this.contourWidth[a]*this.pixelRatio),s.uniforms.permutation=k[a],n=0;n<this.contourLevels[a].length;++n)this._contourCounts[a][n]&&(s.uniforms.height=this.contourLevels[a][n],l.draw(e.LINES,this._contourCounts[a][n],this._contourOffsets[a][n]));for(n=0;n<3;++n)for(s.uniforms.model=o.projections[n],s.uniforms.clipBounds=o.clipBounds[n],a=0;a<3;++a)if(this.contourProject[n][a]){s.uniforms.permutation=k[a],e.lineWidth(this.contourWidth[a]*this.pixelRatio);for(var c=0;c<this.contourLevels[a].length;++c)this._contourCounts[a][c]&&(s.uniforms.height=this.contourLevels[a][c],l.draw(e.LINES,this._contourCounts[a][c],this._contourOffsets[a][c]))}l.unbind()}},S.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=this._field[2].shape,r=this._pickResult,n=e[0]*(t.value[0]+(t.value[2]>>4)/16)/255,i=Math.floor(n),a=n-i,o=e[1]*(t.value[1]+(15&t.value[2])/16)/255,s=Math.floor(o),l=o-s;i+=1,s+=1;var c=r.position;c[0]=c[1]=c[2]=0;for(var u=0;u<2;++u)for(var f=u?a:1-a,h=0;h<2;++h)for(var p=i+u,d=s+h,m=f*(h?l:1-l),v=0;v<3;++v)c[v]+=this._field[v].get(p,d)*m;for(var y=this._pickResult.level,x=0;x<3;++x)if(y[x]=g.le(this.contourLevels[x],c[x]),y[x]<0)this.contourLevels[x].length>0&&(y[x]=0);else if(y[x]<this.contourLevels[x].length-1){var b=this.contourLevels[x][y[x]],_=this.contourLevels[x][y[x]+1];Math.abs(b-c[x])>Math.abs(_-c[x])&&(y[x]+=1)}for(r.index[0]=a<.5?i:i+1,r.index[1]=l<.5?s:s+1,r.uv[0]=n/e[0],r.uv[1]=o/e[1],v=0;v<3;++v)r.dataCoordinate[v]=this._field[v].get(r.index[0],r.index[1]);return r},S.padField=function(t,e){var r=e.shape.slice(),n=t.shape.slice();c.assign(t.lo(1,1).hi(r[0],r[1]),e),c.assign(t.lo(1).hi(r[0],1),e.hi(r[0],1)),c.assign(t.lo(1,n[1]-1).hi(r[0],1),e.lo(0,r[1]-1).hi(r[0],1)),c.assign(t.lo(0,1).hi(1,r[1]),e.hi(1)),c.assign(t.lo(n[0]-1,1).hi(1,r[1]),e.lo(r[0]-1)),t.set(0,0,e.get(0,0)),t.set(0,n[1]-1,e.get(0,r[1]-1)),t.set(n[0]-1,0,e.get(r[0]-1,0)),t.set(n[0]-1,n[1]-1,e.get(r[0]-1,r[1]-1))},S.update=function(t){t=t||{},this.objectOffset=t.objectOffset||this.objectOffset,this.dirty=!0,"contourWidth"in t&&(this.contourWidth=R(t.contourWidth,Number)),"showContour"in t&&(this.showContour=R(t.showContour,Boolean)),"showSurface"in t&&(this.showSurface=!!t.showSurface),"contourTint"in t&&(this.contourTint=R(t.contourTint,Boolean)),"contourColor"in t&&(this.contourColor=B(t.contourColor)),"contourProject"in t&&(this.contourProject=R(t.contourProject,(function(t){return R(t,Boolean)}))),"surfaceProject"in t&&(this.surfaceProject=t.surfaceProject),"dynamicColor"in t&&(this.dynamicColor=B(t.dynamicColor)),"dynamicTint"in t&&(this.dynamicTint=R(t.dynamicTint,Number)),"dynamicWidth"in t&&(this.dynamicWidth=R(t.dynamicWidth,Number)),"opacity"in t&&(this.opacity=t.opacity),"opacityscale"in t&&(this.opacityscale=t.opacityscale),"colorBounds"in t&&(this.colorBounds=t.colorBounds),"vertexColor"in t&&(this.vertexColor=t.vertexColor?1:0),"colormap"in t&&this._colorMap.setPixels(this.genColormap(t.colormap,this.opacityscale));var e=t.field||t.coords&&t.coords[2]||null,r=!1;if(e||(e=this._field[2].shape[0]||this._field[2].shape[2]?this._field[2].lo(1,1).hi(this._field[2].shape[0]-2,this._field[2].shape[1]-2):this._field[2].hi(0,0)),"field"in t||"coords"in t){var i=(e.shape[0]+2)*(e.shape[1]+2);i>this._field[2].data.length&&(s.freeFloat(this._field[2].data),this._field[2].data=s.mallocFloat(n.nextPow2(i))),this._field[2]=f(this._field[2].data,[e.shape[0]+2,e.shape[1]+2]),this.padField(this._field[2],e),this.shape=e.shape.slice();for(var a=this.shape,o=0;o<2;++o)this._field[2].size>this._field[o].data.length&&(s.freeFloat(this._field[o].data),this._field[o].data=s.mallocFloat(this._field[2].size)),this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2]);if(t.coords){var l=t.coords;if(!Array.isArray(l)||3!==l.length)throw new Error("gl-surface: invalid coordinates for x/y");for(o=0;o<2;++o){var c=l[o];for(v=0;v<2;++v)if(c.shape[v]!==a[v])throw new Error("gl-surface: coords have incorrect shape");this.padField(this._field[o],c)}}else if(t.ticks){var u=t.ticks;if(!Array.isArray(u)||2!==u.length)throw new Error("gl-surface: invalid ticks");for(o=0;o<2;++o){var p=u[o];if((Array.isArray(p)||p.length)&&(p=f(p)),p.shape[0]!==a[o])throw new Error("gl-surface: invalid tick length");var d=f(p.data,a);d.stride[o]=p.stride[0],d.stride[1^o]=0,this.padField(this._field[o],d)}}else{for(o=0;o<2;++o){var g=[0,0];g[o]=1,this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2],g,0)}this._field[0].set(0,0,0);for(var v=0;v<a[0];++v)this._field[0].set(v+1,0,v);for(this._field[0].set(a[0]+1,0,a[0]-1),this._field[1].set(0,0,0),v=0;v<a[1];++v)this._field[1].set(0,v+1,v);this._field[1].set(0,a[1]+1,a[1]-1)}var y=this._field,x=f(s.mallocFloat(3*y[2].size*2),[3,a[0]+2,a[1]+2,2]);for(o=0;o<3;++o)m(x.pick(o),y[o],"mirror");var b=f(s.mallocFloat(3*y[2].size),[a[0]+2,a[1]+2,3]);for(o=0;o<a[0]+2;++o)for(v=0;v<a[1]+2;++v){var _=x.get(0,o,v,0),w=x.get(0,o,v,1),k=x.get(1,o,v,0),A=x.get(1,o,v,1),M=x.get(2,o,v,0),S=x.get(2,o,v,1),E=k*S-A*M,L=M*w-S*_,C=_*A-w*k,P=Math.sqrt(E*E+L*L+C*C);P<1e-8?(P=Math.max(Math.abs(E),Math.abs(L),Math.abs(C)))<1e-8?(C=1,L=E=0,P=1):P=1/P:P=1/Math.sqrt(P),b.set(o,v,0,E*P),b.set(o,v,1,L*P),b.set(o,v,2,C*P)}s.free(x.data);var I=[1/0,1/0,1/0],O=[-1/0,-1/0,-1/0],z=1/0,D=-1/0,F=(a[0]-1)*(a[1]-1)*6,N=s.mallocFloat(n.nextPow2(10*F)),j=0,U=0;for(o=0;o<a[0]-1;++o)t:for(v=0;v<a[1]-1;++v){for(var V=0;V<2;++V)for(var H=0;H<2;++H)for(var q=0;q<3;++q){var G=this._field[q].get(1+o+V,1+v+H);if(isNaN(G)||!isFinite(G))continue t}for(q=0;q<6;++q){var Y=o+T[q][0],W=v+T[q][1],X=this._field[0].get(Y+1,W+1),Z=this._field[1].get(Y+1,W+1);G=this._field[2].get(Y+1,W+1),E=b.get(Y+1,W+1,0),L=b.get(Y+1,W+1,1),C=b.get(Y+1,W+1,2),t.intensity&&(J=t.intensity.get(Y,W));var J=t.intensity?t.intensity.get(Y,W):G+this.objectOffset[2];N[j++]=Y,N[j++]=W,N[j++]=X,N[j++]=Z,N[j++]=G,N[j++]=0,N[j++]=J,N[j++]=E,N[j++]=L,N[j++]=C,I[0]=Math.min(I[0],X+this.objectOffset[0]),I[1]=Math.min(I[1],Z+this.objectOffset[1]),I[2]=Math.min(I[2],G+this.objectOffset[2]),z=Math.min(z,J),O[0]=Math.max(O[0],X+this.objectOffset[0]),O[1]=Math.max(O[1],Z+this.objectOffset[1]),O[2]=Math.max(O[2],G+this.objectOffset[2]),D=Math.max(D,J),U+=1}}for(t.intensityBounds&&(z=+t.intensityBounds[0],D=+t.intensityBounds[1]),o=6;o<j;o+=10)N[o]=(N[o]-z)/(D-z);this._vertexCount=U,this._coordinateBuffer.update(N.subarray(0,j)),s.freeFloat(N),s.free(b.data),this.bounds=[I,O],this.intensity=t.intensity||this._field[2],this.intensityBounds[0]===z&&this.intensityBounds[1]===D||(r=!0),this.intensityBounds=[z,D]}if("levels"in t){var K=t.levels;for(K=Array.isArray(K[0])?K.slice():[[],[],K],o=0;o<3;++o)K[o]=K[o].slice(),K[o].sort((function(t,e){return t-e}));for(o=0;o<3;++o)for(v=0;v<K[o].length;++v)K[o][v]-=this.objectOffset[o];t:for(o=0;o<3;++o){if(K[o].length!==this.contourLevels[o].length){r=!0;break}for(v=0;v<K[o].length;++v)if(K[o][v]!==this.contourLevels[o][v]){r=!0;break t}}this.contourLevels=K}if(r){y=this._field,a=this.shape;for(var Q=[],$=0;$<3;++$){var tt=this.contourLevels[$],et=[],rt=[],nt=[0,0,0];for(o=0;o<tt.length;++o){var it=h(this._field[$],tt[o]);et.push(Q.length/5|0),U=0;t:for(v=0;v<it.cells.length;++v){var at=it.cells[v];for(q=0;q<2;++q){var ot=it.positions[at[q]],st=ot[0],lt=0|Math.floor(st),ct=st-lt,ut=ot[1],ft=0|Math.floor(ut),ht=ut-ft,pt=!1;e:for(var dt=0;dt<3;++dt){nt[dt]=0;var gt=($+dt+1)%3;for(V=0;V<2;++V){var mt=V?ct:1-ct;for(Y=0|Math.min(Math.max(lt+V,0),a[0]),H=0;H<2;++H){var vt=H?ht:1-ht;if(W=0|Math.min(Math.max(ft+H,0),a[1]),G=dt<2?this._field[gt].get(Y,W):(this.intensity.get(Y,W)-this.intensityBounds[0])/(this.intensityBounds[1]-this.intensityBounds[0]),!isFinite(G)||isNaN(G)){pt=!0;break e}var yt=mt*vt;nt[dt]+=yt*G}}}if(pt){if(q>0){for(var xt=0;xt<5;++xt)Q.pop();U-=1}continue t}Q.push(nt[0],nt[1],ot[0],ot[1],nt[2]),U+=1}}rt.push(U)}this._contourOffsets[$]=et,this._contourCounts[$]=rt}var bt=s.mallocFloat(Q.length);for(o=0;o<Q.length;++o)bt[o]=Q[o];this._contourBuffer.update(bt),s.freeFloat(bt)}},S.dispose=function(){this._shader.dispose(),this._vao.dispose(),this._coordinateBuffer.dispose(),this._colorMap.dispose(),this._contourBuffer.dispose(),this._contourVAO.dispose(),this._contourShader.dispose(),this._contourPickShader.dispose(),this._dynamicBuffer.dispose(),this._dynamicVAO.dispose();for(var t=0;t<3;++t)s.freeFloat(this._field[t].data)},S.highlight=function(t){var e,r;if(!t)return this._dynamicCounts=[0,0,0],this.dyanamicLevel=[NaN,NaN,NaN],void(this.highlightLevel=[-1,-1,-1]);for(e=0;e<3;++e)this.enableHighlight[e]?this.highlightLevel[e]=t.level[e]:this.highlightLevel[e]=-1;for(r=this.snapToData?t.dataCoordinate:t.position,e=0;e<3;++e)r[e]-=this.objectOffset[e];if(this.enableDynamic[0]&&r[0]!==this.dynamicLevel[0]||this.enableDynamic[1]&&r[1]!==this.dynamicLevel[1]||this.enableDynamic[2]&&r[2]!==this.dynamicLevel[2]){for(var n=0,i=this.shape,a=s.mallocFloat(12*i[0]*i[1]),o=0;o<3;++o)if(this.enableDynamic[o]){this.dynamicLevel[o]=r[o];var l=(o+1)%3,c=(o+2)%3,u=this._field[o],f=this._field[l],p=this._field[c],d=h(u,r[o]),g=d.cells,m=d.positions;for(this._dynamicOffsets[o]=n,e=0;e<g.length;++e)for(var v=g[e],y=0;y<2;++y){var x=m[v[y]],b=+x[0],_=0|b,w=0|Math.min(_+1,i[0]),T=b-_,k=1-T,A=+x[1],M=0|A,S=0|Math.min(M+1,i[1]),E=A-M,L=1-E,C=k*L,P=k*E,I=T*L,O=T*E,z=C*f.get(_,M)+P*f.get(_,S)+I*f.get(w,M)+O*f.get(w,S),D=C*p.get(_,M)+P*p.get(_,S)+I*p.get(w,M)+O*p.get(w,S);if(isNaN(z)||isNaN(D)){y&&(n-=1);break}a[2*n+0]=z,a[2*n+1]=D,n+=1}this._dynamicCounts[o]=n-this._dynamicOffsets[o]}else this.dynamicLevel[o]=NaN,this._dynamicCounts[o]=0;this._dynamicBuffer.update(a.subarray(0,2*n)),s.freeFloat(a)}}},{"./lib/shaders":144,"binary-search-bounds":31,"bit-twiddle":32,colormap:53,"gl-buffer":78,"gl-mat4/invert":98,"gl-mat4/multiply":100,"gl-texture2d":146,"gl-vao":150,ndarray:259,"ndarray-gradient":252,"ndarray-ops":254,"ndarray-pack":255,"surface-nets":302,"typedarray-pool":308}],146:[function(t,e,r){"use strict";var n=t("ndarray"),i=t("ndarray-ops"),a=t("typedarray-pool");e.exports=function(t){if(arguments.length<=1)throw new Error("gl-texture2d: Missing arguments for texture2d constructor");o||c(t);if("number"==typeof arguments[1])return v(t,arguments[1],arguments[2],arguments[3]||t.RGBA,arguments[4]||t.UNSIGNED_BYTE);if(Array.isArray(arguments[1]))return v(t,0|arguments[1][0],0|arguments[1][1],arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if("object"==typeof arguments[1]){var e=arguments[1],r=u(e)?e:e.raw;if(r)return y(t,r,0|e.width,0|e.height,arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if(e.shape&&e.data&&e.stride)return x(t,e)}throw new Error("gl-texture2d: Invalid arguments for texture2d constructor")};var o=null,s=null,l=null;function c(t){o=[t.LINEAR,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_NEAREST],s=[t.NEAREST,t.LINEAR,t.NEAREST_MIPMAP_NEAREST,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_LINEAR],l=[t.REPEAT,t.CLAMP_TO_EDGE,t.MIRRORED_REPEAT]}function u(t){return"undefined"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||"undefined"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||"undefined"!=typeof HTMLVideoElement&&t instanceof HTMLVideoElement||"undefined"!=typeof ImageData&&t instanceof ImageData}var f=function(t,e){i.muls(t,e,255)};function h(t,e,r){var n=t.gl,i=n.getParameter(n.MAX_TEXTURE_SIZE);if(e<0||e>i||r<0||r>i)throw new Error("gl-texture2d: Invalid texture size");return t._shape=[e,r],t.bind(),n.texImage2D(n.TEXTURE_2D,0,t.format,e,r,0,t.format,t.type,null),t._mipLevels=[0],t}function p(t,e,r,n,i,a){this.gl=t,this.handle=e,this.format=i,this.type=a,this._shape=[r,n],this._mipLevels=[0],this._magFilter=t.NEAREST,this._minFilter=t.NEAREST,this._wrapS=t.CLAMP_TO_EDGE,this._wrapT=t.CLAMP_TO_EDGE,this._anisoSamples=1;var o=this,s=[this._wrapS,this._wrapT];Object.defineProperties(s,[{get:function(){return o._wrapS},set:function(t){return o.wrapS=t}},{get:function(){return o._wrapT},set:function(t){return o.wrapT=t}}]),this._wrapVector=s;var l=[this._shape[0],this._shape[1]];Object.defineProperties(l,[{get:function(){return o._shape[0]},set:function(t){return o.width=t}},{get:function(){return o._shape[1]},set:function(t){return o.height=t}}]),this._shapeVector=l}var d=p.prototype;function g(t,e){return 3===t.length?1===e[2]&&e[1]===t[0]*t[2]&&e[0]===t[2]:1===e[0]&&e[1]===t[0]}function m(t){var e=t.createTexture();return t.bindTexture(t.TEXTURE_2D,e),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),e}function v(t,e,r,n,i){var a=t.getParameter(t.MAX_TEXTURE_SIZE);if(e<0||e>a||r<0||r>a)throw new Error("gl-texture2d: Invalid texture shape");if(i===t.FLOAT&&!t.getExtension("OES_texture_float"))throw new Error("gl-texture2d: Floating point textures not supported on this platform");var o=m(t);return t.texImage2D(t.TEXTURE_2D,0,n,e,r,0,n,i,null),new p(t,o,e,r,n,i)}function y(t,e,r,n,i,a){var o=m(t);return t.texImage2D(t.TEXTURE_2D,0,i,i,a,e),new 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sMTuJLff/zjH1qyZIkCAwM1YcIE7du3T5mZmTp58qTzfVbQS1rW99kbb7yhHTt26MUXX9SNN96o8PBw7d69W++8846uueYaPfzww+WOG0DJKP4AoBx69uyp2NhYnT592vlX64JbPocNG+Z8Xqwkhw4duugyHx8fRURESJLz1kmp7LcIlteFYjx/+fk9C6tXr9a+ffvk7e2tWbNm6cYbbyzSi3L06FGXYyu45t27d9fbb7+t5s2bF/mSWxHHKY9atWo5/7+ycmXF66GiFOQpMzOzxHVSU1PdFU6FiIiIcJ7XhebpvNh7ytMUvM+efvppPfzww6pXr16R4taV91lCQoLGjBmjOXPm6OTJk1q9erX69esnSXrzzTf17bfflnvfAIpH8QcA5eDt7e0c0GXixImFnv+70EAvBUoafOP8ZS1atHDeWuXr6+t8Pu27775zKfbSOnDgQImTmJ8+fdo5SMk111xTaBtJio6OLvEWt4ULF1ZIbJJKHKjEGKPFixe7fJzyqF+/vvPcy5urgi/YJfWmWPF6qCjVq1eX9L8c/t7p06e1bds2d4bkMj8/PzVr1kySLjh5/YWWeaKLvc/27t2r3bt3V8ixvLy8dN1112nq1KmqV6+epHM9/AAqFsUfAJTT8OHD5ePjox9//NF5i2HHjh3VqFGji2773nvvFfs82I4dOzR16lRJ50YNPF9BUTlnzhzNmTPngvsv70AXv/f8888X2z5+/HhlZGTIx8dHAwcOdLYXjDpZMHLq7x08eFD//Oc/XY6r4DibN28udvl7772nX3/91eXjlNddd90l6dyInBs3brzgusXlKjQ0VJIKPTf5e1a8HipCy5YtJanEUWJfe+015/OMl5KCUXq//PLLYv9ocvLkSefzwZeKi73PHn/88XLt90L59fb2dj63e7HnfgGUHe8qACin2rVrq2/fvpKk5cuXS7r4QC8FcnJy1LVrV61bt07SuR6ehQsXqnv37srKylJsbKzuueeeQtvcdttt6tKli4wx6t+/v1544YVCt5idPXtWS5Ys0V//+lc1aNDA5fMLCwvTp59+qoceeshZqJ4+fVovvviinnvuOUnSX//610IDXLRv317BwcEyxmjw4MHauXOnpHPPqH3//fdKTEyskGeiCqZxmDt3rp5//nnnc5cpKSl68cUX9cADDygyMtLl45TXI488oiuuuEKZmZnq1KmT/vWvf+nkyZPO5SkpKZo7d66GDBmiDh06FNm+efPmkqTPPvusxIFa3P16qCh/+tOfJEnff/+9nnnmGaWlpUk6NzjOE088oRdeeME5FcCl5P7771fNmjWVmZmpHj16aNmyZc6e2/Xr16tr167Kzc21OMqyKXifvfDCC5o+fboz/qSkJP35z3/WV1995ezJLYs2bdrowQcf1NKlSwsNFHP48GE98MADzt7Enj17VsBZACikMiYPBABPc/7E5RdzscmTz7dw4cJCE7dnZmaWar9ffPGFqVatmpFkQkJCTFBQkHNZeHi4WbduXbH7SE1NNb1793auK8mEhoaa8PBw43A4nG0+Pj5Fti1pwuXfK7hWHTt2NI899piRZBwOh6levbrx9vZ2HqNLly4mIyOjyPbvvvtuofhCQkJMQECAc0Lz8yfK/v2k0gWTvMfFxV0wxuzsbNOhQwfnfgri8/LyMpJMr169nBPRd+zYscj2F5pEvCzXq+D4xU1YfujQIXPdddcVijE8PNyEhoYWuj4JCQlFtv3Pf/7jXO7r62vq1q1r4uLiTLt27Qqt547XQ1ldbJL33Nxc06lTpyK5czgcxuFwmFdffbVUk7y7Okn8hSZ5v9Bk5xe6bitWrDAhISHO/QQFBTl/Dg8PN19//bVz2ZEjR0o8RmmUdpL3oUOHlno/vz/vvXv3mpo1axZ6HYWFhTl/fvHFFy94rUu6VgXt578vgoODC72OR44cWbYLAqBU6PkDABfccMMNzkFZSjPQS4E2bdpo/fr1GjJkiMLCwpSbm6u6devqrrvu0i+//FLoObrzhYaG6rvvvtOcOXN0yy23qF69esrKylJ6errq1q2rbt266aWXXnLO7eaqV155RV988YXat28vY4z8/Px05ZVX6s0339S8efMUEBBQZJt77rlHs2fPVmJiokJCQpzn9sADD2jz5s264oorXI7L19dX8+fP1zPPPKNGjRrJ19dXxhi1bt1a7777rr799lvLR7GsU6eOfvjhB02ZMkV9+vRR7dq1lZ6eruzsbNWvX1833XST3njjDWev8fluu+02/ec//1H79u0VFBSkI0eOaN++fUXmUnP366EieHt7a/bs2Ro7dqyaNGkiPz8/ORwOdevWTQsWLNDo0aOtDrHc2rdvr59//ll/+ctfVKdOHeXm5io8PFx33HGHNmzYoIYNGzrXvRR6N+Pi4rR+/XoNHz7c2cMfEBCg3r176/vvv9eYMWPKtd8vvvhCY8eOVefOnRUfH6/s7Gzl5OQoLi5Ot9xyixYtWqTXX3+9Ik8FwP9zGOPmMbABoAr56aefnIXajh07Lvi83969exUfHy/p3G1T9evXd0eIZfbss89q7Nix6tix4yU3QAXgyd5//33dfffdatCgQYmDKQFAZbJFz9/KlSvVs2dPRUREKCQkRK1bt9akSZPKta/U1FQ98cQTatasmYKCghQQEKDGjRtr5MiRRSY6rqwYAHiOt956S9K5HsDSDPQCwJ4yMzP1xhtvSPrfs3QA4G5VvvibNm2aOnbsqHnz5qlFixbq0aOHdu3apaFDh5b51pITJ07o2muv1UsvvaTk5GR17dpV3bt3V1pamt544w1deeWVxc63VJExAPAcc+bM0eTJkyWJ9zIAffHFF3ryySe1ZcsWZWdnS5Jyc3O1fPly3XDDDdq6dasCAgL00EMPWRwpANuy9pHDynXy5Enng/XTpk1zth89etQkJCRc9IHx3xs5cqSRZPr06VNokIOMjAzTv39/I8kMGTKkUmMAYK0DBw6YuLg4Ex0d7RyYoHfv3qXatrQDSljt/AFfAJTehAkTCg1kEhERYfz8/Jxtfn5+ZsqUKVaHCcDGqnTP3wcffKC0tDT17dtXAwYMcLbXrFlT48aNk3RurqrSKngof8yYMYUGOQgICNBTTz0lSc5h2ysrBgDWys3N1b59+3TixAnFxMTo4Ycf1pQpU6wOC4AH6N27t5544gldf/31qlOnjs6ePStfX181adJE9957r3755Rf98Y9/tDpMADbmY3UAlWn27NmS/jfx6vl69eqlgIAALVy4UJmZmcWOWPd7pRnF7/fzSlV0DACsVb9+fefcXe7c1p2effZZPfvss1aHAVxyEhIS9I9//MPqMACgRFW652/z5s2SpFatWhVZ5ufnp+bNmyszM9M5CfHFdOvWTZL08ssvKzMz09memZmp559/XpI0fPjwSo0BAAAAAMqjyvb8paWlKTU1VZIUExNT7DoxMTFav3699u3bpxYtWlx0n6NHj9ayZcs0c+ZMxcfHq02bNpKktWvXKjMzU6+99pqGDRtW4TFkZWUpKyvL+XN+fr6Sk5MVGRkph8Nx0bgBAAAAVE3GGJ0+fVp16tSRl9eF+/aqbPF35swZ5/8HBQUVu05wcLAk6fTp06XaZ3BwsGbPnq27775bkydP1syZM53LOnXqpPbt21dKDC+99JLGjh1bqhgBAAAA2M+BAwdK7HAq4NHFX//+/bVt27YybTNp0iS1bt26UuLZv3+/evXqpSNHjmjSpEnOeXrmzp2rkSNHKjExUfPnz1eHDh0q9LhjxozRqFGjnD+npqaqXr16OnDggEJDQyv0WBeSnJEsSYoIjHDbMXFx5MXzkBPPQ048E3nxPOTEM5EXz+NJOUlLS1NsbKyqVat20XU9uvhLSkrSjh07yrRNenq6JCkkJKRQW3FF0tmzZyWpVBdKkoYOHaotW7ZoxowZ6tevn7N9yJAhCgkJ0cCBAzV69GitXbu2QmPw9/cvdrCZ0NBQtxZ/ub65544b6L5j4uLIi+chJ56HnHgm8uJ5yIlnIi+exxNzUprHwTx6wJdNmzbJGFOmf4mJiZLOFUZhYWGSpIMHDxa7/4L2uLi4i8Zy4MABLV26VP7+/rrpppuKLO/bt6/8/Py0bt0652AwFR0DAAAAAJSXRxd/rmrZsqUkacOGDUWW5eTkaMuWLQoICFCjRo0uuq+CIi04OFje3t5Flnt7eys4OFjGGKWkpFRKDAAAAABQXlW6+OvVq5ckaerUqUWWzZo1S5mZmerSpUup5terVauWJCk5OVlJSUlFlu/Zs0enTp1ScHCwoqKiKiUGAAAAACivKl383XnnnQoNDdXMmTM1ffp0Z/vx48f12GOPSZIeeeSRIts1adJETZo00aFDh5xt8fHxzqkYRowY4ZzCQZJSUlI0YsQISVK/fv3k4/O/RynLGwMAAAAAVCSPHvDFVREREfroo480ePBgDRo0SImJiYqMjNTChQuVkpKiUaNGOZ8RPF/BIDM5OTmF2idOnKguXbpowYIFSkhIcM7zt2bNGp08eVL169fXuHHjKiQGAAAAAKhIVbrnT5IGDhyo5cuXq3v37tq4caPmzJmjhIQEffLJJxo/fnyZ9tWmTRtt2rRJd911l0JDQ7Vw4UItXrxYNWvW1OOPP66ffvpJderUqdQYAAAAAKA8HMYYY3UQKJu0tDSFhYUpNTWVef5AXjwQOfE85MQzkRfPQ048E3nxPJ6Uk7LUBlW+5w8AAAAAQPEHAAAAALZA8QcAAAAANkDxBwAAAAA2QPEHAAAAADZA8QcAAAAANkDxBwAAAAA2QPEHAAAAADZA8QcAAAAANkDxBwAAAAA2QPEHAAAAADZA8QcAAAAANkDxBwAAAAA2QPEHAAAAADZA8QcAAAAANkDxBwAAAAA2QPEHAAAAADZA8QcAAAAANkDxBwAAAAA2QPEHAAAAADZA8QcAAAAANkDxBwAAAAA2QPEHAAAAADZA8QcAAAAANkDxBwAAAAA2QPEHAAAAADZA8QcAAAAANkDxBwAAAAA2QPEHAAAAADZA8QcAAAAANkDxBwAAAAA2QPEHAAAAADbgY3UAsK/9qak6kZ7u/DkqKEiSbN9WLyysTNdmT/JxSVJYQJbHnUtFt5X12ljV5ik5uVSulxU54dp4xmvJU94rFd12Kb++Kjsnl/K1sfK1VFXfK660Wf1aKu73yqWA4g+W2J+aqkZvvaWsvDxnm7+3tySVq83Py0sOh6NQm4/DIW8vr0Jt3v/flv27Ni+HQzn5+YW2lcOh3PPafL28ZKRCbf7e3sozpkibkQodo7TnEeDjo8VDhuiGSZOUmZtb5u19vbzkVcrr8Ptz9nY45CjFOft5eSm/mOuQb0yh/blyHYrLp5+3t5YOHVrk2hS3rq/DIa9LLPcV/br28fLS39u31yurVhW6XqXOvcMh87s2d+Te38tL+n0+i3ldlzafxZ2bn7e3Hm7TRhPWrKmSuS/unIs7t9LmM8DHR9/+8Y/q+fnnl3zuy5LP4s6tNOfh6ue4J+W+wl/XJXyOl/b3mbfDIZ8K/H1W0bmvjM/x5zt10thly4p8jl9qua/ofBZ8jr+xdu1Fr4O73vc77r//kigAKf5giRPp6YXeNJKK/FyWtuzz3qgFco1R7u/WzTNGecW1GVNkW/2uLaeYY7gSc3Ftmbm5+jUlpdCHfFm2Ly7GEq/D784vr5TnXNy1rujrUNwxsvPyir02xa2bY4x0ieW+ol/Xufn5+te6dUWuV6lzX0ybO3KfVVw+i2krbT6LO7fsvDyNW7WqyD6rSu6LO+fizq20+czMzdUvx44V+pJU0rqenvtL4XPck3Jf4a/rEj7HS/v7zNNzXxmf4x9u2FD85/gllnt3fY5b+b4/kZ5+SRR/PPMHAFVUi5o1rQ7BY11Tu7bVIVxS/Hz4WzFghcujoqwOwWPxOV4+FH8AUEUNb9XK6hA81sNt21odwiWlemCg1SEAtnTLFVdYHYLH4nO8fCj+YImooCAF/O4vyf7e3s77qO3aFuDjowbh4Vwbro3LbVyvktu4NmVr43qVrY3rVXIb16ZsbVyvkts87doE+Pg4B4LxdA5jfndjLDxeWlqawsLClJqaqtDQULcdNzkjWZIUERhRIftjtK+KGb1qT/IRSVJYQJjHnUtFt1k9stellpNL5XpZkROujWe8ljzlvVLRbZfy66uyc3IpXxsrX0tV9b3iSpvVr6Xifq9YpSy1AcXfJaiqFH//XLtW72/YoFuvuEKPt29fIfu0o4rOC1xHTjwPOfFM5MXzkBPPRF48jyflpCy1Abd9wjJHTp/WluPHdfTMGatDAQAAAKo8hu+CZe5s1UpdGjRQjBt7LwEAAAC7oviDZRpGRKhhhPVd5QAAAIAdcNsnAAAAANgAPX+wzM/HjmlvSooaR0aqMZOYAgAAAJWKnj9Y5t/r16vvF1/os19+sToUAAAAoMqj+INl6oeHq03duoplwBcAAACg0nHbJyzzaLt2erRdO6vDAAAAAGyBnj8AAAAAsAGKPwAAAACwAYo/WObpJUvU5oMPNIUBXwAAAIBKR/EHy+xKTtaPhw7p2NmzVocCAAAAVHkM+ALLPHr99fpT8+ZqFh1tdSgAAABAlUfxB8u0ql1brWrXtjoMAAAAwBa47RMAAAAAbICeP1hm09GjSs7I0OVRUapdrZrV4QAAAABVGj1/sMzfFy9W50mTNHf3bqtDAQAAAKo8ij9YJi4sTM1r1FBEYKDVoQAAAABVHrd9wjLv9OpldQgAAACAbdDzBwAAAAA2QPEHAAAAADZA8QfLPDh3rjpPmqQV+/ZZHQoAAABQ5VH8wTLrDh/W4qQknczIsDoUAAAAoMpjwBdY5vlOnfTb2bO6pk4dq0MBAAAAqjyKP1imS4MGVocAAAAA2Aa3fQIAAACADdDzB8tsOnpUmbm5ujwqSmEBAVaHAwAAAFRp9PzBMrdNn662H36on44csToUAAAAoMqj5w+WiQkNVXpOjoJ8fa0OBQAAAKjyKP5gmXm33WZ1CAAAAIBtcNsnAAAAANgAxR8AAAAA2ADFHywz9JtvdNOUKdqdnGx1KAAAAECVR/EHyyz89VfN2rlTp7OyrA4FAAAAqPIY8AWWGd+tm85kZ6teWJjVoQAAAABVHsUfLPPH5s2tDgEAAACwDW77BAAAAAAboOcPlvnl2DFJUuOoKPl5e1scDQAAAFC10fMHy7T98EO1eO89HUxLszoUAAAAoMqj5w+WqREcrLM5OfL14m8QAAAAQGWj+INlfn3oIatDAAAAAGyDLhcAAAAAsAGKPwAAAACwAYo/WCLfGA3++mv9cepUnc7KsjocAAAAoMqj+IMl8vLz9fXWrfryv/9Vbn6+1eEAAAAAVR4DvsASXg6H/nXjjcozRkG+vlaHAwAAAFR5FH+whLeXl/7aurXVYQAAAAC2wW2fAAAAAGAD9PzBEnn5+fr11Cl5e3kpPjxcDofD6pAAAACAKo2eP1jiVGamGv3rX2r4z3/KWB0MAAAAYAP0/MESxhiF+vsr3xh50esHAAAAVDqKP1giOjhYqY8/bnUYAAAAgG3Y4rbPlStXqmfPnoqIiFBISIhat26tSZMmlWtfqampeuKJJ9SsWTMFBQUpICBAjRs31siRI3X8+PEi62/fvl2vvPKKOnXqpKioKPn6+qpWrVoaMGCAVqxY4eqpAQAAAECpVPmev2nTpumWW25Rfn6+/vCHPygqKkqLFi3S0KFD9fPPP+u1114r9b5OnDih66+/Xrt27VKtWrXUtWtXSdKPP/6oN954Q19++aVWr16tuLg45zZdunTRoUOHFBISouuuu04RERHaunWrZsyYoW+++Uavv/66Hn744Yo+bQAAAAAopEr3/CUnJ+uOO+5QXl6epk6dqqVLl2rq1Knavn27EhISNH78eC1durTU+3vxxRe1a9cu9enTR0lJSZo5c6ZmzpyppKQk9e/fX0eOHNHTTz9daJsmTZpo0qRJ+u2337RgwQJ9+eWX+uWXX/Tee+/JGKPRo0dr69atFXzmnu/42bMa9s03+uvs2VaHAgAAANhClS7+PvjgA6Wlpalv374aMGCAs71mzZoaN26cJGn8+PGl3t/y5cslSWPGjFFAQICzPSAgQE899ZQkad26dYW2WbhwoW6//fZC60vSiBEj1K1bN+Xl5enrr78u24lVAamZmfp082ZN/uUXq0MBAAAAbKFK3/Y5+/97lQYNGlRkWa9evRQQEKCFCxcqMzOzSHFWHH9//4uuExkZWer4WrZsqfnz5+vw4cOl3qaqiAwK0sudO8vX29vqUAAAAABbqNI9f5s3b5YktWrVqsgyPz8/NW/eXJmZmdq5c2ep9tetWzdJ0ssvv6zMzExne2Zmpp5//nlJ0vDhw0sd36+//ipJqlWrVqm3qSoiAgP1t/btNaptW6tDAQAAAGyhyvb8paWlKTU1VZIUExNT7DoxMTFav3699u3bpxYtWlx0n6NHj9ayZcs0c+ZMxcfHq02bNpKktWvXKjMzU6+99pqGDRtWqvj27NmjWbNmSZL69OlzwXWzsrKUlZXl/DktLU2SlJyRrFzf3FIdryKcyjjltmOh9MiL5yEnnoeceCby4nnIiWciL57Hk3KSlpFW6nWrbM/fmTNnnP8fFBRU7DrBwcGSpNOnT5dqn8HBwZo9e7Zuu+02HT161Dngy9GjR3XVVVepffv2pdpPbm6uhg0bpqysLN1yyy26+uqrL7j+Sy+9pLCwMOe/2NjYUh3Hk2Xn5enI6TM6mZ5hdSgAAACALXh0z1///v21bdu2Mm0zadIktW7dulLi2b9/v3r16qUjR45o0qRJ6tGjhyRp7ty5GjlypBITEzV//nx16NDhgvt58MEH9cMPP6hBgwZ65513LnrcMWPGaNSoUc6f09LSFBsbq4jACIUGhrp2UuUQERjh8j7WHz6sa9//WLGhodo/cmQFRIWKyAsqFjnxPOTEM5EXz0NOPBN58TyekBOfnNKXdB5d/CUlJWnHjh1l2iY9PV2SFBISUqgtNLRokXT27FlJUrVq1Uq176FDh2rLli2aMWOG+vXr52wfMmSIQkJCNHDgQI0ePVpr164tcR//+Mc/9O6776pmzZr6/vvvFRFx8ReMv79/qQabuZQYY+TtcMjHq8p2PgMAAAAexaOLv02bNpV729DQUIWFhSk1NVUHDx5U06ZNi6xz8OBBSSo0KXtJDhw4oKVLl8rf31833XRTkeV9+/aVn5+f1q1bV+Looe+9956efPJJhYWFad68eUpISCjHmVUN19atq9zfzYkIAAAAoPJU6W6Xli1bSpI2bNhQZFlOTo62bNmigIAANWrU6KL7KigUg4OD5V3M9ATe3t4KDg6WMUYpKSlFln/xxRf661//qqCgIM2ePVtXXnll2U4GAAAAAFxQpYu/Xr16SZKmTp1aZNmsWbOUmZmpLl26lGqOv4LpGJKTk5WUlFRk+Z49e3Tq1CkFBwcrKiqq0LI5c+ZoyJAh8vHx0YwZM9SuXbvynA4AAAAAlFuVLv7uvPNOhYaGaubMmZo+fbqz/fjx43rsscckSY888kiR7Zo0aaImTZro0KFDzrb4+HjndBAjRoxwTiMhSSkpKRoxYoQkqV+/fvLx+d/dtCtXrtSgQYNkjNGXX37pnCvQ7nacOKG/zp6tl3/4wepQAAAAAFvw6Gf+XBUREaGPPvpIgwcP1qBBg5SYmKjIyEgtXLhQKSkpGjVqlBITE4tsVzDITE5OTqH2iRMnqkuXLlqwYIESEhKc8/ytWbNGJ0+eVP369TVu3LhC2/Tu3VsZGRmKj4/XN998o2+++abI8dq3b68777yzYk76ErE/NVXvrF+vFjVr6vFSTpEBAAAAoPyqdPEnSQMHDtTy5cv1wgsvaM2aNcrOzlbTpk11//33a+jQoWXaV5s2bbRp0ya98sorWrRokRYuXCgvLy/Fx8frrrvu0qOPPlpk9M6C5/+SkpKKvV20gN2KvwbVq+vpP/xBNf5/rkUAAAAAlcthjDFWB4GySUtLc45kWtwUFpUlOSNZkmfMZ4L/IS+eh5x4HnLimciL5yEnnom8eB5PyklZaoMq/cwfAAAAAOAcij9YIjsvTymZmUr/3XOVAAAAACoHxR8s8e2OHar+yivqMXmy1aEAAAAAtkDxB0vk//+jpt5evAQBAAAAd6jyo33CMw1q2lRZTz4pxhsCAAAA3IPiD5bwcjjk5+1tdRgAAACAbXDPHQAAAADYAD1/sMSPhw5p6tatahYdraFXXml1OAAAAECVR88fLLH56FG9umqVpm/fbnUoAAAAgC3Q8wdLXFGzpkZdd52a1ahhdSgAAACALVD8wRLXxcToupgYq8MAAAAAbIPbPgEAAADABuj5gyXy8vNlJHk7HHI4HFaHAwAAAFR59PzBEm+sWSPf55/XkG++sToUAAAAwBYo/mCJPGMknev5AwAAAFD5uO0TlniwTRvd2aqVfL34+wMAAADgDhR/sESAj48CfHj5AQAAAO5CtwsAAAAA2ABdL7DE4qQkrdi3T21iYtQjIcHqcAAAAIAqj54/WGLRr7/q2WXLNHfXLqtDAQAAAGyB4g+WaF23ru65+mq1q1fP6lAAAAAAW+C2T1iib5Mm6tukidVhAAAAALZBzx8AAAAA2ADFHwAAAADYAMUfLPHwvHnye/55PbdsmdWhAAAAALZA8QdLZOflKSc/X/nGWB0KAAAAYAsM+AJLvNi5s57o0EHV/PysDgUAAACwBYo/WCI8IEDhAQFWhwEAAADYBrd9AgAAAIAN0PMHS8zeuVNbf/tNneLjdU2dOlaHAwAAAFR59PzBEp9v2aLHFi7U8n37rA4FAAAAsAV6/mCJDvXqydvhUNPoaKtDAQAAAGyB4g+WuOeaa3TPNddYHQYAAABgG9z2CQAAAAA2QPEHAAAAADZA8QdL/HHqVFV/5RVN/vlnq0MBAAAAbIHiD5ZIy8pSSmamcvLyrA4FAAAAsAUGfIEl3r/pJp3JzlbNkBCrQwEAAABsgeIPlqgbGmp1CAAAAICtcNsnAAAAANgAPX+wxPRt23TszBl1T0hQg+rVrQ4HAAAAqPLo+YMlxq9erfvmzNGmo0etDgUAAACwBXr+YIlO9eurVkiI6larZnUoAAAAgC1Q/MESL9xwg9UhAAAAALbCbZ8AAAAAYAMUfwAAAABgAxR/sMQNn36qmNdf1w/791sdCgAAAGALFH+wxNEzZ3To9Gnl5OVZHQoAAABgCwz4AktMv+UWpefk6LKICKtDAQAAAGyB4g+WaBIVZXUIAAAAgK1w2ycAAAAA2AA9f7DEV//9rzJyctS7USNFBgVZHQ4AAABQ5dHzB0uM+v57DZs5U/tSU60OBQAAALAFev5giU7x8frt7FmF+ftbHQoAAABgCxR/sMR/+ve3OgQAAADAVrjtEwAAAABsgOIPAAAAAGyA4g+WuPK999T4X//SfgZ8AQAAANyCZ/5giZ0nTyojN1d5+flWhwIAAADYAsUfLLF46FDl5OWpdrVqVocCAAAA2ALFHyxxXUyM1SEAAAAAtsIzfwAAAABgA/T8we2MMfp661Z5ORy6qVEj+fvwMgQAAAAqG9+64XZ5xuiWqVMlSScefZTiDwAAAHADvnXD7YwxSqxfX3n5+fLz9rY6HAAAAMAWKP7gdr7e3loydKjVYQAAAAC2woAvAAAAAGADFH8AAAAAYAMUf3C71MxMtXzvPbX697+Vl59vdTgAAACALfDMH9wuOy9PPx87JknycjgsjgYAAACwB4o/uF1YQIDm33ab8oyRg+IPAAAAcAuKP7idn7e3ujZsaHUYAAAAgK3wzB8AAAAA2AA9f3C79JwcLU5Kkp+3t7rRAwgAAAC4BT1/cLsjp0/rpilTNPCrr6wOBQAAALANev7gdr7e3rqmTh0F+fpaHQoAAABgGxR/cLt6YWFad9ddVocBAAAA2Aq3fQIAAACADVD8AQAAAIANUPzB7XacOKHrP/yQAV8AAAAAN+KZP7hdWlaWVh88qHphYVaHAgAAANgGxR/cLiEiQjNuuUUBPrz8AAAAAHfh2zfcrnpgoPo1aWJ1GAAAAICt8MwfAAAAANgAPX9wu1MZGdp09Kiq+fvrmjp1rA4HAAAAsAV6/uB2G44c0Q2TJukvM2daHQoAAABgGxR/cLsgX181jY5Ww+rVrQ4FAAAAsA1u+4TbtY2N1X/vu8/qMAAAAABbqbTib968edqyZYtiY2M1YMAA+fr6VtahAAAAAAAX4dJtn++8844aNGiglStXFmofPHiwevXqpb/97W/685//rA4dOigzM9OlQAEAAAAA5edS8Tdjxgylp6erbdu2zrZ58+Zp6tSpqlu3rh5//HG1bt1a69at0/vvv+9ysKgaVuzbpy6TJumR77+3OhQAAADANly67XPHjh1q3ry5vLz+V0N+8cUXcjgcmjp1qlq3bq3MzEzFxcVp8uTJeuCBB1wOGJe+o2fOaFFSknLy860OBQAAALANl3r+fvvtN9WqVatQ27JlyxQbG6vWrVtLkgICAnT99dcrKSnJlUO5ZOXKlerZs6ciIiIUEhKi1q1ba9KkSeXaV2pqqp544gk1a9ZMQUFBCggIUOPGjTVy5EgdP368VPt4/vnn5XA45HA4NHny5HLFcSlrExOjzwYM0N87dLA6FAAAAMA2XOr5CwsL04kTJ5w/JyUlad++fRoyZEih9YKDg3X27FlXDlVu06ZN0y233KL8/Hz94Q9/UFRUlBYtWqShQ4fq559/1muvvVbqfZ04cULXX3+9du3apVq1aqlr166SpB9//FFvvPGGvvzyS61evVpxcXEl7mPHjh36xz/+IYfDIWOMy+d3KaoXFqY/X3GF1WEAAAAAtuJSz19CQoKWL1+u/fv3S5ImTpwoh8OhHj16FFrv4MGDRXoI3SE5OVl33HGH8vLyNHXqVC1dulRTp07V9u3blZCQoPHjx2vp0qWl3t+LL76oXbt2qU+fPkpKStLMmTM1c+ZMJSUlqX///jpy5IiefvrpErc3xujuu+9WeHi4+vTpUwFnCAAAAACl41Lxd++99yozM1MtWrTQ1VdfrXHjxik6Olq9e/d2rpORkaH169eradOmLgdbVh988IHS0tLUt29fDRgwwNles2ZNjRs3TpI0fvz4Uu9v+fLlkqQxY8YoICDA2R4QEKCnnnpKkrRu3boLxrN8+XKNHz9e4eHhZTmVKuW3s2f146FD2nXypNWhAAAAALbhUvF366236pFHHlFWVpY2btyounXrasqUKQoJCXGu89VXXyk9PV2dO3d2Odiymj17tiRp0KBBRZb16tVLAQEBWrhwYamnofD397/oOpGRkcW2Hz16VI899pg6d+6sW2+9tVTHq6q+3bFDbT74QKPmz7c6FAAAAMA2XCr+JOnVV19VSkqKjh07pv3796tTp06Flt9www3auHGj7rzzTlcPVWabN2+WJLVq1arIMj8/PzVv3lyZmZnauXNnqfbXrVs3SdLLL79cqGDMzMzU888/L0kaPnx4sds++OCDysjI0Lvvvlumc6iKgv38VC8sTDWCgqwOBQAAALANlwZ8KeDv76/o6Ohil8XGxio2NrYiDlMmaWlpSk1NlSTFxMQUu05MTIzWr1+vffv2qUWLFhfd5+jRo7Vs2TLNnDlT8fHxatOmjSRp7dq1yszM1GuvvaZhw4YV2W7WrFn6+uuvNXbsWF122WVlPpesrCxlZWUVOjdJSs5IVq5vbpn3V16nMk5VyH66Nayjbg3PDQqUnJFcIfu0s4rKCyoOOfE85MQzkRfPQ048E3nxPJ6Uk7SMtFKvWyHF3+/l5ubqgw8+0JYtW1SvXj3dddddql69emUcqkRnzpxx/n9QCT1MwcHBkqTTp0+Xap/BwcGaPXu27r77bk2ePFkzZ850LuvUqZPat29fbBz33XefGjVqpL/97W9lOQWnl156SWPHji3XtgAAAAAguVj8Pffccxo7dqyWLFmiP/zhD5Kk/Px8JSYmavXq1TLGyOFwaOLEiVq/fn2ZBznp37+/tm3bVqZtJk2a5JxjsKLt379fvXr10pEjRzRp0iTnqKZz587VyJEjlZiYqPnz56vDefPXPfHEEzpw4IAWLVpUqmcGizNmzBiNGjXK+XNaWppiY2MVERih0MBQ106qHCICI9x+TFwcefE85MTzkBPPRF48DznxTOTF83hCTnxySl/SuVT8LViwQDExMc7CT5KmTp2qVatWqUWLFvrrX/+quXPn6ptvvtHbb7+tv//972Xaf1JSknbs2FGmbdLT0yWp0KAz6enpCg0tWiQVzD1YrVq1Uu176NCh2rJli2bMmKF+/fo524cMGaKQkBANHDhQo0eP1tq1ayWdm//v7bff1u23364bbrihTOdxPn9//3IXjp7om+3b9fGmTepUv74evu46q8MBAAAAbMGl4u/XX3/VFb+brHv69OlyOByaMmWKLr/8ct15552qX7++pk6dWubib9OmTeWOLTQ0VGFhYUpNTdXBgweLnWri4MGDknTBSdkLHDhwQEuXLpW/v79uuummIsv79u0rPz8/rVu3TpmZmQoICNCcOXOUn5+vX375RYmJiYXW3759uyTpH//4hz744AP16NFDjz/+eDnO9NKzOzlZ3+7YobAqVNACAAAAns6l4u/kyZOKiooq1LZs2TJddtlluvzyyyVJDodD1157rZYsWeLKocqlZcuWWr58uTZs2FCk+MvJydGWLVsUEBCgRo0aXXRfBYVicHCwvL29iyz39vZWcHCwTp06pZSUlEKT2l+oiN2+fbu2b9+u+vXrl+6kqoBuDRsq1N9fl0VY300OAAAA2IVLUz1ERUXp0KFDzp+3bt2qY8eOFenl8vPzU3Z2tiuHKpdevXpJOncr6u/NmjVLmZmZ6tKlS6EJ20tSUMwlJycrKSmpyPI9e/bo1KlTCg4OdhbEzz77rIwxxf4bOnSoJOk///mPjDH65JNPynual5wWNWvq7quvVqf4eKtDAQAAAGzDpeLv8ssv18qVK7Vx40ZJ0uuvvy6Hw6GePXsWWm/v3r2qXbu2K4cqlzvvvFOhoaGaOXOmpk+f7mw/fvy4HnvsMUnSI488UmS7Jk2aqEmTJoUK2/j4eOd0ECNGjHBOIyFJKSkpGjFihCSpX79+8vGplEFUAQAAAKDcXKpSRo4cqUWLFunaa69VeHi4Tp06pfj4eOcomJKUmpqqn376SX369HE52LKKiIjQRx99pMGDB2vQoEFKTExUZGSkFi5cqJSUFI0aNapIL6Uk5yAzOTk5hdonTpyoLl26aMGCBUpISHDO87dmzRqdPHlS9evX17hx4yr9vC51x86c0Yn0dEUGBanWeQPzAAAAAKg8LvX89ezZU2+99Zbq1q2rjIwMtWvXTjNmzJCfn59znUmTJiknJ0edO3d2OdjyGDhwoJYvX67u3btr48aNmjNnjhISEvTJJ59o/PjxZdpXmzZttGnTJt11110KDQ3VwoULtXjxYtWsWVOPP/64fvrpJ9WpU6eSzqTq+NePP6r5u+/qH8uXWx0KAAAAYBsOY4ypzANkZGQoOztbISEhxQ6UgrJLS0tzjmRa3BQWlSU5I1mS6/OZvLRihV5fs0Z3tWqlFy36o0BVUlF5QcUhJ56HnHgm8uJ5yIlnIi+ex5NyUpbaoNIfTgsMDFRgYGBlHwaXkDEdOmhMhw5WhwEAAADYSoUVf6tXr9aKFSucg6TUrVtXHTp0UNu2bSvqEAAAAACAcnK5+Nu5c6duv/12rV+/XpJUcBepw+GQJF1zzTWaPHmyLrvsMlcPBQAAAAAoJ5eKvyNHjqhjx446duyY6tSpo5tvvln169eXw+HQ3r179fXXX2vdunVKTEzU+vXrLZnuAZ7n002b9P2ePerfpIlubtbM6nAAAAAAW3Cp+HvhhRd07NgxjRw5Ui+99FKhUT4l6ZVXXtGYMWP0+uuv68UXX9Rbb73lUrCoGtYdPqwpW7YoISKC4g8AAABwE5emepgzZ44aN26s8ePHFyn8JMnX11evvvqqGjdurFmzZrlyKFQhg5o21YTu3XVjQoLVoQAAAAC24VLxd+TIEbVq1eqC6zgcDrVq1UpHjhxx5VCoQhLr19fD112ntrGxVocCAAAA2IZLxV9oaKgOHDhw0fUOHDjg1vnoAAAAAACFuVT8tW3bVitXrtTs2bNLXGfOnDlauXKlrr/+elcOhSrk+NmzSjp1SmlZWVaHAgAAANiGS8Xf448/Li8vL/Xv31+333675s6dq23btmnbtm2aN2+ehg4dqv79+8vLy0uPP/54RcWMS9wj8+erwT//qfd/+snqUAAAAADbcGm0z7Zt2+rjjz/WiBEj9Nlnn+nzzz8vtNwYo8DAQP373//Wdddd51KgqDr8vLwU6OMjP29vq0MBAAAAbMPlSd5vu+02JSYm6v3339cPP/ygw4cPS5Lq1KmjDh06aPjw4YplYA+c58O+ffVh375WhwEAAADYisvFnyTFxMRo7NixFbErAAAAAEAlcOmZPwAAAADApYHiD2731tq1uvPbb/XD/v1WhwIAAADYRplu+/R2YYAOh8Oh3Nzccm+PquP7PXs0e9cuXR8bq/b16lkdDgAAAGALZSr+YmNj5XA4KisW2MSQli3VNiZGV9eubXUoAAAAgG2Uqfjbu3dvJYUBOxncrJnVIQAAAAC2Y9kzfzt37tTy5cutOjwAAAAA2Iplxd9LL72kTp06WXV4WCg5I0O/nT2rLJ4BBQAAANyG0T7hdn2/+EI1XntNs3butDoUAAAAwDYo/uB2xhhJkrcXLz8AAADAXco04AtQEX644w5nAQgAAADAPSj+YAmmDAEAAADci/vuAAAAAMAGKP7gdi//8IMenjdPO0+etDoUAAAAwDYo/uB2k3/+WW+uXauDaWlWhwIAAADYBs/8we3uvvpqHT1zRvXDw60OBQAAALANij+43YNt2lgdAgAAAGA7lhV/7du3t+rQAAAAAGA7lj3zN3z4cH388cdWHR4WOpOdrbPZ2cpnrj8AAADAbVzq+bvhhhtKtZ6fn58iIyN15ZVX6o9//KNiY2NdOSwucZe//bYOpqVp3V136Zo6dawOBwAAALAFl4q/pUuXSjo3YbcpoRfn/GVTpkzRk08+qVdeeUUPP/ywK4fGJaygx8+Lid4BAAAAt3Hpts+kpCQ99NBD8vHx0a233qpvv/1WmzZt0qZNm/Tdd9/ptttuk4+Pjx544AH98MMPevHFFxUQEKBHHnlE8+fPr6hzwCVm9wMP6PSYMWpZs6bVoQAAAAC24VLP35o1a/TWW29p7ty56tq1a6FlLVq0UK9evXT77berZ8+euu666/T444+rTZs26ty5s9566y1169bNpeBxaQr09bU6BAAAAMB2XOr5e+2119ShQ4cihd/5unbtqvbt22v8+PGSpE6dOqlly5b68ccfXTk0AAAAAKAMXCr+tm3bpjqlGLCjTp062r59u/Pnyy67TCkpKa4cGpewZ5Ys0ROLFulEerrVoQAAAAC24dJtn0FBQVq/fr2MMXKUMHiHMUbr169XUFCQsy0zM1OhoaGuHBqXsPGrV+tsTo6GX3WVos57XQAAAACoPC71/HXp0kW7d+/WAw88oPRienEyMjL00EMPaffu3YWe79u1axfTPdjYX6+9Vg+2bq3wgACrQwEAAABsw6Wev5deekkLFy7Uu+++qylTpqhHjx7Oou7AgQP6/vvvderUKUVHR+sf//iHpHO3iu7YsUOPPvqo69HjkvTKBZ4RBQAAAFA5XCr+4uLitHr1ao0YMUKLFy/WlClTiqzTuXNnvfvuu4qLi5MkNWjQQEeOHFFYWJgrhwYAAAAAlIFLxZ8kNWzYUAsXLtSePXu0cuVKHTlyRJJUu3ZtXX/99UpISCi0vr+/v2oyv5ttGWNkJDmkEp8TBQAAAFDxXC7+CjRs2FANGzasqN2hijKSvJ97TpJ0fPRoRQcHWxsQAAAAYBMVVvxJ0vHjx3Xo0CFJUt26dVWjRo2K3D2qgLz8fOf/e3u5NN4QAAAAgDKokOLvnXfe0Ztvvqndu3cXar/sssv00EMP6d57762Iw6AK8PHy0olHH1WeMYz2CQAAALiRS8Vffn6+Bg8erBkzZsgYo/DwcMXFxcnhcGjfvn3auXOn7r//fi1atEhff/01z3hBDodDkcztBwAAALidS/fdTZw4UdOnT1ejRo307bffKjk5WRs3btSGDRt08uRJfffdd2rcuLFmzJihiRMnVlTMAAAAAIAycqn4+/jjjxUaGqqlS5eqd+/eRZb36tVLixcvVkhIiD766CNXDoUqIiMnR88vW6YXV6wo9PwfAAAAgMrlUvG3detW3XDDDRecuqFWrVrq3Lmztm7d6sqhUEWczcnR00uX6u+LF1sdCgAAAGArLg/4Uprn+HjWDwX8vb11d6tWyjNGXrwuAAAAALdxqfhr3LixFi9erBMnTigqKqrYdU6cOKHFixercePGrhwKVUQ1f3/9+6abrA4DAAAAsB2XbvscOnSoUlNT1blzZy1atKjI8iVLlqhr165KS0vTsGHDXDkUAAAAAMAFLvX83XfffZo3b57mzp2rbt26KTo6WnFxcZKkffv26bfffpMxRj179tR9991XIQEDAAAAAMrOpZ4/b29vfffdd3r11VcVExOj48ePa926dVq3bp2OHz+u2NhYvfrqq/r222/l5eXSoVBFJJ06pYAXXlCNV1+1OhQAAADAVlwe8MXLy0uPPPKIHnnkER04cECHDx+WJNWpU0exsbEuB4iqJTc/X1l5ecrMzbU6FAAAAMBWXC7+zhcbG0vBhwuqHx6ufQ8/LGOM1aEAAAAAtlKhxR9wMb7e3qoXFmZ1GAAAAIDtlKn4u+OOO8p9IIfDoQ8//LDc2wMAAAAAyq9Mxd8nn3xS7gNR/EGSjp89q/9s3qywgADd2aqV1eEAAAAAtlGm4m/JkiWVFQds4kBqqkYvWKCY0FCKPwAAAMCNylT8dezYsbLigE1UDwzUrVdcoeoBAVaHAgAAANiKZQO+PProo5o+fbr27NljVQiwQIPq1TV5wACrwwAAAABsx7KZ10+cOKG9e/dadXgAAAAAsBXLij8AAAAAgPtQ/MGtfti/X5HjxukPH39sdSgAAACArVD8wa0yc3OVnJGhlMxMq0MBAAAAbMWyAV9gT21jYrT1vvvk6+1tdSgAAACArVD8wa2C/fx0eXS01WEAAAAAtsNtnwAAAABgA/T8wa2STp3S93v2qE61aurTuLHV4QAAAAC2Qc8f3GrDkSO6d/ZsvbpqldWhAAAAALZiWc+fMUbGGKsOD4vUqVZN/Zs0UZOoKKtDAQAAAGzFsp6/Tz75RPn5+VYdHhZpGxur6bfcohc7d7Y6FAAAAMBWXOr5mzRpUqnW8/PzU2RkpFq2bKkaNWq4ckgAAAAAQDm4VPwNGzZMDoej1Os7HA516dJFb731li677DJXDg0AAAAAKAOXir+nn35ae/fu1aRJkxQSEqJu3bqpXr16kqQDBw5o/vz5On36tG6//Xb5+/tr1apVmj9/vjp06KCffvpJdevWrZCTwKXjq//+V6Pnz1eXBg30Ud++VocDAAAA2IZLxd/tt9+u1q1b64477tD48eMVFhZWaHlaWppGjRqlGTNmaO3atWrQoIEeffRRTZgwQS+//LLeeustl4LHpSctK0sH0tJ0Ij3d6lAAAAAAW3EYF4bcHDx4sDZs2KCdO3fKy6v4sWPy8/PVqFEjtWrVSl999ZWys7MVHx+voKAg7dq1q9yB21laWprCwsKUmpqq0NBQtx03OSNZkhQRGFHufZxIT9felBSF+vurUWRkRYVmaxWRF1QscuJ5yIlnIi+eh5x4JvLieTwpJ2WpDVwa7XPJkiVq06ZNiYWfJHl5eal169ZavHixpHODv7Rs2VKHDh1y5dC4REUFBemaOnUo/AAAAAA3c6n4S09P19GjRy+63rFjx5SZmen8OTQ0VD4+lk0xCAAAAAC241Lxd8UVV2j58uVavnx5ieusWLFCy5Yt0xVXXOFsO3DggKKjo105NC5RW3/7Tf/ZvFkr9++3OhQAAADAVlwq/h577DHl5eWpe/fuGjFihBYsWKDt27dr+/btWrBgge655x51795dxhg99thjkqTU1FT99NNPuu666yrkBHBpmbd7t4Z8843eXb/e6lAAAAAAW3Hp3ssBAwZowoQJ+tvf/qb3339fH3zwQaHlxhj5+flpwoQJ6t+/vyTp5MmTGjt2rDp37uzKoXGJqhcWpq4NGuiKGjWsDgUAAACwFZdG+yyQlJSkDz/8UKtWrdKRI0ckSbVr11a7du30l7/8RQ0aNHA5UPzPpTzaJyoeefE85MTzkBPPRF48DznxTOTF83hSTspSG1TIqCvx8fF64YUXKmJXAAAAAIBK4NIzfwAAAACAS0OFFH9bt27VyJEj1a5dOzVu3Ng5uIskrVq1Sv/85z+VnJxcEYfCJe6ttWt1+dtv6x8XGCEWAAAAQMVz+bbP119/XY8//rhyc3MlSQ6HQydOnCi0zsiRI+Xv768RI0a4ejhc4o6dPavtJ07o2NmzVocCAAAA2IpLPX+zZ8/W6NGjFRsbq+nTp+v48eP6/fgx119/vaKjozVz5kyXAkXVcGerVlo6dKgeaN3a6lAAAAAAW3Gp5+/1119XcHCwFixYcMERPa+88krt2LHDlUOhiqgfHq764eFWhwEAAADYjks9fwWTtV9sKoeoqCgdPXrUlUMBAAAAAFzgUs9fdna2qlWrdtH1jh8/Lh+fCplVApe4zUePas+pU2oSFaWm0dFWhwMAAADYhks9f/Hx8dq8efMF18nOztbPP/+sRo0auXIol6xcuVI9e/ZURESEQkJC1Lp1a02aNKlc+0pNTdUTTzyhZs2aKSgoSAEBAWrcuLFGjhyp48ePX3Dbb775Rj169FB0dLQCAgIUGxur/v3764cffihXLJeiDzdu1MCvvtLnv/xidSgAAACArbhU/PXp00d79+7V66+/XuI648aN02+//aYBAwa4cqhymzZtmjp27Kh58+apRYsW6tGjh3bt2qWhQ4dq9OjRZdrXiRMndO211+qll15ScnKyunbtqu7duystLU1vvPGGrrzySu3bt6/Idvn5+Ro+fLiz0Lv66qvVr18/xcbGas6cOVq6dGkFna3na1C9utrFxvLcHwAAAOBmLt2L+dhjj+mzzz7To48+qrVr16p///6SpGPHjmnGjBmaMWOGPvvsM8XHx+v++++vkIDLIjk5WXfccYfy8vI0bdo0ZwF67NgxtW/fXuPHj1fv3r2VmJhYqv29+OKL2rVrl/r06aMvv/xSAQEBkqTMzEz9+c9/1owZM/T000/r008/LbTdc889p48++kg33XSTPvnkE0VERDiXnTp1qsjUGFXZw9ddp4evu87qMAAAAADbcannr3r16lq4cKGaNWumr7/+Wrfeeqskad68eRo0aJAmT56syy+/XPPmzSvVs4EV7YMPPlBaWpr69u1bqOexZs2aGjdunCRp/Pjxpd7f8v+fmHzMmDHOwk+SAgIC9NRTT0mS1q1bV2ibgwcP6qWXXlK9evX05ZdfFir8pHPX8LLLLivbiQEAAABAGbk8CkujRo20adMmffvtt1qwYIH27t2r/Px8xcTEqGvXrho4cKC8vb0rItYymz17tiRp0KBBRZb16tVLAQEBWrhwoTIzMwsVcyXx9/e/6DqRkZGFfv7000+VnZ2tO++8U4GBgaWMHAAAAAAqVoUMwenl5aV+/fqpX79+FbG7ClMwGE2rVq2KLPPz81Pz5s21fv167dy5Uy1atLjo/rp166ZVq1bp5Zdf1hdffFHots/nn39ekjR8+PBC2yxevFjSucnujxw5os8++0y7d+9WWFiYOnXqpO7du8vhcLh0npeSp5cs0exduzTyuut0WymuOQAAAICKUSHFX3p6utavX68jR44oKyurxPWGDBlSEYcrlbS0NKWmpkqSYmJiil0nJiZG69ev1759+0pV/I0ePVrLli3TzJkzFR8frzZt2kiS1q5dq8zMTL322msaNmxYoW22bt3q/O/AgQOdMUnnBsNJTEzUjBkzFH6BAVCysrIKXde0tDRJUnJGsnJ9cy8ad0U5lXHK5X1sO3FMG44c0d6U35SckVwBUaEi8oKKRU48DznxTOTF85ATz0RePI8n5SQtI63U67pc/D399NOaMGGC0tPTS1zHGCOHw+HW4u/MmTPO/w8KCip2neDgYEnS6dOnS7XP4OBgzZ49W3fffbcmT56smTNnOpd16tRJ7du3L7LNqVPnXhijRo1S27Zt9eabbyohIUE//vij7rrrLi1dulR33XWXvv766xKP+9JLL2ns2LGlitHTPdi6lW5u2kiXRVS3OhQAAADAVlwq/saNG6cXXnhB3t7e6tWrlxo1alShA7v0799f27ZtK9M2kyZNUuvWrSsshvPt379fvXr10pEjRzRp0iT16NFDkjR37lyNHDlSiYmJmj9/vjp06ODcJj8/X9K5gV3mzp3rLDg7d+6sb7/9Vi1atNDUqVO1c+fOEudCHDNmjEaNGuX8OS0tTbGxsYoIjFBoYGilnOuFRARGXHylEnSIK/+2uDBX8oLKQU48DznxTOTF85ATz0RePI8n5MQnp/QlnUvF3/vvv6/AwECtWLGi2OfqXJWUlKQdO3aUaZuCHsiQkJBCbaGhRYuks2fPSlKpC9ahQ4dqy5YtmjFjRqHnG4cMGaKQkBANHDhQo0eP1tq1a53LQkJCdOrUKd18883Owq9A8+bNde211+rHH3/U8uXLSyz+/P39SzXYDAAAAACUxKWpHg4cOKCOHTtWSuEnSZs2bZIxpkz/CubsCw0NVVhYmKRz0y0Up6A9Li7uorEcOHBAS5culb+/v2666aYiy/v27Ss/Pz+tW7dOmZmZzvaCfdevX7/Y/Ra0Hz9+/KIxVAU/HT6sebt362Ba6e9NBgAAAOA6l4q/WrVqFenN8iQtW7aUJG3YsKHIspycHG3ZskUBAQEl9ridr6BQDA4OLnbqCm9vbwUHB8sYo5SUFGf7VVddJel/z/79XnLyuUFPzu+prMrGLlumGz/7TPN277Y6FAAAAMBWXCr+/vjHP2rp0qXO2yc9Ta9evSRJU6dOLbJs1qxZyszMVJcuXUo1x1+tWrUknSvWkpKSiizfs2ePTp06peDgYEVFRTnb+/TpI0latmxZkW3OnDnjLEwLisSqLj48XFfWqqWoEgbhAQAAAFA5XCr+nn32WV1++eXq06ePdntgT86dd96p0NBQzZw5U9OnT3e2Hz9+XI899pgk6ZFHHimyXZMmTdSkSRMdOnTI2RYfH++cDmLEiBGFpmxISUnRiBEjJEn9+vWTj8//HqW86aabdPnll2vVqlV65513nO15eXkaNWqUkpOT1bx582JHCq2K3rzxRm0cMUL9mjSxOhQAAADAVhzGGFPejW+44QZlZ2dr9erV8vLyUlxcnGJiYuTlVbSmdDgcWrRokUvBlse0adM0ePBg5/OAkZGRWrhwoVJSUjRq1CiNHz++2FilcwPOnP+s3tq1a9WlSxedOXNGUVFRznn+1qxZo5MnT6p+/fpauXKl6tSpU2h/mzZtUseOHZWWlqaWLVsqISFBGzdu1K+//qrIyEgtWbJEV1xxRanPKS0tTWFhYUpNTS12IJvKUjAvnyeMaoT/IS+eh5x4HnLimciL5yEnnom8eB5PyklZagOXir/iirwSD+RwKC8vr7yHcsnKlSv1wgsvaM2aNcrOzlbTpk11//33a+jQocWuX1LxJ527vfOVV17RokWLdOjQIXl5eSk+Pl59+vTRo48+qoiI4l8ASUlJevbZZzV//nydPHlSNWvWVI8ePfTkk0+WasCZ81H84XzkxfOQE89DTjwTefE85MQzkRfP40k5cVvxt2/fvjKtX9YiB8W7lIu/B+bM0c/Hj+u5xER1LGEEVJSNJ3344Bxy4nnIiWciL56HnHgm8uJ5PCknZakNXJrnj2IOZbXx6FGtPHBAJzMyrA4FAAAAsBWXij+grF644Qb9dvasWteta3UoAAAAgK1Q/MGtErnVEwAAALCES1M9AAAAAAAuDfT8wa1+OnxYGbm5al6jhsIDAqwOBwAAALANev7gVn+ZOVMdPv5Y6w8ftjoUAAAAwFbo+YNbxYWHKzM3VyF+flaHAgAAANgKxR/c6rs//cnqEAAAAABb4rZPAAAAALABij8AAAAAsAGKP7jV7TNm6MbPPtPOkyetDgUAAACwFZ75g1st3btXB9PSdCY72+pQAAAAAFuh+INbvdG9u85kZysuLMzqUAAAAABbofiDWw1s2tTqEAAAAABb4pk/AAAAALABev7gVhuPHJGR1Cw6Wv4+vPwAAAAAd6HnD27V8ZNPdPXEiTqQlmZ1KAAAAICt0PUCt6pTrZpOZ2fLz9vb6lAAAAAAW6H4g1ttv/9+q0MAAAAAbInbPgEAAADABij+AAAAAMAGKP7gNsYYDfjySw366iulZWVZHQ4AAABgKxR/cJt8YzRj+3ZN27ZNOXl5VocDAAAA2AoDvsBtHA6H3uvVS3nGKMTPz+pwAAAAAFuh+IPbeDkcGnHNNVaHAQAAANgSt30CAAAAgA3Q8we3ycvP186TJ+Xt5aXLIiLkcDisDgkAAACwDXr+4DapWVlq+s47avyvfyk3P9/qcAAAAABboecPbmOMUURgoPKNkbcXf3cAAAAA3IniD24TGRSkk489ZnUYAAAAgC3R/QIAAAAANkDxBwAAAAA2QPEHt/nt7FndNn267vr2W6tDAQAAAGyH4g9uk5aVpc9++UVTtmyxOhQAAADAdhjwBW4TGRSk8d26yYeRPgEAAAC3o/iD24QHBGhU27ZWhwEAAADYEl0wAAAAAGADFH9wm+y8PO1LSdGR06etDgUAAACwHYo/uM2W48dV/803dc3771sdCgAAAGA7FH9wK39vb/l7e1sdBgAAAGA7DPgCt2lVu7Yyn3zS6jAAAAAAW6LnDwAAAABsgOIPAAAAAGyA4g9us+PECY347js9v2yZ1aEAAAAAtkPxB7c5mJamiRs26Mv//tfqUAAAAADbYcAXuE2D6tX1XGKiooKCrA4FAAAAsB2KP7hNfPXqeqpjR6vDAAAAAGyJ2z4BAAAAwAbo+YPbZOXm6nR2tvy9vVXN39/qcAAAAABboecPbjN71y5Fv/qqenz2mdWhAAAAALZD8Qe3yTdGkuTtcFgcCQAAAGA/3PYJtxnUtKnynn7aWQQCAAAAcB+KP7iVl8MhL3r+AAAAALfjtk8AAAAAsAGKP7jNj4cOadT33+uDDRusDgUAAACwHYo/uM0vx45pwpo1+mb7dqtDAQAAAGyHZ/7gNi1q1tTf2rVTk6goq0MBAAAAbIfiD25zbd26urZuXavDAAAAAGyJ2z4BAAAAwAYo/uA2OXl5ysjJUW5+vtWhAAAAALZD8Qe3eXvdOgW9+KKGfvON1aEAAAAAtkPxB7fJ+/8ePyZ5BwAAANyPAV/gNg+0aaO7rr5a3hR/AAAAgNtR/MFt/Ly95eftbXUYAAAAgC1x2ycAAAAA2AA9f3CbxUlJWpKUpDYxMerdqJHV4QAAAAC2Qs8f3Gbp3r16YcUKzdu92+pQAAAAANuh5w9u06ZuXd1/7bX6Q1yc1aEAAAAAtkPxB7fp1aiRenG7JwAAAGAJbvsEAAAAABug+AMAAAAAG6D4g9s8PG+evMaO1VOLF1sdCgAAAGA7FH9wm7z8fBmrgwAAAABsigFf4DYvdu6sv//hDwry9bU6FAAAAMB2KP7gNtX8/VXN39/qMAAAAABb4rZPAAAAALABev7gNt/t2KEtx4/rhvh4tYmJsTocAAAAwFbo+YPbfL11q55YvFgr9u+3OhQAAADAduj5g9t0jIuTr5eXrqhRw+pQAAAAANuh+IPbDG/VSsNbtbI6DAAAAMCWuO0TAAAAAGyA4g8AAAAAbIDiD25zy9SpCnnxRX2yaZPVoQAAAAC2Q/EHt0nPydHZnBzl5edbHQoAAABgOwz4Ard5/6abdDY7W1FBQVaHAgAAANgOxR/cplZIiNUhAAAAALbFbZ8AAAAAYAP0/MFtpm3dqsOnT+vGyy5TQkSE1eEAAAAAtkLxB7d5c+1ardi/X19Xq0bxBwAAALgZxR/cpnN8vGpXq6bY0FCrQwEAAABsh+IPbvNMYqLVIQAAAAC2xYAvAAAAAGADtij+Vq5cqZ49eyoiIkIhISFq3bq1Jk2aVK59paam6oknnlCzZs0UFBSkgIAANW7cWCNHjtTx48eL3SYrK0uvvPKKWrVqpZCQEPn7+ys+Pl533XWXfv31V1dODQAAAABKpcoXf9OmTVPHjh01b948tWjRQj169NCuXbs0dOhQjR49ukz7OnHihK699lq99NJLSk5OVteuXdW9e3elpaXpjTfe0JVXXql9+/YV2iYzM1OJiYl6/PHHlZSUpI4dO6p3796SpA8++EBXXnmlfvrppwo7X092w6efqtZrr2nZ3r1WhwIAAADYTpUu/pKTk3XHHXcoLy9PU6dO1dKlSzV16lRt375dCQkJGj9+vJYuXVrq/b344ovatWuX+vTpo6SkJM2cOVMzZ85UUlKS+vfvryNHjujpp58utM3EiRO1Zs0aXXvttdq7d69mz56tadOmaffu3br//vt1+vRpjRo1qoLP3DP9lp6uY2fPKjc/3+pQAAAAANup0sXfBx98oLS0NPXt21cDBgxwttesWVPjxo2TJI0fP77U+1u+fLkkacyYMQoICHC2BwQE6KmnnpIkrVu3rthtRo0apbCwMGe7t7e3nnvuuWK3qapm3HKLNt9zj1rXrWt1KAAAAIDtVOnRPmfPni1JGjRoUJFlvXr1UkBAgBYuXKjMzMxCxVxJ/P39L7pOZGSky9tUVcztBwAAAFinSvf8bd68WZLUqlWrIsv8/PzUvHlzZWZmaufOnaXaX7du3SRJL7/8sjIzM53tmZmZev755yVJw4cPL3ab119/Xampqc72vLw85y2iv98GAAAAACpale35S0tLcxZbMTExxa4TExOj9evXa9++fWrRosVF9zl69GgtW7ZMM2fOVHx8vNq0aSNJWrt2rTIzM/Xaa69p2LBhhba57bbbNG/ePH3xxReqX7++2rVrp4CAAP300086duyYHn30UectoyXJyspSVlZWoXOTpOSMZOX65l407opyKuOUS9tP37ZT6Tk56pHQQFFBgRUUFVzNCyoeOfE85MQzkRfPQ048E3nxPJ6Uk7SMtFKvW2WLvzNnzjj/PygoqNh1goODJUmnT58u1T6Dg4M1e/Zs3X333Zo8ebJmzpzpXNapUye1b9++yDbe3t6aPHmy6tWrp3HjxjlvRZXO9Uh27txZ3t7eFzzuSy+9pLFjx5YqRk/27NKVOnT6jBbeHkXxBwAAALiZRxd//fv317Zt28q0zaRJk9S6detKiWf//v3q1auXjhw5okmTJqlHjx6SpLlz52rkyJFKTEzU/Pnz1aFDB+c2p06dUv/+/bVu3Tq9+eabGjhwoIKCgrR8+XI98MAD6tmzpz7//HPdcsstJR53zJgxhUYETUtLU2xsrCICIxQaGFop53ohEYHle3ave8MEHU9PV1x4zXLvAyXjmnoecuJ5yIlnIi+eh5x4JvLieTwhJz45pS/pPLr4S0pK0o4dO8q0TXp6uiQpJCSkUFtoaNEi6ezZs5KkatWqlWrfQ4cO1ZYtWzRjxgz169fP2T5kyBCFhIRo4MCBGj16tNauXetcNnLkSC1btkwTJkzQgw8+6Gzv27ev6tatq9atW+uRRx7RgAED5OvrW+xx/f39SzVwjKf7sG9fq0MAAAAAbMujB3zZtGmTjDFl+peYmChJCg0NdU6tcPDgwWL3X9AeFxd30VgOHDigpUuXyt/fXzfddFOR5X379pWfn5/WrVvnHAwmLy9PU6ZMkVT8iKPXXHON4uPjdejQIf36668XvyAAAAAAUE4eXfy5qmXLlpKkDRs2FFmWk5OjLVu2KCAgQI0aNbrovgoKxeDg4GKf0fP29lZwcLCMMUpJSZEkHT9+XNnZ2ZJUaI6/8xW0nzrlOQ+NAgAAAKh6qnTx16tXL0nS1KlTiyybNWuWMjMz1aVLl1LN8VerVi1JUnJyspKSkoos37Nnj06dOqXg4GBFRUVJkiIiIuTn5ydJWr9+fZFt0tLSnLe1lqb38VLX4t131eDNN5VEoQsAAAC4XZUu/u68806FhoZq5syZmj59urP9+PHjeuyxxyRJjzzySJHtmjRpoiZNmujQoUPOtvj4eOd0ECNGjCg0Z19KSopGjBghSerXr598fM49Sunv7+8cFGbUqFE6cuSIc5vMzEzdd999Sk9PV7t27VS7du2KOm2PlZSSoqSUFOUbY3UoAAAAgO149IAvroqIiNBHH32kwYMHa9CgQUpMTFRkZKQWLlyolJQUjRo1yvmM4PkKeuNycnIKtU+cOFFdunTRggULlJCQ4Jznb82aNTp58qTq16+vcePGFdrm9ddf19q1a7Vp0yY1btxYbdu2VWBgoNatW6fDhw8rIiJC7733XuVcAA+zZOhQ5eTlqW4xg+8AAAAAqFxVuudPkgYOHKjly5ere/fu2rhxo+bMmaOEhAR98sknGj9+fJn21aZNG23atEl33XWXQkNDtXDhQi1evFg1a9bU448/rp9++kl16tQptE3Dhg21efNmjRw5UnXr1tXy5cs1b948BQUF6a9//as2b96s5s2bV+Qpe6xr6tRR29hYBfhU6b85AAAAAB7JYQz34F1q0tLSFBYWptTU1GKnsKgsyRnJkjxjPhP8D3nxPOTE85ATz0RePA858UzkxfN4Uk7KUhvQBQO3+eznn+XlcKj/5ZfT+wcAAAC4Gd/A4RbGGN02Y4Yk6fjo0RR/AAAAgJvxDRxukW+MujRooLz8fPlT+AEAAABux7dwuIW3l5cW3H671WEAAAAAtlXlR/sEAAAAAFD8AQAAAIAtUPzBLU5nZanZO++oxbvvKicvz+pwAAAAANvhmT+4RXZenrb+9pskycvhsDgaAAAAwH4o/uAWof7+WjxkiPKNofgDAAAALEDxB7fw9fZWp/h4q8MAAAAAbItn/gAAAADABuj5g1uk5+Ro/p498vP2Vs/LLrM6HAAAAMB26PmDWxw7c0b9v/xSN3/9tdWhAAAAALZEzx/cws/bW21jYhTgw0sOAAAAsALfxOEWdUNDtWr4cKvDAAAAAGyL2z4BAAAAwAYo/gAAAADABij+4BY7T57Ute+/r5umTLE6FAAAAMCWeOYPbnE6K0vrDx9WTGio1aEAAAAAtkTxB7doGBGh7/70J/l7e1sdCgAAAGBLFH9wi/CAAPVu1MjqMAAAAADb4pk/AAAAALABev7gFskZGVp/+LBC/f11XUyM1eEAAAAAtkPPH9xi89Gj6j55su6YOdPqUAAAAABboucPbhHk66sWNWuqQfXqVocCAAAA2BLFH9yiTUyMNt9zj9VhAAAAALbFbZ8AAAAAYAMUfwAAAABgAxR/cIsf9u9X4ief6IE5c6wOBQAAALAlnvmDWxw7c0bL9u1Tbn6+1aEAAAAAtkTxB7doExOjLwcNUkRgoNWhAAAAALZE8Qe3iAkN1eBmzawOAwAAALAtnvkDAAAAABug5w9ucfzsWe1OTlZEYKCaREVZHQ4AAABgO/T8wS1m79ypdh99pFHff291KAAAAIAtUfzBLUL8/NSwenXVqVbN6lAAAAAAW+K2T7jFzc2a6WYGfAEAAAAsQ88fAAAAANgAxR8AAAAA2ADFH9zim+3b1evzz/XqypVWhwIAAADYEs/8wS1+PXVKc3btUnhAgNWhAAAAALZE8Qe36N6woar36aMG1atbHQoAAABgSxR/cItmNWqoWY0aVocBAAAA2BbP/AEAAACADdDzB7c4cvq0jp09q+igINUNDbU6HAAAAMB2KP5QbvtTU3UiPd35c1RQkCQVaasXFqZXfvhBb/74owZdfrnGdOjgbAcAAADgHhR/KJf9qalq/K9/KTM319nm5+Ulh8OhrLy8/7V5e2vp0KF6e/16SdLUbds0dds2Bfj4aMf991MAAgAAAG7CM38olxPp6YUKP0nKzs8vVPhJUnZenn5NSVFufn6h9szc3EI9hAAAAAAqF8UfAAAAANgAxR8AAAAA2ADFH8olKihIAT6FHxn19/aWv7d3obYAHx81CA8vsm6Aj49zgBgAAAAAlY8BX1Au9cLCtOP++0s92mdx6zLYCwAAAOA+FH8ot3phYcUWcCW1UewBAAAA1uG2TwAAAACwAYo/AAAAALABij8AAAAAsAGKPwAAAACwAYo/AAAAALABij8AAAAAsAGKPwAAAACwAYo/AAAAALABij8AAAAAsAGKPwAAAACwAYo/AAAAALABij8AAAAAsAGKPwAAAACwAYo/AAAAALABij8AAAAAsAGKPwAAAACwAYo/AAAAALABij8AAAAAsAGKPwAAAACwAYo/AAAAALABij8AAAAAsAGKPwAAAACwAYo/AAAAALABij8AAAAAsAGKPwAAAACwAYo/AAAAALABij8AAAAAsAGKPwAAAACwAYo/AAAAALABij8AAAAAsAGKPwAAAACwAYo/AAAAALABij8AAAAAsAGKPwAAAACwAYo/AAAAALABij8AAAAAsAGKPwAAAACwAYo/AAAAALABij8AAAAAsAEfqwNA2RljJElpaWluPW5axrnj+eTwsvEk5MXzkBPPQ048E3nxPOTEM5EXz+NJOSmoCQpqhAuxPlqU2enTpyVJsbGxFkcCAAAAwBOcPn1aYWFhF1zHYUpTIsKj5Ofn6/Dhw6pWrZocDofbjpuWlqbY2FgdOHBAoaGhbjsuLoy8eB5y4nnIiWciL56HnHgm8uJ5PCknxhidPn1aderUkZfXhZ/qo+fvEuTl5aWYmBjLjh8aGmr5ixxFkRfPQ048DznxTOTF85ATz0RePI+n5ORiPX4FGPAFAAAAAGyA4g8AAAAAbIDiD6Xm7++vZ555Rv7+/laHgvOQF89DTjwPOfFM5MXzkBPPRF48z6WaEwZ8AQAAAAAboOcPAAAAAGyA4g8AAAAAbIDiDwAAAABsgOIPF5WRkaGnn35ajRo1UkBAgOrUqaM77rhDhw4dsjq0Kis9PV3ffPONhg8frsaNGysgIEDBwcFq2bKlnnvuOZ05c6bEbT/55BO1bt1aISEhioiIUM+ePbVq1So3Rm8fJ0+eVI0aNeRwOJSQkHDBdclL5fvtt980evRoNW7cWIGBgYqIiFCrVq306KOPFrv+d999p44dOzrnaEpMTNTs2bPdHHXVtW7dOg0ePFh16tSRr6+vwsPD1aFDB3388ccqbriBvLw8TZgwQVdccYUCAwMVHR2twYMHa9u2bRZEf+n66aef9PLLL2vAgAGKiYmRw+GQw+G46Hbl+YxauXKlevbsqYiICIWEhKh169aaNGlSRZ1KlVGWnOTn52vFihV67LHHdPXVV6tatWry9/dXw4YNdc899ygpKemCxyInpVPe98n5unTp4tzu4MGDJa7ncTkxwAVkZGSY6667zkgytWvXNoMHDzatW7c2kkx0dLTZs2eP1SFWSe+//76RZCSZyy+/3Nx8882me/fuplq1akaSadKkiTl27FiR7R566CEjyQQGBpq+ffua7t27Gx8fH+Pt7W1mzJjh/hOp4oYOHWocDoeRZBo2bFjieuSl8q1fv95ERkYaSaZZs2bmlltuMTfeeKOJi4sz3t7eRdafMGGCkWR8fHxMjx49TN++fU1gYKCRZN566y0LzqBqmTp1qvH29jaSTKtWrczgwYNNp06djI+Pj5Fk/vznPxdaPy8vz/Tv399IMuHh4WbgwIGmY8eOxuFwmKCgILN27VqLzuTS07dvX+fvj/P/XUh5PqMKcuxwOEzHjh3NwIEDTXh4uJFkHnnkkUo4s0tXWXKya9cu5/JatWqZPn36mP79+5u6desaSaZatWpmxYoVxW5LTkqvPO+T83388cdGkvM7wIEDB4pdzxNzQvGHC/r73/9uJJm2bdua06dPO9vHjx9vJJmOHTtaF1wV9sknn5i7777bbN26tVD74cOHzVVXXWUkmT/96U+Fli1YsMBIMpGRkWbnzp3O9lWrVhk/Pz8THh5uTp065Y7wbWHhwoVGkrn77rsvWPyRl8p3/PhxExUVZYKCgszMmTOLLP994bB9+3bj7e1t/P39zapVq5ztO3bsMJGRkcbHx8fs2rWr0uOuqnJyckyNGjWMJPPZZ58VWrZ161YTERFhJJnFixc72wv+4HXZZZeZo0ePOtunTp1qJJmEhASTk5PjtnO4lL388svmqaeeMt9++605cuSI8ff3v+CX2vJ8Rp08edKEhoYaSWbatGnO9qNHj5qEhAQjySxZsqSiT+2SVZac7N6923Tt2tUsWrTI5OfnO9szMzPNsGHDjCRTr149k52dXWg7clI2ZX2fnO/48eMmIiLCdOvWzcTFxZVY/HlqTij+UKKsrCwTFhZmJJkNGzYUWd6iRQsjyaxfv96C6Oxr1apVRpLx9/c3WVlZzvYbb7zRSDITJkwoss2DDz5oJJnXXnvNjZFWXenp6aZhw4amadOmZufOnRcs/shL5bv33nuNJPP222+Xaf2HHnqoyLLXX3/dSDL3339/BUdpH7/88ouRZBo3blzs8oLX/SuvvOJsu/zyy42kYnuZ+vTpYySZqVOnVlbIVdrFvtSW5zPqlVdeMZJM3759i2wzffp0I8n07t3b1dCrrLIUGudLT093fi9bunRpoWXkxDVlycmf//xnExAQYHbv3n3B4s9Tc8IzfyjRypUrlZqaqoYNG+qqq64qsnzQoEGSzj03A/dp2bKlJCkrK0snT56UdO65zMWLF0v6X17OR64q1tixY/Xrr7/qvffek6+vb4nrkZfKl5GRocmTJys4OFh/+ctfSrVNwXN95KRylHbC48jISElSUlKStm3bpsDAQPXq1avIeuSk8pT3M+pC76FevXopICBACxcuVGZmZkWHbGuBgYFq1KiRJOnw4cOFlpET95g3b54+//xz/f3vf1fDhg0vuK6n5oTiDyXavHmzJKlVq1bFLi9o//nnn90WE6Rff/1VkuTr66uIiAhJ0o4dO5SVlaXo6GjFxMQU2YZcVZyff/5Z48eP11/+8hd16NDhguuSl8q3fv16nT59WldddZUCAwM1d+5cjRo1Svfdd5/eeOONIl+QUlJStH//fkkq9o9asbGxioqK0r59+5SWluaWc6hqGjRooIYNG2rHjh36/PPPCy3btm2bJk+erOrVq6t///6S/ve7pnnz5sX+MYX3SeUp72fUhb4f+Pn5qXnz5srMzNTOnTsrIWr7ys/P1759+yRJtWrVKrSMnFS+s2fP6t5771WTJk302GOPXXR9T80JxR9KVPAFqbhfCOe3F3wQwT3efPNNSVKPHj2cf2G/WK6Cg4MVHh6uU6dO6fTp0+4JtArKz8/XnXfeqfDwcI0bN+6i65OXyrd161ZJUo0aNdSvXz/17NlTEyZM0LvvvquRI0cqISFBU6ZMca5fkJPq1asrODi42H3y2eYab29vffrppwoPD9ett96qq6++Wn/84x91ww03qEWLFoqJidGiRYucf7zid411yvMZlZaWptTU1AtuR84qx5QpU3T8+HFFR0fr+uuvd7aTE/d4+umntXfvXr333nvy8/O74LqenBOKP5SoYDqBoKCgYpcXfHHiS6v7zJkzRx9++KF8fX31/PPPO9svliuJfFWEt956S+vWrdOrr77qvGXtQshL5Tt16pQk6dtvv9W8efP09ttv6/jx49q7d69Gjx6tjIwMDR06VJs2bZJETtylXbt2WrZsmRo0aKANGzboyy+/1JIlS+Tl5aWuXbuqQYMGznX5XWOd8rwfzp9qiJy5z4EDB/Twww9Lkp577rlCt1eTk8q3YcMGvfnmmxo6dKg6dux40fU9OScUf8AlYvv27brttttkjNGrr77qfPYP7rF//349+eST6tixo4YNG2Z1OPh/+fn5kqTc3Fw999xzuu+++xQdHa24uDi9+uqruvnmm5WTk6NXX33V4kjtZcqUKWrdurViY2O1du1anTlzRjt37tSwYcM0fvx43XDDDcrKyrI6TOCScPbsWQ0YMEAnTpxQv379dM8991gdkq3k5eU57/p57bXXrA7HZRR/KFFISIikcxOOF+fs2bOSpGrVqrktJrs6dOiQevTooVOnTmnUqFF66KGHCi2/WK4k8uWqv/71r8rOztZ7771X6m3IS+UruMaSih3wpaBt2bJlhdYnJ5Vn165dGjp0qKKiojRr1iy1bt1awcHBuuyyy/Tvf/9bvXv31oYNG/TRRx9J4neNlcrzfjj/PUfOKl9OTo5uvvlmrV+/Xu3bty/yHK1ETirbG2+8oY0bN2rcuHGKiooq1TaenBOKP5SoXr16kqSDBw8Wu7ygPS4uzm0x2VFycrK6deumffv26S9/+Uuxf3W6WK7Onj2rlJQUVa9enQ/+cpo1a5aCgoJ0zz33KDEx0fnvj3/8o6RzBXpB29GjRyWRF3co+PwJCgpSdHR0keX169eXJB0/flzS/3Jy6tQp5y/e3+OzzTVffPGFcnJy1KNHj0JfgAoMHjxYkrR8+XJJ/K6xUnk+o0JDQxUWFnbB7chZxcjPz9fQoUM1d+5cXXnllfruu+8UGBhYZD1yUrm+++47ORwOffrpp4V+/5//+/7mm29WYmKi5s2bJ8mzc+Lj1qPhklJwW+GGDRuKXV7Q3qJFC7fFZDdnzpzRjTfeqK1bt2rAgAF6//335XA4iqzXuHFj+fv767ffftOhQ4dUt27dQsvJVcVISUlx9iD9XmZmpnNZwbDN5KXyFYzYmZGRoaysrCLTDCQnJ0v6319hw8PDVa9ePe3fv18bN25U+/btC61/4MABnThxQnFxcQoNDXXDGVQ9BV9oCr74/F5Be8HzmgW/a7Zs2aKcnJwiI37yPqk85f2MatmypZYvX64NGzaoadOmhZbl5ORoy5YtCggIcE5LgPJ54IEHNGXKFDVq1Ejff/+9wsPDS1yXnFQuY4zzD1bFWbNmjSQVeizEU3NCzx9K1K5dO4WFhWnPnj3OwRLON3XqVEnSTTfd5ObI7CErK0t9+/bVjz/+qO7du2vKlCny9vYudt3AwEDdcMMNkqSvv/66yHJy5TpjTLH/kpKSJEkNGzZ0thX0NpGXylevXj21bNlSxphiC/OCtvOndSiYS67g+p+PnLiuYAj69evXF7t83bp1kv7XKxsfH6/LL79cGRkZznmxzkdOKk95P6Mu9B6aNWuWMjMz1aVLFwUEBFR0yLbx5JNP6p133lG9evW0YMEC1ahR44Lrk5PKs3Tp0hK/AxT02h04cEDGmELFn8fmxN2zyuPS8ve//91IMtdff705c+aMs338+PFGkunYsaN1wVVhubm5pn///kaS6dChgzl79uxFt1mwYIGRZCIjI83OnTud7atWrTL+/v4mPDzcnDp1qhKjtqekpCQjyTRs2LDY5eSl8n322WdGkrniiivM4cOHne0bN240ERERRpL56quvnO3bt2833t7ext/f36xevdrZvnPnThMZGWl8fHzMrl273HoOVclPP/1kJBlJ5p133im0bPXq1SY4ONhIMgsWLHC2v//++0aSueyyy8yxY8ec7dOmTTOSTEJCgsnJyXHbOVQl/v7+5kJf98rzGXXy5EkTGhpqJJlp06Y5248dO2YSEhKMJLNkyZKKPpUq42I5ef31140kU6tWrUI5uRBy4pqL5aQkcXFxRpI5cOBAkWWemhOKP1xQRkaGadOmjZFkateubQYPHuz8OTo62uzZs8fqEKukN954w/nlqX///mbo0KHF/vvtt98KbffQQw8ZSSYoKMj07dvX3HjjjcbHx8d4e3ubGTNmWHMyVdzFij9jyIs7DB061Egy4eHhpmfPnqZTp07OX+Z33XVXkfULvlz5+PiYG2+80fTt29cEBgYaSeaf//ynBWdQtYwePdr5GdasWTNz8803m3bt2hkvLy8jydx9992F1s/Ly3P+wat69epm0KBBJjEx0TgcDhMYGGjWrFlj0ZlcembNmmXatGnj/OdwOIykQm2zZs0qtE15PqOmTp1qvLy8jMPhMJ06dTKDBg0y4eHhRpIZNWqUG8700lGWnGzcuNG5vG3btiX+/l+xYkWR45CT0ivP+6Q4Fyr+jPHMnFD84aLS09PNU089ZRo2bGj8/PxMrVq1zLBhw0p8ocN1zzzzjPOL04X+JSUlFdn2448/NldffbUJCgoy4eHhpkePHmblypXuPwmbKE3xZwx5qWz5+flm4sSJzmscHBxs2rZtaz755JMSt/n2229Nhw4dTEhIiAkJCTEdOnQw3333nRujrtqmT59uunXr5uxNrV69uunUqZP5/PPPi10/NzfXjB8/3jRr1swEBASYyMhIM2jQIPPf//7XzZFf2j7++OOL/u74+OOPi92urJ9RP/zwg+nRo4cJDw83QUFB5pprrrnge86uypKTJUuWlOr3f3E5NIaclFZ53ye/d7HizxjPy4nDGGNKdX8oAAAAAOCSxYAvAAAAAGADFH8AAAAAYAMUfwAAAABgAxR/AAAAAGADFH8AAAAAYAMUfwAAAABgAxR/AAAAAGADFH8AAAAAYAMUfwAAAABgAxR/AABbcTgccjgcVodRaRwOh+rXr291GAAAD0TxBwCwvb1798rhcCgxMdHqUC7oUokTAOCZfKwOAAAAVJxt27bJ19fX6jAAAB6I4g8AgCqkSZMmVocAAPBQ3PYJALC1Z599VvHx8ZKkZcuWOZ8JdDgcGjZsWKF1Dxw4oPvvv18NGzZUQECAIiIi1Lt3b61atarIfs+/RTMtLU2jRo1SfHy8fH199fDDD0uSZs+erTvuuEOXX365QkNDFRwcrJYtW+rFF19UVlZWueK80DN/q1evVt++fRUdHS1/f3/Vr19f9913nw4fPnzB+DMyMvT4448rLi5O/v7+SkhI0CuvvCJjTCmv8jm7d++Ww+FQ165ddfbsWY0dO1aXX365goKC1LBhQ40bN67M+wQAlB49fwAAW7vyyis1cOBATZs2TTVr1lSPHj2cy9q3b+/8/9WrV6tXr146deqUGjdurF69eum3337T999/r3nz5umzzz7TLbfcUmT/GRkZ6tixo/bt26eOHTuqVatWql69uiRp+PDhysjIUPPmzdWiRQulpqbqxx9/1N///nctWrRI8+fPl7e3d5niLMnkyZM1bNgw5eXlqV27doqNjdWGDRv07rvvavr06Vq6dGmxvYbZ2dnq1q2btm7dqsTERJ09e1bLli3T448/rtOnT+uFF14o9bXevHmzJCkiIkItW7ZUVlaW2rZtq9jYWC1atEh/+9vfFBUVpTvuuKPU+wQAlIEBAMBGJJnf//pLSkoykkzHjh2L3SY1NdXUrl3beHt7m8mTJxdatm7dOlO9enUTEhJijh8/XmSfkkzbtm3NqVOniuz3m2++Menp6YXa0tLSTO/evY0k8+mnn5YpzoLzi4uLK9S2f/9+ExgYaLy9vc3MmTOd7Xl5eebhhx82ksw111xT7LEKjpeamlronL29vU1QUJA5ffp0ibH83lNPPeXc5xNPPGFycnKcyz7++GMjyfTq1avU+wMAlA23fQIAcBEfffSRjhw5oocffli33nproWXXXHONnnrqKZ05c0aTJ08udvt//vOfCg8PL9Let29fBQYGFmqrVq2aJkyYIEmaOXNmhcT/wQcfKCMjQ4MHD1afPn2c7V5eXnr55ZdVp04drV+/XitXriyyrZeXl/79738rNDTU2XbNNdfoxhtvVHp6utavX1/qODZt2iRJGjhwoP7xj3/Ix+d/NyAV9GQeP368rKcHACglbvsEAOAi5s+fL0kaMGBAscs7dOggSfrxxx+LLKtdu7auueaaEve9a9cuzZkzR7t379bZs2eVn5/vfO5t165droYuSVqxYoUkFSlcJcnf318333yz3nzzTa1YsULt2rUrtDwuLk6NGzcusl2jRo0kSUeOHCl1HAW3fT799NNFlp04cUKSVKdOnVLvDwBQNhR/AABcxN69eyWpSGH0ewUFzPnq1atX7LrGGI0ePVoTJkwocZCT06dPly3QEhQM6FLSQDAF7YcOHSqyLCYmpthtqlWrJklFBqYpSUpKivbv36/4+Hi1aNGiyPKff/5ZktS8efNS7Q8AUHYUfwAAXER+fr4kadCgQQoODi5xveIGTAkICCh23S+//FKvv/66YmNjNWHCBLVt21bR0dHy9fVVdna2/P393TbypcPhKHGZl1fFPCFScMtn69ati12+ceNGSecGtgEAVA6KPwAALiImJkY7duzQ448/rquvvrpC9jljxgxJ0rvvvqtevXoVWvbrr79WyDEK1KlTRzt27NC+ffvUrFmzIssLejbr1q1bocc9X8Etn1dddVWxyyn+AKDyMeALAMD2/Pz8JEm5ubnFLu/atauk/xVsFeHUqVOSir+t8quvvip2m4vFWZKCZxKnTJlSZFl2dra+/vrrQutVhtIUf9WqVVPDhg0rLQYAsDuKPwCA7UVFRcnX11d79uxRXl5ekeUjRoxQjRo1NG7cOE2cONF5G2iB3Nxcff/999qyZUupj1kwYMrEiRML3d65YsUKvfrqq+WKsyTDhw9XYGCgvvjiC82ePdvZnp+fryeeeEKHDh3S1VdffdFnGl1RcNtnccXf/v37lZycrBYtWlzwFlQAgGso/gAAtufn56cePXro6NGjatmypYYMGaI777xTH3/8sSQpPDxcM2fOVFhYmEaMGKH69eurZ8+euvXWW9W5c2dFR0erR48e2r17d6mP+eCDDyo4OFjvvPOOmjdvrj/96U/6wx/+oI4dO+qee+4pV5wlqVevnv79738rPz9fN910kzp06KA///nPatq0qcaPH6+aNWuWOE1FRcjNzdXWrVtVt25dRUdHF1nOLZ8A4B4UfwAA6NxceLfffrtOnjypzz//XB9++KGWLVvmXH7dddfpl19+0WOPPabQ0FAtW7ZM33zzjfbt26eOHTvqk08+UZcuXUp9vEaNGmn9+vW66aabdOLECX377bc6c+aM/v3vf5fY81eaOEty++23a8WKFerdu7e2bdumqVOnKiMjQ/fee69++umnYgerqSjbt29XVlZWicUdxR8AuIfDuGsoMQAAAACAZf6vPTuQAQAAABDmbx1Iv0XL+QMAABgQfwAAAAPiDwAAYED8AQAADIg/AACAAfEHAAAwIP4AAAAGxB8AAMCA+AMAABgQfwAAAAPiDwAAYED8AQAADARb2+bYmpwDrgAAAABJRU5ErkJggg==" class="
+">
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p><a id="advanced"></a></p>
+<h3 id="Advanced-AutoML-Configuration">Advanced AutoML Configuration<a class="anchor-link" href="http://automl.oraclecorp.com/multiversion/v24.2.0/_static/notebooks/OracleAutoMLx_Classification_Text.html#Advanced-AutoML-Configuration">¶</a></h3><p>You can also configure the pipeline with suitable parameters according to your needs.</p>
+
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[15]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">custom_pipeline</span> <span class="o">=</span> <span class="n">automlx</span><span class="o">.</span><span class="n">Pipeline</span><span class="p">(</span>
+    <span class="n">task</span><span class="o">=</span><span class="s1">'classification'</span><span class="p">,</span>
+    <span class="n">model_list</span><span class="o">=</span><span class="p">[</span>                 <span class="c1"># Specify the models you want the AutoML to consider</span>
+        <span class="s1">'GaussianNB'</span><span class="p">,</span>
+        <span class="s1">'LGBMClassifier'</span><span class="p">,</span>
+<span class="p">],</span>
+    <span class="n">min_features</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>            <span class="c1"># Specify minimum features to force the model to use. It can take 3 possible types of values:</span>
+                                 <span class="c1"># If int, 0 &lt; min_features &lt;= n_features,</span>
+                                 <span class="c1"># If float, 0 &lt; min_features &lt;= 1.0, 1.0 means disabling feature selection</span>
+                                 <span class="c1"># If list, names of features to keep, for example ['a', 'b'] means keep features 'a' and 'b'</span>
+
+    <span class="n">n_algos_tuned</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>             <span class="c1"># Choose how many models to tune</span>
+    <span class="n">adaptive_sampling</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>     <span class="c1"># Disable or enable Adaptive Sampling step. Default to `True`</span>
+    <span class="n">preprocessing</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>          <span class="c1"># Disable or enable Preprocessing step. Default to `True`</span>
+    <span class="n">search_space</span><span class="o">=</span><span class="p">{},</span>             <span class="c1"># You can specify the hyper-parameters and ranges AutoML searches</span>
+    <span class="n">max_tuning_trials</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>         <span class="c1"># The maximum number of tuning trials. Can be integer or Dict (max number for each model)</span>
+    <span class="n">score_metric</span><span class="o">=</span><span class="s1">'f1_macro'</span><span class="p">,</span>     <span class="c1"># Any scikit-learn metric or a custom function</span>
+<span class="p">)</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>A few of the advanced settings can be passed directly to the pipeline's fit method, instead of its constructor.</p>
+
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[16]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">custom_pipeline</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span>
+    <span class="n">X_train</span><span class="p">,</span>
+    <span class="n">y_train</span><span class="p">,</span>
+    <span class="n">X_valid</span><span class="p">,</span>
+    <span class="n">y_valid</span><span class="p">,</span>
+    <span class="n">col_types</span><span class="o">=</span><span class="p">[</span><span class="s2">"text"</span><span class="p">],</span>
+    <span class="n">time_budget</span><span class="o">=</span> <span class="mi">50</span><span class="p">,</span>    <span class="c1"># Specify time budget in seconds</span>
+    <span class="n">cv</span><span class="o">=</span><span class="s1">'auto'</span>           <span class="c1"># Automatically pick a good cross-validation (cv) strategy for the user's dataset.</span>
+                        <span class="c1"># Ignored if X_valid and y_valid are provided.</span>
+                        <span class="c1"># Can also be:</span>
+                        <span class="c1">#   - An integer (for example, to use 5-fold cross validation)</span>
+                        <span class="c1">#   - A list of data indices to use as splits (for advanced, such as time-based splitting)</span>
+<span class="p">)</span>
+<span class="n">y_proba</span> <span class="o">=</span> <span class="n">custom_pipeline</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
+<span class="n">score_default</span> <span class="o">=</span> <span class="n">f1_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_predict</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="s2">"micro"</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Score on test data : </span><span class="si">{</span><span class="n">score_default</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
 </div>
 
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
 
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
 <div class="jp-OutputArea-child">
 
     
     <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
 
 
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>[2024-05-23 10:42:32,537] [automlx.interface] Dataset shape: (3394,1)
+[2024-05-23 10:42:32,598] [automlx.interface] Model Tune disabled.
+[2024-05-23 10:42:32,885] [automlx.data_transform] Running preprocessing. Number of features: 2
+[2024-05-23 10:42:52,004] [automlx.data_transform] Preprocessing completed. Took 19.119 secs
+[2024-05-23 10:42:52,334] [automlx.process] Running Model Generation
+[2024-05-23 10:43:10,641] [automlx.process] Model Generation completed.
+[2024-05-23 10:43:11,010] [automlx.model_selection] Running Model Selection
+[2024-05-23 10:43:11,011] [automlx.trials] Fewer models (2) than top_k (2) provided, skipping model selection
+[2024-05-23 10:43:22,393] [automlx.interface] Re-fitting pipeline
+[2024-05-23 10:44:20,154] [automlx.interface] AutoMLx completed.
+Score on test data : 0.6987519915029209
+</pre>
+</div>
+</div>
 
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+</div>
 
-                        })                };                });            </script>        </div>
 </div>
 
 </div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p><a id="MLX"></a></p>
+<h2 id="Machine-Learning-Explainability">Machine Learning Explainability<a class="anchor-link" href="http://automl.oraclecorp.com/multiversion/v24.2.0/_static/notebooks/OracleAutoMLx_Classification_Text.html#Machine-Learning-Explainability">¶</a></h2>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p>For a variety of decision-making tasks, getting only a prediction as model output is not sufficient. A user may wish to know why the model outputs that prediction, or which data features are relevant for that prediction. For that purpose the Oracle AutoMLx solution defines the MLExplainer object, which allows to compute a variety of model explanations</p>
+<p><a id="MLX-initializing"></a></p>
+<h3 id="Initializing-an-MLExplainer">Initializing an MLExplainer<a class="anchor-link" href="http://automl.oraclecorp.com/multiversion/v24.2.0/_static/notebooks/OracleAutoMLx_Classification_Text.html#Initializing-an-MLExplainer">¶</a></h3><p>The MLExplainer object takes as argument the trained model, the training data and labels, as well as the task.</p>
 
 </div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[17]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">explainer</span> <span class="o">=</span> <span class="n">automlx</span><span class="o">.</span><span class="n">MLExplainer</span><span class="p">(</span><span class="n">est1</span><span class="p">,</span>
+                              <span class="n">X_train</span><span class="p">,</span>
+                              <span class="n">y_train</span><span class="p">,</span>
+                              <span class="n">target_names</span><span class="o">=</span><span class="n">target_names</span><span class="p">,</span>
+                              <span class="n">task</span><span class="o">=</span><span class="s2">"classification"</span><span class="p">,</span>
+                              <span class="n">col_types</span><span class="o">=</span><span class="p">[</span><span class="s2">"text"</span><span class="p">])</span>
+</pre></div>
 
+     </div>
+</div>
+</div>
 </div>
 
 </div>
@@ -16645,8 +19943,8 @@ <h4 id="Visualization">Visualization<a class="anchor-link" href="#Visualization"
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
 </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
-<p><a id='MLX-local'></a></p>
-<h2 id="Local-Token-importance">Local Token importance<a class="anchor-link" href="#Local-Token-importance">&#182;</a></h2>
+<p><a id="MLX-global"></a></p>
+<h3 id="Model-Explanations-(Global-Token-Importance)">Model Explanations (Global Token Importance)<a class="anchor-link" href="http://automl.oraclecorp.com/multiversion/v24.2.0/_static/notebooks/OracleAutoMLx_Classification_Text.html#Model-Explanations-(Global-Token-Importance)">¶</a></h3>
 </div>
 </div>
 </div>
@@ -16657,7 +19955,7 @@ <h2 id="Local-Token-importance">Local Token importance<a class="anchor-link" hre
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
 </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
-<p>For text classification tasks, since we first extract tokens (features), the Oracle AutoMLx solution offers a single way to compute a notion of token importance: Local Token Importance. The notion of Local Token Importance intuitively measures how much a token impacts an instance's predictions (relative to the provided train labels). This notion of token importance considers each token independently from all other tokens.</p>
+<p>For text classification tasks, since we first extract tokens (features), the Oracle AutoMLx solution offers a single way to compute a notion of token importance: Global Token Importance. The notion of Global Token Importance intuitively measures how much a token impacts the model's predictions (relative to the provided train labels). Tokens are the most fine-grained building blocks of the NLP model, such as sentences, words, or characters.</p>
 
 </div>
 </div>
@@ -16669,7 +19967,7 @@ <h2 id="Local-Token-importance">Local Token importance<a class="anchor-link" hre
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
 </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
-<h4 id="Compute-the-importance">Compute the importance<a class="anchor-link" href="#Compute-the-importance">&#182;</a></h4>
+<h4 id="Computing-the-importance">Computing the importance<a class="anchor-link" href="http://automl.oraclecorp.com/multiversion/v24.2.0/_static/notebooks/OracleAutoMLx_Classification_Text.html#Computing-the-importance">¶</a></h4>
 </div>
 </div>
 </div>
@@ -16680,8 +19978,9 @@ <h4 id="Compute-the-importance">Compute the importance<a class="anchor-link" hre
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
 </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
-<p>By default we use a surrogate method to successively measure the importance of each token in a given instance. Such a method therefore runs in linear time with respect to the number of features in the dataset.</p>
-<p>The method <code>explain_prediction()</code> allows to compute such feature importances. It also provides 95% confidence intervals for each feature importance attribution.</p>
+<p>We use a permutation-based method to successively measure the importance of each token (feature). Such a method therefore runs in linear time with respect to the
+number of features (tokens) in the dataset.</p>
+<p>The method <code>explain_model()</code> allows to compute such feature importances.</p>
 
 </div>
 </div>
@@ -16691,11 +19990,10 @@ <h4 id="Compute-the-importance">Compute the importance<a class="anchor-link" hre
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[24]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[18]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">index</span> <span class="o">=</span> <span class="mi">0</span>
-<span class="n">result_explain_prediction_default</span> <span class="o">=</span> <span class="n">explainer</span><span class="o">.</span><span class="n">explain_prediction</span><span class="p">(</span><span class="n">X_train</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">index</span><span class="p">:</span><span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="p">:])</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">result_explain_model_default</span> <span class="o">=</span> <span class="n">explainer</span><span class="o">.</span><span class="n">explain_model</span><span class="p">()</span>
 </pre></div>
 
      </div>
@@ -16710,13 +20008,7 @@ <h4 id="Compute-the-importance">Compute the importance<a class="anchor-link" hre
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
 </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
-<p>There are two options to show the explanation's results:</p>
-<ul>
-<li><code>to_dataframe()</code> will return a dataframe of the results.</li>
-<li><code>show_in_notebook()</code> will show the results as a bar plot.</li>
-</ul>
-<p>The features are returned in decreasing order of importance.</p>
-
+<h4 id="Visualization">Visualization<a class="anchor-link" href="http://automl.oraclecorp.com/multiversion/v24.2.0/_static/notebooks/OracleAutoMLx_Classification_Text.html#Visualization">¶</a></h4>
 </div>
 </div>
 </div>
@@ -16727,7 +20019,13 @@ <h4 id="Compute-the-importance">Compute the importance<a class="anchor-link" hre
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
 </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
-<h4 id="Visualization">Visualization<a class="anchor-link" href="#Visualization">&#182;</a></h4>
+<p>There are two options to show the explanation's results:</p>
+<ul>
+<li><code>to_dataframe()</code> will return a dataframe of the results.</li>
+<li><code>show_in_notebook()</code> will show the results as a bar plot.</li>
+</ul>
+<p>The features are returned in decreasing order of importance.</p>
+
 </div>
 </div>
 </div>
@@ -16736,10 +20034,10 @@ <h4 id="Visualization">Visualization<a class="anchor-link" href="#Visualization"
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[25]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[19]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">result_explain_prediction_default</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">to_dataframe</span><span class="p">()</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">result_explain_model_default</span><span class="o">.</span><span class="n">to_dataframe</span><span class="p">(</span><span class="n">n_tokens</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
 </pre></div>
 
      </div>
@@ -16757,13 +20055,13 @@ <h4 id="Visualization">Visualization<a class="anchor-link" href="#Visualization"
 <div class="jp-OutputArea-child">
 
     
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[25]:</div>
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[19]:</div>
 
 
 
 <div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
 <div>
-<style scoped>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -16780,81 +20078,155 @@ <h4 id="Visualization">Visualization<a class="anchor-link" href="#Visualization"
   <thead>
     <tr style="text-align: right;">
       <th></th>
-      <th>Attribution</th>
-      <th>Token</th>
-      <th>Target</th>
+      <th>token</th>
+      <th>attribution</th>
+      <th>upper_bound</th>
+      <th>lower_bound</th>
     </tr>
   </thead>
   <tbody>
     <tr>
       <th>0</th>
-      <td>1.347056e-01</td>
-      <td>space</td>
-      <td>14</td>
+      <td>israeli</td>
+      <td>0.014393</td>
+      <td>0.014393</td>
+      <td>0.014393</td>
     </tr>
     <tr>
       <th>1</th>
-      <td>1.291750e-01</td>
-      <td>location</td>
-      <td>14</td>
+      <td>atheism</td>
+      <td>0.012633</td>
+      <td>0.012633</td>
+      <td>0.012633</td>
     </tr>
     <tr>
       <th>2</th>
-      <td>1.062831e-01</td>
-      <td>rattling</td>
-      <td>14</td>
+      <td>hockey</td>
+      <td>0.012515</td>
+      <td>0.012515</td>
+      <td>0.012515</td>
     </tr>
     <tr>
       <th>3</th>
-      <td>7.680890e-02</td>
-      <td>launch</td>
-      <td>14</td>
+      <td>bike</td>
+      <td>0.011961</td>
+      <td>0.011961</td>
+      <td>0.011961</td>
     </tr>
     <tr>
       <th>4</th>
-      <td>6.210265e-02</td>
-      <td>wrench</td>
-      <td>14</td>
+      <td>sale</td>
+      <td>0.010798</td>
+      <td>0.010800</td>
+      <td>0.010797</td>
     </tr>
     <tr>
-      <th>...</th>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
+      <th>5</th>
+      <td>baseball</td>
+      <td>0.010190</td>
+      <td>0.010190</td>
+      <td>0.010190</td>
     </tr>
     <tr>
-      <th>83</th>
-      <td>2.211368e-09</td>
-      <td>com</td>
-      <td>14</td>
+      <th>6</th>
+      <td>cars</td>
+      <td>0.010052</td>
+      <td>0.010055</td>
+      <td>0.010050</td>
     </tr>
     <tr>
-      <th>84</th>
-      <td>-1.906403e-09</td>
-      <td>Thiokol</td>
-      <td>14</td>
+      <th>7</th>
+      <td>moon</td>
+      <td>0.009464</td>
+      <td>0.009464</td>
+      <td>0.009464</td>
     </tr>
     <tr>
-      <th>85</th>
-      <td>-5.362070e-10</td>
-      <td>had</td>
-      <td>14</td>
+      <th>8</th>
+      <td>atheists</td>
+      <td>0.009376</td>
+      <td>0.009376</td>
+      <td>0.009376</td>
     </tr>
     <tr>
-      <th>86</th>
-      <td>9.392158e-12</td>
-      <td>I</td>
-      <td>14</td>
+      <th>9</th>
+      <td>graphics</td>
+      <td>0.008399</td>
+      <td>0.008466</td>
+      <td>0.008332</td>
     </tr>
     <tr>
-      <th>87</th>
-      <td>4.174225e-12</td>
-      <td>some</td>
-      <td>14</td>
+      <th>10</th>
+      <td>windows</td>
+      <td>0.007966</td>
+      <td>0.007966</td>
+      <td>0.007966</td>
+    </tr>
+    <tr>
+      <th>11</th>
+      <td>encryption</td>
+      <td>0.006958</td>
+      <td>0.006958</td>
+      <td>0.006958</td>
+    </tr>
+    <tr>
+      <th>12</th>
+      <td>pitching</td>
+      <td>0.005990</td>
+      <td>0.005990</td>
+      <td>0.005990</td>
+    </tr>
+    <tr>
+      <th>13</th>
+      <td>guns</td>
+      <td>0.005813</td>
+      <td>0.005813</td>
+      <td>0.005813</td>
+    </tr>
+    <tr>
+      <th>14</th>
+      <td>israel</td>
+      <td>0.005534</td>
+      <td>0.005534</td>
+      <td>0.005534</td>
+    </tr>
+    <tr>
+      <th>15</th>
+      <td>playoff</td>
+      <td>0.004587</td>
+      <td>0.004587</td>
+      <td>0.004587</td>
+    </tr>
+    <tr>
+      <th>16</th>
+      <td>christians</td>
+      <td>0.004034</td>
+      <td>0.004034</td>
+      <td>0.004034</td>
+    </tr>
+    <tr>
+      <th>17</th>
+      <td>orbit</td>
+      <td>0.003647</td>
+      <td>0.003647</td>
+      <td>0.003647</td>
+    </tr>
+    <tr>
+      <th>18</th>
+      <td>clipper</td>
+      <td>0.003382</td>
+      <td>0.003382</td>
+      <td>0.003382</td>
+    </tr>
+    <tr>
+      <th>19</th>
+      <td>space</td>
+      <td>0.003147</td>
+      <td>0.003147</td>
+      <td>0.003147</td>
     </tr>
   </tbody>
 </table>
-<p>88 rows × 3 columns</p>
 </div>
 </div>
 
@@ -16869,10 +20241,10 @@ <h4 id="Visualization">Visualization<a class="anchor-link" href="#Visualization"
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[26]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[20]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">result_explain_prediction_default</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">show_in_notebook</span><span class="p">()</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">result_explain_model_default</span><span class="o">.</span><span class="n">show_in_notebook</span><span class="p">(</span><span class="n">n_tokens</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
 </pre></div>
 
      </div>
@@ -16895,66 +20267,17 @@ <h4 id="Visualization">Visualization<a class="anchor-link" href="#Visualization"
 
 
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-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
-<p><a id='ref'></a></p>
-<h2 id="References">References<a class="anchor-link" href="#References">&#182;</a></h2><ul>
-<li>More examples and details: <a href="http://automl.oraclecorp.com/">http://automl.oraclecorp.com/</a></li>
-<li>Oracle AutoML <a href="http://www.vldb.org/pvldb/vol13/p3166-yakovlev.pdf">http://www.vldb.org/pvldb/vol13/p3166-yakovlev.pdf</a></li>
-<li>scikit-learn <a href="https://scikit-learn.org/stable/">https://scikit-learn.org/stable/</a></li>
-<li>Interpretable Machine Learning <a href="https://christophm.github.io/interpretable-ml-book/">https://christophm.github.io/interpretable-ml-book/</a></li>
-<li>LIME <a href="https://arxiv.org/pdf/1602.04938">https://arxiv.org/pdf/1602.04938</a></li>
-<li>20newsgroup <a href="http://qwone.com/~jason/20Newsgroups/">http://qwone.com/~jason/20Newsgroups/</a></li>
-</ul>
-
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n=r(39898),i=r(7901),a=r(82884),o=r(71828),s=o.strScale,l=o.strRotate,u=o.strTranslate;t.exports=function(t,e,r){var o,c,f,h,p=t.node(),d=a[r.arrowhead||0],v=a[r.startarrowhead||0],g=(r.arrowwidth||1)*(r.arrowsize||1),y=(r.arrowwidth||1)*(r.startarrowsize||1),m=e.indexOf("start")>=0,x=e.indexOf("end")>=0,b=d.backoff*g+r.standoff,_=v.backoff*y+r.startstandoff;if("line"===p.nodeName){o={x:+t.attr("x1"),y:+t.attr("y1")},c={x:+t.attr("x2"),y:+t.attr("y2")};var w=o.x-c.x,T=o.y-c.y;if(h=(f=Math.atan2(T,w))+Math.PI,b&&_&&b+_>Math.sqrt(w*w+T*T))return void D();if(b){if(b*b>w*w+T*T)return void D();var k=b*Math.cos(f),A=b*Math.sin(f);c.x+=k,c.y+=A,t.attr({x2:c.x,y2:c.y})}if(_){if(_*_>w*w+T*T)return void D();var M=_*Math.cos(f),S=_*Math.sin(f);o.x-=M,o.y-=S,t.attr({x1:o.x,y1:o.y})}}else if("path"===p.nodeName){var E=p.getTotalLength(),L="";if(E<b+_)return void D();var C=p.getPointAtLength(0),P=p.getPointAtLength(.1);f=Math.atan2(C.y-P.y,C.x-P.x),o=p.getPointAtLength(Math.min(_,E)),L="0px,"+_+"px,";var O=p.getPointAtLength(E),I=p.getPointAtLength(E-.1);h=Math.atan2(O.y-I.y,O.x-I.x),c=p.getPointAtLength(Math.max(0,E-b)),L+=E-(L?_+b:b)+"px,"+E+"px",t.style("stroke-dasharray",L)}function D(){t.style("stroke-dasharray","0px,100px")}function z(e,a,o,c){e.path&&(e.noRotate&&(o=0),n.select(p.parentNode).append("path").attr({class:t.attr("class"),d:e.path,transform:u(a.x,a.y)+l(180*o/Math.PI)+s(c)}).style({fill:i.rgb(r.arrowcolor),"stroke-width":0}))}m&&z(v,o,f,y),x&&z(d,c,h,g)}},32745:function(t,e,r){"use strict";var n=r(92605),i=r(44317);t.exports={moduleType:"component",name:"annotations",layoutAttributes:r(50215),supplyLayoutDefaults:r(84046),includeBasePlot:r(76325)("annotations"),calcAutorange:r(3749),draw:n.draw,drawOne:n.drawOne,drawRaw:n.drawRaw,hasClickToShow:i.hasClickToShow,onClick:i.onClick,convertCoords:r(94128)}},26997:function(t,e,r){"use 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n=r(73972),i=r(71828);t.exports={moduleType:"component",name:"annotations3d",schema:{subplots:{scene:{annotations:r(26997)}}},layoutAttributes:r(26997),handleDefaults:r(20226),includeBasePlot:function(t,e){var r=n.subplotsRegistry.gl3d;if(r)for(var a=r.attrRegex,o=Object.keys(t),s=0;s<o.length;s++){var l=o[s];a.test(l)&&(t[l].annotations||[]).length&&(i.pushUnique(e._basePlotModules,r),i.pushUnique(e._subplots.gl3d,l))}},convert:r(5485),draw:r(82188)}},7561:function(t,e,r){"use strict";t.exports=r(63489),r(94338),r(3961),r(38751),r(86825),r(37715),r(99384),r(43805),r(88874),r(83290),r(29108),r(55422),r(94320),r(31320),r(51367),r(21457)},72201:function(t,e,r){"use strict";var n=r(7561),i=r(71828),a=r(50606),o=a.EPOCHJD,s=a.ONEDAY,l={valType:"enumerated",values:i.sortObjectKeys(n.calendars),editType:"calc",dflt:"gregorian"},u=function(t,e,r,n){var a={};return 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n=r(41940),i=r(22399);t.exports={_isSubplotObj:!0,visible:{valType:"boolean",dflt:!0,editType:"legend"},bgcolor:{valType:"color",editType:"legend"},bordercolor:{valType:"color",dflt:i.defaultLine,editType:"legend"},borderwidth:{valType:"number",min:0,dflt:0,editType:"legend"},font:n({editType:"legend"}),grouptitlefont:n({editType:"legend"}),orientation:{valType:"enumerated",values:["v","h"],dflt:"v",editType:"legend"},traceorder:{valType:"flaglist",flags:["reversed","grouped"],extras:["normal"],editType:"legend"},tracegroupgap:{valType:"number",min:0,dflt:10,editType:"legend"},entrywidth:{valType:"number",min:0,editType:"legend"},entrywidthmode:{valType:"enumerated",values:["fraction","pixels"],dflt:"pixels",editType:"legend"},itemsizing:{valType:"enumerated",values:["trace","constant"],dflt:"trace",editType:"legend"},itemwidth:{valType:"number",min:30,dflt:30,editType:"legend"},itemclick:{valType:"enumerated",values:["toggle","toggleothers",!1],dflt:"toggle",editType:"legend"},itemdoubleclick:{valType:"enumerated",values:["toggle","toggleothers",!1],dflt:"toggleothers",editType:"legend"},groupclick:{valType:"enumerated",values:["toggleitem","togglegroup"],dflt:"togglegroup",editType:"legend"},x:{valType:"number",editType:"legend"},xref:{valType:"enumerated",dflt:"paper",values:["container","paper"],editType:"layoutstyle"},xanchor:{valType:"enumerated",values:["auto","left","center","right"],dflt:"left",editType:"legend"},y:{valType:"number",editType:"legend"},yref:{valType:"enumerated",dflt:"paper",values:["container","paper"],editType:"layoutstyle"},yanchor:{valType:"enumerated",values:["auto","top","middle","bottom"],editType:"legend"},uirevision:{valType:"any",editType:"none"},valign:{valType:"enumerated",values:["top","middle","bottom"],dflt:"middle",editType:"legend"},title:{text:{valType:"string",dflt:"",editType:"legend"},font:n({editType:"legend"}),side:{valType:"enumerated",values:["top","left","top 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V,H=i.ensureSingle(N,"g","scrollbox"),q=h.title;h._titleWidth=0,h._titleHeight=0,q.text?((V=i.ensureSingle(H,"text",w+"titletext")).attr("text-anchor","start").call(u.font,q.font).text(q.text),L(V,H,t,h,_)):H.selectAll("."+w+"titletext").remove();var G=i.ensureSingle(N,"rect","scrollbar",(function(t){t.attr(p.scrollBarEnterAttrs).call(c.fill,p.scrollBarColor)})),Z=H.selectAll("g.groups").data(S);Z.enter().append("g").attr("class","groups"),Z.exit().remove();var Y=Z.selectAll("g.traces").data(i.identity);Y.enter().append("g").attr("class","traces"),Y.exit().remove(),Y.style("opacity",(function(t){var e=t[0].trace;return o.traceIs(e,"pie-like")?-1!==B.indexOf(t[0].label)?.5:1:"legendonly"===e.visible?.5:1})).each((function(){n.select(this).call(M,t,h)})).call(x,t,h).each((function(){T||n.select(this).call(E,t,w)})),i.syncOrAsync([a.previousPromises,function(){return function(t,e,r,i){var a=t._fullLayout,o=O(i);i||(i=a[o]);var 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n=r(73972),i=r(71828),a=i.pushUnique,o=!0;t.exports=function(t,e,r){var s=e._fullLayout;if(!e._dragged&&!e._editing){var l,u=s.legend.itemclick,c=s.legend.itemdoubleclick,f=s.legend.groupclick;if(1===r&&"toggle"===u&&"toggleothers"===c&&o&&e.data&&e._context.showTips?(i.notifier(i._(e,"Double-click on legend to isolate one trace"),"long"),o=!1):o=!1,1===r?l=u:2===r&&(l=c),l){var h="togglegroup"===f,p=s.hiddenlabels?s.hiddenlabels.slice():[],d=t.data()[0][0];if(!d.groupTitle||!d.noClick){var v=e._fullData,g=(s.shapes||[]).filter((function(t){return t.showlegend})),y=v.concat(g),m=d.trace;m._isShape&&(m=m._fullInput);var x,b,_,w,T,k=m.legendgroup,A={},M=[],S=[],E=[],L=(s.shapes||[]).map((function(t){return t._input})),C=!1,P=m.legend,O=m._fullInput;if(O&&O._isShape||!n.traceIs(m,"pie-like")){var I,D=k&&k.length,z=[];if(D)for(x=0;x<y.length;x++)(I=y[x]).visible&&I.legendgroup===k&&z.push(x);if("toggle"===l){var 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n}(t))}},c.resetViewMapbox={name:"resetViewMapbox",_cat:"resetView",title:function(t){return u(t,"Reset view")},attr:"reset",icon:o.home,click:function(t){x(t,"mapbox")}},c.zoomInMapbox={name:"zoomInMapbox",_cat:"zoomin",title:function(t){return u(t,"Zoom in")},attr:"zoom",val:"in",icon:o.zoom_plus,click:m},c.zoomOutMapbox={name:"zoomOutMapbox",_cat:"zoomout",title:function(t){return u(t,"Zoom out")},attr:"zoom",val:"out",icon:o.zoom_minus,click:m}},93348:function(t,e,r){"use strict";var n=r(26023),i=Object.keys(n),a=["drawline","drawopenpath","drawclosedpath","drawcircle","drawrect","eraseshape"],o=["v1hovermode","hoverclosest","hovercompare","togglehover","togglespikelines"].concat(a),s=[];i.forEach((function(t){!function(t){if(-1===o.indexOf(t._cat||t.name)){var e=t.name,r=(t._cat||t.name).toLowerCase();-1===s.indexOf(e)&&s.push(e),-1===s.indexOf(r)&&s.push(r)}}(n[t])})),s.sort(),t.exports={DRAW_MODES:a,backButtons:o,foreButtons:s}},35750:function(t,e,r){"use strict";var n=r(71828),i=r(7901),a=r(44467),o=r(42068);t.exports=function(t,e){var r=t.modebar||{},s=a.newContainer(e,"modebar");function l(t,e){return n.coerce(r,s,o,t,e)}l("orientation"),l("bgcolor",i.addOpacity(e.paper_bgcolor,.5));var u=i.contrast(i.rgb(e.modebar.bgcolor));l("color",i.addOpacity(u,.3)),l("activecolor",i.addOpacity(u,.7)),l("uirevision",e.uirevision),l("add"),l("remove")}},64168:function(t,e,r){"use strict";t.exports={moduleType:"component",name:"modebar",layoutAttributes:r(42068),supplyLayoutDefaults:r(35750),manage:r(14192)}},14192:function(t,e,r){"use strict";var n=r(41675),i=r(34098),a=r(73972),o=r(23469).isUnifiedHover,s=r(37676),l=r(26023),u=r(93348).DRAW_MODES,c=r(71828).extendDeep;t.exports=function(t){var e=t._fullLayout,r=t._context,f=e._modeBar;if(r.displayModeBar||r.watermark){if(!Array.isArray(r.modeBarButtonsToRemove))throw new Error(["*modeBarButtonsToRemove* configuration options","must be an array."].join(" "));if(!Array.isArray(r.modeBarButtonsToAdd))throw new 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P=[],O=[],I=[],D=[];(v||b||m||x||_)+y+g+w+T+k>1?(O=["toggleHover"],I=["resetViews"]):y?(P=["zoomInGeo","zoomOutGeo"],O=["hoverClosestGeo"],I=["resetGeo"]):g?(O=["hoverClosest3d"],I=["resetCameraDefault3d","resetCameraLastSave3d"]):w?(P=["zoomInMapbox","zoomOutMapbox"],O=["toggleHover"],I=["resetViewMapbox"]):b?O=["hoverClosestGl2d"]:m?O=["hoverClosestPie"]:A?(O=["hoverClosestCartesian","hoverCompareCartesian"],I=["resetViewSankey"]):O=["toggleHover"],v&&(O=["toggleSpikelines","hoverClosestCartesian","hoverCompareCartesian"]),(function(t){for(var e=0;e<t.length;e++)if(!a.traceIs(t[e],"noHover"))return!1;return!0}(r)||S)&&(O=[]),!v&&!b||M||(P=["zoomIn2d","zoomOut2d","autoScale2d"],"resetViews"!==I[0]&&(I=["resetScale2d"])),g?D=["zoom3d","pan3d","orbitRotation","tableRotation"]:(v||b)&&!M||_?D=["zoom2d","pan2d"]:w||y?D=["pan2d"]:T&&(D=["zoom2d"]),function(t){for(var e=!1,r=0;r<t.length&&!e;r++){var 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n=r(41940),i=r(22399),a=(0,r(44467).templatedArray)("button",{visible:{valType:"boolean",dflt:!0,editType:"plot"},step:{valType:"enumerated",values:["month","year","day","hour","minute","second","all"],dflt:"month",editType:"plot"},stepmode:{valType:"enumerated",values:["backward","todate"],dflt:"backward",editType:"plot"},count:{valType:"number",min:0,dflt:1,editType:"plot"},label:{valType:"string",editType:"plot"},editType:"plot"});t.exports={visible:{valType:"boolean",editType:"plot"},buttons:a,x:{valType:"number",min:-2,max:3,editType:"plot"},xanchor:{valType:"enumerated",values:["auto","left","center","right"],dflt:"left",editType:"plot"},y:{valType:"number",min:-2,max:3,editType:"plot"},yanchor:{valType:"enumerated",values:["auto","top","middle","bottom"],dflt:"bottom",editType:"plot"},font:n({editType:"plot"}),bgcolor:{valType:"color",dflt:i.lightLine,editType:"plot"},activecolor:{valType:"color",editType:"plot"},bordercolor:{valType:"color",dflt:i.defaultLine,editType:"plot"},borderwidth:{valType:"number",min:0,dflt:0,editType:"plot"},editType:"plot"}},89573:function(t){"use 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e=t._fullLayout._infolayer.selectAll(".rangeselector").data(function(t){for(var e=f.list(t,"x",!0),r=[],n=0;n<e.length;n++){var i=e[n];i.rangeselector&&i.rangeselector.visible&&r.push(i)}return r}(t),m);e.enter().append("g").classed("rangeselector",!0),e.exit().remove(),e.style({cursor:"pointer","pointer-events":"all"}),e.each((function(e){var r=n.select(this),o=e,f=o.rangeselector,h=r.selectAll("g.button").data(l.filterVisible(f.buttons));h.enter().append("g").classed("button",!0),h.exit().remove(),h.each((function(e){var r=n.select(this),a=y(o,e);e._isActive=function(t,e,r){if("all"===e.step)return!0===t.autorange;var n=Object.keys(r);return t.range[0]===r[n[0]]&&t.range[1]===r[n[1]]}(o,e,a),r.call(x,f,e),r.call(b,f,e,t),r.on("click",(function(){t._dragged||i.call("_guiRelayout",t,a)})),r.on("mouseover",(function(){e._isHovered=!0,r.call(x,f,e)})),r.on("mouseout",(function(){e._isHovered=!1,r.call(x,f,e)}))})),function(t,e,r,i,o){var f=0,h=0,y=r.borderwidth;e.each((function(){var t=n.select(this).select(".selector-text"),e=r.font.size*p,i=Math.max(e*c.lineCount(t),16)+3;h=Math.max(h,i)})),e.each((function(){var t=n.select(this),e=t.select(".selector-rect"),i=t.select(".selector-text"),a=i.node()&&s.bBox(i.node()).width,o=r.font.size*p,l=c.lineCount(i),d=Math.max(a+10,g.minButtonWidth);t.attr("transform",u(y+f,y)),e.attr({x:0,y:0,width:d,height:h}),c.positionText(i,d/2,h/2-(l-1)*o/2+3),f+=d+5}));var m=t._fullLayout._size,x=m.l+m.w*r.x,b=m.t+m.h*(1-r.y),_="left";l.isRightAnchor(r)&&(x-=f,_="right"),l.isCenterAnchor(r)&&(x-=f/2,_="center");var w="top";l.isBottomAnchor(r)&&(b-=h,w="bottom"),l.isMiddleAnchor(r)&&(b-=h/2,w="middle"),f=Math.ceil(f),h=Math.ceil(h),x=Math.round(x),b=Math.round(b),a.autoMargin(t,i+"-range-selector",{x:r.x,y:r.y,l:f*d[_],r:f*v[_],b:h*v[w],t:h*d[w]}),o.attr("transform",u(x,b))}(t,h,f,o._name,r)}))}},70565:function(t,e,r){"use strict";var n=r(81041),i=r(71828).titleCase;t.exports=function(t,e){var 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i=o.ensureSingle(t,"g",v.grabberMinClassName),a=o.ensureSingle(t,"g",v.grabberMaxClassName),s={x:0,width:v.handleWidth,rx:v.handleRadius,fill:u.background,stroke:u.defaultLine,"stroke-width":v.handleStrokeWidth,"shape-rendering":"crispEdges"},l={y:Math.round(n._height/4),height:Math.round(n._height/2)};o.ensureSingle(i,"rect",v.handleMinClassName,(function(t){t.attr(s)})).attr(l),o.ensureSingle(a,"rect",v.handleMaxClassName,(function(t){t.attr(s)})).attr(l);var c={width:v.grabAreaWidth,x:0,y:0,fill:v.grabAreaFill,cursor:e._context.staticPlot?void 0:v.grabAreaCursor};o.ensureSingle(i,"rect",v.grabAreaMinClassName,(function(t){t.attr(c)})).attr("height",n._height),o.ensureSingle(a,"rect",v.grabAreaMaxClassName,(function(t){t.attr(c)})).attr("height",n._height)}t.exports=function(t){for(var e=t._fullLayout,r=e._rangeSliderData,a=0;a<r.length;a++){var l=r[a][v.name];l._clipId=l._id+"-"+e._uid}var u=e._infolayer.selectAll("g."+v.containerClassName).data(r,(function(t){return t._name}));u.exit().each((function(t){var r=t[v.name];e._topdefs.select("#"+r._clipId).remove()})).remove(),0!==r.length&&(u.enter().append("g").classed(v.containerClassName,!0).attr("pointer-events","all"),u.each((function(r){var a=n.select(this),l=r[v.name],u=e[h.id2name(r.anchor)],f=l[h.id2name(r.anchor)];if(l.range){var w,T=o.simpleMap(l.range,r.r2l),k=o.simpleMap(r.range,r.r2l);w=k[0]<k[1]?[Math.min(T[0],k[0]),Math.max(T[1],k[1])]:[Math.max(T[0],k[0]),Math.min(T[1],k[1])],l.range=l._input.range=o.simpleMap(w,r.l2r)}r.cleanRange("rangeslider.range");var A=e._size,M=r.domain;l._width=A.w*(M[1]-M[0]);var S=Math.round(A.l+A.w*M[0]),E=Math.round(A.t+A.h*(1-r._counterDomainMin)+("bottom"===r.side?r._depth:0)+l._offsetShift+v.extraPad);a.attr("transform",s(S,E)),l._rl=o.simpleMap(l.range,r.r2l);var L=l._rl[0],C=l._rl[1],P=C-L;if(l.p2d=function(t){return t/l._width*P+L},l.d2p=function(t){return(t-L)/P*l._width},r.rangebreaks){var O=r.locateBreaks(L,C);if(O.length){var I,D,z=0;for(I=0;I<O.length;I++)z+=(D=O[I]).max-D.min;var R=l._width/(C-L-z),F=[-R*L];for(I=0;I<O.length;I++)D=O[I],F.push(F[F.length-1]-R*(D.max-D.min));for(l.d2p=function(t){for(var e=F[0],r=0;r<O.length;r++){var n=O[r];if(t>=n.max)e=F[r+1];else if(t<n.min)break}return e+R*t},I=0;I<O.length;I++)(D=O[I]).pmin=l.d2p(D.min),D.pmax=l.d2p(D.max);l.p2d=function(t){for(var e=F[0],r=0;r<O.length;r++){var n=O[r];if(t>=n.pmax)e=F[r+1];else if(t<n.pmin)break}return(t-e)/R}}}if("match"!==f.rangemode){var B=u.r2l(f.range[0]),N=u.r2l(f.range[1])-B;l.d2pOppAxis=function(t){return(t-B)/N*l._height}}a.call(g,t,r,l).call(y,t,r,l).call(m,t,r,l).call(x,t,r,l,f).call(b,t,r,l).call(_,t,r,l),function(t,e,r,a){if(!e._context.staticPlot){var s=t.select("rect."+v.slideBoxClassName).node(),l=t.select("rect."+v.grabAreaMinClassName).node(),u=t.select("rect."+v.grabAreaMaxClassName).node();t.on("mousedown",c),t.on("touchstart",c)}function c(){var c=n.event,f=c.target,h=c.clientX||c.touches[0].clientX,v=h-t.node().getBoundingClientRect().left,g=a.d2p(r._rl[0]),y=a.d2p(r._rl[1]),m=p.coverSlip();function x(t){var c,p,x,b=+(t.clientX||t.touches[0].clientX)-h;switch(f){case s:if(x="ew-resize",g+b>r._length||y+b<0)return;c=g+b,p=y+b;break;case l:if(x="col-resize",g+b>r._length)return;c=g+b,p=y;break;case u:if(x="col-resize",y+b<0)return;c=g,p=y+b;break;default:x="ew-resize",c=v,p=v+b}if(p<c){var _=p;p=c,c=_}a._pixelMin=c,a._pixelMax=p,d(n.select(m),x),function(t,e,r,n){function a(t){return r.l2r(o.constrain(t,n._rl[0],n._rl[1]))}var s=a(n.p2d(n._pixelMin)),l=a(n.p2d(n._pixelMax));window.requestAnimationFrame((function(){i.call("_guiRelayout",e,r._name+".range",[s,l])}))}(0,e,r,a)}function 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d=n._height-c(n.d2pOppAxis(i._rl[1])),g=n._height-c(n.d2pOppAxis(i._rl[0]));t.select("rect."+v.maskMinOppAxisClassName).attr("x",h).attr("height",d).attr("width",p-h),t.select("rect."+v.maskMaxOppAxisClassName).attr("x",h).attr("y",g).attr("height",n._height-g).attr("width",p-h),t.select("rect."+v.slideBoxClassName).attr("y",d).attr("height",g-d)}var y=.5,m=Math.round(f(h-l))-y,x=Math.round(f(p-l))+y;t.select("g."+v.grabberMinClassName).attr("transform",s(m,y)),t.select("g."+v.grabberMaxClassName).attr("transform",s(x,y))}(a,0,r,l,u,f),"bottom"===r.side&&c.draw(t,r._id+"title",{propContainer:r,propName:r._name+".title",placeholder:e._dfltTitle.x,attributes:{x:r._offset+r._length/2,y:E+l._height+l._offsetShift+10+1.5*r.title.font.size,"text-anchor":"middle"}})})))}},549:function(t,e,r){"use strict";var n=r(41675),i=r(63893),a=r(73251),o=r(18783).LINE_SPACING,s=a.name;function l(t){var e=t&&t[s];return e&&e.visible}e.isVisible=l,e.makeData=function(t){var e=n.list({_fullLayout:t},"x",!0),r=t.margin,i=[];if(!t._has("gl2d"))for(var a=0;a<e.length;a++){var o=e[a];if(l(o)){i.push(o);var u=o[s];u._id=s+o._id,u._height=(t.height-r.b-r.t)*u.thickness,u._offsetShift=Math.floor(u.borderwidth/2)}}t._rangeSliderData=i},e.autoMarginOpts=function(t,e){var r=t._fullLayout,n=e[s],l=e._id.charAt(0),u=0,c=0;return"bottom"===e.side&&(u=e._depth,e.title.text!==r._dfltTitle[l]&&(c=1.5*e.title.font.size+10+n._offsetShift,c+=(e.title.text.match(i.BR_TAG_ALL)||[]).length*e.title.font.size*o)),{x:0,y:e._counterDomainMin,l:0,r:0,t:0,b:n._height+u+Math.max(r.margin.b,c),pad:a.extraPad+2*n._offsetShift}}},13137:function(t,e,r){"use strict";var n=r(71828),i=r(75148),a=r(47850),o=r(549);t.exports={moduleType:"component",name:"rangeslider",schema:{subplots:{xaxis:{rangeslider:n.extendFlat({},i,{yaxis:a})}}},layoutAttributes:r(75148),handleDefaults:r(26377),calcAutorange:r(88443),draw:r(72413),isVisible:o.isVisible,makeData:o.makeData,autoMarginOpts:o.autoMarginOpts}},47850:function(t){"use strict";t.exports={_isSubplotObj:!0,rangemode:{valType:"enumerated",values:["auto","fixed","match"],dflt:"match",editType:"calc"},range:{valType:"info_array",items:[{valType:"any",editType:"plot"},{valType:"any",editType:"plot"}],editType:"plot"},editType:"calc"}},8389:function(t,e,r){"use strict";var n=r(50215),i=r(82196).line,a=r(79952).P,o=r(1426).extendFlat,s=r(30962).overrideAll,l=r(44467).templatedArray;r(24695),t.exports=s(l("selection",{type:{valType:"enumerated",values:["rect","path"]},xref:o({},n.xref,{}),yref:o({},n.yref,{}),x0:{valType:"any"},x1:{valType:"any"},y0:{valType:"any"},y1:{valType:"any"},path:{valType:"string",editType:"arraydraw"},opacity:{valType:"number",min:0,max:1,dflt:.7,editType:"arraydraw"},line:{color:i.color,width:o({},i.width,{min:1,dflt:1}),dash:o({},a,{dflt:"dot"})}}),"arraydraw","from-root")},34122:function(t){"use strict";t.exports={BENDPX:1.5,MINSELECT:12,SELECTDELAY:100,SELECTID:"-select"}},59402:function(t,e,r){"use strict";var n=r(71828),i=r(89298),a=r(85501),o=r(8389),s=r(30477);function l(t,e,r){function a(r,i){return n.coerce(t,e,o,r,i)}var l=a("path"),u="path"!==a("type",l?"path":"rect");u&&delete e.path,a("opacity"),a("line.color"),a("line.width"),a("line.dash");for(var c=["x","y"],f=0;f<2;f++){var h,p,d,v=c[f],g={_fullLayout:r},y=i.coerceRef(t,e,g,v);if((h=i.getFromId(g,y))._selectionIndices.push(e._index),d=s.rangeToShapePosition(h),p=s.shapePositionToRange(h),u){var m=v+"0",x=v+"1",b=t[m],_=t[x];t[m]=p(t[m],!0),t[x]=p(t[x],!0),i.coercePosition(e,g,a,y,m),i.coercePosition(e,g,a,y,x);var w=e[m],T=e[x];void 0!==w&&void 0!==T&&(e[m]=d(w),e[x]=d(T),t[m]=b,t[x]=_)}}u&&n.noneOrAll(t,e,["x0","x1","y0","y1"])}t.exports=function(t,e){a(t,e,{name:"selections",handleItemDefaults:l});for(var r=e.selections,n=0;n<r.length;n++){var i=r[n];i&&void 0===i.path&&(void 0!==i.x0&&void 0!==i.x1&&void 0!==i.y0&&void 0!==i.y1||(e.selections[n]=null))}}},32485:function(t,e,r){"use strict";var n=r(60165).readPaths,i=r(42359),a=r(51873).clearOutlineControllers,o=r(7901),s=r(91424),l=r(44467).arrayEditor,u=r(30477),c=u.getPathString;function f(t){var e=t._fullLayout;for(var r in a(t),e._selectionLayer.selectAll("path").remove(),e._plots){var n=e._plots[r].selectionLayer;n&&n.selectAll("path").remove()}for(var i=0;i<e.selections.length;i++)p(t,i)}function h(t){return t._context.editSelection}function p(t,e){t._fullLayout._paperdiv.selectAll('.selectionlayer [data-index="'+e+'"]').remove();var r=u.makeSelectionsOptionsAndPlotinfo(t,e),a=r.options,p=r.plotinfo;a._input&&function(r){var u=c(t,a),g={"data-index":e,"fill-rule":"evenodd",d:u},y=a.opacity,m="rgba(0,0,0,0)",x=a.line.color||o.contrast(t._fullLayout.plot_bgcolor),b=a.line.width,_=a.line.dash;b||(b=5,_="solid");var w=h(t)&&t._fullLayout._activeSelectionIndex===e;w&&(m=t._fullLayout.activeselection.fillcolor,y=t._fullLayout.activeselection.opacity);for(var T=[],k=1;k>=0;k--){var A=r.append("path").attr(g).style("opacity",k?.1:y).call(o.stroke,x).call(o.fill,m).call(s.dashLine,k?"solid":_,k?4+b:b);if(d(A,t,a),w){var M=l(t.layout,"selections",a);A.style({cursor:"move"});var S={element:A.node(),plotinfo:p,gd:t,editHelpers:M,isActiveSelection:!0},E=n(u,t);i(E,A,S)}else A.style("pointer-events",k?"all":"none");T[k]=A}var L=T[0];T[1].node().addEventListener("click",(function(){return function(t,e){if(h(t)){var r=+e.node().getAttribute("data-index");if(r>=0){if(r===t._fullLayout._activeSelectionIndex)return void v(t);t._fullLayout._activeSelectionIndex=r,t._fullLayout._deactivateSelection=v,f(t)}}}(t,L)}))}(t._fullLayout._selectionLayer)}function d(t,e,r){var n=r.xref+r.yref;s.setClipUrl(t,"clip"+e._fullLayout._uid+n,e)}function v(t){h(t)&&t._fullLayout._activeSelectionIndex>=0&&(a(t),delete t._fullLayout._activeSelectionIndex,f(t))}t.exports={draw:f,drawOne:p,activateLastSelection:function(t){if(h(t)){var e=t._fullLayout.selections.length-1;t._fullLayout._activeSelectionIndex=e,t._fullLayout._deactivateSelection=v,f(t)}}}},53777:function(t,e,r){"use strict";var n=r(79952).P,i=r(1426).extendFlat;t.exports={newselection:{mode:{valType:"enumerated",values:["immediate","gradual"],dflt:"immediate",editType:"none"},line:{color:{valType:"color",editType:"none"},width:{valType:"number",min:1,dflt:1,editType:"none"},dash:i({},n,{dflt:"dot",editType:"none"}),editType:"none"},editType:"none"},activeselection:{fillcolor:{valType:"color",dflt:"rgba(0,0,0,0)",editType:"none"},opacity:{valType:"number",min:0,max:1,dflt:.5,editType:"none"},editType:"none"}}},90849:function(t){"use strict";t.exports=function(t,e,r){r("newselection.mode"),r("newselection.line.width")&&(r("newselection.line.color"),r("newselection.line.dash")),r("activeselection.fillcolor"),r("activeselection.opacity")}},35855:function(t,e,r){"use strict";var n=r(64505).selectMode,i=r(51873).clearOutline,a=r(60165),o=a.readPaths,s=a.writePaths,l=a.fixDatesForPaths;t.exports=function(t,e){if(t.length){var r=t[0][0];if(r){var 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s=R(t[o]);s.subtract=!!t[o].subtract,e.push(s),r=Math.min(r,s.xmin),n=Math.max(n,s.xmax),i=Math.min(i,s.ymin),a=Math.max(a,s.ymax)}return{xmin:r,xmax:n,ymin:i,ymax:a,pts:[],contains:function(t,r,n,i){for(var a=!1,o=0;o<e.length;o++)e[o].contains(t,r,n,i)&&(a=!e[o].subtract);return a},isRect:!1,degenerate:!1}}}function Z(t,e,r){var n=e._fullLayout,i=r.plotinfo,a=r.dragmode,o=n._lastSelectedSubplot&&n._lastSelectedSubplot===i.id,s=(t.shiftKey||t.altKey)&&!(p(a)&&d(a));o&&s&&i.selection&&i.selection.selectionDefs&&!r.selectionDefs?(r.selectionDefs=i.selection.selectionDefs,r.mergedPolygons=i.selection.mergedPolygons):s&&i.selection||Y(r),o||(x(e),n._lastSelectedSubplot=i.id)}function Y(t,e){var r=t.dragmode,n=t.plotinfo,i=t.gd;(function(t){return t._fullLayout._activeShapeIndex>=0})(i)&&i._fullLayout._deactivateShape(i),function(t){return t._fullLayout._activeSelectionIndex>=0}(i)&&i._fullLayout._deactivateSelection(i);var o=i._fullLayout._zoomlayer,s=p(r),l=v(r);if(s||l){var u,c,f=o.selectAll(".select-outline-"+n.id);f&&i._fullLayout._outlining&&(s&&(u=T(f,t)),u&&a.call("_guiRelayout",i,{shapes:u}),l&&!U(t)&&(c=k(f,t)),c&&(i._fullLayout._noEmitSelectedAtStart=!0,a.call("_guiRelayout",i,{selections:c}).then((function(){e&&A(i)}))),i._fullLayout._outlining=!1)}n.selection={},n.selection.selectionDefs=t.selectionDefs=[],n.selection.mergedPolygons=t.mergedPolygons=[]}function W(t){return t._id}function X(t,e,r,n){if(!t.calcdata)return[];var i,a,o,s=[],l=e.map(W),u=r.map(W);for(o=0;o<t.calcdata.length;o++)if(!0===(a=(i=t.calcdata[o])[0].trace).visible&&a._module&&a._module.selectPoints)if(!U({subplot:n})||a.subplot!==n&&a.geo!==n)if("splom"===a.type){if(a._xaxes[l[0]]&&a._yaxes[u[0]]){var c=J(a._module,i,e[0],r[0]);c.scene=t._fullLayout._splomScenes[a.uid],s.push(c)}}else if("sankey"===a.type){var 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c=l[u],f=c.data,h=c.fullData,p=c.pointIndex,d=c.pointIndices;d?([].push.apply(f.selectedpoints,d),i._fullInput!==i&&[].push.apply(h.selectedpoints,d)):(f.selectedpoints.push(p),i._fullInput!==i&&h.selectedpoints.push(p))}}else for(n=0;n<e.length;n++)delete(i=e[n].cd[0].trace).selectedpoints,delete i._input.selectedpoints,i._fullInput!==i&&delete i._fullInput.selectedpoints;!function(t,e){for(var r=!1,n=0;n<e.length;n++){var i=e[n],o=i.cd;a.traceIs(o[0].trace,"regl")&&(r=!0);var s=i._module,l=s.styleOnSelect||s.style;l&&(l(t,o,o[0].node3),o[0].nodeRangePlot3&&l(t,o,o[0].nodeRangePlot3))}r&&(P(t),O(t))}(t,e)}function Q(t,e,r){for(var i=(r?n.difference:n.union)({regions:t},{regions:[e]}).regions.reverse(),a=0;a<i.length;a++){var o=i[a];o.subtract=st(o,i.slice(0,a))}return i}function tt(t,e){if(Array.isArray(t))for(var r=e.cd,n=e.cd[0].trace,i=0;i<t.length;i++)t[i]=u(t[i],n,r);return t}function et(t,e){for(var r=[],n=0;n<t.length;n++){r[n]=[];for(var 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p=2*h*Math.PI/a;f.push([s+u*Math.cos(p),l+c*Math.sin(p)])}return f},e.ellipseOver=function(t){var e=t.x0,r=t.y0,n=t.x1,i=t.y1,a=n-e,s=i-r,l=((e-=a)+n)/2,u=((r-=s)+i)/2;return{x0:l-(a*=o),y0:u-(s*=o),x1:l+a,y1:u+s}},e.fixDatesForPaths=function(t,e,r){var n="date"===e.type,i="date"===r.type;if(!n&&!i)return t;for(var a=0;a<t.length;a++)for(var o=0;o<t[a].length;o++)for(var s=0;s+2<t[a][o].length;s+=2)n&&(t[a][o][s+1]=t[a][o][s+1].replace(" ","_")),i&&(t[a][o][s+2]=t[a][o][s+2].replace(" ","_"));return t}},90551:function(t,e,r){"use strict";var n=r(64505),i=n.drawMode,a=n.openMode,o=r(89995),s=o.i000,l=o.i090,u=o.i180,c=o.i270,f=o.cos45,h=o.sin45,p=r(75549),d=p.p2r,v=p.r2p,g=r(51873).clearOutline,y=r(60165),m=y.readPaths,x=y.writePaths,b=y.ellipseOver,_=y.fixDatesForPaths;function w(t,e,r){var n,i=t[0][0],o=e.gd,p=i.getAttribute("d"),g=o._fullLayout.newshape,y=e.plotinfo,w=e.isActiveShape,T=y.xaxis,k=y.yaxis,A=!!y.domain||!y.xaxis,M=!!y.domain||!y.yaxis,S=a(r),E=m(p,o,y,w),L={editable:!0,visible:g.visible,name:g.name,showlegend:g.showlegend,legend:g.legend,legendwidth:g.legendwidth,legendgroup:g.legendgroup,legendgrouptitle:{text:g.legendgrouptitle.text,font:g.legendgrouptitle.font},legendrank:g.legendrank,label:g.label,xref:A?"paper":T._id,yref:M?"paper":k._id,layer:g.layer,opacity:g.opacity,line:{color:g.line.color,width:g.line.width,dash:g.line.dash}};if(S||(L.fillcolor=g.fillcolor,L.fillrule=g.fillrule),1===E.length&&(n=E[0]),n&&5===n.length&&"drawrect"===r)L.type="rect",L.x0=n[0][1],L.y0=n[0][2],L.x1=n[2][1],L.y1=n[2][2];else if(n&&"drawline"===r)L.type="line",L.x0=n[0][1],L.y0=n[0][2],L.x1=n[1][1],L.y1=n[1][2];else if(n&&"drawcircle"===r){L.type="circle";var C=n[s][1],P=n[l][1],O=n[u][1],I=n[c][1],D=n[s][2],z=n[l][2],R=n[u][2],F=n[c][2],B=y.xaxis&&("date"===y.xaxis.type||"log"===y.xaxis.type),N=y.yaxis&&("date"===y.yaxis.type||"log"===y.yaxis.type);B&&(C=v(y.xaxis,C),P=v(y.xaxis,P),O=v(y.xaxis,O),I=v(y.xaxis,I)),N&&(D=v(y.yaxis,D),z=v(y.yaxis,z),R=v(y.yaxis,R),F=v(y.yaxis,F));var j=(P+I)/2,U=(D+R)/2,V=b({x0:j,y0:U,x1:j+(I-P+O-C)/2*f,y1:U+(F-z+R-D)/2*h});B&&(V.x0=d(y.xaxis,V.x0),V.x1=d(y.xaxis,V.x1)),N&&(V.y0=d(y.yaxis,V.y0),V.y1=d(y.yaxis,V.y1)),L.x0=V.x0,L.y0=V.y0,L.x1=V.x1,L.y1=V.y1}else L.type="path",T&&k&&_(E,T,k),L.path=x(E),n=null;return L}t.exports={newShapes:function(t,e){if(t.length&&t[0][0]){var r=e.gd,n=e.isActiveShape,a=e.dragmode,o=(r.layout||{}).shapes||[];if(!i(a)&&void 0!==n){var s=r._fullLayout._activeShapeIndex;if(s<o.length)switch(r._fullLayout.shapes[s].type){case"rect":a="drawrect";break;case"circle":a="drawcircle";break;case"line":a="drawline";break;case"path":var l=o[s].path||"";a="Z"===l[l.length-1]?"drawclosedpath":"drawopenpath"}}var u=w(t,e,a);g(r);for(var c=e.editHelpers,f=(c||{}).modifyItem,h=[],p=0;p<o.length;p++){var d=r._fullLayout.shapes[p];if(h[p]=d._input,void 0!==n&&p===r._fullLayout._activeShapeIndex){var v=u;switch(d.type){case"line":case"rect":case"circle":f("x0",v.x0),f("x1",v.x1),f("y0",v.y0),f("y1",v.y1);break;case"path":f("path",v.path)}}}return void 0===n?(h.push(u),h):c?c.getUpdateObj():{}}},createShapeObj:w}},51873:function(t){"use strict";t.exports={clearOutlineControllers:function(t){var e=t._fullLayout._zoomlayer;e&&e.selectAll(".outline-controllers").remove()},clearOutline:function(t){var e=t._fullLayout._zoomlayer;e&&e.selectAll(".select-outline").remove(),t._fullLayout._outlining=!1}}},30477:function(t,e,r){"use strict";var n=r(21459),i=r(71828),a=r(89298);e.rangeToShapePosition=function(t){return"log"===t.type?t.r2d:function(t){return 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s=e.rangeToShapePosition(r);a=function(t){return s(r.p2r(t-r._offset))}}else a=n?function(t){return 1-(t-o.t)/o.h}:function(t){return(t-o.l)/o.w};return a},e.roundPositionForSharpStrokeRendering=function(t,e){var r=1===Math.round(e%2),n=Math.round(t);return r?n+.5:n},e.makeShapesOptionsAndPlotinfo=function(t,e){var r=t._fullLayout.shapes[e]||{},n=t._fullLayout._plots[r.xref+r.yref];return n?n._hadPlotinfo=!0:(n={},r.xref&&"paper"!==r.xref&&(n.xaxis=t._fullLayout[r.xref+"axis"]),r.yref&&"paper"!==r.yref&&(n.yaxis=t._fullLayout[r.yref+"axis"])),n.xsizemode=r.xsizemode,n.ysizemode=r.ysizemode,n.xanchor=r.xanchor,n.yanchor=r.yanchor,{options:r,plotinfo:n}},e.makeSelectionsOptionsAndPlotinfo=function(t,e){var r=t._fullLayout.selections[e]||{},n=t._fullLayout._plots[r.xref+r.yref];return n?n._hadPlotinfo=!0:(n={},r.xref&&(n.xaxis=t._fullLayout[r.xref+"axis"]),r.yref&&(n.yaxis=t._fullLayout[r.yref+"axis"])),{options:r,plotinfo:n}},e.getPathString=function(t,r){var o,s,l,u,c,f,h,p,d=r.type,v=a.getRefType(r.xref),g=a.getRefType(r.yref),y=a.getFromId(t,r.xref),m=a.getFromId(t,r.yref),x=t._fullLayout._size;if(y?"domain"===v?s=function(t){return y._offset+y._length*t}:(o=e.shapePositionToRange(y),s=function(t){return y._offset+y.r2p(o(t,!0))}):s=function(t){return x.l+x.w*t},m?"domain"===g?u=function(t){return m._offset+m._length*(1-t)}:(l=e.shapePositionToRange(m),u=function(t){return m._offset+m.r2p(l(t,!0))}):u=function(t){return x.t+x.h*(1-t)},"path"===d)return y&&"date"===y.type&&(s=e.decodeDate(s)),m&&"date"===m.type&&(u=e.decodeDate(u)),function(t,e,r){var a=t.path,o=t.xsizemode,s=t.ysizemode,l=t.xanchor,u=t.yanchor;return a.replace(n.segmentRE,(function(t){var a=0,c=t.charAt(0),f=n.paramIsX[c],h=n.paramIsY[c],p=n.numParams[c],d=t.substr(1).replace(n.paramRE,(function(t){return f[a]?t="pixel"===o?e(l)+Number(t):e(t):h[a]&&(t="pixel"===s?r(u)-Number(t):r(t)),++a>p&&(t="X"),t}));return a>p&&(d=d.replace(/[\s,]*X.*/,""),i.log("Ignoring extra 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t.x1},y0:function(t){return t.y0},y1:function(t){return t.y1},slope:function(t,e,r){return"line"!==t.type?void 0:i(t,0,r)/n(t,e)},dx:n,dy:i,width:function(t,e){return Math.abs(n(t,e))},height:function(t,e,r){return Math.abs(i(t,0,r))},length:function(t,e,r){return"line"!==t.type?void 0:Math.sqrt(Math.pow(n(t,e),2)+Math.pow(i(t,0,r),2))},xcenter:function(t,n){return r((e(t.x1,n)+e(t.x0,n))/2,n)},ycenter:function(t,n,i){return r((e(t.y1,i)+e(t.y0,i))/2,i)}}},75067:function(t,e,r){"use strict";var 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Promise.reject();r||i||o.isPlotDiv(t)||o.warn("Calling _doPlot as if redrawing but this container doesn't yet have a plot.",t),P(t,a),i||(i={}),n.select(t).classed("js-plotly-plot",!0),g.makeTester(),Array.isArray(t._promises)||(t._promises=[]);var f=0===(t.data||[]).length&&Array.isArray(r);Array.isArray(r)&&(T.cleanData(r),f?t.data=r:t.data.push.apply(t.data,r),t.empty=!1),t.layout&&!f||(t.layout=T.cleanLayout(i)),h.supplyDefaults(t);var d=t._fullLayout,v=d._has("cartesian");d._replotting=!0,(f||d._shouldCreateBgLayer)&&(function(t){var e=n.select(t),r=t._fullLayout;if(r._calcInverseTransform=lt,r._calcInverseTransform(t),r._container=e.selectAll(".plot-container").data([0]),r._container.enter().insert("div",":first-child").classed("plot-container",!0).classed("plotly",!0),r._paperdiv=r._container.selectAll(".svg-container").data([0]),r._paperdiv.enter().append("div").classed("user-select-none",!0).classed("svg-container",!0).style("position","relative"),r._glcontainer=r._paperdiv.selectAll(".gl-container").data([{}]),r._glcontainer.enter().append("div").classed("gl-container",!0),r._paperdiv.selectAll(".main-svg").remove(),r._paperdiv.select(".modebar-container").remove(),r._paper=r._paperdiv.insert("svg",":first-child").classed("main-svg",!0),r._toppaper=r._paperdiv.append("svg").classed("main-svg",!0),r._modebardiv=r._paperdiv.append("div"),delete r._modeBar,r._hoverpaper=r._paperdiv.append("svg").classed("main-svg",!0),!r._uid){var i={};n.selectAll("defs").each((function(){this.id&&(i[this.id.split("-")[1]]=1)})),r._uid=o.randstr(i)}r._paperdiv.selectAll(".main-svg").attr(x.svgAttrs),r._defs=r._paper.append("defs").attr("id","defs-"+r._uid),r._clips=r._defs.append("g").classed("clips",!0),r._topdefs=r._toppaper.append("defs").attr("id","topdefs-"+r._uid),r._topclips=r._topdefs.append("g").classed("clips",!0),r._bgLayer=r._paper.append("g").classed("bglayer",!0),r._draggers=r._paper.append("g").classed("draglayer",!0);var a=r._paper.append("g").classed("layer-below",!0);r._imageLowerLayer=a.append("g").classed("imagelayer",!0),r._shapeLowerLayer=a.append("g").classed("shapelayer",!0),r._cartesianlayer=r._paper.append("g").classed("cartesianlayer",!0),r._polarlayer=r._paper.append("g").classed("polarlayer",!0),r._smithlayer=r._paper.append("g").classed("smithlayer",!0),r._ternarylayer=r._paper.append("g").classed("ternarylayer",!0),r._geolayer=r._paper.append("g").classed("geolayer",!0),r._funnelarealayer=r._paper.append("g").classed("funnelarealayer",!0),r._pielayer=r._paper.append("g").classed("pielayer",!0),r._iciclelayer=r._paper.append("g").classed("iciclelayer",!0),r._treemaplayer=r._paper.append("g").classed("treemaplayer",!0),r._sunburstlayer=r._paper.append("g").classed("sunburstlayer",!0),r._indicatorlayer=r._toppaper.append("g").classed("indicatorlayer",!0),r._glimages=r._paper.append("g").classed("glimages",!0);var s=r._toppaper.append("g").classed("layer-above",!0);r._imageUpperLayer=s.append("g").classed("imagelayer",!0),r._shapeUpperLayer=s.append("g").classed("shapelayer",!0),r._selectionLayer=r._toppaper.append("g").classed("selectionlayer",!0),r._infolayer=r._toppaper.append("g").classed("infolayer",!0),r._menulayer=r._toppaper.append("g").classed("menulayer",!0),r._zoomlayer=r._toppaper.append("g").classed("zoomlayer",!0),r._hoverlayer=r._hoverpaper.append("g").classed("hoverlayer",!0),r._modebardiv.classed("modebar-container",!0).style("position","absolute").style("top","0px").style("right","0px"),t.emit("plotly_framework")}(t),d._shouldCreateBgLayer&&delete d._shouldCreateBgLayer),g.initGradients(t),g.initPatterns(t),f&&p.saveShowSpikeInitial(t);var y=!t.calcdata||t.calcdata.length!==(t._fullData||[]).length;y&&h.doCalcdata(t);for(var b=0;b<t.calcdata.length;b++)t.calcdata[b][0].trace=t._fullData[b];t._context.responsive?t._responsiveChartHandler||(t._responsiveChartHandler=function(){o.isHidden(t)||h.resize(t)},window.addEventListener("resize",t._responsiveChartHandler)):o.clearResponsive(t);var _=o.extendFlat({},d._size),w=0;function A(){if(h.clearAutoMarginIds(t),k.drawMarginPushers(t),p.allowAutoMargin(t),t._fullLayout.title.text&&t._fullLayout.title.automargin&&h.allowAutoMargin(t,"title.automargin"),d._has("pie"))for(var e=t._fullData,r=0;r<e.length;r++){var n=e[r];"pie"===n.type&&n.automargin&&h.allowAutoMargin(t,"pie."+n.uid+".automargin")}return h.doAutoMargin(t),h.previousPromises(t)}function M(){t._transitioning||(k.doAutoRangeAndConstraints(t),f&&p.saveRangeInitial(t),c.getComponentMethod("rangeslider","calcAutorange")(t))}var S=[h.previousPromises,function(){if(s)return e.addFrames(t,s)},function e(){for(var r=d._basePlotModules,n=0;n<r.length;n++)r[n].drawFramework&&r[n].drawFramework(t);!d._glcanvas&&d._has("gl")&&(d._glcanvas=d._glcontainer.selectAll(".gl-canvas").data([{key:"contextLayer",context:!0,pick:!1},{key:"focusLayer",context:!1,pick:!1},{key:"pickLayer",context:!1,pick:!0}],(function(t){return t.key})),d._glcanvas.enter().append("canvas").attr("class",(function(t){return"gl-canvas gl-canvas-"+t.key.replace("Layer","")})).style({position:"absolute",top:0,left:0,overflow:"visible","pointer-events":"none"}));var i=t._context.plotGlPixelRatio;if(d._glcanvas){d._glcanvas.attr("width",d.width*i).attr("height",d.height*i).style("width",d.width+"px").style("height",d.height+"px");var a=d._glcanvas.data()[0].regl;if(a&&(Math.floor(d.width*i)!==a._gl.drawingBufferWidth||Math.floor(d.height*i)!==a._gl.drawingBufferHeight)){var s="WebGL context buffer and canvas dimensions do not match due to browser/WebGL bug.";if(!w)return o.log(s+" Clearing graph and plotting again."),h.cleanPlot([],{},t._fullData,d),h.supplyDefaults(t),d=t._fullLayout,h.doCalcdata(t),w++,e();o.error(s)}}return"h"===d.modebar.orientation?d._modebardiv.style("height",null).style("width","100%"):d._modebardiv.style("width",null).style("height",d.height+"px"),h.previousPromises(t)},A,function(){if(h.didMarginChange(_,d._size))return o.syncOrAsync([A,k.layoutStyles],t)}];v&&S.push((function(){if(y)return o.syncOrAsync([c.getComponentMethod("shapes","calcAutorange"),c.getComponentMethod("annotations","calcAutorange"),M],t);M()})),S.push(k.layoutStyles),v&&S.push((function(){return p.draw(t,f?"":"redraw")}),(function(t){var e=t._fullLayout._insideTickLabelsUpdaterange;if(e)return t._fullLayout._insideTickLabelsUpdaterange=void 0,H(t,e).then((function(){p.saveRangeInitial(t,!0)}))})),S.push(k.drawData,k.finalDraw,m,h.addLinks,h.rehover,h.redrag,h.reselect,h.doAutoMargin,h.previousPromises);var L=o.syncOrAsync(S,t);return L&&L.then||(L=Promise.resolve()),L.then((function(){return E(t),t}))},e.purge=function(t){var e=(t=o.getGraphDiv(t))._fullLayout||{},r=t._fullData||[];return h.cleanPlot([],{},r,e),h.purge(t),l.purge(t),e._container&&e._container.remove(),delete t._context,t},e.react=function(t,r,n,i){var a,l;t=o.getGraphDiv(t),T.clearPromiseQueue(t);var u=t._fullData,p=t._fullLayout;if(o.isPlotDiv(t)&&u&&p){if(o.isPlainObject(r)){var d=r;r=d.data,n=d.layout,i=d.config,a=d.frames}var v=!1;if(i){var g=o.extendDeep({},t._context);t._context=void 0,P(t,i),v=st(g,t._context)}t.data=r||[],T.cleanData(t.data),t.layout=n||{},T.cleanLayout(t.layout),function(t,e,r,n){var i,a,l,u,c,f,h,p,d,v,g=n._preGUI,y=[],m={},x={};for(i in g){if(c=et(i,Q)){if(d=c.head,v=c.tail,a=c.attr||d+".uirevision",(u=(l=s(n,a).get())&&rt(a,e))&&u===l){if(null===(f=g[i])&&(f=void 0),at(p=(h=s(e,i)).get(),f)){void 0===p&&"autorange"===v&&y.push(d),h.set(B(s(n,i).get()));continue}if("autorange"===v||"range["===v.substr(0,6)){var 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y=t._fullData,m=t._fullLayout,x=void 0===m.datarevision,b=m.transition,_=function(t,e,r,n,i){var a=A.layoutFlags();return a.arrays={},a.rangesAltered={},a.nChanges=0,a.nChangesAnim=0,ot(e,r,[],{getValObject:function(t){return f.getLayoutValObject(r,t)},flags:a,immutable:n,transition:i,gd:t}),(a.plot||a.calc)&&(a.layoutReplot=!0),i&&a.nChanges&&a.nChangesAnim&&(a.anim=a.nChanges===a.nChangesAnim?"all":"some"),a}(t,p,m,x,b),w=_.newDataRevision,M=function(t,e,r,n,i,a){var o=e.length===r.length;if(!i&&!o)return{fullReplot:!0,calc:!0};var s,l,u=A.traceFlags();u.arrays={},u.nChanges=0,u.nChangesAnim=0;var c={getValObject:function(t){var 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if(M.fullReplot||_.layoutReplot||v)t._fullLayout._skipDefaults=!0,D.push(e._doPlot);else{for(var z in _.arrays){var R=_.arrays[z];if(R.length){var F=c.getComponentMethod(z,"drawOne");if(F!==o.noop)for(var N=0;N<R.length;N++)F(t,R[N]);else{var j=c.getComponentMethod(z,"draw");if(j===o.noop)throw new Error("cannot draw components: "+z);j(t)}}}D.push(h.previousPromises),M.style&&D.push(k.doTraceStyle),(M.colorbars||_.colorbars)&&D.push(k.doColorBars),_.legend&&D.push(k.doLegend),_.layoutstyle&&D.push(k.layoutStyles),_.axrange&&G(D),_.ticks&&D.push(k.doTicksRelayout),_.modebar&&D.push(k.doModeBar),_.camera&&D.push(k.doCamera),D.push(E)}D.push(h.rehover,h.redrag,h.reselect),(l=o.syncOrAsync(D,t))&&l.then||(l=Promise.resolve(t))}else l=e.newPlot(t,r,n,i);return l.then((function(){return t.emit("plotly_react",{data:r,layout:n}),t}))},e.redraw=function(t){if(t=o.getGraphDiv(t),!o.isPlotDiv(t))throw new Error("This element is not a Plotly plot: "+t);return T.cleanData(t.data),T.cleanLayout(t.layout),t.calcdata=void 0,e._doPlot(t).then((function(){return t.emit("plotly_redraw"),t}))},e.relayout=H,e.restyle=F,e.setPlotConfig=function(t){return o.extendFlat(_,t)},e.update=K,e._guiRelayout=$(H),e._guiRestyle=$(F),e._guiUpdate=$(K),e._storeDirectGUIEdit=function(t,e,r){for(var n in r)j(n,s(t,n).get(),r[n],e)}},72075:function(t){"use strict";var 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0===e.minor._tick0Init&&(t.tick0=e.tick0)},q.prepTicks=function(t,e){var r=s.simpleMap(t.range,t.r2l,void 0,void 0,e);if("auto"===t.tickmode||!t.dtick){var n,a=t.nticks;a||("category"===t.type||"multicategory"===t.type?(n=t.tickfont?s.bigFont(t.tickfont.size||12):15,a=t._length/n):(n="y"===t._id.charAt(0)?40:80,a=s.constrain(t._length/n,4,9)+1),"radialaxis"===t._name&&(a*=2)),t.minor&&"array"!==t.minor.tickmode||"array"===t.tickmode&&(a*=100),t._roughDTick=Math.abs(r[1]-r[0])/a,q.autoTicks(t,t._roughDTick),t._minDtick>0&&t.dtick<2*t._minDtick&&(t.dtick=t._minDtick,t.tick0=t.l2r(t._forceTick0))}"period"===t.ticklabelmode&&function(t){var e;function r(){return!(i(t.dtick)||"M"!==t.dtick.charAt(0))}var n=r(),a=q.getTickFormat(t);if(a){var 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L=(v+.5)/84;e.maskBreaks(i*(1-L)+L*p)!==O&&d++}(h*=d/84)||(t[n].drop=!0),s&&c>A&&(h=c)}(h>0||0===n)&&(t[n].periodX=i+h/2)}}(C,t,t._definedDelta),t.rangebreaks){var it="y"===t._id.charAt(0),st=1;"auto"===t.tickmode&&(st=t.tickfont?t.tickfont.size:12);var lt=NaN;for(r=C.length-1;r>-1;r--)if(C[r].drop)C.splice(r,1);else{C[r].value=zt(C[r].value,t);var ut=t.c2p(C[r].value);(it?lt>ut-st:lt<ut+st)?C.splice(f?r+1:r,1):lt=ut}}Dt(t)&&360===Math.abs(c[1]-c[0])&&C.pop(),t._tmax=(C[C.length-1]||{}).value,t._prevDateHead="",t._inCalcTicks=!0;var ct,ft,ht=function(e){e.text="",t._prevDateHead=n};for(C=C.concat(P),r=0;r<C.length;r++){var pt=C[r].minor,dt=C[r].value;pt?L.push({x:dt,minor:!0}):(n=t._prevDateHead,ct=q.tickText(t,dt,!1,C[r].simpleLabel),void 0!==(ft=C[r].periodX)&&(ct.periodX=ft,(ft>p||ft<h)&&(ft>p&&(ct.periodX=p),ft<h&&(ct.periodX=h),ht(ct))),C[r].skipLabel&&ht(ct),v.push(ct))}return v=v.concat(L),t._inCalcTicks=!1,u&&v.length&&(v[0].noTick=!0),v};var st=[2,5,10],lt=[1,2,3,6,12],ut=[1,2,5,10,15,30],ct=[1,2,3,7,14],ft=[-.046,0,.301,.477,.602,.699,.778,.845,.903,.954,1],ht=[-.301,0,.301,.699,1],pt=[15,30,45,90,180];function dt(t,e,r){return e*s.roundUp(t/e,r)}function vt(t){var e=t.dtick;if(t._tickexponent=0,i(e)||"string"==typeof e||(e=1),"category"!==t.type&&"multicategory"!==t.type||(t._tickround=null),"date"===t.type){var r=t.r2l(t.tick0),n=t.l2r(r).replace(/(^-|i)/g,""),a=n.length;if("M"===String(e).charAt(0))a>10||"01-01"!==n.substr(5)?t._tickround="d":t._tickround=+e.substr(1)%12==0?"y":"m";else if(e>=M&&a<=10||e>=15*M)t._tickround="d";else if(e>=L&&a<=16||e>=E)t._tickround="M";else if(e>=C&&a<=19||e>=L)t._tickround="S";else{var o=t.l2r(r+e).replace(/^-/,"").length;t._tickround=Math.max(a,o)-20,t._tickround<0&&(t._tickround=4)}}else if(i(e)||"L"===e.charAt(0)){var s=t.range.map(t.r2d||Number);i(e)||(e=Number(e.substr(1))),t._tickround=2-Math.floor(Math.log(e)/Math.LN10+.01);var l=Math.max(Math.abs(s[0]),Math.abs(s[1])),u=Math.floor(Math.log(l)/Math.LN10+.01),c=void 0===t.minexponent?3:t.minexponent;Math.abs(u)>c&&(mt(t.exponentformat)&&!xt(u)?t._tickexponent=3*Math.round((u-1)/3):t._tickexponent=u)}else t._tickround=null}function gt(t,e,r){var n=t.tickfont||{};return{x:e,dx:0,dy:0,text:r||"",fontSize:n.size,font:n.family,fontColor:n.color}}q.autoTicks=function(t,e,r){var n;function a(t){return Math.pow(t,Math.floor(Math.log(e)/Math.LN10))}if("date"===t.type){t.tick0=s.dateTick0(t.calendar,0);var o=2*e;if(o>y)e/=y,n=a(10),t.dtick="M"+12*dt(e,n,st);else if(o>T)e/=T,t.dtick="M"+dt(e,1,lt);else if(o>M){if(t.dtick=dt(e,M,t._hasDayOfWeekBreaks?[1,2,7,14]:ct),!r){var l=q.getTickFormat(t),u="period"===t.ticklabelmode;u&&(t._rawTick0=t.tick0),/%[uVW]/.test(l)?t.tick0=s.dateTick0(t.calendar,2):t.tick0=s.dateTick0(t.calendar,1),u&&(t._dowTick0=t.tick0)}}else o>E?t.dtick=dt(e,E,lt):o>L?t.dtick=dt(e,L,ut):o>C?t.dtick=dt(e,C,ut):(n=a(10),t.dtick=dt(e,n,st))}else if("log"===t.type){t.tick0=0;var c=s.simpleMap(t.range,t.r2l);if(t._isMinor&&(e*=1.5),e>.7)t.dtick=Math.ceil(e);else if(Math.abs(c[1]-c[0])<1){var f=1.5*Math.abs((c[1]-c[0])/e);e=Math.abs(Math.pow(10,c[1])-Math.pow(10,c[0]))/f,n=a(10),t.dtick="L"+dt(e,n,st)}else t.dtick=e>.3?"D2":"D1"}else"category"===t.type||"multicategory"===t.type?(t.tick0=0,t.dtick=Math.ceil(Math.max(e,1))):Dt(t)?(t.tick0=0,n=1,t.dtick=dt(e,n,pt)):(t.tick0=0,n=a(10),t.dtick=dt(e,n,st));if(0===t.dtick&&(t.dtick=1),!i(t.dtick)&&"string"!=typeof t.dtick){var h=t.dtick;throw t.dtick=1,"ax.dtick error: "+String(h)}},q.tickIncrement=function(t,e,r,a){var o=r?-1:1;if(i(e))return s.increment(t,o*e);var l=e.charAt(0),u=o*Number(e.substr(1));if("M"===l)return s.incrementMonth(t,u,a);if("L"===l)return Math.log(Math.pow(10,t)+u)/Math.LN10;if("D"===l){var c="D2"===e?ht:ft,f=t+.01*o,h=s.roundUp(s.mod(f,1),c,r);return Math.floor(f)+Math.log(n.round(Math.pow(10,h),1))/Math.LN10}throw"unrecognized dtick 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a,o=gt(t,e),l="array"===t.tickmode,u=r||l,c=t.type,f="category"===c?t.d2l_noadd:t.d2l;if(l&&Array.isArray(t.ticktext)){var h=s.simpleMap(t.range,t.r2l),p=(Math.abs(h[1]-h[0])-(t._lBreaks||0))/1e4;for(a=0;a<t.ticktext.length&&!(Math.abs(e-f(t.tickvals[a]))<p);a++);if(a<t.ticktext.length)return o.text=String(t.ticktext[a]),o}function d(n){if(void 0===n)return!0;if(r)return"none"===n;var i={first:t._tmin,last:t._tmax}[n];return"all"!==n&&e!==i}var v=r?"never":"none"!==t.exponentformat&&d(t.showexponent)?"hide":"";if("date"===c?function(t,e,r,n){var a=t._tickround,o=r&&t.hoverformat||q.getTickFormat(t);(n=!o&&n)&&(a=i(a)?4:{y:"m",m:"d",d:"M",M:"S",S:4}[a]);var l,u=s.formatDate(e.x,o,a,t._dateFormat,t.calendar,t._extraFormat),c=u.indexOf("\n");if(-1!==c&&(l=u.substr(c+1),u=u.substr(0,c)),n&&(void 0===l||"00:00:00"!==u&&"00:00"!==u?8===u.length&&(u=u.replace(/:00$/,"")):(u=l,l="")),l)if(r)"d"===a?u+=", "+l:u=l+(u?", "+u:"");else if(t._inCalcTicks&&t._prevDateHead===l){var 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F,B,N=Math.abs(l._m),j=i.concatExtremes(t,l),U=j.min,V=j.max;for(B=0;B<U.length;B++)(F=U[B].val-z(U[B])/N)>I&&F<C&&(C=F);for(B=0;B<V.length;B++)(F=V[B].val+R(V[B])/N)<D&&F>P&&(P=F);h/=(P-C)/(2*O),C=l.l2r(C),P=l.l2r(P),l.range=l._input.range=S<E?[C,P]:[P,C]}y(l,h)}}},e.getAxisGroup=function(t,e){for(var r=t._axisMatchGroups,n=0;n<r.length;n++)if(r[n][e])return"g"+n;return e},e.clean=function(t,e){if(e._inputDomain){for(var r=!1,n=e._id,i=t._fullLayout._axisConstraintGroups,a=0;a<i.length;a++)if(i[a][n]){r=!0;break}r&&"domain"===e.constrain||(e._input.domain=e.domain=e._inputDomain,delete e._inputDomain)}}},29323:function(t,e,r){"use strict";var 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O,I,V,X,J,K,$,Q,tt,et,rt,nt,it,at,ot,st,lt,ut,ct,ft,ht,pt,dt,vt=t._fullLayout._zoomlayer,gt=v+y==="nsew",yt=1===(v+y).length;function mt(){if(O=e.xaxis,I=e.yaxis,tt=O._length,et=I._length,$=O._offset,Q=I._offset,(V={})[O._id]=O,(X={})[I._id]=I,v&&y)for(var r=e.overlays,n=0;n<r.length;n++){var i=r[n].xaxis;V[i._id]=i;var a=r[n].yaxis;X[a._id]=a}J=W(V),K=W(X),it=z(J,y),at=z(K,v),ot=!at&&!it,nt=Z(t,t._fullLayout._axisMatchGroups,V,X);var o=(rt=Z(t,t._fullLayout._axisConstraintGroups,V,X,nt)).isSubplotConstrained||nt.isSubplotConstrained;st=y||o,lt=v||o;var s=t._fullLayout;ut=s._has("scattergl"),ct=s._has("splom"),ft=s._has("svg")}r+=e.yaxis._shift,mt();var xt=function(t,e,r){return t?"nsew"===t?r?"":"pan"===e?"move":"crosshair":t.toLowerCase()+"-resize":"pointer"}(at+it,t._fullLayout.dragmode,gt),bt=D(e,v+y+"drag",xt,r,s,u,f);if(ot&&!gt)return bt.onmousedown=null,bt.style.pointerEvents="none",bt;var _t,wt,Tt,kt,At,Mt,St,Et,Lt,Ct,Pt={element:bt,gd:t,plotinfo:e};function Ot(){Pt.plotinfo.selection=!1,M(t)}function It(t,r){var i=Pt.gd;if(i._fullLayout._activeShapeIndex>=0)i._fullLayout._deactivateShape(i);else{var o=i._fullLayout.clickmode;if(H(i),2!==t||yt||qt(),gt)o.indexOf("select")>-1&&S(r,i,J,K,e.id,Pt),o.indexOf("event")>-1&&p.click(i,r,e.id);else if(1===t&&yt){var s=v?I:O,u="s"===v||"w"===y?0:1,f=s._name+".range["+u+"]",h=function(t,e){var r,n=t.range[e],i=Math.abs(n-t.range[1-e]);return"date"===t.type?n:"log"===t.type?(r=Math.ceil(Math.max(0,-Math.log(i)/Math.LN10))+3,a("."+r+"g")(Math.pow(10,n))):(r=Math.floor(Math.log(Math.abs(n))/Math.LN10)-Math.floor(Math.log(i)/Math.LN10)+4,a("."+String(r)+"g")(n))}(s,u),d="left",g="middle";if(s.fixedrange)return;v?(g="n"===v?"top":"bottom","right"===s.side&&(d="right")):"e"===y&&(d="right"),i._context.showAxisRangeEntryBoxes&&n.select(bt).call(c.makeEditable,{gd:i,immediate:!0,background:i._fullLayout.paper_bgcolor,text:String(h),fill:s.tickfont?s.tickfont.color:"#444",horizontalAlign:d,verticalAlign:g}).on("edit",(function(t){var e=s.d2r(t);void 0!==e&&l.call("_guiRelayout",i,f,e)}))}}}function Dt(e,r){if(t._transitioningWithDuration)return!1;var n=Math.max(0,Math.min(tt,pt*e+_t)),i=Math.max(0,Math.min(et,dt*r+wt)),a=Math.abs(n-_t),o=Math.abs(i-wt);function 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a=Pt.dragmode,s=t._fullLayout.dragmode;s!==a&&(Pt.dragmode=s),mt(),pt=t._fullLayout._invScaleX,dt=t._fullLayout._invScaleY,ot||(gt?e.shiftKey?"pan"===s?s="zoom":m(s)||(s="pan"):e.ctrlKey&&(s="pan"):s="pan"),x(s)?Pt.minDrag=1:Pt.minDrag=void 0,m(s)?(Pt.xaxes=J,Pt.yaxes=K,A(e,r,n,Pt,s)):(Pt.clickFn=It,m(a)&&Ot(),ot||("zoom"===s?(Pt.moveFn=Dt,Pt.doneFn=Rt,Pt.minDrag=1,function(e,r,n){var a=bt.getBoundingClientRect();_t=r-a.left,wt=n-a.top,t._fullLayout._calcInverseTransform(t);var s=i.apply3DTransform(t._fullLayout._invTransform)(_t,wt);_t=s[0],wt=s[1],Tt={l:_t,r:_t,w:0,t:wt,b:wt,h:0},kt=t._hmpixcount?t._hmlumcount/t._hmpixcount:o(t._fullLayout.plot_bgcolor).getLuminance(),Mt=!1,St="xy",Ct=!1,Et=N(vt,kt,$,Q,At="M0,0H"+tt+"V"+et+"H0V0"),Lt=j(vt,$,Q)}(0,r,n)):"pan"===s&&(Pt.moveFn=Ut,Pt.doneFn=Gt))),t._fullLayout._redrag=function(){var e=t._dragdata;if(e&&e.element===bt){var r=t._fullLayout.dragmode;m(r)||(mt(),Zt([0,0,tt,et]),Pt.moveFn(e.dx,e.dy))}}},g.init(Pt);var 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c;if(nt.isSubplotConstrained||!it&&1===at.length){for(c=0;c<J.length;c++)J[c].range=J[c]._r.slice(),E(J[c],1-r/et);o=(e=r*tt/et)/2}if(nt.isSubplotConstrained||!at&&1===it.length){for(c=0;c<K.length;c++)K[c].range=K[c]._r.slice(),E(K[c],1-e/tt);s=(r=e*et/tt)/2}}nt.isSubplotConstrained&&at||Vt("x"),nt.isSubplotConstrained&&it||Vt("y");var f=tt-e,h=et-r;!nt.isSubplotConstrained||it&&at||(it?(s=o?0:e*et/tt,h=f*et/tt):(o=s?0:r*tt/et,f=h*tt/et)),Zt([o,s,f,h]),Ht(),t.emit("plotly_relayouting",ht)}function p(t,e,r){for(var n,i,a=1-e,o=0;o<t.length;o++){var s=t[o];if(!s.fixedrange){n=s,i=s._rl[a]+(s._rl[e]-s._rl[a])/B(r/s._length);var l=s.l2r(i);!1!==l&&void 0!==l&&(s.range[e]=l)}}return n._length*(n._rl[e]-i)/(n._rl[e]-n._rl[a])}}function Vt(t,e){for(var r=nt.isSubplotConstrained?{x:K,y:J}[t]:nt[t+"axes"],n=nt.isSubplotConstrained?{x:J,y:K}[t]:[],i=0;i<r.length;i++){var 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n=r(22399),i=r(27670).Y,a=r(79952).P,o=r(78776),s=r(30962).overrideAll,l=r(78607),u={range:{valType:"info_array",items:[{valType:"number"},{valType:"number"}]},showgrid:{valType:"boolean",dflt:!1},tick0:{valType:"number",dflt:0},dtick:{valType:"number"},gridcolor:{valType:"color",dflt:n.lightLine},gridwidth:{valType:"number",min:0,dflt:1},griddash:a};(t.exports=s({domain:i({name:"geo"},{}),fitbounds:{valType:"enumerated",values:[!1,"locations","geojson"],dflt:!1,editType:"plot"},resolution:{valType:"enumerated",values:[110,50],dflt:110,coerceNumber:!0},scope:{valType:"enumerated",values:l(o.scopeDefaults),dflt:"world"},projection:{type:{valType:"enumerated",values:l(o.projNames)},rotation:{lon:{valType:"number"},lat:{valType:"number"},roll:{valType:"number"}},tilt:{valType:"number",dflt:0},distance:{valType:"number",min:1.001,dflt:2},parallels:{valType:"info_array",items:[{valType:"number"},{valType:"number"}]},scale:{valType:"number",min:0,dflt:1}},center:{lon:{valType:"number"},lat:{valType:"number"}},visible:{valType:"boolean",dflt:!0},showcoastlines:{valType:"boolean"},coastlinecolor:{valType:"color",dflt:n.defaultLine},coastlinewidth:{valType:"number",min:0,dflt:1},showland:{valType:"boolean",dflt:!1},landcolor:{valType:"color",dflt:o.landColor},showocean:{valType:"boolean",dflt:!1},oceancolor:{valType:"color",dflt:o.waterColor},showlakes:{valType:"boolean",dflt:!1},lakecolor:{valType:"color",dflt:o.waterColor},showrivers:{valType:"boolean",dflt:!1},rivercolor:{valType:"color",dflt:o.waterColor},riverwidth:{valType:"number",min:0,dflt:1},showcountries:{valType:"boolean"},countrycolor:{valType:"color",dflt:n.defaultLine},countrywidth:{valType:"number",min:0,dflt:1},showsubunits:{valType:"boolean"},subunitcolor:{valType:"color",dflt:n.defaultLine},subunitwidth:{valType:"number",min:0,dflt:1},showframe:{valType:"boolean"},framecolor:{valType:"color",dflt:n.defaultLine},framewidth:{valType:"number",min:0,dflt:1},bgcolor:{valType:"color",dflt:n.background},lonaxis:u,lataxis:u},"plot","from-root")).uirevision={valType:"any",editType:"none"}},82161:function(t,e,r){"use 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n&&[Math.cos(i),r[2]*a,-r[1]*a,r[0]*a]}}(r,_),T=function(t){return[Math.atan2(2*(t[0]*t[1]+t[2]*t[3]),1-2*(t[1]*t[1]+t[2]*t[2]))*s,Math.asin(Math.max(-1,Math.min(1,2*(t[0]*t[2]-t[3]*t[1]))))*s,Math.atan2(2*(t[0]*t[3]+t[1]*t[2]),1-2*(t[2]*t[2]+t[3]*t[3]))*s]}((o=(t=C)[0],l=t[1],u=t[2],c=t[3],[o*(f=(a=w)[0])-l*(p=a[1])-u*(d=a[2])-c*(y=a[3]),o*p+l*f+u*y-c*d,o*d-l*y+u*f+c*p,o*y+l*d-u*p+c*f])),k=i.r=function(t,e,r){var n=m(e,2,t[0]);n=m(n,1,t[1]),n=m(n,0,t[2]-r[2]);var i,a,o=e[0],l=e[1],u=e[2],c=n[0],f=n[1],h=n[2],p=Math.atan2(l,o)*s,d=Math.sqrt(o*o+l*l);Math.abs(f)>d?(a=(f>0?90:-90)-p,i=0):(a=Math.asin(f/d)*s-p,i=Math.sqrt(d*d-f*f));var v=180-a-2*p,y=(Math.atan2(h,c)-Math.atan2(u,i))*s,x=(Math.atan2(h,c)-Math.atan2(u,-i))*s;return g(r[0],r[1],a,y)<=g(r[0],r[1],v,x)?[a,y,r[2]]:[v,x,r[2]]}(T,r,E);isFinite(k[0])&&isFinite(k[1])&&isFinite(k[2])||(k=E),e.rotate(k),E=k}}else r=v(e,M=b);h.of(this,arguments)({type:"zoom"})})),A=h.of(this,arguments),p++||A({type:"zoomstart"})})).on("zoomend",(function(){var r;n.select(this).style(u),d.call(a,"zoom",null),r=h.of(this,arguments),--p||r({type:"zoomend"}),f(t,e,y)})).on("zoom.redraw",(function(){t.render(!0);var r=e.rotate();t.graphDiv.emit("plotly_relayouting",{"geo.projection.scale":e.scale()/t.fitScale,"geo.projection.rotation.lon":-r[0],"geo.projection.rotation.lat":-r[1]})})),n.rebind(a,h,"on")}function v(t,e){var r=t.invert(e);return r&&isFinite(r[0])&&isFinite(r[1])&&function(t){var e=t[0]*o,r=t[1]*o,n=Math.cos(r);return[n*Math.cos(e),n*Math.sin(e),Math.sin(r)]}(r)}function g(t,e,r,n){var i=y(r-t),a=y(n-e);return Math.sqrt(i*i+a*a)}function y(t){return(t%360+540)%360-180}function m(t,e,r){var n=r*o,i=t.slice(),a=0===e?1:0,s=2===e?1:2,l=Math.cos(n),u=Math.sin(n);return i[a]=t[a]*l-t[s]*u,i[s]=t[s]*l+t[a]*u,i}function x(t,e){for(var r=0,n=0,i=t.length;n<i;++n)r+=t[n]*e[n];return r}t.exports=function(t,e){var r=t.projection;return(e._isScoped?h:e._isClipped?d:p)(t,r)}},27659:function(t,e,r){"use strict";var n=r(73972),i=r(85555).SUBPLOT_PATTERN;e.AU=function(t,e,r){var i=n.subplotsRegistry[e];if(!i)return[];for(var a=i.attr,o=[],s=0;s<t.length;s++){var l=t[s];l[0].trace[a]===r&&o.push(l)}return o},e.a0=function(t,e){var r,i=[],a=[];if(!(r="string"==typeof e?n.getModule(e).plot:"function"==typeof e?e:e.plot))return[i,t];for(var o=0;o<t.length;o++){var s=t[o],l=s[0].trace;!0===l.visible&&0!==l._length&&(l._module&&l._module.plot===r?i.push(s):a.push(s))}return[i,a]},e.NG=function(t,e,r){if(!n.subplotsRegistry[e])return[];var a,o,s,l=n.subplotsRegistry[e].attr,u=[];if("gl2d"===e){var c=r.match(i);o="x"+c[1],s="y"+c[2]}for(var f=0;f<t.length;f++)a=t[f],"gl2d"===e&&n.traceIs(a,"gl2d")?a[l[0]]===o&&a[l[1]]===s&&u.push(a):a[l]===r&&u.push(a);return u}},75071:function(t,e,r){"use strict";var n=r(16825),i=r(1195),a=r(48956),o=r(85555),s=r(38520);function l(t,e){this.element=t,this.plot=e,this.mouseListener=null,this.wheelListener=null,this.lastInputTime=Date.now(),this.lastPos=[0,0],this.boxEnabled=!1,this.boxInited=!1,this.boxStart=[0,0],this.boxEnd=[0,0],this.dragStart=[0,0]}t.exports=function(t){var e=t.mouseContainer,r=t.glplot,u=new l(e,r);function c(){t.xaxis.autorange=!1,t.yaxis.autorange=!1}function f(e,n,i){var a,s,l=t.calcDataBox(),f=r.viewBox,h=u.lastPos[0],p=u.lastPos[1],d=o.MINDRAG*r.pixelRatio,v=o.MINZOOM*r.pixelRatio;function g(e,r,n){var i=Math.min(r,n),a=Math.max(r,n);i!==a?(l[e]=i,l[e+2]=a,u.dataBox=l,t.setRanges(l)):(t.selectBox.selectBox=[0,0,1,1],t.glplot.setDirty())}switch(n*=r.pixelRatio,i*=r.pixelRatio,i=f[3]-f[1]-i,t.fullLayout.dragmode){case"zoom":if(e){var y=n/(f[2]-f[0])*(l[2]-l[0])+l[0],m=i/(f[3]-f[1])*(l[3]-l[1])+l[1];u.boxInited||(u.boxStart[0]=y,u.boxStart[1]=m,u.dragStart[0]=n,u.dragStart[1]=i),u.boxEnd[0]=y,u.boxEnd[1]=m,u.boxInited=!0,u.boxEnabled||u.boxStart[0]===u.boxEnd[0]&&u.boxStart[1]===u.boxEnd[1]||(u.boxEnabled=!0);var x=Math.abs(u.dragStart[0]-n)<v,b=Math.abs(u.dragStart[1]-i)<v;if(!function(){for(var e=t.graphDiv._fullLayout._axisConstraintGroups,r=t.xaxis._id,n=t.yaxis._id,i=0;i<e.length;i++)if(-1!==e[i][r]){if(-1!==e[i][n])return!0;break}return!1}()||x&&b)x&&(u.boxEnd[0]=u.boxStart[0]),b&&(u.boxEnd[1]=u.boxStart[1]);else{a=u.boxEnd[0]-u.boxStart[0],s=u.boxEnd[1]-u.boxStart[1];var _=(l[3]-l[1])/(l[2]-l[0]);Math.abs(a*_)>Math.abs(s)?(u.boxEnd[1]=u.boxStart[1]+Math.abs(a)*_*(s>=0?1:-1),u.boxEnd[1]<l[1]?(u.boxEnd[1]=l[1],u.boxEnd[0]=u.boxStart[0]+(l[1]-u.boxStart[1])/Math.abs(_)):u.boxEnd[1]>l[3]&&(u.boxEnd[1]=l[3],u.boxEnd[0]=u.boxStart[0]+(l[3]-u.boxStart[1])/Math.abs(_))):(u.boxEnd[0]=u.boxStart[0]+Math.abs(s)/_*(a>=0?1:-1),u.boxEnd[0]<l[0]?(u.boxEnd[0]=l[0],u.boxEnd[1]=u.boxStart[1]+(l[0]-u.boxStart[0])*Math.abs(_)):u.boxEnd[0]>l[2]&&(u.boxEnd[0]=l[2],u.boxEnd[1]=u.boxStart[1]+(l[2]-u.boxStart[0])*Math.abs(_)))}}else u.boxEnabled?(a=u.boxStart[0]!==u.boxEnd[0],s=u.boxStart[1]!==u.boxEnd[1],a||s?(a&&(g(0,u.boxStart[0],u.boxEnd[0]),t.xaxis.autorange=!1),s&&(g(1,u.boxStart[1],u.boxEnd[1]),t.yaxis.autorange=!1),t.relayoutCallback()):t.glplot.setDirty(),u.boxEnabled=!1,u.boxInited=!1):u.boxInited&&(u.boxInited=!1);break;case"pan":u.boxEnabled=!1,u.boxInited=!1,e?(u.panning||(u.dragStart[0]=n,u.dragStart[1]=i),Math.abs(u.dragStart[0]-n)<d&&(n=u.dragStart[0]),Math.abs(u.dragStart[1]-i)<d&&(i=u.dragStart[1]),a=(h-n)*(l[2]-l[0])/(r.viewBox[2]-r.viewBox[0]),s=(p-i)*(l[3]-l[1])/(r.viewBox[3]-r.viewBox[1]),l[0]+=a,l[2]+=a,l[1]+=s,l[3]+=s,t.setRanges(l),u.panning=!0,u.lastInputTime=Date.now(),c(),t.cameraChanged(),t.handleAnnotations()):u.panning&&(u.panning=!1,t.relayoutCallback())}u.lastPos[0]=n,u.lastPos[1]=i}return u.mouseListener=n(e,f),e.addEventListener("touchstart",(function(t){var r=a(t.changedTouches[0],e);f(0,r[0],r[1]),f(1,r[0],r[1]),t.preventDefault()}),!!s&&{passive:!1}),e.addEventListener("touchmove",(function(t){t.preventDefault();var r=a(t.changedTouches[0],e);f(1,r[0],r[1]),t.preventDefault()}),!!s&&{passive:!1}),e.addEventListener("touchend",(function(t){f(0,u.lastPos[0],u.lastPos[1]),t.preventDefault()}),!!s&&{passive:!1}),u.wheelListener=i(e,(function(e,n){if(!t.scrollZoom)return!1;var i=t.calcDataBox(),a=r.viewBox,o=u.lastPos[0],s=u.lastPos[1],l=Math.exp(5*n/(a[3]-a[1])),f=o/(a[2]-a[0])*(i[2]-i[0])+i[0],h=s/(a[3]-a[1])*(i[3]-i[1])+i[1];return i[0]=(i[0]-f)*l+f,i[2]=(i[2]-f)*l+f,i[1]=(i[1]-h)*l+h,i[3]=(i[3]-h)*l+h,t.setRanges(i),u.lastInputTime=Date.now(),c(),t.cameraChanged(),t.handleAnnotations(),t.relayoutCallback(),!0}),!0),u}},82961:function(t,e,r){"use strict";var n=r(89298),i=r(78614);function a(t){this.scene=t,this.gl=t.gl,this.pixelRatio=t.pixelRatio,this.screenBox=[0,0,1,1],this.viewBox=[0,0,1,1],this.dataBox=[-1,-1,1,1],this.borderLineEnable=[!1,!1,!1,!1],this.borderLineWidth=[1,1,1,1],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.ticks=[[],[]],this.tickEnable=[!0,!0,!1,!1],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labels=["x","y"],this.labelEnable=[!0,!0,!1,!1],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelPad=[15,15,15,15],this.labelSize=[12,12],this.labelFont=["sans-serif","sans-serif"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.title="",this.titleEnable=!0,this.titleCenter=[0,0,0,0],this.titleAngle=0,this.titleColor=[0,0,0,1],this.titleFont="sans-serif",this.titleSize=18,this.gridLineEnable=[!0,!0],this.gridLineColor=[[0,0,0,.5],[0,0,0,.5]],this.gridLineWidth=[1,1],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[1,1],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.static=this.scene.staticPlot}var o=a.prototype,s=["xaxis","yaxis"];o.merge=function(t){var e,r,n,a,o,l,u,c,f,h,p;for(this.titleEnable=!1,this.backgroundColor=i(t.plot_bgcolor),h=0;h<2;++h){var d=(e=s[h]).charAt(0);for(n=(r=t[this.scene[e]._name]).title.text===this.scene.fullLayout._dfltTitle[d]?"":r.title.text,p=0;p<=2;p+=2)this.labelEnable[h+p]=!1,this.labels[h+p]=n,this.labelColor[h+p]=i(r.title.font.color),this.labelFont[h+p]=r.title.font.family,this.labelSize[h+p]=r.title.font.size,this.labelPad[h+p]=this.getLabelPad(e,r),this.tickEnable[h+p]=!1,this.tickColor[h+p]=i((r.tickfont||{}).color),this.tickAngle[h+p]="auto"===r.tickangle?0:Math.PI*-r.tickangle/180,this.tickPad[h+p]=this.getTickPad(r),this.tickMarkLength[h+p]=0,this.tickMarkWidth[h+p]=r.tickwidth||0,this.tickMarkColor[h+p]=i(r.tickcolor),this.borderLineEnable[h+p]=!1,this.borderLineColor[h+p]=i(r.linecolor),this.borderLineWidth[h+p]=r.linewidth||0;u=this.hasSharedAxis(r),o=this.hasAxisInDfltPos(e,r)&&!u,l=this.hasAxisInAltrPos(e,r)&&!u,a=r.mirror||!1,c=u?-1!==String(a).indexOf("all"):!!a,f=u?"allticks"===a:-1!==String(a).indexOf("ticks"),o?this.labelEnable[h]=!0:l&&(this.labelEnable[h+2]=!0),o?this.tickEnable[h]=r.showticklabels:l&&(this.tickEnable[h+2]=r.showticklabels),(o||c)&&(this.borderLineEnable[h]=r.showline),(l||c)&&(this.borderLineEnable[h+2]=r.showline),(o||f)&&(this.tickMarkLength[h]=this.getTickMarkLength(r)),(l||f)&&(this.tickMarkLength[h+2]=this.getTickMarkLength(r)),this.gridLineEnable[h]=r.showgrid,this.gridLineColor[h]=i(r.gridcolor),this.gridLineWidth[h]=r.gridwidth,this.zeroLineEnable[h]=r.zeroline,this.zeroLineColor[h]=i(r.zerolinecolor),this.zeroLineWidth[h]=r.zerolinewidth}},o.hasSharedAxis=function(t){var e=this.scene,r=e.fullLayout._subplots.gl2d;return 0!==n.findSubplotsWithAxis(r,t).indexOf(e.id)},o.hasAxisInDfltPos=function(t,e){var r=e.side;return"xaxis"===t?"bottom"===r:"yaxis"===t?"left"===r:void 0},o.hasAxisInAltrPos=function(t,e){var r=e.side;return"xaxis"===t?"top"===r:"yaxis"===t?"right"===r:void 0},o.getLabelPad=function(t,e){var r=1.5,n=e.title.font.size,i=e.showticklabels;return"xaxis"===t?"top"===e.side?n*(r+(i?1:0))-10:n*(r+(i?.5:0))-10:"yaxis"===t?"right"===e.side?10+n*(r+(i?1:.5)):10+n*(r+(i?.5:0)):void 0},o.getTickPad=function(t){return"outside"===t.ticks?10+t.ticklen:15},o.getTickMarkLength=function(t){if(!t.ticks)return 0;var e=t.ticklen;return"inside"===t.ticks?-e:e},t.exports=function(t){return new a(t)}},4796:function(t,e,r){"use strict";var n=r(30962).overrideAll,i=r(92918),a=r(10820),o=r(77922),s=r(85555),l=r(93612),u=r(528),c=r(27659).NG;e.name="gl2d",e.attr=["xaxis","yaxis"],e.idRoot=["x","y"],e.idRegex=s.idRegex,e.attrRegex=s.attrRegex,e.attributes=r(89502),e.supplyLayoutDefaults=function(t,e,r){e._has("cartesian")||l.supplyLayoutDefaults(t,e,r)},e.layoutAttrOverrides=n(l.layoutAttributes,"plot","from-root"),e.baseLayoutAttrOverrides=n({plot_bgcolor:a.plot_bgcolor,hoverlabel:u.hoverlabel},"plot","nested"),e.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots.gl2d,a=0;a<n.length;a++){var o=n[a],s=e._plots[o],l=c(r,"gl2d",o),u=s._scene2d;void 0===u&&(u=new i({id:o,graphDiv:t,container:t.querySelector(".gl-container"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio},e),s._scene2d=u),u.plot(l,t.calcdata,e,t.layout)}},e.clean=function(t,e,r,n){for(var i=n._subplots.gl2d||[],a=0;a<i.length;a++){var o=i[a],s=n._plots[o];s._scene2d&&0===c(t,"gl2d",o).length&&(s._scene2d.destroy(),delete n._plots[o])}l.clean.apply(this,arguments)},e.drawFramework=function(t){t._context.staticPlot||l.drawFramework(t)},e.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++){var i=e._plots[r[n]]._scene2d,a=i.toImage("png");e._glimages.append("svg:image").attr({xmlns:o.svg,"xlink:href":a,x:0,y:0,width:"100%",height:"100%",preserveAspectRatio:"none"}),i.destroy()}},e.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++)e._plots[r[n]]._scene2d.updateFx(e.dragmode)}},92918:function(t,e,r){"use strict";var n,i,a=r(73972),o=r(89298),s=r(30211),l=r(9330).gl_plot2d,u=r(9330).gl_spikes2d,c=r(9330).gl_select_box,f=r(40372),h=r(82961),p=r(75071),d=r(58617),v=r(99082),g=v.enforce,y=v.clean,m=r(71739).doAutoRange,x=r(64505),b=x.drawMode,_=x.selectMode,w=["xaxis","yaxis"],T=r(85555).SUBPLOT_PATTERN;function k(t,e){this.container=t.container,this.graphDiv=t.graphDiv,this.pixelRatio=t.plotGlPixelRatio||window.devicePixelRatio,this.id=t.id,this.staticPlot=!!t.staticPlot,this.scrollZoom=this.graphDiv._context._scrollZoom.cartesian,this.fullData=null,this.updateRefs(e),this.makeFramework(),this.stopped||(this.glplotOptions=h(this),this.glplotOptions.merge(e),this.glplot=l(this.glplotOptions),this.camera=p(this),this.traces={},this.spikes=u(this.glplot),this.selectBox=c(this.glplot,{innerFill:!1,outerFill:!0}),this.lastButtonState=0,this.pickResult=null,this.isMouseOver=!0,this.stopped=!1,this.redraw=this.draw.bind(this),this.redraw())}t.exports=k;var A=k.prototype;A.makeFramework=function(){if(this.staticPlot){if(!(i||(n=document.createElement("canvas"),i=f({canvas:n,preserveDrawingBuffer:!1,premultipliedAlpha:!0,antialias:!0}))))throw new Error("Error creating static canvas/context for image server");this.canvas=n,this.gl=i}else{var t=this.container.querySelector(".gl-canvas-focus"),e=f({canvas:t,preserveDrawingBuffer:!0,premultipliedAlpha:!0});if(!e)return d(this),void(this.stopped=!0);this.canvas=t,this.gl=e}var r=this.canvas;r.style.width="100%",r.style.height="100%",r.style.position="absolute",r.style.top="0px",r.style.left="0px",r.style["pointer-events"]="none",this.updateSize(r);var a=this.svgContainer=document.createElementNS("http://www.w3.org/2000/svg","svg");a.style.position="absolute",a.style.top=a.style.left="0px",a.style.width=a.style.height="100%",a.style["z-index"]=20,a.style["pointer-events"]="none";var o=this.mouseContainer=document.createElement("div");o.style.position="absolute",o.style["pointer-events"]="auto",this.pickCanvas=this.container.querySelector(".gl-canvas-pick");var s=this.container;s.appendChild(a),s.appendChild(o);var l=this;o.addEventListener("mouseout",(function(){l.isMouseOver=!1,l.unhover()})),o.addEventListener("mouseover",(function(){l.isMouseOver=!0}))},A.toImage=function(t){t||(t="png"),this.stopped=!0,this.staticPlot&&this.container.appendChild(n),this.updateSize(this.canvas);var e=this.glplot.gl,r=e.drawingBufferWidth,i=e.drawingBufferHeight;e.clearColor(1,1,1,0),e.clear(e.COLOR_BUFFER_BIT|e.DEPTH_BUFFER_BIT),this.glplot.setDirty(),this.glplot.draw(),e.bindFramebuffer(e.FRAMEBUFFER,null);var a=new Uint8Array(r*i*4);e.readPixels(0,0,r,i,e.RGBA,e.UNSIGNED_BYTE,a);for(var o=0,s=i-1;o<s;++o,--s)for(var l=0;l<r;++l)for(var u=0;u<4;++u){var c=a[4*(r*o+l)+u];a[4*(r*o+l)+u]=a[4*(r*s+l)+u],a[4*(r*s+l)+u]=c}var f=document.createElement("canvas");f.width=r,f.height=i;var h,p=f.getContext("2d",{willReadFrequently:!0}),d=p.createImageData(r,i);switch(d.data.set(a),p.putImageData(d,0,0),t){case"jpeg":h=f.toDataURL("image/jpeg");break;case"webp":h=f.toDataURL("image/webp");break;default:h=f.toDataURL("image/png")}return this.staticPlot&&this.container.removeChild(n),h},A.updateSize=function(t){t||(t=this.canvas);var e=this.pixelRatio,r=this.fullLayout,n=r.width,i=r.height,a=0|Math.ceil(e*n),o=0|Math.ceil(e*i);return t.width===a&&t.height===o||(t.width=a,t.height=o),t},A.computeTickMarks=function(){this.xaxis.setScale(),this.yaxis.setScale();for(var t=[o.calcTicks(this.xaxis),o.calcTicks(this.yaxis)],e=0;e<2;++e)for(var r=0;r<t[e].length;++r)t[e][r].text=t[e][r].text+"";return t},A.updateRefs=function(t){this.fullLayout=t;var e=this.id.match(T),r="xaxis"+e[1],n="yaxis"+e[2];this.xaxis=this.fullLayout[r],this.yaxis=this.fullLayout[n]},A.relayoutCallback=function(){var t=this.graphDiv,e=this.xaxis,r=this.yaxis,n=t.layout,i={},o=i[e._name+".range"]=e.range.slice(),s=i[r._name+".range"]=r.range.slice();i[e._name+".autorange"]=e.autorange,i[r._name+".autorange"]=r.autorange,a.call("_storeDirectGUIEdit",t.layout,t._fullLayout._preGUI,i);var l=n[e._name];l.range=o,l.autorange=e.autorange;var u=n[r._name];u.range=s,u.autorange=r.autorange,i.lastInputTime=this.camera.lastInputTime,t.emit("plotly_relayout",i)},A.cameraChanged=function(){var t=this.camera;this.glplot.setDataBox(this.calcDataBox());var e=this.computeTickMarks();(function(t,e){for(var r=0;r<2;++r){var n=t[r],i=e[r];if(n.length!==i.length)return!0;for(var a=0;a<n.length;++a)if(n[a].x!==i[a].x)return!0}return!1})(e,this.glplotOptions.ticks)&&(this.glplotOptions.ticks=e,this.glplotOptions.dataBox=t.dataBox,this.glplot.update(this.glplotOptions),this.handleAnnotations())},A.handleAnnotations=function(){for(var t=this.graphDiv,e=this.fullLayout.annotations,r=0;r<e.length;r++){var n=e[r];n.xref===this.xaxis._id&&n.yref===this.yaxis._id&&a.getComponentMethod("annotations","drawOne")(t,r)}},A.destroy=function(){if(this.glplot){var t=this.traces;t&&Object.keys(t).map((function(e){t[e].dispose(),delete t[e]})),this.glplot.dispose(),this.container.removeChild(this.svgContainer),this.container.removeChild(this.mouseContainer),this.fullData=null,this.glplot=null,this.stopped=!0,this.camera.mouseListener.enabled=!1,this.mouseContainer.removeEventListener("wheel",this.camera.wheelListener),this.camera=null}},A.plot=function(t,e,r){var n=this.glplot;this.updateRefs(r),this.xaxis.clearCalc(),this.yaxis.clearCalc(),this.updateTraces(t,e),this.updateFx(r.dragmode);var i=r.width,a=r.height;this.updateSize(this.canvas);var o=this.glplotOptions;o.merge(r),o.screenBox=[0,0,i,a];var s={_fullLayout:{_axisConstraintGroups:r._axisConstraintGroups,xaxis:this.xaxis,yaxis:this.yaxis,_size:r._size}};y(s,this.xaxis),y(s,this.yaxis);var l,u,c=r._size,f=this.xaxis.domain,h=this.yaxis.domain;for(o.viewBox=[c.l+f[0]*c.w,c.b+h[0]*c.h,i-c.r-(1-f[1])*c.w,a-c.t-(1-h[1])*c.h],this.mouseContainer.style.width=c.w*(f[1]-f[0])+"px",this.mouseContainer.style.height=c.h*(h[1]-h[0])+"px",this.mouseContainer.height=c.h*(h[1]-h[0]),this.mouseContainer.style.left=c.l+f[0]*c.w+"px",this.mouseContainer.style.top=c.t+(1-h[1])*c.h+"px",u=0;u<2;++u)(l=this[w[u]])._length=o.viewBox[u+2]-o.viewBox[u],m(this.graphDiv,l),l.setScale();g(s),o.ticks=this.computeTickMarks(),o.dataBox=this.calcDataBox(),o.merge(r),n.update(o),this.glplot.draw()},A.calcDataBox=function(){var t=this.xaxis,e=this.yaxis,r=t.range,n=e.range,i=t.r2l,a=e.r2l;return[i(r[0]),a(n[0]),i(r[1]),a(n[1])]},A.setRanges=function(t){var e=this.xaxis,r=this.yaxis,n=e.l2r,i=r.l2r;e.range=[n(t[0]),n(t[2])],r.range=[i(t[1]),i(t[3])]},A.updateTraces=function(t,e){var r,n,i,a=Object.keys(this.traces);this.fullData=t;t:for(r=0;r<a.length;r++){var o=a[r],s=this.traces[o];for(n=0;n<t.length;n++)if((i=t[n]).uid===o&&i.type===s.type)continue t;s.dispose(),delete this.traces[o]}for(r=0;r<t.length;r++){i=t[r];var l=e[r],u=this.traces[i.uid];u?u.update(i,l):(u=i._module.plot(this,i,l),this.traces[i.uid]=u)}this.glplot.objects.sort((function(t,e){return t._trace.index-e._trace.index}))},A.updateFx=function(t){_(t)||b(t)?(this.pickCanvas.style["pointer-events"]="none",this.mouseContainer.style["pointer-events"]="none"):(this.pickCanvas.style["pointer-events"]="auto",this.mouseContainer.style["pointer-events"]="auto"),this.mouseContainer.style.cursor="pan"===t?"move":"zoom"===t?"crosshair":null},A.emitPointAction=function(t,e){for(var r,n=t.trace.uid,i=t.pointIndex,a=0;a<this.fullData.length;a++)this.fullData[a].uid===n&&(r=this.fullData[a]);var o={x:t.traceCoord[0],y:t.traceCoord[1],curveNumber:r.index,pointNumber:i,data:r._input,fullData:this.fullData,xaxis:this.xaxis,yaxis:this.yaxis};s.appendArrayPointValue(o,r,i),this.graphDiv.emit(e,{points:[o]})},A.draw=function(){if(!this.stopped){requestAnimationFrame(this.redraw);var t=this.glplot,e=this.camera,r=e.mouseListener,n=1===this.lastButtonState&&0===r.buttons,i=this.fullLayout;this.lastButtonState=r.buttons,this.cameraChanged();var a,o=r.x*t.pixelRatio,l=this.canvas.height-t.pixelRatio*r.y;if(e.boxEnabled&&"zoom"===i.dragmode){this.selectBox.enabled=!0;for(var u=this.selectBox.selectBox=[Math.min(e.boxStart[0],e.boxEnd[0]),Math.min(e.boxStart[1],e.boxEnd[1]),Math.max(e.boxStart[0],e.boxEnd[0]),Math.max(e.boxStart[1],e.boxEnd[1])],c=0;c<2;c++)e.boxStart[c]===e.boxEnd[c]&&(u[c]=t.dataBox[c],u[c+2]=t.dataBox[c+2]);t.setDirty()}else if(!e.panning&&this.isMouseOver){this.selectBox.enabled=!1;var f=i._size,h=this.xaxis.domain,p=this.yaxis.domain,d=(a=t.pick(o/t.pixelRatio+f.l+h[0]*f.w,l/t.pixelRatio-(f.t+(1-p[1])*f.h)))&&a.object._trace.handlePick(a);if(d&&n&&this.emitPointAction(d,"plotly_click"),a&&"skip"!==a.object._trace.hoverinfo&&i.hovermode&&d&&(!this.lastPickResult||this.lastPickResult.traceUid!==d.trace.uid||this.lastPickResult.dataCoord[0]!==d.dataCoord[0]||this.lastPickResult.dataCoord[1]!==d.dataCoord[1])){var v=d;this.lastPickResult={traceUid:d.trace?d.trace.uid:null,dataCoord:d.dataCoord.slice()},this.spikes.update({center:a.dataCoord}),v.screenCoord=[((t.viewBox[2]-t.viewBox[0])*(a.dataCoord[0]-t.dataBox[0])/(t.dataBox[2]-t.dataBox[0])+t.viewBox[0])/t.pixelRatio,(this.canvas.height-(t.viewBox[3]-t.viewBox[1])*(a.dataCoord[1]-t.dataBox[1])/(t.dataBox[3]-t.dataBox[1])-t.viewBox[1])/t.pixelRatio],this.emitPointAction(d,"plotly_hover");var g=this.fullData[v.trace.index]||{},y=v.pointIndex,m=s.castHoverinfo(g,i,y);if(m&&"all"!==m){var x=m.split("+");-1===x.indexOf("x")&&(v.traceCoord[0]=void 0),-1===x.indexOf("y")&&(v.traceCoord[1]=void 0),-1===x.indexOf("z")&&(v.traceCoord[2]=void 0),-1===x.indexOf("text")&&(v.textLabel=void 0),-1===x.indexOf("name")&&(v.name=void 0)}s.loneHover({x:v.screenCoord[0],y:v.screenCoord[1],xLabel:this.hoverFormatter("xaxis",v.traceCoord[0]),yLabel:this.hoverFormatter("yaxis",v.traceCoord[1]),zLabel:v.traceCoord[2],text:v.textLabel,name:v.name,color:s.castHoverOption(g,y,"bgcolor")||v.color,borderColor:s.castHoverOption(g,y,"bordercolor"),fontFamily:s.castHoverOption(g,y,"font.family"),fontSize:s.castHoverOption(g,y,"font.size"),fontColor:s.castHoverOption(g,y,"font.color"),nameLength:s.castHoverOption(g,y,"namelength"),textAlign:s.castHoverOption(g,y,"align")},{container:this.svgContainer,gd:this.graphDiv})}}a||this.unhover(),t.draw()}},A.unhover=function(){this.lastPickResult&&(this.spikes.update({}),this.lastPickResult=null,this.graphDiv.emit("plotly_unhover"),s.loneUnhover(this.svgContainer))},A.hoverFormatter=function(t,e){if(void 0!==e){var r=this[t];return o.tickText(r,r.c2l(e),"hover").text}}},58547:function(t,e,r){"use strict";var n=r(30962).overrideAll,i=r(528),a=r(33539),o=r(27659).NG,s=r(71828),l=r(77922),u="gl3d",c="scene";e.name=u,e.attr=c,e.idRoot=c,e.idRegex=e.attrRegex=s.counterRegex("scene"),e.attributes=r(59084),e.layoutAttributes=r(65500),e.baseLayoutAttrOverrides=n({hoverlabel:i.hoverlabel},"plot","nested"),e.supplyLayoutDefaults=r(24682),e.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots[u],i=0;i<n.length;i++){var s=n[i],l=o(r,u,s),c=e[s],f=c.camera,h=c._scene;h||(h=new a({id:s,graphDiv:t,container:t.querySelector(".gl-container"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio,camera:f},e),c._scene=h),h.viewInitial||(h.viewInitial={up:{x:f.up.x,y:f.up.y,z:f.up.z},eye:{x:f.eye.x,y:f.eye.y,z:f.eye.z},center:{x:f.center.x,y:f.center.y,z:f.center.z}}),h.plot(l,e,t.layout)}},e.clean=function(t,e,r,n){for(var i=n._subplots[u]||[],a=0;a<i.length;a++){var o=i[a];!e[o]&&n[o]._scene&&(n[o]._scene.destroy(),n._infolayer&&n._infolayer.selectAll(".annotation-"+o).remove())}},e.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots[u],n=e._size,i=0;i<r.length;i++){var a=e[r[i]],o=a.domain,s=a._scene,c=s.toImage("png");e._glimages.append("svg:image").attr({xmlns:l.svg,"xlink:href":c,x:n.l+n.w*o.x[0],y:n.t+n.h*(1-o.y[1]),width:n.w*(o.x[1]-o.x[0]),height:n.h*(o.y[1]-o.y[0]),preserveAspectRatio:"none"}),s.destroy()}},e.cleanId=function(t){if(t.match(/^scene[0-9]*$/)){var e=t.substr(5);return"1"===e&&(e=""),c+e}},e.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots[u],n=0;n<r.length;n++)e[r[n]]._scene.updateFx(e.dragmode,e.hovermode)}},59084:function(t){"use strict";t.exports={scene:{valType:"subplotid",dflt:"scene",editType:"calc+clearAxisTypes"}}},77894:function(t,e,r){"use strict";var n=r(7901),i=r(13838),a=r(1426).extendFlat,o=r(30962).overrideAll;t.exports=o({visible:i.visible,showspikes:{valType:"boolean",dflt:!0},spikesides:{valType:"boolean",dflt:!0},spikethickness:{valType:"number",min:0,dflt:2},spikecolor:{valType:"color",dflt:n.defaultLine},showbackground:{valType:"boolean",dflt:!1},backgroundcolor:{valType:"color",dflt:"rgba(204, 204, 204, 0.5)"},showaxeslabels:{valType:"boolean",dflt:!0},color:i.color,categoryorder:i.categoryorder,categoryarray:i.categoryarray,title:{text:i.title.text,font:i.title.font},type:a({},i.type,{values:["-","linear","log","date","category"]}),autotypenumbers:i.autotypenumbers,autorange:i.autorange,autorangeoptions:{minallowed:i.autorangeoptions.minallowed,maxallowed:i.autorangeoptions.maxallowed,clipmin:i.autorangeoptions.clipmin,clipmax:i.autorangeoptions.clipmax,include:i.autorangeoptions.include,editType:"plot"},rangemode:i.rangemode,minallowed:i.minallowed,maxallowed:i.maxallowed,range:a({},i.range,{items:[{valType:"any",editType:"plot",impliedEdits:{"^autorange":!1}},{valType:"any",editType:"plot",impliedEdits:{"^autorange":!1}}],anim:!1}),tickmode:i.minor.tickmode,nticks:i.nticks,tick0:i.tick0,dtick:i.dtick,tickvals:i.tickvals,ticktext:i.ticktext,ticks:i.ticks,mirror:i.mirror,ticklen:i.ticklen,tickwidth:i.tickwidth,tickcolor:i.tickcolor,showticklabels:i.showticklabels,labelalias:i.labelalias,tickfont:i.tickfont,tickangle:i.tickangle,tickprefix:i.tickprefix,showtickprefix:i.showtickprefix,ticksuffix:i.ticksuffix,showticksuffix:i.showticksuffix,showexponent:i.showexponent,exponentformat:i.exponentformat,minexponent:i.minexponent,separatethousands:i.separatethousands,tickformat:i.tickformat,tickformatstops:i.tickformatstops,hoverformat:i.hoverformat,showline:i.showline,linecolor:i.linecolor,linewidth:i.linewidth,showgrid:i.showgrid,gridcolor:a({},i.gridcolor,{dflt:"rgb(204, 204, 204)"}),gridwidth:i.gridwidth,zeroline:i.zeroline,zerolinecolor:i.zerolinecolor,zerolinewidth:i.zerolinewidth,_deprecated:{title:i._deprecated.title,titlefont:i._deprecated.titlefont}},"plot","from-root")},3277:function(t,e,r){"use strict";var n=r(84267).mix,i=r(71828),a=r(44467),o=r(77894),s=r(951),l=r(71453),u=["xaxis","yaxis","zaxis"];t.exports=function(t,e,r){var c,f;function h(t,e){return i.coerce(c,f,o,t,e)}for(var p=0;p<u.length;p++){var d=u[p];c=t[d]||{},(f=a.newContainer(e,d))._id=d[0]+r.scene,f._name=d,s(c,f,h,r),l(c,f,h,{font:r.font,letter:d[0],data:r.data,showGrid:!0,noTickson:!0,noTicklabelmode:!0,noTicklabelstep:!0,noTicklabelposition:!0,noTicklabeloverflow:!0,noInsiderange:!0,bgColor:r.bgColor,calendar:r.calendar},r.fullLayout),h("gridcolor",n(f.color,r.bgColor,72.72727272727273).toRgbString()),h("title.text",d[0]),f.setScale=i.noop,h("showspikes")&&(h("spikesides"),h("spikethickness"),h("spikecolor",f.color)),h("showaxeslabels"),h("showbackground")&&h("backgroundcolor")}}},30422:function(t,e,r){"use strict";var n=r(78614),i=r(71828),a=["xaxis","yaxis","zaxis"];function o(){this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.tickEnable=[!0,!0,!0],this.tickFont=["sans-serif","sans-serif","sans-serif"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[18,18,18],this.labels=["x","y","z"],this.labelEnable=[!0,!0,!0],this.labelFont=["Open Sans","Open Sans","Open Sans"],this.labelSize=[20,20,20],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[30,30,30],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[10,10,10],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!0,!0,!0],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._defaultTickPad=this.tickPad.slice(),this._defaultLabelPad=this.labelPad.slice(),this._defaultLineTickLength=this.lineTickLength.slice()}o.prototype.merge=function(t,e){for(var r=this,o=0;o<3;++o){var s=e[a[o]];s.visible?(r.labels[o]=t._meta?i.templateString(s.title.text,t._meta):s.title.text,"font"in s.title&&(s.title.font.color&&(r.labelColor[o]=n(s.title.font.color)),s.title.font.family&&(r.labelFont[o]=s.title.font.family),s.title.font.size&&(r.labelSize[o]=s.title.font.size)),"showline"in s&&(r.lineEnable[o]=s.showline),"linecolor"in s&&(r.lineColor[o]=n(s.linecolor)),"linewidth"in s&&(r.lineWidth[o]=s.linewidth),"showgrid"in s&&(r.gridEnable[o]=s.showgrid),"gridcolor"in s&&(r.gridColor[o]=n(s.gridcolor)),"gridwidth"in s&&(r.gridWidth[o]=s.gridwidth),"log"===s.type?r.zeroEnable[o]=!1:"zeroline"in s&&(r.zeroEnable[o]=s.zeroline),"zerolinecolor"in s&&(r.zeroLineColor[o]=n(s.zerolinecolor)),"zerolinewidth"in s&&(r.zeroLineWidth[o]=s.zerolinewidth),"ticks"in s&&s.ticks?r.lineTickEnable[o]=!0:r.lineTickEnable[o]=!1,"ticklen"in s&&(r.lineTickLength[o]=r._defaultLineTickLength[o]=s.ticklen),"tickcolor"in s&&(r.lineTickColor[o]=n(s.tickcolor)),"tickwidth"in s&&(r.lineTickWidth[o]=s.tickwidth),"tickangle"in s&&(r.tickAngle[o]="auto"===s.tickangle?-3600:Math.PI*-s.tickangle/180),"showticklabels"in s&&(r.tickEnable[o]=s.showticklabels),"tickfont"in s&&(s.tickfont.color&&(r.tickColor[o]=n(s.tickfont.color)),s.tickfont.family&&(r.tickFont[o]=s.tickfont.family),s.tickfont.size&&(r.tickSize[o]=s.tickfont.size)),"mirror"in s?-1!==["ticks","all","allticks"].indexOf(s.mirror)?(r.lineTickMirror[o]=!0,r.lineMirror[o]=!0):!0===s.mirror?(r.lineTickMirror[o]=!1,r.lineMirror[o]=!0):(r.lineTickMirror[o]=!1,r.lineMirror[o]=!1):r.lineMirror[o]=!1,"showbackground"in s&&!1!==s.showbackground?(r.backgroundEnable[o]=!0,r.backgroundColor[o]=n(s.backgroundcolor)):r.backgroundEnable[o]=!1):(r.tickEnable[o]=!1,r.labelEnable[o]=!1,r.lineEnable[o]=!1,r.lineTickEnable[o]=!1,r.gridEnable[o]=!1,r.zeroEnable[o]=!1,r.backgroundEnable[o]=!1)}},t.exports=function(t,e){var r=new o;return r.merge(t,e),r}},24682:function(t,e,r){"use strict";var n=r(71828),i=r(7901),a=r(73972),o=r(49119),s=r(3277),l=r(65500),u=r(27659).NG,c="gl3d";function f(t,e,r,n){for(var o=r("bgcolor"),l=i.combine(o,n.paper_bgcolor),f=["up","center","eye"],h=0;h<f.length;h++)r("camera."+f[h]+".x"),r("camera."+f[h]+".y"),r("camera."+f[h]+".z");r("camera.projection.type");var p=!!r("aspectratio.x")&&!!r("aspectratio.y")&&!!r("aspectratio.z"),d=r("aspectmode",p?"manual":"auto");p||(t.aspectratio=e.aspectratio={x:1,y:1,z:1},"manual"===d&&(e.aspectmode="auto"),t.aspectmode=e.aspectmode);var v=u(n.fullData,c,n.id);s(t,e,{font:n.font,scene:n.id,data:v,bgColor:l,calendar:n.calendar,autotypenumbersDflt:n.autotypenumbersDflt,fullLayout:n.fullLayout}),a.getComponentMethod("annotations3d","handleDefaults")(t,e,n);var g=n.getDfltFromLayout("dragmode");if(!1!==g&&!g)if(g="orbit",t.camera&&t.camera.up){var y=t.camera.up.x,m=t.camera.up.y,x=t.camera.up.z;0!==x&&(y&&m&&x?x/Math.sqrt(y*y+m*m+x*x)>.999&&(g="turntable"):g="turntable")}else g="turntable";r("dragmode",g),r("hovermode",n.getDfltFromLayout("hovermode"))}t.exports=function(t,e,r){var i=e._basePlotModules.length>1;o(t,e,r,{type:c,attributes:l,handleDefaults:f,fullLayout:e,font:e.font,fullData:r,getDfltFromLayout:function(e){if(!i)return n.validate(t[e],l[e])?t[e]:void 0},autotypenumbersDflt:e.autotypenumbers,paper_bgcolor:e.paper_bgcolor,calendar:e.calendar})}},65500:function(t,e,r){"use strict";var n=r(77894),i=r(27670).Y,a=r(1426).extendFlat,o=r(71828).counterRegex;function s(t,e,r){return{x:{valType:"number",dflt:t,editType:"camera"},y:{valType:"number",dflt:e,editType:"camera"},z:{valType:"number",dflt:r,editType:"camera"},editType:"camera"}}t.exports={_arrayAttrRegexps:[o("scene",".annotations",!0)],bgcolor:{valType:"color",dflt:"rgba(0,0,0,0)",editType:"plot"},camera:{up:a(s(0,0,1),{}),center:a(s(0,0,0),{}),eye:a(s(1.25,1.25,1.25),{}),projection:{type:{valType:"enumerated",values:["perspective","orthographic"],dflt:"perspective",editType:"calc"},editType:"calc"},editType:"camera"},domain:i({name:"scene",editType:"plot"}),aspectmode:{valType:"enumerated",values:["auto","cube","data","manual"],dflt:"auto",editType:"plot",impliedEdits:{"aspectratio.x":void 0,"aspectratio.y":void 0,"aspectratio.z":void 0}},aspectratio:{x:{valType:"number",min:0,editType:"plot",impliedEdits:{"^aspectmode":"manual"}},y:{valType:"number",min:0,editType:"plot",impliedEdits:{"^aspectmode":"manual"}},z:{valType:"number",min:0,editType:"plot",impliedEdits:{"^aspectmode":"manual"}},editType:"plot",impliedEdits:{aspectmode:"manual"}},xaxis:n,yaxis:n,zaxis:n,dragmode:{valType:"enumerated",values:["orbit","turntable","zoom","pan",!1],editType:"plot"},hovermode:{valType:"enumerated",values:["closest",!1],dflt:"closest",editType:"modebar"},uirevision:{valType:"any",editType:"none"},editType:"plot",_deprecated:{cameraposition:{valType:"info_array",editType:"camera"}}}},13133:function(t,e,r){"use strict";var n=r(78614),i=["xaxis","yaxis","zaxis"];function a(){this.enabled=[!0,!0,!0],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.drawSides=[!0,!0,!0],this.lineWidth=[1,1,1]}a.prototype.merge=function(t){for(var e=0;e<3;++e){var r=t[i[e]];r.visible?(this.enabled[e]=r.showspikes,this.colors[e]=n(r.spikecolor),this.drawSides[e]=r.spikesides,this.lineWidth[e]=r.spikethickness):(this.enabled[e]=!1,this.drawSides[e]=!1)}},t.exports=function(t){var e=new a;return e.merge(t),e}},96085:function(t,e,r){"use strict";t.exports=function(t){for(var e=t.axesOptions,r=t.glplot.axesPixels,s=t.fullSceneLayout,l=[[],[],[]],u=0;u<3;++u){var c=s[a[u]];if(c._length=(r[u].hi-r[u].lo)*r[u].pixelsPerDataUnit/t.dataScale[u],Math.abs(c._length)===1/0||isNaN(c._length))l[u]=[];else{c._input_range=c.range.slice(),c.range[0]=r[u].lo/t.dataScale[u],c.range[1]=r[u].hi/t.dataScale[u],c._m=1/(t.dataScale[u]*r[u].pixelsPerDataUnit),c.range[0]===c.range[1]&&(c.range[0]-=1,c.range[1]+=1);var f=c.tickmode;if("auto"===c.tickmode){c.tickmode="linear";var h=c.nticks||i.constrain(c._length/40,4,9);n.autoTicks(c,Math.abs(c.range[1]-c.range[0])/h)}for(var p=n.calcTicks(c,{msUTC:!0}),d=0;d<p.length;++d)p[d].x=p[d].x*t.dataScale[u],"date"===c.type&&(p[d].text=p[d].text.replace(/\<br\>/g," "));l[u]=p,c.tickmode=f}}for(e.ticks=l,u=0;u<3;++u)for(o[u]=.5*(t.glplot.bounds[0][u]+t.glplot.bounds[1][u]),d=0;d<2;++d)e.bounds[d][u]=t.glplot.bounds[d][u];t.contourLevels=function(t){for(var e=new Array(3),r=0;r<3;++r){for(var n=t[r],i=new Array(n.length),a=0;a<n.length;++a)i[a]=n[a].x;e[r]=i}return e}(l)};var n=r(89298),i=r(71828),a=["xaxis","yaxis","zaxis"],o=[0,0,0]},63538:function(t){"use strict";function e(t,e){var r,n,i=[0,0,0,0];for(r=0;r<4;++r)for(n=0;n<4;++n)i[n]+=t[4*r+n]*e[r];return i}t.exports=function(t,r){return e(t.projection,e(t.view,e(t.model,[r[0],r[1],r[2],1])))}},33539:function(t,e,r){"use strict";var n,i,a=r(9330).gl_plot3d,o=a.createCamera,s=a.createScene,l=r(40372),u=r(38520),c=r(73972),f=r(71828),h=f.preserveDrawingBuffer(),p=r(89298),d=r(30211),v=r(78614),g=r(58617),y=r(63538),m=r(30422),x=r(13133),b=r(96085),_=r(71739).applyAutorangeOptions,w=!1;function T(t,e){var r=document.createElement("div"),n=t.container;this.graphDiv=t.graphDiv;var i=document.createElementNS("http://www.w3.org/2000/svg","svg");i.style.position="absolute",i.style.top=i.style.left="0px",i.style.width=i.style.height="100%",i.style["z-index"]=20,i.style["pointer-events"]="none",r.appendChild(i),this.svgContainer=i,r.id=t.id,r.style.position="absolute",r.style.top=r.style.left="0px",r.style.width=r.style.height="100%",n.appendChild(r),this.fullLayout=e,this.id=t.id||"scene",this.fullSceneLayout=e[this.id],this.plotArgs=[[],{},{}],this.axesOptions=m(e,e[this.id]),this.spikeOptions=x(e[this.id]),this.container=r,this.staticMode=!!t.staticPlot,this.pixelRatio=this.pixelRatio||t.plotGlPixelRatio||2,this.dataScale=[1,1,1],this.contourLevels=[[],[],[]],this.convertAnnotations=c.getComponentMethod("annotations3d","convert"),this.drawAnnotations=c.getComponentMethod("annotations3d","draw"),this.initializeGLPlot()}var k=T.prototype;k.prepareOptions=function(){var t=this,e={canvas:t.canvas,gl:t.gl,glOptions:{preserveDrawingBuffer:h,premultipliedAlpha:!0,antialias:!0},container:t.container,axes:t.axesOptions,spikes:t.spikeOptions,pickRadius:10,snapToData:!0,autoScale:!0,autoBounds:!1,cameraObject:t.camera,pixelRatio:t.pixelRatio};if(t.staticMode){if(!(i||(n=document.createElement("canvas"),i=l({canvas:n,preserveDrawingBuffer:!0,premultipliedAlpha:!0,antialias:!0}))))throw new Error("error creating static canvas/context for image server");e.gl=i,e.canvas=n}return e};var A=!0;k.tryCreatePlot=function(){var t=this,e=t.prepareOptions(),r=!0;try{t.glplot=s(e)}catch(n){if(t.staticMode||!A||h)r=!1;else{f.warn(["webgl setup failed possibly due to","false preserveDrawingBuffer config.","The mobile/tablet device may not be detected by is-mobile module.","Enabling preserveDrawingBuffer in second attempt to create webgl scene..."].join(" "));try{h=e.glOptions.preserveDrawingBuffer=!0,t.glplot=s(e)}catch(t){h=e.glOptions.preserveDrawingBuffer=!1,r=!1}}}return A=!1,r},k.initializeGLCamera=function(){var t=this,e=t.fullSceneLayout.camera,r="orthographic"===e.projection.type;t.camera=o(t.container,{center:[e.center.x,e.center.y,e.center.z],eye:[e.eye.x,e.eye.y,e.eye.z],up:[e.up.x,e.up.y,e.up.z],_ortho:r,zoomMin:.01,zoomMax:100,mode:"orbit"})},k.initializeGLPlot=function(){var t=this;if(t.initializeGLCamera(),!t.tryCreatePlot())return g(t);t.traces={},t.make4thDimension();var e=t.graphDiv,r=e.layout,n=function(){var e={};return t.isCameraChanged(r)&&(e[t.id+".camera"]=t.getCamera()),t.isAspectChanged(r)&&(e[t.id+".aspectratio"]=t.glplot.getAspectratio(),"manual"!==r[t.id].aspectmode&&(t.fullSceneLayout.aspectmode=r[t.id].aspectmode=e[t.id+".aspectmode"]="manual")),e},i=function(t){if(!1!==t.fullSceneLayout.dragmode){var e=n();t.saveLayout(r),t.graphDiv.emit("plotly_relayout",e)}};return t.glplot.canvas&&(t.glplot.canvas.addEventListener("mouseup",(function(){i(t)})),t.glplot.canvas.addEventListener("touchstart",(function(){w=!0})),t.glplot.canvas.addEventListener("wheel",(function(r){if(e._context._scrollZoom.gl3d){if(t.camera._ortho){var n=r.deltaX>r.deltaY?1.1:1/1.1,a=t.glplot.getAspectratio();t.glplot.setAspectratio({x:n*a.x,y:n*a.y,z:n*a.z})}i(t)}}),!!u&&{passive:!1}),t.glplot.canvas.addEventListener("mousemove",(function(){if(!1!==t.fullSceneLayout.dragmode&&0!==t.camera.mouseListener.buttons){var e=n();t.graphDiv.emit("plotly_relayouting",e)}})),t.staticMode||t.glplot.canvas.addEventListener("webglcontextlost",(function(r){e&&e.emit&&e.emit("plotly_webglcontextlost",{event:r,layer:t.id})}),!1)),t.glplot.oncontextloss=function(){t.recoverContext()},t.glplot.onrender=function(){t.render()},!0},k.render=function(){var t,e=this,r=e.graphDiv,n=e.svgContainer,i=e.container.getBoundingClientRect();r._fullLayout._calcInverseTransform(r);var a=r._fullLayout._invScaleX,o=r._fullLayout._invScaleY,s=i.width*a,l=i.height*o;n.setAttributeNS(null,"viewBox","0 0 "+s+" "+l),n.setAttributeNS(null,"width",s),n.setAttributeNS(null,"height",l),b(e),e.glplot.axes.update(e.axesOptions);for(var u=Object.keys(e.traces),c=null,h=e.glplot.selection,v=0;v<u.length;++v)"skip"!==(t=e.traces[u[v]]).data.hoverinfo&&t.handlePick(h)&&(c=t),t.setContourLevels&&t.setContourLevels();function g(t,r,n){var i=e.fullSceneLayout[t+"axis"];return"log"!==i.type&&(r=i.d2l(r)),p.hoverLabelText(i,r,n)}if(null!==c){var m=y(e.glplot.cameraParams,h.dataCoordinate);t=c.data;var x,_=r._fullData[t.index],T=h.index,k={xLabel:g("x",h.traceCoordinate[0],t.xhoverformat),yLabel:g("y",h.traceCoordinate[1],t.yhoverformat),zLabel:g("z",h.traceCoordinate[2],t.zhoverformat)},A=d.castHoverinfo(_,e.fullLayout,T),M=(A||"").split("+"),S=A&&"all"===A;_.hovertemplate||S||(-1===M.indexOf("x")&&(k.xLabel=void 0),-1===M.indexOf("y")&&(k.yLabel=void 0),-1===M.indexOf("z")&&(k.zLabel=void 0),-1===M.indexOf("text")&&(h.textLabel=void 0),-1===M.indexOf("name")&&(c.name=void 0));var E=[];"cone"===t.type||"streamtube"===t.type?(k.uLabel=g("x",h.traceCoordinate[3],t.uhoverformat),(S||-1!==M.indexOf("u"))&&E.push("u: "+k.uLabel),k.vLabel=g("y",h.traceCoordinate[4],t.vhoverformat),(S||-1!==M.indexOf("v"))&&E.push("v: "+k.vLabel),k.wLabel=g("z",h.traceCoordinate[5],t.whoverformat),(S||-1!==M.indexOf("w"))&&E.push("w: "+k.wLabel),k.normLabel=h.traceCoordinate[6].toPrecision(3),(S||-1!==M.indexOf("norm"))&&E.push("norm: "+k.normLabel),"streamtube"===t.type&&(k.divergenceLabel=h.traceCoordinate[7].toPrecision(3),(S||-1!==M.indexOf("divergence"))&&E.push("divergence: "+k.divergenceLabel)),h.textLabel&&E.push(h.textLabel),x=E.join("<br>")):"isosurface"===t.type||"volume"===t.type?(k.valueLabel=p.hoverLabelText(e._mockAxis,e._mockAxis.d2l(h.traceCoordinate[3]),t.valuehoverformat),E.push("value: "+k.valueLabel),h.textLabel&&E.push(h.textLabel),x=E.join("<br>")):x=h.textLabel;var L={x:h.traceCoordinate[0],y:h.traceCoordinate[1],z:h.traceCoordinate[2],data:_._input,fullData:_,curveNumber:_.index,pointNumber:T};d.appendArrayPointValue(L,_,T),t._module.eventData&&(L=_._module.eventData(L,h,_,{},T));var C={points:[L]};if(e.fullSceneLayout.hovermode){var P=[];d.loneHover({trace:_,x:(.5+.5*m[0]/m[3])*s,y:(.5-.5*m[1]/m[3])*l,xLabel:k.xLabel,yLabel:k.yLabel,zLabel:k.zLabel,text:x,name:c.name,color:d.castHoverOption(_,T,"bgcolor")||c.color,borderColor:d.castHoverOption(_,T,"bordercolor"),fontFamily:d.castHoverOption(_,T,"font.family"),fontSize:d.castHoverOption(_,T,"font.size"),fontColor:d.castHoverOption(_,T,"font.color"),nameLength:d.castHoverOption(_,T,"namelength"),textAlign:d.castHoverOption(_,T,"align"),hovertemplate:f.castOption(_,T,"hovertemplate"),hovertemplateLabels:f.extendFlat({},L,k),eventData:[L]},{container:n,gd:r,inOut_bbox:P}),L.bbox=P[0]}h.distance<5&&(h.buttons||w)?r.emit("plotly_click",C):r.emit("plotly_hover",C),this.oldEventData=C}else d.loneUnhover(n),this.oldEventData&&r.emit("plotly_unhover",this.oldEventData),this.oldEventData=void 0;e.drawAnnotations(e)},k.recoverContext=function(){var t=this;t.glplot.dispose();var e=function(){t.glplot.gl.isContextLost()?requestAnimationFrame(e):t.initializeGLPlot()?t.plot.apply(t,t.plotArgs):f.error("Catastrophic and unrecoverable WebGL error. Context lost.")};requestAnimationFrame(e)};var M=["xaxis","yaxis","zaxis"];function S(t,e,r){for(var n=t.fullSceneLayout,i=0;i<3;i++){var a=M[i],o=a.charAt(0),s=n[a],l=e[o],u=e[o+"calendar"],c=e["_"+o+"length"];if(f.isArrayOrTypedArray(l))for(var h,p=0;p<(c||l.length);p++)if(f.isArrayOrTypedArray(l[p]))for(var d=0;d<l[p].length;++d)h=s.d2l(l[p][d],0,u),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else h=s.d2l(l[p],0,u),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else r[0][i]=Math.min(r[0][i],0),r[1][i]=Math.max(r[1][i],c-1)}}k.plot=function(t,e,r){var n=this;if(n.plotArgs=[t,e,r],!n.glplot.contextLost){var i,a,o,s,l,u,c=e[n.id],f=r[n.id];n.fullLayout=e,n.fullSceneLayout=c,n.axesOptions.merge(e,c),n.spikeOptions.merge(c),n.setViewport(c),n.updateFx(c.dragmode,c.hovermode),n.camera.enableWheel=n.graphDiv._context._scrollZoom.gl3d,n.glplot.setClearColor(v(c.bgcolor)),n.setConvert(l),t?Array.isArray(t)||(t=[t]):t=[];var h=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(o=0;o<t.length;++o)!0===(i=t[o]).visible&&0!==i._length&&S(this,i,h);!function(t,e){for(var r=t.fullSceneLayout,n=r.annotations||[],i=0;i<3;i++)for(var a=M[i],o=a.charAt(0),s=r[a],l=0;l<n.length;l++){var u=n[l];if(u.visible){var c=s.r2l(u[o]);!isNaN(c)&&isFinite(c)&&(e[0][i]=Math.min(e[0][i],c),e[1][i]=Math.max(e[1][i],c))}}}(this,h);var p=[1,1,1];for(s=0;s<3;++s)h[1][s]===h[0][s]?p[s]=1:p[s]=1/(h[1][s]-h[0][s]);for(n.dataScale=p,n.convertAnnotations(this),o=0;o<t.length;++o)!0===(i=t[o]).visible&&0!==i._length&&((a=n.traces[i.uid])?a.data.type===i.type?a.update(i):(a.dispose(),a=i._module.plot(this,i),n.traces[i.uid]=a):(a=i._module.plot(this,i),n.traces[i.uid]=a),a.name=i.name);var d=Object.keys(n.traces);t:for(o=0;o<d.length;++o){for(s=0;s<t.length;++s)if(t[s].uid===d[o]&&!0===t[s].visible&&0!==t[s]._length)continue t;(a=n.traces[d[o]]).dispose(),delete n.traces[d[o]]}n.glplot.objects.sort((function(t,e){return t._trace.data.index-e._trace.data.index}));var g,y=[[0,0,0],[0,0,0]],m=[],x={};for(o=0;o<3;++o){var b;if((u=(l=c[M[o]]).type)in x?(x[u].acc*=p[o],x[u].count+=1):x[u]={acc:p[o],count:1},l.autorange){y[0][o]=1/0,y[1][o]=-1/0;var w=n.glplot.objects,T=n.fullSceneLayout.annotations||[],k=l._name.charAt(0);for(s=0;s<w.length;s++){var A=w[s],E=A.bounds,L=A._trace.data._pad||0;"ErrorBars"===A.constructor.name&&l._lowerLogErrorBound?y[0][o]=Math.min(y[0][o],l._lowerLogErrorBound):y[0][o]=Math.min(y[0][o],E[0][o]/p[o]-L),y[1][o]=Math.max(y[1][o],E[1][o]/p[o]+L)}for(s=0;s<T.length;s++){var C=T[s];if(C.visible){var P=l.r2l(C[k]);y[0][o]=Math.min(y[0][o],P),y[1][o]=Math.max(y[1][o],P)}}if("rangemode"in l&&"tozero"===l.rangemode&&(y[0][o]=Math.min(y[0][o],0),y[1][o]=Math.max(y[1][o],0)),y[0][o]>y[1][o])y[0][o]=-1,y[1][o]=1;else{var O=y[1][o]-y[0][o];y[0][o]-=O/32,y[1][o]+=O/32}if(b=[y[0][o],y[1][o]],b=_(b,l),y[0][o]=b[0],y[1][o]=b[1],l.isReversed()){var I=y[0][o];y[0][o]=y[1][o],y[1][o]=I}}else b=l.range,y[0][o]=l.r2l(b[0]),y[1][o]=l.r2l(b[1]);y[0][o]===y[1][o]&&(y[0][o]-=1,y[1][o]+=1),m[o]=y[1][o]-y[0][o],l.range=[y[0][o],y[1][o]],l.limitRange(),n.glplot.setBounds(o,{min:l.range[0]*p[o],max:l.range[1]*p[o]})}var D=c.aspectmode;if("cube"===D)g=[1,1,1];else if("manual"===D){var 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o=e.x,s=e.y,l=n.bdPos,u=n.bPos,c=r.boxpoints||r.points;i.seedPseudoRandom();var f=t.selectAll("g.points").data(c?function(t){return t.forEach((function(t){t.t=n,t.trace=r})),t}:[]);f.enter().append("g").attr("class","points"),f.exit().remove();var h=f.selectAll("path").data((function(t){var e,n,a=t.pts2,o=Math.max((t.max-t.min)/10,t.q3-t.q1),s=1e-9*o,f=.01*o,h=[],p=0;if(r.jitter){if(0===o)for(p=1,h=new Array(a.length),e=0;e<a.length;e++)h[e]=1;else for(e=0;e<a.length;e++){var d=Math.max(0,e-5),v=a[d].v,g=Math.min(a.length-1,e+5),y=a[g].v;"all"!==c&&(a[e].v<t.lf?y=Math.min(y,t.lf):v=Math.max(v,t.uf));var m=Math.sqrt(f*(g-d)/(y-v+s))||0;m=i.constrain(Math.abs(m),0,1),h.push(m),p=Math.max(m,p)}n=2*r.jitter/(p||1)}for(e=0;e<a.length;e++){var x=a[e],b=x.v,_=r.jitter?n*h[e]*(i.pseudoRandom()-.5):0,w=t.pos+u+l*(r.pointpos+_);"h"===r.orientation?(x.y=w,x.x=b):(x.x=w,x.y=b),"suspectedoutliers"===c&&b<t.uo&&b>t.lo&&(x.so=!0)}return 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Y=_.selectAll("g.gauge-outline").data([x]);Y.enter().append("g").classed("gauge-outline",!0).append("path"),Y.select("path").call(P).call(T),Y.exit().remove()}(t,0,e,{radius:U,innerRadius:V,gauge:Z,layer:Y,size:B,gaugeBg:C,gaugeOutline:P,transitionOpts:r,onComplete:g});var W=I.selectAll("g.bullet").data(R?e:[]);W.exit().remove();var X=I.selectAll("g.bulletaxis").data(R?e:[]);X.exit().remove(),R&&function(t,e,r,n){var i,a,o,s,u,c=r[0].trace,f=n.gauge,p=n.layer,v=n.gaugeBg,g=n.gaugeOutline,y=n.size,x=c.domain,b=n.transitionOpts,_=n.onComplete;f.enter().append("g").classed("bullet",!0),f.attr("transform",l(y.l,y.t)),p.enter().append("g").classed("bulletaxis",!0).classed("crisp",!0),p.selectAll("g.xbulletaxistick,path,text").remove();var A=y.h,M=c.gauge.bar.thickness*A,S=x.x[0],E=x.x[0]+(x.x[1]-x.x[0])*(c._hasNumber||c._hasDelta?1-h.bulletNumberDomainSize:1);function L(t){t.attr("width",(function(t){return Math.max(0,i.c2p(t.range[1])-i.c2p(t.range[0]))})).attr("x",(function(t){return 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0===e?t.parcatsViewModel.model.dimensions[t.model.dimensionInd].dimensionLabel:null})).each((function(t){u.font(n.select(this),t.parcatsViewModel.labelfont)})),M.selectAll("rect.bandrect").on("mouseover",L).on("mouseout",C),M.exit().remove(),A.call(n.behavior.drag().origin((function(t){return{x:t.x,y:0}})).on("dragstart",P).on("drag",O).on("dragend",I)),h.each((function(t){t.traceSelection=n.select(this),t.pathSelection=n.select(this).selectAll("g.paths").selectAll("path.path"),t.dimensionSelection=n.select(this).selectAll("g.dimensions").selectAll("g.dimension")})),h.exit().remove()}function p(t){return t.key}function d(t){var e=t.parcatsViewModel.dimensions.length,r=t.parcatsViewModel.dimensions[e-1].model.dimensionInd;return t.model.dimensionInd===r}function v(t,e){return t.model.rawColor>e.model.rawColor?1:t.model.rawColor<e.model.rawColor?-1:0}function g(t){if(!t.parcatsViewModel.dragDimension&&-1===t.parcatsViewModel.hoverinfoItems.indexOf("skip")){s.raiseToTop(this),w(n.select(this));var e=m(t),r=x(t);if(t.parcatsViewModel.graphDiv.emit("plotly_hover",{points:e,event:n.event,constraints:r}),-1===t.parcatsViewModel.hoverinfoItems.indexOf("none")){var i,a,l,u=n.mouse(this)[0],f=t.parcatsViewModel.graphDiv,h=t.parcatsViewModel.trace,p=f._fullLayout,d=p._paperdiv.node().getBoundingClientRect(),v=t.parcatsViewModel.graphDiv.getBoundingClientRect();for(l=0;l<t.leftXs.length-1;l++)if(t.leftXs[l]+t.dimWidths[l]-2<=u&&u<=t.leftXs[l+1]+2){var g=t.parcatsViewModel.dimensions[l],y=t.parcatsViewModel.dimensions[l+1];i=(g.x+g.width+y.x)/2,a=(t.topYs[l]+t.topYs[l+1]+t.height)/2;break}var b=t.parcatsViewModel.x+i,_=t.parcatsViewModel.y+a,T=c.mostReadable(t.model.color,["black","white"]),k=t.model.count,A=k/t.parcatsViewModel.model.count,M={countLabel:k,probabilityLabel:A.toFixed(3)},S=[];-1!==t.parcatsViewModel.hoverinfoItems.indexOf("count")&&S.push(["Count:",M.countLabel].join(" ")),-1!==t.parcatsViewModel.hoverinfoItems.indexOf("probability")&&S.push(["P:",M.probabilityLabel].join(" "));var E=S.join("<br>"),L=n.mouse(f)[0];o.loneHover({trace:h,x:b-d.left+v.left,y:_-d.top+v.top,text:E,color:t.model.color,borderColor:"black",fontFamily:'Monaco, "Courier New", monospace',fontSize:10,fontColor:T,idealAlign:L<b?"right":"left",hovertemplate:(h.line||{}).hovertemplate,hovertemplateLabels:M,eventData:[{data:h._input,fullData:h,count:k,probability:A}]},{container:p._hoverlayer.node(),outerContainer:p._paper.node(),gd:f})}}}function y(t){if(!t.parcatsViewModel.dragDimension&&(_(n.select(this)),o.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()),t.parcatsViewModel.pathSelection.sort(v),-1===t.parcatsViewModel.hoverinfoItems.indexOf("skip"))){var e=m(t),r=x(t);t.parcatsViewModel.graphDiv.emit("plotly_unhover",{points:e,event:n.event,constraints:r})}}function m(t){for(var e=[],r=D(t.parcatsViewModel),n=0;n<t.model.valueInds.length;n++){var i=t.model.valueInds[n];e.push({curveNumber:r,pointNumber:i})}return e}function x(t){for(var e={},r=t.parcatsViewModel.model.dimensions,n=0;n<r.length;n++){var i=r[n],a=i.categories[t.model.categoryInds[n]];e[i.containerInd]=a.categoryValue}return void 0!==t.model.rawColor&&(e.color=t.model.rawColor),e}function b(t){if(-1===t.parcatsViewModel.hoverinfoItems.indexOf("skip")){var e=m(t),r=x(t);t.parcatsViewModel.graphDiv.emit("plotly_click",{points:e,event:n.event,constraints:r})}}function _(t){t.attr("fill",(function(t){return t.model.color})).attr("fill-opacity",.6).attr("stroke","lightgray").attr("stroke-width",.2).attr("stroke-opacity",1)}function w(t){t.attr("fill-opacity",.8).attr("stroke",(function(t){return c.mostReadable(t.model.color,["black","white"])})).attr("stroke-width",.3)}function T(t){t.select("rect.catrect").attr("stroke","black").attr("stroke-width",1).attr("stroke-opacity",1)}function k(t){t.attr("stroke","black").attr("stroke-width",.2).attr("stroke-opacity",1).attr("fill-opacity",1)}function A(t){var e=t.parcatsViewModel.pathSelection,r=t.categoryViewModel.model.dimensionInd,n=t.categoryViewModel.model.categoryInd;return e.filter((function(e){return e.model.categoryInds[r]===n&&e.model.color===t.color}))}function M(t,e,r){var i=n.select(t).datum(),a=i.categoryViewModel.model,o=i.parcatsViewModel.graphDiv,s=n.select(t.parentNode).selectAll("rect.bandrect"),l=[];s.each((function(t){A(t).each((function(t){Array.prototype.push.apply(l,m(t))}))}));var u={};u[a.dimensionInd]=a.categoryValue,o.emit(e,{points:l,event:r,constraints:u})}function S(t,e,r){var i=n.select(t).datum(),a=i.categoryViewModel.model,o=i.parcatsViewModel.graphDiv,s=A(i),l=[];s.each((function(t){Array.prototype.push.apply(l,m(t))}));var u={};u[a.dimensionInd]=a.categoryValue,void 0!==i.rawColor&&(u.color=i.rawColor),o.emit(e,{points:l,event:r,constraints:u})}function E(t,e,r){t._fullLayout._calcInverseTransform(t);var i,a,o=t._fullLayout._invScaleX,s=t._fullLayout._invScaleY,l=n.select(r.parentNode).select("rect.catrect"),u=l.node().getBoundingClientRect(),c=l.datum(),f=c.parcatsViewModel,h=f.model.dimensions[c.model.dimensionInd],p=f.trace,d=u.top+u.height/2;f.dimensions.length>1&&h.displayInd===f.dimensions.length-1?(i=u.left,a="left"):(i=u.left+u.width,a="right");var v=c.model.count,g=c.model.categoryLabel,y=v/c.parcatsViewModel.model.count,m={countLabel:v,categoryLabel:g,probabilityLabel:y.toFixed(3)},x=[];-1!==c.parcatsViewModel.hoverinfoItems.indexOf("count")&&x.push(["Count:",m.countLabel].join(" ")),-1!==c.parcatsViewModel.hoverinfoItems.indexOf("probability")&&x.push(["P("+m.categoryLabel+"):",m.probabilityLabel].join(" "));var b=x.join("<br>");return{trace:p,x:o*(i-e.left),y:s*(d-e.top),text:b,color:"lightgray",borderColor:"black",fontFamily:'Monaco, "Courier New", monospace',fontSize:12,fontColor:"black",idealAlign:a,hovertemplate:p.hovertemplate,hovertemplateLabels:m,eventData:[{data:p._input,fullData:p,count:v,category:g,probability:y}]}}function L(t){if(!t.parcatsViewModel.dragDimension&&-1===t.parcatsViewModel.hoverinfoItems.indexOf("skip")){if(n.mouse(this)[1]<-1)return;var e,r=t.parcatsViewModel.graphDiv,i=r._fullLayout,a=i._paperdiv.node().getBoundingClientRect(),l=t.parcatsViewModel.hoveron,u=this;"color"===l?(function(t){var e=n.select(t).datum(),r=A(e);w(r),r.each((function(){s.raiseToTop(this)})),n.select(t.parentNode).selectAll("rect.bandrect").filter((function(t){return t.color===e.color})).each((function(){s.raiseToTop(this),n.select(this).attr("stroke","black").attr("stroke-width",1.5)}))}(u),S(u,"plotly_hover",n.event)):(function(t){n.select(t.parentNode).selectAll("rect.bandrect").each((function(t){var e=A(t);w(e),e.each((function(){s.raiseToTop(this)}))})),n.select(t.parentNode).select("rect.catrect").attr("stroke","black").attr("stroke-width",2.5)}(u),M(u,"plotly_hover",n.event)),-1===t.parcatsViewModel.hoverinfoItems.indexOf("none")&&("category"===l?e=E(r,a,u):"color"===l?e=function(t,e,r){t._fullLayout._calcInverseTransform(t);var i,a,o=t._fullLayout._invScaleX,s=t._fullLayout._invScaleY,l=r.getBoundingClientRect(),u=n.select(r).datum(),f=u.categoryViewModel,h=f.parcatsViewModel,p=h.model.dimensions[f.model.dimensionInd],d=h.trace,v=l.y+l.height/2;h.dimensions.length>1&&p.displayInd===h.dimensions.length-1?(i=l.left,a="left"):(i=l.left+l.width,a="right");var g=f.model.categoryLabel,y=u.parcatsViewModel.model.count,m=0;u.categoryViewModel.bands.forEach((function(t){t.color===u.color&&(m+=t.count)}));var x=f.model.count,b=0;h.pathSelection.each((function(t){t.model.color===u.color&&(b+=t.model.count)}));var _=m/y,w=m/b,T=m/x,k={countLabel:y,categoryLabel:g,probabilityLabel:_.toFixed(3)},A=[];-1!==f.parcatsViewModel.hoverinfoItems.indexOf("count")&&A.push(["Count:",k.countLabel].join(" ")),-1!==f.parcatsViewModel.hoverinfoItems.indexOf("probability")&&(A.push("P(color ∩ "+g+"): "+k.probabilityLabel),A.push("P("+g+" | color): "+w.toFixed(3)),A.push("P(color | "+g+"): "+T.toFixed(3)));var M=A.join("<br>"),S=c.mostReadable(u.color,["black","white"]);return{trace:d,x:o*(i-e.left),y:s*(v-e.top),text:M,color:u.color,borderColor:"black",fontFamily:'Monaco, "Courier New", monospace',fontColor:S,fontSize:10,idealAlign:a,hovertemplate:d.hovertemplate,hovertemplateLabels:k,eventData:[{data:d._input,fullData:d,category:g,count:y,probability:_,categorycount:x,colorcount:b,bandcolorcount:m}]}}(r,a,u):"dimension"===l&&(e=function(t,e,r){var i=[];return n.select(r.parentNode.parentNode).selectAll("g.category").select("rect.catrect").each((function(){i.push(E(t,e,this))})),i}(r,a,u)),e&&o.loneHover(e,{container:i._hoverlayer.node(),outerContainer:i._paper.node(),gd:r}))}}function C(t){var e=t.parcatsViewModel;e.dragDimension||(_(e.pathSelection),T(e.dimensionSelection.selectAll("g.category")),k(e.dimensionSelection.selectAll("g.category").selectAll("rect.bandrect")),o.loneUnhover(e.graphDiv._fullLayout._hoverlayer.node()),e.pathSelection.sort(v),-1!==e.hoverinfoItems.indexOf("skip"))||("color"===t.parcatsViewModel.hoveron?S(this,"plotly_unhover",n.event):M(this,"plotly_unhover",n.event))}function P(t){"fixed"!==t.parcatsViewModel.arrangement&&(t.dragDimensionDisplayInd=t.model.displayInd,t.initialDragDimensionDisplayInds=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),t.dragHasMoved=!1,t.dragCategoryDisplayInd=null,n.select(this).selectAll("g.category").select("rect.catrect").each((function(e){var r=n.mouse(this)[0],i=n.mouse(this)[1];-2<=r&&r<=e.width+2&&-2<=i&&i<=e.height+2&&(t.dragCategoryDisplayInd=e.model.displayInd,t.initialDragCategoryDisplayInds=t.model.categories.map((function(t){return t.displayInd})),e.model.dragY=e.y,s.raiseToTop(this.parentNode),n.select(this.parentNode).selectAll("rect.bandrect").each((function(e){e.y<i&&i<=e.y+e.height&&(t.potentialClickBand=this)})))})),t.parcatsViewModel.dragDimension=t,o.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()))}function O(t){if("fixed"!==t.parcatsViewModel.arrangement&&(t.dragHasMoved=!0,null!==t.dragDimensionDisplayInd)){var e=t.dragDimensionDisplayInd,r=e-1,i=e+1,a=t.parcatsViewModel.dimensions[e];if(null!==t.dragCategoryDisplayInd){var o=a.categories[t.dragCategoryDisplayInd];o.model.dragY+=n.event.dy;var s=o.model.dragY,l=o.model.displayInd,u=a.categories,c=u[l-1],f=u[l+1];void 0!==c&&s<c.y+c.height/2&&(o.model.displayInd=c.model.displayInd,c.model.displayInd=l),void 0!==f&&s+o.height>f.y+f.height/2&&(o.model.displayInd=f.model.displayInd,f.model.displayInd=l),t.dragCategoryDisplayInd=o.model.displayInd}if(null===t.dragCategoryDisplayInd||"freeform"===t.parcatsViewModel.arrangement){a.model.dragX=n.event.x;var h=t.parcatsViewModel.dimensions[r],p=t.parcatsViewModel.dimensions[i];void 0!==h&&a.model.dragX<h.x+h.width&&(a.model.displayInd=h.model.displayInd,h.model.displayInd=e),void 0!==p&&a.model.dragX+a.width>p.x&&(a.model.displayInd=p.model.displayInd,p.model.displayInd=t.dragDimensionDisplayInd),t.dragDimensionDisplayInd=a.model.displayInd}j(t.parcatsViewModel),N(t.parcatsViewModel),R(t.parcatsViewModel),z(t.parcatsViewModel)}}function I(t){if("fixed"!==t.parcatsViewModel.arrangement&&null!==t.dragDimensionDisplayInd){n.select(this).selectAll("text").attr("font-weight","normal");var e={},r=D(t.parcatsViewModel),i=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),o=t.initialDragDimensionDisplayInds.some((function(t,e){return t!==i[e]}));o&&i.forEach((function(r,n){var i=t.parcatsViewModel.model.dimensions[n].containerInd;e["dimensions["+i+"].displayindex"]=r}));var s=!1;if(null!==t.dragCategoryDisplayInd){var l=t.model.categories.map((function(t){return t.displayInd}));if(s=t.initialDragCategoryDisplayInds.some((function(t,e){return t!==l[e]}))){var u=t.model.categories.slice().sort((function(t,e){return t.displayInd-e.displayInd})),c=u.map((function(t){return t.categoryValue})),f=u.map((function(t){return t.categoryLabel}));e["dimensions["+t.model.containerInd+"].categoryarray"]=[c],e["dimensions["+t.model.containerInd+"].ticktext"]=[f],e["dimensions["+t.model.containerInd+"].categoryorder"]="array"}}-1===t.parcatsViewModel.hoverinfoItems.indexOf("skip")&&!t.dragHasMoved&&t.potentialClickBand&&("color"===t.parcatsViewModel.hoveron?S(t.potentialClickBand,"plotly_click",n.event.sourceEvent):M(t.potentialClickBand,"plotly_click",n.event.sourceEvent)),t.model.dragX=null,null!==t.dragCategoryDisplayInd&&(t.parcatsViewModel.dimensions[t.dragDimensionDisplayInd].categories[t.dragCategoryDisplayInd].model.dragY=null,t.dragCategoryDisplayInd=null),t.dragDimensionDisplayInd=null,t.parcatsViewModel.dragDimension=null,t.dragHasMoved=null,t.potentialClickBand=null,j(t.parcatsViewModel),N(t.parcatsViewModel),n.transition().duration(300).ease("cubic-in-out").each((function(){R(t.parcatsViewModel,!0),z(t.parcatsViewModel,!0)})).each("end",(function(){(o||s)&&a.restyle(t.parcatsViewModel.graphDiv,e,[r])}))}}function 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e,r;d(t)?(e=t.width+5,r="start"):(e=-5,r="end"),n.select(this).selectAll("tspan").attr("x",e).attr("text-anchor",r)}));var a=i.selectAll("rect.bandrect").data((function(t){return t.bands}),p),o=a.enter().append("rect").attr("class","bandrect").attr("cursor","move").attr("stroke-opacity",0).attr("fill",(function(t){return t.color})).attr("fill-opacity",0);a.attr("fill",(function(t){return t.color})).attr("width",(function(t){return t.width})).attr("height",(function(t){return t.height})).attr("y",(function(t){return t.y})),k(o),a.each((function(){s.raiseToTop(this)})),a.exit().remove()}function F(t,e,r){var n,i=r[0],a=e.margin||{l:80,r:80,t:100,b:80},o=i.trace,s=o.domain,l=e.width,u=e.height,c=Math.floor(l*(s.x[1]-s.x[0])),f=Math.floor(u*(s.y[1]-s.y[0])),h=s.x[0]*l+a.l,p=e.height-s.y[1]*e.height+a.t,d=o.line.shape;n="all"===o.hoverinfo?["count","probability"]:(o.hoverinfo||"").split("+");var v={trace:o,key:o.uid,model:i,x:h,y:p,width:c,height:f,hoveron:o.hoveron,hoverinfoItems:n,arrangement:o.arrangement,bundlecolors:o.bundlecolors,sortpaths:o.sortpaths,labelfont:o.labelfont,categorylabelfont:o.tickfont,pathShape:d,dragDimension:null,margin:a,paths:[],dimensions:[],graphDiv:t,traceSelection:null,pathSelection:null,dimensionSelection:null};return i.dimensions&&(j(v),N(v)),v}function B(t,e,r,n,a){var o,s,l=[],u=[];for(s=0;s<r.length-1;s++)o=i(r[s]+t[s],t[s+1]),l.push(o(a)),u.push(o(1-a));var c="M "+t[0]+","+e[0];for(c+="l"+r[0]+",0 ",s=1;s<r.length;s++)c+="C"+l[s-1]+","+e[s-1]+" "+u[s-1]+","+e[s]+" "+t[s]+","+e[s],c+="l"+r[s]+",0 ";for(c+="l0,"+n+" ",c+="l -"+r[r.length-1]+",0 ",s=r.length-2;s>=0;s--)c+="C"+u[s]+","+(e[s+1]+n)+" "+l[s]+","+(e[s]+n)+" "+(t[s]+r[s])+","+(e[s]+n),c+="l-"+r[s]+",0 ";return c+"Z"}function N(t){var e=t.dimensions,r=t.model,n=e.map((function(t){return t.categories.map((function(t){return t.y}))})),i=t.model.dimensions.map((function(t){return t.categories.map((function(t){return t.displayInd}))})),a=t.model.dimensions.map((function(t){return t.displayInd})),o=t.dimensions.map((function(t){return t.model.dimensionInd})),s=e.map((function(t){return t.x})),l=e.map((function(t){return t.width})),u=[];for(var c in r.paths)r.paths.hasOwnProperty(c)&&u.push(r.paths[c]);function f(t){var e=t.categoryInds.map((function(t,e){return i[e][t]}));return o.map((function(t){return e[t]}))}u.sort((function(e,r){var n=f(e),i=f(r);return"backward"===t.sortpaths&&(n.reverse(),i.reverse()),n.push(e.valueInds[0]),i.push(r.valueInds[0]),t.bundlecolors&&(n.unshift(e.rawColor),i.unshift(r.rawColor)),n<i?-1:n>i?1:0}));for(var h=new Array(u.length),p=e[0].model.count,d=e[0].categories.map((function(t){return t.height})).reduce((function(t,e){return t+e})),v=0;v<u.length;v++){var g,y=u[v];g=p>0?d*(y.count/p):0;for(var m,x=new Array(n.length),b=0;b<y.categoryInds.length;b++){var _=y.categoryInds[b],w=i[b][_],T=a[b];x[T]=n[T][w],n[T][w]+=g;var 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n=r(50693),i=r(13838),a=r(41940),o=r(27670).Y,s=r(1426).extendFlat,l=r(44467).templatedArray;t.exports={domain:o({name:"parcoords",trace:!0,editType:"plot"}),labelangle:{valType:"angle",dflt:0,editType:"plot"},labelside:{valType:"enumerated",values:["top","bottom"],dflt:"top",editType:"plot"},labelfont:a({editType:"plot"}),tickfont:a({editType:"plot"}),rangefont:a({editType:"plot"}),dimensions:l("dimension",{label:{valType:"string",editType:"plot"},tickvals:s({},i.tickvals,{editType:"plot"}),ticktext:s({},i.ticktext,{editType:"plot"}),tickformat:s({},i.tickformat,{editType:"plot"}),visible:{valType:"boolean",dflt:!0,editType:"plot"},range:{valType:"info_array",items:[{valType:"number",editType:"plot"},{valType:"number",editType:"plot"}],editType:"plot"},constraintrange:{valType:"info_array",freeLength:!0,dimensions:"1-2",items:[{valType:"any",editType:"plot"},{valType:"any",editType:"plot"}],editType:"plot"},multiselect:{valType:"boolean",dflt:!0,editType:"plot"},values:{valType:"data_array",editType:"calc"},editType:"calc"}),line:s({editType:"calc"},n("line",{colorscaleDflt:"Viridis",autoColorDflt:!1,editTypeOverride:"calc"})),unselected:{line:{color:{valType:"color",dflt:"#7f7f7f",editType:"plot"},opacity:{valType:"number",min:0,max:1,dflt:"auto",editType:"plot"},editType:"plot"},editType:"plot"}}},57920:function(t,e,r){"use 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k(t){t.on("mousemove",(function(t){i.event.preventDefault(),t.parent.inBrushDrag||T(this,t)})).on("mouseleave",(function(t){t.parent.inBrushDrag||m()})).call(i.behavior.drag().on("dragstart",(function(t){!function(t,e){i.event.sourceEvent.stopPropagation();var r=e.height-i.mouse(t)[1]-2*n.verticalPadding,a=e.unitToPaddedPx.invert(r),o=e.brush,s=_(e,r),l=s.interval,u=o.svgBrush;if(u.wasDragged=!1,u.grabbingBar="ns"===s.region,u.grabbingBar){var c=l.map(e.unitToPaddedPx);u.grabPoint=r-c[0]-n.verticalPadding,u.barLength=c[1]-c[0]}u.clickableOrdinalRange=s.clickableOrdinalRange,u.stayingIntervals=e.multiselect&&o.filterSpecified?o.filter.getConsolidated():[],l&&(u.stayingIntervals=u.stayingIntervals.filter((function(t){return t[0]!==l[0]&&t[1]!==l[1]}))),u.startExtent=s.region?l["s"===s.region?1:0]:a,e.parent.inBrushDrag=!0,u.brushStartCallback()}(this,t)})).on("drag",(function(t){w(this,t)})).on("dragend",(function(t){!function(t,e){var 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u=t.selectAll(".highlight").data(o);u.enter().append("line").classed("highlight",!0).attr("x",-n.bar.width/2).attr("stroke-width",n.bar.width-n.bar.strokeWidth).attr("stroke",n.bar.fillColor).attr("opacity",n.bar.fillOpacity).attr("stroke-linecap","butt"),u.attr("y1",(function(t){return t.height})).call(x)}(i,e,r)},cleanRanges:function(t,e){if(Array.isArray(t[0])?(t=t.map((function(t){return t.sort(s)})),t=e.multiselect?S(t.sort(A)):[t[0]]):t=[t.sort(s)],e.tickvals){var r=e.tickvals.slice().sort(s);if(!(t=t.map((function(t){var e=[p(0,r,t[0],[]),p(1,r,t[1],[])];if(e[1]>e[0])return e})).filter((function(t){return t}))).length)return}return t.length>1?t:t[0]}}},71791:function(t,e,r){"use strict";t.exports={attributes:r(73362),supplyDefaults:r(3633),calc:r(24639),colorbar:{container:"line",min:"cmin",max:"cmax"},moduleType:"trace",name:"parcoords",basePlotModule:r(49351),categories:["gl","regl","noOpacity","noHover"],meta:{}}},49351:function(t,e,r){"use strict";var n=r(39898),i=r(27659).a0,a=r(21341),o=r(77922);e.name="parcoords",e.plot=function(t){var e=i(t.calcdata,"parcoords")[0];e.length&&a(t,e)},e.clean=function(t,e,r,n){var i=n._has&&n._has("parcoords"),a=e._has&&e._has("parcoords");i&&!a&&(n._paperdiv.selectAll(".parcoords").remove(),n._glimages.selectAll("*").remove())},e.toSVG=function(t){var e=t._fullLayout._glimages,r=n.select(t).selectAll(".svg-container");r.filter((function(t,e){return e===r.size()-1})).selectAll(".gl-canvas-context, .gl-canvas-focus").each((function(){var t=this,r=t.toDataURL("image/png");e.append("svg:image").attr({xmlns:o.svg,"xlink:href":r,preserveAspectRatio:"none",x:0,y:0,width:t.style.width,height:t.style.height})})),window.setTimeout((function(){n.selectAll("#filterBarPattern").attr("id","filterBarPattern")}),60)}},24639:function(t,e,r){"use strict";var n=r(71828).isArrayOrTypedArray,i=r(21081),a=r(28984).wrap;t.exports=function(t,e){var r,o;return i.hasColorscale(e,"line")&&n(e.line.color)?(r=e.line.color,o=i.extractOpts(e.line).colorscale,i.calc(t,e,{vals:r,containerStr:"line",cLetter:"c"})):(r=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=.5;return e}(e._length),o=[[0,e.line.color],[1,e.line.color]]),a({lineColor:r,cscale:o})}},25706:function(t){"use 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n=r(71828),i=r(52075).hasColorscale,a=r(1586),o=r(27670).c,s=r(85501),l=r(89298),u=r(73362),c=r(57920),f=r(25706).maxDimensionCount,h=r(94397);function p(t,e,r,i){function a(r,i){return n.coerce(t,e,u.dimensions,r,i)}var o=a("values"),s=a("visible");if(o&&o.length||(s=e.visible=!1),s){a("label"),a("tickvals"),a("ticktext"),a("tickformat");var f=a("range");e._ax={_id:"y",type:"linear",showexponent:"all",exponentformat:"B",range:f},l.setConvert(e._ax,i.layout),a("multiselect");var h=a("constraintrange");h&&(e.constraintrange=c.cleanRanges(h,e))}}t.exports=function(t,e,r,l){function c(r,i){return n.coerce(t,e,u,r,i)}var d=t.dimensions;Array.isArray(d)&&d.length>f&&(n.log("parcoords traces support up to "+f+" dimensions at the moment"),d.splice(f));var v=s(t,e,{name:"dimensions",layout:l,handleItemDefaults:p}),g=function(t,e,r,o,s){var l=s("line.color",r);if(i(t,"line")&&n.isArrayOrTypedArray(l)){if(l.length)return 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p33_36, p37_40, p41_44, p45_48,\n               p49_52, p53_56, p57_60, colors;\n\nuniform mat4 dim0A, dim1A, dim0B, dim1B, dim0C, dim1C, dim0D, dim1D,\n             loA, hiA, loB, hiB, loC, hiC, loD, hiD;\n\nuniform vec2 resolution, viewBoxPos, viewBoxSize;\nuniform float maskHeight;\nuniform float drwLayer; // 0: context, 1: focus, 2: pick\nuniform vec4 contextColor;\nuniform sampler2D maskTexture, palette;\n\nbool isPick    = (drwLayer > 1.5);\nbool isContext = (drwLayer < 0.5);\n\nconst vec4 ZEROS = vec4(0.0, 0.0, 0.0, 0.0);\nconst vec4 UNITS = vec4(1.0, 1.0, 1.0, 1.0);\n\nfloat val(mat4 p, mat4 v) {\n    return dot(matrixCompMult(p, v) * UNITS, UNITS);\n}\n\nfloat axisY(float ratio, mat4 A, mat4 B, mat4 C, mat4 D) {\n    float y1 = val(A, dim0A) + val(B, dim0B) + val(C, dim0C) + val(D, dim0D);\n    float y2 = val(A, dim1A) + val(B, dim1B) + val(C, dim1C) + val(D, dim1D);\n    return y1 * (1.0 - ratio) + y2 * ratio;\n}\n\nint iMod(int a, int b) {\n    return a - b * (a / b);\n}\n\nbool fOutside(float p, float lo, float hi) {\n    return (lo < hi) && (lo > p || p > hi);\n}\n\nbool vOutside(vec4 p, vec4 lo, vec4 hi) {\n    return (\n        fOutside(p[0], lo[0], hi[0]) ||\n        fOutside(p[1], lo[1], hi[1]) ||\n        fOutside(p[2], lo[2], hi[2]) ||\n        fOutside(p[3], lo[3], hi[3])\n    );\n}\n\nbool mOutside(mat4 p, mat4 lo, mat4 hi) {\n    return (\n        vOutside(p[0], lo[0], hi[0]) ||\n        vOutside(p[1], lo[1], hi[1]) ||\n        vOutside(p[2], lo[2], hi[2]) ||\n        vOutside(p[3], lo[3], hi[3])\n    );\n}\n\nbool outsideBoundingBox(mat4 A, mat4 B, mat4 C, mat4 D) {\n    return mOutside(A, loA, hiA) ||\n           mOutside(B, loB, hiB) ||\n           mOutside(C, loC, hiC) ||\n           mOutside(D, loD, hiD);\n}\n\nbool outsideRasterMask(mat4 A, mat4 B, mat4 C, mat4 D) {\n    mat4 pnts[4];\n    pnts[0] = A;\n    pnts[1] = B;\n    pnts[2] = C;\n    pnts[3] = D;\n\n    for(int i = 0; i < 4; ++i) {\n        for(int j = 0; j < 4; ++j) {\n            for(int k = 0; k < 4; ++k) {\n                if(0 == iMod(\n                    int(255.0 * texture2D(maskTexture,\n                        vec2(\n                            (float(i * 2 + j / 2) + 0.5) / 8.0,\n                            (pnts[i][j][k] * (maskHeight - 1.0) + 1.0) / maskHeight\n                        ))[3]\n                    ) / int(pow(2.0, float(iMod(j * 4 + k, 8)))),\n                    2\n                )) return true;\n            }\n        }\n    }\n    return false;\n}\n\nvec4 position(bool isContext, float v, mat4 A, mat4 B, mat4 C, mat4 D) {\n    float x = 0.5 * sign(v) + 0.5;\n    float y = axisY(x, A, B, C, D);\n    float z = 1.0 - abs(v);\n\n    z += isContext ? 0.0 : 2.0 * float(\n        outsideBoundingBox(A, B, C, D) ||\n        outsideRasterMask(A, B, C, D)\n    );\n\n    return vec4(\n        2.0 * (vec2(x, y) * viewBoxSize + viewBoxPos) / resolution - 1.0,\n        z,\n        1.0\n    );\n}\n\nvoid main() {\n    mat4 A = mat4(p01_04, p05_08, p09_12, p13_16);\n    mat4 B = mat4(p17_20, p21_24, p25_28, p29_32);\n    mat4 C = mat4(p33_36, p37_40, p41_44, p45_48);\n    mat4 D = mat4(p49_52, p53_56, p57_60, ZEROS);\n\n    float v = colors[3];\n\n    gl_Position = position(isContext, v, A, B, C, D);\n\n    fragColor =\n        isContext ? vec4(contextColor) :\n        isPick ? vec4(colors.rgb, 1.0) : texture2D(palette, vec2(abs(v), 0.5));\n}\n"]),a=n(["precision highp float;\n#define GLSLIFY 1\n\nvarying vec4 fragColor;\n\nvoid main() {\n    gl_FragColor = fragColor;\n}\n"]),o=r(25706).maxDimensionCount,s=r(71828),l=1e-6,u=new Uint8Array(4),c=new Uint8Array(4),f={shape:[256,1],format:"rgba",type:"uint8",mag:"nearest",min:"nearest"};function h(t,e,r,n,i){var a=t._gl;a.enable(a.SCISSOR_TEST),a.scissor(e,r,n,i),t.clear({color:[0,0,0,0],depth:1})}function p(t,e,r,n,i,a){var o=a.key;r.drawCompleted||(function(t){t.read({x:0,y:0,width:1,height:1,data:u})}(t),r.drawCompleted=!0),function s(l){var 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y=t.lines.canvasOverdrag*d,m=t.domain,x=t.canvasWidth*d,b=t.canvasHeight*d,_=t.pad.l*d,w=t.pad.b*d,T=t.layoutHeight*d,k=t.layoutWidth*d,A=t.deselectedLines.color,M=t.deselectedLines.opacity;return s.extendFlat({key:f,resolution:[x,b],viewBoxPos:[o+y,l],viewBoxSize:[u,c],i0:i,i1:a,dim0A:v[0].slice(0,16),dim0B:v[0].slice(16,32),dim0C:v[0].slice(32,48),dim0D:v[0].slice(48,64),dim1A:v[1].slice(0,16),dim1B:v[1].slice(16,32),dim1C:v[1].slice(32,48),dim1D:v[1].slice(48,64),drwLayer:h,contextColor:[A[0]/255,A[1]/255,A[2]/255,"auto"!==M?A[3]*M:Math.max(1/255,Math.pow(1/t.lines.color.length,1/3))],scissorX:(n===e?0:o+y)+(_-y)+k*m.x[0],scissorWidth:(n===r?x-o+y:u+.5)+(n===e?o+y:0),scissorY:l+w+T*m.y[0],scissorHeight:c,viewportX:_-y+k*m.x[0],viewportY:w+T*m.y[0],viewportWidth:x,viewportHeight:b},p)}function b(t){var e=2047,r=Math.max(0,Math.floor(t[0]*e),0),n=Math.min(e,Math.ceil(t[1]*e),e);return[Math.min(r,n),Math.max(r,n)]}t.exports=function(t,e){var r,n,u,y,_,w=e.context,T=e.pick,k=e.regl,A=k._gl,M=A.getParameter(A.ALIASED_LINE_WIDTH_RANGE),S=Math.max(M[0],Math.min(M[1],e.viewModel.plotGlPixelRatio)),E={currentRafs:{},drawCompleted:!0,clearOnly:!1},L=function(t){for(var e={},r=0;r<=o;r+=4)e[m(r)]=t.buffer({usage:"dynamic",type:"float",data:new Uint8Array(0)});return e}(k),C=k.texture(f),P=[];I(e);var O=k({profile:!1,blend:{enable:w,func:{srcRGB:"src alpha",dstRGB:"one minus src 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I(t){r=t.model,n=t.viewModel,u=n.dimensions.slice(),y=u[0]?u[0].values.length:0;var e=r.lines,i=T?e.color.map((function(t,r){return r/e.color.length})):e.color,a=function(t,e,r){for(var n,i=new Array(t*(o+4)),a=0,s=0;s<t;s++){for(var u=0;u<o;u++)i[a++]=u<e.length?e[u].paddedUnitValues[s]:.5;i[a++]=v(s,2),i[a++]=v(s,1),i[a++]=v(s,0),i[a++]=(n=r[s],Math.max(l,Math.min(.999999,n)))}return i}(y,u,i);!function(t,e,r){for(var n=0;n<=o;n+=4)t[m(n)](g(n/4,e,r))}(L,y,a),w||T||(C=k.texture(s.extendFlat({data:d(r.unitToColor,255)},f)))}return{render:function(t,e,n){var i,a,o,s=t.length,l=1/0,c=-1/0;for(i=0;i<s;i++)t[i].dim0.canvasX<l&&(l=t[i].dim0.canvasX,a=i),t[i].dim1.canvasX>c&&(c=t[i].dim1.canvasX,o=i);0===s&&h(k,0,0,r.canvasWidth,r.canvasHeight);var f=function(t){var e,r,n,i=[[],[]];for(n=0;n<64;n++){var a=!t&&n<u.length?u[n].brush.filter.getBounds():[-1/0,1/0];i[0][n]=a[0],i[1][n]=a[1]}var o=new Array(16384);for(e=0;e<16384;e++)o[e]=255;if(!t)for(e=0;e<u.length;e++){var s=e%8,l=(e-s)/8,c=Math.pow(2,s),f=u[e].brush.filter.get();if(!(f.length<2)){var h=b(f[0])[1];for(r=1;r<f.length;r++){var p=b(f[r]);for(n=h+1;n<p[0];n++)o[8*n+l]&=~c;h=Math.max(h,p[1])}}}var d={shape:[8,2048],format:"alpha",type:"uint8",mag:"nearest",min:"nearest",data:o};return _?_(d):_=k.texture(d),{maskTexture:_,maskHeight:2048,loA:i[0].slice(0,16),loB:i[0].slice(16,32),loC:i[0].slice(32,48),loD:i[0].slice(48,64),hiA:i[1].slice(0,16),hiB:i[1].slice(16,32),hiC:i[1].slice(32,48),hiD:i[1].slice(48,64)}}(w);for(i=0;i<s;i++){var d=t[i],v=d.dim0.crossfilterDimensionIndex,g=d.dim1.crossfilterDimensionIndex,m=d.canvasX,A=d.canvasY,M=m+d.panelSizeX,S=d.plotGlPixelRatio;if(e||!P[v]||P[v][0]!==m||P[v][1]!==M){P[v]=[m,M];var L=x(r,a,o,i,v,g,m,A,d.panelSizeX,d.panelSizeY,d.dim0.crossfilterDimensionIndex,w?0:T?2:1,f,S);E.clearOnly=n;var C=e?r.lines.blockLineCount:y;p(k,O,E,C,y,L)}}},readPixel:function(t,e){return k.read({x:t,y:e,width:1,height:1,data:c}),c},readPixels:function(t,e,r,n){var i=new 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A(t,e,r,i,o){var s,l,u=T(r);return i?n.scale.ordinal().domain(i.map((s=a(r.tickformat),l=o,l?function(t,e){var r=l[e];return null==r?s(t):r}:s))).range(i.map((function(r){var n=(r-u[0])/(u[1]-u[0]);return t-e+n*(2*e-t)}))):n.scale.linear().domain(u).range([t-e,e])}function M(t){if(t.tickvals){var e=T(t);return n.scale.ordinal().domain(t.tickvals).range(t.tickvals.map((function(t){return(t-e[0])/(e[1]-e[0])})))}}function S(t){var e=t.map((function(t){return t[0]})),r=t.map((function(t){var e=o(t[1]);return n.rgb("rgb("+e[0]+","+e[1]+","+e[2]+")")})),i="rgb".split("").map((function(t){return n.scale.linear().clamp(!0).domain(e).range(r.map((i=t,function(t){return t[i]})));var i}));return function(t){return i.map((function(e){return e(t)}))}}function E(t){return t.dimensions.some((function(t){return t.brush.filterSpecified}))}function L(t,e,r){var 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t.map(n).sort(i.sorterAsc)})).sort((function(t,e){return t[0]-e[0]}));e.filterChanged(p.key,o._index,a)}}))}})),p}function P(t){t.classed(m.cn.axisExtentText,!0).attr("text-anchor","middle").style("cursor","default")}function O(t,e){var r="top"===e?1:-1,n=t*Math.PI/180;return{dir:r,dx:Math.sin(n),dy:Math.cos(n),degrees:t}}function I(t,e,r){for(var n=e.panels||(e.panels=[]),i=t.data(),a=0;a<i.length-1;a++){var o=n[a]||(n[a]={}),s=i[a],l=i[a+1];o.dim0=s,o.dim1=l,o.canvasX=s.canvasX,o.panelSizeX=l.canvasX-s.canvasX,o.panelSizeY=e.model.canvasHeight,o.y=0,o.canvasY=0,o.plotGlPixelRatio=r}}function D(t,e){return s.tickText(t._ax,e,!1).text}function z(t,e){if(t.ordinal)return"";var r=t.domainScale.domain(),n=r[e?r.length-1:0];return D(t.model.dimensions[t.visibleIndex],n)}t.exports=function(t,e,r,a){var o=t._context.staticPlot,h=t._fullLayout,p=h._toppaper,_=h._glcontainer,T=t._context.plotGlPixelRatio,A=t._fullLayout.paper_bgcolor;!function(t){for(var e=0;e<t.length;e++)for(var r=0;r<t[e].length;r++)for(var n=t[e][r].trace,i=n.dimensions,a=0;a<i.length;a++){var o=i[a].values,l=i[a]._ax;l&&(l.range?l.range=k(l.range[0],l.range[1]):l.range=w(o,n._length),l.dtick||(l.dtick=.01*(Math.abs(l.range[1]-l.range[0])||1)),l.tickformat=i[a].tickformat,s.calcTicks(l),l.cleanRange())}}(e);var M,S,R=(M=!0,S=!1,{linePickActive:function(t){return arguments.length?M=!!t:M},contextShown:function(t){return arguments.length?S=!!t:S}}),F=e.filter((function(t){return g(t).trace.visible})).map(L.bind(0,r)).map(C.bind(0,R,a));_.each((function(t,e){return i.extendFlat(t,F[e])}));var B=_.selectAll(".gl-canvas").each((function(t){t.viewModel=F[0],t.viewModel.plotGlPixelRatio=T,t.viewModel.paperColor=A,t.model=t.viewModel?t.viewModel.model:null})),N=null;B.filter((function(t){return t.pick})).style("pointer-events",o?"none":"auto").on("mousemove",(function(t){if(R.linePickActive()&&t.lineLayer&&a&&a.hover){var 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J=Y.selectAll("."+m.cn.axisExtentBottom).data(v,d);J.enter().append("g").classed(m.cn.axisExtentBottom,!0),J.attr("transform",(function(t){return u(0,t.model.height+m.axisExtentOffset)}));var K=J.selectAll("."+m.cn.axisExtentBottomText).data(v,d);K.enter().append("text").classed(m.cn.axisExtentBottomText,!0).attr("dy","0.75em").call(P),K.text((function(t){return z(t,!1)})).each((function(t){f.font(n.select(this),t.model.rangeFont)})),x.ensureAxisBrush(H,A,t)}},21341:function(t,e,r){"use strict";var n=r(17171),i=r(79749),a=r(1602).isVisible,o={};function s(t,e,r){var n=e.indexOf(r),i=t.indexOf(n);return-1===i&&(i+=e.length),i}(t.exports=function(t,e){var r=t._fullLayout;if(i(t,[],o)){var l={},u={},c={},f={},h=r._size;e.forEach((function(e,r){var n=e[0].trace;c[r]=n.index;var i=f[r]=n._fullInput.index;l[r]=t.data[i].dimensions,u[r]=t.data[i].dimensions.slice()})),n(t,e,{width:h.w,height:h.h,margin:{t:h.t,r:h.r,b:h.b,l:h.l}},{filterChanged:function(e,n,i){var 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n=r(92770),i=r(84267),a=r(7901),o={};function s(t){return function(e,r){return!!e&&!!(e=i(e)).isValid()&&(e=a.addOpacity(e,e.getAlpha()),t[r]||(t[r]=e),e)}}function l(t,e){var r,n=JSON.stringify(t),a=e[n];if(!a){for(a=t.slice(),r=0;r<t.length;r++)a.push(i(t[r]).lighten(20).toHexString());for(r=0;r<t.length;r++)a.push(i(t[r]).darken(20).toHexString());e[n]=a}return a}t.exports={calc:function(t,e){var r,i,a=[],o=t._fullLayout,l=o.hiddenlabels||[],u=e.labels,c=e.marker.colors||[],f=e.values,h=e._length,p=e._hasValues&&h;if(e.dlabel)for(u=new Array(h),r=0;r<h;r++)u[r]=String(e.label0+r*e.dlabel);var d={},v=s(o["_"+e.type+"colormap"]),g=0,y=!1;for(r=0;r<h;r++){var m,x,b;if(p){if(m=f[r],!n(m))continue;m=+m}else m=1;void 0!==(x=u[r])&&""!==x||(x=r);var _=d[x=String(x)];void 0===_?(d[x]=a.length,(b=-1!==l.indexOf(x))||(g+=m),a.push({v:m,label:x,color:v(c[r],x),i:r,pts:[r],hidden:b})):(y=!0,(i=a[_]).v+=m,i.pts.push(r),i.hidden||(g+=m),!1===i.color&&c[r]&&(i.color=v(c[r],x)))}return a=a.filter((function(t){return t.v>=0})),("funnelarea"===e.type?y:e.sort)&&a.sort((function(t,e){return e.v-t.v})),a[0]&&(a[0].vTotal=g),a},crossTraceCalc:function(t,e){var r=(e||{}).type;r||(r="pie");var n=t._fullLayout,i=t.calcdata,a=n[r+"colorway"],s=n["_"+r+"colormap"];n["extend"+r+"colors"]&&(a=l(a,o));for(var u=0,c=0;c<i.length;c++){var f=i[c];if(f[0].trace.type===r)for(var h=0;h<f.length;h++){var p=f[h];!1===p.color&&(s[p.label]?p.color=s[p.label]:(s[p.label]=p.color=a[u%a.length],u++))}}},makePullColorFn:s,generateExtendedColors:l}},37434:function(t,e,r){"use strict";var n=r(92770),i=r(71828),a=r(34e3),o=r(27670).c,s=r(90769).handleText,l=r(71828).coercePattern;function u(t,e){var r=Array.isArray(t),a=i.isArrayOrTypedArray(e),o=Math.min(r?t.length:1/0,a?e.length:1/0);if(isFinite(o)||(o=0),o&&a){for(var s,l=0;l<o;l++){var u=e[l];if(n(u)&&u>0){s=!0;break}}s||(o=0)}return{hasLabels:r,hasValues:a,len:o}}function c(t,e,r,n,i){n("marker.line.width")&&n("marker.line.color",i?void 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n=r(91424),i=r(7901);t.exports=function(t,e,r,a){var o=r.marker.pattern;o&&o.shape?n.pointStyle(t,r,a,e):i.fill(t,e.color)}},53581:function(t,e,r){"use strict";var n=r(71828);function i(t){return-1!==t.indexOf("e")?t.replace(/[.]?0+e/,"e"):-1!==t.indexOf(".")?t.replace(/[.]?0+$/,""):t}e.formatPiePercent=function(t,e){var r=i((100*t).toPrecision(3));return n.numSeparate(r,e)+"%"},e.formatPieValue=function(t,e){var r=i(t.toPrecision(10));return n.numSeparate(r,e)},e.getFirstFilled=function(t,e){if(Array.isArray(t))for(var r=0;r<e.length;r++){var n=t[e[r]];if(n||0===n||""===n)return n}},e.castOption=function(t,r){return Array.isArray(t)?e.getFirstFilled(t,r):t||void 0},e.getRotationAngle=function(t){return("auto"===t?0:t)*Math.PI/180}},58810:function(t,e,r){"use 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n=r(39898),i=r(74875),a=r(30211),o=r(7901),s=r(91424),l=r(71828),u=l.strScale,c=l.strTranslate,f=r(63893),h=r(72597),p=h.recordMinTextSize,d=h.clearMinTextSize,v=r(97313).TEXTPAD,g=r(53581),y=r(20007),m=r(71828).isValidTextValue;function x(t,e,r){var i=r[0],o=i.cx,s=i.cy,u=i.trace,c="funnelarea"===u.type;"_hasHoverLabel"in u||(u._hasHoverLabel=!1),"_hasHoverEvent"in u||(u._hasHoverEvent=!1),t.on("mouseover",(function(t){var r=e._fullLayout,f=e._fullData[u.index];if(!e._dragging&&!1!==r.hovermode){var h=f.hoverinfo;if(Array.isArray(h)&&(h=a.castHoverinfo({hoverinfo:[g.castOption(h,t.pts)],_module:u._module},r,0)),"all"===h&&(h="label+text+value+percent+name"),f.hovertemplate||"none"!==h&&"skip"!==h&&h){var p=t.rInscribed||0,d=o+t.pxmid[0]*(1-p),v=s+t.pxmid[1]*(1-p),m=r.separators,x=[];if(h&&-1!==h.indexOf("label")&&x.push(t.label),t.text=g.castOption(f.hovertext||f.text,t.pts),h&&-1!==h.indexOf("text")){var b=t.text;l.isValidTextValue(b)&&x.push(b)}t.value=t.v,t.valueLabel=g.formatPieValue(t.v,m),h&&-1!==h.indexOf("value")&&x.push(t.valueLabel),t.percent=t.v/i.vTotal,t.percentLabel=g.formatPiePercent(t.percent,m),h&&-1!==h.indexOf("percent")&&x.push(t.percentLabel);var _=f.hoverlabel,w=_.font,T=[];a.loneHover({trace:u,x0:d-p*i.r,x1:d+p*i.r,y:v,_x0:c?o+t.TL[0]:d-p*i.r,_x1:c?o+t.TR[0]:d+p*i.r,_y0:c?s+t.TL[1]:v-p*i.r,_y1:c?s+t.BL[1]:v+p*i.r,text:x.join("<br>"),name:f.hovertemplate||-1!==h.indexOf("name")?f.name:void 0,idealAlign:t.pxmid[0]<0?"left":"right",color:g.castOption(_.bgcolor,t.pts)||t.color,borderColor:g.castOption(_.bordercolor,t.pts),fontFamily:g.castOption(w.family,t.pts),fontSize:g.castOption(w.size,t.pts),fontColor:g.castOption(w.color,t.pts),nameLength:g.castOption(_.namelength,t.pts),textAlign:g.castOption(_.align,t.pts),hovertemplate:g.castOption(f.hovertemplate,t.pts),hovertemplateLabels:t,eventData:[y(t,f)]},{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:e,inOut_bbox:T}),t.bbox=T[0],u._hasHoverLabel=!0}u._hasHoverEvent=!0,e.emit("plotly_hover",{points:[y(t,f)],event:n.event})}})),t.on("mouseout",(function(t){var r=e._fullLayout,i=e._fullData[u.index],o=n.select(this).datum();u._hasHoverEvent&&(t.originalEvent=n.event,e.emit("plotly_unhover",{points:[y(o,i)],event:n.event}),u._hasHoverEvent=!1),u._hasHoverLabel&&(a.loneUnhover(r._hoverlayer.node()),u._hasHoverLabel=!1)})),t.on("click",(function(t){var r=e._fullLayout,i=e._fullData[u.index];e._dragging||!1===r.hovermode||(e._hoverdata=[y(t,i)],a.click(e,n.event))}))}function b(t,e,r){var n=g.castOption(t.insidetextfont.color,e.pts);!n&&t._input.textfont&&(n=g.castOption(t._input.textfont.color,e.pts));var i=g.castOption(t.insidetextfont.family,e.pts)||g.castOption(t.textfont.family,e.pts)||r.family,a=g.castOption(t.insidetextfont.size,e.pts)||g.castOption(t.textfont.size,e.pts)||r.size;return{color:n||o.contrast(e.color),family:i,size:a}}function _(t,e){for(var r,n,i=0;i<t.length;i++)if((n=(r=t[i][0]).trace).title.text){var a=n.title.text;n._meta&&(a=l.templateString(a,n._meta));var o=s.tester.append("text").attr("data-notex",1).text(a).call(s.font,n.title.font).call(f.convertToTspans,e),u=s.bBox(o.node(),!0);r.titleBox={width:u.width,height:u.height},o.remove()}}function w(t,e,r){var n=r.r||e.rpx1,i=e.rInscribed;if(e.startangle===e.stopangle)return{rCenter:1-i,scale:0,rotate:0,textPosAngle:0};var a,o=e.ring,s=1===o&&Math.abs(e.startangle-e.stopangle)===2*Math.PI,l=e.halfangle,u=e.midangle,c=r.trace.insidetextorientation,f="horizontal"===c,h="tangential"===c,p="radial"===c,d="auto"===c,v=[];if(!d){var g,y=function(r,i){if(function(t,e){var r=t.startangle,n=t.stopangle;return r>e&&e>n||r<e&&e<n}(e,r)){var s=Math.abs(r-e.startangle),l=Math.abs(r-e.stopangle),u=s<l?s:l;(a="tan"===i?k(t,n,o,u,0):T(t,n,o,u,Math.PI/2)).textPosAngle=r,v.push(a)}};if(f||h){for(g=4;g>=-4;g-=2)y(Math.PI*g,"tan");for(g=4;g>=-4;g-=2)y(Math.PI*(g+1),"tan")}if(f||p){for(g=4;g>=-4;g-=2)y(Math.PI*(g+1.5),"rad");for(g=4;g>=-4;g-=2)y(Math.PI*(g+.5),"rad")}}if(s||d||f){var m=Math.sqrt(t.width*t.width+t.height*t.height);if((a={scale:i*n*2/m,rCenter:1-i,rotate:0}).textPosAngle=(e.startangle+e.stopangle)/2,a.scale>=1)return a;v.push(a)}(d||p)&&((a=T(t,n,o,l,u)).textPosAngle=(e.startangle+e.stopangle)/2,v.push(a)),(d||h)&&((a=k(t,n,o,l,u)).textPosAngle=(e.startangle+e.stopangle)/2,v.push(a));for(var 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r,n,i,a=t.trace,o={x:t.cx,y:t.cy},s={tx:0,ty:0};s.ty+=a.title.font.size,i=O(a),-1!==a.title.position.indexOf("top")?(o.y-=(1+i)*t.r,s.ty-=t.titleBox.height):-1!==a.title.position.indexOf("bottom")&&(o.y+=(1+i)*t.r);var l,u=t.r/(void 0===(l=t.trace.aspectratio)?1:l),c=e.w*(a.domain.x[1]-a.domain.x[0])/2;return-1!==a.title.position.indexOf("left")?(c+=u,o.x-=(1+i)*u,s.tx+=t.titleBox.width/2):-1!==a.title.position.indexOf("center")?c*=2:-1!==a.title.position.indexOf("right")&&(c+=u,o.x+=(1+i)*u,s.tx-=t.titleBox.width/2),r=c/t.titleBox.width,n=P(t,e)/t.titleBox.height,{x:o.x,y:o.y,scale:Math.min(r,n),tx:s.tx,ty:s.ty}}function P(t,e){var r=t.trace,n=e.h*(r.domain.y[1]-r.domain.y[0]);return Math.min(t.titleBox.height,n/2)}function O(t){var e,r=t.pull;if(!r)return 0;if(Array.isArray(r))for(r=0,e=0;e<t.pull.length;e++)t.pull[e]>r&&(r=t.pull[e]);return r}function I(t,e){for(var r=[],n=0;n<t.length;n++){var i=t[n][0],a=i.trace,o=a.domain,s=e.w*(o.x[1]-o.x[0]),l=e.h*(o.y[1]-o.y[0]);a.title.text&&"middle center"!==a.title.position&&(l-=P(i,e));var u=s/2,c=l/2;"funnelarea"!==a.type||a.scalegroup||(c/=a.aspectratio),i.r=Math.min(u,c)/(1+O(a)),i.cx=e.l+e.w*(a.domain.x[1]+a.domain.x[0])/2,i.cy=e.t+e.h*(1-a.domain.y[0])-l/2,a.title.text&&-1!==a.title.position.indexOf("bottom")&&(i.cy-=P(i,e)),a.scalegroup&&-1===r.indexOf(a.scalegroup)&&r.push(a.scalegroup)}!function(t,e){for(var r,n,i,a=0;a<e.length;a++){var o=1/0,s=e[a];for(n=0;n<t.length;n++)if((i=(r=t[n][0]).trace).scalegroup===s){var l;if("pie"===i.type)l=r.r*r.r;else if("funnelarea"===i.type){var u,c;i.aspectratio>1?c=(u=r.r)/i.aspectratio:u=(c=r.r)*i.aspectratio,l=(u*=(1+i.baseratio)/2)*c}o=Math.min(o,l/r.vTotal)}for(n=0;n<t.length;n++)if((i=(r=t[n][0]).trace).scalegroup===s){var f=o*r.vTotal;"funnelarea"===i.type&&(f/=(1+i.baseratio)/2,f/=i.aspectratio),r.r=Math.sqrt(f)}}}(t,r)}function D(t,e){return[t*Math.sin(e),-t*Math.cos(e)]}function 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r=t.rotate*Math.PI/180,n=Math.cos(r),i=Math.sin(r),a=(e.left+e.right)/2,o=(e.top+e.bottom)/2;t.textX=a*n-o*i,t.textY=a*i+o*n,t.noCenter=!0}t.exports={plot:function(t,e){var r=t._context.staticPlot,a=t._fullLayout,h=a._size;d("pie",a),_(e,t),I(e,h);var v=l.makeTraceGroups(a._pielayer,e,"trace").each((function(e){var d=n.select(this),v=e[0],y=v.trace;!function(t){var e,r,n,i=t[0],a=i.r,o=i.trace,s=g.getRotationAngle(o.rotation),l=2*Math.PI/i.vTotal,u="px0",c="px1";if("counterclockwise"===o.direction){for(e=0;e<t.length&&t[e].hidden;e++);if(e===t.length)return;s+=l*t[e].v,l*=-1,u="px1",c="px0"}for(n=D(a,s),e=0;e<t.length;e++)(r=t[e]).hidden||(r[u]=n,r.startangle=s,s+=l*r.v/2,r.pxmid=D(a,s),r.midangle=s,n=D(a,s+=l*r.v/2),r.stopangle=s,r[c]=n,r.largeArc=r.v>i.vTotal/2?1:0,r.halfangle=Math.PI*Math.min(r.v/i.vTotal,.5),r.ring=1-o.hole,r.rInscribed=E(r,i))}(e),d.attr("stroke-linejoin","round"),d.each((function(){var m=n.select(this).selectAll("g.slice").data(e);m.enter().append("g").classed("slice",!0),m.exit().remove();var _=[[[],[]],[[],[]]],T=!1;m.each((function(i,o){if(i.hidden)n.select(this).selectAll("path,g").remove();else{i.pointNumber=i.i,i.curveNumber=y.index,_[i.pxmid[1]<0?0:1][i.pxmid[0]<0?0:1].push(i);var u=v.cx,c=v.cy,h=n.select(this),d=h.selectAll("path.surface").data([i]);if(d.enter().append("path").classed("surface",!0).style({"pointer-events":r?"none":"all"}),h.call(x,t,e),y.pull){var m=+g.castOption(y.pull,i.pts)||0;m>0&&(u+=m*i.pxmid[0],c+=m*i.pxmid[1])}i.cxFinal=u,i.cyFinal=c;var k=y.hole;if(i.v===v.vTotal){var A="M"+(u+i.px0[0])+","+(c+i.px0[1])+P(i.px0,i.pxmid,!0,1)+P(i.pxmid,i.px0,!0,1)+"Z";k?d.attr("d","M"+(u+k*i.px0[0])+","+(c+k*i.px0[1])+P(i.px0,i.pxmid,!1,k)+P(i.pxmid,i.px0,!1,k)+"Z"+A):d.attr("d",A)}else{var M=P(i.px0,i.px1,!0,1);if(k){var S=1-k;d.attr("d","M"+(u+k*i.px1[0])+","+(c+k*i.px1[1])+P(i.px1,i.px0,!1,k)+"l"+S*i.px0[0]+","+S*i.px0[1]+M+"Z")}else d.attr("d","M"+u+","+c+"l"+i.px0[0]+","+i.px0[1]+M+"Z")}z(t,i,v);var E=g.castOption(y.textposition,i.pts),C=h.selectAll("g.slicetext").data(i.text&&"none"!==E?[0]:[]);C.enter().append("g").classed("slicetext",!0),C.exit().remove(),C.each((function(){var r=l.ensureSingle(n.select(this),"text","",(function(t){t.attr("data-notex",1)})),h=l.ensureUniformFontSize(t,"outside"===E?function(t,e,r){return{color:g.castOption(t.outsidetextfont.color,e.pts)||g.castOption(t.textfont.color,e.pts)||r.color,family:g.castOption(t.outsidetextfont.family,e.pts)||g.castOption(t.textfont.family,e.pts)||r.family,size:g.castOption(t.outsidetextfont.size,e.pts)||g.castOption(t.textfont.size,e.pts)||r.size}}(y,i,a.font):b(y,i,a.font));r.text(i.text).attr({class:"slicetext",transform:"","text-anchor":"middle"}).call(s.font,h).call(f.convertToTspans,t);var d,m=s.bBox(r.node());if("outside"===E)d=L(m,i);else if(d=w(m,i,v),"auto"===E&&d.scale<1){var x=l.ensureUniformFontSize(t,y.outsidetextfont);r.call(s.font,x),d=L(m=s.bBox(r.node()),i)}var _=d.textPosAngle,k=void 0===_?i.pxmid:D(v.r,_);if(d.targetX=u+k[0]*d.rCenter+(d.x||0),d.targetY=c+k[1]*d.rCenter+(d.y||0),R(d,m),d.outside){var A=d.targetY;i.yLabelMin=A-m.height/2,i.yLabelMid=A,i.yLabelMax=A+m.height/2,i.labelExtraX=0,i.labelExtraY=0,T=!0}d.fontSize=h.size,p(y.type,d,a),e[o].transform=d,l.setTransormAndDisplay(r,d)}))}function P(t,e,r,n){var a=n*(e[0]-t[0]),o=n*(e[1]-t[1]);return"a"+n*v.r+","+n*v.r+" 0 "+i.largeArc+(r?" 1 ":" 0 ")+a+","+o}}));var k=n.select(this).selectAll("g.titletext").data(y.title.text?[0]:[]);if(k.enter().append("g").classed("titletext",!0),k.exit().remove(),k.each((function(){var e,r=l.ensureSingle(n.select(this),"text","",(function(t){t.attr("data-notex",1)})),i=y.title.text;y._meta&&(i=l.templateString(i,y._meta)),r.text(i).attr({class:"titletext",transform:"","text-anchor":"middle"}).call(s.font,y.title.font).call(f.convertToTspans,t),e="middle center"===y.title.position?function(t){var e=Math.sqrt(t.titleBox.width*t.titleBox.width+t.titleBox.height*t.titleBox.height);return{x:t.cx,y:t.cy,scale:t.trace.hole*t.r*2/e,tx:0,ty:-t.titleBox.height/2+t.trace.title.font.size}}(v):C(v,h),r.attr("transform",c(e.x,e.y)+u(Math.min(1,e.scale))+c(e.tx,e.ty))})),T&&function(t,e){var r,n,i,a,o,s,l,u,c,f,h,p,d;function v(t,e){return t.pxmid[1]-e.pxmid[1]}function y(t,e){return e.pxmid[1]-t.pxmid[1]}function m(t,r){r||(r={});var i,u,c,h,p=r.labelExtraY+(n?r.yLabelMax:r.yLabelMin),d=n?t.yLabelMin:t.yLabelMax,v=n?t.yLabelMax:t.yLabelMin,y=t.cyFinal+o(t.px0[1],t.px1[1]),m=p-d;if(m*l>0&&(t.labelExtraY=m),Array.isArray(e.pull))for(u=0;u<f.length;u++)(c=f[u])===t||(g.castOption(e.pull,t.pts)||0)>=(g.castOption(e.pull,c.pts)||0)||((t.pxmid[1]-c.pxmid[1])*l>0?(m=c.cyFinal+o(c.px0[1],c.px1[1])-d-t.labelExtraY)*l>0&&(t.labelExtraY+=m):(v+t.labelExtraY-y)*l>0&&(i=3*s*Math.abs(u-f.indexOf(t)),(h=c.cxFinal+a(c.px0[0],c.px1[0])+i-(t.cxFinal+t.pxmid[0])-t.labelExtraX)*s>0&&(t.labelExtraX+=h)))}for(n=0;n<2;n++)for(i=n?v:y,o=n?Math.max:Math.min,l=n?1:-1,r=0;r<2;r++){for(a=r?Math.max:Math.min,s=r?1:-1,(u=t[n][r]).sort(i),c=t[1-n][r],f=c.concat(u),p=[],h=0;h<u.length;h++)void 0!==u[h].yLabelMid&&p.push(u[h]);for(d=!1,h=0;n&&h<c.length;h++)if(void 0!==c[h].yLabelMid){d=c[h];break}for(h=0;h<p.length;h++){var x=h&&p[h-1];d&&!h&&(x=d),m(p[h],x)}}}(_,y),function(t,e){t.each((function(t){var r=n.select(this);if(t.labelExtraX||t.labelExtraY){var i=r.select("g.slicetext text");t.transform.targetX+=t.labelExtraX,t.transform.targetY+=t.labelExtraY,l.setTransormAndDisplay(i,t.transform);var a=t.cxFinal+t.pxmid[0],s="M"+a+","+(t.cyFinal+t.pxmid[1]),u=(t.yLabelMax-t.yLabelMin)*(t.pxmid[0]<0?-1:1)/4;if(t.labelExtraX){var c=t.labelExtraX*t.pxmid[1]/t.pxmid[0],f=t.yLabelMid+t.labelExtraY-(t.cyFinal+t.pxmid[1]);Math.abs(c)>Math.abs(f)?s+="l"+f*t.pxmid[0]/t.pxmid[1]+","+f+"H"+(a+t.labelExtraX+u):s+="l"+t.labelExtraX+","+c+"v"+(f-c)+"h"+u}else s+="V"+(t.yLabelMid+t.labelExtraY)+"h"+u;l.ensureSingle(r,"path","textline").call(o.stroke,e.outsidetextfont.color).attr({"stroke-width":Math.min(2,e.outsidetextfont.size/8),d:s,fill:"none"})}else r.select("path.textline").remove()}))}(m,y),T&&y.automargin){var A=s.bBox(d.node()),M=y.domain,S=h.w*(M.x[1]-M.x[0]),E=h.h*(M.y[1]-M.y[0]),P=(.5*S-v.r)/h.w,O=(.5*E-v.r)/h.h;i.autoMargin(t,"pie."+y.uid+".automargin",{xl:M.x[0]-P,xr:M.x[1]+P,yb:M.y[0]-O,yt:M.y[1]+O,l:Math.max(v.cx-v.r-A.left,0),r:Math.max(A.right-(v.cx+v.r),0),b:Math.max(A.bottom-(v.cy+v.r),0),t:Math.max(v.cy-v.r-A.top,0),pad:5})}}))}));setTimeout((function(){v.selectAll("tspan").each((function(){var t=n.select(this);t.attr("dy")&&t.attr("dy",t.attr("dy"))}))}),0)},formatSliceLabel:z,transformInsideText:w,determineInsideTextFont:b,positionTitleOutside:C,prerenderTitles:_,layoutAreas:I,attachFxHandlers:x,computeTransform:R}},68357:function(t,e,r){"use strict";var n=r(39898),i=r(63463),a=r(72597).resizeText;t.exports=function(t){var e=t._fullLayout._pielayer.selectAll(".trace");a(t,e,"pie"),e.each((function(e){var r=e[0].trace,a=n.select(this);a.style({opacity:r.opacity}),a.selectAll("path.surface").each((function(e){n.select(this).call(i,e,r,t)}))}))}},63463:function(t,e,r){"use 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a=r.node;a.originalEvent=n.event,t._hoverdata=[a],n.select(e).call(m,r,i),s.click(t,{target:!0})}}})}},3393:function(t,e,r){"use strict";var n=r(49887),i=r(81684).k4,a=r(39898),o=r(30838),s=r(86781),l=r(85247),u=r(84267),c=r(7901),f=r(91424),h=r(71828),p=h.strTranslate,d=h.strRotate,v=r(28984),g=v.keyFun,y=v.repeat,m=v.unwrap,x=r(63893),b=r(73972),_=r(18783),w=_.CAP_SHIFT,T=_.LINE_SPACING;function k(t,e,r){var n,i=m(e),a=i.trace,c=a.domain,f="h"===a.orientation,p=a.node.pad,d=a.node.thickness,v=t.width*(c.x[1]-c.x[0]),g=t.height*(c.y[1]-c.y[0]),y=i._nodes,x=i._links,b=i.circular;(n=b?s.sankeyCircular().circularLinkGap(0):o.sankey()).iterations(l.sankeyIterations).size(f?[v,g]:[g,v]).nodeWidth(d).nodePadding(p).nodeId((function(t){return t.pointNumber})).nodes(y).links(x);var _,w,T,k=n();for(var A in n.nodePadding()<p&&h.warn("node.pad was reduced to ",n.nodePadding()," to fit within the figure."),i._groupLookup){var 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e.trace=t.trace,e.curveNumber=t.trace.index,{circular:t.circular,key:i,traceId:t.key,pointNumber:e.pointNumber,link:e,tinyColorHue:c.tinyRGB(n),tinyColorAlpha:n.getAlpha(),linkPath:M,linkLineColor:t.linkLineColor,linkLineWidth:t.linkLineWidth,linkArrowLength:t.linkArrowLength,valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,parent:t,interactionState:t.interactionState,flow:e.flow}}function M(){return function(t){var e=t.linkArrowLength;if(t.link.circular)return function(t,e){var r=t.width/2,n=t.circularPathData;return"top"===t.circularLinkType?"M "+(n.targetX-e)+" "+(n.targetY+r)+" L"+(n.rightInnerExtent-e)+" "+(n.targetY+r)+"A"+(n.rightLargeArcRadius+r)+" "+(n.rightSmallArcRadius+r)+" 0 0 1 "+(n.rightFullExtent-r-e)+" "+(n.targetY-n.rightSmallArcRadius)+"L"+(n.rightFullExtent-r-e)+" "+n.verticalRightInnerExtent+"A"+(n.rightLargeArcRadius+r)+" "+(n.rightLargeArcRadius+r)+" 0 0 1 "+(n.rightInnerExtent-e)+" "+(n.verticalFullExtent-r)+"L"+n.leftInnerExtent+" 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n=t.link.source.x1,a=t.link.target.x0-e,o=i(n,a),s=o(.5),l=o(.5),u=t.link.y0-t.link.width/2,c=t.link.y0+t.link.width/2,f=t.link.y1-t.link.width/2,h=t.link.y1+t.link.width/2,p="M"+n+","+u,d="C"+s+","+u+" "+l+","+f+" "+a+","+f,v="C"+l+","+h+" "+s+","+c+" "+n+","+c,g=e>0?"L"+(a+e)+","+(f+t.link.width/2):"";return p+d+(g+="L"+a+","+h)+v+"Z"}}function S(t,e){var r=u(e.color),n=l.nodePadAcross,i=t.nodePad/2;e.dx=e.x1-e.x0,e.dy=e.y1-e.y0;var a=e.dx,o=Math.max(.5,e.dy),s="node_"+e.pointNumber;return e.group&&(s=h.randstr()),e.trace=t.trace,e.curveNumber=t.trace.index,{index:e.pointNumber,key:s,partOfGroup:e.partOfGroup||!1,group:e.group,traceId:t.key,trace:t.trace,node:e,nodePad:t.nodePad,nodeLineColor:t.nodeLineColor,nodeLineWidth:t.nodeLineWidth,textFont:t.textFont,size:t.horizontal?t.height:t.width,visibleWidth:Math.ceil(a),visibleHeight:o,zoneX:-n,zoneY:-i,zoneWidth:a+2*n,zoneHeight:o+2*i,labelY:t.horizontal?e.dy/2+1:e.dx/2+1,left:1===e.originalLayer,sizeAcross:t.width,forceLayouts:t.forceLayouts,horizontal:t.horizontal,darkBackground:r.getBrightness()<=128,tinyColorHue:c.tinyRGB(r),tinyColorAlpha:r.getAlpha(),valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,graph:t.graph,arrangement:t.arrangement,uniqueNodeLabelPathId:[t.guid,t.key,s].join("_"),interactionState:t.interactionState,figure:t}}function E(t){t.attr("transform",(function(t){return p(t.node.x0.toFixed(3),t.node.y0.toFixed(3))}))}function L(t){t.call(E)}function C(t,e){t.call(L),e.attr("d",M())}function P(t){t.attr("width",(function(t){return t.node.x1-t.node.x0})).attr("height",(function(t){return t.visibleHeight}))}function O(t){return t.link.width>1||t.linkLineWidth>0}function I(t){return p(t.translateX,t.translateY)+(t.horizontal?"matrix(1 0 0 1 0 0)":"matrix(0 1 1 0 0 0)")}function D(t,e,r){t.on(".basic",null).on("mouseover.basic",(function(t){t.interactionState.dragInProgress||t.partOfGroup||(r.hover(this,t,e),t.interactionState.hovered=[this,t])})).on("mousemove.basic",(function(t){t.interactionState.dragInProgress||t.partOfGroup||(r.follow(this,t),t.interactionState.hovered=[this,t])})).on("mouseout.basic",(function(t){t.interactionState.dragInProgress||t.partOfGroup||(r.unhover(this,t,e),t.interactionState.hovered=!1)})).on("click.basic",(function(t){t.interactionState.hovered&&(r.unhover(this,t,e),t.interactionState.hovered=!1),t.interactionState.dragInProgress||t.partOfGroup||r.select(this,t,e)}))}function z(t,e,r,i){var o=a.behavior.drag().origin((function(t){return{x:t.node.x0+t.visibleWidth/2,y:t.node.y0+t.visibleHeight/2}})).on("dragstart",(function(a){if("fixed"!==a.arrangement&&(h.ensureSingle(i._fullLayout._infolayer,"g","dragcover",(function(t){i._fullLayout._dragCover=t})),h.raiseToTop(this),a.interactionState.dragInProgress=a.node,F(a.node),a.interactionState.hovered&&(r.nodeEvents.unhover.apply(0,a.interactionState.hovered),a.interactionState.hovered=!1),"snap"===a.arrangement)){var o=a.traceId+"|"+a.key;a.forceLayouts[o]?a.forceLayouts[o].alpha(1):function(t,e,r,i){!function(t){for(var e=0;e<t.length;e++)t[e].y=(t[e].y0+t[e].y1)/2,t[e].x=(t[e].x0+t[e].x1)/2}(r.graph.nodes);var a=r.graph.nodes.filter((function(t){return t.originalX===r.node.originalX})).filter((function(t){return!t.partOfGroup}));r.forceLayouts[e]=n.forceSimulation(a).alphaDecay(0).force("collide",n.forceCollide().radius((function(t){return t.dy/2+r.nodePad/2})).strength(1).iterations(l.forceIterations)).force("constrain",function(t,e,r,n){return function(){for(var t=0,i=0;i<r.length;i++){var a=r[i];a===n.interactionState.dragInProgress?(a.x=a.lastDraggedX,a.y=a.lastDraggedY):(a.vx=(a.originalX-a.x)/l.forceTicksPerFrame,a.y=Math.min(n.size-a.dy/2,Math.max(a.dy/2,a.y))),t=Math.max(t,Math.abs(a.vx),Math.abs(a.vy))}!n.interactionState.dragInProgress&&t<.1&&n.forceLayouts[e].alpha()>0&&n.forceLayouts[e].alpha(0)}}(0,e,a,r)).stop()}(0,o,a),function(t,e,r,n,i){window.requestAnimationFrame((function a(){var o;for(o=0;o<l.forceTicksPerFrame;o++)r.forceLayouts[n].tick();if(function(t){for(var e=0;e<t.length;e++)t[e].y0=t[e].y-t[e].dy/2,t[e].y1=t[e].y0+t[e].dy,t[e].x0=t[e].x-t[e].dx/2,t[e].x1=t[e].x0+t[e].dx}(r.graph.nodes),r.sankey.update(r.graph),C(t.filter(B(r)),e),r.forceLayouts[n].alpha()>0)window.requestAnimationFrame(a);else{var 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n=r(52075).hasColorscale,i=r(78803),a=r(34098);t.exports=function(t,e){a.hasLines(e)&&n(e,"line")&&i(t,e,{vals:e.line.color,containerStr:"line",cLetter:"c"}),a.hasMarkers(e)&&(n(e,"marker")&&i(t,e,{vals:e.marker.color,containerStr:"marker",cLetter:"c"}),n(e,"marker.line")&&i(t,e,{vals:e.marker.line.color,containerStr:"marker.line",cLetter:"c"}))}},47581:function(t){"use strict";t.exports={PTS_LINESONLY:20,minTolerance:.2,toleranceGrowth:10,maxScreensAway:20,eventDataKeys:[]}},72626:function(t,e,r){"use strict";var n=r(47761),i=r(11661).setGroupPositions;function a(t,e,r,n,i,a,o){i[n]=!0;var s={i:null,gap:!0,s:0};if(s[o]=r,t.splice(e,0,s),e&&r===t[e-1][o]){var l=t[e-1];s.s=l.s,s.i=l.i,s.gap=l.gap}else a&&(s.s=function(t,e,r,n){var i=t[e-1],a=t[e+1];return a?i?i.s+(a.s-i.s)*(r-i[n])/(a[n]-i[n]):a.s:i.s}(t,e,r,o));e||(t[0].t=t[1].t,t[0].trace=t[1].trace,delete t[1].t,delete t[1].trace)}t.exports=function(t,e){"group"===t._fullLayout.scattermode&&function(t,e){for(var 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n=r(5386).fF,i=r(5386).si,a=r(82196),o=r(9012),s=r(50693),l=r(79952).P,u=r(1426).extendFlat,c=r(30962).overrideAll,f=a.marker,h=a.line,p=f.line;t.exports=c({lon:{valType:"data_array"},lat:{valType:"data_array"},locations:{valType:"data_array"},locationmode:{valType:"enumerated",values:["ISO-3","USA-states","country 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n;if(f.hasText(e)&&(n.text=m(t,e),n.textSel=_(t,e,e.selected),n.textUnsel=_(t,e,e.unselected)),f.hasMarkers(e)&&(n.marker=x(0,e),n.markerSel=b(0,e,e.selected),n.markerUnsel=b(0,e,e.unselected),!e.unselected&&s.isArrayOrTypedArray(e.marker.opacity))){var a=e.marker.opacity;for(n.markerUnsel.opacity=new Array(a.length),r=0;r<a.length;r++)n.markerUnsel.opacity[r]=v*a[r]}if(f.hasLines(e)){n.line={overlay:!0,thickness:e.line.width*i,color:e.line.color,opacity:e.opacity};var o=(d.DASHES[e.line.dash]||[1]).slice();for(r=0;r<o.length;++r)o[r]*=e.line.width*i;n.line.dashes=o}return e.error_x&&e.error_x.visible&&(n.errorX=w(e,e.error_x,i)),e.error_y&&e.error_y.visible&&(n.errorY=w(e,e.error_y,i)),e.fill&&"none"!==e.fill&&(n.fill={closed:!0,fill:e.fillcolor,thickness:0}),n},markerStyle:x,markerSelection:b,linePositions:function(t,e,r){var 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r=e._scene,i={count:0,dirty:!0,lineOptions:[],fillOptions:[],markerOptions:[],markerSelectedOptions:[],markerUnselectedOptions:[],errorXOptions:[],errorYOptions:[],textOptions:[],textSelectedOptions:[],textUnselectedOptions:[],selectBatch:[],unselectBatch:[]},a={fill2d:!1,scatter2d:!1,error2d:!1,line2d:!1,glText:!1,select2d:!1};return e._scene||((r=e._scene={}).init=function(){n.extendFlat(r,a,i)},r.init(),r.update=function(t){var e=n.repeat(t,r.count);if(r.fill2d&&r.fill2d.update(e),r.scatter2d&&r.scatter2d.update(e),r.line2d&&r.line2d.update(e),r.error2d&&r.error2d.update(e.concat(e)),r.select2d&&r.select2d.update(e),r.glText)for(var i=0;i<r.count;i++)r.glText[i].update(t)},r.draw=function(){for(var t=r.count,e=r.fill2d,i=r.error2d,a=r.line2d,o=r.scatter2d,s=r.glText,l=r.select2d,u=r.selectBatch,c=r.unselectBatch,f=0;f<t;f++){if(e&&r.fillOrder[f]&&e.draw(r.fillOrder[f]),a&&r.lineOptions[f]&&a.draw(f),i&&(r.errorXOptions[f]&&i.draw(f),r.errorYOptions[f]&&i.draw(f+t)),o&&r.markerOptions[f])if(c[f].length){var h=n.repeat([],r.count);h[f]=c[f],o.draw(h)}else 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v=n.hasText(l),g=n.hasMarkers(l),y=!g&&!v;if(!0!==l.visible||y)return s;var m=[],x=[];if(!1!==e&&!e.degenerate)for(var b=0;b<c;b++)e.contains([u.xpx[b],u.ypx[b]],!1,b,t)?(m.push(b),s.push({pointNumber:b,x:a.c2d(f[b]),y:o.c2d(h[b])})):x.push(b);if(g){var _=p.scatter2d;if(m.length||x.length){if(!p.selectBatch[d].length&&!p.unselectBatch[d].length){var w=new Array(p.count);w[d]=p.markerUnselectedOptions[d],_.update.apply(_,w)}}else{var T=new Array(p.count);T[d]=p.markerOptions[d],_.update.apply(_,T)}}return p.selectBatch[d]=m,p.unselectBatch[d]=x,v&&i(r),s}},99181:function(t,e,r){"use strict";var n=r(5386).fF,i=r(5386).si,a=r(19316),o=r(82196),s=r(23585),l=r(9012),u=r(50693),c=r(1426).extendFlat,f=r(30962).overrideAll,h=r(23585),p=a.line,d=a.marker;t.exports=f({lon:a.lon,lat:a.lat,cluster:{enabled:{valType:"boolean"},maxzoom:c({},h.layers.maxzoom,{}),step:{valType:"number",arrayOk:!0,dflt:-1,min:-1},size:{valType:"number",arrayOk:!0,dflt:20,min:0},color:{valType:"color",arrayOk:!0},opacity:c({},d.opacity,{dflt:1})},mode:c({},o.mode,{dflt:"markers"}),text:c({},o.text,{}),texttemplate:i({editType:"plot"},{keys:["lat","lon","text"]}),hovertext:c({},o.hovertext,{}),line:{color:p.color,width:p.width},connectgaps:o.connectgaps,marker:c({symbol:{valType:"string",dflt:"circle",arrayOk:!0},angle:{valType:"number",dflt:"auto",arrayOk:!0},allowoverlap:{valType:"boolean",dflt:!1},opacity:d.opacity,size:d.size,sizeref:d.sizeref,sizemin:d.sizemin,sizemode:d.sizemode},u("marker")),fill:a.fill,fillcolor:o.fillcolor,textfont:s.layers.symbol.textfont,textposition:s.layers.symbol.textposition,below:{valType:"string"},selected:{marker:o.selected.marker},unselected:{marker:o.unselected.marker},hoverinfo:c({},l.hoverinfo,{flags:["lon","lat","text","name"]}),hovertemplate:n()},"calc","nested")},15790:function(t,e,r){"use 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n=r(71828),i=n.pushUnique,a=r(34098),o=r(35948);t.exports=function(t,e){var r=t.cd,s=r[0].trace,l=r[0].t,u=t.scene,c=u.matrixOptions.cdata,f=t.xaxis,h=t.yaxis,p=[];if(!u)return p;var d=!a.hasMarkers(s)&&!a.hasText(s);if(!0!==s.visible||d)return p;var v=o.getDimIndex(s,f),g=o.getDimIndex(s,h);if(!1===v||!1===g)return p;var y=l.xpx[v],m=l.ypx[g],x=c[v],b=c[g],_=(t.scene.selectBatch||[]).slice(),w=[];if(!1!==e&&!e.degenerate)for(var T=0;T<x.length;T++)e.contains([y[T],m[T]],null,T,t)?(p.push({pointNumber:T,x:x[T],y:b[T]}),i(_,T)):-1!==_.indexOf(T)?i(_,T):w.push(T);var k=u.matrixOptions;return _.length||w.length?u.selectBatch.length||u.unselectBatch.length||u.matrix.update(u.unselectedOptions,n.extendFlat({},k,u.selectedOptions,u.viewOpts)):u.matrix.update(k,null),u.selectBatch=_,u.unselectBatch=w,p}},21850:function(t,e,r){"use strict";var n=r(50693),i=r(12663).axisHoverFormat,a=r(5386).fF,o=r(2418),s=r(9012),l=r(1426).extendFlat,u={x:{valType:"data_array",editType:"calc+clearAxisTypes"},y:{valType:"data_array",editType:"calc+clearAxisTypes"},z:{valType:"data_array",editType:"calc+clearAxisTypes"},u:{valType:"data_array",editType:"calc"},v:{valType:"data_array",editType:"calc"},w:{valType:"data_array",editType:"calc"},starts:{x:{valType:"data_array",editType:"calc"},y:{valType:"data_array",editType:"calc"},z:{valType:"data_array",editType:"calc"},editType:"calc"},maxdisplayed:{valType:"integer",min:0,dflt:1e3,editType:"calc"},sizeref:{valType:"number",editType:"calc",min:0,dflt:1},text:{valType:"string",dflt:"",editType:"calc"},hovertext:{valType:"string",dflt:"",editType:"calc"},hovertemplate:a({editType:"calc"},{keys:["tubex","tubey","tubez","tubeu","tubev","tubew","norm","divergence"]}),uhoverformat:i("u",1),vhoverformat:i("v",1),whoverformat:i("w",1),xhoverformat:i("x"),yhoverformat:i("y"),zhoverformat:i("z"),showlegend:l({},s.showlegend,{dflt:!1})};l(u,n("",{colorAttr:"u/v/w 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L=function(){m=0,M=[],S=[],E=[]};(!m||m<M.length*S.length*E.length)&&L();var C=function(t){return"x"===t?v:"y"===t?g:y},P=function(t){return"x"===t?M:"y"===t?S:E},O=function(t){return t[m-1]<t[0]?-1:1},I=C(A[1]),D=C(A[3]),z=C(A[5]),R=P(A[1]).length,F=P(A[3]).length,B=P(A[5]).length,N=!1,j=function(t,e,r){return R*(F*t+e)+r},U=O(C(A[1])),V=O(C(A[3])),H=O(C(A[5]));for(e=0;e<B-1;e++){for(r=0;r<F-1;r++){for(i=0;i<R-1;i++){var q=j(e,r,i),G=j(e,r,i+1),Z=j(e,r+1,i),Y=j(e+1,r,i);if(I[q]*U<I[G]*U&&D[q]*V<D[Z]*V&&z[q]*H<z[Y]*H||(N=!0),N)break}if(N)break}if(N)break}return N&&(n.warn("Encountered arbitrary coordinates! 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y=n[c];r.xMax=Math.max(r.xMax,y),r.xMin=Math.min(r.xMin,y);var m=o[c];r.yMax=Math.max(r.yMax,m),r.yMin=Math.min(r.yMin,m);var x=l[c];r.zMax=Math.max(r.zMax,x),r.zMin=Math.min(r.zMin,x)}e._slen=u,e._normMax=f,e._xbnds=[r.xMin,r.xMax],e._ybnds=[r.yMin,r.yMax],e._zbnds=[r.zMin,r.zMax]},filter:s,processGrid:a}},90154:function(t,e,r){"use strict";var n=r(9330).gl_streamtube3d,i=n.createTubeMesh,a=r(71828),o=r(81697).parseColorScale,s=r(21081).extractOpts,l=r(90060),u={xaxis:0,yaxis:1,zaxis:2};function c(t,e){this.scene=t,this.uid=e,this.mesh=null,this.data=null}var f=c.prototype;function h(t){var e=t.length;return e>2?t.slice(1,e-1):2===e?[(t[0]+t[1])/2]:t}function p(t){var e=t.length;return 1===e?[.5,.5]:[t[1]-t[0],t[e-1]-t[e-2]]}function d(t,e){var r=t.fullSceneLayout,i=t.dataScale,c=e._len,f={};function d(t,e){var n=r[e],o=i[u[e]];return a.simpleMap(t,(function(t){return n.d2l(t)*o}))}if(f.vectors=l(d(e._u,"xaxis"),d(e._v,"yaxis"),d(e._w,"zaxis"),c),!c)return{positions:[],cells:[]};var v=d(e._Xs,"xaxis"),g=d(e._Ys,"yaxis"),y=d(e._Zs,"zaxis");if(f.meshgrid=[v,g,y],f.gridFill=e._gridFill,e._slen)f.startingPositions=l(d(e._startsX,"xaxis"),d(e._startsY,"yaxis"),d(e._startsZ,"zaxis"));else{for(var m=g[0],x=h(v),b=h(y),_=new Array(x.length*b.length),w=0,T=0;T<x.length;T++)for(var k=0;k<b.length;k++)_[w++]=[x[T],m,b[k]];f.startingPositions=_}f.colormap=o(e),f.tubeSize=e.sizeref,f.maxLength=e.maxdisplayed;var A=d(e._xbnds,"xaxis"),M=d(e._ybnds,"yaxis"),S=d(e._zbnds,"zaxis"),E=p(v),L=p(g),C=p(y),P=[[A[0]-E[0],M[0]-L[0],S[0]-C[0]],[A[1]+E[1],M[1]+L[1],S[1]+C[1]]],O=n(f,P),I=s(e);O.vertexIntensityBounds=[I.min/e._normMax,I.max/e._normMax];var D=e.lightposition;return O.lightPosition=[D.x,D.y,D.z],O.ambient=e.lighting.ambient,O.diffuse=e.lighting.diffuse,O.specular=e.lighting.specular,O.roughness=e.lighting.roughness,O.fresnel=e.lighting.fresnel,O.opacity=e.opacity,e._pad=O.tubeScale*e.sizeref*2,O}f.handlePick=function(t){var e=this.scene.fullSceneLayout,r=this.scene.dataScale;function n(t,n){var i=e[n],a=r[u[n]];return i.l2c(t)/a}if(t.object===this.mesh){var i=t.data.position,a=t.data.velocity;return t.traceCoordinate=[n(i[0],"xaxis"),n(i[1],"yaxis"),n(i[2],"zaxis"),n(a[0],"xaxis"),n(a[1],"yaxis"),n(a[2],"zaxis"),t.data.intensity*this.data._normMax,t.data.divergence],t.textLabel=this.data.hovertext||this.data.text,!0}},f.update=function(t){this.data=t;var e=d(this.scene,t);this.mesh.update(e)},f.dispose=function(){this.scene.glplot.remove(this.mesh),this.mesh.dispose()},t.exports=function(t,e){var r=t.glplot.gl,n=d(t,e),a=i(r,n),o=new c(t,e.uid);return o.mesh=a,o.data=e,a._trace=o,t.glplot.add(a),o}},22459:function(t,e,r){"use strict";var n=r(71828),i=r(1586),a=r(21850);t.exports=function(t,e,r,o){function s(r,i){return n.coerce(t,e,a,r,i)}var l=s("u"),u=s("v"),c=s("w"),f=s("x"),h=s("y"),p=s("z");l&&l.length&&u&&u.length&&c&&c.length&&f&&f.length&&h&&h.length&&p&&p.length?(s("starts.x"),s("starts.y"),s("starts.z"),s("maxdisplayed"),s("sizeref"),s("lighting.ambient"),s("lighting.diffuse"),s("lighting.specular"),s("lighting.roughness"),s("lighting.fresnel"),s("lightposition.x"),s("lightposition.y"),s("lightposition.z"),i(t,e,o,s,{prefix:"",cLetter:"c"}),s("text"),s("hovertext"),s("hovertemplate"),s("uhoverformat"),s("vhoverformat"),s("whoverformat"),s("xhoverformat"),s("yhoverformat"),s("zhoverformat"),e._length=null):e.visible=!1}},61510:function(t,e,r){"use strict";t.exports={moduleType:"trace",name:"streamtube",basePlotModule:r(58547),categories:["gl3d","showLegend"],attributes:r(21850),supplyDefaults:r(22459),colorbar:{min:"cmin",max:"cmax"},calc:r(88489).calc,plot:r(90154),eventData:function(t,e){return t.tubex=t.x,t.tubey=t.y,t.tubez=t.z,t.tubeu=e.traceCoordinate[3],t.tubev=e.traceCoordinate[4],t.tubew=e.traceCoordinate[5],t.norm=e.traceCoordinate[6],t.divergence=e.traceCoordinate[7],delete t.x,delete t.y,delete t.z,t},meta:{}}},57564:function(t,e,r){"use strict";var n=r(9012),i=r(5386).fF,a=r(5386).si,o=r(50693),s=r(27670).Y,l=r(34e3),u=r(7055),c=r(1426).extendFlat,f=r(79952).u;t.exports={labels:{valType:"data_array",editType:"calc"},parents:{valType:"data_array",editType:"calc"},values:{valType:"data_array",editType:"calc"},branchvalues:{valType:"enumerated",values:["remainder","total"],dflt:"remainder",editType:"calc"},count:{valType:"flaglist",flags:["branches","leaves"],dflt:"leaves",editType:"calc"},level:{valType:"any",editType:"plot",anim:!0},maxdepth:{valType:"integer",editType:"plot",dflt:-1},marker:c({colors:{valType:"data_array",editType:"calc"},line:{color:c({},l.marker.line.color,{dflt:null}),width:c({},l.marker.line.width,{dflt:1}),editType:"calc"},pattern:f,editType:"calc"},o("marker",{colorAttr:"colors",anim:!1})),leaf:{opacity:{valType:"number",editType:"style",min:0,max:1},editType:"plot"},text:l.text,textinfo:{valType:"flaglist",flags:["label","text","value","current path","percent root","percent entry","percent 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n=r(674),i=r(92770),a=r(71828),o=r(21081).makeColorScaleFuncFromTrace,s=r(32354).makePullColorFn,l=r(32354).generateExtendedColors,u=r(21081).calc,c=r(50606).ALMOST_EQUAL,f={},h={},p={};function d(t,e,r){var n=0,i=t.children;if(i){for(var a=i.length,o=0;o<a;o++)n+=d(i[o],e,r);r.branches&&n++}else r.leaves&&n++;return t.value=t.data.data.value=n,e._values||(e._values=[]),e._values[t.data.data.i]=n,n}e.calc=function(t,e){var r,l,f,h,p,v,g=t._fullLayout,y=e.ids,m=a.isArrayOrTypedArray(y),x=e.labels,b=e.parents,_=e.values,w=a.isArrayOrTypedArray(_),T=[],k={},A={},M=function(t){return t||"number"==typeof t},S=function(t){return!w||i(_[t])&&_[t]>=0};m?(r=Math.min(y.length,b.length),l=function(t){return M(y[t])&&S(t)},f=function(t){return String(y[t])}):(r=Math.min(x.length,b.length),l=function(t){return M(x[t])&&S(t)},f=function(t){return String(x[t])}),w&&(r=Math.min(r,_.length));for(var E=0;E<r;E++)if(l(E)){var L=f(E),C=M(b[E])?String(b[E]):"",P={i:E,id:L,pid:C,label:M(x[E])?String(x[E]):""};w&&(P.v=+_[E]),T.push(P),p=L,k[h=C]?k[h].push(p):k[h]=[p],A[p]=1}if(k[""]){if(k[""].length>1){for(var O=a.randstr(),I=0;I<T.length;I++)""===T[I].pid&&(T[I].pid=O);T.unshift({hasMultipleRoots:!0,id:O,pid:"",label:""})}}else{var D,z=[];for(D in k)A[D]||z.push(D);if(1!==z.length)return a.warn(["Multiple implied roots, cannot build",e.type,"hierarchy of",e.name+".","These roots include:",z.join(", ")].join(" "));D=z[0],T.unshift({hasImpliedRoot:!0,id:D,pid:"",label:D})}try{v=n.stratify().id((function(t){return t.id})).parentId((function(t){return t.pid}))(T)}catch(t){return a.warn(["Failed to build",e.type,"hierarchy of",e.name+".","Error:",t.message].join(" "))}var R=n.hierarchy(v),F=!1;if(w)switch(e.branchvalues){case"remainder":R.sum((function(t){return t.data.v}));break;case"total":R.each((function(t){var r=t.data.data,n=r.v;if(t.children){var i=t.children.reduce((function(t,e){return t+e.data.data.v}),0);if((r.hasImpliedRoot||r.hasMultipleRoots)&&(n=i),n<i*c)return F=!0,a.warn(["Total value for node",t.data.data.id,"of",e.name,"is smaller than the sum of its children.","\nparent value =",n,"\nchildren sum =",i].join(" "))}t.value=n}))}else d(R,e,{branches:-1!==e.count.indexOf("branches"),leaves:-1!==e.count.indexOf("leaves")});if(!F){var B,N;e.sort&&R.sort((function(t,e){return e.value-t.value}));var j=e.marker.colors||[],U=!!j.length;return e._hasColorscale?(U||(j=w?e.values:e._values),u(t,e,{vals:j,containerStr:"marker",cLetter:"c"}),N=o(e.marker)):B=s(g["_"+e.type+"colormap"]),R.each((function(t){var r=t.data.data;r.color=e._hasColorscale?N(j[r.i]):B(j[r.i],r.id)})),T[0].hierarchy=R,T}},e._runCrossTraceCalc=function(t,e){var r=e._fullLayout,n=e.calcdata,i=r[t+"colorway"],a=r["_"+t+"colormap"];r["extend"+t+"colors"]&&(i=l(i,"icicle"===t?p:"treemap"===t?h:f));var o,s=0;function u(t){var e=t.data.data,r=e.id;!1===e.color&&(a[r]?e.color=a[r]:t.parent?t.parent.parent?e.color=t.parent.data.data.color:(a[r]=e.color=i[s%i.length],s++):e.color=o)}for(var c=0;c<n.length;c++){var d=n[c][0];d.trace.type===t&&d.hierarchy&&(o=d.trace.root.color,d.hierarchy.each(u))}},e.crossTraceCalc=function(t){return e._runCrossTraceCalc("sunburst",t)}},7055:function(t){"use strict";t.exports={CLICK_TRANSITION_TIME:750,CLICK_TRANSITION_EASING:"linear",eventDataKeys:["currentPath","root","entry","percentRoot","percentEntry","percentParent"]}},17094:function(t,e,r){"use strict";var n=r(71828),i=r(57564),a=r(27670).c,o=r(90769).handleText,s=r(37434).handleMarkerDefaults,l=r(21081),u=l.hasColorscale,c=l.handleDefaults;t.exports=function(t,e,r,l){function f(r,a){return n.coerce(t,e,i,r,a)}var h=f("labels"),p=f("parents");if(h&&h.length&&p&&p.length){var d=f("values");d&&d.length?f("branchvalues"):f("count"),f("level"),f("maxdepth"),s(t,e,l,f);var v=e._hasColorscale=u(t,"marker","colors")||(t.marker||{}).coloraxis;v&&c(t,e,l,f,{prefix:"marker.",cLetter:"c"}),f("leaf.opacity",v?1:.7);var g=f("text");f("texttemplate"),e.texttemplate||f("textinfo",Array.isArray(g)?"text+label":"label"),f("hovertext"),f("hovertemplate"),o(t,e,l,f,"auto",{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!1,moduleHasCliponaxis:!1,moduleHasTextangle:!1,moduleHasInsideanchor:!1}),f("insidetextorientation"),f("sort"),f("rotation"),f("root.color"),a(e,l,f),e._length=null}else e.visible=!1}},43467:function(t,e,r){"use strict";var n=r(91424),i=r(7901);t.exports=function(t,e,r,a,o){var s=e.data.data,l=s.i,u=o||s.color;if(l>=0){e.i=s.i;var c=r.marker;c.pattern&&c.colors&&c.pattern.shape||(c.color=u,e.color=u),n.pointStyle(t,r,a,e)}else i.fill(t,u)}},83523:function(t,e,r){"use strict";var n=r(39898),i=r(73972),a=r(23469).appendArrayPointValue,o=r(30211),s=r(71828),l=r(11086),u=r(2791),c=r(53581).formatPieValue;function f(t,e,r){for(var n=t.data.data,i={curveNumber:e.index,pointNumber:n.i,data:e._input,fullData:e},o=0;o<r.length;o++){var s=r[o];s in t&&(i[s]=t[s])}return"parentString"in t&&!u.isHierarchyRoot(t)&&(i.parent=t.parentString),a(i,e,n.i),i}t.exports=function(t,e,r,a,h){var p=a[0],d=p.trace,v=p.hierarchy,g="sunburst"===d.type,y="treemap"===d.type||"icicle"===d.type;"_hasHoverLabel"in d||(d._hasHoverLabel=!1),"_hasHoverEvent"in d||(d._hasHoverEvent=!1),t.on("mouseover",(function(i){var a=r._fullLayout;if(!r._dragging&&!1!==a.hovermode){var l,m=r._fullData[d.index],x=i.data.data,b=x.i,_=u.isHierarchyRoot(i),w=u.getParent(v,i),T=u.getValue(i),k=function(t){return s.castOption(m,b,t)},A=k("hovertemplate"),M=o.castHoverinfo(m,a,b),S=a.separators;if(A||M&&"none"!==M&&"skip"!==M){var E,L;g&&(E=p.cx+i.pxmid[0]*(1-i.rInscribed),L=p.cy+i.pxmid[1]*(1-i.rInscribed)),y&&(E=i._hoverX,L=i._hoverY);var C,P={},O=[],I=[],D=function(t){return-1!==O.indexOf(t)};M&&(O="all"===M?m._module.attributes.hoverinfo.flags:M.split("+")),P.label=x.label,D("label")&&P.label&&I.push(P.label),x.hasOwnProperty("v")&&(P.value=x.v,P.valueLabel=c(P.value,S),D("value")&&I.push(P.valueLabel)),P.currentPath=i.currentPath=u.getPath(i.data),D("current path")&&!_&&I.push(P.currentPath);var z=[],R=function(){-1===z.indexOf(C)&&(I.push(C),z.push(C))};P.percentParent=i.percentParent=T/u.getValue(w),P.parent=i.parentString=u.getPtLabel(w),D("percent parent")&&(C=u.formatPercent(P.percentParent,S)+" of "+P.parent,R()),P.percentEntry=i.percentEntry=T/u.getValue(e),P.entry=i.entry=u.getPtLabel(e),!D("percent entry")||_||i.onPathbar||(C=u.formatPercent(P.percentEntry,S)+" of "+P.entry,R()),P.percentRoot=i.percentRoot=T/u.getValue(v),P.root=i.root=u.getPtLabel(v),D("percent root")&&!_&&(C=u.formatPercent(P.percentRoot,S)+" of 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N=t.select("path.surface");h.styleOne(N,i,m,r,{hovered:!0})}d._hasHoverEvent=!0,r.emit("plotly_hover",{points:l||[f(i,m,h.eventDataKeys)],event:n.event})}})),t.on("mouseout",(function(e){var i=r._fullLayout,a=r._fullData[d.index],s=n.select(this).datum();if(d._hasHoverEvent&&(e.originalEvent=n.event,r.emit("plotly_unhover",{points:[f(s,a,h.eventDataKeys)],event:n.event}),d._hasHoverEvent=!1),d._hasHoverLabel&&(o.loneUnhover(i._hoverlayer.node()),d._hasHoverLabel=!1),y){var l=t.select("path.surface");h.styleOne(l,s,a,r,{hovered:!1})}})),t.on("click",(function(t){var e=r._fullLayout,a=r._fullData[d.index],s=g&&(u.isHierarchyRoot(t)||u.isLeaf(t)),c=u.getPtId(t),p=u.isEntry(t)?u.findEntryWithChild(v,c):u.findEntryWithLevel(v,c),y=u.getPtId(p),m={points:[f(t,a,h.eventDataKeys)],event:n.event};s||(m.nextLevel=y);var x=l.triggerHandler(r,"plotly_"+d.type+"click",m);if(!1!==x&&e.hovermode&&(r._hoverdata=[f(t,a,h.eventDataKeys)],o.click(r,n.event)),!s&&!1!==x&&!r._dragging&&!r._transitioning){i.call("_storeDirectGUIEdit",a,e._tracePreGUI[a.uid],{level:a.level});var b={data:[{level:y}],traces:[d.index]},_={frame:{redraw:!1,duration:h.transitionTime},transition:{duration:h.transitionTime,easing:h.transitionEasing},mode:"immediate",fromcurrent:!0};o.loneUnhover(e._hoverlayer.node()),i.call("animate",r,b,_)}}))}},2791:function(t,e,r){"use strict";var n=r(71828),i=r(7901),a=r(6964),o=r(53581);function s(t){return t.data.data.pid}e.findEntryWithLevel=function(t,r){var n;return r&&t.eachAfter((function(t){if(e.getPtId(t)===r)return n=t.copy()})),n||t},e.findEntryWithChild=function(t,r){var n;return t.eachAfter((function(t){for(var i=t.children||[],a=0;a<i.length;a++){var o=i[a];if(e.getPtId(o)===r)return 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s,l=b.getPtId(t.parent);T.each((function(t){if(b.getPtId(t)===l)return s=t}));var u,c=s.children;c.forEach((function(t,e){if(b.getPtId(t)===r)return u=e}));var f=c.length,h=a(s.x0,s.x1);e={rpx0:I,rpx1:I,x0:h(u/f),x1:h((u+1)/f)}}return a(n,e)}(t);return function(t){return Z(e(t))}})),t.select("g.slicetext").attr("opacity",0)})).remove():T.exit().remove(),T.order();var X=null;if(_&&R){var J=b.getPtId(R);T.each((function(t){null===X&&b.getPtId(t)===J&&(X=t.x1)}))}var K=T;function $(t){var e=t.parent,r=F[b.getPtId(e)],n={};if(r){var i=e.children,o=i.indexOf(t),s=i.length,l=a(r.x0,r.x1);n.x0=l(o/s),n.x1=l(o/s)}else n.x0=n.x1=0;return n}_&&(K=K.transition().each("end",(function(){var e=n.select(this);b.setSliceCursor(e,t,{hideOnRoot:!0,hideOnLeaves:!0,isTransitioning:!1})}))),K.each((function(i){var u=n.select(this),f=s.ensureSingle(u,"path","surface",(function(t){t.style("pointer-events",h?"none":"all")}));i.rpx0=q(i.y0),i.rpx1=q(i.y1),i.xmid=(i.x0+i.x1)/2,i.pxmid=G(i.rpx1,i.xmid),i.midangle=-(i.xmid-Math.PI/2),i.startangle=-(i.x0-Math.PI/2),i.stopangle=-(i.x1-Math.PI/2),i.halfangle=.5*Math.min(s.angleDelta(i.x0,i.x1)||Math.PI,Math.PI),i.ring=1-i.rpx0/i.rpx1,i.rInscribed=function(t){return 0===t.rpx0&&s.isFullCircle([t.x0,t.x1])?1:Math.max(0,Math.min(1/(1+1/Math.sin(t.halfangle)),t.ring/2))}(i),_?f.transition().attrTween("d",(function(t){var e=function(t){var e,r=F[b.getPtId(t)],n={x0:t.x0,x1:t.x1,rpx0:t.rpx0,rpx1:t.rpx1};if(r)e=r;else if(R)if(t.parent)if(X){var i=(t.x1>X?2*Math.PI:0)+V;e={x0:i,x1:i}}else e={rpx0:I,rpx1:I},s.extendFlat(e,$(t));else e={rpx0:0,rpx1:0};else e={x0:V,x1:V};return a(e,n)}(t);return function(t){return 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a,o,s=t._fullLayout,l=s._sunburstlayer,u=!r,c=!s.uniformtext.mode&&b.hasTransition(r);f("sunburst",s),(a=l.selectAll("g.trace.sunburst").data(e,(function(t){return t[0].trace.uid}))).enter().append("g").classed("trace",!0).classed("sunburst",!0).attr("stroke-linejoin","round"),a.order(),c?(i&&(o=i()),n.transition().duration(r.duration).ease(r.easing).each("end",(function(){o&&o()})).each("interrupt",(function(){o&&o()})).each((function(){l.selectAll("g.trace").each((function(e){_(t,e,this,r)}))}))):(a.each((function(e){_(t,e,this,r)})),s.uniformtext.mode&&y(t,s._sunburstlayer.selectAll(".trace"),"sunburst")),u&&a.exit().remove()},e.formatSliceLabel=function(t,e,r,n,i){var a=r.texttemplate,o=r.textinfo;if(!(a||o&&"none"!==o))return"";var l=i.separators,u=n[0],c=t.data.data,f=u.hierarchy,h=b.isHierarchyRoot(t),p=b.getParent(f,t),d=b.getValue(t);if(!a){var v,g=o.split("+"),y=function(t){return-1!==g.indexOf(t)},m=[];if(y("label")&&c.label&&m.push(c.label),c.hasOwnProperty("v")&&y("value")&&m.push(b.formatValue(c.v,l)),!h){y("current path")&&m.push(b.getPath(t.data));var x=0;y("percent parent")&&x++,y("percent entry")&&x++,y("percent root")&&x++;var _=x>1;if(x){var w,T=function(t){v=b.formatPercent(w,l),_&&(v+=" of "+t),m.push(v)};y("percent parent")&&!h&&(w=d/b.getValue(p),T("parent")),y("percent entry")&&(w=d/b.getValue(e),T("entry")),y("percent root")&&(w=d/b.getValue(f),T("root"))}}return y("text")&&(v=s.castOption(r,c.i,"text"),s.isValidTextValue(v)&&m.push(v)),m.join("<br>")}var k=s.castOption(r,c.i,"texttemplate");if(!k)return"";var A={};c.label&&(A.label=c.label),c.hasOwnProperty("v")&&(A.value=c.v,A.valueLabel=b.formatValue(c.v,l)),A.currentPath=b.getPath(t.data),h||(A.percentParent=d/b.getValue(p),A.percentParentLabel=b.formatPercent(A.percentParent,l),A.parent=b.getPtLabel(p)),A.percentEntry=d/b.getValue(e),A.percentEntryLabel=b.formatPercent(A.percentEntry,l),A.entry=b.getPtLabel(e),A.percentRoot=d/b.getValue(f),A.percentRootLabel=b.formatPercent(A.percentRoot,l),A.root=b.getPtLabel(f),c.hasOwnProperty("color")&&(A.color=c.color);var M=s.castOption(r,c.i,"text");return(s.isValidTextValue(M)||""===M)&&(A.text=M),A.customdata=s.castOption(r,c.i,"customdata"),s.texttemplateString(k,A,i._d3locale,A,r._meta||{})}},29969:function(t,e,r){"use strict";var n=r(39898),i=r(7901),a=r(71828),o=r(72597).resizeText,s=r(43467);function l(t,e,r,n){var 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n=r(78803);t.exports=function(t,e){e.surfacecolor?n(t,e,{vals:e.surfacecolor,containerStr:"",cLetter:"c"}):n(t,e,{vals:e.z,containerStr:"",cLetter:"c"})}},43768:function(t,e,r){"use strict";var n=r(9330).gl_surface3d,i=r(9330).ndarray,a=r(9330).ndarray_linear_interpolate.d2,o=r(824),s=r(43907),l=r(71828).isArrayOrTypedArray,u=r(81697).parseColorScale,c=r(78614),f=r(21081).extractOpts;function h(t,e,r){this.scene=t,this.uid=r,this.surface=e,this.data=null,this.showContour=[!1,!1,!1],this.contourStart=[null,null,null],this.contourEnd=[null,null,null],this.contourSize=[0,0,0],this.minValues=[1/0,1/0,1/0],this.maxValues=[-1/0,-1/0,-1/0],this.dataScaleX=1,this.dataScaleY=1,this.refineData=!0,this.objectOffset=[0,0,0]}var p=h.prototype;p.getXat=function(t,e,r,n){var i=l(this.data.x)?l(this.data.x[0])?this.data.x[e][t]:this.data.x[t]:t;return void 0===r?i:n.d2l(i,0,r)},p.getYat=function(t,e,r,n){var i=l(this.data.y)?l(this.data.y[0])?this.data.y[e][t]:this.data.y[e]:e;return void 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t,e,r,n=[[],[],[]],i=[!1,!1,!1],a=!1;for(t=0;t<3;++t)if(this.showContour[t]&&(a=!0,this.contourSize[t]>0&&null!==this.contourStart[t]&&null!==this.contourEnd[t]&&this.contourEnd[t]>this.contourStart[t]))for(i[t]=!0,e=this.contourStart[t];e<this.contourEnd[t];e+=this.contourSize[t])r=e*this.scene.dataScale[t],k(n[t],r);if(a){var o=[[],[],[]];for(t=0;t<3;++t)this.showContour[t]&&(o[t]=i[t]?n[t]:this.scene.contourLevels[t]);this.surface.update({levels:o})}},p.update=function(t){var e,r,n,a,l=this.scene,h=l.fullSceneLayout,p=this.surface,d=u(t),v=l.dataScale,g=t.z[0].length,y=t._ylength,m=l.contourLevels;this.data=t;var x=[];for(e=0;e<3;e++)for(x[e]=[],r=0;r<g;r++)x[e][r]=[];for(r=0;r<g;r++)for(n=0;n<y;n++)x[0][r][n]=this.getXat(r,n,t.xcalendar,h.xaxis),x[1][r][n]=this.getYat(r,n,t.ycalendar,h.yaxis),x[2][r][n]=this.getZat(r,n,t.zcalendar,h.zaxis);if(t.connectgaps)for(t._emptypoints=s(x[2]),o(x[2],t._emptypoints),t._interpolatedZ=[],r=0;r<g;r++)for(t._interpolatedZ[r]=[],n=0;n<y;n++)t._interpolatedZ[r][n]=x[2][r][n];for(e=0;e<3;e++)for(r=0;r<g;r++)for(n=0;n<y;n++)null==(a=x[e][r][n])?x[e][r][n]=NaN:a=x[e][r][n]*=v[e];for(e=0;e<3;e++)for(r=0;r<g;r++)for(n=0;n<y;n++)null!=(a=x[e][r][n])&&(this.minValues[e]>a&&(this.minValues[e]=a),this.maxValues[e]<a&&(this.maxValues[e]=a));for(e=0;e<3;e++)this.objectOffset[e]=.5*(this.minValues[e]+this.maxValues[e]);for(e=0;e<3;e++)for(r=0;r<g;r++)for(n=0;n<y;n++)null!=(a=x[e][r][n])&&(x[e][r][n]-=this.objectOffset[e]);var b=[i(new Float32Array(g*y),[g,y]),i(new Float32Array(g*y),[g,y]),i(new 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A=[!0,!0,!0],M=["x","y","z"];for(e=0;e<3;++e){var S=t.contours[M[e]];A[e]=S.highlight,w.showContour[e]=S.show||S.highlight,w.showContour[e]&&(w.contourProject[e]=[S.project.x,S.project.y,S.project.z],S.show?(this.showContour[e]=!0,w.levels[e]=m[e],p.highlightColor[e]=w.contourColor[e]=c(S.color),S.usecolormap?p.highlightTint[e]=w.contourTint[e]=0:p.highlightTint[e]=w.contourTint[e]=1,w.contourWidth[e]=S.width,this.contourStart[e]=S.start,this.contourEnd[e]=S.end,this.contourSize[e]=S.size):(this.showContour[e]=!1,this.contourStart[e]=null,this.contourEnd[e]=null,this.contourSize[e]=0),S.highlight&&(w.dynamicColor[e]=c(S.highlightcolor),w.dynamicWidth[e]=S.highlightwidth))}(function(t){var e=t[0].rgb,r=t[t.length-1].rgb;return e[0]===r[0]&&e[1]===r[1]&&e[2]===r[2]&&e[3]===r[3]})(d)&&(w.vertexColor=!0),w.objectOffset=this.objectOffset,w.coords=b,p.update(w),p.visible=t.visible,p.enableDynamic=A,p.enableHighlight=A,p.snapToData=!0,"lighting"in 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diff --git a/demos/OracleAutoMLx_Classification_Text.ipynb b/demos/OracleAutoMLx_Classification_Text.ipynb
index a0980fe..7c9a587 100644
--- a/demos/OracleAutoMLx_Classification_Text.ipynb
+++ b/demos/OracleAutoMLx_Classification_Text.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "20b4dde3",
+   "id": "2e6a94d9",
    "metadata": {},
    "source": [
     "***\n",
@@ -14,25 +14,25 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1da93244",
+   "id": "01dbd4a3",
    "metadata": {},
    "source": [
-    "AutoMLx Text Classification Demo version 23.2.0.\n",
+    "AutoMLx Text Classification.\n",
     "\n",
-    "Copyright © 2023, Oracle and/or its affiliates.\n",
+    "Copyright © 2025, Oracle and/or its affiliates.\n",
     "\n",
     "Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "05d5d8d5",
+   "id": "2ed053f8",
    "metadata": {},
    "source": [
     "## Overview of this Notebook\n",
     "\n",
     "In this notebook we will build a classifier using the Oracle AutoMLx tool for the public 20newsgroup dataset. The dataset is a binary classification dataset, and more details about the dataset can be found at http://qwone.com/~jason/20Newsgroups/.\n",
-    "We explore the various options provided by the Oracle AutoMLx tool, allowing the user to exercise control over the AutoML training process. We then evaluate the different models trained by AutoML. Finally we provide an overview of the possibilites that Oracle AutoMLx offers for explaining the predictions of the tuned model.\n",
+    "We explore the various options provided by the Oracle AutoMLx tool, allowing the user to exercise control over the AutoML training process. We then evaluate the different models trained by AutoML. Finally we provide an overview of the possibilities that Oracle AutoMLx offers for explaining the predictions of the tuned model.\n",
     "\n",
     "---\n",
     "## Prerequisites\n",
@@ -43,38 +43,37 @@
     "\n",
     "## Business Use\n",
     "\n",
-    "Data analytics and modeling problems using Machine Learning (ML) are becoming popular and often rely on data science expertise to build accurate ML models. Such modeling tasks primarily involve the following steps:\n",
-    "- Preprocess dataset (clean, impute, engineer features, normalize).\n",
+    "Data analytics and modeling problems using Machine Learning (ML) are becoming popular and often rely on data science expertise to build accurate ML models. Such modeling tasks differs according to type of problem to be solved, eg. Text classification primarily involve the following steps:\n",
+    "- Preprocess dataset (vectorize).\n",
     "- Pick an appropriate model for the given dataset and prediction task at hand.\n",
     "- Tune the chosen model’s hyperparameters for the given dataset.\n",
     "\n",
-    "All of these steps are significantly time consuming and heavily rely on data scientist expertise. Unfortunately, to make this problem harder, the best feature subset, model, and hyperparameter choice widely varies with the dataset and the prediction task. Hence, there is no one-size-fits-all solution to achieve reasonably good model performance. Using a simple Python API, AutoML can quickly (faster) jump-start the datascience process with an accurately-tuned model and appropriate features for a given prediction task.\n",
+    "All of these steps are significantly time consuming and heavily rely on data scientist expertise. Unfortunately, to make this problem harder, the best feature subset, model, and hyperparameter choice widely varies with the dataset and the prediction task. Hence, there is no one-size-fits-all solution to achieve reasonably good model performance. Using a simple Python API, AutoML can quickly jump-start the datascience process with an accurately-tuned model and appropriate features for a given prediction task.\n",
     "\n",
     "## Table of Contents\n",
     "\n",
-    "- <a href='#setup'>0. Setup</a>\n",
-    "- <a href='#load-data'>1. Load the 20newsgroup Income dataset</a>\n",
-    "- <a href='#AutoML'>2. AutoML</a>\n",
-    "  - <a href='#Engine'>2.0. Set the engine and deprecation warnings</a>\n",
-    "  - <a href='#provider'>2.1. Create an Instance of Oracle AutoMLx</a>\n",
-    "  - <a href='#default'>2.2. Train a Model using AutoML</a>\n",
-    "  - <a href='#analyze'>2.3. Analyze the AutoML optimization process </a>\n",
-    "      - <a href='#algorithm-selection'>2.3.1. Algorithm Selection</a>\n",
-    "      - <a href='#adaptive-sampling'>2.3.2. Adaptive Sampling</a>\n",
-    "      - <a href='#feature-selection'>2.3.3. Feature Selection</a>\n",
-    "      - <a href='#hyperparameter-tuning'>2.3.4. Hyperparameter Tuning</a>\n",
-    "  - <a href='#timebudget'>2.4. Specify a time budget to AutoML</a>\n",
-    "  - <a href='#scoringfn'>2.5. Specify a different scoring metric to AutoML</a>\n",
-    "- <a href='#MLX'>3. Machine Learning Explainability (MLX)</a>\n",
-    "  - <a href='#MLX-initialization'> 3.1. Initialize an MLExplainer</a>\n",
-    "  - <a href='#MLX-global'> 3.2. Global Token Importance</a>\n",
-    "  - <a href='#MLX-local'> 3.3. Local Token Importance</a>\n",
+    "- <a href='#setup'>Setup</a>\n",
+    "- <a href='#load-data'>Load the 20newsgroup Income dataset</a>\n",
+    "- <a href='#AutoML'>AutoML</a>\n",
+    "  - <a href='#Engine'>Set the engine</a>\n",
+    "  - <a href='#provider'>Create an Instance of Oracle AutoMLx</a>\n",
+    "  - <a href='#default'>Train a Model using AutoMLx</a>\n",
+    "  - <a href='#analyze'>Analyze the AutoMLX optimization process </a>\n",
+    "      - <a href='#algorithm-selection'>Algorithm Selection</a>\n",
+    "      - <a href='#adaptive-sampling'>Adaptive Sampling</a>\n",
+    "      - <a href='#feature-selection'>Feature Selection</a>\n",
+    "      - <a href='#hyperparameter-tuning'>Hyperparameter Tuning</a>\n",
+    "  - <a href='#advanced'>Advanced AutoML Configuration</a>\n",
+    "- <a href='#MLX'>Machine Learning Explainability (MLX)</a>\n",
+    "  - <a href='#MLX-initialization'>Initialize an MLExplainer</a>\n",
+    "  - <a href='#MLX-global'>Global Token Importance</a>\n",
+    "  - <a href='#MLX-local'>Local Token Importance</a>\n",
     "- <a href='#ref'>References</a>"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "81f5984a",
+   "id": "49247493",
    "metadata": {},
    "source": [
     "<a id='setup'></a>\n",
@@ -86,43 +85,17 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "2229d7c3",
+   "id": "8f9a92c8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-03-22T16:11:19.435183Z",
-     "iopub.status.busy": "2023-03-22T16:11:19.434823Z",
-     "iopub.status.idle": "2023-03-22T16:11:22.573797Z",
-     "shell.execute_reply": "2023-03-22T16:11:22.573119Z"
+     "iopub.execute_input": "2024-05-23T16:56:48.512098Z",
+     "iopub.status.busy": "2024-05-23T16:56:48.511633Z",
+     "iopub.status.idle": "2024-05-23T16:56:49.018079Z",
+     "shell.execute_reply": "2024-05-23T16:56:49.017355Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Requirement already satisfied: seaborn==0.12.1 in /scratch_user/ypushak/automl-3/automl/py_3.8.7/lib/python3.8/site-packages (0.12.1)\r\n",
-      "Requirement already satisfied: pandas>=0.25 in /scratch_user/ypushak/automl-3/automl/py_3.8.7/lib/python3.8/site-packages (from seaborn==0.12.1) (1.4.1)\r\n",
-      "Requirement already satisfied: matplotlib!=3.6.1,>=3.1 in /scratch_user/ypushak/automl-3/automl/py_3.8.7/lib/python3.8/site-packages (from seaborn==0.12.1) (3.5.1)\r\n",
-      "Requirement already satisfied: numpy>=1.17 in /scratch_user/ypushak/automl-3/automl/py_3.8.7/lib/python3.8/site-packages (from seaborn==0.12.1) (1.22.2)\r\n",
-      "Requirement already satisfied: kiwisolver>=1.0.1 in /scratch_user/ypushak/automl-3/automl/py_3.8.7/lib/python3.8/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn==0.12.1) (1.1.0)\r\n",
-      "Requirement already satisfied: pyparsing>=2.2.1 in /scratch_user/ypushak/automl-3/automl/py_3.8.7/lib/python3.8/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn==0.12.1) (2.4.7)\r\n",
-      "Requirement already satisfied: python-dateutil>=2.7 in /scratch_user/ypushak/automl-3/automl/py_3.8.7/lib/python3.8/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn==0.12.1) (2.8.2)\r\n",
-      "Requirement already satisfied: packaging>=20.0 in /scratch_user/ypushak/automl-3/automl/py_3.8.7/lib/python3.8/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn==0.12.1) (21.3)\r\n",
-      "Requirement already satisfied: pillow>=6.2.0 in /scratch_user/ypushak/automl-3/automl/py_3.8.7/lib/python3.8/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn==0.12.1) (9.4.0)\r\n",
-      "Requirement already satisfied: fonttools>=4.22.0 in /scratch_user/ypushak/automl-3/automl/py_3.8.7/lib/python3.8/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn==0.12.1) (4.29.1)\r\n",
-      "Requirement already satisfied: cycler>=0.10 in /scratch_user/ypushak/automl-3/automl/py_3.8.7/lib/python3.8/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn==0.12.1) (0.10.0)\r\n",
-      "Requirement already satisfied: pytz>=2020.1 in /scratch_user/ypushak/automl-3/automl/py_3.8.7/lib/python3.8/site-packages (from pandas>=0.25->seaborn==0.12.1) (2022.1)\r\n",
-      "Requirement already satisfied: six in /scratch_user/ypushak/automl-3/automl/py_3.8.7/lib/python3.8/site-packages (from cycler>=0.10->matplotlib!=3.6.1,>=3.1->seaborn==0.12.1) (1.16.0)\r\n",
-      "Requirement already satisfied: setuptools in /scratch_user/ypushak/automl-3/automl/py_3.8.7/lib/python3.8/site-packages (from kiwisolver>=1.0.1->matplotlib!=3.6.1,>=3.1->seaborn==0.12.1) (62.3.4)\r\n",
-      "\r\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.2.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.0.1\u001b[0m\r\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\r\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "! pip install seaborn==0.12.1\n",
-    "\n",
     "%matplotlib inline\n",
     "%load_ext autoreload\n",
     "%autoreload 2"
@@ -130,7 +103,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "01b859b4",
+   "id": "8f3d242e",
    "metadata": {},
    "source": [
     "Load the required modules."
@@ -139,57 +112,54 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "946981e4",
+   "id": "4ba6a94d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-03-22T16:11:22.576916Z",
-     "iopub.status.busy": "2023-03-22T16:11:22.576448Z",
-     "iopub.status.idle": "2023-03-22T16:11:26.984078Z",
-     "shell.execute_reply": "2023-03-22T16:11:26.983466Z"
+     "iopub.execute_input": "2024-05-23T16:56:49.021627Z",
+     "iopub.status.busy": "2024-05-23T16:56:49.020971Z",
+     "iopub.status.idle": "2024-05-23T16:56:51.742900Z",
+     "shell.execute_reply": "2024-05-23T16:56:51.742146Z"
     }
    },
    "outputs": [],
    "source": [
-    "import gzip\n",
+    "import numpy as np\n",
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
     "from sklearn.metrics import f1_score\n",
     "from sklearn.model_selection import train_test_split\n",
     "from sklearn.datasets import fetch_20newsgroups\n",
+    "import time\n",
+    "import datetime\n",
     "\n",
     "# Settings for plots\n",
     "plt.rcParams['figure.figsize'] = [10, 7]\n",
     "plt.rcParams['font.size'] = 15\n",
-    "sns.set(color_codes=True)\n",
-    "sns.set(font_scale=1.5)\n",
-    "sns.set_palette(\"bright\")\n",
-    "sns.set_style(\"whitegrid\")\n",
     "\n",
-    "import automl\n",
-    "from automl import init"
+    "import automlx\n",
+    "from automlx import init"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "b608b70a",
+   "id": "a4ef0c9e",
    "metadata": {},
    "source": [
     "<a id='load-data'></a>\n",
     "## Load the 20 News Group dataset\n",
-    "We start by reading in the dataset from sklearn. The dataset has already been pre-split into training and test sets. The training set will be used to create a Machine Learning model using AutoML, and the test set will be used to evaluate the model's performance on unseen data."
+    "We start by reading in the dataset from sklearn. The dataset has already been pre-split into training and test sets. The training set will be used to create a Machine Learning model using AutoMLx, and the test set will be used to evaluate the model's performance on unseen data."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "6df745eb",
+   "id": "3da28df4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-03-22T16:11:26.986880Z",
-     "iopub.status.busy": "2023-03-22T16:11:26.986567Z",
-     "iopub.status.idle": "2023-03-22T16:11:27.394745Z",
-     "shell.execute_reply": "2023-03-22T16:11:27.394130Z"
+     "iopub.execute_input": "2024-05-23T16:56:51.746115Z",
+     "iopub.status.busy": "2024-05-23T16:56:51.745377Z",
+     "iopub.status.idle": "2024-05-23T16:56:52.192214Z",
+     "shell.execute_reply": "2024-05-23T16:56:52.191498Z"
     }
    },
    "outputs": [],
@@ -209,7 +179,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "622c215f",
+   "id": "28128e81",
    "metadata": {},
    "source": [
     "Lets look at a few of the values in the data. 20 NewsGroup is a classification dataset made of text samples. Each sample has an associated class (also called topic), which can be one of the followings:"
@@ -218,13 +188,13 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "d1d90cc5",
+   "id": "fda0d5e4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-03-22T16:11:27.397449Z",
-     "iopub.status.busy": "2023-03-22T16:11:27.397243Z",
-     "iopub.status.idle": "2023-03-22T16:11:27.434118Z",
-     "shell.execute_reply": "2023-03-22T16:11:27.433600Z"
+     "iopub.execute_input": "2024-05-23T16:56:52.195256Z",
+     "iopub.status.busy": "2024-05-23T16:56:52.194690Z",
+     "iopub.status.idle": "2024-05-23T16:56:52.219168Z",
+     "shell.execute_reply": "2024-05-23T16:56:52.218591Z"
     }
    },
    "outputs": [
@@ -264,7 +234,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5a974537",
+   "id": "673dd16b",
    "metadata": {},
    "source": [
     "We display some examples of data samples."
@@ -273,13 +243,13 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "ab414a3c",
+   "id": "99b7330c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-03-22T16:11:27.436227Z",
-     "iopub.status.busy": "2023-03-22T16:11:27.436031Z",
-     "iopub.status.idle": "2023-03-22T16:11:27.473418Z",
-     "shell.execute_reply": "2023-03-22T16:11:27.472922Z"
+     "iopub.execute_input": "2024-05-23T16:56:52.221599Z",
+     "iopub.status.busy": "2024-05-23T16:56:52.221127Z",
+     "iopub.status.idle": "2024-05-23T16:56:52.245471Z",
+     "shell.execute_reply": "2024-05-23T16:56:52.244906Z"
     }
    },
    "outputs": [
@@ -350,58 +320,26 @@
     "X_train.head()"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "744fde7b",
-   "metadata": {},
-   "source": [
-    "We restrict the data to the 4 topics that are related to science."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "229444e9",
+   "id": "05116d26",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-03-22T16:11:27.475498Z",
-     "iopub.status.busy": "2023-03-22T16:11:27.475315Z",
-     "iopub.status.idle": "2023-03-22T16:11:28.504105Z",
-     "shell.execute_reply": "2023-03-22T16:11:28.503591Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "science_labels = [i for i in range(len(target_names)) if 'sci' in target_names[i]]\n",
-    "train_science_indices = [i for i in range(len(y_train)) if y_train.iloc[i][0] in science_labels]\n",
-    "test_science_indices = [i for i in range(len(y_test)) if y_test.iloc[i][0] in science_labels]\n",
-    "\n",
-    "X_train = X_train.iloc[train_science_indices]\n",
-    "y_train = y_train.iloc[train_science_indices]\n",
-    "X_test = X_test.iloc[test_science_indices]\n",
-    "y_test = y_test.iloc[test_science_indices]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "b92db20d",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-03-22T16:11:28.506573Z",
-     "iopub.status.busy": "2023-03-22T16:11:28.506388Z",
-     "iopub.status.idle": "2023-03-22T16:11:28.538643Z",
-     "shell.execute_reply": "2023-03-22T16:11:28.538173Z"
+     "iopub.execute_input": "2024-05-23T16:56:52.247918Z",
+     "iopub.status.busy": "2024-05-23T16:56:52.247381Z",
+     "iopub.status.idle": "2024-05-23T16:56:52.267227Z",
+     "shell.execute_reply": "2024-05-23T16:56:52.266658Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "2373"
+       "11314"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -412,7 +350,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ba03ae30",
+   "id": "00b0acfa",
    "metadata": {},
    "source": [
     "We further downsample the train set to have a reasonable training time for this demonstration."
@@ -420,27 +358,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "af6a0f95",
+   "execution_count": 7,
+   "id": "dda64981",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-03-22T16:11:28.540896Z",
-     "iopub.status.busy": "2023-03-22T16:11:28.540508Z",
-     "iopub.status.idle": "2023-03-22T16:11:28.581307Z",
-     "shell.execute_reply": "2023-03-22T16:11:28.580812Z"
+     "iopub.execute_input": "2024-05-23T16:56:52.270760Z",
+     "iopub.status.busy": "2024-05-23T16:56:52.270253Z",
+     "iopub.status.idle": "2024-05-23T16:56:52.328462Z",
+     "shell.execute_reply": "2024-05-23T16:56:52.327880Z"
     }
    },
    "outputs": [],
    "source": [
     "X_train, _, y_train, _ = train_test_split(X_train, y_train,\n",
-    "                                          test_size=0.5,\n",
+    "                                          test_size=0.7,\n",
     "                                          stratify=y_train,\n",
     "                                          random_state=42)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "bc5c2df0",
+   "id": "e4709814",
    "metadata": {},
    "source": [
     "Finally we generate a validation set and only use that for internal pipeline validation."
@@ -448,14 +386,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "a14c960d",
+   "execution_count": 8,
+   "id": "946ed316",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-03-22T16:11:28.583968Z",
-     "iopub.status.busy": "2023-03-22T16:11:28.583417Z",
-     "iopub.status.idle": "2023-03-22T16:11:28.619430Z",
-     "shell.execute_reply": "2023-03-22T16:11:28.618945Z"
+     "iopub.execute_input": "2024-05-23T16:56:52.331229Z",
+     "iopub.status.busy": "2024-05-23T16:56:52.330710Z",
+     "iopub.status.idle": "2024-05-23T16:56:52.362869Z",
+     "shell.execute_reply": "2024-05-23T16:56:52.362251Z"
     }
    },
    "outputs": [],
@@ -468,7 +406,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6c77c416",
+   "id": "a5ae1fe8",
    "metadata": {},
    "source": [
     "<a id='AutoML'></a>\n",
@@ -477,69 +415,24 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2040b0bc",
+   "id": "7198a9a6",
    "metadata": {},
    "source": [
     "<a id='Engine'></a>\n",
-    "### Setting the engine and deprecation warnings\n",
-    "The AutoML pipeline offers the function `init`, which allows to initialize the parallelization engine. "
+    "### Setting the execution engine\n",
+    "The AutoML package offers the function `init`, which allows to initialize the parallelization engine."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "4d327047",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-03-22T16:11:28.622162Z",
-     "iopub.status.busy": "2023-03-22T16:11:28.621585Z",
-     "iopub.status.idle": "2023-03-22T16:11:30.432257Z",
-     "shell.execute_reply": "2023-03-22T16:11:30.430994Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[2023-03-22 09:11:28,653] [automl.xengine] Using Dask Execution\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/scratch_user/ypushak/automl-3/automl/package/automl/interface/default.py:79: AutoMLxDeprecationWarning: engine dask is deprecated and will be removed in version 23.3.0.\n",
-      "  warn(f'engine {engine} is deprecated and will be removed in version 23.3.0.', AutoMLxDeprecationWarning)\n",
-      "/scratch_user/ypushak/automl-3/automl/package/automl/interface/default.py:82: AutoMLxDeprecationWarning: engine option dask_scheduler is deprecated and will be removed in version 23.3.0\n",
-      "  warn(f'engine option {engine_opt} is deprecated and will be removed in version 23.3.0', AutoMLxDeprecationWarning)\n"
-     ]
-    }
-   ],
-   "source": [
-    "init(engine='dask')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "481e8580",
-   "metadata": {},
-   "source": [
-    "As you can see from the above deprecation warning, dask and it's related configuration options are deprecated, and will be removed in AutoMLx version 23.3.0. This, along with a few other API changes, are coming in 23.3.0. By default, the AutoMLx package is configured to display deprecation warnings for all such changes. However, they can be disabled for any newly-created AutoMLx objects via `init`. \n",
-    "\n",
-    "We will also switch to the `'local'` parallelization engine, which uses python's multiprocessing library for parallelism instead."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "ea49a184",
+   "execution_count": 9,
+   "id": "ddd270d4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-03-22T16:11:30.435850Z",
-     "iopub.status.busy": "2023-03-22T16:11:30.435200Z",
-     "iopub.status.idle": "2023-03-22T16:11:31.081375Z",
-     "shell.execute_reply": "2023-03-22T16:11:31.080315Z"
+     "iopub.execute_input": "2024-05-23T16:56:52.365693Z",
+     "iopub.status.busy": "2024-05-23T16:56:52.365183Z",
+     "iopub.status.idle": "2024-05-23T16:56:57.425907Z",
+     "shell.execute_reply": "2024-05-23T16:56:57.425055Z"
     }
    },
    "outputs": [
@@ -547,70 +440,71 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2023-03-22 09:11:30,484] [automl.interface] Execution engine (local) has already been initialized. Reinitializing!\n",
-      "[2023-03-22 09:11:30,925] [automl.xengine] Local ProcessPool execution (n_jobs=40)\n"
+      "[2024-05-23 09:56:52,788] [automlx.backend] Overwriting ray session directory to /tmp/zdvafa74/ray, which will be deleted at engine shutdown. If you wish to retain ray logs, provide _temp_dir in ray_setup dict of engine_opts when initializing the AutoMLx engine.\n"
      ]
     }
    ],
    "source": [
-    "init(engine='local', check_deprecation_warnings=False)"
+    "init(engine='ray')"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "90ea60bc",
+   "id": "eb107a8f",
    "metadata": {},
    "source": [
     "<a id='provider'></a>\n",
     "### Create an instance of Oracle AutoMLx\n",
     "\n",
-    "The Oracle AutoMLx solution provides a pipeline that automatically finds a tuned model given a prediction task and a training dataset. In particular it allows to find a tuned model for any supervised prediction task, e.g. classification or regression where the target can be binary, categorical or real-valued.\n",
+    "The Oracle AutoMLx solution provides a pipeline that automatically finds a tuned model given a prediction task and a training dataset. In particular it allows to find a tuned model for any supervised prediction task, for example: classification or regression where the target can be binary, categorical or real-valued.\n",
     "\n",
-    "AutoML consists of five main modules:\n",
+    "AutoML consists of five main steps:\n",
     "- **Preprocessing** (Feature extraction and selection) : The pipeline extracts tabular features using [TFIDF](https://en.wikipedia.org/wiki/Tf%E2%80%93idf) that it then selects between.\n",
-    "- **Algorithm Selection** : Identify the right classification algorithm -in this notebook- for a given dataset, choosing from amongst:\n",
-    "    - AdaBoostClassifier\n",
-    "    - DecisionTreeClassifier\n",
-    "    - ExtraTreesClassifier\n",
-    "    - TorchMLPClassifier\n",
-    "    - KNeighborsClassifier\n",
-    "    - LGBMClassifier\n",
-    "    - LinearSVC\n",
-    "    - LogisticRegression\n",
-    "    - RandomForestClassifier\n",
-    "    - SVC\n",
-    "    - XGBClassifier\n",
-    "    - GaussianNB\n",
+    "- **Algorithm Selection** : Identify the right classification algorithm for a given dataset, choosing from amongst:\n",
+    "   - AdaBoostClassifier\n",
+    "   - CatBoostClassifier\n",
+    "   - DecisionTreeClassifier\n",
+    "   - ExtraTreesClassifier\n",
+    "   - TorchMLPClassifier\n",
+    "   - KNeighborsClassifier\n",
+    "   - LGBMClassifier\n",
+    "   - LinearSVC\n",
+    "   - LogisticRegression\n",
+    "   - RandomForestClassifier\n",
+    "   - SVC\n",
+    "   - XGBClassifier\n",
+    "   - GaussianNB\n",
+    "   - TabNetClassifier\n",
     "- **Adaptive Sampling** : Select a subset of the data samples for the model to be trained on.\n",
     "- **Feature Selection** : Select a subset of the features (extracted with TFIDF), based on the previously selected model. In the rest of this notebook, we will implicitly refer to the extracted features as data features.\n",
     "- **Hyperparameter Tuning** : Find the right model parameters that maximize score for the given dataset.\n",
     "\n",
-    "All these pieces are readily combined into a simple AutoML pipeline which automates the entire Machine Learning process with minimal user input/interaction."
+    "All these pieces are readily combined into a simple AutoMLx pipeline which automates the entire Machine Learning process with minimal user input/interaction."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "6c5e0a78",
+   "id": "d88c0b3e",
    "metadata": {},
    "source": [
     "<a id='default'></a>\n",
-    "### Train a model using AutoML\n",
+    "### Train a model using AutoMLx\n",
     "\n",
-    "The AutoML API is quite simple to work with. We create an instance of the pipeline. Next, the training data is passed to the `fit()` function which executes the three previously mentioned steps.\n",
+    "The AutoMLx API is quite simple to work with. We create an instance of the pipeline. Next, the training data is passed to the `fit()` function which executes the four previously mentioned steps.\n",
     "\n",
     "A model is then generated and can be used for prediction tasks. We use the roc_auc scoring metric to evaluate the performance of this model on unseen data (`X_test`)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "63e719f7",
+   "execution_count": 10,
+   "id": "3a680a1e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-03-22T16:11:31.084897Z",
-     "iopub.status.busy": "2023-03-22T16:11:31.084656Z",
-     "iopub.status.idle": "2023-03-22T16:18:08.290853Z",
-     "shell.execute_reply": "2023-03-22T16:18:08.290182Z"
+     "iopub.execute_input": "2024-05-23T16:56:57.430120Z",
+     "iopub.status.busy": "2024-05-23T16:56:57.429823Z",
+     "iopub.status.idle": "2024-05-23T17:42:29.815427Z",
+     "shell.execute_reply": "2024-05-23T17:42:29.814592Z"
     }
    },
    "outputs": [
@@ -618,29 +512,42 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2023-03-22 09:11:31,698] [automl.pipeline] Random state (7) is used for model builds\n",
-      "[2023-03-22 09:11:31,965] [automl.pipeline] Dataset shape: (948, 1)\n",
-      "[2023-03-22 09:11:32,011] [automl.pipeline] Running Auto-Preprocessing\n",
-      "[2023-03-22 09:11:34,034] [automl.pipeline] Preprocessing completed. Updated Dataset shape: (948, 11026), cv: None\n",
-      "[2023-03-22 09:11:38,804] [automl.pipeline] SVC, KNeighborsClassifier are disabled for datasets with > 10K samples or > 1K features\n",
-      "[2023-03-22 09:11:38,805] [automl.pipeline] Running Model Selection\n",
-      "[2023-03-22 09:12:22,758] [automl.pipeline] Model Selection completed. Selected model: ['TorchMLPClassifier']\n",
-      "[2023-03-22 09:12:22,760] [automl.pipeline] Running Adaptive Sampling. Dataset Shape: (948, 11026), Valid Shape: (238, 11026), CV: None, Class counts: [237 237 237 237]\n",
-      "[2023-03-22 09:13:40,825] [automl.pipeline] Adaptive Sampling Completed. Updated Dataset Shape: (948, 11026), Valid Shape: (238, 11026), CV: None, Class counts: [237 237 237 237]\n",
-      "[2023-03-22 09:13:40,826] [automl.pipeline] Starting Feature Selection 0. Dataset Shape: (948, 11026)\n",
-      "[2023-03-22 09:16:13,915] [automl.pipeline] Feature Selection 0 completed. Updated Dataset shape: (948, 3972)\n",
-      "[2023-03-22 09:16:13,987] [automl.pipeline] Tuning TorchMLPClassifier\n",
-      "[2023-03-22 09:17:36,774] [automl.pipeline] Tuning completed. Best params: {'activation': 'relu', 'class_weight': None, 'dropout': 0.1, 'l2_reg': 0.0, 'nr_layers': 3, 'nr_neurons': 100, 'optimizer': 'adagrad'}\n",
-      "[2023-03-22 09:17:42,692] [automl.pipeline] (Re)fitting Pipeline\n",
-      "[2023-03-22 09:18:05,836] [automl.xengine] Local ProcessPool execution (n_jobs=40)\n",
-      "[2023-03-22 09:18:06,041] [automl.pipeline] AutoML completed. Time taken - 387.478 sec\n",
-      "F1 Micro Score on test data: 0.913\n"
+      "[2024-05-23 09:56:57,946] [automlx.interface] Dataset shape: (3394,1)\n",
+      "[2024-05-23 09:57:01,123] [sanerec.autotuning.parameter] Hyperparameter epsilon autotune range is set to its validation range. This could lead to long training times\n",
+      "[2024-05-23 09:57:01,147] [sanerec.autotuning.parameter] Hyperparameter repeat_quality_threshold autotune range is set to its validation range. This could lead to long training times\n",
+      "[2024-05-23 09:57:01,154] [sanerec.autotuning.parameter] Hyperparameter scope autotune range is set to its validation range. This could lead to long training times\n",
+      "[2024-05-23 09:57:01,487] [automlx.data_transform] Running preprocessing. Number of features: 2\n",
+      "[2024-05-23 09:57:16,738] [automlx.data_transform] Preprocessing completed. Took 15.251 secs\n",
+      "[2024-05-23 09:57:50,052] [automlx.process] Running Model Generation\n",
+      "[2024-05-23 09:57:50,345] [automlx.process] CatBoostClassifier is disabled. The CatBoostClassifier model is only recommended for datasets with less than 10000 features.\n",
+      "[2024-05-23 09:57:50,349] [automlx.process] ExtraTreesClassifier is disabled. The ExtraTreesClassifier model is only recommended for datasets with less than 10000 features.\n",
+      "[2024-05-23 09:57:50,353] [automlx.process] KNeighborsClassifier is disabled. The KNeighborsClassifier model is only recommended for datasets with less than 10000 samples and 1000 features.\n",
+      "[2024-05-23 09:57:50,358] [automlx.process] RandomForestClassifier is disabled. The RandomForestClassifier model is only recommended for datasets with less than 10000 features.\n",
+      "[2024-05-23 09:57:50,360] [automlx.process] SVC is disabled. The SVC model is only recommended for datasets with less than 10000 samples and 1000 features.\n",
+      "[2024-05-23 09:57:50,364] [automlx.process] XGBClassifier is disabled. The XGBClassifier model is only recommended for datasets with less than 5000 features.\n",
+      "[2024-05-23 09:57:50,366] [automlx.process] LogisticRegressionClassifier is disabled. The LogisticRegressionClassifier model is only recommended for datasets with less than 5000 features.\n",
+      "[2024-05-23 09:57:50,368] [automlx.process] Model Generation completed.\n",
+      "[2024-05-23 09:57:50,472] [automlx.model_selection] Running Model Selection\n",
+      "[2024-05-23 10:13:18,091] [automlx.model_selection] Model Selection completed - Took 927.619 sec - Selected models: ['LGBMClassifier']\n",
+      "[2024-05-23 10:13:18,704] [automlx.adaptive_sampling] Running Adaptive Sampling. Dataset shape: (3394,25239).\n",
+      "[2024-05-23 10:13:21,982] [automlx.adaptive_sampling] Adaptive Sampling: top_limit: 144 < bottom_limit: 1000,\n",
+      "sampling process will be skipped\n",
+      "[2024-05-23 10:13:21,995] [automlx.adaptive_sampling] Adaptive Sampling: top_limit: 36 < bottom_limit: 1000,\n",
+      "sampling process will be skipped\n",
+      "[2024-05-23 10:13:22,295] [automlx.feature_selection] Starting feature ranking for LGBMClassifier\n",
+      "[2024-05-23 10:37:23,506] [automlx.feature_selection] Feature Selection completed. Took 1441.215 secs.\n",
+      "[2024-05-23 10:37:27,037] [automlx.trials] Running Model Tuning for ['LGBMClassifier']\n",
+      "[2024-05-23 10:40:58,453] [automlx.trials] Best parameters for LGBMClassifier: {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.00119999, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}\n",
+      "[2024-05-23 10:40:58,455] [automlx.trials] Model Tuning completed. Took: 211.419 secs\n",
+      "[2024-05-23 10:41:25,079] [automlx.interface] Re-fitting pipeline\n",
+      "[2024-05-23 10:42:06,170] [automlx.interface] AutoMLx completed.\n",
+      "F1 Micro Score on test data: 0.699\n"
      ]
     }
    ],
    "source": [
-    "est1 = automl.Pipeline(task='classification')\n",
-    "est1.fit(X_train, y_train, X_valid, y_valid, cv=None, col_types=['text'])\n",
+    "est1 = automlx.Pipeline(task='classification')\n",
+    "est1.fit(X_train, y_train, X_valid, y_valid, col_types=['text'])\n",
     "\n",
     "y_predict = est1.predict(X_test)\n",
     "score_default = f1_score(y_test, y_predict, average=\"micro\")\n",
@@ -650,93 +557,133 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cbe060bd",
+   "id": "3f9e6169",
    "metadata": {},
    "source": [
     "<a id='analyze'></a>\n",
-    "### Analyze the AutoML optimization process\n",
+    "### Analyze the AutoMLx optimization process\n",
     "\n",
     "During the AutoML process, a summary of the optimization process is logged. It consists of:\n",
     "- Information about the training data\n",
-    "- Information about the AutoML Pipeline, such as:\n",
-    "    - selected features that AutoML found to be most predictive in the training data;\n",
+    "- Information about the AutoMLx Pipeline, such as:\n",
+    "    - selected features that AutoMLx found to be most predictive in the training data;\n",
     "    - selected algorithm that was the best choice for this data;\n",
     "    - hyperparameters for the selected algorithm."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "a2fb7117",
+   "id": "8fd98140",
    "metadata": {},
    "source": [
-    "AutoML provides a print_summary API to output all the different trials performed."
+    "AutoMLx provides a print_summary API to output all the different trials performed."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "139fb7f1",
+   "execution_count": 11,
+   "id": "9aa8532f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-03-22T16:18:08.294355Z",
-     "iopub.status.busy": "2023-03-22T16:18:08.294161Z",
-     "iopub.status.idle": "2023-03-22T16:18:10.792688Z",
-     "shell.execute_reply": "2023-03-22T16:18:10.791994Z"
+     "iopub.execute_input": "2024-05-23T17:42:29.819395Z",
+     "iopub.status.busy": "2024-05-23T17:42:29.818682Z",
+     "iopub.status.idle": "2024-05-23T17:42:31.322947Z",
+     "shell.execute_reply": "2024-05-23T17:42:31.322230Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
+       "<br><div style='text-align: center; font-weight: bold; font-size: 16px;'>General Summary</div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <td>Training Dataset size</td>\n",
-       "      <td>(948, 1)</td>\n",
+       "      <th>Training Dataset size</th>\n",
+       "      <td>(2715, 1)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>Validation Dataset size</td>\n",
-       "      <td>(238, 1)</td>\n",
+       "      <th>Validation Dataset size</th>\n",
+       "      <td>(679, 1)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>CV</td>\n",
-       "      <td>None</td>\n",
+       "      <th>CV</th>\n",
+       "      <td>ManualSplit(Shuffle=False, Seed=7)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>Optimization Metric</td>\n",
+       "      <th>Optimization Metric</th>\n",
        "      <td>neg_log_loss</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>Selected Features</td>\n",
-       "      <td>Index(['001230', '004418', '00r', '01', '01wb', '02', '022922', '032623',\n",
-       "       '0358', '0366',\n",
-       "       ...\n",
-       "       'za', 'zabriskie', 'zcomm', 'zenier', 'zikzak', 'zisfein', 'zmodem',\n",
-       "       'zoo', 'zoology', 'zrepachol'],\n",
-       "      dtype='object', length=3972)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>Selected Algorithm</td>\n",
-       "      <td>TorchMLPClassifier</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>Time taken</td>\n",
-       "      <td>364.896</td>\n",
+       "      <th>Selected Algorithm</th>\n",
+       "      <td>LGBMClassifier</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>Selected Hyperparameters</td>\n",
-       "      <td>{'hidden_layer_sizes': [100, 100, 100], 'dropout': 0.1, 'activation': 'relu', 'loss': 'default', 'optimizer': 'adagrad', 'n_epochs': 200, 'batch_size': 256, 'l2_reg': 0.0, 'class_weight': None, 'random_state': 7}</td>\n",
+       "      <th>Selected Hyperparameters</th>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.00119999, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>AutoML version</td>\n",
-       "      <td>23.1.1</td>\n",
+       "      <th>AutoMLx version</th>\n",
+       "      <td>24.2.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>Python version</td>\n",
+       "      <th>Python version</th>\n",
        "      <td>3.8.7 (default, Aug 25 2022, 13:59:56) \\n[GCC 8.5.0 20210514 (Red Hat 8.5.0-10.1.0.1)]</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
-       "</table>"
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                                                                                                                                                                                              \n",
+       "Training Dataset size                                                                                                                                                                                                (2715, 1)\n",
+       "Validation Dataset size                                                                                                                                                                                               (679, 1)\n",
+       "CV                                                                                                                                                                                          ManualSplit(Shuffle=False, Seed=7)\n",
+       "Optimization Metric                                                                                                                                                                                               neg_log_loss\n",
+       "Selected Algorithm                                                                                                                                                                                              LGBMClassifier\n",
+       "Selected Hyperparameters  {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.00119999, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}\n",
+       "AutoMLx version                                                                                                                                                                                                         24.2.0\n",
+       "Python version                                                                                                                          3.8.7 (default, Aug 25 2022, 13:59:56) \\n[GCC 8.5.0 20210514 (Red Hat 8.5.0-10.1.0.1)]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<br><div style='text-align: center; font-weight: bold; font-size: 16px;'>Trials Summary</div>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -748,287 +695,5487 @@
     {
      "data": {
       "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Step</th>\n",
+       "      <th># Samples</th>\n",
+       "      <th># Features</th>\n",
        "      <th>Algorithm</th>\n",
-       "      <th>#Samples</th>\n",
-       "      <th>#Features</th>\n",
-       "      <th>Mean Validation Score</th>\n",
        "      <th>Hyperparameters</th>\n",
-       "      <th>CPU Time</th>\n",
+       "      <th>Score (neg_log_loss)</th>\n",
+       "      <th>Runtime (Seconds)</th>\n",
        "      <th>Memory Usage (GB)</th>\n",
+       "      <th>Finished</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <td>TorchMLPClassifier_HT</td>\n",
-       "      <td>948</td>\n",
-       "      <td>3972</td>\n",
-       "      <td>-0.0589</td>\n",
-       "      <td>{'activation': 'relu', 'class_weight': None, 'dropout': 0.1, 'l2_reg': 0.0, 'nr_layers': 3, 'nr_neurons': 100, 'optimizer': 'adagrad'}</td>\n",
-       "      <td>15.5464</td>\n",
-       "      <td>(0.007289886474609375, None)</td>\n",
+       "      <th>0</th>\n",
+       "      <td>Model Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>25238</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8177</td>\n",
+       "      <td>13.8053</td>\n",
+       "      <td>1.2231</td>\n",
+       "      <td>Thu May 23 09:58:12 2024</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>TorchMLPClassifier_HT</td>\n",
-       "      <td>948</td>\n",
-       "      <td>3972</td>\n",
-       "      <td>-0.0740</td>\n",
-       "      <td>{'activation': 'relu', 'class_weight': None, 'dropout': 0.0012425681319433632, 'l2_reg': 0.0, 'nr_layers': 3, 'nr_neurons': 100, 'optimizer': 'adam'}</td>\n",
-       "      <td>14.4130</td>\n",
-       "      <td>(6.103515625e-05, None)</td>\n",
+       "      <th>1</th>\n",
+       "      <td>Model Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>25238</td>\n",
+       "      <td>TorchMLPClassifier</td>\n",
+       "      <td>{'optimizer_class': 'Adam', 'shuffle_dataset_each_epoch': True, 'optimizer_params': {}, 'criterion_class': None, 'criterion_params': {}, 'scheduler_class': None, 'scheduler_params': {}, 'batch_size': 128, 'lr': 0.001, 'epochs': 18, 'input_transform': 'auto', 'tensorboard_dir': None, 'use_tqdm': None, 'prediction_batch_size': 128, 'prediction_input_transform': 'auto', 'shuffling_buffer_size': None, 'depth': 4, 'num_logits': 1000, 'div_factor': 2, 'activation': 'ReLU', 'dropout': 0.1}</td>\n",
+       "      <td>-2.9938</td>\n",
+       "      <td>915.1965</td>\n",
+       "      <td>1.5376</td>\n",
+       "      <td>Thu May 23 10:13:14 2024</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>TorchMLPClassifier_HT</td>\n",
-       "      <td>948</td>\n",
-       "      <td>3972</td>\n",
-       "      <td>-0.0740</td>\n",
-       "      <td>{'activation': 'relu', 'class_weight': None, 'dropout': 1e-05, 'l2_reg': 0.0, 'nr_layers': 3, 'nr_neurons': 100, 'optimizer': 'adam'}</td>\n",
-       "      <td>15.2612</td>\n",
-       "      <td>(0.000148773193359375, None)</td>\n",
+       "      <th>2</th>\n",
+       "      <td>Model Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>25238</td>\n",
+       "      <td>GaussianNB</td>\n",
+       "      <td>{}</td>\n",
+       "      <td>-7.644</td>\n",
+       "      <td>11.5540</td>\n",
+       "      <td>1.4871</td>\n",
+       "      <td>Thu May 23 09:58:09 2024</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>TorchMLPClassifier_HT</td>\n",
-       "      <td>948</td>\n",
-       "      <td>3972</td>\n",
-       "      <td>-0.0740</td>\n",
-       "      <td>{'activation': 'relu', 'class_weight': 'balanced', 'dropout': 0.1, 'l2_reg': 0.0, 'nr_layers': 3, 'nr_neurons': 100, 'optimizer': 'adam'}</td>\n",
-       "      <td>15.6056</td>\n",
-       "      <td>(0.0, None)</td>\n",
+       "      <th>3</th>\n",
+       "      <td>Model Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>25238</td>\n",
+       "      <td>DecisionTreeClassifier</td>\n",
+       "      <td>{'min_samples_split': 0.00125, 'min_samples_leaf': 0.000625, 'max_features': 1.0, 'class_weight': None}</td>\n",
+       "      <td>-15.4821</td>\n",
+       "      <td>12.6717</td>\n",
+       "      <td>1.2525</td>\n",
+       "      <td>Thu May 23 09:58:10 2024</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>TorchMLPClassifier_HT</td>\n",
-       "      <td>948</td>\n",
-       "      <td>3972</td>\n",
-       "      <td>-0.0740</td>\n",
-       "      <td>{'activation': 'relu', 'class_weight': None, 'dropout': 0.1, 'l2_reg': 0.0, 'nr_layers': 3, 'nr_neurons': 100, 'optimizer': 'adam'}</td>\n",
-       "      <td>17.4901</td>\n",
-       "      <td>(3.0517578125e-05, None)</td>\n",
+       "      <th>4</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8081</td>\n",
+       "      <td>25.3911</td>\n",
+       "      <td>1.8933</td>\n",
+       "      <td>Thu May 23 10:29:20 2024</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
+       "      <th>5</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>9885</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8153</td>\n",
+       "      <td>20.3971</td>\n",
+       "      <td>2.6275</td>\n",
+       "      <td>Thu May 23 10:31:25 2024</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>TorchMLPClassifier_AdaBoostClassifier_FS</td>\n",
-       "      <td>948</td>\n",
-       "      <td>1</td>\n",
-       "      <td>-1.4784</td>\n",
-       "      <td>{'activation': 'relu', 'class_weight': None, 'dropout': 0.1, 'l2_reg': 0.0, 'nr_layers': 1, 'nr_neurons': 100, 'optimizer': 'adam'}</td>\n",
-       "      <td>0.5320</td>\n",
-       "      <td>0.0</td>\n",
+       "      <th>6</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>11862</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8173</td>\n",
+       "      <td>22.7057</td>\n",
+       "      <td>1.8509</td>\n",
+       "      <td>Thu May 23 10:31:16 2024</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>GaussianNB_AS</td>\n",
-       "      <td>948</td>\n",
-       "      <td>11026</td>\n",
-       "      <td>-2.6122</td>\n",
-       "      <td>{}</td>\n",
-       "      <td>0.2168</td>\n",
-       "      <td>0.0</td>\n",
+       "      <th>7</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>20499</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8188</td>\n",
+       "      <td>27.9225</td>\n",
+       "      <td>2.7320</td>\n",
+       "      <td>Thu May 23 10:28:37 2024</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>TorchMLPClassifier_HT</td>\n",
-       "      <td>948</td>\n",
-       "      <td>3972</td>\n",
-       "      <td>-8.2554</td>\n",
-       "      <td>{'activation': 'tanh', 'class_weight': 'balanced', 'dropout': 5e-06, 'l2_reg': 0.0, 'nr_layers': 3, 'nr_neurons': 133, 'optimizer': 'rmsprop'}</td>\n",
-       "      <td>20.4976</td>\n",
-       "      <td>(0.0020904541015625, None)</td>\n",
+       "      <th>8</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.819</td>\n",
+       "      <td>23.8457</td>\n",
+       "      <td>1.8576</td>\n",
+       "      <td>Thu May 23 10:31:04 2024</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>DecisionTreeClassifier_AS</td>\n",
-       "      <td>948</td>\n",
-       "      <td>11026</td>\n",
-       "      <td>-10.0133</td>\n",
-       "      <td>{'class_weight': None, 'max_features': 1.0, 'min_samples_leaf': 0.000625, 'min_samples_split': 0.00125}</td>\n",
-       "      <td>0.5121</td>\n",
-       "      <td>0.0</td>\n",
+       "      <th>9</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>17082</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8193</td>\n",
+       "      <td>28.2084</td>\n",
+       "      <td>2.8665</td>\n",
+       "      <td>Thu May 23 10:29:08 2024</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>TorchMLPClassifier_HT</td>\n",
-       "      <td>948</td>\n",
-       "      <td>3972</td>\n",
-       "      <td>-12.1224</td>\n",
-       "      <td>{'activation': 'relu', 'class_weight': None, 'dropout': 0.1, 'l2_reg': 0.0, 'nr_layers': 3, 'nr_neurons': 100, 'optimizer': 'rmsprop'}</td>\n",
-       "      <td>16.5498</td>\n",
-       "      <td>(0.0, None)</td>\n",
+       "      <th>10</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>5720</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8235</td>\n",
+       "      <td>16.3118</td>\n",
+       "      <td>1.7139</td>\n",
+       "      <td>Thu May 23 10:30:16 2024</td>\n",
        "    </tr>\n",
-       "  </tbody>\n",
-       "</table>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "est1.print_summary()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7b58eda6",
-   "metadata": {},
-   "source": [
-    "We also provide the capability to visualize the results of each stage of the AutoML pipeline. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0fd7f7ee",
-   "metadata": {},
-   "source": [
-    "<a id='algorithm-selection'></a>\n",
-    "#### Algorithm Selection\n",
-    "\n",
-    "The plot below shows the scores predicted by Algorithm Selection for each algorithm. The horizontal line shows the average score across all algorithms. Algorithms below the line are colored turquoise, whereas those with a score higher than the mean are colored teal. Here we can see that the `TorchMLPClassifier` achieved the highest predicted score (orange bar), and is chosen for subsequent stages of the Pipeline."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "ec43a674",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-03-22T16:18:10.796215Z",
-     "iopub.status.busy": "2023-03-22T16:18:10.795612Z",
-     "iopub.status.idle": "2023-03-22T16:18:11.255779Z",
-     "shell.execute_reply": "2023-03-22T16:18:11.255174Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x504 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Each trial is a tuple of\n",
-    "# (algorithm, no. samples, no. features, mean CV score, hyperparameters, \n",
-    "# all CV scores, total CV time (s), memory usage (Gb))\n",
-    "trials = est1.model_selection_trials_\n",
-    "colors = []\n",
-    "scores = [x[3] for x in trials]\n",
-    "models = [x[0] for x in trials]\n",
-    "y_margin = 0.10 * (max(scores) - min(scores))\n",
-    "s = pd.Series(scores, index=models).sort_values(ascending=False)\n",
-    "\n",
-    "for f in s.keys():\n",
-    "    if f == '{}_AS'.format(est1.selected_model_):\n",
-    "        colors.append('orange')\n",
-    "    elif s[f] >= s.mean():\n",
-    "        colors.append('teal')\n",
-    "    else:\n",
-    "        colors.append('turquoise')\n",
-    "        \n",
-    "\n",
-    "fig, ax = plt.subplots(1)\n",
-    "ax.set_title(\"Algorithm Selection Trials\")\n",
-    "ax.set_ylim(min(scores) - y_margin, max(scores) + y_margin)\n",
-    "ax.set_ylabel(est1.inferred_score_metric[0])\n",
-    "s.plot.bar(ax=ax, color=colors, edgecolor='black')\n",
-    "ax.axhline(y=s.mean(), color='black', linewidth=0.5)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b0935f33",
-   "metadata": {},
-   "source": [
-    "<a id='adaptive-sampling'></a>\n",
-    "#### Adaptive Sampling\n",
-    "\n",
-    "Following Algorithm Selection, Adaptive Sampling aims to find the smallest dataset sample that can be created without compromising validation set score for the chosen model. Given the small size of the training data (948 samples), Adaptive Sampling is not relevant here."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "43974044",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-03-22T16:18:11.258977Z",
-     "iopub.status.busy": "2023-03-22T16:18:11.258722Z",
-     "iopub.status.idle": "2023-03-22T16:18:11.465718Z",
-     "shell.execute_reply": "2023-03-22T16:18:11.465123Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x504 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Each trial is a tuple of\n",
-    "# (algorithm, no. samples, no. features, mean CV score, hyperparameters, \n",
-    "# all CV scores, total CV time (s), memory usage (Gb))\n",
-    "trials = est1.adaptive_sampling_trials_\n",
-    "scores = [x[3] for x in trials]\n",
-    "n_samples = [x[1] for x in trials]\n",
-    "y_margin = 0.10 * (max(scores) - min(scores))\n",
-    "\n",
-    "fig, ax = plt.subplots(1)\n",
-    "ax.set_title(\"Adaptive Sampling ({})\".format(trials[0][0]))\n",
-    "ax.set_xlabel('Dataset sample size')\n",
-    "ax.set_ylabel(est1.inferred_score_metric[0])\n",
-    "ax.grid(color='g', linestyle='-', linewidth=0.1)\n",
-    "ax.set_ylim(min(scores) - y_margin, max(scores) + y_margin)\n",
-    "ax.plot(n_samples, scores, 'k:', marker=\"s\", color='teal', markersize=3)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9552e459",
-   "metadata": {},
-   "source": [
-    "<a id='feature-selection'></a>\n",
-    "#### Feature Selection\n",
-    "After finding a sample subset, the next step is to find a relevant feature subset to maximize score for the chosen algorithm. The Feature Selection step identifies the smallest feature subset that does not compromise on the score of the chosen algorithm. The orange line shows the optimal number of features chosen by Feature Selection (in this case, retaining only about 4 000 of the more than 11 000 available words)."
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>2800</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8242</td>\n",
+       "      <td>13.4597</td>\n",
+       "      <td>2.5051</td>\n",
+       "      <td>Thu May 23 10:30:25 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>24599</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8251</td>\n",
+       "      <td>31.1346</td>\n",
+       "      <td>2.2462</td>\n",
+       "      <td>Thu May 23 10:28:11 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>11862</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8253</td>\n",
+       "      <td>23.1930</td>\n",
+       "      <td>1.8396</td>\n",
+       "      <td>Thu May 23 10:29:30 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>20499</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8255</td>\n",
+       "      <td>28.9252</td>\n",
+       "      <td>2.0524</td>\n",
+       "      <td>Thu May 23 10:28:54 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>17082</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8259</td>\n",
+       "      <td>27.1348</td>\n",
+       "      <td>2.7111</td>\n",
+       "      <td>Thu May 23 10:30:54 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>24599</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.826</td>\n",
+       "      <td>32.5141</td>\n",
+       "      <td>3.0002</td>\n",
+       "      <td>Thu May 23 10:27:55 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>2757</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8305</td>\n",
+       "      <td>13.5560</td>\n",
+       "      <td>1.7122</td>\n",
+       "      <td>Thu May 23 10:32:43 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>8237</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8315</td>\n",
+       "      <td>19.6689</td>\n",
+       "      <td>1.8418</td>\n",
+       "      <td>Thu May 23 10:31:34 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>6864</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8315</td>\n",
+       "      <td>18.0505</td>\n",
+       "      <td>2.5070</td>\n",
+       "      <td>Thu May 23 10:30:09 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>4766</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8318</td>\n",
+       "      <td>12.5170</td>\n",
+       "      <td>1.7119</td>\n",
+       "      <td>Thu May 23 10:30:19 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>9885</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8326</td>\n",
+       "      <td>21.3303</td>\n",
+       "      <td>1.7615</td>\n",
+       "      <td>Thu May 23 10:29:52 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>8237</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.834</td>\n",
+       "      <td>20.3790</td>\n",
+       "      <td>1.8199</td>\n",
+       "      <td>Thu May 23 10:30:02 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>3309</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8374</td>\n",
+       "      <td>12.2541</td>\n",
+       "      <td>1.7265</td>\n",
+       "      <td>Thu May 23 10:32:19 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>6864</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8377</td>\n",
+       "      <td>17.6653</td>\n",
+       "      <td>1.7352</td>\n",
+       "      <td>Thu May 23 10:31:42 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>1914</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8378</td>\n",
+       "      <td>10.8578</td>\n",
+       "      <td>1.7405</td>\n",
+       "      <td>Thu May 23 10:32:56 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>5720</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8383</td>\n",
+       "      <td>15.5746</td>\n",
+       "      <td>1.7591</td>\n",
+       "      <td>Thu May 23 10:31:55 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>1595</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8403</td>\n",
+       "      <td>11.4344</td>\n",
+       "      <td>1.7144</td>\n",
+       "      <td>Thu May 23 10:33:00 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>2297</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8418</td>\n",
+       "      <td>11.2165</td>\n",
+       "      <td>1.1772</td>\n",
+       "      <td>Thu May 23 10:32:53 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>4766</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8441</td>\n",
+       "      <td>14.2588</td>\n",
+       "      <td>2.5272</td>\n",
+       "      <td>Thu May 23 10:32:03 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>3309</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.845</td>\n",
+       "      <td>11.1807</td>\n",
+       "      <td>1.7050</td>\n",
+       "      <td>Thu May 23 10:32:12 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>2757</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8463</td>\n",
+       "      <td>11.5175</td>\n",
+       "      <td>2.5268</td>\n",
+       "      <td>Thu May 23 10:32:23 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>1329</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8471</td>\n",
+       "      <td>10.7158</td>\n",
+       "      <td>2.5423</td>\n",
+       "      <td>Thu May 23 10:33:19 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>3971</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8474</td>\n",
+       "      <td>10.0837</td>\n",
+       "      <td>1.7367</td>\n",
+       "      <td>Thu May 23 10:32:05 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>3971</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8509</td>\n",
+       "      <td>14.6562</td>\n",
+       "      <td>2.5236</td>\n",
+       "      <td>Thu May 23 10:31:46 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>2757</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8575</td>\n",
+       "      <td>9.8742</td>\n",
+       "      <td>1.7469</td>\n",
+       "      <td>Thu May 23 10:32:44 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>1329</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8596</td>\n",
+       "      <td>9.6186</td>\n",
+       "      <td>2.5307</td>\n",
+       "      <td>Thu May 23 10:32:35 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>1595</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8598</td>\n",
+       "      <td>10.0047</td>\n",
+       "      <td>1.7409</td>\n",
+       "      <td>Thu May 23 10:32:32 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>1914</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8646</td>\n",
+       "      <td>10.9595</td>\n",
+       "      <td>1.1232</td>\n",
+       "      <td>Thu May 23 10:32:30 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>11862</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8662</td>\n",
+       "      <td>21.0217</td>\n",
+       "      <td>2.5132</td>\n",
+       "      <td>Thu May 23 10:29:40 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>2297</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8669</td>\n",
+       "      <td>9.3428</td>\n",
+       "      <td>2.5347</td>\n",
+       "      <td>Thu May 23 10:32:48 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>2297</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8679</td>\n",
+       "      <td>10.5597</td>\n",
+       "      <td>1.7073</td>\n",
+       "      <td>Thu May 23 10:32:26 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>13407</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8725</td>\n",
+       "      <td>22.8446</td>\n",
+       "      <td>1.7808</td>\n",
+       "      <td>Thu May 23 10:28:16 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>1107</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8795</td>\n",
+       "      <td>9.4064</td>\n",
+       "      <td>1.7200</td>\n",
+       "      <td>Thu May 23 10:33:25 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>1107</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8817</td>\n",
+       "      <td>10.1807</td>\n",
+       "      <td>1.2341</td>\n",
+       "      <td>Thu May 23 10:33:21 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>922</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8867</td>\n",
+       "      <td>9.4246</td>\n",
+       "      <td>1.7571</td>\n",
+       "      <td>Thu May 23 10:33:22 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>922</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8874</td>\n",
+       "      <td>8.5782</td>\n",
+       "      <td>0.5493</td>\n",
+       "      <td>Thu May 23 10:33:26 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>1914</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8974</td>\n",
+       "      <td>8.5941</td>\n",
+       "      <td>2.5387</td>\n",
+       "      <td>Thu May 23 10:33:00 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>640</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.9003</td>\n",
+       "      <td>7.8711</td>\n",
+       "      <td>0.5570</td>\n",
+       "      <td>Thu May 23 10:33:36 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>768</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.9037</td>\n",
+       "      <td>8.7857</td>\n",
+       "      <td>1.7596</td>\n",
+       "      <td>Thu May 23 10:33:32 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>9885</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.9057</td>\n",
+       "      <td>19.1298</td>\n",
+       "      <td>1.0824</td>\n",
+       "      <td>Thu May 23 10:34:23 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>768</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.9157</td>\n",
+       "      <td>8.8506</td>\n",
+       "      <td>0.5383</td>\n",
+       "      <td>Thu May 23 10:33:28 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>52</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>1595</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.9177</td>\n",
+       "      <td>6.8742</td>\n",
+       "      <td>1.2233</td>\n",
+       "      <td>Thu May 23 10:33:02 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>1329</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.9346</td>\n",
+       "      <td>7.1818</td>\n",
+       "      <td>1.7467</td>\n",
+       "      <td>Thu May 23 10:33:05 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>640</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.9355</td>\n",
+       "      <td>8.0854</td>\n",
+       "      <td>2.5462</td>\n",
+       "      <td>Thu May 23 10:33:30 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>6864</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.9442</td>\n",
+       "      <td>15.6325</td>\n",
+       "      <td>1.8140</td>\n",
+       "      <td>Thu May 23 10:34:38 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>8237</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.9559</td>\n",
+       "      <td>19.4176</td>\n",
+       "      <td>2.5739</td>\n",
+       "      <td>Thu May 23 10:34:33 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>57</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>533</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.9581</td>\n",
+       "      <td>7.4298</td>\n",
+       "      <td>0.5434</td>\n",
+       "      <td>Thu May 23 10:33:38 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>58</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>1107</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.9609</td>\n",
+       "      <td>6.4102</td>\n",
+       "      <td>0.5082</td>\n",
+       "      <td>Thu May 23 10:33:16 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>59</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>533</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.9718</td>\n",
+       "      <td>7.9872</td>\n",
+       "      <td>1.2181</td>\n",
+       "      <td>Thu May 23 10:33:33 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>922</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.9788</td>\n",
+       "      <td>6.2687</td>\n",
+       "      <td>0.4773</td>\n",
+       "      <td>Thu May 23 10:33:16 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>61</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>5720</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.9839</td>\n",
+       "      <td>12.0948</td>\n",
+       "      <td>0.9443</td>\n",
+       "      <td>Thu May 23 10:34:42 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>444</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.9901</td>\n",
+       "      <td>6.8942</td>\n",
+       "      <td>2.5539</td>\n",
+       "      <td>Thu May 23 10:33:39 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>63</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>768</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.015</td>\n",
+       "      <td>4.8162</td>\n",
+       "      <td>1.2038</td>\n",
+       "      <td>Thu May 23 10:33:09 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>64</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>4766</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.0206</td>\n",
+       "      <td>12.8944</td>\n",
+       "      <td>2.5746</td>\n",
+       "      <td>Thu May 23 10:34:50 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>65</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>370</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.0231</td>\n",
+       "      <td>6.4792</td>\n",
+       "      <td>1.7241</td>\n",
+       "      <td>Thu May 23 10:33:43 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>444</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.026</td>\n",
+       "      <td>7.5108</td>\n",
+       "      <td>1.7203</td>\n",
+       "      <td>Thu May 23 10:33:34 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>67</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>3971</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.0487</td>\n",
+       "      <td>11.7980</td>\n",
+       "      <td>0.9098</td>\n",
+       "      <td>Thu May 23 10:34:55 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>68</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>2757</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.0631</td>\n",
+       "      <td>9.5604</td>\n",
+       "      <td>2.5782</td>\n",
+       "      <td>Thu May 23 10:35:03 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>69</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>370</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.0693</td>\n",
+       "      <td>6.8228</td>\n",
+       "      <td>0.4255</td>\n",
+       "      <td>Thu May 23 10:33:43 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>70</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>3309</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.0706</td>\n",
+       "      <td>10.1032</td>\n",
+       "      <td>1.7449</td>\n",
+       "      <td>Thu May 23 10:34:58 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>308</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.0747</td>\n",
+       "      <td>4.8309</td>\n",
+       "      <td>0.5516</td>\n",
+       "      <td>Thu May 23 10:33:43 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>640</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.0868</td>\n",
+       "      <td>5.4962</td>\n",
+       "      <td>1.7518</td>\n",
+       "      <td>Thu May 23 10:33:11 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>256</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.0918</td>\n",
+       "      <td>5.6216</td>\n",
+       "      <td>2.5555</td>\n",
+       "      <td>Thu May 23 10:33:46 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>2297</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.1067</td>\n",
+       "      <td>8.5754</td>\n",
+       "      <td>0.8990</td>\n",
+       "      <td>Thu May 23 10:35:06 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>1914</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.1302</td>\n",
+       "      <td>7.5573</td>\n",
+       "      <td>1.7469</td>\n",
+       "      <td>Thu May 23 10:35:09 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>76</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>308</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.133</td>\n",
+       "      <td>6.4522</td>\n",
+       "      <td>1.2210</td>\n",
+       "      <td>Thu May 23 10:33:41 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>77</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>256</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.147</td>\n",
+       "      <td>6.0581</td>\n",
+       "      <td>0.5699</td>\n",
+       "      <td>Thu May 23 10:33:43 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>78</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>533</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.1487</td>\n",
+       "      <td>4.9926</td>\n",
+       "      <td>0.5538</td>\n",
+       "      <td>Thu May 23 10:33:51 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>79</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>213</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.1505</td>\n",
+       "      <td>5.7629</td>\n",
+       "      <td>1.2248</td>\n",
+       "      <td>Thu May 23 10:33:48 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>444</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.1718</td>\n",
+       "      <td>4.7018</td>\n",
+       "      <td>1.2313</td>\n",
+       "      <td>Thu May 23 10:33:54 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>81</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>1329</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.1741</td>\n",
+       "      <td>5.8695</td>\n",
+       "      <td>1.2676</td>\n",
+       "      <td>Thu May 23 10:35:13 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>82</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>213</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.1905</td>\n",
+       "      <td>4.7250</td>\n",
+       "      <td>0.3950</td>\n",
+       "      <td>Thu May 23 10:33:49 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>83</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>1595</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.2008</td>\n",
+       "      <td>7.2515</td>\n",
+       "      <td>2.5813</td>\n",
+       "      <td>Thu May 23 10:35:12 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>84</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>177</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.2023</td>\n",
+       "      <td>4.5543</td>\n",
+       "      <td>2.5595</td>\n",
+       "      <td>Thu May 23 10:33:51 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>85</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>177</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.2085</td>\n",
+       "      <td>5.4495</td>\n",
+       "      <td>1.7271</td>\n",
+       "      <td>Thu May 23 10:33:49 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>86</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>1107</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.2119</td>\n",
+       "      <td>6.8125</td>\n",
+       "      <td>0.9248</td>\n",
+       "      <td>Thu May 23 10:35:16 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>87</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>370</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.2418</td>\n",
+       "      <td>4.5516</td>\n",
+       "      <td>1.7326</td>\n",
+       "      <td>Thu May 23 10:33:56 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>88</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>768</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.2567</td>\n",
+       "      <td>6.0074</td>\n",
+       "      <td>2.5853</td>\n",
+       "      <td>Thu May 23 10:35:19 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>89</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>922</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.2606</td>\n",
+       "      <td>6.2707</td>\n",
+       "      <td>1.7489</td>\n",
+       "      <td>Thu May 23 10:35:18 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>90</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>147</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.2657</td>\n",
+       "      <td>4.1396</td>\n",
+       "      <td>0.6062</td>\n",
+       "      <td>Thu May 23 10:35:34 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>91</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>147</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.274</td>\n",
+       "      <td>5.1502</td>\n",
+       "      <td>0.5799</td>\n",
+       "      <td>Thu May 23 10:33:53 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>92</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>308</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.2995</td>\n",
+       "      <td>3.9022</td>\n",
+       "      <td>2.5635</td>\n",
+       "      <td>Thu May 23 10:33:56 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>93</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>256</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.3103</td>\n",
+       "      <td>4.0943</td>\n",
+       "      <td>0.6180</td>\n",
+       "      <td>Thu May 23 10:35:40 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>94</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>213</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.3271</td>\n",
+       "      <td>4.7975</td>\n",
+       "      <td>0.9252</td>\n",
+       "      <td>Thu May 23 10:35:42 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>640</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.3282</td>\n",
+       "      <td>5.6817</td>\n",
+       "      <td>1.2557</td>\n",
+       "      <td>Thu May 23 10:35:21 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>122</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.3316</td>\n",
+       "      <td>4.0702</td>\n",
+       "      <td>1.7594</td>\n",
+       "      <td>Thu May 23 10:35:36 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>122</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.3586</td>\n",
+       "      <td>4.8518</td>\n",
+       "      <td>0.9120</td>\n",
+       "      <td>Thu May 23 10:35:36 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>444</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.3692</td>\n",
+       "      <td>5.0455</td>\n",
+       "      <td>0.9041</td>\n",
+       "      <td>Thu May 23 10:35:23 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>533</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.3852</td>\n",
+       "      <td>5.3884</td>\n",
+       "      <td>0.6020</td>\n",
+       "      <td>Thu May 23 10:35:22 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>370</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.421</td>\n",
+       "      <td>4.4831</td>\n",
+       "      <td>1.7529</td>\n",
+       "      <td>Thu May 23 10:35:24 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>101</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>147</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.4254</td>\n",
+       "      <td>3.8612</td>\n",
+       "      <td>0.6319</td>\n",
+       "      <td>Thu May 23 10:35:50 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>102</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>101</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.4297</td>\n",
+       "      <td>4.7787</td>\n",
+       "      <td>0.4363</td>\n",
+       "      <td>Thu May 23 10:35:56 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>103</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>177</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.4351</td>\n",
+       "      <td>3.5339</td>\n",
+       "      <td>2.6053</td>\n",
+       "      <td>Thu May 23 10:35:48 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>104</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>101</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.4378</td>\n",
+       "      <td>4.1025</td>\n",
+       "      <td>2.5972</td>\n",
+       "      <td>Thu May 23 10:35:37 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>105</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>308</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.4792</td>\n",
+       "      <td>5.1696</td>\n",
+       "      <td>2.5894</td>\n",
+       "      <td>Thu May 23 10:35:26 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>106</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>122</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.5403</td>\n",
+       "      <td>3.0611</td>\n",
+       "      <td>0.4371</td>\n",
+       "      <td>Thu May 23 10:35:50 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>107</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>256</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.5427</td>\n",
+       "      <td>4.6979</td>\n",
+       "      <td>1.2626</td>\n",
+       "      <td>Thu May 23 10:35:27 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>108</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>84</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.5441</td>\n",
+       "      <td>4.5536</td>\n",
+       "      <td>1.2702</td>\n",
+       "      <td>Thu May 23 10:35:39 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>109</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>84</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.5765</td>\n",
+       "      <td>4.3546</td>\n",
+       "      <td>0.6318</td>\n",
+       "      <td>Thu May 23 10:35:57 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>110</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>101</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.5962</td>\n",
+       "      <td>3.7936</td>\n",
+       "      <td>0.9268</td>\n",
+       "      <td>Thu May 23 10:35:52 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>213</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.6081</td>\n",
+       "      <td>4.5165</td>\n",
+       "      <td>0.6061</td>\n",
+       "      <td>Thu May 23 10:35:28 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>112</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>70</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.6246</td>\n",
+       "      <td>4.7872</td>\n",
+       "      <td>2.6133</td>\n",
+       "      <td>Thu May 23 10:35:59 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>113</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>84</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.6468</td>\n",
+       "      <td>3.3117</td>\n",
+       "      <td>2.6093</td>\n",
+       "      <td>Thu May 23 10:35:53 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>114</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>177</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.6859</td>\n",
+       "      <td>4.4852</td>\n",
+       "      <td>0.9052</td>\n",
+       "      <td>Thu May 23 10:35:30 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>115</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>70</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.7001</td>\n",
+       "      <td>3.3399</td>\n",
+       "      <td>0.6615</td>\n",
+       "      <td>Thu May 23 10:36:25 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>116</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>58</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.762</td>\n",
+       "      <td>3.0358</td>\n",
+       "      <td>0.9763</td>\n",
+       "      <td>Thu May 23 10:36:26 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>117</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>70</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.7848</td>\n",
+       "      <td>4.0995</td>\n",
+       "      <td>1.2841</td>\n",
+       "      <td>Thu May 23 10:35:59 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>118</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>58</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.793</td>\n",
+       "      <td>4.2778</td>\n",
+       "      <td>0.4462</td>\n",
+       "      <td>Thu May 23 10:36:01 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>119</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>147</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.8009</td>\n",
+       "      <td>4.4626</td>\n",
+       "      <td>1.7556</td>\n",
+       "      <td>Thu May 23 10:35:31 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>120</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>48</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.801</td>\n",
+       "      <td>3.1682</td>\n",
+       "      <td>0.4796</td>\n",
+       "      <td>Thu May 23 10:36:20 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>121</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>51</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.8163</td>\n",
+       "      <td>2.9546</td>\n",
+       "      <td>0.5748</td>\n",
+       "      <td>Thu May 23 10:33:56 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>122</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>40</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.8358</td>\n",
+       "      <td>2.6926</td>\n",
+       "      <td>0.6556</td>\n",
+       "      <td>Thu May 23 10:36:20 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>123</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>48</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.8889</td>\n",
+       "      <td>3.0477</td>\n",
+       "      <td>1.3058</td>\n",
+       "      <td>Thu May 23 10:36:19 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>124</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>122</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.8891</td>\n",
+       "      <td>4.5973</td>\n",
+       "      <td>2.5934</td>\n",
+       "      <td>Thu May 23 10:35:32 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>125</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>48</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.8955</td>\n",
+       "      <td>3.0244</td>\n",
+       "      <td>0.6552</td>\n",
+       "      <td>Thu May 23 10:36:01 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>126</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>33</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.913</td>\n",
+       "      <td>2.9698</td>\n",
+       "      <td>0.9724</td>\n",
+       "      <td>Thu May 23 10:36:22 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>127</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>58</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.9231</td>\n",
+       "      <td>4.1955</td>\n",
+       "      <td>0.9366</td>\n",
+       "      <td>Thu May 23 10:36:00 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>128</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>101</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.9252</td>\n",
+       "      <td>4.4641</td>\n",
+       "      <td>1.2652</td>\n",
+       "      <td>Thu May 23 10:35:33 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>129</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>48</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.9401</td>\n",
+       "      <td>3.0640</td>\n",
+       "      <td>1.3055</td>\n",
+       "      <td>Thu May 23 10:36:27 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>130</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>84</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.9515</td>\n",
+       "      <td>4.5635</td>\n",
+       "      <td>1.7627</td>\n",
+       "      <td>Thu May 23 10:35:43 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>131</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>27</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.9782</td>\n",
+       "      <td>2.8900</td>\n",
+       "      <td>1.3096</td>\n",
+       "      <td>Thu May 23 10:36:22 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>132</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>40</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.9929</td>\n",
+       "      <td>3.6222</td>\n",
+       "      <td>1.7660</td>\n",
+       "      <td>Thu May 23 10:36:03 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>133</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>70</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.0381</td>\n",
+       "      <td>4.4439</td>\n",
+       "      <td>2.6012</td>\n",
+       "      <td>Thu May 23 10:35:44 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>134</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>40</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.0503</td>\n",
+       "      <td>3.0116</td>\n",
+       "      <td>1.7699</td>\n",
+       "      <td>Thu May 23 10:36:28 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>135</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>33</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.0699</td>\n",
+       "      <td>4.1546</td>\n",
+       "      <td>1.2980</td>\n",
+       "      <td>Thu May 23 10:36:04 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>136</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>22</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.0727</td>\n",
+       "      <td>2.8416</td>\n",
+       "      <td>0.6702</td>\n",
+       "      <td>Thu May 23 10:36:32 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>137</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>40</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.0826</td>\n",
+       "      <td>2.7600</td>\n",
+       "      <td>0.9562</td>\n",
+       "      <td>Thu May 23 10:36:17 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>138</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>33</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.1023</td>\n",
+       "      <td>2.7728</td>\n",
+       "      <td>0.4761</td>\n",
+       "      <td>Thu May 23 10:36:29 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>139</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>58</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.1095</td>\n",
+       "      <td>4.4697</td>\n",
+       "      <td>1.2739</td>\n",
+       "      <td>Thu May 23 10:35:45 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>140</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>48</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.1411</td>\n",
+       "      <td>3.6205</td>\n",
+       "      <td>0.6216</td>\n",
+       "      <td>Thu May 23 10:35:45 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>141</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>27</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.1411</td>\n",
+       "      <td>2.5351</td>\n",
+       "      <td>0.9802</td>\n",
+       "      <td>Thu May 23 10:36:29 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>142</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>22</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.1458</td>\n",
+       "      <td>2.9758</td>\n",
+       "      <td>0.4945</td>\n",
+       "      <td>Thu May 23 10:36:37 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>143</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>33</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.1561</td>\n",
+       "      <td>2.7518</td>\n",
+       "      <td>0.6710</td>\n",
+       "      <td>Thu May 23 10:36:16 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>144</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>18</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.1817</td>\n",
+       "      <td>2.8651</td>\n",
+       "      <td>0.4852</td>\n",
+       "      <td>Thu May 23 10:36:33 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>145</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>40</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.1938</td>\n",
+       "      <td>3.8338</td>\n",
+       "      <td>0.4333</td>\n",
+       "      <td>Thu May 23 10:35:46 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>146</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>18</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.215</td>\n",
+       "      <td>2.3860</td>\n",
+       "      <td>0.9673</td>\n",
+       "      <td>Thu May 23 10:36:38 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>147</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>15</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.2183</td>\n",
+       "      <td>2.7508</td>\n",
+       "      <td>0.6656</td>\n",
+       "      <td>Thu May 23 10:36:36 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>148</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>27</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.2236</td>\n",
+       "      <td>3.6306</td>\n",
+       "      <td>0.4883</td>\n",
+       "      <td>Thu May 23 10:36:24 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>149</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>15</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.2565</td>\n",
+       "      <td>2.4850</td>\n",
+       "      <td>0.5074</td>\n",
+       "      <td>Thu May 23 10:36:44 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>150</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>22</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.3029</td>\n",
+       "      <td>2.9781</td>\n",
+       "      <td>1.3064</td>\n",
+       "      <td>Thu May 23 10:36:31 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>151</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>12</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.3109</td>\n",
+       "      <td>2.5892</td>\n",
+       "      <td>1.7721</td>\n",
+       "      <td>Thu May 23 10:36:39 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>152</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>27</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.3112</td>\n",
+       "      <td>2.6588</td>\n",
+       "      <td>0.4688</td>\n",
+       "      <td>Thu May 23 10:36:15 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>153</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>12</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.3525</td>\n",
+       "      <td>2.6103</td>\n",
+       "      <td>0.6733</td>\n",
+       "      <td>Thu May 23 10:36:40 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>154</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>12</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.3792</td>\n",
+       "      <td>2.7457</td>\n",
+       "      <td>0.6697</td>\n",
+       "      <td>Thu May 23 10:36:45 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>155</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>18</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.4022</td>\n",
+       "      <td>3.2052</td>\n",
+       "      <td>0.9690</td>\n",
+       "      <td>Thu May 23 10:36:34 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>156</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>33</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.4071</td>\n",
+       "      <td>3.6131</td>\n",
+       "      <td>0.9412</td>\n",
+       "      <td>Thu May 23 10:35:47 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>157</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>10</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.431</td>\n",
+       "      <td>2.2560</td>\n",
+       "      <td>0.5033</td>\n",
+       "      <td>Thu May 23 10:36:40 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>158</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>22</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.4472</td>\n",
+       "      <td>2.5898</td>\n",
+       "      <td>1.3019</td>\n",
+       "      <td>Thu May 23 10:36:14 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>159</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>15</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.4729</td>\n",
+       "      <td>3.4469</td>\n",
+       "      <td>1.3117</td>\n",
+       "      <td>Thu May 23 10:36:35 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>160</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>27</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.4812</td>\n",
+       "      <td>3.7719</td>\n",
+       "      <td>0.9786</td>\n",
+       "      <td>Thu May 23 10:36:43 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>161</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>10</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.5027</td>\n",
+       "      <td>2.8314</td>\n",
+       "      <td>0.4960</td>\n",
+       "      <td>Thu May 23 10:36:48 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>162</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>8</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.5188</td>\n",
+       "      <td>2.1882</td>\n",
+       "      <td>0.6821</td>\n",
+       "      <td>Thu May 23 10:36:49 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>163</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>8</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.5313</td>\n",
+       "      <td>2.6851</td>\n",
+       "      <td>0.5192</td>\n",
+       "      <td>Thu May 23 10:36:56 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>164</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>18</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.5424</td>\n",
+       "      <td>2.3904</td>\n",
+       "      <td>0.9496</td>\n",
+       "      <td>Thu May 23 10:36:13 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>165</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>8</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.5501</td>\n",
+       "      <td>2.5698</td>\n",
+       "      <td>0.5057</td>\n",
+       "      <td>Thu May 23 10:36:52 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>166</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>15</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.5725</td>\n",
+       "      <td>2.3022</td>\n",
+       "      <td>0.6671</td>\n",
+       "      <td>Thu May 23 10:36:12 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>167</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>22</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.6016</td>\n",
+       "      <td>3.2742</td>\n",
+       "      <td>1.7780</td>\n",
+       "      <td>Thu May 23 10:36:44 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>168</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>6</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.6036</td>\n",
+       "      <td>2.3810</td>\n",
+       "      <td>0.6845</td>\n",
+       "      <td>Thu May 23 10:36:53 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>169</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>5</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.6066</td>\n",
+       "      <td>2.2897</td>\n",
+       "      <td>1.7814</td>\n",
+       "      <td>Thu May 23 10:36:54 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>170</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>6</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.6299</td>\n",
+       "      <td>2.2368</td>\n",
+       "      <td>0.9856</td>\n",
+       "      <td>Thu May 23 10:36:55 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>171</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>5</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.6518</td>\n",
+       "      <td>2.3437</td>\n",
+       "      <td>1.7899</td>\n",
+       "      <td>Thu May 23 10:36:58 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>172</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>6</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.6546</td>\n",
+       "      <td>2.5006</td>\n",
+       "      <td>0.6908</td>\n",
+       "      <td>Thu May 23 10:36:57 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>173</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>12</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.6615</td>\n",
+       "      <td>2.3334</td>\n",
+       "      <td>0.4765</td>\n",
+       "      <td>Thu May 23 10:36:10 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>174</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>4</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.6717</td>\n",
+       "      <td>2.3237</td>\n",
+       "      <td>0.5135</td>\n",
+       "      <td>Thu May 23 10:37:01 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>18</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.6718</td>\n",
+       "      <td>2.8963</td>\n",
+       "      <td>1.3165</td>\n",
+       "      <td>Thu May 23 10:36:46 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>176</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>4</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.6834</td>\n",
+       "      <td>2.3249</td>\n",
+       "      <td>1.7896</td>\n",
+       "      <td>Thu May 23 10:37:02 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>10</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.692</td>\n",
+       "      <td>3.3026</td>\n",
+       "      <td>0.9810</td>\n",
+       "      <td>Thu May 23 10:36:52 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>178</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>4</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.7024</td>\n",
+       "      <td>2.1647</td>\n",
+       "      <td>0.6942</td>\n",
+       "      <td>Thu May 23 10:37:01 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>179</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>10</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.7094</td>\n",
+       "      <td>2.0194</td>\n",
+       "      <td>0.9606</td>\n",
+       "      <td>Thu May 23 10:36:09 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>180</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>15</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.7318</td>\n",
+       "      <td>3.1588</td>\n",
+       "      <td>1.7826</td>\n",
+       "      <td>Thu May 23 10:36:48 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>181</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>3</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.7357</td>\n",
+       "      <td>2.1384</td>\n",
+       "      <td>1.3207</td>\n",
+       "      <td>Thu May 23 10:37:03 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>182</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>8</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.758</td>\n",
+       "      <td>1.9295</td>\n",
+       "      <td>0.6628</td>\n",
+       "      <td>Thu May 23 10:36:08 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>183</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>3</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.7777</td>\n",
+       "      <td>2.1884</td>\n",
+       "      <td>0.9740</td>\n",
+       "      <td>Thu May 23 10:37:02 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>184</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>12</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.7778</td>\n",
+       "      <td>3.6926</td>\n",
+       "      <td>1.3224</td>\n",
+       "      <td>Thu May 23 10:36:51 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>185</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>3</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.7811</td>\n",
+       "      <td>2.1009</td>\n",
+       "      <td>1.0076</td>\n",
+       "      <td>Thu May 23 10:37:05 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>186</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>2</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.8153</td>\n",
+       "      <td>2.4933</td>\n",
+       "      <td>2.6172</td>\n",
+       "      <td>Thu May 23 10:37:09 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>187</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>6</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.8435</td>\n",
+       "      <td>2.0641</td>\n",
+       "      <td>0.4708</td>\n",
+       "      <td>Thu May 23 10:36:07 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>188</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>2</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.8592</td>\n",
+       "      <td>2.1753</td>\n",
+       "      <td>1.3369</td>\n",
+       "      <td>Thu May 23 10:37:06 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>189</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>2</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.8717</td>\n",
+       "      <td>2.0603</td>\n",
+       "      <td>2.6133</td>\n",
+       "      <td>Thu May 23 10:37:04 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>190</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>5</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.8946</td>\n",
+       "      <td>1.6382</td>\n",
+       "      <td>0.9568</td>\n",
+       "      <td>Thu May 23 10:36:06 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>191</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>1</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.9047</td>\n",
+       "      <td>1.7968</td>\n",
+       "      <td>1.7984</td>\n",
+       "      <td>Thu May 23 10:37:07 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>192</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>1</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.922</td>\n",
+       "      <td>1.8707</td>\n",
+       "      <td>1.0115</td>\n",
+       "      <td>Thu May 23 10:37:09 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>193</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>4</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.9311</td>\n",
+       "      <td>1.9900</td>\n",
+       "      <td>0.6433</td>\n",
+       "      <td>Thu May 23 10:36:05 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>194</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>3</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.946</td>\n",
+       "      <td>1.9046</td>\n",
+       "      <td>0.4669</td>\n",
+       "      <td>Thu May 23 10:36:04 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>195</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>2</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.97</td>\n",
+       "      <td>1.9467</td>\n",
+       "      <td>0.9528</td>\n",
+       "      <td>Thu May 23 10:36:03 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>196</th>\n",
+       "      <td>Feature Selection</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>1</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.9959</td>\n",
+       "      <td>1.9442</td>\n",
+       "      <td>1.2903</td>\n",
+       "      <td>Thu May 23 10:35:52 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>197</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8081</td>\n",
+       "      <td>20.2350</td>\n",
+       "      <td>1.0069</td>\n",
+       "      <td>Thu May 23 10:37:52 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>198</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.00119999, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8081</td>\n",
+       "      <td>20.3789</td>\n",
+       "      <td>0.9145</td>\n",
+       "      <td>Thu May 23 10:39:42 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>199</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': 0, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8081</td>\n",
+       "      <td>25.8773</td>\n",
+       "      <td>0.9123</td>\n",
+       "      <td>Thu May 23 10:38:05 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>200</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8081</td>\n",
+       "      <td>26.2765</td>\n",
+       "      <td>0.8674</td>\n",
+       "      <td>Thu May 23 10:38:06 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>201</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.00119999, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8081</td>\n",
+       "      <td>26.6993</td>\n",
+       "      <td>0.8137</td>\n",
+       "      <td>Thu May 23 10:38:09 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>202</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8081</td>\n",
+       "      <td>28.0649</td>\n",
+       "      <td>0.9741</td>\n",
+       "      <td>Thu May 23 10:38:31 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>203</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8081</td>\n",
+       "      <td>32.8766</td>\n",
+       "      <td>0.7946</td>\n",
+       "      <td>Thu May 23 10:38:24 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>204</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8081</td>\n",
+       "      <td>32.9077</td>\n",
+       "      <td>0.8929</td>\n",
+       "      <td>Thu May 23 10:38:25 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>205</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8081</td>\n",
+       "      <td>33.4196</td>\n",
+       "      <td>0.8874</td>\n",
+       "      <td>Thu May 23 10:38:15 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>206</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8081</td>\n",
+       "      <td>37.2014</td>\n",
+       "      <td>0.8310</td>\n",
+       "      <td>Thu May 23 10:38:25 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>207</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 0.999990000000001, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8081</td>\n",
+       "      <td>28.7169</td>\n",
+       "      <td>2.0712</td>\n",
+       "      <td>Thu May 23 10:38:30 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>208</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 101, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8084</td>\n",
+       "      <td>33.3972</td>\n",
+       "      <td>2.8085</td>\n",
+       "      <td>Thu May 23 10:38:17 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>209</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 125, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8091</td>\n",
+       "      <td>27.4936</td>\n",
+       "      <td>1.3298</td>\n",
+       "      <td>Thu May 23 10:39:37 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>210</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 127, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8094</td>\n",
+       "      <td>28.5509</td>\n",
+       "      <td>0.8472</td>\n",
+       "      <td>Thu May 23 10:39:40 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>211</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 128, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8096</td>\n",
+       "      <td>28.6981</td>\n",
+       "      <td>1.7672</td>\n",
+       "      <td>Thu May 23 10:39:42 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>212</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 126, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8096</td>\n",
+       "      <td>27.2710</td>\n",
+       "      <td>1.3253</td>\n",
+       "      <td>Thu May 23 10:39:38 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>213</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 9.999999999e-06, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8113</td>\n",
+       "      <td>30.9955</td>\n",
+       "      <td>0.8170</td>\n",
+       "      <td>Thu May 23 10:38:30 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>214</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 32, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8128</td>\n",
+       "      <td>15.1494</td>\n",
+       "      <td>1.1774</td>\n",
+       "      <td>Thu May 23 10:39:45 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>215</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 32, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8128</td>\n",
+       "      <td>33.5865</td>\n",
+       "      <td>0.8409</td>\n",
+       "      <td>Thu May 23 10:38:26 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>216</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 177, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8131</td>\n",
+       "      <td>36.7752</td>\n",
+       "      <td>0.8498</td>\n",
+       "      <td>Thu May 23 10:39:40 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>217</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 178, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8131</td>\n",
+       "      <td>21.8527</td>\n",
+       "      <td>0.8002</td>\n",
+       "      <td>Thu May 23 10:40:05 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>218</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 178, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8131</td>\n",
+       "      <td>37.2306</td>\n",
+       "      <td>0.9073</td>\n",
+       "      <td>Thu May 23 10:39:42 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>219</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 174, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8133</td>\n",
+       "      <td>36.2606</td>\n",
+       "      <td>0.8474</td>\n",
+       "      <td>Thu May 23 10:39:39 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>220</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 173, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8135</td>\n",
+       "      <td>36.2719</td>\n",
+       "      <td>0.8471</td>\n",
+       "      <td>Thu May 23 10:39:38 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>221</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.10241113615453983, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8149</td>\n",
+       "      <td>19.9035</td>\n",
+       "      <td>1.9263</td>\n",
+       "      <td>Thu May 23 10:37:53 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>222</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 252, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8157</td>\n",
+       "      <td>51.4688</td>\n",
+       "      <td>0.9162</td>\n",
+       "      <td>Thu May 23 10:38:41 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>223</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 253, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8157</td>\n",
+       "      <td>52.1458</td>\n",
+       "      <td>0.8702</td>\n",
+       "      <td>Thu May 23 10:38:43 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>224</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.09939959374943852, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8174</td>\n",
+       "      <td>21.5247</td>\n",
+       "      <td>0.8437</td>\n",
+       "      <td>Thu May 23 10:39:07 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>225</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.09926466710275127, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8185</td>\n",
+       "      <td>19.0557</td>\n",
+       "      <td>0.8452</td>\n",
+       "      <td>Thu May 23 10:39:02 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.09922325977512633, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8186</td>\n",
+       "      <td>19.7455</td>\n",
+       "      <td>0.8507</td>\n",
+       "      <td>Thu May 23 10:39:02 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>227</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.09914044511987648, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8186</td>\n",
+       "      <td>21.6535</td>\n",
+       "      <td>1.3283</td>\n",
+       "      <td>Thu May 23 10:39:07 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>228</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0.05623427310243317, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8187</td>\n",
+       "      <td>31.1631</td>\n",
+       "      <td>0.8408</td>\n",
+       "      <td>Thu May 23 10:38:27 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>229</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': None}</td>\n",
+       "      <td>-0.8189</td>\n",
+       "      <td>24.7093</td>\n",
+       "      <td>2.2353</td>\n",
+       "      <td>Thu May 23 10:37:55 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>230</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.10242113515453982, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8195</td>\n",
+       "      <td>30.5136</td>\n",
+       "      <td>0.8853</td>\n",
+       "      <td>Thu May 23 10:38:10 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>231</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.09927537176656373, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8204</td>\n",
+       "      <td>23.9768</td>\n",
+       "      <td>0.8466</td>\n",
+       "      <td>Thu May 23 10:39:11 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>232</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.09921326077512634, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8214</td>\n",
+       "      <td>19.5727</td>\n",
+       "      <td>0.8485</td>\n",
+       "      <td>Thu May 23 10:39:01 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>233</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 511, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8229</td>\n",
+       "      <td>25.5383</td>\n",
+       "      <td>1.2588</td>\n",
+       "      <td>Thu May 23 10:39:24 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>234</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 523, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8229</td>\n",
+       "      <td>26.0735</td>\n",
+       "      <td>1.2678</td>\n",
+       "      <td>Thu May 23 10:39:26 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>235</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 510, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8229</td>\n",
+       "      <td>27.2237</td>\n",
+       "      <td>1.2769</td>\n",
+       "      <td>Thu May 23 10:39:24 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>236</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 522, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8229</td>\n",
+       "      <td>28.2897</td>\n",
+       "      <td>1.2703</td>\n",
+       "      <td>Thu May 23 10:39:28 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>237</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 564, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8229</td>\n",
+       "      <td>29.8900</td>\n",
+       "      <td>1.2834</td>\n",
+       "      <td>Thu May 23 10:38:25 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>238</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 190, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8229</td>\n",
+       "      <td>31.0321</td>\n",
+       "      <td>0.8975</td>\n",
+       "      <td>Thu May 23 10:38:24 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>239</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 563, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8229</td>\n",
+       "      <td>32.3063</td>\n",
+       "      <td>1.3149</td>\n",
+       "      <td>Thu May 23 10:38:27 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>240</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 189, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8229</td>\n",
+       "      <td>33.7906</td>\n",
+       "      <td>1.7289</td>\n",
+       "      <td>Thu May 23 10:38:22 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>241</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.07370106425810359, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.824</td>\n",
+       "      <td>23.1302</td>\n",
+       "      <td>0.8315</td>\n",
+       "      <td>Thu May 23 10:39:42 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>242</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0.056244273102432164, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8241</td>\n",
+       "      <td>32.7792</td>\n",
+       "      <td>0.8992</td>\n",
+       "      <td>Thu May 23 10:38:29 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>243</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.09925466810275127, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8243</td>\n",
+       "      <td>21.6087</td>\n",
+       "      <td>0.8517</td>\n",
+       "      <td>Thu May 23 10:39:04 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>244</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0.13554964757452176, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8264</td>\n",
+       "      <td>22.2450</td>\n",
+       "      <td>0.8670</td>\n",
+       "      <td>Thu May 23 10:39:28 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>245</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0.0001778292746984456, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8272</td>\n",
+       "      <td>33.6113</td>\n",
+       "      <td>0.8963</td>\n",
+       "      <td>Thu May 23 10:38:27 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>246</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0.00018782927469744562, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8272</td>\n",
+       "      <td>34.7928</td>\n",
+       "      <td>0.8317</td>\n",
+       "      <td>Thu May 23 10:38:30 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>247</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.100009999, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8274</td>\n",
+       "      <td>20.5852</td>\n",
+       "      <td>0.8955</td>\n",
+       "      <td>Thu May 23 10:37:53 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>248</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.09928537076656373, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8276</td>\n",
+       "      <td>24.9883</td>\n",
+       "      <td>1.7325</td>\n",
+       "      <td>Thu May 23 10:39:12 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>249</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0.13555964757452077, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8284</td>\n",
+       "      <td>24.3892</td>\n",
+       "      <td>0.8488</td>\n",
+       "      <td>Thu May 23 10:39:32 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>250</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0.13727546589824888, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8331</td>\n",
+       "      <td>24.0493</td>\n",
+       "      <td>0.8336</td>\n",
+       "      <td>Thu May 23 10:39:14 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>251</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0.14413873919316136, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8334</td>\n",
+       "      <td>23.6209</td>\n",
+       "      <td>0.8506</td>\n",
+       "      <td>Thu May 23 10:39:32 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>252</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0.14412873919316235, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8335</td>\n",
+       "      <td>23.4433</td>\n",
+       "      <td>0.9310</td>\n",
+       "      <td>Thu May 23 10:39:31 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>253</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0.13726546589824987, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8347</td>\n",
+       "      <td>23.0090</td>\n",
+       "      <td>0.9776</td>\n",
+       "      <td>Thu May 23 10:39:12 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>254</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.14397169870175522, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8358</td>\n",
+       "      <td>15.7275</td>\n",
+       "      <td>1.1751</td>\n",
+       "      <td>Thu May 23 10:38:59 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>255</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.1323499720056148, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8373</td>\n",
+       "      <td>24.0774</td>\n",
+       "      <td>1.0237</td>\n",
+       "      <td>Thu May 23 10:39:17 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>256</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0.1321280109270645, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8384</td>\n",
+       "      <td>21.7320</td>\n",
+       "      <td>1.3201</td>\n",
+       "      <td>Thu May 23 10:39:10 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>257</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0.1321180109270655, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8404</td>\n",
+       "      <td>22.4910</td>\n",
+       "      <td>2.1311</td>\n",
+       "      <td>Thu May 23 10:39:10 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>258</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.1325499620056148, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8409</td>\n",
+       "      <td>22.4901</td>\n",
+       "      <td>0.8413</td>\n",
+       "      <td>Thu May 23 10:39:17 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>259</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.15920068096327575, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8428</td>\n",
+       "      <td>23.0640</td>\n",
+       "      <td>0.8597</td>\n",
+       "      <td>Thu May 23 10:39:19 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>260</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.15328982261520555, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8445</td>\n",
+       "      <td>17.4436</td>\n",
+       "      <td>0.8408</td>\n",
+       "      <td>Thu May 23 10:39:02 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>261</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.15491868834972752, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8454</td>\n",
+       "      <td>21.1098</td>\n",
+       "      <td>0.8806</td>\n",
+       "      <td>Thu May 23 10:38:04 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>262</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.15348981261520556, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8455</td>\n",
+       "      <td>18.8633</td>\n",
+       "      <td>0.8435</td>\n",
+       "      <td>Thu May 23 10:39:04 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.1437717087017552, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8463</td>\n",
+       "      <td>16.0854</td>\n",
+       "      <td>0.8449</td>\n",
+       "      <td>Thu May 23 10:38:59 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>264</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.15511867834972753, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8499</td>\n",
+       "      <td>24.6760</td>\n",
+       "      <td>0.9141</td>\n",
+       "      <td>Thu May 23 10:38:09 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>265</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.15900069096327574, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.85</td>\n",
+       "      <td>23.7525</td>\n",
+       "      <td>2.8674</td>\n",
+       "      <td>Thu May 23 10:39:20 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>266</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 0.068760000093124, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8703</td>\n",
+       "      <td>23.4299</td>\n",
+       "      <td>0.8458</td>\n",
+       "      <td>Thu May 23 10:39:41 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>267</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 0.018760000098124004, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8783</td>\n",
+       "      <td>24.7752</td>\n",
+       "      <td>0.8998</td>\n",
+       "      <td>Thu May 23 10:39:40 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>268</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 0.068750000093125, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8844</td>\n",
+       "      <td>24.4053</td>\n",
+       "      <td>2.1321</td>\n",
+       "      <td>Thu May 23 10:39:41 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>269</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 0.01250000009875, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8845</td>\n",
+       "      <td>31.2005</td>\n",
+       "      <td>1.9282</td>\n",
+       "      <td>Thu May 23 10:39:21 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>270</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 0.012510000098749, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8861</td>\n",
+       "      <td>30.5409</td>\n",
+       "      <td>0.8413</td>\n",
+       "      <td>Thu May 23 10:39:22 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>271</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 0.018750000098125004, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.887</td>\n",
+       "      <td>26.0495</td>\n",
+       "      <td>0.9392</td>\n",
+       "      <td>Thu May 23 10:39:40 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>272</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'goss', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.8906</td>\n",
+       "      <td>11.5748</td>\n",
+       "      <td>2.8070</td>\n",
+       "      <td>Thu May 23 10:37:41 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>273</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'goss', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 177, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.9051</td>\n",
+       "      <td>12.0602</td>\n",
+       "      <td>0.8516</td>\n",
+       "      <td>Thu May 23 10:39:55 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>274</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 0.025010000097499003, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.9125</td>\n",
+       "      <td>26.9627</td>\n",
+       "      <td>0.9389</td>\n",
+       "      <td>Thu May 23 10:39:20 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>275</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'goss', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 253, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.9154</td>\n",
+       "      <td>27.2352</td>\n",
+       "      <td>1.1787</td>\n",
+       "      <td>Thu May 23 10:39:29 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>276</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 0.025000000097500003, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.9154</td>\n",
+       "      <td>27.6526</td>\n",
+       "      <td>0.8441</td>\n",
+       "      <td>Thu May 23 10:39:19 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>277</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': 6, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.9358</td>\n",
+       "      <td>16.5386</td>\n",
+       "      <td>0.8969</td>\n",
+       "      <td>Thu May 23 10:37:57 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>278</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': 5, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.9575</td>\n",
+       "      <td>12.7152</td>\n",
+       "      <td>0.9137</td>\n",
+       "      <td>Thu May 23 10:37:53 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>279</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1.0000099999e-05, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.9639</td>\n",
+       "      <td>34.5683</td>\n",
+       "      <td>2.2728</td>\n",
+       "      <td>Thu May 23 10:38:32 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>280</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 2.0000099998e-05, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.9664</td>\n",
+       "      <td>32.5344</td>\n",
+       "      <td>0.8386</td>\n",
+       "      <td>Thu May 23 10:38:31 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>281</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 0.0001778292746984456, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.9674</td>\n",
+       "      <td>32.2133</td>\n",
+       "      <td>0.8406</td>\n",
+       "      <td>Thu May 23 10:38:31 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>282</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 1.7602198064430905, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.9702</td>\n",
+       "      <td>16.4763</td>\n",
+       "      <td>0.9145</td>\n",
+       "      <td>Thu May 23 10:38:01 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>283</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 0.00018782927469744562, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.9718</td>\n",
+       "      <td>30.9401</td>\n",
+       "      <td>1.3249</td>\n",
+       "      <td>Thu May 23 10:38:31 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>284</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': 4, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.9725</td>\n",
+       "      <td>12.0292</td>\n",
+       "      <td>0.9135</td>\n",
+       "      <td>Thu May 23 10:37:52 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>285</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'dart', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.9785</td>\n",
+       "      <td>25.2995</td>\n",
+       "      <td>1.2987</td>\n",
+       "      <td>Thu May 23 10:37:55 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>286</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 1.7604197964430905, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-0.9788</td>\n",
+       "      <td>15.2991</td>\n",
+       "      <td>0.9009</td>\n",
+       "      <td>Thu May 23 10:38:01 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>287</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': 3, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.0059</td>\n",
+       "      <td>10.9781</td>\n",
+       "      <td>0.8135</td>\n",
+       "      <td>Thu May 23 10:37:51 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>288</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 6, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.6527</td>\n",
+       "      <td>8.1246</td>\n",
+       "      <td>0.8817</td>\n",
+       "      <td>Thu May 23 10:37:55 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>289</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 5, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-1.7479</td>\n",
+       "      <td>7.9676</td>\n",
+       "      <td>0.8152</td>\n",
+       "      <td>Thu May 23 10:37:53 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>290</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.10242113515453982, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.1368</td>\n",
+       "      <td>9.2758</td>\n",
+       "      <td>0.8781</td>\n",
+       "      <td>Thu May 23 10:38:24 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>291</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.10241113615453983, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.1368</td>\n",
+       "      <td>10.3302</td>\n",
+       "      <td>0.9744</td>\n",
+       "      <td>Thu May 23 10:38:23 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>292</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.100009999, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.1502</td>\n",
+       "      <td>10.7863</td>\n",
+       "      <td>0.8417</td>\n",
+       "      <td>Thu May 23 10:38:23 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>293</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.1502</td>\n",
+       "      <td>10.2856</td>\n",
+       "      <td>1.1617</td>\n",
+       "      <td>Thu May 23 10:38:19 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>294</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 253, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.9114</td>\n",
+       "      <td>8.2499</td>\n",
+       "      <td>0.8578</td>\n",
+       "      <td>Thu May 23 10:38:39 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>295</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 252, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.9117</td>\n",
+       "      <td>7.0266</td>\n",
+       "      <td>0.8430</td>\n",
+       "      <td>Thu May 23 10:38:37 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>296</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.000119998, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.9481</td>\n",
+       "      <td>20.8551</td>\n",
+       "      <td>0.9050</td>\n",
+       "      <td>Thu May 23 10:37:52 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>297</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.000109999, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.9534</td>\n",
+       "      <td>15.2459</td>\n",
+       "      <td>1.6288</td>\n",
+       "      <td>Thu May 23 10:37:46 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>298</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 101, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.9693</td>\n",
+       "      <td>5.7732</td>\n",
+       "      <td>1.7309</td>\n",
+       "      <td>Thu May 23 10:38:36 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>299</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 100, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-2.9697</td>\n",
+       "      <td>5.6526</td>\n",
+       "      <td>0.8149</td>\n",
+       "      <td>Thu May 23 10:38:35 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>300</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': None}</td>\n",
+       "      <td>-2.9877</td>\n",
+       "      <td>10.6908</td>\n",
+       "      <td>0.9739</td>\n",
+       "      <td>Thu May 23 10:38:17 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>301</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 0, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-3.0112</td>\n",
+       "      <td>6.4505</td>\n",
+       "      <td>0.7836</td>\n",
+       "      <td>Thu May 23 10:38:29 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>302</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 6, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-3.0112</td>\n",
+       "      <td>6.9859</td>\n",
+       "      <td>1.1621</td>\n",
+       "      <td>Thu May 23 10:38:29 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>303</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-3.0112</td>\n",
+       "      <td>7.3460</td>\n",
+       "      <td>0.8478</td>\n",
+       "      <td>Thu May 23 10:38:28 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>304</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 4, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-3.0112</td>\n",
+       "      <td>8.2352</td>\n",
+       "      <td>0.8335</td>\n",
+       "      <td>Thu May 23 10:38:27 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>305</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 3, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-3.0112</td>\n",
+       "      <td>8.4559</td>\n",
+       "      <td>0.8229</td>\n",
+       "      <td>Thu May 23 10:38:27 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>306</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 5, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-3.0112</td>\n",
+       "      <td>8.4749</td>\n",
+       "      <td>2.8363</td>\n",
+       "      <td>Thu May 23 10:38:29 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>307</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.000119998, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-3.0116</td>\n",
+       "      <td>10.9574</td>\n",
+       "      <td>0.9758</td>\n",
+       "      <td>Thu May 23 10:38:20 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>308</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 6, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-3.0116</td>\n",
+       "      <td>4.3542</td>\n",
+       "      <td>1.1986</td>\n",
+       "      <td>Thu May 23 10:38:33 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>309</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.000109999, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-3.0118</td>\n",
+       "      <td>11.2326</td>\n",
+       "      <td>0.9755</td>\n",
+       "      <td>Thu May 23 10:38:19 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>310</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 0, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-3.0121</td>\n",
+       "      <td>4.5213</td>\n",
+       "      <td>0.8368</td>\n",
+       "      <td>Thu May 23 10:38:39 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>311</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 564, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-3.0121</td>\n",
+       "      <td>4.2517</td>\n",
+       "      <td>0.8345</td>\n",
+       "      <td>Thu May 23 10:38:38 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>312</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 189, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-3.0121</td>\n",
+       "      <td>4.3668</td>\n",
+       "      <td>0.8418</td>\n",
+       "      <td>Thu May 23 10:38:37 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>313</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 32, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-3.0121</td>\n",
+       "      <td>4.3696</td>\n",
+       "      <td>1.3160</td>\n",
+       "      <td>Thu May 23 10:38:36 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>314</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-3.0121</td>\n",
+       "      <td>4.3753</td>\n",
+       "      <td>0.8418</td>\n",
+       "      <td>Thu May 23 10:38:36 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>315</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 563, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-3.0121</td>\n",
+       "      <td>4.3863</td>\n",
+       "      <td>1.1756</td>\n",
+       "      <td>Thu May 23 10:38:38 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>316</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 190, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-3.0121</td>\n",
+       "      <td>4.4110</td>\n",
+       "      <td>2.1299</td>\n",
+       "      <td>Thu May 23 10:38:37 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>317</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-3.0121</td>\n",
+       "      <td>4.8695</td>\n",
+       "      <td>0.8391</td>\n",
+       "      <td>Thu May 23 10:38:33 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>318</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.15511867834972753, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-3.0121</td>\n",
+       "      <td>5.5249</td>\n",
+       "      <td>0.8365</td>\n",
+       "      <td>Thu May 23 10:38:31 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>319</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.15491868834972752, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-3.0121</td>\n",
+       "      <td>6.0223</td>\n",
+       "      <td>0.8328</td>\n",
+       "      <td>Thu May 23 10:38:30 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>320</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-3.0121</td>\n",
+       "      <td>6.2898</td>\n",
+       "      <td>1.7299</td>\n",
+       "      <td>Thu May 23 10:38:29 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>321</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.00119999, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-3.0121</td>\n",
+       "      <td>6.4380</td>\n",
+       "      <td>0.8360</td>\n",
+       "      <td>Thu May 23 10:38:29 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>322</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 3, 'boosting_type': 'dart', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-3.0121</td>\n",
+       "      <td>10.9715</td>\n",
+       "      <td>0.9731</td>\n",
+       "      <td>Thu May 23 10:38:14 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>323</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 3, 'boosting_type': 'goss', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-3.0121</td>\n",
+       "      <td>11.4807</td>\n",
+       "      <td>0.8321</td>\n",
+       "      <td>Thu May 23 10:38:16 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>324</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 9.999999999e-06, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-3.0121</td>\n",
+       "      <td>4.2741</td>\n",
+       "      <td>1.3259</td>\n",
+       "      <td>Thu May 23 10:38:40 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>325</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1.0000099999e-05, 'n_estimators': 5, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-3.0121</td>\n",
+       "      <td>4.4722</td>\n",
+       "      <td>0.9776</td>\n",
+       "      <td>Thu May 23 10:38:42 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>326</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 0.0001778292746984456, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-3.0121</td>\n",
+       "      <td>4.4987</td>\n",
+       "      <td>0.8284</td>\n",
+       "      <td>Thu May 23 10:38:40 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>327</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 0.00018782927469744562, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-3.0121</td>\n",
+       "      <td>4.5715</td>\n",
+       "      <td>1.7346</td>\n",
+       "      <td>Thu May 23 10:38:41 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>328</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 2.0000099998e-05, 'n_estimators': 5, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-3.0121</td>\n",
+       "      <td>4.2560</td>\n",
+       "      <td>1.1777</td>\n",
+       "      <td>Thu May 23 10:38:43 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>329</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 0.0001778292746984456, 'n_estimators': 5, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-3.0121</td>\n",
+       "      <td>4.1203</td>\n",
+       "      <td>0.8478</td>\n",
+       "      <td>Thu May 23 10:38:43 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>330</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 0.00018782927469744562, 'n_estimators': 5, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-3.0121</td>\n",
+       "      <td>4.3616</td>\n",
+       "      <td>0.8424</td>\n",
+       "      <td>Thu May 23 10:38:44 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>331</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 0.05623427310243317, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-3.0121</td>\n",
+       "      <td>4.4524</td>\n",
+       "      <td>0.8340</td>\n",
+       "      <td>Thu May 23 10:38:41 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>332</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 0.056244273102432164, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-3.0121</td>\n",
+       "      <td>4.4193</td>\n",
+       "      <td>0.8345</td>\n",
+       "      <td>Thu May 23 10:38:42 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>333</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 1.7604197964430905, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-3.0121</td>\n",
+       "      <td>4.7082</td>\n",
+       "      <td>0.8322</td>\n",
+       "      <td>Thu May 23 10:38:32 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>334</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 1.7602198064430905, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-3.0121</td>\n",
+       "      <td>5.0464</td>\n",
+       "      <td>1.3080</td>\n",
+       "      <td>Thu May 23 10:38:31 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>335</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 0.999990000000001, 'n_estimators': 5, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-3.0126</td>\n",
+       "      <td>4.3677</td>\n",
+       "      <td>1.3344</td>\n",
+       "      <td>Thu May 23 10:38:45 2024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>336</th>\n",
+       "      <td>Model Tuning</td>\n",
+       "      <td>2715</td>\n",
+       "      <td>14235</td>\n",
+       "      <td>LGBMClassifier</td>\n",
+       "      <td>{'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1, 'n_estimators': 5, 'class_weight': 'balanced'}</td>\n",
+       "      <td>-3.0126</td>\n",
+       "      <td>4.4191</td>\n",
+       "      <td>0.7945</td>\n",
+       "      <td>Thu May 23 10:38:45 2024</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  Step # Samples  # Features                Algorithm  \\\n",
+       "0      Model Selection      2715       25238          LGBMClassifier    \n",
+       "1      Model Selection      2715       25238      TorchMLPClassifier    \n",
+       "2      Model Selection      2715       25238              GaussianNB    \n",
+       "3      Model Selection      2715       25238  DecisionTreeClassifier    \n",
+       "4    Feature Selection      2715       14235          LGBMClassifier    \n",
+       "5    Feature Selection      2715        9885          LGBMClassifier    \n",
+       "6    Feature Selection      2715       11862          LGBMClassifier    \n",
+       "7    Feature Selection      2715       20499          LGBMClassifier    \n",
+       "8    Feature Selection      2715       14235          LGBMClassifier    \n",
+       "9    Feature Selection      2715       17082          LGBMClassifier    \n",
+       "10   Feature Selection      2715        5720          LGBMClassifier    \n",
+       "11   Feature Selection      2715        2800          LGBMClassifier    \n",
+       "12   Feature Selection      2715       24599          LGBMClassifier    \n",
+       "13   Feature Selection      2715       11862          LGBMClassifier    \n",
+       "14   Feature Selection      2715       20499          LGBMClassifier    \n",
+       "15   Feature Selection      2715       17082          LGBMClassifier    \n",
+       "16   Feature Selection      2715       24599          LGBMClassifier    \n",
+       "17   Feature Selection      2715        2757          LGBMClassifier    \n",
+       "18   Feature Selection      2715        8237          LGBMClassifier    \n",
+       "19   Feature Selection      2715        6864          LGBMClassifier    \n",
+       "20   Feature Selection      2715        4766          LGBMClassifier    \n",
+       "21   Feature Selection      2715        9885          LGBMClassifier    \n",
+       "22   Feature Selection      2715        8237          LGBMClassifier    \n",
+       "23   Feature Selection      2715        3309          LGBMClassifier    \n",
+       "24   Feature Selection      2715        6864          LGBMClassifier    \n",
+       "25   Feature Selection      2715        1914          LGBMClassifier    \n",
+       "26   Feature Selection      2715        5720          LGBMClassifier    \n",
+       "27   Feature Selection      2715        1595          LGBMClassifier    \n",
+       "28   Feature Selection      2715        2297          LGBMClassifier    \n",
+       "29   Feature Selection      2715        4766          LGBMClassifier    \n",
+       "30   Feature Selection      2715        3309          LGBMClassifier    \n",
+       "31   Feature Selection      2715        2757          LGBMClassifier    \n",
+       "32   Feature Selection      2715        1329          LGBMClassifier    \n",
+       "33   Feature Selection      2715        3971          LGBMClassifier    \n",
+       "34   Feature Selection      2715        3971          LGBMClassifier    \n",
+       "35   Feature Selection      2715        2757          LGBMClassifier    \n",
+       "36   Feature Selection      2715        1329          LGBMClassifier    \n",
+       "37   Feature Selection      2715        1595          LGBMClassifier    \n",
+       "38   Feature Selection      2715        1914          LGBMClassifier    \n",
+       "39   Feature Selection      2715       11862          LGBMClassifier    \n",
+       "40   Feature Selection      2715        2297          LGBMClassifier    \n",
+       "41   Feature Selection      2715        2297          LGBMClassifier    \n",
+       "42   Feature Selection      2715       13407          LGBMClassifier    \n",
+       "43   Feature Selection      2715        1107          LGBMClassifier    \n",
+       "44   Feature Selection      2715        1107          LGBMClassifier    \n",
+       "45   Feature Selection      2715         922          LGBMClassifier    \n",
+       "46   Feature Selection      2715         922          LGBMClassifier    \n",
+       "47   Feature Selection      2715        1914          LGBMClassifier    \n",
+       "48   Feature Selection      2715         640          LGBMClassifier    \n",
+       "49   Feature Selection      2715         768          LGBMClassifier    \n",
+       "50   Feature Selection      2715        9885          LGBMClassifier    \n",
+       "51   Feature Selection      2715         768          LGBMClassifier    \n",
+       "52   Feature Selection      2715        1595          LGBMClassifier    \n",
+       "53   Feature Selection      2715        1329          LGBMClassifier    \n",
+       "54   Feature Selection      2715         640          LGBMClassifier    \n",
+       "55   Feature Selection      2715        6864          LGBMClassifier    \n",
+       "56   Feature Selection      2715        8237          LGBMClassifier    \n",
+       "57   Feature Selection      2715         533          LGBMClassifier    \n",
+       "58   Feature Selection      2715        1107          LGBMClassifier    \n",
+       "59   Feature Selection      2715         533          LGBMClassifier    \n",
+       "60   Feature Selection      2715         922          LGBMClassifier    \n",
+       "61   Feature Selection      2715        5720          LGBMClassifier    \n",
+       "62   Feature Selection      2715         444          LGBMClassifier    \n",
+       "63   Feature Selection      2715         768          LGBMClassifier    \n",
+       "64   Feature Selection      2715        4766          LGBMClassifier    \n",
+       "65   Feature Selection      2715         370          LGBMClassifier    \n",
+       "66   Feature Selection      2715         444          LGBMClassifier    \n",
+       "67   Feature Selection      2715        3971          LGBMClassifier    \n",
+       "68   Feature Selection      2715        2757          LGBMClassifier    \n",
+       "69   Feature Selection      2715         370          LGBMClassifier    \n",
+       "70   Feature Selection      2715        3309          LGBMClassifier    \n",
+       "71   Feature Selection      2715         308          LGBMClassifier    \n",
+       "72   Feature Selection      2715         640          LGBMClassifier    \n",
+       "73   Feature Selection      2715         256          LGBMClassifier    \n",
+       "74   Feature Selection      2715        2297          LGBMClassifier    \n",
+       "75   Feature Selection      2715        1914          LGBMClassifier    \n",
+       "76   Feature Selection      2715         308          LGBMClassifier    \n",
+       "77   Feature Selection      2715         256          LGBMClassifier    \n",
+       "78   Feature Selection      2715         533          LGBMClassifier    \n",
+       "79   Feature Selection      2715         213          LGBMClassifier    \n",
+       "80   Feature Selection      2715         444          LGBMClassifier    \n",
+       "81   Feature Selection      2715        1329          LGBMClassifier    \n",
+       "82   Feature Selection      2715         213          LGBMClassifier    \n",
+       "83   Feature Selection      2715        1595          LGBMClassifier    \n",
+       "84   Feature Selection      2715         177          LGBMClassifier    \n",
+       "85   Feature Selection      2715         177          LGBMClassifier    \n",
+       "86   Feature Selection      2715        1107          LGBMClassifier    \n",
+       "87   Feature Selection      2715         370          LGBMClassifier    \n",
+       "88   Feature Selection      2715         768          LGBMClassifier    \n",
+       "89   Feature Selection      2715         922          LGBMClassifier    \n",
+       "90   Feature Selection      2715         147          LGBMClassifier    \n",
+       "91   Feature Selection      2715         147          LGBMClassifier    \n",
+       "92   Feature Selection      2715         308          LGBMClassifier    \n",
+       "93   Feature Selection      2715         256          LGBMClassifier    \n",
+       "94   Feature Selection      2715         213          LGBMClassifier    \n",
+       "95   Feature Selection      2715         640          LGBMClassifier    \n",
+       "96   Feature Selection      2715         122          LGBMClassifier    \n",
+       "97   Feature Selection      2715         122          LGBMClassifier    \n",
+       "98   Feature Selection      2715         444          LGBMClassifier    \n",
+       "99   Feature Selection      2715         533          LGBMClassifier    \n",
+       "100  Feature Selection      2715         370          LGBMClassifier    \n",
+       "101  Feature Selection      2715         147          LGBMClassifier    \n",
+       "102  Feature Selection      2715         101          LGBMClassifier    \n",
+       "103  Feature Selection      2715         177          LGBMClassifier    \n",
+       "104  Feature Selection      2715         101          LGBMClassifier    \n",
+       "105  Feature Selection      2715         308          LGBMClassifier    \n",
+       "106  Feature Selection      2715         122          LGBMClassifier    \n",
+       "107  Feature Selection      2715         256          LGBMClassifier    \n",
+       "108  Feature Selection      2715          84          LGBMClassifier    \n",
+       "109  Feature Selection      2715          84          LGBMClassifier    \n",
+       "110  Feature Selection      2715         101          LGBMClassifier    \n",
+       "111  Feature Selection      2715         213          LGBMClassifier    \n",
+       "112  Feature Selection      2715          70          LGBMClassifier    \n",
+       "113  Feature Selection      2715          84          LGBMClassifier    \n",
+       "114  Feature Selection      2715         177          LGBMClassifier    \n",
+       "115  Feature Selection      2715          70          LGBMClassifier    \n",
+       "116  Feature Selection      2715          58          LGBMClassifier    \n",
+       "117  Feature Selection      2715          70          LGBMClassifier    \n",
+       "118  Feature Selection      2715          58          LGBMClassifier    \n",
+       "119  Feature Selection      2715         147          LGBMClassifier    \n",
+       "120  Feature Selection      2715          48          LGBMClassifier    \n",
+       "121  Feature Selection      2715          51          LGBMClassifier    \n",
+       "122  Feature Selection      2715          40          LGBMClassifier    \n",
+       "123  Feature Selection      2715          48          LGBMClassifier    \n",
+       "124  Feature Selection      2715         122          LGBMClassifier    \n",
+       "125  Feature Selection      2715          48          LGBMClassifier    \n",
+       "126  Feature Selection      2715          33          LGBMClassifier    \n",
+       "127  Feature Selection      2715          58          LGBMClassifier    \n",
+       "128  Feature Selection      2715         101          LGBMClassifier    \n",
+       "129  Feature Selection      2715          48          LGBMClassifier    \n",
+       "130  Feature Selection      2715          84          LGBMClassifier    \n",
+       "131  Feature Selection      2715          27          LGBMClassifier    \n",
+       "132  Feature Selection      2715          40          LGBMClassifier    \n",
+       "133  Feature Selection      2715          70          LGBMClassifier    \n",
+       "134  Feature Selection      2715          40          LGBMClassifier    \n",
+       "135  Feature Selection      2715          33          LGBMClassifier    \n",
+       "136  Feature Selection      2715          22          LGBMClassifier    \n",
+       "137  Feature Selection      2715          40          LGBMClassifier    \n",
+       "138  Feature Selection      2715          33          LGBMClassifier    \n",
+       "139  Feature Selection      2715          58          LGBMClassifier    \n",
+       "140  Feature Selection      2715          48          LGBMClassifier    \n",
+       "141  Feature Selection      2715          27          LGBMClassifier    \n",
+       "142  Feature Selection      2715          22          LGBMClassifier    \n",
+       "143  Feature Selection      2715          33          LGBMClassifier    \n",
+       "144  Feature Selection      2715          18          LGBMClassifier    \n",
+       "145  Feature Selection      2715          40          LGBMClassifier    \n",
+       "146  Feature Selection      2715          18          LGBMClassifier    \n",
+       "147  Feature Selection      2715          15          LGBMClassifier    \n",
+       "148  Feature Selection      2715          27          LGBMClassifier    \n",
+       "149  Feature Selection      2715          15          LGBMClassifier    \n",
+       "150  Feature Selection      2715          22          LGBMClassifier    \n",
+       "151  Feature Selection      2715          12          LGBMClassifier    \n",
+       "152  Feature Selection      2715          27          LGBMClassifier    \n",
+       "153  Feature Selection      2715          12          LGBMClassifier    \n",
+       "154  Feature Selection      2715          12          LGBMClassifier    \n",
+       "155  Feature Selection      2715          18          LGBMClassifier    \n",
+       "156  Feature Selection      2715          33          LGBMClassifier    \n",
+       "157  Feature Selection      2715          10          LGBMClassifier    \n",
+       "158  Feature Selection      2715          22          LGBMClassifier    \n",
+       "159  Feature Selection      2715          15          LGBMClassifier    \n",
+       "160  Feature Selection      2715          27          LGBMClassifier    \n",
+       "161  Feature Selection      2715          10          LGBMClassifier    \n",
+       "162  Feature Selection      2715           8          LGBMClassifier    \n",
+       "163  Feature Selection      2715           8          LGBMClassifier    \n",
+       "164  Feature Selection      2715          18          LGBMClassifier    \n",
+       "165  Feature Selection      2715           8          LGBMClassifier    \n",
+       "166  Feature Selection      2715          15          LGBMClassifier    \n",
+       "167  Feature Selection      2715          22          LGBMClassifier    \n",
+       "168  Feature Selection      2715           6          LGBMClassifier    \n",
+       "169  Feature Selection      2715           5          LGBMClassifier    \n",
+       "170  Feature Selection      2715           6          LGBMClassifier    \n",
+       "171  Feature Selection      2715           5          LGBMClassifier    \n",
+       "172  Feature Selection      2715           6          LGBMClassifier    \n",
+       "173  Feature Selection      2715          12          LGBMClassifier    \n",
+       "174  Feature Selection      2715           4          LGBMClassifier    \n",
+       "175  Feature Selection      2715          18          LGBMClassifier    \n",
+       "176  Feature Selection      2715           4          LGBMClassifier    \n",
+       "177  Feature Selection      2715          10          LGBMClassifier    \n",
+       "178  Feature Selection      2715           4          LGBMClassifier    \n",
+       "179  Feature Selection      2715          10          LGBMClassifier    \n",
+       "180  Feature Selection      2715          15          LGBMClassifier    \n",
+       "181  Feature Selection      2715           3          LGBMClassifier    \n",
+       "182  Feature Selection      2715           8          LGBMClassifier    \n",
+       "183  Feature Selection      2715           3          LGBMClassifier    \n",
+       "184  Feature Selection      2715          12          LGBMClassifier    \n",
+       "185  Feature Selection      2715           3          LGBMClassifier    \n",
+       "186  Feature Selection      2715           2          LGBMClassifier    \n",
+       "187  Feature Selection      2715           6          LGBMClassifier    \n",
+       "188  Feature Selection      2715           2          LGBMClassifier    \n",
+       "189  Feature Selection      2715           2          LGBMClassifier    \n",
+       "190  Feature Selection      2715           5          LGBMClassifier    \n",
+       "191  Feature Selection      2715           1          LGBMClassifier    \n",
+       "192  Feature Selection      2715           1          LGBMClassifier    \n",
+       "193  Feature Selection      2715           4          LGBMClassifier    \n",
+       "194  Feature Selection      2715           3          LGBMClassifier    \n",
+       "195  Feature Selection      2715           2          LGBMClassifier    \n",
+       "196  Feature Selection      2715           1          LGBMClassifier    \n",
+       "197       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "198       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "199       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "200       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "201       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "202       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "203       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "204       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "205       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "206       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "207       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "208       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "209       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "210       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "211       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "212       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "213       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "214       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "215       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "216       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "217       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "218       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "219       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "220       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "221       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "222       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "223       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "224       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "225       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "226       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "227       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "228       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "229       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "230       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "231       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "232       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "233       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "234       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "235       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "236       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "237       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "238       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "239       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "240       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "241       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "242       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "243       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "244       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "245       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "246       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "247       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "248       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "249       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "250       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "251       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "252       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "253       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "254       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "255       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "256       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "257       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "258       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "259       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "260       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "261       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "262       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "263       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "264       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "265       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "266       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "267       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "268       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "269       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "270       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "271       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "272       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "273       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "274       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "275       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "276       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "277       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "278       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "279       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "280       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "281       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "282       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "283       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "284       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "285       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "286       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "287       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "288       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "289       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "290       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "291       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "292       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "293       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "294       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "295       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "296       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "297       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "298       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "299       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "300       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "301       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "302       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "303       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "304       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "305       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "306       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "307       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "308       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "309       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "310       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "311       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "312       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "313       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "314       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "315       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "316       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "317       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "318       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "319       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "320       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "321       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "322       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "323       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "324       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "325       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "326       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "327       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "328       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "329       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "330       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "331       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "332       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "333       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "334       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "335       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "336       Model Tuning      2715       14235          LGBMClassifier    \n",
+       "\n",
+       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             Hyperparameters  \\\n",
+       "0                                                                                                                                                                                                                                                                                                            {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "1    {'optimizer_class': 'Adam', 'shuffle_dataset_each_epoch': True, 'optimizer_params': {}, 'criterion_class': None, 'criterion_params': {}, 'scheduler_class': None, 'scheduler_params': {}, 'batch_size': 128, 'lr': 0.001, 'epochs': 18, 'input_transform': 'auto', 'tensorboard_dir': None, 'use_tqdm': None, 'prediction_batch_size': 128, 'prediction_input_transform': 'auto', 'shuffling_buffer_size': None, 'depth': 4, 'num_logits': 1000, 'div_factor': 2, 'activation': 'ReLU', 'dropout': 0.1}   \n",
+       "2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         {}   \n",
+       "3                                                                                                                                                                                                                                                                                                                                                                                                    {'min_samples_split': 0.00125, 'min_samples_leaf': 0.000625, 'max_features': 1.0, 'class_weight': None}   \n",
+       "4                                                                                                                                                                                                                                                                                                            {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "5                                                                                                                                                                                                                                                                                                            {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "6                                                                                                                                                                                                                                                                                                            {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "7                                                                                                                                                                                                                                                                                                            {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "8                                                                                                                                                                                                                                                                                                            {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "9                                                                                                                                                                                                                                                                                                            {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "10                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "11                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "12                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "13                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "14                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "15                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "16                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "17                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "18                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "19                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "20                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "21                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "22                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "23                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "24                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "25                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "26                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "27                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "28                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "29                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "30                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "31                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "32                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "33                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "34                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "35                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "36                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "37                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "38                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "39                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "40                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "41                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "42                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "43                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "44                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "45                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "46                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "47                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "48                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "49                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "50                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "51                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "52                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "53                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "54                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "55                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "56                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "57                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "58                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "59                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "60                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "61                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "62                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "63                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "64                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "65                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "66                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "67                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "68                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "69                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "70                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "71                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "72                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "73                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "74                                                                                                                                                                                                                                                                                                           {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
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+       "100                                                                                                                                                                                                                                                                                                          {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "101                                                                                                                                                                                                                                                                                                          {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "102                                                                                                                                                                                                                                                                                                          {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "103                                                                                                                                                                                                                                                                                                          {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "104                                                                                                                                                                                                                                                                                                          {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "105                                                                                                                                                                                                                                                                                                          {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "106                                                                                                                                                                                                                                                                                                          {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "107                                                                                                                                                                                                                                                                                                          {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "108                                                                                                                                                                                                                                                                                                          {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "109                                                                                                                                                                                                                                                                                                          {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "110                                                                                                                                                                                                                                                                                                          {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "111                                                                                                                                                                                                                                                                                                          {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "112                                                                                                                                                                                                                                                                                                          {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "113                                                                                                                                                                                                                                                                                                          {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "114                                                                                                                                                                                                                                                                                                          {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "115                                                                                                                                                                                                                                                                                                          {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "116                                                                                                                                                                                                                                                                                                          {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "117                                                                                                                                                                                                                                                                                                          {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "118                                                                                                                                                                                                                                                                                                          {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "119                                                                                                                                                                                                                                                                                                          {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "120                                                                                                                                                                                                                                                                                                          {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "121                                                                                                                                                                                                                                                                                                          {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "122                                                                                                                                                                                                                                                                                                          {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "123                                                                                                                                                                                                                                                                                                          {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "124                                                                                                                                                                                                                                                                                                          {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "125                                                                                                                                                                                                                                                                                                          {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "126                                                                                                                                                                                                                                                                                                          {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "127                                                                                                                                                                                                                                                                                                          {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "128                                                                                                                                                                                                                                                                                                          {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "129                                                                                                                                                                                                                                                                                                          {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "130                                                                                                                                                                                                                                                                                                          {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "131                                                                                                                                                                                                                                                                                                          {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "132                                                                                                                                                                                                                                                                                                          {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "133                                                                                                                                                                                                                                                                                                          {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "134                                                                                                                                                                                                                                                                                                          {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "135                                                                                                                                                                                                                                                                                                          {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "136                                                                                                                                                                                                                                                                                                          {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "137                                                                                                                                                                                                                                                                                                          {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "138                                                                                                                                                                                                                                                                                                          {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "139                                                                                                                                                                                                                                                                                                          {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "140                                                                                                                                                                                                                                                                                                          {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "141                                                                                                                                                                                                                                                                                                          {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "142                                                                                                                                                                                                                                                                                                          {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "143                                                                                                                                                                                                                                                                                                          {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "144                                                                                                                                                                                                                                                                                                          {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "145                                                                                                                                                                                                                                                                                                          {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "146                                                                                                                                                                                                                                                                                                          {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "147                                                                                                                                                                                                                                                                                                          {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "148                                                                                                                                                                                                                                                                                                          {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "149                                                                                                                                                                                                                                                                                                          {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "150                                                                                                                                                                                                                                                                                                          {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
+       "151                                                                                                                                                                                                                                                                                                          {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}   \n",
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+       "307                                                                                                                                                                                                                                                                                              {'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.000119998, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}   \n",
+       "308                                                                                                                                                                                                                                                                                                   {'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 6, 'class_weight': 'balanced'}   \n",
+       "309                                                                                                                                                                                                                                                                                              {'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.000109999, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}   \n",
+       "310                                                                                                                                                                                                                                                                                                       {'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 0, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}   \n",
+       "311                                                                                                                                                                                                                                                                                                 {'num_leaves': 564, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}   \n",
+       "312                                                                                                                                                                                                                                                                                                 {'num_leaves': 189, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}   \n",
+       "313                                                                                                                                                                                                                                                                                                  {'num_leaves': 32, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}   \n",
+       "314                                                                                                                                                                                                                                                                                                  {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}   \n",
+       "315                                                                                                                                                                                                                                                                                                 {'num_leaves': 563, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}   \n",
+       "316                                                                                                                                                                                                                                                                                                 {'num_leaves': 190, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}   \n",
+       "317                                                                                                                                                                                                                                                                                                   {'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}   \n",
+       "318                                                                                                                                                                                                                                                                                     {'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.15511867834972753, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}   \n",
+       "319                                                                                                                                                                                                                                                                                     {'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.15491868834972752, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}   \n",
+       "320                                                                                                                                                                                                                                                                                                   {'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}   \n",
+       "321                                                                                                                                                                                                                                                                                              {'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.00119999, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}   \n",
+       "322                                                                                                                                                                                                                                                                                                   {'num_leaves': 3, 'boosting_type': 'dart', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}   \n",
+       "323                                                                                                                                                                                                                                                                                                   {'num_leaves': 3, 'boosting_type': 'goss', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}   \n",
+       "324                                                                                                                                                                                                                                                                                         {'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 9.999999999e-06, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}   \n",
+       "325                                                                                                                                                                                                                                                                                        {'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1.0000099999e-05, 'n_estimators': 5, 'class_weight': 'balanced'}   \n",
+       "326                                                                                                                                                                                                                                                                                   {'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 0.0001778292746984456, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}   \n",
+       "327                                                                                                                                                                                                                                                                                  {'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 0.00018782927469744562, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}   \n",
+       "328                                                                                                                                                                                                                                                                                        {'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 2.0000099998e-05, 'n_estimators': 5, 'class_weight': 'balanced'}   \n",
+       "329                                                                                                                                                                                                                                                                                   {'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 0.0001778292746984456, 'n_estimators': 5, 'class_weight': 'balanced'}   \n",
+       "330                                                                                                                                                                                                                                                                                  {'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 0.00018782927469744562, 'n_estimators': 5, 'class_weight': 'balanced'}   \n",
+       "331                                                                                                                                                                                                                                                                                     {'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 0.05623427310243317, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}   \n",
+       "332                                                                                                                                                                                                                                                                                    {'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 0.056244273102432164, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}   \n",
+       "333                                                                                                                                                                                                                                                                                      {'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 1.7604197964430905, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}   \n",
+       "334                                                                                                                                                                                                                                                                                      {'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 1.7602198064430905, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1e-10, 'n_estimators': 5, 'class_weight': 'balanced'}   \n",
+       "335                                                                                                                                                                                                                                                                                       {'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 0.999990000000001, 'n_estimators': 5, 'class_weight': 'balanced'}   \n",
+       "336                                                                                                                                                                                                                                                                                                       {'num_leaves': 3, 'boosting_type': 'gbdt', 'learning_rate': 0.0001, 'min_child_weight': 0.001, 'max_depth': 1, 'reg_alpha': 1e-10, 'reg_lambda': 1, 'n_estimators': 5, 'class_weight': 'balanced'}   \n",
+       "\n",
+       "    Score (neg_log_loss)  Runtime (Seconds)  Memory Usage (GB)  \\\n",
+       "0                -0.8177            13.8053             1.2231   \n",
+       "1                -2.9938           915.1965             1.5376   \n",
+       "2                 -7.644            11.5540             1.4871   \n",
+       "3               -15.4821            12.6717             1.2525   \n",
+       "4                -0.8081            25.3911             1.8933   \n",
+       "5                -0.8153            20.3971             2.6275   \n",
+       "6                -0.8173            22.7057             1.8509   \n",
+       "7                -0.8188            27.9225             2.7320   \n",
+       "8                 -0.819            23.8457             1.8576   \n",
+       "9                -0.8193            28.2084             2.8665   \n",
+       "10               -0.8235            16.3118             1.7139   \n",
+       "11               -0.8242            13.4597             2.5051   \n",
+       "12               -0.8251            31.1346             2.2462   \n",
+       "13               -0.8253            23.1930             1.8396   \n",
+       "14               -0.8255            28.9252             2.0524   \n",
+       "15               -0.8259            27.1348             2.7111   \n",
+       "16                -0.826            32.5141             3.0002   \n",
+       "17               -0.8305            13.5560             1.7122   \n",
+       "18               -0.8315            19.6689             1.8418   \n",
+       "19               -0.8315            18.0505             2.5070   \n",
+       "20               -0.8318            12.5170             1.7119   \n",
+       "21               -0.8326            21.3303             1.7615   \n",
+       "22                -0.834            20.3790             1.8199   \n",
+       "23               -0.8374            12.2541             1.7265   \n",
+       "24               -0.8377            17.6653             1.7352   \n",
+       "25               -0.8378            10.8578             1.7405   \n",
+       "26               -0.8383            15.5746             1.7591   \n",
+       "27               -0.8403            11.4344             1.7144   \n",
+       "28               -0.8418            11.2165             1.1772   \n",
+       "29               -0.8441            14.2588             2.5272   \n",
+       "30                -0.845            11.1807             1.7050   \n",
+       "31               -0.8463            11.5175             2.5268   \n",
+       "32               -0.8471            10.7158             2.5423   \n",
+       "33               -0.8474            10.0837             1.7367   \n",
+       "34               -0.8509            14.6562             2.5236   \n",
+       "35               -0.8575             9.8742             1.7469   \n",
+       "36               -0.8596             9.6186             2.5307   \n",
+       "37               -0.8598            10.0047             1.7409   \n",
+       "38               -0.8646            10.9595             1.1232   \n",
+       "39               -0.8662            21.0217             2.5132   \n",
+       "40               -0.8669             9.3428             2.5347   \n",
+       "41               -0.8679            10.5597             1.7073   \n",
+       "42               -0.8725            22.8446             1.7808   \n",
+       "43               -0.8795             9.4064             1.7200   \n",
+       "44               -0.8817            10.1807             1.2341   \n",
+       "45               -0.8867             9.4246             1.7571   \n",
+       "46               -0.8874             8.5782             0.5493   \n",
+       "47               -0.8974             8.5941             2.5387   \n",
+       "48               -0.9003             7.8711             0.5570   \n",
+       "49               -0.9037             8.7857             1.7596   \n",
+       "50               -0.9057            19.1298             1.0824   \n",
+       "51               -0.9157             8.8506             0.5383   \n",
+       "52               -0.9177             6.8742             1.2233   \n",
+       "53               -0.9346             7.1818             1.7467   \n",
+       "54               -0.9355             8.0854             2.5462   \n",
+       "55               -0.9442            15.6325             1.8140   \n",
+       "56               -0.9559            19.4176             2.5739   \n",
+       "57               -0.9581             7.4298             0.5434   \n",
+       "58               -0.9609             6.4102             0.5082   \n",
+       "59               -0.9718             7.9872             1.2181   \n",
+       "60               -0.9788             6.2687             0.4773   \n",
+       "61               -0.9839            12.0948             0.9443   \n",
+       "62               -0.9901             6.8942             2.5539   \n",
+       "63                -1.015             4.8162             1.2038   \n",
+       "64               -1.0206            12.8944             2.5746   \n",
+       "65               -1.0231             6.4792             1.7241   \n",
+       "66                -1.026             7.5108             1.7203   \n",
+       "67               -1.0487            11.7980             0.9098   \n",
+       "68               -1.0631             9.5604             2.5782   \n",
+       "69               -1.0693             6.8228             0.4255   \n",
+       "70               -1.0706            10.1032             1.7449   \n",
+       "71               -1.0747             4.8309             0.5516   \n",
+       "72               -1.0868             5.4962             1.7518   \n",
+       "73               -1.0918             5.6216             2.5555   \n",
+       "74               -1.1067             8.5754             0.8990   \n",
+       "75               -1.1302             7.5573             1.7469   \n",
+       "76                -1.133             6.4522             1.2210   \n",
+       "77                -1.147             6.0581             0.5699   \n",
+       "78               -1.1487             4.9926             0.5538   \n",
+       "79               -1.1505             5.7629             1.2248   \n",
+       "80               -1.1718             4.7018             1.2313   \n",
+       "81               -1.1741             5.8695             1.2676   \n",
+       "82               -1.1905             4.7250             0.3950   \n",
+       "83               -1.2008             7.2515             2.5813   \n",
+       "84               -1.2023             4.5543             2.5595   \n",
+       "85               -1.2085             5.4495             1.7271   \n",
+       "86               -1.2119             6.8125             0.9248   \n",
+       "87               -1.2418             4.5516             1.7326   \n",
+       "88               -1.2567             6.0074             2.5853   \n",
+       "89               -1.2606             6.2707             1.7489   \n",
+       "90               -1.2657             4.1396             0.6062   \n",
+       "91                -1.274             5.1502             0.5799   \n",
+       "92               -1.2995             3.9022             2.5635   \n",
+       "93               -1.3103             4.0943             0.6180   \n",
+       "94               -1.3271             4.7975             0.9252   \n",
+       "95               -1.3282             5.6817             1.2557   \n",
+       "96               -1.3316             4.0702             1.7594   \n",
+       "97               -1.3586             4.8518             0.9120   \n",
+       "98               -1.3692             5.0455             0.9041   \n",
+       "99               -1.3852             5.3884             0.6020   \n",
+       "100               -1.421             4.4831             1.7529   \n",
+       "101              -1.4254             3.8612             0.6319   \n",
+       "102              -1.4297             4.7787             0.4363   \n",
+       "103              -1.4351             3.5339             2.6053   \n",
+       "104              -1.4378             4.1025             2.5972   \n",
+       "105              -1.4792             5.1696             2.5894   \n",
+       "106              -1.5403             3.0611             0.4371   \n",
+       "107              -1.5427             4.6979             1.2626   \n",
+       "108              -1.5441             4.5536             1.2702   \n",
+       "109              -1.5765             4.3546             0.6318   \n",
+       "110              -1.5962             3.7936             0.9268   \n",
+       "111              -1.6081             4.5165             0.6061   \n",
+       "112              -1.6246             4.7872             2.6133   \n",
+       "113              -1.6468             3.3117             2.6093   \n",
+       "114              -1.6859             4.4852             0.9052   \n",
+       "115              -1.7001             3.3399             0.6615   \n",
+       "116               -1.762             3.0358             0.9763   \n",
+       "117              -1.7848             4.0995             1.2841   \n",
+       "118               -1.793             4.2778             0.4462   \n",
+       "119              -1.8009             4.4626             1.7556   \n",
+       "120               -1.801             3.1682             0.4796   \n",
+       "121              -1.8163             2.9546             0.5748   \n",
+       "122              -1.8358             2.6926             0.6556   \n",
+       "123              -1.8889             3.0477             1.3058   \n",
+       "124              -1.8891             4.5973             2.5934   \n",
+       "125              -1.8955             3.0244             0.6552   \n",
+       "126               -1.913             2.9698             0.9724   \n",
+       "127              -1.9231             4.1955             0.9366   \n",
+       "128              -1.9252             4.4641             1.2652   \n",
+       "129              -1.9401             3.0640             1.3055   \n",
+       "130              -1.9515             4.5635             1.7627   \n",
+       "131              -1.9782             2.8900             1.3096   \n",
+       "132              -1.9929             3.6222             1.7660   \n",
+       "133              -2.0381             4.4439             2.6012   \n",
+       "134              -2.0503             3.0116             1.7699   \n",
+       "135              -2.0699             4.1546             1.2980   \n",
+       "136              -2.0727             2.8416             0.6702   \n",
+       "137              -2.0826             2.7600             0.9562   \n",
+       "138              -2.1023             2.7728             0.4761   \n",
+       "139              -2.1095             4.4697             1.2739   \n",
+       "140              -2.1411             3.6205             0.6216   \n",
+       "141              -2.1411             2.5351             0.9802   \n",
+       "142              -2.1458             2.9758             0.4945   \n",
+       "143              -2.1561             2.7518             0.6710   \n",
+       "144              -2.1817             2.8651             0.4852   \n",
+       "145              -2.1938             3.8338             0.4333   \n",
+       "146               -2.215             2.3860             0.9673   \n",
+       "147              -2.2183             2.7508             0.6656   \n",
+       "148              -2.2236             3.6306             0.4883   \n",
+       "149              -2.2565             2.4850             0.5074   \n",
+       "150              -2.3029             2.9781             1.3064   \n",
+       "151              -2.3109             2.5892             1.7721   \n",
+       "152              -2.3112             2.6588             0.4688   \n",
+       "153              -2.3525             2.6103             0.6733   \n",
+       "154              -2.3792             2.7457             0.6697   \n",
+       "155              -2.4022             3.2052             0.9690   \n",
+       "156              -2.4071             3.6131             0.9412   \n",
+       "157               -2.431             2.2560             0.5033   \n",
+       "158              -2.4472             2.5898             1.3019   \n",
+       "159              -2.4729             3.4469             1.3117   \n",
+       "160              -2.4812             3.7719             0.9786   \n",
+       "161              -2.5027             2.8314             0.4960   \n",
+       "162              -2.5188             2.1882             0.6821   \n",
+       "163              -2.5313             2.6851             0.5192   \n",
+       "164              -2.5424             2.3904             0.9496   \n",
+       "165              -2.5501             2.5698             0.5057   \n",
+       "166              -2.5725             2.3022             0.6671   \n",
+       "167              -2.6016             3.2742             1.7780   \n",
+       "168              -2.6036             2.3810             0.6845   \n",
+       "169              -2.6066             2.2897             1.7814   \n",
+       "170              -2.6299             2.2368             0.9856   \n",
+       "171              -2.6518             2.3437             1.7899   \n",
+       "172              -2.6546             2.5006             0.6908   \n",
+       "173              -2.6615             2.3334             0.4765   \n",
+       "174              -2.6717             2.3237             0.5135   \n",
+       "175              -2.6718             2.8963             1.3165   \n",
+       "176              -2.6834             2.3249             1.7896   \n",
+       "177               -2.692             3.3026             0.9810   \n",
+       "178              -2.7024             2.1647             0.6942   \n",
+       "179              -2.7094             2.0194             0.9606   \n",
+       "180              -2.7318             3.1588             1.7826   \n",
+       "181              -2.7357             2.1384             1.3207   \n",
+       "182               -2.758             1.9295             0.6628   \n",
+       "183              -2.7777             2.1884             0.9740   \n",
+       "184              -2.7778             3.6926             1.3224   \n",
+       "185              -2.7811             2.1009             1.0076   \n",
+       "186              -2.8153             2.4933             2.6172   \n",
+       "187              -2.8435             2.0641             0.4708   \n",
+       "188              -2.8592             2.1753             1.3369   \n",
+       "189              -2.8717             2.0603             2.6133   \n",
+       "190              -2.8946             1.6382             0.9568   \n",
+       "191              -2.9047             1.7968             1.7984   \n",
+       "192               -2.922             1.8707             1.0115   \n",
+       "193              -2.9311             1.9900             0.6433   \n",
+       "194               -2.946             1.9046             0.4669   \n",
+       "195                -2.97             1.9467             0.9528   \n",
+       "196              -2.9959             1.9442             1.2903   \n",
+       "197              -0.8081            20.2350             1.0069   \n",
+       "198              -0.8081            20.3789             0.9145   \n",
+       "199              -0.8081            25.8773             0.9123   \n",
+       "200              -0.8081            26.2765             0.8674   \n",
+       "201              -0.8081            26.6993             0.8137   \n",
+       "202              -0.8081            28.0649             0.9741   \n",
+       "203              -0.8081            32.8766             0.7946   \n",
+       "204              -0.8081            32.9077             0.8929   \n",
+       "205              -0.8081            33.4196             0.8874   \n",
+       "206              -0.8081            37.2014             0.8310   \n",
+       "207              -0.8081            28.7169             2.0712   \n",
+       "208              -0.8084            33.3972             2.8085   \n",
+       "209              -0.8091            27.4936             1.3298   \n",
+       "210              -0.8094            28.5509             0.8472   \n",
+       "211              -0.8096            28.6981             1.7672   \n",
+       "212              -0.8096            27.2710             1.3253   \n",
+       "213              -0.8113            30.9955             0.8170   \n",
+       "214              -0.8128            15.1494             1.1774   \n",
+       "215              -0.8128            33.5865             0.8409   \n",
+       "216              -0.8131            36.7752             0.8498   \n",
+       "217              -0.8131            21.8527             0.8002   \n",
+       "218              -0.8131            37.2306             0.9073   \n",
+       "219              -0.8133            36.2606             0.8474   \n",
+       "220              -0.8135            36.2719             0.8471   \n",
+       "221              -0.8149            19.9035             1.9263   \n",
+       "222              -0.8157            51.4688             0.9162   \n",
+       "223              -0.8157            52.1458             0.8702   \n",
+       "224              -0.8174            21.5247             0.8437   \n",
+       "225              -0.8185            19.0557             0.8452   \n",
+       "226              -0.8186            19.7455             0.8507   \n",
+       "227              -0.8186            21.6535             1.3283   \n",
+       "228              -0.8187            31.1631             0.8408   \n",
+       "229              -0.8189            24.7093             2.2353   \n",
+       "230              -0.8195            30.5136             0.8853   \n",
+       "231              -0.8204            23.9768             0.8466   \n",
+       "232              -0.8214            19.5727             0.8485   \n",
+       "233              -0.8229            25.5383             1.2588   \n",
+       "234              -0.8229            26.0735             1.2678   \n",
+       "235              -0.8229            27.2237             1.2769   \n",
+       "236              -0.8229            28.2897             1.2703   \n",
+       "237              -0.8229            29.8900             1.2834   \n",
+       "238              -0.8229            31.0321             0.8975   \n",
+       "239              -0.8229            32.3063             1.3149   \n",
+       "240              -0.8229            33.7906             1.7289   \n",
+       "241               -0.824            23.1302             0.8315   \n",
+       "242              -0.8241            32.7792             0.8992   \n",
+       "243              -0.8243            21.6087             0.8517   \n",
+       "244              -0.8264            22.2450             0.8670   \n",
+       "245              -0.8272            33.6113             0.8963   \n",
+       "246              -0.8272            34.7928             0.8317   \n",
+       "247              -0.8274            20.5852             0.8955   \n",
+       "248              -0.8276            24.9883             1.7325   \n",
+       "249              -0.8284            24.3892             0.8488   \n",
+       "250              -0.8331            24.0493             0.8336   \n",
+       "251              -0.8334            23.6209             0.8506   \n",
+       "252              -0.8335            23.4433             0.9310   \n",
+       "253              -0.8347            23.0090             0.9776   \n",
+       "254              -0.8358            15.7275             1.1751   \n",
+       "255              -0.8373            24.0774             1.0237   \n",
+       "256              -0.8384            21.7320             1.3201   \n",
+       "257              -0.8404            22.4910             2.1311   \n",
+       "258              -0.8409            22.4901             0.8413   \n",
+       "259              -0.8428            23.0640             0.8597   \n",
+       "260              -0.8445            17.4436             0.8408   \n",
+       "261              -0.8454            21.1098             0.8806   \n",
+       "262              -0.8455            18.8633             0.8435   \n",
+       "263              -0.8463            16.0854             0.8449   \n",
+       "264              -0.8499            24.6760             0.9141   \n",
+       "265                -0.85            23.7525             2.8674   \n",
+       "266              -0.8703            23.4299             0.8458   \n",
+       "267              -0.8783            24.7752             0.8998   \n",
+       "268              -0.8844            24.4053             2.1321   \n",
+       "269              -0.8845            31.2005             1.9282   \n",
+       "270              -0.8861            30.5409             0.8413   \n",
+       "271               -0.887            26.0495             0.9392   \n",
+       "272              -0.8906            11.5748             2.8070   \n",
+       "273              -0.9051            12.0602             0.8516   \n",
+       "274              -0.9125            26.9627             0.9389   \n",
+       "275              -0.9154            27.2352             1.1787   \n",
+       "276              -0.9154            27.6526             0.8441   \n",
+       "277              -0.9358            16.5386             0.8969   \n",
+       "278              -0.9575            12.7152             0.9137   \n",
+       "279              -0.9639            34.5683             2.2728   \n",
+       "280              -0.9664            32.5344             0.8386   \n",
+       "281              -0.9674            32.2133             0.8406   \n",
+       "282              -0.9702            16.4763             0.9145   \n",
+       "283              -0.9718            30.9401             1.3249   \n",
+       "284              -0.9725            12.0292             0.9135   \n",
+       "285              -0.9785            25.2995             1.2987   \n",
+       "286              -0.9788            15.2991             0.9009   \n",
+       "287              -1.0059            10.9781             0.8135   \n",
+       "288              -1.6527             8.1246             0.8817   \n",
+       "289              -1.7479             7.9676             0.8152   \n",
+       "290              -2.1368             9.2758             0.8781   \n",
+       "291              -2.1368            10.3302             0.9744   \n",
+       "292              -2.1502            10.7863             0.8417   \n",
+       "293              -2.1502            10.2856             1.1617   \n",
+       "294              -2.9114             8.2499             0.8578   \n",
+       "295              -2.9117             7.0266             0.8430   \n",
+       "296              -2.9481            20.8551             0.9050   \n",
+       "297              -2.9534            15.2459             1.6288   \n",
+       "298              -2.9693             5.7732             1.7309   \n",
+       "299              -2.9697             5.6526             0.8149   \n",
+       "300              -2.9877            10.6908             0.9739   \n",
+       "301              -3.0112             6.4505             0.7836   \n",
+       "302              -3.0112             6.9859             1.1621   \n",
+       "303              -3.0112             7.3460             0.8478   \n",
+       "304              -3.0112             8.2352             0.8335   \n",
+       "305              -3.0112             8.4559             0.8229   \n",
+       "306              -3.0112             8.4749             2.8363   \n",
+       "307              -3.0116            10.9574             0.9758   \n",
+       "308              -3.0116             4.3542             1.1986   \n",
+       "309              -3.0118            11.2326             0.9755   \n",
+       "310              -3.0121             4.5213             0.8368   \n",
+       "311              -3.0121             4.2517             0.8345   \n",
+       "312              -3.0121             4.3668             0.8418   \n",
+       "313              -3.0121             4.3696             1.3160   \n",
+       "314              -3.0121             4.3753             0.8418   \n",
+       "315              -3.0121             4.3863             1.1756   \n",
+       "316              -3.0121             4.4110             2.1299   \n",
+       "317              -3.0121             4.8695             0.8391   \n",
+       "318              -3.0121             5.5249             0.8365   \n",
+       "319              -3.0121             6.0223             0.8328   \n",
+       "320              -3.0121             6.2898             1.7299   \n",
+       "321              -3.0121             6.4380             0.8360   \n",
+       "322              -3.0121            10.9715             0.9731   \n",
+       "323              -3.0121            11.4807             0.8321   \n",
+       "324              -3.0121             4.2741             1.3259   \n",
+       "325              -3.0121             4.4722             0.9776   \n",
+       "326              -3.0121             4.4987             0.8284   \n",
+       "327              -3.0121             4.5715             1.7346   \n",
+       "328              -3.0121             4.2560             1.1777   \n",
+       "329              -3.0121             4.1203             0.8478   \n",
+       "330              -3.0121             4.3616             0.8424   \n",
+       "331              -3.0121             4.4524             0.8340   \n",
+       "332              -3.0121             4.4193             0.8345   \n",
+       "333              -3.0121             4.7082             0.8322   \n",
+       "334              -3.0121             5.0464             1.3080   \n",
+       "335              -3.0126             4.3677             1.3344   \n",
+       "336              -3.0126             4.4191             0.7945   \n",
+       "\n",
+       "                     Finished  \n",
+       "0    Thu May 23 09:58:12 2024  \n",
+       "1    Thu May 23 10:13:14 2024  \n",
+       "2    Thu May 23 09:58:09 2024  \n",
+       "3    Thu May 23 09:58:10 2024  \n",
+       "4    Thu May 23 10:29:20 2024  \n",
+       "5    Thu May 23 10:31:25 2024  \n",
+       "6    Thu May 23 10:31:16 2024  \n",
+       "7    Thu May 23 10:28:37 2024  \n",
+       "8    Thu May 23 10:31:04 2024  \n",
+       "9    Thu May 23 10:29:08 2024  \n",
+       "10   Thu May 23 10:30:16 2024  \n",
+       "11   Thu May 23 10:30:25 2024  \n",
+       "12   Thu May 23 10:28:11 2024  \n",
+       "13   Thu May 23 10:29:30 2024  \n",
+       "14   Thu May 23 10:28:54 2024  \n",
+       "15   Thu May 23 10:30:54 2024  \n",
+       "16   Thu May 23 10:27:55 2024  \n",
+       "17   Thu May 23 10:32:43 2024  \n",
+       "18   Thu May 23 10:31:34 2024  \n",
+       "19   Thu May 23 10:30:09 2024  \n",
+       "20   Thu May 23 10:30:19 2024  \n",
+       "21   Thu May 23 10:29:52 2024  \n",
+       "22   Thu May 23 10:30:02 2024  \n",
+       "23   Thu May 23 10:32:19 2024  \n",
+       "24   Thu May 23 10:31:42 2024  \n",
+       "25   Thu May 23 10:32:56 2024  \n",
+       "26   Thu May 23 10:31:55 2024  \n",
+       "27   Thu May 23 10:33:00 2024  \n",
+       "28   Thu May 23 10:32:53 2024  \n",
+       "29   Thu May 23 10:32:03 2024  \n",
+       "30   Thu May 23 10:32:12 2024  \n",
+       "31   Thu May 23 10:32:23 2024  \n",
+       "32   Thu May 23 10:33:19 2024  \n",
+       "33   Thu May 23 10:32:05 2024  \n",
+       "34   Thu May 23 10:31:46 2024  \n",
+       "35   Thu May 23 10:32:44 2024  \n",
+       "36   Thu May 23 10:32:35 2024  \n",
+       "37   Thu May 23 10:32:32 2024  \n",
+       "38   Thu May 23 10:32:30 2024  \n",
+       "39   Thu May 23 10:29:40 2024  \n",
+       "40   Thu May 23 10:32:48 2024  \n",
+       "41   Thu May 23 10:32:26 2024  \n",
+       "42   Thu May 23 10:28:16 2024  \n",
+       "43   Thu May 23 10:33:25 2024  \n",
+       "44   Thu May 23 10:33:21 2024  \n",
+       "45   Thu May 23 10:33:22 2024  \n",
+       "46   Thu May 23 10:33:26 2024  \n",
+       "47   Thu May 23 10:33:00 2024  \n",
+       "48   Thu May 23 10:33:36 2024  \n",
+       "49   Thu May 23 10:33:32 2024  \n",
+       "50   Thu May 23 10:34:23 2024  \n",
+       "51   Thu May 23 10:33:28 2024  \n",
+       "52   Thu May 23 10:33:02 2024  \n",
+       "53   Thu May 23 10:33:05 2024  \n",
+       "54   Thu May 23 10:33:30 2024  \n",
+       "55   Thu May 23 10:34:38 2024  \n",
+       "56   Thu May 23 10:34:33 2024  \n",
+       "57   Thu May 23 10:33:38 2024  \n",
+       "58   Thu May 23 10:33:16 2024  \n",
+       "59   Thu May 23 10:33:33 2024  \n",
+       "60   Thu May 23 10:33:16 2024  \n",
+       "61   Thu May 23 10:34:42 2024  \n",
+       "62   Thu May 23 10:33:39 2024  \n",
+       "63   Thu May 23 10:33:09 2024  \n",
+       "64   Thu May 23 10:34:50 2024  \n",
+       "65   Thu May 23 10:33:43 2024  \n",
+       "66   Thu May 23 10:33:34 2024  \n",
+       "67   Thu May 23 10:34:55 2024  \n",
+       "68   Thu May 23 10:35:03 2024  \n",
+       "69   Thu May 23 10:33:43 2024  \n",
+       "70   Thu May 23 10:34:58 2024  \n",
+       "71   Thu May 23 10:33:43 2024  \n",
+       "72   Thu May 23 10:33:11 2024  \n",
+       "73   Thu May 23 10:33:46 2024  \n",
+       "74   Thu May 23 10:35:06 2024  \n",
+       "75   Thu May 23 10:35:09 2024  \n",
+       "76   Thu May 23 10:33:41 2024  \n",
+       "77   Thu May 23 10:33:43 2024  \n",
+       "78   Thu May 23 10:33:51 2024  \n",
+       "79   Thu May 23 10:33:48 2024  \n",
+       "80   Thu May 23 10:33:54 2024  \n",
+       "81   Thu May 23 10:35:13 2024  \n",
+       "82   Thu May 23 10:33:49 2024  \n",
+       "83   Thu May 23 10:35:12 2024  \n",
+       "84   Thu May 23 10:33:51 2024  \n",
+       "85   Thu May 23 10:33:49 2024  \n",
+       "86   Thu May 23 10:35:16 2024  \n",
+       "87   Thu May 23 10:33:56 2024  \n",
+       "88   Thu May 23 10:35:19 2024  \n",
+       "89   Thu May 23 10:35:18 2024  \n",
+       "90   Thu May 23 10:35:34 2024  \n",
+       "91   Thu May 23 10:33:53 2024  \n",
+       "92   Thu May 23 10:33:56 2024  \n",
+       "93   Thu May 23 10:35:40 2024  \n",
+       "94   Thu May 23 10:35:42 2024  \n",
+       "95   Thu May 23 10:35:21 2024  \n",
+       "96   Thu May 23 10:35:36 2024  \n",
+       "97   Thu May 23 10:35:36 2024  \n",
+       "98   Thu May 23 10:35:23 2024  \n",
+       "99   Thu May 23 10:35:22 2024  \n",
+       "100  Thu May 23 10:35:24 2024  \n",
+       "101  Thu May 23 10:35:50 2024  \n",
+       "102  Thu May 23 10:35:56 2024  \n",
+       "103  Thu May 23 10:35:48 2024  \n",
+       "104  Thu May 23 10:35:37 2024  \n",
+       "105  Thu May 23 10:35:26 2024  \n",
+       "106  Thu May 23 10:35:50 2024  \n",
+       "107  Thu May 23 10:35:27 2024  \n",
+       "108  Thu May 23 10:35:39 2024  \n",
+       "109  Thu May 23 10:35:57 2024  \n",
+       "110  Thu May 23 10:35:52 2024  \n",
+       "111  Thu May 23 10:35:28 2024  \n",
+       "112  Thu May 23 10:35:59 2024  \n",
+       "113  Thu May 23 10:35:53 2024  \n",
+       "114  Thu May 23 10:35:30 2024  \n",
+       "115  Thu May 23 10:36:25 2024  \n",
+       "116  Thu May 23 10:36:26 2024  \n",
+       "117  Thu May 23 10:35:59 2024  \n",
+       "118  Thu May 23 10:36:01 2024  \n",
+       "119  Thu May 23 10:35:31 2024  \n",
+       "120  Thu May 23 10:36:20 2024  \n",
+       "121  Thu May 23 10:33:56 2024  \n",
+       "122  Thu May 23 10:36:20 2024  \n",
+       "123  Thu May 23 10:36:19 2024  \n",
+       "124  Thu May 23 10:35:32 2024  \n",
+       "125  Thu May 23 10:36:01 2024  \n",
+       "126  Thu May 23 10:36:22 2024  \n",
+       "127  Thu May 23 10:36:00 2024  \n",
+       "128  Thu May 23 10:35:33 2024  \n",
+       "129  Thu May 23 10:36:27 2024  \n",
+       "130  Thu May 23 10:35:43 2024  \n",
+       "131  Thu May 23 10:36:22 2024  \n",
+       "132  Thu May 23 10:36:03 2024  \n",
+       "133  Thu May 23 10:35:44 2024  \n",
+       "134  Thu May 23 10:36:28 2024  \n",
+       "135  Thu May 23 10:36:04 2024  \n",
+       "136  Thu May 23 10:36:32 2024  \n",
+       "137  Thu May 23 10:36:17 2024  \n",
+       "138  Thu May 23 10:36:29 2024  \n",
+       "139  Thu May 23 10:35:45 2024  \n",
+       "140  Thu May 23 10:35:45 2024  \n",
+       "141  Thu May 23 10:36:29 2024  \n",
+       "142  Thu May 23 10:36:37 2024  \n",
+       "143  Thu May 23 10:36:16 2024  \n",
+       "144  Thu May 23 10:36:33 2024  \n",
+       "145  Thu May 23 10:35:46 2024  \n",
+       "146  Thu May 23 10:36:38 2024  \n",
+       "147  Thu May 23 10:36:36 2024  \n",
+       "148  Thu May 23 10:36:24 2024  \n",
+       "149  Thu May 23 10:36:44 2024  \n",
+       "150  Thu May 23 10:36:31 2024  \n",
+       "151  Thu May 23 10:36:39 2024  \n",
+       "152  Thu May 23 10:36:15 2024  \n",
+       "153  Thu May 23 10:36:40 2024  \n",
+       "154  Thu May 23 10:36:45 2024  \n",
+       "155  Thu May 23 10:36:34 2024  \n",
+       "156  Thu May 23 10:35:47 2024  \n",
+       "157  Thu May 23 10:36:40 2024  \n",
+       "158  Thu May 23 10:36:14 2024  \n",
+       "159  Thu May 23 10:36:35 2024  \n",
+       "160  Thu May 23 10:36:43 2024  \n",
+       "161  Thu May 23 10:36:48 2024  \n",
+       "162  Thu May 23 10:36:49 2024  \n",
+       "163  Thu May 23 10:36:56 2024  \n",
+       "164  Thu May 23 10:36:13 2024  \n",
+       "165  Thu May 23 10:36:52 2024  \n",
+       "166  Thu May 23 10:36:12 2024  \n",
+       "167  Thu May 23 10:36:44 2024  \n",
+       "168  Thu May 23 10:36:53 2024  \n",
+       "169  Thu May 23 10:36:54 2024  \n",
+       "170  Thu May 23 10:36:55 2024  \n",
+       "171  Thu May 23 10:36:58 2024  \n",
+       "172  Thu May 23 10:36:57 2024  \n",
+       "173  Thu May 23 10:36:10 2024  \n",
+       "174  Thu May 23 10:37:01 2024  \n",
+       "175  Thu May 23 10:36:46 2024  \n",
+       "176  Thu May 23 10:37:02 2024  \n",
+       "177  Thu May 23 10:36:52 2024  \n",
+       "178  Thu May 23 10:37:01 2024  \n",
+       "179  Thu May 23 10:36:09 2024  \n",
+       "180  Thu May 23 10:36:48 2024  \n",
+       "181  Thu May 23 10:37:03 2024  \n",
+       "182  Thu May 23 10:36:08 2024  \n",
+       "183  Thu May 23 10:37:02 2024  \n",
+       "184  Thu May 23 10:36:51 2024  \n",
+       "185  Thu May 23 10:37:05 2024  \n",
+       "186  Thu May 23 10:37:09 2024  \n",
+       "187  Thu May 23 10:36:07 2024  \n",
+       "188  Thu May 23 10:37:06 2024  \n",
+       "189  Thu May 23 10:37:04 2024  \n",
+       "190  Thu May 23 10:36:06 2024  \n",
+       "191  Thu May 23 10:37:07 2024  \n",
+       "192  Thu May 23 10:37:09 2024  \n",
+       "193  Thu May 23 10:36:05 2024  \n",
+       "194  Thu May 23 10:36:04 2024  \n",
+       "195  Thu May 23 10:36:03 2024  \n",
+       "196  Thu May 23 10:35:52 2024  \n",
+       "197  Thu May 23 10:37:52 2024  \n",
+       "198  Thu May 23 10:39:42 2024  \n",
+       "199  Thu May 23 10:38:05 2024  \n",
+       "200  Thu May 23 10:38:06 2024  \n",
+       "201  Thu May 23 10:38:09 2024  \n",
+       "202  Thu May 23 10:38:31 2024  \n",
+       "203  Thu May 23 10:38:24 2024  \n",
+       "204  Thu May 23 10:38:25 2024  \n",
+       "205  Thu May 23 10:38:15 2024  \n",
+       "206  Thu May 23 10:38:25 2024  \n",
+       "207  Thu May 23 10:38:30 2024  \n",
+       "208  Thu May 23 10:38:17 2024  \n",
+       "209  Thu May 23 10:39:37 2024  \n",
+       "210  Thu May 23 10:39:40 2024  \n",
+       "211  Thu May 23 10:39:42 2024  \n",
+       "212  Thu May 23 10:39:38 2024  \n",
+       "213  Thu May 23 10:38:30 2024  \n",
+       "214  Thu May 23 10:39:45 2024  \n",
+       "215  Thu May 23 10:38:26 2024  \n",
+       "216  Thu May 23 10:39:40 2024  \n",
+       "217  Thu May 23 10:40:05 2024  \n",
+       "218  Thu May 23 10:39:42 2024  \n",
+       "219  Thu May 23 10:39:39 2024  \n",
+       "220  Thu May 23 10:39:38 2024  \n",
+       "221  Thu May 23 10:37:53 2024  \n",
+       "222  Thu May 23 10:38:41 2024  \n",
+       "223  Thu May 23 10:38:43 2024  \n",
+       "224  Thu May 23 10:39:07 2024  \n",
+       "225  Thu May 23 10:39:02 2024  \n",
+       "226  Thu May 23 10:39:02 2024  \n",
+       "227  Thu May 23 10:39:07 2024  \n",
+       "228  Thu May 23 10:38:27 2024  \n",
+       "229  Thu May 23 10:37:55 2024  \n",
+       "230  Thu May 23 10:38:10 2024  \n",
+       "231  Thu May 23 10:39:11 2024  \n",
+       "232  Thu May 23 10:39:01 2024  \n",
+       "233  Thu May 23 10:39:24 2024  \n",
+       "234  Thu May 23 10:39:26 2024  \n",
+       "235  Thu May 23 10:39:24 2024  \n",
+       "236  Thu May 23 10:39:28 2024  \n",
+       "237  Thu May 23 10:38:25 2024  \n",
+       "238  Thu May 23 10:38:24 2024  \n",
+       "239  Thu May 23 10:38:27 2024  \n",
+       "240  Thu May 23 10:38:22 2024  \n",
+       "241  Thu May 23 10:39:42 2024  \n",
+       "242  Thu May 23 10:38:29 2024  \n",
+       "243  Thu May 23 10:39:04 2024  \n",
+       "244  Thu May 23 10:39:28 2024  \n",
+       "245  Thu May 23 10:38:27 2024  \n",
+       "246  Thu May 23 10:38:30 2024  \n",
+       "247  Thu May 23 10:37:53 2024  \n",
+       "248  Thu May 23 10:39:12 2024  \n",
+       "249  Thu May 23 10:39:32 2024  \n",
+       "250  Thu May 23 10:39:14 2024  \n",
+       "251  Thu May 23 10:39:32 2024  \n",
+       "252  Thu May 23 10:39:31 2024  \n",
+       "253  Thu May 23 10:39:12 2024  \n",
+       "254  Thu May 23 10:38:59 2024  \n",
+       "255  Thu May 23 10:39:17 2024  \n",
+       "256  Thu May 23 10:39:10 2024  \n",
+       "257  Thu May 23 10:39:10 2024  \n",
+       "258  Thu May 23 10:39:17 2024  \n",
+       "259  Thu May 23 10:39:19 2024  \n",
+       "260  Thu May 23 10:39:02 2024  \n",
+       "261  Thu May 23 10:38:04 2024  \n",
+       "262  Thu May 23 10:39:04 2024  \n",
+       "263  Thu May 23 10:38:59 2024  \n",
+       "264  Thu May 23 10:38:09 2024  \n",
+       "265  Thu May 23 10:39:20 2024  \n",
+       "266  Thu May 23 10:39:41 2024  \n",
+       "267  Thu May 23 10:39:40 2024  \n",
+       "268  Thu May 23 10:39:41 2024  \n",
+       "269  Thu May 23 10:39:21 2024  \n",
+       "270  Thu May 23 10:39:22 2024  \n",
+       "271  Thu May 23 10:39:40 2024  \n",
+       "272  Thu May 23 10:37:41 2024  \n",
+       "273  Thu May 23 10:39:55 2024  \n",
+       "274  Thu May 23 10:39:20 2024  \n",
+       "275  Thu May 23 10:39:29 2024  \n",
+       "276  Thu May 23 10:39:19 2024  \n",
+       "277  Thu May 23 10:37:57 2024  \n",
+       "278  Thu May 23 10:37:53 2024  \n",
+       "279  Thu May 23 10:38:32 2024  \n",
+       "280  Thu May 23 10:38:31 2024  \n",
+       "281  Thu May 23 10:38:31 2024  \n",
+       "282  Thu May 23 10:38:01 2024  \n",
+       "283  Thu May 23 10:38:31 2024  \n",
+       "284  Thu May 23 10:37:52 2024  \n",
+       "285  Thu May 23 10:37:55 2024  \n",
+       "286  Thu May 23 10:38:01 2024  \n",
+       "287  Thu May 23 10:37:51 2024  \n",
+       "288  Thu May 23 10:37:55 2024  \n",
+       "289  Thu May 23 10:37:53 2024  \n",
+       "290  Thu May 23 10:38:24 2024  \n",
+       "291  Thu May 23 10:38:23 2024  \n",
+       "292  Thu May 23 10:38:23 2024  \n",
+       "293  Thu May 23 10:38:19 2024  \n",
+       "294  Thu May 23 10:38:39 2024  \n",
+       "295  Thu May 23 10:38:37 2024  \n",
+       "296  Thu May 23 10:37:52 2024  \n",
+       "297  Thu May 23 10:37:46 2024  \n",
+       "298  Thu May 23 10:38:36 2024  \n",
+       "299  Thu May 23 10:38:35 2024  \n",
+       "300  Thu May 23 10:38:17 2024  \n",
+       "301  Thu May 23 10:38:29 2024  \n",
+       "302  Thu May 23 10:38:29 2024  \n",
+       "303  Thu May 23 10:38:28 2024  \n",
+       "304  Thu May 23 10:38:27 2024  \n",
+       "305  Thu May 23 10:38:27 2024  \n",
+       "306  Thu May 23 10:38:29 2024  \n",
+       "307  Thu May 23 10:38:20 2024  \n",
+       "308  Thu May 23 10:38:33 2024  \n",
+       "309  Thu May 23 10:38:19 2024  \n",
+       "310  Thu May 23 10:38:39 2024  \n",
+       "311  Thu May 23 10:38:38 2024  \n",
+       "312  Thu May 23 10:38:37 2024  \n",
+       "313  Thu May 23 10:38:36 2024  \n",
+       "314  Thu May 23 10:38:36 2024  \n",
+       "315  Thu May 23 10:38:38 2024  \n",
+       "316  Thu May 23 10:38:37 2024  \n",
+       "317  Thu May 23 10:38:33 2024  \n",
+       "318  Thu May 23 10:38:31 2024  \n",
+       "319  Thu May 23 10:38:30 2024  \n",
+       "320  Thu May 23 10:38:29 2024  \n",
+       "321  Thu May 23 10:38:29 2024  \n",
+       "322  Thu May 23 10:38:14 2024  \n",
+       "323  Thu May 23 10:38:16 2024  \n",
+       "324  Thu May 23 10:38:40 2024  \n",
+       "325  Thu May 23 10:38:42 2024  \n",
+       "326  Thu May 23 10:38:40 2024  \n",
+       "327  Thu May 23 10:38:41 2024  \n",
+       "328  Thu May 23 10:38:43 2024  \n",
+       "329  Thu May 23 10:38:43 2024  \n",
+       "330  Thu May 23 10:38:44 2024  \n",
+       "331  Thu May 23 10:38:41 2024  \n",
+       "332  Thu May 23 10:38:42 2024  \n",
+       "333  Thu May 23 10:38:32 2024  \n",
+       "334  Thu May 23 10:38:31 2024  \n",
+       "335  Thu May 23 10:38:45 2024  \n",
+       "336  Thu May 23 10:38:45 2024  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "est1.print_summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a7a95e9a",
+   "metadata": {},
+   "source": [
+    "We also provide the capability to visualize the results of each stage of the AutoMLx pipeline."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a58a2d54",
+   "metadata": {},
+   "source": [
+    "<a id='algorithm-selection'></a>\n",
+    "#### Algorithm Selection\n",
+    "\n",
+    "The plot below shows the scores predicted by Algorithm Selection for each algorithm. The horizontal line shows the average score across all algorithms. Algorithms below the line are colored turquoise, whereas those with a score higher than the mean are colored teal."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "id": "f0ba2e77",
+   "execution_count": 12,
+   "id": "9cf102ab",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-03-22T16:18:11.468709Z",
-     "iopub.status.busy": "2023-03-22T16:18:11.468490Z",
-     "iopub.status.idle": "2023-03-22T16:18:11.753963Z",
-     "shell.execute_reply": "2023-03-22T16:18:11.753373Z"
+     "iopub.execute_input": "2024-05-23T17:42:31.326867Z",
+     "iopub.status.busy": "2024-05-23T17:42:31.326235Z",
+     "iopub.status.idle": "2024-05-23T17:42:31.629207Z",
+     "shell.execute_reply": "2024-05-23T17:42:31.628476Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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nAADMjEzfciWpA+u7HO3J3Xot/b27rO9ywu93+WJ9lz/WedEyMzNx//59MX8piAlhCcm6iU1MTGBqWjZfPqlB7hKRpsb8cpcH1nc5yn4JAJCamLC+ywm/3+WL9V3+WOfFU9gwN04qISIiItJzTAiJiIiI9BwTQiIiIiI9p5UJYWZmJhYtWoT27dvDx8cHAQEBOHfuXLHujYqKwuTJk9G8eXM0bdoUn3zyCZ4/f17GERMRERFVXFqZEH755ZdYv349evfujVmzZsHAwAAff/wxrl27VuR9KSkpCAwMxJUrVzBu3Dh8+umnuHPnDgIDA5GQkFBO0RMRERFVLFo3yzg0NBQHDhzAzJkzMWrUKABA37590bNnTwQFBWHjxo2F3rtp0yY8ffoUO3fuRP369QEAHTp0QK9evbBu3TpMnjy5PN4CERERUYWidS2EISEhMDY2xqBBg8QyU1NTDBw4EFeuXEF0dHSh9x46dAiNGzcWk0EAcHd3R5s2bXDw4MEyjZuIiIiootK6FsKwsDDUqlULlpaWcuU+Pj4QBAFhYWFwcnJSuE8qleLevXsYPHiwwrmGDRvizJkzSEtLg7m5uUrxpGdniGsfqVtaVnqZPJeUY32XH9mfMtZ5+WFdly/Wd/ljnRctMzuzyPNa10IYExOjNOFzdHQEgEJbCOPj45GZmSleV/BeQRAQExOj3mCJiIiItIDWtRCmp6cr3XZFtltIRkaG0vtk5cq2mZPdm56u+r8uzIxMy3xVdHNjszJ9PsljfZcfc2Mz1nc5Y32XL9Z3+WOdK2cgVb5DiXi+nOJQGzMzM2RlZSmUyxK+wraRk5VnZio2mcruNTPjl4iIiIj0j9YlhI6Ojkq7hWXdvcq6kwHA1tYWJiYmSruFY2JiIJFIlHYnExEREek6rUsI69ati8ePHyMlJUWu/MaNG+J5ZQwMDODp6Ylbt24pnAsNDYWbm5vKE0qIiIiIdIHWJYTdu3dHVlYWtm/fLpZlZmZi586daNq0KZydnQEAkZGRCA8Pl7vXz88P169fx507d8SyR48e4fz58+jevXv5vAEiIiKiCkbrJpU0atQI3bt3R1BQEGJiYlCjRg3s2rULkZGRmD9/vnjdjBkzcPHiRdy7d08sGzZsGLZv344xY8bggw8+gKGhIdatWwdHR0dxkWsiIiIifaN1CSEALFy4EL/99hv27NmDhIQEeHl5YeXKlWjWrFmR91lZWSE4OBg//vgjfv/9d0ilUrRq1QqzZs2CnZ1dOUVPREREVLFIBEEom1WVdVxGRgZu3boFb2/vQmc2l5ZskU1OoS8frO9ytCl3+YO0QWms73LC73f5Yn2XP9Z50d6Wt2jdGEIiIiIiUi8mhERERER6jgkhERERkZ5jQkhERESk55gQEhEREek5JoREREREeo4JIREREZGeY0JIREREpOeYEBIRERHpOSaERERERHqOCSERaYzFjwtQJShI02EQEek9JoREeiwjO1vTISAqJQX3X7/Gm7Q0TYdCFUBmTg6ycnI0HQaR3jHSdABEmpKSmQnnoCCkZGUBAJwsLfFk8kQNR5WrSlAQolJSAADOlpZ4NXWq2l8jKycHZj/8IB6X1esUx7j9+/EmPR3Xxo4FALxITISLtTUkEolG4qnokjIyUOmnn8RjTX526pL/Ow8AkZ9/jqrW1th08yaWXbyIg8OHw8bMDMcePcKRR4/wXZcuMDE0RFhMDJ7Ex8OvTh0YSCRIzsyEgUQCC2NjDb4bIu3DFkLSW2GxsWIyCADR+f4y0rT8fzFGlVFchgbyf/zL6nXextnSEj927YoF3boBAARBQMvVq/HR3r3iNan5PqeyIAgCHBctgmTuXEjmzoXZvHn45dw58fzKK1dw5tkz8TgiMRFpZRxTUQomO5r67EpDKgji55qVk6PwHuzNzQEAJoaGsDQxgXnee74cGYlfz5+HYd4/Fv4ODUWvzZsh+6fDdydPwmHhQvE580+fRqd168TjTTdvYvbx4+Lx+YgIhDx8KB6/Tk1FfHq62t4nkbZgQkh6q7mLi1qeYz1/vphIlOV4uPTsbPx89ixi1PSXv4GS1rf0cu5CTv1qBl5NnYrWrq54190dAJAjCPjR1xcjfHwAAPHp6XBYuBArr1wBkJu8CYKg1jgEALGpqeJxRk4OpPle47OQEOy6e1d8/TpLluDbEyfE41arV2PttWsAchOdn/77D1ciI8XjB69fqzWpLZjMa4OQhw9x7NEj8bjGr79i2uHDAABjQ0OF602NcjuwBtavjyMjR8Ik75oZ7dsjfdYssQ4mtmyJcx9+KLYm9/T0xMK8f1wAgIOFBdxsbMTjCxER2H3vnni8+MIFfHrwoHg8dv9+tP3rL/F4zL59GLhtm3gcdPYsFp45Ix7/++CB3Pt6npCA1/m+S8VRJShI/B1SOS+ZDYuJwdh9+/Dg9WsAwK3oaHxy4ACexMcDAG68eoVJ//6LF4mJAIBrL19iSkgIXiUnA8hNnKcePizGcvHFC8w4ckRMds9HROCrY8eQnJkJADj7/DlmHz8u/g7479kzfHvihNh9f+rpU3x/8qT45+Lkkyf46b//xPdw4skT/Hz2rHh8/PFjLL1wQTw+9ugR/rx8WTw++uiR+GcGAI6Eh+Pv0FC54223b8sd7woLU6gvbR6DXNHeh/b9ViEqoDR/qJwtLeWOt9++o/Lry36hArktNYfDwxGV90u5pKxNTMSfZTGeePIEU48cwc3oaABAWlYWcqTSEr9GVk4OKpmaypV9cehQiZ+nLkYGBni/cWN0qVULQG6cn7dujZbVqgEArr58ieq//opzz5+r7TWVdUxPbdtW/PnlF1/g644dAeQmj7+/9x7616sHIDeJdjA3FxOYN2lpmHnsGM7kxRebmgrPZcvEv/yikpPRaMUK7M1LSuLS0vDdyZO4ExMDIPdzvR0dXWQC+SYtTa6VsOD3WBOycnLwMilJPJ559CimhISIx7OOH8eifAnDF23awN/Do0SvlX8oQVVra7TI+24AQEc3N0xq1Uo8HtOsGTb06yceL/b3x83x48XjX959F/uGDpW7/ptOncTjOvb2qFu5snh88cULnIuIEI/nnjwp9776bt2K93fvFo9916/HJwcOiMdTQkKwIl9iFHzjhlzr6Ou8sbQxqanYe/8+4vKOXyUnY/udO+JxRGIiNt68iTd5Cd7j+HisuX4dCXnH91+/xorLl5GYkQEAuB0djSUXL4q/r669fImgs2fF79nFFy/w43//ieOKzzx7hrknTyI773fMiSdP8E3eP4IA4MijR/g6X0vrgfv3Mft//xOP99y9K3f9jjt35I433byJOfmO19+4If4jCwBWXr0qd7zs0iV8d+oUAMUelPC4OFjPn48dd3ITxruxsXBYuBB78v4RdzMqCi4//4xDeS3B116+RK3Fi3HyyRMAwKUXL+C1bBnO532u554/h88ff+Day5cAcpPjFqtWiX9GTz55gnZr1uBhXByA3OTXd/16PEtIAJCb7Hb/+2/xz8Ph8HD02bJFTM7tFyyAZO5chfeh6eSQYwhJ65W0e/X+69foVrs2ZrRrB08HB3x38gS613EvdTxD//kH79SujS0DB5b4Gf+NHo2zz5/jg8aNxUSje506uDthAjwcHAAAv54/j5VXruDm+PGwLpDYFUdmTg4SMzKwsFs3TGvXDv/cuYP2NWqUOOay4mhpiR+6dhWPDQ0M0KZ6dbjb2wMAtt++jeWXLmHrwIFwtrIq0WtIJBI4W1rKjdvMz8bMTPzZQCLB6CZNxGNzY2P8O3y4eOxgYYGUr74Sjy2MjbGhb18xoc2WSlHT1lZM+p8nJGDOiRNo6OSE+o6OuBUdjZarV2PPkCHo7eWFqy9fYvjOnVjTuzfaVK+O8Lg4/HD6dJl3o7/NqadPcScmBuOaNwcABOzYgQevX+PWJ58AAFKysuRanLcNHIjKFhbi8ZQ2beSeV1T9l6Wq1taoam0tHstaqmWmt2snd7xt0CC54z1DhohJEwB826kTzIz+/6/WNq6uqFapknh87dUrGOVr4f0ir5W0oI5ubnj5xRficbfatREzbZp4/J6nJ+JmzBCP+9erJ/4jBQCGNWyIYQ0biscfNGmCD/J9b8e3aIHxLVqIx5+1bo3PWrcWj2e0b48Z7duLrfFfd+yIWR06iP94+rZzZ8zO+0cSAPzYtSvmdukiHi985x25P7e/+Pnhp3wtt0v8/eUmD/3+3ntyxyt79kRWvnr9q3dvuXrOr5KpKcY0bYradnbi8VBvb9TIaxmuZGqKnp6e4u8Ha1NTdHRzg13esARLExM0qVJF/DNpbmyMOvb24jAFE0NDOFlaip+bgUQCcyMjsZdFEARkS6ViXWVkZ+NNejpkfQxJGRl4Gh+PnLzzb94yJEFTQ0Akgrr7XvRERkYGbt26BW9vb5iW4C/j4kjLyv3SmBubveVK/SaZO1fueFP//nidloZPWrRQ2i0qcyUyEoN37MC6vn3RvkYNsb6NDIwx4+hRTGvbVu4visI4Llokdjc6W1ri1AcfwEAiQR17e7xMSsLXx49jTufO4i8ndQl5+BDHHz/GwnfeAQD8fukS3Gxs8J6nZ7HulwoCIpOSYGNqKpdQCoKABWfO4KOmTeX+AlerTbmfS9qgtFJ/v7fdvo3fL13CscBAGBoYYNnFi7gdHY3l771X5OefnyAIuBsbi8oWFnDUQGub7C9CY0NDxKam4uijR+jk5oaq1ta4GRWFuSdP4vsuXVDP0REHHzxAz82b5bq0AUCYM6fI11D190laVhYexMXBx9kZQO44ylVXr+LiRx9BIpFgSkgI/rp2DQlffgmJRIKQhw8Rl5Yml4Tos+LWd0xKCpwKtAi97bPUdwV/58vqS1v+ziwYvzJl8R14W97CLmPSevm/xM6Wlth3/z623Lr11vuaubjg4aefKrSK3YqOxsorV/C/vO6Et+nj5YVq1tYQ5szBq6lT4enggDp5rVcXX7zAtjt3xL/wC/sXbkGJGRl4lpCg8Jd+ft3r1BGTQakgYMmFC9iRN8YGgFz3nTIGEglcK1VSaF28ExODuSdPYtPNm8WKVdMCGjTAiVGjxDFlL5OS8Dg+XkwG558+jVV54w8LIwCo//vv+CNfV155MjY0FMfRVbawwBBvb/EfIw2dnbEjIAD1HB0BAP4eHsj8+mu1x3AzKgozjx4VuxR/v3QJjVasELu5rE1MUMPGBml5rX7fdOqEqKlTxe7b7nXqMBksAUdLS7kW0YrQ/V/RaXt9FYxfmDOnQrwnthCWEFsIK47jjx/D2dISDZycAOS29iRkZMDWzAxJGRkIOnsW09q1g1W+cXnK5K/vl0lJ4l/Ib9LSxK4FZUKjohCbmgrfvDFvBSVnZoqv/eGePYjPyMCOQYOKXFLl13Pn8Pnhw4ifMUOuu7Io2VIpEjMyYG9ujifx8XBfsgSre/WS6ybKLysnByuvXEHb6tXRpGpVuXP3X7+Gh709qv78s8LyN0suXICngwO616kDAPjr6lV4Va4sJtZbb92Cp4OD+MwD9++jjr09vPLGYZ1++hQdztQEoJ4WwrfpvG4datraYl3fvgCAeadOoXPNmnL/EBAEAdtu34a3k5P4ParIwuPiUGfpUvG4OMvOxKUm4WZ0NHycXWBnbo6TT57g/d27sXfoUPg4O2P33bsI2L4dl8eMgY+zM+6/fo3rr17hPQ8PWL7lzw4p4u/v8sc6LxpbCEnn+daqJfeXuEQigW1eEnUoPBzzTp/G7byJGPndjY1FwPbtCI2KUjgnSwbD4+LgsXSp3Gy4gnycnQtNBgHIJaL1HB3R0MlJTAYvR0YqnTH7jrs7/urdWxzDUhxGBgbiUh3WJib4rnNndK1dG0DuzMopISFysx8zc3Iw8eBBHM03Q1LG08EBEolE6fjMH06fxu68wdoAMPXIEWzPNxvwgz175FoX+2zZguC82YNSQUDHfEuAlMdOJSdGjcKqXr0A5La8LjxzBqefPgWQmxT/dv48IhITMdjbWyuSQQDixIJ33d3RtGpVfJpvEoWsVTkqORmfhYTg4osXAIDbMbHovP5vnMhr+a5qbY12NWqIy7f08PBAyldfiV3Eng4OCGjQgMkgkZ7gpBKqcFRdlPnc8+eobGEhTrbIb2D9+ng4aRJq5Q023nb7Ntq4uqK6jQ1Ss7JwKzpabpZwQS7W1hhYvz461axZ6DUPXr/Gm/R0cdJAUfLPXL0VHY0Wq1Zhmb8/JrRsKXedt5MTvEuRnDhYWGBWvgHfV16+RHBoKOb5+gIAHr15A2dLSzhaWGD60aOYfvRosRc3fj5litzx/YkTxYkvABA6frzc7OXzH32EKvkmexwZORI4/614XB4DqGXdsZVMTRE7fToy87rwr758iSmHDqGGjQ2a/PmnOMOzoi/03KJaNThbWuJweDiA3Pcx6/hxSJA7WH9iy5YwMjDAqqtX0bRqVbSsVg0NHCtj+6AB6ODmBiA34dvYv7/4TBMlS78Qkf5gCyFVOAVbpVKzsvDTf/8hspAxcf22bsXP+RYRLkiWDCZnZmLc/v3i0ghNq1bFnQkT0LZ69ULvNTc2xoqePcXZa79fuiS3Xh0ALDhzBv23bi3em8vH08EBa3r3xhBvbwC54w3XX7+OrJwcvElLw4PXr0u1rEx+n7RogedTpoitPaP37EHHdesQk++9KEvMlI1rMTE0lEseHC0t5RLAOvb2cMp3X3MXF7jmzbI0kEjQLa/VUlNMDA3FVttWrq54Mnky3nV3F5NBQDsWelYWowCIS6Q4WFggaeZMBDZqBCD3u/yeR52ymyhERFqNLYRUoSTlrZmV35lnzzDz2DG0qlYNLtbWiElJQUpWFmra2gIAdgQEFOsvOSsTE1wdOxbmea1ZEYmJeJGYiFaursWK7dGbN/gib6HX2fnWKfu8TRu8n/eXripMDA3lxvdtuHED2+/cQUCDBlh3/brKYwjfJn/38/dduiA2NRX98y24q0x5tJJpelC4m62t2hLviiB/wl3cWdZEREwIqUJ5WWBBZ2dLS7zj7o4nkyeLrUyrr17FrOPHEfnFF6hiZYU2rq7F3rlBlkQCuS2Ll/N2kwDevpdxbTs7XP74Y7EFJisnB8aGhqifN/uztJb6++OLNm1gbmwsrk1mu2ABDADkqHkJgg5ubkqT7/KW+tWMCjEAvGDipA1dJ/nX7ctfRkRUEkwIqUJxs7FB6LhxqG5jI04MAXJbcWSGNmyIqtbW4rg0k3nzxIH0qoz9WtitG3w3bBCPi7OXsWzSQUJ6OjqsXYtJLVuifY0aeJ2WVupFnSUSidi9nX+aSVm1XZVkMWtdVXDGtza0F1bkMY5EpH204R/CpEdMjYzQ0NlZLhksqKatLUY1biwe51+rT5WxX12KmBn8NoYGBuJ6gz+fO4chO3aU+Fn6rDxmGRMR0duxhZCKTdXZv8okZ2bicmQk6js65nbRxsfj90uX8FHTpvB0cMCR8HC8+/ff4vVlOdtTtim8jCqjraxMTLAjIAAA0HXDBgjIXX2+LLp3y5KmtgvLr6JM4KgIdUFEpClMCKnYSrpncHJmJirNny/XDbpj0CAMqF8fcWlpWHLhArrUrAlPBwc8zdscXJXXKelf5AcePJA7LukK7eXRvVtW2O34/1gXRKTPmBBSmfv2xAmFZEu2rl+TKlWQnm8brsBGjfDxvn0qPT81KwsfNG6MNX36qHRfcxcXla6nssHWOCIizWNCqOeUdQNb/vADUvP2K83fZWthbIzUrCyxPCsnBx/t24epbdqgYd7uBjLJmZlIzsxEFSsrzOrQQWGdQNkyMQUH85sYGqrc4peUmYm116+rnBA2rVpV7rWc1JCYqGtQrr50X1aUWcZERPpOKxPCxMRELFq0CEeOHEF6ejp8fHwwc+ZM1KtXr8j7pFIpdu3ahSNHjiAsLAwJCQlwdXVFz549MXr0aJjo4RZNyrqBZclgwfMpX30ld29YTAwOPXyI3p6ecglhjlSKdmvWoIqVFQ6NGAE7c3NI8P9dq0UlTVciIxGVkoL5Xbviy/bti/Uero4ZU+Rew4V5npCAT1u1wlBvb9SysxP3wVRVZQsLcbFqRzUlb+y+JCKi8qR1CaFUKsWYMWNw//59jB49GnZ2dti0aRNGjhyJnTt3okYRS3+kpaXhq6++QuPGjTFkyBA4ODjg2rVrWLx4Mc6fP491+fZYpber5+iIB5MmwdrUVKGlcXH37qiWt24gACR/9RXepKXB2coKRkWsGZiel4y+LrAbSFGaVK1aovj/ffAAs44fh4FEUuzkU5nYt+z2QUREVNFpXUIYEhKCa9euYfny5ejWrRsAwN/fH35+fli2bBkWLlxY6L3GxsbYvHkzmjZtKpYFBASgWrVqWLp0KS5cuIBW+TaJ1wdOlpbi+ntOed24jhYW4pZmJoaGePzmDWrZ2aHywoUY26wZfujaVbxftpZdwZbGwXnbsclEp6Tg4osX6OHhIW4bpkzLatXwePJklbbXksydK/6syqzk1q6uaFmtGroUsU8xERGRPtC6dQgPHToEJycndM2XlNjb28Pf3x9Hjx5FVt4YN2VMTEzkkkGZd955BwAQnrdRvD6JmjoV0VOnIvzTTxE1bRoAwM7cHEO8vXF/4kQ4WVri/uvXAIAxzZoVue9vUVZduYLBO3aIzyqMsaEhatraFpk0FkWVFrpGVargwkcfFXvrusIo2++XiIhIm2hdC2FYWBgaNGigMBmhYcOG2Lp1K549ewZ3d3eVnhkbGwsAsMvbJUKfbL55E8aGhhhYvz6A3PF/87p0gYOFBTwcHPBw0iSY5u39+2O+JFxVsl1FZPsIF+bSixdouXo1vuvcWW6/4LLwLCEBa65dw0gfH7jb25f4ORzvR0RE2k7rEsKYmBi0bt1aodwpb0ux6OholRPC1atXw9raGu1LMI4sPTsDUoOSrmBXtJJOcihKzcXL/r+L2NIStWxtYGVigvc8amPakaN4mpCIbQP7y71+WlY29ty7j1vR0ZjVQXkd2ZqZIT49XXxuwdhH+DRA37oesDMzK/J9JWemAQDi09OK/f7lur2VvHZhzjx7grknT2LuyZO591pYIGzCx8W6l0pHNgWoLL7jpBzrunyxvssf67xomdmZRZ7XaEIolUqL7OLNzzRvrFp6errS2cCysvR01b4QK1aswNmzZ/Hdd9/B2tpapXu1Uf79eqNTUnB7/BixtbWGjQ2MDAzwND4BZkZGcLb6/+7P44+fYNXVa/jh9BkAuYnXk8kTxfOyZDD1qxlKX/dlUjLOv3iBnh51itxDt7mLC8I/nQBbFfbZLc4exMr08fKUf44KE1mIiIh0iUYTwkuXLiEwMLBY1547dw729vYwMzNDZqZilisrMytiD9yC/v33X/z2228YPHgwBg8eXOz78jMzMoWpcfGTl5Ioy3XaKlv+/0zg6e06AAC8li1D06pVsXnAAPHc8h7vYdXVa+JxdEqKXFzNXVzQ1tW10FiDQ2/hpzNncPnjj9HMxabQeARBgMfS30u8RV7BuFRlbmzGdfHKEeu7/LG+yxfru/yxzpUzkBa9QatGE8LatWtj/vz5xbrWKm8MmqOjI6KjoxXOy8pkXcdvc+bMGUyfPh1dunTBHC3ae7a08nevOltaYsONGzAxNMSQfLOCXyQm4v7r19hy65aYkBkbGhb53EsfF93VuvzSJQBA81WrikzyLr54UeIt8kpC3QtTExERaSONJoSOjo7o37+/SvfUrVsX165dgyAIchNLQkNDYWFhUeQ6hDI3btzAxIkT0bBhQ/z6668wfEuyo0uipk5FVk6OmOC1/esvWJmYiAlhjlSKlHzd+PkTMkOJBDlC7njJgrNpQx4+RGULi0K3g0vK16pbVJInFVQfj1maXT3yJ6Ycf0JERPpK6yaVdO/eHYcOHcKxY8fEdQjj4uIQEhKCrl27wtjYWLz22bNnACCXJIaHh2PMmDGoVq0aVqxYoVIXszZyDgqSaxH8sn171LCxQf969SAIAk6MGiV3vWERi0bnCAJcK1XC8ylTFM75b9wIABBK2draslo1uXUQi5PgcZYvERFR6WhdQujn54fGjRtj+vTp4k4lmzdvhlQqxaRJk+SuHZWX7Bw/fhwAkJycjA8//BCJiYn48MMPceLECbnrvby8ULdu3fJ4G+UmukD365ILF9C+Rg3suXcP2VIpNippoS2sJfAHX1+0rFZN6esYGxggSyqFZO5cpV3CBfdBLoxEIsGzKVNgbGBQZHJKRERE6qN1CaGhoSFWrlyJhQsXIjg4GBkZGWjYsCEWLFgANze3Iu+Nj4/Hy5cvAQA///yzwvmJEyfqXEJY0KPJkwEA806dQo5Uir+uXoWJoSFGNmokXvNJixaoZWuLKW3ayN37VYcOhT43SyoVf1bWJfzzu+9i/IEDuDthArwqVy70ORdfvECbv/4Sj1WdVEJERESq07qEEABsbGzwww8/4IcffijyOlnLoIyrqyvu3btXlqFVOPnH19mbm4vlX3fsCOD/xxDmTwgPhYejqZL9gd1++w2jGzfGnM6dVY7j27zW2LrLlxeZ5DUsMCmIewMTERGVPa1MCKn4HkyahFvR0TA0MEALFxcsPHMGbjY2GOztDUEQsKFfP9QpsEtHdEoKtty6JTfLGMjd2ePbkyfx7cmTRSZ1yrqEiztz2LKEW9YRERFRyXGQlo47FxGBtmvWIFsqhUQiwZpr13A4b8/mBWfOoN7y5YgtsCBzfL7FvQtL3gqW9/DwwDcdO0KYM6fUXbzcG5iIiKh8sYVQB2Xm5MD2p5+Qlp0NALAxNYWdmRk+P3QImwYMELuD+9Wti+137mDzzZuY1KpVqV7zSmQk/n3wAN+dOqW09VCVpWE4ZpCIiKh8MSHUQZFJSWIyCAAJGRmo//vvkABo4OgoJoRelSvjZlQUPg0JwachIWIiV9gsYwkA2SqBBZO6t3UJM8kjIiKquJgQ6iAzI+UfqwDgZXIyNt28iWENGwJQPjt45+DBsDc3R/sCi3wLANzt7PDw00/LJG4iIiLSDI4h1EFP4uMLPbft9m2cePKkyPunHTkibjWX35Lu3bG2Tx+l93DcHxERkfZiC6EOalKlitJyZ0tL3H/9Gjejo7Hq6lU4W1rCzMgI6Xndy7JEzs/dHbVsbRXuL2qcIbuEiYiItBcTQh3z6M0bJKTL78nrYG6OZ1OmwMLYGJK5c8XyqJQUdK1VC5YmJtgzZIhYXtg6hKbz5mFSy5YIevfdsnsDREREVO6YEOqYSQcP4t8HDwAAy3v0QGULCwzesQOnnj5F9zp1FK5f+M47MJRI5MoWvfOO3CLWMpk5Odh3/z4TQiIiIh3DhFDH/Ojri35162LvvXvwcnBA/23bAAD+GzcqjO0zANB69WpxYolslvG0I0fQtGpVhUkllUxMcP/160L3KyYiIiLtxIRQhxjOnQvZnGEJgFW9eiExI0M8X3A5GCkAqZJZxt3d3VHLzk7h+YmZmYU+i4iIiLQXE0IdIs33swDg4MOHJXpOSCFjCImIiEg3MSHUEa8LbD8HAKlZWbAxNUVCXiuhs6UlkjMzkZKVJR5nSaWIS0sTj4HCxxCqstsIERERaQ8mhDrCysREoaxe5cpY1qMHRu7ahX1Dh6Knpyfa/PUXWrq4YLG/PwCg7V9/wcrEBIdHjhTvK2wMIccMEhER6SYmhDrC1MgIwpw5AIC0rCycj4hA3cqV0ahKFfzv/ffFtQmtTUxgbmws3lfbzg4W+Y6BwtchJCIiIt3EnUp0QI5UihWXL+PggwfYGBqKqJQU+G7YgEPh4TCQSGBmZASDvKVlPB0c4FqpknhvQkaG3MQTIHcdwouRkeX6HoiIiEhz2EKoA668fInxBw6Ixw8mTQIAfLBnDz7YswcAcGDYMPTw8ICZkRFMDA3Fa79s1w6GBvL/LggqZAwhERER6SYmhDqg9+bNcsceS5cqXCMIAgDg4osXkOb9DOSOFyw4hnBq3hjCdgXGEBIREZFuYkKoA4qzJmAHNzcAgKGBgdh9DOSOITQ3kv8acAwhERGRfmFCqOUqL1z41mvszMzEFsLGzs6oY28vnkvNyoKkwNZ1he1lTERERLqJCaEWe5aQgNd5awgqs7xHD1QyNcXIXbtw+tkz9PT0RI4gICff7iSTWraEUYExhIWtQ0hERES6iQmhFiuYyAGAmZERnk+ZgmlHjsDH2RnPExIAAIZ5rYB3YmLkuohnHT+uMIaw/9atyMlrUeSexURERLqPCaEWc7G2lts9BADSs7NhYWyMfnXrooaNDXycnXH544/hVbkyACAtOxsZOTni9crWIczJN+mEexYTERHpPq5DqOWUtd5Fp6Sgz5YtOP74MbJychCXlobsvG7i9tWrw9vJSbw2SypFllSq8AwiIiLSH0wItVh4XBxq/vabQrks+UvNysK/Dx7g3b//xumnTwEAiRkZSMnMFK/9qEkTBPr4yN2ff59i7llMRESk+9hlrMUsjI3RuWZNrL9xQ67czswMY5s1Q5MqVfAsbwyhcd5i1E8SEuQmjMw5cQJWJiboUquWWMYxg0RERPqFCaGWcly4ELFKZhg7W1rCzMgI3WrXRlVra3g7OSF03DjUtrMDAMSmpiIh31Z1ytYhJCIiIv3CLmMtpSwZBICpbdsiJjUVg7Zvx4knT5CenY1nCQnIzJtI0t3dHc1dXOTuKbgOIREREekXJoQ6ZtqRIwpjCHtu3oxTeWMII5OSEJcvmRzq7Y2B9etrJFYiIiKqGNhXqKUKLjcD5I4d/LhpU9iZmWFSy5ZoVrWqOIbQLK9bODYtDfHp6eI9P5w+DSsTE7zr7l5+wRMREVGFwoRQSx0LDMSWW7fw++XLYoufiaEhFrzzDpIzM9GqWjVUtrCAt5MT7k2ciOqVKgEAnsTHo6qVlfgcZesQEhERkX5hl7GWCg4NxaKzZ+W6f6NSUjDnf/9DbGoqRuRtV5ecmYnb0dFIzcoCAAysVw/ta9QQ7zE2NISxkh1PiIiISH9oZSaQmJiI2bNno3Xr1mjcuDECAwMRFham8nNycnLQq1cveHl5Yd26deoPtAx92qoV/uzZU6H8u1OnFMYQ9t+2DaefPQMA3I+Lw4vERPH6vl5e6OHhUT5BExERUYWkdQmhVCrFmDFjcODAAYwYMQLTpk3D69evMXLkSDzLS3qKa8uWLYiIiCijSMuWi7U1auUtJSNjZ2aG77t0gb25Oaa2aYMWLi6oZGoKALDM6xZOz84WZxwDwIIzZ7D4woXyC5yIiIgqHK0bQxgSEoJr165h+fLl6NatGwDA398ffn5+WLZsGRYuXFis58THx2PJkiX48MMPsXTp0rIMuUyERkXhWUICLn/8MSyMjZEtlcLbyQkSiQRJGRmo7+gIWzMzeDs54fHkyaiSN27wdnQ0bPKSRIBjCImIiEgLWwgPHToEJycndO3aVSyzt7eHv78/jh49iqy8sXJvs3jxYri6uqJPnz5lFWqZ2nTzJj7auxfNXFwwZv9++KxYgQMPHuCLQ4fwOi0No/fuxZnnzxGfno7zERFIztuuLrBRI3SrXVt8zs6wMKy6ehWSuXNRJShIU2+HiIiINEjrEsKwsDA0aNBAYTHlhg0bIiUlpVjdxvfu3cPWrVsxc+ZMrV2UeUrr1vird29I5s7Ff3nvudfmzfjl/Hm5MYQHHz7E0H/+EdchvP7qFcLj4sTnpGVniz8XXMaGiIiI9IPWdRnHxMSgdevWCuVOTk4AgOjoaLi/ZU29efPmoVu3bmjevHmpxxCmZ2dAaiCU6hmFSctKL/RcJVMjOFvm7kn8WauW+LFrF5x7HoErL1/CwsgA09q2QeMqjniWkDuBxNRQgrSsdAiCFDlCTqHPLuo1dZ0+v/fyJttNm3VefljX5Yv1Xf5Y50XLzM4s8rxGE0KpVFrsLl7TvHFv6enpMDExUTgvK0tPL/oLIRuDePDgQRWjrViuvXqF8DdvcPGj0TCUSHDl5Uu0dq2GNtVdkZSRgeqVKsHS2Bh+7rXxcNInqGxhAQC48vIVTAwNxec4WVoiOq9l0MnSUiPvhYiIiDRLownhpUuXEBgYWKxrz507B3t7e5iZmSEzUzHLlZWZmZkV+oyMjAwsXLgQgYGBqF69esmCLsDMyBSmxqZvv7AUzI0V39Peew/x2/nzSP/6a7T96y+ci4jAPwEBCHn4EDPbt8enIYewpndvOFpWwpnnkehepw5szMwwvnlzuNnais+Mmjq1TGPXRsrqm8qGubEZ67ucsb7LF+u7/LHOlTOQFj1ETqMJYe3atTF//vxiXWuVN0vW0dER0dHRCudlZbKuY2U2bdqEN2/eoHfv3mJX8atXrwAACQkJiIiIgLOzM4wr+KzbKkFB4ng/ydy5YvmAbdsAANPbtQOQOz7w3wcP8PG+fdgxaBAG1K+PM8+fIzkzk/sXExERkUijCaGjoyP69++v0j1169bFtWvXIAiC3ISQ0NBQWFhYoEa+XTgKioyMRGpqqtKZxb///jt+//13/Pvvv28dg6hpBSd/WBobI/mrr3DpxQtcf/UKlS0s8G2nTmhbvTqexMcDAOzMc0dtHXz4ELvu3sW3J0/C2dISr9hCSEREpPe0blJJ9+7dcejQIRw7dkxchzAuLg4hISHo2rWrXOuebMaxLEkcOHAgWrVqJfe8169f45tvvsGAAQPg6+uLKlWqlNM7UZ+UrCycff4cbVxd0aJaNSRmZMDO3Bwmhobo4eGB2GnTxAWq0zmrmIiIiArQuoTQz88PjRs3xvTp0zF69GjY2dlh8+bNkEqlmDRpkty1o0aNAgAcP34cAODl5QUvLy+5a2Rdx56enmKCWdGZGhoiI99uIwDQbs0abOrfH3vv38ePvr6YHBKC1b16oZKpKQ4+eIDeXl5wzut2JyIiIspP69YhNDQ0xMqVK+Hv74/g4GAsWrQIdnZ22LBhA9zc3DQdXrnYP2wYlA0NHbZzJ7bcugWpkLsMTrZUin8fPMCY/fvFdQid880kduasYiIiIoIWthACgI2NDX744Qf88MMPRV4naxksiqurK+7du6eu0MpFt9q1UXDlQ2HOHFx9+RI3o6JQ2cIC87t2Rdvq1fE4bwyhbNkZjhkkIiKigrSuhZCAl0lJCmUnnjxBkypV8H7jxgAAIwMDSCQSjNm3DwDgu2EDt6YjIiIipZgQaqFP/v1X7tjIwABd1q/Hhhs30GvzZrxOS8O0I0dw9vlzuYkjnERCREREyjAh1EKftWqFLQMGYN/QoQAg7l08as8e7L9/X7xOO3dpJiIiovKmlWMI9V2nmjWx+upVjDtwAAAwq0MHzPP1xY1Xr3AnJgYO5ub4+d130bZ6dThbWootg5xEQkRERMowIdRCTosWISY1FQDgaGGBwQ0a4NDDh3jX3R2NqlRBfHo60rKykC2VchIJERERvRW7jLWQLBmU/Tz0n3/QfeNGrLxyBZ3XrcPr1FR8/b//4VzeGotERERERWELoQ64HRMDABh/4AAEAAZ5W/qZGBpqMCoiIiLSFkwIdcA3HTtibpcuuB0djXuvX8PBwgLL/P3RxtVV06ERERGRFmCXsQ4YWL8+9t27h/qOjuhfrx5ypFJEp6TI7VtMREREVBgmhFrO0cICA7dvR+8tW7Ds4kU0X7kSr9PS8N2pUxxDSERERMXCLmMt9HuPHohMSkLnmjXRLThYnGTy+eHDyJZKYWSQm+dbGBtrMkwiIiLSEkwItUx4XByA3Akj/hs3AgC+7dQJczp3xt3YWDyMi4ODuTkqmZjggz178MGePXC2tOTyM0RERFQoJoRa5vSzZ3Jb1yV++SUevXmDHXfuYEC9eqhbuXJueWameA23rCMiIqKicAyhlgls1Ej82VAiQXJmJvpu3YpB27fj53Pn4LVsmQajIyIiIm3EFkItY/zdd+LPUkGAyy+/AMjdt/jr48eRkZMDANyyjoiIiIqNCaGWkeb7Wcj7/3edO2N2p0548Po1nsTHAwDHDBIREVGxMSHUcumzZiEsNhabbt7EUG9veDg4aDokIiIi0jJMCLWMAeRbCV8mJ6Pnpk14kZSEJ/HxWHbxIiK/+EJT4REREZEWYkKoZXLmzMHWW7fwJj0dDZ2cUGvxYkgAVDI1xY+nTyMlK0vTIRIREZGWYUKoZXbfvYu4tDSkZWej/dq1AIDvu3TBrI4d8fjNGzxLSNBwhERERKRtmBBqmW23b2PzrVvisTBnDq6/eoV116/j/UaNUMvOToPRERERkTbiOoRaZkO/fuLPFsbGuP/6Nfz+/hsf7NmDb0+cQKX58zUYHREREWkjthBqGSMDA+wZMgQJ6emoZWcHr2XLYGxgABdra/x24QKS8u1QQkRERFQcTAi1SJWgIHGxaSsTEyTnJX/fd+mCGe3b41lCAiISEzUZIhEREWkhdhlrkfx7EsuSQWHOHHStXRt/Xr6M6pUqoW316poKj4iIiLQUWwi1mI2pKW5GReGd4GDEp6fjcXw8Fpw5A2HOHE2HRkRERFpE5YQwJycHmZmZMDc3F8sSExOxY8cOJCQkoEePHvDy8lJrkKTcvqFD4bNiBcyNjODl4IBVV69qOiQiIiLSQip3GX/zzTcYNGiQeJyVlYVhw4Zh4cKF+PPPPzFo0CCEhYWpNUjK5WhhIf5sZWKCjuvWAQDm+fri7sSJMJRIAACSuXNRJShIEyESERGRFlI5Ibxy5Qp8fX3F40OHDuHhw4f45ptvsGXLFlSuXBkrV65Ua5CU69LHH8PJ0hKA/BjCdtWrY+mFC4hJTRWvzT/ekIiIiKgoKncZx8TEwNXVVTw+ceIEPDw8MGzYMABAQEAAtm7dqr4ISbTn3j1E50v0KltY4EpkJLpu2MAt64iIiKjEVG4hFAQBOTk54vHFixfRqlUr8djR0RGvX79WT3QkZ1D9+rAxNUW9ypXx77Bh+CcgAM1XrYKhgQGau7jAOa/1EIDcz0RERERFUbmF0NXVFf/99x+GDh2KK1euICYmRi4hjI6OhrW1tVqDpFxVra1hbWqK+PR0XHn5ErP/9z8AuesQfprvMyAiIiJShcothP3798exY8fQs2dPjBs3Dg4ODmjfvr14/saNG6hdu7ZagywoMTERs2fPRuvWrdG4cWMEBgaqNJFFKpXi77//Rq9eveDj44PWrVvjww8/xLNnz8ow6tJ79OYNXiQm4mVyspgMCnPmoLmLC4LOnoUgCBqOkIiIiLSRyi2E77//PlJSUnDs2DHUq1cPn3/+ubgEzZs3b3Djxg2MHj1a7YHKSKVSjBkzBvfv38fo0aNhZ2eHTZs2YeTIkdi5cydq1Kjx1mdMnz4dR48excCBAxEYGIjk5GSEhoYiPj6+WPdryv7791Ew5Tv3/Dk6r1uHLKkUn7dpA4lGIiMiIiJtpnJCKJFIMGHCBEyYMEHhnJ2dHc6dO6eWwAoTEhKCa9euYfny5ejWrRsAwN/fH35+fli2bBkWLlxY5P379+9HSEgINm7ciEaNGpVprOo2xNsbk0NC5MrarlkDe3NzNHJ2ZjJIREREJaK2revi4uLw5MkTdT2uUIcOHYKTkxO6du0qltnb28Pf3x9Hjx5F1ltm265fvx7dunVDo0aNkJ2djbS0tLIOWW2cLC1hbKD4kc3r0gXH338fEglTQiIiIlKdyi2Eu3fvxpUrV/D999+LZT///DNWr14NAGjUqBFWr14NKysr9UWZT1hYGBo0aKCQ/DRs2BBbt27Fs2fP4O7urvTe5ORk3Lx5E76+vvjmm2+wa9cuZGZmwsPDA19++aXcWMjiSs/OgNSgbMbupWWlyx0/jIuDlYkJ3qT/f3nqVzNw5tlzzD3xP0xr25pJYSkUrG8qO7J9jljn5Yd1Xb5Y3+WPdV60zOzMIs+r3EK4ZcsWZGdni8c3b97EqlWr0Lx5cwwaNAg3b97EurwdNMpCTEwMnJycFMplZdHR0YXe++zZMwiCgHXr1uH8+fP49ttvsWDBAgDA2LFjERoaWjZBq4nPilVyyWANm0o49fQZ3vl7E749eUphfCERERFRcajcQvjs2TN0795dPA4JCYGNjQ3++usvmJiYQCKR4ODBg5g4ceJbnyWVSt/axStjamoKAEhPT4eJiYnCeVlZenrh/0JIzdvJIyUlBbt370bVqlUBAB06dEC3bt3w559/Yvny5cWKR8bMyBSmxqYq3aMqc2MzvFHStb2+bz90Wb8eTpaWaO3qCgtjM7YQqoG5sZmmQ9Ab5sZmrO9yxvouX6zv8sc6V85AWnR+oHJCmJSUJLfO4Llz59C2bVsxIfP29sbevXuL9axLly4hMDCwWNeeO3cO9vb2MDMzQ2amYrOnrMzMrPAvgiypbNq0qZgMAoCDgwPatm2Lq1evFiuWiqLL+vUAcscQftysmYajISIiIm2lckLo6OiIp0+fAsidSHL37l0MGDBAPJ+amgpDQ8NiPat27dqYP39+sa6VjUl0dHRU2i0sK1PWnSwjO1e5cmWFcw4ODkhMTCxWLJqQmW93mPyEOXNw4skTfHviBOZ06sQWQiIiIlKZyglhq1atsHHjRtjY2ODChQuQSCTo1KmTeP7x48dwdnYu1rMcHR3Rv39/lV6/bt26uHbtGgRBkEt+QkNDYWFhUeQ6gs7OzqhcuTKioqIUzkVFRcHOzk6lWMpTjpJFpz3s7XE4PBzd//4bAoC5J0/C2dISr6ZOLf8AiYiISGupPKlk8uTJcHR0xKJFi3Dq1CmMGTMGrq6uAIDs7GwcPnwYLVq0UHugMt27d0d0dDSOHTsmlsXFxSEkJARdu3aFsbGxWP7s2TOF3Ue6d++Oa9euITw8XCyLiIjAmTNn0LZt2zKLu7SMCiw3IwGwrm9f+OUlgzJRKSnlGhcRERFpP5VbCKtUqYIDBw7g4cOHsLa2houLi3guPT0d3333HerWravWIPPz8/ND48aNMX36dHGnks2bN0MqlWLSpEly144aNQoAcPz4cbFs7NixCAkJwfvvv4+RI0fC0NAQf//9N0xNTZUutl1RGBToChYAvBscrJlgiIiISKeonBACgKGhIby8vBTKraysxN1DyoqhoSFWrlyJhQsXIjg4GBkZGWjYsCEWLFgANze3t97v5OSEjRs34qeffsKff/4JQRDQtGlTTJ8+vVj3a4qyWcYpSmZoO1talkc4REREpEMkgqBkcFoxnD9/HkePHsXz588BANWrV8c777yDVq1aqTXAiiojIwO3bt2Ct7e3OHtZ3WSLbJobm+FZQgLcfvtN7nwLFxc8S0gQu4k5frB08tc3lbFNuS3eaYPSWN/lhN/v8sX6Ln+s86K9LW9RuYVQKpVixowZ2L9/PwRBgEHe2DapVIqNGzeiV69eWLBgAWe7qpmZ0f9/VKaGhrg7cSJq2NgodCUTERERqUrlSSVr1qzBvn374Ofnh927d+PGjRu4ceMGdu/eDX9/f+zbtw9r164ti1j1Wv5lZzJycrDi8mVY/PCDBiMiIiIiXaFyQrhr1y60a9cOv/32G+rWrQtjY2MYGxujbt26+OWXX9C2bVv8888/ZRGrXrsTEyN3vODMGWQUsjYhERERkSpUTgifP38OX1/fQs/7+vqK4wpJfWrZ2iqUtatevfwDISIiIp2j8hhCc3NzxMbGFno+JiYG5ubmpQqKFNnk25LPwtgYDydNgh3rmYiIiNRA5RbC5s2bY+PGjXjw4IHCuYcPH2LTpk1lujC1PkrLyoJzUJB4nJqVhcUXLsD2p580GBURERHpCpVbCD/99FMMHjwY/fr1g6+vL+rUqQMgNxk8fvw4jI2NFRaIptJJysxUKFtw5owGIiEiIiJdpHJC6OXlheDgYPzwww84fPgwDh8+LJ5r0qQJZs2apXTRaio5m0LWOfStVaucIyEiIiJdVKKdSho2bIgtW7YgLi4OERERAABXV1fY29urNTjKlZ6drVAWN306DA1U7vEnIiIiUlCihFDG3t6eSWA5SFOSEC44cwa/nT+P9K+/1kBEREREpEtKlRBS+VC2GwnHEBIREZG6vDUhrFu3rsrb0EkkEty5c6fEQZE8WzPl+zL6ubuXcyRERESki96aEPbt25f7EmuY/YIFcsdVrKxwb+JEZCjpSiYiIiJS1VsTwp+41p3GpWRlyR2/Sk7Gj6dPcwwhERERqUWZT1NNSEhAYGAgu5DVjHsZExERkbqUeUKYlZWFixcvIiEhoaxfSmc5FLFFnWTuXFTJt4sJERERkaq4kJ0WaFmtWpHno1JSyikSIiIi0kVMCLWAo6WlpkMgIiIiHcaEUAvM7dxZoSz/B+fMhJGIiIhKgQtTa4GE9HSFMikAYc6c8g+GiIiIdA5bCLXAtCNH5I5drK2ZDBIREZHaMCHUAq6VKskdRyYlaSgSIiIi0kXsMtYC33XpgrXXr8uVSebOBZA7fvDV1KkaiIqIiIh0RZm3EJqZmaFfv35wcnIq65fSWdFFLCvDJWeIiIiotMq8hdDKygrz588v65fRaQXHEBIRERGpk8oJYdeuXYs8L5FIYGZmhqpVq6Jdu3YICAiAhYVFiQMkoLatLY4Xco5LzhAREVFpqZwQuri4IDo6Gk+fPoW1tTVcXV0BABEREUhKSoKbmxtMTU1x48YNnD59Glu2bMGmTZtgb2+v9uD1xfR27bD62jXx2MzICGmzZmkwIiIiItIlKo8h/OqrrxAfH485c+bg7Nmz2LVrF3bt2oVz585h9uzZSEhIwA8//IDz589j9uzZePr0KRYvXlwWseuNJ/Hxcsfp2dmaCYSIiIh0ksoJ4YIFC9CjRw8MHToUxsbGYrmRkRGGDx+O7t27Y8GCBTAwMMDw4cPx3nvv4eTJk2oNWt/MO31a0yEQERGRDlM5Ibxx4wa8vLwKPe/l5YXr+ZZIadKkCWJjY0sUHOXyZHc7ERERlSGVE0ITExPcvHmz0POhoaEwMTERj7OysmDJiQ+lMrl1a7nj0Y0bayYQIiIi0kkqJ4S+vr7YuXMnVq5cibS0NLE8LS0Nf/75J3bv3g1fX1+x/OrVq6hZs6ZagpVJTEzE7Nmz0bp1azRu3BiBgYEICwsr9v3//vsvBg0ahGbNmqF169YIDAzE2bNn1RqjOnVcu1bueGzz5hqKhIiIiHSRygnhjBkz4OXlhV9++QUtWrSAr68vfH190aJFC/z666/w8PDA9OnTAQAZGRkwNTXFsGHD1BawVCrFmDFjcODAAYwYMQLTpk3D69evMXLkSDx79uyt92/cuBFTpkyBvb09pk6dinHjxuHNmzcYPXo0zpw5o7Y41elNerrccZvVqzUUCREREekilZedsbW1xbZt27B9+3acOHECERERAIDWrVujS5cuGDRokNhlbGpqikWLFqk14JCQEFy7dg3Lly9Ht27dAAD+/v7w8/PDsmXLsHDhwiLv//vvv9GwYUOsWLECEokEANC3b1+0b98ee/fuRbt27dQab1mQajoAIiIi0ikl2qnExMQEw4cPx/Dhw9Udz1sdOnQITk5Ocgtk29vbw9/fH/v370dWVpbc7OeCkpOTUaNGDTEZBIBKlSrB1NQUpqamZRo7ERERUUVU6r2M4+LiEBcXp45YiiUsLAwNGjSQS+gAoGHDhkhJSXlrt3HLli1x+vRpBAcHIyIiAuHh4fjmm28gCIJGEtzisDMzkzu2L3BMREREVBolaiGMiorCL7/8gmPHjiElJQVA7p7FXbt2xZQpU+Ds7KzWIPOLiYlB6wKzbgHAyckJABAdHQ13d/dC7//qq6/w+vVrzJs3D/PmzQMAVK5cGRs2bChyOZ3CpGdnQGogqHxfcaRl5Y4dLDiG0NjQUDxH6sM6LT/mef9nnZcf1nX5Yn2XP9Z50TKzM4s8r3JCGBkZiYCAAMTGxqJevXqoU6cOACA8PBy7d+/GmTNnsG3bNlStWvWtz5JKpcjKyirW68q6c9PT0+WWtZGRlaWnF/2FMDc3R+3atVG1alV06tQJKSkpWLduHcaPH49NmzahevXqxYpHk6LyknAiIiIidVA5IVy8eDESExPx559/olOnTnLnTp48iUmTJmHx4sX46aef3vqsS5cuITAwsFive+7cOdjb28PMzAyZmYpZrqzM7C3dqZ9++ilMTU2xfPlysaxr167w8/PDb7/9hp9//rlY8ciYGZnC1Lhsxx7WsbfHw3zd8pNatoS5MbuNywrrtvyYG5uxvssZ67t8sb7LH+tcOQOppMjzKieEZ86cwbBhwxSSQQDo1KkThg4div379xfrWbVr18b8+fOLda2VlRUAwNHREdHR0QrnZWWyrmNlnj9/jtOnT+PHH3+UK7e1tUXTpk1x7dq1YsVS3pb36AG/v/8Wj/vVravBaIiIiEjXqJwQJiQkwM3NrdDzbm5uSExMLNazHB0d0b9/f5Vev27durh27RoEQZCbWBIaGgoLCwvUqFGj0HtlW+hJpYoLt2RnZyM7O1ulWMpL/mQQAIbt3ImXX3yhoWiIiIhI16g8y7hKlSq4ePFioecvX76MKlWqlCqoonTv3h3R0dE4duyYWBYXF4eQkBB07dpVbsmZZ8+eyc06dnNzg4GBAf7991+5Z7569QqXL19G/fr1yyxudXqVnKzpEIiIiEiHqNxC2L17d6xevRqurq4YM2YMrK2tAeSu77dy5UocPHgQY8aMUXugMn5+fmjcuDGmT5+O0aNHw87ODps3b4ZUKsWkSZPkrh01ahQA4Pjx4wBy1yscMGAAtm/fjvfffx/vvvsukpOTsWnTJmRmZuLjjz8us7hLo76jI+7ExIjHU5TMsiYiIiIqKZUTwk8++QSXL1/GqlWrsGbNGrnlXnJyctC0aVOMHz9e7YHKGBoaYuXKlVi4cCGCg4ORkZGBhg0bYsGCBUV2Zct8++23qFu3Lnbs2IGgoCAAgI+PDxYtWoRmzZqVWdylEfTOO+ixaZN47J83s5uIiIhIHSSCIKi8iF52djZ27tyJo0ePilvXVa9eHd26dUO/fv1gZFSi5Q21SkZGBm7dugVvb+8y2+FEtqaSxY8L5MqdLS3xaurUMnlNfSarb85QKwebcsf/pg1KY32XE36/yxfru/yxzov2trylRJmbkZERAgICEBAQUOoASXVch5CIiIjUqdRb11HZa6xkko5k7lxUyevyJiIiIiqNt7YQ7t69u0QP7tu3b4nuI0XzunRBz82bFcrZUkhERETq8NaE8Msvv4REIoEqQw0lEgkTQjVSlgwSERERqctbE8INGzaURxxUAs6WlpoOgYiIiHTAWxPCli1bluoFpFIpXr16hcqVK8PExKRUz9JXLatVw8UXLxTKOdOYiIiI1KHMJ5XExcWha9euuHLlSlm/lM6a3bGjpkMgIiIiHVYus4xLsNQh5dNLyRhCdhcTERGRuuj+CtI6SJgzR9MhEBERkQ7hOoQVXGJGhkIZ1x8kIiIidWJCWMGlZ2crlHH9QSIiIlInJoQVXKUy2ieZiIiISIYJYQVnZqR8mCe7jYmIiEhdmBBWcAnpimMIAXYbExERkfowIazgMnIUxxASERERqVOZLztjY2ODDRs2oF69emX9UjrJppAxhFyHkIiIiNRF5YTw0qVLRZ6XSCQwNTWFi4sLHBwcYGxsXOrt7/SZiaGh3LGzpSW3rCMiIiK1UjkhHDlyJCQSSbGu9fT0xBdffIGO3HqtxBIzMuWOmQwSERGRuqmcEP7444/YuHEjnj59il69eqFWrVoAgEePHmH//v2oVasW+vTpg8ePH2PPnj0YP348/vrrL7Ru3VrtweuDhitWij9zwCcRERGVBZUTwrS0NLx58waHDh2Cg4OD3LkJEyZg8ODBMDAwwOzZszF27Fj07dsXK1euZEJYQrGpqeLPUg3GQURERLpL5UanDRs2ICAgQCEZBABHR0cMGjQI69evBwA4OTlh0KBBuHnzZukjJSIiIqIyoXJCGBkZCTMzs0LPm5ub4+XLl+Kxq6srMpTsx0vFw51KiIiIqKypnBBWq1YN+/fvR2ZmpsK5zMxM7N27Fy4uLmLZq1evYGtrW6og9dmNsR+LP9ewsdFgJERERKSrVB5DGBgYiO+++w4BAQEYOnQoatasCQB4/PgxNm/ejPv372P27Nni9YcPH4aPj4/aAtY3d2JixJ9HNGyowUiIiIhIV6mcEA4bNgzJyclYvnw55syZIy5BIwgCTExMMGXKFAwbNgxAbovh9OnT4ebmpt6o9UhcWpr4809nzuCHrl01GA0RERHpohLtVDJmzBgMHjwYZ86cQUREBIDcsYJt27aV6x42MTFBhw4d1BKovsoRBPFnqSCgSlAQ1yIkIiIitSrx1nU2Njbo0aOHOmMhJbrkdcnLRKWkaCYQIiIi0lklTggjIiJw7tw5xMbGolevXnB1dUVmZiZiY2NRuXJlmJiYqDNOvRUaHaXpEIiIiEjHlSghXLRoEdatW4ecnBxIJBI0btxYTAjfe+89TJ48GaNGjVJzqPopPi1d7tjZ0lJDkRAREZGuUnnZmS1btuCvv/7CsGHDsGbNGgj5xrhZWVnB19cX//vf/9QapD6T5qtfgHsZExERkfqp3EK4adMmvPPOO5g1axbevHmjcN7LywuXLl1SS3AEdKlVU/y5buXKmguEiIiIdJbKLYRPnjxB27ZtCz1vZ2enNFGkkrn+6v/HEN6NjYVk7lxUCQrSYERERESka1RuITQ1NUVavrXxCoqMjESlSpVKFVRRHj16hC1btiA0NBR37txBRkYGjh07BldX12I/4+rVq1i0aBHu3LkDKysr+Pv744svvoC5uXmZxV1SfbduVyjjTGMiIiJSJ5VbCH18fHDkyBGl5zIyMrBnzx40bdq01IEV5vr16wgODkZycjLc3d1Vvj8sLAyjRo1CRkYGvvzySwwcOBBbt27FlClTyiDa0is4hpCIiIhI3VRuIfzwww/x4YcfYtq0aRgwYAAAIDY2FqdPn8bSpUsRFRWFn3/+We2Byvj6+uLSpUuwsrLCunXrcOfOHZXu/+WXX2Bra4vg4GBY5s3YdXV1xddff41z586hTZs2ZRE2ERERUYWlcgth27Zt8e233+LQoUP44IMPAADTp0/HmDFjcPfuXXz//fdo0qSJ2gOVsbW1hZWVVYnuTU5OxtmzZ9G3b18xGQSAPn36wMLCAgcPHlRXmGpjxfUciYiIqIyVaB3CwYMHw9fXFyEhIXj06BEEQUDNmjXh7+8PZ2dndceoNvfu3UN2dja8vb3lyk1MTFCvXj2EhYWp/Mz07AxIDcqmWzctKx3JmZkK5U6WlkjLSldyB5UG67T8yEbrss7LD+u6fLG+yx/rvGiZ2Yr5RH4l3qnE0dERI0eOLOntGhETEwMgN/aCHB0dcf369XKO6O0MJIA0X77pZGmJJ5Mnai4gIiIi0jklSgivXbuGv//+G0+fPkV8fLzc4tQAIJFIcPTo0bc+RyqVIisrq1ivaWpqWpJQ5aSn5/7rQdm2eqampuJ5VZgZmcLUuPSxFUZaoPExigtTlzlzYzNNh6A3zI3NWN/ljPVdvljf5Y91rpyBVFLkeZUTwt27d2PmzJkwMjJCzZo1UbVq1RIHd+nSJQQGBhbr2nPnzsHe3r7ErwUAZma5X5JMJd2wGRkZ4nkiIiIifaJyQvjHH3+gVq1aWLt2banHC9auXRvz588v1rUlnUiSn6yrWNZ1nF9MTAycnJxK/RplifsYExERUVlQOSGMjIzE9OnT1TJ5xNHREf379y/1c4rL09MTRkZGuHXrFt59912xPDMzE2FhYejVq1e5xVJcHvb2eBAXB4D7GBMREVHZUHnZmSpVqijtcq2IwsPDERkZKR5bW1ujTZs22LNnD1Ly7faxZ88epKamonv37poIs0jbBuYmzH4lWISbiIiIqDhUbiEcMmQI9u3bh1GjRsHQ0LAsYipSUlISgoODAUCcFbxx40ZYW1vDxcUFffv2Fa/t0aMHWrZsKV4PAFOmTMGQIUMwcuRIDBo0CK9evcLatWvRsWPHIvdo1pRO6zcAAA6Fh2s4EiIiItJVKieEDRo0wOHDhzFo0CAMGzYMrq6uShPDFi1aqCXAghISErB48WK5sjVr1gAAWrZsKZcQKtOgQQOsXbsWQUFBmD9/PqysrBAQEIDPP/+8TOItrcSM/2+NrRIUxG5jIiIiUjuVE8JRo0aJP3/99deQSOSnMQuCAIlEUqJFnovD1dUV9+7dK9a1hV3XvHlzbNmyRZ1hlYuofN3cREREROqickJY3FnBRERERKQdVE4I+/XrVxZxEBEREZGGqDzLmDSH6xASERFRWWBCqEWiUlJQJShI02EQERGRjmFCWMEV3HmQE0uIiIhI3ZgQVnCCpgMgIiIinceEkIiIiEjPMSEkIiIi0nNMCImIiIj0HBNCIiIiIj3HhFDLcC1CIiIiUjcmhFrm1dSpmg6BiIiIdAwTQi3DhamJiIhI3ZgQahkuTE1ERETqxoSQiIiISM8xIdQynFRCRERE6saEUMtwUgkRERGpGxNCIiIiIj3HhJCIiIhIzzEh1DJcdoaIiIjUjQmhluGyM0RERKRuTAiJiIiI9BwTQi3DZWeIiIhI3ZgQahkuO0NERETqxoRQi/DDIiIiorLAHEOLSDUdABEREekkJoREREREeo4JIREREZGeY0JYwUny/cwZxkRERFQWjDQdABVNyPv/0ZEj0bV2bY3GQkRERLqJLYRaYuHZs5oOgYiIiHSU1rUQPnr0CFu2bEFoaCju3LmDjIwMHDt2DK6urm+9Ny0tDTt37sTRo0fx4MEDpKSkoGbNmggICEBAQAAMDQ3L4R2UjJ2ZmaZDICIiIh2ldS2E169fR3BwMJKTk+Hu7q7Svc+fP8f3338PABg1ahRmzJiBatWq4dtvv8Xs2bPLItxSk31AW2/fRpWgII3GQkRERLpJ61oIfX19cenSJVhZWWHdunW4c+dOse+tXLky9u3bBw8PD7FsyJAhmDlzJv755x+MHz8e1atXL4uwSyz/2oNRKSkai4OIiIh0l9a1ENra2sLKyqpE99rb28slgzLvvPMOgNzuaCIiIiJ9o3UJYVmIjY0FANjZ2Wk4EiIiIqLyp3VdxuqWmZmJ9evXo0aNGvD29lb5/vTsDEgNhLdfWAJpWekwkEggFXKf72RpibSs9DJ5LQLrthyZ5/2fdV5+WNfli/Vd/ljnRcvMzizyvEYTQqlUiqysrGJda2pqWiYxfP/99wgPD8eqVatgYFDxGkxlyWDI8KHo6FZDw9EQERGRLtJoQnjp0iUEBgYW69pz587B3t5era+/evVqbNu2DV988QU6dOhQomeYGZnC1LhsktX8fjpzDn51PMv8dQgwN+YSP+XF3NiM9V3OWN/li/Vd/ljnyhlIJUWe12hCWLt2bcyfP79Y15Z0Iklhdu7ciaCgIAwfPhxjxoxR67PLwoknTyCZOxfOlpZ4NXWqpsMhIiIiHaLRhNDR0RH9+/cv99c9evQovv76a7z77rv4+uuvy/31S4NLzxAREZG6VbxBc2oUHh6OyMhIubJLly7h888/R/PmzREUFFQhxw0SERERlSetm2WclJSE4OBgALm7lgDAxo0bYW1tDRcXF/Tt21e8tkePHmjZsqV4/YsXLzB+/HhIJBL4+fnh4MGDcs9u2rRphVuYuiBnS0tNh0BEREQ6RusSwoSEBCxevFiubM2aNQCAli1byiWEBUVERCApKQkA8N133ymcnz9/foVPCDl+kIiIiNRN6xJCV1dX3Lt3r1jXFryuVatWxb6XiIiISF9wAJ0W4YdFREREZYE5hhaRajoAIiIi0klMCImIiIj0HBNCIiIiIj3HhJCIiIhIzzEhJCIiItJzTAiJiIiI9BwTwgpMEARNh0BERER6gAlhBRaZnKzpEIiIiEgPMCGswFysrDQdAhEREekBJoQVmEQi0XQIREREpAeYEFZgr9hlTEREROWACWEFlsNJJURERFQOmBBWYNWsrTUdAhEREekBJoREREREeo4JYQX2kmMIiYiIqBwwIdQi/LCIiIioLBhpOgAqXBVLS/w7bAi8Kjuhtp2dpsMhIiIiHcVGpwpMANBj0xYE37ih6VCIiIhIhzEhrMBkYwi/PXkSkrlzUSUoSMMRERERkS5iQliBGRbYqSQqJUVDkRAREZEuY0JYgTlZWmo6BCIiItIDTAgrsLi0NE2HQERERHqACWEFdjs6RtMhEBERkR5gQliB1XOsrOkQiIiISA8wIazA7M3NNR0CERER6QEmhBXY2ecRmg6BiIiI9AATwgrMgS2EREREVA6YEFZgDZwcNR0CERER6QEmhBVYYkaGpkMgIiIiPcCEsAILOnte0yEQERGRHmBCWIF91LSxpkMgIiIiPaB1CeGjR4/w448/YsiQIfDx8YGXlxciIko2Gzc5ORnt2rWDl5cXjh49quZIS6+GjY2mQyAiIiI9oHUJ4fXr1xEcHIzk5GS4u7uX6lnLly9HamqqmiJTvzXXrms6BCIiItIDWpcQ+vr64tKlS9i/fz/69OlT4uc8fvwYwcHB+PDDD9UYnXo9jo+XO3a2tNRMIERERKTTjDQdgKpsbW3V8pz58+ejS5cuaNGihVqeVxY+atIELxKTMLVtOzRzcdF0OERERKSjtC4hVIeTJ0/i7Nmz+Pfff/Hy5UtNh1MoN1sbrOnTC+bGZpoOhYiIiHSY3iWEWVlZ+PHHHzFy5EjUqFGj1AlhenYGpAaCmqKTt+ziBUw/egLLe3THB40blclr0P9Ly0rXdAh6Q7YHD+u8/LCuyxfru/yxzouWmZ1Z5HmNjiGUSqXIyMgo1n/qsmHDBiQkJGD8+PFqe2ZZeZ6QBAC4FBmp4UiIiIhIl2m0hfDSpUsIDAws1rXnzp2Dvb19qV4vNjYWv//+Oz7//HNUqlSpVM+SMTMyhamxqVqeVdD4Fs0Rn5GJya3asNu4HLGuy4+5sRnru5yxvssX67v8sc6VM5BKijyv0YSwdu3amD9/frGutbKyKvXrrVixAtbW1mjfvr24dmFsbCwA4PXr14iIiEC1atUgkRRdaeWlpq0tDoU/QnDoTQC5s4xfTZ2q4aiIiIhI12g0IXR0dET//v3L7fUiIyPx8uVLvPvuuwrnvvnmGwBAaGgoTE3LpsVPVcsvXkZ0Sop4HJXvZyIiIiJ10elJJeHh4TA3N4dL3pItY8eOVUhA79+/j8WLF2PMmDFo1KgRjI2NNRGqUhFJiZoOgYiIiPSA1iWESUlJCA4OBpC7awkAbNy4EdbW1nBxcUHfvn3Fa3v06IGWLVuK1zdqpDhT19raWjzXrVu3sg1eRWObNcXKK9eQlp0NgAtTExERUdnQuoQwISEBixcvlitbs2YNAKBly5ZyCaG267QuWC4Z5PhBIiIiKgtalxC6urri3r17xbq2ONe1atWq2M8rbzH59lnm+EEiIiIqK1q3lzERERERqRcTQiIiIiI9x4SQiIiISM8xISQiIiLSc0wIKzAzI0PxZy45Q0RERGVF62YZ65MrH3+EVVevYULLVqhTyn2ciYiIiArDFsIKzNzYCG2ru6KyhYWmQyEiIiIdxhbCCmzg9n9w9eUr8ZiLUxMREVFZYAthBTakQX25Yy5OTURERGWBCWEFFnTugqZDICIiIj3AhLACiy7QIsiZxkRERFQWmBBqEY4fJCIiorLAhLACc8rXIsjWQSIiIiornGVcgT2ZPBEAYG5spuFIiIiISJexhZCIiIhIzzEhJCIiItJzTAiJiIiI9BwTQiIiIiI9x4SQiIiISM8xISQiIiLSc0wIiYiIiPQcE0IiIiIiPceEkIiIiEjPMSEkIiIi0nNMCImIiIj0HBNCIiIiIj3HhJCIiIhIzzEhJCIiItJzTAiJiIiI9BwTQiIiIiI9x4SQiIiISM8xISQiIiLSc0aaDkBVjx49wpYtWxAaGoo7d+4gIyMDx44dg6ura7GfkZmZiVWrVmHv3r148eIFbG1t0ahRI/z444+wsbEpw+iJiIiIKh6tSwivX7+O4OBguLu7w93dHXfu3FHp/szMTHz00Ue4d+8eAgIC4Obmhjdv3uDq1atIT09nQkhERER6R+sSQl9fX1y6dAlWVlZYt26dygnh2rVrERYWhp07d6J69eplFCURERGR9tC6hNDW1rbE90qlUgQHByMgIADVq1dHZmYmBEGAqamp+gIkIiIi0jJalxCWxoMHDxATEwM3Nzd8+umnOHr0KHJyctCoUSN888038Pb2VvmZ6dkZkBoIZRAtkJaVXibPJeVY3+XHPO//rPPyw7ouX6zv8sc6L1pmdmaR5/VqlvGzZ88AAD///DMiIyPx008/4bvvvsOrV6/w/vvv48WLFxqOkIiIiKj8abSFUCqVIisrq1jXqqNbNyUlBQAgkUiwfv16WFpaAgCaNGmC3r17Y/369fjqq69UeqaZkSlMjcu2y9nc2KxMn0/yWN/lx9zYjPVdzljf5Yv1Xf5Y58oZSCVFntdoQnjp0iUEBgYW69pz587B3t6+VK9nZpb7JenSpYuYDAKAp6cn6tati6tXr5bq+URERETaSKMJYe3atTF//vxiXWtlZVXq13N0dAQAVK5cWeGcg4MDnj9/XuxnCULuuMHMzKL75EtD1t//tqye1IP1XY6MqgLI/fPD+i4f/H6XL9Z3+WOdF02Wr8jyl4I0mhA6Ojqif//+5fZ6Xl5eMDY2RlRUlMK5qKgolVogZV3d9+/fV1t8RHrDfV/u/+8+0GwcRER6JisrS+wxzU+nZxmHh4fD3NwcLi4uAHJbGdu3b49jx44hLi5OTACvXbuGBw8e4JNPPin2sy0tLeHp6QljY2NIJPzXCBEREVVcgiAgKytLbshcfhKhsLbDCiopKQnBwcEAcnctOXnyJEaPHg1ra2u4uLigb9++4rVeXl5o2bKleD2Q26IXEBAAZ2dnDBkyBKmpqVi/fj0sLCywe/fuUq1zSERERKSNtC4hjIiIQNeuXZWeK5j8KUsIASA0NBSLFi3CzZs3YWhoiHbt2mHGjBmoVq1amcZOREREVBFpXUJIREREROqlVwtTExEREZEiJoREREREeo4JIREREZGeY0JIREREpOeYEBIRERHpOSaERERERHqOCWEFlJmZiUWLFqF9+/bw8fFBQEAAzp07p+mwKqzQ0FDMnTsXPXr0QOPGjdG5c2dMmTIFT58+Vbj26tWrGDp0KBo1aoR27dph3rx5SEtLU7hOlc+guM/UVatWrYKXlxf69OmjcI71rT6hoaEYM2YMWrRogSZNmqB3797YuXOn3DXHjh1Dv3790LBhQ3Tu3BnLli1Ddna2wrMSExMxe/ZstG7dGo0bN0ZgYCDCwsKUvm5xn6lLnjx5gs8++wwdO3ZE48aN0aNHD6xcuVJh73p+v1UTHR2NoKAgjBw5Ek2aNIGXlxcuXLig9FpNfpdVeaYu4TqEFdDnn3+Ow4cPIzAwEG5ubti1axdu3bqF4OBgNGnSRNPhVTiffvoprl69iu7du8PLywsxMTHYuHEjUlNTsWPHDri7uwMAwsLCMHjwYNSpUweDBg3Cq1evsGbNGrRr1w4rVqyQe2ZxPwNVnqmLYmJi4OfnB0EQUKNGDezZs0c8x/pWn5MnT2LChAlo2bIlfH19YWRkhCdPnsDa2hoTJkwQrxk7dixat26NHj164P79+9i4cSOGDRuG2bNni8+SSqUYNmwY7t+/j9GjR8POzg6bNm1CVFQUdu7ciRo1asi9bnGeqUuioqLQs2dPWFtbY8iQIbCxscHly5exd+9e9O7dG4sWLQLA73dJXLhwQawDe3t7XLt2DRs2bECrVq3krtPkd1mVZ+ocgSqUGzduCJ6ensLatWvFsvT0dKFbt27CsGHDNBdYBXblyhUhIyNDruzx48eCt7e3MGPGDLHso48+Ejp06CAkJyeLZdu2bRM8PT2Fs2fPimWqfAbFfaaumjFjhjBy5EhhxIgRQu/eveXOsb7VIzExUWjTpo3w/fffF3ldjx49hH79+gnZ2dli2S+//CLUrVtXePz4sVh24MABwdPTUzhy5IhY9vr1a6F58+bCtGnTSvRMXfLnn38Knp6ewv379+XKJ02aJNSvX1/IzMwUBIHf75JISkoS4uLiBEEQhCNHjgienp7C+fPnFa7T5HdZlWfqGnYZVzAhISEwNjbGoEGDxDJTU1MMHDgQV65cQXR0tAajq5iaNm0KExMTubKaNWvCw8MD4eHhAIDk5GScPXsWffv2ldvYu0+fPrCwsMDBgwfFsuJ+Bqo8UxeFhoZi7969mDlzpsI51rf67Nu3D4mJiZg8eTKA3HoQCnTsPHz4EA8fPsTgwYNhaGgolg8bNgxSqRSHDx8Wyw4dOgQnJye5LUDt7e3h7++Po0ePIisrS+Vn6pKUlBQAgIODg1x55cqVYWRkBENDQ36/S8jKygp2dnZFXqPp73Jxn6mLmBBWMGFhYahVq5bcLwQA8PHxgSAIejGOQR0EQUBsbKz4y+fevXvIzs6Gt7e33HUmJiaoV6+eXL0W9zNQ5Zm6RhAEfP/99+jbty/q1auncJ71rT7nzp1D7dq1cfLkSXTq1AnNmjVDy5YtERQUhJycHADAnTt3AEChbpydnVGlShXxPJBb3w0aNIBEIpG7tmHDhkhJScGzZ89UfqYuadGiBQBg1qxZuHv3Ll6+fIm9e/di165d+Pjjj2FgYMDvdxnS9He5uM/URUwIK5iYmBg4OTkplDs6OgIAWwiLae/evYiKioK/vz+A3HoF/r8e83N0dJSr1+J+Bqo8U9fs3r0bDx8+xGeffab0POtbfZ4+fYpXr17hyy+/RL9+/bB06VJ069YNq1atwk8//QRAPfUtK9P3+m7fvj0mT56Ms2fPok+fPujcuTOmTZuGjz76CBMnTgTA73dZ0vR3ubjP1EVGmg6A5KWnp8PY2Fih3NTUFACQkZFR3iFpnfDwcHz33Xdo1qyZOPM1PT0dABS6loHcupWdl11bnM9AlWfqkuTkZPz8888YM2aM0l+cAOtbnVJTU5GQkIAvvvgCY8aMAQC8++67SE1NxebNmzF+/Pi31k3+Warp6elKr5OVyZ6lyjN1jaurK1q2bIl33nkHtra2OHHiBJYuXQp7e3sMHTqU3+8ypOnvcnGfqYuYEFYwZmZmSscoyH5pyH6JkHIxMTEYO3YsbGxssHjxYhgY5DaCm5mZAYDCshFAbt3KzsuuLc5noMozdckff/wBY2NjfPDBB4Vew/pWH9n76tmzp1x5r169EBISgps3b6pc38quk5XJrtXX+j5w4ADmzJmDkJAQODs7A8hNwAVBwMKFC9GjRw9+v8uQpr/LxX2mLmKXcQVTWNeArMm7sBYZApKSkvDxxx8jKSkJq1evlusekP0sq8f8CnYRFPczUOWZuiI6Ohrr16/HsGHDEBsbi4iICERERCAjIwNZWVmIiIhAQkIC61uNZO+7cuXKcuWyY3XVt6xM3+t706ZNaNCggZgMyvj6+iI1NRV3797l97sMafq7XNxn6iImhBVM3bp18fjxY3Gmm8yNGzfE86QoIyMD48aNw5MnT/Dnn3+idu3acuc9PT1hZGSEW7duyZVnZmYiLCxMbmJEcT8DVZ6pK16/fo2srCwEBQWha9eu4n83btxAeHg4unbtilWrVrG+1ahBgwYActfHy+/Vq1cAcmdAyt57wbqJiorCq1evFOr79u3bCjOVQ0NDYWFhIa6zpsozdUlsbKw4WSc/WStfTk4Ov99lSNPf5eI+UxcxIaxgunfvjqysLGzfvl0sy8zMxM6dO9G0aVOFf7VS7i/ozz77DNevX8fixYvRuHFjhWusra3Rpk0b7NmzR+4X8549e5Camoru3buLZcX9DFR5pq5wdXXF8uXLFf7z8PBAtWrVsHz5cvTt25f1rUay97Vjxw6xTBAEbN++HRYWFmjcuDE8PDxQu3ZtbN26VS6Z2bx5MwwMDPDuu+/KPS86OhrHjh0Ty+Li4hASEoKuXbuK491UeaYuqVWrFm7duqUwm/TAgQMwNDSEl5cXv99lSNPf5eI+Uxdxp5IKaPLkyTh27Bjef/991KhRQ1zVfv369WjWrJmmw6twfvjhB2zYsAFdunQRZxXLWFpaolu3bgCA27dvY8iQIfDw8BB3AVi7di1atWqFVatWyd1X3M9AlWfqspEjRyIxMVFupxLWt/rMmDEDe/bswcCBA1G/fn2cPHkSJ06cEGe/AsD//vc/jB8/XmEnhsGDB+Pbb78Vn5WTk4Nhw4bhwYMH4k4MmzdvxsuXL7Fz5064ubmJ1xb3mbrk0qVLeP/992FnZ4fhw4fDxsYGJ06cwKlTpzBkyBDMnTsXAL/fJfX7778DyJ38t3//fgwYMACurq6oVKkSRowYAUCz32VVnqlrmBBWQBkZGfjtt9+wb98+JCQkwMvLC59//jnatm2r6dAqpJEjR+LixYtKz1WrVg3Hjx8Xjy9fvoygoCDcuXMHVlZW6NGjBz7//HNYWFjI3afKZ1DcZ+oyZQkhwPpWl8zMTPz+++/YvXs3YmNj4erqilGjRmHIkCFy1x09ehTLli1DeHg47O3tMWDAAHzyyScwMpKfP5iQkICFCxfi6NGjyMjIQMOGDfHll1+K3dMleaYuCQ0NxdKlSxEWFob4+HhUq1YNAwYMwIcffii3sDG/36rz8vJSWl7wd7Umv8uqPFOXMCEkIiIi0nMcQ0hERESk55gQEhEREek5JoREREREeo4JIREREZGeY0JIREREpOeYEBIRERHpOSaERERERHqOCSER6TQvLy98+eWXmg6jRNLS0jBv3jx07twZ9erVg6+vr6ZDIiIdpbtLzRNRmblw4QICAwMBAN9//z0CAgIUrvHy8kLnzp3x559/lnd4OmPVqlUIDg7G6NGj4eXlBSsrqyKvL2wXCAD44osvMGbMGHWHKDp69CjCwsIwadKkMnsNIio7TAiJqFSWLl2K3r17w8zMTNOh6JyzZ8/C09MTM2bMKPY99erVwwcffKBQXr9+fXWGpuDo0aPYtWsXE0IiLcWEkIhKzNvbG7du3cL69esxduxYTYejcTk5OcjMzIS5ublanhcTEwMXFxeV7nF2dkafPn3U8voVhSAISE1NhaWlpaZDIdJZHENIRCXm7++PBg0aYNWqVXjz5s1bry9sPN/OnTvh5eWFCxcuiGVLly6Fl5cXHj58iB9++AHt27dHo0aN8P777+PRo0cAgMOHD6Nfv37w8fGBr68vtm7dWuhrnz17FgEBAWjUqBHatWuHefPmISUlReG6pKQkLFq0CO+88w68vb3RunVrfP7553j+/LnSmM+ePYvly5ejW7du8PHxwcGDB4usg+zsbKxcuRI9evRAw4YN0apVK0yYMAH37t1TeHZERAQuXrwILy8veHl5YenSpUU+u7hu3ryJCRMmoFWrVvD29oafnx/++OMPZGdny10XGhqKL7/8En5+fmjUqBGaNGmCIUOG4MiRI3LXjRw5Ert27QIAMVYvLy/s3LlTPK9s/GNERITC+7pw4YJ478aNG8V6WrNmjXjNv//+i6FDh6JJkyZo1KgRBg0ahJCQEIXnnzhxAiNGjECrVq3g4+ODzp07Y+LEiXj8+HHJK49IR7GFkIhKTCKRYOrUqfjggw+wYsUKzJw5U+2vMWPGDFhYWGDs2LGIi4vD2rVr8dFHH+HTTz9FUFAQhgwZggEDBmDHjh345ptv4O7ujubNm8s94/bt2zh06BAGDRqEPn364MKFCwgODsaDBw+wdu1aGBjk/ts4KSkJQ4YMQWRkJAYMGAAPDw/ExMRg06ZNGDRoEP755x9Uq1ZN7tkLFixAdnY2AgICYGlpiVq1ahX5fqZOnYqDBw+iXbt2GDp0KGJjY7Fx40YMGTIEGzduRP369dGiRQssXLgQ8+fPh52dHcaNGweg6DGCMtnZ2YiLi5MrMzAwgK2tLYDcJGnixIlwc3PD6NGjYWNjg+vXr2PJkiUICwvDkiVLxPuOHDmCR48eoXv37qhWrRri4+Oxa9cuTJw4EUFBQejVqxcAYNy4cZBKpbh8+TIWLlwo3t+0adO3xluY9evXIz4+HoMGDYKjoyOqVKkCAPj111+xYsUKdOjQAZMnT4aBgQGOHDmCyZMn45tvvsHw4cMBABcvXsT48ePh4eGBsWPHwtraGtHR0Th37hyePXv21s+JSO8IREQqOn/+vODp6SmsXr1aEARB+OCDDwRvb28hIiJCvMbT01MYM2aM3H2enp7CjBkzFJ73zz//CJ6ensL58+fFsiVLlgienp7C2LFjBalUKpavX79e8PT0FJo0aSJERkaK5a9fvxa8vb2FKVOmKLymp6encOTIEbny77//XvD09BT2798vV9awYUMhLCxM7tqIiAihSZMmcrHLYn733XeF1NTUwisrn//++0/w9PQUJk+eLPeewsLChHr16glDhw6Vu75Lly7CiBEjivXs/O+14H9t27YVBEEQ0tPThbZt2wrDhg0TsrKy5O5du3atwmeQkpKi8BqpqanCu+++K/j7+8uVz5gxQ/D09FQa14gRI4QuXboolD9//lzw9PQUlixZIpbJvlstWrQQYmNj5a6/deuW4OnpKfz8888Kzxo/frzQpEkTISkpSRAEQfjxxx8FT09PhWcQkXLsMiaiUps6dSqysrKwePFitT975MiRkEgk4rGs9c/X1xdVq1YVy+3t7VGrVi08efJE4Rm1atVCt27d5MpkM25l3Z+CIGDfvn1o0aIFnJycEBcXJ/5nbm6Oxo0b47///lN49tChQ4s9ZlD2WuPGjZN7T3Xr1kWXLl1w5coVhdY9VTVq1Ahr166V+0/2uZw5cwaxsbHo378/EhMT5d5jx44dxWtkLCwsxJ/T0tLw5s0bpKWloXXr1ggPD0dycnKpYi1Knz594ODgIFe2b98+SCQS9O3bVy72uLg4+Pr6IiUlBdevXwcAWFtbAwAOHTqk0BVORIrYZUxEpVa/fn2899572LdvH0aPHo26deuq7dnVq1eXO65UqRIAwNXVVeFaGxsbvHjxQqHc3d1doczJyQmVKlUSxwbGxcUhPj4e//33H9q0aaM0FlnXcn6qdD1GRETAwMBAaTx16tTB0aNHERERAXt7+2I/syA7Ozu0bdtW6bnw8HAAwFdffVXo/bGxseLPr1+/xm+//YZjx47h9evXCtcmJia+dSmckqpZs6ZCWXh4OARBgL+/f6H3yeIfPnw4jh07hrlz5yIoKAjNmjVDhw4d0LNnz1LVL5GuYkJIRGrx2Wef4dChQwgKCsLq1atVujcnJ6fQc8qSMAAwNDRU6TXeRhAEAEDbtm3x8ccfF/s+bVpuR/Yep0+fjnr16im9xsnJSbx29OjRCA8PR2BgILy9vWFtbQ1DQ0P8888/2L9/P6RSaaniKepzV9bqKggCJBIJVq1aVejnX6dOHQC5ifGOHTtw+fJlnD17FpcuXcL8+fOxdOlSrFy5Ek2aNClV7ES6hgkhEalF9erVMXToUGzYsEFutnB+tra2iI+PVygvOINX3WQtY/lFR0cjMTFRbIG0t7dHpUqVkJycXGgLW2lVr14dUqkU4eHhCq2oshiVtXyqi6zVzdzc/K3v8d69e7h79y4mTJiATz/9VO7c9u3bFa7P3wVekK2tLW7fvq1QrurnXrNmTZw+fRouLi5KW1kLMjQ0RKtWrdCqVSsAwN27dzFgwAD88ccfWLlypUqvTaTrOIaQiNRm/PjxsLKywqJFi5Ser1mzJq5fv460tDSxLCEhQVyepKw8fvwYR48elStbtWoVAIhjCw0MDNCrVy+EhoYqXcIEgNJuU1XIXmvlypViax0A3L9/H8ePH0ezZs3KtDuzffv2cHBwwKpVq5Qm5unp6eK4QFnLbP44ZbEWXHYG+P/xhsqeW7NmTaSkpCA0NFQsk0qlWLdunUrx9+7dGwDwyy+/KG1dzN/drWwsZu3atWFqaoqEhASVXpdIH7CFkIjUxt7eHh9++GGhk0uGDx+OadOm4f3330efPn2QmJiI7du3w8XFBTExMWUWl6enJ6ZNm4ZBgwbBzc0NFy5cwKFDh9CyZUv06NFDvG7KlCm4evUqPvvsM/j7+6NRo0YwNjZGZGQkTp06hQYNGuCnn34qcRzt2rWDv78/Dhw4gISEBHTp0kVc1sbU1BRff/21Ot5uoSwsLLBgwQJMmDAB3bt3x4ABA+Dm5obExEQ8evQIR44cwbJly9CqVSu4u7vDw8MDq1evRnp6OmrVqoXHjx9j69at8PT0VGjxa9SoEf7++2/MnTsXnTp1grGxMXx8fFC9enUEBARg7dq1mDBhAgIDA2FsbIxDhw4V2WWsjI+PDyZNmoSlS5eib9++8PPzg7OzM6Kjo3H79m2cOnUKt27dAgDMnj0br169Qvv27eHi4oL09HQcPHgQKSkpOrdwN5E6MCEkIrX64IMPsGnTJqUJXu/evREdHY2NGzdi/vz5qF69Oj755BMYGBjgxo0bZRZTgwYNMHPmTPz666/YsmULrKysMGLECEyZMkVujKK1tTU2b96MNWvWICQkBMeOHYOhoSGqVKmCZs2aYdCgQaWOJSgoCPXr18euXbvw008/wcLCAi1atMDkyZOLtc5gaXXo0AE7duzAypUrsXfvXrx58waVKlVCjRo1MGrUKDEGQ0ND/Pnnn1iwYAF27dqFtLQ0eHh4YMGCBbh7965CQtizZ0+EhYXhwIEDCAkJgVQqFT/j6tWrY/ny5fjll1+wePFi2Nraok+fPhgwYECRE0SUmThxIry9vREcHIwNGzYgNTUVDg4O8PDwwKxZs8Tr+vTpg507d2LXrl2Ii4uDlZUV6tSpgyVLlsDPz6/0FUmkYyRCwf4AIiIiItIrHENIREREpOeYEBIRERHpOSaERERERHqOCSERERGRnmNCSERERKTnmBASERER6TkmhERERER6jgkhERERkZ5jQkhERESk55gQEhEREem5/wMNvorYuJS3oAAAAABJRU5ErkJggg==\n",
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yn3zyyeumKTcMQ506dZIknT9/Xt9++22O95HVWaC84u4P9Ha1UqVK6emnn9bGjRsdH9YnT57suBzyn2cxlixZIvPq/eA3/JPVZa3XSn/v7t27Z+t9/zmtvtU/d+7oVo+lU6dOqU+fPkpOTla7du20evVqJSUlKSEhQXFxcTp+/Li+++67W3rv2rVra9euXfrhhx/Uv39/1axZUxcvXtTy5cv17LPPqnr16vrzzz9v6b0BWI+yBsCj5fTSxpxsX7x4cUlX78m5fPlylttl9ZDl9GJws8ui0tfn5Rk0T1SyZEnHfXdnzpzJcOY0/SxPdi9vzK5SpUrd8vumj73V8c7gZzejxYsXKzExUUWLFtVPP/2k1q1bX/e8vn+eCc0pHx8f3X///friiy/0559/6uTJk/r8888VEhKiw4cPq3fv3s7+FQBYhLIGwGOdPHlSCxYskCR9//33OnfuXJZ/Nm3aJEnasGGD476jm6lXr56kq5dpZTU5iWmaWrt2babrGjRoIOnqB9o9e/Zkuk1qaqpjsoqGDRtmK1dO/PN+I9OJBzq7i39eAuvr6yvp6j2JjRo1kiT99NNPubq/9Huktm7dqtjY2ByNbdCggePy25zmSv++3+r3PP1nd9WqVZlOyCJJu3btcvyiwhU/u3Zy+PBhSVK1atWyvCRx+fLluba/YsWK6emnn9aYMWMkSdu3b2cCEiCfoqwB8FgzZszQlStXFBwcrM6dOysgICDLPw0bNlT16tUlZf/sWp06dRyTbbz33nuZfvCdOXNmlmc9OnTooGLFiknKejbIL774wnFfU/qMgLkpKCjI8f/Pnj2b6+9vF7/++utN7yM7f/685s6dK+nqZCTpM/FJUv/+/SVdPYOyePHiG75PTibEeOihh1SkSBFduXJFAwcOvGF5SktLy/A98vf31yOPPCLp6s9femHIjvTv+61+z9P3e/ToUU2cODHTbV5//XVJV2fivPPOO29pP/lF+gyhe/bsyfTew8jIyCxnc72Rf97/lpl/nr3LaqIXAPbGkQvAY6WXrq5du95wApB0Dz30kCRp+vTpSklJuen2hmHozTfflHT13rjevXtnmDBi0qRJevrpp1W0aNFMxxcqVMhR0mbPnq1nnnlGcXFxkq5OJPHxxx87prnv3r276tevf9NMOVWkSBHHJBFTpkzJ1t87P/roo48UHh6uAQMGaPny5UpMTHSsS0xM1LfffqtmzZo5ivWgQYMyjO/Ro4fuvPNOmaapbt266e2333Z8r6Wr0+ivWrVKzz33nCpWrJjtXEWKFNH//d//SZLmzJmje+65Rxs3bnScrUpLS1NUVJTGjRun22+/XQsXLsww/p133lFoaKhOnTql5s2b69tvv9XFixclXT1r9tdff2nw4MGaMWNGhnE1a9aUdLV8ZnWZ7o00atRIDzzwgCRpwIAB+vTTTx1l+Pjx43rqqacc92i99dZb8vPzy/E+8pOOHTvKy8tLp0+f1uOPP+74ml6+fFnffvutOnbseMPJVLIyZ84cNW/eXF988YUOHDjgWJ6amqqff/5ZQ4cOlSQ1bdo0y//OALA3yhoAj/T7779r586dkv5Xwm4mfbu4uDgtWrQoW2Mee+wxR6GaMWOGypUrp5CQEAUFBenJJ59U06ZN9cwzz0hSph9Yn3/+eb388suSrp5FK126tEJCQhQcHKwXX3xRV65cUdu2bfXVV19lK8+tSM/3ySefKCAgQOHh4Spfvrzj7Ik7KFiwoE6dOqVPP/1UHTp0UHBwsIKCghQYGKjg4GB1795df/75p+OZdc8991yG8d7e3vrhhx9077336vLlyxoxYoTKli2r4OBgFS1aVIGBgWrXrp0+++yzHM32J12djXTChAny8fHRkiVL1KRJE/n7+ys0NFR+fn667bbb9Morr2jXrl3XTSpSrlw5/fzzzypbtqwOHz6s7t27KzAwUKGhofL399cdd9yhDz744LpL5Hr37i0/Pz/t27dP4eHhKlWqlMqXL6/y5ctne2r5SZMmqXXr1rp8+bIGDBig4OBghYSEqEyZMo6zba+88orj58udValSRYMHD5YkzZ07V+XKlVORIkUUEBCg7t27KyAgQB9//HGO39c0TW3YsEHPPPOMKlWqJD8/P4WGhsrHx0edOnXSkSNHVKZMGU2ePDm3/0oA8ghlDYBHSj+rFhwcrI4dO2ZrzB133OGY0j0nE418+OGHmjt3rtq0aaPAwEAlJyerRo0aev/99/Xzzz87Prz/87K6fxo/frxWrlypBx54QCVLltT58+cVGBiotm3bavLkyVq2bNkt/VY+u4YPH66PPvpIDRo0UMGCBXXkyBEdPHjQqQkR7GbGjBlauXKlhg0bpvbt26tcuXK6fPmyLl26pKJFi6phw4Z6+eWXtX37dr333nuZvkdQUJB++uknLV68WN27d1d4eLiSk5OVlJSksmXLqmPHjho9enS273n8p2eeeUa7d+/WK6+8otq1a8vX11dnz55VQECAGjRooAEDBmjZsmWZXgpbr149RUVF6b333lOTJk0UGBioc+fOqXjx4mrTpo3Gjx9/3UPGq1SpolWrVqlLly4qXry4Tp06pYMHD+rgwYPZPrsaHBysFStWaNKkSY6f/fPnz6tUqVJ64IEHtGrVKr3//vs5/lrkV++9956mT5+uRo0aqVChQrpy5YoqV66s4cOHa/v27RmeQ5hdXbp00fTp09W3b1/Vrl1bwcHBSkhIUGBgoBo1aqS33npLf//9t+MSbgD5j2FyxzgAWKp58+basGGDRo0apREjRlgdBwAA2ARn1gDAQmvWrHHMFJn+PDcAAACJsgYALvfcc89p6tSpOn78uGM2v7Nnz+qLL75wPLerXbt2bj99OQAAyBkugwQAF6tTp4527Ngh6eqzufz9/XX27FlHcbvtttv0yy+/OGZdBAAAkChrAOByCxYs0Pz587Vx40bFxcUpISFBQUFBuv3223X//ferf//+WT4oFwAAeC7KGgAAAADYEPesAQAAAIANFbA6gCdIS0vTsWPHFBgYeN0DSwEAAAB4DtM0de7cOZUpU0ZeXjc+d0ZZywPHjh1TWFiY1TEAAAAA2MThw4dVrly5G25DWcsDgYGBkq5+Q4KCgixOAwAAAMAqiYmJCgsLc3SEG6Gs5YH0Sx+DgoIoawAAAACydXsUE4wAAAAAgA1R1gAAAADAhihrAAAAAGBDlDUAAAAAsCHKGgAAAADYEGUNAAAAAGyIsgYAAAAANkRZAwAAAAAboqwBAAAAgA1R1m7g4sWLev3111W1alX5+fmpTJkyeuKJJ3T06FGrowEAAABwc5S1LFy6dEnt2rXTW2+9pfPnz6tr164KCwvTlClTVLduXR04cMDqiAAAAADcGGUtC2+//bZ+//13NW3aVHv27NE333yjjRs3aty4cTp58qSeeOIJqyMCAAAAcGOGaZqm1SHs5vLlyypRooQSEhK0bds21a1bN8P62rVr648//tCWLVtUv379m75fYmKigoODlZCQoKCgIFfFBgAAAGBzOekGnFnLxPr165WQkKBKlSpdV9Qk6cEHH5Qk/fTTT3kdDQAAAICHoKxlYseOHZKkevXqZbo+ffkff/yRZ5kAAAAAeBbKWiYOHTokSSpXrlym69OXHzx4MM8yAQAAAPAsBawOYEfnz5+XJPn7+2e6vnDhwpKkc+fOZbo+OTlZycnJjteJiYm5nNAahw4dUnx8vNUxPFpoaKjCw8OtjuHROA6sxTFgPY4B63EcWI/jwHqechxQ1lxg9OjRevPNN62OkasOHTqkGtWrKeniJaujeDT/Qn6K2rXbI/7jZEeHDh1SterVdeniRaujeCy/QoW0e9cujgGLHDp0SNVq1NClpCSro3g0P39/7Y6K4jiwCMeBPXjKcUBZy0RAQIAkKSmLg/DChQuSpMDAwEzXDxs2TAMHDnS8TkxMVFhYWC6nzFvx8fFKunhJM5+VapSxOo1nijom9fjskuLj493+P0x2FR8ff7Wo3X+/FBpqdRzPEx+vS3PncgxYKD4+XpeSklTj/96Rf+WKVsfxSEn7Dijqpf9wHFiI48B6nnQcUNYykf5NP3LkSKbr05dHRERkut7X11e+vr6uCWexGmWkehWsTgFYLDRUKsNvLeC5/CtXVGDNGlbHACzFcYC8wAQjmahdu7Ykadu2bZmuT19eq1atPMsEAAAAwLNQ1jLRvHlzBQcHa//+/YqMjLxu/ffffy9J6ty5cx4nAwAAAOApKGuZ8PHx0fPPPy9Jeu655xz3qEnS+PHj9ccff6h169aqX7++VREBAAAAuDnuWcvCa6+9puXLl2vDhg2qUqWKWrZsqYMHD2rjxo0qXry4Jk+ebHVEAAAAAG6MM2tZ8PPz06pVqzRixAj5+/tr/vz5OnjwoPr06aNt27apYkVm/wEAAADgOpxZu4FChQpp1KhRGjVqlNVRAAAAAHgYzqwBAAAAgA1R1gAAAADAhihrAAAAAGBDlDUAAAAAsCHKGgAAAADYEGUNAAAAAGyIsgYAAAAANkRZAwAAAAAboqwBAAAAgA1R1gAAAADAhihrAAAAAGBDlDUAAAAAsCHKGgAAAADYEGUNAAAAAGyIsgYAAAAANlTA6gDIX6KOWZ3Ac/G1BwAA8CyUNWRLaGio/Av5qcdnl6yO4tH8C/kpNDTU6hgAAADIA5Q1ZEt4eLiidu1WfHy81VE8WmhoqMLDw62OAQAAgDxAWUO2hYeHUxQAAACAPMIEIwAAAABgQ5Q1AAAAALAhyhoAAAAA2BBlDQAAAABsiLIGAAAAADZEWQMAAAAAG6KsAQAAAIANUdYAAAAAwIYoawAAAABgQ5Q1AAAAALAhyhoAAAAA2BBlDQAAAABsiLIGAAAAADZEWQMAAAAAG6KsAQAAAIANUdYAAAAAwIYoawAAAABgQ5Q1AAAAALAhyhoAAAAA2BBlDQAAAABsiLIGAAAAADZEWQMAAAAAG6KsAQAAAIANUdYAAAAAwIYoawAAAABgQ5S1a+zatUtjxoxR27ZtFRoaqoIFC6pUqVK6//779euvv1odDwAAAICHKGB1ALu58847dfToUQUEBKhJkyYKCQnRzp07NW/ePM2fP1/jx4/XSy+9ZHVMAAAAAG6OM2vXqF69uqZPn66TJ09q2bJl+uabb/Tnn3/q888/l2maeuWVV7Rz506rYwIAAABwc5S1ayxfvlw9e/aUn59fhuVPP/20OnbsqNTUVH333XcWpQMAAADgKShrOVC7dm1J0rFjxyxOAgAAAMDdUdZy4MCBA5KkUqVKWZwEAAAAgLujrGXT/v37tXDhQklSly5dLE4DAAAAwN0xG2Q2pKSkqE+fPkpOTlb37t1Vv379G26fnJys5ORkx+vExERXRwQAAADgZtyurHXr1k1RUVE5GjN9+nQ1atQoy/UvvPCC1q1bp4oVK+qzzz676fuNHj1ab775Zo4yAAAAAMA/uV1Zi46O1u7du3M0JikpKct177zzjiZMmKCSJUvq559/VkhIyE3fb9iwYRo4cKDjdWJiosLCwnKUCQAAAIBnc7uyFhkZmWvv9fnnn+u1115TcHCwli5dqsqVK2drnK+vr3x9fXMtBwAAAADPwwQjWZgzZ46ee+45+fv7a9GiRapTp47VkQAAAAB4EMpaJhYvXqxevXqpQIECmjdvnpo3b251JAAAAAAexu0ug3TW+vXr9eCDD8o0TX377bfq2LGj1ZEAAAAAeCDK2jXuvfdeXbx4URUqVND8+fM1f/7867Zp0aKFnnzyybwPBwAAAMBjUNaucfbsWUlXZ5WMjo7OcjvKGgAAAABXoqxdwzRNqyMAAAAAABOMAAAAAIAdUdYAAAAAwIYoawAAAABgQ5Q1AAAAALAhyhoAAAAA2BBlDQAAAABsiLIGAAAAADZEWQMAAAAAG6KsAQAAAIANUdYAAAAAwIYoawAAAABgQ5Q1AAAAALAhyhoAAAAA2BBlDQAAAABsiLIGAAAAADZEWQMAAAAAG6KsAQAAAIANUdYAAAAAwIYoawAAAABgQ5Q1AAAAALChAlYHAIB8Jz7e6gSeia87AMDDUNYAIJtCQ0PlV6iQLs2da3UUj+VXqJBCQ0OtjgEAQJ6grAFANoWHh2v3rl2K5wyPZUJDQxUeHm51DAAA8gRlDQByIDw8nLIAAADyBBOMAAAAAIANUdYAAAAAwIYoawAAAABgQ5Q1AAAAALAhyhoAAAAA2BBlDQAAAABsiLIGAAAAADZEWQMAAAAAG6KsAQAAAIANUdYAAAAAwIYoawAAAABgQ5Q1AAAAALAhyhoAAAAA2BBlDQAAAABsiLIGAAAAADZEWQMAAAAAG6KsAQAAAIANUdYAAAAAwIYoawAAAABgQ5Q1AAAAALAhyhoAAAAA2BBlLRveeustGYYhwzA0c+ZMq+MAAAAA8ACUtZvYvXu33nnnHRmGYXUUAAAAAB6EsnYDpmmqf//+KlKkiLp06WJ1HAAAAAAehLJ2AxMnTtTatWs1btw4FSlSxOo4AAAAADwIZS0Lx48f16uvvqr27dvr8ccftzoOAAAAAA9DWcvCCy+8oIsXL2rChAlWRwEAAADggQpYHcCOFi5cqO+++05vvvmmqlSpkuPxycnJSk5OdrxOTEzMzXgAAAAAPABn1q5x/vx5Pfvss6pataqGDBlyS+8xevRoBQcHO/6EhYXlckoAAAAA7s7tzqx169ZNUVFRORozffp0NWrUSJI0fPhwHT58WCtWrJCvr+8tZRg2bJgGDhzoeJ2YmEhhAwAAAJAjblfWoqOjtXv37hyNSUpKkiRt2rRJ//3vf9WzZ0+1a9fuljP4+vrectEDAAAAAMkNy1pkZOQtj128eLHS0tL0559/qk2bNhnW7dq1S5L0zjvvaOLEierUqZOGDh3qRFIAAAAAyJrblbXccKPCt2vXLu3atUvly5fPszwAAAAAPA8TjPzDyJEjZZpmpn969+4tSZoxY4ZM09TUqVOtDQsAAADArVHWAAAAAMCGKGsAAAAAYEOUNQAAAACwIcpaNk2dOlWmaapHjx5WRwEAAADgAShrAAAAAGBDlDUAAAAAsCHKGgAAAADYEGUNAAAAAGyIsgYAAAAANkRZAwAAAAAboqwBAAAAgA1R1gAAAADAhihrAAAAAGBDlDUAAAAAsCHKGgAAAADYEGUNAAAAAGyIsgYAAAAANkRZAwAAAAAboqwBAAAAgA1R1gAAAADAhihrAAAAAGBDlDUAAAAAsCHKGgAAAADYEGUNAAAAAGyIsgYAAAAANkRZAwAAAAAboqwBAAAAgA1R1gAAAADAhihrAAAAAGBDlDUAAAAAsCHKGgAAAADYEGUNAAAAAGyIsgYAAAAANkRZAwAAAAAboqwBAAAAgA1R1gAAAADAhihrAAAAAGBDlDUAAAAAsCHKGgAAAADYEGUNAAAAAGyIsgYAAAAANkRZAwAAAAAboqwBAAAAgA1R1gAAAADAhihrAAAAAGBDlDUAAAAAsCHKGgAAAADYEGUNAAAAAGyIsnYD8+fPV6dOnVS8eHH5+fkpLCxM3bp107p166yOBgAAAMDNFbA6gB2lpaXpqaee0uTJk1W4cGG1aNFCRYoU0aFDh7R48WLVr19fLVq0sDomAAAAADdGWcvEqFGjNHnyZHXu3FlTp05VSEiIY92ZM2cUHx9vYToAAAAAnoCydo0jR45o9OjRCg8P1zfffKNChQplWF+0aFEVLVrUonQAAAAAPAX3rF1j2rRpunz5sp588snrihoAAAAA5BXOrF1j5cqVkqRmzZopNjZWs2bN0r59+xQcHKy2bdvqrrvukmEYFqcEAAAA4O4oa9fYuXOn438feOABJSQkONaNHTtWbdq00bx581SkSJEs3yM5OVnJycmO14mJiS7LCwAAAMA9cRnkNc6cOSNJGjhwoGrVqqVt27YpMTFRy5cvV4UKFbR69Wo99dRTN3yP0aNHKzg42PEnLCwsL6IDAAAAcCNud2atW7duioqKytGY6dOnq1GjRpKuTtsvXZ1IZMmSJSpcuLAkqX379lqwYIFq1aql77//Xnv27FHVqlUzfb9hw4Zp4MCBjteJiYkUNgAAAAA54nZlLTo6Wrt3787RmKSkJMf/DwgI0JkzZ/TQQw85ilq6mjVrqmHDhtq0aZPWrl2bZVnz9fWVr69vzsMDAAAAwP/ndmUtMjLSqfERERE6c+aMypcvn+n68uXLa9OmTTpx4oRT+wEAAACAG3HZPWtLly7VBx98oG+++UZXrlxx1W5yXd26dSX97961a50+fVrS1TNwAAAAAOAqTpW1zz77TBUrVtT69eszLH/44Yd1zz33aMiQIXrsscfUsmVLXbp0yamgeaVLly6SpDVr1ly37vz589q2bZuk/5U6AAAAAHAFp8ravHnzlJSUpKZNmzqWLV26VN9//73Kli2roUOHqlGjRtq8ebO++uorp8Pmhc6dO6tGjRrasGGDPvvsM8fy1NRUDRw4UKdPn1bNmjXVokULC1MCAAAAcHdO3bO2e/du1axZU15e/+t8c+bMkWEY+v7779WoUSNdunRJERERmjlzpgYMGOB0YFfz9vbW119/rdatW+u5557Tl19+qcqVK2v79u06cOCAihUrpq+//poHYwMAAABwKafOrJ08eVKlSpXKsGzNmjUKCwtzTIXv5+enZs2aKTo62pld5ak6deooMjJSvXr1UlxcnBYsWKDLly/rySef1NatW3XHHXdYHREAAACAm3PqzFpwcLDi4+Mdr6Ojo3Xw4EH16tUrw3aFCxfWhQsXnNlVnqtQoYKmTZtmdQwAAAAAHsqpM2uVK1fW2rVrdejQIUnSl19+KcMw1KlTpwzbHTly5LozcAAAAACArDlV1v7973/r0qVLqlWrlurXr6+xY8eqePHiuvfeex3bXLx4UVu2bNFtt93mdFgAAAAA8BROlbXHH39cgwYNUnJysrZv366yZctq9uzZGZ5B9u233yopKUnt27d3OiwAAAAAeAqn7lmTpPfff19vv/22EhMTVbx48evWt2vXTtu3b1elSpWc3RUAAAAAeAyny5ok+fr6ZlrUJCksLExhYWG5sRsAAAAA8Bi5UtaulZKSookTJ+qvv/5SeHi4nnrqKRUtWtQVuwIAAAAAt+TUPWujRo2St7e31q5d61iWlpamNm3a6LnnntNnn32mYcOGqWHDhjp79qyzWQEAAADAYzhV1pYtW6Zy5cqpVatWjmXff/+9NmzYoDvuuENffPGFunbtqgMHDui///2v02EBAAAAwFM4VdYOHDigGjVqZFg2d+5cGYah2bNn66mnntIPP/ygsLAwff/9904FBQAAAABP4lRZO3XqlEJDQzMsW7NmjapUqeIocYZhqGHDho4HZwMAAAAAbs6pshYaGqqjR486Xu/cuVNxcXFq06ZNhu18fHx0+fJlZ3YFAAAAAB7FqbJWo0YNrV+/Xtu3b5ckjR8/XoZh6O67786wXUxMjEqXLu3MrgAAAADAozhV1l5++WWlpKSoYcOGCg0N1ZQpU1ShQgV16tTJsU1CQoK2bt2q2rVrOx0WAAAAADyFU89Zu/vuu/XJJ59o7Nixio+PV/PmzfXf//5XPj4+jm2mT5+uK1euqH379k6Hze+6d++uggULWh0DAIBbkpCQIEnaN+p9FQgKtDiNZ0pJPCfp6i/Mg4ODLU7jmTgOrJffj4MrV65ke1vDNE3ThVl08eJFXb58WQEBAfL29nblrmwrMTFRwcHBSkhIUFBQkNVxAAC4Jdu2bVP9+vVVf+FsBdascfMByHXn/orS1nsf1datW1WvXj2r43gkjgPr5ffjICfdwKkza9lRqFAhFSpUyNW7AQAAAAC3kmtl7bffftOvv/7qmB2ybNmyatmypZo2bZpbuwAAAAAAj+F0WduzZ4969uypLVu2SJLSr6o0DEOS1KBBA82cOVNVqlRxdlcAAAAA4DGcKmuxsbFq3bq14uLiVKZMGT300EMqX768DMNQTEyMvvvuO23evFlt2rTRli1bmL4fAAAAALLJqbL29ttvKy4uTi+//LJGjx6dYRZISRozZoyGDRum8ePH691339Unn3ziVFgAAAAA8BROPWdt8eLFqlatmsaNG3ddUZOkggUL6v3331e1atW0cOFCZ3YFAAAAAB7FqbIWGxt70+kyDcNQvXr1FBsb68yuAAAAAMCjOFXWgoKCdPjw4Ztud/jwYZ4vBgAAAAA54FRZa9q0qdavX69FixZluc3ixYu1fv16NWvWzJldAQAAAIBHcWqCkaFDh2rx4sXq1q2bunfvrscee0zly5eXJB08eFCzZ8/WnDlz5OXlpaFDh+ZGXgAAAADwCE6VtaZNm2rKlCl6+umnNWvWLH399dcZ1pumqUKFCumLL75QkyZNnAoKAAAAAJ7E6Ydi9+jRQ23atNFXX32ldevW6dixY5KkMmXKqGXLlurXr5/CwsKcDgoAAAAAnsTpsiZJ5cqV05tvvpkbbwUAAAAAkJMTjAAAAAAAXIOyBgAAAAA2lKPLIL29vW95R4ZhKCUl5ZbHAwAAAIAnyVFZCwsLk2EYrsoCAAAAAPj/clTWYmJiXBQDAAAAAPBPlt2ztmfPHq1du9aq3QMAAACArVlW1kaPHq22bdtatXsAAAAAsDVmgwQAAAAAG6KsAQAAAIANUdYAAAAAwIYoawAAAABgQ5Q1AAAAALChHD1nDQAAIGnfAasjeCy+9oBnoawBAIBsCQ0NlZ+/v6Je+o/VUTyan7+/QkNDrY4BIA9Q1gAAQLaEh4drd1SU4uPjrY7i0UJDQxUeHm51DAB5gLIGAACyLTw8nKIAAHnEsglGWrRooV69elm1+xtKTk7WmDFjVK9ePQUEBMjX11cVKlTQU089pQMHuFYcAAAAgOtZVtb69eunKVOmWLX7LF26dElt2rTR0KFDFR0drdatW+vee++VJE2cOFF16tTR1q1bLU4JAAAAwN05dRlku3btsrWdj4+PihUrpjp16uiRRx5RWFiYM7t1qS+//FK///67GjZsqGXLlik4OFiSlJqaqpdeekmffvqpBg4cqDVr1licFAAAAIA7c6qsrV69WpJkGIZM08x0m3+umz17tl577TWNGTNGL730kjO7dpm1a9dKkgYOHOgoapLk7e2tUaNG6dNPP9XmzZutigcAAADAQzh1GWR0dLRefPFFFShQQI8//rgWLFigyMhIRUZG6qefflKPHj1UoEABDRgwQOvWrdO7774rPz8/DRo0SL/88ktu/R1yla+v7023KVasWB4kAQAAAODJnCprv//+uz755BMtXLhQM2bM0L333qtatWqpVq1auueeezR9+nQtXLhQn332mQ4ePKihQ4dq/vz5Mk1Tn3zySW79HXJVx44dJUnjx49XQkKCY3lqaqpef/11SVfvtwMAAAAAV3LqMsgPPvhALVu2VIcOHbLcpkOHDmrRooXGjRunRx99VG3btlXt2rW1adMmZ3btMj169NDSpUs1Z84clS9fXs2bN5efn5+2bt2quLg4DR48WCNGjLA6JgAAAAA351RZi4qKUteuXW+6XZkyZfTjjz86XlepUkU7d+50Ztcu4+3trZkzZyo8PFxjx47VokWLHOvq1aun9u3by9vb+4bvkZycrOTkZMfrxMREl+UFAAAA4J6cKmv+/v7asmWLTNOUYRiZbmOaprZs2SJ/f3/HskuXLikoKMiZXWepW7duioqKytGY6dOnq1GjRpKkM2fOqFu3btq8ebM++ugjPfDAA/L399fatWs1YMAA3X333fr666/VvXv3LN9v9OjRevPNN536ewAAAADwbE6VtTvvvFPffPONBgwYoLFjx2YoZJJ08eJFDRkyRPv27dOjjz7qWL53716XTd8fHR2t3bt352hMUlKS4/+//PLLWrNmjT788EO98MILjuVdu3ZV2bJl1ahRIw0aNEj333+/ChYsmOn7DRs2TAMHDnS8TkxMtPXjCgAAAADYj1NlbfTo0Vq+fLkmTJig2bNnq1OnTo5ScvjwYf388886c+aMihcvrnfeeUfS1Usnd+/ercGDBzufPhORkZG3PDY1NVWzZ8+WJD344IPXrW/QoIEqVKigAwcO6MCBA6pWrVqm7+Pr65utWSUBAAAAICtOlbWIiAj99ttvevrpp7Vy5UpH0fmn9u3ba8KECYqIiJAkVaxYUbGxsRmeYWYXJ06c0OXLlyUpy3zpy8+cOZNnuQAAAAB4HqfKmiRVqlRJy5cv1/79+7V+/XrFxsZKkkqXLq1mzZqpcuXKGbb39fVVyZIlnd2tS4SEhMjHx0eXL1/Wli1b1LZt2wzrExMTHZdYppdPAAAAAHAFp8taukqVKqlSpUq59XaW8PX1VadOnbRgwQINHDhQixcvVunSpSVdnRTl2WefVVJSkpo3b+5YDgAAAACukGtlTbp6GeHRo0clSWXLllWJEiVy8+3zxPjx47Vx40ZFRkaqWrVqatq0qQoVKqTNmzfr2LFjCgkJ0eeff251TAAAAABuzis33uSzzz5TtWrVVLp0aTVo0EANGjRQ6dKlVb16dU2YMCE3dpFnKlWqpB07dujll19W2bJltXbtWi1dulT+/v567rnntGPHDtWsWdPqmAAAAADcnFNn1tLS0vTwww9r3rx5Mk1TRYoUUUREhAzD0MGDB7Vnzx49//zzWrFihb777rssn8VmNyVLltT48eM1fvx4q6MAAAAA8FBOnVn78ssvNXfuXFWtWlULFizQ6dOntX37dm3btk2nTp3STz/9pGrVqmnevHn68ssvcyszAAAAALg9p8ralClTFBQUpNWrV+vee++9bv0999yjlStXKiAgQJMnT3ZmVwAAAADgUZwqazt37lS7du1uOBV/qVKl1L59e+3cudOZXQEAAACAR3F6gpHs3IeWX+5VAwAAAAC7cKqsVatWTStXrlR8fHyW28THx2vlypWqVq2aM7sCAAAAAI/iVFnr3bu3EhIS1L59e61YseK69atWrVKHDh2UmJioPn36OLMrAAAAAPAoTk3d/+yzz2rp0qVasmSJOnbsqOLFiysiIkKSdPDgQZ08eVKmaeruu+/Ws88+myuBAQAAAMATOHVmzdvbWz/99JPef/99lStXTidOnNDmzZu1efNmnThxQmFhYXr//fe1YMECeXnlyvO3AQAAAMAjOHVmTZK8vLw0aNAgDRo0SIcPH9axY8ckSWXKlFFYWJjTAQEAAADAEzld1v4pLCyMggYAAAAAuYBrEwEAAADAhnJ0Zu2JJ5645R0ZhqFJkybd8ngAAAAA8CQ5KmtTp0695R1R1gAAAAAg+3JU1latWuWqHAAAAACAf8hRWWvdurWrcgAAAAAA/sGyCUYGDx6sSpUqWbV7AAAAALA1y8pafHy8YmJirNo9AAAAANgaU/cDAAAAgA1R1gAAAADAhihrAAAAAGBDlDUAAAAAsCHKGgAAAADYEGUNAAAAAGyIsgYAAAAANkRZAwAAAAAbsqysmaYp0zSt2j0AAAAA2JplZW3q1KlKS0uzavcAAAAAYGsFnBk8ffr0bG3n4+OjYsWKqXbt2ipRooQzuwQAAAAAj+BUWevTp48Mw8j29oZh6M4779Qnn3yiKlWqOLNrAAAAAHBrTpW1119/XTExMZo+fboCAgLUsWNHhYeHS5IOHz6sX375RefOnVPPnj3l6+urDRs26JdfflHLli21detWlS1bNlf+EgAAAADgbpwqaz179lSjRo30xBNPaNy4cQoODs6wPjExUQMHDtS8efO0ceNGVaxYUYMHD9aHH36o9957T5988olT4QEAAADAXTk1wciwYcNUtGhRffnll9cVNUkKCgrSl19+qaJFi2r48OHy8vLS6NGjVbp0aS1dutSZXQMAAACAW3OqrK1atUqNGzeWl1fWb+Pl5aVGjRpp5cqVkq5ONlK7dm0dPXrUmV0DAAAAgFtzqqwlJSXp+PHjN90uLi5Oly5dcrwOCgpSgQJOXYEJAAAAAG7NqbJ2xx13aO3atVq7dm2W2/z6669as2aN7rjjDseyw4cPq3jx4s7sGgAAAADcmlNl7dVXX1VqaqruuusuPf3001q2bJl27dqlXbt2admyZXrmmWd01113yTRNvfrqq5KkhIQEbd26VU2aNMmVvwAAAAAAuCOnrkW8//779eGHH2rIkCH66quvNHHixAzrTdOUj4+PPvzwQ3Xr1k2SdOrUKb355ptq3769M7sGAAAAALfm9I1jL774orp06aJJkyZpw4YNio2NlSSVLl1azZs3V9++fVWxYkXH9hUrVtSQIUOc3S0AAAAAuLVcmeWjQoUKevvtt3PjrQAAAAAAcvKeNQAAAACAa+RKWdu5c6defvllNW/eXNWqVXNMJiJJGzZs0Mcff6zTp0/nxq4AAAAAwCM4fRnk+PHjNXToUKWkpEiSDMNQfHx8hm1efvll+fr66umnn3Z2dwAAAADgEZw6s7Zo0SK98sorCgsL09y5c3XixAmZpplhm2bNmql48eL68ccfnQoKAAAAAJ7EqTNr48ePV+HChbVs2bIMMz5eq06dOtq9e7czuwIAAAAAj+LUmbX0h1vfqKhJUmhoqI4fP+7MrgAAAADAozhV1i5fvqzAwMCbbnfixAkVKJArTwnIkQsXLmjGjBkaMGCAGjduLF9fXxmGoZEjR9507JEjR9S3b1+VKVNGfn5+qlq1qt544w1dunTJ9cEBAAAAeDynGlSFChW0Y8eOG25z+fJl/fHHH6pataozu7ole/fuVa9evXI8bt++fWratKni4+NVs2ZNtWzZUlu2bNGoUaO0YsUKrVixQr6+vi5IDAAAAABXOXVmrUuXLoqJidH48eOz3Gbs2LE6efKk7r//fmd2dUsCAwPVr18/ff7559q6datGjRqVrXF9+vRRfHy8XnjhBf3555/65ptvtHv3bnXr1k3r16/X6NGjXZwcAAAAgKdzqqy9+uqrKlu2rAYPHqzu3btrzpw5kqS4uDjNmzdPvXr10htvvKEKFSro+eefz5XAOVGpUiVNnDhRTz/9tOrVq6eCBQvedMymTZu0fv16lShRQmPHjnUsL1CggCZMmKCCBQvq448/djyqAAAAAABcwamyVrRoUS1fvly33367vvvuOz3++OOSpKVLl+rBBx/UzJkzVaNGDS1dujRb97bZwaJFiyRJnTt3vu5Sx5IlS6ply5Y6c+aM1q1bZ0U8AAAAAB7C6Vk/qlatqsjISC1YsEDLli1TTEyM0tLSVK5cOXXo0EEPPPCAvL29cyNrnki/B69evXqZrq9Xr55WrlypP/74Q23atMnDZAAAAAA8Sa5M0ejl5aX77rtP9913X268naUOHTokSSpXrlym69OXHzx4MMv3SE5OVnJysuN1YmJiLiYEAAAA4AlypawlJSVpy5Ytio2NzVBSrnUrMzPmtfPnz0uS/P39M11fuHBhSdK5c+eyfI/Ro0frzTffzP1wAAAAADyG02Xt9ddf14cffqikpKQstzFNU4Zh5LisdevWTVFRUTkaM336dDVq1ChHY3LbsGHDNHDgQMfrxMREhYWFWZgIAAAAQH7jVFkbO3as3n77bXl7e+uee+5R1apVc3UikejoaO3evTtHY25UGrMjICDghu9z4cIFSbrh39PX15fnsAEAAABwilNl7auvvlKhQoX066+/ZjkhhzMiIyNz/T1vJjw8XNu3b9eRI0cyXZ++PCIiIi9jAQAAAPAwTk3df/jwYbVu3dolRc0qtWvXliRt27Yt0/Xpy2vVqpVnmQAAAAB4HqfKWqlSpRwTbriLe+65R5L0008/XTdZSlxcnH799VcVLVpUzZs3tyIeAAAAAA/hVFl75JFHtHr1asd9XO6gUaNGat68uU6cOKEhQ4Y4lqekpOjZZ5/VlStX9MILL6hgwYIWpgQAAADg7py6Z23kyJHasGGDunTpoi+++EKVK1fOrVy5plu3boqNjZUkHTt2TJI0ceJELV26VJJUunRpzZs3L8OYKVOmqGnTpvroo4+0cuVK3Xbbbdq8ebMOHDigZs2aadiwYXn7lwAAAADgcZwqa3fffbfS0tK0evVq1ahRQxERESpXrpy8vK4/YWcYhlasWOHM7m7J9u3br3uA9dGjR3X06FFJmU8UUqVKFW3fvl2vv/66li5dqnnz5ik8PFwjRozQ8OHDmekRAAAAgMs5VdZWr17t+P+pqak6cOCADhw4kOm2hmE4s6tbFhMTc0vjwsLCNGXKlNwNAwAAAADZ5FRZi46Ozq0cAAAAAIB/cKqs8awxAAAAAHANp2aDBAAAAAC4BmUNAAAAAGyIsgYAAAAANkRZAwAAAAAboqwBAAAAgA1R1gAAAADAhihrAAAAAGBDlDUAAAAAsCHKGgAAAADYEGUNAAAAAGyIsgYAAAAANkRZAwAAAAAboqwBAAAAgA1R1gAAAADAhihrAAAAAGBDlDUAAAAAsCHKGgAAAADYEGUNAAAAAGyIsgYAAAAANkRZAwAAAAAboqwBAAAAgA1R1gAAAADAhihrAAAAAGBDlDUAAAAAsCHKGgAAAADYEGUNAAAAAGyIsgYAAAAANkRZAwAAAAAboqwBAAAAgA1R1gAAAADAhihrAAAAAGBDlDUAAAAAsCHKGgAAAADYEGUNAAAAAGyIsgYAAAAANkRZAwAAAAAboqwBAAAAgA1R1gAAAADAhihrAAAAAGBDlDUAAAAAsCHKGgAAAADYEGUNAAAAAGzIrcvahQsXNGPGDA0YMECNGzeWr6+vDMPQyJEjsxwTFxenSZMmqVu3bipXrpx8fHxUpEgRtW7dWtOmTZNpmnn3FwAAAADgsQpYHcCV9u7dq169euVozKBBgzRr1iwVKFBADRo0UIsWLXT06FGtW7dOa9eu1cKFCzVnzhx5e3u7KDUAAAAAuPmZtcDAQPXr10+ff/65tm7dqlGjRt10TLFixfTOO+/o2LFj+u233zRnzhz9+uuv+v333xUUFKTvv/9ekyZNyoP0AAAAADyZW59Zq1SpkiZOnOh4/csvv9x0zEcffZTp8oYNG2ro0KEaPny4Zs+erf79++daTgAAAAC4llufWctttWvXliQdO3bM4iQAAAAA3B1lLQcOHDggSSpVqpTFSQAAAAC4O7e+DDI3XblyRZ999pkkqWvXrjfcNjk5WcnJyY7XiYmJLs0GAAAAwP1wZi2bRowYoaioKFWoUEHPPPPMDbcdPXq0goODHX/CwsLyKCUAAAAAd2HrM2vdunVTVFRUjsZMnz5djRo1ytUcc+bM0dixY+Xn56evv/5a/v7+N9x+2LBhGjhwoON1YmIihQ0AAABAjti6rEVHR2v37t05GpOUlJSrGVauXKk+ffrIy8tLs2fPVpMmTW46xtfXV76+vrmaAwAAAIBnsXVZi4yMtHT/mzdvVteuXXX58mVNmjRJ9913n6V5AAAAAHgOW5c1K+3cuVP/+te/dP78eX344Yfq27ev1ZEAAAAAeBAmGMlETEyMOnbsqFOnTmnkyJF66aWXrI4EAAAAwMNQ1q5x4sQJdezYUUePHtWgQYP0xhtvWB0JAAAAgAdy+8sgu3XrptjYWEnSsWPHJEkTJ07U0qVLJUmlS5fWvHnzHNs//fTT2rt3r/z9/RUfH68+ffpc956hoaH64IMPXB8eAAAAgMdy+7K2fft2HTx4MMOyo0eP6ujRo5KkiIiIDOvOnDkj6eqsktOmTcv0PSMiIihrAAAAAFzK7ctaTExMjrZfvXq1S3IAAAAAQE5wzxoAAAAA2BBlDQAAAABsiLIGAAAAADZEWQMAAAAAG6KsAQAAAIANUdYAAAAAwIYoawAAAABgQ5Q1AAAAALAhyhoAAAAA2BBlDQAAAABsiLIGAAAAADZEWQMAAAAAG6KsAQAAAIANUdYAAAAAwIYoawAAAABgQ5Q1AAAAALAhyhoAAAAA2BBlDQAAAABsiLIGAAAAADZEWQMAAAAAG6KsAQAAAIANUdYAAAAAwIYoawAAAABgQ5Q1AAAAALAhyhoAAAAA2BBlDQAAAABsiLIGAAAAADZEWQMAAAAAG6KsAQAAAIANUdYAAAAAwIYoawAAAABgQ5Q1AAAAALAhyhoAAAAA2BBlDQAAAABsiLIGAAAAADZEWQMAAAAAG6KsAQAAAIANUdYAAAAAwIYoawAAAABgQ5Q1AAAAALAhyhoAAAAA2BBlDQAAAABsiLIGAAAAADZEWQMAAAAAG3LrsnbhwgXNmDFDAwYMUOPGjeXr6yvDMDRy5Mgcvc/atWvl5eUlwzD05JNPuiYsAAAAAPxDAasDuNLevXvVq1cvp94jOTlZ/fv3z6VEAAAAAJA9bn1mLTAwUP369dPnn3+urVu3atSoUTl+j7ffflt79uxRv379XJAQAAAAADLn1mWtUqVKmjhxop5++mnVq1dPBQsWzNH4v//+W2PHjlW/fv3UvHlzF6UEAAAAgOu5dVlzhmma6t+/v4KDgzVmzBir4wAAAADwMG59z5ozJkyYoA0bNmj69OkKCQmxOg4AAAAAD0NZy8TRo0c1bNgwtW3bVj179szx+OTkZCUnJzteJyYm5mY8AAAAAB6AyyAz8fzzz+vSpUuaMGHCLY0fPXq0goODHX/CwsJyOSEAAAAAd2frM2vdunVTVFRUjsZMnz5djRo1uuV9zp07V/Pnz9frr7+uatWq3dJ7DBs2TAMHDnS8TkxMpLABAAAAyBFbl7Xo6Gjt3r07R2OSkpJueX+JiYkaMGCAqlSpouHDh9/y+/j6+srX1/eWxwMAAACArctaZGRknu5v27ZtOnbsmMqXL6+77rorw7rjx49LkhYtWqQ2bdqoVKlSmjNnTp7mAwAAAOA5bF3WrBITE6OYmJhM1x0/flzHjx9XRERE3oYCAAAA4FGYYOQf2rRpI9M0M/0zZcoUSVK/fv1kmmaWZQ4AAAAAcgNlDQAAAABsyO0vg+zWrZtiY2MlSceOHZMkTZw4UUuXLpUklS5dWvPmzbMsHwAAAABkxu3L2vbt23Xw4MEMy44ePaqjR49KEveeAQAAALAlty9ruXVvWZ8+fdSnT59ceS8AAAAAuBnuWQMAAAAAG6KsAQAAAIANUdYAAAAAwIYoawAAAABgQ5Q1AAAAALAhyhoAAAAA2BBlDQAAAABsiLIGAAAAADZEWQMAAAAAG6KsAQAAAIANUdYAAAAAwIYoawAAAABgQ5Q1AAAAALAhyhoAAAAA2BBlDQAAAABsiLIGAAAAADZEWQMAAAAAG6KsAQAAAIANUdYAAAAAwIYoawAAAABgQ5Q1AAAAALAhyhoAAAAA2BBlDQAAAABsiLIGAAAAADZEWQMAAAAAG6KsAQAAAIANUdYAAAAAwIYoawAAAABgQ5Q1AAAAALAhyhoAAAAA2BBlDQAAAABsiLIGAAAAADZEWQMAAAAAG6KsAQAAAIANUdYAAAAAwIYKWB0AAAAAyG+S9h2wOoLH8qSvPWUNAAAAyKbQ0FD5+fsr6qX/WB3Fo/n5+ys0NNTqGC5HWQMAAACyKTw8XLujohQfH291FI8WGhqq8PBwq2O4HGUNAAAAyIHw8HCPKAqwHhOMAAAAAIANUdYAAAAAwIYoawAAAABgQ5Q1AAAAALAhyhoAAAAA2JBbl7ULFy5oxowZGjBggBo3bixfX18ZhqGRI0dma/yOHTvUs2dPlStXTr6+vipZsqTatGmjKVOmuDY4AAAAAI/n1lP37927V7169bqlsRMnTtS///1vmaapJk2aqGXLloqLi1NkZKRmzZqlvn375nJaAAAAAPgfty5rgYGB6tevnxo2bKiGDRtq0aJFev311286buXKlerfv78qVaqkBQsWqEaNGo51ly9f1t9//+3K2AAAAADg3mWtUqVKmjhxouP1L7/8kq1xAwYMkGEYmjt3boaiJkk+Pj6qW7duruYEAAAAgGu59T1rt2L9+vXauXOn2rRpozvuuMPqOAAAAAA8lFufWbsVK1eulCQ1a9ZMFy9e1Jw5c7R161Z5e3urfv36euihh1SoUCGLUwIAAABwd5S1a+zcuVOSlJaWprp162r37t0Z1o8YMUILFy7krBsAAAAAl+IyyGucOXNGkjR27FhduHBBixcvVkJCgv7880916NBBhw4dUufOnZWUlJTleyQnJysxMTHDHwAAAADICVufWevWrZuioqJyNGb69Olq1KjRLe8zLS1NkpSSkqIffvjB8V41a9bUTz/9pMqVK+vgwYOaNWuWnnrqqUzfY/To0XrzzTdvOQMAAAAA2LqsRUdHX3cZ4s3c6IxXdgQEBEiSbrvttutKn6+vrx577DGNHTtWa9asybKsDRs2TAMHDnS8TkxMVFhYmFO5AAAAAHgWW5e1yMjIPN9nRESEJKl8+fKZrk9ffuLEiSzfw9fXV76+vrkdDQAAAIAH4Z61a6Q/Qy393rVrnT59WtL/zsABAAAAgCtQ1q5x9913q0CBAvrzzz8dxeyf1qxZI0k8GBsAAACAS1HWrhEaGqq+ffvq/PnzeuGFF3T58mXHumnTpmnZsmXy8/NTnz59rAsJAAAAwO0ZpmmaVodwpW7duik2NlaSdOzYMR0+fFhly5ZVuXLlJEmlS5fWvHnzMoxJSEhQy5Yt9eeffyo8PFwNGjTQoUOHtGXLFnl7e2vKlCnq2bNntjMkJiYqODhYCQkJCgoKyr2/HAAAAIB8JSfdwO3LWvny5XXw4MEs10dERCgmJua65RcuXNC7776rb7/9VocOHVJAQICaNWumoUOHqnnz5jnKQFkDAAAAIFHWbIeyBgAAAEDKWTfgnjUAAAAAsCHKGgAAAADYEGUNAAAAAGyIsgYAAAAANkRZAwAAAAAboqwBAAAAgA1R1gAAAADAhihrAAAAAGBDlDUAAAAAsCHKGgAAAADYEGUNAAAAAGyIsgYAAAAANkRZAwAAAAAboqwBAAAAgA1R1gAAAADAhihrAAAAAGBDlDUAAAAAsCHKGgAAAADYEGUNAAAAAGyogNUBPIFpmpKkxMREi5MAAAAAsFJ6J0jvCDdCWcsD586dkySFhYVZnAQAAACAHZw7d07BwcE33MYws1Pp4JS0tDQdO3ZMgYGBMgzD6jgeKTExUWFhYTp8+LCCgoKsjgNYguMA4DgAJI4Dq5mmqXPnzqlMmTLy8rrxXWmcWcsDXl5eKleunNUxICkoKIj/KMHjcRwAHAeAxHFgpZudUUvHBCMAAAAAYEOUNQAAAACwIcoaPIKvr6/eeOMN+fr6Wh0FsAzHAcBxAEgcB/kJE4wAAAAAgA1xZg0AAAAAbIiyBgAAAAA2RFkDAAAAABuirAEAAACADVHWAAAAAMCGKGsAAAAAYEOUNQAAAACwIcoaAAAAANgQZQ0AAAAAbIiyBgAAAAA2RFmDW1mwYIGWLFlidQzAUhwHAAC4B8M0TdPqEEBu8fb2VseOHfmgCo/GcQAAkKSQkBDdcccdWrNmjdVRcIsKWB0AyE3FixdX0aJFrY4BWIrjAJ4sNTVVf//9t1JSUlS5cmUFBQU51u3du1cTJkzQ3r17FRAQoHvuuUc9evSwMC3gWikpKSpXrpzVMeAEyhrcSps2bbRp0yaZpinDMKyOA1iC4wCeas6cOXrhhRd06tQpSVLBggX17LPPavz48Vq6dKm6du2qlJQUpV9U9O233+qHH37QvHnzrIwNuMztt9+uo0ePWh0DTuCeNbiVt956S/Hx8Xr55Zd16dIlq+MAluA4gCf67bff9Pjjjys+Pl7e3t4KCQnR5cuX9dFHH+mzzz5T79695efnp0GDBum///2vBg0apICAAC1YsEDTpk2zOj7gEgMGDNC6deu0bt06q6PgFnHPGtzKqFGjtGvXLn3zzTcqXry47rzzToWHh8vPz++6bQ3D0IgRIyxICbgWxwE80YMPPqi5c+dq6NChevPNN1WwYEFFR0ere/fu2rNnjy5cuKAtW7aodu3ajjHbt29Xw4YN1bp1a61YscLC9IBrHDp0SO+++65mzJihJ598Up07d87y3wNJCg8Pz+OEuBnKGtyKl5eXDMNQdn6sDcNQampqHqQC8hbHATxR2bJlVbhwYe3ZsyfD8nXr1qlVq1Zq1qxZpmcXWrRood27d+vkyZN5FRXIM//89+Bml8UbhqGUlJQ8Sobs4p41uJUpU6ZYHQGwHMcBPNHJkyfVrFmz65bXrVtXkhQREZHpuIiICG3cuNGl2QCrtGrVinuX8znKGtxK7969rY4AWI7jAJ4oJSVFgYGB1y0vXLiwJMnX1zfTcT4+PkpLS3NpNsAqq1evtjoCnMQEIwAAAABgQ5xZg1tKSUnRokWLtGnTJsXHx6tx48Z64oknJEnHjh1TfHy8brvtNhUowCEA98VxAAD4p7179yo+Pl7FihVT1apVrY6DbODMGtzOunXrVLlyZd1///0aPXq0Jk6cmOGm8t9++01169bVggULLEwJuBbHATzRtGnT5O3tfd0fwzCyXDd9+nSrYwMulZycrOHDhys0NFTVq1dXixYt9N577znWz5w5U/Xq1VNkZKR1IZElyhrcys6dO9WpUyfFxsZqwIAB+vbbb6+bEa9z587y9/fXDz/8YFFKwLU4DuCpTNO8pT+Au7p48aLatGmjMWPGyMfHR3ffffd1P/Pt2rXTjh079O2331qUEjfCtS9wK2+99ZYuXbqkxYsXq2PHjplu4+Pjo3r16mn79u15nA7IGxwH8ERMEgJcb+zYsdq4caP69eunTz75RH5+fvLyyniupkyZMrrtttu0fPlyvfvuuxYlRVY4swa3smrVKjVq1CjLD6jpypYtq2PHjuVRKiBvcRwAACTpm2++UXh4uCZMmJDlg7AlqVq1ajp8+HAeJkN2UdbgVs6ePauwsLCbbnfhwgVduXIlDxIBeY/jAAAgSdHR0WrQoMFNJ5Ly8fHRmTNn8igVcoKyBrdSokQJ7du376bbRUVFZevDLJAfcRwAACSpUKFC2Sph0dHRKlq0aB4kQk5R1uBW2rVrp8jISK1atSrLbebNm6d9+/apQ4cOeZgMyDscB/BEmc30mN0/PL4C7qpOnTrasmWLTp48meU20dHR2r59uxo2bJiHyZBdlDW4laFDh8rHx0f33XefJkyYoOPHjzvWnTlzRpMnT1a/fv1UuHBhDRw40MKkgOtwHMATFSxYUD4+Pjn6YxgGM0LCrT311FM6d+6cHn30UcXHx1+3/uzZs3riiSd05coV9e/f34KEuBnD5L9QcDPz589Xz549lZSUlOl6Pz8/zZ49W126dMnjZEDe4TgAshYbG6t3331XEydOVHJysooUKaLTp09bHQtwiccee0xz5sxRQECAmjVrpl9++UVVqlRR9erVtWbNGiUmJqpXr16aOnWq1VGRCcoa3NLBgwf14YcfatmyZYqJiVFaWprKlSunDh06aNCgQapUqZLVEQGX4zgAMjpx4oRGjx6tL7/8UpcuXVJgYKBefPFFDRw4UMHBwVbHA1zCNE198MEHev/99687uxYcHKxXX31VQ4cOlWEYFiXEjVDWAACAW4uPj9eYMWM0YcIEJSUlKTAwUAMGDNCgQYOYVAEeIzU1Vdu2bcvwy7uGDRvKx8fH6mi4AcoaAABwS6dPn9bYsWP13//+VxcuXFDhwoX1/PPPa/DgwQoJCbE6HgDcFGUNAAC4lbNnz+r999/Xp59+qnPnzsnf31/PPfecBg8erNDQUKvjAUC2UdaQr1WsWFGGYWj58uWqUKGCKlasmO2xhmFo//79LkwH5A2OA+CqhIQEjR8/Xh999JESExNVqFAh/fvf/9aQIUNUvHhxq+MBLjdq1CgZhqHnnntOISEhGjVqVLbHGoahESNGuDAdbgVlDfmal9fVp0/s2rVLVatWdbzOrrS0NFfEAvIUxwFw9UPq//3f/ykhIUG+vr565plnNGTIEJUsWdLqaECe8fLykmEYioqKcvx7kP6IipsxDEOpqal5kBI5QVkDAAD5XvqH0kKFCqlPnz4qU6ZMjsYPHz7cRcmAvDN16lQZhqH7779fgYGBmjZtWo7G9+7d20XJcKsoa8jXpk+frsqVK6tZs2ZWRwEsw3EAKMMZ5fQpyNM/4txoSnLTNDmjAMC2ClgdAHBGnz591KdPH8eH1IoVK+qhhx7SmDFjLE4G5B2OA0B64403rI4AWM7b21t9+vTRpEmTJF29PLhOnTrq0qWLxclwqyhryNe8vLyUkpLieB0TE6OTJ09amAjIexwHAGUNkK6eKf7nRXMjR45Unz59KGv5WM7uQgdspkSJEvrzzz+tjgFYiuMAACBJAQEBOnHihNUxkIs4s4Z87c4779TMmTNVqVIlRURESJKWLl2qdu3a3XSsYRhasWKFqyMCLsdxAACQpFq1amn58uV68803VaFCBUnSvn37NH369GyN79Wrlyvj4RYwwQjytfj4eD3xxBNasmSJUlNTsz09rcQUtXAfHAdA5pKTkzV79mytXbtWsbGxSk5OznQ7fmkBd7Fs2TLdf//9unDhguPfghtNsJOOiXbsi7IGt3DlyhXFxsaqfPnyevDBB/X+++9na1z6WQjAHXAcAP9z9OhRtW/fXnv37r3pLy/4kAp3cvjwYS1fvlyHDx/WyJEjVadOHXXt2jVbY7n30364DBJuoWDBggoPD1d4eLjKly/Ph094JI4D4H8GDx6sPXv2qFmzZho4cKCqVq2qwMBAq2MBLhcWFqa+fftKkqOsUcLyL86sAQAAt1OsWDEFBgZq165d8vPzszoOYIk1a9aoVKlSqlatmtVRcIs4swYAANxOcnKy7rzzTooaPFrr1q2tjgAnUdaQrz3xxBMyDEPvvvuuSpYsqSeeeCLbYw3DcDw0EsjPOA6A691xxx2Kj4+3OgaQp9auXStJatSokfz8/Byvs6tVq1auiAUncBkk8jUvLy8ZhqGoqChVrVpVXl7Zf3QgN5TDXXAcANebP3++HnroIa1fv16NGjWyOg6QJzL79yA7s0Gm498D++HMGvK1VatWSZLCw8MzvAY8CccBcL169epp4MCBat++vQYOHKgOHTqoXLlyWf4yI/34AfKzXr16yTAMBQcHZ3iN/IszawAAwO2kn1HIznOmDMNQSkpKHiUDgOzjzBoAAHA7rVq14owCgHyPM2twK0lJSYqPj1exYsVUuHBhx/IzZ85ozJgx+uuvvxQeHq5BgwapUqVKFiYFXIfjAABwM1FRUfr7778VFhamxo0bWx0HWaCswa0MGzZMY8eO1aZNm1S/fn1JV6dvrlWrlvbt26f0H/fQ0FDt2LFDpUuXtjIu4BIcBwAASfrmm2/0xRdfaPTo0RkK2eDBgzV+/HjH6y5duuj777+Xt7e3FTFxA9mfMgzIB1auXKlKlSo5PqBK0syZM7V37161bdtWP//8s1544QXFx8frww8/tDAp4DocBwAA6ep/+yMjI1W3bl3Hsg0bNmjcuHEKDAzUI488ovLly2vBggWaNWuWhUmRFc6swa2ULl1a9erV06JFixzLunbtqoULFyomJkZhYWGSpOrVq6tgwYL6888/rYoKuAzHAfA/SUlJWrVqlfbu3atz584ps489hmFoxIgRFqQDXKtChQqKiIjQ6tWrHcv+/e9/68svv9SSJUvUsWNHnT59WhUqVFDt2rVz/Fw2uB4TjMCtnDlzRkWKFHG8Nk1T69atU61atRwfUCWpdu3a+vnnny1ICLgexwFw1dSpU/Xyyy8rMTHRseza2SHTX1PW4I5OnDihZs2aZVi2atUqlShRQh07dpQkhYSEqFWrVtq6dasVEXETXAYJt1KqVClFR0c7Xm/dulVnzpxR69atM2zHDGFwZxwHgLR8+XL169dPhmFo+PDhatq0qSTpiy++0ODBg1W5cmWZpqnnn39ekydPtjgt4BqFChXK8MuK2NhY7dmz57p/D4oUKaIzZ87kdTxkA2UNbqVOnTratGmT5s+fr3Pnzumtt96SYRi69957M2y3d+9elSlTxqKUgGtxHADSuHHjZBiGVq1apbfeektVqlSRJD311FN677339Pfff+ull17S5MmTM9zfCbiTihUr6tdff9XZs2clSbNmzZJhGI6zaumOHz+uEiVKWJAQN8M9a3ArGzZsUKtWrRz3JJimqTp16mjLli3y8rr6u4m4uDiVLVtWjz76qGbMmGFlXMAlOA6Aq7OdVq9eXevWrZMk9e3bV9OnT1dqaqpjm7S0NFWuXFl169bVDz/8YFVUwGU+++wzPf/886pQoYLq1KmjRYsWydfXVwcOHFCxYsUkSVeuXFGJEiXUoEEDLVu2zOLEuBZn1uBWmjVrpnnz5qlFixaqXr26evTooQULFjg+oErS7NmzFRgYqE6dOlmYFHAdjgNAOn/+vMLDwx2vfX19JUnnzp1zLPPy8lLjxo3166+/5nk+IC889dRTeuCBBxQdHa158+apYMGC+uKLLxxFTZIWLlyohIQEtWvXzsKkyApn1gAAgNspX768qlevrqVLl0qS3nzzTY0aNSrD8wcl6a677tKGDRsylDjA3cTExOjkyZOqXr26AgMDM6yLjIzUwYMH1aRJE5UsWdKihMgKZ9YAAIDbqV69uvbu3et43axZM5mmqbFjxzouEd6wYYNWrlypatWqWRUTyBPly5dXw4YNrytq0tX7nLt27UpRsynKGtxKXFyc1q5dq7i4uAzL9+/fr0ceeUQ1a9bU3Xffrd9++82ihIDrcRwA0j333KPo6Ght2rRJktS+fXvVqlVL33//vcqWLav69eurbdu2SktL00svvWRtWMACS5cu1QcffKBvvvlGV65csToOssBlkHArL7/8sj7++GNFRUWpatWqkqTExERVq1ZNJ06ccPw2tVChQoqMjHTMDga4E44DQEpISNDvv/+u6tWrKyIiQpJ09OhR9evXT8uXL1daWpqCg4P16quvatiwYRanBVzjs88+0wcffKAZM2aoefPmjuUPP/xwhkl1GjZsqNWrV8vPz8+KmLgBzqzBraxevVq33Xab4wOqdPWhqHFxcXr00Ue1e/dujR8/XhcvXtS4ceMsTAq4DscBIAUHB+uuu+5yFDVJKlu2rJYuXarExEQdPXpU8fHxFDW4tXnz5ikpKcnxnEHp6hm19DPMQ4cOVaNGjbR582Z99dVXFiZFVjizBrdSokQJNW3aVD/++KNj2V133aVVq1bp2LFjCg0NlSTVrVtXly5dUlRUlFVRAZfhOAAASFJ4eLiqVq2q5cuXO5b16dNHM2bM0G+//aZGjRrp0qVLioiIUPny5bVx40YL0yIznFmDWzl37pz8/f0dr1NTU/Xbb7+pfv36jg+o0tUbz48cOWJFRMDlOA4AAJJ08uRJlSpVKsOyNWvWKCwsTI0aNZIk+fn5qVmzZoqOjrYiIm6igNUBgNxUpkwZ7dq1y/F63bp1On/+vNq0aZNhu5SUFPn4+ORxOiBvcBzAEz3xxBMyDEPvvvuuSpYsqSeeeCLbYw3D0KRJk1yYDrBGcHCw4uPjHa+jo6N18OBB9erVK8N2hQsX1oULF/I6HrKBsga30rRpU82ePVv/93//p/bt2+u1116TYRjq3Llzhu2ioqJUtmxZi1ICrsVxAE80depUGYahIUOGqGTJkpo6dWq2x1LW4K4qV66stWvX6tChQwoPD9eXX34pwzDUqVOnDNsdOXLkujNwsAfuWYNb+fvvv9WwYUMlJydLkkzTVNu2bbVixQrHNjExMapYsaL69evHzbRwSxwH8ERr1qyRJDVu3Fh+fn6O19nVunVrV8QCLDVr1iz17NlTQUFBqlSpkiIjI1W8eHHt27dPAQEBkqSLFy+qePHiatu2rX766SeLE+NanFmDW7n99tu1bt06ffTRR4qPj1f9+vU1ePDgDNv8/PPPql27tu677z5rQgIuxnEAT3Rt2aJ8AdLjjz+uyMhIffrpp9q+fbvKlSunadOmOYqaJH377bdKSkpS+/btLUyKrHBmDQAAAHBjycnJSkxMVPHixa9bd/jwYZ0+fVqVKlXKUOJgD5Q1AADgdpKSkhQfH69ixYqpcOHCjuVnzpzRmDFj9Ndffyk8PFyDBg1SpUqVLEwKAFmjrMGtnT17VufOnVNWP+bh4eF5nAjIexwH8ETDhg3T2LFjtWnTJtWvX1/S1bMLtWrV0r59+xzHQ2hoqHbs2KHSpUtbGRcAMsU9a3A7x48f12uvvaYFCxbo1KlTWW5nGIZSUlLyMBmQdzgO4OlWrlypSpUqOYqaJM2cOVN79+5Vu3btNGTIEC1atEgff/yxPvzwQ40dO9bCtIBrrVu3Tj/++KP27t2b5S/vDMPIMBEV7IEza3ArsbGxatiwoY4dO6ayZcvqypUrOnHihJo2baoDBw4oLi5OhmGoadOmKliwoFatWmV1ZCDXcRwAUunSpVWvXj0tWrTIsaxr165auHChYmJiFBYWJunqw+ELFiyoP//806qogMuYpql+/fpp2rRpjoJmGEaGspb+2jAMpaamWhUVWfCyOgCQm95++20dO3ZMo0aN0uHDh/Wvf/1LhmFo/fr1io2N1erVq1W9enUZhqElS5ZYHRdwCY4D4Oq9aUWKFHG8Nk1T69atU61atRxFTZJq166tw4cPW5AQcL3PP/9cU6dOVf369bVs2TLdf//9kqTdu3dryZIl6tOnj7y8vDR48GAdOHDA4rTIDGUNbmXp0qWqUKGCXnvttUzXt2rVSr/88ou2b9+ut956K4/TAXmD4wCQSpUqpejoaMfrrVu36syZM9dN6W8YRl5HA/LM1KlTVbhwYS1ZskTt27dXYGCgJKlKlSq66667NHnyZM2ePVsffPCBIiMjrQ2LTFHW4FaOHj2qOnXqOF57e3tLkuPhwJJUtmxZtW3bVt9++21exwPyBMcBINWpU0ebNm3S/Pnzde7cOb311lsyDEP33ntvhu327t2rMmXKWJQScK2oqCg1a9ZMxYoVk/S/X07883LHBx98UPXr19cHH3xgSUbcGGUNbiUoKCjD6/RLYI4ePZphuZ+f33XLAHfBcQBIr776qiTpgQceUJEiRfTTTz+pdu3aateunWObuLg47dixI8MkJIA7SUtLcxQ1SfL395d09TLhf6pSpQr3bdoUZQ1uJTw8XIcOHXK8rlmzpiRp8eLFjmVJSUlav3490zTDbXEcAFKzZs00b948tWjRQtWrV1ePHj20YMECeXn976PP7NmzFRgYqE6dOlmYFHCdsmXL6tixY47XERERkqTt27dn2G7Pnj0qUIBJ4u2I2SDhVl599VV99NFHOnLkiIoXL67Tp0+rfPnyunLlil588UWVLVtWM2fO1JYtW/Tvf/9bn376qdWRgVzHcQAAkKRHH31UK1asUGxsrLy9vbVjxw7VrVtXtWvX1uzZs1W2bFl9/vnnGjJkiNq3b69ly5ZZHRnXoKzBrezYsUPvvfeennnmGcdN5LNnz1bfvn11+fJlx/S0t99+u9atW6fg4GCLEwO5j+MAACBd/W//448/rvnz56tLly6SpMcee0xz5szJMLmOt7e31q1bp0aNGlkVFVmgrMEjHDp0SIsXL9aZM2dUtWpVdenSRQULFrQ6FpCnOA7gSeLi4rR7925Vq1ZNJUuWdCzfv3+//vOf/+ivv/5SeHi4RowYoaZNm1qYFHCt5ORkFShQwDHZ1JUrVzRu3DjNnz/f8e/Bq6++qpYtW1qcFJmhrAEAALfz8ssv6+OPP1ZUVJSqVq0qSUpMTFS1atV04sQJx0OBCxUqpMjISFWpUsXKuACQKSYYAQAAbmf16tW67bbbHEVNuvrMqbi4OD366KPavXu3xo8fr4sXL2rcuHEWJgWArHFmDfna2rVrnRrfqlWrXEoCWIfjALheiRIl1LRpU/3444+OZXfddZdWrVqlY8eOKTQ0VJJUt25dXbp0SVFRUVZFBYAsMUcn8rU2bdpkuEE2p/75UEggv+I4AK537tw5xzOlpKs/57/99pvq16/vKGqSVL16dS1cuNCKiECuq1ix4i2PNQxD+/fvz8U0yA2UNeRrvXr1cupDKuAOOA6A65UpU0a7du1yvF63bp3Onz+vNm3aZNguJSVFPj4+eZwOcI2YmBirIyCXcRkkAABwOz169NDs2bM1btw4tW/fXs8++6w2bNigX3/9Vc2aNXNsV7NmTXl5eemPP/6wMC0AZI6yBgAA3M7ff/+thg0bKjk5WZJkmqbatm2rFStWOLaJiYlRxYoV1a9fP3311VdWRQWALHEZJPK9qKgonTx5UuXLl1d4ePgNtz148KAOHjyoEiVKqHr16nmUEHA9jgMgo/SHvn/00UeKj49X/fr1NXjw4Azb/Pzzz6pdu7buu+8+a0ICwE1wZg35Wnx8vCpXrix/f39t3bpVpUuXvuH2sbGxql+/vi5fvqx9+/apSJEieRMUcCGOAwCAdPUMcseOHRUdHa0ZM2bc9IHvv/32m3r27KkqVapoyZIleZQSOcFz1pCvTZkyRYmJiXr33Xdv+gFVkkqXLq333ntPp0+f1pQpU/IgIeB6HAcAAEn68ccftWLFCnXs2PGmRU2SmjZtqk6dOumXX37RokWL8iAhcooza8jX2rRpox07dujkyZMqUCB7V/WmpqaqePHiql27tlatWuXihIDrcRwA1zt06FCOtr/Z5cNAftC9e3fNnTtXhw8fVqlSpbI1Ji4uTuXKldNDDz2kr7/+2sUJkVPcs4Z87e+//1aTJk2y/QFVkry9vdW4cWNt3brVhcmAvMNxAFyvfPny2X6khWEYSklJcXEiwPU2bdqk+vXrZ7uoSVLJkiXVoEED/f777y5MhltFWUO+lpCQoGLFiuV4XLFixZSQkOCCREDe4zgArteqVatMy1paWpoOHz6sQ4cOKS0tTU2bNuU5a3Abx48fV5MmTXI8rnz58tqxY4cLEsFZlDXka0WKFNHp06dzPO706dMKCgpyQSIg73EcANdbvXr1Ddfv2bNHTz75pEzTZGIFuI2CBQvq8uXLOR535coVeXt7uyARnMUEI8jXKleurI0bNyo1NTXbY1JSUvT777+rSpUqLkwG5B2OAyDnqlatqrlz52rnzp164403rI4D5IrSpUsrKioqx+N27typMmXKuCARnEVZQ77WqVMnnT17Vp9++mm2x3z66adKSEjQv/71LxcmA/IOxwFwa0JDQ9W4cWPNmTPH6ihArmjZsqV2796tjRs3ZnvM77//rl27dqlVq1YuTIZbxWyQyNdOnz6tChUq6NKlS5o0aZJ69Ohxw+1nzJihfv36yd/fXwcOHFBISEgeJQVch+MAuHX/+te/tGrVKl26dMnqKIDTNm7cqKZNm6p69epau3atQkNDb7h9fHy8WrZsqT179mjDhg1q3LhxHiVFdlHWkO/Nnz9fDz74oEzTVJMmTfToo4+qXr16Kl68uCTp5MmT2rZtm2bPnq3ff/9dhmHohx9+UNeuXS1ODuQejgMg57Zv367mzZsrLCxMu3fvtjoOkCuefPJJTZ48WaVLl9bIkSPVvXv36+5PTkxM1Jw5czRy5EjFxcXpiSee0FdffWVRYtwIZQ1uYcmSJerdu7fi4+OznKrZNE0VL15cU6dO5dIvuCWOA+B/Ro0aleW68+fPa8+ePVqyZIlSUlL0/vvva+DAgXmYDnCdlJQU9ezZU998840Mw5BhGKpYsWKGX94dOHBApmnKNE098sgjmjFjBhOM2BRlDW4jKSlJ06ZN0+LFixUZGalTp05Jujo9eZ06dXTPPfeoV69e8vf3tzgp4DocB8BVXl5eMgxDN/qY4+/vr1deeUUjR47Mu2BAHvnuu+/0wQcfaPPmzZmub9SokV555RU9+OCDeZwMOUFZAwAAbmfatGlZrvPx8VHp0qXVsGFDFS5cOA9TAXnv1KlT1/3yrnbt2je9nw32QFkDADeSmpqq+Ph4+fr6qkiRIlbHAQAATmDqfgBwA4cOHdLDDz+soKAglSlTRsWKFVN4eLjee++9G14GBgDwDKdPn9ayZcs0e/Zsbdiwweo4yCbOrCFfq1ix4i2PNQxD+/fvz8U0gDXi4uJUr149HT9+/LpiZhiG+vTpo0mTJlmUDgBgpZMnT+rFF1/U999/r9TUVElS7969NXnyZEnSxIkT9eqrr2rBggVq0aKFlVGRiQJWBwCcERMTc9MbyLOS1Wx5QH4zZswYxcbGqkGDBho3bpzq1q2rc+fOacGCBRoyZIimTp2qF198UbVq1bI6KpCnkpKSNH78eP3444/au3evzp07l+l2hmEoJSUlj9MBrnf69Gk1a9ZM+/fvV506ddS8eXP997//zbDN/fffr3//+9/6/vvvKWs2RFmDW6hfv7569Oihrl27qlChQlbHAfLUkiVLFBISoiVLlqhYsWKSpICAAD399NPy8fFRv379tGTJEsoaPEpCQoJatmypv//+W97e3vLx8ZFpmipdunSGs9AREREWJwVc55133tH+/fv1+uuvO2Y9vbashYSEqFatWlqzZo0FCXEz3LOGfG3OnDm699579ccff2jgwIGqXbu2hg4dqr/++kslSpRQyZIlb/gHcAcHDx5UkyZNHEXtnzp37uzYBvAk7733nv766y/1799fiYmJevDBB2UYho4ePaoLFy5o6tSpKlWqlBo3bqwDBw5YHRdwifnz56tq1ao3fTxFpUqVdPTo0bwJhRyhrCFfe/jhh/Xjjz8qNjZWn3zyiW6//XZNmzZNHTt2VFhYmAYPHqzIyEirYwIudenSJZUoUSLTdelTM1+6dCkvIwGWmz9/vsqUKaOPP/5Yfn5+GS599/PzU69evbR8+XLNmzdP48aNszAp4DpHjx5V7dq1b7qdYRhKTEzMg0TIKcoa3EJISIieffZZbdiwQfv379fIkSMVGBiocePGqX79+qpZs6bGjBmjw4cPWx0VAJAHDh48qHr16qlgwYKSrj4kW5KuXLni2Oa2225T69atNXXqVCsiAi4XFBSk2NjYm263f/9+FS9ePA8SIae4Zw1up0KFChoxYoRGjBihzZs3a9asWfrmm280fPhwffjhhzp+/LjVEYFct2/fPk2fPv2W1vfq1ctVsQDL+Pn5yc/Pz/E6KChIknT8+HGFhYU5loeEhGj9+vV5ng/ICw0bNtTKlSsVHR2tChUqZLrNjh07FBkZqQcffDCP0yE7KGtwaxEREapYsaLKlCmjuLg4paWlWR0JcIn169ff8APnunXrslxPWYM7CgsLy3A1RfXq1SVJa9asUY8ePSRJKSkp2rx5c6b3ewLuYMCAAVqyZIm6deum2bNnq0aNGhnW79u3Tz179pRpmnr++ectSokboazB7SQlJWnu3LmaNWuWVqxYodTUVAUHB+upp55Sz549rY4H5LrevXtbHQGwnZYtW2rKlCk6d+6cAgMD1blzZ73wwgt64YUXdP78eZUtW1aTJk1STEyMHnvsMavjAi7RqVMnvfrqqxo7dqxq1qypKlWqyDAM/fzzz6pdu7Z27typ1NRU/ec//2HafpviodhwC2lpafr55581c+ZMLViwQElJSfLx8dE999yjHj166O6775aPj4/VMQEAeWTdunUaPHiwRo4cqbvuukuSNH78eL3yyiuOyUZM01SpUqW0ZcsWlSlTxsq4gEt99913euedd/THH39kWF69enWNGDFCjz76qEXJcDOUNeRrGzdudNyTdvLkSRmGoVatWqlHjx568MEHFRwcbHVEwLYiIyOVmJioVq1aWR0FyDMbN27UvHnzdObMGVWtWlV9+/ZVSEiI1bGAPHHy5EnFxMQoLS1N5cqVU9myZa2OhJugrCFf8/LykmEYuuOOO/T444/rscce4z88QDY1bdpUmzdvVkpKitVRAABAJihryNfSy5q3t3eOxxqGoeTkZBekAvKHpk2batOmTUpNTbU6CgDAxXbs2KFNmzYpPj5et99+u7p06SJJSk5OVnJysmPGVNgLE4wg3zNNkzMDAIAsbdiwQfv27ct0XYMGDXTbbbflcSIg7+zevVt9+/bVxo0bHct69+7tKGtff/21nnzySS1atEidOnWyKiayQFlDvsZU/AAA6eq/B7Vq1VJ0dLTWrVununXrOtZ99dVXWT5nsEaNGvrjjz8cD80G3Mnhw4fVqlUrnTx5Ul26dFHLli01ePDgDNs8/PDDevbZZ/XDDz9Q1myIsgYAAPK9RYsWaefOnXr55ZczFLV/6t+/f4bXu3bt0tq1a7Vo0SJ17tw5L2ICeWrUqFGKj4/XxIkT9cQTT0jSdWWtcOHCqlOnToYzb7APyhoAAMj35s6dK8MwNHDgwCy3mTBhQobXp06dUpkyZfTdd99R1uCWli5dqlq1ajmKWlbKly+vX375JY9SIScoa3B7qampWrRokfbu3auAgAB17NhRFSpUsDoWACAXbdq0SXfccUeOZgQuVqyYateurS1btrgwGWCdEydOqHnz5jfd7sqVK0pKSsqDRMgpyhryvd27d2vIkCFau3atUlJSdNttt2no0KG67777dODAAXXq1En79+93bO/t7a3Ro0dr0KBBFqYGck+7du1uadzOnTtzOQlgncOHD6tDhw6ZrvPy8srynrRy5cppxYoVrowGWKZYsWI6dOjQTbfbs2ePSpcunQeJkFOUNeRrhw8fVrNmzXT27FmlP4Vi06ZNevDBB/Xzzz/rlVde0b59+9SgQQNVqFBBBw4c0NatW/Xqq6+qYcOGPAwYbmH16tW3PNYwjNwLAljo8uXL8vX1zXTdpEmTNGnSpEzX+fn58RgXuK3mzZtr/vz5ioyMVJ06dTLdZs2aNfrrr7/Up0+fPM2G7KGsIV8bPXq0zpw5o3bt2uk///mPSpQooc2bN2vo0KF69tlntW/fPk2aNEl9+/Z1jJk0aZKeeuopffrpp5Q1uIVVq1ZZHQGwXEhIiI4dO5bjcceOHVNISIgLEgHWe+WVVzRv3jx17dpVn3/+uTp27Jhh/cqVK9WnTx8VKFBAL730kjUhcUM8FBv5WpUqVZSYmKjo6Gj5+/s7ln/77bd65JFHdNttt+mvv/66blzNmjWVkJCgw4cP52VcAICL3HXXXVq3bp3i4uIUEBCQrTGJiYkqWbKkWrZsyeQKcFufffaZXnzxRaWlpcnf319JSUkKCAiQl5eXEhMTZRiGPvvss+tmS4U98FAR5GtHjhxRw4YNMxQ1SWrfvr2kq8/PyUyNGjV04sQJl+cDAOSNe+65RxcvXtQ777yT7THvvPOOLl++7Hg4MOCOnn32Wf3666/q3LmzDMOQaZo6d+6ckpOTddddd2nNmjUUNRvjMkjka8nJySpRosR1y4sVKyZJCgwMzHRcQECAUlJSXJoNAJB3nnrqKb333nsaO3asgoKCNHTo0CzvyTRNU6NHj9b777+v0qVL68knn8zjtEDeSH/ge5MmTTR//nyZpqn4+HilpaUpNDRU3t7eVkfETVDWACCfu9XZIKWrE4wwEx7cQaFChfTdd9/pzjvv1GuvvaYvv/xSDz30kOrWravQ0FBJUnx8vLZv367vvvtOhw4dkq+vr7777jv5+flZnB5wjTp16qh169aOe5sNw1Dx4sUtToWcoKwBQD63evVqx6UtOcVskHAnzZs31/r169WjRw/t2rVL48aNy3Q70zRVo0YNzZgxQ/Xq1cvjlEDeCQkJUZkyZayOAScwwQjyNS8vL5UqVUrVq1e/bt3q1auzXLdr1y7FxcUpNTU1L2ICLuXl5SXDMFSvXj316NFDTZo0ydH4xo0buygZYA3TNLV48WItWbJEO3bs0KlTpyT97yHY//rXv3T33Xfzywq4vXvvvVeHDh3SH3/8YXUU3CLKGvK1rB5ymh2GYVDW4BbGjh2rWbNm6c8//5RhGKpUqZIef/xxPf7446pcubLV8QAAFtm8ebNatGihd999V4MGDbI6Dm4BZQ352po1a5wa37p161xKAljvzz//1IwZMzRnzhwdOXJEhmGoUaNG6tGjh7p37+64bwcA4BmmT5+uNWvWaOrUqapVq5buuecehYeHZ3mfZq9evfI4IW6GsgYAbsY0Ta1evVozZ87U3LlzlZCQoIIFC6pDhw7q0aOH7rvvPiZUAAA31K5dO3Xq1EmvvvqqpP9dJv/Pj/uZXf5rmiZXHNkUZQ0A3FhycrIWLFigmTNnaunSpUpJSVFYWJhiYmKsjgYAyGVeXl7q06ePJk+eLEkaOXJkju7NfOONN1wVDbeI2SABwI35+vqqW7du8vHx0YULF7Ry5UolJSVZHQsAkAdGjhxpdQQ46dZnZwBs4v3331f//v21Y8eODMv79u0rb2/vTP9wTTY8wbp16/Tvf/9bpUqV0v333681a9aoQ4cO+uijj6yOBgAAsoEza8jXoqKiNHToULVo0UK1a9e+br1pmvL19c2wLDU1VV9//bWGDx+e6bT+QH4WFRWlmTNn6uuvv9ahQ4dkmqbq1aun1157TY888ohKlSpldUQAAJBNlDXka19//bWkrE/zG4ahixcvZli2adMmNWnSRNOnT9e7777r6oiAy8XGxmr27NmaOXOmduzYIdM0VaFCBQ0fPlyPP/44v5QAAA+yb98+TZ8+/ZbGcuWR/TDBCPK1li1bau/evTp+/Ph16/r27avp06dnOrNRlSpVVLJkSa1bty4vYgIuVaBAAZmmqWLFiumhhx5Sjx491LRpU6tjAQDyWPrsj7eK2SDthzNryNd27dqlRo0aZbruRr+HqFSpkrZt2+aqWECeSktLk2EYKly4sJYtW6Zly5Zle6xhGNq9e7cL0wEA8lLJkiVVrVo1q2Mgl1DWkK8lJCQoJCQk03XPPPOMOnTokOm60NBQJSQkuDIakKdM09TBgwdzPM6Z38ACAOynU6dOjqn7kf9R1pCvBQQEZFm6mjRpoiZNmmS6LjExUYULF3ZlNCDPREdHWx0BAAC4AGUN+Vp4ePgtXc64ZcsWhYeHuyARkPciIiKsjgAAAFyA56whX2vbtq1iY2O1aNGibI9ZsGCBjh8/rnbt2rkwGQAAAOAcZoNEvrZnzx7dfvvtKlGihNasWaPKlSvfcPu9e/eqVatWio+P199//62qVavmUVLAGikpKTp16tT/a+/eY3O8/z+Ov65LWVfae3XoFDVVakqMTk3n8KVqCOIcmzHiMBJL5jSzLJNMhrHWYbJszllYMDMThxjFjCnaEZ2WUWup45TOjVrT9r5/fyyan0OrRe+r9+X5SPxx3df7Sl5JQ73v9+fzuZSXl1dsDVNmALAH0zQ1YsQI9qzZCMsg4dXCw8M1bdo0zZw5U5GRkZowYYLefPNNRURE3FOXlpamtWvXasGCBbp9+7Y+/vhjGjXYWkJCgj777DMdPHhQ+fn5xdYZhqGCggIPJgMAlJeMjAxVq1bN6hh4ipiswRamTp2q+Pj4ouvnnntOgYGBkqScnJyiqYLb7dYHH3ygOXPmWJIT8IQtW7aoX79+KiwsVGBgoEJDQ+Xv719s/Z49ezyYDgBgBVZaeCeaNdjGwYMHFRcXp4SEBDmdznvu+fv764033tDkyZOLPSESsIuoqCgdOXJE8+bN03vvvadKlSpZHQkAYBFWWng3mjXYjsvlUkZGhq5duyZJqlGjhkJDQ2WanKeDZ4Ofn58iIyO1f/9+q6MAACzESgvvx5412I5pmgoLC1NYWFixNStWrND58+c1ffp0DyYDPKNatWosZQEA6NNPP5XL5dL8+fNZaeGlmKzhmRQdHa3Dhw+rsLDQ6ijAUzdkyBAlJyfr1KlTVkcBAFiIlRbej3VhAGAzc+bMkdPp1Icffsj+AwB4hrHSwvuxDBIAvNyMGTMe+KxHjx6Ki4vThg0b1KlTJ9WrV++h+zYNw9Ann3ziiZgAAA+LjY1VcnKy1THwBFgGiWcSyyBhJ6ZpyjAMPc4/54Zh8PcAAGwqKytLUVFRGj58uGbOnCkfH+Y03oafGAB4uZUrV1odAQBQAa1cuZKVFl6OyRqeSUzWAACA3ZVl5QUrLSomJmsAAACADbHywvsxWYNXe9L3hfANEuwoPT1d27ZtU0xMjJo3b/7QmuPHj2v37t3q1auXGjZs6OGEAACgNDi6H17N7XY/9h/ArhYsWKDJkycrICCg2Bp/f39NmjRJX375pQeTAQCAsmCyBgA207RpU1WrVk1JSUkl1rVu3Vp37txRamqqh5IBAKySmJioffv26cKFC5KkunXrqkOHDoqOjrY4GUrCnjUAsJmsrCz17NnzkXVhYWHavn27BxIBAKxy6tQpDRs2rOh9a3fnNIZhSPrvi7vVq1ercePGlmVE8WjWAMBmKlWqpLy8vEfW5eXlsW8TAGzs0qVL+t///qcrV66oTp06GjRokBo0aCDDMJSZman169crKSlJnTp1UnJysoKDg62OjPuwDBIAbCYqKkoZGRk6d+6c/Pz8HlqTm5ur+vXrKyQkREePHvVwQgCAJ4wfP15ff/21Jk6cqNmzZ6tKlSr33M/Pz9dHH32kefPmafz48Vq0aJFFSVEcDhgBAJsZOHCgrl+/rtGjR+v27dsP3M/NzdWYMWOUk5OjgQMHWpAQAOAJ27ZtU5MmTRQfH/9AoyZJlStX1hdffKEmTZpoy5YtFiTEozBZAwCbyc3NVZs2bXTixAkFBQXprbfeUlhYmCTpzJkzWrNmjf7++281adJESUlJqlq1qsWJAQDlwdfXVwMGDNB3331XYt3bb7+tDRs26N9///VQMpQWe9YAwGb8/PyUkJCgoUOHavfu3VqwYEHRRvK738917txZq1atolEDABsLCAhQVlbWI+uysrJKfN0LrEOzBgA2VLt2bSUkJCgpKUkJCQlFv6xDQkIUGxurqKgoixMCAMpbdHS0tmzZoq1btxZ7SvC2bdv022+/qXfv3h5Oh9JgGSQA2Ez//v0VHBysr776yuooAAALJSYmqmPHjjIMQ4MHD9aQIUPUoEEDSdLZs2e1Zs0arV27Vi6XS/v27VPbtm2tDYwH0KwBgM34+vqqb9++Wrt2rdVRAAAWW716tcaOHas7d+4ULYm/y+126/nnn9fixYs1dOhQixKiJDRrAGAzTZs2VaNGjbR582arowAAKoDz589r6dKl2r9/vy5evChJqlOnjjp06KBRo0YpJCTE4oQoDs0aANjMjBkzFBcXp1OnTql27dpWxwEAAI+JZg0AbCY/P199+/ZVenq6Pv/8c/Xq1UuVK1e2OhYAACgjmjUAsJmGDRvK5XIVnQBpGIaCgoLk6+v7QK1hGDpz5oynIwIAgFKgWQMAmzFNs0z1LpernJIAADzJNE2Zpqm0tDSFh4erUqVKpX7WMAwVFBSUYzo8Dt6zBgA2Q/MFAM+m+vXryzCMoqXvISEhD5wACe/CZA0AAAAAKqCyrZUBAAAAAHgEzRoA2FRKSorGjh2riIgIORwOORwORUREaNy4cUpJSbE6HgDAYtnZ2exTq+Bo1gDAhhYuXKjWrVtr2bJlOnnypG7evKmbN2/q5MmTWrJkiVq3bq2FCxdaHRMAUI6Sk5M1Y8YMpaWl3fP5xo0bVbt2bb344ouqUaMGvw8qMPasAYDN7Ny5U926dZOfn5/GjRunYcOGqUGDBjIMQ5mZmVq1apW++eYb5ebmaseOHerSpYvVkQEA5WD48OFat26dLly4oBo1akiSMjIy9PLLLys/P1/BwcG6cuWK3G63du3apU6dOlkbGA9gsgYANjNv3jz5+Phox44diouL0yuvvCKHw6GAgAC1aNFCX3zxhXbs2CHTNBUfH291XABAOTl48KBatWpV1KhJ0ooVK5Sfn6+4uDhduHBBhw4dkmmaTNcqKCZrAGAzNWrUUGRkpHbu3FliXdeuXXXkyBFdu3bNQ8kAAJ70wgsvqFu3blq3bl3RZ23btlVqaqquXbumKlWqSJJiYmKUmZmpv/76y6qoKAaTNQCwmdzcXNWqVeuRdbVq1VJubq4HEgEArFBYWHjPASK3bt3SkSNH1K5du6JGTZLq1Kmjy5cvWxERj0CzBgA2ExISosTExBJP+CooKFBiYqJCQkI8mAwA4En169fX77//XnS9detWFRQUKDY29p46p9Mph8Ph6XgoBZo1ALCZPn366OzZsxo5cqT++eefB+47nU6NGTNG586dU9++fT2eDwDgGb1799a5c+fUv39/LVq0SFOmTJFpmurTp889dUePHtVLL71kUUqUhD1rAODlGjZsqEGDBmnOnDmSpOvXrysqKkqZmZmqVq2aunfvrgYNGkiSzp49q+3bt8vpdKphw4ZKSkpSYGCghekBAOUlOztbbdq0UWZmZtFnkyZNUlxcXNH1oUOHFB0drSlTpmju3LkWpERJfKwOAAB4MpmZmbp69WrRdfXq1bVv3z6NHTtWW7du1fr16x94pmfPnlq8eDGNGgDYWM2aNZWSkqIffvhBV69e1auvvqqYmJh7ai5fvqz3339fQ4cOtSglSsJkDQC8nGmaGjFihFasWPHAvYyMDO3fv18XL16U9N8m8vbt2ys0NNTTMQEAQBkxWQMAGwsNDaUxAwDAS9GsAQAAADbw66+/SpLatGkjX1/fouvS6tixY3nEwhNgGSQAeDnTNNWyZcvHPtlx+vTpTzcQAMASpmnKMAydOHFC4eHhRdelVVhYWI7p8DiYrAGADRw7dkzHjh0r0zNut1uGYdCsAYBNvPPOOzIMo+idaXev4b2YrAGAlzNNU40aNVK7du0e6/mVK1c+5UQAAOBpYLIGADbQvn37h54GCQAAvJdpdQAAAAAAT5/L5ZLT6VR+fn6xNfn5+XI6nXK5XB5MhtKiWQMAAABsaP78+QoMDNTevXuLrdm7d68CAwO1aNEiDyZDadGsAQAAADa0ceNGhYSEKDY2ttia2NhY1atXTxs2bPBgMpQWzRoAAABgQ6dPn1azZs0eWde8eXOdPn3aA4lQVhwwAgBejn0GAICHuXHjRtEx/iVxOBzKycnxQCKUFZM1AAAAwIaCg4OVkpLyyLqUlBQFBQV5IBHKimYNAAAAsKGYmBidOHFC69atK7bm+++/V1pamjp37uzBZCgtXooNAAAA2NDJkyfVqlUruVwujRkzRu+++67CwsIkSWfOnNGSJUu0dOlSGYah5ORkNW/e3OLEuB/NGgAAAGBT69ev1/Dhw5WXl/fAPbfbLV9fX61cuVKDBw+2IB0ehWYNAAAAsLH09HTNmzdPu3btUlZWliQVHek/YcIENW7c2OKEKA7NGgAAAABUQBwwAgAAAAAVEO9ZAwAAAGzs2rVrWr16tQ4fPqzs7Gx16dJFU6dOlSSlpqbqzJkzio2NlZ+fn8VJcT+aNQAAAMCm1q9fr9GjR+vWrVtyu90yDEN169Ytun/hwgX169dP3377rYYOHWphUjwMyyABAAAAG0pMTNSQIUPk4+Oj+Ph4HT58WPcfV9GlSxc5HA79+OOPFqVESZisAQAAADY0a9YsmaapnTt3KjIy8qE1lSpVUmRkpI4fP+7hdCgNJmsAAACADR04cEDR0dHFNmp31a5dW5cuXfJQKpQFzRoAAABgQ7m5uapVq9Yj63JycjyQBo+DZg0AAACwobp16yo1NbXEGrfbrePHjys0NNRDqVAWNGsAAACADXXv3l1//vmn1q5dW2zNsmXLlJWVpZ49e3owGUrLcN9/JAwAAAAAr3f+/Hm1aNFCt27d0sSJE9WvXz+9/vrrGjRokKZNm6aNGzdq7ty5cjgc+uOPPxQUFGR1ZNyHZg0AAACwqcTERA0YMECXL1+WYRj33HO73QoKCtKmTZv02muvWZQQJaFZAwAAAGzs5s2bWr58uXbu3KnMzEy5XC7Vq1dPXbt21dixY+VwOKyOiGLQrAEAAABABcQBIwAAAABQAflYHQAAAADA03fgwAHt2bNHJ06cUE5OjgzDUPXq1RUREaHOnTuzT80L0KwBAAAANpKSkqKRI0fq6NGjkv47SOT/u3vQSJs2bbR8+XJFRER4PCNKhz1rAAAAgE0kJSUpJiZGt2/fVtWqVdWjRw+1bNlSNWvWlNvtVnZ2to4ePaqff/5Zt2/flr+/v3755Re1atXK6uh4CJo1AAAAwAYKCwvVtGlTpaena9SoUYqPj1dAQMBDa51OpyZNmqQVK1aoSZMmSktLe+Bof1iPA0YAAAAAG9i0aZPS09M1ePBgLV26tNhGTZICAgK0bNkyDRo0SKdOndLmzZs9mBSlRbMGAAAA2MDmzZtlmqZmzZpV6mdmz54tSfrpp5/KKRWeBMsgAQAAABto0aKFCgsLlZqaWqbnmjVrJh8fHx07dqyckuFxMVkDAAAAbODSpUsKDw8v83Ph4eG6ePFiOSTCk6JZAwAAAGzgxo0bcjgcZX4uICBATqezHBLhSdGsAQAAADZQUFAg0yz7f+9N01RBQUE5JMKTolkDAAAAgAqIA0YAAAAAGzBN84nelVZYWPgU0+Bp8LE6AAAAAICn43HnMLwQu2JisgYAAAAAFRB71gAAAACgAqJZAwAAAIAKiGYNAAAAACogmjUAAAAAqIBo1gAAAACgAqJZAwAAAIAKiGYNAAAAACogmjUAAAAAqIBo1gAAAACgAvo/2D3aFtbIJcMAAAAASUVORK5CYII=",
       "text/plain": [
-       "<Figure size 720x504 with 1 Axes>"
+       "<Figure size 1000x700 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1036,19 +6183,87 @@
     }
    ],
    "source": [
-    "# Each trial is a tuple of\n",
-    "# (algorithm, no. samples, no. features, mean CV score, hyperparameters, \n",
-    "# all CV scores, total CV time (s), memory usage (Gb))\n",
-    "trials = est1.feature_selection_trials_\n",
-    "scores = [x[3] for x in trials]\n",
-    "n_features = [x[2] for x in trials]\n",
+    "# Each trial is a row in a dataframe that contains\n",
+    "# Algorithm, Number of Samples, Number of Features, Hyperparameters, Score, Runtime, Memory Usage, Step as features\n",
+    "trials = est1.completed_trials_summary_[est1.completed_trials_summary_[\"Step\"].str.contains('Model Selection')]\n",
+    "name_of_score_column = f\"Score ({est1._inferred_score_metric[0].name})\"\n",
+    "trials.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
+    "trials.dropna(subset=[name_of_score_column], inplace = True)\n",
+    "scores = trials[name_of_score_column].tolist()\n",
+    "models = trials['Algorithm'].tolist()\n",
+    "colors = []\n",
+    "\n",
     "y_margin = 0.10 * (max(scores) - min(scores))\n",
+    "s = pd.Series(scores, index=models).sort_values(ascending=False)\n",
+    "s = s.dropna()\n",
+    "for f in s.keys():\n",
+    "    if f.strip()  ==  est1.selected_model_.strip():\n",
+    "        colors.append('orange')\n",
+    "    elif s[f] >= s.mean():\n",
+    "        colors.append('teal')\n",
+    "    else:\n",
+    "        colors.append('turquoise')\n",
     "\n",
+    "fig, ax = plt.subplots(1)\n",
+    "ax.set_title(\"Algorithm Selection Trials\")\n",
+    "ax.set_ylim(min(scores) - y_margin, max(scores) + y_margin)\n",
+    "ax.set_ylabel(est1._inferred_score_metric[0].name)\n",
+    "s.plot.bar(ax=ax, color=colors, edgecolor='black')\n",
+    "ax.axhline(y=s.mean(), color='black', linewidth=0.5)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "81d2a610",
+   "metadata": {
+    "lines_to_next_cell": 0
+   },
+   "source": [
+    "<a id='feature-selection'></a>\n",
+    "#### Feature Selection\n",
+    "After finding a sample subset, the next step is to find a relevant feature subset to maximize score for the chosen algorithm. The feature selection step identifies the smallest feature subset that does not compromise on the score of the chosen algorithm. The orange line shows the optimal number of features chosen by Feature Selection."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "d25aa96e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-23T17:42:31.632302Z",
+     "iopub.status.busy": "2024-05-23T17:42:31.631588Z",
+     "iopub.status.idle": "2024-05-23T17:42:31.852559Z",
+     "shell.execute_reply": "2024-05-23T17:42:31.851751Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Each trial is a row in a dataframe that contains\n",
+    "# Algorithm, Number of Samples, Number of Features, Hyperparameters, Score, Runtime, Memory Usage, Step as features\n",
+    "trials = est1.completed_trials_summary_[est1.completed_trials_summary_[\"Step\"].str.contains('Feature Selection')]\n",
+    "trials.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
+    "trials.dropna(subset=[name_of_score_column], inplace = True)\n",
+    "trials.sort_values(by=['# Features'],ascending=True, inplace = True)\n",
+    "scores = trials[name_of_score_column].tolist()\n",
+    "n_features = trials['# Features'].tolist()\n",
     "\n",
+    "y_margin = 0.10 * (max(scores) - min(scores))\n",
     "fig, ax = plt.subplots(1)\n",
     "ax.set_title(\"Feature Selection Trials\")\n",
     "ax.set_xlabel(\"Number of Features\")\n",
-    "ax.set_ylabel(est1.inferred_score_metric[0])\n",
+    "ax.set_ylabel(est1._inferred_score_metric[0].name)\n",
     "ax.grid(color='g', linestyle='-', linewidth=0.1)\n",
     "ax.set_ylim(min(scores) - y_margin, max(scores) + y_margin)\n",
     "ax.plot(n_features, scores, 'k:', marker=\"s\", color='teal', markersize=3)\n",
@@ -1058,33 +6273,33 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0f2b48c8",
+   "id": "12eb2da2",
    "metadata": {},
    "source": [
     "<a id='hyperparameter-tuning'></a>\n",
     "#### Hyperparameter Tuning\n",
     "\n",
-    "Hyperparameter Tuning is the last stage of the AutoML pipeline, and focuses on improving the chosen algorithm's score on the reduced dataset (after Adaptive Sampling and Feature Selection). We use a novel algorithm to search across many hyperparameters dimensions, and converge automatically when optimal hyperparameters are identified. Each trial in the graph below represents a particular hyperparameters configuration for the selected model."
+    "Hyperparameter Tuning is the last stage of the AutoMLx pipeline, and focuses on improving the chosen algorithm's score on the reduced dataset (after Adaptive Sampling and Feature Selection). We use a novel algorithm to search across many hyperparameters dimensions, and converge automatically when optimal hyperparameters are identified. Each trial in the graph below represents a particular hyperparameters configuration for the selected model."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "id": "c73d9647",
+   "execution_count": 14,
+   "id": "808a2303",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-03-22T16:18:11.757614Z",
-     "iopub.status.busy": "2023-03-22T16:18:11.757030Z",
-     "iopub.status.idle": "2023-03-22T16:18:12.281556Z",
-     "shell.execute_reply": "2023-03-22T16:18:12.280807Z"
+     "iopub.execute_input": "2024-05-23T17:42:31.855883Z",
+     "iopub.status.busy": "2024-05-23T17:42:31.855110Z",
+     "iopub.status.idle": "2024-05-23T17:42:32.176593Z",
+     "shell.execute_reply": "2024-05-23T17:42:32.175759Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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E7du3T5mZmTp58qTzfVbQS1rW99kbb7yhHTt26MUXX9SNN96o8PBw7d69W++8846uueYaPfzww+WOG0DJKP4AoBx69uyp2NhYnT592vlX64JbPocNG+Z8Xqwkhw4duugyHx8fRURESJLz1kmp7LcIlteFYjx/+fk9C6tXr9a+ffvk7e2tWbNm6cYbbyzSi3L06FGXYyu45t27d9fbb7+t5s2bF/mSWxHHKY9atWo5/7+ycmXF66GiFOQpMzOzxHVSU1PdFU6FiIiIcJ7XhebpvNh7ytMUvM+efvppPfzww6pXr16R4taV91lCQoLGjBmjOXPm6OTJk1q9erX69esnSXrzzTf17bfflnvfAIpH8QcA5eDt7e0c0GXixImFnv+70EAvBUoafOP8ZS1atHDeWuXr6+t8Pu27775zKfbSOnDgQImTmJ8+fdo5SMk111xTaBtJio6OLvEWt4ULF1ZIbJJKHKjEGKPFixe7fJzyqF+/vvPcy5urgi/YJfWmWPF6qCjVq1eX9L8c/t7p06e1bds2d4bkMj8/PzVr1kySLjh5/YWWeaKLvc/27t2r3bt3V8ixvLy8dN1112nq1KmqV6+epHM9/AAqFsUfAJTT8OHD5ePjox9//NF5i2HHjh3VqFGji2773nvvFfs82I4dOzR16lRJ50YNPF9BUTlnzhzNmTPngvsv70AXv/f8888X2z5+/HhlZGTIx8dHAwcOdLYXjDpZMHLq7x08eFD//Oc/XY6r4DibN28udvl7772nX3/91eXjlNddd90l6dyInBs3brzgusXlKjQ0VJIKPTf5e1a8HipCy5YtJanEUWJfe+015/OMl5KCUXq//PLLYv9ocvLkSefzwZeKi73PHn/88XLt90L59fb2dj63e7HnfgGUHe8qACin2rVrq2/fvpKk5cuXS7r4QC8FcnJy1LVrV61bt07SuR6ehQsXqnv37srKylJsbKzuueeeQtvcdttt6tKli4wx6t+/v1544YVCt5idPXtWS5Ys0V//+lc1aNDA5fMLCwvTp59+qoceeshZqJ4+fVovvviinnvuOUnSX//610IDXLRv317BwcEyxmjw4MHauXOnpHPPqH3//fdKTEyskGeiCqZxmDt3rp5//nnnc5cpKSl68cUX9cADDygyMtLl45TXI488oiuuuEKZmZnq1KmT/vWvf+nkyZPO5SkpKZo7d66GDBmiDh06FNm+efPmkqTPPvusxIFa3P16qCh/+tOfJEnff/+9nnnmGaWlpUk6NzjOE088oRdeeME5FcCl5P7771fNmjWVmZmpHj16aNmyZc6e2/Xr16tr167Kzc21OMqyKXifvfDCC5o+fboz/qSkJP35z3/WV1995ezJLYs2bdrowQcf1NKlSwsNFHP48GE98MADzt7Enj17VsBZACikMiYPBABPc/7E5RdzscmTz7dw4cJCE7dnZmaWar9ffPGFqVatmpFkQkJCTFBQkHNZeHi4WbduXbH7SE1NNb1793auK8mEhoaa8PBw43A4nG0+Pj5Fti1pwuXfK7hWHTt2NI899piRZBwOh6levbrx9vZ2HqNLly4mIyOjyPbvvvtuofhCQkJMQECAc0Lz8yfK/v2k0gWTvMfFxV0wxuzsbNOhQwfnfgri8/LyMpJMr169nBPRd+zYscj2F5pEvCzXq+D4xU1YfujQIXPdddcVijE8PNyEhoYWuj4JCQlFtv3Pf/7jXO7r62vq1q1r4uLiTLt27Qqt547XQ1ldbJL33Nxc06lTpyK5czgcxuFwmFdffbVUk7y7Okn8hSZ5v9Bk5xe6bitWrDAhISHO/QQFBTl/Dg8PN19//bVz2ZEjR0o8RmmUdpL3oUOHlno/vz/vvXv3mpo1axZ6HYWFhTl/fvHFFy94rUu6VgXt578vgoODC72OR44cWbYLAqBU6PkDABfccMMNzkFZSjPQS4E2bdpo/fr1GjJkiMLCwpSbm6u6devqrrvu0i+//FLoObrzhYaG6rvvvtOcOXN0yy23qF69esrKylJ6errq1q2rbt266aWXXnLO7eaqV155RV988YXat28vY4z8/Px05ZVX6s0339S8efMUEBBQZJt77rlHs2fPVmJiokJCQpzn9sADD2jz5s264oorXI7L19dX8+fP1zPPPKNGjRrJ19dXxhi1bt1a7777rr799lvLR7GsU6eOfvjhB02ZMkV9+vRR7dq1lZ6eruzsbNWvX1833XST3njjDWev8fluu+02/ec//1H79u0VFBSkI0eOaN++fUXmUnP366EieHt7a/bs2Ro7dqyaNGkiPz8/ORwOdevWTQsWLNDo0aOtDrHc2rdvr59//ll/+ctfVKdOHeXm5io8PFx33HGHNmzYoIYNGzrXvRR6N+Pi4rR+/XoNHz7c2cMfEBCg3r176/vvv9eYMWPKtd8vvvhCY8eOVefOnRUfH6/s7Gzl5OQoLi5Ot9xyixYtWqTXX3+9Ik8FwP9zGOPmMbABoAr56aefnIXajh07Lvi83969exUfHy/p3G1T9evXd0eIZfbss89q7Nix6tix4yU3QAXgyd5//33dfffdatCgQYmDKQFAZbJFz9/KlSvVs2dPRUREKCQkRK1bt9akSZPKta/U1FQ98cQTatasmYKCghQQEKDGjRtr5MiRRSY6rqwYAHiOt956S9K5HsDSDPQCwJ4yMzP1xhtvSPrfs3QA4G5VvvibNm2aOnbsqHnz5qlFixbq0aOHdu3apaFDh5b51pITJ07o2muv1UsvvaTk5GR17dpV3bt3V1pamt544w1deeWVxc63VJExAPAcc+bM0eTJkyWJ9zIAffHFF3ryySe1ZcsWZWdnS5Jyc3O1fPly3XDDDdq6dasCAgL00EMPWRwpANuy9pHDynXy5Enng/XTpk1zth89etQkJCRc9IHx3xs5cqSRZPr06VNokIOMjAzTv39/I8kMGTKkUmMAYK0DBw6YuLg4Ex0d7RyYoHfv3qXatrQDSljt/AFfAJTehAkTCg1kEhERYfz8/Jxtfn5+ZsqUKVaHCcDGqnTP3wcffKC0tDT17dtXAwYMcLbXrFlT48aNk3RurqrSKngof8yYMYUGOQgICNBTTz0lSc5h2ysrBgDWys3N1b59+3TixAnFxMTo4Ycf1pQpU6wOC4AH6N27t5544gldf/31qlOnjs6ePStfX181adJE9957r3755Rf98Y9/tDpMADbmY3UAlWn27NmS/jfx6vl69eqlgIAALVy4UJmZmcWOWPd7pRnF7/fzSlV0DACsVb9+fefcXe7c1p2effZZPfvss1aHAVxyEhIS9I9//MPqMACgRFW652/z5s2SpFatWhVZ5ufnp+bNmyszM9M5CfHFdOvWTZL08ssvKzMz09memZmp559/XpI0fPjwSo0BAAAAAMqjyvb8paWlKTU1VZIUExNT7DoxMTFav3699u3bpxYtWlx0n6NHj9ayZcs0c+ZMxcfHq02bNpKktWvXKjMzU6+99pqGDRtW4TFkZWUpKyvL+XN+fr6Sk5MVGRkph8Nx0bgBAAAAVE3GGJ0+fVp16tSRl9eF+/aqbPF35swZ5/8HBQUVu05wcLAk6fTp06XaZ3BwsGbPnq27775bkydP1syZM53LOnXqpPbt21dKDC+99JLGjh1bqhgBAAAA2M+BAwdK7HAq4NHFX//+/bVt27YybTNp0iS1bt26UuLZv3+/evXqpSNHjmjSpEnOeXrmzp2rkSNHKjExUfPnz1eHDh0q9LhjxozRqFGjnD+npqaqXr16OnDggEJDQyv0WBeSnJEsSYoIjHDbMXFx5MXzkBPPQ048E3nxPOTEM5EXz+NJOUlLS1NsbKyqVat20XU9uvhLSkrSjh07yrRNenq6JCkkJKRQW3FF0tmzZyWpVBdKkoYOHaotW7ZoxowZ6tevn7N9yJAhCgkJ0cCBAzV69GitXbu2QmPw9/cvdrCZ0NBQtxZ/ub65544b6L5j4uLIi+chJ56HnHgm8uJ5yIlnIi+exxNzUprHwTx6wJdNmzbJGFOmf4mJiZLOFUZhYWGSpIMHDxa7/4L2uLi4i8Zy4MABLV26VP7+/rrpppuKLO/bt6/8/Py0bt0652AwFR0DAAAAAJSXRxd/rmrZsqUkacOGDUWW5eTkaMuWLQoICFCjRo0uuq+CIi04OFje3t5Flnt7eys4OFjGGKWkpFRKDAAAAABQXlW6+OvVq5ckaerUqUWWzZo1S5mZmerSpUup5terVauWJCk5OVlJSUlFlu/Zs0enTp1ScHCwoqKiKiUGAAAAACivKl383XnnnQoNDdXMmTM1ffp0Z/vx48f12GOPSZIeeeSRIts1adJETZo00aFDh5xt8fHxzqkYRowY4ZzCQZJSUlI0YsQISVK/fv3k4/O/RynLGwMAAAAAVCSPHvDFVREREfroo480ePBgDRo0SImJiYqMjNTChQuVkpKiUaNGOZ8RPF/BIDM5OTmF2idOnKguXbpowYIFSkhIcM7zt2bNGp08eVL169fXuHHjKiQGAAAAAKhIVbrnT5IGDhyo5cuXq3v37tq4caPmzJmjhIQEffLJJxo/fnyZ9tWmTRtt2rRJd911l0JDQ7Vw4UItXrxYNWvW1OOPP66ffvpJderUqdQYAAAAAKA8HMYYY3UQKJu0tDSFhYUpNTWVef5AXjwQOfE85MQzkRfPQ048E3nxPJ6Uk7LUBlW+5w8AAAAAQPEHAAAAALZA8QcAAAAANkDxBwAAAAA2QPEHAAAAADZA8QcAAAAANkDxBwAAAAA2QPEHAAAAADZA8QcAAAAANkDxBwAAAAA2QPEHAAAAADZA8QcAAAAANkDxBwAAAAA2QPEHAAAAADZA8QcAAAAANkDxBwAAAAA2QPEHAAAAADZA8QcAAAAANkDxBwAAAAA2QPEHAAAAADZA8QcAAAAANkDxBwAAAAA2QPEHAAAAADZA8QcAAAAANkDxBwAAAAA2QPEHAAAAADZA8QcAAAAANkDxBwAAAAA2QPEHAAAAADZA8QcAAAAANkDxBwAAAAA2QPEHAAAAADbgY3UAsK/9qak6kZ7u/DkqKEiSbN9WLyysTNdmT/JxSVJYQJbHnUtFt5X12ljV5ik5uVSulxU54dp4xmvJU94rFd12Kb++Kjsnl/K1sfK1VFXfK660Wf1aKu73yqWA4g+W2J+aqkZvvaWsvDxnm7+3tySVq83Py0sOh6NQm4/DIW8vr0Jt3v/flv27Ni+HQzn5+YW2lcOh3PPafL28ZKRCbf7e3sozpkibkQodo7TnEeDjo8VDhuiGSZOUmZtb5u19vbzkVcrr8Ptz9nY45CjFOft5eSm/mOuQb0yh/blyHYrLp5+3t5YOHVrk2hS3rq/DIa9LLPcV/br28fLS39u31yurVhW6XqXOvcMh87s2d+Te38tL+n0+i3ldlzafxZ2bn7e3Hm7TRhPWrKmSuS/unIs7t9LmM8DHR9/+8Y/q+fnnl3zuy5LP4s6tNOfh6ue4J+W+wl/XJXyOl/b3mbfDIZ8K/H1W0bmvjM/x5zt10thly4p8jl9qua/ofBZ8jr+xdu1Fr4O73vc77r//kigAKf5giRPp6YXeNJKK/FyWtuzz3qgFco1R7u/WzTNGecW1GVNkW/2uLaeYY7gSc3Ftmbm5+jUlpdCHfFm2Ly7GEq/D784vr5TnXNy1rujrUNwxsvPyir02xa2bY4x0ieW+ol/Xufn5+te6dUWuV6lzX0ybO3KfVVw+i2krbT6LO7fsvDyNW7WqyD6rSu6LO+fizq20+czMzdUvx44V+pJU0rqenvtL4XPck3Jf4a/rEj7HS/v7zNNzXxmf4x9u2FD85/gllnt3fY5b+b4/kZ5+SRR/PPMHAFVUi5o1rQ7BY11Tu7bVIVxS/Hz4WzFghcujoqwOwWPxOV4+FH8AUEUNb9XK6hA81sNt21odwiWlemCg1SEAtnTLFVdYHYLH4nO8fCj+YImooCAF/O4vyf7e3s77qO3aFuDjowbh4Vwbro3LbVyvktu4NmVr43qVrY3rVXIb16ZsbVyvkts87doE+Pg4B4LxdA5jfndjLDxeWlqawsLClJqaqtDQULcdNzkjWZIUERhRIftjtK+KGb1qT/IRSVJYQJjHnUtFt1k9stellpNL5XpZkROujWe8ljzlvVLRbZfy66uyc3IpXxsrX0tV9b3iSpvVr6Xifq9YpSy1AcXfJaiqFH//XLtW72/YoFuvuEKPt29fIfu0o4rOC1xHTjwPOfFM5MXzkBPPRF48jyflpCy1Abd9wjJHTp/WluPHdfTMGatDAQAAAKo8hu+CZe5s1UpdGjRQjBt7LwEAAAC7oviDZRpGRKhhhPVd5QAAAIAdcNsnAAAAANgAPX+wzM/HjmlvSooaR0aqMZOYAgAAAJWKnj9Y5t/r16vvF1/os19+sToUAAAAoMqj+INl6oeHq03duoplwBcAAACg0nHbJyzzaLt2erRdO6vDAAAAAGyBnj8AAAAAsAGKPwAAAACwAYo/WObpJUvU5oMPNIUBXwAAAIBKR/EHy+xKTtaPhw7p2NmzVocCAAAAVHkM+ALLPHr99fpT8+ZqFh1tdSgAAABAlUfxB8u0ql1brWrXtjoMAAAAwBa47RMAAAAAbICeP1hm09GjSs7I0OVRUapdrZrV4QAAAABVGj1/sMzfFy9W50mTNHf3bqtDAQAAAKo8ij9YJi4sTM1r1FBEYKDVoQAAAABVHrd9wjLv9OpldQgAAACAbdDzBwAAAAA2QPEHAAAAADZA8QfLPDh3rjpPmqQV+/ZZHQoAAABQ5VH8wTLrDh/W4qQknczIsDoUAAAAoMpjwBdY5vlOnfTb2bO6pk4dq0MBAAAAqjyKP1imS4MGVocAAAAA2Aa3fQIAAACADdDzB8tsOnpUmbm5ujwqSmEBAVaHAwAAAFRp9PzBMrdNn662H36on44csToUAAAAoMqj5w+WiQkNVXpOjoJ8fa0OBQAAAKjyKP5gmXm33WZ1CAAAAIBtcNsnAAAAANgAxR8AAAAA2ADFHywz9JtvdNOUKdqdnGx1KAAAAECVR/EHyyz89VfN2rlTp7OyrA4FAAAAqPIY8AWWGd+tm85kZ6teWJjVoQAAAABVHsUfLPPH5s2tDgEAAACwDW77BAAAAAAboOcPlvnl2DFJUuOoKPl5e1scDQAAAFC10fMHy7T98EO1eO89HUxLszoUAAAAoMqj5w+WqREcrLM5OfL14m8QAAAAQGWj+INlfn3oIatDAAAAAGyDLhcAAAAAsAGKPwAAAACwAYo/WCLfGA3++mv9cepUnc7KsjocAAAAoMqj+IMl8vLz9fXWrfryv/9Vbn6+1eEAAAAAVR4DvsASXg6H/nXjjcozRkG+vlaHAwAAAFR5FH+whLeXl/7aurXVYQAAAAC2wW2fAAAAAGAD9PzBEnn5+fr11Cl5e3kpPjxcDofD6pAAAACAKo2eP1jiVGamGv3rX2r4z3/KWB0MAAAAYAP0/MESxhiF+vsr3xh50esHAAAAVDqKP1giOjhYqY8/bnUYAAAAgG3Y4rbPlStXqmfPnoqIiFBISIhat26tSZMmlWtfqampeuKJJ9SsWTMFBQUpICBAjRs31siRI3X8+PEi62/fvl2vvPKKOnXqpKioKPn6+qpWrVoaMGCAVqxY4eqpAQAAAECpVPmev2nTpumWW25Rfn6+/vCHPygqKkqLFi3S0KFD9fPPP+u1114r9b5OnDih66+/Xrt27VKtWrXUtWtXSdKPP/6oN954Q19++aVWr16tuLg45zZdunTRoUOHFBISouuuu04RERHaunWrZsyYoW+++Uavv/66Hn744Yo+bQAAAAAopEr3/CUnJ+uOO+5QXl6epk6dqqVLl2rq1Knavn27EhISNH78eC1durTU+3vxxRe1a9cu9enTR0lJSZo5c6ZmzpyppKQk9e/fX0eOHNHTTz9daJsmTZpo0qRJ+u2337RgwQJ9+eWX+uWXX/Tee+/JGKPRo0dr69atFXzmnu/42bMa9s03+uvs2VaHAgAAANhClS7+PvjgA6Wlpalv374aMGCAs71mzZoaN26cJGn8+PGl3t/y5cslSWPGjFFAQICzPSAgQE899ZQkad26dYW2WbhwoW6//fZC60vSiBEj1K1bN+Xl5enrr78u24lVAamZmfp082ZN/uUXq0MBAAAAbKFK3/Y5+/97lQYNGlRkWa9evRQQEKCFCxcqMzOzSHFWHH9//4uuExkZWer4WrZsqfnz5+vw4cOl3qaqiAwK0sudO8vX29vqUAAAAABbqNI9f5s3b5YktWrVqsgyPz8/NW/eXJmZmdq5c2ep9tetWzdJ0ssvv6zMzExne2Zmpp5//nlJ0vDhw0sd36+//ipJqlWrVqm3qSoiAgP1t/btNaptW6tDAQAAAGyhyvb8paWlKTU1VZIUExNT7DoxMTFav3699u3bpxYtWlx0n6NHj9ayZcs0c+ZMxcfHq02bNpKktWvXKjMzU6+99pqGDRtWqvj27NmjWbNmSZL69OlzwXWzsrKUlZXl/DktLU2SlJyRrFzf3FIdryKcyjjltmOh9MiL5yEnnoeceCby4nnIiWciL57Hk3KSlpFW6nWrbM/fmTNnnP8fFBRU7DrBwcGSpNOnT5dqn8HBwZo9e7Zuu+02HT161Dngy9GjR3XVVVepffv2pdpPbm6uhg0bpqysLN1yyy26+uqrL7j+Sy+9pLCwMOe/2NjYUh3Hk2Xn5enI6TM6mZ5hdSgAAACALXh0z1///v21bdu2Mm0zadIktW7dulLi2b9/v3r16qUjR45o0qRJ6tGjhyRp7ty5GjlypBITEzV//nx16NDhgvt58MEH9cMPP6hBgwZ65513LnrcMWPGaNSoUc6f09LSFBsbq4jACIUGhrp2UuUQERjh8j7WHz6sa9//WLGhodo/cmQFRIWKyAsqFjnxPOTEM5EXz0NOPBN58TyekBOfnNKXdB5d/CUlJWnHjh1l2iY9PV2SFBISUqgtNLRokXT27FlJUrVq1Uq176FDh2rLli2aMWOG+vXr52wfMmSIQkJCNHDgQI0ePVpr164tcR//+Mc/9O6776pmzZr6/vvvFRFx8ReMv79/qQabuZQYY+TtcMjHq8p2PgMAAAAexaOLv02bNpV729DQUIWFhSk1NVUHDx5U06ZNi6xz8OBBSSo0KXtJDhw4oKVLl8rf31833XRTkeV9+/aVn5+f1q1bV+Looe+9956efPJJhYWFad68eUpISCjHmVUN19atq9zfzYkIAAAAoPJU6W6Xli1bSpI2bNhQZFlOTo62bNmigIAANWrU6KL7KigUg4OD5V3M9ATe3t4KDg6WMUYpKSlFln/xxRf661//qqCgIM2ePVtXXnll2U4GAAAAAFxQpYu/Xr16SZKmTp1aZNmsWbOUmZmpLl26lGqOv4LpGJKTk5WUlFRk+Z49e3Tq1CkFBwcrKiqq0LI5c+ZoyJAh8vHx0YwZM9SuXbvynA4AAAAAlFuVLv7uvPNOhYaGaubMmZo+fbqz/fjx43rsscckSY888kiR7Zo0aaImTZro0KFDzrb4+HjndBAjRoxwTiMhSSkpKRoxYoQkqV+/fvLx+d/dtCtXrtSgQYNkjNGXX37pnCvQ7nacOKG/zp6tl3/4wepQAAAAAFvw6Gf+XBUREaGPPvpIgwcP1qBBg5SYmKjIyEgtXLhQKSkpGjVqlBITE4tsVzDITE5OTqH2iRMnqkuXLlqwYIESEhKc8/ytWbNGJ0+eVP369TVu3LhC2/Tu3VsZGRmKj4/XN998o2+++abI8dq3b68777yzYk76ErE/NVXvrF+vFjVr6vFSTpEBAAAAoPyqdPEnSQMHDtTy5cv1wgsvaM2aNcrOzlbTpk11//33a+jQoWXaV5s2bbRp0ya98sorWrRokRYuXCgvLy/Fx8frrrvu0qOPPlpk9M6C5/+SkpKKvV20gN2KvwbVq+vpP/xBNf5/rkUAAAAAlcthjDFWB4GySUtLc45kWtwUFpUlOSNZkmfMZ4L/IS+eh5x4HnLimciL5yEnnom8eB5PyklZaoMq/cwfAAAAAOAcij9YIjsvTymZmUr/3XOVAAAAACoHxR8s8e2OHar+yivqMXmy1aEAAAAAtkDxB0vk//+jpt5evAQBAAAAd6jyo33CMw1q2lRZTz4pxhsCAAAA3IPiD5bwcjjk5+1tdRgAAACAbXDPHQAAAADYAD1/sMSPhw5p6tatahYdraFXXml1OAAAAECVR88fLLH56FG9umqVpm/fbnUoAAAAgC3Q8wdLXFGzpkZdd52a1ahhdSgAAACALVD8wRLXxcToupgYq8MAAAAAbIPbPgEAAADABuj5gyXy8vNlJHk7HHI4HFaHAwAAAFR59PzBEm+sWSPf55/XkG++sToUAAAAwBYo/mCJPGMknev5AwAAAFD5uO0TlniwTRvd2aqVfL34+wMAAADgDhR/sESAj48CfHj5AQAAAO5CtwsAAAAA2ABdL7DE4qQkrdi3T21iYtQjIcHqcAAAAIAqj54/WGLRr7/q2WXLNHfXLqtDAQAAAGyB4g+WaF23ru65+mq1q1fP6lAAAAAAW+C2T1iib5Mm6tukidVhAAAAALZBzx8AAAAA2ADFHwAAAADYAMUfLPHwvHnye/55PbdsmdWhAAAAALZA8QdLZOflKSc/X/nGWB0KAAAAYAsM+AJLvNi5s57o0EHV/PysDgUAAACwBYo/WCI8IEDhAQFWhwEAAADYBrd9AgAAAIAN0PMHS8zeuVNbf/tNneLjdU2dOlaHAwAAAFR59PzBEp9v2aLHFi7U8n37rA4FAAAAsAV6/mCJDvXqydvhUNPoaKtDAQAAAGyB4g+WuOeaa3TPNddYHQYAAABgG9z2CQAAAAA2QPEHAAAAADZA8QdL/HHqVFV/5RVN/vlnq0MBAAAAbIHiD5ZIy8pSSmamcvLyrA4FAAAAsAUGfIEl3r/pJp3JzlbNkBCrQwEAAABsgeIPlqgbGmp1CAAAAICtcNsnAAAAANgAPX+wxPRt23TszBl1T0hQg+rVrQ4HAAAAqPLo+YMlxq9erfvmzNGmo0etDgUAAACwBXr+YIlO9eurVkiI6larZnUoAAAAgC1Q/MESL9xwg9UhAAAAALbCbZ8AAAAAYAMUfwAAAABgAxR/sMQNn36qmNdf1w/791sdCgAAAGALFH+wxNEzZ3To9Gnl5OVZHQoAAABgCwz4AktMv+UWpefk6LKICKtDAQAAAGyB4g+WaBIVZXUIAAAAgK1w2ycAAAAA2AA9f7DEV//9rzJyctS7USNFBgVZHQ4AAABQ5dHzB0uM+v57DZs5U/tSU60OBQAAALAFev5giU7x8frt7FmF+ftbHQoAAABgCxR/sMR/+ve3OgQAAADAVrjtEwAAAABsgOIPAAAAAGyA4g+WuPK999T4X//SfgZ8AQAAANyCZ/5giZ0nTyojN1d5+flWhwIAAADYAsUfLLF46FDl5OWpdrVqVocCAAAA2ALFHyxxXUyM1SEAAAAAtsIzfwAAAABgA/T8we2MMfp661Z5ORy6qVEj+fvwMgQAAAAqG9+64XZ5xuiWqVMlSScefZTiDwAAAHADvnXD7YwxSqxfX3n5+fLz9rY6HAAAAMAWKP7gdr7e3loydKjVYQAAAAC2woAvAAAAAGADFH8AAAAAYAMUf3C71MxMtXzvPbX697+Vl59vdTgAAACALfDMH9wuOy9PPx87JknycjgsjgYAAACwB4o/uF1YQIDm33ab8oyRg+IPAAAAcAuKP7idn7e3ujZsaHUYAAAAgK3wzB8AAAAA2AA9f3C79JwcLU5Kkp+3t7rRAwgAAAC4BT1/cLsjp0/rpilTNPCrr6wOBQAAALANev7gdr7e3rqmTh0F+fpaHQoAAABgGxR/cLt6YWFad9ddVocBAAAA2Aq3fQIAAACADVD8AQAAAIANUPzB7XacOKHrP/yQAV8AAAAAN+KZP7hdWlaWVh88qHphYVaHAgAAANgGxR/cLiEiQjNuuUUBPrz8AAAAAHfh2zfcrnpgoPo1aWJ1GAAAAICt8MwfAAAAANgAPX9wu1MZGdp09Kiq+fvrmjp1rA4HAAAAsAV6/uB2G44c0Q2TJukvM2daHQoAAABgGxR/cLsgX181jY5Ww+rVrQ4FAAAAsA1u+4TbtY2N1X/vu8/qMAAAAABbqbTib968edqyZYtiY2M1YMAA+fr6VtahAAAAAAAX4dJtn++8844aNGiglStXFmofPHiwevXqpb/97W/685//rA4dOigzM9OlQAEAAAAA5edS8Tdjxgylp6erbdu2zrZ58+Zp6tSpqlu3rh5//HG1bt1a69at0/vvv+9ysKgaVuzbpy6TJumR77+3OhQAAADANly67XPHjh1q3ry5vLz+V0N+8cUXcjgcmjp1qlq3bq3MzEzFxcVp8uTJeuCBB1wOGJe+o2fOaFFSknLy860OBQAAALANl3r+fvvtN9WqVatQ27JlyxQbG6vWrVtLkgICAnT99dcrKSnJlUO5ZOXKlerZs6ciIiIUEhKi1q1ba9KkSeXaV2pqqp544gk1a9ZMQUFBCggIUOPGjTVy5EgdP368VPt4/vnn5XA45HA4NHny5HLFcSlrExOjzwYM0N87dLA6FAAAAMA2XOr5CwsL04kTJ5w/JyUlad++fRoyZEih9YKDg3X27FlXDlVu06ZN0y233KL8/Hz94Q9/UFRUlBYtWqShQ4fq559/1muvvVbqfZ04cULXX3+9du3apVq1aqlr166SpB9//FFvvPGGvvzyS61evVpxcXEl7mPHjh36xz/+IYfDIWOMy+d3KaoXFqY/X3GF1WEAAAAAtuJSz19CQoKWL1+u/fv3S5ImTpwoh8OhHj16FFrv4MGDRXoI3SE5OVl33HGH8vLyNHXqVC1dulRTp07V9u3blZCQoPHjx2vp0qWl3t+LL76oXbt2qU+fPkpKStLMmTM1c+ZMJSUlqX///jpy5IiefvrpErc3xujuu+9WeHi4+vTpUwFnCAAAAACl41Lxd++99yozM1MtWrTQ1VdfrXHjxik6Olq9e/d2rpORkaH169eradOmLgdbVh988IHS0tLUt29fDRgwwNles2ZNjRs3TpI0fvz4Uu9v+fLlkqQxY8YoICDA2R4QEKCnnnpKkrRu3boLxrN8+XKNHz9e4eHhZTmVKuW3s2f146FD2nXypNWhAAAAALbhUvF366236pFHHlFWVpY2btyounXrasqUKQoJCXGu89VXXyk9PV2dO3d2Odiymj17tiRp0KBBRZb16tVLAQEBWrhwYamnofD397/oOpGRkcW2Hz16VI899pg6d+6sW2+9tVTHq6q+3bFDbT74QKPmz7c6FAAAAMA2XCr+JOnVV19VSkqKjh07pv3796tTp06Flt9www3auHGj7rzzTlcPVWabN2+WJLVq1arIMj8/PzVv3lyZmZnauXNnqfbXrVs3SdLLL79cqGDMzMzU888/L0kaPnx4sds++OCDysjI0Lvvvlumc6iKgv38VC8sTDWCgqwOBQAAALANlwZ8KeDv76/o6Ohil8XGxio2NrYiDlMmaWlpSk1NlSTFxMQUu05MTIzWr1+vffv2qUWLFhfd5+jRo7Vs2TLNnDlT8fHxatOmjSRp7dq1yszM1GuvvaZhw4YV2W7WrFn6+uuvNXbsWF122WVlPpesrCxlZWUVOjdJSs5IVq5vbpn3V16nMk5VyH66Nayjbg3PDQqUnJFcIfu0s4rKCyoOOfE85MQzkRfPQ048E3nxPJ6Uk7SMtFKvWyHF3+/l5ubqgw8+0JYtW1SvXj3dddddql69emUcqkRnzpxx/n9QCT1MwcHBkqTTp0+Xap/BwcGaPXu27r77bk2ePFkzZ850LuvUqZPat29fbBz33XefGjVqpL/97W9lOQWnl156SWPHji3XtgAAAAAguVj8Pffccxo7dqyWLFmiP/zhD5Kk/Px8JSYmavXq1TLGyOFwaOLEiVq/fn2ZBznp37+/tm3bVqZtJk2a5JxjsKLt379fvXr10pEjRzRp0iTnqKZz587VyJEjlZiYqPnz56vDefPXPfHEEzpw4IAWLVpUqmcGizNmzBiNGjXK+XNaWppiY2MVERih0MBQ106qHCICI9x+TFwcefE85MTzkBPPRF48DznxTOTF83hCTnxySl/SuVT8LViwQDExMc7CT5KmTp2qVatWqUWLFvrrX/+quXPn6ptvvtHbb7+tv//972Xaf1JSknbs2FGmbdLT0yWp0KAz6enpCg0tWiQVzD1YrVq1Uu176NCh2rJli2bMmKF+/fo524cMGaKQkBANHDhQo0eP1tq1ayWdm//v7bff1u23364bbrihTOdxPn9//3IXjp7om+3b9fGmTepUv74evu46q8MBAAAAbMGl4u/XX3/VFb+brHv69OlyOByaMmWKLr/8ct15552qX7++pk6dWubib9OmTeWOLTQ0VGFhYUpNTdXBgweLnWri4MGDknTBSdkLHDhwQEuXLpW/v79uuummIsv79u0rPz8/rVu3TpmZmQoICNCcOXOUn5+vX375RYmJiYXW3759uyTpH//4hz744AP16NFDjz/+eDnO9NKzOzlZ3+7YobAqVNACAAAAns6l4u/kyZOKiooq1LZs2TJddtlluvzyyyVJDodD1157rZYsWeLKocqlZcuWWr58uTZs2FCk+MvJydGWLVsUEBCgRo0aXXRfBYVicHCwvL29iyz39vZWcHCwTp06pZSUlEKT2l+oiN2+fbu2b9+u+vXrl+6kqoBuDRsq1N9fl0VY300OAAAA2IVLUz1ERUXp0KFDzp+3bt2qY8eOFenl8vPzU3Z2tiuHKpdevXpJOncr6u/NmjVLmZmZ6tKlS6EJ20tSUMwlJycrKSmpyPI9e/bo1KlTCg4OdhbEzz77rIwxxf4bOnSoJOk///mPjDH65JNPynual5wWNWvq7quvVqf4eKtDAQAAAGzDpeLv8ssv18qVK7Vx40ZJ0uuvvy6Hw6GePXsWWm/v3r2qXbu2K4cqlzvvvFOhoaGaOXOmpk+f7mw/fvy4HnvsMUnSI488UmS7Jk2aqEmTJoUK2/j4eOd0ECNGjHBOIyFJKSkpGjFihCSpX79+8vGplEFUAQAAAKDcXKpSRo4cqUWLFunaa69VeHi4Tp06pfj4eOcomJKUmpqqn376SX369HE52LKKiIjQRx99pMGDB2vQoEFKTExUZGSkFi5cqJSUFI0aNapIL6Uk5yAzOTk5hdonTpyoLl26aMGCBUpISHDO87dmzRqdPHlS9evX17hx4yr9vC51x86c0Yn0dEUGBanWeQPzAAAAAKg8LvX89ezZU2+99Zbq1q2rjIwMtWvXTjNmzJCfn59znUmTJiknJ0edO3d2OdjyGDhwoJYvX67u3btr48aNmjNnjhISEvTJJ59o/PjxZdpXmzZttGnTJt11110KDQ3VwoULtXjxYtWsWVOPP/64fvrpJ9WpU6eSzqTq+NePP6r5u+/qH8uXWx0KAAAAYBsOY4ypzANkZGQoOztbISEhxQ6UgrJLS0tzjmRa3BQWlSU5I1mS6/OZvLRihV5fs0Z3tWqlFy36o0BVUlF5QcUhJ56HnHgm8uJ5yIlnIi+ex5NyUpbaoNIfTgsMDFRgYGBlHwaXkDEdOmhMhw5WhwEAAADYSoUVf6tXr9aKFSucg6TUrVtXHTp0UNu2bSvqEAAAAACAcnK5+Nu5c6duv/12rV+/XpJUcBepw+GQJF1zzTWaPHmyLrvsMlcPBQAAAAAoJ5eKvyNHjqhjx446duyY6tSpo5tvvln169eXw+HQ3r179fXXX2vdunVKTEzU+vXrLZnuAZ7n002b9P2ePerfpIlubtbM6nAAAAAAW3Cp+HvhhRd07NgxjRw5Ui+99FKhUT4l6ZVXXtGYMWP0+uuv68UXX9Rbb73lUrCoGtYdPqwpW7YoISKC4g8AAABwE5emepgzZ44aN26s8ePHFyn8JMnX11evvvqqGjdurFmzZrlyKFQhg5o21YTu3XVjQoLVoQAAAAC24VLxd+TIEbVq1eqC6zgcDrVq1UpHjhxx5VCoQhLr19fD112ntrGxVocCAAAA2IZLxV9oaKgOHDhw0fUOHDjg1vnoAAAAAACFuVT8tW3bVitXrtTs2bNLXGfOnDlauXKlrr/+elcOhSrk+NmzSjp1SmlZWVaHAgAAANiGS8Xf448/Li8vL/Xv31+333675s6dq23btmnbtm2aN2+ehg4dqv79+8vLy0uPP/54RcWMS9wj8+erwT//qfd/+snqUAAAAADbcGm0z7Zt2+rjjz/WiBEj9Nlnn+nzzz8vtNwYo8DAQP373//Wdddd51KgqDr8vLwU6OMjP29vq0MBAAAAbMPlSd5vu+02JSYm6v3339cPP/ygw4cPS5Lq1KmjDh06aPjw4YplYA+c58O+ffVh375WhwEAAADYisvFnyTFxMRo7NixFbErAAAAAEAlcOmZPwAAAADApYHiD2731tq1uvPbb/XD/v1WhwIAAADYRplu+/R2YYAOh8Oh3Nzccm+PquP7PXs0e9cuXR8bq/b16lkdDgAAAGALZSr+YmNj5XA4KisW2MSQli3VNiZGV9eubXUoAAAAgG2Uqfjbu3dvJYUBOxncrJnVIQAAAAC2Y9kzfzt37tTy5cutOjwAAAAA2Iplxd9LL72kTp06WXV4WCg5I0O/nT2rLJ4BBQAAANyG0T7hdn2/+EI1XntNs3butDoUAAAAwDYo/uB2xhhJkrcXLz8AAADAXco04AtQEX644w5nAQgAAADAPSj+YAmmDAEAAADci/vuAAAAAMAGKP7gdi//8IMenjdPO0+etDoUAAAAwDYo/uB2k3/+WW+uXauDaWlWhwIAAADYBs/8we3uvvpqHT1zRvXDw60OBQAAALANij+43YNt2lgdAgAAAGA7lhV/7du3t+rQAAAAAGA7lj3zN3z4cH388cdWHR4WOpOdrbPZ2cpnrj8AAADAbVzq+bvhhhtKtZ6fn58iIyN15ZVX6o9//KNiY2NdOSwucZe//bYOpqVp3V136Zo6dawOBwAAALAFl4q/pUuXSjo3YbcpoRfn/GVTpkzRk08+qVdeeUUPP/ywK4fGJaygx8+Lid4BAAAAt3Hpts+kpCQ99NBD8vHx0a233qpvv/1WmzZt0qZNm/Tdd9/ptttuk4+Pjx544AH98MMPevHFFxUQEKBHHnlE8+fPr6hzwCVm9wMP6PSYMWpZs6bVoQAAAAC24VLP35o1a/TWW29p7ty56tq1a6FlLVq0UK9evXT77berZ8+euu666/T444+rTZs26ty5s9566y1169bNpeBxaQr09bU6BAAAAMB2XOr5e+2119ShQ4cihd/5unbtqvbt22v8+PGSpE6dOqlly5b68ccfXTk0AAAAAKAMXCr+tm3bpjqlGLCjTp062r59u/Pnyy67TCkpKa4cGpewZ5Ys0ROLFulEerrVoQAAAAC24dJtn0FBQVq/fr2MMXKUMHiHMUbr169XUFCQsy0zM1OhoaGuHBqXsPGrV+tsTo6GX3WVos57XQAAAACoPC71/HXp0kW7d+/WAw88oPRienEyMjL00EMPaffu3YWe79u1axfTPdjYX6+9Vg+2bq3wgACrQwEAAABsw6Wev5deekkLFy7Uu+++qylTpqhHjx7Oou7AgQP6/vvvderUKUVHR+sf//iHpHO3iu7YsUOPPvqo69HjkvTKBZ4RBQAAAFA5XCr+4uLitHr1ao0YMUKLFy/WlClTiqzTuXNnvfvuu4qLi5MkNWjQQEeOHFFYWJgrhwYAAAAAlIFLxZ8kNWzYUAsXLtSePXu0cuVKHTlyRJJUu3ZtXX/99UpISCi0vr+/v2oyv5ttGWNkJDmkEp8TBQAAAFDxXC7+CjRs2FANGzasqN2hijKSvJ97TpJ0fPRoRQcHWxsQAAAAYBMVVvxJ0vHjx3Xo0CFJUt26dVWjRo2K3D2qgLz8fOf/e3u5NN4QAAAAgDKokOLvnXfe0Ztvvqndu3cXar/sssv00EMP6d57762Iw6AK8PHy0olHH1WeMYz2CQAAALiRS8Vffn6+Bg8erBkzZsgYo/DwcMXFxcnhcGjfvn3auXOn7r//fi1atEhff/01z3hBDodDkcztBwAAALidS/fdTZw4UdOnT1ejRo307bffKjk5WRs3btSGDRt08uRJfffdd2rcuLFmzJihiRMnVlTMAAAAAIAycqn4+/jjjxUaGqqlS5eqd+/eRZb36tVLixcvVkhIiD766CNXDoUqIiMnR88vW6YXV6wo9PwfAAAAgMrlUvG3detW3XDDDRecuqFWrVrq3Lmztm7d6sqhUEWczcnR00uX6u+LF1sdCgAAAGArLg/4Uprn+HjWDwX8vb11d6tWyjNGXrwuAAAAALdxqfhr3LixFi9erBMnTigqKqrYdU6cOKHFixercePGrhwKVUQ1f3/9+6abrA4DAAAAsB2XbvscOnSoUlNT1blzZy1atKjI8iVLlqhr165KS0vTsGHDXDkUAAAAAMAFLvX83XfffZo3b57mzp2rbt26KTo6WnFxcZKkffv26bfffpMxRj179tR9991XIQEDAAAAAMrOpZ4/b29vfffdd3r11VcVExOj48ePa926dVq3bp2OHz+u2NhYvfrqq/r222/l5eXSoVBFJJ06pYAXXlCNV1+1OhQAAADAVlwe8MXLy0uPPPKIHnnkER04cECHDx+WJNWpU0exsbEuB4iqJTc/X1l5ecrMzbU6FAAAAMBWXC7+zhcbG0vBhwuqHx6ufQ8/LGOM1aEAAAAAtlKhxR9wMb7e3qoXFmZ1GAAAAIDtlKn4u+OOO8p9IIfDoQ8//LDc2wMAAAAAyq9Mxd8nn3xS7gNR/EGSjp89q/9s3qywgADd2aqV1eEAAAAAtlGm4m/JkiWVFQds4kBqqkYvWKCY0FCKPwAAAMCNylT8dezYsbLigE1UDwzUrVdcoeoBAVaHAgAAANiKZQO+PProo5o+fbr27NljVQiwQIPq1TV5wACrwwAAAABsx7KZ10+cOKG9e/dadXgAAAAAsBXLij8AAAAAgPtQ/MGtfti/X5HjxukPH39sdSgAAACArVD8wa0yc3OVnJGhlMxMq0MBAAAAbMWyAV9gT21jYrT1vvvk6+1tdSgAAACArVD8wa2C/fx0eXS01WEAAAAAtsNtnwAAAABgA/T8wa2STp3S93v2qE61aurTuLHV4QAAAAC2Qc8f3GrDkSO6d/ZsvbpqldWhAAAAALZiWc+fMUbGGKsOD4vUqVZN/Zs0UZOoKKtDAQAAAGzFsp6/Tz75RPn5+VYdHhZpGxur6bfcohc7d7Y6FAAAAMBWXOr5mzRpUqnW8/PzU2RkpFq2bKkaNWq4ckgAAAAAQDm4VPwNGzZMDoej1Os7HA516dJFb731li677DJXDg0AAAAAKAOXir+nn35ae/fu1aRJkxQSEqJu3bqpXr16kqQDBw5o/vz5On36tG6//Xb5+/tr1apVmj9/vjp06KCffvpJdevWrZCTwKXjq//+V6Pnz1eXBg30Ud++VocDAAAA2IZLxd/tt9+u1q1b64477tD48eMVFhZWaHlaWppGjRqlGTNmaO3atWrQoIEeffRRTZgwQS+//LLeeustl4LHpSctK0sH0tJ0Ij3d6lAAAAAAW3EYF4bcHDx4sDZs2KCdO3fKy6v4sWPy8/PVqFEjtWrVSl999ZWys7MVHx+voKAg7dq1q9yB21laWprCwsKUmpqq0NBQtx03OSNZkhQRGFHufZxIT9felBSF+vurUWRkRYVmaxWRF1QscuJ5yIlnIi+eh5x4JvLieTwpJ2WpDVwa7XPJkiVq06ZNiYWfJHl5eal169ZavHixpHODv7Rs2VKHDh1y5dC4REUFBemaOnUo/AAAAAA3c6n4S09P19GjRy+63rFjx5SZmen8OTQ0VD4+lk0xCAAAAAC241Lxd8UVV2j58uVavnx5ieusWLFCy5Yt0xVXXOFsO3DggKKjo105NC5RW3/7Tf/ZvFkr9++3OhQAAADAVlwq/h577DHl5eWpe/fuGjFihBYsWKDt27dr+/btWrBgge655x51795dxhg99thjkqTU1FT99NNPuu666yrkBHBpmbd7t4Z8843eXb/e6lAAAAAAW3Hp3ssBAwZowoQJ+tvf/qb3339fH3zwQaHlxhj5+flpwoQJ6t+/vyTp5MmTGjt2rDp37uzKoXGJqhcWpq4NGuiKGjWsDgUAAACwFZdG+yyQlJSkDz/8UKtWrdKRI0ckSbVr11a7du30l7/8RQ0aNHA5UPzPpTzaJyoeefE85MTzkBPPRF48DznxTOTF83hSTspSG1TIqCvx8fF64YUXKmJXAAAAAIBK4NIzfwAAAACAS0OFFH9bt27VyJEj1a5dOzVu3Ng5uIskrVq1Sv/85z+VnJxcEYfCJe6ttWt1+dtv6x8XGCEWAAAAQMVz+bbP119/XY8//rhyc3MlSQ6HQydOnCi0zsiRI+Xv768RI0a4ejhc4o6dPavtJ07o2NmzVocCAAAA2IpLPX+zZ8/W6NGjFRsbq+nTp+v48eP6/fgx119/vaKjozVz5kyXAkXVcGerVlo6dKgeaN3a6lAAAAAAW3Gp5+/1119XcHCwFixYcMERPa+88krt2LHDlUOhiqgfHq764eFWhwEAAADYjks9fwWTtV9sKoeoqCgdPXrUlUMBAAAAAFzgUs9fdna2qlWrdtH1jh8/Lh+fCplVApe4zUePas+pU2oSFaWm0dFWhwMAAADYhks9f/Hx8dq8efMF18nOztbPP/+sRo0auXIol6xcuVI9e/ZURESEQkJC1Lp1a02aNKlc+0pNTdUTTzyhZs2aKSgoSAEBAWrcuLFGjhyp48ePX3Dbb775Rj169FB0dLQCAgIUGxur/v3764cffihXLJeiDzdu1MCvvtLnv/xidSgAAACArbhU/PXp00d79+7V66+/XuI648aN02+//aYBAwa4cqhymzZtmjp27Kh58+apRYsW6tGjh3bt2qWhQ4dq9OjRZdrXiRMndO211+qll15ScnKyunbtqu7duystLU1vvPGGrrzySu3bt6/Idvn5+Ro+fLiz0Lv66qvVr18/xcbGas6cOVq6dGkFna3na1C9utrFxvLcHwAAAOBmLt2L+dhjj+mzzz7To48+qrVr16p///6SpGPHjmnGjBmaMWOGPvvsM8XHx+v++++vkIDLIjk5WXfccYfy8vI0bdo0ZwF67NgxtW/fXuPHj1fv3r2VmJhYqv29+OKL2rVrl/r06aMvv/xSAQEBkqTMzEz9+c9/1owZM/T000/r008/LbTdc889p48++kg33XSTPvnkE0VERDiXnTp1qsjUGFXZw9ddp4evu87qMAAAAADbcannr3r16lq4cKGaNWumr7/+Wrfeeqskad68eRo0aJAmT56syy+/XPPmzSvVs4EV7YMPPlBaWpr69u1bqOexZs2aGjdunCRp/Pjxpd7f8v+fmHzMmDHOwk+SAgIC9NRTT0mS1q1bV2ibgwcP6qWXXlK9evX05ZdfFir8pHPX8LLLLivbiQEAAABAGbk8CkujRo20adMmffvtt1qwYIH27t2r/Px8xcTEqGvXrho4cKC8vb0rItYymz17tiRp0KBBRZb16tVLAQEBWrhwoTIzMwsVcyXx9/e/6DqRkZGFfv7000+VnZ2tO++8U4GBgaWMHAAAAAAqVoUMwenl5aV+/fqpX79+FbG7ClMwGE2rVq2KLPPz81Pz5s21fv167dy5Uy1atLjo/rp166ZVq1bp5Zdf1hdffFHots/nn39ekjR8+PBC2yxevFjSucnujxw5os8++0y7d+9WWFiYOnXqpO7du8vhcLh0npeSp5cs0exduzTyuut0WymuOQAAAICKUSHFX3p6utavX68jR44oKyurxPWGDBlSEYcrlbS0NKWmpkqSYmJiil0nJiZG69ev1759+0pV/I0ePVrLli3TzJkzFR8frzZt2kiS1q5dq8zMTL322msaNmxYoW22bt3q/O/AgQOdMUnnBsNJTEzUjBkzFH6BAVCysrIKXde0tDRJUnJGsnJ9cy8ad0U5lXHK5X1sO3FMG44c0d6U35SckVwBUaEi8oKKRU48DznxTOTF85ATz0RePI8n5SQtI63U67pc/D399NOaMGGC0tPTS1zHGCOHw+HW4u/MmTPO/w8KCip2neDgYEnS6dOnS7XP4OBgzZ49W3fffbcmT56smTNnOpd16tRJ7du3L7LNqVPnXhijRo1S27Zt9eabbyohIUE//vij7rrrLi1dulR33XWXvv766xKP+9JLL2ns2LGlitHTPdi6lW5u2kiXRVS3OhQAAADAVlwq/saNG6cXXnhB3t7e6tWrlxo1alShA7v0799f27ZtK9M2kyZNUuvWrSsshvPt379fvXr10pEjRzRp0iT16NFDkjR37lyNHDlSiYmJmj9/vjp06ODcJj8/X9K5gV3mzp3rLDg7d+6sb7/9Vi1atNDUqVO1c+fOEudCHDNmjEaNGuX8OS0tTbGxsYoIjFBoYGilnOuFRARGXHylEnSIK/+2uDBX8oLKQU48DznxTOTF85ATz0RePI8n5MQnp/QlnUvF3/vvv6/AwECtWLGi2OfqXJWUlKQdO3aUaZuCHsiQkJBCbaGhRYuks2fPSlKpC9ahQ4dqy5YtmjFjRqHnG4cMGaKQkBANHDhQo0eP1tq1a53LQkJCdOrUKd18883Owq9A8+bNde211+rHH3/U8uXLSyz+/P39SzXYDAAAAACUxKWpHg4cOKCOHTtWSuEnSZs2bZIxpkz/CubsCw0NVVhYmKRz0y0Up6A9Li7uorEcOHBAS5culb+/v2666aYiy/v27Ss/Pz+tW7dOmZmZzvaCfdevX7/Y/Ra0Hz9+/KIxVAU/HT6sebt362Ba6e9NBgAAAOA6l4q/WrVqFenN8iQtW7aUJG3YsKHIspycHG3ZskUBAQEl9ridr6BQDA4OLnbqCm9vbwUHB8sYo5SUFGf7VVddJel/z/79XnLyuUFPzu+prMrGLlumGz/7TPN277Y6FAAAAMBWXCr+/vjHP2rp0qXO2yc9Ta9evSRJU6dOLbJs1qxZyszMVJcuXUo1x1+tWrUknSvWkpKSiizfs2ePTp06peDgYEVFRTnb+/TpI0latmxZkW3OnDnjLEwLisSqLj48XFfWqqWoEgbhAQAAAFA5XCr+nn32WV1++eXq06ePdntgT86dd96p0NBQzZw5U9OnT3e2Hz9+XI899pgk6ZFHHimyXZMmTdSkSRMdOnTI2RYfH++cDmLEiBGFpmxISUnRiBEjJEn9+vWTj8//HqW86aabdPnll2vVqlV65513nO15eXkaNWqUkpOT1bx582JHCq2K3rzxRm0cMUL9mjSxOhQAAADAVhzGGFPejW+44QZlZ2dr9erV8vLyUlxcnGJiYuTlVbSmdDgcWrRokUvBlse0adM0ePBg5/OAkZGRWrhwoVJSUjRq1CiNHz++2FilcwPOnP+s3tq1a9WlSxedOXNGUVFRznn+1qxZo5MnT6p+/fpauXKl6tSpU2h/mzZtUseOHZWWlqaWLVsqISFBGzdu1K+//qrIyEgtWbJEV1xxRanPKS0tTWFhYUpNTS12IJvKUjAvnyeMaoT/IS+eh5x4HnLimciL5yEnnom8eB5PyklZagOXir/iirwSD+RwKC8vr7yHcsnKlSv1wgsvaM2aNcrOzlbTpk11//33a+jQocWuX1LxJ527vfOVV17RokWLdOjQIXl5eSk+Pl59+vTRo48+qoiI4l8ASUlJevbZZzV//nydPHlSNWvWVI8ePfTkk0+WasCZ81H84XzkxfOQE89DTjwTefE85MQzkRfP40k5cVvxt2/fvjKtX9YiB8W7lIu/B+bM0c/Hj+u5xER1LGEEVJSNJ3344Bxy4nnIiWciL56HnHgm8uJ5PCknZakNXJrnj2IOZbXx6FGtPHBAJzMyrA4FAAAAsBWXij+grF644Qb9dvasWteta3UoAAAAgK1Q/MGtErnVEwAAALCES1M9AAAAAAAuDfT8wa1+OnxYGbm5al6jhsIDAqwOBwAAALANev7gVn+ZOVMdPv5Y6w8ftjoUAAAAwFbo+YNbxYWHKzM3VyF+flaHAgAAANgKxR/c6rs//cnqEAAAAABb4rZPAAAAALABij8AAAAAsAGKP7jV7TNm6MbPPtPOkyetDgUAAACwFZ75g1st3btXB9PSdCY72+pQAAAAAFuh+INbvdG9u85kZysuLMzqUAAAAABbofiDWw1s2tTqEAAAAABb4pk/AAAAALABev7gVhuPHJGR1Cw6Wv4+vPwAAAAAd6HnD27V8ZNPdPXEiTqQlmZ1KAAAAICt0PUCt6pTrZpOZ2fLz9vb6lAAAAAAW6H4g1ttv/9+q0MAAAAAbInbPgEAAADABij+AAAAAMAGKP7gNsYYDfjySw366iulZWVZHQ4AAABgKxR/cJt8YzRj+3ZN27ZNOXl5VocDAAAA2AoDvsBtHA6H3uvVS3nGKMTPz+pwAAAAAFuh+IPbeDkcGnHNNVaHAQAAANgSt30CAAAAgA3Q8we3ycvP186TJ+Xt5aXLIiLkcDisDgkAAACwDXr+4DapWVlq+s47avyvfyk3P9/qcAAAAABboecPbmOMUURgoPKNkbcXf3cAAAAA3IniD24TGRSkk489ZnUYAAAAgC3R/QIAAAAANkDxBwAAAAA2QPEHt/nt7FndNn267vr2W6tDAQAAAGyH4g9uk5aVpc9++UVTtmyxOhQAAADAdhjwBW4TGRSk8d26yYeRPgEAAAC3o/iD24QHBGhU27ZWhwEAAADYEl0wAAAAAGADFH9wm+y8PO1LSdGR06etDgUAAACwHYo/uM2W48dV/803dc3771sdCgAAAGA7FH9wK39vb/l7e1sdBgAAAGA7DPgCt2lVu7Yyn3zS6jAAAAAAW6LnDwAAAABsgOIPAAAAAGyA4g9us+PECY347js9v2yZ1aEAAAAAtkPxB7c5mJamiRs26Mv//tfqUAAAAADbYcAXuE2D6tX1XGKiooKCrA4FAAAAsB2KP7hNfPXqeqpjR6vDAAAAAGyJ2z4BAAAAwAbo+YPbZOXm6nR2tvy9vVXN39/qcAAAAABboecPbjN71y5Fv/qqenz2mdWhAAAAALZD8Qe3yTdGkuTtcFgcCQAAAGA/3PYJtxnUtKnynn7aWQQCAAAAcB+KP7iVl8MhL3r+AAAAALfjtk8AAAAAsAGKP7jNj4cOadT33+uDDRusDgUAAACwHYo/uM0vx45pwpo1+mb7dqtDAQAAAGyHZ/7gNi1q1tTf2rVTk6goq0MBAAAAbIfiD25zbd26urZuXavDAAAAAGyJ2z4BAAAAwAYo/uA2OXl5ysjJUW5+vtWhAAAAALZD8Qe3eXvdOgW9+KKGfvON1aEAAAAAtkPxB7fJ+/8ePyZ5BwAAANyPAV/gNg+0aaO7rr5a3hR/AAAAgNtR/MFt/Ly95eftbXUYAAAAgC1x2ycAAAAA2AA9f3CbxUlJWpKUpDYxMerdqJHV4QAAAAC2Qs8f3Gbp3r16YcUKzdu92+pQAAAAANuh5w9u06ZuXd1/7bX6Q1yc1aEAAAAAtkPxB7fp1aiRenG7JwAAAGAJbvsEAAAAABug+AMAAAAAG6D4g9s8PG+evMaO1VOLF1sdCgAAAGA7FH9wm7z8fBmrgwAAAABsigFf4DYvdu6sv//hDwry9bU6FAAAAMB2KP7gNtX8/VXN39/qMAAAAABb4rZPAAAAALABev7gNt/t2KEtx4/rhvh4tYmJsTocAAAAwFbo+YPbfL11q55YvFgr9u+3OhQAAADAduj5g9t0jIuTr5eXrqhRw+pQAAAAANuh+IPbDG/VSsNbtbI6DAAAAMCWuO0TAAAAAGyA4g8AAAAAbIDiD25zy9SpCnnxRX2yaZPVoQAAAAC2Q/EHt0nPydHZnBzl5edbHQoAAABgOwz4Ard5/6abdDY7W1FBQVaHAgAAANgOxR/cplZIiNUhAAAAALbFbZ8AAAAAYAP0/MFtpm3dqsOnT+vGyy5TQkSE1eEAAAAAtkLxB7d5c+1ardi/X19Xq0bxBwAAALgZxR/cpnN8vGpXq6bY0FCrQwEAAABsh+IPbvNMYqLVIQAAAAC2xYAvAAAAAGADtij+Vq5cqZ49eyoiIkIhISFq3bq1Jk2aVK59paam6oknnlCzZs0UFBSkgIAANW7cWCNHjtTx48eL3SYrK0uvvPKKWrVqpZCQEPn7+ys+Pl533XWXfv31V1dODQAAAABKpcoXf9OmTVPHjh01b948tWjRQj169NCuXbs0dOhQjR49ukz7OnHihK699lq99NJLSk5OVteuXdW9e3elpaXpjTfe0JVXXql9+/YV2iYzM1OJiYl6/PHHlZSUpI4dO6p3796SpA8++EBXXnmlfvrppwo7X092w6efqtZrr2nZ3r1WhwIAAADYTpUu/pKTk3XHHXcoLy9PU6dO1dKlSzV16lRt375dCQkJGj9+vJYuXVrq/b344ovatWuX+vTpo6SkJM2cOVMzZ85UUlKS+vfvryNHjujpp58utM3EiRO1Zs0aXXvttdq7d69mz56tadOmaffu3br//vt1+vRpjRo1qoLP3DP9lp6uY2fPKjc/3+pQAAAAANup0sXfBx98oLS0NPXt21cDBgxwttesWVPjxo2TJI0fP77U+1u+fLkkacyYMQoICHC2BwQE6KmnnpIkrVu3rthtRo0apbCwMGe7t7e3nnvuuWK3qapm3HKLNt9zj1rXrWt1KAAAAIDtVOnRPmfPni1JGjRoUJFlvXr1UkBAgBYuXKjMzMxCxVxJ/P39L7pOZGSky9tUVcztBwAAAFinSvf8bd68WZLUqlWrIsv8/PzUvHlzZWZmaufOnaXaX7du3SRJL7/8sjIzM53tmZmZev755yVJw4cPL3ab119/Xampqc72vLw85y2iv98GAAAAACpale35S0tLcxZbMTExxa4TExOj9evXa9++fWrRosVF9zl69GgtW7ZMM2fOVHx8vNq0aSNJWrt2rTIzM/Xaa69p2LBhhba57bbbNG/ePH3xxReqX7++2rVrp4CAAP300086duyYHn30UectoyXJyspSVlZWoXOTpOSMZOX65l407opyKuOUS9tP37ZT6Tk56pHQQFFBgRUUFVzNCyoeOfE85MQzkRfPQ048E3nxPJ6Uk7SMtFKvW2WLvzNnzjj/PygoqNh1goODJUmnT58u1T6Dg4M1e/Zs3X333Zo8ebJmzpzpXNapUye1b9++yDbe3t6aPHmy6tWrp3HjxjlvRZXO9Uh27txZ3t7eFzzuSy+9pLFjx5YqRk/27NKVOnT6jBbeHkXxBwAAALiZRxd//fv317Zt28q0zaRJk9S6detKiWf//v3q1auXjhw5okmTJqlHjx6SpLlz52rkyJFKTEzU/Pnz1aFDB+c2p06dUv/+/bVu3Tq9+eabGjhwoIKCgrR8+XI98MAD6tmzpz7//HPdcsstJR53zJgxhUYETUtLU2xsrCICIxQaGFop53ohEYHle3ave8MEHU9PV1x4zXLvAyXjmnoecuJ5yIlnIi+eh5x4JvLieTwhJz45pS/pPLr4S0pK0o4dO8q0TXp6uiQpJCSkUFtoaNEi6ezZs5KkatWqlWrfQ4cO1ZYtWzRjxgz169fP2T5kyBCFhIRo4MCBGj16tNauXetcNnLkSC1btkwTJkzQgw8+6Gzv27ev6tatq9atW+uRRx7RgAED5OvrW+xx/f39SzVwjKf7sG9fq0MAAAAAbMujB3zZtGmTjDFl+peYmChJCg0NdU6tcPDgwWL3X9AeFxd30VgOHDigpUuXyt/fXzfddFOR5X379pWfn5/WrVvnHAwmLy9PU6ZMkVT8iKPXXHON4uPjdejQIf36668XvyAAAAAAUE4eXfy5qmXLlpKkDRs2FFmWk5OjLVu2KCAgQI0aNbrovgoKxeDg4GKf0fP29lZwcLCMMUpJSZEkHT9+XNnZ2ZJUaI6/8xW0nzrlOQ+NAgAAAKh6qnTx16tXL0nS1KlTiyybNWuWMjMz1aVLl1LN8VerVi1JUnJyspKSkoos37Nnj06dOqXg4GBFRUVJkiIiIuTn5ydJWr9+fZFt0tLSnLe1lqb38VLX4t131eDNN5VEoQsAAAC4XZUu/u68806FhoZq5syZmj59urP9+PHjeuyxxyRJjzzySJHtmjRpoiZNmujQoUPOtvj4eOd0ECNGjCg0Z19KSopGjBghSerXr598fM49Sunv7+8cFGbUqFE6cuSIc5vMzEzdd999Sk9PV7t27VS7du2KOm2PlZSSoqSUFOUbY3UoAAAAgO149IAvroqIiNBHH32kwYMHa9CgQUpMTFRkZKQWLlyolJQUjRo1yvmM4PkKeuNycnIKtU+cOFFdunTRggULlJCQ4Jznb82aNTp58qTq16+vcePGFdrm9ddf19q1a7Vp0yY1btxYbdu2VWBgoNatW6fDhw8rIiJC7733XuVcAA+zZOhQ5eTlqW4xg+8AAAAAqFxVuudPkgYOHKjly5ere/fu2rhxo+bMmaOEhAR98sknGj9+fJn21aZNG23atEl33XWXQkNDtXDhQi1evFg1a9bU448/rp9++kl16tQptE3Dhg21efNmjRw5UnXr1tXy5cs1b948BQUF6a9//as2b96s5s2bV+Qpe6xr6tRR29hYBfhU6b85AAAAAB7JYQz34F1q0tLSFBYWptTU1GKnsKgsyRnJkjxjPhP8D3nxPOTE85ATz0RePA858UzkxfN4Uk7KUhvQBQO3+eznn+XlcKj/5ZfT+wcAAAC4Gd/A4RbGGN02Y4Yk6fjo0RR/AAAAgJvxDRxukW+MujRooLz8fPlT+AEAAABux7dwuIW3l5cW3H671WEAAAAAtlXlR/sEAAAAAFD8AQAAAIAtUPzBLU5nZanZO++oxbvvKicvz+pwAAAAANvhmT+4RXZenrb+9pskycvhsDgaAAAAwH4o/uAWof7+WjxkiPKNofgDAAAALEDxB7fw9fZWp/h4q8MAAAAAbItn/gAAAADABuj5g1uk5+Ro/p498vP2Vs/LLrM6HAAAAMB26PmDWxw7c0b9v/xSN3/9tdWhAAAAALZEzx/cws/bW21jYhTgw0sOAAAAsALfxOEWdUNDtWr4cKvDAAAAAGyL2z4BAAAAwAYo/gAAAADABij+4BY7T57Ute+/r5umTLE6FAAAAMCWeOYPbnE6K0vrDx9WTGio1aEAAAAAtkTxB7doGBGh7/70J/l7e1sdCgAAAGBLFH9wi/CAAPVu1MjqMAAAAADb4pk/AAAAALABev7gFskZGVp/+LBC/f11XUyM1eEAAAAAtkPPH9xi89Gj6j55su6YOdPqUAAAAABboucPbhHk66sWNWuqQfXqVocCAAAA2BLFH9yiTUyMNt9zj9VhAAAAALbFbZ8AAAAAYAMUfwAAAABgAxR/cIsf9u9X4ief6IE5c6wOBQAAALAlnvmDWxw7c0bL9u1Tbn6+1aEAAAAAtkTxB7doExOjLwcNUkRgoNWhAAAAALZE8Qe3iAkN1eBmzawOAwAAALAtnvkDAAAAABug5w9ucfzsWe1OTlZEYKCaREVZHQ4AAABgO/T8wS1m79ypdh99pFHff291KAAAAIAtUfzBLUL8/NSwenXVqVbN6lAAAAAAW+K2T7jFzc2a6WYGfAEAAAAsQ88fAAAAANgAxR8AAAAA2ADFH9zim+3b1evzz/XqypVWhwIAAADYEs/8wS1+PXVKc3btUnhAgNWhAAAAALZE8Qe36N6woar36aMG1atbHQoAAABgSxR/cItmNWqoWY0aVocBAAAA2BbP/AEAAACADdDzB7c4cvq0jp09q+igINUNDbU6HAAAAMB2KP5QbvtTU3UiPd35c1RQkCQVaasXFqZXfvhBb/74owZdfrnGdOjgbAcAAADgHhR/KJf9qalq/K9/KTM319nm5+Ulh8OhrLy8/7V5e2vp0KF6e/16SdLUbds0dds2Bfj4aMf991MAAgAAAG7CM38olxPp6YUKP0nKzs8vVPhJUnZenn5NSVFufn6h9szc3EI9hAAAAAAqF8UfAAAAANgAxR8AAAAA2ADFH8olKihIAT6FHxn19/aWv7d3obYAHx81CA8vsm6Aj49zgBgAAAAAlY8BX1Au9cLCtOP++0s92mdx6zLYCwAAAOA+FH8ot3phYcUWcCW1UewBAAAA1uG2TwAAAACwAYo/AAAAALABij8AAAAAsAGKPwAAAACwAYo/AAAAALABij8AAAAAsAGKPwAAAACwAYo/AAAAALABij8AAAAAsAGKPwAAAACwAYo/AAAAALABij8AAAAAsAGKPwAAAACwAYo/AAAAALABij8AAAAAsAGKPwAAAACwAYo/AAAAALABij8AAAAAsAGKPwAAAACwAYo/AAAAALABij8AAAAAsAGKPwAAAACwAYo/AAAAALABij8AAAAAsAGKPwAAAACwAYo/AAAAALABij8AAAAAsAGKPwAAAACwAYo/AAAAALABij8AAAAAsAGKPwAAAACwAYo/AAAAALABij8AAAAAsAGKPwAAAACwAYo/AAAAALABij8AAAAAsAGKPwAAAACwAYo/AAAAALABij8AAAAAsAEfqwNA2RljJElpaWluPW5axrnj+eTwsvEk5MXzkBPPQ048E3nxPOTEM5EXz+NJOSmoCQpqhAuxPlqU2enTpyVJsbGxFkcCAAAAwBOcPn1aYWFhF1zHYUpTIsKj5Ofn6/Dhw6pWrZocDofbjpuWlqbY2FgdOHBAoaGhbjsuLoy8eB5y4nnIiWciL56HnHgm8uJ5PCknxhidPn1aderUkZfXhZ/qo+fvEuTl5aWYmBjLjh8aGmr5ixxFkRfPQ048DznxTOTF85ATz0RePI+n5ORiPX4FGPAFAAAAAGyA4g8AAAAAbIDiD6Xm7++vZ555Rv7+/laHgvOQF89DTjwPOfFM5MXzkBPPRF48z6WaEwZ8AQAAAAAboOcPAAAAAGyA4g8AAAAAbIDiDwAAAABsgOIPF5WRkaGnn35ajRo1UkBAgOrUqaM77rhDhw4dsjq0Kis9PV3ffPONhg8frsaNGysgIEDBwcFq2bKlnnvuOZ05c6bEbT/55BO1bt1aISEhioiIUM+ePbVq1So3Rm8fJ0+eVI0aNeRwOJSQkHDBdclL5fvtt980evRoNW7cWIGBgYqIiFCrVq306KOPFrv+d999p44dOzrnaEpMTNTs2bPdHHXVtW7dOg0ePFh16tSRr6+vwsPD1aFDB3388ccqbriBvLw8TZgwQVdccYUCAwMVHR2twYMHa9u2bRZEf+n66aef9PLLL2vAgAGKiYmRw+GQw+G46Hbl+YxauXKlevbsqYiICIWEhKh169aaNGlSRZ1KlVGWnOTn52vFihV67LHHdPXVV6tatWry9/dXw4YNdc899ygpKemCxyInpVPe98n5unTp4tzu4MGDJa7ncTkxwAVkZGSY6667zkgytWvXNoMHDzatW7c2kkx0dLTZs2eP1SFWSe+//76RZCSZyy+/3Nx8882me/fuplq1akaSadKkiTl27FiR7R566CEjyQQGBpq+ffua7t27Gx8fH+Pt7W1mzJjh/hOp4oYOHWocDoeRZBo2bFjieuSl8q1fv95ERkYaSaZZs2bmlltuMTfeeKOJi4sz3t7eRdafMGGCkWR8fHxMjx49TN++fU1gYKCRZN566y0LzqBqmTp1qvH29jaSTKtWrczgwYNNp06djI+Pj5Fk/vznPxdaPy8vz/Tv399IMuHh4WbgwIGmY8eOxuFwmKCgILN27VqLzuTS07dvX+fvj/P/XUh5PqMKcuxwOEzHjh3NwIEDTXh4uJFkHnnkkUo4s0tXWXKya9cu5/JatWqZPn36mP79+5u6desaSaZatWpmxYoVxW5LTkqvPO+T83388cdGkvM7wIEDB4pdzxNzQvGHC/r73/9uJJm2bdua06dPO9vHjx9vJJmOHTtaF1wV9sknn5i7777bbN26tVD74cOHzVVXXWUkmT/96U+Fli1YsMBIMpGRkWbnzp3O9lWrVhk/Pz8THh5uTp065Y7wbWHhwoVGkrn77rsvWPyRl8p3/PhxExUVZYKCgszMmTOLLP994bB9+3bj7e1t/P39zapVq5ztO3bsMJGRkcbHx8fs2rWr0uOuqnJyckyNGjWMJPPZZ58VWrZ161YTERFhJJnFixc72wv+4HXZZZeZo0ePOtunTp1qJJmEhASTk5PjtnO4lL388svmqaeeMt9++605cuSI8ff3v+CX2vJ8Rp08edKEhoYaSWbatGnO9qNHj5qEhAQjySxZsqSiT+2SVZac7N6923Tt2tUsWrTI5OfnO9szMzPNsGHDjCRTr149k52dXWg7clI2ZX2fnO/48eMmIiLCdOvWzcTFxZVY/HlqTij+UKKsrCwTFhZmJJkNGzYUWd6iRQsjyaxfv96C6Oxr1apVRpLx9/c3WVlZzvYbb7zRSDITJkwoss2DDz5oJJnXXnvNjZFWXenp6aZhw4amadOmZufOnRcs/shL5bv33nuNJPP222+Xaf2HHnqoyLLXX3/dSDL3339/BUdpH7/88ouRZBo3blzs8oLX/SuvvOJsu/zyy42kYnuZ+vTpYySZqVOnVlbIVdrFvtSW5zPqlVdeMZJM3759i2wzffp0I8n07t3b1dCrrLIUGudLT093fi9bunRpoWXkxDVlycmf//xnExAQYHbv3n3B4s9Tc8IzfyjRypUrlZqaqoYNG+qqq64qsnzQoEGSzj03A/dp2bKlJCkrK0snT56UdO65zMWLF0v6X17OR64q1tixY/Xrr7/qvffek6+vb4nrkZfKl5GRocmTJys4OFh/+ctfSrVNwXN95KRylHbC48jISElSUlKStm3bpsDAQPXq1avIeuSk8pT3M+pC76FevXopICBACxcuVGZmZkWHbGuBgYFq1KiRJOnw4cOFlpET95g3b54+//xz/f3vf1fDhg0vuK6n5oTiDyXavHmzJKlVq1bFLi9o//nnn90WE6Rff/1VkuTr66uIiAhJ0o4dO5SVlaXo6GjFxMQU2YZcVZyff/5Z48eP11/+8hd16NDhguuSl8q3fv16nT59WldddZUCAwM1d+5cjRo1Svfdd5/eeOONIl+QUlJStH//fkkq9o9asbGxioqK0r59+5SWluaWc6hqGjRooIYNG2rHjh36/PPPCy3btm2bJk+erOrVq6t///6S/ve7pnnz5sX+MYX3SeUp72fUhb4f+Pn5qXnz5srMzNTOnTsrIWr7ys/P1759+yRJtWrVKrSMnFS+s2fP6t5771WTJk302GOPXXR9T80JxR9KVPAFqbhfCOe3F3wQwT3efPNNSVKPHj2cf2G/WK6Cg4MVHh6uU6dO6fTp0+4JtArKz8/XnXfeqfDwcI0bN+6i65OXyrd161ZJUo0aNdSvXz/17NlTEyZM0LvvvquRI0cqISFBU6ZMca5fkJPq1asrODi42H3y2eYab29vffrppwoPD9ett96qq6++Wn/84x91ww03qEWLFoqJidGiRYucf7zid411yvMZlZaWptTU1AtuR84qx5QpU3T8+HFFR0fr+uuvd7aTE/d4+umntXfvXr333nvy8/O74LqenBOKP5SoYDqBoKCgYpcXfHHiS6v7zJkzRx9++KF8fX31/PPPO9svliuJfFWEt956S+vWrdOrr77qvGXtQshL5Tt16pQk6dtvv9W8efP09ttv6/jx49q7d69Gjx6tjIwMDR06VJs2bZJETtylXbt2WrZsmRo0aKANGzboyy+/1JIlS+Tl5aWuXbuqQYMGznX5XWOd8rwfzp9qiJy5z4EDB/Twww9Lkp577rlCt1eTk8q3YcMGvfnmmxo6dKg6dux40fU9OScUf8AlYvv27brttttkjNGrr77qfPYP7rF//349+eST6tixo4YNG2Z1OPh/+fn5kqTc3Fw999xzuu+++xQdHa24uDi9+uqruvnmm5WTk6NXX33V4kjtZcqUKWrdurViY2O1du1anTlzRjt37tSwYcM0fvx43XDDDcrKyrI6TOCScPbsWQ0YMEAnTpxQv379dM8991gdkq3k5eU57/p57bXXrA7HZRR/KFFISIikcxOOF+fs2bOSpGrVqrktJrs6dOiQevTooVOnTmnUqFF66KGHCi2/WK4k8uWqv/71r8rOztZ7771X6m3IS+UruMaSih3wpaBt2bJlhdYnJ5Vn165dGjp0qKKiojRr1iy1bt1awcHBuuyyy/Tvf/9bvXv31oYNG/TRRx9J4neNlcrzfjj/PUfOKl9OTo5uvvlmrV+/Xu3bty/yHK1ETirbG2+8oY0bN2rcuHGKiooq1TaenBOKP5SoXr16kqSDBw8Wu7ygPS4uzm0x2VFycrK6deumffv26S9/+Uuxf3W6WK7Onj2rlJQUVa9enQ/+cpo1a5aCgoJ0zz33KDEx0fnvj3/8o6RzBXpB29GjRyWRF3co+PwJCgpSdHR0keX169eXJB0/flzS/3Jy6tQp5y/e3+OzzTVffPGFcnJy1KNHj0JfgAoMHjxYkrR8+XJJ/K6xUnk+o0JDQxUWFnbB7chZxcjPz9fQoUM1d+5cXXnllfruu+8UGBhYZD1yUrm+++47ORwOffrpp4V+/5//+/7mm29WYmKi5s2bJ8mzc+Lj1qPhklJwW+GGDRuKXV7Q3qJFC7fFZDdnzpzRjTfeqK1bt2rAgAF6//335XA4iqzXuHFj+fv767ffftOhQ4dUt27dQsvJVcVISUlx9iD9XmZmpnNZwbDN5KXyFYzYmZGRoaysrCLTDCQnJ0v6319hw8PDVa9ePe3fv18bN25U+/btC61/4MABnThxQnFxcQoNDXXDGVQ9BV9oCr74/F5Be8HzmgW/a7Zs2aKcnJwiI37yPqk85f2MatmypZYvX64NGzaoadOmhZbl5ORoy5YtCggIcE5LgPJ54IEHNGXKFDVq1Ejff/+9wsPDS1yXnFQuY4zzD1bFWbNmjSQVeizEU3NCzx9K1K5dO4WFhWnPnj3OwRLON3XqVEnSTTfd5ObI7CErK0t9+/bVjz/+qO7du2vKlCny9vYudt3AwEDdcMMNkqSvv/66yHJy5TpjTLH/kpKSJEkNGzZ0thX0NpGXylevXj21bNlSxphiC/OCtvOndSiYS67g+p+PnLiuYAj69evXF7t83bp1kv7XKxsfH6/LL79cGRkZznmxzkdOKk95P6Mu9B6aNWuWMjMz1aVLFwUEBFR0yLbx5JNP6p133lG9evW0YMEC1ahR44Lrk5PKs3Tp0hK/AxT02h04cEDGmELFn8fmxN2zyuPS8ve//91IMtdff705c+aMs338+PFGkunYsaN1wVVhubm5pn///kaS6dChgzl79uxFt1mwYIGRZCIjI83OnTud7atWrTL+/v4mPDzcnDp1qhKjtqekpCQjyTRs2LDY5eSl8n322WdGkrniiivM4cOHne0bN240ERERRpL56quvnO3bt2833t7ext/f36xevdrZvnPnThMZGWl8fHzMrl273HoOVclPP/1kJBlJ5p133im0bPXq1SY4ONhIMgsWLHC2v//++0aSueyyy8yxY8ec7dOmTTOSTEJCgsnJyXHbOVQl/v7+5kJf98rzGXXy5EkTGhpqJJlp06Y5248dO2YSEhKMJLNkyZKKPpUq42I5ef31140kU6tWrUI5uRBy4pqL5aQkcXFxRpI5cOBAkWWemhOKP1xQRkaGadOmjZFkateubQYPHuz8OTo62uzZs8fqEKukN954w/nlqX///mbo0KHF/vvtt98KbffQQw8ZSSYoKMj07dvX3HjjjcbHx8d4e3ubGTNmWHMyVdzFij9jyIs7DB061Egy4eHhpmfPnqZTp07OX+Z33XVXkfULvlz5+PiYG2+80fTt29cEBgYaSeaf//ynBWdQtYwePdr5GdasWTNz8803m3bt2hkvLy8jydx9992F1s/Ly3P+wat69epm0KBBJjEx0TgcDhMYGGjWrFlj0ZlcembNmmXatGnj/OdwOIykQm2zZs0qtE15PqOmTp1qvLy8jMPhMJ06dTKDBg0y4eHhRpIZNWqUG8700lGWnGzcuNG5vG3btiX+/l+xYkWR45CT0ivP+6Q4Fyr+jPHMnFD84aLS09PNU089ZRo2bGj8/PxMrVq1zLBhw0p8ocN1zzzzjPOL04X+JSUlFdn2448/NldffbUJCgoy4eHhpkePHmblypXuPwmbKE3xZwx5qWz5+flm4sSJzmscHBxs2rZtaz755JMSt/n2229Nhw4dTEhIiAkJCTEdOnQw3333nRujrtqmT59uunXr5uxNrV69uunUqZP5/PPPi10/NzfXjB8/3jRr1swEBASYyMhIM2jQIPPf//7XzZFf2j7++OOL/u74+OOPi92urJ9RP/zwg+nRo4cJDw83QUFB5pprrrnge86uypKTJUuWlOr3f3E5NIaclFZ53ye/d7HizxjPy4nDGGNKdX8oAAAAAOCSxYAvAAAAAGADFH8AAAAAYAMUfwAAAABgAxR/AAAAAGADFH8AAAAAYAMUfwAAAABgAxR/AAAAAGADFH8AAAAAYAMUfwAAAABgAxR/AABbcTgccjgcVodRaRwOh+rXr291GAAAD0TxBwCwvb1798rhcCgxMdHqUC7oUokTAOCZfKwOAAAAVJxt27bJ19fX6jAAAB6I4g8AgCqkSZMmVocAAPBQ3PYJALC1Z599VvHx8ZKkZcuWOZ8JdDgcGjZsWKF1Dxw4oPvvv18NGzZUQECAIiIi1Lt3b61atarIfs+/RTMtLU2jRo1SfHy8fH199fDDD0uSZs+erTvuuEOXX365QkNDFRwcrJYtW+rFF19UVlZWueK80DN/q1evVt++fRUdHS1/f3/Vr19f9913nw4fPnzB+DMyMvT4448rLi5O/v7+SkhI0CuvvCJjTCmv8jm7d++Ww+FQ165ddfbsWY0dO1aXX365goKC1LBhQ40bN67M+wQAlB49fwAAW7vyyis1cOBATZs2TTVr1lSPHj2cy9q3b+/8/9WrV6tXr146deqUGjdurF69eum3337T999/r3nz5umzzz7TLbfcUmT/GRkZ6tixo/bt26eOHTuqVatWql69uiRp+PDhysjIUPPmzdWiRQulpqbqxx9/1N///nctWrRI8+fPl7e3d5niLMnkyZM1bNgw5eXlqV27doqNjdWGDRv07rvvavr06Vq6dGmxvYbZ2dnq1q2btm7dqsTERJ09e1bLli3T448/rtOnT+uFF14o9bXevHmzJCkiIkItW7ZUVlaW2rZtq9jYWC1atEh/+9vfFBUVpTvuuKPU+wQAlIEBAMBGJJnf//pLSkoykkzHjh2L3SY1NdXUrl3beHt7m8mTJxdatm7dOlO9enUTEhJijh8/XmSfkkzbtm3NqVOniuz3m2++Menp6YXa0tLSTO/evY0k8+mnn5YpzoLzi4uLK9S2f/9+ExgYaLy9vc3MmTOd7Xl5eebhhx82ksw111xT7LEKjpeamlronL29vU1QUJA5ffp0ibH83lNPPeXc5xNPPGFycnKcyz7++GMjyfTq1avU+wMAlA23fQIAcBEfffSRjhw5oocffli33nproWXXXHONnnrqKZ05c0aTJ08udvt//vOfCg8PL9Let29fBQYGFmqrVq2aJkyYIEmaOXNmhcT/wQcfKCMjQ4MHD1afPn2c7V5eXnr55ZdVp04drV+/XitXriyyrZeXl/79738rNDTU2XbNNdfoxhtvVHp6utavX1/qODZt2iRJGjhwoP7xj3/Ix+d/NyAV9GQeP368rKcHACglbvsEAOAi5s+fL0kaMGBAscs7dOggSfrxxx+LLKtdu7auueaaEve9a9cuzZkzR7t379bZs2eVn5/vfO5t165droYuSVqxYoUkFSlcJcnf318333yz3nzzTa1YsULt2rUrtDwuLk6NGzcusl2jRo0kSUeOHCl1HAW3fT799NNFlp04cUKSVKdOnVLvDwBQNhR/AABcxN69eyWpSGH0ewUFzPnq1atX7LrGGI0ePVoTJkwocZCT06dPly3QEhQM6FLSQDAF7YcOHSqyLCYmpthtqlWrJklFBqYpSUpKivbv36/4+Hi1aNGiyPKff/5ZktS8efNS7Q8AUHYUfwAAXER+fr4kadCgQQoODi5xveIGTAkICCh23S+//FKvv/66YmNjNWHCBLVt21bR0dHy9fVVdna2/P393TbypcPhKHGZl1fFPCFScMtn69ati12+ceNGSecGtgEAVA6KPwAALiImJkY7duzQ448/rquvvrpC9jljxgxJ0rvvvqtevXoVWvbrr79WyDEK1KlTRzt27NC+ffvUrFmzIssLejbr1q1bocc9X8Etn1dddVWxyyn+AKDyMeALAMD2/Pz8JEm5ubnFLu/atauk/xVsFeHUqVOSir+t8quvvip2m4vFWZKCZxKnTJlSZFl2dra+/vrrQutVhtIUf9WqVVPDhg0rLQYAsDuKPwCA7UVFRcnX11d79uxRXl5ekeUjRoxQjRo1NG7cOE2cONF5G2iB3Nxcff/999qyZUupj1kwYMrEiRML3d65YsUKvfrqq+WKsyTDhw9XYGCgvvjiC82ePdvZnp+fryeeeEKHDh3S1VdffdFnGl1RcNtnccXf/v37lZycrBYtWlzwFlQAgGso/gAAtufn56cePXro6NGjatmypYYMGaI777xTH3/8sSQpPDxcM2fOVFhYmEaMGKH69eurZ8+euvXWW9W5c2dFR0erR48e2r17d6mP+eCDDyo4OFjvvPOOmjdvrj/96U/6wx/+oI4dO+qee+4pV5wlqVevnv79738rPz9fN910kzp06KA///nPatq0qcaPH6+aNWuWOE1FRcjNzdXWrVtVt25dRUdHF1nOLZ8A4B4UfwAA6NxceLfffrtOnjypzz//XB9++KGWLVvmXH7dddfpl19+0WOPPabQ0FAtW7ZM33zzjfbt26eOHTvqk08+UZcuXUp9vEaNGmn9+vW66aabdOLECX377bc6c+aM/v3vf5fY81eaOEty++23a8WKFerdu7e2bdumqVOnKiMjQ/fee69++umnYgerqSjbt29XVlZWicUdxR8AuIfDuGsoMQAAAACAZf6vPTuQAQAAABDmbx1Iv0XL+QMAABgQfwAAAAPiDwAAYED8AQAADIg/AACAAfEHAAAwIP4AAAAGxB8AAMCA+AMAABgQfwAAAAPiDwAAYED8AQAADARb2+bYmpwDrgAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<Figure size 720x504 with 1 Axes>"
+       "<Figure size 1000x700 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1092,129 +6307,98 @@
     }
    ],
    "source": [
-    "# Each trial is a tuple of\n",
-    "# (algorithm, no. samples, no. features, mean CV score, hyperparameters, \n",
-    "# all CV scores, total CV time (s), memory usage (Gb))\n",
-    "trials = est1.tuning_trials_\n",
-    "scores = [x[3] for x in reversed(trials)]\n",
-    "y_margin = 0.10 * (max(scores) - min(scores))\n",
-    "\n",
+    "# Each trial is a row in a dataframe that contains\n",
+    "# Algorithm, Number of Samples, Number of Features, Hyperparameters, Score, Runtime, Memory Usage, Step as features\n",
+    "trials = est1.completed_trials_summary_[est1.completed_trials_summary_[\"Step\"].str.contains('Model Tuning')]\n",
+    "trials.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
+    "trials.dropna(subset=[name_of_score_column], inplace = True)\n",
+    "trials.drop(trials[trials['Finished'] == -1].index, inplace = True)\n",
+    "trials['Finished']= trials['Finished'].apply(lambda x: time.mktime(datetime.datetime.strptime(x,\n",
+    "                                             \"%a %b %d %H:%M:%S %Y\").timetuple()))\n",
+    "trials.sort_values(by=['Finished'],ascending=True, inplace = True)\n",
+    "scores = trials[name_of_score_column].tolist()\n",
+    "score = []\n",
+    "score.append(scores[0])\n",
+    "for i in range(1,len(scores)):\n",
+    "    if scores[i]>= score[i-1]:\n",
+    "        score.append(scores[i])\n",
+    "    else:\n",
+    "        score.append(score[i-1])\n",
+    "y_margin = 0.10 * (max(score) - min(score))\n",
     "\n",
     "fig, ax = plt.subplots(1)\n",
     "ax.set_title(\"Hyperparameter Tuning Trials\")\n",
     "ax.set_xlabel(\"Iteration $n$\")\n",
-    "ax.set_ylabel(est1.inferred_score_metric[0])\n",
+    "ax.set_ylabel(est1._inferred_score_metric[0].name)\n",
     "ax.grid(color='g', linestyle='-', linewidth=0.1)\n",
-    "ax.set_ylim(min(scores) - y_margin, max(scores) + y_margin)\n",
-    "ax.plot(range(1, len(trials) + 1), scores, 'k:', marker=\"s\", color='teal', markersize=3)\n",
+    "ax.set_ylim(min(score) - y_margin, max(score) + y_margin)\n",
+    "ax.plot(range(1, len(trials) + 1), score, 'k:', marker=\"s\", color='teal', markersize=3)\n",
     "plt.show()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "8d8c129d",
+   "id": "ccb78790",
    "metadata": {},
    "source": [
-    "<a id='timebudget'></a>\n",
-    "### Specify a time budget to AutoML\n",
-    "The Oracle AutoMLx tool also supports a user given time budget in seconds. Given the small size of this dataset, we give a small `time budget` of 10 seconds using the time_budget argument."
+    "<a id='advanced'></a>\n",
+    "### Advanced AutoML Configuration\n",
+    "\n",
+    "You can also configure the pipeline with suitable parameters according to your needs."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "c94c23dd",
+   "execution_count": 15,
+   "id": "6415885e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-03-22T16:18:12.285303Z",
-     "iopub.status.busy": "2023-03-22T16:18:12.284699Z",
-     "iopub.status.idle": "2023-03-22T16:18:32.453811Z",
-     "shell.execute_reply": "2023-03-22T16:18:32.453108Z"
+     "iopub.execute_input": "2024-05-23T17:42:32.179637Z",
+     "iopub.status.busy": "2024-05-23T17:42:32.178946Z",
+     "iopub.status.idle": "2024-05-23T17:42:32.243319Z",
+     "shell.execute_reply": "2024-05-23T17:42:32.242423Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[2023-03-22 09:18:12,531] [automl.pipeline] Random state (7) is used for model builds\n",
-      "[2023-03-22 09:18:12,794] [automl.pipeline] Dataset shape: (948, 1)\n",
-      "[2023-03-22 09:18:12,841] [automl.pipeline] Running Auto-Preprocessing\n",
-      "[2023-03-22 09:18:14,757] [automl.pipeline] Preprocessing completed. Updated Dataset shape: (948, 11026), cv: None\n",
-      "[2023-03-22 09:18:19,069] [automl.pipeline] SVC, KNeighborsClassifier are disabled for datasets with > 10K samples or > 1K features\n",
-      "[2023-03-22 09:18:19,070] [automl.pipeline] Running Model Selection\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Time budget exceeded by 0.05s, resetting XEngine\n",
-      "Timebudget Exceeded or Timedout completed 1/9, 0 tasks timedout\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[2023-03-22 09:18:22,915] [automl.pipeline] Model Selection completed. Selected model: ['DecisionTreeClassifier']\n",
-      "[2023-03-22 09:18:22,917] [automl.pipeline] Running Adaptive Sampling. Dataset Shape: (948, 11026), Valid Shape: (238, 11026), CV: None, Class counts: [237 237 237 237]\n",
-      "[2023-03-22 09:18:22,969] [automl.pipeline] Timebudget exhausted. Skipping Adaptive Sampling\n",
-      "[2023-03-22 09:18:22,987] [automl.pipeline] Adaptive Sampling Completed. Updated Dataset Shape: (948, 11026), Valid Shape: (238, 11026), CV: None, Class counts: [237 237 237 237]\n",
-      "[2023-03-22 09:18:22,987] [automl.pipeline] Starting Feature Selection 0. Dataset Shape: (948, 11026)\n",
-      "[2023-03-22 09:18:23,036] [automl.pipeline] Timebudget exhausted. Skipping Feature Selection\n",
-      "[2023-03-22 09:18:23,037] [automl.pipeline] Using all features: Index(['00', '000', '0000', '00000', '00041032', '0004422', '001230', '001321',\n",
-      "       '0029', '004418',\n",
-      "       ...\n",
-      "       'zinc', 'zion', 'zip', 'zipping', 'zisfein', 'zmodem', 'zone', 'zoo',\n",
-      "       'zoology', 'zrepachol'],\n",
-      "      dtype='object', length=11026)\n",
-      "[2023-03-22 09:18:23,058] [automl.pipeline] Feature Selection 0 completed. Updated Dataset shape: (948, 11026)\n",
-      "[2023-03-22 09:18:23,107] [automl.pipeline] Timebudget exhausted. Skipping Hyperparameter Optimization for DecisionTreeClassifier\n",
-      "[2023-03-22 09:18:23,564] [automl.pipeline] (Re)fitting Pipeline\n",
-      "[2023-03-22 09:18:29,310] [automl.xengine] Local ProcessPool execution (n_jobs=40)\n",
-      "[2023-03-22 09:18:29,512] [automl.pipeline] AutoML completed. Time taken - 15.504 sec\n",
-      "F1 micro Score on test data: 0.716\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "est2 = automl.Pipeline(task=\"classification\")\n",
-    "est2.fit(X_train, y_train, X_valid, y_valid, cv=None, col_types=['text'], time_budget=10)\n",
+    "custom_pipeline = automlx.Pipeline(\n",
+    "    task='classification',\n",
+    "    model_list=[                 # Specify the models you want the AutoML to consider\n",
+    "        'GaussianNB',\n",
+    "        'LGBMClassifier',\n",
+    "],\n",
+    "    min_features=1.0,            # Specify minimum features to force the model to use. It can take 3 possible types of values:\n",
+    "                                 # If int, 0 < min_features <= n_features,\n",
+    "                                 # If float, 0 < min_features <= 1.0, 1.0 means disabling feature selection\n",
+    "                                 # If list, names of features to keep, for example ['a', 'b'] means keep features 'a' and 'b'\n",
     "\n",
-    "y_predict = est2.predict(X_test)\n",
-    "score_default = f1_score(y_test, y_predict, average=\"micro\")\n",
-    "\n",
-    "print('F1 micro Score on test data: {:3.3f}'.format(score_default))"
+    "    n_algos_tuned=2,             # Choose how many models to tune\n",
+    "    adaptive_sampling=False,     # Disable or enable Adaptive Sampling step. Default to `True`\n",
+    "    preprocessing=True,          # Disable or enable Preprocessing step. Default to `True`\n",
+    "    search_space={},             # You can specify the hyper-parameters and ranges AutoML searches\n",
+    "    max_tuning_trials=2,         # The maximum number of tuning trials. Can be integer or Dict (max number for each model)\n",
+    "    score_metric='f1_macro',     # Any scikit-learn metric or a custom function\n",
+    ")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "c7b0491c",
+   "id": "71303d28",
    "metadata": {},
    "source": [
-    "<a id='scoringstr'></a>\n",
-    "### Specify a different scoring metric to AutoML\n",
-    "By default, the score metric is set to `neg_log_loss` for classifcation and `neg_mean_squared_error` for regression.\n",
-    "\n",
-    "The user can also choose another scoring metric. The list of possible metrics is given by:\n",
-    "- For binary classification, one of 'roc_auc', 'accuracy', 'f1', 'precision', 'recall', 'f1_micro', 'f1_macro', 'f1_weighted', 'f1_samples', 'recall_micro', 'recall_macro', 'recall_weighted', 'recall_samples', 'precision_micro', 'precision_macro', 'precision_weighted', 'precision_samples'\n",
-    "- For multiclass classification , one of  'neg_log_loss', 'recall_macro', 'accuracy','f1_micro', 'f1_macro', 'f1_weighted', 'f1_samples', 'recall_micro', 'recall_weighted', 'recall_samples', 'precision_micro', 'precision_macro', 'precision_weighted', 'precision_samples'\n",
-    "- For regression, one of 'neg_mean_squared_error', 'r2', 'neg_mean_absolute_error', 'neg_mean_squared_log_error', 'neg_median_absolute_error'\n",
-    "\n",
-    "Here, we ask AutoML to optimize for the 'f1_micro' scoring metric."
+    "A few of the advanced settings can be passed directly to the pipeline's fit method, instead of its constructor."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "id": "0794e86c",
+   "execution_count": 16,
+   "id": "d8cfa11b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-03-22T16:18:32.456828Z",
-     "iopub.status.busy": "2023-03-22T16:18:32.456492Z",
-     "iopub.status.idle": "2023-03-22T16:20:34.878493Z",
-     "shell.execute_reply": "2023-03-22T16:20:34.877888Z"
+     "iopub.execute_input": "2024-05-23T17:42:32.246363Z",
+     "iopub.status.busy": "2024-05-23T17:42:32.245836Z",
+     "iopub.status.idle": "2024-05-23T17:45:33.358021Z",
+     "shell.execute_reply": "2024-05-23T17:45:33.357152Z"
     }
    },
    "outputs": [
@@ -1222,59 +6406,43 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2023-03-22 09:18:32,696] [automl.pipeline] Random state (7) is used for model builds\n",
-      "[2023-03-22 09:18:32,931] [automl.pipeline] Dataset shape: (948, 1)\n",
-      "[2023-03-22 09:18:32,976] [automl.pipeline] Running Auto-Preprocessing\n",
-      "[2023-03-22 09:18:34,815] [automl.pipeline] Preprocessing completed. Updated Dataset shape: (948, 11026), cv: None\n",
-      "[2023-03-22 09:18:39,467] [automl.pipeline] SVC, KNeighborsClassifier are disabled for datasets with > 10K samples or > 1K features\n",
-      "[2023-03-22 09:18:39,468] [automl.pipeline] Running Model Selection\n",
-      "[2023-03-22 09:19:22,252] [automl.pipeline] Model Selection completed. Selected model: ['TorchMLPClassifier']\n",
-      "[2023-03-22 09:19:22,253] [automl.pipeline] Running Adaptive Sampling. Dataset Shape: (948, 11026), Valid Shape: (238, 11026), CV: None, Class counts: [237 237 237 237]\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Time budget exceeded by 4.87s, resetting XEngine\n",
-      "Timebudget Exceeded or Timedout completed 0/1, 0 tasks timedout\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[2023-03-22 09:19:37,891] [automl.pipeline] Adaptive Sampling Completed. Updated Dataset Shape: (948, 11026), Valid Shape: (238, 11026), CV: None, Class counts: [237 237 237 237]\n",
-      "[2023-03-22 09:19:37,891] [automl.pipeline] Starting Feature Selection 0. Dataset Shape: (948, 11026)\n",
-      "[2023-03-22 09:19:37,935] [automl.pipeline] Timebudget exhausted. Skipping Feature Selection\n",
-      "[2023-03-22 09:19:37,936] [automl.pipeline] Using all features: Index(['00', '000', '0000', '00000', '00041032', '0004422', '001230', '001321',\n",
-      "       '0029', '004418',\n",
-      "       ...\n",
-      "       'zinc', 'zion', 'zip', 'zipping', 'zisfein', 'zmodem', 'zone', 'zoo',\n",
-      "       'zoology', 'zrepachol'],\n",
-      "      dtype='object', length=11026)\n",
-      "[2023-03-22 09:19:37,955] [automl.pipeline] Feature Selection 0 completed. Updated Dataset shape: (948, 11026)\n",
-      "[2023-03-22 09:19:37,999] [automl.pipeline] Timebudget exhausted. Skipping Hyperparameter Optimization for TorchMLPClassifier\n",
-      "[2023-03-22 09:19:38,594] [automl.pipeline] (Re)fitting Pipeline\n",
-      "[2023-03-22 09:20:31,302] [automl.xengine] Local ProcessPool execution (n_jobs=40)\n",
-      "[2023-03-22 09:20:31,487] [automl.pipeline] AutoML completed. Time taken - 117.251 sec\n",
-      "F1 micro Score on test data: 0.908\n"
+      "[2024-05-23 10:42:32,537] [automlx.interface] Dataset shape: (3394,1)\n",
+      "[2024-05-23 10:42:32,598] [automlx.interface] Model Tune disabled.\n",
+      "[2024-05-23 10:42:32,885] [automlx.data_transform] Running preprocessing. Number of features: 2\n",
+      "[2024-05-23 10:42:52,004] [automlx.data_transform] Preprocessing completed. Took 19.119 secs\n",
+      "[2024-05-23 10:42:52,334] [automlx.process] Running Model Generation\n",
+      "[2024-05-23 10:43:10,641] [automlx.process] Model Generation completed.\n",
+      "[2024-05-23 10:43:11,010] [automlx.model_selection] Running Model Selection\n",
+      "[2024-05-23 10:43:11,011] [automlx.trials] Fewer models (2) than top_k (2) provided, skipping model selection\n",
+      "[2024-05-23 10:43:22,393] [automlx.interface] Re-fitting pipeline\n",
+      "[2024-05-23 10:44:20,154] [automlx.interface] AutoMLx completed.\n",
+      "Score on test data : 0.6987519915029209\n"
      ]
     }
    ],
    "source": [
-    "est3 = automl.Pipeline(task=\"classification\", score_metric='f1_micro')\n",
-    "est3.fit(X_train, y_train, X_valid, y_valid, cv=None, col_types=['text'], time_budget=60)\n",
-    "\n",
-    "y_predict = est3.predict(X_test)\n",
+    "custom_pipeline.fit(\n",
+    "    X_train,\n",
+    "    y_train,\n",
+    "    X_valid,\n",
+    "    y_valid,\n",
+    "    col_types=[\"text\"],\n",
+    "    time_budget= 50,    # Specify time budget in seconds\n",
+    "    cv='auto'           # Automatically pick a good cross-validation (cv) strategy for the user's dataset.\n",
+    "                        # Ignored if X_valid and y_valid are provided.\n",
+    "                        # Can also be:\n",
+    "                        #   - An integer (for example, to use 5-fold cross validation)\n",
+    "                        #   - A list of data indices to use as splits (for advanced, such as time-based splitting)\n",
+    ")\n",
+    "y_proba = custom_pipeline.predict_proba(X_test)\n",
     "score_default = f1_score(y_test, y_predict, average=\"micro\")\n",
     "\n",
-    "print('F1 micro Score on test data: {:3.3f}'.format(score_default))"
+    "print(f'Score on test data : {score_default}')"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "eb28f2b7",
+   "id": "188bfa32",
    "metadata": {},
    "source": [
     "<a id='MLX'></a>\n",
@@ -1283,7 +6451,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "49fd5de1",
+   "id": "c58cc43a",
    "metadata": {},
    "source": [
     "For a variety of decision-making tasks, getting only a prediction as model output is not sufficient. A user may wish to know why the model outputs that prediction, or which data features are relevant for that prediction. For that purpose the Oracle AutoMLx solution defines the MLExplainer object, which allows to compute a variety of model explanations\n",
@@ -1296,29 +6464,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "id": "ba07c5fd",
+   "execution_count": 17,
+   "id": "bcae799c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-03-22T16:20:34.881516Z",
-     "iopub.status.busy": "2023-03-22T16:20:34.881316Z",
-     "iopub.status.idle": "2023-03-22T16:20:37.236789Z",
-     "shell.execute_reply": "2023-03-22T16:20:37.235888Z"
+     "iopub.execute_input": "2024-05-23T17:45:33.361050Z",
+     "iopub.status.busy": "2024-05-23T17:45:33.360753Z",
+     "iopub.status.idle": "2024-05-23T17:45:35.659421Z",
+     "shell.execute_reply": "2024-05-23T17:45:35.658637Z"
     }
    },
    "outputs": [],
    "source": [
-    "explainer = automl.MLExplainer(est1,\n",
-    "                               X_train,\n",
-    "                               y_train,\n",
-    "                               target_names=['sci.crypt', 'sci.electronics', 'sci.med', 'sci.space'],\n",
-    "                               task=\"classification\",\n",
-    "                               col_types=[\"text\"])"
+    "explainer = automlx.MLExplainer(est1,\n",
+    "                              X_train,\n",
+    "                              y_train,\n",
+    "                              target_names=target_names,\n",
+    "                              task=\"classification\",\n",
+    "                              col_types=[\"text\"])"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e6d7c3f1",
+   "id": "d6592d01",
    "metadata": {},
    "source": [
     "<a id='MLX-global'></a>\n",
@@ -1327,15 +6495,17 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2af6d83e",
-   "metadata": {},
+   "id": "4d3724cd",
+   "metadata": {
+    "lines_to_next_cell": 0
+   },
    "source": [
-    "For text classification tasks, since we first extract tokens (features), the Oracle AutoMLx solution offers a single way to compute a notion of token importance: Global Token Importance. The notion of Global Token Importance intuitively measures how much a token impacts the model's predictions (relative to the provided train labels). This notion of token importance considers each token independently from all other tokens. Tokens are the most fine-grained building blocks of the NLP model, such as sentences, words, or characters."
+    "For text classification tasks, since we first extract tokens (features), the Oracle AutoMLx solution offers a single way to compute a notion of token importance: Global Token Importance. The notion of Global Token Importance intuitively measures how much a token impacts the model's predictions (relative to the provided train labels). Tokens are the most fine-grained building blocks of the NLP model, such as sentences, words, or characters."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "0216c9a0",
+   "id": "c88de134",
    "metadata": {},
    "source": [
     "#### Computing the importance"
@@ -1343,25 +6513,25 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7bebefd2",
+   "id": "0b738491",
    "metadata": {},
    "source": [
     "We use a permutation-based method to successively measure the importance of each token (feature). Such a method therefore runs in linear time with respect to the\n",
-    "number of features (tokens) in the dataset. \n",
+    "number of features (tokens) in the dataset.\n",
     "\n",
-    "The method `explain_model()` allows to compute such feature importances. It also provides 95% confidence intervals for each token importance attribution."
+    "The method `explain_model()` allows to compute such feature importances."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "id": "7f6f5004",
+   "execution_count": 18,
+   "id": "d9795b26",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-03-22T16:20:37.240651Z",
-     "iopub.status.busy": "2023-03-22T16:20:37.240367Z",
-     "iopub.status.idle": "2023-03-22T16:28:24.148789Z",
-     "shell.execute_reply": "2023-03-22T16:28:24.148117Z"
+     "iopub.execute_input": "2024-05-23T17:45:35.662764Z",
+     "iopub.status.busy": "2024-05-23T17:45:35.662465Z",
+     "iopub.status.idle": "2024-05-23T19:05:24.194926Z",
+     "shell.execute_reply": "2024-05-23T19:05:24.194225Z"
     }
    },
    "outputs": [],
@@ -1371,7 +6541,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "376d3e4c",
+   "id": "e00d21e6",
    "metadata": {},
    "source": [
     "#### Visualization"
@@ -1379,7 +6549,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7bd8305c",
+   "id": "368b179a",
    "metadata": {},
    "source": [
     "There are two options to show the explanation's results:\n",
@@ -1391,14 +6561,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "id": "ef66760e",
+   "execution_count": 19,
+   "id": "83b3e115",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-03-22T16:28:24.151692Z",
-     "iopub.status.busy": "2023-03-22T16:28:24.151510Z",
-     "iopub.status.idle": "2023-03-22T16:28:25.776789Z",
-     "shell.execute_reply": "2023-03-22T16:28:25.776219Z"
+     "iopub.execute_input": "2024-05-23T19:05:24.198760Z",
+     "iopub.status.busy": "2024-05-23T19:05:24.198068Z",
+     "iopub.status.idle": "2024-05-23T19:05:26.476882Z",
+     "shell.execute_reply": "2024-05-23T19:05:26.476238Z"
     }
    },
    "outputs": [
@@ -1432,143 +6602,143 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>security</td>\n",
-       "      <td>0.000048</td>\n",
-       "      <td>0.000049</td>\n",
-       "      <td>0.000048</td>\n",
+       "      <td>israeli</td>\n",
+       "      <td>0.014393</td>\n",
+       "      <td>0.014393</td>\n",
+       "      <td>0.014393</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>orbit</td>\n",
-       "      <td>0.000026</td>\n",
-       "      <td>0.000046</td>\n",
-       "      <td>0.000006</td>\n",
+       "      <td>atheism</td>\n",
+       "      <td>0.012633</td>\n",
+       "      <td>0.012633</td>\n",
+       "      <td>0.012633</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>space</td>\n",
-       "      <td>0.000025</td>\n",
-       "      <td>0.000036</td>\n",
-       "      <td>0.000013</td>\n",
+       "      <td>hockey</td>\n",
+       "      <td>0.012515</td>\n",
+       "      <td>0.012515</td>\n",
+       "      <td>0.012515</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>prb</td>\n",
-       "      <td>0.000021</td>\n",
-       "      <td>0.000025</td>\n",
-       "      <td>0.000017</td>\n",
+       "      <td>bike</td>\n",
+       "      <td>0.011961</td>\n",
+       "      <td>0.011961</td>\n",
+       "      <td>0.011961</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>gtoal</td>\n",
-       "      <td>0.000017</td>\n",
-       "      <td>0.000028</td>\n",
-       "      <td>0.000006</td>\n",
+       "      <td>sale</td>\n",
+       "      <td>0.010798</td>\n",
+       "      <td>0.010800</td>\n",
+       "      <td>0.010797</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
-       "      <td>moon</td>\n",
-       "      <td>0.000016</td>\n",
-       "      <td>0.000016</td>\n",
-       "      <td>0.000016</td>\n",
+       "      <td>baseball</td>\n",
+       "      <td>0.010190</td>\n",
+       "      <td>0.010190</td>\n",
+       "      <td>0.010190</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
-       "      <td>encryption</td>\n",
-       "      <td>0.000015</td>\n",
-       "      <td>0.000018</td>\n",
-       "      <td>0.000013</td>\n",
+       "      <td>cars</td>\n",
+       "      <td>0.010052</td>\n",
+       "      <td>0.010055</td>\n",
+       "      <td>0.010050</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
-       "      <td>aurora</td>\n",
-       "      <td>0.000012</td>\n",
-       "      <td>0.000013</td>\n",
-       "      <td>0.000010</td>\n",
+       "      <td>moon</td>\n",
+       "      <td>0.009464</td>\n",
+       "      <td>0.009464</td>\n",
+       "      <td>0.009464</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
-       "      <td>tapped</td>\n",
-       "      <td>0.000011</td>\n",
-       "      <td>0.000012</td>\n",
-       "      <td>0.000010</td>\n",
+       "      <td>atheists</td>\n",
+       "      <td>0.009376</td>\n",
+       "      <td>0.009376</td>\n",
+       "      <td>0.009376</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
-       "      <td>circuit</td>\n",
-       "      <td>0.000009</td>\n",
-       "      <td>0.000012</td>\n",
-       "      <td>0.000006</td>\n",
+       "      <td>graphics</td>\n",
+       "      <td>0.008399</td>\n",
+       "      <td>0.008466</td>\n",
+       "      <td>0.008332</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
-       "      <td>alaska</td>\n",
-       "      <td>0.000007</td>\n",
-       "      <td>0.000009</td>\n",
-       "      <td>0.000005</td>\n",
+       "      <td>windows</td>\n",
+       "      <td>0.007966</td>\n",
+       "      <td>0.007966</td>\n",
+       "      <td>0.007966</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
-       "      <td>disease</td>\n",
-       "      <td>0.000006</td>\n",
-       "      <td>0.000006</td>\n",
-       "      <td>0.000006</td>\n",
+       "      <td>encryption</td>\n",
+       "      <td>0.006958</td>\n",
+       "      <td>0.006958</td>\n",
+       "      <td>0.006958</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
-       "      <td>launch</td>\n",
-       "      <td>0.000005</td>\n",
-       "      <td>0.000009</td>\n",
-       "      <td>0.000002</td>\n",
+       "      <td>pitching</td>\n",
+       "      <td>0.005990</td>\n",
+       "      <td>0.005990</td>\n",
+       "      <td>0.005990</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
-       "      <td>shuttle</td>\n",
-       "      <td>0.000005</td>\n",
-       "      <td>0.000006</td>\n",
-       "      <td>0.000004</td>\n",
+       "      <td>guns</td>\n",
+       "      <td>0.005813</td>\n",
+       "      <td>0.005813</td>\n",
+       "      <td>0.005813</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
-       "      <td>crypto</td>\n",
-       "      <td>0.000005</td>\n",
-       "      <td>0.000005</td>\n",
-       "      <td>0.000005</td>\n",
+       "      <td>israel</td>\n",
+       "      <td>0.005534</td>\n",
+       "      <td>0.005534</td>\n",
+       "      <td>0.005534</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
-       "      <td>power</td>\n",
-       "      <td>0.000005</td>\n",
-       "      <td>0.000005</td>\n",
-       "      <td>0.000004</td>\n",
+       "      <td>playoff</td>\n",
+       "      <td>0.004587</td>\n",
+       "      <td>0.004587</td>\n",
+       "      <td>0.004587</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
-       "      <td>pitt</td>\n",
-       "      <td>0.000005</td>\n",
-       "      <td>0.000006</td>\n",
-       "      <td>0.000003</td>\n",
+       "      <td>christians</td>\n",
+       "      <td>0.004034</td>\n",
+       "      <td>0.004034</td>\n",
+       "      <td>0.004034</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
-       "      <td>encrypted</td>\n",
-       "      <td>0.000004</td>\n",
-       "      <td>0.000004</td>\n",
-       "      <td>0.000003</td>\n",
+       "      <td>orbit</td>\n",
+       "      <td>0.003647</td>\n",
+       "      <td>0.003647</td>\n",
+       "      <td>0.003647</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
-       "      <td>spacecraft</td>\n",
-       "      <td>0.000004</td>\n",
-       "      <td>0.000004</td>\n",
-       "      <td>0.000003</td>\n",
+       "      <td>clipper</td>\n",
+       "      <td>0.003382</td>\n",
+       "      <td>0.003382</td>\n",
+       "      <td>0.003382</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
-       "      <td>clipper</td>\n",
-       "      <td>0.000004</td>\n",
-       "      <td>0.000004</td>\n",
-       "      <td>0.000003</td>\n",
+       "      <td>space</td>\n",
+       "      <td>0.003147</td>\n",
+       "      <td>0.003147</td>\n",
+       "      <td>0.003147</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1576,29 +6746,29 @@
       ],
       "text/plain": [
        "         token  attribution  upper_bound  lower_bound\n",
-       "0     security     0.000048     0.000049     0.000048\n",
-       "1        orbit     0.000026     0.000046     0.000006\n",
-       "2        space     0.000025     0.000036     0.000013\n",
-       "3          prb     0.000021     0.000025     0.000017\n",
-       "4        gtoal     0.000017     0.000028     0.000006\n",
-       "5         moon     0.000016     0.000016     0.000016\n",
-       "6   encryption     0.000015     0.000018     0.000013\n",
-       "7       aurora     0.000012     0.000013     0.000010\n",
-       "8       tapped     0.000011     0.000012     0.000010\n",
-       "9      circuit     0.000009     0.000012     0.000006\n",
-       "10      alaska     0.000007     0.000009     0.000005\n",
-       "11     disease     0.000006     0.000006     0.000006\n",
-       "12      launch     0.000005     0.000009     0.000002\n",
-       "13     shuttle     0.000005     0.000006     0.000004\n",
-       "14      crypto     0.000005     0.000005     0.000005\n",
-       "15       power     0.000005     0.000005     0.000004\n",
-       "16        pitt     0.000005     0.000006     0.000003\n",
-       "17   encrypted     0.000004     0.000004     0.000003\n",
-       "18  spacecraft     0.000004     0.000004     0.000003\n",
-       "19     clipper     0.000004     0.000004     0.000003"
+       "0      israeli     0.014393     0.014393     0.014393\n",
+       "1      atheism     0.012633     0.012633     0.012633\n",
+       "2       hockey     0.012515     0.012515     0.012515\n",
+       "3         bike     0.011961     0.011961     0.011961\n",
+       "4         sale     0.010798     0.010800     0.010797\n",
+       "5     baseball     0.010190     0.010190     0.010190\n",
+       "6         cars     0.010052     0.010055     0.010050\n",
+       "7         moon     0.009464     0.009464     0.009464\n",
+       "8     atheists     0.009376     0.009376     0.009376\n",
+       "9     graphics     0.008399     0.008466     0.008332\n",
+       "10     windows     0.007966     0.007966     0.007966\n",
+       "11  encryption     0.006958     0.006958     0.006958\n",
+       "12    pitching     0.005990     0.005990     0.005990\n",
+       "13        guns     0.005813     0.005813     0.005813\n",
+       "14      israel     0.005534     0.005534     0.005534\n",
+       "15     playoff     0.004587     0.004587     0.004587\n",
+       "16  christians     0.004034     0.004034     0.004034\n",
+       "17       orbit     0.003647     0.003647     0.003647\n",
+       "18     clipper     0.003382     0.003382     0.003382\n",
+       "19       space     0.003147     0.003147     0.003147"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1609,14 +6779,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "id": "19fea0fa",
+   "execution_count": 20,
+   "id": "67cdd489",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-03-22T16:28:25.779157Z",
-     "iopub.status.busy": "2023-03-22T16:28:25.778695Z",
-     "iopub.status.idle": "2023-03-22T16:28:26.053080Z",
-     "shell.execute_reply": "2023-03-22T16:28:26.052447Z"
+     "iopub.execute_input": "2024-05-23T19:05:26.479841Z",
+     "iopub.status.busy": "2024-05-23T19:05:26.479210Z",
+     "iopub.status.idle": "2024-05-23T19:05:29.887479Z",
+     "shell.execute_reply": "2024-05-23T19:05:29.886855Z"
     },
     "lines_to_next_cell": 2
    },
@@ -1626,75 +6796,18 @@
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        "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
-       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
+       "        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
        "        if (typeof require !== 'undefined') {\n",
        "        require.undef(\"plotly\");\n",
        "        define('plotly', function(require, exports, module) {\n",
        "            /**\n",
-       "* plotly.js v2.6.3\n",
-       "* Copyright 2012-2021, Plotly, Inc.\n",
+       "* plotly.js v2.27.0\n",
+       "* Copyright 2012-2023, Plotly, Inc.\n",
        "* All rights reserved.\n",
        "* Licensed under the MIT license\n",
        "*/\n",
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r=L(8/(3*y)),i=e/r;return[t/(r*(1-n(i)/y)),i]},gt.invert=function(t,e){var r=2-n(e)/L(2*y/3);return[t*L(6*y)/(2*r),d(e)*S((4-r*r)/3)]},mt.invert=function(t,e){var r=L(y*(4+y))/2;return[t*r/(1+L(1-e*e*(4+y)/(4*y))),e*r/2]},vt.invert=function(t,e){var r=e*L((4+y)/y)/2,n=S(r),i=o(n);return[t/(2/L(y*(4+y))*(1+i)),S((n+r*(i+2))/(2+x))]},yt.invert=function(t,e){var r=L(2+y),n=e*r/2;return[r*t/(1+o(n)),n]},xt.invert=function(t,e){var r=1+x,n=L(r/2);return[2*t*n/(1+o(e*=n)),S((e+g(e))/r)]};var bt=3+2*w;function _t(t,e){var r=g(t/=2),n=o(t),a=L(o(e)),s=o(e/=2),l=g(e)/(s+w*n*a),u=L(2/(1+l*l)),f=L((w*s+(n+r)*a)/(w*s+(n-r)*a));return[bt*(u*(f-1/f)-2*c(f)),bt*(u*l*(f+1/f)-2*i(l))]}_t.invert=function(t,e){if(!(r=D.invert(t/1.2,1.065*e)))return null;var r,a=r[0],s=r[1],l=20;t/=bt,e/=bt;do{var 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a=V(s,i);return[r/n*(t+e/o(a)),a]},a}function Mt(t){return[t[0]/2,S(m(t[1]/2*M))*A]}function St(t){return[2*t[0],2*i(g(t[1]*M))*A]}function Et(t,r){var i=2*y/r,s=t*t;function l(r,l){var c=e.geoAzimuthalEquidistantRaw(r,l),u=c[0],f=c[1],h=u*u+f*f;if(h>s){var d=L(h),m=a(f,u),b=i*p(m/i),_=m-b,w=t*o(_),T=(t*g(_)-_*g(w))/(x-w),k=Lt(_,T),A=(y-t)/Ct(k,w,y);u=d;var M,S=50;do{u-=M=(t+Ct(k,w,u)*A-d)/(k(u)*A)}while(n(M)>v&&--S>0);f=_*g(u),u<x&&(f-=T*(u-x));var E=g(b),C=o(b);c[0]=u*C-f*E,c[1]=u*E+f*C}return c}return l.invert=function(r,l){var c=r*r+l*l;if(c>s){var u=L(c),f=a(l,r),h=i*p(f/i),d=f-h;r=u*o(d),l=u*g(d);for(var m=r-x,v=g(r),b=l/v,_=r<x?1/0:0,w=10;;){var T=t*g(b),k=t*o(b),A=g(k),M=x-k,S=(T-b*A)/M,E=Lt(b,S);if(n(_)<1e-12||!--w)break;b-=_=(b*v-S*m-l)/(v-2*m*(M*(k+b*T*o(k)-A)-T*(T-b*A))/(M*M))}r=(u=t+Ct(E,k,r)*(y-t)/Ct(E,k,y))*o(f=h+b),l=u*g(f)}return e.geoAzimuthalEquidistantRaw.invert(r,l)},l}function Lt(t,e){return function(r){var n=t*o(r);return r<x&&(n-=e),L(1+n*n)}}function 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float fontSize, charStep, em, align, baseline;\\n\\t\\t\\tuniform vec4 viewport;\\n\\t\\t\\tuniform vec4 color;\\n\\t\\t\\tuniform vec2 atlasSize, atlasDim, scale, translate, positionOffset;\\n\\t\\t\\tvarying vec2 charCoord, charId;\\n\\t\\t\\tvarying float charWidth;\\n\\t\\t\\tvarying vec4 fontColor;\\n\\t\\t\\tvoid main () {\\n\\t\\t\\t\\tvec2 offset = floor(em * (vec2(align + charOffset, baseline)\\n\\t\\t\\t\\t\\t+ vec2(positionOffset.x, -positionOffset.y)))\\n\\t\\t\\t\\t\\t/ (viewport.zw * scale.xy);\\n\\n\\t\\t\\t\\tvec2 position = (position + translate) * scale;\\n\\t\\t\\t\\tposition += offset * scale;\\n\\n\\t\\t\\t\\tcharCoord = position * viewport.zw + viewport.xy;\\n\\n\\t\\t\\t\\tgl_Position = vec4(position * 2. - 1., 0, 1);\\n\\n\\t\\t\\t\\tgl_PointSize = charStep;\\n\\n\\t\\t\\t\\tcharId.x = mod(char, atlasDim.x);\\n\\t\\t\\t\\tcharId.y = floor(char / atlasDim.x);\\n\\n\\t\\t\\t\\tcharWidth = width * em;\\n\\n\\t\\t\\t\\tfontColor = color / 255.;\\n\\t\\t\\t}\",frag:\"\\n\\t\\t\\tprecision highp float;\\n\\t\\t\\tuniform float fontSize, charStep, opacity;\\n\\t\\t\\tuniform vec2 atlasSize;\\n\\t\\t\\tuniform vec4 viewport;\\n\\t\\t\\tuniform sampler2D atlas;\\n\\t\\t\\tvarying vec4 fontColor;\\n\\t\\t\\tvarying vec2 charCoord, charId;\\n\\t\\t\\tvarying float charWidth;\\n\\n\\t\\t\\tfloat lightness(vec4 color) {\\n\\t\\t\\t\\treturn color.r * 0.299 + color.g * 0.587 + color.b * 0.114;\\n\\t\\t\\t}\\n\\n\\t\\t\\tvoid main () {\\n\\t\\t\\t\\tvec2 uv = gl_FragCoord.xy - charCoord + charStep * .5;\\n\\t\\t\\t\\tfloat halfCharStep = floor(charStep * .5 + .5);\\n\\n\\t\\t\\t\\t// invert y and shift by 1px (FF expecially needs that)\\n\\t\\t\\t\\tuv.y = charStep - uv.y;\\n\\n\\t\\t\\t\\t// ignore points outside of character bounding box\\n\\t\\t\\t\\tfloat halfCharWidth = ceil(charWidth * .5);\\n\\t\\t\\t\\tif (floor(uv.x) > halfCharStep + halfCharWidth ||\\n\\t\\t\\t\\t\\tfloor(uv.x) < halfCharStep - halfCharWidth) return;\\n\\n\\t\\t\\t\\tuv += charId * charStep;\\n\\t\\t\\t\\tuv = uv / atlasSize;\\n\\n\\t\\t\\t\\tvec4 color = fontColor;\\n\\t\\t\\t\\tvec4 mask = texture2D(atlas, uv);\\n\\n\\t\\t\\t\\tfloat maskY = lightness(mask);\\n\\t\\t\\t\\t// float colorY = lightness(color);\\n\\t\\t\\t\\tcolor.a *= maskY;\\n\\t\\t\\t\\tcolor.a *= opacity;\\n\\n\\t\\t\\t\\t// color.a += .1;\\n\\n\\t\\t\\t\\t// antialiasing, see yiq color space y-channel formula\\n\\t\\t\\t\\t// color.rgb += (1. - color.rgb) * (1. - mask.rgb);\\n\\n\\t\\t\\t\\tgl_FragColor = color;\\n\\t\\t\\t}\"});return{regl:t,draw:e,atlas:{}}},T.prototype.update=function(t){var e=this;if(\"string\"==typeof t)t={text:t};else if(!t)return;null!=(t=i(t,{position:\"position positions coord coords coordinates\",font:\"font fontFace fontface typeface cssFont css-font family fontFamily\",fontSize:\"fontSize fontsize size font-size\",text:\"text texts chars characters value values symbols\",align:\"align alignment textAlign textbaseline\",baseline:\"baseline textBaseline textbaseline\",direction:\"dir direction textDirection\",color:\"color colour fill fill-color fillColor textColor textcolor\",kerning:\"kerning kern\",range:\"range dataBox\",viewport:\"vp viewport viewBox viewbox viewPort\",opacity:\"opacity alpha transparency visible visibility opaque\",offset:\"offset positionOffset padding shift indent indentation\"},!0)).opacity&&(Array.isArray(t.opacity)?this.opacity=t.opacity.map((function(t){return parseFloat(t)})):this.opacity=parseFloat(t.opacity)),null!=t.viewport&&(this.viewport=f(t.viewport),this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),null==this.viewport&&(this.viewport={x:0,y:0,width:this.gl.drawingBufferWidth,height:this.gl.drawingBufferHeight},this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),null!=t.kerning&&(this.kerning=t.kerning),null!=t.offset&&(\"number\"==typeof t.offset&&(t.offset=[t.offset,0]),this.positionOffset=y(t.offset)),t.direction&&(this.direction=t.direction),t.range&&(this.range=t.range,this.scale=[1/(t.range[2]-t.range[0]),1/(t.range[3]-t.range[1])],this.translate=[-t.range[0],-t.range[1]]),t.scale&&(this.scale=t.scale),t.translate&&(this.translate=t.translate),this.scale||(this.scale=[1/this.viewport.width,1/this.viewport.height]),this.translate||(this.translate=[0,0]),this.font.length||t.font||(t.font=T.baseFontSize+\"px sans-serif\");var r,a=!1,o=!1;if(t.font&&(Array.isArray(t.font)?t.font:[t.font]).forEach((function(t,r){if(\"string\"==typeof t)try{t=n.parse(t)}catch(e){t=n.parse(T.baseFontSize+\"px \"+t)}else t=n.parse(n.stringify(t));var i=n.stringify({size:T.baseFontSize,family:t.family,stretch:_?t.stretch:void 0,variant:t.variant,weight:t.weight,style:t.style}),s=p(t.size),l=Math.round(s[0]*d(s[1]));if(l!==e.fontSize[r]&&(o=!0,e.fontSize[r]=l),!(e.font[r]&&i==e.font[r].baseString||(a=!0,e.font[r]=T.fonts[i],e.font[r]))){var c=t.family.join(\", \"),u=[t.style];t.style!=t.variant&&u.push(t.variant),t.variant!=t.weight&&u.push(t.weight),_&&t.weight!=t.stretch&&u.push(t.stretch),e.font[r]={baseString:i,family:c,weight:t.weight,stretch:t.stretch,style:t.style,variant:t.variant,width:{},kerning:{},metrics:v(c,{origin:\"top\",fontSize:T.baseFontSize,fontStyle:u.join(\" \")})},T.fonts[i]=e.font[r]}})),(a||o)&&this.font.forEach((function(r,i){var a=n.stringify({size:e.fontSize[i],family:r.family,stretch:_?r.stretch:void 0,variant:r.variant,weight:r.weight,style:r.style});if(e.fontAtlas[i]=e.shader.atlas[a],!e.fontAtlas[i]){var o=r.metrics;e.shader.atlas[a]=e.fontAtlas[i]={fontString:a,step:2*Math.ceil(e.fontSize[i]*o.bottom*.5),em:e.fontSize[i],cols:0,rows:0,height:0,width:0,chars:[],ids:{},texture:e.regl.texture()}}null==t.text&&(t.text=e.text)})),\"string\"==typeof t.text&&t.position&&t.position.length>2){for(var s=Array(.5*t.position.length),h=0;h<s.length;h++)s[h]=t.text;t.text=s}if(null!=t.text||a){if(this.textOffsets=[0],Array.isArray(t.text)){this.count=t.text[0].length,this.counts=[this.count];for(var b=1;b<t.text.length;b++)this.textOffsets[b]=this.textOffsets[b-1]+t.text[b-1].length,this.count+=t.text[b].length,this.counts.push(t.text[b].length);this.text=t.text.join(\"\")}else this.text=t.text,this.count=this.text.length,this.counts=[this.count];r=[],this.font.forEach((function(t,n){T.atlasContext.font=t.baseString;for(var i=e.fontAtlas[n],a=0;a<e.text.length;a++){var o=e.text.charAt(a);if(null==i.ids[o]&&(i.ids[o]=i.chars.length,i.chars.push(o),r.push(o)),null==t.width[o]&&(t.width[o]=T.atlasContext.measureText(o).width/T.baseFontSize,e.kerning)){var s=[];for(var l in t.width)s.push(l+o,o+l);m(t.kerning,g(t.family,{pairs:s}))}}}))}if(t.position)if(t.position.length>2){for(var w=!t.position[0].length,k=u.mallocFloat(2*this.count),A=0,M=0;A<this.counts.length;A++){var S=this.counts[A];if(w)for(var E=0;E<S;E++)k[M++]=t.position[2*A],k[M++]=t.position[2*A+1];else for(var L=0;L<S;L++)k[M++]=t.position[A][0],k[M++]=t.position[A][1]}this.position.call?this.position({type:\"float\",data:k}):this.position=this.regl.buffer({type:\"float\",data:k}),u.freeFloat(k)}else this.position.destroy&&this.position.destroy(),this.position={constant:t.position};if(t.text||a){var C=u.mallocUint8(this.count),P=u.mallocFloat(2*this.count);this.textWidth=[];for(var I=0,O=0;I<this.counts.length;I++){for(var z=this.counts[I],D=this.font[I]||this.font[0],R=this.fontAtlas[I]||this.fontAtlas[0],F=0;F<z;F++){var B=this.text.charAt(O),N=this.text.charAt(O-1);if(C[O]=R.ids[B],P[2*O]=D.width[B],F){var j=P[2*O-2],U=P[2*O],V=P[2*O-1]+.5*j+.5*U;if(this.kerning){var H=D.kerning[N+B];H&&(V+=.001*H)}P[2*O+1]=V}else P[2*O+1]=.5*P[2*O];O++}this.textWidth.push(P.length?.5*P[2*O-2]+P[2*O-1]:0)}t.align||(t.align=this.align),this.charBuffer({data:C,type:\"uint8\",usage:\"stream\"}),this.sizeBuffer({data:P,type:\"float\",usage:\"stream\"}),u.freeUint8(C),u.freeFloat(P),r.length&&this.font.forEach((function(t,r){var n=e.fontAtlas[r],i=n.step,a=Math.floor(T.maxAtlasSize/i),o=Math.min(a,n.chars.length),s=Math.ceil(n.chars.length/o),l=x(o*i),u=x(s*i);n.width=l,n.height=u,n.rows=s,n.cols=o,n.em&&n.texture({data:c({canvas:T.atlasCanvas,font:n.fontString,chars:n.chars,shape:[l,u],step:[i,i]})})}))}if(t.align&&(this.align=t.align,this.alignOffset=this.textWidth.map((function(t,r){var n=Array.isArray(e.align)?e.align.length>1?e.align[r]:e.align[0]:e.align;if(\"number\"==typeof n)return n;switch(n){case\"right\":case\"end\":return-t;case\"center\":case\"centre\":case\"middle\":return.5*-t}return 0}))),null==this.baseline&&null==t.baseline&&(t.baseline=0),null!=t.baseline&&(this.baseline=t.baseline,Array.isArray(this.baseline)||(this.baseline=[this.baseline]),this.baselineOffset=this.baseline.map((function(t,r){var n=(e.font[r]||e.font[0]).metrics,i=0;return i+=.5*n.bottom,i+=\"number\"==typeof t?t-n.baseline:-n[t],i*=-1}))),null!=t.color)if(t.color||(t.color=\"transparent\"),\"string\"!=typeof t.color&&isNaN(t.color)){var q;if(\"number\"==typeof t.color[0]&&t.color.length>this.counts.length){var G=t.color.length;q=u.mallocUint8(G);for(var Y=(t.color.subarray||t.color.slice).bind(t.color),W=0;W<G;W+=4)q.set(l(Y(W,W+4),\"uint8\"),W)}else{var X=t.color.length;q=u.mallocUint8(4*X);for(var Z=0;Z<X;Z++)q.set(l(t.color[Z]||0,\"uint8\"),4*Z)}this.color=q}else this.color=l(t.color,\"uint8\");if(t.position||t.text||t.color||t.baseline||t.align||t.font||t.offset||t.opacity)if(this.color.length>4||this.baselineOffset.length>1||this.align&&this.align.length>1||this.fontAtlas.length>1||this.positionOffset.length>2){var J=Math.max(.5*this.position.length||0,.25*this.color.length||0,this.baselineOffset.length||0,this.alignOffset.length||0,this.font.length||0,this.opacity.length||0,.5*this.positionOffset.length||0);this.batch=Array(J);for(var K=0;K<this.batch.length;K++)this.batch[K]={count:this.counts.length>1?this.counts[K]:this.counts[0],offset:this.textOffsets.length>1?this.textOffsets[K]:this.textOffsets[0],color:this.color?this.color.length<=4?this.color:this.color.subarray(4*K,4*K+4):[0,0,0,255],opacity:Array.isArray(this.opacity)?this.opacity[K]:this.opacity,baseline:null!=this.baselineOffset[K]?this.baselineOffset[K]:this.baselineOffset[0],align:this.align?null!=this.alignOffset[K]?this.alignOffset[K]:this.alignOffset[0]:0,atlas:this.fontAtlas[K]||this.fontAtlas[0],positionOffset:this.positionOffset.length>2?this.positionOffset.subarray(2*K,2*K+2):this.positionOffset}}else this.count?this.batch=[{count:this.count,offset:0,color:this.color||[0,0,0,255],opacity:Array.isArray(this.opacity)?this.opacity[0]:this.opacity,baseline:this.baselineOffset[0],align:this.alignOffset?this.alignOffset[0]:0,atlas:this.fontAtlas[0],positionOffset:this.positionOffset}]:this.batch=[]},T.prototype.destroy=function(){},T.prototype.kerning=!0,T.prototype.position={constant:new Float32Array(2)},T.prototype.translate=null,T.prototype.scale=null,T.prototype.font=null,T.prototype.text=\"\",T.prototype.positionOffset=[0,0],T.prototype.opacity=1,T.prototype.color=new Uint8Array([0,0,0,255]),T.prototype.alignOffset=[0,0],T.maxAtlasSize=1024,T.atlasCanvas=document.createElement(\"canvas\"),T.atlasContext=T.atlasCanvas.getContext(\"2d\",{alpha:!1}),T.baseFontSize=64,T.fonts={},e.exports=T},{\"bit-twiddle\":81,\"color-normalize\":89,\"css-font\":99,\"detect-kerning\":125,\"es6-weak-map\":183,\"flatten-vertex-data\":191,\"font-atlas\":192,\"font-measure\":193,\"gl-util/context\":226,\"is-plain-obj\":236,\"object-assign\":247,\"parse-rect\":249,\"parse-unit\":251,\"pick-by-alias\":253,regl:283,\"to-px\":314,\"typedarray-pool\":327}],226:[function(t,e,r){(function(r){(function(){\"use strict\";var n=t(\"pick-by-alias\");function i(t){if(t.container)if(t.container==document.body)document.body.style.width||(t.canvas.width=t.width||t.pixelRatio*r.innerWidth),document.body.style.height||(t.canvas.height=t.height||t.pixelRatio*r.innerHeight);else{var 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r=e.parse(t[1],1,Bt);if(!r)return null;var n=[],i=null;e.expectedType&&\"value\"!==e.expectedType.kind&&(i=e.expectedType);for(var a=1;a<t.length;a+=2){var o=1===a?-1/0:t[a],s=t[a+1],l=a,c=a+1;if(\"number\"!=typeof o)return e.error('Input/output pairs for \"step\" expressions must be defined using literal numeric values (not computed expressions) for the input values.',l);if(n.length&&n[n.length-1][0]>=o)return e.error('Input/output pairs for \"step\" expressions must be arranged with input values in strictly ascending order.',l);var u=e.parse(s,c,i);if(!u)return null;i=i||u.type,n.push([o,u])}return new He(i,r,n)},He.prototype.evaluate=function(t){var e=this.labels,r=this.outputs;if(1===e.length)return r[0].evaluate(t);var n=this.input.evaluate(t);if(n<=e[0])return r[0].evaluate(t);var i=e.length;return n>=e[i-1]?r[i-1].evaluate(t):r[Ve(e,n)].evaluate(t)},He.prototype.eachChild=function(t){t(this.input);for(var e=0,r=this.outputs;e<r.length;e+=1){t(r[e])}},He.prototype.outputDefined=function(){return this.outputs.every((function(t){return t.outputDefined()}))},He.prototype.serialize=function(){for(var t=[\"step\",this.input.serialize()],e=0;e<this.labels.length;e++)e>0&&t.push(this.labels[e]),t.push(this.outputs[e].serialize());return t};var Ge=Object.freeze({__proto__:null,number:qe,color:function(t,e,r){return new te(qe(t.r,e.r,r),qe(t.g,e.g,r),qe(t.b,e.b,r),qe(t.a,e.a,r))},array:function(t,e,r){return t.map((function(t,n){return qe(t,e[n],r)}))}}),Ye=6/29,We=3*Ye*Ye,Xe=Math.PI/180,Ze=180/Math.PI;function Je(t){return t>.008856451679035631?Math.pow(t,1/3):t/We+4/29}function Ke(t){return t>Ye?t*t*t:We*(t-4/29)}function Qe(t){return 255*(t<=.0031308?12.92*t:1.055*Math.pow(t,1/2.4)-.055)}function $e(t){return(t/=255)<=.04045?t/12.92:Math.pow((t+.055)/1.055,2.4)}function tr(t){var e=$e(t.r),r=$e(t.g),n=$e(t.b),i=Je((.4124564*e+.3575761*r+.1804375*n)/.95047),a=Je((.2126729*e+.7151522*r+.072175*n)/1);return{l:116*a-16,a:500*(i-a),b:200*(a-Je((.0193339*e+.119192*r+.9503041*n)/1.08883)),alpha:t.a}}function er(t){var e=(t.l+16)/116,r=isNaN(t.a)?e:e+t.a/500,n=isNaN(t.b)?e:e-t.b/200;return e=1*Ke(e),r=.95047*Ke(r),n=1.08883*Ke(n),new te(Qe(3.2404542*r-1.5371385*e-.4985314*n),Qe(-.969266*r+1.8760108*e+.041556*n),Qe(.0556434*r-.2040259*e+1.0572252*n),t.alpha)}function rr(t,e,r){var n=e-t;return t+r*(n>180||n<-180?n-360*Math.round(n/360):n)}var nr={forward:tr,reverse:er,interpolate:function(t,e,r){return{l:qe(t.l,e.l,r),a:qe(t.a,e.a,r),b:qe(t.b,e.b,r),alpha:qe(t.alpha,e.alpha,r)}}},ir={forward:function(t){var e=tr(t),r=e.l,n=e.a,i=e.b,a=Math.atan2(i,n)*Ze;return{h:a<0?a+360:a,c:Math.sqrt(n*n+i*i),l:r,alpha:t.a}},reverse:function(t){var e=t.h*Xe,r=t.c;return er({l:t.l,a:Math.cos(e)*r,b:Math.sin(e)*r,alpha:t.alpha})},interpolate:function(t,e,r){return{h:rr(t.h,e.h,r),c:qe(t.c,e.c,r),l:qe(t.l,e.l,r),alpha:qe(t.alpha,e.alpha,r)}}},ar=Object.freeze({__proto__:null,lab:nr,hcl:ir}),or=function(t,e,r,n,i){this.type=t,this.operator=e,this.interpolation=r,this.input=n,this.labels=[],this.outputs=[];for(var a=0,o=i;a<o.length;a+=1){var s=o[a],l=s[0],c=s[1];this.labels.push(l),this.outputs.push(c)}};function sr(t,e,r,n){var i=n-r,a=t-r;return 0===i?0:1===e?a/i:(Math.pow(e,a)-1)/(Math.pow(e,i)-1)}or.interpolationFactor=function(t,e,n,i){var a=0;if(\"exponential\"===t.name)a=sr(e,t.base,n,i);else if(\"linear\"===t.name)a=sr(e,1,n,i);else if(\"cubic-bezier\"===t.name){var o=t.controlPoints;a=new r(o[0],o[1],o[2],o[3]).solve(sr(e,1,n,i))}return a},or.parse=function(t,e){var r=t[0],n=t[1],i=t[2],a=t.slice(3);if(!Array.isArray(n)||0===n.length)return e.error(\"Expected an interpolation type expression.\",1);if(\"linear\"===n[0])n={name:\"linear\"};else if(\"exponential\"===n[0]){var o=n[1];if(\"number\"!=typeof o)return e.error(\"Exponential interpolation requires a numeric base.\",1,1);n={name:\"exponential\",base:o}}else{if(\"cubic-bezier\"!==n[0])return e.error(\"Unknown interpolation type \"+String(n[0]),1,0);var s=n.slice(1);if(4!==s.length||s.some((function(t){return\"number\"!=typeof t||t<0||t>1})))return e.error(\"Cubic bezier interpolation requires four numeric arguments with values between 0 and 1.\",1);n={name:\"cubic-bezier\",controlPoints:s}}if(t.length-1<4)return e.error(\"Expected at least 4 arguments, but found only \"+(t.length-1)+\".\");if((t.length-1)%2!=0)return e.error(\"Expected an even number of arguments.\");if(!(i=e.parse(i,2,Bt)))return null;var l=[],c=null;\"interpolate-hcl\"===r||\"interpolate-lab\"===r?c=Ut:e.expectedType&&\"value\"!==e.expectedType.kind&&(c=e.expectedType);for(var u=0;u<a.length;u+=2){var f=a[u],h=a[u+1],p=u+3,d=u+4;if(\"number\"!=typeof f)return e.error('Input/output pairs for 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a=Ve(e,n),o=e[a],s=e[a+1],l=or.interpolationFactor(this.interpolation,n,o,s),c=r[a].evaluate(t),u=r[a+1].evaluate(t);return\"interpolate\"===this.operator?Ge[this.type.kind.toLowerCase()](c,u,l):\"interpolate-hcl\"===this.operator?ir.reverse(ir.interpolate(ir.forward(c),ir.forward(u),l)):nr.reverse(nr.interpolate(nr.forward(c),nr.forward(u),l))},or.prototype.eachChild=function(t){t(this.input);for(var e=0,r=this.outputs;e<r.length;e+=1){t(r[e])}},or.prototype.outputDefined=function(){return this.outputs.every((function(t){return t.outputDefined()}))},or.prototype.serialize=function(){var t;t=\"linear\"===this.interpolation.name?[\"linear\"]:\"exponential\"===this.interpolation.name?1===this.interpolation.base?[\"linear\"]:[\"exponential\",this.interpolation.base]:[\"cubic-bezier\"].concat(this.interpolation.controlPoints);for(var e=[this.operator,t,this.input.serialize()],r=0;r<this.labels.length;r++)e.push(this.labels[r],this.outputs[r].serialize());return e};var lr=function(t,e){this.type=t,this.args=e};lr.parse=function(t,e){if(t.length<2)return e.error(\"Expectected at least one argument.\");var r=null,n=e.expectedType;n&&\"value\"!==n.kind&&(r=n);for(var i=[],a=0,o=t.slice(1);a<o.length;a+=1){var s=o[a],l=e.parse(s,1+i.length,r,void 0,{typeAnnotation:\"omit\"});if(!l)return null;r=r||l.type,i.push(l)}var c=n&&i.some((function(t){return Jt(n,t.type)}));return new lr(c?Ht:r,i)},lr.prototype.evaluate=function(t){for(var e,r=null,n=0,i=0,a=this.args;i<a.length;i+=1){if(n++,(r=a[i].evaluate(t))&&r instanceof ie&&!r.available&&(e||(e=r.name),r=null,n===this.args.length&&(r=e)),null!==r)break}return r},lr.prototype.eachChild=function(t){this.args.forEach(t)},lr.prototype.outputDefined=function(){return this.args.every((function(t){return t.outputDefined()}))},lr.prototype.serialize=function(){var t=[\"coalesce\"];return this.eachChild((function(e){t.push(e.serialize())})),t};var cr=function(t,e){this.type=e.type,this.bindings=[].concat(t),this.result=e};cr.prototype.evaluate=function(t){return this.result.evaluate(t)},cr.prototype.eachChild=function(t){for(var e=0,r=this.bindings;e<r.length;e+=1){t(r[e][1])}t(this.result)},cr.parse=function(t,e){if(t.length<4)return e.error(\"Expected at least 3 arguments, but found \"+(t.length-1)+\" instead.\");for(var r=[],n=1;n<t.length-1;n+=2){var i=t[n];if(\"string\"!=typeof i)return e.error(\"Expected string, but found \"+typeof i+\" instead.\",n);if(/[^a-zA-Z0-9_]/.test(i))return e.error(\"Variable names must contain only alphanumeric characters or '_'.\",n);var a=e.parse(t[n+1],n+1);if(!a)return null;r.push([i,a])}var o=e.parse(t[t.length-1],t.length-1,e.expectedType,r);return o?new cr(r,o):null},cr.prototype.outputDefined=function(){return this.result.outputDefined()},cr.prototype.serialize=function(){for(var t=[\"let\"],e=0,r=this.bindings;e<r.length;e+=1){var n=r[e],i=n[0],a=n[1];t.push(i,a.serialize())}return t.push(this.result.serialize()),t};var ur=function(t,e,r){this.type=t,this.index=e,this.input=r};ur.parse=function(t,e){if(3!==t.length)return e.error(\"Expected 2 arguments, but found \"+(t.length-1)+\" instead.\");var r=e.parse(t[1],1,Bt),n=e.parse(t[2],2,Wt(e.expectedType||Ht));if(!r||!n)return null;var i=n.type;return new ur(i.itemType,r,n)},ur.prototype.evaluate=function(t){var e=this.index.evaluate(t),r=this.input.evaluate(t);if(e<0)throw new ue(\"Array index out of bounds: \"+e+\" < 0.\");if(e>=r.length)throw new ue(\"Array index out of bounds: \"+e+\" > \"+(r.length-1)+\".\");if(e!==Math.floor(e))throw new ue(\"Array index must be an integer, but found \"+e+\" instead.\");return r[e]},ur.prototype.eachChild=function(t){t(this.index),t(this.input)},ur.prototype.outputDefined=function(){return!1},ur.prototype.serialize=function(){return[\"at\",this.index.serialize(),this.input.serialize()]};var fr=function(t,e){this.type=jt,this.needle=t,this.haystack=e};fr.parse=function(t,e){if(3!==t.length)return e.error(\"Expected 2 arguments, but found \"+(t.length-1)+\" instead.\");var r=e.parse(t[1],1,Ht),n=e.parse(t[2],2,Ht);return r&&n?Kt(r.type,[jt,Nt,Bt,Ft,Ht])?new fr(r,n):e.error(\"Expected first argument to be of type boolean, string, number or null, but found \"+Xt(r.type)+\" instead\"):null},fr.prototype.evaluate=function(t){var e=this.needle.evaluate(t),r=this.haystack.evaluate(t);if(!r)return!1;if(!Qt(e,[\"boolean\",\"string\",\"number\",\"null\"]))throw new ue(\"Expected first argument to be of type boolean, string, number or null, but found \"+Xt(se(e))+\" instead.\");if(!Qt(r,[\"string\",\"array\"]))throw new ue(\"Expected second argument to be of type array or string, but found \"+Xt(se(r))+\" instead.\");return r.indexOf(e)>=0},fr.prototype.eachChild=function(t){t(this.needle),t(this.haystack)},fr.prototype.outputDefined=function(){return!0},fr.prototype.serialize=function(){return[\"in\",this.needle.serialize(),this.haystack.serialize()]};var hr=function(t,e,r){this.type=Bt,this.needle=t,this.haystack=e,this.fromIndex=r};hr.parse=function(t,e){if(t.length<=2||t.length>=5)return e.error(\"Expected 3 or 4 arguments, but found \"+(t.length-1)+\" instead.\");var r=e.parse(t[1],1,Ht),n=e.parse(t[2],2,Ht);if(!r||!n)return null;if(!Kt(r.type,[jt,Nt,Bt,Ft,Ht]))return e.error(\"Expected first argument to be of type boolean, string, number or null, but found \"+Xt(r.type)+\" instead\");if(4===t.length){var i=e.parse(t[3],3,Bt);return i?new hr(r,n,i):null}return new hr(r,n)},hr.prototype.evaluate=function(t){var e=this.needle.evaluate(t),r=this.haystack.evaluate(t);if(!Qt(e,[\"boolean\",\"string\",\"number\",\"null\"]))throw new ue(\"Expected first argument to be of type boolean, string, number or null, but found \"+Xt(se(e))+\" instead.\");if(!Qt(r,[\"string\",\"array\"]))throw new ue(\"Expected second argument to be of type array or string, but found \"+Xt(se(r))+\" instead.\");if(this.fromIndex){var n=this.fromIndex.evaluate(t);return r.indexOf(e,n)}return r.indexOf(e)},hr.prototype.eachChild=function(t){t(this.needle),t(this.haystack),this.fromIndex&&t(this.fromIndex)},hr.prototype.outputDefined=function(){return!1},hr.prototype.serialize=function(){if(null!=this.fromIndex&&void 0!==this.fromIndex){var t=this.fromIndex.serialize();return[\"index-of\",this.needle.serialize(),this.haystack.serialize(),t]}return[\"index-of\",this.needle.serialize(),this.haystack.serialize()]};var pr=function(t,e,r,n,i,a){this.inputType=t,this.type=e,this.input=r,this.cases=n,this.outputs=i,this.otherwise=a};pr.parse=function(t,e){if(t.length<5)return e.error(\"Expected at least 4 arguments, but found only \"+(t.length-1)+\".\");if(t.length%2!=1)return e.error(\"Expected an even number of 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g?\"value\"!==d.type.kind&&e.concat(1).checkSubtype(r,d.type)?null:new pr(r,n,d,i,a,g):null},pr.prototype.evaluate=function(t){var e=this.input.evaluate(t);return(se(e)===this.inputType&&this.outputs[this.cases[e]]||this.otherwise).evaluate(t)},pr.prototype.eachChild=function(t){t(this.input),this.outputs.forEach(t),t(this.otherwise)},pr.prototype.outputDefined=function(){return this.outputs.every((function(t){return t.outputDefined()}))&&this.otherwise.outputDefined()},pr.prototype.serialize=function(){for(var t=this,e=[\"match\",this.input.serialize()],r=[],n={},i=0,a=Object.keys(this.cases).sort();i<a.length;i+=1){var o=a[i];void 0===(f=n[this.cases[o]])?(n[this.cases[o]]=r.length,r.push([this.cases[o],[o]])):r[f][1].push(o)}for(var s=function(e){return\"number\"===t.inputType.kind?Number(e):e},l=0,c=r;l<c.length;l+=1){var u=c[l],f=u[0],h=u[1];1===h.length?e.push(s(h[0])):e.push(h.map(s)),e.push(this.outputs[outputIndex$1].serialize())}return e.push(this.otherwise.serialize()),e};var dr=function(t,e,r){this.type=t,this.branches=e,this.otherwise=r};dr.parse=function(t,e){if(t.length<4)return e.error(\"Expected at least 3 arguments, but found only \"+(t.length-1)+\".\");if(t.length%2!=0)return e.error(\"Expected an odd number of arguments.\");var r;e.expectedType&&\"value\"!==e.expectedType.kind&&(r=e.expectedType);for(var n=[],i=1;i<t.length-1;i+=2){var a=e.parse(t[i],i,jt);if(!a)return null;var o=e.parse(t[i+1],i+1,r);if(!o)return null;n.push([a,o]),r=r||o.type}var s=e.parse(t[t.length-1],t.length-1,r);return s?new dr(r,n,s):null},dr.prototype.evaluate=function(t){for(var e=0,r=this.branches;e<r.length;e+=1){var n=r[e],i=n[0],a=n[1];if(i.evaluate(t))return a.evaluate(t)}return this.otherwise.evaluate(t)},dr.prototype.eachChild=function(t){for(var e=0,r=this.branches;e<r.length;e+=1){var n=r[e],i=n[0],a=n[1];t(i),t(a)}t(this.otherwise)},dr.prototype.outputDefined=function(){return this.branches.every((function(t){t[0];return 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or?a.interpolation:void 0;return Ir(new Zr(n?\"camera\":\"composite\",t.value,a.labels,o))}Zr.prototype.evaluateWithoutErrorHandling=function(t,e,r,n,i,a){return this._styleExpression.evaluateWithoutErrorHandling(t,e,r,n,i,a)},Zr.prototype.evaluate=function(t,e,r,n,i,a){return this._styleExpression.evaluate(t,e,r,n,i,a)},Zr.prototype.interpolationFactor=function(t,e,r){return this.interpolationType?or.interpolationFactor(this.interpolationType,t,e,r):0};var Kr=function(t,e){this._parameters=t,this._specification=e,It(this,function t(e,r){var n,i,a,o=\"color\"===r.type,s=e.stops&&\"object\"==typeof e.stops[0][0],l=s||void 0!==e.property,c=s||!l,u=e.type||(Rr(r)?\"exponential\":\"interval\");if(o&&((e=It({},e)).stops&&(e.stops=e.stops.map((function(t){return[t[0],te.parse(t[1])]}))),e.default?e.default=te.parse(e.default):e.default=te.parse(r.default)),e.colorSpace&&\"rgb\"!==e.colorSpace&&!ar[e.colorSpace])throw new Error(\"Unknown color space: 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n&&r<n.minimum?[new Ct(e,r,r+\" is less than the minimum value \"+n.minimum)]:\"maximum\"in n&&r>n.maximum?[new Ct(e,r,r+\" is greater than the maximum value \"+n.maximum)]:[]}function en(t){var e,r,n,i=t.valueSpec,a=Ot(t.value.type),o={},s=\"categorical\"!==a&&void 0===t.value.property,l=!s,c=\"array\"===Fr(t.value.stops)&&\"array\"===Fr(t.value.stops[0])&&\"object\"===Fr(t.value.stops[0][0]),u=Qr({key:t.key,value:t.value,valueSpec:t.styleSpec.function,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{stops:function(t){if(\"identity\"===a)return[new Ct(t.key,t.value,'identity function may not have a \"stops\" property')];var e=[],r=t.value;e=e.concat($r({key:t.key,value:r,valueSpec:t.valueSpec,style:t.style,styleSpec:t.styleSpec,arrayElementValidator:f})),\"array\"===Fr(r)&&0===r.length&&e.push(new Ct(t.key,r,\"array must have at least one stop\"));return e},default:function(t){return kn({key:t.key,value:t.value,valueSpec:i,style:t.style,styleSpec:t.styleSpec})}}});return\"identity\"===a&&s&&u.push(new Ct(t.key,t.value,'missing required property \"property\"')),\"identity\"===a||t.value.stops||u.push(new Ct(t.key,t.value,'missing required property \"stops\"')),\"exponential\"===a&&t.valueSpec.expression&&!Rr(t.valueSpec)&&u.push(new Ct(t.key,t.value,\"exponential functions not supported\")),t.styleSpec.$version>=8&&(l&&!zr(t.valueSpec)?u.push(new Ct(t.key,t.value,\"property functions not supported\")):s&&!Dr(t.valueSpec)&&u.push(new Ct(t.key,t.value,\"zoom functions not supported\"))),\"categorical\"!==a&&!c||void 0!==t.value.property||u.push(new Ct(t.key,t.value,'\"property\" property is required')),u;function f(t){var e=[],a=t.value,s=t.key;if(\"array\"!==Fr(a))return[new Ct(s,a,\"array expected, \"+Fr(a)+\" found\")];if(2!==a.length)return[new Ct(s,a,\"array length 2 expected, length \"+a.length+\" found\")];if(c){if(\"object\"!==Fr(a[0]))return[new Ct(s,a,\"object expected, \"+Fr(a[0])+\" found\")];if(void 0===a[0].zoom)return[new Ct(s,a,\"object stop key must have zoom\")];if(void 0===a[0].value)return[new Ct(s,a,\"object stop key must have value\")];if(n&&n>Ot(a[0].zoom))return[new Ct(s,a[0].zoom,\"stop zoom values must appear in ascending order\")];Ot(a[0].zoom)!==n&&(n=Ot(a[0].zoom),r=void 0,o={}),e=e.concat(Qr({key:s+\"[0]\",value:a[0],valueSpec:{zoom:{}},style:t.style,styleSpec:t.styleSpec,objectElementValidators:{zoom:tn,value:h}}))}else e=e.concat(h({key:s+\"[0]\",value:a[0],valueSpec:{},style:t.style,styleSpec:t.styleSpec},a));return Yr(zt(a[1]))?e.concat([new Ct(s+\"[1]\",a[1],\"expressions are not allowed in function stops.\")]):e.concat(kn({key:s+\"[1]\",value:a[1],valueSpec:i,style:t.style,styleSpec:t.styleSpec}))}function h(t,n){var s=Fr(t.value),l=Ot(t.value),c=null!==t.value?t.value:n;if(e){if(s!==e)return[new Ct(t.key,c,s+\" stop domain type must match previous stop domain type \"+e)]}else e=s;if(\"number\"!==s&&\"string\"!==s&&\"boolean\"!==s)return[new Ct(t.key,c,\"stop domain value must be a number, string, or boolean\")];if(\"number\"!==s&&\"categorical\"!==a){var u=\"number expected, \"+s+\" found\";return zr(i)&&void 0===a&&(u+='\\nIf you intended to use a categorical function, specify `\"type\": \"categorical\"`.'),[new Ct(t.key,c,u)]}return\"categorical\"!==a||\"number\"!==s||isFinite(l)&&Math.floor(l)===l?\"categorical\"!==a&&\"number\"===s&&void 0!==r&&l<r?[new Ct(t.key,c,\"stop domain values must appear in ascending order\")]:(r=l,\"categorical\"===a&&l in o?[new Ct(t.key,c,\"stop domain values must be unique\")]:(o[l]=!0,[])):[new Ct(t.key,c,\"integer expected, found \"+l)]}}function rn(t){var e=(\"property\"===t.expressionContext?Jr:Wr)(zt(t.value),t.valueSpec);if(\"error\"===e.result)return e.value.map((function(e){return new Ct(\"\"+t.key+e.key,t.value,e.message)}));var r=e.value.expression||e.value._styleExpression.expression;if(\"property\"===t.expressionContext&&\"text-font\"===t.propertyKey&&!r.outputDefined())return[new Ct(t.key,t.value,'Invalid data expression for \"'+t.propertyKey+'\". Output values must be contained as literals within the expression.')];if(\"property\"===t.expressionContext&&\"layout\"===t.propertyType&&!Be(r))return[new Ct(t.key,t.value,'\"feature-state\" data expressions are not supported with layout properties.')];if(\"filter\"===t.expressionContext&&!Be(r))return[new Ct(t.key,t.value,'\"feature-state\" data expressions are not supported with filters.')];if(t.expressionContext&&0===t.expressionContext.indexOf(\"cluster\")){if(!Ne(r,[\"zoom\",\"feature-state\"]))return[new Ct(t.key,t.value,'\"zoom\" and \"feature-state\" expressions are not supported with cluster properties.')];if(\"cluster-initial\"===t.expressionContext&&!Fe(r))return[new Ct(t.key,t.value,\"Feature data expressions are not supported with initial expression part of cluster properties.\")]}return[]}function nn(t){var e=t.key,r=t.value,n=t.valueSpec,i=[];return Array.isArray(n.values)?-1===n.values.indexOf(Ot(r))&&i.push(new Ct(e,r,\"expected one of [\"+n.values.join(\", \")+\"], 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on={type:\"boolean\",default:!1,transition:!1,\"property-type\":\"data-driven\",expression:{interpolated:!1,parameters:[\"zoom\",\"feature\"]}};function sn(t){if(null==t)return{filter:function(){return!0},needGeometry:!1};an(t)||(t=cn(t));var e=Wr(t,on);if(\"error\"===e.result)throw new Error(e.value.map((function(t){return t.key+\": \"+t.message})).join(\", \"));return{filter:function(t,r,n){return e.value.evaluate(t,r,{},n)},needGeometry:function t(e){if(!Array.isArray(e))return!1;if(\"within\"===e[0])return!0;for(var r=1;r<e.length;r++)if(t(e[r]))return!0;return!1}(t)}}function ln(t,e){return t<e?-1:t>e?1:0}function cn(t){if(!t)return!0;var e,r=t[0];return t.length<=1?\"any\"!==r:\"==\"===r?un(t[1],t[2],\"==\"):\"!=\"===r?pn(un(t[1],t[2],\"==\")):\"<\"===r||\">\"===r||\"<=\"===r||\">=\"===r?un(t[1],t[2],r):\"any\"===r?(e=t.slice(1),[\"any\"].concat(e.map(cn))):\"all\"===r?[\"all\"].concat(t.slice(1).map(cn)):\"none\"===r?[\"all\"].concat(t.slice(1).map(cn).map(pn)):\"in\"===r?fn(t[1],t.slice(2)):\"!in\"===r?pn(fn(t[1],t.slice(2))):\"has\"===r?hn(t[1]):\"!has\"===r?pn(hn(t[1])):\"within\"!==r||t}function un(t,e,r){switch(t){case\"$type\":return[\"filter-type-\"+r,e];case\"$id\":return[\"filter-id-\"+r,e];default:return[\"filter-\"+r,t,e]}}function fn(t,e){if(0===e.length)return!1;switch(t){case\"$type\":return[\"filter-type-in\",[\"literal\",e]];case\"$id\":return[\"filter-id-in\",[\"literal\",e]];default:return e.length>200&&!e.some((function(t){return typeof t!=typeof e[0]}))?[\"filter-in-large\",t,[\"literal\",e.sort(ln)]]:[\"filter-in-small\",t,[\"literal\",e]]}}function hn(t){switch(t){case\"$type\":return!0;case\"$id\":return[\"filter-has-id\"];default:return[\"filter-has\",t]}}function pn(t){return[\"!\",t]}function dn(t){return an(zt(t.value))?rn(It({},t,{expressionContext:\"filter\",valueSpec:{value:\"boolean\"}})):function t(e){var r=e.value,n=e.key;if(\"array\"!==Fr(r))return[new Ct(n,r,\"array expected, \"+Fr(r)+\" found\")];var i,a=e.styleSpec,o=[];if(r.length<1)return[new Ct(n,r,\"filter array must have at least 1 element\")];switch(o=o.concat(nn({key:n+\"[0]\",value:r[0],valueSpec:a.filter_operator,style:e.style,styleSpec:e.styleSpec})),Ot(r[0])){case\"<\":case\"<=\":case\">\":case\">=\":r.length>=2&&\"$type\"===Ot(r[1])&&o.push(new Ct(n,r,'\"$type\" cannot be use with operator \"'+r[0]+'\"'));case\"==\":case\"!=\":3!==r.length&&o.push(new Ct(n,r,'filter array for operator \"'+r[0]+'\" must have 3 elements'));case\"in\":case\"!in\":r.length>=2&&\"string\"!==(i=Fr(r[1]))&&o.push(new Ct(n+\"[1]\",r[1],\"string expected, \"+i+\" 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r=t.key,n=t.style,i=t.styleSpec,a=t.value,o=t.objectKey,s=i[e+\"_\"+t.layerType];if(!s)return[];var l=o.match(/^(.*)-transition$/);if(\"paint\"===e&&l&&s[l[1]]&&s[l[1]].transition)return kn({key:r,value:a,valueSpec:i.transition,style:n,styleSpec:i});var c,u=t.valueSpec||s[o];if(!u)return[new Ct(r,a,'unknown property \"'+o+'\"')];if(\"string\"===Fr(a)&&zr(u)&&!u.tokens&&(c=/^{([^}]+)}$/.exec(a)))return[new Ct(r,a,'\"'+o+'\" does not support interpolation syntax\\nUse an identity property function instead: `{ \"type\": \"identity\", \"property\": '+JSON.stringify(c[1])+\" }`.\")];var f=[];return\"symbol\"===t.layerType&&(\"text-field\"===o&&n&&!n.glyphs&&f.push(new Ct(r,a,'use of \"text-field\" requires a style \"glyphs\" property')),\"text-font\"===o&&Br(zt(a))&&\"identity\"===Ot(a.type)&&f.push(new Ct(r,a,'\"text-font\" does not support identity 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Kr(t,e);if(Yr(t)){var r=Jr(t,e);if(\"error\"===r.result)throw new Error(r.value.map((function(t){return t.key+\": \"+t.message})).join(\", \"));return r.value}var n=t;return\"string\"==typeof t&&\"color\"===e.type&&(n=te.parse(t)),{kind:\"constant\",evaluate:function(){return n}}}(void 0===e?t.specification.default:e,t.specification)};di.prototype.isDataDriven=function(){return\"source\"===this.expression.kind||\"composite\"===this.expression.kind},di.prototype.possiblyEvaluate=function(t,e,r){return this.property.possiblyEvaluate(this,t,e,r)};var gi=function(t){this.property=t,this.value=new di(t,void 0)};gi.prototype.transitioned=function(t,e){return new vi(this.property,this.value,e,u({},t.transition,this.transition),t.now)},gi.prototype.untransitioned=function(){return new vi(this.property,this.value,null,{},0)};var mi=function(t){this._properties=t,this._values=Object.create(t.defaultTransitionablePropertyValues)};mi.prototype.getValue=function(t){return x(this._values[t].value.value)},mi.prototype.setValue=function(t,e){this._values.hasOwnProperty(t)||(this._values[t]=new gi(this._values[t].property)),this._values[t].value=new di(this._values[t].property,null===e?void 0:x(e))},mi.prototype.getTransition=function(t){return x(this._values[t].transition)},mi.prototype.setTransition=function(t,e){this._values.hasOwnProperty(t)||(this._values[t]=new gi(this._values[t].property)),this._values[t].transition=x(e)||void 0},mi.prototype.serialize=function(){for(var t={},e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e],i=this.getValue(n);void 0!==i&&(t[n]=i);var a=this.getTransition(n);void 0!==a&&(t[n+\"-transition\"]=a)}return t},mi.prototype.transitioned=function(t,e){for(var r=new yi(this._properties),n=0,i=Object.keys(this._values);n<i.length;n+=1){var a=i[n];r._values[a]=this._values[a].transitioned(t,e._values[a])}return r},mi.prototype.untransitioned=function(){for(var t=new yi(this._properties),e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e];t._values[n]=this._values[n].untransitioned()}return t};var vi=function(t,e,r,n,i){this.property=t,this.value=e,this.begin=i+n.delay||0,this.end=this.begin+n.duration||0,t.specification.transition&&(n.delay||n.duration)&&(this.prior=r)};vi.prototype.possiblyEvaluate=function(t,e,r){var n=t.now||0,i=this.value.possiblyEvaluate(t,e,r),a=this.prior;if(a){if(n>this.end)return this.prior=null,i;if(this.value.isDataDriven())return this.prior=null,i;if(n<this.begin)return a.possiblyEvaluate(t,e,r);var o=(n-this.begin)/(this.end-this.begin);return this.property.interpolate(a.possiblyEvaluate(t,e,r),i,function(t){if(t<=0)return 0;if(t>=1)return 1;var e=t*t,r=e*t;return 4*(t<.5?r:3*(t-e)+r-.75)}(o))}return i};var yi=function(t){this._properties=t,this._values=Object.create(t.defaultTransitioningPropertyValues)};yi.prototype.possiblyEvaluate=function(t,e,r){for(var n=new _i(this._properties),i=0,a=Object.keys(this._values);i<a.length;i+=1){var o=a[i];n._values[o]=this._values[o].possiblyEvaluate(t,e,r)}return n},yi.prototype.hasTransition=function(){for(var t=0,e=Object.keys(this._values);t<e.length;t+=1){var r=e[t];if(this._values[r].prior)return!0}return!1};var xi=function(t){this._properties=t,this._values=Object.create(t.defaultPropertyValues)};xi.prototype.getValue=function(t){return x(this._values[t].value)},xi.prototype.setValue=function(t,e){this._values[t]=new di(this._values[t].property,null===e?void 0:x(e))},xi.prototype.serialize=function(){for(var t={},e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e],i=this.getValue(n);void 0!==i&&(t[n]=i)}return t},xi.prototype.possiblyEvaluate=function(t,e,r){for(var n=new _i(this._properties),i=0,a=Object.keys(this._values);i<a.length;i+=1){var o=a[i];n._values[o]=this._values[o].possiblyEvaluate(t,e,r)}return n};var bi=function(t,e,r){this.property=t,this.value=e,this.parameters=r};bi.prototype.isConstant=function(){return\"constant\"===this.value.kind},bi.prototype.constantOr=function(t){return\"constant\"===this.value.kind?this.value.value:t},bi.prototype.evaluate=function(t,e,r,n){return this.property.evaluate(this.value,this.parameters,t,e,r,n)};var _i=function(t){this._properties=t,this._values=Object.create(t.defaultPossiblyEvaluatedValues)};_i.prototype.get=function(t){return this._values[t]};var wi=function(t){this.specification=t};wi.prototype.possiblyEvaluate=function(t,e){return t.expression.evaluate(e)},wi.prototype.interpolate=function(t,e,r){var n=Ge[this.specification.type];return n?n(t,e,r):t};var Ti=function(t,e){this.specification=t,this.overrides=e};Ti.prototype.possiblyEvaluate=function(t,e,r,n){return\"constant\"===t.expression.kind||\"camera\"===t.expression.kind?new bi(this,{kind:\"constant\",value:t.expression.evaluate(e,null,{},r,n)},e):new bi(this,t.expression,e)},Ti.prototype.interpolate=function(t,e,r){if(\"constant\"!==t.value.kind||\"constant\"!==e.value.kind)return t;if(void 0===t.value.value||void 0===e.value.value)return new bi(this,{kind:\"constant\",value:void 0},t.parameters);var n=Ge[this.specification.type];return n?new bi(this,{kind:\"constant\",value:n(t.value.value,e.value.value,r)},t.parameters):t},Ti.prototype.evaluate=function(t,e,r,n,i,a){return\"constant\"===t.kind?t.value:t.evaluate(e,r,n,i,a)};var ki=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.possiblyEvaluate=function(t,e,r,n){if(void 0===t.value)return new bi(this,{kind:\"constant\",value:void 0},e);if(\"constant\"===t.expression.kind){var i=t.expression.evaluate(e,null,{},r,n),a=\"resolvedImage\"===t.property.specification.type&&\"string\"!=typeof i?i.name:i,o=this._calculate(a,a,a,e);return new bi(this,{kind:\"constant\",value:o},e)}if(\"camera\"===t.expression.kind){var s=this._calculate(t.expression.evaluate({zoom:e.zoom-1}),t.expression.evaluate({zoom:e.zoom}),t.expression.evaluate({zoom:e.zoom+1}),e);return new bi(this,{kind:\"constant\",value:s},e)}return new bi(this,t.expression,e)},e.prototype.evaluate=function(t,e,r,n,i,a){if(\"source\"===t.kind){var o=t.evaluate(e,r,n,i,a);return this._calculate(o,o,o,e)}return\"composite\"===t.kind?this._calculate(t.evaluate({zoom:Math.floor(e.zoom)-1},r,n),t.evaluate({zoom:Math.floor(e.zoom)},r,n),t.evaluate({zoom:Math.floor(e.zoom)+1},r,n),e):t.value},e.prototype._calculate=function(t,e,r,n){return n.zoom>n.zoomHistory.lastIntegerZoom?{from:t,to:e}:{from:r,to:e}},e.prototype.interpolate=function(t){return t},e}(Ti),Ai=function(t){this.specification=t};Ai.prototype.possiblyEvaluate=function(t,e,r,n){if(void 0!==t.value){if(\"constant\"===t.expression.kind){var i=t.expression.evaluate(e,null,{},r,n);return this._calculate(i,i,i,e)}return this._calculate(t.expression.evaluate(new pi(Math.floor(e.zoom-1),e)),t.expression.evaluate(new pi(Math.floor(e.zoom),e)),t.expression.evaluate(new pi(Math.floor(e.zoom+1),e)),e)}},Ai.prototype._calculate=function(t,e,r,n){return n.zoom>n.zoomHistory.lastIntegerZoom?{from:t,to:e}:{from:r,to:e}},Ai.prototype.interpolate=function(t){return t};var Mi=function(t){this.specification=t};Mi.prototype.possiblyEvaluate=function(t,e,r,n){return!!t.expression.evaluate(e,null,{},r,n)},Mi.prototype.interpolate=function(){return!1};var Si=function(t){for(var e in this.properties=t,this.defaultPropertyValues={},this.defaultTransitionablePropertyValues={},this.defaultTransitioningPropertyValues={},this.defaultPossiblyEvaluatedValues={},this.overridableProperties=[],t){var r=t[e];r.specification.overridable&&this.overridableProperties.push(e);var n=this.defaultPropertyValues[e]=new di(r,void 0),i=this.defaultTransitionablePropertyValues[e]=new gi(r);this.defaultTransitioningPropertyValues[e]=i.untransitioned(),this.defaultPossiblyEvaluatedValues[e]=n.possiblyEvaluate({})}};Nn(\"DataDrivenProperty\",Ti),Nn(\"DataConstantProperty\",wi),Nn(\"CrossFadedDataDrivenProperty\",ki),Nn(\"CrossFadedProperty\",Ai),Nn(\"ColorRampProperty\",Mi);var Ei=function(t){function e(e,r){if(t.call(this),this.id=e.id,this.type=e.type,this._featureFilter={filter:function(){return!0},needGeometry:!1},\"custom\"!==e.type&&(e=e,this.metadata=e.metadata,this.minzoom=e.minzoom,this.maxzoom=e.maxzoom,\"background\"!==e.type&&(this.source=e.source,this.sourceLayer=e[\"source-layer\"],this.filter=e.filter),r.layout&&(this._unevaluatedLayout=new xi(r.layout)),r.paint)){for(var n in this._transitionablePaint=new mi(r.paint),e.paint)this.setPaintProperty(n,e.paint[n],{validate:!1});for(var i in e.layout)this.setLayoutProperty(i,e.layout[i],{validate:!1});this._transitioningPaint=this._transitionablePaint.untransitioned(),this.paint=new _i(r.paint)}}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getCrossfadeParameters=function(){return this._crossfadeParameters},e.prototype.getLayoutProperty=function(t){return\"visibility\"===t?this.visibility:this._unevaluatedLayout.getValue(t)},e.prototype.setLayoutProperty=function(t,e,r){if(void 0===r&&(r={}),null!=e){var n=\"layers.\"+this.id+\".layout.\"+t;if(this._validate(In,n,t,e,r))return}\"visibility\"!==t?this._unevaluatedLayout.setValue(t,e):this.visibility=e},e.prototype.getPaintProperty=function(t){return m(t,\"-transition\")?this._transitionablePaint.getTransition(t.slice(0,-\"-transition\".length)):this._transitionablePaint.getValue(t)},e.prototype.setPaintProperty=function(t,e,r){if(void 0===r&&(r={}),null!=e){var n=\"layers.\"+this.id+\".paint.\"+t;if(this._validate(Pn,n,t,e,r))return!1}if(m(t,\"-transition\"))return this._transitionablePaint.setTransition(t.slice(0,-\"-transition\".length),e||void 0),!1;var i=this._transitionablePaint._values[t],a=\"cross-faded-data-driven\"===i.property.specification[\"property-type\"],o=i.value.isDataDriven(),s=i.value;this._transitionablePaint.setValue(t,e),this._handleSpecialPaintPropertyUpdate(t);var l=this._transitionablePaint._values[t].value;return l.isDataDriven()||o||a||this._handleOverridablePaintPropertyUpdate(t,s,l)},e.prototype._handleSpecialPaintPropertyUpdate=function(t){},e.prototype._handleOverridablePaintPropertyUpdate=function(t,e,r){return!1},e.prototype.isHidden=function(t){return!!(this.minzoom&&t<this.minzoom)||(!!(this.maxzoom&&t>=this.maxzoom)||\"none\"===this.visibility)},e.prototype.updateTransitions=function(t){this._transitioningPaint=this._transitionablePaint.transitioned(t,this._transitioningPaint)},e.prototype.hasTransition=function(){return this._transitioningPaint.hasTransition()},e.prototype.recalculate=function(t,e){t.getCrossfadeParameters&&(this._crossfadeParameters=t.getCrossfadeParameters()),this._unevaluatedLayout&&(this.layout=this._unevaluatedLayout.possiblyEvaluate(t,void 0,e)),this.paint=this._transitioningPaint.possiblyEvaluate(t,void 0,e)},e.prototype.serialize=function(){var t={id:this.id,type:this.type,source:this.source,\"source-layer\":this.sourceLayer,metadata:this.metadata,minzoom:this.minzoom,maxzoom:this.maxzoom,filter:this.filter,layout:this._unevaluatedLayout&&this._unevaluatedLayout.serialize(),paint:this._transitionablePaint&&this._transitionablePaint.serialize()};return this.visibility&&(t.layout=t.layout||{},t.layout.visibility=this.visibility),y(t,(function(t,e){return!(void 0===t||\"layout\"===e&&!Object.keys(t).length||\"paint\"===e&&!Object.keys(t).length)}))},e.prototype._validate=function(t,e,r,n,i){return void 0===i&&(i={}),(!i||!1!==i.validate)&&On(this,t.call(Ln,{key:e,layerType:this.type,objectKey:r,value:n,styleSpec:Lt,style:{glyphs:!0,sprite:!0}}))},e.prototype.is3D=function(){return!1},e.prototype.isTileClipped=function(){return!1},e.prototype.hasOffscreenPass=function(){return!1},e.prototype.resize=function(){},e.prototype.isStateDependent=function(){for(var t in this.paint._values){var e=this.paint.get(t);if(e instanceof bi&&zr(e.property.specification)&&((\"source\"===e.value.kind||\"composite\"===e.value.kind)&&e.value.isStateDependent))return!0}return!1},e}(Et),Li={Int8:Int8Array,Uint8:Uint8Array,Int16:Int16Array,Uint16:Uint16Array,Int32:Int32Array,Uint32:Uint32Array,Float32:Float32Array},Ci=function(t,e){this._structArray=t,this._pos1=e*this.size,this._pos2=this._pos1/2,this._pos4=this._pos1/4,this._pos8=this._pos1/8},Pi=function(){this.isTransferred=!1,this.capacity=-1,this.resize(0)};function Ii(t,e){void 0===e&&(e=1);var r=0,n=0;return{members:t.map((function(t){var i,a=(i=t.type,Li[i].BYTES_PER_ELEMENT),o=r=Oi(r,Math.max(e,a)),s=t.components||1;return n=Math.max(n,a),r+=a*s,{name:t.name,type:t.type,components:s,offset:o}})),size:Oi(r,Math.max(n,e)),alignment:e}}function Oi(t,e){return Math.ceil(t/e)*e}Pi.serialize=function(t,e){return t._trim(),e&&(t.isTransferred=!0,e.push(t.arrayBuffer)),{length:t.length,arrayBuffer:t.arrayBuffer}},Pi.deserialize=function(t){var e=Object.create(this.prototype);return e.arrayBuffer=t.arrayBuffer,e.length=t.length,e.capacity=t.arrayBuffer.byteLength/e.bytesPerElement,e._refreshViews(),e},Pi.prototype._trim=function(){this.length!==this.capacity&&(this.capacity=this.length,this.arrayBuffer=this.arrayBuffer.slice(0,this.length*this.bytesPerElement),this._refreshViews())},Pi.prototype.clear=function(){this.length=0},Pi.prototype.resize=function(t){this.reserve(t),this.length=t},Pi.prototype.reserve=function(t){if(t>this.capacity){this.capacity=Math.max(t,Math.floor(5*this.capacity),128),this.arrayBuffer=new ArrayBuffer(this.capacity*this.bytesPerElement);var e=this.uint8;this._refreshViews(),e&&this.uint8.set(e)}},Pi.prototype._refreshViews=function(){throw new Error(\"_refreshViews() must be implemented by each concrete StructArray layout\")};var zi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.int16[n+0]=e,this.int16[n+1]=r,t},e}(Pi);zi.prototype.bytesPerElement=4,Nn(\"StructArrayLayout2i4\",zi);var Di=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=4*t;return this.int16[a+0]=e,this.int16[a+1]=r,this.int16[a+2]=n,this.int16[a+3]=i,t},e}(Pi);Di.prototype.bytesPerElement=8,Nn(\"StructArrayLayout4i8\",Di);var Ri=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=6*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t},e}(Pi);Ri.prototype.bytesPerElement=12,Nn(\"StructArrayLayout2i4i12\",Ri);var Fi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=4*t,l=8*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.uint8[l+4]=n,this.uint8[l+5]=i,this.uint8[l+6]=a,this.uint8[l+7]=o,t},e}(Pi);Fi.prototype.bytesPerElement=8,Nn(\"StructArrayLayout2i4ub8\",Fi);var Bi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c){var u=this.length;return this.resize(u+1),this.emplace(u,t,e,r,n,i,a,o,s,l,c)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u){var f=9*t,h=18*t;return this.uint16[f+0]=e,this.uint16[f+1]=r,this.uint16[f+2]=n,this.uint16[f+3]=i,this.uint16[f+4]=a,this.uint16[f+5]=o,this.uint16[f+6]=s,this.uint16[f+7]=l,this.uint8[h+16]=c,this.uint8[h+17]=u,t},e}(Pi);Bi.prototype.bytesPerElement=18,Nn(\"StructArrayLayout8ui2ub18\",Bi);var Ni=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f){var h=this.length;return this.resize(h+1),this.emplace(h,t,e,r,n,i,a,o,s,l,c,u,f)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h){var p=12*t;return this.int16[p+0]=e,this.int16[p+1]=r,this.int16[p+2]=n,this.int16[p+3]=i,this.uint16[p+4]=a,this.uint16[p+5]=o,this.uint16[p+6]=s,this.uint16[p+7]=l,this.int16[p+8]=c,this.int16[p+9]=u,this.int16[p+10]=f,this.int16[p+11]=h,t},e}(Pi);Ni.prototype.bytesPerElement=24,Nn(\"StructArrayLayout4i4ui4i24\",Ni);var ji=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.float32[i+0]=e,this.float32[i+1]=r,this.float32[i+2]=n,t},e}(Pi);ji.prototype.bytesPerElement=12,Nn(\"StructArrayLayout3f12\",ji);var Ui=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.uint32[r+0]=e,t},e}(Pi);Ui.prototype.bytesPerElement=4,Nn(\"StructArrayLayout1ul4\",Ui);var Vi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l){var c=this.length;return this.resize(c+1),this.emplace(c,t,e,r,n,i,a,o,s,l)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c){var u=10*t,f=5*t;return this.int16[u+0]=e,this.int16[u+1]=r,this.int16[u+2]=n,this.int16[u+3]=i,this.int16[u+4]=a,this.int16[u+5]=o,this.uint32[f+3]=s,this.uint16[u+8]=l,this.uint16[u+9]=c,t},e}(Pi);Vi.prototype.bytesPerElement=20,Nn(\"StructArrayLayout6i1ul2ui20\",Vi);var Hi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=6*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t},e}(Pi);Hi.prototype.bytesPerElement=12,Nn(\"StructArrayLayout2i2i2i12\",Hi);var qi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i){var a=this.length;return this.resize(a+1),this.emplace(a,t,e,r,n,i)},e.prototype.emplace=function(t,e,r,n,i,a){var o=4*t,s=8*t;return this.float32[o+0]=e,this.float32[o+1]=r,this.float32[o+2]=n,this.int16[s+6]=i,this.int16[s+7]=a,t},e}(Pi);qi.prototype.bytesPerElement=16,Nn(\"StructArrayLayout2f1f2i16\",qi);var Gi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=12*t,o=3*t;return this.uint8[a+0]=e,this.uint8[a+1]=r,this.float32[o+1]=n,this.float32[o+2]=i,t},e}(Pi);Gi.prototype.bytesPerElement=12,Nn(\"StructArrayLayout2ub2f12\",Gi);var Yi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.uint16[i+0]=e,this.uint16[i+1]=r,this.uint16[i+2]=n,t},e}(Pi);Yi.prototype.bytesPerElement=6,Nn(\"StructArrayLayout3ui6\",Yi);var Wi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g,m){var v=this.length;return this.resize(v+1),this.emplace(v,t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g,m)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g,m,v){var y=24*t,x=12*t,b=48*t;return this.int16[y+0]=e,this.int16[y+1]=r,this.uint16[y+2]=n,this.uint16[y+3]=i,this.uint32[x+2]=a,this.uint32[x+3]=o,this.uint32[x+4]=s,this.uint16[y+10]=l,this.uint16[y+11]=c,this.uint16[y+12]=u,this.float32[x+7]=f,this.float32[x+8]=h,this.uint8[b+36]=p,this.uint8[b+37]=d,this.uint8[b+38]=g,this.uint32[x+10]=m,this.int16[y+22]=v,t},e}(Pi);Wi.prototype.bytesPerElement=48,Nn(\"StructArrayLayout2i2ui3ul3ui2f3ub1ul1i48\",Wi);var Xi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g,m,v,y,x,b,_,w,T,k,A,M,S){var E=this.length;return this.resize(E+1),this.emplace(E,t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g,m,v,y,x,b,_,w,T,k,A,M,S)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g,m,v,y,x,b,_,w,T,k,A,M,S,E){var L=34*t,C=17*t;return this.int16[L+0]=e,this.int16[L+1]=r,this.int16[L+2]=n,this.int16[L+3]=i,this.int16[L+4]=a,this.int16[L+5]=o,this.int16[L+6]=s,this.int16[L+7]=l,this.uint16[L+8]=c,this.uint16[L+9]=u,this.uint16[L+10]=f,this.uint16[L+11]=h,this.uint16[L+12]=p,this.uint16[L+13]=d,this.uint16[L+14]=g,this.uint16[L+15]=m,this.uint16[L+16]=v,this.uint16[L+17]=y,this.uint16[L+18]=x,this.uint16[L+19]=b,this.uint16[L+20]=_,this.uint16[L+21]=w,this.uint16[L+22]=T,this.uint32[C+12]=k,this.float32[C+13]=A,this.float32[C+14]=M,this.float32[C+15]=S,this.float32[C+16]=E,t},e}(Pi);Xi.prototype.bytesPerElement=68,Nn(\"StructArrayLayout8i15ui1ul4f68\",Xi);var Zi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.float32[r+0]=e,t},e}(Pi);Zi.prototype.bytesPerElement=4,Nn(\"StructArrayLayout1f4\",Zi);var Ji=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.int16[i+0]=e,this.int16[i+1]=r,this.int16[i+2]=n,t},e}(Pi);Ji.prototype.bytesPerElement=6,Nn(\"StructArrayLayout3i6\",Ji);var Ki=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=2*t,a=4*t;return this.uint32[i+0]=e,this.uint16[a+2]=r,this.uint16[a+3]=n,t},e}(Pi);Ki.prototype.bytesPerElement=8,Nn(\"StructArrayLayout1ul2ui8\",Ki);var Qi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.uint16[n+0]=e,this.uint16[n+1]=r,t},e}(Pi);Qi.prototype.bytesPerElement=4,Nn(\"StructArrayLayout2ui4\",Qi);var $i=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.uint16[r+0]=e,t},e}(Pi);$i.prototype.bytesPerElement=2,Nn(\"StructArrayLayout1ui2\",$i);var ta=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.float32[n+0]=e,this.float32[n+1]=r,t},e}(Pi);ta.prototype.bytesPerElement=8,Nn(\"StructArrayLayout2f8\",ta);var ea=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=4*t;return this.float32[a+0]=e,this.float32[a+1]=r,this.float32[a+2]=n,this.float32[a+3]=i,t},e}(Pi);ea.prototype.bytesPerElement=16,Nn(\"StructArrayLayout4f16\",ea);var ra=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorPointX:{configurable:!0},anchorPointY:{configurable:!0},x1:{configurable:!0},y1:{configurable:!0},x2:{configurable:!0},y2:{configurable:!0},featureIndex:{configurable:!0},sourceLayerIndex:{configurable:!0},bucketIndex:{configurable:!0},anchorPoint:{configurable:!0}};return r.anchorPointX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorPointY.get=function(){return this._structArray.int16[this._pos2+1]},r.x1.get=function(){return this._structArray.int16[this._pos2+2]},r.y1.get=function(){return this._structArray.int16[this._pos2+3]},r.x2.get=function(){return this._structArray.int16[this._pos2+4]},r.y2.get=function(){return this._structArray.int16[this._pos2+5]},r.featureIndex.get=function(){return this._structArray.uint32[this._pos4+3]},r.sourceLayerIndex.get=function(){return this._structArray.uint16[this._pos2+8]},r.bucketIndex.get=function(){return this._structArray.uint16[this._pos2+9]},r.anchorPoint.get=function(){return new i(this.anchorPointX,this.anchorPointY)},Object.defineProperties(e.prototype,r),e}(Ci);ra.prototype.size=20;var na=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new ra(this,t)},e}(Vi);Nn(\"CollisionBoxArray\",na);var ia=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorX:{configurable:!0},anchorY:{configurable:!0},glyphStartIndex:{configurable:!0},numGlyphs:{configurable:!0},vertexStartIndex:{configurable:!0},lineStartIndex:{configurable:!0},lineLength:{configurable:!0},segment:{configurable:!0},lowerSize:{configurable:!0},upperSize:{configurable:!0},lineOffsetX:{configurable:!0},lineOffsetY:{configurable:!0},writingMode:{configurable:!0},placedOrientation:{configurable:!0},hidden:{configurable:!0},crossTileID:{configurable:!0},associatedIconIndex:{configurable:!0}};return r.anchorX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorY.get=function(){return this._structArray.int16[this._pos2+1]},r.glyphStartIndex.get=function(){return this._structArray.uint16[this._pos2+2]},r.numGlyphs.get=function(){return this._structArray.uint16[this._pos2+3]},r.vertexStartIndex.get=function(){return this._structArray.uint32[this._pos4+2]},r.lineStartIndex.get=function(){return this._structArray.uint32[this._pos4+3]},r.lineLength.get=function(){return this._structArray.uint32[this._pos4+4]},r.segment.get=function(){return this._structArray.uint16[this._pos2+10]},r.lowerSize.get=function(){return this._structArray.uint16[this._pos2+11]},r.upperSize.get=function(){return this._structArray.uint16[this._pos2+12]},r.lineOffsetX.get=function(){return this._structArray.float32[this._pos4+7]},r.lineOffsetY.get=function(){return this._structArray.float32[this._pos4+8]},r.writingMode.get=function(){return this._structArray.uint8[this._pos1+36]},r.placedOrientation.get=function(){return this._structArray.uint8[this._pos1+37]},r.placedOrientation.set=function(t){this._structArray.uint8[this._pos1+37]=t},r.hidden.get=function(){return this._structArray.uint8[this._pos1+38]},r.hidden.set=function(t){this._structArray.uint8[this._pos1+38]=t},r.crossTileID.get=function(){return this._structArray.uint32[this._pos4+10]},r.crossTileID.set=function(t){this._structArray.uint32[this._pos4+10]=t},r.associatedIconIndex.get=function(){return this._structArray.int16[this._pos2+22]},Object.defineProperties(e.prototype,r),e}(Ci);ia.prototype.size=48;var aa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new ia(this,t)},e}(Wi);Nn(\"PlacedSymbolArray\",aa);var oa=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorX:{configurable:!0},anchorY:{configurable:!0},rightJustifiedTextSymbolIndex:{configurable:!0},centerJustifiedTextSymbolIndex:{configurable:!0},leftJustifiedTextSymbolIndex:{configurable:!0},verticalPlacedTextSymbolIndex:{configurable:!0},placedIconSymbolIndex:{configurable:!0},verticalPlacedIconSymbolIndex:{configurable:!0},key:{configurable:!0},textBoxStartIndex:{configurable:!0},textBoxEndIndex:{configurable:!0},verticalTextBoxStartIndex:{configurable:!0},verticalTextBoxEndIndex:{configurable:!0},iconBoxStartIndex:{configurable:!0},iconBoxEndIndex:{configurable:!0},verticalIconBoxStartIndex:{configurable:!0},verticalIconBoxEndIndex:{configurable:!0},featureIndex:{configurable:!0},numHorizontalGlyphVertices:{configurable:!0},numVerticalGlyphVertices:{configurable:!0},numIconVertices:{configurable:!0},numVerticalIconVertices:{configurable:!0},useRuntimeCollisionCircles:{configurable:!0},crossTileID:{configurable:!0},textBoxScale:{configurable:!0},textOffset0:{configurable:!0},textOffset1:{configurable:!0},collisionCircleDiameter:{configurable:!0}};return r.anchorX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorY.get=function(){return this._structArray.int16[this._pos2+1]},r.rightJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+2]},r.centerJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+3]},r.leftJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+4]},r.verticalPlacedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+5]},r.placedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+6]},r.verticalPlacedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+7]},r.key.get=function(){return this._structArray.uint16[this._pos2+8]},r.textBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+9]},r.textBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+10]},r.verticalTextBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+11]},r.verticalTextBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+12]},r.iconBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+13]},r.iconBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+14]},r.verticalIconBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+15]},r.verticalIconBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+16]},r.featureIndex.get=function(){return this._structArray.uint16[this._pos2+17]},r.numHorizontalGlyphVertices.get=function(){return this._structArray.uint16[this._pos2+18]},r.numVerticalGlyphVertices.get=function(){return this._structArray.uint16[this._pos2+19]},r.numIconVertices.get=function(){return this._structArray.uint16[this._pos2+20]},r.numVerticalIconVertices.get=function(){return this._structArray.uint16[this._pos2+21]},r.useRuntimeCollisionCircles.get=function(){return this._structArray.uint16[this._pos2+22]},r.crossTileID.get=function(){return this._structArray.uint32[this._pos4+12]},r.crossTileID.set=function(t){this._structArray.uint32[this._pos4+12]=t},r.textBoxScale.get=function(){return this._structArray.float32[this._pos4+13]},r.textOffset0.get=function(){return this._structArray.float32[this._pos4+14]},r.textOffset1.get=function(){return this._structArray.float32[this._pos4+15]},r.collisionCircleDiameter.get=function(){return this._structArray.float32[this._pos4+16]},Object.defineProperties(e.prototype,r),e}(Ci);oa.prototype.size=68;var sa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new oa(this,t)},e}(Xi);Nn(\"SymbolInstanceArray\",sa);var la=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getoffsetX=function(t){return this.float32[1*t+0]},e}(Zi);Nn(\"GlyphOffsetArray\",la);var ca=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getx=function(t){return this.int16[3*t+0]},e.prototype.gety=function(t){return this.int16[3*t+1]},e.prototype.gettileUnitDistanceFromAnchor=function(t){return this.int16[3*t+2]},e}(Ji);Nn(\"SymbolLineVertexArray\",ca);var ua=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={featureIndex:{configurable:!0},sourceLayerIndex:{configurable:!0},bucketIndex:{configurable:!0}};return r.featureIndex.get=function(){return this._structArray.uint32[this._pos4+0]},r.sourceLayerIndex.get=function(){return 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t.paint._values)if(r(a)){var o=t.paint.get(a);if(o instanceof bi&&zr(o.property.specification)){var s=Va(a,t.type),l=o.value,c=o.property.specification.type,u=o.property.useIntegerZoom,f=o.property.specification[\"property-type\"],h=\"cross-faded\"===f||\"cross-faded-data-driven\"===f;if(\"constant\"===l.kind)this.binders[a]=h?new Ra(l.value,s):new Da(l.value,s,c),i.push(\"/u_\"+a);else if(\"source\"===l.kind||h){var p=Ha(a,c,\"source\");this.binders[a]=h?new Na(l,c,u,e,p,t.id):new Fa(l,s,c,p),i.push(\"/a_\"+a)}else{var d=Ha(a,c,\"composite\");this.binders[a]=new Ba(l,s,c,u,e,d),i.push(\"/z_\"+a)}}}this.cacheKey=i.sort().join(\"\")};ja.prototype.getMaxValue=function(t){var e=this.binders[t];return e instanceof Fa||e instanceof Ba?e.maxValue:0},ja.prototype.populatePaintArrays=function(t,e,r,n,i){for(var a in this.binders){var o=this.binders[a];(o instanceof Fa||o instanceof Ba||o instanceof Na)&&o.populatePaintArray(t,e,r,n,i)}},ja.prototype.setConstantPatternPositions=function(t,e){for(var r in this.binders){var n=this.binders[r];n instanceof Ra&&n.setConstantPatternPositions(t,e)}},ja.prototype.updatePaintArrays=function(t,e,r,n,i){var a=!1;for(var o in t)for(var s=0,l=e.getPositions(o);s<l.length;s+=1){var c=l[s],u=r.feature(c.index);for(var f in this.binders){var h=this.binders[f];if((h instanceof Fa||h instanceof Ba||h instanceof Na)&&!0===h.expression.isStateDependent){var p=n.paint.get(f);h.expression=p.value,h.updatePaintArray(c.start,c.end,u,t[o],i),a=!0}}}return a},ja.prototype.defines=function(){var t=[];for(var e in this.binders){var r=this.binders[e];(r instanceof Da||r instanceof Ra)&&t.push.apply(t,r.uniformNames.map((function(t){return\"#define HAS_UNIFORM_\"+t})))}return t},ja.prototype.getPaintVertexBuffers=function(){return this._buffers},ja.prototype.getUniforms=function(t,e){var r=[];for(var n in this.binders){var i=this.binders[n];if(i instanceof Da||i instanceof Ra||i instanceof Ba)for(var a=0,o=i.uniformNames;a<o.length;a+=1){var s=o[a];if(e[s]){var l=i.getBinding(t,e[s],s);r.push({name:s,property:n,binding:l})}}}return r},ja.prototype.setUniforms=function(t,e,r,n){for(var i=0,a=e;i<a.length;i+=1){var o=a[i],s=o.name,l=o.property,c=o.binding;this.binders[l].setUniform(c,n,r.get(l),s)}},ja.prototype.updatePaintBuffers=function(t){for(var e in this._buffers=[],this.binders){var r=this.binders[e];if(t&&r instanceof Na){var n=2===t.fromScale?r.zoomInPaintVertexBuffer:r.zoomOutPaintVertexBuffer;n&&this._buffers.push(n)}else(r instanceof Fa||r instanceof Ba)&&r.paintVertexBuffer&&this._buffers.push(r.paintVertexBuffer)}},ja.prototype.upload=function(t){for(var e in this.binders){var r=this.binders[e];(r instanceof Fa||r instanceof Ba||r instanceof Na)&&r.upload(t)}this.updatePaintBuffers()},ja.prototype.destroy=function(){for(var t in this.binders){var e=this.binders[t];(e instanceof Fa||e instanceof Ba||e instanceof Na)&&e.destroy()}};var Ua=function(t,e,r,n){void 0===n&&(n=function(){return!0}),this.programConfigurations={};for(var i=0,a=e;i<a.length;i+=1){var o=a[i];this.programConfigurations[o.id]=new ja(o,r,n,t)}this.needsUpload=!1,this._featureMap=new _a,this._bufferOffset=0};function Va(t,e){return{\"text-opacity\":[\"opacity\"],\"icon-opacity\":[\"opacity\"],\"text-color\":[\"fill_color\"],\"icon-color\":[\"fill_color\"],\"text-halo-color\":[\"halo_color\"],\"icon-halo-color\":[\"halo_color\"],\"text-halo-blur\":[\"halo_blur\"],\"icon-halo-blur\":[\"halo_blur\"],\"text-halo-width\":[\"halo_width\"],\"icon-halo-width\":[\"halo_width\"],\"line-gap-width\":[\"gapwidth\"],\"line-pattern\":[\"pattern_to\",\"pattern_from\",\"pixel_ratio_to\",\"pixel_ratio_from\"],\"fill-pattern\":[\"pattern_to\",\"pattern_from\",\"pixel_ratio_to\",\"pixel_ratio_from\"],\"fill-extrusion-pattern\":[\"pattern_to\",\"pattern_from\",\"pixel_ratio_to\",\"pixel_ratio_from\"]}[t]||[t.replace(e+\"-\",\"\").replace(/-/g,\"_\")]}function Ha(t,e,r){var n={color:{source:ta,composite:ea},number:{source:Zi,composite:ta}},i=function(t){return{\"line-pattern\":{source:Bi,composite:Bi},\"fill-pattern\":{source:Bi,composite:Bi},\"fill-extrusion-pattern\":{source:Bi,composite:Bi}}[t]}(t);return 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c=t.readVarint();n=7&c,a=c>>3}if(a--,1===n||2===n)o+=t.readSVarint(),s+=t.readSVarint(),1===n&&(e&&l.push(e),e=[]),e.push(new i(o,s));else{if(7!==n)throw new Error(\"unknown command \"+n);e&&e.push(e[0].clone())}}return e&&l.push(e),l},ws.prototype.bbox=function(){var t=this._pbf;t.pos=this._geometry;for(var e=t.readVarint()+t.pos,r=1,n=0,i=0,a=0,o=1/0,s=-1/0,l=1/0,c=-1/0;t.pos<e;){if(n<=0){var u=t.readVarint();r=7&u,n=u>>3}if(n--,1===r||2===r)(i+=t.readSVarint())<o&&(o=i),i>s&&(s=i),(a+=t.readSVarint())<l&&(l=a),a>c&&(c=a);else if(7!==r)throw new Error(\"unknown command \"+r)}return[o,l,s,c]},ws.prototype.toGeoJSON=function(t,e,r){var n,i,a=this.extent*Math.pow(2,r),o=this.extent*t,s=this.extent*e,l=this.loadGeometry(),c=ws.types[this.type];function u(t){for(var e=0;e<t.length;e++){var r=t[e],n=180-360*(r.y+s)/a;t[e]=[360*(r.x+o)/a-180,360/Math.PI*Math.atan(Math.exp(n*Math.PI/180))-90]}}switch(this.type){case 1:var f=[];for(n=0;n<l.length;n++)f[n]=l[n][0];u(l=f);break;case 2:for(n=0;n<l.length;n++)u(l[n]);break;case 3:for(l=function(t){var e=t.length;if(e<=1)return[t];for(var r,n,i=[],a=0;a<e;a++){var o=ks(t[a]);0!==o&&(void 0===n&&(n=o<0),n===o<0?(r&&i.push(r),r=[t[a]]):r.push(t[a]))}r&&i.push(r);return i}(l),n=0;n<l.length;n++)for(i=0;i<l[n].length;i++)u(l[n][i])}1===l.length?l=l[0]:c=\"Multi\"+c;var h={type:\"Feature\",geometry:{type:c,coordinates:l},properties:this.properties};return\"id\"in this&&(h.id=this.id),h};var As=Ms;function Ms(t,e){this.version=1,this.name=null,this.extent=4096,this.length=0,this._pbf=t,this._keys=[],this._values=[],this._features=[],t.readFields(Ss,this,e),this.length=this._features.length}function Ss(t,e,r){15===t?e.version=r.readVarint():1===t?e.name=r.readString():5===t?e.extent=r.readVarint():2===t?e._features.push(r.pos):3===t?e._keys.push(r.readString()):4===t&&e._values.push(function(t){var e=null,r=t.readVarint()+t.pos;for(;t.pos<r;){var n=t.readVarint()>>3;e=1===n?t.readString():2===n?t.readFloat():3===n?t.readDouble():4===n?t.readVarint64():5===n?t.readVarint():6===n?t.readSVarint():7===n?t.readBoolean():null}return e}(r))}function Es(t,e,r){if(3===t){var n=new As(r,r.readVarint()+r.pos);n.length&&(e[n.name]=n)}}Ms.prototype.feature=function(t){if(t<0||t>=this._features.length)throw new Error(\"feature index out of bounds\");this._pbf.pos=this._features[t];var e=this._pbf.readVarint()+this._pbf.pos;return new _s(this._pbf,e,this.extent,this._keys,this._values)};var Ls={VectorTile:function(t,e){this.layers=t.readFields(Es,{},e)},VectorTileFeature:_s,VectorTileLayer:As},Cs=Ls.VectorTileFeature.types,Ps=Math.pow(2,13);function Is(t,e,r,n,i,a,o,s){t.emplaceBack(e,r,2*Math.floor(n*Ps)+o,i*Ps*2,a*Ps*2,Math.round(s))}var Os=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.layoutVertexArray=new Ri,this.indexArray=new Yi,this.programConfigurations=new Ua(bs,t.layers,t.zoom),this.segments=new pa,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};function zs(t,e){return t.x===e.x&&(t.x<0||t.x>8192)||t.y===e.y&&(t.y<0||t.y>8192)}function Ds(t){return t.every((function(t){return t.x<0}))||t.every((function(t){return t.x>8192}))||t.every((function(t){return t.y<0}))||t.every((function(t){return t.y>8192}))}Os.prototype.populate=function(t,e,r){this.features=[],this.hasPattern=ds(\"fill-extrusion\",this.layers,e);for(var n=0,i=t;n<i.length;n+=1){var a=i[n],o=a.feature,s=a.id,l=a.index,c=a.sourceLayerIndex,u=this.layers[0]._featureFilter.needGeometry,f={type:o.type,id:s,properties:o.properties,geometry:u?Ya(o):[]};if(this.layers[0]._featureFilter.filter(new pi(this.zoom),f,r)){var h={id:s,sourceLayerIndex:c,index:l,geometry:u?f.geometry:Ya(o),properties:o.properties,type:o.type,patterns:{}};void 0!==o.id&&(h.id=o.id),this.hasPattern?this.features.push(gs(\"fill-extrusion\",this.layers,h,this.zoom,e)):this.addFeature(h,h.geometry,l,r,{}),e.featureIndex.insert(o,h.geometry,l,c,this.index,!0)}}},Os.prototype.addFeatures=function(t,e,r){for(var n=0,i=this.features;n<i.length;n+=1){var a=i[n],o=a.geometry;this.addFeature(a,o,a.index,e,r)}},Os.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},Os.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},Os.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},Os.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,bs),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},Os.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},Os.prototype.addFeature=function(t,e,r,n,i){for(var a=0,o=hs(e,500);a<o.length;a+=1){for(var s=o[a],l=0,c=0,u=s;c<u.length;c+=1){l+=u[c].length}for(var f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray),h=0,p=s;h<p.length;h+=1){var d=p[h];if(0!==d.length&&!Ds(d))for(var g=0,m=0;m<d.length;m++){var v=d[m];if(m>=1){var y=d[m-1];if(!zs(v,y)){f.vertexLength+4>pa.MAX_VERTEX_ARRAY_LENGTH&&(f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray));var x=v.sub(y)._perp()._unit(),b=y.dist(v);g+b>32768&&(g=0),Is(this.layoutVertexArray,v.x,v.y,x.x,x.y,0,0,g),Is(this.layoutVertexArray,v.x,v.y,x.x,x.y,0,1,g),g+=b,Is(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,0,g),Is(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,1,g);var _=f.vertexLength;this.indexArray.emplaceBack(_,_+2,_+1),this.indexArray.emplaceBack(_+1,_+2,_+3),f.vertexLength+=4,f.primitiveLength+=2}}}}if(f.vertexLength+l>pa.MAX_VERTEX_ARRAY_LENGTH&&(f=this.segments.prepareSegment(l,this.layoutVertexArray,this.indexArray)),\"Polygon\"===Cs[t.type]){for(var w=[],T=[],k=f.vertexLength,A=0,M=s;A<M.length;A+=1){var S=M[A];if(0!==S.length){S!==s[0]&&T.push(w.length/2);for(var E=0;E<S.length;E++){var L=S[E];Is(this.layoutVertexArray,L.x,L.y,0,0,1,1,0),w.push(L.x),w.push(L.y)}}}for(var C=Do(w,T),P=0;P<C.length;P+=3)this.indexArray.emplaceBack(k+C[P],k+C[P+2],k+C[P+1]);f.primitiveLength+=C.length/3,f.vertexLength+=l}}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,i,n)},Nn(\"FillExtrusionBucket\",Os,{omit:[\"layers\",\"features\"]});var Rs={paint:new Si({\"fill-extrusion-opacity\":new wi(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-opacity\"]),\"fill-extrusion-color\":new Ti(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-color\"]),\"fill-extrusion-translate\":new wi(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-translate\"]),\"fill-extrusion-translate-anchor\":new wi(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-translate-anchor\"]),\"fill-extrusion-pattern\":new ki(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-pattern\"]),\"fill-extrusion-height\":new Ti(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-height\"]),\"fill-extrusion-base\":new Ti(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-base\"]),\"fill-extrusion-vertical-gradient\":new wi(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-vertical-gradient\"])})},Fs=function(t){function e(e){t.call(this,e,Rs)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.createBucket=function(t){return new Os(t)},e.prototype.queryRadius=function(){return so(this.paint.get(\"fill-extrusion-translate\"))},e.prototype.is3D=function(){return!0},e.prototype.queryIntersectsFeature=function(t,e,r,n,a,o,s,l){var c=lo(t,this.paint.get(\"fill-extrusion-translate\"),this.paint.get(\"fill-extrusion-translate-anchor\"),o.angle,s),u=this.paint.get(\"fill-extrusion-height\").evaluate(e,r),f=this.paint.get(\"fill-extrusion-base\").evaluate(e,r),h=function(t,e,r,n){for(var a=[],o=0,s=t;o<s.length;o+=1){var l=s[o],c=[l.x,l.y,n,1];xo(c,c,e),a.push(new i(c[0]/c[3],c[1]/c[3]))}return a}(c,l,0,0),p=function(t,e,r,n){for(var a=[],o=[],s=n[8]*e,l=n[9]*e,c=n[10]*e,u=n[11]*e,f=n[8]*r,h=n[9]*r,p=n[10]*r,d=n[11]*r,g=0,m=t;g<m.length;g+=1){for(var v=m[g],y=[],x=[],b=0,_=v;b<_.length;b+=1){var w=_[b],T=w.x,k=w.y,A=n[0]*T+n[4]*k+n[12],M=n[1]*T+n[5]*k+n[13],S=n[2]*T+n[6]*k+n[14],E=n[3]*T+n[7]*k+n[15],L=S+c,C=E+u,P=A+f,I=M+h,O=S+p,z=E+d,D=new i((A+s)/C,(M+l)/C);D.z=L/C,y.push(D);var R=new i(P/z,I/z);R.z=O/z,x.push(R)}a.push(y),o.push(x)}return[a,o]}(n,f,u,l);return function(t,e,r){var n=1/0;Ka(r,e)&&(n=Ns(r,e[0]));for(var i=0;i<e.length;i++)for(var a=e[i],o=t[i],s=0;s<a.length-1;s++){var l=a[s],c=a[s+1],u=o[s],f=o[s+1],h=[l,c,f,u,l];Za(r,h)&&(n=Math.min(n,Ns(r,h)))}return n!==1/0&&n}(p[0],p[1],h)},e}(Ei);function Bs(t,e){return t.x*e.x+t.y*e.y}function Ns(t,e){if(1===t.length){for(var r,n=0,i=e[n++];!r||i.equals(r);)if(!(r=e[n++]))return 1/0;for(;n<e.length;n++){var a=e[n],o=t[0],s=r.sub(i),l=a.sub(i),c=o.sub(i),u=Bs(s,s),f=Bs(s,l),h=Bs(l,l),p=Bs(c,s),d=Bs(c,l),g=u*h-f*f,m=(h*p-f*d)/g,v=(u*d-f*p)/g,y=1-m-v,x=i.z*y+r.z*m+a.z*v;if(isFinite(x))return x}return 1/0}for(var b=1/0,_=0,w=e;_<w.length;_+=1){var T=w[_];b=Math.min(b,T.z)}return b}var js=Ii([{name:\"a_pos_normal\",components:2,type:\"Int16\"},{name:\"a_data\",components:4,type:\"Uint8\"}],4).members,Us=Ls.VectorTileFeature.types,Vs=Math.cos(Math.PI/180*37.5),Hs=Math.pow(2,14)/.5,qs=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.patternFeatures=[],this.layoutVertexArray=new Fi,this.indexArray=new Yi,this.programConfigurations=new Ua(js,t.layers,t.zoom),this.segments=new pa,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};qs.prototype.populate=function(t,e,r){this.hasPattern=ds(\"line\",this.layers,e);for(var n=this.layers[0].layout.get(\"line-sort-key\"),i=[],a=0,o=t;a<o.length;a+=1){var s=o[a],l=s.feature,c=s.id,u=s.index,f=s.sourceLayerIndex,h=this.layers[0]._featureFilter.needGeometry,p={type:l.type,id:c,properties:l.properties,geometry:h?Ya(l):[]};if(this.layers[0]._featureFilter.filter(new pi(this.zoom),p,r)){h||(p.geometry=Ya(l));var d=n?n.evaluate(p,{},r):void 0,g={id:c,properties:l.properties,type:l.type,sourceLayerIndex:f,index:u,geometry:p.geometry,patterns:{},sortKey:d};i.push(g)}}n&&i.sort((function(t,e){return t.sortKey-e.sortKey}));for(var m=0,v=i;m<v.length;m+=1){var y=v[m],x=y,b=x.geometry,_=x.index,w=x.sourceLayerIndex;if(this.hasPattern){var T=gs(\"line\",this.layers,y,this.zoom,e);this.patternFeatures.push(T)}else this.addFeature(y,b,_,r,{});var k=t[_].feature;e.featureIndex.insert(k,b,_,w,this.index)}},qs.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},qs.prototype.addFeatures=function(t,e,r){for(var n=0,i=this.patternFeatures;n<i.length;n+=1){var a=i[n];this.addFeature(a,a.geometry,a.index,e,r)}},qs.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},qs.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},qs.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,js),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},qs.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},qs.prototype.addFeature=function(t,e,r,n,i){for(var a=this.layers[0].layout,o=a.get(\"line-join\").evaluate(t,{}),s=a.get(\"line-cap\"),l=a.get(\"line-miter-limit\"),c=a.get(\"line-round-limit\"),u=0,f=e;u<f.length;u+=1){var h=f[u];this.addLine(h,t,o,s,l,c)}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,i,n)},qs.prototype.addLine=function(t,e,r,n,i,a){if(this.distance=0,this.scaledDistance=0,this.totalDistance=0,e.properties&&e.properties.hasOwnProperty(\"mapbox_clip_start\")&&e.properties.hasOwnProperty(\"mapbox_clip_end\")){this.clipStart=+e.properties.mapbox_clip_start,this.clipEnd=+e.properties.mapbox_clip_end;for(var o=0;o<t.length-1;o++)this.totalDistance+=t[o].dist(t[o+1]);this.updateScaledDistance()}for(var s=\"Polygon\"===Us[e.type],l=t.length;l>=2&&t[l-1].equals(t[l-2]);)l--;for(var c=0;c<l-1&&t[c].equals(t[c+1]);)c++;if(!(l<(s?3:2))){\"bevel\"===r&&(i=1.05);var u,f=this.overscaling<=16?122880/(512*this.overscaling):0,h=this.segments.prepareSegment(10*l,this.layoutVertexArray,this.indexArray),p=void 0,d=void 0,g=void 0,m=void 0;this.e1=this.e2=-1,s&&(u=t[l-2],m=t[c].sub(u)._unit()._perp());for(var v=c;v<l;v++)if(!(d=v===l-1?s?t[c+1]:void 0:t[v+1])||!t[v].equals(d)){m&&(g=m),u&&(p=u),u=t[v],m=d?d.sub(u)._unit()._perp():g;var y=(g=g||m).add(m);0===y.x&&0===y.y||y._unit();var x=g.x*m.x+g.y*m.y,b=y.x*m.x+y.y*m.y,_=0!==b?1/b:1/0,w=2*Math.sqrt(2-2*b),T=b<Vs&&p&&d,k=g.x*m.y-g.y*m.x>0;if(T&&v>c){var A=u.dist(p);if(A>2*f){var M=u.sub(u.sub(p)._mult(f/A)._round());this.updateDistance(p,M),this.addCurrentVertex(M,g,0,0,h),p=M}}var S=p&&d,E=S?r:s?\"butt\":n;if(S&&\"round\"===E&&(_<a?E=\"miter\":_<=2&&(E=\"fakeround\")),\"miter\"===E&&_>i&&(E=\"bevel\"),\"bevel\"===E&&(_>2&&(E=\"flipbevel\"),_<i&&(E=\"miter\")),p&&this.updateDistance(p,u),\"miter\"===E)y._mult(_),this.addCurrentVertex(u,y,0,0,h);else if(\"flipbevel\"===E){if(_>100)y=m.mult(-1);else{var L=_*g.add(m).mag()/g.sub(m).mag();y._perp()._mult(L*(k?-1:1))}this.addCurrentVertex(u,y,0,0,h),this.addCurrentVertex(u,y.mult(-1),0,0,h)}else if(\"bevel\"===E||\"fakeround\"===E){var C=-Math.sqrt(_*_-1),P=k?C:0,I=k?0:C;if(p&&this.addCurrentVertex(u,g,P,I,h),\"fakeround\"===E)for(var O=Math.round(180*w/Math.PI/20),z=1;z<O;z++){var D=z/O;if(.5!==D){var R=D-.5;D+=D*R*(D-1)*((1.0904+x*(x*(3.55645-1.43519*x)-3.2452))*R*R+(.848013+x*(.215638*x-1.06021)))}var F=m.sub(g)._mult(D)._add(g)._unit()._mult(k?-1:1);this.addHalfVertex(u,F.x,F.y,!1,k,0,h)}d&&this.addCurrentVertex(u,m,-P,-I,h)}else if(\"butt\"===E)this.addCurrentVertex(u,y,0,0,h);else if(\"square\"===E){var B=p?1:-1;this.addCurrentVertex(u,y,B,B,h)}else\"round\"===E&&(p&&(this.addCurrentVertex(u,g,0,0,h),this.addCurrentVertex(u,g,1,1,h,!0)),d&&(this.addCurrentVertex(u,m,-1,-1,h,!0),this.addCurrentVertex(u,m,0,0,h)));if(T&&v<l-1){var N=u.dist(d);if(N>2*f){var j=u.add(d.sub(u)._mult(f/N)._round());this.updateDistance(u,j),this.addCurrentVertex(j,m,0,0,h),u=j}}}}},qs.prototype.addCurrentVertex=function(t,e,r,n,i,a){void 0===a&&(a=!1);var o=e.x+e.y*r,s=e.y-e.x*r,l=-e.x+e.y*n,c=-e.y-e.x*n;this.addHalfVertex(t,o,s,a,!1,r,i),this.addHalfVertex(t,l,c,a,!0,-n,i),this.distance>Hs/2&&0===this.totalDistance&&(this.distance=0,this.addCurrentVertex(t,e,r,n,i,a))},qs.prototype.addHalfVertex=function(t,e,r,n,i,a,o){var s=t.x,l=t.y,c=.5*this.scaledDistance;this.layoutVertexArray.emplaceBack((s<<1)+(n?1:0),(l<<1)+(i?1:0),Math.round(63*e)+128,Math.round(63*r)+128,1+(0===a?0:a<0?-1:1)|(63&c)<<2,c>>6);var u=o.vertexLength++;this.e1>=0&&this.e2>=0&&(this.indexArray.emplaceBack(this.e1,this.e2,u),o.primitiveLength++),i?this.e2=u:this.e1=u},qs.prototype.updateScaledDistance=function(){this.scaledDistance=this.totalDistance>0?(this.clipStart+(this.clipEnd-this.clipStart)*this.distance/this.totalDistance)*(Hs-1):this.distance},qs.prototype.updateDistance=function(t,e){this.distance+=t.dist(e),this.updateScaledDistance()},Nn(\"LineBucket\",qs,{omit:[\"layers\",\"patternFeatures\"]});var Gs=new Si({\"line-cap\":new wi(Lt.layout_line[\"line-cap\"]),\"line-join\":new Ti(Lt.layout_line[\"line-join\"]),\"line-miter-limit\":new wi(Lt.layout_line[\"line-miter-limit\"]),\"line-round-limit\":new wi(Lt.layout_line[\"line-round-limit\"]),\"line-sort-key\":new Ti(Lt.layout_line[\"line-sort-key\"])}),Ys={paint:new Si({\"line-opacity\":new Ti(Lt.paint_line[\"line-opacity\"]),\"line-color\":new Ti(Lt.paint_line[\"line-color\"]),\"line-translate\":new wi(Lt.paint_line[\"line-translate\"]),\"line-translate-anchor\":new wi(Lt.paint_line[\"line-translate-anchor\"]),\"line-width\":new Ti(Lt.paint_line[\"line-width\"]),\"line-gap-width\":new Ti(Lt.paint_line[\"line-gap-width\"]),\"line-offset\":new Ti(Lt.paint_line[\"line-offset\"]),\"line-blur\":new Ti(Lt.paint_line[\"line-blur\"]),\"line-dasharray\":new Ai(Lt.paint_line[\"line-dasharray\"]),\"line-pattern\":new ki(Lt.paint_line[\"line-pattern\"]),\"line-gradient\":new Mi(Lt.paint_line[\"line-gradient\"])}),layout:Gs},Ws=new(function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.possiblyEvaluate=function(e,r){return r=new pi(Math.floor(r.zoom),{now:r.now,fadeDuration:r.fadeDuration,zoomHistory:r.zoomHistory,transition:r.transition}),t.prototype.possiblyEvaluate.call(this,e,r)},e.prototype.evaluate=function(e,r,n,i){return r=u({},r,{zoom:Math.floor(r.zoom)}),t.prototype.evaluate.call(this,e,r,n,i)},e}(Ti))(Ys.paint.properties[\"line-width\"].specification);Ws.useIntegerZoom=!0;var Xs=function(t){function e(e){t.call(this,e,Ys)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._handleSpecialPaintPropertyUpdate=function(t){\"line-gradient\"===t&&this._updateGradient()},e.prototype._updateGradient=function(){var 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o=t[a],s=[],l=0;l<o.length;l++){var c=o[l-1],u=o[l],f=o[l+1],h=0===l?n:u.sub(c)._unit()._perp(),p=l===o.length-1?n:f.sub(u)._unit()._perp(),d=h._add(p)._unit(),g=d.x*p.x+d.y*p.y;d._mult(1/g),s.push(d._mult(e)._add(u))}r.push(s)}return r}(n,u*s)),function(t,e,r){for(var n=0;n<e.length;n++){var i=e[n];if(t.length>=3)for(var a=0;a<i.length;a++)if(io(t,i[a]))return!0;if(Qa(t,i,r))return!0}return!1}(l,n,c)},e.prototype.isTileClipped=function(){return!0},e}(Ei);function Zs(t,e){return e>0?e+2*t:t}var 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nl=function(t,e,r,n,i){var a,o,s=8*i-n-1,l=(1<<s)-1,c=l>>1,u=-7,f=r?i-1:0,h=r?-1:1,p=t[e+f];for(f+=h,a=p&(1<<-u)-1,p>>=-u,u+=s;u>0;a=256*a+t[e+f],f+=h,u-=8);for(o=a&(1<<-u)-1,a>>=-u,u+=n;u>0;o=256*o+t[e+f],f+=h,u-=8);if(0===a)a=1-c;else{if(a===l)return o?NaN:1/0*(p?-1:1);o+=Math.pow(2,n),a-=c}return(p?-1:1)*o*Math.pow(2,a-n)},il=function(t,e,r,n,i,a){var o,s,l,c=8*a-i-1,u=(1<<c)-1,f=u>>1,h=23===i?Math.pow(2,-24)-Math.pow(2,-77):0,p=n?0:a-1,d=n?1:-1,g=e<0||0===e&&1/e<0?1:0;for(e=Math.abs(e),isNaN(e)||e===1/0?(s=isNaN(e)?1:0,o=u):(o=Math.floor(Math.log(e)/Math.LN2),e*(l=Math.pow(2,-o))<1&&(o--,l*=2),(e+=o+f>=1?h/l:h*Math.pow(2,1-f))*l>=2&&(o++,l/=2),o+f>=u?(s=0,o=u):o+f>=1?(s=(e*l-1)*Math.pow(2,i),o+=f):(s=e*Math.pow(2,f-1)*Math.pow(2,i),o=0));i>=8;t[r+p]=255&s,p+=d,s/=256,i-=8);for(o=o<<i|s,c+=i;c>0;t[r+p]=255&o,p+=d,o/=256,c-=8);t[r+p-d]|=128*g},al=ol;function ol(t){this.buf=ArrayBuffer.isView&&ArrayBuffer.isView(t)?t:new 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e=ll(this);for(t=t||[];this.pos<e;)t.push(this.readSFixed32());return t},readPackedFixed64:function(t){if(this.type!==ol.Bytes)return t.push(this.readFixed64());var e=ll(this);for(t=t||[];this.pos<e;)t.push(this.readFixed64());return t},readPackedSFixed64:function(t){if(this.type!==ol.Bytes)return t.push(this.readSFixed64());var e=ll(this);for(t=t||[];this.pos<e;)t.push(this.readSFixed64());return t},skip:function(t){var e=7&t;if(e===ol.Varint)for(;this.buf[this.pos++]>127;);else if(e===ol.Bytes)this.pos=this.readVarint()+this.pos;else if(e===ol.Fixed32)this.pos+=4;else{if(e!==ol.Fixed64)throw new Error(\"Unimplemented type: \"+e);this.pos+=8}},writeTag:function(t,e){this.writeVarint(t<<3|e)},realloc:function(t){for(var e=this.length||16;e<this.pos+t;)e*=2;if(e!==this.length){var r=new Uint8Array(e);r.set(this.buf),this.buf=r,this.length=e}},finish:function(){return 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r=(7&t)<<4;if(e.buf[e.pos++]|=r|((t>>>=3)?128:0),!t)return;if(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),!t)return;if(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),!t)return;if(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),!t)return;if(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),!t)return;e.buf[e.pos++]=127&t}(n,e)}(t,this):(this.realloc(4),this.buf[this.pos++]=127&t|(t>127?128:0),t<=127||(this.buf[this.pos++]=127&(t>>>=7)|(t>127?128:0),t<=127||(this.buf[this.pos++]=127&(t>>>=7)|(t>127?128:0),t<=127||(this.buf[this.pos++]=t>>>7&127))))},writeSVarint:function(t){this.writeVarint(t<0?2*-t-1:2*t)},writeBoolean:function(t){this.writeVarint(Boolean(t))},writeString:function(t){t=String(t),this.realloc(4*t.length),this.pos++;var e=this.pos;this.pos=function(t,e,r){for(var n,i,a=0;a<e.length;a++){if((n=e.charCodeAt(a))>55295&&n<57344){if(!i){n>56319||a+1===e.length?(t[r++]=239,t[r++]=191,t[r++]=189):i=n;continue}if(n<56320){t[r++]=239,t[r++]=191,t[r++]=189,i=n;continue}n=i-55296<<10|n-56320|65536,i=null}else i&&(t[r++]=239,t[r++]=191,t[r++]=189,i=null);n<128?t[r++]=n:(n<2048?t[r++]=n>>6|192:(n<65536?t[r++]=n>>12|224:(t[r++]=n>>18|240,t[r++]=n>>12&63|128),t[r++]=n>>6&63|128),t[r++]=63&n|128)}return r}(this.buf,t,this.pos);var r=this.pos-e;r>=128&&ul(e,r,this),this.pos=e-1,this.writeVarint(r),this.pos+=r},writeFloat:function(t){this.realloc(4),il(this.buf,t,this.pos,!0,23,4),this.pos+=4},writeDouble:function(t){this.realloc(8),il(this.buf,t,this.pos,!0,52,8),this.pos+=8},writeBytes:function(t){var e=t.length;this.writeVarint(e),this.realloc(e);for(var r=0;r<e;r++)this.buf[this.pos++]=t[r]},writeRawMessage:function(t,e){this.pos++;var r=this.pos;t(e,this);var n=this.pos-r;n>=128&&ul(r,n,this),this.pos=r-1,this.writeVarint(n),this.pos+=n},writeMessage:function(t,e,r){this.writeTag(t,ol.Bytes),this.writeRawMessage(e,r)},writePackedVarint:function(t,e){e.length&&this.writeMessage(t,fl,e)},writePackedSVarint:function(t,e){e.length&&this.writeMessage(t,hl,e)},writePackedBoolean:function(t,e){e.length&&this.writeMessage(t,gl,e)},writePackedFloat:function(t,e){e.length&&this.writeMessage(t,pl,e)},writePackedDouble:function(t,e){e.length&&this.writeMessage(t,dl,e)},writePackedFixed32:function(t,e){e.length&&this.writeMessage(t,ml,e)},writePackedSFixed32:function(t,e){e.length&&this.writeMessage(t,vl,e)},writePackedFixed64:function(t,e){e.length&&this.writeMessage(t,yl,e)},writePackedSFixed64:function(t,e){e.length&&this.writeMessage(t,xl,e)},writeBytesField:function(t,e){this.writeTag(t,ol.Bytes),this.writeBytes(e)},writeFixed32Field:function(t,e){this.writeTag(t,ol.Fixed32),this.writeFixed32(e)},writeSFixed32Field:function(t,e){this.writeTag(t,ol.Fixed32),this.writeSFixed32(e)},writeFixed64Field:function(t,e){this.writeTag(t,ol.Fixed64),this.writeFixed64(e)},writeSFixed64Field:function(t,e){this.writeTag(t,ol.Fixed64),this.writeSFixed64(e)},writeVarintField:function(t,e){this.writeTag(t,ol.Varint),this.writeVarint(e)},writeSVarintField:function(t,e){this.writeTag(t,ol.Varint),this.writeSVarint(e)},writeStringField:function(t,e){this.writeTag(t,ol.Bytes),this.writeString(e)},writeFloatField:function(t,e){this.writeTag(t,ol.Fixed32),this.writeFloat(e)},writeDoubleField:function(t,e){this.writeTag(t,ol.Fixed64),this.writeDouble(e)},writeBooleanField:function(t,e){this.writeVarintField(t,Boolean(e))}};function Tl(t,e,r){1===t&&r.readMessage(kl,e)}function kl(t,e,r){if(3===t){var n=r.readMessage(Al,{}),i=n.id,a=n.bitmap,o=n.width,s=n.height,l=n.left,c=n.top,u=n.advance;e.push({id:i,bitmap:new So({width:o+6,height:s+6},a),metrics:{width:o,height:s,left:l,top:c,advance:u}})}}function Al(t,e,r){1===t?e.id=r.readVarint():2===t?e.bitmap=r.readBytes():3===t?e.width=r.readVarint():4===t?e.height=r.readVarint():5===t?e.left=r.readSVarint():6===t?e.top=r.readSVarint():7===t&&(e.advance=r.readVarint())}function Ml(t){for(var e=0,r=0,n=0,i=t;n<i.length;n+=1){var a=i[n];e+=a.w*a.h,r=Math.max(r,a.w)}t.sort((function(t,e){return e.h-t.h}));for(var o=[{x:0,y:0,w:Math.max(Math.ceil(Math.sqrt(e/.95)),r),h:1/0}],s=0,l=0,c=0,u=t;c<u.length;c+=1)for(var f=u[c],h=o.length-1;h>=0;h--){var p=o[h];if(!(f.w>p.w||f.h>p.h)){if(f.x=p.x,f.y=p.y,l=Math.max(l,f.y+f.h),s=Math.max(s,f.x+f.w),f.w===p.w&&f.h===p.h){var d=o.pop();h<o.length&&(o[h]=d)}else 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Eo({width:o||1,height:s||1});for(var c in t){var u=t[c],f=r[c].paddedRect;Eo.copy(u.data,l,{x:0,y:0},{x:f.x+1,y:f.y+1},u.data)}for(var h in e){var p=e[h],d=n[h].paddedRect,g=d.x+1,m=d.y+1,v=p.data.width,y=p.data.height;Eo.copy(p.data,l,{x:0,y:0},{x:g,y:m},p.data),Eo.copy(p.data,l,{x:0,y:y-1},{x:g,y:m-1},{width:v,height:1}),Eo.copy(p.data,l,{x:0,y:0},{x:g,y:m+y},{width:v,height:1}),Eo.copy(p.data,l,{x:v-1,y:0},{x:g-1,y:m},{width:1,height:y}),Eo.copy(p.data,l,{x:0,y:0},{x:g+v,y:m},{width:1,height:y})}this.image=l,this.iconPositions=r,this.patternPositions=n};Ll.prototype.addImages=function(t,e,r){for(var n in t){var i=t[n],a={x:0,y:0,w:i.data.width+2,h:i.data.height+2};r.push(a),e[n]=new Sl(a,i),i.hasRenderCallback&&this.haveRenderCallbacks.push(n)}},Ll.prototype.patchUpdatedImages=function(t,e){for(var r in t.dispatchRenderCallbacks(this.haveRenderCallbacks),t.updatedImages)this.patchUpdatedImage(this.iconPositions[r],t.getImage(r),e),this.patchUpdatedImage(this.patternPositions[r],t.getImage(r),e)},Ll.prototype.patchUpdatedImage=function(t,e,r){if(t&&e&&t.version!==e.version){t.version=e.version;var n=t.tl,i=n[0],a=n[1];r.update(e.data,void 0,{x:i,y:a})}},Nn(\"ImagePosition\",Sl),Nn(\"ImageAtlas\",Ll);var Cl={horizontal:1,vertical:2,horizontalOnly:3};var Pl=function(){this.scale=1,this.fontStack=\"\",this.imageName=null};Pl.forText=function(t,e){var r=new Pl;return r.scale=t||1,r.fontStack=e,r},Pl.forImage=function(t){var e=new Pl;return e.imageName=t,e};var Il=function(){this.text=\"\",this.sectionIndex=[],this.sections=[],this.imageSectionID=null};function Ol(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g){var m,v=Il.fromFeature(t,i);f===Cl.vertical&&v.verticalizePunctuation();var y=hi.processBidirectionalText,x=hi.processStyledBidirectionalText;if(y&&1===v.sections.length){m=[];for(var b=0,_=y(v.toString(),jl(v,c,a,e,n,p,d));b<_.length;b+=1){var w=_[b],T=new Il;T.text=w,T.sections=v.sections;for(var k=0;k<w.length;k++)T.sectionIndex.push(0);m.push(T)}}else if(x){m=[];for(var A=0,M=x(v.text,v.sectionIndex,jl(v,c,a,e,n,p,d));A<M.length;A+=1){var S=M[A],E=new Il;E.text=S[0],E.sectionIndex=S[1],E.sections=v.sections,m.push(E)}}else m=function(t,e){for(var r=[],n=t.text,i=0,a=0,o=e;a<o.length;a+=1){var s=o[a];r.push(t.substring(i,s)),i=s}return i<n.length&&r.push(t.substring(i,n.length)),r}(v,jl(v,c,a,e,n,p,d));var L=[],C={positionedLines:L,text:v.toString(),top:u[1],bottom:u[1],left:u[0],right:u[0],writingMode:f,iconsInText:!1,verticalizable:!1};return function(t,e,r,n,i,a,o,s,l,c,u,f){for(var h=0,p=-17,d=0,g=0,m=\"right\"===s?1:\"left\"===s?0:.5,v=0,y=0,x=i;y<x.length;y+=1){var b=x[y];b.trim();var _=b.getMaxScale(),w=24*(_-1),T={positionedGlyphs:[],lineOffset:0};t.positionedLines[v]=T;var k=T.positionedGlyphs,A=0;if(b.length()){for(var M=0;M<b.length();M++){var S=b.getSection(M),E=b.getSectionIndex(M),L=b.getCharCode(M),C=0,P=null,I=null,O=null,z=24,D=!(l===Cl.horizontal||!u&&!Zn(L)||u&&(zl[L]||(Y=L,Yn.Arabic(Y)||Yn[\"Arabic Supplement\"](Y)||Yn[\"Arabic Extended-A\"](Y)||Yn[\"Arabic Presentation Forms-A\"](Y)||Yn[\"Arabic Presentation Forms-B\"](Y))));if(S.imageName){var R=n[S.imageName];if(!R)continue;O=S.imageName,t.iconsInText=t.iconsInText||!0,I=R.paddedRect;var F=R.displaySize;S.scale=24*S.scale/f,P={width:F[0],height:F[1],left:1,top:-3,advance:D?F[1]:F[0]};var B=24-F[1]*S.scale;C=w+B,z=P.advance;var N=D?F[0]*S.scale-24*_:F[1]*S.scale-24*_;N>0&&N>A&&(A=N)}else{var j=r[S.fontStack],U=j&&j[L];if(U&&U.rect)I=U.rect,P=U.metrics;else{var V=e[S.fontStack],H=V&&V[L];if(!H)continue;P=H.metrics}C=24*(_-S.scale)}D?(t.verticalizable=!0,k.push({glyph:L,imageName:O,x:h,y:p+C,vertical:D,scale:S.scale,fontStack:S.fontStack,sectionIndex:E,metrics:P,rect:I}),h+=z*S.scale+c):(k.push({glyph:L,imageName:O,x:h,y:p+C,vertical:D,scale:S.scale,fontStack:S.fontStack,sectionIndex:E,metrics:P,rect:I}),h+=P.advance*S.scale+c)}if(0!==k.length){var q=h-c;d=Math.max(q,d),Vl(k,0,k.length-1,m,A)}h=0;var G=a*_+A;T.lineOffset=Math.max(A,w),p+=G,g=Math.max(G,g),++v}else p+=a,++v}var Y;var W=p- -17,X=Ul(o),Z=X.horizontalAlign,J=X.verticalAlign;(function(t,e,r,n,i,a,o,s,l){var c=(e-r)*i,u=0;u=a!==o?-s*n- -17:(-n*l+.5)*o;for(var f=0,h=t;f<h.length;f+=1)for(var p=h[f],d=0,g=p.positionedGlyphs;d<g.length;d+=1){var m=g[d];m.x+=c,m.y+=u}})(t.positionedLines,m,Z,J,d,g,a,W,i.length),t.top+=-J*W,t.bottom=t.top+W,t.left+=-Z*d,t.right=t.left+d}(C,e,r,n,m,o,s,l,f,c,h,g),!function(t){for(var e=0,r=t;e<r.length;e+=1){if(0!==r[e].positionedGlyphs.length)return!1}return!0}(L)&&C}Il.fromFeature=function(t,e){for(var r=new Il,n=0;n<t.sections.length;n++){var i=t.sections[n];i.image?r.addImageSection(i):r.addTextSection(i,e)}return r},Il.prototype.length=function(){return this.text.length},Il.prototype.getSection=function(t){return this.sections[this.sectionIndex[t]]},Il.prototype.getSectionIndex=function(t){return this.sectionIndex[t]},Il.prototype.getCharCode=function(t){return this.text.charCodeAt(t)},Il.prototype.verticalizePunctuation=function(){this.text=function(t){for(var e=\"\",r=0;r<t.length;r++){var n=t.charCodeAt(r+1)||null,i=t.charCodeAt(r-1)||null;(!n||!Jn(n)||rl[t[r+1]])&&(!i||!Jn(i)||rl[t[r-1]])&&rl[t[r]]?e+=rl[t[r]]:e+=t[r]}return e}(this.text)},Il.prototype.trim=function(){for(var t=0,e=0;e<this.text.length&&zl[this.text.charCodeAt(e)];e++)t++;for(var 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e=this.data,r=this.compare,n=this.length>>1,i=e[t];t<n;){var a=1+(t<<1),o=e[a],s=a+1;if(s<this.length&&r(e[s],o)<0&&(a=s,o=e[s]),r(o,i)>=0)break;e[t]=o,t=a}e[t]=i};var pc=Number.POSITIVE_INFINITY;function dc(t,e){return e[1]!==pc?function(t,e,r){var n=0,i=0;switch(e=Math.abs(e),r=Math.abs(r),t){case\"top-right\":case\"top-left\":case\"top\":i=r-7;break;case\"bottom-right\":case\"bottom-left\":case\"bottom\":i=7-r}switch(t){case\"top-right\":case\"bottom-right\":case\"right\":n=-e;break;case\"top-left\":case\"bottom-left\":case\"left\":n=e}return[n,i]}(t,e[0],e[1]):function(t,e){var r=0,n=0;e<0&&(e=0);var i=e/Math.sqrt(2);switch(t){case\"top-right\":case\"top-left\":n=i-7;break;case\"bottom-right\":case\"bottom-left\":n=7-i;break;case\"bottom\":n=7-e;break;case\"top\":n=e-7}switch(t){case\"top-right\":case\"bottom-right\":r=-i;break;case\"top-left\":case\"bottom-left\":r=i;break;case\"left\":r=e;break;case\"right\":r=-e}return[r,n]}(t,e[0])}function gc(t){switch(t){case\"right\":case\"top-right\":case\"bottom-right\":return\"right\";case\"left\":case\"top-left\":case\"bottom-left\":return\"left\"}return\"center\"}function mc(t,e,r,n,a,o,s,l,c,u,f,h,p,d,g){var m=function(t,e,r,n,a,o,s,l){for(var c=n.layout.get(\"text-rotate\").evaluate(o,{})*Math.PI/180,u=[],f=0,h=e.positionedLines;f<h.length;f+=1)for(var p=h[f],d=0,g=p.positionedGlyphs;d<g.length;d+=1){var m=g[d];if(m.rect){var v=m.rect||{},y=4,x=!0,b=1,_=0,w=(a||l)&&m.vertical,T=m.metrics.advance*m.scale/2;if(l&&e.verticalizable){var k=24*(m.scale-1),A=(24-m.metrics.width*m.scale)/2;_=p.lineOffset/2-(m.imageName?-A:k)}if(m.imageName){var M=s[m.imageName];x=M.sdf,y=1/(b=M.pixelRatio)}var S=a?[m.x+T,m.y]:[0,0],E=a?[0,0]:[m.x+T+r[0],m.y+r[1]-_],L=[0,0];w&&(L=E,E=[0,0]);var C=(m.metrics.left-y)*m.scale-T+E[0],P=(-m.metrics.top-y)*m.scale+E[1],I=C+v.w*m.scale/b,O=P+v.h*m.scale/b,z=new i(C,P),D=new i(I,P),R=new i(C,O),F=new i(I,O);if(w){var B=new i(-T,T- -17),N=-Math.PI/2,j=12-T,U=m.imageName?j:0,V=new i(22-j,-U),H=new(Function.prototype.bind.apply(i,[null].concat(L)));z._rotateAround(N,B)._add(V)._add(H),D._rotateAround(N,B)._add(V)._add(H),R._rotateAround(N,B)._add(V)._add(H),F._rotateAround(N,B)._add(V)._add(H)}if(c){var q=Math.sin(c),G=Math.cos(c),Y=[G,-q,q,G];z._matMult(Y),D._matMult(Y),R._matMult(Y),F._matMult(Y)}var W=new i(0,0),X=new i(0,0);u.push({tl:z,tr:D,bl:R,br:F,tex:v,writingMode:e.writingMode,glyphOffset:S,sectionIndex:m.sectionIndex,isSDF:x,pixelOffsetTL:W,pixelOffsetBR:X,minFontScaleX:0,minFontScaleY:0})}}return u}(0,r,l,a,o,s,n,t.allowVerticalPlacement),v=t.textSizeData,y=null;\"source\"===v.kind?(y=[128*a.layout.get(\"text-size\").evaluate(s,{})])[0]>32640&&_(t.layerIds[0]+': Value for \"text-size\" is >= 255. Reduce your \"text-size\".'):\"composite\"===v.kind&&((y=[128*d.compositeTextSizes[0].evaluate(s,{},g),128*d.compositeTextSizes[1].evaluate(s,{},g)])[0]>32640||y[1]>32640)&&_(t.layerIds[0]+': Value for \"text-size\" is >= 255. Reduce your \"text-size\".'),t.addSymbols(t.text,m,y,l,o,s,u,e,c.lineStartIndex,c.lineLength,p,g);for(var x=0,b=f;x<b.length;x+=1){h[b[x]]=t.text.placedSymbolArray.length-1}return 4*m.length}function vc(t){for(var e in t)return t[e];return null}function yc(t,e,r,n){var i=t.compareText;if(e in i){for(var a=i[e],o=a.length-1;o>=0;o--)if(n.dist(a[o])<r)return!0}else i[e]=[];return i[e].push(n),!1}var xc=Ls.VectorTileFeature.types,bc=[{name:\"a_fade_opacity\",components:1,type:\"Uint8\",offset:0}];function _c(t,e,r,n,i,a,o,s,l,c,u,f,h){var p=s?Math.min(32640,Math.round(s[0])):0,d=s?Math.min(32640,Math.round(s[1])):0;t.emplaceBack(e,r,Math.round(32*n),Math.round(32*i),a,o,(p<<1)+(l?1:0),d,16*c,16*u,256*f,256*h)}function wc(t,e,r){t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r)}function Tc(t){for(var e=0,r=t.sections;e<r.length;e+=1){if($n(r[e].text))return!0}return!1}var kc=function(t){this.layoutVertexArray=new Ni,this.indexArray=new Yi,this.programConfigurations=t,this.segments=new pa,this.dynamicLayoutVertexArray=new ji,this.opacityVertexArray=new Ui,this.placedSymbolArray=new aa};kc.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length&&0===this.indexArray.length&&0===this.dynamicLayoutVertexArray.length&&0===this.opacityVertexArray.length},kc.prototype.upload=function(t,e,r,n){this.isEmpty()||(r&&(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,Js.members),this.indexBuffer=t.createIndexBuffer(this.indexArray,e),this.dynamicLayoutVertexBuffer=t.createVertexBuffer(this.dynamicLayoutVertexArray,Ks.members,!0),this.opacityVertexBuffer=t.createVertexBuffer(this.opacityVertexArray,bc,!0),this.opacityVertexBuffer.itemSize=1),(r||n)&&this.programConfigurations.upload(t))},kc.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy(),this.dynamicLayoutVertexBuffer.destroy(),this.opacityVertexBuffer.destroy())},Nn(\"SymbolBuffers\",kc);var Ac=function(t,e,r){this.layoutVertexArray=new t,this.layoutAttributes=e,this.indexArray=new r,this.segments=new pa,this.collisionVertexArray=new Gi};Ac.prototype.upload=function(t){this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,this.layoutAttributes),this.indexBuffer=t.createIndexBuffer(this.indexArray),this.collisionVertexBuffer=t.createVertexBuffer(this.collisionVertexArray,Qs.members,!0)},Ac.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.segments.destroy(),this.collisionVertexBuffer.destroy())},Nn(\"CollisionBuffers\",Ac);var Mc=function(t){this.collisionBoxArray=t.collisionBoxArray,this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.pixelRatio=t.pixelRatio,this.sourceLayerIndex=t.sourceLayerIndex,this.hasPattern=!1,this.hasRTLText=!1,this.sortKeyRanges=[],this.collisionCircleArray=[],this.placementInvProjMatrix=ho([]),this.placementViewportMatrix=ho([]);var e=this.layers[0]._unevaluatedLayout._values;this.textSizeData=Gl(this.zoom,e[\"text-size\"]),this.iconSizeData=Gl(this.zoom,e[\"icon-size\"]);var r=this.layers[0].layout,n=r.get(\"symbol-sort-key\"),i=r.get(\"symbol-z-order\");this.sortFeaturesByKey=\"viewport-y\"!==i&&void 0!==n.constantOr(1);var a=\"viewport-y\"===i||\"auto\"===i&&!this.sortFeaturesByKey;this.sortFeaturesByY=a&&(r.get(\"text-allow-overlap\")||r.get(\"icon-allow-overlap\")||r.get(\"text-ignore-placement\")||r.get(\"icon-ignore-placement\")),\"point\"===r.get(\"symbol-placement\")&&(this.writingModes=r.get(\"text-writing-mode\").map((function(t){return Cl[t]}))),this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id})),this.sourceID=t.sourceID};Mc.prototype.createArrays=function(){this.text=new kc(new Ua(Js.members,this.layers,this.zoom,(function(t){return/^text/.test(t)}))),this.icon=new kc(new Ua(Js.members,this.layers,this.zoom,(function(t){return/^icon/.test(t)}))),this.glyphOffsetArray=new la,this.lineVertexArray=new ca,this.symbolInstances=new sa},Mc.prototype.calculateGlyphDependencies=function(t,e,r,n,i){for(var a=0;a<t.length;a++)if(e[t.charCodeAt(a)]=!0,(r||n)&&i){var o=rl[t.charAt(a)];o&&(e[o.charCodeAt(0)]=!0)}},Mc.prototype.populate=function(t,e,r){var n=this.layers[0],i=n.layout,a=i.get(\"text-font\"),o=i.get(\"text-field\"),s=i.get(\"icon-image\"),l=(\"constant\"!==o.value.kind||o.value.value instanceof ne&&!o.value.value.isEmpty()||o.value.value.toString().length>0)&&(\"constant\"!==a.value.kind||a.value.value.length>0),c=\"constant\"!==s.value.kind||!!s.value.value||Object.keys(s.parameters).length>0,u=i.get(\"symbol-sort-key\");if(this.features=[],l||c){for(var f=e.iconDependencies,h=e.glyphDependencies,p=e.availableImages,d=new pi(this.zoom),g=0,m=t;g<m.length;g+=1){var v=m[g],y=v.feature,x=v.id,b=v.index,_=v.sourceLayerIndex,w=n._featureFilter.needGeometry,T={type:y.type,id:x,properties:y.properties,geometry:w?Ya(y):[]};if(n._featureFilter.filter(d,T,r)){w||(T.geometry=Ya(y));var k=void 0;if(l){var A=n.getValueAndResolveTokens(\"text-field\",T,r,p),M=ne.factory(A);Tc(M)&&(this.hasRTLText=!0),(!this.hasRTLText||\"unavailable\"===ui()||this.hasRTLText&&hi.isParsed())&&(k=el(M,n,T))}var S=void 0;if(c){var E=n.getValueAndResolveTokens(\"icon-image\",T,r,p);S=E instanceof ie?E:ie.fromString(E)}if(k||S){var L=this.sortFeaturesByKey?u.evaluate(T,{},r):void 0,C={id:x,text:k,icon:S,index:b,sourceLayerIndex:_,geometry:Ya(y),properties:y.properties,type:xc[y.type],sortKey:L};if(this.features.push(C),S&&(f[S.name]=!0),k){var P=a.evaluate(T,{},r).join(\",\"),I=\"map\"===i.get(\"text-rotation-alignment\")&&\"point\"!==i.get(\"symbol-placement\");this.allowVerticalPlacement=this.writingModes&&this.writingModes.indexOf(Cl.vertical)>=0;for(var O=0,z=k.sections;O<z.length;O+=1){var D=z[O];if(D.image)f[D.image.name]=!0;else{var R=Wn(k.toString()),F=D.fontStack||P,B=h[F]=h[F]||{};this.calculateGlyphDependencies(D.text,B,I,this.allowVerticalPlacement,R)}}}}}}\"line\"===i.get(\"symbol-placement\")&&(this.features=function(t){var e={},r={},n=[],i=0;function a(e){n.push(t[e]),i++}function o(t,e,i){var a=r[t];return delete r[t],r[e]=a,n[a].geometry[0].pop(),n[a].geometry[0]=n[a].geometry[0].concat(i[0]),a}function s(t,r,i){var a=e[r];return delete e[r],e[t]=a,n[a].geometry[0].shift(),n[a].geometry[0]=i[0].concat(n[a].geometry[0]),a}function l(t,e,r){var n=r?e[0][e[0].length-1]:e[0][0];return t+\":\"+n.x+\":\"+n.y}for(var c=0;c<t.length;c++){var u=t[c],f=u.geometry,h=u.text?u.text.toString():null;if(h){var p=l(h,f),d=l(h,f,!0);if(p in r&&d in e&&r[p]!==e[d]){var g=s(p,d,f),m=o(p,d,n[g].geometry);delete e[p],delete r[d],r[l(h,n[m].geometry,!0)]=m,n[g].geometry=null}else p in r?o(p,d,f):d in e?s(p,d,f):(a(c),e[p]=i-1,r[d]=i-1)}else a(c)}return n.filter((function(t){return t.geometry}))}(this.features)),this.sortFeaturesByKey&&this.features.sort((function(t,e){return t.sortKey-e.sortKey}))}},Mc.prototype.update=function(t,e,r){this.stateDependentLayers.length&&(this.text.programConfigurations.updatePaintArrays(t,e,this.layers,r),this.icon.programConfigurations.updatePaintArrays(t,e,this.layers,r))},Mc.prototype.isEmpty=function(){return 0===this.symbolInstances.length&&!this.hasRTLText},Mc.prototype.uploadPending=function(){return!this.uploaded||this.text.programConfigurations.needsUpload||this.icon.programConfigurations.needsUpload},Mc.prototype.upload=function(t){!this.uploaded&&this.hasDebugData()&&(this.textCollisionBox.upload(t),this.iconCollisionBox.upload(t)),this.text.upload(t,this.sortFeaturesByY,!this.uploaded,this.text.programConfigurations.needsUpload),this.icon.upload(t,this.sortFeaturesByY,!this.uploaded,this.icon.programConfigurations.needsUpload),this.uploaded=!0},Mc.prototype.destroyDebugData=function(){this.textCollisionBox.destroy(),this.iconCollisionBox.destroy()},Mc.prototype.destroy=function(){this.text.destroy(),this.icon.destroy(),this.hasDebugData()&&this.destroyDebugData()},Mc.prototype.addToLineVertexArray=function(t,e){var r=this.lineVertexArray.length;if(void 0!==t.segment){for(var n=t.dist(e[t.segment+1]),i=t.dist(e[t.segment]),a={},o=t.segment+1;o<e.length;o++)a[o]={x:e[o].x,y:e[o].y,tileUnitDistanceFromAnchor:n},o<e.length-1&&(n+=e[o+1].dist(e[o]));for(var s=t.segment||0;s>=0;s--)a[s]={x:e[s].x,y:e[s].y,tileUnitDistanceFromAnchor:i},s>0&&(i+=e[s-1].dist(e[s]));for(var l=0;l<e.length;l++){var c=a[l];this.lineVertexArray.emplaceBack(c.x,c.y,c.tileUnitDistanceFromAnchor)}}return{lineStartIndex:r,lineLength:this.lineVertexArray.length-r}},Mc.prototype.addSymbols=function(t,e,r,n,i,a,o,s,l,c,u,f){for(var h=t.indexArray,p=t.layoutVertexArray,d=t.segments.prepareSegment(4*e.length,p,h,a.sortKey),g=this.glyphOffsetArray.length,m=d.vertexLength,v=this.allowVerticalPlacement&&o===Cl.vertical?Math.PI/2:0,y=a.text&&a.text.sections,x=0;x<e.length;x++){var b=e[x],_=b.tl,w=b.tr,T=b.bl,k=b.br,A=b.tex,M=b.pixelOffsetTL,S=b.pixelOffsetBR,E=b.minFontScaleX,L=b.minFontScaleY,C=b.glyphOffset,P=b.isSDF,I=b.sectionIndex,O=d.vertexLength,z=C[1];_c(p,s.x,s.y,_.x,z+_.y,A.x,A.y,r,P,M.x,M.y,E,L),_c(p,s.x,s.y,w.x,z+w.y,A.x+A.w,A.y,r,P,S.x,M.y,E,L),_c(p,s.x,s.y,T.x,z+T.y,A.x,A.y+A.h,r,P,M.x,S.y,E,L),_c(p,s.x,s.y,k.x,z+k.y,A.x+A.w,A.y+A.h,r,P,S.x,S.y,E,L),wc(t.dynamicLayoutVertexArray,s,v),h.emplaceBack(O,O+1,O+2),h.emplaceBack(O+1,O+2,O+3),d.vertexLength+=4,d.primitiveLength+=2,this.glyphOffsetArray.emplaceBack(C[0]),x!==e.length-1&&I===e[x+1].sectionIndex||t.programConfigurations.populatePaintArrays(p.length,a,a.index,{},f,y&&y[I])}t.placedSymbolArray.emplaceBack(s.x,s.y,g,this.glyphOffsetArray.length-g,m,l,c,s.segment,r?r[0]:0,r?r[1]:0,n[0],n[1],o,0,!1,0,u)},Mc.prototype._addCollisionDebugVertex=function(t,e,r,n,i,a){return e.emplaceBack(0,0),t.emplaceBack(r.x,r.y,n,i,Math.round(a.x),Math.round(a.y))},Mc.prototype.addCollisionDebugVertices=function(t,e,r,n,a,o,s){var l=a.segments.prepareSegment(4,a.layoutVertexArray,a.indexArray),c=l.vertexLength,u=a.layoutVertexArray,f=a.collisionVertexArray,h=s.anchorX,p=s.anchorY;this._addCollisionDebugVertex(u,f,o,h,p,new i(t,e)),this._addCollisionDebugVertex(u,f,o,h,p,new i(r,e)),this._addCollisionDebugVertex(u,f,o,h,p,new i(r,n)),this._addCollisionDebugVertex(u,f,o,h,p,new i(t,n)),l.vertexLength+=4;var d=a.indexArray;d.emplaceBack(c,c+1),d.emplaceBack(c+1,c+2),d.emplaceBack(c+2,c+3),d.emplaceBack(c+3,c),l.primitiveLength+=4},Mc.prototype.addDebugCollisionBoxes=function(t,e,r,n){for(var i=t;i<e;i++){var a=this.collisionBoxArray.get(i),o=a.x1,s=a.y1,l=a.x2,c=a.y2;this.addCollisionDebugVertices(o,s,l,c,n?this.textCollisionBox:this.iconCollisionBox,a.anchorPoint,r)}},Mc.prototype.generateCollisionDebugBuffers=function(){this.hasDebugData()&&this.destroyDebugData(),this.textCollisionBox=new Ac(Hi,$s.members,Qi),this.iconCollisionBox=new Ac(Hi,$s.members,Qi);for(var t=0;t<this.symbolInstances.length;t++){var e=this.symbolInstances.get(t);this.addDebugCollisionBoxes(e.textBoxStartIndex,e.textBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.verticalTextBoxStartIndex,e.verticalTextBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.iconBoxStartIndex,e.iconBoxEndIndex,e,!1),this.addDebugCollisionBoxes(e.verticalIconBoxStartIndex,e.verticalIconBoxEndIndex,e,!1)}},Mc.prototype._deserializeCollisionBoxesForSymbol=function(t,e,r,n,i,a,o,s,l){for(var c={},u=e;u<r;u++){var f=t.get(u);c.textBox={x1:f.x1,y1:f.y1,x2:f.x2,y2:f.y2,anchorPointX:f.anchorPointX,anchorPointY:f.anchorPointY},c.textFeatureIndex=f.featureIndex;break}for(var h=n;h<i;h++){var p=t.get(h);c.verticalTextBox={x1:p.x1,y1:p.y1,x2:p.x2,y2:p.y2,anchorPointX:p.anchorPointX,anchorPointY:p.anchorPointY},c.verticalTextFeatureIndex=p.featureIndex;break}for(var d=a;d<o;d++){var g=t.get(d);c.iconBox={x1:g.x1,y1:g.y1,x2:g.x2,y2:g.y2,anchorPointX:g.anchorPointX,anchorPointY:g.anchorPointY},c.iconFeatureIndex=g.featureIndex;break}for(var m=s;m<l;m++){var v=t.get(m);c.verticalIconBox={x1:v.x1,y1:v.y1,x2:v.x2,y2:v.y2,anchorPointX:v.anchorPointX,anchorPointY:v.anchorPointY},c.verticalIconFeatureIndex=v.featureIndex;break}return c},Mc.prototype.deserializeCollisionBoxes=function(t){this.collisionArrays=[];for(var e=0;e<this.symbolInstances.length;e++){var r=this.symbolInstances.get(e);this.collisionArrays.push(this._deserializeCollisionBoxesForSymbol(t,r.textBoxStartIndex,r.textBoxEndIndex,r.verticalTextBoxStartIndex,r.verticalTextBoxEndIndex,r.iconBoxStartIndex,r.iconBoxEndIndex,r.verticalIconBoxStartIndex,r.verticalIconBoxEndIndex))}},Mc.prototype.hasTextData=function(){return this.text.segments.get().length>0},Mc.prototype.hasIconData=function(){return this.icon.segments.get().length>0},Mc.prototype.hasDebugData=function(){return this.textCollisionBox&&this.iconCollisionBox},Mc.prototype.hasTextCollisionBoxData=function(){return this.hasDebugData()&&this.textCollisionBox.segments.get().length>0},Mc.prototype.hasIconCollisionBoxData=function(){return this.hasDebugData()&&this.iconCollisionBox.segments.get().length>0},Mc.prototype.addIndicesForPlacedSymbol=function(t,e){for(var r=t.placedSymbolArray.get(e),n=r.vertexStartIndex+4*r.numGlyphs,i=r.vertexStartIndex;i<n;i+=4)t.indexArray.emplaceBack(i,i+1,i+2),t.indexArray.emplaceBack(i+1,i+2,i+3)},Mc.prototype.getSortedSymbolIndexes=function(t){if(this.sortedAngle===t&&void 0!==this.symbolInstanceIndexes)return this.symbolInstanceIndexes;for(var e=Math.sin(t),r=Math.cos(t),n=[],i=[],a=[],o=0;o<this.symbolInstances.length;++o){a.push(o);var s=this.symbolInstances.get(o);n.push(0|Math.round(e*s.anchorX+r*s.anchorY)),i.push(s.featureIndex)}return a.sort((function(t,e){return n[t]-n[e]||i[e]-i[t]})),a},Mc.prototype.addToSortKeyRanges=function(t,e){var r=this.sortKeyRanges[this.sortKeyRanges.length-1];r&&r.sortKey===e?r.symbolInstanceEnd=t+1:this.sortKeyRanges.push({sortKey:e,symbolInstanceStart:t,symbolInstanceEnd:t+1})},Mc.prototype.sortFeatures=function(t){var e=this;if(this.sortFeaturesByY&&this.sortedAngle!==t&&!(this.text.segments.get().length>1||this.icon.segments.get().length>1)){this.symbolInstanceIndexes=this.getSortedSymbolIndexes(t),this.sortedAngle=t,this.text.indexArray.clear(),this.icon.indexArray.clear(),this.featureSortOrder=[];for(var r=0,n=this.symbolInstanceIndexes;r<n.length;r+=1){var i=n[r],a=this.symbolInstances.get(i);this.featureSortOrder.push(a.featureIndex),[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t,r,n){t>=0&&n.indexOf(t)===r&&e.addIndicesForPlacedSymbol(e.text,t)})),a.verticalPlacedTextSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.text,a.verticalPlacedTextSymbolIndex),a.placedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,a.placedIconSymbolIndex),a.verticalPlacedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,a.verticalPlacedIconSymbolIndex)}this.text.indexBuffer&&this.text.indexBuffer.updateData(this.text.indexArray),this.icon.indexBuffer&&this.icon.indexBuffer.updateData(this.icon.indexArray)}},Nn(\"SymbolBucket\",Mc,{omit:[\"layers\",\"collisionBoxArray\",\"features\",\"compareText\"]}),Mc.MAX_GLYPHS=65535,Mc.addDynamicAttributes=wc;var Sc=new Si({\"symbol-placement\":new wi(Lt.layout_symbol[\"symbol-placement\"]),\"symbol-spacing\":new wi(Lt.layout_symbol[\"symbol-spacing\"]),\"symbol-avoid-edges\":new wi(Lt.layout_symbol[\"symbol-avoid-edges\"]),\"symbol-sort-key\":new Ti(Lt.layout_symbol[\"symbol-sort-key\"]),\"symbol-z-order\":new wi(Lt.layout_symbol[\"symbol-z-order\"]),\"icon-allow-overlap\":new wi(Lt.layout_symbol[\"icon-allow-overlap\"]),\"icon-ignore-placement\":new wi(Lt.layout_symbol[\"icon-ignore-placement\"]),\"icon-optional\":new wi(Lt.layout_symbol[\"icon-optional\"]),\"icon-rotation-alignment\":new wi(Lt.layout_symbol[\"icon-rotation-alignment\"]),\"icon-size\":new Ti(Lt.layout_symbol[\"icon-size\"]),\"icon-text-fit\":new wi(Lt.layout_symbol[\"icon-text-fit\"]),\"icon-text-fit-padding\":new wi(Lt.layout_symbol[\"icon-text-fit-padding\"]),\"icon-image\":new Ti(Lt.layout_symbol[\"icon-image\"]),\"icon-rotate\":new Ti(Lt.layout_symbol[\"icon-rotate\"]),\"icon-padding\":new wi(Lt.layout_symbol[\"icon-padding\"]),\"icon-keep-upright\":new wi(Lt.layout_symbol[\"icon-keep-upright\"]),\"icon-offset\":new Ti(Lt.layout_symbol[\"icon-offset\"]),\"icon-anchor\":new Ti(Lt.layout_symbol[\"icon-anchor\"]),\"icon-pitch-alignment\":new wi(Lt.layout_symbol[\"icon-pitch-alignment\"]),\"text-pitch-alignment\":new wi(Lt.layout_symbol[\"text-pitch-alignment\"]),\"text-rotation-alignment\":new wi(Lt.layout_symbol[\"text-rotation-alignment\"]),\"text-field\":new Ti(Lt.layout_symbol[\"text-field\"]),\"text-font\":new Ti(Lt.layout_symbol[\"text-font\"]),\"text-size\":new Ti(Lt.layout_symbol[\"text-size\"]),\"text-max-width\":new Ti(Lt.layout_symbol[\"text-max-width\"]),\"text-line-height\":new wi(Lt.layout_symbol[\"text-line-height\"]),\"text-letter-spacing\":new Ti(Lt.layout_symbol[\"text-letter-spacing\"]),\"text-justify\":new Ti(Lt.layout_symbol[\"text-justify\"]),\"text-radial-offset\":new Ti(Lt.layout_symbol[\"text-radial-offset\"]),\"text-variable-anchor\":new wi(Lt.layout_symbol[\"text-variable-anchor\"]),\"text-anchor\":new Ti(Lt.layout_symbol[\"text-anchor\"]),\"text-max-angle\":new wi(Lt.layout_symbol[\"text-max-angle\"]),\"text-writing-mode\":new wi(Lt.layout_symbol[\"text-writing-mode\"]),\"text-rotate\":new Ti(Lt.layout_symbol[\"text-rotate\"]),\"text-padding\":new wi(Lt.layout_symbol[\"text-padding\"]),\"text-keep-upright\":new wi(Lt.layout_symbol[\"text-keep-upright\"]),\"text-transform\":new Ti(Lt.layout_symbol[\"text-transform\"]),\"text-offset\":new Ti(Lt.layout_symbol[\"text-offset\"]),\"text-allow-overlap\":new wi(Lt.layout_symbol[\"text-allow-overlap\"]),\"text-ignore-placement\":new wi(Lt.layout_symbol[\"text-ignore-placement\"]),\"text-optional\":new wi(Lt.layout_symbol[\"text-optional\"])}),Ec={paint:new Si({\"icon-opacity\":new Ti(Lt.paint_symbol[\"icon-opacity\"]),\"icon-color\":new Ti(Lt.paint_symbol[\"icon-color\"]),\"icon-halo-color\":new Ti(Lt.paint_symbol[\"icon-halo-color\"]),\"icon-halo-width\":new Ti(Lt.paint_symbol[\"icon-halo-width\"]),\"icon-halo-blur\":new Ti(Lt.paint_symbol[\"icon-halo-blur\"]),\"icon-translate\":new wi(Lt.paint_symbol[\"icon-translate\"]),\"icon-translate-anchor\":new wi(Lt.paint_symbol[\"icon-translate-anchor\"]),\"text-opacity\":new Ti(Lt.paint_symbol[\"text-opacity\"]),\"text-color\":new Ti(Lt.paint_symbol[\"text-color\"],{runtimeType:Ut,getOverride:function(t){return t.textColor},hasOverride:function(t){return!!t.textColor}}),\"text-halo-color\":new Ti(Lt.paint_symbol[\"text-halo-color\"]),\"text-halo-width\":new Ti(Lt.paint_symbol[\"text-halo-width\"]),\"text-halo-blur\":new Ti(Lt.paint_symbol[\"text-halo-blur\"]),\"text-translate\":new wi(Lt.paint_symbol[\"text-translate\"]),\"text-translate-anchor\":new wi(Lt.paint_symbol[\"text-translate-anchor\"])}),layout:Sc},Lc=function(t){this.type=t.property.overrides?t.property.overrides.runtimeType:Ft,this.defaultValue=t};Lc.prototype.evaluate=function(t){if(t.formattedSection){var e=this.defaultValue.property.overrides;if(e&&e.hasOverride(t.formattedSection))return e.getOverride(t.formattedSection)}return t.feature&&t.featureState?this.defaultValue.evaluate(t.feature,t.featureState):this.defaultValue.property.specification.default},Lc.prototype.eachChild=function(t){this.defaultValue.isConstant()||t(this.defaultValue.value._styleExpression.expression)},Lc.prototype.outputDefined=function(){return!1},Lc.prototype.serialize=function(){return null},Nn(\"FormatSectionOverride\",Lc,{omit:[\"defaultValue\"]});var Cc=function(t){function e(e){t.call(this,e,Ec)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.recalculate=function(e,r){if(t.prototype.recalculate.call(this,e,r),\"auto\"===this.layout.get(\"icon-rotation-alignment\")&&(\"point\"!==this.layout.get(\"symbol-placement\")?this.layout._values[\"icon-rotation-alignment\"]=\"map\":this.layout._values[\"icon-rotation-alignment\"]=\"viewport\"),\"auto\"===this.layout.get(\"text-rotation-alignment\")&&(\"point\"!==this.layout.get(\"symbol-placement\")?this.layout._values[\"text-rotation-alignment\"]=\"map\":this.layout._values[\"text-rotation-alignment\"]=\"viewport\"),\"auto\"===this.layout.get(\"text-pitch-alignment\")&&(this.layout._values[\"text-pitch-alignment\"]=this.layout.get(\"text-rotation-alignment\")),\"auto\"===this.layout.get(\"icon-pitch-alignment\")&&(this.layout._values[\"icon-pitch-alignment\"]=this.layout.get(\"icon-rotation-alignment\")),\"point\"===this.layout.get(\"symbol-placement\")){var n=this.layout.get(\"text-writing-mode\");if(n){for(var i=[],a=0,o=n;a<o.length;a+=1){var s=o[a];i.indexOf(s)<0&&i.push(s)}this.layout._values[\"text-writing-mode\"]=i}else this.layout._values[\"text-writing-mode\"]=[\"horizontal\"]}this._setPaintOverrides()},e.prototype.getValueAndResolveTokens=function(t,e,r,n){var i=this.layout.get(t).evaluate(e,{},r,n),a=this._unevaluatedLayout._values[t];return a.isDataDriven()||Yr(a.value)||!i?i:function(t,e){return e.replace(/{([^{}]+)}/g,(function(e,r){return r in t?String(t[r]):\"\"}))}(e.properties,i)},e.prototype.createBucket=function(t){return new Mc(t)},e.prototype.queryRadius=function(){return 0},e.prototype.queryIntersectsFeature=function(){return!1},e.prototype._setPaintOverrides=function(){for(var t=0,r=Ec.paint.overridableProperties;t<r.length;t+=1){var n=r[t];if(e.hasPaintOverride(this.layout,n)){var i=this.paint.get(n),a=new Lc(i),o=new Gr(a,i.property.specification),s=null;s=\"constant\"===i.value.kind||\"source\"===i.value.kind?new Xr(\"source\",o):new Zr(\"composite\",o,i.value.zoomStops,i.value._interpolationType),this.paint._values[n]=new bi(i.property,s,i.parameters)}}},e.prototype._handleOverridablePaintPropertyUpdate=function(t,r,n){return!(!this.layout||r.isDataDriven()||n.isDataDriven())&&e.hasPaintOverride(this.layout,t)},e.hasPaintOverride=function(t,e){var r=t.get(\"text-field\"),n=Ec.paint.properties[e],i=!1,a=function(t){for(var e=0,r=t;e<r.length;e+=1){var a=r[e];if(n.overrides&&n.overrides.hasOverride(a))return void(i=!0)}};if(\"constant\"===r.value.kind&&r.value.value instanceof ne)a(r.value.value.sections);else if(\"source\"===r.value.kind){var o=function(t){if(!i)if(t instanceof ce&&se(t.value)===Gt){var e=t.value;a(e.sections)}else t instanceof pe?a(t.sections):t.eachChild(o)},s=r.value;s._styleExpression&&o(s._styleExpression.expression)}return i},e}(Ei),Pc={paint:new Si({\"background-color\":new wi(Lt.paint_background[\"background-color\"]),\"background-pattern\":new Ai(Lt.paint_background[\"background-pattern\"]),\"background-opacity\":new wi(Lt.paint_background[\"background-opacity\"])})},Ic=function(t){function e(e){t.call(this,e,Pc)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(Ei),Oc={paint:new Si({\"raster-opacity\":new wi(Lt.paint_raster[\"raster-opacity\"]),\"raster-hue-rotate\":new wi(Lt.paint_raster[\"raster-hue-rotate\"]),\"raster-brightness-min\":new wi(Lt.paint_raster[\"raster-brightness-min\"]),\"raster-brightness-max\":new wi(Lt.paint_raster[\"raster-brightness-max\"]),\"raster-saturation\":new wi(Lt.paint_raster[\"raster-saturation\"]),\"raster-contrast\":new wi(Lt.paint_raster[\"raster-contrast\"]),\"raster-resampling\":new wi(Lt.paint_raster[\"raster-resampling\"]),\"raster-fade-duration\":new wi(Lt.paint_raster[\"raster-fade-duration\"])})},zc=function(t){function e(e){t.call(this,e,Oc)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(Ei);var Dc=function(t){function e(e){t.call(this,e,{}),this.implementation=e}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.is3D=function(){return\"3d\"===this.implementation.renderingMode},e.prototype.hasOffscreenPass=function(){return void 0!==this.implementation.prerender},e.prototype.recalculate=function(){},e.prototype.updateTransitions=function(){},e.prototype.hasTransition=function(){},e.prototype.serialize=function(){},e.prototype.onAdd=function(t){this.implementation.onAdd&&this.implementation.onAdd(t,t.painter.context.gl)},e.prototype.onRemove=function(t){this.implementation.onRemove&&this.implementation.onRemove(t,t.painter.context.gl)},e}(Ei),Rc={circle:_o,heatmap:Po,hillshade:Oo,fill:xs,\"fill-extrusion\":Fs,line:Xs,symbol:Cc,background:Ic,raster:zc};var Fc=self.HTMLImageElement,Bc=self.HTMLCanvasElement,Nc=self.HTMLVideoElement,jc=self.ImageData,Uc=self.ImageBitmap,Vc=function(t,e,r,n){this.context=t,this.format=r,this.texture=t.gl.createTexture(),this.update(e,n)};Vc.prototype.update=function(t,e,r){var n=t.width,i=t.height,a=!(this.size&&this.size[0]===n&&this.size[1]===i||r),o=this.context,s=o.gl;if(this.useMipmap=Boolean(e&&e.useMipmap),s.bindTexture(s.TEXTURE_2D,this.texture),o.pixelStoreUnpackFlipY.set(!1),o.pixelStoreUnpack.set(1),o.pixelStoreUnpackPremultiplyAlpha.set(this.format===s.RGBA&&(!e||!1!==e.premultiply)),a)this.size=[n,i],t instanceof Fc||t instanceof Bc||t instanceof Nc||t instanceof jc||Uc&&t instanceof Uc?s.texImage2D(s.TEXTURE_2D,0,this.format,this.format,s.UNSIGNED_BYTE,t):s.texImage2D(s.TEXTURE_2D,0,this.format,n,i,0,this.format,s.UNSIGNED_BYTE,t.data);else{var l=r||{x:0,y:0},c=l.x,u=l.y;t instanceof Fc||t instanceof Bc||t instanceof Nc||t instanceof jc||Uc&&t instanceof Uc?s.texSubImage2D(s.TEXTURE_2D,0,c,u,s.RGBA,s.UNSIGNED_BYTE,t):s.texSubImage2D(s.TEXTURE_2D,0,c,u,n,i,s.RGBA,s.UNSIGNED_BYTE,t.data)}this.useMipmap&&this.isSizePowerOfTwo()&&s.generateMipmap(s.TEXTURE_2D)},Vc.prototype.bind=function(t,e,r){var n=this.context.gl;n.bindTexture(n.TEXTURE_2D,this.texture),r!==n.LINEAR_MIPMAP_NEAREST||this.isSizePowerOfTwo()||(r=n.LINEAR),t!==this.filter&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,t),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,r||t),this.filter=t),e!==this.wrap&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,e),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,e),this.wrap=e)},Vc.prototype.isSizePowerOfTwo=function(){return this.size[0]===this.size[1]&&Math.log(this.size[0])/Math.LN2%1==0},Vc.prototype.destroy=function(){this.context.gl.deleteTexture(this.texture),this.texture=null};var Hc=function(t){var e=this;this._callback=t,this._triggered=!1,\"undefined\"!=typeof MessageChannel&&(this._channel=new MessageChannel,this._channel.port2.onmessage=function(){e._triggered=!1,e._callback()})};Hc.prototype.trigger=function(){var t=this;this._triggered||(this._triggered=!0,this._channel?this._channel.port1.postMessage(!0):setTimeout((function(){t._triggered=!1,t._callback()}),0))},Hc.prototype.remove=function(){delete this._channel,this._callback=function(){}};var qc=function(t,e,r){this.target=t,this.parent=e,this.mapId=r,this.callbacks={},this.tasks={},this.taskQueue=[],this.cancelCallbacks={},g([\"receive\",\"process\"],this),this.invoker=new Hc(this.process),this.target.addEventListener(\"message\",this.receive,!1),this.globalScope=k()?t:self};function Gc(t,e,r){var n=2*Math.PI*6378137/256/Math.pow(2,r);return[t*n-2*Math.PI*6378137/2,e*n-2*Math.PI*6378137/2]}qc.prototype.send=function(t,e,r,n,i){var a=this;void 0===i&&(i=!1);var o=Math.round(1e18*Math.random()).toString(36).substring(0,10);r&&(this.callbacks[o]=r);var s=S(this.globalScope)?void 0:[];return this.target.postMessage({id:o,type:t,hasCallback:!!r,targetMapId:n,mustQueue:i,sourceMapId:this.mapId,data:Hn(e,s)},s),{cancel:function(){r&&delete a.callbacks[o],a.target.postMessage({id:o,type:\"<cancel>\",targetMapId:n,sourceMapId:a.mapId})}}},qc.prototype.receive=function(t){var e=t.data,r=e.id;if(r&&(!e.targetMapId||this.mapId===e.targetMapId))if(\"<cancel>\"===e.type){delete this.tasks[r];var n=this.cancelCallbacks[r];delete this.cancelCallbacks[r],n&&n()}else k()||e.mustQueue?(this.tasks[r]=e,this.taskQueue.push(r),this.invoker.trigger()):this.processTask(r,e)},qc.prototype.process=function(){if(this.taskQueue.length){var t=this.taskQueue.shift(),e=this.tasks[t];delete this.tasks[t],this.taskQueue.length&&this.invoker.trigger(),e&&this.processTask(t,e)}},qc.prototype.processTask=function(t,e){var r=this;if(\"<response>\"===e.type){var n=this.callbacks[t];delete this.callbacks[t],n&&(e.error?n(qn(e.error)):n(null,qn(e.data)))}else{var i=!1,a=S(this.globalScope)?void 0:[],o=e.hasCallback?function(e,n){i=!0,delete r.cancelCallbacks[t],r.target.postMessage({id:t,type:\"<response>\",sourceMapId:r.mapId,error:e?Hn(e):null,data:Hn(n,a)},a)}:function(t){i=!0},s=null,l=qn(e.data);if(this.parent[e.type])s=this.parent[e.type](e.sourceMapId,l,o);else if(this.parent.getWorkerSource){var c=e.type.split(\".\");s=this.parent.getWorkerSource(e.sourceMapId,c[0],l.source)[c[1]](l,o)}else o(new Error(\"Could not find function \"+e.type));!i&&s&&s.cancel&&(this.cancelCallbacks[t]=s.cancel)}},qc.prototype.remove=function(){this.invoker.remove(),this.target.removeEventListener(\"message\",this.receive,!1)};var Yc=function(t,e){t&&(e?this.setSouthWest(t).setNorthEast(e):4===t.length?this.setSouthWest([t[0],t[1]]).setNorthEast([t[2],t[3]]):this.setSouthWest(t[0]).setNorthEast(t[1]))};Yc.prototype.setNorthEast=function(t){return this._ne=t instanceof Wc?new Wc(t.lng,t.lat):Wc.convert(t),this},Yc.prototype.setSouthWest=function(t){return this._sw=t instanceof Wc?new Wc(t.lng,t.lat):Wc.convert(t),this},Yc.prototype.extend=function(t){var e,r,n=this._sw,i=this._ne;if(t instanceof Wc)e=t,r=t;else{if(!(t instanceof Yc)){if(Array.isArray(t)){if(4===t.length||t.every(Array.isArray)){var a=t;return this.extend(Yc.convert(a))}var o=t;return this.extend(Wc.convert(o))}return this}if(e=t._sw,r=t._ne,!e||!r)return this}return n||i?(n.lng=Math.min(e.lng,n.lng),n.lat=Math.min(e.lat,n.lat),i.lng=Math.max(r.lng,i.lng),i.lat=Math.max(r.lat,i.lat)):(this._sw=new Wc(e.lng,e.lat),this._ne=new Wc(r.lng,r.lat)),this},Yc.prototype.getCenter=function(){return new Wc((this._sw.lng+this._ne.lng)/2,(this._sw.lat+this._ne.lat)/2)},Yc.prototype.getSouthWest=function(){return this._sw},Yc.prototype.getNorthEast=function(){return this._ne},Yc.prototype.getNorthWest=function(){return new Wc(this.getWest(),this.getNorth())},Yc.prototype.getSouthEast=function(){return new Wc(this.getEast(),this.getSouth())},Yc.prototype.getWest=function(){return this._sw.lng},Yc.prototype.getSouth=function(){return this._sw.lat},Yc.prototype.getEast=function(){return this._ne.lng},Yc.prototype.getNorth=function(){return this._ne.lat},Yc.prototype.toArray=function(){return[this._sw.toArray(),this._ne.toArray()]},Yc.prototype.toString=function(){return\"LngLatBounds(\"+this._sw.toString()+\", \"+this._ne.toString()+\")\"},Yc.prototype.isEmpty=function(){return!(this._sw&&this._ne)},Yc.prototype.contains=function(t){var e=Wc.convert(t),r=e.lng,n=e.lat,i=this._sw.lat<=n&&n<=this._ne.lat,a=this._sw.lng<=r&&r<=this._ne.lng;return this._sw.lng>this._ne.lng&&(a=this._sw.lng>=r&&r>=this._ne.lng),i&&a},Yc.convert=function(t){return!t||t instanceof Yc?t:new Yc(t)};var Wc=function(t,e){if(isNaN(t)||isNaN(e))throw new Error(\"Invalid LngLat object: (\"+t+\", \"+e+\")\");if(this.lng=+t,this.lat=+e,this.lat>90||this.lat<-90)throw new Error(\"Invalid LngLat latitude value: must be between -90 and 90\")};Wc.prototype.wrap=function(){return new Wc(c(this.lng,-180,180),this.lat)},Wc.prototype.toArray=function(){return[this.lng,this.lat]},Wc.prototype.toString=function(){return\"LngLat(\"+this.lng+\", \"+this.lat+\")\"},Wc.prototype.distanceTo=function(t){var e=Math.PI/180,r=this.lat*e,n=t.lat*e,i=Math.sin(r)*Math.sin(n)+Math.cos(r)*Math.cos(n)*Math.cos((t.lng-this.lng)*e);return 6371008.8*Math.acos(Math.min(i,1))},Wc.prototype.toBounds=function(t){void 0===t&&(t=0);var e=360*t/40075017,r=e/Math.cos(Math.PI/180*this.lat);return new Yc(new Wc(this.lng-r,this.lat-e),new Wc(this.lng+r,this.lat+e))},Wc.convert=function(t){if(t instanceof Wc)return t;if(Array.isArray(t)&&(2===t.length||3===t.length))return new Wc(Number(t[0]),Number(t[1]));if(!Array.isArray(t)&&\"object\"==typeof t&&null!==t)return new Wc(Number(\"lng\"in t?t.lng:t.lon),Number(t.lat));throw new Error(\"`LngLatLike` argument must be specified as a LngLat instance, an object {lng: <lng>, lat: <lat>}, an object {lon: <lng>, lat: <lat>}, or an array of [<lng>, <lat>]\")};var Xc=2*Math.PI*6371008.8;function Zc(t){return Xc*Math.cos(t*Math.PI/180)}function Jc(t){return(180+t)/360}function Kc(t){return(180-180/Math.PI*Math.log(Math.tan(Math.PI/4+t*Math.PI/360)))/360}function Qc(t,e){return t/Zc(e)}function $c(t){var e=180-360*t;return 360/Math.PI*Math.atan(Math.exp(e*Math.PI/180))-90}var tu=function(t,e,r){void 0===r&&(r=0),this.x=+t,this.y=+e,this.z=+r};tu.fromLngLat=function(t,e){void 0===e&&(e=0);var r=Wc.convert(t);return new tu(Jc(r.lng),Kc(r.lat),Qc(e,r.lat))},tu.prototype.toLngLat=function(){return new Wc(360*this.x-180,$c(this.y))},tu.prototype.toAltitude=function(){return t=this.z,e=this.y,t*Zc($c(e));var t,e},tu.prototype.meterInMercatorCoordinateUnits=function(){return 1/Xc*(t=$c(this.y),1/Math.cos(t*Math.PI/180));var t};var eu=function(t,e,r){this.z=t,this.x=e,this.y=r,this.key=iu(0,t,t,e,r)};eu.prototype.equals=function(t){return this.z===t.z&&this.x===t.x&&this.y===t.y},eu.prototype.url=function(t,e){var r,n,i,a,o,s=(r=this.x,n=this.y,i=this.z,a=Gc(256*r,256*(n=Math.pow(2,i)-n-1),i),o=Gc(256*(r+1),256*(n+1),i),a[0]+\",\"+a[1]+\",\"+o[0]+\",\"+o[1]),l=function(t,e,r){for(var n,i=\"\",a=t;a>0;a--)i+=(e&(n=1<<a-1)?1:0)+(r&n?2:0);return i}(this.z,this.x,this.y);return t[(this.x+this.y)%t.length].replace(\"{prefix}\",(this.x%16).toString(16)+(this.y%16).toString(16)).replace(\"{z}\",String(this.z)).replace(\"{x}\",String(this.x)).replace(\"{y}\",String(\"tms\"===e?Math.pow(2,this.z)-this.y-1:this.y)).replace(\"{quadkey}\",l).replace(\"{bbox-epsg-3857}\",s)},eu.prototype.getTilePoint=function(t){var e=Math.pow(2,this.z);return new i(8192*(t.x*e-this.x),8192*(t.y*e-this.y))},eu.prototype.toString=function(){return this.z+\"/\"+this.x+\"/\"+this.y};var ru=function(t,e){this.wrap=t,this.canonical=e,this.key=iu(t,e.z,e.z,e.x,e.y)},nu=function(t,e,r,n,i){this.overscaledZ=t,this.wrap=e,this.canonical=new eu(r,+n,+i),this.key=iu(e,t,r,n,i)};function iu(t,e,r,n,i){(t*=2)<0&&(t=-1*t-1);var a=1<<r;return(a*a*t+a*i+n).toString(36)+r.toString(36)+e.toString(36)}nu.prototype.equals=function(t){return this.overscaledZ===t.overscaledZ&&this.wrap===t.wrap&&this.canonical.equals(t.canonical)},nu.prototype.scaledTo=function(t){var e=this.canonical.z-t;return t>this.canonical.z?new nu(t,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y):new nu(t,this.wrap,t,this.canonical.x>>e,this.canonical.y>>e)},nu.prototype.calculateScaledKey=function(t,e){var r=this.canonical.z-t;return t>this.canonical.z?iu(this.wrap*+e,t,this.canonical.z,this.canonical.x,this.canonical.y):iu(this.wrap*+e,t,t,this.canonical.x>>r,this.canonical.y>>r)},nu.prototype.isChildOf=function(t){if(t.wrap!==this.wrap)return!1;var e=this.canonical.z-t.canonical.z;return 0===t.overscaledZ||t.overscaledZ<this.overscaledZ&&t.canonical.x===this.canonical.x>>e&&t.canonical.y===this.canonical.y>>e},nu.prototype.children=function(t){if(this.overscaledZ>=t)return[new nu(this.overscaledZ+1,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y)];var e=this.canonical.z+1,r=2*this.canonical.x,n=2*this.canonical.y;return[new nu(e,this.wrap,e,r,n),new nu(e,this.wrap,e,r+1,n),new nu(e,this.wrap,e,r,n+1),new nu(e,this.wrap,e,r+1,n+1)]},nu.prototype.isLessThan=function(t){return this.wrap<t.wrap||!(this.wrap>t.wrap)&&(this.overscaledZ<t.overscaledZ||!(this.overscaledZ>t.overscaledZ)&&(this.canonical.x<t.canonical.x||!(this.canonical.x>t.canonical.x)&&this.canonical.y<t.canonical.y))},nu.prototype.wrapped=function(){return new nu(this.overscaledZ,0,this.canonical.z,this.canonical.x,this.canonical.y)},nu.prototype.unwrapTo=function(t){return new nu(this.overscaledZ,t,this.canonical.z,this.canonical.x,this.canonical.y)},nu.prototype.overscaleFactor=function(){return Math.pow(2,this.overscaledZ-this.canonical.z)},nu.prototype.toUnwrapped=function(){return new ru(this.wrap,this.canonical)},nu.prototype.toString=function(){return this.overscaledZ+\"/\"+this.canonical.x+\"/\"+this.canonical.y},nu.prototype.getTilePoint=function(t){return this.canonical.getTilePoint(new tu(t.x-this.wrap,t.y))},Nn(\"CanonicalTileID\",eu),Nn(\"OverscaledTileID\",nu,{omit:[\"posMatrix\"]});var au=function(t,e,r){if(this.uid=t,e.height!==e.width)throw new RangeError(\"DEM tiles must be square\");if(r&&\"mapbox\"!==r&&\"terrarium\"!==r)return _('\"'+r+'\" is not a valid encoding type. Valid types include \"mapbox\" and \"terrarium\".');this.stride=e.height;var n=this.dim=e.height-2;this.data=new Uint32Array(e.data.buffer),this.encoding=r||\"mapbox\";for(var i=0;i<n;i++)this.data[this._idx(-1,i)]=this.data[this._idx(0,i)],this.data[this._idx(n,i)]=this.data[this._idx(n-1,i)],this.data[this._idx(i,-1)]=this.data[this._idx(i,0)],this.data[this._idx(i,n)]=this.data[this._idx(i,n-1)];this.data[this._idx(-1,-1)]=this.data[this._idx(0,0)],this.data[this._idx(n,-1)]=this.data[this._idx(n-1,0)],this.data[this._idx(-1,n)]=this.data[this._idx(0,n-1)],this.data[this._idx(n,n)]=this.data[this._idx(n-1,n-1)]};au.prototype.get=function(t,e){var r=new Uint8Array(this.data.buffer),n=4*this._idx(t,e);return(\"terrarium\"===this.encoding?this._unpackTerrarium:this._unpackMapbox)(r[n],r[n+1],r[n+2])},au.prototype.getUnpackVector=function(){return\"terrarium\"===this.encoding?[256,1,1/256,32768]:[6553.6,25.6,.1,1e4]},au.prototype._idx=function(t,e){if(t<-1||t>=this.dim+1||e<-1||e>=this.dim+1)throw new RangeError(\"out of range source coordinates for DEM data\");return(e+1)*this.stride+(t+1)},au.prototype._unpackMapbox=function(t,e,r){return(256*t*256+256*e+r)/10-1e4},au.prototype._unpackTerrarium=function(t,e,r){return 256*t+e+r/256-32768},au.prototype.getPixels=function(){return new Eo({width:this.stride,height:this.stride},new Uint8Array(this.data.buffer))},au.prototype.backfillBorder=function(t,e,r){if(this.dim!==t.dim)throw new Error(\"dem dimension mismatch\");var n=e*this.dim,i=e*this.dim+this.dim,a=r*this.dim,o=r*this.dim+this.dim;switch(e){case-1:n=i-1;break;case 1:i=n+1}switch(r){case-1:a=o-1;break;case 1:o=a+1}for(var s=-e*this.dim,l=-r*this.dim,c=a;c<o;c++)for(var u=n;u<i;u++)this.data[this._idx(u,c)]=t.data[this._idx(u+s,c+l)]},Nn(\"DEMData\",au);var ou=function(t){this._stringToNumber={},this._numberToString=[];for(var e=0;e<t.length;e++){var r=t[e];this._stringToNumber[r]=e,this._numberToString[e]=r}};ou.prototype.encode=function(t){return this._stringToNumber[t]},ou.prototype.decode=function(t){return this._numberToString[t]};var su=function(t,e,r,n,i){this.type=\"Feature\",this._vectorTileFeature=t,t._z=e,t._x=r,t._y=n,this.properties=t.properties,this.id=i},lu={geometry:{configurable:!0}};lu.geometry.get=function(){return void 0===this._geometry&&(this._geometry=this._vectorTileFeature.toGeoJSON(this._vectorTileFeature._x,this._vectorTileFeature._y,this._vectorTileFeature._z).geometry),this._geometry},lu.geometry.set=function(t){this._geometry=t},su.prototype.toJSON=function(){var t={geometry:this.geometry};for(var e in this)\"_geometry\"!==e&&\"_vectorTileFeature\"!==e&&(t[e]=this[e]);return t},Object.defineProperties(su.prototype,lu);var cu=function(){this.state={},this.stateChanges={},this.deletedStates={}};cu.prototype.updateState=function(t,e,r){var n=String(e);if(this.stateChanges[t]=this.stateChanges[t]||{},this.stateChanges[t][n]=this.stateChanges[t][n]||{},u(this.stateChanges[t][n],r),null===this.deletedStates[t])for(var i in this.deletedStates[t]={},this.state[t])i!==n&&(this.deletedStates[t][i]=null);else if(this.deletedStates[t]&&null===this.deletedStates[t][n])for(var a in this.deletedStates[t][n]={},this.state[t][n])r[a]||(this.deletedStates[t][n][a]=null);else for(var o in r){this.deletedStates[t]&&this.deletedStates[t][n]&&null===this.deletedStates[t][n][o]&&delete this.deletedStates[t][n][o]}},cu.prototype.removeFeatureState=function(t,e,r){if(!(null===this.deletedStates[t])){var n=String(e);if(this.deletedStates[t]=this.deletedStates[t]||{},r&&void 0!==e)null!==this.deletedStates[t][n]&&(this.deletedStates[t][n]=this.deletedStates[t][n]||{},this.deletedStates[t][n][r]=null);else if(void 0!==e){if(this.stateChanges[t]&&this.stateChanges[t][n])for(r in this.deletedStates[t][n]={},this.stateChanges[t][n])this.deletedStates[t][n][r]=null;else this.deletedStates[t][n]=null}else this.deletedStates[t]=null}},cu.prototype.getState=function(t,e){var r=String(e),n=this.state[t]||{},i=this.stateChanges[t]||{},a=u({},n[r],i[r]);if(null===this.deletedStates[t])return{};if(this.deletedStates[t]){var o=this.deletedStates[t][e];if(null===o)return{};for(var s in o)delete a[s]}return a},cu.prototype.initializeTileState=function(t,e){t.setFeatureState(this.state,e)},cu.prototype.coalesceChanges=function(t,e){var r={};for(var n in this.stateChanges){this.state[n]=this.state[n]||{};var i={};for(var a in this.stateChanges[n])this.state[n][a]||(this.state[n][a]={}),u(this.state[n][a],this.stateChanges[n][a]),i[a]=this.state[n][a];r[n]=i}for(var o in this.deletedStates){this.state[o]=this.state[o]||{};var s={};if(null===this.deletedStates[o])for(var l in this.state[o])s[l]={},this.state[o][l]={};else for(var c in this.deletedStates[o]){if(null===this.deletedStates[o][c])this.state[o][c]={};else for(var f=0,h=Object.keys(this.deletedStates[o][c]);f<h.length;f+=1){var p=h[f];delete this.state[o][c][p]}s[c]=this.state[o][c]}r[o]=r[o]||{},u(r[o],s)}if(this.stateChanges={},this.deletedStates={},0!==Object.keys(r).length)for(var d in t){t[d].setFeatureState(r,e)}};var uu=function(t,e){this.tileID=t,this.x=t.canonical.x,this.y=t.canonical.y,this.z=t.canonical.z,this.grid=new zn(8192,16,0),this.grid3D=new zn(8192,16,0),this.featureIndexArray=new fa,this.promoteId=e};function fu(t,e,r,n,i){return v(t,(function(t,a){var o=e instanceof _i?e.get(a):null;return o&&o.evaluate?o.evaluate(r,n,i):o}))}function hu(t){for(var e=1/0,r=1/0,n=-1/0,i=-1/0,a=0,o=t;a<o.length;a+=1){var s=o[a];e=Math.min(e,s.x),r=Math.min(r,s.y),n=Math.max(n,s.x),i=Math.max(i,s.y)}return{minX:e,minY:r,maxX:n,maxY:i}}function pu(t,e){return e-t}uu.prototype.insert=function(t,e,r,n,i,a){var o=this.featureIndexArray.length;this.featureIndexArray.emplaceBack(r,n,i);for(var s=a?this.grid3D:this.grid,l=0;l<e.length;l++){for(var c=e[l],u=[1/0,1/0,-1/0,-1/0],f=0;f<c.length;f++){var h=c[f];u[0]=Math.min(u[0],h.x),u[1]=Math.min(u[1],h.y),u[2]=Math.max(u[2],h.x),u[3]=Math.max(u[3],h.y)}u[0]<8192&&u[1]<8192&&u[2]>=0&&u[3]>=0&&s.insert(o,u[0],u[1],u[2],u[3])}},uu.prototype.loadVTLayers=function(){return this.vtLayers||(this.vtLayers=new Ls.VectorTile(new al(this.rawTileData)).layers,this.sourceLayerCoder=new ou(this.vtLayers?Object.keys(this.vtLayers).sort():[\"_geojsonTileLayer\"])),this.vtLayers},uu.prototype.query=function(t,e,r,n){var a=this;this.loadVTLayers();for(var o=t.params||{},s=8192/t.tileSize/t.scale,l=sn(o.filter),c=t.queryGeometry,u=t.queryPadding*s,f=hu(c),h=this.grid.query(f.minX-u,f.minY-u,f.maxX+u,f.maxY+u),p=hu(t.cameraQueryGeometry),d=this.grid3D.query(p.minX-u,p.minY-u,p.maxX+u,p.maxY+u,(function(e,r,n,a){return function(t,e,r,n,a){for(var o=0,s=t;o<s.length;o+=1){var l=s[o];if(e<=l.x&&r<=l.y&&n>=l.x&&a>=l.y)return!0}var c=[new i(e,r),new i(e,a),new i(n,a),new i(n,r)];if(t.length>2)for(var u=0,f=c;u<f.length;u+=1){if(io(t,f[u]))return!0}for(var h=0;h<t.length-1;h++){if(ao(t[h],t[h+1],c))return!0}return!1}(t.cameraQueryGeometry,e-u,r-u,n+u,a+u)})),g=0,m=d;g<m.length;g+=1){var v=m[g];h.push(v)}h.sort(pu);for(var y,x={},b=function(i){var u=h[i];if(u!==y){y=u;var f=a.featureIndexArray.get(u),p=null;a.loadMatchingFeature(x,f.bucketIndex,f.sourceLayerIndex,f.featureIndex,l,o.layers,o.availableImages,e,r,n,(function(e,r,n){return p||(p=Ya(e)),r.queryIntersectsFeature(c,e,n,p,a.z,t.transform,s,t.pixelPosMatrix)}))}},_=0;_<h.length;_++)b(_);return x},uu.prototype.loadMatchingFeature=function(t,e,r,n,i,a,o,s,l,c,u){var f=this.bucketLayerIDs[e];if(!a||function(t,e){for(var r=0;r<t.length;r++)if(e.indexOf(t[r])>=0)return!0;return!1}(a,f)){var h=this.sourceLayerCoder.decode(r),p=this.vtLayers[h].feature(n);if(i.filter(new pi(this.tileID.overscaledZ),p))for(var d=this.getId(p,h),g=0;g<f.length;g++){var m=f[g];if(!(a&&a.indexOf(m)<0)){var v=s[m];if(v){var y={};void 0!==d&&c&&(y=c.getState(v.sourceLayer||\"_geojsonTileLayer\",d));var x=l[m];x.paint=fu(x.paint,v.paint,p,y,o),x.layout=fu(x.layout,v.layout,p,y,o);var b=!u||u(p,v,y);if(b){var _=new su(p,this.z,this.x,this.y,d);_.layer=x;var w=t[m];void 0===w&&(w=t[m]=[]),w.push({featureIndex:n,feature:_,intersectionZ:b})}}}}}},uu.prototype.lookupSymbolFeatures=function(t,e,r,n,i,a,o,s){var l={};this.loadVTLayers();for(var c=sn(i),u=0,f=t;u<f.length;u+=1){var h=f[u];this.loadMatchingFeature(l,r,n,h,c,a,o,s,e)}return l},uu.prototype.hasLayer=function(t){for(var e=0,r=this.bucketLayerIDs;e<r.length;e+=1)for(var n=0,i=r[e];n<i.length;n+=1){if(t===i[n])return!0}return!1},uu.prototype.getId=function(t,e){var r=t.id;if(this.promoteId){var n=\"string\"==typeof this.promoteId?this.promoteId:this.promoteId[e];\"boolean\"==typeof(r=t.properties[n])&&(r=Number(r))}return r},Nn(\"FeatureIndex\",uu,{omit:[\"rawTileData\",\"sourceLayerCoder\"]});var du=function(t,e){this.tileID=t,this.uid=h(),this.uses=0,this.tileSize=e,this.buckets={},this.expirationTime=null,this.queryPadding=0,this.hasSymbolBuckets=!1,this.hasRTLText=!1,this.dependencies={},this.expiredRequestCount=0,this.state=\"loading\"};du.prototype.registerFadeDuration=function(t){var e=t+this.timeAdded;e<R.now()||this.fadeEndTime&&e<this.fadeEndTime||(this.fadeEndTime=e)},du.prototype.wasRequested=function(){return\"errored\"===this.state||\"loaded\"===this.state||\"reloading\"===this.state},du.prototype.loadVectorData=function(t,e,r){if(this.hasData()&&this.unloadVectorData(),this.state=\"loaded\",t){for(var n in t.featureIndex&&(this.latestFeatureIndex=t.featureIndex,t.rawTileData?(this.latestRawTileData=t.rawTileData,this.latestFeatureIndex.rawTileData=t.rawTileData):this.latestRawTileData&&(this.latestFeatureIndex.rawTileData=this.latestRawTileData)),this.collisionBoxArray=t.collisionBoxArray,this.buckets=function(t,e){var r={};if(!e)return r;for(var n=function(){var t=a[i],n=t.layerIds.map((function(t){return e.getLayer(t)})).filter(Boolean);if(0!==n.length){t.layers=n,t.stateDependentLayerIds&&(t.stateDependentLayers=t.stateDependentLayerIds.map((function(t){return n.filter((function(e){return e.id===t}))[0]})));for(var o=0,s=n;o<s.length;o+=1){var l=s[o];r[l.id]=t}}},i=0,a=t;i<a.length;i+=1)n();return r}(t.buckets,e.style),this.hasSymbolBuckets=!1,this.buckets){var i=this.buckets[n];if(i instanceof Mc){if(this.hasSymbolBuckets=!0,!r)break;i.justReloaded=!0}}if(this.hasRTLText=!1,this.hasSymbolBuckets)for(var a in this.buckets){var o=this.buckets[a];if(o instanceof Mc&&o.hasRTLText){this.hasRTLText=!0,hi.isLoading()||hi.isLoaded()||\"deferred\"!==ui()||fi();break}}for(var s in this.queryPadding=0,this.buckets){var l=this.buckets[s];this.queryPadding=Math.max(this.queryPadding,e.style.getLayer(s).queryRadius(l))}t.imageAtlas&&(this.imageAtlas=t.imageAtlas),t.glyphAtlasImage&&(this.glyphAtlasImage=t.glyphAtlasImage)}else this.collisionBoxArray=new na},du.prototype.unloadVectorData=function(){for(var t in this.buckets)this.buckets[t].destroy();this.buckets={},this.imageAtlasTexture&&this.imageAtlasTexture.destroy(),this.imageAtlas&&(this.imageAtlas=null),this.glyphAtlasTexture&&this.glyphAtlasTexture.destroy(),this.latestFeatureIndex=null,this.state=\"unloaded\"},du.prototype.getBucket=function(t){return this.buckets[t.id]},du.prototype.upload=function(t){for(var e in this.buckets){var r=this.buckets[e];r.uploadPending()&&r.upload(t)}var n=t.gl;this.imageAtlas&&!this.imageAtlas.uploaded&&(this.imageAtlasTexture=new Vc(t,this.imageAtlas.image,n.RGBA),this.imageAtlas.uploaded=!0),this.glyphAtlasImage&&(this.glyphAtlasTexture=new Vc(t,this.glyphAtlasImage,n.ALPHA),this.glyphAtlasImage=null)},du.prototype.prepare=function(t){this.imageAtlas&&this.imageAtlas.patchUpdatedImages(t,this.imageAtlasTexture)},du.prototype.queryRenderedFeatures=function(t,e,r,n,i,a,o,s,l,c){return this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData?this.latestFeatureIndex.query({queryGeometry:n,cameraQueryGeometry:i,scale:a,tileSize:this.tileSize,pixelPosMatrix:c,transform:s,params:o,queryPadding:this.queryPadding*l},t,e,r):{}},du.prototype.querySourceFeatures=function(t,e){var r=this.latestFeatureIndex;if(r&&r.rawTileData){var n=r.loadVTLayers(),i=e?e.sourceLayer:\"\",a=n._geojsonTileLayer||n[i];if(a)for(var o=sn(e&&e.filter),s=this.tileID.canonical,l=s.z,c=s.x,u=s.y,f={z:l,x:c,y:u},h=0;h<a.length;h++){var p=a.feature(h);if(o.filter(new pi(this.tileID.overscaledZ),p)){var d=r.getId(p,i),g=new su(p,l,c,u,d);g.tile=f,t.push(g)}}}},du.prototype.hasData=function(){return\"loaded\"===this.state||\"reloading\"===this.state||\"expired\"===this.state},du.prototype.patternsLoaded=function(){return this.imageAtlas&&!!Object.keys(this.imageAtlas.patternPositions).length},du.prototype.setExpiryData=function(t){var e=this.expirationTime;if(t.cacheControl){var r=A(t.cacheControl);r[\"max-age\"]&&(this.expirationTime=Date.now()+1e3*r[\"max-age\"])}else t.expires&&(this.expirationTime=new Date(t.expires).getTime());if(this.expirationTime){var n=Date.now(),i=!1;if(this.expirationTime>n)i=!1;else if(e)if(this.expirationTime<e)i=!0;else{var a=this.expirationTime-e;a?this.expirationTime=n+Math.max(a,3e4):i=!0}else i=!0;i?(this.expiredRequestCount++,this.state=\"expired\"):this.expiredRequestCount=0}},du.prototype.getExpiryTimeout=function(){if(this.expirationTime)return this.expiredRequestCount?1e3*(1<<Math.min(this.expiredRequestCount-1,31)):Math.min(this.expirationTime-(new Date).getTime(),Math.pow(2,31)-1)},du.prototype.setFeatureState=function(t,e){if(this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData&&0!==Object.keys(t).length){var r=this.latestFeatureIndex.loadVTLayers();for(var n in this.buckets)if(e.style.hasLayer(n)){var i=this.buckets[n],a=i.layers[0].sourceLayer||\"_geojsonTileLayer\",o=r[a],s=t[a];if(o&&s&&0!==Object.keys(s).length){i.update(s,o,this.imageAtlas&&this.imageAtlas.patternPositions||{});var l=e&&e.style&&e.style.getLayer(n);l&&(this.queryPadding=Math.max(this.queryPadding,l.queryRadius(i)))}}}},du.prototype.holdingForFade=function(){return void 0!==this.symbolFadeHoldUntil},du.prototype.symbolFadeFinished=function(){return!this.symbolFadeHoldUntil||this.symbolFadeHoldUntil<R.now()},du.prototype.clearFadeHold=function(){this.symbolFadeHoldUntil=void 0},du.prototype.setHoldDuration=function(t){this.symbolFadeHoldUntil=R.now()+t},du.prototype.setDependencies=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1){r[i[n]]=!0}this.dependencies[t]=r},du.prototype.hasDependency=function(t,e){for(var r=0,n=t;r<n.length;r+=1){var i=n[r],a=this.dependencies[i];if(a)for(var o=0,s=e;o<s.length;o+=1){if(a[s[o]])return!0}}return!1};var 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r},t.makeRequest=yt,t.mapObject=v,t.mercatorXfromLng=Jc,t.mercatorYfromLat=Kc,t.mercatorZfromAltitude=Qc,t.mul=go,t.multiply=po,t.mvt=Ls,t.normalize=function(t,e){var r=e[0],n=e[1],i=e[2],a=r*r+n*n+i*i;return a>0&&(a=1/Math.sqrt(a)),t[0]=e[0]*a,t[1]=e[1]*a,t[2]=e[2]*a,t},t.number=qe,t.offscreenCanvasSupported=pt,t.ortho=function(t,e,r,n,i,a,o){var s=1/(e-r),l=1/(n-i),c=1/(a-o);return t[0]=-2*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*l,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*c,t[11]=0,t[12]=(e+r)*s,t[13]=(i+n)*l,t[14]=(o+a)*c,t[15]=1,t},t.parseGlyphPBF=function(t){return new al(t).readFields(Tl,[])},t.pbf=al,t.performSymbolLayout=function(t,e,r,n,i,a,o){t.createArrays();var s=512*t.overscaling;t.tilePixelRatio=8192/s,t.compareText={},t.iconsNeedLinear=!1;var l=t.layers[0].layout,c=t.layers[0]._unevaluatedLayout._values,u={};if(\"composite\"===t.textSizeData.kind){var f=t.textSizeData,h=f.minZoom,p=f.maxZoom;u.compositeTextSizes=[c[\"text-size\"].possiblyEvaluate(new 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m=(c+1)%2;(0===c?r-i<=d:n-i<=g)&&(o.push(f),o.push(p-1),o.push(m)),(0===c?r+i>=d:n+i>=g)&&(o.push(p+1),o.push(u),o.push(m))}}return s}(this.ids,this.coords,t,e,r,this.nodeSize)};var j={minZoom:0,maxZoom:16,radius:40,extent:512,nodeSize:64,log:!1,generateId:!1,reduce:null,map:function(t){return t}},U=function(t){this.options=X(Object.create(j),t),this.trees=new Array(this.options.maxZoom+1)};function V(t,e,r,n,i){return{x:t,y:e,zoom:1/0,id:r,parentId:-1,numPoints:n,properties:i}}function H(t,e){var r=t.geometry.coordinates,n=r[0],i=r[1];return{x:Y(n),y:W(i),zoom:1/0,index:e,parentId:-1}}function q(t){return{type:\"Feature\",id:t.id,properties:G(t),geometry:{type:\"Point\",coordinates:[(n=t.x,360*(n-.5)),(e=t.y,r=(180-360*e)*Math.PI/180,360*Math.atan(Math.exp(r))/Math.PI-90)]}};var e,r,n}function G(t){var e=t.numPoints,r=e>=1e4?Math.round(e/1e3)+\"k\":e>=1e3?Math.round(e/100)/10+\"k\":e;return 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r=0;r<e.length;r+=3)t.minX=Math.min(t.minX,e[r]),t.minY=Math.min(t.minY,e[r+1]),t.maxX=Math.max(t.maxX,e[r]),t.maxY=Math.max(t.maxY,e[r+1])}function tt(t,e,r,n){if(e.geometry){var i=e.geometry.coordinates,a=e.geometry.type,o=Math.pow(r.tolerance/((1<<r.maxZoom)*r.extent),2),s=[],l=e.id;if(r.promoteId?l=e.properties[r.promoteId]:r.generateId&&(l=n||0),\"Point\"===a)et(i,s);else if(\"MultiPoint\"===a)for(var c=0;c<i.length;c++)et(i[c],s);else if(\"LineString\"===a)rt(i,s,o,!1);else if(\"MultiLineString\"===a){if(r.lineMetrics){for(c=0;c<i.length;c++)s=[],rt(i[c],s,o,!1),t.push(Q(l,\"LineString\",s,e.properties));return}nt(i,s,o,!1)}else if(\"Polygon\"===a)nt(i,s,o,!0);else{if(\"MultiPolygon\"!==a){if(\"GeometryCollection\"===a){for(c=0;c<e.geometry.geometries.length;c++)tt(t,{id:l,geometry:e.geometry.geometries[c],properties:e.properties},r,n);return}throw new Error(\"Input data is not a valid GeoJSON object.\")}for(c=0;c<i.length;c++){var 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u=t[c],f=u.geometry,h=u.type,p=0===i?u.minX:u.minY,d=0===i?u.maxX:u.maxY;if(p>=r&&d<n)l.push(u);else if(!(d<r||p>=n)){var g=[];if(\"Point\"===h||\"MultiPoint\"===h)st(f,g,r,n,i);else if(\"LineString\"===h)lt(f,g,r,n,i,!1,s.lineMetrics);else if(\"MultiLineString\"===h)ut(f,g,r,n,i,!1);else if(\"Polygon\"===h)ut(f,g,r,n,i,!0);else if(\"MultiPolygon\"===h)for(var m=0;m<f.length;m++){var v=[];ut(f[m],v,r,n,i,!0),v.length&&g.push(v)}if(g.length){if(s.lineMetrics&&\"LineString\"===h){for(m=0;m<g.length;m++)l.push(Q(u.id,h,g[m],u.tags));continue}\"LineString\"!==h&&\"MultiLineString\"!==h||(1===g.length?(h=\"LineString\",g=g[0]):h=\"MultiLineString\"),\"Point\"!==h&&\"MultiPoint\"!==h||(h=3===g.length?\"Point\":\"MultiPoint\"),l.push(Q(u.id,h,g,u.tags))}}}return l.length?l:null}function st(t,e,r,n,i){for(var a=0;a<t.length;a+=3){var o=t[a+i];o>=r&&o<=n&&(e.push(t[a]),e.push(t[a+1]),e.push(t[a+2]))}}function lt(t,e,r,n,i,a,o){for(var 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r=[],n=0;n<t.length;n++){var i,a=t[n],o=a.type;if(\"Point\"===o||\"MultiPoint\"===o||\"LineString\"===o)i=gt(a.geometry,e);else if(\"MultiLineString\"===o||\"Polygon\"===o){i=[];for(var s=0;s<a.geometry.length;s++)i.push(gt(a.geometry[s],e))}else if(\"MultiPolygon\"===o)for(i=[],s=0;s<a.geometry.length;s++){for(var l=[],c=0;c<a.geometry[s].length;c++)l.push(gt(a.geometry[s][c],e));i.push(l)}r.push(Q(a.id,o,i,a.tags))}return r}function gt(t,e){var r=[];r.size=t.size,void 0!==t.start&&(r.start=t.start,r.end=t.end);for(var n=0;n<t.length;n+=3)r.push(t[n]+e,t[n+1],t[n+2]);return r}function mt(t,e){if(t.transformed)return t;var r,n,i,a=1<<t.z,o=t.x,s=t.y;for(r=0;r<t.features.length;r++){var l=t.features[r],c=l.geometry,u=l.type;if(l.geometry=[],1===u)for(n=0;n<c.length;n+=2)l.geometry.push(vt(c[n],c[n+1],e,a,o,s));else for(n=0;n<c.length;n++){var f=[];for(i=0;i<c[n].length;i+=2)f.push(vt(c[n][i],c[n][i+1],e,a,o,s));l.geometry.push(f)}}return t.transformed=!0,t}function 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u={},e.tags)u[f]=e.tags[f];u.mapbox_clip_start=i.start/i.size,u.mapbox_clip_end=i.end/i.size}var h={geometry:o,type:\"Polygon\"===a||\"MultiPolygon\"===a?3:\"LineString\"===a||\"MultiLineString\"===a?2:1,tags:u};null!==e.id&&(h.id=e.id),t.features.push(h)}}function bt(t,e,r,n,i,a){var o=n*n;if(n>0&&e.size<(i?o:n))r.numPoints+=e.length/3;else{for(var s=[],l=0;l<e.length;l+=3)(0===n||e[l+2]>o)&&(r.numSimplified++,s.push(e[l]),s.push(e[l+1])),r.numPoints++;i&&function(t,e){for(var r=0,n=0,i=t.length,a=i-2;n<i;a=n,n+=2)r+=(t[n]-t[a])*(t[n+1]+t[a+1]);if(r>0===e)for(n=0,i=t.length;n<i/2;n+=2){var o=t[n],s=t[n+1];t[n]=t[i-2-n],t[n+1]=t[i-1-n],t[i-2-n]=o,t[i-1-n]=s}}(s,a),t.push(s)}}function _t(t,e){var r=(e=this.options=function(t,e){for(var r in e)t[r]=e[r];return t}(Object.create(this.options),e)).debug;if(r&&console.time(\"preprocess data\"),e.maxZoom<0||e.maxZoom>24)throw new Error(\"maxZoom should be in the 0-24 range\");if(e.promoteId&&e.generateId)throw new Error(\"promoteId and generateId cannot be used together.\");var n=function(t,e){var r=[];if(\"FeatureCollection\"===t.type)for(var n=0;n<t.features.length;n++)tt(r,t.features[n],e,n);else\"Feature\"===t.type?tt(r,t,e):tt(r,{geometry:t},e);return r}(t,e);this.tiles={},this.tileCoords=[],r&&(console.timeEnd(\"preprocess data\"),console.log(\"index: maxZoom: %d, maxPoints: %d\",e.indexMaxZoom,e.indexMaxPoints),console.time(\"generate tiles\"),this.stats={},this.total=0),(n=function(t,e){var r=e.buffer/e.extent,n=t,i=ot(t,1,-1-r,r,0,-1,2,e),a=ot(t,1,1-r,2+r,0,-1,2,e);return(i||a)&&(n=ot(t,1,-r,1+r,0,-1,2,e)||[],i&&(n=dt(i,1).concat(n)),a&&(n=n.concat(dt(a,-1)))),n}(n,e)).length&&this.splitTile(n,0,0,0),r&&(n.length&&console.log(\"features: %d, points: %d\",this.tiles[0].numFeatures,this.tiles[0].numPoints),console.timeEnd(\"generate tiles\"),console.log(\"tiles generated:\",this.total,JSON.stringify(this.stats)))}function wt(t,e,r){return 32*((1<<t)*r+e)+t}function Tt(t,e){var r=t.tileID.canonical;if(!this._geoJSONIndex)return e(null,null);var n=this._geoJSONIndex.getTile(r.z,r.x,r.y);if(!n)return e(null,null);var i=new m(n.features),a=_(i);0===a.byteOffset&&a.byteLength===a.buffer.byteLength||(a=new Uint8Array(a)),e(null,{vectorTile:i,rawData:a.buffer})}U.prototype.load=function(t){var e=this.options,r=e.log,n=e.minZoom,i=e.maxZoom,a=e.nodeSize;r&&console.time(\"total time\");var o=\"prepare \"+t.length+\" points\";r&&console.time(o),this.points=t;for(var s=[],l=0;l<t.length;l++)t[l].geometry&&s.push(H(t[l],l));this.trees[i+1]=new N(s,Z,J,a,Float32Array),r&&console.timeEnd(o);for(var c=i;c>=n;c--){var u=+Date.now();s=this._cluster(s,c),this.trees[c]=new N(s,Z,J,a,Float32Array),r&&console.log(\"z%d: %d clusters in %dms\",c,s.length,+Date.now()-u)}return r&&console.timeEnd(\"total time\"),this},U.prototype.getClusters=function(t,e){var r=((t[0]+180)%360+360)%360-180,n=Math.max(-90,Math.min(90,t[1])),i=180===t[2]?180:((t[2]+180)%360+360)%360-180,a=Math.max(-90,Math.min(90,t[3]));if(t[2]-t[0]>=360)r=-180,i=180;else if(r>i){var o=this.getClusters([r,n,180,a],e),s=this.getClusters([-180,n,i,a],e);return o.concat(s)}for(var l=this.trees[this._limitZoom(e)],c=[],u=0,f=l.range(Y(r),W(a),Y(i),W(n));u<f.length;u+=1){var h=f[u],p=l.points[h];c.push(p.numPoints?q(p):this.points[p.index])}return c},U.prototype.getChildren=function(t){var e=this._getOriginId(t),r=this._getOriginZoom(t),n=\"No cluster with the specified id.\",i=this.trees[r];if(!i)throw new Error(n);var a=i.points[e];if(!a)throw new Error(n);for(var o=this.options.radius/(this.options.extent*Math.pow(2,r-1)),s=[],l=0,c=i.within(a.x,a.y,o);l<c.length;l+=1){var u=c[l],f=i.points[u];f.parentId===t&&s.push(f.numPoints?q(f):this.points[f.index])}if(0===s.length)throw new Error(n);return s},U.prototype.getLeaves=function(t,e,r){e=e||10,r=r||0;var n=[];return this._appendLeaves(n,t,e,r,0),n},U.prototype.getTile=function(t,e,r){var n=this.trees[this._limitZoom(t)],i=Math.pow(2,t),a=this.options,o=a.extent,s=a.radius/o,l=(r-s)/i,c=(r+1+s)/i,u={features:[]};return this._addTileFeatures(n.range((e-s)/i,l,(e+1+s)/i,c),n.points,e,r,i,u),0===e&&this._addTileFeatures(n.range(1-s/i,l,1,c),n.points,i,r,i,u),e===i-1&&this._addTileFeatures(n.range(0,l,s/i,c),n.points,-1,r,i,u),u.features.length?u:null},U.prototype.getClusterExpansionZoom=function(t){for(var e=this._getOriginZoom(t)-1;e<=this.options.maxZoom;){var r=this.getChildren(t);if(e++,1!==r.length)break;t=r[0].properties.cluster_id}return e},U.prototype._appendLeaves=function(t,e,r,n,i){for(var a=0,o=this.getChildren(e);a<o.length;a+=1){var s=o[a],l=s.properties;if(l&&l.cluster?i+l.point_count<=n?i+=l.point_count:i=this._appendLeaves(t,l.cluster_id,r,n,i):i<n?i++:t.push(s),t.length===r)break}return i},U.prototype._addTileFeatures=function(t,e,r,n,i,a){for(var o=0,s=t;o<s.length;o+=1){var l=e[s[o]],c=l.numPoints,u={type:1,geometry:[[Math.round(this.options.extent*(l.x*i-r)),Math.round(this.options.extent*(l.y*i-n))]],tags:c?G(l):this.points[l.index].properties},f=void 0;c?f=l.id:this.options.generateId?f=l.index:this.points[l.index].id&&(f=this.points[l.index].id),void 0!==f&&(u.id=f),a.features.push(u)}},U.prototype._limitZoom=function(t){return Math.max(this.options.minZoom,Math.min(t,this.options.maxZoom+1))},U.prototype._cluster=function(t,e){for(var r=[],n=this.options,i=n.radius,a=n.extent,o=n.reduce,s=i/(a*Math.pow(2,e)),l=0;l<t.length;l++){var c=t[l];if(!(c.zoom<=e)){c.zoom=e;for(var u=this.trees[e+1],f=u.within(c.x,c.y,s),h=c.numPoints||1,p=c.x*h,d=c.y*h,g=o&&h>1?this._map(c,!0):null,m=(l<<5)+(e+1)+this.points.length,v=0,y=f;v<y.length;v+=1){var x=y[v],b=u.points[x];if(!(b.zoom<=e)){b.zoom=e;var _=b.numPoints||1;p+=b.x*_,d+=b.y*_,h+=_,b.parentId=m,o&&(g||(g=this._map(c,!0)),o(g,this._map(b)))}}1===h?r.push(c):(c.parentId=m,r.push(V(p/h,d/h,m,h,g)))}}return r},U.prototype._getOriginId=function(t){return t-this.points.length>>5},U.prototype._getOriginZoom=function(t){return(t-this.points.length)%32},U.prototype._map=function(t,e){if(t.numPoints)return e?X({},t.properties):t.properties;var r=this.points[t.index].properties,n=this.options.map(r);return e&&n===r?X({},n):n},_t.prototype.options={maxZoom:14,indexMaxZoom:5,indexMaxPoints:1e5,tolerance:3,extent:4096,buffer:64,lineMetrics:!1,promoteId:null,generateId:!1,debug:0},_t.prototype.splitTile=function(t,e,r,n,i,a,o){for(var s=[t,e,r,n],l=this.options,c=l.debug;s.length;){n=s.pop(),r=s.pop(),e=s.pop(),t=s.pop();var u=1<<e,f=wt(e,r,n),h=this.tiles[f];if(!h&&(c>1&&console.time(\"creation\"),h=this.tiles[f]=yt(t,e,r,n,l),this.tileCoords.push({z:e,x:r,y:n}),c)){c>1&&(console.log(\"tile z%d-%d-%d (features: %d, points: %d, simplified: %d)\",e,r,n,h.numFeatures,h.numPoints,h.numSimplified),console.timeEnd(\"creation\"));var p=\"z\"+e;this.stats[p]=(this.stats[p]||0)+1,this.total++}if(h.source=t,i){if(e===l.maxZoom||e===i)continue;var d=1<<i-e;if(r!==Math.floor(a/d)||n!==Math.floor(o/d))continue}else if(e===l.indexMaxZoom||h.numPoints<=l.indexMaxPoints)continue;if(h.source=null,0!==t.length){c>1&&console.time(\"clipping\");var g,m,v,y,x,b,_=.5*l.buffer/l.extent,w=.5-_,T=.5+_,k=1+_;g=m=v=y=null,x=ot(t,u,r-_,r+T,0,h.minX,h.maxX,l),b=ot(t,u,r+w,r+k,0,h.minX,h.maxX,l),t=null,x&&(g=ot(x,u,n-_,n+T,1,h.minY,h.maxY,l),m=ot(x,u,n+w,n+k,1,h.minY,h.maxY,l),x=null),b&&(v=ot(b,u,n-_,n+T,1,h.minY,h.maxY,l),y=ot(b,u,n+w,n+k,1,h.minY,h.maxY,l),b=null),c>1&&console.timeEnd(\"clipping\"),s.push(g||[],e+1,2*r,2*n),s.push(m||[],e+1,2*r,2*n+1),s.push(v||[],e+1,2*r+1,2*n),s.push(y||[],e+1,2*r+1,2*n+1)}}},_t.prototype.getTile=function(t,e,r){var n=this.options,i=n.extent,a=n.debug;if(t<0||t>24)return null;var o=1<<t,s=wt(t,e=(e%o+o)%o,r);if(this.tiles[s])return mt(this.tiles[s],i);a>1&&console.log(\"drilling down to z%d-%d-%d\",t,e,r);for(var l,c=t,u=e,f=r;!l&&c>0;)c--,u=Math.floor(u/2),f=Math.floor(f/2),l=this.tiles[wt(c,u,f)];return l&&l.source?(a>1&&console.log(\"found parent tile z%d-%d-%d\",c,u,f),a>1&&console.time(\"drilling down\"),this.splitTile(l.source,c,u,f,t,e,r),a>1&&console.timeEnd(\"drilling down\"),this.tiles[s]?mt(this.tiles[s],i):null):null};var kt=function(e){function r(t,r,n,i){e.call(this,t,r,n,Tt),i&&(this.loadGeoJSON=i)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadData=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),this._pendingCallback=e,this._pendingLoadDataParams=t,this._state&&\"Idle\"!==this._state?this._state=\"NeedsLoadData\":(this._state=\"Coalescing\",this._loadData())},r.prototype._loadData=function(){var e=this;if(this._pendingCallback&&this._pendingLoadDataParams){var r=this._pendingCallback,n=this._pendingLoadDataParams;delete this._pendingCallback,delete this._pendingLoadDataParams;var i=!!(n&&n.request&&n.request.collectResourceTiming)&&new t.RequestPerformance(n.request);this.loadGeoJSON(n,(function(a,o){if(a||!o)return r(a);if(\"object\"!=typeof o)return r(new Error(\"Input data given to '\"+n.source+\"' is not a valid GeoJSON object.\"));f(o,!0);try{e._geoJSONIndex=n.cluster?new U(function(e){var r=e.superclusterOptions,n=e.clusterProperties;if(!n||!r)return r;for(var i={},a={},o={accumulated:null,zoom:0},s={properties:null},l=Object.keys(n),c=0,u=l;c<u.length;c+=1){var f=u[c],h=n[f],p=h[0],d=h[1],g=t.createExpression(d),m=t.createExpression(\"string\"==typeof p?[p,[\"accumulated\"],[\"get\",f]]:p);i[f]=g.value,a[f]=m.value}return r.map=function(t){s.properties=t;for(var e={},r=0,n=l;r<n.length;r+=1){var a=n[r];e[a]=i[a].evaluate(o,s)}return e},r.reduce=function(t,e){s.properties=e;for(var r=0,n=l;r<n.length;r+=1){var i=n[r];o.accumulated=t[i],t[i]=a[i].evaluate(o,s)}},r}(n)).load(o.features):function(t,e){return new _t(t,e)}(o,n.geojsonVtOptions)}catch(a){return r(a)}e.loaded={};var s={};if(i){var l=i.finish();l&&(s.resourceTiming={},s.resourceTiming[n.source]=JSON.parse(JSON.stringify(l)))}r(null,s)}))}},r.prototype.coalesce=function(){\"Coalescing\"===this._state?this._state=\"Idle\":\"NeedsLoadData\"===this._state&&(this._state=\"Coalescing\",this._loadData())},r.prototype.reloadTile=function(t,r){var n=this.loaded,i=t.uid;return n&&n[i]?e.prototype.reloadTile.call(this,t,r):this.loadTile(t,r)},r.prototype.loadGeoJSON=function(e,r){if(e.request)t.getJSON(e.request,r);else{if(\"string\"!=typeof e.data)return r(new Error(\"Input data given to '\"+e.source+\"' is not a valid GeoJSON object.\"));try{return r(null,JSON.parse(e.data))}catch(t){return r(new Error(\"Input data given to '\"+e.source+\"' is not a valid GeoJSON 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t.Point(r.clientX-n.left-e.clientLeft,r.clientY-n.top-e.clientTop)},r.touchPos=function(e,r){for(var n=e.getBoundingClientRect(),i=[],a=0;a<r.length;a++)i.push(new t.Point(r[a].clientX-n.left-e.clientLeft,r[a].clientY-n.top-e.clientTop));return i},r.mouseButton=function(e){return void 0!==t.window.InstallTrigger&&2===e.button&&e.ctrlKey&&t.window.navigator.platform.toUpperCase().indexOf(\"MAC\")>=0?0:e.button},r.remove=function(t){t.parentNode&&t.parentNode.removeChild(t)};var h=function(e){function r(){e.call(this),this.images={},this.updatedImages={},this.callbackDispatchedThisFrame={},this.loaded=!1,this.requestors=[],this.patterns={},this.atlasImage=new t.RGBAImage({width:1,height:1}),this.dirty=!0}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.isLoaded=function(){return this.loaded},r.prototype.setLoaded=function(t){if(this.loaded!==t&&(this.loaded=t,t)){for(var e=0,r=this.requestors;e<r.length;e+=1){var n=r[e],i=n.ids,a=n.callback;this._notify(i,a)}this.requestors=[]}},r.prototype.getImage=function(t){return this.images[t]},r.prototype.addImage=function(t,e){this._validate(t,e)&&(this.images[t]=e)},r.prototype._validate=function(e,r){var n=!0;return this._validateStretch(r.stretchX,r.data&&r.data.width)||(this.fire(new t.ErrorEvent(new Error('Image \"'+e+'\" has invalid \"stretchX\" value'))),n=!1),this._validateStretch(r.stretchY,r.data&&r.data.height)||(this.fire(new t.ErrorEvent(new Error('Image \"'+e+'\" has invalid \"stretchY\" value'))),n=!1),this._validateContent(r.content,r)||(this.fire(new t.ErrorEvent(new Error('Image \"'+e+'\" has invalid \"content\" value'))),n=!1),n},r.prototype._validateStretch=function(t,e){if(!t)return!0;for(var r=0,n=0,i=t;n<i.length;n+=1){var a=i[n];if(a[0]<r||a[1]<a[0]||e<a[1])return!1;r=a[1]}return!0},r.prototype._validateContent=function(t,e){return!t||4===t.length&&(!(t[0]<0||e.data.width<t[0])&&(!(t[1]<0||e.data.height<t[1])&&(!(t[2]<0||e.data.width<t[2])&&(!(t[3]<0||e.data.height<t[3])&&(!(t[2]<t[0])&&!(t[3]<t[1]))))))},r.prototype.updateImage=function(t,e){var r=this.images[t];e.version=r.version+1,this.images[t]=e,this.updatedImages[t]=!0},r.prototype.removeImage=function(t){var e=this.images[t];delete this.images[t],delete this.patterns[t],e.userImage&&e.userImage.onRemove&&e.userImage.onRemove()},r.prototype.listImages=function(){return Object.keys(this.images)},r.prototype.getImages=function(t,e){var r=!0;if(!this.isLoaded())for(var n=0,i=t;n<i.length;n+=1){var a=i[n];this.images[a]||(r=!1)}this.isLoaded()||r?this._notify(t,e):this.requestors.push({ids:t,callback:e})},r.prototype._notify=function(e,r){for(var n={},i=0,a=e;i<a.length;i+=1){var o=a[i];this.images[o]||this.fire(new t.Event(\"styleimagemissing\",{id:o}));var s=this.images[o];s?n[o]={data:s.data.clone(),pixelRatio:s.pixelRatio,sdf:s.sdf,version:s.version,stretchX:s.stretchX,stretchY:s.stretchY,content:s.content,hasRenderCallback:Boolean(s.userImage&&s.userImage.render)}:t.warnOnce('Image \"'+o+'\" could not be loaded. Please make sure you have added the image with map.addImage() or a \"sprite\" property in your style. You can provide missing images by listening for the \"styleimagemissing\" map event.')}r(null,n)},r.prototype.getPixelSize=function(){var t=this.atlasImage;return{width:t.width,height:t.height}},r.prototype.getPattern=function(e){var r=this.patterns[e],n=this.getImage(e);if(!n)return null;if(r&&r.position.version===n.version)return r.position;if(r)r.position.version=n.version;else{var i={w:n.data.width+2,h:n.data.height+2,x:0,y:0},a=new t.ImagePosition(i,n);this.patterns[e]={bin:i,position:a}}return this._updatePatternAtlas(),this.patterns[e].position},r.prototype.bind=function(e){var r=e.gl;this.atlasTexture?this.dirty&&(this.atlasTexture.update(this.atlasImage),this.dirty=!1):this.atlasTexture=new t.Texture(e,this.atlasImage,r.RGBA),this.atlasTexture.bind(r.LINEAR,r.CLAMP_TO_EDGE)},r.prototype._updatePatternAtlas=function(){var e=[];for(var r in this.patterns)e.push(this.patterns[r].bin);var n=t.potpack(e),i=n.w,a=n.h,o=this.atlasImage;for(var s in o.resize({width:i||1,height:a||1}),this.patterns){var l=this.patterns[s].bin,c=l.x+1,u=l.y+1,f=this.images[s].data,h=f.width,p=f.height;t.RGBAImage.copy(f,o,{x:0,y:0},{x:c,y:u},{width:h,height:p}),t.RGBAImage.copy(f,o,{x:0,y:p-1},{x:c,y:u-1},{width:h,height:1}),t.RGBAImage.copy(f,o,{x:0,y:0},{x:c,y:u+p},{width:h,height:1}),t.RGBAImage.copy(f,o,{x:h-1,y:0},{x:c-1,y:u},{width:1,height:p}),t.RGBAImage.copy(f,o,{x:0,y:0},{x:c+h,y:u},{width:1,height:p})}this.dirty=!0},r.prototype.beginFrame=function(){this.callbackDispatchedThisFrame={}},r.prototype.dispatchRenderCallbacks=function(t){for(var e=0,r=t;e<r.length;e+=1){var n=r[e];if(!this.callbackDispatchedThisFrame[n]){this.callbackDispatchedThisFrame[n]=!0;var i=this.images[n];f(i)&&this.updateImage(n,i)}}},r}(t.Evented);var p=m,d=m,g=1e20;function m(t,e,r,n,i,a){this.fontSize=t||24,this.buffer=void 0===e?3:e,this.cutoff=n||.25,this.fontFamily=i||\"sans-serif\",this.fontWeight=a||\"normal\",this.radius=r||8;var o=this.size=this.fontSize+2*this.buffer;this.canvas=document.createElement(\"canvas\"),this.canvas.width=this.canvas.height=o,this.ctx=this.canvas.getContext(\"2d\"),this.ctx.font=this.fontWeight+\" \"+this.fontSize+\"px \"+this.fontFamily,this.ctx.textBaseline=\"middle\",this.ctx.fillStyle=\"black\",this.gridOuter=new Float64Array(o*o),this.gridInner=new Float64Array(o*o),this.f=new Float64Array(o),this.d=new Float64Array(o),this.z=new Float64Array(o+1),this.v=new Int16Array(o),this.middle=Math.round(o/2*(navigator.userAgent.indexOf(\"Gecko/\")>=0?1.2:1))}function v(t,e,r,n,i,a,o){for(var s=0;s<e;s++){for(var l=0;l<r;l++)n[l]=t[l*e+s];for(y(n,i,a,o,r),l=0;l<r;l++)t[l*e+s]=i[l]}for(l=0;l<r;l++){for(s=0;s<e;s++)n[s]=t[l*e+s];for(y(n,i,a,o,e),s=0;s<e;s++)t[l*e+s]=Math.sqrt(i[s])}}function y(t,e,r,n,i){r[0]=0,n[0]=-g,n[1]=+g;for(var a=1,o=0;a<i;a++){for(var s=(t[a]+a*a-(t[r[o]]+r[o]*r[o]))/(2*a-2*r[o]);s<=n[o];)o--,s=(t[a]+a*a-(t[r[o]]+r[o]*r[o]))/(2*a-2*r[o]);r[++o]=a,n[o]=s,n[o+1]=+g}for(a=0,o=0;a<i;a++){for(;n[o+1]<a;)o++;e[a]=(a-r[o])*(a-r[o])+t[r[o]]}}m.prototype.draw=function(t){this.ctx.clearRect(0,0,this.size,this.size),this.ctx.fillText(t,this.buffer,this.middle);for(var e=this.ctx.getImageData(0,0,this.size,this.size),r=new Uint8ClampedArray(this.size*this.size),n=0;n<this.size*this.size;n++){var i=e.data[4*n+3]/255;this.gridOuter[n]=1===i?0:0===i?g:Math.pow(Math.max(0,.5-i),2),this.gridInner[n]=1===i?g:0===i?0:Math.pow(Math.max(0,i-.5),2)}for(v(this.gridOuter,this.size,this.size,this.f,this.d,this.v,this.z),v(this.gridInner,this.size,this.size,this.f,this.d,this.v,this.z),n=0;n<this.size*this.size;n++){var a=this.gridOuter[n]-this.gridInner[n];r[n]=Math.max(0,Math.min(255,Math.round(255-255*(a/this.radius+this.cutoff))))}return r},p.default=d;var x=function(t,e){this.requestManager=t,this.localIdeographFontFamily=e,this.entries={}};x.prototype.setURL=function(t){this.url=t},x.prototype.getGlyphs=function(e,r){var n=this,i=[];for(var a in e)for(var o=0,s=e[a];o<s.length;o+=1){var l=s[o];i.push({stack:a,id:l})}t.asyncAll(i,(function(t,e){var r=t.stack,i=t.id,a=n.entries[r];a||(a=n.entries[r]={glyphs:{},requests:{},ranges:{}});var o=a.glyphs[i];if(void 0===o){if(o=n._tinySDF(a,r,i))return a.glyphs[i]=o,void e(null,{stack:r,id:i,glyph:o});var s=Math.floor(i/256);if(256*s>65535)e(new Error(\"glyphs > 65535 not supported\"));else if(a.ranges[s])e(null,{stack:r,id:i,glyph:o});else{var l=a.requests[s];l||(l=a.requests[s]=[],x.loadGlyphRange(r,s,n.url,n.requestManager,(function(t,e){if(e){for(var r in e)n._doesCharSupportLocalGlyph(+r)||(a.glyphs[+r]=e[+r]);a.ranges[s]=!0}for(var i=0,o=l;i<o.length;i+=1){(0,o[i])(t,e)}delete a.requests[s]}))),l.push((function(t,n){t?e(t):n&&e(null,{stack:r,id:i,glyph:n[i]||null})}))}}else e(null,{stack:r,id:i,glyph:o})}),(function(t,e){if(t)r(t);else if(e){for(var n={},i=0,a=e;i<a.length;i+=1){var o=a[i],s=o.stack,l=o.id,c=o.glyph;(n[s]||(n[s]={}))[l]=c&&{id:c.id,bitmap:c.bitmap.clone(),metrics:c.metrics}}r(null,n)}}))},x.prototype._doesCharSupportLocalGlyph=function(e){return!!this.localIdeographFontFamily&&(t.isChar[\"CJK Unified Ideographs\"](e)||t.isChar[\"Hangul Syllables\"](e)||t.isChar.Hiragana(e)||t.isChar.Katakana(e))},x.prototype._tinySDF=function(e,r,n){var i=this.localIdeographFontFamily;if(i&&this._doesCharSupportLocalGlyph(n)){var a=e.tinySDF;if(!a){var o=\"400\";/bold/i.test(r)?o=\"900\":/medium/i.test(r)?o=\"500\":/light/i.test(r)&&(o=\"200\"),a=e.tinySDF=new x.TinySDF(24,3,8,.25,i,o)}return{id:n,bitmap:new t.AlphaImage({width:30,height:30},a.draw(String.fromCharCode(n))),metrics:{width:24,height:24,left:0,top:-8,advance:24}}}},x.loadGlyphRange=function(e,r,n,i,a){var o=256*r,s=o+255,l=i.transformRequest(i.normalizeGlyphsURL(n).replace(\"{fontstack}\",e).replace(\"{range}\",o+\"-\"+s),t.ResourceType.Glyphs);t.getArrayBuffer(l,(function(e,r){if(e)a(e);else if(r){for(var n={},i=0,o=t.parseGlyphPBF(r);i<o.length;i+=1){var s=o[i];n[s.id]=s}a(null,n)}}))},x.TinySDF=p;var b=function(){this.specification=t.styleSpec.light.position};b.prototype.possiblyEvaluate=function(e,r){return t.sphericalToCartesian(e.expression.evaluate(r))},b.prototype.interpolate=function(e,r,n){return{x:t.number(e.x,r.x,n),y:t.number(e.y,r.y,n),z:t.number(e.z,r.z,n)}};var _=new t.Properties({anchor:new t.DataConstantProperty(t.styleSpec.light.anchor),position:new b,color:new t.DataConstantProperty(t.styleSpec.light.color),intensity:new t.DataConstantProperty(t.styleSpec.light.intensity)}),w=function(e){function r(r){e.call(this),this._transitionable=new t.Transitionable(_),this.setLight(r),this._transitioning=this._transitionable.untransitioned()}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getLight=function(){return this._transitionable.serialize()},r.prototype.setLight=function(e,r){if(void 0===r&&(r={}),!this._validate(t.validateLight,e,r))for(var n in e){var i=e[n];t.endsWith(n,\"-transition\")?this._transitionable.setTransition(n.slice(0,-\"-transition\".length),i):this._transitionable.setValue(n,i)}},r.prototype.updateTransitions=function(t){this._transitioning=this._transitionable.transitioned(t,this._transitioning)},r.prototype.hasTransition=function(){return this._transitioning.hasTransition()},r.prototype.recalculate=function(t){this.properties=this._transitioning.possiblyEvaluate(t)},r.prototype._validate=function(e,r,n){return(!n||!1!==n.validate)&&t.emitValidationErrors(this,e.call(t.validateStyle,t.extend({value:r,style:{glyphs:!0,sprite:!0},styleSpec:t.styleSpec})))},r}(t.Evented),T=function(t,e){this.width=t,this.height=e,this.nextRow=0,this.data=new Uint8Array(this.width*this.height),this.dashEntry={}};T.prototype.getDash=function(t,e){var r=t.join(\",\")+String(e);return this.dashEntry[r]||(this.dashEntry[r]=this.addDash(t,e)),this.dashEntry[r]},T.prototype.getDashRanges=function(t,e,r){var n=[],i=t.length%2==1?-t[t.length-1]*r:0,a=t[0]*r,o=!0;n.push({left:i,right:a,isDash:o,zeroLength:0===t[0]});for(var s=t[0],l=1;l<t.length;l++){o=!o;var c=t[l];i=s*r,a=(s+=c)*r,n.push({left:i,right:a,isDash:o,zeroLength:0===c})}return n},T.prototype.addRoundDash=function(t,e,r){for(var n=e/2,i=-r;i<=r;i++)for(var a=this.nextRow+r+i,o=this.width*a,s=0,l=t[s],c=0;c<this.width;c++){c/l.right>1&&(l=t[++s]);var u=Math.abs(c-l.left),f=Math.abs(c-l.right),h=Math.min(u,f),p=void 0,d=i/r*(n+1);if(l.isDash){var g=n-Math.abs(d);p=Math.sqrt(h*h+g*g)}else p=n-Math.sqrt(h*h+d*d);this.data[o+c]=Math.max(0,Math.min(255,p+128))}},T.prototype.addRegularDash=function(t){for(var e=t.length-1;e>=0;--e){var r=t[e],n=t[e+1];r.zeroLength?t.splice(e,1):n&&n.isDash===r.isDash&&(n.left=r.left,t.splice(e,1))}var i=t[0],a=t[t.length-1];i.isDash===a.isDash&&(i.left=a.left-this.width,a.right=i.right+this.width);for(var o=this.width*this.nextRow,s=0,l=t[s],c=0;c<this.width;c++){c/l.right>1&&(l=t[++s]);var u=Math.abs(c-l.left),f=Math.abs(c-l.right),h=Math.min(u,f),p=l.isDash?h:-h;this.data[o+c]=Math.max(0,Math.min(255,p+128))}},T.prototype.addDash=function(e,r){var n=r?7:0,i=2*n+1;if(this.nextRow+i>this.height)return t.warnOnce(\"LineAtlas out of space\"),null;for(var a=0,o=0;o<e.length;o++)a+=e[o];if(0!==a){var s=this.width/a,l=this.getDashRanges(e,this.width,s);r?this.addRoundDash(l,s,n):this.addRegularDash(l)}var c={y:(this.nextRow+n+.5)/this.height,height:2*n/this.height,width:a};return this.nextRow+=i,this.dirty=!0,c},T.prototype.bind=function(t){var e=t.gl;this.texture?(e.bindTexture(e.TEXTURE_2D,this.texture),this.dirty&&(this.dirty=!1,e.texSubImage2D(e.TEXTURE_2D,0,0,0,this.width,this.height,e.ALPHA,e.UNSIGNED_BYTE,this.data))):(this.texture=e.createTexture(),e.bindTexture(e.TEXTURE_2D,this.texture),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_S,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_T,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,e.LINEAR),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,e.LINEAR),e.texImage2D(e.TEXTURE_2D,0,e.ALPHA,this.width,this.height,0,e.ALPHA,e.UNSIGNED_BYTE,this.data))};var k=function e(r,n){this.workerPool=r,this.actors=[],this.currentActor=0,this.id=t.uniqueId();for(var i=this.workerPool.acquire(this.id),a=0;a<i.length;a++){var o=i[a],s=new e.Actor(o,n,this.id);s.name=\"Worker \"+a,this.actors.push(s)}};function A(e,r,n){var i=function(i,a){if(i)return n(i);if(a){var o=t.pick(t.extend(a,e),[\"tiles\",\"minzoom\",\"maxzoom\",\"attribution\",\"mapbox_logo\",\"bounds\",\"scheme\",\"tileSize\",\"encoding\"]);a.vector_layers&&(o.vectorLayers=a.vector_layers,o.vectorLayerIds=o.vectorLayers.map((function(t){return t.id}))),o.tiles=r.canonicalizeTileset(o,e.url),n(null,o)}};return e.url?t.getJSON(r.transformRequest(r.normalizeSourceURL(e.url),t.ResourceType.Source),i):t.browser.frame((function(){return i(null,e)}))}k.prototype.broadcast=function(e,r,n){n=n||function(){},t.asyncAll(this.actors,(function(t,n){t.send(e,r,n)}),n)},k.prototype.getActor=function(){return this.currentActor=(this.currentActor+1)%this.actors.length,this.actors[this.currentActor]},k.prototype.remove=function(){this.actors.forEach((function(t){t.remove()})),this.actors=[],this.workerPool.release(this.id)},k.Actor=t.Actor;var M=function(e,r,n){this.bounds=t.LngLatBounds.convert(this.validateBounds(e)),this.minzoom=r||0,this.maxzoom=n||24};M.prototype.validateBounds=function(t){return Array.isArray(t)&&4===t.length?[Math.max(-180,t[0]),Math.max(-90,t[1]),Math.min(180,t[2]),Math.min(90,t[3])]:[-180,-90,180,90]},M.prototype.contains=function(e){var r=Math.pow(2,e.z),n=Math.floor(t.mercatorXfromLng(this.bounds.getWest())*r),i=Math.floor(t.mercatorYfromLat(this.bounds.getNorth())*r),a=Math.ceil(t.mercatorXfromLng(this.bounds.getEast())*r),o=Math.ceil(t.mercatorYfromLat(this.bounds.getSouth())*r);return e.x>=n&&e.x<a&&e.y>=i&&e.y<o};var S=function(e){function r(r,n,i,a){if(e.call(this),this.id=r,this.dispatcher=i,this.type=\"vector\",this.minzoom=0,this.maxzoom=22,this.scheme=\"xyz\",this.tileSize=512,this.reparseOverscaled=!0,this.isTileClipped=!0,this._loaded=!1,t.extend(this,t.pick(n,[\"url\",\"scheme\",\"tileSize\",\"promoteId\"])),this._options=t.extend({type:\"vector\"},n),this._collectResourceTiming=n.collectResourceTiming,512!==this.tileSize)throw new Error(\"vector tile sources must have a tileSize of 512\");this.setEventedParent(a)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._tileJSONRequest=A(this._options,this.map._requestManager,(function(r,n){e._tileJSONRequest=null,e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new M(n.bounds,e.minzoom,e.maxzoom)),t.postTurnstileEvent(n.tiles,e.map._requestManager._customAccessToken),t.postMapLoadEvent(n.tiles,e.map._getMapId(),e.map._requestManager._skuToken,e.map._requestManager._customAccessToken),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.loadTile=function(e,r){var n=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme)),i={request:this.map._requestManager.transformRequest(n,t.ResourceType.Tile),uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,tileSize:this.tileSize*e.tileID.overscaleFactor(),type:this.type,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};function a(n,i){return delete e.request,e.aborted?r(null):n&&404!==n.status?r(n):(i&&i.resourceTiming&&(e.resourceTiming=i.resourceTiming),this.map._refreshExpiredTiles&&i&&e.setExpiryData(i),e.loadVectorData(i,this.map.painter),t.cacheEntryPossiblyAdded(this.dispatcher),r(null),void(e.reloadCallback&&(this.loadTile(e,e.reloadCallback),e.reloadCallback=null)))}i.request.collectResourceTiming=this._collectResourceTiming,e.actor&&\"expired\"!==e.state?\"loading\"===e.state?e.reloadCallback=r:e.request=e.actor.send(\"reloadTile\",i,a.bind(this)):(e.actor=this.dispatcher.getActor(),e.request=e.actor.send(\"loadTile\",i,a.bind(this)))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.actor&&t.actor.send(\"abortTile\",{uid:t.uid,type:this.type,source:this.id},void 0)},r.prototype.unloadTile=function(t){t.unloadVectorData(),t.actor&&t.actor.send(\"removeTile\",{uid:t.uid,type:this.type,source:this.id},void 0)},r.prototype.hasTransition=function(){return!1},r}(t.Evented),E=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.dispatcher=i,this.setEventedParent(a),this.type=\"raster\",this.minzoom=0,this.maxzoom=22,this.roundZoom=!0,this.scheme=\"xyz\",this.tileSize=512,this._loaded=!1,this._options=t.extend({type:\"raster\"},n),t.extend(this,t.pick(n,[\"url\",\"scheme\",\"tileSize\"]))}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._tileJSONRequest=A(this._options,this.map._requestManager,(function(r,n){e._tileJSONRequest=null,e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new M(n.bounds,e.minzoom,e.maxzoom)),t.postTurnstileEvent(n.tiles),t.postMapLoadEvent(n.tiles,e.map._getMapId(),e.map._requestManager._skuToken),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.loadTile=function(e,r){var n=this,i=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);e.request=t.getImage(this.map._requestManager.transformRequest(i,t.ResourceType.Tile),(function(i,a){if(delete e.request,e.aborted)e.state=\"unloaded\",r(null);else if(i)e.state=\"errored\",r(i);else if(a){n.map._refreshExpiredTiles&&e.setExpiryData(a),delete a.cacheControl,delete a.expires;var o=n.map.painter.context,s=o.gl;e.texture=n.map.painter.getTileTexture(a.width),e.texture?e.texture.update(a,{useMipmap:!0}):(e.texture=new t.Texture(o,a,s.RGBA,{useMipmap:!0}),e.texture.bind(s.LINEAR,s.CLAMP_TO_EDGE,s.LINEAR_MIPMAP_NEAREST),o.extTextureFilterAnisotropic&&s.texParameterf(s.TEXTURE_2D,o.extTextureFilterAnisotropic.TEXTURE_MAX_ANISOTROPY_EXT,o.extTextureFilterAnisotropicMax)),e.state=\"loaded\",t.cacheEntryPossiblyAdded(n.dispatcher),r(null)}}))},r.prototype.abortTile=function(t,e){t.request&&(t.request.cancel(),delete t.request),e()},r.prototype.unloadTile=function(t,e){t.texture&&this.map.painter.saveTileTexture(t.texture),e()},r.prototype.hasTransition=function(){return!1},r}(t.Evented),L=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),this.type=\"raster-dem\",this.maxzoom=22,this._options=t.extend({type:\"raster-dem\"},n),this.encoding=n.encoding||\"mapbox\"}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.serialize=function(){return{type:\"raster-dem\",url:this.url,tileSize:this.tileSize,tiles:this.tiles,bounds:this.bounds,encoding:this.encoding}},r.prototype.loadTile=function(e,r){var n=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);function i(t,n){t&&(e.state=\"errored\",r(t)),n&&(e.dem=n,e.needsHillshadePrepare=!0,e.state=\"loaded\",r(null))}e.request=t.getImage(this.map._requestManager.transformRequest(n,t.ResourceType.Tile),function(n,a){if(delete e.request,e.aborted)e.state=\"unloaded\",r(null);else if(n)e.state=\"errored\",r(n);else if(a){this.map._refreshExpiredTiles&&e.setExpiryData(a),delete a.cacheControl,delete a.expires;var o=t.window.ImageBitmap&&a instanceof t.window.ImageBitmap&&t.offscreenCanvasSupported()?a:t.browser.getImageData(a,1),s={uid:e.uid,coord:e.tileID,source:this.id,rawImageData:o,encoding:this.encoding};e.actor&&\"expired\"!==e.state||(e.actor=this.dispatcher.getActor(),e.actor.send(\"loadDEMTile\",s,i.bind(this)))}}.bind(this)),e.neighboringTiles=this._getNeighboringTiles(e.tileID)},r.prototype._getNeighboringTiles=function(e){var r=e.canonical,n=Math.pow(2,r.z),i=(r.x-1+n)%n,a=0===r.x?e.wrap-1:e.wrap,o=(r.x+1+n)%n,s=r.x+1===n?e.wrap+1:e.wrap,l={};return l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y).key]={backfilled:!1},r.y>0&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y-1).key]={backfilled:!1}),r.y+1<n&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y+1).key]={backfilled:!1}),l},r.prototype.unloadTile=function(t){t.demTexture&&this.map.painter.saveTileTexture(t.demTexture),t.fbo&&(t.fbo.destroy(),delete t.fbo),t.dem&&delete t.dem,delete t.neighboringTiles,t.state=\"unloaded\",t.actor&&t.actor.send(\"removeDEMTile\",{uid:t.uid,source:this.id})},r}(E),C=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.type=\"geojson\",this.minzoom=0,this.maxzoom=18,this.tileSize=512,this.isTileClipped=!0,this.reparseOverscaled=!0,this._removed=!1,this._loaded=!1,this.actor=i.getActor(),this.setEventedParent(a),this._data=n.data,this._options=t.extend({},n),this._collectResourceTiming=n.collectResourceTiming,this._resourceTiming=[],void 0!==n.maxzoom&&(this.maxzoom=n.maxzoom),n.type&&(this.type=n.type),n.attribution&&(this.attribution=n.attribution),this.promoteId=n.promoteId;var o=t.EXTENT/this.tileSize;this.workerOptions=t.extend({source:this.id,cluster:n.cluster||!1,geojsonVtOptions:{buffer:(void 0!==n.buffer?n.buffer:128)*o,tolerance:(void 0!==n.tolerance?n.tolerance:.375)*o,extent:t.EXTENT,maxZoom:this.maxzoom,lineMetrics:n.lineMetrics||!1,generateId:n.generateId||!1},superclusterOptions:{maxZoom:void 0!==n.clusterMaxZoom?Math.min(n.clusterMaxZoom,this.maxzoom-1):this.maxzoom-1,extent:t.EXTENT,radius:(n.clusterRadius||50)*o,log:!1,generateId:n.generateId||!1},clusterProperties:n.clusterProperties},n.workerOptions)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._updateWorkerData((function(r){if(r)e.fire(new t.ErrorEvent(r));else{var n={dataType:\"source\",sourceDataType:\"metadata\"};e._collectResourceTiming&&e._resourceTiming&&e._resourceTiming.length>0&&(n.resourceTiming=e._resourceTiming,e._resourceTiming=[]),e.fire(new t.Event(\"data\",n))}}))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setData=function(e){var r=this;return this._data=e,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._updateWorkerData((function(e){if(e)r.fire(new t.ErrorEvent(e));else{var n={dataType:\"source\",sourceDataType:\"content\"};r._collectResourceTiming&&r._resourceTiming&&r._resourceTiming.length>0&&(n.resourceTiming=r._resourceTiming,r._resourceTiming=[]),r.fire(new t.Event(\"data\",n))}})),this},r.prototype.getClusterExpansionZoom=function(t,e){return this.actor.send(\"geojson.getClusterExpansionZoom\",{clusterId:t,source:this.id},e),this},r.prototype.getClusterChildren=function(t,e){return this.actor.send(\"geojson.getClusterChildren\",{clusterId:t,source:this.id},e),this},r.prototype.getClusterLeaves=function(t,e,r,n){return this.actor.send(\"geojson.getClusterLeaves\",{source:this.id,clusterId:t,limit:e,offset:r},n),this},r.prototype._updateWorkerData=function(e){var r=this;this._loaded=!1;var n=t.extend({},this.workerOptions),i=this._data;\"string\"==typeof i?(n.request=this.map._requestManager.transformRequest(t.browser.resolveURL(i),t.ResourceType.Source),n.request.collectResourceTiming=this._collectResourceTiming):n.data=JSON.stringify(i),this.actor.send(this.type+\".loadData\",n,(function(t,i){r._removed||i&&i.abandoned||(r._loaded=!0,i&&i.resourceTiming&&i.resourceTiming[r.id]&&(r._resourceTiming=i.resourceTiming[r.id].slice(0)),r.actor.send(r.type+\".coalesce\",{source:n.source},null),e(t))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.loadTile=function(e,r){var n=this,i=e.actor?\"reloadTile\":\"loadTile\";e.actor=this.actor;var a={type:this.type,uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,maxZoom:this.maxzoom,tileSize:this.tileSize,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};e.request=this.actor.send(i,a,(function(t,a){return delete e.request,e.unloadVectorData(),e.aborted?r(null):t?r(t):(e.loadVectorData(a,n.map.painter,\"reloadTile\"===i),r(null))}))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.aborted=!0},r.prototype.unloadTile=function(t){t.unloadVectorData(),this.actor.send(\"removeTile\",{uid:t.uid,type:this.type,source:this.id})},r.prototype.onRemove=function(){this._removed=!0,this.actor.send(\"removeSource\",{type:this.type,source:this.id})},r.prototype.serialize=function(){return t.extend({},this._options,{type:this.type,data:this._data})},r.prototype.hasTransition=function(){return!1},r}(t.Evented),P=t.createLayout([{name:\"a_pos\",type:\"Int16\",components:2},{name:\"a_texture_pos\",type:\"Int16\",components:2}]),I=function(e){function r(t,r,n,i){e.call(this),this.id=t,this.dispatcher=n,this.coordinates=r.coordinates,this.type=\"image\",this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.tiles={},this._loaded=!1,this.setEventedParent(i),this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(e,r){var n=this;this._loaded=!1,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this.url=this.options.url,t.getImage(this.map._requestManager.transformRequest(this.url,t.ResourceType.Image),(function(i,a){n._loaded=!0,i?n.fire(new t.ErrorEvent(i)):a&&(n.image=a,e&&(n.coordinates=e),r&&r(),n._finishLoading())}))},r.prototype.loaded=function(){return this._loaded},r.prototype.updateImage=function(t){var e=this;return this.image&&t.url?(this.options.url=t.url,this.load(t.coordinates,(function(){e.texture=null})),this):this},r.prototype._finishLoading=function(){this.map&&(this.setCoordinates(this.coordinates),this.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setCoordinates=function(e){var r=this;this.coordinates=e;var n=e.map(t.MercatorCoordinate.fromLngLat);this.tileID=function(e){for(var r=1/0,n=1/0,i=-1/0,a=-1/0,o=0,s=e;o<s.length;o+=1){var l=s[o];r=Math.min(r,l.x),n=Math.min(n,l.y),i=Math.max(i,l.x),a=Math.max(a,l.y)}var c=i-r,u=a-n,f=Math.max(c,u),h=Math.max(0,Math.floor(-Math.log(f)/Math.LN2)),p=Math.pow(2,h);return new t.CanonicalTileID(h,Math.floor((r+i)/2*p),Math.floor((n+a)/2*p))}(n),this.minzoom=this.maxzoom=this.tileID.z;var i=n.map((function(t){return r.tileID.getTilePoint(t)._round()}));return this._boundsArray=new t.StructArrayLayout4i8,this._boundsArray.emplaceBack(i[0].x,i[0].y,0,0),this._boundsArray.emplaceBack(i[1].x,i[1].y,t.EXTENT,0),this._boundsArray.emplaceBack(i[3].x,i[3].y,0,t.EXTENT),this._boundsArray.emplaceBack(i[2].x,i[2].y,t.EXTENT,t.EXTENT),this.boundsBuffer&&(this.boundsBuffer.destroy(),delete this.boundsBuffer),this.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})),this},r.prototype.prepare=function(){if(0!==Object.keys(this.tiles).length&&this.image){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture||(this.texture=new t.Texture(e,this.image,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];\"loaded\"!==i.state&&(i.state=\"loaded\",i.texture=this.texture)}}},r.prototype.loadTile=function(t,e){this.tileID&&this.tileID.equals(t.tileID.canonical)?(this.tiles[String(t.tileID.wrap)]=t,t.buckets={},e(null)):(t.state=\"errored\",e(null))},r.prototype.serialize=function(){return{type:\"image\",url:this.options.url,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return!1},r}(t.Evented);var O=function(e){function r(t,r,n,i){e.call(this,t,r,n,i),this.roundZoom=!0,this.type=\"video\",this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1;var r=this.options;this.urls=[];for(var n=0,i=r.urls;n<i.length;n+=1){var a=i[n];this.urls.push(this.map._requestManager.transformRequest(a,t.ResourceType.Source).url)}t.getVideo(this.urls,(function(r,n){e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(e.video=n,e.video.loop=!0,e.video.addEventListener(\"playing\",(function(){e.map.triggerRepaint()})),e.map&&e.video.play(),e._finishLoading())}))},r.prototype.pause=function(){this.video&&this.video.pause()},r.prototype.play=function(){this.video&&this.video.play()},r.prototype.seek=function(e){if(this.video){var r=this.video.seekable;e<r.start(0)||e>r.end(0)?this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+this.id,null,\"Playback for this video can be set only between the \"+r.start(0)+\" and \"+r.end(0)+\"-second mark.\"))):this.video.currentTime=e}},r.prototype.getVideo=function(){return this.video},r.prototype.onAdd=function(t){this.map||(this.map=t,this.load(),this.video&&(this.video.play(),this.setCoordinates(this.coordinates)))},r.prototype.prepare=function(){if(!(0===Object.keys(this.tiles).length||this.video.readyState<2)){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?this.video.paused||(this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE),r.texSubImage2D(r.TEXTURE_2D,0,0,0,r.RGBA,r.UNSIGNED_BYTE,this.video)):(this.texture=new t.Texture(e,this.video,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];\"loaded\"!==i.state&&(i.state=\"loaded\",i.texture=this.texture)}}},r.prototype.serialize=function(){return{type:\"video\",urls:this.urls,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this.video&&!this.video.paused},r}(I),z=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),n.coordinates?Array.isArray(n.coordinates)&&4===n.coordinates.length&&!n.coordinates.some((function(t){return!Array.isArray(t)||2!==t.length||t.some((function(t){return\"number\"!=typeof t}))}))||this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'\"coordinates\" property must be an array of 4 longitude/latitude array pairs'))):this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'missing required property \"coordinates\"'))),n.animate&&\"boolean\"!=typeof n.animate&&this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'optional \"animate\" property must be a boolean value'))),n.canvas?\"string\"==typeof n.canvas||n.canvas instanceof t.window.HTMLCanvasElement||this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'\"canvas\" must be either a string representing the ID of the canvas element from which to read, or an HTMLCanvasElement instance'))):this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'missing required property \"canvas\"'))),this.options=n,this.animate=void 0===n.animate||n.animate}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){this._loaded=!0,this.canvas||(this.canvas=this.options.canvas instanceof t.window.HTMLCanvasElement?this.options.canvas:t.window.document.getElementById(this.options.canvas)),this.width=this.canvas.width,this.height=this.canvas.height,this._hasInvalidDimensions()?this.fire(new t.ErrorEvent(new Error(\"Canvas dimensions cannot be less than or equal to zero.\"))):(this.play=function(){this._playing=!0,this.map.triggerRepaint()},this.pause=function(){this._playing&&(this.prepare(),this._playing=!1)},this._finishLoading())},r.prototype.getCanvas=function(){return this.canvas},r.prototype.onAdd=function(t){this.map=t,this.load(),this.canvas&&this.animate&&this.play()},r.prototype.onRemove=function(){this.pause()},r.prototype.prepare=function(){var e=!1;if(this.canvas.width!==this.width&&(this.width=this.canvas.width,e=!0),this.canvas.height!==this.height&&(this.height=this.canvas.height,e=!0),!this._hasInvalidDimensions()&&0!==Object.keys(this.tiles).length){var r=this.map.painter.context,n=r.gl;for(var i in this.boundsBuffer||(this.boundsBuffer=r.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?(e||this._playing)&&this.texture.update(this.canvas,{premultiply:!0}):this.texture=new t.Texture(r,this.canvas,n.RGBA,{premultiply:!0}),this.tiles){var a=this.tiles[i];\"loaded\"!==a.state&&(a.state=\"loaded\",a.texture=this.texture)}}},r.prototype.serialize=function(){return{type:\"canvas\",coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this._playing},r.prototype._hasInvalidDimensions=function(){for(var t=0,e=[this.canvas.width,this.canvas.height];t<e.length;t+=1){var 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e=this.gl;e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,t,0),this.current=t,this.dirty=!1}},e}(_t),Tt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.set=function(t){if(t!==this.current||this.dirty){this.context.bindFramebuffer.set(this.parent);var e=this.gl;e.framebufferRenderbuffer(e.FRAMEBUFFER,e.DEPTH_ATTACHMENT,e.RENDERBUFFER,t),this.current=t,this.dirty=!1}},e}(_t),kt=function(t,e,r,n){this.context=t,this.width=e,this.height=r;var i=t.gl,a=this.framebuffer=i.createFramebuffer();this.colorAttachment=new wt(t,a),n&&(this.depthAttachment=new Tt(t,a))};kt.prototype.destroy=function(){var t=this.context.gl,e=this.colorAttachment.get();if(e&&t.deleteTexture(e),this.depthAttachment){var r=this.depthAttachment.get();r&&t.deleteRenderbuffer(r)}t.deleteFramebuffer(this.framebuffer)};var At=function(t,e,r){this.func=t,this.mask=e,this.range=r};At.ReadOnly=!1,At.ReadWrite=!0,At.disabled=new At(519,At.ReadOnly,[0,1]);var Mt=function(t,e,r,n,i,a){this.test=t,this.ref=e,this.mask=r,this.fail=n,this.depthFail=i,this.pass=a};Mt.disabled=new Mt({func:519,mask:0},0,0,7680,7680,7680);var St=function(t,e,r){this.blendFunction=t,this.blendColor=e,this.mask=r};St.disabled=new St(St.Replace=[1,0],t.Color.transparent,[!1,!1,!1,!1]),St.unblended=new St(St.Replace,t.Color.transparent,[!0,!0,!0,!0]),St.alphaBlended=new St([1,771],t.Color.transparent,[!0,!0,!0,!0]);var Et=function(t,e,r){this.enable=t,this.mode=e,this.frontFace=r};Et.disabled=new Et(!1,1029,2305),Et.backCCW=new Et(!0,1029,2305);var Lt=function(t){this.gl=t,this.extVertexArrayObject=this.gl.getExtension(\"OES_vertex_array_object\"),this.clearColor=new q(this),this.clearDepth=new G(this),this.clearStencil=new Y(this),this.colorMask=new W(this),this.depthMask=new X(this),this.stencilMask=new Z(this),this.stencilFunc=new J(this),this.stencilOp=new K(this),this.stencilTest=new Q(this),this.depthRange=new $(this),this.depthTest=new tt(this),this.depthFunc=new et(this),this.blend=new rt(this),this.blendFunc=new nt(this),this.blendColor=new it(this),this.blendEquation=new at(this),this.cullFace=new ot(this),this.cullFaceSide=new st(this),this.frontFace=new lt(this),this.program=new ct(this),this.activeTexture=new ut(this),this.viewport=new ft(this),this.bindFramebuffer=new ht(this),this.bindRenderbuffer=new pt(this),this.bindTexture=new dt(this),this.bindVertexBuffer=new gt(this),this.bindElementBuffer=new mt(this),this.bindVertexArrayOES=this.extVertexArrayObject&&new vt(this),this.pixelStoreUnpack=new yt(this),this.pixelStoreUnpackPremultiplyAlpha=new xt(this),this.pixelStoreUnpackFlipY=new bt(this),this.extTextureFilterAnisotropic=t.getExtension(\"EXT_texture_filter_anisotropic\")||t.getExtension(\"MOZ_EXT_texture_filter_anisotropic\")||t.getExtension(\"WEBKIT_EXT_texture_filter_anisotropic\"),this.extTextureFilterAnisotropic&&(this.extTextureFilterAnisotropicMax=t.getParameter(this.extTextureFilterAnisotropic.MAX_TEXTURE_MAX_ANISOTROPY_EXT)),this.extTextureHalfFloat=t.getExtension(\"OES_texture_half_float\"),this.extTextureHalfFloat&&(t.getExtension(\"OES_texture_half_float_linear\"),this.extRenderToTextureHalfFloat=t.getExtension(\"EXT_color_buffer_half_float\")),this.extTimerQuery=t.getExtension(\"EXT_disjoint_timer_query\")};Lt.prototype.setDefault=function(){this.unbindVAO(),this.clearColor.setDefault(),this.clearDepth.setDefault(),this.clearStencil.setDefault(),this.colorMask.setDefault(),this.depthMask.setDefault(),this.stencilMask.setDefault(),this.stencilFunc.setDefault(),this.stencilOp.setDefault(),this.stencilTest.setDefault(),this.depthRange.setDefault(),this.depthTest.setDefault(),this.depthFunc.setDefault(),this.blend.setDefault(),this.blendFunc.setDefault(),this.blendColor.setDefault(),this.blendEquation.setDefault(),this.cullFace.setDefault(),this.cullFaceSide.setDefault(),this.frontFace.setDefault(),this.program.setDefault(),this.activeTexture.setDefault(),this.bindFramebuffer.setDefault(),this.pixelStoreUnpack.setDefault(),this.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.pixelStoreUnpackFlipY.setDefault()},Lt.prototype.setDirty=function(){this.clearColor.dirty=!0,this.clearDepth.dirty=!0,this.clearStencil.dirty=!0,this.colorMask.dirty=!0,this.depthMask.dirty=!0,this.stencilMask.dirty=!0,this.stencilFunc.dirty=!0,this.stencilOp.dirty=!0,this.stencilTest.dirty=!0,this.depthRange.dirty=!0,this.depthTest.dirty=!0,this.depthFunc.dirty=!0,this.blend.dirty=!0,this.blendFunc.dirty=!0,this.blendColor.dirty=!0,this.blendEquation.dirty=!0,this.cullFace.dirty=!0,this.cullFaceSide.dirty=!0,this.frontFace.dirty=!0,this.program.dirty=!0,this.activeTexture.dirty=!0,this.viewport.dirty=!0,this.bindFramebuffer.dirty=!0,this.bindRenderbuffer.dirty=!0,this.bindTexture.dirty=!0,this.bindVertexBuffer.dirty=!0,this.bindElementBuffer.dirty=!0,this.extVertexArrayObject&&(this.bindVertexArrayOES.dirty=!0),this.pixelStoreUnpack.dirty=!0,this.pixelStoreUnpackPremultiplyAlpha.dirty=!0,this.pixelStoreUnpackFlipY.dirty=!0},Lt.prototype.createIndexBuffer=function(t,e){return new j(this,t,e)},Lt.prototype.createVertexBuffer=function(t,e,r){return new V(this,t,e,r)},Lt.prototype.createRenderbuffer=function(t,e,r){var n=this.gl,i=n.createRenderbuffer();return this.bindRenderbuffer.set(i),n.renderbufferStorage(n.RENDERBUFFER,t,e,r),this.bindRenderbuffer.set(null),i},Lt.prototype.createFramebuffer=function(t,e,r){return new kt(this,t,e,r)},Lt.prototype.clear=function(t){var e=t.color,r=t.depth,n=this.gl,i=0;e&&(i|=n.COLOR_BUFFER_BIT,this.clearColor.set(e),this.colorMask.set([!0,!0,!0,!0])),void 0!==r&&(i|=n.DEPTH_BUFFER_BIT,this.depthRange.set([0,1]),this.clearDepth.set(r),this.depthMask.set(!0)),n.clear(i)},Lt.prototype.setCullFace=function(t){!1===t.enable?this.cullFace.set(!1):(this.cullFace.set(!0),this.cullFaceSide.set(t.mode),this.frontFace.set(t.frontFace))},Lt.prototype.setDepthMode=function(t){t.func!==this.gl.ALWAYS||t.mask?(this.depthTest.set(!0),this.depthFunc.set(t.func),this.depthMask.set(t.mask),this.depthRange.set(t.range)):this.depthTest.set(!1)},Lt.prototype.setStencilMode=function(t){t.test.func!==this.gl.ALWAYS||t.mask?(this.stencilTest.set(!0),this.stencilMask.set(t.mask),this.stencilOp.set([t.fail,t.depthFail,t.pass]),this.stencilFunc.set({func:t.test.func,ref:t.ref,mask:t.test.mask})):this.stencilTest.set(!1)},Lt.prototype.setColorMode=function(e){t.deepEqual(e.blendFunction,St.Replace)?this.blend.set(!1):(this.blend.set(!0),this.blendFunc.set(e.blendFunction),this.blendColor.set(e.blendColor)),this.colorMask.set(e.mask)},Lt.prototype.unbindVAO=function(){this.extVertexArrayObject&&this.bindVertexArrayOES.set(null)};var Ct=function(e){function r(r,n,i){var a=this;e.call(this),this.id=r,this.dispatcher=i,this.on(\"data\",(function(t){\"source\"===t.dataType&&\"metadata\"===t.sourceDataType&&(a._sourceLoaded=!0),a._sourceLoaded&&!a._paused&&\"source\"===t.dataType&&\"content\"===t.sourceDataType&&(a.reload(),a.transform&&a.update(a.transform))})),this.on(\"error\",(function(){a._sourceErrored=!0})),this._source=function(e,r,n,i){var a=new D[r.type](e,r,n,i);if(a.id!==e)throw new Error(\"Expected Source id to be \"+e+\" instead of \"+a.id);return t.bindAll([\"load\",\"abort\",\"unload\",\"serialize\",\"prepare\"],a),a}(r,n,i,this),this._tiles={},this._cache=new N(0,this._unloadTile.bind(this)),this._timers={},this._cacheTimers={},this._maxTileCacheSize=null,this._loadedParentTiles={},this._coveredTiles={},this._state=new t.SourceFeatureState}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.onAdd=function(t){this.map=t,this._maxTileCacheSize=t?t._maxTileCacheSize:null,this._source&&this._source.onAdd&&this._source.onAdd(t)},r.prototype.onRemove=function(t){this._source&&this._source.onRemove&&this._source.onRemove(t)},r.prototype.loaded=function(){if(this._sourceErrored)return!0;if(!this._sourceLoaded)return!1;if(!this._source.loaded())return!1;for(var t in this._tiles){var e=this._tiles[t];if(\"loaded\"!==e.state&&\"errored\"!==e.state)return!1}return!0},r.prototype.getSource=function(){return this._source},r.prototype.pause=function(){this._paused=!0},r.prototype.resume=function(){if(this._paused){var t=this._shouldReloadOnResume;this._paused=!1,this._shouldReloadOnResume=!1,t&&this.reload(),this.transform&&this.update(this.transform)}},r.prototype._loadTile=function(t,e){return this._source.loadTile(t,e)},r.prototype._unloadTile=function(t){if(this._source.unloadTile)return this._source.unloadTile(t,(function(){}))},r.prototype._abortTile=function(t){if(this._source.abortTile)return this._source.abortTile(t,(function(){}))},r.prototype.serialize=function(){return this._source.serialize()},r.prototype.prepare=function(t){for(var e in this._source.prepare&&this._source.prepare(),this._state.coalesceChanges(this._tiles,this.map?this.map.painter:null),this._tiles){var r=this._tiles[e];r.upload(t),r.prepare(this.map.style.imageManager)}},r.prototype.getIds=function(){return t.values(this._tiles).map((function(t){return t.tileID})).sort(Pt).map((function(t){return t.key}))},r.prototype.getRenderableIds=function(e){var r=this,n=[];for(var i in this._tiles)this._isIdRenderable(i,e)&&n.push(this._tiles[i]);return e?n.sort((function(e,n){var i=e.tileID,a=n.tileID,o=new t.Point(i.canonical.x,i.canonical.y)._rotate(r.transform.angle),s=new t.Point(a.canonical.x,a.canonical.y)._rotate(r.transform.angle);return i.overscaledZ-a.overscaledZ||s.y-o.y||s.x-o.x})).map((function(t){return t.tileID.key})):n.map((function(t){return t.tileID})).sort(Pt).map((function(t){return t.key}))},r.prototype.hasRenderableParent=function(t){var e=this.findLoadedParent(t,0);return!!e&&this._isIdRenderable(e.tileID.key)},r.prototype._isIdRenderable=function(t,e){return this._tiles[t]&&this._tiles[t].hasData()&&!this._coveredTiles[t]&&(e||!this._tiles[t].holdingForFade())},r.prototype.reload=function(){if(this._paused)this._shouldReloadOnResume=!0;else for(var t in this._cache.reset(),this._tiles)\"errored\"!==this._tiles[t].state&&this._reloadTile(t,\"reloading\")},r.prototype._reloadTile=function(t,e){var r=this._tiles[t];r&&(\"loading\"!==r.state&&(r.state=e),this._loadTile(r,this._tileLoaded.bind(this,r,t,e)))},r.prototype._tileLoaded=function(e,r,n,i){if(i)return e.state=\"errored\",void(404!==i.status?this._source.fire(new t.ErrorEvent(i,{tile:e})):this.update(this.transform));e.timeAdded=t.browser.now(),\"expired\"===n&&(e.refreshedUponExpiration=!0),this._setTileReloadTimer(r,e),\"raster-dem\"===this.getSource().type&&e.dem&&this._backfillDEM(e),this._state.initializeTileState(e,this.map?this.map.painter:null),this._source.fire(new t.Event(\"data\",{dataType:\"source\",tile:e,coord:e.tileID}))},r.prototype._backfillDEM=function(t){for(var e=this.getRenderableIds(),r=0;r<e.length;r++){var n=e[r];if(t.neighboringTiles&&t.neighboringTiles[n]){var i=this.getTileByID(n);a(t,i),a(i,t)}}function a(t,e){t.needsHillshadePrepare=!0;var r=e.tileID.canonical.x-t.tileID.canonical.x,n=e.tileID.canonical.y-t.tileID.canonical.y,i=Math.pow(2,t.tileID.canonical.z),a=e.tileID.key;0===r&&0===n||Math.abs(n)>1||(Math.abs(r)>1&&(1===Math.abs(r+i)?r+=i:1===Math.abs(r-i)&&(r-=i)),e.dem&&t.dem&&(t.dem.backfillBorder(e.dem,r,n),t.neighboringTiles&&t.neighboringTiles[a]&&(t.neighboringTiles[a].backfilled=!0)))}},r.prototype.getTile=function(t){return this.getTileByID(t.key)},r.prototype.getTileByID=function(t){return this._tiles[t]},r.prototype._retainLoadedChildren=function(t,e,r,n){for(var i in this._tiles){var a=this._tiles[i];if(!(n[i]||!a.hasData()||a.tileID.overscaledZ<=e||a.tileID.overscaledZ>r)){for(var o=a.tileID;a&&a.tileID.overscaledZ>e+1;){var s=a.tileID.scaledTo(a.tileID.overscaledZ-1);(a=this._tiles[s.key])&&a.hasData()&&(o=s)}for(var l=o;l.overscaledZ>e;)if(t[(l=l.scaledTo(l.overscaledZ-1)).key]){n[o.key]=o;break}}}},r.prototype.findLoadedParent=function(t,e){if(t.key in this._loadedParentTiles){var r=this._loadedParentTiles[t.key];return r&&r.tileID.overscaledZ>=e?r:null}for(var n=t.overscaledZ-1;n>=e;n--){var i=t.scaledTo(n),a=this._getLoadedTile(i);if(a)return a}},r.prototype._getLoadedTile=function(t){var e=this._tiles[t.key];return e&&e.hasData()?e:this._cache.getByKey(t.wrapped().key)},r.prototype.updateCacheSize=function(t){var e=(Math.ceil(t.width/this._source.tileSize)+1)*(Math.ceil(t.height/this._source.tileSize)+1),r=Math.floor(5*e),n=\"number\"==typeof this._maxTileCacheSize?Math.min(this._maxTileCacheSize,r):r;this._cache.setMaxSize(n)},r.prototype.handleWrapJump=function(t){var e=(t-(void 0===this._prevLng?t:this._prevLng))/360,r=Math.round(e);if(this._prevLng=t,r){var n={};for(var i in this._tiles){var a=this._tiles[i];a.tileID=a.tileID.unwrapTo(a.tileID.wrap+r),n[a.tileID.key]=a}for(var o in this._tiles=n,this._timers)clearTimeout(this._timers[o]),delete this._timers[o];for(var s in this._tiles){var l=this._tiles[s];this._setTileReloadTimer(s,l)}}},r.prototype.update=function(e){var n=this;if(this.transform=e,this._sourceLoaded&&!this._paused){var i;this.updateCacheSize(e),this.handleWrapJump(this.transform.center.lng),this._coveredTiles={},this.used?this._source.tileID?i=e.getVisibleUnwrappedCoordinates(this._source.tileID).map((function(e){return new t.OverscaledTileID(e.canonical.z,e.wrap,e.canonical.z,e.canonical.x,e.canonical.y)})):(i=e.coveringTiles({tileSize:this._source.tileSize,minzoom:this._source.minzoom,maxzoom:this._source.maxzoom,roundZoom:this._source.roundZoom,reparseOverscaled:this._source.reparseOverscaled}),this._source.hasTile&&(i=i.filter((function(t){return n._source.hasTile(t)})))):i=[];var a=e.coveringZoomLevel(this._source),o=Math.max(a-r.maxOverzooming,this._source.minzoom),s=Math.max(a+r.maxUnderzooming,this._source.minzoom),l=this._updateRetainedTiles(i,a);if(It(this._source.type)){for(var c={},u={},f=0,h=Object.keys(l);f<h.length;f+=1){var p=h[f],d=l[p],g=this._tiles[p];if(g&&!(g.fadeEndTime&&g.fadeEndTime<=t.browser.now())){var m=this.findLoadedParent(d,o);m&&(this._addTile(m.tileID),c[m.tileID.key]=m.tileID),u[p]=d}}for(var v in this._retainLoadedChildren(u,a,s,l),c)l[v]||(this._coveredTiles[v]=!0,l[v]=c[v])}for(var y in l)this._tiles[y].clearFadeHold();for(var x=0,b=t.keysDifference(this._tiles,l);x<b.length;x+=1){var _=b[x],w=this._tiles[_];w.hasSymbolBuckets&&!w.holdingForFade()?w.setHoldDuration(this.map._fadeDuration):w.hasSymbolBuckets&&!w.symbolFadeFinished()||this._removeTile(_)}this._updateLoadedParentTileCache()}},r.prototype.releaseSymbolFadeTiles=function(){for(var t in this._tiles)this._tiles[t].holdingForFade()&&this._removeTile(t)},r.prototype._updateRetainedTiles=function(t,e){for(var n={},i={},a=Math.max(e-r.maxOverzooming,this._source.minzoom),o=Math.max(e+r.maxUnderzooming,this._source.minzoom),s={},l=0,c=t;l<c.length;l+=1){var u=c[l],f=this._addTile(u);n[u.key]=u,f.hasData()||e<this._source.maxzoom&&(s[u.key]=u)}this._retainLoadedChildren(s,e,o,n);for(var h=0,p=t;h<p.length;h+=1){var d=p[h],g=this._tiles[d.key];if(!g.hasData()){if(e+1>this._source.maxzoom){var m=d.children(this._source.maxzoom)[0],v=this.getTile(m);if(v&&v.hasData()){n[m.key]=m;continue}}else{var y=d.children(this._source.maxzoom);if(n[y[0].key]&&n[y[1].key]&&n[y[2].key]&&n[y[3].key])continue}for(var x=g.wasRequested(),b=d.overscaledZ-1;b>=a;--b){var _=d.scaledTo(b);if(i[_.key])break;if(i[_.key]=!0,!(g=this.getTile(_))&&x&&(g=this._addTile(_)),g&&(n[_.key]=_,x=g.wasRequested(),g.hasData()))break}}}return n},r.prototype._updateLoadedParentTileCache=function(){for(var t in this._loadedParentTiles={},this._tiles){for(var e=[],r=void 0,n=this._tiles[t].tileID;n.overscaledZ>0;){if(n.key in this._loadedParentTiles){r=this._loadedParentTiles[n.key];break}e.push(n.key);var i=n.scaledTo(n.overscaledZ-1);if(r=this._getLoadedTile(i))break;n=i}for(var a=0,o=e;a<o.length;a+=1){var s=o[a];this._loadedParentTiles[s]=r}}},r.prototype._addTile=function(e){var r=this._tiles[e.key];if(r)return r;(r=this._cache.getAndRemove(e))&&(this._setTileReloadTimer(e.key,r),r.tileID=e,this._state.initializeTileState(r,this.map?this.map.painter:null),this._cacheTimers[e.key]&&(clearTimeout(this._cacheTimers[e.key]),delete this._cacheTimers[e.key],this._setTileReloadTimer(e.key,r)));var n=Boolean(r);return n||(r=new t.Tile(e,this._source.tileSize*e.overscaleFactor()),this._loadTile(r,this._tileLoaded.bind(this,r,e.key,r.state))),r?(r.uses++,this._tiles[e.key]=r,n||this._source.fire(new t.Event(\"dataloading\",{tile:r,coord:r.tileID,dataType:\"source\"})),r):null},r.prototype._setTileReloadTimer=function(t,e){var r=this;t in this._timers&&(clearTimeout(this._timers[t]),delete this._timers[t]);var n=e.getExpiryTimeout();n&&(this._timers[t]=setTimeout((function(){r._reloadTile(t,\"expired\"),delete r._timers[t]}),n))},r.prototype._removeTile=function(t){var e=this._tiles[t];e&&(e.uses--,delete this._tiles[t],this._timers[t]&&(clearTimeout(this._timers[t]),delete this._timers[t]),e.uses>0||(e.hasData()&&\"reloading\"!==e.state?this._cache.add(e.tileID,e,e.getExpiryTimeout()):(e.aborted=!0,this._abortTile(e),this._unloadTile(e))))},r.prototype.clearTiles=function(){for(var t in this._shouldReloadOnResume=!1,this._paused=!1,this._tiles)this._removeTile(t);this._cache.reset()},r.prototype.tilesIn=function(e,r,n){var i=this,a=[],o=this.transform;if(!o)return a;for(var s=n?o.getCameraQueryGeometry(e):e,l=e.map((function(t){return o.pointCoordinate(t)})),c=s.map((function(t){return o.pointCoordinate(t)})),u=this.getIds(),f=1/0,h=1/0,p=-1/0,d=-1/0,g=0,m=c;g<m.length;g+=1){var v=m[g];f=Math.min(f,v.x),h=Math.min(h,v.y),p=Math.max(p,v.x),d=Math.max(d,v.y)}for(var y=function(e){var n=i._tiles[u[e]];if(!n.holdingForFade()){var s=n.tileID,g=Math.pow(2,o.zoom-n.tileID.overscaledZ),m=r*n.queryPadding*t.EXTENT/n.tileSize/g,v=[s.getTilePoint(new t.MercatorCoordinate(f,h)),s.getTilePoint(new t.MercatorCoordinate(p,d))];if(v[0].x-m<t.EXTENT&&v[0].y-m<t.EXTENT&&v[1].x+m>=0&&v[1].y+m>=0){var y=l.map((function(t){return s.getTilePoint(t)})),x=c.map((function(t){return s.getTilePoint(t)}));a.push({tile:n,tileID:s,queryGeometry:y,cameraQueryGeometry:x,scale:g})}}},x=0;x<u.length;x++)y(x);return a},r.prototype.getVisibleCoordinates=function(t){for(var e=this,r=this.getRenderableIds(t).map((function(t){return e._tiles[t].tileID})),n=0,i=r;n<i.length;n+=1){var a=i[n];a.posMatrix=this.transform.calculatePosMatrix(a.toUnwrapped())}return r},r.prototype.hasTransition=function(){if(this._source.hasTransition())return!0;if(It(this._source.type))for(var e in this._tiles){var r=this._tiles[e];if(void 0!==r.fadeEndTime&&r.fadeEndTime>=t.browser.now())return!0}return!1},r.prototype.setFeatureState=function(t,e,r){t=t||\"_geojsonTileLayer\",this._state.updateState(t,e,r)},r.prototype.removeFeatureState=function(t,e,r){t=t||\"_geojsonTileLayer\",this._state.removeFeatureState(t,e,r)},r.prototype.getFeatureState=function(t,e){return t=t||\"_geojsonTileLayer\",this._state.getState(t,e)},r.prototype.setDependencies=function(t,e,r){var n=this._tiles[t];n&&n.setDependencies(e,r)},r.prototype.reloadTilesForDependencies=function(t,e){for(var r in this._tiles){this._tiles[r].hasDependency(t,e)&&this._reloadTile(r,\"reloading\")}this._cache.filter((function(r){return!r.hasDependency(t,e)}))},r}(t.Evented);function Pt(t,e){var r=Math.abs(2*t.wrap)-+(t.wrap<0),n=Math.abs(2*e.wrap)-+(e.wrap<0);return t.overscaledZ-e.overscaledZ||n-r||e.canonical.y-t.canonical.y||e.canonical.x-t.canonical.x}function It(t){return\"raster\"===t||\"image\"===t||\"video\"===t}function Ot(){return new t.window.Worker(Zi.workerUrl)}Ct.maxOverzooming=10,Ct.maxUnderzooming=3;var zt=\"mapboxgl_preloaded_worker_pool\",Dt=function(){this.active={}};Dt.prototype.acquire=function(t){if(!this.workers)for(this.workers=[];this.workers.length<Dt.workerCount;)this.workers.push(new Ot);return this.active[t]=!0,this.workers.slice()},Dt.prototype.release=function(t){delete this.active[t],0===this.numActive()&&(this.workers.forEach((function(t){t.terminate()})),this.workers=null)},Dt.prototype.isPreloaded=function(){return!!this.active[zt]},Dt.prototype.numActive=function(){return Object.keys(this.active).length};var Rt,Ft=Math.floor(t.browser.hardwareConcurrency/2);function Bt(){return Rt||(Rt=new Dt),Rt}function Nt(e,r){var n={};for(var i in e)\"ref\"!==i&&(n[i]=e[i]);return t.refProperties.forEach((function(t){t in r&&(n[t]=r[t])})),n}function jt(t){t=t.slice();for(var e=Object.create(null),r=0;r<t.length;r++)e[t[r].id]=t[r];for(var n=0;n<t.length;n++)\"ref\"in t[n]&&(t[n]=Nt(t[n],e[t[n].ref]));return t}Dt.workerCount=Math.max(Math.min(Ft,6),1);var Ut={setStyle:\"setStyle\",addLayer:\"addLayer\",removeLayer:\"removeLayer\",setPaintProperty:\"setPaintProperty\",setLayoutProperty:\"setLayoutProperty\",setFilter:\"setFilter\",addSource:\"addSource\",removeSource:\"removeSource\",setGeoJSONSourceData:\"setGeoJSONSourceData\",setLayerZoomRange:\"setLayerZoomRange\",setLayerProperty:\"setLayerProperty\",setCenter:\"setCenter\",setZoom:\"setZoom\",setBearing:\"setBearing\",setPitch:\"setPitch\",setSprite:\"setSprite\",setGlyphs:\"setGlyphs\",setTransition:\"setTransition\",setLight:\"setLight\"};function Vt(t,e,r){r.push({command:Ut.addSource,args:[t,e[t]]})}function Ht(t,e,r){e.push({command:Ut.removeSource,args:[t]}),r[t]=!0}function qt(t,e,r,n){Ht(t,r,n),Vt(t,e,r)}function Gt(e,r,n){var i;for(i in e[n])if(e[n].hasOwnProperty(i)&&\"data\"!==i&&!t.deepEqual(e[n][i],r[n][i]))return!1;for(i in r[n])if(r[n].hasOwnProperty(i)&&\"data\"!==i&&!t.deepEqual(e[n][i],r[n][i]))return!1;return!0}function Yt(e,r,n,i,a,o){var s;for(s in r=r||{},e=e||{})e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}));for(s in r)r.hasOwnProperty(s)&&!e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}))}function Wt(t){return t.id}function Xt(t,e){return t[e.id]=e,t}function Zt(e,r){if(!e)return[{command:Ut.setStyle,args:[r]}];var n=[];try{if(!t.deepEqual(e.version,r.version))return[{command:Ut.setStyle,args:[r]}];t.deepEqual(e.center,r.center)||n.push({command:Ut.setCenter,args:[r.center]}),t.deepEqual(e.zoom,r.zoom)||n.push({command:Ut.setZoom,args:[r.zoom]}),t.deepEqual(e.bearing,r.bearing)||n.push({command:Ut.setBearing,args:[r.bearing]}),t.deepEqual(e.pitch,r.pitch)||n.push({command:Ut.setPitch,args:[r.pitch]}),t.deepEqual(e.sprite,r.sprite)||n.push({command:Ut.setSprite,args:[r.sprite]}),t.deepEqual(e.glyphs,r.glyphs)||n.push({command:Ut.setGlyphs,args:[r.glyphs]}),t.deepEqual(e.transition,r.transition)||n.push({command:Ut.setTransition,args:[r.transition]}),t.deepEqual(e.light,r.light)||n.push({command:Ut.setLight,args:[r.light]});var i={},a=[];!function(e,r,n,i){var a;for(a in r=r||{},e=e||{})e.hasOwnProperty(a)&&(r.hasOwnProperty(a)||Ht(a,n,i));for(a in r)r.hasOwnProperty(a)&&(e.hasOwnProperty(a)?t.deepEqual(e[a],r[a])||(\"geojson\"===e[a].type&&\"geojson\"===r[a].type&&Gt(e,r,a)?n.push({command:Ut.setGeoJSONSourceData,args:[a,r[a].data]}):qt(a,r,n,i)):Vt(a,r,n))}(e.sources,r.sources,a,i);var o=[];e.layers&&e.layers.forEach((function(t){i[t.source]?n.push({command:Ut.removeLayer,args:[t.id]}):o.push(t)})),n=n.concat(a),function(e,r,n){r=r||[];var i,a,o,s,l,c,u,f=(e=e||[]).map(Wt),h=r.map(Wt),p=e.reduce(Xt,{}),d=r.reduce(Xt,{}),g=f.slice(),m=Object.create(null);for(i=0,a=0;i<f.length;i++)o=f[i],d.hasOwnProperty(o)?a++:(n.push({command:Ut.removeLayer,args:[o]}),g.splice(g.indexOf(o,a),1));for(i=0,a=0;i<h.length;i++)o=h[h.length-1-i],g[g.length-1-i]!==o&&(p.hasOwnProperty(o)?(n.push({command:Ut.removeLayer,args:[o]}),g.splice(g.lastIndexOf(o,g.length-a),1)):a++,c=g[g.length-i],n.push({command:Ut.addLayer,args:[d[o],c]}),g.splice(g.length-i,0,o),m[o]=!0);for(i=0;i<h.length;i++)if(s=p[o=h[i]],l=d[o],!m[o]&&!t.deepEqual(s,l))if(t.deepEqual(s.source,l.source)&&t.deepEqual(s[\"source-layer\"],l[\"source-layer\"])&&t.deepEqual(s.type,l.type)){for(u in Yt(s.layout,l.layout,n,o,null,Ut.setLayoutProperty),Yt(s.paint,l.paint,n,o,null,Ut.setPaintProperty),t.deepEqual(s.filter,l.filter)||n.push({command:Ut.setFilter,args:[o,l.filter]}),t.deepEqual(s.minzoom,l.minzoom)&&t.deepEqual(s.maxzoom,l.maxzoom)||n.push({command:Ut.setLayerZoomRange,args:[o,l.minzoom,l.maxzoom]}),s)s.hasOwnProperty(u)&&\"layout\"!==u&&\"paint\"!==u&&\"filter\"!==u&&\"metadata\"!==u&&\"minzoom\"!==u&&\"maxzoom\"!==u&&(0===u.indexOf(\"paint.\")?Yt(s[u],l[u],n,o,u.slice(6),Ut.setPaintProperty):t.deepEqual(s[u],l[u])||n.push({command:Ut.setLayerProperty,args:[o,u,l[u]]}));for(u in l)l.hasOwnProperty(u)&&!s.hasOwnProperty(u)&&\"layout\"!==u&&\"paint\"!==u&&\"filter\"!==u&&\"metadata\"!==u&&\"minzoom\"!==u&&\"maxzoom\"!==u&&(0===u.indexOf(\"paint.\")?Yt(s[u],l[u],n,o,u.slice(6),Ut.setPaintProperty):t.deepEqual(s[u],l[u])||n.push({command:Ut.setLayerProperty,args:[o,u,l[u]]}))}else n.push({command:Ut.removeLayer,args:[o]}),c=g[g.lastIndexOf(o)+1],n.push({command:Ut.addLayer,args:[l,c]})}(o,r.layers,n)}catch(t){console.warn(\"Unable to compute style diff:\",t),n=[{command:Ut.setStyle,args:[r]}]}return n}var Jt=function(t,e){this.reset(t,e)};Jt.prototype.reset=function(t,e){this.points=t||[],this._distances=[0];for(var r=1;r<this.points.length;r++)this._distances[r]=this._distances[r-1]+this.points[r].dist(this.points[r-1]);this.length=this._distances[this._distances.length-1],this.padding=Math.min(e||0,.5*this.length),this.paddedLength=this.length-2*this.padding},Jt.prototype.lerp=function(e){if(1===this.points.length)return this.points[0];e=t.clamp(e,0,1);for(var r=1,n=this._distances[r],i=e*this.paddedLength+this.padding;n<i&&r<this._distances.length;)n=this._distances[++r];var a=r-1,o=this._distances[a],s=n-o,l=s>0?(i-o)/s:0;return this.points[a].mult(1-l).add(this.points[r].mult(l))};var Kt=function(t,e,r){var n=this.boxCells=[],i=this.circleCells=[];this.xCellCount=Math.ceil(t/r),this.yCellCount=Math.ceil(e/r);for(var a=0;a<this.xCellCount*this.yCellCount;a++)n.push([]),i.push([]);this.circleKeys=[],this.boxKeys=[],this.bboxes=[],this.circles=[],this.width=t,this.height=e,this.xScale=this.xCellCount/t,this.yScale=this.yCellCount/e,this.boxUid=0,this.circleUid=0};function Qt(e,r,n,i,a){var o=t.create();return r?(t.scale(o,o,[1/a,1/a,1]),n||t.rotateZ(o,o,i.angle)):t.multiply(o,i.labelPlaneMatrix,e),o}function $t(e,r,n,i,a){if(r){var o=t.clone(e);return t.scale(o,o,[a,a,1]),n||t.rotateZ(o,o,-i.angle),o}return i.glCoordMatrix}function te(e,r){var n=[e.x,e.y,0,1];fe(n,n,r);var i=n[3];return{point:new t.Point(n[0]/i,n[1]/i),signedDistanceFromCamera:i}}function ee(t,e){return.5+t/e*.5}function re(t,e){var r=t[0]/t[3],n=t[1]/t[3];return r>=-e[0]&&r<=e[0]&&n>=-e[1]&&n<=e[1]}function ne(e,r,n,i,a,o,s,l){var c=i?e.textSizeData:e.iconSizeData,u=t.evaluateSizeForZoom(c,n.transform.zoom),f=[256/n.width*2+1,256/n.height*2+1],h=i?e.text.dynamicLayoutVertexArray:e.icon.dynamicLayoutVertexArray;h.clear();for(var p=e.lineVertexArray,d=i?e.text.placedSymbolArray:e.icon.placedSymbolArray,g=n.transform.width/n.transform.height,m=!1,v=0;v<d.length;v++){var y=d.get(v);if(y.hidden||y.writingMode===t.WritingMode.vertical&&!m)ue(y.numGlyphs,h);else{m=!1;var x=[y.anchorX,y.anchorY,0,1];if(t.transformMat4(x,x,r),re(x,f)){var b=x[3],_=ee(n.transform.cameraToCenterDistance,b),w=t.evaluateSizeForFeature(c,u,y),T=s?w/_:w*_,k=new t.Point(y.anchorX,y.anchorY),A=te(k,a).point,M={},S=oe(y,T,!1,l,r,a,o,e.glyphOffsetArray,p,h,A,k,M,g);m=S.useVertical,(S.notEnoughRoom||m||S.needsFlipping&&oe(y,T,!0,l,r,a,o,e.glyphOffsetArray,p,h,A,k,M,g).notEnoughRoom)&&ue(y.numGlyphs,h)}else ue(y.numGlyphs,h)}}i?e.text.dynamicLayoutVertexBuffer.updateData(h):e.icon.dynamicLayoutVertexBuffer.updateData(h)}function ie(t,e,r,n,i,a,o,s,l,c,u){var f=s.glyphStartIndex+s.numGlyphs,h=s.lineStartIndex,p=s.lineStartIndex+s.lineLength,d=e.getoffsetX(s.glyphStartIndex),g=e.getoffsetX(f-1),m=le(t*d,r,n,i,a,o,s.segment,h,p,l,c,u);if(!m)return null;var v=le(t*g,r,n,i,a,o,s.segment,h,p,l,c,u);return v?{first:m,last:v}:null}function ae(e,r,n,i){if(e===t.WritingMode.horizontal&&Math.abs(n.y-r.y)>Math.abs(n.x-r.x)*i)return{useVertical:!0};return(e===t.WritingMode.vertical?r.y<n.y:r.x>n.x)?{needsFlipping:!0}:null}function oe(e,r,n,i,a,o,s,l,c,u,f,h,p,d){var g,m=r/24,v=e.lineOffsetX*m,y=e.lineOffsetY*m;if(e.numGlyphs>1){var x=e.glyphStartIndex+e.numGlyphs,b=e.lineStartIndex,_=e.lineStartIndex+e.lineLength,w=ie(m,l,v,y,n,f,h,e,c,o,p);if(!w)return{notEnoughRoom:!0};var T=te(w.first.point,s).point,k=te(w.last.point,s).point;if(i&&!n){var A=ae(e.writingMode,T,k,d);if(A)return A}g=[w.first];for(var M=e.glyphStartIndex+1;M<x-1;M++)g.push(le(m*l.getoffsetX(M),v,y,n,f,h,e.segment,b,_,c,o,p));g.push(w.last)}else{if(i&&!n){var S=te(h,a).point,E=e.lineStartIndex+e.segment+1,L=new t.Point(c.getx(E),c.gety(E)),C=te(L,a),P=C.signedDistanceFromCamera>0?C.point:se(h,L,S,1,a),I=ae(e.writingMode,S,P,d);if(I)return I}var O=le(m*l.getoffsetX(e.glyphStartIndex),v,y,n,f,h,e.segment,e.lineStartIndex,e.lineStartIndex+e.lineLength,c,o,p);if(!O)return{notEnoughRoom:!0};g=[O]}for(var z=0,D=g;z<D.length;z+=1){var R=D[z];t.addDynamicAttributes(u,R.point,R.angle)}return{}}function se(t,e,r,n,i){var a=te(t.add(t.sub(e)._unit()),i).point,o=r.sub(a);return r.add(o._mult(n/o.mag()))}function le(e,r,n,i,a,o,s,l,c,u,f,h){var p=i?e-r:e+r,d=p>0?1:-1,g=0;i&&(d*=-1,g=Math.PI),d<0&&(g+=Math.PI);for(var m=d>0?l+s:l+s+1,v=a,y=a,x=0,b=0,_=Math.abs(p),w=[];x+b<=_;){if((m+=d)<l||m>=c)return null;if(y=v,w.push(v),void 0===(v=h[m])){var T=new t.Point(u.getx(m),u.gety(m)),k=te(T,f);if(k.signedDistanceFromCamera>0)v=h[m]=k.point;else{var A=m-d;v=se(0===x?o:new t.Point(u.getx(A),u.gety(A)),T,y,_-x+1,f)}}x+=b,b=y.dist(v)}var M=(_-x)/b,S=v.sub(y),E=S.mult(M)._add(y);E._add(S._unit()._perp()._mult(n*d));var L=g+Math.atan2(v.y-y.y,v.x-y.x);return w.push(E),{point:E,angle:L,path:w}}Kt.prototype.keysLength=function(){return this.boxKeys.length+this.circleKeys.length},Kt.prototype.insert=function(t,e,r,n,i){this._forEachCell(e,r,n,i,this._insertBoxCell,this.boxUid++),this.boxKeys.push(t),this.bboxes.push(e),this.bboxes.push(r),this.bboxes.push(n),this.bboxes.push(i)},Kt.prototype.insertCircle=function(t,e,r,n){this._forEachCell(e-n,r-n,e+n,r+n,this._insertCircleCell,this.circleUid++),this.circleKeys.push(t),this.circles.push(e),this.circles.push(r),this.circles.push(n)},Kt.prototype._insertBoxCell=function(t,e,r,n,i,a){this.boxCells[i].push(a)},Kt.prototype._insertCircleCell=function(t,e,r,n,i,a){this.circleCells[i].push(a)},Kt.prototype._query=function(t,e,r,n,i,a){if(r<0||t>this.width||n<0||e>this.height)return!i&&[];var o=[];if(t<=0&&e<=0&&this.width<=r&&this.height<=n){if(i)return!0;for(var s=0;s<this.boxKeys.length;s++)o.push({key:this.boxKeys[s],x1:this.bboxes[4*s],y1:this.bboxes[4*s+1],x2:this.bboxes[4*s+2],y2:this.bboxes[4*s+3]});for(var l=0;l<this.circleKeys.length;l++){var c=this.circles[3*l],u=this.circles[3*l+1],f=this.circles[3*l+2];o.push({key:this.circleKeys[l],x1:c-f,y1:u-f,x2:c+f,y2:u+f})}return a?o.filter(a):o}var h={hitTest:i,seenUids:{box:{},circle:{}}};return this._forEachCell(t,e,r,n,this._queryCell,o,h,a),i?o.length>0:o},Kt.prototype._queryCircle=function(t,e,r,n,i){var a=t-r,o=t+r,s=e-r,l=e+r;if(o<0||a>this.width||l<0||s>this.height)return!n&&[];var c=[],u={hitTest:n,circle:{x:t,y:e,radius:r},seenUids:{box:{},circle:{}}};return this._forEachCell(a,s,o,l,this._queryCellCircle,c,u,i),n?c.length>0:c},Kt.prototype.query=function(t,e,r,n,i){return this._query(t,e,r,n,!1,i)},Kt.prototype.hitTest=function(t,e,r,n,i){return this._query(t,e,r,n,!0,i)},Kt.prototype.hitTestCircle=function(t,e,r,n){return this._queryCircle(t,e,r,!0,n)},Kt.prototype._queryCell=function(t,e,r,n,i,a,o,s){var l=o.seenUids,c=this.boxCells[i];if(null!==c)for(var u=this.bboxes,f=0,h=c;f<h.length;f+=1){var p=h[f];if(!l.box[p]){l.box[p]=!0;var d=4*p;if(t<=u[d+2]&&e<=u[d+3]&&r>=u[d+0]&&n>=u[d+1]&&(!s||s(this.boxKeys[p]))){if(o.hitTest)return a.push(!0),!0;a.push({key:this.boxKeys[p],x1:u[d],y1:u[d+1],x2:u[d+2],y2:u[d+3]})}}}var g=this.circleCells[i];if(null!==g)for(var m=this.circles,v=0,y=g;v<y.length;v+=1){var x=y[v];if(!l.circle[x]){l.circle[x]=!0;var b=3*x;if(this._circleAndRectCollide(m[b],m[b+1],m[b+2],t,e,r,n)&&(!s||s(this.circleKeys[x]))){if(o.hitTest)return a.push(!0),!0;var _=m[b],w=m[b+1],T=m[b+2];a.push({key:this.circleKeys[x],x1:_-T,y1:w-T,x2:_+T,y2:w+T})}}}},Kt.prototype._queryCellCircle=function(t,e,r,n,i,a,o,s){var l=o.circle,c=o.seenUids,u=this.boxCells[i];if(null!==u)for(var f=this.bboxes,h=0,p=u;h<p.length;h+=1){var d=p[h];if(!c.box[d]){c.box[d]=!0;var g=4*d;if(this._circleAndRectCollide(l.x,l.y,l.radius,f[g+0],f[g+1],f[g+2],f[g+3])&&(!s||s(this.boxKeys[d])))return a.push(!0),!0}}var m=this.circleCells[i];if(null!==m)for(var v=this.circles,y=0,x=m;y<x.length;y+=1){var b=x[y];if(!c.circle[b]){c.circle[b]=!0;var _=3*b;if(this._circlesCollide(v[_],v[_+1],v[_+2],l.x,l.y,l.radius)&&(!s||s(this.circleKeys[b])))return a.push(!0),!0}}},Kt.prototype._forEachCell=function(t,e,r,n,i,a,o,s){for(var l=this._convertToXCellCoord(t),c=this._convertToYCellCoord(e),u=this._convertToXCellCoord(r),f=this._convertToYCellCoord(n),h=l;h<=u;h++)for(var p=c;p<=f;p++){var d=this.xCellCount*p+h;if(i.call(this,t,e,r,n,d,a,o,s))return}},Kt.prototype._convertToXCellCoord=function(t){return Math.max(0,Math.min(this.xCellCount-1,Math.floor(t*this.xScale)))},Kt.prototype._convertToYCellCoord=function(t){return Math.max(0,Math.min(this.yCellCount-1,Math.floor(t*this.yScale)))},Kt.prototype._circlesCollide=function(t,e,r,n,i,a){var o=n-t,s=i-e,l=r+a;return l*l>o*o+s*s},Kt.prototype._circleAndRectCollide=function(t,e,r,n,i,a,o){var s=(a-n)/2,l=Math.abs(t-(n+s));if(l>s+r)return!1;var c=(o-i)/2,u=Math.abs(e-(i+c));if(u>c+r)return!1;if(l<=s||u<=c)return!0;var f=l-s,h=u-c;return f*f+h*h<=r*r};var ce=new Float32Array([-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0]);function ue(t,e){for(var r=0;r<t;r++){var n=e.length;e.resize(n+4),e.float32.set(ce,3*n)}}function fe(t,e,r){var n=e[0],i=e[1];return t[0]=r[0]*n+r[4]*i+r[12],t[1]=r[1]*n+r[5]*i+r[13],t[3]=r[3]*n+r[7]*i+r[15],t}var he=function(t,e,r){void 0===e&&(e=new Kt(t.width+200,t.height+200,25)),void 0===r&&(r=new Kt(t.width+200,t.height+200,25)),this.transform=t,this.grid=e,this.ignoredGrid=r,this.pitchfactor=Math.cos(t._pitch)*t.cameraToCenterDistance,this.screenRightBoundary=t.width+100,this.screenBottomBoundary=t.height+100,this.gridRightBoundary=t.width+200,this.gridBottomBoundary=t.height+200};function pe(e,r,n){return r*(t.EXTENT/(e.tileSize*Math.pow(2,n-e.tileID.overscaledZ)))}he.prototype.placeCollisionBox=function(t,e,r,n,i){var a=this.projectAndGetPerspectiveRatio(n,t.anchorPointX,t.anchorPointY),o=r*a.perspectiveRatio,s=t.x1*o+a.point.x,l=t.y1*o+a.point.y,c=t.x2*o+a.point.x,u=t.y2*o+a.point.y;return!this.isInsideGrid(s,l,c,u)||!e&&this.grid.hitTest(s,l,c,u,i)?{box:[],offscreen:!1}:{box:[s,l,c,u],offscreen:this.isOffscreen(s,l,c,u)}},he.prototype.placeCollisionCircles=function(e,r,n,i,a,o,s,l,c,u,f,h,p){var d=[],g=new t.Point(r.anchorX,r.anchorY),m=te(g,o),v=ee(this.transform.cameraToCenterDistance,m.signedDistanceFromCamera),y=(u?a/v:a*v)/t.ONE_EM,x=te(g,s).point,b=ie(y,i,r.lineOffsetX*y,r.lineOffsetY*y,!1,x,g,r,n,s,{}),_=!1,w=!1,T=!0;if(b){for(var k=.5*h*v+p,A=new t.Point(-100,-100),M=new t.Point(this.screenRightBoundary,this.screenBottomBoundary),S=new Jt,E=b.first,L=b.last,C=[],P=E.path.length-1;P>=1;P--)C.push(E.path[P]);for(var I=1;I<L.path.length;I++)C.push(L.path[I]);var O=2.5*k;if(l){var z=C.map((function(t){return te(t,l)}));C=z.some((function(t){return t.signedDistanceFromCamera<=0}))?[]:z.map((function(t){return t.point}))}var D=[];if(C.length>0){for(var R=C[0].clone(),F=C[0].clone(),B=1;B<C.length;B++)R.x=Math.min(R.x,C[B].x),R.y=Math.min(R.y,C[B].y),F.x=Math.max(F.x,C[B].x),F.y=Math.max(F.y,C[B].y);D=R.x>=A.x&&F.x<=M.x&&R.y>=A.y&&F.y<=M.y?[C]:F.x<A.x||R.x>M.x||F.y<A.y||R.y>M.y?[]:t.clipLine([C],A.x,A.y,M.x,M.y)}for(var N=0,j=D;N<j.length;N+=1){var U=j[N];S.reset(U,.25*k);var V=0;V=S.length<=.5*k?1:Math.ceil(S.paddedLength/O)+1;for(var H=0;H<V;H++){var q=H/Math.max(V-1,1),G=S.lerp(q),Y=G.x+100,W=G.y+100;d.push(Y,W,k,0);var X=Y-k,Z=W-k,J=Y+k,K=W+k;if(T=T&&this.isOffscreen(X,Z,J,K),w=w||this.isInsideGrid(X,Z,J,K),!e&&this.grid.hitTestCircle(Y,W,k,f)&&(_=!0,!c))return{circles:[],offscreen:!1,collisionDetected:_}}}}return{circles:!c&&_||!w?[]:d,offscreen:T,collisionDetected:_}},he.prototype.queryRenderedSymbols=function(e){if(0===e.length||0===this.grid.keysLength()&&0===this.ignoredGrid.keysLength())return{};for(var r=[],n=1/0,i=1/0,a=-1/0,o=-1/0,s=0,l=e;s<l.length;s+=1){var c=l[s],u=new t.Point(c.x+100,c.y+100);n=Math.min(n,u.x),i=Math.min(i,u.y),a=Math.max(a,u.x),o=Math.max(o,u.y),r.push(u)}for(var f={},h={},p=0,d=this.grid.query(n,i,a,o).concat(this.ignoredGrid.query(n,i,a,o));p<d.length;p+=1){var g=d[p],m=g.key;if(void 0===f[m.bucketInstanceId]&&(f[m.bucketInstanceId]={}),!f[m.bucketInstanceId][m.featureIndex]){var v=[new t.Point(g.x1,g.y1),new t.Point(g.x2,g.y1),new t.Point(g.x2,g.y2),new t.Point(g.x1,g.y2)];t.polygonIntersectsPolygon(r,v)&&(f[m.bucketInstanceId][m.featureIndex]=!0,void 0===h[m.bucketInstanceId]&&(h[m.bucketInstanceId]=[]),h[m.bucketInstanceId].push(m.featureIndex))}}return h},he.prototype.insertCollisionBox=function(t,e,r,n,i){var a={bucketInstanceId:r,featureIndex:n,collisionGroupID:i};(e?this.ignoredGrid:this.grid).insert(a,t[0],t[1],t[2],t[3])},he.prototype.insertCollisionCircles=function(t,e,r,n,i){for(var a=e?this.ignoredGrid:this.grid,o={bucketInstanceId:r,featureIndex:n,collisionGroupID:i},s=0;s<t.length;s+=4)a.insertCircle(o,t[s],t[s+1],t[s+2])},he.prototype.projectAndGetPerspectiveRatio=function(e,r,n){var i=[r,n,0,1];return fe(i,i,e),{point:new t.Point((i[0]/i[3]+1)/2*this.transform.width+100,(-i[1]/i[3]+1)/2*this.transform.height+100),perspectiveRatio:.5+this.transform.cameraToCenterDistance/i[3]*.5}},he.prototype.isOffscreen=function(t,e,r,n){return r<100||t>=this.screenRightBoundary||n<100||e>this.screenBottomBoundary},he.prototype.isInsideGrid=function(t,e,r,n){return r>=0&&t<this.gridRightBoundary&&n>=0&&e<this.gridBottomBoundary},he.prototype.getViewportMatrix=function(){var e=t.identity([]);return t.translate(e,e,[-100,-100,0]),e};var de=function(t,e,r,n){this.opacity=t?Math.max(0,Math.min(1,t.opacity+(t.placed?e:-e))):n&&r?1:0,this.placed=r};de.prototype.isHidden=function(){return 0===this.opacity&&!this.placed};var ge=function(t,e,r,n,i){this.text=new de(t?t.text:null,e,r,i),this.icon=new de(t?t.icon:null,e,n,i)};ge.prototype.isHidden=function(){return this.text.isHidden()&&this.icon.isHidden()};var me=function(t,e,r){this.text=t,this.icon=e,this.skipFade=r},ve=function(){this.invProjMatrix=t.create(),this.viewportMatrix=t.create(),this.circles=[]},ye=function(t,e,r,n,i){this.bucketInstanceId=t,this.featureIndex=e,this.sourceLayerIndex=r,this.bucketIndex=n,this.tileID=i},xe=function(t){this.crossSourceCollisions=t,this.maxGroupID=0,this.collisionGroups={}};function be(e,r,n,i,a){var o=t.getAnchorAlignment(e),s=-(o.horizontalAlign-.5)*r,l=-(o.verticalAlign-.5)*n,c=t.evaluateVariableOffset(e,i);return new t.Point(s+c[0]*a,l+c[1]*a)}function _e(e,r,n,i,a,o){var s=e.x1,l=e.x2,c=e.y1,u=e.y2,f=e.anchorPointX,h=e.anchorPointY,p=new t.Point(r,n);return i&&p._rotate(a?o:-o),{x1:s+p.x,y1:c+p.y,x2:l+p.x,y2:u+p.y,anchorPointX:f,anchorPointY:h}}xe.prototype.get=function(t){if(this.crossSourceCollisions)return{ID:0,predicate:null};if(!this.collisionGroups[t]){var e=++this.maxGroupID;this.collisionGroups[t]={ID:e,predicate:function(t){return t.collisionGroupID===e}}}return this.collisionGroups[t]};var we=function(t,e,r,n){this.transform=t.clone(),this.collisionIndex=new he(this.transform),this.placements={},this.opacities={},this.variableOffsets={},this.stale=!1,this.commitTime=0,this.fadeDuration=e,this.retainedQueryData={},this.collisionGroups=new xe(r),this.collisionCircleArrays={},this.prevPlacement=n,n&&(n.prevPlacement=void 0),this.placedOrientations={}};function Te(t,e,r,n,i){t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0)}we.prototype.getBucketParts=function(e,r,n,i){var a=n.getBucket(r),o=n.latestFeatureIndex;if(a&&o&&r.id===a.layerIds[0]){var s=n.collisionBoxArray,l=a.layers[0].layout,c=Math.pow(2,this.transform.zoom-n.tileID.overscaledZ),u=n.tileSize/t.EXTENT,f=this.transform.calculatePosMatrix(n.tileID.toUnwrapped()),h=\"map\"===l.get(\"text-pitch-alignment\"),p=\"map\"===l.get(\"text-rotation-alignment\"),d=pe(n,1,this.transform.zoom),g=Qt(f,h,p,this.transform,d),m=null;if(h){var v=$t(f,h,p,this.transform,d);m=t.multiply([],this.transform.labelPlaneMatrix,v)}this.retainedQueryData[a.bucketInstanceId]=new ye(a.bucketInstanceId,o,a.sourceLayerIndex,a.index,n.tileID);var y={bucket:a,layout:l,posMatrix:f,textLabelPlaneMatrix:g,labelToScreenMatrix:m,scale:c,textPixelRatio:u,holdingForFade:n.holdingForFade(),collisionBoxArray:s,partiallyEvaluatedTextSize:t.evaluateSizeForZoom(a.textSizeData,this.transform.zoom),collisionGroup:this.collisionGroups.get(a.sourceID)};if(i)for(var x=0,b=a.sortKeyRanges;x<b.length;x+=1){var _=b[x],w=_.sortKey,T=_.symbolInstanceStart,k=_.symbolInstanceEnd;e.push({sortKey:w,symbolInstanceStart:T,symbolInstanceEnd:k,parameters:y})}else e.push({symbolInstanceStart:0,symbolInstanceEnd:a.symbolInstances.length,parameters:y})}},we.prototype.attemptAnchorPlacement=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d){var g,m=[f.textOffset0,f.textOffset1],v=be(t,r,n,m,i),y=this.collisionIndex.placeCollisionBox(_e(e,v.x,v.y,a,o,this.transform.angle),u,s,l,c.predicate);if(d&&0===this.collisionIndex.placeCollisionBox(_e(d,v.x,v.y,a,o,this.transform.angle),u,s,l,c.predicate).box.length)return;if(y.box.length>0)return this.prevPlacement&&this.prevPlacement.variableOffsets[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID].text&&(g=this.prevPlacement.variableOffsets[f.crossTileID].anchor),this.variableOffsets[f.crossTileID]={textOffset:m,width:r,height:n,anchor:t,textBoxScale:i,prevAnchor:g},this.markUsedJustification(h,t,f,p),h.allowVerticalPlacement&&(this.markUsedOrientation(h,p,f),this.placedOrientations[f.crossTileID]=p),{shift:v,placedGlyphBoxes:y}},we.prototype.placeLayerBucketPart=function(e,r,n){var i=this,a=e.parameters,o=a.bucket,s=a.layout,l=a.posMatrix,c=a.textLabelPlaneMatrix,u=a.labelToScreenMatrix,f=a.textPixelRatio,h=a.holdingForFade,p=a.collisionBoxArray,d=a.partiallyEvaluatedTextSize,g=a.collisionGroup,m=s.get(\"text-optional\"),v=s.get(\"icon-optional\"),y=s.get(\"text-allow-overlap\"),x=s.get(\"icon-allow-overlap\"),b=\"map\"===s.get(\"text-rotation-alignment\"),_=\"map\"===s.get(\"text-pitch-alignment\"),w=\"none\"!==s.get(\"icon-text-fit\"),T=\"viewport-y\"===s.get(\"symbol-z-order\"),k=y&&(x||!o.hasIconData()||v),A=x&&(y||!o.hasTextData()||m);!o.collisionArrays&&p&&o.deserializeCollisionBoxes(p);var M=function(e,a){if(!r[e.crossTileID])if(h)i.placements[e.crossTileID]=new me(!1,!1,!1);else{var p,T=!1,M=!1,S=!0,E=null,L={box:null,offscreen:null},C={box:null,offscreen:null},P=null,I=null,O=0,z=0,D=0;a.textFeatureIndex?O=a.textFeatureIndex:e.useRuntimeCollisionCircles&&(O=e.featureIndex),a.verticalTextFeatureIndex&&(z=a.verticalTextFeatureIndex);var R=a.textBox;if(R){var F=function(r){var n=t.WritingMode.horizontal;if(o.allowVerticalPlacement&&!r&&i.prevPlacement){var a=i.prevPlacement.placedOrientations[e.crossTileID];a&&(i.placedOrientations[e.crossTileID]=a,n=a,i.markUsedOrientation(o,n,e))}return n},B=function(r,n){if(o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&a.verticalTextBox)for(var i=0,s=o.writingModes;i<s.length;i+=1){if(s[i]===t.WritingMode.vertical?(L=n(),C=L):L=r(),L&&L.box&&L.box.length)break}else L=r()};if(s.get(\"text-variable-anchor\")){var N=s.get(\"text-variable-anchor\");if(i.prevPlacement&&i.prevPlacement.variableOffsets[e.crossTileID]){var j=i.prevPlacement.variableOffsets[e.crossTileID];N.indexOf(j.anchor)>0&&(N=N.filter((function(t){return t!==j.anchor}))).unshift(j.anchor)}var U=function(t,r,n){for(var a=t.x2-t.x1,s=t.y2-t.y1,c=e.textBoxScale,u=w&&!x?r:null,h={box:[],offscreen:!1},p=y?2*N.length:N.length,d=0;d<p;++d){var m=N[d%N.length],v=d>=N.length,k=i.attemptAnchorPlacement(m,t,a,s,c,b,_,f,l,g,v,e,o,n,u);if(k&&(h=k.placedGlyphBoxes)&&h.box&&h.box.length){T=!0,E=k.shift;break}}return h};B((function(){return U(R,a.iconBox,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox,n=L&&L.box&&L.box.length;return o.allowVerticalPlacement&&!n&&e.numVerticalGlyphVertices>0&&r?U(r,a.verticalIconBox,t.WritingMode.vertical):{box:null,offscreen:null}})),L&&(T=L.box,S=L.offscreen);var V=F(L&&L.box);if(!T&&i.prevPlacement){var H=i.prevPlacement.variableOffsets[e.crossTileID];H&&(i.variableOffsets[e.crossTileID]=H,i.markUsedJustification(o,H.anchor,e,V))}}else{var q=function(t,r){var n=i.collisionIndex.placeCollisionBox(t,y,f,l,g.predicate);return n&&n.box&&n.box.length&&(i.markUsedOrientation(o,r,e),i.placedOrientations[e.crossTileID]=r),n};B((function(){return q(R,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox;return o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&r?q(r,t.WritingMode.vertical):{box:null,offscreen:null}})),F(L&&L.box&&L.box.length)}}if(T=(p=L)&&p.box&&p.box.length>0,S=p&&p.offscreen,e.useRuntimeCollisionCircles){var G=o.text.placedSymbolArray.get(e.centerJustifiedTextSymbolIndex),Y=t.evaluateSizeForFeature(o.textSizeData,d,G),W=s.get(\"text-padding\"),X=e.collisionCircleDiameter;P=i.collisionIndex.placeCollisionCircles(y,G,o.lineVertexArray,o.glyphOffsetArray,Y,l,c,u,n,_,g.predicate,X,W),T=y||P.circles.length>0&&!P.collisionDetected,S=S&&P.offscreen}if(a.iconFeatureIndex&&(D=a.iconFeatureIndex),a.iconBox){var Z=function(t){var e=w&&E?_e(t,E.x,E.y,b,_,i.transform.angle):t;return i.collisionIndex.placeCollisionBox(e,x,f,l,g.predicate)};M=C&&C.box&&C.box.length&&a.verticalIconBox?(I=Z(a.verticalIconBox)).box.length>0:(I=Z(a.iconBox)).box.length>0,S=S&&I.offscreen}var J=m||0===e.numHorizontalGlyphVertices&&0===e.numVerticalGlyphVertices,K=v||0===e.numIconVertices;if(J||K?K?J||(M=M&&T):T=M&&T:M=T=M&&T,T&&p&&p.box&&(C&&C.box&&z?i.collisionIndex.insertCollisionBox(p.box,s.get(\"text-ignore-placement\"),o.bucketInstanceId,z,g.ID):i.collisionIndex.insertCollisionBox(p.box,s.get(\"text-ignore-placement\"),o.bucketInstanceId,O,g.ID)),M&&I&&i.collisionIndex.insertCollisionBox(I.box,s.get(\"icon-ignore-placement\"),o.bucketInstanceId,D,g.ID),P&&(T&&i.collisionIndex.insertCollisionCircles(P.circles,s.get(\"text-ignore-placement\"),o.bucketInstanceId,O,g.ID),n)){var Q=o.bucketInstanceId,$=i.collisionCircleArrays[Q];void 0===$&&($=i.collisionCircleArrays[Q]=new ve);for(var tt=0;tt<P.circles.length;tt+=4)$.circles.push(P.circles[tt+0]),$.circles.push(P.circles[tt+1]),$.circles.push(P.circles[tt+2]),$.circles.push(P.collisionDetected?1:0)}i.placements[e.crossTileID]=new me(T||k,M||A,S||o.justReloaded),r[e.crossTileID]=!0}};if(T)for(var S=o.getSortedSymbolIndexes(this.transform.angle),E=S.length-1;E>=0;--E){var L=S[E];M(o.symbolInstances.get(L),o.collisionArrays[L])}else for(var C=e.symbolInstanceStart;C<e.symbolInstanceEnd;C++)M(o.symbolInstances.get(C),o.collisionArrays[C]);if(n&&o.bucketInstanceId in this.collisionCircleArrays){var P=this.collisionCircleArrays[o.bucketInstanceId];t.invert(P.invProjMatrix,l),P.viewportMatrix=this.collisionIndex.getViewportMatrix()}o.justReloaded=!1},we.prototype.markUsedJustification=function(e,r,n,i){var a,o={left:n.leftJustifiedTextSymbolIndex,center:n.centerJustifiedTextSymbolIndex,right:n.rightJustifiedTextSymbolIndex};a=i===t.WritingMode.vertical?n.verticalPlacedTextSymbolIndex:o[t.getAnchorJustification(r)];for(var s=0,l=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex,n.verticalPlacedTextSymbolIndex];s<l.length;s+=1){var c=l[s];c>=0&&(e.text.placedSymbolArray.get(c).crossTileID=a>=0&&c!==a?0:n.crossTileID)}},we.prototype.markUsedOrientation=function(e,r,n){for(var i=r===t.WritingMode.horizontal||r===t.WritingMode.horizontalOnly?r:0,a=r===t.WritingMode.vertical?r:0,o=0,s=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex];o<s.length;o+=1){var l=s[o];e.text.placedSymbolArray.get(l).placedOrientation=i}n.verticalPlacedTextSymbolIndex&&(e.text.placedSymbolArray.get(n.verticalPlacedTextSymbolIndex).placedOrientation=a)},we.prototype.commit=function(t){this.commitTime=t,this.zoomAtLastRecencyCheck=this.transform.zoom;var e=this.prevPlacement,r=!1;this.prevZoomAdjustment=e?e.zoomAdjustment(this.transform.zoom):0;var n=e?e.symbolFadeChange(t):1,i=e?e.opacities:{},a=e?e.variableOffsets:{},o=e?e.placedOrientations:{};for(var s in this.placements){var l=this.placements[s],c=i[s];c?(this.opacities[s]=new ge(c,n,l.text,l.icon),r=r||l.text!==c.text.placed||l.icon!==c.icon.placed):(this.opacities[s]=new ge(null,n,l.text,l.icon,l.skipFade),r=r||l.text||l.icon)}for(var u in i){var f=i[u];if(!this.opacities[u]){var h=new ge(f,n,!1,!1);h.isHidden()||(this.opacities[u]=h,r=r||f.text.placed||f.icon.placed)}}for(var p in a)this.variableOffsets[p]||!this.opacities[p]||this.opacities[p].isHidden()||(this.variableOffsets[p]=a[p]);for(var d in o)this.placedOrientations[d]||!this.opacities[d]||this.opacities[d].isHidden()||(this.placedOrientations[d]=o[d]);r?this.lastPlacementChangeTime=t:\"number\"!=typeof this.lastPlacementChangeTime&&(this.lastPlacementChangeTime=e?e.lastPlacementChangeTime:t)},we.prototype.updateLayerOpacities=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1){var a=i[n],o=a.getBucket(t);o&&a.latestFeatureIndex&&t.id===o.layerIds[0]&&this.updateBucketOpacities(o,r,a.collisionBoxArray)}},we.prototype.updateBucketOpacities=function(e,r,n){var i=this;e.hasTextData()&&e.text.opacityVertexArray.clear(),e.hasIconData()&&e.icon.opacityVertexArray.clear(),e.hasIconCollisionBoxData()&&e.iconCollisionBox.collisionVertexArray.clear(),e.hasTextCollisionBoxData()&&e.textCollisionBox.collisionVertexArray.clear();var a=e.layers[0].layout,o=new ge(null,0,!1,!1,!0),s=a.get(\"text-allow-overlap\"),l=a.get(\"icon-allow-overlap\"),c=a.get(\"text-variable-anchor\"),u=\"map\"===a.get(\"text-rotation-alignment\"),f=\"map\"===a.get(\"text-pitch-alignment\"),h=\"none\"!==a.get(\"icon-text-fit\"),p=new ge(null,0,s&&(l||!e.hasIconData()||a.get(\"icon-optional\")),l&&(s||!e.hasTextData()||a.get(\"text-optional\")),!0);!e.collisionArrays&&n&&(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData())&&e.deserializeCollisionBoxes(n);for(var d=function(t,e,r){for(var n=0;n<e/4;n++)t.opacityVertexArray.emplaceBack(r)},g=function(n){var a=e.symbolInstances.get(n),s=a.numHorizontalGlyphVertices,l=a.numVerticalGlyphVertices,g=a.crossTileID,m=r[g],v=i.opacities[g];m?v=o:v||(v=p,i.opacities[g]=v),r[g]=!0;var y=s>0||l>0,x=a.numIconVertices>0,b=i.placedOrientations[a.crossTileID],_=b===t.WritingMode.vertical,w=b===t.WritingMode.horizontal||b===t.WritingMode.horizontalOnly;if(y){var T=Pe(v.text),k=_?Ie:T;d(e.text,s,k);var A=w?Ie:T;d(e.text,l,A);var M=v.text.isHidden();[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t){t>=0&&(e.text.placedSymbolArray.get(t).hidden=M||_?1:0)})),a.verticalPlacedTextSymbolIndex>=0&&(e.text.placedSymbolArray.get(a.verticalPlacedTextSymbolIndex).hidden=M||w?1:0);var S=i.variableOffsets[a.crossTileID];S&&i.markUsedJustification(e,S.anchor,a,b);var E=i.placedOrientations[a.crossTileID];E&&(i.markUsedJustification(e,\"left\",a,E),i.markUsedOrientation(e,E,a))}if(x){var L=Pe(v.icon),C=!(h&&a.verticalPlacedIconSymbolIndex&&_);if(a.placedIconSymbolIndex>=0){var P=C?L:Ie;d(e.icon,a.numIconVertices,P),e.icon.placedSymbolArray.get(a.placedIconSymbolIndex).hidden=v.icon.isHidden()}if(a.verticalPlacedIconSymbolIndex>=0){var I=C?Ie:L;d(e.icon,a.numVerticalIconVertices,I),e.icon.placedSymbolArray.get(a.verticalPlacedIconSymbolIndex).hidden=v.icon.isHidden()}}if(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData()){var O=e.collisionArrays[n];if(O){var z=new t.Point(0,0);if(O.textBox||O.verticalTextBox){var D=!0;if(c){var R=i.variableOffsets[g];R?(z=be(R.anchor,R.width,R.height,R.textOffset,R.textBoxScale),u&&z._rotate(f?i.transform.angle:-i.transform.angle)):D=!1}O.textBox&&Te(e.textCollisionBox.collisionVertexArray,v.text.placed,!D||_,z.x,z.y),O.verticalTextBox&&Te(e.textCollisionBox.collisionVertexArray,v.text.placed,!D||w,z.x,z.y)}var F=Boolean(!w&&O.verticalIconBox);O.iconBox&&Te(e.iconCollisionBox.collisionVertexArray,v.icon.placed,F,h?z.x:0,h?z.y:0),O.verticalIconBox&&Te(e.iconCollisionBox.collisionVertexArray,v.icon.placed,!F,h?z.x:0,h?z.y:0)}}},m=0;m<e.symbolInstances.length;m++)g(m);if(e.sortFeatures(this.transform.angle),this.retainedQueryData[e.bucketInstanceId]&&(this.retainedQueryData[e.bucketInstanceId].featureSortOrder=e.featureSortOrder),e.hasTextData()&&e.text.opacityVertexBuffer&&e.text.opacityVertexBuffer.updateData(e.text.opacityVertexArray),e.hasIconData()&&e.icon.opacityVertexBuffer&&e.icon.opacityVertexBuffer.updateData(e.icon.opacityVertexArray),e.hasIconCollisionBoxData()&&e.iconCollisionBox.collisionVertexBuffer&&e.iconCollisionBox.collisionVertexBuffer.updateData(e.iconCollisionBox.collisionVertexArray),e.hasTextCollisionBoxData()&&e.textCollisionBox.collisionVertexBuffer&&e.textCollisionBox.collisionVertexBuffer.updateData(e.textCollisionBox.collisionVertexArray),e.bucketInstanceId in this.collisionCircleArrays){var v=this.collisionCircleArrays[e.bucketInstanceId];e.placementInvProjMatrix=v.invProjMatrix,e.placementViewportMatrix=v.viewportMatrix,e.collisionCircleArray=v.circles,delete this.collisionCircleArrays[e.bucketInstanceId]}},we.prototype.symbolFadeChange=function(t){return 0===this.fadeDuration?1:(t-this.commitTime)/this.fadeDuration+this.prevZoomAdjustment},we.prototype.zoomAdjustment=function(t){return Math.max(0,(this.transform.zoom-t)/1.5)},we.prototype.hasTransitions=function(t){return this.stale||t-this.lastPlacementChangeTime<this.fadeDuration},we.prototype.stillRecent=function(t,e){var r=this.zoomAtLastRecencyCheck===e?1-this.zoomAdjustment(e):1;return this.zoomAtLastRecencyCheck=e,this.commitTime+this.fadeDuration*r>t},we.prototype.setStale=function(){this.stale=!0};var ke=Math.pow(2,25),Ae=Math.pow(2,24),Me=Math.pow(2,17),Se=Math.pow(2,16),Ee=Math.pow(2,9),Le=Math.pow(2,8),Ce=Math.pow(2,1);function Pe(t){if(0===t.opacity&&!t.placed)return 0;if(1===t.opacity&&t.placed)return 4294967295;var e=t.placed?1:0,r=Math.floor(127*t.opacity);return r*ke+e*Ae+r*Me+e*Se+r*Ee+e*Le+r*Ce+e}var Ie=0,Oe=function(t){this._sortAcrossTiles=\"viewport-y\"!==t.layout.get(\"symbol-z-order\")&&void 0!==t.layout.get(\"symbol-sort-key\").constantOr(1),this._currentTileIndex=0,this._currentPartIndex=0,this._seenCrossTileIDs={},this._bucketParts=[]};Oe.prototype.continuePlacement=function(t,e,r,n,i){for(var a=this._bucketParts;this._currentTileIndex<t.length;){var o=t[this._currentTileIndex];if(e.getBucketParts(a,n,o,this._sortAcrossTiles),this._currentTileIndex++,i())return!0}for(this._sortAcrossTiles&&(this._sortAcrossTiles=!1,a.sort((function(t,e){return t.sortKey-e.sortKey})));this._currentPartIndex<a.length;){var s=a[this._currentPartIndex];if(e.placeLayerBucketPart(s,this._seenCrossTileIDs,r),this._currentPartIndex++,i())return!0}return!1};var ze=function(t,e,r,n,i,a,o){this.placement=new we(t,i,a,o),this._currentPlacementIndex=e.length-1,this._forceFullPlacement=r,this._showCollisionBoxes=n,this._done=!1};ze.prototype.isDone=function(){return this._done},ze.prototype.continuePlacement=function(e,r,n){for(var i=this,a=t.browser.now(),o=function(){var e=t.browser.now()-a;return!i._forceFullPlacement&&e>2};this._currentPlacementIndex>=0;){var s=r[e[this._currentPlacementIndex]],l=this.placement.collisionIndex.transform.zoom;if(\"symbol\"===s.type&&(!s.minzoom||s.minzoom<=l)&&(!s.maxzoom||s.maxzoom>l)){if(this._inProgressLayer||(this._inProgressLayer=new Oe(s)),this._inProgressLayer.continuePlacement(n[s.source],this.placement,this._showCollisionBoxes,s,o))return;delete this._inProgressLayer}this._currentPlacementIndex--}this._done=!0},ze.prototype.commit=function(t){return this.placement.commit(t),this.placement};var De=512/t.EXTENT/2,Re=function(t,e,r){this.tileID=t,this.indexedSymbolInstances={},this.bucketInstanceId=r;for(var n=0;n<e.length;n++){var i=e.get(n),a=i.key;this.indexedSymbolInstances[a]||(this.indexedSymbolInstances[a]=[]),this.indexedSymbolInstances[a].push({crossTileID:i.crossTileID,coord:this.getScaledCoordinates(i,t)})}};Re.prototype.getScaledCoordinates=function(e,r){var n=r.canonical.z-this.tileID.canonical.z,i=De/Math.pow(2,n);return{x:Math.floor((r.canonical.x*t.EXTENT+e.anchorX)*i),y:Math.floor((r.canonical.y*t.EXTENT+e.anchorY)*i)}},Re.prototype.findMatches=function(t,e,r){for(var n=this.tileID.canonical.z<e.canonical.z?1:Math.pow(2,this.tileID.canonical.z-e.canonical.z),i=0;i<t.length;i++){var a=t.get(i);if(!a.crossTileID){var o=this.indexedSymbolInstances[a.key];if(o)for(var s=this.getScaledCoordinates(a,e),l=0,c=o;l<c.length;l+=1){var u=c[l];if(Math.abs(u.coord.x-s.x)<=n&&Math.abs(u.coord.y-s.y)<=n&&!r[u.crossTileID]){r[u.crossTileID]=!0,a.crossTileID=u.crossTileID;break}}}}};var Fe=function(){this.maxCrossTileID=0};Fe.prototype.generate=function(){return++this.maxCrossTileID};var Be=function(){this.indexes={},this.usedCrossTileIDs={},this.lng=0};Be.prototype.handleWrapJump=function(t){var e=Math.round((t-this.lng)/360);if(0!==e)for(var r in this.indexes){var n=this.indexes[r],i={};for(var a in n){var o=n[a];o.tileID=o.tileID.unwrapTo(o.tileID.wrap+e),i[o.tileID.key]=o}this.indexes[r]=i}this.lng=t},Be.prototype.addBucket=function(t,e,r){if(this.indexes[t.overscaledZ]&&this.indexes[t.overscaledZ][t.key]){if(this.indexes[t.overscaledZ][t.key].bucketInstanceId===e.bucketInstanceId)return!1;this.removeBucketCrossTileIDs(t.overscaledZ,this.indexes[t.overscaledZ][t.key])}for(var n=0;n<e.symbolInstances.length;n++){e.symbolInstances.get(n).crossTileID=0}this.usedCrossTileIDs[t.overscaledZ]||(this.usedCrossTileIDs[t.overscaledZ]={});var i=this.usedCrossTileIDs[t.overscaledZ];for(var a in this.indexes){var o=this.indexes[a];if(Number(a)>t.overscaledZ)for(var s in o){var l=o[s];l.tileID.isChildOf(t)&&l.findMatches(e.symbolInstances,t,i)}else{var c=o[t.scaledTo(Number(a)).key];c&&c.findMatches(e.symbolInstances,t,i)}}for(var u=0;u<e.symbolInstances.length;u++){var f=e.symbolInstances.get(u);f.crossTileID||(f.crossTileID=r.generate(),i[f.crossTileID]=!0)}return void 0===this.indexes[t.overscaledZ]&&(this.indexes[t.overscaledZ]={}),this.indexes[t.overscaledZ][t.key]=new Re(t,e.symbolInstances,e.bucketInstanceId),!0},Be.prototype.removeBucketCrossTileIDs=function(t,e){for(var r in e.indexedSymbolInstances)for(var n=0,i=e.indexedSymbolInstances[r];n<i.length;n+=1){var a=i[n];delete this.usedCrossTileIDs[t][a.crossTileID]}},Be.prototype.removeStaleBuckets=function(t){var e=!1;for(var r in this.indexes){var n=this.indexes[r];for(var i in n)t[n[i].bucketInstanceId]||(this.removeBucketCrossTileIDs(r,n[i]),delete n[i],e=!0)}return e};var Ne=function(){this.layerIndexes={},this.crossTileIDs=new Fe,this.maxBucketInstanceId=0,this.bucketsInCurrentPlacement={}};Ne.prototype.addLayer=function(t,e,r){var n=this.layerIndexes[t.id];void 0===n&&(n=this.layerIndexes[t.id]=new Be);var i=!1,a={};n.handleWrapJump(r);for(var o=0,s=e;o<s.length;o+=1){var l=s[o],c=l.getBucket(t);c&&t.id===c.layerIds[0]&&(c.bucketInstanceId||(c.bucketInstanceId=++this.maxBucketInstanceId),n.addBucket(l.tileID,c,this.crossTileIDs)&&(i=!0),a[c.bucketInstanceId]=!0)}return n.removeStaleBuckets(a)&&(i=!0),i},Ne.prototype.pruneUnusedLayers=function(t){var e={};for(var r in t.forEach((function(t){e[t]=!0})),this.layerIndexes)e[r]||delete this.layerIndexes[r]};var je=function(e,r){return t.emitValidationErrors(e,r&&r.filter((function(t){return\"source.canvas\"!==t.identifier})))},Ue=t.pick(Ut,[\"addLayer\",\"removeLayer\",\"setPaintProperty\",\"setLayoutProperty\",\"setFilter\",\"addSource\",\"removeSource\",\"setLayerZoomRange\",\"setLight\",\"setTransition\",\"setGeoJSONSourceData\"]),Ve=t.pick(Ut,[\"setCenter\",\"setZoom\",\"setBearing\",\"setPitch\"]),He=function(){var e={},r=t.styleSpec.$version;for(var n in t.styleSpec.$root){var i=t.styleSpec.$root[n];if(i.required){var a=null;null!=(a=\"version\"===n?r:\"array\"===i.type?[]:{})&&(e[n]=a)}}return e}(),qe=function(e){function r(n,i){var a=this;void 0===i&&(i={}),e.call(this),this.map=n,this.dispatcher=new k(Bt(),this),this.imageManager=new h,this.imageManager.setEventedParent(this),this.glyphManager=new x(n._requestManager,i.localIdeographFontFamily),this.lineAtlas=new T(256,512),this.crossTileSymbolIndex=new Ne,this._layers={},this._serializedLayers={},this._order=[],this.sourceCaches={},this.zoomHistory=new t.ZoomHistory,this._loaded=!1,this._availableImages=[],this._resetUpdates(),this.dispatcher.broadcast(\"setReferrer\",t.getReferrer());var o=this;this._rtlTextPluginCallback=r.registerForPluginStateChange((function(e){var r={pluginStatus:e.pluginStatus,pluginURL:e.pluginURL};o.dispatcher.broadcast(\"syncRTLPluginState\",r,(function(e,r){if((t.triggerPluginCompletionEvent(e),r)&&r.every((function(t){return t})))for(var n in o.sourceCaches)o.sourceCaches[n].reload()}))})),this.on(\"data\",(function(t){if(\"source\"===t.dataType&&\"metadata\"===t.sourceDataType){var e=a.sourceCaches[t.sourceId];if(e){var r=e.getSource();if(r&&r.vectorLayerIds)for(var n in a._layers){var i=a._layers[n];i.source===r.id&&a._validateLayer(i)}}}}))}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadURL=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event(\"dataloading\",{dataType:\"style\"}));var i=\"boolean\"==typeof r.validate?r.validate:!t.isMapboxURL(e);e=this.map._requestManager.normalizeStyleURL(e,r.accessToken);var a=this.map._requestManager.transformRequest(e,t.ResourceType.Style);this._request=t.getJSON(a,(function(e,r){n._request=null,e?n.fire(new t.ErrorEvent(e)):r&&n._load(r,i)}))},r.prototype.loadJSON=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event(\"dataloading\",{dataType:\"style\"})),this._request=t.browser.frame((function(){n._request=null,n._load(e,!1!==r.validate)}))},r.prototype.loadEmpty=function(){this.fire(new t.Event(\"dataloading\",{dataType:\"style\"})),this._load(He,!1)},r.prototype._load=function(e,r){if(!r||!je(this,t.validateStyle(e))){for(var n in this._loaded=!0,this.stylesheet=e,e.sources)this.addSource(n,e.sources[n],{validate:!1});e.sprite?this._loadSprite(e.sprite):this.imageManager.setLoaded(!0),this.glyphManager.setURL(e.glyphs);var i=jt(this.stylesheet.layers);this._order=i.map((function(t){return t.id})),this._layers={},this._serializedLayers={};for(var a=0,o=i;a<o.length;a+=1){var s=o[a];(s=t.createStyleLayer(s)).setEventedParent(this,{layer:{id:s.id}}),this._layers[s.id]=s,this._serializedLayers[s.id]=s.serialize()}this.dispatcher.broadcast(\"setLayers\",this._serializeLayers(this._order)),this.light=new w(this.stylesheet.light),this.fire(new t.Event(\"data\",{dataType:\"style\"})),this.fire(new t.Event(\"style.load\"))}},r.prototype._loadSprite=function(e){var r=this;this._spriteRequest=function(e,r,n){var i,a,o,s=t.browser.devicePixelRatio>1?\"@2x\":\"\",l=t.getJSON(r.transformRequest(r.normalizeSpriteURL(e,s,\".json\"),t.ResourceType.SpriteJSON),(function(t,e){l=null,o||(o=t,i=e,u())})),c=t.getImage(r.transformRequest(r.normalizeSpriteURL(e,s,\".png\"),t.ResourceType.SpriteImage),(function(t,e){c=null,o||(o=t,a=e,u())}));function u(){if(o)n(o);else if(i&&a){var e=t.browser.getImageData(a),r={};for(var s in i){var l=i[s],c=l.width,u=l.height,f=l.x,h=l.y,p=l.sdf,d=l.pixelRatio,g=l.stretchX,m=l.stretchY,v=l.content,y=new t.RGBAImage({width:c,height:u});t.RGBAImage.copy(e,y,{x:f,y:h},{x:0,y:0},{width:c,height:u}),r[s]={data:y,pixelRatio:d,sdf:p,stretchX:g,stretchY:m,content:v}}n(null,r)}}return{cancel:function(){l&&(l.cancel(),l=null),c&&(c.cancel(),c=null)}}}(e,this.map._requestManager,(function(e,n){if(r._spriteRequest=null,e)r.fire(new t.ErrorEvent(e));else if(n)for(var i in n)r.imageManager.addImage(i,n[i]);r.imageManager.setLoaded(!0),r._availableImages=r.imageManager.listImages(),r.dispatcher.broadcast(\"setImages\",r._availableImages),r.fire(new t.Event(\"data\",{dataType:\"style\"}))}))},r.prototype._validateLayer=function(e){var r=this.sourceCaches[e.source];if(r){var n=e.sourceLayer;if(n){var i=r.getSource();(\"geojson\"===i.type||i.vectorLayerIds&&-1===i.vectorLayerIds.indexOf(n))&&this.fire(new t.ErrorEvent(new Error('Source layer \"'+n+'\" does not exist on source \"'+i.id+'\" as specified by style layer \"'+e.id+'\"')))}}},r.prototype.loaded=function(){if(!this._loaded)return!1;if(Object.keys(this._updatedSources).length)return!1;for(var t in this.sourceCaches)if(!this.sourceCaches[t].loaded())return!1;return!!this.imageManager.isLoaded()},r.prototype._serializeLayers=function(t){for(var e=[],r=0,n=t;r<n.length;r+=1){var i=n[r],a=this._layers[i];\"custom\"!==a.type&&e.push(a.serialize())}return e},r.prototype.hasTransitions=function(){if(this.light&&this.light.hasTransition())return!0;for(var t in this.sourceCaches)if(this.sourceCaches[t].hasTransition())return!0;for(var e in this._layers)if(this._layers[e].hasTransition())return!0;return!1},r.prototype._checkLoaded=function(){if(!this._loaded)throw new Error(\"Style is not done loading\")},r.prototype.update=function(e){if(this._loaded){var r=this._changed;if(this._changed){var n=Object.keys(this._updatedLayers),i=Object.keys(this._removedLayers);for(var a in(n.length||i.length)&&this._updateWorkerLayers(n,i),this._updatedSources){var o=this._updatedSources[a];\"reload\"===o?this._reloadSource(a):\"clear\"===o&&this._clearSource(a)}for(var s in this._updateTilesForChangedImages(),this._updatedPaintProps)this._layers[s].updateTransitions(e);this.light.updateTransitions(e),this._resetUpdates()}for(var l in this.sourceCaches)this.sourceCaches[l].used=!1;for(var c=0,u=this._order;c<u.length;c+=1){var f=u[c],h=this._layers[f];h.recalculate(e,this._availableImages),!h.isHidden(e.zoom)&&h.source&&(this.sourceCaches[h.source].used=!0)}this.light.recalculate(e),this.z=e.zoom,r&&this.fire(new t.Event(\"data\",{dataType:\"style\"}))}},r.prototype._updateTilesForChangedImages=function(){var t=Object.keys(this._changedImages);if(t.length){for(var e in this.sourceCaches)this.sourceCaches[e].reloadTilesForDependencies([\"icons\",\"patterns\"],t);this._changedImages={}}},r.prototype._updateWorkerLayers=function(t,e){this.dispatcher.broadcast(\"updateLayers\",{layers:this._serializeLayers(t),removedIds:e})},r.prototype._resetUpdates=function(){this._changed=!1,this._updatedLayers={},this._removedLayers={},this._updatedSources={},this._updatedPaintProps={},this._changedImages={}},r.prototype.setState=function(e){var r=this;if(this._checkLoaded(),je(this,t.validateStyle(e)))return!1;(e=t.clone$1(e)).layers=jt(e.layers);var n=Zt(this.serialize(),e).filter((function(t){return!(t.command in Ve)}));if(0===n.length)return!1;var i=n.filter((function(t){return!(t.command in Ue)}));if(i.length>0)throw new Error(\"Unimplemented: \"+i.map((function(t){return t.command})).join(\", \")+\".\");return n.forEach((function(t){\"setTransition\"!==t.command&&r[t.command].apply(r,t.args)})),this.stylesheet=e,!0},r.prototype.addImage=function(e,r){if(this.getImage(e))return this.fire(new t.ErrorEvent(new Error(\"An image with this name already exists.\")));this.imageManager.addImage(e,r),this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.fire(new t.Event(\"data\",{dataType:\"style\"}))},r.prototype.updateImage=function(t,e){this.imageManager.updateImage(t,e)},r.prototype.getImage=function(t){return this.imageManager.getImage(t)},r.prototype.removeImage=function(e){if(!this.getImage(e))return this.fire(new t.ErrorEvent(new Error(\"No image with this name exists.\")));this.imageManager.removeImage(e),this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.fire(new t.Event(\"data\",{dataType:\"style\"}))},r.prototype.listImages=function(){return this._checkLoaded(),this.imageManager.listImages()},r.prototype.addSource=function(e,r,n){var i=this;if(void 0===n&&(n={}),this._checkLoaded(),void 0!==this.sourceCaches[e])throw new Error(\"There is already a source with this ID\");if(!r.type)throw new Error(\"The type property must be defined, but the only the following properties were given: \"+Object.keys(r).join(\", \")+\".\");if(!([\"vector\",\"raster\",\"geojson\",\"video\",\"image\"].indexOf(r.type)>=0)||!this._validate(t.validateStyle.source,\"sources.\"+e,r,null,n)){this.map&&this.map._collectResourceTiming&&(r.collectResourceTiming=!0);var a=this.sourceCaches[e]=new Ct(e,r,this.dispatcher);a.style=this,a.setEventedParent(this,(function(){return{isSourceLoaded:i.loaded(),source:a.serialize(),sourceId:e}})),a.onAdd(this.map),this._changed=!0}},r.prototype.removeSource=function(e){if(this._checkLoaded(),void 0===this.sourceCaches[e])throw new Error(\"There is no source with this ID\");for(var r in this._layers)if(this._layers[r].source===e)return this.fire(new t.ErrorEvent(new Error('Source \"'+e+'\" cannot be removed while layer \"'+r+'\" is using it.')));var n=this.sourceCaches[e];delete this.sourceCaches[e],delete this._updatedSources[e],n.fire(new t.Event(\"data\",{sourceDataType:\"metadata\",dataType:\"source\",sourceId:e})),n.setEventedParent(null),n.clearTiles(),n.onRemove&&n.onRemove(this.map),this._changed=!0},r.prototype.setGeoJSONSourceData=function(t,e){this._checkLoaded(),this.sourceCaches[t].getSource().setData(e),this._changed=!0},r.prototype.getSource=function(t){return this.sourceCaches[t]&&this.sourceCaches[t].getSource()},r.prototype.addLayer=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=e.id;if(this.getLayer(i))this.fire(new t.ErrorEvent(new Error('Layer with id \"'+i+'\" already exists on this map')));else{var a;if(\"custom\"===e.type){if(je(this,t.validateCustomStyleLayer(e)))return;a=t.createStyleLayer(e)}else{if(\"object\"==typeof e.source&&(this.addSource(i,e.source),e=t.clone$1(e),e=t.extend(e,{source:i})),this._validate(t.validateStyle.layer,\"layers.\"+i,e,{arrayIndex:-1},n))return;a=t.createStyleLayer(e),this._validateLayer(a),a.setEventedParent(this,{layer:{id:i}}),this._serializedLayers[a.id]=a.serialize()}var o=r?this._order.indexOf(r):this._order.length;if(r&&-1===o)this.fire(new t.ErrorEvent(new Error('Layer with id \"'+r+'\" does not exist on this map.')));else{if(this._order.splice(o,0,i),this._layerOrderChanged=!0,this._layers[i]=a,this._removedLayers[i]&&a.source&&\"custom\"!==a.type){var s=this._removedLayers[i];delete this._removedLayers[i],s.type!==a.type?this._updatedSources[a.source]=\"clear\":(this._updatedSources[a.source]=\"reload\",this.sourceCaches[a.source].pause())}this._updateLayer(a),a.onAdd&&a.onAdd(this.map)}}},r.prototype.moveLayer=function(e,r){if(this._checkLoaded(),this._changed=!0,this._layers[e]){if(e!==r){var n=this._order.indexOf(e);this._order.splice(n,1);var i=r?this._order.indexOf(r):this._order.length;r&&-1===i?this.fire(new t.ErrorEvent(new Error('Layer with id \"'+r+'\" does not exist on this map.'))):(this._order.splice(i,0,e),this._layerOrderChanged=!0)}}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be moved.\")))},r.prototype.removeLayer=function(e){this._checkLoaded();var r=this._layers[e];if(r){r.setEventedParent(null);var n=this._order.indexOf(e);this._order.splice(n,1),this._layerOrderChanged=!0,this._changed=!0,this._removedLayers[e]=r,delete this._layers[e],delete this._serializedLayers[e],delete this._updatedLayers[e],delete this._updatedPaintProps[e],r.onRemove&&r.onRemove(this.map)}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be removed.\")))},r.prototype.getLayer=function(t){return this._layers[t]},r.prototype.hasLayer=function(t){return t in this._layers},r.prototype.setLayerZoomRange=function(e,r,n){this._checkLoaded();var i=this.getLayer(e);i?i.minzoom===r&&i.maxzoom===n||(null!=r&&(i.minzoom=r),null!=n&&(i.maxzoom=n),this._updateLayer(i)):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot have zoom extent.\")))},r.prototype.setFilter=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=this.getLayer(e);if(i){if(!t.deepEqual(i.filter,r))return null==r?(i.filter=void 0,void this._updateLayer(i)):void(this._validate(t.validateStyle.filter,\"layers.\"+i.id+\".filter\",r,null,n)||(i.filter=t.clone$1(r),this._updateLayer(i)))}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be filtered.\")))},r.prototype.getFilter=function(e){return t.clone$1(this.getLayer(e).filter)},r.prototype.setLayoutProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getLayoutProperty(r),n)||(a.setLayoutProperty(r,n,i),this._updateLayer(a)):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be styled.\")))},r.prototype.getLayoutProperty=function(e,r){var n=this.getLayer(e);if(n)return n.getLayoutProperty(r);this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style.\")))},r.prototype.setPaintProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getPaintProperty(r),n)||(a.setPaintProperty(r,n,i)&&this._updateLayer(a),this._changed=!0,this._updatedPaintProps[e]=!0):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be styled.\")))},r.prototype.getPaintProperty=function(t,e){return this.getLayer(t).getPaintProperty(e)},r.prototype.setFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=e.sourceLayer,a=this.sourceCaches[n];if(void 0!==a){var o=a.getSource().type;\"geojson\"===o&&i?this.fire(new t.ErrorEvent(new Error(\"GeoJSON sources cannot have a sourceLayer parameter.\"))):\"vector\"!==o||i?(void 0===e.id&&this.fire(new t.ErrorEvent(new Error(\"The feature id parameter must be provided.\"))),a.setFeatureState(i,e.id,r)):this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+n+\"' does not exist in the map's style.\")))},r.prototype.removeFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=this.sourceCaches[n];if(void 0!==i){var a=i.getSource().type,o=\"vector\"===a?e.sourceLayer:void 0;\"vector\"!==a||o?r&&\"string\"!=typeof e.id&&\"number\"!=typeof e.id?this.fire(new t.ErrorEvent(new Error(\"A feature id is requred to remove its specific state property.\"))):i.removeFeatureState(o,e.id,r):this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+n+\"' does not exist in the map's style.\")))},r.prototype.getFeatureState=function(e){this._checkLoaded();var r=e.source,n=e.sourceLayer,i=this.sourceCaches[r];if(void 0!==i){if(\"vector\"!==i.getSource().type||n)return void 0===e.id&&this.fire(new t.ErrorEvent(new Error(\"The feature id parameter must be provided.\"))),i.getFeatureState(n,e.id);this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+r+\"' does not exist in the map's style.\")))},r.prototype.getTransition=function(){return t.extend({duration:300,delay:0},this.stylesheet&&this.stylesheet.transition)},r.prototype.serialize=function(){return t.filterObject({version:this.stylesheet.version,name:this.stylesheet.name,metadata:this.stylesheet.metadata,light:this.stylesheet.light,center:this.stylesheet.center,zoom:this.stylesheet.zoom,bearing:this.stylesheet.bearing,pitch:this.stylesheet.pitch,sprite:this.stylesheet.sprite,glyphs:this.stylesheet.glyphs,transition:this.stylesheet.transition,sources:t.mapObject(this.sourceCaches,(function(t){return t.serialize()})),layers:this._serializeLayers(this._order)},(function(t){return void 0!==t}))},r.prototype._updateLayer=function(t){this._updatedLayers[t.id]=!0,t.source&&!this._updatedSources[t.source]&&\"raster\"!==this.sourceCaches[t.source].getSource().type&&(this._updatedSources[t.source]=\"reload\",this.sourceCaches[t.source].pause()),this._changed=!0},r.prototype._flattenAndSortRenderedFeatures=function(t){for(var e=this,r=function(t){return\"fill-extrusion\"===e._layers[t].type},n={},i=[],a=this._order.length-1;a>=0;a--){var o=this._order[a];if(r(o)){n[o]=a;for(var s=0,l=t;s<l.length;s+=1){var c=l[s][o];if(c)for(var u=0,f=c;u<f.length;u+=1){var h=f[u];i.push(h)}}}}i.sort((function(t,e){return e.intersectionZ-t.intersectionZ}));for(var p=[],d=this._order.length-1;d>=0;d--){var g=this._order[d];if(r(g))for(var m=i.length-1;m>=0;m--){var v=i[m].feature;if(n[v.layer.id]<d)break;p.push(v),i.pop()}else for(var y=0,x=t;y<x.length;y+=1){var b=x[y][g];if(b)for(var _=0,w=b;_<w.length;_+=1){var T=w[_];p.push(T.feature)}}}return p},r.prototype.queryRenderedFeatures=function(e,r,n){r&&r.filter&&this._validate(t.validateStyle.filter,\"queryRenderedFeatures.filter\",r.filter,null,r);var i={};if(r&&r.layers){if(!Array.isArray(r.layers))return this.fire(new t.ErrorEvent(new Error(\"parameters.layers must be an Array.\"))),[];for(var a=0,o=r.layers;a<o.length;a+=1){var s=o[a],l=this._layers[s];if(!l)return this.fire(new t.ErrorEvent(new Error(\"The layer '\"+s+\"' does not exist in the map's style and cannot be queried for features.\"))),[];i[l.source]=!0}}var c=[];for(var u in r.availableImages=this._availableImages,this.sourceCaches)r.layers&&!i[u]||c.push(F(this.sourceCaches[u],this._layers,this._serializedLayers,e,r,n));return this.placement&&c.push(function(t,e,r,n,i,a,o){for(var s={},l=a.queryRenderedSymbols(n),c=[],u=0,f=Object.keys(l).map(Number);u<f.length;u+=1){var h=f[u];c.push(o[h])}c.sort(B);for(var p=function(){var r=g[d],n=r.featureIndex.lookupSymbolFeatures(l[r.bucketInstanceId],e,r.bucketIndex,r.sourceLayerIndex,i.filter,i.layers,i.availableImages,t);for(var a in n){var o=s[a]=s[a]||[],c=n[a];c.sort((function(t,e){var n=r.featureSortOrder;if(n){var i=n.indexOf(t.featureIndex);return n.indexOf(e.featureIndex)-i}return e.featureIndex-t.featureIndex}));for(var u=0,f=c;u<f.length;u+=1){var h=f[u];o.push(h)}}},d=0,g=c;d<g.length;d+=1)p();var m=function(e){s[e].forEach((function(n){var i=n.feature,a=t[e],o=r[a.source].getFeatureState(i.layer[\"source-layer\"],i.id);i.source=i.layer.source,i.layer[\"source-layer\"]&&(i.sourceLayer=i.layer[\"source-layer\"]),i.state=o}))};for(var v in s)m(v);return s}(this._layers,this._serializedLayers,this.sourceCaches,e,r,this.placement.collisionIndex,this.placement.retainedQueryData)),this._flattenAndSortRenderedFeatures(c)},r.prototype.querySourceFeatures=function(e,r){r&&r.filter&&this._validate(t.validateStyle.filter,\"querySourceFeatures.filter\",r.filter,null,r);var n=this.sourceCaches[e];return n?function(t,e){for(var r=t.getRenderableIds().map((function(e){return t.getTileByID(e)})),n=[],i={},a=0;a<r.length;a++){var o=r[a],s=o.tileID.canonical.key;i[s]||(i[s]=!0,o.querySourceFeatures(n,e))}return n}(n,r):[]},r.prototype.addSourceType=function(t,e,n){return r.getSourceType(t)?n(new Error('A source type called \"'+t+'\" already exists.')):(r.setSourceType(t,e),e.workerSourceURL?void this.dispatcher.broadcast(\"loadWorkerSource\",{name:t,url:e.workerSourceURL},n):n(null,null))},r.prototype.getLight=function(){return this.light.getLight()},r.prototype.setLight=function(e,r){void 0===r&&(r={}),this._checkLoaded();var n=this.light.getLight(),i=!1;for(var a in e)if(!t.deepEqual(e[a],n[a])){i=!0;break}if(i){var o={now:t.browser.now(),transition:t.extend({duration:300,delay:0},this.stylesheet.transition)};this.light.setLight(e,r),this.light.updateTransitions(o)}},r.prototype._validate=function(e,r,n,i,a){return void 0===a&&(a={}),(!a||!1!==a.validate)&&je(this,e.call(t.validateStyle,t.extend({key:r,style:this.serialize(),value:n,styleSpec:t.styleSpec},i)))},r.prototype._remove=function(){for(var e in this._request&&(this._request.cancel(),this._request=null),this._spriteRequest&&(this._spriteRequest.cancel(),this._spriteRequest=null),t.evented.off(\"pluginStateChange\",this._rtlTextPluginCallback),this._layers){this._layers[e].setEventedParent(null)}for(var r in this.sourceCaches)this.sourceCaches[r].clearTiles(),this.sourceCaches[r].setEventedParent(null);this.imageManager.setEventedParent(null),this.setEventedParent(null),this.dispatcher.remove()},r.prototype._clearSource=function(t){this.sourceCaches[t].clearTiles()},r.prototype._reloadSource=function(t){this.sourceCaches[t].resume(),this.sourceCaches[t].reload()},r.prototype._updateSources=function(t){for(var e in this.sourceCaches)this.sourceCaches[e].update(t)},r.prototype._generateCollisionBoxes=function(){for(var t in this.sourceCaches)this._reloadSource(t)},r.prototype._updatePlacement=function(e,r,n,i,a){void 0===a&&(a=!1);for(var o=!1,s=!1,l={},c=0,u=this._order;c<u.length;c+=1){var f=u[c],h=this._layers[f];if(\"symbol\"===h.type){if(!l[h.source]){var p=this.sourceCaches[h.source];l[h.source]=p.getRenderableIds(!0).map((function(t){return p.getTileByID(t)})).sort((function(t,e){return e.tileID.overscaledZ-t.tileID.overscaledZ||(t.tileID.isLessThan(e.tileID)?-1:1)}))}var d=this.crossTileSymbolIndex.addLayer(h,l[h.source],e.center.lng);o=o||d}}if(this.crossTileSymbolIndex.pruneUnusedLayers(this._order),((a=a||this._layerOrderChanged||0===n)||!this.pauseablePlacement||this.pauseablePlacement.isDone()&&!this.placement.stillRecent(t.browser.now(),e.zoom))&&(this.pauseablePlacement=new ze(e,this._order,a,r,n,i,this.placement),this._layerOrderChanged=!1),this.pauseablePlacement.isDone()?this.placement.setStale():(this.pauseablePlacement.continuePlacement(this._order,this._layers,l),this.pauseablePlacement.isDone()&&(this.placement=this.pauseablePlacement.commit(t.browser.now()),s=!0),o&&this.pauseablePlacement.placement.setStale()),s||o)for(var g=0,m=this._order;g<m.length;g+=1){var v=m[g],y=this._layers[v];\"symbol\"===y.type&&this.placement.updateLayerOpacities(y,l[y.source])}return!this.pauseablePlacement.isDone()||this.placement.hasTransitions(t.browser.now())},r.prototype._releaseSymbolFadeTiles=function(){for(var t in this.sourceCaches)this.sourceCaches[t].releaseSymbolFadeTiles()},r.prototype.getImages=function(t,e,r){this.imageManager.getImages(e.icons,r),this._updateTilesForChangedImages();var n=this.sourceCaches[e.source];n&&n.setDependencies(e.tileID.key,e.type,e.icons)},r.prototype.getGlyphs=function(t,e,r){this.glyphManager.getGlyphs(e.stacks,r)},r.prototype.getResource=function(e,r,n){return t.makeRequest(r,n)},r}(t.Evented);qe.getSourceType=function(t){return D[t]},qe.setSourceType=function(t,e){D[t]=e},qe.registerForPluginStateChange=t.registerForPluginStateChange;var Ge=t.createLayout([{name:\"a_pos\",type:\"Int16\",components:2}]),Ye=yr(\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#if !defined(lowp)\\n#define lowp\\n#endif\\n#if !defined(mediump)\\n#define mediump\\n#endif\\n#if !defined(highp)\\n#define highp\\n#endif\\n#endif\",\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#if !defined(lowp)\\n#define lowp\\n#endif\\n#if !defined(mediump)\\n#define mediump\\n#endif\\n#if !defined(highp)\\n#define highp\\n#endif\\n#endif\\nvec2 unpack_float(const float packedValue) {int packedIntValue=int(packedValue);int v0=packedIntValue/256;return vec2(v0,packedIntValue-v0*256);}vec2 unpack_opacity(const float packedOpacity) {int intOpacity=int(packedOpacity)/2;return vec2(float(intOpacity)/127.0,mod(packedOpacity,2.0));}vec4 decode_color(const vec2 encodedColor) {return vec4(unpack_float(encodedColor[0])/255.0,unpack_float(encodedColor[1])/255.0\\n);}float unpack_mix_vec2(const vec2 packedValue,const float t) {return mix(packedValue[0],packedValue[1],t);}vec4 unpack_mix_color(const vec4 packedColors,const float t) {vec4 minColor=decode_color(vec2(packedColors[0],packedColors[1]));vec4 maxColor=decode_color(vec2(packedColors[2],packedColors[3]));return mix(minColor,maxColor,t);}vec2 get_pattern_pos(const vec2 pixel_coord_upper,const vec2 pixel_coord_lower,const vec2 pattern_size,const float tile_units_to_pixels,const vec2 pos) {vec2 offset=mod(mod(mod(pixel_coord_upper,pattern_size)*256.0,pattern_size)*256.0+pixel_coord_lower,pattern_size);return (tile_units_to_pixels*pos+offset)/pattern_size;}\"),We=yr(\"uniform vec4 u_color;uniform float u_opacity;void main() {gl_FragColor=u_color*u_opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}\"),Xe=yr(\"uniform vec2 u_pattern_tl_a;uniform vec2 u_pattern_br_a;uniform vec2 u_pattern_tl_b;uniform vec2 u_pattern_br_b;uniform vec2 u_texsize;uniform float u_mix;uniform float u_opacity;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(u_pattern_tl_a/u_texsize,u_pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(u_pattern_tl_b/u_texsize,u_pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_mix)*u_opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pattern_size_a;uniform vec2 u_pattern_size_b;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_scale_a;uniform float u_scale_b;uniform float u_tile_units_to_pixels;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_a*u_pattern_size_a,u_tile_units_to_pixels,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_b*u_pattern_size_b,u_tile_units_to_pixels,a_pos);}\"),Ze=yr(\"varying vec3 v_data;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define mediump float radius\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define highp vec4 stroke_color\\n#pragma mapbox: define mediump float stroke_width\\n#pragma mapbox: define lowp float stroke_opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize mediump float radius\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize highp vec4 stroke_color\\n#pragma mapbox: initialize mediump float stroke_width\\n#pragma mapbox: initialize lowp float stroke_opacity\\nvec2 extrude=v_data.xy;float extrude_length=length(extrude);lowp float antialiasblur=v_data.z;float antialiased_blur=-max(blur,antialiasblur);float opacity_t=smoothstep(0.0,antialiased_blur,extrude_length-1.0);float color_t=stroke_width < 0.01 ? 0.0 : smoothstep(antialiased_blur,0.0,extrude_length-radius/(radius+stroke_width));gl_FragColor=opacity_t*mix(color*opacity,stroke_color*stroke_opacity,color_t);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform bool u_scale_with_map;uniform bool u_pitch_with_map;uniform vec2 u_extrude_scale;uniform lowp float u_device_pixel_ratio;uniform highp float u_camera_to_center_distance;attribute vec2 a_pos;varying vec3 v_data;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define mediump float radius\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define highp vec4 stroke_color\\n#pragma mapbox: define mediump float stroke_width\\n#pragma mapbox: define lowp float stroke_opacity\\nvoid main(void) {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize mediump float radius\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize highp vec4 stroke_color\\n#pragma mapbox: initialize mediump float stroke_width\\n#pragma mapbox: initialize lowp float stroke_opacity\\nvec2 extrude=vec2(mod(a_pos,2.0)*2.0-1.0);vec2 circle_center=floor(a_pos*0.5);if (u_pitch_with_map) {vec2 corner_position=circle_center;if (u_scale_with_map) {corner_position+=extrude*(radius+stroke_width)*u_extrude_scale;} else {vec4 projected_center=u_matrix*vec4(circle_center,0,1);corner_position+=extrude*(radius+stroke_width)*u_extrude_scale*(projected_center.w/u_camera_to_center_distance);}gl_Position=u_matrix*vec4(corner_position,0,1);} else {gl_Position=u_matrix*vec4(circle_center,0,1);if (u_scale_with_map) {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*u_camera_to_center_distance;} else {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*gl_Position.w;}}lowp float antialiasblur=1.0/u_device_pixel_ratio/(radius+stroke_width);v_data=vec3(extrude.x,extrude.y,antialiasblur);}\"),Je=yr(\"void main() {gl_FragColor=vec4(1.0);}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}\"),Ke=yr(\"uniform highp float u_intensity;varying vec2 v_extrude;\\n#pragma mapbox: define highp float weight\\n#define GAUSS_COEF 0.3989422804014327\\nvoid main() {\\n#pragma mapbox: initialize highp float weight\\nfloat d=-0.5*3.0*3.0*dot(v_extrude,v_extrude);float val=weight*u_intensity*GAUSS_COEF*exp(d);gl_FragColor=vec4(val,1.0,1.0,1.0);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform float u_extrude_scale;uniform float u_opacity;uniform float u_intensity;attribute vec2 a_pos;varying vec2 v_extrude;\\n#pragma mapbox: define highp float weight\\n#pragma mapbox: define mediump float radius\\nconst highp float ZERO=1.0/255.0/16.0;\\n#define GAUSS_COEF 0.3989422804014327\\nvoid main(void) {\\n#pragma mapbox: initialize highp float weight\\n#pragma mapbox: initialize mediump float radius\\nvec2 unscaled_extrude=vec2(mod(a_pos,2.0)*2.0-1.0);float S=sqrt(-2.0*log(ZERO/weight/u_intensity/GAUSS_COEF))/3.0;v_extrude=S*unscaled_extrude;vec2 extrude=v_extrude*radius*u_extrude_scale;vec4 pos=vec4(floor(a_pos*0.5)+extrude,0,1);gl_Position=u_matrix*pos;}\"),Qe=yr(\"uniform sampler2D u_image;uniform sampler2D u_color_ramp;uniform float u_opacity;varying vec2 v_pos;void main() {float t=texture2D(u_image,v_pos).r;vec4 color=texture2D(u_color_ramp,vec2(t,0.5));gl_FragColor=color*u_opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(0.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_world;attribute vec2 a_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos*u_world,0,1);v_pos.x=a_pos.x;v_pos.y=1.0-a_pos.y;}\"),$e=yr(\"varying float v_placed;varying float v_notUsed;void main() {float alpha=0.5;gl_FragColor=vec4(1.0,0.0,0.0,1.0)*alpha;if (v_placed > 0.5) {gl_FragColor=vec4(0.0,0.0,1.0,0.5)*alpha;}if (v_notUsed > 0.5) {gl_FragColor*=.1;}}\",\"attribute vec2 a_pos;attribute vec2 a_anchor_pos;attribute vec2 a_extrude;attribute vec2 a_placed;attribute vec2 a_shift;uniform mat4 u_matrix;uniform vec2 u_extrude_scale;uniform float u_camera_to_center_distance;varying float v_placed;varying float v_notUsed;void main() {vec4 projectedPoint=u_matrix*vec4(a_anchor_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);gl_Position=u_matrix*vec4(a_pos,0.0,1.0);gl_Position.xy+=(a_extrude+a_shift)*u_extrude_scale*gl_Position.w*collision_perspective_ratio;v_placed=a_placed.x;v_notUsed=a_placed.y;}\"),tr=yr(\"varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;void main() {float alpha=0.5*min(v_perspective_ratio,1.0);float stroke_radius=0.9*max(v_perspective_ratio,1.0);float distance_to_center=length(v_extrude);float distance_to_edge=abs(distance_to_center-v_radius);float opacity_t=smoothstep(-stroke_radius,0.0,-distance_to_edge);vec4 color=mix(vec4(0.0,0.0,1.0,0.5),vec4(1.0,0.0,0.0,1.0),v_collision);gl_FragColor=color*alpha*opacity_t;}\",\"attribute vec2 a_pos;attribute float a_radius;attribute vec2 a_flags;uniform mat4 u_matrix;uniform mat4 u_inv_matrix;uniform vec2 u_viewport_size;uniform float u_camera_to_center_distance;varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;vec3 toTilePosition(vec2 screenPos) {vec4 rayStart=u_inv_matrix*vec4(screenPos,-1.0,1.0);vec4 rayEnd  =u_inv_matrix*vec4(screenPos, 1.0,1.0);rayStart.xyz/=rayStart.w;rayEnd.xyz  /=rayEnd.w;highp float t=(0.0-rayStart.z)/(rayEnd.z-rayStart.z);return mix(rayStart.xyz,rayEnd.xyz,t);}void main() {vec2 quadCenterPos=a_pos;float radius=a_radius;float collision=a_flags.x;float vertexIdx=a_flags.y;vec2 quadVertexOffset=vec2(mix(-1.0,1.0,float(vertexIdx >=2.0)),mix(-1.0,1.0,float(vertexIdx >=1.0 && vertexIdx <=2.0)));vec2 quadVertexExtent=quadVertexOffset*radius;vec3 tilePos=toTilePosition(quadCenterPos);vec4 clipPos=u_matrix*vec4(tilePos,1.0);highp float camera_to_anchor_distance=clipPos.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);float padding_factor=1.2;v_radius=radius;v_extrude=quadVertexExtent*padding_factor;v_perspective_ratio=collision_perspective_ratio;v_collision=collision;gl_Position=vec4(clipPos.xyz/clipPos.w,1.0)+vec4(quadVertexExtent*padding_factor/u_viewport_size*2.0,0.0,0.0);}\"),er=yr(\"uniform highp vec4 u_color;uniform sampler2D u_overlay;varying vec2 v_uv;void main() {vec4 overlay_color=texture2D(u_overlay,v_uv);gl_FragColor=mix(u_color,overlay_color,overlay_color.a);}\",\"attribute vec2 a_pos;varying vec2 v_uv;uniform mat4 u_matrix;uniform float u_overlay_scale;void main() {v_uv=a_pos/8192.0;gl_Position=u_matrix*vec4(a_pos*u_overlay_scale,0,1);}\"),rr=yr(\"#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float opacity\\ngl_FragColor=color*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float opacity\\ngl_Position=u_matrix*vec4(a_pos,0,1);}\"),nr=yr(\"varying vec2 v_pos;\\n#pragma mapbox: define highp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 outline_color\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=outline_color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;uniform vec2 u_world;varying vec2 v_pos;\\n#pragma mapbox: define highp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 outline_color\\n#pragma mapbox: initialize lowp float opacity\\ngl_Position=u_matrix*vec4(a_pos,0,1);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}\"),ir=yr(\"uniform vec2 u_texsize;uniform sampler2D u_image;uniform float u_fade;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);float dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=mix(color1,color2,u_fade)*alpha*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_world;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;gl_Position=u_matrix*vec4(a_pos,0,1);vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,a_pos);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}\"),ar=yr(\"uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_fade)*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileZoomRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileZoomRatio,a_pos);}\"),or=yr(\"varying vec4 v_color;void main() {gl_FragColor=v_color;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;uniform float u_vertical_gradient;uniform lowp float u_opacity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec4 v_color;\\n#pragma mapbox: define highp float base\\n#pragma mapbox: define highp float height\\n#pragma mapbox: define highp vec4 color\\nvoid main() {\\n#pragma mapbox: initialize highp float base\\n#pragma mapbox: initialize highp float height\\n#pragma mapbox: initialize highp vec4 color\\nvec3 normal=a_normal_ed.xyz;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);gl_Position=u_matrix*vec4(a_pos,t > 0.0 ? height : base,1);float colorvalue=color.r*0.2126+color.g*0.7152+color.b*0.0722;v_color=vec4(0.0,0.0,0.0,1.0);vec4 ambientlight=vec4(0.03,0.03,0.03,1.0);color+=ambientlight;float directional=clamp(dot(normal/16384.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((1.0-colorvalue+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_color.r+=clamp(color.r*directional*u_lightcolor.r,mix(0.0,0.3,1.0-u_lightcolor.r),1.0);v_color.g+=clamp(color.g*directional*u_lightcolor.g,mix(0.0,0.3,1.0-u_lightcolor.g),1.0);v_color.b+=clamp(color.b*directional*u_lightcolor.b,mix(0.0,0.3,1.0-u_lightcolor.b),1.0);v_color*=u_opacity;}\"),sr=yr(\"uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\\n#pragma mapbox: define lowp float base\\n#pragma mapbox: define lowp float height\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float base\\n#pragma mapbox: initialize lowp float height\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);vec4 mixedColor=mix(color1,color2,u_fade);gl_FragColor=mixedColor*v_lighting;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_height_factor;uniform vec3 u_scale;uniform float u_vertical_gradient;uniform lowp float u_opacity;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\\n#pragma mapbox: define lowp float base\\n#pragma mapbox: define lowp float height\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float base\\n#pragma mapbox: initialize lowp float height\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec3 normal=a_normal_ed.xyz;float edgedistance=a_normal_ed.w;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);float z=t > 0.0 ? height : base;gl_Position=u_matrix*vec4(a_pos,z,1);vec2 pos=normal.x==1.0 && normal.y==0.0 && normal.z==16384.0\\n? a_pos\\n: vec2(edgedistance,z*u_height_factor);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,pos);v_lighting=vec4(0.0,0.0,0.0,1.0);float directional=clamp(dot(normal/16383.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((0.5+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_lighting.rgb+=clamp(directional*u_lightcolor,mix(vec3(0.0),vec3(0.3),1.0-u_lightcolor),vec3(1.0));v_lighting*=u_opacity;}\"),lr=yr(\"#ifdef GL_ES\\nprecision highp float;\\n#endif\\nuniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_dimension;uniform float u_zoom;uniform float u_maxzoom;uniform vec4 u_unpack;float getElevation(vec2 coord,float bias) {vec4 data=texture2D(u_image,coord)*255.0;data.a=-1.0;return dot(data,u_unpack)/4.0;}void main() {vec2 epsilon=1.0/u_dimension;float a=getElevation(v_pos+vec2(-epsilon.x,-epsilon.y),0.0);float b=getElevation(v_pos+vec2(0,-epsilon.y),0.0);float c=getElevation(v_pos+vec2(epsilon.x,-epsilon.y),0.0);float d=getElevation(v_pos+vec2(-epsilon.x,0),0.0);float e=getElevation(v_pos,0.0);float f=getElevation(v_pos+vec2(epsilon.x,0),0.0);float g=getElevation(v_pos+vec2(-epsilon.x,epsilon.y),0.0);float h=getElevation(v_pos+vec2(0,epsilon.y),0.0);float i=getElevation(v_pos+vec2(epsilon.x,epsilon.y),0.0);float exaggeration=u_zoom < 2.0 ? 0.4 : u_zoom < 4.5 ? 0.35 : 0.3;vec2 deriv=vec2((c+f+f+i)-(a+d+d+g),(g+h+h+i)-(a+b+b+c))/ pow(2.0,(u_zoom-u_maxzoom)*exaggeration+19.2562-u_zoom);gl_FragColor=clamp(vec4(deriv.x/2.0+0.5,deriv.y/2.0+0.5,1.0,1.0),0.0,1.0);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_dimension;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);highp vec2 epsilon=1.0/u_dimension;float scale=(u_dimension.x-2.0)/u_dimension.x;v_pos=(a_texture_pos/8192.0)*scale+epsilon;}\"),cr=yr(\"uniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_latrange;uniform vec2 u_light;uniform vec4 u_shadow;uniform vec4 u_highlight;uniform vec4 u_accent;\\n#define PI 3.141592653589793\\nvoid main() {vec4 pixel=texture2D(u_image,v_pos);vec2 deriv=((pixel.rg*2.0)-1.0);float scaleFactor=cos(radians((u_latrange[0]-u_latrange[1])*(1.0-v_pos.y)+u_latrange[1]));float slope=atan(1.25*length(deriv)/scaleFactor);float aspect=deriv.x !=0.0 ? atan(deriv.y,-deriv.x) : PI/2.0*(deriv.y > 0.0 ? 1.0 :-1.0);float intensity=u_light.x;float azimuth=u_light.y+PI;float base=1.875-intensity*1.75;float maxValue=0.5*PI;float scaledSlope=intensity !=0.5 ? ((pow(base,slope)-1.0)/(pow(base,maxValue)-1.0))*maxValue : slope;float accent=cos(scaledSlope);vec4 accent_color=(1.0-accent)*u_accent*clamp(intensity*2.0,0.0,1.0);float shade=abs(mod((aspect+azimuth)/PI+0.5,2.0)-1.0);vec4 shade_color=mix(u_shadow,u_highlight,shade)*sin(scaledSlope)*clamp(intensity*2.0,0.0,1.0);gl_FragColor=accent_color*(1.0-shade_color.a)+shade_color;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos=a_texture_pos/8192.0;}\"),ur=yr(\"uniform lowp float u_device_pixel_ratio;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform vec2 u_units_to_pixels;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_linesofar;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}\"),fr=yr(\"uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;varying highp float v_lineprogress;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);vec4 color=texture2D(u_image,vec2(v_lineprogress,0.5));gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define MAX_LINE_DISTANCE 32767.0\\n#define scale 0.015873016\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_lineprogress;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_lineprogress=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0/MAX_LINE_DISTANCE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}\"),hr=yr(\"uniform lowp float u_device_pixel_ratio;uniform vec2 u_texsize;uniform float u_fade;uniform mediump vec3 u_scale;uniform sampler2D u_image;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;vec2 pattern_size_a=vec2(display_size_a.x*fromScale/tileZoomRatio,display_size_a.y);vec2 pattern_size_b=vec2(display_size_b.x*toScale/tileZoomRatio,display_size_b.y);float aspect_a=display_size_a.y/v_width;float aspect_b=display_size_b.y/v_width;float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float x_a=mod(v_linesofar/pattern_size_a.x*aspect_a,1.0);float x_b=mod(v_linesofar/pattern_size_b.x*aspect_b,1.0);float y=0.5*v_normal.y+0.5;vec2 texel_size=1.0/u_texsize;vec2 pos_a=mix(pattern_tl_a*texel_size-texel_size,pattern_br_a*texel_size+texel_size,vec2(x_a,y));vec2 pos_b=mix(pattern_tl_b*texel_size-texel_size,pattern_br_b*texel_size+texel_size,vec2(x_b,y));vec4 color=mix(texture2D(u_image,pos_a),texture2D(u_image,pos_b),u_fade);gl_FragColor=color*alpha*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\n#define LINE_DISTANCE_SCALE 2.0\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform vec2 u_units_to_pixels;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_linesofar=a_linesofar;v_width2=vec2(outset,inset);v_width=floorwidth;}\"),pr=yr(\"uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;uniform float u_sdfgamma;uniform float u_mix;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float sdfdist_a=texture2D(u_image,v_tex_a).a;float sdfdist_b=texture2D(u_image,v_tex_b).a;float sdfdist=mix(sdfdist_a,sdfdist_b,u_mix);alpha*=smoothstep(0.5-u_sdfgamma/floorwidth,0.5+u_sdfgamma/floorwidth,sdfdist);gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\n#define LINE_DISTANCE_SCALE 2.0\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_patternscale_a;uniform float u_tex_y_a;uniform vec2 u_patternscale_b;uniform float u_tex_y_b;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_tex_a=vec2(a_linesofar*u_patternscale_a.x/floorwidth,normal.y*u_patternscale_a.y+u_tex_y_a);v_tex_b=vec2(a_linesofar*u_patternscale_b.x/floorwidth,normal.y*u_patternscale_b.y+u_tex_y_b);v_width2=vec2(outset,inset);}\"),dr=yr(\"uniform float u_fade_t;uniform float u_opacity;uniform sampler2D u_image0;uniform sampler2D u_image1;varying vec2 v_pos0;varying vec2 v_pos1;uniform float u_brightness_low;uniform float u_brightness_high;uniform float u_saturation_factor;uniform float u_contrast_factor;uniform vec3 u_spin_weights;void main() {vec4 color0=texture2D(u_image0,v_pos0);vec4 color1=texture2D(u_image1,v_pos1);if (color0.a > 0.0) {color0.rgb=color0.rgb/color0.a;}if (color1.a > 0.0) {color1.rgb=color1.rgb/color1.a;}vec4 color=mix(color0,color1,u_fade_t);color.a*=u_opacity;vec3 rgb=color.rgb;rgb=vec3(dot(rgb,u_spin_weights.xyz),dot(rgb,u_spin_weights.zxy),dot(rgb,u_spin_weights.yzx));float average=(color.r+color.g+color.b)/3.0;rgb+=(average-rgb)*u_saturation_factor;rgb=(rgb-0.5)*u_contrast_factor+0.5;vec3 u_high_vec=vec3(u_brightness_low,u_brightness_low,u_brightness_low);vec3 u_low_vec=vec3(u_brightness_high,u_brightness_high,u_brightness_high);gl_FragColor=vec4(mix(u_high_vec,u_low_vec,rgb)*color.a,color.a);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_tl_parent;uniform float u_scale_parent;uniform float u_buffer_scale;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos0;varying vec2 v_pos1;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos0=(((a_texture_pos/8192.0)-0.5)/u_buffer_scale )+0.5;v_pos1=(v_pos0*u_scale_parent)+u_tl_parent;}\"),gr=yr(\"uniform sampler2D u_texture;varying vec2 v_tex;varying float v_fade_opacity;\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\nlowp float alpha=opacity*v_fade_opacity;gl_FragColor=texture2D(u_texture,v_tex)*alpha;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform highp float u_camera_to_center_distance;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform float u_fade_change;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform vec2 u_texsize;varying vec2 v_tex;varying float v_fade_opacity;\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;vec2 a_minFontScale=a_pixeloffset.zw/256.0;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*max(a_minFontScale,fontScale)+a_pxoffset/16.0),0.0,1.0);v_tex=a_tex/u_texsize;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;v_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));}\"),mr=yr(\"#define SDF_PX 8.0\\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;uniform bool u_is_text;varying vec2 v_data0;varying vec3 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nfloat EDGE_GAMMA=0.105/u_device_pixel_ratio;vec2 tex=v_data0.xy;float gamma_scale=v_data1.x;float size=v_data1.y;float fade_opacity=v_data1[2];float fontScale=u_is_text ? size/24.0 : size;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;varying vec2 v_data0;varying vec3 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale+a_pxoffset),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0=a_tex/u_texsize;v_data1=vec3(gamma_scale,size,interpolated_fade_opacity);}\"),vr=yr(\"#define SDF_PX 8.0\\n#define SDF 1.0\\n#define ICON 0.0\\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform sampler2D u_texture_icon;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;varying vec4 v_data0;varying vec4 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nfloat fade_opacity=v_data1[2];if (v_data1.w==ICON) {vec2 tex_icon=v_data0.zw;lowp float alpha=opacity*fade_opacity;gl_FragColor=texture2D(u_texture_icon,tex_icon)*alpha;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\nreturn;}vec2 tex=v_data0.xy;float EDGE_GAMMA=0.105/u_device_pixel_ratio;float gamma_scale=v_data1.x;float size=v_data1.y;float fontScale=size/24.0;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;uniform vec2 u_texsize_icon;varying vec4 v_data0;varying vec4 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);float is_sdf=a_size[0]-2.0*a_size_min;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=size/24.0;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0.xy=a_tex/u_texsize;v_data0.zw=a_tex/u_texsize_icon;v_data1=vec4(gamma_scale,size,interpolated_fade_opacity,is_sdf);}\");function yr(t,e){var r=/#pragma mapbox: ([\\w]+) ([\\w]+) ([\\w]+) ([\\w]+)/g,n={};return{fragmentSource:t=t.replace(r,(function(t,e,r,i,a){return n[a]=!0,\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\nvarying \"+r+\" \"+i+\" \"+a+\";\\n#else\\nuniform \"+r+\" \"+i+\" u_\"+a+\";\\n#endif\\n\":\"\\n#ifdef HAS_UNIFORM_u_\"+a+\"\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\"})),vertexSource:e=e.replace(r,(function(t,e,r,i,a){var o=\"float\"===i?\"vec2\":\"vec4\",s=a.match(/color/)?\"color\":o;return n[a]?\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\nuniform lowp float u_\"+a+\"_t;\\nattribute \"+r+\" \"+o+\" a_\"+a+\";\\nvarying \"+r+\" \"+i+\" \"+a+\";\\n#else\\nuniform \"+r+\" \"+i+\" u_\"+a+\";\\n#endif\\n\":\"vec4\"===s?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+a+\" = a_\"+a+\";\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\":\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+a+\" = unpack_mix_\"+s+\"(a_\"+a+\", u_\"+a+\"_t);\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\":\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\nuniform lowp float u_\"+a+\"_t;\\nattribute \"+r+\" \"+o+\" a_\"+a+\";\\n#else\\nuniform \"+r+\" \"+i+\" u_\"+a+\";\\n#endif\\n\":\"vec4\"===s?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+r+\" \"+i+\" \"+a+\" = a_\"+a+\";\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\":\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+r+\" \"+i+\" \"+a+\" = unpack_mix_\"+s+\"(a_\"+a+\", u_\"+a+\"_t);\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\"}))}}var xr=Object.freeze({__proto__:null,prelude:Ye,background:We,backgroundPattern:Xe,circle:Ze,clippingMask:Je,heatmap:Ke,heatmapTexture:Qe,collisionBox:$e,collisionCircle:tr,debug:er,fill:rr,fillOutline:nr,fillOutlinePattern:ir,fillPattern:ar,fillExtrusion:or,fillExtrusionPattern:sr,hillshadePrepare:lr,hillshade:cr,line:ur,lineGradient:fr,linePattern:hr,lineSDF:pr,raster:dr,symbolIcon:gr,symbolSDF:mr,symbolTextAndIcon:vr}),br=function(){this.boundProgram=null,this.boundLayoutVertexBuffer=null,this.boundPaintVertexBuffers=[],this.boundIndexBuffer=null,this.boundVertexOffset=null,this.boundDynamicVertexBuffer=null,this.vao=null};br.prototype.bind=function(t,e,r,n,i,a,o,s){this.context=t;for(var l=this.boundPaintVertexBuffers.length!==n.length,c=0;!l&&c<n.length;c++)this.boundPaintVertexBuffers[c]!==n[c]&&(l=!0);var u=!this.vao||this.boundProgram!==e||this.boundLayoutVertexBuffer!==r||l||this.boundIndexBuffer!==i||this.boundVertexOffset!==a||this.boundDynamicVertexBuffer!==o||this.boundDynamicVertexBuffer2!==s;!t.extVertexArrayObject||u?this.freshBind(e,r,n,i,a,o,s):(t.bindVertexArrayOES.set(this.vao),o&&o.bind(),i&&i.dynamicDraw&&i.bind(),s&&s.bind())},br.prototype.freshBind=function(t,e,r,n,i,a,o){var s,l=t.numAttributes,c=this.context,u=c.gl;if(c.extVertexArrayObject)this.vao&&this.destroy(),this.vao=c.extVertexArrayObject.createVertexArrayOES(),c.bindVertexArrayOES.set(this.vao),s=0,this.boundProgram=t,this.boundLayoutVertexBuffer=e,this.boundPaintVertexBuffers=r,this.boundIndexBuffer=n,this.boundVertexOffset=i,this.boundDynamicVertexBuffer=a,this.boundDynamicVertexBuffer2=o;else{s=c.currentNumAttributes||0;for(var f=l;f<s;f++)u.disableVertexAttribArray(f)}e.enableAttributes(u,t);for(var h=0,p=r;h<p.length;h+=1){p[h].enableAttributes(u,t)}a&&a.enableAttributes(u,t),o&&o.enableAttributes(u,t),e.bind(),e.setVertexAttribPointers(u,t,i);for(var d=0,g=r;d<g.length;d+=1){var m=g[d];m.bind(),m.setVertexAttribPointers(u,t,i)}a&&(a.bind(),a.setVertexAttribPointers(u,t,i)),n&&n.bind(),o&&(o.bind(),o.setVertexAttribPointers(u,t,i)),c.currentNumAttributes=l},br.prototype.destroy=function(){this.vao&&(this.context.extVertexArrayObject.deleteVertexArrayOES(this.vao),this.vao=null)};var _r=function(t,e,r,n,i){var a=t.gl;this.program=a.createProgram();var o=r?r.defines():[];i&&o.push(\"#define OVERDRAW_INSPECTOR;\");var s=o.concat(Ye.fragmentSource,e.fragmentSource).join(\"\\n\"),l=o.concat(Ye.vertexSource,e.vertexSource).join(\"\\n\"),c=a.createShader(a.FRAGMENT_SHADER);if(a.isContextLost())this.failedToCreate=!0;else{a.shaderSource(c,s),a.compileShader(c),a.attachShader(this.program,c);var u=a.createShader(a.VERTEX_SHADER);if(a.isContextLost())this.failedToCreate=!0;else{a.shaderSource(u,l),a.compileShader(u),a.attachShader(this.program,u);for(var f=r?r.layoutAttributes:[],h=0;h<f.length;h++)a.bindAttribLocation(this.program,h,f[h].name);a.linkProgram(this.program),a.deleteShader(u),a.deleteShader(c),this.numAttributes=a.getProgramParameter(this.program,a.ACTIVE_ATTRIBUTES),this.attributes={};for(var p={},d=0;d<this.numAttributes;d++){var g=a.getActiveAttrib(this.program,d);g&&(this.attributes[g.name]=a.getAttribLocation(this.program,g.name))}for(var m=a.getProgramParameter(this.program,a.ACTIVE_UNIFORMS),v=0;v<m;v++){var y=a.getActiveUniform(this.program,v);y&&(p[y.name]=a.getUniformLocation(this.program,y.name))}this.fixedUniforms=n(t,p),this.binderUniforms=r?r.getUniforms(t,p):[]}}};function wr(t,e,r){var n=1/pe(r,1,e.transform.tileZoom),i=Math.pow(2,r.tileID.overscaledZ),a=r.tileSize*Math.pow(2,e.transform.tileZoom)/i,o=a*(r.tileID.canonical.x+r.tileID.wrap*i),s=a*r.tileID.canonical.y;return{u_image:0,u_texsize:r.imageAtlasTexture.size,u_scale:[n,t.fromScale,t.toScale],u_fade:t.t,u_pixel_coord_upper:[o>>16,s>>16],u_pixel_coord_lower:[65535&o,65535&s]}}_r.prototype.draw=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g){var m,v=t.gl;if(!this.failedToCreate){for(var y in t.program.set(this.program),t.setDepthMode(r),t.setStencilMode(n),t.setColorMode(i),t.setCullFace(a),this.fixedUniforms)this.fixedUniforms[y].set(o[y]);p&&p.setUniforms(t,this.binderUniforms,f,{zoom:h});for(var x=(m={},m[v.LINES]=2,m[v.TRIANGLES]=3,m[v.LINE_STRIP]=1,m)[e],b=0,_=u.get();b<_.length;b+=1){var w=_[b],T=w.vaos||(w.vaos={});(T[s]||(T[s]=new br)).bind(t,this,l,p?p.getPaintVertexBuffers():[],c,w.vertexOffset,d,g),v.drawElements(e,w.primitiveLength*x,v.UNSIGNED_SHORT,w.primitiveOffset*x*2)}}};var Tr=function(e,r,n,i){var a=r.style.light,o=a.properties.get(\"position\"),s=[o.x,o.y,o.z],l=t.create$1();\"viewport\"===a.properties.get(\"anchor\")&&t.fromRotation(l,-r.transform.angle),t.transformMat3(s,s,l);var c=a.properties.get(\"color\");return{u_matrix:e,u_lightpos:s,u_lightintensity:a.properties.get(\"intensity\"),u_lightcolor:[c.r,c.g,c.b],u_vertical_gradient:+n,u_opacity:i}},kr=function(e,r,n,i,a,o,s){return t.extend(Tr(e,r,n,i),wr(o,r,s),{u_height_factor:-Math.pow(2,a.overscaledZ)/s.tileSize/8})},Ar=function(t){return{u_matrix:t}},Mr=function(e,r,n,i){return t.extend(Ar(e),wr(n,r,i))},Sr=function(t,e){return{u_matrix:t,u_world:e}},Er=function(e,r,n,i,a){return t.extend(Mr(e,r,n,i),{u_world:a})},Lr=function(e,r,n,i){var a,o,s=e.transform;if(\"map\"===i.paint.get(\"circle-pitch-alignment\")){var l=pe(n,1,s.zoom);a=!0,o=[l,l]}else a=!1,o=s.pixelsToGLUnits;return{u_camera_to_center_distance:s.cameraToCenterDistance,u_scale_with_map:+(\"map\"===i.paint.get(\"circle-pitch-scale\")),u_matrix:e.translatePosMatrix(r.posMatrix,n,i.paint.get(\"circle-translate\"),i.paint.get(\"circle-translate-anchor\")),u_pitch_with_map:+a,u_device_pixel_ratio:t.browser.devicePixelRatio,u_extrude_scale:o}},Cr=function(t,e,r){var n=pe(r,1,e.zoom),i=Math.pow(2,e.zoom-r.tileID.overscaledZ),a=r.tileID.overscaleFactor();return{u_matrix:t,u_camera_to_center_distance:e.cameraToCenterDistance,u_pixels_to_tile_units:n,u_extrude_scale:[e.pixelsToGLUnits[0]/(n*i),e.pixelsToGLUnits[1]/(n*i)],u_overscale_factor:a}},Pr=function(t,e,r){return{u_matrix:t,u_inv_matrix:e,u_camera_to_center_distance:r.cameraToCenterDistance,u_viewport_size:[r.width,r.height]}},Ir=function(t,e,r){return void 0===r&&(r=1),{u_matrix:t,u_color:e,u_overlay:0,u_overlay_scale:r}},Or=function(t){return{u_matrix:t}},zr=function(t,e,r,n){return{u_matrix:t,u_extrude_scale:pe(e,1,r),u_intensity:n}};function Dr(e,r){var n=Math.pow(2,r.canonical.z),i=r.canonical.y;return[new t.MercatorCoordinate(0,i/n).toLngLat().lat,new t.MercatorCoordinate(0,(i+1)/n).toLngLat().lat]}var Rr=function(e,r,n){var i=e.transform;return{u_matrix:Ur(e,r,n),u_ratio:1/pe(r,1,i.zoom),u_device_pixel_ratio:t.browser.devicePixelRatio,u_units_to_pixels:[1/i.pixelsToGLUnits[0],1/i.pixelsToGLUnits[1]]}},Fr=function(e,r,n){return t.extend(Rr(e,r,n),{u_image:0})},Br=function(e,r,n,i){var a=e.transform,o=jr(r,a);return{u_matrix:Ur(e,r,n),u_texsize:r.imageAtlasTexture.size,u_ratio:1/pe(r,1,a.zoom),u_device_pixel_ratio:t.browser.devicePixelRatio,u_image:0,u_scale:[o,i.fromScale,i.toScale],u_fade:i.t,u_units_to_pixels:[1/a.pixelsToGLUnits[0],1/a.pixelsToGLUnits[1]]}},Nr=function(e,r,n,i,a){var o=e.transform,s=e.lineAtlas,l=jr(r,o),c=\"round\"===n.layout.get(\"line-cap\"),u=s.getDash(i.from,c),f=s.getDash(i.to,c),h=u.width*a.fromScale,p=f.width*a.toScale;return t.extend(Rr(e,r,n),{u_patternscale_a:[l/h,-u.height/2],u_patternscale_b:[l/p,-f.height/2],u_sdfgamma:s.width/(256*Math.min(h,p)*t.browser.devicePixelRatio)/2,u_image:0,u_tex_y_a:u.y,u_tex_y_b:f.y,u_mix:a.t})};function jr(t,e){return 1/pe(t,1,e.tileZoom)}function Ur(t,e,r){return t.translatePosMatrix(e.tileID.posMatrix,e,r.paint.get(\"line-translate\"),r.paint.get(\"line-translate-anchor\"))}var Vr=function(t,e,r,n,i){return{u_matrix:t,u_tl_parent:e,u_scale_parent:r,u_buffer_scale:1,u_fade_t:n.mix,u_opacity:n.opacity*i.paint.get(\"raster-opacity\"),u_image0:0,u_image1:1,u_brightness_low:i.paint.get(\"raster-brightness-min\"),u_brightness_high:i.paint.get(\"raster-brightness-max\"),u_saturation_factor:(o=i.paint.get(\"raster-saturation\"),o>0?1-1/(1.001-o):-o),u_contrast_factor:(a=i.paint.get(\"raster-contrast\"),a>0?1/(1-a):1+a),u_spin_weights:Hr(i.paint.get(\"raster-hue-rotate\"))};var a,o};function Hr(t){t*=Math.PI/180;var e=Math.sin(t),r=Math.cos(t);return[(2*r+1)/3,(-Math.sqrt(3)*e-r+1)/3,(Math.sqrt(3)*e-r+1)/3]}var qr,Gr=function(t,e,r,n,i,a,o,s,l,c){var u=i.transform;return{u_is_size_zoom_constant:+(\"constant\"===t||\"source\"===t),u_is_size_feature_constant:+(\"constant\"===t||\"camera\"===t),u_size_t:e?e.uSizeT:0,u_size:e?e.uSize:0,u_camera_to_center_distance:u.cameraToCenterDistance,u_pitch:u.pitch/360*2*Math.PI,u_rotate_symbol:+r,u_aspect_ratio:u.width/u.height,u_fade_change:i.options.fadeDuration?i.symbolFadeChange:1,u_matrix:a,u_label_plane_matrix:o,u_coord_matrix:s,u_is_text:+l,u_pitch_with_map:+n,u_texsize:c,u_texture:0}},Yr=function(e,r,n,i,a,o,s,l,c,u,f){var h=a.transform;return t.extend(Gr(e,r,n,i,a,o,s,l,c,u),{u_gamma_scale:i?Math.cos(h._pitch)*h.cameraToCenterDistance:1,u_device_pixel_ratio:t.browser.devicePixelRatio,u_is_halo:+f})},Wr=function(e,r,n,i,a,o,s,l,c,u){return t.extend(Yr(e,r,n,i,a,o,s,l,!0,c,!0),{u_texsize_icon:u,u_texture_icon:1})},Xr=function(t,e,r){return{u_matrix:t,u_opacity:e,u_color:r}},Zr=function(e,r,n,i,a,o){return t.extend(function(t,e,r,n){var i=r.imageManager.getPattern(t.from.toString()),a=r.imageManager.getPattern(t.to.toString()),o=r.imageManager.getPixelSize(),s=o.width,l=o.height,c=Math.pow(2,n.tileID.overscaledZ),u=n.tileSize*Math.pow(2,r.transform.tileZoom)/c,f=u*(n.tileID.canonical.x+n.tileID.wrap*c),h=u*n.tileID.canonical.y;return{u_image:0,u_pattern_tl_a:i.tl,u_pattern_br_a:i.br,u_pattern_tl_b:a.tl,u_pattern_br_b:a.br,u_texsize:[s,l],u_mix:e.t,u_pattern_size_a:i.displaySize,u_pattern_size_b:a.displaySize,u_scale_a:e.fromScale,u_scale_b:e.toScale,u_tile_units_to_pixels:1/pe(n,1,r.transform.tileZoom),u_pixel_coord_upper:[f>>16,h>>16],u_pixel_coord_lower:[65535&f,65535&h]}}(i,o,n,a),{u_matrix:e,u_opacity:r})},Jr={fillExtrusion:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fillExtrusionPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_height_factor:new t.Uniform1f(e,r.u_height_factor),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fill:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},fillPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},fillOutline:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world)}},fillOutlinePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},circle:function(e,r){return{u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_scale_with_map:new t.Uniform1i(e,r.u_scale_with_map),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},collisionBox:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pixels_to_tile_units:new t.Uniform1f(e,r.u_pixels_to_tile_units),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_overscale_factor:new t.Uniform1f(e,r.u_overscale_factor)}},collisionCircle:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_inv_matrix:new t.UniformMatrix4f(e,r.u_inv_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_viewport_size:new t.Uniform2f(e,r.u_viewport_size)}},debug:function(e,r){return{u_color:new t.UniformColor(e,r.u_color),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_overlay:new t.Uniform1i(e,r.u_overlay),u_overlay_scale:new t.Uniform1f(e,r.u_overlay_scale)}},clippingMask:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmap:function(e,r){return{u_extrude_scale:new t.Uniform1f(e,r.u_extrude_scale),u_intensity:new t.Uniform1f(e,r.u_intensity),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmapTexture:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_color_ramp:new t.Uniform1i(e,r.u_color_ramp),u_opacity:new t.Uniform1f(e,r.u_opacity)}},hillshade:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_latrange:new t.Uniform2f(e,r.u_latrange),u_light:new t.Uniform2f(e,r.u_light),u_shadow:new t.UniformColor(e,r.u_shadow),u_highlight:new t.UniformColor(e,r.u_highlight),u_accent:new t.UniformColor(e,r.u_accent)}},hillshadePrepare:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_dimension:new t.Uniform2f(e,r.u_dimension),u_zoom:new t.Uniform1f(e,r.u_zoom),u_maxzoom:new t.Uniform1f(e,r.u_maxzoom),u_unpack:new t.Uniform4f(e,r.u_unpack)}},line:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels)}},lineGradient:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_image:new t.Uniform1i(e,r.u_image)}},linePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_texsize:new t.Uniform2f(e,r.u_texsize),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_image:new t.Uniform1i(e,r.u_image),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},lineSDF:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_patternscale_a:new t.Uniform2f(e,r.u_patternscale_a),u_patternscale_b:new t.Uniform2f(e,r.u_patternscale_b),u_sdfgamma:new t.Uniform1f(e,r.u_sdfgamma),u_image:new t.Uniform1i(e,r.u_image),u_tex_y_a:new t.Uniform1f(e,r.u_tex_y_a),u_tex_y_b:new t.Uniform1f(e,r.u_tex_y_b),u_mix:new t.Uniform1f(e,r.u_mix)}},raster:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_tl_parent:new t.Uniform2f(e,r.u_tl_parent),u_scale_parent:new t.Uniform1f(e,r.u_scale_parent),u_buffer_scale:new t.Uniform1f(e,r.u_buffer_scale),u_fade_t:new t.Uniform1f(e,r.u_fade_t),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image0:new t.Uniform1i(e,r.u_image0),u_image1:new t.Uniform1i(e,r.u_image1),u_brightness_low:new t.Uniform1f(e,r.u_brightness_low),u_brightness_high:new t.Uniform1f(e,r.u_brightness_high),u_saturation_factor:new t.Uniform1f(e,r.u_saturation_factor),u_contrast_factor:new t.Uniform1f(e,r.u_contrast_factor),u_spin_weights:new t.Uniform3f(e,r.u_spin_weights)}},symbolIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture)}},symbolSDF:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},symbolTextAndIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texsize_icon:new t.Uniform2f(e,r.u_texsize_icon),u_texture:new t.Uniform1i(e,r.u_texture),u_texture_icon:new t.Uniform1i(e,r.u_texture_icon),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},background:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_color:new t.UniformColor(e,r.u_color)}},backgroundPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image:new t.Uniform1i(e,r.u_image),u_pattern_tl_a:new t.Uniform2f(e,r.u_pattern_tl_a),u_pattern_br_a:new t.Uniform2f(e,r.u_pattern_br_a),u_pattern_tl_b:new t.Uniform2f(e,r.u_pattern_tl_b),u_pattern_br_b:new t.Uniform2f(e,r.u_pattern_br_b),u_texsize:new t.Uniform2f(e,r.u_texsize),u_mix:new t.Uniform1f(e,r.u_mix),u_pattern_size_a:new t.Uniform2f(e,r.u_pattern_size_a),u_pattern_size_b:new t.Uniform2f(e,r.u_pattern_size_b),u_scale_a:new t.Uniform1f(e,r.u_scale_a),u_scale_b:new t.Uniform1f(e,r.u_scale_b),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_tile_units_to_pixels:new t.Uniform1f(e,r.u_tile_units_to_pixels)}}};function Kr(e,r,n,i,a,o,s){for(var l=e.context,c=l.gl,u=e.useProgram(\"collisionBox\"),f=[],h=0,p=0,d=0;d<i.length;d++){var g=i[d],m=r.getTile(g),v=m.getBucket(n);if(v){var y=g.posMatrix;0===a[0]&&0===a[1]||(y=e.translatePosMatrix(g.posMatrix,m,a,o));var x=s?v.textCollisionBox:v.iconCollisionBox,b=v.collisionCircleArray;if(b.length>0){var _=t.create(),w=y;t.mul(_,v.placementInvProjMatrix,e.transform.glCoordMatrix),t.mul(_,_,v.placementViewportMatrix),f.push({circleArray:b,circleOffset:p,transform:w,invTransform:_}),p=h+=b.length/4}x&&u.draw(l,c.LINES,At.disabled,Mt.disabled,e.colorModeForRenderPass(),Et.disabled,Cr(y,e.transform,m),n.id,x.layoutVertexBuffer,x.indexBuffer,x.segments,null,e.transform.zoom,null,null,x.collisionVertexBuffer)}}if(s&&f.length){var T=e.useProgram(\"collisionCircle\"),k=new t.StructArrayLayout2f1f2i16;k.resize(4*h),k._trim();for(var A=0,M=0,S=f;M<S.length;M+=1)for(var E=S[M],L=0;L<E.circleArray.length/4;L++){var C=4*L,P=E.circleArray[C+0],I=E.circleArray[C+1],O=E.circleArray[C+2],z=E.circleArray[C+3];k.emplace(A++,P,I,O,z,0),k.emplace(A++,P,I,O,z,1),k.emplace(A++,P,I,O,z,2),k.emplace(A++,P,I,O,z,3)}(!qr||qr.length<2*h)&&(qr=function(e){var r=2*e,n=new t.StructArrayLayout3ui6;n.resize(r),n._trim();for(var i=0;i<r;i++){var a=6*i;n.uint16[a+0]=4*i+0,n.uint16[a+1]=4*i+1,n.uint16[a+2]=4*i+2,n.uint16[a+3]=4*i+2,n.uint16[a+4]=4*i+3,n.uint16[a+5]=4*i+0}return n}(h));for(var D=l.createIndexBuffer(qr,!0),R=l.createVertexBuffer(k,t.collisionCircleLayout.members,!0),F=0,B=f;F<B.length;F+=1){var N=B[F],j=Pr(N.transform,N.invTransform,e.transform);T.draw(l,c.TRIANGLES,At.disabled,Mt.disabled,e.colorModeForRenderPass(),Et.disabled,j,n.id,R,D,t.SegmentVector.simpleSegment(0,2*N.circleOffset,N.circleArray.length,N.circleArray.length/2),null,e.transform.zoom,null,null,null)}R.destroy(),D.destroy()}}var Qr=t.identity(new Float32Array(16));function $r(e,r,n,i,a,o){var s=t.getAnchorAlignment(e),l=-(s.horizontalAlign-.5)*r,c=-(s.verticalAlign-.5)*n,u=t.evaluateVariableOffset(e,i);return new t.Point((l/a+u[0])*o,(c/a+u[1])*o)}function tn(e,r,n,i,a,o,s,l,c,u,f){var h=e.text.placedSymbolArray,p=e.text.dynamicLayoutVertexArray,d=e.icon.dynamicLayoutVertexArray,g={};p.clear();for(var m=0;m<h.length;m++){var v=h.get(m),y=e.allowVerticalPlacement&&!v.placedOrientation,x=v.hidden||!v.crossTileID||y?null:i[v.crossTileID];if(x){var b=new t.Point(v.anchorX,v.anchorY),_=te(b,n?l:s),w=ee(o.cameraToCenterDistance,_.signedDistanceFromCamera),T=a.evaluateSizeForFeature(e.textSizeData,u,v)*w/t.ONE_EM;n&&(T*=e.tilePixelRatio/c);for(var k=x.width,A=x.height,M=$r(x.anchor,k,A,x.textOffset,x.textBoxScale,T),S=n?te(b.add(M),s).point:_.point.add(r?M.rotate(-o.angle):M),E=e.allowVerticalPlacement&&v.placedOrientation===t.WritingMode.vertical?Math.PI/2:0,L=0;L<v.numGlyphs;L++)t.addDynamicAttributes(p,S,E);f&&v.associatedIconIndex>=0&&(g[v.associatedIconIndex]={shiftedAnchor:S,angle:E})}else ue(v.numGlyphs,p)}if(f){d.clear();for(var C=e.icon.placedSymbolArray,P=0;P<C.length;P++){var I=C.get(P);if(I.hidden)ue(I.numGlyphs,d);else{var O=g[P];if(O)for(var z=0;z<I.numGlyphs;z++)t.addDynamicAttributes(d,O.shiftedAnchor,O.angle);else ue(I.numGlyphs,d)}}e.icon.dynamicLayoutVertexBuffer.updateData(d)}e.text.dynamicLayoutVertexBuffer.updateData(p)}function en(t,e,r){return r.iconsInText&&e?\"symbolTextAndIcon\":t?\"symbolSDF\":\"symbolIcon\"}function rn(e,r,n,i,a,o,s,l,c,u,f,h){for(var p=e.context,d=p.gl,g=e.transform,m=\"map\"===l,v=\"map\"===c,y=m&&\"point\"!==n.layout.get(\"symbol-placement\"),x=m&&!v&&!y,b=void 0!==n.layout.get(\"symbol-sort-key\").constantOr(1),_=e.depthModeForSublayer(0,At.ReadOnly),w=n.layout.get(\"text-variable-anchor\"),T=[],k=0,A=i;k<A.length;k+=1){var M=A[k],S=r.getTile(M),E=S.getBucket(n);if(E){var L=a?E.text:E.icon;if(L&&L.segments.get().length){var C=L.programConfigurations.get(n.id),P=a||E.sdfIcons,I=a?E.textSizeData:E.iconSizeData,O=v||0!==g.pitch,z=e.useProgram(en(P,a,E),C),D=t.evaluateSizeForZoom(I,g.zoom),R=void 0,F=[0,0],B=void 0,N=void 0,j=null,U=void 0;if(a){if(B=S.glyphAtlasTexture,N=d.LINEAR,R=S.glyphAtlasTexture.size,E.iconsInText){F=S.imageAtlasTexture.size,j=S.imageAtlasTexture;var V=\"composite\"===I.kind||\"camera\"===I.kind;U=O||e.options.rotating||e.options.zooming||V?d.LINEAR:d.NEAREST}}else{var H=1!==n.layout.get(\"icon-size\").constantOr(0)||E.iconsNeedLinear;B=S.imageAtlasTexture,N=P||e.options.rotating||e.options.zooming||H||O?d.LINEAR:d.NEAREST,R=S.imageAtlasTexture.size}var q=pe(S,1,e.transform.zoom),G=Qt(M.posMatrix,v,m,e.transform,q),Y=$t(M.posMatrix,v,m,e.transform,q),W=w&&E.hasTextData(),X=\"none\"!==n.layout.get(\"icon-text-fit\")&&W&&E.hasIconData();y&&ne(E,M.posMatrix,e,a,G,Y,v,u);var Z=e.translatePosMatrix(M.posMatrix,S,o,s),J=y||a&&w||X?Qr:G,K=e.translatePosMatrix(Y,S,o,s,!0),Q=P&&0!==n.paint.get(a?\"text-halo-width\":\"icon-halo-width\").constantOr(1),$={program:z,buffers:L,uniformValues:P?E.iconsInText?Wr(I.kind,D,x,v,e,Z,J,K,R,F):Yr(I.kind,D,x,v,e,Z,J,K,a,R,!0):Gr(I.kind,D,x,v,e,Z,J,K,a,R),atlasTexture:B,atlasTextureIcon:j,atlasInterpolation:N,atlasInterpolationIcon:U,isSDF:P,hasHalo:Q};if(b)for(var tt=0,et=L.segments.get();tt<et.length;tt+=1){var rt=et[tt];T.push({segments:new t.SegmentVector([rt]),sortKey:rt.sortKey,state:$})}else T.push({segments:L.segments,sortKey:0,state:$})}}}b&&T.sort((function(t,e){return t.sortKey-e.sortKey}));for(var nt=0,it=T;nt<it.length;nt+=1){var at=it[nt],ot=at.state;if(p.activeTexture.set(d.TEXTURE0),ot.atlasTexture.bind(ot.atlasInterpolation,d.CLAMP_TO_EDGE),ot.atlasTextureIcon&&(p.activeTexture.set(d.TEXTURE1),ot.atlasTextureIcon&&ot.atlasTextureIcon.bind(ot.atlasInterpolationIcon,d.CLAMP_TO_EDGE)),ot.isSDF){var st=ot.uniformValues;ot.hasHalo&&(st.u_is_halo=1,nn(ot.buffers,at.segments,n,e,ot.program,_,f,h,st)),st.u_is_halo=0}nn(ot.buffers,at.segments,n,e,ot.program,_,f,h,ot.uniformValues)}}function nn(t,e,r,n,i,a,o,s,l){var c=n.context,u=c.gl;i.draw(c,u.TRIANGLES,a,o,s,Et.disabled,l,r.id,t.layoutVertexBuffer,t.indexBuffer,e,r.paint,n.transform.zoom,t.programConfigurations.get(r.id),t.dynamicLayoutVertexBuffer,t.opacityVertexBuffer)}function an(t,e,r,n,i,a,o){var s,l,c,u,f,h=t.context.gl,p=r.paint.get(\"fill-pattern\"),d=p&&p.constantOr(1),g=r.getCrossfadeParameters();o?(l=d&&!r.getPaintProperty(\"fill-outline-color\")?\"fillOutlinePattern\":\"fillOutline\",s=h.LINES):(l=d?\"fillPattern\":\"fill\",s=h.TRIANGLES);for(var m=0,v=n;m<v.length;m+=1){var y=v[m],x=e.getTile(y);if(!d||x.patternsLoaded()){var b=x.getBucket(r);if(b){var _=b.programConfigurations.get(r.id),w=t.useProgram(l,_);d&&(t.context.activeTexture.set(h.TEXTURE0),x.imageAtlasTexture.bind(h.LINEAR,h.CLAMP_TO_EDGE),_.updatePaintBuffers(g));var T=p.constantOr(null);if(T&&x.imageAtlas){var k=x.imageAtlas,A=k.patternPositions[T.to.toString()],M=k.patternPositions[T.from.toString()];A&&M&&_.setConstantPatternPositions(A,M)}var S=t.translatePosMatrix(y.posMatrix,x,r.paint.get(\"fill-translate\"),r.paint.get(\"fill-translate-anchor\"));if(o){u=b.indexBuffer2,f=b.segments2;var E=[h.drawingBufferWidth,h.drawingBufferHeight];c=\"fillOutlinePattern\"===l&&d?Er(S,t,g,x,E):Sr(S,E)}else u=b.indexBuffer,f=b.segments,c=d?Mr(S,t,g,x):Ar(S);w.draw(t.context,s,i,t.stencilModeForClipping(y),a,Et.disabled,c,r.id,b.layoutVertexBuffer,u,f,r.paint,t.transform.zoom,_)}}}}function on(t,e,r,n,i,a,o){for(var s=t.context,l=s.gl,c=r.paint.get(\"fill-extrusion-pattern\"),u=c.constantOr(1),f=r.getCrossfadeParameters(),h=r.paint.get(\"fill-extrusion-opacity\"),p=0,d=n;p<d.length;p+=1){var g=d[p],m=e.getTile(g),v=m.getBucket(r);if(v){var y=v.programConfigurations.get(r.id),x=t.useProgram(u?\"fillExtrusionPattern\":\"fillExtrusion\",y);u&&(t.context.activeTexture.set(l.TEXTURE0),m.imageAtlasTexture.bind(l.LINEAR,l.CLAMP_TO_EDGE),y.updatePaintBuffers(f));var b=c.constantOr(null);if(b&&m.imageAtlas){var _=m.imageAtlas,w=_.patternPositions[b.to.toString()],T=_.patternPositions[b.from.toString()];w&&T&&y.setConstantPatternPositions(w,T)}var k=t.translatePosMatrix(g.posMatrix,m,r.paint.get(\"fill-extrusion-translate\"),r.paint.get(\"fill-extrusion-translate-anchor\")),A=r.paint.get(\"fill-extrusion-vertical-gradient\"),M=u?kr(k,t,A,h,g,f,m):Tr(k,t,A,h);x.draw(s,s.gl.TRIANGLES,i,a,o,Et.backCCW,M,r.id,v.layoutVertexBuffer,v.indexBuffer,v.segments,r.paint,t.transform.zoom,y)}}}function sn(t,e,r,n,i,a){var o=t.context,s=o.gl,l=e.fbo;if(l){var c=t.useProgram(\"hillshade\");o.activeTexture.set(s.TEXTURE0),s.bindTexture(s.TEXTURE_2D,l.colorAttachment.get());var u=function(t,e,r){var n=r.paint.get(\"hillshade-shadow-color\"),i=r.paint.get(\"hillshade-highlight-color\"),a=r.paint.get(\"hillshade-accent-color\"),o=r.paint.get(\"hillshade-illumination-direction\")*(Math.PI/180);\"viewport\"===r.paint.get(\"hillshade-illumination-anchor\")&&(o-=t.transform.angle);var s=!t.options.moving;return{u_matrix:t.transform.calculatePosMatrix(e.tileID.toUnwrapped(),s),u_image:0,u_latrange:Dr(t,e.tileID),u_light:[r.paint.get(\"hillshade-exaggeration\"),o],u_shadow:n,u_highlight:i,u_accent:a}}(t,e,r);c.draw(o,s.TRIANGLES,n,i,a,Et.disabled,u,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}}function ln(e,r,n,i,a,o,s){var l=e.context,c=l.gl,u=r.dem;if(u&&u.data){var f=u.dim,h=u.stride,p=u.getPixels();if(l.activeTexture.set(c.TEXTURE1),l.pixelStoreUnpackPremultiplyAlpha.set(!1),r.demTexture=r.demTexture||e.getTileTexture(h),r.demTexture){var d=r.demTexture;d.update(p,{premultiply:!1}),d.bind(c.NEAREST,c.CLAMP_TO_EDGE)}else r.demTexture=new t.Texture(l,p,c.RGBA,{premultiply:!1}),r.demTexture.bind(c.NEAREST,c.CLAMP_TO_EDGE);l.activeTexture.set(c.TEXTURE0);var g=r.fbo;if(!g){var m=new t.Texture(l,{width:f,height:f,data:null},c.RGBA);m.bind(c.LINEAR,c.CLAMP_TO_EDGE),(g=r.fbo=l.createFramebuffer(f,f,!0)).colorAttachment.set(m.texture)}l.bindFramebuffer.set(g.framebuffer),l.viewport.set([0,0,f,f]),e.useProgram(\"hillshadePrepare\").draw(l,c.TRIANGLES,a,o,s,Et.disabled,function(e,r,n){var i=r.stride,a=t.create();return t.ortho(a,0,t.EXTENT,-t.EXTENT,0,0,1),t.translate(a,a,[0,-t.EXTENT,0]),{u_matrix:a,u_image:1,u_dimension:[i,i],u_zoom:e.overscaledZ,u_maxzoom:n,u_unpack:r.getUnpackVector()}}(r.tileID,u,i),n.id,e.rasterBoundsBuffer,e.quadTriangleIndexBuffer,e.rasterBoundsSegments),r.needsHillshadePrepare=!1}}function cn(e,r,n,i,a){var o=i.paint.get(\"raster-fade-duration\");if(o>0){var s=t.browser.now(),l=(s-e.timeAdded)/o,c=r?(s-r.timeAdded)/o:-1,u=n.getSource(),f=a.coveringZoomLevel({tileSize:u.tileSize,roundZoom:u.roundZoom}),h=!r||Math.abs(r.tileID.overscaledZ-f)>Math.abs(e.tileID.overscaledZ-f),p=h&&e.refreshedUponExpiration?1:t.clamp(h?l:1-c,0,1);return e.refreshedUponExpiration&&l>=1&&(e.refreshedUponExpiration=!1),r?{opacity:1,mix:1-p}:{opacity:p,mix:0}}return{opacity:1,mix:0}}var un=new t.Color(1,0,0,1),fn=new t.Color(0,1,0,1),hn=new t.Color(0,0,1,1),pn=new t.Color(1,0,1,1),dn=new t.Color(0,1,1,1);function gn(t){var e=t.transform.padding;mn(t,t.transform.height-(e.top||0),3,un),mn(t,e.bottom||0,3,fn),vn(t,e.left||0,3,hn),vn(t,t.transform.width-(e.right||0),3,pn);var r=t.transform.centerPoint;!function(t,e,r,n){yn(t,e-1,r-10,2,20,n),yn(t,e-10,r-1,20,2,n)}(t,r.x,t.transform.height-r.y,dn)}function mn(t,e,r,n){yn(t,0,e+r/2,t.transform.width,r,n)}function vn(t,e,r,n){yn(t,e-r/2,0,r,t.transform.height,n)}function yn(e,r,n,i,a,o){var s=e.context,l=s.gl;l.enable(l.SCISSOR_TEST),l.scissor(r*t.browser.devicePixelRatio,n*t.browser.devicePixelRatio,i*t.browser.devicePixelRatio,a*t.browser.devicePixelRatio),s.clear({color:o}),l.disable(l.SCISSOR_TEST)}function xn(e,r,n){var i=e.context,a=i.gl,o=n.posMatrix,s=e.useProgram(\"debug\"),l=At.disabled,c=Mt.disabled,u=e.colorModeForRenderPass();i.activeTexture.set(a.TEXTURE0),e.emptyTexture.bind(a.LINEAR,a.CLAMP_TO_EDGE),s.draw(i,a.LINE_STRIP,l,c,u,Et.disabled,Ir(o,t.Color.red),\"$debug\",e.debugBuffer,e.tileBorderIndexBuffer,e.debugSegments);var f=r.getTileByID(n.key).latestRawTileData,h=f&&f.byteLength||0,p=Math.floor(h/1024),d=r.getTile(n).tileSize,g=512/Math.min(d,512)*(n.overscaledZ/e.transform.zoom)*.5,m=n.canonical.toString();n.overscaledZ!==n.canonical.z&&(m+=\" => \"+n.overscaledZ),function(t,e){t.initDebugOverlayCanvas();var r=t.debugOverlayCanvas,n=t.context.gl,i=t.debugOverlayCanvas.getContext(\"2d\");i.clearRect(0,0,r.width,r.height),i.shadowColor=\"white\",i.shadowBlur=2,i.lineWidth=1.5,i.strokeStyle=\"white\",i.textBaseline=\"top\",i.font=\"bold 36px Open Sans, sans-serif\",i.fillText(e,5,5),i.strokeText(e,5,5),t.debugOverlayTexture.update(r),t.debugOverlayTexture.bind(n.LINEAR,n.CLAMP_TO_EDGE)}(e,m+\" \"+p+\"kb\"),s.draw(i,a.TRIANGLES,l,c,St.alphaBlended,Et.disabled,Ir(o,t.Color.transparent,g),\"$debug\",e.debugBuffer,e.quadTriangleIndexBuffer,e.debugSegments)}var bn={symbol:function(e,r,n,i,a){if(\"translucent\"===e.renderPass){var o=Mt.disabled,s=e.colorModeForRenderPass();n.layout.get(\"text-variable-anchor\")&&function(e,r,n,i,a,o,s){for(var l=r.transform,c=\"map\"===a,u=\"map\"===o,f=0,h=e;f<h.length;f+=1){var p=h[f],d=i.getTile(p),g=d.getBucket(n);if(g&&g.text&&g.text.segments.get().length){var m=g.textSizeData,v=t.evaluateSizeForZoom(m,l.zoom),y=pe(d,1,r.transform.zoom),x=Qt(p.posMatrix,u,c,r.transform,y),b=\"none\"!==n.layout.get(\"icon-text-fit\")&&g.hasIconData();if(v){var _=Math.pow(2,l.zoom-d.tileID.overscaledZ);tn(g,c,u,s,t.symbolSize,l,x,p.posMatrix,_,v,b)}}}}(i,e,n,r,n.layout.get(\"text-rotation-alignment\"),n.layout.get(\"text-pitch-alignment\"),a),0!==n.paint.get(\"icon-opacity\").constantOr(1)&&rn(e,r,n,i,!1,n.paint.get(\"icon-translate\"),n.paint.get(\"icon-translate-anchor\"),n.layout.get(\"icon-rotation-alignment\"),n.layout.get(\"icon-pitch-alignment\"),n.layout.get(\"icon-keep-upright\"),o,s),0!==n.paint.get(\"text-opacity\").constantOr(1)&&rn(e,r,n,i,!0,n.paint.get(\"text-translate\"),n.paint.get(\"text-translate-anchor\"),n.layout.get(\"text-rotation-alignment\"),n.layout.get(\"text-pitch-alignment\"),n.layout.get(\"text-keep-upright\"),o,s),r.map.showCollisionBoxes&&(Kr(e,r,n,i,n.paint.get(\"text-translate\"),n.paint.get(\"text-translate-anchor\"),!0),Kr(e,r,n,i,n.paint.get(\"icon-translate\"),n.paint.get(\"icon-translate-anchor\"),!1))}},circle:function(e,r,n,i){if(\"translucent\"===e.renderPass){var a=n.paint.get(\"circle-opacity\"),o=n.paint.get(\"circle-stroke-width\"),s=n.paint.get(\"circle-stroke-opacity\"),l=void 0!==n.layout.get(\"circle-sort-key\").constantOr(1);if(0!==a.constantOr(1)||0!==o.constantOr(1)&&0!==s.constantOr(1)){for(var c=e.context,u=c.gl,f=e.depthModeForSublayer(0,At.ReadOnly),h=Mt.disabled,p=e.colorModeForRenderPass(),d=[],g=0;g<i.length;g++){var m=i[g],v=r.getTile(m),y=v.getBucket(n);if(y){var x=y.programConfigurations.get(n.id),b={programConfiguration:x,program:e.useProgram(\"circle\",x),layoutVertexBuffer:y.layoutVertexBuffer,indexBuffer:y.indexBuffer,uniformValues:Lr(e,m,v,n)};if(l)for(var _=0,w=y.segments.get();_<w.length;_+=1){var T=w[_];d.push({segments:new t.SegmentVector([T]),sortKey:T.sortKey,state:b})}else d.push({segments:y.segments,sortKey:0,state:b})}}l&&d.sort((function(t,e){return t.sortKey-e.sortKey}));for(var k=0,A=d;k<A.length;k+=1){var M=A[k],S=M.state,E=S.programConfiguration,L=S.program,C=S.layoutVertexBuffer,P=S.indexBuffer,I=S.uniformValues,O=M.segments;L.draw(c,u.TRIANGLES,f,h,p,Et.disabled,I,n.id,C,P,O,n.paint,e.transform.zoom,E)}}}},heatmap:function(e,r,n,i){if(0!==n.paint.get(\"heatmap-opacity\"))if(\"offscreen\"===e.renderPass){var a=e.context,o=a.gl,s=Mt.disabled,l=new St([o.ONE,o.ONE],t.Color.transparent,[!0,!0,!0,!0]);!function(t,e,r){var n=t.gl;t.activeTexture.set(n.TEXTURE1),t.viewport.set([0,0,e.width/4,e.height/4]);var i=r.heatmapFbo;if(i)n.bindTexture(n.TEXTURE_2D,i.colorAttachment.get()),t.bindFramebuffer.set(i.framebuffer);else{var a=n.createTexture();n.bindTexture(n.TEXTURE_2D,a),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,n.LINEAR),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,n.LINEAR),i=r.heatmapFbo=t.createFramebuffer(e.width/4,e.height/4,!1),function(t,e,r,n){var i=t.gl,a=t.extRenderToTextureHalfFloat?t.extTextureHalfFloat.HALF_FLOAT_OES:i.UNSIGNED_BYTE;i.texImage2D(i.TEXTURE_2D,0,i.RGBA,e.width/4,e.height/4,0,i.RGBA,a,null),n.colorAttachment.set(r)}(t,e,a,i)}}(a,e,n),a.clear({color:t.Color.transparent});for(var c=0;c<i.length;c++){var u=i[c];if(!r.hasRenderableParent(u)){var f=r.getTile(u),h=f.getBucket(n);if(h){var p=h.programConfigurations.get(n.id),d=e.useProgram(\"heatmap\",p),g=e.transform.zoom;d.draw(a,o.TRIANGLES,At.disabled,s,l,Et.disabled,zr(u.posMatrix,f,g,n.paint.get(\"heatmap-intensity\")),n.id,h.layoutVertexBuffer,h.indexBuffer,h.segments,n.paint,e.transform.zoom,p)}}}a.viewport.set([0,0,e.width,e.height])}else\"translucent\"===e.renderPass&&(e.context.setColorMode(e.colorModeForRenderPass()),function(e,r){var n=e.context,i=n.gl,a=r.heatmapFbo;if(!a)return;n.activeTexture.set(i.TEXTURE0),i.bindTexture(i.TEXTURE_2D,a.colorAttachment.get()),n.activeTexture.set(i.TEXTURE1);var o=r.colorRampTexture;o||(o=r.colorRampTexture=new t.Texture(n,r.colorRamp,i.RGBA));o.bind(i.LINEAR,i.CLAMP_TO_EDGE),e.useProgram(\"heatmapTexture\").draw(n,i.TRIANGLES,At.disabled,Mt.disabled,e.colorModeForRenderPass(),Et.disabled,function(e,r,n,i){var a=t.create();t.ortho(a,0,e.width,e.height,0,0,1);var o=e.context.gl;return{u_matrix:a,u_world:[o.drawingBufferWidth,o.drawingBufferHeight],u_image:n,u_color_ramp:i,u_opacity:r.paint.get(\"heatmap-opacity\")}}(e,r,0,1),r.id,e.viewportBuffer,e.quadTriangleIndexBuffer,e.viewportSegments,r.paint,e.transform.zoom)}(e,n))},line:function(e,r,n,i){if(\"translucent\"===e.renderPass){var a=n.paint.get(\"line-opacity\"),o=n.paint.get(\"line-width\");if(0!==a.constantOr(1)&&0!==o.constantOr(1)){var s=e.depthModeForSublayer(0,At.ReadOnly),l=e.colorModeForRenderPass(),c=n.paint.get(\"line-dasharray\"),u=n.paint.get(\"line-pattern\"),f=u.constantOr(1),h=n.paint.get(\"line-gradient\"),p=n.getCrossfadeParameters(),d=f?\"linePattern\":c?\"lineSDF\":h?\"lineGradient\":\"line\",g=e.context,m=g.gl,v=!0;if(h){g.activeTexture.set(m.TEXTURE0);var y=n.gradientTexture;if(!n.gradient)return;y||(y=n.gradientTexture=new t.Texture(g,n.gradient,m.RGBA)),y.bind(m.LINEAR,m.CLAMP_TO_EDGE)}for(var x=0,b=i;x<b.length;x+=1){var _=b[x],w=r.getTile(_);if(!f||w.patternsLoaded()){var T=w.getBucket(n);if(T){var k=T.programConfigurations.get(n.id),A=e.context.program.get(),M=e.useProgram(d,k),S=v||M.program!==A,E=u.constantOr(null);if(E&&w.imageAtlas){var L=w.imageAtlas,C=L.patternPositions[E.to.toString()],P=L.patternPositions[E.from.toString()];C&&P&&k.setConstantPatternPositions(C,P)}var I=f?Br(e,w,n,p):c?Nr(e,w,n,c,p):h?Fr(e,w,n):Rr(e,w,n);f?(g.activeTexture.set(m.TEXTURE0),w.imageAtlasTexture.bind(m.LINEAR,m.CLAMP_TO_EDGE),k.updatePaintBuffers(p)):c&&(S||e.lineAtlas.dirty)&&(g.activeTexture.set(m.TEXTURE0),e.lineAtlas.bind(g)),M.draw(g,m.TRIANGLES,s,e.stencilModeForClipping(_),l,Et.disabled,I,n.id,T.layoutVertexBuffer,T.indexBuffer,T.segments,n.paint,e.transform.zoom,k),v=!1}}}}}},fill:function(e,r,n,i){var a=n.paint.get(\"fill-color\"),o=n.paint.get(\"fill-opacity\");if(0!==o.constantOr(1)){var s=e.colorModeForRenderPass(),l=n.paint.get(\"fill-pattern\"),c=e.opaquePassEnabledForLayer()&&!l.constantOr(1)&&1===a.constantOr(t.Color.transparent).a&&1===o.constantOr(0)?\"opaque\":\"translucent\";if(e.renderPass===c){var u=e.depthModeForSublayer(1,\"opaque\"===e.renderPass?At.ReadWrite:At.ReadOnly);an(e,r,n,i,u,s,!1)}if(\"translucent\"===e.renderPass&&n.paint.get(\"fill-antialias\")){var f=e.depthModeForSublayer(n.getPaintProperty(\"fill-outline-color\")?2:0,At.ReadOnly);an(e,r,n,i,f,s,!0)}}},\"fill-extrusion\":function(t,e,r,n){var i=r.paint.get(\"fill-extrusion-opacity\");if(0!==i&&\"translucent\"===t.renderPass){var a=new At(t.context.gl.LEQUAL,At.ReadWrite,t.depthRangeFor3D);if(1!==i||r.paint.get(\"fill-extrusion-pattern\").constantOr(1))on(t,e,r,n,a,Mt.disabled,St.disabled),on(t,e,r,n,a,t.stencilModeFor3D(),t.colorModeForRenderPass());else{var o=t.colorModeForRenderPass();on(t,e,r,n,a,Mt.disabled,o)}}},hillshade:function(t,e,r,n){if(\"offscreen\"===t.renderPass||\"translucent\"===t.renderPass){for(var i=t.context,a=e.getSource().maxzoom,o=t.depthModeForSublayer(0,At.ReadOnly),s=t.colorModeForRenderPass(),l=\"translucent\"===t.renderPass?t.stencilConfigForOverlap(n):[{},n],c=l[0],u=0,f=l[1];u<f.length;u+=1){var h=f[u],p=e.getTile(h);p.needsHillshadePrepare&&\"offscreen\"===t.renderPass?ln(t,p,r,a,o,Mt.disabled,s):\"translucent\"===t.renderPass&&sn(t,p,r,o,c[h.overscaledZ],s)}i.viewport.set([0,0,t.width,t.height])}},raster:function(t,e,r,n){if(\"translucent\"===t.renderPass&&0!==r.paint.get(\"raster-opacity\")&&n.length)for(var i=t.context,a=i.gl,o=e.getSource(),s=t.useProgram(\"raster\"),l=t.colorModeForRenderPass(),c=o instanceof I?[{},n]:t.stencilConfigForOverlap(n),u=c[0],f=c[1],h=f[f.length-1].overscaledZ,p=!t.options.moving,d=0,g=f;d<g.length;d+=1){var m=g[d],v=t.depthModeForSublayer(m.overscaledZ-h,1===r.paint.get(\"raster-opacity\")?At.ReadWrite:At.ReadOnly,a.LESS),y=e.getTile(m),x=t.transform.calculatePosMatrix(m.toUnwrapped(),p);y.registerFadeDuration(r.paint.get(\"raster-fade-duration\"));var b=e.findLoadedParent(m,0),_=cn(y,b,e,r,t.transform),w=void 0,T=void 0,k=\"nearest\"===r.paint.get(\"raster-resampling\")?a.NEAREST:a.LINEAR;i.activeTexture.set(a.TEXTURE0),y.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),i.activeTexture.set(a.TEXTURE1),b?(b.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),w=Math.pow(2,b.tileID.overscaledZ-y.tileID.overscaledZ),T=[y.tileID.canonical.x*w%1,y.tileID.canonical.y*w%1]):y.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST);var A=Vr(x,T||[0,0],w||1,_,r);o instanceof I?s.draw(i,a.TRIANGLES,v,Mt.disabled,l,Et.disabled,A,r.id,o.boundsBuffer,t.quadTriangleIndexBuffer,o.boundsSegments):s.draw(i,a.TRIANGLES,v,u[m.overscaledZ],l,Et.disabled,A,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}},background:function(t,e,r){var n=r.paint.get(\"background-color\"),i=r.paint.get(\"background-opacity\");if(0!==i){var a=t.context,o=a.gl,s=t.transform,l=s.tileSize,c=r.paint.get(\"background-pattern\");if(!t.isPatternMissing(c)){var u=!c&&1===n.a&&1===i&&t.opaquePassEnabledForLayer()?\"opaque\":\"translucent\";if(t.renderPass===u){var f=Mt.disabled,h=t.depthModeForSublayer(0,\"opaque\"===u?At.ReadWrite:At.ReadOnly),p=t.colorModeForRenderPass(),d=t.useProgram(c?\"backgroundPattern\":\"background\"),g=s.coveringTiles({tileSize:l});c&&(a.activeTexture.set(o.TEXTURE0),t.imageManager.bind(t.context));for(var m=r.getCrossfadeParameters(),v=0,y=g;v<y.length;v+=1){var x=y[v],b=t.transform.calculatePosMatrix(x.toUnwrapped()),_=c?Zr(b,i,t,c,{tileID:x,tileSize:l},m):Xr(b,i,n);d.draw(a,o.TRIANGLES,h,f,p,Et.disabled,_,r.id,t.tileExtentBuffer,t.quadTriangleIndexBuffer,t.tileExtentSegments)}}}}},debug:function(t,e,r){for(var n=0;n<r.length;n++)xn(t,e,r[n])},custom:function(t,e,r){var n=t.context,i=r.implementation;if(\"offscreen\"===t.renderPass){var a=i.prerender;a&&(t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),a.call(i,n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState())}else if(\"translucent\"===t.renderPass){t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),n.setStencilMode(Mt.disabled);var o=\"3d\"===i.renderingMode?new At(t.context.gl.LEQUAL,At.ReadWrite,t.depthRangeFor3D):t.depthModeForSublayer(0,At.ReadOnly);n.setDepthMode(o),i.render(n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState(),n.bindFramebuffer.set(null)}}},_n=function(t,e){this.context=new Lt(t),this.transform=e,this._tileTextures={},this.setup(),this.numSublayers=Ct.maxUnderzooming+Ct.maxOverzooming+1,this.depthEpsilon=1/Math.pow(2,16),this.crossTileSymbolIndex=new Ne,this.gpuTimers={}};_n.prototype.resize=function(e,r){if(this.width=e*t.browser.devicePixelRatio,this.height=r*t.browser.devicePixelRatio,this.context.viewport.set([0,0,this.width,this.height]),this.style)for(var n=0,i=this.style._order;n<i.length;n+=1){var a=i[n];this.style._layers[a].resize()}},_n.prototype.setup=function(){var e=this.context,r=new t.StructArrayLayout2i4;r.emplaceBack(0,0),r.emplaceBack(t.EXTENT,0),r.emplaceBack(0,t.EXTENT),r.emplaceBack(t.EXTENT,t.EXTENT),this.tileExtentBuffer=e.createVertexBuffer(r,Ge.members),this.tileExtentSegments=t.SegmentVector.simpleSegment(0,0,4,2);var n=new t.StructArrayLayout2i4;n.emplaceBack(0,0),n.emplaceBack(t.EXTENT,0),n.emplaceBack(0,t.EXTENT),n.emplaceBack(t.EXTENT,t.EXTENT),this.debugBuffer=e.createVertexBuffer(n,Ge.members),this.debugSegments=t.SegmentVector.simpleSegment(0,0,4,5);var i=new t.StructArrayLayout4i8;i.emplaceBack(0,0,0,0),i.emplaceBack(t.EXTENT,0,t.EXTENT,0),i.emplaceBack(0,t.EXTENT,0,t.EXTENT),i.emplaceBack(t.EXTENT,t.EXTENT,t.EXTENT,t.EXTENT),this.rasterBoundsBuffer=e.createVertexBuffer(i,P.members),this.rasterBoundsSegments=t.SegmentVector.simpleSegment(0,0,4,2);var a=new t.StructArrayLayout2i4;a.emplaceBack(0,0),a.emplaceBack(1,0),a.emplaceBack(0,1),a.emplaceBack(1,1),this.viewportBuffer=e.createVertexBuffer(a,Ge.members),this.viewportSegments=t.SegmentVector.simpleSegment(0,0,4,2);var o=new t.StructArrayLayout1ui2;o.emplaceBack(0),o.emplaceBack(1),o.emplaceBack(3),o.emplaceBack(2),o.emplaceBack(0),this.tileBorderIndexBuffer=e.createIndexBuffer(o);var s=new t.StructArrayLayout3ui6;s.emplaceBack(0,1,2),s.emplaceBack(2,1,3),this.quadTriangleIndexBuffer=e.createIndexBuffer(s),this.emptyTexture=new t.Texture(e,{width:1,height:1,data:new Uint8Array([0,0,0,0])},e.gl.RGBA);var l=this.context.gl;this.stencilClearMode=new Mt({func:l.ALWAYS,mask:0},0,255,l.ZERO,l.ZERO,l.ZERO)},_n.prototype.clearStencil=function(){var e=this.context,r=e.gl;this.nextStencilID=1,this.currentStencilSource=void 0;var n=t.create();t.ortho(n,0,this.width,this.height,0,0,1),t.scale(n,n,[r.drawingBufferWidth,r.drawingBufferHeight,0]),this.useProgram(\"clippingMask\").draw(e,r.TRIANGLES,At.disabled,this.stencilClearMode,St.disabled,Et.disabled,Or(n),\"$clipping\",this.viewportBuffer,this.quadTriangleIndexBuffer,this.viewportSegments)},_n.prototype._renderTileClippingMasks=function(t,e){if(this.currentStencilSource!==t.source&&t.isTileClipped()&&e&&e.length){this.currentStencilSource=t.source;var r=this.context,n=r.gl;this.nextStencilID+e.length>256&&this.clearStencil(),r.setColorMode(St.disabled),r.setDepthMode(At.disabled);var i=this.useProgram(\"clippingMask\");this._tileClippingMaskIDs={};for(var a=0,o=e;a<o.length;a+=1){var s=o[a],l=this._tileClippingMaskIDs[s.key]=this.nextStencilID++;i.draw(r,n.TRIANGLES,At.disabled,new Mt({func:n.ALWAYS,mask:0},l,255,n.KEEP,n.KEEP,n.REPLACE),St.disabled,Et.disabled,Or(s.posMatrix),\"$clipping\",this.tileExtentBuffer,this.quadTriangleIndexBuffer,this.tileExtentSegments)}}},_n.prototype.stencilModeFor3D=function(){this.currentStencilSource=void 0,this.nextStencilID+1>256&&this.clearStencil();var t=this.nextStencilID++,e=this.context.gl;return new Mt({func:e.NOTEQUAL,mask:255},t,255,e.KEEP,e.KEEP,e.REPLACE)},_n.prototype.stencilModeForClipping=function(t){var e=this.context.gl;return new Mt({func:e.EQUAL,mask:255},this._tileClippingMaskIDs[t.key],0,e.KEEP,e.KEEP,e.REPLACE)},_n.prototype.stencilConfigForOverlap=function(t){var e,r=this.context.gl,n=t.sort((function(t,e){return e.overscaledZ-t.overscaledZ})),i=n[n.length-1].overscaledZ,a=n[0].overscaledZ-i+1;if(a>1){this.currentStencilSource=void 0,this.nextStencilID+a>256&&this.clearStencil();for(var o={},s=0;s<a;s++)o[s+i]=new Mt({func:r.GEQUAL,mask:255},s+this.nextStencilID,255,r.KEEP,r.KEEP,r.REPLACE);return this.nextStencilID+=a,[o,n]}return[(e={},e[i]=Mt.disabled,e),n]},_n.prototype.colorModeForRenderPass=function(){var e=this.context.gl;if(this._showOverdrawInspector){return new St([e.CONSTANT_COLOR,e.ONE],new t.Color(1/8,1/8,1/8,0),[!0,!0,!0,!0])}return\"opaque\"===this.renderPass?St.unblended:St.alphaBlended},_n.prototype.depthModeForSublayer=function(t,e,r){if(!this.opaquePassEnabledForLayer())return At.disabled;var n=1-((1+this.currentLayer)*this.numSublayers+t)*this.depthEpsilon;return new At(r||this.context.gl.LEQUAL,e,[n,n])},_n.prototype.opaquePassEnabledForLayer=function(){return this.currentLayer<this.opaquePassCutoff},_n.prototype.render=function(e,r){var n=this;this.style=e,this.options=r,this.lineAtlas=e.lineAtlas,this.imageManager=e.imageManager,this.glyphManager=e.glyphManager,this.symbolFadeChange=e.placement.symbolFadeChange(t.browser.now()),this.imageManager.beginFrame();var i=this.style._order,a=this.style.sourceCaches;for(var o in a){var s=a[o];s.used&&s.prepare(this.context)}var l,c,u={},f={},h={};for(var p in a){var d=a[p];u[p]=d.getVisibleCoordinates(),f[p]=u[p].slice().reverse(),h[p]=d.getVisibleCoordinates(!0).reverse()}this.opaquePassCutoff=1/0;for(var g=0;g<i.length;g++){var m=i[g];if(this.style._layers[m].is3D()){this.opaquePassCutoff=g;break}}this.renderPass=\"offscreen\";for(var v=0,y=i;v<y.length;v+=1){var x=y[v],b=this.style._layers[x];if(b.hasOffscreenPass()&&!b.isHidden(this.transform.zoom)){var _=f[b.source];(\"custom\"===b.type||_.length)&&this.renderLayer(this,a[b.source],b,_)}}for(this.context.bindFramebuffer.set(null),this.context.clear({color:r.showOverdrawInspector?t.Color.black:t.Color.transparent,depth:1}),this.clearStencil(),this._showOverdrawInspector=r.showOverdrawInspector,this.depthRangeFor3D=[0,1-(e._order.length+2)*this.numSublayers*this.depthEpsilon],this.renderPass=\"opaque\",this.currentLayer=i.length-1;this.currentLayer>=0;this.currentLayer--){var w=this.style._layers[i[this.currentLayer]],T=a[w.source],k=u[w.source];this._renderTileClippingMasks(w,k),this.renderLayer(this,T,w,k)}for(this.renderPass=\"translucent\",this.currentLayer=0;this.currentLayer<i.length;this.currentLayer++){var A=this.style._layers[i[this.currentLayer]],M=a[A.source],S=(\"symbol\"===A.type?h:f)[A.source];this._renderTileClippingMasks(A,u[A.source]),this.renderLayer(this,M,A,S)}this.options.showTileBoundaries&&(t.values(this.style._layers).forEach((function(t){t.source&&!t.isHidden(n.transform.zoom)&&(t.source!==(c&&c.id)&&(c=n.style.sourceCaches[t.source]),(!l||l.getSource().maxzoom<c.getSource().maxzoom)&&(l=c))})),l&&bn.debug(this,l,l.getVisibleCoordinates()));this.options.showPadding&&gn(this),this.context.setDefault()},_n.prototype.renderLayer=function(t,e,r,n){r.isHidden(this.transform.zoom)||(\"background\"===r.type||\"custom\"===r.type||n.length)&&(this.id=r.id,this.gpuTimingStart(r),bn[r.type](t,e,r,n,this.style.placement.variableOffsets),this.gpuTimingEnd())},_n.prototype.gpuTimingStart=function(t){if(this.options.gpuTiming){var e=this.context.extTimerQuery,r=this.gpuTimers[t.id];r||(r=this.gpuTimers[t.id]={calls:0,cpuTime:0,query:e.createQueryEXT()}),r.calls++,e.beginQueryEXT(e.TIME_ELAPSED_EXT,r.query)}},_n.prototype.gpuTimingEnd=function(){if(this.options.gpuTiming){var t=this.context.extTimerQuery;t.endQueryEXT(t.TIME_ELAPSED_EXT)}},_n.prototype.collectGpuTimers=function(){var t=this.gpuTimers;return this.gpuTimers={},t},_n.prototype.queryGpuTimers=function(t){var e={};for(var r in t){var n=t[r],i=this.context.extTimerQuery,a=i.getQueryObjectEXT(n.query,i.QUERY_RESULT_EXT)/1e6;i.deleteQueryEXT(n.query),e[r]=a}return e},_n.prototype.translatePosMatrix=function(e,r,n,i,a){if(!n[0]&&!n[1])return e;var o=a?\"map\"===i?this.transform.angle:0:\"viewport\"===i?-this.transform.angle:0;if(o){var s=Math.sin(o),l=Math.cos(o);n=[n[0]*l-n[1]*s,n[0]*s+n[1]*l]}var c=[a?n[0]:pe(r,n[0],this.transform.zoom),a?n[1]:pe(r,n[1],this.transform.zoom),0],u=new Float32Array(16);return t.translate(u,e,c),u},_n.prototype.saveTileTexture=function(t){var e=this._tileTextures[t.size[0]];e?e.push(t):this._tileTextures[t.size[0]]=[t]},_n.prototype.getTileTexture=function(t){var e=this._tileTextures[t];return e&&e.length>0?e.pop():null},_n.prototype.isPatternMissing=function(t){if(!t)return!1;if(!t.from||!t.to)return!0;var e=this.imageManager.getPattern(t.from.toString()),r=this.imageManager.getPattern(t.to.toString());return!e||!r},_n.prototype.useProgram=function(t,e){this.cache=this.cache||{};var r=\"\"+t+(e?e.cacheKey:\"\")+(this._showOverdrawInspector?\"/overdraw\":\"\");return this.cache[r]||(this.cache[r]=new _r(this.context,xr[t],e,Jr[t],this._showOverdrawInspector)),this.cache[r]},_n.prototype.setCustomLayerDefaults=function(){this.context.unbindVAO(),this.context.cullFace.setDefault(),this.context.activeTexture.setDefault(),this.context.pixelStoreUnpack.setDefault(),this.context.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.context.pixelStoreUnpackFlipY.setDefault()},_n.prototype.setBaseState=function(){var t=this.context.gl;this.context.cullFace.set(!1),this.context.viewport.set([0,0,this.width,this.height]),this.context.blendEquation.set(t.FUNC_ADD)},_n.prototype.initDebugOverlayCanvas=function(){if(null==this.debugOverlayCanvas){this.debugOverlayCanvas=t.window.document.createElement(\"canvas\"),this.debugOverlayCanvas.width=512,this.debugOverlayCanvas.height=512;var e=this.context.gl;this.debugOverlayTexture=new t.Texture(this.context,this.debugOverlayCanvas,e.RGBA)}},_n.prototype.destroy=function(){this.emptyTexture.destroy(),this.debugOverlayTexture&&this.debugOverlayTexture.destroy()};var wn=function(t,e){this.points=t,this.planes=e};wn.fromInvProjectionMatrix=function(e,r,n){var i=Math.pow(2,n),a=[[-1,1,-1,1],[1,1,-1,1],[1,-1,-1,1],[-1,-1,-1,1],[-1,1,1,1],[1,1,1,1],[1,-1,1,1],[-1,-1,1,1]].map((function(r){return t.transformMat4([],r,e)})).map((function(e){return t.scale$1([],e,1/e[3]/r*i)})),o=[[0,1,2],[6,5,4],[0,3,7],[2,1,5],[3,2,6],[0,4,5]].map((function(e){var r=t.sub([],a[e[0]],a[e[1]]),n=t.sub([],a[e[2]],a[e[1]]),i=t.normalize([],t.cross([],r,n)),o=-t.dot(i,a[e[1]]);return i.concat(o)}));return new wn(a,o)};var Tn=function(e,r){this.min=e,this.max=r,this.center=t.scale$2([],t.add([],this.min,this.max),.5)};Tn.prototype.quadrant=function(e){for(var r=[e%2==0,e<2],n=t.clone$2(this.min),i=t.clone$2(this.max),a=0;a<r.length;a++)n[a]=r[a]?this.min[a]:this.center[a],i[a]=r[a]?this.center[a]:this.max[a];return i[2]=this.max[2],new Tn(n,i)},Tn.prototype.distanceX=function(t){return Math.max(Math.min(this.max[0],t[0]),this.min[0])-t[0]},Tn.prototype.distanceY=function(t){return Math.max(Math.min(this.max[1],t[1]),this.min[1])-t[1]},Tn.prototype.intersects=function(e){for(var r=[[this.min[0],this.min[1],0,1],[this.max[0],this.min[1],0,1],[this.max[0],this.max[1],0,1],[this.min[0],this.max[1],0,1]],n=!0,i=0;i<e.planes.length;i++){for(var a=e.planes[i],o=0,s=0;s<r.length;s++)o+=t.dot$1(a,r[s])>=0;if(0===o)return 0;o!==r.length&&(n=!1)}if(n)return 2;for(var l=0;l<3;l++){for(var c=Number.MAX_VALUE,u=-Number.MAX_VALUE,f=0;f<e.points.length;f++){var h=e.points[f][l]-this.min[l];c=Math.min(c,h),u=Math.max(u,h)}if(u<0||c>this.max[l]-this.min[l])return 0}return 1};var kn=function(t,e,r,n){if(void 0===t&&(t=0),void 0===e&&(e=0),void 0===r&&(r=0),void 0===n&&(n=0),isNaN(t)||t<0||isNaN(e)||e<0||isNaN(r)||r<0||isNaN(n)||n<0)throw new Error(\"Invalid value for edge-insets, top, bottom, left and right must all be numbers\");this.top=t,this.bottom=e,this.left=r,this.right=n};kn.prototype.interpolate=function(e,r,n){return null!=r.top&&null!=e.top&&(this.top=t.number(e.top,r.top,n)),null!=r.bottom&&null!=e.bottom&&(this.bottom=t.number(e.bottom,r.bottom,n)),null!=r.left&&null!=e.left&&(this.left=t.number(e.left,r.left,n)),null!=r.right&&null!=e.right&&(this.right=t.number(e.right,r.right,n)),this},kn.prototype.getCenter=function(e,r){var n=t.clamp((this.left+e-this.right)/2,0,e),i=t.clamp((this.top+r-this.bottom)/2,0,r);return new t.Point(n,i)},kn.prototype.equals=function(t){return this.top===t.top&&this.bottom===t.bottom&&this.left===t.left&&this.right===t.right},kn.prototype.clone=function(){return new kn(this.top,this.bottom,this.left,this.right)},kn.prototype.toJSON=function(){return{top:this.top,bottom:this.bottom,left:this.left,right:this.right}};var An=function(e,r,n,i,a){this.tileSize=512,this.maxValidLatitude=85.051129,this._renderWorldCopies=void 0===a||a,this._minZoom=e||0,this._maxZoom=r||22,this._minPitch=null==n?0:n,this._maxPitch=null==i?60:i,this.setMaxBounds(),this.width=0,this.height=0,this._center=new t.LngLat(0,0),this.zoom=0,this.angle=0,this._fov=.6435011087932844,this._pitch=0,this._unmodified=!0,this._edgeInsets=new kn,this._posMatrixCache={},this._alignedPosMatrixCache={}},Mn={minZoom:{configurable:!0},maxZoom:{configurable:!0},minPitch:{configurable:!0},maxPitch:{configurable:!0},renderWorldCopies:{configurable:!0},worldSize:{configurable:!0},centerOffset:{configurable:!0},size:{configurable:!0},bearing:{configurable:!0},pitch:{configurable:!0},fov:{configurable:!0},zoom:{configurable:!0},center:{configurable:!0},padding:{configurable:!0},centerPoint:{configurable:!0},unmodified:{configurable:!0},point:{configurable:!0}};An.prototype.clone=function(){var t=new An(this._minZoom,this._maxZoom,this._minPitch,this.maxPitch,this._renderWorldCopies);return t.tileSize=this.tileSize,t.latRange=this.latRange,t.width=this.width,t.height=this.height,t._center=this._center,t.zoom=this.zoom,t.angle=this.angle,t._fov=this._fov,t._pitch=this._pitch,t._unmodified=this._unmodified,t._edgeInsets=this._edgeInsets.clone(),t._calcMatrices(),t},Mn.minZoom.get=function(){return this._minZoom},Mn.minZoom.set=function(t){this._minZoom!==t&&(this._minZoom=t,this.zoom=Math.max(this.zoom,t))},Mn.maxZoom.get=function(){return this._maxZoom},Mn.maxZoom.set=function(t){this._maxZoom!==t&&(this._maxZoom=t,this.zoom=Math.min(this.zoom,t))},Mn.minPitch.get=function(){return this._minPitch},Mn.minPitch.set=function(t){this._minPitch!==t&&(this._minPitch=t,this.pitch=Math.max(this.pitch,t))},Mn.maxPitch.get=function(){return this._maxPitch},Mn.maxPitch.set=function(t){this._maxPitch!==t&&(this._maxPitch=t,this.pitch=Math.min(this.pitch,t))},Mn.renderWorldCopies.get=function(){return this._renderWorldCopies},Mn.renderWorldCopies.set=function(t){void 0===t?t=!0:null===t&&(t=!1),this._renderWorldCopies=t},Mn.worldSize.get=function(){return this.tileSize*this.scale},Mn.centerOffset.get=function(){return this.centerPoint._sub(this.size._div(2))},Mn.size.get=function(){return new t.Point(this.width,this.height)},Mn.bearing.get=function(){return-this.angle/Math.PI*180},Mn.bearing.set=function(e){var r=-t.wrap(e,-180,180)*Math.PI/180;this.angle!==r&&(this._unmodified=!1,this.angle=r,this._calcMatrices(),this.rotationMatrix=t.create$2(),t.rotate(this.rotationMatrix,this.rotationMatrix,this.angle))},Mn.pitch.get=function(){return this._pitch/Math.PI*180},Mn.pitch.set=function(e){var r=t.clamp(e,this.minPitch,this.maxPitch)/180*Math.PI;this._pitch!==r&&(this._unmodified=!1,this._pitch=r,this._calcMatrices())},Mn.fov.get=function(){return this._fov/Math.PI*180},Mn.fov.set=function(t){t=Math.max(.01,Math.min(60,t)),this._fov!==t&&(this._unmodified=!1,this._fov=t/180*Math.PI,this._calcMatrices())},Mn.zoom.get=function(){return this._zoom},Mn.zoom.set=function(t){var e=Math.min(Math.max(t,this.minZoom),this.maxZoom);this._zoom!==e&&(this._unmodified=!1,this._zoom=e,this.scale=this.zoomScale(e),this.tileZoom=Math.floor(e),this.zoomFraction=e-this.tileZoom,this._constrain(),this._calcMatrices())},Mn.center.get=function(){return this._center},Mn.center.set=function(t){t.lat===this._center.lat&&t.lng===this._center.lng||(this._unmodified=!1,this._center=t,this._constrain(),this._calcMatrices())},Mn.padding.get=function(){return this._edgeInsets.toJSON()},Mn.padding.set=function(t){this._edgeInsets.equals(t)||(this._unmodified=!1,this._edgeInsets.interpolate(this._edgeInsets,t,1),this._calcMatrices())},Mn.centerPoint.get=function(){return this._edgeInsets.getCenter(this.width,this.height)},An.prototype.isPaddingEqual=function(t){return this._edgeInsets.equals(t)},An.prototype.interpolatePadding=function(t,e,r){this._unmodified=!1,this._edgeInsets.interpolate(t,e,r),this._constrain(),this._calcMatrices()},An.prototype.coveringZoomLevel=function(t){var e=(t.roundZoom?Math.round:Math.floor)(this.zoom+this.scaleZoom(this.tileSize/t.tileSize));return Math.max(0,e)},An.prototype.getVisibleUnwrappedCoordinates=function(e){var r=[new t.UnwrappedTileID(0,e)];if(this._renderWorldCopies)for(var n=this.pointCoordinate(new t.Point(0,0)),i=this.pointCoordinate(new t.Point(this.width,0)),a=this.pointCoordinate(new t.Point(this.width,this.height)),o=this.pointCoordinate(new t.Point(0,this.height)),s=Math.floor(Math.min(n.x,i.x,a.x,o.x)),l=Math.floor(Math.max(n.x,i.x,a.x,o.x)),c=s-1;c<=l+1;c++)0!==c&&r.push(new t.UnwrappedTileID(c,e));return r},An.prototype.coveringTiles=function(e){var r=this.coveringZoomLevel(e),n=r;if(void 0!==e.minzoom&&r<e.minzoom)return[];void 0!==e.maxzoom&&r>e.maxzoom&&(r=e.maxzoom);var i=t.MercatorCoordinate.fromLngLat(this.center),a=Math.pow(2,r),o=[a*i.x,a*i.y,0],s=wn.fromInvProjectionMatrix(this.invProjMatrix,this.worldSize,r),l=e.minzoom||0;this.pitch<=60&&this._edgeInsets.top<.1&&(l=r);var c=function(t){return{aabb:new Tn([t*a,0,0],[(t+1)*a,a,0]),zoom:0,x:0,y:0,wrap:t,fullyVisible:!1}},u=[],f=[],h=r,p=e.reparseOverscaled?n:r;if(this._renderWorldCopies)for(var d=1;d<=3;d++)u.push(c(-d)),u.push(c(d));for(u.push(c(0));u.length>0;){var g=u.pop(),m=g.x,v=g.y,y=g.fullyVisible;if(!y){var x=g.aabb.intersects(s);if(0===x)continue;y=2===x}var b=g.aabb.distanceX(o),_=g.aabb.distanceY(o),w=Math.max(Math.abs(b),Math.abs(_)),T=3+(1<<h-g.zoom)-2;if(g.zoom===h||w>T&&g.zoom>=l)f.push({tileID:new t.OverscaledTileID(g.zoom===h?p:g.zoom,g.wrap,g.zoom,m,v),distanceSq:t.sqrLen([o[0]-.5-m,o[1]-.5-v])});else for(var k=0;k<4;k++){var A=(m<<1)+k%2,M=(v<<1)+(k>>1);u.push({aabb:g.aabb.quadrant(k),zoom:g.zoom+1,x:A,y:M,wrap:g.wrap,fullyVisible:y})}}return f.sort((function(t,e){return t.distanceSq-e.distanceSq})).map((function(t){return t.tileID}))},An.prototype.resize=function(t,e){this.width=t,this.height=e,this.pixelsToGLUnits=[2/t,-2/e],this._constrain(),this._calcMatrices()},Mn.unmodified.get=function(){return this._unmodified},An.prototype.zoomScale=function(t){return Math.pow(2,t)},An.prototype.scaleZoom=function(t){return Math.log(t)/Math.LN2},An.prototype.project=function(e){var r=t.clamp(e.lat,-this.maxValidLatitude,this.maxValidLatitude);return new t.Point(t.mercatorXfromLng(e.lng)*this.worldSize,t.mercatorYfromLat(r)*this.worldSize)},An.prototype.unproject=function(e){return new t.MercatorCoordinate(e.x/this.worldSize,e.y/this.worldSize).toLngLat()},Mn.point.get=function(){return this.project(this.center)},An.prototype.setLocationAtPoint=function(e,r){var n=this.pointCoordinate(r),i=this.pointCoordinate(this.centerPoint),a=this.locationCoordinate(e),o=new t.MercatorCoordinate(a.x-(n.x-i.x),a.y-(n.y-i.y));this.center=this.coordinateLocation(o),this._renderWorldCopies&&(this.center=this.center.wrap())},An.prototype.locationPoint=function(t){return this.coordinatePoint(this.locationCoordinate(t))},An.prototype.pointLocation=function(t){return this.coordinateLocation(this.pointCoordinate(t))},An.prototype.locationCoordinate=function(e){return t.MercatorCoordinate.fromLngLat(e)},An.prototype.coordinateLocation=function(t){return t.toLngLat()},An.prototype.pointCoordinate=function(e){var r=[e.x,e.y,0,1],n=[e.x,e.y,1,1];t.transformMat4(r,r,this.pixelMatrixInverse),t.transformMat4(n,n,this.pixelMatrixInverse);var i=r[3],a=n[3],o=r[0]/i,s=n[0]/a,l=r[1]/i,c=n[1]/a,u=r[2]/i,f=n[2]/a,h=u===f?0:(0-u)/(f-u);return new t.MercatorCoordinate(t.number(o,s,h)/this.worldSize,t.number(l,c,h)/this.worldSize)},An.prototype.coordinatePoint=function(e){var r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix),new t.Point(r[0]/r[3],r[1]/r[3])},An.prototype.getBounds=function(){return(new t.LngLatBounds).extend(this.pointLocation(new t.Point(0,0))).extend(this.pointLocation(new t.Point(this.width,0))).extend(this.pointLocation(new t.Point(this.width,this.height))).extend(this.pointLocation(new t.Point(0,this.height)))},An.prototype.getMaxBounds=function(){return this.latRange&&2===this.latRange.length&&this.lngRange&&2===this.lngRange.length?new t.LngLatBounds([this.lngRange[0],this.latRange[0]],[this.lngRange[1],this.latRange[1]]):null},An.prototype.setMaxBounds=function(t){t?(this.lngRange=[t.getWest(),t.getEast()],this.latRange=[t.getSouth(),t.getNorth()],this._constrain()):(this.lngRange=null,this.latRange=[-this.maxValidLatitude,this.maxValidLatitude])},An.prototype.calculatePosMatrix=function(e,r){void 0===r&&(r=!1);var n=e.key,i=r?this._alignedPosMatrixCache:this._posMatrixCache;if(i[n])return i[n];var a=e.canonical,o=this.worldSize/this.zoomScale(a.z),s=a.x+Math.pow(2,a.z)*e.wrap,l=t.identity(new Float64Array(16));return t.translate(l,l,[s*o,a.y*o,0]),t.scale(l,l,[o/t.EXTENT,o/t.EXTENT,1]),t.multiply(l,r?this.alignedProjMatrix:this.projMatrix,l),i[n]=new Float32Array(l),i[n]},An.prototype.customLayerMatrix=function(){return this.mercatorMatrix.slice()},An.prototype._constrain=function(){if(this.center&&this.width&&this.height&&!this._constraining){this._constraining=!0;var e,r,n,i,a=-90,o=90,s=-180,l=180,c=this.size,u=this._unmodified;if(this.latRange){var f=this.latRange;a=t.mercatorYfromLat(f[1])*this.worldSize,e=(o=t.mercatorYfromLat(f[0])*this.worldSize)-a<c.y?c.y/(o-a):0}if(this.lngRange){var h=this.lngRange;s=t.mercatorXfromLng(h[0])*this.worldSize,r=(l=t.mercatorXfromLng(h[1])*this.worldSize)-s<c.x?c.x/(l-s):0}var p=this.point,d=Math.max(r||0,e||0);if(d)return this.center=this.unproject(new t.Point(r?(l+s)/2:p.x,e?(o+a)/2:p.y)),this.zoom+=this.scaleZoom(d),this._unmodified=u,void(this._constraining=!1);if(this.latRange){var g=p.y,m=c.y/2;g-m<a&&(i=a+m),g+m>o&&(i=o-m)}if(this.lngRange){var v=p.x,y=c.x/2;v-y<s&&(n=s+y),v+y>l&&(n=l-y)}void 0===n&&void 0===i||(this.center=this.unproject(new t.Point(void 0!==n?n:p.x,void 0!==i?i:p.y))),this._unmodified=u,this._constraining=!1}},An.prototype._calcMatrices=function(){if(this.height){var e=this._fov/2,r=this.centerOffset;this.cameraToCenterDistance=.5/Math.tan(e)*this.height;var n=Math.PI/2+this._pitch,i=this._fov*(.5+r.y/this.height),a=Math.sin(i)*this.cameraToCenterDistance/Math.sin(t.clamp(Math.PI-n-i,.01,Math.PI-.01)),o=this.point,s=o.x,l=o.y,c=1.01*(Math.cos(Math.PI/2-this._pitch)*a+this.cameraToCenterDistance),u=this.height/50,f=new Float64Array(16);t.perspective(f,this._fov,this.width/this.height,u,c),f[8]=2*-r.x/this.width,f[9]=2*r.y/this.height,t.scale(f,f,[1,-1,1]),t.translate(f,f,[0,0,-this.cameraToCenterDistance]),t.rotateX(f,f,this._pitch),t.rotateZ(f,f,this.angle),t.translate(f,f,[-s,-l,0]),this.mercatorMatrix=t.scale([],f,[this.worldSize,this.worldSize,this.worldSize]),t.scale(f,f,[1,1,t.mercatorZfromAltitude(1,this.center.lat)*this.worldSize,1]),this.projMatrix=f,this.invProjMatrix=t.invert([],this.projMatrix);var h=this.width%2/2,p=this.height%2/2,d=Math.cos(this.angle),g=Math.sin(this.angle),m=s-Math.round(s)+d*h+g*p,v=l-Math.round(l)+d*p+g*h,y=new Float64Array(f);if(t.translate(y,y,[m>.5?m-1:m,v>.5?v-1:v,0]),this.alignedProjMatrix=y,f=t.create(),t.scale(f,f,[this.width/2,-this.height/2,1]),t.translate(f,f,[1,-1,0]),this.labelPlaneMatrix=f,f=t.create(),t.scale(f,f,[1,-1,1]),t.translate(f,f,[-1,-1,0]),t.scale(f,f,[2/this.width,2/this.height,1]),this.glCoordMatrix=f,this.pixelMatrix=t.multiply(new Float64Array(16),this.labelPlaneMatrix,this.projMatrix),!(f=t.invert(new Float64Array(16),this.pixelMatrix)))throw new Error(\"failed to invert matrix\");this.pixelMatrixInverse=f,this._posMatrixCache={},this._alignedPosMatrixCache={}}},An.prototype.maxPitchScaleFactor=function(){if(!this.pixelMatrixInverse)return 1;var e=this.pointCoordinate(new t.Point(0,0)),r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix)[3]/this.cameraToCenterDistance},An.prototype.getCameraPoint=function(){var e=this._pitch,r=Math.tan(e)*(this.cameraToCenterDistance||1);return this.centerPoint.add(new t.Point(0,r))},An.prototype.getCameraQueryGeometry=function(e){var r=this.getCameraPoint();if(1===e.length)return[e[0],r];for(var n=r.x,i=r.y,a=r.x,o=r.y,s=0,l=e;s<l.length;s+=1){var c=l[s];n=Math.min(n,c.x),i=Math.min(i,c.y),a=Math.max(a,c.x),o=Math.max(o,c.y)}return[new t.Point(n,i),new t.Point(a,i),new t.Point(a,o),new t.Point(n,o),new t.Point(n,i)]},Object.defineProperties(An.prototype,Mn);var Sn=function(e){var r,n,i,a,o;this._hashName=e&&encodeURIComponent(e),t.bindAll([\"_getCurrentHash\",\"_onHashChange\",\"_updateHash\"],this),this._updateHash=(r=this._updateHashUnthrottled.bind(this),n=300,i=!1,a=null,o=function(){a=null,i&&(r(),a=setTimeout(o,n),i=!1)},function(){return i=!0,a||o(),a})};Sn.prototype.addTo=function(e){return this._map=e,t.window.addEventListener(\"hashchange\",this._onHashChange,!1),this._map.on(\"moveend\",this._updateHash),this},Sn.prototype.remove=function(){return t.window.removeEventListener(\"hashchange\",this._onHashChange,!1),this._map.off(\"moveend\",this._updateHash),clearTimeout(this._updateHash()),delete this._map,this},Sn.prototype.getHashString=function(e){var r=this._map.getCenter(),n=Math.round(100*this._map.getZoom())/100,i=Math.ceil((n*Math.LN2+Math.log(512/360/.5))/Math.LN10),a=Math.pow(10,i),o=Math.round(r.lng*a)/a,s=Math.round(r.lat*a)/a,l=this._map.getBearing(),c=this._map.getPitch(),u=\"\";if(u+=e?\"/\"+o+\"/\"+s+\"/\"+n:n+\"/\"+s+\"/\"+o,(l||c)&&(u+=\"/\"+Math.round(10*l)/10),c&&(u+=\"/\"+Math.round(c)),this._hashName){var f=this._hashName,h=!1,p=t.window.location.hash.slice(1).split(\"&\").map((function(t){var e=t.split(\"=\")[0];return e===f?(h=!0,e+\"=\"+u):t})).filter((function(t){return t}));return h||p.push(f+\"=\"+u),\"#\"+p.join(\"&\")}return\"#\"+u},Sn.prototype._getCurrentHash=function(){var e,r=this,n=t.window.location.hash.replace(\"#\",\"\");return this._hashName?(n.split(\"&\").map((function(t){return t.split(\"=\")})).forEach((function(t){t[0]===r._hashName&&(e=t)})),(e&&e[1]||\"\").split(\"/\")):n.split(\"/\")},Sn.prototype._onHashChange=function(){var t=this._getCurrentHash();if(t.length>=3&&!t.some((function(t){return isNaN(t)}))){var e=this._map.dragRotate.isEnabled()&&this._map.touchZoomRotate.isEnabled()?+(t[3]||0):this._map.getBearing();return this._map.jumpTo({center:[+t[2],+t[1]],zoom:+t[0],bearing:e,pitch:+(t[4]||0)}),!0}return!1},Sn.prototype._updateHashUnthrottled=function(){var e=this.getHashString();try{t.window.history.replaceState(t.window.history.state,\"\",e)}catch(t){}};var En={linearity:.3,easing:t.bezier(0,0,.3,1)},Ln=t.extend({deceleration:2500,maxSpeed:1400},En),Cn=t.extend({deceleration:20,maxSpeed:1400},En),Pn=t.extend({deceleration:1e3,maxSpeed:360},En),In=t.extend({deceleration:1e3,maxSpeed:90},En),On=function(t){this._map=t,this.clear()};function zn(t,e){(!t.duration||t.duration<e.duration)&&(t.duration=e.duration,t.easing=e.easing)}function Dn(e,r,n){var i=n.maxSpeed,a=n.linearity,o=n.deceleration,s=t.clamp(e*a/(r/1e3),-i,i),l=Math.abs(s)/(o*a);return{easing:n.easing,duration:1e3*l,amount:s*(l/2)}}On.prototype.clear=function(){this._inertiaBuffer=[]},On.prototype.record=function(e){this._drainInertiaBuffer(),this._inertiaBuffer.push({time:t.browser.now(),settings:e})},On.prototype._drainInertiaBuffer=function(){for(var e=this._inertiaBuffer,r=t.browser.now();e.length>0&&r-e[0].time>160;)e.shift()},On.prototype._onMoveEnd=function(e){if(this._drainInertiaBuffer(),!(this._inertiaBuffer.length<2)){for(var r={zoom:0,bearing:0,pitch:0,pan:new t.Point(0,0),pinchAround:void 0,around:void 0},n=0,i=this._inertiaBuffer;n<i.length;n+=1){var a=i[n].settings;r.zoom+=a.zoomDelta||0,r.bearing+=a.bearingDelta||0,r.pitch+=a.pitchDelta||0,a.panDelta&&r.pan._add(a.panDelta),a.around&&(r.around=a.around),a.pinchAround&&(r.pinchAround=a.pinchAround)}var o=this._inertiaBuffer[this._inertiaBuffer.length-1].time-this._inertiaBuffer[0].time,s={};if(r.pan.mag()){var l=Dn(r.pan.mag(),o,t.extend({},Ln,e||{}));s.offset=r.pan.mult(l.amount/r.pan.mag()),s.center=this._map.transform.center,zn(s,l)}if(r.zoom){var c=Dn(r.zoom,o,Cn);s.zoom=this._map.transform.zoom+c.amount,zn(s,c)}if(r.bearing){var u=Dn(r.bearing,o,Pn);s.bearing=this._map.transform.bearing+t.clamp(u.amount,-179,179),zn(s,u)}if(r.pitch){var f=Dn(r.pitch,o,In);s.pitch=this._map.transform.pitch+f.amount,zn(s,f)}if(s.zoom||s.bearing){var h=void 0===r.pinchAround?r.around:r.pinchAround;s.around=h?this._map.unproject(h):this._map.getCenter()}return this.clear(),t.extend(s,{noMoveStart:!0})}};var Rn=function(e){function n(n,i,a,o){void 0===o&&(o={});var s=r.mousePos(i.getCanvasContainer(),a),l=i.unproject(s);e.call(this,n,t.extend({point:s,lngLat:l,originalEvent:a},o)),this._defaultPrevented=!1,this.target=i}e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n;var i={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},i.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,i),n}(t.Event),Fn=function(e){function n(n,i,a){var o=\"touchend\"===n?a.changedTouches:a.touches,s=r.touchPos(i.getCanvasContainer(),o),l=s.map((function(t){return i.unproject(t)})),c=s.reduce((function(t,e,r,n){return t.add(e.div(n.length))}),new t.Point(0,0)),u=i.unproject(c);e.call(this,n,{points:s,point:c,lngLats:l,lngLat:u,originalEvent:a}),this._defaultPrevented=!1}e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n;var i={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},i.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,i),n}(t.Event),Bn=function(t){function e(e,r,n){t.call(this,e,{originalEvent:n}),this._defaultPrevented=!1}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={defaultPrevented:{configurable:!0}};return e.prototype.preventDefault=function(){this._defaultPrevented=!0},r.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(e.prototype,r),e}(t.Event),Nn=function(t,e){this._map=t,this._clickTolerance=e.clickTolerance};Nn.prototype.reset=function(){delete this._mousedownPos},Nn.prototype.wheel=function(t){return this._firePreventable(new Bn(t.type,this._map,t))},Nn.prototype.mousedown=function(t,e){return this._mousedownPos=e,this._firePreventable(new Rn(t.type,this._map,t))},Nn.prototype.mouseup=function(t){this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.click=function(t,e){this._mousedownPos&&this._mousedownPos.dist(e)>=this._clickTolerance||this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.dblclick=function(t){return this._firePreventable(new Rn(t.type,this._map,t))},Nn.prototype.mouseover=function(t){this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.mouseout=function(t){this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.touchstart=function(t){return this._firePreventable(new Fn(t.type,this._map,t))},Nn.prototype.touchmove=function(t){this._map.fire(new Fn(t.type,this._map,t))},Nn.prototype.touchend=function(t){this._map.fire(new Fn(t.type,this._map,t))},Nn.prototype.touchcancel=function(t){this._map.fire(new Fn(t.type,this._map,t))},Nn.prototype._firePreventable=function(t){if(this._map.fire(t),t.defaultPrevented)return{}},Nn.prototype.isEnabled=function(){return!0},Nn.prototype.isActive=function(){return!1},Nn.prototype.enable=function(){},Nn.prototype.disable=function(){};var jn=function(t){this._map=t};jn.prototype.reset=function(){this._delayContextMenu=!1,delete this._contextMenuEvent},jn.prototype.mousemove=function(t){this._map.fire(new Rn(t.type,this._map,t))},jn.prototype.mousedown=function(){this._delayContextMenu=!0},jn.prototype.mouseup=function(){this._delayContextMenu=!1,this._contextMenuEvent&&(this._map.fire(new Rn(\"contextmenu\",this._map,this._contextMenuEvent)),delete this._contextMenuEvent)},jn.prototype.contextmenu=function(t){this._delayContextMenu?this._contextMenuEvent=t:this._map.fire(new Rn(t.type,this._map,t)),this._map.listens(\"contextmenu\")&&t.preventDefault()},jn.prototype.isEnabled=function(){return!0},jn.prototype.isActive=function(){return!1},jn.prototype.enable=function(){},jn.prototype.disable=function(){};var Un=function(t,e){this._map=t,this._el=t.getCanvasContainer(),this._container=t.getContainer(),this._clickTolerance=e.clickTolerance||1};function Vn(t,e){for(var r={},n=0;n<t.length;n++)r[t[n].identifier]=e[n];return r}Un.prototype.isEnabled=function(){return!!this._enabled},Un.prototype.isActive=function(){return!!this._active},Un.prototype.enable=function(){this.isEnabled()||(this._enabled=!0)},Un.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},Un.prototype.mousedown=function(t,e){this.isEnabled()&&t.shiftKey&&0===t.button&&(r.disableDrag(),this._startPos=this._lastPos=e,this._active=!0)},Un.prototype.mousemoveWindow=function(t,e){if(this._active){var n=e;if(!(this._lastPos.equals(n)||!this._box&&n.dist(this._startPos)<this._clickTolerance)){var i=this._startPos;this._lastPos=n,this._box||(this._box=r.create(\"div\",\"mapboxgl-boxzoom\",this._container),this._container.classList.add(\"mapboxgl-crosshair\"),this._fireEvent(\"boxzoomstart\",t));var a=Math.min(i.x,n.x),o=Math.max(i.x,n.x),s=Math.min(i.y,n.y),l=Math.max(i.y,n.y);r.setTransform(this._box,\"translate(\"+a+\"px,\"+s+\"px)\"),this._box.style.width=o-a+\"px\",this._box.style.height=l-s+\"px\"}}},Un.prototype.mouseupWindow=function(e,n){var i=this;if(this._active&&0===e.button){var a=this._startPos,o=n;if(this.reset(),r.suppressClick(),a.x!==o.x||a.y!==o.y)return this._map.fire(new t.Event(\"boxzoomend\",{originalEvent:e})),{cameraAnimation:function(t){return t.fitScreenCoordinates(a,o,i._map.getBearing(),{linear:!0})}};this._fireEvent(\"boxzoomcancel\",e)}},Un.prototype.keydown=function(t){this._active&&27===t.keyCode&&(this.reset(),this._fireEvent(\"boxzoomcancel\",t))},Un.prototype.reset=function(){this._active=!1,this._container.classList.remove(\"mapboxgl-crosshair\"),this._box&&(r.remove(this._box),this._box=null),r.enableDrag(),delete this._startPos,delete this._lastPos},Un.prototype._fireEvent=function(e,r){return this._map.fire(new t.Event(e,{originalEvent:r}))};var Hn=function(t){this.reset(),this.numTouches=t.numTouches};Hn.prototype.reset=function(){delete this.centroid,delete this.startTime,delete this.touches,this.aborted=!1},Hn.prototype.touchstart=function(e,r,n){(this.centroid||n.length>this.numTouches)&&(this.aborted=!0),this.aborted||(void 0===this.startTime&&(this.startTime=e.timeStamp),n.length===this.numTouches&&(this.centroid=function(e){for(var r=new t.Point(0,0),n=0,i=e;n<i.length;n+=1){var a=i[n];r._add(a)}return r.div(e.length)}(r),this.touches=Vn(n,r)))},Hn.prototype.touchmove=function(t,e,r){if(!this.aborted&&this.centroid){var n=Vn(r,e);for(var i in this.touches){var a=this.touches[i],o=n[i];(!o||o.dist(a)>30)&&(this.aborted=!0)}}},Hn.prototype.touchend=function(t,e,r){if((!this.centroid||t.timeStamp-this.startTime>500)&&(this.aborted=!0),0===r.length){var n=!this.aborted&&this.centroid;if(this.reset(),n)return n}};var qn=function(t){this.singleTap=new Hn(t),this.numTaps=t.numTaps,this.reset()};qn.prototype.reset=function(){this.lastTime=1/0,delete this.lastTap,this.count=0,this.singleTap.reset()},qn.prototype.touchstart=function(t,e,r){this.singleTap.touchstart(t,e,r)},qn.prototype.touchmove=function(t,e,r){this.singleTap.touchmove(t,e,r)},qn.prototype.touchend=function(t,e,r){var n=this.singleTap.touchend(t,e,r);if(n){var i=t.timeStamp-this.lastTime<500,a=!this.lastTap||this.lastTap.dist(n)<30;if(i&&a||this.reset(),this.count++,this.lastTime=t.timeStamp,this.lastTap=n,this.count===this.numTaps)return this.reset(),n}};var Gn=function(){this._zoomIn=new qn({numTouches:1,numTaps:2}),this._zoomOut=new qn({numTouches:2,numTaps:1}),this.reset()};Gn.prototype.reset=function(){this._active=!1,this._zoomIn.reset(),this._zoomOut.reset()},Gn.prototype.touchstart=function(t,e,r){this._zoomIn.touchstart(t,e,r),this._zoomOut.touchstart(t,e,r)},Gn.prototype.touchmove=function(t,e,r){this._zoomIn.touchmove(t,e,r),this._zoomOut.touchmove(t,e,r)},Gn.prototype.touchend=function(t,e,r){var n=this,i=this._zoomIn.touchend(t,e,r),a=this._zoomOut.touchend(t,e,r);return i?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()+1,around:e.unproject(i)},{originalEvent:t})}}):a?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()-1,around:e.unproject(a)},{originalEvent:t})}}):void 0},Gn.prototype.touchcancel=function(){this.reset()},Gn.prototype.enable=function(){this._enabled=!0},Gn.prototype.disable=function(){this._enabled=!1,this.reset()},Gn.prototype.isEnabled=function(){return this._enabled},Gn.prototype.isActive=function(){return this._active};var Yn=function(t){this.reset(),this._clickTolerance=t.clickTolerance||1};Yn.prototype.reset=function(){this._active=!1,this._moved=!1,delete this._lastPoint,delete this._eventButton},Yn.prototype._correctButton=function(t,e){return!1},Yn.prototype._move=function(t,e){return{}},Yn.prototype.mousedown=function(t,e){if(!this._lastPoint){var n=r.mouseButton(t);this._correctButton(t,n)&&(this._lastPoint=e,this._eventButton=n)}},Yn.prototype.mousemoveWindow=function(t,e){var r=this._lastPoint;if(r&&(t.preventDefault(),this._moved||!(e.dist(r)<this._clickTolerance)))return this._moved=!0,this._lastPoint=e,this._move(r,e)},Yn.prototype.mouseupWindow=function(t){r.mouseButton(t)===this._eventButton&&(this._moved&&r.suppressClick(),this.reset())},Yn.prototype.enable=function(){this._enabled=!0},Yn.prototype.disable=function(){this._enabled=!1,this.reset()},Yn.prototype.isEnabled=function(){return this._enabled},Yn.prototype.isActive=function(){return this._active};var Wn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.mousedown=function(e,r){t.prototype.mousedown.call(this,e,r),this._lastPoint&&(this._active=!0)},e.prototype._correctButton=function(t,e){return 0===e&&!t.ctrlKey},e.prototype._move=function(t,e){return{around:e,panDelta:e.sub(t)}},e}(Yn),Xn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._correctButton=function(t,e){return 0===e&&t.ctrlKey||2===e},e.prototype._move=function(t,e){var r=.8*(e.x-t.x);if(r)return this._active=!0,{bearingDelta:r}},e.prototype.contextmenu=function(t){t.preventDefault()},e}(Yn),Zn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._correctButton=function(t,e){return 0===e&&t.ctrlKey||2===e},e.prototype._move=function(t,e){var r=-.5*(e.y-t.y);if(r)return this._active=!0,{pitchDelta:r}},e.prototype.contextmenu=function(t){t.preventDefault()},e}(Yn),Jn=function(t){this._minTouches=1,this._clickTolerance=t.clickTolerance||1,this.reset()};Jn.prototype.reset=function(){this._active=!1,this._touches={},this._sum=new t.Point(0,0)},Jn.prototype.touchstart=function(t,e,r){return this._calculateTransform(t,e,r)},Jn.prototype.touchmove=function(t,e,r){if(this._active)return t.preventDefault(),this._calculateTransform(t,e,r)},Jn.prototype.touchend=function(t,e,r){this._calculateTransform(t,e,r),this._active&&r.length<this._minTouches&&this.reset()},Jn.prototype.touchcancel=function(){this.reset()},Jn.prototype._calculateTransform=function(e,r,n){n.length>0&&(this._active=!0);var i=Vn(n,r),a=new t.Point(0,0),o=new t.Point(0,0),s=0;for(var l in i){var c=i[l],u=this._touches[l];u&&(a._add(c),o._add(c.sub(u)),s++,i[l]=c)}if(this._touches=i,!(s<this._minTouches)&&o.mag()){var f=o.div(s);if(this._sum._add(f),!(this._sum.mag()<this._clickTolerance))return{around:a.div(s),panDelta:f}}},Jn.prototype.enable=function(){this._enabled=!0},Jn.prototype.disable=function(){this._enabled=!1,this.reset()},Jn.prototype.isEnabled=function(){return this._enabled},Jn.prototype.isActive=function(){return this._active};var Kn=function(){this.reset()};function Qn(t,e,r){for(var n=0;n<t.length;n++)if(t[n].identifier===r)return e[n]}Kn.prototype.reset=function(){this._active=!1,delete this._firstTwoTouches},Kn.prototype._start=function(t){},Kn.prototype._move=function(t,e,r){return{}},Kn.prototype.touchstart=function(t,e,r){this._firstTwoTouches||r.length<2||(this._firstTwoTouches=[r[0].identifier,r[1].identifier],this._start([e[0],e[1]]))},Kn.prototype.touchmove=function(t,e,r){if(this._firstTwoTouches){t.preventDefault();var n=this._firstTwoTouches,i=n[0],a=n[1],o=Qn(r,e,i),s=Qn(r,e,a);if(o&&s){var l=this._aroundCenter?null:o.add(s).div(2);return this._move([o,s],l,t)}}},Kn.prototype.touchend=function(t,e,n){if(this._firstTwoTouches){var i=this._firstTwoTouches,a=i[0],o=i[1],s=Qn(n,e,a),l=Qn(n,e,o);s&&l||(this._active&&r.suppressClick(),this.reset())}},Kn.prototype.touchcancel=function(){this.reset()},Kn.prototype.enable=function(t){this._enabled=!0,this._aroundCenter=!!t&&\"center\"===t.around},Kn.prototype.disable=function(){this._enabled=!1,this.reset()},Kn.prototype.isEnabled=function(){return this._enabled},Kn.prototype.isActive=function(){return this._active};function $n(t,e){return Math.log(t/e)/Math.LN2}var ti=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),delete this._distance,delete this._startDistance},e.prototype._start=function(t){this._startDistance=this._distance=t[0].dist(t[1])},e.prototype._move=function(t,e){var r=this._distance;if(this._distance=t[0].dist(t[1]),this._active||!(Math.abs($n(this._distance,this._startDistance))<.1))return this._active=!0,{zoomDelta:$n(this._distance,r),pinchAround:e}},e}(Kn);function ei(t,e){return 180*t.angleWith(e)/Math.PI}var ri=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),delete this._minDiameter,delete this._startVector,delete this._vector},e.prototype._start=function(t){this._startVector=this._vector=t[0].sub(t[1]),this._minDiameter=t[0].dist(t[1])},e.prototype._move=function(t,e){var r=this._vector;if(this._vector=t[0].sub(t[1]),this._active||!this._isBelowThreshold(this._vector))return this._active=!0,{bearingDelta:ei(this._vector,r),pinchAround:e}},e.prototype._isBelowThreshold=function(t){this._minDiameter=Math.min(this._minDiameter,t.mag());var e=25/(Math.PI*this._minDiameter)*360,r=ei(t,this._startVector);return Math.abs(r)<e},e}(Kn);function ni(t){return Math.abs(t.y)>Math.abs(t.x)}var ii=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),this._valid=void 0,delete this._firstMove,delete this._lastPoints},e.prototype._start=function(t){this._lastPoints=t,ni(t[0].sub(t[1]))&&(this._valid=!1)},e.prototype._move=function(t,e,r){var n=t[0].sub(this._lastPoints[0]),i=t[1].sub(this._lastPoints[1]);if(this._valid=this.gestureBeginsVertically(n,i,r.timeStamp),this._valid){this._lastPoints=t,this._active=!0;return{pitchDelta:-.5*((n.y+i.y)/2)}}},e.prototype.gestureBeginsVertically=function(t,e,r){if(void 0!==this._valid)return this._valid;var n=t.mag()>=2,i=e.mag()>=2;if(n||i){if(!n||!i)return void 0===this._firstMove&&(this._firstMove=r),r-this._firstMove<100&&void 0;var a=t.y>0==e.y>0;return ni(t)&&ni(e)&&a}},e}(Kn),ai={panStep:100,bearingStep:15,pitchStep:10},oi=function(){var t=ai;this._panStep=t.panStep,this._bearingStep=t.bearingStep,this._pitchStep=t.pitchStep};function si(t){return t*(2-t)}oi.prototype.reset=function(){this._active=!1},oi.prototype.keydown=function(t){var e=this;if(!(t.altKey||t.ctrlKey||t.metaKey)){var r=0,n=0,i=0,a=0,o=0;switch(t.keyCode){case 61:case 107:case 171:case 187:r=1;break;case 189:case 109:case 173:r=-1;break;case 37:t.shiftKey?n=-1:(t.preventDefault(),a=-1);break;case 39:t.shiftKey?n=1:(t.preventDefault(),a=1);break;case 38:t.shiftKey?i=1:(t.preventDefault(),o=-1);break;case 40:t.shiftKey?i=-1:(t.preventDefault(),o=1);break;default:return}return{cameraAnimation:function(s){var l=s.getZoom();s.easeTo({duration:300,easeId:\"keyboardHandler\",easing:si,zoom:r?Math.round(l)+r*(t.shiftKey?2:1):l,bearing:s.getBearing()+n*e._bearingStep,pitch:s.getPitch()+i*e._pitchStep,offset:[-a*e._panStep,-o*e._panStep],center:s.getCenter()},{originalEvent:t})}}}},oi.prototype.enable=function(){this._enabled=!0},oi.prototype.disable=function(){this._enabled=!1,this.reset()},oi.prototype.isEnabled=function(){return this._enabled},oi.prototype.isActive=function(){return this._active};var li=function(e,r){this._map=e,this._el=e.getCanvasContainer(),this._handler=r,this._delta=0,this._defaultZoomRate=.01,this._wheelZoomRate=1/450,t.bindAll([\"_onWheel\",\"_onTimeout\",\"_onScrollFrame\",\"_onScrollFinished\"],this)};li.prototype.setZoomRate=function(t){this._defaultZoomRate=t},li.prototype.setWheelZoomRate=function(t){this._wheelZoomRate=t},li.prototype.isEnabled=function(){return!!this._enabled},li.prototype.isActive=function(){return!!this._active||void 0!==this._finishTimeout},li.prototype.isZooming=function(){return!!this._zooming},li.prototype.enable=function(t){this.isEnabled()||(this._enabled=!0,this._aroundCenter=t&&\"center\"===t.around)},li.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},li.prototype.wheel=function(e){if(this.isEnabled()){var r=e.deltaMode===t.window.WheelEvent.DOM_DELTA_LINE?40*e.deltaY:e.deltaY,n=t.browser.now(),i=n-(this._lastWheelEventTime||0);this._lastWheelEventTime=n,0!==r&&r%4.000244140625==0?this._type=\"wheel\":0!==r&&Math.abs(r)<4?this._type=\"trackpad\":i>400?(this._type=null,this._lastValue=r,this._timeout=setTimeout(this._onTimeout,40,e)):this._type||(this._type=Math.abs(i*r)<200?\"trackpad\":\"wheel\",this._timeout&&(clearTimeout(this._timeout),this._timeout=null,r+=this._lastValue)),e.shiftKey&&r&&(r/=4),this._type&&(this._lastWheelEvent=e,this._delta-=r,this._active||this._start(e)),e.preventDefault()}},li.prototype._onTimeout=function(t){this._type=\"wheel\",this._delta-=this._lastValue,this._active||this._start(t)},li.prototype._start=function(e){if(this._delta){this._frameId&&(this._frameId=null),this._active=!0,this.isZooming()||(this._zooming=!0),this._finishTimeout&&(clearTimeout(this._finishTimeout),delete this._finishTimeout);var n=r.mousePos(this._el,e);this._around=t.LngLat.convert(this._aroundCenter?this._map.getCenter():this._map.unproject(n)),this._aroundPoint=this._map.transform.locationPoint(this._around),this._frameId||(this._frameId=!0,this._handler._triggerRenderFrame())}},li.prototype.renderFrame=function(){return this._onScrollFrame()},li.prototype._onScrollFrame=function(){var e=this;if(this._frameId&&(this._frameId=null,this.isActive())){var r=this._map.transform;if(0!==this._delta){var n=\"wheel\"===this._type&&Math.abs(this._delta)>4.000244140625?this._wheelZoomRate:this._defaultZoomRate,i=2/(1+Math.exp(-Math.abs(this._delta*n)));this._delta<0&&0!==i&&(i=1/i);var a=\"number\"==typeof this._targetZoom?r.zoomScale(this._targetZoom):r.scale;this._targetZoom=Math.min(r.maxZoom,Math.max(r.minZoom,r.scaleZoom(a*i))),\"wheel\"===this._type&&(this._startZoom=r.zoom,this._easing=this._smoothOutEasing(200)),this._delta=0}var o,s=\"number\"==typeof this._targetZoom?this._targetZoom:r.zoom,l=this._startZoom,c=this._easing,u=!1;if(\"wheel\"===this._type&&l&&c){var f=Math.min((t.browser.now()-this._lastWheelEventTime)/200,1),h=c(f);o=t.number(l,s,h),f<1?this._frameId||(this._frameId=!0):u=!0}else o=s,u=!0;return this._active=!0,u&&(this._active=!1,this._finishTimeout=setTimeout((function(){e._zooming=!1,e._handler._triggerRenderFrame(),delete e._targetZoom,delete e._finishTimeout}),200)),{noInertia:!0,needsRenderFrame:!u,zoomDelta:o-r.zoom,around:this._aroundPoint,originalEvent:this._lastWheelEvent}}},li.prototype._smoothOutEasing=function(e){var r=t.ease;if(this._prevEase){var n=this._prevEase,i=(t.browser.now()-n.start)/n.duration,a=n.easing(i+.01)-n.easing(i),o=.27/Math.sqrt(a*a+1e-4)*.01,s=Math.sqrt(.0729-o*o);r=t.bezier(o,s,.25,1)}return this._prevEase={start:t.browser.now(),duration:e,easing:r},r},li.prototype.reset=function(){this._active=!1};var ci=function(t,e){this._clickZoom=t,this._tapZoom=e};ci.prototype.enable=function(){this._clickZoom.enable(),this._tapZoom.enable()},ci.prototype.disable=function(){this._clickZoom.disable(),this._tapZoom.disable()},ci.prototype.isEnabled=function(){return this._clickZoom.isEnabled()&&this._tapZoom.isEnabled()},ci.prototype.isActive=function(){return this._clickZoom.isActive()||this._tapZoom.isActive()};var ui=function(){this.reset()};ui.prototype.reset=function(){this._active=!1},ui.prototype.dblclick=function(t,e){return t.preventDefault(),{cameraAnimation:function(r){r.easeTo({duration:300,zoom:r.getZoom()+(t.shiftKey?-1:1),around:r.unproject(e)},{originalEvent:t})}}},ui.prototype.enable=function(){this._enabled=!0},ui.prototype.disable=function(){this._enabled=!1,this.reset()},ui.prototype.isEnabled=function(){return this._enabled},ui.prototype.isActive=function(){return this._active};var fi=function(){this._tap=new qn({numTouches:1,numTaps:1}),this.reset()};fi.prototype.reset=function(){this._active=!1,delete this._swipePoint,delete this._swipeTouch,delete this._tapTime,this._tap.reset()},fi.prototype.touchstart=function(t,e,r){this._swipePoint||(this._tapTime&&t.timeStamp-this._tapTime>500&&this.reset(),this._tapTime?r.length>0&&(this._swipePoint=e[0],this._swipeTouch=r[0].identifier):this._tap.touchstart(t,e,r))},fi.prototype.touchmove=function(t,e,r){if(this._tapTime){if(this._swipePoint){if(r[0].identifier!==this._swipeTouch)return;var n=e[0],i=n.y-this._swipePoint.y;return this._swipePoint=n,t.preventDefault(),this._active=!0,{zoomDelta:i/128}}}else this._tap.touchmove(t,e,r)},fi.prototype.touchend=function(t,e,r){this._tapTime?this._swipePoint&&0===r.length&&this.reset():this._tap.touchend(t,e,r)&&(this._tapTime=t.timeStamp)},fi.prototype.touchcancel=function(){this.reset()},fi.prototype.enable=function(){this._enabled=!0},fi.prototype.disable=function(){this._enabled=!1,this.reset()},fi.prototype.isEnabled=function(){return this._enabled},fi.prototype.isActive=function(){return this._active};var hi=function(t,e,r){this._el=t,this._mousePan=e,this._touchPan=r};hi.prototype.enable=function(t){this._inertiaOptions=t||{},this._mousePan.enable(),this._touchPan.enable(),this._el.classList.add(\"mapboxgl-touch-drag-pan\")},hi.prototype.disable=function(){this._mousePan.disable(),this._touchPan.disable(),this._el.classList.remove(\"mapboxgl-touch-drag-pan\")},hi.prototype.isEnabled=function(){return this._mousePan.isEnabled()&&this._touchPan.isEnabled()},hi.prototype.isActive=function(){return this._mousePan.isActive()||this._touchPan.isActive()};var pi=function(t,e,r){this._pitchWithRotate=t.pitchWithRotate,this._mouseRotate=e,this._mousePitch=r};pi.prototype.enable=function(){this._mouseRotate.enable(),this._pitchWithRotate&&this._mousePitch.enable()},pi.prototype.disable=function(){this._mouseRotate.disable(),this._mousePitch.disable()},pi.prototype.isEnabled=function(){return this._mouseRotate.isEnabled()&&(!this._pitchWithRotate||this._mousePitch.isEnabled())},pi.prototype.isActive=function(){return this._mouseRotate.isActive()||this._mousePitch.isActive()};var di=function(t,e,r,n){this._el=t,this._touchZoom=e,this._touchRotate=r,this._tapDragZoom=n,this._rotationDisabled=!1,this._enabled=!0};di.prototype.enable=function(t){this._touchZoom.enable(t),this._rotationDisabled||this._touchRotate.enable(t),this._tapDragZoom.enable(),this._el.classList.add(\"mapboxgl-touch-zoom-rotate\")},di.prototype.disable=function(){this._touchZoom.disable(),this._touchRotate.disable(),this._tapDragZoom.disable(),this._el.classList.remove(\"mapboxgl-touch-zoom-rotate\")},di.prototype.isEnabled=function(){return this._touchZoom.isEnabled()&&(this._rotationDisabled||this._touchRotate.isEnabled())&&this._tapDragZoom.isEnabled()},di.prototype.isActive=function(){return this._touchZoom.isActive()||this._touchRotate.isActive()||this._tapDragZoom.isActive()},di.prototype.disableRotation=function(){this._rotationDisabled=!0,this._touchRotate.disable()},di.prototype.enableRotation=function(){this._rotationDisabled=!1,this._touchZoom.isEnabled()&&this._touchRotate.enable()};var gi=function(t){return t.zoom||t.drag||t.pitch||t.rotate},mi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(t.Event);function vi(t){return t.panDelta&&t.panDelta.mag()||t.zoomDelta||t.bearingDelta||t.pitchDelta}var yi=function(e,n){this._map=e,this._el=this._map.getCanvasContainer(),this._handlers=[],this._handlersById={},this._changes=[],this._inertia=new On(e),this._bearingSnap=n.bearingSnap,this._previousActiveHandlers={},this._eventsInProgress={},this._addDefaultHandlers(n),t.bindAll([\"handleEvent\",\"handleWindowEvent\"],this);var i=this._el;this._listeners=[[i,\"touchstart\",{passive:!1}],[i,\"touchmove\",{passive:!1}],[i,\"touchend\",void 0],[i,\"touchcancel\",void 0],[i,\"mousedown\",void 0],[i,\"mousemove\",void 0],[i,\"mouseup\",void 0],[t.window.document,\"mousemove\",{capture:!0}],[t.window.document,\"mouseup\",void 0],[i,\"mouseover\",void 0],[i,\"mouseout\",void 0],[i,\"dblclick\",void 0],[i,\"click\",void 0],[i,\"keydown\",{capture:!1}],[i,\"keyup\",void 0],[i,\"wheel\",{passive:!1}],[i,\"contextmenu\",void 0],[t.window,\"blur\",void 0]];for(var a=0,o=this._listeners;a<o.length;a+=1){var s=o[a],l=s[0],c=s[1],u=s[2];r.addEventListener(l,c,l===t.window.document?this.handleWindowEvent:this.handleEvent,u)}};yi.prototype.destroy=function(){for(var e=0,n=this._listeners;e<n.length;e+=1){var i=n[e],a=i[0],o=i[1],s=i[2];r.removeEventListener(a,o,a===t.window.document?this.handleWindowEvent:this.handleEvent,s)}},yi.prototype._addDefaultHandlers=function(t){var e=this._map,r=e.getCanvasContainer();this._add(\"mapEvent\",new Nn(e,t));var n=e.boxZoom=new Un(e,t);this._add(\"boxZoom\",n);var i=new Gn,a=new ui;e.doubleClickZoom=new ci(a,i),this._add(\"tapZoom\",i),this._add(\"clickZoom\",a);var o=new fi;this._add(\"tapDragZoom\",o);var s=e.touchPitch=new ii;this._add(\"touchPitch\",s);var l=new Xn(t),c=new Zn(t);e.dragRotate=new pi(t,l,c),this._add(\"mouseRotate\",l,[\"mousePitch\"]),this._add(\"mousePitch\",c,[\"mouseRotate\"]);var u=new Wn(t),f=new Jn(t);e.dragPan=new hi(r,u,f),this._add(\"mousePan\",u),this._add(\"touchPan\",f,[\"touchZoom\",\"touchRotate\"]);var h=new ri,p=new ti;e.touchZoomRotate=new di(r,p,h,o),this._add(\"touchRotate\",h,[\"touchPan\",\"touchZoom\"]),this._add(\"touchZoom\",p,[\"touchPan\",\"touchRotate\"]);var d=e.scrollZoom=new li(e,this);this._add(\"scrollZoom\",d,[\"mousePan\"]);var g=e.keyboard=new oi;this._add(\"keyboard\",g),this._add(\"blockableMapEvent\",new jn(e));for(var m=0,v=[\"boxZoom\",\"doubleClickZoom\",\"tapDragZoom\",\"touchPitch\",\"dragRotate\",\"dragPan\",\"touchZoomRotate\",\"scrollZoom\",\"keyboard\"];m<v.length;m+=1){var y=v[m];t.interactive&&t[y]&&e[y].enable(t[y])}},yi.prototype._add=function(t,e,r){this._handlers.push({handlerName:t,handler:e,allowed:r}),this._handlersById[t]=e},yi.prototype.stop=function(){if(!this._updatingCamera){for(var t=0,e=this._handlers;t<e.length;t+=1){e[t].handler.reset()}this._inertia.clear(),this._fireEvents({},{}),this._changes=[]}},yi.prototype.isActive=function(){for(var t=0,e=this._handlers;t<e.length;t+=1){if(e[t].handler.isActive())return!0}return!1},yi.prototype.isZooming=function(){return!!this._eventsInProgress.zoom||this._map.scrollZoom.isZooming()},yi.prototype.isRotating=function(){return!!this._eventsInProgress.rotate},yi.prototype.isMoving=function(){return Boolean(gi(this._eventsInProgress))||this.isZooming()},yi.prototype._blockedByActive=function(t,e,r){for(var n in t)if(n!==r&&(!e||e.indexOf(n)<0))return!0;return!1},yi.prototype.handleWindowEvent=function(t){this.handleEvent(t,t.type+\"Window\")},yi.prototype._getMapTouches=function(t){for(var e=[],r=0,n=t;r<n.length;r+=1){var i=n[r],a=i.target;this._el.contains(a)&&e.push(i)}return e},yi.prototype.handleEvent=function(t,e){if(\"blur\"!==t.type){this._updatingCamera=!0;for(var n=\"renderFrame\"===t.type?void 0:t,i={needsRenderFrame:!1},a={},o={},s=t.touches?this._getMapTouches(t.touches):void 0,l=s?r.touchPos(this._el,s):r.mousePos(this._el,t),c=0,u=this._handlers;c<u.length;c+=1){var f=u[c],h=f.handlerName,p=f.handler,d=f.allowed;if(p.isEnabled()){var g=void 0;this._blockedByActive(o,d,h)?p.reset():p[e||t.type]&&(g=p[e||t.type](t,l,s),this.mergeHandlerResult(i,a,g,h,n),g&&g.needsRenderFrame&&this._triggerRenderFrame()),(g||p.isActive())&&(o[h]=p)}}var m={};for(var v in this._previousActiveHandlers)o[v]||(m[v]=n);this._previousActiveHandlers=o,(Object.keys(m).length||vi(i))&&(this._changes.push([i,a,m]),this._triggerRenderFrame()),(Object.keys(o).length||vi(i))&&this._map._stop(!0),this._updatingCamera=!1;var y=i.cameraAnimation;y&&(this._inertia.clear(),this._fireEvents({},{}),this._changes=[],y(this._map))}else this.stop()},yi.prototype.mergeHandlerResult=function(e,r,n,i,a){if(n){t.extend(e,n);var o={handlerName:i,originalEvent:n.originalEvent||a};void 0!==n.zoomDelta&&(r.zoom=o),void 0!==n.panDelta&&(r.drag=o),void 0!==n.pitchDelta&&(r.pitch=o),void 0!==n.bearingDelta&&(r.rotate=o)}},yi.prototype._applyChanges=function(){for(var e={},r={},n={},i=0,a=this._changes;i<a.length;i+=1){var o=a[i],s=o[0],l=o[1],c=o[2];s.panDelta&&(e.panDelta=(e.panDelta||new t.Point(0,0))._add(s.panDelta)),s.zoomDelta&&(e.zoomDelta=(e.zoomDelta||0)+s.zoomDelta),s.bearingDelta&&(e.bearingDelta=(e.bearingDelta||0)+s.bearingDelta),s.pitchDelta&&(e.pitchDelta=(e.pitchDelta||0)+s.pitchDelta),void 0!==s.around&&(e.around=s.around),void 0!==s.pinchAround&&(e.pinchAround=s.pinchAround),s.noInertia&&(e.noInertia=s.noInertia),t.extend(r,l),t.extend(n,c)}this._updateMapTransform(e,r,n),this._changes=[]},yi.prototype._updateMapTransform=function(t,e,r){var n=this._map,i=n.transform;if(!vi(t))return this._fireEvents(e,r);var a=t.panDelta,o=t.zoomDelta,s=t.bearingDelta,l=t.pitchDelta,c=t.around,u=t.pinchAround;void 0!==u&&(c=u),n._stop(!0),c=c||n.transform.centerPoint;var f=i.pointLocation(a?c.sub(a):c);s&&(i.bearing+=s),l&&(i.pitch+=l),o&&(i.zoom+=o),i.setLocationAtPoint(f,c),this._map._update(),t.noInertia||this._inertia.record(t),this._fireEvents(e,r)},yi.prototype._fireEvents=function(e,r){var n=this,i=gi(this._eventsInProgress),a=gi(e),o={};for(var s in e){var l=e[s].originalEvent;this._eventsInProgress[s]||(o[s+\"start\"]=l),this._eventsInProgress[s]=e[s]}for(var c in!i&&a&&this._fireEvent(\"movestart\",a.originalEvent),o)this._fireEvent(c,o[c]);for(var u in e.rotate&&(this._bearingChanged=!0),a&&this._fireEvent(\"move\",a.originalEvent),e){var f=e[u].originalEvent;this._fireEvent(u,f)}var h,p={};for(var d in this._eventsInProgress){var g=this._eventsInProgress[d],m=g.handlerName,v=g.originalEvent;this._handlersById[m].isActive()||(delete this._eventsInProgress[d],h=r[m]||v,p[d+\"end\"]=h)}for(var y in p)this._fireEvent(y,p[y]);var x=gi(this._eventsInProgress);if((i||a)&&!x){this._updatingCamera=!0;var b=this._inertia._onMoveEnd(this._map.dragPan._inertiaOptions),_=function(t){return 0!==t&&-n._bearingSnap<t&&t<n._bearingSnap};b?(_(b.bearing||this._map.getBearing())&&(b.bearing=0),this._map.easeTo(b,{originalEvent:h})):(this._map.fire(new t.Event(\"moveend\",{originalEvent:h})),_(this._map.getBearing())&&this._map.resetNorth()),this._bearingChanged=!1,this._updatingCamera=!1}},yi.prototype._fireEvent=function(e,r){this._map.fire(new t.Event(e,r?{originalEvent:r}:{}))},yi.prototype._triggerRenderFrame=function(){var t=this;void 0===this._frameId&&(this._frameId=this._map._requestRenderFrame((function(e){delete t._frameId,t.handleEvent(new mi(\"renderFrame\",{timeStamp:e})),t._applyChanges()})))};var xi=function(e){function r(r,n){e.call(this),this._moving=!1,this._zooming=!1,this.transform=r,this._bearingSnap=n.bearingSnap,t.bindAll([\"_renderFrameCallback\"],this)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getCenter=function(){return new t.LngLat(this.transform.center.lng,this.transform.center.lat)},r.prototype.setCenter=function(t,e){return this.jumpTo({center:t},e)},r.prototype.panBy=function(e,r,n){return e=t.Point.convert(e).mult(-1),this.panTo(this.transform.center,t.extend({offset:e},r),n)},r.prototype.panTo=function(e,r,n){return this.easeTo(t.extend({center:e},r),n)},r.prototype.getZoom=function(){return this.transform.zoom},r.prototype.setZoom=function(t,e){return this.jumpTo({zoom:t},e),this},r.prototype.zoomTo=function(e,r,n){return this.easeTo(t.extend({zoom:e},r),n)},r.prototype.zoomIn=function(t,e){return this.zoomTo(this.getZoom()+1,t,e),this},r.prototype.zoomOut=function(t,e){return this.zoomTo(this.getZoom()-1,t,e),this},r.prototype.getBearing=function(){return this.transform.bearing},r.prototype.setBearing=function(t,e){return this.jumpTo({bearing:t},e),this},r.prototype.getPadding=function(){return this.transform.padding},r.prototype.setPadding=function(t,e){return this.jumpTo({padding:t},e),this},r.prototype.rotateTo=function(e,r,n){return this.easeTo(t.extend({bearing:e},r),n)},r.prototype.resetNorth=function(e,r){return this.rotateTo(0,t.extend({duration:1e3},e),r),this},r.prototype.resetNorthPitch=function(e,r){return this.easeTo(t.extend({bearing:0,pitch:0,duration:1e3},e),r),this},r.prototype.snapToNorth=function(t,e){return Math.abs(this.getBearing())<this._bearingSnap?this.resetNorth(t,e):this},r.prototype.getPitch=function(){return this.transform.pitch},r.prototype.setPitch=function(t,e){return this.jumpTo({pitch:t},e),this},r.prototype.cameraForBounds=function(e,r){return e=t.LngLatBounds.convert(e),this._cameraForBoxAndBearing(e.getNorthWest(),e.getSouthEast(),0,r)},r.prototype._cameraForBoxAndBearing=function(e,r,n,i){var a={top:0,bottom:0,right:0,left:0};if(\"number\"==typeof(i=t.extend({padding:a,offset:[0,0],maxZoom:this.transform.maxZoom},i)).padding){var o=i.padding;i.padding={top:o,bottom:o,right:o,left:o}}i.padding=t.extend(a,i.padding);var s=this.transform,l=s.padding,c=s.project(t.LngLat.convert(e)),u=s.project(t.LngLat.convert(r)),f=c.rotate(-n*Math.PI/180),h=u.rotate(-n*Math.PI/180),p=new t.Point(Math.max(f.x,h.x),Math.max(f.y,h.y)),d=new t.Point(Math.min(f.x,h.x),Math.min(f.y,h.y)),g=p.sub(d),m=(s.width-(l.left+l.right+i.padding.left+i.padding.right))/g.x,v=(s.height-(l.top+l.bottom+i.padding.top+i.padding.bottom))/g.y;if(!(v<0||m<0)){var y=Math.min(s.scaleZoom(s.scale*Math.min(m,v)),i.maxZoom),x=t.Point.convert(i.offset),b=(i.padding.left-i.padding.right)/2,_=(i.padding.top-i.padding.bottom)/2,w=new t.Point(x.x+b,x.y+_).mult(s.scale/s.zoomScale(y));return{center:s.unproject(c.add(u).div(2).sub(w)),zoom:y,bearing:n}}t.warnOnce(\"Map cannot fit within canvas with the given bounds, padding, and/or offset.\")},r.prototype.fitBounds=function(t,e,r){return this._fitInternal(this.cameraForBounds(t,e),e,r)},r.prototype.fitScreenCoordinates=function(e,r,n,i,a){return this._fitInternal(this._cameraForBoxAndBearing(this.transform.pointLocation(t.Point.convert(e)),this.transform.pointLocation(t.Point.convert(r)),n,i),i,a)},r.prototype._fitInternal=function(e,r,n){return e?(delete(r=t.extend(e,r)).padding,r.linear?this.easeTo(r,n):this.flyTo(r,n)):this},r.prototype.jumpTo=function(e,r){this.stop();var n=this.transform,i=!1,a=!1,o=!1;return\"zoom\"in e&&n.zoom!==+e.zoom&&(i=!0,n.zoom=+e.zoom),void 0!==e.center&&(n.center=t.LngLat.convert(e.center)),\"bearing\"in e&&n.bearing!==+e.bearing&&(a=!0,n.bearing=+e.bearing),\"pitch\"in e&&n.pitch!==+e.pitch&&(o=!0,n.pitch=+e.pitch),null==e.padding||n.isPaddingEqual(e.padding)||(n.padding=e.padding),this.fire(new t.Event(\"movestart\",r)).fire(new t.Event(\"move\",r)),i&&this.fire(new t.Event(\"zoomstart\",r)).fire(new t.Event(\"zoom\",r)).fire(new t.Event(\"zoomend\",r)),a&&this.fire(new t.Event(\"rotatestart\",r)).fire(new t.Event(\"rotate\",r)).fire(new t.Event(\"rotateend\",r)),o&&this.fire(new t.Event(\"pitchstart\",r)).fire(new t.Event(\"pitch\",r)).fire(new t.Event(\"pitchend\",r)),this.fire(new t.Event(\"moveend\",r))},r.prototype.easeTo=function(e,r){var n=this;this._stop(!1,e.easeId),(!1===(e=t.extend({offset:[0,0],duration:500,easing:t.ease},e)).animate||!e.essential&&t.browser.prefersReducedMotion)&&(e.duration=0);var i=this.transform,a=this.getZoom(),o=this.getBearing(),s=this.getPitch(),l=this.getPadding(),c=\"zoom\"in e?+e.zoom:a,u=\"bearing\"in e?this._normalizeBearing(e.bearing,o):o,f=\"pitch\"in e?+e.pitch:s,h=\"padding\"in e?e.padding:i.padding,p=t.Point.convert(e.offset),d=i.centerPoint.add(p),g=i.pointLocation(d),m=t.LngLat.convert(e.center||g);this._normalizeCenter(m);var v,y,x=i.project(g),b=i.project(m).sub(x),_=i.zoomScale(c-a);e.around&&(v=t.LngLat.convert(e.around),y=i.locationPoint(v));var w={moving:this._moving,zooming:this._zooming,rotating:this._rotating,pitching:this._pitching};return this._zooming=this._zooming||c!==a,this._rotating=this._rotating||o!==u,this._pitching=this._pitching||f!==s,this._padding=!i.isPaddingEqual(h),this._easeId=e.easeId,this._prepareEase(r,e.noMoveStart,w),clearTimeout(this._easeEndTimeoutID),this._ease((function(e){if(n._zooming&&(i.zoom=t.number(a,c,e)),n._rotating&&(i.bearing=t.number(o,u,e)),n._pitching&&(i.pitch=t.number(s,f,e)),n._padding&&(i.interpolatePadding(l,h,e),d=i.centerPoint.add(p)),v)i.setLocationAtPoint(v,y);else{var g=i.zoomScale(i.zoom-a),m=c>a?Math.min(2,_):Math.max(.5,_),w=Math.pow(m,1-e),T=i.unproject(x.add(b.mult(e*w)).mult(g));i.setLocationAtPoint(i.renderWorldCopies?T.wrap():T,d)}n._fireMoveEvents(r)}),(function(t){n._afterEase(r,t)}),e),this},r.prototype._prepareEase=function(e,r,n){void 0===n&&(n={}),this._moving=!0,r||n.moving||this.fire(new t.Event(\"movestart\",e)),this._zooming&&!n.zooming&&this.fire(new t.Event(\"zoomstart\",e)),this._rotating&&!n.rotating&&this.fire(new t.Event(\"rotatestart\",e)),this._pitching&&!n.pitching&&this.fire(new t.Event(\"pitchstart\",e))},r.prototype._fireMoveEvents=function(e){this.fire(new t.Event(\"move\",e)),this._zooming&&this.fire(new t.Event(\"zoom\",e)),this._rotating&&this.fire(new t.Event(\"rotate\",e)),this._pitching&&this.fire(new t.Event(\"pitch\",e))},r.prototype._afterEase=function(e,r){if(!this._easeId||!r||this._easeId!==r){delete this._easeId;var n=this._zooming,i=this._rotating,a=this._pitching;this._moving=!1,this._zooming=!1,this._rotating=!1,this._pitching=!1,this._padding=!1,n&&this.fire(new t.Event(\"zoomend\",e)),i&&this.fire(new t.Event(\"rotateend\",e)),a&&this.fire(new t.Event(\"pitchend\",e)),this.fire(new t.Event(\"moveend\",e))}},r.prototype.flyTo=function(e,r){var n=this;if(!e.essential&&t.browser.prefersReducedMotion){var i=t.pick(e,[\"center\",\"zoom\",\"bearing\",\"pitch\",\"around\"]);return this.jumpTo(i,r)}this.stop(),e=t.extend({offset:[0,0],speed:1.2,curve:1.42,easing:t.ease},e);var a=this.transform,o=this.getZoom(),s=this.getBearing(),l=this.getPitch(),c=this.getPadding(),u=\"zoom\"in e?t.clamp(+e.zoom,a.minZoom,a.maxZoom):o,f=\"bearing\"in e?this._normalizeBearing(e.bearing,s):s,h=\"pitch\"in e?+e.pitch:l,p=\"padding\"in e?e.padding:a.padding,d=a.zoomScale(u-o),g=t.Point.convert(e.offset),m=a.centerPoint.add(g),v=a.pointLocation(m),y=t.LngLat.convert(e.center||v);this._normalizeCenter(y);var x=a.project(v),b=a.project(y).sub(x),_=e.curve,w=Math.max(a.width,a.height),T=w/d,k=b.mag();if(\"minZoom\"in e){var A=t.clamp(Math.min(e.minZoom,o,u),a.minZoom,a.maxZoom),M=w/a.zoomScale(A-o);_=Math.sqrt(M/k*2)}var S=_*_;function E(t){var e=(T*T-w*w+(t?-1:1)*S*S*k*k)/(2*(t?T:w)*S*k);return Math.log(Math.sqrt(e*e+1)-e)}function L(t){return(Math.exp(t)-Math.exp(-t))/2}function C(t){return(Math.exp(t)+Math.exp(-t))/2}var P=E(0),I=function(t){return C(P)/C(P+_*t)},O=function(t){return w*((C(P)*(L(e=P+_*t)/C(e))-L(P))/S)/k;var e},z=(E(1)-P)/_;if(Math.abs(k)<1e-6||!isFinite(z)){if(Math.abs(w-T)<1e-6)return this.easeTo(e,r);var D=T<w?-1:1;z=Math.abs(Math.log(T/w))/_,O=function(){return 0},I=function(t){return Math.exp(D*_*t)}}if(\"duration\"in e)e.duration=+e.duration;else{var R=\"screenSpeed\"in e?+e.screenSpeed/_:+e.speed;e.duration=1e3*z/R}return e.maxDuration&&e.duration>e.maxDuration&&(e.duration=0),this._zooming=!0,this._rotating=s!==f,this._pitching=h!==l,this._padding=!a.isPaddingEqual(p),this._prepareEase(r,!1),this._ease((function(e){var i=e*z,d=1/I(i);a.zoom=1===e?u:o+a.scaleZoom(d),n._rotating&&(a.bearing=t.number(s,f,e)),n._pitching&&(a.pitch=t.number(l,h,e)),n._padding&&(a.interpolatePadding(c,p,e),m=a.centerPoint.add(g));var v=1===e?y:a.unproject(x.add(b.mult(O(i))).mult(d));a.setLocationAtPoint(a.renderWorldCopies?v.wrap():v,m),n._fireMoveEvents(r)}),(function(){return n._afterEase(r)}),e),this},r.prototype.isEasing=function(){return!!this._easeFrameId},r.prototype.stop=function(){return this._stop()},r.prototype._stop=function(t,e){if(this._easeFrameId&&(this._cancelRenderFrame(this._easeFrameId),delete this._easeFrameId,delete this._onEaseFrame),this._onEaseEnd){var r=this._onEaseEnd;delete this._onEaseEnd,r.call(this,e)}if(!t){var n=this.handlers;n&&n.stop()}return this},r.prototype._ease=function(e,r,n){!1===n.animate||0===n.duration?(e(1),r()):(this._easeStart=t.browser.now(),this._easeOptions=n,this._onEaseFrame=e,this._onEaseEnd=r,this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback))},r.prototype._renderFrameCallback=function(){var e=Math.min((t.browser.now()-this._easeStart)/this._easeOptions.duration,1);this._onEaseFrame(this._easeOptions.easing(e)),e<1?this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback):this.stop()},r.prototype._normalizeBearing=function(e,r){e=t.wrap(e,-180,180);var n=Math.abs(e-r);return Math.abs(e-360-r)<n&&(e-=360),Math.abs(e+360-r)<n&&(e+=360),e},r.prototype._normalizeCenter=function(t){var e=this.transform;if(e.renderWorldCopies&&!e.lngRange){var r=t.lng-e.center.lng;t.lng+=r>180?-360:r<-180?360:0}},r}(t.Evented),bi=function(e){void 0===e&&(e={}),this.options=e,t.bindAll([\"_updateEditLink\",\"_updateData\",\"_updateCompact\"],this)};bi.prototype.getDefaultPosition=function(){return\"bottom-right\"},bi.prototype.onAdd=function(t){var e=this.options&&this.options.compact;return this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-attrib\"),this._innerContainer=r.create(\"div\",\"mapboxgl-ctrl-attrib-inner\",this._container),e&&this._container.classList.add(\"mapboxgl-compact\"),this._updateAttributions(),this._updateEditLink(),this._map.on(\"styledata\",this._updateData),this._map.on(\"sourcedata\",this._updateData),this._map.on(\"moveend\",this._updateEditLink),void 0===e&&(this._map.on(\"resize\",this._updateCompact),this._updateCompact()),this._container},bi.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"styledata\",this._updateData),this._map.off(\"sourcedata\",this._updateData),this._map.off(\"moveend\",this._updateEditLink),this._map.off(\"resize\",this._updateCompact),this._map=void 0,this._attribHTML=void 0},bi.prototype._updateEditLink=function(){var e=this._editLink;e||(e=this._editLink=this._container.querySelector(\".mapbox-improve-map\"));var r=[{key:\"owner\",value:this.styleOwner},{key:\"id\",value:this.styleId},{key:\"access_token\",value:this._map._requestManager._customAccessToken||t.config.ACCESS_TOKEN}];if(e){var n=r.reduce((function(t,e,n){return e.value&&(t+=e.key+\"=\"+e.value+(n<r.length-1?\"&\":\"\")),t}),\"?\");e.href=t.config.FEEDBACK_URL+\"/\"+n+(this._map._hash?this._map._hash.getHashString(!0):\"\"),e.rel=\"noopener nofollow\"}},bi.prototype._updateData=function(t){!t||\"metadata\"!==t.sourceDataType&&\"style\"!==t.dataType||(this._updateAttributions(),this._updateEditLink())},bi.prototype._updateAttributions=function(){if(this._map.style){var t=[];if(this.options.customAttribution&&(Array.isArray(this.options.customAttribution)?t=t.concat(this.options.customAttribution.map((function(t){return\"string\"!=typeof t?\"\":t}))):\"string\"==typeof this.options.customAttribution&&t.push(this.options.customAttribution)),this._map.style.stylesheet){var e=this._map.style.stylesheet;this.styleOwner=e.owner,this.styleId=e.id}var r=this._map.style.sourceCaches;for(var n in r){var i=r[n];if(i.used){var a=i.getSource();a.attribution&&t.indexOf(a.attribution)<0&&t.push(a.attribution)}}t.sort((function(t,e){return t.length-e.length}));var o=(t=t.filter((function(e,r){for(var n=r+1;n<t.length;n++)if(t[n].indexOf(e)>=0)return!1;return!0}))).join(\" | \");o!==this._attribHTML&&(this._attribHTML=o,t.length?(this._innerContainer.innerHTML=o,this._container.classList.remove(\"mapboxgl-attrib-empty\")):this._container.classList.add(\"mapboxgl-attrib-empty\"),this._editLink=null)}},bi.prototype._updateCompact=function(){this._map.getCanvasContainer().offsetWidth<=640?this._container.classList.add(\"mapboxgl-compact\"):this._container.classList.remove(\"mapboxgl-compact\")};var _i=function(){t.bindAll([\"_updateLogo\"],this),t.bindAll([\"_updateCompact\"],this)};_i.prototype.onAdd=function(t){this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl\");var e=r.create(\"a\",\"mapboxgl-ctrl-logo\");return e.target=\"_blank\",e.rel=\"noopener nofollow\",e.href=\"https://www.mapbox.com/\",e.setAttribute(\"aria-label\",this._map._getUIString(\"LogoControl.Title\")),e.setAttribute(\"rel\",\"noopener nofollow\"),this._container.appendChild(e),this._container.style.display=\"none\",this._map.on(\"sourcedata\",this._updateLogo),this._updateLogo(),this._map.on(\"resize\",this._updateCompact),this._updateCompact(),this._container},_i.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"sourcedata\",this._updateLogo),this._map.off(\"resize\",this._updateCompact)},_i.prototype.getDefaultPosition=function(){return\"bottom-left\"},_i.prototype._updateLogo=function(t){t&&\"metadata\"!==t.sourceDataType||(this._container.style.display=this._logoRequired()?\"block\":\"none\")},_i.prototype._logoRequired=function(){if(this._map.style){var t=this._map.style.sourceCaches;for(var e in t){if(t[e].getSource().mapbox_logo)return!0}return!1}},_i.prototype._updateCompact=function(){var t=this._container.children;if(t.length){var e=t[0];this._map.getCanvasContainer().offsetWidth<250?e.classList.add(\"mapboxgl-compact\"):e.classList.remove(\"mapboxgl-compact\")}};var wi=function(){this._queue=[],this._id=0,this._cleared=!1,this._currentlyRunning=!1};wi.prototype.add=function(t){var e=++this._id;return this._queue.push({callback:t,id:e,cancelled:!1}),e},wi.prototype.remove=function(t){for(var e=this._currentlyRunning,r=0,n=e?this._queue.concat(e):this._queue;r<n.length;r+=1){var i=n[r];if(i.id===t)return void(i.cancelled=!0)}},wi.prototype.run=function(t){void 0===t&&(t=0);var e=this._currentlyRunning=this._queue;this._queue=[];for(var r=0,n=e;r<n.length;r+=1){var i=n[r];if(!i.cancelled&&(i.callback(t),this._cleared))break}this._cleared=!1,this._currentlyRunning=!1},wi.prototype.clear=function(){this._currentlyRunning&&(this._cleared=!0),this._queue=[]};var Ti={\"FullscreenControl.Enter\":\"Enter fullscreen\",\"FullscreenControl.Exit\":\"Exit fullscreen\",\"GeolocateControl.FindMyLocation\":\"Find my location\",\"GeolocateControl.LocationNotAvailable\":\"Location not available\",\"LogoControl.Title\":\"Mapbox logo\",\"NavigationControl.ResetBearing\":\"Reset bearing to north\",\"NavigationControl.ZoomIn\":\"Zoom in\",\"NavigationControl.ZoomOut\":\"Zoom out\",\"ScaleControl.Feet\":\"ft\",\"ScaleControl.Meters\":\"m\",\"ScaleControl.Kilometers\":\"km\",\"ScaleControl.Miles\":\"mi\",\"ScaleControl.NauticalMiles\":\"nm\"},ki=t.window.HTMLImageElement,Ai=t.window.HTMLElement,Mi=t.window.ImageBitmap,Si={center:[0,0],zoom:0,bearing:0,pitch:0,minZoom:-2,maxZoom:22,minPitch:0,maxPitch:60,interactive:!0,scrollZoom:!0,boxZoom:!0,dragRotate:!0,dragPan:!0,keyboard:!0,doubleClickZoom:!0,touchZoomRotate:!0,touchPitch:!0,bearingSnap:7,clickTolerance:3,pitchWithRotate:!0,hash:!1,attributionControl:!0,failIfMajorPerformanceCaveat:!1,preserveDrawingBuffer:!1,trackResize:!0,renderWorldCopies:!0,refreshExpiredTiles:!0,maxTileCacheSize:null,localIdeographFontFamily:\"sans-serif\",transformRequest:null,accessToken:null,fadeDuration:300,crossSourceCollisions:!0},Ei=function(n){function i(e){var r=this;if(null!=(e=t.extend({},Si,e)).minZoom&&null!=e.maxZoom&&e.minZoom>e.maxZoom)throw new Error(\"maxZoom must be greater than or equal to minZoom\");if(null!=e.minPitch&&null!=e.maxPitch&&e.minPitch>e.maxPitch)throw new Error(\"maxPitch must be greater than or equal to minPitch\");if(null!=e.minPitch&&e.minPitch<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(null!=e.maxPitch&&e.maxPitch>60)throw new Error(\"maxPitch must be less than or equal to 60\");var i=new An(e.minZoom,e.maxZoom,e.minPitch,e.maxPitch,e.renderWorldCopies);if(n.call(this,i,e),this._interactive=e.interactive,this._maxTileCacheSize=e.maxTileCacheSize,this._failIfMajorPerformanceCaveat=e.failIfMajorPerformanceCaveat,this._preserveDrawingBuffer=e.preserveDrawingBuffer,this._antialias=e.antialias,this._trackResize=e.trackResize,this._bearingSnap=e.bearingSnap,this._refreshExpiredTiles=e.refreshExpiredTiles,this._fadeDuration=e.fadeDuration,this._crossSourceCollisions=e.crossSourceCollisions,this._crossFadingFactor=1,this._collectResourceTiming=e.collectResourceTiming,this._renderTaskQueue=new wi,this._controls=[],this._mapId=t.uniqueId(),this._locale=t.extend({},Ti,e.locale),this._requestManager=new t.RequestManager(e.transformRequest,e.accessToken),\"string\"==typeof e.container){if(this._container=t.window.document.getElementById(e.container),!this._container)throw new Error(\"Container '\"+e.container+\"' not found.\")}else{if(!(e.container instanceof Ai))throw new Error(\"Invalid type: 'container' must be a String or HTMLElement.\");this._container=e.container}if(e.maxBounds&&this.setMaxBounds(e.maxBounds),t.bindAll([\"_onWindowOnline\",\"_onWindowResize\",\"_contextLost\",\"_contextRestored\"],this),this._setupContainer(),this._setupPainter(),void 0===this.painter)throw new Error(\"Failed to initialize WebGL.\");this.on(\"move\",(function(){return r._update(!1)})),this.on(\"moveend\",(function(){return r._update(!1)})),this.on(\"zoom\",(function(){return r._update(!0)})),void 0!==t.window&&(t.window.addEventListener(\"online\",this._onWindowOnline,!1),t.window.addEventListener(\"resize\",this._onWindowResize,!1)),this.handlers=new yi(this,e);var a=\"string\"==typeof e.hash&&e.hash||void 0;this._hash=e.hash&&new Sn(a).addTo(this),this._hash&&this._hash._onHashChange()||(this.jumpTo({center:e.center,zoom:e.zoom,bearing:e.bearing,pitch:e.pitch}),e.bounds&&(this.resize(),this.fitBounds(e.bounds,t.extend({},e.fitBoundsOptions,{duration:0})))),this.resize(),this._localIdeographFontFamily=e.localIdeographFontFamily,e.style&&this.setStyle(e.style,{localIdeographFontFamily:e.localIdeographFontFamily}),e.attributionControl&&this.addControl(new bi({customAttribution:e.customAttribution})),this.addControl(new _i,e.logoPosition),this.on(\"style.load\",(function(){r.transform.unmodified&&r.jumpTo(r.style.stylesheet)})),this.on(\"data\",(function(e){r._update(\"style\"===e.dataType),r.fire(new t.Event(e.dataType+\"data\",e))})),this.on(\"dataloading\",(function(e){r.fire(new t.Event(e.dataType+\"dataloading\",e))}))}n&&(i.__proto__=n),i.prototype=Object.create(n&&n.prototype),i.prototype.constructor=i;var a={showTileBoundaries:{configurable:!0},showPadding:{configurable:!0},showCollisionBoxes:{configurable:!0},showOverdrawInspector:{configurable:!0},repaint:{configurable:!0},vertices:{configurable:!0},version:{configurable:!0}};return i.prototype._getMapId=function(){return this._mapId},i.prototype.addControl=function(e,r){if(void 0===r&&e.getDefaultPosition&&(r=e.getDefaultPosition()),void 0===r&&(r=\"top-right\"),!e||!e.onAdd)return this.fire(new t.ErrorEvent(new Error(\"Invalid argument to map.addControl(). Argument must be a control with onAdd and onRemove methods.\")));var n=e.onAdd(this);this._controls.push(e);var i=this._controlPositions[r];return-1!==r.indexOf(\"bottom\")?i.insertBefore(n,i.firstChild):i.appendChild(n),this},i.prototype.removeControl=function(e){if(!e||!e.onRemove)return this.fire(new t.ErrorEvent(new Error(\"Invalid argument to map.removeControl(). Argument must be a control with onAdd and onRemove methods.\")));var r=this._controls.indexOf(e);return r>-1&&this._controls.splice(r,1),e.onRemove(this),this},i.prototype.resize=function(e){var r=this._containerDimensions(),n=r[0],i=r[1];this._resizeCanvas(n,i),this.transform.resize(n,i),this.painter.resize(n,i);var a=!this._moving;return a&&(this.stop(),this.fire(new t.Event(\"movestart\",e)).fire(new t.Event(\"move\",e))),this.fire(new t.Event(\"resize\",e)),a&&this.fire(new t.Event(\"moveend\",e)),this},i.prototype.getBounds=function(){return this.transform.getBounds()},i.prototype.getMaxBounds=function(){return this.transform.getMaxBounds()},i.prototype.setMaxBounds=function(e){return this.transform.setMaxBounds(t.LngLatBounds.convert(e)),this._update()},i.prototype.setMinZoom=function(t){if((t=null==t?-2:t)>=-2&&t<=this.transform.maxZoom)return this.transform.minZoom=t,this._update(),this.getZoom()<t&&this.setZoom(t),this;throw new Error(\"minZoom must be between -2 and the current maxZoom, inclusive\")},i.prototype.getMinZoom=function(){return this.transform.minZoom},i.prototype.setMaxZoom=function(t){if((t=null==t?22:t)>=this.transform.minZoom)return this.transform.maxZoom=t,this._update(),this.getZoom()>t&&this.setZoom(t),this;throw new Error(\"maxZoom must be greater than the current minZoom\")},i.prototype.getMaxZoom=function(){return this.transform.maxZoom},i.prototype.setMinPitch=function(t){if((t=null==t?0:t)<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(t>=0&&t<=this.transform.maxPitch)return this.transform.minPitch=t,this._update(),this.getPitch()<t&&this.setPitch(t),this;throw new Error(\"minPitch must be between 0 and the current maxPitch, inclusive\")},i.prototype.getMinPitch=function(){return this.transform.minPitch},i.prototype.setMaxPitch=function(t){if((t=null==t?60:t)>60)throw new Error(\"maxPitch must be less than or equal to 60\");if(t>=this.transform.minPitch)return this.transform.maxPitch=t,this._update(),this.getPitch()>t&&this.setPitch(t),this;throw new Error(\"maxPitch must be greater than the current minPitch\")},i.prototype.getMaxPitch=function(){return this.transform.maxPitch},i.prototype.getRenderWorldCopies=function(){return this.transform.renderWorldCopies},i.prototype.setRenderWorldCopies=function(t){return this.transform.renderWorldCopies=t,this._update()},i.prototype.project=function(e){return this.transform.locationPoint(t.LngLat.convert(e))},i.prototype.unproject=function(e){return this.transform.pointLocation(t.Point.convert(e))},i.prototype.isMoving=function(){return this._moving||this.handlers.isMoving()},i.prototype.isZooming=function(){return this._zooming||this.handlers.isZooming()},i.prototype.isRotating=function(){return this._rotating||this.handlers.isRotating()},i.prototype._createDelegatedListener=function(t,e,r){var n,i=this;if(\"mouseenter\"===t||\"mouseover\"===t){var a=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){var o=i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[];o.length?a||(a=!0,r.call(i,new Rn(t,i,n.originalEvent,{features:o}))):a=!1},mouseout:function(){a=!1}}}}if(\"mouseleave\"===t||\"mouseout\"===t){var o=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){(i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[]).length?o=!0:o&&(o=!1,r.call(i,new Rn(t,i,n.originalEvent)))},mouseout:function(e){o&&(o=!1,r.call(i,new Rn(t,i,e.originalEvent)))}}}}return{layer:e,listener:r,delegates:(n={},n[t]=function(t){var n=i.getLayer(e)?i.queryRenderedFeatures(t.point,{layers:[e]}):[];n.length&&(t.features=n,r.call(i,t),delete t.features)},n)}},i.prototype.on=function(t,e,r){if(void 0===r)return n.prototype.on.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in this._delegatedListeners=this._delegatedListeners||{},this._delegatedListeners[t]=this._delegatedListeners[t]||[],this._delegatedListeners[t].push(i),i.delegates)this.on(a,i.delegates[a]);return this},i.prototype.once=function(t,e,r){if(void 0===r)return n.prototype.once.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in i.delegates)this.once(a,i.delegates[a]);return this},i.prototype.off=function(t,e,r){var i=this;if(void 0===r)return n.prototype.off.call(this,t,e);return this._delegatedListeners&&this._delegatedListeners[t]&&function(n){for(var a=n[t],o=0;o<a.length;o++){var s=a[o];if(s.layer===e&&s.listener===r){for(var l in s.delegates)i.off(l,s.delegates[l]);return a.splice(o,1),i}}}(this._delegatedListeners),this},i.prototype.queryRenderedFeatures=function(e,r){if(!this.style)return[];var n;if(void 0!==r||void 0===e||e instanceof t.Point||Array.isArray(e)||(r=e,e=void 0),r=r||{},(e=e||[[0,0],[this.transform.width,this.transform.height]])instanceof t.Point||\"number\"==typeof e[0])n=[t.Point.convert(e)];else{var i=t.Point.convert(e[0]),a=t.Point.convert(e[1]);n=[i,new t.Point(a.x,i.y),a,new t.Point(i.x,a.y),i]}return this.style.queryRenderedFeatures(n,r,this.transform)},i.prototype.querySourceFeatures=function(t,e){return this.style.querySourceFeatures(t,e)},i.prototype.setStyle=function(e,r){return!1!==(r=t.extend({},{localIdeographFontFamily:this._localIdeographFontFamily},r)).diff&&r.localIdeographFontFamily===this._localIdeographFontFamily&&this.style&&e?(this._diffStyle(e,r),this):(this._localIdeographFontFamily=r.localIdeographFontFamily,this._updateStyle(e,r))},i.prototype._getUIString=function(t){var e=this._locale[t];if(null==e)throw new Error(\"Missing UI string '\"+t+\"'\");return e},i.prototype._updateStyle=function(t,e){return this.style&&(this.style.setEventedParent(null),this.style._remove()),t?(this.style=new qe(this,e||{}),this.style.setEventedParent(this,{style:this.style}),\"string\"==typeof t?this.style.loadURL(t):this.style.loadJSON(t),this):(delete this.style,this)},i.prototype._lazyInitEmptyStyle=function(){this.style||(this.style=new qe(this,{}),this.style.setEventedParent(this,{style:this.style}),this.style.loadEmpty())},i.prototype._diffStyle=function(e,r){var n=this;if(\"string\"==typeof e){var i=this._requestManager.normalizeStyleURL(e),a=this._requestManager.transformRequest(i,t.ResourceType.Style);t.getJSON(a,(function(e,i){e?n.fire(new t.ErrorEvent(e)):i&&n._updateDiff(i,r)}))}else\"object\"==typeof e&&this._updateDiff(e,r)},i.prototype._updateDiff=function(e,r){try{this.style.setState(e)&&this._update(!0)}catch(n){t.warnOnce(\"Unable to perform style diff: \"+(n.message||n.error||n)+\".  Rebuilding the style from scratch.\"),this._updateStyle(e,r)}},i.prototype.getStyle=function(){if(this.style)return this.style.serialize()},i.prototype.isStyleLoaded=function(){return this.style?this.style.loaded():t.warnOnce(\"There is no style added to the map.\")},i.prototype.addSource=function(t,e){return this._lazyInitEmptyStyle(),this.style.addSource(t,e),this._update(!0)},i.prototype.isSourceLoaded=function(e){var r=this.style&&this.style.sourceCaches[e];if(void 0!==r)return r.loaded();this.fire(new t.ErrorEvent(new Error(\"There is no source with ID '\"+e+\"'\")))},i.prototype.areTilesLoaded=function(){var t=this.style&&this.style.sourceCaches;for(var e in t){var r=t[e]._tiles;for(var n in r){var i=r[n];if(\"loaded\"!==i.state&&\"errored\"!==i.state)return!1}}return!0},i.prototype.addSourceType=function(t,e,r){return this._lazyInitEmptyStyle(),this.style.addSourceType(t,e,r)},i.prototype.removeSource=function(t){return this.style.removeSource(t),this._update(!0)},i.prototype.getSource=function(t){return this.style.getSource(t)},i.prototype.addImage=function(e,r,n){void 0===n&&(n={});var i=n.pixelRatio;void 0===i&&(i=1);var a=n.sdf;void 0===a&&(a=!1);var o=n.stretchX,s=n.stretchY,l=n.content;this._lazyInitEmptyStyle();if(r instanceof ki||Mi&&r instanceof Mi){var c=t.browser.getImageData(r),u=c.width,f=c.height,h=c.data;this.style.addImage(e,{data:new t.RGBAImage({width:u,height:f},h),pixelRatio:i,stretchX:o,stretchY:s,content:l,sdf:a,version:0})}else{if(void 0===r.width||void 0===r.height)return this.fire(new t.ErrorEvent(new Error(\"Invalid arguments to map.addImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`\")));var p=r.width,d=r.height,g=r.data,m=r;this.style.addImage(e,{data:new t.RGBAImage({width:p,height:d},new Uint8Array(g)),pixelRatio:i,stretchX:o,stretchY:s,content:l,sdf:a,version:0,userImage:m}),m.onAdd&&m.onAdd(this,e)}},i.prototype.updateImage=function(e,r){var n=this.style.getImage(e);if(!n)return this.fire(new t.ErrorEvent(new Error(\"The map has no image with that id. If you are adding a new image use `map.addImage(...)` instead.\")));var i=r instanceof ki||Mi&&r instanceof Mi?t.browser.getImageData(r):r,a=i.width,o=i.height,s=i.data;if(void 0===a||void 0===o)return this.fire(new t.ErrorEvent(new Error(\"Invalid arguments to map.updateImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`\")));if(a!==n.data.width||o!==n.data.height)return this.fire(new t.ErrorEvent(new Error(\"The width and height of the updated image must be that same as the previous version of the image\")));var l=!(r instanceof ki||Mi&&r instanceof Mi);n.data.replace(s,l),this.style.updateImage(e,n)},i.prototype.hasImage=function(e){return e?!!this.style.getImage(e):(this.fire(new t.ErrorEvent(new Error(\"Missing required image id\"))),!1)},i.prototype.removeImage=function(t){this.style.removeImage(t)},i.prototype.loadImage=function(e,r){t.getImage(this._requestManager.transformRequest(e,t.ResourceType.Image),r)},i.prototype.listImages=function(){return this.style.listImages()},i.prototype.addLayer=function(t,e){return this._lazyInitEmptyStyle(),this.style.addLayer(t,e),this._update(!0)},i.prototype.moveLayer=function(t,e){return this.style.moveLayer(t,e),this._update(!0)},i.prototype.removeLayer=function(t){return this.style.removeLayer(t),this._update(!0)},i.prototype.getLayer=function(t){return this.style.getLayer(t)},i.prototype.setLayerZoomRange=function(t,e,r){return this.style.setLayerZoomRange(t,e,r),this._update(!0)},i.prototype.setFilter=function(t,e,r){return void 0===r&&(r={}),this.style.setFilter(t,e,r),this._update(!0)},i.prototype.getFilter=function(t){return this.style.getFilter(t)},i.prototype.setPaintProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setPaintProperty(t,e,r,n),this._update(!0)},i.prototype.getPaintProperty=function(t,e){return this.style.getPaintProperty(t,e)},i.prototype.setLayoutProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setLayoutProperty(t,e,r,n),this._update(!0)},i.prototype.getLayoutProperty=function(t,e){return this.style.getLayoutProperty(t,e)},i.prototype.setLight=function(t,e){return void 0===e&&(e={}),this._lazyInitEmptyStyle(),this.style.setLight(t,e),this._update(!0)},i.prototype.getLight=function(){return this.style.getLight()},i.prototype.setFeatureState=function(t,e){return this.style.setFeatureState(t,e),this._update()},i.prototype.removeFeatureState=function(t,e){return this.style.removeFeatureState(t,e),this._update()},i.prototype.getFeatureState=function(t){return this.style.getFeatureState(t)},i.prototype.getContainer=function(){return this._container},i.prototype.getCanvasContainer=function(){return this._canvasContainer},i.prototype.getCanvas=function(){return this._canvas},i.prototype._containerDimensions=function(){var t=0,e=0;return this._container&&(t=this._container.clientWidth||400,e=this._container.clientHeight||300),[t,e]},i.prototype._detectMissingCSS=function(){\"rgb(250, 128, 114)\"!==t.window.getComputedStyle(this._missingCSSCanary).getPropertyValue(\"background-color\")&&t.warnOnce(\"This page appears to be missing CSS declarations for Mapbox GL JS, which may cause the map to display incorrectly. Please ensure your page includes mapbox-gl.css, as described in https://www.mapbox.com/mapbox-gl-js/api/.\")},i.prototype._setupContainer=function(){var t=this._container;t.classList.add(\"mapboxgl-map\"),(this._missingCSSCanary=r.create(\"div\",\"mapboxgl-canary\",t)).style.visibility=\"hidden\",this._detectMissingCSS();var e=this._canvasContainer=r.create(\"div\",\"mapboxgl-canvas-container\",t);this._interactive&&e.classList.add(\"mapboxgl-interactive\"),this._canvas=r.create(\"canvas\",\"mapboxgl-canvas\",e),this._canvas.addEventListener(\"webglcontextlost\",this._contextLost,!1),this._canvas.addEventListener(\"webglcontextrestored\",this._contextRestored,!1),this._canvas.setAttribute(\"tabindex\",\"0\"),this._canvas.setAttribute(\"aria-label\",\"Map\");var n=this._containerDimensions();this._resizeCanvas(n[0],n[1]);var i=this._controlContainer=r.create(\"div\",\"mapboxgl-control-container\",t),a=this._controlPositions={};[\"top-left\",\"top-right\",\"bottom-left\",\"bottom-right\"].forEach((function(t){a[t]=r.create(\"div\",\"mapboxgl-ctrl-\"+t,i)}))},i.prototype._resizeCanvas=function(e,r){var n=t.browser.devicePixelRatio||1;this._canvas.width=n*e,this._canvas.height=n*r,this._canvas.style.width=e+\"px\",this._canvas.style.height=r+\"px\"},i.prototype._setupPainter=function(){var r=t.extend({},e.webGLContextAttributes,{failIfMajorPerformanceCaveat:this._failIfMajorPerformanceCaveat,preserveDrawingBuffer:this._preserveDrawingBuffer,antialias:this._antialias||!1}),n=this._canvas.getContext(\"webgl\",r)||this._canvas.getContext(\"experimental-webgl\",r);n?(this.painter=new _n(n,this.transform),t.webpSupported.testSupport(n)):this.fire(new t.ErrorEvent(new Error(\"Failed to initialize WebGL\")))},i.prototype._contextLost=function(e){e.preventDefault(),this._frame&&(this._frame.cancel(),this._frame=null),this.fire(new t.Event(\"webglcontextlost\",{originalEvent:e}))},i.prototype._contextRestored=function(e){this._setupPainter(),this.resize(),this._update(),this.fire(new t.Event(\"webglcontextrestored\",{originalEvent:e}))},i.prototype.loaded=function(){return!this._styleDirty&&!this._sourcesDirty&&!!this.style&&this.style.loaded()},i.prototype._update=function(t){return this.style?(this._styleDirty=this._styleDirty||t,this._sourcesDirty=!0,this.triggerRepaint(),this):this},i.prototype._requestRenderFrame=function(t){return this._update(),this._renderTaskQueue.add(t)},i.prototype._cancelRenderFrame=function(t){this._renderTaskQueue.remove(t)},i.prototype._render=function(e){var r,n=this,i=0,a=this.painter.context.extTimerQuery;if(this.listens(\"gpu-timing-frame\")&&(r=a.createQueryEXT(),a.beginQueryEXT(a.TIME_ELAPSED_EXT,r),i=t.browser.now()),this.painter.context.setDirty(),this.painter.setBaseState(),this._renderTaskQueue.run(e),!this._removed){var o=!1;if(this.style&&this._styleDirty){this._styleDirty=!1;var s=this.transform.zoom,l=t.browser.now();this.style.zoomHistory.update(s,l);var c=new t.EvaluationParameters(s,{now:l,fadeDuration:this._fadeDuration,zoomHistory:this.style.zoomHistory,transition:this.style.getTransition()}),u=c.crossFadingFactor();1===u&&u===this._crossFadingFactor||(o=!0,this._crossFadingFactor=u),this.style.update(c)}if(this.style&&this._sourcesDirty&&(this._sourcesDirty=!1,this.style._updateSources(this.transform)),this._placementDirty=this.style&&this.style._updatePlacement(this.painter.transform,this.showCollisionBoxes,this._fadeDuration,this._crossSourceCollisions),this.painter.render(this.style,{showTileBoundaries:this.showTileBoundaries,showOverdrawInspector:this._showOverdrawInspector,rotating:this.isRotating(),zooming:this.isZooming(),moving:this.isMoving(),fadeDuration:this._fadeDuration,showPadding:this.showPadding,gpuTiming:!!this.listens(\"gpu-timing-layer\")}),this.fire(new t.Event(\"render\")),this.loaded()&&!this._loaded&&(this._loaded=!0,this.fire(new t.Event(\"load\"))),this.style&&(this.style.hasTransitions()||o)&&(this._styleDirty=!0),this.style&&!this._placementDirty&&this.style._releaseSymbolFadeTiles(),this.listens(\"gpu-timing-frame\")){var f=t.browser.now()-i;a.endQueryEXT(a.TIME_ELAPSED_EXT,r),setTimeout((function(){var e=a.getQueryObjectEXT(r,a.QUERY_RESULT_EXT)/1e6;a.deleteQueryEXT(r),n.fire(new t.Event(\"gpu-timing-frame\",{cpuTime:f,gpuTime:e}))}),50)}if(this.listens(\"gpu-timing-layer\")){var h=this.painter.collectGpuTimers();setTimeout((function(){var e=n.painter.queryGpuTimers(h);n.fire(new t.Event(\"gpu-timing-layer\",{layerTimes:e}))}),50)}return this._sourcesDirty||this._styleDirty||this._placementDirty||this._repaint?this.triggerRepaint():!this.isMoving()&&this.loaded()&&(this._fullyLoaded||(this._fullyLoaded=!0),this.fire(new t.Event(\"idle\"))),this}},i.prototype.remove=function(){this._hash&&this._hash.remove();for(var e=0,r=this._controls;e<r.length;e+=1){r[e].onRemove(this)}this._controls=[],this._frame&&(this._frame.cancel(),this._frame=null),this._renderTaskQueue.clear(),this.painter.destroy(),this.handlers.destroy(),delete this.handlers,this.setStyle(null),void 0!==t.window&&(t.window.removeEventListener(\"resize\",this._onWindowResize,!1),t.window.removeEventListener(\"online\",this._onWindowOnline,!1));var n=this.painter.context.gl.getExtension(\"WEBGL_lose_context\");n&&n.loseContext(),Li(this._canvasContainer),Li(this._controlContainer),Li(this._missingCSSCanary),this._container.classList.remove(\"mapboxgl-map\"),this._removed=!0,this.fire(new t.Event(\"remove\"))},i.prototype.triggerRepaint=function(){var e=this;this.style&&!this._frame&&(this._frame=t.browser.frame((function(t){e._frame=null,e._render(t)})))},i.prototype._onWindowOnline=function(){this._update()},i.prototype._onWindowResize=function(t){this._trackResize&&this.resize({originalEvent:t})._update()},a.showTileBoundaries.get=function(){return!!this._showTileBoundaries},a.showTileBoundaries.set=function(t){this._showTileBoundaries!==t&&(this._showTileBoundaries=t,this._update())},a.showPadding.get=function(){return!!this._showPadding},a.showPadding.set=function(t){this._showPadding!==t&&(this._showPadding=t,this._update())},a.showCollisionBoxes.get=function(){return!!this._showCollisionBoxes},a.showCollisionBoxes.set=function(t){this._showCollisionBoxes!==t&&(this._showCollisionBoxes=t,t?this.style._generateCollisionBoxes():this._update())},a.showOverdrawInspector.get=function(){return!!this._showOverdrawInspector},a.showOverdrawInspector.set=function(t){this._showOverdrawInspector!==t&&(this._showOverdrawInspector=t,this._update())},a.repaint.get=function(){return!!this._repaint},a.repaint.set=function(t){this._repaint!==t&&(this._repaint=t,this.triggerRepaint())},a.vertices.get=function(){return!!this._vertices},a.vertices.set=function(t){this._vertices=t,this._update()},i.prototype._setCacheLimits=function(e,r){t.setCacheLimits(e,r)},a.version.get=function(){return t.version},Object.defineProperties(i.prototype,a),i}(xi);function Li(t){t.parentNode&&t.parentNode.removeChild(t)}var Ci={showCompass:!0,showZoom:!0,visualizePitch:!1},Pi=function(e){var n=this;this.options=t.extend({},Ci,e),this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),this._container.addEventListener(\"contextmenu\",(function(t){return t.preventDefault()})),this.options.showZoom&&(t.bindAll([\"_setButtonTitle\",\"_updateZoomButtons\"],this),this._zoomInButton=this._createButton(\"mapboxgl-ctrl-zoom-in\",(function(t){return n._map.zoomIn({},{originalEvent:t})})),r.create(\"span\",\"mapboxgl-ctrl-icon\",this._zoomInButton).setAttribute(\"aria-hidden\",!0),this._zoomOutButton=this._createButton(\"mapboxgl-ctrl-zoom-out\",(function(t){return n._map.zoomOut({},{originalEvent:t})})),r.create(\"span\",\"mapboxgl-ctrl-icon\",this._zoomOutButton).setAttribute(\"aria-hidden\",!0)),this.options.showCompass&&(t.bindAll([\"_rotateCompassArrow\"],this),this._compass=this._createButton(\"mapboxgl-ctrl-compass\",(function(t){n.options.visualizePitch?n._map.resetNorthPitch({},{originalEvent:t}):n._map.resetNorth({},{originalEvent:t})})),this._compassIcon=r.create(\"span\",\"mapboxgl-ctrl-icon\",this._compass),this._compassIcon.setAttribute(\"aria-hidden\",!0))};Pi.prototype._updateZoomButtons=function(){var t=this._map.getZoom();this._zoomInButton.disabled=t===this._map.getMaxZoom(),this._zoomOutButton.disabled=t===this._map.getMinZoom()},Pi.prototype._rotateCompassArrow=function(){var t=this.options.visualizePitch?\"scale(\"+1/Math.pow(Math.cos(this._map.transform.pitch*(Math.PI/180)),.5)+\") rotateX(\"+this._map.transform.pitch+\"deg) rotateZ(\"+this._map.transform.angle*(180/Math.PI)+\"deg)\":\"rotate(\"+this._map.transform.angle*(180/Math.PI)+\"deg)\";this._compassIcon.style.transform=t},Pi.prototype.onAdd=function(t){return this._map=t,this.options.showZoom&&(this._setButtonTitle(this._zoomInButton,\"ZoomIn\"),this._setButtonTitle(this._zoomOutButton,\"ZoomOut\"),this._map.on(\"zoom\",this._updateZoomButtons),this._updateZoomButtons()),this.options.showCompass&&(this._setButtonTitle(this._compass,\"ResetBearing\"),this.options.visualizePitch&&this._map.on(\"pitch\",this._rotateCompassArrow),this._map.on(\"rotate\",this._rotateCompassArrow),this._rotateCompassArrow(),this._handler=new Ii(this._map,this._compass,this.options.visualizePitch)),this._container},Pi.prototype.onRemove=function(){r.remove(this._container),this.options.showZoom&&this._map.off(\"zoom\",this._updateZoomButtons),this.options.showCompass&&(this.options.visualizePitch&&this._map.off(\"pitch\",this._rotateCompassArrow),this._map.off(\"rotate\",this._rotateCompassArrow),this._handler.off(),delete this._handler),delete this._map},Pi.prototype._createButton=function(t,e){var n=r.create(\"button\",t,this._container);return n.type=\"button\",n.addEventListener(\"click\",e),n},Pi.prototype._setButtonTitle=function(t,e){var r=this._map._getUIString(\"NavigationControl.\"+e);t.title=r,t.setAttribute(\"aria-label\",r)};var Ii=function(e,n,i){void 0===i&&(i=!1),this._clickTolerance=10,this.element=n,this.mouseRotate=new Xn({clickTolerance:e.dragRotate._mouseRotate._clickTolerance}),this.map=e,i&&(this.mousePitch=new Zn({clickTolerance:e.dragRotate._mousePitch._clickTolerance})),t.bindAll([\"mousedown\",\"mousemove\",\"mouseup\",\"touchstart\",\"touchmove\",\"touchend\",\"reset\"],this),r.addEventListener(n,\"mousedown\",this.mousedown),r.addEventListener(n,\"touchstart\",this.touchstart,{passive:!1}),r.addEventListener(n,\"touchmove\",this.touchmove),r.addEventListener(n,\"touchend\",this.touchend),r.addEventListener(n,\"touchcancel\",this.reset)};function Oi(e,r,n){if(e=new t.LngLat(e.lng,e.lat),r){var i=new t.LngLat(e.lng-360,e.lat),a=new t.LngLat(e.lng+360,e.lat),o=n.locationPoint(e).distSqr(r);n.locationPoint(i).distSqr(r)<o?e=i:n.locationPoint(a).distSqr(r)<o&&(e=a)}for(;Math.abs(e.lng-n.center.lng)>180;){var s=n.locationPoint(e);if(s.x>=0&&s.y>=0&&s.x<=n.width&&s.y<=n.height)break;e.lng>n.center.lng?e.lng-=360:e.lng+=360}return e}Ii.prototype.down=function(t,e){this.mouseRotate.mousedown(t,e),this.mousePitch&&this.mousePitch.mousedown(t,e),r.disableDrag()},Ii.prototype.move=function(t,e){var r=this.map,n=this.mouseRotate.mousemoveWindow(t,e);if(n&&n.bearingDelta&&r.setBearing(r.getBearing()+n.bearingDelta),this.mousePitch){var i=this.mousePitch.mousemoveWindow(t,e);i&&i.pitchDelta&&r.setPitch(r.getPitch()+i.pitchDelta)}},Ii.prototype.off=function(){var t=this.element;r.removeEventListener(t,\"mousedown\",this.mousedown),r.removeEventListener(t,\"touchstart\",this.touchstart,{passive:!1}),r.removeEventListener(t,\"touchmove\",this.touchmove),r.removeEventListener(t,\"touchend\",this.touchend),r.removeEventListener(t,\"touchcancel\",this.reset),this.offTemp()},Ii.prototype.offTemp=function(){r.enableDrag(),r.removeEventListener(t.window,\"mousemove\",this.mousemove),r.removeEventListener(t.window,\"mouseup\",this.mouseup)},Ii.prototype.mousedown=function(e){this.down(t.extend({},e,{ctrlKey:!0,preventDefault:function(){return e.preventDefault()}}),r.mousePos(this.element,e)),r.addEventListener(t.window,\"mousemove\",this.mousemove),r.addEventListener(t.window,\"mouseup\",this.mouseup)},Ii.prototype.mousemove=function(t){this.move(t,r.mousePos(this.element,t))},Ii.prototype.mouseup=function(t){this.mouseRotate.mouseupWindow(t),this.mousePitch&&this.mousePitch.mouseupWindow(t),this.offTemp()},Ii.prototype.touchstart=function(t){1!==t.targetTouches.length?this.reset():(this._startPos=this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.down({type:\"mousedown\",button:0,ctrlKey:!0,preventDefault:function(){return t.preventDefault()}},this._startPos))},Ii.prototype.touchmove=function(t){1!==t.targetTouches.length?this.reset():(this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.move({preventDefault:function(){return t.preventDefault()}},this._lastPos))},Ii.prototype.touchend=function(t){0===t.targetTouches.length&&this._startPos&&this._lastPos&&this._startPos.dist(this._lastPos)<this._clickTolerance&&this.element.click(),this.reset()},Ii.prototype.reset=function(){this.mouseRotate.reset(),this.mousePitch&&this.mousePitch.reset(),delete this._startPos,delete this._lastPos,this.offTemp()};var zi={center:\"translate(-50%,-50%)\",top:\"translate(-50%,0)\",\"top-left\":\"translate(0,0)\",\"top-right\":\"translate(-100%,0)\",bottom:\"translate(-50%,-100%)\",\"bottom-left\":\"translate(0,-100%)\",\"bottom-right\":\"translate(-100%,-100%)\",left:\"translate(0,-50%)\",right:\"translate(-100%,-50%)\"};function Di(t,e,r){var n=t.classList;for(var i in zi)n.remove(\"mapboxgl-\"+r+\"-anchor-\"+i);n.add(\"mapboxgl-\"+r+\"-anchor-\"+e)}var Ri,Fi=function(e){function n(n,i){var a=this;if(e.call(this),(n instanceof t.window.HTMLElement||i)&&(n=t.extend({element:n},i)),t.bindAll([\"_update\",\"_onMove\",\"_onUp\",\"_addDragHandler\",\"_onMapClick\",\"_onKeyPress\"],this),this._anchor=n&&n.anchor||\"center\",this._color=n&&n.color||\"#3FB1CE\",this._draggable=n&&n.draggable||!1,this._state=\"inactive\",this._rotation=n&&n.rotation||0,this._rotationAlignment=n&&n.rotationAlignment||\"auto\",this._pitchAlignment=n&&n.pitchAlignment&&\"auto\"!==n.pitchAlignment?n.pitchAlignment:this._rotationAlignment,n&&n.element)this._element=n.element,this._offset=t.Point.convert(n&&n.offset||[0,0]);else{this._defaultMarker=!0,this._element=r.create(\"div\"),this._element.setAttribute(\"aria-label\",\"Map marker\");var o=r.createNS(\"http://www.w3.org/2000/svg\",\"svg\");o.setAttributeNS(null,\"display\",\"block\"),o.setAttributeNS(null,\"height\",\"41px\"),o.setAttributeNS(null,\"width\",\"27px\"),o.setAttributeNS(null,\"viewBox\",\"0 0 27 41\");var s=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");s.setAttributeNS(null,\"stroke\",\"none\"),s.setAttributeNS(null,\"stroke-width\",\"1\"),s.setAttributeNS(null,\"fill\",\"none\"),s.setAttributeNS(null,\"fill-rule\",\"evenodd\");var l=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");l.setAttributeNS(null,\"fill-rule\",\"nonzero\");var c=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");c.setAttributeNS(null,\"transform\",\"translate(3.0, 29.0)\"),c.setAttributeNS(null,\"fill\",\"#000000\");for(var u=0,f=[{rx:\"10.5\",ry:\"5.25002273\"},{rx:\"10.5\",ry:\"5.25002273\"},{rx:\"9.5\",ry:\"4.77275007\"},{rx:\"8.5\",ry:\"4.29549936\"},{rx:\"7.5\",ry:\"3.81822308\"},{rx:\"6.5\",ry:\"3.34094679\"},{rx:\"5.5\",ry:\"2.86367051\"},{rx:\"4.5\",ry:\"2.38636864\"}];u<f.length;u+=1){var h=f[u],p=r.createNS(\"http://www.w3.org/2000/svg\",\"ellipse\");p.setAttributeNS(null,\"opacity\",\"0.04\"),p.setAttributeNS(null,\"cx\",\"10.5\"),p.setAttributeNS(null,\"cy\",\"5.80029008\"),p.setAttributeNS(null,\"rx\",h.rx),p.setAttributeNS(null,\"ry\",h.ry),c.appendChild(p)}var d=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");d.setAttributeNS(null,\"fill\",this._color);var g=r.createNS(\"http://www.w3.org/2000/svg\",\"path\");g.setAttributeNS(null,\"d\",\"M27,13.5 C27,19.074644 20.250001,27.000002 14.75,34.500002 C14.016665,35.500004 12.983335,35.500004 12.25,34.500002 C6.7499993,27.000002 0,19.222562 0,13.5 C0,6.0441559 6.0441559,0 13.5,0 C20.955844,0 27,6.0441559 27,13.5 Z\"),d.appendChild(g);var m=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");m.setAttributeNS(null,\"opacity\",\"0.25\"),m.setAttributeNS(null,\"fill\",\"#000000\");var v=r.createNS(\"http://www.w3.org/2000/svg\",\"path\");v.setAttributeNS(null,\"d\",\"M13.5,0 C6.0441559,0 0,6.0441559 0,13.5 C0,19.222562 6.7499993,27 12.25,34.5 C13,35.522727 14.016664,35.500004 14.75,34.5 C20.250001,27 27,19.074644 27,13.5 C27,6.0441559 20.955844,0 13.5,0 Z M13.5,1 C20.415404,1 26,6.584596 26,13.5 C26,15.898657 24.495584,19.181431 22.220703,22.738281 C19.945823,26.295132 16.705119,30.142167 13.943359,33.908203 C13.743445,34.180814 13.612715,34.322738 13.5,34.441406 C13.387285,34.322738 13.256555,34.180814 13.056641,33.908203 C10.284481,30.127985 7.4148684,26.314159 5.015625,22.773438 C2.6163816,19.232715 1,15.953538 1,13.5 C1,6.584596 6.584596,1 13.5,1 Z\"),m.appendChild(v);var y=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");y.setAttributeNS(null,\"transform\",\"translate(6.0, 7.0)\"),y.setAttributeNS(null,\"fill\",\"#FFFFFF\");var x=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");x.setAttributeNS(null,\"transform\",\"translate(8.0, 8.0)\");var b=r.createNS(\"http://www.w3.org/2000/svg\",\"circle\");b.setAttributeNS(null,\"fill\",\"#000000\"),b.setAttributeNS(null,\"opacity\",\"0.25\"),b.setAttributeNS(null,\"cx\",\"5.5\"),b.setAttributeNS(null,\"cy\",\"5.5\"),b.setAttributeNS(null,\"r\",\"5.4999962\");var _=r.createNS(\"http://www.w3.org/2000/svg\",\"circle\");_.setAttributeNS(null,\"fill\",\"#FFFFFF\"),_.setAttributeNS(null,\"cx\",\"5.5\"),_.setAttributeNS(null,\"cy\",\"5.5\"),_.setAttributeNS(null,\"r\",\"5.4999962\"),x.appendChild(b),x.appendChild(_),l.appendChild(c),l.appendChild(d),l.appendChild(m),l.appendChild(y),l.appendChild(x),o.appendChild(l),this._element.appendChild(o),this._offset=t.Point.convert(n&&n.offset||[0,-14])}this._element.classList.add(\"mapboxgl-marker\"),this._element.addEventListener(\"dragstart\",(function(t){t.preventDefault()})),this._element.addEventListener(\"mousedown\",(function(t){t.preventDefault()})),this._element.addEventListener(\"focus\",(function(){var t=a._map.getContainer();t.scrollTop=0,t.scrollLeft=0})),Di(this._element,this._anchor,\"marker\"),this._popup=null}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.addTo=function(t){return this.remove(),this._map=t,t.getCanvasContainer().appendChild(this._element),t.on(\"move\",this._update),t.on(\"moveend\",this._update),this.setDraggable(this._draggable),this._update(),this._map.on(\"click\",this._onMapClick),this},n.prototype.remove=function(){return this._map&&(this._map.off(\"click\",this._onMapClick),this._map.off(\"move\",this._update),this._map.off(\"moveend\",this._update),this._map.off(\"mousedown\",this._addDragHandler),this._map.off(\"touchstart\",this._addDragHandler),this._map.off(\"mouseup\",this._onUp),this._map.off(\"touchend\",this._onUp),this._map.off(\"mousemove\",this._onMove),this._map.off(\"touchmove\",this._onMove),delete this._map),r.remove(this._element),this._popup&&this._popup.remove(),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._popup&&this._popup.setLngLat(this._lngLat),this._update(),this},n.prototype.getElement=function(){return this._element},n.prototype.setPopup=function(t){if(this._popup&&(this._popup.remove(),this._popup=null,this._element.removeEventListener(\"keypress\",this._onKeyPress),this._originalTabIndex||this._element.removeAttribute(\"tabindex\")),t){if(!(\"offset\"in t.options)){var e=Math.sqrt(Math.pow(13.5,2)/2);t.options.offset=this._defaultMarker?{top:[0,0],\"top-left\":[0,0],\"top-right\":[0,0],bottom:[0,-38.1],\"bottom-left\":[e,-1*(24.6+e)],\"bottom-right\":[-e,-1*(24.6+e)],left:[13.5,-24.6],right:[-13.5,-24.6]}:this._offset}this._popup=t,this._lngLat&&this._popup.setLngLat(this._lngLat),this._originalTabIndex=this._element.getAttribute(\"tabindex\"),this._originalTabIndex||this._element.setAttribute(\"tabindex\",\"0\"),this._element.addEventListener(\"keypress\",this._onKeyPress)}return this},n.prototype._onKeyPress=function(t){var e=t.code,r=t.charCode||t.keyCode;\"Space\"!==e&&\"Enter\"!==e&&32!==r&&13!==r||this.togglePopup()},n.prototype._onMapClick=function(t){var e=t.originalEvent.target,r=this._element;this._popup&&(e===r||r.contains(e))&&this.togglePopup()},n.prototype.getPopup=function(){return this._popup},n.prototype.togglePopup=function(){var t=this._popup;return t?(t.isOpen()?t.remove():t.addTo(this._map),this):this},n.prototype._update=function(t){if(this._map){this._map.transform.renderWorldCopies&&(this._lngLat=Oi(this._lngLat,this._pos,this._map.transform)),this._pos=this._map.project(this._lngLat)._add(this._offset);var e=\"\";\"viewport\"===this._rotationAlignment||\"auto\"===this._rotationAlignment?e=\"rotateZ(\"+this._rotation+\"deg)\":\"map\"===this._rotationAlignment&&(e=\"rotateZ(\"+(this._rotation-this._map.getBearing())+\"deg)\");var n=\"\";\"viewport\"===this._pitchAlignment||\"auto\"===this._pitchAlignment?n=\"rotateX(0deg)\":\"map\"===this._pitchAlignment&&(n=\"rotateX(\"+this._map.getPitch()+\"deg)\"),t&&\"moveend\"!==t.type||(this._pos=this._pos.round()),r.setTransform(this._element,zi[this._anchor]+\" translate(\"+this._pos.x+\"px, \"+this._pos.y+\"px) \"+n+\" \"+e)}},n.prototype.getOffset=function(){return this._offset},n.prototype.setOffset=function(e){return this._offset=t.Point.convert(e),this._update(),this},n.prototype._onMove=function(e){this._pos=e.point.sub(this._positionDelta),this._lngLat=this._map.unproject(this._pos),this.setLngLat(this._lngLat),this._element.style.pointerEvents=\"none\",\"pending\"===this._state&&(this._state=\"active\",this.fire(new t.Event(\"dragstart\"))),this.fire(new t.Event(\"drag\"))},n.prototype._onUp=function(){this._element.style.pointerEvents=\"auto\",this._positionDelta=null,this._map.off(\"mousemove\",this._onMove),this._map.off(\"touchmove\",this._onMove),\"active\"===this._state&&this.fire(new t.Event(\"dragend\")),this._state=\"inactive\"},n.prototype._addDragHandler=function(t){this._element.contains(t.originalEvent.target)&&(t.preventDefault(),this._positionDelta=t.point.sub(this._pos).add(this._offset),this._state=\"pending\",this._map.on(\"mousemove\",this._onMove),this._map.on(\"touchmove\",this._onMove),this._map.once(\"mouseup\",this._onUp),this._map.once(\"touchend\",this._onUp))},n.prototype.setDraggable=function(t){return this._draggable=!!t,this._map&&(t?(this._map.on(\"mousedown\",this._addDragHandler),this._map.on(\"touchstart\",this._addDragHandler)):(this._map.off(\"mousedown\",this._addDragHandler),this._map.off(\"touchstart\",this._addDragHandler))),this},n.prototype.isDraggable=function(){return this._draggable},n.prototype.setRotation=function(t){return this._rotation=t||0,this._update(),this},n.prototype.getRotation=function(){return this._rotation},n.prototype.setRotationAlignment=function(t){return this._rotationAlignment=t||\"auto\",this._update(),this},n.prototype.getRotationAlignment=function(){return this._rotationAlignment},n.prototype.setPitchAlignment=function(t){return this._pitchAlignment=t&&\"auto\"!==t?t:this._rotationAlignment,this._update(),this},n.prototype.getPitchAlignment=function(){return this._pitchAlignment},n}(t.Evented),Bi={positionOptions:{enableHighAccuracy:!1,maximumAge:0,timeout:6e3},fitBoundsOptions:{maxZoom:15},trackUserLocation:!1,showAccuracyCircle:!0,showUserLocation:!0};var Ni=0,ji=!1,Ui=function(e){function n(r){e.call(this),this.options=t.extend({},Bi,r),t.bindAll([\"_onSuccess\",\"_onError\",\"_onZoom\",\"_finish\",\"_setupUI\",\"_updateCamera\",\"_updateMarker\"],this)}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.onAdd=function(e){var n;return this._map=e,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),n=this._setupUI,void 0!==Ri?n(Ri):void 0!==t.window.navigator.permissions?t.window.navigator.permissions.query({name:\"geolocation\"}).then((function(t){Ri=\"denied\"!==t.state,n(Ri)})):(Ri=!!t.window.navigator.geolocation,n(Ri)),this._container},n.prototype.onRemove=function(){void 0!==this._geolocationWatchID&&(t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0),this.options.showUserLocation&&this._userLocationDotMarker&&this._userLocationDotMarker.remove(),this.options.showAccuracyCircle&&this._accuracyCircleMarker&&this._accuracyCircleMarker.remove(),r.remove(this._container),this._map.off(\"zoom\",this._onZoom),this._map=void 0,Ni=0,ji=!1},n.prototype._isOutOfMapMaxBounds=function(t){var e=this._map.getMaxBounds(),r=t.coords;return e&&(r.longitude<e.getWest()||r.longitude>e.getEast()||r.latitude<e.getSouth()||r.latitude>e.getNorth())},n.prototype._setErrorState=function(){switch(this._watchState){case\"WAITING_ACTIVE\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\");break;case\"ACTIVE_LOCK\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\");break;case\"BACKGROUND\":this._watchState=\"BACKGROUND_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\")}},n.prototype._onSuccess=function(e){if(this._map){if(this._isOutOfMapMaxBounds(e))return this._setErrorState(),this.fire(new t.Event(\"outofmaxbounds\",e)),this._updateMarker(),void this._finish();if(this.options.trackUserLocation)switch(this._lastKnownPosition=e,this._watchState){case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"BACKGROUND\":case\"BACKGROUND_ERROR\":this._watchState=\"BACKGROUND\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\")}this.options.showUserLocation&&\"OFF\"!==this._watchState&&this._updateMarker(e),this.options.trackUserLocation&&\"ACTIVE_LOCK\"!==this._watchState||this._updateCamera(e),this.options.showUserLocation&&this._dotElement.classList.remove(\"mapboxgl-user-location-dot-stale\"),this.fire(new t.Event(\"geolocate\",e)),this._finish()}},n.prototype._updateCamera=function(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude),n=e.coords.accuracy,i=this._map.getBearing(),a=t.extend({bearing:i},this.options.fitBoundsOptions);this._map.fitBounds(r.toBounds(n),a,{geolocateSource:!0})},n.prototype._updateMarker=function(e){if(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude);this._accuracyCircleMarker.setLngLat(r).addTo(this._map),this._userLocationDotMarker.setLngLat(r).addTo(this._map),this._accuracy=e.coords.accuracy,this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()}else this._userLocationDotMarker.remove(),this._accuracyCircleMarker.remove()},n.prototype._updateCircleRadius=function(){var t=this._map._container.clientHeight/2,e=this._map.unproject([0,t]),r=this._map.unproject([1,t]),n=e.distanceTo(r),i=Math.ceil(2*this._accuracy/n);this._circleElement.style.width=i+\"px\",this._circleElement.style.height=i+\"px\"},n.prototype._onZoom=function(){this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()},n.prototype._onError=function(e){if(this._map){if(this.options.trackUserLocation)if(1===e.code){this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.disabled=!0;var r=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.title=r,this._geolocateButton.setAttribute(\"aria-label\",r),void 0!==this._geolocationWatchID&&this._clearWatch()}else{if(3===e.code&&ji)return;this._setErrorState()}\"OFF\"!==this._watchState&&this.options.showUserLocation&&this._dotElement.classList.add(\"mapboxgl-user-location-dot-stale\"),this.fire(new t.Event(\"error\",e)),this._finish()}},n.prototype._finish=function(){this._timeoutId&&clearTimeout(this._timeoutId),this._timeoutId=void 0},n.prototype._setupUI=function(e){var n=this;if(this._container.addEventListener(\"contextmenu\",(function(t){return t.preventDefault()})),this._geolocateButton=r.create(\"button\",\"mapboxgl-ctrl-geolocate\",this._container),r.create(\"span\",\"mapboxgl-ctrl-icon\",this._geolocateButton).setAttribute(\"aria-hidden\",!0),this._geolocateButton.type=\"button\",!1===e){t.warnOnce(\"Geolocation support is not available so the GeolocateControl will be disabled.\");var i=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.disabled=!0,this._geolocateButton.title=i,this._geolocateButton.setAttribute(\"aria-label\",i)}else{var a=this._map._getUIString(\"GeolocateControl.FindMyLocation\");this._geolocateButton.title=a,this._geolocateButton.setAttribute(\"aria-label\",a)}this.options.trackUserLocation&&(this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this._watchState=\"OFF\"),this.options.showUserLocation&&(this._dotElement=r.create(\"div\",\"mapboxgl-user-location-dot\"),this._userLocationDotMarker=new Fi(this._dotElement),this._circleElement=r.create(\"div\",\"mapboxgl-user-location-accuracy-circle\"),this._accuracyCircleMarker=new Fi({element:this._circleElement,pitchAlignment:\"map\"}),this.options.trackUserLocation&&(this._watchState=\"OFF\"),this._map.on(\"zoom\",this._onZoom)),this._geolocateButton.addEventListener(\"click\",this.trigger.bind(this)),this._setup=!0,this.options.trackUserLocation&&this._map.on(\"movestart\",(function(e){var r=e.originalEvent&&\"resize\"===e.originalEvent.type;e.geolocateSource||\"ACTIVE_LOCK\"!==n._watchState||r||(n._watchState=\"BACKGROUND\",n._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\"),n._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),n.fire(new t.Event(\"trackuserlocationend\")))}))},n.prototype.trigger=function(){if(!this._setup)return t.warnOnce(\"Geolocate control triggered before added to a map\"),!1;if(this.options.trackUserLocation){switch(this._watchState){case\"OFF\":this._watchState=\"WAITING_ACTIVE\",this.fire(new t.Event(\"trackuserlocationstart\"));break;case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":case\"BACKGROUND_ERROR\":Ni--,ji=!1,this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this.fire(new t.Event(\"trackuserlocationend\"));break;case\"BACKGROUND\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._lastKnownPosition&&this._updateCamera(this._lastKnownPosition),this.fire(new t.Event(\"trackuserlocationstart\"))}switch(this._watchState){case\"WAITING_ACTIVE\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"ACTIVE_LOCK\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"ACTIVE_ERROR\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\");break;case\"BACKGROUND\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\");break;case\"BACKGROUND_ERROR\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background-error\")}if(\"OFF\"===this._watchState&&void 0!==this._geolocationWatchID)this._clearWatch();else if(void 0===this._geolocationWatchID){var e;this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"true\"),++Ni>1?(e={maximumAge:6e5,timeout:0},ji=!0):(e=this.options.positionOptions,ji=!1),this._geolocationWatchID=t.window.navigator.geolocation.watchPosition(this._onSuccess,this._onError,e)}}else t.window.navigator.geolocation.getCurrentPosition(this._onSuccess,this._onError,this.options.positionOptions),this._timeoutId=setTimeout(this._finish,1e4);return!0},n.prototype._clearWatch=function(){t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0,this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this.options.showUserLocation&&this._updateMarker(null)},n}(t.Evented),Vi={maxWidth:100,unit:\"metric\"},Hi=function(e){this.options=t.extend({},Vi,e),t.bindAll([\"_onMove\",\"setUnit\"],this)};function qi(t,e,r){var n=r&&r.maxWidth||100,i=t._container.clientHeight/2,a=t.unproject([0,i]),o=t.unproject([n,i]),s=a.distanceTo(o);if(r&&\"imperial\"===r.unit){var l=3.2808*s;if(l>5280)Gi(e,n,l/5280,t._getUIString(\"ScaleControl.Miles\"));else Gi(e,n,l,t._getUIString(\"ScaleControl.Feet\"))}else if(r&&\"nautical\"===r.unit){Gi(e,n,s/1852,t._getUIString(\"ScaleControl.NauticalMiles\"))}else s>=1e3?Gi(e,n,s/1e3,t._getUIString(\"ScaleControl.Kilometers\")):Gi(e,n,s,t._getUIString(\"ScaleControl.Meters\"))}function Gi(t,e,r,n){var i,a,o,s=(i=r,a=Math.pow(10,(\"\"+Math.floor(i)).length-1),o=(o=i/a)>=10?10:o>=5?5:o>=3?3:o>=2?2:o>=1?1:function(t){var e=Math.pow(10,Math.ceil(-Math.log(t)/Math.LN10));return Math.round(t*e)/e}(o),a*o),l=s/r;t.style.width=e*l+\"px\",t.innerHTML=s+\"&nbsp;\"+n}Hi.prototype.getDefaultPosition=function(){return\"bottom-left\"},Hi.prototype._onMove=function(){qi(this._map,this._container,this.options)},Hi.prototype.onAdd=function(t){return this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-scale\",t.getContainer()),this._map.on(\"move\",this._onMove),this._onMove(),this._container},Hi.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"move\",this._onMove),this._map=void 0},Hi.prototype.setUnit=function(t){this.options.unit=t,qi(this._map,this._container,this.options)};var Yi=function(e){this._fullscreen=!1,e&&e.container&&(e.container instanceof t.window.HTMLElement?this._container=e.container:t.warnOnce(\"Full screen control 'container' must be a DOM element.\")),t.bindAll([\"_onClickFullscreen\",\"_changeIcon\"],this),\"onfullscreenchange\"in t.window.document?this._fullscreenchange=\"fullscreenchange\":\"onmozfullscreenchange\"in t.window.document?this._fullscreenchange=\"mozfullscreenchange\":\"onwebkitfullscreenchange\"in t.window.document?this._fullscreenchange=\"webkitfullscreenchange\":\"onmsfullscreenchange\"in t.window.document&&(this._fullscreenchange=\"MSFullscreenChange\")};Yi.prototype.onAdd=function(e){return this._map=e,this._container||(this._container=this._map.getContainer()),this._controlContainer=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),this._checkFullscreenSupport()?this._setupUI():(this._controlContainer.style.display=\"none\",t.warnOnce(\"This device does not support fullscreen mode.\")),this._controlContainer},Yi.prototype.onRemove=function(){r.remove(this._controlContainer),this._map=null,t.window.document.removeEventListener(this._fullscreenchange,this._changeIcon)},Yi.prototype._checkFullscreenSupport=function(){return!!(t.window.document.fullscreenEnabled||t.window.document.mozFullScreenEnabled||t.window.document.msFullscreenEnabled||t.window.document.webkitFullscreenEnabled)},Yi.prototype._setupUI=function(){var e=this._fullscreenButton=r.create(\"button\",\"mapboxgl-ctrl-fullscreen\",this._controlContainer);r.create(\"span\",\"mapboxgl-ctrl-icon\",e).setAttribute(\"aria-hidden\",!0),e.type=\"button\",this._updateTitle(),this._fullscreenButton.addEventListener(\"click\",this._onClickFullscreen),t.window.document.addEventListener(this._fullscreenchange,this._changeIcon)},Yi.prototype._updateTitle=function(){var t=this._getTitle();this._fullscreenButton.setAttribute(\"aria-label\",t),this._fullscreenButton.title=t},Yi.prototype._getTitle=function(){return this._map._getUIString(this._isFullscreen()?\"FullscreenControl.Exit\":\"FullscreenControl.Enter\")},Yi.prototype._isFullscreen=function(){return this._fullscreen},Yi.prototype._changeIcon=function(){(t.window.document.fullscreenElement||t.window.document.mozFullScreenElement||t.window.document.webkitFullscreenElement||t.window.document.msFullscreenElement)===this._container!==this._fullscreen&&(this._fullscreen=!this._fullscreen,this._fullscreenButton.classList.toggle(\"mapboxgl-ctrl-shrink\"),this._fullscreenButton.classList.toggle(\"mapboxgl-ctrl-fullscreen\"),this._updateTitle())},Yi.prototype._onClickFullscreen=function(){this._isFullscreen()?t.window.document.exitFullscreen?t.window.document.exitFullscreen():t.window.document.mozCancelFullScreen?t.window.document.mozCancelFullScreen():t.window.document.msExitFullscreen?t.window.document.msExitFullscreen():t.window.document.webkitCancelFullScreen&&t.window.document.webkitCancelFullScreen():this._container.requestFullscreen?this._container.requestFullscreen():this._container.mozRequestFullScreen?this._container.mozRequestFullScreen():this._container.msRequestFullscreen?this._container.msRequestFullscreen():this._container.webkitRequestFullscreen&&this._container.webkitRequestFullscreen()};var Wi={closeButton:!0,closeOnClick:!0,className:\"\",maxWidth:\"240px\"},Xi=function(e){function n(r){e.call(this),this.options=t.extend(Object.create(Wi),r),t.bindAll([\"_update\",\"_onClose\",\"remove\",\"_onMouseMove\",\"_onMouseUp\",\"_onDrag\"],this)}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.addTo=function(e){return this._map&&this.remove(),this._map=e,this.options.closeOnClick&&this._map.on(\"click\",this._onClose),this.options.closeOnMove&&this._map.on(\"move\",this._onClose),this._map.on(\"remove\",this.remove),this._update(),this._trackPointer?(this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"mouseup\",this._onMouseUp),this._container&&this._container.classList.add(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"mapboxgl-track-pointer\")):this._map.on(\"move\",this._update),this.fire(new t.Event(\"open\")),this},n.prototype.isOpen=function(){return!!this._map},n.prototype.remove=function(){return this._content&&r.remove(this._content),this._container&&(r.remove(this._container),delete this._container),this._map&&(this._map.off(\"move\",this._update),this._map.off(\"move\",this._onClose),this._map.off(\"click\",this._onClose),this._map.off(\"remove\",this.remove),this._map.off(\"mousemove\",this._onMouseMove),this._map.off(\"mouseup\",this._onMouseUp),this._map.off(\"drag\",this._onDrag),delete this._map),this.fire(new t.Event(\"close\")),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._trackPointer=!1,this._update(),this._map&&(this._map.on(\"move\",this._update),this._map.off(\"mousemove\",this._onMouseMove),this._container&&this._container.classList.remove(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.remove(\"mapboxgl-track-pointer\")),this},n.prototype.trackPointer=function(){return this._trackPointer=!0,this._pos=null,this._update(),this._map&&(this._map.off(\"move\",this._update),this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"drag\",this._onDrag),this._container&&this._container.classList.add(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"mapboxgl-track-pointer\")),this},n.prototype.getElement=function(){return this._container},n.prototype.setText=function(e){return this.setDOMContent(t.window.document.createTextNode(e))},n.prototype.setHTML=function(e){var r,n=t.window.document.createDocumentFragment(),i=t.window.document.createElement(\"body\");for(i.innerHTML=e;r=i.firstChild;)n.appendChild(r);return this.setDOMContent(n)},n.prototype.getMaxWidth=function(){return this._container&&this._container.style.maxWidth},n.prototype.setMaxWidth=function(t){return this.options.maxWidth=t,this._update(),this},n.prototype.setDOMContent=function(t){return this._createContent(),this._content.appendChild(t),this._update(),this},n.prototype.addClassName=function(t){this._container&&this._container.classList.add(t)},n.prototype.removeClassName=function(t){this._container&&this._container.classList.remove(t)},n.prototype.toggleClassName=function(t){if(this._container)return this._container.classList.toggle(t)},n.prototype._createContent=function(){this._content&&r.remove(this._content),this._content=r.create(\"div\",\"mapboxgl-popup-content\",this._container),this.options.closeButton&&(this._closeButton=r.create(\"button\",\"mapboxgl-popup-close-button\",this._content),this._closeButton.type=\"button\",this._closeButton.setAttribute(\"aria-label\",\"Close popup\"),this._closeButton.innerHTML=\"&#215;\",this._closeButton.addEventListener(\"click\",this._onClose))},n.prototype._onMouseUp=function(t){this._update(t.point)},n.prototype._onMouseMove=function(t){this._update(t.point)},n.prototype._onDrag=function(t){this._update(t.point)},n.prototype._update=function(e){var n=this,i=this._lngLat||this._trackPointer;if(this._map&&i&&this._content&&(this._container||(this._container=r.create(\"div\",\"mapboxgl-popup\",this._map.getContainer()),this._tip=r.create(\"div\",\"mapboxgl-popup-tip\",this._container),this._container.appendChild(this._content),this.options.className&&this.options.className.split(\" \").forEach((function(t){return n._container.classList.add(t)})),this._trackPointer&&this._container.classList.add(\"mapboxgl-popup-track-pointer\")),this.options.maxWidth&&this._container.style.maxWidth!==this.options.maxWidth&&(this._container.style.maxWidth=this.options.maxWidth),this._map.transform.renderWorldCopies&&!this._trackPointer&&(this._lngLat=Oi(this._lngLat,this._pos,this._map.transform)),!this._trackPointer||e)){var a=this._pos=this._trackPointer&&e?e:this._map.project(this._lngLat),o=this.options.anchor,s=function e(r){if(r){if(\"number\"==typeof r){var n=Math.round(Math.sqrt(.5*Math.pow(r,2)));return{center:new t.Point(0,0),top:new t.Point(0,r),\"top-left\":new t.Point(n,n),\"top-right\":new t.Point(-n,n),bottom:new t.Point(0,-r),\"bottom-left\":new t.Point(n,-n),\"bottom-right\":new t.Point(-n,-n),left:new t.Point(r,0),right:new t.Point(-r,0)}}if(r instanceof t.Point||Array.isArray(r)){var i=t.Point.convert(r);return{center:i,top:i,\"top-left\":i,\"top-right\":i,bottom:i,\"bottom-left\":i,\"bottom-right\":i,left:i,right:i}}return{center:t.Point.convert(r.center||[0,0]),top:t.Point.convert(r.top||[0,0]),\"top-left\":t.Point.convert(r[\"top-left\"]||[0,0]),\"top-right\":t.Point.convert(r[\"top-right\"]||[0,0]),bottom:t.Point.convert(r.bottom||[0,0]),\"bottom-left\":t.Point.convert(r[\"bottom-left\"]||[0,0]),\"bottom-right\":t.Point.convert(r[\"bottom-right\"]||[0,0]),left:t.Point.convert(r.left||[0,0]),right:t.Point.convert(r.right||[0,0])}}return e(new t.Point(0,0))}(this.options.offset);if(!o){var l,c=this._container.offsetWidth,u=this._container.offsetHeight;l=a.y+s.bottom.y<u?[\"top\"]:a.y>this._map.transform.height-u?[\"bottom\"]:[],a.x<c/2?l.push(\"left\"):a.x>this._map.transform.width-c/2&&l.push(\"right\"),o=0===l.length?\"bottom\":l.join(\"-\")}var f=a.add(s[o]).round();r.setTransform(this._container,zi[o]+\" translate(\"+f.x+\"px,\"+f.y+\"px)\"),Di(this._container,o,\"popup\")}},n.prototype._onClose=function(){this.remove()},n}(t.Evented);var Zi={version:t.version,supported:e,setRTLTextPlugin:t.setRTLTextPlugin,getRTLTextPluginStatus:t.getRTLTextPluginStatus,Map:Ei,NavigationControl:Pi,GeolocateControl:Ui,AttributionControl:bi,ScaleControl:Hi,FullscreenControl:Yi,Popup:Xi,Marker:Fi,Style:qe,LngLat:t.LngLat,LngLatBounds:t.LngLatBounds,Point:t.Point,MercatorCoordinate:t.MercatorCoordinate,Evented:t.Evented,config:t.config,prewarm:function(){Bt().acquire(zt)},clearPrewarmedResources:function(){var t=Rt;t&&(t.isPreloaded()&&1===t.numActive()?(t.release(zt),Rt=null):console.warn(\"Could not clear WebWorkers since there are active Map instances that still reference it. The pre-warmed WebWorker pool can only be cleared when all map instances have been removed with map.remove()\"))},get accessToken(){return t.config.ACCESS_TOKEN},set accessToken(e){t.config.ACCESS_TOKEN=e},get baseApiUrl(){return t.config.API_URL},set baseApiUrl(e){t.config.API_URL=e},get workerCount(){return Dt.workerCount},set workerCount(t){Dt.workerCount=t},get maxParallelImageRequests(){return t.config.MAX_PARALLEL_IMAGE_REQUESTS},set maxParallelImageRequests(e){t.config.MAX_PARALLEL_IMAGE_REQUESTS=e},clearStorage:function(e){t.clearTileCache(e)},workerUrl:\"\"};return Zi})),r}))},{}],240:[function(t,e,r){\"use strict\";e.exports=Math.log2||function(t){return Math.log(t)*Math.LOG2E}},{}],241:[function(t,e,r){\"use strict\";e.exports=function(t,e){e||(e=t,t=window);var r=0,i=0,a=0,o={shift:!1,alt:!1,control:!1,meta:!1},s=!1;function l(t){var e=!1;return\"altKey\"in t&&(e=e||t.altKey!==o.alt,o.alt=!!t.altKey),\"shiftKey\"in t&&(e=e||t.shiftKey!==o.shift,o.shift=!!t.shiftKey),\"ctrlKey\"in t&&(e=e||t.ctrlKey!==o.control,o.control=!!t.ctrlKey),\"metaKey\"in t&&(e=e||t.metaKey!==o.meta,o.meta=!!t.metaKey),e}function c(t,s){var c=n.x(s),u=n.y(s);\"buttons\"in s&&(t=0|s.buttons),(t!==r||c!==i||u!==a||l(s))&&(r=0|t,i=c||0,a=u||0,e&&e(r,i,a,o))}function u(t){c(0,t)}function f(){(r||i||a||o.shift||o.alt||o.meta||o.control)&&(i=a=0,r=0,o.shift=o.alt=o.control=o.meta=!1,e&&e(0,0,0,o))}function h(t){l(t)&&e&&e(r,i,a,o)}function p(t){0===n.buttons(t)?c(0,t):c(r,t)}function d(t){c(r|n.buttons(t),t)}function g(t){c(r&~n.buttons(t),t)}function m(){s||(s=!0,t.addEventListener(\"mousemove\",p),t.addEventListener(\"mousedown\",d),t.addEventListener(\"mouseup\",g),t.addEventListener(\"mouseleave\",u),t.addEventListener(\"mouseenter\",u),t.addEventListener(\"mouseout\",u),t.addEventListener(\"mouseover\",u),t.addEventListener(\"blur\",f),t.addEventListener(\"keyup\",h),t.addEventListener(\"keydown\",h),t.addEventListener(\"keypress\",h),t!==window&&(window.addEventListener(\"blur\",f),window.addEventListener(\"keyup\",h),window.addEventListener(\"keydown\",h),window.addEventListener(\"keypress\",h)))}m();var v={element:t};return Object.defineProperties(v,{enabled:{get:function(){return s},set:function(e){e?m():function(){if(!s)return;s=!1,t.removeEventListener(\"mousemove\",p),t.removeEventListener(\"mousedown\",d),t.removeEventListener(\"mouseup\",g),t.removeEventListener(\"mouseleave\",u),t.removeEventListener(\"mouseenter\",u),t.removeEventListener(\"mouseout\",u),t.removeEventListener(\"mouseover\",u),t.removeEventListener(\"blur\",f),t.removeEventListener(\"keyup\",h),t.removeEventListener(\"keydown\",h),t.removeEventListener(\"keypress\",h),t!==window&&(window.removeEventListener(\"blur\",f),window.removeEventListener(\"keyup\",h),window.removeEventListener(\"keydown\",h),window.removeEventListener(\"keypress\",h))}()},enumerable:!0},buttons:{get:function(){return r},enumerable:!0},x:{get:function(){return i},enumerable:!0},y:{get:function(){return a},enumerable:!0},mods:{get:function(){return o},enumerable:!0}}),v};var n=t(\"mouse-event\")},{\"mouse-event\":243}],242:[function(t,e,r){var n={left:0,top:0};e.exports=function(t,e,r){e=e||t.currentTarget||t.srcElement,Array.isArray(r)||(r=[0,0]);var i=t.clientX||0,a=t.clientY||0,o=(s=e,s===window||s===document||s===document.body?n:s.getBoundingClientRect());var s;return r[0]=i-o.left,r[1]=a-o.top,r}},{}],243:[function(t,e,r){\"use strict\";function n(t){return t.target||t.srcElement||window}r.buttons=function(t){if(\"object\"==typeof t){if(\"buttons\"in t)return t.buttons;if(\"which\"in t){if(2===(e=t.which))return 4;if(3===e)return 2;if(e>0)return 1<<e-1}else if(\"button\"in t){var e;if(1===(e=t.button))return 4;if(2===e)return 2;if(e>=0)return 1<<e}}return 0},r.element=n,r.x=function(t){if(\"object\"==typeof t){if(\"offsetX\"in t)return t.offsetX;var e=n(t).getBoundingClientRect();return t.clientX-e.left}return 0},r.y=function(t){if(\"object\"==typeof t){if(\"offsetY\"in t)return t.offsetY;var e=n(t).getBoundingClientRect();return t.clientY-e.top}return 0}},{}],244:[function(t,e,r){\"use strict\";var n=t(\"to-px\");e.exports=function(t,e,r){\"function\"==typeof t&&(r=!!e,e=t,t=window);var i=n(\"ex\",t),a=function(t){r&&t.preventDefault();var n=t.deltaX||0,a=t.deltaY||0,o=t.deltaZ||0,s=1;switch(t.deltaMode){case 1:s=i;break;case 2:s=window.innerHeight}if(a*=s,o*=s,(n*=s)||a||o)return e(n,a,o,t)};return t.addEventListener(\"wheel\",a),a}},{\"to-px\":314}],245:[function(t,e,r){(function(t,r){(function(){\n",
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-       "/*\n",
-       " * @copyright 2016 Sean Connelly (@voidqk), http://syntheti.cc\n",
-       " * @license MIT\n",
-       " * @preserve Project Home: https://github.com/voidqk/polybooljs\n",
-       " */\n",
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r=0,n=t[t.length-1][0],i=t[t.length-1][1],a=[],o=0;o<t.length;o++){var s=t[o][0],l=t[o][1];a.push([s,l]),r+=l*n-s*i,n=s,i=l}return r<0!==e&&a.reverse(),a.push([a[0][0],a[0][1]]),a}var u=[];function f(t){var e=[c(t.region,!1)];u.push(e);for(var r=0;r<t.children.length;r++)e.push(h(t.children[r]))}function h(t){for(var e=0;e<t.children.length;e++)f(t.children[e]);return c(t.region,!0)}for(s=0;s<a.children.length;s++)f(a.children[s]);return u.length<=0?{type:\"Polygon\",coordinates:[]}:1==u.length?{type:\"Polygon\",coordinates:u[0]}:{type:\"MultiPolygon\",coordinates:u}}};e.exports=n},{}],258:[function(t,e,r){var n=t(\"./linked-list\");e.exports=function(t,e,r){function i(t,e,n){return{id:r?r.segmentId():-1,start:t,end:e,myFill:{above:n.myFill.above,below:n.myFill.below},otherFill:null}}var a=n.create();function o(t,r){a.insertBefore(t,(function(n){return function(t,r,n,i,a,o){var s=e.pointsCompare(r,a);return 0!==s?s:e.pointsSame(n,o)?0:t!==i?t?1:-1:e.pointAboveOrOnLine(n,i?a:o,i?o:a)?1:-1}(t.isStart,t.pt,r,n.isStart,n.pt,n.other.pt)<0}))}function s(t,e){var r=function(t,e){var r=n.node({isStart:!0,pt:t.start,seg:t,primary:e,other:null,status:null});return o(r,t.end),r}(t,e);return function(t,e,r){var i=n.node({isStart:!1,pt:e.end,seg:e,primary:r,other:t,status:null});t.other=i,o(i,t.pt)}(r,t,e),r}function l(t,e){var n=i(e,t.seg.end,t.seg);return function(t,e){r&&r.segmentChop(t.seg,e),t.other.remove(),t.seg.end=e,t.other.pt=e,o(t.other,t.pt)}(t,e),s(n,t.primary)}function c(i,o){var s=n.create();function c(t){return s.findTransition((function(r){var n,i,a,o,s,l;return(n=t,i=r.ev,a=n.seg.start,o=n.seg.end,s=i.seg.start,l=i.seg.end,e.pointsCollinear(a,s,l)?e.pointsCollinear(o,s,l)||e.pointAboveOrOnLine(o,s,l)?1:-1:e.pointAboveOrOnLine(a,s,l)?1:-1)>0}))}function u(t,n){var i=t.seg,a=n.seg,o=i.start,s=i.end,c=a.start,u=a.end;r&&r.checkIntersection(i,a);var f=e.linesIntersect(o,s,c,u);if(!1===f){if(!e.pointsCollinear(o,s,c))return!1;if(e.pointsSame(o,u)||e.pointsSame(s,c))return!1;var h=e.pointsSame(o,c),p=e.pointsSame(s,u);if(h&&p)return n;var d=!h&&e.pointBetween(o,c,u),g=!p&&e.pointBetween(s,c,u);if(h)return g?l(n,s):l(t,u),n;d&&(p||(g?l(n,s):l(t,u)),l(n,o))}else 0===f.alongA&&(-1===f.alongB?l(t,c):0===f.alongB?l(t,f.pt):1===f.alongB&&l(t,u)),0===f.alongB&&(-1===f.alongA?l(n,o):0===f.alongA?l(n,f.pt):1===f.alongA&&l(n,s));return!1}for(var f=[];!a.isEmpty();){var h=a.getHead();if(r&&r.vert(h.pt[0]),h.isStart){r&&r.segmentNew(h.seg,h.primary);var p=c(h),d=p.before?p.before.ev:null,g=p.after?p.after.ev:null;function m(){if(d){var t=u(h,d);if(t)return t}return!!g&&u(h,g)}r&&r.tempStatus(h.seg,!!d&&d.seg,!!g&&g.seg);var v,y=m();if(y){var x;if(t)(x=null===h.seg.myFill.below||h.seg.myFill.above!==h.seg.myFill.below)&&(y.seg.myFill.above=!y.seg.myFill.above);else y.seg.otherFill=h.seg.myFill;r&&r.segmentUpdate(y.seg),h.other.remove(),h.remove()}if(a.getHead()!==h){r&&r.rewind(h.seg);continue}if(t)x=null===h.seg.myFill.below||h.seg.myFill.above!==h.seg.myFill.below,h.seg.myFill.below=g?g.seg.myFill.above:i,h.seg.myFill.above=x?!h.seg.myFill.below:h.seg.myFill.below;else if(null===h.seg.otherFill)v=g?h.primary===g.primary?g.seg.otherFill.above:g.seg.myFill.above:h.primary?o:i,h.seg.otherFill={above:v,below:v};r&&r.status(h.seg,!!d&&d.seg,!!g&&g.seg),h.other.status=p.insert(n.node({ev:h}))}else{var b=h.status;if(null===b)throw new Error(\"PolyBool: Zero-length segment detected; your epsilon is probably too small or too large\");if(s.exists(b.prev)&&s.exists(b.next)&&u(b.prev.ev,b.next.ev),r&&r.statusRemove(b.ev.seg),b.remove(),!h.primary){var _=h.seg.myFill;h.seg.myFill=h.seg.otherFill,h.seg.otherFill=_}f.push(h.seg)}a.getHead().remove()}return r&&r.done(),f}return t?{addRegion:function(t){for(var n,i,a,o=t[t.length-1],l=0;l<t.length;l++){n=o,o=t[l];var c=e.pointsCompare(n,o);0!==c&&s((i=c<0?n:o,a=c<0?o:n,{id:r?r.segmentId():-1,start:i,end:a,myFill:{above:null,below:null},otherFill:null}),!0)}},calculate:function(t){return c(t,!1)}}:{calculate:function(t,e,r,n){return t.forEach((function(t){s(i(t.start,t.end,t),!0)})),r.forEach((function(t){s(i(t.start,t.end,t),!1)})),c(e,n)}}}},{\"./linked-list\":259}],259:[function(t,e,r){e.exports={create:function(){var t={root:{root:!0,next:null},exists:function(e){return null!==e&&e!==t.root},isEmpty:function(){return null===t.root.next},getHead:function(){return t.root.next},insertBefore:function(e,r){for(var n=t.root,i=t.root.next;null!==i;){if(r(i))return e.prev=i.prev,e.next=i,i.prev.next=e,void(i.prev=e);n=i,i=i.next}n.next=e,e.prev=n,e.next=null},findTransition:function(e){for(var r=t.root,n=t.root.next;null!==n&&!e(n);)r=n,n=n.next;return{before:r===t.root?null:r,after:n,insert:function(t){return t.prev=r,t.next=n,r.next=t,null!==n&&(n.prev=t),t}}}};return t},node:function(t){return t.prev=null,t.next=null,t.remove=function(){t.prev.next=t.next,t.next&&(t.next.prev=t.prev),t.prev=null,t.next=null},t}}},{}],260:[function(t,e,r){e.exports=function(t,e,r){var n=[],i=[];return t.forEach((function(t){var a=t.start,o=t.end;if(e.pointsSame(a,o))console.warn(\"PolyBool: Warning: Zero-length segment detected; your epsilon is probably too small or too large\");else{r&&r.chainStart(t);for(var s={index:0,matches_head:!1,matches_pt1:!1},l={index:0,matches_head:!1,matches_pt1:!1},c=s,u=0;u<n.length;u++){var f=(m=n[u])[0],h=(m[1],m[m.length-1]);m[m.length-2];if(e.pointsSame(f,a)){if(k(u,!0,!0))break}else if(e.pointsSame(f,o)){if(k(u,!0,!1))break}else if(e.pointsSame(h,a)){if(k(u,!1,!0))break}else if(e.pointsSame(h,o)&&k(u,!1,!1))break}if(c===s)return n.push([a,o]),void(r&&r.chainNew(a,o));if(c===l){r&&r.chainMatch(s.index);var p=s.index,d=s.matches_pt1?o:a,g=s.matches_head,m=n[p],v=g?m[0]:m[m.length-1],y=g?m[1]:m[m.length-2],x=g?m[m.length-1]:m[0],b=g?m[m.length-2]:m[1];return e.pointsCollinear(y,v,d)&&(g?(r&&r.chainRemoveHead(s.index,d),m.shift()):(r&&r.chainRemoveTail(s.index,d),m.pop()),v=y),e.pointsSame(x,d)?(n.splice(p,1),e.pointsCollinear(b,x,v)&&(g?(r&&r.chainRemoveTail(s.index,v),m.pop()):(r&&r.chainRemoveHead(s.index,v),m.shift())),r&&r.chainClose(s.index),void i.push(m)):void(g?(r&&r.chainAddHead(s.index,d),m.unshift(d)):(r&&r.chainAddTail(s.index,d),m.push(d)))}var _=s.index,w=l.index;r&&r.chainConnect(_,w);var T=n[_].length<n[w].length;s.matches_head?l.matches_head?T?(A(_),M(_,w)):(A(w),M(w,_)):M(w,_):l.matches_head?M(_,w):T?(A(_),M(w,_)):(A(w),M(_,w))}function k(t,e,r){return c.index=t,c.matches_head=e,c.matches_pt1=r,c===s?(c=l,!1):(c=null,!0)}function A(t){r&&r.chainReverse(t),n[t].reverse()}function M(t,i){var a=n[t],o=n[i],s=a[a.length-1],l=a[a.length-2],c=o[0],u=o[1];e.pointsCollinear(l,s,c)&&(r&&r.chainRemoveTail(t,s),a.pop(),s=l),e.pointsCollinear(s,c,u)&&(r&&r.chainRemoveHead(i,c),o.shift()),r&&r.chainJoin(t,i),n[t]=a.concat(o),n.splice(i,1)}})),i}},{}],261:[function(t,e,r){function n(t,e,r){var n=[];return t.forEach((function(t){var i=(t.myFill.above?8:0)+(t.myFill.below?4:0)+(t.otherFill&&t.otherFill.above?2:0)+(t.otherFill&&t.otherFill.below?1:0);0!==e[i]&&n.push({id:r?r.segmentId():-1,start:t.start,end:t.end,myFill:{above:1===e[i],below:2===e[i]},otherFill:null})})),r&&r.selected(n),n}var i={union:function(t,e){return n(t,[0,2,1,0,2,2,0,0,1,0,1,0,0,0,0,0],e)},intersect:function(t,e){return n(t,[0,0,0,0,0,2,0,2,0,0,1,1,0,2,1,0],e)},difference:function(t,e){return n(t,[0,0,0,0,2,0,2,0,1,1,0,0,0,1,2,0],e)},differenceRev:function(t,e){return n(t,[0,2,1,0,0,0,1,1,0,2,0,2,0,0,0,0],e)},xor:function(t,e){return n(t,[0,2,1,0,2,0,0,1,1,0,0,2,0,1,2,0],e)}};e.exports=i},{}],262:[function(t,e,r){\"use strict\";var n=t(\"stream\").Transform,i=t(\"stream-parser\");function a(){n.call(this,{readableObjectMode:!0})}function o(t,e,r){Error.call(this),Error.captureStackTrace(this,this.constructor),this.name=this.constructor.name,this.message=t,e&&(this.code=e),r&&(this.statusCode=r)}a.prototype=Object.create(n.prototype),a.prototype.constructor=a,i(a.prototype),r.ParserStream=a,r.sliceEq=function(t,e,r){for(var n=e,i=0;i<r.length;)if(t[n++]!==r[i++])return!1;return!0},r.str2arr=function(t,e){var r=[],n=0;if(e&&\"hex\"===e)for(;n<t.length;)r.push(parseInt(t.slice(n,n+2),16)),n+=2;else for(;n<t.length;n++)r.push(255&t.charCodeAt(n));return r},r.readUInt16LE=function(t,e){return t[e]|t[e+1]<<8},r.readUInt16BE=function(t,e){return t[e+1]|t[e]<<8},r.readUInt32LE=function(t,e){return t[e]|t[e+1]<<8|t[e+2]<<16|16777216*t[e+3]},r.readUInt32BE=function(t,e){return t[e+3]|t[e+2]<<8|t[e+1]<<16|16777216*t[e]},o.prototype=Object.create(Error.prototype),o.prototype.constructor=o,r.ProbeError=o},{stream:285,\"stream-parser\":301}],263:[function(t,e,r){\"use strict\";function n(t,e){var r=new Error(t);return r.code=e,r}function i(t){try{return decodeURIComponent(escape(t))}catch(e){return t}}function a(t,e,r){this.input=t.subarray(e,r),this.start=e;var i=String.fromCharCode.apply(null,this.input.subarray(0,4));if(\"II*\\0\"!==i&&\"MM\\0*\"!==i)throw n(\"invalid TIFF signature\",\"EBADDATA\");this.big_endian=\"M\"===i[0]}a.prototype.each=function(t){this.aborted=!1;var e=this.read_uint32(4);for(this.ifds_to_read=[{id:0,offset:e}];this.ifds_to_read.length>0&&!this.aborted;){var r=this.ifds_to_read.shift();r.offset&&this.scan_ifd(r.id,r.offset,t)}},a.prototype.read_uint16=function(t){var e=this.input;if(t+2>e.length)throw n(\"unexpected EOF\",\"EBADDATA\");return this.big_endian?256*e[t]+e[t+1]:e[t]+256*e[t+1]},a.prototype.read_uint32=function(t){var e=this.input;if(t+4>e.length)throw n(\"unexpected EOF\",\"EBADDATA\");return this.big_endian?16777216*e[t]+65536*e[t+1]+256*e[t+2]+e[t+3]:e[t]+256*e[t+1]+65536*e[t+2]+16777216*e[t+3]},a.prototype.is_subifd_link=function(t,e){return 0===t&&34665===e||0===t&&34853===e||34665===t&&40965===e},a.prototype.exif_format_length=function(t){switch(t){case 1:case 2:case 6:case 7:return 1;case 3:case 8:return 2;case 4:case 9:case 11:return 4;case 5:case 10:case 12:return 8;default:return 0}},a.prototype.exif_format_read=function(t,e){var r;switch(t){case 1:case 2:return r=this.input[e];case 6:return(r=this.input[e])|33554430*(128&r);case 3:return r=this.read_uint16(e);case 8:return(r=this.read_uint16(e))|131070*(32768&r);case 4:return r=this.read_uint32(e);case 9:return 0|(r=this.read_uint32(e));case 5:case 10:case 11:case 12:case 7:default:return null}},a.prototype.scan_ifd=function(t,e,r){var a=this.read_uint16(e);e+=2;for(var o=0;o<a;o++){var s=this.read_uint16(e),l=this.read_uint16(e+2),c=this.read_uint32(e+4),u=this.exif_format_length(l),f=c*u,h=f<=4?e+8:this.read_uint32(e+8),p=!1;if(h+f>this.input.length)throw n(\"unexpected EOF\",\"EBADDATA\");for(var d=[],g=h,m=0;m<c;m++,g+=u){var v=this.exif_format_read(l,g);if(null===v){d=null;break}d.push(v)}if(Array.isArray(d)&&2===l&&(d=i(String.fromCharCode.apply(null,d)))&&\"\\0\"===d[d.length-1]&&(d=d.slice(0,-1)),this.is_subifd_link(t,s)&&Array.isArray(d)&&Number.isInteger(d[0])&&d[0]>0&&(this.ifds_to_read.push({id:s,offset:d[0]}),p=!0),!1===r({is_big_endian:this.big_endian,ifd:t,tag:s,format:l,count:c,entry_offset:e+this.start,data_length:f,data_offset:h+this.start,value:d,is_subifd_link:p}))return void(this.aborted=!0);e+=12}0===t&&this.ifds_to_read.push({id:1,offset:this.read_uint32(e)})},e.exports.ExifParser=a,e.exports.get_orientation=function(t){var e=0;try{return new a(t,0,t.length).each((function(t){if(0===t.ifd&&274===t.tag&&Array.isArray(t.value))return e=t.value[0],!1})),e}catch(t){return-1}}},{}],264:[function(t,e,r){\"use strict\";var n=t(\"./common\").readUInt16BE,i=t(\"./common\").readUInt32BE;function a(t,e){if(t.length<4+e)return null;var r=i(t,e);return t.length<r+e||r<8?null:{boxtype:String.fromCharCode.apply(null,t.slice(e+4,e+8)),data:t.slice(e+8,e+r),end:e+r}}function o(t,e){for(var r=0;;){var n=a(t,r);if(!n)break;switch(n.boxtype){case\"ispe\":e.sizes.push({width:i(n.data,4),height:i(n.data,8)});break;case\"irot\":e.transforms.push({type:\"irot\",value:3&n.data[0]});break;case\"imir\":e.transforms.push({type:\"imir\",value:1&n.data[0]})}r=n.end}}function s(t,e,r){for(var n=0,i=0;i<r;i++)n=256*n+(t[e+i]||0);return n}function l(t,e){for(var r=t[4]>>4&15,i=15&t[4],a=t[5]>>4&15,o=n(t,6),l=8,c=0;c<o;c++){var u=n(t,l),f=n(t,l+=2),h=s(t,l+=2,a),p=n(t,l+=a);if(l+=2,0===f&&1===p){var d=s(t,l,r),g=s(t,l+r,i);e.item_loc[u]={length:g,offset:d+h}}l+=p*(r+i)}}function c(t,e){for(var r=n(t,4),i=6,o=0;o<r;o++){var s=a(t,i);if(!s)break;if(\"infe\"===s.boxtype){for(var l=n(s.data,4),c=\"\",u=8;u<s.data.length&&s.data[u];u++)c+=String.fromCharCode(s.data[u]);e.item_inf[c]=l}i=s.end}}function u(t,e){for(var r=0;;){var n=a(t,r);if(!n)break;\"ipco\"===n.boxtype&&o(n.data,e),r=n.end}}e.exports.unbox=a,e.exports.readSizeFromMeta=function(t){var e={sizes:[],transforms:[],item_inf:{},item_loc:{}};if(function(t,e){for(var r=4;;){var n=a(t,r);if(!n)break;\"iprp\"===n.boxtype&&u(n.data,e),\"iloc\"===n.boxtype&&l(n.data,e),\"iinf\"===n.boxtype&&c(n.data,e),r=n.end}}(t,e),e.sizes.length){var r,n,i,o=(r=e.sizes,n=r.reduce((function(t,e){return t.width>e.width||t.width===e.width&&t.height>e.height?t:e})),i=r.reduce((function(t,e){return t.height>e.height||t.height===e.height&&t.width>e.width?t:e})),n.width>i.height||n.width===i.height&&n.height>i.width?n:i),s=1;e.transforms.forEach((function(t){var e={1:6,2:5,3:8,4:7,5:4,6:3,7:2,8:1},r={1:4,2:3,3:2,4:1,5:6,6:5,7:8,8:7};if(\"imir\"===t.type&&(s=0===t.value?r[s]:e[s=e[s=r[s]]]),\"irot\"===t.type)for(var n=0;n<t.value;n++)s=e[s]}));var f=null;return e.item_inf.Exif&&(f=e.item_loc[e.item_inf.Exif]),{width:o.width,height:o.height,orientation:e.transforms.length?s:null,variants:e.sizes,exif_location:f}}},e.exports.getMimeType=function(t){var e=String.fromCharCode.apply(null,t.slice(0,4)),r={};r[e]=!0;for(var 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n=t(\"../common\").str2arr,i=t(\"../common\").sliceEq,a=t(\"../common\").readUInt16LE,o=n(\"GIF87a\"),s=n(\"GIF89a\");e.exports=function(t){if(!(t.length<10)&&(i(t,0,o)||i(t,0,s)))return{width:a(t,6),height:a(t,8),type:\"gif\",mime:\"image/gif\",wUnits:\"px\",hUnits:\"px\"}}},{\"../common\":262}],268:[function(t,e,r){\"use strict\";var n=t(\"../common\").readUInt16LE;e.exports=function(t){var e=n(t,0),r=n(t,2),i=n(t,4);if(0===e&&1===r&&i){for(var a=[],o={width:0,height:0},s=0;s<i;s++){var l=t[6+16*s]||256,c=t[6+16*s+1]||256,u={width:l,height:c};a.push(u),(l>o.width||c>o.height)&&(o=u)}return{width:o.width,height:o.height,variants:a,type:\"ico\",mime:\"image/x-icon\",wUnits:\"px\",hUnits:\"px\"}}}},{\"../common\":262}],269:[function(t,e,r){\"use strict\";var n=t(\"../common\").readUInt16BE,i=t(\"../common\").str2arr,a=t(\"../common\").sliceEq,o=t(\"../exif_utils\"),s=i(\"Exif\\0\\0\");e.exports=function(t){if(!(t.length<2)&&255===t[0]&&216===t[1])for(var 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n=t(\"../common\").str2arr,i=t(\"../common\").sliceEq,a=t(\"../common\").readUInt32BE,o=n(\"8BPS\\0\\x01\");e.exports=function(t){if(!(t.length<22)&&i(t,0,o))return{width:a(t,18),height:a(t,14),type:\"psd\",mime:\"image/vnd.adobe.photoshop\",wUnits:\"px\",hUnits:\"px\"}}},{\"../common\":262}],272:[function(t,e,r){\"use strict\";function n(t){return\"number\"==typeof t&&isFinite(t)&&t>0}var i=/<[-_.:a-zA-Z0-9][^>]*>/,a=/^<([-_.:a-zA-Z0-9]+:)?svg\\s/,o=/[^-]\\bwidth=\"([^%]+?)\"|[^-]\\bwidth='([^%]+?)'/,s=/\\bheight=\"([^%]+?)\"|\\bheight='([^%]+?)'/,l=/\\bview[bB]ox=\"(.+?)\"|\\bview[bB]ox='(.+?)'/,c=/in$|mm$|cm$|pt$|pc$|px$|em$|ex$/;function u(t){return c.test(t)?t.match(c)[0]:\"px\"}e.exports=function(t){if(function(t){var e,r=0,n=t.length;for(239===t[0]&&187===t[1]&&191===t[2]&&(r=3);r<n&&(32===(e=t[r])||9===e||13===e||10===e);)r++;return r!==n&&60===t[r]}(t)){for(var e=\"\",r=0;r<t.length;r++)e+=String.fromCharCode(t[r]);var c=(e.match(i)||[\"\"])[0];if(a.test(c)){var 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h=[[1,0,0,1,0,0],[1,0,0,-1,0,0],[-1,0,0,-1,0,0],[-1,0,0,-1,0,0],[-1,0,0,1,0,0],[1,0,0,1,0,0],[1,0,-1,0,0,1],[1,0,-1,0,0,-1],[1,0,1,0,0,-1],[1,0,1,0,0,-1],[1,0,1,0,0,1],[1,0,-1,0,0,1],[-1,0,-1,0,0,1],[-1,0,-1,0,0,-1],[-1,0,1,0,0,-1],[-1,0,1,0,0,-1],[-1,0,1,0,0,1],[-1,0,-1,0,0,1],[0,1,1,0,0,0],[0,1,-1,0,0,0],[0,-1,-1,0,0,0],[0,-1,-1,0,0,0],[0,1,1,0,0,0],[0,-1,1,0,0,0],[0,1,0,-1,1,0],[0,1,0,-1,-1,0],[0,1,0,1,-1,0],[0,1,0,1,1,0],[0,1,0,-1,1,0],[0,1,0,1,-1,0],[0,-1,0,-1,1,0],[0,-1,0,-1,-1,0],[0,-1,0,1,-1,0],[0,-1,0,1,1,0],[0,-1,0,-1,1,0],[0,-1,0,1,-1,0]]},{\"array-bounds\":71,\"color-normalize\":89,\"flatten-vertex-data\":191,\"object-assign\":247,\"pick-by-alias\":253,\"to-float32\":313,\"update-diff\":329}],280:[function(t,e,r){\"use strict\";var n=t(\"color-normalize\"),i=t(\"array-bounds\"),a=t(\"object-assign\"),o=t(\"glslify\"),s=t(\"pick-by-alias\"),l=t(\"flatten-vertex-data\"),c=t(\"earcut\"),u=t(\"array-normalize\"),f=t(\"to-float32\"),h=f.float32,p=f.fract32,d=t(\"es6-weak-map\"),g=t(\"parse-rect\"),m=t(\"array-find-index\");function v(t,e){if(!(this instanceof v))return new v(t,e);if(\"function\"==typeof t?(e||(e={}),e.regl=t):e=t,e.length&&(e.positions=e),!(t=e.regl).hasExtension(\"ANGLE_instanced_arrays\"))throw Error(\"regl-error2d: `ANGLE_instanced_arrays` extension should be enabled\");this.gl=t._gl,this.regl=t,this.passes=[],this.shaders=v.shaders.has(t)?v.shaders.get(t):v.shaders.set(t,v.createShaders(t)).get(t),this.update(e)}e.exports=v,v.dashMult=2,v.maxPatternLength=256,v.precisionThreshold=3e6,v.maxPoints=1e4,v.maxLines=2048,v.shaders=new d,v.createShaders=function(t){var e,r=t.buffer({usage:\"static\",type:\"float\",data:[0,1,0,0,1,1,1,0]}),n={primitive:\"triangle strip\",instances:t.prop(\"count\"),count:4,offset:0,uniforms:{miterMode:function(t,e){return\"round\"===e.join?2:1},miterLimit:t.prop(\"miterLimit\"),scale:t.prop(\"scale\"),scaleFract:t.prop(\"scaleFract\"),translateFract:t.prop(\"translateFract\"),translate:t.prop(\"translate\"),thickness:t.prop(\"thickness\"),dashTexture:t.prop(\"dashTexture\"),opacity:t.prop(\"opacity\"),pixelRatio:t.context(\"pixelRatio\"),id:t.prop(\"id\"),dashLength:t.prop(\"dashLength\"),viewport:function(t,e){return[e.viewport.x,e.viewport.y,t.viewportWidth,t.viewportHeight]},depth:t.prop(\"depth\")},blend:{enable:!0,color:[0,0,0,0],equation:{rgb:\"add\",alpha:\"add\"},func:{srcRGB:\"src alpha\",dstRGB:\"one minus src alpha\",srcAlpha:\"one minus dst alpha\",dstAlpha:\"one\"}},depth:{enable:function(t,e){return!e.overlay}},stencil:{enable:!1},scissor:{enable:!0,box:t.prop(\"viewport\")},viewport:t.prop(\"viewport\")},i=t(a({vert:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 aCoord, bCoord, aCoordFract, bCoordFract;\\nattribute vec4 color;\\nattribute float lineEnd, lineTop;\\n\\nuniform vec2 scale, scaleFract, translate, translateFract;\\nuniform float thickness, pixelRatio, id, depth;\\nuniform vec4 viewport;\\n\\nvarying vec4 fragColor;\\nvarying vec2 tangent;\\n\\nvec2 project(vec2 position, vec2 positionFract, vec2 scale, vec2 scaleFract, vec2 translate, vec2 translateFract) {\\n\\t// the order is important\\n\\treturn position * scale + translate\\n       + positionFract * scale + translateFract\\n       + position * scaleFract\\n       + positionFract * scaleFract;\\n}\\n\\nvoid main() {\\n\\tfloat lineStart = 1. - lineEnd;\\n\\tfloat lineOffset = lineTop * 2. - 1.;\\n\\n\\tvec2 diff = (bCoord + bCoordFract - aCoord - aCoordFract);\\n\\ttangent = normalize(diff * scale * viewport.zw);\\n\\tvec2 normal = vec2(-tangent.y, tangent.x);\\n\\n\\tvec2 position = project(aCoord, aCoordFract, scale, scaleFract, translate, translateFract) * lineStart\\n\\t\\t+ project(bCoord, bCoordFract, scale, scaleFract, translate, translateFract) * lineEnd\\n\\n\\t\\t+ thickness * normal * .5 * lineOffset / viewport.zw;\\n\\n\\tgl_Position = vec4(position * 2.0 - 1.0, depth, 1);\\n\\n\\tfragColor = color / 255.;\\n}\\n\"]),frag:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform float dashLength, pixelRatio, thickness, opacity, id;\\nuniform sampler2D dashTexture;\\n\\nvarying vec4 fragColor;\\nvarying vec2 tangent;\\n\\nvoid main() {\\n\\tfloat alpha = 1.;\\n\\n\\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashLength) * .5 + .25;\\n\\tfloat dash = texture2D(dashTexture, vec2(t, .5)).r;\\n\\n\\tgl_FragColor = fragColor;\\n\\tgl_FragColor.a *= alpha * opacity * dash;\\n}\\n\"]),attributes:{lineEnd:{buffer:r,divisor:0,stride:8,offset:0},lineTop:{buffer:r,divisor:0,stride:8,offset:4},aCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:8,divisor:1},bCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:16,divisor:1},aCoordFract:{buffer:t.prop(\"positionFractBuffer\"),stride:8,offset:8,divisor:1},bCoordFract:{buffer:t.prop(\"positionFractBuffer\"),stride:8,offset:16,divisor:1},color:{buffer:t.prop(\"colorBuffer\"),stride:4,offset:0,divisor:1}}},n));try{e=t(a({cull:{enable:!0,face:\"back\"},vert:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 aCoord, bCoord, nextCoord, prevCoord;\\nattribute vec4 aColor, bColor;\\nattribute float lineEnd, lineTop;\\n\\nuniform vec2 scale, translate;\\nuniform float thickness, pixelRatio, id, depth;\\nuniform vec4 viewport;\\nuniform float miterLimit, miterMode;\\n\\nvarying vec4 fragColor;\\nvarying vec4 startCutoff, endCutoff;\\nvarying vec2 tangent;\\nvarying vec2 startCoord, endCoord;\\nvarying float enableStartMiter, enableEndMiter;\\n\\nconst float REVERSE_THRESHOLD = -.875;\\nconst float MIN_DIFF = 1e-6;\\n\\n// TODO: possible optimizations: avoid overcalculating all for vertices and calc just one instead\\n// TODO: precalculate dot products, normalize things beforehead etc.\\n// TODO: refactor to rectangular algorithm\\n\\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\\n\\tvec2 diff = b - a;\\n\\tvec2 perp = normalize(vec2(-diff.y, diff.x));\\n\\treturn dot(p - a, perp);\\n}\\n\\nbool isNaN( float val ){\\n  return ( val < 0.0 || 0.0 < val || val == 0.0 ) ? false : true;\\n}\\n\\nvoid main() {\\n\\tvec2 aCoord = aCoord, bCoord = bCoord, prevCoord = prevCoord, nextCoord = nextCoord;\\n\\n  vec2 adjustedScale;\\n  adjustedScale.x = (abs(scale.x) < MIN_DIFF) ? MIN_DIFF : scale.x;\\n  adjustedScale.y = (abs(scale.y) < MIN_DIFF) ? MIN_DIFF : scale.y;\\n\\n  vec2 scaleRatio = adjustedScale * viewport.zw;\\n\\tvec2 normalWidth = thickness / scaleRatio;\\n\\n\\tfloat lineStart = 1. - lineEnd;\\n\\tfloat lineBot = 1. - lineTop;\\n\\n\\tfragColor = (lineStart * aColor + lineEnd * bColor) / 255.;\\n\\n\\tif (isNaN(aCoord.x) || isNaN(aCoord.y) || isNaN(bCoord.x) || isNaN(bCoord.y)) return;\\n\\n\\tif (aCoord == prevCoord) prevCoord = aCoord + normalize(bCoord - aCoord);\\n\\tif (bCoord == nextCoord) nextCoord = bCoord - normalize(bCoord - aCoord);\\n\\n\\tvec2 prevDiff = aCoord - prevCoord;\\n\\tvec2 currDiff = bCoord - aCoord;\\n\\tvec2 nextDiff = nextCoord - bCoord;\\n\\n\\tvec2 prevTangent = normalize(prevDiff * scaleRatio);\\n\\tvec2 currTangent = normalize(currDiff * scaleRatio);\\n\\tvec2 nextTangent = normalize(nextDiff * scaleRatio);\\n\\n\\tvec2 prevNormal = vec2(-prevTangent.y, prevTangent.x);\\n\\tvec2 currNormal = vec2(-currTangent.y, currTangent.x);\\n\\tvec2 nextNormal = vec2(-nextTangent.y, nextTangent.x);\\n\\n\\tvec2 startJoinDirection = normalize(prevTangent - currTangent);\\n\\tvec2 endJoinDirection = normalize(currTangent - nextTangent);\\n\\n\\t// collapsed/unidirectional segment cases\\n\\t// FIXME: there should be more elegant solution\\n\\tvec2 prevTanDiff = abs(prevTangent - currTangent);\\n\\tvec2 nextTanDiff = abs(nextTangent - currTangent);\\n\\tif (max(prevTanDiff.x, prevTanDiff.y) < MIN_DIFF) {\\n\\t\\tstartJoinDirection = currNormal;\\n\\t}\\n\\tif (max(nextTanDiff.x, nextTanDiff.y) < MIN_DIFF) {\\n\\t\\tendJoinDirection = currNormal;\\n\\t}\\n\\tif (aCoord == bCoord) {\\n\\t\\tendJoinDirection = startJoinDirection;\\n\\t\\tcurrNormal = prevNormal;\\n\\t\\tcurrTangent = prevTangent;\\n\\t}\\n\\n\\ttangent = currTangent;\\n\\n\\t//calculate join shifts relative to normals\\n\\tfloat startJoinShift = dot(currNormal, startJoinDirection);\\n\\tfloat endJoinShift = dot(currNormal, endJoinDirection);\\n\\n\\tfloat startMiterRatio = abs(1. / startJoinShift);\\n\\tfloat endMiterRatio = abs(1. / endJoinShift);\\n\\n\\tvec2 startJoin = startJoinDirection * startMiterRatio;\\n\\tvec2 endJoin = endJoinDirection * endMiterRatio;\\n\\n\\tvec2 startTopJoin, startBotJoin, endTopJoin, endBotJoin;\\n\\tstartTopJoin = sign(startJoinShift) * startJoin * .5;\\n\\tstartBotJoin = -startTopJoin;\\n\\n\\tendTopJoin = sign(endJoinShift) * endJoin * .5;\\n\\tendBotJoin = -endTopJoin;\\n\\n\\tvec2 aTopCoord = aCoord + normalWidth * startTopJoin;\\n\\tvec2 bTopCoord = bCoord + normalWidth * endTopJoin;\\n\\tvec2 aBotCoord = aCoord + normalWidth * startBotJoin;\\n\\tvec2 bBotCoord = bCoord + normalWidth * endBotJoin;\\n\\n\\t//miter anti-clipping\\n\\tfloat baClipping = distToLine(bCoord, aCoord, aBotCoord) / dot(normalize(normalWidth * endBotJoin), normalize(normalWidth.yx * vec2(-startBotJoin.y, startBotJoin.x)));\\n\\tfloat abClipping = distToLine(aCoord, bCoord, bTopCoord) / dot(normalize(normalWidth * startBotJoin), normalize(normalWidth.yx * vec2(-endBotJoin.y, endBotJoin.x)));\\n\\n\\t//prevent close to reverse direction switch\\n\\tbool prevReverse = dot(currTangent, prevTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, prevNormal)) * min(length(prevDiff), length(currDiff)) <  length(normalWidth * currNormal);\\n\\tbool nextReverse = dot(currTangent, nextTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, nextNormal)) * min(length(nextDiff), length(currDiff)) <  length(normalWidth * currNormal);\\n\\n\\tif (prevReverse) {\\n\\t\\t//make join rectangular\\n\\t\\tvec2 miterShift = normalWidth * startJoinDirection * miterLimit * .5;\\n\\t\\tfloat normalAdjust = 1. - min(miterLimit / startMiterRatio, 1.);\\n\\t\\taBotCoord = aCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\\n\\t\\taTopCoord = aCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\\n\\t}\\n\\telse if (!nextReverse && baClipping > 0. && baClipping < length(normalWidth * endBotJoin)) {\\n\\t\\t//handle miter clipping\\n\\t\\tbTopCoord -= normalWidth * endTopJoin;\\n\\t\\tbTopCoord += normalize(endTopJoin * normalWidth) * baClipping;\\n\\t}\\n\\n\\tif (nextReverse) {\\n\\t\\t//make join rectangular\\n\\t\\tvec2 miterShift = normalWidth * endJoinDirection * miterLimit * .5;\\n\\t\\tfloat normalAdjust = 1. - min(miterLimit / endMiterRatio, 1.);\\n\\t\\tbBotCoord = bCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\\n\\t\\tbTopCoord = bCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\\n\\t}\\n\\telse if (!prevReverse && abClipping > 0. && abClipping < length(normalWidth * startBotJoin)) {\\n\\t\\t//handle miter clipping\\n\\t\\taBotCoord -= normalWidth * startBotJoin;\\n\\t\\taBotCoord += normalize(startBotJoin * normalWidth) * abClipping;\\n\\t}\\n\\n\\tvec2 aTopPosition = (aTopCoord) * adjustedScale + translate;\\n\\tvec2 aBotPosition = (aBotCoord) * adjustedScale + translate;\\n\\n\\tvec2 bTopPosition = (bTopCoord) * adjustedScale + translate;\\n\\tvec2 bBotPosition = (bBotCoord) * adjustedScale + translate;\\n\\n\\t//position is normalized 0..1 coord on the screen\\n\\tvec2 position = (aTopPosition * lineTop + aBotPosition * lineBot) * lineStart + (bTopPosition * lineTop + bBotPosition * lineBot) * lineEnd;\\n\\n\\tstartCoord = aCoord * scaleRatio + translate * viewport.zw + viewport.xy;\\n\\tendCoord = bCoord * scaleRatio + translate * viewport.zw + viewport.xy;\\n\\n\\tgl_Position = vec4(position  * 2.0 - 1.0, depth, 1);\\n\\n\\tenableStartMiter = step(dot(currTangent, prevTangent), .5);\\n\\tenableEndMiter = step(dot(currTangent, nextTangent), .5);\\n\\n\\t//bevel miter cutoffs\\n\\tif (miterMode == 1.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * miterLimit * .5;\\n\\t\\t\\tstartCutoff = vec4(aCoord, aCoord);\\n\\t\\t\\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\\n\\t\\t\\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tstartCutoff += viewport.xyxy;\\n\\t\\t\\tstartCutoff += startMiterWidth.xyxy;\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * miterLimit * .5;\\n\\t\\t\\tendCutoff = vec4(bCoord, bCoord);\\n\\t\\t\\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\\n\\t\\t\\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tendCutoff += viewport.xyxy;\\n\\t\\t\\tendCutoff += endMiterWidth.xyxy;\\n\\t\\t}\\n\\t}\\n\\n\\t//round miter cutoffs\\n\\telse if (miterMode == 2.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * abs(dot(startJoinDirection, currNormal)) * .5;\\n\\t\\t\\tstartCutoff = vec4(aCoord, aCoord);\\n\\t\\t\\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\\n\\t\\t\\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tstartCutoff += viewport.xyxy;\\n\\t\\t\\tstartCutoff += startMiterWidth.xyxy;\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * abs(dot(endJoinDirection, currNormal)) * .5;\\n\\t\\t\\tendCutoff = vec4(bCoord, bCoord);\\n\\t\\t\\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\\n\\t\\t\\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tendCutoff += viewport.xyxy;\\n\\t\\t\\tendCutoff += endMiterWidth.xyxy;\\n\\t\\t}\\n\\t}\\n}\\n\"]),frag:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform float dashLength, pixelRatio, thickness, opacity, id, miterMode;\\nuniform sampler2D dashTexture;\\n\\nvarying vec4 fragColor;\\nvarying vec2 tangent;\\nvarying vec4 startCutoff, endCutoff;\\nvarying vec2 startCoord, endCoord;\\nvarying float enableStartMiter, enableEndMiter;\\n\\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\\n\\tvec2 diff = b - a;\\n\\tvec2 perp = normalize(vec2(-diff.y, diff.x));\\n\\treturn dot(p - a, perp);\\n}\\n\\nvoid main() {\\n\\tfloat alpha = 1., distToStart, distToEnd;\\n\\tfloat cutoff = thickness * .5;\\n\\n\\t//bevel miter\\n\\tif (miterMode == 1.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\\n\\t\\t\\tif (distToStart < -1.) {\\n\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\treturn;\\n\\t\\t\\t}\\n\\t\\t\\talpha *= min(max(distToStart + 1., 0.), 1.);\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\\n\\t\\t\\tif (distToEnd < -1.) {\\n\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\treturn;\\n\\t\\t\\t}\\n\\t\\t\\talpha *= min(max(distToEnd + 1., 0.), 1.);\\n\\t\\t}\\n\\t}\\n\\n\\t// round miter\\n\\telse if (miterMode == 2.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\\n\\t\\t\\tif (distToStart < 0.) {\\n\\t\\t\\t\\tfloat radius = length(gl_FragCoord.xy - startCoord);\\n\\n\\t\\t\\t\\tif(radius > cutoff + .5) {\\n\\t\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\t\\treturn;\\n\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\\n\\t\\t\\t}\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\\n\\t\\t\\tif (distToEnd < 0.) {\\n\\t\\t\\t\\tfloat radius = length(gl_FragCoord.xy - endCoord);\\n\\n\\t\\t\\t\\tif(radius > cutoff + .5) {\\n\\t\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\t\\treturn;\\n\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\\n\\t\\t\\t}\\n\\t\\t}\\n\\t}\\n\\n\\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashLength) * .5 + .25;\\n\\tfloat dash = texture2D(dashTexture, vec2(t, .5)).r;\\n\\n\\tgl_FragColor = fragColor;\\n\\tgl_FragColor.a *= alpha * opacity * 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t=this,e=[],r=arguments.length;r--;)e[r]=arguments[r];return(e.length?e:this.passes).forEach((function(e,r){var n;if(e&&Array.isArray(e))return(n=t).draw.apply(n,e);\"number\"==typeof e&&(e=t.passes[e]),e&&e.count>1&&e.opacity&&(t.regl._refresh(),e.fill&&e.triangles&&e.triangles.length>2&&t.shaders.fill(e),e.thickness&&(e.scale[0]*e.viewport.width>v.precisionThreshold||e.scale[1]*e.viewport.height>v.precisionThreshold||\"rect\"===e.join||!e.join&&(e.thickness<=2||e.count>=v.maxPoints)?t.shaders.rect(e):t.shaders.miter(e)))})),this},v.prototype.update=function(t){var e=this;if(t){null!=t.length?\"number\"==typeof t[0]&&(t=[{positions:t}]):Array.isArray(t)||(t=[t]);var r=this.regl,o=this.gl;if(t.forEach((function(t,f){var d=e.passes[f];if(void 0!==t)if(null!==t){if(\"number\"==typeof t[0]&&(t={positions:t}),t=s(t,{positions:\"positions points data coords\",thickness:\"thickness lineWidth lineWidths line-width linewidth width stroke-width strokewidth strokeWidth\",join:\"lineJoin linejoin 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C=y[2*S],P=y[2*S+1];isNaN(C)||isNaN(P)||null==C||null==P?(C=y[2*M],P=y[2*M+1],A[S]=M):M=S,k[E++]=C,k[E++]=P}if(t.splitNull){d.count-1 in A||(A[d.count]=d.count-1);var I=Object.keys(A).map(Number).sort((function(t,e){return t-e})),O=[],z=0,D=null!=d.hole?d.hole[0]:null;if(null!=D){var R=m(I,(function(t){return t>=D}));(I=I.slice(0,R)).push(D)}for(var F=function(t){var e=k.slice(2*z,2*I[t]).concat(D?k.slice(2*D):[]),r=(d.hole||[]).map((function(e){return e-D+(I[t]-z)})),n=c(e,r);n=n.map((function(e){return e+z+(e+z<I[t]?0:D-I[t])})),O.push.apply(O,n),z=I[t]+1},B=0;B<I.length;B++)F(B);for(var N=0,j=O.length;N<j;N++)null!=A[O[N]]&&(O[N]=A[O[N]]);d.triangles=O}else{for(var U=c(k,d.hole||[]),V=0,H=U.length;V<H;V++)null!=A[U[V]]&&(U[V]=A[U[V]]);d.triangles=U}}var q=new Float64Array(y);u(q,2,T);var G=new 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at=0;at<t.dashes.length;++at){for(var ot=0,st=t.dashes[at]*v.dashMult*.5;ot<st;++ot)$[rt++]=nt;nt^=255}}d.dashLength=tt,d.dashTexture({channels:1,data:$,width:$.length,height:1,mag:\"linear\",min:\"linear\"},0,0)}if(t.color){var lt=d.count,ct=t.color;ct||(ct=\"transparent\");var ut=new Uint8Array(4*lt+4);if(Array.isArray(ct)&&\"number\"!=typeof ct[0]){for(var ft=0;ft<lt;ft++){var ht=n(ct[ft],\"uint8\");ut.set(ht,4*ft)}ut.set(n(ct[0],\"uint8\"),4*lt)}else for(var pt=n(ct,\"uint8\"),dt=0;dt<lt+1;dt++)ut.set(pt,4*dt);d.colorBuffer({usage:\"dynamic\",type:\"uint8\",data:ut})}}else e.passes[f]=null})),t.length<this.passes.length){for(var f=t.length;f<this.passes.length;f++){var d=this.passes[f];d&&(d.colorBuffer.destroy(),d.positionBuffer.destroy(),d.dashTexture.destroy())}this.passes.length=t.length}for(var y=[],x=0;x<this.passes.length;x++)null!==this.passes[x]&&y.push(this.passes[x]);return this.passes=y,this}},v.prototype.destroy=function(){return 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o={uniforms:{constPointSize:!!e.constPointSize,opacity:t.prop(\"opacity\"),paletteSize:function(t,e){return[r.tooManyColors?0:255,n.height]},pixelRatio:t.context(\"pixelRatio\"),scale:t.prop(\"scale\"),scaleFract:t.prop(\"scaleFract\"),translate:t.prop(\"translate\"),translateFract:t.prop(\"translateFract\"),markerTexture:t.prop(\"markerTexture\"),paletteTexture:n},attributes:{x:function(t,e){return e.xAttr||{buffer:e.positionBuffer,stride:8,offset:0}},y:function(t,e){return e.yAttr||{buffer:e.positionBuffer,stride:8,offset:4}},xFract:function(t,e){return e.xAttr?{constant:[0,0]}:{buffer:e.positionFractBuffer,stride:8,offset:0}},yFract:function(t,e){return e.yAttr?{constant:[0,0]}:{buffer:e.positionFractBuffer,stride:8,offset:4}},size:function(t,e){return e.size.length?{buffer:e.sizeBuffer,stride:2,offset:0}:{constant:[Math.round(255*e.size/r.maxSize)]}},borderSize:function(t,e){return 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mix(color, fragColor, colorAmt);\\n    color.a *= opacity;\\n\\n    gl_FragColor = color;\\n  }\\n\\n}\\n\"]),s.vert=h([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute float x, y, xFract, yFract;\\nattribute float size, borderSize;\\nattribute vec4 colorId, borderColorId;\\nattribute float isActive;\\n\\nuniform bool constPointSize;\\nuniform float pixelRatio;\\nuniform vec2 scale, scaleFract, translate, translateFract, paletteSize;\\nuniform sampler2D paletteTexture;\\n\\nconst float maxSize = 100.;\\nconst float borderLevel = .5;\\n\\nvarying vec4 fragColor, fragBorderColor;\\nvarying float fragPointSize, fragBorderRadius, fragWidth, fragBorderColorLevel, fragColorLevel;\\n\\nfloat pointSizeScale = (constPointSize) ? 2. : pixelRatio;\\n\\nbool isDirect = (paletteSize.x < 1.);\\n\\nvec4 getColor(vec4 id) {\\n  return isDirect ? id / 255. : texture2D(paletteTexture,\\n    vec2(\\n      (id.x + .5) / paletteSize.x,\\n      (id.y + .5) / paletteSize.y\\n    )\\n  );\\n}\\n\\nvoid main() {\\n  // ignore inactive points\\n  if (isActive == 0.) return;\\n\\n  vec2 position = vec2(x, y);\\n  vec2 positionFract = vec2(xFract, yFract);\\n\\n  vec4 color = getColor(colorId);\\n  vec4 borderColor = getColor(borderColorId);\\n\\n  float size = size * maxSize / 255.;\\n  float borderSize = borderSize * maxSize / 255.;\\n\\n  gl_PointSize = 2. * size * pointSizeScale;\\n  fragPointSize = size * pixelRatio;\\n\\n  vec2 pos = (position + translate) * scale\\n      + (positionFract + translateFract) * scale\\n      + (position + translate) * scaleFract\\n      + (positionFract + translateFract) * scaleFract;\\n\\n  gl_Position = vec4(pos * 2. - 1., 0., 1.);\\n\\n  fragColor = color;\\n  fragBorderColor = borderColor;\\n  fragWidth = 1. / gl_PointSize;\\n\\n  fragBorderColorLevel = clamp(borderLevel - borderLevel * borderSize / size, 0., 1.);\\n  fragColorLevel = clamp(borderLevel + (1. - borderLevel) * borderSize / size, 0., 1.);\\n}\"]),this.drawMarker=t(s);var l=f({},o);l.frag=h([\"precision highp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor, fragBorderColor;\\nvarying float fragBorderRadius, fragWidth;\\n\\nuniform float opacity;\\n\\nfloat smoothStep(float edge0, float edge1, float x) {\\n\\tfloat t;\\n\\tt = clamp((x - edge0) / (edge1 - edge0), 0.0, 1.0);\\n\\treturn t * t * (3.0 - 2.0 * t);\\n}\\n\\nvoid main() {\\n\\tfloat radius, alpha = 1.0, delta = fragWidth;\\n\\n\\tradius = length(2.0 * gl_PointCoord.xy - 1.0);\\n\\n\\tif (radius > 1.0 + delta) {\\n\\t\\tdiscard;\\n\\t}\\n\\n\\talpha -= smoothstep(1.0 - delta, 1.0 + delta, radius);\\n\\n\\tfloat borderRadius = fragBorderRadius;\\n\\tfloat ratio = smoothstep(borderRadius - delta, borderRadius + delta, radius);\\n\\tvec4 color = mix(fragColor, fragBorderColor, ratio);\\n\\tcolor.a *= alpha * opacity;\\n\\tgl_FragColor = color;\\n}\\n\"]),l.vert=h([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute float x, y, xFract, yFract;\\nattribute float size, borderSize;\\nattribute vec4 colorId, borderColorId;\\nattribute float isActive;\\n\\nuniform bool constPointSize;\\nuniform float pixelRatio;\\nuniform vec2 paletteSize, scale, scaleFract, translate, translateFract;\\nuniform sampler2D paletteTexture;\\n\\nconst float maxSize = 100.;\\n\\nvarying vec4 fragColor, fragBorderColor;\\nvarying float fragBorderRadius, fragWidth;\\n\\nfloat pointSizeScale = (constPointSize) ? 2. : pixelRatio;\\n\\nbool isDirect = (paletteSize.x < 1.);\\n\\nvec4 getColor(vec4 id) {\\n  return isDirect ? id / 255. : texture2D(paletteTexture,\\n    vec2(\\n      (id.x + .5) / paletteSize.x,\\n      (id.y + .5) / paletteSize.y\\n    )\\n  );\\n}\\n\\nvoid main() {\\n  // ignore inactive points\\n  if (isActive == 0.) return;\\n\\n  vec2 position = vec2(x, y);\\n  vec2 positionFract = vec2(xFract, yFract);\\n\\n  vec4 color = getColor(colorId);\\n  vec4 borderColor = getColor(borderColorId);\\n\\n  float size = size * maxSize / 255.;\\n  float borderSize = borderSize * maxSize / 255.;\\n\\n  gl_PointSize = (size + borderSize) * pointSizeScale;\\n\\n  vec2 pos = (position + translate) * scale\\n      + (positionFract + translateFract) * scale\\n      + (position + translate) * scaleFract\\n      + (positionFract + translateFract) * scaleFract;\\n\\n  gl_Position = vec4(pos * 2. - 1., 0., 1.);\\n\\n  fragBorderRadius = 1. - 2. * borderSize / (size + borderSize);\\n  fragColor = color;\\n  fragBorderColor = borderColor.a == 0. || borderSize == 0. ? vec4(color.rgb, 0.) : borderColor;\\n  fragWidth = 1. / gl_PointSize;\\n}\\n\"]),m&&(l.frag=l.frag.replace(\"smoothstep\",\"smoothStep\"),s.frag=s.frag.replace(\"smoothstep\",\"smoothStep\")),this.drawCircle=t(l)}b.defaults={color:\"black\",borderColor:\"transparent\",borderSize:0,size:12,opacity:1,marker:void 0,viewport:null,range:null,pixelSize:null,count:0,offset:0,bounds:null,positions:[],snap:1e4},b.prototype.render=function(){return arguments.length&&this.update.apply(this,arguments),this.draw(),this},b.prototype.draw=function(){for(var t=this,e=arguments.length,r=new Array(e),n=0;n<e;n++)r[n]=arguments[n];var i=this.groups;if(1===r.length&&Array.isArray(r[0])&&(null===r[0][0]||Array.isArray(r[0][0]))&&(r=r[0]),this.regl._refresh(),r.length)for(var a=0;a<r.length;a++)this.drawItem(a,r[a]);else i.forEach((function(e,r){t.drawItem(r)}));return this},b.prototype.drawItem=function(t,e){var r=this.groups,n=r[t];if(\"number\"==typeof e&&(t=e,n=r[e],e=null),n&&n.count&&n.opacity){n.activation[0]&&this.drawCircle(this.getMarkerDrawOptions(0,n,e));for(var a=[],o=1;o<n.activation.length;o++)n.activation[o]&&(!0===n.activation[o]||n.activation[o].data.length)&&a.push.apply(a,i(this.getMarkerDrawOptions(o,n,e)));a.length&&this.drawMarker(a)}},b.prototype.getMarkerDrawOptions=function(t,e,r){var i=e.range,a=e.tree,o=e.viewport,s=e.activation,l=e.selectionBuffer,c=e.count;this.regl;if(!a)return r?[f({},e,{markerTexture:this.markerTextures[t],activation:s[t],count:r.length,elements:r,offset:0})]:[f({},e,{markerTexture:this.markerTextures[t],activation:s[t],offset:0})];var u=[],h=a.range(i,{lod:!0,px:[(i[2]-i[0])/o.width,(i[3]-i[1])/o.height]});if(r){for(var p=s[t].data,d=new Uint8Array(c),g=0;g<r.length;g++){var m=r[g];d[m]=p?p[m]:1}l.subdata(d)}for(var v=h.length;v--;){var y=n(h[v],2),x=y[0],b=y[1];u.push(f({},e,{markerTexture:this.markerTextures[t],activation:r?l:s[t],offset:x,count:b-x}))}return u},b.prototype.update=function(){for(var t=this,e=arguments.length,r=new Array(e),n=0;n<e;n++)r[n]=arguments[n];if(r.length){1===r.length&&Array.isArray(r[0])&&(r=r[0]);var i=this.groups,a=this.gl,o=this.regl,s=this.maxSize,c=this.maxColors,h=this.palette;this.groups=i=r.map((function(e,r){var n=i[r];if(void 0===e)return n;null===e?e={positions:null}:\"function\"==typeof e?e={ondraw:e}:\"number\"==typeof e[0]&&(e={positions:e}),null===(e=p(e,{positions:\"positions data points\",snap:\"snap cluster lod tree\",size:\"sizes size radius\",borderSize:\"borderSizes borderSize border-size bordersize borderWidth borderWidths border-width borderwidth stroke-width strokeWidth strokewidth outline\",color:\"colors color fill fill-color fillColor\",borderColor:\"borderColors borderColor stroke stroke-color strokeColor\",marker:\"markers marker shape\",range:\"range dataBox databox\",viewport:\"viewport viewPort viewBox viewbox\",opacity:\"opacity alpha transparency\",bounds:\"bound bounds boundaries limits\",tooManyColors:\"tooManyColors palette paletteMode optimizePalette enablePalette\"})).positions&&(e.positions=[]),null!=e.tooManyColors&&(t.tooManyColors=e.tooManyColors),n||(i[r]=n={id:r,scale:null,translate:null,scaleFract:null,translateFract:null,activation:[],selectionBuffer:o.buffer({data:new Uint8Array(0),usage:\"stream\",type:\"uint8\"}),sizeBuffer:o.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"uint8\"}),colorBuffer:o.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"uint8\"}),positionBuffer:o.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"float\"}),positionFractBuffer:o.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"float\"})},e=f({},b.defaults,e)),e.positions&&!(\"marker\"in e)&&(e.marker=n.marker,delete n.marker),e.marker&&!(\"positions\"in e)&&(e.positions=n.positions,delete n.positions);var m=0,x=0;if(d(n,e,[{snap:!0,size:function(t,e){return null==t&&(t=b.defaults.size),m+=t&&t.length?1:0,t},borderSize:function(t,e){return null==t&&(t=b.defaults.borderSize),m+=t&&t.length?1:0,t},opacity:parseFloat,color:function(e,r){return null==e&&(e=b.defaults.color),e=t.updateColor(e),x++,e},borderColor:function(e,r){return null==e&&(e=b.defaults.borderColor),e=t.updateColor(e),x++,e},bounds:function(t,e,r){return\"range\"in r||(r.range=null),t},positions:function(t,e,r){var n=e.snap,i=e.positionBuffer,a=e.positionFractBuffer,s=e.selectionBuffer;if(t.x||t.y)return t.x.length?e.xAttr={buffer:o.buffer(t.x),offset:0,stride:4,count:t.x.length}:e.xAttr={buffer:t.x.buffer,offset:4*t.x.offset||0,stride:4*(t.x.stride||1),count:t.x.count},t.y.length?e.yAttr={buffer:o.buffer(t.y),offset:0,stride:4,count:t.y.length}:e.yAttr={buffer:t.y.buffer,offset:4*t.y.offset||0,stride:4*(t.y.stride||1),count:t.y.count},e.count=Math.max(e.xAttr.count,e.yAttr.count),t;t=g(t,\"float64\");var c=e.count=Math.floor(t.length/2),f=e.bounds=c?l(t,2):null;if(r.range||e.range||(delete e.range,r.range=f),r.marker||e.marker||(delete e.marker,r.marker=null),n&&(!0===n||c>n)?e.tree=u(t,{bounds:f}):n&&n.length&&(e.tree=n),e.tree){var h={primitive:\"points\",usage:\"static\",data:e.tree,type:\"uint32\"};e.elements?e.elements(h):e.elements=o.elements(h)}var p=v.float32(t);return i({data:p,usage:\"dynamic\"}),a({data:v.fract32(t,p),usage:\"dynamic\"}),s({data:new Uint8Array(c),type:\"uint8\",usage:\"stream\"}),t}},{marker:function(e,r,n){var i=r.activation;if(i.forEach((function(t){return t&&t.destroy&&t.destroy()})),i.length=0,e&&\"number\"!=typeof e[0]){for(var a=[],s=0,l=Math.min(e.length,r.count);s<l;s++){var c=t.addMarker(e[s]);a[c]||(a[c]=new Uint8Array(r.count)),a[c][s]=1}for(var u=0;u<a.length;u++)if(a[u]){var f={data:a[u],type:\"uint8\",usage:\"static\"};i[u]?i[u](f):i[u]=o.buffer(f),i[u].data=a[u]}}else{i[t.addMarker(e)]=!0}return e},range:function(t,e,r){var n=e.bounds;if(n)return t||(t=n),e.scale=[1/(t[2]-t[0]),1/(t[3]-t[1])],e.translate=[-t[0],-t[1]],e.scaleFract=v.fract(e.scale),e.translateFract=v.fract(e.translate),t},viewport:function(t){return y(t||[a.drawingBufferWidth,a.drawingBufferHeight])}}]),m){var _=n,w=_.count,T=_.size,k=_.borderSize,A=_.sizeBuffer,M=new Uint8Array(2*w);if(T.length||k.length)for(var S=0;S<w;S++)M[2*S]=Math.round(255*(null==T[S]?T:T[S])/s),M[2*S+1]=Math.round(255*(null==k[S]?k:k[S])/s);A({data:M,usage:\"dynamic\"})}if(x){var E,L=n,C=L.count,P=L.color,I=L.borderColor,O=L.colorBuffer;if(t.tooManyColors){if(P.length||I.length){E=new Uint8Array(8*C);for(var z=0;z<C;z++){var D=P[z];E[8*z]=h[4*D],E[8*z+1]=h[4*D+1],E[8*z+2]=h[4*D+2],E[8*z+3]=h[4*D+3];var R=I[z];E[8*z+4]=h[4*R],E[8*z+5]=h[4*R+1],E[8*z+6]=h[4*R+2],E[8*z+7]=h[4*R+3]}}}else if(P.length||I.length){E=new Uint8Array(4*C+2);for(var F=0;F<C;F++)null!=P[F]&&(E[4*F]=P[F]%c,E[4*F+1]=Math.floor(P[F]/c)),null!=I[F]&&(E[4*F+2]=I[F]%c,E[4*F+3]=Math.floor(I[F]/c))}O({data:E||new Uint8Array(0),type:\"uint8\",usage:\"dynamic\"})}return n}))}},b.prototype.addMarker=function(t){var e,r=this.markerTextures,n=this.regl,i=this.markerCache,a=null==t?0:i.indexOf(t);if(a>=0)return a;if(t instanceof Uint8Array||t instanceof Uint8ClampedArray)e=t;else{e=new Uint8Array(t.length);for(var o=0,s=t.length;o<s;o++)e[o]=255*t[o]}var l=Math.floor(Math.sqrt(e.length));return a=r.length,i.push(t),r.push(n.texture({channels:1,data:e,radius:l,mag:\"linear\",min:\"linear\"})),a},b.prototype.updateColor=function(t){var e=this.paletteIds,r=this.palette,n=this.maxColors;Array.isArray(t)||(t=[t]);var i=[];if(\"number\"==typeof t[0]){var a=[];if(Array.isArray(t))for(var o=0;o<t.length;o+=4)a.push(t.slice(o,o+4));else for(var l=0;l<t.length;l+=4)a.push(t.subarray(l,l+4));t=a}for(var u=0;u<t.length;u++){var f=t[u];f=s(f,\"uint8\");var h=c(f,!1);if(null==e[h]){var p=r.length;e[h]=Math.floor(p/4),r[p]=f[0],r[p+1]=f[1],r[p+2]=f[2],r[p+3]=f[3]}i[u]=e[h]}return!this.tooManyColors&&r.length>4*n&&(this.tooManyColors=!0),this.updatePalette(r),1===i.length?i[0]:i},b.prototype.updatePalette=function(t){if(!this.tooManyColors){var e=this.maxColors,r=this.paletteTexture,n=Math.ceil(.25*t.length/e);if(n>1)for(var i=.25*(t=t.slice()).length%e;i<n*e;i++)t.push(0,0,0,0);r.height<n&&r.resize(e,n),r.subimage({width:Math.min(.25*t.length,e),height:n,data:t},0,0)}},b.prototype.destroy=function(){return this.groups.forEach((function(t){t.sizeBuffer.destroy(),t.positionBuffer.destroy(),t.positionFractBuffer.destroy(),t.colorBuffer.destroy(),t.activation.forEach((function(t){return t&&t.destroy&&t.destroy()})),t.selectionBuffer.destroy(),t.elements&&t.elements.destroy()})),this.groups.length=0,this.paletteTexture.destroy(),this.markerTextures.forEach((function(t){return t&&t.destroy&&t.destroy()})),this};var _=t(\"object-assign\");e.exports=function(t,e){var r=new x(t,e),n=r.render.bind(r);return _(n,{render:n,update:r.update.bind(r),draw:r.draw.bind(r),destroy:r.destroy.bind(r),regl:r.regl,gl:r.gl,canvas:r.gl.canvas,groups:r.groups,markers:r.markerCache,palette:r.palette}),n}},{\"@plotly/point-cluster\":59,\"array-bounds\":71,\"color-id\":87,\"color-normalize\":89,\"flatten-vertex-data\":191,glslify:227,\"is-iexplorer\":233,\"object-assign\":247,\"parse-rect\":249,\"pick-by-alias\":253,\"to-float32\":313,\"update-diff\":329}],282:[function(t,e,r){\"use strict\";var n=t(\"regl-scatter2d\"),i=t(\"pick-by-alias\"),a=t(\"array-bounds\"),o=t(\"raf\"),s=t(\"array-range\"),l=t(\"parse-rect\"),c=t(\"flatten-vertex-data\");function u(t,e){if(!(this instanceof u))return new u(t,e);this.traces=[],this.passes={},this.regl=t,this.scatter=n(t),this.canvas=this.scatter.canvas}function f(t,e,r){return(null!=t.id?t.id:t)<<16|(255&e)<<8|255&r}function h(t,e,r){var n,i,a,o,s=t[e],l=t[r];return s.length>2?(s[0],s[2],n=s[1],i=s[3]):s.length?(n=s[0],i=s[1]):(s.x,n=s.y,s.x+s.width,i=s.y+s.height),l.length>2?(a=l[0],o=l[2],l[1],l[3]):l.length?(a=l[0],o=l[1]):(a=l.x,l.y,o=l.x+l.width,l.y+l.height),[a,n,o,i]}function p(t){if(\"number\"==typeof t)return[t,t,t,t];if(2===t.length)return[t[0],t[1],t[0],t[1]];var e=l(t);return[e.x,e.y,e.x+e.width,e.y+e.height]}e.exports=u,u.prototype.render=function(){for(var t,e=this,r=[],n=arguments.length;n--;)r[n]=arguments[n];return r.length&&(t=this).update.apply(t,r),this.regl.attributes.preserveDrawingBuffer?this.draw():(this.dirty?null==this.planned&&(this.planned=o((function(){e.draw(),e.dirty=!0,e.planned=null}))):(this.draw(),this.dirty=!0,o((function(){e.dirty=!1}))),this)},u.prototype.update=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];if(e.length){for(var n=0;n<e.length;n++)this.updateItem(n,e[n]);this.traces=this.traces.filter(Boolean);for(var i=[],a=0,o=0;o<this.traces.length;o++){for(var s=this.traces[o],l=this.traces[o].passes,c=0;c<l.length;c++)i.push(this.passes[l[c]]);s.passOffset=a,a+=s.passes.length}return(t=this.scatter).update.apply(t,i),this}},u.prototype.updateItem=function(t,e){var r=this.regl;if(null===e)return this.traces[t]=null,this;if(!e)return this;var n,o=i(e,{data:\"data items columns rows values dimensions samples x\",snap:\"snap cluster\",size:\"sizes size radius\",color:\"colors color fill fill-color fillColor\",opacity:\"opacity alpha transparency opaque\",borderSize:\"borderSizes borderSize border-size bordersize borderWidth borderWidths border-width borderwidth stroke-width strokeWidth strokewidth outline\",borderColor:\"borderColors borderColor bordercolor stroke stroke-color strokeColor\",marker:\"markers marker shape\",range:\"range ranges databox dataBox\",viewport:\"viewport viewBox viewbox\",domain:\"domain domains area areas\",padding:\"pad padding paddings pads margin margins\",transpose:\"transpose transposed\",diagonal:\"diagonal diag showDiagonal\",upper:\"upper up top upperhalf upperHalf showupperhalf showUpper showUpperHalf\",lower:\"lower low bottom lowerhalf lowerHalf showlowerhalf showLowerHalf showLower\"}),s=this.traces[t]||(this.traces[t]={id:t,buffer:r.buffer({usage:\"dynamic\",type:\"float\",data:new Uint8Array}),color:\"black\",marker:null,size:12,borderColor:\"transparent\",borderSize:1,viewport:l([r._gl.drawingBufferWidth,r._gl.drawingBufferHeight]),padding:[0,0,0,0],opacity:1,diagonal:!0,upper:!0,lower:!0});if(null!=o.color&&(s.color=o.color),null!=o.size&&(s.size=o.size),null!=o.marker&&(s.marker=o.marker),null!=o.borderColor&&(s.borderColor=o.borderColor),null!=o.borderSize&&(s.borderSize=o.borderSize),null!=o.opacity&&(s.opacity=o.opacity),o.viewport&&(s.viewport=l(o.viewport)),null!=o.diagonal&&(s.diagonal=o.diagonal),null!=o.upper&&(s.upper=o.upper),null!=o.lower&&(s.lower=o.lower),o.data){s.buffer(c(o.data)),s.columns=o.data.length,s.count=o.data[0].length,s.bounds=[];for(var u=0;u<s.columns;u++)s.bounds[u]=a(o.data[u],1)}o.range&&(s.range=o.range,n=s.range&&\"number\"!=typeof s.range[0]),o.domain&&(s.domain=o.domain);var d=!1;null!=o.padding&&(Array.isArray(o.padding)&&o.padding.length===s.columns&&\"number\"==typeof o.padding[o.padding.length-1]?(s.padding=o.padding.map(p),d=!0):s.padding=p(o.padding));var g=s.columns,m=s.count,v=s.viewport.width,y=s.viewport.height,x=s.viewport.x,b=s.viewport.y,_=v/g,w=y/g;s.passes=[];for(var T=0;T<g;T++)for(var k=0;k<g;k++)if((s.diagonal||k!==T)&&(s.upper||!(T>k))&&(s.lower||!(T<k))){var A=f(s.id,T,k),M=this.passes[A]||(this.passes[A]={});if(o.data&&(o.transpose?M.positions={x:{buffer:s.buffer,offset:k,count:m,stride:g},y:{buffer:s.buffer,offset:T,count:m,stride:g}}:M.positions={x:{buffer:s.buffer,offset:k*m,count:m},y:{buffer:s.buffer,offset:T*m,count:m}},M.bounds=h(s.bounds,T,k)),o.domain||o.viewport||o.data){var S=d?h(s.padding,T,k):s.padding;if(s.domain){var E=h(s.domain,T,k),L=E[0],C=E[1],P=E[2],I=E[3];M.viewport=[x+L*v+S[0],b+C*y+S[1],x+P*v-S[2],b+I*y-S[3]]}else M.viewport=[x+k*_+_*S[0],b+T*w+w*S[1],x+(k+1)*_-_*S[2],b+(T+1)*w-w*S[3]]}o.color&&(M.color=s.color),o.size&&(M.size=s.size),o.marker&&(M.marker=s.marker),o.borderSize&&(M.borderSize=s.borderSize),o.borderColor&&(M.borderColor=s.borderColor),o.opacity&&(M.opacity=s.opacity),o.range&&(M.range=n?h(s.range,T,k):s.range||M.bounds),s.passes.push(A)}return this},u.prototype.draw=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];if(e.length){for(var n=[],i=0;i<e.length;i++)if(\"number\"==typeof e[i]){var a=this.traces[e[i]],o=a.passes,l=a.passOffset;n.push.apply(n,s(l,l+o.length))}else if(e[i].length){var c=e[i],u=this.traces[i],f=u.passes,h=u.passOffset;f=f.map((function(t,e){n[h+e]=c}))}(t=this.scatter).draw.apply(t,n)}else this.scatter.draw();return this},u.prototype.destroy=function(){return 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e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return(t=e.year()+(e.year()<0?1:0))%4==3||t%4==-1},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear||n.regionalOptions[\"\"].invalidYear),13},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(13===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return(t=i.year())<0&&t++,i.day()+30*(i.month()-1)+365*(t-1)+Math.floor(t/4)+this.jdEpoch-1},fromJD:function(t){var e=Math.floor(t)+.5-this.jdEpoch,r=Math.floor((e-Math.floor((e+366)/1461))/365)+1;r<=0&&r--,e=Math.floor(t)+.5-this.newDate(r,1,1).toJD();var n=Math.floor(e/30)+1,i=e-30*(n-1)+1;return 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this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),13},daysInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),400},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/8)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]},daysInWeek:function(){return 8},dayOfWeek:function(t,e,r){return(this._validate(t,e,r,n.local.invalidDate).day()+1)%8},weekDay:function(t,e,r){var n=this.dayOfWeek(t,e,r);return n>=2&&n<=6},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return{century:o[Math.floor((i.year()-1)/100)+1]||\"\"}},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=i.year()+(i.year()<0?1:0),e=i.month(),(r=i.day())+(e>1?16:0)+(e>2?32*(e-2):0)+400*(t-1)+this.jdEpoch-1},fromJD:function(t){t=Math.floor(t+.5)-Math.floor(this.jdEpoch)-1;var e=Math.floor(t/400)+1;t-=400*(e-1),t+=t>15?16:0;var r=Math.floor(t/32)+1,n=t-32*(r-1)+1;return this.newDate(e<=0?e-1:e,r,n)}});var o={20:\"Fruitbat\",21:\"Anchovy\"};n.calendars.discworld=a},{\"../main\":346,\"object-assign\":247}],335:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new 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a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}function o(t,e){return t-e*Math.floor(t/e)}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Hebrew\",jdEpoch:347995.5,daysPerMonth:[30,29,30,29,30,29,30,29,30,29,30,29,29],hasYearZero:!1,minMonth:1,firstMonth:7,minDay:1,regionalOptions:{\"\":{name:\"Hebrew\",epochs:[\"BAM\",\"AM\"],monthNames:[\"Nisan\",\"Iyar\",\"Sivan\",\"Tammuz\",\"Av\",\"Elul\",\"Tishrei\",\"Cheshvan\",\"Kislev\",\"Tevet\",\"Shevat\",\"Adar\",\"Adar II\"],monthNamesShort:[\"Nis\",\"Iya\",\"Siv\",\"Tam\",\"Av\",\"Elu\",\"Tis\",\"Che\",\"Kis\",\"Tev\",\"She\",\"Ada\",\"Ad2\"],dayNames:[\"Yom Rishon\",\"Yom Sheni\",\"Yom Shlishi\",\"Yom Revi'i\",\"Yom Chamishi\",\"Yom Shishi\",\"Yom Shabbat\"],dayNamesShort:[\"Ris\",\"She\",\"Shl\",\"Rev\",\"Cha\",\"Shi\",\"Sha\"],dayNamesMin:[\"Ri\",\"She\",\"Shl\",\"Re\",\"Ch\",\"Shi\",\"Sha\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return this._leapYear(e.year())},_leapYear:function(t){return o(7*(t=t<0?t+1:t)+1,19)<7},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),this._leapYear(t.year?t.year():t)?13:12},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){return t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year(),this.toJD(-1===t?1:t+1,7,1)-this.toJD(t,7,1)},daysInMonth:function(t,e){return t.year&&(e=t.month(),t=t.year()),this._validate(t,e,this.minDay,n.local.invalidMonth),12===e&&this.leapYear(t)||8===e&&5===o(this.daysInYear(t),10)?30:9===e&&3===o(this.daysInYear(t),10)?29:this.daysPerMonth[e-1]},weekDay:function(t,e,r){return 6!==this.dayOfWeek(t,e,r)},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return{yearType:(this.leapYear(i)?\"embolismic\":\"common\")+\" \"+[\"deficient\",\"regular\",\"complete\"][this.daysInYear(i)%10-3]}},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=t<=0?t+1:t,o=this.jdEpoch+this._delay1(a)+this._delay2(a)+r+1;if(e<7){for(var s=7;s<=this.monthsInYear(t);s++)o+=this.daysInMonth(t,s);for(s=1;s<e;s++)o+=this.daysInMonth(t,s)}else for(s=7;s<e;s++)o+=this.daysInMonth(t,s);return o},_delay1:function(t){var e=Math.floor((235*t-234)/19),r=12084+13753*e,n=29*e+Math.floor(r/25920);return o(3*(n+1),7)<3&&n++,n},_delay2:function(t){var e=this._delay1(t-1),r=this._delay1(t);return this._delay1(t+1)-r==356?2:r-e==382?1:0},fromJD:function(t){t=Math.floor(t)+.5;for(var e=Math.floor(98496*(t-this.jdEpoch)/35975351)-1;t>=this.toJD(-1===e?1:e+1,7,1);)e++;for(var r=t<this.toJD(e,1,1)?7:1;t>this.toJD(e,r,this.daysInMonth(e,r));)r++;var n=t-this.toJD(e,r,1)+1;return 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n.add(4-(n.dayOfWeek()||7),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=i.year(),e=i.month(),r=i.day(),t<0&&t++,e<=2&&(t--,e+=12),Math.floor(365.25*(t+4716))+Math.floor(30.6001*(e+1))+r-1524.5},fromJD:function(t){var e=Math.floor(t+.5)+1524,r=Math.floor((e-122.1)/365.25),n=Math.floor(365.25*r),i=Math.floor((e-n)/30.6001),a=i-Math.floor(i<14?1:13),o=r-Math.floor(a>2?4716:4715),s=e-n-Math.floor(30.6001*i);return o<=0&&o--,this.newDate(o,a,s)}}),n.calendars.julian=a},{\"../main\":346,\"object-assign\":247}],339:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}function o(t,e){return 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this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),!1},formatYear:function(t){t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year();var e=Math.floor(t/400);return t%=400,t+=t<0?400:0,e+\".\"+Math.floor(t/20)+\".\"+t%20},forYear:function(t){if((t=t.split(\".\")).length<3)throw\"Invalid Mayan year\";for(var e=0,r=0;r<t.length;r++){var n=parseInt(t[r],10);if(Math.abs(n)>19||r>0&&n<0)throw\"Invalid Mayan year\";e=20*e+n}return e},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),18},weekOfYear:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate),0},daysInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),360},daysInMonth:function(t,e){return this._validate(t,e,this.minDay,n.local.invalidMonth),20},daysInWeek:function(){return 5},dayOfWeek:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate).day()},weekDay:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate),!0},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate).toJD(),a=this._toHaab(i),o=this._toTzolkin(i);return{haabMonthName:this.local.haabMonths[a[0]-1],haabMonth:a[0],haabDay:a[1],tzolkinDayName:this.local.tzolkinMonths[o[0]-1],tzolkinDay:o[0],tzolkinTrecena:o[1]}},_toHaab:function(t){var e=o((t-=this.jdEpoch)+8+340,365);return[Math.floor(e/20)+1,o(e,20)]},_toTzolkin:function(t){return[s((t-=this.jdEpoch)+20,20),s(t+4,13)]},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return i.day()+20*i.month()+360*i.year()+this.jdEpoch},fromJD:function(t){t=Math.floor(t)+.5-this.jdEpoch;var e=Math.floor(t/360);t%=360,t+=t<0?360:0;var r=Math.floor(t/20),n=t%20;return this.newDate(e,r,n)}}),n.calendars.mayan=a},{\"../main\":346,\"object-assign\":247}],340:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new 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a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Nepali\",jdEpoch:1700709.5,daysPerMonth:[31,31,32,32,31,30,30,29,30,29,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,daysPerYear:365,regionalOptions:{\"\":{name:\"Nepali\",epochs:[\"BBS\",\"ABS\"],monthNames:[\"Baisakh\",\"Jestha\",\"Ashadh\",\"Shrawan\",\"Bhadra\",\"Ashwin\",\"Kartik\",\"Mangsir\",\"Paush\",\"Mangh\",\"Falgun\",\"Chaitra\"],monthNamesShort:[\"Bai\",\"Je\",\"As\",\"Shra\",\"Bha\",\"Ash\",\"Kar\",\"Mang\",\"Pau\",\"Ma\",\"Fal\",\"Chai\"],dayNames:[\"Aaitabaar\",\"Sombaar\",\"Manglbaar\",\"Budhabaar\",\"Bihibaar\",\"Shukrabaar\",\"Shanibaar\"],dayNamesShort:[\"Aaita\",\"Som\",\"Mangl\",\"Budha\",\"Bihi\",\"Shukra\",\"Shani\"],dayNamesMin:[\"Aai\",\"So\",\"Man\",\"Bu\",\"Bi\",\"Shu\",\"Sha\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:1,isRTL:!1}},leapYear:function(t){return 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9===e?(o+=r-this.NEPALI_CALENDAR_DATA[l][0])<0&&(o+=a.daysInYear(c)):o+=this.NEPALI_CALENDAR_DATA[l][9]-this.NEPALI_CALENDAR_DATA[l][0],a.newDate(c,1,1).add(o,\"d\").toJD()},fromJD:function(t){var e=n.instance().fromJD(t),r=e.year(),i=e.dayOfYear(),a=r+56;this._createMissingCalendarData(a);for(var o=9,s=this.NEPALI_CALENDAR_DATA[a][0],l=this.NEPALI_CALENDAR_DATA[a][o]-s+1;i>l;)++o>12&&(o=1,a++),l+=this.NEPALI_CALENDAR_DATA[a][o];var c=this.NEPALI_CALENDAR_DATA[a][o]-(l-i);return this.newDate(a,o,c)},_createMissingCalendarData:function(t){var e=this.daysPerMonth.slice(0);e.unshift(17);for(var r=t-1;r<t+2;r++)void 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e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return 682*((e.year()-(e.year()>0?474:473))%2820+474+38)%2816<682},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-(n.dayOfWeek()+1)%7,\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=t-(t>=0?474:473),s=474+o(a,2820);return r+(e<=7?31*(e-1):30*(e-1)+6)+Math.floor((682*s-110)/2816)+365*(s-1)+1029983*Math.floor(a/2820)+this.jdEpoch-1},fromJD:function(t){var e=(t=Math.floor(t)+.5)-this.toJD(475,1,1),r=Math.floor(e/1029983),n=o(e,1029983),i=2820;if(1029982!==n){var a=Math.floor(n/366),s=o(n,366);i=Math.floor((2134*a+2816*s+2815)/1028522)+a+1}var l=i+2820*r+474;l=l<=0?l-1:l;var 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n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"UmmAlQura\",hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Umm al-Qura\",epochs:[\"BH\",\"AH\"],monthNames:[\"Al-Muharram\",\"Safar\",\"Rabi' al-awwal\",\"Rabi' Al-Thani\",\"Jumada Al-Awwal\",\"Jumada Al-Thani\",\"Rajab\",\"Sha'aban\",\"Ramadan\",\"Shawwal\",\"Dhu al-Qi'dah\",\"Dhu al-Hijjah\"],monthNamesShort:[\"Muh\",\"Saf\",\"Rab1\",\"Rab2\",\"Jum1\",\"Jum2\",\"Raj\",\"Sha'\",\"Ram\",\"Shaw\",\"DhuQ\",\"DhuH\"],dayNames:[\"Yawm al-Ahad\",\"Yawm al-Ithnain\",\"Yawm al-Thal\\u0101th\\u0101\\u2019\",\"Yawm al-Arba\\u2018\\u0101\\u2019\",\"Yawm al-Kham\\u012bs\",\"Yawm al-Jum\\u2018a\",\"Yawm al-Sabt\"],dayNamesMin:[\"Ah\",\"Ith\",\"Th\",\"Ar\",\"Kh\",\"Ju\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:6,isRTL:!0}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return 355===this.daysInYear(e.year())},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){for(var e=0,r=1;r<=12;r++)e+=this.daysInMonth(t,r);return e},daysInMonth:function(t,e){for(var r=this._validate(t,e,this.minDay,n.local.invalidMonth).toJD()-24e5+.5,i=0,a=0;a<o.length;a++){if(o[a]>r)return o[i]-o[i-1];i++}return 30},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate),a=12*(i.year()-1)+i.month()-15292;return i.day()+o[a-1]-1+24e5-.5},fromJD:function(t){for(var e=t-24e5+.5,r=0,n=0;n<o.length&&!(o[n]>e);n++)r++;var i=r+15292,a=Math.floor((i-1)/12),s=a+1,l=i-12*a,c=e-o[r-1]+1;return this.newDate(s,l,c)},isValid:function(t,e,r){var i=n.baseCalendar.prototype.isValid.apply(this,arguments);return 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n=t(\"object-assign\");function i(){this.regionalOptions=[],this.regionalOptions[\"\"]={invalidCalendar:\"Calendar {0} not found\",invalidDate:\"Invalid {0} date\",invalidMonth:\"Invalid {0} month\",invalidYear:\"Invalid {0} year\",differentCalendars:\"Cannot mix {0} and {1} dates\"},this.local=this.regionalOptions[\"\"],this.calendars={},this._localCals={}}function a(t,e,r,n){if(this._calendar=t,this._year=e,this._month=r,this._day=n,0===this._calendar._validateLevel&&!this._calendar.isValid(this._year,this._month,this._day))throw(c.local.invalidDate||c.regionalOptions[\"\"].invalidDate).replace(/\\{0\\}/,this._calendar.local.name)}function o(t,e){return\"000000\".substring(0,e-(t=\"\"+t).length)+t}function s(){this.shortYearCutoff=\"+10\"}function l(t){this.local=this.regionalOptions[t]||this.regionalOptions[\"\"]}n(i.prototype,{instance:function(t,e){t=(t||\"gregorian\").toLowerCase(),e=e||\"\";var r=this._localCals[t+\"-\"+e];if(!r&&this.calendars[t]&&(r=new this.calendars[t](e),this._localCals[t+\"-\"+e]=r),!r)throw(this.local.invalidCalendar||this.regionalOptions[\"\"].invalidCalendar).replace(/\\{0\\}/,t);return r},newDate:function(t,e,r,n,i){return(n=(null!=t&&t.year?t.calendar():\"string\"==typeof n?this.instance(n,i):n)||this.instance()).newDate(t,e,r)},substituteDigits:function(t){return function(e){return(e+\"\").replace(/[0-9]/g,(function(e){return t[e]}))}},substituteChineseDigits:function(t,e){return function(r){for(var n=\"\",i=0;r>0;){var a=r%10;n=(0===a?\"\":t[a]+e[i])+n,i++,r=Math.floor(r/10)}return 0===n.indexOf(t[1]+e[1])&&(n=n.substr(1)),n||t[0]}}}),n(a.prototype,{newDate:function(t,e,r){return this._calendar.newDate(null==t?this:t,e,r)},year:function(t){return 0===arguments.length?this._year:this.set(t,\"y\")},month:function(t){return 0===arguments.length?this._month:this.set(t,\"m\")},day:function(t){return 0===arguments.length?this._day:this.set(t,\"d\")},date:function(t,e,r){if(!this._calendar.isValid(t,e,r))throw(c.local.invalidDate||c.regionalOptions[\"\"].invalidDate).replace(/\\{0\\}/,this._calendar.local.name);return this._year=t,this._month=e,this._day=r,this},leapYear:function(){return this._calendar.leapYear(this)},epoch:function(){return this._calendar.epoch(this)},formatYear:function(){return this._calendar.formatYear(this)},monthOfYear:function(){return this._calendar.monthOfYear(this)},weekOfYear:function(){return this._calendar.weekOfYear(this)},daysInYear:function(){return this._calendar.daysInYear(this)},dayOfYear:function(){return this._calendar.dayOfYear(this)},daysInMonth:function(){return this._calendar.daysInMonth(this)},dayOfWeek:function(){return this._calendar.dayOfWeek(this)},weekDay:function(){return this._calendar.weekDay(this)},extraInfo:function(){return this._calendar.extraInfo(this)},add:function(t,e){return this._calendar.add(this,t,e)},set:function(t,e){return this._calendar.set(this,t,e)},compareTo:function(t){if(this._calendar.name!==t._calendar.name)throw(c.local.differentCalendars||c.regionalOptions[\"\"].differentCalendars).replace(/\\{0\\}/,this._calendar.local.name).replace(/\\{1\\}/,t._calendar.local.name);var e=this._year!==t._year?this._year-t._year:this._month!==t._month?this.monthOfYear()-t.monthOfYear():this._day-t._day;return 0===e?0:e<0?-1:1},calendar:function(){return this._calendar},toJD:function(){return this._calendar.toJD(this)},fromJD:function(t){return this._calendar.fromJD(t)},toJSDate:function(){return this._calendar.toJSDate(this)},fromJSDate:function(t){return this._calendar.fromJSDate(t)},toString:function(){return(this.year()<0?\"-\":\"\")+o(Math.abs(this.year()),4)+\"-\"+o(this.month(),2)+\"-\"+o(this.day(),2)}}),n(s.prototype,{_validateLevel:0,newDate:function(t,e,r){return null==t?this.today():(t.year&&(this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate),r=t.day(),e=t.month(),t=t.year()),new a(this,t,e,r))},today:function(){return this.fromJSDate(new Date)},epoch:function(t){return this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear).year()<0?this.local.epochs[0]:this.local.epochs[1]},formatYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear);return(e.year()<0?\"-\":\"\")+o(Math.abs(e.year()),4)},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear),12},monthOfYear:function(t,e){var r=this._validate(t,e,this.minDay,c.local.invalidMonth||c.regionalOptions[\"\"].invalidMonth);return(r.month()+this.monthsInYear(r)-this.firstMonth)%this.monthsInYear(r)+this.minMonth},fromMonthOfYear:function(t,e){var r=(e+this.firstMonth-2*this.minMonth)%this.monthsInYear(t)+this.minMonth;return this._validate(t,r,this.minDay,c.local.invalidMonth||c.regionalOptions[\"\"].invalidMonth),r},daysInYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear);return this.leapYear(e)?366:365},dayOfYear:function(t,e,r){var n=this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate);return n.toJD()-this.newDate(n.year(),this.fromMonthOfYear(n.year(),this.minMonth),this.minDay).toJD()+1},daysInWeek:function(){return 7},dayOfWeek:function(t,e,r){var n=this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate);return(Math.floor(this.toJD(n))+2)%this.daysInWeek()},extraInfo:function(t,e,r){return this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate),{}},add:function(t,e,r){return this._validate(t,this.minMonth,this.minDay,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate),this._correctAdd(t,this._add(t,e,r),e,r)},_add:function(t,e,r){if(this._validateLevel++,\"d\"===r||\"w\"===r){var n=t.toJD()+e*(\"w\"===r?this.daysInWeek():1),i=t.calendar().fromJD(n);return this._validateLevel--,[i.year(),i.month(),i.day()]}try{var a=t.year()+(\"y\"===r?e:0),o=t.monthOfYear()+(\"m\"===r?e:0);i=t.day();\"y\"===r?(t.month()!==this.fromMonthOfYear(a,o)&&(o=this.newDate(a,t.month(),this.minDay).monthOfYear()),o=Math.min(o,this.monthsInYear(a)),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o)))):\"m\"===r&&(!function(t){for(;o<t.minMonth;)a--,o+=t.monthsInYear(a);for(var e=t.monthsInYear(a);o>e-1+t.minMonth;)a++,o-=e,e=t.monthsInYear(a)}(this),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o))));var s=[a,this.fromMonthOfYear(a,o),i];return this._validateLevel--,s}catch(t){throw 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r,s,l=o(t,e),c=l.on,u=l.off.concat(l.explicitOff),f={},h=t._fullLayout.annotations;if(!c.length&&!u.length)return;for(r=0;r<c.length;r++)(s=a(t.layout,\"annotations\",h[c[r]])).modifyItem(\"visible\",!0),n.extendFlat(f,s.getUpdateObj());for(r=0;r<u.length;r++)(s=a(t.layout,\"annotations\",h[u[r]])).modifyItem(\"visible\",!1),n.extendFlat(f,s.getUpdateObj());return i.call(\"update\",t,{},f)}}},{\"../../lib\":503,\"../../plot_api/plot_template\":543,\"../../registry\":638}],352:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../color\");e.exports=function(t,e,r,a){a(\"opacity\");var o=a(\"bgcolor\"),s=a(\"bordercolor\"),l=i.opacity(s);a(\"borderpad\");var c=a(\"borderwidth\"),u=a(\"showarrow\");if(a(\"text\",u?\" \":r._dfltTitle.annotation),a(\"textangle\"),n.coerceFont(a,\"font\",r.font),a(\"width\"),a(\"align\"),a(\"height\")&&a(\"valign\"),u){var f,h,p=a(\"arrowside\");-1!==p.indexOf(\"end\")&&(f=a(\"arrowhead\"),h=a(\"arrowsize\")),-1!==p.indexOf(\"start\")&&(a(\"startarrowhead\",f),a(\"startarrowsize\",h)),a(\"arrowcolor\",l?e.bordercolor:i.defaultLine),a(\"arrowwidth\",2*(l&&c||1)),a(\"standoff\"),a(\"startstandoff\")}var d=a(\"hovertext\"),g=r.hoverlabel||{};if(d){var m=a(\"hoverlabel.bgcolor\",g.bgcolor||(i.opacity(o)?i.rgb(o):i.defaultLine)),v=a(\"hoverlabel.bordercolor\",g.bordercolor||i.contrast(m));n.coerceFont(a,\"hoverlabel.font\",{family:g.font.family,size:g.font.size,color:g.font.color||v})}a(\"captureevents\",!!d)}},{\"../../lib\":503,\"../color\":366}],353:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),i=t(\"../../lib/to_log_range\");e.exports=function(t,e,r,a){e=e||{};var o=\"log\"===r&&\"linear\"===e.type,s=\"linear\"===r&&\"log\"===e.type;if(o||s)for(var l,c,u=t._fullLayout.annotations,f=e._id.charAt(0),h=0;h<u.length;h++)l=u[h],c=\"annotations[\"+h+\"].\",l[f+\"ref\"]===e._id&&p(f),l[\"a\"+f+\"ref\"]===e._id&&p(\"a\"+f);function p(t){var r=l[t],s=null;s=o?i(r,e.range):Math.pow(10,r),n(s)||(s=null),a(c+t,s)}}},{\"../../lib/to_log_range\":531,\"fast-isnumeric\":190}],354:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../plots/cartesian/axes\"),a=t(\"../../plots/array_container_defaults\"),o=t(\"./common_defaults\"),s=t(\"./attributes\");function l(t,e,r){function a(r,i){return n.coerce(t,e,s,r,i)}var l=a(\"visible\"),c=a(\"clicktoshow\");if(l||c){o(t,e,r,a);for(var u=e.showarrow,f=[\"x\",\"y\"],h=[-10,-30],p={_fullLayout:r},d=0;d<2;d++){var g=f[d],m=i.coerceRef(t,e,p,g,\"\",\"paper\");if(\"paper\"!==m)i.getFromId(p,m)._annIndices.push(e._index);if(i.coercePosition(e,p,a,m,g,.5),u){var v=\"a\"+g,y=i.coerceRef(t,e,p,v,\"pixel\",[\"pixel\",\"paper\"]);\"pixel\"!==y&&y!==m&&(y=e[v]=\"pixel\");var x=\"pixel\"===y?h[d]:.4;i.coercePosition(e,p,a,y,v,x)}a(g+\"anchor\"),a(g+\"shift\")}if(n.noneOrAll(t,e,[\"x\",\"y\"]),u&&n.noneOrAll(t,e,[\"ax\",\"ay\"]),c){var b=a(\"xclick\"),_=a(\"yclick\");e._xclick=void 0===b?e.x:i.cleanPosition(b,p,e.xref),e._yclick=void 0===_?e.y:i.cleanPosition(_,p,e.yref)}}}e.exports=function(t,e){a(t,e,{name:\"annotations\",handleItemDefaults:l})}},{\"../../lib\":503,\"../../plots/array_container_defaults\":549,\"../../plots/cartesian/axes\":554,\"./attributes\":349,\"./common_defaults\":352}],355:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"../../registry\"),a=t(\"../../plots/plots\"),o=t(\"../../lib\"),s=o.strTranslate,l=t(\"../../plots/cartesian/axes\"),c=t(\"../color\"),u=t(\"../drawing\"),f=t(\"../fx\"),h=t(\"../../lib/svg_text_utils\"),p=t(\"../../lib/setcursor\"),d=t(\"../dragelement\"),g=t(\"../../plot_api/plot_template\").arrayEditor,m=t(\"./draw_arrow_head\");function v(t,e){var r=t._fullLayout.annotations[e]||{},n=l.getFromId(t,r.xref),i=l.getFromId(t,r.yref);n&&n.setScale(),i&&i.setScale(),x(t,r,e,!1,n,i)}function y(t,e,r,n,i){var a=i[r],o=i[r+\"ref\"],s=-1!==r.indexOf(\"y\"),c=\"domain\"===l.getRefType(o),u=s?n.h:n.w;return t?c?a+(s?-e:e)/t._length:t.p2r(t.r2p(a)+e):a+(s?-e:e)/u}function x(t,e,r,a,v,x){var b,_,w=t._fullLayout,T=t._fullLayout._size,k=t._context.edits;a?(b=\"annotation-\"+a,_=a+\".annotations\"):(b=\"annotation\",_=\"annotations\");var A=g(t.layout,_,e),M=A.modifyBase,S=A.modifyItem,E=A.getUpdateObj;w._infolayer.selectAll(\".\"+b+'[data-index=\"'+r+'\"]').remove();var L=\"clip\"+w._uid+\"_ann\"+r;if(e._input&&!1!==e.visible){var C={x:{},y:{}},P=+e.textangle||0,I=w._infolayer.append(\"g\").classed(b,!0).attr(\"data-index\",String(r)).style(\"opacity\",e.opacity),O=I.append(\"g\").classed(\"annotation-text-g\",!0),z=k[e.showarrow?\"annotationTail\":\"annotationPosition\"],D=e.captureevents||k.annotationText||z,R=O.append(\"g\").style(\"pointer-events\",D?\"all\":null).call(p,\"pointer\").on(\"click\",(function(){t._dragging=!1,t.emit(\"plotly_clickannotation\",Y(n.event))}));e.hovertext&&R.on(\"mouseover\",(function(){var r=e.hoverlabel,n=r.font,i=this.getBoundingClientRect(),a=t.getBoundingClientRect();f.loneHover({x0:i.left-a.left,x1:i.right-a.left,y:(i.top+i.bottom)/2-a.top,text:e.hovertext,color:r.bgcolor,borderColor:r.bordercolor,fontFamily:n.family,fontSize:n.size,fontColor:n.color},{container:w._hoverlayer.node(),outerContainer:w._paper.node(),gd:t})})).on(\"mouseout\",(function(){f.loneUnhover(w._hoverlayer.node())}));var F=e.borderwidth,B=e.borderpad,N=F+B,j=R.append(\"rect\").attr(\"class\",\"bg\").style(\"stroke-width\",F+\"px\").call(c.stroke,e.bordercolor).call(c.fill,e.bgcolor),U=e.width||e.height,V=w._topclips.selectAll(\"#\"+L).data(U?[0]:[]);V.enter().append(\"clipPath\").classed(\"annclip\",!0).attr(\"id\",L).append(\"rect\"),V.exit().remove();var H=e.font,q=w._meta?o.templateString(e.text,w._meta):e.text,G=R.append(\"text\").classed(\"annotation-text\",!0).text(q);k.annotationText?G.call(h.makeEditable,{delegate:R,gd:t}).call(W).on(\"edit\",(function(r){e.text=r,this.call(W),S(\"text\",r),v&&v.autorange&&M(v._name+\".autorange\",!0),x&&x.autorange&&M(x._name+\".autorange\",!0),i.call(\"_guiRelayout\",t,E())})):G.call(W)}else n.selectAll(\"#\"+L).remove();function Y(t){var n={index:r,annotation:e._input,fullAnnotation:e,event:t};return a&&(n.subplotId=a),n}function W(r){return r.call(u.font,H).attr({\"text-anchor\":{left:\"start\",right:\"end\"}[e.align]||\"middle\"}),h.convertToTspans(r,t,X),r}function X(){var r=G.selectAll(\"a\");1===r.size()&&r.text()===G.text()&&R.insert(\"a\",\":first-child\").attr({\"xlink:xlink:href\":r.attr(\"xlink:href\"),\"xlink:xlink:show\":r.attr(\"xlink:show\")}).style({cursor:\"pointer\"}).node().appendChild(j.node());var n=R.select(\".annotation-text-math-group\"),f=!n.empty(),g=u.bBox((f?n:G).node()),b=g.width,_=g.height,A=e.width||b,D=e.height||_,B=Math.round(A+2*N),H=Math.round(D+2*N);function q(t,e){return\"auto\"===e&&(e=t<1/3?\"left\":t>2/3?\"right\":\"center\"),{center:0,middle:0,left:.5,bottom:-.5,right:-.5,top:.5}[e]}for(var W=!1,X=[\"x\",\"y\"],Z=0;Z<X.length;Z++){var J,K,Q,$,tt,et=X[Z],rt=e[et+\"ref\"]||et,nt=e[\"a\"+et+\"ref\"],it={x:v,y:x}[et],at=(P+(\"x\"===et?0:-90))*Math.PI/180,ot=B*Math.cos(at),st=H*Math.sin(at),lt=Math.abs(ot)+Math.abs(st),ct=e[et+\"anchor\"],ut=e[et+\"shift\"]*(\"x\"===et?1:-1),ft=C[et],ht=l.getRefType(rt);if(it&&\"domain\"!==ht){var pt=it.r2fraction(e[et]);(pt<0||pt>1)&&(nt===rt?((pt=it.r2fraction(e[\"a\"+et]))<0||pt>1)&&(W=!0):W=!0),J=it._offset+it.r2p(e[et]),$=.5}else{var dt=\"domain\"===ht;\"x\"===et?(Q=e[et],J=dt?it._offset+it._length*Q:J=T.l+T.w*Q):(Q=1-e[et],J=dt?it._offset+it._length*Q:J=T.t+T.h*Q),$=e.showarrow?.5:Q}if(e.showarrow){ft.head=J;var gt=e[\"a\"+et];if(tt=ot*q(.5,e.xanchor)-st*q(.5,e.yanchor),nt===rt){var mt=l.getRefType(nt);\"domain\"===mt?(\"y\"===et&&(gt=1-gt),ft.tail=it._offset+it._length*gt):\"paper\"===mt?\"y\"===et?(gt=1-gt,ft.tail=T.t+T.h*gt):ft.tail=T.l+T.w*gt:ft.tail=it._offset+it.r2p(gt),K=tt}else ft.tail=J+gt,K=tt+gt;ft.text=ft.tail+tt;var vt=w[\"x\"===et?\"width\":\"height\"];if(\"paper\"===rt&&(ft.head=o.constrain(ft.head,1,vt-1)),\"pixel\"===nt){var yt=-Math.max(ft.tail-3,ft.text),xt=Math.min(ft.tail+3,ft.text)-vt;yt>0?(ft.tail+=yt,ft.text+=yt):xt>0&&(ft.tail-=xt,ft.text-=xt)}ft.tail+=ut,ft.head+=ut}else K=tt=lt*q($,ct),ft.text=J+tt;ft.text+=ut,tt+=ut,K+=ut,e[\"_\"+et+\"padplus\"]=lt/2+K,e[\"_\"+et+\"padminus\"]=lt/2-K,e[\"_\"+et+\"size\"]=lt,e[\"_\"+et+\"shift\"]=tt}if(W)R.remove();else{var bt=0,_t=0;if(\"left\"!==e.align&&(bt=(A-b)*(\"center\"===e.align?.5:1)),\"top\"!==e.valign&&(_t=(D-_)*(\"middle\"===e.valign?.5:1)),f)n.select(\"svg\").attr({x:N+bt-1,y:N+_t}).call(u.setClipUrl,U?L:null,t);else{var wt=N+_t-g.top,Tt=N+bt-g.left;G.call(h.positionText,Tt,wt).call(u.setClipUrl,U?L:null,t)}V.select(\"rect\").call(u.setRect,N,N,A,D),j.call(u.setRect,F/2,F/2,B-F,H-F),R.call(u.setTranslate,Math.round(C.x.text-B/2),Math.round(C.y.text-H/2)),O.attr({transform:\"rotate(\"+P+\",\"+C.x.text+\",\"+C.y.text+\")\"});var kt,At=function(r,n){I.selectAll(\".annotation-arrow-g\").remove();var l=C.x.head,f=C.y.head,h=C.x.tail+r,p=C.y.tail+n,g=C.x.text+r,b=C.y.text+n,_=o.rotationXYMatrix(P,g,b),w=o.apply2DTransform(_),A=o.apply2DTransform2(_),L=+j.attr(\"width\"),z=+j.attr(\"height\"),D=g-.5*L,F=D+L,B=b-.5*z,N=B+z,U=[[D,B,D,N],[D,N,F,N],[F,N,F,B],[F,B,D,B]].map(A);if(!U.reduce((function(t,e){return t^!!o.segmentsIntersect(l,f,l+1e6,f+1e6,e[0],e[1],e[2],e[3])}),!1)){U.forEach((function(t){var e=o.segmentsIntersect(h,p,l,f,t[0],t[1],t[2],t[3]);e&&(h=e.x,p=e.y)}));var V=e.arrowwidth,H=e.arrowcolor,q=e.arrowside,G=I.append(\"g\").style({opacity:c.opacity(H)}).classed(\"annotation-arrow-g\",!0),Y=G.append(\"path\").attr(\"d\",\"M\"+h+\",\"+p+\"L\"+l+\",\"+f).style(\"stroke-width\",V+\"px\").call(c.stroke,c.rgb(H));if(m(Y,q,e),k.annotationPosition&&Y.node().parentNode&&!a){var W=l,X=f;if(e.standoff){var Z=Math.sqrt(Math.pow(l-h,2)+Math.pow(f-p,2));W+=e.standoff*(h-l)/Z,X+=e.standoff*(p-f)/Z}var J,K,Q=G.append(\"path\").classed(\"annotation-arrow\",!0).classed(\"anndrag\",!0).classed(\"cursor-move\",!0).attr({d:\"M3,3H-3V-3H3ZM0,0L\"+(h-W)+\",\"+(p-X),transform:s(W,X)}).style(\"stroke-width\",V+6+\"px\").call(c.stroke,\"rgba(0,0,0,0)\").call(c.fill,\"rgba(0,0,0,0)\");d.init({element:Q.node(),gd:t,prepFn:function(){var t=u.getTranslate(R);J=t.x,K=t.y,v&&v.autorange&&M(v._name+\".autorange\",!0),x&&x.autorange&&M(x._name+\".autorange\",!0)},moveFn:function(t,r){var n=w(J,K),i=n[0]+t,a=n[1]+r;R.call(u.setTranslate,i,a),S(\"x\",y(v,t,\"x\",T,e)),S(\"y\",y(x,r,\"y\",T,e)),e.axref===e.xref&&S(\"ax\",y(v,t,\"ax\",T,e)),e.ayref===e.yref&&S(\"ay\",y(x,r,\"ay\",T,e)),G.attr(\"transform\",s(t,r)),O.attr({transform:\"rotate(\"+P+\",\"+i+\",\"+a+\")\"})},doneFn:function(){i.call(\"_guiRelayout\",t,E());var e=document.querySelector(\".js-notes-box-panel\");e&&e.redraw(e.selectedObj)}})}}};if(e.showarrow&&At(0,0),z)d.init({element:R.node(),gd:t,prepFn:function(){kt=O.attr(\"transform\")},moveFn:function(t,r){var n=\"pointer\";if(e.showarrow)e.axref===e.xref?S(\"ax\",y(v,t,\"ax\",T,e)):S(\"ax\",e.ax+t),e.ayref===e.yref?S(\"ay\",y(x,r,\"ay\",T.w,e)):S(\"ay\",e.ay+r),At(t,r);else{if(a)return;var i,o;if(v)i=y(v,t,\"x\",T,e);else{var l=e._xsize/T.w,c=e.x+(e._xshift-e.xshift)/T.w-l/2;i=d.align(c+t/T.w,l,0,1,e.xanchor)}if(x)o=y(x,r,\"y\",T,e);else{var u=e._ysize/T.h,f=e.y-(e._yshift+e.yshift)/T.h-u/2;o=d.align(f-r/T.h,u,0,1,e.yanchor)}S(\"x\",i),S(\"y\",o),v&&x||(n=d.getCursor(v?.5:i,x?.5:o,e.xanchor,e.yanchor))}O.attr({transform:s(t,r)+kt}),p(R,n)},clickFn:function(r,n){e.captureevents&&t.emit(\"plotly_clickannotation\",Y(n))},doneFn:function(){p(R),i.call(\"_guiRelayout\",t,E());var e=document.querySelector(\".js-notes-box-panel\");e&&e.redraw(e.selectedObj)}})}}}e.exports={draw:function(t){var e=t._fullLayout;e._infolayer.selectAll(\".annotation\").remove();for(var r=0;r<e.annotations.length;r++)e.annotations[r].visible&&v(t,r);return a.previousPromises(t)},drawOne:v,drawRaw:x}},{\"../../lib\":503,\"../../lib/setcursor\":524,\"../../lib/svg_text_utils\":529,\"../../plot_api/plot_template\":543,\"../../plots/cartesian/axes\":554,\"../../plots/plots\":619,\"../../registry\":638,\"../color\":366,\"../dragelement\":385,\"../drawing\":388,\"../fx\":406,\"./draw_arrow_head\":356,\"@plotly/d3\":58}],356:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"../color\"),a=t(\"./arrow_paths\"),o=t(\"../../lib\"),s=o.strScale,l=o.strRotate,c=o.strTranslate;e.exports=function(t,e,r){var o,u,f,h,p=t.node(),d=a[r.arrowhead||0],g=a[r.startarrowhead||0],m=(r.arrowwidth||1)*(r.arrowsize||1),v=(r.arrowwidth||1)*(r.startarrowsize||1),y=e.indexOf(\"start\")>=0,x=e.indexOf(\"end\")>=0,b=d.backoff*m+r.standoff,_=g.backoff*v+r.startstandoff;if(\"line\"===p.nodeName){o={x:+t.attr(\"x1\"),y:+t.attr(\"y1\")},u={x:+t.attr(\"x2\"),y:+t.attr(\"y2\")};var w=o.x-u.x,T=o.y-u.y;if(h=(f=Math.atan2(T,w))+Math.PI,b&&_&&b+_>Math.sqrt(w*w+T*T))return void z();if(b){if(b*b>w*w+T*T)return void z();var k=b*Math.cos(f),A=b*Math.sin(f);u.x+=k,u.y+=A,t.attr({x2:u.x,y2:u.y})}if(_){if(_*_>w*w+T*T)return void z();var M=_*Math.cos(f),S=_*Math.sin(f);o.x-=M,o.y-=S,t.attr({x1:o.x,y1:o.y})}}else if(\"path\"===p.nodeName){var E=p.getTotalLength(),L=\"\";if(E<b+_)return void z();var C=p.getPointAtLength(0),P=p.getPointAtLength(.1);f=Math.atan2(C.y-P.y,C.x-P.x),o=p.getPointAtLength(Math.min(_,E)),L=\"0px,\"+_+\"px,\";var 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n=t(\"./draw\"),i=t(\"./click\");e.exports={moduleType:\"component\",name:\"annotations\",layoutAttributes:t(\"./attributes\"),supplyLayoutDefaults:t(\"./defaults\"),includeBasePlot:t(\"../../plots/cartesian/include_components\")(\"annotations\"),calcAutorange:t(\"./calc_autorange\"),draw:n.draw,drawOne:n.drawOne,drawRaw:n.drawRaw,hasClickToShow:i.hasClickToShow,onClick:i.onClick,convertCoords:t(\"./convert_coords\")}},{\"../../plots/cartesian/include_components\":567,\"./attributes\":349,\"./calc_autorange\":350,\"./click\":351,\"./convert_coords\":353,\"./defaults\":354,\"./draw\":355}],358:[function(t,e,r){\"use strict\";var 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c=!1;return{color:t.color||p.defaultLine,x0:t.x0||t.x||0,x1:t.x1||t.x||0,y0:t.y0||t.y||0,y1:t.y1||t.y||0,xLabel:t.xLabel,yLabel:t.yLabel,zLabel:t.zLabel,text:t.text,name:t.name,idealAlign:t.idealAlign,borderColor:t.borderColor,fontFamily:t.fontFamily,fontSize:t.fontSize,fontColor:t.fontColor,nameLength:t.nameLength,textAlign:t.textAlign,trace:t.trace||{index:0,hoverinfo:\"\"},xa:{_offset:0},ya:{_offset:0},index:0,hovertemplate:t.hovertemplate||!1,hovertemplateLabels:t.hovertemplateLabels||!1,eventData:c}})),{gd:i,hovermode:\"closest\",rotateLabels:!1,bgColor:e.bgColor||p.background,container:n.select(e.container),outerContainer:e.outerContainer||e.container}),l=0,c=0;return s.sort((function(t,e){return t.y0-e.y0})).each((function(t,r){var n=t.y0-t.by/2;t.offset=n-5<l?l-n+5:0,l=n+t.by+t.offset,r===e.anchorIndex&&(c=t.offset)})).each((function(t){t.offset-=c})),z(s,!1,i._fullLayout._invScaleX,i._fullLayout._invScaleY),r?s:s.node()};var P=/<extra>([\\s\\S]*)<\\/extra>/;function I(t,e){var r=e.gd,i=r._fullLayout,a=e.hovermode,c=e.rotateLabels,f=e.bgColor,d=e.container,g=e.outerContainer,w=e.commonLabelOpts||{};if(0===t.length)return[[]];var T=e.fontFamily||y.HOVERFONT,k=e.fontSize||y.HOVERFONTSIZE,A=t[0],E=A.xa,L=A.ya,P=a.charAt(0),I=A[P+\"Label\"],z=V(r,g),D=z.top,R=z.width,F=z.height,B=void 0!==I&&A.distance<=e.hoverdistance&&(\"x\"===a||\"y\"===a);if(B){var N,j,U=!0;for(N=0;N<t.length;N++)if(U&&void 0===t[N].zLabel&&(U=!1),j=t[N].hoverinfo||t[N].trace.hoverinfo){var H=Array.isArray(j)?j:j.split(\"+\");if(-1===H.indexOf(\"all\")&&-1===H.indexOf(a)){B=!1;break}}U&&(B=!1)}var q=d.selectAll(\"g.axistext\").data(B?[0]:[]);if(q.enter().append(\"g\").classed(\"axistext\",!0),q.exit().remove(),q.each((function(){var t=n.select(this),e=o.ensureSingle(t,\"path\",\"\",(function(t){t.style({\"stroke-width\":\"1px\"})})),l=o.ensureSingle(t,\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),c=w.bgcolor||p.defaultLine,f=w.bordercolor||p.contrast(c),d=p.contrast(c),g={family:w.font.family||T,size:w.font.size||k,color:w.font.color||d};e.style({fill:c,stroke:f}),l.text(I).call(h.font,g).call(u.positionText,0,0).call(u.convertToTspans,r),t.attr(\"transform\",\"\");var m,v,y=V(r,l.node());if(\"x\"===a){var x=\"top\"===E.side?\"-\":\"\";l.attr(\"text-anchor\",\"middle\").call(u.positionText,0,\"top\"===E.side?D-y.bottom-M-S:D-y.top+M+S),m=E._offset+(A.x0+A.x1)/2,v=L._offset+(\"top\"===E.side?0:L._length);var b=y.width/2+S;m<b?(m=b,e.attr(\"d\",\"M-\"+(b-M)+\",0L-\"+(b-2*M)+\",\"+x+M+\"H\"+(S+y.width/2)+\"v\"+x+(2*S+y.height)+\"H-\"+b+\"V\"+x+M+\"Z\")):m>i.width-b?(m=i.width-b,e.attr(\"d\",\"M\"+(b-M)+\",0L\"+b+\",\"+x+M+\"v\"+x+(2*S+y.height)+\"H-\"+b+\"V\"+x+M+\"H\"+(b-2*M)+\"Z\")):e.attr(\"d\",\"M0,0L\"+M+\",\"+x+M+\"H\"+(S+y.width/2)+\"v\"+x+(2*S+y.height)+\"H-\"+(S+y.width/2)+\"V\"+x+M+\"H-\"+M+\"Z\")}else{var _,C,P;\"right\"===L.side?(_=\"start\",C=1,P=\"\",m=E._offset+E._length):(_=\"end\",C=-1,P=\"-\",m=E._offset),v=L._offset+(A.y0+A.y1)/2,l.attr(\"text-anchor\",_),e.attr(\"d\",\"M0,0L\"+P+M+\",\"+M+\"V\"+(S+y.height/2)+\"h\"+P+(2*S+y.width)+\"V-\"+(S+y.height/2)+\"H\"+P+M+\"V-\"+M+\"Z\");var O,z=y.height/2,R=D-y.top-z,F=\"clip\"+i._uid+\"commonlabel\"+L._id;if(m<y.width+2*S+M){O=\"M-\"+(M+S)+\"-\"+z+\"h-\"+(y.width-S)+\"V\"+z+\"h\"+(y.width-S)+\"Z\";var B=y.width-m+S;u.positionText(l,B,R),\"end\"===_&&l.selectAll(\"tspan\").each((function(){var t=n.select(this),e=h.tester.append(\"text\").text(t.text()).call(h.font,g),i=V(r,e.node());Math.round(i.width)<Math.round(y.width)&&t.attr(\"x\",B-i.width),e.remove()}))}else u.positionText(l,C*(S+M),R),O=null;var N=i._topclips.selectAll(\"#\"+F).data(O?[0]:[]);N.enter().append(\"clipPath\").attr(\"id\",F).append(\"path\"),N.exit().remove(),N.select(\"path\").attr(\"d\",O),h.setClipUrl(l,O?F:null,r)}t.attr(\"transform\",s(m,v))})),v.isUnifiedHover(a)){d.selectAll(\"g.hovertext\").remove();var G=t.filter((function(t){return\"none\"!==t.hoverinfo}));if(0===G.length)return;var Y=i.hoverlabel,W=Y.font,X={showlegend:!0,legend:{title:{text:I,font:W},font:W,bgcolor:Y.bgcolor,bordercolor:Y.bordercolor,borderwidth:1,tracegroupgap:7,traceorder:i.legend?i.legend.traceorder:void 0,orientation:\"v\"}},Z={};x(X,Z,r._fullData);var J=Z.legend;J.entries=[];for(var K=0;K<G.length;K++){var Q=G[K];if(\"none\"!==Q.hoverinfo){var $=O(Q,!0,a,i,I),tt=$[0],et=$[1];Q.name=et,Q.text=\"\"!==et?et+\" : 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d=t.bgcolor||o,g=p.combine(p.opacity(d)?d:p.defaultLine,f),m=p.combine(p.opacity(o)?o:p.defaultLine,f),v=t.borderColor||p.contrast(g),y=O(t,B,a,i,I,e),x=y[0],b=y[1],w=e.select(\"text.nums\").call(h.font,t.fontFamily||T,t.fontSize||k,t.fontColor||v).text(x).attr(\"data-notex\",1).call(u.positionText,0,0).call(u.convertToTspans,r),A=e.select(\"text.name\"),E=0,L=0;if(b&&b!==x){A.call(h.font,t.fontFamily||T,t.fontSize||k,m).text(b).attr(\"data-notex\",1).call(u.positionText,0,0).call(u.convertToTspans,r);var C=V(r,A.node());E=C.width+2*S,L=C.height+2*S}else A.remove(),e.select(\"rect\").remove();e.select(\"path\").style({fill:g,stroke:v});var P=t.xa._offset+(t.x0+t.x1)/2,z=t.ya._offset+(t.y0+t.y1)/2,N=Math.abs(t.x1-t.x0),j=Math.abs(t.y1-t.y0),U=V(r,w.node()),H=U.width/i._invScaleX,q=U.height/i._invScaleY;t.ty0=(D-U.top)/i._invScaleY,t.bx=H+2*S,t.by=Math.max(q+2*S,L),t.anchor=\"start\",t.txwidth=H,t.tx2width=E,t.offset=0;var 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r=e.currentTarget;r.setAttribute(\"data-attr\",\"zoom\"),r.setAttribute(\"data-val\",\"reset\"),f(t,e),r.setAttribute(\"data-attr\",\"resetLastSave\"),p(t,e),x(t,\"geo\"),x(t,\"mapbox\")}},u.toggleSpikelines={name:\"toggleSpikelines\",title:function(t){return c(t,\"Toggle Spike Lines\")},icon:o.spikeline,attr:\"_cartesianSpikesEnabled\",val:\"on\",click:function(t){var e=t._fullLayout,r=e._cartesianSpikesEnabled;e._cartesianSpikesEnabled=\"on\"===r?\"off\":\"on\",n.call(\"_guiRelayout\",t,function(t){for(var e=\"on\"===t._fullLayout._cartesianSpikesEnabled,r=a.list(t,null,!0),n={},i=0;i<r.length;i++){var o=r[i];n[o._name+\".showspikes\"]=!!e||o._showSpikeInitial}return n}(t))}},u.resetViewMapbox={name:\"resetViewMapbox\",_cat:\"resetView\",title:function(t){return c(t,\"Reset view\")},attr:\"reset\",icon:o.home,click:function(t){x(t,\"mapbox\")}},u.zoomInMapbox={name:\"zoomInMapbox\",_cat:\"zoomin\",title:function(t){return c(t,\"Zoom 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n=t(\"../../plots/font_attributes\"),i=t(\"../../plots/pad_attributes\"),a=t(\"../../lib/extend\").extendDeepAll,o=t(\"../../plot_api/edit_types\").overrideAll,s=t(\"../../plots/animation_attributes\"),l=t(\"../../plot_api/plot_template\").templatedArray,c=t(\"./constants\"),u=l(\"step\",{visible:{valType:\"boolean\",dflt:!0},method:{valType:\"enumerated\",values:[\"restyle\",\"relayout\",\"animate\",\"update\",\"skip\"],dflt:\"restyle\"},args:{valType:\"info_array\",freeLength:!0,items:[{valType:\"any\"},{valType:\"any\"},{valType:\"any\"}]},label:{valType:\"string\"},value:{valType:\"string\"},execute:{valType:\"boolean\",dflt:!0}});e.exports=o(l(\"slider\",{visible:{valType:\"boolean\",dflt:!0},active:{valType:\"number\",min:0,dflt:0},steps:u,lenmode:{valType:\"enumerated\",values:[\"fraction\",\"pixels\"],dflt:\"fraction\"},len:{valType:\"number\",min:0,dflt:1},x:{valType:\"number\",min:-2,max:3,dflt:0},pad:a(i({editType:\"arraydraw\"}),{},{t:{dflt:20}}),xanchor:{valType:\"enumerated\",values:[\"auto\",\"left\",\"center\",\"right\"],dflt:\"left\"},y:{valType:\"number\",min:-2,max:3,dflt:0},yanchor:{valType:\"enumerated\",values:[\"auto\",\"top\",\"middle\",\"bottom\"],dflt:\"top\"},transition:{duration:{valType:\"number\",min:0,dflt:150},easing:{valType:\"enumerated\",values:s.transition.easing.values,dflt:\"cubic-in-out\"}},currentvalue:{visible:{valType:\"boolean\",dflt:!0},xanchor:{valType:\"enumerated\",values:[\"left\",\"center\",\"right\"],dflt:\"left\"},offset:{valType:\"number\",dflt:10},prefix:{valType:\"string\"},suffix:{valType:\"string\"},font:n({})},font:n({}),activebgcolor:{valType:\"color\",dflt:c.gripBgActiveColor},bgcolor:{valType:\"color\",dflt:c.railBgColor},bordercolor:{valType:\"color\",dflt:c.railBorderColor},borderwidth:{valType:\"number\",min:0,dflt:c.railBorderWidth},ticklen:{valType:\"number\",min:0,dflt:c.tickLength},tickcolor:{valType:\"color\",dflt:c.tickColor},tickwidth:{valType:\"number\",min:0,dflt:1},minorticklen:{valType:\"number\",min:0,dflt:c.minorTickLength}}),\"arraydraw\",\"from-root\")},{\"../../lib/extend\":493,\"../../plot_api/edit_types\":536,\"../../plot_api/plot_template\":543,\"../../plots/animation_attributes\":548,\"../../plots/font_attributes\":585,\"../../plots/pad_attributes\":618,\"./constants\":460}],460:[function(t,e,r){\"use 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T={y:e.y,b:u.height*g[w],t:u.height*d[w]};\"fraction\"===e.lenmode?(T.l=0,T.xl=e.x-e.len*d[b],T.r=0,T.xr=e.x+e.len*g[b]):(T.x=e.x,T.l=u.outerLength*d[b],T.r=u.outerLength*g[b]),i.autoMargin(t,m(e),T)}function x(t,e,r){if(e.currentvalue.visible){var n,i,a=e._dims;switch(e.currentvalue.xanchor){case\"right\":n=a.inputAreaLength-f.currentValueInset-a.currentValueMaxWidth,i=\"left\";break;case\"center\":n=.5*a.inputAreaLength,i=\"middle\";break;default:n=f.currentValueInset,i=\"left\"}var l=s.ensureSingle(t,\"text\",f.labelClass,(function(t){t.attr({\"text-anchor\":i,\"data-notex\":1})})),u=e.currentvalue.prefix?e.currentvalue.prefix:\"\";if(\"string\"==typeof r)u+=r;else{var h=e.steps[e.active].label,d=e._gd._fullLayout._meta;d&&(h=s.templateString(h,d)),u+=h}e.currentvalue.suffix&&(u+=e.currentvalue.suffix),l.call(o.font,e.currentvalue.font).text(u).call(c.convertToTspans,e._gd);var g=c.lineCount(l),m=(a.currentValueMaxLines+1-g)*e.currentvalue.font.size*p;return 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t=s(),a=L(t,n.mouse(i)[0]);T(e,r,t,a,!1)}function h(){var t=s();t._dragging=!1,l.call(a.fill,t.bgcolor),o.on(\"mouseup\",null),o.on(\"mousemove\",null),o.on(\"touchend\",null),o.on(\"touchmove\",null),e.emit(\"plotly_sliderend\",{slider:t,step:t.steps[t.active]})}T(e,r,t,c,!0),t._dragging=!0,o.on(\"mousemove\",u),o.on(\"touchmove\",u),o.on(\"mouseup\",h),o.on(\"touchend\",h)}t.on(\"mousedown\",l),t.on(\"touchstart\",l)}function M(t,e){var r=t.selectAll(\"rect.\"+f.tickRectClass).data(e._visibleSteps),i=e._dims;r.enter().append(\"rect\").classed(f.tickRectClass,!0),r.exit().remove(),r.attr({width:e.tickwidth+\"px\",\"shape-rendering\":\"crispEdges\"}),r.each((function(t,r){var s=r%i.labelStride==0,l=n.select(this);l.attr({height:s?e.ticklen:e.minorticklen}).call(a.fill,e.tickcolor),o.setTranslate(l,E(e,r/(e._stepCount-1))-.5*e.tickwidth,(s?f.tickOffset:f.minorTickOffset)+i.currentValueTotalHeight)}))}function S(t,e,r){for(var 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l=a.selectAll(\"g.\"+f.groupClassName).data(r,v);l.enter().append(\"g\").classed(f.groupClassName,!0),l.exit().each(s).remove();for(var c=0;c<r.length;c++){var u=r[c];y(t,u)}l.each((function(e){var r=n.select(this);!function(t){var e=t._dims;e.labelSteps=[];for(var r=t._stepCount,n=0;n<r;n+=e.labelStride)e.labelSteps.push({fraction:n/(r-1),step:t._visibleSteps[n]})}(e),i.manageCommandObserver(t,e,e._visibleSteps,(function(e){var n=r.data()[0];n.active!==e.index&&(n._dragging||k(t,r,n,e.index,!1,!0))})),function(t,e,r){(r.steps[r.active]||{}).visible||(r.active=r._visibleSteps[0]._index);e.call(x,r).call(P,r).call(w,r).call(M,r).call(C,t,r).call(b,t,r);var n=r._dims;o.setTranslate(e,n.lx+r.pad.l,n.ly+r.pad.t),e.call(S,r,!1),e.call(x,r)}(t,n.select(this),e)}))}}},{\"../../constants/alignment\":471,\"../../lib\":503,\"../../lib/svg_text_utils\":529,\"../../plot_api/plot_template\":543,\"../../plots/plots\":619,\"../color\":366,\"../drawing\":388,\"./constants\":460,\"@plotly/d3\":58}],463:[function(t,e,r){\"use strict\";var n=t(\"./constants\");e.exports={moduleType:\"component\",name:n.name,layoutAttributes:t(\"./attributes\"),supplyLayoutDefaults:t(\"./defaults\"),draw:t(\"./draw\")}},{\"./attributes\":459,\"./constants\":460,\"./defaults\":461,\"./draw\":462}],464:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"fast-isnumeric\"),a=t(\"../../plots/plots\"),o=t(\"../../registry\"),s=t(\"../../lib\"),l=s.strTranslate,c=t(\"../drawing\"),u=t(\"../color\"),f=t(\"../../lib/svg_text_utils\"),h=t(\"../../constants/interactions\"),p=t(\"../../constants/alignment\").OPPOSITE_SIDE,d=/ [XY][0-9]* /;e.exports={draw:function(t,e,r){var g,m=r.propContainer,v=r.propName,y=r.placeholder,x=r.traceIndex,b=r.avoid||{},_=r.attributes,w=r.transform,T=r.containerGroup,k=t._fullLayout,A=1,M=!1,S=m.title,E=(S&&S.text?S.text:\"\").trim(),L=S&&S.font?S.font:{},C=L.family,P=L.size,I=L.color;\"title.text\"===v?g=\"titleText\":-1!==v.indexOf(\"axis\")?g=\"axisTitleText\":v.indexOf(!0)&&(g=\"colorbarTitleText\");var O=t._context.edits[g];\"\"===E?A=0:E.replace(d,\" % \")===y.replace(d,\" % \")&&(A=.2,M=!0,O||(E=\"\")),r._meta?E=s.templateString(E,r._meta):k._meta&&(E=s.templateString(E,k._meta));var z=E||O;T||(T=s.ensureSingle(k._infolayer,\"g\",\"g-\"+e));var D=T.selectAll(\"text\").data(z?[0]:[]);if(D.enter().append(\"text\"),D.text(E).attr(\"class\",e),D.exit().remove(),!z)return T;function R(t){s.syncOrAsync([F,B],t)}function F(e){var r;return 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n=t(\"../../plots/font_attributes\"),i=t(\"../color/attributes\"),a=t(\"../../lib/extend\").extendFlat,o=t(\"../../plot_api/edit_types\").overrideAll,s=t(\"../../plots/pad_attributes\"),l=t(\"../../plot_api/plot_template\").templatedArray,c=l(\"button\",{visible:{valType:\"boolean\"},method:{valType:\"enumerated\",values:[\"restyle\",\"relayout\",\"animate\",\"update\",\"skip\"],dflt:\"restyle\"},args:{valType:\"info_array\",freeLength:!0,items:[{valType:\"any\"},{valType:\"any\"},{valType:\"any\"}]},args2:{valType:\"info_array\",freeLength:!0,items:[{valType:\"any\"},{valType:\"any\"},{valType:\"any\"}]},label:{valType:\"string\",dflt:\"\"},execute:{valType:\"boolean\",dflt:!0}});e.exports=o(l(\"updatemenu\",{_arrayAttrRegexps:[/^updatemenus\\[(0|[1-9][0-9]+)\\]\\.buttons/],visible:{valType:\"boolean\"},type:{valType:\"enumerated\",values:[\"dropdown\",\"buttons\"],dflt:\"dropdown\"},direction:{valType:\"enumerated\",values:[\"left\",\"right\",\"up\",\"down\"],dflt:\"down\"},active:{valType:\"integer\",min:-1,dflt:0},showactive:{valType:\"boolean\",dflt:!0},buttons:c,x:{valType:\"number\",min:-2,max:3,dflt:-.05},xanchor:{valType:\"enumerated\",values:[\"auto\",\"left\",\"center\",\"right\"],dflt:\"right\"},y:{valType:\"number\",min:-2,max:3,dflt:1},yanchor:{valType:\"enumerated\",values:[\"auto\",\"top\",\"middle\",\"bottom\"],dflt:\"top\"},pad:a(s({editType:\"arraydraw\"}),{}),font:n({}),bgcolor:{valType:\"color\"},bordercolor:{valType:\"color\",dflt:i.borderLine},borderwidth:{valType:\"number\",min:0,dflt:1,editType:\"arraydraw\"}}),\"arraydraw\",\"from-root\")},{\"../../lib/extend\":493,\"../../plot_api/edit_types\":536,\"../../plot_api/plot_template\":543,\"../../plots/font_attributes\":585,\"../../plots/pad_attributes\":618,\"../color/attributes\":365}],466:[function(t,e,r){\"use strict\";e.exports={name:\"updatemenus\",containerClassName:\"updatemenu-container\",headerGroupClassName:\"updatemenu-header-group\",headerClassName:\"updatemenu-header\",headerArrowClassName:\"updatemenu-header-arrow\",dropdownButtonGroupClassName:\"updatemenu-dropdown-button-group\",dropdownButtonClassName:\"updatemenu-dropdown-button\",buttonClassName:\"updatemenu-button\",itemRectClassName:\"updatemenu-item-rect\",itemTextClassName:\"updatemenu-item-text\",menuIndexAttrName:\"updatemenu-active-index\",autoMarginIdRoot:\"updatemenu-\",blankHeaderOpts:{label:\"  \"},minWidth:30,minHeight:30,textPadX:24,arrowPadX:16,rx:2,ry:2,textOffsetX:12,textOffsetY:3,arrowOffsetX:4,gapButtonHeader:5,gapButton:2,activeColor:\"#F4FAFF\",hoverColor:\"#F4FAFF\",arrowSymbol:{left:\"\\u25c4\",right:\"\\u25ba\",up:\"\\u25b2\",down:\"\\u25bc\"}}},{}],467:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../plots/array_container_defaults\"),a=t(\"./attributes\"),o=t(\"./constants\").name,s=a.buttons;function l(t,e,r){function o(r,i){return n.coerce(t,e,a,r,i)}o(\"visible\",i(t,e,{name:\"buttons\",handleItemDefaults:c}).length>0)&&(o(\"active\"),o(\"direction\"),o(\"type\"),o(\"showactive\"),o(\"x\"),o(\"y\"),n.noneOrAll(t,e,[\"x\",\"y\"]),o(\"xanchor\"),o(\"yanchor\"),o(\"pad.t\"),o(\"pad.r\"),o(\"pad.b\"),o(\"pad.l\"),n.coerceFont(o,\"font\",r.font),o(\"bgcolor\",r.paper_bgcolor),o(\"bordercolor\"),o(\"borderwidth\"))}function c(t,e){function r(r,i){return n.coerce(t,e,s,r,i)}r(\"visible\",\"skip\"===t.method||Array.isArray(t.args))&&(r(\"method\"),r(\"args\"),r(\"args2\"),r(\"label\"),r(\"execute\"))}e.exports=function(t,e){i(t,e,{name:o,handleItemDefaults:l})}},{\"../../lib\":503,\"../../plots/array_container_defaults\":549,\"./attributes\":465,\"./constants\":466}],468:[function(t,e,r){\"use strict\";var 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l=function(t){return-1==+t.attr(f.menuIndexAttrName)}(r)&&\"buttons\"!==o.type?[]:o.buttons,c=\"dropdown\"===o.type?f.dropdownButtonClassName:f.buttonClassName,u=r.selectAll(\"g.\"+c).data(s.filterVisible(l)),h=u.enter().append(\"g\").classed(c,!0),p=u.exit();\"dropdown\"===o.type?(h.attr(\"opacity\",\"0\").transition().attr(\"opacity\",\"1\"),p.transition().attr(\"opacity\",\"0\").remove()):p.remove();var d=0,m=0,v=o._dims,x=-1!==[\"up\",\"down\"].indexOf(o.direction);\"dropdown\"===o.type&&(x?m=v.headerHeight+f.gapButtonHeader:d=v.headerWidth+f.gapButtonHeader),\"dropdown\"===o.type&&\"up\"===o.direction&&(m=-f.gapButtonHeader+f.gapButton-v.openHeight),\"dropdown\"===o.type&&\"left\"===o.direction&&(d=-f.gapButtonHeader+f.gapButton-v.openWidth);var b={x:v.lx+d+o.pad.l,y:v.ly+m+o.pad.t,yPad:f.gapButton,xPad:f.gapButton,index:0},k={l:b.x+o.borderwidth,t:b.y+o.borderwidth};u.each((function(s,l){var 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h=s.select(\".\"+f.itemTextClassName),p=h.node()&&o.bBox(h.node()).width,d=Math.max(p+f.textPadX,f.minWidth),g=e.font.size*u,m=l.lineCount(h),v=Math.max(g*m,f.minHeight)+f.textOffsetY;v=Math.ceil(v),d=Math.ceil(d),r.widths[a]=d,r.heights[a]=v,r.height1=Math.max(r.height1,v),r.width1=Math.max(r.width1,d),c?(r.totalWidth=Math.max(r.totalWidth,d),r.openWidth=r.totalWidth,r.totalHeight+=v+f.gapButton,r.openHeight+=v+f.gapButton):(r.totalWidth+=d+f.gapButton,r.openWidth+=d+f.gapButton,r.totalHeight=Math.max(r.totalHeight,v),r.openHeight=r.totalHeight)})),c?r.totalHeight-=f.gapButton:r.totalWidth-=f.gapButton,r.headerWidth=r.width1+f.arrowPadX,r.headerHeight=r.height1,\"dropdown\"===e.type&&(c?(r.width1+=f.arrowPadX,r.totalHeight=r.height1):r.totalWidth=r.width1,r.totalWidth+=f.arrowPadX),a.remove();var h=r.totalWidth+e.pad.l+e.pad.r,p=r.totalHeight+e.pad.t+e.pad.b,d=t._fullLayout._size;r.lx=d.l+d.w*e.x,r.ly=d.t+d.h*(1-e.y);var 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s(t,e,r){this.gd=t,this.container=e,this.id=r,this.position=null,this.translateX=null,this.translateY=null,this.hbar=null,this.vbar=null,this.bg=this.container.selectAll(\"rect.scrollbox-bg\").data([0]),this.bg.exit().on(\".drag\",null).on(\"wheel\",null).remove(),this.bg.enter().append(\"rect\").classed(\"scrollbox-bg\",!0).style(\"pointer-events\",\"all\").attr({opacity:0,x:0,y:0,width:0,height:0})}s.barWidth=2,s.barLength=20,s.barRadius=2,s.barPad=1,s.barColor=\"#808BA4\",s.prototype.enable=function(t,e,r){var o=this.gd._fullLayout,l=o.width,c=o.height;this.position=t;var u,f,h,p,d=this.position.l,g=this.position.w,m=this.position.t,v=this.position.h,y=this.position.direction,x=\"down\"===y,b=\"left\"===y,_=\"up\"===y,w=g,T=v;x||b||\"right\"===y||_||(this.position.direction=\"down\",x=!0),x||_?(f=(u=d)+w,x?(h=m,T=(p=Math.min(h+T,c))-h):T=(p=m+T)-(h=Math.max(p-T,0))):(p=(h=m)+T,b?w=(f=d+w)-(u=Math.max(f-w,0)):(u=d,w=(f=Math.min(u+w,l))-u)),this._box={l:u,t:h,w:w,h:T};var k=g>w,A=s.barLength+2*s.barPad,M=s.barWidth+2*s.barPad,S=d,E=m+v;E+M>c&&(E=c-M);var L=this.container.selectAll(\"rect.scrollbar-horizontal\").data(k?[0]:[]);L.exit().on(\".drag\",null).remove(),L.enter().append(\"rect\").classed(\"scrollbar-horizontal\",!0).call(i.fill,s.barColor),k?(this.hbar=L.attr({rx:s.barRadius,ry:s.barRadius,x:S,y:E,width:A,height:M}),this._hbarXMin=S+A/2,this._hbarTranslateMax=w-A):(delete this.hbar,delete this._hbarXMin,delete this._hbarTranslateMax);var C=v>T,P=s.barWidth+2*s.barPad,I=s.barLength+2*s.barPad,O=d+g,z=m;O+P>l&&(O=l-P);var D=this.container.selectAll(\"rect.scrollbar-vertical\").data(C?[0]:[]);D.exit().on(\".drag\",null).remove(),D.enter().append(\"rect\").classed(\"scrollbar-vertical\",!0).call(i.fill,s.barColor),C?(this.vbar=D.attr({rx:s.barRadius,ry:s.barRadius,x:O,y:z,width:P,height:I}),this._vbarYMin=z+I/2,this._vbarTranslateMax=T-I):(delete this.vbar,delete this._vbarYMin,delete this._vbarTranslateMax);var 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t.dtick=e>.3?\"D2\":\"D1\"}else\"category\"===t.type||\"multicategory\"===t.type?(t.tick0=0,t.dtick=Math.ceil(Math.max(e,1))):At(t)?(t.tick0=0,r=1,t.dtick=ot(e,r,at)):(t.tick0=0,r=n(10),t.dtick=ot(e,r,$));if(0===t.dtick&&(t.dtick=1),!i(t.dtick)&&\"string\"!=typeof t.dtick){var f=t.dtick;throw t.dtick=1,\"ax.dtick error: \"+String(f)}},V.tickIncrement=function(t,e,r,a){var o=r?-1:1;if(i(e))return s.increment(t,o*e);var l=e.charAt(0),c=o*Number(e.substr(1));if(\"M\"===l)return s.incrementMonth(t,c,a);if(\"L\"===l)return Math.log(Math.pow(10,t)+c)/Math.LN10;if(\"D\"===l){var u=\"D2\"===e?it:nt,f=t+.01*o,h=s.roundUp(s.mod(f,1),u,r);return Math.floor(f)+Math.log(n.round(Math.pow(10,h),1))/Math.LN10}throw\"unrecognized dtick \"+String(e)},V.tickFirst=function(t,e){var r=t.r2l||Number,a=s.simpleMap(t.range,r,void 0,void 0,e),o=a[1]<a[0],l=o?Math.floor:Math.ceil,c=X(a)[0],u=t.dtick,f=r(t.tick0);if(i(u)){var 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h=s.simpleMap(t.range,t.r2l),p=(Math.abs(h[1]-h[0])-(t._lBreaks||0))/1e4;for(a=0;a<t.ticktext.length&&!(Math.abs(e-f(t.tickvals[a]))<p);a++);if(a<t.ticktext.length)return o.text=String(t.ticktext[a]),o}function d(n){if(void 0===n)return!0;if(r)return\"none\"===n;var i={first:t._tmin,last:t._tmax}[n];return\"all\"!==n&&e!==i}var g=r?\"never\":\"none\"!==t.exponentformat&&d(t.showexponent)?\"hide\":\"\";if(\"date\"===u?function(t,e,r,n){var a=t._tickround,o=r&&t.hoverformat||V.getTickFormat(t);n&&(a=i(a)?4:{y:\"m\",m:\"d\",d:\"M\",M:\"S\",S:4}[a]);var l,c=s.formatDate(e.x,o,a,t._dateFormat,t.calendar,t._extraFormat),u=c.indexOf(\"\\n\");-1!==u&&(l=c.substr(u+1),c=c.substr(0,u));n&&(\"00:00:00\"===c||\"00:00\"===c?(c=l,l=\"\"):8===c.length&&(c=c.replace(/:00$/,\"\")));if(l)if(r)\"d\"===a?c+=\", \"+l:c=l+(c?\", \"+c:\"\");else if(t._inCalcTicks&&t._prevDateHead===l){var f=St(t),h=t._trueSide||t.side;(!f&&\"top\"===h||f&&\"bottom\"===h)&&(c+=\"<br> \")}else 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a=n._fullLayout._zoomlayer.selectAll(\".select-outline-\"+r.id);if(a&&n._fullLayout._drawing){var o=v(a,t);o&&i.call(\"_guiRelayout\",n,{shapes:o}),n._fullLayout._drawing=!1}}r.selection={},r.selection.selectionDefs=t.selectionDefs=[],r.selection.mergedPolygons=t.mergedPolygons=[]}function N(t,e,r,n){var i,a,o,s=[],l=e.map((function(t){return t._id})),c=r.map((function(t){return t._id}));for(o=0;o<t.calcdata.length;o++)if(!0===(a=(i=t.calcdata[o])[0].trace).visible&&a._module&&a._module.selectPoints)if(!n||a.subplot!==n&&a.geo!==n)if(\"splom\"===a.type&&a._xaxes[l[0]]&&a._yaxes[c[0]]){var u=h(a._module,i,e[0],r[0]);u.scene=t._fullLayout._splomScenes[a.uid],s.push(u)}else if(\"sankey\"===a.type){var f=h(a._module,i,e[0],r[0]);s.push(f)}else{if(-1===l.indexOf(a.xaxis))continue;if(-1===c.indexOf(a.yaxis))continue;s.push(h(a._module,i,_(t,a.xaxis),_(t,a.yaxis)))}else s.push(h(a._module,i,e[0],r[0]));return s;function h(t,e,r,n){return{_module:t,cd:e,xaxis:r,yaxis:n}}}function j(t){var 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Boolean(w.lineLayers[t])}function c(t){return Boolean(w.fillLayers[t])}var u=(this.hasChoropleth?w.layersForChoropleth:w.layers).filter((function(t){return l(t)||c(t)?e[\"show\"+t]:!s(t)||e[t].showgrid})),p=r.framework.selectAll(\".layer\").data(u,String);p.exit().each((function(t){delete a[t],delete o[t],n.select(this).remove()})),p.enter().append(\"g\").attr(\"class\",(function(t){return\"layer \"+t})).each((function(t){var e=a[t]=n.select(this);\"bg\"===t?r.bgRect=e.append(\"rect\").style(\"pointer-events\",\"all\"):s(t)?o[t]=e.append(\"path\").style(\"fill\",\"none\"):\"backplot\"===t?e.append(\"g\").classed(\"choroplethlayer\",!0):\"frontplot\"===t?e.append(\"g\").classed(\"scatterlayer\",!0):l(t)?o[t]=e.append(\"path\").style(\"fill\",\"none\").style(\"stroke-miterlimit\",2):c(t)&&(o[t]=e.append(\"path\").style(\"stroke\",\"none\"))})),p.order(),p.each((function(r){var 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r=this.bounds,n=(e.framewidth||0)/2,i=r[0][0]-n,a=r[0][1]-n,o=r[1][0]-i+n,s=r[1][1]-a+n;h.setRect(this.clipRect,i,a,o,s),this.bgRect.call(h.setRect,i,a,o,s).call(f.fill,e.bgcolor),this.xaxis._offset=i,this.xaxis._length=o,this.yaxis._offset=a,this.yaxis._length=s},S.updateFx=function(t,e){var r=this,i=r.graphDiv,a=r.bgRect,o=t.dragmode,s=t.clickmode;if(!r.isStatic){var u;\"select\"===o?u=function(t,e){(t.range={})[r.id]=[h([e.xmin,e.ymin]),h([e.xmax,e.ymax])]}:\"lasso\"===o&&(u=function(t,e,n){(t.lassoPoints={})[r.id]=n.filtered.map(h)});var f={element:r.bgRect.node(),gd:i,plotinfo:{id:r.id,xaxis:r.xaxis,yaxis:r.yaxis,fillRangeItems:u},xaxes:[r.xaxis],yaxes:[r.yaxis],subplot:r.id,clickFn:function(t){2===t&&x(i)}};\"pan\"===o?(a.node().onmousedown=null,a.call(_(r,e)),a.on(\"dblclick.zoom\",(function(){var t=r.viewInitial,e={};for(var n in 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t=this,e=t.graphDiv,r=e._fullLayout,i=\"clip\"+r._uid+t.id;t.clipDef=r._clips.append(\"clipPath\").attr(\"id\",i),t.clipRect=t.clipDef.append(\"rect\"),t.framework=n.select(t.container).append(\"g\").attr(\"class\",\"geo \"+t.id).call(h.setClipUrl,i,e),t.project=function(e){var r=t.projection(e);return r?[r[0]-t.xaxis._offset,r[1]-t.yaxis._offset]:[null,null]},t.xaxis={_id:\"x\",c2p:function(e){return t.project(e)[0]}},t.yaxis={_id:\"y\",c2p:function(e){return t.project(e)[1]}},t.mockAxis={type:\"linear\",showexponent:\"all\",exponentformat:\"B\"},g.setConvert(t.mockAxis,r)},S.saveViewInitial=function(t){var 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_=(l[3]-l[1])/(l[2]-l[0]);Math.abs(a*_)>Math.abs(s)?(c.boxEnd[1]=c.boxStart[1]+Math.abs(a)*_*(s>=0?1:-1),c.boxEnd[1]<l[1]?(c.boxEnd[1]=l[1],c.boxEnd[0]=c.boxStart[0]+(l[1]-c.boxStart[1])/Math.abs(_)):c.boxEnd[1]>l[3]&&(c.boxEnd[1]=l[3],c.boxEnd[0]=c.boxStart[0]+(l[3]-c.boxStart[1])/Math.abs(_))):(c.boxEnd[0]=c.boxStart[0]+Math.abs(s)/_*(a>=0?1:-1),c.boxEnd[0]<l[0]?(c.boxEnd[0]=l[0],c.boxEnd[1]=c.boxStart[1]+(l[0]-c.boxStart[0])*Math.abs(_)):c.boxEnd[0]>l[2]&&(c.boxEnd[0]=l[2],c.boxEnd[1]=c.boxStart[1]+(l[2]-c.boxStart[0])*Math.abs(_)))}}else c.boxEnabled?(a=c.boxStart[0]!==c.boxEnd[0],s=c.boxStart[1]!==c.boxEnd[1],a||s?(a&&(m(0,c.boxStart[0],c.boxEnd[0]),t.xaxis.autorange=!1),s&&(m(1,c.boxStart[1],c.boxEnd[1]),t.yaxis.autorange=!1),t.relayoutCallback()):t.glplot.setDirty(),c.boxEnabled=!1,c.boxInited=!1):c.boxInited&&(c.boxInited=!1);break;case\"pan\":c.boxEnabled=!1,c.boxInited=!1,e?(c.panning||(c.dragStart[0]=n,c.dragStart[1]=i),Math.abs(c.dragStart[0]-n)<d&&(n=c.dragStart[0]),Math.abs(c.dragStart[1]-i)<d&&(i=c.dragStart[1]),a=(h-n)*(l[2]-l[0])/(r.viewBox[2]-r.viewBox[0]),s=(p-i)*(l[3]-l[1])/(r.viewBox[3]-r.viewBox[1]),l[0]+=a,l[2]+=a,l[1]+=s,l[3]+=s,t.setRanges(l),c.panning=!0,c.lastInputTime=Date.now(),u(),t.cameraChanged(),t.handleAnnotations()):c.panning&&(c.panning=!1,t.relayoutCallback())}c.lastPos[0]=n,c.lastPos[1]=i}return c.mouseListener=n(e,f),e.addEventListener(\"touchstart\",(function(t){var r=a(t.changedTouches[0],e);f(0,r[0],r[1]),f(1,r[0],r[1]),t.preventDefault()}),!!s&&{passive:!1}),e.addEventListener(\"touchmove\",(function(t){t.preventDefault();var r=a(t.changedTouches[0],e);f(1,r[0],r[1]),t.preventDefault()}),!!s&&{passive:!1}),e.addEventListener(\"touchend\",(function(t){f(0,c.lastPos[0],c.lastPos[1]),t.preventDefault()}),!!s&&{passive:!1}),c.wheelListener=i(e,(function(e,n){if(!t.scrollZoom)return!1;var i=t.calcDataBox(),a=r.viewBox,o=c.lastPos[0],s=c.lastPos[1],l=Math.exp(5*n/(a[3]-a[1])),f=o/(a[2]-a[0])*(i[2]-i[0])+i[0],h=s/(a[3]-a[1])*(i[3]-i[1])+i[1];return i[0]=(i[0]-f)*l+f,i[2]=(i[2]-f)*l+f,i[1]=(i[1]-h)*l+h,i[3]=(i[3]-h)*l+h,t.setRanges(i),c.lastInputTime=Date.now(),u(),t.cameraChanged(),t.handleAnnotations(),t.relayoutCallback(),!0}),!0),c}},{\"../cartesian/constants\":561,\"has-passive-events\":229,\"mouse-change\":241,\"mouse-event-offset\":242,\"mouse-wheel\":244}],595:[function(t,e,r){\"use strict\";var n=t(\"../cartesian/axes\"),i=t(\"../../lib/str2rgbarray\");function a(t){this.scene=t,this.gl=t.gl,this.pixelRatio=t.pixelRatio,this.screenBox=[0,0,1,1],this.viewBox=[0,0,1,1],this.dataBox=[-1,-1,1,1],this.borderLineEnable=[!1,!1,!1,!1],this.borderLineWidth=[1,1,1,1],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.ticks=[[],[]],this.tickEnable=[!0,!0,!1,!1],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labels=[\"x\",\"y\"],this.labelEnable=[!0,!0,!1,!1],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelPad=[15,15,15,15],this.labelSize=[12,12],this.labelFont=[\"sans-serif\",\"sans-serif\"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.title=\"\",this.titleEnable=!0,this.titleCenter=[0,0,0,0],this.titleAngle=0,this.titleColor=[0,0,0,1],this.titleFont=\"sans-serif\",this.titleSize=18,this.gridLineEnable=[!0,!0],this.gridLineColor=[[0,0,0,.5],[0,0,0,.5]],this.gridLineWidth=[1,1],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[1,1],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.static=this.scene.staticPlot}var o=a.prototype,s=[\"xaxis\",\"yaxis\"];o.merge=function(t){var e,r,n,a,o,l,c,u,f,h,p;for(this.titleEnable=!1,this.backgroundColor=i(t.plot_bgcolor),h=0;h<2;++h){var d=(e=s[h]).charAt(0);for(n=(r=t[this.scene[e]._name]).title.text===this.scene.fullLayout._dfltTitle[d]?\"\":r.title.text,p=0;p<=2;p+=2)this.labelEnable[h+p]=!1,this.labels[h+p]=n,this.labelColor[h+p]=i(r.title.font.color),this.labelFont[h+p]=r.title.font.family,this.labelSize[h+p]=r.title.font.size,this.labelPad[h+p]=this.getLabelPad(e,r),this.tickEnable[h+p]=!1,this.tickColor[h+p]=i((r.tickfont||{}).color),this.tickAngle[h+p]=\"auto\"===r.tickangle?0:Math.PI*-r.tickangle/180,this.tickPad[h+p]=this.getTickPad(r),this.tickMarkLength[h+p]=0,this.tickMarkWidth[h+p]=r.tickwidth||0,this.tickMarkColor[h+p]=i(r.tickcolor),this.borderLineEnable[h+p]=!1,this.borderLineColor[h+p]=i(r.linecolor),this.borderLineWidth[h+p]=r.linewidth||0;c=this.hasSharedAxis(r),o=this.hasAxisInDfltPos(e,r)&&!c,l=this.hasAxisInAltrPos(e,r)&&!c,a=r.mirror||!1,u=c?-1!==String(a).indexOf(\"all\"):!!a,f=c?\"allticks\"===a:-1!==String(a).indexOf(\"ticks\"),o?this.labelEnable[h]=!0:l&&(this.labelEnable[h+2]=!0),o?this.tickEnable[h]=r.showticklabels:l&&(this.tickEnable[h+2]=r.showticklabels),(o||u)&&(this.borderLineEnable[h]=r.showline),(l||u)&&(this.borderLineEnable[h+2]=r.showline),(o||f)&&(this.tickMarkLength[h]=this.getTickMarkLength(r)),(l||f)&&(this.tickMarkLength[h+2]=this.getTickMarkLength(r)),this.gridLineEnable[h]=r.showgrid,this.gridLineColor[h]=i(r.gridcolor),this.gridLineWidth[h]=r.gridwidth,this.zeroLineEnable[h]=r.zeroline,this.zeroLineColor[h]=i(r.zerolinecolor),this.zeroLineWidth[h]=r.zerolinewidth}},o.hasSharedAxis=function(t){var e=this.scene,r=e.fullLayout._subplots.gl2d;return 0!==n.findSubplotsWithAxis(r,t).indexOf(e.id)},o.hasAxisInDfltPos=function(t,e){var r=e.side;return\"xaxis\"===t?\"bottom\"===r:\"yaxis\"===t?\"left\"===r:void 0},o.hasAxisInAltrPos=function(t,e){var r=e.side;return\"xaxis\"===t?\"top\"===r:\"yaxis\"===t?\"right\"===r:void 0},o.getLabelPad=function(t,e){var r=e.title.font.size,n=e.showticklabels;return\"xaxis\"===t?\"top\"===e.side?r*(1.5+(n?1:0))-10:r*(1.5+(n?.5:0))-10:\"yaxis\"===t?\"right\"===e.side?10+r*(1.5+(n?1:.5)):10+r*(1.5+(n?.5:0)):void 0},o.getTickPad=function(t){return\"outside\"===t.ticks?10+t.ticklen:15},o.getTickMarkLength=function(t){if(!t.ticks)return 0;var e=t.ticklen;return\"inside\"===t.ticks?-e:e},e.exports=function(t){return new a(t)}},{\"../../lib/str2rgbarray\":528,\"../cartesian/axes\":554}],596:[function(t,e,r){\"use strict\";var n=t(\"../../plot_api/edit_types\").overrideAll,i=t(\"./scene2d\"),a=t(\"../layout_attributes\"),o=t(\"../../constants/xmlns_namespaces\"),s=t(\"../cartesian/constants\"),l=t(\"../cartesian\"),c=t(\"../../components/fx/layout_attributes\"),u=t(\"../get_data\").getSubplotData;r.name=\"gl2d\",r.attr=[\"xaxis\",\"yaxis\"],r.idRoot=[\"x\",\"y\"],r.idRegex=s.idRegex,r.attrRegex=s.attrRegex,r.attributes=t(\"../cartesian/attributes\"),r.supplyLayoutDefaults=function(t,e,r){e._has(\"cartesian\")||l.supplyLayoutDefaults(t,e,r)},r.layoutAttrOverrides=n(l.layoutAttributes,\"plot\",\"from-root\"),r.baseLayoutAttrOverrides=n({plot_bgcolor:a.plot_bgcolor,hoverlabel:c.hoverlabel},\"plot\",\"nested\"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots.gl2d,a=0;a<n.length;a++){var o=n[a],s=e._plots[o],l=u(r,\"gl2d\",o),c=s._scene2d;void 0===c&&(c=new i({id:o,graphDiv:t,container:t.querySelector(\".gl-container\"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio},e),s._scene2d=c),c.plot(l,t.calcdata,e,t.layout)}},r.clean=function(t,e,r,n){for(var i=n._subplots.gl2d||[],a=0;a<i.length;a++){var o=i[a],s=n._plots[o];if(s._scene2d){var c=u(t,\"gl2d\",o);0===c.length&&(s._scene2d.destroy(),delete n._plots[o])}}l.clean.apply(this,arguments)},r.drawFramework=function(t){t._context.staticPlot||l.drawFramework(t)},r.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++){var i=e._plots[r[n]]._scene2d,a=i.toImage(\"png\");e._glimages.append(\"svg:image\").attr({xmlns:o.svg,\"xlink:href\":a,x:0,y:0,width:\"100%\",height:\"100%\",preserveAspectRatio:\"none\"}),i.destroy()}},r.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++){e._plots[r[n]]._scene2d.updateFx(e.dragmode)}}},{\"../../components/fx/layout_attributes\":407,\"../../constants/xmlns_namespaces\":480,\"../../plot_api/edit_types\":536,\"../cartesian\":568,\"../cartesian/attributes\":552,\"../cartesian/constants\":561,\"../get_data\":593,\"../layout_attributes\":610,\"./scene2d\":597}],597:[function(t,e,r){\"use strict\";var n,i,a=t(\"../../registry\"),o=t(\"../../plots/cartesian/axes\"),s=t(\"../../components/fx\"),l=t(\"../../../stackgl_modules\").gl_plot2d,c=t(\"../../../stackgl_modules\").gl_spikes2d,u=t(\"../../../stackgl_modules\").gl_select_box,f=t(\"webgl-context\"),h=t(\"./convert\"),p=t(\"./camera\"),d=t(\"../../lib/show_no_webgl_msg\"),g=t(\"../cartesian/constraints\"),m=g.enforce,v=g.clean,y=t(\"../cartesian/autorange\").doAutoRange,x=t(\"../../components/dragelement/helpers\"),b=x.drawMode,_=x.selectMode,w=[\"xaxis\",\"yaxis\"],T=t(\"../cartesian/constants\").SUBPLOT_PATTERN;function k(t,e){this.container=t.container,this.graphDiv=t.graphDiv,this.pixelRatio=t.plotGlPixelRatio||window.devicePixelRatio,this.id=t.id,this.staticPlot=!!t.staticPlot,this.scrollZoom=this.graphDiv._context._scrollZoom.cartesian,this.fullData=null,this.updateRefs(e),this.makeFramework(),this.stopped||(this.glplotOptions=h(this),this.glplotOptions.merge(e),this.glplot=l(this.glplotOptions),this.camera=p(this),this.traces={},this.spikes=c(this.glplot),this.selectBox=u(this.glplot,{innerFill:!1,outerFill:!0}),this.lastButtonState=0,this.pickResult=null,this.isMouseOver=!0,this.stopped=!1,this.redraw=this.draw.bind(this),this.redraw())}e.exports=k;var A=k.prototype;A.makeFramework=function(){if(this.staticPlot){if(!(i||(n=document.createElement(\"canvas\"),i=f({canvas:n,preserveDrawingBuffer:!1,premultipliedAlpha:!0,antialias:!0}))))throw new Error(\"Error creating static canvas/context for image server\");this.canvas=n,this.gl=i}else{var t=this.container.querySelector(\".gl-canvas-focus\"),e=f({canvas:t,preserveDrawingBuffer:!0,premultipliedAlpha:!0});if(!e)return d(this),void(this.stopped=!0);this.canvas=t,this.gl=e}var r=this.canvas;r.style.width=\"100%\",r.style.height=\"100%\",r.style.position=\"absolute\",r.style.top=\"0px\",r.style.left=\"0px\",r.style[\"pointer-events\"]=\"none\",this.updateSize(r);var a=this.svgContainer=document.createElementNS(\"http://www.w3.org/2000/svg\",\"svg\");a.style.position=\"absolute\",a.style.top=a.style.left=\"0px\",a.style.width=a.style.height=\"100%\",a.style[\"z-index\"]=20,a.style[\"pointer-events\"]=\"none\";var o=this.mouseContainer=document.createElement(\"div\");o.style.position=\"absolute\",o.style[\"pointer-events\"]=\"auto\",this.pickCanvas=this.container.querySelector(\".gl-canvas-pick\");var s=this.container;s.appendChild(a),s.appendChild(o);var l=this;o.addEventListener(\"mouseout\",(function(){l.isMouseOver=!1,l.unhover()})),o.addEventListener(\"mouseover\",(function(){l.isMouseOver=!0}))},A.toImage=function(t){t||(t=\"png\"),this.stopped=!0,this.staticPlot&&this.container.appendChild(n),this.updateSize(this.canvas);var e=this.glplot.gl,r=e.drawingBufferWidth,i=e.drawingBufferHeight;e.clearColor(1,1,1,0),e.clear(e.COLOR_BUFFER_BIT|e.DEPTH_BUFFER_BIT),this.glplot.setDirty(),this.glplot.draw(),e.bindFramebuffer(e.FRAMEBUFFER,null);var a=new Uint8Array(r*i*4);e.readPixels(0,0,r,i,e.RGBA,e.UNSIGNED_BYTE,a);for(var o=0,s=i-1;o<s;++o,--s)for(var l=0;l<r;++l)for(var c=0;c<4;++c){var u=a[4*(r*o+l)+c];a[4*(r*o+l)+c]=a[4*(r*s+l)+c],a[4*(r*s+l)+c]=u}var f=document.createElement(\"canvas\");f.width=r,f.height=i;var h,p=f.getContext(\"2d\"),d=p.createImageData(r,i);switch(d.data.set(a),p.putImageData(d,0,0),t){case\"jpeg\":h=f.toDataURL(\"image/jpeg\");break;case\"webp\":h=f.toDataURL(\"image/webp\");break;default:h=f.toDataURL(\"image/png\")}return this.staticPlot&&this.container.removeChild(n),h},A.updateSize=function(t){t||(t=this.canvas);var e=this.pixelRatio,r=this.fullLayout,n=r.width,i=r.height,a=0|Math.ceil(e*n),o=0|Math.ceil(e*i);return t.width===a&&t.height===o||(t.width=a,t.height=o),t},A.computeTickMarks=function(){this.xaxis.setScale(),this.yaxis.setScale();for(var t=[o.calcTicks(this.xaxis),o.calcTicks(this.yaxis)],e=0;e<2;++e)for(var r=0;r<t[e].length;++r)t[e][r].text=t[e][r].text+\"\";return t},A.updateRefs=function(t){this.fullLayout=t;var e=this.id.match(T),r=\"xaxis\"+e[1],n=\"yaxis\"+e[2];this.xaxis=this.fullLayout[r],this.yaxis=this.fullLayout[n]},A.relayoutCallback=function(){var t=this.graphDiv,e=this.xaxis,r=this.yaxis,n=t.layout,i={},o=i[e._name+\".range\"]=e.range.slice(),s=i[r._name+\".range\"]=r.range.slice();i[e._name+\".autorange\"]=e.autorange,i[r._name+\".autorange\"]=r.autorange,a.call(\"_storeDirectGUIEdit\",t.layout,t._fullLayout._preGUI,i);var l=n[e._name];l.range=o,l.autorange=e.autorange;var c=n[r._name];c.range=s,c.autorange=r.autorange,i.lastInputTime=this.camera.lastInputTime,t.emit(\"plotly_relayout\",i)},A.cameraChanged=function(){var t=this.camera;this.glplot.setDataBox(this.calcDataBox());var e=this.computeTickMarks();(function(t,e){for(var r=0;r<2;++r){var n=t[r],i=e[r];if(n.length!==i.length)return!0;for(var a=0;a<n.length;++a)if(n[a].x!==i[a].x)return!0}return!1})(e,this.glplotOptions.ticks)&&(this.glplotOptions.ticks=e,this.glplotOptions.dataBox=t.dataBox,this.glplot.update(this.glplotOptions),this.handleAnnotations())},A.handleAnnotations=function(){for(var t=this.graphDiv,e=this.fullLayout.annotations,r=0;r<e.length;r++){var n=e[r];n.xref===this.xaxis._id&&n.yref===this.yaxis._id&&a.getComponentMethod(\"annotations\",\"drawOne\")(t,r)}},A.destroy=function(){if(this.glplot){var t=this.traces;t&&Object.keys(t).map((function(e){t[e].dispose(),delete t[e]})),this.glplot.dispose(),this.container.removeChild(this.svgContainer),this.container.removeChild(this.mouseContainer),this.fullData=null,this.glplot=null,this.stopped=!0,this.camera.mouseListener.enabled=!1,this.mouseContainer.removeEventListener(\"wheel\",this.camera.wheelListener),this.camera=null}},A.plot=function(t,e,r){var n=this.glplot;this.updateRefs(r),this.xaxis.clearCalc(),this.yaxis.clearCalc(),this.updateTraces(t,e),this.updateFx(r.dragmode);var i=r.width,a=r.height;this.updateSize(this.canvas);var o=this.glplotOptions;o.merge(r),o.screenBox=[0,0,i,a];var s={_fullLayout:{_axisConstraintGroups:r._axisConstraintGroups,xaxis:this.xaxis,yaxis:this.yaxis,_size:r._size}};v(s,this.xaxis),v(s,this.yaxis);var l,c,u=r._size,f=this.xaxis.domain,h=this.yaxis.domain;for(o.viewBox=[u.l+f[0]*u.w,u.b+h[0]*u.h,i-u.r-(1-f[1])*u.w,a-u.t-(1-h[1])*u.h],this.mouseContainer.style.width=u.w*(f[1]-f[0])+\"px\",this.mouseContainer.style.height=u.h*(h[1]-h[0])+\"px\",this.mouseContainer.height=u.h*(h[1]-h[0]),this.mouseContainer.style.left=u.l+f[0]*u.w+\"px\",this.mouseContainer.style.top=u.t+(1-h[1])*u.h+\"px\",c=0;c<2;++c)(l=this[w[c]])._length=o.viewBox[c+2]-o.viewBox[c],y(this.graphDiv,l),l.setScale();m(s),o.ticks=this.computeTickMarks(),o.dataBox=this.calcDataBox(),o.merge(r),n.update(o),this.glplot.draw()},A.calcDataBox=function(){var t=this.xaxis,e=this.yaxis,r=t.range,n=e.range,i=t.r2l,a=e.r2l;return[i(r[0]),a(n[0]),i(r[1]),a(n[1])]},A.setRanges=function(t){var e=this.xaxis,r=this.yaxis,n=e.l2r,i=r.l2r;e.range=[n(t[0]),n(t[2])],r.range=[i(t[1]),i(t[3])]},A.updateTraces=function(t,e){var r,n,i,a=Object.keys(this.traces);this.fullData=t;t:for(r=0;r<a.length;r++){var o=a[r],s=this.traces[o];for(n=0;n<t.length;n++)if((i=t[n]).uid===o&&i.type===s.type)continue t;s.dispose(),delete this.traces[o]}for(r=0;r<t.length;r++){i=t[r];var l=e[r],c=this.traces[i.uid];c?c.update(i,l):(c=i._module.plot(this,i,l),this.traces[i.uid]=c)}this.glplot.objects.sort((function(t,e){return t._trace.index-e._trace.index}))},A.updateFx=function(t){_(t)||b(t)?(this.pickCanvas.style[\"pointer-events\"]=\"none\",this.mouseContainer.style[\"pointer-events\"]=\"none\"):(this.pickCanvas.style[\"pointer-events\"]=\"auto\",this.mouseContainer.style[\"pointer-events\"]=\"auto\"),this.mouseContainer.style.cursor=\"pan\"===t?\"move\":\"zoom\"===t?\"crosshair\":null},A.emitPointAction=function(t,e){for(var r,n=t.trace.uid,i=t.pointIndex,a=0;a<this.fullData.length;a++)this.fullData[a].uid===n&&(r=this.fullData[a]);var o={x:t.traceCoord[0],y:t.traceCoord[1],curveNumber:r.index,pointNumber:i,data:r._input,fullData:this.fullData,xaxis:this.xaxis,yaxis:this.yaxis};s.appendArrayPointValue(o,r,i),this.graphDiv.emit(e,{points:[o]})},A.draw=function(){if(!this.stopped){requestAnimationFrame(this.redraw);var t=this.glplot,e=this.camera,r=e.mouseListener,n=1===this.lastButtonState&&0===r.buttons,i=this.fullLayout;this.lastButtonState=r.buttons,this.cameraChanged();var a,o=r.x*t.pixelRatio,l=this.canvas.height-t.pixelRatio*r.y;if(e.boxEnabled&&\"zoom\"===i.dragmode){this.selectBox.enabled=!0;for(var c=this.selectBox.selectBox=[Math.min(e.boxStart[0],e.boxEnd[0]),Math.min(e.boxStart[1],e.boxEnd[1]),Math.max(e.boxStart[0],e.boxEnd[0]),Math.max(e.boxStart[1],e.boxEnd[1])],u=0;u<2;u++)e.boxStart[u]===e.boxEnd[u]&&(c[u]=t.dataBox[u],c[u+2]=t.dataBox[u+2]);t.setDirty()}else if(!e.panning&&this.isMouseOver){this.selectBox.enabled=!1;var f=i._size,h=this.xaxis.domain,p=this.yaxis.domain,d=(a=t.pick(o/t.pixelRatio+f.l+h[0]*f.w,l/t.pixelRatio-(f.t+(1-p[1])*f.h)))&&a.object._trace.handlePick(a);if(d&&n&&this.emitPointAction(d,\"plotly_click\"),a&&\"skip\"!==a.object._trace.hoverinfo&&i.hovermode&&d&&(!this.lastPickResult||this.lastPickResult.traceUid!==d.trace.uid||this.lastPickResult.dataCoord[0]!==d.dataCoord[0]||this.lastPickResult.dataCoord[1]!==d.dataCoord[1])){var g=d;this.lastPickResult={traceUid:d.trace?d.trace.uid:null,dataCoord:d.dataCoord.slice()},this.spikes.update({center:a.dataCoord}),g.screenCoord=[((t.viewBox[2]-t.viewBox[0])*(a.dataCoord[0]-t.dataBox[0])/(t.dataBox[2]-t.dataBox[0])+t.viewBox[0])/t.pixelRatio,(this.canvas.height-(t.viewBox[3]-t.viewBox[1])*(a.dataCoord[1]-t.dataBox[1])/(t.dataBox[3]-t.dataBox[1])-t.viewBox[1])/t.pixelRatio],this.emitPointAction(d,\"plotly_hover\");var m=this.fullData[g.trace.index]||{},v=g.pointIndex,y=s.castHoverinfo(m,i,v);if(y&&\"all\"!==y){var x=y.split(\"+\");-1===x.indexOf(\"x\")&&(g.traceCoord[0]=void 0),-1===x.indexOf(\"y\")&&(g.traceCoord[1]=void 0),-1===x.indexOf(\"z\")&&(g.traceCoord[2]=void 0),-1===x.indexOf(\"text\")&&(g.textLabel=void 0),-1===x.indexOf(\"name\")&&(g.name=void 0)}s.loneHover({x:g.screenCoord[0],y:g.screenCoord[1],xLabel:this.hoverFormatter(\"xaxis\",g.traceCoord[0]),yLabel:this.hoverFormatter(\"yaxis\",g.traceCoord[1]),zLabel:g.traceCoord[2],text:g.textLabel,name:g.name,color:s.castHoverOption(m,v,\"bgcolor\")||g.color,borderColor:s.castHoverOption(m,v,\"bordercolor\"),fontFamily:s.castHoverOption(m,v,\"font.family\"),fontSize:s.castHoverOption(m,v,\"font.size\"),fontColor:s.castHoverOption(m,v,\"font.color\"),nameLength:s.castHoverOption(m,v,\"namelength\"),textAlign:s.castHoverOption(m,v,\"align\")},{container:this.svgContainer,gd:this.graphDiv})}}a||this.unhover(),t.draw()}},A.unhover=function(){this.lastPickResult&&(this.spikes.update({}),this.lastPickResult=null,this.graphDiv.emit(\"plotly_unhover\"),s.loneUnhover(this.svgContainer))},A.hoverFormatter=function(t,e){if(void 0!==e){var r=this[t];return o.tickText(r,r.c2l(e),\"hover\").text}}},{\"../../../stackgl_modules\":1119,\"../../components/dragelement/helpers\":384,\"../../components/fx\":406,\"../../lib/show_no_webgl_msg\":525,\"../../plots/cartesian/axes\":554,\"../../registry\":638,\"../cartesian/autorange\":553,\"../cartesian/constants\":561,\"../cartesian/constraints\":562,\"./camera\":594,\"./convert\":595,\"webgl-context\":331}],598:[function(t,e,r){\"use strict\";var n=t(\"../../plot_api/edit_types\").overrideAll,i=t(\"../../components/fx/layout_attributes\"),a=t(\"./scene\"),o=t(\"../get_data\").getSubplotData,s=t(\"../../lib\"),l=t(\"../../constants/xmlns_namespaces\");r.name=\"gl3d\",r.attr=\"scene\",r.idRoot=\"scene\",r.idRegex=r.attrRegex=s.counterRegex(\"scene\"),r.attributes=t(\"./layout/attributes\"),r.layoutAttributes=t(\"./layout/layout_attributes\"),r.baseLayoutAttrOverrides=n({hoverlabel:i.hoverlabel},\"plot\",\"nested\"),r.supplyLayoutDefaults=t(\"./layout/defaults\"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots.gl3d,i=0;i<n.length;i++){var s=n[i],l=o(r,\"gl3d\",s),c=e[s],u=c.camera,f=c._scene;f||(f=new a({id:s,graphDiv:t,container:t.querySelector(\".gl-container\"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio,camera:u},e),c._scene=f),f.viewInitial||(f.viewInitial={up:{x:u.up.x,y:u.up.y,z:u.up.z},eye:{x:u.eye.x,y:u.eye.y,z:u.eye.z},center:{x:u.center.x,y:u.center.y,z:u.center.z}}),f.plot(l,e,t.layout)}},r.clean=function(t,e,r,n){for(var i=n._subplots.gl3d||[],a=0;a<i.length;a++){var o=i[a];!e[o]&&n[o]._scene&&(n[o]._scene.destroy(),n._infolayer&&n._infolayer.selectAll(\".annotation-\"+o).remove())}},r.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots.gl3d,n=e._size,i=0;i<r.length;i++){var a=e[r[i]],o=a.domain,s=a._scene,c=s.toImage(\"png\");e._glimages.append(\"svg:image\").attr({xmlns:l.svg,\"xlink:href\":c,x:n.l+n.w*o.x[0],y:n.t+n.h*(1-o.y[1]),width:n.w*(o.x[1]-o.x[0]),height:n.h*(o.y[1]-o.y[0]),preserveAspectRatio:\"none\"}),s.destroy()}},r.cleanId=function(t){if(t.match(/^scene[0-9]*$/)){var e=t.substr(5);return\"1\"===e&&(e=\"\"),\"scene\"+e}},r.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots.gl3d,n=0;n<r.length;n++){e[r[n]]._scene.updateFx(e.dragmode,e.hovermode)}}},{\"../../components/fx/layout_attributes\":407,\"../../constants/xmlns_namespaces\":480,\"../../lib\":503,\"../../plot_api/edit_types\":536,\"../get_data\":593,\"./layout/attributes\":599,\"./layout/defaults\":603,\"./layout/layout_attributes\":604,\"./scene\":608}],599:[function(t,e,r){\"use strict\";e.exports={scene:{valType:\"subplotid\",dflt:\"scene\",editType:\"calc+clearAxisTypes\"}}},{}],600:[function(t,e,r){\"use strict\";var n=t(\"../../../components/color\"),i=t(\"../../cartesian/layout_attributes\"),a=t(\"../../../lib/extend\").extendFlat,o=t(\"../../../plot_api/edit_types\").overrideAll;e.exports=o({visible:i.visible,showspikes:{valType:\"boolean\",dflt:!0},spikesides:{valType:\"boolean\",dflt:!0},spikethickness:{valType:\"number\",min:0,dflt:2},spikecolor:{valType:\"color\",dflt:n.defaultLine},showbackground:{valType:\"boolean\",dflt:!1},backgroundcolor:{valType:\"color\",dflt:\"rgba(204, 204, 204, 0.5)\"},showaxeslabels:{valType:\"boolean\",dflt:!0},color:i.color,categoryorder:i.categoryorder,categoryarray:i.categoryarray,title:{text:i.title.text,font:i.title.font},type:a({},i.type,{values:[\"-\",\"linear\",\"log\",\"date\",\"category\"]}),autotypenumbers:i.autotypenumbers,autorange:i.autorange,rangemode:i.rangemode,range:a({},i.range,{items:[{valType:\"any\",editType:\"plot\",impliedEdits:{\"^autorange\":!1}},{valType:\"any\",editType:\"plot\",impliedEdits:{\"^autorange\":!1}}],anim:!1}),tickmode:i.tickmode,nticks:i.nticks,tick0:i.tick0,dtick:i.dtick,tickvals:i.tickvals,ticktext:i.ticktext,ticks:i.ticks,mirror:i.mirror,ticklen:i.ticklen,tickwidth:i.tickwidth,tickcolor:i.tickcolor,showticklabels:i.showticklabels,tickfont:i.tickfont,tickangle:i.tickangle,tickprefix:i.tickprefix,showtickprefix:i.showtickprefix,ticksuffix:i.ticksuffix,showticksuffix:i.showticksuffix,showexponent:i.showexponent,exponentformat:i.exponentformat,minexponent:i.minexponent,separatethousands:i.separatethousands,tickformat:i.tickformat,tickformatstops:i.tickformatstops,hoverformat:i.hoverformat,showline:i.showline,linecolor:i.linecolor,linewidth:i.linewidth,showgrid:i.showgrid,gridcolor:a({},i.gridcolor,{dflt:\"rgb(204, 204, 204)\"}),gridwidth:i.gridwidth,zeroline:i.zeroline,zerolinecolor:i.zerolinecolor,zerolinewidth:i.zerolinewidth,_deprecated:{title:i._deprecated.title,titlefont:i._deprecated.titlefont}},\"plot\",\"from-root\")},{\"../../../components/color\":366,\"../../../lib/extend\":493,\"../../../plot_api/edit_types\":536,\"../../cartesian/layout_attributes\":569}],601:[function(t,e,r){\"use strict\";var n=t(\"tinycolor2\").mix,i=t(\"../../../lib\"),a=t(\"../../../plot_api/plot_template\"),o=t(\"./axis_attributes\"),s=t(\"../../cartesian/type_defaults\"),l=t(\"../../cartesian/axis_defaults\"),c=[\"xaxis\",\"yaxis\",\"zaxis\"];e.exports=function(t,e,r){var u,f;function h(t,e){return i.coerce(u,f,o,t,e)}for(var p=0;p<c.length;p++){var d=c[p];u=t[d]||{},(f=a.newContainer(e,d))._id=d[0]+r.scene,f._name=d,s(u,f,h,r),l(u,f,h,{font:r.font,letter:d[0],data:r.data,showGrid:!0,noTickson:!0,noTicklabelmode:!0,noTicklabelposition:!0,noTicklabeloverflow:!0,bgColor:r.bgColor,calendar:r.calendar},r.fullLayout),h(\"gridcolor\",n(f.color,r.bgColor,13600/187).toRgbString()),h(\"title.text\",d[0]),f.setScale=i.noop,h(\"showspikes\")&&(h(\"spikesides\"),h(\"spikethickness\"),h(\"spikecolor\",f.color)),h(\"showaxeslabels\"),h(\"showbackground\")&&h(\"backgroundcolor\")}}},{\"../../../lib\":503,\"../../../plot_api/plot_template\":543,\"../../cartesian/axis_defaults\":556,\"../../cartesian/type_defaults\":582,\"./axis_attributes\":600,tinycolor2:312}],602:[function(t,e,r){\"use strict\";var n=t(\"../../../lib/str2rgbarray\"),i=t(\"../../../lib\"),a=[\"xaxis\",\"yaxis\",\"zaxis\"];function o(){this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.tickEnable=[!0,!0,!0],this.tickFont=[\"sans-serif\",\"sans-serif\",\"sans-serif\"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[18,18,18],this.labels=[\"x\",\"y\",\"z\"],this.labelEnable=[!0,!0,!0],this.labelFont=[\"Open Sans\",\"Open Sans\",\"Open Sans\"],this.labelSize=[20,20,20],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[30,30,30],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[10,10,10],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!0,!0,!0],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._defaultTickPad=this.tickPad.slice(),this._defaultLabelPad=this.labelPad.slice(),this._defaultLineTickLength=this.lineTickLength.slice()}o.prototype.merge=function(t,e){for(var r=0;r<3;++r){var o=e[a[r]];o.visible?(this.labels[r]=t._meta?i.templateString(o.title.text,t._meta):o.title.text,\"font\"in o.title&&(o.title.font.color&&(this.labelColor[r]=n(o.title.font.color)),o.title.font.family&&(this.labelFont[r]=o.title.font.family),o.title.font.size&&(this.labelSize[r]=o.title.font.size)),\"showline\"in o&&(this.lineEnable[r]=o.showline),\"linecolor\"in o&&(this.lineColor[r]=n(o.linecolor)),\"linewidth\"in o&&(this.lineWidth[r]=o.linewidth),\"showgrid\"in o&&(this.gridEnable[r]=o.showgrid),\"gridcolor\"in o&&(this.gridColor[r]=n(o.gridcolor)),\"gridwidth\"in o&&(this.gridWidth[r]=o.gridwidth),\"log\"===o.type?this.zeroEnable[r]=!1:\"zeroline\"in o&&(this.zeroEnable[r]=o.zeroline),\"zerolinecolor\"in o&&(this.zeroLineColor[r]=n(o.zerolinecolor)),\"zerolinewidth\"in o&&(this.zeroLineWidth[r]=o.zerolinewidth),\"ticks\"in o&&o.ticks?this.lineTickEnable[r]=!0:this.lineTickEnable[r]=!1,\"ticklen\"in o&&(this.lineTickLength[r]=this._defaultLineTickLength[r]=o.ticklen),\"tickcolor\"in o&&(this.lineTickColor[r]=n(o.tickcolor)),\"tickwidth\"in o&&(this.lineTickWidth[r]=o.tickwidth),\"tickangle\"in o&&(this.tickAngle[r]=\"auto\"===o.tickangle?-3600:Math.PI*-o.tickangle/180),\"showticklabels\"in o&&(this.tickEnable[r]=o.showticklabels),\"tickfont\"in o&&(o.tickfont.color&&(this.tickColor[r]=n(o.tickfont.color)),o.tickfont.family&&(this.tickFont[r]=o.tickfont.family),o.tickfont.size&&(this.tickSize[r]=o.tickfont.size)),\"mirror\"in o?-1!==[\"ticks\",\"all\",\"allticks\"].indexOf(o.mirror)?(this.lineTickMirror[r]=!0,this.lineMirror[r]=!0):!0===o.mirror?(this.lineTickMirror[r]=!1,this.lineMirror[r]=!0):(this.lineTickMirror[r]=!1,this.lineMirror[r]=!1):this.lineMirror[r]=!1,\"showbackground\"in o&&!1!==o.showbackground?(this.backgroundEnable[r]=!0,this.backgroundColor[r]=n(o.backgroundcolor)):this.backgroundEnable[r]=!1):(this.tickEnable[r]=!1,this.labelEnable[r]=!1,this.lineEnable[r]=!1,this.lineTickEnable[r]=!1,this.gridEnable[r]=!1,this.zeroEnable[r]=!1,this.backgroundEnable[r]=!1)}},e.exports=function(t,e){var r=new o;return r.merge(t,e),r}},{\"../../../lib\":503,\"../../../lib/str2rgbarray\":528}],603:[function(t,e,r){\"use strict\";var n=t(\"../../../lib\"),i=t(\"../../../components/color\"),a=t(\"../../../registry\"),o=t(\"../../subplot_defaults\"),s=t(\"./axis_defaults\"),l=t(\"./layout_attributes\"),c=t(\"../../get_data\").getSubplotData;function u(t,e,r,n){for(var o=r(\"bgcolor\"),l=i.combine(o,n.paper_bgcolor),u=[\"up\",\"center\",\"eye\"],f=0;f<u.length;f++)r(\"camera.\"+u[f]+\".x\"),r(\"camera.\"+u[f]+\".y\"),r(\"camera.\"+u[f]+\".z\");r(\"camera.projection.type\");var h=!!r(\"aspectratio.x\")&&!!r(\"aspectratio.y\")&&!!r(\"aspectratio.z\"),p=r(\"aspectmode\",h?\"manual\":\"auto\");h||(t.aspectratio=e.aspectratio={x:1,y:1,z:1},\"manual\"===p&&(e.aspectmode=\"auto\"),t.aspectmode=e.aspectmode);var d=c(n.fullData,\"gl3d\",n.id);s(t,e,{font:n.font,scene:n.id,data:d,bgColor:l,calendar:n.calendar,autotypenumbersDflt:n.autotypenumbersDflt,fullLayout:n.fullLayout}),a.getComponentMethod(\"annotations3d\",\"handleDefaults\")(t,e,n);var g=n.getDfltFromLayout(\"dragmode\");if(!1!==g&&!g)if(g=\"orbit\",t.camera&&t.camera.up){var m=t.camera.up.x,v=t.camera.up.y,y=t.camera.up.z;0!==y&&(m&&v&&y?y/Math.sqrt(m*m+v*v+y*y)>.999&&(g=\"turntable\"):g=\"turntable\")}else g=\"turntable\";r(\"dragmode\",g),r(\"hovermode\",n.getDfltFromLayout(\"hovermode\"))}e.exports=function(t,e,r){var i=e._basePlotModules.length>1;o(t,e,r,{type:\"gl3d\",attributes:l,handleDefaults:u,fullLayout:e,font:e.font,fullData:r,getDfltFromLayout:function(e){if(!i)return n.validate(t[e],l[e])?t[e]:void 0},autotypenumbersDflt:e.autotypenumbers,paper_bgcolor:e.paper_bgcolor,calendar:e.calendar})}},{\"../../../components/color\":366,\"../../../lib\":503,\"../../../registry\":638,\"../../get_data\":593,\"../../subplot_defaults\":632,\"./axis_defaults\":601,\"./layout_attributes\":604}],604:[function(t,e,r){\"use strict\";var n=t(\"./axis_attributes\"),i=t(\"../../domain\").attributes,a=t(\"../../../lib/extend\").extendFlat,o=t(\"../../../lib\").counterRegex;function s(t,e,r){return{x:{valType:\"number\",dflt:t,editType:\"camera\"},y:{valType:\"number\",dflt:e,editType:\"camera\"},z:{valType:\"number\",dflt:r,editType:\"camera\"},editType:\"camera\"}}e.exports={_arrayAttrRegexps:[o(\"scene\",\".annotations\",!0)],bgcolor:{valType:\"color\",dflt:\"rgba(0,0,0,0)\",editType:\"plot\"},camera:{up:a(s(0,0,1),{}),center:a(s(0,0,0),{}),eye:a(s(1.25,1.25,1.25),{}),projection:{type:{valType:\"enumerated\",values:[\"perspective\",\"orthographic\"],dflt:\"perspective\",editType:\"calc\"},editType:\"calc\"},editType:\"camera\"},domain:i({name:\"scene\",editType:\"plot\"}),aspectmode:{valType:\"enumerated\",values:[\"auto\",\"cube\",\"data\",\"manual\"],dflt:\"auto\",editType:\"plot\",impliedEdits:{\"aspectratio.x\":void 0,\"aspectratio.y\":void 0,\"aspectratio.z\":void 0}},aspectratio:{x:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},y:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},z:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},editType:\"plot\",impliedEdits:{aspectmode:\"manual\"}},xaxis:n,yaxis:n,zaxis:n,dragmode:{valType:\"enumerated\",values:[\"orbit\",\"turntable\",\"zoom\",\"pan\",!1],editType:\"plot\"},hovermode:{valType:\"enumerated\",values:[\"closest\",!1],dflt:\"closest\",editType:\"modebar\"},uirevision:{valType:\"any\",editType:\"none\"},editType:\"plot\",_deprecated:{cameraposition:{valType:\"info_array\",editType:\"camera\"}}}},{\"../../../lib\":503,\"../../../lib/extend\":493,\"../../domain\":584,\"./axis_attributes\":600}],605:[function(t,e,r){\"use strict\";var n=t(\"../../../lib/str2rgbarray\"),i=[\"xaxis\",\"yaxis\",\"zaxis\"];function a(){this.enabled=[!0,!0,!0],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.drawSides=[!0,!0,!0],this.lineWidth=[1,1,1]}a.prototype.merge=function(t){for(var e=0;e<3;++e){var r=t[i[e]];r.visible?(this.enabled[e]=r.showspikes,this.colors[e]=n(r.spikecolor),this.drawSides[e]=r.spikesides,this.lineWidth[e]=r.spikethickness):(this.enabled[e]=!1,this.drawSides[e]=!1)}},e.exports=function(t){var e=new a;return e.merge(t),e}},{\"../../../lib/str2rgbarray\":528}],606:[function(t,e,r){\"use strict\";e.exports=function(t){for(var e=t.axesOptions,r=t.glplot.axesPixels,s=t.fullSceneLayout,l=[[],[],[]],c=0;c<3;++c){var u=s[a[c]];if(u._length=(r[c].hi-r[c].lo)*r[c].pixelsPerDataUnit/t.dataScale[c],Math.abs(u._length)===1/0||isNaN(u._length))l[c]=[];else{u._input_range=u.range.slice(),u.range[0]=r[c].lo/t.dataScale[c],u.range[1]=r[c].hi/t.dataScale[c],u._m=1/(t.dataScale[c]*r[c].pixelsPerDataUnit),u.range[0]===u.range[1]&&(u.range[0]-=1,u.range[1]+=1);var f=u.tickmode;if(\"auto\"===u.tickmode){u.tickmode=\"linear\";var h=u.nticks||i.constrain(u._length/40,4,9);n.autoTicks(u,Math.abs(u.range[1]-u.range[0])/h)}for(var p=n.calcTicks(u,{msUTC:!0}),d=0;d<p.length;++d)p[d].x=p[d].x*t.dataScale[c],\"date\"===u.type&&(p[d].text=p[d].text.replace(/\\<br\\>/g,\" \"));l[c]=p,u.tickmode=f}}e.ticks=l;for(c=0;c<3;++c){o[c]=.5*(t.glplot.bounds[0][c]+t.glplot.bounds[1][c]);for(d=0;d<2;++d)e.bounds[d][c]=t.glplot.bounds[d][c]}t.contourLevels=function(t){for(var e=new Array(3),r=0;r<3;++r){for(var n=t[r],i=new Array(n.length),a=0;a<n.length;++a)i[a]=n[a].x;e[r]=i}return e}(l)};var n=t(\"../../cartesian/axes\"),i=t(\"../../../lib\"),a=[\"xaxis\",\"yaxis\",\"zaxis\"],o=[0,0,0]},{\"../../../lib\":503,\"../../cartesian/axes\":554}],607:[function(t,e,r){\"use strict\";function n(t,e){var r,n,i=[0,0,0,0];for(r=0;r<4;++r)for(n=0;n<4;++n)i[n]+=t[4*r+n]*e[r];return i}e.exports=function(t,e){return n(t.projection,n(t.view,n(t.model,[e[0],e[1],e[2],1])))}},{}],608:[function(t,e,r){\"use strict\";var n,i,a=t(\"../../../stackgl_modules\").gl_plot3d,o=a.createCamera,s=a.createScene,l=t(\"webgl-context\"),c=t(\"has-passive-events\"),u=t(\"../../registry\"),f=t(\"../../lib\"),h=f.preserveDrawingBuffer(),p=t(\"../../plots/cartesian/axes\"),d=t(\"../../components/fx\"),g=t(\"../../lib/str2rgbarray\"),m=t(\"../../lib/show_no_webgl_msg\"),v=t(\"./project\"),y=t(\"./layout/convert\"),x=t(\"./layout/spikes\"),b=t(\"./layout/tick_marks\");function _(t,e){var r=document.createElement(\"div\"),n=t.container;this.graphDiv=t.graphDiv;var i=document.createElementNS(\"http://www.w3.org/2000/svg\",\"svg\");i.style.position=\"absolute\",i.style.top=i.style.left=\"0px\",i.style.width=i.style.height=\"100%\",i.style[\"z-index\"]=20,i.style[\"pointer-events\"]=\"none\",r.appendChild(i),this.svgContainer=i,r.id=t.id,r.style.position=\"absolute\",r.style.top=r.style.left=\"0px\",r.style.width=r.style.height=\"100%\",n.appendChild(r),this.fullLayout=e,this.id=t.id||\"scene\",this.fullSceneLayout=e[this.id],this.plotArgs=[[],{},{}],this.axesOptions=y(e,e[this.id]),this.spikeOptions=x(e[this.id]),this.container=r,this.staticMode=!!t.staticPlot,this.pixelRatio=this.pixelRatio||t.plotGlPixelRatio||2,this.dataScale=[1,1,1],this.contourLevels=[[],[],[]],this.convertAnnotations=u.getComponentMethod(\"annotations3d\",\"convert\"),this.drawAnnotations=u.getComponentMethod(\"annotations3d\",\"draw\"),this.initializeGLPlot()}var w=_.prototype;w.prepareOptions=function(){var t={canvas:this.canvas,gl:this.gl,glOptions:{preserveDrawingBuffer:h,premultipliedAlpha:!0,antialias:!0},container:this.container,axes:this.axesOptions,spikes:this.spikeOptions,pickRadius:10,snapToData:!0,autoScale:!0,autoBounds:!1,cameraObject:this.camera,pixelRatio:this.pixelRatio};if(this.staticMode){if(!(i||(n=document.createElement(\"canvas\"),i=l({canvas:n,preserveDrawingBuffer:!0,premultipliedAlpha:!0,antialias:!0}))))throw new Error(\"error creating static canvas/context for image server\");t.gl=i,t.canvas=n}return t};var T=!0;w.tryCreatePlot=function(){var t=this.prepareOptions(),e=!0;try{this.glplot=s(t)}catch(r){if(this.staticMode||!T||h)e=!1;else{f.warn([\"webgl setup failed possibly due to\",\"false preserveDrawingBuffer config.\",\"The mobile/tablet device may not be detected by is-mobile module.\",\"Enabling preserveDrawingBuffer in second attempt to create webgl scene...\"].join(\" \"));try{h=t.glOptions.preserveDrawingBuffer=!0,this.glplot=s(t)}catch(r){h=t.glOptions.preserveDrawingBuffer=!1,e=!1}}}return T=!1,e},w.initializeGLCamera=function(){var t=this.fullSceneLayout.camera,e=\"orthographic\"===t.projection.type;this.camera=o(this.container,{center:[t.center.x,t.center.y,t.center.z],eye:[t.eye.x,t.eye.y,t.eye.z],up:[t.up.x,t.up.y,t.up.z],_ortho:e,zoomMin:.01,zoomMax:100,mode:\"orbit\"})},w.initializeGLPlot=function(){var t=this;if(t.initializeGLCamera(),!t.tryCreatePlot())return m(t);t.traces={},t.make4thDimension();var e=t.graphDiv,r=e.layout,n=function(){var e={};return t.isCameraChanged(r)&&(e[t.id+\".camera\"]=t.getCamera()),t.isAspectChanged(r)&&(e[t.id+\".aspectratio\"]=t.glplot.getAspectratio(),\"manual\"!==r[t.id].aspectmode&&(t.fullSceneLayout.aspectmode=r[t.id].aspectmode=e[t.id+\".aspectmode\"]=\"manual\")),e},i=function(t){if(!1!==t.fullSceneLayout.dragmode){var e=n();t.saveLayout(r),t.graphDiv.emit(\"plotly_relayout\",e)}};return t.glplot.canvas&&(t.glplot.canvas.addEventListener(\"mouseup\",(function(){i(t)})),t.glplot.canvas.addEventListener(\"wheel\",(function(r){if(e._context._scrollZoom.gl3d){if(t.camera._ortho){var n=r.deltaX>r.deltaY?1.1:1/1.1,a=t.glplot.getAspectratio();t.glplot.setAspectratio({x:n*a.x,y:n*a.y,z:n*a.z})}i(t)}}),!!c&&{passive:!1}),t.glplot.canvas.addEventListener(\"mousemove\",(function(){if(!1!==t.fullSceneLayout.dragmode&&0!==t.camera.mouseListener.buttons){var e=n();t.graphDiv.emit(\"plotly_relayouting\",e)}})),t.staticMode||t.glplot.canvas.addEventListener(\"webglcontextlost\",(function(r){e&&e.emit&&e.emit(\"plotly_webglcontextlost\",{event:r,layer:t.id})}),!1)),t.glplot.oncontextloss=function(){t.recoverContext()},t.glplot.onrender=function(){t.render()},!0},w.render=function(){var t,e=this,r=e.graphDiv,n=e.svgContainer,i=e.container.getBoundingClientRect();r._fullLayout._calcInverseTransform(r);var a=r._fullLayout._invScaleX,o=r._fullLayout._invScaleY,s=i.width*a,l=i.height*o;n.setAttributeNS(null,\"viewBox\",\"0 0 \"+s+\" \"+l),n.setAttributeNS(null,\"width\",s),n.setAttributeNS(null,\"height\",l),b(e),e.glplot.axes.update(e.axesOptions);for(var c=Object.keys(e.traces),u=null,h=e.glplot.selection,g=0;g<c.length;++g)\"skip\"!==(t=e.traces[c[g]]).data.hoverinfo&&t.handlePick(h)&&(u=t),t.setContourLevels&&t.setContourLevels();function m(t,r,n){var i=e.fullSceneLayout[t+\"axis\"];return\"log\"!==i.type&&(r=i.d2l(r)),p.hoverLabelText(i,r,n)}if(null!==u){var y=v(e.glplot.cameraParams,h.dataCoordinate);t=u.data;var x,_=r._fullData[t.index],w=h.index,T={xLabel:m(\"x\",h.traceCoordinate[0],t.xhoverformat),yLabel:m(\"y\",h.traceCoordinate[1],t.yhoverformat),zLabel:m(\"z\",h.traceCoordinate[2],t.zhoverformat)},k=d.castHoverinfo(_,e.fullLayout,w),A=(k||\"\").split(\"+\"),M=k&&\"all\"===k;_.hovertemplate||M||(-1===A.indexOf(\"x\")&&(T.xLabel=void 0),-1===A.indexOf(\"y\")&&(T.yLabel=void 0),-1===A.indexOf(\"z\")&&(T.zLabel=void 0),-1===A.indexOf(\"text\")&&(h.textLabel=void 0),-1===A.indexOf(\"name\")&&(u.name=void 0));var S=[];\"cone\"===t.type||\"streamtube\"===t.type?(T.uLabel=m(\"x\",h.traceCoordinate[3],t.uhoverformat),(M||-1!==A.indexOf(\"u\"))&&S.push(\"u: \"+T.uLabel),T.vLabel=m(\"y\",h.traceCoordinate[4],t.vhoverformat),(M||-1!==A.indexOf(\"v\"))&&S.push(\"v: \"+T.vLabel),T.wLabel=m(\"z\",h.traceCoordinate[5],t.whoverformat),(M||-1!==A.indexOf(\"w\"))&&S.push(\"w: \"+T.wLabel),T.normLabel=h.traceCoordinate[6].toPrecision(3),(M||-1!==A.indexOf(\"norm\"))&&S.push(\"norm: \"+T.normLabel),\"streamtube\"===t.type&&(T.divergenceLabel=h.traceCoordinate[7].toPrecision(3),(M||-1!==A.indexOf(\"divergence\"))&&S.push(\"divergence: \"+T.divergenceLabel)),h.textLabel&&S.push(h.textLabel),x=S.join(\"<br>\")):\"isosurface\"===t.type||\"volume\"===t.type?(T.valueLabel=p.hoverLabelText(e._mockAxis,e._mockAxis.d2l(h.traceCoordinate[3]),t.valuehoverformat),S.push(\"value: \"+T.valueLabel),h.textLabel&&S.push(h.textLabel),x=S.join(\"<br>\")):x=h.textLabel;var E={x:h.traceCoordinate[0],y:h.traceCoordinate[1],z:h.traceCoordinate[2],data:_._input,fullData:_,curveNumber:_.index,pointNumber:w};d.appendArrayPointValue(E,_,w),t._module.eventData&&(E=_._module.eventData(E,h,_,{},w));var L={points:[E]};if(e.fullSceneLayout.hovermode){var C=[];d.loneHover({trace:_,x:(.5+.5*y[0]/y[3])*s,y:(.5-.5*y[1]/y[3])*l,xLabel:T.xLabel,yLabel:T.yLabel,zLabel:T.zLabel,text:x,name:u.name,color:d.castHoverOption(_,w,\"bgcolor\")||u.color,borderColor:d.castHoverOption(_,w,\"bordercolor\"),fontFamily:d.castHoverOption(_,w,\"font.family\"),fontSize:d.castHoverOption(_,w,\"font.size\"),fontColor:d.castHoverOption(_,w,\"font.color\"),nameLength:d.castHoverOption(_,w,\"namelength\"),textAlign:d.castHoverOption(_,w,\"align\"),hovertemplate:f.castOption(_,w,\"hovertemplate\"),hovertemplateLabels:f.extendFlat({},E,T),eventData:[E]},{container:n,gd:r,inOut_bbox:C}),E.bbox=C[0]}h.buttons&&h.distance<5?r.emit(\"plotly_click\",L):r.emit(\"plotly_hover\",L),this.oldEventData=L}else d.loneUnhover(n),this.oldEventData&&r.emit(\"plotly_unhover\",this.oldEventData),this.oldEventData=void 0;e.drawAnnotations(e)},w.recoverContext=function(){var t=this;t.glplot.dispose();var e=function(){t.glplot.gl.isContextLost()?requestAnimationFrame(e):t.initializeGLPlot()?t.plot.apply(t,t.plotArgs):f.error(\"Catastrophic and unrecoverable WebGL error. Context lost.\")};requestAnimationFrame(e)};var k=[\"xaxis\",\"yaxis\",\"zaxis\"];function A(t,e,r){for(var n=t.fullSceneLayout,i=0;i<3;i++){var a=k[i],o=a.charAt(0),s=n[a],l=e[o],c=e[o+\"calendar\"],u=e[\"_\"+o+\"length\"];if(f.isArrayOrTypedArray(l))for(var h,p=0;p<(u||l.length);p++)if(f.isArrayOrTypedArray(l[p]))for(var d=0;d<l[p].length;++d)h=s.d2l(l[p][d],0,c),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else h=s.d2l(l[p],0,c),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else r[0][i]=Math.min(r[0][i],0),r[1][i]=Math.max(r[1][i],u-1)}}w.plot=function(t,e,r){if(this.plotArgs=[t,e,r],!this.glplot.contextLost){var n,i,a,o,s,l,c=e[this.id],u=r[this.id];this.fullLayout=e,this.fullSceneLayout=c,this.axesOptions.merge(e,c),this.spikeOptions.merge(c),this.setViewport(c),this.updateFx(c.dragmode,c.hovermode),this.camera.enableWheel=this.graphDiv._context._scrollZoom.gl3d,this.glplot.setClearColor(g(c.bgcolor)),this.setConvert(s),t?Array.isArray(t)||(t=[t]):t=[];var f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(a=0;a<t.length;++a)!0===(n=t[a]).visible&&0!==n._length&&A(this,n,f);!function(t,e){for(var r=t.fullSceneLayout,n=r.annotations||[],i=0;i<3;i++)for(var a=k[i],o=a.charAt(0),s=r[a],l=0;l<n.length;l++){var c=n[l];if(c.visible){var u=s.r2l(c[o]);!isNaN(u)&&isFinite(u)&&(e[0][i]=Math.min(e[0][i],u),e[1][i]=Math.max(e[1][i],u))}}}(this,f);var h=[1,1,1];for(o=0;o<3;++o)f[1][o]===f[0][o]?h[o]=1:h[o]=1/(f[1][o]-f[0][o]);for(this.dataScale=h,this.convertAnnotations(this),a=0;a<t.length;++a)!0===(n=t[a]).visible&&0!==n._length&&((i=this.traces[n.uid])?i.data.type===n.type?i.update(n):(i.dispose(),i=n._module.plot(this,n),this.traces[n.uid]=i):(i=n._module.plot(this,n),this.traces[n.uid]=i),i.name=n.name);var p=Object.keys(this.traces);t:for(a=0;a<p.length;++a){for(o=0;o<t.length;++o)if(t[o].uid===p[a]&&!0===t[o].visible&&0!==t[o]._length)continue t;(i=this.traces[p[a]]).dispose(),delete this.traces[p[a]]}this.glplot.objects.sort((function(t,e){return t._trace.data.index-e._trace.data.index}));var d,m=[[0,0,0],[0,0,0]],v=[],y={};for(a=0;a<3;++a){if((l=(s=c[k[a]]).type)in y?(y[l].acc*=h[a],y[l].count+=1):y[l]={acc:h[a],count:1},s.autorange){m[0][a]=1/0,m[1][a]=-1/0;var x=this.glplot.objects,b=this.fullSceneLayout.annotations||[],_=s._name.charAt(0);for(o=0;o<x.length;o++){var w=x[o],T=w.bounds,M=w._trace.data._pad||0;\"ErrorBars\"===w.constructor.name&&s._lowerLogErrorBound?m[0][a]=Math.min(m[0][a],s._lowerLogErrorBound):m[0][a]=Math.min(m[0][a],T[0][a]/h[a]-M),m[1][a]=Math.max(m[1][a],T[1][a]/h[a]+M)}for(o=0;o<b.length;o++){var S=b[o];if(S.visible){var E=s.r2l(S[_]);m[0][a]=Math.min(m[0][a],E),m[1][a]=Math.max(m[1][a],E)}}if(\"rangemode\"in s&&\"tozero\"===s.rangemode&&(m[0][a]=Math.min(m[0][a],0),m[1][a]=Math.max(m[1][a],0)),m[0][a]>m[1][a])m[0][a]=-1,m[1][a]=1;else{var L=m[1][a]-m[0][a];m[0][a]-=L/32,m[1][a]+=L/32}if(\"reversed\"===s.autorange){var C=m[0][a];m[0][a]=m[1][a],m[1][a]=C}}else{var P=s.range;m[0][a]=s.r2l(P[0]),m[1][a]=s.r2l(P[1])}m[0][a]===m[1][a]&&(m[0][a]-=1,m[1][a]+=1),v[a]=m[1][a]-m[0][a],this.glplot.setBounds(a,{min:m[0][a]*h[a],max:m[1][a]*h[a]})}var I=c.aspectmode;if(\"cube\"===I)d=[1,1,1];else if(\"manual\"===I){var O=c.aspectratio;d=[O.x,O.y,O.z]}else{if(\"auto\"!==I&&\"data\"!==I)throw new Error(\"scene.js aspectRatio was not one of the enumerated types\");var z=[1,1,1];for(a=0;a<3;++a){var D=y[l=(s=c[k[a]]).type];z[a]=Math.pow(D.acc,1/D.count)/h[a]}d=\"data\"===I||Math.max.apply(null,z)/Math.min.apply(null,z)<=4?z:[1,1,1]}c.aspectratio.x=u.aspectratio.x=d[0],c.aspectratio.y=u.aspectratio.y=d[1],c.aspectratio.z=u.aspectratio.z=d[2],this.glplot.setAspectratio(c.aspectratio),this.viewInitial.aspectratio||(this.viewInitial.aspectratio={x:c.aspectratio.x,y:c.aspectratio.y,z:c.aspectratio.z}),this.viewInitial.aspectmode||(this.viewInitial.aspectmode=c.aspectmode);var R=c.domain||null,F=e._size||null;if(R&&F){var B=this.container.style;B.position=\"absolute\",B.left=F.l+R.x[0]*F.w+\"px\",B.top=F.t+(1-R.y[1])*F.h+\"px\",B.width=F.w*(R.x[1]-R.x[0])+\"px\",B.height=F.h*(R.y[1]-R.y[0])+\"px\"}this.glplot.redraw()}},w.destroy=function(){this.glplot&&(this.camera.mouseListener.enabled=!1,this.container.removeEventListener(\"wheel\",this.camera.wheelListener),this.camera=null,this.glplot.dispose(),this.container.parentNode.removeChild(this.container),this.glplot=null)},w.getCamera=function(){var t;return this.camera.view.recalcMatrix(this.camera.view.lastT()),{up:{x:(t=this.camera).up[0],y:t.up[1],z:t.up[2]},center:{x:t.center[0],y:t.center[1],z:t.center[2]},eye:{x:t.eye[0],y:t.eye[1],z:t.eye[2]},projection:{type:!0===t._ortho?\"orthographic\":\"perspective\"}}},w.setViewport=function(t){var e,r=t.camera;this.camera.lookAt.apply(this,[[(e=r).eye.x,e.eye.y,e.eye.z],[e.center.x,e.center.y,e.center.z],[e.up.x,e.up.y,e.up.z]]),this.glplot.setAspectratio(t.aspectratio),\"orthographic\"===r.projection.type!==this.camera._ortho&&(this.glplot.redraw(),this.glplot.clearRGBA(),this.glplot.dispose(),this.initializeGLPlot())},w.isCameraChanged=function(t){var e=this.getCamera(),r=f.nestedProperty(t,this.id+\".camera\").get();function n(t,e,r,n){var i=[\"up\",\"center\",\"eye\"],a=[\"x\",\"y\",\"z\"];return e[i[r]]&&t[i[r]][a[n]]===e[i[r]][a[n]]}var i=!1;if(void 0===r)i=!0;else{for(var a=0;a<3;a++)for(var o=0;o<3;o++)if(!n(e,r,a,o)){i=!0;break}(!r.projection||e.projection&&e.projection.type!==r.projection.type)&&(i=!0)}return i},w.isAspectChanged=function(t){var e=this.glplot.getAspectratio(),r=f.nestedProperty(t,this.id+\".aspectratio\").get();return void 0===r||r.x!==e.x||r.y!==e.y||r.z!==e.z},w.saveLayout=function(t){var e,r,n,i,a,o,s=this.fullLayout,l=this.isCameraChanged(t),c=this.isAspectChanged(t),h=l||c;if(h){var p={};if(l&&(e=this.getCamera(),n=(r=f.nestedProperty(t,this.id+\".camera\")).get(),p[this.id+\".camera\"]=n),c&&(i=this.glplot.getAspectratio(),o=(a=f.nestedProperty(t,this.id+\".aspectratio\")).get(),p[this.id+\".aspectratio\"]=o),u.call(\"_storeDirectGUIEdit\",t,s._preGUI,p),l)r.set(e),f.nestedProperty(s,this.id+\".camera\").set(e);if(c)a.set(i),f.nestedProperty(s,this.id+\".aspectratio\").set(i),this.glplot.redraw()}return h},w.updateFx=function(t,e){var r=this.camera;if(r)if(\"orbit\"===t)r.mode=\"orbit\",r.keyBindingMode=\"rotate\";else if(\"turntable\"===t){r.up=[0,0,1],r.mode=\"turntable\",r.keyBindingMode=\"rotate\";var n=this.graphDiv,i=n._fullLayout,a=this.fullSceneLayout.camera,o=a.up.x,s=a.up.y,l=a.up.z;if(l/Math.sqrt(o*o+s*s+l*l)<.999){var c=this.id+\".camera.up\",h={x:0,y:0,z:1},p={};p[c]=h;var d=n.layout;u.call(\"_storeDirectGUIEdit\",d,i._preGUI,p),a.up=h,f.nestedProperty(d,c).set(h)}}else r.keyBindingMode=t;this.fullSceneLayout.hovermode=e},w.toImage=function(t){t||(t=\"png\"),this.staticMode&&this.container.appendChild(n),this.glplot.redraw();var e=this.glplot.gl,r=e.drawingBufferWidth,i=e.drawingBufferHeight;e.bindFramebuffer(e.FRAMEBUFFER,null);var a=new Uint8Array(r*i*4);e.readPixels(0,0,r,i,e.RGBA,e.UNSIGNED_BYTE,a),function(t,e,r){for(var n=0,i=r-1;n<i;++n,--i)for(var a=0;a<e;++a)for(var o=0;o<4;++o){var s=4*(e*n+a)+o,l=4*(e*i+a)+o,c=t[s];t[s]=t[l],t[l]=c}}(a,r,i),function(t,e,r){for(var n=0;n<r;++n)for(var i=0;i<e;++i){var a=4*(e*n+i),o=t[a+3];if(o>0)for(var s=255/o,l=0;l<3;++l)t[a+l]=Math.min(s*t[a+l],255)}}(a,r,i);var o=document.createElement(\"canvas\");o.width=r,o.height=i;var 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n,i=this,a=e[i.id];i.map&&a.accesstoken!==i.accessToken&&(i.map.remove(),i.map=null,i.styleObj=null,i.traceHash={},i.layerList=[]),n=i.map?new Promise((function(r,n){i.updateMap(t,e,r,n)})):new Promise((function(r,n){i.createMap(t,e,r,n)})),r.push(n)},_.createMap=function(t,e,r,i){var o=this,s=e[o.id],l=o.styleObj=T(s.style);o.accessToken=s.accesstoken;var c=o.map=new n.Map({container:o.div,style:l.style,center:A(s.center),zoom:s.zoom,bearing:s.bearing,pitch:s.pitch,interactive:!o.isStatic,preserveDrawingBuffer:o.isStatic,doubleClickZoom:!1,boxZoom:!1,attributionControl:!1}).addControl(new n.AttributionControl({compact:!0}));c._canvas.style.left=\"0px\",c._canvas.style.top=\"0px\",o.rejectOnError(i),o.isStatic||o.initFx(t,e);var u=[];u.push(new Promise((function(t){c.once(\"load\",t)}))),u=u.concat(a.fetchTraceGeoData(t)),Promise.all(u).then((function(){o.fillBelowLookup(t,e),o.updateData(t),o.updateLayout(e),o.resolveOnRender(r)})).catch(i)},_.updateMap=function(t,e,r,n){var 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e[o.findIndexOfMin(e,(function(e){return o.angleDist(t,e)}))]}function V(t,e,r){return e?(t.attr(\"display\",null),t.attr(r)):t&&t.attr(\"display\",\"none\"),t}e.exports=function(t,e,r){return new B(t,e,r)},N.plot=function(t,e){for(var r=e[this.id],n=!1,i=0;i<t.length;i++){if(!1===t[i][0].trace.cliponaxis){n=!0;break}}this._hasClipOnAxisFalse=n,this.updateLayers(e,r),this.updateLayout(e,r),f.generalUpdatePerTraceModule(this.gd,this,t,r),this.updateFx(e,r),this.isSmith&&(delete r.realaxis.range,delete r.imaginaryaxis.range)},N.updateLayers=function(t,e){var r=this.isSmith,i=this.layers,a=this.getRadial(e),o=this.getAngular(e),s=S.layerNames,l=s.indexOf(\"frontplot\"),c=s.slice(0,l),u=\"below traces\"===o.layer,f=\"below traces\"===a.layer;u&&c.push(\"angular-line\"),f&&c.push(\"radial-line\"),u&&c.push(\"angular-axis\"),f&&c.push(\"radial-axis\"),c.push(\"frontplot\"),u||c.push(\"angular-line\"),f||c.push(\"radial-line\"),u||c.push(\"angular-axis\"),f||c.push(\"radial-axis\");var h=(r?\"smith\":\"polar\")+\"sublayer\",p=this.framework.selectAll(\".\"+h).data(c,String);p.enter().append(\"g\").attr(\"class\",(function(t){return h+\" \"+t})).each((function(t){var e=i[t]=n.select(this);switch(t){case\"frontplot\":r||e.append(\"g\").classed(\"barlayer\",!0),e.append(\"g\").classed(\"scatterlayer\",!0);break;case\"backplot\":e.append(\"g\").classed(\"maplayer\",!0);break;case\"plotbg\":i.bg=e.append(\"path\");break;case\"radial-grid\":case\"angular-grid\":e.style(\"fill\",\"none\");break;case\"radial-line\":e.append(\"line\").style(\"fill\",\"none\");break;case\"angular-line\":e.append(\"path\").style(\"fill\",\"none\")}})),p.order()},N.updateLayout=function(t,e){var r=this.layers,n=t._size,i=this.getRadial(e),a=this.getAngular(e),o=e.domain.x,s=e.domain.y;this.xOffset=n.l+n.w*o[0],this.yOffset=n.t+n.h*(1-s[1]);var f=this.xLength=n.w*(o[1]-o[0]),h=this.yLength=n.h*(s[1]-s[0]),p=this.getSector(e);this.sectorInRad=p.map(R);var d,g,m,v,y,x=this.sectorBBox=function(t){var e,r,n,i,a=t[0],o=t[1]-a,s=D(a,360),l=s+o,c=Math.cos(R(s)),u=Math.sin(R(s)),f=Math.cos(R(l)),h=Math.sin(R(l));i=s<=90&&l>=90||s>90&&l>=450?1:u<=0&&h<=0?0:Math.max(u,h);e=s<=180&&l>=180||s>180&&l>=540?-1:c>=0&&f>=0?0:Math.min(c,f);r=s<=270&&l>=270||s>270&&l>=630?-1:u>=0&&h>=0?0:Math.min(u,h);n=l>=360?1:c<=0&&f<=0?0:Math.max(c,f);return[e,r,n,i]}(p),b=x[2]-x[0],_=x[3]-x[1],w=h/f,T=Math.abs(_/b);w>T?(d=f,y=(h-(g=f*T))/n.h/2,m=[o[0],o[1]],v=[s[0]+y,s[1]-y]):(g=h,y=(f-(d=h/T))/n.w/2,m=[o[0]+y,o[1]-y],v=[s[0],s[1]]),this.xLength2=d,this.yLength2=g,this.xDomain2=m,this.yDomain2=v;var k,A=this.xOffset2=n.l+n.w*m[0],M=this.yOffset2=n.t+n.h*(1-v[1]),S=this.radius=d/b,E=this.innerRadius=this.getHole(e)*S,L=this.cx=A-S*x[0],C=this.cy=M+S*x[3],P=this.cxx=L-A,I=this.cyy=C-M,O=i.side;\"counterclockwise\"===O?(k=O,O=\"top\"):\"clockwise\"===O&&(k=O,O=\"bottom\"),this.radialAxis=this.mockAxis(t,e,i,{_id:\"x\",side:O,_trueSide:k,domain:[E/n.w,S/n.w]}),this.angularAxis=this.mockAxis(t,e,a,{side:\"right\",domain:[0,Math.PI],autorange:!1}),this.doAutoRange(t,e),this.updateAngularAxis(t,e),this.updateRadialAxis(t,e),this.updateRadialAxisTitle(t,e),this.xaxis=this.mockCartesianAxis(t,e,{_id:\"x\",domain:m}),this.yaxis=this.mockCartesianAxis(t,e,{_id:\"y\",domain:v});var z=this.pathSubplot();this.clipPaths.forTraces.select(\"path\").attr(\"d\",z).attr(\"transform\",l(P,I)),r.frontplot.attr(\"transform\",l(A,M)).call(u.setClipUrl,this._hasClipOnAxisFalse?null:this.clipIds.forTraces,this.gd),r.bg.attr(\"d\",z).attr(\"transform\",l(L,C)).call(c.fill,e.bgcolor)},N.mockAxis=function(t,e,r,n){var i=o.extendFlat({},r,n);return d(i,e,t),i},N.mockCartesianAxis=function(t,e,r){var n=this,i=n.isSmith,a=r._id,s=o.extendFlat({type:\"linear\"},r);p(s,t);var l={x:[0,2],y:[1,3]};return s.setRange=function(){var t=n.sectorBBox,r=l[a],i=n.radialAxis._rl,o=(i[1]-i[0])/(1-n.getHole(e));s.range=[t[r[0]]*o,t[r[1]]*o]},s.isPtWithinRange=\"x\"!==a||i?function(){return!0}:function(t){return n.isPtInside(t)},s.setRange(),s.setScale(),s},N.doAutoRange=function(t,e){var r=this.gd,n=this.radialAxis,i=this.getRadial(e);g(r,n);var 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h.tickText(m,t,!0,!1)})):h.calcTicks(m),k=y?T:h.clipEnds(m,T),A=h.getTickSigns(m)[2];y&&((\"top\"===m.ticks&&\"bottom\"===m.side||\"bottom\"===m.ticks&&\"top\"===m.side)&&(A=-A),\"top\"===m.ticks&&\"top\"===m.side&&(w=-m.ticklen),\"bottom\"===m.ticks&&\"bottom\"===m.side&&(w=m.ticklen)),h.drawTicks(n,m,{vals:T,layer:i[\"radial-axis\"],path:h.makeTickPath(m,0,A),transFn:x,crisp:!1}),h.drawGrid(n,m,{vals:k,layer:i[\"radial-grid\"],path:b,transFn:o.noop,crisp:!1}),h.drawLabels(n,m,{vals:T,layer:i[\"radial-axis\"],transFn:x,labelFns:h.makeLabelFns(m,w)})}var M=r.radialAxisAngle=r.vangles?F(U(R(d.angle),r.vangles)):d.angle,S=l(f,p),E=S+s(-M);V(i[\"radial-axis\"],v&&(d.showticklabels||d.ticks),{transform:E}),V(i[\"radial-grid\"],v&&d.showgrid,{transform:y?\"\":S}),V(i[\"radial-line\"].select(\"line\"),v&&d.showline,{x1:y?-a:u,y1:0,x2:a,y2:0,transform:E}).attr(\"stroke-width\",d.linewidth).call(c.stroke,d.linecolor)},N.updateRadialAxisTitle=function(t,e,r){if(!this.isSmith){var n=this.gd,i=this.radius,a=this.cx,o=this.cy,s=this.getRadial(e),l=this.id+\"title\",c=0;if(s.title){var f=u.bBox(this.layers[\"radial-axis\"].node()).height,h=s.title.font.size,p=s.side;c=\"top\"===p?h:\"counterclockwise\"===p?-(f+.4*h):f+.8*h}var d=void 0!==r?r:this.radialAxisAngle,g=R(d),m=Math.cos(g),v=Math.sin(g),y=a+i/2*m+c*v,b=o-i/2*v+c*m;this.layers[\"radial-axis-title\"]=x.draw(n,l,{propContainer:s,propName:this.id+\".radialaxis.title\",placeholder:z(n,\"Click to enter radial axis title\"),attributes:{x:y,y:b,\"text-anchor\":\"middle\"},transform:{rotate:-d}})}},N.updateAngularAxis=function(t,e){var r=this,n=r.gd,i=r.layers,a=r.radius,u=r.innerRadius,f=r.cx,p=r.cy,d=r.getAngular(e),g=r.angularAxis,m=r.isSmith;m||(r.fillViewInitialKey(\"angularaxis.rotation\",d.rotation),g.setGeometry(),g.setScale());var v=m?function(t){var e=O(r,C([0,t.x]));return Math.atan2(e[0]-f,e[1]-p)-Math.PI/2}:function(t){return g.t2g(t.x)};\"linear\"===g.type&&\"radians\"===g.thetaunit&&(g.tick0=F(g.tick0),g.dtick=F(g.dtick));var y=function(t){return l(f+a*Math.cos(t),p-a*Math.sin(t))},x=m?function(t){var e=O(r,C([0,t.x]));return l(e[0],e[1])}:function(t){return y(v(t))},b=m?function(t){var e=O(r,C([0,t.x])),n=Math.atan2(e[0]-f,e[1]-p)-Math.PI/2;return l(e[0],e[1])+s(-F(n))}:function(t){var e=v(t);return y(e)+s(-F(e))},_=m?function(t){return P(r,t.x,0,1/0)}:function(t){var e=v(t),r=Math.cos(e),n=Math.sin(e);return\"M\"+[f+u*r,p-u*n]+\"L\"+[f+a*r,p-a*n]},w=h.makeLabelFns(g,0).labelStandoff,T={xFn:function(t){var e=v(t);return Math.cos(e)*w},yFn:function(t){var e=v(t),r=Math.sin(e)>0?.2:1;return-Math.sin(e)*(w+t.fontSize*r)+Math.abs(Math.cos(e))*(t.fontSize*M)},anchorFn:function(t){var e=v(t),r=Math.cos(e);return Math.abs(r)<.1?\"middle\":r>0?\"start\":\"end\"},heightFn:function(t,e,r){var n=v(t);return-.5*(1+Math.sin(n))*r}},k=j(d);r.angularTickLayout!==k&&(i[\"angular-axis\"].selectAll(\".\"+g._id+\"tick\").remove(),r.angularTickLayout=k);var A,S=m?[1/0].concat(g.tickvals||[]).map((function(t){return h.tickText(g,t,!0,!1)})):h.calcTicks(g);if(m&&(S[0].text=\"\\u221e\",S[0].fontSize*=1.75),\"linear\"===e.gridshape?(A=S.map(v),o.angleDelta(A[0],A[1])<0&&(A=A.slice().reverse())):A=null,r.vangles=A,\"category\"===g.type&&(S=S.filter((function(t){return o.isAngleInsideSector(v(t),r.sectorInRad)}))),g.visible){var E=\"inside\"===g.ticks?-1:1,L=(g.linewidth||1)/2;h.drawTicks(n,g,{vals:S,layer:i[\"angular-axis\"],path:\"M\"+E*L+\",0h\"+E*g.ticklen,transFn:b,crisp:!1}),h.drawGrid(n,g,{vals:S,layer:i[\"angular-grid\"],path:_,transFn:o.noop,crisp:!1}),h.drawLabels(n,g,{vals:S,layer:i[\"angular-axis\"],repositionOnUpdate:!0,transFn:x,labelFns:T})}V(i[\"angular-line\"].select(\"path\"),d.showline,{d:r.pathSubplot(),transform:l(f,p)}).attr(\"stroke-width\",d.linewidth).call(c.stroke,d.linecolor)},N.updateFx=function(t,e){this.gd._context.staticPlot||(!this.isSmith&&(this.updateAngularDrag(t),this.updateRadialDrag(t,e,0),this.updateRadialDrag(t,e,1)),this.updateHoverAndMainDrag(t))},N.updateHoverAndMainDrag=function(t){var e,r,s=this,c=s.isSmith,u=s.gd,f=s.layers,h=t._zoomlayer,p=S.MINZOOM,d=S.OFFEDGE,g=s.radius,x=s.innerRadius,T=s.cx,k=s.cy,A=s.cxx,M=s.cyy,L=s.sectorInRad,C=s.vangles,P=s.radialAxis,I=E.clampTiny,O=E.findXYatLength,z=E.findEnclosingVertexAngles,D=S.cornerHalfWidth,R=S.cornerLen/2,F=m.makeDragger(f,\"path\",\"maindrag\",\"crosshair\");n.select(F).attr(\"d\",s.pathSubplot()).attr(\"transform\",l(T,k)),F.onmousemove=function(t){y.hover(u,t,s.id),u._fullLayout._lasthover=F,u._fullLayout._hoversubplot=s.id},F.onmouseout=function(t){u._dragging||v.unhover(u,t)};var B,N,j,U,V,H,q,G,Y,W={element:F,gd:u,subplot:s.id,plotinfo:{id:s.id,xaxis:s.xaxis,yaxis:s.yaxis},xaxes:[s.xaxis],yaxes:[s.yaxis]};function X(t,e){return Math.sqrt(t*t+e*e)}function Z(t,e){return X(t-A,e-M)}function J(t,e){return Math.atan2(M-e,t-A)}function K(t,e){return[t*Math.cos(e),t*Math.sin(-e)]}function Q(t,e){if(0===t)return s.pathSector(2*D);var r=R/t,n=e-r,i=e+r,a=Math.max(0,Math.min(t,g)),o=a-D,l=a+D;return\"M\"+K(o,n)+\"A\"+[o,o]+\" 0,0,0 \"+K(o,i)+\"L\"+K(l,i)+\"A\"+[l,l]+\" 0,0,1 \"+K(l,n)+\"Z\"}function $(t,e,r){if(0===t)return s.pathSector(2*D);var n,i,a=K(t,e),o=K(t,r),l=I((a[0]+o[0])/2),c=I((a[1]+o[1])/2);if(l&&c){var u=c/l,f=-1/u,h=O(D,u,l,c);n=O(R,f,h[0][0],h[0][1]),i=O(R,f,h[1][0],h[1][1])}else{var p,d;c?(p=R,d=D):(p=D,d=R),n=[[l-p,c-d],[l+p,c-d]],i=[[l-p,c+d],[l+p,c+d]]}return\"M\"+n.join(\"L\")+\"L\"+i.reverse().join(\"L\")+\"Z\"}function tt(t,e){return e=Math.max(Math.min(e,g),x),t<d?t=0:g-t<d?t=g:e<d?e=0:g-e<d&&(e=g),Math.abs(e-t)>p?(t<e?(j=t,U=e):(j=e,U=t),!0):(j=null,U=null,!1)}function et(t,e){t=t||V,e=e||\"M0,0Z\",G.attr(\"d\",t),Y.attr(\"d\",e),m.transitionZoombox(G,Y,H,q),H=!0;var r={};ot(r),u.emit(\"plotly_relayouting\",r)}function rt(t,n){var i,a,o=B+(t*=e),l=N+(n*=r),c=Z(B,N),u=Math.min(Z(o,l),g),f=J(B,N);tt(c,u)&&(i=V+s.pathSector(U),j&&(i+=s.pathSector(j)),a=Q(j,f)+Q(U,f)),et(i,a)}function 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n=t(\"../../components/color/attributes\"),i=t(\"../domain\").attributes,a=t(\"../cartesian/layout_attributes\"),o=t(\"../../plot_api/edit_types\").overrideAll,s=t(\"../../lib/extend\").extendFlat,l={title:{text:a.title.text,font:a.title.font},color:a.color,tickmode:a.tickmode,nticks:s({},a.nticks,{dflt:6,min:1}),tick0:a.tick0,dtick:a.dtick,tickvals:a.tickvals,ticktext:a.ticktext,ticks:a.ticks,ticklen:a.ticklen,tickwidth:a.tickwidth,tickcolor:a.tickcolor,showticklabels:a.showticklabels,showtickprefix:a.showtickprefix,tickprefix:a.tickprefix,showticksuffix:a.showticksuffix,ticksuffix:a.ticksuffix,showexponent:a.showexponent,exponentformat:a.exponentformat,minexponent:a.minexponent,separatethousands:a.separatethousands,tickfont:a.tickfont,tickangle:a.tickangle,tickformat:a.tickformat,tickformatstops:a.tickformatstops,hoverformat:a.hoverformat,showline:s({},a.showline,{dflt:!0}),linecolor:a.linecolor,linewidth:a.linewidth,showgrid:s({},a.showgrid,{dflt:!0}),gridcolor:a.gridcolor,gridwidth:a.gridwidth,layer:a.layer,min:{valType:\"number\",dflt:0,min:0},_deprecated:{title:a._deprecated.title,titlefont:a._deprecated.titlefont}},c=e.exports=o({domain:i({name:\"ternary\"}),bgcolor:{valType:\"color\",dflt:n.background},sum:{valType:\"number\",dflt:1,min:0},aaxis:l,baxis:l,caxis:l},\"plot\",\"from-root\");c.uirevision={valType:\"any\",editType:\"none\"},c.aaxis.uirevision=c.baxis.uirevision=c.caxis.uirevision={valType:\"any\",editType:\"none\"}},{\"../../components/color/attributes\":365,\"../../lib/extend\":493,\"../../plot_api/edit_types\":536,\"../cartesian/layout_attributes\":569,\"../domain\":584}],636:[function(t,e,r){\"use strict\";var n=t(\"../../components/color\"),i=t(\"../../plot_api/plot_template\"),a=t(\"../../lib\"),o=t(\"../subplot_defaults\"),s=t(\"../cartesian/tick_label_defaults\"),l=t(\"../cartesian/prefix_suffix_defaults\"),c=t(\"../cartesian/tick_mark_defaults\"),u=t(\"../cartesian/tick_value_defaults\"),f=t(\"../cartesian/line_grid_defaults\"),h=t(\"./layout_attributes\"),p=[\"aaxis\",\"baxis\",\"caxis\"];function d(t,e,r,a){var o,s,l,c=r(\"bgcolor\"),u=r(\"sum\");a.bgColor=n.combine(c,a.paper_bgcolor);for(var f=0;f<p.length;f++)s=t[o=p[f]]||{},(l=i.newContainer(e,o))._name=o,g(s,l,a,e);var h=e.aaxis,d=e.baxis,m=e.caxis;h.min+d.min+m.min>=u&&(h.min=0,d.min=0,m.min=0,t.aaxis&&delete t.aaxis.min,t.baxis&&delete t.baxis.min,t.caxis&&delete t.caxis.min)}function g(t,e,r,n){var i=h[e._name];function o(r,n){return a.coerce(t,e,i,r,n)}o(\"uirevision\",n.uirevision),e.type=\"linear\";var p=o(\"color\"),d=p!==i.color.dflt?p:r.font.color,g=e._name.charAt(0).toUpperCase(),m=\"Component \"+g,v=o(\"title.text\",m);e._hovertitle=v===m?v:g,a.coerceFont(o,\"title.font\",{family:r.font.family,size:a.bigFont(r.font.size),color:d}),o(\"min\"),u(t,e,o,\"linear\"),l(t,e,o,\"linear\"),s(t,e,o,\"linear\"),c(t,e,o,{outerTicks:!0}),o(\"showticklabels\")&&(a.coerceFont(o,\"tickfont\",{family:r.font.family,size:r.font.size,color:d}),o(\"tickangle\"),o(\"tickformat\")),f(t,e,o,{dfltColor:p,bgColor:r.bgColor,blend:60,showLine:!0,showGrid:!0,noZeroLine:!0,attributes:i}),o(\"hoverformat\"),o(\"layer\")}e.exports=function(t,e,r){o(t,e,r,{type:\"ternary\",attributes:h,handleDefaults:d,font:e.font,paper_bgcolor:e.paper_bgcolor})}},{\"../../components/color\":366,\"../../lib\":503,\"../../plot_api/plot_template\":543,\"../cartesian/line_grid_defaults\":571,\"../cartesian/prefix_suffix_defaults\":573,\"../cartesian/tick_label_defaults\":578,\"../cartesian/tick_mark_defaults\":579,\"../cartesian/tick_value_defaults\":580,\"../subplot_defaults\":632,\"./layout_attributes\":635}],637:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"tinycolor2\"),a=t(\"../../registry\"),o=t(\"../../lib\"),s=o.strTranslate,l=o._,c=t(\"../../components/color\"),u=t(\"../../components/drawing\"),f=t(\"../cartesian/set_convert\"),h=t(\"../../lib/extend\").extendFlat,p=t(\"../plots\"),d=t(\"../cartesian/axes\"),g=t(\"../../components/dragelement\"),m=t(\"../../components/fx\"),v=t(\"../../components/dragelement/helpers\"),y=v.freeMode,x=v.rectMode,b=t(\"../../components/titles\"),_=t(\"../cartesian/select\").prepSelect,w=t(\"../cartesian/select\").selectOnClick,T=t(\"../cartesian/select\").clearSelect,k=t(\"../cartesian/select\").clearSelectionsCache,A=t(\"../cartesian/constants\");function M(t,e){this.id=t.id,this.graphDiv=t.graphDiv,this.init(e),this.makeFramework(e),this.aTickLayout=null,this.bTickLayout=null,this.cTickLayout=null}e.exports=M;var S=M.prototype;S.init=function(t){this.container=t._ternarylayer,this.defs=t._defs,this.layoutId=t._uid,this.traceHash={},this.layers={}},S.plot=function(t,e){var r=e[this.id],n=e._size;this._hasClipOnAxisFalse=!1;for(var i=0;i<t.length;i++){if(!1===t[i][0].trace.cliponaxis){this._hasClipOnAxisFalse=!0;break}}this.updateLayers(r),this.adjustLayout(r,n),p.generalUpdatePerTraceModule(this.graphDiv,this,t,r),this.layers.plotbg.select(\"path\").call(c.fill,r.bgcolor)},S.makeFramework=function(t){var e=this.graphDiv,r=t[this.id],n=this.clipId=\"clip\"+this.layoutId+this.id,i=this.clipIdRelative=\"clip-relative\"+this.layoutId+this.id;this.clipDef=o.ensureSingleById(t._clips,\"clipPath\",n,(function(t){t.append(\"path\").attr(\"d\",\"M0,0Z\")})),this.clipDefRelative=o.ensureSingleById(t._clips,\"clipPath\",i,(function(t){t.append(\"path\").attr(\"d\",\"M0,0Z\")})),this.plotContainer=o.ensureSingle(this.container,\"g\",this.id),this.updateLayers(r),u.setClipUrl(this.layers.backplot,n,e),u.setClipUrl(this.layers.grids,n,e)},S.updateLayers=function(t){var e=this.layers,r=[\"draglayer\",\"plotbg\",\"backplot\",\"grids\"];\"below traces\"===t.aaxis.layer&&r.push(\"aaxis\",\"aline\"),\"below traces\"===t.baxis.layer&&r.push(\"baxis\",\"bline\"),\"below traces\"===t.caxis.layer&&r.push(\"caxis\",\"cline\"),r.push(\"frontplot\"),\"above traces\"===t.aaxis.layer&&r.push(\"aaxis\",\"aline\"),\"above traces\"===t.baxis.layer&&r.push(\"baxis\",\"bline\"),\"above traces\"===t.caxis.layer&&r.push(\"caxis\",\"cline\");var i=this.plotContainer.selectAll(\"g.toplevel\").data(r,String),a=[\"agrid\",\"bgrid\",\"cgrid\"];i.enter().append(\"g\").attr(\"class\",(function(t){return\"toplevel \"+t})).each((function(t){var r=n.select(this);e[t]=r,\"frontplot\"===t?r.append(\"g\").classed(\"scatterlayer\",!0):\"backplot\"===t?r.append(\"g\").classed(\"maplayer\",!0):\"plotbg\"===t?r.append(\"path\").attr(\"d\",\"M0,0Z\"):\"aline\"===t||\"bline\"===t||\"cline\"===t?r.append(\"path\"):\"grids\"===t&&a.forEach((function(t){e[t]=r.append(\"g\").classed(\"grid \"+t,!0)}))})),i.order()};var E=Math.sqrt(4/3);S.adjustLayout=function(t,e){var r,n,i,a,o,l,p=this,d=t.domain,g=(d.x[0]+d.x[1])/2,m=(d.y[0]+d.y[1])/2,v=d.x[1]-d.x[0],y=d.y[1]-d.y[0],x=v*e.w,b=y*e.h,_=t.sum,w=t.aaxis.min,T=t.baxis.min,k=t.caxis.min;x>E*b?i=(a=b)*E:a=(i=x)/E,o=v*i/x,l=y*a/b,r=e.l+e.w*g-i/2,n=e.t+e.h*(1-m)-a/2,p.x0=r,p.y0=n,p.w=i,p.h=a,p.sum=_,p.xaxis={type:\"linear\",range:[w+2*k-_,_-w-2*T],domain:[g-o/2,g+o/2],_id:\"x\"},f(p.xaxis,p.graphDiv._fullLayout),p.xaxis.setScale(),p.xaxis.isPtWithinRange=function(t){return t.a>=p.aaxis.range[0]&&t.a<=p.aaxis.range[1]&&t.b>=p.baxis.range[1]&&t.b<=p.baxis.range[0]&&t.c>=p.caxis.range[1]&&t.c<=p.caxis.range[0]},p.yaxis={type:\"linear\",range:[w,_-T-k],domain:[m-l/2,m+l/2],_id:\"y\"},f(p.yaxis,p.graphDiv._fullLayout),p.yaxis.setScale(),p.yaxis.isPtWithinRange=function(){return!0};var A=p.yaxis.domain[0],M=p.aaxis=h({},t.aaxis,{range:[w,_-T-k],side:\"left\",tickangle:(+t.aaxis.tickangle||0)-30,domain:[A,A+l*E],anchor:\"free\",position:0,_id:\"y\",_length:i});f(M,p.graphDiv._fullLayout),M.setScale();var S=p.baxis=h({},t.baxis,{range:[_-w-k,T],side:\"bottom\",domain:p.xaxis.domain,anchor:\"free\",position:0,_id:\"x\",_length:i});f(S,p.graphDiv._fullLayout),S.setScale();var L=p.caxis=h({},t.caxis,{range:[_-w-T,k],side:\"right\",tickangle:(+t.caxis.tickangle||0)+30,domain:[A,A+l*E],anchor:\"free\",position:0,_id:\"y\",_length:i});f(L,p.graphDiv._fullLayout),L.setScale();var C=\"M\"+r+\",\"+(n+a)+\"h\"+i+\"l-\"+i/2+\",-\"+a+\"Z\";p.clipDef.select(\"path\").attr(\"d\",C),p.layers.plotbg.select(\"path\").attr(\"d\",C);var P=\"M0,\"+a+\"h\"+i+\"l-\"+i/2+\",-\"+a+\"Z\";p.clipDefRelative.select(\"path\").attr(\"d\",P);var I=s(r,n);p.plotContainer.selectAll(\".scatterlayer,.maplayer\").attr(\"transform\",I),p.clipDefRelative.select(\"path\").attr(\"transform\",null);var O=s(r-S._offset,n+a);p.layers.baxis.attr(\"transform\",O),p.layers.bgrid.attr(\"transform\",O);var z=s(r+i/2,n)+\"rotate(30)\"+s(0,-M._offset);p.layers.aaxis.attr(\"transform\",z),p.layers.agrid.attr(\"transform\",z);var D=s(r+i/2,n)+\"rotate(-30)\"+s(0,-L._offset);p.layers.caxis.attr(\"transform\",D),p.layers.cgrid.attr(\"transform\",D),p.drawAxes(!0),p.layers.aline.select(\"path\").attr(\"d\",M.showline?\"M\"+r+\",\"+(n+a)+\"l\"+i/2+\",-\"+a:\"M0,0\").call(c.stroke,M.linecolor||\"#000\").style(\"stroke-width\",(M.linewidth||0)+\"px\"),p.layers.bline.select(\"path\").attr(\"d\",S.showline?\"M\"+r+\",\"+(n+a)+\"h\"+i:\"M0,0\").call(c.stroke,S.linecolor||\"#000\").style(\"stroke-width\",(S.linewidth||0)+\"px\"),p.layers.cline.select(\"path\").attr(\"d\",L.showline?\"M\"+(r+i/2)+\",\"+n+\"l\"+i/2+\",\"+a:\"M0,0\").call(c.stroke,L.linecolor||\"#000\").style(\"stroke-width\",(L.linewidth||0)+\"px\"),p.graphDiv._context.staticPlot||p.initInteractions(),u.setClipUrl(p.layers.frontplot,p._hasClipOnAxisFalse?null:p.clipId,p.graphDiv)},S.drawAxes=function(t){var e=this.graphDiv,r=this.id.substr(7)+\"title\",n=this.layers,i=this.aaxis,a=this.baxis,o=this.caxis;if(this.drawAx(i),this.drawAx(a),this.drawAx(o),t){var s=Math.max(i.showticklabels?i.tickfont.size/2:0,(o.showticklabels?.75*o.tickfont.size:0)+(\"outside\"===o.ticks?.87*o.ticklen:0)),c=(a.showticklabels?a.tickfont.size:0)+(\"outside\"===a.ticks?a.ticklen:0)+3;n[\"a-title\"]=b.draw(e,\"a\"+r,{propContainer:i,propName:this.id+\".aaxis.title\",placeholder:l(e,\"Click to enter Component A title\"),attributes:{x:this.x0+this.w/2,y:this.y0-i.title.font.size/3-s,\"text-anchor\":\"middle\"}}),n[\"b-title\"]=b.draw(e,\"b\"+r,{propContainer:a,propName:this.id+\".baxis.title\",placeholder:l(e,\"Click to enter Component B title\"),attributes:{x:this.x0-c,y:this.y0+this.h+.83*a.title.font.size+c,\"text-anchor\":\"middle\"}}),n[\"c-title\"]=b.draw(e,\"c\"+r,{propContainer:o,propName:this.id+\".caxis.title\",placeholder:l(e,\"Click to enter Component C title\"),attributes:{x:this.x0+this.w+c,y:this.y0+this.h+.83*o.title.font.size+c,\"text-anchor\":\"middle\"}})}},S.drawAx=function(t){var e,r=this.graphDiv,n=t._name,i=n.charAt(0),a=t._id,s=this.layers[n],l=i+\"tickLayout\",c=(e=t).ticks+String(e.ticklen)+String(e.showticklabels);this[l]!==c&&(s.selectAll(\".\"+a+\"tick\").remove(),this[l]=c),t.setScale();var u=d.calcTicks(t),f=d.clipEnds(t,u),h=d.makeTransTickFn(t),p=d.getTickSigns(t)[2],g=o.deg2rad(30),m=p*(t.linewidth||1)/2,v=p*t.ticklen,y=this.w,x=this.h,b=\"b\"===i?\"M0,\"+m+\"l\"+Math.sin(g)*v+\",\"+Math.cos(g)*v:\"M\"+m+\",0l\"+Math.cos(g)*v+\",\"+-Math.sin(g)*v,_={a:\"M0,0l\"+x+\",-\"+y/2,b:\"M0,0l-\"+y/2+\",-\"+x,c:\"M0,0l-\"+x+\",\"+y/2}[i];d.drawTicks(r,t,{vals:\"inside\"===t.ticks?f:u,layer:s,path:b,transFn:h,crisp:!1}),d.drawGrid(r,t,{vals:f,layer:this.layers[i+\"grid\"],path:_,transFn:h,crisp:!1}),d.drawLabels(r,t,{vals:u,layer:s,transFn:h,labelFns:d.makeLabelFns(t,0,30)})};var L=A.MINZOOM/2+.87,C=\"m-0.87,.5h\"+L+\"v3h-\"+(L+5.2)+\"l\"+(L/2+2.6)+\",-\"+(.87*L+4.5)+\"l2.6,1.5l-\"+L/2+\",\"+.87*L+\"Z\",P=\"m0.87,.5h-\"+L+\"v3h\"+(L+5.2)+\"l-\"+(L/2+2.6)+\",-\"+(.87*L+4.5)+\"l-2.6,1.5l\"+L/2+\",\"+.87*L+\"Z\",I=\"m0,1l\"+L/2+\",\"+.87*L+\"l2.6,-1.5l-\"+(L/2+2.6)+\",-\"+(.87*L+4.5)+\"l-\"+(L/2+2.6)+\",\"+(.87*L+4.5)+\"l2.6,1.5l\"+L/2+\",-\"+.87*L+\"Z\",O=!0;function z(t){n.select(t).selectAll(\".zoombox,.js-zoombox-backdrop,.js-zoombox-menu,.zoombox-corners\").remove()}S.clearSelect=function(){k(this.dragOptions),T(this.dragOptions.gd)},S.initInteractions=function(){var t,e,r,n,f,h,p,d,v,b,T,k,M=this,S=M.layers.plotbg.select(\"path\").node(),L=M.graphDiv,D=L._fullLayout._zoomlayer;function R(t){var e={};return e[M.id+\".aaxis.min\"]=t.a,e[M.id+\".baxis.min\"]=t.b,e[M.id+\".caxis.min\"]=t.c,e}function F(t,e){var r=L._fullLayout.clickmode;z(L),2===t&&(L.emit(\"plotly_doubleclick\",null),a.call(\"_guiRelayout\",L,R({a:0,b:0,c:0}))),r.indexOf(\"select\")>-1&&1===t&&w(e,L,[M.xaxis],[M.yaxis],M.id,M.dragOptions),r.indexOf(\"event\")>-1&&m.click(L,e,M.id)}function B(t,e){return 1-e/M.h}function N(t,e){return 1-(t+(M.h-e)/Math.sqrt(3))/M.w}function j(t,e){return(t-(M.h-e)/Math.sqrt(3))/M.w}function U(i,a){var o=r+i*t,s=n+a*e,l=Math.max(0,Math.min(1,B(0,n),B(0,s))),c=Math.max(0,Math.min(1,N(r,n),N(o,s))),u=Math.max(0,Math.min(1,j(r,n),j(o,s))),g=(l/2+u)*M.w,m=(1-l/2-c)*M.w,y=(g+m)/2,x=m-g,_=(1-l)*M.h,w=_-x/E;x<A.MINZOOM?(p=f,T.attr(\"d\",v),k.attr(\"d\",\"M0,0Z\")):(p={a:f.a+l*h,b:f.b+c*h,c:f.c+u*h},T.attr(\"d\",v+\"M\"+g+\",\"+_+\"H\"+m+\"L\"+y+\",\"+w+\"L\"+g+\",\"+_+\"Z\"),k.attr(\"d\",\"M\"+r+\",\"+n+\"m0.5,0.5h5v-2h-5v-5h-2v5h-5v2h5v5h2ZM\"+g+\",\"+_+C+\"M\"+m+\",\"+_+P+\"M\"+y+\",\"+w+I)),b||(T.transition().style(\"fill\",d>.2?\"rgba(0,0,0,0.4)\":\"rgba(255,255,255,0.3)\").duration(200),k.transition().style(\"opacity\",1).duration(200),b=!0),L.emit(\"plotly_relayouting\",R(p))}function V(){z(L),p!==f&&(a.call(\"_guiRelayout\",L,R(p)),O&&L.data&&L._context.showTips&&(o.notifier(l(L,\"Double-click to zoom back out\"),\"long\"),O=!1))}function H(t,e){var 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n=t(\"../heatmap/attributes\"),i=t(\"../scatter/attributes\"),a=t(\"../../plots/cartesian/axis_format_attributes\"),o=a.axisHoverFormat,s=a.descriptionOnlyNumbers,l=t(\"../../components/colorscale/attributes\"),c=t(\"../../components/drawing/attributes\").dash,u=t(\"../../plots/font_attributes\"),f=t(\"../../lib/extend\").extendFlat,h=t(\"../../constants/filter_ops\"),p=h.COMPARISON_OPS2,d=h.INTERVAL_OPS,g=i.line;e.exports=f({z:n.z,x:n.x,x0:n.x0,dx:n.dx,y:n.y,y0:n.y0,dy:n.dy,xperiod:n.xperiod,yperiod:n.yperiod,xperiod0:i.xperiod0,yperiod0:i.yperiod0,xperiodalignment:n.xperiodalignment,yperiodalignment:n.yperiodalignment,text:n.text,hovertext:n.hovertext,transpose:n.transpose,xtype:n.xtype,ytype:n.ytype,xhoverformat:o(\"x\"),yhoverformat:o(\"y\"),zhoverformat:o(\"z\",1),hovertemplate:n.hovertemplate,hoverongaps:n.hoverongaps,connectgaps:f({},n.connectgaps,{}),fillcolor:{valType:\"color\",editType:\"calc\"},autocontour:{valType:\"boolean\",dflt:!0,editType:\"calc\",impliedEdits:{\"contours.start\":void 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a}(u,h),[l]}}},{\"../../components/color\":366,\"../../lib\":503,\"../bar/hover\":655}],778:[function(t,e,r){\"use 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s=r||n.select(t).selectAll(\"g.funnellayer\").selectAll(\"g.trace\");l(t,s,\"funnel\"),s.style(\"opacity\",(function(t){return t[0].trace.opacity})),s.each((function(e){var r=n.select(this),s=e[0].trace;r.selectAll(\".point > path\").each((function(t){if(!t.isBlank){var e=s.marker;n.select(this).call(a.fill,t.mc||e.color).call(a.stroke,t.mlc||e.line.color).call(i.dashLine,e.line.dash,t.mlw||e.line.width).style(\"opacity\",s.selectedpoints&&!t.selected?o:1)}})),c(r,s,t),r.selectAll(\".regions\").each((function(){n.select(this).selectAll(\"path\").style(\"stroke-width\",0).call(a.fill,s.connector.fillcolor)})),r.selectAll(\".lines\").each((function(){var t=s.connector.line;i.lineGroupStyle(n.select(this).selectAll(\"path\"),t.width,t.color,t.dash)}))}))}}},{\"../../components/color\":366,\"../../components/drawing\":388,\"../../constants/interactions\":478,\"../bar/style\":662,\"../bar/uniform_text\":664,\"@plotly/d3\":58}],783:[function(t,e,r){\"use strict\";var n=t(\"../pie/attributes\"),i=t(\"../../plots/attributes\"),a=t(\"../../plots/domain\").attributes,o=t(\"../../plots/template_attributes\").hovertemplateAttrs,s=t(\"../../plots/template_attributes\").texttemplateAttrs,l=t(\"../../lib/extend\").extendFlat;e.exports={labels:n.labels,label0:n.label0,dlabel:n.dlabel,values:n.values,marker:{colors:n.marker.colors,line:{color:l({},n.marker.line.color,{dflt:null}),width:l({},n.marker.line.width,{dflt:1}),editType:\"calc\"},editType:\"calc\"},text:n.text,hovertext:n.hovertext,scalegroup:l({},n.scalegroup,{}),textinfo:l({},n.textinfo,{flags:[\"label\",\"text\",\"value\",\"percent\"]}),texttemplate:s({editType:\"plot\"},{keys:[\"label\",\"color\",\"value\",\"text\",\"percent\"]}),hoverinfo:l({},i.hoverinfo,{flags:[\"label\",\"text\",\"value\",\"percent\",\"name\"]}),hovertemplate:o({},{keys:[\"label\",\"color\",\"value\",\"text\",\"percent\"]}),textposition:l({},n.textposition,{values:[\"inside\",\"none\"],dflt:\"inside\"}),textfont:n.textfont,insidetextfont:n.insidetextfont,title:{text:n.title.text,font:n.title.font,position:l({},n.title.position,{values:[\"top 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n=t(\"../../lib\"),i=t(\"./attributes\"),a=t(\"../../plots/domain\").defaults,o=t(\"../bar/defaults\").handleText,s=t(\"../pie/defaults\").handleLabelsAndValues;e.exports=function(t,e,r,l){function c(r,a){return n.coerce(t,e,i,r,a)}var u=c(\"labels\"),f=c(\"values\"),h=s(u,f),p=h.len;if(e._hasLabels=h.hasLabels,e._hasValues=h.hasValues,!e._hasLabels&&e._hasValues&&(c(\"label0\"),c(\"dlabel\")),p){e._length=p,c(\"marker.line.width\")&&c(\"marker.line.color\",l.paper_bgcolor),c(\"marker.colors\"),c(\"scalegroup\");var d,g=c(\"text\"),m=c(\"texttemplate\");if(m||(d=c(\"textinfo\",Array.isArray(g)?\"text+percent\":\"percent\")),c(\"hovertext\"),c(\"hovertemplate\"),m||d&&\"none\"!==d){var v=c(\"textposition\");o(t,e,l,c,v,{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!1,moduleHasCliponaxis:!1,moduleHasTextangle:!1,moduleHasInsideanchor:!1})}a(e,l,c),c(\"title.text\")&&(c(\"title.position\"),n.coerceFont(c,\"title.font\",l.font)),c(\"aspectratio\"),c(\"baseratio\")}else e.visible=!1}},{\"../../lib\":503,\"../../plots/domain\":584,\"../bar/defaults\":652,\"../pie/defaults\":902,\"./attributes\":783}],787:[function(t,e,r){\"use strict\";e.exports={moduleType:\"trace\",name:\"funnelarea\",basePlotModule:t(\"./base_plot\"),categories:[\"pie-like\",\"funnelarea\",\"showLegend\"],attributes:t(\"./attributes\"),layoutAttributes:t(\"./layout_attributes\"),supplyDefaults:t(\"./defaults\"),supplyLayoutDefaults:t(\"./layout_defaults\"),calc:t(\"./calc\").calc,crossTraceCalc:t(\"./calc\").crossTraceCalc,plot:t(\"./plot\"),style:t(\"./style\"),styleOne:t(\"../pie/style_one\"),meta:{}}},{\"../pie/style_one\":910,\"./attributes\":783,\"./base_plot\":784,\"./calc\":785,\"./defaults\":786,\"./layout_attributes\":788,\"./layout_defaults\":789,\"./plot\":790,\"./style\":791}],788:[function(t,e,r){\"use strict\";var n=t(\"../pie/layout_attributes\").hiddenlabels;e.exports={hiddenlabels:n,funnelareacolorway:{valType:\"colorlist\",editType:\"calc\"},extendfunnelareacolors:{valType:\"boolean\",dflt:!0,editType:\"calc\"}}},{\"../pie/layout_attributes\":906}],789:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"./layout_attributes\");e.exports=function(t,e){function r(r,a){return n.coerce(t,e,i,r,a)}r(\"hiddenlabels\"),r(\"funnelareacolorway\",e.colorway),r(\"extendfunnelareacolors\")}},{\"../../lib\":503,\"./layout_attributes\":788}],790:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"../../components/drawing\"),a=t(\"../../lib\"),o=a.strScale,s=a.strTranslate,l=t(\"../../lib/svg_text_utils\"),c=t(\"../bar/plot\").toMoveInsideBar,u=t(\"../bar/uniform_text\"),f=u.recordMinTextSize,h=u.clearMinTextSize,p=t(\"../pie/helpers\"),d=t(\"../pie/plot\"),g=d.attachFxHandlers,m=d.determineInsideTextFont,v=d.layoutAreas,y=d.prerenderTitles,x=d.positionTitleOutside,b=d.formatSliceLabel;function _(t,e){return\"l\"+(e[0]-t[0])+\",\"+(e[1]-t[1])}e.exports=function(t,e){var r=t._fullLayout;h(\"funnelarea\",r),y(e,t),v(e,r._size),a.makeTraceGroups(r._funnelarealayer,e,\"trace\").each((function(e){var u=n.select(this),h=e[0],d=h.trace;!function(t){if(!t.length)return;var e=t[0],r=e.trace,n=r.aspectratio,i=r.baseratio;i>.999&&(i=.999);var a,o=Math.pow(i,2),s=e.vTotal,l=s,c=s*o/(1-o)/s;function u(){var t,e={x:t=Math.sqrt(c),y:-t};return[e.x,e.y]}var f,h,p=[];for(p.push(u()),f=t.length-1;f>-1;f--)if(!(h=t[f]).hidden){var d=h.v/l;c+=d,p.push(u())}var g=1/0,m=-1/0;for(f=0;f<p.length;f++)a=p[f],g=Math.min(g,a[1]),m=Math.max(m,a[1]);for(f=0;f<p.length;f++)p[f][1]-=(m+g)/2;var v=p[p.length-1][0],y=e.r,x=(m-g)/2,b=y/v,_=y/x*n;for(e.r=_*x,f=0;f<p.length;f++)p[f][0]*=b,p[f][1]*=_;var w=[-(a=p[0])[0],a[1]],T=[a[0],a[1]],k=0;for(f=t.length-1;f>-1;f--)if(!(h=t[f]).hidden){var A=p[k+=1][0],M=p[k][1];h.TL=[-A,M],h.TR=[A,M],h.BL=w,h.BR=T,h.pxmid=(S=h.TR,E=h.BR,[.5*(S[0]+E[0]),.5*(S[1]+E[1])]),w=h.TL,T=h.TR}var S,E}(e),u.each((function(){var u=n.select(this).selectAll(\"g.slice\").data(e);u.enter().append(\"g\").classed(\"slice\",!0),u.exit().remove(),u.each((function(o,s){if(o.hidden)n.select(this).selectAll(\"path,g\").remove();else{o.pointNumber=o.i,o.curveNumber=d.index;var u=h.cx,v=h.cy,y=n.select(this),x=y.selectAll(\"path.surface\").data([o]);x.enter().append(\"path\").classed(\"surface\",!0).style({\"pointer-events\":\"all\"}),y.call(g,t,e);var w=\"M\"+(u+o.TR[0])+\",\"+(v+o.TR[1])+_(o.TR,o.BR)+_(o.BR,o.BL)+_(o.BL,o.TL)+\"Z\";x.attr(\"d\",w),b(t,o,h);var T=p.castOption(d.textposition,o.pts),k=y.selectAll(\"g.slicetext\").data(o.text&&\"none\"!==T?[0]:[]);k.enter().append(\"g\").classed(\"slicetext\",!0),k.exit().remove(),k.each((function(){var h=a.ensureSingle(n.select(this),\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),p=a.ensureUniformFontSize(t,m(d,o,r.font));h.text(o.text).attr({class:\"slicetext\",transform:\"\",\"text-anchor\":\"middle\"}).call(i.font,p).call(l.convertToTspans,t);var g,y,x,b=i.bBox(h.node()),_=Math.min(o.BL[1],o.BR[1])+v,w=Math.max(o.TL[1],o.TR[1])+v;y=Math.max(o.TL[0],o.BL[0])+u,x=Math.min(o.TR[0],o.BR[0])+u,(g=c(y,x,_,w,b,{isHorizontal:!0,constrained:!0,angle:0,anchor:\"middle\"})).fontSize=p.size,f(d.type,g,r),e[s].transform=g,h.attr(\"transform\",a.getTextTransform(g))}))}}));var 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W=_.selectAll(\"g.gauge-outline\").data([x]);W.enter().append(\"g\").classed(\"gauge-outline\",!0).append(\"path\"),W.select(\"path\").call(P).call(T),W.exit().remove()}(t,0,e,{radius:U,innerRadius:V,gauge:Y,layer:W,size:B,gaugeBg:C,gaugeOutline:P,transitionOpts:r,onComplete:m});var X=O.selectAll(\"g.bullet\").data(R?e:[]);X.exit().remove();var Z=O.selectAll(\"g.bulletaxis\").data(R?e:[]);Z.exit().remove(),R&&function(t,e,r,n){var i,a,o,s,c,u=r[0].trace,f=n.gauge,p=n.layer,g=n.gaugeBg,m=n.gaugeOutline,v=n.size,x=u.domain,b=n.transitionOpts,_=n.onComplete;f.enter().append(\"g\").classed(\"bullet\",!0),f.attr(\"transform\",l(v.l,v.t)),p.enter().append(\"g\").classed(\"bulletaxis\",!0).classed(\"crisp\",!0),p.selectAll(\"g.xbulletaxistick,path,text\").remove();var A=v.h,M=u.gauge.bar.thickness*A,S=x.x[0],E=x.x[0]+(x.x[1]-x.x[0])*(u._hasNumber||u._hasDelta?1-h.bulletNumberDomainSize:1);(i=k(t,u.gauge.axis))._id=\"xbulletaxis\",i.domain=[S,E],i.setScale(),a=d.calcTicks(i),o=d.makeTransTickFn(i),s=d.getTickSigns(i)[2],c=v.t+v.h,i.visible&&(d.drawTicks(t,i,{vals:\"inside\"===i.ticks?d.clipEnds(i,a):a,layer:p,path:d.makeTickPath(i,c,s),transFn:o}),d.drawLabels(t,i,{vals:a,layer:p,transFn:o,labelFns:d.makeLabelFns(i,c)}));function L(t){t.attr(\"width\",(function(t){return Math.max(0,i.c2p(t.range[1])-i.c2p(t.range[0]))})).attr(\"x\",(function(t){return i.c2p(t.range[0])})).attr(\"y\",(function(t){return.5*(1-t.thickness)*A})).attr(\"height\",(function(t){return t.thickness*A}))}var C=[g].concat(u.gauge.steps),P=f.selectAll(\"g.bg-bullet\").data(C);P.enter().append(\"g\").classed(\"bg-bullet\",!0).append(\"rect\"),P.select(\"rect\").call(L).call(T),P.exit().remove();var 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n=t(\"../../components/colorscale/attributes\"),i=t(\"../../plots/cartesian/axis_format_attributes\").axisHoverFormat,a=t(\"../../plots/template_attributes\").hovertemplateAttrs,o=t(\"../surface/attributes\"),s=t(\"../../plots/attributes\"),l=t(\"../../lib/extend\").extendFlat;e.exports=l({x:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},y:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},z:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},i:{valType:\"data_array\",editType:\"calc\"},j:{valType:\"data_array\",editType:\"calc\"},k:{valType:\"data_array\",editType:\"calc\"},text:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"calc\"},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"calc\"},hovertemplate:a({editType:\"calc\"}),xhoverformat:i(\"x\"),yhoverformat:i(\"y\"),zhoverformat:i(\"z\"),delaunayaxis:{valType:\"enumerated\",values:[\"x\",\"y\",\"z\"],dflt:\"z\",editType:\"calc\"},alphahull:{valType:\"number\",dflt:-1,editType:\"calc\"},intensity:{valType:\"data_array\",editType:\"calc\"},intensitymode:{valType:\"enumerated\",values:[\"vertex\",\"cell\"],dflt:\"vertex\",editType:\"calc\"},color:{valType:\"color\",editType:\"calc\"},vertexcolor:{valType:\"data_array\",editType:\"calc\"},facecolor:{valType:\"data_array\",editType:\"calc\"},transforms:void 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n=t(\"../../components/colorscale/calc\");e.exports=function(t,e){e.intensity&&n(t,e,{vals:e.intensity,containerStr:\"\",cLetter:\"c\"})}},{\"../../components/colorscale/calc\":374}],868:[function(t,e,r){\"use strict\";var n=t(\"../../../stackgl_modules\").gl_mesh3d,i=t(\"../../../stackgl_modules\").delaunay_triangulate,a=t(\"../../../stackgl_modules\").alpha_shape,o=t(\"../../../stackgl_modules\").convex_hull,s=t(\"../../lib/gl_format_color\").parseColorScale,l=t(\"../../lib/str2rgbarray\"),c=t(\"../../components/colorscale\").extractOpts,u=t(\"../../plots/gl3d/zip3\");function f(t,e,r){this.scene=t,this.uid=r,this.mesh=e,this.name=\"\",this.color=\"#fff\",this.data=null,this.showContour=!1}var h=f.prototype;function p(t){for(var e=[],r=t.length,n=0;n<r;n++)e[n]=l(t[n]);return e}function d(t,e,r,n){for(var i=[],a=e.length,o=0;o<a;o++)i[o]=t.d2l(e[o],0,n)*r;return i}function g(t){for(var e=[],r=t.length,n=0;n<r;n++)e[n]=Math.round(t[n]);return e}function m(t,e){for(var r=t.length,n=0;n<r;n++)if(t[n]<=-.5||t[n]>=e-.5)return!1;return!0}h.handlePick=function(t){if(t.object===this.mesh){var e=t.index=t.data.index;t.data._cellCenter?t.traceCoordinate=t.data.dataCoordinate:t.traceCoordinate=[this.data.x[e],this.data.y[e],this.data.z[e]];var r=this.data.hovertext||this.data.text;return Array.isArray(r)&&void 0!==r[e]?t.textLabel=r[e]:r&&(t.textLabel=r),!0}},h.update=function(t){var e=this.scene,r=e.fullSceneLayout;this.data=t;var n,f=t.x.length,h=u(d(r.xaxis,t.x,e.dataScale[0],t.xcalendar),d(r.yaxis,t.y,e.dataScale[1],t.ycalendar),d(r.zaxis,t.z,e.dataScale[2],t.zcalendar));if(t.i&&t.j&&t.k){if(t.i.length!==t.j.length||t.j.length!==t.k.length||!m(t.i,f)||!m(t.j,f)||!m(t.k,f))return;n=u(g(t.i),g(t.j),g(t.k))}else n=0===t.alphahull?o(h):t.alphahull>0?a(t.alphahull,h):function(t,e){for(var r=[\"x\",\"y\",\"z\"].indexOf(t),n=[],a=e.length,o=0;o<a;o++)n[o]=[e[o][(r+1)%3],e[o][(r+2)%3]];return i(n)}(t.delaunayaxis,h);var v={positions:h,cells:n,lightPosition:[t.lightposition.x,t.lightposition.y,t.lightposition.z],ambient:t.lighting.ambient,diffuse:t.lighting.diffuse,specular:t.lighting.specular,roughness:t.lighting.roughness,fresnel:t.lighting.fresnel,vertexNormalsEpsilon:t.lighting.vertexnormalsepsilon,faceNormalsEpsilon:t.lighting.facenormalsepsilon,opacity:t.opacity,contourEnable:t.contour.show,contourColor:l(t.contour.color).slice(0,3),contourWidth:t.contour.width,useFacetNormals:t.flatshading};if(t.intensity){var y=c(t);this.color=\"#fff\";var x=t.intensitymode;v[x+\"Intensity\"]=t.intensity,v[x+\"IntensityBounds\"]=[y.min,y.max],v.colormap=s(t)}else t.vertexcolor?(this.color=t.vertexcolor[0],v.vertexColors=p(t.vertexcolor)):t.facecolor?(this.color=t.facecolor[0],v.cellColors=p(t.facecolor)):(this.color=t.color,v.meshColor=l(t.color));this.mesh.update(v)},h.dispose=function(){this.scene.glplot.remove(this.mesh),this.mesh.dispose()},e.exports=function(t,e){var r=t.glplot.gl,i=n({gl:r}),a=new 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n=t(\"../../lib\").extendFlat,i=t(\"../scatter/attributes\"),a=t(\"../../plots/cartesian/axis_format_attributes\").axisHoverFormat,o=t(\"../../components/drawing/attributes\").dash,s=t(\"../../components/fx/attributes\"),l=t(\"../../constants/delta.js\"),c=l.INCREASING.COLOR,u=l.DECREASING.COLOR,f=i.line;function 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E=S.enter().append(\"rect\").attr(\"class\",\"bandrect\").attr(\"stroke-opacity\",0).attr(\"fill\",(function(t){return t.color})).attr(\"fill-opacity\",0);S.attr(\"fill\",(function(t){return t.color})).attr(\"width\",(function(t){return t.width})).attr(\"height\",(function(t){return t.height})).attr(\"y\",(function(t){return t.y})).attr(\"cursor\",(function(t){return\"fixed\"===t.parcatsViewModel.arrangement?\"default\":\"perpendicular\"===t.parcatsViewModel.arrangement?\"ns-resize\":\"move\"})),k(E),S.exit().remove(),M.append(\"text\").attr(\"class\",\"catlabel\").attr(\"pointer-events\",\"none\");var z=e._fullLayout.paper_bgcolor;A.select(\"text.catlabel\").attr(\"text-anchor\",(function(t){return d(t)?\"start\":\"end\"})).attr(\"alignment-baseline\",\"middle\").style(\"text-shadow\",f.makeTextShadow(z)).style(\"fill\",\"rgb(0, 0, 0)\").attr(\"x\",(function(t){return d(t)?t.width+5:-5})).attr(\"y\",(function(t){return t.height/2})).text((function(t){return t.model.categoryLabel})).each((function(t){c.font(n.select(this),t.parcatsViewModel.categorylabelfont),f.convertToTspans(n.select(this),e)})),M.append(\"text\").attr(\"class\",\"dimlabel\"),A.select(\"text.dimlabel\").attr(\"text-anchor\",\"middle\").attr(\"alignment-baseline\",\"baseline\").attr(\"cursor\",(function(t){return\"fixed\"===t.parcatsViewModel.arrangement?\"default\":\"ew-resize\"})).attr(\"x\",(function(t){return t.width/2})).attr(\"y\",-5).text((function(t,e){return 0===e?t.parcatsViewModel.model.dimensions[t.model.dimensionInd].dimensionLabel:null})).each((function(t){c.font(n.select(this),t.parcatsViewModel.labelfont)})),A.selectAll(\"rect.bandrect\").on(\"mouseover\",L).on(\"mouseout\",C),A.exit().remove(),w.call(n.behavior.drag().origin((function(t){return{x:t.x,y:0}})).on(\"dragstart\",P).on(\"drag\",I).on(\"dragend\",O)),u.each((function(t){t.traceSelection=n.select(this),t.pathSelection=n.select(this).selectAll(\"g.paths\").selectAll(\"path.path\"),t.dimensionSelection=n.select(this).selectAll(\"g.dimensions\").selectAll(\"g.dimension\")})),u.exit().remove()}function p(t){return t.key}function d(t){var e=t.parcatsViewModel.dimensions.length,r=t.parcatsViewModel.dimensions[e-1].model.dimensionInd;return t.model.dimensionInd===r}function g(t,e){return t.model.rawColor>e.model.rawColor?1:t.model.rawColor<e.model.rawColor?-1:0}function m(t){if(!t.parcatsViewModel.dragDimension&&-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")){s.raiseToTop(this),w(n.select(this));var e=y(t),r=x(t);if(t.parcatsViewModel.graphDiv.emit(\"plotly_hover\",{points:e,event:n.event,constraints:r}),-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"none\")){var i,a,l,c=n.mouse(this)[0],f=t.parcatsViewModel.graphDiv,h=t.parcatsViewModel.trace,p=f._fullLayout,d=p._paperdiv.node().getBoundingClientRect(),g=t.parcatsViewModel.graphDiv.getBoundingClientRect();for(l=0;l<t.leftXs.length-1;l++)if(t.leftXs[l]+t.dimWidths[l]-2<=c&&c<=t.leftXs[l+1]+2){var m=t.parcatsViewModel.dimensions[l],v=t.parcatsViewModel.dimensions[l+1];i=(m.x+m.width+v.x)/2,a=(t.topYs[l]+t.topYs[l+1]+t.height)/2;break}var b=t.parcatsViewModel.x+i,_=t.parcatsViewModel.y+a,T=u.mostReadable(t.model.color,[\"black\",\"white\"]),k=t.model.count,A=k/t.parcatsViewModel.model.count,M={countLabel:k,probabilityLabel:A.toFixed(3)},S=[];-1!==t.parcatsViewModel.hoverinfoItems.indexOf(\"count\")&&S.push([\"Count:\",M.countLabel].join(\" \")),-1!==t.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")&&S.push([\"P:\",M.probabilityLabel].join(\" \"));var E=S.join(\"<br>\"),L=n.mouse(f)[0];o.loneHover({trace:h,x:b-d.left+g.left,y:_-d.top+g.top,text:E,color:t.model.color,borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontSize:10,fontColor:T,idealAlign:L<b?\"right\":\"left\",hovertemplate:(h.line||{}).hovertemplate,hovertemplateLabels:M,eventData:[{data:h._input,fullData:h,count:k,probability:A}]},{container:p._hoverlayer.node(),outerContainer:p._paper.node(),gd:f})}}}function v(t){if(!t.parcatsViewModel.dragDimension&&(_(n.select(this)),o.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()),t.parcatsViewModel.pathSelection.sort(g),-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\"))){var e=y(t),r=x(t);t.parcatsViewModel.graphDiv.emit(\"plotly_unhover\",{points:e,event:n.event,constraints:r})}}function y(t){for(var e=[],r=z(t.parcatsViewModel),n=0;n<t.model.valueInds.length;n++){var i=t.model.valueInds[n];e.push({curveNumber:r,pointNumber:i})}return e}function x(t){for(var e={},r=t.parcatsViewModel.model.dimensions,n=0;n<r.length;n++){var i=r[n],a=i.categories[t.model.categoryInds[n]];e[i.containerInd]=a.categoryValue}return void 0!==t.model.rawColor&&(e.color=t.model.rawColor),e}function b(t){if(-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")){var e=y(t),r=x(t);t.parcatsViewModel.graphDiv.emit(\"plotly_click\",{points:e,event:n.event,constraints:r})}}function _(t){t.attr(\"fill\",(function(t){return t.model.color})).attr(\"fill-opacity\",.6).attr(\"stroke\",\"lightgray\").attr(\"stroke-width\",.2).attr(\"stroke-opacity\",1)}function w(t){t.attr(\"fill-opacity\",.8).attr(\"stroke\",(function(t){return u.mostReadable(t.model.color,[\"black\",\"white\"])})).attr(\"stroke-width\",.3)}function T(t){t.select(\"rect.catrect\").attr(\"stroke\",\"black\").attr(\"stroke-width\",1).attr(\"stroke-opacity\",1)}function k(t){t.attr(\"stroke\",\"black\").attr(\"stroke-width\",.2).attr(\"stroke-opacity\",1).attr(\"fill-opacity\",1)}function A(t){var e=t.parcatsViewModel.pathSelection,r=t.categoryViewModel.model.dimensionInd,n=t.categoryViewModel.model.categoryInd;return e.filter((function(e){return e.model.categoryInds[r]===n&&e.model.color===t.color}))}function M(t,e,r){var i=n.select(t).datum(),a=i.categoryViewModel.model,o=i.parcatsViewModel.graphDiv,s=n.select(t.parentNode).selectAll(\"rect.bandrect\"),l=[];s.each((function(t){A(t).each((function(t){Array.prototype.push.apply(l,y(t))}))}));var c={};c[a.dimensionInd]=a.categoryValue,o.emit(e,{points:l,event:r,constraints:c})}function S(t,e,r){var i=n.select(t).datum(),a=i.categoryViewModel.model,o=i.parcatsViewModel.graphDiv,s=A(i),l=[];s.each((function(t){Array.prototype.push.apply(l,y(t))}));var c={};c[a.dimensionInd]=a.categoryValue,void 0!==i.rawColor&&(c.color=i.rawColor),o.emit(e,{points:l,event:r,constraints:c})}function E(t,e,r){t._fullLayout._calcInverseTransform(t);var i,a,o=t._fullLayout._invScaleX,s=t._fullLayout._invScaleY,l=n.select(r.parentNode).select(\"rect.catrect\"),c=l.node().getBoundingClientRect(),u=l.datum(),f=u.parcatsViewModel,h=f.model.dimensions[u.model.dimensionInd],p=f.trace,d=c.top+c.height/2;f.dimensions.length>1&&h.displayInd===f.dimensions.length-1?(i=c.left,a=\"left\"):(i=c.left+c.width,a=\"right\");var g=u.model.count,m=u.model.categoryLabel,v=g/u.parcatsViewModel.model.count,y={countLabel:g,categoryLabel:m,probabilityLabel:v.toFixed(3)},x=[];-1!==u.parcatsViewModel.hoverinfoItems.indexOf(\"count\")&&x.push([\"Count:\",y.countLabel].join(\" \")),-1!==u.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")&&x.push([\"P(\"+y.categoryLabel+\"):\",y.probabilityLabel].join(\" \"));var b=x.join(\"<br>\");return{trace:p,x:o*(i-e.left),y:s*(d-e.top),text:b,color:\"lightgray\",borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontSize:12,fontColor:\"black\",idealAlign:a,hovertemplate:p.hovertemplate,hovertemplateLabels:y,eventData:[{data:p._input,fullData:p,count:g,category:m,probability:v}]}}function L(t){if(!t.parcatsViewModel.dragDimension&&-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")){if(n.mouse(this)[1]<-1)return;var e,r=t.parcatsViewModel.graphDiv,i=r._fullLayout,a=i._paperdiv.node().getBoundingClientRect(),l=t.parcatsViewModel.hoveron;if(\"color\"===l?(!function(t){var e=n.select(t).datum(),r=A(e);w(r),r.each((function(){s.raiseToTop(this)})),n.select(t.parentNode).selectAll(\"rect.bandrect\").filter((function(t){return t.color===e.color})).each((function(){s.raiseToTop(this),n.select(this).attr(\"stroke\",\"black\").attr(\"stroke-width\",1.5)}))}(this),S(this,\"plotly_hover\",n.event)):(!function(t){n.select(t.parentNode).selectAll(\"rect.bandrect\").each((function(t){var e=A(t);w(e),e.each((function(){s.raiseToTop(this)}))})),n.select(t.parentNode).select(\"rect.catrect\").attr(\"stroke\",\"black\").attr(\"stroke-width\",2.5)}(this),M(this,\"plotly_hover\",n.event)),-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"none\"))\"category\"===l?e=E(r,a,this):\"color\"===l?e=function(t,e,r){t._fullLayout._calcInverseTransform(t);var i,a,o=t._fullLayout._invScaleX,s=t._fullLayout._invScaleY,l=r.getBoundingClientRect(),c=n.select(r).datum(),f=c.categoryViewModel,h=f.parcatsViewModel,p=h.model.dimensions[f.model.dimensionInd],d=h.trace,g=l.y+l.height/2;h.dimensions.length>1&&p.displayInd===h.dimensions.length-1?(i=l.left,a=\"left\"):(i=l.left+l.width,a=\"right\");var m=f.model.categoryLabel,v=c.parcatsViewModel.model.count,y=0;c.categoryViewModel.bands.forEach((function(t){t.color===c.color&&(y+=t.count)}));var x=f.model.count,b=0;h.pathSelection.each((function(t){t.model.color===c.color&&(b+=t.model.count)}));var _=y/v,w=y/b,T=y/x,k={countLabel:v,categoryLabel:m,probabilityLabel:_.toFixed(3)},A=[];-1!==f.parcatsViewModel.hoverinfoItems.indexOf(\"count\")&&A.push([\"Count:\",k.countLabel].join(\" \")),-1!==f.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")&&(A.push(\"P(color \\u2229 \"+m+\"): \"+k.probabilityLabel),A.push(\"P(\"+m+\" | color): \"+w.toFixed(3)),A.push(\"P(color | \"+m+\"): \"+T.toFixed(3)));var M=A.join(\"<br>\"),S=u.mostReadable(c.color,[\"black\",\"white\"]);return{trace:d,x:o*(i-e.left),y:s*(g-e.top),text:M,color:c.color,borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontColor:S,fontSize:10,idealAlign:a,hovertemplate:d.hovertemplate,hovertemplateLabels:k,eventData:[{data:d._input,fullData:d,category:m,count:v,probability:_,categorycount:x,colorcount:b,bandcolorcount:y}]}}(r,a,this):\"dimension\"===l&&(e=function(t,e,r){var i=[];return n.select(r.parentNode.parentNode).selectAll(\"g.category\").select(\"rect.catrect\").each((function(){i.push(E(t,e,this))})),i}(r,a,this)),e&&o.loneHover(e,{container:i._hoverlayer.node(),outerContainer:i._paper.node(),gd:r})}}function C(t){var e=t.parcatsViewModel;if(!e.dragDimension&&(_(e.pathSelection),T(e.dimensionSelection.selectAll(\"g.category\")),k(e.dimensionSelection.selectAll(\"g.category\").selectAll(\"rect.bandrect\")),o.loneUnhover(e.graphDiv._fullLayout._hoverlayer.node()),e.pathSelection.sort(g),-1===e.hoverinfoItems.indexOf(\"skip\"))){\"color\"===t.parcatsViewModel.hoveron?S(this,\"plotly_unhover\",n.event):M(this,\"plotly_unhover\",n.event)}}function P(t){\"fixed\"!==t.parcatsViewModel.arrangement&&(t.dragDimensionDisplayInd=t.model.displayInd,t.initialDragDimensionDisplayInds=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),t.dragHasMoved=!1,t.dragCategoryDisplayInd=null,n.select(this).selectAll(\"g.category\").select(\"rect.catrect\").each((function(e){var r=n.mouse(this)[0],i=n.mouse(this)[1];-2<=r&&r<=e.width+2&&-2<=i&&i<=e.height+2&&(t.dragCategoryDisplayInd=e.model.displayInd,t.initialDragCategoryDisplayInds=t.model.categories.map((function(t){return t.displayInd})),e.model.dragY=e.y,s.raiseToTop(this.parentNode),n.select(this.parentNode).selectAll(\"rect.bandrect\").each((function(e){e.y<i&&i<=e.y+e.height&&(t.potentialClickBand=this)})))})),t.parcatsViewModel.dragDimension=t,o.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()))}function I(t){if(\"fixed\"!==t.parcatsViewModel.arrangement&&(t.dragHasMoved=!0,null!==t.dragDimensionDisplayInd)){var e=t.dragDimensionDisplayInd,r=e-1,i=e+1,a=t.parcatsViewModel.dimensions[e];if(null!==t.dragCategoryDisplayInd){var o=a.categories[t.dragCategoryDisplayInd];o.model.dragY+=n.event.dy;var s=o.model.dragY,l=o.model.displayInd,c=a.categories,u=c[l-1],f=c[l+1];void 0!==u&&s<u.y+u.height/2&&(o.model.displayInd=u.model.displayInd,u.model.displayInd=l),void 0!==f&&s+o.height>f.y+f.height/2&&(o.model.displayInd=f.model.displayInd,f.model.displayInd=l),t.dragCategoryDisplayInd=o.model.displayInd}if(null===t.dragCategoryDisplayInd||\"freeform\"===t.parcatsViewModel.arrangement){a.model.dragX=n.event.x;var h=t.parcatsViewModel.dimensions[r],p=t.parcatsViewModel.dimensions[i];void 0!==h&&a.model.dragX<h.x+h.width&&(a.model.displayInd=h.model.displayInd,h.model.displayInd=e),void 0!==p&&a.model.dragX+a.width>p.x&&(a.model.displayInd=p.model.displayInd,p.model.displayInd=t.dragDimensionDisplayInd),t.dragDimensionDisplayInd=a.model.displayInd}j(t.parcatsViewModel),N(t.parcatsViewModel),R(t.parcatsViewModel),D(t.parcatsViewModel)}}function O(t){if(\"fixed\"!==t.parcatsViewModel.arrangement&&null!==t.dragDimensionDisplayInd){n.select(this).selectAll(\"text\").attr(\"font-weight\",\"normal\");var e={},r=z(t.parcatsViewModel),i=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),o=t.initialDragDimensionDisplayInds.some((function(t,e){return t!==i[e]}));o&&i.forEach((function(r,n){var i=t.parcatsViewModel.model.dimensions[n].containerInd;e[\"dimensions[\"+i+\"].displayindex\"]=r}));var s=!1;if(null!==t.dragCategoryDisplayInd){var l=t.model.categories.map((function(t){return t.displayInd}));if(s=t.initialDragCategoryDisplayInds.some((function(t,e){return t!==l[e]}))){var c=t.model.categories.slice().sort((function(t,e){return t.displayInd-e.displayInd})),u=c.map((function(t){return t.categoryValue})),f=c.map((function(t){return t.categoryLabel}));e[\"dimensions[\"+t.model.containerInd+\"].categoryarray\"]=[u],e[\"dimensions[\"+t.model.containerInd+\"].ticktext\"]=[f],e[\"dimensions[\"+t.model.containerInd+\"].categoryorder\"]=\"array\"}}if(-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")&&!t.dragHasMoved&&t.potentialClickBand&&(\"color\"===t.parcatsViewModel.hoveron?S(t.potentialClickBand,\"plotly_click\",n.event.sourceEvent):M(t.potentialClickBand,\"plotly_click\",n.event.sourceEvent)),t.model.dragX=null,null!==t.dragCategoryDisplayInd)t.parcatsViewModel.dimensions[t.dragDimensionDisplayInd].categories[t.dragCategoryDisplayInd].model.dragY=null,t.dragCategoryDisplayInd=null;t.dragDimensionDisplayInd=null,t.parcatsViewModel.dragDimension=null,t.dragHasMoved=null,t.potentialClickBand=null,j(t.parcatsViewModel),N(t.parcatsViewModel),n.transition().duration(300).ease(\"cubic-in-out\").each((function(){R(t.parcatsViewModel,!0),D(t.parcatsViewModel,!0)})).each(\"end\",(function(){(o||s)&&a.restyle(t.parcatsViewModel.graphDiv,e,[r])}))}}function z(t){for(var e,r=t.graphDiv._fullData,n=0;n<r.length;n++)if(t.key===r[n].uid){e=n;break}return e}function D(t,e){var r;void 0===e&&(e=!1),t.pathSelection.data((function(t){return t.paths}),p),(r=t.pathSelection,e?r.transition():r).attr(\"d\",(function(t){return t.svgD}))}function R(t,e){function r(t){return e?t.transition():t}void 0===e&&(e=!1),t.dimensionSelection.data((function(t){return t.dimensions}),p);var i=t.dimensionSelection.selectAll(\"g.category\").data((function(t){return t.categories}),p);r(t.dimensionSelection).attr(\"transform\",(function(t){return l(t.x,0)})),r(i).attr(\"transform\",(function(t){return l(0,t.y)})),i.select(\".dimlabel\").text((function(t,e){return 0===e?t.parcatsViewModel.model.dimensions[t.model.dimensionInd].dimensionLabel:null})),i.select(\".catlabel\").attr(\"text-anchor\",(function(t){return d(t)?\"start\":\"end\"})).attr(\"x\",(function(t){return d(t)?t.width+5:-5})).each((function(t){var e,r;d(t)?(e=t.width+5,r=\"start\"):(e=-5,r=\"end\"),n.select(this).selectAll(\"tspan\").attr(\"x\",e).attr(\"text-anchor\",r)}));var a=i.selectAll(\"rect.bandrect\").data((function(t){return t.bands}),p),o=a.enter().append(\"rect\").attr(\"class\",\"bandrect\").attr(\"cursor\",\"move\").attr(\"stroke-opacity\",0).attr(\"fill\",(function(t){return t.color})).attr(\"fill-opacity\",0);a.attr(\"fill\",(function(t){return t.color})).attr(\"width\",(function(t){return t.width})).attr(\"height\",(function(t){return t.height})).attr(\"y\",(function(t){return t.y})),k(o),a.each((function(){s.raiseToTop(this)})),a.exit().remove()}function F(t,e,r){var n,i=r[0],a=e.margin||{l:80,r:80,t:100,b:80},o=i.trace,s=o.domain,l=e.width,c=e.height,u=Math.floor(l*(s.x[1]-s.x[0])),f=Math.floor(c*(s.y[1]-s.y[0])),h=s.x[0]*l+a.l,p=e.height-s.y[1]*e.height+a.t,d=o.line.shape;n=\"all\"===o.hoverinfo?[\"count\",\"probability\"]:(o.hoverinfo||\"\").split(\"+\");var g={trace:o,key:o.uid,model:i,x:h,y:p,width:u,height:f,hoveron:o.hoveron,hoverinfoItems:n,arrangement:o.arrangement,bundlecolors:o.bundlecolors,sortpaths:o.sortpaths,labelfont:o.labelfont,categorylabelfont:o.tickfont,pathShape:d,dragDimension:null,margin:a,paths:[],dimensions:[],graphDiv:t,traceSelection:null,pathSelection:null,dimensionSelection:null};return i.dimensions&&(j(g),N(g)),g}function B(t,e,r,n,a){var o,s,l=[],c=[];for(s=0;s<r.length-1;s++)o=i(r[s]+t[s],t[s+1]),l.push(o(a)),c.push(o(1-a));var u=\"M \"+t[0]+\",\"+e[0];for(u+=\"l\"+r[0]+\",0 \",s=1;s<r.length;s++)u+=\"C\"+l[s-1]+\",\"+e[s-1]+\" \"+c[s-1]+\",\"+e[s]+\" \"+t[s]+\",\"+e[s],u+=\"l\"+r[s]+\",0 \";for(u+=\"l0,\"+n+\" \",u+=\"l -\"+r[r.length-1]+\",0 \",s=r.length-2;s>=0;s--)u+=\"C\"+c[s]+\",\"+(e[s+1]+n)+\" \"+l[s]+\",\"+(e[s]+n)+\" \"+(t[s]+r[s])+\",\"+(e[s]+n),u+=\"l-\"+r[s]+\",0 \";return u+=\"Z\"}function N(t){var e=t.dimensions,r=t.model,n=e.map((function(t){return t.categories.map((function(t){return t.y}))})),i=t.model.dimensions.map((function(t){return t.categories.map((function(t){return t.displayInd}))})),a=t.model.dimensions.map((function(t){return t.displayInd})),o=t.dimensions.map((function(t){return t.model.dimensionInd})),s=e.map((function(t){return t.x})),l=e.map((function(t){return t.width})),c=[];for(var u in r.paths)r.paths.hasOwnProperty(u)&&c.push(r.paths[u]);function f(t){var e=t.categoryInds.map((function(t,e){return i[e][t]}));return o.map((function(t){return e[t]}))}c.sort((function(e,r){var n=f(e),i=f(r);return\"backward\"===t.sortpaths&&(n.reverse(),i.reverse()),n.push(e.valueInds[0]),i.push(r.valueInds[0]),t.bundlecolors&&(n.unshift(e.rawColor),i.unshift(r.rawColor)),n<i?-1:n>i?1:0}));for(var h=new Array(c.length),p=e[0].model.count,d=e[0].categories.map((function(t){return t.height})).reduce((function(t,e){return t+e})),g=0;g<c.length;g++){var m,v=c[g];m=p>0?d*(v.count/p):0;for(var y,x=new Array(n.length),b=0;b<v.categoryInds.length;b++){var _=v.categoryInds[b],w=i[b][_],T=a[b];x[T]=n[T][w],n[T][w]+=m;var k=t.dimensions[T].categories[w],A=k.bands.length,M=k.bands[A-1];if(void 0===M||v.rawColor!==M.rawColor){var S=void 0===M?0:M.y+M.height;k.bands.push({key:S,color:v.color,rawColor:v.rawColor,height:m,width:k.width,count:v.count,y:S,categoryViewModel:k,parcatsViewModel:t})}else{var E=k.bands[A-1];E.height+=m,E.count+=v.count}}y=\"hspline\"===t.pathShape?B(s,x,l,m,.5):B(s,x,l,m,0),h[g]={key:v.valueInds[0],model:v,height:m,leftXs:s,topYs:x,dimWidths:l,svgD:y,parcatsViewModel:t}}t.paths=h}function j(t){var e=t.model.dimensions.map((function(t){return{displayInd:t.displayInd,dimensionInd:t.dimensionInd}}));e.sort((function(t,e){return t.displayInd-e.displayInd}));var r=[];for(var n in e){var i=e[n].dimensionInd,a=t.model.dimensions[i];r.push(U(t,a))}t.dimensions=r}function U(t,e){var r,n=t.model.dimensions.length,i=e.displayInd;r=40+(n>1?(t.width-80-16)/(n-1):0)*i;var 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n=t(\"../../components/colorscale/attributes\"),i=t(\"../../plots/cartesian/layout_attributes\"),a=t(\"../../plots/font_attributes\"),o=t(\"../../plots/domain\").attributes,s=t(\"../../lib/extend\").extendFlat,l=t(\"../../plot_api/plot_template\").templatedArray;e.exports={domain:o({name:\"parcoords\",trace:!0,editType:\"plot\"}),labelangle:{valType:\"angle\",dflt:0,editType:\"plot\"},labelside:{valType:\"enumerated\",values:[\"top\",\"bottom\"],dflt:\"top\",editType:\"plot\"},labelfont:a({editType:\"plot\"}),tickfont:a({editType:\"plot\"}),rangefont:a({editType:\"plot\"}),dimensions:l(\"dimension\",{label:{valType:\"string\",editType:\"plot\"},tickvals:s({},i.tickvals,{editType:\"plot\"}),ticktext:s({},i.ticktext,{editType:\"plot\"}),tickformat:s({},i.tickformat,{editType:\"plot\"}),visible:{valType:\"boolean\",dflt:!0,editType:\"plot\"},range:{valType:\"info_array\",items:[{valType:\"number\",editType:\"plot\"},{valType:\"number\",editType:\"plot\"}],editType:\"plot\"},constraintrange:{valType:\"info_array\",freeLength:!0,dimensions:\"1-2\",items:[{valType:\"any\",editType:\"plot\"},{valType:\"any\",editType:\"plot\"}],editType:\"plot\"},multiselect:{valType:\"boolean\",dflt:!0,editType:\"plot\"},values:{valType:\"data_array\",editType:\"calc\"},editType:\"calc\"}),line:s({editType:\"calc\"},n(\"line\",{colorscaleDflt:\"Viridis\",autoColorDflt:!1,editTypeOverride:\"calc\"}))}},{\"../../components/colorscale/attributes\":373,\"../../lib/extend\":493,\"../../plot_api/plot_template\":543,\"../../plots/cartesian/layout_attributes\":569,\"../../plots/domain\":584,\"../../plots/font_attributes\":585}],888:[function(t,e,r){\"use 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t.slice()}))}(e).slice();e.filter.set(r),o()}),brushEndCallback:a}}},ensureAxisBrush:function(t,e){var r=t.selectAll(\".\"+n.cn.axisBrush).data(o,a);r.enter().append(\"g\").classed(n.cn.axisBrush,!0),function(t,e){var r=t.selectAll(\".background\").data(o);r.enter().append(\"rect\").classed(\"background\",!0).call(d).call(g).style(\"pointer-events\",\"auto\").attr(\"transform\",l(0,n.verticalPadding)),r.call(k).attr(\"height\",(function(t){return t.height-n.verticalPadding}));var i=t.selectAll(\".highlight-shadow\").data(o);i.enter().append(\"line\").classed(\"highlight-shadow\",!0).attr(\"x\",-n.bar.width/2).attr(\"stroke-width\",n.bar.width+n.bar.strokeWidth).attr(\"stroke\",e).attr(\"opacity\",n.bar.strokeOpacity).attr(\"stroke-linecap\",\"butt\"),i.attr(\"y1\",(function(t){return t.height})).call(x);var a=t.selectAll(\".highlight\").data(o);a.enter().append(\"line\").classed(\"highlight\",!0).attr(\"x\",-n.bar.width/2).attr(\"stroke-width\",n.bar.width-n.bar.strokeWidth).attr(\"stroke\",n.bar.fillColor).attr(\"opacity\",n.bar.fillOpacity).attr(\"stroke-linecap\",\"butt\"),a.attr(\"y1\",(function(t){return t.height})).call(x)}(r,e)},cleanRanges:function(t,e){if(Array.isArray(t[0])?(t=t.map((function(t){return t.sort(s)})),t=e.multiselect?S(t.sort(A)):[t[0]]):t=[t.sort(s)],e.tickvals){var r=e.tickvals.slice().sort(s);if(!(t=t.map((function(t){var e=[p(0,r,t[0],[]),p(1,r,t[1],[])];if(e[1]>e[0])return e})).filter((function(t){return t}))).length)return}return t.length>1?t:t[0]}}},{\"../../lib\":503,\"../../lib/gup\":500,\"./constants\":891,\"@plotly/d3\":58}],889:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"../../plots/get_data\").getModuleCalcData,a=t(\"./plot\"),o=t(\"../../constants/xmlns_namespaces\");r.name=\"parcoords\",r.plot=function(t){var e=i(t.calcdata,\"parcoords\")[0];e.length&&a(t,e)},r.clean=function(t,e,r,n){var i=n._has&&n._has(\"parcoords\"),a=e._has&&e._has(\"parcoords\");i&&!a&&(n._paperdiv.selectAll(\".parcoords\").remove(),n._glimages.selectAll(\"*\").remove())},r.toSVG=function(t){var e=t._fullLayout._glimages,r=n.select(t).selectAll(\".svg-container\");r.filter((function(t,e){return e===r.size()-1})).selectAll(\".gl-canvas-context, .gl-canvas-focus\").each((function(){var t=this.toDataURL(\"image/png\");e.append(\"svg:image\").attr({xmlns:o.svg,\"xlink:href\":t,preserveAspectRatio:\"none\",x:0,y:0,width:this.style.width,height:this.style.height})})),window.setTimeout((function(){n.selectAll(\"#filterBarPattern\").attr(\"id\",\"filterBarPattern\")}),60)}},{\"../../constants/xmlns_namespaces\":480,\"../../plots/get_data\":593,\"./plot\":898,\"@plotly/d3\":58}],890:[function(t,e,r){\"use strict\";var n=t(\"../../lib\").isArrayOrTypedArray,i=t(\"../../components/colorscale\"),a=t(\"../../lib/gup\").wrap;e.exports=function(t,e){var r,o;return i.hasColorscale(e,\"line\")&&n(e.line.color)?(r=e.line.color,o=i.extractOpts(e.line).colorscale,i.calc(t,e,{vals:r,containerStr:\"line\",cLetter:\"c\"})):(r=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=.5;return e}(e._length),o=[[0,e.line.color],[1,e.line.color]]),a({lineColor:r,cscale:o})}},{\"../../components/colorscale\":378,\"../../lib\":503,\"../../lib/gup\":500}],891:[function(t,e,r){\"use 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n=t(\"../../lib\").isTypedArray;r.convertTypedArray=function(t){return n(t)?Array.prototype.slice.call(t):t},r.isOrdinal=function(t){return!!t.tickvals},r.isVisible=function(t){return t.visible||!(\"visible\"in t)}},{\"../../lib\":503}],894:[function(t,e,r){\"use strict\";e.exports={attributes:t(\"./attributes\"),supplyDefaults:t(\"./defaults\"),calc:t(\"./calc\"),plot:t(\"./plot\"),colorbar:{container:\"line\",min:\"cmin\",max:\"cmax\"},moduleType:\"trace\",name:\"parcoords\",basePlotModule:t(\"./base_plot\"),categories:[\"gl\",\"regl\",\"noOpacity\",\"noHover\"],meta:{}}},{\"./attributes\":887,\"./base_plot\":889,\"./calc\":890,\"./defaults\":892,\"./plot\":898}],895:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor;\\n\\nattribute vec4 p01_04, p05_08, p09_12, p13_16,\\n               p17_20, p21_24, p25_28, p29_32,\\n               p33_36, p37_40, p41_44, p45_48,\\n               p49_52, p53_56, p57_60, colors;\\n\\nuniform mat4 dim0A, dim1A, dim0B, dim1B, dim0C, dim1C, dim0D, dim1D,\\n             loA, hiA, loB, hiB, loC, hiC, loD, hiD;\\n\\nuniform vec2 resolution, viewBoxPos, viewBoxSize;\\nuniform float maskHeight;\\nuniform float drwLayer; // 0: context, 1: focus, 2: pick\\nuniform vec4 contextColor;\\nuniform sampler2D maskTexture, palette;\\n\\nbool isPick    = (drwLayer > 1.5);\\nbool isContext = (drwLayer < 0.5);\\n\\nconst vec4 ZEROS = vec4(0.0, 0.0, 0.0, 0.0);\\nconst vec4 UNITS = vec4(1.0, 1.0, 1.0, 1.0);\\n\\nfloat val(mat4 p, mat4 v) {\\n    return dot(matrixCompMult(p, v) * UNITS, UNITS);\\n}\\n\\nfloat axisY(float ratio, mat4 A, mat4 B, mat4 C, mat4 D) {\\n    float y1 = val(A, dim0A) + val(B, dim0B) + val(C, dim0C) + val(D, dim0D);\\n    float y2 = val(A, dim1A) + val(B, dim1B) + val(C, dim1C) + val(D, dim1D);\\n    return y1 * (1.0 - ratio) + y2 * ratio;\\n}\\n\\nint iMod(int a, int b) {\\n    return a - b * (a / b);\\n}\\n\\nbool fOutside(float p, float lo, float hi) {\\n    return (lo < hi) && (lo > p || p > hi);\\n}\\n\\nbool vOutside(vec4 p, vec4 lo, vec4 hi) {\\n    return (\\n        fOutside(p[0], lo[0], hi[0]) ||\\n        fOutside(p[1], lo[1], hi[1]) ||\\n        fOutside(p[2], lo[2], hi[2]) ||\\n        fOutside(p[3], lo[3], hi[3])\\n    );\\n}\\n\\nbool mOutside(mat4 p, mat4 lo, mat4 hi) {\\n    return (\\n        vOutside(p[0], lo[0], hi[0]) ||\\n        vOutside(p[1], lo[1], hi[1]) ||\\n        vOutside(p[2], lo[2], hi[2]) ||\\n        vOutside(p[3], lo[3], hi[3])\\n    );\\n}\\n\\nbool outsideBoundingBox(mat4 A, mat4 B, mat4 C, mat4 D) {\\n    return mOutside(A, loA, hiA) ||\\n           mOutside(B, loB, hiB) ||\\n           mOutside(C, loC, hiC) ||\\n           mOutside(D, loD, hiD);\\n}\\n\\nbool outsideRasterMask(mat4 A, mat4 B, mat4 C, mat4 D) {\\n    mat4 pnts[4];\\n    pnts[0] = A;\\n    pnts[1] = B;\\n    pnts[2] = C;\\n    pnts[3] = D;\\n\\n    for(int i = 0; i < 4; ++i) {\\n        for(int j = 0; j < 4; ++j) {\\n            for(int k = 0; k < 4; ++k) {\\n                if(0 == iMod(\\n                    int(255.0 * texture2D(maskTexture,\\n                        vec2(\\n                            (float(i * 2 + j / 2) + 0.5) / 8.0,\\n                            (pnts[i][j][k] * (maskHeight - 1.0) + 1.0) / maskHeight\\n                        ))[3]\\n                    ) / int(pow(2.0, float(iMod(j * 4 + k, 8)))),\\n                    2\\n                )) return true;\\n            }\\n        }\\n    }\\n    return false;\\n}\\n\\nvec4 position(bool isContext, float v, mat4 A, mat4 B, mat4 C, mat4 D) {\\n    float x = 0.5 * sign(v) + 0.5;\\n    float y = axisY(x, A, B, C, D);\\n    float z = 1.0 - abs(v);\\n\\n    z += isContext ? 0.0 : 2.0 * float(\\n        outsideBoundingBox(A, B, C, D) ||\\n        outsideRasterMask(A, B, C, D)\\n    );\\n\\n    return vec4(\\n        2.0 * (vec2(x, y) * viewBoxSize + viewBoxPos) / resolution - 1.0,\\n        z,\\n        1.0\\n    );\\n}\\n\\nvoid main() {\\n    mat4 A = mat4(p01_04, p05_08, p09_12, p13_16);\\n    mat4 B = mat4(p17_20, p21_24, p25_28, p29_32);\\n    mat4 C = mat4(p33_36, p37_40, p41_44, p45_48);\\n    mat4 D = mat4(p49_52, p53_56, p57_60, ZEROS);\\n\\n    float v = colors[3];\\n\\n    gl_Position = position(isContext, v, A, B, C, D);\\n\\n    fragColor =\\n        isContext ? vec4(contextColor) :\\n        isPick ? vec4(colors.rgb, 1.0) : texture2D(palette, vec2(abs(v), 0.5));\\n}\\n\"]),a=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n    gl_FragColor = fragColor;\\n}\\n\"]),o=t(\"./constants\").maxDimensionCount,s=t(\"../../lib\"),l=new Uint8Array(4),c=new Uint8Array(4),u={shape:[256,1],format:\"rgba\",type:\"uint8\",mag:\"nearest\",min:\"nearest\"};function f(t,e,r,n,i){var a=t._gl;a.enable(a.SCISSOR_TEST),a.scissor(e,r,n,i),t.clear({color:[0,0,0,0],depth:1})}function h(t,e,r,n,i,a){var o=a.key;r.drawCompleted||(!function(t){t.read({x:0,y:0,width:1,height:1,data:l})}(t),r.drawCompleted=!0),function s(l){var c=Math.min(n,i-l*n);0===l&&(window.cancelAnimationFrame(r.currentRafs[o]),delete r.currentRafs[o],f(t,a.scissorX,a.scissorY,a.scissorWidth,a.viewBoxSize[1])),r.clearOnly||(a.count=2*c,a.offset=2*l*n,e(a),l*n+c<i&&(r.currentRafs[o]=window.requestAnimationFrame((function(){s(l+1)}))),r.drawCompleted=!1)}(0)}function p(t,e){for(var r=new Array(256),n=0;n<256;n++)r[n]=t(n/255).concat(e);return r}function d(t,e){return(t>>>8*e)%256/255}function g(t,e,r){for(var n=new Array(8*e),i=0,a=0;a<e;a++)for(var o=0;o<2;o++)for(var s=0;s<4;s++){var l=4*t+s,c=r[64*a+l];63===l&&0===o&&(c*=-1),n[i++]=c}return n}function m(t){var e=\"0\"+t;return e.substr(e.length-2)}function v(t){return t<o?\"p\"+m(t+1)+\"_\"+m(t+4):\"colors\"}function y(t,e,r,n,i,a,o,l,c,u,f,h,p,d){for(var g=[[],[]],m=0;m<64;m++)g[0][m]=m===i?1:0,g[1][m]=m===a?1:0;o*=d,l*=d,c*=d,u*=d;var 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r,n,l,m,b,_=e.context,w=e.pick,T=e.regl,k=T._gl,A=k.getParameter(k.ALIASED_LINE_WIDTH_RANGE),M=Math.max(A[0],Math.min(A[1],e.viewModel.plotGlPixelRatio)),S={currentRafs:{},drawCompleted:!0,clearOnly:!1},E=function(t){for(var e={},r=0;r<=o;r+=4)e[v(r)]=t.buffer({usage:\"dynamic\",type:\"float\",data:new Uint8Array(0)});return e}(T),L=T.texture(u),C=[];I(e);var P=T({profile:!1,blend:{enable:_,func:{srcRGB:\"src alpha\",dstRGB:\"one minus src 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I(t){r=t.model,n=t.viewModel,l=n.dimensions.slice(),m=l[0]?l[0].values.length:0;var e=r.lines,i=w?e.color.map((function(t,r){return r/e.color.length})):e.color,a=function(t,e,r){for(var n,i=new Array(t*(o+4)),a=0,s=0;s<t;s++){for(var l=0;l<o;l++)i[a++]=l<e.length?e[l].paddedUnitValues[s]:.5;i[a++]=d(s,2),i[a++]=d(s,1),i[a++]=d(s,0),i[a++]=(n=r[s],Math.max(1e-6,Math.min(.999999,n)))}return i}(m,l,i);!function(t,e,r){for(var n=0;n<=o;n+=4)t[v(n)](g(n/4,e,r))}(E,m,a),_||w||(L=T.texture(s.extendFlat({data:p(r.unitToColor,255)},u)))}return{render:function(t,e,n){var i,a,o,s=t.length,c=1/0,u=-1/0;for(i=0;i<s;i++)t[i].dim0.canvasX<c&&(c=t[i].dim0.canvasX,a=i),t[i].dim1.canvasX>u&&(u=t[i].dim1.canvasX,o=i);0===s&&f(T,0,0,r.canvasWidth,r.canvasHeight);var p=function(t){var e,r,n,i=[[],[]];for(n=0;n<64;n++){var a=!t&&n<l.length?l[n].brush.filter.getBounds():[-1/0,1/0];i[0][n]=a[0],i[1][n]=a[1]}var o=new Array(16384);for(e=0;e<16384;e++)o[e]=255;if(!t)for(e=0;e<l.length;e++){var s=e%8,c=(e-s)/8,u=Math.pow(2,s),f=l[e].brush.filter.get();if(!(f.length<2)){var h=x(f[0])[1];for(r=1;r<f.length;r++){var p=x(f[r]);for(n=h+1;n<p[0];n++)o[8*n+c]&=~u;h=Math.max(h,p[1])}}}var d={shape:[8,2048],format:\"alpha\",type:\"uint8\",mag:\"nearest\",min:\"nearest\",data:o};return b?b(d):b=T.texture(d),{maskTexture:b,maskHeight:2048,loA:i[0].slice(0,16),loB:i[0].slice(16,32),loC:i[0].slice(32,48),loD:i[0].slice(48,64),hiA:i[1].slice(0,16),hiB:i[1].slice(16,32),hiC:i[1].slice(32,48),hiD:i[1].slice(48,64)}}(_);for(i=0;i<s;i++){var d=t[i],g=d.dim0.crossfilterDimensionIndex,v=d.dim1.crossfilterDimensionIndex,k=d.canvasX,A=d.canvasY,M=k+d.panelSizeX,E=d.plotGlPixelRatio;if(e||!C[g]||C[g][0]!==k||C[g][1]!==M){C[g]=[k,M];var L=y(r,a,o,i,g,v,k,A,d.panelSizeX,d.panelSizeY,d.dim0.crossfilterDimensionIndex,_?0:w?2:1,p,E);S.clearOnly=n;var I=e?r.lines.blockLineCount:m;h(T,P,S,I,m,L)}}},readPixel:function(t,e){return T.read({x:t,y:e,width:1,height:1,data:c}),c},readPixels:function(t,e,r,n){var i=new Uint8Array(4*r*n);return T.read({x:t,y:e,width:r,height:n,data:i}),i},destroy:function(){for(var e in t.style[\"pointer-events\"]=\"none\",L.destroy(),b&&b.destroy(),E)E[e].destroy()},update:I}}},{\"../../lib\":503,\"./constants\":891,glslify:227}],896:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,n){var i,a;for(n||(n=1/0),i=0;i<e.length;i++)(a=e[i]).visible&&(n=Math.min(n,a[r].length));for(n===1/0&&(n=0),t._length=n,i=0;i<e.length;i++)(a=e[i]).visible&&(a._length=n);return n}},{}],897:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"../../lib\"),a=i.numberFormat,o=t(\"color-rgba\"),s=t(\"../../plots/cartesian/axes\"),l=i.strRotate,c=i.strTranslate,u=t(\"../../lib/svg_text_utils\"),f=t(\"../../components/drawing\"),h=t(\"../../components/colorscale\"),p=t(\"../../lib/gup\"),d=p.keyFun,g=p.repeat,m=p.unwrap,v=t(\"./helpers\"),y=t(\"./constants\"),x=t(\"./axisbrush\"),b=t(\"./lines\");function _(t,e,r){return i.aggNums(t,null,e,r)}function w(t,e){return k(_(Math.min,t,e),_(Math.max,t,e))}function T(t){var e=t.range;return e?k(e[0],e[1]):w(t.values,t._length)}function k(t,e){return!isNaN(t)&&isFinite(t)||(t=0),!isNaN(e)&&isFinite(e)||(e=0),t===e&&(0===t?(t-=1,e+=1):(t*=.9,e*=1.1)),[t,e]}function A(t,e,r,i,o){var s,l,c=T(r);return i?n.scale.ordinal().domain(i.map((s=a(r.tickformat),l=o,l?function(t,e){var r=l[e];return null==r?s(t):r}:s))).range(i.map((function(r){var n=(r-c[0])/(c[1]-c[0]);return t-e+n*(2*e-t)}))):n.scale.linear().domain(c).range([t-e,e])}function M(t){if(t.tickvals){var e=T(t);return n.scale.ordinal().domain(t.tickvals).range(t.tickvals.map((function(t){return(t-e[0])/(e[1]-e[0])})))}}function S(t){var e=t.map((function(t){return t[0]})),r=t.map((function(t){var e=o(t[1]);return n.rgb(\"rgb(\"+e[0]+\",\"+e[1]+\",\"+e[2]+\")\")})),i=\"rgb\".split(\"\").map((function(t){return n.scale.linear().clamp(!0).domain(e).range(r.map((i=t,function(t){return t[i]})));var i}));return function(t){return i.map((function(e){return e(t)}))}}function E(t){return t.dimensions.some((function(t){return t.brush.filterSpecified}))}function L(t,e,r){var a=m(e),s=a.trace,l=v.convertTypedArray(a.lineColor),c=s.line,u={color:o(y.deselectedLineColor)},f=h.extractOpts(c),p=f.reversescale?h.flipScale(a.cscale):a.cscale,d=s.domain,g=s.dimensions,x=t.width,b=s.labelangle,_=s.labelside,w=s.labelfont,k=s.tickfont,A=s.rangefont,M=i.extendDeepNoArrays({},c,{color:l.map(n.scale.linear().domain(T({values:l,range:[f.min,f.max],_length:s._length}))),blockLineCount:y.blockLineCount,canvasOverdrag:y.overdrag*y.canvasPixelRatio}),E=Math.floor(x*(d.x[1]-d.x[0])),L=Math.floor(t.height*(d.y[1]-d.y[0])),C=t.margin||{l:80,r:80,t:100,b:80},P=E,I=L;return{key:r,colCount:g.filter(v.isVisible).length,dimensions:g,tickDistance:y.tickDistance,unitToColor:S(p),lines:M,deselectedLines:u,labelAngle:b,labelSide:_,labelFont:w,tickFont:k,rangeFont:A,layoutWidth:x,layoutHeight:t.height,domain:d,translateX:d.x[0]*x,translateY:t.height-d.y[1]*t.height,pad:C,canvasWidth:P*y.canvasPixelRatio+2*M.canvasOverdrag,canvasHeight:I*y.canvasPixelRatio,width:P,height:I,canvasPixelRatio:y.canvasPixelRatio}}function C(t,e,r){var o=r.width,s=r.height,l=r.dimensions,c=r.canvasPixelRatio,u=function(t){return o*t/Math.max(1,r.colCount-1)},f=y.verticalPadding/s,h=function(t,e){return n.scale.linear().range([e,t-e])}(s,y.verticalPadding),p={key:r.key,xScale:u,model:r,inBrushDrag:!1},d={};return p.dimensions=l.filter(v.isVisible).map((function(o,l){var g=function(t,e){return n.scale.linear().domain(T(t)).range([e,1-e])}(o,f),m=d[o.label];d[o.label]=(m||0)+1;var b=o.label+(m?\"__\"+m:\"\"),_=o.constraintrange,w=_&&_.length;w&&!Array.isArray(_[0])&&(_=[_]);var k=w?_.map((function(t){return t.map(g)})):[[-1/0,1/0]],S=o.values;S.length>o._length&&(S=S.slice(0,o._length));var L,C=o.tickvals;function P(t,e){return{val:t,text:L[e]}}function I(t,e){return t.val-e.val}if(Array.isArray(C)&&C.length){L=o.ticktext,Array.isArray(L)&&L.length?L.length>C.length?L=L.slice(0,C.length):C.length>L.length&&(C=C.slice(0,L.length)):L=C.map(a(o.tickformat));for(var O=1;O<C.length;O++)if(C[O]<C[O-1]){for(var 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t.map(n).sort(i.sorterAsc)})).sort((function(t,e){return t[0]-e[0]}));e.filterChanged(p.key,o._index,a)}}))}})),p}function P(t){t.classed(y.cn.axisExtentText,!0).attr(\"text-anchor\",\"middle\").style(\"cursor\",\"default\")}function I(t,e){var r=\"top\"===e?1:-1,n=t*Math.PI/180;return{dir:r,dx:Math.sin(n),dy:Math.cos(n),degrees:t}}function O(t,e,r){for(var n=e.panels||(e.panels=[]),i=t.data(),a=0;a<i.length-1;a++){var o=n[a]||(n[a]={}),s=i[a],l=i[a+1];o.dim0=s,o.dim1=l,o.canvasX=s.canvasX,o.panelSizeX=l.canvasX-s.canvasX,o.panelSizeY=e.model.canvasHeight,o.y=0,o.canvasY=0,o.plotGlPixelRatio=r}}function z(t,e){return s.tickText(t._ax,e,!1).text}function D(t,e){if(t.ordinal)return\"\";var r=t.domainScale.domain(),n=r[e?r.length-1:0];return z(t.model.dimensions[t.visibleIndex],n)}e.exports=function(t,e,r,a){var o=t._fullLayout,h=o._toppaper,p=o._glcontainer,_=t._context.plotGlPixelRatio,T=t._fullLayout.paper_bgcolor;!function(t){for(var e=0;e<t.length;e++)for(var 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r=t._fullLayout;if(i(t)){var s={},l={},c={},u={},f=r._size;e.forEach((function(e,r){var n=e[0].trace;c[r]=n.index;var i=u[r]=n._fullInput.index;s[r]=t.data[i].dimensions,l[r]=t.data[i].dimensions.slice()}));n(t,e,{width:f.w,height:f.h,margin:{t:f.t,r:f.r,b:f.b,l:f.l}},{filterChanged:function(e,n,i){var a=l[e][n],o=i.map((function(t){return t.slice()})),s=\"dimensions[\"+n+\"].constraintrange\",f=r._tracePreGUI[t._fullData[c[e]]._fullInput.uid];if(void 0===f[s]){var h=a.constraintrange;f[s]=h||null}var p=t._fullData[c[e]].dimensions[n];o.length?(1===o.length&&(o=o[0]),a.constraintrange=o,p.constraintrange=o.slice(),o=[o]):(delete a.constraintrange,delete p.constraintrange,o=null);var d={};d[s]=o,t.emit(\"plotly_restyle\",[d,[u[e]]])},hover:function(e){t.emit(\"plotly_hover\",e)},unhover:function(e){t.emit(\"plotly_unhover\",e)},axesMoved:function(e,r){var n=function(t,e){return function(r,n){return 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n=t(\"../../plots/attributes\"),i=t(\"../../plots/domain\").attributes,a=t(\"../../plots/font_attributes\"),o=t(\"../../components/color/attributes\"),s=t(\"../../plots/template_attributes\").hovertemplateAttrs,l=t(\"../../plots/template_attributes\").texttemplateAttrs,c=t(\"../../lib/extend\").extendFlat,u=a({editType:\"plot\",arrayOk:!0,colorEditType:\"plot\"});e.exports={labels:{valType:\"data_array\",editType:\"calc\"},label0:{valType:\"number\",dflt:0,editType:\"calc\"},dlabel:{valType:\"number\",dflt:1,editType:\"calc\"},values:{valType:\"data_array\",editType:\"calc\"},marker:{colors:{valType:\"data_array\",editType:\"calc\"},line:{color:{valType:\"color\",dflt:o.defaultLine,arrayOk:!0,editType:\"style\"},width:{valType:\"number\",min:0,dflt:0,arrayOk:!0,editType:\"style\"},editType:\"calc\"},editType:\"calc\"},text:{valType:\"data_array\",editType:\"plot\"},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"style\"},scalegroup:{valType:\"string\",dflt:\"\",editType:\"calc\"},textinfo:{valType:\"flaglist\",flags:[\"label\",\"text\",\"value\",\"percent\"],extras:[\"none\"],editType:\"calc\"},hoverinfo:c({},n.hoverinfo,{flags:[\"label\",\"text\",\"value\",\"percent\",\"name\"]}),hovertemplate:s({},{keys:[\"label\",\"color\",\"value\",\"percent\",\"text\"]}),texttemplate:l({editType:\"plot\"},{keys:[\"label\",\"color\",\"value\",\"percent\",\"text\"]}),textposition:{valType:\"enumerated\",values:[\"inside\",\"outside\",\"auto\",\"none\"],dflt:\"auto\",arrayOk:!0,editType:\"plot\"},textfont:c({},u,{}),insidetextorientation:{valType:\"enumerated\",values:[\"horizontal\",\"radial\",\"tangential\",\"auto\"],dflt:\"auto\",editType:\"plot\"},insidetextfont:c({},u,{}),outsidetextfont:c({},u,{}),automargin:{valType:\"boolean\",dflt:!1,editType:\"plot\"},title:{text:{valType:\"string\",dflt:\"\",editType:\"plot\"},font:c({},u,{}),position:{valType:\"enumerated\",values:[\"top 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r,i,a=[],o=t._fullLayout,l=o.hiddenlabels||[],c=e.labels,u=e.marker.colors||[],f=e.values,h=e._length,p=e._hasValues&&h;if(e.dlabel)for(c=new Array(h),r=0;r<h;r++)c[r]=String(e.label0+r*e.dlabel);var d={},g=s(o[\"_\"+e.type+\"colormap\"]),m=0,v=!1;for(r=0;r<h;r++){var y,x,b;if(p){if(y=f[r],!n(y))continue;if((y=+y)<0)continue}else y=1;void 0!==(x=c[r])&&\"\"!==x||(x=r);var _=d[x=String(x)];void 0===_?(d[x]=a.length,(b=-1!==l.indexOf(x))||(m+=y),a.push({v:y,label:x,color:g(u[r],x),i:r,pts:[r],hidden:b})):(v=!0,(i=a[_]).v+=y,i.pts.push(r),i.hidden||(m+=y),!1===i.color&&u[r]&&(i.color=g(u[r],x)))}return(\"funnelarea\"===e.type?v:e.sort)&&a.sort((function(t,e){return e.v-t.v})),a[0]&&(a[0].vTotal=m),a},crossTraceCalc:function(t,e){var r=(e||{}).type;r||(r=\"pie\");var n=t._fullLayout,i=t.calcdata,a=n[r+\"colorway\"],s=n[\"_\"+r+\"colormap\"];n[\"extend\"+r+\"colors\"]&&(a=l(a,o));for(var c=0,u=0;u<i.length;u++){var f=i[u];if(f[0].trace.type===r)for(var h=0;h<f.length;h++){var p=f[h];!1===p.color&&(s[p.label]?p.color=s[p.label]:(s[p.label]=p.color=a[c%a.length],c++))}}},makePullColorFn:s,generateExtendedColors:l}},{\"../../components/color\":366,\"fast-isnumeric\":190,tinycolor2:312}],902:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),i=t(\"../../lib\"),a=t(\"./attributes\"),o=t(\"../../plots/domain\").defaults,s=t(\"../bar/defaults\").handleText;function l(t,e){var r=Array.isArray(t),a=i.isArrayOrTypedArray(e),o=Math.min(r?t.length:1/0,a?e.length:1/0);if(isFinite(o)||(o=0),o&&a){for(var s,l=0;l<o;l++){var c=e[l];if(n(c)&&c>0){s=!0;break}}s||(o=0)}return{hasLabels:r,hasValues:a,len:o}}e.exports={handleLabelsAndValues:l,supplyDefaults:function(t,e,r,n){function c(r,n){return i.coerce(t,e,a,r,n)}var u=l(c(\"labels\"),c(\"values\")),f=u.len;if(e._hasLabels=u.hasLabels,e._hasValues=u.hasValues,!e._hasLabels&&e._hasValues&&(c(\"label0\"),c(\"dlabel\")),f){e._length=f,c(\"marker.line.width\")&&c(\"marker.line.color\"),c(\"marker.colors\"),c(\"scalegroup\");var h,p=c(\"text\"),d=c(\"texttemplate\");if(d||(h=c(\"textinfo\",Array.isArray(p)?\"text+percent\":\"percent\")),c(\"hovertext\"),c(\"hovertemplate\"),d||h&&\"none\"!==h){var g=c(\"textposition\");s(t,e,n,c,g,{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!1,moduleHasCliponaxis:!1,moduleHasTextangle:!1,moduleHasInsideanchor:!1}),(Array.isArray(g)||\"auto\"===g||\"outside\"===g)&&c(\"automargin\"),(\"inside\"===g||\"auto\"===g||Array.isArray(g))&&c(\"insidetextorientation\")}o(e,n,c);var m=c(\"hole\");if(c(\"title.text\")){var v=c(\"title.position\",m?\"middle center\":\"top center\");m||\"middle center\"!==v||(e.title.position=\"top center\"),i.coerceFont(c,\"title.font\",n.font)}c(\"sort\"),c(\"direction\"),c(\"rotation\"),c(\"pull\")}else e.visible=!1}}},{\"../../lib\":503,\"../../plots/domain\":584,\"../bar/defaults\":652,\"./attributes\":899,\"fast-isnumeric\":190}],903:[function(t,e,r){\"use strict\";var n=t(\"../../components/fx/helpers\").appendArrayMultiPointValues;e.exports=function(t,e){var r={curveNumber:e.index,pointNumbers:t.pts,data:e._input,fullData:e,label:t.label,color:t.color,value:t.v,percent:t.percent,text:t.text,bbox:t.bbox,v:t.v};return 1===t.pts.length&&(r.pointNumber=r.i=t.pts[0]),n(r,e,t.pts),\"funnelarea\"===e.type&&(delete r.v,delete r.i),r}},{\"../../components/fx/helpers\":402}],904:[function(t,e,r){\"use strict\";var n=t(\"../../lib\");function i(t){return-1!==t.indexOf(\"e\")?t.replace(/[.]?0+e/,\"e\"):-1!==t.indexOf(\".\")?t.replace(/[.]?0+$/,\"\"):t}r.formatPiePercent=function(t,e){var r=i((100*t).toPrecision(3));return n.numSeparate(r,e)+\"%\"},r.formatPieValue=function(t,e){var r=i(t.toPrecision(10));return n.numSeparate(r,e)},r.getFirstFilled=function(t,e){if(Array.isArray(t))for(var r=0;r<e.length;r++){var n=t[e[r]];if(n||0===n||\"\"===n)return n}},r.castOption=function(t,e){return Array.isArray(t)?r.getFirstFilled(t,e):t||void 0},r.getRotationAngle=function(t){return(\"auto\"===t?0:t)*Math.PI/180}},{\"../../lib\":503}],905:[function(t,e,r){\"use strict\";e.exports={attributes:t(\"./attributes\"),supplyDefaults:t(\"./defaults\").supplyDefaults,supplyLayoutDefaults:t(\"./layout_defaults\"),layoutAttributes:t(\"./layout_attributes\"),calc:t(\"./calc\").calc,crossTraceCalc:t(\"./calc\").crossTraceCalc,plot:t(\"./plot\").plot,style:t(\"./style\"),styleOne:t(\"./style_one\"),moduleType:\"trace\",name:\"pie\",basePlotModule:t(\"./base_plot\"),categories:[\"pie-like\",\"pie\",\"showLegend\"],meta:{}}},{\"./attributes\":899,\"./base_plot\":900,\"./calc\":901,\"./defaults\":902,\"./layout_attributes\":906,\"./layout_defaults\":907,\"./plot\":908,\"./style\":909,\"./style_one\":910}],906:[function(t,e,r){\"use strict\";e.exports={hiddenlabels:{valType:\"data_array\",editType:\"calc\"},piecolorway:{valType:\"colorlist\",editType:\"calc\"},extendpiecolors:{valType:\"boolean\",dflt:!0,editType:\"calc\"}}},{}],907:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"./layout_attributes\");e.exports=function(t,e){function r(r,a){return n.coerce(t,e,i,r,a)}r(\"hiddenlabels\"),r(\"piecolorway\",e.colorway),r(\"extendpiecolors\")}},{\"../../lib\":503,\"./layout_attributes\":906}],908:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"../../plots/plots\"),a=t(\"../../components/fx\"),o=t(\"../../components/color\"),s=t(\"../../components/drawing\"),l=t(\"../../lib\"),c=l.strScale,u=l.strTranslate,f=t(\"../../lib/svg_text_utils\"),h=t(\"../bar/uniform_text\"),p=h.recordMinTextSize,d=h.clearMinTextSize,g=t(\"../bar/constants\").TEXTPAD,m=t(\"./helpers\"),v=t(\"./event_data\"),y=t(\"../../lib\").isValidTextValue;function x(t,e,r){var i=r[0],o=i.cx,s=i.cy,c=i.trace,u=\"funnelarea\"===c.type;\"_hasHoverLabel\"in c||(c._hasHoverLabel=!1),\"_hasHoverEvent\"in c||(c._hasHoverEvent=!1),t.on(\"mouseover\",(function(t){var r=e._fullLayout,f=e._fullData[c.index];if(!e._dragging&&!1!==r.hovermode){var h=f.hoverinfo;if(Array.isArray(h)&&(h=a.castHoverinfo({hoverinfo:[m.castOption(h,t.pts)],_module:c._module},r,0)),\"all\"===h&&(h=\"label+text+value+percent+name\"),f.hovertemplate||\"none\"!==h&&\"skip\"!==h&&h){var p=t.rInscribed||0,d=o+t.pxmid[0]*(1-p),g=s+t.pxmid[1]*(1-p),y=r.separators,x=[];if(h&&-1!==h.indexOf(\"label\")&&x.push(t.label),t.text=m.castOption(f.hovertext||f.text,t.pts),h&&-1!==h.indexOf(\"text\")){var b=t.text;l.isValidTextValue(b)&&x.push(b)}t.value=t.v,t.valueLabel=m.formatPieValue(t.v,y),h&&-1!==h.indexOf(\"value\")&&x.push(t.valueLabel),t.percent=t.v/i.vTotal,t.percentLabel=m.formatPiePercent(t.percent,y),h&&-1!==h.indexOf(\"percent\")&&x.push(t.percentLabel);var _=f.hoverlabel,w=_.font,T=[];a.loneHover({trace:c,x0:d-p*i.r,x1:d+p*i.r,y:g,_x0:u?o+t.TL[0]:d-p*i.r,_x1:u?o+t.TR[0]:d+p*i.r,_y0:u?s+t.TL[1]:g-p*i.r,_y1:u?s+t.BL[1]:g+p*i.r,text:x.join(\"<br>\"),name:f.hovertemplate||-1!==h.indexOf(\"name\")?f.name:void 0,idealAlign:t.pxmid[0]<0?\"left\":\"right\",color:m.castOption(_.bgcolor,t.pts)||t.color,borderColor:m.castOption(_.bordercolor,t.pts),fontFamily:m.castOption(w.family,t.pts),fontSize:m.castOption(w.size,t.pts),fontColor:m.castOption(w.color,t.pts),nameLength:m.castOption(_.namelength,t.pts),textAlign:m.castOption(_.align,t.pts),hovertemplate:m.castOption(f.hovertemplate,t.pts),hovertemplateLabels:t,eventData:[v(t,f)]},{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:e,inOut_bbox:T}),t.bbox=T[0],c._hasHoverLabel=!0}c._hasHoverEvent=!0,e.emit(\"plotly_hover\",{points:[v(t,f)],event:n.event})}})),t.on(\"mouseout\",(function(t){var r=e._fullLayout,i=e._fullData[c.index],o=n.select(this).datum();c._hasHoverEvent&&(t.originalEvent=n.event,e.emit(\"plotly_unhover\",{points:[v(o,i)],event:n.event}),c._hasHoverEvent=!1),c._hasHoverLabel&&(a.loneUnhover(r._hoverlayer.node()),c._hasHoverLabel=!1)})),t.on(\"click\",(function(t){var r=e._fullLayout,i=e._fullData[c.index];e._dragging||!1===r.hovermode||(e._hoverdata=[v(t,i)],a.click(e,n.event))}))}function b(t,e,r){var n=m.castOption(t.insidetextfont.color,e.pts);!n&&t._input.textfont&&(n=m.castOption(t._input.textfont.color,e.pts));var i=m.castOption(t.insidetextfont.family,e.pts)||m.castOption(t.textfont.family,e.pts)||r.family,a=m.castOption(t.insidetextfont.size,e.pts)||m.castOption(t.textfont.size,e.pts)||r.size;return{color:n||o.contrast(e.color),family:i,size:a}}function _(t,e){for(var r,n,i=0;i<t.length;i++)if((n=(r=t[i][0]).trace).title.text){var a=n.title.text;n._meta&&(a=l.templateString(a,n._meta));var o=s.tester.append(\"text\").attr(\"data-notex\",1).text(a).call(s.font,n.title.font).call(f.convertToTspans,e),c=s.bBox(o.node(),!0);r.titleBox={width:c.width,height:c.height},o.remove()}}function w(t,e,r){var n=r.r||e.rpx1,i=e.rInscribed;if(e.startangle===e.stopangle)return{rCenter:1-i,scale:0,rotate:0,textPosAngle:0};var a,o=e.ring,s=1===o&&Math.abs(e.startangle-e.stopangle)===2*Math.PI,l=e.halfangle,c=e.midangle,u=r.trace.insidetextorientation,f=\"horizontal\"===u,h=\"tangential\"===u,p=\"radial\"===u,d=\"auto\"===u,g=[];if(!d){var m,v=function(r,i){if(function(t,e){var r=t.startangle,n=t.stopangle;return r>e&&e>n||r<e&&e<n}(e,r)){var s=Math.abs(r-e.startangle),l=Math.abs(r-e.stopangle),c=s<l?s:l;(a=\"tan\"===i?k(t,n,o,c,0):T(t,n,o,c,Math.PI/2)).textPosAngle=r,g.push(a)}};if(f||h){for(m=4;m>=-4;m-=2)v(Math.PI*m,\"tan\");for(m=4;m>=-4;m-=2)v(Math.PI*(m+1),\"tan\")}if(f||p){for(m=4;m>=-4;m-=2)v(Math.PI*(m+1.5),\"rad\");for(m=4;m>=-4;m-=2)v(Math.PI*(m+.5),\"rad\")}}if(s||d||f){var y=Math.sqrt(t.width*t.width+t.height*t.height);if((a={scale:i*n*2/y,rCenter:1-i,rotate:0}).textPosAngle=(e.startangle+e.stopangle)/2,a.scale>=1)return a;g.push(a)}(d||p)&&((a=T(t,n,o,l,c)).textPosAngle=(e.startangle+e.stopangle)/2,g.push(a)),(d||h)&&((a=k(t,n,o,l,c)).textPosAngle=(e.startangle+e.stopangle)/2,g.push(a));for(var x=0,b=0,_=0;_<g.length;_++){var w=g[_].scale;if(b<w&&(b=w,x=_),!d&&b>=1)break}return g[x]}function T(t,e,r,n,i){e=Math.max(0,e-2*g);var a=t.width/t.height,o=S(a,n,e,r);return{scale:2*o/t.height,rCenter:A(a,o/e),rotate:M(i)}}function k(t,e,r,n,i){e=Math.max(0,e-2*g);var a=t.height/t.width,o=S(a,n,e,r);return{scale:2*o/t.width,rCenter:A(a,o/e),rotate:M(i+Math.PI/2)}}function A(t,e){return Math.cos(e)-t*e}function M(t){return(180/Math.PI*t+720)%180-90}function S(t,e,r,n){var i=t+1/(2*Math.tan(e));return r*Math.min(1/(Math.sqrt(i*i+.5)+i),n/(Math.sqrt(t*t+n/2)+t))}function E(t,e){return t.v!==e.vTotal||e.trace.hole?Math.min(1/(1+1/Math.sin(t.halfangle)),t.ring/2):1}function L(t,e){var r=e.pxmid[0],n=e.pxmid[1],i=t.width/2,a=t.height/2;return r<0&&(i*=-1),n<0&&(a*=-1),{scale:1,rCenter:1,rotate:0,x:i+Math.abs(a)*(i>0?1:-1)/2,y:a/(1+r*r/(n*n)),outside:!0}}function C(t,e){var r,n,i,a=t.trace,o={x:t.cx,y:t.cy},s={tx:0,ty:0};s.ty+=a.title.font.size,i=I(a),-1!==a.title.position.indexOf(\"top\")?(o.y-=(1+i)*t.r,s.ty-=t.titleBox.height):-1!==a.title.position.indexOf(\"bottom\")&&(o.y+=(1+i)*t.r);var l,c,u=(l=t.r,c=t.trace.aspectratio,l/(void 0===c?1:c)),f=e.w*(a.domain.x[1]-a.domain.x[0])/2;return-1!==a.title.position.indexOf(\"left\")?(f+=u,o.x-=(1+i)*u,s.tx+=t.titleBox.width/2):-1!==a.title.position.indexOf(\"center\")?f*=2:-1!==a.title.position.indexOf(\"right\")&&(f+=u,o.x+=(1+i)*u,s.tx-=t.titleBox.width/2),r=f/t.titleBox.width,n=P(t,e)/t.titleBox.height,{x:o.x,y:o.y,scale:Math.min(r,n),tx:s.tx,ty:s.ty}}function P(t,e){var r=t.trace,n=e.h*(r.domain.y[1]-r.domain.y[0]);return Math.min(t.titleBox.height,n/2)}function I(t){var e,r=t.pull;if(!r)return 0;if(Array.isArray(r))for(r=0,e=0;e<t.pull.length;e++)t.pull[e]>r&&(r=t.pull[e]);return r}function O(t,e){for(var r=[],n=0;n<t.length;n++){var i=t[n][0],a=i.trace,o=a.domain,s=e.w*(o.x[1]-o.x[0]),l=e.h*(o.y[1]-o.y[0]);a.title.text&&\"middle center\"!==a.title.position&&(l-=P(i,e));var c=s/2,u=l/2;\"funnelarea\"!==a.type||a.scalegroup||(u/=a.aspectratio),i.r=Math.min(c,u)/(1+I(a)),i.cx=e.l+e.w*(a.domain.x[1]+a.domain.x[0])/2,i.cy=e.t+e.h*(1-a.domain.y[0])-l/2,a.title.text&&-1!==a.title.position.indexOf(\"bottom\")&&(i.cy-=P(i,e)),a.scalegroup&&-1===r.indexOf(a.scalegroup)&&r.push(a.scalegroup)}!function(t,e){for(var r,n,i,a=0;a<e.length;a++){var o=1/0,s=e[a];for(n=0;n<t.length;n++)if(r=t[n][0],(i=r.trace).scalegroup===s){var l;if(\"pie\"===i.type)l=r.r*r.r;else if(\"funnelarea\"===i.type){var c,u;i.aspectratio>1?(c=r.r,u=c/i.aspectratio):(u=r.r,c=u*i.aspectratio),c*=(1+i.baseratio)/2,l=c*u}o=Math.min(o,l/r.vTotal)}for(n=0;n<t.length;n++)if(r=t[n][0],(i=r.trace).scalegroup===s){var f=o*r.vTotal;\"funnelarea\"===i.type&&(f/=(1+i.baseratio)/2,f/=i.aspectratio),r.r=Math.sqrt(f)}}}(t,r)}function z(t,e){return[t*Math.sin(e),-t*Math.cos(e)]}function D(t,e,r){var n=t._fullLayout,i=r.trace,a=i.texttemplate,o=i.textinfo;if(!a&&o&&\"none\"!==o){var s,c=o.split(\"+\"),u=function(t){return-1!==c.indexOf(t)},f=u(\"label\"),h=u(\"text\"),p=u(\"value\"),d=u(\"percent\"),g=n.separators;if(s=f?[e.label]:[],h){var v=m.getFirstFilled(i.text,e.pts);y(v)&&s.push(v)}p&&s.push(m.formatPieValue(e.v,g)),d&&s.push(m.formatPiePercent(e.v/r.vTotal,g)),e.text=s.join(\"<br>\")}if(a){var x=l.castOption(i,e.i,\"texttemplate\");if(x){var b=function(t){return{label:t.label,value:t.v,valueLabel:m.formatPieValue(t.v,n.separators),percent:t.v/r.vTotal,percentLabel:m.formatPiePercent(t.v/r.vTotal,n.separators),color:t.color,text:t.text,customdata:l.castOption(i,t.i,\"customdata\")}}(e),_=m.getFirstFilled(i.text,e.pts);(y(_)||\"\"===_)&&(b.text=_),e.text=l.texttemplateString(x,b,t._fullLayout._d3locale,b,i._meta||{})}else e.text=\"\"}}function R(t,e){var r=t.rotate*Math.PI/180,n=Math.cos(r),i=Math.sin(r),a=(e.left+e.right)/2,o=(e.top+e.bottom)/2;t.textX=a*n-o*i,t.textY=a*i+o*n,t.noCenter=!0}e.exports={plot:function(t,e){var r=t._fullLayout,a=r._size;d(\"pie\",r),_(e,t),O(e,a);var h=l.makeTraceGroups(r._pielayer,e,\"trace\").each((function(e){var h=n.select(this),d=e[0],g=d.trace;!function(t){var e,r,n,i=t[0],a=i.r,o=i.trace,s=m.getRotationAngle(o.rotation),l=2*Math.PI/i.vTotal,c=\"px0\",u=\"px1\";if(\"counterclockwise\"===o.direction){for(e=0;e<t.length&&t[e].hidden;e++);if(e===t.length)return;s+=l*t[e].v,l*=-1,c=\"px1\",u=\"px0\"}for(n=z(a,s),e=0;e<t.length;e++)(r=t[e]).hidden||(r[c]=n,r.startangle=s,s+=l*r.v/2,r.pxmid=z(a,s),r.midangle=s,s+=l*r.v/2,n=z(a,s),r.stopangle=s,r[u]=n,r.largeArc=r.v>i.vTotal/2?1:0,r.halfangle=Math.PI*Math.min(r.v/i.vTotal,.5),r.ring=1-o.hole,r.rInscribed=E(r,i))}(e),h.attr(\"stroke-linejoin\",\"round\"),h.each((function(){var v=n.select(this).selectAll(\"g.slice\").data(e);v.enter().append(\"g\").classed(\"slice\",!0),v.exit().remove();var y=[[[],[]],[[],[]]],_=!1;v.each((function(i,a){if(i.hidden)n.select(this).selectAll(\"path,g\").remove();else{i.pointNumber=i.i,i.curveNumber=g.index,y[i.pxmid[1]<0?0:1][i.pxmid[0]<0?0:1].push(i);var o=d.cx,c=d.cy,u=n.select(this),h=u.selectAll(\"path.surface\").data([i]);if(h.enter().append(\"path\").classed(\"surface\",!0).style({\"pointer-events\":\"all\"}),u.call(x,t,e),g.pull){var v=+m.castOption(g.pull,i.pts)||0;v>0&&(o+=v*i.pxmid[0],c+=v*i.pxmid[1])}i.cxFinal=o,i.cyFinal=c;var T=g.hole;if(i.v===d.vTotal){var k=\"M\"+(o+i.px0[0])+\",\"+(c+i.px0[1])+C(i.px0,i.pxmid,!0,1)+C(i.pxmid,i.px0,!0,1)+\"Z\";T?h.attr(\"d\",\"M\"+(o+T*i.px0[0])+\",\"+(c+T*i.px0[1])+C(i.px0,i.pxmid,!1,T)+C(i.pxmid,i.px0,!1,T)+\"Z\"+k):h.attr(\"d\",k)}else{var A=C(i.px0,i.px1,!0,1);if(T){var M=1-T;h.attr(\"d\",\"M\"+(o+T*i.px1[0])+\",\"+(c+T*i.px1[1])+C(i.px1,i.px0,!1,T)+\"l\"+M*i.px0[0]+\",\"+M*i.px0[1]+A+\"Z\")}else h.attr(\"d\",\"M\"+o+\",\"+c+\"l\"+i.px0[0]+\",\"+i.px0[1]+A+\"Z\")}D(t,i,d);var S=m.castOption(g.textposition,i.pts),E=u.selectAll(\"g.slicetext\").data(i.text&&\"none\"!==S?[0]:[]);E.enter().append(\"g\").classed(\"slicetext\",!0),E.exit().remove(),E.each((function(){var u=l.ensureSingle(n.select(this),\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),h=l.ensureUniformFontSize(t,\"outside\"===S?function(t,e,r){var n=m.castOption(t.outsidetextfont.color,e.pts)||m.castOption(t.textfont.color,e.pts)||r.color,i=m.castOption(t.outsidetextfont.family,e.pts)||m.castOption(t.textfont.family,e.pts)||r.family,a=m.castOption(t.outsidetextfont.size,e.pts)||m.castOption(t.textfont.size,e.pts)||r.size;return{color:n,family:i,size:a}}(g,i,r.font):b(g,i,r.font));u.text(i.text).attr({class:\"slicetext\",transform:\"\",\"text-anchor\":\"middle\"}).call(s.font,h).call(f.convertToTspans,t);var v,y=s.bBox(u.node());if(\"outside\"===S)v=L(y,i);else if(v=w(y,i,d),\"auto\"===S&&v.scale<1){var x=l.ensureUniformFontSize(t,g.outsidetextfont);u.call(s.font,x),v=L(y=s.bBox(u.node()),i)}var T=v.textPosAngle,k=void 0===T?i.pxmid:z(d.r,T);if(v.targetX=o+k[0]*v.rCenter+(v.x||0),v.targetY=c+k[1]*v.rCenter+(v.y||0),R(v,y),v.outside){var A=v.targetY;i.yLabelMin=A-y.height/2,i.yLabelMid=A,i.yLabelMax=A+y.height/2,i.labelExtraX=0,i.labelExtraY=0,_=!0}v.fontSize=h.size,p(g.type,v,r),e[a].transform=v,u.attr(\"transform\",l.getTextTransform(v))}))}function C(t,e,r,n){var a=n*(e[0]-t[0]),o=n*(e[1]-t[1]);return\"a\"+n*d.r+\",\"+n*d.r+\" 0 \"+i.largeArc+(r?\" 1 \":\" 0 \")+a+\",\"+o}}));var T=n.select(this).selectAll(\"g.titletext\").data(g.title.text?[0]:[]);if(T.enter().append(\"g\").classed(\"titletext\",!0),T.exit().remove(),T.each((function(){var e,r=l.ensureSingle(n.select(this),\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),i=g.title.text;g._meta&&(i=l.templateString(i,g._meta)),r.text(i).attr({class:\"titletext\",transform:\"\",\"text-anchor\":\"middle\"}).call(s.font,g.title.font).call(f.convertToTspans,t),e=\"middle center\"===g.title.position?function(t){var e=Math.sqrt(t.titleBox.width*t.titleBox.width+t.titleBox.height*t.titleBox.height);return{x:t.cx,y:t.cy,scale:t.trace.hole*t.r*2/e,tx:0,ty:-t.titleBox.height/2+t.trace.title.font.size}}(d):C(d,a),r.attr(\"transform\",u(e.x,e.y)+c(Math.min(1,e.scale))+u(e.tx,e.ty))})),_&&function(t,e){var r,n,i,a,o,s,l,c,u,f,h,p,d;function g(t,e){return t.pxmid[1]-e.pxmid[1]}function v(t,e){return e.pxmid[1]-t.pxmid[1]}function y(t,r){r||(r={});var 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n=t(\"@plotly/d3\"),i=t(\"../../lib\"),a=i.numberFormat,o=t(\"./render\"),s=t(\"../../components/fx\"),l=t(\"../../components/color\"),c=t(\"./constants\").cn,u=i._;function f(t){return\"\"!==t}function h(t,e){return t.filter((function(t){return t.key===e.traceId}))}function p(t,e){n.select(t).select(\"path\").style(\"fill-opacity\",e),n.select(t).select(\"rect\").style(\"fill-opacity\",e)}function d(t){n.select(t).select(\"text.name\").style(\"fill\",\"black\")}function g(t){return function(e){return-1!==t.node.sourceLinks.indexOf(e.link)||-1!==t.node.targetLinks.indexOf(e.link)}}function m(t){return function(e){return-1!==e.node.sourceLinks.indexOf(t.link)||-1!==e.node.targetLinks.indexOf(t.link)}}function v(t,e,r){e&&r&&h(r,e).selectAll(\".\"+c.sankeyLink).filter(g(e)).call(x.bind(0,e,r,!1))}function y(t,e,r){e&&r&&h(r,e).selectAll(\".\"+c.sankeyLink).filter(g(e)).call(b.bind(0,e,r,!1))}function x(t,e,r,n){var i=n.datum().link.label;n.style(\"fill-opacity\",(function(t){if(!t.link.concentrationscale)return.4})),i&&h(e,t).selectAll(\".\"+c.sankeyLink).filter((function(t){return t.link.label===i})).style(\"fill-opacity\",(function(t){if(!t.link.concentrationscale)return.4})),r&&h(e,t).selectAll(\".\"+c.sankeyNode).filter(m(t)).call(v)}function b(t,e,r,n){var i=n.datum().link.label;n.style(\"fill-opacity\",(function(t){return t.tinyColorAlpha})),i&&h(e,t).selectAll(\".\"+c.sankeyLink).filter((function(t){return t.link.label===i})).style(\"fill-opacity\",(function(t){return t.tinyColorAlpha})),r&&h(e,t).selectAll(c.sankeyNode).filter(m(t)).call(y)}function _(t,e){var r=t.hoverlabel||{},n=i.nestedProperty(r,e).get();return!Array.isArray(n)&&n}e.exports=function(t,e){for(var r=t._fullLayout,i=r._paper,h=r._size,g=0;g<t._fullData.length;g++)if(t._fullData[g].visible&&t._fullData[g].type===c.sankey&&!t._fullData[g]._viewInitial){var m=t._fullData[g].node;t._fullData[g]._viewInitial={node:{groups:m.groups.slice(),x:m.x.slice(),y:m.y.slice()}}}var w=u(t,\"source:\")+\" \",T=u(t,\"target:\")+\" \",k=u(t,\"concentration:\")+\" \",A=u(t,\"incoming flow count:\")+\" \",M=u(t,\"outgoing flow count:\")+\" \";o(t,i,e,{width:h.w,height:h.h,margin:{t:h.t,r:h.r,b:h.b,l:h.l}},{linkEvents:{hover:function(e,r,i){!1!==t._fullLayout.hovermode&&(n.select(e).call(x.bind(0,r,i,!0)),\"skip\"!==r.link.trace.link.hoverinfo&&(r.link.fullData=r.link.trace,t.emit(\"plotly_hover\",{event:n.event,points:[r.link]})))},follow:function(e,i){if(!1!==t._fullLayout.hovermode){var o=i.link.trace.link;if(\"none\"!==o.hoverinfo&&\"skip\"!==o.hoverinfo){for(var c=[],u=0,h=0;h<i.flow.links.length;h++){var g=i.flow.links[h];if(\"closest\"!==t._fullLayout.hovermode||i.link.pointNumber===g.pointNumber){i.link.pointNumber===g.pointNumber&&(u=h),g.fullData=g.trace,o=i.link.trace.link;var m=y(g),v={valueLabel:a(i.valueFormat)(g.value)+i.valueSuffix};c.push({x:m[0],y:m[1],name:v.valueLabel,text:[g.label||\"\",w+g.source.label,T+g.target.label,g.concentrationscale?k+a(\"%0.2f\")(g.flow.labelConcentration):\"\"].filter(f).join(\"<br>\"),color:_(o,\"bgcolor\")||l.addOpacity(g.color,1),borderColor:_(o,\"bordercolor\"),fontFamily:_(o,\"font.family\"),fontSize:_(o,\"font.size\"),fontColor:_(o,\"font.color\"),nameLength:_(o,\"namelength\"),textAlign:_(o,\"align\"),idealAlign:n.event.x<m[0]?\"right\":\"left\",hovertemplate:o.hovertemplate,hovertemplateLabels:v,eventData:[g]})}}s.loneHover(c,{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:t,anchorIndex:u}).each((function(){i.link.concentrationscale||p(this,.65),d(this)}))}}function y(t){var e,r;t.circular?(e=(t.circularPathData.leftInnerExtent+t.circularPathData.rightInnerExtent)/2,r=t.circularPathData.verticalFullExtent):(e=(t.source.x1+t.target.x0)/2,r=(t.y0+t.y1)/2);var n=[e,r];return\"v\"===t.trace.orientation&&n.reverse(),n[0]+=i.parent.translateX,n[1]+=i.parent.translateY,n}},unhover:function(e,i,a){!1!==t._fullLayout.hovermode&&(n.select(e).call(b.bind(0,i,a,!0)),\"skip\"!==i.link.trace.link.hoverinfo&&(i.link.fullData=i.link.trace,t.emit(\"plotly_unhover\",{event:n.event,points:[i.link]})),s.loneUnhover(r._hoverlayer.node()))},select:function(e,r){var i=r.link;i.originalEvent=n.event,t._hoverdata=[i],s.click(t,{target:!0})}},nodeEvents:{hover:function(e,r,i){!1!==t._fullLayout.hovermode&&(n.select(e).call(v,r,i),\"skip\"!==r.node.trace.node.hoverinfo&&(r.node.fullData=r.node.trace,t.emit(\"plotly_hover\",{event:n.event,points:[r.node]})))},follow:function(e,i){if(!1!==t._fullLayout.hovermode){var o=i.node.trace.node;if(\"none\"!==o.hoverinfo&&\"skip\"!==o.hoverinfo){var l=n.select(e).select(\".\"+c.nodeRect),u=t._fullLayout._paperdiv.node().getBoundingClientRect(),h=l.node().getBoundingClientRect(),g=h.left-2-u.left,m=h.right+2-u.left,v=h.top+h.height/4-u.top,y={valueLabel:a(i.valueFormat)(i.node.value)+i.valueSuffix};i.node.fullData=i.node.trace,t._fullLayout._calcInverseTransform(t);var x=t._fullLayout._invScaleX,b=t._fullLayout._invScaleY,w=s.loneHover({x0:x*g,x1:x*m,y:b*v,name:a(i.valueFormat)(i.node.value)+i.valueSuffix,text:[i.node.label,A+i.node.targetLinks.length,M+i.node.sourceLinks.length].filter(f).join(\"<br>\"),color:_(o,\"bgcolor\")||i.tinyColorHue,borderColor:_(o,\"bordercolor\"),fontFamily:_(o,\"font.family\"),fontSize:_(o,\"font.size\"),fontColor:_(o,\"font.color\"),nameLength:_(o,\"namelength\"),textAlign:_(o,\"align\"),idealAlign:\"left\",hovertemplate:o.hovertemplate,hovertemplateLabels:y,eventData:[i.node]},{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:t});p(w,.85),d(w)}}},unhover:function(e,i,a){!1!==t._fullLayout.hovermode&&(n.select(e).call(y,i,a),\"skip\"!==i.node.trace.node.hoverinfo&&(i.node.fullData=i.node.trace,t.emit(\"plotly_unhover\",{event:n.event,points:[i.node]})),s.loneUnhover(r._hoverlayer.node()))},select:function(e,r,i){var a=r.node;a.originalEvent=n.event,t._hoverdata=[a],n.select(e).call(y,r,i),s.click(t,{target:!0})}}})}},{\"../../components/color\":366,\"../../components/fx\":406,\"../../lib\":503,\"./constants\":918,\"./render\":922,\"@plotly/d3\":58}],922:[function(t,e,r){\"use strict\";var n=t(\"d3-force\"),i=t(\"d3-interpolate\").interpolateNumber,a=t(\"@plotly/d3\"),o=t(\"@plotly/d3-sankey\"),s=t(\"@plotly/d3-sankey-circular\"),l=t(\"./constants\"),c=t(\"tinycolor2\"),u=t(\"../../components/color\"),f=t(\"../../components/drawing\"),h=t(\"../../lib\"),p=h.strTranslate,d=h.strRotate,g=t(\"../../lib/gup\"),m=g.keyFun,v=g.repeat,y=g.unwrap,x=t(\"../../lib/svg_text_utils\"),b=t(\"../../registry\"),_=t(\"../../constants/alignment\"),w=_.CAP_SHIFT,T=_.LINE_SPACING;function k(t,e,r){var n,i=y(e),a=i.trace,u=a.domain,f=\"h\"===a.orientation,p=a.node.pad,d=a.node.thickness,g=t.width*(u.x[1]-u.x[0]),m=t.height*(u.y[1]-u.y[0]),v=i._nodes,x=i._links,b=i.circular;(n=b?s.sankeyCircular().circularLinkGap(0):o.sankey()).iterations(l.sankeyIterations).size(f?[g,m]:[m,g]).nodeWidth(d).nodePadding(p).nodeId((function(t){return t.pointNumber})).nodes(v).links(x);var _,w,T,k=n();for(var A in n.nodePadding()<p&&h.warn(\"node.pad was reduced to \",n.nodePadding(),\" to fit within the figure.\"),i._groupLookup){var M,S=parseInt(i._groupLookup[A]);for(_=0;_<k.nodes.length;_++)if(k.nodes[_].pointNumber===S){M=k.nodes[_];break}if(M){var E={pointNumber:parseInt(A),x0:M.x0,x1:M.x1,y0:M.y0,y1:M.y1,partOfGroup:!0,sourceLinks:[],targetLinks:[]};k.nodes.unshift(E),M.childrenNodes.unshift(E)}}if(function(){for(_=0;_<k.nodes.length;_++){var t,e,r=k.nodes[_],n={};for(w=0;w<r.targetLinks.length;w++)t=(e=r.targetLinks[w]).source.pointNumber+\":\"+e.target.pointNumber,n.hasOwnProperty(t)||(n[t]=[]),n[t].push(e);var i=Object.keys(n);for(w=0;w<i.length;w++){var a=n[t=i[w]],o=0,s={};for(T=0;T<a.length;T++)s[(e=a[T]).label]||(s[e.label]=0),s[e.label]+=e.value,o+=e.value;for(T=0;T<a.length;T++)(e=a[T]).flow={value:o,labelConcentration:s[e.label]/o,concentration:e.value/o,links:a},e.concentrationscale&&(e.color=c(e.concentrationscale(e.flow.labelConcentration)))}var l=0;for(w=0;w<r.sourceLinks.length;w++)l+=r.sourceLinks[w].value;for(w=0;w<r.sourceLinks.length;w++)(e=r.sourceLinks[w]).concentrationOut=e.value/l;var u=0;for(w=0;w<r.targetLinks.length;w++)u+=r.targetLinks[w].value;for(w=0;w<r.targetLinks.length;w++)(e=r.targetLinks[w]).concenrationIn=e.value/u}}(),a.node.x.length&&a.node.y.length){for(_=0;_<Math.min(a.node.x.length,a.node.y.length,k.nodes.length);_++)if(a.node.x[_]&&a.node.y[_]){var L=[a.node.x[_]*g,a.node.y[_]*m];k.nodes[_].x0=L[0]-d/2,k.nodes[_].x1=L[0]+d/2;var C=k.nodes[_].y1-k.nodes[_].y0;k.nodes[_].y0=L[1]-C/2,k.nodes[_].y1=L[1]+C/2}if(\"snap\"===a.arrangement)!function(t){t.forEach((function(t){var e,r,n,i=0,a=t.length;for(t.sort((function(t,e){return t.y0-e.y0})),n=0;n<a;++n)(e=t[n]).y0>=i||(r=i-e.y0)>1e-6&&(e.y0+=r,e.y1+=r),i=e.y1+p}))}(function(t){var e,r,n=t.map((function(t,e){return{x0:t.x0,index:e}})).sort((function(t,e){return t.x0-e.x0})),i=[],a=-1,o=-1/0;for(_=0;_<n.length;_++){var s=t[n[_].index];s.x0>o+d&&(a+=1,e=s.x0),o=s.x0,i[a]||(i[a]=[]),i[a].push(s),r=e-s.x0,s.x0+=r,s.x1+=r}return i}(v=k.nodes));n.update(k)}return{circular:b,key:r,trace:a,guid:h.randstr(),horizontal:f,width:g,height:m,nodePad:a.node.pad,nodeLineColor:a.node.line.color,nodeLineWidth:a.node.line.width,linkLineColor:a.link.line.color,linkLineWidth:a.link.line.width,valueFormat:a.valueformat,valueSuffix:a.valuesuffix,textFont:a.textfont,translateX:u.x[0]*t.width+t.margin.l,translateY:t.height-u.y[1]*t.height+t.margin.t,dragParallel:f?m:g,dragPerpendicular:f?g:m,arrangement:a.arrangement,sankey:n,graph:k,forceLayouts:{},interactionState:{dragInProgress:!1,hovered:!1}}}function A(t,e,r){var n=c(e.color),i=e.source.label+\"|\"+e.target.label+\"__\"+r;return e.trace=t.trace,e.curveNumber=t.trace.index,{circular:t.circular,key:i,traceId:t.key,pointNumber:e.pointNumber,link:e,tinyColorHue:u.tinyRGB(n),tinyColorAlpha:n.getAlpha(),linkPath:M,linkLineColor:t.linkLineColor,linkLineWidth:t.linkLineWidth,valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,parent:t,interactionState:t.interactionState,flow:e.flow}}function M(){return function(t){if(t.link.circular)return e=t.link,r=e.width/2,n=e.circularPathData,\"top\"===e.circularLinkType?\"M \"+n.targetX+\" \"+(n.targetY+r)+\" L\"+n.rightInnerExtent+\" \"+(n.targetY+r)+\"A\"+(n.rightLargeArcRadius+r)+\" \"+(n.rightSmallArcRadius+r)+\" 0 0 1 \"+(n.rightFullExtent-r)+\" \"+(n.targetY-n.rightSmallArcRadius)+\"L\"+(n.rightFullExtent-r)+\" \"+n.verticalRightInnerExtent+\"A\"+(n.rightLargeArcRadius+r)+\" \"+(n.rightLargeArcRadius+r)+\" 0 0 1 \"+n.rightInnerExtent+\" \"+(n.verticalFullExtent-r)+\"L\"+n.leftInnerExtent+\" \"+(n.verticalFullExtent-r)+\"A\"+(n.leftLargeArcRadius+r)+\" \"+(n.leftLargeArcRadius+r)+\" 0 0 1 \"+(n.leftFullExtent+r)+\" \"+n.verticalLeftInnerExtent+\"L\"+(n.leftFullExtent+r)+\" \"+(n.sourceY-n.leftSmallArcRadius)+\"A\"+(n.leftLargeArcRadius+r)+\" \"+(n.leftSmallArcRadius+r)+\" 0 0 1 \"+n.leftInnerExtent+\" \"+(n.sourceY+r)+\"L\"+n.sourceX+\" \"+(n.sourceY+r)+\"L\"+n.sourceX+\" \"+(n.sourceY-r)+\"L\"+n.leftInnerExtent+\" \"+(n.sourceY-r)+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftSmallArcRadius-r)+\" 0 0 0 \"+(n.leftFullExtent-r)+\" \"+(n.sourceY-n.leftSmallArcRadius)+\"L\"+(n.leftFullExtent-r)+\" \"+n.verticalLeftInnerExtent+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftLargeArcRadius-r)+\" 0 0 0 \"+n.leftInnerExtent+\" \"+(n.verticalFullExtent+r)+\"L\"+n.rightInnerExtent+\" \"+(n.verticalFullExtent+r)+\"A\"+(n.rightLargeArcRadius-r)+\" \"+(n.rightLargeArcRadius-r)+\" 0 0 0 \"+(n.rightFullExtent+r)+\" \"+n.verticalRightInnerExtent+\"L\"+(n.rightFullExtent+r)+\" \"+(n.targetY-n.rightSmallArcRadius)+\"A\"+(n.rightLargeArcRadius-r)+\" \"+(n.rightSmallArcRadius-r)+\" 0 0 0 \"+n.rightInnerExtent+\" \"+(n.targetY-r)+\"L\"+n.targetX+\" \"+(n.targetY-r)+\"Z\":\"M \"+n.targetX+\" \"+(n.targetY-r)+\" L\"+n.rightInnerExtent+\" \"+(n.targetY-r)+\"A\"+(n.rightLargeArcRadius+r)+\" \"+(n.rightSmallArcRadius+r)+\" 0 0 0 \"+(n.rightFullExtent-r)+\" \"+(n.targetY+n.rightSmallArcRadius)+\"L\"+(n.rightFullExtent-r)+\" \"+n.verticalRightInnerExtent+\"A\"+(n.rightLargeArcRadius+r)+\" \"+(n.rightLargeArcRadius+r)+\" 0 0 0 \"+n.rightInnerExtent+\" \"+(n.verticalFullExtent+r)+\"L\"+n.leftInnerExtent+\" \"+(n.verticalFullExtent+r)+\"A\"+(n.leftLargeArcRadius+r)+\" \"+(n.leftLargeArcRadius+r)+\" 0 0 0 \"+(n.leftFullExtent+r)+\" \"+n.verticalLeftInnerExtent+\"L\"+(n.leftFullExtent+r)+\" \"+(n.sourceY+n.leftSmallArcRadius)+\"A\"+(n.leftLargeArcRadius+r)+\" \"+(n.leftSmallArcRadius+r)+\" 0 0 0 \"+n.leftInnerExtent+\" \"+(n.sourceY-r)+\"L\"+n.sourceX+\" \"+(n.sourceY-r)+\"L\"+n.sourceX+\" \"+(n.sourceY+r)+\"L\"+n.leftInnerExtent+\" \"+(n.sourceY+r)+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftSmallArcRadius-r)+\" 0 0 1 \"+(n.leftFullExtent-r)+\" \"+(n.sourceY+n.leftSmallArcRadius)+\"L\"+(n.leftFullExtent-r)+\" \"+n.verticalLeftInnerExtent+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftLargeArcRadius-r)+\" 0 0 1 \"+n.leftInnerExtent+\" \"+(n.verticalFullExtent-r)+\"L\"+n.rightInnerExtent+\" \"+(n.verticalFullExtent-r)+\"A\"+(n.rightLargeArcRadius-r)+\" \"+(n.rightLargeArcRadius-r)+\" 0 0 1 \"+(n.rightFullExtent+r)+\" \"+n.verticalRightInnerExtent+\"L\"+(n.rightFullExtent+r)+\" \"+(n.targetY+n.rightSmallArcRadius)+\"A\"+(n.rightLargeArcRadius-r)+\" \"+(n.rightSmallArcRadius-r)+\" 0 0 1 \"+n.rightInnerExtent+\" \"+(n.targetY+r)+\"L\"+n.targetX+\" \"+(n.targetY+r)+\"Z\";var e,r,n,a=t.link.source.x1,o=t.link.target.x0,s=i(a,o),l=s(.5),c=s(.5),u=t.link.y0-t.link.width/2,f=t.link.y0+t.link.width/2,h=t.link.y1-t.link.width/2,p=t.link.y1+t.link.width/2;return\"M\"+a+\",\"+u+\"C\"+l+\",\"+u+\" \"+c+\",\"+h+\" \"+o+\",\"+h+\"L\"+o+\",\"+p+\"C\"+c+\",\"+p+\" \"+l+\",\"+f+\" \"+a+\",\"+f+\"Z\"}}function S(t,e){var r=c(e.color),n=l.nodePadAcross,i=t.nodePad/2;e.dx=e.x1-e.x0,e.dy=e.y1-e.y0;var a=e.dx,o=Math.max(.5,e.dy),s=\"node_\"+e.pointNumber;return e.group&&(s=h.randstr()),e.trace=t.trace,e.curveNumber=t.trace.index,{index:e.pointNumber,key:s,partOfGroup:e.partOfGroup||!1,group:e.group,traceId:t.key,trace:t.trace,node:e,nodePad:t.nodePad,nodeLineColor:t.nodeLineColor,nodeLineWidth:t.nodeLineWidth,textFont:t.textFont,size:t.horizontal?t.height:t.width,visibleWidth:Math.ceil(a),visibleHeight:o,zoneX:-n,zoneY:-i,zoneWidth:a+2*n,zoneHeight:o+2*i,labelY:t.horizontal?e.dy/2+1:e.dx/2+1,left:1===e.originalLayer,sizeAcross:t.width,forceLayouts:t.forceLayouts,horizontal:t.horizontal,darkBackground:r.getBrightness()<=128,tinyColorHue:u.tinyRGB(r),tinyColorAlpha:r.getAlpha(),valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,graph:t.graph,arrangement:t.arrangement,uniqueNodeLabelPathId:[t.guid,t.key,s].join(\"_\"),interactionState:t.interactionState,figure:t}}function E(t){t.attr(\"transform\",(function(t){return p(t.node.x0.toFixed(3),t.node.y0.toFixed(3))}))}function L(t){t.call(E)}function 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n=t(\"../scatter/attributes\"),i=t(\"../../plots/attributes\"),a=t(\"../../plots/template_attributes\").hovertemplateAttrs,o=t(\"../../plots/template_attributes\").texttemplateAttrs,s=t(\"../../components/colorscale/attributes\"),l=t(\"../../lib/extend\").extendFlat,c=n.marker,u=n.line,f=c.line;e.exports={carpet:{valType:\"string\",editType:\"calc\"},a:{valType:\"data_array\",editType:\"calc\"},b:{valType:\"data_array\",editType:\"calc\"},mode:l({},n.mode,{dflt:\"markers\"}),text:l({},n.text,{}),texttemplate:o({editType:\"plot\"},{keys:[\"a\",\"b\",\"text\"]}),hovertext:l({},n.hovertext,{}),line:{color:u.color,width:u.width,dash:u.dash,shape:l({},u.shape,{values:[\"linear\",\"spline\"]}),smoothing:u.smoothing,editType:\"calc\"},connectgaps:n.connectgaps,fill:l({},n.fill,{values:[\"none\",\"toself\",\"tonext\"],dflt:\"none\"}),fillcolor:n.fillcolor,marker:l({symbol:c.symbol,opacity:c.opacity,maxdisplayed:c.maxdisplayed,size:c.size,sizeref:c.sizeref,sizemin:c.sizemin,sizemode:c.sizemode,line:l({width:f.width,editType:\"calc\"},s(\"marker.line\")),gradient:c.gradient,editType:\"calc\"},s(\"marker\")),textfont:n.textfont,textposition:n.textposition,selected:n.selected,unselected:n.unselected,hoverinfo:l({},i.hoverinfo,{flags:[\"a\",\"b\",\"text\",\"name\"]}),hoveron:n.hoveron,hovertemplate:a()}},{\"../../components/colorscale/attributes\":373,\"../../lib/extend\":493,\"../../plots/attributes\":550,\"../../plots/template_attributes\":633,\"../scatter/attributes\":925}],960:[function(t,e,r){\"use 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n=t(\"../../lib\"),i=t(\"../scatter/constants\"),a=t(\"../scatter/subtypes\"),o=t(\"../scatter/marker_defaults\"),s=t(\"../scatter/line_defaults\"),l=t(\"../scatter/line_shape_defaults\"),c=t(\"../scatter/text_defaults\"),u=t(\"../scatter/fillcolor_defaults\"),f=t(\"./attributes\");e.exports=function(t,e,r,h){function p(r,i){return n.coerce(t,e,f,r,i)}p(\"carpet\"),e.xaxis=\"x\",e.yaxis=\"y\";var d=p(\"a\"),g=p(\"b\"),m=Math.min(d.length,g.length);if(m){e._length=m,p(\"text\"),p(\"texttemplate\"),p(\"hovertext\"),p(\"mode\",m<i.PTS_LINESONLY?\"lines+markers\":\"lines\"),a.hasLines(e)&&(s(t,e,r,h,p),l(t,e,p),p(\"connectgaps\")),a.hasMarkers(e)&&o(t,e,r,h,p,{gradient:!0}),a.hasText(e)&&c(t,e,h,p);var v=[];(a.hasMarkers(e)||a.hasText(e))&&(p(\"marker.maxdisplayed\"),v.push(\"points\")),p(\"fill\"),\"none\"!==e.fill&&(u(t,e,r,p),a.hasLines(e)||l(t,e,p)),\"tonext\"!==e.fill&&\"toself\"!==e.fill||v.push(\"fills\"),\"fills\"!==p(\"hoveron\",v.join(\"+\")||\"points\")&&p(\"hovertemplate\"),n.coerceSelectionMarkerOpacity(e,p)}else e.visible=!1}},{\"../../lib\":503,\"../scatter/constants\":929,\"../scatter/fillcolor_defaults\":933,\"../scatter/line_defaults\":938,\"../scatter/line_shape_defaults\":940,\"../scatter/marker_defaults\":944,\"../scatter/subtypes\":950,\"../scatter/text_defaults\":951,\"./attributes\":959}],962:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,n,i){var a=n[i];return t.a=a.a,t.b=a.b,t.y=a.y,t}},{}],963:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r={},n=e._carpet,i=n.ab2ij([t.a,t.b]),a=Math.floor(i[0]),o=i[0]-a,s=Math.floor(i[1]),l=i[1]-s,c=n.evalxy([],a,s,o,l);return r.yLabel=c[1].toFixed(3),r}},{}],964:[function(t,e,r){\"use strict\";var n=t(\"../scatter/hover\"),i=t(\"../../lib\").fillText;e.exports=function(t,e,r,a){var o=n(t,e,r,a);if(o&&!1!==o[0].index){var s=o[0];if(void 0===s.index){var l=1-s.y0/t.ya._length,c=t.xa._length,u=c*l/2,f=c-u;return s.x0=Math.max(Math.min(s.x0,f),u),s.x1=Math.max(Math.min(s.x1,f),u),o}var h=s.cd[s.index];s.a=h.a,s.b=h.b,s.xLabelVal=void 0,s.yLabelVal=void 0;var p=s.trace,d=p._carpet,g=p._module.formatLabels(h,p);s.yLabel=g.yLabel,delete s.text;var m=[];if(!p.hovertemplate){var v=(h.hi||p.hoverinfo).split(\"+\");-1!==v.indexOf(\"all\")&&(v=[\"a\",\"b\",\"text\"]),-1!==v.indexOf(\"a\")&&y(d.aaxis,h.a),-1!==v.indexOf(\"b\")&&y(d.baxis,h.b),m.push(\"y: \"+s.yLabel),-1!==v.indexOf(\"text\")&&i(h,p,m),s.extraText=m.join(\"<br>\")}return o}function y(t,e){var r;r=t.labelprefix&&t.labelprefix.length>0?t.labelprefix.replace(/ = $/,\"\"):t._hovertitle,m.push(r+\": \"+e.toFixed(3)+t.labelsuffix)}}},{\"../../lib\":503,\"../scatter/hover\":936}],965:[function(t,e,r){\"use 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s,l,c,u=r[0][0].carpet,f={xaxis:i.getFromId(t,u.xaxis||\"x\"),yaxis:i.getFromId(t,u.yaxis||\"y\"),plot:e.plot};for(n(t,f,r,o),s=0;s<r.length;s++)l=r[s][0].trace,c=o.selectAll(\"g.trace\"+l.uid+\" .js-line\"),a.setClipUrl(c,r[s][0].carpet._clipPathId,t)}},{\"../../components/drawing\":388,\"../../plots/cartesian/axes\":554,\"../scatter/plot\":946}],967:[function(t,e,r){\"use strict\";var n=t(\"../../plots/template_attributes\").hovertemplateAttrs,i=t(\"../../plots/template_attributes\").texttemplateAttrs,a=t(\"../scatter/attributes\"),o=t(\"../../plots/attributes\"),s=t(\"../../components/colorscale/attributes\"),l=t(\"../../components/drawing/attributes\").dash,c=t(\"../../lib/extend\").extendFlat,u=t(\"../../plot_api/edit_types\").overrideAll,f=a.marker,h=a.line,p=f.line;e.exports=u({lon:{valType:\"data_array\"},lat:{valType:\"data_array\"},locations:{valType:\"data_array\"},locationmode:{valType:\"enumerated\",values:[\"ISO-3\",\"USA-states\",\"country 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n=t(\"../../lib\"),i=t(\"../scatter/subtypes\"),a=t(\"../scatter/marker_defaults\"),o=t(\"../scatter/line_defaults\"),s=t(\"../scatter/text_defaults\"),l=t(\"../scatter/fillcolor_defaults\"),c=t(\"./attributes\");e.exports=function(t,e,r,u){function f(r,i){return n.coerce(t,e,c,r,i)}var h,p=f(\"locations\");if(p&&p.length){var d,g=f(\"geojson\");(\"string\"==typeof g&&\"\"!==g||n.isPlainObject(g))&&(d=\"geojson-id\"),\"geojson-id\"===f(\"locationmode\",d)&&f(\"featureidkey\"),h=p.length}else{var m=f(\"lon\")||[],v=f(\"lat\")||[];h=Math.min(m.length,v.length)}h?(e._length=h,f(\"text\"),f(\"hovertext\"),f(\"hovertemplate\"),f(\"mode\"),i.hasLines(e)&&(o(t,e,r,u,f),f(\"connectgaps\")),i.hasMarkers(e)&&a(t,e,r,u,f,{gradient:!0}),i.hasText(e)&&(f(\"texttemplate\"),s(t,e,u,f)),f(\"fill\"),\"none\"!==e.fill&&l(t,e,r,f),n.coerceSelectionMarkerOpacity(e,f)):e.visible=!1}},{\"../../lib\":503,\"../scatter/fillcolor_defaults\":933,\"../scatter/line_defaults\":938,\"../scatter/marker_defaults\":944,\"../scatter/subtypes\":950,\"../scatter/text_defaults\":951,\"./attributes\":967}],970:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,n,i){t.lon=e.lon,t.lat=e.lat,t.location=e.loc?e.loc:null;var a=n[i];return a.fIn&&a.fIn.properties&&(t.properties=a.fIn.properties),t}},{}],971:[function(t,e,r){\"use strict\";var n=t(\"../../plots/cartesian/axes\");e.exports=function(t,e,r){var i={},a=r[e.geo]._subplot.mockAxis,o=t.lonlat;return 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t.lonLabel=b.lonLabel,t.latLabel=b.latLabel,t.color=a(c,g),t.extraText=function(t,e,r,n){if(t.hovertemplate)return;var i=e.hi||t.hoverinfo,a=\"all\"===i?s.hoverinfo.flags:i.split(\"+\"),l=-1!==a.indexOf(\"location\")&&Array.isArray(t.locations),c=-1!==a.indexOf(\"lon\"),u=-1!==a.indexOf(\"lat\"),f=-1!==a.indexOf(\"text\"),h=[];function p(t){return t+\"\\xb0\"}l?h.push(e.loc):c&&u?h.push(\"(\"+p(r.latLabel)+\", \"+p(r.lonLabel)+\")\"):c?h.push(n.lon+p(r.lonLabel)):u&&h.push(n.lat+p(r.latLabel));f&&o(e,t,h);return h.join(\"<br>\")}(c,g,t,l[0].t.labels),t.hovertemplate=c.hovertemplate,[t]}}},{\"../../components/fx\":406,\"../../constants/numerical\":479,\"../../lib\":503,\"../scatter/get_trace_color\":935,\"./attributes\":967}],973:[function(t,e,r){\"use 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n=t(\"@plotly/d3\"),i=t(\"../../lib\"),a=t(\"../../lib/topojson_utils\").getTopojsonFeatures,o=t(\"../../lib/geojson_utils\"),s=t(\"../../lib/geo_location_utils\"),l=t(\"../../plots/cartesian/autorange\").findExtremes,c=t(\"../../constants/numerical\").BADNUM,u=t(\"../scatter/calc\").calcMarkerSize,f=t(\"../scatter/subtypes\"),h=t(\"./style\");e.exports={calcGeoJSON:function(t,e){var r,n,i=t[0].trace,o=e[i.geo],f=o._subplot,h=i._length;if(Array.isArray(i.locations)){var p=i.locationmode,d=\"geojson-id\"===p?s.extractTraceFeature(t):a(i,f.topojson);for(r=0;r<h;r++){n=t[r];var g=\"geojson-id\"===p?n.fOut:s.locationToFeature(p,n.loc,d);n.lonlat=g?g.properties.ct:[c,c]}}var m,v,y={padded:!0};if(\"geojson\"===o.fitbounds&&\"geojson-id\"===i.locationmode){var x=s.computeBbox(s.getTraceGeojson(i));m=[x[0],x[2]],v=[x[1],x[3]]}else{for(m=new Array(h),v=new Array(h),r=0;r<h;r++)n=t[r],m[r]=n.lonlat[0],v[r]=n.lonlat[1];y.ppad=u(i,h)}i._extremes.lon=l(o.lonaxis._ax,m,y),i._extremes.lat=l(o.lataxis._ax,v,y)},plot:function(t,e,r){var a=e.layers.frontplot.select(\".scatterlayer\"),s=i.makeTraceGroups(a,r,\"trace scattergeo\");function l(t,e){t.lonlat[0]===c&&n.select(e).remove()}s.selectAll(\"*\").remove(),s.each((function(e){var r=n.select(this),a=e[0].trace;if(f.hasLines(a)||\"none\"!==a.fill){var s=o.calcTraceToLineCoords(e),c=\"none\"!==a.fill?o.makePolygon(s):o.makeLine(s);r.selectAll(\"path.js-line\").data([{geojson:c,trace:a}]).enter().append(\"path\").classed(\"js-line\",!0).style(\"stroke-miterlimit\",2)}f.hasMarkers(a)&&r.selectAll(\"path.point\").data(i.identity).enter().append(\"path\").classed(\"point\",!0).each((function(t){l(t,this)})),f.hasText(a)&&r.selectAll(\"g\").data(i.identity).enter().append(\"g\").append(\"text\").each((function(t){l(t,this)})),h(t,e)}))}}},{\"../../constants/numerical\":479,\"../../lib\":503,\"../../lib/geo_location_utils\":496,\"../../lib/geojson_utils\":497,\"../../lib/topojson_utils\":532,\"../../plots/cartesian/autorange\":553,\"../scatter/calc\":926,\"../scatter/subtypes\":950,\"./style\":976,\"@plotly/d3\":58}],975:[function(t,e,r){\"use strict\";var n=t(\"../scatter/subtypes\"),i=t(\"../../constants/numerical\").BADNUM;e.exports=function(t,e){var 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e=n.select(this),r=t.trace,o=r.line||{};e.call(a.stroke,o.color).call(i.dashLine,o.dash||\"\",o.width||0),\"none\"!==r.fill&&e.call(a.fill,r.fillcolor)}))}(t,e)}},{\"../../components/color\":366,\"../../components/drawing\":388,\"../scatter/style\":949,\"@plotly/d3\":58}],977:[function(t,e,r){\"use strict\";var n=t(\"../../plots/attributes\"),i=t(\"../scatter/attributes\"),a=t(\"../../plots/cartesian/axis_format_attributes\").axisHoverFormat,o=t(\"../../components/colorscale/attributes\"),s=t(\"../../lib/sort_object_keys\"),l=t(\"../../lib/extend\").extendFlat,c=t(\"../../plot_api/edit_types\").overrideAll,u=t(\"./constants\").DASHES,f=i.line,h=i.marker,p=h.line,d=e.exports=c({x:i.x,x0:i.x0,dx:i.dx,y:i.y,y0:i.y0,dy:i.dy,xperiod:i.xperiod,yperiod:i.yperiod,xperiod0:i.xperiod0,yperiod0:i.yperiod0,xperiodalignment:i.xperiodalignment,yperiodalignment:i.yperiodalignment,xhoverformat:a(\"x\"),yhoverformat:a(\"y\"),text:i.text,hovertext:i.hovertext,textposition:i.textposition,textfont:i.textfont,mode:{valType:\"flaglist\",flags:[\"lines\",\"markers\",\"text\"],extras:[\"none\"]},line:{color:f.color,width:f.width,shape:{valType:\"enumerated\",values:[\"linear\",\"hv\",\"vh\",\"hvh\",\"vhv\"],dflt:\"linear\",editType:\"plot\"},dash:{valType:\"enumerated\",values:s(u),dflt:\"solid\"}},marker:l({},o(\"marker\"),{symbol:h.symbol,size:h.size,sizeref:h.sizeref,sizemin:h.sizemin,sizemode:h.sizemode,opacity:h.opacity,colorbar:h.colorbar,line:l({},o(\"marker.line\"),{width:p.width})}),connectgaps:i.connectgaps,fill:l({},i.fill,{dflt:\"none\"}),fillcolor:i.fillcolor,selected:{marker:i.selected.marker,textfont:i.selected.textfont},unselected:{marker:i.unselected.marker,textfont:i.unselected.textfont},opacity:n.opacity},\"calc\",\"nested\");d.x.editType=d.y.editType=d.x0.editType=d.y0.editType=\"calc+clearAxisTypes\",d.hovertemplate=i.hovertemplate,d.texttemplate=i.texttemplate},{\"../../components/colorscale/attributes\":373,\"../../lib/extend\":493,\"../../lib/sort_object_keys\":526,\"../../plot_api/edit_types\":536,\"../../plots/attributes\":550,\"../../plots/cartesian/axis_format_attributes\":557,\"../scatter/attributes\":925,\"./constants\":979}],978:[function(t,e,r){\"use 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T=y-_,k=y+w;p[v+u[0]]=x-i.c2l(T),p[v+u[1]]=i.c2l(k)-x,p[v+u[2]]=0,p[v+u[3]]=0,d=Math.min(d,y-_),g=Math.max(g,y+w)}}}h[a]={positions:r,errors:p,_bnds:[d,g]}}}return p(i,l),p(a,u),h},textPosition:function(t,e,r,n){var i,a=e._length,o={};if(f.hasMarkers(e)){var s=r.font,l=r.align,c=r.baseline;for(o.offset=new Array(a),i=0;i<a;i++){var u=n.sizes?n.sizes[i]:n.size,h=Array.isArray(s)?s[i].size:s.size,p=Array.isArray(l)?l.length>1?l[i]:l[0]:l,d=Array.isArray(c)?c.length>1?c[i]:c[0]:c,g=m[p],v=m[d],y=u?u/.8+1:0,x=-v*y-.5*v;o.offset[i]=[g*y/h,x/h]}}return o}}},{\"../../components/drawing\":388,\"../../components/fx/helpers\":402,\"../../constants/interactions\":478,\"../../lib\":503,\"../../lib/gl_format_color\":499,\"../../plots/cartesian/axis_ids\":558,\"../../registry\":638,\"../scatter/make_bubble_size_func\":942,\"../scatter/subtypes\":950,\"./constants\":979,\"./helpers\":984,\"color-normalize\":89,\"fast-isnumeric\":190,\"svg-path-sdf\":310}],981:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../registry\"),a=t(\"./helpers\"),o=t(\"./attributes\"),s=t(\"../scatter/constants\"),l=t(\"../scatter/subtypes\"),c=t(\"../scatter/xy_defaults\"),u=t(\"../scatter/period_defaults\"),f=t(\"../scatter/marker_defaults\"),h=t(\"../scatter/line_defaults\"),p=t(\"../scatter/fillcolor_defaults\"),d=t(\"../scatter/text_defaults\");e.exports=function(t,e,r,g){function m(r,i){return n.coerce(t,e,o,r,i)}var v=!!t.marker&&a.isOpenSymbol(t.marker.symbol),y=l.isBubble(t),x=c(t,e,g,m);if(x){u(t,e,g,m),m(\"xhoverformat\"),m(\"yhoverformat\");var b=x<s.PTS_LINESONLY?\"lines+markers\":\"lines\";m(\"text\"),m(\"hovertext\"),m(\"hovertemplate\"),m(\"mode\",b),l.hasLines(e)&&(m(\"connectgaps\"),h(t,e,r,g,m),m(\"line.shape\")),l.hasMarkers(e)&&(f(t,e,r,g,m),m(\"marker.line.width\",v||y?1:0)),l.hasText(e)&&(m(\"texttemplate\"),d(t,e,g,m));var _=(e.line||{}).color,w=(e.marker||{}).color;m(\"fill\"),\"none\"!==e.fill&&p(t,e,r,m);var T=i.getComponentMethod(\"errorbars\",\"supplyDefaults\");T(t,e,_||w||r,{axis:\"y\"}),T(t,e,_||w||r,{axis:\"x\",inherit:\"y\"}),n.coerceSelectionMarkerOpacity(e,m)}else e.visible=!1}},{\"../../lib\":503,\"../../registry\":638,\"../scatter/constants\":929,\"../scatter/fillcolor_defaults\":933,\"../scatter/line_defaults\":938,\"../scatter/marker_defaults\":944,\"../scatter/period_defaults\":945,\"../scatter/subtypes\":950,\"../scatter/text_defaults\":951,\"../scatter/xy_defaults\":952,\"./attributes\":977,\"./helpers\":984}],982:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../components/color\"),a=t(\"../../constants/interactions\").DESELECTDIM;e.exports={styleTextSelection:function(t){var e,r,o=t[0],s=o.trace,l=o.t,c=l._scene,u=l.index,f=c.selectBatch[u],h=c.unselectBatch[u],p=c.textOptions[u],d=c.textSelectedOptions[u]||{},g=c.textUnselectedOptions[u]||{},m=n.extendFlat({},p);if(f.length||h.length){var v=d.color,y=g.color,x=p.color,b=Array.isArray(x);for(m.color=new Array(s._length),e=0;e<f.length;e++)r=f[e],m.color[r]=v||(b?x[r]:x);for(e=0;e<h.length;e++){r=h[e];var _=b?x[r]:x;m.color[r]=y||(v?_:i.addOpacity(_,a))}}c.glText[u].update(m)}}},{\"../../components/color\":366,\"../../constants/interactions\":478,\"../../lib\":503}],983:[function(t,e,r){\"use strict\";var n=t(\"../scatter/format_labels\");e.exports=function(t,e,r){var i=t.i;return\"x\"in t||(t.x=e._x[i]),\"y\"in t||(t.y=e._y[i]),n(t,e,r)}},{\"../scatter/format_labels\":934}],984:[function(t,e,r){\"use strict\";var n=t(\"./constants\");r.isOpenSymbol=function(t){return\"string\"==typeof t?n.OPEN_RE.test(t):t%200>100},r.isDotSymbol=function(t){return\"string\"==typeof t?n.DOT_RE.test(t):t>200}},{\"./constants\":979}],985:[function(t,e,r){\"use strict\";var n=t(\"../../registry\"),i=t(\"../../lib\"),a=t(\"../scatter/get_trace_color\");function o(t,e,r,o){var s=t.xa,l=t.ya,c=t.distance,u=t.dxy,f=t.index,h={pointNumber:f,x:e[f],y:r[f]};h.tx=Array.isArray(o.text)?o.text[f]:o.text,h.htx=Array.isArray(o.hovertext)?o.hovertext[f]:o.hovertext,h.data=Array.isArray(o.customdata)?o.customdata[f]:o.customdata,h.tp=Array.isArray(o.textposition)?o.textposition[f]:o.textposition;var p=o.textfont;p&&(h.ts=i.isArrayOrTypedArray(p.size)?p.size[f]:p.size,h.tc=Array.isArray(p.color)?p.color[f]:p.color,h.tf=Array.isArray(p.family)?p.family[f]:p.family);var d=o.marker;d&&(h.ms=i.isArrayOrTypedArray(d.size)?d.size[f]:d.size,h.mo=i.isArrayOrTypedArray(d.opacity)?d.opacity[f]:d.opacity,h.mx=i.isArrayOrTypedArray(d.symbol)?d.symbol[f]:d.symbol,h.mc=i.isArrayOrTypedArray(d.color)?d.color[f]:d.color);var g=d&&d.line;g&&(h.mlc=Array.isArray(g.color)?g.color[f]:g.color,h.mlw=i.isArrayOrTypedArray(g.width)?g.width[f]:g.width);var m=d&&d.gradient;m&&\"none\"!==m.type&&(h.mgt=Array.isArray(m.type)?m.type[f]:m.type,h.mgc=Array.isArray(m.color)?m.color[f]:m.color);var v=s.c2p(h.x,!0),y=l.c2p(h.y,!0),x=h.mrc||1,b=o.hoverlabel;b&&(h.hbg=Array.isArray(b.bgcolor)?b.bgcolor[f]:b.bgcolor,h.hbc=Array.isArray(b.bordercolor)?b.bordercolor[f]:b.bordercolor,h.hts=i.isArrayOrTypedArray(b.font.size)?b.font.size[f]:b.font.size,h.htc=Array.isArray(b.font.color)?b.font.color[f]:b.font.color,h.htf=Array.isArray(b.font.family)?b.font.family[f]:b.font.family,h.hnl=i.isArrayOrTypedArray(b.namelength)?b.namelength[f]:b.namelength);var _=o.hoverinfo;_&&(h.hi=Array.isArray(_)?_[f]:_);var w=o.hovertemplate;w&&(h.ht=Array.isArray(w)?w[f]:w);var T={};T[t.index]=h;var k=o._origX,A=o._origY,M=i.extendFlat({},t,{color:a(o,h),x0:v-x,x1:v+x,xLabelVal:k?k[f]:h.x,y0:y-x,y1:y+x,yLabelVal:A?A[f]:h.y,cd:T,distance:c,spikeDistance:u,hovertemplate:h.ht});return h.htx?M.text=h.htx:h.tx?M.text=h.tx:o.text&&(M.text=o.text),i.fillText(h,o,M),n.getComponentMethod(\"errorbars\",\"hoverInfo\")(h,o,M),M}e.exports={hoverPoints:function(t,e,r,n){var i,a,s,l,c,u,f,h,p,d,g=t.cd,m=g[0].t,v=g[0].trace,y=t.xa,x=t.ya,b=m.x,_=m.y,w=y.c2p(e),T=x.c2p(r),k=t.distance;if(m.tree){var A=y.p2c(w-k),M=y.p2c(w+k),S=x.p2c(T-k),E=x.p2c(T+k);i=\"x\"===n?m.tree.range(Math.min(A,M),Math.min(x._rl[0],x._rl[1]),Math.max(A,M),Math.max(x._rl[0],x._rl[1])):m.tree.range(Math.min(A,M),Math.min(S,E),Math.max(A,M),Math.max(S,E))}else i=m.ids;var L=k;if(\"x\"===n){var C=!!v.xperiodalignment,P=!!v.yperiodalignment;for(u=0;u<i.length;u++){if(l=b[a=i[u]],f=Math.abs(y.c2p(l)-w),C){var I=y.c2p(v._xStarts[a]),O=y.c2p(v._xEnds[a]);f=w>=Math.min(I,O)&&w<=Math.max(I,O)?0:1/0}if(f<L){if(L=f,c=_[a],h=x.c2p(c)-T,P){var z=x.c2p(v._yStarts[a]),D=x.c2p(v._yEnds[a]);h=T>=Math.min(z,D)&&T<=Math.max(z,D)?0:1/0}d=Math.sqrt(f*f+h*h),s=i[u]}}}else for(u=i.length-1;u>-1;u--)l=b[a=i[u]],c=_[a],f=y.c2p(l)-w,h=x.c2p(c)-T,(p=Math.sqrt(f*f+h*h))<L&&(L=d=p,s=a);return t.index=s,t.distance=L,t.dxy=d,void 0===s?[t]:[o(t,b,_,v)]},calcHover:o}},{\"../../lib\":503,\"../../registry\":638,\"../scatter/get_trace_color\":935}],986:[function(t,e,r){\"use strict\";var n=t(\"./hover\");e.exports={moduleType:\"trace\",name:\"scattergl\",basePlotModule:t(\"../../plots/cartesian\"),categories:[\"gl\",\"regl\",\"cartesian\",\"symbols\",\"errorBarsOK\",\"showLegend\",\"scatter-like\"],attributes:t(\"./attributes\"),supplyDefaults:t(\"./defaults\"),crossTraceDefaults:t(\"../scatter/cross_trace_defaults\"),colorbar:t(\"../scatter/marker_colorbar\"),formatLabels:t(\"./format_labels\"),calc:t(\"./calc\"),plot:t(\"./plot\"),hoverPoints:n.hoverPoints,selectPoints:t(\"./select\"),meta:{}}},{\"../../plots/cartesian\":568,\"../scatter/cross_trace_defaults\":931,\"../scatter/marker_colorbar\":943,\"./attributes\":977,\"./calc\":978,\"./defaults\":981,\"./format_labels\":983,\"./hover\":985,\"./plot\":987,\"./select\":989}],987:[function(t,e,r){\"use strict\";var n=t(\"regl-scatter2d\"),i=t(\"regl-line2d\"),a=t(\"regl-error2d\"),o=t(\"gl-text\"),s=t(\"../../lib\"),l=t(\"../../components/dragelement/helpers\").selectMode,c=t(\"../../lib/prepare_regl\"),u=t(\"../scatter/subtypes\"),f=t(\"../scatter/link_traces\"),h=t(\"./edit_style\").styleTextSelection;function p(t,e,r,n){var i=t._size,a=t.width*n,o=t.height*n,s=i.l*n,l=i.b*n,c=i.r*n,u=i.t*n,f=i.w*n,h=i.h*n;return[s+e.domain[0]*f,l+r.domain[0]*h,a-c-(1-e.domain[1])*f,o-u-(1-r.domain[1])*h]}e.exports=function(t,e,r){if(r.length){var d,g,m=t._fullLayout,v=e._scene,y=e.xaxis,x=e.yaxis;if(v)if(c(t,[\"ANGLE_instanced_arrays\",\"OES_element_index_uint\"])){var b=v.count,_=m._glcanvas.data()[0].regl;if(f(t,e,r),v.dirty){if(!0===v.error2d&&(v.error2d=a(_)),!0===v.line2d&&(v.line2d=i(_)),!0===v.scatter2d&&(v.scatter2d=n(_)),!0===v.fill2d&&(v.fill2d=i(_)),!0===v.glText)for(v.glText=new Array(b),d=0;d<b;d++)v.glText[d]=new o(_);if(v.glText){if(b>v.glText.length){var w=b-v.glText.length;for(d=0;d<w;d++)v.glText.push(new o(_))}else if(b<v.glText.length){var T=v.glText.length-b;v.glText.splice(b,T).forEach((function(t){t.destroy()}))}for(d=0;d<b;d++)v.glText[d].update(v.textOptions[d])}if(v.line2d&&(v.line2d.update(v.lineOptions),v.lineOptions=v.lineOptions.map((function(t){if(t&&t.positions){for(var e=t.positions,r=0;r<e.length&&(isNaN(e[r])||isNaN(e[r+1]));)r+=2;for(var n=e.length-2;n>r&&(isNaN(e[n])||isNaN(e[n+1]));)n-=2;t.positions=e.slice(r,n+2)}return t})),v.line2d.update(v.lineOptions)),v.error2d){var k=(v.errorXOptions||[]).concat(v.errorYOptions||[]);v.error2d.update(k)}v.scatter2d&&v.scatter2d.update(v.markerOptions),v.fillOrder=s.repeat(null,b),v.fill2d&&(v.fillOptions=v.fillOptions.map((function(t,e){var n=r[e];if(t&&n&&n[0]&&n[0].trace){var i,a,o=n[0],s=o.trace,l=o.t,c=v.lineOptions[e],u=[];s._ownfill&&u.push(e),s._nexttrace&&u.push(e+1),u.length&&(v.fillOrder[e]=u);var f,h,p=[],d=c&&c.positions||l.positions;if(\"tozeroy\"===s.fill){for(f=0;f<d.length&&isNaN(d[f+1]);)f+=2;for(h=d.length-2;h>f&&isNaN(d[h+1]);)h-=2;0!==d[f+1]&&(p=[d[f],0]),p=p.concat(d.slice(f,h+2)),0!==d[h+1]&&(p=p.concat([d[h],0]))}else if(\"tozerox\"===s.fill){for(f=0;f<d.length&&isNaN(d[f]);)f+=2;for(h=d.length-2;h>f&&isNaN(d[h]);)h-=2;0!==d[f]&&(p=[0,d[f+1]]),p=p.concat(d.slice(f,h+2)),0!==d[h]&&(p=p.concat([0,d[h+1]]))}else if(\"toself\"===s.fill||\"tonext\"===s.fill){for(p=[],i=0,t.splitNull=!0,a=0;a<d.length;a+=2)(isNaN(d[a])||isNaN(d[a+1]))&&((p=p.concat(d.slice(i,a))).push(d[i],d[i+1]),p.push(null,null),i=a+2);p=p.concat(d.slice(i)),i&&p.push(d[i],d[i+1])}else{var g=s._nexttrace;if(g){var m=v.lineOptions[e+1];if(m){var y=m.positions;if(\"tonexty\"===s.fill){for(p=d.slice(),e=Math.floor(y.length/2);e--;){var x=y[2*e],b=y[2*e+1];isNaN(x)||isNaN(b)||p.push(x,b)}t.fill=g.fillcolor}}}}if(s._prevtrace&&\"tonext\"===s._prevtrace.fill){var _=v.lineOptions[e-1].positions,w=p.length/2,T=[i=w];for(a=0;a<_.length;a+=2)(isNaN(_[a])||isNaN(_[a+1]))&&(T.push(a/2+w+1),i=a+2);p=p.concat(_),t.hole=T}return t.fillmode=s.fill,t.opacity=s.opacity,t.positions=p,t}})),v.fill2d.update(v.fillOptions))}var A=m.dragmode,M=l(A),S=m.clickmode.indexOf(\"select\")>-1;for(d=0;d<b;d++){var E=r[d][0],L=E.trace,C=E.t,P=C.index,I=L._length,O=C.x,z=C.y;if(L.selectedpoints||M||S){if(M||(M=!0),L.selectedpoints){var D=v.selectBatch[P]=s.selIndices2selPoints(L),R={};for(g=0;g<D.length;g++)R[D[g]]=1;var F=[];for(g=0;g<I;g++)R[g]||F.push(g);v.unselectBatch[P]=F}var B=C.xpx=new Array(I),N=C.ypx=new Array(I);for(g=0;g<I;g++)B[g]=y.c2p(O[g]),N[g]=x.c2p(z[g])}else C.xpx=C.ypx=null}if(M){if(v.select2d||(v.select2d=n(m._glcanvas.data()[1].regl)),v.scatter2d){var j=new Array(b);for(d=0;d<b;d++)j[d]=v.selectBatch[d].length||v.unselectBatch[d].length?v.markerUnselectedOptions[d]:{};v.scatter2d.update(j)}v.select2d&&(v.select2d.update(v.markerOptions),v.select2d.update(v.markerSelectedOptions)),v.glText&&r.forEach((function(t){var e=((t||[])[0]||{}).trace||{};u.hasText(e)&&h(t)}))}else v.scatter2d&&v.scatter2d.update(v.markerOptions);var U={viewport:p(m,y,x,t._context.plotGlPixelRatio),range:[(y._rl||y.range)[0],(x._rl||x.range)[0],(y._rl||y.range)[1],(x._rl||x.range)[1]]},V=s.repeat(U,v.count);v.fill2d&&v.fill2d.update(V),v.line2d&&v.line2d.update(V),v.error2d&&v.error2d.update(V.concat(V)),v.scatter2d&&v.scatter2d.update(V),v.select2d&&v.select2d.update(V),v.glText&&v.glText.forEach((function(t){t.update(U)}))}else v.init()}}},{\"../../components/dragelement/helpers\":384,\"../../lib\":503,\"../../lib/prepare_regl\":516,\"../scatter/link_traces\":941,\"../scatter/subtypes\":950,\"./edit_style\":982,\"gl-text\":225,\"regl-error2d\":279,\"regl-line2d\":280,\"regl-scatter2d\":281}],988:[function(t,e,r){\"use strict\";var n=t(\"../../lib\");e.exports=function(t,e){var r=e._scene,i={count:0,dirty:!0,lineOptions:[],fillOptions:[],markerOptions:[],markerSelectedOptions:[],markerUnselectedOptions:[],errorXOptions:[],errorYOptions:[],textOptions:[],textSelectedOptions:[],textUnselectedOptions:[],selectBatch:[],unselectBatch:[]},a={fill2d:!1,scatter2d:!1,error2d:!1,line2d:!1,glText:!1,select2d:!1};return e._scene||((r=e._scene={}).init=function(){n.extendFlat(r,a,i)},r.init(),r.update=function(t){var e=n.repeat(t,r.count);if(r.fill2d&&r.fill2d.update(e),r.scatter2d&&r.scatter2d.update(e),r.line2d&&r.line2d.update(e),r.error2d&&r.error2d.update(e.concat(e)),r.select2d&&r.select2d.update(e),r.glText)for(var i=0;i<r.count;i++)r.glText[i].update(t)},r.draw=function(){for(var t=r.count,e=r.fill2d,i=r.error2d,a=r.line2d,o=r.scatter2d,s=r.glText,l=r.select2d,c=r.selectBatch,u=r.unselectBatch,f=0;f<t;f++){if(e&&r.fillOrder[f]&&e.draw(r.fillOrder[f]),a&&r.lineOptions[f]&&a.draw(f),i&&(r.errorXOptions[f]&&i.draw(f),r.errorYOptions[f]&&i.draw(f+t)),o&&r.markerOptions[f])if(u[f].length){var h=n.repeat([],r.count);h[f]=u[f],o.draw(h)}else c[f].length||o.draw(f);s[f]&&r.textOptions[f]&&s[f].render()}l&&l.draw(c),r.dirty=!1},r.destroy=function(){r.fill2d&&r.fill2d.destroy&&r.fill2d.destroy(),r.scatter2d&&r.scatter2d.destroy&&r.scatter2d.destroy(),r.error2d&&r.error2d.destroy&&r.error2d.destroy(),r.line2d&&r.line2d.destroy&&r.line2d.destroy(),r.select2d&&r.select2d.destroy&&r.select2d.destroy(),r.glText&&r.glText.forEach((function(t){t.destroy&&t.destroy()})),r.lineOptions=null,r.fillOptions=null,r.markerOptions=null,r.markerSelectedOptions=null,r.markerUnselectedOptions=null,r.errorXOptions=null,r.errorYOptions=null,r.textOptions=null,r.textSelectedOptions=null,r.textUnselectedOptions=null,r.selectBatch=null,r.unselectBatch=null,e._scene=null}),r.dirty||n.extendFlat(r,i),r}},{\"../../lib\":503}],989:[function(t,e,r){\"use strict\";var n=t(\"../scatter/subtypes\"),i=t(\"./edit_style\").styleTextSelection;e.exports=function(t,e){var r=t.cd,a=t.xaxis,o=t.yaxis,s=[],l=r[0].trace,c=r[0].t,u=l._length,f=c.x,h=c.y,p=c._scene,d=c.index;if(!p)return s;var g=n.hasText(l),m=n.hasMarkers(l),v=!m&&!g;if(!0!==l.visible||v)return s;var y=[],x=[];if(!1!==e&&!e.degenerate)for(var b=0;b<u;b++)e.contains([c.xpx[b],c.ypx[b]],!1,b,t)?(y.push(b),s.push({pointNumber:b,x:a.c2d(f[b]),y:o.c2d(h[b])})):x.push(b);if(m){var _=p.scatter2d;if(y.length||x.length){if(!p.selectBatch[d].length&&!p.unselectBatch[d].length){var w=new Array(p.count);w[d]=p.markerUnselectedOptions[d],_.update.apply(_,w)}}else{var T=new Array(p.count);T[d]=p.markerOptions[d],_.update.apply(_,T)}}return p.selectBatch[d]=y,p.unselectBatch[d]=x,g&&i(r),s}},{\"../scatter/subtypes\":950,\"./edit_style\":982}],990:[function(t,e,r){\"use strict\";var n=t(\"../../plots/template_attributes\").hovertemplateAttrs,i=t(\"../../plots/template_attributes\").texttemplateAttrs,a=t(\"../scattergeo/attributes\"),o=t(\"../scatter/attributes\"),s=t(\"../../plots/mapbox/layout_attributes\"),l=t(\"../../plots/attributes\"),c=t(\"../../components/colorscale/attributes\"),u=t(\"../../lib/extend\").extendFlat,f=t(\"../../plot_api/edit_types\").overrideAll,h=a.line,p=a.marker;e.exports=f({lon:a.lon,lat:a.lat,mode:u({},o.mode,{dflt:\"markers\"}),text:u({},o.text,{}),texttemplate:i({editType:\"plot\"},{keys:[\"lat\",\"lon\",\"text\"]}),hovertext:u({},o.hovertext,{}),line:{color:h.color,width:h.width},connectgaps:o.connectgaps,marker:u({symbol:{valType:\"string\",dflt:\"circle\",arrayOk:!0},angle:{valType:\"number\",dflt:\"auto\",arrayOk:!0},allowoverlap:{valType:\"boolean\",dflt:!1},opacity:p.opacity,size:p.size,sizeref:p.sizeref,sizemin:p.sizemin,sizemode:p.sizemode},c(\"marker\")),fill:a.fill,fillcolor:o.fillcolor,textfont:s.layers.symbol.textfont,textposition:s.layers.symbol.textposition,below:{valType:\"string\"},selected:{marker:o.selected.marker},unselected:{marker:o.unselected.marker},hoverinfo:u({},l.hoverinfo,{flags:[\"lon\",\"lat\",\"text\",\"name\"]}),hovertemplate:n()},\"calc\",\"nested\")},{\"../../components/colorscale/attributes\":373,\"../../lib/extend\":493,\"../../plot_api/edit_types\":536,\"../../plots/attributes\":550,\"../../plots/mapbox/layout_attributes\":615,\"../../plots/template_attributes\":633,\"../scatter/attributes\":925,\"../scattergeo/attributes\":967}],991:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),i=t(\"../../lib\"),a=t(\"../../constants/numerical\").BADNUM,o=t(\"../../lib/geojson_utils\"),s=t(\"../../components/colorscale\"),l=t(\"../../components/drawing\"),c=t(\"../scatter/make_bubble_size_func\"),u=t(\"../scatter/subtypes\"),f=t(\"../../plots/mapbox/convert_text_opts\"),h=t(\"../../components/fx/helpers\").appendArrayPointValue,p=t(\"../../lib/svg_text_utils\").NEWLINES,d=t(\"../../lib/svg_text_utils\").BR_TAG_ALL;function g(){return{geojson:o.makeBlank(),layout:{visibility:\"none\"},paint:{}}}function m(t,e){return i.isArrayOrTypedArray(t)?e?function(e){return n(t[e])?+t[e]:0}:function(e){return t[e]}:t?function(){return t}:v}function v(){return\"\"}function y(t){return t[0]===a}e.exports=function(t,e){var r,a=e[0].trace,x=!0===a.visible&&0!==a._length,b=\"none\"!==a.fill,_=u.hasLines(a),w=u.hasMarkers(a),T=u.hasText(a),k=w&&\"circle\"===a.marker.symbol,A=w&&\"circle\"!==a.marker.symbol,M=g(),S=g(),E=g(),L=g(),C={fill:M,line:S,circle:E,symbol:L};if(!x)return C;if((b||_)&&(r=o.calcTraceToLineCoords(e)),b&&(M.geojson=o.makePolygon(r),M.layout.visibility=\"visible\",i.extendFlat(M.paint,{\"fill-color\":a.fillcolor})),_&&(S.geojson=o.makeLine(r),S.layout.visibility=\"visible\",i.extendFlat(S.paint,{\"line-width\":a.line.width,\"line-color\":a.line.color,\"line-opacity\":a.opacity})),k){var P=function(t){var e,r,a,o,u=t[0].trace,f=u.marker,h=u.selectedpoints,p=i.isArrayOrTypedArray(f.color),d=i.isArrayOrTypedArray(f.size),g=i.isArrayOrTypedArray(f.opacity);function m(t){return u.opacity*t}p&&(r=s.hasColorscale(u,\"marker\")?s.makeColorScaleFuncFromTrace(f):i.identity);d&&(a=c(u));g&&(o=function(t){return m(n(t)?+i.constrain(t,0,1):0)});var v,x=[];for(e=0;e<t.length;e++){var 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A={};u.label&&(A.label=u.label),u.hasOwnProperty(\"v\")&&(A.value=u.v,A.valueLabel=b.formatValue(u.v,l)),A.currentPath=b.getPath(t.data),h||(A.percentParent=d/b.getValue(p),A.percentParentLabel=b.formatPercent(A.percentParent,l),A.parent=b.getPtLabel(p)),A.percentEntry=d/b.getValue(e),A.percentEntryLabel=b.formatPercent(A.percentEntry,l),A.entry=b.getPtLabel(e),A.percentRoot=d/b.getValue(f),A.percentRootLabel=b.formatPercent(A.percentRoot,l),A.root=b.getPtLabel(f),u.hasOwnProperty(\"color\")&&(A.color=u.color);var M=s.castOption(r,u.i,\"text\");return(s.isValidTextValue(M)||\"\"===M)&&(A.text=M),A.customdata=s.castOption(r,u.i,\"customdata\"),s.texttemplateString(k,A,i._d3locale,A,r._meta||{})}},{\"../../components/drawing\":388,\"../../lib\":503,\"../../lib/svg_text_utils\":529,\"../bar/style\":662,\"../bar/uniform_text\":664,\"../pie/helpers\":904,\"../pie/plot\":908,\"./constants\":1047,\"./fx\":1049,\"./helpers\":1050,\"./style\":1055,\"@plotly/d3\":58,\"d3-hierarchy\":115,\"d3-interpolate\":116}],1055:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"../../components/color\"),a=t(\"../../lib\"),o=t(\"../bar/uniform_text\").resizeText;function s(t,e,r){var n=e.data.data,o=!e.children,s=n.i,l=a.castOption(r,s,\"marker.line.color\")||i.defaultLine,c=a.castOption(r,s,\"marker.line.width\")||0;t.style(\"stroke-width\",c).call(i.fill,n.color).call(i.stroke,l).style(\"opacity\",o?r.leaf.opacity:null)}e.exports={style:function(t){var e=t._fullLayout._sunburstlayer.selectAll(\".trace\");o(t,e,\"sunburst\"),e.each((function(t){var e=n.select(this),r=t[0].trace;e.style(\"opacity\",r.opacity),e.selectAll(\"path.surface\").each((function(t){n.select(this).call(s,t,r)}))}))},styleOne:s}},{\"../../components/color\":366,\"../../lib\":503,\"../bar/uniform_text\":664,\"@plotly/d3\":58}],1056:[function(t,e,r){\"use strict\";var n=t(\"../../components/color\"),i=t(\"../../components/colorscale/attributes\"),a=t(\"../../plots/cartesian/axis_format_attributes\").axisHoverFormat,o=t(\"../../plots/template_attributes\").hovertemplateAttrs,s=t(\"../../plots/attributes\"),l=t(\"../../lib/extend\").extendFlat,c=t(\"../../plot_api/edit_types\").overrideAll;function u(t){return{show:{valType:\"boolean\",dflt:!1},start:{valType:\"number\",dflt:null,editType:\"plot\"},end:{valType:\"number\",dflt:null,editType:\"plot\"},size:{valType:\"number\",dflt:null,min:0,editType:\"plot\"},project:{x:{valType:\"boolean\",dflt:!1},y:{valType:\"boolean\",dflt:!1},z:{valType:\"boolean\",dflt:!1}},color:{valType:\"color\",dflt:n.defaultLine},usecolormap:{valType:\"boolean\",dflt:!1},width:{valType:\"number\",min:1,max:16,dflt:2},highlight:{valType:\"boolean\",dflt:!0},highlightcolor:{valType:\"color\",dflt:n.defaultLine},highlightwidth:{valType:\"number\",min:1,max:16,dflt:2}}}var f=e.exports=c(l({z:{valType:\"data_array\"},x:{valType:\"data_array\"},y:{valType:\"data_array\"},text:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertemplate:o(),xhoverformat:a(\"x\"),yhoverformat:a(\"y\"),zhoverformat:a(\"z\"),connectgaps:{valType:\"boolean\",dflt:!1,editType:\"calc\"},surfacecolor:{valType:\"data_array\"}},i(\"\",{colorAttr:\"z or surfacecolor\",showScaleDflt:!0,autoColorDflt:!1,editTypeOverride:\"calc\"}),{contours:{x:u(),y:u(),z:u()},hidesurface:{valType:\"boolean\",dflt:!1},lightposition:{x:{valType:\"number\",min:-1e5,max:1e5,dflt:10},y:{valType:\"number\",min:-1e5,max:1e5,dflt:1e4},z:{valType:\"number\",min:-1e5,max:1e5,dflt:0}},lighting:{ambient:{valType:\"number\",min:0,max:1,dflt:.8},diffuse:{valType:\"number\",min:0,max:1,dflt:.8},specular:{valType:\"number\",min:0,max:2,dflt:.05},roughness:{valType:\"number\",min:0,max:1,dflt:.5},fresnel:{valType:\"number\",min:0,max:5,dflt:.2}},opacity:{valType:\"number\",min:0,max:1,dflt:1},opacityscale:{valType:\"any\",editType:\"calc\"},_deprecated:{zauto:l({},i.zauto,{}),zmin:l({},i.zmin,{}),zmax:l({},i.zmax,{})},hoverinfo:l({},s.hoverinfo),showlegend:l({},s.showlegend,{dflt:!1})}),\"calc\",\"nested\");f.x.editType=f.y.editType=f.z.editType=\"calc+clearAxisTypes\",f.transforms=void 0},{\"../../components/color\":366,\"../../components/colorscale/attributes\":373,\"../../lib/extend\":493,\"../../plot_api/edit_types\":536,\"../../plots/attributes\":550,\"../../plots/cartesian/axis_format_attributes\":557,\"../../plots/template_attributes\":633}],1057:[function(t,e,r){\"use strict\";var n=t(\"../../components/colorscale/calc\");e.exports=function(t,e){e.surfacecolor?n(t,e,{vals:e.surfacecolor,containerStr:\"\",cLetter:\"c\"}):n(t,e,{vals:e.z,containerStr:\"\",cLetter:\"c\"})}},{\"../../components/colorscale/calc\":374}],1058:[function(t,e,r){\"use strict\";var n=t(\"../../../stackgl_modules\").gl_surface3d,i=t(\"../../../stackgl_modules\").ndarray,a=t(\"../../../stackgl_modules\").ndarray_linear_interpolate.d2,o=t(\"../heatmap/interp2d\"),s=t(\"../heatmap/find_empties\"),l=t(\"../../lib\").isArrayOrTypedArray,c=t(\"../../lib/gl_format_color\").parseColorScale,u=t(\"../../lib/str2rgbarray\"),f=t(\"../../components/colorscale\").extractOpts;function h(t,e,r){this.scene=t,this.uid=r,this.surface=e,this.data=null,this.showContour=[!1,!1,!1],this.contourStart=[null,null,null],this.contourEnd=[null,null,null],this.contourSize=[0,0,0],this.minValues=[1/0,1/0,1/0],this.maxValues=[-1/0,-1/0,-1/0],this.dataScaleX=1,this.dataScaleY=1,this.refineData=!0,this.objectOffset=[0,0,0]}var p=h.prototype;p.getXat=function(t,e,r,n){var i=l(this.data.x)?l(this.data.x[0])?this.data.x[e][t]:this.data.x[t]:t;return void 0===r?i:n.d2l(i,0,r)},p.getYat=function(t,e,r,n){var i=l(this.data.y)?l(this.data.y[0])?this.data.y[e][t]:this.data.y[e]:e;return void 0===r?i:n.d2l(i,0,r)},p.getZat=function(t,e,r,n){var i=this.data.z[e][t];return null===i&&this.data.connectgaps&&this.data._interpolatedZ&&(i=this.data._interpolatedZ[e][t]),void 0===r?i:n.d2l(i,0,r)},p.handlePick=function(t){if(t.object===this.surface){var e=(t.data.index[0]-1)/this.dataScaleX-1,r=(t.data.index[1]-1)/this.dataScaleY-1,n=Math.max(Math.min(Math.round(e),this.data.z[0].length-1),0),i=Math.max(Math.min(Math.round(r),this.data._ylength-1),0);t.index=[n,i],t.traceCoordinate=[this.getXat(n,i),this.getYat(n,i),this.getZat(n,i)],t.dataCoordinate=[this.getXat(n,i,this.data.xcalendar,this.scene.fullSceneLayout.xaxis),this.getYat(n,i,this.data.ycalendar,this.scene.fullSceneLayout.yaxis),this.getZat(n,i,this.data.zcalendar,this.scene.fullSceneLayout.zaxis)];for(var a=0;a<3;a++){var o=t.dataCoordinate[a];null!=o&&(t.dataCoordinate[a]*=this.scene.dataScale[a])}var s=this.data.hovertext||this.data.text;return Array.isArray(s)&&s[i]&&void 0!==s[i][n]?t.textLabel=s[i][n]:t.textLabel=s||\"\",t.data.dataCoordinate=t.dataCoordinate.slice(),this.surface.highlight(t.data),this.scene.glplot.spikes.position=t.dataCoordinate,!0}};var d=[2,3,5,7,11,13,17,19,23,29,31,37,41,43,47,53,59,61,67,71,73,79,83,89,97,101,103,107,109,113,127,131,137,139,149,151,157,163,167,173,179,181,191,193,197,199,211,223,227,229,233,239,241,251,257,263,269,271,277,281,283,293,307,311,313,317,331,337,347,349,353,359,367,373,379,383,389,397,401,409,419,421,431,433,439,443,449,457,461,463,467,479,487,491,499,503,509,521,523,541,547,557,563,569,571,577,587,593,599,601,607,613,617,619,631,641,643,647,653,659,661,673,677,683,691,701,709,719,727,733,739,743,751,757,761,769,773,787,797,809,811,821,823,827,829,839,853,857,859,863,877,881,883,887,907,911,919,929,937,941,947,953,967,971,977,983,991,997,1009,1013,1019,1021,1031,1033,1039,1049,1051,1061,1063,1069,1087,1091,1093,1097,1103,1109,1117,1123,1129,1151,1153,1163,1171,1181,1187,1193,1201,1213,1217,1223,1229,1231,1237,1249,1259,1277,1279,1283,1289,1291,1297,1301,1303,1307,1319,1321,1327,1361,1367,1373,1381,1399,1409,1423,1427,1429,1433,1439,1447,1451,1453,1459,1471,1481,1483,1487,1489,1493,1499,1511,1523,1531,1543,1549,1553,1559,1567,1571,1579,1583,1597,1601,1607,1609,1613,1619,1621,1627,1637,1657,1663,1667,1669,1693,1697,1699,1709,1721,1723,1733,1741,1747,1753,1759,1777,1783,1787,1789,1801,1811,1823,1831,1847,1861,1867,1871,1873,1877,1879,1889,1901,1907,1913,1931,1933,1949,1951,1973,1979,1987,1993,1997,1999,2003,2011,2017,2027,2029,2039,2053,2063,2069,2081,2083,2087,2089,2099,2111,2113,2129,2131,2137,2141,2143,2153,2161,2179,2203,2207,2213,2221,2237,2239,2243,2251,2267,2269,2273,2281,2287,2293,2297,2309,2311,2333,2339,2341,2347,2351,2357,2371,2377,2381,2383,2389,2393,2399,2411,2417,2423,2437,2441,2447,2459,2467,2473,2477,2503,2521,2531,2539,2543,2549,2551,2557,2579,2591,2593,2609,2617,2621,2633,2647,2657,2659,2663,2671,2677,2683,2687,2689,2693,2699,2707,2711,2713,2719,2729,2731,2741,2749,2753,2767,2777,2789,2791,2797,2801,2803,2819,2833,2837,2843,2851,2857,2861,2879,2887,2897,2903,2909,2917,2927,2939,2953,2957,2963,2969,2971,2999];function g(t,e){if(t<e)return 0;for(var r=0;0===Math.floor(t%e);)t/=e,r++;return r}function m(t){for(var e=[],r=0;r<d.length;r++){var n=d[r];e.push(g(t,n))}return e}function v(t){for(var e=m(t),r=t,n=0;n<d.length;n++)if(e[n]>0){r=d[n];break}return r}function y(t,e){if(!(t<1||e<1)){for(var r=m(t),n=m(e),i=1,a=0;a<d.length;a++)i*=Math.pow(d[a],Math.max(r[a],n[a]));return i}}p.calcXnums=function(t){var e,r=[];for(e=1;e<t;e++){var n=this.getXat(e-1,0),i=this.getXat(e,0);r[e-1]=i!==n&&null!=n&&null!=i?Math.abs(i-n):0}var a=0;for(e=1;e<t;e++)a+=r[e-1];for(e=1;e<t;e++)0===r[e-1]?r[e-1]=1:r[e-1]=Math.round(a/r[e-1]);return r},p.calcYnums=function(t){var e,r=[];for(e=1;e<t;e++){var n=this.getYat(0,e-1),i=this.getYat(0,e);r[e-1]=i!==n&&null!=n&&null!=i?Math.abs(i-n):0}var a=0;for(e=1;e<t;e++)a+=r[e-1];for(e=1;e<t;e++)0===r[e-1]?r[e-1]=1:r[e-1]=Math.round(a/r[e-1]);return r};var x=[1,2,4,6,12,24,36,48,60,120,180,240,360,720,840,1260],b=x[9],_=x[13];function w(t,e,r){var n=r[8]+r[2]*e[0]+r[5]*e[1];return t[0]=(r[6]+r[0]*e[0]+r[3]*e[1])/n,t[1]=(r[7]+r[1]*e[0]+r[4]*e[1])/n,t}function T(t,e,r){return function(t,e,r,n){for(var i=[0,0],o=t.shape[0],s=t.shape[1],l=0;l<o;l++)for(var c=0;c<s;c++)r(i,[l,c],n),t.set(l,c,a(e,i[0],i[1]))}(t,e,w,r),t}function k(t,e){for(var r=!1,n=0;n<t.length;n++)if(e===t[n]){r=!0;break}!1===r&&t.push(e)}p.estimateScale=function(t,e){for(var r=1+function(t){if(0!==t.length){for(var e=1,r=0;r<t.length;r++)e=y(e,t[r]);return e}}(0===e?this.calcXnums(t):this.calcYnums(t));r<b;)r*=2;for(;r>_;)r--,r/=v(r),++r<b&&(r=_);var n=Math.round(r/t);return n>1?n:1},p.refineCoords=function(t){for(var e=this.dataScaleX,r=this.dataScaleY,n=t[0].shape[0],a=t[0].shape[1],o=0|Math.floor(t[0].shape[0]*e+1),s=0|Math.floor(t[0].shape[1]*r+1),l=1+n+1,c=1+a+1,u=i(new Float32Array(l*c),[l,c]),f=[1/e,0,0,0,1/r,0,0,0,1],h=0;h<t.length;++h){this.surface.padField(u,t[h]);var p=i(new Float32Array(o*s),[o,s]);T(p,u,f),t[h]=p}},p.setContourLevels=function(){var t,e,r,n=[[],[],[]],i=[!1,!1,!1],a=!1;for(t=0;t<3;++t)if(this.showContour[t]&&(a=!0,this.contourSize[t]>0&&null!==this.contourStart[t]&&null!==this.contourEnd[t]&&this.contourEnd[t]>this.contourStart[t]))for(i[t]=!0,e=this.contourStart[t];e<this.contourEnd[t];e+=this.contourSize[t])r=e*this.scene.dataScale[t],k(n[t],r);if(a){var o=[[],[],[]];for(t=0;t<3;++t)this.showContour[t]&&(o[t]=i[t]?n[t]:this.scene.contourLevels[t]);this.surface.update({levels:o})}},p.update=function(t){var e,r,n,a,l=this.scene,h=l.fullSceneLayout,p=this.surface,d=c(t),g=l.dataScale,m=t.z[0].length,v=t._ylength,y=l.contourLevels;this.data=t;var x=[];for(e=0;e<3;e++)for(x[e]=[],r=0;r<m;r++)x[e][r]=[];for(r=0;r<m;r++)for(n=0;n<v;n++)x[0][r][n]=this.getXat(r,n,t.xcalendar,h.xaxis),x[1][r][n]=this.getYat(r,n,t.ycalendar,h.yaxis),x[2][r][n]=this.getZat(r,n,t.zcalendar,h.zaxis);if(t.connectgaps)for(t._emptypoints=s(x[2]),o(x[2],t._emptypoints),t._interpolatedZ=[],r=0;r<m;r++)for(t._interpolatedZ[r]=[],n=0;n<v;n++)t._interpolatedZ[r][n]=x[2][r][n];for(e=0;e<3;e++)for(r=0;r<m;r++)for(n=0;n<v;n++)null==(a=x[e][r][n])?x[e][r][n]=NaN:a=x[e][r][n]*=g[e];for(e=0;e<3;e++)for(r=0;r<m;r++)for(n=0;n<v;n++)null!=(a=x[e][r][n])&&(this.minValues[e]>a&&(this.minValues[e]=a),this.maxValues[e]<a&&(this.maxValues[e]=a));for(e=0;e<3;e++)this.objectOffset[e]=.5*(this.minValues[e]+this.maxValues[e]);for(e=0;e<3;e++)for(r=0;r<m;r++)for(n=0;n<v;n++)null!=(a=x[e][r][n])&&(x[e][r][n]-=this.objectOffset[e]);var b=[i(new Float32Array(m*v),[m,v]),i(new Float32Array(m*v),[m,v]),i(new Float32Array(m*v),[m,v])];for(e=0;e<3;e++)for(r=0;r<m;r++)for(n=0;n<v;n++)b[e].set(r,n,x[e][r][n]);x=[];var w={colormap:d,levels:[[],[],[]],showContour:[!0,!0,!0],showSurface:!t.hidesurface,contourProject:[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],contourWidth:[1,1,1],contourColor:[[1,1,1,1],[1,1,1,1],[1,1,1,1]],contourTint:[1,1,1],dynamicColor:[[1,1,1,1],[1,1,1,1],[1,1,1,1]],dynamicWidth:[1,1,1],dynamicTint:[1,1,1],opacityscale:t.opacityscale,opacity:t.opacity},T=f(t);if(w.intensityBounds=[T.min,T.max],t.surfacecolor){var k=i(new Float32Array(m*v),[m,v]);for(r=0;r<m;r++)for(n=0;n<v;n++)k.set(r,n,t.surfacecolor[n][r]);b.push(k)}else w.intensityBounds[0]*=g[2],w.intensityBounds[1]*=g[2];(_<b[0].shape[0]||_<b[0].shape[1])&&(this.refineData=!1),!0===this.refineData&&(this.dataScaleX=this.estimateScale(b[0].shape[0],0),this.dataScaleY=this.estimateScale(b[0].shape[1],1),1===this.dataScaleX&&1===this.dataScaleY||this.refineCoords(b)),t.surfacecolor&&(w.intensity=b.pop());var A=[!0,!0,!0],M=[\"x\",\"y\",\"z\"];for(e=0;e<3;++e){var S=t.contours[M[e]];A[e]=S.highlight,w.showContour[e]=S.show||S.highlight,w.showContour[e]&&(w.contourProject[e]=[S.project.x,S.project.y,S.project.z],S.show?(this.showContour[e]=!0,w.levels[e]=y[e],p.highlightColor[e]=w.contourColor[e]=u(S.color),S.usecolormap?p.highlightTint[e]=w.contourTint[e]=0:p.highlightTint[e]=w.contourTint[e]=1,w.contourWidth[e]=S.width,this.contourStart[e]=S.start,this.contourEnd[e]=S.end,this.contourSize[e]=S.size):(this.showContour[e]=!1,this.contourStart[e]=null,this.contourEnd[e]=null,this.contourSize[e]=0),S.highlight&&(w.dynamicColor[e]=u(S.highlightcolor),w.dynamicWidth[e]=S.highlightwidth))}(function(t){var e=t[0].rgb,r=t[t.length-1].rgb;return e[0]===r[0]&&e[1]===r[1]&&e[2]===r[2]&&e[3]===r[3]})(d)&&(w.vertexColor=!0),w.objectOffset=this.objectOffset,w.coords=b,p.update(w),p.visible=t.visible,p.enableDynamic=A,p.enableHighlight=A,p.snapToData=!0,\"lighting\"in t&&(p.ambientLight=t.lighting.ambient,p.diffuseLight=t.lighting.diffuse,p.specularLight=t.lighting.specular,p.roughness=t.lighting.roughness,p.fresnel=t.lighting.fresnel),\"lightposition\"in t&&(p.lightPosition=[t.lightposition.x,t.lightposition.y,t.lightposition.z])},p.dispose=function(){this.scene.glplot.remove(this.surface),this.surface.dispose()},e.exports=function(t,e){var r=t.glplot.gl,i=n({gl:r}),a=new h(t,i,e.uid);return i._trace=a,a.update(e),t.glplot.add(i),a}},{\"../../../stackgl_modules\":1119,\"../../components/colorscale\":378,\"../../lib\":503,\"../../lib/gl_format_color\":499,\"../../lib/str2rgbarray\":528,\"../heatmap/find_empties\":798,\"../heatmap/interp2d\":801}],1059:[function(t,e,r){\"use strict\";var n=t(\"../../registry\"),i=t(\"../../lib\"),a=t(\"../../components/colorscale/defaults\"),o=t(\"./attributes\");function s(t,e,r,n){var 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this._expand(r+1),this.words[r]=e?this.words[r]|1<<i:this.words[r]&~(1<<i),this.strip()},a.prototype.iadd=function(t){var e,r,n;if(0!==this.negative&&0===t.negative)return this.negative=0,e=this.isub(t),this.negative^=1,this._normSign();if(0===this.negative&&0!==t.negative)return t.negative=0,e=this.isub(t),t.negative=1,e._normSign();this.length>t.length?(r=this,n=t):(r=t,n=this);for(var i=0,a=0;a<n.length;a++)e=(0|r.words[a])+(0|n.words[a])+i,this.words[a]=67108863&e,i=e>>>26;for(;0!==i&&a<r.length;a++)e=(0|r.words[a])+i,this.words[a]=67108863&e,i=e>>>26;if(this.length=r.length,0!==i)this.words[this.length]=i,this.length++;else if(r!==this)for(;a<r.length;a++)this.words[a]=r.words[a];return this},a.prototype.add=function(t){var e;return 0!==t.negative&&0===this.negative?(t.negative=0,e=this.sub(t),t.negative^=1,e):0===t.negative&&0!==this.negative?(this.negative=0,e=t.sub(this),this.negative=1,e):this.length>t.length?this.clone().iadd(t):t.clone().iadd(this)},a.prototype.isub=function(t){if(0!==t.negative){t.negative=0;var e=this.iadd(t);return t.negative=1,e._normSign()}if(0!==this.negative)return this.negative=0,this.iadd(t),this.negative=1,this._normSign();var r,n,i=this.cmp(t);if(0===i)return this.negative=0,this.length=1,this.words[0]=0,this;i>0?(r=this,n=t):(r=t,n=this);for(var a=0,o=0;o<n.length;o++)a=(e=(0|r.words[o])-(0|n.words[o])+a)>>26,this.words[o]=67108863&e;for(;0!==a&&o<r.length;o++)a=(e=(0|r.words[o])+a)>>26,this.words[o]=67108863&e;if(0===a&&o<r.length&&r!==this)for(;o<r.length;o++)this.words[o]=r.words[o];return this.length=Math.max(this.length,o),r!==this&&(this.negative=1),this.strip()},a.prototype.sub=function(t){return this.clone().isub(t)};var d=function(t,e,r){var n,i,a,o=t.words,s=e.words,l=r.words,c=0,u=0|o[0],f=8191&u,h=u>>>13,p=0|o[1],d=8191&p,g=p>>>13,m=0|o[2],v=8191&m,y=m>>>13,x=0|o[3],b=8191&x,_=x>>>13,w=0|o[4],T=8191&w,k=w>>>13,A=0|o[5],M=8191&A,S=A>>>13,E=0|o[6],L=8191&E,C=E>>>13,P=0|o[7],I=8191&P,O=P>>>13,z=0|o[8],D=8191&z,R=z>>>13,F=0|o[9],B=8191&F,N=F>>>13,j=0|s[0],U=8191&j,V=j>>>13,H=0|s[1],q=8191&H,G=H>>>13,Y=0|s[2],W=8191&Y,X=Y>>>13,Z=0|s[3],J=8191&Z,K=Z>>>13,Q=0|s[4],$=8191&Q,tt=Q>>>13,et=0|s[5],rt=8191&et,nt=et>>>13,it=0|s[6],at=8191&it,ot=it>>>13,st=0|s[7],lt=8191&st,ct=st>>>13,ut=0|s[8],ft=8191&ut,ht=ut>>>13,pt=0|s[9],dt=8191&pt,gt=pt>>>13;r.negative=t.negative^e.negative,r.length=19;var mt=(c+(n=Math.imul(f,U))|0)+((8191&(i=(i=Math.imul(f,V))+Math.imul(h,U)|0))<<13)|0;c=((a=Math.imul(h,V))+(i>>>13)|0)+(mt>>>26)|0,mt&=67108863,n=Math.imul(d,U),i=(i=Math.imul(d,V))+Math.imul(g,U)|0,a=Math.imul(g,V);var vt=(c+(n=n+Math.imul(f,q)|0)|0)+((8191&(i=(i=i+Math.imul(f,G)|0)+Math.imul(h,q)|0))<<13)|0;c=((a=a+Math.imul(h,G)|0)+(i>>>13)|0)+(vt>>>26)|0,vt&=67108863,n=Math.imul(v,U),i=(i=Math.imul(v,V))+Math.imul(y,U)|0,a=Math.imul(y,V),n=n+Math.imul(d,q)|0,i=(i=i+Math.imul(d,G)|0)+Math.imul(g,q)|0,a=a+Math.imul(g,G)|0;var yt=(c+(n=n+Math.imul(f,W)|0)|0)+((8191&(i=(i=i+Math.imul(f,X)|0)+Math.imul(h,W)|0))<<13)|0;c=((a=a+Math.imul(h,X)|0)+(i>>>13)|0)+(yt>>>26)|0,yt&=67108863,n=Math.imul(b,U),i=(i=Math.imul(b,V))+Math.imul(_,U)|0,a=Math.imul(_,V),n=n+Math.imul(v,q)|0,i=(i=i+Math.imul(v,G)|0)+Math.imul(y,q)|0,a=a+Math.imul(y,G)|0,n=n+Math.imul(d,W)|0,i=(i=i+Math.imul(d,X)|0)+Math.imul(g,W)|0,a=a+Math.imul(g,X)|0;var xt=(c+(n=n+Math.imul(f,J)|0)|0)+((8191&(i=(i=i+Math.imul(f,K)|0)+Math.imul(h,J)|0))<<13)|0;c=((a=a+Math.imul(h,K)|0)+(i>>>13)|0)+(xt>>>26)|0,xt&=67108863,n=Math.imul(T,U),i=(i=Math.imul(T,V))+Math.imul(k,U)|0,a=Math.imul(k,V),n=n+Math.imul(b,q)|0,i=(i=i+Math.imul(b,G)|0)+Math.imul(_,q)|0,a=a+Math.imul(_,G)|0,n=n+Math.imul(v,W)|0,i=(i=i+Math.imul(v,X)|0)+Math.imul(y,W)|0,a=a+Math.imul(y,X)|0,n=n+Math.imul(d,J)|0,i=(i=i+Math.imul(d,K)|0)+Math.imul(g,J)|0,a=a+Math.imul(g,K)|0;var bt=(c+(n=n+Math.imul(f,$)|0)|0)+((8191&(i=(i=i+Math.imul(f,tt)|0)+Math.imul(h,$)|0))<<13)|0;c=((a=a+Math.imul(h,tt)|0)+(i>>>13)|0)+(bt>>>26)|0,bt&=67108863,n=Math.imul(M,U),i=(i=Math.imul(M,V))+Math.imul(S,U)|0,a=Math.imul(S,V),n=n+Math.imul(T,q)|0,i=(i=i+Math.imul(T,G)|0)+Math.imul(k,q)|0,a=a+Math.imul(k,G)|0,n=n+Math.imul(b,W)|0,i=(i=i+Math.imul(b,X)|0)+Math.imul(_,W)|0,a=a+Math.imul(_,X)|0,n=n+Math.imul(v,J)|0,i=(i=i+Math.imul(v,K)|0)+Math.imul(y,J)|0,a=a+Math.imul(y,K)|0,n=n+Math.imul(d,$)|0,i=(i=i+Math.imul(d,tt)|0)+Math.imul(g,$)|0,a=a+Math.imul(g,tt)|0;var _t=(c+(n=n+Math.imul(f,rt)|0)|0)+((8191&(i=(i=i+Math.imul(f,nt)|0)+Math.imul(h,rt)|0))<<13)|0;c=((a=a+Math.imul(h,nt)|0)+(i>>>13)|0)+(_t>>>26)|0,_t&=67108863,n=Math.imul(L,U),i=(i=Math.imul(L,V))+Math.imul(C,U)|0,a=Math.imul(C,V),n=n+Math.imul(M,q)|0,i=(i=i+Math.imul(M,G)|0)+Math.imul(S,q)|0,a=a+Math.imul(S,G)|0,n=n+Math.imul(T,W)|0,i=(i=i+Math.imul(T,X)|0)+Math.imul(k,W)|0,a=a+Math.imul(k,X)|0,n=n+Math.imul(b,J)|0,i=(i=i+Math.imul(b,K)|0)+Math.imul(_,J)|0,a=a+Math.imul(_,K)|0,n=n+Math.imul(v,$)|0,i=(i=i+Math.imul(v,tt)|0)+Math.imul(y,$)|0,a=a+Math.imul(y,tt)|0,n=n+Math.imul(d,rt)|0,i=(i=i+Math.imul(d,nt)|0)+Math.imul(g,rt)|0,a=a+Math.imul(g,nt)|0;var wt=(c+(n=n+Math.imul(f,at)|0)|0)+((8191&(i=(i=i+Math.imul(f,ot)|0)+Math.imul(h,at)|0))<<13)|0;c=((a=a+Math.imul(h,ot)|0)+(i>>>13)|0)+(wt>>>26)|0,wt&=67108863,n=Math.imul(I,U),i=(i=Math.imul(I,V))+Math.imul(O,U)|0,a=Math.imul(O,V),n=n+Math.imul(L,q)|0,i=(i=i+Math.imul(L,G)|0)+Math.imul(C,q)|0,a=a+Math.imul(C,G)|0,n=n+Math.imul(M,W)|0,i=(i=i+Math.imul(M,X)|0)+Math.imul(S,W)|0,a=a+Math.imul(S,X)|0,n=n+Math.imul(T,J)|0,i=(i=i+Math.imul(T,K)|0)+Math.imul(k,J)|0,a=a+Math.imul(k,K)|0,n=n+Math.imul(b,$)|0,i=(i=i+Math.imul(b,tt)|0)+Math.imul(_,$)|0,a=a+Math.imul(_,tt)|0,n=n+Math.imul(v,rt)|0,i=(i=i+Math.imul(v,nt)|0)+Math.imul(y,rt)|0,a=a+Math.imul(y,nt)|0,n=n+Math.imul(d,at)|0,i=(i=i+Math.imul(d,ot)|0)+Math.imul(g,at)|0,a=a+Math.imul(g,ot)|0;var Tt=(c+(n=n+Math.imul(f,lt)|0)|0)+((8191&(i=(i=i+Math.imul(f,ct)|0)+Math.imul(h,lt)|0))<<13)|0;c=((a=a+Math.imul(h,ct)|0)+(i>>>13)|0)+(Tt>>>26)|0,Tt&=67108863,n=Math.imul(D,U),i=(i=Math.imul(D,V))+Math.imul(R,U)|0,a=Math.imul(R,V),n=n+Math.imul(I,q)|0,i=(i=i+Math.imul(I,G)|0)+Math.imul(O,q)|0,a=a+Math.imul(O,G)|0,n=n+Math.imul(L,W)|0,i=(i=i+Math.imul(L,X)|0)+Math.imul(C,W)|0,a=a+Math.imul(C,X)|0,n=n+Math.imul(M,J)|0,i=(i=i+Math.imul(M,K)|0)+Math.imul(S,J)|0,a=a+Math.imul(S,K)|0,n=n+Math.imul(T,$)|0,i=(i=i+Math.imul(T,tt)|0)+Math.imul(k,$)|0,a=a+Math.imul(k,tt)|0,n=n+Math.imul(b,rt)|0,i=(i=i+Math.imul(b,nt)|0)+Math.imul(_,rt)|0,a=a+Math.imul(_,nt)|0,n=n+Math.imul(v,at)|0,i=(i=i+Math.imul(v,ot)|0)+Math.imul(y,at)|0,a=a+Math.imul(y,ot)|0,n=n+Math.imul(d,lt)|0,i=(i=i+Math.imul(d,ct)|0)+Math.imul(g,lt)|0,a=a+Math.imul(g,ct)|0;var kt=(c+(n=n+Math.imul(f,ft)|0)|0)+((8191&(i=(i=i+Math.imul(f,ht)|0)+Math.imul(h,ft)|0))<<13)|0;c=((a=a+Math.imul(h,ht)|0)+(i>>>13)|0)+(kt>>>26)|0,kt&=67108863,n=Math.imul(B,U),i=(i=Math.imul(B,V))+Math.imul(N,U)|0,a=Math.imul(N,V),n=n+Math.imul(D,q)|0,i=(i=i+Math.imul(D,G)|0)+Math.imul(R,q)|0,a=a+Math.imul(R,G)|0,n=n+Math.imul(I,W)|0,i=(i=i+Math.imul(I,X)|0)+Math.imul(O,W)|0,a=a+Math.imul(O,X)|0,n=n+Math.imul(L,J)|0,i=(i=i+Math.imul(L,K)|0)+Math.imul(C,J)|0,a=a+Math.imul(C,K)|0,n=n+Math.imul(M,$)|0,i=(i=i+Math.imul(M,tt)|0)+Math.imul(S,$)|0,a=a+Math.imul(S,tt)|0,n=n+Math.imul(T,rt)|0,i=(i=i+Math.imul(T,nt)|0)+Math.imul(k,rt)|0,a=a+Math.imul(k,nt)|0,n=n+Math.imul(b,at)|0,i=(i=i+Math.imul(b,ot)|0)+Math.imul(_,at)|0,a=a+Math.imul(_,ot)|0,n=n+Math.imul(v,lt)|0,i=(i=i+Math.imul(v,ct)|0)+Math.imul(y,lt)|0,a=a+Math.imul(y,ct)|0,n=n+Math.imul(d,ft)|0,i=(i=i+Math.imul(d,ht)|0)+Math.imul(g,ft)|0,a=a+Math.imul(g,ht)|0;var At=(c+(n=n+Math.imul(f,dt)|0)|0)+((8191&(i=(i=i+Math.imul(f,gt)|0)+Math.imul(h,dt)|0))<<13)|0;c=((a=a+Math.imul(h,gt)|0)+(i>>>13)|0)+(At>>>26)|0,At&=67108863,n=Math.imul(B,q),i=(i=Math.imul(B,G))+Math.imul(N,q)|0,a=Math.imul(N,G),n=n+Math.imul(D,W)|0,i=(i=i+Math.imul(D,X)|0)+Math.imul(R,W)|0,a=a+Math.imul(R,X)|0,n=n+Math.imul(I,J)|0,i=(i=i+Math.imul(I,K)|0)+Math.imul(O,J)|0,a=a+Math.imul(O,K)|0,n=n+Math.imul(L,$)|0,i=(i=i+Math.imul(L,tt)|0)+Math.imul(C,$)|0,a=a+Math.imul(C,tt)|0,n=n+Math.imul(M,rt)|0,i=(i=i+Math.imul(M,nt)|0)+Math.imul(S,rt)|0,a=a+Math.imul(S,nt)|0,n=n+Math.imul(T,at)|0,i=(i=i+Math.imul(T,ot)|0)+Math.imul(k,at)|0,a=a+Math.imul(k,ot)|0,n=n+Math.imul(b,lt)|0,i=(i=i+Math.imul(b,ct)|0)+Math.imul(_,lt)|0,a=a+Math.imul(_,ct)|0,n=n+Math.imul(v,ft)|0,i=(i=i+Math.imul(v,ht)|0)+Math.imul(y,ft)|0,a=a+Math.imul(y,ht)|0;var Mt=(c+(n=n+Math.imul(d,dt)|0)|0)+((8191&(i=(i=i+Math.imul(d,gt)|0)+Math.imul(g,dt)|0))<<13)|0;c=((a=a+Math.imul(g,gt)|0)+(i>>>13)|0)+(Mt>>>26)|0,Mt&=67108863,n=Math.imul(B,W),i=(i=Math.imul(B,X))+Math.imul(N,W)|0,a=Math.imul(N,X),n=n+Math.imul(D,J)|0,i=(i=i+Math.imul(D,K)|0)+Math.imul(R,J)|0,a=a+Math.imul(R,K)|0,n=n+Math.imul(I,$)|0,i=(i=i+Math.imul(I,tt)|0)+Math.imul(O,$)|0,a=a+Math.imul(O,tt)|0,n=n+Math.imul(L,rt)|0,i=(i=i+Math.imul(L,nt)|0)+Math.imul(C,rt)|0,a=a+Math.imul(C,nt)|0,n=n+Math.imul(M,at)|0,i=(i=i+Math.imul(M,ot)|0)+Math.imul(S,at)|0,a=a+Math.imul(S,ot)|0,n=n+Math.imul(T,lt)|0,i=(i=i+Math.imul(T,ct)|0)+Math.imul(k,lt)|0,a=a+Math.imul(k,ct)|0,n=n+Math.imul(b,ft)|0,i=(i=i+Math.imul(b,ht)|0)+Math.imul(_,ft)|0,a=a+Math.imul(_,ht)|0;var St=(c+(n=n+Math.imul(v,dt)|0)|0)+((8191&(i=(i=i+Math.imul(v,gt)|0)+Math.imul(y,dt)|0))<<13)|0;c=((a=a+Math.imul(y,gt)|0)+(i>>>13)|0)+(St>>>26)|0,St&=67108863,n=Math.imul(B,J),i=(i=Math.imul(B,K))+Math.imul(N,J)|0,a=Math.imul(N,K),n=n+Math.imul(D,$)|0,i=(i=i+Math.imul(D,tt)|0)+Math.imul(R,$)|0,a=a+Math.imul(R,tt)|0,n=n+Math.imul(I,rt)|0,i=(i=i+Math.imul(I,nt)|0)+Math.imul(O,rt)|0,a=a+Math.imul(O,nt)|0,n=n+Math.imul(L,at)|0,i=(i=i+Math.imul(L,ot)|0)+Math.imul(C,at)|0,a=a+Math.imul(C,ot)|0,n=n+Math.imul(M,lt)|0,i=(i=i+Math.imul(M,ct)|0)+Math.imul(S,lt)|0,a=a+Math.imul(S,ct)|0,n=n+Math.imul(T,ft)|0,i=(i=i+Math.imul(T,ht)|0)+Math.imul(k,ft)|0,a=a+Math.imul(k,ht)|0;var Et=(c+(n=n+Math.imul(b,dt)|0)|0)+((8191&(i=(i=i+Math.imul(b,gt)|0)+Math.imul(_,dt)|0))<<13)|0;c=((a=a+Math.imul(_,gt)|0)+(i>>>13)|0)+(Et>>>26)|0,Et&=67108863,n=Math.imul(B,$),i=(i=Math.imul(B,tt))+Math.imul(N,$)|0,a=Math.imul(N,tt),n=n+Math.imul(D,rt)|0,i=(i=i+Math.imul(D,nt)|0)+Math.imul(R,rt)|0,a=a+Math.imul(R,nt)|0,n=n+Math.imul(I,at)|0,i=(i=i+Math.imul(I,ot)|0)+Math.imul(O,at)|0,a=a+Math.imul(O,ot)|0,n=n+Math.imul(L,lt)|0,i=(i=i+Math.imul(L,ct)|0)+Math.imul(C,lt)|0,a=a+Math.imul(C,ct)|0,n=n+Math.imul(M,ft)|0,i=(i=i+Math.imul(M,ht)|0)+Math.imul(S,ft)|0,a=a+Math.imul(S,ht)|0;var Lt=(c+(n=n+Math.imul(T,dt)|0)|0)+((8191&(i=(i=i+Math.imul(T,gt)|0)+Math.imul(k,dt)|0))<<13)|0;c=((a=a+Math.imul(k,gt)|0)+(i>>>13)|0)+(Lt>>>26)|0,Lt&=67108863,n=Math.imul(B,rt),i=(i=Math.imul(B,nt))+Math.imul(N,rt)|0,a=Math.imul(N,nt),n=n+Math.imul(D,at)|0,i=(i=i+Math.imul(D,ot)|0)+Math.imul(R,at)|0,a=a+Math.imul(R,ot)|0,n=n+Math.imul(I,lt)|0,i=(i=i+Math.imul(I,ct)|0)+Math.imul(O,lt)|0,a=a+Math.imul(O,ct)|0,n=n+Math.imul(L,ft)|0,i=(i=i+Math.imul(L,ht)|0)+Math.imul(C,ft)|0,a=a+Math.imul(C,ht)|0;var Ct=(c+(n=n+Math.imul(M,dt)|0)|0)+((8191&(i=(i=i+Math.imul(M,gt)|0)+Math.imul(S,dt)|0))<<13)|0;c=((a=a+Math.imul(S,gt)|0)+(i>>>13)|0)+(Ct>>>26)|0,Ct&=67108863,n=Math.imul(B,at),i=(i=Math.imul(B,ot))+Math.imul(N,at)|0,a=Math.imul(N,ot),n=n+Math.imul(D,lt)|0,i=(i=i+Math.imul(D,ct)|0)+Math.imul(R,lt)|0,a=a+Math.imul(R,ct)|0,n=n+Math.imul(I,ft)|0,i=(i=i+Math.imul(I,ht)|0)+Math.imul(O,ft)|0,a=a+Math.imul(O,ht)|0;var Pt=(c+(n=n+Math.imul(L,dt)|0)|0)+((8191&(i=(i=i+Math.imul(L,gt)|0)+Math.imul(C,dt)|0))<<13)|0;c=((a=a+Math.imul(C,gt)|0)+(i>>>13)|0)+(Pt>>>26)|0,Pt&=67108863,n=Math.imul(B,lt),i=(i=Math.imul(B,ct))+Math.imul(N,lt)|0,a=Math.imul(N,ct),n=n+Math.imul(D,ft)|0,i=(i=i+Math.imul(D,ht)|0)+Math.imul(R,ft)|0,a=a+Math.imul(R,ht)|0;var It=(c+(n=n+Math.imul(I,dt)|0)|0)+((8191&(i=(i=i+Math.imul(I,gt)|0)+Math.imul(O,dt)|0))<<13)|0;c=((a=a+Math.imul(O,gt)|0)+(i>>>13)|0)+(It>>>26)|0,It&=67108863,n=Math.imul(B,ft),i=(i=Math.imul(B,ht))+Math.imul(N,ft)|0,a=Math.imul(N,ht);var Ot=(c+(n=n+Math.imul(D,dt)|0)|0)+((8191&(i=(i=i+Math.imul(D,gt)|0)+Math.imul(R,dt)|0))<<13)|0;c=((a=a+Math.imul(R,gt)|0)+(i>>>13)|0)+(Ot>>>26)|0,Ot&=67108863;var zt=(c+(n=Math.imul(B,dt))|0)+((8191&(i=(i=Math.imul(B,gt))+Math.imul(N,dt)|0))<<13)|0;return c=((a=Math.imul(N,gt))+(i>>>13)|0)+(zt>>>26)|0,zt&=67108863,l[0]=mt,l[1]=vt,l[2]=yt,l[3]=xt,l[4]=bt,l[5]=_t,l[6]=wt,l[7]=Tt,l[8]=kt,l[9]=At,l[10]=Mt,l[11]=St,l[12]=Et,l[13]=Lt,l[14]=Ct,l[15]=Pt,l[16]=It,l[17]=Ot,l[18]=zt,0!==c&&(l[19]=c,r.length++),r};function g(t,e,r){return(new m).mulp(t,e,r)}function m(t,e){this.x=t,this.y=e}Math.imul||(d=p),a.prototype.mulTo=function(t,e){var r=this.length+t.length;return 10===this.length&&10===t.length?d(this,t,e):r<63?p(this,t,e):r<1024?function(t,e,r){r.negative=e.negative^t.negative,r.length=t.length+e.length;for(var n=0,i=0,a=0;a<r.length-1;a++){var o=i;i=0;for(var s=67108863&n,l=Math.min(a,e.length-1),c=Math.max(0,a-t.length+1);c<=l;c++){var u=a-c,f=(0|t.words[u])*(0|e.words[c]),h=67108863&f;s=67108863&(h=h+s|0),i+=(o=(o=o+(f/67108864|0)|0)+(h>>>26)|0)>>>26,o&=67108863}r.words[a]=s,n=o,o=i}return 0!==n?r.words[a]=n:r.length--,r.strip()}(this,t,e):g(this,t,e)},m.prototype.makeRBT=function(t){for(var e=new Array(t),r=a.prototype._countBits(t)-1,n=0;n<t;n++)e[n]=this.revBin(n,r,t);return e},m.prototype.revBin=function(t,e,r){if(0===t||t===r-1)return t;for(var n=0,i=0;i<e;i++)n|=(1&t)<<e-i-1,t>>=1;return n},m.prototype.permute=function(t,e,r,n,i,a){for(var o=0;o<a;o++)n[o]=e[t[o]],i[o]=r[t[o]]},m.prototype.transform=function(t,e,r,n,i,a){this.permute(a,t,e,r,n,i);for(var o=1;o<i;o<<=1)for(var s=o<<1,l=Math.cos(2*Math.PI/s),c=Math.sin(2*Math.PI/s),u=0;u<i;u+=s)for(var f=l,h=c,p=0;p<o;p++){var d=r[u+p],g=n[u+p],m=r[u+p+o],v=n[u+p+o],y=f*m-h*v;v=f*v+h*m,m=y,r[u+p]=d+m,n[u+p]=g+v,r[u+p+o]=d-m,n[u+p+o]=g-v,p!==s&&(y=l*f-c*h,h=l*h+c*f,f=y)}},m.prototype.guessLen13b=function(t,e){var r=1|Math.max(e,t),n=1&r,i=0;for(r=r/2|0;r;r>>>=1)i++;return 1<<i+1+n},m.prototype.conjugate=function(t,e,r){if(!(r<=1))for(var n=0;n<r/2;n++){var i=t[n];t[n]=t[r-n-1],t[r-n-1]=i,i=e[n],e[n]=-e[r-n-1],e[r-n-1]=-i}},m.prototype.normalize13b=function(t,e){for(var r=0,n=0;n<e/2;n++){var i=8192*Math.round(t[2*n+1]/e)+Math.round(t[2*n]/e)+r;t[n]=67108863&i,r=i<67108864?0:i/67108864|0}return t},m.prototype.convert13b=function(t,e,r,i){for(var a=0,o=0;o<e;o++)a+=0|t[o],r[2*o]=8191&a,a>>>=13,r[2*o+1]=8191&a,a>>>=13;for(o=2*e;o<i;++o)r[o]=0;n(0===a),n(0==(-8192&a))},m.prototype.stub=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=0;return e},m.prototype.mulp=function(t,e,r){var n=2*this.guessLen13b(t.length,e.length),i=this.makeRBT(n),a=this.stub(n),o=new Array(n),s=new Array(n),l=new Array(n),c=new Array(n),u=new Array(n),f=new Array(n),h=r.words;h.length=n,this.convert13b(t.words,t.length,o,n),this.convert13b(e.words,e.length,c,n),this.transform(o,a,s,l,n,i),this.transform(c,a,u,f,n,i);for(var p=0;p<n;p++){var d=s[p]*u[p]-l[p]*f[p];l[p]=s[p]*f[p]+l[p]*u[p],s[p]=d}return this.conjugate(s,l,n),this.transform(s,l,h,a,n,i),this.conjugate(h,a,n),this.normalize13b(h,n),r.negative=t.negative^e.negative,r.length=t.length+e.length,r.strip()},a.prototype.mul=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),this.mulTo(t,e)},a.prototype.mulf=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),g(this,t,e)},a.prototype.imul=function(t){return this.clone().mulTo(t,this)},a.prototype.imuln=function(t){n(\"number\"==typeof t),n(t<67108864);for(var e=0,r=0;r<this.length;r++){var i=(0|this.words[r])*t,a=(67108863&i)+(67108863&e);e>>=26,e+=i/67108864|0,e+=a>>>26,this.words[r]=67108863&a}return 0!==e&&(this.words[r]=e,this.length++),this},a.prototype.muln=function(t){return this.clone().imuln(t)},a.prototype.sqr=function(){return this.mul(this)},a.prototype.isqr=function(){return this.imul(this.clone())},a.prototype.pow=function(t){var e=function(t){for(var e=new Array(t.bitLength()),r=0;r<e.length;r++){var n=r/26|0,i=r%26;e[r]=(t.words[n]&1<<i)>>>i}return e}(t);if(0===e.length)return new a(1);for(var r=this,n=0;n<e.length&&0===e[n];n++,r=r.sqr());if(++n<e.length)for(var i=r.sqr();n<e.length;n++,i=i.sqr())0!==e[n]&&(r=r.mul(i));return r},a.prototype.iushln=function(t){n(\"number\"==typeof t&&t>=0);var e,r=t%26,i=(t-r)/26,a=67108863>>>26-r<<26-r;if(0!==r){var o=0;for(e=0;e<this.length;e++){var s=this.words[e]&a,l=(0|this.words[e])-s<<r;this.words[e]=l|o,o=s>>>26-r}o&&(this.words[e]=o,this.length++)}if(0!==i){for(e=this.length-1;e>=0;e--)this.words[e+i]=this.words[e];for(e=0;e<i;e++)this.words[e]=0;this.length+=i}return this.strip()},a.prototype.ishln=function(t){return n(0===this.negative),this.iushln(t)},a.prototype.iushrn=function(t,e,r){var i;n(\"number\"==typeof t&&t>=0),i=e?(e-e%26)/26:0;var a=t%26,o=Math.min((t-a)/26,this.length),s=67108863^67108863>>>a<<a,l=r;if(i-=o,i=Math.max(0,i),l){for(var c=0;c<o;c++)l.words[c]=this.words[c];l.length=o}if(0===o);else if(this.length>o)for(this.length-=o,c=0;c<this.length;c++)this.words[c]=this.words[c+o];else this.words[0]=0,this.length=1;var u=0;for(c=this.length-1;c>=0&&(0!==u||c>=i);c--){var f=0|this.words[c];this.words[c]=u<<26-a|f>>>a,u=f&s}return l&&0!==u&&(l.words[l.length++]=u),0===this.length&&(this.words[0]=0,this.length=1),this.strip()},a.prototype.ishrn=function(t,e,r){return n(0===this.negative),this.iushrn(t,e,r)},a.prototype.shln=function(t){return this.clone().ishln(t)},a.prototype.ushln=function(t){return this.clone().iushln(t)},a.prototype.shrn=function(t){return this.clone().ishrn(t)},a.prototype.ushrn=function(t){return this.clone().iushrn(t)},a.prototype.testn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26,i=1<<e;return!(this.length<=r)&&!!(this.words[r]&i)},a.prototype.imaskn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26;if(n(0===this.negative,\"imaskn works only with positive numbers\"),this.length<=r)return this;if(0!==e&&r++,this.length=Math.min(r,this.length),0!==e){var i=67108863^67108863>>>e<<e;this.words[this.length-1]&=i}return this.strip()},a.prototype.maskn=function(t){return this.clone().imaskn(t)},a.prototype.iaddn=function(t){return n(\"number\"==typeof t),n(t<67108864),t<0?this.isubn(-t):0!==this.negative?1===this.length&&(0|this.words[0])<t?(this.words[0]=t-(0|this.words[0]),this.negative=0,this):(this.negative=0,this.isubn(t),this.negative=1,this):this._iaddn(t)},a.prototype._iaddn=function(t){this.words[0]+=t;for(var e=0;e<this.length&&this.words[e]>=67108864;e++)this.words[e]-=67108864,e===this.length-1?this.words[e+1]=1:this.words[e+1]++;return this.length=Math.max(this.length,e+1),this},a.prototype.isubn=function(t){if(n(\"number\"==typeof t),n(t<67108864),t<0)return this.iaddn(-t);if(0!==this.negative)return this.negative=0,this.iaddn(t),this.negative=1,this;if(this.words[0]-=t,1===this.length&&this.words[0]<0)this.words[0]=-this.words[0],this.negative=1;else for(var e=0;e<this.length&&this.words[e]<0;e++)this.words[e]+=67108864,this.words[e+1]-=1;return this.strip()},a.prototype.addn=function(t){return this.clone().iaddn(t)},a.prototype.subn=function(t){return this.clone().isubn(t)},a.prototype.iabs=function(){return this.negative=0,this},a.prototype.abs=function(){return this.clone().iabs()},a.prototype._ishlnsubmul=function(t,e,r){var i,a,o=t.length+r;this._expand(o);var s=0;for(i=0;i<t.length;i++){a=(0|this.words[i+r])+s;var l=(0|t.words[i])*e;s=((a-=67108863&l)>>26)-(l/67108864|0),this.words[i+r]=67108863&a}for(;i<this.length-r;i++)s=(a=(0|this.words[i+r])+s)>>26,this.words[i+r]=67108863&a;if(0===s)return this.strip();for(n(-1===s),s=0,i=0;i<this.length;i++)s=(a=-(0|this.words[i])+s)>>26,this.words[i]=67108863&a;return this.negative=1,this.strip()},a.prototype._wordDiv=function(t,e){var r=(this.length,t.length),n=this.clone(),i=t,o=0|i.words[i.length-1];0!==(r=26-this._countBits(o))&&(i=i.ushln(r),n.iushln(r),o=0|i.words[i.length-1]);var s,l=n.length-i.length;if(\"mod\"!==e){(s=new a(null)).length=l+1,s.words=new Array(s.length);for(var c=0;c<s.length;c++)s.words[c]=0}var u=n.clone()._ishlnsubmul(i,1,l);0===u.negative&&(n=u,s&&(s.words[l]=1));for(var f=l-1;f>=0;f--){var h=67108864*(0|n.words[i.length+f])+(0|n.words[i.length+f-1]);for(h=Math.min(h/o|0,67108863),n._ishlnsubmul(i,h,f);0!==n.negative;)h--,n.negative=0,n._ishlnsubmul(i,1,f),n.isZero()||(n.negative^=1);s&&(s.words[f]=h)}return s&&s.strip(),n.strip(),\"div\"!==e&&0!==r&&n.iushrn(r),{div:s||null,mod:n}},a.prototype.divmod=function(t,e,r){return n(!t.isZero()),this.isZero()?{div:new a(0),mod:new a(0)}:0!==this.negative&&0===t.negative?(s=this.neg().divmod(t,e),\"mod\"!==e&&(i=s.div.neg()),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.iadd(t)),{div:i,mod:o}):0===this.negative&&0!==t.negative?(s=this.divmod(t.neg(),e),\"mod\"!==e&&(i=s.div.neg()),{div:i,mod:s.mod}):0!=(this.negative&t.negative)?(s=this.neg().divmod(t.neg(),e),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.isub(t)),{div:s.div,mod:o}):t.length>this.length||this.cmp(t)<0?{div:new a(0),mod:this}:1===t.length?\"div\"===e?{div:this.divn(t.words[0]),mod:null}:\"mod\"===e?{div:null,mod:new a(this.modn(t.words[0]))}:{div:this.divn(t.words[0]),mod:new a(this.modn(t.words[0]))}:this._wordDiv(t,e);var i,o,s},a.prototype.div=function(t){return this.divmod(t,\"div\",!1).div},a.prototype.mod=function(t){return this.divmod(t,\"mod\",!1).mod},a.prototype.umod=function(t){return this.divmod(t,\"mod\",!0).mod},a.prototype.divRound=function(t){var e=this.divmod(t);if(e.mod.isZero())return e.div;var r=0!==e.div.negative?e.mod.isub(t):e.mod,n=t.ushrn(1),i=t.andln(1),a=r.cmp(n);return a<0||1===i&&0===a?e.div:0!==e.div.negative?e.div.isubn(1):e.div.iaddn(1)},a.prototype.modn=function(t){n(t<=67108863);for(var e=(1<<26)%t,r=0,i=this.length-1;i>=0;i--)r=(e*r+(0|this.words[i]))%t;return r},a.prototype.idivn=function(t){n(t<=67108863);for(var e=0,r=this.length-1;r>=0;r--){var i=(0|this.words[r])+67108864*e;this.words[r]=i/t|0,e=i%t}return this.strip()},a.prototype.divn=function(t){return this.clone().idivn(t)},a.prototype.egcd=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i=new a(1),o=new a(0),s=new a(0),l=new a(1),c=0;e.isEven()&&r.isEven();)e.iushrn(1),r.iushrn(1),++c;for(var u=r.clone(),f=e.clone();!e.isZero();){for(var h=0,p=1;0==(e.words[0]&p)&&h<26;++h,p<<=1);if(h>0)for(e.iushrn(h);h-- >0;)(i.isOdd()||o.isOdd())&&(i.iadd(u),o.isub(f)),i.iushrn(1),o.iushrn(1);for(var d=0,g=1;0==(r.words[0]&g)&&d<26;++d,g<<=1);if(d>0)for(r.iushrn(d);d-- >0;)(s.isOdd()||l.isOdd())&&(s.iadd(u),l.isub(f)),s.iushrn(1),l.iushrn(1);e.cmp(r)>=0?(e.isub(r),i.isub(s),o.isub(l)):(r.isub(e),s.isub(i),l.isub(o))}return{a:s,b:l,gcd:r.iushln(c)}},a.prototype._invmp=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i,o=new a(1),s=new a(0),l=r.clone();e.cmpn(1)>0&&r.cmpn(1)>0;){for(var c=0,u=1;0==(e.words[0]&u)&&c<26;++c,u<<=1);if(c>0)for(e.iushrn(c);c-- >0;)o.isOdd()&&o.iadd(l),o.iushrn(1);for(var f=0,h=1;0==(r.words[0]&h)&&f<26;++f,h<<=1);if(f>0)for(r.iushrn(f);f-- >0;)s.isOdd()&&s.iadd(l),s.iushrn(1);e.cmp(r)>=0?(e.isub(r),o.isub(s)):(r.isub(e),s.isub(o))}return(i=0===e.cmpn(1)?o:s).cmpn(0)<0&&i.iadd(t),i},a.prototype.gcd=function(t){if(this.isZero())return t.abs();if(t.isZero())return this.abs();var e=this.clone(),r=t.clone();e.negative=0,r.negative=0;for(var n=0;e.isEven()&&r.isEven();n++)e.iushrn(1),r.iushrn(1);for(;;){for(;e.isEven();)e.iushrn(1);for(;r.isEven();)r.iushrn(1);var i=e.cmp(r);if(i<0){var a=e;e=r,r=a}else if(0===i||0===r.cmpn(1))break;e.isub(r)}return r.iushln(n)},a.prototype.invm=function(t){return this.egcd(t).a.umod(t)},a.prototype.isEven=function(){return 0==(1&this.words[0])},a.prototype.isOdd=function(){return 1==(1&this.words[0])},a.prototype.andln=function(t){return this.words[0]&t},a.prototype.bincn=function(t){n(\"number\"==typeof t);var e=t%26,r=(t-e)/26,i=1<<e;if(this.length<=r)return this._expand(r+1),this.words[r]|=i,this;for(var a=i,o=r;0!==a&&o<this.length;o++){var s=0|this.words[o];a=(s+=a)>>>26,s&=67108863,this.words[o]=s}return 0!==a&&(this.words[o]=a,this.length++),this},a.prototype.isZero=function(){return 1===this.length&&0===this.words[0]},a.prototype.cmpn=function(t){var e,r=t<0;if(0!==this.negative&&!r)return-1;if(0===this.negative&&r)return 1;if(this.strip(),this.length>1)e=1;else{r&&(t=-t),n(t<=67108863,\"Number is too big\");var i=0|this.words[0];e=i===t?0:i<t?-1:1}return 0!==this.negative?0|-e:e},a.prototype.cmp=function(t){if(0!==this.negative&&0===t.negative)return-1;if(0===this.negative&&0!==t.negative)return 1;var e=this.ucmp(t);return 0!==this.negative?0|-e:e},a.prototype.ucmp=function(t){if(this.length>t.length)return 1;if(this.length<t.length)return-1;for(var e=0,r=this.length-1;r>=0;r--){var n=0|this.words[r],i=0|t.words[r];if(n!==i){n<i?e=-1:n>i&&(e=1);break}}return 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fffffffe fffffc2f\")}function b(){y.call(this,\"p224\",\"ffffffff ffffffff ffffffff ffffffff 00000000 00000000 00000001\")}function _(){y.call(this,\"p192\",\"ffffffff ffffffff ffffffff fffffffe ffffffff ffffffff\")}function w(){y.call(this,\"25519\",\"7fffffffffffffff ffffffffffffffff ffffffffffffffff ffffffffffffffed\")}function T(t){if(\"string\"==typeof t){var e=a._prime(t);this.m=e.p,this.prime=e}else n(t.gtn(1),\"modulus must be greater than 1\"),this.m=t,this.prime=null}function k(t){T.call(this,t),this.shift=this.m.bitLength(),this.shift%26!=0&&(this.shift+=26-this.shift%26),this.r=new a(1).iushln(this.shift),this.r2=this.imod(this.r.sqr()),this.rinv=this.r._invmp(this.m),this.minv=this.rinv.mul(this.r).isubn(1).div(this.m),this.minv=this.minv.umod(this.r),this.minv=this.r.sub(this.minv)}y.prototype._tmp=function(){var t=new a(null);return t.words=new Array(Math.ceil(this.n/13)),t},y.prototype.ireduce=function(t){var e,r=t;do{this.split(r,this.tmp),e=(r=(r=this.imulK(r)).iadd(this.tmp)).bitLength()}while(e>this.n);var n=e<this.n?-1:r.ucmp(this.p);return 0===n?(r.words[0]=0,r.length=1):n>0?r.isub(this.p):void 0!==r.strip?r.strip():r._strip(),r},y.prototype.split=function(t,e){t.iushrn(this.n,0,e)},y.prototype.imulK=function(t){return t.imul(this.k)},i(x,y),x.prototype.split=function(t,e){for(var r=Math.min(t.length,9),n=0;n<r;n++)e.words[n]=t.words[n];if(e.length=r,t.length<=9)return t.words[0]=0,void(t.length=1);var i=t.words[9];for(e.words[e.length++]=4194303&i,n=10;n<t.length;n++){var a=0|t.words[n];t.words[n-10]=(4194303&a)<<4|i>>>22,i=a}i>>>=22,t.words[n-10]=i,0===i&&t.length>10?t.length-=10:t.length-=9},x.prototype.imulK=function(t){t.words[t.length]=0,t.words[t.length+1]=0,t.length+=2;for(var e=0,r=0;r<t.length;r++){var n=0|t.words[r];e+=977*n,t.words[r]=67108863&e,e=64*n+(e/67108864|0)}return 0===t.words[t.length-1]&&(t.length--,0===t.words[t.length-1]&&t.length--),t},i(b,y),i(_,y),i(w,y),w.prototype.imulK=function(t){for(var e=0,r=0;r<t.length;r++){var n=19*(0|t.words[r])+e,i=67108863&n;n>>>=26,t.words[r]=i,e=n}return 0!==e&&(t.words[t.length++]=e),t},a._prime=function(t){if(v[t])return v[t];var e;if(\"k256\"===t)e=new x;else if(\"p224\"===t)e=new b;else if(\"p192\"===t)e=new _;else{if(\"p25519\"!==t)throw new Error(\"Unknown prime \"+t);e=new w}return v[t]=e,e},T.prototype._verify1=function(t){n(0===t.negative,\"red works only with positives\"),n(t.red,\"red works only with red numbers\")},T.prototype._verify2=function(t,e){n(0==(t.negative|e.negative),\"red works only with positives\"),n(t.red&&t.red===e.red,\"red works only with red numbers\")},T.prototype.imod=function(t){return this.prime?this.prime.ireduce(t)._forceRed(this):t.umod(this.m)._forceRed(this)},T.prototype.neg=function(t){return t.isZero()?t.clone():this.m.sub(t)._forceRed(this)},T.prototype.add=function(t,e){this._verify2(t,e);var r=t.add(e);return r.cmp(this.m)>=0&&r.isub(this.m),r._forceRed(this)},T.prototype.iadd=function(t,e){this._verify2(t,e);var r=t.iadd(e);return r.cmp(this.m)>=0&&r.isub(this.m),r},T.prototype.sub=function(t,e){this._verify2(t,e);var r=t.sub(e);return r.cmpn(0)<0&&r.iadd(this.m),r._forceRed(this)},T.prototype.isub=function(t,e){this._verify2(t,e);var r=t.isub(e);return r.cmpn(0)<0&&r.iadd(this.m),r},T.prototype.shl=function(t,e){return this._verify1(t),this.imod(t.ushln(e))},T.prototype.imul=function(t,e){return this._verify2(t,e),this.imod(t.imul(e))},T.prototype.mul=function(t,e){return this._verify2(t,e),this.imod(t.mul(e))},T.prototype.isqr=function(t){return this.imul(t,t.clone())},T.prototype.sqr=function(t){return this.mul(t,t)},T.prototype.sqrt=function(t){if(t.isZero())return t.clone();var e=this.m.andln(3);if(n(e%2==1),3===e){var r=this.m.add(new a(1)).iushrn(2);return this.pow(t,r)}for(var i=this.m.subn(1),o=0;!i.isZero()&&0===i.andln(1);)o++,i.iushrn(1);n(!i.isZero());var s=new a(1).toRed(this),l=s.redNeg(),c=this.m.subn(1).iushrn(1),u=this.m.bitLength();for(u=new a(2*u*u).toRed(this);0!==this.pow(u,c).cmp(l);)u.redIAdd(l);for(var f=this.pow(u,i),h=this.pow(t,i.addn(1).iushrn(1)),p=this.pow(t,i),d=o;0!==p.cmp(s);){for(var g=p,m=0;0!==g.cmp(s);m++)g=g.redSqr();n(m<d);var v=this.pow(f,new a(1).iushln(d-m-1));h=h.redMul(v),f=v.redSqr(),p=p.redMul(f),d=m}return h},T.prototype.invm=function(t){var e=t._invmp(this.m);return 0!==e.negative?(e.negative=0,this.imod(e).redNeg()):this.imod(e)},T.prototype.pow=function(t,e){if(e.isZero())return new a(1).toRed(this);if(0===e.cmpn(1))return t.clone();var r=new Array(16);r[0]=new a(1).toRed(this),r[1]=t;for(var n=2;n<r.length;n++)r[n]=this.mul(r[n-1],t);var i=r[0],o=0,s=0,l=e.bitLength()%26;for(0===l&&(l=26),n=e.length-1;n>=0;n--){for(var c=e.words[n],u=l-1;u>=0;u--){var f=c>>u&1;i!==r[0]&&(i=this.sqr(i)),0!==f||0!==o?(o<<=1,o|=f,(4===++s||0===n&&0===u)&&(i=this.mul(i,r[o]),s=0,o=0)):s=0}l=26}return i},T.prototype.convertTo=function(t){var e=t.umod(this.m);return e===t?e.clone():e},T.prototype.convertFrom=function(t){var e=t.clone();return e.red=null,e},a.mont=function(t){return new k(t)},i(k,T),k.prototype.convertTo=function(t){return this.imod(t.ushln(this.shift))},k.prototype.convertFrom=function(t){var e=this.imod(t.mul(this.rinv));return e.red=null,e},k.prototype.imul=function(t,e){if(t.isZero()||e.isZero())return t.words[0]=0,t.length=1,t;var r=t.imul(e),n=r.maskn(this.shift).mul(this.minv).imaskn(this.shift).mul(this.m),i=r.isub(n).iushrn(this.shift),a=i;return i.cmp(this.m)>=0?a=i.isub(this.m):i.cmpn(0)<0&&(a=i.iadd(this.m)),a._forceRed(this)},k.prototype.mul=function(t,e){if(t.isZero()||e.isZero())return new a(0)._forceRed(this);var r=t.mul(e),n=r.maskn(this.shift).mul(this.minv).imaskn(this.shift).mul(this.m),i=r.isub(n).iushrn(this.shift),o=i;return i.cmp(this.m)>=0?o=i.isub(this.m):i.cmpn(0)<0&&(o=i.iadd(this.m)),o._forceRed(this)},k.prototype.invm=function(t){return this.imod(t._invmp(this.m).mul(this.r2))._forceRed(this)}}(void 0===e||e,this)},{buffer:2}],34:[function(t,e,r){\"use strict\";e.exports=function(t){var e,r,n,i=t.length,a=0;for(e=0;e<i;++e)a+=t[e].length;var o=new Array(a),s=0;for(e=0;e<i;++e){var l=t[e],c=l.length;for(r=0;r<c;++r){var u=o[s++]=new Array(c-1),f=0;for(n=0;n<c;++n)n!==r&&(u[f++]=l[n]);if(1&r){var h=u[1];u[1]=u[0],u[0]=h}}}return o}},{}],35:[function(t,e,r){\"use strict\";e.exports=function(t,e,r){switch(arguments.length){case 1:return f(t);case 2:return\"function\"==typeof e?c(t,t,e,!0):h(t,e);case 3:return c(t,e,r,!1);default:throw new Error(\"box-intersect: Invalid arguments\")}};var n,i=t(\"typedarray-pool\"),a=t(\"./lib/sweep\"),o=t(\"./lib/intersect\");function s(t,e){for(var r=0;r<t;++r)if(!(e[r]<=e[r+t]))return!0;return!1}function l(t,e,r,n){for(var i=0,a=0,o=0,l=t.length;o<l;++o){var c=t[o];if(!s(e,c)){for(var u=0;u<2*e;++u)r[i++]=c[u];n[a++]=o}}return a}function c(t,e,r,n){var s=t.length,c=e.length;if(!(s<=0||c<=0)){var u=t[0].length>>>1;if(!(u<=0)){var f,h=i.mallocDouble(2*u*s),p=i.mallocInt32(s);if((s=l(t,u,h,p))>0){if(1===u&&n)a.init(s),f=a.sweepComplete(u,r,0,s,h,p,0,s,h,p);else{var d=i.mallocDouble(2*u*c),g=i.mallocInt32(c);(c=l(e,u,d,g))>0&&(a.init(s+c),f=1===u?a.sweepBipartite(u,r,0,s,h,p,0,c,d,g):o(u,r,n,s,h,p,c,d,g),i.free(d),i.free(g))}i.free(h),i.free(p)}return f}}}function u(t,e){n.push([t,e])}function f(t){return n=[],c(t,t,u,!0),n}function h(t,e){return n=[],c(t,e,u,!1),n}},{\"./lib/intersect\":37,\"./lib/sweep\":41,\"typedarray-pool\":308}],36:[function(t,e,r){\"use strict\";function n(t){return t?function(t,e,r,n,i,a,o,s,l,c,u){return i-n>l-s?function(t,e,r,n,i,a,o,s,l,c,u){for(var f=2*t,h=n,p=f*n;h<i;++h,p+=f){var d=a[e+p],g=a[e+p+t],m=o[h];t:for(var v=s,y=f*s;v<l;++v,y+=f){var x=c[e+y],b=c[e+y+t],_=u[v];if(!(b<d||g<x)){for(var w=e+1;w<t;++w){var T=a[w+p],k=a[w+t+p],A=c[w+y],M=c[w+t+y];if(k<A||M<T)continue t}var S=r(m,_);if(void 0!==S)return S}}}}(t,e,r,n,i,a,o,s,l,c,u):function(t,e,r,n,i,a,o,s,l,c,u){for(var f=2*t,h=s,p=f*s;h<l;++h,p+=f){var d=c[e+p],g=c[e+p+t],m=u[h];t:for(var v=n,y=f*n;v<i;++v,y+=f){var x=a[e+y],b=a[e+y+t],_=o[v];if(!(g<x||b<d)){for(var w=e+1;w<t;++w){var T=a[w+y],k=a[w+t+y],A=c[w+p],M=c[w+t+p];if(k<A||M<T)continue t}var S=r(_,m);if(void 0!==S)return S}}}}(t,e,r,n,i,a,o,s,l,c,u)}:function(t,e,r,n,i,a,o,s,l,c,u,f){return a-i>c-l?n?function(t,e,r,n,i,a,o,s,l,c,u){for(var f=2*t,h=n,p=f*n;h<i;++h,p+=f){var d=a[e+p],g=a[e+p+t],m=o[h];t:for(var v=s,y=f*s;v<l;++v,y+=f){var x=c[e+y],b=u[v];if(!(x<=d||g<x)){for(var _=e+1;_<t;++_){var w=a[_+p],T=a[_+t+p],k=c[_+y],A=c[_+t+y];if(T<k||A<w)continue t}var M=r(b,m);if(void 0!==M)return 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e=i.nextPow2(t);o.length<e&&(n.free(o),o=n.mallocInt32(e));s.length<e&&(n.free(s),s=n.mallocInt32(e));l.length<e&&(n.free(l),l=n.mallocInt32(e));c.length<e&&(n.free(c),c=n.mallocInt32(e));u.length<e&&(n.free(u),u=n.mallocInt32(e));f.length<e&&(n.free(f),f=n.mallocInt32(e));var r=8*e;h.length<r&&(n.free(h),h=n.mallocDouble(r))},sweepBipartite:function(t,e,r,n,i,u,f,g,m,v){for(var y=0,x=2*t,b=t-1,_=x-1,w=r;w<n;++w){var T=u[w],k=x*w;h[y++]=i[k+b],h[y++]=-(T+1),h[y++]=i[k+_],h[y++]=T}for(w=f;w<g;++w){T=v[w]+(1<<28);var A=x*w;h[y++]=m[A+b],h[y++]=-T,h[y++]=m[A+_],h[y++]=T}var M=y>>>1;a(h,M);var S=0,E=0;for(w=0;w<M;++w){var L=0|h[2*w+1];if(L>=1<<28)p(l,c,E--,L=L-(1<<28)|0);else if(L>=0)p(o,s,S--,L);else if(L<=-(1<<28)){L=-L-(1<<28)|0;for(var C=0;C<S;++C){if(void 0!==(P=e(o[C],L)))return P}d(l,c,E++,L)}else{L=-L-1|0;for(C=0;C<E;++C){var P;if(void 0!==(P=e(L,l[C])))return P}d(o,s,S++,L)}}},sweepComplete:function(t,e,r,n,i,g,m,v,y,x){for(var b=0,_=2*t,w=t-1,T=_-1,k=r;k<n;++k){var 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e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a<=0&&(i=n.length),r=a<=0?r.left:r.right}return n.length=i,new h(this,n)},l.gt=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a<0&&(i=n.length),r=a<0?r.left:r.right}return n.length=i,new h(this,n)},l.lt=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a>0&&(i=n.length),r=a<=0?r.left:r.right}return n.length=i,new h(this,n)},l.le=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a>=0&&(i=n.length),r=a<0?r.left:r.right}return n.length=i,new h(this,n)},l.find=function(t){for(var e=this._compare,r=this.root,n=[];r;){var i=e(t,r.key);if(n.push(r),0===i)return new h(this,n);r=i<=0?r.left:r.right}return new h(this,[])},l.remove=function(t){var e=this.find(t);return e?e.remove():this},l.get=function(t){for(var e=this._compare,r=this.root;r;){var n=e(t,r.key);if(0===n)return r.value;r=n<=0?r.left:r.right}};var p=h.prototype;function d(t,e){t.key=e.key,t.value=e.value,t.left=e.left,t.right=e.right,t._color=e._color,t._count=e._count}function g(t,e){return t<e?-1:t>e?1:0}Object.defineProperty(p,\"valid\",{get:function(){return this._stack.length>0}}),Object.defineProperty(p,\"node\",{get:function(){return this._stack.length>0?this._stack[this._stack.length-1]:null},enumerable:!0}),p.clone=function(){return new h(this.tree,this._stack.slice())},p.remove=function(){var t=this._stack;if(0===t.length)return this.tree;var e=new Array(t.length),r=t[t.length-1];e[e.length-1]=new n(r._color,r.key,r.value,r.left,r.right,r._count);for(var l=t.length-2;l>=0;--l){(r=t[l]).left===t[l+1]?e[l]=new n(r._color,r.key,r.value,e[l+1],r.right,r._count):e[l]=new n(r._color,r.key,r.value,r.left,e[l+1],r._count)}if((r=e[e.length-1]).left&&r.right){var c=e.length;for(r=r.left;r.right;)e.push(r),r=r.right;var u=e[c-1];e.push(new n(r._color,u.key,u.value,r.left,r.right,r._count)),e[c-1].key=r.key,e[c-1].value=r.value;for(l=e.length-2;l>=c;--l)r=e[l],e[l]=new n(r._color,r.key,r.value,r.left,e[l+1],r._count);e[c-1].left=e[c]}if(0===(r=e[e.length-1])._color){var f=e[e.length-2];f.left===r?f.left=null:f.right===r&&(f.right=null),e.pop();for(l=0;l<e.length;++l)e[l]._count--;return new s(this.tree._compare,e[0])}if(r.left||r.right){r.left?d(r,r.left):r.right&&d(r,r.right),r._color=1;for(l=0;l<e.length-1;++l)e[l]._count--;return new s(this.tree._compare,e[0])}if(1===e.length)return new s(this.tree._compare,null);for(l=0;l<e.length;++l)e[l]._count--;var h=e[e.length-2];return function(t){for(var e,r,n,s,l=t.length-1;l>=0;--l){if(e=t[l],0===l)return void(e._color=1);if((r=t[l-1]).left===e){if((n=r.right).right&&0===n.right._color){if(s=(n=r.right=i(n)).right=i(n.right),r.right=n.left,n.left=r,n.right=s,n._color=r._color,e._color=1,r._color=1,s._color=1,o(r),o(n),l>1)(c=t[l-2]).left===r?c.left=n:c.right=n;return void(t[l-1]=n)}if(n.left&&0===n.left._color){if(s=(n=r.right=i(n)).left=i(n.left),r.right=s.left,n.left=s.right,s.left=r,s.right=n,s._color=r._color,r._color=1,n._color=1,e._color=1,o(r),o(n),o(s),l>1)(c=t[l-2]).left===r?c.left=s:c.right=s;return void(t[l-1]=s)}if(1===n._color){if(0===r._color)return r._color=1,void(r.right=a(0,n));r.right=a(0,n);continue}n=i(n),r.right=n.left,n.left=r,n._color=r._color,r._color=0,o(r),o(n),l>1&&((c=t[l-2]).left===r?c.left=n:c.right=n),t[l-1]=n,t[l]=r,l+1<t.length?t[l+1]=e:t.push(e),l+=2}else{if((n=r.left).left&&0===n.left._color){if(s=(n=r.left=i(n)).left=i(n.left),r.left=n.right,n.right=r,n.left=s,n._color=r._color,e._color=1,r._color=1,s._color=1,o(r),o(n),l>1)(c=t[l-2]).right===r?c.right=n:c.left=n;return void(t[l-1]=n)}if(n.right&&0===n.right._color){if(s=(n=r.left=i(n)).right=i(n.right),r.left=s.right,n.right=s.left,s.right=r,s.left=n,s._color=r._color,r._color=1,n._color=1,e._color=1,o(r),o(n),o(s),l>1)(c=t[l-2]).right===r?c.right=s:c.left=s;return void(t[l-1]=s)}if(1===n._color){if(0===r._color)return r._color=1,void(r.left=a(0,n));r.left=a(0,n);continue}var c;n=i(n),r.left=n.right,n.right=r,n._color=r._color,r._color=0,o(r),o(n),l>1&&((c=t[l-2]).right===r?c.right=n:c.left=n),t[l-1]=n,t[l]=r,l+1<t.length?t[l+1]=e:t.push(e),l+=2}}}(e),h.left===r?h.left=null:h.right=null,new s(this.tree._compare,e[0])},Object.defineProperty(p,\"key\",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].key},enumerable:!0}),Object.defineProperty(p,\"value\",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].value},enumerable:!0}),Object.defineProperty(p,\"index\",{get:function(){var t=0,e=this._stack;if(0===e.length){var r=this.tree.root;return r?r._count:0}e[e.length-1].left&&(t=e[e.length-1].left._count);for(var n=e.length-2;n>=0;--n)e[n+1]===e[n].right&&(++t,e[n].left&&(t+=e[n].left._count));return t},enumerable:!0}),p.next=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.right)for(e=e.right;e;)t.push(e),e=e.left;else for(t.pop();t.length>0&&t[t.length-1].right===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(p,\"hasNext\",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].right)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].left===t[e])return!0;return!1}}),p.update=function(t){var e=this._stack;if(0===e.length)throw new Error(\"Can't update empty node!\");var r=new Array(e.length),i=e[e.length-1];r[r.length-1]=new n(i._color,i.key,t,i.left,i.right,i._count);for(var a=e.length-2;a>=0;--a)(i=e[a]).left===e[a+1]?r[a]=new n(i._color,i.key,i.value,r[a+1],i.right,i._count):r[a]=new n(i._color,i.key,i.value,i.left,r[a+1],i._count);return new s(this.tree._compare,r[0])},p.prev=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.left)for(e=e.left;e;)t.push(e),e=e.right;else for(t.pop();t.length>0&&t[t.length-1].left===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(p,\"hasPrev\",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].left)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].right===t[e])return!0;return!1}})},{}],70:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r=new u(t);return r.update(e),r};var n=t(\"./lib/text.js\"),i=t(\"./lib/lines.js\"),a=t(\"./lib/background.js\"),o=t(\"./lib/cube.js\"),s=t(\"./lib/ticks.js\"),l=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]);function c(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function u(t){this.gl=t,this.pixelRatio=1,this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.autoTicks=!0,this.tickSpacing=[1,1,1],this.tickEnable=[!0,!0,!0],this.tickFont=[\"sans-serif\",\"sans-serif\",\"sans-serif\"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickAlign=[\"auto\",\"auto\",\"auto\"],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[10,10,10],this.lastCubeProps={cubeEdges:[0,0,0],axis:[0,0,0]},this.labels=[\"x\",\"y\",\"z\"],this.labelEnable=[!0,!0,!0],this.labelFont=\"sans-serif\",this.labelSize=[20,20,20],this.labelAngle=[0,0,0],this.labelAlign=[\"auto\",\"auto\",\"auto\"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[10,10,10],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[0,0,0],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!1,!1,!1],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._firstInit=!0,this._text=null,this._lines=null,this._background=a(t)}var f=u.prototype;function h(){this.primalOffset=[0,0,0],this.primalMinor=[0,0,0],this.mirrorOffset=[0,0,0],this.mirrorMinor=[0,0,0]}f.update=function(t){function e(e,r,n){if(n in t){var i,a=t[n],o=this[n];(e?Array.isArray(a)&&Array.isArray(a[0]):Array.isArray(a))?this[n]=i=[r(a[0]),r(a[1]),r(a[2])]:this[n]=i=[r(a),r(a),r(a)];for(var s=0;s<3;++s)if(i[s]!==o[s])return!0}return!1}t=t||{};var r,a=e.bind(this,!1,Number),o=e.bind(this,!1,Boolean),l=e.bind(this,!1,String),c=e.bind(this,!0,(function(t){if(Array.isArray(t)){if(3===t.length)return[+t[0],+t[1],+t[2],1];if(4===t.length)return[+t[0],+t[1],+t[2],+t[3]]}return[0,0,0,1]})),u=!1,f=!1;if(\"bounds\"in t)for(var h=t.bounds,p=0;p<2;++p)for(var d=0;d<3;++d)h[p][d]!==this.bounds[p][d]&&(f=!0),this.bounds[p][d]=h[p][d];if(\"ticks\"in t){r=t.ticks,u=!0,this.autoTicks=!1;for(p=0;p<3;++p)this.tickSpacing[p]=0}else a(\"tickSpacing\")&&(this.autoTicks=!0,f=!0);if(this._firstInit&&(\"ticks\"in t||\"tickSpacing\"in t||(this.autoTicks=!0),f=!0,u=!0,this._firstInit=!1),f&&this.autoTicks&&(r=s.create(this.bounds,this.tickSpacing),u=!0),u){for(p=0;p<3;++p)r[p].sort((function(t,e){return t.x-e.x}));s.equal(r,this.ticks)?u=!1:this.ticks=r}o(\"tickEnable\"),l(\"tickFont\")&&(u=!0),a(\"tickSize\"),a(\"tickAngle\"),a(\"tickPad\"),c(\"tickColor\");var g=l(\"labels\");l(\"labelFont\")&&(g=!0),o(\"labelEnable\"),a(\"labelSize\"),a(\"labelPad\"),c(\"labelColor\"),o(\"lineEnable\"),o(\"lineMirror\"),a(\"lineWidth\"),c(\"lineColor\"),o(\"lineTickEnable\"),o(\"lineTickMirror\"),a(\"lineTickLength\"),a(\"lineTickWidth\"),c(\"lineTickColor\"),o(\"gridEnable\"),a(\"gridWidth\"),c(\"gridColor\"),o(\"zeroEnable\"),c(\"zeroLineColor\"),a(\"zeroLineWidth\"),o(\"backgroundEnable\"),c(\"backgroundColor\"),this._text?this._text&&(g||u)&&this._text.update(this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont):this._text=n(this.gl,this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont),this._lines&&u&&(this._lines.dispose(),this._lines=null),this._lines||(this._lines=i(this.gl,this.bounds,this.ticks))};var p=[new h,new h,new h];function d(t,e,r,n,i){for(var a=t.primalOffset,o=t.primalMinor,s=t.mirrorOffset,l=t.mirrorMinor,c=n[e],u=0;u<3;++u)if(e!==u){var f=a,h=s,p=o,d=l;c&1<<u&&(f=s,h=a,p=l,d=o),f[u]=r[0][u],h[u]=r[1][u],i[u]>0?(p[u]=-1,d[u]=0):(p[u]=0,d[u]=1)}}var g=[0,0,0],m={model:l,view:l,projection:l,_ortho:!1};f.isOpaque=function(){return!0},f.isTransparent=function(){return!1},f.drawTransparent=function(t){};var v=[0,0,0],y=[0,0,0],x=[0,0,0];f.draw=function(t){t=t||m;for(var e=this.gl,r=t.model||l,n=t.view||l,i=t.projection||l,a=this.bounds,s=t._ortho||!1,u=o(r,n,i,a,s),f=u.cubeEdges,h=u.axis,b=n[12],_=n[13],w=n[14],T=n[15],k=(s?2:1)*this.pixelRatio*(i[3]*b+i[7]*_+i[11]*w+i[15]*T)/e.drawingBufferHeight,A=0;A<3;++A)this.lastCubeProps.cubeEdges[A]=f[A],this.lastCubeProps.axis[A]=h[A];var M=p;for(A=0;A<3;++A)d(p[A],A,this.bounds,f,h);e=this.gl;var S,E=g;for(A=0;A<3;++A)this.backgroundEnable[A]?E[A]=h[A]:E[A]=0;this._background.draw(r,n,i,a,E,this.backgroundColor),this._lines.bind(r,n,i,this);for(A=0;A<3;++A){var L=[0,0,0];h[A]>0?L[A]=a[1][A]:L[A]=a[0][A];for(var C=0;C<2;++C){var P=(A+1+C)%3,I=(A+1+(1^C))%3;this.gridEnable[P]&&this._lines.drawGrid(P,I,this.bounds,L,this.gridColor[P],this.gridWidth[P]*this.pixelRatio)}for(C=0;C<2;++C){P=(A+1+C)%3,I=(A+1+(1^C))%3;this.zeroEnable[I]&&Math.min(a[0][I],a[1][I])<=0&&Math.max(a[0][I],a[1][I])>=0&&this._lines.drawZero(P,I,this.bounds,L,this.zeroLineColor[I],this.zeroLineWidth[I]*this.pixelRatio)}}for(A=0;A<3;++A){this.lineEnable[A]&&this._lines.drawAxisLine(A,this.bounds,M[A].primalOffset,this.lineColor[A],this.lineWidth[A]*this.pixelRatio),this.lineMirror[A]&&this._lines.drawAxisLine(A,this.bounds,M[A].mirrorOffset,this.lineColor[A],this.lineWidth[A]*this.pixelRatio);var O=c(v,M[A].primalMinor),z=c(y,M[A].mirrorMinor),D=this.lineTickLength;for(C=0;C<3;++C){var R=k/r[5*C];O[C]*=D[C]*R,z[C]*=D[C]*R}this.lineTickEnable[A]&&this._lines.drawAxisTicks(A,M[A].primalOffset,O,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio),this.lineTickMirror[A]&&this._lines.drawAxisTicks(A,M[A].mirrorOffset,z,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio)}this._lines.unbind(),this._text.bind(r,n,i,this.pixelRatio);var F,B;function N(t){(B=[0,0,0])[t]=1}function j(t,e,r){var n=(t+1)%3,i=(t+2)%3,a=e[n],o=e[i],s=r[n],l=r[i];a>0&&l>0||a>0&&l<0||a<0&&l>0||a<0&&l<0?N(n):(o>0&&s>0||o>0&&s<0||o<0&&s>0||o<0&&s<0)&&N(i)}for(A=0;A<3;++A){var U=M[A].primalMinor,V=M[A].mirrorMinor,H=c(x,M[A].primalOffset);for(C=0;C<3;++C)this.lineTickEnable[A]&&(H[C]+=k*U[C]*Math.max(this.lineTickLength[C],0)/r[5*C]);var q=[0,0,0];if(q[A]=1,this.tickEnable[A]){-3600===this.tickAngle[A]?(this.tickAngle[A]=0,this.tickAlign[A]=\"auto\"):this.tickAlign[A]=-1,F=1,\"auto\"===(S=[this.tickAlign[A],.5,F])[0]?S[0]=0:S[0]=parseInt(\"\"+S[0]),B=[0,0,0],j(A,U,V);for(C=0;C<3;++C)H[C]+=k*U[C]*this.tickPad[C]/r[5*C];this._text.drawTicks(A,this.tickSize[A],this.tickAngle[A],H,this.tickColor[A],q,B,S)}if(this.labelEnable[A]){F=0,B=[0,0,0],this.labels[A].length>4&&(N(A),F=1),\"auto\"===(S=[this.labelAlign[A],.5,F])[0]?S[0]=0:S[0]=parseInt(\"\"+S[0]);for(C=0;C<3;++C)H[C]+=k*U[C]*this.labelPad[C]/r[5*C];H[A]+=.5*(a[0][A]+a[1][A]),this._text.drawLabel(A,this.labelSize[A],this.labelAngle[A],H,this.labelColor[A],[0,0,0],B,S)}}this._text.unbind()},f.dispose=function(){this._text.dispose(),this._lines.dispose(),this._background.dispose(),this._lines=null,this._text=null,this._background=null,this.gl=null}},{\"./lib/background.js\":71,\"./lib/cube.js\":72,\"./lib/lines.js\":73,\"./lib/text.js\":75,\"./lib/ticks.js\":76}],71:[function(t,e,r){\"use strict\";e.exports=function(t){for(var e=[],r=[],s=0,l=0;l<3;++l)for(var c=(l+1)%3,u=(l+2)%3,f=[0,0,0],h=[0,0,0],p=-1;p<=1;p+=2){r.push(s,s+2,s+1,s+1,s+2,s+3),f[l]=p,h[l]=p;for(var d=-1;d<=1;d+=2){f[c]=d;for(var g=-1;g<=1;g+=2)f[u]=g,e.push(f[0],f[1],f[2],h[0],h[1],h[2]),s+=1}var m=c;c=u,u=m}var v=n(t,new Float32Array(e)),y=n(t,new Uint16Array(r),t.ELEMENT_ARRAY_BUFFER),x=i(t,[{buffer:v,type:t.FLOAT,size:3,offset:0,stride:24},{buffer:v,type:t.FLOAT,size:3,offset:12,stride:24}],y),b=a(t);return b.attributes.position.location=0,b.attributes.normal.location=1,new o(t,v,x,b)};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"./shaders\").bg;function o(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n}var s=o.prototype;s.draw=function(t,e,r,n,i,a){for(var o=!1,s=0;s<3;++s)o=o||i[s];if(o){var l=this.gl;l.enable(l.POLYGON_OFFSET_FILL),l.polygonOffset(1,2),this.shader.bind(),this.shader.uniforms={model:t,view:e,projection:r,bounds:n,enable:i,colors:a},this.vao.bind(),this.vao.draw(this.gl.TRIANGLES,36),this.vao.unbind(),l.disable(l.POLYGON_OFFSET_FILL)}},s.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},{\"./shaders\":74,\"gl-buffer\":78,\"gl-vao\":150}],72:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,a,p){i(s,e,t),i(s,r,s);for(var y=0,x=0;x<2;++x){u[2]=a[x][2];for(var b=0;b<2;++b){u[1]=a[b][1];for(var _=0;_<2;++_)u[0]=a[_][0],h(l[y],u,s),y+=1}}var w=-1;for(x=0;x<8;++x){for(var T=l[x][3],k=0;k<3;++k)c[x][k]=l[x][k]/T;p&&(c[x][2]*=-1),T<0&&(w<0||c[x][2]<c[w][2])&&(w=x)}if(w<0){w=0;for(var A=0;A<3;++A){for(var M=(A+2)%3,S=(A+1)%3,E=-1,L=-1,C=0;C<2;++C){var P=(O=C<<A)+(C<<M)+(1-C<<S),I=O+(1-C<<M)+(C<<S);o(c[O],c[P],c[I],f)<0||(C?E=1:L=1)}if(E<0||L<0)L>E&&(w|=1<<A);else{for(C=0;C<2;++C){P=(O=C<<A)+(C<<M)+(1-C<<S),I=O+(1-C<<M)+(C<<S);var O,z=d([l[O],l[P],l[I],l[O+(1<<M)+(1<<S)]]);C?E=z:L=z}L>E&&(w|=1<<A)}}}var D=7^w,R=-1;for(x=0;x<8;++x)x!==w&&x!==D&&(R<0||c[R][1]>c[x][1])&&(R=x);var F=-1;for(x=0;x<3;++x){if((N=R^1<<x)!==w&&N!==D)F<0&&(F=N),(S=c[N])[0]<c[F][0]&&(F=N)}var B=-1;for(x=0;x<3;++x){var N;if((N=R^1<<x)!==w&&N!==D&&N!==F)B<0&&(B=N),(S=c[N])[0]>c[B][0]&&(B=N)}var j=g;j[0]=j[1]=j[2]=0,j[n.log2(F^R)]=R&F,j[n.log2(R^B)]=R&B;var U=7^B;U===w||U===D?(U=7^F,j[n.log2(B^U)]=U&B):j[n.log2(F^U)]=U&F;var V=m,H=w;for(A=0;A<3;++A)V[A]=H&1<<A?-1:1;return v};var n=t(\"bit-twiddle\"),i=t(\"gl-mat4/multiply\"),a=t(\"split-polygon\"),o=t(\"robust-orientation\"),s=new Array(16),l=new Array(8),c=new Array(8),u=new Array(3),f=[0,0,0];function h(t,e,r){for(var n=0;n<4;++n){t[n]=r[12+n];for(var i=0;i<3;++i)t[n]+=e[i]*r[4*i+n]}}!function(){for(var t=0;t<8;++t)l[t]=[1,1,1,1],c[t]=[1,1,1]}();var p=[[0,0,1,0,0],[0,0,-1,1,0],[0,-1,0,1,0],[0,1,0,1,0],[-1,0,0,1,0],[1,0,0,1,0]];function d(t){for(var e=0;e<p.length;++e)if((t=a.positive(t,p[e])).length<3)return 0;var r=t[0],n=r[0]/r[3],i=r[1]/r[3],o=0;for(e=1;e+1<t.length;++e){var s=t[e],l=t[e+1],c=s[0]/s[3]-n,u=s[1]/s[3]-i,f=l[0]/l[3]-n,h=l[1]/l[3]-i;o+=Math.abs(c*h-u*f)}return o}var g=[1,1,1],m=[0,0,0],v={cubeEdges:g,axis:m}},{\"bit-twiddle\":32,\"gl-mat4/multiply\":100,\"robust-orientation\":284,\"split-polygon\":300}],73:[function(t,e,r){\"use strict\";e.exports=function(t,e,r){var o=[],s=[0,0,0],l=[0,0,0],c=[0,0,0],u=[0,0,0];o.push(0,0,1,0,1,1,0,0,-1,0,0,-1,0,1,1,0,1,-1);for(var f=0;f<3;++f){for(var h=o.length/3|0,d=0;d<r[f].length;++d){var g=+r[f][d].x;o.push(g,0,1,g,1,1,g,0,-1,g,0,-1,g,1,1,g,1,-1)}var m=o.length/3|0;s[f]=h,l[f]=m-h;h=o.length/3|0;for(var v=0;v<r[f].length;++v){g=+r[f][v].x;o.push(g,0,1,g,1,1,g,0,-1,g,0,-1,g,1,1,g,1,-1)}m=o.length/3|0;c[f]=h,u[f]=m-h}var y=n(t,new Float32Array(o)),x=i(t,[{buffer:y,type:t.FLOAT,size:3,stride:0,offset:0}]),b=a(t);return b.attributes.position.location=0,new p(t,y,x,b,l,s,u,c)};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"./shaders\").line,o=[0,0,0],s=[0,0,0],l=[0,0,0],c=[0,0,0],u=[1,1];function f(t){return t[0]=t[1]=t[2]=0,t}function h(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function p(t,e,r,n,i,a,o,s){this.gl=t,this.vertBuffer=e,this.vao=r,this.shader=n,this.tickCount=i,this.tickOffset=a,this.gridCount=o,this.gridOffset=s}var d=p.prototype;d.bind=function(t,e,r){this.shader.bind(),this.shader.uniforms.model=t,this.shader.uniforms.view=e,this.shader.uniforms.projection=r,u[0]=this.gl.drawingBufferWidth,u[1]=this.gl.drawingBufferHeight,this.shader.uniforms.screenShape=u,this.vao.bind()},d.unbind=function(){this.vao.unbind()},d.drawAxisLine=function(t,e,r,n,i){var a=f(s);this.shader.uniforms.majorAxis=s,a[t]=e[1][t]-e[0][t],this.shader.uniforms.minorAxis=a;var o,u=h(c,r);u[t]+=e[0][t],this.shader.uniforms.offset=u,this.shader.uniforms.lineWidth=i,this.shader.uniforms.color=n,(o=f(l))[(t+2)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6),(o=f(l))[(t+1)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6)},d.drawAxisTicks=function(t,e,r,n,i){if(this.tickCount[t]){var a=f(o);a[t]=1,this.shader.uniforms.majorAxis=a,this.shader.uniforms.offset=e,this.shader.uniforms.minorAxis=r,this.shader.uniforms.color=n,this.shader.uniforms.lineWidth=i;var s=f(l);s[t]=1,this.shader.uniforms.screenAxis=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t])}},d.drawGrid=function(t,e,r,n,i,a){if(this.gridCount[t]){var u=f(s);u[e]=r[1][e]-r[0][e],this.shader.uniforms.minorAxis=u;var p=h(c,n);p[e]+=r[0][e],this.shader.uniforms.offset=p;var d=f(o);d[t]=1,this.shader.uniforms.majorAxis=d;var g=f(l);g[t]=1,this.shader.uniforms.screenAxis=g,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,this.gridCount[t],this.gridOffset[t])}},d.drawZero=function(t,e,r,n,i,a){var o=f(s);this.shader.uniforms.majorAxis=o,o[t]=r[1][t]-r[0][t],this.shader.uniforms.minorAxis=o;var u=h(c,n);u[t]+=r[0][t],this.shader.uniforms.offset=u;var p=f(l);p[e]=1,this.shader.uniforms.screenAxis=p,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,6)},d.dispose=function(){this.vao.dispose(),this.vertBuffer.dispose(),this.shader.dispose()}},{\"./shaders\":74,\"gl-buffer\":78,\"gl-vao\":150}],74:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 offset, majorAxis, minorAxis, screenAxis;\\nuniform float lineWidth;\\nuniform vec2 screenShape;\\n\\nvec3 project(vec3 p) {\\n  vec4 pp = projection * view * model * vec4(p, 1.0);\\n  return pp.xyz / max(pp.w, 0.0001);\\n}\\n\\nvoid main() {\\n  vec3 major = position.x * majorAxis;\\n  vec3 minor = position.y * minorAxis;\\n\\n  vec3 vPosition = major + minor + offset;\\n  vec3 pPosition = project(vPosition);\\n  vec3 offset = project(vPosition + screenAxis * position.z);\\n\\n  vec2 screen = normalize((offset - pPosition).xy * screenShape) / screenShape;\\n\\n  gl_Position = vec4(pPosition + vec3(0.5 * screen * lineWidth, 0), 1.0);\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\nvoid main() {\\n  gl_FragColor = color;\\n}\"]);r.line=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec3\"}])};var s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 offset, axis, alignDir, alignOpt;\\nuniform float scale, angle, pixelScale;\\nuniform vec2 resolution;\\n\\nvec3 project(vec3 p) {\\n  vec4 pp = projection * view * model * vec4(p, 1.0);\\n  return pp.xyz / max(pp.w, 0.0001);\\n}\\n\\nfloat computeViewAngle(vec3 a, vec3 b) {\\n  vec3 A = project(a);\\n  vec3 B = project(b);\\n\\n  return atan(\\n    (B.y - A.y) * resolution.y,\\n    (B.x - A.x) * resolution.x\\n  );\\n}\\n\\nconst float PI = 3.141592;\\nconst float TWO_PI = 2.0 * PI;\\nconst float HALF_PI = 0.5 * PI;\\nconst float ONE_AND_HALF_PI = 1.5 * PI;\\n\\nint option = int(floor(alignOpt.x + 0.001));\\nfloat hv_ratio =       alignOpt.y;\\nbool enableAlign =    (alignOpt.z != 0.0);\\n\\nfloat mod_angle(float a) {\\n  return mod(a, PI);\\n}\\n\\nfloat positive_angle(float a) {\\n  return mod_angle((a < 0.0) ?\\n    a + TWO_PI :\\n    a\\n  );\\n}\\n\\nfloat look_upwards(float a) {\\n  float b = positive_angle(a);\\n  return ((b > HALF_PI) && (b <= ONE_AND_HALF_PI)) ?\\n    b - PI :\\n    b;\\n}\\n\\nfloat look_horizontal_or_vertical(float a, float ratio) {\\n  // ratio controls the ratio between being horizontal to (vertical + horizontal)\\n  // if ratio is set to 0.5 then it is 50%, 50%.\\n  // when using a higher ratio e.g. 0.75 the result would\\n  // likely be more horizontal than vertical.\\n\\n  float b = positive_angle(a);\\n\\n  return\\n    (b < (      ratio) * HALF_PI) ? 0.0 :\\n    (b < (2.0 - ratio) * HALF_PI) ? -HALF_PI :\\n    (b < (2.0 + ratio) * HALF_PI) ? 0.0 :\\n    (b < (4.0 - ratio) * HALF_PI) ? HALF_PI :\\n                                    0.0;\\n}\\n\\nfloat roundTo(float a, float b) {\\n  return float(b * floor((a + 0.5 * b) / b));\\n}\\n\\nfloat look_round_n_directions(float a, int n) {\\n  float b = positive_angle(a);\\n  float div = TWO_PI / float(n);\\n  float c = roundTo(b, div);\\n  return look_upwards(c);\\n}\\n\\nfloat applyAlignOption(float rawAngle, float delta) {\\n  return\\n    (option >  2) ? look_round_n_directions(rawAngle + delta, option) :       // option 3-n: round to n directions\\n    (option == 2) ? look_horizontal_or_vertical(rawAngle + delta, hv_ratio) : // horizontal or vertical\\n    (option == 1) ? rawAngle + delta :       // use free angle, and flip to align with one direction of the axis\\n    (option == 0) ? look_upwards(rawAngle) : // use free angle, and stay upwards\\n    (option ==-1) ? 0.0 :                    // useful for backward compatibility, all texts remains horizontal\\n                    rawAngle;                // otherwise return back raw input angle\\n}\\n\\nbool isAxisTitle = (axis.x == 0.0) &&\\n                   (axis.y == 0.0) &&\\n                   (axis.z == 0.0);\\n\\nvoid main() {\\n  //Compute world offset\\n  float axisDistance = position.z;\\n  vec3 dataPosition = axisDistance * axis + offset;\\n\\n  float beta = angle; // i.e. user defined attributes for each tick\\n\\n  float axisAngle;\\n  float clipAngle;\\n  float flip;\\n\\n  if (enableAlign) {\\n    axisAngle = (isAxisTitle) ? HALF_PI :\\n                      computeViewAngle(dataPosition, dataPosition + axis);\\n    clipAngle = computeViewAngle(dataPosition, dataPosition + alignDir);\\n\\n    axisAngle += (sin(axisAngle) < 0.0) ? PI : 0.0;\\n    clipAngle += (sin(clipAngle) < 0.0) ? PI : 0.0;\\n\\n    flip = (dot(vec2(cos(axisAngle), sin(axisAngle)),\\n                vec2(sin(clipAngle),-cos(clipAngle))) > 0.0) ? 1.0 : 0.0;\\n\\n    beta += applyAlignOption(clipAngle, flip * PI);\\n  }\\n\\n  //Compute plane offset\\n  vec2 planeCoord = position.xy * pixelScale;\\n\\n  mat2 planeXform = scale * mat2(\\n     cos(beta), sin(beta),\\n    -sin(beta), cos(beta)\\n  );\\n\\n  vec2 viewOffset = 2.0 * planeXform * planeCoord / resolution;\\n\\n  //Compute clip position\\n  vec3 clipPosition = project(dataPosition);\\n\\n  //Apply text offset in clip coordinates\\n  clipPosition += vec3(viewOffset, 0.0);\\n\\n  //Done\\n  gl_Position = vec4(clipPosition, 1.0);\\n}\"]),l=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\nvoid main() {\\n  gl_FragColor = color;\\n}\"]);r.text=function(t){return i(t,s,l,null,[{name:\"position\",type:\"vec3\"}])};var c=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec3 normal;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 enable;\\nuniform vec3 bounds[2];\\n\\nvarying vec3 colorChannel;\\n\\nvoid main() {\\n\\n  vec3 signAxis = sign(bounds[1] - bounds[0]);\\n\\n  vec3 realNormal = signAxis * normal;\\n\\n  if(dot(realNormal, enable) > 0.0) {\\n    vec3 minRange = min(bounds[0], bounds[1]);\\n    vec3 maxRange = max(bounds[0], bounds[1]);\\n    vec3 nPosition = mix(minRange, maxRange, 0.5 * (position + 1.0));\\n    gl_Position = projection * view * model * vec4(nPosition, 1.0);\\n  } else {\\n    gl_Position = vec4(0,0,0,0);\\n  }\\n\\n  colorChannel = abs(realNormal);\\n}\"]),u=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 colors[3];\\n\\nvarying vec3 colorChannel;\\n\\nvoid main() {\\n  gl_FragColor = colorChannel.x * colors[0] +\\n                 colorChannel.y * colors[1] +\\n                 colorChannel.z * colors[2];\\n}\"]);r.bg=function(t){return i(t,c,u,null,[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}])}},{\"gl-shader\":132,glslify:231}],75:[function(t,e,r){(function(r){(function(){\"use strict\";e.exports=function(t,e,r,a,s,l){var u=n(t),f=i(t,[{buffer:u,size:3}]),h=o(t);h.attributes.position.location=0;var p=new c(t,h,u,f);return p.update(e,r,a,s,l),p};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"vectorize-text\"),o=t(\"./shaders\").text,s=window||r.global||{},l=s.__TEXT_CACHE||{};s.__TEXT_CACHE={};function c(t,e,r,n){this.gl=t,this.shader=e,this.buffer=r,this.vao=n,this.tickOffset=this.tickCount=this.labelOffset=this.labelCount=null}var u=c.prototype,f=[0,0];u.bind=function(t,e,r,n){this.vao.bind(),this.shader.bind();var i=this.shader.uniforms;i.model=t,i.view=e,i.projection=r,i.pixelScale=n,f[0]=this.gl.drawingBufferWidth,f[1]=this.gl.drawingBufferHeight,this.shader.uniforms.resolution=f},u.unbind=function(){this.vao.unbind()},u.update=function(t,e,r,n,i){var o=[];function s(t,e,r,n,i,s){var c=l[r];c||(c=l[r]={});var u=c[e];u||(u=c[e]=function(t,e){try{return a(t,e)}catch(e){return console.warn('error vectorizing text:\"'+t+'\" error:',e),{cells:[],positions:[]}}}(e,{triangles:!0,font:r,textAlign:\"center\",textBaseline:\"middle\",lineSpacing:i,styletags:s}));for(var f=(n||12)/12,h=u.positions,p=u.cells,d=0,g=p.length;d<g;++d)for(var m=p[d],v=2;v>=0;--v){var y=h[m[v]];o.push(f*y[0],-f*y[1],t)}}for(var c=[0,0,0],u=[0,0,0],f=[0,0,0],h=[0,0,0],p={breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},d=0;d<3;++d){f[d]=o.length/3|0,s(.5*(t[0][d]+t[1][d]),e[d],r[d],12,1.25,p),h[d]=(o.length/3|0)-f[d],c[d]=o.length/3|0;for(var g=0;g<n[d].length;++g)n[d][g].text&&s(n[d][g].x,n[d][g].text,n[d][g].font||i,n[d][g].fontSize||12,1.25,p);u[d]=(o.length/3|0)-c[d]}this.buffer.update(o),this.tickOffset=c,this.tickCount=u,this.labelOffset=f,this.labelCount=h},u.drawTicks=function(t,e,r,n,i,a,o,s){this.tickCount[t]&&(this.shader.uniforms.axis=a,this.shader.uniforms.color=i,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.shader.uniforms.alignDir=o,this.shader.uniforms.alignOpt=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t]))},u.drawLabel=function(t,e,r,n,i,a,o,s){this.labelCount[t]&&(this.shader.uniforms.axis=a,this.shader.uniforms.color=i,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.shader.uniforms.alignDir=o,this.shader.uniforms.alignOpt=s,this.vao.draw(this.gl.TRIANGLES,this.labelCount[t],this.labelOffset[t]))},u.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()}}).call(this)}).call(this,t(\"_process\"))},{\"./shaders\":74,_process:5,\"gl-buffer\":78,\"gl-vao\":150,\"vectorize-text\":311}],76:[function(t,e,r){\"use strict\";function n(t,e){var r=t+\"\",n=r.indexOf(\".\"),i=0;n>=0&&(i=r.length-n-1);var a=Math.pow(10,i),o=Math.round(t*e*a),s=o+\"\";if(s.indexOf(\"e\")>=0)return s;var l=o/a,c=o%a;o<0?(l=0|-Math.ceil(l),c=0|-c):(l=0|Math.floor(l),c|=0);var u=\"\"+l;if(o<0&&(u=\"-\"+u),i){for(var f=\"\"+c;f.length<i;)f=\"0\"+f;return u+\".\"+f}return u}r.create=function(t,e){for(var r=[],i=0;i<3;++i){for(var a=[],o=(t[0][i],t[1][i],0);o*e[i]<=t[1][i];++o)a.push({x:o*e[i],text:n(e[i],o)});for(o=-1;o*e[i]>=t[0][i];--o)a.push({x:o*e[i],text:n(e[i],o)});r.push(a)}return r},r.equal=function(t,e){for(var r=0;r<3;++r){if(t[r].length!==e[r].length)return!1;for(var n=0;n<t[r].length;++n){var i=t[r][n],a=e[r][n];if(i.x!==a.x||i.text!==a.text||i.font!==a.font||i.fontColor!==a.fontColor||i.fontSize!==a.fontSize||i.dx!==a.dx||i.dy!==a.dy)return!1}}return!0}},{}],77:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,l,f){var h=e.model||c,p=e.view||c,v=e.projection||c,y=e._ortho||!1,x=t.bounds,b=(f=f||a(h,p,v,x,y)).axis;o(u,p,h),o(u,v,u);for(var _=g,w=0;w<3;++w)_[w].lo=1/0,_[w].hi=-1/0,_[w].pixelsPerDataUnit=1/0;var T=n(s(u,u));s(u,u);for(var k=0;k<3;++k){var A=(k+1)%3,M=(k+2)%3,S=m;t:for(w=0;w<2;++w){var E=[];if(b[k]<0!=!!w){S[k]=x[w][k];for(var L=0;L<2;++L){S[A]=x[L^w][A];for(var C=0;C<2;++C)S[M]=x[C^L^w][M],E.push(S.slice())}var P=y?5:4;for(L=P;L===P;++L){if(0===E.length)continue t;E=i.positive(E,T[L])}for(L=0;L<E.length;++L){M=E[L];var I=d(m,u,M,r,l);for(C=0;C<3;++C)_[C].lo=Math.min(_[C].lo,M[C]),_[C].hi=Math.max(_[C].hi,M[C]),C!==k&&(_[C].pixelsPerDataUnit=Math.min(_[C].pixelsPerDataUnit,Math.abs(I[C])))}}}}return _};var n=t(\"extract-frustum-planes\"),i=t(\"split-polygon\"),a=t(\"./lib/cube.js\"),o=t(\"gl-mat4/multiply\"),s=t(\"gl-mat4/transpose\"),l=t(\"gl-vec4/transformMat4\"),c=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]),u=new Float32Array(16);function f(t,e,r){this.lo=t,this.hi=e,this.pixelsPerDataUnit=r}var h=[0,0,0,1],p=[0,0,0,1];function d(t,e,r,n,i){for(var a=0;a<3;++a){for(var o=h,s=p,c=0;c<3;++c)s[c]=o[c]=r[c];s[3]=o[3]=1,s[a]+=1,l(s,s,e),s[3]<0&&(t[a]=1/0),o[a]-=1,l(o,o,e),o[3]<0&&(t[a]=1/0);var u=(o[0]/o[3]-s[0]/s[3])*n,f=(o[1]/o[3]-s[1]/s[3])*i;t[a]=.25*Math.sqrt(u*u+f*f)}return t}var g=[new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0)],m=[0,0,0]},{\"./lib/cube.js\":72,\"extract-frustum-planes\":67,\"gl-mat4/multiply\":100,\"gl-mat4/transpose\":109,\"gl-vec4/transformMat4\":221,\"split-polygon\":300}],78:[function(t,e,r){\"use strict\";var n=t(\"typedarray-pool\"),i=t(\"ndarray-ops\"),a=t(\"ndarray\"),o=[\"uint8\",\"uint8_clamped\",\"uint16\",\"uint32\",\"int8\",\"int16\",\"int32\",\"float32\"];function s(t,e,r,n,i){this.gl=t,this.type=e,this.handle=r,this.length=n,this.usage=i}var l=s.prototype;function c(t,e,r,n,i,a){var o=i.length*i.BYTES_PER_ELEMENT;if(a<0)return t.bufferData(e,i,n),o;if(o+a>r)throw new Error(\"gl-buffer: If resizing buffer, must not specify offset\");return t.bufferSubData(e,a,i),r}function u(t,e){for(var r=n.malloc(t.length,e),i=t.length,a=0;a<i;++a)r[a]=t[a];return r}l.bind=function(){this.gl.bindBuffer(this.type,this.handle)},l.unbind=function(){this.gl.bindBuffer(this.type,null)},l.dispose=function(){this.gl.deleteBuffer(this.handle)},l.update=function(t,e){if(\"number\"!=typeof e&&(e=-1),this.bind(),\"object\"==typeof t&&void 0!==t.shape){var r=t.dtype;if(o.indexOf(r)<0&&(r=\"float32\"),this.type===this.gl.ELEMENT_ARRAY_BUFFER)r=gl.getExtension(\"OES_element_index_uint\")&&\"uint16\"!==r?\"uint32\":\"uint16\";if(r===t.dtype&&function(t,e){for(var r=1,n=e.length-1;n>=0;--n){if(e[n]!==r)return!1;r*=t[n]}return!0}(t.shape,t.stride))0===t.offset&&t.data.length===t.shape[0]?this.length=c(this.gl,this.type,this.length,this.usage,t.data,e):this.length=c(this.gl,this.type,this.length,this.usage,t.data.subarray(t.offset,t.shape[0]),e);else{var s=n.malloc(t.size,r),l=a(s,t.shape);i.assign(l,t),this.length=c(this.gl,this.type,this.length,this.usage,e<0?s:s.subarray(0,t.size),e),n.free(s)}}else if(Array.isArray(t)){var f;f=this.type===this.gl.ELEMENT_ARRAY_BUFFER?u(t,\"uint16\"):u(t,\"float32\"),this.length=c(this.gl,this.type,this.length,this.usage,e<0?f:f.subarray(0,t.length),e),n.free(f)}else if(\"object\"==typeof t&&\"number\"==typeof t.length)this.length=c(this.gl,this.type,this.length,this.usage,t,e);else{if(\"number\"!=typeof t&&void 0!==t)throw new Error(\"gl-buffer: Invalid data type\");if(e>=0)throw new Error(\"gl-buffer: Cannot specify offset when resizing buffer\");(t|=0)<=0&&(t=1),this.gl.bufferData(this.type,0|t,this.usage),this.length=t}},e.exports=function(t,e,r,n){if(r=r||t.ARRAY_BUFFER,n=n||t.DYNAMIC_DRAW,r!==t.ARRAY_BUFFER&&r!==t.ELEMENT_ARRAY_BUFFER)throw new Error(\"gl-buffer: Invalid type for webgl buffer, must be either gl.ARRAY_BUFFER or gl.ELEMENT_ARRAY_BUFFER\");if(n!==t.DYNAMIC_DRAW&&n!==t.STATIC_DRAW&&n!==t.STREAM_DRAW)throw new Error(\"gl-buffer: Invalid usage for buffer, must be either gl.DYNAMIC_DRAW, gl.STATIC_DRAW or gl.STREAM_DRAW\");var i=t.createBuffer(),a=new s(t,r,i,0,n);return a.update(e),a}},{ndarray:259,\"ndarray-ops\":254,\"typedarray-pool\":308}],79:[function(t,e,r){\"use strict\";var n=t(\"gl-vec3\");e.exports=function(t,e){var r=t.positions,i=t.vectors,a={positions:[],vertexIntensity:[],vertexIntensityBounds:t.vertexIntensityBounds,vectors:[],cells:[],coneOffset:t.coneOffset,colormap:t.colormap};if(0===t.positions.length)return e&&(e[0]=[0,0,0],e[1]=[0,0,0]),a;for(var o=0,s=1/0,l=-1/0,c=1/0,u=-1/0,f=1/0,h=-1/0,p=null,d=null,g=[],m=1/0,v=!1,y=0;y<r.length;y++){var x=r[y];s=Math.min(x[0],s),l=Math.max(x[0],l),c=Math.min(x[1],c),u=Math.max(x[1],u),f=Math.min(x[2],f),h=Math.max(x[2],h);var b=i[y];if(n.length(b)>o&&(o=n.length(b)),y){var _=2*n.distance(p,x)/(n.length(d)+n.length(b));_?(m=Math.min(m,_),v=!1):v=!0}v||(p=x,d=b),g.push(b)}var w=[s,c,f],T=[l,u,h];e&&(e[0]=w,e[1]=T),0===o&&(o=1);var k=1/o;isFinite(m)||(m=1),a.vectorScale=m;var A=t.coneSize||.5;t.absoluteConeSize&&(A=t.absoluteConeSize*k),a.coneScale=A;y=0;for(var M=0;y<r.length;y++)for(var S=(x=r[y])[0],E=x[1],L=x[2],C=g[y],P=n.length(C)*k,I=0;I<8;I++){a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vertexIntensity.push(P,P,P),a.vertexIntensity.push(P,P,P);var O=a.positions.length;a.cells.push([O-6,O-5,O-4],[O-3,O-2,O-1])}return a};var i=t(\"./lib/shaders\");e.exports.createMesh=t(\"./create_mesh\"),e.exports.createConeMesh=function(t,r){return e.exports.createMesh(t,r,{shaders:i,traceType:\"cone\"})}},{\"./create_mesh\":80,\"./lib/shaders\":81,\"gl-vec3\":169}],80:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"gl-texture2d\"),s=t(\"gl-mat4/multiply\"),l=t(\"gl-mat4/invert\"),c=t(\"ndarray\"),u=t(\"colormap\"),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e,r,n,i,a,o,s,l,c,u){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.pickShader=n,this.trianglePositions=i,this.triangleVectors=a,this.triangleColors=s,this.triangleUVs=l,this.triangleIds=o,this.triangleVAO=c,this.triangleCount=0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.traceType=u,this.tubeScale=1,this.coneScale=2,this.vectorScale=1,this.coneOffset=.25,this._model=f,this._view=f,this._projection=f,this._resolution=[1,1]}var p=h.prototype;function d(t,e){var r=n(t,e.meshShader.vertex,e.meshShader.fragment,null,e.meshShader.attributes);return r.attributes.position.location=0,r.attributes.color.location=2,r.attributes.uv.location=3,r.attributes.vector.location=4,r}function g(t,e){var r=n(t,e.pickShader.vertex,e.pickShader.fragment,null,e.pickShader.attributes);return r.attributes.position.location=0,r.attributes.id.location=1,r.attributes.vector.location=4,r}p.isOpaque=function(){return this.opacity>=1},p.isTransparent=function(){return this.opacity<1},p.pickSlots=1,p.setPickBase=function(t){this.pickId=t},p.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,\"lightPosition\"in t&&(this.lightPosition=t.lightPosition),\"opacity\"in t&&(this.opacity=t.opacity),\"ambient\"in t&&(this.ambientLight=t.ambient),\"diffuse\"in t&&(this.diffuseLight=t.diffuse),\"specular\"in t&&(this.specularLight=t.specular),\"roughness\"in t&&(this.roughness=t.roughness),\"fresnel\"in t&&(this.fresnel=t.fresnel),void 0!==t.tubeScale&&(this.tubeScale=t.tubeScale),void 0!==t.vectorScale&&(this.vectorScale=t.vectorScale),void 0!==t.coneScale&&(this.coneScale=t.coneScale),void 0!==t.coneOffset&&(this.coneOffset=t.coneOffset),t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t){for(var e=u({colormap:t,nshades:256,format:\"rgba\"}),r=new Uint8Array(1024),n=0;n<256;++n){for(var i=e[n],a=0;a<3;++a)r[4*n+a]=i[a];r[4*n+3]=255*i[3]}return c(r,[256,256,4],[4,0,1])}(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions,i=t.vectors;if(n&&r&&i){var a=[],o=[],s=[],l=[],f=[];this.cells=r,this.positions=n,this.vectors=i;var h=t.meshColor||[1,1,1,1],p=t.vertexIntensity,d=1/0,g=-1/0;if(p)if(t.vertexIntensityBounds)d=+t.vertexIntensityBounds[0],g=+t.vertexIntensityBounds[1];else for(var m=0;m<p.length;++m){var v=p[m];d=Math.min(d,v),g=Math.max(g,v)}else for(m=0;m<n.length;++m){v=n[m][2];d=Math.min(d,v),g=Math.max(g,v)}this.intensity=p||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n),this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(m=0;m<n.length;++m)for(var y=n[m],x=0;x<3;++x)!isNaN(y[x])&&isFinite(y[x])&&(this.bounds[0][x]=Math.min(this.bounds[0][x],y[x]),this.bounds[1][x]=Math.max(this.bounds[1][x],y[x]));var b=0;t:for(m=0;m<r.length;++m){var _=r[m];switch(_.length){case 3:for(x=0;x<3;++x){y=n[T=_[x]];for(var w=0;w<3;++w)if(isNaN(y[w])||!isFinite(y[w]))continue t}for(x=0;x<3;++x){var T;y=n[T=_[2-x]];a.push(y[0],y[1],y[2],y[3]);var k=i[T];o.push(k[0],k[1],k[2],k[3]||0);var A,M=h;3===M.length?s.push(M[0],M[1],M[2],1):s.push(M[0],M[1],M[2],M[3]),A=p?[(p[T]-d)/(g-d),0]:[(y[2]-d)/(g-d),0],l.push(A[0],A[1]),f.push(m)}b+=1}}this.triangleCount=b,this.trianglePositions.update(a),this.triangleVectors.update(o),this.triangleColors.update(s),this.triangleUVs.update(l),this.triangleIds.update(new Uint32Array(f))}},p.drawTransparent=p.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var c={model:r,view:n,projection:i,inverseModel:f.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,texture:0};c.inverseModel=l(c.inverseModel,c.model),e.disable(e.CULL_FACE),this.texture.bind(0);var u=new Array(16);s(u,c.view,c.model),s(u,c.projection,u),l(u,u);for(o=0;o<3;++o)c.eyePosition[o]=u[12+o]/u[15];var h=u[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*u[4*o+3];for(o=0;o<3;++o){for(var p=u[12+o],d=0;d<3;++d)p+=u[4*d+o]*this.lightPosition[d];c.lightPosition[o]=p/h}if(this.triangleCount>0){var g=this.triShader;g.bind(),g.uniforms=c,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()}},p.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s={model:r,view:n,projection:i,clipBounds:a,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,pickId:this.pickId/255},l=this.pickShader;l.bind(),l.uniforms=s,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind())},p.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions[r[1]].slice(0,3),i={position:n,dataCoordinate:n,index:Math.floor(r[1]/48)};return\"cone\"===this.traceType?i.index=Math.floor(r[1]/48):\"streamtube\"===this.traceType&&(i.intensity=this.intensity[r[1]],i.velocity=this.vectors[r[1]].slice(0,3),i.divergence=this.vectors[r[1]][3],i.index=e),i},p.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.pickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleVectors.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleIds.dispose()},e.exports=function(t,e,r){var n=r.shaders;1===arguments.length&&(t=(e=t).gl);var s=d(t,n),l=g(t,n),u=o(t,c(new Uint8Array([255,255,255,255]),[1,1,4]));u.generateMipmap(),u.minFilter=t.LINEAR_MIPMAP_LINEAR,u.magFilter=t.LINEAR;var f=i(t),p=i(t),m=i(t),v=i(t),y=i(t),x=a(t,[{buffer:f,type:t.FLOAT,size:4},{buffer:y,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:m,type:t.FLOAT,size:4},{buffer:v,type:t.FLOAT,size:2},{buffer:p,type:t.FLOAT,size:4}]),b=new h(t,u,s,l,f,p,y,m,v,x,r.traceType||\"cone\");return b.update(e),b}},{colormap:53,\"gl-buffer\":78,\"gl-mat4/invert\":98,\"gl-mat4/multiply\":100,\"gl-shader\":132,\"gl-texture2d\":146,\"gl-vao\":150,ndarray:259}],81:[function(t,e,r){var n=t(\"glslify\"),i=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the cone vertex and normal at the given index.\\n//\\n// The returned vertex is for a cone with its top at origin and height of 1.0,\\n// pointing in the direction of the vector attribute.\\n//\\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\\n// These vertices are used to make up the triangles of the cone by the following:\\n//   segment + 0 top vertex\\n//   segment + 1 perimeter vertex a+1\\n//   segment + 2 perimeter vertex a\\n//   segment + 3 center base vertex\\n//   segment + 4 perimeter vertex a\\n//   segment + 5 perimeter vertex a+1\\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\\n// To go from index to segment, floor(index / 6)\\n// To go from segment to angle, 2*pi * (segment/segmentCount)\\n// To go from index to segment index, index - (segment*6)\\n//\\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\\n\\n  const float segmentCount = 8.0;\\n\\n  float index = rawIndex - floor(rawIndex /\\n    (segmentCount * 6.0)) *\\n    (segmentCount * 6.0);\\n\\n  float segment = floor(0.001 + index/6.0);\\n  float segmentIndex = index - (segment*6.0);\\n\\n  normal = -normalize(d);\\n\\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\\n    return mix(vec3(0.0), -d, coneOffset);\\n  }\\n\\n  float nextAngle = (\\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\\n  ) ? 1.0 : 0.0;\\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\\n\\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\\n  vec3 v2 = v1 - d;\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d)*0.25;\\n  vec3 y = v * sin(angle) * length(d)*0.25;\\n  vec3 v3 = v2 + x + y;\\n  if (segmentIndex < 3.0) {\\n    vec3 tx = u * sin(angle);\\n    vec3 ty = v * -cos(angle);\\n    vec3 tangent = tx + ty;\\n    normal = normalize(cross(v3 - v1, tangent));\\n  }\\n\\n  if (segmentIndex == 0.0) {\\n    return mix(d, vec3(0.0), coneOffset);\\n  }\\n  return v3;\\n}\\n\\nattribute vec3 vector;\\nattribute vec4 color, position;\\nattribute vec2 uv;\\n\\nuniform float vectorScale, coneScale, coneOffset;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 eyePosition, lightPosition;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  // Scale the vector magnitude to stay constant with\\n  // model & view changes.\\n  vec3 normal;\\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector), position.w, coneOffset, normal);\\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * conePosition;\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n  // vec4 m_position  = model * vec4(conePosition, 1.0);\\n  vec4 t_position  = view * conePosition;\\n  gl_Position      = projection * t_position;\\n\\n  f_color          = color;\\n  f_data           = conePosition.xyz;\\n  f_position       = position.xyz;\\n  f_uv             = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness,\\n  float fresnel) {\\n\\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n  //Half angle vector\\n  vec3 H = normalize(lightDirection + viewDirection);\\n\\n  //Geometric term\\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\\n  float VdotH = max(dot(viewDirection, H), 0.000001);\\n  float LdotH = max(dot(lightDirection, H), 0.000001);\\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n  float G = min(1.0, min(G1, G2));\\n  \\n  //Distribution term\\n  float D = beckmannDistribution(NdotH, roughness);\\n\\n  //Fresnel term\\n  float F = pow(1.0 - VdotN, fresnel);\\n\\n  //Multiply terms and done\\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n  vec3 N = normalize(f_normal);\\n  vec3 L = normalize(f_lightDirection);\\n  vec3 V = normalize(f_eyeDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = litColor * opacity;\\n}\\n\"]),o=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the cone vertex and normal at the given index.\\n//\\n// The returned vertex is for a cone with its top at origin and height of 1.0,\\n// pointing in the direction of the vector attribute.\\n//\\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\\n// These vertices are used to make up the triangles of the cone by the following:\\n//   segment + 0 top vertex\\n//   segment + 1 perimeter vertex a+1\\n//   segment + 2 perimeter vertex a\\n//   segment + 3 center base vertex\\n//   segment + 4 perimeter vertex a\\n//   segment + 5 perimeter vertex a+1\\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\\n// To go from index to segment, floor(index / 6)\\n// To go from segment to angle, 2*pi * (segment/segmentCount)\\n// To go from index to segment index, index - (segment*6)\\n//\\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\\n\\n  const float segmentCount = 8.0;\\n\\n  float index = rawIndex - floor(rawIndex /\\n    (segmentCount * 6.0)) *\\n    (segmentCount * 6.0);\\n\\n  float segment = floor(0.001 + index/6.0);\\n  float segmentIndex = index - (segment*6.0);\\n\\n  normal = -normalize(d);\\n\\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\\n    return mix(vec3(0.0), -d, coneOffset);\\n  }\\n\\n  float nextAngle = (\\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\\n  ) ? 1.0 : 0.0;\\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\\n\\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\\n  vec3 v2 = v1 - d;\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d)*0.25;\\n  vec3 y = v * sin(angle) * length(d)*0.25;\\n  vec3 v3 = v2 + x + y;\\n  if (segmentIndex < 3.0) {\\n    vec3 tx = u * sin(angle);\\n    vec3 ty = v * -cos(angle);\\n    vec3 tangent = tx + ty;\\n    normal = normalize(cross(v3 - v1, tangent));\\n  }\\n\\n  if (segmentIndex == 0.0) {\\n    return mix(d, vec3(0.0), coneOffset);\\n  }\\n  return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform float vectorScale, coneScale, coneOffset;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  vec3 normal;\\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector.xyz), position.w, coneOffset, normal);\\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n  gl_Position = projection * view * conePosition;\\n  f_id        = id;\\n  f_position  = position.xyz;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3  clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n  gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec4\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"vector\",type:\"vec3\"}]},r.pickShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec4\"},{name:\"id\",type:\"vec4\"},{name:\"vector\",type:\"vec3\"}]}},{glslify:231}],82:[function(t,e,r){e.exports={0:\"NONE\",1:\"ONE\",2:\"LINE_LOOP\",3:\"LINE_STRIP\",4:\"TRIANGLES\",5:\"TRIANGLE_STRIP\",6:\"TRIANGLE_FAN\",256:\"DEPTH_BUFFER_BIT\",512:\"NEVER\",513:\"LESS\",514:\"EQUAL\",515:\"LEQUAL\",516:\"GREATER\",517:\"NOTEQUAL\",518:\"GEQUAL\",519:\"ALWAYS\",768:\"SRC_COLOR\",769:\"ONE_MINUS_SRC_COLOR\",770:\"SRC_ALPHA\",771:\"ONE_MINUS_SRC_ALPHA\",772:\"DST_ALPHA\",773:\"ONE_MINUS_DST_ALPHA\",774:\"DST_COLOR\",775:\"ONE_MINUS_DST_COLOR\",776:\"SRC_ALPHA_SATURATE\",1024:\"STENCIL_BUFFER_BIT\",1028:\"FRONT\",1029:\"BACK\",1032:\"FRONT_AND_BACK\",1280:\"INVALID_ENUM\",1281:\"INVALID_VALUE\",1282:\"INVALID_OPERATION\",1285:\"OUT_OF_MEMORY\",1286:\"INVALID_FRAMEBUFFER_OPERATION\",2304:\"CW\",2305:\"CCW\",2849:\"LINE_WIDTH\",2884:\"CULL_FACE\",2885:\"CULL_FACE_MODE\",2886:\"FRONT_FACE\",2928:\"DEPTH_RANGE\",2929:\"DEPTH_TEST\",2930:\"DEPTH_WRITEMASK\",2931:\"DEPTH_CLEAR_VALUE\",2932:\"DEPTH_FUNC\",2960:\"STENCIL_TEST\",2961:\"STENCIL_CLEAR_VALUE\",2962:\"STENCIL_FUNC\",2963:\"STENCIL_VALUE_MASK\",2964:\"STENCIL_FAIL\",2965:\"STENCIL_PASS_DEPTH_FAIL\",2966:\"STENCIL_PASS_DEPTH_PASS\",2967:\"STENCIL_REF\",2968:\"STENCIL_WRITEMASK\",2978:\"VIEWPORT\",3024:\"DITHER\",3042:\"BLEND\",3088:\"SCISSOR_BOX\",3089:\"SCISSOR_TEST\",3106:\"COLOR_CLEAR_VALUE\",3107:\"COLOR_WRITEMASK\",3317:\"UNPACK_ALIGNMENT\",3333:\"PACK_ALIGNMENT\",3379:\"MAX_TEXTURE_SIZE\",3386:\"MAX_VIEWPORT_DIMS\",3408:\"SUBPIXEL_BITS\",3410:\"RED_BITS\",3411:\"GREEN_BITS\",3412:\"BLUE_BITS\",3413:\"ALPHA_BITS\",3414:\"DEPTH_BITS\",3415:\"STENCIL_BITS\",3553:\"TEXTURE_2D\",4352:\"DONT_CARE\",4353:\"FASTEST\",4354:\"NICEST\",5120:\"BYTE\",5121:\"UNSIGNED_BYTE\",5122:\"SHORT\",5123:\"UNSIGNED_SHORT\",5124:\"INT\",5125:\"UNSIGNED_INT\",5126:\"FLOAT\",5386:\"INVERT\",5890:\"TEXTURE\",6401:\"STENCIL_INDEX\",6402:\"DEPTH_COMPONENT\",6406:\"ALPHA\",6407:\"RGB\",6408:\"RGBA\",6409:\"LUMINANCE\",6410:\"LUMINANCE_ALPHA\",7680:\"KEEP\",7681:\"REPLACE\",7682:\"INCR\",7683:\"DECR\",7936:\"VENDOR\",7937:\"RENDERER\",7938:\"VERSION\",9728:\"NEAREST\",9729:\"LINEAR\",9984:\"NEAREST_MIPMAP_NEAREST\",9985:\"LINEAR_MIPMAP_NEAREST\",9986:\"NEAREST_MIPMAP_LINEAR\",9987:\"LINEAR_MIPMAP_LINEAR\",10240:\"TEXTURE_MAG_FILTER\",10241:\"TEXTURE_MIN_FILTER\",10242:\"TEXTURE_WRAP_S\",10243:\"TEXTURE_WRAP_T\",10497:\"REPEAT\",10752:\"POLYGON_OFFSET_UNITS\",16384:\"COLOR_BUFFER_BIT\",32769:\"CONSTANT_COLOR\",32770:\"ONE_MINUS_CONSTANT_COLOR\",32771:\"CONSTANT_ALPHA\",32772:\"ONE_MINUS_CONSTANT_ALPHA\",32773:\"BLEND_COLOR\",32774:\"FUNC_ADD\",32777:\"BLEND_EQUATION_RGB\",32778:\"FUNC_SUBTRACT\",32779:\"FUNC_REVERSE_SUBTRACT\",32819:\"UNSIGNED_SHORT_4_4_4_4\",32820:\"UNSIGNED_SHORT_5_5_5_1\",32823:\"POLYGON_OFFSET_FILL\",32824:\"POLYGON_OFFSET_FACTOR\",32854:\"RGBA4\",32855:\"RGB5_A1\",32873:\"TEXTURE_BINDING_2D\",32926:\"SAMPLE_ALPHA_TO_COVERAGE\",32928:\"SAMPLE_COVERAGE\",32936:\"SAMPLE_BUFFERS\",32937:\"SAMPLES\",32938:\"SAMPLE_COVERAGE_VALUE\",32939:\"SAMPLE_COVERAGE_INVERT\",32968:\"BLEND_DST_RGB\",32969:\"BLEND_SRC_RGB\",32970:\"BLEND_DST_ALPHA\",32971:\"BLEND_SRC_ALPHA\",33071:\"CLAMP_TO_EDGE\",33170:\"GENERATE_MIPMAP_HINT\",33189:\"DEPTH_COMPONENT16\",33306:\"DEPTH_STENCIL_ATTACHMENT\",33635:\"UNSIGNED_SHORT_5_6_5\",33648:\"MIRRORED_REPEAT\",33901:\"ALIASED_POINT_SIZE_RANGE\",33902:\"ALIASED_LINE_WIDTH_RANGE\",33984:\"TEXTURE0\",33985:\"TEXTURE1\",33986:\"TEXTURE2\",33987:\"TEXTURE3\",33988:\"TEXTURE4\",33989:\"TEXTURE5\",33990:\"TEXTURE6\",33991:\"TEXTURE7\",33992:\"TEXTURE8\",33993:\"TEXTURE9\",33994:\"TEXTURE10\",33995:\"TEXTURE11\",33996:\"TEXTURE12\",33997:\"TEXTURE13\",33998:\"TEXTURE14\",33999:\"TEXTURE15\",34e3:\"TEXTURE16\",34001:\"TEXTURE17\",34002:\"TEXTURE18\",34003:\"TEXTURE19\",34004:\"TEXTURE20\",34005:\"TEXTURE21\",34006:\"TEXTURE22\",34007:\"TEXTURE23\",34008:\"TEXTURE24\",34009:\"TEXTURE25\",34010:\"TEXTURE26\",34011:\"TEXTURE27\",34012:\"TEXTURE28\",34013:\"TEXTURE29\",34014:\"TEXTURE30\",34015:\"TEXTURE31\",34016:\"ACTIVE_TEXTURE\",34024:\"MAX_RENDERBUFFER_SIZE\",34041:\"DEPTH_STENCIL\",34055:\"INCR_WRAP\",34056:\"DECR_WRAP\",34067:\"TEXTURE_CUBE_MAP\",34068:\"TEXTURE_BINDING_CUBE_MAP\",34069:\"TEXTURE_CUBE_MAP_POSITIVE_X\",34070:\"TEXTURE_CUBE_MAP_NEGATIVE_X\",34071:\"TEXTURE_CUBE_MAP_POSITIVE_Y\",34072:\"TEXTURE_CUBE_MAP_NEGATIVE_Y\",34073:\"TEXTURE_CUBE_MAP_POSITIVE_Z\",34074:\"TEXTURE_CUBE_MAP_NEGATIVE_Z\",34076:\"MAX_CUBE_MAP_TEXTURE_SIZE\",34338:\"VERTEX_ATTRIB_ARRAY_ENABLED\",34339:\"VERTEX_ATTRIB_ARRAY_SIZE\",34340:\"VERTEX_ATTRIB_ARRAY_STRIDE\",34341:\"VERTEX_ATTRIB_ARRAY_TYPE\",34342:\"CURRENT_VERTEX_ATTRIB\",34373:\"VERTEX_ATTRIB_ARRAY_POINTER\",34466:\"NUM_COMPRESSED_TEXTURE_FORM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t.bindRenderbuffer(t.RENDERBUFFER,a),t.renderbufferStorage(t.RENDERBUFFER,n,e,r),t.framebufferRenderbuffer(t.FRAMEBUFFER,i,t.RENDERBUFFER,a),a}function d(t,e,r,n,i,a,o,s){this.gl=t,this._shape=[0|e,0|r],this._destroyed=!1,this._ext=s,this.color=new Array(i);for(var d=0;d<i;++d)this.color[d]=null;this._color_rb=null,this.depth=null,this._depth_rb=null,this._colorType=n,this._useDepth=a,this._useStencil=o;var g=this,m=[0|e,0|r];Object.defineProperties(m,{0:{get:function(){return g._shape[0]},set:function(t){return g.width=t}},1:{get:function(){return g._shape[1]},set:function(t){return g.height=t}}}),this._shapeVector=m,function(t){var e=c(t.gl),r=t.gl,n=t.handle=r.createFramebuffer(),i=t._shape[0],a=t._shape[1],o=t.color.length,s=t._ext,d=t._useStencil,g=t._useDepth,m=t._colorType;r.bindFramebuffer(r.FRAMEBUFFER,n);for(var v=0;v<o;++v)t.color[v]=h(r,i,a,m,r.RGBA,r.COLOR_ATTACHMENT0+v);0===o?(t._color_rb=p(r,i,a,r.RGBA4,r.COLOR_ATTACHMENT0),s&&s.drawBuffersWEBGL(l[0])):o>1&&s.drawBuffersWEBGL(l[o]);var y=r.getExtension(\"WEBGL_depth_texture\");y?d?t.depth=h(r,i,a,y.UNSIGNED_INT_24_8_WEBGL,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):g&&(t.depth=h(r,i,a,r.UNSIGNED_SHORT,r.DEPTH_COMPONENT,r.DEPTH_ATTACHMENT)):g&&d?t._depth_rb=p(r,i,a,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):g?t._depth_rb=p(r,i,a,r.DEPTH_COMPONENT16,r.DEPTH_ATTACHMENT):d&&(t._depth_rb=p(r,i,a,r.STENCIL_INDEX,r.STENCIL_ATTACHMENT));var x=r.checkFramebufferStatus(r.FRAMEBUFFER);if(x!==r.FRAMEBUFFER_COMPLETE){t._destroyed=!0,r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteFramebuffer(t.handle),t.handle=null,t.depth&&(t.depth.dispose(),t.depth=null),t._depth_rb&&(r.deleteRenderbuffer(t._depth_rb),t._depth_rb=null);for(v=0;v<t.color.length;++v)t.color[v].dispose(),t.color[v]=null;t._color_rb&&(r.deleteRenderbuffer(t._color_rb),t._color_rb=null),u(r,e),f(x)}u(r,e)}(this)}var g=d.prototype;function m(t,e,r){if(t._destroyed)throw new Error(\"gl-fbo: Can't resize destroyed FBO\");if(t._shape[0]!==e||t._shape[1]!==r){var n=t.gl,i=n.getParameter(n.MAX_RENDERBUFFER_SIZE);if(e<0||e>i||r<0||r>i)throw new Error(\"gl-fbo: Can't resize FBO, invalid dimensions\");t._shape[0]=e,t._shape[1]=r;for(var a=c(n),o=0;o<t.color.length;++o)t.color[o].shape=t._shape;t._color_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._color_rb),n.renderbufferStorage(n.RENDERBUFFER,n.RGBA4,t._shape[0],t._shape[1])),t.depth&&(t.depth.shape=t._shape),t._depth_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._depth_rb),t._useDepth&&t._useStencil?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_STENCIL,t._shape[0],t._shape[1]):t._useDepth?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_COMPONENT16,t._shape[0],t._shape[1]):t._useStencil&&n.renderbufferStorage(n.RENDERBUFFER,n.STENCIL_INDEX,t._shape[0],t._shape[1])),n.bindFramebuffer(n.FRAMEBUFFER,t.handle);var s=n.checkFramebufferStatus(n.FRAMEBUFFER);s!==n.FRAMEBUFFER_COMPLETE&&(t.dispose(),u(n,a),f(s)),u(n,a)}}Object.defineProperties(g,{shape:{get:function(){return this._destroyed?[0,0]:this._shapeVector},set:function(t){if(Array.isArray(t)||(t=[0|t,0|t]),2!==t.length)throw new Error(\"gl-fbo: Shape vector must be length 2\");var e=0|t[0],r=0|t[1];return m(this,e,r),[e,r]},enumerable:!1},width:{get:function(){return this._destroyed?0:this._shape[0]},set:function(t){return m(this,t|=0,this._shape[1]),t},enumerable:!1},height:{get:function(){return this._destroyed?0:this._shape[1]},set:function(t){return t|=0,m(this,this._shape[0],t),t},enumerable:!1}}),g.bind=function(){if(!this._destroyed){var t=this.gl;t.bindFramebuffer(t.FRAMEBUFFER,this.handle),t.viewport(0,0,this._shape[0],this._shape[1])}},g.dispose=function(){if(!this._destroyed){this._destroyed=!0;var t=this.gl;t.deleteFramebuffer(this.handle),this.handle=null,this.depth&&(this.depth.dispose(),this.depth=null),this._depth_rb&&(t.deleteRenderbuffer(this._depth_rb),this._depth_rb=null);for(var e=0;e<this.color.length;++e)this.color[e].dispose(),this.color[e]=null;this._color_rb&&(t.deleteRenderbuffer(this._color_rb),this._color_rb=null)}}},{\"gl-texture2d\":146}],87:[function(t,e,r){var n=t(\"sprintf-js\").sprintf,i=t(\"gl-constants/lookup\"),a=t(\"glsl-shader-name\"),o=t(\"add-line-numbers\");e.exports=function(t,e,r){\"use strict\";var s=a(e)||\"of unknown name (see npm glsl-shader-name)\",l=\"unknown type\";void 0!==r&&(l=r===i.FRAGMENT_SHADER?\"fragment\":\"vertex\");for(var c=n(\"Error compiling %s shader %s:\\n\",l,s),u=n(\"%s%s\",c,t),f=t.split(\"\\n\"),h={},p=0;p<f.length;p++){var d=f[p];if(\"\"!==d&&\"\\0\"!==d){var g=parseInt(d.split(\":\")[2]);if(isNaN(g))throw new Error(n(\"Could not parse error: %s\",d));h[g]=d}}var m=o(e).split(\"\\n\");for(p=0;p<m.length;p++)if(h[p+3]||h[p+2]||h[p+1]){var v=m[p];if(c+=v+\"\\n\",h[p+1]){var y=h[p+1];y=y.substr(y.split(\":\",3).join(\":\").length+1).trim(),c+=n(\"^^^ %s\\n\\n\",y)}}return{long:c.trim(),short:u.trim()}}},{\"add-line-numbers\":9,\"gl-constants/lookup\":83,\"glsl-shader-name\":223,\"sprintf-js\":301}],88:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r=t.gl,n=o(r,l.vertex,l.fragment),i=o(r,l.pickVertex,l.pickFragment),a=s(r),u=s(r),f=s(r),h=s(r),p=new c(t,n,i,a,u,f,h);return p.update(e),t.addObject(p),p};var n=t(\"binary-search-bounds\"),i=t(\"iota-array\"),a=t(\"typedarray-pool\"),o=t(\"gl-shader\"),s=t(\"gl-buffer\"),l=t(\"./lib/shaders\");function c(t,e,r,n,i,a,o){this.plot=t,this.shader=e,this.pickShader=r,this.positionBuffer=n,this.weightBuffer=i,this.colorBuffer=a,this.idBuffer=o,this.xData=[],this.yData=[],this.shape=[0,0],this.bounds=[1/0,1/0,-1/0,-1/0],this.pickOffset=0}var u,f=c.prototype,h=[0,0,1,0,0,1,1,0,1,1,0,1];f.draw=(u=[1,0,0,0,1,0,0,0,1],function(){var t=this.plot,e=this.shader,r=this.bounds,n=this.numVertices;if(!(n<=0)){var i=t.gl,a=t.dataBox,o=r[2]-r[0],s=r[3]-r[1],l=a[2]-a[0],c=a[3]-a[1];u[0]=2*o/l,u[4]=2*s/c,u[6]=2*(r[0]-a[0])/l-1,u[7]=2*(r[1]-a[1])/c-1,e.bind();var f=e.uniforms;f.viewTransform=u,f.shape=this.shape;var h=e.attributes;this.positionBuffer.bind(),h.position.pointer(),this.weightBuffer.bind(),h.weight.pointer(i.UNSIGNED_BYTE,!1),this.colorBuffer.bind(),h.color.pointer(i.UNSIGNED_BYTE,!0),i.drawArrays(i.TRIANGLES,0,n)}}),f.drawPick=function(){var t=[1,0,0,0,1,0,0,0,1],e=[0,0,0,0];return function(r){var n=this.plot,i=this.pickShader,a=this.bounds,o=this.numVertices;if(!(o<=0)){var s=n.gl,l=n.dataBox,c=a[2]-a[0],u=a[3]-a[1],f=l[2]-l[0],h=l[3]-l[1];t[0]=2*c/f,t[4]=2*u/h,t[6]=2*(a[0]-l[0])/f-1,t[7]=2*(a[1]-l[1])/h-1;for(var p=0;p<4;++p)e[p]=r>>8*p&255;this.pickOffset=r,i.bind();var d=i.uniforms;d.viewTransform=t,d.pickOffset=e,d.shape=this.shape;var g=i.attributes;return this.positionBuffer.bind(),g.position.pointer(),this.weightBuffer.bind(),g.weight.pointer(s.UNSIGNED_BYTE,!1),this.idBuffer.bind(),g.pickId.pointer(s.UNSIGNED_BYTE,!1),s.drawArrays(s.TRIANGLES,0,o),r+this.shape[0]*this.shape[1]}}}(),f.pick=function(t,e,r){var n=this.pickOffset,i=this.shape[0]*this.shape[1];if(r<n||r>=n+i)return null;var a=r-n,o=this.xData,s=this.yData;return{object:this,pointId:a,dataCoord:[o[a%this.shape[0]],s[a/this.shape[0]|0]]}},f.update=function(t){var e=(t=t||{}).shape||[0,0],r=t.x||i(e[0]),o=t.y||i(e[1]),s=t.z||new Float32Array(e[0]*e[1]),l=!1!==t.zsmooth;this.xData=r,this.yData=o;var c,u,f,p,d=t.colorLevels||[0],g=t.colorValues||[0,0,0,1],m=d.length,v=this.bounds;l?(c=v[0]=r[0],u=v[1]=o[0],f=v[2]=r[r.length-1],p=v[3]=o[o.length-1]):(c=v[0]=r[0]+(r[1]-r[0])/2,u=v[1]=o[0]+(o[1]-o[0])/2,f=v[2]=r[r.length-1]+(r[r.length-1]-r[r.length-2])/2,p=v[3]=o[o.length-1]+(o[o.length-1]-o[o.length-2])/2);var y=1/(f-c),x=1/(p-u),b=e[0],_=e[1];this.shape=[b,_];var w=(l?(b-1)*(_-1):b*_)*(h.length>>>1);this.numVertices=w;for(var T=a.mallocUint8(4*w),k=a.mallocFloat32(2*w),A=a.mallocUint8(2*w),M=a.mallocUint32(w),S=0,E=l?b-1:b,L=l?_-1:_,C=0;C<L;++C){var P,I;l?(P=x*(o[C]-u),I=x*(o[C+1]-u)):(P=C<_-1?x*(o[C]-(o[C+1]-o[C])/2-u):x*(o[C]-(o[C]-o[C-1])/2-u),I=C<_-1?x*(o[C]+(o[C+1]-o[C])/2-u):x*(o[C]+(o[C]-o[C-1])/2-u));for(var O=0;O<E;++O){var z,D;l?(z=y*(r[O]-c),D=y*(r[O+1]-c)):(z=O<b-1?y*(r[O]-(r[O+1]-r[O])/2-c):y*(r[O]-(r[O]-r[O-1])/2-c),D=O<b-1?y*(r[O]+(r[O+1]-r[O])/2-c):y*(r[O]+(r[O]-r[O-1])/2-c));for(var R=0;R<h.length;R+=2){var F,B,N,j,U=h[R],V=h[R+1],H=s[l?(C+V)*b+(O+U):C*b+O],q=n.le(d,H);if(q<0)F=g[0],B=g[1],N=g[2],j=g[3];else if(q===m-1)F=g[4*m-4],B=g[4*m-3],N=g[4*m-2],j=g[4*m-1];else{var G=(H-d[q])/(d[q+1]-d[q]),Y=1-G,W=4*q,X=4*(q+1);F=Y*g[W]+G*g[X],B=Y*g[W+1]+G*g[X+1],N=Y*g[W+2]+G*g[X+2],j=Y*g[W+3]+G*g[X+3]}T[4*S]=255*F,T[4*S+1]=255*B,T[4*S+2]=255*N,T[4*S+3]=255*j,k[2*S]=.5*z+.5*D,k[2*S+1]=.5*P+.5*I,A[2*S]=U,A[2*S+1]=V,M[S]=C*b+O,S+=1}}}this.positionBuffer.update(k),this.weightBuffer.update(A),this.colorBuffer.update(T),this.idBuffer.update(M),a.free(k),a.free(T),a.free(A),a.free(M)},f.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.positionBuffer.dispose(),this.weightBuffer.dispose(),this.colorBuffer.dispose(),this.idBuffer.dispose(),this.plot.removeObject(this)}},{\"./lib/shaders\":89,\"binary-search-bounds\":31,\"gl-buffer\":78,\"gl-shader\":132,\"iota-array\":235,\"typedarray-pool\":308}],89:[function(t,e,r){\"use strict\";var n=t(\"glslify\");e.exports={fragment:n([\"precision lowp float;\\n#define GLSLIFY 1\\nvarying vec4 fragColor;\\nvoid main() {\\n  gl_FragColor = vec4(fragColor.rgb * fragColor.a, fragColor.a);\\n}\\n\"]),vertex:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 color;\\nattribute vec2 weight;\\n\\nuniform vec2 shape;\\nuniform mat3 viewTransform;\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\\n  fragColor = color;\\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\\n}\\n\"]),pickFragment:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragId;\\nvarying vec2 vWeight;\\n\\nuniform vec2 shape;\\nuniform vec4 pickOffset;\\n\\nvoid main() {\\n  vec2 d = step(.5, vWeight);\\n  vec4 id = fragId + pickOffset;\\n  id.x += d.x + d.y*shape.x;\\n\\n  id.y += floor(id.x / 256.0);\\n  id.x -= floor(id.x / 256.0) * 256.0;\\n\\n  id.z += floor(id.y / 256.0);\\n  id.y -= floor(id.y / 256.0) * 256.0;\\n\\n  id.w += floor(id.z / 256.0);\\n  id.z -= floor(id.z / 256.0) * 256.0;\\n\\n  gl_FragColor = id/255.;\\n}\\n\"]),pickVertex:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 pickId;\\nattribute vec2 weight;\\n\\nuniform vec2 shape;\\nuniform mat3 viewTransform;\\n\\nvarying vec4 fragId;\\nvarying vec2 vWeight;\\n\\nvoid main() {\\n  vWeight = weight;\\n\\n  fragId = pickId;\\n\\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\\n}\\n\"])}},{glslify:231}],90:[function(t,e,r){var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, nextPosition;\\nattribute float arcLength, lineWidth;\\nattribute vec4 color;\\n\\nuniform vec2 screenShape;\\nuniform float pixelRatio;\\nuniform mat4 model, view, projection;\\n\\nvarying vec4 fragColor;\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\n\\nvec4 project(vec3 p) {\\n  return projection * view * model * vec4(p, 1.0);\\n}\\n\\nvoid main() {\\n  vec4 startPoint = project(position);\\n  vec4 endPoint   = project(nextPosition);\\n\\n  vec2 A = startPoint.xy / startPoint.w;\\n  vec2 B =   endPoint.xy /   endPoint.w;\\n\\n  float clipAngle = atan(\\n    (B.y - A.y) * screenShape.y,\\n    (B.x - A.x) * screenShape.x\\n  );\\n\\n  vec2 offset = 0.5 * pixelRatio * lineWidth * vec2(\\n    sin(clipAngle),\\n    -cos(clipAngle)\\n  ) / screenShape;\\n\\n  gl_Position = vec4(startPoint.xy + startPoint.w * offset, startPoint.zw);\\n\\n  worldPosition = position;\\n  pixelArcLength = arcLength;\\n  fragColor = color;\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3      clipBounds[2];\\nuniform sampler2D dashTexture;\\nuniform float     dashScale;\\nuniform float     opacity;\\n\\nvarying vec3    worldPosition;\\nvarying float   pixelArcLength;\\nvarying vec4    fragColor;\\n\\nvoid main() {\\n  if (\\n    outOfRange(clipBounds[0], clipBounds[1], worldPosition) ||\\n    fragColor.a * opacity == 0.\\n  ) discard;\\n\\n  float dashWeight = texture2D(dashTexture, vec2(dashScale * pixelArcLength, 0)).r;\\n  if(dashWeight < 0.5) {\\n    discard;\\n  }\\n  gl_FragColor = fragColor * opacity;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\n#define FLOAT_MAX  1.70141184e38\\n#define FLOAT_MIN  1.17549435e-38\\n\\n// https://github.com/mikolalysenko/glsl-read-float/blob/master/index.glsl\\nvec4 packFloat(float v) {\\n  float av = abs(v);\\n\\n  //Handle special cases\\n  if(av < FLOAT_MIN) {\\n    return vec4(0.0, 0.0, 0.0, 0.0);\\n  } else if(v > FLOAT_MAX) {\\n    return vec4(127.0, 128.0, 0.0, 0.0) / 255.0;\\n  } else if(v < -FLOAT_MAX) {\\n    return vec4(255.0, 128.0, 0.0, 0.0) / 255.0;\\n  }\\n\\n  vec4 c = vec4(0,0,0,0);\\n\\n  //Compute exponent and mantissa\\n  float e = floor(log2(av));\\n  float m = av * pow(2.0, -e) - 1.0;\\n\\n  //Unpack mantissa\\n  c[1] = floor(128.0 * m);\\n  m -= c[1] / 128.0;\\n  c[2] = floor(32768.0 * m);\\n  m -= c[2] / 32768.0;\\n  c[3] = floor(8388608.0 * m);\\n\\n  //Unpack exponent\\n  float ebias = e + 127.0;\\n  c[0] = floor(ebias / 2.0);\\n  ebias -= c[0] * 2.0;\\n  c[1] += floor(ebias) * 128.0;\\n\\n  //Unpack sign bit\\n  c[0] += 128.0 * step(0.0, -v);\\n\\n  //Scale back to range\\n  return c / 255.0;\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform float pickId;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], worldPosition)) discard;\\n\\n  gl_FragColor = vec4(pickId/255.0, packFloat(pixelArcLength).xyz);\\n}\"]),l=[{name:\"position\",type:\"vec3\"},{name:\"nextPosition\",type:\"vec3\"},{name:\"arcLength\",type:\"float\"},{name:\"lineWidth\",type:\"float\"},{name:\"color\",type:\"vec4\"}];r.createShader=function(t){return i(t,a,o,null,l)},r.createPickShader=function(t){return i(t,a,s,null,l)}},{\"gl-shader\":132,glslify:231}],91:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl||t.scene&&t.scene.gl,r=f(e);r.attributes.position.location=0,r.attributes.nextPosition.location=1,r.attributes.arcLength.location=2,r.attributes.lineWidth.location=3,r.attributes.color.location=4;var o=h(e);o.attributes.position.location=0,o.attributes.nextPosition.location=1,o.attributes.arcLength.location=2,o.attributes.lineWidth.location=3,o.attributes.color.location=4;for(var s=n(e),l=i(e,[{buffer:s,size:3,offset:0,stride:48},{buffer:s,size:3,offset:12,stride:48},{buffer:s,size:1,offset:24,stride:48},{buffer:s,size:1,offset:28,stride:48},{buffer:s,size:4,offset:32,stride:48}]),u=c(new Array(1024),[256,1,4]),p=0;p<1024;++p)u.data[p]=255;var d=a(e,u);d.wrap=e.REPEAT;var g=new v(e,r,o,s,l,d);return g.update(t),g};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"gl-texture2d\"),o=new Uint8Array(4),s=new Float32Array(o.buffer);var l=t(\"binary-search-bounds\"),c=t(\"ndarray\"),u=t(\"./lib/shaders\"),f=u.createShader,h=u.createPickShader,p=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function d(t,e){for(var r=0,n=0;n<3;++n){var i=t[n]-e[n];r+=i*i}return Math.sqrt(r)}function g(t){for(var e=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],r=0;r<3;++r)e[0][r]=Math.max(t[0][r],e[0][r]),e[1][r]=Math.min(t[1][r],e[1][r]);return e}function m(t,e,r,n){this.arcLength=t,this.position=e,this.index=r,this.dataCoordinate=n}function v(t,e,r,n,i,a){this.gl=t,this.shader=e,this.pickShader=r,this.buffer=n,this.vao=i,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.points=[],this.arcLength=[],this.vertexCount=0,this.bounds=[[0,0,0],[0,0,0]],this.pickId=0,this.lineWidth=1,this.texture=a,this.dashScale=1,this.opacity=1,this.hasAlpha=!1,this.dirty=!0,this.pixelRatio=1}var y=v.prototype;y.isTransparent=function(){return this.hasAlpha},y.isOpaque=function(){return!this.hasAlpha},y.pickSlots=1,y.setPickBase=function(t){this.pickId=t},y.drawTransparent=y.draw=function(t){if(this.vertexCount){var e=this.gl,r=this.shader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,clipBounds:g(this.clipBounds),dashTexture:this.texture.bind(),dashScale:this.dashScale/this.arcLength[this.arcLength.length-1],opacity:this.opacity,screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},y.drawPick=function(t){if(this.vertexCount){var e=this.gl,r=this.pickShader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,pickId:this.pickId,clipBounds:g(this.clipBounds),screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},y.update=function(t){var e,r;this.dirty=!0;var n=!!t.connectGaps;\"dashScale\"in t&&(this.dashScale=t.dashScale),this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=+t.opacity,this.opacity<1&&(this.hasAlpha=!0));var i=[],a=[],o=[],s=0,u=0,f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],h=t.position||t.positions;if(h){var p=t.color||t.colors||[0,0,0,1],g=t.lineWidth||1,m=!1;t:for(e=1;e<h.length;++e){var v,y,x,b=h[e-1],_=h[e];for(a.push(s),o.push(b.slice()),r=0;r<3;++r){if(isNaN(b[r])||isNaN(_[r])||!isFinite(b[r])||!isFinite(_[r])){if(!n&&i.length>0){for(var w=0;w<24;++w)i.push(i[i.length-12]);u+=2,m=!0}continue t}f[0][r]=Math.min(f[0][r],b[r],_[r]),f[1][r]=Math.max(f[1][r],b[r],_[r])}Array.isArray(p[0])?(v=p.length>e-1?p[e-1]:p.length>0?p[p.length-1]:[0,0,0,1],y=p.length>e?p[e]:p.length>0?p[p.length-1]:[0,0,0,1]):v=y=p,3===v.length&&(v=[v[0],v[1],v[2],1]),3===y.length&&(y=[y[0],y[1],y[2],1]),!this.hasAlpha&&v[3]<1&&(this.hasAlpha=!0),x=Array.isArray(g)?g.length>e-1?g[e-1]:g.length>0?g[g.length-1]:[0,0,0,1]:g;var T=s;if(s+=d(b,_),m){for(r=0;r<2;++r)i.push(b[0],b[1],b[2],_[0],_[1],_[2],T,x,v[0],v[1],v[2],v[3]);u+=2,m=!1}i.push(b[0],b[1],b[2],_[0],_[1],_[2],T,x,v[0],v[1],v[2],v[3],b[0],b[1],b[2],_[0],_[1],_[2],T,-x,v[0],v[1],v[2],v[3],_[0],_[1],_[2],b[0],b[1],b[2],s,-x,y[0],y[1],y[2],y[3],_[0],_[1],_[2],b[0],b[1],b[2],s,x,y[0],y[1],y[2],y[3]),u+=4}}if(this.buffer.update(i),a.push(s),o.push(h[h.length-1].slice()),this.bounds=f,this.vertexCount=u,this.points=o,this.arcLength=a,\"dashes\"in t){var k=t.dashes.slice();for(k.unshift(0),e=1;e<k.length;++e)k[e]=k[e-1]+k[e];var A=c(new Array(1024),[256,1,4]);for(e=0;e<256;++e){for(r=0;r<4;++r)A.set(e,0,r,0);1&l.le(k,k[k.length-1]*e/255)?A.set(e,0,0,0):A.set(e,0,0,255)}this.texture.setPixels(A)}},y.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()},y.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=function(t,e,r,n){return o[0]=n,o[1]=r,o[2]=e,o[3]=t,s[0]}(t.value[0],t.value[1],t.value[2],0),r=l.le(this.arcLength,e);if(r<0)return null;if(r===this.arcLength.length-1)return new m(this.arcLength[this.arcLength.length-1],this.points[this.points.length-1].slice(),r);for(var n=this.points[r],i=this.points[Math.min(r+1,this.points.length-1)],a=(e-this.arcLength[r])/(this.arcLength[r+1]-this.arcLength[r]),c=1-a,u=[0,0,0],f=0;f<3;++f)u[f]=c*n[f]+a*i[f];var h=Math.min(a<.5?r:r+1,this.points.length-1);return new m(e,u,h,this.points[h])}},{\"./lib/shaders\":90,\"binary-search-bounds\":31,\"gl-buffer\":78,\"gl-texture2d\":146,\"gl-vao\":150,ndarray:259}],92:[function(t,e,r){e.exports=function(t){var e=new Float32Array(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e}},{}],93:[function(t,e,r){e.exports=function(){var t=new Float32Array(16);return 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t[0]=1-(p+g),t[1]=f+y,t[2]=h-v,t[3]=0,t[4]=f-y,t[5]=1-(u+g),t[6]=d+m,t[7]=0,t[8]=h+v,t[9]=d-m,t[10]=1-(u+p),t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t}},{}],97:[function(t,e,r){e.exports=function(t){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],98:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],c=e[7],u=e[8],f=e[9],h=e[10],p=e[11],d=e[12],g=e[13],m=e[14],v=e[15],y=r*s-n*o,x=r*l-i*o,b=r*c-a*o,_=n*l-i*s,w=n*c-a*s,T=i*c-a*l,k=u*g-f*d,A=u*m-h*d,M=u*v-p*d,S=f*m-h*g,E=f*v-p*g,L=h*v-p*m,C=y*L-x*E+b*S+_*M-w*A+T*k;if(!C)return null;return C=1/C,t[0]=(s*L-l*E+c*S)*C,t[1]=(i*E-n*L-a*S)*C,t[2]=(g*T-m*w+v*_)*C,t[3]=(h*w-f*T-p*_)*C,t[4]=(l*M-o*L-c*A)*C,t[5]=(r*L-i*M+a*A)*C,t[6]=(m*b-d*T-v*x)*C,t[7]=(u*T-h*b+p*x)*C,t[8]=(o*E-s*M+c*k)*C,t[9]=(n*M-r*E-a*k)*C,t[10]=(d*w-g*b+v*y)*C,t[11]=(f*b-u*w-p*y)*C,t[12]=(s*A-o*S-l*k)*C,t[13]=(r*S-n*A+i*k)*C,t[14]=(g*x-d*_-m*y)*C,t[15]=(u*_-f*x+h*y)*C,t}},{}],99:[function(t,e,r){var n=t(\"./identity\");e.exports=function(t,e,r,i){var a,o,s,l,c,u,f,h,p,d,g=e[0],m=e[1],v=e[2],y=i[0],x=i[1],b=i[2],_=r[0],w=r[1],T=r[2];if(Math.abs(g-_)<1e-6&&Math.abs(m-w)<1e-6&&Math.abs(v-T)<1e-6)return n(t);f=g-_,h=m-w,p=v-T,d=1/Math.sqrt(f*f+h*h+p*p),a=x*(p*=d)-b*(h*=d),o=b*(f*=d)-y*p,s=y*h-x*f,(d=Math.sqrt(a*a+o*o+s*s))?(a*=d=1/d,o*=d,s*=d):(a=0,o=0,s=0);l=h*s-p*o,c=p*a-f*s,u=f*o-h*a,(d=Math.sqrt(l*l+c*c+u*u))?(l*=d=1/d,c*=d,u*=d):(l=0,c=0,u=0);return t[0]=a,t[1]=l,t[2]=f,t[3]=0,t[4]=o,t[5]=c,t[6]=h,t[7]=0,t[8]=s,t[9]=u,t[10]=p,t[11]=0,t[12]=-(a*g+o*m+s*v),t[13]=-(l*g+c*m+u*v),t[14]=-(f*g+h*m+p*v),t[15]=1,t}},{\"./identity\":97}],100:[function(t,e,r){e.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],g=e[12],m=e[13],v=e[14],y=e[15],x=r[0],b=r[1],_=r[2],w=r[3];return t[0]=x*n+b*s+_*f+w*g,t[1]=x*i+b*l+_*h+w*m,t[2]=x*a+b*c+_*p+w*v,t[3]=x*o+b*u+_*d+w*y,x=r[4],b=r[5],_=r[6],w=r[7],t[4]=x*n+b*s+_*f+w*g,t[5]=x*i+b*l+_*h+w*m,t[6]=x*a+b*c+_*p+w*v,t[7]=x*o+b*u+_*d+w*y,x=r[8],b=r[9],_=r[10],w=r[11],t[8]=x*n+b*s+_*f+w*g,t[9]=x*i+b*l+_*h+w*m,t[10]=x*a+b*c+_*p+w*v,t[11]=x*o+b*u+_*d+w*y,x=r[12],b=r[13],_=r[14],w=r[15],t[12]=x*n+b*s+_*f+w*g,t[13]=x*i+b*l+_*h+w*m,t[14]=x*a+b*c+_*p+w*v,t[15]=x*o+b*u+_*d+w*y,t}},{}],101:[function(t,e,r){e.exports=function(t,e,r,n,i,a,o){var s=1/(e-r),l=1/(n-i),c=1/(a-o);return t[0]=-2*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*l,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*c,t[11]=0,t[12]=(e+r)*s,t[13]=(i+n)*l,t[14]=(o+a)*c,t[15]=1,t}},{}],102:[function(t,e,r){e.exports=function(t,e,r,n,i){var a=1/Math.tan(e/2),o=1/(n-i);return t[0]=a/r,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=a,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=(i+n)*o,t[11]=-1,t[12]=0,t[13]=0,t[14]=2*i*n*o,t[15]=0,t}},{}],103:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c,u,f,h,p,d,g,m,v,y,x,b,_,w,T,k,A,M,S,E=n[0],L=n[1],C=n[2],P=Math.sqrt(E*E+L*L+C*C);if(Math.abs(P)<1e-6)return null;E*=P=1/P,L*=P,C*=P,i=Math.sin(r),a=Math.cos(r),o=1-a,s=e[0],l=e[1],c=e[2],u=e[3],f=e[4],h=e[5],p=e[6],d=e[7],g=e[8],m=e[9],v=e[10],y=e[11],x=E*E*o+a,b=L*E*o+C*i,_=C*E*o-L*i,w=E*L*o-C*i,T=L*L*o+a,k=C*L*o+E*i,A=E*C*o+L*i,M=L*C*o-E*i,S=C*C*o+a,t[0]=s*x+f*b+g*_,t[1]=l*x+h*b+m*_,t[2]=c*x+p*b+v*_,t[3]=u*x+d*b+y*_,t[4]=s*w+f*T+g*k,t[5]=l*w+h*T+m*k,t[6]=c*w+p*T+v*k,t[7]=u*w+d*T+y*k,t[8]=s*A+f*M+g*S,t[9]=l*A+h*M+m*S,t[10]=c*A+p*M+v*S,t[11]=u*A+d*M+y*S,e!==t&&(t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t}},{}],104:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[4],o=e[5],s=e[6],l=e[7],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[4]=a*i+c*n,t[5]=o*i+u*n,t[6]=s*i+f*n,t[7]=l*i+h*n,t[8]=c*i-a*n,t[9]=u*i-o*n,t[10]=f*i-s*n,t[11]=h*i-l*n,t}},{}],105:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i-c*n,t[1]=o*i-u*n,t[2]=s*i-f*n,t[3]=l*i-h*n,t[8]=a*n+c*i,t[9]=o*n+u*i,t[10]=s*n+f*i,t[11]=l*n+h*i,t}},{}],106:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[4],u=e[5],f=e[6],h=e[7];e!==t&&(t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i+c*n,t[1]=o*i+u*n,t[2]=s*i+f*n,t[3]=l*i+h*n,t[4]=c*i-a*n,t[5]=u*i-o*n,t[6]=f*i-s*n,t[7]=h*i-l*n,t}},{}],107:[function(t,e,r){e.exports=function(t,e,r){var n=r[0],i=r[1],a=r[2];return t[0]=e[0]*n,t[1]=e[1]*n,t[2]=e[2]*n,t[3]=e[3]*n,t[4]=e[4]*i,t[5]=e[5]*i,t[6]=e[6]*i,t[7]=e[7]*i,t[8]=e[8]*a,t[9]=e[9]*a,t[10]=e[10]*a,t[11]=e[11]*a,t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t}},{}],108:[function(t,e,r){e.exports=function(t,e,r){var n,i,a,o,s,l,c,u,f,h,p,d,g=r[0],m=r[1],v=r[2];e===t?(t[12]=e[0]*g+e[4]*m+e[8]*v+e[12],t[13]=e[1]*g+e[5]*m+e[9]*v+e[13],t[14]=e[2]*g+e[6]*m+e[10]*v+e[14],t[15]=e[3]*g+e[7]*m+e[11]*v+e[15]):(n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],t[0]=n,t[1]=i,t[2]=a,t[3]=o,t[4]=s,t[5]=l,t[6]=c,t[7]=u,t[8]=f,t[9]=h,t[10]=p,t[11]=d,t[12]=n*g+s*m+f*v+e[12],t[13]=i*g+l*m+h*v+e[13],t[14]=a*g+c*m+p*v+e[14],t[15]=o*g+u*m+d*v+e[15]);return t}},{}],109:[function(t,e,r){e.exports=function(t,e){if(t===e){var r=e[1],n=e[2],i=e[3],a=e[6],o=e[7],s=e[11];t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=r,t[6]=e[9],t[7]=e[13],t[8]=n,t[9]=a,t[11]=e[14],t[12]=i,t[13]=o,t[14]=s}else t[0]=e[0],t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=e[1],t[5]=e[5],t[6]=e[9],t[7]=e[13],t[8]=e[2],t[9]=e[6],t[10]=e[10],t[11]=e[14],t[12]=e[3],t[13]=e[7],t[14]=e[11],t[15]=e[15];return t}},{}],110:[function(t,e,r){\"use strict\";var n=t(\"barycentric\"),i=t(\"polytope-closest-point/lib/closest_point_2d.js\");function a(t,e){for(var r=[0,0,0,0],n=0;n<4;++n)for(var i=0;i<4;++i)r[i]+=t[4*n+i]*e[n];return r}function o(t,e,r,n,i){for(var o=a(n,a(r,a(e,[t[0],t[1],t[2],1]))),s=0;s<3;++s)o[s]/=o[3];return[.5*i[0]*(1+o[0]),.5*i[1]*(1-o[1])]}function s(t,e){for(var r=[0,0,0],n=0;n<t.length;++n)for(var i=t[n],a=e[n],o=0;o<3;++o)r[o]+=a*i[o];return r}e.exports=function(t,e,r,a,l,c){if(1===t.length)return[0,t[0].slice()];for(var u=new Array(t.length),f=0;f<t.length;++f)u[f]=o(t[f],r,a,l,c);var h=0,p=1/0;for(f=0;f<u.length;++f){for(var d=0,g=0;g<2;++g)d+=Math.pow(u[f][g]-e[g],2);d<p&&(p=d,h=f)}var m=function(t,e){if(2===t.length){for(var r=0,a=0,o=0;o<2;++o)r+=Math.pow(e[o]-t[0][o],2),a+=Math.pow(e[o]-t[1][o],2);return r=Math.sqrt(r),a=Math.sqrt(a),r+a<1e-6?[1,0]:[a/(r+a),r/(a+r)]}if(3===t.length){var s=[0,0];return i(t[0],t[1],t[2],e,s),n(t,s)}return[]}(u,e),v=0;for(f=0;f<3;++f){if(m[f]<-.001||m[f]>1.0001)return null;v+=m[f]}if(Math.abs(v-1)>.001)return null;return[h,s(t,m),m]}},{barycentric:14,\"polytope-closest-point/lib/closest_point_2d.js\":270}],111:[function(t,e,r){var n=t(\"glslify\"),i=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, normal;\\nattribute vec4 color;\\nattribute vec2 uv;\\n\\nuniform mat4 model\\n           , view\\n           , projection\\n           , inverseModel;\\nuniform vec3 eyePosition\\n           , lightPosition;\\n\\nvarying vec3 f_normal\\n           , f_lightDirection\\n           , f_eyeDirection\\n           , f_data;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvec4 project(vec3 p) {\\n  return projection * view * model * vec4(p, 1.0);\\n}\\n\\nvoid main() {\\n  gl_Position      = project(position);\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * vec4(position , 1.0);\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  f_normal  = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n  f_color          = color;\\n  f_data           = position;\\n  f_uv             = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness,\\n  float fresnel) {\\n\\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n  //Half angle vector\\n  vec3 H = normalize(lightDirection + viewDirection);\\n\\n  //Geometric term\\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\\n  float VdotH = max(dot(viewDirection, H), 0.000001);\\n  float LdotH = max(dot(lightDirection, H), 0.000001);\\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n  float G = min(1.0, min(G1, G2));\\n  \\n  //Distribution term\\n  float D = beckmannDistribution(NdotH, roughness);\\n\\n  //Fresnel term\\n  float F = pow(1.0 - VdotN, fresnel);\\n\\n  //Multiply terms and done\\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\n//#pragma glslify: beckmann = require(glsl-specular-beckmann) // used in gl-surface3d\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness\\n            , fresnel\\n            , kambient\\n            , kdiffuse\\n            , kspecular;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal\\n           , f_lightDirection\\n           , f_eyeDirection\\n           , f_data;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (f_color.a == 0.0 ||\\n    outOfRange(clipBounds[0], clipBounds[1], f_data)\\n  ) discard;\\n\\n  vec3 N = normalize(f_normal);\\n  vec3 L = normalize(f_lightDirection);\\n  vec3 V = normalize(f_eyeDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n  //float specular = max(0.0, beckmann(L, V, N, roughness)); // used in gl-surface3d\\n\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  vec4 surfaceColor = vec4(f_color.rgb, 1.0) * texture2D(texture, f_uv);\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = litColor * f_color.a;\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 uv;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  gl_Position = projection * view * model * vec4(position, 1.0);\\n  f_color = color;\\n  f_data  = position;\\n  f_uv    = uv;\\n}\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform sampler2D texture;\\nuniform float opacity;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_data)) discard;\\n\\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\\n}\"]),l=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 uv;\\nattribute float pointSize;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0.0, 0.0 ,0.0 ,0.0);\\n  } else {\\n    gl_Position = projection * view * model * vec4(position, 1.0);\\n  }\\n  gl_PointSize = pointSize;\\n  f_color = color;\\n  f_uv = uv;\\n}\"]),c=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D texture;\\nuniform float opacity;\\n\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  vec2 pointR = gl_PointCoord.xy - vec2(0.5, 0.5);\\n  if(dot(pointR, pointR) > 0.25) {\\n    discard;\\n  }\\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\\n}\"]),u=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  gl_Position = projection * view * model * vec4(position, 1.0);\\n  f_id        = id;\\n  f_position  = position;\\n}\"]),f=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3  clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n  gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]),h=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3  position;\\nattribute float pointSize;\\nattribute vec4  id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0.0, 0.0, 0.0, 0.0);\\n  } else {\\n    gl_Position  = projection * view * model * vec4(position, 1.0);\\n    gl_PointSize = pointSize;\\n  }\\n  f_id         = id;\\n  f_position   = position;\\n}\"]),p=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\n\\nvoid main() {\\n  gl_Position = projection * view * model * vec4(position, 1.0);\\n}\"]),d=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec3 contourColor;\\n\\nvoid main() {\\n  gl_FragColor = vec4(contourColor, 1.0);\\n}\\n\"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},r.wireShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},r.pointShader={vertex:l,fragment:c,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"pointSize\",type:\"float\"}]},r.pickShader={vertex:u,fragment:f,attributes:[{name:\"position\",type:\"vec3\"},{name:\"id\",type:\"vec4\"}]},r.pointPickShader={vertex:h,fragment:f,attributes:[{name:\"position\",type:\"vec3\"},{name:\"pointSize\",type:\"float\"},{name:\"id\",type:\"vec4\"}]},r.contourShader={vertex:p,fragment:d,attributes:[{name:\"position\",type:\"vec3\"}]}},{glslify:231}],112:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"gl-texture2d\"),s=t(\"normals\"),l=t(\"gl-mat4/multiply\"),c=t(\"gl-mat4/invert\"),u=t(\"ndarray\"),f=t(\"colormap\"),h=t(\"simplicial-complex-contour\"),p=t(\"typedarray-pool\"),d=t(\"./lib/shaders\"),g=t(\"./lib/closest-point\"),m=d.meshShader,v=d.wireShader,y=d.pointShader,x=d.pickShader,b=d.pointPickShader,_=d.contourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function T(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g,m,v,y,x,b,_,T,k,A,M,S){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.lineShader=n,this.pointShader=i,this.pickShader=a,this.pointPickShader=o,this.contourShader=s,this.trianglePositions=l,this.triangleColors=u,this.triangleNormals=h,this.triangleUVs=f,this.triangleIds=c,this.triangleVAO=p,this.triangleCount=0,this.lineWidth=1,this.edgePositions=d,this.edgeColors=m,this.edgeUVs=v,this.edgeIds=g,this.edgeVAO=y,this.edgeCount=0,this.pointPositions=x,this.pointColors=_,this.pointUVs=T,this.pointSizes=k,this.pointIds=b,this.pointVAO=A,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=M,this.contourVAO=S,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!0,this.pickVertex=!0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.hasAlpha=!1,this.opacityscale=!1,this._model=w,this._view=w,this._projection=w,this._resolution=[1,1]}var k=T.prototype;function A(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;r<e.length;++r){if(e.length<2)return 1;if(e[r][0]===t)return e[r][1];if(e[r][0]>t&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}function M(t){var e=n(t,m.vertex,m.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.normal.location=4,e}function S(t){var e=n(t,v.vertex,v.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e}function E(t){var e=n(t,y.vertex,y.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.pointSize.location=4,e}function L(t){var e=n(t,x.vertex,x.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e}function C(t){var e=n(t,b.vertex,b.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e.attributes.pointSize.location=4,e}function P(t){var e=n(t,_.vertex,_.fragment);return e.attributes.position.location=0,e}k.isOpaque=function(){return!this.hasAlpha},k.isTransparent=function(){return this.hasAlpha},k.pickSlots=1,k.setPickBase=function(t){this.pickId=t},k.highlight=function(t){if(t&&this.contourEnable){for(var e=h(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,a=r.length,o=p.mallocFloat32(6*a),s=0,l=0;l<a;++l)for(var c=r[l],u=0;u<2;++u){var f=c[0];2===c.length&&(f=c[u]);for(var d=n[f][0],g=n[f][1],m=i[f],v=1-m,y=this.positions[d],x=this.positions[g],b=0;b<3;++b)o[s++]=m*y[b]+v*x[b]}this.contourCount=s/3|0,this.contourPositions.update(o.subarray(0,s)),p.free(o)}else this.contourCount=0},k.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,\"contourEnable\"in t&&(this.contourEnable=t.contourEnable),\"contourColor\"in t&&(this.contourColor=t.contourColor),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"lightPosition\"in t&&(this.lightPosition=t.lightPosition),this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=t.opacity,this.opacity<1&&(this.hasAlpha=!0)),\"opacityscale\"in t&&(this.opacityscale=t.opacityscale,this.hasAlpha=!0),\"ambient\"in t&&(this.ambientLight=t.ambient),\"diffuse\"in t&&(this.diffuseLight=t.diffuse),\"specular\"in t&&(this.specularLight=t.specular),\"roughness\"in t&&(this.roughness=t.roughness),\"fresnel\"in t&&(this.fresnel=t.fresnel),t.texture?(this.texture.dispose(),this.texture=o(e,t.texture)):t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t,e){for(var r=f({colormap:t,nshades:256,format:\"rgba\"}),n=new Uint8Array(1024),i=0;i<256;++i){for(var a=r[i],o=0;o<3;++o)n[4*i+o]=a[o];n[4*i+3]=e?255*A(i/255,e):255*a[3]}return u(n,[256,256,4],[4,0,1])}(t.colormap,this.opacityscale)),this.texture.generateMipmap());var r=t.cells,n=t.positions;if(n&&r){var i=[],a=[],l=[],c=[],h=[],p=[],d=[],g=[],m=[],v=[],y=[],x=[],b=[],_=[];this.cells=r,this.positions=n;var w=t.vertexNormals,T=t.cellNormals,k=void 0===t.vertexNormalsEpsilon?1e-6:t.vertexNormalsEpsilon,M=void 0===t.faceNormalsEpsilon?1e-6:t.faceNormalsEpsilon;t.useFacetNormals&&!T&&(T=s.faceNormals(r,n,M)),T||w||(w=s.vertexNormals(r,n,k));var S=t.vertexColors,E=t.cellColors,L=t.meshColor||[1,1,1,1],C=t.vertexUVs,P=t.vertexIntensity,I=t.cellUVs,O=t.cellIntensity,z=1/0,D=-1/0;if(!C&&!I)if(P)if(t.vertexIntensityBounds)z=+t.vertexIntensityBounds[0],D=+t.vertexIntensityBounds[1];else for(var R=0;R<P.length;++R){var F=P[R];z=Math.min(z,F),D=Math.max(D,F)}else if(O)if(t.cellIntensityBounds)z=+t.cellIntensityBounds[0],D=+t.cellIntensityBounds[1];else for(R=0;R<O.length;++R){F=O[R];z=Math.min(z,F),D=Math.max(D,F)}else for(R=0;R<n.length;++R){F=n[R][2];z=Math.min(z,F),D=Math.max(D,F)}this.intensity=P||(O||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n)),this.pickVertex=!(O||E);var B=t.pointSizes,N=t.pointSize||1;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(R=0;R<n.length;++R)for(var j=n[R],U=0;U<3;++U)!isNaN(j[U])&&isFinite(j[U])&&(this.bounds[0][U]=Math.min(this.bounds[0][U],j[U]),this.bounds[1][U]=Math.max(this.bounds[1][U],j[U]));var V=0,H=0,q=0;t:for(R=0;R<r.length;++R){var G=r[R];switch(G.length){case 1:for(j=n[W=G[0]],U=0;U<3;++U)if(isNaN(j[U])||!isFinite(j[U]))continue t;v.push(j[0],j[1],j[2]),X=S?S[W]:E?E[R]:L,this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[W]-z)/(D-z),this.opacityscale)):3===X.length?y.push(X[0],X[1],X[2],this.opacity):(y.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)),Z=C?C[W]:P?[(P[W]-z)/(D-z),0]:I?I[R]:O?[(O[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],x.push(Z[0],Z[1]),B?b.push(B[W]):b.push(N),_.push(R),q+=1;break;case 2:for(U=0;U<2;++U){j=n[W=G[U]];for(var Y=0;Y<3;++Y)if(isNaN(j[Y])||!isFinite(j[Y]))continue t}for(U=0;U<2;++U){j=n[W=G[U]];p.push(j[0],j[1],j[2]),X=S?S[W]:E?E[R]:L,this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[W]-z)/(D-z),this.opacityscale)):3===X.length?d.push(X[0],X[1],X[2],this.opacity):(d.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)),Z=C?C[W]:P?[(P[W]-z)/(D-z),0]:I?I[R]:O?[(O[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],g.push(Z[0],Z[1]),m.push(R)}H+=1;break;case 3:for(U=0;U<3;++U)for(j=n[W=G[U]],Y=0;Y<3;++Y)if(isNaN(j[Y])||!isFinite(j[Y]))continue t;for(U=0;U<3;++U){var W,X,Z,J;j=n[W=G[2-U]];i.push(j[0],j[1],j[2]),(X=S?S[W]:E?E[R]:L)?this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[W]-z)/(D-z),this.opacityscale)):3===X.length?a.push(X[0],X[1],X[2],this.opacity):(a.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)):a.push(.5,.5,.5,1),Z=C?C[W]:P?[(P[W]-z)/(D-z),0]:I?I[R]:O?[(O[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],c.push(Z[0],Z[1]),J=w?w[W]:T[R],l.push(J[0],J[1],J[2]),h.push(R)}V+=1}}this.pointCount=q,this.edgeCount=H,this.triangleCount=V,this.pointPositions.update(v),this.pointColors.update(y),this.pointUVs.update(x),this.pointSizes.update(b),this.pointIds.update(new Uint32Array(_)),this.edgePositions.update(p),this.edgeColors.update(d),this.edgeUVs.update(g),this.edgeIds.update(new Uint32Array(m)),this.trianglePositions.update(i),this.triangleColors.update(a),this.triangleUVs.update(c),this.triangleNormals.update(l),this.triangleIds.update(new Uint32Array(h))}},k.drawTransparent=k.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var s={model:r,view:n,projection:i,inverseModel:w.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],contourColor:this.contourColor,texture:0};s.inverseModel=c(s.inverseModel,s.model),e.disable(e.CULL_FACE),this.texture.bind(0);var u=new Array(16);l(u,s.view,s.model),l(u,s.projection,u),c(u,u);for(o=0;o<3;++o)s.eyePosition[o]=u[12+o]/u[15];var f,h=u[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*u[4*o+3];for(o=0;o<3;++o){for(var p=u[12+o],d=0;d<3;++d)p+=u[4*d+o]*this.lightPosition[d];s.lightPosition[o]=p/h}this.triangleCount>0&&((f=this.triShader).bind(),f.uniforms=s,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind());this.edgeCount>0&&this.lineWidth>0&&((f=this.lineShader).bind(),f.uniforms=s,this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind());this.pointCount>0&&((f=this.pointShader).bind(),f.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind());this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0&&((f=this.contourShader).bind(),f.uniforms=s,this.contourVAO.bind(),e.drawArrays(e.LINES,0,this.contourCount),this.contourVAO.unbind())},k.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s,l={model:r,view:n,projection:i,clipBounds:a,pickId:this.pickId/255};((s=this.pickShader).bind(),s.uniforms=l,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0)&&((s=this.pointPickShader).bind(),s.uniforms=l,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind())},k.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;for(var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions,i=new Array(r.length),a=0;a<r.length;++a)i[a]=n[r[a]];var o=t.coord[0],s=t.coord[1];if(!this.pickVertex){var l=this.positions[r[0]],c=this.positions[r[1]],u=this.positions[r[2]],f=[(l[0]+c[0]+u[0])/3,(l[1]+c[1]+u[1])/3,(l[2]+c[2]+u[2])/3];return{_cellCenter:!0,position:[o,s],index:e,cell:r,cellId:e,intensity:this.intensity[e],dataCoordinate:f}}var h=g(i,[o*this.pixelRatio,this._resolution[1]-s*this.pixelRatio],this._model,this._view,this._projection,this._resolution);if(!h)return null;var p=h[2],d=0;for(a=0;a<r.length;++a)d+=p[a]*this.intensity[r[a]];return{position:h[1],index:r[h[0]],cell:r,cellId:e,intensity:d,dataCoordinate:this.positions[r[h[0]]]}},k.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.lineShader.dispose(),this.pointShader.dispose(),this.pickShader.dispose(),this.pointPickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleNormals.dispose(),this.triangleIds.dispose(),this.edgeVAO.dispose(),this.edgePositions.dispose(),this.edgeColors.dispose(),this.edgeUVs.dispose(),this.edgeIds.dispose(),this.pointVAO.dispose(),this.pointPositions.dispose(),this.pointColors.dispose(),this.pointUVs.dispose(),this.pointSizes.dispose(),this.pointIds.dispose(),this.contourVAO.dispose(),this.contourPositions.dispose(),this.contourShader.dispose()},e.exports=function(t,e){1===arguments.length&&(t=(e=t).gl);var r=t.getExtension(\"OES_standard_derivatives\")||t.getExtension(\"MOZ_OES_standard_derivatives\")||t.getExtension(\"WEBKIT_OES_standard_derivatives\");if(!r)throw new Error(\"derivatives not supported\");var n=M(t),s=S(t),l=E(t),c=L(t),f=C(t),h=P(t),p=o(t,u(new Uint8Array([255,255,255,255]),[1,1,4]));p.generateMipmap(),p.minFilter=t.LINEAR_MIPMAP_LINEAR,p.magFilter=t.LINEAR;var d=i(t),g=i(t),m=i(t),v=i(t),y=i(t),x=a(t,[{buffer:d,type:t.FLOAT,size:3},{buffer:y,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:g,type:t.FLOAT,size:4},{buffer:m,type:t.FLOAT,size:2},{buffer:v,type:t.FLOAT,size:3}]),b=i(t),_=i(t),w=i(t),k=i(t),A=a(t,[{buffer:b,type:t.FLOAT,size:3},{buffer:k,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:_,type:t.FLOAT,size:4},{buffer:w,type:t.FLOAT,size:2}]),I=i(t),O=i(t),z=i(t),D=i(t),R=i(t),F=a(t,[{buffer:I,type:t.FLOAT,size:3},{buffer:R,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:O,type:t.FLOAT,size:4},{buffer:z,type:t.FLOAT,size:2},{buffer:D,type:t.FLOAT,size:1}]),B=i(t),N=a(t,[{buffer:B,type:t.FLOAT,size:3}]),j=new T(t,p,n,s,l,c,f,h,d,y,g,m,v,x,b,k,_,w,A,I,R,O,z,D,F,B,N);return j.update(e),j}},{\"./lib/closest-point\":110,\"./lib/shaders\":111,colormap:53,\"gl-buffer\":78,\"gl-mat4/invert\":98,\"gl-mat4/multiply\":100,\"gl-shader\":132,\"gl-texture2d\":146,\"gl-vao\":150,ndarray:259,normals:261,\"simplicial-complex-contour\":291,\"typedarray-pool\":308}],113:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e,[0,0,0,1,1,0,1,1]),s=i(e,a.boxVert,a.lineFrag);return new o(t,r,s)};var n=t(\"gl-buffer\"),i=t(\"gl-shader\"),a=t(\"./shaders\");function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,c=o.prototype;c.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},c.drawBox=(s=[0,0],l=[0,0],function(t,e,r,n,i){var a=this.plot,o=this.shader,c=a.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,o.uniforms.lo=s,o.uniforms.hi=l,o.uniforms.color=i,c.drawArrays(c.TRIANGLE_STRIP,0,4)}),c.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":116,\"gl-buffer\":78,\"gl-shader\":132}],114:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e),a=i(e,o.gridVert,o.gridFrag),l=i(e,o.tickVert,o.gridFrag);return new s(t,r,a,l)};var n=t(\"gl-buffer\"),i=t(\"gl-shader\"),a=t(\"binary-search-bounds\"),o=t(\"./shaders\");function s(t,e,r,n){this.plot=t,this.vbo=e,this.shader=r,this.tickShader=n,this.ticks=[[],[]]}function l(t,e){return t-e}var c,u,f,h,p,d=s.prototype;d.draw=(c=[0,0],u=[0,0],f=[0,0],function(){for(var t=this.plot,e=this.vbo,r=this.shader,n=this.ticks,i=t.gl,a=t._tickBounds,o=t.dataBox,s=t.viewBox,l=t.gridLineWidth,h=t.gridLineColor,p=t.gridLineEnable,d=t.pixelRatio,g=0;g<2;++g){var m=a[g],v=a[g+2]-m,y=.5*(o[g+2]+o[g]),x=o[g+2]-o[g];u[g]=2*v/x,c[g]=2*(m-y)/x}r.bind(),e.bind(),r.attributes.dataCoord.pointer(),r.uniforms.dataShift=c,r.uniforms.dataScale=u;var b=0;for(g=0;g<2;++g){f[0]=f[1]=0,f[g]=1,r.uniforms.dataAxis=f,r.uniforms.lineWidth=l[g]/(s[g+2]-s[g])*d,r.uniforms.color=h[g];var _=6*n[g].length;p[g]&&_&&i.drawArrays(i.TRIANGLES,b,_),b+=_}}),d.drawTickMarks=function(){var t=[0,0],e=[0,0],r=[1,0],n=[0,1],i=[0,0],o=[0,0];return function(){for(var s=this.plot,c=this.vbo,u=this.tickShader,f=this.ticks,h=s.gl,p=s._tickBounds,d=s.dataBox,g=s.viewBox,m=s.pixelRatio,v=s.screenBox,y=v[2]-v[0],x=v[3]-v[1],b=g[2]-g[0],_=g[3]-g[1],w=0;w<2;++w){var T=p[w],k=p[w+2]-T,A=.5*(d[w+2]+d[w]),M=d[w+2]-d[w];e[w]=2*k/M,t[w]=2*(T-A)/M}e[0]*=b/y,t[0]*=b/y,e[1]*=_/x,t[1]*=_/x,u.bind(),c.bind(),u.attributes.dataCoord.pointer();var S=u.uniforms;S.dataShift=t,S.dataScale=e;var E=s.tickMarkLength,L=s.tickMarkWidth,C=s.tickMarkColor,P=6*f[0].length,I=Math.min(a.ge(f[0],(d[0]-p[0])/(p[2]-p[0]),l),f[0].length),O=Math.min(a.gt(f[0],(d[2]-p[0])/(p[2]-p[0]),l),f[0].length),z=0+6*I,D=6*Math.max(0,O-I),R=Math.min(a.ge(f[1],(d[1]-p[1])/(p[3]-p[1]),l),f[1].length),F=Math.min(a.gt(f[1],(d[3]-p[1])/(p[3]-p[1]),l),f[1].length),B=P+6*R,N=6*Math.max(0,F-R);i[0]=2*(g[0]-E[1])/y-1,i[1]=(g[3]+g[1])/x-1,o[0]=E[1]*m/y,o[1]=L[1]*m/x,N&&(S.color=C[1],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,B,N)),i[0]=(g[2]+g[0])/y-1,i[1]=2*(g[1]-E[0])/x-1,o[0]=L[0]*m/y,o[1]=E[0]*m/x,D&&(S.color=C[0],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,z,D)),i[0]=2*(g[2]+E[3])/y-1,i[1]=(g[3]+g[1])/x-1,o[0]=E[3]*m/y,o[1]=L[3]*m/x,N&&(S.color=C[3],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,B,N)),i[0]=(g[2]+g[0])/y-1,i[1]=2*(g[3]+E[2])/x-1,o[0]=L[2]*m/y,o[1]=E[2]*m/x,D&&(S.color=C[2],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,z,D))}}(),d.update=(h=[1,1,-1,-1,1,-1],p=[1,-1,1,1,-1,-1],function(t){for(var e=t.ticks,r=t.bounds,n=new Float32Array(18*(e[0].length+e[1].length)),i=(this.plot.zeroLineEnable,0),a=[[],[]],o=0;o<2;++o)for(var s=a[o],l=e[o],c=r[o],u=r[o+2],f=0;f<l.length;++f){var d=(l[f].x-c)/(u-c);s.push(d);for(var g=0;g<6;++g)n[i++]=d,n[i++]=h[g],n[i++]=p[g]}this.ticks=a,this.vbo.update(n)}),d.dispose=function(){this.vbo.dispose(),this.shader.dispose(),this.tickShader.dispose()}},{\"./shaders\":116,\"binary-search-bounds\":31,\"gl-buffer\":78,\"gl-shader\":132}],115:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e,[-1,-1,-1,1,1,-1,1,1]),s=i(e,a.lineVert,a.lineFrag);return new o(t,r,s)};var n=t(\"gl-buffer\"),i=t(\"gl-shader\"),a=t(\"./shaders\");function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,c=o.prototype;c.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},c.drawLine=(s=[0,0],l=[0,0],function(t,e,r,n,i,a){var o=this.plot,c=this.shader,u=o.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,c.uniforms.start=s,c.uniforms.end=l,c.uniforms.width=i*o.pixelRatio,c.uniforms.color=a,u.drawArrays(u.TRIANGLE_STRIP,0,4)}),c.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":116,\"gl-buffer\":78,\"gl-shader\":132}],116:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=n([\"precision lowp float;\\n#define GLSLIFY 1\\nuniform vec4 color;\\nvoid main() {\\n  gl_FragColor = vec4(color.xyz * color.w, color.w);\\n}\\n\"]);e.exports={lineVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 coord;\\n\\nuniform vec4 screenBox;\\nuniform vec2 start, end;\\nuniform float width;\\n\\nvec2 perp(vec2 v) {\\n  return vec2(v.y, -v.x);\\n}\\n\\nvec2 screen(vec2 v) {\\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\\n}\\n\\nvoid main() {\\n  vec2 delta = normalize(perp(start - end));\\n  vec2 offset = mix(start, end, 0.5 * (coord.y+1.0));\\n  gl_Position = vec4(screen(offset + 0.5 * width * delta * coord.x), 0, 1);\\n}\\n\"]),lineFrag:i,textVert:n([\"#define GLSLIFY 1\\nattribute vec3 textCoordinate;\\n\\nuniform vec2 dataScale, dataShift, dataAxis, screenOffset, textScale;\\nuniform float angle;\\n\\nvoid main() {\\n  float dataOffset  = textCoordinate.z;\\n  vec2 glyphOffset  = textCoordinate.xy;\\n  mat2 glyphMatrix = mat2(cos(angle), sin(angle), -sin(angle), cos(angle));\\n  vec2 screenCoordinate = dataAxis * (dataScale * dataOffset + dataShift) +\\n    glyphMatrix * glyphOffset * textScale + screenOffset;\\n  gl_Position = vec4(screenCoordinate, 0, 1);\\n}\\n\"]),textFrag:i,gridVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 dataCoord;\\n\\nuniform vec2 dataAxis, dataShift, dataScale;\\nuniform float lineWidth;\\n\\nvoid main() {\\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\\n  pos += 10.0 * dataCoord.y * vec2(dataAxis.y, -dataAxis.x) + dataCoord.z * lineWidth;\\n  gl_Position = vec4(pos, 0, 1);\\n}\\n\"]),gridFrag:i,boxVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 coord;\\n\\nuniform vec4 screenBox;\\nuniform vec2 lo, hi;\\n\\nvec2 screen(vec2 v) {\\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\\n}\\n\\nvoid main() {\\n  gl_Position = vec4(screen(mix(lo, hi, coord)), 0, 1);\\n}\\n\"]),tickVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 dataCoord;\\n\\nuniform vec2 dataAxis, dataShift, dataScale, screenOffset, tickScale;\\n\\nvoid main() {\\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\\n  gl_Position = vec4(pos + tickScale*dataCoord.yz + screenOffset, 0, 1);\\n}\\n\"])}},{glslify:231}],117:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e),a=i(e,s.textVert,s.textFrag);return new l(t,r,a)};var n=t(\"gl-buffer\"),i=t(\"gl-shader\"),a=t(\"text-cache\"),o=t(\"binary-search-bounds\"),s=t(\"./shaders\");function l(t,e,r){this.plot=t,this.vbo=e,this.shader=r,this.tickOffset=[[],[]],this.tickX=[[],[]],this.labelOffset=[0,0],this.labelCount=[0,0]}var c,u,f,h,p,d,g=l.prototype;g.drawTicks=(c=[0,0],u=[0,0],f=[0,0],function(t){var e=this.plot,r=this.shader,n=this.tickX[t],i=this.tickOffset[t],a=e.gl,s=e.viewBox,l=e.dataBox,h=e.screenBox,p=e.pixelRatio,d=e.tickEnable,g=e.tickPad,m=e.tickColor,v=e.tickAngle,y=e.labelEnable,x=e.labelPad,b=e.labelColor,_=e.labelAngle,w=this.labelOffset[t],T=this.labelCount[t],k=o.lt(n,l[t]),A=o.le(n,l[t+2]);c[0]=c[1]=0,c[t]=1,u[t]=(s[2+t]+s[t])/(h[2+t]-h[t])-1;var M=2/h[2+(1^t)]-h[1^t];u[1^t]=M*s[1^t]-1,d[t]&&(u[1^t]-=M*p*g[t],k<A&&i[A]>i[k]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=m[t],r.uniforms.angle=v[t],a.drawArrays(a.TRIANGLES,i[k],i[A]-i[k]))),y[t]&&T&&(u[1^t]-=M*p*x[t],r.uniforms.dataAxis=f,r.uniforms.screenOffset=u,r.uniforms.color=b[t],r.uniforms.angle=_[t],a.drawArrays(a.TRIANGLES,w,T)),u[1^t]=M*s[2+(1^t)]-1,d[t+2]&&(u[1^t]+=M*p*g[t+2],k<A&&i[A]>i[k]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=m[t+2],r.uniforms.angle=v[t+2],a.drawArrays(a.TRIANGLES,i[k],i[A]-i[k]))),y[t+2]&&T&&(u[1^t]+=M*p*x[t+2],r.uniforms.dataAxis=f,r.uniforms.screenOffset=u,r.uniforms.color=b[t+2],r.uniforms.angle=_[t+2],a.drawArrays(a.TRIANGLES,w,T))}),g.drawTitle=function(){var t=[0,0],e=[0,0];return function(){var r=this.plot,n=this.shader,i=r.gl,a=r.screenBox,o=r.titleCenter,s=r.titleAngle,l=r.titleColor,c=r.pixelRatio;if(this.titleCount){for(var u=0;u<2;++u)e[u]=2*(o[u]*c-a[u])/(a[2+u]-a[u])-1;n.bind(),n.uniforms.dataAxis=t,n.uniforms.screenOffset=e,n.uniforms.angle=s,n.uniforms.color=l,i.drawArrays(i.TRIANGLES,this.titleOffset,this.titleCount)}}}(),g.bind=(h=[0,0],p=[0,0],d=[0,0],function(){var t=this.plot,e=this.shader,r=t._tickBounds,n=t.dataBox,i=t.screenBox,a=t.viewBox;e.bind();for(var o=0;o<2;++o){var s=r[o],l=r[o+2]-s,c=.5*(n[o+2]+n[o]),u=n[o+2]-n[o],f=a[o],g=a[o+2]-f,m=i[o],v=i[o+2]-m;p[o]=2*l/u*g/v,h[o]=2*(s-c)/u*g/v}d[1]=2*t.pixelRatio/(i[3]-i[1]),d[0]=d[1]*(i[3]-i[1])/(i[2]-i[0]),e.uniforms.dataScale=p,e.uniforms.dataShift=h,e.uniforms.textScale=d,this.vbo.bind(),e.attributes.textCoordinate.pointer()}),g.update=function(t){var e,r,n,i,o,s=[],l=t.ticks,c=t.bounds;for(o=0;o<2;++o){var u=[Math.floor(s.length/3)],f=[-1/0],h=l[o];for(e=0;e<h.length;++e){var p=h[e],d=p.x,g=p.text,m=p.font||\"sans-serif\";i=p.fontSize||12;for(var v=1/(c[o+2]-c[o]),y=c[o],x=g.split(\"\\n\"),b=0;b<x.length;b++)for(n=a(m,x[b]).data,r=0;r<n.length;r+=2)s.push(n[r]*i,-n[r+1]*i-b*i*1.2,(d-y)*v);u.push(Math.floor(s.length/3)),f.push(d)}this.tickOffset[o]=u,this.tickX[o]=f}for(o=0;o<2;++o){for(this.labelOffset[o]=Math.floor(s.length/3),n=a(t.labelFont[o],t.labels[o],{textAlign:\"center\"}).data,i=t.labelSize[o],e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.labelCount[o]=Math.floor(s.length/3)-this.labelOffset[o]}for(this.titleOffset=Math.floor(s.length/3),n=a(t.titleFont,t.title).data,i=t.titleSize,e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.titleCount=Math.floor(s.length/3)-this.titleOffset,this.vbo.update(s)},g.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":116,\"binary-search-bounds\":31,\"gl-buffer\":78,\"gl-shader\":132,\"text-cache\":303}],118:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e,[e.drawingBufferWidth,e.drawingBufferHeight]),c=new l(e,r);return c.grid=i(c),c.text=a(c),c.line=o(c),c.box=s(c),c.update(t),c};var n=t(\"gl-select-static\"),i=t(\"./lib/grid\"),a=t(\"./lib/text\"),o=t(\"./lib/line\"),s=t(\"./lib/box\");function l(t,e){this.gl=t,this.pickBuffer=e,this.screenBox=[0,0,t.drawingBufferWidth,t.drawingBufferHeight],this.viewBox=[0,0,0,0],this.dataBox=[-10,-10,10,10],this.gridLineEnable=[!0,!0],this.gridLineWidth=[1,1],this.gridLineColor=[[0,0,0,1],[0,0,0,1]],this.pixelRatio=1,this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickEnable=[!0,!0,!0,!0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[15,15,15,15],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelEnable=[!0,!0,!0,!0],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.titleCenter=[0,0],this.titleEnable=!0,this.titleAngle=0,this.titleColor=[0,0,0,1],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[4,4],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderLineEnable=[!0,!0,!0,!0],this.borderLineWidth=[2,2,2,2],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.grid=null,this.text=null,this.line=null,this.box=null,this.objects=[],this.overlays=[],this._tickBounds=[1/0,1/0,-1/0,-1/0],this.static=!1,this.dirty=!1,this.pickDirty=!1,this.pickDelay=120,this.pickRadius=10,this._pickTimeout=null,this._drawPick=this.drawPick.bind(this),this._depthCounter=0}var c=l.prototype;function u(t){for(var e=t.slice(),r=0;r<e.length;++r)e[r]=e[r].slice();return e}function f(t,e){return t.x-e.x}c.setDirty=function(){this.dirty=this.pickDirty=!0},c.setOverlayDirty=function(){this.dirty=!0},c.nextDepthValue=function(){return this._depthCounter++/65536},c.draw=function(){var t=this.gl,e=this.screenBox,r=this.viewBox,n=this.dataBox,i=this.pixelRatio,a=this.grid,o=this.line,s=this.text,l=this.objects;if(this._depthCounter=0,this.pickDirty&&(this._pickTimeout&&clearTimeout(this._pickTimeout),this.pickDirty=!1,this._pickTimeout=setTimeout(this._drawPick,this.pickDelay)),this.dirty){if(this.dirty=!1,t.bindFramebuffer(t.FRAMEBUFFER,null),t.enable(t.SCISSOR_TEST),t.disable(t.DEPTH_TEST),t.depthFunc(t.LESS),t.depthMask(!1),t.enable(t.BLEND),t.blendEquation(t.FUNC_ADD,t.FUNC_ADD),t.blendFunc(t.ONE,t.ONE_MINUS_SRC_ALPHA),this.borderColor){t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]);var c=this.borderColor;t.clearColor(c[0]*c[3],c[1]*c[3],c[2]*c[3],c[3]),t.clear(t.COLOR_BUFFER_BIT|t.DEPTH_BUFFER_BIT)}t.scissor(r[0],r[1],r[2]-r[0],r[3]-r[1]),t.viewport(r[0],r[1],r[2]-r[0],r[3]-r[1]);var u=this.backgroundColor;t.clearColor(u[0]*u[3],u[1]*u[3],u[2]*u[3],u[3]),t.clear(t.COLOR_BUFFER_BIT),a.draw();var f=this.zeroLineEnable,h=this.zeroLineColor,p=this.zeroLineWidth;if(f[0]||f[1]){o.bind();for(var d=0;d<2;++d)if(f[d]&&n[d]<=0&&n[d+2]>=0){var g=e[d]-n[d]*(e[d+2]-e[d])/(n[d+2]-n[d]);0===d?o.drawLine(g,e[1],g,e[3],p[d],h[d]):o.drawLine(e[0],g,e[2],g,p[d],h[d])}}for(d=0;d<l.length;++d)l[d].draw();t.viewport(e[0],e[1],e[2]-e[0],e[3]-e[1]),t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]),this.grid.drawTickMarks(),o.bind();var m=this.borderLineEnable,v=this.borderLineWidth,y=this.borderLineColor;for(m[1]&&o.drawLine(r[0],r[1]-.5*v[1]*i,r[0],r[3]+.5*v[3]*i,v[1],y[1]),m[0]&&o.drawLine(r[0]-.5*v[0]*i,r[1],r[2]+.5*v[2]*i,r[1],v[0],y[0]),m[3]&&o.drawLine(r[2],r[1]-.5*v[1]*i,r[2],r[3]+.5*v[3]*i,v[3],y[3]),m[2]&&o.drawLine(r[0]-.5*v[0]*i,r[3],r[2]+.5*v[2]*i,r[3],v[2],y[2]),s.bind(),d=0;d<2;++d)s.drawTicks(d);this.titleEnable&&s.drawTitle();var x=this.overlays;for(d=0;d<x.length;++d)x[d].draw();t.disable(t.SCISSOR_TEST),t.disable(t.BLEND),t.depthMask(!0)}},c.drawPick=function(){if(!this.static){var t=this.pickBuffer;this.gl,this._pickTimeout=null,t.begin();for(var e=1,r=this.objects,n=0;n<r.length;++n)e=r[n].drawPick(e);t.end()}},c.pick=function(t,e){if(!this.static){var r=this.pixelRatio,n=this.pickPixelRatio,i=this.viewBox,a=0|Math.round((t-i[0]/r)*n),o=0|Math.round((e-i[1]/r)*n),s=this.pickBuffer.query(a,o,this.pickRadius);if(!s)return null;for(var l=s.id+(s.value[0]<<8)+(s.value[1]<<16)+(s.value[2]<<24),c=this.objects,u=0;u<c.length;++u){var f=c[u].pick(a,o,l);if(f)return f}return null}},c.setScreenBox=function(t){var e=this.screenBox,r=this.pixelRatio;e[0]=0|Math.round(t[0]*r),e[1]=0|Math.round(t[1]*r),e[2]=0|Math.round(t[2]*r),e[3]=0|Math.round(t[3]*r),this.setDirty()},c.setDataBox=function(t){var e=this.dataBox;(e[0]!==t[0]||e[1]!==t[1]||e[2]!==t[2]||e[3]!==t[3])&&(e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],this.setDirty())},c.setViewBox=function(t){var e=this.pixelRatio,r=this.viewBox;r[0]=0|Math.round(t[0]*e),r[1]=0|Math.round(t[1]*e),r[2]=0|Math.round(t[2]*e),r[3]=0|Math.round(t[3]*e);var n=this.pickPixelRatio;this.pickBuffer.shape=[0|Math.round((t[2]-t[0])*n),0|Math.round((t[3]-t[1])*n)],this.setDirty()},c.update=function(t){t=t||{};var e=this.gl;this.pixelRatio=t.pixelRatio||1;var r=this.pixelRatio;this.pickPixelRatio=Math.max(r,1),this.setScreenBox(t.screenBox||[0,0,e.drawingBufferWidth/r,e.drawingBufferHeight/r]);this.screenBox;this.setViewBox(t.viewBox||[.125*(this.screenBox[2]-this.screenBox[0])/r,.125*(this.screenBox[3]-this.screenBox[1])/r,.875*(this.screenBox[2]-this.screenBox[0])/r,.875*(this.screenBox[3]-this.screenBox[1])/r]);var n=this.viewBox,i=(n[2]-n[0])/(n[3]-n[1]);this.setDataBox(t.dataBox||[-10,-10/i,10,10/i]),this.borderColor=!1!==t.borderColor&&(t.borderColor||[0,0,0,0]).slice(),this.backgroundColor=(t.backgroundColor||[0,0,0,0]).slice(),this.gridLineEnable=(t.gridLineEnable||[!0,!0]).slice(),this.gridLineWidth=(t.gridLineWidth||[1,1]).slice(),this.gridLineColor=u(t.gridLineColor||[[.5,.5,.5,1],[.5,.5,.5,1]]),this.zeroLineEnable=(t.zeroLineEnable||[!0,!0]).slice(),this.zeroLineWidth=(t.zeroLineWidth||[4,4]).slice(),this.zeroLineColor=u(t.zeroLineColor||[[0,0,0,1],[0,0,0,1]]),this.tickMarkLength=(t.tickMarkLength||[0,0,0,0]).slice(),this.tickMarkWidth=(t.tickMarkWidth||[0,0,0,0]).slice(),this.tickMarkColor=u(t.tickMarkColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.titleCenter=(t.titleCenter||[.5*(n[0]+n[2])/r,(n[3]+120)/r]).slice(),this.titleEnable=!(\"titleEnable\"in t)||!!t.titleEnable,this.titleAngle=t.titleAngle||0,this.titleColor=(t.titleColor||[0,0,0,1]).slice(),this.labelPad=(t.labelPad||[15,15,15,15]).slice(),this.labelAngle=(t.labelAngle||[0,Math.PI/2,0,3*Math.PI/2]).slice(),this.labelEnable=(t.labelEnable||[!0,!0,!0,!0]).slice(),this.labelColor=u(t.labelColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.tickPad=(t.tickPad||[15,15,15,15]).slice(),this.tickAngle=(t.tickAngle||[0,0,0,0]).slice(),this.tickEnable=(t.tickEnable||[!0,!0,!0,!0]).slice(),this.tickColor=u(t.tickColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.borderLineEnable=(t.borderLineEnable||[!0,!0,!0,!0]).slice(),this.borderLineWidth=(t.borderLineWidth||[2,2,2,2]).slice(),this.borderLineColor=u(t.borderLineColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]);var a=t.ticks||[[],[]],o=this._tickBounds;o[0]=o[1]=1/0,o[2]=o[3]=-1/0;for(var s=0;s<2;++s){var l=a[s].slice(0);0!==l.length&&(l.sort(f),o[s]=Math.min(o[s],l[0].x),o[s+2]=Math.max(o[s+2],l[l.length-1].x))}this.grid.update({bounds:o,ticks:a}),this.text.update({bounds:o,ticks:a,labels:t.labels||[\"x\",\"y\"],labelSize:t.labelSize||[12,12],labelFont:t.labelFont||[\"sans-serif\",\"sans-serif\"],title:t.title||\"\",titleSize:t.titleSize||18,titleFont:t.titleFont||\"sans-serif\"}),this.static=!!t.static,this.setDirty()},c.dispose=function(){this.box.dispose(),this.grid.dispose(),this.text.dispose(),this.line.dispose();for(var t=this.objects.length-1;t>=0;--t)this.objects[t].dispose();this.objects.length=0;for(t=this.overlays.length-1;t>=0;--t)this.overlays[t].dispose();this.overlays.length=0,this.gl=null},c.addObject=function(t){this.objects.indexOf(t)<0&&(this.objects.push(t),this.setDirty())},c.removeObject=function(t){for(var e=this.objects,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setDirty();break}},c.addOverlay=function(t){this.overlays.indexOf(t)<0&&(this.overlays.push(t),this.setOverlayDirty())},c.removeOverlay=function(t){for(var e=this.overlays,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setOverlayDirty();break}}},{\"./lib/box\":113,\"./lib/grid\":114,\"./lib/line\":115,\"./lib/text\":117,\"gl-select-static\":131}],119:[function(t,e,r){\"use strict\";e.exports=function(t,e){t=t||document.body,e=e||{};var r=[.01,1/0];\"distanceLimits\"in e&&(r[0]=e.distanceLimits[0],r[1]=e.distanceLimits[1]);\"zoomMin\"in e&&(r[0]=e.zoomMin);\"zoomMax\"in e&&(r[1]=e.zoomMax);var c=i({center:e.center||[0,0,0],up:e.up||[0,1,0],eye:e.eye||[0,0,10],mode:e.mode||\"orbit\",distanceLimits:r}),u=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],f=0,h=t.clientWidth,p=t.clientHeight,d={keyBindingMode:\"rotate\",enableWheel:!0,view:c,element:t,delay:e.delay||16,rotateSpeed:e.rotateSpeed||1,zoomSpeed:e.zoomSpeed||1,translateSpeed:e.translateSpeed||1,flipX:!!e.flipX,flipY:!!e.flipY,modes:c.modes,_ortho:e._ortho||e.projection&&\"orthographic\"===e.projection.type||!1,tick:function(){var e=n(),r=this.delay,i=e-2*r;c.idle(e-r),c.recalcMatrix(i),c.flush(e-(100+2*r));for(var a=!0,o=c.computedMatrix,s=0;s<16;++s)a=a&&u[s]===o[s],u[s]=o[s];var l=t.clientWidth===h&&t.clientHeight===p;return h=t.clientWidth,p=t.clientHeight,a?!l:(f=Math.exp(c.computedRadius[0]),!0)},lookAt:function(t,e,r){c.lookAt(c.lastT(),t,e,r)},rotate:function(t,e,r){c.rotate(c.lastT(),t,e,r)},pan:function(t,e,r){c.pan(c.lastT(),t,e,r)},translate:function(t,e,r){c.translate(c.lastT(),t,e,r)}};return Object.defineProperties(d,{matrix:{get:function(){return c.computedMatrix},set:function(t){return c.setMatrix(c.lastT(),t),c.computedMatrix},enumerable:!0},mode:{get:function(){return c.getMode()},set:function(t){var e=c.computedUp.slice(),r=c.computedEye.slice(),i=c.computedCenter.slice();if(c.setMode(t),\"turntable\"===t){var a=n();c._active.lookAt(a,r,i,e),c._active.lookAt(a+500,r,i,[0,0,1]),c._active.flush(a)}return c.getMode()},enumerable:!0},center:{get:function(){return c.computedCenter},set:function(t){return c.lookAt(c.lastT(),null,t),c.computedCenter},enumerable:!0},eye:{get:function(){return c.computedEye},set:function(t){return c.lookAt(c.lastT(),t),c.computedEye},enumerable:!0},up:{get:function(){return c.computedUp},set:function(t){return c.lookAt(c.lastT(),null,null,t),c.computedUp},enumerable:!0},distance:{get:function(){return f},set:function(t){return c.setDistance(c.lastT(),t),t},enumerable:!0},distanceLimits:{get:function(){return c.getDistanceLimits(r)},set:function(t){return c.setDistanceLimits(t),t},enumerable:!0}}),t.addEventListener(\"contextmenu\",(function(t){return t.preventDefault(),!1})),d._lastX=-1,d._lastY=-1,d._lastMods={shift:!1,control:!1,alt:!1,meta:!1},d.enableMouseListeners=function(){function e(e,r,i,a){var o=d.keyBindingMode;if(!1!==o){var s=\"rotate\"===o,l=\"pan\"===o,u=\"zoom\"===o,h=!!a.control,p=!!a.alt,g=!!a.shift,m=!!(1&e),v=!!(2&e),y=!!(4&e),x=1/t.clientHeight,b=x*(r-d._lastX),_=x*(i-d._lastY),w=d.flipX?1:-1,T=d.flipY?1:-1,k=Math.PI*d.rotateSpeed,A=n();if(-1!==d._lastX&&-1!==d._lastY&&((s&&m&&!h&&!p&&!g||m&&!h&&!p&&g)&&c.rotate(A,w*k*b,-T*k*_,0),(l&&m&&!h&&!p&&!g||v||m&&h&&!p&&!g)&&c.pan(A,-d.translateSpeed*b*f,d.translateSpeed*_*f,0),u&&m&&!h&&!p&&!g||y||m&&!h&&p&&!g)){var M=-d.zoomSpeed*_/window.innerHeight*(A-c.lastT())*100;c.pan(A,0,0,f*(Math.exp(M)-1))}return d._lastX=r,d._lastY=i,d._lastMods=a,!0}}d.mouseListener=a(t,e),t.addEventListener(\"touchstart\",(function(r){var n=s(r.changedTouches[0],t);e(0,n[0],n[1],d._lastMods),e(1,n[0],n[1],d._lastMods)}),!!l&&{passive:!0}),t.addEventListener(\"touchmove\",(function(r){var n=s(r.changedTouches[0],t);e(1,n[0],n[1],d._lastMods),r.preventDefault()}),!!l&&{passive:!1}),t.addEventListener(\"touchend\",(function(t){e(0,d._lastX,d._lastY,d._lastMods)}),!!l&&{passive:!0}),d.wheelListener=o(t,(function(t,e){if(!1!==d.keyBindingMode&&d.enableWheel){var r=d.flipX?1:-1,i=d.flipY?1:-1,a=n();if(Math.abs(t)>Math.abs(e))c.rotate(a,0,0,-t*r*Math.PI*d.rotateSpeed/window.innerWidth);else if(!d._ortho){var o=-d.zoomSpeed*i*e/window.innerHeight*(a-c.lastT())/20;c.pan(a,0,0,f*(Math.exp(o)-1))}}}),!0)},d.enableMouseListeners(),d};var n=t(\"right-now\"),i=t(\"3d-view\"),a=t(\"mouse-change\"),o=t(\"mouse-wheel\"),s=t(\"mouse-event-offset\"),l=t(\"has-passive-events\")},{\"3d-view\":7,\"has-passive-events\":232,\"mouse-change\":247,\"mouse-event-offset\":248,\"mouse-wheel\":250,\"right-now\":278}],120:[function(t,e,r){var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision mediump float;\\n#define GLSLIFY 1\\nattribute vec2 position;\\nvarying vec2 uv;\\nvoid main() {\\n  uv = position;\\n  gl_Position = vec4(position, 0, 1);\\n}\"]),o=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D accumBuffer;\\nvarying vec2 uv;\\n\\nvoid main() {\\n  vec4 accum = texture2D(accumBuffer, 0.5 * (uv + 1.0));\\n  gl_FragColor = min(vec4(1,1,1,1), accum);\\n}\"]);e.exports=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec2\"}])}},{\"gl-shader\":132,glslify:231}],121:[function(t,e,r){\"use strict\";var n=t(\"./camera.js\"),i=t(\"gl-axes3d\"),a=t(\"gl-axes3d/properties\"),o=t(\"gl-spikes3d\"),s=t(\"gl-select-static\"),l=t(\"gl-fbo\"),c=t(\"a-big-triangle\"),u=t(\"mouse-change\"),f=t(\"gl-mat4/perspective\"),h=t(\"gl-mat4/ortho\"),p=t(\"./lib/shader\"),d=t(\"is-mobile\")({tablet:!0,featureDetect:!0});function g(){this.mouse=[-1,-1],this.screen=null,this.distance=1/0,this.index=null,this.dataCoordinate=null,this.dataPosition=null,this.object=null,this.data=null}function m(t){var e=Math.round(Math.log(Math.abs(t))/Math.log(10));if(e<0){var r=Math.round(Math.pow(10,-e));return Math.ceil(t*r)/r}if(e>0){r=Math.round(Math.pow(10,e));return Math.ceil(t/r)*r}return Math.ceil(t)}function v(t){return\"boolean\"!=typeof t||t}e.exports={createScene:function(t){(t=t||{}).camera=t.camera||{};var e=t.canvas;if(!e){if(e=document.createElement(\"canvas\"),t.container)t.container.appendChild(e);else document.body.appendChild(e)}var r=t.gl;r||(t.glOptions&&(d=!!t.glOptions.preserveDrawingBuffer),r=function(t,e){var r=null;try{(r=t.getContext(\"webgl\",e))||(r=t.getContext(\"experimental-webgl\",e))}catch(t){return null}return r}(e,t.glOptions||{premultipliedAlpha:!0,antialias:!0,preserveDrawingBuffer:d}));if(!r)throw new Error(\"webgl not supported\");var y=t.bounds||[[-10,-10,-10],[10,10,10]],x=new g,b=l(r,r.drawingBufferWidth,r.drawingBufferHeight,{preferFloat:!d}),_=p(r),w=t.cameraObject&&!0===t.cameraObject._ortho||t.camera.projection&&\"orthographic\"===t.camera.projection.type||!1,T={eye:t.camera.eye||[2,0,0],center:t.camera.center||[0,0,0],up:t.camera.up||[0,1,0],zoomMin:t.camera.zoomMax||.1,zoomMax:t.camera.zoomMin||100,mode:t.camera.mode||\"turntable\",_ortho:w},k=t.axes||{},A=i(r,k);A.enable=!k.disable;var M=t.spikes||{},S=o(r,M),E=[],L=[],C=[],P=[],I=!0,O=!0,z=new Array(16),D=new Array(16),R={view:null,projection:z,model:D,_ortho:!1},F=(O=!0,[r.drawingBufferWidth,r.drawingBufferHeight]),B=t.cameraObject||n(e,T),N={gl:r,contextLost:!1,pixelRatio:t.pixelRatio||1,canvas:e,selection:x,camera:B,axes:A,axesPixels:null,spikes:S,bounds:y,objects:E,shape:F,aspect:t.aspectRatio||[1,1,1],pickRadius:t.pickRadius||10,zNear:t.zNear||.01,zFar:t.zFar||1e3,fovy:t.fovy||Math.PI/4,clearColor:t.clearColor||[0,0,0,0],autoResize:v(t.autoResize),autoBounds:v(t.autoBounds),autoScale:!!t.autoScale,autoCenter:v(t.autoCenter),clipToBounds:v(t.clipToBounds),snapToData:!!t.snapToData,onselect:t.onselect||null,onrender:t.onrender||null,onclick:t.onclick||null,cameraParams:R,oncontextloss:null,mouseListener:null,_stopped:!1,getAspectratio:function(){return{x:this.aspect[0],y:this.aspect[1],z:this.aspect[2]}},setAspectratio:function(t){this.aspect[0]=t.x,this.aspect[1]=t.y,this.aspect[2]=t.z,O=!0},setBounds:function(t,e){this.bounds[0][t]=e.min,this.bounds[1][t]=e.max},setClearColor:function(t){this.clearColor=t},clearRGBA:function(){this.gl.clearColor(this.clearColor[0],this.clearColor[1],this.clearColor[2],this.clearColor[3]),this.gl.clear(this.gl.COLOR_BUFFER_BIT|this.gl.DEPTH_BUFFER_BIT)}},j=[r.drawingBufferWidth/N.pixelRatio|0,r.drawingBufferHeight/N.pixelRatio|0];function U(){if(!N._stopped&&N.autoResize){var t=e.parentNode,r=1,n=1;t&&t!==document.body?(r=t.clientWidth,n=t.clientHeight):(r=window.innerWidth,n=window.innerHeight);var i=0|Math.ceil(r*N.pixelRatio),a=0|Math.ceil(n*N.pixelRatio);if(i!==e.width||a!==e.height){e.width=i,e.height=a;var o=e.style;o.position=o.position||\"absolute\",o.left=\"0px\",o.top=\"0px\",o.width=r+\"px\",o.height=n+\"px\",I=!0}}}N.autoResize&&U();function V(){for(var t=E.length,e=P.length,n=0;n<e;++n)C[n]=0;t:for(n=0;n<t;++n){var i=E[n],a=i.pickSlots;if(a){for(var o=0;o<e;++o)if(C[o]+a<255){L[n]=o,i.setPickBase(C[o]+1),C[o]+=a;continue t}var l=s(r,F);L[n]=e,P.push(l),C.push(a),i.setPickBase(1),e+=1}else L[n]=-1}for(;e>0&&0===C[e-1];)C.pop(),P.pop().dispose()}function H(){if(N.contextLost)return!0;r.isContextLost()&&(N.contextLost=!0,N.mouseListener.enabled=!1,N.selection.object=null,N.oncontextloss&&N.oncontextloss())}window.addEventListener(\"resize\",U),N.update=function(t){N._stopped||(t=t||{},I=!0,O=!0)},N.add=function(t){N._stopped||(t.axes=A,E.push(t),L.push(-1),I=!0,O=!0,V())},N.remove=function(t){if(!N._stopped){var e=E.indexOf(t);e<0||(E.splice(e,1),L.pop(),I=!0,O=!0,V())}},N.dispose=function(){if(!N._stopped&&(N._stopped=!0,window.removeEventListener(\"resize\",U),e.removeEventListener(\"webglcontextlost\",H),N.mouseListener.enabled=!1,!N.contextLost)){A.dispose(),S.dispose();for(var t=0;t<E.length;++t)E[t].dispose();b.dispose();for(t=0;t<P.length;++t)P[t].dispose();_.dispose(),r=null,A=null,S=null,E=[]}},N._mouseRotating=!1,N._prevButtons=0,N.enableMouseListeners=function(){N.mouseListener=u(e,(function(t,e,r){if(!N._stopped){var n=P.length,i=E.length,a=x.object;x.distance=1/0,x.mouse[0]=e,x.mouse[1]=r,x.object=null,x.screen=null,x.dataCoordinate=x.dataPosition=null;var o=!1;if(t&&N._prevButtons)N._mouseRotating=!0;else{N._mouseRotating&&(O=!0),N._mouseRotating=!1;for(var s=0;s<n;++s){var l=P[s].query(e,j[1]-r-1,N.pickRadius);if(l){if(l.distance>x.distance)continue;for(var c=0;c<i;++c){var u=E[c];if(L[c]===s){var f=u.pick(l);f&&(x.buttons=t,x.screen=l.coord,x.distance=l.distance,x.object=u,x.index=f.distance,x.dataPosition=f.position,x.dataCoordinate=f.dataCoordinate,x.data=f,o=!0)}}}}}a&&a!==x.object&&(a.highlight&&a.highlight(null),I=!0),x.object&&(x.object.highlight&&x.object.highlight(x.data),I=!0),(o=o||x.object!==a)&&N.onselect&&N.onselect(x),1&t&&!(1&N._prevButtons)&&N.onclick&&N.onclick(x),N._prevButtons=t}}))},e.addEventListener(\"webglcontextlost\",H);var q=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],G=[q[0].slice(),q[1].slice()];function Y(){if(!H()){U();var t=N.camera.tick();R.view=N.camera.matrix,I=I||t,O=O||t,A.pixelRatio=N.pixelRatio,S.pixelRatio=N.pixelRatio;var e=E.length,n=q[0],i=q[1];n[0]=n[1]=n[2]=1/0,i[0]=i[1]=i[2]=-1/0;for(var o=0;o<e;++o){(C=E[o]).pixelRatio=N.pixelRatio,C.axes=N.axes,I=I||!!C.dirty,O=O||!!C.dirty;var s=C.bounds;if(s)for(var l=s[0],u=s[1],p=0;p<3;++p)n[p]=Math.min(n[p],l[p]),i[p]=Math.max(i[p],u[p])}var d=N.bounds;if(N.autoBounds)for(p=0;p<3;++p){if(i[p]<n[p])n[p]=-1,i[p]=1;else{n[p]===i[p]&&(n[p]-=1,i[p]+=1);var g=.05*(i[p]-n[p]);n[p]=n[p]-g,i[p]=i[p]+g}d[0][p]=n[p],d[1][p]=i[p]}var v=!1;for(p=0;p<3;++p)v=v||G[0][p]!==d[0][p]||G[1][p]!==d[1][p],G[0][p]=d[0][p],G[1][p]=d[1][p];if(O=O||v,I=I||v){if(v){var y=[0,0,0];for(o=0;o<3;++o)y[o]=m((d[1][o]-d[0][o])/10);A.autoTicks?A.update({bounds:d,tickSpacing:y}):A.update({bounds:d})}var T=r.drawingBufferWidth,k=r.drawingBufferHeight;F[0]=T,F[1]=k,j[0]=0|Math.max(T/N.pixelRatio,1),j[1]=0|Math.max(k/N.pixelRatio,1),function(t,e){var r=t.bounds,n=t.cameraParams,i=n.projection,a=n.model,o=t.gl.drawingBufferWidth,s=t.gl.drawingBufferHeight,l=t.zNear,c=t.zFar,u=t.fovy,p=o/s;e?(h(i,-p,p,-1,1,l,c),n._ortho=!0):(f(i,u,p,l,c),n._ortho=!1);for(var d=0;d<16;++d)a[d]=0;a[15]=1;var g=0;for(d=0;d<3;++d)g=Math.max(g,r[1][d]-r[0][d]);for(d=0;d<3;++d)t.autoScale?a[5*d]=t.aspect[d]/(r[1][d]-r[0][d]):a[5*d]=1/g,t.autoCenter&&(a[12+d]=.5*-a[5*d]*(r[0][d]+r[1][d]))}(N,w);for(o=0;o<e;++o){(C=E[o]).axesBounds=d,N.clipToBounds&&(C.clipBounds=d)}x.object&&(N.snapToData?S.position=x.dataCoordinate:S.position=x.dataPosition,S.bounds=d),O&&(O=!1,function(){if(!H()){r.colorMask(!0,!0,!0,!0),r.depthMask(!0),r.disable(r.BLEND),r.enable(r.DEPTH_TEST),r.depthFunc(r.LEQUAL);for(var t=E.length,e=P.length,n=0;n<e;++n){var i=P[n];i.shape=j,i.begin();for(var a=0;a<t;++a)if(L[a]===n){var o=E[a];o.drawPick&&(o.pixelRatio=1,o.drawPick(R))}i.end()}}}()),N.axesPixels=a(N.axes,R,T,k),N.onrender&&N.onrender(),r.bindFramebuffer(r.FRAMEBUFFER,null),r.viewport(0,0,T,k),N.clearRGBA(),r.depthMask(!0),r.colorMask(!0,!0,!0,!0),r.enable(r.DEPTH_TEST),r.depthFunc(r.LEQUAL),r.disable(r.BLEND),r.disable(r.CULL_FACE);var M=!1;A.enable&&(M=M||A.isTransparent(),A.draw(R)),S.axes=A,x.object&&S.draw(R),r.disable(r.CULL_FACE);for(o=0;o<e;++o){(C=E[o]).axes=A,C.pixelRatio=N.pixelRatio,C.isOpaque&&C.isOpaque()&&C.draw(R),C.isTransparent&&C.isTransparent()&&(M=!0)}if(M){b.shape=F,b.bind(),r.clear(r.DEPTH_BUFFER_BIT),r.colorMask(!1,!1,!1,!1),r.depthMask(!0),r.depthFunc(r.LESS),A.enable&&A.isTransparent()&&A.drawTransparent(R);for(o=0;o<e;++o){(C=E[o]).isOpaque&&C.isOpaque()&&C.draw(R)}r.enable(r.BLEND),r.blendEquation(r.FUNC_ADD),r.blendFunc(r.ONE,r.ONE_MINUS_SRC_ALPHA),r.colorMask(!0,!0,!0,!0),r.depthMask(!1),r.clearColor(0,0,0,0),r.clear(r.COLOR_BUFFER_BIT),A.isTransparent()&&A.drawTransparent(R);for(o=0;o<e;++o){var C;(C=E[o]).isTransparent&&C.isTransparent()&&C.drawTransparent(R)}r.bindFramebuffer(r.FRAMEBUFFER,null),r.blendFunc(r.ONE,r.ONE_MINUS_SRC_ALPHA),r.disable(r.DEPTH_TEST),_.bind(),b.color[0].bind(0),_.uniforms.accumBuffer=0,c(r),r.disable(r.BLEND)}I=!1;for(o=0;o<e;++o)E[o].dirty=!1}}}return N.enableMouseListeners(),function t(){if(N._stopped||N.contextLost)return;Y(),requestAnimationFrame(t)}(),N.redraw=function(){N._stopped||(I=!0,Y())},N},createCamera:n}},{\"./camera.js\":119,\"./lib/shader\":120,\"a-big-triangle\":8,\"gl-axes3d\":70,\"gl-axes3d/properties\":77,\"gl-fbo\":86,\"gl-mat4/ortho\":101,\"gl-mat4/perspective\":102,\"gl-select-static\":131,\"gl-spikes3d\":141,\"is-mobile\":238,\"mouse-change\":247}],122:[function(t,e,r){var n=t(\"glslify\");r.pointVertex=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\n\\nuniform mat3 matrix;\\nuniform float pointSize;\\nuniform float pointCloud;\\n\\nhighp float rand(vec2 co) {\\n  highp float a = 12.9898;\\n  highp float b = 78.233;\\n  highp float c = 43758.5453;\\n  highp float d = dot(co.xy, vec2(a, b));\\n  highp float e = mod(d, 3.14);\\n  return fract(sin(e) * c);\\n}\\n\\nvoid main() {\\n  vec3 hgPosition = matrix * vec3(position, 1);\\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\\n    // if we don't jitter the point size a bit, overall point cloud\\n    // saturation 'jumps' on zooming, which is disturbing and confusing\\n  gl_PointSize = pointSize * ((19.5 + rand(position)) / 20.0);\\n  if(pointCloud != 0.0) { // pointCloud is truthy\\n    // get the same square surface as circle would be\\n    gl_PointSize *= 0.886;\\n  }\\n}\"]),r.pointFragment=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color, borderColor;\\nuniform float centerFraction;\\nuniform float pointCloud;\\n\\nvoid main() {\\n  float radius;\\n  vec4 baseColor;\\n  if(pointCloud != 0.0) { // pointCloud is truthy\\n    if(centerFraction == 1.0) {\\n      gl_FragColor = color;\\n    } else {\\n      gl_FragColor = mix(borderColor, color, centerFraction);\\n    }\\n  } else {\\n    radius = length(2.0 * gl_PointCoord.xy - 1.0);\\n    if(radius > 1.0) {\\n      discard;\\n    }\\n    baseColor = mix(borderColor, color, step(radius, centerFraction));\\n    gl_FragColor = vec4(baseColor.rgb * baseColor.a, baseColor.a);\\n  }\\n}\\n\"]),r.pickVertex=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 pickId;\\n\\nuniform mat3 matrix;\\nuniform float pointSize;\\nuniform vec4 pickOffset;\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n  vec3 hgPosition = matrix * vec3(position, 1);\\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\\n  gl_PointSize = pointSize;\\n\\n  vec4 id = pickId + pickOffset;\\n  id.y += floor(id.x / 256.0);\\n  id.x -= floor(id.x / 256.0) * 256.0;\\n\\n  id.z += floor(id.y / 256.0);\\n  id.y -= floor(id.y / 256.0) * 256.0;\\n\\n  id.w += floor(id.z / 256.0);\\n  id.z -= floor(id.z / 256.0) * 256.0;\\n\\n  fragId = id;\\n}\\n\"]),r.pickFragment=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n  float radius = length(2.0 * gl_PointCoord.xy - 1.0);\\n  if(radius > 1.0) {\\n    discard;\\n  }\\n  gl_FragColor = fragId / 255.0;\\n}\\n\"])},{glslify:231}],123:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"typedarray-pool\"),o=t(\"./lib/shader\");function s(t,e,r,n,i){this.plot=t,this.offsetBuffer=e,this.pickBuffer=r,this.shader=n,this.pickShader=i,this.sizeMin=.5,this.sizeMinCap=2,this.sizeMax=20,this.areaRatio=1,this.pointCount=0,this.color=[1,0,0,1],this.borderColor=[0,0,0,1],this.blend=!1,this.pickOffset=0,this.points=null}e.exports=function(t,e){var r=t.gl,a=i(r),l=i(r),c=n(r,o.pointVertex,o.pointFragment),u=n(r,o.pickVertex,o.pickFragment),f=new s(t,a,l,c,u);return f.update(e),t.addObject(f),f};var l,c,u=s.prototype;u.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.offsetBuffer.dispose(),this.pickBuffer.dispose(),this.plot.removeObject(this)},u.update=function(t){var e;function r(e,r){return e in t?t[e]:r}t=t||{},this.sizeMin=r(\"sizeMin\",.5),this.sizeMax=r(\"sizeMax\",20),this.color=r(\"color\",[1,0,0,1]).slice(),this.areaRatio=r(\"areaRatio\",1),this.borderColor=r(\"borderColor\",[0,0,0,1]).slice(),this.blend=r(\"blend\",!1);var n=t.positions.length>>>1,i=t.positions instanceof Float32Array,o=t.idToIndex instanceof Int32Array&&t.idToIndex.length>=n,s=t.positions,l=i?s:a.mallocFloat32(s.length),c=o?t.idToIndex:a.mallocInt32(n);if(i||l.set(s),!o)for(l.set(s),e=0;e<n;e++)c[e]=e;this.points=s,this.offsetBuffer.update(l),this.pickBuffer.update(c),i||a.free(l),o||a.free(c),this.pointCount=n,this.pickOffset=0},u.unifiedDraw=(l=[1,0,0,0,1,0,0,0,1],c=[0,0,0,0],function(t){var e=void 0!==t,r=e?this.pickShader:this.shader,n=this.plot.gl,i=this.plot.dataBox;if(0===this.pointCount)return t;var a=i[2]-i[0],o=i[3]-i[1],s=function(t,e){var r,n=0,i=t.length>>>1;for(r=0;r<i;r++){var a=t[2*r],o=t[2*r+1];a>=e[0]&&a<=e[2]&&o>=e[1]&&o<=e[3]&&n++}return n}(this.points,i),u=this.plot.pickPixelRatio*Math.max(Math.min(this.sizeMinCap,this.sizeMin),Math.min(this.sizeMax,this.sizeMax/Math.pow(s,.33333)));l[0]=2/a,l[4]=2/o,l[6]=-2*i[0]/a-1,l[7]=-2*i[1]/o-1,this.offsetBuffer.bind(),r.bind(),r.attributes.position.pointer(),r.uniforms.matrix=l,r.uniforms.color=this.color,r.uniforms.borderColor=this.borderColor,r.uniforms.pointCloud=u<5,r.uniforms.pointSize=u,r.uniforms.centerFraction=Math.min(1,Math.max(0,Math.sqrt(1-this.areaRatio))),e&&(c[0]=255&t,c[1]=t>>8&255,c[2]=t>>16&255,c[3]=t>>24&255,this.pickBuffer.bind(),r.attributes.pickId.pointer(n.UNSIGNED_BYTE),r.uniforms.pickOffset=c,this.pickOffset=t);var f=n.getParameter(n.BLEND),h=n.getParameter(n.DITHER);return f&&!this.blend&&n.disable(n.BLEND),h&&n.disable(n.DITHER),n.drawArrays(n.POINTS,0,this.pointCount),f&&!this.blend&&n.enable(n.BLEND),h&&n.enable(n.DITHER),t+this.pointCount}),u.draw=u.unifiedDraw,u.drawPick=u.unifiedDraw,u.pick=function(t,e,r){var n=this.pickOffset,i=this.pointCount;if(r<n||r>=n+i)return null;var a=r-n,o=this.points;return{object:this,pointId:a,dataCoord:[o[2*a],o[2*a+1]]}}},{\"./lib/shader\":122,\"gl-buffer\":78,\"gl-shader\":132,\"typedarray-pool\":308}],124:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c=e[0],u=e[1],f=e[2],h=e[3],p=r[0],d=r[1],g=r[2],m=r[3];(a=c*p+u*d+f*g+h*m)<0&&(a=-a,p=-p,d=-d,g=-g,m=-m);1-a>1e-6?(i=Math.acos(a),o=Math.sin(i),s=Math.sin((1-n)*i)/o,l=Math.sin(n*i)/o):(s=1-n,l=n);return t[0]=s*c+l*p,t[1]=s*u+l*d,t[2]=s*f+l*g,t[3]=s*h+l*m,t}},{}],125:[function(t,e,r){\"use strict\";e.exports=function(t){return t||0===t?t.toString():\"\"}},{}],126:[function(t,e,r){\"use strict\";var n=t(\"vectorize-text\");e.exports=function(t,e,r){var a=i[e];a||(a=i[e]={});if(t in a)return a[t];var o={textAlign:\"center\",textBaseline:\"middle\",lineHeight:1,font:e,lineSpacing:1.25,styletags:{breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},triangles:!0},s=n(t,o);o.triangles=!1;var l,c,u=n(t,o);if(r&&1!==r){for(l=0;l<s.positions.length;++l)for(c=0;c<s.positions[l].length;++c)s.positions[l][c]/=r;for(l=0;l<u.positions.length;++l)for(c=0;c<u.positions[l].length;++c)u.positions[l][c]/=r}var f=[[1/0,1/0],[-1/0,-1/0]],h=u.positions.length;for(l=0;l<h;++l){var p=u.positions[l];for(c=0;c<2;++c)f[0][c]=Math.min(f[0][c],p[c]),f[1][c]=Math.max(f[1][c],p[c])}return a[t]=[s,u,f]};var i={}},{\"vectorize-text\":311}],127:[function(t,e,r){var n=t(\"gl-shader\"),i=t(\"glslify\"),a=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform vec4 highlightId;\\nuniform float highlightScale;\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float scale = 1.0;\\n    if(distance(highlightId, id) < 0.0001) {\\n      scale = highlightScale;\\n    }\\n\\n    vec4 worldPosition = model * vec4(position, 1);\\n    vec4 viewPosition = view * worldPosition;\\n    viewPosition = viewPosition / viewPosition.w;\\n    vec4 clipPosition = projection * (viewPosition + scale * vec4(glyph.x, -glyph.y, 0, 0));\\n\\n    gl_Position = clipPosition;\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = position;\\n  }\\n}\"]),o=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float highlightScale, pixelRatio;\\nuniform vec4 highlightId;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float scale = pixelRatio;\\n    if(distance(highlightId.bgr, id.bgr) < 0.001) {\\n      scale *= highlightScale;\\n    }\\n\\n    vec4 worldPosition = model * vec4(position, 1.0);\\n    vec4 viewPosition = view * worldPosition;\\n    vec4 clipPosition = projection * viewPosition;\\n    clipPosition /= clipPosition.w;\\n\\n    gl_Position = clipPosition + vec4(screenSize * scale * vec2(glyph.x, -glyph.y), 0.0, 0.0);\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = position;\\n  }\\n}\"]),s=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform float highlightScale;\\nuniform vec4 highlightId;\\nuniform vec3 axes[2];\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float scale, pixelRatio;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float lscale = pixelRatio * scale;\\n    if(distance(highlightId, id) < 0.0001) {\\n      lscale *= highlightScale;\\n    }\\n\\n    vec4 clipCenter   = projection * view * model * vec4(position, 1);\\n    vec3 dataPosition = position + 0.5*lscale*(axes[0] * glyph.x + axes[1] * glyph.y) * clipCenter.w * screenSize.y;\\n    vec4 clipPosition = projection * view * model * vec4(dataPosition, 1);\\n\\n    gl_Position = clipPosition;\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = dataPosition;\\n  }\\n}\\n\"]),l=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float opacity;\\n\\nvarying vec4 interpColor;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (\\n    outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate) ||\\n    interpColor.a * opacity == 0.\\n  ) discard;\\n  gl_FragColor = interpColor * opacity;\\n}\\n\"]),c=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float pickGroup;\\n\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate)) discard;\\n\\n  gl_FragColor = vec4(pickGroup, pickId.bgr);\\n}\"]),u=[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"glyph\",type:\"vec2\"},{name:\"id\",type:\"vec4\"}],f={vertex:a,fragment:l,attributes:u},h={vertex:o,fragment:l,attributes:u},p={vertex:s,fragment:l,attributes:u},d={vertex:a,fragment:c,attributes:u},g={vertex:o,fragment:c,attributes:u},m={vertex:s,fragment:c,attributes:u};function v(t,e){var r=n(t,e),i=r.attributes;return i.position.location=0,i.color.location=1,i.glyph.location=2,i.id.location=3,r}r.createPerspective=function(t){return v(t,f)},r.createOrtho=function(t){return v(t,h)},r.createProject=function(t){return v(t,p)},r.createPickPerspective=function(t){return v(t,d)},r.createPickOrtho=function(t){return v(t,g)},r.createPickProject=function(t){return v(t,m)}},{\"gl-shader\":132,glslify:231}],128:[function(t,e,r){\"use strict\";var n=t(\"is-string-blank\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"typedarray-pool\"),s=t(\"gl-mat4/multiply\"),l=t(\"./lib/shaders\"),c=t(\"./lib/glyphs\"),u=t(\"./lib/get-simple-string\"),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e){var r=t[0],n=t[1],i=t[2],a=t[3];return t[0]=e[0]*r+e[4]*n+e[8]*i+e[12]*a,t[1]=e[1]*r+e[5]*n+e[9]*i+e[13]*a,t[2]=e[2]*r+e[6]*n+e[10]*i+e[14]*a,t[3]=e[3]*r+e[7]*n+e[11]*i+e[15]*a,t}function p(t,e,r,n){return h(n,n),h(n,n),h(n,n)}function d(t,e){this.index=t,this.dataCoordinate=this.position=e}function g(t){return!0===t||t>1?1:t}function m(t,e,r,n,i,a,o,s,l,c,u,f){this.gl=t,this.pixelRatio=1,this.shader=e,this.orthoShader=r,this.projectShader=n,this.pointBuffer=i,this.colorBuffer=a,this.glyphBuffer=o,this.idBuffer=s,this.vao=l,this.vertexCount=0,this.lineVertexCount=0,this.opacity=1,this.hasAlpha=!1,this.lineWidth=0,this.projectScale=[2/3,2/3,2/3],this.projectOpacity=[1,1,1],this.projectHasAlpha=!1,this.pickId=0,this.pickPerspectiveShader=c,this.pickOrthoShader=u,this.pickProjectShader=f,this.points=[],this._selectResult=new d(0,[0,0,0]),this.useOrtho=!0,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.axesProject=[!0,!0,!0],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.highlightId=[1,1,1,1],this.highlightScale=2,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.dirty=!0}e.exports=function(t){var e=t.gl,r=l.createPerspective(e),n=l.createOrtho(e),o=l.createProject(e),s=l.createPickPerspective(e),c=l.createPickOrtho(e),u=l.createPickProject(e),f=i(e),h=i(e),p=i(e),d=i(e),g=a(e,[{buffer:f,size:3,type:e.FLOAT},{buffer:h,size:4,type:e.FLOAT},{buffer:p,size:2,type:e.FLOAT},{buffer:d,size:4,type:e.UNSIGNED_BYTE,normalized:!0}]),v=new m(e,r,n,o,f,h,p,d,g,s,c,u);return v.update(t),v};var v=m.prototype;v.pickSlots=1,v.setPickBase=function(t){this.pickId=t},v.isTransparent=function(){if(this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&this.projectHasAlpha)return!0;return!1},v.isOpaque=function(){if(!this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&!this.projectHasAlpha)return!0;return!1};var y=[0,0],x=[0,0,0],b=[0,0,0],_=[0,0,0,1],w=[0,0,0,1],T=f.slice(),k=[0,0,0],A=[[0,0,0],[0,0,0]];function M(t){return t[0]=t[1]=t[2]=0,t}function S(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=1,t}function E(t,e,r,n){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[r]=n,t}function L(t,e,r,n){var i,a=e.axesProject,o=e.gl,l=t.uniforms,c=r.model||f,u=r.view||f,h=r.projection||f,d=e.axesBounds,g=function(t){for(var e=A,r=0;r<2;++r)for(var n=0;n<3;++n)e[r][n]=Math.max(Math.min(t[r][n],1e8),-1e8);return e}(e.clipBounds);i=e.axes&&e.axes.lastCubeProps?e.axes.lastCubeProps.axis:[1,1,1],y[0]=2/o.drawingBufferWidth,y[1]=2/o.drawingBufferHeight,t.bind(),l.view=u,l.projection=h,l.screenSize=y,l.highlightId=e.highlightId,l.highlightScale=e.highlightScale,l.clipBounds=g,l.pickGroup=e.pickId/255,l.pixelRatio=n;for(var m=0;m<3;++m)if(a[m]){l.scale=e.projectScale[m],l.opacity=e.projectOpacity[m];for(var v=T,L=0;L<16;++L)v[L]=0;for(L=0;L<4;++L)v[5*L]=1;v[5*m]=0,i[m]<0?v[12+m]=d[0][m]:v[12+m]=d[1][m],s(v,c,v),l.model=v;var C=(m+1)%3,P=(m+2)%3,I=M(x),O=M(b);I[C]=1,O[P]=1;var z=p(0,0,0,S(_,I)),D=p(0,0,0,S(w,O));if(Math.abs(z[1])>Math.abs(D[1])){var R=z;z=D,D=R,R=I,I=O,O=R;var F=C;C=P,P=F}z[0]<0&&(I[C]=-1),D[1]>0&&(O[P]=-1);var B=0,N=0;for(L=0;L<4;++L)B+=Math.pow(c[4*C+L],2),N+=Math.pow(c[4*P+L],2);I[C]/=Math.sqrt(B),O[P]/=Math.sqrt(N),l.axes[0]=I,l.axes[1]=O,l.fragClipBounds[0]=E(k,g[0],m,-1e8),l.fragClipBounds[1]=E(k,g[1],m,1e8),e.vao.bind(),e.vao.draw(o.TRIANGLES,e.vertexCount),e.lineWidth>0&&(o.lineWidth(e.lineWidth*n),e.vao.draw(o.LINES,e.lineVertexCount,e.vertexCount)),e.vao.unbind()}}var C=[[-1e8,-1e8,-1e8],[1e8,1e8,1e8]];function P(t,e,r,n,i,a,o){var s=r.gl;if((a===r.projectHasAlpha||o)&&L(e,r,n,i),a===r.hasAlpha||o){t.bind();var l=t.uniforms;l.model=n.model||f,l.view=n.view||f,l.projection=n.projection||f,y[0]=2/s.drawingBufferWidth,y[1]=2/s.drawingBufferHeight,l.screenSize=y,l.highlightId=r.highlightId,l.highlightScale=r.highlightScale,l.fragClipBounds=C,l.clipBounds=r.axes.bounds,l.opacity=r.opacity,l.pickGroup=r.pickId/255,l.pixelRatio=i,r.vao.bind(),r.vao.draw(s.TRIANGLES,r.vertexCount),r.lineWidth>0&&(s.lineWidth(r.lineWidth*i),r.vao.draw(s.LINES,r.lineVertexCount,r.vertexCount)),r.vao.unbind()}}function I(t,e,r,i){var a;a=Array.isArray(t)?e<t.length?t[e]:void 0:t,a=u(a);var o=!0;n(a)&&(a=\"\\u25bc\",o=!1);var s=c(a,r,i);return{mesh:s[0],lines:s[1],bounds:s[2],visible:o}}v.draw=function(t){P(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!1,!1)},v.drawTransparent=function(t){P(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!0,!1)},v.drawPick=function(t){P(this.useOrtho?this.pickOrthoShader:this.pickPerspectiveShader,this.pickProjectShader,this,t,1,!0,!0)},v.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[2]+(t.value[1]<<8)+(t.value[0]<<16);if(e>=this.pointCount||e<0)return null;var r=this.points[e],n=this._selectResult;n.index=e;for(var i=0;i<3;++i)n.position[i]=n.dataCoordinate[i]=r[i];return n},v.highlight=function(t){if(t){var e=t.index,r=255&e,n=e>>8&255,i=e>>16&255;this.highlightId=[r/255,n/255,i/255,0]}else this.highlightId=[1,1,1,1]},v.update=function(t){if(\"perspective\"in(t=t||{})&&(this.useOrtho=!t.perspective),\"orthographic\"in t&&(this.useOrtho=!!t.orthographic),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"project\"in t)if(Array.isArray(t.project))this.axesProject=t.project;else{var e=!!t.project;this.axesProject=[e,e,e]}if(\"projectScale\"in t)if(Array.isArray(t.projectScale))this.projectScale=t.projectScale.slice();else{var r=+t.projectScale;this.projectScale=[r,r,r]}if(this.projectHasAlpha=!1,\"projectOpacity\"in t){if(Array.isArray(t.projectOpacity))this.projectOpacity=t.projectOpacity.slice();else{r=+t.projectOpacity;this.projectOpacity=[r,r,r]}for(var n=0;n<3;++n)this.projectOpacity[n]=g(this.projectOpacity[n]),this.projectOpacity[n]<1&&(this.projectHasAlpha=!0)}this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=g(t.opacity),this.opacity<1&&(this.hasAlpha=!0)),this.dirty=!0;var i,a,s=t.position,l=t.font||\"normal\",c=t.alignment||[0,0];if(2===c.length)i=c[0],a=c[1];else{i=[],a=[];for(n=0;n<c.length;++n)i[n]=c[n][0],a[n]=c[n][1]}var u=[1/0,1/0,1/0],f=[-1/0,-1/0,-1/0],h=t.glyph,p=t.color,d=t.size,m=t.angle,v=t.lineColor,y=-1,x=0,b=0,_=0;if(s.length){_=s.length;t:for(n=0;n<_;++n){for(var w=s[n],T=0;T<3;++T)if(isNaN(w[T])||!isFinite(w[T]))continue t;var k=(N=I(h,n,l,this.pixelRatio)).mesh,A=N.lines,M=N.bounds;x+=3*k.cells.length,b+=2*A.edges.length}}var S=x+b,E=o.mallocFloat(3*S),L=o.mallocFloat(4*S),C=o.mallocFloat(2*S),P=o.mallocUint32(S);if(S>0){var O=0,z=x,D=[0,0,0,1],R=[0,0,0,1],F=Array.isArray(p)&&Array.isArray(p[0]),B=Array.isArray(v)&&Array.isArray(v[0]);t:for(n=0;n<_;++n){y+=1;for(w=s[n],T=0;T<3;++T){if(isNaN(w[T])||!isFinite(w[T]))continue t;f[T]=Math.max(f[T],w[T]),u[T]=Math.min(u[T],w[T])}k=(N=I(h,n,l,this.pixelRatio)).mesh,A=N.lines,M=N.bounds;var N,j=N.visible;if(j)if(Array.isArray(p)){if(3===(U=F?n<p.length?p[n]:[0,0,0,0]:p).length){for(T=0;T<3;++T)D[T]=U[T];D[3]=1}else if(4===U.length){for(T=0;T<4;++T)D[T]=U[T];!this.hasAlpha&&U[3]<1&&(this.hasAlpha=!0)}}else D[0]=D[1]=D[2]=0,D[3]=1;else D=[1,1,1,0];if(j)if(Array.isArray(v)){var U;if(3===(U=B?n<v.length?v[n]:[0,0,0,0]:v).length){for(T=0;T<3;++T)R[T]=U[T];R[T]=1}else if(4===U.length){for(T=0;T<4;++T)R[T]=U[T];!this.hasAlpha&&U[3]<1&&(this.hasAlpha=!0)}}else R[0]=R[1]=R[2]=0,R[3]=1;else R=[1,1,1,0];var V=.5;j?Array.isArray(d)?V=n<d.length?+d[n]:12:d?V=+d:this.useOrtho&&(V=12):V=0;var H=0;Array.isArray(m)?H=n<m.length?+m[n]:0:m&&(H=+m);var q=Math.cos(H),G=Math.sin(H);for(w=s[n],T=0;T<3;++T)f[T]=Math.max(f[T],w[T]),u[T]=Math.min(u[T],w[T]);var Y=i,W=a;Y=0;Array.isArray(i)?Y=n<i.length?i[n]:0:i&&(Y=i);W=0;Array.isArray(a)?W=n<a.length?a[n]:0:a&&(W=a);var X=[Y*=Y>0?1-M[0][0]:Y<0?1+M[1][0]:1,W*=W>0?1-M[0][1]:W<0?1+M[1][1]:1],Z=k.cells||[],J=k.positions||[];for(T=0;T<Z.length;++T)for(var K=Z[T],Q=0;Q<3;++Q){for(var $=0;$<3;++$)E[3*O+$]=w[$];for($=0;$<4;++$)L[4*O+$]=D[$];P[O]=y;var tt=J[K[Q]];C[2*O]=V*(q*tt[0]-G*tt[1]+X[0]),C[2*O+1]=V*(G*tt[0]+q*tt[1]+X[1]),O+=1}for(Z=A.edges,J=A.positions,T=0;T<Z.length;++T)for(K=Z[T],Q=0;Q<2;++Q){for($=0;$<3;++$)E[3*z+$]=w[$];for($=0;$<4;++$)L[4*z+$]=R[$];P[z]=y;tt=J[K[Q]];C[2*z]=V*(q*tt[0]-G*tt[1]+X[0]),C[2*z+1]=V*(G*tt[0]+q*tt[1]+X[1]),z+=1}}}this.bounds=[u,f],this.points=s,this.pointCount=s.length,this.vertexCount=x,this.lineVertexCount=b,this.pointBuffer.update(E),this.colorBuffer.update(L),this.glyphBuffer.update(C),this.idBuffer.update(P),o.free(E),o.free(L),o.free(C),o.free(P)},v.dispose=function(){this.shader.dispose(),this.orthoShader.dispose(),this.pickPerspectiveShader.dispose(),this.pickOrthoShader.dispose(),this.vao.dispose(),this.pointBuffer.dispose(),this.colorBuffer.dispose(),this.glyphBuffer.dispose(),this.idBuffer.dispose()}},{\"./lib/get-simple-string\":125,\"./lib/glyphs\":126,\"./lib/shaders\":127,\"gl-buffer\":78,\"gl-mat4/multiply\":100,\"gl-vao\":150,\"is-string-blank\":239,\"typedarray-pool\":308}],129:[function(t,e,r){\"use strict\";var n=t(\"glslify\");r.boxVertex=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 vertex;\\n\\nuniform vec2 cornerA, cornerB;\\n\\nvoid main() {\\n  gl_Position = vec4(mix(cornerA, cornerB, vertex), 0, 1);\\n}\\n\"]),r.boxFragment=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\n\\nvoid main() {\\n  gl_FragColor = color;\\n}\\n\"])},{glslify:231}],130:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"./lib/shaders\");function o(t,e,r){this.plot=t,this.boxBuffer=e,this.boxShader=r,this.enabled=!0,this.selectBox=[1/0,1/0,-1/0,-1/0],this.borderColor=[0,0,0,1],this.innerFill=!1,this.innerColor=[0,0,0,.25],this.outerFill=!0,this.outerColor=[0,0,0,.5],this.borderWidth=10}e.exports=function(t,e){var r=t.gl,s=i(r,[0,0,0,1,1,0,1,1]),l=n(r,a.boxVertex,a.boxFragment),c=new o(t,s,l);return c.update(e),t.addOverlay(c),c};var s=o.prototype;s.draw=function(){if(this.enabled){var t=this.plot,e=this.selectBox,r=this.borderWidth,n=(this.innerFill,this.innerColor),i=(this.outerFill,this.outerColor),a=this.borderColor,o=t.box,s=t.screenBox,l=t.dataBox,c=t.viewBox,u=t.pixelRatio,f=(e[0]-l[0])*(c[2]-c[0])/(l[2]-l[0])+c[0],h=(e[1]-l[1])*(c[3]-c[1])/(l[3]-l[1])+c[1],p=(e[2]-l[0])*(c[2]-c[0])/(l[2]-l[0])+c[0],d=(e[3]-l[1])*(c[3]-c[1])/(l[3]-l[1])+c[1];if(f=Math.max(f,c[0]),h=Math.max(h,c[1]),p=Math.min(p,c[2]),d=Math.min(d,c[3]),!(p<f||d<h)){o.bind();var g=s[2]-s[0],m=s[3]-s[1];if(this.outerFill&&(o.drawBox(0,0,g,h,i),o.drawBox(0,h,f,d,i),o.drawBox(0,d,g,m,i),o.drawBox(p,h,g,d,i)),this.innerFill&&o.drawBox(f,h,p,d,n),r>0){var v=r*u;o.drawBox(f-v,h-v,p+v,h+v,a),o.drawBox(f-v,d-v,p+v,d+v,a),o.drawBox(f-v,h-v,f+v,d+v,a),o.drawBox(p-v,h-v,p+v,d+v,a)}}}},s.update=function(t){t=t||{},this.innerFill=!!t.innerFill,this.outerFill=!!t.outerFill,this.innerColor=(t.innerColor||[0,0,0,.5]).slice(),this.outerColor=(t.outerColor||[0,0,0,.5]).slice(),this.borderColor=(t.borderColor||[0,0,0,1]).slice(),this.borderWidth=t.borderWidth||0,this.selectBox=(t.selectBox||this.selectBox).slice()},s.dispose=function(){this.boxBuffer.dispose(),this.boxShader.dispose(),this.plot.removeOverlay(this)}},{\"./lib/shaders\":129,\"gl-buffer\":78,\"gl-shader\":132}],131:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r=e[0],a=e[1],o=n(t,r,a,{}),s=i.mallocUint8(r*a*4);return new l(t,o,s)};var n=t(\"gl-fbo\"),i=t(\"typedarray-pool\"),a=t(\"ndarray\"),o=t(\"bit-twiddle\").nextPow2;function s(t,e,r,n,i){this.coord=[t,e],this.id=r,this.value=n,this.distance=i}function l(t,e,r){this.gl=t,this.fbo=e,this.buffer=r,this._readTimeout=null;var n=this;this._readCallback=function(){n.gl&&(e.bind(),t.readPixels(0,0,e.shape[0],e.shape[1],t.RGBA,t.UNSIGNED_BYTE,n.buffer),n._readTimeout=null)}}var c=l.prototype;Object.defineProperty(c,\"shape\",{get:function(){return this.gl?this.fbo.shape.slice():[0,0]},set:function(t){if(this.gl){this.fbo.shape=t;var e=this.fbo.shape[0],r=this.fbo.shape[1];if(r*e*4>this.buffer.length){i.free(this.buffer);for(var n=this.buffer=i.mallocUint8(o(r*e*4)),a=0;a<r*e*4;++a)n[a]=255}return t}}}),c.begin=function(){var t=this.gl;this.shape;t&&(this.fbo.bind(),t.clearColor(1,1,1,1),t.clear(t.COLOR_BUFFER_BIT|t.DEPTH_BUFFER_BIT))},c.end=function(){var t=this.gl;t&&(t.bindFramebuffer(t.FRAMEBUFFER,null),this._readTimeout||clearTimeout(this._readTimeout),this._readTimeout=setTimeout(this._readCallback,1))},c.query=function(t,e,r){if(!this.gl)return null;var n=this.fbo.shape.slice();t|=0,e|=0,\"number\"!=typeof r&&(r=1);var i=0|Math.min(Math.max(t-r,0),n[0]),o=0|Math.min(Math.max(t+r,0),n[0]),l=0|Math.min(Math.max(e-r,0),n[1]),c=0|Math.min(Math.max(e+r,0),n[1]);if(o<=i||c<=l)return null;var u=[o-i,c-l],f=a(this.buffer,[u[0],u[1],4],[4,4*n[0],1],4*(i+n[0]*l)),h=function(t,e,r){for(var n=1e8,i=-1,a=-1,o=t.shape[0],s=t.shape[1],l=0;l<o;l++)for(var c=0;c<s;c++){var u=t.get(l,c,0),f=t.get(l,c,1),h=t.get(l,c,2),p=t.get(l,c,3);if(u<255||f<255||h<255||p<255){var d=e-l,g=r-c,m=d*d+g*g;m<n&&(n=m,i=l,a=c)}}return[i,a,n]}(f.hi(u[0],u[1],1),r,r),p=h[0],d=h[1];return p<0||Math.pow(this.radius,2)<h[2]?null:new s(p+i|0,d+l|0,f.get(p,d,0),[f.get(p,d,1),f.get(p,d,2),f.get(p,d,3)],Math.sqrt(h[2]))},c.dispose=function(){this.gl&&(this.fbo.dispose(),i.free(this.buffer),this.gl=null,this._readTimeout&&clearTimeout(this._readTimeout))}},{\"bit-twiddle\":32,\"gl-fbo\":86,ndarray:259,\"typedarray-pool\":308}],132:[function(t,e,r){\"use strict\";var n=t(\"./lib/create-uniforms\"),i=t(\"./lib/create-attributes\"),a=t(\"./lib/reflect\"),o=t(\"./lib/shader-cache\"),s=t(\"./lib/runtime-reflect\"),l=t(\"./lib/GLError\");function c(t){this.gl=t,this.gl.lastAttribCount=0,this._vref=this._fref=this._relink=this.vertShader=this.fragShader=this.program=this.attributes=this.uniforms=this.types=null}var u=c.prototype;function f(t,e){return t.name<e.name?-1:1}u.bind=function(){var t;this.program||this._relink();var e=this.gl.getProgramParameter(this.program,this.gl.ACTIVE_ATTRIBUTES),r=this.gl.lastAttribCount;if(e>r)for(t=r;t<e;t++)this.gl.enableVertexAttribArray(t);else if(r>e)for(t=e;t<r;t++)this.gl.disableVertexAttribArray(t);this.gl.lastAttribCount=e,this.gl.useProgram(this.program)},u.dispose=function(){for(var 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w=c[y];if(w.type.indexOf(\"mat\")>=0){for(var T=0|w.type.charAt(w.type.length-1),k=new Array(T),A=0;A<T;++A)k[A]=_.length,b.push(w.name+\"[\"+A+\"]\"),\"number\"==typeof w.location?_.push(w.location+A):Array.isArray(w.location)&&w.location.length===T&&\"number\"==typeof w.location[A]?_.push(0|w.location[A]):_.push(-1);x.push({name:w.name,type:w.type,locations:k})}else x.push({name:w.name,type:w.type,locations:[_.length]}),b.push(w.name),\"number\"==typeof w.location?_.push(0|w.location):_.push(-1)}var M=0;for(y=0;y<_.length;++y)if(_[y]<0){for(;_.indexOf(M)>=0;)M+=1;_[y]=M}var S=new Array(r.length);function E(){h.program=o.program(p,h._vref,h._fref,b,_);for(var t=0;t<r.length;++t)S[t]=p.getUniformLocation(h.program,r[t].name)}E(),h._relink=E,h.types={uniforms:a(r),attributes:a(c)},h.attributes=i(p,h,x,_),Object.defineProperty(h,\"uniforms\",n(p,h,r,S))},e.exports=function(t,e,r,n,i){var a=new c(t);return 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h=c,p=u,d=0;d<3;++d)i&&i[d]<0?(h[d]=this.bounds[0][d],p[d]=this.bounds[1][d]):(h[d]=this.bounds[1][d],p[d]=this.bounds[0][d]);f[0]=e.drawingBufferWidth,f[1]=e.drawingBufferHeight,n.uniforms.model=a,n.uniforms.view=s,n.uniforms.projection=l,n.uniforms.coordinates=[this.position,h,p],n.uniforms.colors=this.colors,n.uniforms.screenShape=f;for(d=0;d<3;++d)n.uniforms.lineWidth=this.lineWidth[d]*this.pixelRatio,this.enabled[d]&&(r.draw(e.TRIANGLES,6,6*d),this.drawSides[d]&&r.draw(e.TRIANGLES,12,18+12*d));r.unbind()},l.update=function(t){t&&(\"bounds\"in t&&(this.bounds=t.bounds),\"position\"in t&&(this.position=t.position),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"colors\"in t&&(this.colors=t.colors),\"enabled\"in t&&(this.enabled=t.enabled),\"drawSides\"in t&&(this.drawSides=t.drawSides))},l.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},{\"./shaders/index\":140,\"gl-buffer\":78,\"gl-vao\":150}],142:[function(t,e,r){var n=t(\"glslify\"),i=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the tube vertex and normal at the given index.\\n//\\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\\n//\\n// Each tube segment is made up of a ring of vertices.\\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\\n// The indexes of tube segments run from 0 to 8.\\n//\\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\\n  float segmentCount = 8.0;\\n\\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d);\\n  vec3 y = v * sin(angle) * length(d);\\n  vec3 v3 = x + y;\\n\\n  normal = normalize(v3);\\n\\n  return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 color, position;\\nattribute vec2 uv;\\n\\nuniform float vectorScale, tubeScale;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 eyePosition, lightPosition;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  // Scale the vector magnitude to stay constant with\\n  // model & view changes.\\n  vec3 normal;\\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * tubePosition;\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n  // vec4 m_position  = model * vec4(tubePosition, 1.0);\\n  vec4 t_position  = view * tubePosition;\\n  gl_Position      = projection * t_position;\\n\\n  f_color          = color;\\n  f_data           = tubePosition.xyz;\\n  f_position       = position.xyz;\\n  f_uv             = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness,\\n  float fresnel) {\\n\\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n  //Half angle vector\\n  vec3 H = normalize(lightDirection + viewDirection);\\n\\n  //Geometric term\\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\\n  float VdotH = max(dot(viewDirection, H), 0.000001);\\n  float LdotH = max(dot(lightDirection, H), 0.000001);\\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n  float G = min(1.0, min(G1, G2));\\n  \\n  //Distribution term\\n  float D = beckmannDistribution(NdotH, roughness);\\n\\n  //Fresnel term\\n  float F = pow(1.0 - VdotN, fresnel);\\n\\n  //Multiply terms and done\\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n  vec3 N = normalize(f_normal);\\n  vec3 L = normalize(f_lightDirection);\\n  vec3 V = normalize(f_eyeDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = litColor * opacity;\\n}\\n\"]),o=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the tube vertex and normal at the given index.\\n//\\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\\n//\\n// Each tube segment is made up of a ring of vertices.\\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\\n// The indexes of tube segments run from 0 to 8.\\n//\\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\\n  float segmentCount = 8.0;\\n\\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d);\\n  vec3 y = v * sin(angle) * length(d);\\n  vec3 v3 = x + y;\\n\\n  normal = normalize(v3);\\n\\n  return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform float tubeScale;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  vec3 normal;\\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n  gl_Position = projection * view * tubePosition;\\n  f_id        = id;\\n  f_position  = position.xyz;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3  clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n  gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec4\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"vector\",type:\"vec4\"}]},r.pickShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec4\"},{name:\"id\",type:\"vec4\"},{name:\"vector\",type:\"vec4\"}]}},{glslify:231}],143:[function(t,e,r){\"use strict\";var n=t(\"gl-vec3\"),i=t(\"gl-vec4\"),a=[\"xyz\",\"xzy\",\"yxz\",\"yzx\",\"zxy\",\"zyx\"],o=function(t,e,r,a){for(var o=0,s=0;s<t.length;s++)for(var l=t[s].velocities,c=0;c<l.length;c++)o=Math.max(o,n.length(l[c]));var u=t.map((function(t){return function(t,e,r,a){for(var o=t.points,s=t.velocities,l=t.divergences,c=[],u=[],f=[],h=[],p=[],d=[],g=0,m=0,v=i.create(),y=i.create(),x=0;x<o.length;x++){var b=o[x],_=s[x],w=l[x];0===e&&(w=.05*r),m=n.length(_)/a,v=i.create(),n.copy(v,_),v[3]=w;for(var T=0;T<8;T++)p[T]=[b[0],b[1],b[2],T];if(h.length>0)for(T=0;T<8;T++){var k=(T+1)%8;c.push(h[T],p[T],p[k],p[k],h[k],h[T]),f.push(y,v,v,v,y,y),d.push(g,m,m,m,g,g);var A=c.length;u.push([A-6,A-5,A-4],[A-3,A-2,A-1])}var M=h;h=p,p=M;var S=y;y=v,v=S;var E=g;g=m,m=E}return{positions:c,cells:u,vectors:f,vertexIntensity:d}}(t,r,a,o)})),f=[],h=[],p=[],d=[];for(s=0;s<u.length;s++){var g=u[s],m=f.length;f=f.concat(g.positions),p=p.concat(g.vectors),d=d.concat(g.vertexIntensity);for(c=0;c<g.cells.length;c++){var v=g.cells[c],y=[];h.push(y);for(var x=0;x<v.length;x++)y.push(v[x]+m)}}return{positions:f,cells:h,vectors:p,vertexIntensity:d,colormap:e}},s=function(t,e){var r,n=t.length;for(r=0;r<n;r++){var i=t[r];if(i===e)return r;if(i>e)return r-1}return r},l=function(t,e,r){return t<e?e:t>r?r:t},c=function(t){var e=1/0;t.sort((function(t,e){return t-e}));for(var r=t.length,n=1;n<r;n++){var i=Math.abs(t[n]-t[n-1]);i<e&&(e=i)}return e};e.exports=function(t,e){var r=t.startingPositions,i=t.maxLength||1e3,u=t.tubeSize||1,f=t.absoluteTubeSize,h=t.gridFill||\"+x+y+z\",p={};-1!==h.indexOf(\"-x\")&&(p.reversedX=!0),-1!==h.indexOf(\"-y\")&&(p.reversedY=!0),-1!==h.indexOf(\"-z\")&&(p.reversedZ=!0),p.filled=a.indexOf(h.replace(/-/g,\"\").replace(/\\+/g,\"\"));var d=t.getVelocity||function(e){return function(t,e,r){var i=e.vectors,a=e.meshgrid,o=t[0],c=t[1],u=t[2],f=a[0].length,h=a[1].length,p=a[2].length,d=s(a[0],o),g=s(a[1],c),m=s(a[2],u),v=d+1,y=g+1,x=m+1;if(d=l(d,0,f-1),v=l(v,0,f-1),g=l(g,0,h-1),y=l(y,0,h-1),m=l(m,0,p-1),x=l(x,0,p-1),d<0||g<0||m<0||v>f-1||y>h-1||x>p-1)return n.create();var b,_,w,T,k,A,M=a[0][d],S=a[0][v],E=a[1][g],L=a[1][y],C=a[2][m],P=(o-M)/(S-M),I=(c-E)/(L-E),O=(u-C)/(a[2][x]-C);switch(isFinite(P)||(P=.5),isFinite(I)||(I=.5),isFinite(O)||(O=.5),r.reversedX&&(d=f-1-d,v=f-1-v),r.reversedY&&(g=h-1-g,y=h-1-y),r.reversedZ&&(m=p-1-m,x=p-1-x),r.filled){case 5:k=m,A=x,w=g*p,T=y*p,b=d*p*h,_=v*p*h;break;case 4:k=m,A=x,b=d*p,_=v*p,w=g*p*f,T=y*p*f;break;case 3:w=g,T=y,k=m*h,A=x*h,b=d*h*p,_=v*h*p;break;case 2:w=g,T=y,b=d*h,_=v*h,k=m*h*f,A=x*h*f;break;case 1:b=d,_=v,k=m*f,A=x*f,w=g*f*p,T=y*f*p;break;default:b=d,_=v,w=g*f,T=y*f,k=m*f*h,A=x*f*h}var z=i[b+w+k],D=i[b+w+A],R=i[b+T+k],F=i[b+T+A],B=i[_+w+k],N=i[_+w+A],j=i[_+T+k],U=i[_+T+A],V=n.create(),H=n.create(),q=n.create(),G=n.create();n.lerp(V,z,B,P),n.lerp(H,D,N,P),n.lerp(q,R,j,P),n.lerp(G,F,U,P);var Y=n.create(),W=n.create();n.lerp(Y,V,q,I),n.lerp(W,H,G,I);var X=n.create();return n.lerp(X,Y,W,O),X}(e,t,p)},g=t.getDivergence||function(t,e){var r=n.create(),i=1e-4;n.add(r,t,[i,0,0]);var a=d(r);n.subtract(a,a,e),n.scale(a,a,1/i),n.add(r,t,[0,i,0]);var o=d(r);n.subtract(o,o,e),n.scale(o,o,1/i),n.add(r,t,[0,0,i]);var s=d(r);return n.subtract(s,s,e),n.scale(s,s,1/i),n.add(r,a,o),n.add(r,r,s),r},m=[],v=e[0][0],y=e[0][1],x=e[0][2],b=e[1][0],_=e[1][1],w=e[1][2],T=function(t){var e=t[0],r=t[1],n=t[2];return!(e<v||e>b||r<y||r>_||n<x||n>w)},k=10*n.distance(e[0],e[1])/i,A=k*k,M=1,S=0,E=r.length;E>1&&(M=function(t){for(var e=[],r=[],n=[],i={},a={},o={},s=t.length,l=0;l<s;l++){var u=t[l],f=u[0],h=u[1],p=u[2];i[f]||(e.push(f),i[f]=!0),a[h]||(r.push(h),a[h]=!0),o[p]||(n.push(p),o[p]=!0)}var d=c(e),g=c(r),m=c(n),v=Math.min(d,g,m);return isFinite(v)?v:1}(r));for(var L=0;L<E;L++){var C=n.create();n.copy(C,r[L]);var P=[C],I=[],O=d(C),z=C;I.push(O);var D=[],R=g(C,O),F=n.length(R);isFinite(F)&&F>S&&(S=F),D.push(F),m.push({points:P,velocities:I,divergences:D});for(var B=0;B<100*i&&P.length<i&&T(C);){B++;var N=n.clone(O),j=n.squaredLength(N);if(0===j)break;if(j>A&&n.scale(N,N,k/Math.sqrt(j)),n.add(N,N,C),O=d(N),n.squaredDistance(z,N)-A>-1e-4*A){P.push(N),z=N,I.push(O);R=g(N,O),F=n.length(R);isFinite(F)&&F>S&&(S=F),D.push(F)}C=N}}var U=o(m,t.colormap,S,M);return f?U.tubeScale=f:(0===S&&(S=1),U.tubeScale=.5*u*M/S),U};var u=t(\"./lib/shaders\"),f=t(\"gl-cone3d\").createMesh;e.exports.createTubeMesh=function(t,e){return f(t,e,{shaders:u,traceType:\"streamtube\"})}},{\"./lib/shaders\":142,\"gl-cone3d\":79,\"gl-vec3\":169,\"gl-vec4\":205}],144:[function(t,e,r){var n=t(\"gl-shader\"),i=t(\"glslify\"),a=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec4 uv;\\nattribute vec3 f;\\nattribute vec3 normal;\\n\\nuniform vec3 objectOffset;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 lightPosition, eyePosition;\\nuniform sampler2D colormap;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n  vec3 localCoordinate = vec3(uv.zw, f.x);\\n  worldCoordinate = objectOffset + localCoordinate;\\n  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\\n  vec4 clipPosition = projection * view * worldPosition;\\n  gl_Position = clipPosition;\\n  kill = f.y;\\n  value = f.z;\\n  planeCoordinate = uv.xy;\\n\\n  vColor = texture2D(colormap, vec2(value, value));\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * worldPosition;\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  lightDirection = lightPosition - cameraCoordinate.xyz;\\n  eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  surfaceNormal  = normalize((vec4(normal,0) * inverseModel).xyz);\\n}\\n\"]),o=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat beckmannSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness) {\\n  return beckmannDistribution(dot(surfaceNormal, normalize(lightDirection + viewDirection)), roughness);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 lowerBound, upperBound;\\nuniform float contourTint;\\nuniform vec4 contourColor;\\nuniform sampler2D colormap;\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform float vertexColor;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n  if (\\n    kill > 0.0 ||\\n    vColor.a == 0.0 ||\\n    outOfRange(clipBounds[0], clipBounds[1], worldCoordinate)\\n  ) discard;\\n\\n  vec3 N = normalize(surfaceNormal);\\n  vec3 V = normalize(eyeDirection);\\n  vec3 L = normalize(lightDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = max(beckmannSpecular(L, V, N, roughness), 0.);\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  //decide how to interpolate color \\u2014 in vertex or in fragment\\n  vec4 surfaceColor =\\n    step(vertexColor, .5) * texture2D(colormap, vec2(value, value)) +\\n    step(.5, vertexColor) * vColor;\\n\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = mix(litColor, contourColor, contourTint) * opacity;\\n}\\n\"]),s=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec4 uv;\\nattribute float f;\\n\\nuniform vec3 objectOffset;\\nuniform mat3 permutation;\\nuniform mat4 model, view, projection;\\nuniform float height, zOffset;\\nuniform sampler2D colormap;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n  vec3 dataCoordinate = permutation * vec3(uv.xy, height);\\n  worldCoordinate = objectOffset + dataCoordinate;\\n  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\\n\\n  vec4 clipPosition = projection * view * worldPosition;\\n  clipPosition.z += zOffset;\\n\\n  gl_Position = clipPosition;\\n  value = f + objectOffset.z;\\n  kill = -1.0;\\n  planeCoordinate = uv.zw;\\n\\n  vColor = texture2D(colormap, vec2(value, value));\\n\\n  //Don't do lighting for contours\\n  surfaceNormal   = vec3(1,0,0);\\n  eyeDirection    = vec3(0,1,0);\\n  lightDirection  = vec3(0,0,1);\\n}\\n\"]),l=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec2 shape;\\nuniform vec3 clipBounds[2];\\nuniform float pickId;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 surfaceNormal;\\n\\nvec2 splitFloat(float v) {\\n  float vh = 255.0 * v;\\n  float upper = floor(vh);\\n  float lower = fract(vh);\\n  return vec2(upper / 255.0, floor(lower * 16.0) / 16.0);\\n}\\n\\nvoid main() {\\n  if ((kill > 0.0) ||\\n      (outOfRange(clipBounds[0], clipBounds[1], worldCoordinate))) discard;\\n\\n  vec2 ux = splitFloat(planeCoordinate.x / shape.x);\\n  vec2 uy = splitFloat(planeCoordinate.y / shape.y);\\n  gl_FragColor = vec4(pickId, ux.x, uy.x, ux.y + (uy.y/16.0));\\n}\\n\"]);r.createShader=function(t){var e=n(t,a,o,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createPickShader=function(t){var e=n(t,a,l,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createContourShader=function(t){var e=n(t,s,o,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e},r.createPickContourShader=function(t){var e=n(t,s,l,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e}},{\"gl-shader\":132,glslify:231}],145:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=y(e),n=b(e),s=x(e),l=_(e),c=i(e),u=a(e,[{buffer:c,size:4,stride:40,offset:0},{buffer:c,size:3,stride:40,offset:16},{buffer:c,size:3,stride:40,offset:28}]),f=i(e),h=a(e,[{buffer:f,size:4,stride:20,offset:0},{buffer:f,size:1,stride:20,offset:16}]),p=i(e),d=a(e,[{buffer:p,size:2,type:e.FLOAT}]),g=o(e,1,256,e.RGBA,e.UNSIGNED_BYTE);g.minFilter=e.LINEAR,g.magFilter=e.LINEAR;var m=new M(e,[0,0],[[0,0,0],[0,0,0]],r,n,c,u,g,s,l,f,h,p,d,[0,0,0]),v={levels:[[],[],[]]};for(var w in t)v[w]=t[w];return v.colormap=v.colormap||\"jet\",m.update(v),m};var n=t(\"bit-twiddle\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"gl-texture2d\"),s=t(\"typedarray-pool\"),l=t(\"colormap\"),c=t(\"ndarray-ops\"),u=t(\"ndarray-pack\"),f=t(\"ndarray\"),h=t(\"surface-nets\"),p=t(\"gl-mat4/multiply\"),d=t(\"gl-mat4/invert\"),g=t(\"binary-search-bounds\"),m=t(\"ndarray-gradient\"),v=t(\"./lib/shaders\"),y=v.createShader,x=v.createContourShader,b=v.createPickShader,_=v.createPickContourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],T=[[0,0],[0,1],[1,0],[1,1],[1,0],[0,1]],k=[[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0]];function A(t,e,r,n,i){this.position=t,this.index=e,this.uv=r,this.level=n,this.dataCoordinate=i}!function(){for(var t=0;t<3;++t){var e=k[t],r=(t+2)%3;e[(t+1)%3+0]=1,e[r+3]=1,e[t+6]=1}}();function M(t,e,r,n,i,a,o,l,c,u,h,p,d,g,m){this.gl=t,this.shape=e,this.bounds=r,this.objectOffset=m,this.intensityBounds=[],this._shader=n,this._pickShader=i,this._coordinateBuffer=a,this._vao=o,this._colorMap=l,this._contourShader=c,this._contourPickShader=u,this._contourBuffer=h,this._contourVAO=p,this._contourOffsets=[[],[],[]],this._contourCounts=[[],[],[]],this._vertexCount=0,this._pickResult=new A([0,0,0],[0,0],[0,0],[0,0,0],[0,0,0]),this._dynamicBuffer=d,this._dynamicVAO=g,this._dynamicOffsets=[0,0,0],this._dynamicCounts=[0,0,0],this.contourWidth=[1,1,1],this.contourLevels=[[1],[1],[1]],this.contourTint=[0,0,0],this.contourColor=[[.5,.5,.5,1],[.5,.5,.5,1],[.5,.5,.5,1]],this.showContour=!0,this.showSurface=!0,this.enableHighlight=[!0,!0,!0],this.highlightColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.highlightTint=[1,1,1],this.highlightLevel=[-1,-1,-1],this.enableDynamic=[!0,!0,!0],this.dynamicLevel=[NaN,NaN,NaN],this.dynamicColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.dynamicTint=[1,1,1],this.dynamicWidth=[1,1,1],this.axesBounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.surfaceProject=[!1,!1,!1],this.contourProject=[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],this.colorBounds=[!1,!1],this._field=[f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0])],this.pickId=1,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.snapToData=!1,this.pixelRatio=1,this.opacity=1,this.lightPosition=[10,1e4,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.vertexColor=0,this.dirty=!0}var S=M.prototype;S.genColormap=function(t,e){var r=!1,n=u([l({colormap:t,nshades:256,format:\"rgba\"}).map((function(t,n){var i=e?function(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;r<e.length;++r){if(e.length<2)return 1;if(e[r][0]===t)return e[r][1];if(e[r][0]>t&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}(n/255,e):t[3];return i<1&&(r=!0),[t[0],t[1],t[2],255*i]}))]);return c.divseq(n,255),this.hasAlphaScale=r,n},S.isTransparent=function(){return this.opacity<1||this.hasAlphaScale},S.isOpaque=function(){return!this.isTransparent()},S.pickSlots=1,S.setPickBase=function(t){this.pickId=t};var E=[0,0,0],L={showSurface:!1,showContour:!1,projections:[w.slice(),w.slice(),w.slice()],clipBounds:[[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]]]};function C(t,e){var r,n,i,a=e.axes&&e.axes.lastCubeProps.axis||E,o=e.showSurface,s=e.showContour;for(r=0;r<3;++r)for(o=o||e.surfaceProject[r],n=0;n<3;++n)s=s||e.contourProject[r][n];for(r=0;r<3;++r){var l=L.projections[r];for(n=0;n<16;++n)l[n]=0;for(n=0;n<4;++n)l[5*n]=1;l[5*r]=0,l[12+r]=e.axesBounds[+(a[r]>0)][r],p(l,t.model,l);var c=L.clipBounds[r];for(i=0;i<2;++i)for(n=0;n<3;++n)c[i][n]=t.clipBounds[i][n];c[0][r]=-1e8,c[1][r]=1e8}return L.showSurface=o,L.showContour=s,L}var P={model:w,view:w,projection:w,inverseModel:w.slice(),lowerBound:[0,0,0],upperBound:[0,0,0],colorMap:0,clipBounds:[[0,0,0],[0,0,0]],height:0,contourTint:0,contourColor:[0,0,0,1],permutation:[1,0,0,0,1,0,0,0,1],zOffset:-1e-4,objectOffset:[0,0,0],kambient:1,kdiffuse:1,kspecular:1,lightPosition:[1e3,1e3,1e3],eyePosition:[0,0,0],roughness:1,fresnel:1,opacity:1,vertexColor:0},I=w.slice(),O=[1,0,0,0,1,0,0,0,1];function z(t,e){t=t||{};var r=this.gl;r.disable(r.CULL_FACE),this._colorMap.bind(0);var n=P;n.model=t.model||w,n.view=t.view||w,n.projection=t.projection||w,n.lowerBound=[this.bounds[0][0],this.bounds[0][1],this.colorBounds[0]||this.bounds[0][2]],n.upperBound=[this.bounds[1][0],this.bounds[1][1],this.colorBounds[1]||this.bounds[1][2]],n.objectOffset=this.objectOffset,n.contourColor=this.contourColor[0],n.inverseModel=d(n.inverseModel,n.model);for(var i=0;i<2;++i)for(var a=n.clipBounds[i],o=0;o<3;++o)a[o]=Math.min(Math.max(this.clipBounds[i][o],-1e8),1e8);n.kambient=this.ambientLight,n.kdiffuse=this.diffuseLight,n.kspecular=this.specularLight,n.roughness=this.roughness,n.fresnel=this.fresnel,n.opacity=this.opacity,n.height=0,n.permutation=O,n.vertexColor=this.vertexColor;var s=I;for(p(s,n.view,n.model),p(s,n.projection,s),d(s,s),i=0;i<3;++i)n.eyePosition[i]=s[12+i]/s[15];var l=s[15];for(i=0;i<3;++i)l+=this.lightPosition[i]*s[4*i+3];for(i=0;i<3;++i){var c=s[12+i];for(o=0;o<3;++o)c+=s[4*o+i]*this.lightPosition[o];n.lightPosition[i]=c/l}var u=C(n,this);if(u.showSurface){for(this._shader.bind(),this._shader.uniforms=n,this._vao.bind(),this.showSurface&&this._vertexCount&&this._vao.draw(r.TRIANGLES,this._vertexCount),i=0;i<3;++i)this.surfaceProject[i]&&this.vertexCount&&(this._shader.uniforms.model=u.projections[i],this._shader.uniforms.clipBounds=u.clipBounds[i],this._vao.draw(r.TRIANGLES,this._vertexCount));this._vao.unbind()}if(u.showContour){var f=this._contourShader;n.kambient=1,n.kdiffuse=0,n.kspecular=0,n.opacity=1,f.bind(),f.uniforms=n;var h=this._contourVAO;for(h.bind(),i=0;i<3;++i)for(f.uniforms.permutation=k[i],r.lineWidth(this.contourWidth[i]*this.pixelRatio),o=0;o<this.contourLevels[i].length;++o)o===this.highlightLevel[i]?(f.uniforms.contourColor=this.highlightColor[i],f.uniforms.contourTint=this.highlightTint[i]):0!==o&&o-1!==this.highlightLevel[i]||(f.uniforms.contourColor=this.contourColor[i],f.uniforms.contourTint=this.contourTint[i]),this._contourCounts[i][o]&&(f.uniforms.height=this.contourLevels[i][o],h.draw(r.LINES,this._contourCounts[i][o],this._contourOffsets[i][o]));for(i=0;i<3;++i)for(f.uniforms.model=u.projections[i],f.uniforms.clipBounds=u.clipBounds[i],o=0;o<3;++o)if(this.contourProject[i][o]){f.uniforms.permutation=k[o],r.lineWidth(this.contourWidth[o]*this.pixelRatio);for(var g=0;g<this.contourLevels[o].length;++g)g===this.highlightLevel[o]?(f.uniforms.contourColor=this.highlightColor[o],f.uniforms.contourTint=this.highlightTint[o]):0!==g&&g-1!==this.highlightLevel[o]||(f.uniforms.contourColor=this.contourColor[o],f.uniforms.contourTint=this.contourTint[o]),this._contourCounts[o][g]&&(f.uniforms.height=this.contourLevels[o][g],h.draw(r.LINES,this._contourCounts[o][g],this._contourOffsets[o][g]))}for(h.unbind(),(h=this._dynamicVAO).bind(),i=0;i<3;++i)if(0!==this._dynamicCounts[i])for(f.uniforms.model=n.model,f.uniforms.clipBounds=n.clipBounds,f.uniforms.permutation=k[i],r.lineWidth(this.dynamicWidth[i]*this.pixelRatio),f.uniforms.contourColor=this.dynamicColor[i],f.uniforms.contourTint=this.dynamicTint[i],f.uniforms.height=this.dynamicLevel[i],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]),o=0;o<3;++o)this.contourProject[o][i]&&(f.uniforms.model=u.projections[o],f.uniforms.clipBounds=u.clipBounds[o],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]));h.unbind()}}S.draw=function(t){return z.call(this,t,!1)},S.drawTransparent=function(t){return z.call(this,t,!0)};var D={model:w,view:w,projection:w,inverseModel:w,clipBounds:[[0,0,0],[0,0,0]],height:0,shape:[0,0],pickId:0,lowerBound:[0,0,0],upperBound:[0,0,0],zOffset:0,objectOffset:[0,0,0],permutation:[1,0,0,0,1,0,0,0,1],lightPosition:[0,0,0],eyePosition:[0,0,0]};function R(t,e){return Array.isArray(t)?[e(t[0]),e(t[1]),e(t[2])]:[e(t),e(t),e(t)]}function F(t){return Array.isArray(t)?3===t.length?[t[0],t[1],t[2],1]:[t[0],t[1],t[2],t[3]]:[0,0,0,1]}function B(t){if(Array.isArray(t)){if(Array.isArray(t))return[F(t[0]),F(t[1]),F(t[2])];var e=F(t);return[e.slice(),e.slice(),e.slice()]}}S.drawPick=function(t){t=t||{};var e=this.gl;e.disable(e.CULL_FACE);var r=D;r.model=t.model||w,r.view=t.view||w,r.projection=t.projection||w,r.shape=this._field[2].shape,r.pickId=this.pickId/255,r.lowerBound=this.bounds[0],r.upperBound=this.bounds[1],r.objectOffset=this.objectOffset,r.permutation=O;for(var n=0;n<2;++n)for(var i=r.clipBounds[n],a=0;a<3;++a)i[a]=Math.min(Math.max(this.clipBounds[n][a],-1e8),1e8);var o=C(r,this);if(o.showSurface){for(this._pickShader.bind(),this._pickShader.uniforms=r,this._vao.bind(),this._vao.draw(e.TRIANGLES,this._vertexCount),n=0;n<3;++n)this.surfaceProject[n]&&(this._pickShader.uniforms.model=o.projections[n],this._pickShader.uniforms.clipBounds=o.clipBounds[n],this._vao.draw(e.TRIANGLES,this._vertexCount));this._vao.unbind()}if(o.showContour){var s=this._contourPickShader;s.bind(),s.uniforms=r;var l=this._contourVAO;for(l.bind(),a=0;a<3;++a)for(e.lineWidth(this.contourWidth[a]*this.pixelRatio),s.uniforms.permutation=k[a],n=0;n<this.contourLevels[a].length;++n)this._contourCounts[a][n]&&(s.uniforms.height=this.contourLevels[a][n],l.draw(e.LINES,this._contourCounts[a][n],this._contourOffsets[a][n]));for(n=0;n<3;++n)for(s.uniforms.model=o.projections[n],s.uniforms.clipBounds=o.clipBounds[n],a=0;a<3;++a)if(this.contourProject[n][a]){s.uniforms.permutation=k[a],e.lineWidth(this.contourWidth[a]*this.pixelRatio);for(var c=0;c<this.contourLevels[a].length;++c)this._contourCounts[a][c]&&(s.uniforms.height=this.contourLevels[a][c],l.draw(e.LINES,this._contourCounts[a][c],this._contourOffsets[a][c]))}l.unbind()}},S.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=this._field[2].shape,r=this._pickResult,n=e[0]*(t.value[0]+(t.value[2]>>4)/16)/255,i=Math.floor(n),a=n-i,o=e[1]*(t.value[1]+(15&t.value[2])/16)/255,s=Math.floor(o),l=o-s;i+=1,s+=1;var c=r.position;c[0]=c[1]=c[2]=0;for(var u=0;u<2;++u)for(var f=u?a:1-a,h=0;h<2;++h)for(var p=i+u,d=s+h,m=f*(h?l:1-l),v=0;v<3;++v)c[v]+=this._field[v].get(p,d)*m;for(var y=this._pickResult.level,x=0;x<3;++x)if(y[x]=g.le(this.contourLevels[x],c[x]),y[x]<0)this.contourLevels[x].length>0&&(y[x]=0);else if(y[x]<this.contourLevels[x].length-1){var b=this.contourLevels[x][y[x]],_=this.contourLevels[x][y[x]+1];Math.abs(b-c[x])>Math.abs(_-c[x])&&(y[x]+=1)}for(r.index[0]=a<.5?i:i+1,r.index[1]=l<.5?s:s+1,r.uv[0]=n/e[0],r.uv[1]=o/e[1],v=0;v<3;++v)r.dataCoordinate[v]=this._field[v].get(r.index[0],r.index[1]);return r},S.padField=function(t,e){var r=e.shape.slice(),n=t.shape.slice();c.assign(t.lo(1,1).hi(r[0],r[1]),e),c.assign(t.lo(1).hi(r[0],1),e.hi(r[0],1)),c.assign(t.lo(1,n[1]-1).hi(r[0],1),e.lo(0,r[1]-1).hi(r[0],1)),c.assign(t.lo(0,1).hi(1,r[1]),e.hi(1)),c.assign(t.lo(n[0]-1,1).hi(1,r[1]),e.lo(r[0]-1)),t.set(0,0,e.get(0,0)),t.set(0,n[1]-1,e.get(0,r[1]-1)),t.set(n[0]-1,0,e.get(r[0]-1,0)),t.set(n[0]-1,n[1]-1,e.get(r[0]-1,r[1]-1))},S.update=function(t){t=t||{},this.objectOffset=t.objectOffset||this.objectOffset,this.dirty=!0,\"contourWidth\"in t&&(this.contourWidth=R(t.contourWidth,Number)),\"showContour\"in t&&(this.showContour=R(t.showContour,Boolean)),\"showSurface\"in t&&(this.showSurface=!!t.showSurface),\"contourTint\"in t&&(this.contourTint=R(t.contourTint,Boolean)),\"contourColor\"in t&&(this.contourColor=B(t.contourColor)),\"contourProject\"in t&&(this.contourProject=R(t.contourProject,(function(t){return R(t,Boolean)}))),\"surfaceProject\"in t&&(this.surfaceProject=t.surfaceProject),\"dynamicColor\"in t&&(this.dynamicColor=B(t.dynamicColor)),\"dynamicTint\"in t&&(this.dynamicTint=R(t.dynamicTint,Number)),\"dynamicWidth\"in t&&(this.dynamicWidth=R(t.dynamicWidth,Number)),\"opacity\"in t&&(this.opacity=t.opacity),\"opacityscale\"in t&&(this.opacityscale=t.opacityscale),\"colorBounds\"in t&&(this.colorBounds=t.colorBounds),\"vertexColor\"in t&&(this.vertexColor=t.vertexColor?1:0),\"colormap\"in t&&this._colorMap.setPixels(this.genColormap(t.colormap,this.opacityscale));var e=t.field||t.coords&&t.coords[2]||null,r=!1;if(e||(e=this._field[2].shape[0]||this._field[2].shape[2]?this._field[2].lo(1,1).hi(this._field[2].shape[0]-2,this._field[2].shape[1]-2):this._field[2].hi(0,0)),\"field\"in t||\"coords\"in t){var i=(e.shape[0]+2)*(e.shape[1]+2);i>this._field[2].data.length&&(s.freeFloat(this._field[2].data),this._field[2].data=s.mallocFloat(n.nextPow2(i))),this._field[2]=f(this._field[2].data,[e.shape[0]+2,e.shape[1]+2]),this.padField(this._field[2],e),this.shape=e.shape.slice();for(var a=this.shape,o=0;o<2;++o)this._field[2].size>this._field[o].data.length&&(s.freeFloat(this._field[o].data),this._field[o].data=s.mallocFloat(this._field[2].size)),this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2]);if(t.coords){var l=t.coords;if(!Array.isArray(l)||3!==l.length)throw new Error(\"gl-surface: invalid coordinates for x/y\");for(o=0;o<2;++o){var c=l[o];for(v=0;v<2;++v)if(c.shape[v]!==a[v])throw new Error(\"gl-surface: coords have incorrect shape\");this.padField(this._field[o],c)}}else if(t.ticks){var u=t.ticks;if(!Array.isArray(u)||2!==u.length)throw new Error(\"gl-surface: invalid ticks\");for(o=0;o<2;++o){var p=u[o];if((Array.isArray(p)||p.length)&&(p=f(p)),p.shape[0]!==a[o])throw new Error(\"gl-surface: invalid tick length\");var d=f(p.data,a);d.stride[o]=p.stride[0],d.stride[1^o]=0,this.padField(this._field[o],d)}}else{for(o=0;o<2;++o){var g=[0,0];g[o]=1,this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2],g,0)}this._field[0].set(0,0,0);for(var v=0;v<a[0];++v)this._field[0].set(v+1,0,v);for(this._field[0].set(a[0]+1,0,a[0]-1),this._field[1].set(0,0,0),v=0;v<a[1];++v)this._field[1].set(0,v+1,v);this._field[1].set(0,a[1]+1,a[1]-1)}var y=this._field,x=f(s.mallocFloat(3*y[2].size*2),[3,a[0]+2,a[1]+2,2]);for(o=0;o<3;++o)m(x.pick(o),y[o],\"mirror\");var b=f(s.mallocFloat(3*y[2].size),[a[0]+2,a[1]+2,3]);for(o=0;o<a[0]+2;++o)for(v=0;v<a[1]+2;++v){var _=x.get(0,o,v,0),w=x.get(0,o,v,1),k=x.get(1,o,v,0),A=x.get(1,o,v,1),M=x.get(2,o,v,0),S=x.get(2,o,v,1),E=k*S-A*M,L=M*w-S*_,C=_*A-w*k,P=Math.sqrt(E*E+L*L+C*C);P<1e-8?(P=Math.max(Math.abs(E),Math.abs(L),Math.abs(C)))<1e-8?(C=1,L=E=0,P=1):P=1/P:P=1/Math.sqrt(P),b.set(o,v,0,E*P),b.set(o,v,1,L*P),b.set(o,v,2,C*P)}s.free(x.data);var I=[1/0,1/0,1/0],O=[-1/0,-1/0,-1/0],z=1/0,D=-1/0,F=(a[0]-1)*(a[1]-1)*6,N=s.mallocFloat(n.nextPow2(10*F)),j=0,U=0;for(o=0;o<a[0]-1;++o)t:for(v=0;v<a[1]-1;++v){for(var V=0;V<2;++V)for(var H=0;H<2;++H)for(var q=0;q<3;++q){var G=this._field[q].get(1+o+V,1+v+H);if(isNaN(G)||!isFinite(G))continue t}for(q=0;q<6;++q){var Y=o+T[q][0],W=v+T[q][1],X=this._field[0].get(Y+1,W+1),Z=this._field[1].get(Y+1,W+1);G=this._field[2].get(Y+1,W+1),E=b.get(Y+1,W+1,0),L=b.get(Y+1,W+1,1),C=b.get(Y+1,W+1,2),t.intensity&&(J=t.intensity.get(Y,W));var J=t.intensity?t.intensity.get(Y,W):G+this.objectOffset[2];N[j++]=Y,N[j++]=W,N[j++]=X,N[j++]=Z,N[j++]=G,N[j++]=0,N[j++]=J,N[j++]=E,N[j++]=L,N[j++]=C,I[0]=Math.min(I[0],X+this.objectOffset[0]),I[1]=Math.min(I[1],Z+this.objectOffset[1]),I[2]=Math.min(I[2],G+this.objectOffset[2]),z=Math.min(z,J),O[0]=Math.max(O[0],X+this.objectOffset[0]),O[1]=Math.max(O[1],Z+this.objectOffset[1]),O[2]=Math.max(O[2],G+this.objectOffset[2]),D=Math.max(D,J),U+=1}}for(t.intensityBounds&&(z=+t.intensityBounds[0],D=+t.intensityBounds[1]),o=6;o<j;o+=10)N[o]=(N[o]-z)/(D-z);this._vertexCount=U,this._coordinateBuffer.update(N.subarray(0,j)),s.freeFloat(N),s.free(b.data),this.bounds=[I,O],this.intensity=t.intensity||this._field[2],this.intensityBounds[0]===z&&this.intensityBounds[1]===D||(r=!0),this.intensityBounds=[z,D]}if(\"levels\"in t){var K=t.levels;for(K=Array.isArray(K[0])?K.slice():[[],[],K],o=0;o<3;++o)K[o]=K[o].slice(),K[o].sort((function(t,e){return t-e}));for(o=0;o<3;++o)for(v=0;v<K[o].length;++v)K[o][v]-=this.objectOffset[o];t:for(o=0;o<3;++o){if(K[o].length!==this.contourLevels[o].length){r=!0;break}for(v=0;v<K[o].length;++v)if(K[o][v]!==this.contourLevels[o][v]){r=!0;break t}}this.contourLevels=K}if(r){y=this._field,a=this.shape;for(var Q=[],$=0;$<3;++$){var tt=this.contourLevels[$],et=[],rt=[],nt=[0,0,0];for(o=0;o<tt.length;++o){var it=h(this._field[$],tt[o]);et.push(Q.length/5|0),U=0;t:for(v=0;v<it.cells.length;++v){var at=it.cells[v];for(q=0;q<2;++q){var ot=it.positions[at[q]],st=ot[0],lt=0|Math.floor(st),ct=st-lt,ut=ot[1],ft=0|Math.floor(ut),ht=ut-ft,pt=!1;e:for(var dt=0;dt<3;++dt){nt[dt]=0;var gt=($+dt+1)%3;for(V=0;V<2;++V){var mt=V?ct:1-ct;for(Y=0|Math.min(Math.max(lt+V,0),a[0]),H=0;H<2;++H){var vt=H?ht:1-ht;if(W=0|Math.min(Math.max(ft+H,0),a[1]),G=dt<2?this._field[gt].get(Y,W):(this.intensity.get(Y,W)-this.intensityBounds[0])/(this.intensityBounds[1]-this.intensityBounds[0]),!isFinite(G)||isNaN(G)){pt=!0;break e}var yt=mt*vt;nt[dt]+=yt*G}}}if(pt){if(q>0){for(var xt=0;xt<5;++xt)Q.pop();U-=1}continue t}Q.push(nt[0],nt[1],ot[0],ot[1],nt[2]),U+=1}}rt.push(U)}this._contourOffsets[$]=et,this._contourCounts[$]=rt}var bt=s.mallocFloat(Q.length);for(o=0;o<Q.length;++o)bt[o]=Q[o];this._contourBuffer.update(bt),s.freeFloat(bt)}},S.dispose=function(){this._shader.dispose(),this._vao.dispose(),this._coordinateBuffer.dispose(),this._colorMap.dispose(),this._contourBuffer.dispose(),this._contourVAO.dispose(),this._contourShader.dispose(),this._contourPickShader.dispose(),this._dynamicBuffer.dispose(),this._dynamicVAO.dispose();for(var t=0;t<3;++t)s.freeFloat(this._field[t].data)},S.highlight=function(t){var e,r;if(!t)return this._dynamicCounts=[0,0,0],this.dyanamicLevel=[NaN,NaN,NaN],void(this.highlightLevel=[-1,-1,-1]);for(e=0;e<3;++e)this.enableHighlight[e]?this.highlightLevel[e]=t.level[e]:this.highlightLevel[e]=-1;for(r=this.snapToData?t.dataCoordinate:t.position,e=0;e<3;++e)r[e]-=this.objectOffset[e];if(this.enableDynamic[0]&&r[0]!==this.dynamicLevel[0]||this.enableDynamic[1]&&r[1]!==this.dynamicLevel[1]||this.enableDynamic[2]&&r[2]!==this.dynamicLevel[2]){for(var n=0,i=this.shape,a=s.mallocFloat(12*i[0]*i[1]),o=0;o<3;++o)if(this.enableDynamic[o]){this.dynamicLevel[o]=r[o];var l=(o+1)%3,c=(o+2)%3,u=this._field[o],f=this._field[l],p=this._field[c],d=h(u,r[o]),g=d.cells,m=d.positions;for(this._dynamicOffsets[o]=n,e=0;e<g.length;++e)for(var v=g[e],y=0;y<2;++y){var x=m[v[y]],b=+x[0],_=0|b,w=0|Math.min(_+1,i[0]),T=b-_,k=1-T,A=+x[1],M=0|A,S=0|Math.min(M+1,i[1]),E=A-M,L=1-E,C=k*L,P=k*E,I=T*L,O=T*E,z=C*f.get(_,M)+P*f.get(_,S)+I*f.get(w,M)+O*f.get(w,S),D=C*p.get(_,M)+P*p.get(_,S)+I*p.get(w,M)+O*p.get(w,S);if(isNaN(z)||isNaN(D)){y&&(n-=1);break}a[2*n+0]=z,a[2*n+1]=D,n+=1}this._dynamicCounts[o]=n-this._dynamicOffsets[o]}else this.dynamicLevel[o]=NaN,this._dynamicCounts[o]=0;this._dynamicBuffer.update(a.subarray(0,2*n)),s.freeFloat(a)}}},{\"./lib/shaders\":144,\"binary-search-bounds\":31,\"bit-twiddle\":32,colormap:53,\"gl-buffer\":78,\"gl-mat4/invert\":98,\"gl-mat4/multiply\":100,\"gl-texture2d\":146,\"gl-vao\":150,ndarray:259,\"ndarray-gradient\":252,\"ndarray-ops\":254,\"ndarray-pack\":255,\"surface-nets\":302,\"typedarray-pool\":308}],146:[function(t,e,r){\"use strict\";var n=t(\"ndarray\"),i=t(\"ndarray-ops\"),a=t(\"typedarray-pool\");e.exports=function(t){if(arguments.length<=1)throw new Error(\"gl-texture2d: Missing arguments for texture2d constructor\");o||c(t);if(\"number\"==typeof arguments[1])return v(t,arguments[1],arguments[2],arguments[3]||t.RGBA,arguments[4]||t.UNSIGNED_BYTE);if(Array.isArray(arguments[1]))return v(t,0|arguments[1][0],0|arguments[1][1],arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if(\"object\"==typeof arguments[1]){var e=arguments[1],r=u(e)?e:e.raw;if(r)return y(t,r,0|e.width,0|e.height,arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if(e.shape&&e.data&&e.stride)return x(t,e)}throw new Error(\"gl-texture2d: Invalid arguments for texture2d constructor\")};var o=null,s=null,l=null;function c(t){o=[t.LINEAR,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_NEAREST],s=[t.NEAREST,t.LINEAR,t.NEAREST_MIPMAP_NEAREST,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_LINEAR],l=[t.REPEAT,t.CLAMP_TO_EDGE,t.MIRRORED_REPEAT]}function u(t){return\"undefined\"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||\"undefined\"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||\"undefined\"!=typeof HTMLVideoElement&&t instanceof HTMLVideoElement||\"undefined\"!=typeof ImageData&&t instanceof ImageData}var f=function(t,e){i.muls(t,e,255)};function h(t,e,r){var n=t.gl,i=n.getParameter(n.MAX_TEXTURE_SIZE);if(e<0||e>i||r<0||r>i)throw new Error(\"gl-texture2d: Invalid texture size\");return t._shape=[e,r],t.bind(),n.texImage2D(n.TEXTURE_2D,0,t.format,e,r,0,t.format,t.type,null),t._mipLevels=[0],t}function p(t,e,r,n,i,a){this.gl=t,this.handle=e,this.format=i,this.type=a,this._shape=[r,n],this._mipLevels=[0],this._magFilter=t.NEAREST,this._minFilter=t.NEAREST,this._wrapS=t.CLAMP_TO_EDGE,this._wrapT=t.CLAMP_TO_EDGE,this._anisoSamples=1;var o=this,s=[this._wrapS,this._wrapT];Object.defineProperties(s,[{get:function(){return o._wrapS},set:function(t){return o.wrapS=t}},{get:function(){return o._wrapT},set:function(t){return o.wrapT=t}}]),this._wrapVector=s;var l=[this._shape[0],this._shape[1]];Object.defineProperties(l,[{get:function(){return o._shape[0]},set:function(t){return o.width=t}},{get:function(){return o._shape[1]},set:function(t){return o.height=t}}]),this._shapeVector=l}var d=p.prototype;function g(t,e){return 3===t.length?1===e[2]&&e[1]===t[0]*t[2]&&e[0]===t[2]:1===e[0]&&e[1]===t[0]}function m(t){var e=t.createTexture();return t.bindTexture(t.TEXTURE_2D,e),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),e}function v(t,e,r,n,i){var a=t.getParameter(t.MAX_TEXTURE_SIZE);if(e<0||e>a||r<0||r>a)throw new Error(\"gl-texture2d: Invalid texture shape\");if(i===t.FLOAT&&!t.getExtension(\"OES_texture_float\"))throw new Error(\"gl-texture2d: Floating point textures not supported on this platform\");var o=m(t);return t.texImage2D(t.TEXTURE_2D,0,n,e,r,0,n,i,null),new p(t,o,e,r,n,i)}function y(t,e,r,n,i,a){var o=m(t);return t.texImage2D(t.TEXTURE_2D,0,i,i,a,e),new p(t,o,r,n,i,a)}function x(t,e){var r=e.dtype,o=e.shape.slice(),s=t.getParameter(t.MAX_TEXTURE_SIZE);if(o[0]<0||o[0]>s||o[1]<0||o[1]>s)throw new Error(\"gl-texture2d: Invalid texture size\");var l=g(o,e.stride.slice()),c=0;\"float32\"===r?c=t.FLOAT:\"float64\"===r?(c=t.FLOAT,l=!1,r=\"float32\"):\"uint8\"===r?c=t.UNSIGNED_BYTE:(c=t.UNSIGNED_BYTE,l=!1,r=\"uint8\");var u,h,d=0;if(2===o.length)d=t.LUMINANCE,o=[o[0],o[1],1],e=n(e.data,o,[e.stride[0],e.stride[1],1],e.offset);else{if(3!==o.length)throw new Error(\"gl-texture2d: Invalid shape for texture\");if(1===o[2])d=t.ALPHA;else if(2===o[2])d=t.LUMINANCE_ALPHA;else if(3===o[2])d=t.RGB;else{if(4!==o[2])throw new Error(\"gl-texture2d: Invalid shape for pixel coords\");d=t.RGBA}}c!==t.FLOAT||t.getExtension(\"OES_texture_float\")||(c=t.UNSIGNED_BYTE,l=!1);var v=e.size;if(l)u=0===e.offset&&e.data.length===v?e.data:e.data.subarray(e.offset,e.offset+v);else{var y=[o[2],o[2]*o[0],1];h=a.malloc(v,r);var x=n(h,o,y,0);\"float32\"!==r&&\"float64\"!==r||c!==t.UNSIGNED_BYTE?i.assign(x,e):f(x,e),u=h.subarray(0,v)}var b=m(t);return t.texImage2D(t.TEXTURE_2D,0,d,o[0],o[1],0,d,c,u),l||a.free(h),new p(t,b,o[0],o[1],d,c)}Object.defineProperties(d,{minFilter:{get:function(){return this._minFilter},set:function(t){this.bind();var e=this.gl;if(this.type===e.FLOAT&&o.indexOf(t)>=0&&(e.getExtension(\"OES_texture_float_linear\")||(t=e.NEAREST)),s.indexOf(t)<0)throw new Error(\"gl-texture2d: Unknown filter mode \"+t);return e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,t),this._minFilter=t}},magFilter:{get:function(){return this._magFilter},set:function(t){this.bind();var e=this.gl;if(this.type===e.FLOAT&&o.indexOf(t)>=0&&(e.getExtension(\"OES_texture_float_linear\")||(t=e.NEAREST)),s.indexOf(t)<0)throw new Error(\"gl-texture2d: Unknown filter mode \"+t);return e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,t),this._magFilter=t}},mipSamples:{get:function(){return this._anisoSamples},set:function(t){var e=this._anisoSamples;if(this._anisoSamples=0|Math.max(t,1),e!==this._anisoSamples){var r=this.gl.getExtension(\"EXT_texture_filter_anisotropic\");r&&this.gl.texParameterf(this.gl.TEXTURE_2D,r.TEXTURE_MAX_ANISOTROPY_EXT,this._anisoSamples)}return this._anisoSamples}},wrapS:{get:function(){return this._wrapS},set:function(t){if(this.bind(),l.indexOf(t)<0)throw new Error(\"gl-texture2d: Unknown wrap mode \"+t);return this.gl.texParameteri(this.gl.TEXTURE_2D,this.gl.TEXTURE_WRAP_S,t),this._wrapS=t}},wrapT:{get:function(){return this._wrapT},set:function(t){if(this.bind(),l.indexOf(t)<0)throw new Error(\"gl-texture2d: Unknown wrap mode \"+t);return this.gl.texParameteri(this.gl.TEXTURE_2D,this.gl.TEXTURE_WRAP_T,t),this._wrapT=t}},wrap:{get:function(){return this._wrapVector},set:function(t){if(Array.isArray(t)||(t=[t,t]),2!==t.length)throw new Error(\"gl-texture2d: Must specify wrap mode for rows and columns\");for(var 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-       "/*!\n",
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-       " * @license  MIT\n",
-       " */\n",
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l=0;l<3;++l){for(var c=0,f=0;f<3;++f)c+=r[l+4*f]*i[f];r[12+l]=-c}},h.getMatrix=function(t,e){this.recalcMatrix(t);var r=this.computedMatrix;if(e){for(var n=0;n<16;++n)e[n]=r[n];return e}return r},h.idle=function(t){this.center.idle(t),this.radius.idle(t),this.rotation.idle(t)},h.flush=function(t){this.center.flush(t),this.radius.flush(t),this.rotation.flush(t)},h.pan=function(t,e,r,n){e=e||0,r=r||0,n=n||0,this.recalcMatrix(t);var i=this.computedMatrix,a=i[1],o=i[5],s=i[9],c=l(a,o,s);a/=c,o/=c,s/=c;var u=i[0],f=i[4],h=i[8],p=u*a+f*o+h*s,d=l(u-=a*p,f-=o*p,h-=s*p);u/=d,f/=d,h/=d;var g=i[2],m=i[6],v=i[10],y=g*a+m*o+v*s,x=g*u+m*f+v*h,b=l(g-=y*a+x*u,m-=y*o+x*f,v-=y*s+x*h);g/=b,m/=b,v/=b;var _=u*e+a*r,w=f*e+o*r,T=h*e+s*r;this.center.move(t,_,w,T);var k=Math.exp(this.computedRadius[0]);k=Math.max(1e-4,k+n),this.radius.set(t,Math.log(k))},h.rotate=function(t,e,r,n){this.recalcMatrix(t),e=e||0,r=r||0;var i=this.computedMatrix,a=i[0],o=i[4],s=i[8],u=i[1],f=i[5],h=i[9],p=i[2],d=i[6],g=i[10],m=e*a+r*u,v=e*o+r*f,y=e*s+r*h,x=-(d*y-g*v),b=-(g*m-p*y),_=-(p*v-d*m),w=Math.sqrt(Math.max(0,1-Math.pow(x,2)-Math.pow(b,2)-Math.pow(_,2))),T=c(x,b,_,w);T>1e-6?(x/=T,b/=T,_/=T,w/=T):(x=b=_=0,w=1);var k=this.computedRotation,A=k[0],M=k[1],S=k[2],E=k[3],L=A*w+E*x+M*_-S*b,C=M*w+E*b+S*x-A*_,P=S*w+E*_+A*b-M*x,I=E*w-A*x-M*b-S*_;if(n){x=p,b=d,_=g;var O=Math.sin(n)/l(x,b,_);x*=O,b*=O,_*=O,I=I*(w=Math.cos(e))-(L=L*w+I*x+C*_-P*b)*x-(C=C*w+I*b+P*x-L*_)*b-(P=P*w+I*_+L*b-C*x)*_}var z=c(L,C,P,I);z>1e-6?(L/=z,C/=z,P/=z,I/=z):(L=C=P=0,I=1),this.rotation.set(t,L,C,P,I)},h.lookAt=function(t,e,r,n){this.recalcMatrix(t),r=r||this.computedCenter,e=e||this.computedEye,n=n||this.computedUp;var a=this.computedMatrix;i(a,e,r,n);var o=this.computedRotation;s(o,a[0],a[1],a[2],a[4],a[5],a[6],a[8],a[9],a[10]),u(o,o),this.rotation.set(t,o[0],o[1],o[2],o[3]);for(var l=0,c=0;c<3;++c)l+=Math.pow(r[c]-e[c],2);this.radius.set(t,.5*Math.log(Math.max(l,1e-6))),this.center.set(t,r[0],r[1],r[2])},h.translate=function(t,e,r,n){this.center.move(t,e||0,r||0,n||0)},h.setMatrix=function(t,e){var r=this.computedRotation;s(r,e[0],e[1],e[2],e[4],e[5],e[6],e[8],e[9],e[10]),u(r,r),this.rotation.set(t,r[0],r[1],r[2],r[3]);var n=this.computedMatrix;o(n,e);var i=n[15];if(Math.abs(i)>1e-6){var a=n[12]/i,l=n[13]/i,c=n[14]/i;this.recalcMatrix(t);var f=Math.exp(this.computedRadius[0]);this.center.set(t,a-n[2]*f,l-n[6]*f,c-n[10]*f),this.radius.idle(t)}else this.center.idle(t),this.radius.idle(t)},h.setDistance=function(t,e){e>0&&this.radius.set(t,Math.log(e))},h.setDistanceLimits=function(t,e){t=t>0?Math.log(t):-1/0,e=e>0?Math.log(e):1/0,e=Math.max(e,t),this.radius.bounds[0][0]=t,this.radius.bounds[1][0]=e},h.getDistanceLimits=function(t){var e=this.radius.bounds;return 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-       "/*!\n",
-       " * pad-left <https://github.com/jonschlinkert/pad-left>\n",
-       " *\n",
-       " * Copyright (c) 2014-2015, Jon Schlinkert.\n",
-       " * Licensed under the MIT license.\n",
-       " */\n",
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o=(a+2)%3;e||(this.recalcMatrix(t),e=this.computedMatrix);var s=e[a],l=e[a+4],f=e[a+8];if(n){var h=Math.abs(s),p=Math.abs(l),d=Math.abs(f),g=Math.max(h,p,d);h===g?(s=s<0?-1:1,l=f=0):d===g?(f=f<0?-1:1,s=l=0):(l=l<0?-1:1,s=f=0)}else{var m=c(s,l,f);s/=m,l/=m,f/=m}var v,y,x=e[o],b=e[o+4],_=e[o+8],w=x*s+b*l+_*f,T=c(x-=s*w,b-=l*w,_-=f*w),k=l*(_/=T)-f*(b/=T),A=f*(x/=T)-s*_,M=s*b-l*x,S=c(k,A,M);if(k/=S,A/=S,M/=S,this.center.jump(t,q,G,Y),this.radius.idle(t),this.up.jump(t,s,l,f),this.right.jump(t,x,b,_),2===a){var E=e[1],L=e[5],C=e[9],P=E*x+L*b+C*_,I=E*k+L*A+C*M;v=R<0?-Math.PI/2:Math.PI/2,y=Math.atan2(I,P)}else{var O=e[2],z=e[6],D=e[10],R=O*s+z*l+D*f,F=O*x+z*b+D*_,B=O*k+z*A+D*M;v=Math.asin(u(R)),y=Math.atan2(B,F)}this.angle.jump(t,y,v),this.recalcMatrix(t);var N=e[2],j=e[6],U=e[10],V=this.computedMatrix;i(V,e);var H=V[15],q=V[12]/H,G=V[13]/H,Y=V[14]/H,W=Math.exp(this.computedRadius[0]);this.center.jump(t,q-N*W,G-j*W,Y-U*W)},p.lastT=function(){return Math.max(this.center.lastT(),this.up.lastT(),this.right.lastT(),this.radius.lastT(),this.angle.lastT())},p.idle=function(t){this.center.idle(t),this.up.idle(t),this.right.idle(t),this.radius.idle(t),this.angle.idle(t)},p.flush=function(t){this.center.flush(t),this.up.flush(t),this.right.flush(t),this.radius.flush(t),this.angle.flush(t)},p.setDistance=function(t,e){e>0&&this.radius.set(t,Math.log(e))},p.lookAt=function(t,e,r,n){this.recalcMatrix(t),e=e||this.computedEye,r=r||this.computedCenter;var i=(n=n||this.computedUp)[0],a=n[1],o=n[2],s=c(i,a,o);if(!(s<1e-6)){i/=s,a/=s,o/=s;var l=e[0]-r[0],f=e[1]-r[1],h=e[2]-r[2],p=c(l,f,h);if(!(p<1e-6)){l/=p,f/=p,h/=p;var d=this.computedRight,g=d[0],m=d[1],v=d[2],y=i*g+a*m+o*v,x=c(g-=y*i,m-=y*a,v-=y*o);if(!(x<.01&&(x=c(g=a*h-o*f,m=o*l-i*h,v=i*f-a*l))<1e-6)){g/=x,m/=x,v/=x,this.up.set(t,i,a,o),this.right.set(t,g,m,v),this.center.set(t,r[0],r[1],r[2]),this.radius.set(t,Math.log(p));var b=a*v-o*m,_=o*g-i*v,w=i*m-a*g,T=c(b,_,w),k=i*l+a*f+o*h,A=g*l+m*f+v*h,M=(b/=T)*l+(_/=T)*f+(w/=T)*h,S=Math.asin(u(k)),E=Math.atan2(M,A),L=this.angle._state,C=L[L.length-1],P=L[L.length-2];C%=2*Math.PI;var I=Math.abs(C+2*Math.PI-E),O=Math.abs(C-E),z=Math.abs(C-2*Math.PI-E);I<O&&(C+=2*Math.PI),z<O&&(C-=2*Math.PI),this.angle.jump(this.angle.lastT(),C,P),this.angle.set(t,E,S)}}}}},{\"filtered-vector\":68,\"gl-mat4/invert\":98,\"gl-mat4/rotate\":103,\"gl-vec3/cross\":157,\"gl-vec3/dot\":162,\"gl-vec3/normalize\":179}],306:[function(t,e,r){\"use strict\";e.exports=function(t,e,r){var i=t*e,a=n*t,o=a-(a-t),s=t-o,l=n*e,c=l-(l-e),u=e-c,f=s*u-(i-o*c-s*c-o*u);if(r)return r[0]=f,r[1]=i,r;return[f,i]};var n=+(Math.pow(2,27)+1)},{}],307:[function(t,e,r){\"use strict\";e.exports=function(t,e,r){var n=t+e,i=n-t,a=e-i,o=t-(n-i);if(r)return r[0]=o+a,r[1]=n,r;return[o+a,n]}},{}],308:[function(t,e,r){(function(e){(function(){\"use strict\";var 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null},r.mallocArrayBuffer=p,r.mallocUint8=d,r.mallocUint16=g,r.mallocUint32=m,r.mallocInt8=v,r.mallocInt16=y,r.mallocInt32=x,r.mallocFloat32=r.mallocFloat=b,r.mallocFloat64=r.mallocDouble=_,r.mallocUint8Clamped=w,r.mallocBigUint64=T,r.mallocBigInt64=k,r.mallocDataView=A,r.mallocBuffer=M,r.clearCache=function(){for(var t=0;t<32;++t)c.UINT8[t].length=0,c.UINT16[t].length=0,c.UINT32[t].length=0,c.INT8[t].length=0,c.INT16[t].length=0,c.INT32[t].length=0,c.FLOAT[t].length=0,c.DOUBLE[t].length=0,c.BIGUINT64[t].length=0,c.BIGINT64[t].length=0,c.UINT8C[t].length=0,u[t].length=0,f[t].length=0}}).call(this)}).call(this,void 0!==n?n:\"undefined\"!=typeof self?self:\"undefined\"!=typeof window?window:{})},{\"bit-twiddle\":32,buffer:3,dup:65}],309:[function(t,e,r){\"use strict\";function n(t){this.roots=new Array(t),this.ranks=new Array(t);for(var e=0;e<t;++e)this.roots[e]=e,this.ranks[e]=0}e.exports=n;var i=n.prototype;Object.defineProperty(i,\"length\",{get:function(){return this.roots.length}}),i.makeSet=function(){var t=this.roots.length;return this.roots.push(t),this.ranks.push(0),t},i.find=function(t){for(var e=t,r=this.roots;r[t]!==t;)t=r[t];for(;r[e]!==t;){var n=r[e];r[e]=t,e=n}return t},i.link=function(t,e){var r=this.find(t),n=this.find(e);if(r!==n){var i=this.ranks,a=this.roots,o=i[r],s=i[n];o<s?a[r]=n:s<o?a[n]=r:(a[n]=r,++i[r])}}},{}],310:[function(t,e,r){\"use strict\";e.exports=function(t,e,r){return 0===t.length?t:e?(r||t.sort(e),function(t,e){for(var r=1,n=t.length,i=t[0],a=t[0],o=1;o<n;++o)if(a=i,e(i=t[o],a)){if(o===r){r++;continue}t[r++]=i}return t.length=r,t}(t,e)):(r||t.sort(),function(t){for(var e=1,r=t.length,n=t[0],i=t[0],a=1;a<r;++a,i=n)if(i=n,(n=t[a])!==i){if(a===e){e++;continue}t[e++]=n}return t.length=e,t}(t))}},{}],311:[function(t,e,r){\"use strict\";e.exports=function(t,e){\"object\"==typeof e&&null!==e||(e={});return n(t,e.canvas||i,e.context||a,e)};var n=t(\"./lib/vtext\"),i=null,a=null;\"undefined\"!=typeof 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n=t(\"surface-nets\"),i=t(\"ndarray\"),a=t(\"simplify-planar-graph\"),o=t(\"clean-pslg\"),s=t(\"cdt2d\"),l=t(\"planar-graph-to-polyline\");function c(t,e,r,n){for(var i=\"<\"+t+\">\",a=\"</\"+t+\">\",o=i.length,s=a.length,l=\"+\"===e[0]||\"-\"===e[0],c=0,u=-s;c>-1&&-1!==(c=r.indexOf(i,c))&&-1!==(u=r.indexOf(a,c+o))&&!(u<=c);){for(var f=c;f<u+s;++f)if(f<c+o||f>=u)n[f]=null,r=r.substr(0,f)+\" \"+r.substr(f+1);else if(null!==n[f]){var h=n[f].indexOf(e[0]);-1===h?n[f]+=e:l&&(n[f]=n[f].substr(0,h+1)+(1+parseInt(n[f][h+1]))+n[f].substr(h+2))}var p=c+o,d=r.substr(p,u-p).indexOf(i);c=-1!==d?d:u+s}return n}function u(t,e){var r=n(t,128);return e?a(r.cells,r.positions,.25):{edges:r.cells,positions:r.positions}}function f(t,e,r,n){var i=u(t,n),a=function(t,e,r){for(var n=e.textAlign||\"start\",i=e.textBaseline||\"alphabetic\",a=[1<<30,1<<30],o=[0,0],s=t.length,l=0;l<s;++l)for(var c=t[l],u=0;u<2;++u)a[u]=0|Math.min(a[u],c[u]),o[u]=0|Math.max(o[u],c[u]);var 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yt=w[\"x\"===et?\"width\":\"height\"];if(\"paper\"===rt&&(ft.head=o.constrain(ft.head,1,yt-1)),\"pixel\"===nt){var mt=-Math.max(ft.tail-3,ft.text),xt=Math.min(ft.tail+3,ft.text)-yt;mt>0?(ft.tail+=mt,ft.text+=mt):xt>0&&(ft.tail-=xt,ft.text-=xt)}ft.tail+=ct,ft.head+=ct}else K=tt=lt*q(Q,ut),ft.text=J+tt;ft.text+=ct,tt+=ct,K+=ct,e[\"_\"+et+\"padplus\"]=lt/2+K,e[\"_\"+et+\"padminus\"]=lt/2-K,e[\"_\"+et+\"size\"]=lt,e[\"_\"+et+\"shift\"]=tt}if(Y)R.remove();else{var bt=0,_t=0;if(\"left\"!==e.align&&(bt=(A-b)*(\"center\"===e.align?.5:1)),\"top\"!==e.valign&&(_t=(z-_)*(\"middle\"===e.valign?.5:1)),f)n.select(\"svg\").attr({x:N+bt-1,y:N+_t}).call(c.setClipUrl,U?L:null,t);else{var wt=N+_t-v.top,Tt=N+bt-v.left;G.call(h.positionText,Tt,wt).call(c.setClipUrl,U?L:null,t)}V.select(\"rect\").call(c.setRect,N,N,A,z),j.call(c.setRect,F/2,F/2,B-F,H-F),R.call(c.setTranslate,Math.round(C.x.text-B/2),Math.round(C.y.text-H/2)),I.attr({transform:\"rotate(\"+P+\",\"+C.x.text+\",\"+C.y.text+\")\"});var 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x,b,_,w=\"paper\"===h(\"yref\"),T=\"paper\"===h(\"xref\"),k=\"left\";g?(_=\"middle\",k=T?\"left\":\"right\",x=T?1.02:1,b=.5):(_=w?\"bottom\":\"top\",k=\"center\",x=.5,b=w?1.02:1),n.coerce(f,c,{x:{valType:\"number\",min:T?-2:0,max:T?3:1,dflt:x}},\"x\"),n.coerce(f,c,{y:{valType:\"number\",min:w?-2:0,max:w?3:1,dflt:b}},\"y\"),h(\"xanchor\",k),h(\"xpad\"),h(\"yanchor\",_),h(\"ypad\"),n.noneOrAll(f,c,[\"x\",\"y\"]),h(\"outlinecolor\"),h(\"outlinewidth\"),h(\"bordercolor\"),h(\"borderwidth\"),h(\"bgcolor\");var A=n.coerce(f,c,{ticklabelposition:{valType:\"enumerated\",dflt:\"outside\",values:g?[\"outside\",\"inside\",\"outside top\",\"inside top\",\"outside bottom\",\"inside bottom\"]:[\"outside\",\"inside\",\"outside left\",\"inside left\",\"outside right\",\"inside right\"]}},\"ticklabelposition\");h(\"ticklabeloverflow\",-1!==A.indexOf(\"inside\")?\"hide past domain\":\"hide past div\"),a(f,c,h,\"linear\");var 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f(t,{_fillcolor:null,_line:{color:null,width:null,dash:null},_levels:{start:null,end:null,size:null},_filllevels:null,_fillgradient:null,_zrange:null})}function u(){\"function\"==typeof i.calc?i.calc(t,n,e):(e._fillgradient=r.reversescale?y(r.colorscale):r.colorscale,e._zrange=[r[i.min],r[i.max]])}for(var c=0;c<o.length;c++){var h=o[c];if((n=h[0].trace)._module){var p=n._module.colorbar;if(!0===n.visible&&p)for(var d=Array.isArray(p),v=d?p:[p],g=0;g<v.length;g++){var m=(i=v[g]).container;(r=m?n[m]:n)&&r.showscale&&((e=l(r.colorbar))._id=\"cb\"+n.uid+(d&&m?\"-\"+m:\"\"),e._traceIndex=n.index,e._propPrefix=(m?m+\".\":\"\")+\"colorbar.\",e._meta=n._meta,u(),s.push(e))}}}for(var x in a._colorAxes)if((r=a[x]).showscale){var b=a._colorAxes[x];(e=l(r.colorbar))._id=\"cb\"+x,e._propPrefix=x+\".colorbar.\",e._meta=a._meta,i={min:\"cmin\",max:\"cmax\"},\"heatmap\"!==b[0]&&(n=b[1],i.calc=n._module.colorbar.calc),u(),s.push(e)}return s}(t),(function(t){return t._id}));e.enter().append(\"g\").attr(\"class\",(function(t){return t._id})).classed(A.colorbar,!0),e.each((function(e){var r=n.select(this);u.ensureSingle(r,\"rect\",A.cbbg),u.ensureSingle(r,\"g\",A.cbfills),u.ensureSingle(r,\"g\",A.cblines),u.ensureSingle(r,\"g\",A.cbaxis,(function(t){t.classed(A.crisp,!0)})),u.ensureSingle(r,\"g\",A.cbtitleunshift,(function(t){t.append(\"g\").classed(A.cbtitle,!0)})),u.ensureSingle(r,\"rect\",A.cboutline);var y=function(t,e,r){var o=\"v\"===e.orientation,l=e.len,h=e.lenmode,y=e.thickness,_=e.thicknessmode,M=e.outlinewidth,S=e.borderwidth,E=e.bgcolor,L=e.xanchor,C=e.yanchor,P=e.xpad,O=e.ypad,I=e.x,D=o?e.y:1-e.y,z=\"paper\"===e.yref,R=\"paper\"===e.xref,F=r._fullLayout,B=F._size,N=e._fillcolor,j=e._line,U=e.title,V=U.side,H=e._zrange||n.extent((\"function\"==typeof N?N:j.color).domain()),q=\"function\"==typeof j.color?j.color:function(){return j.color},G=\"function\"==typeof N?N:function(){return N},Z=e._levels,Y=function(t,e,r){var n,i,a=e._levels,o=[],s=[],l=a.end+a.size/100,u=a.size,c=1.001*r[0]-.001*r[1],f=1.001*r[1]-.001*r[0];for(i=0;i<1e5&&(n=a.start+i*u,!(u>0?n>=l:n<=l));i++)n>c&&n<f&&o.push(n);if(e._fillgradient)s=[0];else if(\"function\"==typeof e._fillcolor){var h=e._filllevels;if(h)for(l=h.end+h.size/100,u=h.size,i=0;i<1e5&&(n=h.start+i*u,!(u>0?n>=l:n<=l));i++)n>r[0]&&n<r[1]&&s.push(n);else(s=o.map((function(t){return t-a.size/2}))).push(s[s.length-1]+a.size)}else e._fillcolor&&\"string\"==typeof e._fillcolor&&(s=[0]);return a.size<0&&(o.reverse(),s.reverse()),{line:o,fill:s}}(0,e,H),W=Y.fill,X=Y.line,J=Math.round(y*(\"fraction\"===_?o?B.w:B.h:1)),K=J/(o?B.w:B.h),$=Math.round(l*(\"fraction\"===h?o?B.h:B.w:1)),Q=$/(o?B.h:B.w),tt=R?B.w:r._fullLayout.width,et=z?B.h:r._fullLayout.height,rt=Math.round(o?I*tt+P:D*et+O),nt={center:.5,right:1}[L]||0,it={top:1,middle:.5}[C]||0,at=o?I-nt*K:D-it*K,ot=o?D-it*Q:I-nt*Q,st=Math.round(o?et*(1-ot):tt*ot);e._lenFrac=Q,e._thickFrac=K,e._uFrac=at,e._vFrac=ot;var lt=e._axis=function(t,e,r){var n=t._fullLayout,i=\"v\"===e.orientation,a={type:\"linear\",range:r,tickmode:e.tickmode,nticks:e.nticks,tick0:e.tick0,dtick:e.dtick,tickvals:e.tickvals,ticktext:e.ticktext,ticks:e.ticks,ticklen:e.ticklen,tickwidth:e.tickwidth,tickcolor:e.tickcolor,showticklabels:e.showticklabels,labelalias:e.labelalias,ticklabelposition:e.ticklabelposition,ticklabeloverflow:e.ticklabeloverflow,ticklabelstep:e.ticklabelstep,tickfont:e.tickfont,tickangle:e.tickangle,tickformat:e.tickformat,exponentformat:e.exponentformat,minexponent:e.minexponent,separatethousands:e.separatethousands,showexponent:e.showexponent,showtickprefix:e.showtickprefix,tickprefix:e.tickprefix,showticksuffix:e.showticksuffix,ticksuffix:e.ticksuffix,title:e.title,showline:!0,anchor:\"free\",side:i?\"right\":\"bottom\",position:1},o=i?\"y\":\"x\",s={type:\"linear\",_id:o+e._id},l={letter:o,font:n.font,noHover:!0,noTickson:!0,noTicklabelmode:!0,noInsideRange:!0,calendar:n.calendar};function c(t,e){return u.coerce(a,s,b,t,e)}return m(a,s,c,l,n),x(a,s,c,l),s}(r,e,H);lt.position=K+(o?I+P/B.w:D+O/B.h);var ut=-1!==[\"top\",\"bottom\"].indexOf(V);if(o&&ut&&(lt.title.side=V,lt.titlex=I+P/B.w,lt.titley=ot+(\"top\"===U.side?Q-O/B.h:O/B.h)),o||ut||(lt.title.side=V,lt.titley=D+O/B.h,lt.titlex=ot+P/B.w),j.color&&\"auto\"===e.tickmode){lt.tickmode=\"linear\",lt.tick0=Z.start;var ct=Z.size,ft=u.constrain($/50,4,15)+1,ht=(H[1]-H[0])/((e.nticks||ft)*ct);if(ht>1){var pt=Math.pow(10,Math.floor(Math.log(ht)/Math.LN10));ct*=pt*u.roundUp(ht/pt,[2,5,10]),(Math.abs(Z.start)/Z.size+1e-6)%1<2e-6&&(lt.tick0=0)}lt.dtick=ct}lt.domain=o?[ot+O/B.h,ot+Q-O/B.h]:[ot+P/B.w,ot+Q-P/B.w],lt.setScale(),t.attr(\"transform\",c(Math.round(B.l),Math.round(B.t)));var dt,vt=t.select(\".\"+A.cbtitleunshift).attr(\"transform\",c(-Math.round(B.l),-Math.round(B.t))),gt=lt.ticklabelposition,yt=lt.title.font.size,mt=t.select(\".\"+A.cbaxis),xt=0,bt=0;function _t(n,i){var a={propContainer:lt,propName:e._propPrefix+\"title\",traceIndex:e._traceIndex,_meta:e._meta,placeholder:F._dfltTitle.colorbar,containerGroup:t.select(\".\"+A.cbtitle)},o=\"h\"===n.charAt(0)?n.substr(1):\"h\"+n;t.selectAll(\".\"+o+\",.\"+o+\"-math-group\").remove(),v.draw(r,n,f(a,i||{}))}return u.syncOrAsync([a.previousPromises,function(){var t,e;(o&&ut||!o&&!ut)&&(\"top\"===V&&(t=P+B.l+tt*I,e=O+B.t+et*(1-ot-Q)+3+.75*yt),\"bottom\"===V&&(t=P+B.l+tt*I,e=O+B.t+et*(1-ot)-3-.25*yt),\"right\"===V&&(e=O+B.t+et*D+3+.75*yt,t=P+B.l+tt*ot),_t(lt._id+\"title\",{attributes:{x:t,y:e,\"text-anchor\":o?\"start\":\"middle\"}}))},function(){if(!o&&!ut||o&&ut){var a,l=t.select(\".\"+A.cbtitle),f=l.select(\"text\"),h=[-M/2,M/2],d=l.select(\".h\"+lt._id+\"title-math-group\").node(),v=15.6;if(f.node()&&(v=parseInt(f.node().style.fontSize,10)*w),d?(a=p.bBox(d),bt=a.width,(xt=a.height)>v&&(h[1]-=(xt-v)/2)):f.node()&&!f.classed(A.jsPlaceholder)&&(a=p.bBox(f.node()),bt=a.width,xt=a.height),o){if(xt){if(xt+=5,\"top\"===V)lt.domain[1]-=xt/B.h,h[1]*=-1;else{lt.domain[0]+=xt/B.h;var y=g.lineCount(f);h[1]+=(1-y)*v}l.attr(\"transform\",c(h[0],h[1])),lt.setScale()}}else bt&&(\"right\"===V&&(lt.domain[0]+=(bt+yt/2)/B.w),l.attr(\"transform\",c(h[0],h[1])),lt.setScale())}t.selectAll(\".\"+A.cbfills+\",.\"+A.cblines).attr(\"transform\",o?c(0,Math.round(B.h*(1-lt.domain[1]))):c(Math.round(B.w*lt.domain[0]),0)),mt.attr(\"transform\",o?c(0,Math.round(-B.t)):c(Math.round(-B.l),0));var m=t.select(\".\"+A.cbfills).selectAll(\"rect.\"+A.cbfill).attr(\"style\",\"\").data(W);m.enter().append(\"rect\").classed(A.cbfill,!0).attr(\"style\",\"\"),m.exit().remove();var x=H.map(lt.c2p).map(Math.round).sort((function(t,e){return t-e}));m.each((function(t,a){var s=[0===a?H[0]:(W[a]+W[a-1])/2,a===W.length-1?H[1]:(W[a]+W[a+1])/2].map(lt.c2p).map(Math.round);o&&(s[1]=u.constrain(s[1]+(s[1]>s[0])?1:-1,x[0],x[1]));var l=n.select(this).attr(o?\"x\":\"y\",rt).attr(o?\"y\":\"x\",n.min(s)).attr(o?\"width\":\"height\",Math.max(J,2)).attr(o?\"height\":\"width\",Math.max(n.max(s)-n.min(s),2));if(e._fillgradient)p.gradient(l,r,e._id,o?\"vertical\":\"horizontalreversed\",e._fillgradient,\"fill\");else{var c=G(t).replace(\"e-\",\"\");l.attr(\"fill\",i(c).toHexString())}}));var b=t.select(\".\"+A.cblines).selectAll(\"path.\"+A.cbline).data(j.color&&j.width?X:[]);b.enter().append(\"path\").classed(A.cbline,!0),b.exit().remove(),b.each((function(t){var e=rt,r=Math.round(lt.c2p(t))+j.width/2%1;n.select(this).attr(\"d\",\"M\"+(o?e+\",\"+r:r+\",\"+e)+(o?\"h\":\"v\")+J).call(p.lineGroupStyle,j.width,q(t),j.dash)})),mt.selectAll(\"g.\"+lt._id+\"tick,path\").remove();var _=rt+J+(M||0)/2-(\"outside\"===e.ticks?1:0),T=s.calcTicks(lt),k=s.getTickSigns(lt)[2];return s.drawTicks(r,lt,{vals:\"inside\"===lt.ticks?s.clipEnds(lt,T):T,layer:mt,path:s.makeTickPath(lt,_,k),transFn:s.makeTransTickFn(lt)}),s.drawLabels(r,lt,{vals:T,layer:mt,transFn:s.makeTransTickLabelFn(lt),labelFns:s.makeLabelFns(lt,_)})},function(){if(o&&!ut||!o&&ut){var t,i,a=lt.position||0,s=lt._offset+lt._length/2;if(\"right\"===V)i=s,t=B.l+tt*a+10+yt*(lt.showticklabels?1:.5);else if(t=s,\"bottom\"===V&&(i=B.t+et*a+10+(-1===gt.indexOf(\"inside\")?lt.tickfont.size:0)+(\"intside\"!==lt.ticks&&e.ticklen||0)),\"top\"===V){var l=U.text.split(\"<br>\").length;i=B.t+et*a+10-J-w*yt*l}_t((o?\"h\":\"v\")+lt._id+\"title\",{avoid:{selection:n.select(r).selectAll(\"g.\"+lt._id+\"tick\"),side:V,offsetTop:o?0:B.t,offsetLeft:o?B.l:0,maxShift:o?F.width:F.height},attributes:{x:t,y:i,\"text-anchor\":\"middle\"},transform:{rotate:o?-90:0,offset:0}})}},a.previousPromises,function(){var n,s=J+M/2;-1===gt.indexOf(\"inside\")&&(n=p.bBox(mt.node()),s+=o?n.width:n.height),dt=vt.select(\"text\");var u=0,f=o&&\"top\"===V,v=!o&&\"right\"===V,g=0;if(dt.node()&&!dt.classed(A.jsPlaceholder)){var m,x=vt.select(\".h\"+lt._id+\"title-math-group\").node();x&&(o&&ut||!o&&!ut)?(u=(n=p.bBox(x)).width,m=n.height):(u=(n=p.bBox(vt.node())).right-B.l-(o?rt:st),m=n.bottom-B.t-(o?st:rt),o||\"top\"!==V||(s+=n.height,g=n.height)),v&&(dt.attr(\"transform\",c(u/2+yt/2,0)),u*=2),s=Math.max(s,o?u:m)}var b=2*(o?P:O)+s+S+M/2,w=0;!o&&U.text&&\"bottom\"===C&&D<=0&&(b+=w=b/2,g+=w),F._hColorbarMoveTitle=w,F._hColorbarMoveCBTitle=g;var N=S+M,j=(o?rt:st)-N/2-(o?P:0),H=(o?st:rt)-(o?$:O+g-w);t.select(\".\"+A.cbbg).attr(\"x\",j).attr(\"y\",H).attr(o?\"width\":\"height\",Math.max(b-w,2)).attr(o?\"height\":\"width\",Math.max($+N,2)).call(d.fill,E).call(d.stroke,e.bordercolor).style(\"stroke-width\",S);var q=v?Math.max(u-10,0):0;t.selectAll(\".\"+A.cboutline).attr(\"x\",(o?rt:st+P)+q).attr(\"y\",(o?st+O-$:rt)+(f?xt:0)).attr(o?\"width\":\"height\",Math.max(J,2)).attr(o?\"height\":\"width\",Math.max($-(o?2*O+xt:2*P+q),2)).call(d.stroke,e.outlinecolor).style({fill:\"none\",\"stroke-width\":M});var G=o?nt*b:0,Z=o?0:(1-it)*b-g;if(G=R?B.l-G:-G,Z=z?B.t-Z:-Z,t.attr(\"transform\",c(G,Z)),!o&&(S||i(E).getAlpha()&&!i.equals(F.paper_bgcolor,E))){var Y=mt.selectAll(\"text\"),W=Y[0].length,X=t.select(\".\"+A.cbbg).node(),K=p.bBox(X),Q=p.getTranslate(t);Y.each((function(t,e){var r=W-1;if(0===e||e===r){var n,i=p.bBox(this),a=p.getTranslate(this);if(e===r){var o=i.right+a.x;(n=K.right+Q.x+st-S-2+I-o)>0&&(n=0)}else if(0===e){var s=i.left+a.x;(n=K.left+Q.x+st+S+2-s)<0&&(n=0)}n&&(W<3?this.setAttribute(\"transform\",\"translate(\"+n+\",0) \"+this.getAttribute(\"transform\")):this.setAttribute(\"visibility\",\"hidden\"))}}))}var tt={},et=T[L],at=k[L],ot=T[C],ct=k[C],ft=b-J;o?(\"pixels\"===h?(tt.y=D,tt.t=$*ot,tt.b=$*ct):(tt.t=tt.b=0,tt.yt=D+l*ot,tt.yb=D-l*ct),\"pixels\"===_?(tt.x=I,tt.l=b*et,tt.r=b*at):(tt.l=ft*et,tt.r=ft*at,tt.xl=I-y*et,tt.xr=I+y*at)):(\"pixels\"===h?(tt.x=I,tt.l=$*et,tt.r=$*at):(tt.l=tt.r=0,tt.xl=I+l*et,tt.xr=I-l*at),\"pixels\"===_?(tt.y=1-D,tt.t=b*ot,tt.b=b*ct):(tt.t=ft*ot,tt.b=ft*ct,tt.yt=D-y*ot,tt.yb=D+y*ct));var ht=e.y<.5?\"b\":\"t\",pt=e.x<.5?\"l\":\"r\";r._fullLayout._reservedMargin[e._id]={};var bt={r:F.width-j-G,l:j+tt.r,b:F.height-H-Z,t:H+tt.b};R&&z?a.autoMargin(r,e._id,tt):R?r._fullLayout._reservedMargin[e._id][ht]=bt[ht]:z||o?r._fullLayout._reservedMargin[e._id][pt]=bt[pt]:r._fullLayout._reservedMargin[e._id][ht]=bt[ht]}],r)}(r,e,t);y&&y.then&&(t._promises||[]).push(y),t._context.edits.colorbarPosition&&function(t,e,r){var 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g(e,r,\"M-\"+i+\",-\"+n+\"H\"+n+\"V\"+i+\"Z\")}},\"triangle-se\":{n:10,f:function(t,e,r){if(v(e))return u;var n=l(.6*t,2),i=l(1.2*t,2);return g(e,r,\"M\"+n+\",-\"+i+\"V\"+n+\"H-\"+i+\"Z\")}},\"triangle-sw\":{n:11,f:function(t,e,r){if(v(e))return u;var n=l(.6*t,2),i=l(1.2*t,2);return g(e,r,\"M\"+i+\",\"+n+\"H-\"+n+\"V-\"+i+\"Z\")}},\"triangle-nw\":{n:12,f:function(t,e,r){if(v(e))return u;var n=l(.6*t,2),i=l(1.2*t,2);return g(e,r,\"M-\"+n+\",\"+i+\"V-\"+n+\"H\"+i+\"Z\")}},pentagon:{n:13,f:function(t,e,r){if(v(e))return u;var n=l(.951*t,2),i=l(.588*t,2),a=l(-t,2),o=l(-.309*t,2);return g(e,r,\"M\"+n+\",\"+o+\"L\"+i+\",\"+l(.809*t,2)+\"H-\"+i+\"L-\"+n+\",\"+o+\"L0,\"+a+\"Z\")}},hexagon:{n:14,f:function(t,e,r){if(v(e))return u;var n=l(t,2),i=l(t/2,2),a=l(t*f/2,2);return g(e,r,\"M\"+a+\",-\"+i+\"V\"+i+\"L0,\"+n+\"L-\"+a+\",\"+i+\"V-\"+i+\"L0,-\"+n+\"Z\")}},hexagon2:{n:15,f:function(t,e,r){if(v(e))return u;var n=l(t,2),i=l(t/2,2),a=l(t*f/2,2);return 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c=i.traceIs(u,\"2dMap\")?a:n.fillArray;c(u.hoverinfo,l,\"hi\",o(u)),u.hovertemplate&&c(u.hovertemplate,l,\"ht\"),u.hoverlabel&&(c(u.hoverlabel.bgcolor,l,\"hbg\"),c(u.hoverlabel.bordercolor,l,\"hbc\"),c(u.hoverlabel.font.size,l,\"hts\"),c(u.hoverlabel.font.color,l,\"htc\"),c(u.hoverlabel.font.family,l,\"htf\"),c(u.hoverlabel.namelength,l,\"hnl\"),c(u.hoverlabel.align,l,\"hta\"))}}}},75914:function(t,e,r){\"use strict\";var n=r(73972),i=r(88335).hover;t.exports=function(t,e,r){var a=n.getComponentMethod(\"annotations\",\"onClick\")(t,t._hoverdata);function o(){t.emit(\"plotly_click\",{points:t._hoverdata,event:e})}void 0!==r&&i(t,e,r,!0),t._hoverdata&&e&&e.target&&(a&&a.then?a.then(o):o(),e.stopImmediatePropagation&&e.stopImmediatePropagation())}},26675:function(t){\"use strict\";t.exports={YANGLE:60,HOVERARROWSIZE:6,HOVERTEXTPAD:3,HOVERFONTSIZE:13,HOVERFONT:\"Arial, sans-serif\",HOVERMINTIME:50,HOVERID:\"-hover\"}},54268:function(t,e,r){\"use strict\";var n=r(71828),i=r(77914),a=r(38048);t.exports=function(t,e,r,o){var s=n.extendFlat({},o.hoverlabel);e.hovertemplate&&(s.namelength=-1),a(t,e,(function(r,a){return n.coerce(t,e,i,r,a)}),s)}},23469:function(t,e,r){\"use strict\";var n=r(71828);e.getSubplot=function(t){return t.subplot||t.xaxis+t.yaxis||t.geo},e.isTraceInSubplots=function(t,r){if(\"splom\"===t.type){for(var n=t.xaxes||[],i=t.yaxes||[],a=0;a<n.length;a++)for(var o=0;o<i.length;o++)if(-1!==r.indexOf(n[a]+i[o]))return!0;return!1}return-1!==r.indexOf(e.getSubplot(t))},e.flat=function(t,e){for(var r=new Array(t.length),n=0;n<t.length;n++)r[n]=e;return r},e.p2c=function(t,e){for(var r=new Array(t.length),n=0;n<t.length;n++)r[n]=t[n].p2c(e);return r},e.getDistanceFunction=function(t,r,n,i){return\"closest\"===t?i||e.quadrature(r,n):\"x\"===t.charAt(0)?r:n},e.getClosest=function(t,e,r){if(!1!==r.index)r.index>=0&&r.index<t.length?r.distance=0:r.index=!1;else for(var n=0;n<t.length;n++){var 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t.distance-e.distance})),nt=function(t,e){for(var r=e.charAt(0),n=[],i=[],a=[],o=0;o<t.length;o++){var s=t[o];g.traceIs(s.trace,\"bar-like\")||g.traceIs(s.trace,\"box-violin\")?a.push(s):s.trace[r+\"period\"]?i.push(s):n.push(s)}return n.concat(i).concat(a)}(nt,I)};mt();var xt=I.charAt(0),bt=(\"x\"===xt||\"y\"===xt)&&nt[0]&&L[nt[0].trace.type];if(m&&0!==H&&0!==nt.length){var _t=pt(nt.filter((function(t){return t.ya.showspikes})),H,bt);at.hLinePoint=dt(_t);var wt=pt(nt.filter((function(t){return t.xa.showspikes})),H,bt);at.vLinePoint=dt(wt)}if(0===nt.length){var Tt=d.unhoverRaw(t,e);return!m||null===at.hLinePoint&&null===at.vLinePoint||B(gt)&&F(t,at,vt),Tt}if(m&&B(gt)&&F(t,at,vt),y.isXYhover($)&&0!==nt[0].length&&\"splom\"!==nt[0].trace.type){var kt=nt[0],At=(nt=E[kt.trace.type]?nt.filter((function(t){return t.trace.index===kt.trace.index})):[kt]).length;ht(j(\"x\",kt,c),j(\"y\",kt,c));var Mt,St=[],Et={},Lt=0,Ct=function(t){var e=E[t.trace.type]?C(t):t.trace.index;if(Et[e]){var 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n=0;for(s=0;s<t.length;s++)(u=t[s]).pos+u.dp+u.size>e.pmax&&n++;for(s=t.length-1;s>=0&&!(n<=0);s--)(u=t[s]).pos>e.pmax-1&&(u.del=!0,n--);for(s=0;s<t.length&&!(n<=0);s++)if((u=t[s]).pos<e.pmin+1)for(u.del=!0,n--,o=2*u.size,l=t.length-1;l>=0;l--)t[l].dp-=o;for(s=t.length-1;s>=0&&!(n<=0);s--)(u=t[s]).pos+u.dp+u.size>e.pmax&&(u.del=!0,n--)}}}for(t.each((function(t){var n=t[f],i=t[h],a=\"x\"===n._id.charAt(0),o=n.range;0===y&&o&&o[0]>o[1]!==a&&(d=-1);var s=0,l=a?r.width:r.height;if(\"x\"===r.hovermode||\"y\"===r.hovermode){var u,c,p=D(t,e),v=t.anchor,A=\"end\"===v?-1:1;if(\"middle\"===v)c=(u=t.crossPos+(a?k(p.y-t.by/2):w(t.bx/2+t.tx2width/2)))+(a?k(t.by):w(t.bx));else if(a)c=(u=t.crossPos+k(M+p.y)-k(t.by/2-M))+k(t.by);else{var S=w(A*M+p.x),E=S+w(A*t.bx);u=t.crossPos+Math.min(S,E),c=t.crossPos+Math.max(S,E)}a?void 0!==b&&void 0!==_&&Math.min(c,_)-Math.max(u,b)>1&&(\"left\"===i.side?(s=i._mainLinePosition,l=r.width):l=i._mainLinePosition):void 0!==m&&void 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ot,st,lt,ut,ct=d.select(\"g.legend\"),ft=H(r,ct.node()),ht=ft.width+2*S,pt=ft.height+2*S,dt=W[0],vt=(dt.x0+dt.x1)/2,gt=(dt.y0+dt.y1)/2,yt=!(g.traceIs(dt.trace,\"bar-like\")||g.traceIs(dt.trace,\"box-violin\"));\"y\"===P?yt?(st=gt-S,ot=gt+S):(st=Math.min.apply(null,W.map((function(t){return Math.min(t.y0,t.y1)}))),ot=Math.max.apply(null,W.map((function(t){return Math.max(t.y0,t.y1)})))):st=ot=o.mean(W.map((function(t){return(t.y0+t.y1)/2})))-pt/2,\"x\"===P?yt?(lt=vt+S,ut=vt-S):(lt=Math.max.apply(null,W.map((function(t){return Math.max(t.x0,t.x1)}))),ut=Math.min.apply(null,W.map((function(t){return Math.min(t.x0,t.x1)})))):lt=ut=o.mean(W.map((function(t){return(t.x0+t.x1)/2})))-ht/2;var mt,xt,bt=E._offset,_t=L._offset;return ut+=bt-ht,st+=_t-pt,mt=(lt+=bt)+ht<B&&lt>=0?lt:ut+ht<B&&ut>=0?ut:bt+ht<B?bt:lt-vt<vt-ut+ht?B-ht:0,mt+=S,xt=(ot+=_t)+pt<N&&ot>=0?ot:st+pt<N&&st>=0?st:_t+pt<N?_t:ot-gt<gt-st+pt?N-pt:0,xt+=S,ct.attr(\"transform\",s(mt-1,xt-1)),ct}var wt=d.selectAll(\"g.hovertext\").data(t,(function(t){return C(t)}));return wt.enter().append(\"g\").classed(\"hovertext\",!0).each((function(){var t=n.select(this);t.append(\"rect\").call(p.fill,p.addOpacity(f,.8)),t.append(\"text\").classed(\"name\",!0),t.append(\"path\").style(\"stroke-width\",\"1px\"),t.append(\"text\").classed(\"nums\",!0).call(h.font,T,k)})),wt.exit().remove(),wt.each((function(t){var e=n.select(this).attr(\"transform\",\"\"),o=t.color;Array.isArray(o)&&(o=o[t.eventData[0].pointNumber]);var 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n,i,o=r.container,s=r.fullLayout,l=s._size,u=r.event,c=!!e.hLinePoint,f=!!e.vLinePoint;if(o.selectAll(\".spikeline\").remove(),f||c){var d=p.combine(s.plot_bgcolor,s.paper_bgcolor);if(c){var g,y,m=e.hLinePoint;n=m&&m.xa,\"cursor\"===(i=m&&m.ya).spikesnap?(g=u.pointerX,y=u.pointerY):(g=n._offset+m.x,y=i._offset+m.y);var x,b,_=a.readability(m.color,d)<1.5?p.contrast(d):m.color,w=i.spikemode,T=i.spikethickness,k=i.spikecolor||_,A=v.getPxPosition(t,i);if(-1!==w.indexOf(\"toaxis\")||-1!==w.indexOf(\"across\")){if(-1!==w.indexOf(\"toaxis\")&&(x=A,b=g),-1!==w.indexOf(\"across\")){var 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n=r(85555),i=r(44467).templatedArray;r(24695),t.exports=i(\"image\",{visible:{valType:\"boolean\",dflt:!0,editType:\"arraydraw\"},source:{valType:\"string\",editType:\"arraydraw\"},layer:{valType:\"enumerated\",values:[\"below\",\"above\"],dflt:\"above\",editType:\"arraydraw\"},sizex:{valType:\"number\",dflt:0,editType:\"arraydraw\"},sizey:{valType:\"number\",dflt:0,editType:\"arraydraw\"},sizing:{valType:\"enumerated\",values:[\"fill\",\"contain\",\"stretch\"],dflt:\"contain\",editType:\"arraydraw\"},opacity:{valType:\"number\",min:0,max:1,dflt:1,editType:\"arraydraw\"},x:{valType:\"any\",dflt:0,editType:\"arraydraw\"},y:{valType:\"any\",dflt:0,editType:\"arraydraw\"},xanchor:{valType:\"enumerated\",values:[\"left\",\"center\",\"right\"],dflt:\"left\",editType:\"arraydraw\"},yanchor:{valType:\"enumerated\",values:[\"top\",\"middle\",\"bottom\"],dflt:\"top\",editType:\"arraydraw\"},xref:{valType:\"enumerated\",values:[\"paper\",n.idRegex.x.toString()],dflt:\"paper\",editType:\"arraydraw\"},yref:{valType:\"enumerated\",values:[\"paper\",n.idRegex.y.toString()],dflt:\"paper\",editType:\"arraydraw\"},editType:\"arraydraw\"})},75378:function(t,e,r){\"use 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r=n.select(this);if(this._imgSrc!==e.source)if(r.attr(\"xmlns\",s.svg),e.source&&\"data:\"===e.source.slice(0,5))r.attr(\"xlink:href\",e.source),this._imgSrc=e.source;else{var i=new Promise(function(t){var n=new Image;function i(){r.remove(),t()}this.img=n,n.setAttribute(\"crossOrigin\",\"anonymous\"),n.onerror=i,n.onload=function(){var e=document.createElement(\"canvas\");e.width=this.width,e.height=this.height,e.getContext(\"2d\",{willReadFrequently:!0}).drawImage(this,0,0);var n=e.toDataURL(\"image/png\");r.attr(\"xlink:href\",n),t()},r.on(\"error\",i),n.src=e.source,this._imgSrc=e.source}.bind(this));t._promises.push(i)}}function y(e){var r,o,s=n.select(this),u=a.getFromId(t,e.xref),c=a.getFromId(t,e.yref),f=\"domain\"===a.getRefType(e.xref),h=\"domain\"===a.getRefType(e.yref),p=l._size;r=void 0!==u?\"string\"==typeof e.xref&&f?u._length*e.sizex:Math.abs(u.l2p(e.sizex)-u.l2p(0)):e.sizex*p.w,o=void 0!==c?\"string\"==typeof 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b=Object.keys(l._plots);for(r=0;r<b.length;r++){e=b[r];var _=l._plots[e];if(_.imagelayer){var w=_.imagelayer.selectAll(\"image\").data(c[e]||[]);w.enter().append(\"image\"),w.exit().remove(),w.each((function(t){g.bind(this)(t),y.bind(this)(t)}))}}}},68804:function(t,e,r){\"use strict\";t.exports={moduleType:\"component\",name:\"images\",layoutAttributes:r(69819),supplyLayoutDefaults:r(81603),includeBasePlot:r(76325)(\"images\"),draw:r(80750),convertCoords:r(75378)}},33030:function(t,e,r){\"use strict\";var n=r(41940),i=r(22399);t.exports={_isSubplotObj:!0,visible:{valType:\"boolean\",dflt:!0,editType:\"legend\"},bgcolor:{valType:\"color\",editType:\"legend\"},bordercolor:{valType:\"color\",dflt:i.defaultLine,editType:\"legend\"},borderwidth:{valType:\"number\",min:0,dflt:0,editType:\"legend\"},font:n({editType:\"legend\"}),grouptitlefont:n({editType:\"legend\"}),orientation:{valType:\"enumerated\",values:[\"v\",\"h\"],dflt:\"v\",editType:\"legend\"},traceorder:{valType:\"flaglist\",flags:[\"reversed\",\"grouped\"],extras:[\"normal\"],editType:\"legend\"},tracegroupgap:{valType:\"number\",min:0,dflt:10,editType:\"legend\"},entrywidth:{valType:\"number\",min:0,editType:\"legend\"},entrywidthmode:{valType:\"enumerated\",values:[\"fraction\",\"pixels\"],dflt:\"pixels\",editType:\"legend\"},itemsizing:{valType:\"enumerated\",values:[\"trace\",\"constant\"],dflt:\"trace\",editType:\"legend\"},itemwidth:{valType:\"number\",min:30,dflt:30,editType:\"legend\"},itemclick:{valType:\"enumerated\",values:[\"toggle\",\"toggleothers\",!1],dflt:\"toggle\",editType:\"legend\"},itemdoubleclick:{valType:\"enumerated\",values:[\"toggle\",\"toggleothers\",!1],dflt:\"toggleothers\",editType:\"legend\"},groupclick:{valType:\"enumerated\",values:[\"toggleitem\",\"togglegroup\"],dflt:\"togglegroup\",editType:\"legend\"},x:{valType:\"number\",editType:\"legend\"},xref:{valType:\"enumerated\",dflt:\"paper\",values:[\"container\",\"paper\"],editType:\"layoutstyle\"},xanchor:{valType:\"enumerated\",values:[\"auto\",\"left\",\"center\",\"right\"],dflt:\"left\",editType:\"legend\"},y:{valType:\"number\",editType:\"legend\"},yref:{valType:\"enumerated\",dflt:\"paper\",values:[\"container\",\"paper\"],editType:\"layoutstyle\"},yanchor:{valType:\"enumerated\",values:[\"auto\",\"top\",\"middle\",\"bottom\"],editType:\"legend\"},uirevision:{valType:\"any\",editType:\"none\"},valign:{valType:\"enumerated\",values:[\"top\",\"middle\",\"bottom\"],dflt:\"middle\",editType:\"legend\"},title:{text:{valType:\"string\",dflt:\"\",editType:\"legend\"},font:n({editType:\"legend\"}),side:{valType:\"enumerated\",values:[\"top\",\"left\",\"top left\",\"top center\",\"top right\"],editType:\"legend\"},editType:\"legend\"},editType:\"legend\"}},14928:function(t){\"use strict\";t.exports={scrollBarWidth:6,scrollBarMinHeight:20,scrollBarColor:\"#808BA4\",scrollBarMargin:4,scrollBarEnterAttrs:{rx:20,ry:3,width:0,height:0},titlePad:2,itemGap:5}},99017:function(t,e,r){\"use strict\";var n=r(73972),i=r(71828),a=r(44467),o=r(9012),s=r(33030),l=r(10820),u=r(10130);function c(t,e,r,c){var f=e[t]||{},h=a.newContainer(r,t);function p(t,e){return i.coerce(f,h,s,t,e)}var d=i.coerceFont(p,\"font\",r.font);if(p(\"bgcolor\",r.paper_bgcolor),p(\"bordercolor\"),p(\"visible\")){for(var v,g=function(t,e){var r=v._input,n=v;return i.coerce(r,n,o,t,e)},y=r.font||{},m=i.coerceFont(p,\"grouptitlefont\",i.extendFlat({},y,{size:Math.round(1.1*y.size)})),x=0,b=!1,_=\"normal\",w=(r.shapes||[]).filter((function(t){return t.showlegend})),T=c.concat(w).filter((function(e){return t===(e.legend||\"legend\")})),k=0;k<T.length;k++)if((v=T[k]).visible){var A=v._isShape;(v.showlegend||v._dfltShowLegend&&!(v._module&&v._module.attributes&&v._module.attributes.showlegend&&!1===v._module.attributes.showlegend.dflt))&&(x++,v.showlegend&&(b=!0,(!A&&n.traceIs(v,\"pie-like\")||!0===v._input.showlegend)&&x++),i.coerceFont(g,\"legendgrouptitle.font\",m)),(!A&&n.traceIs(v,\"bar\")&&\"stack\"===r.barmode||-1!==[\"tonextx\",\"tonexty\"].indexOf(v.fill))&&(_=u.isGrouped({traceorder:_})?\"grouped+reversed\":\"reversed\"),void 0!==v.legendgroup&&\"\"!==v.legendgroup&&(_=u.isReversed({traceorder:_})?\"reversed+grouped\":\"grouped\")}var M=i.coerce(e,r,l,\"showlegend\",b&&x>(\"legend\"===t?1:0));if(!1===M&&(r[t]=void 0),(!1!==M||f.uirevision)&&(p(\"uirevision\",r.uirevision),!1!==M)){p(\"borderwidth\");var S,E,L,C=\"h\"===p(\"orientation\"),P=\"paper\"===p(\"yref\"),O=\"paper\"===p(\"xref\"),I=\"left\";if(C?(S=0,n.getComponentMethod(\"rangeslider\",\"isVisible\")(e.xaxis)?P?(E=1.1,L=\"bottom\"):(E=1,L=\"top\"):P?(E=-.1,L=\"top\"):(E=0,L=\"bottom\")):(E=1,L=\"auto\",O?S=1.02:(S=1,I=\"right\")),i.coerce(f,h,{x:{valType:\"number\",editType:\"legend\",min:O?-2:0,max:O?3:1,dflt:S}},\"x\"),i.coerce(f,h,{y:{valType:\"number\",editType:\"legend\",min:P?-2:0,max:P?3:1,dflt:E}},\"y\"),p(\"traceorder\",_),u.isGrouped(r[t])&&p(\"tracegroupgap\"),p(\"entrywidth\"),p(\"entrywidthmode\"),p(\"itemsizing\"),p(\"itemwidth\"),p(\"itemclick\"),p(\"itemdoubleclick\"),p(\"groupclick\"),p(\"xanchor\",I),p(\"yanchor\",L),p(\"valign\"),i.noneOrAll(f,h,[\"x\",\"y\"]),p(\"title.text\")){p(\"title.side\",C?\"left\":\"top\");var D=i.extendFlat({},d,{size:i.bigFont(d.size)});i.coerceFont(p,\"title.font\",D)}}}}t.exports=function(t,e,r){var n,a=r.slice(),o=e.shapes;if(o)for(n=0;n<o.length;n++){var 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V,H=i.ensureSingle(N,\"g\",\"scrollbox\"),q=h.title;h._titleWidth=0,h._titleHeight=0,q.text?((V=i.ensureSingle(H,\"text\",w+\"titletext\")).attr(\"text-anchor\",\"start\").call(u.font,q.font).text(q.text),L(V,H,t,h,_)):H.selectAll(\".\"+w+\"titletext\").remove();var G=i.ensureSingle(N,\"rect\",\"scrollbar\",(function(t){t.attr(p.scrollBarEnterAttrs).call(c.fill,p.scrollBarColor)})),Z=H.selectAll(\"g.groups\").data(S);Z.enter().append(\"g\").attr(\"class\",\"groups\"),Z.exit().remove();var Y=Z.selectAll(\"g.traces\").data(i.identity);Y.enter().append(\"g\").attr(\"class\",\"traces\"),Y.exit().remove(),Y.style(\"opacity\",(function(t){var e=t[0].trace;return o.traceIs(e,\"pie-like\")?-1!==B.indexOf(t[0].label)?.5:1:\"legendonly\"===e.visible?.5:1})).each((function(){n.select(this).call(M,t,h)})).call(x,t,h).each((function(){T||n.select(this).call(E,t,w)})),i.syncOrAsync([a.previousPromises,function(){return function(t,e,r,i){var a=t._fullLayout,o=O(i);i||(i=a[o]);var s=a._size,l=b.isVertical(i),c=b.isGrouped(i),f=\"fraction\"===i.entrywidthmode,h=i.borderwidth,d=2*h,v=p.itemGap,g=i.itemwidth+2*v,y=2*(h+v),m=P(i),x=i.y<0||0===i.y&&\"top\"===m,_=i.y>1||1===i.y&&\"bottom\"===m,w=i.tracegroupgap,T={};i._maxHeight=Math.max(x||_?a.height/2:s.h,30);var A=0;i._width=0,i._height=0;var M=function(t){var e=0,r=0,n=t.title.side;return n&&(-1!==n.indexOf(\"left\")&&(e=t._titleWidth),-1!==n.indexOf(\"top\")&&(r=t._titleHeight)),[e,r]}(i);if(l)r.each((function(t){var e=t[0].height;u.setTranslate(this,h+M[0],h+M[1]+i._height+e/2+v),i._height+=e,i._width=Math.max(i._width,t[0].width)})),A=g+i._width,i._width+=v+g+d,i._height+=y,c&&(e.each((function(t,e){u.setTranslate(this,0,e*i.tracegroupgap)})),i._height+=(i._lgroupsLength-1)*i.tracegroupgap);else{var S=C(i),E=i.x<0||0===i.x&&\"right\"===S,L=i.x>1||1===i.x&&\"left\"===S,I=_||x,D=a.width/2;i._maxWidth=Math.max(E?I&&\"left\"===S?s.l+s.w:D:L?I&&\"right\"===S?s.r+s.w:D:s.w,2*g);var z=0,R=0;r.each((function(t){var 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Y=t._context.edits,W=Y.legendText||Y.legendPosition;r.each((function(t){var e=n.select(this).select(\".\"+o+\"toggle\"),r=t[0].height,a=t[0].trace.legendgroup,s=k(t,i,g);c&&\"\"!==a&&(s=T[a]);var h=W?g:A||s;l||f||(h+=v/2),u.setRect(e,0,-r/2,h,r)}))}(t,Z,Y,h)},function(){var e,c,m,x,b=d._size,_=h.borderwidth,k=\"paper\"===h.xref,M=\"paper\"===h.yref;if(q.text&&function(t,e,r){if(\"top center\"===e.title.side||\"top right\"===e.title.side){var n=e.title.font.size*v,i=0,a=t.node(),o=u.bBox(a).width;\"top center\"===e.title.side?i=.5*(e._width-2*r-2*p.titlePad-o):\"top right\"===e.title.side&&(i=e._width-2*r-2*p.titlePad-o),f.positionText(t,r+p.titlePad+i,r+n)}}(V,h,_),!T){var S,E;S=k?b.l+b.w*h.x-g[C(h)]*h._width:d.width*h.x-g[C(h)]*h._width,E=M?b.t+b.h*(1-h.y)-g[P(h)]*h._effHeight:d.height*(1-h.y)-g[P(h)]*h._effHeight;var L=function(t,e,r,n){var i=t._fullLayout,o=i[e],s=C(o),l=P(o),u=\"paper\"===o.xref,c=\"paper\"===o.yref;t._fullLayout._reservedMargin[e]={};var 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n,a,s=O(r),l=t.data()[0][0],c=l.trace,h=o.traceIs(c,\"pie-like\"),d=!r._inHover&&e._context.edits.legendText&&!h,v=r._maxNameLength;l.groupTitle?(n=l.groupTitle.text,a=l.groupTitle.font):(a=r.font,r.entries?n=l.text:(n=h?l.label:c.name,c._meta&&(n=i.templateString(n,c._meta))));var g=i.ensureSingle(t,\"text\",s+\"text\");g.attr(\"text-anchor\",\"start\").call(u.font,a).text(d?S(n,v):n);var y=r.itemwidth+2*p.itemGap;f.positionText(g,y,0),d?g.call(f.makeEditable,{gd:e,text:n}).call(L,t,e,r).on(\"edit\",(function(n){this.text(S(n,v)).call(L,t,e,r);var a=l.trace._fullInput||{},s={};if(o.hasTransform(a,\"groupby\")){var u=o.getTransformIndices(a,\"groupby\"),f=u[u.length-1],h=i.keyedContainer(a,\"transforms[\"+f+\"].styles\",\"target\",\"value.name\");h.set(l.trace._group,n),s=h.constructUpdate()}else s.name=n;return a._isShape?o.call(\"_guiRelayout\",e,\"shapes[\"+c.index+\"].name\",s.name):o.call(\"_guiRestyle\",e,s,c.index)})):L(g,t,e,r)}function S(t,e){var 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h,p=r.modeBarButtons;h=Array.isArray(p)&&p.length?function(t){for(var e=c([],t),r=0;r<e.length;r++)for(var n=e[r],i=0;i<n.length;i++){var a=n[i];if(\"string\"==typeof a){if(void 0===l[a])throw new Error([\"*modeBarButtons* configuration options\",\"invalid button name\"].join(\" \"));e[r][i]=l[a]}}return e}(p):!r.displayModeBar&&r.watermark?[]:function(t){var e=t._fullLayout,r=t._fullData,s=t._context;function c(t,e){if(\"string\"==typeof e){if(e.toLowerCase()===t.toLowerCase())return!0}else{var r=e.name,n=e._cat||e.name;if(r===t||n===t.toLowerCase())return!0}return!1}var f=e.modebar.add;\"string\"==typeof f&&(f=[f]);var h=e.modebar.remove;\"string\"==typeof h&&(h=[h]);var p=s.modeBarButtonsToAdd.concat(f.filter((function(t){for(var e=0;e<s.modeBarButtonsToRemove.length;e++)if(c(t,s.modeBarButtonsToRemove[e]))return!1;return!0}))),d=s.modeBarButtonsToRemove.concat(h.filter((function(t){for(var e=0;e<s.modeBarButtonsToAdd.length;e++)if(c(t,s.modeBarButtonsToAdd[e]))return!1;return!0}))),v=e._has(\"cartesian\"),g=e._has(\"gl3d\"),y=e._has(\"geo\"),m=e._has(\"pie\"),x=e._has(\"funnelarea\"),b=e._has(\"gl2d\"),_=e._has(\"ternary\"),w=e._has(\"mapbox\"),T=e._has(\"polar\"),k=e._has(\"smith\"),A=e._has(\"sankey\"),M=function(t){for(var e=n.list({_fullLayout:t},null,!0),r=0;r<e.length;r++)if(!e[r].fixedrange)return!1;return!0}(e),S=o(e.hovermode),E=[];function L(t){if(t.length){for(var e=[],r=0;r<t.length;r++){for(var n=t[r],i=l[n],a=i.name.toLowerCase(),o=(i._cat||i.name).toLowerCase(),s=!1,u=0;u<d.length;u++){var c=d[u].toLowerCase();if(c===a||c===o){s=!0;break}}s||e.push(l[n])}E.push(e)}}var C=[\"toImage\"];s.showEditInChartStudio?C.push(\"editInChartStudio\"):s.showSendToCloud&&C.push(\"sendDataToCloud\"),L(C);var P=[],O=[],I=[],D=[];(v||b||m||x||_)+y+g+w+T+k>1?(O=[\"toggleHover\"],I=[\"resetViews\"]):y?(P=[\"zoomInGeo\",\"zoomOutGeo\"],O=[\"hoverClosestGeo\"],I=[\"resetGeo\"]):g?(O=[\"hoverClosest3d\"],I=[\"resetCameraDefault3d\",\"resetCameraLastSave3d\"]):w?(P=[\"zoomInMapbox\",\"zoomOutMapbox\"],O=[\"toggleHover\"],I=[\"resetViewMapbox\"]):b?O=[\"hoverClosestGl2d\"]:m?O=[\"hoverClosestPie\"]:A?(O=[\"hoverClosestCartesian\",\"hoverCompareCartesian\"],I=[\"resetViewSankey\"]):O=[\"toggleHover\"],v&&(O=[\"toggleSpikelines\",\"hoverClosestCartesian\",\"hoverCompareCartesian\"]),(function(t){for(var e=0;e<t.length;e++)if(!a.traceIs(t[e],\"noHover\"))return!1;return!0}(r)||S)&&(O=[]),!v&&!b||M||(P=[\"zoomIn2d\",\"zoomOut2d\",\"autoScale2d\"],\"resetViews\"!==I[0]&&(I=[\"resetScale2d\"])),g?D=[\"zoom3d\",\"pan3d\",\"orbitRotation\",\"tableRotation\"]:(v||b)&&!M||_?D=[\"zoom2d\",\"pan2d\"]:w||y?D=[\"pan2d\"]:T&&(D=[\"zoom2d\"]),function(t){for(var e=!1,r=0;r<t.length&&!e;r++){var 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L(D),L(P.concat(I)),L(z),function(t,e){if(e.length)if(Array.isArray(e[0]))for(var r=0;r<e.length;r++)t.push(e[r]);else t.push(e);return t}(E,p)}(t),f?f.update(t,h):e._modeBar=s(t,h)}else f&&(f.destroy(),delete e._modeBar)}},37676:function(t,e,r){\"use strict\";var n=r(39898),i=r(92770),a=r(71828),o=r(24255),s=r(11506).version,l=new DOMParser;function u(t){this.container=t.container,this.element=document.createElement(\"div\"),this.update(t.graphInfo,t.buttons),this.container.appendChild(this.element)}var c=u.prototype;c.update=function(t,e){this.graphInfo=t;var r=this.graphInfo._context,n=this.graphInfo._fullLayout,i=\"modebar-\"+n._uid;this.element.setAttribute(\"id\",i),this._uid=i,this.element.className=\"modebar\",\"hover\"===r.displayModeBar&&(this.element.className+=\" modebar--hover ease-bg\"),\"v\"===n.modebar.orientation&&(this.element.className+=\" vertical\",e=e.reverse());var o=n.modebar,s=\"hover\"===r.displayModeBar?\".js-plotly-plot .plotly:hover 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n=t.name;if(!n)throw new Error(\"must provide button 'name' in button config\");if(-1!==e.buttonsNames.indexOf(n))throw new Error(\"button name '\"+n+\"' is taken\");e.buttonsNames.push(n);var i=e.createButton(t);e.buttonElements.push(i),r.appendChild(i)})),e.element.appendChild(r)}))},c.createGroup=function(){var t=document.createElement(\"div\");return t.className=\"modebar-group\",t},c.createButton=function(t){var e=this,r=document.createElement(\"a\");r.setAttribute(\"rel\",\"tooltip\"),r.className=\"modebar-btn\";var i=t.title;void 0===i?i=t.name:\"function\"==typeof i&&(i=i(this.graphInfo)),(i||0===i)&&r.setAttribute(\"data-title\",i),void 0!==t.attr&&r.setAttribute(\"data-attr\",t.attr);var a=t.val;if(void 0!==a&&(\"function\"==typeof a&&(a=a(this.graphInfo)),r.setAttribute(\"data-val\",a)),\"function\"!=typeof t.click)throw new Error(\"must provide button 'click' function in button 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t.svg&&(e=l.parseFromString(t.svg,\"application/xml\").childNodes[0]),e.setAttribute(\"height\",\"1em\"),e.setAttribute(\"width\",\"1em\"),e},c.updateActiveButton=function(t){var e=this.graphInfo._fullLayout,r=void 0!==t?t.getAttribute(\"data-attr\"):null;this.buttonElements.forEach((function(t){var i=t.getAttribute(\"data-val\")||!0,o=t.getAttribute(\"data-attr\"),s=\"true\"===t.getAttribute(\"data-toggle\"),l=n.select(t);if(s)o===r&&l.classed(\"active\",!l.classed(\"active\"));else{var u=null===o?o:a.nestedProperty(e,o).get();l.classed(\"active\",u===i)}}))},c.hasButtons=function(t){var e=this.buttons;if(!e)return!1;if(t.length!==e.length)return!1;for(var r=0;r<t.length;++r){if(t[r].length!==e[r].length)return!1;for(var n=0;n<t[r].length;n++)if(t[r][n].name!==e[r][n].name)return!1}return!0},c.getLogo=function(){var t=this.createGroup(),e=document.createElement(\"a\");return e.href=\"https://plotly.com/\",e.target=\"_blank\",e.setAttribute(\"data-title\",a._(this.graphInfo,\"Produced with Plotly.js\")+\" (v\"+s+\")\"),e.className=\"modebar-btn plotlyjsicon modebar-btn--logo\",e.appendChild(this.createIcon(o.newplotlylogo)),t.appendChild(e),t},c.removeAllButtons=function(){for(;this.element.firstChild;)this.element.removeChild(this.element.firstChild);this.hasLogo=!1},c.destroy=function(){a.removeElement(this.container.querySelector(\".modebar\")),a.deleteRelatedStyleRule(this._uid)},t.exports=function(t,e){var r=t._fullLayout,i=new u({graphInfo:t,container:r._modebardiv.node(),buttons:e});return r._privateplot&&n.select(i.element).append(\"span\").classed(\"badge-private float--left\",!0).text(\"PRIVATE\"),i}},37113:function(t,e,r){\"use strict\";var n=r(41940),i=r(22399),a=(0,r(44467).templatedArray)(\"button\",{visible:{valType:\"boolean\",dflt:!0,editType:\"plot\"},step:{valType:\"enumerated\",values:[\"month\",\"year\",\"day\",\"hour\",\"minute\",\"second\",\"all\"],dflt:\"month\",editType:\"plot\"},stepmode:{valType:\"enumerated\",values:[\"backward\",\"todate\"],dflt:\"backward\",editType:\"plot\"},count:{valType:\"number\",min:0,dflt:1,editType:\"plot\"},label:{valType:\"string\",editType:\"plot\"},editType:\"plot\"});t.exports={visible:{valType:\"boolean\",editType:\"plot\"},buttons:a,x:{valType:\"number\",min:-2,max:3,editType:\"plot\"},xanchor:{valType:\"enumerated\",values:[\"auto\",\"left\",\"center\",\"right\"],dflt:\"left\",editType:\"plot\"},y:{valType:\"number\",min:-2,max:3,editType:\"plot\"},yanchor:{valType:\"enumerated\",values:[\"auto\",\"top\",\"middle\",\"bottom\"],dflt:\"bottom\",editType:\"plot\"},font:n({editType:\"plot\"}),bgcolor:{valType:\"color\",dflt:i.lightLine,editType:\"plot\"},activecolor:{valType:\"color\",editType:\"plot\"},bordercolor:{valType:\"color\",dflt:i.defaultLine,editType:\"plot\"},borderwidth:{valType:\"number\",min:0,dflt:0,editType:\"plot\"},editType:\"plot\"}},89573:function(t){\"use 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g=d(\"bgcolor\");d(\"activecolor\",i.contrast(g,l.lightAmount,l.darkAmount)),d(\"bordercolor\"),d(\"borderwidth\")}}},21598:function(t,e,r){\"use strict\";var n=r(39898),i=r(73972),a=r(74875),o=r(7901),s=r(91424),l=r(71828),u=l.strTranslate,c=r(63893),f=r(41675),h=r(18783),p=h.LINE_SPACING,d=h.FROM_TL,v=h.FROM_BR,g=r(89573),y=r(70565);function m(t){return t._id}function x(t,e,r){var n=l.ensureSingle(t,\"rect\",\"selector-rect\",(function(t){t.attr(\"shape-rendering\",\"crispEdges\")}));n.attr({rx:g.rx,ry:g.ry}),n.call(o.stroke,e.bordercolor).call(o.fill,function(t,e){return e._isActive||e._isHovered?t.activecolor:t.bgcolor}(e,r)).style(\"stroke-width\",e.borderwidth+\"px\")}function b(t,e,r,n){var i,a;l.ensureSingle(t,\"text\",\"selector-text\",(function(t){t.attr(\"text-anchor\",\"middle\")})).call(s.font,e.font).text((i=r,a=n._fullLayout._meta,i.label?a?l.templateString(i.label,a):i.label:\"all\"===i.step?\"all\":i.count+i.step.charAt(0))).call((function(t){c.convertToTspans(t,n)}))}t.exports=function(t){var e=t._fullLayout._infolayer.selectAll(\".rangeselector\").data(function(t){for(var e=f.list(t,\"x\",!0),r=[],n=0;n<e.length;n++){var i=e[n];i.rangeselector&&i.rangeselector.visible&&r.push(i)}return r}(t),m);e.enter().append(\"g\").classed(\"rangeselector\",!0),e.exit().remove(),e.style({cursor:\"pointer\",\"pointer-events\":\"all\"}),e.each((function(e){var r=n.select(this),o=e,f=o.rangeselector,h=r.selectAll(\"g.button\").data(l.filterVisible(f.buttons));h.enter().append(\"g\").classed(\"button\",!0),h.exit().remove(),h.each((function(e){var r=n.select(this),a=y(o,e);e._isActive=function(t,e,r){if(\"all\"===e.step)return!0===t.autorange;var n=Object.keys(r);return t.range[0]===r[n[0]]&&t.range[1]===r[n[1]]}(o,e,a),r.call(x,f,e),r.call(b,f,e,t),r.on(\"click\",(function(){t._dragged||i.call(\"_guiRelayout\",t,a)})),r.on(\"mouseover\",(function(){e._isHovered=!0,r.call(x,f,e)})),r.on(\"mouseout\",(function(){e._isHovered=!1,r.call(x,f,e)}))})),function(t,e,r,i,o){var f=0,h=0,y=r.borderwidth;e.each((function(){var t=n.select(this).select(\".selector-text\"),e=r.font.size*p,i=Math.max(e*c.lineCount(t),16)+3;h=Math.max(h,i)})),e.each((function(){var t=n.select(this),e=t.select(\".selector-rect\"),i=t.select(\".selector-text\"),a=i.node()&&s.bBox(i.node()).width,o=r.font.size*p,l=c.lineCount(i),d=Math.max(a+10,g.minButtonWidth);t.attr(\"transform\",u(y+f,y)),e.attr({x:0,y:0,width:d,height:h}),c.positionText(i,d/2,h/2-(l-1)*o/2+3),f+=d+5}));var m=t._fullLayout._size,x=m.l+m.w*r.x,b=m.t+m.h*(1-r.y),_=\"left\";l.isRightAnchor(r)&&(x-=f,_=\"right\"),l.isCenterAnchor(r)&&(x-=f/2,_=\"center\");var w=\"top\";l.isBottomAnchor(r)&&(b-=h,w=\"bottom\"),l.isMiddleAnchor(r)&&(b-=h/2,w=\"middle\"),f=Math.ceil(f),h=Math.ceil(h),x=Math.round(x),b=Math.round(b),a.autoMargin(t,i+\"-range-selector\",{x:r.x,y:r.y,l:f*d[_],r:f*v[_],b:h*v[w],t:h*d[w]}),o.attr(\"transform\",u(x,b))}(t,h,f,o._name,r)}))}},70565:function(t,e,r){\"use strict\";var n=r(81041),i=r(71828).titleCase;t.exports=function(t,e){var r=t._name,a={};if(\"all\"===e.step)a[r+\".autorange\"]=!0;else{var o=function(t,e){var r,a=t.range,o=new Date(t.r2l(a[1])),s=e.step,l=n[\"utc\"+i(s)],u=e.count;switch(e.stepmode){case\"backward\":r=t.l2r(+l.offset(o,-u));break;case\"todate\":var c=l.offset(o,-u);r=t.l2r(+l.ceil(c))}return[r,a[1]]}(t,e);a[r+\".range[0]\"]=o[0],a[r+\".range[1]\"]=o[1]}return a}},97218:function(t,e,r){\"use strict\";t.exports={moduleType:\"component\",name:\"rangeselector\",schema:{subplots:{xaxis:{rangeselector:r(37113)}}},layoutAttributes:r(37113),handleDefaults:r(28674),draw:r(21598)}},75148:function(t,e,r){\"use 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o=e[r],s=o[a.name];s&&s.visible&&s.autorange&&(s._input.autorange=!0,s._input.range=s.range=i(t,o))}}},73251:function(t){\"use 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n=r(71828),i=r(44467),a=r(41675),o=r(75148),s=r(47850);t.exports=function(t,e,r){var l=t[r],u=e[r];if(l.rangeslider||e._requestRangeslider[u._id]){n.isPlainObject(l.rangeslider)||(l.rangeslider={});var c,f,h=l.rangeslider,p=i.newContainer(u,\"rangeslider\");if(_(\"visible\")){_(\"bgcolor\",e.plot_bgcolor),_(\"bordercolor\"),_(\"borderwidth\"),_(\"thickness\"),_(\"autorange\",!u.isValidRange(h.range)),_(\"range\");var d=e._subplots;if(d)for(var v=d.cartesian.filter((function(t){return t.substr(0,t.indexOf(\"y\"))===a.name2id(r)})).map((function(t){return t.substr(t.indexOf(\"y\"),t.length)})),g=n.simpleMap(v,a.id2name),y=0;y<g.length;y++){var m=g[y];c=h[m]||{},f=i.newContainer(p,m,\"yaxis\");var x,b=e[m];c.range&&b.isValidRange(c.range)&&(x=\"fixed\"),\"match\"!==w(\"rangemode\",x)&&w(\"range\",b.range.slice())}p._input=h}}function _(t,e){return n.coerce(h,p,o,t,e)}function w(t,e){return n.coerce(c,f,s,t,e)}}},72413:function(t,e,r){\"use strict\";var n=r(39898),i=r(73972),a=r(74875),o=r(71828),s=o.strTranslate,l=r(91424),u=r(7901),c=r(92998),f=r(93612),h=r(41675),p=r(28569),d=r(6964),v=r(73251);function g(t,e,r,n){var i=o.ensureSingle(t,\"rect\",v.bgClassName,(function(t){t.attr({x:0,y:0,\"shape-rendering\":\"crispEdges\"})})),a=n.borderwidth%2==0?n.borderwidth:n.borderwidth-1,c=-n._offsetShift,f=l.crispRound(e,n.borderwidth);i.attr({width:n._width+a,height:n._height+a,transform:s(c,c),\"stroke-width\":f}).call(u.stroke,n.bordercolor).call(u.fill,n.bgcolor)}function y(t,e,r,n){var i=e._fullLayout;o.ensureSingleById(i._topdefs,\"clipPath\",n._clipId,(function(t){t.append(\"rect\").attr({x:0,y:0})})).select(\"rect\").attr({width:n._width,height:n._height})}function m(t,e,r,i){var s,u=e.calcdata,c=t.selectAll(\"g.\"+v.rangePlotClassName).data(r._subplotsWith,o.identity);c.enter().append(\"g\").attr(\"class\",(function(t){return v.rangePlotClassName+\" 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x(t,e,r,n,i){o.ensureSingle(t,\"rect\",v.maskMinClassName,(function(t){t.attr({x:0,y:0,\"shape-rendering\":\"crispEdges\"})})).attr(\"height\",n._height).call(u.fill,v.maskColor),o.ensureSingle(t,\"rect\",v.maskMaxClassName,(function(t){t.attr({y:0,\"shape-rendering\":\"crispEdges\"})})).attr(\"height\",n._height).call(u.fill,v.maskColor),\"match\"!==i.rangemode&&(o.ensureSingle(t,\"rect\",v.maskMinOppAxisClassName,(function(t){t.attr({y:0,\"shape-rendering\":\"crispEdges\"})})).attr(\"width\",n._width).call(u.fill,v.maskOppAxisColor),o.ensureSingle(t,\"rect\",v.maskMaxOppAxisClassName,(function(t){t.attr({y:0,\"shape-rendering\":\"crispEdges\"})})).attr(\"width\",n._width).style(\"border-top\",v.maskOppBorder).call(u.fill,v.maskOppAxisColor))}function b(t,e,r,n){e._context.staticPlot||o.ensureSingle(t,\"rect\",v.slideBoxClassName,(function(t){t.attr({y:0,cursor:v.slideBoxCursor,\"shape-rendering\":\"crispEdges\"})})).attr({height:n._height,fill:v.slideBoxFill})}function _(t,e,r,n){var i=o.ensureSingle(t,\"g\",v.grabberMinClassName),a=o.ensureSingle(t,\"g\",v.grabberMaxClassName),s={x:0,width:v.handleWidth,rx:v.handleRadius,fill:u.background,stroke:u.defaultLine,\"stroke-width\":v.handleStrokeWidth,\"shape-rendering\":\"crispEdges\"},l={y:Math.round(n._height/4),height:Math.round(n._height/2)};o.ensureSingle(i,\"rect\",v.handleMinClassName,(function(t){t.attr(s)})).attr(l),o.ensureSingle(a,\"rect\",v.handleMaxClassName,(function(t){t.attr(s)})).attr(l);var c={width:v.grabAreaWidth,x:0,y:0,fill:v.grabAreaFill,cursor:e._context.staticPlot?void 0:v.grabAreaCursor};o.ensureSingle(i,\"rect\",v.grabAreaMinClassName,(function(t){t.attr(c)})).attr(\"height\",n._height),o.ensureSingle(a,\"rect\",v.grabAreaMaxClassName,(function(t){t.attr(c)})).attr(\"height\",n._height)}t.exports=function(t){for(var e=t._fullLayout,r=e._rangeSliderData,a=0;a<r.length;a++){var l=r[a][v.name];l._clipId=l._id+\"-\"+e._uid}var u=e._infolayer.selectAll(\"g.\"+v.containerClassName).data(r,(function(t){return t._name}));u.exit().each((function(t){var r=t[v.name];e._topdefs.select(\"#\"+r._clipId).remove()})).remove(),0!==r.length&&(u.enter().append(\"g\").classed(v.containerClassName,!0).attr(\"pointer-events\",\"all\"),u.each((function(r){var a=n.select(this),l=r[v.name],u=e[h.id2name(r.anchor)],f=l[h.id2name(r.anchor)];if(l.range){var w,T=o.simpleMap(l.range,r.r2l),k=o.simpleMap(r.range,r.r2l);w=k[0]<k[1]?[Math.min(T[0],k[0]),Math.max(T[1],k[1])]:[Math.max(T[0],k[0]),Math.min(T[1],k[1])],l.range=l._input.range=o.simpleMap(w,r.l2r)}r.cleanRange(\"rangeslider.range\");var A=e._size,M=r.domain;l._width=A.w*(M[1]-M[0]);var S=Math.round(A.l+A.w*M[0]),E=Math.round(A.t+A.h*(1-r._counterDomainMin)+(\"bottom\"===r.side?r._depth:0)+l._offsetShift+v.extraPad);a.attr(\"transform\",s(S,E)),l._rl=o.simpleMap(l.range,r.r2l);var L=l._rl[0],C=l._rl[1],P=C-L;if(l.p2d=function(t){return t/l._width*P+L},l.d2p=function(t){return(t-L)/P*l._width},r.rangebreaks){var O=r.locateBreaks(L,C);if(O.length){var I,D,z=0;for(I=0;I<O.length;I++)z+=(D=O[I]).max-D.min;var R=l._width/(C-L-z),F=[-R*L];for(I=0;I<O.length;I++)D=O[I],F.push(F[F.length-1]-R*(D.max-D.min));for(l.d2p=function(t){for(var e=F[0],r=0;r<O.length;r++){var n=O[r];if(t>=n.max)e=F[r+1];else if(t<n.min)break}return e+R*t},I=0;I<O.length;I++)(D=O[I]).pmin=l.d2p(D.min),D.pmax=l.d2p(D.max);l.p2d=function(t){for(var e=F[0],r=0;r<O.length;r++){var n=O[r];if(t>=n.pmax)e=F[r+1];else if(t<n.pmin)break}return(t-e)/R}}}if(\"match\"!==f.rangemode){var B=u.r2l(f.range[0]),N=u.r2l(f.range[1])-B;l.d2pOppAxis=function(t){return(t-B)/N*l._height}}a.call(g,t,r,l).call(y,t,r,l).call(m,t,r,l).call(x,t,r,l,f).call(b,t,r,l).call(_,t,r,l),function(t,e,r,a){if(!e._context.staticPlot){var s=t.select(\"rect.\"+v.slideBoxClassName).node(),l=t.select(\"rect.\"+v.grabAreaMinClassName).node(),u=t.select(\"rect.\"+v.grabAreaMaxClassName).node();t.on(\"mousedown\",c),t.on(\"touchstart\",c)}function c(){var c=n.event,f=c.target,h=c.clientX||c.touches[0].clientX,v=h-t.node().getBoundingClientRect().left,g=a.d2p(r._rl[0]),y=a.d2p(r._rl[1]),m=p.coverSlip();function x(t){var c,p,x,b=+(t.clientX||t.touches[0].clientX)-h;switch(f){case s:if(x=\"ew-resize\",g+b>r._length||y+b<0)return;c=g+b,p=y+b;break;case l:if(x=\"col-resize\",g+b>r._length)return;c=g+b,p=y;break;case u:if(x=\"col-resize\",y+b<0)return;c=g,p=y+b;break;default:x=\"ew-resize\",c=v,p=v+b}if(p<c){var _=p;p=c,c=_}a._pixelMin=c,a._pixelMax=p,d(n.select(m),x),function(t,e,r,n){function a(t){return r.l2r(o.constrain(t,n._rl[0],n._rl[1]))}var s=a(n.p2d(n._pixelMin)),l=a(n.p2d(n._pixelMax));window.requestAnimationFrame((function(){i.call(\"_guiRelayout\",e,r._name+\".range\",[s,l])}))}(0,e,r,a)}function b(){m.removeEventListener(\"mousemove\",x),m.removeEventListener(\"mouseup\",b),this.removeEventListener(\"touchmove\",x),this.removeEventListener(\"touchend\",b),o.removeElement(m)}this.addEventListener(\"touchmove\",x),this.addEventListener(\"touchend\",b),m.addEventListener(\"mousemove\",x),m.addEventListener(\"mouseup\",b)}}(a,t,r,l),function(t,e,r,n,i,a){var l=v.handleWidth/2;function u(t){return o.constrain(t,0,n._width)}function c(t){return o.constrain(t,0,n._height)}function f(t){return o.constrain(t,-l,n._width+l)}var h=u(n.d2p(r._rl[0])),p=u(n.d2p(r._rl[1]));if(t.select(\"rect.\"+v.slideBoxClassName).attr(\"x\",h).attr(\"width\",p-h),t.select(\"rect.\"+v.maskMinClassName).attr(\"width\",h),t.select(\"rect.\"+v.maskMaxClassName).attr(\"x\",p).attr(\"width\",n._width-p),\"match\"!==a.rangemode){var d=n._height-c(n.d2pOppAxis(i._rl[1])),g=n._height-c(n.d2pOppAxis(i._rl[0]));t.select(\"rect.\"+v.maskMinOppAxisClassName).attr(\"x\",h).attr(\"height\",d).attr(\"width\",p-h),t.select(\"rect.\"+v.maskMaxOppAxisClassName).attr(\"x\",h).attr(\"y\",g).attr(\"height\",n._height-g).attr(\"width\",p-h),t.select(\"rect.\"+v.slideBoxClassName).attr(\"y\",d).attr(\"height\",g-d)}var y=.5,m=Math.round(f(h-l))-y,x=Math.round(f(p-l))+y;t.select(\"g.\"+v.grabberMinClassName).attr(\"transform\",s(m,y)),t.select(\"g.\"+v.grabberMaxClassName).attr(\"transform\",s(x,y))}(a,0,r,l,u,f),\"bottom\"===r.side&&c.draw(t,r._id+\"title\",{propContainer:r,propName:r._name+\".title\",placeholder:e._dfltTitle.x,attributes:{x:r._offset+r._length/2,y:E+l._height+l._offsetShift+10+1.5*r.title.font.size,\"text-anchor\":\"middle\"}})})))}},549:function(t,e,r){\"use strict\";var n=r(41675),i=r(63893),a=r(73251),o=r(18783).LINE_SPACING,s=a.name;function l(t){var e=t&&t[s];return e&&e.visible}e.isVisible=l,e.makeData=function(t){var e=n.list({_fullLayout:t},\"x\",!0),r=t.margin,i=[];if(!t._has(\"gl2d\"))for(var a=0;a<e.length;a++){var o=e[a];if(l(o)){i.push(o);var u=o[s];u._id=s+o._id,u._height=(t.height-r.b-r.t)*u.thickness,u._offsetShift=Math.floor(u.borderwidth/2)}}t._rangeSliderData=i},e.autoMarginOpts=function(t,e){var r=t._fullLayout,n=e[s],l=e._id.charAt(0),u=0,c=0;return\"bottom\"===e.side&&(u=e._depth,e.title.text!==r._dfltTitle[l]&&(c=1.5*e.title.font.size+10+n._offsetShift,c+=(e.title.text.match(i.BR_TAG_ALL)||[]).length*e.title.font.size*o)),{x:0,y:e._counterDomainMin,l:0,r:0,t:0,b:n._height+u+Math.max(r.margin.b,c),pad:a.extraPad+2*n._offsetShift}}},13137:function(t,e,r){\"use strict\";var n=r(71828),i=r(75148),a=r(47850),o=r(549);t.exports={moduleType:\"component\",name:\"rangeslider\",schema:{subplots:{xaxis:{rangeslider:n.extendFlat({},i,{yaxis:a})}}},layoutAttributes:r(75148),handleDefaults:r(26377),calcAutorange:r(88443),draw:r(72413),isVisible:o.isVisible,makeData:o.makeData,autoMarginOpts:o.autoMarginOpts}},47850:function(t){\"use strict\";t.exports={_isSubplotObj:!0,rangemode:{valType:\"enumerated\",values:[\"auto\",\"fixed\",\"match\"],dflt:\"match\",editType:\"calc\"},range:{valType:\"info_array\",items:[{valType:\"any\",editType:\"plot\"},{valType:\"any\",editType:\"plot\"}],editType:\"plot\"},editType:\"calc\"}},8389:function(t,e,r){\"use strict\";var n=r(50215),i=r(82196).line,a=r(79952).P,o=r(1426).extendFlat,s=r(30962).overrideAll,l=r(44467).templatedArray;r(24695),t.exports=s(l(\"selection\",{type:{valType:\"enumerated\",values:[\"rect\",\"path\"]},xref:o({},n.xref,{}),yref:o({},n.yref,{}),x0:{valType:\"any\"},x1:{valType:\"any\"},y0:{valType:\"any\"},y1:{valType:\"any\"},path:{valType:\"string\",editType:\"arraydraw\"},opacity:{valType:\"number\",min:0,max:1,dflt:.7,editType:\"arraydraw\"},line:{color:i.color,width:o({},i.width,{min:1,dflt:1}),dash:o({},a,{dflt:\"dot\"})}}),\"arraydraw\",\"from-root\")},34122:function(t){\"use strict\";t.exports={BENDPX:1.5,MINSELECT:12,SELECTDELAY:100,SELECTID:\"-select\"}},59402:function(t,e,r){\"use strict\";var n=r(71828),i=r(89298),a=r(85501),o=r(8389),s=r(30477);function l(t,e,r){function a(r,i){return n.coerce(t,e,o,r,i)}var l=a(\"path\"),u=\"path\"!==a(\"type\",l?\"path\":\"rect\");u&&delete e.path,a(\"opacity\"),a(\"line.color\"),a(\"line.width\"),a(\"line.dash\");for(var c=[\"x\",\"y\"],f=0;f<2;f++){var h,p,d,v=c[f],g={_fullLayout:r},y=i.coerceRef(t,e,g,v);if((h=i.getFromId(g,y))._selectionIndices.push(e._index),d=s.rangeToShapePosition(h),p=s.shapePositionToRange(h),u){var m=v+\"0\",x=v+\"1\",b=t[m],_=t[x];t[m]=p(t[m],!0),t[x]=p(t[x],!0),i.coercePosition(e,g,a,y,m),i.coercePosition(e,g,a,y,x);var w=e[m],T=e[x];void 0!==w&&void 0!==T&&(e[m]=d(w),e[x]=d(T),t[m]=b,t[x]=_)}}u&&n.noneOrAll(t,e,[\"x0\",\"x1\",\"y0\",\"y1\"])}t.exports=function(t,e){a(t,e,{name:\"selections\",handleItemDefaults:l});for(var r=e.selections,n=0;n<r.length;n++){var i=r[n];i&&void 0===i.path&&(void 0!==i.x0&&void 0!==i.x1&&void 0!==i.y0&&void 0!==i.y1||(e.selections[n]=null))}}},32485:function(t,e,r){\"use strict\";var n=r(60165).readPaths,i=r(42359),a=r(51873).clearOutlineControllers,o=r(7901),s=r(91424),l=r(44467).arrayEditor,u=r(30477),c=u.getPathString;function f(t){var e=t._fullLayout;for(var r in a(t),e._selectionLayer.selectAll(\"path\").remove(),e._plots){var n=e._plots[r].selectionLayer;n&&n.selectAll(\"path\").remove()}for(var i=0;i<e.selections.length;i++)p(t,i)}function h(t){return t._context.editSelection}function p(t,e){t._fullLayout._paperdiv.selectAll('.selectionlayer [data-index=\"'+e+'\"]').remove();var r=u.makeSelectionsOptionsAndPlotinfo(t,e),a=r.options,p=r.plotinfo;a._input&&function(r){var u=c(t,a),g={\"data-index\":e,\"fill-rule\":\"evenodd\",d:u},y=a.opacity,m=\"rgba(0,0,0,0)\",x=a.line.color||o.contrast(t._fullLayout.plot_bgcolor),b=a.line.width,_=a.line.dash;b||(b=5,_=\"solid\");var w=h(t)&&t._fullLayout._activeSelectionIndex===e;w&&(m=t._fullLayout.activeselection.fillcolor,y=t._fullLayout.activeselection.opacity);for(var T=[],k=1;k>=0;k--){var A=r.append(\"path\").attr(g).style(\"opacity\",k?.1:y).call(o.stroke,x).call(o.fill,m).call(s.dashLine,k?\"solid\":_,k?4+b:b);if(d(A,t,a),w){var M=l(t.layout,\"selections\",a);A.style({cursor:\"move\"});var S={element:A.node(),plotinfo:p,gd:t,editHelpers:M,isActiveSelection:!0},E=n(u,t);i(E,A,S)}else A.style(\"pointer-events\",k?\"all\":\"none\");T[k]=A}var L=T[0];T[1].node().addEventListener(\"click\",(function(){return function(t,e){if(h(t)){var r=+e.node().getAttribute(\"data-index\");if(r>=0){if(r===t._fullLayout._activeSelectionIndex)return void v(t);t._fullLayout._activeSelectionIndex=r,t._fullLayout._deactivateSelection=v,f(t)}}}(t,L)}))}(t._fullLayout._selectionLayer)}function d(t,e,r){var n=r.xref+r.yref;s.setClipUrl(t,\"clip\"+e._fullLayout._uid+n,e)}function v(t){h(t)&&t._fullLayout._activeSelectionIndex>=0&&(a(t),delete t._fullLayout._activeSelectionIndex,f(t))}t.exports={draw:f,drawOne:p,activateLastSelection:function(t){if(h(t)){var e=t._fullLayout.selections.length-1;t._fullLayout._activeSelectionIndex=e,t._fullLayout._deactivateSelection=v,f(t)}}}},53777:function(t,e,r){\"use strict\";var n=r(79952).P,i=r(1426).extendFlat;t.exports={newselection:{mode:{valType:\"enumerated\",values:[\"immediate\",\"gradual\"],dflt:\"immediate\",editType:\"none\"},line:{color:{valType:\"color\",editType:\"none\"},width:{valType:\"number\",min:1,dflt:1,editType:\"none\"},dash:i({},n,{dflt:\"dot\",editType:\"none\"}),editType:\"none\"},editType:\"none\"},activeselection:{fillcolor:{valType:\"color\",dflt:\"rgba(0,0,0,0)\",editType:\"none\"},opacity:{valType:\"number\",min:0,max:1,dflt:.5,editType:\"none\"},editType:\"none\"}}},90849:function(t){\"use strict\";t.exports=function(t,e,r){r(\"newselection.mode\"),r(\"newselection.line.width\")&&(r(\"newselection.line.color\"),r(\"newselection.line.dash\")),r(\"activeselection.fillcolor\"),r(\"activeselection.opacity\")}},35855:function(t,e,r){\"use strict\";var n=r(64505).selectMode,i=r(51873).clearOutline,a=r(60165),o=a.readPaths,s=a.writePaths,l=a.fixDatesForPaths;t.exports=function(t,e){if(t.length){var r=t[0][0];if(r){var a=r.getAttribute(\"d\"),u=e.gd,c=u._fullLayout.newselection,f=e.plotinfo,h=f.xaxis,p=f.yaxis,d=e.isActiveSelection,v=e.dragmode,g=(u.layout||{}).selections||[];if(!n(v)&&void 0!==d){var y=u._fullLayout._activeSelectionIndex;if(y<g.length)switch(u._fullLayout.selections[y].type){case\"rect\":v=\"select\";break;case\"path\":v=\"lasso\"}}var m,x=o(a,u,f,d),b={xref:h._id,yref:p._id,opacity:c.opacity,line:{color:c.line.color,width:c.line.width,dash:c.line.dash}};1===x.length&&(m=x[0]),m&&5===m.length&&\"select\"===v?(b.type=\"rect\",b.x0=m[0][1],b.y0=m[0][2],b.x1=m[2][1],b.y1=m[2][2]):(b.type=\"path\",h&&p&&l(x,h,p),b.path=s(x),m=null),i(u);for(var _=e.editHelpers,w=(_||{}).modifyItem,T=[],k=0;k<g.length;k++){var A=u._fullLayout.selections[k];if(A){if(T[k]=A._input,void 0!==d&&k===u._fullLayout._activeSelectionIndex){var M=b;switch(A.type){case\"rect\":w(\"x0\",M.x0),w(\"x1\",M.x1),w(\"y0\",M.y0),w(\"y1\",M.y1);break;case\"path\":w(\"path\",M.path)}}}else T[k]=A}return void 0===d?(T.push(b),T):_?_.getUpdateObj():{}}}}},75549:function(t,e,r){\"use strict\";var n=r(71828).strTranslate;function i(t,e){switch(t.type){case\"log\":return t.p2d(e);case\"date\":return t.p2r(e,0,t.calendar);default:return t.p2r(e)}}t.exports={p2r:i,r2p:function(t,e){switch(t.type){case\"log\":return t.d2p(e);case\"date\":return t.r2p(e,0,t.calendar);default:return t.r2p(e)}},axValue:function(t){var e=\"y\"===t._id.charAt(0)?1:0;return function(r){return i(t,r[e])}},getTransform:function(t){return n(t.xaxis._offset,t.yaxis._offset)}}},47322:function(t,e,r){\"use strict\";var n=r(32485),i=r(3937);t.exports={moduleType:\"component\",name:\"selections\",layoutAttributes:r(8389),supplyLayoutDefaults:r(59402),supplyDrawNewSelectionDefaults:r(90849),includeBasePlot:r(76325)(\"selections\"),draw:n.draw,drawOne:n.drawOne,reselect:i.reselect,prepSelect:i.prepSelect,clearOutline:i.clearOutline,clearSelectionsCache:i.clearSelectionsCache,selectOnClick:i.selectOnClick}},3937:function(t,e,r){\"use strict\";var n=r(52142),i=r(38258),a=r(73972),o=r(91424).dashStyle,s=r(7901),l=r(30211),u=r(23469).makeEventData,c=r(64505),f=c.freeMode,h=c.rectMode,p=c.drawMode,d=c.openMode,v=c.selectMode,g=r(30477),y=r(21459),m=r(42359),x=r(51873).clearOutline,b=r(60165),_=b.handleEllipse,w=b.readPaths,T=r(90551).newShapes,k=r(35855),A=r(32485).activateLastSelection,M=r(71828),S=M.sorterAsc,E=r(61082),L=r(79990),C=r(41675).getFromId,P=r(33306),O=r(61549).redrawReglTraces,I=r(34122),D=I.MINSELECT,z=E.filter,R=E.tester,F=r(75549),B=F.p2r,N=F.axValue,j=F.getTransform;function U(t){return void 0!==t.subplot}function V(t,e,r,n,i,a,o){var s,l,u,c,f,h,p,v,g,y=e._hoverdata,x=e._fullLayout.clickmode.indexOf(\"event\")>-1,b=[];if(function(t){return t&&Array.isArray(t)&&!0!==t[0].hoverOnBox}(y)){Z(t,e,a);var _=function(t,e){var r,n,i=t[0],a=-1,o=[];for(n=0;n<e.length;n++)if(r=e[n],i.fullData._expandedIndex===r.cd[0].trace._expandedIndex){if(!0===i.hoverOnBox)break;void 0!==i.pointNumber?a=i.pointNumber:void 0!==i.binNumber&&(a=i.binNumber,o=i.pointNumbers);break}return{pointNumber:a,pointNumbers:o,searchInfo:r}}(y,s=X(e,r,n,i));if(_.pointNumbers.length>0?function(t,e){var r,n,i,a=[];for(i=0;i<t.length;i++)(r=t[i]).cd[0].trace.selectedpoints&&r.cd[0].trace.selectedpoints.length>0&&a.push(r);if(1===a.length&&a[0]===e.searchInfo&&(n=e.searchInfo.cd[0].trace).selectedpoints.length===e.pointNumbers.length){for(i=0;i<e.pointNumbers.length;i++)if(n.selectedpoints.indexOf(e.pointNumbers[i])<0)return!1;return!0}return!1}(s,_):function(t){var e,r,n=0;for(r=0;r<t.length;r++)if((e=t[r].cd[0].trace).selectedpoints){if(e.selectedpoints.length>1)return!1;if((n+=e.selectedpoints.length)>1)return!1}return 1===n}(s)&&(h=K(_))){for(o&&o.remove(),g=0;g<s.length;g++)(l=s[g])._module.selectPoints(l,!1);$(e,s),Y(a),x&&ht(e)}else{for(p=t.shiftKey&&(void 0!==h?h:K(_)),u=function(t,e,r){return{pointNumber:t,searchInfo:e,subtract:!!r}}(_.pointNumber,_.searchInfo,p),c=G(a.selectionDefs.concat([u])),g=0;g<s.length;g++)if(f=tt(s[g]._module.selectPoints(s[g],c),s[g]),b.length)for(var w=0;w<f.length;w++)b.push(f[w]);else b=f;if($(e,s,v={points:b}),u&&a&&a.selectionDefs.push(u),o){var T=a.mergedPolygons,k=d(a.dragmode);m(et(T,k),o,a)}x&&ft(e,v)}}}function H(t){return\"pointNumber\"in t&&\"searchInfo\"in t}function q(t){return{xmin:0,xmax:0,ymin:0,ymax:0,pts:[],contains:function(e,r,n,i){var a=t.searchInfo.cd[0].trace._expandedIndex;return i.cd[0].trace._expandedIndex===a&&n===t.pointNumber},isRect:!1,degenerate:!1,subtract:!!t.subtract}}function G(t){if(t.length){for(var e=[],r=H(t[0])?0:t[0][0][0],n=r,i=H(t[0])?0:t[0][0][1],a=i,o=0;o<t.length;o++)if(H(t[o]))e.push(q(t[o]));else{var s=R(t[o]);s.subtract=!!t[o].subtract,e.push(s),r=Math.min(r,s.xmin),n=Math.max(n,s.xmax),i=Math.min(i,s.ymin),a=Math.max(a,s.ymax)}return{xmin:r,xmax:n,ymin:i,ymax:a,pts:[],contains:function(t,r,n,i){for(var a=!1,o=0;o<e.length;o++)e[o].contains(t,r,n,i)&&(a=!e[o].subtract);return a},isRect:!1,degenerate:!1}}}function Z(t,e,r){var n=e._fullLayout,i=r.plotinfo,a=r.dragmode,o=n._lastSelectedSubplot&&n._lastSelectedSubplot===i.id,s=(t.shiftKey||t.altKey)&&!(p(a)&&d(a));o&&s&&i.selection&&i.selection.selectionDefs&&!r.selectionDefs?(r.selectionDefs=i.selection.selectionDefs,r.mergedPolygons=i.selection.mergedPolygons):s&&i.selection||Y(r),o||(x(e),n._lastSelectedSubplot=i.id)}function Y(t,e){var r=t.dragmode,n=t.plotinfo,i=t.gd;(function(t){return t._fullLayout._activeShapeIndex>=0})(i)&&i._fullLayout._deactivateShape(i),function(t){return t._fullLayout._activeSelectionIndex>=0}(i)&&i._fullLayout._deactivateSelection(i);var o=i._fullLayout._zoomlayer,s=p(r),l=v(r);if(s||l){var u,c,f=o.selectAll(\".select-outline-\"+n.id);f&&i._fullLayout._outlining&&(s&&(u=T(f,t)),u&&a.call(\"_guiRelayout\",i,{shapes:u}),l&&!U(t)&&(c=k(f,t)),c&&(i._fullLayout._noEmitSelectedAtStart=!0,a.call(\"_guiRelayout\",i,{selections:c}).then((function(){e&&A(i)}))),i._fullLayout._outlining=!1)}n.selection={},n.selection.selectionDefs=t.selectionDefs=[],n.selection.mergedPolygons=t.mergedPolygons=[]}function W(t){return t._id}function X(t,e,r,n){if(!t.calcdata)return[];var i,a,o,s=[],l=e.map(W),u=r.map(W);for(o=0;o<t.calcdata.length;o++)if(!0===(a=(i=t.calcdata[o])[0].trace).visible&&a._module&&a._module.selectPoints)if(!U({subplot:n})||a.subplot!==n&&a.geo!==n)if(\"splom\"===a.type){if(a._xaxes[l[0]]&&a._yaxes[u[0]]){var c=J(a._module,i,e[0],r[0]);c.scene=t._fullLayout._splomScenes[a.uid],s.push(c)}}else if(\"sankey\"===a.type){var f=J(a._module,i,e[0],r[0]);s.push(f)}else{if(-1===l.indexOf(a.xaxis))continue;if(-1===u.indexOf(a.yaxis))continue;s.push(J(a._module,i,C(t,a.xaxis),C(t,a.yaxis)))}else s.push(J(a._module,i,e[0],r[0]));return s}function J(t,e,r,n){return{_module:t,cd:e,xaxis:r,yaxis:n}}function K(t){var e=t.searchInfo.cd[0].trace,r=t.pointNumber,n=t.pointNumbers,i=n.length>0?n[0]:r;return!!e.selectedpoints&&e.selectedpoints.indexOf(i)>-1}function $(t,e,r){var n,i;for(n=0;n<e.length;n++){var o=e[n].cd[0].trace._fullInput,s=t._fullLayout._tracePreGUI[o.uid]||{};void 0===s.selectedpoints&&(s.selectedpoints=o._input.selectedpoints||null)}if(r){var l=r.points||[];for(n=0;n<e.length;n++)(i=e[n].cd[0].trace)._input.selectedpoints=i._fullInput.selectedpoints=[],i._fullInput!==i&&(i.selectedpoints=[]);for(var u=0;u<l.length;u++){var c=l[u],f=c.data,h=c.fullData,p=c.pointIndex,d=c.pointIndices;d?([].push.apply(f.selectedpoints,d),i._fullInput!==i&&[].push.apply(h.selectedpoints,d)):(f.selectedpoints.push(p),i._fullInput!==i&&h.selectedpoints.push(p))}}else for(n=0;n<e.length;n++)delete(i=e[n].cd[0].trace).selectedpoints,delete i._input.selectedpoints,i._fullInput!==i&&delete i._fullInput.selectedpoints;!function(t,e){for(var r=!1,n=0;n<e.length;n++){var i=e[n],o=i.cd;a.traceIs(o[0].trace,\"regl\")&&(r=!0);var s=i._module,l=s.styleOnSelect||s.style;l&&(l(t,o,o[0].node3),o[0].nodeRangePlot3&&l(t,o,o[0].nodeRangePlot3))}r&&(P(t),O(t))}(t,e)}function Q(t,e,r){for(var i=(r?n.difference:n.union)({regions:t},{regions:[e]}).regions.reverse(),a=0;a<i.length;a++){var o=i[a];o.subtract=st(o,i.slice(0,a))}return i}function tt(t,e){if(Array.isArray(t))for(var r=e.cd,n=e.cd[0].trace,i=0;i<t.length;i++)t[i]=u(t[i],n,r);return t}function et(t,e){for(var r=[],n=0;n<t.length;n++){r[n]=[];for(var 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n=0;n<r.length;n++){var i=r[n];if(i.xaxis&&i.xaxis._id===t&&i.yaxis&&i.yaxis._id===e)return!0}return!1})(o=vt.xref,s=vt.yref,c)||it(t,o,s,n),tt&&(Q.points.length?ft(t,Q):ht(t)),h._deselect=!1}return{eventData:Q,selectionTesters:r}}function it(t,e,r,n){n=X(t,[C(t,e,\"x\")],[C(t,r,\"y\")],e+r);for(var i=0;i<n.length;i++){var a=n[i];a._module.selectPoints(a,!1)}$(t,n)}function at(t,e,r,n){for(var i,a=0;a<t.length;a++){var o=t[a];e===o.xref&&r===o.yref&&(i?n=G(i=Q(i,o,!!o.subtract)):(i=[o],n=R(o)))}return n}function ot(t,e){for(var r=[],n=t._fullLayout,i=n.selections,a=i.length,o=0;o<a;o++)if(!e||o===n._activeSelectionIndex){var s=i[o];if(s){var l,u,c,f,h,p=s.xref,d=s.yref,v=C(t,p,\"x\"),m=C(t,d,\"y\");if(\"rect\"===s.type){h=[];var x=lt(v,s.x0),b=lt(v,s.x1),_=lt(m,s.y0),w=lt(m,s.y1);h=[[x,_],[x,w],[b,w],[b,_]],l=Math.min(x,b),u=Math.max(x,b),c=Math.min(_,w),f=Math.max(_,w),h.xmin=l,h.xmax=u,h.ymin=c,h.ymax=f,h.xref=p,h.yref=d,h.subtract=!1,h.isRect=!0,r.push(h)}else if(\"path\"===s.type)for(var T=s.path.split(\"Z\"),k=[],A=0;A<T.length;A++){var M=T[A];if(M){M+=\"Z\";var S=g.extractPathCoords(M,y.paramIsX,\"raw\"),E=g.extractPathCoords(M,y.paramIsY,\"raw\");l=1/0,u=-1/0,c=1/0,f=-1/0,h=[];for(var L=0;L<S.length;L++){var P=lt(v,S[L]),O=lt(m,E[L]);h.push([P,O]),l=Math.min(P,l),u=Math.max(P,u),c=Math.min(O,c),f=Math.max(O,f)}h.xmin=l,h.xmax=u,h.ymin=c,h.ymax=f,h.xref=p,h.yref=d,h.subtract=st(h,k),k.push(h),r.push(h)}}}}return r}function st(t,e){for(var r=!1,n=0;n<e.length;n++)for(var a=e[n],o=0;o<t.length;o++)if(i(t[o],a)){r=!r;break}return r}function lt(t,e){return\"date\"===t.type&&(e=e.replace(\"_\",\" \")),\"log\"===t.type?t.c2p(e):t.r2p(e,null,t.calendar)}function ut(t){for(var e=t.length,r=[],n=0;n<e;n++){var i=t[n];r=(r=r.concat(i)).concat([i[0]])}return(a=r).isRect=5===a.length&&a[0][0]===a[4][0]&&a[0][1]===a[4][1]&&a[0][0]===a[1][0]&&a[2][0]===a[3][0]&&a[0][1]===a[3][1]&&a[1][1]===a[2][1]||a[0][1]===a[1][1]&&a[2][1]===a[3][1]&&a[0][0]===a[3][0]&&a[1][0]===a[2][0],a.isRect&&(a.xmin=Math.min(a[0][0],a[2][0]),a.xmax=Math.max(a[0][0],a[2][0]),a.ymin=Math.min(a[0][1],a[2][1]),a.ymax=Math.max(a[0][1],a[2][1])),a;var a}function ct(t){return function(e,r){for(var n,i,a=0;a<t.length;a++){var o=t[a],s=o._id,l=s.charAt(0);if(r.isRect){n||(n={});var u=r[l+\"min\"],c=r[l+\"max\"];void 0!==u&&void 0!==c&&(n[s]=[B(o,u),B(o,c)].sort(S))}else i||(i={}),i[s]=r.map(N(o))}n&&(e.range=n),i&&(e.lassoPoints=i)}}function ft(t,e){e&&(e.selections=(t.layout||{}).selections||[]),t.emit(\"plotly_selected\",e)}function ht(t){t.emit(\"plotly_deselect\",null)}t.exports={reselect:nt,prepSelect:function(t,e,r,n,i){var u=!U(n),c=f(i),g=h(i),y=d(i),x=p(i),b=v(i),w=\"drawcircle\"===i,T=\"drawline\"===i||w,k=n.gd,A=k._fullLayout,S=b&&\"immediate\"===A.newselection.mode&&u,E=A._zoomlayer,C=n.element.getBoundingClientRect(),P=n.plotinfo,O=j(P),F=e-C.left,B=r-C.top;A._calcInverseTransform(k);var N=M.apply3DTransform(A._invTransform)(F,B);F=N[0],B=N[1];var H,q,W,J,K,tt,at,ot=A._invScaleX,st=A._invScaleY,lt=F,pt=B,dt=\"M\"+F+\",\"+B,vt=n.xaxes[0],gt=n.yaxes[0],yt=vt._length,mt=gt._length,xt=t.altKey&&!(p(i)&&y);Z(t,k,n),c&&(H=z([[F,B]],I.BENDPX));var bt=E.selectAll(\"path.select-outline-\"+P.id).data([1]),_t=x?A.newshape:A.newselection;x&&(n.hasText=_t.label.text||_t.label.texttemplate);var wt=x&&!y?_t.fillcolor:\"rgba(0,0,0,0)\",Tt=_t.line.color||(u?s.contrast(k._fullLayout.plot_bgcolor):\"#7f7f7f\");bt.enter().append(\"path\").attr(\"class\",\"select-outline 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n,i=t[0][0],o=e.gd,p=i.getAttribute(\"d\"),g=o._fullLayout.newshape,y=e.plotinfo,w=e.isActiveShape,T=y.xaxis,k=y.yaxis,A=!!y.domain||!y.xaxis,M=!!y.domain||!y.yaxis,S=a(r),E=m(p,o,y,w),L={editable:!0,visible:g.visible,name:g.name,showlegend:g.showlegend,legend:g.legend,legendwidth:g.legendwidth,legendgroup:g.legendgroup,legendgrouptitle:{text:g.legendgrouptitle.text,font:g.legendgrouptitle.font},legendrank:g.legendrank,label:g.label,xref:A?\"paper\":T._id,yref:M?\"paper\":k._id,layer:g.layer,opacity:g.opacity,line:{color:g.line.color,width:g.line.width,dash:g.line.dash}};if(S||(L.fillcolor=g.fillcolor,L.fillrule=g.fillrule),1===E.length&&(n=E[0]),n&&5===n.length&&\"drawrect\"===r)L.type=\"rect\",L.x0=n[0][1],L.y0=n[0][2],L.x1=n[2][1],L.y1=n[2][2];else if(n&&\"drawline\"===r)L.type=\"line\",L.x0=n[0][1],L.y0=n[0][2],L.x1=n[1][1],L.y1=n[1][2];else if(n&&\"drawcircle\"===r){L.type=\"circle\";var C=n[s][1],P=n[l][1],O=n[u][1],I=n[c][1],D=n[s][2],z=n[l][2],R=n[u][2],F=n[c][2],B=y.xaxis&&(\"date\"===y.xaxis.type||\"log\"===y.xaxis.type),N=y.yaxis&&(\"date\"===y.yaxis.type||\"log\"===y.yaxis.type);B&&(C=v(y.xaxis,C),P=v(y.xaxis,P),O=v(y.xaxis,O),I=v(y.xaxis,I)),N&&(D=v(y.yaxis,D),z=v(y.yaxis,z),R=v(y.yaxis,R),F=v(y.yaxis,F));var j=(P+I)/2,U=(D+R)/2,V=b({x0:j,y0:U,x1:j+(I-P+O-C)/2*f,y1:U+(F-z+R-D)/2*h});B&&(V.x0=d(y.xaxis,V.x0),V.x1=d(y.xaxis,V.x1)),N&&(V.y0=d(y.yaxis,V.y0),V.y1=d(y.yaxis,V.y1)),L.x0=V.x0,L.y0=V.y0,L.x1=V.x1,L.y1=V.y1}else L.type=\"path\",T&&k&&_(E,T,k),L.path=x(E),n=null;return L}t.exports={newShapes:function(t,e){if(t.length&&t[0][0]){var r=e.gd,n=e.isActiveShape,a=e.dragmode,o=(r.layout||{}).shapes||[];if(!i(a)&&void 0!==n){var s=r._fullLayout._activeShapeIndex;if(s<o.length)switch(r._fullLayout.shapes[s].type){case\"rect\":a=\"drawrect\";break;case\"circle\":a=\"drawcircle\";break;case\"line\":a=\"drawline\";break;case\"path\":var l=o[s].path||\"\";a=\"Z\"===l[l.length-1]?\"drawclosedpath\":\"drawopenpath\"}}var u=w(t,e,a);g(r);for(var c=e.editHelpers,f=(c||{}).modifyItem,h=[],p=0;p<o.length;p++){var d=r._fullLayout.shapes[p];if(h[p]=d._input,void 0!==n&&p===r._fullLayout._activeShapeIndex){var v=u;switch(d.type){case\"line\":case\"rect\":case\"circle\":f(\"x0\",v.x0),f(\"x1\",v.x1),f(\"y0\",v.y0),f(\"y1\",v.y1);break;case\"path\":f(\"path\",v.path)}}}return void 0===n?(h.push(u),h):c?c.getUpdateObj():{}}},createShapeObj:w}},51873:function(t){\"use strict\";t.exports={clearOutlineControllers:function(t){var e=t._fullLayout._zoomlayer;e&&e.selectAll(\".outline-controllers\").remove()},clearOutline:function(t){var e=t._fullLayout._zoomlayer;e&&e.selectAll(\".select-outline\").remove(),t._fullLayout._outlining=!1}}},30477:function(t,e,r){\"use strict\";var n=r(21459),i=r(71828),a=r(89298);e.rangeToShapePosition=function(t){return\"log\"===t.type?t.r2d:function(t){return 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n?1-e:e};else{var s=e.rangeToShapePosition(r);a=function(t){return s(r.p2r(t-r._offset))}}else a=n?function(t){return 1-(t-o.t)/o.h}:function(t){return(t-o.l)/o.w};return a},e.roundPositionForSharpStrokeRendering=function(t,e){var r=1===Math.round(e%2),n=Math.round(t);return r?n+.5:n},e.makeShapesOptionsAndPlotinfo=function(t,e){var r=t._fullLayout.shapes[e]||{},n=t._fullLayout._plots[r.xref+r.yref];return n?n._hadPlotinfo=!0:(n={},r.xref&&\"paper\"!==r.xref&&(n.xaxis=t._fullLayout[r.xref+\"axis\"]),r.yref&&\"paper\"!==r.yref&&(n.yaxis=t._fullLayout[r.yref+\"axis\"])),n.xsizemode=r.xsizemode,n.ysizemode=r.ysizemode,n.xanchor=r.xanchor,n.yanchor=r.yanchor,{options:r,plotinfo:n}},e.makeSelectionsOptionsAndPlotinfo=function(t,e){var r=t._fullLayout.selections[e]||{},n=t._fullLayout._plots[r.xref+r.yref];return n?n._hadPlotinfo=!0:(n={},r.xref&&(n.xaxis=t._fullLayout[r.xref+\"axis\"]),r.yref&&(n.yaxis=t._fullLayout[r.yref+\"axis\"])),{options:r,plotinfo:n}},e.getPathString=function(t,r){var o,s,l,u,c,f,h,p,d=r.type,v=a.getRefType(r.xref),g=a.getRefType(r.yref),y=a.getFromId(t,r.xref),m=a.getFromId(t,r.yref),x=t._fullLayout._size;if(y?\"domain\"===v?s=function(t){return y._offset+y._length*t}:(o=e.shapePositionToRange(y),s=function(t){return y._offset+y.r2p(o(t,!0))}):s=function(t){return x.l+x.w*t},m?\"domain\"===g?u=function(t){return m._offset+m._length*(1-t)}:(l=e.shapePositionToRange(m),u=function(t){return m._offset+m.r2p(l(t,!0))}):u=function(t){return x.t+x.h*(1-t)},\"path\"===d)return y&&\"date\"===y.type&&(s=e.decodeDate(s)),m&&\"date\"===m.type&&(u=e.decodeDate(u)),function(t,e,r){var a=t.path,o=t.xsizemode,s=t.ysizemode,l=t.xanchor,u=t.yanchor;return a.replace(n.segmentRE,(function(t){var a=0,c=t.charAt(0),f=n.paramIsX[c],h=n.paramIsY[c],p=n.numParams[c],d=t.substr(1).replace(n.paramRE,(function(t){return f[a]?t=\"pixel\"===o?e(l)+Number(t):e(t):h[a]&&(t=\"pixel\"===s?r(u)-Number(t):r(t)),++a>p&&(t=\"X\"),t}));return a>p&&(d=d.replace(/[\\s,]*X.*/,\"\"),i.log(\"Ignoring extra params in segment \"+t)),c+d}))}(r,s,u);if(\"pixel\"===r.xsizemode){var b=s(r.xanchor);c=b+r.x0,f=b+r.x1}else c=s(r.x0),f=s(r.x1);if(\"pixel\"===r.ysizemode){var _=u(r.yanchor);h=_-r.y0,p=_-r.y1}else h=u(r.y0),p=u(r.y1);if(\"line\"===d)return\"M\"+c+\",\"+h+\"L\"+f+\",\"+p;if(\"rect\"===d)return\"M\"+c+\",\"+h+\"H\"+f+\"V\"+p+\"H\"+c+\"Z\";var w=(c+f)/2,T=(h+p)/2,k=Math.abs(w-c),A=Math.abs(T-h),M=\"A\"+k+\",\"+A,S=w+k+\",\"+T;return\"M\"+S+M+\" 0 1,1 \"+w+\",\"+(T-A)+M+\" 0 0,1 \"+S+\"Z\"}},89853:function(t,e,r){\"use strict\";var n=r(34031);t.exports={moduleType:\"component\",name:\"shapes\",layoutAttributes:r(89827),supplyLayoutDefaults:r(84726),supplyDrawNewShapeDefaults:r(45547),includeBasePlot:r(76325)(\"shapes\"),calcAutorange:r(5627),draw:n.draw,drawOne:n.drawOne}},37281:function(t){\"use strict\";function e(t,e){return e?e.d2l(t):t}function r(t,e){return e?e.l2d(t):t}function n(t,r){return e(t.x1,r)-e(t.x0,r)}function i(t,r,n){return e(t.y1,n)-e(t.y0,n)}t.exports={x0:function(t){return t.x0},x1:function(t){return t.x1},y0:function(t){return t.y0},y1:function(t){return t.y1},slope:function(t,e,r){return\"line\"!==t.type?void 0:i(t,0,r)/n(t,e)},dx:n,dy:i,width:function(t,e){return Math.abs(n(t,e))},height:function(t,e,r){return Math.abs(i(t,0,r))},length:function(t,e,r){return\"line\"!==t.type?void 0:Math.sqrt(Math.pow(n(t,e),2)+Math.pow(i(t,0,r),2))},xcenter:function(t,n){return r((e(t.x1,n)+e(t.x0,n))/2,n)},ycenter:function(t,n,i){return r((e(t.y1,i)+e(t.y0,i))/2,i)}}},75067:function(t,e,r){\"use 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n=r(41940),i=r(35025),a=r(1426).extendDeepAll,o=r(30962).overrideAll,s=r(85594),l=r(44467).templatedArray,u=r(98292),c=l(\"step\",{visible:{valType:\"boolean\",dflt:!0},method:{valType:\"enumerated\",values:[\"restyle\",\"relayout\",\"animate\",\"update\",\"skip\"],dflt:\"restyle\"},args:{valType:\"info_array\",freeLength:!0,items:[{valType:\"any\"},{valType:\"any\"},{valType:\"any\"}]},label:{valType:\"string\"},value:{valType:\"string\"},execute:{valType:\"boolean\",dflt:!0}});t.exports=o(l(\"slider\",{visible:{valType:\"boolean\",dflt:!0},active:{valType:\"number\",min:0,dflt:0},steps:c,lenmode:{valType:\"enumerated\",values:[\"fraction\",\"pixels\"],dflt:\"fraction\"},len:{valType:\"number\",min:0,dflt:1},x:{valType:\"number\",min:-2,max:3,dflt:0},pad:a(i({editType:\"arraydraw\"}),{},{t:{dflt:20}}),xanchor:{valType:\"enumerated\",values:[\"auto\",\"left\",\"center\",\"right\"],dflt:\"left\"},y:{valType:\"number\",min:-2,max:3,dflt:0},yanchor:{valType:\"enumerated\",values:[\"auto\",\"top\",\"middle\",\"bottom\"],dflt:\"top\"},transition:{duration:{valType:\"number\",min:0,dflt:150},easing:{valType:\"enumerated\",values:s.transition.easing.values,dflt:\"cubic-in-out\"}},currentvalue:{visible:{valType:\"boolean\",dflt:!0},xanchor:{valType:\"enumerated\",values:[\"left\",\"center\",\"right\"],dflt:\"left\"},offset:{valType:\"number\",dflt:10},prefix:{valType:\"string\"},suffix:{valType:\"string\"},font:n({})},font:n({}),activebgcolor:{valType:\"color\",dflt:u.gripBgActiveColor},bgcolor:{valType:\"color\",dflt:u.railBgColor},bordercolor:{valType:\"color\",dflt:u.railBorderColor},borderwidth:{valType:\"number\",min:0,dflt:u.railBorderWidth},ticklen:{valType:\"number\",min:0,dflt:u.tickLength},tickcolor:{valType:\"color\",dflt:u.tickColor},tickwidth:{valType:\"number\",min:0,dflt:1},minorticklen:{valType:\"number\",min:0,dflt:u.minorTickLength}}),\"arraydraw\",\"from-root\")},98292:function(t){\"use 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T={y:e.y,b:c.height*v[w],t:c.height*d[w]};\"fraction\"===e.lenmode?(T.l=0,T.xl=e.x-e.len*d[b],T.r=0,T.xr=e.x+e.len*v[b]):(T.x=e.x,T.l=c.outerLength*d[b],T.r=c.outerLength*v[b]),i.autoMargin(t,g(e),T)}function x(t,e,r){if(e.currentvalue.visible){var n,i,a=e._dims;switch(e.currentvalue.xanchor){case\"right\":n=a.inputAreaLength-f.currentValueInset-a.currentValueMaxWidth,i=\"left\";break;case\"center\":n=.5*a.inputAreaLength,i=\"middle\";break;default:n=f.currentValueInset,i=\"left\"}var l=s.ensureSingle(t,\"text\",f.labelClass,(function(t){t.attr({\"text-anchor\":i,\"data-notex\":1})})),c=e.currentvalue.prefix?e.currentvalue.prefix:\"\";if(\"string\"==typeof r)c+=r;else{var h=e.steps[e.active].label,d=e._gd._fullLayout._meta;d&&(h=s.templateString(h,d)),c+=h}e.currentvalue.suffix&&(c+=e.currentvalue.suffix),l.call(o.font,e.currentvalue.font).text(c).call(u.convertToTspans,e._gd);var v=u.lineCount(l),g=(a.currentValueMaxLines+1-v)*e.currentvalue.font.size*p;return 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s(t,e,r){this.gd=t,this.container=e,this.id=r,this.position=null,this.translateX=null,this.translateY=null,this.hbar=null,this.vbar=null,this.bg=this.container.selectAll(\"rect.scrollbox-bg\").data([0]),this.bg.exit().on(\".drag\",null).on(\"wheel\",null).remove(),this.bg.enter().append(\"rect\").classed(\"scrollbox-bg\",!0).style(\"pointer-events\",\"all\").attr({opacity:0,x:0,y:0,width:0,height:0})}s.barWidth=2,s.barLength=20,s.barRadius=2,s.barPad=1,s.barColor=\"#808BA4\",s.prototype.enable=function(t,e,r){var o=this.gd._fullLayout,l=o.width,u=o.height;this.position=t;var c,f,h,p,d=this.position.l,v=this.position.w,g=this.position.t,y=this.position.h,m=this.position.direction,x=\"down\"===m,b=\"left\"===m,_=\"up\"===m,w=v,T=y;x||b||\"right\"===m||_||(this.position.direction=\"down\",x=!0),x||_?(f=(c=d)+w,x?(h=g,T=(p=Math.min(h+T,u))-h):T=(p=g+T)-(h=Math.max(p-T,0))):(p=(h=g)+T,b?w=(f=d+w)-(c=Math.max(f-w,0)):(c=d,w=(f=Math.min(c+w,l))-c)),this._box={l:c,t:h,w:w,h:T};var k=v>w,A=s.barLength+2*s.barPad,M=s.barWidth+2*s.barPad,S=d,E=g+y;E+M>u&&(E=u-M);var L=this.container.selectAll(\"rect.scrollbar-horizontal\").data(k?[0]:[]);L.exit().on(\".drag\",null).remove(),L.enter().append(\"rect\").classed(\"scrollbar-horizontal\",!0).call(i.fill,s.barColor),k?(this.hbar=L.attr({rx:s.barRadius,ry:s.barRadius,x:S,y:E,width:A,height:M}),this._hbarXMin=S+A/2,this._hbarTranslateMax=w-A):(delete this.hbar,delete this._hbarXMin,delete this._hbarTranslateMax);var C=y>T,P=s.barWidth+2*s.barPad,O=s.barLength+2*s.barPad,I=d+v,D=g;I+P>l&&(I=l-P);var z=this.container.selectAll(\"rect.scrollbar-vertical\").data(C?[0]:[]);z.exit().on(\".drag\",null).remove(),z.enter().append(\"rect\").classed(\"scrollbar-vertical\",!0).call(i.fill,s.barColor),C?(this.vbar=z.attr({rx:s.barRadius,ry:s.barRadius,x:I,y:D,width:P,height:O}),this._vbarYMin=D+O/2,this._vbarTranslateMax=T-O):(delete this.vbar,delete this._vbarYMin,delete this._vbarTranslateMax);var R=this.id,F=c-.5,B=C?f+P+.5:f+.5,N=h-.5,j=k?p+M+.5:p+.5,U=o._topdefs.selectAll(\"#\"+R).data(k||C?[0]:[]);if(U.exit().remove(),U.enter().append(\"clipPath\").attr(\"id\",R).append(\"rect\"),k||C?(this._clipRect=U.select(\"rect\").attr({x:Math.floor(F),y:Math.floor(N),width:Math.ceil(B)-Math.floor(F),height:Math.ceil(j)-Math.floor(N)}),this.container.call(a.setClipUrl,R,this.gd),this.bg.attr({x:d,y:g,width:v,height:y})):(this.bg.attr({width:0,height:0}),this.container.on(\"wheel\",null).on(\".drag\",null).call(a.setClipUrl,null),delete this._clipRect),k||C){var V=n.behavior.drag().on(\"dragstart\",(function(){n.event.sourceEvent.preventDefault()})).on(\"drag\",this._onBoxDrag.bind(this));this.container.on(\"wheel\",null).on(\"wheel\",this._onBoxWheel.bind(this)).on(\".drag\",null).call(V);var H=n.behavior.drag().on(\"dragstart\",(function(){n.event.sourceEvent.preventDefault(),n.event.sourceEvent.stopPropagation()})).on(\"drag\",this._onBarDrag.bind(this));k&&this.hbar.on(\".drag\",null).call(H),C&&this.vbar.on(\".drag\",null).call(H)}this.setTranslate(e,r)},s.prototype.disable=function(){(this.hbar||this.vbar)&&(this.bg.attr({width:0,height:0}),this.container.on(\"wheel\",null).on(\".drag\",null).call(a.setClipUrl,null),delete this._clipRect),this.hbar&&(this.hbar.on(\".drag\",null),this.hbar.remove(),delete this.hbar,delete this._hbarXMin,delete this._hbarTranslateMax),this.vbar&&(this.vbar.on(\".drag\",null),this.vbar.remove(),delete this.vbar,delete this._vbarYMin,delete this._vbarTranslateMax)},s.prototype._onBoxDrag=function(){var t=this.translateX,e=this.translateY;this.hbar&&(t-=n.event.dx),this.vbar&&(e-=n.event.dy),this.setTranslate(t,e)},s.prototype._onBoxWheel=function(){var t=this.translateX,e=this.translateY;this.hbar&&(t+=n.event.deltaY),this.vbar&&(e+=n.event.deltaY),this.setTranslate(t,e)},s.prototype._onBarDrag=function(){var t=this.translateX,e=this.translateY;if(this.hbar){var r=t+this._hbarXMin,i=r+this._hbarTranslateMax;t=(o.constrain(n.event.x,r,i)-r)/(i-r)*(this.position.w-this._box.w)}if(this.vbar){var a=e+this._vbarYMin,s=a+this._vbarTranslateMax;e=(o.constrain(n.event.y,a,s)-a)/(s-a)*(this.position.h-this._box.h)}this.setTranslate(t,e)},s.prototype.setTranslate=function(t,e){var r=this.position.w-this._box.w,n=this.position.h-this._box.h;if(t=o.constrain(t||0,0,r),e=o.constrain(e||0,0,n),this.translateX=t,this.translateY=e,this.container.call(a.setTranslate,this._box.l-this.position.l-t,this._box.t-this.position.t-e),this._clipRect&&this._clipRect.attr({x:Math.floor(this.position.l+t-.5),y:Math.floor(this.position.t+e-.5)}),this.hbar){var i=t/r;this.hbar.call(a.setTranslate,t+i*this._hbarTranslateMax,e)}if(this.vbar){var s=e/n;this.vbar.call(a.setTranslate,t,e+s*this._vbarTranslateMax)}}},18783:function(t){\"use strict\";t.exports={FROM_BL:{left:0,center:.5,right:1,bottom:0,middle:.5,top:1},FROM_TL:{left:0,center:.5,right:1,bottom:1,middle:.5,top:0},FROM_BR:{left:1,center:.5,right:0,bottom:0,middle:.5,top:1},LINE_SPACING:1.3,CAP_SHIFT:.7,MID_SHIFT:.35,OPPOSITE_SIDE:{left:\"right\",right:\"left\",top:\"bottom\",bottom:\"top\"}}},24695:function(t){\"use strict\";t.exports={axisRefDescription:function(t,e,r){return[\"If set to a\",t,\"axis id (e.g. *\"+t+\"* or\",\"*\"+t+\"2*), the `\"+t+\"` position refers to a\",t,\"coordinate. 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z=!D;D?(t._dtickInit=t.dtick,t._tick0Init=t.tick0):(t.minor._dtickInit=t.minor.dtick,t.minor._tick0Init=t.minor.tick0);var R=D?t:s.extendFlat({},t,t.minor);if(z?q.prepMinorTicks(R,t,e):q.prepTicks(R,e),\"array\"!==R.tickmode)if(\"sync\"!==R.tickmode){var F=K(c),B=F[0],N=F[1],j=i(R.dtick),U=\"log\"===a&&!(j||\"L\"===R.dtick.charAt(0)),V=q.tickFirst(R,e);if(D){if(t._tmin=V,V<B!==f)break;\"category\"!==a&&\"multicategory\"!==a||(N=f?Math.max(-.5,N):Math.min(t._categories.length-.5,N))}var H,G,Z=null,Y=V;D&&(j?G=t.dtick:\"date\"===a?\"string\"==typeof t.dtick&&\"M\"===t.dtick.charAt(0)&&(G=T*t.dtick.substring(1)):G=t._roughDTick,H=Math.round((t.r2l(Y)-t.r2l(t.tick0))/G)-1);var W=R.dtick;for(R.rangebreaks&&R._tick0Init!==R.tick0&&(Y=zt(Y,t),f||(Y=q.tickIncrement(Y,W,!f,o))),D&&u&&(Y=q.tickIncrement(Y,W,!f,o),H--);f?Y>=N:Y<=N;Y=q.tickIncrement(Y,W,f,o)){if(D&&H++,R.rangebreaks&&!f){if(Y<B)continue;if(R.maskBreaks(Y)===O&&zt(Y,R)>=p)break}if(C.length>d||Y===Z)break;Z=Y;var X={value:Y};D?(U&&Y!==(0|Y)&&(X.simpleLabel=!0),l>1&&H%l&&(X.skipLabel=!0),C.push(X)):(X.minor=!0,P.push(X))}}else C=[],v=at(t);else D?(C=[],v=ot(t)):(P=[],L=ot(t))}if(I&&!(\"inside\"===t.minor.ticks&&\"outside\"===t.ticks||\"outside\"===t.minor.ticks&&\"inside\"===t.ticks)){for(var J=C.map((function(t){return t.value})),$=[],Q=0;Q<P.length;Q++){var tt=P[Q],et=tt.value;if(-1===J.indexOf(et)){for(var rt=!1,nt=0;!rt&&nt<C.length;nt++)1e7+C[nt].value===1e7+et&&(rt=!0);rt||$.push(tt)}}P=$}if(u&&function(t,e,r){for(var n=0;n<t.length;n++){var i=t[n].value,a=n,o=n+1;n<t.length-1?(a=n,o=n+1):n>0?(a=n-1,o=n):(a=n,o=n);var s,l=t[a].value,u=t[o].value,c=Math.abs(u-l),f=r||c,h=0;f>=m?h=c>=m&&c<=g?c:y:r===b&&f>=_?h=c>=_&&c<=x?c:b:f>=k?h=c>=k&&c<=w?c:T:r===A&&f>=A?h=A:f>=M?h=M:r===S&&f>=S?h=S:r===E&&f>=E&&(h=E),h>=c&&(h=c,s=!0);var p=i+h;if(e.rangebreaks&&h>0){for(var d=0,v=0;v<84;v++){var L=(v+.5)/84;e.maskBreaks(i*(1-L)+L*p)!==O&&d++}(h*=d/84)||(t[n].drop=!0),s&&c>A&&(h=c)}(h>0||0===n)&&(t[n].periodX=i+h/2)}}(C,t,t._definedDelta),t.rangebreaks){var it=\"y\"===t._id.charAt(0),st=1;\"auto\"===t.tickmode&&(st=t.tickfont?t.tickfont.size:12);var lt=NaN;for(r=C.length-1;r>-1;r--)if(C[r].drop)C.splice(r,1);else{C[r].value=zt(C[r].value,t);var ut=t.c2p(C[r].value);(it?lt>ut-st:lt<ut+st)?C.splice(f?r+1:r,1):lt=ut}}Dt(t)&&360===Math.abs(c[1]-c[0])&&C.pop(),t._tmax=(C[C.length-1]||{}).value,t._prevDateHead=\"\",t._inCalcTicks=!0;var ct,ft,ht=function(e){e.text=\"\",t._prevDateHead=n};for(C=C.concat(P),r=0;r<C.length;r++){var pt=C[r].minor,dt=C[r].value;pt?L.push({x:dt,minor:!0}):(n=t._prevDateHead,ct=q.tickText(t,dt,!1,C[r].simpleLabel),void 0!==(ft=C[r].periodX)&&(ct.periodX=ft,(ft>p||ft<h)&&(ft>p&&(ct.periodX=p),ft<h&&(ct.periodX=h),ht(ct))),C[r].skipLabel&&ht(ct),v.push(ct))}return v=v.concat(L),t._inCalcTicks=!1,u&&v.length&&(v[0].noTick=!0),v};var st=[2,5,10],lt=[1,2,3,6,12],ut=[1,2,5,10,15,30],ct=[1,2,3,7,14],ft=[-.046,0,.301,.477,.602,.699,.778,.845,.903,.954,1],ht=[-.301,0,.301,.699,1],pt=[15,30,45,90,180];function dt(t,e,r){return e*s.roundUp(t/e,r)}function vt(t){var e=t.dtick;if(t._tickexponent=0,i(e)||\"string\"==typeof e||(e=1),\"category\"!==t.type&&\"multicategory\"!==t.type||(t._tickround=null),\"date\"===t.type){var r=t.r2l(t.tick0),n=t.l2r(r).replace(/(^-|i)/g,\"\"),a=n.length;if(\"M\"===String(e).charAt(0))a>10||\"01-01\"!==n.substr(5)?t._tickround=\"d\":t._tickround=+e.substr(1)%12==0?\"y\":\"m\";else if(e>=M&&a<=10||e>=15*M)t._tickround=\"d\";else if(e>=L&&a<=16||e>=E)t._tickround=\"M\";else if(e>=C&&a<=19||e>=L)t._tickround=\"S\";else{var o=t.l2r(r+e).replace(/^-/,\"\").length;t._tickround=Math.max(a,o)-20,t._tickround<0&&(t._tickround=4)}}else if(i(e)||\"L\"===e.charAt(0)){var s=t.range.map(t.r2d||Number);i(e)||(e=Number(e.substr(1))),t._tickround=2-Math.floor(Math.log(e)/Math.LN10+.01);var l=Math.max(Math.abs(s[0]),Math.abs(s[1])),u=Math.floor(Math.log(l)/Math.LN10+.01),c=void 0===t.minexponent?3:t.minexponent;Math.abs(u)>c&&(mt(t.exponentformat)&&!xt(u)?t._tickexponent=3*Math.round((u-1)/3):t._tickexponent=u)}else t._tickround=null}function gt(t,e,r){var n=t.tickfont||{};return{x:e,dx:0,dy:0,text:r||\"\",fontSize:n.size,font:n.family,fontColor:n.color}}q.autoTicks=function(t,e,r){var n;function a(t){return Math.pow(t,Math.floor(Math.log(e)/Math.LN10))}if(\"date\"===t.type){t.tick0=s.dateTick0(t.calendar,0);var o=2*e;if(o>y)e/=y,n=a(10),t.dtick=\"M\"+12*dt(e,n,st);else if(o>T)e/=T,t.dtick=\"M\"+dt(e,1,lt);else if(o>M){if(t.dtick=dt(e,M,t._hasDayOfWeekBreaks?[1,2,7,14]:ct),!r){var l=q.getTickFormat(t),u=\"period\"===t.ticklabelmode;u&&(t._rawTick0=t.tick0),/%[uVW]/.test(l)?t.tick0=s.dateTick0(t.calendar,2):t.tick0=s.dateTick0(t.calendar,1),u&&(t._dowTick0=t.tick0)}}else o>E?t.dtick=dt(e,E,lt):o>L?t.dtick=dt(e,L,ut):o>C?t.dtick=dt(e,C,ut):(n=a(10),t.dtick=dt(e,n,st))}else if(\"log\"===t.type){t.tick0=0;var c=s.simpleMap(t.range,t.r2l);if(t._isMinor&&(e*=1.5),e>.7)t.dtick=Math.ceil(e);else if(Math.abs(c[1]-c[0])<1){var f=1.5*Math.abs((c[1]-c[0])/e);e=Math.abs(Math.pow(10,c[1])-Math.pow(10,c[0]))/f,n=a(10),t.dtick=\"L\"+dt(e,n,st)}else t.dtick=e>.3?\"D2\":\"D1\"}else\"category\"===t.type||\"multicategory\"===t.type?(t.tick0=0,t.dtick=Math.ceil(Math.max(e,1))):Dt(t)?(t.tick0=0,n=1,t.dtick=dt(e,n,pt)):(t.tick0=0,n=a(10),t.dtick=dt(e,n,st));if(0===t.dtick&&(t.dtick=1),!i(t.dtick)&&\"string\"!=typeof t.dtick){var h=t.dtick;throw t.dtick=1,\"ax.dtick error: \"+String(h)}},q.tickIncrement=function(t,e,r,a){var o=r?-1:1;if(i(e))return s.increment(t,o*e);var l=e.charAt(0),u=o*Number(e.substr(1));if(\"M\"===l)return s.incrementMonth(t,u,a);if(\"L\"===l)return Math.log(Math.pow(10,t)+u)/Math.LN10;if(\"D\"===l){var c=\"D2\"===e?ht:ft,f=t+.01*o,h=s.roundUp(s.mod(f,1),c,r);return Math.floor(f)+Math.log(n.round(Math.pow(10,h),1))/Math.LN10}throw\"unrecognized dtick 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a,o=gt(t,e),l=\"array\"===t.tickmode,u=r||l,c=t.type,f=\"category\"===c?t.d2l_noadd:t.d2l;if(l&&Array.isArray(t.ticktext)){var h=s.simpleMap(t.range,t.r2l),p=(Math.abs(h[1]-h[0])-(t._lBreaks||0))/1e4;for(a=0;a<t.ticktext.length&&!(Math.abs(e-f(t.tickvals[a]))<p);a++);if(a<t.ticktext.length)return o.text=String(t.ticktext[a]),o}function d(n){if(void 0===n)return!0;if(r)return\"none\"===n;var i={first:t._tmin,last:t._tmax}[n];return\"all\"!==n&&e!==i}var v=r?\"never\":\"none\"!==t.exponentformat&&d(t.showexponent)?\"hide\":\"\";if(\"date\"===c?function(t,e,r,n){var a=t._tickround,o=r&&t.hoverformat||q.getTickFormat(t);(n=!o&&n)&&(a=i(a)?4:{y:\"m\",m:\"d\",d:\"M\",M:\"S\",S:4}[a]);var l,u=s.formatDate(e.x,o,a,t._dateFormat,t.calendar,t._extraFormat),c=u.indexOf(\"\\n\");if(-1!==c&&(l=u.substr(c+1),u=u.substr(0,c)),n&&(void 0===l||\"00:00:00\"!==u&&\"00:00\"!==u?8===u.length&&(u=u.replace(/:00$/,\"\")):(u=l,l=\"\")),l)if(r)\"d\"===a?u+=\", \"+l:u=l+(u?\", \"+u:\"\");else if(t._inCalcTicks&&t._prevDateHead===l){var f=Rt(t),h=t._trueSide||t.side;(!f&&\"top\"===h||f&&\"bottom\"===h)&&(u+=\"<br> \")}else t._prevDateHead=l,u+=\"<br>\"+l;e.text=u}(t,o,r,u):\"log\"===c?function(t,e,r,n,a){var o=t.dtick,l=e.x,u=t.tickformat,c=\"string\"==typeof o&&o.charAt(0);if(\"never\"===a&&(a=\"\"),n&&\"L\"!==c&&(o=\"L3\",c=\"L\"),u||\"L\"===c)e.text=bt(Math.pow(10,l),t,a,n);else if(i(o)||\"D\"===c&&s.mod(l+.01,1)<.1){var f=Math.round(l),h=Math.abs(f),p=t.exponentformat;\"power\"===p||mt(p)&&xt(f)?(e.text=0===f?1:1===f?\"10\":\"10<sup>\"+(f>1?\"\":P)+h+\"</sup>\",e.fontSize*=1.25):(\"e\"===p||\"E\"===p)&&h>2?e.text=\"1\"+p+(f>0?\"+\":P)+h:(e.text=bt(Math.pow(10,l),t,\"\",\"fakehover\"),\"D1\"===o&&\"y\"===t._id.charAt(0)&&(e.dy-=e.fontSize/6))}else{if(\"D\"!==c)throw\"unrecognized dtick \"+String(o);e.text=String(Math.round(Math.pow(10,s.mod(l,1)))),e.fontSize*=.75}if(\"D1\"===t.dtick){var d=String(e.text).charAt(0);\"0\"!==d&&\"1\"!==d||(\"y\"===t._id.charAt(0)?e.dx-=e.fontSize/4:(e.dy+=e.fontSize/2,e.dx+=(t.range[1]>t.range[0]?1:-1)*e.fontSize*(l<0?.5:.25)))}}(t,o,0,u,v):\"category\"===c?function(t,e){var r=t._categories[Math.round(e.x)];void 0===r&&(r=\"\"),e.text=String(r)}(t,o):\"multicategory\"===c?function(t,e,r){var n=Math.round(e.x),i=t._categories[n]||[],a=void 0===i[1]?\"\":String(i[1]),o=void 0===i[0]?\"\":String(i[0]);r?e.text=o+\" - \"+a:(e.text=a,e.text2=o)}(t,o,r):Dt(t)?function(t,e,r,n,i){if(\"radians\"!==t.thetaunit||r)e.text=bt(e.x,t,i,n);else{var a=e.x/180;if(0===a)e.text=\"0\";else{var o=function(t){function e(t,e){return Math.abs(t-e)<=1e-6}var r=function(t){for(var r=1;!e(Math.round(t*r)/r,t);)r*=10;return r}(t),n=t*r,i=Math.abs(function t(r,n){return e(n,0)?r:t(n,r%n)}(n,r));return[Math.round(n/i),Math.round(r/i)]}(a);if(o[1]>=100)e.text=bt(s.deg2rad(e.x),t,i,n);else{var 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z=0;z<e.rangebreaks.length;z++)if(e.rangebreaks[z].pattern===y){e._hasDayOfWeekBreaks=!0;break}if(g(e,m),m._has(\"scattergl\")||m._has(\"splom\"))for(var R=0;R<n.data.length;R++){var F=n.data[R];\"scattergl\"!==F.type&&\"splom\"!==F.type||(F.visible=!1,a.warn(F.type+\" traces do not work on axes with rangebreaks. 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k=l._inputDomain,A=(l.domain[1]-l.domain[0])/(k[1]-k[0]),M=(l.r2l(l.range[1])-l.r2l(l.range[0]))/(l.r2l(l._inputRange[1])-l.r2l(l._inputRange[0]));if((h/=A)*M<1){l.domain=l._input.domain=k.slice(),s(l,h);continue}if(M<1&&(l.range=l._input.range=l._inputRange.slice(),h*=M),l.autorange){var S=l.r2l(l.range[0]),E=l.r2l(l.range[1]),L=(S+E)/2,C=L,P=L,O=Math.abs(E-L),I=L-O*h*1.0001,D=L+O*h*1.0001,z=i.makePadFn(p,l,0),R=i.makePadFn(p,l,1);y(l,h);var F,B,N=Math.abs(l._m),j=i.concatExtremes(t,l),U=j.min,V=j.max;for(B=0;B<U.length;B++)(F=U[B].val-z(U[B])/N)>I&&F<C&&(C=F);for(B=0;B<V.length;B++)(F=V[B].val+R(V[B])/N)<D&&F>P&&(P=F);h/=(P-C)/(2*O),C=l.l2r(C),P=l.l2r(P),l.range=l._input.range=S<E?[C,P]:[P,C]}y(l,h)}}},e.getAxisGroup=function(t,e){for(var r=t._axisMatchGroups,n=0;n<r.length;n++)if(r[n][e])return\"g\"+n;return e},e.clean=function(t,e){if(e._inputDomain){for(var 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t.append(\"path\").attr(\"class\",\"zoombox\").style({fill:e>.2?\"rgba(0,0,0,0)\":\"rgba(255,255,255,0)\",\"stroke-width\":0}).attr(\"transform\",u(r,n)).attr(\"d\",i+\"Z\")}function j(t,e,r){return t.append(\"path\").attr(\"class\",\"zoombox-corners\").style({fill:f.background,stroke:f.defaultLine,\"stroke-width\":1,opacity:0}).attr(\"transform\",u(e,r)).attr(\"d\",\"M0,0Z\")}function U(t,e,r,n,i,a){t.attr(\"d\",n+\"M\"+r.l+\",\"+r.t+\"v\"+r.h+\"h\"+r.w+\"v-\"+r.h+\"h-\"+r.w+\"Z\"),V(t,e,i,a)}function V(t,e,r,n){r||(t.transition().style(\"fill\",n>.2?\"rgba(0,0,0,0.4)\":\"rgba(255,255,255,0.3)\").duration(200),e.transition().style(\"opacity\",1).duration(200))}function H(t){n.select(t).selectAll(\".zoombox,.js-zoombox-backdrop,.js-zoombox-menu,.zoombox-corners\").remove()}function q(t){O&&t.data&&t._context.showTips&&(i.notifier(i._(t,\"Double-click to zoom back out\"),\"long\"),O=!1)}function G(t){var e=Math.floor(Math.min(t.b-t.t,t.r-t.l,P)/2);return\"M\"+(t.l-3.5)+\",\"+(t.t-.5+e)+\"h3v\"+-e+\"h\"+e+\"v-3h-\"+(e+3)+\"ZM\"+(t.r+3.5)+\",\"+(t.t-.5+e)+\"h-3v\"+-e+\"h\"+-e+\"v-3h\"+(e+3)+\"ZM\"+(t.r+3.5)+\",\"+(t.b+.5-e)+\"h-3v\"+e+\"h\"+-e+\"v3h\"+(e+3)+\"ZM\"+(t.l-3.5)+\",\"+(t.b+.5-e)+\"h3v\"+e+\"h\"+e+\"v3h-\"+(e+3)+\"Z\"}function Z(t,e,r,n,a){for(var o,s,l,u,c=!1,f={},h={},p=(a||{}).xaHash,d=(a||{}).yaHash,v=0;v<e.length;v++){var g=e[v];for(o in r)if(g[o]){for(l in g)a&&(p[l]||d[l])||(\"x\"===l.charAt(0)?r:n)[l]||(f[l]=o);for(s in n)a&&(p[s]||d[s])||!g[s]||(c=!0)}for(s in n)if(g[s])for(u in g)a&&(p[u]||d[u])||(\"x\"===u.charAt(0)?r:n)[u]||(h[u]=s)}c&&(i.extendFlat(f,h),h={});var y={},m=[];for(l in f){var x=k(t,l);m.push(x),y[x._id]=x}var b={},_=[];for(u in h){var w=k(t,u);_.push(w),b[w._id]=w}return{xaHash:y,yaHash:b,xaxes:m,yaxes:_,xLinks:f,yLinks:h,isSubplotConstrained:c}}function Y(t,e){if(s){var r=void 0!==t.onwheel?\"wheel\":\"mousewheel\";t._onwheel&&t.removeEventListener(r,t._onwheel),t._onwheel=e,t.addEventListener(r,e,{passive:!1})}else void 0!==t.onwheel?t.onwheel=e:void 0!==t.onmousewheel?t.onmousewheel=e:t.isAddedWheelEvent||(t.isAddedWheelEvent=!0,t.addEventListener(\"wheel\",e,{passive:!1}))}function W(t){var e=[];for(var r in t)e.push(t[r]);return e}t.exports={makeDragBox:function(t,e,r,s,u,f,v,y){var O,I,V,X,J,K,$,Q,tt,et,rt,nt,it,at,ot,st,lt,ut,ct,ft,ht,pt,dt,vt=t._fullLayout._zoomlayer,gt=v+y===\"nsew\",yt=1===(v+y).length;function mt(){if(O=e.xaxis,I=e.yaxis,tt=O._length,et=I._length,$=O._offset,Q=I._offset,(V={})[O._id]=O,(X={})[I._id]=I,v&&y)for(var r=e.overlays,n=0;n<r.length;n++){var i=r[n].xaxis;V[i._id]=i;var a=r[n].yaxis;X[a._id]=a}J=W(V),K=W(X),it=z(J,y),at=z(K,v),ot=!at&&!it,nt=Z(t,t._fullLayout._axisMatchGroups,V,X);var o=(rt=Z(t,t._fullLayout._axisConstraintGroups,V,X,nt)).isSubplotConstrained||nt.isSubplotConstrained;st=y||o,lt=v||o;var s=t._fullLayout;ut=s._has(\"scattergl\"),ct=s._has(\"splom\"),ft=s._has(\"svg\")}r+=e.yaxis._shift,mt();var xt=function(t,e,r){return t?\"nsew\"===t?r?\"\":\"pan\"===e?\"move\":\"crosshair\":t.toLowerCase()+\"-resize\":\"pointer\"}(at+it,t._fullLayout.dragmode,gt),bt=D(e,v+y+\"drag\",xt,r,s,u,f);if(ot&&!gt)return bt.onmousedown=null,bt.style.pointerEvents=\"none\",bt;var _t,wt,Tt,kt,At,Mt,St,Et,Lt,Ct,Pt={element:bt,gd:t,plotinfo:e};function Ot(){Pt.plotinfo.selection=!1,M(t)}function It(t,r){var i=Pt.gd;if(i._fullLayout._activeShapeIndex>=0)i._fullLayout._deactivateShape(i);else{var o=i._fullLayout.clickmode;if(H(i),2!==t||yt||qt(),gt)o.indexOf(\"select\")>-1&&S(r,i,J,K,e.id,Pt),o.indexOf(\"event\")>-1&&p.click(i,r,e.id);else if(1===t&&yt){var s=v?I:O,u=\"s\"===v||\"w\"===y?0:1,f=s._name+\".range[\"+u+\"]\",h=function(t,e){var r,n=t.range[e],i=Math.abs(n-t.range[1-e]);return\"date\"===t.type?n:\"log\"===t.type?(r=Math.ceil(Math.max(0,-Math.log(i)/Math.LN10))+3,a(\".\"+r+\"g\")(Math.pow(10,n))):(r=Math.floor(Math.log(Math.abs(n))/Math.LN10)-Math.floor(Math.log(i)/Math.LN10)+4,a(\".\"+String(r)+\"g\")(n))}(s,u),d=\"left\",g=\"middle\";if(s.fixedrange)return;v?(g=\"n\"===v?\"top\":\"bottom\",\"right\"===s.side&&(d=\"right\")):\"e\"===y&&(d=\"right\"),i._context.showAxisRangeEntryBoxes&&n.select(bt).call(c.makeEditable,{gd:i,immediate:!0,background:i._fullLayout.paper_bgcolor,text:String(h),fill:s.tickfont?s.tickfont.color:\"#444\",horizontalAlign:d,verticalAlign:g}).on(\"edit\",(function(t){var e=s.d2r(t);void 0!==e&&l.call(\"_guiRelayout\",i,f,e)}))}}}function Dt(e,r){if(t._transitioningWithDuration)return!1;var n=Math.max(0,Math.min(tt,pt*e+_t)),i=Math.max(0,Math.min(et,dt*r+wt)),a=Math.abs(n-_t),o=Math.abs(i-wt);function s(){St=\"\",Tt.r=Tt.l,Tt.t=Tt.b,Lt.attr(\"d\",\"M0,0Z\")}if(Tt.l=Math.min(_t,n),Tt.r=Math.max(_t,n),Tt.t=Math.min(wt,i),Tt.b=Math.max(wt,i),rt.isSubplotConstrained)a>P||o>P?(St=\"xy\",a/tt>o/et?(o=a*et/tt,wt>i?Tt.t=wt-o:Tt.b=wt+o):(a=o*tt/et,_t>n?Tt.l=_t-a:Tt.r=_t+a),Lt.attr(\"d\",G(Tt))):s();else if(nt.isSubplotConstrained)if(a>P||o>P){St=\"xy\";var l=Math.min(Tt.l/tt,(et-Tt.b)/et),u=Math.max(Tt.r/tt,(et-Tt.t)/et);Tt.l=l*tt,Tt.r=u*tt,Tt.b=(1-l)*et,Tt.t=(1-u)*et,Lt.attr(\"d\",G(Tt))}else 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a=Pt.dragmode,s=t._fullLayout.dragmode;s!==a&&(Pt.dragmode=s),mt(),pt=t._fullLayout._invScaleX,dt=t._fullLayout._invScaleY,ot||(gt?e.shiftKey?\"pan\"===s?s=\"zoom\":m(s)||(s=\"pan\"):e.ctrlKey&&(s=\"pan\"):s=\"pan\"),x(s)?Pt.minDrag=1:Pt.minDrag=void 0,m(s)?(Pt.xaxes=J,Pt.yaxes=K,A(e,r,n,Pt,s)):(Pt.clickFn=It,m(a)&&Ot(),ot||(\"zoom\"===s?(Pt.moveFn=Dt,Pt.doneFn=Rt,Pt.minDrag=1,function(e,r,n){var a=bt.getBoundingClientRect();_t=r-a.left,wt=n-a.top,t._fullLayout._calcInverseTransform(t);var s=i.apply3DTransform(t._fullLayout._invTransform)(_t,wt);_t=s[0],wt=s[1],Tt={l:_t,r:_t,w:0,t:wt,b:wt,h:0},kt=t._hmpixcount?t._hmlumcount/t._hmpixcount:o(t._fullLayout.plot_bgcolor).getLuminance(),Mt=!1,St=\"xy\",Ct=!1,Et=N(vt,kt,$,Q,At=\"M0,0H\"+tt+\"V\"+et+\"H0V0\"),Lt=j(vt,$,Q)}(0,r,n)):\"pan\"===s&&(Pt.moveFn=Ut,Pt.doneFn=Gt))),t._fullLayout._redrag=function(){var e=t._dragdata;if(e&&e.element===bt){var r=t._fullLayout.dragmode;m(r)||(mt(),Zt([0,0,tt,et]),Pt.moveFn(e.dx,e.dy))}}},g.init(Pt);var Ft=[0,0,tt,et],Bt=null,Nt=L.REDRAWDELAY,jt=e.mainplot?t._fullLayout._plots[e.mainplot]:e;function Ut(e,r){if(e*=pt,r*=dt,!t._transitioningWithDuration){if(t._fullLayout._replotting=!0,\"ew\"===it||\"ns\"===at){var n=it?-e:0,i=at?-r:0;if(nt.isSubplotConstrained){if(it&&at){var a=(e/tt-r/et)/2;n=-(e=a*tt),i=-(r=-a*et)}at?n=-i*tt/et:i=-n*et/tt}return it&&(F(J,e),Vt(\"x\")),at&&(F(K,r),Vt(\"y\")),Zt([n,i,tt,et]),Ht(),void t.emit(\"plotly_relayouting\",ht)}var o,s,l=\"w\"===it==(\"n\"===at)?1:-1;if(it&&at&&(rt.isSubplotConstrained||nt.isSubplotConstrained)){var u=(e/tt+l*r/et)/2;e=u*tt,r=l*u*et}if(\"w\"===it?e=p(J,0,e):\"e\"===it?e=p(J,1,-e):it||(e=0),\"n\"===at?r=p(K,1,r):\"s\"===at?r=p(K,0,-r):at||(r=0),o=\"w\"===it?e:0,s=\"n\"===at?r:0,rt.isSubplotConstrained&&!nt.isSubplotConstrained||nt.isSubplotConstrained&&it&&at&&l>0){var c;if(nt.isSubplotConstrained||!it&&1===at.length){for(c=0;c<J.length;c++)J[c].range=J[c]._r.slice(),E(J[c],1-r/et);o=(e=r*tt/et)/2}if(nt.isSubplotConstrained||!at&&1===it.length){for(c=0;c<K.length;c++)K[c].range=K[c]._r.slice(),E(K[c],1-e/tt);s=(r=e*et/tt)/2}}nt.isSubplotConstrained&&at||Vt(\"x\"),nt.isSubplotConstrained&&it||Vt(\"y\");var f=tt-e,h=et-r;!nt.isSubplotConstrained||it&&at||(it?(s=o?0:e*et/tt,h=f*et/tt):(o=s?0:r*tt/et,f=h*tt/et)),Zt([o,s,f,h]),Ht(),t.emit(\"plotly_relayouting\",ht)}function p(t,e,r){for(var n,i,a=1-e,o=0;o<t.length;o++){var s=t[o];if(!s.fixedrange){n=s,i=s._rl[a]+(s._rl[e]-s._rl[a])/B(r/s._length);var l=s.l2r(i);!1!==l&&void 0!==l&&(s.range[e]=l)}}return n._length*(n._rl[e]-i)/(n._rl[e]-n._rl[a])}}function Vt(t,e){for(var r=nt.isSubplotConstrained?{x:K,y:J}[t]:nt[t+\"axes\"],n=nt.isSubplotConstrained?{x:J,y:K}[t]:[],i=0;i<r.length;i++){var a=r[i],o=a._id,s=nt.xLinks[o]||nt.yLinks[o],l=n[0]||V[s]||X[s];l&&(e?(e[a._name+\".range[0]\"]=e[l._name+\".range[0]\"],e[a._name+\".range[1]\"]=e[l._name+\".range[1]\"]):a.range=l.range.slice())}}function Ht(){var r,n=[];function i(t){for(r=0;r<t.length;r++)t[r].fixedrange||n.push(t[r]._id)}function a(t,e){for(r=0;r<t.length;r++){var i=t[r],a=i[e];i.fixedrange||\"sync\"!==a.tickmode||n.push(a._id)}}for(st&&(i(J),i(rt.xaxes),i(nt.xaxes),a(e.overlays,\"xaxis\")),lt&&(i(K),i(rt.yaxes),i(nt.yaxes),a(e.overlays,\"yaxis\")),ht={},r=0;r<n.length;r++){var o=n[r],s=k(t,o);d.drawOne(t,s,{skipTitle:!0}),ht[s._name+\".range[0]\"]=s.range[0],ht[s._name+\".range[1]\"]=s.range[1]}d.redrawComponents(t,n)}function qt(){if(!t._transitioningWithDuration){var e=t._context.doubleClick,r=[];it&&(r=r.concat(J)),at&&(r=r.concat(K)),nt.xaxes&&(r=r.concat(nt.xaxes)),nt.yaxes&&(r=r.concat(nt.yaxes));var n,i,a={};if(\"reset+autosize\"===e)for(e=\"autosize\",i=0;i<r.length;i++){var o=(n=r[i])._rangeInitial0,s=n._rangeInitial1,u=void 0!==o||void 0!==s;if(u&&(void 0!==o&&o!==n.range[0]||void 0!==s&&s!==n.range[1])||!u&&!0!==n.autorange){e=\"reset\";break}}if(\"autosize\"===e)for(i=0;i<r.length;i++)(n=r[i]).fixedrange||(a[n._name+\".autorange\"]=!0);else if(\"reset\"===e)for((it||rt.isSubplotConstrained)&&(r=r.concat(rt.xaxes)),at&&!rt.isSubplotConstrained&&(r=r.concat(rt.yaxes)),rt.isSubplotConstrained&&(it?at||(r=r.concat(K)):r=r.concat(J)),i=0;i<r.length;i++)if(!(n=r[i]).fixedrange){var c=n._name,f=n._autorangeInitial;void 0===n._rangeInitial0&&void 0===n._rangeInitial1?a[c+\".autorange\"]=!0:void 0===n._rangeInitial0?(a[c+\".autorange\"]=f,a[c+\".range\"]=[null,n._rangeInitial1]):void 0===n._rangeInitial1?(a[c+\".range\"]=[n._rangeInitial0,null],a[c+\".autorange\"]=f):a[c+\".range\"]=[n._rangeInitial0,n._rangeInitial1]}t.emit(\"plotly_doubleclick\",null),l.call(\"_guiRelayout\",t,a)}}function Gt(){Zt([0,0,tt,et]),i.syncOrAsync([T.previousPromises,function(){t._fullLayout._replotting=!1,l.call(\"_guiRelayout\",t,ht)}],t)}function Zt(e){var r,n,a,o,s=t._fullLayout,u=s._plots,c=s._subplots.cartesian;if(ct&&l.subplotsRegistry.splom.drag(t),ut)for(r=0;r<c.length;r++)if(a=(n=u[c[r]]).xaxis,o=n.yaxis,n._scene){var f=i.simpleMap(a.range,a.r2l),p=i.simpleMap(o.range,o.r2l);a.limitRange&&a.limitRange(),o.limitRange&&o.limitRange(),f=a.range,p=o.range,n._scene.update({range:[f[0],p[0],f[1],p[1]]})}if((ct||ut)&&(_(t),w(t)),ft){var d=e[2]/O._length,g=e[3]/I._length;for(r=0;r<c.length;r++){a=(n=u[c[r]]).xaxis,o=n.yaxis;var m,x,b,T,k=(st||nt.isSubplotConstrained)&&!a.fixedrange&&V[a._id],A=(lt||nt.isSubplotConstrained)&&!o.fixedrange&&X[o._id];if(k?(m=d,b=y||nt.isSubplotConstrained?e[0]:Xt(a,m)):nt.xaHash[a._id]?(m=d,b=e[0]*a._length/O._length):nt.yaHash[a._id]?(m=g,b=\"ns\"===at?-e[1]*a._length/I._length:Xt(a,m,{n:\"top\",s:\"bottom\"}[at])):b=Wt(a,m=Yt(a,d,g)),m>1&&(void 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r=function(t,e){return[t[1]*e[2]-t[2]*e[1],t[2]*e[0]-t[0]*e[2],t[0]*e[1]-t[1]*e[0]]}(t,e),n=Math.sqrt(x(r,r)),i=.5*Math.acos(Math.max(-1,Math.min(1,x(t,e)))),a=Math.sin(i)/n;return n&&[Math.cos(i),r[2]*a,-r[1]*a,r[0]*a]}}(r,_),T=function(t){return[Math.atan2(2*(t[0]*t[1]+t[2]*t[3]),1-2*(t[1]*t[1]+t[2]*t[2]))*s,Math.asin(Math.max(-1,Math.min(1,2*(t[0]*t[2]-t[3]*t[1]))))*s,Math.atan2(2*(t[0]*t[3]+t[1]*t[2]),1-2*(t[2]*t[2]+t[3]*t[3]))*s]}((o=(t=C)[0],l=t[1],u=t[2],c=t[3],[o*(f=(a=w)[0])-l*(p=a[1])-u*(d=a[2])-c*(y=a[3]),o*p+l*f+u*y-c*d,o*d-l*y+u*f+c*p,o*y+l*d-u*p+c*f])),k=i.r=function(t,e,r){var n=m(e,2,t[0]);n=m(n,1,t[1]),n=m(n,0,t[2]-r[2]);var i,a,o=e[0],l=e[1],u=e[2],c=n[0],f=n[1],h=n[2],p=Math.atan2(l,o)*s,d=Math.sqrt(o*o+l*l);Math.abs(f)>d?(a=(f>0?90:-90)-p,i=0):(a=Math.asin(f/d)*s-p,i=Math.sqrt(d*d-f*f));var v=180-a-2*p,y=(Math.atan2(h,c)-Math.atan2(u,i))*s,x=(Math.atan2(h,c)-Math.atan2(u,-i))*s;return g(r[0],r[1],a,y)<=g(r[0],r[1],v,x)?[a,y,r[2]]:[v,x,r[2]]}(T,r,E);isFinite(k[0])&&isFinite(k[1])&&isFinite(k[2])||(k=E),e.rotate(k),E=k}}else r=v(e,M=b);h.of(this,arguments)({type:\"zoom\"})})),A=h.of(this,arguments),p++||A({type:\"zoomstart\"})})).on(\"zoomend\",(function(){var r;n.select(this).style(u),d.call(a,\"zoom\",null),r=h.of(this,arguments),--p||r({type:\"zoomend\"}),f(t,e,y)})).on(\"zoom.redraw\",(function(){t.render(!0);var r=e.rotate();t.graphDiv.emit(\"plotly_relayouting\",{\"geo.projection.scale\":e.scale()/t.fitScale,\"geo.projection.rotation.lon\":-r[0],\"geo.projection.rotation.lat\":-r[1]})})),n.rebind(a,h,\"on\")}function v(t,e){var r=t.invert(e);return r&&isFinite(r[0])&&isFinite(r[1])&&function(t){var e=t[0]*o,r=t[1]*o,n=Math.cos(r);return[n*Math.cos(e),n*Math.sin(e),Math.sin(r)]}(r)}function g(t,e,r,n){var i=y(r-t),a=y(n-e);return Math.sqrt(i*i+a*a)}function y(t){return(t%360+540)%360-180}function m(t,e,r){var n=r*o,i=t.slice(),a=0===e?1:0,s=2===e?1:2,l=Math.cos(n),u=Math.sin(n);return i[a]=t[a]*l-t[s]*u,i[s]=t[s]*l+t[a]*u,i}function x(t,e){for(var r=0,n=0,i=t.length;n<i;++n)r+=t[n]*e[n];return r}t.exports=function(t,e){var r=t.projection;return(e._isScoped?h:e._isClipped?d:p)(t,r)}},27659:function(t,e,r){\"use strict\";var n=r(73972),i=r(85555).SUBPLOT_PATTERN;e.AU=function(t,e,r){var i=n.subplotsRegistry[e];if(!i)return[];for(var a=i.attr,o=[],s=0;s<t.length;s++){var l=t[s];l[0].trace[a]===r&&o.push(l)}return o},e.a0=function(t,e){var r,i=[],a=[];if(!(r=\"string\"==typeof e?n.getModule(e).plot:\"function\"==typeof e?e:e.plot))return[i,t];for(var o=0;o<t.length;o++){var s=t[o],l=s[0].trace;!0===l.visible&&0!==l._length&&(l._module&&l._module.plot===r?i.push(s):a.push(s))}return[i,a]},e.NG=function(t,e,r){if(!n.subplotsRegistry[e])return[];var a,o,s,l=n.subplotsRegistry[e].attr,u=[];if(\"gl2d\"===e){var c=r.match(i);o=\"x\"+c[1],s=\"y\"+c[2]}for(var f=0;f<t.length;f++)a=t[f],\"gl2d\"===e&&n.traceIs(a,\"gl2d\")?a[l[0]]===o&&a[l[1]]===s&&u.push(a):a[l]===r&&u.push(a);return u}},75071:function(t,e,r){\"use strict\";var n=r(16825),i=r(1195),a=r(48956),o=r(85555),s=r(38520);function l(t,e){this.element=t,this.plot=e,this.mouseListener=null,this.wheelListener=null,this.lastInputTime=Date.now(),this.lastPos=[0,0],this.boxEnabled=!1,this.boxInited=!1,this.boxStart=[0,0],this.boxEnd=[0,0],this.dragStart=[0,0]}t.exports=function(t){var e=t.mouseContainer,r=t.glplot,u=new l(e,r);function c(){t.xaxis.autorange=!1,t.yaxis.autorange=!1}function f(e,n,i){var a,s,l=t.calcDataBox(),f=r.viewBox,h=u.lastPos[0],p=u.lastPos[1],d=o.MINDRAG*r.pixelRatio,v=o.MINZOOM*r.pixelRatio;function g(e,r,n){var i=Math.min(r,n),a=Math.max(r,n);i!==a?(l[e]=i,l[e+2]=a,u.dataBox=l,t.setRanges(l)):(t.selectBox.selectBox=[0,0,1,1],t.glplot.setDirty())}switch(n*=r.pixelRatio,i*=r.pixelRatio,i=f[3]-f[1]-i,t.fullLayout.dragmode){case\"zoom\":if(e){var y=n/(f[2]-f[0])*(l[2]-l[0])+l[0],m=i/(f[3]-f[1])*(l[3]-l[1])+l[1];u.boxInited||(u.boxStart[0]=y,u.boxStart[1]=m,u.dragStart[0]=n,u.dragStart[1]=i),u.boxEnd[0]=y,u.boxEnd[1]=m,u.boxInited=!0,u.boxEnabled||u.boxStart[0]===u.boxEnd[0]&&u.boxStart[1]===u.boxEnd[1]||(u.boxEnabled=!0);var x=Math.abs(u.dragStart[0]-n)<v,b=Math.abs(u.dragStart[1]-i)<v;if(!function(){for(var e=t.graphDiv._fullLayout._axisConstraintGroups,r=t.xaxis._id,n=t.yaxis._id,i=0;i<e.length;i++)if(-1!==e[i][r]){if(-1!==e[i][n])return!0;break}return!1}()||x&&b)x&&(u.boxEnd[0]=u.boxStart[0]),b&&(u.boxEnd[1]=u.boxStart[1]);else{a=u.boxEnd[0]-u.boxStart[0],s=u.boxEnd[1]-u.boxStart[1];var _=(l[3]-l[1])/(l[2]-l[0]);Math.abs(a*_)>Math.abs(s)?(u.boxEnd[1]=u.boxStart[1]+Math.abs(a)*_*(s>=0?1:-1),u.boxEnd[1]<l[1]?(u.boxEnd[1]=l[1],u.boxEnd[0]=u.boxStart[0]+(l[1]-u.boxStart[1])/Math.abs(_)):u.boxEnd[1]>l[3]&&(u.boxEnd[1]=l[3],u.boxEnd[0]=u.boxStart[0]+(l[3]-u.boxStart[1])/Math.abs(_))):(u.boxEnd[0]=u.boxStart[0]+Math.abs(s)/_*(a>=0?1:-1),u.boxEnd[0]<l[0]?(u.boxEnd[0]=l[0],u.boxEnd[1]=u.boxStart[1]+(l[0]-u.boxStart[0])*Math.abs(_)):u.boxEnd[0]>l[2]&&(u.boxEnd[0]=l[2],u.boxEnd[1]=u.boxStart[1]+(l[2]-u.boxStart[0])*Math.abs(_)))}}else u.boxEnabled?(a=u.boxStart[0]!==u.boxEnd[0],s=u.boxStart[1]!==u.boxEnd[1],a||s?(a&&(g(0,u.boxStart[0],u.boxEnd[0]),t.xaxis.autorange=!1),s&&(g(1,u.boxStart[1],u.boxEnd[1]),t.yaxis.autorange=!1),t.relayoutCallback()):t.glplot.setDirty(),u.boxEnabled=!1,u.boxInited=!1):u.boxInited&&(u.boxInited=!1);break;case\"pan\":u.boxEnabled=!1,u.boxInited=!1,e?(u.panning||(u.dragStart[0]=n,u.dragStart[1]=i),Math.abs(u.dragStart[0]-n)<d&&(n=u.dragStart[0]),Math.abs(u.dragStart[1]-i)<d&&(i=u.dragStart[1]),a=(h-n)*(l[2]-l[0])/(r.viewBox[2]-r.viewBox[0]),s=(p-i)*(l[3]-l[1])/(r.viewBox[3]-r.viewBox[1]),l[0]+=a,l[2]+=a,l[1]+=s,l[3]+=s,t.setRanges(l),u.panning=!0,u.lastInputTime=Date.now(),c(),t.cameraChanged(),t.handleAnnotations()):u.panning&&(u.panning=!1,t.relayoutCallback())}u.lastPos[0]=n,u.lastPos[1]=i}return u.mouseListener=n(e,f),e.addEventListener(\"touchstart\",(function(t){var r=a(t.changedTouches[0],e);f(0,r[0],r[1]),f(1,r[0],r[1]),t.preventDefault()}),!!s&&{passive:!1}),e.addEventListener(\"touchmove\",(function(t){t.preventDefault();var r=a(t.changedTouches[0],e);f(1,r[0],r[1]),t.preventDefault()}),!!s&&{passive:!1}),e.addEventListener(\"touchend\",(function(t){f(0,u.lastPos[0],u.lastPos[1]),t.preventDefault()}),!!s&&{passive:!1}),u.wheelListener=i(e,(function(e,n){if(!t.scrollZoom)return!1;var i=t.calcDataBox(),a=r.viewBox,o=u.lastPos[0],s=u.lastPos[1],l=Math.exp(5*n/(a[3]-a[1])),f=o/(a[2]-a[0])*(i[2]-i[0])+i[0],h=s/(a[3]-a[1])*(i[3]-i[1])+i[1];return i[0]=(i[0]-f)*l+f,i[2]=(i[2]-f)*l+f,i[1]=(i[1]-h)*l+h,i[3]=(i[3]-h)*l+h,t.setRanges(i),u.lastInputTime=Date.now(),c(),t.cameraChanged(),t.handleAnnotations(),t.relayoutCallback(),!0}),!0),u}},82961:function(t,e,r){\"use strict\";var n=r(89298),i=r(78614);function a(t){this.scene=t,this.gl=t.gl,this.pixelRatio=t.pixelRatio,this.screenBox=[0,0,1,1],this.viewBox=[0,0,1,1],this.dataBox=[-1,-1,1,1],this.borderLineEnable=[!1,!1,!1,!1],this.borderLineWidth=[1,1,1,1],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.ticks=[[],[]],this.tickEnable=[!0,!0,!1,!1],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labels=[\"x\",\"y\"],this.labelEnable=[!0,!0,!1,!1],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelPad=[15,15,15,15],this.labelSize=[12,12],this.labelFont=[\"sans-serif\",\"sans-serif\"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.title=\"\",this.titleEnable=!0,this.titleCenter=[0,0,0,0],this.titleAngle=0,this.titleColor=[0,0,0,1],this.titleFont=\"sans-serif\",this.titleSize=18,this.gridLineEnable=[!0,!0],this.gridLineColor=[[0,0,0,.5],[0,0,0,.5]],this.gridLineWidth=[1,1],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[1,1],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.static=this.scene.staticPlot}var o=a.prototype,s=[\"xaxis\",\"yaxis\"];o.merge=function(t){var e,r,n,a,o,l,u,c,f,h,p;for(this.titleEnable=!1,this.backgroundColor=i(t.plot_bgcolor),h=0;h<2;++h){var d=(e=s[h]).charAt(0);for(n=(r=t[this.scene[e]._name]).title.text===this.scene.fullLayout._dfltTitle[d]?\"\":r.title.text,p=0;p<=2;p+=2)this.labelEnable[h+p]=!1,this.labels[h+p]=n,this.labelColor[h+p]=i(r.title.font.color),this.labelFont[h+p]=r.title.font.family,this.labelSize[h+p]=r.title.font.size,this.labelPad[h+p]=this.getLabelPad(e,r),this.tickEnable[h+p]=!1,this.tickColor[h+p]=i((r.tickfont||{}).color),this.tickAngle[h+p]=\"auto\"===r.tickangle?0:Math.PI*-r.tickangle/180,this.tickPad[h+p]=this.getTickPad(r),this.tickMarkLength[h+p]=0,this.tickMarkWidth[h+p]=r.tickwidth||0,this.tickMarkColor[h+p]=i(r.tickcolor),this.borderLineEnable[h+p]=!1,this.borderLineColor[h+p]=i(r.linecolor),this.borderLineWidth[h+p]=r.linewidth||0;u=this.hasSharedAxis(r),o=this.hasAxisInDfltPos(e,r)&&!u,l=this.hasAxisInAltrPos(e,r)&&!u,a=r.mirror||!1,c=u?-1!==String(a).indexOf(\"all\"):!!a,f=u?\"allticks\"===a:-1!==String(a).indexOf(\"ticks\"),o?this.labelEnable[h]=!0:l&&(this.labelEnable[h+2]=!0),o?this.tickEnable[h]=r.showticklabels:l&&(this.tickEnable[h+2]=r.showticklabels),(o||c)&&(this.borderLineEnable[h]=r.showline),(l||c)&&(this.borderLineEnable[h+2]=r.showline),(o||f)&&(this.tickMarkLength[h]=this.getTickMarkLength(r)),(l||f)&&(this.tickMarkLength[h+2]=this.getTickMarkLength(r)),this.gridLineEnable[h]=r.showgrid,this.gridLineColor[h]=i(r.gridcolor),this.gridLineWidth[h]=r.gridwidth,this.zeroLineEnable[h]=r.zeroline,this.zeroLineColor[h]=i(r.zerolinecolor),this.zeroLineWidth[h]=r.zerolinewidth}},o.hasSharedAxis=function(t){var e=this.scene,r=e.fullLayout._subplots.gl2d;return 0!==n.findSubplotsWithAxis(r,t).indexOf(e.id)},o.hasAxisInDfltPos=function(t,e){var r=e.side;return\"xaxis\"===t?\"bottom\"===r:\"yaxis\"===t?\"left\"===r:void 0},o.hasAxisInAltrPos=function(t,e){var r=e.side;return\"xaxis\"===t?\"top\"===r:\"yaxis\"===t?\"right\"===r:void 0},o.getLabelPad=function(t,e){var r=1.5,n=e.title.font.size,i=e.showticklabels;return\"xaxis\"===t?\"top\"===e.side?n*(r+(i?1:0))-10:n*(r+(i?.5:0))-10:\"yaxis\"===t?\"right\"===e.side?10+n*(r+(i?1:.5)):10+n*(r+(i?.5:0)):void 0},o.getTickPad=function(t){return\"outside\"===t.ticks?10+t.ticklen:15},o.getTickMarkLength=function(t){if(!t.ticks)return 0;var e=t.ticklen;return\"inside\"===t.ticks?-e:e},t.exports=function(t){return new a(t)}},4796:function(t,e,r){\"use strict\";var n=r(30962).overrideAll,i=r(92918),a=r(10820),o=r(77922),s=r(85555),l=r(93612),u=r(528),c=r(27659).NG;e.name=\"gl2d\",e.attr=[\"xaxis\",\"yaxis\"],e.idRoot=[\"x\",\"y\"],e.idRegex=s.idRegex,e.attrRegex=s.attrRegex,e.attributes=r(89502),e.supplyLayoutDefaults=function(t,e,r){e._has(\"cartesian\")||l.supplyLayoutDefaults(t,e,r)},e.layoutAttrOverrides=n(l.layoutAttributes,\"plot\",\"from-root\"),e.baseLayoutAttrOverrides=n({plot_bgcolor:a.plot_bgcolor,hoverlabel:u.hoverlabel},\"plot\",\"nested\"),e.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots.gl2d,a=0;a<n.length;a++){var o=n[a],s=e._plots[o],l=c(r,\"gl2d\",o),u=s._scene2d;void 0===u&&(u=new i({id:o,graphDiv:t,container:t.querySelector(\".gl-container\"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio},e),s._scene2d=u),u.plot(l,t.calcdata,e,t.layout)}},e.clean=function(t,e,r,n){for(var i=n._subplots.gl2d||[],a=0;a<i.length;a++){var o=i[a],s=n._plots[o];s._scene2d&&0===c(t,\"gl2d\",o).length&&(s._scene2d.destroy(),delete n._plots[o])}l.clean.apply(this,arguments)},e.drawFramework=function(t){t._context.staticPlot||l.drawFramework(t)},e.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++){var i=e._plots[r[n]]._scene2d,a=i.toImage(\"png\");e._glimages.append(\"svg:image\").attr({xmlns:o.svg,\"xlink:href\":a,x:0,y:0,width:\"100%\",height:\"100%\",preserveAspectRatio:\"none\"}),i.destroy()}},e.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++)e._plots[r[n]]._scene2d.updateFx(e.dragmode)}},92918:function(t,e,r){\"use strict\";var n,i,a=r(73972),o=r(89298),s=r(30211),l=r(9330).gl_plot2d,u=r(9330).gl_spikes2d,c=r(9330).gl_select_box,f=r(40372),h=r(82961),p=r(75071),d=r(58617),v=r(99082),g=v.enforce,y=v.clean,m=r(71739).doAutoRange,x=r(64505),b=x.drawMode,_=x.selectMode,w=[\"xaxis\",\"yaxis\"],T=r(85555).SUBPLOT_PATTERN;function k(t,e){this.container=t.container,this.graphDiv=t.graphDiv,this.pixelRatio=t.plotGlPixelRatio||window.devicePixelRatio,this.id=t.id,this.staticPlot=!!t.staticPlot,this.scrollZoom=this.graphDiv._context._scrollZoom.cartesian,this.fullData=null,this.updateRefs(e),this.makeFramework(),this.stopped||(this.glplotOptions=h(this),this.glplotOptions.merge(e),this.glplot=l(this.glplotOptions),this.camera=p(this),this.traces={},this.spikes=u(this.glplot),this.selectBox=c(this.glplot,{innerFill:!1,outerFill:!0}),this.lastButtonState=0,this.pickResult=null,this.isMouseOver=!0,this.stopped=!1,this.redraw=this.draw.bind(this),this.redraw())}t.exports=k;var A=k.prototype;A.makeFramework=function(){if(this.staticPlot){if(!(i||(n=document.createElement(\"canvas\"),i=f({canvas:n,preserveDrawingBuffer:!1,premultipliedAlpha:!0,antialias:!0}))))throw new Error(\"Error creating static canvas/context for image server\");this.canvas=n,this.gl=i}else{var t=this.container.querySelector(\".gl-canvas-focus\"),e=f({canvas:t,preserveDrawingBuffer:!0,premultipliedAlpha:!0});if(!e)return d(this),void(this.stopped=!0);this.canvas=t,this.gl=e}var r=this.canvas;r.style.width=\"100%\",r.style.height=\"100%\",r.style.position=\"absolute\",r.style.top=\"0px\",r.style.left=\"0px\",r.style[\"pointer-events\"]=\"none\",this.updateSize(r);var a=this.svgContainer=document.createElementNS(\"http://www.w3.org/2000/svg\",\"svg\");a.style.position=\"absolute\",a.style.top=a.style.left=\"0px\",a.style.width=a.style.height=\"100%\",a.style[\"z-index\"]=20,a.style[\"pointer-events\"]=\"none\";var o=this.mouseContainer=document.createElement(\"div\");o.style.position=\"absolute\",o.style[\"pointer-events\"]=\"auto\",this.pickCanvas=this.container.querySelector(\".gl-canvas-pick\");var s=this.container;s.appendChild(a),s.appendChild(o);var l=this;o.addEventListener(\"mouseout\",(function(){l.isMouseOver=!1,l.unhover()})),o.addEventListener(\"mouseover\",(function(){l.isMouseOver=!0}))},A.toImage=function(t){t||(t=\"png\"),this.stopped=!0,this.staticPlot&&this.container.appendChild(n),this.updateSize(this.canvas);var e=this.glplot.gl,r=e.drawingBufferWidth,i=e.drawingBufferHeight;e.clearColor(1,1,1,0),e.clear(e.COLOR_BUFFER_BIT|e.DEPTH_BUFFER_BIT),this.glplot.setDirty(),this.glplot.draw(),e.bindFramebuffer(e.FRAMEBUFFER,null);var a=new Uint8Array(r*i*4);e.readPixels(0,0,r,i,e.RGBA,e.UNSIGNED_BYTE,a);for(var o=0,s=i-1;o<s;++o,--s)for(var l=0;l<r;++l)for(var u=0;u<4;++u){var c=a[4*(r*o+l)+u];a[4*(r*o+l)+u]=a[4*(r*s+l)+u],a[4*(r*s+l)+u]=c}var f=document.createElement(\"canvas\");f.width=r,f.height=i;var h,p=f.getContext(\"2d\",{willReadFrequently:!0}),d=p.createImageData(r,i);switch(d.data.set(a),p.putImageData(d,0,0),t){case\"jpeg\":h=f.toDataURL(\"image/jpeg\");break;case\"webp\":h=f.toDataURL(\"image/webp\");break;default:h=f.toDataURL(\"image/png\")}return this.staticPlot&&this.container.removeChild(n),h},A.updateSize=function(t){t||(t=this.canvas);var e=this.pixelRatio,r=this.fullLayout,n=r.width,i=r.height,a=0|Math.ceil(e*n),o=0|Math.ceil(e*i);return t.width===a&&t.height===o||(t.width=a,t.height=o),t},A.computeTickMarks=function(){this.xaxis.setScale(),this.yaxis.setScale();for(var t=[o.calcTicks(this.xaxis),o.calcTicks(this.yaxis)],e=0;e<2;++e)for(var r=0;r<t[e].length;++r)t[e][r].text=t[e][r].text+\"\";return t},A.updateRefs=function(t){this.fullLayout=t;var e=this.id.match(T),r=\"xaxis\"+e[1],n=\"yaxis\"+e[2];this.xaxis=this.fullLayout[r],this.yaxis=this.fullLayout[n]},A.relayoutCallback=function(){var t=this.graphDiv,e=this.xaxis,r=this.yaxis,n=t.layout,i={},o=i[e._name+\".range\"]=e.range.slice(),s=i[r._name+\".range\"]=r.range.slice();i[e._name+\".autorange\"]=e.autorange,i[r._name+\".autorange\"]=r.autorange,a.call(\"_storeDirectGUIEdit\",t.layout,t._fullLayout._preGUI,i);var l=n[e._name];l.range=o,l.autorange=e.autorange;var u=n[r._name];u.range=s,u.autorange=r.autorange,i.lastInputTime=this.camera.lastInputTime,t.emit(\"plotly_relayout\",i)},A.cameraChanged=function(){var t=this.camera;this.glplot.setDataBox(this.calcDataBox());var e=this.computeTickMarks();(function(t,e){for(var r=0;r<2;++r){var n=t[r],i=e[r];if(n.length!==i.length)return!0;for(var a=0;a<n.length;++a)if(n[a].x!==i[a].x)return!0}return!1})(e,this.glplotOptions.ticks)&&(this.glplotOptions.ticks=e,this.glplotOptions.dataBox=t.dataBox,this.glplot.update(this.glplotOptions),this.handleAnnotations())},A.handleAnnotations=function(){for(var t=this.graphDiv,e=this.fullLayout.annotations,r=0;r<e.length;r++){var n=e[r];n.xref===this.xaxis._id&&n.yref===this.yaxis._id&&a.getComponentMethod(\"annotations\",\"drawOne\")(t,r)}},A.destroy=function(){if(this.glplot){var t=this.traces;t&&Object.keys(t).map((function(e){t[e].dispose(),delete t[e]})),this.glplot.dispose(),this.container.removeChild(this.svgContainer),this.container.removeChild(this.mouseContainer),this.fullData=null,this.glplot=null,this.stopped=!0,this.camera.mouseListener.enabled=!1,this.mouseContainer.removeEventListener(\"wheel\",this.camera.wheelListener),this.camera=null}},A.plot=function(t,e,r){var n=this.glplot;this.updateRefs(r),this.xaxis.clearCalc(),this.yaxis.clearCalc(),this.updateTraces(t,e),this.updateFx(r.dragmode);var i=r.width,a=r.height;this.updateSize(this.canvas);var o=this.glplotOptions;o.merge(r),o.screenBox=[0,0,i,a];var s={_fullLayout:{_axisConstraintGroups:r._axisConstraintGroups,xaxis:this.xaxis,yaxis:this.yaxis,_size:r._size}};y(s,this.xaxis),y(s,this.yaxis);var l,u,c=r._size,f=this.xaxis.domain,h=this.yaxis.domain;for(o.viewBox=[c.l+f[0]*c.w,c.b+h[0]*c.h,i-c.r-(1-f[1])*c.w,a-c.t-(1-h[1])*c.h],this.mouseContainer.style.width=c.w*(f[1]-f[0])+\"px\",this.mouseContainer.style.height=c.h*(h[1]-h[0])+\"px\",this.mouseContainer.height=c.h*(h[1]-h[0]),this.mouseContainer.style.left=c.l+f[0]*c.w+\"px\",this.mouseContainer.style.top=c.t+(1-h[1])*c.h+\"px\",u=0;u<2;++u)(l=this[w[u]])._length=o.viewBox[u+2]-o.viewBox[u],m(this.graphDiv,l),l.setScale();g(s),o.ticks=this.computeTickMarks(),o.dataBox=this.calcDataBox(),o.merge(r),n.update(o),this.glplot.draw()},A.calcDataBox=function(){var t=this.xaxis,e=this.yaxis,r=t.range,n=e.range,i=t.r2l,a=e.r2l;return[i(r[0]),a(n[0]),i(r[1]),a(n[1])]},A.setRanges=function(t){var e=this.xaxis,r=this.yaxis,n=e.l2r,i=r.l2r;e.range=[n(t[0]),n(t[2])],r.range=[i(t[1]),i(t[3])]},A.updateTraces=function(t,e){var r,n,i,a=Object.keys(this.traces);this.fullData=t;t:for(r=0;r<a.length;r++){var o=a[r],s=this.traces[o];for(n=0;n<t.length;n++)if((i=t[n]).uid===o&&i.type===s.type)continue t;s.dispose(),delete this.traces[o]}for(r=0;r<t.length;r++){i=t[r];var l=e[r],u=this.traces[i.uid];u?u.update(i,l):(u=i._module.plot(this,i,l),this.traces[i.uid]=u)}this.glplot.objects.sort((function(t,e){return t._trace.index-e._trace.index}))},A.updateFx=function(t){_(t)||b(t)?(this.pickCanvas.style[\"pointer-events\"]=\"none\",this.mouseContainer.style[\"pointer-events\"]=\"none\"):(this.pickCanvas.style[\"pointer-events\"]=\"auto\",this.mouseContainer.style[\"pointer-events\"]=\"auto\"),this.mouseContainer.style.cursor=\"pan\"===t?\"move\":\"zoom\"===t?\"crosshair\":null},A.emitPointAction=function(t,e){for(var r,n=t.trace.uid,i=t.pointIndex,a=0;a<this.fullData.length;a++)this.fullData[a].uid===n&&(r=this.fullData[a]);var o={x:t.traceCoord[0],y:t.traceCoord[1],curveNumber:r.index,pointNumber:i,data:r._input,fullData:this.fullData,xaxis:this.xaxis,yaxis:this.yaxis};s.appendArrayPointValue(o,r,i),this.graphDiv.emit(e,{points:[o]})},A.draw=function(){if(!this.stopped){requestAnimationFrame(this.redraw);var t=this.glplot,e=this.camera,r=e.mouseListener,n=1===this.lastButtonState&&0===r.buttons,i=this.fullLayout;this.lastButtonState=r.buttons,this.cameraChanged();var a,o=r.x*t.pixelRatio,l=this.canvas.height-t.pixelRatio*r.y;if(e.boxEnabled&&\"zoom\"===i.dragmode){this.selectBox.enabled=!0;for(var u=this.selectBox.selectBox=[Math.min(e.boxStart[0],e.boxEnd[0]),Math.min(e.boxStart[1],e.boxEnd[1]),Math.max(e.boxStart[0],e.boxEnd[0]),Math.max(e.boxStart[1],e.boxEnd[1])],c=0;c<2;c++)e.boxStart[c]===e.boxEnd[c]&&(u[c]=t.dataBox[c],u[c+2]=t.dataBox[c+2]);t.setDirty()}else if(!e.panning&&this.isMouseOver){this.selectBox.enabled=!1;var f=i._size,h=this.xaxis.domain,p=this.yaxis.domain,d=(a=t.pick(o/t.pixelRatio+f.l+h[0]*f.w,l/t.pixelRatio-(f.t+(1-p[1])*f.h)))&&a.object._trace.handlePick(a);if(d&&n&&this.emitPointAction(d,\"plotly_click\"),a&&\"skip\"!==a.object._trace.hoverinfo&&i.hovermode&&d&&(!this.lastPickResult||this.lastPickResult.traceUid!==d.trace.uid||this.lastPickResult.dataCoord[0]!==d.dataCoord[0]||this.lastPickResult.dataCoord[1]!==d.dataCoord[1])){var v=d;this.lastPickResult={traceUid:d.trace?d.trace.uid:null,dataCoord:d.dataCoord.slice()},this.spikes.update({center:a.dataCoord}),v.screenCoord=[((t.viewBox[2]-t.viewBox[0])*(a.dataCoord[0]-t.dataBox[0])/(t.dataBox[2]-t.dataBox[0])+t.viewBox[0])/t.pixelRatio,(this.canvas.height-(t.viewBox[3]-t.viewBox[1])*(a.dataCoord[1]-t.dataBox[1])/(t.dataBox[3]-t.dataBox[1])-t.viewBox[1])/t.pixelRatio],this.emitPointAction(d,\"plotly_hover\");var g=this.fullData[v.trace.index]||{},y=v.pointIndex,m=s.castHoverinfo(g,i,y);if(m&&\"all\"!==m){var x=m.split(\"+\");-1===x.indexOf(\"x\")&&(v.traceCoord[0]=void 0),-1===x.indexOf(\"y\")&&(v.traceCoord[1]=void 0),-1===x.indexOf(\"z\")&&(v.traceCoord[2]=void 0),-1===x.indexOf(\"text\")&&(v.textLabel=void 0),-1===x.indexOf(\"name\")&&(v.name=void 0)}s.loneHover({x:v.screenCoord[0],y:v.screenCoord[1],xLabel:this.hoverFormatter(\"xaxis\",v.traceCoord[0]),yLabel:this.hoverFormatter(\"yaxis\",v.traceCoord[1]),zLabel:v.traceCoord[2],text:v.textLabel,name:v.name,color:s.castHoverOption(g,y,\"bgcolor\")||v.color,borderColor:s.castHoverOption(g,y,\"bordercolor\"),fontFamily:s.castHoverOption(g,y,\"font.family\"),fontSize:s.castHoverOption(g,y,\"font.size\"),fontColor:s.castHoverOption(g,y,\"font.color\"),nameLength:s.castHoverOption(g,y,\"namelength\"),textAlign:s.castHoverOption(g,y,\"align\")},{container:this.svgContainer,gd:this.graphDiv})}}a||this.unhover(),t.draw()}},A.unhover=function(){this.lastPickResult&&(this.spikes.update({}),this.lastPickResult=null,this.graphDiv.emit(\"plotly_unhover\"),s.loneUnhover(this.svgContainer))},A.hoverFormatter=function(t,e){if(void 0!==e){var r=this[t];return o.tickText(r,r.c2l(e),\"hover\").text}}},58547:function(t,e,r){\"use strict\";var n=r(30962).overrideAll,i=r(528),a=r(33539),o=r(27659).NG,s=r(71828),l=r(77922),u=\"gl3d\",c=\"scene\";e.name=u,e.attr=c,e.idRoot=c,e.idRegex=e.attrRegex=s.counterRegex(\"scene\"),e.attributes=r(59084),e.layoutAttributes=r(65500),e.baseLayoutAttrOverrides=n({hoverlabel:i.hoverlabel},\"plot\",\"nested\"),e.supplyLayoutDefaults=r(24682),e.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots[u],i=0;i<n.length;i++){var s=n[i],l=o(r,u,s),c=e[s],f=c.camera,h=c._scene;h||(h=new a({id:s,graphDiv:t,container:t.querySelector(\".gl-container\"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio,camera:f},e),c._scene=h),h.viewInitial||(h.viewInitial={up:{x:f.up.x,y:f.up.y,z:f.up.z},eye:{x:f.eye.x,y:f.eye.y,z:f.eye.z},center:{x:f.center.x,y:f.center.y,z:f.center.z}}),h.plot(l,e,t.layout)}},e.clean=function(t,e,r,n){for(var i=n._subplots[u]||[],a=0;a<i.length;a++){var o=i[a];!e[o]&&n[o]._scene&&(n[o]._scene.destroy(),n._infolayer&&n._infolayer.selectAll(\".annotation-\"+o).remove())}},e.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots[u],n=e._size,i=0;i<r.length;i++){var a=e[r[i]],o=a.domain,s=a._scene,c=s.toImage(\"png\");e._glimages.append(\"svg:image\").attr({xmlns:l.svg,\"xlink:href\":c,x:n.l+n.w*o.x[0],y:n.t+n.h*(1-o.y[1]),width:n.w*(o.x[1]-o.x[0]),height:n.h*(o.y[1]-o.y[0]),preserveAspectRatio:\"none\"}),s.destroy()}},e.cleanId=function(t){if(t.match(/^scene[0-9]*$/)){var e=t.substr(5);return\"1\"===e&&(e=\"\"),c+e}},e.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots[u],n=0;n<r.length;n++)e[r[n]]._scene.updateFx(e.dragmode,e.hovermode)}},59084:function(t){\"use strict\";t.exports={scene:{valType:\"subplotid\",dflt:\"scene\",editType:\"calc+clearAxisTypes\"}}},77894:function(t,e,r){\"use strict\";var n=r(7901),i=r(13838),a=r(1426).extendFlat,o=r(30962).overrideAll;t.exports=o({visible:i.visible,showspikes:{valType:\"boolean\",dflt:!0},spikesides:{valType:\"boolean\",dflt:!0},spikethickness:{valType:\"number\",min:0,dflt:2},spikecolor:{valType:\"color\",dflt:n.defaultLine},showbackground:{valType:\"boolean\",dflt:!1},backgroundcolor:{valType:\"color\",dflt:\"rgba(204, 204, 204, 0.5)\"},showaxeslabels:{valType:\"boolean\",dflt:!0},color:i.color,categoryorder:i.categoryorder,categoryarray:i.categoryarray,title:{text:i.title.text,font:i.title.font},type:a({},i.type,{values:[\"-\",\"linear\",\"log\",\"date\",\"category\"]}),autotypenumbers:i.autotypenumbers,autorange:i.autorange,autorangeoptions:{minallowed:i.autorangeoptions.minallowed,maxallowed:i.autorangeoptions.maxallowed,clipmin:i.autorangeoptions.clipmin,clipmax:i.autorangeoptions.clipmax,include:i.autorangeoptions.include,editType:\"plot\"},rangemode:i.rangemode,minallowed:i.minallowed,maxallowed:i.maxallowed,range:a({},i.range,{items:[{valType:\"any\",editType:\"plot\",impliedEdits:{\"^autorange\":!1}},{valType:\"any\",editType:\"plot\",impliedEdits:{\"^autorange\":!1}}],anim:!1}),tickmode:i.minor.tickmode,nticks:i.nticks,tick0:i.tick0,dtick:i.dtick,tickvals:i.tickvals,ticktext:i.ticktext,ticks:i.ticks,mirror:i.mirror,ticklen:i.ticklen,tickwidth:i.tickwidth,tickcolor:i.tickcolor,showticklabels:i.showticklabels,labelalias:i.labelalias,tickfont:i.tickfont,tickangle:i.tickangle,tickprefix:i.tickprefix,showtickprefix:i.showtickprefix,ticksuffix:i.ticksuffix,showticksuffix:i.showticksuffix,showexponent:i.showexponent,exponentformat:i.exponentformat,minexponent:i.minexponent,separatethousands:i.separatethousands,tickformat:i.tickformat,tickformatstops:i.tickformatstops,hoverformat:i.hoverformat,showline:i.showline,linecolor:i.linecolor,linewidth:i.linewidth,showgrid:i.showgrid,gridcolor:a({},i.gridcolor,{dflt:\"rgb(204, 204, 204)\"}),gridwidth:i.gridwidth,zeroline:i.zeroline,zerolinecolor:i.zerolinecolor,zerolinewidth:i.zerolinewidth,_deprecated:{title:i._deprecated.title,titlefont:i._deprecated.titlefont}},\"plot\",\"from-root\")},3277:function(t,e,r){\"use strict\";var n=r(84267).mix,i=r(71828),a=r(44467),o=r(77894),s=r(951),l=r(71453),u=[\"xaxis\",\"yaxis\",\"zaxis\"];t.exports=function(t,e,r){var c,f;function h(t,e){return i.coerce(c,f,o,t,e)}for(var p=0;p<u.length;p++){var d=u[p];c=t[d]||{},(f=a.newContainer(e,d))._id=d[0]+r.scene,f._name=d,s(c,f,h,r),l(c,f,h,{font:r.font,letter:d[0],data:r.data,showGrid:!0,noTickson:!0,noTicklabelmode:!0,noTicklabelstep:!0,noTicklabelposition:!0,noTicklabeloverflow:!0,noInsiderange:!0,bgColor:r.bgColor,calendar:r.calendar},r.fullLayout),h(\"gridcolor\",n(f.color,r.bgColor,72.72727272727273).toRgbString()),h(\"title.text\",d[0]),f.setScale=i.noop,h(\"showspikes\")&&(h(\"spikesides\"),h(\"spikethickness\"),h(\"spikecolor\",f.color)),h(\"showaxeslabels\"),h(\"showbackground\")&&h(\"backgroundcolor\")}}},30422:function(t,e,r){\"use strict\";var n=r(78614),i=r(71828),a=[\"xaxis\",\"yaxis\",\"zaxis\"];function o(){this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.tickEnable=[!0,!0,!0],this.tickFont=[\"sans-serif\",\"sans-serif\",\"sans-serif\"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[18,18,18],this.labels=[\"x\",\"y\",\"z\"],this.labelEnable=[!0,!0,!0],this.labelFont=[\"Open Sans\",\"Open Sans\",\"Open Sans\"],this.labelSize=[20,20,20],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[30,30,30],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[10,10,10],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!0,!0,!0],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._defaultTickPad=this.tickPad.slice(),this._defaultLabelPad=this.labelPad.slice(),this._defaultLineTickLength=this.lineTickLength.slice()}o.prototype.merge=function(t,e){for(var r=this,o=0;o<3;++o){var s=e[a[o]];s.visible?(r.labels[o]=t._meta?i.templateString(s.title.text,t._meta):s.title.text,\"font\"in s.title&&(s.title.font.color&&(r.labelColor[o]=n(s.title.font.color)),s.title.font.family&&(r.labelFont[o]=s.title.font.family),s.title.font.size&&(r.labelSize[o]=s.title.font.size)),\"showline\"in s&&(r.lineEnable[o]=s.showline),\"linecolor\"in s&&(r.lineColor[o]=n(s.linecolor)),\"linewidth\"in s&&(r.lineWidth[o]=s.linewidth),\"showgrid\"in s&&(r.gridEnable[o]=s.showgrid),\"gridcolor\"in s&&(r.gridColor[o]=n(s.gridcolor)),\"gridwidth\"in s&&(r.gridWidth[o]=s.gridwidth),\"log\"===s.type?r.zeroEnable[o]=!1:\"zeroline\"in s&&(r.zeroEnable[o]=s.zeroline),\"zerolinecolor\"in s&&(r.zeroLineColor[o]=n(s.zerolinecolor)),\"zerolinewidth\"in s&&(r.zeroLineWidth[o]=s.zerolinewidth),\"ticks\"in s&&s.ticks?r.lineTickEnable[o]=!0:r.lineTickEnable[o]=!1,\"ticklen\"in s&&(r.lineTickLength[o]=r._defaultLineTickLength[o]=s.ticklen),\"tickcolor\"in s&&(r.lineTickColor[o]=n(s.tickcolor)),\"tickwidth\"in s&&(r.lineTickWidth[o]=s.tickwidth),\"tickangle\"in s&&(r.tickAngle[o]=\"auto\"===s.tickangle?-3600:Math.PI*-s.tickangle/180),\"showticklabels\"in s&&(r.tickEnable[o]=s.showticklabels),\"tickfont\"in s&&(s.tickfont.color&&(r.tickColor[o]=n(s.tickfont.color)),s.tickfont.family&&(r.tickFont[o]=s.tickfont.family),s.tickfont.size&&(r.tickSize[o]=s.tickfont.size)),\"mirror\"in s?-1!==[\"ticks\",\"all\",\"allticks\"].indexOf(s.mirror)?(r.lineTickMirror[o]=!0,r.lineMirror[o]=!0):!0===s.mirror?(r.lineTickMirror[o]=!1,r.lineMirror[o]=!0):(r.lineTickMirror[o]=!1,r.lineMirror[o]=!1):r.lineMirror[o]=!1,\"showbackground\"in s&&!1!==s.showbackground?(r.backgroundEnable[o]=!0,r.backgroundColor[o]=n(s.backgroundcolor)):r.backgroundEnable[o]=!1):(r.tickEnable[o]=!1,r.labelEnable[o]=!1,r.lineEnable[o]=!1,r.lineTickEnable[o]=!1,r.gridEnable[o]=!1,r.zeroEnable[o]=!1,r.backgroundEnable[o]=!1)}},t.exports=function(t,e){var r=new o;return r.merge(t,e),r}},24682:function(t,e,r){\"use strict\";var n=r(71828),i=r(7901),a=r(73972),o=r(49119),s=r(3277),l=r(65500),u=r(27659).NG,c=\"gl3d\";function f(t,e,r,n){for(var o=r(\"bgcolor\"),l=i.combine(o,n.paper_bgcolor),f=[\"up\",\"center\",\"eye\"],h=0;h<f.length;h++)r(\"camera.\"+f[h]+\".x\"),r(\"camera.\"+f[h]+\".y\"),r(\"camera.\"+f[h]+\".z\");r(\"camera.projection.type\");var p=!!r(\"aspectratio.x\")&&!!r(\"aspectratio.y\")&&!!r(\"aspectratio.z\"),d=r(\"aspectmode\",p?\"manual\":\"auto\");p||(t.aspectratio=e.aspectratio={x:1,y:1,z:1},\"manual\"===d&&(e.aspectmode=\"auto\"),t.aspectmode=e.aspectmode);var v=u(n.fullData,c,n.id);s(t,e,{font:n.font,scene:n.id,data:v,bgColor:l,calendar:n.calendar,autotypenumbersDflt:n.autotypenumbersDflt,fullLayout:n.fullLayout}),a.getComponentMethod(\"annotations3d\",\"handleDefaults\")(t,e,n);var g=n.getDfltFromLayout(\"dragmode\");if(!1!==g&&!g)if(g=\"orbit\",t.camera&&t.camera.up){var y=t.camera.up.x,m=t.camera.up.y,x=t.camera.up.z;0!==x&&(y&&m&&x?x/Math.sqrt(y*y+m*m+x*x)>.999&&(g=\"turntable\"):g=\"turntable\")}else g=\"turntable\";r(\"dragmode\",g),r(\"hovermode\",n.getDfltFromLayout(\"hovermode\"))}t.exports=function(t,e,r){var i=e._basePlotModules.length>1;o(t,e,r,{type:c,attributes:l,handleDefaults:f,fullLayout:e,font:e.font,fullData:r,getDfltFromLayout:function(e){if(!i)return n.validate(t[e],l[e])?t[e]:void 0},autotypenumbersDflt:e.autotypenumbers,paper_bgcolor:e.paper_bgcolor,calendar:e.calendar})}},65500:function(t,e,r){\"use strict\";var n=r(77894),i=r(27670).Y,a=r(1426).extendFlat,o=r(71828).counterRegex;function s(t,e,r){return{x:{valType:\"number\",dflt:t,editType:\"camera\"},y:{valType:\"number\",dflt:e,editType:\"camera\"},z:{valType:\"number\",dflt:r,editType:\"camera\"},editType:\"camera\"}}t.exports={_arrayAttrRegexps:[o(\"scene\",\".annotations\",!0)],bgcolor:{valType:\"color\",dflt:\"rgba(0,0,0,0)\",editType:\"plot\"},camera:{up:a(s(0,0,1),{}),center:a(s(0,0,0),{}),eye:a(s(1.25,1.25,1.25),{}),projection:{type:{valType:\"enumerated\",values:[\"perspective\",\"orthographic\"],dflt:\"perspective\",editType:\"calc\"},editType:\"calc\"},editType:\"camera\"},domain:i({name:\"scene\",editType:\"plot\"}),aspectmode:{valType:\"enumerated\",values:[\"auto\",\"cube\",\"data\",\"manual\"],dflt:\"auto\",editType:\"plot\",impliedEdits:{\"aspectratio.x\":void 0,\"aspectratio.y\":void 0,\"aspectratio.z\":void 0}},aspectratio:{x:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},y:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},z:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},editType:\"plot\",impliedEdits:{aspectmode:\"manual\"}},xaxis:n,yaxis:n,zaxis:n,dragmode:{valType:\"enumerated\",values:[\"orbit\",\"turntable\",\"zoom\",\"pan\",!1],editType:\"plot\"},hovermode:{valType:\"enumerated\",values:[\"closest\",!1],dflt:\"closest\",editType:\"modebar\"},uirevision:{valType:\"any\",editType:\"none\"},editType:\"plot\",_deprecated:{cameraposition:{valType:\"info_array\",editType:\"camera\"}}}},13133:function(t,e,r){\"use strict\";var n=r(78614),i=[\"xaxis\",\"yaxis\",\"zaxis\"];function a(){this.enabled=[!0,!0,!0],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.drawSides=[!0,!0,!0],this.lineWidth=[1,1,1]}a.prototype.merge=function(t){for(var e=0;e<3;++e){var r=t[i[e]];r.visible?(this.enabled[e]=r.showspikes,this.colors[e]=n(r.spikecolor),this.drawSides[e]=r.spikesides,this.lineWidth[e]=r.spikethickness):(this.enabled[e]=!1,this.drawSides[e]=!1)}},t.exports=function(t){var e=new a;return e.merge(t),e}},96085:function(t,e,r){\"use strict\";t.exports=function(t){for(var e=t.axesOptions,r=t.glplot.axesPixels,s=t.fullSceneLayout,l=[[],[],[]],u=0;u<3;++u){var c=s[a[u]];if(c._length=(r[u].hi-r[u].lo)*r[u].pixelsPerDataUnit/t.dataScale[u],Math.abs(c._length)===1/0||isNaN(c._length))l[u]=[];else{c._input_range=c.range.slice(),c.range[0]=r[u].lo/t.dataScale[u],c.range[1]=r[u].hi/t.dataScale[u],c._m=1/(t.dataScale[u]*r[u].pixelsPerDataUnit),c.range[0]===c.range[1]&&(c.range[0]-=1,c.range[1]+=1);var f=c.tickmode;if(\"auto\"===c.tickmode){c.tickmode=\"linear\";var h=c.nticks||i.constrain(c._length/40,4,9);n.autoTicks(c,Math.abs(c.range[1]-c.range[0])/h)}for(var p=n.calcTicks(c,{msUTC:!0}),d=0;d<p.length;++d)p[d].x=p[d].x*t.dataScale[u],\"date\"===c.type&&(p[d].text=p[d].text.replace(/\\<br\\>/g,\" \"));l[u]=p,c.tickmode=f}}for(e.ticks=l,u=0;u<3;++u)for(o[u]=.5*(t.glplot.bounds[0][u]+t.glplot.bounds[1][u]),d=0;d<2;++d)e.bounds[d][u]=t.glplot.bounds[d][u];t.contourLevels=function(t){for(var e=new Array(3),r=0;r<3;++r){for(var n=t[r],i=new Array(n.length),a=0;a<n.length;++a)i[a]=n[a].x;e[r]=i}return e}(l)};var n=r(89298),i=r(71828),a=[\"xaxis\",\"yaxis\",\"zaxis\"],o=[0,0,0]},63538:function(t){\"use strict\";function e(t,e){var r,n,i=[0,0,0,0];for(r=0;r<4;++r)for(n=0;n<4;++n)i[n]+=t[4*r+n]*e[r];return i}t.exports=function(t,r){return e(t.projection,e(t.view,e(t.model,[r[0],r[1],r[2],1])))}},33539:function(t,e,r){\"use strict\";var n,i,a=r(9330).gl_plot3d,o=a.createCamera,s=a.createScene,l=r(40372),u=r(38520),c=r(73972),f=r(71828),h=f.preserveDrawingBuffer(),p=r(89298),d=r(30211),v=r(78614),g=r(58617),y=r(63538),m=r(30422),x=r(13133),b=r(96085),_=r(71739).applyAutorangeOptions,w=!1;function T(t,e){var r=document.createElement(\"div\"),n=t.container;this.graphDiv=t.graphDiv;var i=document.createElementNS(\"http://www.w3.org/2000/svg\",\"svg\");i.style.position=\"absolute\",i.style.top=i.style.left=\"0px\",i.style.width=i.style.height=\"100%\",i.style[\"z-index\"]=20,i.style[\"pointer-events\"]=\"none\",r.appendChild(i),this.svgContainer=i,r.id=t.id,r.style.position=\"absolute\",r.style.top=r.style.left=\"0px\",r.style.width=r.style.height=\"100%\",n.appendChild(r),this.fullLayout=e,this.id=t.id||\"scene\",this.fullSceneLayout=e[this.id],this.plotArgs=[[],{},{}],this.axesOptions=m(e,e[this.id]),this.spikeOptions=x(e[this.id]),this.container=r,this.staticMode=!!t.staticPlot,this.pixelRatio=this.pixelRatio||t.plotGlPixelRatio||2,this.dataScale=[1,1,1],this.contourLevels=[[],[],[]],this.convertAnnotations=c.getComponentMethod(\"annotations3d\",\"convert\"),this.drawAnnotations=c.getComponentMethod(\"annotations3d\",\"draw\"),this.initializeGLPlot()}var k=T.prototype;k.prepareOptions=function(){var t=this,e={canvas:t.canvas,gl:t.gl,glOptions:{preserveDrawingBuffer:h,premultipliedAlpha:!0,antialias:!0},container:t.container,axes:t.axesOptions,spikes:t.spikeOptions,pickRadius:10,snapToData:!0,autoScale:!0,autoBounds:!1,cameraObject:t.camera,pixelRatio:t.pixelRatio};if(t.staticMode){if(!(i||(n=document.createElement(\"canvas\"),i=l({canvas:n,preserveDrawingBuffer:!0,premultipliedAlpha:!0,antialias:!0}))))throw new Error(\"error creating static canvas/context for image server\");e.gl=i,e.canvas=n}return e};var A=!0;k.tryCreatePlot=function(){var t=this,e=t.prepareOptions(),r=!0;try{t.glplot=s(e)}catch(n){if(t.staticMode||!A||h)r=!1;else{f.warn([\"webgl setup failed possibly due to\",\"false preserveDrawingBuffer config.\",\"The mobile/tablet device may not be detected by is-mobile module.\",\"Enabling preserveDrawingBuffer in second attempt to create webgl scene...\"].join(\" \"));try{h=e.glOptions.preserveDrawingBuffer=!0,t.glplot=s(e)}catch(t){h=e.glOptions.preserveDrawingBuffer=!1,r=!1}}}return A=!1,r},k.initializeGLCamera=function(){var t=this,e=t.fullSceneLayout.camera,r=\"orthographic\"===e.projection.type;t.camera=o(t.container,{center:[e.center.x,e.center.y,e.center.z],eye:[e.eye.x,e.eye.y,e.eye.z],up:[e.up.x,e.up.y,e.up.z],_ortho:r,zoomMin:.01,zoomMax:100,mode:\"orbit\"})},k.initializeGLPlot=function(){var t=this;if(t.initializeGLCamera(),!t.tryCreatePlot())return g(t);t.traces={},t.make4thDimension();var e=t.graphDiv,r=e.layout,n=function(){var e={};return t.isCameraChanged(r)&&(e[t.id+\".camera\"]=t.getCamera()),t.isAspectChanged(r)&&(e[t.id+\".aspectratio\"]=t.glplot.getAspectratio(),\"manual\"!==r[t.id].aspectmode&&(t.fullSceneLayout.aspectmode=r[t.id].aspectmode=e[t.id+\".aspectmode\"]=\"manual\")),e},i=function(t){if(!1!==t.fullSceneLayout.dragmode){var e=n();t.saveLayout(r),t.graphDiv.emit(\"plotly_relayout\",e)}};return t.glplot.canvas&&(t.glplot.canvas.addEventListener(\"mouseup\",(function(){i(t)})),t.glplot.canvas.addEventListener(\"touchstart\",(function(){w=!0})),t.glplot.canvas.addEventListener(\"wheel\",(function(r){if(e._context._scrollZoom.gl3d){if(t.camera._ortho){var n=r.deltaX>r.deltaY?1.1:1/1.1,a=t.glplot.getAspectratio();t.glplot.setAspectratio({x:n*a.x,y:n*a.y,z:n*a.z})}i(t)}}),!!u&&{passive:!1}),t.glplot.canvas.addEventListener(\"mousemove\",(function(){if(!1!==t.fullSceneLayout.dragmode&&0!==t.camera.mouseListener.buttons){var e=n();t.graphDiv.emit(\"plotly_relayouting\",e)}})),t.staticMode||t.glplot.canvas.addEventListener(\"webglcontextlost\",(function(r){e&&e.emit&&e.emit(\"plotly_webglcontextlost\",{event:r,layer:t.id})}),!1)),t.glplot.oncontextloss=function(){t.recoverContext()},t.glplot.onrender=function(){t.render()},!0},k.render=function(){var t,e=this,r=e.graphDiv,n=e.svgContainer,i=e.container.getBoundingClientRect();r._fullLayout._calcInverseTransform(r);var a=r._fullLayout._invScaleX,o=r._fullLayout._invScaleY,s=i.width*a,l=i.height*o;n.setAttributeNS(null,\"viewBox\",\"0 0 \"+s+\" \"+l),n.setAttributeNS(null,\"width\",s),n.setAttributeNS(null,\"height\",l),b(e),e.glplot.axes.update(e.axesOptions);for(var u=Object.keys(e.traces),c=null,h=e.glplot.selection,v=0;v<u.length;++v)\"skip\"!==(t=e.traces[u[v]]).data.hoverinfo&&t.handlePick(h)&&(c=t),t.setContourLevels&&t.setContourLevels();function g(t,r,n){var i=e.fullSceneLayout[t+\"axis\"];return\"log\"!==i.type&&(r=i.d2l(r)),p.hoverLabelText(i,r,n)}if(null!==c){var m=y(e.glplot.cameraParams,h.dataCoordinate);t=c.data;var x,_=r._fullData[t.index],T=h.index,k={xLabel:g(\"x\",h.traceCoordinate[0],t.xhoverformat),yLabel:g(\"y\",h.traceCoordinate[1],t.yhoverformat),zLabel:g(\"z\",h.traceCoordinate[2],t.zhoverformat)},A=d.castHoverinfo(_,e.fullLayout,T),M=(A||\"\").split(\"+\"),S=A&&\"all\"===A;_.hovertemplate||S||(-1===M.indexOf(\"x\")&&(k.xLabel=void 0),-1===M.indexOf(\"y\")&&(k.yLabel=void 0),-1===M.indexOf(\"z\")&&(k.zLabel=void 0),-1===M.indexOf(\"text\")&&(h.textLabel=void 0),-1===M.indexOf(\"name\")&&(c.name=void 0));var E=[];\"cone\"===t.type||\"streamtube\"===t.type?(k.uLabel=g(\"x\",h.traceCoordinate[3],t.uhoverformat),(S||-1!==M.indexOf(\"u\"))&&E.push(\"u: \"+k.uLabel),k.vLabel=g(\"y\",h.traceCoordinate[4],t.vhoverformat),(S||-1!==M.indexOf(\"v\"))&&E.push(\"v: \"+k.vLabel),k.wLabel=g(\"z\",h.traceCoordinate[5],t.whoverformat),(S||-1!==M.indexOf(\"w\"))&&E.push(\"w: \"+k.wLabel),k.normLabel=h.traceCoordinate[6].toPrecision(3),(S||-1!==M.indexOf(\"norm\"))&&E.push(\"norm: \"+k.normLabel),\"streamtube\"===t.type&&(k.divergenceLabel=h.traceCoordinate[7].toPrecision(3),(S||-1!==M.indexOf(\"divergence\"))&&E.push(\"divergence: \"+k.divergenceLabel)),h.textLabel&&E.push(h.textLabel),x=E.join(\"<br>\")):\"isosurface\"===t.type||\"volume\"===t.type?(k.valueLabel=p.hoverLabelText(e._mockAxis,e._mockAxis.d2l(h.traceCoordinate[3]),t.valuehoverformat),E.push(\"value: \"+k.valueLabel),h.textLabel&&E.push(h.textLabel),x=E.join(\"<br>\")):x=h.textLabel;var L={x:h.traceCoordinate[0],y:h.traceCoordinate[1],z:h.traceCoordinate[2],data:_._input,fullData:_,curveNumber:_.index,pointNumber:T};d.appendArrayPointValue(L,_,T),t._module.eventData&&(L=_._module.eventData(L,h,_,{},T));var C={points:[L]};if(e.fullSceneLayout.hovermode){var P=[];d.loneHover({trace:_,x:(.5+.5*m[0]/m[3])*s,y:(.5-.5*m[1]/m[3])*l,xLabel:k.xLabel,yLabel:k.yLabel,zLabel:k.zLabel,text:x,name:c.name,color:d.castHoverOption(_,T,\"bgcolor\")||c.color,borderColor:d.castHoverOption(_,T,\"bordercolor\"),fontFamily:d.castHoverOption(_,T,\"font.family\"),fontSize:d.castHoverOption(_,T,\"font.size\"),fontColor:d.castHoverOption(_,T,\"font.color\"),nameLength:d.castHoverOption(_,T,\"namelength\"),textAlign:d.castHoverOption(_,T,\"align\"),hovertemplate:f.castOption(_,T,\"hovertemplate\"),hovertemplateLabels:f.extendFlat({},L,k),eventData:[L]},{container:n,gd:r,inOut_bbox:P}),L.bbox=P[0]}h.distance<5&&(h.buttons||w)?r.emit(\"plotly_click\",C):r.emit(\"plotly_hover\",C),this.oldEventData=C}else d.loneUnhover(n),this.oldEventData&&r.emit(\"plotly_unhover\",this.oldEventData),this.oldEventData=void 0;e.drawAnnotations(e)},k.recoverContext=function(){var t=this;t.glplot.dispose();var e=function(){t.glplot.gl.isContextLost()?requestAnimationFrame(e):t.initializeGLPlot()?t.plot.apply(t,t.plotArgs):f.error(\"Catastrophic and unrecoverable WebGL error. Context lost.\")};requestAnimationFrame(e)};var M=[\"xaxis\",\"yaxis\",\"zaxis\"];function S(t,e,r){for(var n=t.fullSceneLayout,i=0;i<3;i++){var a=M[i],o=a.charAt(0),s=n[a],l=e[o],u=e[o+\"calendar\"],c=e[\"_\"+o+\"length\"];if(f.isArrayOrTypedArray(l))for(var h,p=0;p<(c||l.length);p++)if(f.isArrayOrTypedArray(l[p]))for(var d=0;d<l[p].length;++d)h=s.d2l(l[p][d],0,u),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else h=s.d2l(l[p],0,u),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else r[0][i]=Math.min(r[0][i],0),r[1][i]=Math.max(r[1][i],c-1)}}k.plot=function(t,e,r){var n=this;if(n.plotArgs=[t,e,r],!n.glplot.contextLost){var i,a,o,s,l,u,c=e[n.id],f=r[n.id];n.fullLayout=e,n.fullSceneLayout=c,n.axesOptions.merge(e,c),n.spikeOptions.merge(c),n.setViewport(c),n.updateFx(c.dragmode,c.hovermode),n.camera.enableWheel=n.graphDiv._context._scrollZoom.gl3d,n.glplot.setClearColor(v(c.bgcolor)),n.setConvert(l),t?Array.isArray(t)||(t=[t]):t=[];var h=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(o=0;o<t.length;++o)!0===(i=t[o]).visible&&0!==i._length&&S(this,i,h);!function(t,e){for(var r=t.fullSceneLayout,n=r.annotations||[],i=0;i<3;i++)for(var a=M[i],o=a.charAt(0),s=r[a],l=0;l<n.length;l++){var u=n[l];if(u.visible){var c=s.r2l(u[o]);!isNaN(c)&&isFinite(c)&&(e[0][i]=Math.min(e[0][i],c),e[1][i]=Math.max(e[1][i],c))}}}(this,h);var p=[1,1,1];for(s=0;s<3;++s)h[1][s]===h[0][s]?p[s]=1:p[s]=1/(h[1][s]-h[0][s]);for(n.dataScale=p,n.convertAnnotations(this),o=0;o<t.length;++o)!0===(i=t[o]).visible&&0!==i._length&&((a=n.traces[i.uid])?a.data.type===i.type?a.update(i):(a.dispose(),a=i._module.plot(this,i),n.traces[i.uid]=a):(a=i._module.plot(this,i),n.traces[i.uid]=a),a.name=i.name);var d=Object.keys(n.traces);t:for(o=0;o<d.length;++o){for(s=0;s<t.length;++s)if(t[s].uid===d[o]&&!0===t[s].visible&&0!==t[s]._length)continue t;(a=n.traces[d[o]]).dispose(),delete n.traces[d[o]]}n.glplot.objects.sort((function(t,e){return t._trace.data.index-e._trace.data.index}));var g,y=[[0,0,0],[0,0,0]],m=[],x={};for(o=0;o<3;++o){var b;if((u=(l=c[M[o]]).type)in x?(x[u].acc*=p[o],x[u].count+=1):x[u]={acc:p[o],count:1},l.autorange){y[0][o]=1/0,y[1][o]=-1/0;var w=n.glplot.objects,T=n.fullSceneLayout.annotations||[],k=l._name.charAt(0);for(s=0;s<w.length;s++){var 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n=r(7901),i=r(52075).hasColorscale,a=r(1586),o=r(71828).coercePattern;t.exports=function(t,e,r,s,l){var u=r(\"marker.color\",s),c=i(t,\"marker\");c&&a(t,e,l,r,{prefix:\"marker.\",cLetter:\"c\"}),r(\"marker.line.color\",n.defaultLine),i(t,\"marker.line\")&&a(t,e,l,r,{prefix:\"marker.line.\",cLetter:\"c\"}),r(\"marker.line.width\"),r(\"marker.opacity\"),o(r,\"marker.pattern\",u,c),r(\"selected.marker.color\"),r(\"unselected.marker.color\")}},72597:function(t,e,r){\"use strict\";var n=r(39898),i=r(71828);function a(t){return\"_\"+t+\"Text_minsize\"}t.exports={recordMinTextSize:function(t,e,r){if(r.uniformtext.mode){var n=a(t),i=r.uniformtext.minsize,o=e.scale*e.fontSize;e.hide=o<i,r[n]=r[n]||1/0,e.hide||(r[n]=Math.min(r[n],Math.max(o,i)))}},clearMinTextSize:function(t,e){e[a(t)]=void 0},resizeText:function(t,e,r){var a=t._fullLayout,o=a[\"_\"+r+\"Text_minsize\"];if(o){var 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r=t._fullLayout,o=e.subplot,l=r[o].radialaxis,u=r[o].angularaxis,c=l.makeCalcdata(e,\"r\"),f=u.makeCalcdata(e,\"theta\"),h=e._length,p=new Array(h),d=c,v=f,g=0;g<h;g++)p[g]={p:v[g],s:d[g]};function y(t){var r=e[t];void 0!==r&&(e[\"_\"+t]=Array.isArray(r)?u.makeCalcdata(e,t):u.d2c(r,e.thetaunit))}return\"linear\"===u.type&&(y(\"width\"),y(\"offset\")),n(e,\"marker\")&&i(t,e,{vals:e.marker.color,containerStr:\"marker\",cLetter:\"c\"}),n(e,\"marker.line\")&&i(t,e,{vals:e.marker.line.color,containerStr:\"marker.line\",cLetter:\"c\"}),a(p,e),s(p,e),p},crossTraceCalc:function(t,e,r){for(var n=t.calcdata,i=[],a=0;a<n.length;a++){var s=n[a],c=s[0].trace;!0===c.visible&&l(c,\"bar\")&&c.subplot===r&&i.push(s)}var f=u({},e.radialaxis,{_id:\"x\"}),h=e.angularaxis;o(t,h,f,i,{mode:e.barmode,norm:e.barnorm,gap:e.bargap,groupgap:e.bargroupgap})}}},6135:function(t,e,r){\"use strict\";var n=r(71828),i=r(22184).handleRThetaDefaults,a=r(98340),o=r(55023);t.exports=function(t,e,r,s){function l(r,i){return n.coerce(t,e,o,r,i)}i(t,e,s,l)?(l(\"thetaunit\"),l(\"base\"),l(\"offset\"),l(\"width\"),l(\"text\"),l(\"hovertext\"),l(\"hovertemplate\"),a(t,e,l,r,s),n.coerceSelectionMarkerOpacity(e,l)):e.visible=!1}},27379:function(t,e,r){\"use strict\";var n=r(30211),i=r(71828),a=r(95423).getTraceColor,o=i.fillText,s=r(59150).makeHoverPointText,l=r(10869).isPtInsidePolygon;t.exports=function(t,e,r){var u=t.cd,c=u[0].trace,f=t.subplot,h=f.radialAxis,p=f.angularAxis,d=f.vangles,v=d?l:i.isPtInsideSector,g=t.maxHoverDistance,y=p._period||2*Math.PI,m=Math.abs(h.g2p(Math.sqrt(e*e+r*r))),x=Math.atan2(r,e);if(h.range[0]>h.range[1]&&(x+=Math.PI),n.getClosest(u,(function(t){return v(m,x,[t.rp0,t.rp1],[t.thetag0,t.thetag1],d)?g+Math.min(1,Math.abs(t.thetag1-t.thetag0)/y)-1+(t.rp1-m)/(t.rp1-t.rp0)-1:1/0}),t),!1!==t.index){var b=u[t.index];t.x0=t.x1=b.ct[0],t.y0=t.y1=b.ct[1];var _=i.extendFlat({},b,{r:b.s,theta:b.p});return o(b,c,t),s(_,c,f,t),t.hovertemplate=c.hovertemplate,t.color=a(c,b),t.xLabelVal=t.yLabelVal=void 0,b.s<0&&(t.idealAlign=\"left\"),[t]}}},23381:function(t,e,r){\"use strict\";t.exports={moduleType:\"trace\",name:\"barpolar\",basePlotModule:r(23580),categories:[\"polar\",\"bar\",\"showLegend\"],attributes:r(55023),layoutAttributes:r(40151),supplyDefaults:r(6135),supplyLayoutDefaults:r(19860),calc:r(74692).calc,crossTraceCalc:r(74692).crossTraceCalc,plot:r(60173),colorbar:r(4898),formatLabels:r(98608),style:r(16688).style,styleOnSelect:r(16688).styleOnSelect,hoverPoints:r(27379),selectPoints:r(81974),meta:{}}},40151:function(t){\"use strict\";t.exports={barmode:{valType:\"enumerated\",values:[\"stack\",\"overlay\"],dflt:\"stack\",editType:\"calc\"},bargap:{valType:\"number\",dflt:.1,min:0,max:1,editType:\"calc\"}}},19860:function(t,e,r){\"use strict\";var n=r(71828),i=r(40151);t.exports=function(t,e,r){var a,o={};function s(r,o){return n.coerce(t[a]||{},e[a],i,r,o)}for(var 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R,F,B,N,j,U,V=k+T,H=Boolean(C);if(\"positive\"===P?(M=w*(C?1:.5),R=V,S=R=k):\"negative\"===P?(M=R=k,S=w*(C?1:.5),F=V):(M=S=w,R=F=V),(E.boxpoints||E.points)&&v>0){var q=E.pointpos,G=E.jitter,Z=E.marker.size/2,Y=0;q+G>=0&&((Y=V*(q+G))>M?(H=!0,j=Z,B=Y):Y>R&&(j=Z,B=M)),Y<=M&&(B=M);var W=0;q-G<=0&&((W=-V*(q-G))>S?(H=!0,U=Z,N=W):W>F&&(U=Z,N=S)),W<=S&&(N=S)}else B=M,N=S;var X=new Array(u.length);for(l=0;l<u.length;l++)X[l]=u[l].pos;E._extremes[h]=n.findExtremes(o,X,{padded:H,vpadminus:N,vpadplus:B,vpadLinearized:!0,ppadminus:{x:U,y:j}[p],ppadplus:{x:j,y:U}[p]})}}}t.exports={crossTraceCalc:function(t,e){for(var r=t.calcdata,n=e.xaxis,i=e.yaxis,a=0;a<o.length;a++){for(var l=o[a],u=\"h\"===l?i:n,c=[],f=0;f<r.length;f++){var h=r[f],p=h[0].t,d=h[0].trace;!0!==d.visible||\"box\"!==d.type&&\"candlestick\"!==d.type||p.empty||(d.orientation||\"v\")!==l||d.xaxis!==n._id||d.yaxis!==i._id||c.push(f)}s(\"box\",t,c,u)}},setPositionOffset:s}},36411:function(t,e,r){\"use strict\";var n=r(71828),i=r(73972),a=r(7901),o=r(73927),s=r(26125),l=r(4322),u=r(53522);function c(t,e,r,a){function o(t){var e=0;return t&&t.length&&(e+=1,n.isArrayOrTypedArray(t[0])&&t[0].length&&(e+=1)),e}function s(e){return n.validate(t[e],u[e])}var c,f=r(\"y\"),h=r(\"x\");if(\"box\"===e.type){var p=r(\"q1\"),d=r(\"median\"),v=r(\"q3\");e._hasPreCompStats=p&&p.length&&d&&d.length&&v&&v.length,c=Math.min(n.minRowLength(p),n.minRowLength(d),n.minRowLength(v))}var g,y,m=o(f),x=o(h),b=m&&n.minRowLength(f),_=x&&n.minRowLength(h),w=a.calendar,T={autotypenumbers:a.autotypenumbers};if(e._hasPreCompStats)switch(String(x)+String(m)){case\"00\":var k=s(\"x0\")||s(\"dx\");g=!s(\"y0\")&&!s(\"dy\")||k?\"v\":\"h\",y=c;break;case\"10\":g=\"v\",y=Math.min(c,_);break;case\"20\":g=\"h\",y=Math.min(c,h.length);break;case\"01\":g=\"h\",y=Math.min(c,b);break;case\"02\":g=\"v\",y=Math.min(c,f.length);break;case\"12\":g=\"v\",y=Math.min(c,_,f.length);break;case\"21\":g=\"h\",y=Math.min(c,h.length,b);break;case\"11\":y=0;break;case\"22\":var A,M=!1;for(A=0;A<h.length;A++)if(\"category\"===l(h[A],w,T)){M=!0;break}if(M)g=\"v\",y=Math.min(c,_,f.length);else{for(A=0;A<f.length;A++)if(\"category\"===l(f[A],w,T)){M=!0;break}M?(g=\"h\",y=Math.min(c,h.length,b)):(g=\"v\",y=Math.min(c,_,f.length))}}else m>0?(g=\"v\",y=x>0?Math.min(_,b):Math.min(b)):x>0?(g=\"h\",y=Math.min(_)):y=0;if(y){e._length=y;var 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b=t.selectAll(\"path.box\").data(\"violin\"!==r.type||r.box.visible?i.identity:[]);b.enter().append(\"path\").style(\"vector-effect\",o?\"none\":\"non-scaling-stroke\").attr(\"class\",\"box\"),b.exit().remove(),b.each((function(t){if(t.empty)return n.select(this).attr(\"d\",\"M0,0Z\");var e=f.c2l(t.pos+p,!0),a=f.l2p(e-s)+v,o=f.l2p(e+l)+v,b=h?(a+o)/2:f.l2p(e)+v,_=r.whiskerwidth,w=h?a*_+(1-_)*b:f.l2p(e-d)+v,T=h?o*_+(1-_)*b:f.l2p(e+d)+v,k=f.l2p(e-s*x)+v,A=f.l2p(e+l*x)+v,M=\"sd\"===r.sizemode,S=c.c2p(M?t.mean-t.sd:t.q1,!0),E=M?c.c2p(t.mean+t.sd,!0):c.c2p(t.q3,!0),L=i.constrain(M?c.c2p(t.mean,!0):c.c2p(t.med,!0),Math.min(S,E)+1,Math.max(S,E)-1),C=void 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t.width})).attr(\"height\",(function(t){return t.height})).attr(\"y\",(function(t){return t.y})).attr(\"cursor\",(function(t){return\"fixed\"===t.parcatsViewModel.arrangement?\"default\":\"perpendicular\"===t.parcatsViewModel.arrangement?\"ns-resize\":\"move\"})),k(D),E.exit().remove(),S.append(\"text\").attr(\"class\",\"catlabel\").attr(\"pointer-events\",\"none\");var z=e._fullLayout.paper_bgcolor;M.select(\"text.catlabel\").attr(\"text-anchor\",(function(t){return d(t)?\"start\":\"end\"})).attr(\"alignment-baseline\",\"middle\").style(\"text-shadow\",f.makeTextShadow(z)).style(\"fill\",\"rgb(0, 0, 0)\").attr(\"x\",(function(t){return d(t)?t.width+5:-5})).attr(\"y\",(function(t){return t.height/2})).text((function(t){return t.model.categoryLabel})).each((function(t){u.font(n.select(this),t.parcatsViewModel.categorylabelfont),f.convertToTspans(n.select(this),e)})),S.append(\"text\").attr(\"class\",\"dimlabel\"),M.select(\"text.dimlabel\").attr(\"text-anchor\",\"middle\").attr(\"alignment-baseline\",\"baseline\").attr(\"cursor\",(function(t){return\"fixed\"===t.parcatsViewModel.arrangement?\"default\":\"ew-resize\"})).attr(\"x\",(function(t){return t.width/2})).attr(\"y\",-5).text((function(t,e){return 0===e?t.parcatsViewModel.model.dimensions[t.model.dimensionInd].dimensionLabel:null})).each((function(t){u.font(n.select(this),t.parcatsViewModel.labelfont)})),M.selectAll(\"rect.bandrect\").on(\"mouseover\",L).on(\"mouseout\",C),M.exit().remove(),A.call(n.behavior.drag().origin((function(t){return{x:t.x,y:0}})).on(\"dragstart\",P).on(\"drag\",O).on(\"dragend\",I)),h.each((function(t){t.traceSelection=n.select(this),t.pathSelection=n.select(this).selectAll(\"g.paths\").selectAll(\"path.path\"),t.dimensionSelection=n.select(this).selectAll(\"g.dimensions\").selectAll(\"g.dimension\")})),h.exit().remove()}function p(t){return t.key}function d(t){var e=t.parcatsViewModel.dimensions.length,r=t.parcatsViewModel.dimensions[e-1].model.dimensionInd;return t.model.dimensionInd===r}function v(t,e){return t.model.rawColor>e.model.rawColor?1:t.model.rawColor<e.model.rawColor?-1:0}function g(t){if(!t.parcatsViewModel.dragDimension&&-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")){s.raiseToTop(this),w(n.select(this));var e=m(t),r=x(t);if(t.parcatsViewModel.graphDiv.emit(\"plotly_hover\",{points:e,event:n.event,constraints:r}),-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"none\")){var i,a,l,u=n.mouse(this)[0],f=t.parcatsViewModel.graphDiv,h=t.parcatsViewModel.trace,p=f._fullLayout,d=p._paperdiv.node().getBoundingClientRect(),v=t.parcatsViewModel.graphDiv.getBoundingClientRect();for(l=0;l<t.leftXs.length-1;l++)if(t.leftXs[l]+t.dimWidths[l]-2<=u&&u<=t.leftXs[l+1]+2){var g=t.parcatsViewModel.dimensions[l],y=t.parcatsViewModel.dimensions[l+1];i=(g.x+g.width+y.x)/2,a=(t.topYs[l]+t.topYs[l+1]+t.height)/2;break}var b=t.parcatsViewModel.x+i,_=t.parcatsViewModel.y+a,T=c.mostReadable(t.model.color,[\"black\",\"white\"]),k=t.model.count,A=k/t.parcatsViewModel.model.count,M={countLabel:k,probabilityLabel:A.toFixed(3)},S=[];-1!==t.parcatsViewModel.hoverinfoItems.indexOf(\"count\")&&S.push([\"Count:\",M.countLabel].join(\" \")),-1!==t.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")&&S.push([\"P:\",M.probabilityLabel].join(\" \"));var E=S.join(\"<br>\"),L=n.mouse(f)[0];o.loneHover({trace:h,x:b-d.left+v.left,y:_-d.top+v.top,text:E,color:t.model.color,borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontSize:10,fontColor:T,idealAlign:L<b?\"right\":\"left\",hovertemplate:(h.line||{}).hovertemplate,hovertemplateLabels:M,eventData:[{data:h._input,fullData:h,count:k,probability:A}]},{container:p._hoverlayer.node(),outerContainer:p._paper.node(),gd:f})}}}function y(t){if(!t.parcatsViewModel.dragDimension&&(_(n.select(this)),o.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()),t.parcatsViewModel.pathSelection.sort(v),-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\"))){var e=m(t),r=x(t);t.parcatsViewModel.graphDiv.emit(\"plotly_unhover\",{points:e,event:n.event,constraints:r})}}function m(t){for(var e=[],r=D(t.parcatsViewModel),n=0;n<t.model.valueInds.length;n++){var i=t.model.valueInds[n];e.push({curveNumber:r,pointNumber:i})}return e}function x(t){for(var e={},r=t.parcatsViewModel.model.dimensions,n=0;n<r.length;n++){var i=r[n],a=i.categories[t.model.categoryInds[n]];e[i.containerInd]=a.categoryValue}return void 0!==t.model.rawColor&&(e.color=t.model.rawColor),e}function b(t){if(-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")){var e=m(t),r=x(t);t.parcatsViewModel.graphDiv.emit(\"plotly_click\",{points:e,event:n.event,constraints:r})}}function _(t){t.attr(\"fill\",(function(t){return t.model.color})).attr(\"fill-opacity\",.6).attr(\"stroke\",\"lightgray\").attr(\"stroke-width\",.2).attr(\"stroke-opacity\",1)}function w(t){t.attr(\"fill-opacity\",.8).attr(\"stroke\",(function(t){return c.mostReadable(t.model.color,[\"black\",\"white\"])})).attr(\"stroke-width\",.3)}function T(t){t.select(\"rect.catrect\").attr(\"stroke\",\"black\").attr(\"stroke-width\",1).attr(\"stroke-opacity\",1)}function k(t){t.attr(\"stroke\",\"black\").attr(\"stroke-width\",.2).attr(\"stroke-opacity\",1).attr(\"fill-opacity\",1)}function A(t){var e=t.parcatsViewModel.pathSelection,r=t.categoryViewModel.model.dimensionInd,n=t.categoryViewModel.model.categoryInd;return e.filter((function(e){return e.model.categoryInds[r]===n&&e.model.color===t.color}))}function M(t,e,r){var i=n.select(t).datum(),a=i.categoryViewModel.model,o=i.parcatsViewModel.graphDiv,s=n.select(t.parentNode).selectAll(\"rect.bandrect\"),l=[];s.each((function(t){A(t).each((function(t){Array.prototype.push.apply(l,m(t))}))}));var u={};u[a.dimensionInd]=a.categoryValue,o.emit(e,{points:l,event:r,constraints:u})}function S(t,e,r){var i=n.select(t).datum(),a=i.categoryViewModel.model,o=i.parcatsViewModel.graphDiv,s=A(i),l=[];s.each((function(t){Array.prototype.push.apply(l,m(t))}));var u={};u[a.dimensionInd]=a.categoryValue,void 0!==i.rawColor&&(u.color=i.rawColor),o.emit(e,{points:l,event:r,constraints:u})}function E(t,e,r){t._fullLayout._calcInverseTransform(t);var i,a,o=t._fullLayout._invScaleX,s=t._fullLayout._invScaleY,l=n.select(r.parentNode).select(\"rect.catrect\"),u=l.node().getBoundingClientRect(),c=l.datum(),f=c.parcatsViewModel,h=f.model.dimensions[c.model.dimensionInd],p=f.trace,d=u.top+u.height/2;f.dimensions.length>1&&h.displayInd===f.dimensions.length-1?(i=u.left,a=\"left\"):(i=u.left+u.width,a=\"right\");var v=c.model.count,g=c.model.categoryLabel,y=v/c.parcatsViewModel.model.count,m={countLabel:v,categoryLabel:g,probabilityLabel:y.toFixed(3)},x=[];-1!==c.parcatsViewModel.hoverinfoItems.indexOf(\"count\")&&x.push([\"Count:\",m.countLabel].join(\" \")),-1!==c.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")&&x.push([\"P(\"+m.categoryLabel+\"):\",m.probabilityLabel].join(\" \"));var b=x.join(\"<br>\");return{trace:p,x:o*(i-e.left),y:s*(d-e.top),text:b,color:\"lightgray\",borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontSize:12,fontColor:\"black\",idealAlign:a,hovertemplate:p.hovertemplate,hovertemplateLabels:m,eventData:[{data:p._input,fullData:p,count:v,category:g,probability:y}]}}function L(t){if(!t.parcatsViewModel.dragDimension&&-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")){if(n.mouse(this)[1]<-1)return;var e,r=t.parcatsViewModel.graphDiv,i=r._fullLayout,a=i._paperdiv.node().getBoundingClientRect(),l=t.parcatsViewModel.hoveron,u=this;\"color\"===l?(function(t){var e=n.select(t).datum(),r=A(e);w(r),r.each((function(){s.raiseToTop(this)})),n.select(t.parentNode).selectAll(\"rect.bandrect\").filter((function(t){return t.color===e.color})).each((function(){s.raiseToTop(this),n.select(this).attr(\"stroke\",\"black\").attr(\"stroke-width\",1.5)}))}(u),S(u,\"plotly_hover\",n.event)):(function(t){n.select(t.parentNode).selectAll(\"rect.bandrect\").each((function(t){var e=A(t);w(e),e.each((function(){s.raiseToTop(this)}))})),n.select(t.parentNode).select(\"rect.catrect\").attr(\"stroke\",\"black\").attr(\"stroke-width\",2.5)}(u),M(u,\"plotly_hover\",n.event)),-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"none\")&&(\"category\"===l?e=E(r,a,u):\"color\"===l?e=function(t,e,r){t._fullLayout._calcInverseTransform(t);var i,a,o=t._fullLayout._invScaleX,s=t._fullLayout._invScaleY,l=r.getBoundingClientRect(),u=n.select(r).datum(),f=u.categoryViewModel,h=f.parcatsViewModel,p=h.model.dimensions[f.model.dimensionInd],d=h.trace,v=l.y+l.height/2;h.dimensions.length>1&&p.displayInd===h.dimensions.length-1?(i=l.left,a=\"left\"):(i=l.left+l.width,a=\"right\");var g=f.model.categoryLabel,y=u.parcatsViewModel.model.count,m=0;u.categoryViewModel.bands.forEach((function(t){t.color===u.color&&(m+=t.count)}));var x=f.model.count,b=0;h.pathSelection.each((function(t){t.model.color===u.color&&(b+=t.model.count)}));var _=m/y,w=m/b,T=m/x,k={countLabel:y,categoryLabel:g,probabilityLabel:_.toFixed(3)},A=[];-1!==f.parcatsViewModel.hoverinfoItems.indexOf(\"count\")&&A.push([\"Count:\",k.countLabel].join(\" \")),-1!==f.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")&&(A.push(\"P(color ∩ \"+g+\"): \"+k.probabilityLabel),A.push(\"P(\"+g+\" | color): \"+w.toFixed(3)),A.push(\"P(color | \"+g+\"): \"+T.toFixed(3)));var M=A.join(\"<br>\"),S=c.mostReadable(u.color,[\"black\",\"white\"]);return{trace:d,x:o*(i-e.left),y:s*(v-e.top),text:M,color:u.color,borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontColor:S,fontSize:10,idealAlign:a,hovertemplate:d.hovertemplate,hovertemplateLabels:k,eventData:[{data:d._input,fullData:d,category:g,count:y,probability:_,categorycount:x,colorcount:b,bandcolorcount:m}]}}(r,a,u):\"dimension\"===l&&(e=function(t,e,r){var i=[];return n.select(r.parentNode.parentNode).selectAll(\"g.category\").select(\"rect.catrect\").each((function(){i.push(E(t,e,this))})),i}(r,a,u)),e&&o.loneHover(e,{container:i._hoverlayer.node(),outerContainer:i._paper.node(),gd:r}))}}function C(t){var e=t.parcatsViewModel;e.dragDimension||(_(e.pathSelection),T(e.dimensionSelection.selectAll(\"g.category\")),k(e.dimensionSelection.selectAll(\"g.category\").selectAll(\"rect.bandrect\")),o.loneUnhover(e.graphDiv._fullLayout._hoverlayer.node()),e.pathSelection.sort(v),-1!==e.hoverinfoItems.indexOf(\"skip\"))||(\"color\"===t.parcatsViewModel.hoveron?S(this,\"plotly_unhover\",n.event):M(this,\"plotly_unhover\",n.event))}function P(t){\"fixed\"!==t.parcatsViewModel.arrangement&&(t.dragDimensionDisplayInd=t.model.displayInd,t.initialDragDimensionDisplayInds=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),t.dragHasMoved=!1,t.dragCategoryDisplayInd=null,n.select(this).selectAll(\"g.category\").select(\"rect.catrect\").each((function(e){var r=n.mouse(this)[0],i=n.mouse(this)[1];-2<=r&&r<=e.width+2&&-2<=i&&i<=e.height+2&&(t.dragCategoryDisplayInd=e.model.displayInd,t.initialDragCategoryDisplayInds=t.model.categories.map((function(t){return t.displayInd})),e.model.dragY=e.y,s.raiseToTop(this.parentNode),n.select(this.parentNode).selectAll(\"rect.bandrect\").each((function(e){e.y<i&&i<=e.y+e.height&&(t.potentialClickBand=this)})))})),t.parcatsViewModel.dragDimension=t,o.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()))}function O(t){if(\"fixed\"!==t.parcatsViewModel.arrangement&&(t.dragHasMoved=!0,null!==t.dragDimensionDisplayInd)){var e=t.dragDimensionDisplayInd,r=e-1,i=e+1,a=t.parcatsViewModel.dimensions[e];if(null!==t.dragCategoryDisplayInd){var o=a.categories[t.dragCategoryDisplayInd];o.model.dragY+=n.event.dy;var s=o.model.dragY,l=o.model.displayInd,u=a.categories,c=u[l-1],f=u[l+1];void 0!==c&&s<c.y+c.height/2&&(o.model.displayInd=c.model.displayInd,c.model.displayInd=l),void 0!==f&&s+o.height>f.y+f.height/2&&(o.model.displayInd=f.model.displayInd,f.model.displayInd=l),t.dragCategoryDisplayInd=o.model.displayInd}if(null===t.dragCategoryDisplayInd||\"freeform\"===t.parcatsViewModel.arrangement){a.model.dragX=n.event.x;var h=t.parcatsViewModel.dimensions[r],p=t.parcatsViewModel.dimensions[i];void 0!==h&&a.model.dragX<h.x+h.width&&(a.model.displayInd=h.model.displayInd,h.model.displayInd=e),void 0!==p&&a.model.dragX+a.width>p.x&&(a.model.displayInd=p.model.displayInd,p.model.displayInd=t.dragDimensionDisplayInd),t.dragDimensionDisplayInd=a.model.displayInd}j(t.parcatsViewModel),N(t.parcatsViewModel),R(t.parcatsViewModel),z(t.parcatsViewModel)}}function I(t){if(\"fixed\"!==t.parcatsViewModel.arrangement&&null!==t.dragDimensionDisplayInd){n.select(this).selectAll(\"text\").attr(\"font-weight\",\"normal\");var e={},r=D(t.parcatsViewModel),i=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),o=t.initialDragDimensionDisplayInds.some((function(t,e){return t!==i[e]}));o&&i.forEach((function(r,n){var i=t.parcatsViewModel.model.dimensions[n].containerInd;e[\"dimensions[\"+i+\"].displayindex\"]=r}));var s=!1;if(null!==t.dragCategoryDisplayInd){var l=t.model.categories.map((function(t){return t.displayInd}));if(s=t.initialDragCategoryDisplayInds.some((function(t,e){return t!==l[e]}))){var u=t.model.categories.slice().sort((function(t,e){return t.displayInd-e.displayInd})),c=u.map((function(t){return t.categoryValue})),f=u.map((function(t){return t.categoryLabel}));e[\"dimensions[\"+t.model.containerInd+\"].categoryarray\"]=[c],e[\"dimensions[\"+t.model.containerInd+\"].ticktext\"]=[f],e[\"dimensions[\"+t.model.containerInd+\"].categoryorder\"]=\"array\"}}-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")&&!t.dragHasMoved&&t.potentialClickBand&&(\"color\"===t.parcatsViewModel.hoveron?S(t.potentialClickBand,\"plotly_click\",n.event.sourceEvent):M(t.potentialClickBand,\"plotly_click\",n.event.sourceEvent)),t.model.dragX=null,null!==t.dragCategoryDisplayInd&&(t.parcatsViewModel.dimensions[t.dragDimensionDisplayInd].categories[t.dragCategoryDisplayInd].model.dragY=null,t.dragCategoryDisplayInd=null),t.dragDimensionDisplayInd=null,t.parcatsViewModel.dragDimension=null,t.dragHasMoved=null,t.potentialClickBand=null,j(t.parcatsViewModel),N(t.parcatsViewModel),n.transition().duration(300).ease(\"cubic-in-out\").each((function(){R(t.parcatsViewModel,!0),z(t.parcatsViewModel,!0)})).each(\"end\",(function(){(o||s)&&a.restyle(t.parcatsViewModel.graphDiv,e,[r])}))}}function D(t){for(var e,r=t.graphDiv._fullData,n=0;n<r.length;n++)if(t.key===r[n].uid){e=n;break}return e}function z(t,e){var r;void 0===e&&(e=!1),t.pathSelection.data((function(t){return t.paths}),p),(r=t.pathSelection,e?r.transition():r).attr(\"d\",(function(t){return t.svgD}))}function R(t,e){function r(t){return e?t.transition():t}void 0===e&&(e=!1),t.dimensionSelection.data((function(t){return t.dimensions}),p);var i=t.dimensionSelection.selectAll(\"g.category\").data((function(t){return t.categories}),p);r(t.dimensionSelection).attr(\"transform\",(function(t){return l(t.x,0)})),r(i).attr(\"transform\",(function(t){return l(0,t.y)})),i.select(\".dimlabel\").text((function(t,e){return 0===e?t.parcatsViewModel.model.dimensions[t.model.dimensionInd].dimensionLabel:null})),i.select(\".catlabel\").attr(\"text-anchor\",(function(t){return d(t)?\"start\":\"end\"})).attr(\"x\",(function(t){return d(t)?t.width+5:-5})).each((function(t){var e,r;d(t)?(e=t.width+5,r=\"start\"):(e=-5,r=\"end\"),n.select(this).selectAll(\"tspan\").attr(\"x\",e).attr(\"text-anchor\",r)}));var a=i.selectAll(\"rect.bandrect\").data((function(t){return t.bands}),p),o=a.enter().append(\"rect\").attr(\"class\",\"bandrect\").attr(\"cursor\",\"move\").attr(\"stroke-opacity\",0).attr(\"fill\",(function(t){return t.color})).attr(\"fill-opacity\",0);a.attr(\"fill\",(function(t){return t.color})).attr(\"width\",(function(t){return t.width})).attr(\"height\",(function(t){return t.height})).attr(\"y\",(function(t){return t.y})),k(o),a.each((function(){s.raiseToTop(this)})),a.exit().remove()}function F(t,e,r){var n,i=r[0],a=e.margin||{l:80,r:80,t:100,b:80},o=i.trace,s=o.domain,l=e.width,u=e.height,c=Math.floor(l*(s.x[1]-s.x[0])),f=Math.floor(u*(s.y[1]-s.y[0])),h=s.x[0]*l+a.l,p=e.height-s.y[1]*e.height+a.t,d=o.line.shape;n=\"all\"===o.hoverinfo?[\"count\",\"probability\"]:(o.hoverinfo||\"\").split(\"+\");var v={trace:o,key:o.uid,model:i,x:h,y:p,width:c,height:f,hoveron:o.hoveron,hoverinfoItems:n,arrangement:o.arrangement,bundlecolors:o.bundlecolors,sortpaths:o.sortpaths,labelfont:o.labelfont,categorylabelfont:o.tickfont,pathShape:d,dragDimension:null,margin:a,paths:[],dimensions:[],graphDiv:t,traceSelection:null,pathSelection:null,dimensionSelection:null};return i.dimensions&&(j(v),N(v)),v}function B(t,e,r,n,a){var o,s,l=[],u=[];for(s=0;s<r.length-1;s++)o=i(r[s]+t[s],t[s+1]),l.push(o(a)),u.push(o(1-a));var c=\"M \"+t[0]+\",\"+e[0];for(c+=\"l\"+r[0]+\",0 \",s=1;s<r.length;s++)c+=\"C\"+l[s-1]+\",\"+e[s-1]+\" \"+u[s-1]+\",\"+e[s]+\" \"+t[s]+\",\"+e[s],c+=\"l\"+r[s]+\",0 \";for(c+=\"l0,\"+n+\" \",c+=\"l -\"+r[r.length-1]+\",0 \",s=r.length-2;s>=0;s--)c+=\"C\"+u[s]+\",\"+(e[s+1]+n)+\" \"+l[s]+\",\"+(e[s]+n)+\" \"+(t[s]+r[s])+\",\"+(e[s]+n),c+=\"l-\"+r[s]+\",0 \";return c+\"Z\"}function N(t){var e=t.dimensions,r=t.model,n=e.map((function(t){return t.categories.map((function(t){return t.y}))})),i=t.model.dimensions.map((function(t){return t.categories.map((function(t){return t.displayInd}))})),a=t.model.dimensions.map((function(t){return t.displayInd})),o=t.dimensions.map((function(t){return t.model.dimensionInd})),s=e.map((function(t){return t.x})),l=e.map((function(t){return t.width})),u=[];for(var c in r.paths)r.paths.hasOwnProperty(c)&&u.push(r.paths[c]);function f(t){var e=t.categoryInds.map((function(t,e){return i[e][t]}));return o.map((function(t){return e[t]}))}u.sort((function(e,r){var n=f(e),i=f(r);return\"backward\"===t.sortpaths&&(n.reverse(),i.reverse()),n.push(e.valueInds[0]),i.push(r.valueInds[0]),t.bundlecolors&&(n.unshift(e.rawColor),i.unshift(r.rawColor)),n<i?-1:n>i?1:0}));for(var h=new Array(u.length),p=e[0].model.count,d=e[0].categories.map((function(t){return t.height})).reduce((function(t,e){return t+e})),v=0;v<u.length;v++){var g,y=u[v];g=p>0?d*(y.count/p):0;for(var m,x=new Array(n.length),b=0;b<y.categoryInds.length;b++){var 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n=r(50693),i=r(13838),a=r(41940),o=r(27670).Y,s=r(1426).extendFlat,l=r(44467).templatedArray;t.exports={domain:o({name:\"parcoords\",trace:!0,editType:\"plot\"}),labelangle:{valType:\"angle\",dflt:0,editType:\"plot\"},labelside:{valType:\"enumerated\",values:[\"top\",\"bottom\"],dflt:\"top\",editType:\"plot\"},labelfont:a({editType:\"plot\"}),tickfont:a({editType:\"plot\"}),rangefont:a({editType:\"plot\"}),dimensions:l(\"dimension\",{label:{valType:\"string\",editType:\"plot\"},tickvals:s({},i.tickvals,{editType:\"plot\"}),ticktext:s({},i.ticktext,{editType:\"plot\"}),tickformat:s({},i.tickformat,{editType:\"plot\"}),visible:{valType:\"boolean\",dflt:!0,editType:\"plot\"},range:{valType:\"info_array\",items:[{valType:\"number\",editType:\"plot\"},{valType:\"number\",editType:\"plot\"}],editType:\"plot\"},constraintrange:{valType:\"info_array\",freeLength:!0,dimensions:\"1-2\",items:[{valType:\"any\",editType:\"plot\"},{valType:\"any\",editType:\"plot\"}],editType:\"plot\"},multiselect:{valType:\"boolean\",dflt:!0,editType:\"plot\"},values:{valType:\"data_array\",editType:\"calc\"},editType:\"calc\"}),line:s({editType:\"calc\"},n(\"line\",{colorscaleDflt:\"Viridis\",autoColorDflt:!1,editTypeOverride:\"calc\"})),unselected:{line:{color:{valType:\"color\",dflt:\"#7f7f7f\",editType:\"plot\"},opacity:{valType:\"number\",min:0,max:1,dflt:\"auto\",editType:\"plot\"},editType:\"plot\"},editType:\"plot\"}}},57920:function(t,e,r){\"use 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s(\"line.colorscale\"),a(t,e,o,s,{prefix:\"line.\",cLetter:\"c\"}),l.length;e.line.color=r}return 1/0}(t,e,r,l,c);o(e,l,c),Array.isArray(v)&&v.length||(e.visible=!1),h(e,v,\"values\",g);var y={family:l.font.family,size:Math.round(l.font.size/1.2),color:l.font.color};n.coerceFont(c,\"labelfont\",y),n.coerceFont(c,\"tickfont\",y),n.coerceFont(c,\"rangefont\",y),c(\"labelangle\"),c(\"labelside\"),c(\"unselected.line.color\"),c(\"unselected.line.opacity\")}},1602:function(t,e,r){\"use strict\";var n=r(71828).isTypedArray;e.convertTypedArray=function(t){return n(t)?Array.prototype.slice.call(t):t},e.isOrdinal=function(t){return!!t.tickvals},e.isVisible=function(t){return t.visible||!(\"visible\"in t)}},67618:function(t,e,r){\"use strict\";var n=r(71791);n.plot=r(21341),t.exports=n},83398:function(t,e,r){\"use strict\";var n=r(56068),i=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor;\\n\\nattribute vec4 p01_04, p05_08, p09_12, p13_16,\\n               p17_20, p21_24, p25_28, p29_32,\\n               p33_36, p37_40, p41_44, p45_48,\\n               p49_52, p53_56, p57_60, colors;\\n\\nuniform mat4 dim0A, dim1A, dim0B, dim1B, dim0C, dim1C, dim0D, dim1D,\\n             loA, hiA, loB, hiB, loC, hiC, loD, hiD;\\n\\nuniform vec2 resolution, viewBoxPos, viewBoxSize;\\nuniform float maskHeight;\\nuniform float drwLayer; // 0: context, 1: focus, 2: pick\\nuniform vec4 contextColor;\\nuniform sampler2D maskTexture, palette;\\n\\nbool isPick    = (drwLayer > 1.5);\\nbool isContext = (drwLayer < 0.5);\\n\\nconst vec4 ZEROS = vec4(0.0, 0.0, 0.0, 0.0);\\nconst vec4 UNITS = vec4(1.0, 1.0, 1.0, 1.0);\\n\\nfloat val(mat4 p, mat4 v) {\\n    return dot(matrixCompMult(p, v) * UNITS, UNITS);\\n}\\n\\nfloat axisY(float ratio, mat4 A, mat4 B, mat4 C, mat4 D) {\\n    float y1 = val(A, dim0A) + val(B, dim0B) + val(C, dim0C) + val(D, dim0D);\\n    float y2 = val(A, dim1A) + val(B, dim1B) + val(C, dim1C) + val(D, dim1D);\\n    return y1 * (1.0 - ratio) + y2 * ratio;\\n}\\n\\nint iMod(int a, int b) {\\n    return a - b * (a / b);\\n}\\n\\nbool fOutside(float p, float lo, float hi) {\\n    return (lo < hi) && (lo > p || p > hi);\\n}\\n\\nbool vOutside(vec4 p, vec4 lo, vec4 hi) {\\n    return (\\n        fOutside(p[0], lo[0], hi[0]) ||\\n        fOutside(p[1], lo[1], hi[1]) ||\\n        fOutside(p[2], lo[2], hi[2]) ||\\n        fOutside(p[3], lo[3], hi[3])\\n    );\\n}\\n\\nbool mOutside(mat4 p, mat4 lo, mat4 hi) {\\n    return (\\n        vOutside(p[0], lo[0], hi[0]) ||\\n        vOutside(p[1], lo[1], hi[1]) ||\\n        vOutside(p[2], lo[2], hi[2]) ||\\n        vOutside(p[3], lo[3], hi[3])\\n    );\\n}\\n\\nbool outsideBoundingBox(mat4 A, mat4 B, mat4 C, mat4 D) {\\n    return mOutside(A, loA, hiA) ||\\n           mOutside(B, loB, hiB) ||\\n           mOutside(C, loC, hiC) ||\\n           mOutside(D, loD, hiD);\\n}\\n\\nbool outsideRasterMask(mat4 A, mat4 B, mat4 C, mat4 D) {\\n    mat4 pnts[4];\\n    pnts[0] = A;\\n    pnts[1] = B;\\n    pnts[2] = C;\\n    pnts[3] = D;\\n\\n    for(int i = 0; i < 4; ++i) {\\n        for(int j = 0; j < 4; ++j) {\\n            for(int k = 0; k < 4; ++k) {\\n                if(0 == iMod(\\n                    int(255.0 * texture2D(maskTexture,\\n                        vec2(\\n                            (float(i * 2 + j / 2) + 0.5) / 8.0,\\n                            (pnts[i][j][k] * (maskHeight - 1.0) + 1.0) / maskHeight\\n                        ))[3]\\n                    ) / int(pow(2.0, float(iMod(j * 4 + k, 8)))),\\n                    2\\n                )) return true;\\n            }\\n        }\\n    }\\n    return false;\\n}\\n\\nvec4 position(bool isContext, float v, mat4 A, mat4 B, mat4 C, mat4 D) {\\n    float x = 0.5 * sign(v) + 0.5;\\n    float y = axisY(x, A, B, C, D);\\n    float z = 1.0 - abs(v);\\n\\n    z += isContext ? 0.0 : 2.0 * float(\\n        outsideBoundingBox(A, B, C, D) ||\\n        outsideRasterMask(A, B, C, D)\\n    );\\n\\n    return vec4(\\n        2.0 * (vec2(x, y) * viewBoxSize + viewBoxPos) / resolution - 1.0,\\n        z,\\n        1.0\\n    );\\n}\\n\\nvoid main() {\\n    mat4 A = mat4(p01_04, p05_08, p09_12, p13_16);\\n    mat4 B = mat4(p17_20, p21_24, p25_28, p29_32);\\n    mat4 C = mat4(p33_36, p37_40, p41_44, p45_48);\\n    mat4 D = mat4(p49_52, p53_56, p57_60, ZEROS);\\n\\n    float v = colors[3];\\n\\n    gl_Position = position(isContext, v, A, B, C, D);\\n\\n    fragColor =\\n        isContext ? vec4(contextColor) :\\n        isPick ? vec4(colors.rgb, 1.0) : texture2D(palette, vec2(abs(v), 0.5));\\n}\\n\"]),a=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n    gl_FragColor = fragColor;\\n}\\n\"]),o=r(25706).maxDimensionCount,s=r(71828),l=1e-6,u=new Uint8Array(4),c=new Uint8Array(4),f={shape:[256,1],format:\"rgba\",type:\"uint8\",mag:\"nearest\",min:\"nearest\"};function h(t,e,r,n,i){var a=t._gl;a.enable(a.SCISSOR_TEST),a.scissor(e,r,n,i),t.clear({color:[0,0,0,0],depth:1})}function p(t,e,r,n,i,a){var o=a.key;r.drawCompleted||(function(t){t.read({x:0,y:0,width:1,height:1,data:u})}(t),r.drawCompleted=!0),function s(l){var u=Math.min(n,i-l*n);0===l&&(window.cancelAnimationFrame(r.currentRafs[o]),delete r.currentRafs[o],h(t,a.scissorX,a.scissorY,a.scissorWidth,a.viewBoxSize[1])),r.clearOnly||(a.count=2*u,a.offset=2*l*n,e(a),l*n+u<i&&(r.currentRafs[o]=window.requestAnimationFrame((function(){s(l+1)}))),r.drawCompleted=!1)}(0)}function d(t,e){for(var r=new Array(256),n=0;n<256;n++)r[n]=t(n/255).concat(e);return r}function v(t,e){return(t>>>8*e)%256/255}function g(t,e,r){for(var n=new Array(8*e),i=0,a=0;a<e;a++)for(var o=0;o<2;o++)for(var s=0;s<4;s++){var l=4*t+s,u=r[64*a+l];63===l&&0===o&&(u*=-1),n[i++]=u}return n}function y(t){var e=\"0\"+t;return e.substr(e.length-2)}function m(t){return t<o?\"p\"+y(t+1)+\"_\"+y(t+4):\"colors\"}function x(t,e,r,n,i,a,o,l,u,c,f,h,p,d){for(var v=[[],[]],g=0;g<64;g++)v[0][g]=g===i?1:0,v[1][g]=g===a?1:0;o*=d,l*=d,u*=d,c*=d;var y=t.lines.canvasOverdrag*d,m=t.domain,x=t.canvasWidth*d,b=t.canvasHeight*d,_=t.pad.l*d,w=t.pad.b*d,T=t.layoutHeight*d,k=t.layoutWidth*d,A=t.deselectedLines.color,M=t.deselectedLines.opacity;return s.extendFlat({key:f,resolution:[x,b],viewBoxPos:[o+y,l],viewBoxSize:[u,c],i0:i,i1:a,dim0A:v[0].slice(0,16),dim0B:v[0].slice(16,32),dim0C:v[0].slice(32,48),dim0D:v[0].slice(48,64),dim1A:v[1].slice(0,16),dim1B:v[1].slice(16,32),dim1C:v[1].slice(32,48),dim1D:v[1].slice(48,64),drwLayer:h,contextColor:[A[0]/255,A[1]/255,A[2]/255,\"auto\"!==M?A[3]*M:Math.max(1/255,Math.pow(1/t.lines.color.length,1/3))],scissorX:(n===e?0:o+y)+(_-y)+k*m.x[0],scissorWidth:(n===r?x-o+y:u+.5)+(n===e?o+y:0),scissorY:l+w+T*m.y[0],scissorHeight:c,viewportX:_-y+k*m.x[0],viewportY:w+T*m.y[0],viewportWidth:x,viewportHeight:b},p)}function b(t){var e=2047,r=Math.max(0,Math.floor(t[0]*e),0),n=Math.min(e,Math.ceil(t[1]*e),e);return[Math.min(r,n),Math.max(r,n)]}t.exports=function(t,e){var r,n,u,y,_,w=e.context,T=e.pick,k=e.regl,A=k._gl,M=A.getParameter(A.ALIASED_LINE_WIDTH_RANGE),S=Math.max(M[0],Math.min(M[1],e.viewModel.plotGlPixelRatio)),E={currentRafs:{},drawCompleted:!0,clearOnly:!1},L=function(t){for(var e={},r=0;r<=o;r+=4)e[m(r)]=t.buffer({usage:\"dynamic\",type:\"float\",data:new Uint8Array(0)});return e}(k),C=k.texture(f),P=[];I(e);var O=k({profile:!1,blend:{enable:w,func:{srcRGB:\"src alpha\",dstRGB:\"one minus src 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I(t){r=t.model,n=t.viewModel,u=n.dimensions.slice(),y=u[0]?u[0].values.length:0;var e=r.lines,i=T?e.color.map((function(t,r){return r/e.color.length})):e.color,a=function(t,e,r){for(var n,i=new Array(t*(o+4)),a=0,s=0;s<t;s++){for(var u=0;u<o;u++)i[a++]=u<e.length?e[u].paddedUnitValues[s]:.5;i[a++]=v(s,2),i[a++]=v(s,1),i[a++]=v(s,0),i[a++]=(n=r[s],Math.max(l,Math.min(.999999,n)))}return i}(y,u,i);!function(t,e,r){for(var n=0;n<=o;n+=4)t[m(n)](g(n/4,e,r))}(L,y,a),w||T||(C=k.texture(s.extendFlat({data:d(r.unitToColor,255)},f)))}return{render:function(t,e,n){var i,a,o,s=t.length,l=1/0,c=-1/0;for(i=0;i<s;i++)t[i].dim0.canvasX<l&&(l=t[i].dim0.canvasX,a=i),t[i].dim1.canvasX>c&&(c=t[i].dim1.canvasX,o=i);0===s&&h(k,0,0,r.canvasWidth,r.canvasHeight);var f=function(t){var e,r,n,i=[[],[]];for(n=0;n<64;n++){var a=!t&&n<u.length?u[n].brush.filter.getBounds():[-1/0,1/0];i[0][n]=a[0],i[1][n]=a[1]}var o=new Array(16384);for(e=0;e<16384;e++)o[e]=255;if(!t)for(e=0;e<u.length;e++){var s=e%8,l=(e-s)/8,c=Math.pow(2,s),f=u[e].brush.filter.get();if(!(f.length<2)){var h=b(f[0])[1];for(r=1;r<f.length;r++){var p=b(f[r]);for(n=h+1;n<p[0];n++)o[8*n+l]&=~c;h=Math.max(h,p[1])}}}var d={shape:[8,2048],format:\"alpha\",type:\"uint8\",mag:\"nearest\",min:\"nearest\",data:o};return _?_(d):_=k.texture(d),{maskTexture:_,maskHeight:2048,loA:i[0].slice(0,16),loB:i[0].slice(16,32),loC:i[0].slice(32,48),loD:i[0].slice(48,64),hiA:i[1].slice(0,16),hiB:i[1].slice(16,32),hiC:i[1].slice(32,48),hiD:i[1].slice(48,64)}}(w);for(i=0;i<s;i++){var d=t[i],v=d.dim0.crossfilterDimensionIndex,g=d.dim1.crossfilterDimensionIndex,m=d.canvasX,A=d.canvasY,M=m+d.panelSizeX,S=d.plotGlPixelRatio;if(e||!P[v]||P[v][0]!==m||P[v][1]!==M){P[v]=[m,M];var L=x(r,a,o,i,v,g,m,A,d.panelSizeX,d.panelSizeY,d.dim0.crossfilterDimensionIndex,w?0:T?2:1,f,S);E.clearOnly=n;var C=e?r.lines.blockLineCount:y;p(k,O,E,C,y,L)}}},readPixel:function(t,e){return k.read({x:t,y:e,width:1,height:1,data:c}),c},readPixels:function(t,e,r,n){var i=new Uint8Array(4*r*n);return k.read({x:t,y:e,width:r,height:n,data:i}),i},destroy:function(){for(var e in t.style[\"pointer-events\"]=\"none\",C.destroy(),_&&_.destroy(),L)L[e].destroy()},update:I}}},94397:function(t){\"use strict\";t.exports=function(t,e,r,n){var i,a;for(n||(n=1/0),i=0;i<e.length;i++)(a=e[i]).visible&&(n=Math.min(n,a[r].length));for(n===1/0&&(n=0),t._length=n,i=0;i<e.length;i++)(a=e[i]).visible&&(a._length=n);return n}},17171:function(t,e,r){\"use strict\";var n=r(39898),i=r(71828),a=i.numberFormat,o=r(36652),s=r(89298),l=i.strRotate,u=i.strTranslate,c=r(63893),f=r(91424),h=r(21081),p=r(28984),d=p.keyFun,v=p.repeat,g=p.unwrap,y=r(1602),m=r(25706),x=r(57920),b=r(83398);function _(t,e,r){return i.aggNums(t,null,e,r)}function w(t,e){return k(_(Math.min,t,e),_(Math.max,t,e))}function T(t){var e=t.range;return e?k(e[0],e[1]):w(t.values,t._length)}function k(t,e){return!isNaN(t)&&isFinite(t)||(t=0),!isNaN(e)&&isFinite(e)||(e=0),t===e&&(0===t?(t-=1,e+=1):(t*=.9,e*=1.1)),[t,e]}function A(t,e,r,i,o){var s,l,u=T(r);return i?n.scale.ordinal().domain(i.map((s=a(r.tickformat),l=o,l?function(t,e){var r=l[e];return null==r?s(t):r}:s))).range(i.map((function(r){var n=(r-u[0])/(u[1]-u[0]);return t-e+n*(2*e-t)}))):n.scale.linear().domain(u).range([t-e,e])}function M(t){if(t.tickvals){var e=T(t);return n.scale.ordinal().domain(t.tickvals).range(t.tickvals.map((function(t){return(t-e[0])/(e[1]-e[0])})))}}function S(t){var e=t.map((function(t){return t[0]})),r=t.map((function(t){var e=o(t[1]);return n.rgb(\"rgb(\"+e[0]+\",\"+e[1]+\",\"+e[2]+\")\")})),i=\"rgb\".split(\"\").map((function(t){return n.scale.linear().clamp(!0).domain(e).range(r.map((i=t,function(t){return t[i]})));var i}));return function(t){return i.map((function(e){return e(t)}))}}function E(t){return t.dimensions.some((function(t){return t.brush.filterSpecified}))}function L(t,e,r){var a=g(e),s=a.trace,l=y.convertTypedArray(a.lineColor),u=s.line,c={color:o(s.unselected.line.color),opacity:s.unselected.line.opacity},f=h.extractOpts(u),p=f.reversescale?h.flipScale(a.cscale):a.cscale,d=s.domain,v=s.dimensions,x=t.width,b=s.labelangle,_=s.labelside,w=s.labelfont,k=s.tickfont,A=s.rangefont,M=i.extendDeepNoArrays({},u,{color:l.map(n.scale.linear().domain(T({values:l,range:[f.min,f.max],_length:s._length}))),blockLineCount:m.blockLineCount,canvasOverdrag:m.overdrag*m.canvasPixelRatio}),E=Math.floor(x*(d.x[1]-d.x[0])),L=Math.floor(t.height*(d.y[1]-d.y[0])),C=t.margin||{l:80,r:80,t:100,b:80},P=E,O=L;return{key:r,colCount:v.filter(y.isVisible).length,dimensions:v,tickDistance:m.tickDistance,unitToColor:S(p),lines:M,deselectedLines:c,labelAngle:b,labelSide:_,labelFont:w,tickFont:k,rangeFont:A,layoutWidth:x,layoutHeight:t.height,domain:d,translateX:d.x[0]*x,translateY:t.height-d.y[1]*t.height,pad:C,canvasWidth:P*m.canvasPixelRatio+2*M.canvasOverdrag,canvasHeight:O*m.canvasPixelRatio,width:P,height:O,canvasPixelRatio:m.canvasPixelRatio}}function C(t,e,r){var o=r.width,s=r.height,l=r.dimensions,u=r.canvasPixelRatio,c=function(t){return o*t/Math.max(1,r.colCount-1)},f=m.verticalPadding/s,h=function(t,e){return n.scale.linear().range([e,t-e])}(s,m.verticalPadding),p={key:r.key,xScale:c,model:r,inBrushDrag:!1},d={};return p.dimensions=l.filter(y.isVisible).map((function(o,l){var v=function(t,e){return n.scale.linear().domain(T(t)).range([e,1-e])}(o,f),g=d[o.label];d[o.label]=(g||0)+1;var b=o.label+(g?\"__\"+g:\"\"),_=o.constraintrange,w=_&&_.length;w&&!Array.isArray(_[0])&&(_=[_]);var k=w?_.map((function(t){return t.map(v)})):[[-1/0,1/0]],S=o.values;S.length>o._length&&(S=S.slice(0,o._length));var L,C=o.tickvals;function P(t,e){return{val:t,text:L[e]}}function O(t,e){return t.val-e.val}if(Array.isArray(C)&&C.length){L=o.ticktext,Array.isArray(L)&&L.length?L.length>C.length?L=L.slice(0,C.length):C.length>L.length&&(C=C.slice(0,L.length)):L=C.map(a(o.tickformat));for(var I=1;I<C.length;I++)if(C[I]<C[I-1]){for(var D=C.map(P).sort(O),z=0;z<C.length;z++)C[z]=D[z].val,L[z]=D[z].text;break}}else C=void 0;return S=y.convertTypedArray(S),{key:b,label:o.label,tickFormat:o.tickformat,tickvals:C,ticktext:L,ordinal:y.isOrdinal(o),multiselect:o.multiselect,xIndex:l,crossfilterDimensionIndex:l,visibleIndex:o._index,height:s,values:S,paddedUnitValues:S.map(v),unitTickvals:C&&C.map(v),xScale:c,x:c(l),canvasX:c(l)*u,unitToPaddedPx:h,domainScale:A(s,m.verticalPadding,o,C,L),ordinalScale:M(o),parent:p,model:r,brush:x.makeBrush(t,w,k,(function(){t.linePickActive(!1)}),(function(){var e=p;e.focusLayer&&e.focusLayer.render(e.panels,!0);var r=E(e);!t.contextShown()&&r?(e.contextLayer&&e.contextLayer.render(e.panels,!0),t.contextShown(!0)):t.contextShown()&&!r&&(e.contextLayer&&e.contextLayer.render(e.panels,!0,!0),t.contextShown(!1))}),(function(r){if(p.focusLayer.render(p.panels,!0),p.pickLayer&&p.pickLayer.render(p.panels,!0),t.linePickActive(!0),e&&e.filterChanged){var n=v.invert,a=r.map((function(t){return t.map(n).sort(i.sorterAsc)})).sort((function(t,e){return t[0]-e[0]}));e.filterChanged(p.key,o._index,a)}}))}})),p}function P(t){t.classed(m.cn.axisExtentText,!0).attr(\"text-anchor\",\"middle\").style(\"cursor\",\"default\")}function O(t,e){var r=\"top\"===e?1:-1,n=t*Math.PI/180;return{dir:r,dx:Math.sin(n),dy:Math.cos(n),degrees:t}}function I(t,e,r){for(var n=e.panels||(e.panels=[]),i=t.data(),a=0;a<i.length-1;a++){var o=n[a]||(n[a]={}),s=i[a],l=i[a+1];o.dim0=s,o.dim1=l,o.canvasX=s.canvasX,o.panelSizeX=l.canvasX-s.canvasX,o.panelSizeY=e.model.canvasHeight,o.y=0,o.canvasY=0,o.plotGlPixelRatio=r}}function D(t,e){return s.tickText(t._ax,e,!1).text}function z(t,e){if(t.ordinal)return\"\";var r=t.domainScale.domain(),n=r[e?r.length-1:0];return D(t.model.dimensions[t.visibleIndex],n)}t.exports=function(t,e,r,a){var o=t._context.staticPlot,h=t._fullLayout,p=h._toppaper,_=h._glcontainer,T=t._context.plotGlPixelRatio,A=t._fullLayout.paper_bgcolor;!function(t){for(var e=0;e<t.length;e++)for(var r=0;r<t[e].length;r++)for(var n=t[e][r].trace,i=n.dimensions,a=0;a<i.length;a++){var o=i[a].values,l=i[a]._ax;l&&(l.range?l.range=k(l.range[0],l.range[1]):l.range=w(o,n._length),l.dtick||(l.dtick=.01*(Math.abs(l.range[1]-l.range[0])||1)),l.tickformat=i[a].tickformat,s.calcTicks(l),l.cleanRange())}}(e);var M,S,R=(M=!0,S=!1,{linePickActive:function(t){return arguments.length?M=!!t:M},contextShown:function(t){return arguments.length?S=!!t:S}}),F=e.filter((function(t){return g(t).trace.visible})).map(L.bind(0,r)).map(C.bind(0,R,a));_.each((function(t,e){return i.extendFlat(t,F[e])}));var B=_.selectAll(\".gl-canvas\").each((function(t){t.viewModel=F[0],t.viewModel.plotGlPixelRatio=T,t.viewModel.paperColor=A,t.model=t.viewModel?t.viewModel.model:null})),N=null;B.filter((function(t){return t.pick})).style(\"pointer-events\",o?\"none\":\"auto\").on(\"mousemove\",(function(t){if(R.linePickActive()&&t.lineLayer&&a&&a.hover){var e=n.event,r=this.width,i=this.height,o=n.mouse(this),s=o[0],l=o[1];if(s<0||l<0||s>=r||l>=i)return;var u=t.lineLayer.readPixel(s,i-1-l),c=0!==u[3],f=c?u[2]+256*(u[1]+256*u[0]):null,h={x:s,y:l,clientX:e.clientX,clientY:e.clientY,dataIndex:t.model.key,curveNumber:f};f!==N&&(c?a.hover(h):a.unhover&&a.unhover(h),N=f)}})),B.style(\"opacity\",(function(t){return t.pick?0:1})),p.style(\"background\",\"rgba(255, 255, 255, 0)\");var j=p.selectAll(\".\"+m.cn.parcoords).data(F,d);j.exit().remove(),j.enter().append(\"g\").classed(m.cn.parcoords,!0).style(\"shape-rendering\",\"crispEdges\").style(\"pointer-events\",\"none\"),j.attr(\"transform\",(function(t){return u(t.model.translateX,t.model.translateY)}));var U=j.selectAll(\".\"+m.cn.parcoordsControlView).data(v,d);U.enter().append(\"g\").classed(m.cn.parcoordsControlView,!0),U.attr(\"transform\",(function(t){return u(t.model.pad.l,t.model.pad.t)}));var V=U.selectAll(\".\"+m.cn.yAxis).data((function(t){return 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e=t.model.height/t.model.tickDistance,r=t.domainScale,i=r.domain();n.select(this).call(n.svg.axis().orient(\"left\").tickSize(4).outerTickSize(2).ticks(e,t.tickFormat).tickValues(t.ordinal?i:null).tickFormat((function(e){return y.isOrdinal(t)?e:D(t.model.dimensions[t.visibleIndex],e)})).scale(r)),f.font(q.selectAll(\"text\"),t.model.tickFont)})),q.selectAll(\".domain, .tick>line\").attr(\"fill\",\"none\").attr(\"stroke\",\"black\").attr(\"stroke-opacity\",.25).attr(\"stroke-width\",\"1px\"),q.selectAll(\"text\").style(\"text-shadow\",c.makeTextShadow(A)).style(\"cursor\",\"default\");var G=H.selectAll(\".\"+m.cn.axisHeading).data(v,d);G.enter().append(\"g\").classed(m.cn.axisHeading,!0);var Z=G.selectAll(\".\"+m.cn.axisTitle).data(v,d);Z.enter().append(\"text\").classed(m.cn.axisTitle,!0).attr(\"text-anchor\",\"middle\").style(\"cursor\",\"ew-resize\").style(\"pointer-events\",o?\"none\":\"auto\"),Z.text((function(t){return t.label})).each((function(e){var 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J=Y.selectAll(\".\"+m.cn.axisExtentBottom).data(v,d);J.enter().append(\"g\").classed(m.cn.axisExtentBottom,!0),J.attr(\"transform\",(function(t){return u(0,t.model.height+m.axisExtentOffset)}));var K=J.selectAll(\".\"+m.cn.axisExtentBottomText).data(v,d);K.enter().append(\"text\").classed(m.cn.axisExtentBottomText,!0).attr(\"dy\",\"0.75em\").call(P),K.text((function(t){return z(t,!1)})).each((function(t){f.font(n.select(this),t.model.rangeFont)})),x.ensureAxisBrush(H,A,t)}},21341:function(t,e,r){\"use strict\";var n=r(17171),i=r(79749),a=r(1602).isVisible,o={};function s(t,e,r){var n=e.indexOf(r),i=t.indexOf(n);return-1===i&&(i+=e.length),i}(t.exports=function(t,e){var r=t._fullLayout;if(i(t,[],o)){var l={},u={},c={},f={},h=r._size;e.forEach((function(e,r){var n=e[0].trace;c[r]=n.index;var i=f[r]=n._fullInput.index;l[r]=t.data[i].dimensions,u[r]=t.data[i].dimensions.slice()})),n(t,e,{width:h.w,height:h.h,margin:{t:h.t,r:h.r,b:h.b,l:h.l}},{filterChanged:function(e,n,i){var a=u[e][n],o=i.map((function(t){return t.slice()})),s=\"dimensions[\"+n+\"].constraintrange\",l=r._tracePreGUI[t._fullData[c[e]]._fullInput.uid];if(void 0===l[s]){var h=a.constraintrange;l[s]=h||null}var p=t._fullData[c[e]].dimensions[n];o.length?(1===o.length&&(o=o[0]),a.constraintrange=o,p.constraintrange=o.slice(),o=[o]):(delete a.constraintrange,delete p.constraintrange,o=null);var d={};d[s]=o,t.emit(\"plotly_restyle\",[d,[f[e]]])},hover:function(e){t.emit(\"plotly_hover\",e)},unhover:function(e){t.emit(\"plotly_unhover\",e)},axesMoved:function(e,r){var n=function(t,e){return function(r,n){return s(t,e,r)-s(t,e,n)}}(r,u[e].filter(a));l[e].sort(n),u[e].filter((function(t){return!a(t)})).sort((function(t){return u[e].indexOf(t)})).forEach((function(t){l[e].splice(l[e].indexOf(t),1),l[e].splice(u[e].indexOf(t),0,t)})),t.emit(\"plotly_restyle\",[{dimensions:[l[e]]},[f[e]]])}})}}).reglPrecompiled=o},34e3:function(t,e,r){\"use strict\";var n=r(9012),i=r(27670).Y,a=r(41940),o=r(22399),s=r(5386).fF,l=r(5386).si,u=r(1426).extendFlat,c=r(79952).u,f=a({editType:\"plot\",arrayOk:!0,colorEditType:\"plot\"});t.exports={labels:{valType:\"data_array\",editType:\"calc\"},label0:{valType:\"number\",dflt:0,editType:\"calc\"},dlabel:{valType:\"number\",dflt:1,editType:\"calc\"},values:{valType:\"data_array\",editType:\"calc\"},marker:{colors:{valType:\"data_array\",editType:\"calc\"},line:{color:{valType:\"color\",dflt:o.defaultLine,arrayOk:!0,editType:\"style\"},width:{valType:\"number\",min:0,dflt:0,arrayOk:!0,editType:\"style\"},editType:\"calc\"},pattern:c,editType:\"calc\"},text:{valType:\"data_array\",editType:\"plot\"},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"style\"},scalegroup:{valType:\"string\",dflt:\"\",editType:\"calc\"},textinfo:{valType:\"flaglist\",flags:[\"label\",\"text\",\"value\",\"percent\"],extras:[\"none\"],editType:\"calc\"},hoverinfo:u({},n.hoverinfo,{flags:[\"label\",\"text\",\"value\",\"percent\",\"name\"]}),hovertemplate:s({},{keys:[\"label\",\"color\",\"value\",\"percent\",\"text\"]}),texttemplate:l({editType:\"plot\"},{keys:[\"label\",\"color\",\"value\",\"percent\",\"text\"]}),textposition:{valType:\"enumerated\",values:[\"inside\",\"outside\",\"auto\",\"none\"],dflt:\"auto\",arrayOk:!0,editType:\"plot\"},textfont:u({},f,{}),insidetextorientation:{valType:\"enumerated\",values:[\"horizontal\",\"radial\",\"tangential\",\"auto\"],dflt:\"auto\",editType:\"plot\"},insidetextfont:u({},f,{}),outsidetextfont:u({},f,{}),automargin:{valType:\"boolean\",dflt:!1,editType:\"plot\"},title:{text:{valType:\"string\",dflt:\"\",editType:\"plot\"},font:u({},f,{}),position:{valType:\"enumerated\",values:[\"top 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n=r(74875);e.name=\"pie\",e.plot=function(t,r,i,a){n.plotBasePlot(e.name,t,r,i,a)},e.clean=function(t,r,i,a){n.cleanBasePlot(e.name,t,r,i,a)}},32354:function(t,e,r){\"use strict\";var n=r(92770),i=r(84267),a=r(7901),o={};function s(t){return function(e,r){return!!e&&!!(e=i(e)).isValid()&&(e=a.addOpacity(e,e.getAlpha()),t[r]||(t[r]=e),e)}}function l(t,e){var r,n=JSON.stringify(t),a=e[n];if(!a){for(a=t.slice(),r=0;r<t.length;r++)a.push(i(t[r]).lighten(20).toHexString());for(r=0;r<t.length;r++)a.push(i(t[r]).darken(20).toHexString());e[n]=a}return a}t.exports={calc:function(t,e){var r,i,a=[],o=t._fullLayout,l=o.hiddenlabels||[],u=e.labels,c=e.marker.colors||[],f=e.values,h=e._length,p=e._hasValues&&h;if(e.dlabel)for(u=new Array(h),r=0;r<h;r++)u[r]=String(e.label0+r*e.dlabel);var d={},v=s(o[\"_\"+e.type+\"colormap\"]),g=0,y=!1;for(r=0;r<h;r++){var m,x,b;if(p){if(m=f[r],!n(m))continue;m=+m}else m=1;void 0!==(x=u[r])&&\"\"!==x||(x=r);var _=d[x=String(x)];void 0===_?(d[x]=a.length,(b=-1!==l.indexOf(x))||(g+=m),a.push({v:m,label:x,color:v(c[r],x),i:r,pts:[r],hidden:b})):(y=!0,(i=a[_]).v+=m,i.pts.push(r),i.hidden||(g+=m),!1===i.color&&c[r]&&(i.color=v(c[r],x)))}return a=a.filter((function(t){return t.v>=0})),(\"funnelarea\"===e.type?y:e.sort)&&a.sort((function(t,e){return e.v-t.v})),a[0]&&(a[0].vTotal=g),a},crossTraceCalc:function(t,e){var r=(e||{}).type;r||(r=\"pie\");var n=t._fullLayout,i=t.calcdata,a=n[r+\"colorway\"],s=n[\"_\"+r+\"colormap\"];n[\"extend\"+r+\"colors\"]&&(a=l(a,o));for(var u=0,c=0;c<i.length;c++){var f=i[c];if(f[0].trace.type===r)for(var h=0;h<f.length;h++){var p=f[h];!1===p.color&&(s[p.label]?p.color=s[p.label]:(s[p.label]=p.color=a[u%a.length],u++))}}},makePullColorFn:s,generateExtendedColors:l}},37434:function(t,e,r){\"use strict\";var n=r(92770),i=r(71828),a=r(34e3),o=r(27670).c,s=r(90769).handleText,l=r(71828).coercePattern;function u(t,e){var r=Array.isArray(t),a=i.isArrayOrTypedArray(e),o=Math.min(r?t.length:1/0,a?e.length:1/0);if(isFinite(o)||(o=0),o&&a){for(var s,l=0;l<o;l++){var u=e[l];if(n(u)&&u>0){s=!0;break}}s||(o=0)}return{hasLabels:r,hasValues:a,len:o}}function c(t,e,r,n,i){n(\"marker.line.width\")&&n(\"marker.line.color\",i?void 0:r.paper_bgcolor);var a=n(\"marker.colors\");l(n,\"marker.pattern\",a),t.marker&&!e.marker.pattern.fgcolor&&(e.marker.pattern.fgcolor=t.marker.colors),e.marker.pattern.bgcolor||(e.marker.pattern.bgcolor=r.paper_bgcolor)}t.exports={handleLabelsAndValues:u,handleMarkerDefaults:c,supplyDefaults:function(t,e,r,n){function l(r,n){return i.coerce(t,e,a,r,n)}var f=u(l(\"labels\"),l(\"values\")),h=f.len;if(e._hasLabels=f.hasLabels,e._hasValues=f.hasValues,!e._hasLabels&&e._hasValues&&(l(\"label0\"),l(\"dlabel\")),h){e._length=h,c(t,e,n,l,!0),l(\"scalegroup\");var p,d=l(\"text\"),v=l(\"texttemplate\");if(v||(p=l(\"textinfo\",Array.isArray(d)?\"text+percent\":\"percent\")),l(\"hovertext\"),l(\"hovertemplate\"),v||p&&\"none\"!==p){var g=l(\"textposition\");s(t,e,n,l,g,{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!1,moduleHasCliponaxis:!1,moduleHasTextangle:!1,moduleHasInsideanchor:!1}),(Array.isArray(g)||\"auto\"===g||\"outside\"===g)&&l(\"automargin\"),(\"inside\"===g||\"auto\"===g||Array.isArray(g))&&l(\"insidetextorientation\")}o(e,n,l);var y=l(\"hole\");if(l(\"title.text\")){var m=l(\"title.position\",y?\"middle center\":\"top center\");y||\"middle center\"!==m||(e.title.position=\"top center\"),i.coerceFont(l,\"title.font\",n.font)}l(\"sort\"),l(\"direction\"),l(\"rotation\"),l(\"pull\")}else e.visible=!1}}},20007:function(t,e,r){\"use strict\";var n=r(23469).appendArrayMultiPointValues;t.exports=function(t,e){var r={curveNumber:e.index,pointNumbers:t.pts,data:e._input,fullData:e,label:t.label,color:t.color,value:t.v,percent:t.percent,text:t.text,bbox:t.bbox,v:t.v};return 1===t.pts.length&&(r.pointNumber=r.i=t.pts[0]),n(r,e,t.pts),\"funnelarea\"===e.type&&(delete r.v,delete r.i),r}},22209:function(t,e,r){\"use strict\";var n=r(91424),i=r(7901);t.exports=function(t,e,r,a){var o=r.marker.pattern;o&&o.shape?n.pointStyle(t,r,a,e):i.fill(t,e.color)}},53581:function(t,e,r){\"use strict\";var n=r(71828);function i(t){return-1!==t.indexOf(\"e\")?t.replace(/[.]?0+e/,\"e\"):-1!==t.indexOf(\".\")?t.replace(/[.]?0+$/,\"\"):t}e.formatPiePercent=function(t,e){var r=i((100*t).toPrecision(3));return n.numSeparate(r,e)+\"%\"},e.formatPieValue=function(t,e){var r=i(t.toPrecision(10));return n.numSeparate(r,e)},e.getFirstFilled=function(t,e){if(Array.isArray(t))for(var r=0;r<e.length;r++){var n=t[e[r]];if(n||0===n||\"\"===n)return n}},e.castOption=function(t,r){return Array.isArray(t)?e.getFirstFilled(t,r):t||void 0},e.getRotationAngle=function(t){return(\"auto\"===t?0:t)*Math.PI/180}},58810:function(t,e,r){\"use strict\";t.exports={attributes:r(34e3),supplyDefaults:r(37434).supplyDefaults,supplyLayoutDefaults:r(92097),layoutAttributes:r(92774),calc:r(32354).calc,crossTraceCalc:r(32354).crossTraceCalc,plot:r(14575).plot,style:r(68357),styleOne:r(63463),moduleType:\"trace\",name:\"pie\",basePlotModule:r(13584),categories:[\"pie-like\",\"pie\",\"showLegend\"],meta:{}}},92774:function(t){\"use strict\";t.exports={hiddenlabels:{valType:\"data_array\",editType:\"calc\"},piecolorway:{valType:\"colorlist\",editType:\"calc\"},extendpiecolors:{valType:\"boolean\",dflt:!0,editType:\"calc\"}}},92097:function(t,e,r){\"use strict\";var n=r(71828),i=r(92774);t.exports=function(t,e){function r(r,a){return n.coerce(t,e,i,r,a)}r(\"hiddenlabels\"),r(\"piecolorway\",e.colorway),r(\"extendpiecolors\")}},14575:function(t,e,r){\"use strict\";var n=r(39898),i=r(74875),a=r(30211),o=r(7901),s=r(91424),l=r(71828),u=l.strScale,c=l.strTranslate,f=r(63893),h=r(72597),p=h.recordMinTextSize,d=h.clearMinTextSize,v=r(97313).TEXTPAD,g=r(53581),y=r(20007),m=r(71828).isValidTextValue;function x(t,e,r){var i=r[0],o=i.cx,s=i.cy,u=i.trace,c=\"funnelarea\"===u.type;\"_hasHoverLabel\"in u||(u._hasHoverLabel=!1),\"_hasHoverEvent\"in u||(u._hasHoverEvent=!1),t.on(\"mouseover\",(function(t){var r=e._fullLayout,f=e._fullData[u.index];if(!e._dragging&&!1!==r.hovermode){var h=f.hoverinfo;if(Array.isArray(h)&&(h=a.castHoverinfo({hoverinfo:[g.castOption(h,t.pts)],_module:u._module},r,0)),\"all\"===h&&(h=\"label+text+value+percent+name\"),f.hovertemplate||\"none\"!==h&&\"skip\"!==h&&h){var p=t.rInscribed||0,d=o+t.pxmid[0]*(1-p),v=s+t.pxmid[1]*(1-p),m=r.separators,x=[];if(h&&-1!==h.indexOf(\"label\")&&x.push(t.label),t.text=g.castOption(f.hovertext||f.text,t.pts),h&&-1!==h.indexOf(\"text\")){var b=t.text;l.isValidTextValue(b)&&x.push(b)}t.value=t.v,t.valueLabel=g.formatPieValue(t.v,m),h&&-1!==h.indexOf(\"value\")&&x.push(t.valueLabel),t.percent=t.v/i.vTotal,t.percentLabel=g.formatPiePercent(t.percent,m),h&&-1!==h.indexOf(\"percent\")&&x.push(t.percentLabel);var _=f.hoverlabel,w=_.font,T=[];a.loneHover({trace:u,x0:d-p*i.r,x1:d+p*i.r,y:v,_x0:c?o+t.TL[0]:d-p*i.r,_x1:c?o+t.TR[0]:d+p*i.r,_y0:c?s+t.TL[1]:v-p*i.r,_y1:c?s+t.BL[1]:v+p*i.r,text:x.join(\"<br>\"),name:f.hovertemplate||-1!==h.indexOf(\"name\")?f.name:void 0,idealAlign:t.pxmid[0]<0?\"left\":\"right\",color:g.castOption(_.bgcolor,t.pts)||t.color,borderColor:g.castOption(_.bordercolor,t.pts),fontFamily:g.castOption(w.family,t.pts),fontSize:g.castOption(w.size,t.pts),fontColor:g.castOption(w.color,t.pts),nameLength:g.castOption(_.namelength,t.pts),textAlign:g.castOption(_.align,t.pts),hovertemplate:g.castOption(f.hovertemplate,t.pts),hovertemplateLabels:t,eventData:[y(t,f)]},{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:e,inOut_bbox:T}),t.bbox=T[0],u._hasHoverLabel=!0}u._hasHoverEvent=!0,e.emit(\"plotly_hover\",{points:[y(t,f)],event:n.event})}})),t.on(\"mouseout\",(function(t){var r=e._fullLayout,i=e._fullData[u.index],o=n.select(this).datum();u._hasHoverEvent&&(t.originalEvent=n.event,e.emit(\"plotly_unhover\",{points:[y(o,i)],event:n.event}),u._hasHoverEvent=!1),u._hasHoverLabel&&(a.loneUnhover(r._hoverlayer.node()),u._hasHoverLabel=!1)})),t.on(\"click\",(function(t){var r=e._fullLayout,i=e._fullData[u.index];e._dragging||!1===r.hovermode||(e._hoverdata=[y(t,i)],a.click(e,n.event))}))}function b(t,e,r){var n=g.castOption(t.insidetextfont.color,e.pts);!n&&t._input.textfont&&(n=g.castOption(t._input.textfont.color,e.pts));var i=g.castOption(t.insidetextfont.family,e.pts)||g.castOption(t.textfont.family,e.pts)||r.family,a=g.castOption(t.insidetextfont.size,e.pts)||g.castOption(t.textfont.size,e.pts)||r.size;return{color:n||o.contrast(e.color),family:i,size:a}}function _(t,e){for(var r,n,i=0;i<t.length;i++)if((n=(r=t[i][0]).trace).title.text){var a=n.title.text;n._meta&&(a=l.templateString(a,n._meta));var o=s.tester.append(\"text\").attr(\"data-notex\",1).text(a).call(s.font,n.title.font).call(f.convertToTspans,e),u=s.bBox(o.node(),!0);r.titleBox={width:u.width,height:u.height},o.remove()}}function w(t,e,r){var n=r.r||e.rpx1,i=e.rInscribed;if(e.startangle===e.stopangle)return{rCenter:1-i,scale:0,rotate:0,textPosAngle:0};var a,o=e.ring,s=1===o&&Math.abs(e.startangle-e.stopangle)===2*Math.PI,l=e.halfangle,u=e.midangle,c=r.trace.insidetextorientation,f=\"horizontal\"===c,h=\"tangential\"===c,p=\"radial\"===c,d=\"auto\"===c,v=[];if(!d){var g,y=function(r,i){if(function(t,e){var r=t.startangle,n=t.stopangle;return r>e&&e>n||r<e&&e<n}(e,r)){var s=Math.abs(r-e.startangle),l=Math.abs(r-e.stopangle),u=s<l?s:l;(a=\"tan\"===i?k(t,n,o,u,0):T(t,n,o,u,Math.PI/2)).textPosAngle=r,v.push(a)}};if(f||h){for(g=4;g>=-4;g-=2)y(Math.PI*g,\"tan\");for(g=4;g>=-4;g-=2)y(Math.PI*(g+1),\"tan\")}if(f||p){for(g=4;g>=-4;g-=2)y(Math.PI*(g+1.5),\"rad\");for(g=4;g>=-4;g-=2)y(Math.PI*(g+.5),\"rad\")}}if(s||d||f){var m=Math.sqrt(t.width*t.width+t.height*t.height);if((a={scale:i*n*2/m,rCenter:1-i,rotate:0}).textPosAngle=(e.startangle+e.stopangle)/2,a.scale>=1)return a;v.push(a)}(d||p)&&((a=T(t,n,o,l,u)).textPosAngle=(e.startangle+e.stopangle)/2,v.push(a)),(d||h)&&((a=k(t,n,o,l,u)).textPosAngle=(e.startangle+e.stopangle)/2,v.push(a));for(var x=0,b=0,_=0;_<v.length;_++){var w=v[_].scale;if(b<w&&(b=w,x=_),!d&&b>=1)break}return v[x]}function T(t,e,r,n,i){e=Math.max(0,e-2*v);var a=t.width/t.height,o=S(a,n,e,r);return{scale:2*o/t.height,rCenter:A(a,o/e),rotate:M(i)}}function k(t,e,r,n,i){e=Math.max(0,e-2*v);var a=t.height/t.width,o=S(a,n,e,r);return{scale:2*o/t.width,rCenter:A(a,o/e),rotate:M(i+Math.PI/2)}}function A(t,e){return Math.cos(e)-t*e}function M(t){return(180/Math.PI*t+720)%180-90}function S(t,e,r,n){var i=t+1/(2*Math.tan(e));return r*Math.min(1/(Math.sqrt(i*i+.5)+i),n/(Math.sqrt(t*t+n/2)+t))}function E(t,e){return t.v!==e.vTotal||e.trace.hole?Math.min(1/(1+1/Math.sin(t.halfangle)),t.ring/2):1}function L(t,e){var r=e.pxmid[0],n=e.pxmid[1],i=t.width/2,a=t.height/2;return r<0&&(i*=-1),n<0&&(a*=-1),{scale:1,rCenter:1,rotate:0,x:i+Math.abs(a)*(i>0?1:-1)/2,y:a/(1+r*r/(n*n)),outside:!0}}function C(t,e){var r,n,i,a=t.trace,o={x:t.cx,y:t.cy},s={tx:0,ty:0};s.ty+=a.title.font.size,i=O(a),-1!==a.title.position.indexOf(\"top\")?(o.y-=(1+i)*t.r,s.ty-=t.titleBox.height):-1!==a.title.position.indexOf(\"bottom\")&&(o.y+=(1+i)*t.r);var l,u=t.r/(void 0===(l=t.trace.aspectratio)?1:l),c=e.w*(a.domain.x[1]-a.domain.x[0])/2;return-1!==a.title.position.indexOf(\"left\")?(c+=u,o.x-=(1+i)*u,s.tx+=t.titleBox.width/2):-1!==a.title.position.indexOf(\"center\")?c*=2:-1!==a.title.position.indexOf(\"right\")&&(c+=u,o.x+=(1+i)*u,s.tx-=t.titleBox.width/2),r=c/t.titleBox.width,n=P(t,e)/t.titleBox.height,{x:o.x,y:o.y,scale:Math.min(r,n),tx:s.tx,ty:s.ty}}function P(t,e){var r=t.trace,n=e.h*(r.domain.y[1]-r.domain.y[0]);return Math.min(t.titleBox.height,n/2)}function O(t){var e,r=t.pull;if(!r)return 0;if(Array.isArray(r))for(r=0,e=0;e<t.pull.length;e++)t.pull[e]>r&&(r=t.pull[e]);return r}function I(t,e){for(var r=[],n=0;n<t.length;n++){var i=t[n][0],a=i.trace,o=a.domain,s=e.w*(o.x[1]-o.x[0]),l=e.h*(o.y[1]-o.y[0]);a.title.text&&\"middle center\"!==a.title.position&&(l-=P(i,e));var u=s/2,c=l/2;\"funnelarea\"!==a.type||a.scalegroup||(c/=a.aspectratio),i.r=Math.min(u,c)/(1+O(a)),i.cx=e.l+e.w*(a.domain.x[1]+a.domain.x[0])/2,i.cy=e.t+e.h*(1-a.domain.y[0])-l/2,a.title.text&&-1!==a.title.position.indexOf(\"bottom\")&&(i.cy-=P(i,e)),a.scalegroup&&-1===r.indexOf(a.scalegroup)&&r.push(a.scalegroup)}!function(t,e){for(var r,n,i,a=0;a<e.length;a++){var o=1/0,s=e[a];for(n=0;n<t.length;n++)if((i=(r=t[n][0]).trace).scalegroup===s){var l;if(\"pie\"===i.type)l=r.r*r.r;else if(\"funnelarea\"===i.type){var u,c;i.aspectratio>1?c=(u=r.r)/i.aspectratio:u=(c=r.r)*i.aspectratio,l=(u*=(1+i.baseratio)/2)*c}o=Math.min(o,l/r.vTotal)}for(n=0;n<t.length;n++)if((i=(r=t[n][0]).trace).scalegroup===s){var f=o*r.vTotal;\"funnelarea\"===i.type&&(f/=(1+i.baseratio)/2,f/=i.aspectratio),r.r=Math.sqrt(f)}}}(t,r)}function D(t,e){return[t*Math.sin(e),-t*Math.cos(e)]}function z(t,e,r){var n=t._fullLayout,i=r.trace,a=i.texttemplate,o=i.textinfo;if(!a&&o&&\"none\"!==o){var s,u=o.split(\"+\"),c=function(t){return-1!==u.indexOf(t)},f=c(\"label\"),h=c(\"text\"),p=c(\"value\"),d=c(\"percent\"),v=n.separators;if(s=f?[e.label]:[],h){var y=g.getFirstFilled(i.text,e.pts);m(y)&&s.push(y)}p&&s.push(g.formatPieValue(e.v,v)),d&&s.push(g.formatPiePercent(e.v/r.vTotal,v)),e.text=s.join(\"<br>\")}if(a){var x=l.castOption(i,e.i,\"texttemplate\");if(x){var b=function(t){return{label:t.label,value:t.v,valueLabel:g.formatPieValue(t.v,n.separators),percent:t.v/r.vTotal,percentLabel:g.formatPiePercent(t.v/r.vTotal,n.separators),color:t.color,text:t.text,customdata:l.castOption(i,t.i,\"customdata\")}}(e),_=g.getFirstFilled(i.text,e.pts);(m(_)||\"\"===_)&&(b.text=_),e.text=l.texttemplateString(x,b,t._fullLayout._d3locale,b,i._meta||{})}else e.text=\"\"}}function R(t,e){var r=t.rotate*Math.PI/180,n=Math.cos(r),i=Math.sin(r),a=(e.left+e.right)/2,o=(e.top+e.bottom)/2;t.textX=a*n-o*i,t.textY=a*i+o*n,t.noCenter=!0}t.exports={plot:function(t,e){var r=t._context.staticPlot,a=t._fullLayout,h=a._size;d(\"pie\",a),_(e,t),I(e,h);var v=l.makeTraceGroups(a._pielayer,e,\"trace\").each((function(e){var d=n.select(this),v=e[0],y=v.trace;!function(t){var e,r,n,i=t[0],a=i.r,o=i.trace,s=g.getRotationAngle(o.rotation),l=2*Math.PI/i.vTotal,u=\"px0\",c=\"px1\";if(\"counterclockwise\"===o.direction){for(e=0;e<t.length&&t[e].hidden;e++);if(e===t.length)return;s+=l*t[e].v,l*=-1,u=\"px1\",c=\"px0\"}for(n=D(a,s),e=0;e<t.length;e++)(r=t[e]).hidden||(r[u]=n,r.startangle=s,s+=l*r.v/2,r.pxmid=D(a,s),r.midangle=s,n=D(a,s+=l*r.v/2),r.stopangle=s,r[c]=n,r.largeArc=r.v>i.vTotal/2?1:0,r.halfangle=Math.PI*Math.min(r.v/i.vTotal,.5),r.ring=1-o.hole,r.rInscribed=E(r,i))}(e),d.attr(\"stroke-linejoin\",\"round\"),d.each((function(){var m=n.select(this).selectAll(\"g.slice\").data(e);m.enter().append(\"g\").classed(\"slice\",!0),m.exit().remove();var _=[[[],[]],[[],[]]],T=!1;m.each((function(i,o){if(i.hidden)n.select(this).selectAll(\"path,g\").remove();else{i.pointNumber=i.i,i.curveNumber=y.index,_[i.pxmid[1]<0?0:1][i.pxmid[0]<0?0:1].push(i);var u=v.cx,c=v.cy,h=n.select(this),d=h.selectAll(\"path.surface\").data([i]);if(d.enter().append(\"path\").classed(\"surface\",!0).style({\"pointer-events\":r?\"none\":\"all\"}),h.call(x,t,e),y.pull){var m=+g.castOption(y.pull,i.pts)||0;m>0&&(u+=m*i.pxmid[0],c+=m*i.pxmid[1])}i.cxFinal=u,i.cyFinal=c;var k=y.hole;if(i.v===v.vTotal){var A=\"M\"+(u+i.px0[0])+\",\"+(c+i.px0[1])+P(i.px0,i.pxmid,!0,1)+P(i.pxmid,i.px0,!0,1)+\"Z\";k?d.attr(\"d\",\"M\"+(u+k*i.px0[0])+\",\"+(c+k*i.px0[1])+P(i.px0,i.pxmid,!1,k)+P(i.pxmid,i.px0,!1,k)+\"Z\"+A):d.attr(\"d\",A)}else{var M=P(i.px0,i.px1,!0,1);if(k){var S=1-k;d.attr(\"d\",\"M\"+(u+k*i.px1[0])+\",\"+(c+k*i.px1[1])+P(i.px1,i.px0,!1,k)+\"l\"+S*i.px0[0]+\",\"+S*i.px0[1]+M+\"Z\")}else d.attr(\"d\",\"M\"+u+\",\"+c+\"l\"+i.px0[0]+\",\"+i.px0[1]+M+\"Z\")}z(t,i,v);var E=g.castOption(y.textposition,i.pts),C=h.selectAll(\"g.slicetext\").data(i.text&&\"none\"!==E?[0]:[]);C.enter().append(\"g\").classed(\"slicetext\",!0),C.exit().remove(),C.each((function(){var r=l.ensureSingle(n.select(this),\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),h=l.ensureUniformFontSize(t,\"outside\"===E?function(t,e,r){return{color:g.castOption(t.outsidetextfont.color,e.pts)||g.castOption(t.textfont.color,e.pts)||r.color,family:g.castOption(t.outsidetextfont.family,e.pts)||g.castOption(t.textfont.family,e.pts)||r.family,size:g.castOption(t.outsidetextfont.size,e.pts)||g.castOption(t.textfont.size,e.pts)||r.size}}(y,i,a.font):b(y,i,a.font));r.text(i.text).attr({class:\"slicetext\",transform:\"\",\"text-anchor\":\"middle\"}).call(s.font,h).call(f.convertToTspans,t);var d,m=s.bBox(r.node());if(\"outside\"===E)d=L(m,i);else if(d=w(m,i,v),\"auto\"===E&&d.scale<1){var x=l.ensureUniformFontSize(t,y.outsidetextfont);r.call(s.font,x),d=L(m=s.bBox(r.node()),i)}var _=d.textPosAngle,k=void 0===_?i.pxmid:D(v.r,_);if(d.targetX=u+k[0]*d.rCenter+(d.x||0),d.targetY=c+k[1]*d.rCenter+(d.y||0),R(d,m),d.outside){var A=d.targetY;i.yLabelMin=A-m.height/2,i.yLabelMid=A,i.yLabelMax=A+m.height/2,i.labelExtraX=0,i.labelExtraY=0,T=!0}d.fontSize=h.size,p(y.type,d,a),e[o].transform=d,l.setTransormAndDisplay(r,d)}))}function P(t,e,r,n){var a=n*(e[0]-t[0]),o=n*(e[1]-t[1]);return\"a\"+n*v.r+\",\"+n*v.r+\" 0 \"+i.largeArc+(r?\" 1 \":\" 0 \")+a+\",\"+o}}));var k=n.select(this).selectAll(\"g.titletext\").data(y.title.text?[0]:[]);if(k.enter().append(\"g\").classed(\"titletext\",!0),k.exit().remove(),k.each((function(){var e,r=l.ensureSingle(n.select(this),\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),i=y.title.text;y._meta&&(i=l.templateString(i,y._meta)),r.text(i).attr({class:\"titletext\",transform:\"\",\"text-anchor\":\"middle\"}).call(s.font,y.title.font).call(f.convertToTspans,t),e=\"middle center\"===y.title.position?function(t){var 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t.link.label===i})).style(\"fill-opacity\",(function(t){return t.tinyColorAlpha})),r&&h(e,t).selectAll(u.sankeyNode).filter(g(t)).call(m)}function _(t,e){var r=t.hoverlabel||{},n=i.nestedProperty(r,e).get();return!Array.isArray(n)&&n}t.exports=function(t,e){for(var r=t._fullLayout,i=r._paper,h=r._size,v=0;v<t._fullData.length;v++)if(t._fullData[v].visible&&t._fullData[v].type===u.sankey&&!t._fullData[v]._viewInitial){var g=t._fullData[v].node;t._fullData[v]._viewInitial={node:{groups:g.groups.slice(),x:g.x.slice(),y:g.y.slice()}}}var w=c(t,\"source:\")+\" \",T=c(t,\"target:\")+\" \",k=c(t,\"concentration:\")+\" \",A=c(t,\"incoming flow count:\")+\" \",M=c(t,\"outgoing flow count:\")+\" \";o(t,i,e,{width:h.w,height:h.h,margin:{t:h.t,r:h.r,b:h.b,l:h.l}},{linkEvents:{hover:function(e,r,i){!1!==t._fullLayout.hovermode&&(n.select(e).call(x.bind(0,r,i,!0)),\"skip\"!==r.link.trace.link.hoverinfo&&(r.link.fullData=r.link.trace,t.emit(\"plotly_hover\",{event:n.event,points:[r.link]})))},follow:function(e,i){if(!1!==t._fullLayout.hovermode){var o=i.link.trace.link;if(\"none\"!==o.hoverinfo&&\"skip\"!==o.hoverinfo){for(var u=[],c=0,h=0;h<i.flow.links.length;h++){var v=i.flow.links[h];if(\"closest\"!==t._fullLayout.hovermode||i.link.pointNumber===v.pointNumber){i.link.pointNumber===v.pointNumber&&(c=h),v.fullData=v.trace,o=i.link.trace.link;var g=m(v),y={valueLabel:a(i.valueFormat)(v.value)+i.valueSuffix};u.push({x:g[0],y:g[1],name:y.valueLabel,text:[v.label||\"\",w+v.source.label,T+v.target.label,v.concentrationscale?k+a(\"%0.2f\")(v.flow.labelConcentration):\"\"].filter(f).join(\"<br>\"),color:_(o,\"bgcolor\")||l.addOpacity(v.color,1),borderColor:_(o,\"bordercolor\"),fontFamily:_(o,\"font.family\"),fontSize:_(o,\"font.size\"),fontColor:_(o,\"font.color\"),nameLength:_(o,\"namelength\"),textAlign:_(o,\"align\"),idealAlign:n.event.x<g[0]?\"right\":\"left\",hovertemplate:o.hovertemplate,hovertemplateLabels:y,eventData:[v]})}}s.loneHover(u,{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:t,anchorIndex:c}).each((function(){i.link.concentrationscale||p(this,.65),d(this)}))}}function m(t){var e,r;t.circular?(e=(t.circularPathData.leftInnerExtent+t.circularPathData.rightInnerExtent)/2,r=t.circularPathData.verticalFullExtent):(e=(t.source.x1+t.target.x0)/2,r=(t.y0+t.y1)/2);var n=[e,r];return\"v\"===t.trace.orientation&&n.reverse(),n[0]+=i.parent.translateX,n[1]+=i.parent.translateY,n}},unhover:function(e,i,a){!1!==t._fullLayout.hovermode&&(n.select(e).call(b.bind(0,i,a,!0)),\"skip\"!==i.link.trace.link.hoverinfo&&(i.link.fullData=i.link.trace,t.emit(\"plotly_unhover\",{event:n.event,points:[i.link]})),s.loneUnhover(r._hoverlayer.node()))},select:function(e,r){var i=r.link;i.originalEvent=n.event,t._hoverdata=[i],s.click(t,{target:!0})}},nodeEvents:{hover:function(e,r,i){!1!==t._fullLayout.hovermode&&(n.select(e).call(y,r,i),\"skip\"!==r.node.trace.node.hoverinfo&&(r.node.fullData=r.node.trace,t.emit(\"plotly_hover\",{event:n.event,points:[r.node]})))},follow:function(e,i){if(!1!==t._fullLayout.hovermode){var o=i.node.trace.node;if(\"none\"!==o.hoverinfo&&\"skip\"!==o.hoverinfo){var l=n.select(e).select(\".\"+u.nodeRect),c=t._fullLayout._paperdiv.node().getBoundingClientRect(),h=l.node().getBoundingClientRect(),v=h.left-2-c.left,g=h.right+2-c.left,y=h.top+h.height/4-c.top,m={valueLabel:a(i.valueFormat)(i.node.value)+i.valueSuffix};i.node.fullData=i.node.trace,t._fullLayout._calcInverseTransform(t);var x=t._fullLayout._invScaleX,b=t._fullLayout._invScaleY,w=s.loneHover({x0:x*v,x1:x*g,y:b*y,name:a(i.valueFormat)(i.node.value)+i.valueSuffix,text:[i.node.label,A+i.node.targetLinks.length,M+i.node.sourceLinks.length].filter(f).join(\"<br>\"),color:_(o,\"bgcolor\")||i.tinyColorHue,borderColor:_(o,\"bordercolor\"),fontFamily:_(o,\"font.family\"),fontSize:_(o,\"font.size\"),fontColor:_(o,\"font.color\"),nameLength:_(o,\"namelength\"),textAlign:_(o,\"align\"),idealAlign:\"left\",hovertemplate:o.hovertemplate,hovertemplateLabels:m,eventData:[i.node]},{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:t});p(w,.85),d(w)}}},unhover:function(e,i,a){!1!==t._fullLayout.hovermode&&(n.select(e).call(m,i,a),\"skip\"!==i.node.trace.node.hoverinfo&&(i.node.fullData=i.node.trace,t.emit(\"plotly_unhover\",{event:n.event,points:[i.node]})),s.loneUnhover(r._hoverlayer.node()))},select:function(e,r,i){var a=r.node;a.originalEvent=n.event,t._hoverdata=[a],n.select(e).call(m,r,i),s.click(t,{target:!0})}}})}},3393:function(t,e,r){\"use strict\";var n=r(49887),i=r(81684).k4,a=r(39898),o=r(30838),s=r(86781),l=r(85247),u=r(84267),c=r(7901),f=r(91424),h=r(71828),p=h.strTranslate,d=h.strRotate,v=r(28984),g=v.keyFun,y=v.repeat,m=v.unwrap,x=r(63893),b=r(73972),_=r(18783),w=_.CAP_SHIFT,T=_.LINE_SPACING;function k(t,e,r){var n,i=m(e),a=i.trace,c=a.domain,f=\"h\"===a.orientation,p=a.node.pad,d=a.node.thickness,v=t.width*(c.x[1]-c.x[0]),g=t.height*(c.y[1]-c.y[0]),y=i._nodes,x=i._links,b=i.circular;(n=b?s.sankeyCircular().circularLinkGap(0):o.sankey()).iterations(l.sankeyIterations).size(f?[v,g]:[g,v]).nodeWidth(d).nodePadding(p).nodeId((function(t){return t.pointNumber})).nodes(y).links(x);var _,w,T,k=n();for(var A in n.nodePadding()<p&&h.warn(\"node.pad was reduced to \",n.nodePadding(),\" to fit within the figure.\"),i._groupLookup){var M,S=parseInt(i._groupLookup[A]);for(_=0;_<k.nodes.length;_++)if(k.nodes[_].pointNumber===S){M=k.nodes[_];break}if(M){var E={pointNumber:parseInt(A),x0:M.x0,x1:M.x1,y0:M.y0,y1:M.y1,partOfGroup:!0,sourceLinks:[],targetLinks:[]};k.nodes.unshift(E),M.childrenNodes.unshift(E)}}if(function(){for(_=0;_<k.nodes.length;_++){var t,e,r=k.nodes[_],n={};for(w=0;w<r.targetLinks.length;w++)t=(e=r.targetLinks[w]).source.pointNumber+\":\"+e.target.pointNumber,n.hasOwnProperty(t)||(n[t]=[]),n[t].push(e);var i=Object.keys(n);for(w=0;w<i.length;w++){var a=n[t=i[w]],o=0,s={};for(T=0;T<a.length;T++)s[(e=a[T]).label]||(s[e.label]=0),s[e.label]+=e.value,o+=e.value;for(T=0;T<a.length;T++)(e=a[T]).flow={value:o,labelConcentration:s[e.label]/o,concentration:e.value/o,links:a},e.concentrationscale&&(e.color=u(e.concentrationscale(e.flow.labelConcentration)))}var l=0;for(w=0;w<r.sourceLinks.length;w++)l+=r.sourceLinks[w].value;for(w=0;w<r.sourceLinks.length;w++)(e=r.sourceLinks[w]).concentrationOut=e.value/l;var c=0;for(w=0;w<r.targetLinks.length;w++)c+=r.targetLinks[w].value;for(w=0;w<r.targetLinks.length;w++)(e=r.targetLinks[w]).concenrationIn=e.value/c}}(),a.node.x.length&&a.node.y.length){for(_=0;_<Math.min(a.node.x.length,a.node.y.length,k.nodes.length);_++)if(a.node.x[_]&&a.node.y[_]){var L=[a.node.x[_]*v,a.node.y[_]*g];k.nodes[_].x0=L[0]-d/2,k.nodes[_].x1=L[0]+d/2;var C=k.nodes[_].y1-k.nodes[_].y0;k.nodes[_].y0=L[1]-C/2,k.nodes[_].y1=L[1]+C/2}\"snap\"===a.arrangement&&function(t){var e,r,n=t.map((function(t,e){return{x0:t.x0,index:e}})).sort((function(t,e){return t.x0-e.x0})),i=[],a=-1,o=-1/0;for(_=0;_<n.length;_++){var s=t[n[_].index];s.x0>o+d&&(a+=1,e=s.x0),o=s.x0,i[a]||(i[a]=[]),i[a].push(s),r=e-s.x0,s.x0+=r,s.x1+=r}return i}(y=k.nodes).forEach((function(t){var e,r,n,i=0,a=t.length;for(t.sort((function(t,e){return t.y0-e.y0})),n=0;n<a;++n)(e=t[n]).y0>=i||(r=i-e.y0)>1e-6&&(e.y0+=r,e.y1+=r),i=e.y1+p})),n.update(k)}return{circular:b,key:r,trace:a,guid:h.randstr(),horizontal:f,width:v,height:g,nodePad:a.node.pad,nodeLineColor:a.node.line.color,nodeLineWidth:a.node.line.width,linkLineColor:a.link.line.color,linkLineWidth:a.link.line.width,linkArrowLength:a.link.arrowlen,valueFormat:a.valueformat,valueSuffix:a.valuesuffix,textFont:a.textfont,translateX:c.x[0]*t.width+t.margin.l,translateY:t.height-c.y[1]*t.height+t.margin.t,dragParallel:f?g:v,dragPerpendicular:f?v:g,arrangement:a.arrangement,sankey:n,graph:k,forceLayouts:{},interactionState:{dragInProgress:!1,hovered:!1}}}function A(t,e,r){var n=u(e.color),i=e.source.label+\"|\"+e.target.label+\"__\"+r;return e.trace=t.trace,e.curveNumber=t.trace.index,{circular:t.circular,key:i,traceId:t.key,pointNumber:e.pointNumber,link:e,tinyColorHue:c.tinyRGB(n),tinyColorAlpha:n.getAlpha(),linkPath:M,linkLineColor:t.linkLineColor,linkLineWidth:t.linkLineWidth,linkArrowLength:t.linkArrowLength,valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,parent:t,interactionState:t.interactionState,flow:e.flow}}function M(){return function(t){var e=t.linkArrowLength;if(t.link.circular)return function(t,e){var r=t.width/2,n=t.circularPathData;return\"top\"===t.circularLinkType?\"M \"+(n.targetX-e)+\" \"+(n.targetY+r)+\" L\"+(n.rightInnerExtent-e)+\" \"+(n.targetY+r)+\"A\"+(n.rightLargeArcRadius+r)+\" \"+(n.rightSmallArcRadius+r)+\" 0 0 1 \"+(n.rightFullExtent-r-e)+\" \"+(n.targetY-n.rightSmallArcRadius)+\"L\"+(n.rightFullExtent-r-e)+\" \"+n.verticalRightInnerExtent+\"A\"+(n.rightLargeArcRadius+r)+\" \"+(n.rightLargeArcRadius+r)+\" 0 0 1 \"+(n.rightInnerExtent-e)+\" \"+(n.verticalFullExtent-r)+\"L\"+n.leftInnerExtent+\" \"+(n.verticalFullExtent-r)+\"A\"+(n.leftLargeArcRadius+r)+\" \"+(n.leftLargeArcRadius+r)+\" 0 0 1 \"+(n.leftFullExtent+r)+\" \"+n.verticalLeftInnerExtent+\"L\"+(n.leftFullExtent+r)+\" \"+(n.sourceY-n.leftSmallArcRadius)+\"A\"+(n.leftLargeArcRadius+r)+\" \"+(n.leftSmallArcRadius+r)+\" 0 0 1 \"+n.leftInnerExtent+\" \"+(n.sourceY+r)+\"L\"+n.sourceX+\" \"+(n.sourceY+r)+\"L\"+n.sourceX+\" \"+(n.sourceY-r)+\"L\"+n.leftInnerExtent+\" \"+(n.sourceY-r)+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftSmallArcRadius-r)+\" 0 0 0 \"+(n.leftFullExtent-r)+\" \"+(n.sourceY-n.leftSmallArcRadius)+\"L\"+(n.leftFullExtent-r)+\" \"+n.verticalLeftInnerExtent+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftLargeArcRadius-r)+\" 0 0 0 \"+n.leftInnerExtent+\" \"+(n.verticalFullExtent+r)+\"L\"+(n.rightInnerExtent-e)+\" \"+(n.verticalFullExtent+r)+\"A\"+(n.rightLargeArcRadius-r)+\" \"+(n.rightLargeArcRadius-r)+\" 0 0 0 \"+(n.rightFullExtent+r-e)+\" \"+n.verticalRightInnerExtent+\"L\"+(n.rightFullExtent+r-e)+\" \"+(n.targetY-n.rightSmallArcRadius)+\"A\"+(n.rightLargeArcRadius-r)+\" \"+(n.rightSmallArcRadius-r)+\" 0 0 0 \"+(n.rightInnerExtent-e)+\" \"+(n.targetY-r)+\"L\"+(n.targetX-e)+\" \"+(n.targetY-r)+(e>0?\"L\"+n.targetX+\" \"+n.targetY:\"\")+\"Z\":\"M \"+(n.targetX-e)+\" \"+(n.targetY-r)+\" L\"+(n.rightInnerExtent-e)+\" \"+(n.targetY-r)+\"A\"+(n.rightLargeArcRadius+r)+\" \"+(n.rightSmallArcRadius+r)+\" 0 0 0 \"+(n.rightFullExtent-r-e)+\" \"+(n.targetY+n.rightSmallArcRadius)+\"L\"+(n.rightFullExtent-r-e)+\" \"+n.verticalRightInnerExtent+\"A\"+(n.rightLargeArcRadius+r)+\" \"+(n.rightLargeArcRadius+r)+\" 0 0 0 \"+(n.rightInnerExtent-e)+\" \"+(n.verticalFullExtent+r)+\"L\"+n.leftInnerExtent+\" \"+(n.verticalFullExtent+r)+\"A\"+(n.leftLargeArcRadius+r)+\" \"+(n.leftLargeArcRadius+r)+\" 0 0 0 \"+(n.leftFullExtent+r)+\" \"+n.verticalLeftInnerExtent+\"L\"+(n.leftFullExtent+r)+\" \"+(n.sourceY+n.leftSmallArcRadius)+\"A\"+(n.leftLargeArcRadius+r)+\" \"+(n.leftSmallArcRadius+r)+\" 0 0 0 \"+n.leftInnerExtent+\" \"+(n.sourceY-r)+\"L\"+n.sourceX+\" \"+(n.sourceY-r)+\"L\"+n.sourceX+\" \"+(n.sourceY+r)+\"L\"+n.leftInnerExtent+\" \"+(n.sourceY+r)+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftSmallArcRadius-r)+\" 0 0 1 \"+(n.leftFullExtent-r)+\" \"+(n.sourceY+n.leftSmallArcRadius)+\"L\"+(n.leftFullExtent-r)+\" \"+n.verticalLeftInnerExtent+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftLargeArcRadius-r)+\" 0 0 1 \"+n.leftInnerExtent+\" \"+(n.verticalFullExtent-r)+\"L\"+(n.rightInnerExtent-e)+\" \"+(n.verticalFullExtent-r)+\"A\"+(n.rightLargeArcRadius-r)+\" \"+(n.rightLargeArcRadius-r)+\" 0 0 1 \"+(n.rightFullExtent+r-e)+\" \"+n.verticalRightInnerExtent+\"L\"+(n.rightFullExtent+r-e)+\" \"+(n.targetY+n.rightSmallArcRadius)+\"A\"+(n.rightLargeArcRadius-r)+\" \"+(n.rightSmallArcRadius-r)+\" 0 0 1 \"+(n.rightInnerExtent-e)+\" \"+(n.targetY+r)+\"L\"+(n.targetX-e)+\" \"+(n.targetY+r)+(e>0?\"L\"+n.targetX+\" \"+n.targetY:\"\")+\"Z\"}(t.link,e);var r=Math.abs((t.link.target.x0-t.link.source.x1)/2);e>r&&(e=r);var n=t.link.source.x1,a=t.link.target.x0-e,o=i(n,a),s=o(.5),l=o(.5),u=t.link.y0-t.link.width/2,c=t.link.y0+t.link.width/2,f=t.link.y1-t.link.width/2,h=t.link.y1+t.link.width/2,p=\"M\"+n+\",\"+u,d=\"C\"+s+\",\"+u+\" \"+l+\",\"+f+\" \"+a+\",\"+f,v=\"C\"+l+\",\"+h+\" \"+s+\",\"+c+\" \"+n+\",\"+c,g=e>0?\"L\"+(a+e)+\",\"+(f+t.link.width/2):\"\";return p+d+(g+=\"L\"+a+\",\"+h)+v+\"Z\"}}function S(t,e){var r=u(e.color),n=l.nodePadAcross,i=t.nodePad/2;e.dx=e.x1-e.x0,e.dy=e.y1-e.y0;var a=e.dx,o=Math.max(.5,e.dy),s=\"node_\"+e.pointNumber;return e.group&&(s=h.randstr()),e.trace=t.trace,e.curveNumber=t.trace.index,{index:e.pointNumber,key:s,partOfGroup:e.partOfGroup||!1,group:e.group,traceId:t.key,trace:t.trace,node:e,nodePad:t.nodePad,nodeLineColor:t.nodeLineColor,nodeLineWidth:t.nodeLineWidth,textFont:t.textFont,size:t.horizontal?t.height:t.width,visibleWidth:Math.ceil(a),visibleHeight:o,zoneX:-n,zoneY:-i,zoneWidth:a+2*n,zoneHeight:o+2*i,labelY:t.horizontal?e.dy/2+1:e.dx/2+1,left:1===e.originalLayer,sizeAcross:t.width,forceLayouts:t.forceLayouts,horizontal:t.horizontal,darkBackground:r.getBrightness()<=128,tinyColorHue:c.tinyRGB(r),tinyColorAlpha:r.getAlpha(),valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,graph:t.graph,arrangement:t.arrangement,uniqueNodeLabelPathId:[t.guid,t.key,s].join(\"_\"),interactionState:t.interactionState,figure:t}}function E(t){t.attr(\"transform\",(function(t){return p(t.node.x0.toFixed(3),t.node.y0.toFixed(3))}))}function L(t){t.call(E)}function 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b=u<2||i[0]!==i[u-1]||o[0]!==o[u-1];b&&(\"tozerox\"===y||\"tonextx\"===y&&(p||\"h\"===d))?m.tozero=!0:(e.error_y||{}).visible||\"tonexty\"!==y&&\"tozeroy\"!==y&&(l.hasMarkers(e)||l.hasText(e))||(m.padded=!1,m.ppad=0),b&&(\"tozeroy\"===y||\"tonexty\"===y&&(p||\"v\"===d))?x.tozero=!0:\"tonextx\"!==y&&\"tozerox\"!==y||(x.padded=!1),f&&(e._extremes[f]=a.findExtremes(r,i,m)),h&&(e._extremes[h]=a.findExtremes(n,o,x))}function p(t,e){if(l.hasMarkers(t)){var r,n=t.marker,o=1.6*(t.marker.sizeref||1);if(r=\"area\"===t.marker.sizemode?function(t){return Math.max(Math.sqrt((t||0)/o),3)}:function(t){return Math.max((t||0)/o,3)},i.isArrayOrTypedArray(n.size)){var s={type:\"linear\"};a.setConvert(s);for(var u=s.makeCalcdata(t.marker,\"size\"),c=new Array(e),f=0;f<e;f++)c[f]=r(u[f]);return c}return r(n.size)}}function d(t,e){var r=v(e),n=t._firstScatter;n[r]||(n[r]=e.uid)}function v(t){var e=t.stackgroup;return t.xaxis+t.yaxis+t.type+(e?\"-\"+e:\"\")}function g(t,e,r,n){var i=t.stackgroup;if(i){var 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n=r(52075).hasColorscale,i=r(78803),a=r(34098);t.exports=function(t,e){a.hasLines(e)&&n(e,\"line\")&&i(t,e,{vals:e.line.color,containerStr:\"line\",cLetter:\"c\"}),a.hasMarkers(e)&&(n(e,\"marker\")&&i(t,e,{vals:e.marker.color,containerStr:\"marker\",cLetter:\"c\"}),n(e,\"marker.line\")&&i(t,e,{vals:e.marker.line.color,containerStr:\"marker.line\",cLetter:\"c\"}))}},47581:function(t){\"use strict\";t.exports={PTS_LINESONLY:20,minTolerance:.2,toleranceGrowth:10,maxScreensAway:20,eventDataKeys:[]}},72626:function(t,e,r){\"use strict\";var n=r(47761),i=r(11661).setGroupPositions;function a(t,e,r,n,i,a,o){i[n]=!0;var s={i:null,gap:!0,s:0};if(s[o]=r,t.splice(e,0,s),e&&r===t[e-1][o]){var l=t[e-1];s.s=l.s,s.i=l.i,s.gap=l.gap}else a&&(s.s=function(t,e,r,n){var i=t[e-1],a=t[e+1];return a?i?i.s+(a.s-i.s)*(r-i[n])/(a[n]-i[n]):a.s:i.s}(t,e,r,o));e||(t[0].t=t[1].t,t[0].trace=t[1].trace,delete t[1].t,delete 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n=r(82196),i=r(9012),a=r(5386).fF,o=r(5386).si,s=r(50693),l=r(1426).extendFlat,u=n.marker,c=n.line,f=u.line;t.exports={carpet:{valType:\"string\",editType:\"calc\"},a:{valType:\"data_array\",editType:\"calc\"},b:{valType:\"data_array\",editType:\"calc\"},mode:l({},n.mode,{dflt:\"markers\"}),text:l({},n.text,{}),texttemplate:o({editType:\"plot\"},{keys:[\"a\",\"b\",\"text\"]}),hovertext:l({},n.hovertext,{}),line:{color:c.color,width:c.width,dash:c.dash,backoff:c.backoff,shape:l({},c.shape,{values:[\"linear\",\"spline\"]}),smoothing:c.smoothing,editType:\"calc\"},connectgaps:n.connectgaps,fill:l({},n.fill,{values:[\"none\",\"toself\",\"tonext\"],dflt:\"none\"}),fillcolor:n.fillcolor,marker:l({symbol:u.symbol,opacity:u.opacity,maxdisplayed:u.maxdisplayed,angle:u.angle,angleref:u.angleref,standoff:u.standoff,size:u.size,sizeref:u.sizeref,sizemin:u.sizemin,sizemode:u.sizemode,line:l({width:f.width,editType:\"calc\"},s(\"marker.line\")),gradient:u.gradient,editType:\"calc\"},s(\"marker\")),textfont:n.textfont,textposition:n.textposition,selected:n.selected,unselected:n.unselected,hoverinfo:l({},i.hoverinfo,{flags:[\"a\",\"b\",\"text\",\"name\"]}),hoveron:n.hoveron,hovertemplate:a()}},34618:function(t,e,r){\"use 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r,n,i=e._length,o=e.marker,l={},u=s.isArrayOrTypedArray(o.symbol),f=s.isArrayOrTypedArray(o.angle),d=s.isArrayOrTypedArray(o.color),v=s.isArrayOrTypedArray(o.line.color),g=s.isArrayOrTypedArray(o.opacity),y=s.isArrayOrTypedArray(o.size),m=s.isArrayOrTypedArray(o.line.width);if(u||(n=p.isOpenSymbol(o.symbol)),u||d||v||g||f){l.symbols=new Array(i),l.angles=new Array(i),l.colors=new Array(i),l.borderColors=new Array(i);var x=o.symbol,b=o.angle,_=c(o,o.opacity,i),w=c(o.line,o.opacity,i);if(!Array.isArray(w[0])){var T=w;for(w=Array(i),r=0;r<i;r++)w[r]=T}if(!Array.isArray(_[0])){var k=_;for(_=Array(i),r=0;r<i;r++)_[r]=k}if(!Array.isArray(x)){var A=x;for(x=Array(i),r=0;r<i;r++)x[r]=A}if(!Array.isArray(b)){var M=b;for(b=Array(i),r=0;r<i;r++)b[r]=M}for(l.symbols=x,l.angles=b,l.colors=_,l.borderColors=w,r=0;r<i;r++)u&&(n=p.isOpenSymbol(o.symbol[r])),n&&(w[r]=_[r].slice(),_[r]=_[r].slice(),_[r][3]=0);for(l.opacity=e.opacity,l.markers=new Array(i),r=0;r<i;r++)l.markers[r]=E({mx:l.symbols[r],ma:l.angles[r]},e)}else n?(l.color=a(o.color,\"uint8\"),l.color[3]=0,l.borderColor=a(o.color,\"uint8\")):(l.color=a(o.color,\"uint8\"),l.borderColor=a(o.line.color,\"uint8\")),l.opacity=e.opacity*o.opacity,l.marker=E({mx:o.symbol,ma:o.angle},e);var S,L=h(e,1);if(y||m){var C,P=l.sizes=new Array(i),O=l.borderSizes=new Array(i),I=0;if(y){for(r=0;r<i;r++)P[r]=L(o.size[r]),I+=P[r];C=I/i}else for(S=L(o.size),r=0;r<i;r++)P[r]=S;if(m)for(r=0;r<i;r++)O[r]=o.line.width[r];else for(S=o.line.width,r=0;r<i;r++)O[r]=S;l.sizeAvg=C}else l.size=L(o&&o.size||10),l.borderSizes=L(o.line.width);return l}function b(t,e,r){var n=e.marker,i={};return r?(r.marker&&r.marker.symbol?i=x(0,s.extendFlat({},n,r.marker)):r.marker&&(r.marker.size&&(i.size=r.marker.size),r.marker.color&&(i.colors=r.marker.color),void 0!==r.marker.opacity&&(i.opacity=r.marker.opacity)),i):i}function _(t,e,r){var n={};if(!r)return n;if(r.textfont){var i={opacity:1,text:e.text,texttemplate:e.texttemplate,textposition:e.textposition,textfont:s.extendFlat({},e.textfont)};r.textfont&&s.extendFlat(i.textfont,r.textfont),n=m(t,i)}return n}function w(t,e,r){var n={capSize:2*e.width*r,lineWidth:e.thickness*r,color:e.color};return e.copy_ystyle&&(n=t.error_y),n}var T=d.SYMBOL_SDF_SIZE,k=d.SYMBOL_SIZE,A=d.SYMBOL_STROKE,M={},S=l.symbolFuncs[0](.05*k);function E(t,e){var r,n,a=t.mx;if(\"circle\"===a)return null;var o=l.symbolNumber(a),s=l.symbolFuncs[o%100],u=!!l.symbolNoDot[o%100],c=!!l.symbolNoFill[o%100],f=p.isDotSymbol(a);if(t.ma&&(a+=\"_\"+t.ma),M[a])return M[a];var h=l.getMarkerAngle(t,e);return r=f&&!u?s(1.1*k,h)+S:s(k,h),n=i(r,{w:T,h:T,viewBox:[-k,-k,k,k],stroke:c?A:-A}),M[a]=n,n||null}t.exports={style:function(t,e){var r,n={marker:void 0,markerSel:void 0,markerUnsel:void 0,line:void 0,fill:void 0,errorX:void 0,errorY:void 0,text:void 0,textSel:void 0,textUnsel:void 0},i=t._context.plotGlPixelRatio;if(!0!==e.visible)return n;if(f.hasText(e)&&(n.text=m(t,e),n.textSel=_(t,e,e.selected),n.textUnsel=_(t,e,e.unselected)),f.hasMarkers(e)&&(n.marker=x(0,e),n.markerSel=b(0,e,e.selected),n.markerUnsel=b(0,e,e.unselected),!e.unselected&&s.isArrayOrTypedArray(e.marker.opacity))){var a=e.marker.opacity;for(n.markerUnsel.opacity=new Array(a.length),r=0;r<a.length;r++)n.markerUnsel.opacity[r]=v*a[r]}if(f.hasLines(e)){n.line={overlay:!0,thickness:e.line.width*i,color:e.line.color,opacity:e.opacity};var o=(d.DASHES[e.line.dash]||[1]).slice();for(r=0;r<o.length;++r)o[r]*=e.line.width*i;n.line.dashes=o}return e.error_x&&e.error_x.visible&&(n.errorX=w(e,e.error_x,i)),e.error_y&&e.error_y.visible&&(n.errorY=w(e,e.error_y,i)),e.fill&&\"none\"!==e.fill&&(n.fill={closed:!0,fill:e.fillcolor,thickness:0}),n},markerStyle:x,markerSelection:b,linePositions:function(t,e,r){var n,i,a=r.length,o=a/2;if(f.hasLines(e)&&o)if(\"hv\"===e.line.shape){for(n=[],i=0;i<o-1;i++)isNaN(r[2*i])||isNaN(r[2*i+1])?n.push(NaN,NaN,NaN,NaN):(n.push(r[2*i],r[2*i+1]),isNaN(r[2*i+2])||isNaN(r[2*i+3])?n.push(NaN,NaN):n.push(r[2*i+2],r[2*i+1]));n.push(r[a-2],r[a-1])}else if(\"hvh\"===e.line.shape){for(n=[],i=0;i<o-1;i++)if(isNaN(r[2*i])||isNaN(r[2*i+1])||isNaN(r[2*i+2])||isNaN(r[2*i+3]))isNaN(r[2*i])||isNaN(r[2*i+1])?n.push(NaN,NaN):n.push(r[2*i],r[2*i+1]),n.push(NaN,NaN);else{var s=(r[2*i]+r[2*i+2])/2;n.push(r[2*i],r[2*i+1],s,r[2*i+1],s,r[2*i+3])}n.push(r[a-2],r[a-1])}else if(\"vhv\"===e.line.shape){for(n=[],i=0;i<o-1;i++)if(isNaN(r[2*i])||isNaN(r[2*i+1])||isNaN(r[2*i+2])||isNaN(r[2*i+3]))isNaN(r[2*i])||isNaN(r[2*i+1])?n.push(NaN,NaN):n.push(r[2*i],r[2*i+1]),n.push(NaN,NaN);else{var l=(r[2*i+1]+r[2*i+3])/2;n.push(r[2*i],r[2*i+1],r[2*i],l,r[2*i+2],l)}n.push(r[a-2],r[a-1])}else if(\"vh\"===e.line.shape){for(n=[],i=0;i<o-1;i++)isNaN(r[2*i])||isNaN(r[2*i+1])?n.push(NaN,NaN,NaN,NaN):(n.push(r[2*i],r[2*i+1]),isNaN(r[2*i+2])||isNaN(r[2*i+3])?n.push(NaN,NaN):n.push(r[2*i],r[2*i+3]));n.push(r[a-2],r[a-1])}else n=r;var u=!1;for(i=0;i<n.length;i++)if(isNaN(n[i])){u=!0;break}var c=u||n.length>d.TOO_MANY_POINTS||f.hasMarkers(e)?\"rect\":\"round\";if(u&&e.connectgaps){var h=n[0],p=n[1];for(i=0;i<n.length;i+=2)isNaN(n[i])||isNaN(n[i+1])?(n[i]=h,n[i+1]=p):(h=n[i],p=n[i+1])}return{join:c,positions:n}},errorBarPositions:function(t,e,r,i,a){var s=o.getComponentMethod(\"errorbars\",\"makeComputeError\"),l=u.getFromId(t,e.xaxis,\"x\"),c=u.getFromId(t,e.yaxis,\"y\"),f=r.length/2,h={};function p(t,i){var a=i._id.charAt(0),o=e[\"error_\"+a];if(o&&o.visible&&(\"linear\"===i.type||\"log\"===i.type)){for(var l=s(o),u={x:0,y:1}[a],c={x:[0,1,2,3],y:[2,3,0,1]}[a],p=new Float64Array(4*f),d=1/0,v=-1/0,g=0,y=0;g<f;g++,y+=4){var m=t[g];if(n(m)){var x=r[2*g+u],b=l(m,g),_=b[0],w=b[1];if(n(_)&&n(w)){var T=m-_,k=m+w;p[y+c[0]]=x-i.c2l(T),p[y+c[1]]=i.c2l(k)-x,p[y+c[2]]=0,p[y+c[3]]=0,d=Math.min(d,m-_),v=Math.max(v,m+w)}}}h[a]={positions:r,errors:p,_bnds:[d,v]}}}return p(i,l),p(a,c),h},textPosition:function(t,e,r,n){var i,a=e._length,o={};if(f.hasMarkers(e)){var s=r.font,l=r.align,u=r.baseline;for(o.offset=new Array(a),i=0;i<a;i++){var c=n.sizes?n.sizes[i]:n.size,h=Array.isArray(s)?s[i].size:s.size,p=Array.isArray(l)?l.length>1?l[i]:l[0]:l,d=Array.isArray(u)?u.length>1?u[i]:u[0]:u,v=g[p],y=g[d],m=c?c/.8+1:0,x=-y*m-.5*y;o.offset[i]=[v*m/h,x/h]}}return o}}},47148:function(t,e,r){\"use strict\";var n=r(71828),i=r(73972),a=r(68645),o=r(42341),s=r(47581),l=r(34098),u=r(67513),c=r(73927),f=r(49508),h=r(11058),p=r(28908),d=r(82410);t.exports=function(t,e,r,v){function g(r,i){return n.coerce(t,e,o,r,i)}var y=!!t.marker&&a.isOpenSymbol(t.marker.symbol),m=l.isBubble(t),x=u(t,e,v,g);if(x){c(t,e,v,g),g(\"xhoverformat\"),g(\"yhoverformat\");var b=x<s.PTS_LINESONLY?\"lines+markers\":\"lines\";g(\"text\"),g(\"hovertext\"),g(\"hovertemplate\"),g(\"mode\",b),l.hasLines(e)&&(g(\"connectgaps\"),h(t,e,r,v,g),g(\"line.shape\")),l.hasMarkers(e)&&(f(t,e,r,v,g,{noAngleRef:!0,noStandOff:!0}),g(\"marker.line.width\",y||m?1:0)),l.hasText(e)&&(g(\"texttemplate\"),d(t,e,v,g));var _=(e.line||{}).color,w=(e.marker||{}).color;g(\"fill\"),\"none\"!==e.fill&&p(t,e,r,g);var T=i.getComponentMethod(\"errorbars\",\"supplyDefaults\");T(t,e,_||w||r,{axis:\"y\"}),T(t,e,_||w||r,{axis:\"x\",inherit:\"y\"}),n.coerceSelectionMarkerOpacity(e,g)}else e.visible=!1}},5345:function(t,e,r){\"use strict\";var n=r(71828),i=r(7901),a=r(37822).DESELECTDIM;t.exports={styleTextSelection:function(t){var e,r,o=t[0],s=o.trace,l=o.t,u=l._scene,c=l.index,f=u.selectBatch[c],h=u.unselectBatch[c],p=u.textOptions[c],d=u.textSelectedOptions[c]||{},v=u.textUnselectedOptions[c]||{},g=n.extendFlat({},p);if(f.length||h.length){var y=d.color,m=v.color,x=p.color,b=Array.isArray(x);for(g.color=new Array(s._length),e=0;e<f.length;e++)r=f[e],g.color[r]=y||(b?x[r]:x);for(e=0;e<h.length;e++){r=h[e];var _=b?x[r]:x;g.color[r]=m||(y?_:i.addOpacity(_,a))}}u.glText[c].update(g)}}},68101:function(t,e,r){\"use strict\";var n=r(8225);t.exports=function(t,e,r){var i=t.i;return\"x\"in t||(t.x=e._x[i]),\"y\"in t||(t.y=e._y[i]),n(t,e,r)}},68645:function(t,e,r){\"use strict\";var n=r(78232);e.isOpenSymbol=function(t){return\"string\"==typeof t?n.OPEN_RE.test(t):t%200>100},e.isDotSymbol=function(t){return\"string\"==typeof t?n.DOT_RE.test(t):t>200}},20794:function(t,e,r){\"use strict\";var n=r(73972),i=r(71828),a=r(34603);function o(t,e,r,o){var s=t.xa,l=t.ya,u=t.distance,c=t.dxy,f=t.index,h={pointNumber:f,x:e[f],y:r[f]};h.tx=Array.isArray(o.text)?o.text[f]:o.text,h.htx=Array.isArray(o.hovertext)?o.hovertext[f]:o.hovertext,h.data=Array.isArray(o.customdata)?o.customdata[f]:o.customdata,h.tp=Array.isArray(o.textposition)?o.textposition[f]:o.textposition;var p=o.textfont;p&&(h.ts=i.isArrayOrTypedArray(p.size)?p.size[f]:p.size,h.tc=Array.isArray(p.color)?p.color[f]:p.color,h.tf=Array.isArray(p.family)?p.family[f]:p.family);var d=o.marker;d&&(h.ms=i.isArrayOrTypedArray(d.size)?d.size[f]:d.size,h.mo=i.isArrayOrTypedArray(d.opacity)?d.opacity[f]:d.opacity,h.mx=i.isArrayOrTypedArray(d.symbol)?d.symbol[f]:d.symbol,h.ma=i.isArrayOrTypedArray(d.angle)?d.angle[f]:d.angle,h.mc=i.isArrayOrTypedArray(d.color)?d.color[f]:d.color);var v=d&&d.line;v&&(h.mlc=Array.isArray(v.color)?v.color[f]:v.color,h.mlw=i.isArrayOrTypedArray(v.width)?v.width[f]:v.width);var g=d&&d.gradient;g&&\"none\"!==g.type&&(h.mgt=Array.isArray(g.type)?g.type[f]:g.type,h.mgc=Array.isArray(g.color)?g.color[f]:g.color);var y=s.c2p(h.x,!0),m=l.c2p(h.y,!0),x=h.mrc||1,b=o.hoverlabel;b&&(h.hbg=Array.isArray(b.bgcolor)?b.bgcolor[f]:b.bgcolor,h.hbc=Array.isArray(b.bordercolor)?b.bordercolor[f]:b.bordercolor,h.hts=i.isArrayOrTypedArray(b.font.size)?b.font.size[f]:b.font.size,h.htc=Array.isArray(b.font.color)?b.font.color[f]:b.font.color,h.htf=Array.isArray(b.font.family)?b.font.family[f]:b.font.family,h.hnl=i.isArrayOrTypedArray(b.namelength)?b.namelength[f]:b.namelength);var _=o.hoverinfo;_&&(h.hi=Array.isArray(_)?_[f]:_);var w=o.hovertemplate;w&&(h.ht=Array.isArray(w)?w[f]:w);var T={};T[t.index]=h;var k=o._origX,A=o._origY,M=i.extendFlat({},t,{color:a(o,h),x0:y-x,x1:y+x,xLabelVal:k?k[f]:h.x,y0:m-x,y1:m+x,yLabelVal:A?A[f]:h.y,cd:T,distance:u,spikeDistance:c,hovertemplate:h.ht});return h.htx?M.text=h.htx:h.tx?M.text=h.tx:o.text&&(M.text=o.text),i.fillText(h,o,M),n.getComponentMethod(\"errorbars\",\"hoverInfo\")(h,o,M),M}t.exports={hoverPoints:function(t,e,r,n){var i,a,s,l,u,c,f,h,p,d,v=t.cd,g=v[0].t,y=v[0].trace,m=t.xa,x=t.ya,b=g.x,_=g.y,w=m.c2p(e),T=x.c2p(r),k=t.distance;if(g.tree){var A=m.p2c(w-k),M=m.p2c(w+k),S=x.p2c(T-k),E=x.p2c(T+k);i=\"x\"===n?g.tree.range(Math.min(A,M),Math.min(x._rl[0],x._rl[1]),Math.max(A,M),Math.max(x._rl[0],x._rl[1])):g.tree.range(Math.min(A,M),Math.min(S,E),Math.max(A,M),Math.max(S,E))}else i=g.ids;var L=k;if(\"x\"===n){var C=!!y.xperiodalignment,P=!!y.yperiodalignment;for(c=0;c<i.length;c++){if(l=b[a=i[c]],f=Math.abs(m.c2p(l)-w),C){var O=m.c2p(y._xStarts[a]),I=m.c2p(y._xEnds[a]);f=w>=Math.min(O,I)&&w<=Math.max(O,I)?0:1/0}if(f<L){if(L=f,u=_[a],h=x.c2p(u)-T,P){var D=x.c2p(y._yStarts[a]),z=x.c2p(y._yEnds[a]);h=T>=Math.min(D,z)&&T<=Math.max(D,z)?0:1/0}d=Math.sqrt(f*f+h*h),s=i[c]}}}else for(c=i.length-1;c>-1;c--)l=b[a=i[c]],u=_[a],f=m.c2p(l)-w,h=x.c2p(u)-T,(p=Math.sqrt(f*f+h*h))<L&&(L=d=p,s=a);return t.index=s,t.distance=L,t.dxy=d,void 0===s?[t]:[o(t,b,_,y)]},calcHover:o}},68868:function(t,e,r){\"use strict\";var n=r(72156);n.plot=r(26787),t.exports=n},26787:function(t,e,r){\"use strict\";var n=r(11870),i=r(46075),a=r(3593),o=r(42505),s=r(71828),l=r(64505).selectMode,u=r(79749),c=r(34098),f=r(68687),h=r(5345).styleTextSelection,p={};function d(t,e,r,n){var i=t._size,a=t.width*n,o=t.height*n,s=i.l*n,l=i.b*n,u=i.r*n,c=i.t*n,f=i.w*n,h=i.h*n;return[s+e.domain[0]*f,l+r.domain[0]*h,a-u-(1-e.domain[1])*f,o-c-(1-r.domain[1])*h]}(t.exports=function(t,e,r){if(r.length){var v,g,y=t._fullLayout,m=e._scene,x=e.xaxis,b=e.yaxis;if(m)if(u(t,[\"ANGLE_instanced_arrays\",\"OES_element_index_uint\"],p)){var 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n=r(5386).fF,i=r(5386).si,a=r(19316),o=r(82196),s=r(23585),l=r(9012),u=r(50693),c=r(1426).extendFlat,f=r(30962).overrideAll,h=r(23585),p=a.line,d=a.marker;t.exports=f({lon:a.lon,lat:a.lat,cluster:{enabled:{valType:\"boolean\"},maxzoom:c({},h.layers.maxzoom,{}),step:{valType:\"number\",arrayOk:!0,dflt:-1,min:-1},size:{valType:\"number\",arrayOk:!0,dflt:20,min:0},color:{valType:\"color\",arrayOk:!0},opacity:c({},d.opacity,{dflt:1})},mode:c({},o.mode,{dflt:\"markers\"}),text:c({},o.text,{}),texttemplate:i({editType:\"plot\"},{keys:[\"lat\",\"lon\",\"text\"]}),hovertext:c({},o.hovertext,{}),line:{color:p.color,width:p.width},connectgaps:o.connectgaps,marker:c({symbol:{valType:\"string\",dflt:\"circle\",arrayOk:!0},angle:{valType:\"number\",dflt:\"auto\",arrayOk:!0},allowoverlap:{valType:\"boolean\",dflt:!1},opacity:d.opacity,size:d.size,sizeref:d.sizeref,sizemin:d.sizemin,sizemode:d.sizemode},u(\"marker\")),fill:a.fill,fillcolor:o.fillcolor,textfont:s.layers.symbol.textfont,textposition:s.layers.symbol.textposition,below:{valType:\"string\"},selected:{marker:o.selected.marker},unselected:{marker:o.unselected.marker},hoverinfo:c({},l.hoverinfo,{flags:[\"lon\",\"lat\",\"text\",\"name\"]}),hovertemplate:n()},\"calc\",\"nested\")},15790:function(t,e,r){\"use 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r,a=e[0].trace,b=!0===a.visible&&0!==a._length,_=\"none\"!==a.fill,w=c.hasLines(a),T=c.hasMarkers(a),k=c.hasText(a),A=T&&\"circle\"===a.marker.symbol,M=T&&\"circle\"!==a.marker.symbol,S=a.cluster&&a.cluster.enabled,E=v(\"fill\"),L=v(\"line\"),C=v(\"circle\"),P=v(\"symbol\"),O={fill:E,line:L,circle:C,symbol:P};if(!b)return O;if((_||w)&&(r=o.calcTraceToLineCoords(e)),_&&(E.geojson=o.makePolygon(r),E.layout.visibility=\"visible\",i.extendFlat(E.paint,{\"fill-color\":a.fillcolor})),w&&(L.geojson=o.makeLine(r),L.layout.visibility=\"visible\",i.extendFlat(L.paint,{\"line-width\":a.line.width,\"line-color\":a.line.color,\"line-opacity\":a.opacity})),A){var I=function(t){var e,r,a,o,c=t[0].trace,f=c.marker,h=c.selectedpoints,p=i.isArrayOrTypedArray(f.color),d=i.isArrayOrTypedArray(f.size),v=i.isArrayOrTypedArray(f.opacity);function g(t){return c.opacity*t}p&&(r=s.hasColorscale(c,\"marker\")?s.makeColorScaleFuncFromTrace(f):i.identity),d&&(a=u(c)),v&&(o=function(t){return 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a.marker&&\"auto\"!==a.marker.angle&&i.extendFlat(P.layout,{\"icon-rotate\":{type:\"identity\",property:\"angle\"},\"icon-rotation-alignment\":\"map\"}),P.layout[\"icon-allow-overlap\"]=a.marker.allowoverlap,i.extendFlat(P.paint,{\"icon-opacity\":a.opacity*a.marker.opacity,\"icon-color\":a.marker.color})),k)){var D=(a.marker||{}).size,z=f(a.textposition,D);i.extendFlat(P.layout,{\"text-size\":a.textfont.size,\"text-anchor\":z.anchor,\"text-offset\":z.offset,\"text-font\":a.textfont.family.split(\", \")}),i.extendFlat(P.paint,{\"text-color\":a.textfont.color,\"text-opacity\":a.opacity})}return O}},76645:function(t,e,r){\"use strict\";var n=r(71828),i=r(34098),a=r(49508),o=r(11058),s=r(82410),l=r(28908),u=r(99181),c=[\"Metropolis Black Italic\",\"Metropolis Black\",\"Metropolis Bold Italic\",\"Metropolis Bold\",\"Metropolis Extra Bold Italic\",\"Metropolis Extra Bold\",\"Metropolis Extra Light Italic\",\"Metropolis Extra Light\",\"Metropolis Light Italic\",\"Metropolis Light\",\"Metropolis Medium Italic\",\"Metropolis Medium\",\"Metropolis Regular Italic\",\"Metropolis Regular\",\"Metropolis Semi Bold Italic\",\"Metropolis Semi Bold\",\"Metropolis Thin Italic\",\"Metropolis Thin\",\"Open Sans Bold Italic\",\"Open Sans Bold\",\"Open Sans Extra Bold Italic\",\"Open Sans Extra Bold\",\"Open Sans Italic\",\"Open Sans Light Italic\",\"Open Sans Light\",\"Open Sans Regular\",\"Open Sans Semibold Italic\",\"Open Sans Semibold\",\"Klokantech Noto Sans Bold\",\"Klokantech Noto Sans CJK Bold\",\"Klokantech Noto Sans CJK Regular\",\"Klokantech Noto Sans Italic\",\"Klokantech Noto Sans Regular\"];t.exports=function(t,e,r,f){function h(r,i){return n.coerce(t,e,u,r,i)}function p(r,i){return n.coerce2(t,e,u,r,i)}var d=function(t,e,r){var n=r(\"lon\")||[],i=r(\"lat\")||[],a=Math.min(n.length,i.length);return 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n=r(35948),i=r(20794).calcHover;t.exports={hoverPoints:function(t,e,r){var a=t.cd[0].trace,o=t.scene.matrixOptions.cdata,s=t.xa,l=t.ya,u=s.c2p(e),c=l.c2p(r),f=t.distance,h=n.getDimIndex(a,s),p=n.getDimIndex(a,l);if(!1===h||!1===p)return[t];for(var d,v,g=o[h],y=o[p],m=f,x=0;x<g.length;x++){var b=g[x],_=y[x],w=s.c2p(b)-u,T=l.c2p(_)-c,k=Math.sqrt(w*w+T*T);k<m&&(m=v=k,d=x)}return t.index=d,t.distance=m,t.dxy=v,void 0===d?[t]:[i(t,g,y,a)]}}},6419:function(t,e,r){\"use strict\";var n=r(65017);n.basePlotModule=r(16947),t.exports=n},79410:function(t,e,r){\"use strict\";var n=r(60487),i=r(71828),a=r(41675),o=r(64505).selectMode;function s(t,e){var r,s,l,u,c,f=t._fullLayout,h=f._size,p=e.trace,d=e.t,v=f._splomScenes[p.uid],g=v.matrixOptions,y=g.cdata,m=f._glcanvas.data()[0].regl,x=f.dragmode;if(0!==y.length){g.lower=p.showupperhalf,g.upper=p.showlowerhalf,g.diagonal=p.diagonal.visible;var b=p._visibleDims,_=y.length,w=v.viewOpts={};for(w.ranges=new Array(_),w.domains=new 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B=i.extendFlat({},g,v.unselectedOptions,w),N=i.extendFlat({},g,v.selectedOptions,w);v.matrix.update(B,N),P=!1}}else d.xpx=d.ypx=null;if(P){var j=i.extendFlat({},g,w);v.matrix.update(j,null)}}}t.exports=function(t,e,r){if(r.length)for(var n=0;n<r.length;n++)s(t,r[n][0])}},10164:function(t,e,r){\"use strict\";var n=r(71828);t.exports=function(t,e){var r=t._fullLayout,i=e.uid,a=r._splomScenes;a||(a=r._splomScenes={});var o={dirty:!0,selectBatch:[],unselectBatch:[]},s=a[e.uid];return s||((s=a[i]=n.extendFlat({},o,{matrix:!1,selectBatch:[],unselectBatch:[]})).draw=function(){s.matrix&&s.matrix.draw&&(s.selectBatch.length||s.unselectBatch.length?s.matrix.draw(s.unselectBatch,s.selectBatch):s.matrix.draw()),s.dirty=!1},s.destroy=function(){s.matrix&&s.matrix.destroy&&s.matrix.destroy(),s.matrixOptions=null,s.selectBatch=null,s.unselectBatch=null,s=null}),s.dirty||n.extendFlat(s,o),s}},8689:function(t,e,r){\"use strict\";var n=r(71828),i=n.pushUnique,a=r(34098),o=r(35948);t.exports=function(t,e){var r=t.cd,s=r[0].trace,l=r[0].t,u=t.scene,c=u.matrixOptions.cdata,f=t.xaxis,h=t.yaxis,p=[];if(!u)return p;var d=!a.hasMarkers(s)&&!a.hasText(s);if(!0!==s.visible||d)return p;var v=o.getDimIndex(s,f),g=o.getDimIndex(s,h);if(!1===v||!1===g)return p;var y=l.xpx[v],m=l.ypx[g],x=c[v],b=c[g],_=(t.scene.selectBatch||[]).slice(),w=[];if(!1!==e&&!e.degenerate)for(var T=0;T<x.length;T++)e.contains([y[T],m[T]],null,T,t)?(p.push({pointNumber:T,x:x[T],y:b[T]}),i(_,T)):-1!==_.indexOf(T)?i(_,T):w.push(T);var k=u.matrixOptions;return _.length||w.length?u.selectBatch.length||u.unselectBatch.length||u.matrix.update(u.unselectedOptions,n.extendFlat({},k,u.selectedOptions,u.viewOpts)):u.matrix.update(k,null),u.selectBatch=_,u.unselectBatch=w,p}},21850:function(t,e,r){\"use strict\";var n=r(50693),i=r(12663).axisHoverFormat,a=r(5386).fF,o=r(2418),s=r(9012),l=r(1426).extendFlat,u={x:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},y:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},z:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},u:{valType:\"data_array\",editType:\"calc\"},v:{valType:\"data_array\",editType:\"calc\"},w:{valType:\"data_array\",editType:\"calc\"},starts:{x:{valType:\"data_array\",editType:\"calc\"},y:{valType:\"data_array\",editType:\"calc\"},z:{valType:\"data_array\",editType:\"calc\"},editType:\"calc\"},maxdisplayed:{valType:\"integer\",min:0,dflt:1e3,editType:\"calc\"},sizeref:{valType:\"number\",editType:\"calc\",min:0,dflt:1},text:{valType:\"string\",dflt:\"\",editType:\"calc\"},hovertext:{valType:\"string\",dflt:\"\",editType:\"calc\"},hovertemplate:a({editType:\"calc\"},{keys:[\"tubex\",\"tubey\",\"tubez\",\"tubeu\",\"tubev\",\"tubew\",\"norm\",\"divergence\"]}),uhoverformat:i(\"u\",1),vhoverformat:i(\"v\",1),whoverformat:i(\"w\",1),xhoverformat:i(\"x\"),yhoverformat:i(\"y\"),zhoverformat:i(\"z\"),showlegend:l({},s.showlegend,{dflt:!1})};l(u,n(\"\",{colorAttr:\"u/v/w 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L=function(){m=0,M=[],S=[],E=[]};(!m||m<M.length*S.length*E.length)&&L();var C=function(t){return\"x\"===t?v:\"y\"===t?g:y},P=function(t){return\"x\"===t?M:\"y\"===t?S:E},O=function(t){return t[m-1]<t[0]?-1:1},I=C(A[1]),D=C(A[3]),z=C(A[5]),R=P(A[1]).length,F=P(A[3]).length,B=P(A[5]).length,N=!1,j=function(t,e,r){return R*(F*t+e)+r},U=O(C(A[1])),V=O(C(A[3])),H=O(C(A[5]));for(e=0;e<B-1;e++){for(r=0;r<F-1;r++){for(i=0;i<R-1;i++){var q=j(e,r,i),G=j(e,r,i+1),Z=j(e,r+1,i),Y=j(e+1,r,i);if(I[q]*U<I[G]*U&&D[q]*V<D[Z]*V&&z[q]*H<z[Y]*H||(N=!0),N)break}if(N)break}if(N)break}return N&&(n.warn(\"Encountered arbitrary coordinates! 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y=n[c];r.xMax=Math.max(r.xMax,y),r.xMin=Math.min(r.xMin,y);var m=o[c];r.yMax=Math.max(r.yMax,m),r.yMin=Math.min(r.yMin,m);var x=l[c];r.zMax=Math.max(r.zMax,x),r.zMin=Math.min(r.zMin,x)}e._slen=u,e._normMax=f,e._xbnds=[r.xMin,r.xMax],e._ybnds=[r.yMin,r.yMax],e._zbnds=[r.zMin,r.zMax]},filter:s,processGrid:a}},90154:function(t,e,r){\"use strict\";var n=r(9330).gl_streamtube3d,i=n.createTubeMesh,a=r(71828),o=r(81697).parseColorScale,s=r(21081).extractOpts,l=r(90060),u={xaxis:0,yaxis:1,zaxis:2};function c(t,e){this.scene=t,this.uid=e,this.mesh=null,this.data=null}var f=c.prototype;function h(t){var e=t.length;return e>2?t.slice(1,e-1):2===e?[(t[0]+t[1])/2]:t}function p(t){var e=t.length;return 1===e?[.5,.5]:[t[1]-t[0],t[e-1]-t[e-2]]}function d(t,e){var r=t.fullSceneLayout,i=t.dataScale,c=e._len,f={};function d(t,e){var n=r[e],o=i[u[e]];return a.simpleMap(t,(function(t){return 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e.listPath(t,\"label\").join(\"/\")+\"/\"},e.formatValue=o.formatPieValue,e.formatPercent=function(t,e){var r=n.formatPercent(t,0);return\"0%\"===r&&(r=o.formatPiePercent(t,e)),r}},87619:function(t,e,r){\"use strict\";t.exports={moduleType:\"trace\",name:\"sunburst\",basePlotModule:r(66888),categories:[],animatable:!0,attributes:r(57564),layoutAttributes:r(2654),supplyDefaults:r(17094),supplyLayoutDefaults:r(57034),calc:r(52147).calc,crossTraceCalc:r(52147).crossTraceCalc,plot:r(24714).plot,style:r(29969).style,colorbar:r(4898),meta:{}}},2654:function(t){\"use strict\";t.exports={sunburstcolorway:{valType:\"colorlist\",editType:\"calc\"},extendsunburstcolors:{valType:\"boolean\",dflt:!0,editType:\"calc\"}}},57034:function(t,e,r){\"use strict\";var n=r(71828),i=r(2654);t.exports=function(t,e){function r(r,a){return n.coerce(t,e,i,r,a)}r(\"sunburstcolorway\",e.colorway),r(\"extendsunburstcolors\")}},24714:function(t,e,r){\"use strict\";var 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s,l=b.getPtId(t.parent);T.each((function(t){if(b.getPtId(t)===l)return s=t}));var u,c=s.children;c.forEach((function(t,e){if(b.getPtId(t)===r)return u=e}));var f=c.length,h=a(s.x0,s.x1);e={rpx0:I,rpx1:I,x0:h(u/f),x1:h((u+1)/f)}}return a(n,e)}(t);return function(t){return Z(e(t))}})),t.select(\"g.slicetext\").attr(\"opacity\",0)})).remove():T.exit().remove(),T.order();var X=null;if(_&&R){var J=b.getPtId(R);T.each((function(t){null===X&&b.getPtId(t)===J&&(X=t.x1)}))}var K=T;function $(t){var e=t.parent,r=F[b.getPtId(e)],n={};if(r){var i=e.children,o=i.indexOf(t),s=i.length,l=a(r.x0,r.x1);n.x0=l(o/s),n.x1=l(o/s)}else n.x0=n.x1=0;return n}_&&(K=K.transition().each(\"end\",(function(){var e=n.select(this);b.setSliceCursor(e,t,{hideOnRoot:!0,hideOnLeaves:!0,isTransitioning:!1})}))),K.each((function(i){var u=n.select(this),f=s.ensureSingle(u,\"path\",\"surface\",(function(t){t.style(\"pointer-events\",h?\"none\":\"all\")}));i.rpx0=q(i.y0),i.rpx1=q(i.y1),i.xmid=(i.x0+i.x1)/2,i.pxmid=G(i.rpx1,i.xmid),i.midangle=-(i.xmid-Math.PI/2),i.startangle=-(i.x0-Math.PI/2),i.stopangle=-(i.x1-Math.PI/2),i.halfangle=.5*Math.min(s.angleDelta(i.x0,i.x1)||Math.PI,Math.PI),i.ring=1-i.rpx0/i.rpx1,i.rInscribed=function(t){return 0===t.rpx0&&s.isFullCircle([t.x0,t.x1])?1:Math.max(0,Math.min(1/(1+1/Math.sin(t.halfangle)),t.ring/2))}(i),_?f.transition().attrTween(\"d\",(function(t){var e=function(t){var e,r=F[b.getPtId(t)],n={x0:t.x0,x1:t.x1,rpx0:t.rpx0,rpx1:t.rpx1};if(r)e=r;else if(R)if(t.parent)if(X){var i=(t.x1>X?2*Math.PI:0)+V;e={x0:i,x1:i}}else e={rpx0:I,rpx1:I},s.extendFlat(e,$(t));else e={rpx0:0,rpx1:0};else e={x0:V,x1:V};return a(e,n)}(t);return function(t){return Z(e(t))}})):f.attr(\"d\",Z),u.call(m,S,t,r,{eventDataKeys:x.eventDataKeys,transitionTime:x.CLICK_TRANSITION_TIME,transitionEasing:x.CLICK_TRANSITION_EASING}).call(b.setSliceCursor,t,{hideOnRoot:!0,hideOnLeaves:!0,isTransitioning:t._transitioning}),f.call(g,i,A,t);var p=s.ensureSingle(u,\"g\",\"slicetext\"),w=s.ensureSingle(p,\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),T=s.ensureUniformFontSize(t,b.determineTextFont(A,i,y.font));w.text(e.formatSliceLabel(i,S,A,r,y)).classed(\"slicetext\",!0).attr(\"text-anchor\",\"middle\").call(o.font,T).call(l.convertToTspans,t);var M=o.bBox(w.node());i.transform=v(M,i,k),i.transform.targetX=Y(i),i.transform.targetY=W(i);var E=function(t,e){var r=t.transform;return d(r,e),r.fontSize=T.size,c(A.type,r,y),s.getTextTransform(r)};_?w.transition().attrTween(\"transform\",(function(t){var e=function(t){var e,r=F[b.getPtId(t)],n=t.transform;if(r)e=r;else if(e={rpx1:t.rpx1,transform:{textPosAngle:n.textPosAngle,scale:0,rotate:n.rotate,rCenter:n.rCenter,x:n.x,y:n.y}},R)if(t.parent)if(X){var i=t.x1>X?2*Math.PI:0;e.x0=e.x1=i}else s.extendFlat(e,$(t));else e.x0=e.x1=V;else e.x0=e.x1=V;var o=a(e.transform.textPosAngle,t.transform.textPosAngle),l=a(e.rpx1,t.rpx1),u=a(e.x0,t.x0),f=a(e.x1,t.x1),h=a(e.transform.scale,n.scale),p=a(e.transform.rotate,n.rotate),d=0===n.rCenter?3:0===e.transform.rCenter?1/3:1,v=a(e.transform.rCenter,n.rCenter);return function(t){var e=l(t),r=u(t),i=f(t),a=function(t){return v(Math.pow(t,d))}(t),s={pxmid:G(e,(r+i)/2),rpx1:e,transform:{textPosAngle:o(t),rCenter:a,x:n.x,y:n.y}};return c(A.type,n,y),{transform:{targetX:Y(s),targetY:W(s),scale:h(t),rotate:p(t),rCenter:a}}}}(t);return function(t){return E(e(t),M)}})):w.attr(\"transform\",E(i,M))}))}function w(t){return e=t.rpx1,r=t.transform.textPosAngle,[e*Math.sin(r),-e*Math.cos(r)];var e,r}e.plot=function(t,e,r,i){var a,o,s=t._fullLayout,l=s._sunburstlayer,u=!r,c=!s.uniformtext.mode&&b.hasTransition(r);f(\"sunburst\",s),(a=l.selectAll(\"g.trace.sunburst\").data(e,(function(t){return t[0].trace.uid}))).enter().append(\"g\").classed(\"trace\",!0).classed(\"sunburst\",!0).attr(\"stroke-linejoin\",\"round\"),a.order(),c?(i&&(o=i()),n.transition().duration(r.duration).ease(r.easing).each(\"end\",(function(){o&&o()})).each(\"interrupt\",(function(){o&&o()})).each((function(){l.selectAll(\"g.trace\").each((function(e){_(t,e,this,r)}))}))):(a.each((function(e){_(t,e,this,r)})),s.uniformtext.mode&&y(t,s._sunburstlayer.selectAll(\".trace\"),\"sunburst\")),u&&a.exit().remove()},e.formatSliceLabel=function(t,e,r,n,i){var a=r.texttemplate,o=r.textinfo;if(!(a||o&&\"none\"!==o))return\"\";var l=i.separators,u=n[0],c=t.data.data,f=u.hierarchy,h=b.isHierarchyRoot(t),p=b.getParent(f,t),d=b.getValue(t);if(!a){var v,g=o.split(\"+\"),y=function(t){return-1!==g.indexOf(t)},m=[];if(y(\"label\")&&c.label&&m.push(c.label),c.hasOwnProperty(\"v\")&&y(\"value\")&&m.push(b.formatValue(c.v,l)),!h){y(\"current path\")&&m.push(b.getPath(t.data));var x=0;y(\"percent parent\")&&x++,y(\"percent entry\")&&x++,y(\"percent root\")&&x++;var _=x>1;if(x){var w,T=function(t){v=b.formatPercent(w,l),_&&(v+=\" of \"+t),m.push(v)};y(\"percent parent\")&&!h&&(w=d/b.getValue(p),T(\"parent\")),y(\"percent entry\")&&(w=d/b.getValue(e),T(\"entry\")),y(\"percent root\")&&(w=d/b.getValue(f),T(\"root\"))}}return y(\"text\")&&(v=s.castOption(r,c.i,\"text\"),s.isValidTextValue(v)&&m.push(v)),m.join(\"<br>\")}var k=s.castOption(r,c.i,\"texttemplate\");if(!k)return\"\";var A={};c.label&&(A.label=c.label),c.hasOwnProperty(\"v\")&&(A.value=c.v,A.valueLabel=b.formatValue(c.v,l)),A.currentPath=b.getPath(t.data),h||(A.percentParent=d/b.getValue(p),A.percentParentLabel=b.formatPercent(A.percentParent,l),A.parent=b.getPtLabel(p)),A.percentEntry=d/b.getValue(e),A.percentEntryLabel=b.formatPercent(A.percentEntry,l),A.entry=b.getPtLabel(e),A.percentRoot=d/b.getValue(f),A.percentRootLabel=b.formatPercent(A.percentRoot,l),A.root=b.getPtLabel(f),c.hasOwnProperty(\"color\")&&(A.color=c.color);var M=s.castOption(r,c.i,\"text\");return(s.isValidTextValue(M)||\"\"===M)&&(A.text=M),A.customdata=s.castOption(r,c.i,\"customdata\"),s.texttemplateString(k,A,i._d3locale,A,r._meta||{})}},29969:function(t,e,r){\"use strict\";var n=r(39898),i=r(7901),a=r(71828),o=r(72597).resizeText,s=r(43467);function l(t,e,r,n){var 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f=t.exports=u(l({z:{valType:\"data_array\"},x:{valType:\"data_array\"},y:{valType:\"data_array\"},text:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertemplate:o(),xhoverformat:a(\"x\"),yhoverformat:a(\"y\"),zhoverformat:a(\"z\"),connectgaps:{valType:\"boolean\",dflt:!1,editType:\"calc\"},surfacecolor:{valType:\"data_array\"}},i(\"\",{colorAttr:\"z or 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n=r(78803);t.exports=function(t,e){e.surfacecolor?n(t,e,{vals:e.surfacecolor,containerStr:\"\",cLetter:\"c\"}):n(t,e,{vals:e.z,containerStr:\"\",cLetter:\"c\"})}},43768:function(t,e,r){\"use strict\";var n=r(9330).gl_surface3d,i=r(9330).ndarray,a=r(9330).ndarray_linear_interpolate.d2,o=r(824),s=r(43907),l=r(71828).isArrayOrTypedArray,u=r(81697).parseColorScale,c=r(78614),f=r(21081).extractOpts;function h(t,e,r){this.scene=t,this.uid=r,this.surface=e,this.data=null,this.showContour=[!1,!1,!1],this.contourStart=[null,null,null],this.contourEnd=[null,null,null],this.contourSize=[0,0,0],this.minValues=[1/0,1/0,1/0],this.maxValues=[-1/0,-1/0,-1/0],this.dataScaleX=1,this.dataScaleY=1,this.refineData=!0,this.objectOffset=[0,0,0]}var p=h.prototype;p.getXat=function(t,e,r,n){var i=l(this.data.x)?l(this.data.x[0])?this.data.x[e][t]:this.data.x[t]:t;return void 0===r?i:n.d2l(i,0,r)},p.getYat=function(t,e,r,n){var i=l(this.data.y)?l(this.data.y[0])?this.data.y[e][t]:this.data.y[e]:e;return void 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A=[!0,!0,!0],M=[\"x\",\"y\",\"z\"];for(e=0;e<3;++e){var S=t.contours[M[e]];A[e]=S.highlight,w.showContour[e]=S.show||S.highlight,w.showContour[e]&&(w.contourProject[e]=[S.project.x,S.project.y,S.project.z],S.show?(this.showContour[e]=!0,w.levels[e]=m[e],p.highlightColor[e]=w.contourColor[e]=c(S.color),S.usecolormap?p.highlightTint[e]=w.contourTint[e]=0:p.highlightTint[e]=w.contourTint[e]=1,w.contourWidth[e]=S.width,this.contourStart[e]=S.start,this.contourEnd[e]=S.end,this.contourSize[e]=S.size):(this.showContour[e]=!1,this.contourStart[e]=null,this.contourEnd[e]=null,this.contourSize[e]=0),S.highlight&&(w.dynamicColor[e]=c(S.highlightcolor),w.dynamicWidth[e]=S.highlightwidth))}(function(t){var e=t[0].rgb,r=t[t.length-1].rgb;return e[0]===r[0]&&e[1]===r[1]&&e[2]===r[2]&&e[3]===r[3]})(d)&&(w.vertexColor=!0),w.objectOffset=this.objectOffset,w.coords=b,p.update(w),p.visible=t.visible,p.enableDynamic=A,p.enableHighlight=A,p.snapToData=!0,\"lighting\"in 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n=r(50215),i=r(1426).extendFlat,a=r(30962).overrideAll,o=r(41940),s=r(27670).Y,l=r(12663).descriptionOnlyNumbers;(t.exports=a({domain:s({name:\"table\",trace:!0}),columnwidth:{valType:\"number\",arrayOk:!0,dflt:null},columnorder:{valType:\"data_array\"},header:{values:{valType:\"data_array\",dflt:[]},format:{valType:\"data_array\",dflt:[],description:l(\"cell value\")},prefix:{valType:\"string\",arrayOk:!0,dflt:null},suffix:{valType:\"string\",arrayOk:!0,dflt:null},height:{valType:\"number\",dflt:28},align:i({},n.align,{arrayOk:!0}),line:{width:{valType:\"number\",arrayOk:!0,dflt:1},color:{valType:\"color\",arrayOk:!0,dflt:\"grey\"}},fill:{color:{valType:\"color\",arrayOk:!0,dflt:\"white\"}},font:i({},o({arrayOk:!0}))},cells:{values:{valType:\"data_array\",dflt:[]},format:{valType:\"data_array\",dflt:[],description:l(\"cell 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L=Math.max(o(e.header.line.width),o(e.cells.line.width)),C={key:e.uid+t._context.staticPlot,translateX:g.x[0]*t._fullLayout._size.w,translateY:t._fullLayout._size.h*(1-g.y[1]),size:t._fullLayout._size,width:y,maxLineWidth:L,height:m,columnOrder:M,groupHeight:m,rowBlocks:k,headerRowBlocks:T,scrollY:0,cells:i({},e.cells,{values:r}),headerCells:i({},e.header,{values:v}),gdColumns:v.map((function(t){return t[0]})),gdColumnsOriginalOrder:v.map((function(t){return t[0]})),prevPages:[0,0],scrollbarState:{scrollbarScrollInProgress:!1},columns:v.map((function(t,e){var r=A[t];return A[t]=(r||0)+1,{key:t+\"__\"+A[t],label:t,specIndex:e,xIndex:M[e],xScale:c,x:void 0,calcdata:void 0,columnWidth:S[e]}}))};return C.columns.forEach((function(t){t.calcdata=C,t.x=c(t)})),C}},56269:function(t,e,r){\"use strict\";var n=r(1426).extendFlat;e.splitToPanels=function(t){var e=[0,0],r=n({},t,{key:\"header\",type:\"header\",page:0,prevPages:e,currentRepaint:[null,null],dragHandle:!0,values:t.calcdata.headerCells.values[t.specIndex],rowBlocks:t.calcdata.headerRowBlocks,calcdata:n({},t.calcdata,{cells:t.calcdata.headerCells})});return[n({},t,{key:\"cells1\",type:\"cells\",page:0,prevPages:e,currentRepaint:[null,null],dragHandle:!1,values:t.calcdata.cells.values[t.specIndex],rowBlocks:t.calcdata.rowBlocks}),n({},t,{key:\"cells2\",type:\"cells\",page:1,prevPages:e,currentRepaint:[null,null],dragHandle:!1,values:t.calcdata.cells.values[t.specIndex],rowBlocks:t.calcdata.rowBlocks}),r]},e.splitToCells=function(t){var e=function(t){var e=t.rowBlocks[t.page],r=e?e.rows[0].rowIndex:0;return[r,e?r+e.rows.length:0]}(t);return(t.values||[]).slice(e[0],e[1]).map((function(r,n){return{keyWithinBlock:n+(\"string\"==typeof r&&r.match(/[<$&> 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a.indexOf(t)})),s=o.length;s<n;s++)o.push(s);e(\"columnorder\",o)}(e,s),s(\"cells.values\"),s(\"cells.format\"),s(\"cells.align\"),s(\"cells.prefix\"),s(\"cells.suffix\"),s(\"cells.height\"),s(\"cells.line.width\"),s(\"cells.line.color\"),s(\"cells.fill.color\"),n.coerceFont(s,\"cells.font\",n.extendFlat({},o.font)),e._length=null}},96595:function(t,e,r){\"use strict\";t.exports={attributes:r(44464),supplyDefaults:r(39754),calc:r(76333),plot:r(36736),moduleType:\"trace\",name:\"table\",basePlotModule:r(99469),categories:[\"noOpacity\"],meta:{}}},36736:function(t,e,r){\"use strict\";var n=r(49850),i=r(39898),a=r(71828).numberFormat,o=r(28984),s=r(91424),l=r(63893),u=r(71828).raiseToTop,c=r(71828).strTranslate,f=r(71828).cancelTransition,h=r(51018),p=r(56269),d=r(7901);function v(t){return Math.ceil(t.calcdata.maxLineWidth/2)}function g(t,e){return\"clip\"+t._fullLayout._uid+\"_scrollAreaBottomClip_\"+e.key}function y(t,e){return\"clip\"+t._fullLayout._uid+\"_columnBoundaryClippath_\"+e.calcdata.key+\"_\"+e.specIndex}function m(t){return[].concat.apply([],t.map((function(t){return t}))).map((function(t){return t.__data__}))}function x(t,e,r){var a=t.selectAll(\".\"+n.cn.scrollbarKit).data(o.repeat,o.keyFun);a.enter().append(\"g\").classed(n.cn.scrollbarKit,!0).style(\"shape-rendering\",\"geometricPrecision\"),a.each((function(t){var e=t.scrollbarState;e.totalHeight=function(t){var e=t.rowBlocks;return z(e,e.length-1)+(e.length?R(e[e.length-1],1/0):1)}(t),e.scrollableAreaHeight=t.groupHeight-S(t),e.currentlyVisibleHeight=Math.min(e.totalHeight,e.scrollableAreaHeight),e.ratio=e.currentlyVisibleHeight/e.totalHeight,e.barLength=Math.max(e.ratio*e.currentlyVisibleHeight,n.goldenRatio*n.scrollbarWidth),e.barWiggleRoom=e.currentlyVisibleHeight-e.barLength,e.wiggleRoom=Math.max(0,e.totalHeight-e.scrollableAreaHeight),e.topY=0===e.barWiggleRoom?0:t.scrollY/e.wiggleRoom*e.barWiggleRoom,e.bottomY=e.topY+e.barLength,e.dragMultiplier=e.wiggleRoom/e.barWiggleRoom})).attr(\"transform\",(function(t){var e=t.width+n.scrollbarWidth/2+n.scrollbarOffset;return c(e,S(t))}));var s=a.selectAll(\".\"+n.cn.scrollbar).data(o.repeat,o.keyFun);s.enter().append(\"g\").classed(n.cn.scrollbar,!0);var l=s.selectAll(\".\"+n.cn.scrollbarSlider).data(o.repeat,o.keyFun);l.enter().append(\"g\").classed(n.cn.scrollbarSlider,!0),l.attr(\"transform\",(function(t){return c(0,t.scrollbarState.topY||0)}));var u=l.selectAll(\".\"+n.cn.scrollbarGlyph).data(o.repeat,o.keyFun);u.enter().append(\"line\").classed(n.cn.scrollbarGlyph,!0).attr(\"stroke\",\"black\").attr(\"stroke-width\",n.scrollbarWidth).attr(\"stroke-linecap\",\"round\").attr(\"y1\",n.scrollbarWidth/2),u.attr(\"y2\",(function(t){return t.scrollbarState.barLength-n.scrollbarWidth/2})).attr(\"stroke-opacity\",(function(t){return t.columnDragInProgress||!t.scrollbarState.barWiggleRoom||r?0:.4})),u.transition().delay(0).duration(0),u.transition().delay(n.scrollbarHideDelay).duration(n.scrollbarHideDuration).attr(\"stroke-opacity\",0);var f=s.selectAll(\".\"+n.cn.scrollbarCaptureZone).data(o.repeat,o.keyFun);f.enter().append(\"line\").classed(n.cn.scrollbarCaptureZone,!0).attr(\"stroke\",\"white\").attr(\"stroke-opacity\",.01).attr(\"stroke-width\",n.scrollbarCaptureWidth).attr(\"stroke-linecap\",\"butt\").attr(\"y1\",0).on(\"mousedown\",(function(r){var n=i.event.y,a=this.getBoundingClientRect(),o=r.scrollbarState,s=n-a.top,l=i.scale.linear().domain([0,o.scrollableAreaHeight]).range([0,o.totalHeight]).clamp(!0);o.topY<=s&&s<=o.bottomY||L(e,t,null,l(s-o.barLength/2))(r)})).call(i.behavior.drag().origin((function(t){return i.event.stopPropagation(),t.scrollbarState.scrollbarScrollInProgress=!0,t})).on(\"drag\",L(e,t)).on(\"dragend\",(function(){}))),f.attr(\"y2\",(function(t){return t.scrollbarState.scrollableAreaHeight})),e._context.staticPlot&&(u.remove(),f.remove())}function b(t,e,r,a){var l=function(t){var e=t.selectAll(\".\"+n.cn.columnCells).data(o.repeat,o.keyFun);return e.enter().append(\"g\").classed(n.cn.columnCells,!0),e.exit().remove(),e}(r),u=function(t){var e=t.selectAll(\".\"+n.cn.columnCell).data(p.splitToCells,(function(t){return t.keyWithinBlock}));return e.enter().append(\"g\").classed(n.cn.columnCell,!0),e.exit().remove(),e}(l);!function(t){t.each((function(t,e){var r=t.calcdata.cells.font,n=t.column.specIndex,i={size:T(r.size,n,e),color:T(r.color,n,e),family:T(r.family,n,e)};t.rowNumber=t.key,t.align=T(t.calcdata.cells.align,n,e),t.cellBorderWidth=T(t.calcdata.cells.line.width,n,e),t.font=i}))}(u);var c=function(t){var e=t.selectAll(\".\"+n.cn.cellRect).data(o.repeat,(function(t){return t.keyWithinBlock}));return e.enter().append(\"rect\").classed(n.cn.cellRect,!0),e}(u);!function(t){t.attr(\"width\",(function(t){return t.column.columnWidth})).attr(\"stroke-width\",(function(t){return t.cellBorderWidth})).each((function(t){var e=i.select(this);d.stroke(e,T(t.calcdata.cells.line.color,t.column.specIndex,t.rowNumber)),d.fill(e,T(t.calcdata.cells.fill.color,t.column.specIndex,t.rowNumber))}))}(c);var f=function(t){var e=t.selectAll(\".\"+n.cn.cellTextHolder).data(o.repeat,(function(t){return t.keyWithinBlock}));return e.enter().append(\"g\").classed(n.cn.cellTextHolder,!0).style(\"shape-rendering\",\"geometricPrecision\"),e}(u),h=function(t){var e=t.selectAll(\".\"+n.cn.cellText).data(o.repeat,(function(t){return t.keyWithinBlock}));return e.enter().append(\"text\").classed(n.cn.cellText,!0).style(\"cursor\",(function(){return\"auto\"})).on(\"mousedown\",(function(){i.event.stopPropagation()})),e}(f);!function(t){t.each((function(t){s.font(i.select(this),t.font)}))}(h),_(h,e,a,t),D(u)}function _(t,e,r,o){t.text((function(t){var e=t.column.specIndex,r=t.rowNumber,i=t.value,o=\"string\"==typeof i,s=o&&i.match(/<br>/i),l=!o||s;t.mayHaveMarkup=o&&i.match(/[<&>]/);var u,c=\"string\"==typeof(u=i)&&u.match(n.latexCheck);t.latex=c;var f,h,p=c?\"\":T(t.calcdata.cells.prefix,e,r)||\"\",d=c?\"\":T(t.calcdata.cells.suffix,e,r)||\"\",v=c?null:T(t.calcdata.cells.format,e,r)||null,g=p+(v?a(v)(t.value):t.value)+d;if(t.wrappingNeeded=!t.wrapped&&!l&&!c&&(f=w(g)),t.cellHeightMayIncrease=s||c||t.mayHaveMarkup||(void 0===f?w(g):f),t.needsConvertToTspans=t.mayHaveMarkup||t.wrappingNeeded||t.latex,t.wrappingNeeded){var y=(\" 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k(t,e,r){t.transition().ease(n.releaseTransitionEase).duration(n.releaseTransitionDuration).attr(\"transform\",c(e.x,r))}function A(t){return\"cells\"===t.type}function M(t){return\"header\"===t.type}function S(t){return(t.rowBlocks.length?t.rowBlocks[0].auxiliaryBlocks:[]).reduce((function(t,e){return t+R(e,1/0)}),0)}function E(t,e,r){var n=m(e)[0];if(void 0!==n){var i=n.rowBlocks,a=n.calcdata,o=z(i,i.length),s=n.calcdata.groupHeight-S(n),l=a.scrollY=Math.max(0,Math.min(o-s,a.scrollY)),u=function(t,e,r){for(var n=[],i=0,a=0;a<t.length;a++){for(var o=t[a],s=o.rows,l=0,u=0;u<s.length;u++)l+=s[u].rowHeight;o.allRowsHeight=l,e<i+l&&e+r>i&&n.push(a),i+=l}return n}(i,l,s);1===u.length&&(u[0]===i.length-1?u.unshift(u[0]-1):u.push(u[0]+1)),u[0]%2&&u.reverse(),e.each((function(t,e){t.page=u[e],t.scrollY=l})),e.attr(\"transform\",(function(t){var e=z(t.rowBlocks,t.page)-t.scrollY;return c(0,e)})),t&&(C(t,r,e,u,n.prevPages,n,0),C(t,r,e,u,n.prevPages,n,1),x(r,t))}}function L(t,e,r,a){return function(o){var s=o.calcdata?o.calcdata:o,l=e.filter((function(t){return s.key===t.key})),u=r||s.scrollbarState.dragMultiplier,c=s.scrollY;s.scrollY=void 0===a?s.scrollY+u*i.event.dy:a;var f=l.selectAll(\".\"+n.cn.yColumn).selectAll(\".\"+n.cn.columnBlock).filter(A);return E(t,f,l),s.scrollY===c}}function C(t,e,r,n,i,a,o){n[o]!==i[o]&&(clearTimeout(a.currentRepaint[o]),a.currentRepaint[o]=setTimeout((function(){var a=r.filter((function(t,e){return e===o&&n[e]!==i[e]}));b(t,e,a,r),i[o]=n[o]})))}function P(t,e,r,a){return function(){var o=i.select(e.parentNode);o.each((function(t){var e=t.fragments;o.selectAll(\"tspan.line\").each((function(t,r){e[r].width=this.getComputedTextLength()}));var 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r=!t._context.staticPlot,a=t._fullLayout._paper.selectAll(\".\"+n.cn.table).data(e.map((function(e){var r=o.unwrap(e).trace;return h(t,r)})),o.keyFun);a.exit().remove(),a.enter().append(\"g\").classed(n.cn.table,!0).attr(\"overflow\",\"visible\").style(\"box-sizing\",\"content-box\").style(\"position\",\"absolute\").style(\"left\",0).style(\"overflow\",\"visible\").style(\"shape-rendering\",\"crispEdges\").style(\"pointer-events\",\"all\"),a.attr(\"width\",(function(t){return t.width+t.size.l+t.size.r})).attr(\"height\",(function(t){return t.height+t.size.t+t.size.b})).attr(\"transform\",(function(t){return c(t.translateX,t.translateY)}));var l=a.selectAll(\".\"+n.cn.tableControlView).data(o.repeat,o.keyFun),d=l.enter().append(\"g\").classed(n.cn.tableControlView,!0).style(\"box-sizing\",\"content-box\");if(r){var _=\"onwheel\"in document?\"wheel\":\"mousewheel\";d.on(\"mousemove\",(function(e){l.filter((function(t){return 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c=[\"\",\"0\",\"00\",\"000\",\"0000\",\"00000\",\"000000\",\"0000000\",\"00000000\",\"000000000\",\"0000000000\",\"00000000000\",\"000000000000\",\"0000000000000\",\"00000000000000\",\"000000000000000\",\"0000000000000000\",\"00000000000000000\",\"000000000000000000\",\"0000000000000000000\",\"00000000000000000000\",\"000000000000000000000\",\"0000000000000000000000\",\"00000000000000000000000\",\"000000000000000000000000\",\"0000000000000000000000000\"],f=[0,0,25,16,12,11,10,9,8,8,7,7,7,7,6,6,6,6,6,6,6,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5],h=[0,0,33554432,43046721,16777216,48828125,60466176,40353607,16777216,43046721,1e7,19487171,35831808,62748517,7529536,11390625,16777216,24137569,34012224,47045881,64e6,4084101,5153632,6436343,7962624,9765625,11881376,14348907,17210368,20511149,243e5,28629151,33554432,39135393,45435424,52521875,60466176];function p(t,e,r){r.negative=e.negative^t.negative;var n=t.length+e.length|0;r.length=n,n=n-1|0;var i=0|t.words[0],a=0|e.words[0],o=i*a,s=67108863&o,l=o/67108864|0;r.words[0]=s;for(var u=1;u<n;u++){for(var c=l>>>26,f=67108863&l,h=Math.min(u,e.length-1),p=Math.max(0,u-t.length+1);p<=h;p++){var d=u-p|0;c+=(o=(i=0|t.words[d])*(a=0|e.words[p])+f)/67108864|0,f=67108863&o}r.words[u]=0|f,l=0|c}return 0!==l?r.words[u]=0|l:r.length--,r.strip()}a.prototype.toString=function(t,e){var r;if(e=0|e||1,16===(t=t||10)||\"hex\"===t){r=\"\";for(var i=0,a=0,o=0;o<this.length;o++){var s=this.words[o],l=(16777215&(s<<i|a)).toString(16);r=0!=(a=s>>>24-i&16777215)||o!==this.length-1?c[6-l.length]+l+r:l+r,(i+=2)>=26&&(i-=26,o--)}for(0!==a&&(r=a.toString(16)+r);r.length%e!=0;)r=\"0\"+r;return 0!==this.negative&&(r=\"-\"+r),r}if(t===(0|t)&&t>=2&&t<=36){var u=f[t],p=h[t];r=\"\";var d=this.clone();for(d.negative=0;!d.isZero();){var v=d.modn(p).toString(t);r=(d=d.idivn(p)).isZero()?v+r:c[u-v.length]+v+r}for(this.isZero()&&(r=\"0\"+r);r.length%e!=0;)r=\"0\"+r;return 0!==this.negative&&(r=\"-\"+r),r}n(!1,\"Base 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32-Math.clz32(t)}:a.prototype._countBits=function(t){var e=t,r=0;return e>=4096&&(r+=13,e>>>=13),e>=64&&(r+=7,e>>>=7),e>=8&&(r+=4,e>>>=4),e>=2&&(r+=2,e>>>=2),r+e},a.prototype._zeroBits=function(t){if(0===t)return 26;var e=t,r=0;return 0==(8191&e)&&(r+=13,e>>>=13),0==(127&e)&&(r+=7,e>>>=7),0==(15&e)&&(r+=4,e>>>=4),0==(3&e)&&(r+=2,e>>>=2),0==(1&e)&&r++,r},a.prototype.bitLength=function(){var t=this.words[this.length-1],e=this._countBits(t);return 26*(this.length-1)+e},a.prototype.zeroBits=function(){if(this.isZero())return 0;for(var t=0,e=0;e<this.length;e++){var r=this._zeroBits(this.words[e]);if(t+=r,26!==r)break}return t},a.prototype.byteLength=function(){return Math.ceil(this.bitLength()/8)},a.prototype.toTwos=function(t){return 0!==this.negative?this.abs().inotn(t).iaddn(1):this.clone()},a.prototype.fromTwos=function(t){return this.testn(t-1)?this.notn(t).iaddn(1).ineg():this.clone()},a.prototype.isNeg=function(){return 0!==this.negative},a.prototype.neg=function(){return this.clone().ineg()},a.prototype.ineg=function(){return this.isZero()||(this.negative^=1),this},a.prototype.iuor=function(t){for(;this.length<t.length;)this.words[this.length++]=0;for(var e=0;e<t.length;e++)this.words[e]=this.words[e]|t.words[e];return this.strip()},a.prototype.ior=function(t){return n(0==(this.negative|t.negative)),this.iuor(t)},a.prototype.or=function(t){return this.length>t.length?this.clone().ior(t):t.clone().ior(this)},a.prototype.uor=function(t){return this.length>t.length?this.clone().iuor(t):t.clone().iuor(this)},a.prototype.iuand=function(t){var e;e=this.length>t.length?t:this;for(var r=0;r<e.length;r++)this.words[r]=this.words[r]&t.words[r];return this.length=e.length,this.strip()},a.prototype.iand=function(t){return n(0==(this.negative|t.negative)),this.iuand(t)},a.prototype.and=function(t){return this.length>t.length?this.clone().iand(t):t.clone().iand(this)},a.prototype.uand=function(t){return this.length>t.length?this.clone().iuand(t):t.clone().iuand(this)},a.prototype.iuxor=function(t){var e,r;this.length>t.length?(e=this,r=t):(e=t,r=this);for(var n=0;n<r.length;n++)this.words[n]=e.words[n]^r.words[n];if(this!==e)for(;n<e.length;n++)this.words[n]=e.words[n];return this.length=e.length,this.strip()},a.prototype.ixor=function(t){return n(0==(this.negative|t.negative)),this.iuxor(t)},a.prototype.xor=function(t){return this.length>t.length?this.clone().ixor(t):t.clone().ixor(this)},a.prototype.uxor=function(t){return this.length>t.length?this.clone().iuxor(t):t.clone().iuxor(this)},a.prototype.inotn=function(t){n(\"number\"==typeof t&&t>=0);var e=0|Math.ceil(t/26),r=t%26;this._expand(e),r>0&&e--;for(var i=0;i<e;i++)this.words[i]=67108863&~this.words[i];return r>0&&(this.words[i]=~this.words[i]&67108863>>26-r),this.strip()},a.prototype.notn=function(t){return this.clone().inotn(t)},a.prototype.setn=function(t,e){n(\"number\"==typeof t&&t>=0);var r=t/26|0,i=t%26;return this._expand(r+1),this.words[r]=e?this.words[r]|1<<i:this.words[r]&~(1<<i),this.strip()},a.prototype.iadd=function(t){var e,r,n;if(0!==this.negative&&0===t.negative)return this.negative=0,e=this.isub(t),this.negative^=1,this._normSign();if(0===this.negative&&0!==t.negative)return t.negative=0,e=this.isub(t),t.negative=1,e._normSign();this.length>t.length?(r=this,n=t):(r=t,n=this);for(var i=0,a=0;a<n.length;a++)e=(0|r.words[a])+(0|n.words[a])+i,this.words[a]=67108863&e,i=e>>>26;for(;0!==i&&a<r.length;a++)e=(0|r.words[a])+i,this.words[a]=67108863&e,i=e>>>26;if(this.length=r.length,0!==i)this.words[this.length]=i,this.length++;else if(r!==this)for(;a<r.length;a++)this.words[a]=r.words[a];return this},a.prototype.add=function(t){var e;return 0!==t.negative&&0===this.negative?(t.negative=0,e=this.sub(t),t.negative^=1,e):0===t.negative&&0!==this.negative?(this.negative=0,e=t.sub(this),this.negative=1,e):this.length>t.length?this.clone().iadd(t):t.clone().iadd(this)},a.prototype.isub=function(t){if(0!==t.negative){t.negative=0;var e=this.iadd(t);return t.negative=1,e._normSign()}if(0!==this.negative)return this.negative=0,this.iadd(t),this.negative=1,this._normSign();var r,n,i=this.cmp(t);if(0===i)return this.negative=0,this.length=1,this.words[0]=0,this;i>0?(r=this,n=t):(r=t,n=this);for(var a=0,o=0;o<n.length;o++)a=(e=(0|r.words[o])-(0|n.words[o])+a)>>26,this.words[o]=67108863&e;for(;0!==a&&o<r.length;o++)a=(e=(0|r.words[o])+a)>>26,this.words[o]=67108863&e;if(0===a&&o<r.length&&r!==this)for(;o<r.length;o++)this.words[o]=r.words[o];return this.length=Math.max(this.length,o),r!==this&&(this.negative=1),this.strip()},a.prototype.sub=function(t){return this.clone().isub(t)};var d=function(t,e,r){var n,i,a,o=t.words,s=e.words,l=r.words,u=0,c=0|o[0],f=8191&c,h=c>>>13,p=0|o[1],d=8191&p,v=p>>>13,g=0|o[2],y=8191&g,m=g>>>13,x=0|o[3],b=8191&x,_=x>>>13,w=0|o[4],T=8191&w,k=w>>>13,A=0|o[5],M=8191&A,S=A>>>13,E=0|o[6],L=8191&E,C=E>>>13,P=0|o[7],O=8191&P,I=P>>>13,D=0|o[8],z=8191&D,R=D>>>13,F=0|o[9],B=8191&F,N=F>>>13,j=0|s[0],U=8191&j,V=j>>>13,H=0|s[1],q=8191&H,G=H>>>13,Z=0|s[2],Y=8191&Z,W=Z>>>13,X=0|s[3],J=8191&X,K=X>>>13,$=0|s[4],Q=8191&$,tt=$>>>13,et=0|s[5],rt=8191&et,nt=et>>>13,it=0|s[6],at=8191&it,ot=it>>>13,st=0|s[7],lt=8191&st,ut=st>>>13,ct=0|s[8],ft=8191&ct,ht=ct>>>13,pt=0|s[9],dt=8191&pt,vt=pt>>>13;r.negative=t.negative^e.negative,r.length=19;var gt=(u+(n=Math.imul(f,U))|0)+((8191&(i=(i=Math.imul(f,V))+Math.imul(h,U)|0))<<13)|0;u=((a=Math.imul(h,V))+(i>>>13)|0)+(gt>>>26)|0,gt&=67108863,n=Math.imul(d,U),i=(i=Math.imul(d,V))+Math.imul(v,U)|0,a=Math.imul(v,V);var yt=(u+(n=n+Math.imul(f,q)|0)|0)+((8191&(i=(i=i+Math.imul(f,G)|0)+Math.imul(h,q)|0))<<13)|0;u=((a=a+Math.imul(h,G)|0)+(i>>>13)|0)+(yt>>>26)|0,yt&=67108863,n=Math.imul(y,U),i=(i=Math.imul(y,V))+Math.imul(m,U)|0,a=Math.imul(m,V),n=n+Math.imul(d,q)|0,i=(i=i+Math.imul(d,G)|0)+Math.imul(v,q)|0,a=a+Math.imul(v,G)|0;var mt=(u+(n=n+Math.imul(f,Y)|0)|0)+((8191&(i=(i=i+Math.imul(f,W)|0)+Math.imul(h,Y)|0))<<13)|0;u=((a=a+Math.imul(h,W)|0)+(i>>>13)|0)+(mt>>>26)|0,mt&=67108863,n=Math.imul(b,U),i=(i=Math.imul(b,V))+Math.imul(_,U)|0,a=Math.imul(_,V),n=n+Math.imul(y,q)|0,i=(i=i+Math.imul(y,G)|0)+Math.imul(m,q)|0,a=a+Math.imul(m,G)|0,n=n+Math.imul(d,Y)|0,i=(i=i+Math.imul(d,W)|0)+Math.imul(v,Y)|0,a=a+Math.imul(v,W)|0;var xt=(u+(n=n+Math.imul(f,J)|0)|0)+((8191&(i=(i=i+Math.imul(f,K)|0)+Math.imul(h,J)|0))<<13)|0;u=((a=a+Math.imul(h,K)|0)+(i>>>13)|0)+(xt>>>26)|0,xt&=67108863,n=Math.imul(T,U),i=(i=Math.imul(T,V))+Math.imul(k,U)|0,a=Math.imul(k,V),n=n+Math.imul(b,q)|0,i=(i=i+Math.imul(b,G)|0)+Math.imul(_,q)|0,a=a+Math.imul(_,G)|0,n=n+Math.imul(y,Y)|0,i=(i=i+Math.imul(y,W)|0)+Math.imul(m,Y)|0,a=a+Math.imul(m,W)|0,n=n+Math.imul(d,J)|0,i=(i=i+Math.imul(d,K)|0)+Math.imul(v,J)|0,a=a+Math.imul(v,K)|0;var bt=(u+(n=n+Math.imul(f,Q)|0)|0)+((8191&(i=(i=i+Math.imul(f,tt)|0)+Math.imul(h,Q)|0))<<13)|0;u=((a=a+Math.imul(h,tt)|0)+(i>>>13)|0)+(bt>>>26)|0,bt&=67108863,n=Math.imul(M,U),i=(i=Math.imul(M,V))+Math.imul(S,U)|0,a=Math.imul(S,V),n=n+Math.imul(T,q)|0,i=(i=i+Math.imul(T,G)|0)+Math.imul(k,q)|0,a=a+Math.imul(k,G)|0,n=n+Math.imul(b,Y)|0,i=(i=i+Math.imul(b,W)|0)+Math.imul(_,Y)|0,a=a+Math.imul(_,W)|0,n=n+Math.imul(y,J)|0,i=(i=i+Math.imul(y,K)|0)+Math.imul(m,J)|0,a=a+Math.imul(m,K)|0,n=n+Math.imul(d,Q)|0,i=(i=i+Math.imul(d,tt)|0)+Math.imul(v,Q)|0,a=a+Math.imul(v,tt)|0;var _t=(u+(n=n+Math.imul(f,rt)|0)|0)+((8191&(i=(i=i+Math.imul(f,nt)|0)+Math.imul(h,rt)|0))<<13)|0;u=((a=a+Math.imul(h,nt)|0)+(i>>>13)|0)+(_t>>>26)|0,_t&=67108863,n=Math.imul(L,U),i=(i=Math.imul(L,V))+Math.imul(C,U)|0,a=Math.imul(C,V),n=n+Math.imul(M,q)|0,i=(i=i+Math.imul(M,G)|0)+Math.imul(S,q)|0,a=a+Math.imul(S,G)|0,n=n+Math.imul(T,Y)|0,i=(i=i+Math.imul(T,W)|0)+Math.imul(k,Y)|0,a=a+Math.imul(k,W)|0,n=n+Math.imul(b,J)|0,i=(i=i+Math.imul(b,K)|0)+Math.imul(_,J)|0,a=a+Math.imul(_,K)|0,n=n+Math.imul(y,Q)|0,i=(i=i+Math.imul(y,tt)|0)+Math.imul(m,Q)|0,a=a+Math.imul(m,tt)|0,n=n+Math.imul(d,rt)|0,i=(i=i+Math.imul(d,nt)|0)+Math.imul(v,rt)|0,a=a+Math.imul(v,nt)|0;var wt=(u+(n=n+Math.imul(f,at)|0)|0)+((8191&(i=(i=i+Math.imul(f,ot)|0)+Math.imul(h,at)|0))<<13)|0;u=((a=a+Math.imul(h,ot)|0)+(i>>>13)|0)+(wt>>>26)|0,wt&=67108863,n=Math.imul(O,U),i=(i=Math.imul(O,V))+Math.imul(I,U)|0,a=Math.imul(I,V),n=n+Math.imul(L,q)|0,i=(i=i+Math.imul(L,G)|0)+Math.imul(C,q)|0,a=a+Math.imul(C,G)|0,n=n+Math.imul(M,Y)|0,i=(i=i+Math.imul(M,W)|0)+Math.imul(S,Y)|0,a=a+Math.imul(S,W)|0,n=n+Math.imul(T,J)|0,i=(i=i+Math.imul(T,K)|0)+Math.imul(k,J)|0,a=a+Math.imul(k,K)|0,n=n+Math.imul(b,Q)|0,i=(i=i+Math.imul(b,tt)|0)+Math.imul(_,Q)|0,a=a+Math.imul(_,tt)|0,n=n+Math.imul(y,rt)|0,i=(i=i+Math.imul(y,nt)|0)+Math.imul(m,rt)|0,a=a+Math.imul(m,nt)|0,n=n+Math.imul(d,at)|0,i=(i=i+Math.imul(d,ot)|0)+Math.imul(v,at)|0,a=a+Math.imul(v,ot)|0;var Tt=(u+(n=n+Math.imul(f,lt)|0)|0)+((8191&(i=(i=i+Math.imul(f,ut)|0)+Math.imul(h,lt)|0))<<13)|0;u=((a=a+Math.imul(h,ut)|0)+(i>>>13)|0)+(Tt>>>26)|0,Tt&=67108863,n=Math.imul(z,U),i=(i=Math.imul(z,V))+Math.imul(R,U)|0,a=Math.imul(R,V),n=n+Math.imul(O,q)|0,i=(i=i+Math.imul(O,G)|0)+Math.imul(I,q)|0,a=a+Math.imul(I,G)|0,n=n+Math.imul(L,Y)|0,i=(i=i+Math.imul(L,W)|0)+Math.imul(C,Y)|0,a=a+Math.imul(C,W)|0,n=n+Math.imul(M,J)|0,i=(i=i+Math.imul(M,K)|0)+Math.imul(S,J)|0,a=a+Math.imul(S,K)|0,n=n+Math.imul(T,Q)|0,i=(i=i+Math.imul(T,tt)|0)+Math.imul(k,Q)|0,a=a+Math.imul(k,tt)|0,n=n+Math.imul(b,rt)|0,i=(i=i+Math.imul(b,nt)|0)+Math.imul(_,rt)|0,a=a+Math.imul(_,nt)|0,n=n+Math.imul(y,at)|0,i=(i=i+Math.imul(y,ot)|0)+Math.imul(m,at)|0,a=a+Math.imul(m,ot)|0,n=n+Math.imul(d,lt)|0,i=(i=i+Math.imul(d,ut)|0)+Math.imul(v,lt)|0,a=a+Math.imul(v,ut)|0;var kt=(u+(n=n+Math.imul(f,ft)|0)|0)+((8191&(i=(i=i+Math.imul(f,ht)|0)+Math.imul(h,ft)|0))<<13)|0;u=((a=a+Math.imul(h,ht)|0)+(i>>>13)|0)+(kt>>>26)|0,kt&=67108863,n=Math.imul(B,U),i=(i=Math.imul(B,V))+Math.imul(N,U)|0,a=Math.imul(N,V),n=n+Math.imul(z,q)|0,i=(i=i+Math.imul(z,G)|0)+Math.imul(R,q)|0,a=a+Math.imul(R,G)|0,n=n+Math.imul(O,Y)|0,i=(i=i+Math.imul(O,W)|0)+Math.imul(I,Y)|0,a=a+Math.imul(I,W)|0,n=n+Math.imul(L,J)|0,i=(i=i+Math.imul(L,K)|0)+Math.imul(C,J)|0,a=a+Math.imul(C,K)|0,n=n+Math.imul(M,Q)|0,i=(i=i+Math.imul(M,tt)|0)+Math.imul(S,Q)|0,a=a+Math.imul(S,tt)|0,n=n+Math.imul(T,rt)|0,i=(i=i+Math.imul(T,nt)|0)+Math.imul(k,rt)|0,a=a+Math.imul(k,nt)|0,n=n+Math.imul(b,at)|0,i=(i=i+Math.imul(b,ot)|0)+Math.imul(_,at)|0,a=a+Math.imul(_,ot)|0,n=n+Math.imul(y,lt)|0,i=(i=i+Math.imul(y,ut)|0)+Math.imul(m,lt)|0,a=a+Math.imul(m,ut)|0,n=n+Math.imul(d,ft)|0,i=(i=i+Math.imul(d,ht)|0)+Math.imul(v,ft)|0,a=a+Math.imul(v,ht)|0;var At=(u+(n=n+Math.imul(f,dt)|0)|0)+((8191&(i=(i=i+Math.imul(f,vt)|0)+Math.imul(h,dt)|0))<<13)|0;u=((a=a+Math.imul(h,vt)|0)+(i>>>13)|0)+(At>>>26)|0,At&=67108863,n=Math.imul(B,q),i=(i=Math.imul(B,G))+Math.imul(N,q)|0,a=Math.imul(N,G),n=n+Math.imul(z,Y)|0,i=(i=i+Math.imul(z,W)|0)+Math.imul(R,Y)|0,a=a+Math.imul(R,W)|0,n=n+Math.imul(O,J)|0,i=(i=i+Math.imul(O,K)|0)+Math.imul(I,J)|0,a=a+Math.imul(I,K)|0,n=n+Math.imul(L,Q)|0,i=(i=i+Math.imul(L,tt)|0)+Math.imul(C,Q)|0,a=a+Math.imul(C,tt)|0,n=n+Math.imul(M,rt)|0,i=(i=i+Math.imul(M,nt)|0)+Math.imul(S,rt)|0,a=a+Math.imul(S,nt)|0,n=n+Math.imul(T,at)|0,i=(i=i+Math.imul(T,ot)|0)+Math.imul(k,at)|0,a=a+Math.imul(k,ot)|0,n=n+Math.imul(b,lt)|0,i=(i=i+Math.imul(b,ut)|0)+Math.imul(_,lt)|0,a=a+Math.imul(_,ut)|0,n=n+Math.imul(y,ft)|0,i=(i=i+Math.imul(y,ht)|0)+Math.imul(m,ft)|0,a=a+Math.imul(m,ht)|0;var Mt=(u+(n=n+Math.imul(d,dt)|0)|0)+((8191&(i=(i=i+Math.imul(d,vt)|0)+Math.imul(v,dt)|0))<<13)|0;u=((a=a+Math.imul(v,vt)|0)+(i>>>13)|0)+(Mt>>>26)|0,Mt&=67108863,n=Math.imul(B,Y),i=(i=Math.imul(B,W))+Math.imul(N,Y)|0,a=Math.imul(N,W),n=n+Math.imul(z,J)|0,i=(i=i+Math.imul(z,K)|0)+Math.imul(R,J)|0,a=a+Math.imul(R,K)|0,n=n+Math.imul(O,Q)|0,i=(i=i+Math.imul(O,tt)|0)+Math.imul(I,Q)|0,a=a+Math.imul(I,tt)|0,n=n+Math.imul(L,rt)|0,i=(i=i+Math.imul(L,nt)|0)+Math.imul(C,rt)|0,a=a+Math.imul(C,nt)|0,n=n+Math.imul(M,at)|0,i=(i=i+Math.imul(M,ot)|0)+Math.imul(S,at)|0,a=a+Math.imul(S,ot)|0,n=n+Math.imul(T,lt)|0,i=(i=i+Math.imul(T,ut)|0)+Math.imul(k,lt)|0,a=a+Math.imul(k,ut)|0,n=n+Math.imul(b,ft)|0,i=(i=i+Math.imul(b,ht)|0)+Math.imul(_,ft)|0,a=a+Math.imul(_,ht)|0;var St=(u+(n=n+Math.imul(y,dt)|0)|0)+((8191&(i=(i=i+Math.imul(y,vt)|0)+Math.imul(m,dt)|0))<<13)|0;u=((a=a+Math.imul(m,vt)|0)+(i>>>13)|0)+(St>>>26)|0,St&=67108863,n=Math.imul(B,J),i=(i=Math.imul(B,K))+Math.imul(N,J)|0,a=Math.imul(N,K),n=n+Math.imul(z,Q)|0,i=(i=i+Math.imul(z,tt)|0)+Math.imul(R,Q)|0,a=a+Math.imul(R,tt)|0,n=n+Math.imul(O,rt)|0,i=(i=i+Math.imul(O,nt)|0)+Math.imul(I,rt)|0,a=a+Math.imul(I,nt)|0,n=n+Math.imul(L,at)|0,i=(i=i+Math.imul(L,ot)|0)+Math.imul(C,at)|0,a=a+Math.imul(C,ot)|0,n=n+Math.imul(M,lt)|0,i=(i=i+Math.imul(M,ut)|0)+Math.imul(S,lt)|0,a=a+Math.imul(S,ut)|0,n=n+Math.imul(T,ft)|0,i=(i=i+Math.imul(T,ht)|0)+Math.imul(k,ft)|0,a=a+Math.imul(k,ht)|0;var Et=(u+(n=n+Math.imul(b,dt)|0)|0)+((8191&(i=(i=i+Math.imul(b,vt)|0)+Math.imul(_,dt)|0))<<13)|0;u=((a=a+Math.imul(_,vt)|0)+(i>>>13)|0)+(Et>>>26)|0,Et&=67108863,n=Math.imul(B,Q),i=(i=Math.imul(B,tt))+Math.imul(N,Q)|0,a=Math.imul(N,tt),n=n+Math.imul(z,rt)|0,i=(i=i+Math.imul(z,nt)|0)+Math.imul(R,rt)|0,a=a+Math.imul(R,nt)|0,n=n+Math.imul(O,at)|0,i=(i=i+Math.imul(O,ot)|0)+Math.imul(I,at)|0,a=a+Math.imul(I,ot)|0,n=n+Math.imul(L,lt)|0,i=(i=i+Math.imul(L,ut)|0)+Math.imul(C,lt)|0,a=a+Math.imul(C,ut)|0,n=n+Math.imul(M,ft)|0,i=(i=i+Math.imul(M,ht)|0)+Math.imul(S,ft)|0,a=a+Math.imul(S,ht)|0;var Lt=(u+(n=n+Math.imul(T,dt)|0)|0)+((8191&(i=(i=i+Math.imul(T,vt)|0)+Math.imul(k,dt)|0))<<13)|0;u=((a=a+Math.imul(k,vt)|0)+(i>>>13)|0)+(Lt>>>26)|0,Lt&=67108863,n=Math.imul(B,rt),i=(i=Math.imul(B,nt))+Math.imul(N,rt)|0,a=Math.imul(N,nt),n=n+Math.imul(z,at)|0,i=(i=i+Math.imul(z,ot)|0)+Math.imul(R,at)|0,a=a+Math.imul(R,ot)|0,n=n+Math.imul(O,lt)|0,i=(i=i+Math.imul(O,ut)|0)+Math.imul(I,lt)|0,a=a+Math.imul(I,ut)|0,n=n+Math.imul(L,ft)|0,i=(i=i+Math.imul(L,ht)|0)+Math.imul(C,ft)|0,a=a+Math.imul(C,ht)|0;var Ct=(u+(n=n+Math.imul(M,dt)|0)|0)+((8191&(i=(i=i+Math.imul(M,vt)|0)+Math.imul(S,dt)|0))<<13)|0;u=((a=a+Math.imul(S,vt)|0)+(i>>>13)|0)+(Ct>>>26)|0,Ct&=67108863,n=Math.imul(B,at),i=(i=Math.imul(B,ot))+Math.imul(N,at)|0,a=Math.imul(N,ot),n=n+Math.imul(z,lt)|0,i=(i=i+Math.imul(z,ut)|0)+Math.imul(R,lt)|0,a=a+Math.imul(R,ut)|0,n=n+Math.imul(O,ft)|0,i=(i=i+Math.imul(O,ht)|0)+Math.imul(I,ft)|0,a=a+Math.imul(I,ht)|0;var Pt=(u+(n=n+Math.imul(L,dt)|0)|0)+((8191&(i=(i=i+Math.imul(L,vt)|0)+Math.imul(C,dt)|0))<<13)|0;u=((a=a+Math.imul(C,vt)|0)+(i>>>13)|0)+(Pt>>>26)|0,Pt&=67108863,n=Math.imul(B,lt),i=(i=Math.imul(B,ut))+Math.imul(N,lt)|0,a=Math.imul(N,ut),n=n+Math.imul(z,ft)|0,i=(i=i+Math.imul(z,ht)|0)+Math.imul(R,ft)|0,a=a+Math.imul(R,ht)|0;var Ot=(u+(n=n+Math.imul(O,dt)|0)|0)+((8191&(i=(i=i+Math.imul(O,vt)|0)+Math.imul(I,dt)|0))<<13)|0;u=((a=a+Math.imul(I,vt)|0)+(i>>>13)|0)+(Ot>>>26)|0,Ot&=67108863,n=Math.imul(B,ft),i=(i=Math.imul(B,ht))+Math.imul(N,ft)|0,a=Math.imul(N,ht);var It=(u+(n=n+Math.imul(z,dt)|0)|0)+((8191&(i=(i=i+Math.imul(z,vt)|0)+Math.imul(R,dt)|0))<<13)|0;u=((a=a+Math.imul(R,vt)|0)+(i>>>13)|0)+(It>>>26)|0,It&=67108863;var Dt=(u+(n=Math.imul(B,dt))|0)+((8191&(i=(i=Math.imul(B,vt))+Math.imul(N,dt)|0))<<13)|0;return u=((a=Math.imul(N,vt))+(i>>>13)|0)+(Dt>>>26)|0,Dt&=67108863,l[0]=gt,l[1]=yt,l[2]=mt,l[3]=xt,l[4]=bt,l[5]=_t,l[6]=wt,l[7]=Tt,l[8]=kt,l[9]=At,l[10]=Mt,l[11]=St,l[12]=Et,l[13]=Lt,l[14]=Ct,l[15]=Pt,l[16]=Ot,l[17]=It,l[18]=Dt,0!==u&&(l[19]=u,r.length++),r};function v(t,e,r){return(new g).mulp(t,e,r)}function g(t,e){this.x=t,this.y=e}Math.imul||(d=p),a.prototype.mulTo=function(t,e){var r,n=this.length+t.length;return r=10===this.length&&10===t.length?d(this,t,e):n<63?p(this,t,e):n<1024?function(t,e,r){r.negative=e.negative^t.negative,r.length=t.length+e.length;for(var n=0,i=0,a=0;a<r.length-1;a++){var o=i;i=0;for(var s=67108863&n,l=Math.min(a,e.length-1),u=Math.max(0,a-t.length+1);u<=l;u++){var c=a-u,f=(0|t.words[c])*(0|e.words[u]),h=67108863&f;s=67108863&(h=h+s|0),i+=(o=(o=o+(f/67108864|0)|0)+(h>>>26)|0)>>>26,o&=67108863}r.words[a]=s,n=o,o=i}return 0!==n?r.words[a]=n:r.length--,r.strip()}(this,t,e):v(this,t,e),r},g.prototype.makeRBT=function(t){for(var e=new Array(t),r=a.prototype._countBits(t)-1,n=0;n<t;n++)e[n]=this.revBin(n,r,t);return e},g.prototype.revBin=function(t,e,r){if(0===t||t===r-1)return t;for(var n=0,i=0;i<e;i++)n|=(1&t)<<e-i-1,t>>=1;return n},g.prototype.permute=function(t,e,r,n,i,a){for(var o=0;o<a;o++)n[o]=e[t[o]],i[o]=r[t[o]]},g.prototype.transform=function(t,e,r,n,i,a){this.permute(a,t,e,r,n,i);for(var o=1;o<i;o<<=1)for(var s=o<<1,l=Math.cos(2*Math.PI/s),u=Math.sin(2*Math.PI/s),c=0;c<i;c+=s)for(var f=l,h=u,p=0;p<o;p++){var d=r[c+p],v=n[c+p],g=r[c+p+o],y=n[c+p+o],m=f*g-h*y;y=f*y+h*g,g=m,r[c+p]=d+g,n[c+p]=v+y,r[c+p+o]=d-g,n[c+p+o]=v-y,p!==s&&(m=l*f-u*h,h=l*h+u*f,f=m)}},g.prototype.guessLen13b=function(t,e){var r=1|Math.max(e,t),n=1&r,i=0;for(r=r/2|0;r;r>>>=1)i++;return 1<<i+1+n},g.prototype.conjugate=function(t,e,r){if(!(r<=1))for(var n=0;n<r/2;n++){var i=t[n];t[n]=t[r-n-1],t[r-n-1]=i,i=e[n],e[n]=-e[r-n-1],e[r-n-1]=-i}},g.prototype.normalize13b=function(t,e){for(var r=0,n=0;n<e/2;n++){var i=8192*Math.round(t[2*n+1]/e)+Math.round(t[2*n]/e)+r;t[n]=67108863&i,r=i<67108864?0:i/67108864|0}return t},g.prototype.convert13b=function(t,e,r,i){for(var a=0,o=0;o<e;o++)a+=0|t[o],r[2*o]=8191&a,a>>>=13,r[2*o+1]=8191&a,a>>>=13;for(o=2*e;o<i;++o)r[o]=0;n(0===a),n(0==(-8192&a))},g.prototype.stub=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=0;return e},g.prototype.mulp=function(t,e,r){var n=2*this.guessLen13b(t.length,e.length),i=this.makeRBT(n),a=this.stub(n),o=new Array(n),s=new Array(n),l=new Array(n),u=new Array(n),c=new Array(n),f=new Array(n),h=r.words;h.length=n,this.convert13b(t.words,t.length,o,n),this.convert13b(e.words,e.length,u,n),this.transform(o,a,s,l,n,i),this.transform(u,a,c,f,n,i);for(var p=0;p<n;p++){var d=s[p]*c[p]-l[p]*f[p];l[p]=s[p]*f[p]+l[p]*c[p],s[p]=d}return this.conjugate(s,l,n),this.transform(s,l,h,a,n,i),this.conjugate(h,a,n),this.normalize13b(h,n),r.negative=t.negative^e.negative,r.length=t.length+e.length,r.strip()},a.prototype.mul=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),this.mulTo(t,e)},a.prototype.mulf=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),v(this,t,e)},a.prototype.imul=function(t){return this.clone().mulTo(t,this)},a.prototype.imuln=function(t){n(\"number\"==typeof t),n(t<67108864);for(var e=0,r=0;r<this.length;r++){var i=(0|this.words[r])*t,a=(67108863&i)+(67108863&e);e>>=26,e+=i/67108864|0,e+=a>>>26,this.words[r]=67108863&a}return 0!==e&&(this.words[r]=e,this.length++),this},a.prototype.muln=function(t){return this.clone().imuln(t)},a.prototype.sqr=function(){return this.mul(this)},a.prototype.isqr=function(){return this.imul(this.clone())},a.prototype.pow=function(t){var e=function(t){for(var e=new Array(t.bitLength()),r=0;r<e.length;r++){var n=r/26|0,i=r%26;e[r]=(t.words[n]&1<<i)>>>i}return e}(t);if(0===e.length)return new a(1);for(var r=this,n=0;n<e.length&&0===e[n];n++,r=r.sqr());if(++n<e.length)for(var i=r.sqr();n<e.length;n++,i=i.sqr())0!==e[n]&&(r=r.mul(i));return r},a.prototype.iushln=function(t){n(\"number\"==typeof t&&t>=0);var e,r=t%26,i=(t-r)/26,a=67108863>>>26-r<<26-r;if(0!==r){var o=0;for(e=0;e<this.length;e++){var s=this.words[e]&a,l=(0|this.words[e])-s<<r;this.words[e]=l|o,o=s>>>26-r}o&&(this.words[e]=o,this.length++)}if(0!==i){for(e=this.length-1;e>=0;e--)this.words[e+i]=this.words[e];for(e=0;e<i;e++)this.words[e]=0;this.length+=i}return this.strip()},a.prototype.ishln=function(t){return n(0===this.negative),this.iushln(t)},a.prototype.iushrn=function(t,e,r){var i;n(\"number\"==typeof t&&t>=0),i=e?(e-e%26)/26:0;var a=t%26,o=Math.min((t-a)/26,this.length),s=67108863^67108863>>>a<<a,l=r;if(i-=o,i=Math.max(0,i),l){for(var u=0;u<o;u++)l.words[u]=this.words[u];l.length=o}if(0===o);else if(this.length>o)for(this.length-=o,u=0;u<this.length;u++)this.words[u]=this.words[u+o];else this.words[0]=0,this.length=1;var c=0;for(u=this.length-1;u>=0&&(0!==c||u>=i);u--){var f=0|this.words[u];this.words[u]=c<<26-a|f>>>a,c=f&s}return l&&0!==c&&(l.words[l.length++]=c),0===this.length&&(this.words[0]=0,this.length=1),this.strip()},a.prototype.ishrn=function(t,e,r){return n(0===this.negative),this.iushrn(t,e,r)},a.prototype.shln=function(t){return this.clone().ishln(t)},a.prototype.ushln=function(t){return this.clone().iushln(t)},a.prototype.shrn=function(t){return this.clone().ishrn(t)},a.prototype.ushrn=function(t){return this.clone().iushrn(t)},a.prototype.testn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26,i=1<<e;return!(this.length<=r||!(this.words[r]&i))},a.prototype.imaskn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26;if(n(0===this.negative,\"imaskn works only with positive numbers\"),this.length<=r)return this;if(0!==e&&r++,this.length=Math.min(r,this.length),0!==e){var i=67108863^67108863>>>e<<e;this.words[this.length-1]&=i}return this.strip()},a.prototype.maskn=function(t){return this.clone().imaskn(t)},a.prototype.iaddn=function(t){return n(\"number\"==typeof t),n(t<67108864),t<0?this.isubn(-t):0!==this.negative?1===this.length&&(0|this.words[0])<t?(this.words[0]=t-(0|this.words[0]),this.negative=0,this):(this.negative=0,this.isubn(t),this.negative=1,this):this._iaddn(t)},a.prototype._iaddn=function(t){this.words[0]+=t;for(var e=0;e<this.length&&this.words[e]>=67108864;e++)this.words[e]-=67108864,e===this.length-1?this.words[e+1]=1:this.words[e+1]++;return this.length=Math.max(this.length,e+1),this},a.prototype.isubn=function(t){if(n(\"number\"==typeof t),n(t<67108864),t<0)return this.iaddn(-t);if(0!==this.negative)return this.negative=0,this.iaddn(t),this.negative=1,this;if(this.words[0]-=t,1===this.length&&this.words[0]<0)this.words[0]=-this.words[0],this.negative=1;else for(var e=0;e<this.length&&this.words[e]<0;e++)this.words[e]+=67108864,this.words[e+1]-=1;return this.strip()},a.prototype.addn=function(t){return this.clone().iaddn(t)},a.prototype.subn=function(t){return this.clone().isubn(t)},a.prototype.iabs=function(){return this.negative=0,this},a.prototype.abs=function(){return this.clone().iabs()},a.prototype._ishlnsubmul=function(t,e,r){var i,a,o=t.length+r;this._expand(o);var s=0;for(i=0;i<t.length;i++){a=(0|this.words[i+r])+s;var l=(0|t.words[i])*e;s=((a-=67108863&l)>>26)-(l/67108864|0),this.words[i+r]=67108863&a}for(;i<this.length-r;i++)s=(a=(0|this.words[i+r])+s)>>26,this.words[i+r]=67108863&a;if(0===s)return this.strip();for(n(-1===s),s=0,i=0;i<this.length;i++)s=(a=-(0|this.words[i])+s)>>26,this.words[i]=67108863&a;return this.negative=1,this.strip()},a.prototype._wordDiv=function(t,e){var r=(this.length,t.length),n=this.clone(),i=t,o=0|i.words[i.length-1];0!=(r=26-this._countBits(o))&&(i=i.ushln(r),n.iushln(r),o=0|i.words[i.length-1]);var s,l=n.length-i.length;if(\"mod\"!==e){(s=new a(null)).length=l+1,s.words=new Array(s.length);for(var u=0;u<s.length;u++)s.words[u]=0}var c=n.clone()._ishlnsubmul(i,1,l);0===c.negative&&(n=c,s&&(s.words[l]=1));for(var f=l-1;f>=0;f--){var h=67108864*(0|n.words[i.length+f])+(0|n.words[i.length+f-1]);for(h=Math.min(h/o|0,67108863),n._ishlnsubmul(i,h,f);0!==n.negative;)h--,n.negative=0,n._ishlnsubmul(i,1,f),n.isZero()||(n.negative^=1);s&&(s.words[f]=h)}return s&&s.strip(),n.strip(),\"div\"!==e&&0!==r&&n.iushrn(r),{div:s||null,mod:n}},a.prototype.divmod=function(t,e,r){return n(!t.isZero()),this.isZero()?{div:new a(0),mod:new a(0)}:0!==this.negative&&0===t.negative?(s=this.neg().divmod(t,e),\"mod\"!==e&&(i=s.div.neg()),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.iadd(t)),{div:i,mod:o}):0===this.negative&&0!==t.negative?(s=this.divmod(t.neg(),e),\"mod\"!==e&&(i=s.div.neg()),{div:i,mod:s.mod}):0!=(this.negative&t.negative)?(s=this.neg().divmod(t.neg(),e),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.isub(t)),{div:s.div,mod:o}):t.length>this.length||this.cmp(t)<0?{div:new a(0),mod:this}:1===t.length?\"div\"===e?{div:this.divn(t.words[0]),mod:null}:\"mod\"===e?{div:null,mod:new a(this.modn(t.words[0]))}:{div:this.divn(t.words[0]),mod:new a(this.modn(t.words[0]))}:this._wordDiv(t,e);var i,o,s},a.prototype.div=function(t){return this.divmod(t,\"div\",!1).div},a.prototype.mod=function(t){return this.divmod(t,\"mod\",!1).mod},a.prototype.umod=function(t){return this.divmod(t,\"mod\",!0).mod},a.prototype.divRound=function(t){var e=this.divmod(t);if(e.mod.isZero())return e.div;var r=0!==e.div.negative?e.mod.isub(t):e.mod,n=t.ushrn(1),i=t.andln(1),a=r.cmp(n);return a<0||1===i&&0===a?e.div:0!==e.div.negative?e.div.isubn(1):e.div.iaddn(1)},a.prototype.modn=function(t){n(t<=67108863);for(var e=(1<<26)%t,r=0,i=this.length-1;i>=0;i--)r=(e*r+(0|this.words[i]))%t;return r},a.prototype.idivn=function(t){n(t<=67108863);for(var e=0,r=this.length-1;r>=0;r--){var i=(0|this.words[r])+67108864*e;this.words[r]=i/t|0,e=i%t}return this.strip()},a.prototype.divn=function(t){return this.clone().idivn(t)},a.prototype.egcd=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i=new a(1),o=new a(0),s=new a(0),l=new a(1),u=0;e.isEven()&&r.isEven();)e.iushrn(1),r.iushrn(1),++u;for(var c=r.clone(),f=e.clone();!e.isZero();){for(var h=0,p=1;0==(e.words[0]&p)&&h<26;++h,p<<=1);if(h>0)for(e.iushrn(h);h-- >0;)(i.isOdd()||o.isOdd())&&(i.iadd(c),o.isub(f)),i.iushrn(1),o.iushrn(1);for(var d=0,v=1;0==(r.words[0]&v)&&d<26;++d,v<<=1);if(d>0)for(r.iushrn(d);d-- >0;)(s.isOdd()||l.isOdd())&&(s.iadd(c),l.isub(f)),s.iushrn(1),l.iushrn(1);e.cmp(r)>=0?(e.isub(r),i.isub(s),o.isub(l)):(r.isub(e),s.isub(i),l.isub(o))}return{a:s,b:l,gcd:r.iushln(u)}},a.prototype._invmp=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i,o=new a(1),s=new a(0),l=r.clone();e.cmpn(1)>0&&r.cmpn(1)>0;){for(var u=0,c=1;0==(e.words[0]&c)&&u<26;++u,c<<=1);if(u>0)for(e.iushrn(u);u-- >0;)o.isOdd()&&o.iadd(l),o.iushrn(1);for(var f=0,h=1;0==(r.words[0]&h)&&f<26;++f,h<<=1);if(f>0)for(r.iushrn(f);f-- >0;)s.isOdd()&&s.iadd(l),s.iushrn(1);e.cmp(r)>=0?(e.isub(r),o.isub(s)):(r.isub(e),s.isub(o))}return(i=0===e.cmpn(1)?o:s).cmpn(0)<0&&i.iadd(t),i},a.prototype.gcd=function(t){if(this.isZero())return t.abs();if(t.isZero())return this.abs();var e=this.clone(),r=t.clone();e.negative=0,r.negative=0;for(var n=0;e.isEven()&&r.isEven();n++)e.iushrn(1),r.iushrn(1);for(;;){for(;e.isEven();)e.iushrn(1);for(;r.isEven();)r.iushrn(1);var i=e.cmp(r);if(i<0){var a=e;e=r,r=a}else if(0===i||0===r.cmpn(1))break;e.isub(r)}return r.iushln(n)},a.prototype.invm=function(t){return this.egcd(t).a.umod(t)},a.prototype.isEven=function(){return 0==(1&this.words[0])},a.prototype.isOdd=function(){return 1==(1&this.words[0])},a.prototype.andln=function(t){return this.words[0]&t},a.prototype.bincn=function(t){n(\"number\"==typeof t);var e=t%26,r=(t-e)/26,i=1<<e;if(this.length<=r)return this._expand(r+1),this.words[r]|=i,this;for(var a=i,o=r;0!==a&&o<this.length;o++){var s=0|this.words[o];a=(s+=a)>>>26,s&=67108863,this.words[o]=s}return 0!==a&&(this.words[o]=a,this.length++),this},a.prototype.isZero=function(){return 1===this.length&&0===this.words[0]},a.prototype.cmpn=function(t){var e,r=t<0;if(0!==this.negative&&!r)return-1;if(0===this.negative&&r)return 1;if(this.strip(),this.length>1)e=1;else{r&&(t=-t),n(t<=67108863,\"Number is too big\");var i=0|this.words[0];e=i===t?0:i<t?-1:1}return 0!==this.negative?0|-e:e},a.prototype.cmp=function(t){if(0!==this.negative&&0===t.negative)return-1;if(0===this.negative&&0!==t.negative)return 1;var e=this.ucmp(t);return 0!==this.negative?0|-e:e},a.prototype.ucmp=function(t){if(this.length>t.length)return 1;if(this.length<t.length)return-1;for(var e=0,r=this.length-1;r>=0;r--){var n=0|this.words[r],i=0|t.words[r];if(n!==i){n<i?e=-1:n>i&&(e=1);break}}return e},a.prototype.gtn=function(t){return 1===this.cmpn(t)},a.prototype.gt=function(t){return 1===this.cmp(t)},a.prototype.gten=function(t){return this.cmpn(t)>=0},a.prototype.gte=function(t){return this.cmp(t)>=0},a.prototype.ltn=function(t){return-1===this.cmpn(t)},a.prototype.lt=function(t){return-1===this.cmp(t)},a.prototype.lten=function(t){return this.cmpn(t)<=0},a.prototype.lte=function(t){return this.cmp(t)<=0},a.prototype.eqn=function(t){return 0===this.cmpn(t)},a.prototype.eq=function(t){return 0===this.cmp(t)},a.red=function(t){return new T(t)},a.prototype.toRed=function(t){return n(!this.red,\"Already a number in reduction context\"),n(0===this.negative,\"red works only with positives\"),t.convertTo(this)._forceRed(t)},a.prototype.fromRed=function(){return n(this.red,\"fromRed works only with numbers in reduction context\"),this.red.convertFrom(this)},a.prototype._forceRed=function(t){return this.red=t,this},a.prototype.forceRed=function(t){return n(!this.red,\"Already a number in reduction context\"),this._forceRed(t)},a.prototype.redAdd=function(t){return n(this.red,\"redAdd works only with red numbers\"),this.red.add(this,t)},a.prototype.redIAdd=function(t){return n(this.red,\"redIAdd works only with red numbers\"),this.red.iadd(this,t)},a.prototype.redSub=function(t){return n(this.red,\"redSub works only with red numbers\"),this.red.sub(this,t)},a.prototype.redISub=function(t){return n(this.red,\"redISub works only with red numbers\"),this.red.isub(this,t)},a.prototype.redShl=function(t){return n(this.red,\"redShl works only with red numbers\"),this.red.shl(this,t)},a.prototype.redMul=function(t){return n(this.red,\"redMul works only with red numbers\"),this.red._verify2(this,t),this.red.mul(this,t)},a.prototype.redIMul=function(t){return n(this.red,\"redMul works only with red numbers\"),this.red._verify2(this,t),this.red.imul(this,t)},a.prototype.redSqr=function(){return n(this.red,\"redSqr works only with red numbers\"),this.red._verify1(this),this.red.sqr(this)},a.prototype.redISqr=function(){return n(this.red,\"redISqr works only with red numbers\"),this.red._verify1(this),this.red.isqr(this)},a.prototype.redSqrt=function(){return n(this.red,\"redSqrt works only with red numbers\"),this.red._verify1(this),this.red.sqrt(this)},a.prototype.redInvm=function(){return n(this.red,\"redInvm works only with red numbers\"),this.red._verify1(this),this.red.invm(this)},a.prototype.redNeg=function(){return n(this.red,\"redNeg works only with red numbers\"),this.red._verify1(this),this.red.neg(this)},a.prototype.redPow=function(t){return n(this.red&&!t.red,\"redPow(normalNum)\"),this.red._verify1(this),this.red.pow(this,t)};var y={k256:null,p224:null,p192:null,p25519:null};function m(t,e){this.name=t,this.p=new a(e,16),this.n=this.p.bitLength(),this.k=new a(1).iushln(this.n).isub(this.p),this.tmp=this._tmp()}function x(){m.call(this,\"k256\",\"ffffffff ffffffff ffffffff ffffffff ffffffff ffffffff fffffffe fffffc2f\")}function b(){m.call(this,\"p224\",\"ffffffff ffffffff ffffffff ffffffff 00000000 00000000 00000001\")}function _(){m.call(this,\"p192\",\"ffffffff ffffffff ffffffff fffffffe ffffffff ffffffff\")}function w(){m.call(this,\"25519\",\"7fffffffffffffff ffffffffffffffff ffffffffffffffff ffffffffffffffed\")}function T(t){if(\"string\"==typeof t){var e=a._prime(t);this.m=e.p,this.prime=e}else n(t.gtn(1),\"modulus must be greater than 1\"),this.m=t,this.prime=null}function k(t){T.call(this,t),this.shift=this.m.bitLength(),this.shift%26!=0&&(this.shift+=26-this.shift%26),this.r=new a(1).iushln(this.shift),this.r2=this.imod(this.r.sqr()),this.rinv=this.r._invmp(this.m),this.minv=this.rinv.mul(this.r).isubn(1).div(this.m),this.minv=this.minv.umod(this.r),this.minv=this.r.sub(this.minv)}m.prototype._tmp=function(){var t=new a(null);return t.words=new Array(Math.ceil(this.n/13)),t},m.prototype.ireduce=function(t){var e,r=t;do{this.split(r,this.tmp),e=(r=(r=this.imulK(r)).iadd(this.tmp)).bitLength()}while(e>this.n);var n=e<this.n?-1:r.ucmp(this.p);return 0===n?(r.words[0]=0,r.length=1):n>0?r.isub(this.p):void 0!==r.strip?r.strip():r._strip(),r},m.prototype.split=function(t,e){t.iushrn(this.n,0,e)},m.prototype.imulK=function(t){return t.imul(this.k)},i(x,m),x.prototype.split=function(t,e){for(var r=4194303,n=Math.min(t.length,9),i=0;i<n;i++)e.words[i]=t.words[i];if(e.length=n,t.length<=9)return t.words[0]=0,void(t.length=1);var a=t.words[9];for(e.words[e.length++]=a&r,i=10;i<t.length;i++){var o=0|t.words[i];t.words[i-10]=(o&r)<<4|a>>>22,a=o}a>>>=22,t.words[i-10]=a,0===a&&t.length>10?t.length-=10:t.length-=9},x.prototype.imulK=function(t){t.words[t.length]=0,t.words[t.length+1]=0,t.length+=2;for(var e=0,r=0;r<t.length;r++){var n=0|t.words[r];e+=977*n,t.words[r]=67108863&e,e=64*n+(e/67108864|0)}return 0===t.words[t.length-1]&&(t.length--,0===t.words[t.length-1]&&t.length--),t},i(b,m),i(_,m),i(w,m),w.prototype.imulK=function(t){for(var e=0,r=0;r<t.length;r++){var n=19*(0|t.words[r])+e,i=67108863&n;n>>>=26,t.words[r]=i,e=n}return 0!==e&&(t.words[t.length++]=e),t},a._prime=function(t){if(y[t])return y[t];var e;if(\"k256\"===t)e=new x;else if(\"p224\"===t)e=new b;else if(\"p192\"===t)e=new _;else{if(\"p25519\"!==t)throw new Error(\"Unknown prime \"+t);e=new w}return y[t]=e,e},T.prototype._verify1=function(t){n(0===t.negative,\"red works only with positives\"),n(t.red,\"red works only with red numbers\")},T.prototype._verify2=function(t,e){n(0==(t.negative|e.negative),\"red works only with positives\"),n(t.red&&t.red===e.red,\"red works only with red numbers\")},T.prototype.imod=function(t){return this.prime?this.prime.ireduce(t)._forceRed(this):t.umod(this.m)._forceRed(this)},T.prototype.neg=function(t){return t.isZero()?t.clone():this.m.sub(t)._forceRed(this)},T.prototype.add=function(t,e){this._verify2(t,e);var r=t.add(e);return r.cmp(this.m)>=0&&r.isub(this.m),r._forceRed(this)},T.prototype.iadd=function(t,e){this._verify2(t,e);var r=t.iadd(e);return r.cmp(this.m)>=0&&r.isub(this.m),r},T.prototype.sub=function(t,e){this._verify2(t,e);var r=t.sub(e);return r.cmpn(0)<0&&r.iadd(this.m),r._forceRed(this)},T.prototype.isub=function(t,e){this._verify2(t,e);var r=t.isub(e);return r.cmpn(0)<0&&r.iadd(this.m),r},T.prototype.shl=function(t,e){return this._verify1(t),this.imod(t.ushln(e))},T.prototype.imul=function(t,e){return this._verify2(t,e),this.imod(t.imul(e))},T.prototype.mul=function(t,e){return this._verify2(t,e),this.imod(t.mul(e))},T.prototype.isqr=function(t){return this.imul(t,t.clone())},T.prototype.sqr=function(t){return this.mul(t,t)},T.prototype.sqrt=function(t){if(t.isZero())return t.clone();var e=this.m.andln(3);if(n(e%2==1),3===e){var r=this.m.add(new a(1)).iushrn(2);return this.pow(t,r)}for(var i=this.m.subn(1),o=0;!i.isZero()&&0===i.andln(1);)o++,i.iushrn(1);n(!i.isZero());var s=new a(1).toRed(this),l=s.redNeg(),u=this.m.subn(1).iushrn(1),c=this.m.bitLength();for(c=new a(2*c*c).toRed(this);0!==this.pow(c,u).cmp(l);)c.redIAdd(l);for(var f=this.pow(c,i),h=this.pow(t,i.addn(1).iushrn(1)),p=this.pow(t,i),d=o;0!==p.cmp(s);){for(var v=p,g=0;0!==v.cmp(s);g++)v=v.redSqr();n(g<d);var y=this.pow(f,new a(1).iushln(d-g-1));h=h.redMul(y),f=y.redSqr(),p=p.redMul(f),d=g}return h},T.prototype.invm=function(t){var e=t._invmp(this.m);return 0!==e.negative?(e.negative=0,this.imod(e).redNeg()):this.imod(e)},T.prototype.pow=function(t,e){if(e.isZero())return new a(1).toRed(this);if(0===e.cmpn(1))return t.clone();var r=new Array(16);r[0]=new a(1).toRed(this),r[1]=t;for(var n=2;n<r.length;n++)r[n]=this.mul(r[n-1],t);var i=r[0],o=0,s=0,l=e.bitLength()%26;for(0===l&&(l=26),n=e.length-1;n>=0;n--){for(var u=e.words[n],c=l-1;c>=0;c--){var 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s=(o=a.left=r(o)).right=r(o.right),a.left=s.right,o.right=s.left,s.right=a,s.left=o,s._color=a._color,a._color=1,o._color=1,e._color=1,i(a),i(o),i(s),l>1&&((u=t[l-2]).right===a?u.right=s:u.left=s),void(t[l-1]=s);if(1===o._color){if(0===a._color)return a._color=1,void(a.left=n(0,o));a.left=n(0,o);continue}var u;o=r(o),a.left=o.right,o.right=a,o._color=a._color,a._color=0,i(a),i(o),l>1&&((u=t[l-2]).right===a?u.right=o:u.left=o),t[l-1]=o,t[l]=a,l+1<t.length?t[l+1]=e:t.push(e),l+=2}}}(o),p.left===s?p.left=null:p.right=null,new a(this.tree._compare,o[0])},Object.defineProperty(f,\"key\",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].key},enumerable:!0}),Object.defineProperty(f,\"value\",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].value},enumerable:!0}),Object.defineProperty(f,\"index\",{get:function(){var t=0,e=this._stack;if(0===e.length){var r=this.tree.root;return r?r._count:0}e[e.length-1].left&&(t=e[e.length-1].left._count);for(var n=e.length-2;n>=0;--n)e[n+1]===e[n].right&&(++t,e[n].left&&(t+=e[n].left._count));return t},enumerable:!0}),f.next=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.right)for(e=e.right;e;)t.push(e),e=e.left;else for(t.pop();t.length>0&&t[t.length-1].right===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(f,\"hasNext\",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].right)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].left===t[e])return!0;return!1}}),f.update=function(t){var r=this._stack;if(0===r.length)throw new Error(\"Can't update empty node!\");var n=new Array(r.length),i=r[r.length-1];n[n.length-1]=new e(i._color,i.key,t,i.left,i.right,i._count);for(var o=r.length-2;o>=0;--o)(i=r[o]).left===r[o+1]?n[o]=new e(i._color,i.key,i.value,n[o+1],i.right,i._count):n[o]=new e(i._color,i.key,i.value,i.left,n[o+1],i._count);return new a(this.tree._compare,n[0])},f.prev=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.left)for(e=e.left;e;)t.push(e),e=e.right;else for(t.pop();t.length>0&&t[t.length-1].left===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(f,\"hasPrev\",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].left)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].right===t[e])return!0;return!1}})},7453:function(t,e,r){\"use strict\";t.exports=function(t,e){var r=new c(t);return r.update(e),r};var n=r(9557),i=r(1681),a=r(1011),o=r(2864),s=r(8468),l=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]);function u(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function c(t){this.gl=t,this.pixelRatio=1,this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.autoTicks=!0,this.tickSpacing=[1,1,1],this.tickEnable=[!0,!0,!0],this.tickFont=[\"sans-serif\",\"sans-serif\",\"sans-serif\"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickAlign=[\"auto\",\"auto\",\"auto\"],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[10,10,10],this.lastCubeProps={cubeEdges:[0,0,0],axis:[0,0,0]},this.labels=[\"x\",\"y\",\"z\"],this.labelEnable=[!0,!0,!0],this.labelFont=\"sans-serif\",this.labelSize=[20,20,20],this.labelAngle=[0,0,0],this.labelAlign=[\"auto\",\"auto\",\"auto\"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[10,10,10],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[0,0,0],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!1,!1,!1],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._firstInit=!0,this._text=null,this._lines=null,this._background=a(t)}var f=c.prototype;function h(){this.primalOffset=[0,0,0],this.primalMinor=[0,0,0],this.mirrorOffset=[0,0,0],this.mirrorMinor=[0,0,0]}f.update=function(t){function e(e,r,n){if(n in t){var i,a=t[n],o=this[n];(e?Array.isArray(a)&&Array.isArray(a[0]):Array.isArray(a))?this[n]=i=[r(a[0]),r(a[1]),r(a[2])]:this[n]=i=[r(a),r(a),r(a)];for(var s=0;s<3;++s)if(i[s]!==o[s])return!0}return!1}t=t||{};var r,a=e.bind(this,!1,Number),o=e.bind(this,!1,Boolean),l=e.bind(this,!1,String),u=e.bind(this,!0,(function(t){if(Array.isArray(t)){if(3===t.length)return[+t[0],+t[1],+t[2],1];if(4===t.length)return[+t[0],+t[1],+t[2],+t[3]]}return[0,0,0,1]})),c=!1,f=!1;if(\"bounds\"in t)for(var h=t.bounds,p=0;p<2;++p)for(var d=0;d<3;++d)h[p][d]!==this.bounds[p][d]&&(f=!0),this.bounds[p][d]=h[p][d];if(\"ticks\"in t)for(r=t.ticks,c=!0,this.autoTicks=!1,p=0;p<3;++p)this.tickSpacing[p]=0;else a(\"tickSpacing\")&&(this.autoTicks=!0,f=!0);if(this._firstInit&&(\"ticks\"in t||\"tickSpacing\"in t||(this.autoTicks=!0),f=!0,c=!0,this._firstInit=!1),f&&this.autoTicks&&(r=s.create(this.bounds,this.tickSpacing),c=!0),c){for(p=0;p<3;++p)r[p].sort((function(t,e){return t.x-e.x}));s.equal(r,this.ticks)?c=!1:this.ticks=r}o(\"tickEnable\"),l(\"tickFont\")&&(c=!0),a(\"tickSize\"),a(\"tickAngle\"),a(\"tickPad\"),u(\"tickColor\");var v=l(\"labels\");l(\"labelFont\")&&(v=!0),o(\"labelEnable\"),a(\"labelSize\"),a(\"labelPad\"),u(\"labelColor\"),o(\"lineEnable\"),o(\"lineMirror\"),a(\"lineWidth\"),u(\"lineColor\"),o(\"lineTickEnable\"),o(\"lineTickMirror\"),a(\"lineTickLength\"),a(\"lineTickWidth\"),u(\"lineTickColor\"),o(\"gridEnable\"),a(\"gridWidth\"),u(\"gridColor\"),o(\"zeroEnable\"),u(\"zeroLineColor\"),a(\"zeroLineWidth\"),o(\"backgroundEnable\"),u(\"backgroundColor\"),this._text?this._text&&(v||c)&&this._text.update(this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont):this._text=n(this.gl,this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont),this._lines&&c&&(this._lines.dispose(),this._lines=null),this._lines||(this._lines=i(this.gl,this.bounds,this.ticks))};var p=[new h,new h,new h];function d(t,e,r,n,i){for(var a=t.primalOffset,o=t.primalMinor,s=t.mirrorOffset,l=t.mirrorMinor,u=n[e],c=0;c<3;++c)if(e!==c){var f=a,h=s,p=o,d=l;u&1<<c&&(f=s,h=a,p=l,d=o),f[c]=r[0][c],h[c]=r[1][c],i[c]>0?(p[c]=-1,d[c]=0):(p[c]=0,d[c]=1)}}var v=[0,0,0],g={model:l,view:l,projection:l,_ortho:!1};f.isOpaque=function(){return!0},f.isTransparent=function(){return!1},f.drawTransparent=function(t){};var y=[0,0,0],m=[0,0,0],x=[0,0,0];f.draw=function(t){t=t||g;for(var e=this.gl,r=t.model||l,n=t.view||l,i=t.projection||l,a=this.bounds,s=t._ortho||!1,c=o(r,n,i,a,s),f=c.cubeEdges,h=c.axis,b=n[12],_=n[13],w=n[14],T=n[15],k=(s?2:1)*this.pixelRatio*(i[3]*b+i[7]*_+i[11]*w+i[15]*T)/e.drawingBufferHeight,A=0;A<3;++A)this.lastCubeProps.cubeEdges[A]=f[A],this.lastCubeProps.axis[A]=h[A];var M=p;for(A=0;A<3;++A)d(p[A],A,this.bounds,f,h);e=this.gl;var S,E,L,C=v;for(A=0;A<3;++A)this.backgroundEnable[A]?C[A]=h[A]:C[A]=0;for(this._background.draw(r,n,i,a,C,this.backgroundColor),this._lines.bind(r,n,i,this),A=0;A<3;++A){var P=[0,0,0];h[A]>0?P[A]=a[1][A]:P[A]=a[0][A];for(var O=0;O<2;++O){var I=(A+1+O)%3,D=(A+1+(1^O))%3;this.gridEnable[I]&&this._lines.drawGrid(I,D,this.bounds,P,this.gridColor[I],this.gridWidth[I]*this.pixelRatio)}for(O=0;O<2;++O)I=(A+1+O)%3,D=(A+1+(1^O))%3,this.zeroEnable[D]&&Math.min(a[0][D],a[1][D])<=0&&Math.max(a[0][D],a[1][D])>=0&&this._lines.drawZero(I,D,this.bounds,P,this.zeroLineColor[D],this.zeroLineWidth[D]*this.pixelRatio)}for(A=0;A<3;++A){this.lineEnable[A]&&this._lines.drawAxisLine(A,this.bounds,M[A].primalOffset,this.lineColor[A],this.lineWidth[A]*this.pixelRatio),this.lineMirror[A]&&this._lines.drawAxisLine(A,this.bounds,M[A].mirrorOffset,this.lineColor[A],this.lineWidth[A]*this.pixelRatio);var z=u(y,M[A].primalMinor),R=u(m,M[A].mirrorMinor),F=this.lineTickLength;for(O=0;O<3;++O){var B=k/r[5*O];z[O]*=F[O]*B,R[O]*=F[O]*B}this.lineTickEnable[A]&&this._lines.drawAxisTicks(A,M[A].primalOffset,z,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio),this.lineTickMirror[A]&&this._lines.drawAxisTicks(A,M[A].mirrorOffset,R,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio)}function N(t){(L=[0,0,0])[t]=1}function j(t,e,r){var n=(t+1)%3,i=(t+2)%3,a=e[n],o=e[i],s=r[n],l=r[i];a>0&&l>0||a>0&&l<0||a<0&&l>0||a<0&&l<0?N(n):(o>0&&s>0||o>0&&s<0||o<0&&s>0||o<0&&s<0)&&N(i)}for(this._lines.unbind(),this._text.bind(r,n,i,this.pixelRatio),A=0;A<3;++A){var U=M[A].primalMinor,V=M[A].mirrorMinor,H=u(x,M[A].primalOffset);for(O=0;O<3;++O)this.lineTickEnable[A]&&(H[O]+=k*U[O]*Math.max(this.lineTickLength[O],0)/r[5*O]);var q=[0,0,0];if(q[A]=1,this.tickEnable[A]){for(-3600===this.tickAngle[A]?(this.tickAngle[A]=0,this.tickAlign[A]=\"auto\"):this.tickAlign[A]=-1,E=1,\"auto\"===(S=[this.tickAlign[A],.5,E])[0]?S[0]=0:S[0]=parseInt(\"\"+S[0]),L=[0,0,0],j(A,U,V),O=0;O<3;++O)H[O]+=k*U[O]*this.tickPad[O]/r[5*O];this._text.drawTicks(A,this.tickSize[A],this.tickAngle[A],H,this.tickColor[A],q,L,S)}if(this.labelEnable[A]){for(E=0,L=[0,0,0],this.labels[A].length>4&&(N(A),E=1),\"auto\"===(S=[this.labelAlign[A],.5,E])[0]?S[0]=0:S[0]=parseInt(\"\"+S[0]),O=0;O<3;++O)H[O]+=k*U[O]*this.labelPad[O]/r[5*O];H[A]+=.5*(a[0][A]+a[1][A]),this._text.drawLabel(A,this.labelSize[A],this.labelAngle[A],H,this.labelColor[A],[0,0,0],L,S)}}this._text.unbind()},f.dispose=function(){this._text.dispose(),this._lines.dispose(),this._background.dispose(),this._lines=null,this._text=null,this._background=null,this.gl=null}},1011:function(t,e,r){\"use strict\";t.exports=function(t){for(var e=[],r=[],s=0,l=0;l<3;++l)for(var u=(l+1)%3,c=(l+2)%3,f=[0,0,0],h=[0,0,0],p=-1;p<=1;p+=2){r.push(s,s+2,s+1,s+1,s+2,s+3),f[l]=p,h[l]=p;for(var d=-1;d<=1;d+=2){f[u]=d;for(var v=-1;v<=1;v+=2)f[c]=v,e.push(f[0],f[1],f[2],h[0],h[1],h[2]),s+=1}var g=u;u=c,c=g}var y=n(t,new Float32Array(e)),m=n(t,new Uint16Array(r),t.ELEMENT_ARRAY_BUFFER),x=i(t,[{buffer:y,type:t.FLOAT,size:3,offset:0,stride:24},{buffer:y,type:t.FLOAT,size:3,offset:12,stride:24}],m),b=a(t);return b.attributes.position.location=0,b.attributes.normal.location=1,new o(t,y,x,b)};var n=r(5827),i=r(2944),a=r(1943).bg;function o(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n}var s=o.prototype;s.draw=function(t,e,r,n,i,a){for(var o=!1,s=0;s<3;++s)o=o||i[s];if(o){var l=this.gl;l.enable(l.POLYGON_OFFSET_FILL),l.polygonOffset(1,2),this.shader.bind(),this.shader.uniforms={model:t,view:e,projection:r,bounds:n,enable:i,colors:a},this.vao.bind(),this.vao.draw(this.gl.TRIANGLES,36),this.vao.unbind(),l.disable(l.POLYGON_OFFSET_FILL)}},s.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},2864:function(t,e,r){\"use strict\";t.exports=function(t,e,r,a,p){i(s,e,t),i(s,r,s);for(var m=0,x=0;x<2;++x){c[2]=a[x][2];for(var b=0;b<2;++b){c[1]=a[b][1];for(var _=0;_<2;++_)c[0]=a[_][0],h(l[m],c,s),m+=1}}var w=-1;for(x=0;x<8;++x){for(var T=l[x][3],k=0;k<3;++k)u[x][k]=l[x][k]/T;p&&(u[x][2]*=-1),T<0&&(w<0||u[x][2]<u[w][2])&&(w=x)}if(w<0){w=0;for(var A=0;A<3;++A){for(var M=(A+2)%3,S=(A+1)%3,E=-1,L=-1,C=0;C<2;++C){var P=(I=C<<A)+(C<<M)+(1-C<<S),O=I+(1-C<<M)+(C<<S);o(u[I],u[P],u[O],f)<0||(C?E=1:L=1)}if(E<0||L<0)L>E&&(w|=1<<A);else{for(C=0;C<2;++C){P=(I=C<<A)+(C<<M)+(1-C<<S),O=I+(1-C<<M)+(C<<S);var I,D=d([l[I],l[P],l[O],l[I+(1<<M)+(1<<S)]]);C?E=D:L=D}L>E&&(w|=1<<A)}}}var z=7^w,R=-1;for(x=0;x<8;++x)x!==w&&x!==z&&(R<0||u[R][1]>u[x][1])&&(R=x);var F=-1;for(x=0;x<3;++x)(N=R^1<<x)!==w&&N!==z&&(F<0&&(F=N),(S=u[N])[0]<u[F][0]&&(F=N));var B=-1;for(x=0;x<3;++x){var N;(N=R^1<<x)!==w&&N!==z&&N!==F&&(B<0&&(B=N),(S=u[N])[0]>u[B][0]&&(B=N))}var j=v;j[0]=j[1]=j[2]=0,j[n.log2(F^R)]=R&F,j[n.log2(R^B)]=R&B;var U=7^B;U===w||U===z?(U=7^F,j[n.log2(B^U)]=U&B):j[n.log2(F^U)]=U&F;var V=g,H=w;for(A=0;A<3;++A)V[A]=H&1<<A?-1:1;return y};var n=r(2288),i=r(104),a=r(4670),o=r(417),s=new Array(16),l=new Array(8),u=new Array(8),c=new Array(3),f=[0,0,0];function h(t,e,r){for(var n=0;n<4;++n){t[n]=r[12+n];for(var i=0;i<3;++i)t[n]+=e[i]*r[4*i+n]}}!function(){for(var t=0;t<8;++t)l[t]=[1,1,1,1],u[t]=[1,1,1]}();var p=[[0,0,1,0,0],[0,0,-1,1,0],[0,-1,0,1,0],[0,1,0,1,0],[-1,0,0,1,0],[1,0,0,1,0]];function d(t){for(var e=0;e<p.length;++e)if((t=a.positive(t,p[e])).length<3)return 0;var r=t[0],n=r[0]/r[3],i=r[1]/r[3],o=0;for(e=1;e+1<t.length;++e){var s=t[e],l=t[e+1],u=s[0]/s[3]-n,c=s[1]/s[3]-i,f=l[0]/l[3]-n,h=l[1]/l[3]-i;o+=Math.abs(u*h-c*f)}return o}var v=[1,1,1],g=[0,0,0],y={cubeEdges:v,axis:g}},1681:function(t,e,r){\"use strict\";t.exports=function(t,e,r){var o=[],s=[0,0,0],l=[0,0,0],u=[0,0,0],c=[0,0,0];o.push(0,0,1,0,1,1,0,0,-1,0,0,-1,0,1,1,0,1,-1);for(var f=0;f<3;++f){for(var h=o.length/3|0,d=0;d<r[f].length;++d){var v=+r[f][d].x;o.push(v,0,1,v,1,1,v,0,-1,v,0,-1,v,1,1,v,1,-1)}var g=o.length/3|0;s[f]=h,l[f]=g-h,h=o.length/3|0;for(var y=0;y<r[f].length;++y)v=+r[f][y].x,o.push(v,0,1,v,1,1,v,0,-1,v,0,-1,v,1,1,v,1,-1);g=o.length/3|0,u[f]=h,c[f]=g-h}var m=n(t,new Float32Array(o)),x=i(t,[{buffer:m,type:t.FLOAT,size:3,stride:0,offset:0}]),b=a(t);return b.attributes.position.location=0,new p(t,m,x,b,l,s,c,u)};var n=r(5827),i=r(2944),a=r(1943).j,o=[0,0,0],s=[0,0,0],l=[0,0,0],u=[0,0,0],c=[1,1];function f(t){return t[0]=t[1]=t[2]=0,t}function h(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function p(t,e,r,n,i,a,o,s){this.gl=t,this.vertBuffer=e,this.vao=r,this.shader=n,this.tickCount=i,this.tickOffset=a,this.gridCount=o,this.gridOffset=s}var d=p.prototype;d.bind=function(t,e,r){this.shader.bind(),this.shader.uniforms.model=t,this.shader.uniforms.view=e,this.shader.uniforms.projection=r,c[0]=this.gl.drawingBufferWidth,c[1]=this.gl.drawingBufferHeight,this.shader.uniforms.screenShape=c,this.vao.bind()},d.unbind=function(){this.vao.unbind()},d.drawAxisLine=function(t,e,r,n,i){var a=f(s);this.shader.uniforms.majorAxis=s,a[t]=e[1][t]-e[0][t],this.shader.uniforms.minorAxis=a;var o,c=h(u,r);c[t]+=e[0][t],this.shader.uniforms.offset=c,this.shader.uniforms.lineWidth=i,this.shader.uniforms.color=n,(o=f(l))[(t+2)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6),(o=f(l))[(t+1)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6)},d.drawAxisTicks=function(t,e,r,n,i){if(this.tickCount[t]){var a=f(o);a[t]=1,this.shader.uniforms.majorAxis=a,this.shader.uniforms.offset=e,this.shader.uniforms.minorAxis=r,this.shader.uniforms.color=n,this.shader.uniforms.lineWidth=i;var s=f(l);s[t]=1,this.shader.uniforms.screenAxis=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t])}},d.drawGrid=function(t,e,r,n,i,a){if(this.gridCount[t]){var c=f(s);c[e]=r[1][e]-r[0][e],this.shader.uniforms.minorAxis=c;var p=h(u,n);p[e]+=r[0][e],this.shader.uniforms.offset=p;var d=f(o);d[t]=1,this.shader.uniforms.majorAxis=d;var v=f(l);v[t]=1,this.shader.uniforms.screenAxis=v,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,this.gridCount[t],this.gridOffset[t])}},d.drawZero=function(t,e,r,n,i,a){var o=f(s);this.shader.uniforms.majorAxis=o,o[t]=r[1][t]-r[0][t],this.shader.uniforms.minorAxis=o;var c=h(u,n);c[t]+=r[0][t],this.shader.uniforms.offset=c;var p=f(l);p[e]=1,this.shader.uniforms.screenAxis=p,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,6)},d.dispose=function(){this.vao.dispose(),this.vertBuffer.dispose(),this.shader.dispose()}},1943:function(t,e,r){\"use strict\";var n=r(6832),i=r(5158),a=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 offset, majorAxis, minorAxis, screenAxis;\\nuniform float lineWidth;\\nuniform vec2 screenShape;\\n\\nvec3 project(vec3 p) {\\n  vec4 pp = projection * view * model * vec4(p, 1.0);\\n  return pp.xyz / max(pp.w, 0.0001);\\n}\\n\\nvoid main() {\\n  vec3 major = position.x * majorAxis;\\n  vec3 minor = position.y * minorAxis;\\n\\n  vec3 vPosition = major + minor + offset;\\n  vec3 pPosition = project(vPosition);\\n  vec3 offset = project(vPosition + screenAxis * position.z);\\n\\n  vec2 screen = normalize((offset - pPosition).xy * screenShape) / screenShape;\\n\\n  gl_Position = vec4(pPosition + vec3(0.5 * screen * lineWidth, 0), 1.0);\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\nvoid main() {\\n  gl_FragColor = color;\\n}\"]);e.j=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec3\"}])};var s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 offset, axis, alignDir, alignOpt;\\nuniform float scale, angle, pixelScale;\\nuniform vec2 resolution;\\n\\nvec3 project(vec3 p) {\\n  vec4 pp = projection * view * model * vec4(p, 1.0);\\n  return pp.xyz / max(pp.w, 0.0001);\\n}\\n\\nfloat computeViewAngle(vec3 a, vec3 b) {\\n  vec3 A = project(a);\\n  vec3 B = project(b);\\n\\n  return atan(\\n    (B.y - A.y) * resolution.y,\\n    (B.x - A.x) * resolution.x\\n  );\\n}\\n\\nconst float PI = 3.141592;\\nconst float TWO_PI = 2.0 * PI;\\nconst float HALF_PI = 0.5 * PI;\\nconst float ONE_AND_HALF_PI = 1.5 * PI;\\n\\nint option = int(floor(alignOpt.x + 0.001));\\nfloat hv_ratio =       alignOpt.y;\\nbool enableAlign =    (alignOpt.z != 0.0);\\n\\nfloat mod_angle(float a) {\\n  return mod(a, PI);\\n}\\n\\nfloat positive_angle(float a) {\\n  return mod_angle((a < 0.0) ?\\n    a + TWO_PI :\\n    a\\n  );\\n}\\n\\nfloat look_upwards(float a) {\\n  float b = positive_angle(a);\\n  return ((b > HALF_PI) && (b <= ONE_AND_HALF_PI)) ?\\n    b - PI :\\n    b;\\n}\\n\\nfloat look_horizontal_or_vertical(float a, float ratio) {\\n  // ratio controls the ratio between being horizontal to (vertical + horizontal)\\n  // if ratio is set to 0.5 then it is 50%, 50%.\\n  // when using a higher ratio e.g. 0.75 the result would\\n  // likely be more horizontal than vertical.\\n\\n  float b = positive_angle(a);\\n\\n  return\\n    (b < (      ratio) * HALF_PI) ? 0.0 :\\n    (b < (2.0 - ratio) * HALF_PI) ? -HALF_PI :\\n    (b < (2.0 + ratio) * HALF_PI) ? 0.0 :\\n    (b < (4.0 - ratio) * HALF_PI) ? HALF_PI :\\n                                    0.0;\\n}\\n\\nfloat roundTo(float a, float b) {\\n  return float(b * floor((a + 0.5 * b) / b));\\n}\\n\\nfloat look_round_n_directions(float a, int n) {\\n  float b = positive_angle(a);\\n  float div = TWO_PI / float(n);\\n  float c = roundTo(b, div);\\n  return look_upwards(c);\\n}\\n\\nfloat applyAlignOption(float rawAngle, float delta) {\\n  return\\n    (option >  2) ? look_round_n_directions(rawAngle + delta, option) :       // option 3-n: round to n directions\\n    (option == 2) ? look_horizontal_or_vertical(rawAngle + delta, hv_ratio) : // horizontal or vertical\\n    (option == 1) ? rawAngle + delta :       // use free angle, and flip to align with one direction of the axis\\n    (option == 0) ? look_upwards(rawAngle) : // use free angle, and stay upwards\\n    (option ==-1) ? 0.0 :                    // useful for backward compatibility, all texts remains horizontal\\n                    rawAngle;                // otherwise return back raw input angle\\n}\\n\\nbool isAxisTitle = (axis.x == 0.0) &&\\n                   (axis.y == 0.0) &&\\n                   (axis.z == 0.0);\\n\\nvoid main() {\\n  //Compute world offset\\n  float axisDistance = position.z;\\n  vec3 dataPosition = axisDistance * axis + offset;\\n\\n  float beta = angle; // i.e. user defined attributes for each tick\\n\\n  float axisAngle;\\n  float clipAngle;\\n  float flip;\\n\\n  if (enableAlign) {\\n    axisAngle = (isAxisTitle) ? HALF_PI :\\n                      computeViewAngle(dataPosition, dataPosition + axis);\\n    clipAngle = computeViewAngle(dataPosition, dataPosition + alignDir);\\n\\n    axisAngle += (sin(axisAngle) < 0.0) ? PI : 0.0;\\n    clipAngle += (sin(clipAngle) < 0.0) ? PI : 0.0;\\n\\n    flip = (dot(vec2(cos(axisAngle), sin(axisAngle)),\\n                vec2(sin(clipAngle),-cos(clipAngle))) > 0.0) ? 1.0 : 0.0;\\n\\n    beta += applyAlignOption(clipAngle, flip * PI);\\n  }\\n\\n  //Compute plane offset\\n  vec2 planeCoord = position.xy * pixelScale;\\n\\n  mat2 planeXform = scale * mat2(\\n     cos(beta), sin(beta),\\n    -sin(beta), cos(beta)\\n  );\\n\\n  vec2 viewOffset = 2.0 * planeXform * planeCoord / resolution;\\n\\n  //Compute clip position\\n  vec3 clipPosition = project(dataPosition);\\n\\n  //Apply text offset in clip coordinates\\n  clipPosition += vec3(viewOffset, 0.0);\\n\\n  //Done\\n  gl_Position = vec4(clipPosition, 1.0);\\n}\"]),l=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\nvoid main() {\\n  gl_FragColor = color;\\n}\"]);e.f=function(t){return i(t,s,l,null,[{name:\"position\",type:\"vec3\"}])};var u=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec3 normal;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 enable;\\nuniform vec3 bounds[2];\\n\\nvarying vec3 colorChannel;\\n\\nvoid main() {\\n\\n  vec3 signAxis = sign(bounds[1] - bounds[0]);\\n\\n  vec3 realNormal = signAxis * normal;\\n\\n  if(dot(realNormal, enable) > 0.0) {\\n    vec3 minRange = min(bounds[0], bounds[1]);\\n    vec3 maxRange = max(bounds[0], bounds[1]);\\n    vec3 nPosition = mix(minRange, maxRange, 0.5 * (position + 1.0));\\n    gl_Position = projection * view * model * vec4(nPosition, 1.0);\\n  } else {\\n    gl_Position = vec4(0,0,0,0);\\n  }\\n\\n  colorChannel = abs(realNormal);\\n}\"]),c=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 colors[3];\\n\\nvarying vec3 colorChannel;\\n\\nvoid main() {\\n  gl_FragColor = colorChannel.x * colors[0] +\\n                 colorChannel.y * colors[1] +\\n                 colorChannel.z * colors[2];\\n}\"]);e.bg=function(t){return i(t,u,c,null,[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}])}},9557:function(t,e,r){\"use strict\";t.exports=function(t,e,r,i,o,l){var u=n(t),f=a(t,[{buffer:u,size:3}]),h=s(t);h.attributes.position.location=0;var p=new c(t,h,u,f);return p.update(e,r,i,o,l),p};var n=r(5827),a=r(2944),o=r(875),s=r(1943).f,l=window||i.global||{},u=l.__TEXT_CACHE||{};function c(t,e,r,n){this.gl=t,this.shader=e,this.buffer=r,this.vao=n,this.tickOffset=this.tickCount=this.labelOffset=this.labelCount=null}l.__TEXT_CACHE={};var f=c.prototype,h=[0,0];f.bind=function(t,e,r,n){this.vao.bind(),this.shader.bind();var i=this.shader.uniforms;i.model=t,i.view=e,i.projection=r,i.pixelScale=n,h[0]=this.gl.drawingBufferWidth,h[1]=this.gl.drawingBufferHeight,this.shader.uniforms.resolution=h},f.unbind=function(){this.vao.unbind()},f.update=function(t,e,r,n,i){var a=[];function s(t,e,r,n,i,s){var l=u[r];l||(l=u[r]={});var c=l[e];c||(c=l[e]=function(t,e){try{return o(t,e)}catch(e){return console.warn('error vectorizing text:\"'+t+'\" error:',e),{cells:[],positions:[]}}}(e,{triangles:!0,font:r,textAlign:\"center\",textBaseline:\"middle\",lineSpacing:i,styletags:s}));for(var f=(n||12)/12,h=c.positions,p=c.cells,d=0,v=p.length;d<v;++d)for(var g=p[d],y=2;y>=0;--y){var m=h[g[y]];a.push(f*m[0],-f*m[1],t)}}for(var l=[0,0,0],c=[0,0,0],f=[0,0,0],h=[0,0,0],p={breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},d=0;d<3;++d){f[d]=a.length/3|0,s(.5*(t[0][d]+t[1][d]),e[d],r[d],12,1.25,p),h[d]=(a.length/3|0)-f[d],l[d]=a.length/3|0;for(var v=0;v<n[d].length;++v)n[d][v].text&&s(n[d][v].x,n[d][v].text,n[d][v].font||i,n[d][v].fontSize||12,1.25,p);c[d]=(a.length/3|0)-l[d]}this.buffer.update(a),this.tickOffset=l,this.tickCount=c,this.labelOffset=f,this.labelCount=h},f.drawTicks=function(t,e,r,n,i,a,o,s){this.tickCount[t]&&(this.shader.uniforms.axis=a,this.shader.uniforms.color=i,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.shader.uniforms.alignDir=o,this.shader.uniforms.alignOpt=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t]))},f.drawLabel=function(t,e,r,n,i,a,o,s){this.labelCount[t]&&(this.shader.uniforms.axis=a,this.shader.uniforms.color=i,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.shader.uniforms.alignDir=o,this.shader.uniforms.alignOpt=s,this.vao.draw(this.gl.TRIANGLES,this.labelCount[t],this.labelOffset[t]))},f.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()}},8468:function(t,e){\"use strict\";function r(t,e){var r=t+\"\",n=r.indexOf(\".\"),i=0;n>=0&&(i=r.length-n-1);var a=Math.pow(10,i),o=Math.round(t*e*a),s=o+\"\";if(s.indexOf(\"e\")>=0)return s;var l=o/a,u=o%a;o<0?(l=0|-Math.ceil(l),u=0|-u):(l=0|Math.floor(l),u|=0);var c=\"\"+l;if(o<0&&(c=\"-\"+c),i){for(var f=\"\"+u;f.length<i;)f=\"0\"+f;return c+\".\"+f}return c}e.create=function(t,e){for(var n=[],i=0;i<3;++i){for(var a=[],o=(t[0][i],t[1][i],0);o*e[i]<=t[1][i];++o)a.push({x:o*e[i],text:r(e[i],o)});for(o=-1;o*e[i]>=t[0][i];--o)a.push({x:o*e[i],text:r(e[i],o)});n.push(a)}return n},e.equal=function(t,e){for(var r=0;r<3;++r){if(t[r].length!==e[r].length)return!1;for(var n=0;n<t[r].length;++n){var i=t[r][n],a=e[r][n];if(i.x!==a.x||i.text!==a.text||i.font!==a.font||i.fontColor!==a.fontColor||i.fontSize!==a.fontSize||i.dx!==a.dx||i.dy!==a.dy)return!1}}return!0}},2771:function(t,e,r){\"use strict\";t.exports=function(t,e,r,l,f){var h=e.model||u,p=e.view||u,y=e.projection||u,m=e._ortho||!1,x=t.bounds,b=(f=f||a(h,p,y,x,m)).axis;o(c,p,h),o(c,y,c);for(var _=v,w=0;w<3;++w)_[w].lo=1/0,_[w].hi=-1/0,_[w].pixelsPerDataUnit=1/0;var T=n(s(c,c));s(c,c);for(var k=0;k<3;++k){var A=(k+1)%3,M=(k+2)%3,S=g;t:for(w=0;w<2;++w){var E=[];if(b[k]<0!=!!w){S[k]=x[w][k];for(var L=0;L<2;++L){S[A]=x[L^w][A];for(var C=0;C<2;++C)S[M]=x[C^L^w][M],E.push(S.slice())}var P=m?5:4;for(L=P;L===P;++L){if(0===E.length)continue t;E=i.positive(E,T[L])}for(L=0;L<E.length;++L){M=E[L];var O=d(g,c,M,r,l);for(C=0;C<3;++C)_[C].lo=Math.min(_[C].lo,M[C]),_[C].hi=Math.max(_[C].hi,M[C]),C!==k&&(_[C].pixelsPerDataUnit=Math.min(_[C].pixelsPerDataUnit,Math.abs(O[C])))}}}}return _};var n=r(5795),i=r(4670),a=r(2864),o=r(104),s=r(2142),l=r(6342),u=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]),c=new Float32Array(16);function f(t,e,r){this.lo=t,this.hi=e,this.pixelsPerDataUnit=r}var h=[0,0,0,1],p=[0,0,0,1];function d(t,e,r,n,i){for(var a=0;a<3;++a){for(var o=h,s=p,u=0;u<3;++u)s[u]=o[u]=r[u];s[3]=o[3]=1,s[a]+=1,l(s,s,e),s[3]<0&&(t[a]=1/0),o[a]-=1,l(o,o,e),o[3]<0&&(t[a]=1/0);var c=(o[0]/o[3]-s[0]/s[3])*n,f=(o[1]/o[3]-s[1]/s[3])*i;t[a]=.25*Math.sqrt(c*c+f*f)}return t}var v=[new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0)],g=[0,0,0]},5827:function(t,e,r){\"use strict\";var n=r(5306),i=r(7498),a=r(5050),o=[\"uint8\",\"uint8_clamped\",\"uint16\",\"uint32\",\"int8\",\"int16\",\"int32\",\"float32\"];function s(t,e,r,n,i){this.gl=t,this.type=e,this.handle=r,this.length=n,this.usage=i}var l=s.prototype;function u(t,e,r,n,i,a){var o=i.length*i.BYTES_PER_ELEMENT;if(a<0)return t.bufferData(e,i,n),o;if(o+a>r)throw new Error(\"gl-buffer: If resizing buffer, must not specify offset\");return t.bufferSubData(e,a,i),r}function c(t,e){for(var r=n.malloc(t.length,e),i=t.length,a=0;a<i;++a)r[a]=t[a];return r}l.bind=function(){this.gl.bindBuffer(this.type,this.handle)},l.unbind=function(){this.gl.bindBuffer(this.type,null)},l.dispose=function(){this.gl.deleteBuffer(this.handle)},l.update=function(t,e){if(\"number\"!=typeof e&&(e=-1),this.bind(),\"object\"==typeof t&&void 0!==t.shape){var r=t.dtype;if(o.indexOf(r)<0&&(r=\"float32\"),this.type===this.gl.ELEMENT_ARRAY_BUFFER&&(r=gl.getExtension(\"OES_element_index_uint\")&&\"uint16\"!==r?\"uint32\":\"uint16\"),r===t.dtype&&function(t,e){for(var r=1,n=e.length-1;n>=0;--n){if(e[n]!==r)return!1;r*=t[n]}return!0}(t.shape,t.stride))0===t.offset&&t.data.length===t.shape[0]?this.length=u(this.gl,this.type,this.length,this.usage,t.data,e):this.length=u(this.gl,this.type,this.length,this.usage,t.data.subarray(t.offset,t.shape[0]),e);else{var s=n.malloc(t.size,r),l=a(s,t.shape);i.assign(l,t),this.length=u(this.gl,this.type,this.length,this.usage,e<0?s:s.subarray(0,t.size),e),n.free(s)}}else if(Array.isArray(t)){var f;f=this.type===this.gl.ELEMENT_ARRAY_BUFFER?c(t,\"uint16\"):c(t,\"float32\"),this.length=u(this.gl,this.type,this.length,this.usage,e<0?f:f.subarray(0,t.length),e),n.free(f)}else if(\"object\"==typeof t&&\"number\"==typeof t.length)this.length=u(this.gl,this.type,this.length,this.usage,t,e);else{if(\"number\"!=typeof t&&void 0!==t)throw new Error(\"gl-buffer: Invalid data type\");if(e>=0)throw new Error(\"gl-buffer: Cannot specify offset when resizing buffer\");(t|=0)<=0&&(t=1),this.gl.bufferData(this.type,0|t,this.usage),this.length=t}},t.exports=function(t,e,r,n){if(r=r||t.ARRAY_BUFFER,n=n||t.DYNAMIC_DRAW,r!==t.ARRAY_BUFFER&&r!==t.ELEMENT_ARRAY_BUFFER)throw new Error(\"gl-buffer: Invalid type for webgl buffer, must be either gl.ARRAY_BUFFER or gl.ELEMENT_ARRAY_BUFFER\");if(n!==t.DYNAMIC_DRAW&&n!==t.STATIC_DRAW&&n!==t.STREAM_DRAW)throw new Error(\"gl-buffer: Invalid usage for buffer, must be either gl.DYNAMIC_DRAW, gl.STATIC_DRAW or gl.STREAM_DRAW\");var i=t.createBuffer(),a=new s(t,r,i,0,n);return a.update(e),a}},1140:function(t,e,r){\"use strict\";var n=r(2858);t.exports=function(t,e){var r=t.positions,i=t.vectors,a={positions:[],vertexIntensity:[],vertexIntensityBounds:t.vertexIntensityBounds,vectors:[],cells:[],coneOffset:t.coneOffset,colormap:t.colormap};if(0===t.positions.length)return e&&(e[0]=[0,0,0],e[1]=[0,0,0]),a;for(var o=0,s=1/0,l=-1/0,u=1/0,c=-1/0,f=1/0,h=-1/0,p=null,d=null,v=[],g=1/0,y=!1,m=0;m<r.length;m++){var x=r[m];s=Math.min(x[0],s),l=Math.max(x[0],l),u=Math.min(x[1],u),c=Math.max(x[1],c),f=Math.min(x[2],f),h=Math.max(x[2],h);var b=i[m];if(n.length(b)>o&&(o=n.length(b)),m){var _=2*n.distance(p,x)/(n.length(d)+n.length(b));_?(g=Math.min(g,_),y=!1):y=!0}y||(p=x,d=b),v.push(b)}var w=[s,u,f],T=[l,c,h];e&&(e[0]=w,e[1]=T),0===o&&(o=1);var k=1/o;isFinite(g)||(g=1),a.vectorScale=g;var A=t.coneSize||.5;t.absoluteConeSize&&(A=t.absoluteConeSize*k),a.coneScale=A,m=0;for(var M=0;m<r.length;m++)for(var S=(x=r[m])[0],E=x[1],L=x[2],C=v[m],P=n.length(C)*k,O=0;O<8;O++){a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vertexIntensity.push(P,P,P),a.vertexIntensity.push(P,P,P);var I=a.positions.length;a.cells.push([I-6,I-5,I-4],[I-3,I-2,I-1])}return a};var i=r(7234);t.exports.createMesh=r(5028),t.exports.createConeMesh=function(e,r){return t.exports.createMesh(e,r,{shaders:i,traceType:\"cone\"})}},5028:function(t,e,r){\"use strict\";var n=r(5158),i=r(5827),a=r(2944),o=r(8931),s=r(104),l=r(7437),u=r(5050),c=r(9156),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e,r,n,i,a,o,s,l,u,c){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.pickShader=n,this.trianglePositions=i,this.triangleVectors=a,this.triangleColors=s,this.triangleUVs=l,this.triangleIds=o,this.triangleVAO=u,this.triangleCount=0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.traceType=c,this.tubeScale=1,this.coneScale=2,this.vectorScale=1,this.coneOffset=.25,this._model=f,this._view=f,this._projection=f,this._resolution=[1,1]}var p=h.prototype;p.isOpaque=function(){return this.opacity>=1},p.isTransparent=function(){return this.opacity<1},p.pickSlots=1,p.setPickBase=function(t){this.pickId=t},p.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,\"lightPosition\"in t&&(this.lightPosition=t.lightPosition),\"opacity\"in t&&(this.opacity=t.opacity),\"ambient\"in t&&(this.ambientLight=t.ambient),\"diffuse\"in t&&(this.diffuseLight=t.diffuse),\"specular\"in t&&(this.specularLight=t.specular),\"roughness\"in t&&(this.roughness=t.roughness),\"fresnel\"in t&&(this.fresnel=t.fresnel),void 0!==t.tubeScale&&(this.tubeScale=t.tubeScale),void 0!==t.vectorScale&&(this.vectorScale=t.vectorScale),void 0!==t.coneScale&&(this.coneScale=t.coneScale),void 0!==t.coneOffset&&(this.coneOffset=t.coneOffset),t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t){for(var e=c({colormap:t,nshades:256,format:\"rgba\"}),r=new Uint8Array(1024),n=0;n<256;++n){for(var i=e[n],a=0;a<3;++a)r[4*n+a]=i[a];r[4*n+3]=255*i[3]}return u(r,[256,256,4],[4,0,1])}(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions,i=t.vectors;if(n&&r&&i){var a=[],o=[],s=[],l=[],f=[];this.cells=r,this.positions=n,this.vectors=i;var h=t.meshColor||[1,1,1,1],p=t.vertexIntensity,d=1/0,v=-1/0;if(p)if(t.vertexIntensityBounds)d=+t.vertexIntensityBounds[0],v=+t.vertexIntensityBounds[1];else for(var g=0;g<p.length;++g){var y=p[g];d=Math.min(d,y),v=Math.max(v,y)}else for(g=0;g<n.length;++g)y=n[g][2],d=Math.min(d,y),v=Math.max(v,y);for(this.intensity=p||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n),this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],g=0;g<n.length;++g)for(var m=n[g],x=0;x<3;++x)!isNaN(m[x])&&isFinite(m[x])&&(this.bounds[0][x]=Math.min(this.bounds[0][x],m[x]),this.bounds[1][x]=Math.max(this.bounds[1][x],m[x]));var b=0;t:for(g=0;g<r.length;++g){var _=r[g];if(3===_.length){for(x=0;x<3;++x){m=n[T=_[x]];for(var w=0;w<3;++w)if(isNaN(m[w])||!isFinite(m[w]))continue t}for(x=0;x<3;++x){var T;m=n[T=_[2-x]],a.push(m[0],m[1],m[2],m[3]);var k=i[T];o.push(k[0],k[1],k[2],k[3]||0);var A,M=h;3===M.length?s.push(M[0],M[1],M[2],1):s.push(M[0],M[1],M[2],M[3]),A=p?[(p[T]-d)/(v-d),0]:[(m[2]-d)/(v-d),0],l.push(A[0],A[1]),f.push(g)}b+=1}}this.triangleCount=b,this.trianglePositions.update(a),this.triangleVectors.update(o),this.triangleColors.update(s),this.triangleUVs.update(l),this.triangleIds.update(new Uint32Array(f))}},p.drawTransparent=p.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var u={model:r,view:n,projection:i,inverseModel:f.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,texture:0};u.inverseModel=l(u.inverseModel,u.model),e.disable(e.CULL_FACE),this.texture.bind(0);var c=new Array(16);for(s(c,u.view,u.model),s(c,u.projection,c),l(c,c),o=0;o<3;++o)u.eyePosition[o]=c[12+o]/c[15];var h=c[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*c[4*o+3];for(o=0;o<3;++o){for(var p=c[12+o],d=0;d<3;++d)p+=c[4*d+o]*this.lightPosition[d];u.lightPosition[o]=p/h}if(this.triangleCount>0){var v=this.triShader;v.bind(),v.uniforms=u,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()}},p.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s={model:r,view:n,projection:i,clipBounds:a,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,pickId:this.pickId/255},l=this.pickShader;l.bind(),l.uniforms=s,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind())},p.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions[r[1]].slice(0,3),i={position:n,dataCoordinate:n,index:Math.floor(r[1]/48)};return\"cone\"===this.traceType?i.index=Math.floor(r[1]/48):\"streamtube\"===this.traceType&&(i.intensity=this.intensity[r[1]],i.velocity=this.vectors[r[1]].slice(0,3),i.divergence=this.vectors[r[1]][3],i.index=e),i},p.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.pickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleVectors.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleIds.dispose()},t.exports=function(t,e,r){var s=r.shaders;1===arguments.length&&(t=(e=t).gl);var l=function(t,e){var r=n(t,e.meshShader.vertex,e.meshShader.fragment,null,e.meshShader.attributes);return r.attributes.position.location=0,r.attributes.color.location=2,r.attributes.uv.location=3,r.attributes.vector.location=4,r}(t,s),c=function(t,e){var r=n(t,e.pickShader.vertex,e.pickShader.fragment,null,e.pickShader.attributes);return r.attributes.position.location=0,r.attributes.id.location=1,r.attributes.vector.location=4,r}(t,s),f=o(t,u(new Uint8Array([255,255,255,255]),[1,1,4]));f.generateMipmap(),f.minFilter=t.LINEAR_MIPMAP_LINEAR,f.magFilter=t.LINEAR;var p=i(t),d=i(t),v=i(t),g=i(t),y=i(t),m=new h(t,f,l,c,p,d,y,v,g,a(t,[{buffer:p,type:t.FLOAT,size:4},{buffer:y,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:v,type:t.FLOAT,size:4},{buffer:g,type:t.FLOAT,size:2},{buffer:d,type:t.FLOAT,size:4}]),r.traceType||\"cone\");return m.update(e),m}},7234:function(t,e,r){var n=r(6832),i=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the cone vertex and normal at the given index.\\n//\\n// The returned vertex is for a cone with its top at origin and height of 1.0,\\n// pointing in the direction of the vector attribute.\\n//\\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\\n// These vertices are used to make up the triangles of the cone by the following:\\n//   segment + 0 top vertex\\n//   segment + 1 perimeter vertex a+1\\n//   segment + 2 perimeter vertex a\\n//   segment + 3 center base vertex\\n//   segment + 4 perimeter vertex a\\n//   segment + 5 perimeter vertex a+1\\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\\n// To go from index to segment, floor(index / 6)\\n// To go from segment to angle, 2*pi * (segment/segmentCount)\\n// To go from index to segment index, index - (segment*6)\\n//\\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\\n\\n  const float segmentCount = 8.0;\\n\\n  float index = rawIndex - floor(rawIndex /\\n    (segmentCount * 6.0)) *\\n    (segmentCount * 6.0);\\n\\n  float segment = floor(0.001 + index/6.0);\\n  float segmentIndex = index - (segment*6.0);\\n\\n  normal = -normalize(d);\\n\\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\\n    return mix(vec3(0.0), -d, coneOffset);\\n  }\\n\\n  float nextAngle = (\\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\\n  ) ? 1.0 : 0.0;\\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\\n\\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\\n  vec3 v2 = v1 - d;\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d)*0.25;\\n  vec3 y = v * sin(angle) * length(d)*0.25;\\n  vec3 v3 = v2 + x + y;\\n  if (segmentIndex < 3.0) {\\n    vec3 tx = u * sin(angle);\\n    vec3 ty = v * -cos(angle);\\n    vec3 tangent = tx + ty;\\n    normal = normalize(cross(v3 - v1, tangent));\\n  }\\n\\n  if (segmentIndex == 0.0) {\\n    return mix(d, vec3(0.0), coneOffset);\\n  }\\n  return v3;\\n}\\n\\nattribute vec3 vector;\\nattribute vec4 color, position;\\nattribute vec2 uv;\\n\\nuniform float vectorScale, coneScale, coneOffset;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 eyePosition, lightPosition;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  // Scale the vector magnitude to stay constant with\\n  // model & view changes.\\n  vec3 normal;\\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector), position.w, coneOffset, normal);\\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * conePosition;\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n  // vec4 m_position  = model * vec4(conePosition, 1.0);\\n  vec4 t_position  = view * conePosition;\\n  gl_Position      = projection * t_position;\\n\\n  f_color          = color;\\n  f_data           = conePosition.xyz;\\n  f_position       = position.xyz;\\n  f_uv             = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness,\\n  float fresnel) {\\n\\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n  //Half angle vector\\n  vec3 H = normalize(lightDirection + viewDirection);\\n\\n  //Geometric term\\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\\n  float VdotH = max(dot(viewDirection, H), 0.000001);\\n  float LdotH = max(dot(lightDirection, H), 0.000001);\\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n  float G = min(1.0, min(G1, G2));\\n  \\n  //Distribution term\\n  float D = beckmannDistribution(NdotH, roughness);\\n\\n  //Fresnel term\\n  float F = pow(1.0 - VdotN, fresnel);\\n\\n  //Multiply terms and done\\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n  vec3 N = normalize(f_normal);\\n  vec3 L = normalize(f_lightDirection);\\n  vec3 V = normalize(f_eyeDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = litColor * opacity;\\n}\\n\"]),o=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the cone vertex and normal at the given index.\\n//\\n// The returned vertex is for a cone with its top at origin and height of 1.0,\\n// pointing in the direction of the vector attribute.\\n//\\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\\n// These vertices are used to make up the triangles of the cone by the following:\\n//   segment + 0 top vertex\\n//   segment + 1 perimeter vertex a+1\\n//   segment + 2 perimeter vertex a\\n//   segment + 3 center base vertex\\n//   segment + 4 perimeter vertex a\\n//   segment + 5 perimeter vertex a+1\\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\\n// To go from index to segment, floor(index / 6)\\n// To go from segment to angle, 2*pi * (segment/segmentCount)\\n// To go from index to segment index, index - (segment*6)\\n//\\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\\n\\n  const float segmentCount = 8.0;\\n\\n  float index = rawIndex - floor(rawIndex /\\n    (segmentCount * 6.0)) *\\n    (segmentCount * 6.0);\\n\\n  float segment = floor(0.001 + index/6.0);\\n  float segmentIndex = index - (segment*6.0);\\n\\n  normal = -normalize(d);\\n\\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\\n    return mix(vec3(0.0), -d, coneOffset);\\n  }\\n\\n  float nextAngle = (\\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\\n  ) ? 1.0 : 0.0;\\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\\n\\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\\n  vec3 v2 = v1 - d;\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d)*0.25;\\n  vec3 y = v * sin(angle) * length(d)*0.25;\\n  vec3 v3 = v2 + x + y;\\n  if (segmentIndex < 3.0) {\\n    vec3 tx = u * sin(angle);\\n    vec3 ty = v * -cos(angle);\\n    vec3 tangent = tx + ty;\\n    normal = normalize(cross(v3 - v1, tangent));\\n  }\\n\\n  if (segmentIndex == 0.0) {\\n    return mix(d, vec3(0.0), coneOffset);\\n  }\\n  return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform float vectorScale, coneScale, coneOffset;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  vec3 normal;\\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector.xyz), position.w, coneOffset, normal);\\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n  gl_Position = projection * view * conePosition;\\n  f_id        = id;\\n  f_position  = position.xyz;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3  clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n  gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]);e.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec4\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"vector\",type:\"vec3\"}]},e.pickShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec4\"},{name:\"id\",type:\"vec4\"},{name:\"vector\",type:\"vec3\"}]}},1950:function(t){t.exports={0:\"NONE\",1:\"ONE\",2:\"LINE_LOOP\",3:\"LINE_STRIP\",4:\"TRIANGLES\",5:\"TRIANGLE_STRIP\",6:\"TRIANGLE_FAN\",256:\"DEPTH_BUFFER_BIT\",512:\"NEVER\",513:\"LESS\",514:\"EQUAL\",515:\"LEQUAL\",516:\"GREATER\",517:\"NOTEQUAL\",518:\"GEQUAL\",519:\"ALWAYS\",768:\"SRC_COLOR\",769:\"ONE_MINUS_SRC_COLOR\",770:\"SRC_ALPHA\",771:\"ONE_MINUS_SRC_ALPHA\",772:\"DST_ALPHA\",773:\"ONE_MINUS_DST_ALPHA\",774:\"DST_COLOR\",775:\"ONE_MINUS_DST_COLOR\",776:\"SRC_ALPHA_SATURATE\",1024:\"STENCIL_BUFFER_BIT\",1028:\"FRONT\",1029:\"BACK\",1032:\"FRONT_AND_BACK\",1280:\"INVALID_ENUM\",1281:\"INVALID_VALUE\",1282:\"INVALID_OPERATION\",1285:\"OUT_OF_MEMORY\",1286:\"INVALID_FRAMEBUFFER_OPERATION\",2304:\"CW\",2305:\"CCW\",2849:\"LINE_WIDTH\",2884:\"CULL_FACE\",2885:\"CULL_FACE_MODE\",2886:\"FRONT_FACE\",2928:\"DEPTH_RANGE\",2929:\"DEPTH_TEST\",2930:\"DEPTH_WRITEMASK\",2931:\"DEPTH_CLEAR_VALUE\",2932:\"DEPTH_FUNC\",2960:\"STENCIL_TEST\",2961:\"STENCIL_CLEAR_VALUE\",2962:\"STENCIL_FUNC\",2963:\"STENCIL_VALUE_MASK\",2964:\"STENCIL_FAIL\",2965:\"STENCIL_PASS_DEPTH_FAIL\",2966:\"STENCIL_PASS_DEPTH_PASS\",2967:\"STENCIL_REF\",2968:\"STENCIL_WRITEMASK\",2978:\"VIEWPORT\",3024:\"DITHER\",3042:\"BLEND\",3088:\"SCISSOR_BOX\",3089:\"SCISSOR_TEST\",3106:\"COLOR_CLEAR_VALUE\",3107:\"COLOR_WRITEMASK\",3317:\"UNPACK_ALIGNMENT\",3333:\"PACK_ALIGNMENT\",3379:\"MAX_TEXTURE_SIZE\",3386:\"MAX_VIEWPORT_DIMS\",3408:\"SUBPIXEL_BITS\",3410:\"RED_BITS\",3411:\"GREEN_BITS\",3412:\"BLUE_BITS\",3413:\"ALPHA_BITS\",3414:\"DEPTH_BITS\",3415:\"STENCIL_BITS\",3553:\"TEXTURE_2D\",4352:\"DONT_CARE\",4353:\"FASTEST\",4354:\"NICEST\",5120:\"BYTE\",5121:\"UNSIGNED_BYTE\",5122:\"SHORT\",5123:\"UNSIGNED_SHORT\",5124:\"INT\",5125:\"UNSIGNED_INT\",5126:\"FLOAT\",5386:\"INVERT\",5890:\"TEXTURE\",6401:\"STENCIL_INDEX\",6402:\"DEPTH_COMPONENT\",6406:\"ALPHA\",6407:\"RGB\",6408:\"RGBA\",6409:\"LUMINANCE\",6410:\"LUMINANCE_ALPHA\",7680:\"KEEP\",7681:\"REPLACE\",7682:\"INCR\",7683:\"DECR\",7936:\"VENDOR\",7937:\"RENDERER\",7938:\"VERSION\",9728:\"NEAREST\",9729:\"LINEAR\",9984:\"NEAREST_MIPMAP_NEAREST\",9985:\"LINEAR_MIPMAP_NEAREST\",9986:\"NEAREST_MIPMAP_LINEAR\",9987:\"LINEAR_MIPMAP_LINEAR\",10240:\"TEXTURE_MAG_FILTER\",10241:\"TEXTURE_MIN_FILTER\",10242:\"TEXTURE_WRAP_S\",10243:\"TEXTURE_WRAP_T\",10497:\"REPEAT\",10752:\"POLYGON_OFFSET_UNITS\",16384:\"COLOR_BUFFER_BIT\",32769:\"CONSTANT_COLOR\",32770:\"ONE_MINUS_CONSTANT_COLOR\",32771:\"CONSTANT_ALPHA\",32772:\"ONE_MINUS_CONSTANT_ALPHA\",32773:\"BLEND_COLOR\",32774:\"FUNC_ADD\",32777:\"BLEND_EQUATION_RGB\",32778:\"FUNC_SUBTRACT\",32779:\"FUNC_REVERSE_SUBTRACT\",32819:\"UNSIGNED_SHORT_4_4_4_4\",32820:\"UNSIGNED_SHORT_5_5_5_1\",32823:\"POLYGON_OFFSET_FILL\",32824:\"POLYGON_OFFSET_FACTOR\",32854:\"RGBA4\",32855:\"RGB5_A1\",32873:\"TEXTURE_BINDING_2D\",32926:\"SAMPLE_ALPHA_TO_COVERAGE\",32928:\"SAMPLE_COVERAGE\",32936:\"SAMPLE_BUFFERS\",32937:\"SAMPLES\",32938:\"SAMPLE_COVERAGE_VALUE\",32939:\"SAMPLE_COVERAGE_INVERT\",32968:\"BLEND_DST_RGB\",32969:\"BLEND_SRC_RGB\",32970:\"BLEND_DST_ALPHA\",32971:\"BLEND_SRC_ALPHA\",33071:\"CLAMP_TO_EDGE\",33170:\"GENERATE_MIPMAP_HINT\",33189:\"DEPTH_COMPONENT16\",33306:\"DEPTH_STENCIL_ATTACHMENT\",33635:\"UNSIGNED_SHORT_5_6_5\",33648:\"MIRRORED_REPEAT\",33901:\"ALIASED_POINT_SIZE_RANGE\",33902:\"ALIASED_LINE_WIDTH_RANGE\",33984:\"TEXTURE0\",33985:\"TEXTURE1\",33986:\"TEXTURE2\",33987:\"TEXTURE3\",33988:\"TEXTURE4\",33989:\"TEXTURE5\",33990:\"TEXTURE6\",33991:\"TEXTURE7\",33992:\"TEXTURE8\",33993:\"TEXTURE9\",33994:\"TEXTURE10\",33995:\"TEXTURE11\",33996:\"TEXTURE12\",33997:\"TEXTURE13\",33998:\"TEXTURE14\",33999:\"TEXTURE15\",34e3:\"TEXTURE16\",34001:\"TEXTURE17\",34002:\"TEXTURE18\",34003:\"TEXTURE19\",34004:\"TEXTURE20\",34005:\"TEXTURE21\",34006:\"TEXTURE22\",34007:\"TEXTURE23\",34008:\"TEXTURE24\",34009:\"TEXTURE25\",34010:\"TEXTURE26\",34011:\"TEXTURE27\",34012:\"TEXTURE28\",34013:\"TEXTURE29\",34014:\"TEXTURE30\",34015:\"TEXTURE31\",34016:\"ACTIVE_TEXTURE\",34024:\"MAX_RENDERBUFFER_SIZE\",34041:\"DEPTH_STENCIL\",34055:\"INCR_WRAP\",34056:\"DECR_WRAP\",34067:\"TEXTURE_CUBE_MAP\",34068:\"TEXTURE_BINDING_CUBE_MAP\",34069:\"TEXTURE_CUBE_MAP_POSITIVE_X\",34070:\"TEXTURE_CUBE_MAP_NEGATIVE_X\",34071:\"TEXTURE_CUBE_MAP_POSITIVE_Y\",34072:\"TEXTURE_CUBE_MAP_NEGATIVE_Y\",34073:\"TEXTURE_CUBE_MAP_POSITIVE_Z\",34074:\"TEXTURE_CUBE_MAP_NEGATIVE_Z\",34076:\"MAX_CUBE_MAP_TEXTURE_SIZE\",34338:\"VERTEX_ATTRIB_ARRAY_ENABLED\",34339:\"VERTEX_ATTRIB_ARRAY_SIZE\",34340:\"VERTEX_ATTRIB_ARRAY_STRIDE\",34341:\"VERTEX_ATTRIB_ARRAY_TYPE\",34342:\"CURRENT_VERTEX_ATTRIB\",34373:\"VERTEX_ATTRIB_ARRAY_POINTER\",34466:\"NUM_COMPRESSED_TEXTURE_FORMATS\",34467:\"COMPRESSED_TEXTURE_FORMATS\",34660:\"BUFFER_SIZE\",34661:\"BUFFER_USAGE\",34816:\"STENCIL_BACK_FUNC\",34817:\"STENCIL_BACK_FAIL\",34818:\"STENCIL_BACK_PASS_DEPTH_FAIL\",34819:\"STENCIL_BACK_PASS_DEPTH_PASS\",34877:\"BLEND_EQUATION_ALPHA\",34921:\"MAX_VERTEX_ATTRIBS\",34922:\"VERTEX_ATTRIB_ARRAY_NORMALIZED\",34930:\"MAX_TEXTURE_IMAGE_UNITS\",34962:\"ARRAY_BUFFER\",34963:\"ELEMENT_ARRAY_BUFFER\",34964:\"ARRAY_BUFFER_BINDING\",34965:\"ELEMENT_ARRAY_BUFFER_BINDING\",34975:\"VERTEX_ATTRIB_ARRAY_BUFFER_BINDING\",35040:\"STREAM_DRAW\",35044:\"STATIC_DRAW\",35048:\"DYNAMIC_DRAW\",35632:\"FRAGMENT_SHADER\",35633:\"VERTEX_SHADER\",35660:\"MAX_VERTEX_TEXTURE_IMAGE_UNITS\",35661:\"MAX_COMBINED_TEXTURE_IMAGE_UNITS\",35663:\"SHADER_TYPE\",35664:\"FLOAT_VEC2\",35665:\"FLOAT_VEC3\",35666:\"FLOAT_VEC4\",35667:\"INT_VEC2\",35668:\"INT_VEC3\",35669:\"INT_VEC4\",35670:\"BOOL\",35671:\"BOOL_VEC2\",35672:\"BOOL_VEC3\",35673:\"BOOL_VEC4\",35674:\"FLOAT_MAT2\",35675:\"FLOAT_MAT3\",35676:\"FLOAT_MAT4\",35678:\"SAMPLER_2D\",35680:\"SAMPLER_CUBE\",35712:\"DELETE_STATUS\",35713:\"COMPILE_STATUS\",35714:\"LINK_STATUS\",35715:\"VALIDATE_STATUS\",35716:\"INFO_LOG_LENGTH\",35717:\"ATTACHED_SHADERS\",35718:\"ACTIVE_UNIFORMS\",35719:\"ACTIVE_UNIFORM_MAX_LENGTH\",35720:\"SHADER_SOURCE_LENGTH\",35721:\"ACTIVE_ATTRIBUTES\",35722:\"ACTIVE_ATTRIBUTE_MAX_LENGTH\",35724:\"SHADING_LANGUAGE_VERSION\",35725:\"CURRENT_PROGRAM\",36003:\"STENCIL_BACK_REF\",36004:\"STENCIL_BACK_VALUE_MASK\",36005:\"STENCIL_BACK_WRITEMASK\",36006:\"FRAMEBUFFER_BINDING\",36007:\"RENDERBUFFER_BINDING\",36048:\"FRAMEBUFFER_ATTACHMENT_OBJECT_TYPE\",36049:\"FRAMEBUFFER_ATTACHMENT_OBJECT_NAME\",36050:\"FRAMEBUFFER_ATTACHMENT_TEXTURE_LEVEL\",36051:\"FRAMEBUFFER_ATTACHMENT_TEXTURE_CUBE_MAP_FACE\",36053:\"FRAMEBUFFER_COMPLETE\",36054:\"FRAMEBUFFER_INCOMPLETE_ATTACHMENT\",36055:\"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT\",36057:\"FRAMEBUFFER_INCOMPLETE_DIMENSIONS\",36061:\"FRAMEBUFFER_UNSUPPORTED\",36064:\"COLOR_ATTACHMENT0\",36096:\"DEPTH_ATTACHMENT\",36128:\"STENCIL_ATTACHMENT\",36160:\"FRAMEBUFFER\",36161:\"RENDERBUFFER\",36162:\"RENDERBUFFER_WIDTH\",36163:\"RENDERBUFFER_HEIGHT\",36164:\"RENDERBUFFER_INTERNAL_FORMAT\",36168:\"STENCIL_INDEX8\",36176:\"RENDERBUFFER_RED_SIZE\",36177:\"RENDERBUFFER_GREEN_SIZE\",36178:\"RENDERBUFFER_BLUE_SIZE\",36179:\"RENDERBUFFER_ALPHA_SIZE\",36180:\"RENDERBUFFER_DEPTH_SIZE\",36181:\"RENDERBUFFER_STENCIL_SIZE\",36194:\"RGB565\",36336:\"LOW_FLOAT\",36337:\"MEDIUM_FLOAT\",36338:\"HIGH_FLOAT\",36339:\"LOW_INT\",36340:\"MEDIUM_INT\",36341:\"HIGH_INT\",36346:\"SHADER_COMPILER\",36347:\"MAX_VERTEX_UNIFORM_VECTORS\",36348:\"MAX_VARYING_VECTORS\",36349:\"MAX_FRAGMENT_UNIFORM_VECTORS\",37440:\"UNPACK_FLIP_Y_WEBGL\",37441:\"UNPACK_PREMULTIPLY_ALPHA_WEBGL\",37442:\"CONTEXT_LOST_WEBGL\",37443:\"UNPACK_COLORSPACE_CONVERSION_WEBGL\",37444:\"BROWSER_DEFAULT_WEBGL\"}},6603:function(t,e,r){var n=r(1950);t.exports=function(t){return n[t]}},3110:function(t,e,r){\"use strict\";t.exports=function(t){var e=t.gl,r=n(e),o=i(e,[{buffer:r,type:e.FLOAT,size:3,offset:0,stride:40},{buffer:r,type:e.FLOAT,size:4,offset:12,stride:40},{buffer:r,type:e.FLOAT,size:3,offset:28,stride:40}]),l=a(e);l.attributes.position.location=0,l.attributes.color.location=1,l.attributes.offset.location=2;var u=new s(e,r,o,l);return u.update(t),u};var n=r(5827),i=r(2944),a=r(7667),o=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function s(t,e,r,n){this.gl=t,this.shader=n,this.buffer=e,this.vao=r,this.pixelRatio=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lineWidth=[1,1,1],this.capSize=[10,10,10],this.lineCount=[0,0,0],this.lineOffset=[0,0,0],this.opacity=1,this.hasAlpha=!1}var l=s.prototype;function u(t,e){for(var r=0;r<3;++r)t[0][r]=Math.min(t[0][r],e[r]),t[1][r]=Math.max(t[1][r],e[r])}l.isOpaque=function(){return!this.hasAlpha},l.isTransparent=function(){return this.hasAlpha},l.drawTransparent=l.draw=function(t){var e=this.gl,r=this.shader.uniforms;this.shader.bind();var n=r.view=t.view||o,i=r.projection=t.projection||o;r.model=t.model||o,r.clipBounds=this.clipBounds,r.opacity=this.opacity;var a=n[12],s=n[13],l=n[14],u=n[15],c=(t._ortho?2:1)*this.pixelRatio*(i[3]*a+i[7]*s+i[11]*l+i[15]*u)/e.drawingBufferHeight;this.vao.bind();for(var f=0;f<3;++f)e.lineWidth(this.lineWidth[f]*this.pixelRatio),r.capSize=this.capSize[f]*c,this.lineCount[f]&&e.drawArrays(e.LINES,this.lineOffset[f],this.lineCount[f]);this.vao.unbind()};var c=function(){for(var t=new Array(3),e=0;e<3;++e){for(var r=[],n=1;n<=2;++n)for(var i=-1;i<=1;i+=2){var a=[0,0,0];a[(n+e)%3]=i,r.push(a)}t[e]=r}return t}();function f(t,e,r,n){for(var i=c[n],a=0;a<i.length;++a){var o=i[a];t.push(e[0],e[1],e[2],r[0],r[1],r[2],r[3],o[0],o[1],o[2])}return i.length}l.update=function(t){\"lineWidth\"in(t=t||{})&&(this.lineWidth=t.lineWidth,Array.isArray(this.lineWidth)||(this.lineWidth=[this.lineWidth,this.lineWidth,this.lineWidth])),\"capSize\"in t&&(this.capSize=t.capSize,Array.isArray(this.capSize)||(this.capSize=[this.capSize,this.capSize,this.capSize])),this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=+t.opacity,this.opacity<1&&(this.hasAlpha=!0));var e=t.color||[[0,0,0],[0,0,0],[0,0,0]],r=t.position,n=t.error;if(Array.isArray(e[0])||(e=[e,e,e]),r&&n){var i=[],a=r.length,o=0;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.lineCount=[0,0,0];for(var s=0;s<3;++s){this.lineOffset[s]=o;t:for(var l=0;l<a;++l){for(var c=r[l],h=0;h<3;++h)if(isNaN(c[h])||!isFinite(c[h]))continue t;var p,d=n[l],v=e[s];Array.isArray(v[0])&&(v=e[l]),3===v.length?v=[v[0],v[1],v[2],1]:4===v.length&&(v=[v[0],v[1],v[2],v[3]],!this.hasAlpha&&v[3]<1&&(this.hasAlpha=!0)),isNaN(d[0][s])||isNaN(d[1][s])||(d[0][s]<0&&((p=c.slice())[s]+=d[0][s],i.push(c[0],c[1],c[2],v[0],v[1],v[2],v[3],0,0,0,p[0],p[1],p[2],v[0],v[1],v[2],v[3],0,0,0),u(this.bounds,p),o+=2+f(i,p,v,s)),d[1][s]>0&&((p=c.slice())[s]+=d[1][s],i.push(c[0],c[1],c[2],v[0],v[1],v[2],v[3],0,0,0,p[0],p[1],p[2],v[0],v[1],v[2],v[3],0,0,0),u(this.bounds,p),o+=2+f(i,p,v,s)))}this.lineCount[s]=o-this.lineOffset[s]}this.buffer.update(i)}},l.dispose=function(){this.shader.dispose(),this.buffer.dispose(),this.vao.dispose()}},7667:function(t,e,r){\"use strict\";var n=r(6832),i=r(5158),a=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, offset;\\nattribute vec4 color;\\nuniform mat4 model, view, projection;\\nuniform float capSize;\\nvarying vec4 fragColor;\\nvarying vec3 fragPosition;\\n\\nvoid main() {\\n  vec4 worldPosition  = model * vec4(position, 1.0);\\n  worldPosition       = (worldPosition / worldPosition.w) + vec4(capSize * offset, 0.0);\\n  gl_Position         = projection * view * worldPosition;\\n  fragColor           = color;\\n  fragPosition        = position;\\n}\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float opacity;\\nvarying vec3 fragPosition;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  if (\\n    outOfRange(clipBounds[0], clipBounds[1], fragPosition) ||\\n    fragColor.a * opacity == 0.\\n  ) discard;\\n\\n  gl_FragColor = opacity * fragColor;\\n}\"]);t.exports=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"offset\",type:\"vec3\"}])}},4234:function(t,e,r){\"use strict\";var n=r(8931);t.exports=function(t,e,r,n){i||(i=t.FRAMEBUFFER_UNSUPPORTED,a=t.FRAMEBUFFER_INCOMPLETE_ATTACHMENT,o=t.FRAMEBUFFER_INCOMPLETE_DIMENSIONS,s=t.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT);var u=t.getExtension(\"WEBGL_draw_buffers\");if(!l&&u&&function(t,e){var r=t.getParameter(e.MAX_COLOR_ATTACHMENTS_WEBGL);l=new Array(r+1);for(var n=0;n<=r;++n){for(var i=new Array(r),a=0;a<n;++a)i[a]=t.COLOR_ATTACHMENT0+a;for(a=n;a<r;++a)i[a]=t.NONE;l[n]=i}}(t,u),Array.isArray(e)&&(n=r,r=0|e[1],e=0|e[0]),\"number\"!=typeof e)throw new Error(\"gl-fbo: Missing shape parameter\");var c=t.getParameter(t.MAX_RENDERBUFFER_SIZE);if(e<0||e>c||r<0||r>c)throw new Error(\"gl-fbo: Parameters are too large for FBO\");var f=1;if(\"color\"in(n=n||{})){if((f=Math.max(0|n.color,0))<0)throw new Error(\"gl-fbo: Must specify a nonnegative number of colors\");if(f>1){if(!u)throw new Error(\"gl-fbo: Multiple draw buffer extension not supported\");if(f>t.getParameter(u.MAX_COLOR_ATTACHMENTS_WEBGL))throw new Error(\"gl-fbo: Context does not support \"+f+\" draw buffers\")}}var h=t.UNSIGNED_BYTE,p=t.getExtension(\"OES_texture_float\");if(n.float&&f>0){if(!p)throw new Error(\"gl-fbo: Context does not support floating point textures\");h=t.FLOAT}else n.preferFloat&&f>0&&p&&(h=t.FLOAT);var v=!0;\"depth\"in n&&(v=!!n.depth);var g=!1;return\"stencil\"in n&&(g=!!n.stencil),new d(t,e,r,h,f,v,g,u)};var i,a,o,s,l=null;function u(t){return[t.getParameter(t.FRAMEBUFFER_BINDING),t.getParameter(t.RENDERBUFFER_BINDING),t.getParameter(t.TEXTURE_BINDING_2D)]}function c(t,e){t.bindFramebuffer(t.FRAMEBUFFER,e[0]),t.bindRenderbuffer(t.RENDERBUFFER,e[1]),t.bindTexture(t.TEXTURE_2D,e[2])}function f(t){switch(t){case i:throw new Error(\"gl-fbo: Framebuffer unsupported\");case a:throw new Error(\"gl-fbo: Framebuffer incomplete attachment\");case o:throw new Error(\"gl-fbo: Framebuffer incomplete dimensions\");case s:throw new Error(\"gl-fbo: Framebuffer incomplete missing attachment\");default:throw new Error(\"gl-fbo: Framebuffer failed for unspecified reason\")}}function h(t,e,r,i,a,o){if(!i)return null;var s=n(t,e,r,a,i);return s.magFilter=t.NEAREST,s.minFilter=t.NEAREST,s.mipSamples=1,s.bind(),t.framebufferTexture2D(t.FRAMEBUFFER,o,t.TEXTURE_2D,s.handle,0),s}function p(t,e,r,n,i){var a=t.createRenderbuffer();return t.bindRenderbuffer(t.RENDERBUFFER,a),t.renderbufferStorage(t.RENDERBUFFER,n,e,r),t.framebufferRenderbuffer(t.FRAMEBUFFER,i,t.RENDERBUFFER,a),a}function d(t,e,r,n,i,a,o,s){this.gl=t,this._shape=[0|e,0|r],this._destroyed=!1,this._ext=s,this.color=new Array(i);for(var d=0;d<i;++d)this.color[d]=null;this._color_rb=null,this.depth=null,this._depth_rb=null,this._colorType=n,this._useDepth=a,this._useStencil=o;var v=this,g=[0|e,0|r];Object.defineProperties(g,{0:{get:function(){return v._shape[0]},set:function(t){return v.width=t}},1:{get:function(){return v._shape[1]},set:function(t){return v.height=t}}}),this._shapeVector=g,function(t){var e=u(t.gl),r=t.gl,n=t.handle=r.createFramebuffer(),i=t._shape[0],a=t._shape[1],o=t.color.length,s=t._ext,d=t._useStencil,v=t._useDepth,g=t._colorType;r.bindFramebuffer(r.FRAMEBUFFER,n);for(var y=0;y<o;++y)t.color[y]=h(r,i,a,g,r.RGBA,r.COLOR_ATTACHMENT0+y);0===o?(t._color_rb=p(r,i,a,r.RGBA4,r.COLOR_ATTACHMENT0),s&&s.drawBuffersWEBGL(l[0])):o>1&&s.drawBuffersWEBGL(l[o]);var m=r.getExtension(\"WEBGL_depth_texture\");m?d?t.depth=h(r,i,a,m.UNSIGNED_INT_24_8_WEBGL,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):v&&(t.depth=h(r,i,a,r.UNSIGNED_SHORT,r.DEPTH_COMPONENT,r.DEPTH_ATTACHMENT)):v&&d?t._depth_rb=p(r,i,a,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):v?t._depth_rb=p(r,i,a,r.DEPTH_COMPONENT16,r.DEPTH_ATTACHMENT):d&&(t._depth_rb=p(r,i,a,r.STENCIL_INDEX,r.STENCIL_ATTACHMENT));var x=r.checkFramebufferStatus(r.FRAMEBUFFER);if(x!==r.FRAMEBUFFER_COMPLETE){for(t._destroyed=!0,r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteFramebuffer(t.handle),t.handle=null,t.depth&&(t.depth.dispose(),t.depth=null),t._depth_rb&&(r.deleteRenderbuffer(t._depth_rb),t._depth_rb=null),y=0;y<t.color.length;++y)t.color[y].dispose(),t.color[y]=null;t._color_rb&&(r.deleteRenderbuffer(t._color_rb),t._color_rb=null),c(r,e),f(x)}c(r,e)}(this)}var v=d.prototype;function g(t,e,r){if(t._destroyed)throw new Error(\"gl-fbo: Can't resize destroyed FBO\");if(t._shape[0]!==e||t._shape[1]!==r){var n=t.gl,i=n.getParameter(n.MAX_RENDERBUFFER_SIZE);if(e<0||e>i||r<0||r>i)throw new Error(\"gl-fbo: Can't resize FBO, invalid dimensions\");t._shape[0]=e,t._shape[1]=r;for(var a=u(n),o=0;o<t.color.length;++o)t.color[o].shape=t._shape;t._color_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._color_rb),n.renderbufferStorage(n.RENDERBUFFER,n.RGBA4,t._shape[0],t._shape[1])),t.depth&&(t.depth.shape=t._shape),t._depth_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._depth_rb),t._useDepth&&t._useStencil?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_STENCIL,t._shape[0],t._shape[1]):t._useDepth?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_COMPONENT16,t._shape[0],t._shape[1]):t._useStencil&&n.renderbufferStorage(n.RENDERBUFFER,n.STENCIL_INDEX,t._shape[0],t._shape[1])),n.bindFramebuffer(n.FRAMEBUFFER,t.handle);var s=n.checkFramebufferStatus(n.FRAMEBUFFER);s!==n.FRAMEBUFFER_COMPLETE&&(t.dispose(),c(n,a),f(s)),c(n,a)}}Object.defineProperties(v,{shape:{get:function(){return this._destroyed?[0,0]:this._shapeVector},set:function(t){if(Array.isArray(t)||(t=[0|t,0|t]),2!==t.length)throw new Error(\"gl-fbo: Shape vector must be length 2\");var e=0|t[0],r=0|t[1];return g(this,e,r),[e,r]},enumerable:!1},width:{get:function(){return this._destroyed?0:this._shape[0]},set:function(t){return g(this,t|=0,this._shape[1]),t},enumerable:!1},height:{get:function(){return this._destroyed?0:this._shape[1]},set:function(t){return t|=0,g(this,this._shape[0],t),t},enumerable:!1}}),v.bind=function(){if(!this._destroyed){var t=this.gl;t.bindFramebuffer(t.FRAMEBUFFER,this.handle),t.viewport(0,0,this._shape[0],this._shape[1])}},v.dispose=function(){if(!this._destroyed){this._destroyed=!0;var t=this.gl;t.deleteFramebuffer(this.handle),this.handle=null,this.depth&&(this.depth.dispose(),this.depth=null),this._depth_rb&&(t.deleteRenderbuffer(this._depth_rb),this._depth_rb=null);for(var e=0;e<this.color.length;++e)this.color[e].dispose(),this.color[e]=null;this._color_rb&&(t.deleteRenderbuffer(this._color_rb),this._color_rb=null)}}},3530:function(t,e,r){var n=r(8974).sprintf,i=r(6603),a=r(9365),o=r(8008);t.exports=function(t,e,r){\"use strict\";var s=a(e)||\"of unknown name (see npm glsl-shader-name)\",l=\"unknown type\";void 0!==r&&(l=r===i.FRAGMENT_SHADER?\"fragment\":\"vertex\");for(var u=n(\"Error compiling %s shader %s:\\n\",l,s),c=n(\"%s%s\",u,t),f=t.split(\"\\n\"),h={},p=0;p<f.length;p++){var d=f[p];if(\"\"!==d&&\"\\0\"!==d){var v=parseInt(d.split(\":\")[2]);if(isNaN(v))throw new Error(n(\"Could not parse error: %s\",d));h[v]=d}}var g=o(e).split(\"\\n\");for(p=0;p<g.length;p++)if((h[p+3]||h[p+2]||h[p+1])&&(u+=g[p]+\"\\n\",h[p+1])){var y=h[p+1];y=y.substr(y.split(\":\",3).join(\":\").length+1).trim(),u+=n(\"^^^ %s\\n\\n\",y)}return{long:u.trim(),short:c.trim()}}},6386:function(t,e,r){\"use strict\";t.exports=function(t,e){var r=t.gl,n=new u(t,o(r,l.vertex,l.fragment),o(r,l.pickVertex,l.pickFragment),s(r),s(r),s(r),s(r));return n.update(e),t.addObject(n),n};var n=r(5070),i=r(9560),a=r(5306),o=r(5158),s=r(5827),l=r(1292);function u(t,e,r,n,i,a,o){this.plot=t,this.shader=e,this.pickShader=r,this.positionBuffer=n,this.weightBuffer=i,this.colorBuffer=a,this.idBuffer=o,this.xData=[],this.yData=[],this.shape=[0,0],this.bounds=[1/0,1/0,-1/0,-1/0],this.pickOffset=0}var c,f=u.prototype,h=[0,0,1,0,0,1,1,0,1,1,0,1];f.draw=(c=[1,0,0,0,1,0,0,0,1],function(){var t=this.plot,e=this.shader,r=this.bounds,n=this.numVertices;if(!(n<=0)){var i=t.gl,a=t.dataBox,o=r[2]-r[0],s=r[3]-r[1],l=a[2]-a[0],u=a[3]-a[1];c[0]=2*o/l,c[4]=2*s/u,c[6]=2*(r[0]-a[0])/l-1,c[7]=2*(r[1]-a[1])/u-1,e.bind();var f=e.uniforms;f.viewTransform=c,f.shape=this.shape;var h=e.attributes;this.positionBuffer.bind(),h.position.pointer(),this.weightBuffer.bind(),h.weight.pointer(i.UNSIGNED_BYTE,!1),this.colorBuffer.bind(),h.color.pointer(i.UNSIGNED_BYTE,!0),i.drawArrays(i.TRIANGLES,0,n)}}),f.drawPick=function(){var t=[1,0,0,0,1,0,0,0,1],e=[0,0,0,0];return function(r){var n=this.plot,i=this.pickShader,a=this.bounds,o=this.numVertices;if(!(o<=0)){var s=n.gl,l=n.dataBox,u=a[2]-a[0],c=a[3]-a[1],f=l[2]-l[0],h=l[3]-l[1];t[0]=2*u/f,t[4]=2*c/h,t[6]=2*(a[0]-l[0])/f-1,t[7]=2*(a[1]-l[1])/h-1;for(var p=0;p<4;++p)e[p]=r>>8*p&255;this.pickOffset=r,i.bind();var d=i.uniforms;d.viewTransform=t,d.pickOffset=e,d.shape=this.shape;var v=i.attributes;return this.positionBuffer.bind(),v.position.pointer(),this.weightBuffer.bind(),v.weight.pointer(s.UNSIGNED_BYTE,!1),this.idBuffer.bind(),v.pickId.pointer(s.UNSIGNED_BYTE,!1),s.drawArrays(s.TRIANGLES,0,o),r+this.shape[0]*this.shape[1]}}}(),f.pick=function(t,e,r){var n=this.pickOffset,i=this.shape[0]*this.shape[1];if(r<n||r>=n+i)return null;var a=r-n,o=this.xData,s=this.yData;return{object:this,pointId:a,dataCoord:[o[a%this.shape[0]],s[a/this.shape[0]|0]]}},f.update=function(t){var e=(t=t||{}).shape||[0,0],r=t.x||i(e[0]),o=t.y||i(e[1]),s=t.z||new Float32Array(e[0]*e[1]),l=!1!==t.zsmooth;this.xData=r,this.yData=o;var u,c,f,p,d=t.colorLevels||[0],v=t.colorValues||[0,0,0,1],g=d.length,y=this.bounds;l?(u=y[0]=r[0],c=y[1]=o[0],f=y[2]=r[r.length-1],p=y[3]=o[o.length-1]):(u=y[0]=r[0]+(r[1]-r[0])/2,c=y[1]=o[0]+(o[1]-o[0])/2,f=y[2]=r[r.length-1]+(r[r.length-1]-r[r.length-2])/2,p=y[3]=o[o.length-1]+(o[o.length-1]-o[o.length-2])/2);var m=1/(f-u),x=1/(p-c),b=e[0],_=e[1];this.shape=[b,_];var w=(l?(b-1)*(_-1):b*_)*(h.length>>>1);this.numVertices=w;for(var T=a.mallocUint8(4*w),k=a.mallocFloat32(2*w),A=a.mallocUint8(2*w),M=a.mallocUint32(w),S=0,E=l?b-1:b,L=l?_-1:_,C=0;C<L;++C){var P,O;l?(P=x*(o[C]-c),O=x*(o[C+1]-c)):(P=C<_-1?x*(o[C]-(o[C+1]-o[C])/2-c):x*(o[C]-(o[C]-o[C-1])/2-c),O=C<_-1?x*(o[C]+(o[C+1]-o[C])/2-c):x*(o[C]+(o[C]-o[C-1])/2-c));for(var I=0;I<E;++I){var D,z;l?(D=m*(r[I]-u),z=m*(r[I+1]-u)):(D=I<b-1?m*(r[I]-(r[I+1]-r[I])/2-u):m*(r[I]-(r[I]-r[I-1])/2-u),z=I<b-1?m*(r[I]+(r[I+1]-r[I])/2-u):m*(r[I]+(r[I]-r[I-1])/2-u));for(var R=0;R<h.length;R+=2){var F,B,N,j,U=h[R],V=h[R+1],H=s[l?(C+V)*b+(I+U):C*b+I],q=n.le(d,H);if(q<0)F=v[0],B=v[1],N=v[2],j=v[3];else if(q===g-1)F=v[4*g-4],B=v[4*g-3],N=v[4*g-2],j=v[4*g-1];else{var G=(H-d[q])/(d[q+1]-d[q]),Z=1-G,Y=4*q,W=4*(q+1);F=Z*v[Y]+G*v[W],B=Z*v[Y+1]+G*v[W+1],N=Z*v[Y+2]+G*v[W+2],j=Z*v[Y+3]+G*v[W+3]}T[4*S]=255*F,T[4*S+1]=255*B,T[4*S+2]=255*N,T[4*S+3]=255*j,k[2*S]=.5*D+.5*z,k[2*S+1]=.5*P+.5*O,A[2*S]=U,A[2*S+1]=V,M[S]=C*b+I,S+=1}}}this.positionBuffer.update(k),this.weightBuffer.update(A),this.colorBuffer.update(T),this.idBuffer.update(M),a.free(k),a.free(T),a.free(A),a.free(M)},f.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.positionBuffer.dispose(),this.weightBuffer.dispose(),this.colorBuffer.dispose(),this.idBuffer.dispose(),this.plot.removeObject(this)}},1292:function(t,e,r){\"use strict\";var n=r(6832);t.exports={fragment:n([\"precision lowp float;\\n#define GLSLIFY 1\\nvarying vec4 fragColor;\\nvoid main() {\\n  gl_FragColor = vec4(fragColor.rgb * fragColor.a, fragColor.a);\\n}\\n\"]),vertex:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 color;\\nattribute vec2 weight;\\n\\nuniform vec2 shape;\\nuniform mat3 viewTransform;\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\\n  fragColor = color;\\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\\n}\\n\"]),pickFragment:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragId;\\nvarying vec2 vWeight;\\n\\nuniform vec2 shape;\\nuniform vec4 pickOffset;\\n\\nvoid main() {\\n  vec2 d = step(.5, vWeight);\\n  vec4 id = fragId + pickOffset;\\n  id.x += d.x + d.y*shape.x;\\n\\n  id.y += floor(id.x / 256.0);\\n  id.x -= floor(id.x / 256.0) * 256.0;\\n\\n  id.z += floor(id.y / 256.0);\\n  id.y -= floor(id.y / 256.0) * 256.0;\\n\\n  id.w += floor(id.z / 256.0);\\n  id.z -= floor(id.z / 256.0) * 256.0;\\n\\n  gl_FragColor = id/255.;\\n}\\n\"]),pickVertex:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 pickId;\\nattribute vec2 weight;\\n\\nuniform vec2 shape;\\nuniform mat3 viewTransform;\\n\\nvarying vec4 fragId;\\nvarying vec2 vWeight;\\n\\nvoid main() {\\n  vWeight = weight;\\n\\n  fragId = pickId;\\n\\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\\n}\\n\"])}},248:function(t,e,r){var n=r(6832),i=r(5158),a=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, nextPosition;\\nattribute float arcLength, lineWidth;\\nattribute vec4 color;\\n\\nuniform vec2 screenShape;\\nuniform float pixelRatio;\\nuniform mat4 model, view, projection;\\n\\nvarying vec4 fragColor;\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\n\\nvec4 project(vec3 p) {\\n  return projection * view * model * vec4(p, 1.0);\\n}\\n\\nvoid main() {\\n  vec4 startPoint = project(position);\\n  vec4 endPoint   = project(nextPosition);\\n\\n  vec2 A = startPoint.xy / startPoint.w;\\n  vec2 B =   endPoint.xy /   endPoint.w;\\n\\n  float clipAngle = atan(\\n    (B.y - A.y) * screenShape.y,\\n    (B.x - A.x) * screenShape.x\\n  );\\n\\n  vec2 offset = 0.5 * pixelRatio * lineWidth * vec2(\\n    sin(clipAngle),\\n    -cos(clipAngle)\\n  ) / screenShape;\\n\\n  gl_Position = vec4(startPoint.xy + startPoint.w * offset, startPoint.zw);\\n\\n  worldPosition = position;\\n  pixelArcLength = arcLength;\\n  fragColor = color;\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3      clipBounds[2];\\nuniform sampler2D dashTexture;\\nuniform float     dashScale;\\nuniform float     opacity;\\n\\nvarying vec3    worldPosition;\\nvarying float   pixelArcLength;\\nvarying vec4    fragColor;\\n\\nvoid main() {\\n  if (\\n    outOfRange(clipBounds[0], clipBounds[1], worldPosition) ||\\n    fragColor.a * opacity == 0.\\n  ) discard;\\n\\n  float dashWeight = texture2D(dashTexture, vec2(dashScale * pixelArcLength, 0)).r;\\n  if(dashWeight < 0.5) {\\n    discard;\\n  }\\n  gl_FragColor = fragColor * opacity;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\n#define FLOAT_MAX  1.70141184e38\\n#define FLOAT_MIN  1.17549435e-38\\n\\n// https://github.com/mikolalysenko/glsl-read-float/blob/master/index.glsl\\nvec4 packFloat(float v) {\\n  float av = abs(v);\\n\\n  //Handle special cases\\n  if(av < FLOAT_MIN) {\\n    return vec4(0.0, 0.0, 0.0, 0.0);\\n  } else if(v > FLOAT_MAX) {\\n    return vec4(127.0, 128.0, 0.0, 0.0) / 255.0;\\n  } else if(v < -FLOAT_MAX) {\\n    return vec4(255.0, 128.0, 0.0, 0.0) / 255.0;\\n  }\\n\\n  vec4 c = vec4(0,0,0,0);\\n\\n  //Compute exponent and mantissa\\n  float e = floor(log2(av));\\n  float m = av * pow(2.0, -e) - 1.0;\\n\\n  //Unpack mantissa\\n  c[1] = floor(128.0 * m);\\n  m -= c[1] / 128.0;\\n  c[2] = floor(32768.0 * m);\\n  m -= c[2] / 32768.0;\\n  c[3] = floor(8388608.0 * m);\\n\\n  //Unpack exponent\\n  float ebias = e + 127.0;\\n  c[0] = floor(ebias / 2.0);\\n  ebias -= c[0] * 2.0;\\n  c[1] += floor(ebias) * 128.0;\\n\\n  //Unpack sign bit\\n  c[0] += 128.0 * step(0.0, -v);\\n\\n  //Scale back to range\\n  return c / 255.0;\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform float pickId;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], worldPosition)) discard;\\n\\n  gl_FragColor = vec4(pickId/255.0, packFloat(pixelArcLength).xyz);\\n}\"]),l=[{name:\"position\",type:\"vec3\"},{name:\"nextPosition\",type:\"vec3\"},{name:\"arcLength\",type:\"float\"},{name:\"lineWidth\",type:\"float\"},{name:\"color\",type:\"vec4\"}];e.createShader=function(t){return i(t,a,o,null,l)},e.createPickShader=function(t){return i(t,a,s,null,l)}},6086:function(t,e,r){\"use strict\";t.exports=function(t){var e=t.gl||t.scene&&t.scene.gl,r=f(e);r.attributes.position.location=0,r.attributes.nextPosition.location=1,r.attributes.arcLength.location=2,r.attributes.lineWidth.location=3,r.attributes.color.location=4;var o=h(e);o.attributes.position.location=0,o.attributes.nextPosition.location=1,o.attributes.arcLength.location=2,o.attributes.lineWidth.location=3,o.attributes.color.location=4;for(var s=n(e),l=i(e,[{buffer:s,size:3,offset:0,stride:48},{buffer:s,size:3,offset:12,stride:48},{buffer:s,size:1,offset:24,stride:48},{buffer:s,size:1,offset:28,stride:48},{buffer:s,size:4,offset:32,stride:48}]),c=u(new Array(1024),[256,1,4]),p=0;p<1024;++p)c.data[p]=255;var d=a(e,c);d.wrap=e.REPEAT;var v=new y(e,r,o,s,l,d);return v.update(t),v};var n=r(5827),i=r(2944),a=r(8931),o=new Uint8Array(4),s=new Float32Array(o.buffer),l=r(5070),u=r(5050),c=r(248),f=c.createShader,h=c.createPickShader,p=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function d(t,e){for(var r=0,n=0;n<3;++n){var i=t[n]-e[n];r+=i*i}return Math.sqrt(r)}function v(t){for(var e=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],r=0;r<3;++r)e[0][r]=Math.max(t[0][r],e[0][r]),e[1][r]=Math.min(t[1][r],e[1][r]);return e}function g(t,e,r,n){this.arcLength=t,this.position=e,this.index=r,this.dataCoordinate=n}function y(t,e,r,n,i,a){this.gl=t,this.shader=e,this.pickShader=r,this.buffer=n,this.vao=i,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.points=[],this.arcLength=[],this.vertexCount=0,this.bounds=[[0,0,0],[0,0,0]],this.pickId=0,this.lineWidth=1,this.texture=a,this.dashScale=1,this.opacity=1,this.hasAlpha=!1,this.dirty=!0,this.pixelRatio=1}var m=y.prototype;m.isTransparent=function(){return this.hasAlpha},m.isOpaque=function(){return!this.hasAlpha},m.pickSlots=1,m.setPickBase=function(t){this.pickId=t},m.drawTransparent=m.draw=function(t){if(this.vertexCount){var e=this.gl,r=this.shader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,clipBounds:v(this.clipBounds),dashTexture:this.texture.bind(),dashScale:this.dashScale/this.arcLength[this.arcLength.length-1],opacity:this.opacity,screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},m.drawPick=function(t){if(this.vertexCount){var e=this.gl,r=this.pickShader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,pickId:this.pickId,clipBounds:v(this.clipBounds),screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},m.update=function(t){var e,r;this.dirty=!0;var n=!!t.connectGaps;\"dashScale\"in t&&(this.dashScale=t.dashScale),this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=+t.opacity,this.opacity<1&&(this.hasAlpha=!0));var i=[],a=[],o=[],s=0,c=0,f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],h=t.position||t.positions;if(h){var p=t.color||t.colors||[0,0,0,1],v=t.lineWidth||1,g=!1;t:for(e=1;e<h.length;++e){var y,m,x,b=h[e-1],_=h[e];for(a.push(s),o.push(b.slice()),r=0;r<3;++r){if(isNaN(b[r])||isNaN(_[r])||!isFinite(b[r])||!isFinite(_[r])){if(!n&&i.length>0){for(var w=0;w<24;++w)i.push(i[i.length-12]);c+=2,g=!0}continue t}f[0][r]=Math.min(f[0][r],b[r],_[r]),f[1][r]=Math.max(f[1][r],b[r],_[r])}Array.isArray(p[0])?(y=p.length>e-1?p[e-1]:p.length>0?p[p.length-1]:[0,0,0,1],m=p.length>e?p[e]:p.length>0?p[p.length-1]:[0,0,0,1]):y=m=p,3===y.length&&(y=[y[0],y[1],y[2],1]),3===m.length&&(m=[m[0],m[1],m[2],1]),!this.hasAlpha&&y[3]<1&&(this.hasAlpha=!0),x=Array.isArray(v)?v.length>e-1?v[e-1]:v.length>0?v[v.length-1]:[0,0,0,1]:v;var T=s;if(s+=d(b,_),g){for(r=0;r<2;++r)i.push(b[0],b[1],b[2],_[0],_[1],_[2],T,x,y[0],y[1],y[2],y[3]);c+=2,g=!1}i.push(b[0],b[1],b[2],_[0],_[1],_[2],T,x,y[0],y[1],y[2],y[3],b[0],b[1],b[2],_[0],_[1],_[2],T,-x,y[0],y[1],y[2],y[3],_[0],_[1],_[2],b[0],b[1],b[2],s,-x,m[0],m[1],m[2],m[3],_[0],_[1],_[2],b[0],b[1],b[2],s,x,m[0],m[1],m[2],m[3]),c+=4}}if(this.buffer.update(i),a.push(s),o.push(h[h.length-1].slice()),this.bounds=f,this.vertexCount=c,this.points=o,this.arcLength=a,\"dashes\"in t){var k=t.dashes.slice();for(k.unshift(0),e=1;e<k.length;++e)k[e]=k[e-1]+k[e];var A=u(new Array(1024),[256,1,4]);for(e=0;e<256;++e){for(r=0;r<4;++r)A.set(e,0,r,0);1&l.le(k,k[k.length-1]*e/255)?A.set(e,0,0,0):A.set(e,0,0,255)}this.texture.setPixels(A)}},m.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()},m.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=function(t,e,r,n){return o[0]=0,o[1]=r,o[2]=e,o[3]=t,s[0]}(t.value[0],t.value[1],t.value[2]),r=l.le(this.arcLength,e);if(r<0)return null;if(r===this.arcLength.length-1)return new g(this.arcLength[this.arcLength.length-1],this.points[this.points.length-1].slice(),r);for(var n=this.points[r],i=this.points[Math.min(r+1,this.points.length-1)],a=(e-this.arcLength[r])/(this.arcLength[r+1]-this.arcLength[r]),u=1-a,c=[0,0,0],f=0;f<3;++f)c[f]=u*n[f]+a*i[f];var h=Math.min(a<.5?r:r+1,this.points.length-1);return new g(e,c,h,this.points[h])}},7332:function(t){t.exports=function(t){var e=new Float32Array(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e}},9823:function(t){t.exports=function(){var t=new Float32Array(16);return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},7787:function(t){t.exports=function(t){var e=t[0],r=t[1],n=t[2],i=t[3],a=t[4],o=t[5],s=t[6],l=t[7],u=t[8],c=t[9],f=t[10],h=t[11],p=t[12],d=t[13],v=t[14],g=t[15];return(e*o-r*a)*(f*g-h*v)-(e*s-n*a)*(c*g-h*d)+(e*l-i*a)*(c*v-f*d)+(r*s-n*o)*(u*g-h*p)-(r*l-i*o)*(u*v-f*p)+(n*l-i*s)*(u*d-c*p)}},5950:function(t){t.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=r+r,s=n+n,l=i+i,u=r*o,c=n*o,f=n*s,h=i*o,p=i*s,d=i*l,v=a*o,g=a*s,y=a*l;return t[0]=1-f-d,t[1]=c+y,t[2]=h-g,t[3]=0,t[4]=c-y,t[5]=1-u-d,t[6]=p+v,t[7]=0,t[8]=h+g,t[9]=p-v,t[10]=1-u-f,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},7280:function(t){t.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=n+n,l=i+i,u=a+a,c=n*s,f=n*l,h=n*u,p=i*l,d=i*u,v=a*u,g=o*s,y=o*l,m=o*u;return t[0]=1-(p+v),t[1]=f+m,t[2]=h-y,t[3]=0,t[4]=f-m,t[5]=1-(c+v),t[6]=d+g,t[7]=0,t[8]=h+y,t[9]=d-g,t[10]=1-(c+p),t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t}},9947:function(t){t.exports=function(t){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},7437:function(t){t.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],u=e[7],c=e[8],f=e[9],h=e[10],p=e[11],d=e[12],v=e[13],g=e[14],y=e[15],m=r*s-n*o,x=r*l-i*o,b=r*u-a*o,_=n*l-i*s,w=n*u-a*s,T=i*u-a*l,k=c*v-f*d,A=c*g-h*d,M=c*y-p*d,S=f*g-h*v,E=f*y-p*v,L=h*y-p*g,C=m*L-x*E+b*S+_*M-w*A+T*k;return C?(C=1/C,t[0]=(s*L-l*E+u*S)*C,t[1]=(i*E-n*L-a*S)*C,t[2]=(v*T-g*w+y*_)*C,t[3]=(h*w-f*T-p*_)*C,t[4]=(l*M-o*L-u*A)*C,t[5]=(r*L-i*M+a*A)*C,t[6]=(g*b-d*T-y*x)*C,t[7]=(c*T-h*b+p*x)*C,t[8]=(o*E-s*M+u*k)*C,t[9]=(n*M-r*E-a*k)*C,t[10]=(d*w-v*b+y*m)*C,t[11]=(f*b-c*w-p*m)*C,t[12]=(s*A-o*S-l*k)*C,t[13]=(r*S-n*A+i*k)*C,t[14]=(v*x-d*_-g*m)*C,t[15]=(c*_-f*x+h*m)*C,t):null}},3012:function(t,e,r){var n=r(9947);t.exports=function(t,e,r,i){var a,o,s,l,u,c,f,h,p,d,v=e[0],g=e[1],y=e[2],m=i[0],x=i[1],b=i[2],_=r[0],w=r[1],T=r[2];return Math.abs(v-_)<1e-6&&Math.abs(g-w)<1e-6&&Math.abs(y-T)<1e-6?n(t):(f=v-_,h=g-w,p=y-T,a=x*(p*=d=1/Math.sqrt(f*f+h*h+p*p))-b*(h*=d),o=b*(f*=d)-m*p,s=m*h-x*f,(d=Math.sqrt(a*a+o*o+s*s))?(a*=d=1/d,o*=d,s*=d):(a=0,o=0,s=0),l=h*s-p*o,u=p*a-f*s,c=f*o-h*a,(d=Math.sqrt(l*l+u*u+c*c))?(l*=d=1/d,u*=d,c*=d):(l=0,u=0,c=0),t[0]=a,t[1]=l,t[2]=f,t[3]=0,t[4]=o,t[5]=u,t[6]=h,t[7]=0,t[8]=s,t[9]=c,t[10]=p,t[11]=0,t[12]=-(a*v+o*g+s*y),t[13]=-(l*v+u*g+c*y),t[14]=-(f*v+h*g+p*y),t[15]=1,t)}},104:function(t){t.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],u=e[6],c=e[7],f=e[8],h=e[9],p=e[10],d=e[11],v=e[12],g=e[13],y=e[14],m=e[15],x=r[0],b=r[1],_=r[2],w=r[3];return t[0]=x*n+b*s+_*f+w*v,t[1]=x*i+b*l+_*h+w*g,t[2]=x*a+b*u+_*p+w*y,t[3]=x*o+b*c+_*d+w*m,x=r[4],b=r[5],_=r[6],w=r[7],t[4]=x*n+b*s+_*f+w*v,t[5]=x*i+b*l+_*h+w*g,t[6]=x*a+b*u+_*p+w*y,t[7]=x*o+b*c+_*d+w*m,x=r[8],b=r[9],_=r[10],w=r[11],t[8]=x*n+b*s+_*f+w*v,t[9]=x*i+b*l+_*h+w*g,t[10]=x*a+b*u+_*p+w*y,t[11]=x*o+b*c+_*d+w*m,x=r[12],b=r[13],_=r[14],w=r[15],t[12]=x*n+b*s+_*f+w*v,t[13]=x*i+b*l+_*h+w*g,t[14]=x*a+b*u+_*p+w*y,t[15]=x*o+b*c+_*d+w*m,t}},5268:function(t){t.exports=function(t,e,r,n,i,a,o){var s=1/(e-r),l=1/(n-i),u=1/(a-o);return t[0]=-2*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*l,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*u,t[11]=0,t[12]=(e+r)*s,t[13]=(i+n)*l,t[14]=(o+a)*u,t[15]=1,t}},1120:function(t){t.exports=function(t,e,r,n,i){var a=1/Math.tan(e/2),o=1/(n-i);return t[0]=a/r,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=a,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=(i+n)*o,t[11]=-1,t[12]=0,t[13]=0,t[14]=2*i*n*o,t[15]=0,t}},4422:function(t){t.exports=function(t,e,r,n){var i,a,o,s,l,u,c,f,h,p,d,v,g,y,m,x,b,_,w,T,k,A,M,S,E=n[0],L=n[1],C=n[2],P=Math.sqrt(E*E+L*L+C*C);return Math.abs(P)<1e-6?null:(E*=P=1/P,L*=P,C*=P,i=Math.sin(r),o=1-(a=Math.cos(r)),s=e[0],l=e[1],u=e[2],c=e[3],f=e[4],h=e[5],p=e[6],d=e[7],v=e[8],g=e[9],y=e[10],m=e[11],x=E*E*o+a,b=L*E*o+C*i,_=C*E*o-L*i,w=E*L*o-C*i,T=L*L*o+a,k=C*L*o+E*i,A=E*C*o+L*i,M=L*C*o-E*i,S=C*C*o+a,t[0]=s*x+f*b+v*_,t[1]=l*x+h*b+g*_,t[2]=u*x+p*b+y*_,t[3]=c*x+d*b+m*_,t[4]=s*w+f*T+v*k,t[5]=l*w+h*T+g*k,t[6]=u*w+p*T+y*k,t[7]=c*w+d*T+m*k,t[8]=s*A+f*M+v*S,t[9]=l*A+h*M+g*S,t[10]=u*A+p*M+y*S,t[11]=c*A+d*M+m*S,e!==t&&(t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]),t)}},6109:function(t){t.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[4],o=e[5],s=e[6],l=e[7],u=e[8],c=e[9],f=e[10],h=e[11];return e!==t&&(t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]),t[4]=a*i+u*n,t[5]=o*i+c*n,t[6]=s*i+f*n,t[7]=l*i+h*n,t[8]=u*i-a*n,t[9]=c*i-o*n,t[10]=f*i-s*n,t[11]=h*i-l*n,t}},7115:function(t){t.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],u=e[8],c=e[9],f=e[10],h=e[11];return e!==t&&(t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]),t[0]=a*i-u*n,t[1]=o*i-c*n,t[2]=s*i-f*n,t[3]=l*i-h*n,t[8]=a*n+u*i,t[9]=o*n+c*i,t[10]=s*n+f*i,t[11]=l*n+h*i,t}},5240:function(t){t.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],u=e[4],c=e[5],f=e[6],h=e[7];return e!==t&&(t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]),t[0]=a*i+u*n,t[1]=o*i+c*n,t[2]=s*i+f*n,t[3]=l*i+h*n,t[4]=u*i-a*n,t[5]=c*i-o*n,t[6]=f*i-s*n,t[7]=h*i-l*n,t}},3668:function(t){t.exports=function(t,e,r){var n=r[0],i=r[1],a=r[2];return t[0]=e[0]*n,t[1]=e[1]*n,t[2]=e[2]*n,t[3]=e[3]*n,t[4]=e[4]*i,t[5]=e[5]*i,t[6]=e[6]*i,t[7]=e[7]*i,t[8]=e[8]*a,t[9]=e[9]*a,t[10]=e[10]*a,t[11]=e[11]*a,t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t}},998:function(t){t.exports=function(t,e,r){var n,i,a,o,s,l,u,c,f,h,p,d,v=r[0],g=r[1],y=r[2];return e===t?(t[12]=e[0]*v+e[4]*g+e[8]*y+e[12],t[13]=e[1]*v+e[5]*g+e[9]*y+e[13],t[14]=e[2]*v+e[6]*g+e[10]*y+e[14],t[15]=e[3]*v+e[7]*g+e[11]*y+e[15]):(n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],u=e[6],c=e[7],f=e[8],h=e[9],p=e[10],d=e[11],t[0]=n,t[1]=i,t[2]=a,t[3]=o,t[4]=s,t[5]=l,t[6]=u,t[7]=c,t[8]=f,t[9]=h,t[10]=p,t[11]=d,t[12]=n*v+s*g+f*y+e[12],t[13]=i*v+l*g+h*y+e[13],t[14]=a*v+u*g+p*y+e[14],t[15]=o*v+c*g+d*y+e[15]),t}},2142:function(t){t.exports=function(t,e){if(t===e){var r=e[1],n=e[2],i=e[3],a=e[6],o=e[7],s=e[11];t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=r,t[6]=e[9],t[7]=e[13],t[8]=n,t[9]=a,t[11]=e[14],t[12]=i,t[13]=o,t[14]=s}else t[0]=e[0],t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=e[1],t[5]=e[5],t[6]=e[9],t[7]=e[13],t[8]=e[2],t[9]=e[6],t[10]=e[10],t[11]=e[14],t[12]=e[3],t[13]=e[7],t[14]=e[11],t[15]=e[15];return t}},4340:function(t,e,r){\"use strict\";var n=r(957),i=r(7309);function a(t,e){for(var r=[0,0,0,0],n=0;n<4;++n)for(var i=0;i<4;++i)r[i]+=t[4*n+i]*e[n];return r}function o(t,e,r,n,i){for(var o=a(n,a(r,a(e,[t[0],t[1],t[2],1]))),s=0;s<3;++s)o[s]/=o[3];return[.5*i[0]*(1+o[0]),.5*i[1]*(1-o[1])]}function s(t,e){for(var r=[0,0,0],n=0;n<t.length;++n)for(var i=t[n],a=e[n],o=0;o<3;++o)r[o]+=a*i[o];return r}t.exports=function(t,e,r,a,l,u){if(1===t.length)return[0,t[0].slice()];for(var c=new Array(t.length),f=0;f<t.length;++f)c[f]=o(t[f],r,a,l,u);var h=0,p=1/0;for(f=0;f<c.length;++f){for(var d=0,v=0;v<2;++v)d+=Math.pow(c[f][v]-e[v],2);d<p&&(p=d,h=f)}var g=function(t,e){if(2===t.length){for(var r=0,a=0,o=0;o<2;++o)r+=Math.pow(e[o]-t[0][o],2),a+=Math.pow(e[o]-t[1][o],2);return(r=Math.sqrt(r))+(a=Math.sqrt(a))<1e-6?[1,0]:[a/(r+a),r/(a+r)]}if(3===t.length){var s=[0,0];return i(t[0],t[1],t[2],e,s),n(t,s)}return[]}(c,e),y=0;for(f=0;f<3;++f){if(g[f]<-.001||g[f]>1.0001)return null;y+=g[f]}return Math.abs(y-1)>.001?null:[h,s(t,g),g]}},2056:function(t,e,r){var n=r(6832),i=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, normal;\\nattribute vec4 color;\\nattribute vec2 uv;\\n\\nuniform mat4 model\\n           , view\\n           , projection\\n           , inverseModel;\\nuniform vec3 eyePosition\\n           , lightPosition;\\n\\nvarying vec3 f_normal\\n           , f_lightDirection\\n           , f_eyeDirection\\n           , f_data;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvec4 project(vec3 p) {\\n  return projection * view * model * vec4(p, 1.0);\\n}\\n\\nvoid main() {\\n  gl_Position      = project(position);\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * vec4(position , 1.0);\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  f_normal  = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n  f_color          = color;\\n  f_data           = position;\\n  f_uv             = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness,\\n  float fresnel) {\\n\\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n  //Half angle vector\\n  vec3 H = normalize(lightDirection + viewDirection);\\n\\n  //Geometric term\\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\\n  float VdotH = max(dot(viewDirection, H), 0.000001);\\n  float LdotH = max(dot(lightDirection, H), 0.000001);\\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n  float G = min(1.0, min(G1, G2));\\n  \\n  //Distribution term\\n  float D = beckmannDistribution(NdotH, roughness);\\n\\n  //Fresnel term\\n  float F = pow(1.0 - VdotN, fresnel);\\n\\n  //Multiply terms and done\\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\n//#pragma glslify: beckmann = require(glsl-specular-beckmann) // used in gl-surface3d\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness\\n            , fresnel\\n            , kambient\\n            , kdiffuse\\n            , kspecular;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal\\n           , f_lightDirection\\n           , f_eyeDirection\\n           , f_data;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (f_color.a == 0.0 ||\\n    outOfRange(clipBounds[0], clipBounds[1], f_data)\\n  ) discard;\\n\\n  vec3 N = normalize(f_normal);\\n  vec3 L = normalize(f_lightDirection);\\n  vec3 V = normalize(f_eyeDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n  //float specular = max(0.0, beckmann(L, V, N, roughness)); // used in gl-surface3d\\n\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  vec4 surfaceColor = vec4(f_color.rgb, 1.0) * texture2D(texture, f_uv);\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = litColor * f_color.a;\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 uv;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  gl_Position = projection * view * model * vec4(position, 1.0);\\n  f_color = color;\\n  f_data  = position;\\n  f_uv    = uv;\\n}\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform sampler2D texture;\\nuniform float opacity;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_data)) discard;\\n\\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\\n}\"]),l=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 uv;\\nattribute float pointSize;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0.0, 0.0 ,0.0 ,0.0);\\n  } else {\\n    gl_Position = projection * view * model * vec4(position, 1.0);\\n  }\\n  gl_PointSize = pointSize;\\n  f_color = color;\\n  f_uv = uv;\\n}\"]),u=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D texture;\\nuniform float opacity;\\n\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  vec2 pointR = gl_PointCoord.xy - vec2(0.5, 0.5);\\n  if(dot(pointR, pointR) > 0.25) {\\n    discard;\\n  }\\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\\n}\"]),c=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  gl_Position = projection * view * model * vec4(position, 1.0);\\n  f_id        = id;\\n  f_position  = position;\\n}\"]),f=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3  clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n  gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]),h=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3  position;\\nattribute float pointSize;\\nattribute vec4  id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0.0, 0.0, 0.0, 0.0);\\n  } else {\\n    gl_Position  = projection * view * model * vec4(position, 1.0);\\n    gl_PointSize = pointSize;\\n  }\\n  f_id         = id;\\n  f_position   = position;\\n}\"]),p=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\n\\nvoid main() {\\n  gl_Position = projection * view * model * vec4(position, 1.0);\\n}\"]),d=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec3 contourColor;\\n\\nvoid main() {\\n  gl_FragColor = vec4(contourColor, 1.0);\\n}\\n\"]);e.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},e.wireShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},e.pointShader={vertex:l,fragment:u,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"pointSize\",type:\"float\"}]},e.pickShader={vertex:c,fragment:f,attributes:[{name:\"position\",type:\"vec3\"},{name:\"id\",type:\"vec4\"}]},e.pointPickShader={vertex:h,fragment:f,attributes:[{name:\"position\",type:\"vec3\"},{name:\"pointSize\",type:\"float\"},{name:\"id\",type:\"vec4\"}]},e.contourShader={vertex:p,fragment:d,attributes:[{name:\"position\",type:\"vec3\"}]}},8116:function(t,e,r){\"use strict\";var n=r(5158),i=r(5827),a=r(2944),o=r(8931),s=r(115),l=r(104),u=r(7437),c=r(5050),f=r(9156),h=r(7212),p=r(5306),d=r(2056),v=r(4340),g=d.meshShader,y=d.wireShader,m=d.pointShader,x=d.pickShader,b=d.pointPickShader,_=d.contourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function T(t,e,r,n,i,a,o,s,l,u,c,f,h,p,d,v,g,y,m,x,b,_,T,k,A,M,S){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.lineShader=n,this.pointShader=i,this.pickShader=a,this.pointPickShader=o,this.contourShader=s,this.trianglePositions=l,this.triangleColors=c,this.triangleNormals=h,this.triangleUVs=f,this.triangleIds=u,this.triangleVAO=p,this.triangleCount=0,this.lineWidth=1,this.edgePositions=d,this.edgeColors=g,this.edgeUVs=y,this.edgeIds=v,this.edgeVAO=m,this.edgeCount=0,this.pointPositions=x,this.pointColors=_,this.pointUVs=T,this.pointSizes=k,this.pointIds=b,this.pointVAO=A,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=M,this.contourVAO=S,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!0,this.pickVertex=!0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.hasAlpha=!1,this.opacityscale=!1,this._model=w,this._view=w,this._projection=w,this._resolution=[1,1]}var k=T.prototype;function A(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;r<e.length;++r){if(e.length<2)return 1;if(e[r][0]===t)return e[r][1];if(e[r][0]>t&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}function M(t){var e=n(t,m.vertex,m.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.pointSize.location=4,e}function S(t){var e=n(t,x.vertex,x.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e}function E(t){var e=n(t,b.vertex,b.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e.attributes.pointSize.location=4,e}function L(t){var e=n(t,_.vertex,_.fragment);return e.attributes.position.location=0,e}k.isOpaque=function(){return!this.hasAlpha},k.isTransparent=function(){return this.hasAlpha},k.pickSlots=1,k.setPickBase=function(t){this.pickId=t},k.highlight=function(t){if(t&&this.contourEnable){for(var e=h(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,a=r.length,o=p.mallocFloat32(6*a),s=0,l=0;l<a;++l)for(var u=r[l],c=0;c<2;++c){var f=u[0];2===u.length&&(f=u[c]);for(var d=n[f][0],v=n[f][1],g=i[f],y=1-g,m=this.positions[d],x=this.positions[v],b=0;b<3;++b)o[s++]=g*m[b]+y*x[b]}this.contourCount=s/3|0,this.contourPositions.update(o.subarray(0,s)),p.free(o)}else this.contourCount=0},k.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,\"contourEnable\"in t&&(this.contourEnable=t.contourEnable),\"contourColor\"in t&&(this.contourColor=t.contourColor),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"lightPosition\"in t&&(this.lightPosition=t.lightPosition),this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=t.opacity,this.opacity<1&&(this.hasAlpha=!0)),\"opacityscale\"in t&&(this.opacityscale=t.opacityscale,this.hasAlpha=!0),\"ambient\"in t&&(this.ambientLight=t.ambient),\"diffuse\"in t&&(this.diffuseLight=t.diffuse),\"specular\"in t&&(this.specularLight=t.specular),\"roughness\"in t&&(this.roughness=t.roughness),\"fresnel\"in t&&(this.fresnel=t.fresnel),t.texture?(this.texture.dispose(),this.texture=o(e,t.texture)):t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t,e){for(var r=f({colormap:t,nshades:256,format:\"rgba\"}),n=new Uint8Array(1024),i=0;i<256;++i){for(var a=r[i],o=0;o<3;++o)n[4*i+o]=a[o];n[4*i+3]=e?255*A(i/255,e):255*a[3]}return c(n,[256,256,4],[4,0,1])}(t.colormap,this.opacityscale)),this.texture.generateMipmap());var r=t.cells,n=t.positions;if(n&&r){var i=[],a=[],l=[],u=[],h=[],p=[],d=[],v=[],g=[],y=[],m=[],x=[],b=[],_=[];this.cells=r,this.positions=n;var w=t.vertexNormals,T=t.cellNormals,k=void 0===t.vertexNormalsEpsilon?1e-6:t.vertexNormalsEpsilon,M=void 0===t.faceNormalsEpsilon?1e-6:t.faceNormalsEpsilon;t.useFacetNormals&&!T&&(T=s.faceNormals(r,n,M)),T||w||(w=s.vertexNormals(r,n,k));var S=t.vertexColors,E=t.cellColors,L=t.meshColor||[1,1,1,1],C=t.vertexUVs,P=t.vertexIntensity,O=t.cellUVs,I=t.cellIntensity,D=1/0,z=-1/0;if(!C&&!O)if(P)if(t.vertexIntensityBounds)D=+t.vertexIntensityBounds[0],z=+t.vertexIntensityBounds[1];else for(var R=0;R<P.length;++R){var F=P[R];D=Math.min(D,F),z=Math.max(z,F)}else if(I)if(t.cellIntensityBounds)D=+t.cellIntensityBounds[0],z=+t.cellIntensityBounds[1];else for(R=0;R<I.length;++R)F=I[R],D=Math.min(D,F),z=Math.max(z,F);else for(R=0;R<n.length;++R)F=n[R][2],D=Math.min(D,F),z=Math.max(z,F);this.intensity=P||I||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n),this.pickVertex=!(I||E);var B=t.pointSizes,N=t.pointSize||1;for(this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],R=0;R<n.length;++R)for(var j=n[R],U=0;U<3;++U)!isNaN(j[U])&&isFinite(j[U])&&(this.bounds[0][U]=Math.min(this.bounds[0][U],j[U]),this.bounds[1][U]=Math.max(this.bounds[1][U],j[U]));var V=0,H=0,q=0;t:for(R=0;R<r.length;++R){var G=r[R];switch(G.length){case 1:for(j=n[Y=G[0]],U=0;U<3;++U)if(isNaN(j[U])||!isFinite(j[U]))continue t;y.push(j[0],j[1],j[2]),W=S?S[Y]:E?E[R]:L,this.opacityscale&&P?a.push(W[0],W[1],W[2],this.opacity*A((P[Y]-D)/(z-D),this.opacityscale)):3===W.length?m.push(W[0],W[1],W[2],this.opacity):(m.push(W[0],W[1],W[2],W[3]*this.opacity),W[3]<1&&(this.hasAlpha=!0)),X=C?C[Y]:P?[(P[Y]-D)/(z-D),0]:O?O[R]:I?[(I[R]-D)/(z-D),0]:[(j[2]-D)/(z-D),0],x.push(X[0],X[1]),B?b.push(B[Y]):b.push(N),_.push(R),q+=1;break;case 2:for(U=0;U<2;++U){j=n[Y=G[U]];for(var Z=0;Z<3;++Z)if(isNaN(j[Z])||!isFinite(j[Z]))continue t}for(U=0;U<2;++U)j=n[Y=G[U]],p.push(j[0],j[1],j[2]),W=S?S[Y]:E?E[R]:L,this.opacityscale&&P?a.push(W[0],W[1],W[2],this.opacity*A((P[Y]-D)/(z-D),this.opacityscale)):3===W.length?d.push(W[0],W[1],W[2],this.opacity):(d.push(W[0],W[1],W[2],W[3]*this.opacity),W[3]<1&&(this.hasAlpha=!0)),X=C?C[Y]:P?[(P[Y]-D)/(z-D),0]:O?O[R]:I?[(I[R]-D)/(z-D),0]:[(j[2]-D)/(z-D),0],v.push(X[0],X[1]),g.push(R);H+=1;break;case 3:for(U=0;U<3;++U)for(j=n[Y=G[U]],Z=0;Z<3;++Z)if(isNaN(j[Z])||!isFinite(j[Z]))continue t;for(U=0;U<3;++U){var Y,W,X,J;j=n[Y=G[2-U]],i.push(j[0],j[1],j[2]),(W=S?S[Y]:E?E[R]:L)?this.opacityscale&&P?a.push(W[0],W[1],W[2],this.opacity*A((P[Y]-D)/(z-D),this.opacityscale)):3===W.length?a.push(W[0],W[1],W[2],this.opacity):(a.push(W[0],W[1],W[2],W[3]*this.opacity),W[3]<1&&(this.hasAlpha=!0)):a.push(.5,.5,.5,1),X=C?C[Y]:P?[(P[Y]-D)/(z-D),0]:O?O[R]:I?[(I[R]-D)/(z-D),0]:[(j[2]-D)/(z-D),0],u.push(X[0],X[1]),J=w?w[Y]:T[R],l.push(J[0],J[1],J[2]),h.push(R)}V+=1}}this.pointCount=q,this.edgeCount=H,this.triangleCount=V,this.pointPositions.update(y),this.pointColors.update(m),this.pointUVs.update(x),this.pointSizes.update(b),this.pointIds.update(new Uint32Array(_)),this.edgePositions.update(p),this.edgeColors.update(d),this.edgeUVs.update(v),this.edgeIds.update(new Uint32Array(g)),this.trianglePositions.update(i),this.triangleColors.update(a),this.triangleUVs.update(u),this.triangleNormals.update(l),this.triangleIds.update(new Uint32Array(h))}},k.drawTransparent=k.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var s={model:r,view:n,projection:i,inverseModel:w.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],contourColor:this.contourColor,texture:0};s.inverseModel=u(s.inverseModel,s.model),e.disable(e.CULL_FACE),this.texture.bind(0);var c=new Array(16);for(l(c,s.view,s.model),l(c,s.projection,c),u(c,c),o=0;o<3;++o)s.eyePosition[o]=c[12+o]/c[15];var f,h=c[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*c[4*o+3];for(o=0;o<3;++o){for(var p=c[12+o],d=0;d<3;++d)p+=c[4*d+o]*this.lightPosition[d];s.lightPosition[o]=p/h}this.triangleCount>0&&((f=this.triShader).bind(),f.uniforms=s,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&this.lineWidth>0&&((f=this.lineShader).bind(),f.uniforms=s,this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0&&((f=this.pointShader).bind(),f.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind()),this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0&&((f=this.contourShader).bind(),f.uniforms=s,this.contourVAO.bind(),e.drawArrays(e.LINES,0,this.contourCount),this.contourVAO.unbind())},k.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s,l={model:r,view:n,projection:i,clipBounds:a,pickId:this.pickId/255};(s=this.pickShader).bind(),s.uniforms=l,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0&&((s=this.pointPickShader).bind(),s.uniforms=l,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind())},k.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;for(var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions,i=new Array(r.length),a=0;a<r.length;++a)i[a]=n[r[a]];var o=t.coord[0],s=t.coord[1];if(!this.pickVertex){var l=this.positions[r[0]],u=this.positions[r[1]],c=this.positions[r[2]],f=[(l[0]+u[0]+c[0])/3,(l[1]+u[1]+c[1])/3,(l[2]+u[2]+c[2])/3];return{_cellCenter:!0,position:[o,s],index:e,cell:r,cellId:e,intensity:this.intensity[e],dataCoordinate:f}}var h=v(i,[o*this.pixelRatio,this._resolution[1]-s*this.pixelRatio],this._model,this._view,this._projection,this._resolution);if(!h)return null;var p=h[2],d=0;for(a=0;a<r.length;++a)d+=p[a]*this.intensity[r[a]];return{position:h[1],index:r[h[0]],cell:r,cellId:e,intensity:d,dataCoordinate:this.positions[r[h[0]]]}},k.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.lineShader.dispose(),this.pointShader.dispose(),this.pickShader.dispose(),this.pointPickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleNormals.dispose(),this.triangleIds.dispose(),this.edgeVAO.dispose(),this.edgePositions.dispose(),this.edgeColors.dispose(),this.edgeUVs.dispose(),this.edgeIds.dispose(),this.pointVAO.dispose(),this.pointPositions.dispose(),this.pointColors.dispose(),this.pointUVs.dispose(),this.pointSizes.dispose(),this.pointIds.dispose(),this.contourVAO.dispose(),this.contourPositions.dispose(),this.contourShader.dispose()},t.exports=function(t,e){if(1===arguments.length&&(t=(e=t).gl),!(t.getExtension(\"OES_standard_derivatives\")||t.getExtension(\"MOZ_OES_standard_derivatives\")||t.getExtension(\"WEBKIT_OES_standard_derivatives\")))throw new Error(\"derivatives not supported\");var r=function(t){var e=n(t,g.vertex,g.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.normal.location=4,e}(t),s=function(t){var e=n(t,y.vertex,y.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e}(t),l=M(t),u=S(t),f=E(t),h=L(t),p=o(t,c(new Uint8Array([255,255,255,255]),[1,1,4]));p.generateMipmap(),p.minFilter=t.LINEAR_MIPMAP_LINEAR,p.magFilter=t.LINEAR;var d=i(t),v=i(t),m=i(t),x=i(t),b=i(t),_=a(t,[{buffer:d,type:t.FLOAT,size:3},{buffer:b,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:v,type:t.FLOAT,size:4},{buffer:m,type:t.FLOAT,size:2},{buffer:x,type:t.FLOAT,size:3}]),w=i(t),k=i(t),A=i(t),C=i(t),P=a(t,[{buffer:w,type:t.FLOAT,size:3},{buffer:C,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:k,type:t.FLOAT,size:4},{buffer:A,type:t.FLOAT,size:2}]),O=i(t),I=i(t),D=i(t),z=i(t),R=i(t),F=a(t,[{buffer:O,type:t.FLOAT,size:3},{buffer:R,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:I,type:t.FLOAT,size:4},{buffer:D,type:t.FLOAT,size:2},{buffer:z,type:t.FLOAT,size:1}]),B=i(t),N=new T(t,p,r,s,l,u,f,h,d,b,v,m,x,_,w,C,k,A,P,O,R,I,D,z,F,B,a(t,[{buffer:B,type:t.FLOAT,size:3}]));return N.update(e),N}},4554:function(t,e,r){\"use strict\";t.exports=function(t){var e=t.gl;return new o(t,n(e,[0,0,0,1,1,0,1,1]),i(e,a.boxVert,a.lineFrag))};var n=r(5827),i=r(5158),a=r(2709);function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,u=o.prototype;u.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},u.drawBox=(s=[0,0],l=[0,0],function(t,e,r,n,i){var a=this.plot,o=this.shader,u=a.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,o.uniforms.lo=s,o.uniforms.hi=l,o.uniforms.color=i,u.drawArrays(u.TRIANGLE_STRIP,0,4)}),u.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},3016:function(t,e,r){\"use strict\";t.exports=function(t){var e=t.gl;return new s(t,n(e),i(e,o.gridVert,o.gridFrag),i(e,o.tickVert,o.gridFrag))};var n=r(5827),i=r(5158),a=r(5070),o=r(2709);function s(t,e,r,n){this.plot=t,this.vbo=e,this.shader=r,this.tickShader=n,this.ticks=[[],[]]}function l(t,e){return t-e}var u,c,f,h,p,d=s.prototype;d.draw=(u=[0,0],c=[0,0],f=[0,0],function(){for(var t=this.plot,e=this.vbo,r=this.shader,n=this.ticks,i=t.gl,a=t._tickBounds,o=t.dataBox,s=t.viewBox,l=t.gridLineWidth,h=t.gridLineColor,p=t.gridLineEnable,d=t.pixelRatio,v=0;v<2;++v){var g=a[v],y=a[v+2]-g,m=.5*(o[v+2]+o[v]),x=o[v+2]-o[v];c[v]=2*y/x,u[v]=2*(g-m)/x}r.bind(),e.bind(),r.attributes.dataCoord.pointer(),r.uniforms.dataShift=u,r.uniforms.dataScale=c;var b=0;for(v=0;v<2;++v){f[0]=f[1]=0,f[v]=1,r.uniforms.dataAxis=f,r.uniforms.lineWidth=l[v]/(s[v+2]-s[v])*d,r.uniforms.color=h[v];var _=6*n[v].length;p[v]&&_&&i.drawArrays(i.TRIANGLES,b,_),b+=_}}),d.drawTickMarks=function(){var t=[0,0],e=[0,0],r=[1,0],n=[0,1],i=[0,0],o=[0,0];return function(){for(var s=this.plot,u=this.vbo,c=this.tickShader,f=this.ticks,h=s.gl,p=s._tickBounds,d=s.dataBox,v=s.viewBox,g=s.pixelRatio,y=s.screenBox,m=y[2]-y[0],x=y[3]-y[1],b=v[2]-v[0],_=v[3]-v[1],w=0;w<2;++w){var T=p[w],k=p[w+2]-T,A=.5*(d[w+2]+d[w]),M=d[w+2]-d[w];e[w]=2*k/M,t[w]=2*(T-A)/M}e[0]*=b/m,t[0]*=b/m,e[1]*=_/x,t[1]*=_/x,c.bind(),u.bind(),c.attributes.dataCoord.pointer();var S=c.uniforms;S.dataShift=t,S.dataScale=e;var E=s.tickMarkLength,L=s.tickMarkWidth,C=s.tickMarkColor,P=6*f[0].length,O=Math.min(a.ge(f[0],(d[0]-p[0])/(p[2]-p[0]),l),f[0].length),I=Math.min(a.gt(f[0],(d[2]-p[0])/(p[2]-p[0]),l),f[0].length),D=0+6*O,z=6*Math.max(0,I-O),R=Math.min(a.ge(f[1],(d[1]-p[1])/(p[3]-p[1]),l),f[1].length),F=Math.min(a.gt(f[1],(d[3]-p[1])/(p[3]-p[1]),l),f[1].length),B=P+6*R,N=6*Math.max(0,F-R);i[0]=2*(v[0]-E[1])/m-1,i[1]=(v[3]+v[1])/x-1,o[0]=E[1]*g/m,o[1]=L[1]*g/x,N&&(S.color=C[1],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,B,N)),i[0]=(v[2]+v[0])/m-1,i[1]=2*(v[1]-E[0])/x-1,o[0]=L[0]*g/m,o[1]=E[0]*g/x,z&&(S.color=C[0],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,D,z)),i[0]=2*(v[2]+E[3])/m-1,i[1]=(v[3]+v[1])/x-1,o[0]=E[3]*g/m,o[1]=L[3]*g/x,N&&(S.color=C[3],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,B,N)),i[0]=(v[2]+v[0])/m-1,i[1]=2*(v[3]+E[2])/x-1,o[0]=L[2]*g/m,o[1]=E[2]*g/x,z&&(S.color=C[2],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,D,z))}}(),d.update=(h=[1,1,-1,-1,1,-1],p=[1,-1,1,1,-1,-1],function(t){for(var e=t.ticks,r=t.bounds,n=new Float32Array(18*(e[0].length+e[1].length)),i=(this.plot.zeroLineEnable,0),a=[[],[]],o=0;o<2;++o)for(var s=a[o],l=e[o],u=r[o],c=r[o+2],f=0;f<l.length;++f){var d=(l[f].x-u)/(c-u);s.push(d);for(var v=0;v<6;++v)n[i++]=d,n[i++]=h[v],n[i++]=p[v]}this.ticks=a,this.vbo.update(n)}),d.dispose=function(){this.vbo.dispose(),this.shader.dispose(),this.tickShader.dispose()}},1154:function(t,e,r){\"use strict\";t.exports=function(t){var e=t.gl;return new o(t,n(e,[-1,-1,-1,1,1,-1,1,1]),i(e,a.lineVert,a.lineFrag))};var n=r(5827),i=r(5158),a=r(2709);function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,u=o.prototype;u.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},u.drawLine=(s=[0,0],l=[0,0],function(t,e,r,n,i,a){var o=this.plot,u=this.shader,c=o.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,u.uniforms.start=s,u.uniforms.end=l,u.uniforms.width=i*o.pixelRatio,u.uniforms.color=a,c.drawArrays(c.TRIANGLE_STRIP,0,4)}),u.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},2709:function(t,e,r){\"use strict\";var n=r(6832),i=n([\"precision lowp float;\\n#define GLSLIFY 1\\nuniform vec4 color;\\nvoid main() {\\n  gl_FragColor = vec4(color.xyz * color.w, color.w);\\n}\\n\"]);t.exports={lineVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 coord;\\n\\nuniform vec4 screenBox;\\nuniform vec2 start, end;\\nuniform float width;\\n\\nvec2 perp(vec2 v) {\\n  return vec2(v.y, -v.x);\\n}\\n\\nvec2 screen(vec2 v) {\\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\\n}\\n\\nvoid main() {\\n  vec2 delta = normalize(perp(start - end));\\n  vec2 offset = mix(start, end, 0.5 * (coord.y+1.0));\\n  gl_Position = vec4(screen(offset + 0.5 * width * delta * coord.x), 0, 1);\\n}\\n\"]),lineFrag:i,textVert:n([\"#define GLSLIFY 1\\nattribute vec3 textCoordinate;\\n\\nuniform vec2 dataScale, dataShift, dataAxis, screenOffset, textScale;\\nuniform float angle;\\n\\nvoid main() {\\n  float dataOffset  = textCoordinate.z;\\n  vec2 glyphOffset  = textCoordinate.xy;\\n  mat2 glyphMatrix = mat2(cos(angle), sin(angle), -sin(angle), cos(angle));\\n  vec2 screenCoordinate = dataAxis * (dataScale * dataOffset + dataShift) +\\n    glyphMatrix * glyphOffset * textScale + screenOffset;\\n  gl_Position = vec4(screenCoordinate, 0, 1);\\n}\\n\"]),textFrag:i,gridVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 dataCoord;\\n\\nuniform vec2 dataAxis, dataShift, dataScale;\\nuniform float lineWidth;\\n\\nvoid main() {\\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\\n  pos += 10.0 * dataCoord.y * vec2(dataAxis.y, -dataAxis.x) + dataCoord.z * lineWidth;\\n  gl_Position = vec4(pos, 0, 1);\\n}\\n\"]),gridFrag:i,boxVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 coord;\\n\\nuniform vec4 screenBox;\\nuniform vec2 lo, hi;\\n\\nvec2 screen(vec2 v) {\\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\\n}\\n\\nvoid main() {\\n  gl_Position = vec4(screen(mix(lo, hi, coord)), 0, 1);\\n}\\n\"]),tickVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 dataCoord;\\n\\nuniform vec2 dataAxis, dataShift, dataScale, screenOffset, tickScale;\\n\\nvoid main() {\\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\\n  gl_Position = vec4(pos + tickScale*dataCoord.yz + screenOffset, 0, 1);\\n}\\n\"])}},5613:function(t,e,r){\"use strict\";t.exports=function(t){var e=t.gl;return new l(t,n(e),i(e,s.textVert,s.textFrag))};var n=r(5827),i=r(5158),a=r(6946),o=r(5070),s=r(2709);function l(t,e,r){this.plot=t,this.vbo=e,this.shader=r,this.tickOffset=[[],[]],this.tickX=[[],[]],this.labelOffset=[0,0],this.labelCount=[0,0]}var u,c,f,h,p,d,v=l.prototype;v.drawTicks=(u=[0,0],c=[0,0],f=[0,0],function(t){var e=this.plot,r=this.shader,n=this.tickX[t],i=this.tickOffset[t],a=e.gl,s=e.viewBox,l=e.dataBox,h=e.screenBox,p=e.pixelRatio,d=e.tickEnable,v=e.tickPad,g=e.tickColor,y=e.tickAngle,m=e.labelEnable,x=e.labelPad,b=e.labelColor,_=e.labelAngle,w=this.labelOffset[t],T=this.labelCount[t],k=o.lt(n,l[t]),A=o.le(n,l[t+2]);u[0]=u[1]=0,u[t]=1,c[t]=(s[2+t]+s[t])/(h[2+t]-h[t])-1;var M=2/h[2+(1^t)]-h[1^t];c[1^t]=M*s[1^t]-1,d[t]&&(c[1^t]-=M*p*v[t],k<A&&i[A]>i[k]&&(r.uniforms.dataAxis=u,r.uniforms.screenOffset=c,r.uniforms.color=g[t],r.uniforms.angle=y[t],a.drawArrays(a.TRIANGLES,i[k],i[A]-i[k]))),m[t]&&T&&(c[1^t]-=M*p*x[t],r.uniforms.dataAxis=f,r.uniforms.screenOffset=c,r.uniforms.color=b[t],r.uniforms.angle=_[t],a.drawArrays(a.TRIANGLES,w,T)),c[1^t]=M*s[2+(1^t)]-1,d[t+2]&&(c[1^t]+=M*p*v[t+2],k<A&&i[A]>i[k]&&(r.uniforms.dataAxis=u,r.uniforms.screenOffset=c,r.uniforms.color=g[t+2],r.uniforms.angle=y[t+2],a.drawArrays(a.TRIANGLES,i[k],i[A]-i[k]))),m[t+2]&&T&&(c[1^t]+=M*p*x[t+2],r.uniforms.dataAxis=f,r.uniforms.screenOffset=c,r.uniforms.color=b[t+2],r.uniforms.angle=_[t+2],a.drawArrays(a.TRIANGLES,w,T))}),v.drawTitle=function(){var t=[0,0],e=[0,0];return function(){var r=this.plot,n=this.shader,i=r.gl,a=r.screenBox,o=r.titleCenter,s=r.titleAngle,l=r.titleColor,u=r.pixelRatio;if(this.titleCount){for(var c=0;c<2;++c)e[c]=2*(o[c]*u-a[c])/(a[2+c]-a[c])-1;n.bind(),n.uniforms.dataAxis=t,n.uniforms.screenOffset=e,n.uniforms.angle=s,n.uniforms.color=l,i.drawArrays(i.TRIANGLES,this.titleOffset,this.titleCount)}}}(),v.bind=(h=[0,0],p=[0,0],d=[0,0],function(){var t=this.plot,e=this.shader,r=t._tickBounds,n=t.dataBox,i=t.screenBox,a=t.viewBox;e.bind();for(var o=0;o<2;++o){var s=r[o],l=r[o+2]-s,u=.5*(n[o+2]+n[o]),c=n[o+2]-n[o],f=a[o],v=a[o+2]-f,g=i[o],y=i[o+2]-g;p[o]=2*l/c*v/y,h[o]=2*(s-u)/c*v/y}d[1]=2*t.pixelRatio/(i[3]-i[1]),d[0]=d[1]*(i[3]-i[1])/(i[2]-i[0]),e.uniforms.dataScale=p,e.uniforms.dataShift=h,e.uniforms.textScale=d,this.vbo.bind(),e.attributes.textCoordinate.pointer()}),v.update=function(t){var e,r,n,i,o,s=[],l=t.ticks,u=t.bounds;for(o=0;o<2;++o){var c=[Math.floor(s.length/3)],f=[-1/0],h=l[o];for(e=0;e<h.length;++e){var p=h[e],d=p.x,v=p.text,g=p.font||\"sans-serif\";i=p.fontSize||12;for(var y=1/(u[o+2]-u[o]),m=u[o],x=v.split(\"\\n\"),b=0;b<x.length;b++)for(n=a(g,x[b]).data,r=0;r<n.length;r+=2)s.push(n[r]*i,-n[r+1]*i-b*i*1.2,(d-m)*y);c.push(Math.floor(s.length/3)),f.push(d)}this.tickOffset[o]=c,this.tickX[o]=f}for(o=0;o<2;++o){for(this.labelOffset[o]=Math.floor(s.length/3),n=a(t.labelFont[o],t.labels[o],{textAlign:\"center\"}).data,i=t.labelSize[o],e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.labelCount[o]=Math.floor(s.length/3)-this.labelOffset[o]}for(this.titleOffset=Math.floor(s.length/3),n=a(t.titleFont,t.title).data,i=t.titleSize,e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.titleCount=Math.floor(s.length/3)-this.titleOffset,this.vbo.update(s)},v.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},2117:function(t,e,r){\"use strict\";t.exports=function(t){var e=t.gl,r=new l(e,n(e,[e.drawingBufferWidth,e.drawingBufferHeight]));return r.grid=i(r),r.text=a(r),r.line=o(r),r.box=s(r),r.update(t),r};var n=r(2611),i=r(3016),a=r(5613),o=r(1154),s=r(4554);function l(t,e){this.gl=t,this.pickBuffer=e,this.screenBox=[0,0,t.drawingBufferWidth,t.drawingBufferHeight],this.viewBox=[0,0,0,0],this.dataBox=[-10,-10,10,10],this.gridLineEnable=[!0,!0],this.gridLineWidth=[1,1],this.gridLineColor=[[0,0,0,1],[0,0,0,1]],this.pixelRatio=1,this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickEnable=[!0,!0,!0,!0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[15,15,15,15],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelEnable=[!0,!0,!0,!0],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.titleCenter=[0,0],this.titleEnable=!0,this.titleAngle=0,this.titleColor=[0,0,0,1],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[4,4],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderLineEnable=[!0,!0,!0,!0],this.borderLineWidth=[2,2,2,2],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.grid=null,this.text=null,this.line=null,this.box=null,this.objects=[],this.overlays=[],this._tickBounds=[1/0,1/0,-1/0,-1/0],this.static=!1,this.dirty=!1,this.pickDirty=!1,this.pickDelay=120,this.pickRadius=10,this._pickTimeout=null,this._drawPick=this.drawPick.bind(this),this._depthCounter=0}var u=l.prototype;function c(t){for(var e=t.slice(),r=0;r<e.length;++r)e[r]=e[r].slice();return e}function f(t,e){return t.x-e.x}u.setDirty=function(){this.dirty=this.pickDirty=!0},u.setOverlayDirty=function(){this.dirty=!0},u.nextDepthValue=function(){return this._depthCounter++/65536},u.draw=function(){var t=this.gl,e=this.screenBox,r=this.viewBox,n=this.dataBox,i=this.pixelRatio,a=this.grid,o=this.line,s=this.text,l=this.objects;if(this._depthCounter=0,this.pickDirty&&(this._pickTimeout&&clearTimeout(this._pickTimeout),this.pickDirty=!1,this._pickTimeout=setTimeout(this._drawPick,this.pickDelay)),this.dirty){if(this.dirty=!1,t.bindFramebuffer(t.FRAMEBUFFER,null),t.enable(t.SCISSOR_TEST),t.disable(t.DEPTH_TEST),t.depthFunc(t.LESS),t.depthMask(!1),t.enable(t.BLEND),t.blendEquation(t.FUNC_ADD,t.FUNC_ADD),t.blendFunc(t.ONE,t.ONE_MINUS_SRC_ALPHA),this.borderColor){t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]);var u=this.borderColor;t.clearColor(u[0]*u[3],u[1]*u[3],u[2]*u[3],u[3]),t.clear(t.COLOR_BUFFER_BIT|t.DEPTH_BUFFER_BIT)}t.scissor(r[0],r[1],r[2]-r[0],r[3]-r[1]),t.viewport(r[0],r[1],r[2]-r[0],r[3]-r[1]);var c=this.backgroundColor;t.clearColor(c[0]*c[3],c[1]*c[3],c[2]*c[3],c[3]),t.clear(t.COLOR_BUFFER_BIT),a.draw();var f=this.zeroLineEnable,h=this.zeroLineColor,p=this.zeroLineWidth;if(f[0]||f[1]){o.bind();for(var d=0;d<2;++d)if(f[d]&&n[d]<=0&&n[d+2]>=0){var v=e[d]-n[d]*(e[d+2]-e[d])/(n[d+2]-n[d]);0===d?o.drawLine(v,e[1],v,e[3],p[d],h[d]):o.drawLine(e[0],v,e[2],v,p[d],h[d])}}for(d=0;d<l.length;++d)l[d].draw();t.viewport(e[0],e[1],e[2]-e[0],e[3]-e[1]),t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]),this.grid.drawTickMarks(),o.bind();var g=this.borderLineEnable,y=this.borderLineWidth,m=this.borderLineColor;for(g[1]&&o.drawLine(r[0],r[1]-.5*y[1]*i,r[0],r[3]+.5*y[3]*i,y[1],m[1]),g[0]&&o.drawLine(r[0]-.5*y[0]*i,r[1],r[2]+.5*y[2]*i,r[1],y[0],m[0]),g[3]&&o.drawLine(r[2],r[1]-.5*y[1]*i,r[2],r[3]+.5*y[3]*i,y[3],m[3]),g[2]&&o.drawLine(r[0]-.5*y[0]*i,r[3],r[2]+.5*y[2]*i,r[3],y[2],m[2]),s.bind(),d=0;d<2;++d)s.drawTicks(d);this.titleEnable&&s.drawTitle();var x=this.overlays;for(d=0;d<x.length;++d)x[d].draw();t.disable(t.SCISSOR_TEST),t.disable(t.BLEND),t.depthMask(!0)}},u.drawPick=function(){if(!this.static){var t=this.pickBuffer;this.gl,this._pickTimeout=null,t.begin();for(var e=1,r=this.objects,n=0;n<r.length;++n)e=r[n].drawPick(e);t.end()}},u.pick=function(t,e){if(!this.static){var r=this.pixelRatio,n=this.pickPixelRatio,i=this.viewBox,a=0|Math.round((t-i[0]/r)*n),o=0|Math.round((e-i[1]/r)*n),s=this.pickBuffer.query(a,o,this.pickRadius);if(!s)return null;for(var l=s.id+(s.value[0]<<8)+(s.value[1]<<16)+(s.value[2]<<24),u=this.objects,c=0;c<u.length;++c){var f=u[c].pick(a,o,l);if(f)return f}return null}},u.setScreenBox=function(t){var e=this.screenBox,r=this.pixelRatio;e[0]=0|Math.round(t[0]*r),e[1]=0|Math.round(t[1]*r),e[2]=0|Math.round(t[2]*r),e[3]=0|Math.round(t[3]*r),this.setDirty()},u.setDataBox=function(t){var e=this.dataBox;(e[0]!==t[0]||e[1]!==t[1]||e[2]!==t[2]||e[3]!==t[3])&&(e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],this.setDirty())},u.setViewBox=function(t){var e=this.pixelRatio,r=this.viewBox;r[0]=0|Math.round(t[0]*e),r[1]=0|Math.round(t[1]*e),r[2]=0|Math.round(t[2]*e),r[3]=0|Math.round(t[3]*e);var n=this.pickPixelRatio;this.pickBuffer.shape=[0|Math.round((t[2]-t[0])*n),0|Math.round((t[3]-t[1])*n)],this.setDirty()},u.update=function(t){t=t||{};var e=this.gl;this.pixelRatio=t.pixelRatio||1;var r=this.pixelRatio;this.pickPixelRatio=Math.max(r,1),this.setScreenBox(t.screenBox||[0,0,e.drawingBufferWidth/r,e.drawingBufferHeight/r]),this.screenBox,this.setViewBox(t.viewBox||[.125*(this.screenBox[2]-this.screenBox[0])/r,.125*(this.screenBox[3]-this.screenBox[1])/r,.875*(this.screenBox[2]-this.screenBox[0])/r,.875*(this.screenBox[3]-this.screenBox[1])/r]);var n=this.viewBox,i=(n[2]-n[0])/(n[3]-n[1]);this.setDataBox(t.dataBox||[-10,-10/i,10,10/i]),this.borderColor=!1!==t.borderColor&&(t.borderColor||[0,0,0,0]).slice(),this.backgroundColor=(t.backgroundColor||[0,0,0,0]).slice(),this.gridLineEnable=(t.gridLineEnable||[!0,!0]).slice(),this.gridLineWidth=(t.gridLineWidth||[1,1]).slice(),this.gridLineColor=c(t.gridLineColor||[[.5,.5,.5,1],[.5,.5,.5,1]]),this.zeroLineEnable=(t.zeroLineEnable||[!0,!0]).slice(),this.zeroLineWidth=(t.zeroLineWidth||[4,4]).slice(),this.zeroLineColor=c(t.zeroLineColor||[[0,0,0,1],[0,0,0,1]]),this.tickMarkLength=(t.tickMarkLength||[0,0,0,0]).slice(),this.tickMarkWidth=(t.tickMarkWidth||[0,0,0,0]).slice(),this.tickMarkColor=c(t.tickMarkColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.titleCenter=(t.titleCenter||[.5*(n[0]+n[2])/r,(n[3]+120)/r]).slice(),this.titleEnable=!(\"titleEnable\"in t)||!!t.titleEnable,this.titleAngle=t.titleAngle||0,this.titleColor=(t.titleColor||[0,0,0,1]).slice(),this.labelPad=(t.labelPad||[15,15,15,15]).slice(),this.labelAngle=(t.labelAngle||[0,Math.PI/2,0,3*Math.PI/2]).slice(),this.labelEnable=(t.labelEnable||[!0,!0,!0,!0]).slice(),this.labelColor=c(t.labelColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.tickPad=(t.tickPad||[15,15,15,15]).slice(),this.tickAngle=(t.tickAngle||[0,0,0,0]).slice(),this.tickEnable=(t.tickEnable||[!0,!0,!0,!0]).slice(),this.tickColor=c(t.tickColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.borderLineEnable=(t.borderLineEnable||[!0,!0,!0,!0]).slice(),this.borderLineWidth=(t.borderLineWidth||[2,2,2,2]).slice(),this.borderLineColor=c(t.borderLineColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]);var a=t.ticks||[[],[]],o=this._tickBounds;o[0]=o[1]=1/0,o[2]=o[3]=-1/0;for(var s=0;s<2;++s){var l=a[s].slice(0);0!==l.length&&(l.sort(f),o[s]=Math.min(o[s],l[0].x),o[s+2]=Math.max(o[s+2],l[l.length-1].x))}this.grid.update({bounds:o,ticks:a}),this.text.update({bounds:o,ticks:a,labels:t.labels||[\"x\",\"y\"],labelSize:t.labelSize||[12,12],labelFont:t.labelFont||[\"sans-serif\",\"sans-serif\"],title:t.title||\"\",titleSize:t.titleSize||18,titleFont:t.titleFont||\"sans-serif\"}),this.static=!!t.static,this.setDirty()},u.dispose=function(){this.box.dispose(),this.grid.dispose(),this.text.dispose(),this.line.dispose();for(var t=this.objects.length-1;t>=0;--t)this.objects[t].dispose();for(this.objects.length=0,t=this.overlays.length-1;t>=0;--t)this.overlays[t].dispose();this.overlays.length=0,this.gl=null},u.addObject=function(t){this.objects.indexOf(t)<0&&(this.objects.push(t),this.setDirty())},u.removeObject=function(t){for(var e=this.objects,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setDirty();break}},u.addOverlay=function(t){this.overlays.indexOf(t)<0&&(this.overlays.push(t),this.setOverlayDirty())},u.removeOverlay=function(t){for(var e=this.overlays,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setOverlayDirty();break}}},4296:function(t,e,r){\"use strict\";t.exports=function(t,e){t=t||document.body;var r=[.01,1/0];\"distanceLimits\"in(e=e||{})&&(r[0]=e.distanceLimits[0],r[1]=e.distanceLimits[1]),\"zoomMin\"in e&&(r[0]=e.zoomMin),\"zoomMax\"in e&&(r[1]=e.zoomMax);var u=i({center:e.center||[0,0,0],up:e.up||[0,1,0],eye:e.eye||[0,0,10],mode:e.mode||\"orbit\",distanceLimits:r}),c=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],f=0,h=t.clientWidth,p=t.clientHeight,d={keyBindingMode:\"rotate\",enableWheel:!0,view:u,element:t,delay:e.delay||16,rotateSpeed:e.rotateSpeed||1,zoomSpeed:e.zoomSpeed||1,translateSpeed:e.translateSpeed||1,flipX:!!e.flipX,flipY:!!e.flipY,modes:u.modes,_ortho:e._ortho||e.projection&&\"orthographic\"===e.projection.type||!1,tick:function(){var e=n(),r=this.delay,i=e-2*r;u.idle(e-r),u.recalcMatrix(i),u.flush(e-(100+2*r));for(var a=!0,o=u.computedMatrix,s=0;s<16;++s)a=a&&c[s]===o[s],c[s]=o[s];var l=t.clientWidth===h&&t.clientHeight===p;return h=t.clientWidth,p=t.clientHeight,a?!l:(f=Math.exp(u.computedRadius[0]),!0)},lookAt:function(t,e,r){u.lookAt(u.lastT(),t,e,r)},rotate:function(t,e,r){u.rotate(u.lastT(),t,e,r)},pan:function(t,e,r){u.pan(u.lastT(),t,e,r)},translate:function(t,e,r){u.translate(u.lastT(),t,e,r)}};return Object.defineProperties(d,{matrix:{get:function(){return u.computedMatrix},set:function(t){return u.setMatrix(u.lastT(),t),u.computedMatrix},enumerable:!0},mode:{get:function(){return u.getMode()},set:function(t){var e=u.computedUp.slice(),r=u.computedEye.slice(),i=u.computedCenter.slice();if(u.setMode(t),\"turntable\"===t){var a=n();u._active.lookAt(a,r,i,e),u._active.lookAt(a+500,r,i,[0,0,1]),u._active.flush(a)}return u.getMode()},enumerable:!0},center:{get:function(){return u.computedCenter},set:function(t){return u.lookAt(u.lastT(),null,t),u.computedCenter},enumerable:!0},eye:{get:function(){return u.computedEye},set:function(t){return u.lookAt(u.lastT(),t),u.computedEye},enumerable:!0},up:{get:function(){return u.computedUp},set:function(t){return u.lookAt(u.lastT(),null,null,t),u.computedUp},enumerable:!0},distance:{get:function(){return f},set:function(t){return u.setDistance(u.lastT(),t),t},enumerable:!0},distanceLimits:{get:function(){return u.getDistanceLimits(r)},set:function(t){return u.setDistanceLimits(t),t},enumerable:!0}}),t.addEventListener(\"contextmenu\",(function(t){return t.preventDefault(),!1})),d._lastX=-1,d._lastY=-1,d._lastMods={shift:!1,control:!1,alt:!1,meta:!1},d.enableMouseListeners=function(){function e(e,r,i,a){var o=d.keyBindingMode;if(!1!==o){var s=\"rotate\"===o,l=\"pan\"===o,c=\"zoom\"===o,h=!!a.control,p=!!a.alt,v=!!a.shift,g=!!(1&e),y=!!(2&e),m=!!(4&e),x=1/t.clientHeight,b=x*(r-d._lastX),_=x*(i-d._lastY),w=d.flipX?1:-1,T=d.flipY?1:-1,k=Math.PI*d.rotateSpeed,A=n();if(-1!==d._lastX&&-1!==d._lastY&&((s&&g&&!h&&!p&&!v||g&&!h&&!p&&v)&&u.rotate(A,w*k*b,-T*k*_,0),(l&&g&&!h&&!p&&!v||y||g&&h&&!p&&!v)&&u.pan(A,-d.translateSpeed*b*f,d.translateSpeed*_*f,0),c&&g&&!h&&!p&&!v||m||g&&!h&&p&&!v)){var M=-d.zoomSpeed*_/window.innerHeight*(A-u.lastT())*100;u.pan(A,0,0,f*(Math.exp(M)-1))}return d._lastX=r,d._lastY=i,d._lastMods=a,!0}}d.mouseListener=a(t,e),t.addEventListener(\"touchstart\",(function(r){var n=s(r.changedTouches[0],t);e(0,n[0],n[1],d._lastMods),e(1,n[0],n[1],d._lastMods)}),!!l&&{passive:!0}),t.addEventListener(\"touchmove\",(function(r){var n=s(r.changedTouches[0],t);e(1,n[0],n[1],d._lastMods),r.preventDefault()}),!!l&&{passive:!1}),t.addEventListener(\"touchend\",(function(t){e(0,d._lastX,d._lastY,d._lastMods)}),!!l&&{passive:!0}),d.wheelListener=o(t,(function(t,e){if(!1!==d.keyBindingMode&&d.enableWheel){var r=d.flipX?1:-1,i=d.flipY?1:-1,a=n();if(Math.abs(t)>Math.abs(e))u.rotate(a,0,0,-t*r*Math.PI*d.rotateSpeed/window.innerWidth);else if(!d._ortho){var o=-d.zoomSpeed*i*e/window.innerHeight*(a-u.lastT())/20;u.pan(a,0,0,f*(Math.exp(o)-1))}}}),!0)},d.enableMouseListeners(),d};var n=r(8161),i=r(1152),a=r(6145),o=r(6475),s=r(2565),l=r(5233)},8245:function(t,e,r){var n=r(6832),i=r(5158),a=n([\"precision mediump float;\\n#define GLSLIFY 1\\nattribute vec2 position;\\nvarying vec2 uv;\\nvoid main() {\\n  uv = position;\\n  gl_Position = vec4(position, 0, 1);\\n}\"]),o=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D accumBuffer;\\nvarying vec2 uv;\\n\\nvoid main() {\\n  vec4 accum = texture2D(accumBuffer, 0.5 * (uv + 1.0));\\n  gl_FragColor = min(vec4(1,1,1,1), accum);\\n}\"]);t.exports=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec2\"}])}},1059:function(t,e,r){\"use strict\";var n=r(4296),i=r(7453),a=r(2771),o=r(6496),s=r(2611),l=r(4234),u=r(8126),c=r(6145),f=r(1120),h=r(5268),p=r(8245),d=r(2321)({tablet:!0,featureDetect:!0});function v(){this.mouse=[-1,-1],this.screen=null,this.distance=1/0,this.index=null,this.dataCoordinate=null,this.dataPosition=null,this.object=null,this.data=null}function g(t){var e=Math.round(Math.log(Math.abs(t))/Math.log(10));if(e<0){var r=Math.round(Math.pow(10,-e));return Math.ceil(t*r)/r}return e>0?(r=Math.round(Math.pow(10,e)),Math.ceil(t/r)*r):Math.ceil(t)}function y(t){return\"boolean\"!=typeof t||t}t.exports={createScene:function(t){(t=t||{}).camera=t.camera||{};var e=t.canvas;e||(e=document.createElement(\"canvas\"),t.container?t.container.appendChild(e):document.body.appendChild(e));var r=t.gl;if(r||(t.glOptions&&(d=!!t.glOptions.preserveDrawingBuffer),r=function(t,e){var r=null;try{(r=t.getContext(\"webgl\",e))||(r=t.getContext(\"experimental-webgl\",e))}catch(t){return null}return r}(e,t.glOptions||{premultipliedAlpha:!0,antialias:!0,preserveDrawingBuffer:d})),!r)throw new Error(\"webgl not supported\");var m=t.bounds||[[-10,-10,-10],[10,10,10]],x=new v,b=l(r,r.drawingBufferWidth,r.drawingBufferHeight,{preferFloat:!d}),_=p(r),w=t.cameraObject&&!0===t.cameraObject._ortho||t.camera.projection&&\"orthographic\"===t.camera.projection.type||!1,T={eye:t.camera.eye||[2,0,0],center:t.camera.center||[0,0,0],up:t.camera.up||[0,1,0],zoomMin:t.camera.zoomMax||.1,zoomMax:t.camera.zoomMin||100,mode:t.camera.mode||\"turntable\",_ortho:w},k=t.axes||{},A=i(r,k);A.enable=!k.disable;var M=t.spikes||{},S=o(r,M),E=[],L=[],C=[],P=[],O=!0,I=!0,D={view:null,projection:new Array(16),model:new Array(16),_ortho:!1},z=(I=!0,[r.drawingBufferWidth,r.drawingBufferHeight]),R=t.cameraObject||n(e,T),F={gl:r,contextLost:!1,pixelRatio:t.pixelRatio||1,canvas:e,selection:x,camera:R,axes:A,axesPixels:null,spikes:S,bounds:m,objects:E,shape:z,aspect:t.aspectRatio||[1,1,1],pickRadius:t.pickRadius||10,zNear:t.zNear||.01,zFar:t.zFar||1e3,fovy:t.fovy||Math.PI/4,clearColor:t.clearColor||[0,0,0,0],autoResize:y(t.autoResize),autoBounds:y(t.autoBounds),autoScale:!!t.autoScale,autoCenter:y(t.autoCenter),clipToBounds:y(t.clipToBounds),snapToData:!!t.snapToData,onselect:t.onselect||null,onrender:t.onrender||null,onclick:t.onclick||null,cameraParams:D,oncontextloss:null,mouseListener:null,_stopped:!1,getAspectratio:function(){return{x:this.aspect[0],y:this.aspect[1],z:this.aspect[2]}},setAspectratio:function(t){this.aspect[0]=t.x,this.aspect[1]=t.y,this.aspect[2]=t.z,I=!0},setBounds:function(t,e){this.bounds[0][t]=e.min,this.bounds[1][t]=e.max},setClearColor:function(t){this.clearColor=t},clearRGBA:function(){this.gl.clearColor(this.clearColor[0],this.clearColor[1],this.clearColor[2],this.clearColor[3]),this.gl.clear(this.gl.COLOR_BUFFER_BIT|this.gl.DEPTH_BUFFER_BIT)}},B=[r.drawingBufferWidth/F.pixelRatio|0,r.drawingBufferHeight/F.pixelRatio|0];function N(){if(!F._stopped&&F.autoResize){var t=e.parentNode,r=1,n=1;t&&t!==document.body?(r=t.clientWidth,n=t.clientHeight):(r=window.innerWidth,n=window.innerHeight);var i=0|Math.ceil(r*F.pixelRatio),a=0|Math.ceil(n*F.pixelRatio);if(i!==e.width||a!==e.height){e.width=i,e.height=a;var o=e.style;o.position=o.position||\"absolute\",o.left=\"0px\",o.top=\"0px\",o.width=r+\"px\",o.height=n+\"px\",O=!0}}}function j(){for(var t=E.length,e=P.length,n=0;n<e;++n)C[n]=0;t:for(n=0;n<t;++n){var i=E[n],a=i.pickSlots;if(a){for(var o=0;o<e;++o)if(C[o]+a<255){L[n]=o,i.setPickBase(C[o]+1),C[o]+=a;continue t}var l=s(r,z);L[n]=e,P.push(l),C.push(a),i.setPickBase(1),e+=1}else L[n]=-1}for(;e>0&&0===C[e-1];)C.pop(),P.pop().dispose()}function U(){if(F.contextLost)return!0;r.isContextLost()&&(F.contextLost=!0,F.mouseListener.enabled=!1,F.selection.object=null,F.oncontextloss&&F.oncontextloss())}F.autoResize&&N(),window.addEventListener(\"resize\",N),F.update=function(t){F._stopped||(t=t||{},O=!0,I=!0)},F.add=function(t){F._stopped||(t.axes=A,E.push(t),L.push(-1),O=!0,I=!0,j())},F.remove=function(t){if(!F._stopped){var e=E.indexOf(t);e<0||(E.splice(e,1),L.pop(),O=!0,I=!0,j())}},F.dispose=function(){if(!F._stopped&&(F._stopped=!0,window.removeEventListener(\"resize\",N),e.removeEventListener(\"webglcontextlost\",U),F.mouseListener.enabled=!1,!F.contextLost)){A.dispose(),S.dispose();for(var t=0;t<E.length;++t)E[t].dispose();for(b.dispose(),t=0;t<P.length;++t)P[t].dispose();_.dispose(),r=null,A=null,S=null,E=[]}},F._mouseRotating=!1,F._prevButtons=0,F.enableMouseListeners=function(){F.mouseListener=c(e,(function(t,e,r){if(!F._stopped){var n=P.length,i=E.length,a=x.object;x.distance=1/0,x.mouse[0]=e,x.mouse[1]=r,x.object=null,x.screen=null,x.dataCoordinate=x.dataPosition=null;var o=!1;if(t&&F._prevButtons)F._mouseRotating=!0;else{F._mouseRotating&&(I=!0),F._mouseRotating=!1;for(var s=0;s<n;++s){var l=P[s].query(e,B[1]-r-1,F.pickRadius);if(l){if(l.distance>x.distance)continue;for(var u=0;u<i;++u){var c=E[u];if(L[u]===s){var f=c.pick(l);f&&(x.buttons=t,x.screen=l.coord,x.distance=l.distance,x.object=c,x.index=f.distance,x.dataPosition=f.position,x.dataCoordinate=f.dataCoordinate,x.data=f,o=!0)}}}}}a&&a!==x.object&&(a.highlight&&a.highlight(null),O=!0),x.object&&(x.object.highlight&&x.object.highlight(x.data),O=!0),(o=o||x.object!==a)&&F.onselect&&F.onselect(x),1&t&&!(1&F._prevButtons)&&F.onclick&&F.onclick(x),F._prevButtons=t}}))},e.addEventListener(\"webglcontextlost\",U);var V=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],H=[V[0].slice(),V[1].slice()];function q(){if(!U()){N();var t=F.camera.tick();D.view=F.camera.matrix,O=O||t,I=I||t,A.pixelRatio=F.pixelRatio,S.pixelRatio=F.pixelRatio;var e=E.length,n=V[0],i=V[1];n[0]=n[1]=n[2]=1/0,i[0]=i[1]=i[2]=-1/0;for(var o=0;o<e;++o){(C=E[o]).pixelRatio=F.pixelRatio,C.axes=F.axes,O=O||!!C.dirty,I=I||!!C.dirty;var s=C.bounds;if(s)for(var l=s[0],c=s[1],p=0;p<3;++p)n[p]=Math.min(n[p],l[p]),i[p]=Math.max(i[p],c[p])}var d=F.bounds;if(F.autoBounds)for(p=0;p<3;++p){if(i[p]<n[p])n[p]=-1,i[p]=1;else{n[p]===i[p]&&(n[p]-=1,i[p]+=1);var v=.05*(i[p]-n[p]);n[p]=n[p]-v,i[p]=i[p]+v}d[0][p]=n[p],d[1][p]=i[p]}var y=!1;for(p=0;p<3;++p)y=y||H[0][p]!==d[0][p]||H[1][p]!==d[1][p],H[0][p]=d[0][p],H[1][p]=d[1][p];if(I=I||y,O=O||y){if(y){var m=[0,0,0];for(o=0;o<3;++o)m[o]=g((d[1][o]-d[0][o])/10);A.autoTicks?A.update({bounds:d,tickSpacing:m}):A.update({bounds:d})}var T=r.drawingBufferWidth,k=r.drawingBufferHeight;for(z[0]=T,z[1]=k,B[0]=0|Math.max(T/F.pixelRatio,1),B[1]=0|Math.max(k/F.pixelRatio,1),function(t,e){var r=t.bounds,n=t.cameraParams,i=n.projection,a=n.model,o=t.gl.drawingBufferWidth,s=t.gl.drawingBufferHeight,l=t.zNear,u=t.zFar,c=t.fovy,p=o/s;e?(h(i,-p,p,-1,1,l,u),n._ortho=!0):(f(i,c,p,l,u),n._ortho=!1);for(var d=0;d<16;++d)a[d]=0;a[15]=1;var v=0;for(d=0;d<3;++d)v=Math.max(v,r[1][d]-r[0][d]);for(d=0;d<3;++d)t.autoScale?a[5*d]=t.aspect[d]/(r[1][d]-r[0][d]):a[5*d]=1/v,t.autoCenter&&(a[12+d]=.5*-a[5*d]*(r[0][d]+r[1][d]))}(F,w),o=0;o<e;++o)(C=E[o]).axesBounds=d,F.clipToBounds&&(C.clipBounds=d);x.object&&(F.snapToData?S.position=x.dataCoordinate:S.position=x.dataPosition,S.bounds=d),I&&(I=!1,function(){if(!U()){r.colorMask(!0,!0,!0,!0),r.depthMask(!0),r.disable(r.BLEND),r.enable(r.DEPTH_TEST),r.depthFunc(r.LEQUAL);for(var t=E.length,e=P.length,n=0;n<e;++n){var i=P[n];i.shape=B,i.begin();for(var a=0;a<t;++a)if(L[a]===n){var o=E[a];o.drawPick&&(o.pixelRatio=1,o.drawPick(D))}i.end()}}}()),F.axesPixels=a(F.axes,D,T,k),F.onrender&&F.onrender(),r.bindFramebuffer(r.FRAMEBUFFER,null),r.viewport(0,0,T,k),F.clearRGBA(),r.depthMask(!0),r.colorMask(!0,!0,!0,!0),r.enable(r.DEPTH_TEST),r.depthFunc(r.LEQUAL),r.disable(r.BLEND),r.disable(r.CULL_FACE);var M=!1;for(A.enable&&(M=M||A.isTransparent(),A.draw(D)),S.axes=A,x.object&&S.draw(D),r.disable(r.CULL_FACE),o=0;o<e;++o)(C=E[o]).axes=A,C.pixelRatio=F.pixelRatio,C.isOpaque&&C.isOpaque()&&C.draw(D),C.isTransparent&&C.isTransparent()&&(M=!0);if(M){for(b.shape=z,b.bind(),r.clear(r.DEPTH_BUFFER_BIT),r.colorMask(!1,!1,!1,!1),r.depthMask(!0),r.depthFunc(r.LESS),A.enable&&A.isTransparent()&&A.drawTransparent(D),o=0;o<e;++o)(C=E[o]).isOpaque&&C.isOpaque()&&C.draw(D);for(r.enable(r.BLEND),r.blendEquation(r.FUNC_ADD),r.blendFunc(r.ONE,r.ONE_MINUS_SRC_ALPHA),r.colorMask(!0,!0,!0,!0),r.depthMask(!1),r.clearColor(0,0,0,0),r.clear(r.COLOR_BUFFER_BIT),A.isTransparent()&&A.drawTransparent(D),o=0;o<e;++o){var C;(C=E[o]).isTransparent&&C.isTransparent()&&C.drawTransparent(D)}r.bindFramebuffer(r.FRAMEBUFFER,null),r.blendFunc(r.ONE,r.ONE_MINUS_SRC_ALPHA),r.disable(r.DEPTH_TEST),_.bind(),b.color[0].bind(0),_.uniforms.accumBuffer=0,u(r),r.disable(r.BLEND)}for(O=!1,o=0;o<e;++o)E[o].dirty=!1}}}return F.enableMouseListeners(),function t(){F._stopped||F.contextLost||(q(),requestAnimationFrame(t))}(),F.redraw=function(){F._stopped||(O=!0,q())},F},createCamera:n}},8023:function(t,e,r){var n=r(6832);e.pointVertex=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\n\\nuniform mat3 matrix;\\nuniform float pointSize;\\nuniform float pointCloud;\\n\\nhighp float rand(vec2 co) {\\n  highp float a = 12.9898;\\n  highp float b = 78.233;\\n  highp float c = 43758.5453;\\n  highp float d = dot(co.xy, vec2(a, b));\\n  highp float e = mod(d, 3.14);\\n  return fract(sin(e) * c);\\n}\\n\\nvoid main() {\\n  vec3 hgPosition = matrix * vec3(position, 1);\\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\\n    // if we don't jitter the point size a bit, overall point cloud\\n    // saturation 'jumps' on zooming, which is disturbing and confusing\\n  gl_PointSize = pointSize * ((19.5 + rand(position)) / 20.0);\\n  if(pointCloud != 0.0) { // pointCloud is truthy\\n    // get the same square surface as circle would be\\n    gl_PointSize *= 0.886;\\n  }\\n}\"]),e.pointFragment=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color, borderColor;\\nuniform float centerFraction;\\nuniform float pointCloud;\\n\\nvoid main() {\\n  float radius;\\n  vec4 baseColor;\\n  if(pointCloud != 0.0) { // pointCloud is truthy\\n    if(centerFraction == 1.0) {\\n      gl_FragColor = color;\\n    } else {\\n      gl_FragColor = mix(borderColor, color, centerFraction);\\n    }\\n  } else {\\n    radius = length(2.0 * gl_PointCoord.xy - 1.0);\\n    if(radius > 1.0) {\\n      discard;\\n    }\\n    baseColor = mix(borderColor, color, step(radius, centerFraction));\\n    gl_FragColor = vec4(baseColor.rgb * baseColor.a, baseColor.a);\\n  }\\n}\\n\"]),e.pickVertex=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 pickId;\\n\\nuniform mat3 matrix;\\nuniform float pointSize;\\nuniform vec4 pickOffset;\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n  vec3 hgPosition = matrix * vec3(position, 1);\\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\\n  gl_PointSize = pointSize;\\n\\n  vec4 id = pickId + pickOffset;\\n  id.y += floor(id.x / 256.0);\\n  id.x -= floor(id.x / 256.0) * 256.0;\\n\\n  id.z += floor(id.y / 256.0);\\n  id.y -= floor(id.y / 256.0) * 256.0;\\n\\n  id.w += floor(id.z / 256.0);\\n  id.z -= floor(id.z / 256.0) * 256.0;\\n\\n  fragId = id;\\n}\\n\"]),e.pickFragment=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n  float radius = length(2.0 * gl_PointCoord.xy - 1.0);\\n  if(radius > 1.0) {\\n    discard;\\n  }\\n  gl_FragColor = fragId / 255.0;\\n}\\n\"])},8271:function(t,e,r){\"use strict\";var n=r(5158),i=r(5827),a=r(5306),o=r(8023);function s(t,e,r,n,i){this.plot=t,this.offsetBuffer=e,this.pickBuffer=r,this.shader=n,this.pickShader=i,this.sizeMin=.5,this.sizeMinCap=2,this.sizeMax=20,this.areaRatio=1,this.pointCount=0,this.color=[1,0,0,1],this.borderColor=[0,0,0,1],this.blend=!1,this.pickOffset=0,this.points=null}t.exports=function(t,e){var r=t.gl,a=new s(t,i(r),i(r),n(r,o.pointVertex,o.pointFragment),n(r,o.pickVertex,o.pickFragment));return a.update(e),t.addObject(a),a};var l,u,c=s.prototype;c.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.offsetBuffer.dispose(),this.pickBuffer.dispose(),this.plot.removeObject(this)},c.update=function(t){var e;function r(e,r){return e in t?t[e]:r}t=t||{},this.sizeMin=r(\"sizeMin\",.5),this.sizeMax=r(\"sizeMax\",20),this.color=r(\"color\",[1,0,0,1]).slice(),this.areaRatio=r(\"areaRatio\",1),this.borderColor=r(\"borderColor\",[0,0,0,1]).slice(),this.blend=r(\"blend\",!1);var n=t.positions.length>>>1,i=t.positions instanceof Float32Array,o=t.idToIndex instanceof Int32Array&&t.idToIndex.length>=n,s=t.positions,l=i?s:a.mallocFloat32(s.length),u=o?t.idToIndex:a.mallocInt32(n);if(i||l.set(s),!o)for(l.set(s),e=0;e<n;e++)u[e]=e;this.points=s,this.offsetBuffer.update(l),this.pickBuffer.update(u),i||a.free(l),o||a.free(u),this.pointCount=n,this.pickOffset=0},c.unifiedDraw=(l=[1,0,0,0,1,0,0,0,1],u=[0,0,0,0],function(t){var e=void 0!==t,r=e?this.pickShader:this.shader,n=this.plot.gl,i=this.plot.dataBox;if(0===this.pointCount)return t;var a=i[2]-i[0],o=i[3]-i[1],s=function(t,e){var r,n=0,i=t.length>>>1;for(r=0;r<i;r++){var a=t[2*r],o=t[2*r+1];a>=e[0]&&a<=e[2]&&o>=e[1]&&o<=e[3]&&n++}return n}(this.points,i),c=this.plot.pickPixelRatio*Math.max(Math.min(this.sizeMinCap,this.sizeMin),Math.min(this.sizeMax,this.sizeMax/Math.pow(s,.33333)));l[0]=2/a,l[4]=2/o,l[6]=-2*i[0]/a-1,l[7]=-2*i[1]/o-1,this.offsetBuffer.bind(),r.bind(),r.attributes.position.pointer(),r.uniforms.matrix=l,r.uniforms.color=this.color,r.uniforms.borderColor=this.borderColor,r.uniforms.pointCloud=c<5,r.uniforms.pointSize=c,r.uniforms.centerFraction=Math.min(1,Math.max(0,Math.sqrt(1-this.areaRatio))),e&&(u[0]=255&t,u[1]=t>>8&255,u[2]=t>>16&255,u[3]=t>>24&255,this.pickBuffer.bind(),r.attributes.pickId.pointer(n.UNSIGNED_BYTE),r.uniforms.pickOffset=u,this.pickOffset=t);var f=n.getParameter(n.BLEND),h=n.getParameter(n.DITHER);return f&&!this.blend&&n.disable(n.BLEND),h&&n.disable(n.DITHER),n.drawArrays(n.POINTS,0,this.pointCount),f&&!this.blend&&n.enable(n.BLEND),h&&n.enable(n.DITHER),t+this.pointCount}),c.draw=c.unifiedDraw,c.drawPick=c.unifiedDraw,c.pick=function(t,e,r){var n=this.pickOffset,i=this.pointCount;if(r<n||r>=n+i)return null;var a=r-n,o=this.points;return{object:this,pointId:a,dataCoord:[o[2*a],o[2*a+1]]}}},6093:function(t){t.exports=function(t,e,r,n){var i,a,o,s,l,u=e[0],c=e[1],f=e[2],h=e[3],p=r[0],d=r[1],v=r[2],g=r[3];return(a=u*p+c*d+f*v+h*g)<0&&(a=-a,p=-p,d=-d,v=-v,g=-g),1-a>1e-6?(i=Math.acos(a),o=Math.sin(i),s=Math.sin((1-n)*i)/o,l=Math.sin(n*i)/o):(s=1-n,l=n),t[0]=s*u+l*p,t[1]=s*c+l*d,t[2]=s*f+l*v,t[3]=s*h+l*g,t}},8240:function(t){\"use strict\";t.exports=function(t){return t||0===t?t.toString():\"\"}},4123:function(t,e,r){\"use strict\";var n=r(875);t.exports=function(t,e,r){var a=i[e];if(a||(a=i[e]={}),t in a)return a[t];var o={textAlign:\"center\",textBaseline:\"middle\",lineHeight:1,font:e,lineSpacing:1.25,styletags:{breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},triangles:!0},s=n(t,o);o.triangles=!1;var l,u,c=n(t,o);if(r&&1!==r){for(l=0;l<s.positions.length;++l)for(u=0;u<s.positions[l].length;++u)s.positions[l][u]/=r;for(l=0;l<c.positions.length;++l)for(u=0;u<c.positions[l].length;++u)c.positions[l][u]/=r}var f=[[1/0,1/0],[-1/0,-1/0]],h=c.positions.length;for(l=0;l<h;++l){var p=c.positions[l];for(u=0;u<2;++u)f[0][u]=Math.min(f[0][u],p[u]),f[1][u]=Math.max(f[1][u],p[u])}return a[t]=[s,c,f]};var i={}},9282:function(t,e,r){var n=r(5158),i=r(6832),a=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform vec4 highlightId;\\nuniform float highlightScale;\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float scale = 1.0;\\n    if(distance(highlightId, id) < 0.0001) {\\n      scale = highlightScale;\\n    }\\n\\n    vec4 worldPosition = model * vec4(position, 1);\\n    vec4 viewPosition = view * worldPosition;\\n    viewPosition = viewPosition / viewPosition.w;\\n    vec4 clipPosition = projection * (viewPosition + scale * vec4(glyph.x, -glyph.y, 0, 0));\\n\\n    gl_Position = clipPosition;\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = position;\\n  }\\n}\"]),o=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float highlightScale, pixelRatio;\\nuniform vec4 highlightId;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float scale = pixelRatio;\\n    if(distance(highlightId.bgr, id.bgr) < 0.001) {\\n      scale *= highlightScale;\\n    }\\n\\n    vec4 worldPosition = model * vec4(position, 1.0);\\n    vec4 viewPosition = view * worldPosition;\\n    vec4 clipPosition = projection * viewPosition;\\n    clipPosition /= clipPosition.w;\\n\\n    gl_Position = clipPosition + vec4(screenSize * scale * vec2(glyph.x, -glyph.y), 0.0, 0.0);\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = position;\\n  }\\n}\"]),s=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform float highlightScale;\\nuniform vec4 highlightId;\\nuniform vec3 axes[2];\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float scale, pixelRatio;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float lscale = pixelRatio * scale;\\n    if(distance(highlightId, id) < 0.0001) {\\n      lscale *= highlightScale;\\n    }\\n\\n    vec4 clipCenter   = projection * view * model * vec4(position, 1);\\n    vec3 dataPosition = position + 0.5*lscale*(axes[0] * glyph.x + axes[1] * glyph.y) * clipCenter.w * screenSize.y;\\n    vec4 clipPosition = projection * view * model * vec4(dataPosition, 1);\\n\\n    gl_Position = clipPosition;\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = dataPosition;\\n  }\\n}\\n\"]),l=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float opacity;\\n\\nvarying vec4 interpColor;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (\\n    outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate) ||\\n    interpColor.a * opacity == 0.\\n  ) discard;\\n  gl_FragColor = interpColor * opacity;\\n}\\n\"]),u=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float pickGroup;\\n\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate)) discard;\\n\\n  gl_FragColor = vec4(pickGroup, pickId.bgr);\\n}\"]),c=[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"glyph\",type:\"vec2\"},{name:\"id\",type:\"vec4\"}],f={vertex:a,fragment:l,attributes:c},h={vertex:o,fragment:l,attributes:c},p={vertex:s,fragment:l,attributes:c},d={vertex:a,fragment:u,attributes:c},v={vertex:o,fragment:u,attributes:c},g={vertex:s,fragment:u,attributes:c};function y(t,e){var r=n(t,e),i=r.attributes;return i.position.location=0,i.color.location=1,i.glyph.location=2,i.id.location=3,r}e.createPerspective=function(t){return y(t,f)},e.createOrtho=function(t){return y(t,h)},e.createProject=function(t){return y(t,p)},e.createPickPerspective=function(t){return y(t,d)},e.createPickOrtho=function(t){return y(t,v)},e.createPickProject=function(t){return y(t,g)}},2182:function(t,e,r){\"use strict\";var n=r(3596),i=r(5827),a=r(2944),o=r(5306),s=r(104),l=r(9282),u=r(4123),c=r(8240),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e){var r=t[0],n=t[1],i=t[2],a=t[3];return t[0]=e[0]*r+e[4]*n+e[8]*i+e[12]*a,t[1]=e[1]*r+e[5]*n+e[9]*i+e[13]*a,t[2]=e[2]*r+e[6]*n+e[10]*i+e[14]*a,t[3]=e[3]*r+e[7]*n+e[11]*i+e[15]*a,t}function p(t,e,r,n){return h(n,n),h(n,n),h(n,n)}function d(t,e){this.index=t,this.dataCoordinate=this.position=e}function v(t){return!0===t||t>1?1:t}function g(t,e,r,n,i,a,o,s,l,u,c,f){this.gl=t,this.pixelRatio=1,this.shader=e,this.orthoShader=r,this.projectShader=n,this.pointBuffer=i,this.colorBuffer=a,this.glyphBuffer=o,this.idBuffer=s,this.vao=l,this.vertexCount=0,this.lineVertexCount=0,this.opacity=1,this.hasAlpha=!1,this.lineWidth=0,this.projectScale=[2/3,2/3,2/3],this.projectOpacity=[1,1,1],this.projectHasAlpha=!1,this.pickId=0,this.pickPerspectiveShader=u,this.pickOrthoShader=c,this.pickProjectShader=f,this.points=[],this._selectResult=new d(0,[0,0,0]),this.useOrtho=!0,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.axesProject=[!0,!0,!0],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.highlightId=[1,1,1,1],this.highlightScale=2,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.dirty=!0}t.exports=function(t){var e=t.gl,r=l.createPerspective(e),n=l.createOrtho(e),o=l.createProject(e),s=l.createPickPerspective(e),u=l.createPickOrtho(e),c=l.createPickProject(e),f=i(e),h=i(e),p=i(e),d=i(e),v=new g(e,r,n,o,f,h,p,d,a(e,[{buffer:f,size:3,type:e.FLOAT},{buffer:h,size:4,type:e.FLOAT},{buffer:p,size:2,type:e.FLOAT},{buffer:d,size:4,type:e.UNSIGNED_BYTE,normalized:!0}]),s,u,c);return v.update(t),v};var y=g.prototype;y.pickSlots=1,y.setPickBase=function(t){this.pickId=t},y.isTransparent=function(){if(this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&this.projectHasAlpha)return!0;return!1},y.isOpaque=function(){if(!this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&!this.projectHasAlpha)return!0;return!1};var m=[0,0],x=[0,0,0],b=[0,0,0],_=[0,0,0,1],w=[0,0,0,1],T=f.slice(),k=[0,0,0],A=[[0,0,0],[0,0,0]];function M(t){return t[0]=t[1]=t[2]=0,t}function S(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=1,t}function E(t,e,r,n){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[r]=n,t}var L=[[-1e8,-1e8,-1e8],[1e8,1e8,1e8]];function C(t,e,r,n,i,a,o){var l=r.gl;if((a===r.projectHasAlpha||o)&&function(t,e,r,n){var i,a=e.axesProject,o=e.gl,l=t.uniforms,u=r.model||f,c=r.view||f,h=r.projection||f,d=e.axesBounds,v=function(t){for(var e=A,r=0;r<2;++r)for(var n=0;n<3;++n)e[r][n]=Math.max(Math.min(t[r][n],1e8),-1e8);return e}(e.clipBounds);i=e.axes&&e.axes.lastCubeProps?e.axes.lastCubeProps.axis:[1,1,1],m[0]=2/o.drawingBufferWidth,m[1]=2/o.drawingBufferHeight,t.bind(),l.view=c,l.projection=h,l.screenSize=m,l.highlightId=e.highlightId,l.highlightScale=e.highlightScale,l.clipBounds=v,l.pickGroup=e.pickId/255,l.pixelRatio=n;for(var g=0;g<3;++g)if(a[g]){l.scale=e.projectScale[g],l.opacity=e.projectOpacity[g];for(var y=T,L=0;L<16;++L)y[L]=0;for(L=0;L<4;++L)y[5*L]=1;y[5*g]=0,i[g]<0?y[12+g]=d[0][g]:y[12+g]=d[1][g],s(y,u,y),l.model=y;var C=(g+1)%3,P=(g+2)%3,O=M(x),I=M(b);O[C]=1,I[P]=1;var D=p(0,0,0,S(_,O)),z=p(0,0,0,S(w,I));if(Math.abs(D[1])>Math.abs(z[1])){var R=D;D=z,z=R,R=O,O=I,I=R;var F=C;C=P,P=F}D[0]<0&&(O[C]=-1),z[1]>0&&(I[P]=-1);var B=0,N=0;for(L=0;L<4;++L)B+=Math.pow(u[4*C+L],2),N+=Math.pow(u[4*P+L],2);O[C]/=Math.sqrt(B),I[P]/=Math.sqrt(N),l.axes[0]=O,l.axes[1]=I,l.fragClipBounds[0]=E(k,v[0],g,-1e8),l.fragClipBounds[1]=E(k,v[1],g,1e8),e.vao.bind(),e.vao.draw(o.TRIANGLES,e.vertexCount),e.lineWidth>0&&(o.lineWidth(e.lineWidth*n),e.vao.draw(o.LINES,e.lineVertexCount,e.vertexCount)),e.vao.unbind()}}(e,r,n,i),a===r.hasAlpha||o){t.bind();var u=t.uniforms;u.model=n.model||f,u.view=n.view||f,u.projection=n.projection||f,m[0]=2/l.drawingBufferWidth,m[1]=2/l.drawingBufferHeight,u.screenSize=m,u.highlightId=r.highlightId,u.highlightScale=r.highlightScale,u.fragClipBounds=L,u.clipBounds=r.axes.bounds,u.opacity=r.opacity,u.pickGroup=r.pickId/255,u.pixelRatio=i,r.vao.bind(),r.vao.draw(l.TRIANGLES,r.vertexCount),r.lineWidth>0&&(l.lineWidth(r.lineWidth*i),r.vao.draw(l.LINES,r.lineVertexCount,r.vertexCount)),r.vao.unbind()}}function P(t,e,r,i){var a;a=Array.isArray(t)?e<t.length?t[e]:void 0:t,a=c(a);var o=!0;n(a)&&(a=\"▼\",o=!1);var s=u(a,r,i);return{mesh:s[0],lines:s[1],bounds:s[2],visible:o}}y.draw=function(t){C(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!1,!1)},y.drawTransparent=function(t){C(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!0,!1)},y.drawPick=function(t){C(this.useOrtho?this.pickOrthoShader:this.pickPerspectiveShader,this.pickProjectShader,this,t,1,!0,!0)},y.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[2]+(t.value[1]<<8)+(t.value[0]<<16);if(e>=this.pointCount||e<0)return null;var r=this.points[e],n=this._selectResult;n.index=e;for(var i=0;i<3;++i)n.position[i]=n.dataCoordinate[i]=r[i];return n},y.highlight=function(t){if(t){var e=t.index,r=255&e,n=e>>8&255,i=e>>16&255;this.highlightId=[r/255,n/255,i/255,0]}else this.highlightId=[1,1,1,1]},y.update=function(t){if(\"perspective\"in(t=t||{})&&(this.useOrtho=!t.perspective),\"orthographic\"in t&&(this.useOrtho=!!t.orthographic),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"project\"in t)if(Array.isArray(t.project))this.axesProject=t.project;else{var e=!!t.project;this.axesProject=[e,e,e]}if(\"projectScale\"in t)if(Array.isArray(t.projectScale))this.projectScale=t.projectScale.slice();else{var r=+t.projectScale;this.projectScale=[r,r,r]}if(this.projectHasAlpha=!1,\"projectOpacity\"in t){Array.isArray(t.projectOpacity)?this.projectOpacity=t.projectOpacity.slice():(r=+t.projectOpacity,this.projectOpacity=[r,r,r]);for(var n=0;n<3;++n)this.projectOpacity[n]=v(this.projectOpacity[n]),this.projectOpacity[n]<1&&(this.projectHasAlpha=!0)}this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=v(t.opacity),this.opacity<1&&(this.hasAlpha=!0)),this.dirty=!0;var i,a,s=t.position,l=t.font||\"normal\",u=t.alignment||[0,0];if(2===u.length)i=u[0],a=u[1];else for(i=[],a=[],n=0;n<u.length;++n)i[n]=u[n][0],a[n]=u[n][1];var c=[1/0,1/0,1/0],f=[-1/0,-1/0,-1/0],h=t.glyph,p=t.color,d=t.size,g=t.angle,y=t.lineColor,m=-1,x=0,b=0,_=0;if(s.length){_=s.length;t:for(n=0;n<_;++n){for(var w=s[n],T=0;T<3;++T)if(isNaN(w[T])||!isFinite(w[T]))continue t;var k=(N=P(h,n,l,this.pixelRatio)).mesh,A=N.lines,M=N.bounds;x+=3*k.cells.length,b+=2*A.edges.length}}var S=x+b,E=o.mallocFloat(3*S),L=o.mallocFloat(4*S),C=o.mallocFloat(2*S),O=o.mallocUint32(S);if(S>0){var I=0,D=x,z=[0,0,0,1],R=[0,0,0,1],F=Array.isArray(p)&&Array.isArray(p[0]),B=Array.isArray(y)&&Array.isArray(y[0]);t:for(n=0;n<_;++n){for(m+=1,w=s[n],T=0;T<3;++T){if(isNaN(w[T])||!isFinite(w[T]))continue t;f[T]=Math.max(f[T],w[T]),c[T]=Math.min(c[T],w[T])}k=(N=P(h,n,l,this.pixelRatio)).mesh,A=N.lines,M=N.bounds;var N,j=N.visible;if(j)if(Array.isArray(p)){if(3===(U=F?n<p.length?p[n]:[0,0,0,0]:p).length){for(T=0;T<3;++T)z[T]=U[T];z[3]=1}else if(4===U.length){for(T=0;T<4;++T)z[T]=U[T];!this.hasAlpha&&U[3]<1&&(this.hasAlpha=!0)}}else z[0]=z[1]=z[2]=0,z[3]=1;else z=[1,1,1,0];if(j)if(Array.isArray(y)){var U;if(3===(U=B?n<y.length?y[n]:[0,0,0,0]:y).length){for(T=0;T<3;++T)R[T]=U[T];R[T]=1}else if(4===U.length){for(T=0;T<4;++T)R[T]=U[T];!this.hasAlpha&&U[3]<1&&(this.hasAlpha=!0)}}else R[0]=R[1]=R[2]=0,R[3]=1;else R=[1,1,1,0];var V=.5;j?Array.isArray(d)?V=n<d.length?+d[n]:12:d?V=+d:this.useOrtho&&(V=12):V=0;var H=0;Array.isArray(g)?H=n<g.length?+g[n]:0:g&&(H=+g);var q=Math.cos(H),G=Math.sin(H);for(w=s[n],T=0;T<3;++T)f[T]=Math.max(f[T],w[T]),c[T]=Math.min(c[T],w[T]);var Z=i,Y=a;Z=0,Array.isArray(i)?Z=n<i.length?i[n]:0:i&&(Z=i),Y=0,Array.isArray(a)?Y=n<a.length?a[n]:0:a&&(Y=a);var W=[Z*=Z>0?1-M[0][0]:Z<0?1+M[1][0]:1,Y*=Y>0?1-M[0][1]:Y<0?1+M[1][1]:1],X=k.cells||[],J=k.positions||[];for(T=0;T<X.length;++T)for(var K=X[T],$=0;$<3;++$){for(var Q=0;Q<3;++Q)E[3*I+Q]=w[Q];for(Q=0;Q<4;++Q)L[4*I+Q]=z[Q];O[I]=m;var tt=J[K[$]];C[2*I]=V*(q*tt[0]-G*tt[1]+W[0]),C[2*I+1]=V*(G*tt[0]+q*tt[1]+W[1]),I+=1}for(X=A.edges,J=A.positions,T=0;T<X.length;++T)for(K=X[T],$=0;$<2;++$){for(Q=0;Q<3;++Q)E[3*D+Q]=w[Q];for(Q=0;Q<4;++Q)L[4*D+Q]=R[Q];O[D]=m,tt=J[K[$]],C[2*D]=V*(q*tt[0]-G*tt[1]+W[0]),C[2*D+1]=V*(G*tt[0]+q*tt[1]+W[1]),D+=1}}}this.bounds=[c,f],this.points=s,this.pointCount=s.length,this.vertexCount=x,this.lineVertexCount=b,this.pointBuffer.update(E),this.colorBuffer.update(L),this.glyphBuffer.update(C),this.idBuffer.update(O),o.free(E),o.free(L),o.free(C),o.free(O)},y.dispose=function(){this.shader.dispose(),this.orthoShader.dispose(),this.pickPerspectiveShader.dispose(),this.pickOrthoShader.dispose(),this.vao.dispose(),this.pointBuffer.dispose(),this.colorBuffer.dispose(),this.glyphBuffer.dispose(),this.idBuffer.dispose()}},1884:function(t,e,r){\"use strict\";var n=r(6832);e.boxVertex=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 vertex;\\n\\nuniform vec2 cornerA, cornerB;\\n\\nvoid main() {\\n  gl_Position = vec4(mix(cornerA, cornerB, vertex), 0, 1);\\n}\\n\"]),e.boxFragment=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\n\\nvoid main() {\\n  gl_FragColor = color;\\n}\\n\"])},6623:function(t,e,r){\"use strict\";var n=r(5158),i=r(5827),a=r(1884);function o(t,e,r){this.plot=t,this.boxBuffer=e,this.boxShader=r,this.enabled=!0,this.selectBox=[1/0,1/0,-1/0,-1/0],this.borderColor=[0,0,0,1],this.innerFill=!1,this.innerColor=[0,0,0,.25],this.outerFill=!0,this.outerColor=[0,0,0,.5],this.borderWidth=10}t.exports=function(t,e){var r=t.gl,s=new o(t,i(r,[0,0,0,1,1,0,1,1]),n(r,a.boxVertex,a.boxFragment));return s.update(e),t.addOverlay(s),s};var s=o.prototype;s.draw=function(){if(this.enabled){var t=this.plot,e=this.selectBox,r=this.borderWidth,n=(this.innerFill,this.innerColor),i=(this.outerFill,this.outerColor),a=this.borderColor,o=t.box,s=t.screenBox,l=t.dataBox,u=t.viewBox,c=t.pixelRatio,f=(e[0]-l[0])*(u[2]-u[0])/(l[2]-l[0])+u[0],h=(e[1]-l[1])*(u[3]-u[1])/(l[3]-l[1])+u[1],p=(e[2]-l[0])*(u[2]-u[0])/(l[2]-l[0])+u[0],d=(e[3]-l[1])*(u[3]-u[1])/(l[3]-l[1])+u[1];if(f=Math.max(f,u[0]),h=Math.max(h,u[1]),p=Math.min(p,u[2]),d=Math.min(d,u[3]),!(p<f||d<h)){o.bind();var v=s[2]-s[0],g=s[3]-s[1];if(this.outerFill&&(o.drawBox(0,0,v,h,i),o.drawBox(0,h,f,d,i),o.drawBox(0,d,v,g,i),o.drawBox(p,h,v,d,i)),this.innerFill&&o.drawBox(f,h,p,d,n),r>0){var y=r*c;o.drawBox(f-y,h-y,p+y,h+y,a),o.drawBox(f-y,d-y,p+y,d+y,a),o.drawBox(f-y,h-y,f+y,d+y,a),o.drawBox(p-y,h-y,p+y,d+y,a)}}}},s.update=function(t){t=t||{},this.innerFill=!!t.innerFill,this.outerFill=!!t.outerFill,this.innerColor=(t.innerColor||[0,0,0,.5]).slice(),this.outerColor=(t.outerColor||[0,0,0,.5]).slice(),this.borderColor=(t.borderColor||[0,0,0,1]).slice(),this.borderWidth=t.borderWidth||0,this.selectBox=(t.selectBox||this.selectBox).slice()},s.dispose=function(){this.boxBuffer.dispose(),this.boxShader.dispose(),this.plot.removeOverlay(this)}},2611:function(t,e,r){\"use strict\";t.exports=function(t,e){var r=e[0],a=e[1];return new l(t,n(t,r,a,{}),i.mallocUint8(r*a*4))};var n=r(4234),i=r(5306),a=r(5050),o=r(2288).nextPow2;function s(t,e,r,n,i){this.coord=[t,e],this.id=r,this.value=n,this.distance=i}function l(t,e,r){this.gl=t,this.fbo=e,this.buffer=r,this._readTimeout=null;var 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h=u,p=c,d=0;d<3;++d)i&&i[d]<0?(h[d]=this.bounds[0][d],p[d]=this.bounds[1][d]):(h[d]=this.bounds[1][d],p[d]=this.bounds[0][d]);for(f[0]=e.drawingBufferWidth,f[1]=e.drawingBufferHeight,n.uniforms.model=a,n.uniforms.view=s,n.uniforms.projection=l,n.uniforms.coordinates=[this.position,h,p],n.uniforms.colors=this.colors,n.uniforms.screenShape=f,d=0;d<3;++d)n.uniforms.lineWidth=this.lineWidth[d]*this.pixelRatio,this.enabled[d]&&(r.draw(e.TRIANGLES,6,6*d),this.drawSides[d]&&r.draw(e.TRIANGLES,12,18+12*d));r.unbind()},l.update=function(t){t&&(\"bounds\"in t&&(this.bounds=t.bounds),\"position\"in t&&(this.position=t.position),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"colors\"in t&&(this.colors=t.colors),\"enabled\"in t&&(this.enabled=t.enabled),\"drawSides\"in t&&(this.drawSides=t.drawSides))},l.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},9578:function(t,e,r){var n=r(6832),i=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the tube vertex and normal at the given index.\\n//\\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\\n//\\n// Each tube segment is made up of a ring of vertices.\\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\\n// The indexes of tube segments run from 0 to 8.\\n//\\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\\n  float segmentCount = 8.0;\\n\\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d);\\n  vec3 y = v * sin(angle) * length(d);\\n  vec3 v3 = x + y;\\n\\n  normal = normalize(v3);\\n\\n  return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 color, position;\\nattribute vec2 uv;\\n\\nuniform float vectorScale, tubeScale;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 eyePosition, lightPosition;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  // Scale the vector magnitude to stay constant with\\n  // model & view changes.\\n  vec3 normal;\\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * tubePosition;\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n  // vec4 m_position  = model * vec4(tubePosition, 1.0);\\n  vec4 t_position  = view * tubePosition;\\n  gl_Position      = projection * t_position;\\n\\n  f_color          = color;\\n  f_data           = tubePosition.xyz;\\n  f_position       = position.xyz;\\n  f_uv             = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness,\\n  float fresnel) {\\n\\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n  //Half angle vector\\n  vec3 H = normalize(lightDirection + viewDirection);\\n\\n  //Geometric term\\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\\n  float VdotH = max(dot(viewDirection, H), 0.000001);\\n  float LdotH = max(dot(lightDirection, H), 0.000001);\\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n  float G = min(1.0, min(G1, G2));\\n  \\n  //Distribution term\\n  float D = beckmannDistribution(NdotH, roughness);\\n\\n  //Fresnel term\\n  float F = pow(1.0 - VdotN, fresnel);\\n\\n  //Multiply terms and done\\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n  vec3 N = normalize(f_normal);\\n  vec3 L = normalize(f_lightDirection);\\n  vec3 V = normalize(f_eyeDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = litColor * opacity;\\n}\\n\"]),o=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the tube vertex and normal at the given index.\\n//\\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\\n//\\n// Each tube segment is made up of a ring of vertices.\\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\\n// The indexes of tube segments run from 0 to 8.\\n//\\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\\n  float segmentCount = 8.0;\\n\\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d);\\n  vec3 y = v * sin(angle) * length(d);\\n  vec3 v3 = x + y;\\n\\n  normal = normalize(v3);\\n\\n  return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform float tubeScale;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  vec3 normal;\\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n  gl_Position = projection * view * tubePosition;\\n  f_id        = id;\\n  f_position  = position.xyz;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3  clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n  gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]);e.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec4\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"vector\",type:\"vec4\"}]},e.pickShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec4\"},{name:\"id\",type:\"vec4\"},{name:\"vector\",type:\"vec4\"}]}},7307:function(t,e,r){\"use strict\";var n=r(2858),i=r(4020),a=[\"xyz\",\"xzy\",\"yxz\",\"yzx\",\"zxy\",\"zyx\"],o=function(t,e){var r,n=t.length;for(r=0;r<n;r++){var i=t[r];if(i===e)return r;if(i>e)return r-1}return r},s=function(t,e,r){return t<e?e:t>r?r:t},l=function(t){var e=1/0;t.sort((function(t,e){return t-e}));for(var r=t.length,n=1;n<r;n++){var i=Math.abs(t[n]-t[n-1]);i<e&&(e=i)}return e};t.exports=function(t,e){var r=t.startingPositions,u=t.maxLength||1e3,c=t.tubeSize||1,f=t.absoluteTubeSize,h=t.gridFill||\"+x+y+z\",p={};-1!==h.indexOf(\"-x\")&&(p.reversedX=!0),-1!==h.indexOf(\"-y\")&&(p.reversedY=!0),-1!==h.indexOf(\"-z\")&&(p.reversedZ=!0),p.filled=a.indexOf(h.replace(/-/g,\"\").replace(/\\+/g,\"\"));var d=t.getVelocity||function(e){return function(t,e,r){var i=e.vectors,a=e.meshgrid,l=t[0],u=t[1],c=t[2],f=a[0].length,h=a[1].length,p=a[2].length,d=o(a[0],l),v=o(a[1],u),g=o(a[2],c),y=d+1,m=v+1,x=g+1;if(d=s(d,0,f-1),y=s(y,0,f-1),v=s(v,0,h-1),m=s(m,0,h-1),g=s(g,0,p-1),x=s(x,0,p-1),d<0||v<0||g<0||y>f-1||m>h-1||x>p-1)return n.create();var b,_,w,T,k,A,M=a[0][d],S=a[0][y],E=a[1][v],L=a[1][m],C=a[2][g],P=(l-M)/(S-M),O=(u-E)/(L-E),I=(c-C)/(a[2][x]-C);switch(isFinite(P)||(P=.5),isFinite(O)||(O=.5),isFinite(I)||(I=.5),r.reversedX&&(d=f-1-d,y=f-1-y),r.reversedY&&(v=h-1-v,m=h-1-m),r.reversedZ&&(g=p-1-g,x=p-1-x),r.filled){case 5:k=g,A=x,w=v*p,T=m*p,b=d*p*h,_=y*p*h;break;case 4:k=g,A=x,b=d*p,_=y*p,w=v*p*f,T=m*p*f;break;case 3:w=v,T=m,k=g*h,A=x*h,b=d*h*p,_=y*h*p;break;case 2:w=v,T=m,b=d*h,_=y*h,k=g*h*f,A=x*h*f;break;case 1:b=d,_=y,k=g*f,A=x*f,w=v*f*p,T=m*f*p;break;default:b=d,_=y,w=v*f,T=m*f,k=g*f*h,A=x*f*h}var D=i[b+w+k],z=i[b+w+A],R=i[b+T+k],F=i[b+T+A],B=i[_+w+k],N=i[_+w+A],j=i[_+T+k],U=i[_+T+A],V=n.create(),H=n.create(),q=n.create(),G=n.create();n.lerp(V,D,B,P),n.lerp(H,z,N,P),n.lerp(q,R,j,P),n.lerp(G,F,U,P);var Z=n.create(),Y=n.create();n.lerp(Z,V,q,O),n.lerp(Y,H,G,O);var W=n.create();return n.lerp(W,Z,Y,I),W}(e,t,p)},v=t.getDivergence||function(t,e){var r=n.create(),i=1e-4;n.add(r,t,[i,0,0]);var a=d(r);n.subtract(a,a,e),n.scale(a,a,1/i),n.add(r,t,[0,i,0]);var o=d(r);n.subtract(o,o,e),n.scale(o,o,1/i),n.add(r,t,[0,0,i]);var s=d(r);return n.subtract(s,s,e),n.scale(s,s,1/i),n.add(r,a,o),n.add(r,r,s),r},g=[],y=e[0][0],m=e[0][1],x=e[0][2],b=e[1][0],_=e[1][1],w=e[1][2],T=function(t){var e=t[0],r=t[1],n=t[2];return!(e<y||e>b||r<m||r>_||n<x||n>w)},k=10*n.distance(e[0],e[1])/u,A=k*k,M=1,S=0,E=r.length;E>1&&(M=function(t){for(var e=[],r=[],n=[],i={},a={},o={},s=t.length,u=0;u<s;u++){var c=t[u],f=c[0],h=c[1],p=c[2];i[f]||(e.push(f),i[f]=!0),a[h]||(r.push(h),a[h]=!0),o[p]||(n.push(p),o[p]=!0)}var d=l(e),v=l(r),g=l(n),y=Math.min(d,v,g);return isFinite(y)?y:1}(r));for(var L=0;L<E;L++){var C=n.create();n.copy(C,r[L]);var P=[C],O=[],I=d(C),D=C;O.push(I);var z=[],R=v(C,I),F=n.length(R);isFinite(F)&&F>S&&(S=F),z.push(F),g.push({points:P,velocities:O,divergences:z});for(var B=0;B<100*u&&P.length<u&&T(C);){B++;var N=n.clone(I),j=n.squaredLength(N);if(0===j)break;j>A&&n.scale(N,N,k/Math.sqrt(j)),n.add(N,N,C),I=d(N),n.squaredDistance(D,N)-A>-1e-4*A&&(P.push(N),D=N,O.push(I),R=v(N,I),F=n.length(R),isFinite(F)&&F>S&&(S=F),z.push(F)),C=N}}var U=function(t,e,r,a){for(var o=0,s=0;s<t.length;s++)for(var l=t[s].velocities,u=0;u<l.length;u++)o=Math.max(o,n.length(l[u]));var c=t.map((function(t){return function(t,e,r,a){for(var o=t.points,s=t.velocities,l=t.divergences,u=[],c=[],f=[],h=[],p=[],d=[],v=0,g=0,y=i.create(),m=i.create(),x=0;x<o.length;x++){var b=o[x],_=s[x],w=l[x];0===e&&(w=.05*r),g=n.length(_)/a,y=i.create(),n.copy(y,_),y[3]=w;for(var T=0;T<8;T++)p[T]=[b[0],b[1],b[2],T];if(h.length>0)for(T=0;T<8;T++){var k=(T+1)%8;u.push(h[T],p[T],p[k],p[k],h[k],h[T]),f.push(m,y,y,y,m,m),d.push(v,g,g,g,v,v);var A=u.length;c.push([A-6,A-5,A-4],[A-3,A-2,A-1])}var M=h;h=p,p=M;var S=m;m=y,y=S;var E=v;v=g,g=E}return{positions:u,cells:c,vectors:f,vertexIntensity:d}}(t,r,a,o)})),f=[],h=[],p=[],d=[];for(s=0;s<c.length;s++){var v=c[s],g=f.length;for(f=f.concat(v.positions),p=p.concat(v.vectors),d=d.concat(v.vertexIntensity),u=0;u<v.cells.length;u++){var y=v.cells[u],m=[];h.push(m);for(var x=0;x<y.length;x++)m.push(y[x]+g)}}return{positions:f,cells:h,vectors:p,vertexIntensity:d,colormap:e}}(g,t.colormap,S,M);return f?U.tubeScale=f:(0===S&&(S=1),U.tubeScale=.5*c*M/S),U};var u=r(9578),c=r(1140).createMesh;t.exports.createTubeMesh=function(t,e){return c(t,e,{shaders:u,traceType:\"streamtube\"})}},9054:function(t,e,r){var n=r(5158),i=r(6832),a=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec4 uv;\\nattribute vec3 f;\\nattribute vec3 normal;\\n\\nuniform vec3 objectOffset;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 lightPosition, eyePosition;\\nuniform sampler2D colormap;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n  vec3 localCoordinate = vec3(uv.zw, f.x);\\n  worldCoordinate = objectOffset + localCoordinate;\\n  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\\n  vec4 clipPosition = projection * view * worldPosition;\\n  gl_Position = clipPosition;\\n  kill = f.y;\\n  value = f.z;\\n  planeCoordinate = uv.xy;\\n\\n  vColor = texture2D(colormap, vec2(value, value));\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * worldPosition;\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  lightDirection = lightPosition - cameraCoordinate.xyz;\\n  eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  surfaceNormal  = normalize((vec4(normal,0) * inverseModel).xyz);\\n}\\n\"]),o=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat beckmannSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness) {\\n  return beckmannDistribution(dot(surfaceNormal, normalize(lightDirection + viewDirection)), roughness);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 lowerBound, upperBound;\\nuniform float contourTint;\\nuniform vec4 contourColor;\\nuniform sampler2D colormap;\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform float vertexColor;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n  if (\\n    kill > 0.0 ||\\n    vColor.a == 0.0 ||\\n    outOfRange(clipBounds[0], clipBounds[1], worldCoordinate)\\n  ) discard;\\n\\n  vec3 N = normalize(surfaceNormal);\\n  vec3 V = normalize(eyeDirection);\\n  vec3 L = normalize(lightDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = max(beckmannSpecular(L, V, N, roughness), 0.);\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  //decide how to interpolate color — in vertex or in fragment\\n  vec4 surfaceColor =\\n    step(vertexColor, .5) * texture2D(colormap, vec2(value, value)) +\\n    step(.5, vertexColor) * vColor;\\n\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = mix(litColor, contourColor, contourTint) * opacity;\\n}\\n\"]),s=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec4 uv;\\nattribute float f;\\n\\nuniform vec3 objectOffset;\\nuniform mat3 permutation;\\nuniform mat4 model, view, projection;\\nuniform float height, zOffset;\\nuniform sampler2D colormap;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n  vec3 dataCoordinate = permutation * vec3(uv.xy, height);\\n  worldCoordinate = objectOffset + dataCoordinate;\\n  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\\n\\n  vec4 clipPosition = projection * view * worldPosition;\\n  clipPosition.z += zOffset;\\n\\n  gl_Position = clipPosition;\\n  value = f + objectOffset.z;\\n  kill = -1.0;\\n  planeCoordinate = uv.zw;\\n\\n  vColor = texture2D(colormap, vec2(value, value));\\n\\n  //Don't do lighting for contours\\n  surfaceNormal   = vec3(1,0,0);\\n  eyeDirection    = vec3(0,1,0);\\n  lightDirection  = vec3(0,0,1);\\n}\\n\"]),l=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec2 shape;\\nuniform vec3 clipBounds[2];\\nuniform float pickId;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 surfaceNormal;\\n\\nvec2 splitFloat(float v) {\\n  float vh = 255.0 * v;\\n  float upper = floor(vh);\\n  float lower = fract(vh);\\n  return vec2(upper / 255.0, floor(lower * 16.0) / 16.0);\\n}\\n\\nvoid main() {\\n  if ((kill > 0.0) ||\\n      (outOfRange(clipBounds[0], clipBounds[1], worldCoordinate))) discard;\\n\\n  vec2 ux = splitFloat(planeCoordinate.x / shape.x);\\n  vec2 uy = splitFloat(planeCoordinate.y / shape.y);\\n  gl_FragColor = vec4(pickId, ux.x, uy.x, ux.y + (uy.y/16.0));\\n}\\n\"]);e.createShader=function(t){var e=n(t,a,o,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},e.createPickShader=function(t){var e=n(t,a,l,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},e.createContourShader=function(t){var e=n(t,s,o,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e},e.createPickContourShader=function(t){var e=n(t,s,l,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e}},3754:function(t,e,r){\"use strict\";t.exports=function(t){var e=t.gl,r=m(e),n=b(e),s=x(e),l=_(e),u=i(e),c=a(e,[{buffer:u,size:4,stride:w,offset:0},{buffer:u,size:3,stride:w,offset:16},{buffer:u,size:3,stride:w,offset:28}]),f=i(e),h=a(e,[{buffer:f,size:4,stride:20,offset:0},{buffer:f,size:1,stride:20,offset:16}]),p=i(e),d=a(e,[{buffer:p,size:2,type:e.FLOAT}]),v=o(e,1,S,e.RGBA,e.UNSIGNED_BYTE);v.minFilter=e.LINEAR,v.magFilter=e.LINEAR;var g=new E(e,[0,0],[[0,0,0],[0,0,0]],r,n,u,c,v,s,l,f,h,p,d,[0,0,0]),y={levels:[[],[],[]]};for(var T in t)y[T]=t[T];return y.colormap=y.colormap||\"jet\",g.update(y),g};var n=r(2288),i=r(5827),a=r(2944),o=r(8931),s=r(5306),l=r(9156),u=r(7498),c=r(7382),f=r(5050),h=r(4162),p=r(104),d=r(7437),v=r(5070),g=r(9144),y=r(9054),m=y.createShader,x=y.createContourShader,b=y.createPickShader,_=y.createPickContourShader,w=40,T=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],k=[[0,0],[0,1],[1,0],[1,1],[1,0],[0,1]],A=[[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0]];function M(t,e,r,n,i){this.position=t,this.index=e,this.uv=r,this.level=n,this.dataCoordinate=i}!function(){for(var t=0;t<3;++t){var e=A[t],r=(t+2)%3;e[(t+1)%3+0]=1,e[r+3]=1,e[t+6]=1}}();var S=256;function E(t,e,r,n,i,a,o,l,u,c,h,p,d,v,g){this.gl=t,this.shape=e,this.bounds=r,this.objectOffset=g,this.intensityBounds=[],this._shader=n,this._pickShader=i,this._coordinateBuffer=a,this._vao=o,this._colorMap=l,this._contourShader=u,this._contourPickShader=c,this._contourBuffer=h,this._contourVAO=p,this._contourOffsets=[[],[],[]],this._contourCounts=[[],[],[]],this._vertexCount=0,this._pickResult=new M([0,0,0],[0,0],[0,0],[0,0,0],[0,0,0]),this._dynamicBuffer=d,this._dynamicVAO=v,this._dynamicOffsets=[0,0,0],this._dynamicCounts=[0,0,0],this.contourWidth=[1,1,1],this.contourLevels=[[1],[1],[1]],this.contourTint=[0,0,0],this.contourColor=[[.5,.5,.5,1],[.5,.5,.5,1],[.5,.5,.5,1]],this.showContour=!0,this.showSurface=!0,this.enableHighlight=[!0,!0,!0],this.highlightColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.highlightTint=[1,1,1],this.highlightLevel=[-1,-1,-1],this.enableDynamic=[!0,!0,!0],this.dynamicLevel=[NaN,NaN,NaN],this.dynamicColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.dynamicTint=[1,1,1],this.dynamicWidth=[1,1,1],this.axesBounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.surfaceProject=[!1,!1,!1],this.contourProject=[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],this.colorBounds=[!1,!1],this._field=[f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0])],this.pickId=1,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.snapToData=!1,this.pixelRatio=1,this.opacity=1,this.lightPosition=[10,1e4,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.vertexColor=0,this.dirty=!0}var L=E.prototype;L.genColormap=function(t,e){var r=!1,n=c([l({colormap:t,nshades:S,format:\"rgba\"}).map((function(t,n){var i=e?function(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;r<e.length;++r){if(e.length<2)return 1;if(e[r][0]===t)return e[r][1];if(e[r][0]>t&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}(n/255,e):t[3];return i<1&&(r=!0),[t[0],t[1],t[2],255*i]}))]);return u.divseq(n,255),this.hasAlphaScale=r,n},L.isTransparent=function(){return this.opacity<1||this.hasAlphaScale},L.isOpaque=function(){return!this.isTransparent()},L.pickSlots=1,L.setPickBase=function(t){this.pickId=t};var C=[0,0,0],P={showSurface:!1,showContour:!1,projections:[T.slice(),T.slice(),T.slice()],clipBounds:[[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]]]};function O(t,e){var r,n,i,a=e.axes&&e.axes.lastCubeProps.axis||C,o=e.showSurface,s=e.showContour;for(r=0;r<3;++r)for(o=o||e.surfaceProject[r],n=0;n<3;++n)s=s||e.contourProject[r][n];for(r=0;r<3;++r){var l=P.projections[r];for(n=0;n<16;++n)l[n]=0;for(n=0;n<4;++n)l[5*n]=1;l[5*r]=0,l[12+r]=e.axesBounds[+(a[r]>0)][r],p(l,t.model,l);var u=P.clipBounds[r];for(i=0;i<2;++i)for(n=0;n<3;++n)u[i][n]=t.clipBounds[i][n];u[0][r]=-1e8,u[1][r]=1e8}return P.showSurface=o,P.showContour=s,P}var I={model:T,view:T,projection:T,inverseModel:T.slice(),lowerBound:[0,0,0],upperBound:[0,0,0],colorMap:0,clipBounds:[[0,0,0],[0,0,0]],height:0,contourTint:0,contourColor:[0,0,0,1],permutation:[1,0,0,0,1,0,0,0,1],zOffset:-1e-4,objectOffset:[0,0,0],kambient:1,kdiffuse:1,kspecular:1,lightPosition:[1e3,1e3,1e3],eyePosition:[0,0,0],roughness:1,fresnel:1,opacity:1,vertexColor:0},D=T.slice(),z=[1,0,0,0,1,0,0,0,1];function R(t,e){t=t||{};var r=this.gl;r.disable(r.CULL_FACE),this._colorMap.bind(0);var n=I;n.model=t.model||T,n.view=t.view||T,n.projection=t.projection||T,n.lowerBound=[this.bounds[0][0],this.bounds[0][1],this.colorBounds[0]||this.bounds[0][2]],n.upperBound=[this.bounds[1][0],this.bounds[1][1],this.colorBounds[1]||this.bounds[1][2]],n.objectOffset=this.objectOffset,n.contourColor=this.contourColor[0],n.inverseModel=d(n.inverseModel,n.model);for(var i=0;i<2;++i)for(var a=n.clipBounds[i],o=0;o<3;++o)a[o]=Math.min(Math.max(this.clipBounds[i][o],-1e8),1e8);n.kambient=this.ambientLight,n.kdiffuse=this.diffuseLight,n.kspecular=this.specularLight,n.roughness=this.roughness,n.fresnel=this.fresnel,n.opacity=this.opacity,n.height=0,n.permutation=z,n.vertexColor=this.vertexColor;var s=D;for(p(s,n.view,n.model),p(s,n.projection,s),d(s,s),i=0;i<3;++i)n.eyePosition[i]=s[12+i]/s[15];var l=s[15];for(i=0;i<3;++i)l+=this.lightPosition[i]*s[4*i+3];for(i=0;i<3;++i){var u=s[12+i];for(o=0;o<3;++o)u+=s[4*o+i]*this.lightPosition[o];n.lightPosition[i]=u/l}var c=O(n,this);if(c.showSurface){for(this._shader.bind(),this._shader.uniforms=n,this._vao.bind(),this.showSurface&&this._vertexCount&&this._vao.draw(r.TRIANGLES,this._vertexCount),i=0;i<3;++i)this.surfaceProject[i]&&this.vertexCount&&(this._shader.uniforms.model=c.projections[i],this._shader.uniforms.clipBounds=c.clipBounds[i],this._vao.draw(r.TRIANGLES,this._vertexCount));this._vao.unbind()}if(c.showContour){var f=this._contourShader;n.kambient=1,n.kdiffuse=0,n.kspecular=0,n.opacity=1,f.bind(),f.uniforms=n;var h=this._contourVAO;for(h.bind(),i=0;i<3;++i)for(f.uniforms.permutation=A[i],r.lineWidth(this.contourWidth[i]*this.pixelRatio),o=0;o<this.contourLevels[i].length;++o)o===this.highlightLevel[i]?(f.uniforms.contourColor=this.highlightColor[i],f.uniforms.contourTint=this.highlightTint[i]):0!==o&&o-1!==this.highlightLevel[i]||(f.uniforms.contourColor=this.contourColor[i],f.uniforms.contourTint=this.contourTint[i]),this._contourCounts[i][o]&&(f.uniforms.height=this.contourLevels[i][o],h.draw(r.LINES,this._contourCounts[i][o],this._contourOffsets[i][o]));for(i=0;i<3;++i)for(f.uniforms.model=c.projections[i],f.uniforms.clipBounds=c.clipBounds[i],o=0;o<3;++o)if(this.contourProject[i][o]){f.uniforms.permutation=A[o],r.lineWidth(this.contourWidth[o]*this.pixelRatio);for(var v=0;v<this.contourLevels[o].length;++v)v===this.highlightLevel[o]?(f.uniforms.contourColor=this.highlightColor[o],f.uniforms.contourTint=this.highlightTint[o]):0!==v&&v-1!==this.highlightLevel[o]||(f.uniforms.contourColor=this.contourColor[o],f.uniforms.contourTint=this.contourTint[o]),this._contourCounts[o][v]&&(f.uniforms.height=this.contourLevels[o][v],h.draw(r.LINES,this._contourCounts[o][v],this._contourOffsets[o][v]))}for(h.unbind(),(h=this._dynamicVAO).bind(),i=0;i<3;++i)if(0!==this._dynamicCounts[i])for(f.uniforms.model=n.model,f.uniforms.clipBounds=n.clipBounds,f.uniforms.permutation=A[i],r.lineWidth(this.dynamicWidth[i]*this.pixelRatio),f.uniforms.contourColor=this.dynamicColor[i],f.uniforms.contourTint=this.dynamicTint[i],f.uniforms.height=this.dynamicLevel[i],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]),o=0;o<3;++o)this.contourProject[o][i]&&(f.uniforms.model=c.projections[o],f.uniforms.clipBounds=c.clipBounds[o],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]));h.unbind()}}L.draw=function(t){return R.call(this,t,!1)},L.drawTransparent=function(t){return R.call(this,t,!0)};var F={model:T,view:T,projection:T,inverseModel:T,clipBounds:[[0,0,0],[0,0,0]],height:0,shape:[0,0],pickId:0,lowerBound:[0,0,0],upperBound:[0,0,0],zOffset:0,objectOffset:[0,0,0],permutation:[1,0,0,0,1,0,0,0,1],lightPosition:[0,0,0],eyePosition:[0,0,0]};function B(t,e){return Array.isArray(t)?[e(t[0]),e(t[1]),e(t[2])]:[e(t),e(t),e(t)]}function N(t){return Array.isArray(t)?3===t.length?[t[0],t[1],t[2],1]:[t[0],t[1],t[2],t[3]]:[0,0,0,1]}function j(t){if(Array.isArray(t)){if(Array.isArray(t))return[N(t[0]),N(t[1]),N(t[2])];var e=N(t);return[e.slice(),e.slice(),e.slice()]}}L.drawPick=function(t){t=t||{};var e=this.gl;e.disable(e.CULL_FACE);var r=F;r.model=t.model||T,r.view=t.view||T,r.projection=t.projection||T,r.shape=this._field[2].shape,r.pickId=this.pickId/255,r.lowerBound=this.bounds[0],r.upperBound=this.bounds[1],r.objectOffset=this.objectOffset,r.permutation=z;for(var n=0;n<2;++n)for(var i=r.clipBounds[n],a=0;a<3;++a)i[a]=Math.min(Math.max(this.clipBounds[n][a],-1e8),1e8);var o=O(r,this);if(o.showSurface){for(this._pickShader.bind(),this._pickShader.uniforms=r,this._vao.bind(),this._vao.draw(e.TRIANGLES,this._vertexCount),n=0;n<3;++n)this.surfaceProject[n]&&(this._pickShader.uniforms.model=o.projections[n],this._pickShader.uniforms.clipBounds=o.clipBounds[n],this._vao.draw(e.TRIANGLES,this._vertexCount));this._vao.unbind()}if(o.showContour){var s=this._contourPickShader;s.bind(),s.uniforms=r;var l=this._contourVAO;for(l.bind(),a=0;a<3;++a)for(e.lineWidth(this.contourWidth[a]*this.pixelRatio),s.uniforms.permutation=A[a],n=0;n<this.contourLevels[a].length;++n)this._contourCounts[a][n]&&(s.uniforms.height=this.contourLevels[a][n],l.draw(e.LINES,this._contourCounts[a][n],this._contourOffsets[a][n]));for(n=0;n<3;++n)for(s.uniforms.model=o.projections[n],s.uniforms.clipBounds=o.clipBounds[n],a=0;a<3;++a)if(this.contourProject[n][a]){s.uniforms.permutation=A[a],e.lineWidth(this.contourWidth[a]*this.pixelRatio);for(var u=0;u<this.contourLevels[a].length;++u)this._contourCounts[a][u]&&(s.uniforms.height=this.contourLevels[a][u],l.draw(e.LINES,this._contourCounts[a][u],this._contourOffsets[a][u]))}l.unbind()}},L.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=this._field[2].shape,r=this._pickResult,n=e[0]*(t.value[0]+(t.value[2]>>4)/16)/255,i=Math.floor(n),a=n-i,o=e[1]*(t.value[1]+(15&t.value[2])/16)/255,s=Math.floor(o),l=o-s;i+=1,s+=1;var u=r.position;u[0]=u[1]=u[2]=0;for(var c=0;c<2;++c)for(var f=c?a:1-a,h=0;h<2;++h)for(var p=i+c,d=s+h,g=f*(h?l:1-l),y=0;y<3;++y)u[y]+=this._field[y].get(p,d)*g;for(var m=this._pickResult.level,x=0;x<3;++x)if(m[x]=v.le(this.contourLevels[x],u[x]),m[x]<0)this.contourLevels[x].length>0&&(m[x]=0);else if(m[x]<this.contourLevels[x].length-1){var b=this.contourLevels[x][m[x]],_=this.contourLevels[x][m[x]+1];Math.abs(b-u[x])>Math.abs(_-u[x])&&(m[x]+=1)}for(r.index[0]=a<.5?i:i+1,r.index[1]=l<.5?s:s+1,r.uv[0]=n/e[0],r.uv[1]=o/e[1],y=0;y<3;++y)r.dataCoordinate[y]=this._field[y].get(r.index[0],r.index[1]);return r},L.padField=function(t,e){var r=e.shape.slice(),n=t.shape.slice();u.assign(t.lo(1,1).hi(r[0],r[1]),e),u.assign(t.lo(1).hi(r[0],1),e.hi(r[0],1)),u.assign(t.lo(1,n[1]-1).hi(r[0],1),e.lo(0,r[1]-1).hi(r[0],1)),u.assign(t.lo(0,1).hi(1,r[1]),e.hi(1)),u.assign(t.lo(n[0]-1,1).hi(1,r[1]),e.lo(r[0]-1)),t.set(0,0,e.get(0,0)),t.set(0,n[1]-1,e.get(0,r[1]-1)),t.set(n[0]-1,0,e.get(r[0]-1,0)),t.set(n[0]-1,n[1]-1,e.get(r[0]-1,r[1]-1))},L.update=function(t){t=t||{},this.objectOffset=t.objectOffset||this.objectOffset,this.dirty=!0,\"contourWidth\"in t&&(this.contourWidth=B(t.contourWidth,Number)),\"showContour\"in t&&(this.showContour=B(t.showContour,Boolean)),\"showSurface\"in t&&(this.showSurface=!!t.showSurface),\"contourTint\"in t&&(this.contourTint=B(t.contourTint,Boolean)),\"contourColor\"in t&&(this.contourColor=j(t.contourColor)),\"contourProject\"in t&&(this.contourProject=B(t.contourProject,(function(t){return B(t,Boolean)}))),\"surfaceProject\"in t&&(this.surfaceProject=t.surfaceProject),\"dynamicColor\"in t&&(this.dynamicColor=j(t.dynamicColor)),\"dynamicTint\"in t&&(this.dynamicTint=B(t.dynamicTint,Number)),\"dynamicWidth\"in t&&(this.dynamicWidth=B(t.dynamicWidth,Number)),\"opacity\"in t&&(this.opacity=t.opacity),\"opacityscale\"in t&&(this.opacityscale=t.opacityscale),\"colorBounds\"in t&&(this.colorBounds=t.colorBounds),\"vertexColor\"in t&&(this.vertexColor=t.vertexColor?1:0),\"colormap\"in t&&this._colorMap.setPixels(this.genColormap(t.colormap,this.opacityscale));var e=t.field||t.coords&&t.coords[2]||null,r=!1;if(e||(e=this._field[2].shape[0]||this._field[2].shape[2]?this._field[2].lo(1,1).hi(this._field[2].shape[0]-2,this._field[2].shape[1]-2):this._field[2].hi(0,0)),\"field\"in t||\"coords\"in t){var i=(e.shape[0]+2)*(e.shape[1]+2);i>this._field[2].data.length&&(s.freeFloat(this._field[2].data),this._field[2].data=s.mallocFloat(n.nextPow2(i))),this._field[2]=f(this._field[2].data,[e.shape[0]+2,e.shape[1]+2]),this.padField(this._field[2],e),this.shape=e.shape.slice();for(var a=this.shape,o=0;o<2;++o)this._field[2].size>this._field[o].data.length&&(s.freeFloat(this._field[o].data),this._field[o].data=s.mallocFloat(this._field[2].size)),this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2]);if(t.coords){var l=t.coords;if(!Array.isArray(l)||3!==l.length)throw new Error(\"gl-surface: invalid coordinates for x/y\");for(o=0;o<2;++o){var u=l[o];for(y=0;y<2;++y)if(u.shape[y]!==a[y])throw new Error(\"gl-surface: coords have incorrect shape\");this.padField(this._field[o],u)}}else if(t.ticks){var c=t.ticks;if(!Array.isArray(c)||2!==c.length)throw new Error(\"gl-surface: invalid ticks\");for(o=0;o<2;++o){var p=c[o];if((Array.isArray(p)||p.length)&&(p=f(p)),p.shape[0]!==a[o])throw new Error(\"gl-surface: invalid tick length\");var d=f(p.data,a);d.stride[o]=p.stride[0],d.stride[1^o]=0,this.padField(this._field[o],d)}}else{for(o=0;o<2;++o){var v=[0,0];v[o]=1,this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2],v,0)}this._field[0].set(0,0,0);for(var y=0;y<a[0];++y)this._field[0].set(y+1,0,y);for(this._field[0].set(a[0]+1,0,a[0]-1),this._field[1].set(0,0,0),y=0;y<a[1];++y)this._field[1].set(0,y+1,y);this._field[1].set(0,a[1]+1,a[1]-1)}var m=this._field,x=f(s.mallocFloat(3*m[2].size*2),[3,a[0]+2,a[1]+2,2]);for(o=0;o<3;++o)g(x.pick(o),m[o],\"mirror\");var b=f(s.mallocFloat(3*m[2].size),[a[0]+2,a[1]+2,3]);for(o=0;o<a[0]+2;++o)for(y=0;y<a[1]+2;++y){var _=x.get(0,o,y,0),w=x.get(0,o,y,1),T=x.get(1,o,y,0),A=x.get(1,o,y,1),M=x.get(2,o,y,0),S=x.get(2,o,y,1),E=T*S-A*M,L=M*w-S*_,C=_*A-w*T,P=Math.sqrt(E*E+L*L+C*C);P<1e-8?(P=Math.max(Math.abs(E),Math.abs(L),Math.abs(C)))<1e-8?(C=1,L=E=0,P=1):P=1/P:P=1/Math.sqrt(P),b.set(o,y,0,E*P),b.set(o,y,1,L*P),b.set(o,y,2,C*P)}s.free(x.data);var O=[1/0,1/0,1/0],I=[-1/0,-1/0,-1/0],D=1/0,z=-1/0,R=(a[0]-1)*(a[1]-1)*6,F=s.mallocFloat(n.nextPow2(10*R)),N=0,U=0;for(o=0;o<a[0]-1;++o)t:for(y=0;y<a[1]-1;++y){for(var V=0;V<2;++V)for(var H=0;H<2;++H)for(var q=0;q<3;++q){var G=this._field[q].get(1+o+V,1+y+H);if(isNaN(G)||!isFinite(G))continue t}for(q=0;q<6;++q){var Z=o+k[q][0],Y=y+k[q][1],W=this._field[0].get(Z+1,Y+1),X=this._field[1].get(Z+1,Y+1);G=this._field[2].get(Z+1,Y+1),E=b.get(Z+1,Y+1,0),L=b.get(Z+1,Y+1,1),C=b.get(Z+1,Y+1,2),t.intensity&&(J=t.intensity.get(Z,Y));var J=t.intensity?t.intensity.get(Z,Y):G+this.objectOffset[2];F[N++]=Z,F[N++]=Y,F[N++]=W,F[N++]=X,F[N++]=G,F[N++]=0,F[N++]=J,F[N++]=E,F[N++]=L,F[N++]=C,O[0]=Math.min(O[0],W+this.objectOffset[0]),O[1]=Math.min(O[1],X+this.objectOffset[1]),O[2]=Math.min(O[2],G+this.objectOffset[2]),D=Math.min(D,J),I[0]=Math.max(I[0],W+this.objectOffset[0]),I[1]=Math.max(I[1],X+this.objectOffset[1]),I[2]=Math.max(I[2],G+this.objectOffset[2]),z=Math.max(z,J),U+=1}}for(t.intensityBounds&&(D=+t.intensityBounds[0],z=+t.intensityBounds[1]),o=6;o<N;o+=10)F[o]=(F[o]-D)/(z-D);this._vertexCount=U,this._coordinateBuffer.update(F.subarray(0,N)),s.freeFloat(F),s.free(b.data),this.bounds=[O,I],this.intensity=t.intensity||this._field[2],this.intensityBounds[0]===D&&this.intensityBounds[1]===z||(r=!0),this.intensityBounds=[D,z]}if(\"levels\"in t){var K=t.levels;for(K=Array.isArray(K[0])?K.slice():[[],[],K],o=0;o<3;++o)K[o]=K[o].slice(),K[o].sort((function(t,e){return t-e}));for(o=0;o<3;++o)for(y=0;y<K[o].length;++y)K[o][y]-=this.objectOffset[o];t:for(o=0;o<3;++o){if(K[o].length!==this.contourLevels[o].length){r=!0;break}for(y=0;y<K[o].length;++y)if(K[o][y]!==this.contourLevels[o][y]){r=!0;break t}}this.contourLevels=K}if(r){m=this._field,a=this.shape;for(var $=[],Q=0;Q<3;++Q){var tt=this.contourLevels[Q],et=[],rt=[],nt=[0,0,0];for(o=0;o<tt.length;++o){var it=h(this._field[Q],tt[o]);et.push($.length/5|0),U=0;t:for(y=0;y<it.cells.length;++y){var at=it.cells[y];for(q=0;q<2;++q){var ot=it.positions[at[q]],st=ot[0],lt=0|Math.floor(st),ut=st-lt,ct=ot[1],ft=0|Math.floor(ct),ht=ct-ft,pt=!1;e:for(var dt=0;dt<3;++dt){nt[dt]=0;var vt=(Q+dt+1)%3;for(V=0;V<2;++V){var gt=V?ut:1-ut;for(Z=0|Math.min(Math.max(lt+V,0),a[0]),H=0;H<2;++H){var yt=H?ht:1-ht;if(Y=0|Math.min(Math.max(ft+H,0),a[1]),G=dt<2?this._field[vt].get(Z,Y):(this.intensity.get(Z,Y)-this.intensityBounds[0])/(this.intensityBounds[1]-this.intensityBounds[0]),!isFinite(G)||isNaN(G)){pt=!0;break e}var mt=gt*yt;nt[dt]+=mt*G}}}if(pt){if(q>0){for(var xt=0;xt<5;++xt)$.pop();U-=1}continue t}$.push(nt[0],nt[1],ot[0],ot[1],nt[2]),U+=1}}rt.push(U)}this._contourOffsets[Q]=et,this._contourCounts[Q]=rt}var bt=s.mallocFloat($.length);for(o=0;o<$.length;++o)bt[o]=$[o];this._contourBuffer.update(bt),s.freeFloat(bt)}},L.dispose=function(){this._shader.dispose(),this._vao.dispose(),this._coordinateBuffer.dispose(),this._colorMap.dispose(),this._contourBuffer.dispose(),this._contourVAO.dispose(),this._contourShader.dispose(),this._contourPickShader.dispose(),this._dynamicBuffer.dispose(),this._dynamicVAO.dispose();for(var t=0;t<3;++t)s.freeFloat(this._field[t].data)},L.highlight=function(t){var e,r;if(!t)return this._dynamicCounts=[0,0,0],this.dyanamicLevel=[NaN,NaN,NaN],void(this.highlightLevel=[-1,-1,-1]);for(e=0;e<3;++e)this.enableHighlight[e]?this.highlightLevel[e]=t.level[e]:this.highlightLevel[e]=-1;for(r=this.snapToData?t.dataCoordinate:t.position,e=0;e<3;++e)r[e]-=this.objectOffset[e];if(this.enableDynamic[0]&&r[0]!==this.dynamicLevel[0]||this.enableDynamic[1]&&r[1]!==this.dynamicLevel[1]||this.enableDynamic[2]&&r[2]!==this.dynamicLevel[2]){for(var n=0,i=this.shape,a=s.mallocFloat(12*i[0]*i[1]),o=0;o<3;++o)if(this.enableDynamic[o]){this.dynamicLevel[o]=r[o];var l=(o+1)%3,u=(o+2)%3,c=this._field[o],f=this._field[l],p=this._field[u],d=h(c,r[o]),v=d.cells,g=d.positions;for(this._dynamicOffsets[o]=n,e=0;e<v.length;++e)for(var y=v[e],m=0;m<2;++m){var x=g[y[m]],b=+x[0],_=0|b,w=0|Math.min(_+1,i[0]),T=b-_,k=1-T,A=+x[1],M=0|A,S=0|Math.min(M+1,i[1]),E=A-M,L=1-E,C=k*L,P=k*E,O=T*L,I=T*E,D=C*f.get(_,M)+P*f.get(_,S)+O*f.get(w,M)+I*f.get(w,S),z=C*p.get(_,M)+P*p.get(_,S)+O*p.get(w,M)+I*p.get(w,S);if(isNaN(D)||isNaN(z)){m&&(n-=1);break}a[2*n+0]=D,a[2*n+1]=z,n+=1}this._dynamicCounts[o]=n-this._dynamicOffsets[o]}else this.dynamicLevel[o]=NaN,this._dynamicCounts[o]=0;this._dynamicBuffer.update(a.subarray(0,2*n)),s.freeFloat(a)}}},8931:function(t,e,r){\"use strict\";var n=r(5050),i=r(7498),a=r(5306);t.exports=function(t){if(arguments.length<=1)throw new Error(\"gl-texture2d: Missing arguments for texture2d constructor\");if(o||function(t){o=[t.LINEAR,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_NEAREST],s=[t.NEAREST,t.LINEAR,t.NEAREST_MIPMAP_NEAREST,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_LINEAR],l=[t.REPEAT,t.CLAMP_TO_EDGE,t.MIRRORED_REPEAT]}(t),\"number\"==typeof arguments[1])return g(t,arguments[1],arguments[2],arguments[3]||t.RGBA,arguments[4]||t.UNSIGNED_BYTE);if(Array.isArray(arguments[1]))return g(t,0|arguments[1][0],0|arguments[1][1],arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if(\"object\"==typeof arguments[1]){var e=arguments[1],r=u(e)?e:e.raw;if(r)return function(t,e,r,n,i,a){var o=v(t);return t.texImage2D(t.TEXTURE_2D,0,i,i,a,e),new h(t,o,r,n,i,a)}(t,r,0|e.width,0|e.height,arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if(e.shape&&e.data&&e.stride)return function(t,e){var r=e.dtype,o=e.shape.slice(),s=t.getParameter(t.MAX_TEXTURE_SIZE);if(o[0]<0||o[0]>s||o[1]<0||o[1]>s)throw new Error(\"gl-texture2d: Invalid texture size\");var l=d(o,e.stride.slice()),u=0;\"float32\"===r?u=t.FLOAT:\"float64\"===r?(u=t.FLOAT,l=!1,r=\"float32\"):\"uint8\"===r?u=t.UNSIGNED_BYTE:(u=t.UNSIGNED_BYTE,l=!1,r=\"uint8\");var f,p,g=0;if(2===o.length)g=t.LUMINANCE,o=[o[0],o[1],1],e=n(e.data,o,[e.stride[0],e.stride[1],1],e.offset);else{if(3!==o.length)throw new Error(\"gl-texture2d: Invalid shape for texture\");if(1===o[2])g=t.ALPHA;else if(2===o[2])g=t.LUMINANCE_ALPHA;else if(3===o[2])g=t.RGB;else{if(4!==o[2])throw new Error(\"gl-texture2d: Invalid shape for pixel coords\");g=t.RGBA}}u!==t.FLOAT||t.getExtension(\"OES_texture_float\")||(u=t.UNSIGNED_BYTE,l=!1);var y=e.size;if(l)f=0===e.offset&&e.data.length===y?e.data:e.data.subarray(e.offset,e.offset+y);else{var m=[o[2],o[2]*o[0],1];p=a.malloc(y,r);var x=n(p,o,m,0);\"float32\"!==r&&\"float64\"!==r||u!==t.UNSIGNED_BYTE?i.assign(x,e):c(x,e),f=p.subarray(0,y)}var b=v(t);return t.texImage2D(t.TEXTURE_2D,0,g,o[0],o[1],0,g,u,f),l||a.free(p),new h(t,b,o[0],o[1],g,u)}(t,e)}throw new Error(\"gl-texture2d: Invalid arguments for texture2d constructor\")};var o=null,s=null,l=null;function u(t){return\"undefined\"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||\"undefined\"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||\"undefined\"!=typeof HTMLVideoElement&&t instanceof HTMLVideoElement||\"undefined\"!=typeof ImageData&&t instanceof ImageData}var c=function(t,e){i.muls(t,e,255)};function f(t,e,r){var n=t.gl,i=n.getParameter(n.MAX_TEXTURE_SIZE);if(e<0||e>i||r<0||r>i)throw new Error(\"gl-texture2d: Invalid texture size\");return t._shape=[e,r],t.bind(),n.texImage2D(n.TEXTURE_2D,0,t.format,e,r,0,t.format,t.type,null),t._mipLevels=[0],t}function h(t,e,r,n,i,a){this.gl=t,this.handle=e,this.format=i,this.type=a,this._shape=[r,n],this._mipLevels=[0],this._magFilter=t.NEAREST,this._minFilter=t.NEAREST,this._wrapS=t.CLAMP_TO_EDGE,this._wrapT=t.CLAMP_TO_EDGE,this._anisoSamples=1;var o=this,s=[this._wrapS,this._wrapT];Object.defineProperties(s,[{get:function(){return o._wrapS},set:function(t){return o.wrapS=t}},{get:function(){return o._wrapT},set:function(t){return o.wrapT=t}}]),this._wrapVector=s;var l=[this._shape[0],this._shape[1]];Object.defineProperties(l,[{get:function(){return o._shape[0]},set:function(t){return o.width=t}},{get:function(){return o._shape[1]},set:function(t){return o.height=t}}]),this._shapeVector=l}var p=h.prototype;function d(t,e){return 3===t.length?1===e[2]&&e[1]===t[0]*t[2]&&e[0]===t[2]:1===e[0]&&e[1]===t[0]}function v(t){var e=t.createTexture();return t.bindTexture(t.TEXTURE_2D,e),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),e}function g(t,e,r,n,i){var a=t.getParameter(t.MAX_TEXTURE_SIZE);if(e<0||e>a||r<0||r>a)throw new Error(\"gl-texture2d: Invalid texture shape\");if(i===t.FLOAT&&!t.getExtension(\"OES_texture_float\"))throw new Error(\"gl-texture2d: Floating point textures not supported on this platform\");var o=v(t);return t.texImage2D(t.TEXTURE_2D,0,n,e,r,0,n,i,null),new h(t,o,e,r,n,i)}Object.defineProperties(p,{minFilter:{get:function(){return this._minFilter},set:function(t){this.bind();var 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n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],u=e[8],c=e[9],f=e[10],h=e[11];return e!==t&&(t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]),t[0]=a*i-u*n,t[1]=o*i-c*n,t[2]=s*i-f*n,t[3]=l*i-h*n,t[8]=a*n+u*i,t[9]=o*n+c*i,t[10]=s*n+f*i,t[11]=l*n+h*i,t}},15692:function(t){t.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],u=e[4],c=e[5],f=e[6],h=e[7];return e!==t&&(t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]),t[0]=a*i+u*n,t[1]=o*i+c*n,t[2]=s*i+f*n,t[3]=l*i+h*n,t[4]=u*i-a*n,t[5]=c*i-o*n,t[6]=f*i-s*n,t[7]=h*i-l*n,t}},10789:function(t){t.exports=function(t,e,r){var n=r[0],i=r[1],a=r[2];return t[0]=e[0]*n,t[1]=e[1]*n,t[2]=e[2]*n,t[3]=e[3]*n,t[4]=e[4]*i,t[5]=e[5]*i,t[6]=e[6]*i,t[7]=e[7]*i,t[8]=e[8]*a,t[9]=e[9]*a,t[10]=e[10]*a,t[11]=e[11]*a,t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t}},6726:function(t){t.exports=function(t){return\"mat4(\"+t[0]+\", \"+t[1]+\", \"+t[2]+\", \"+t[3]+\", \"+t[4]+\", \"+t[5]+\", \"+t[6]+\", \"+t[7]+\", \"+t[8]+\", \"+t[9]+\", \"+t[10]+\", \"+t[11]+\", \"+t[12]+\", \"+t[13]+\", \"+t[14]+\", \"+t[15]+\")\"}},31283:function(t){t.exports=function(t,e,r){var n,i,a,o,s,l,u,c,f,h,p,d,v=r[0],g=r[1],y=r[2];return e===t?(t[12]=e[0]*v+e[4]*g+e[8]*y+e[12],t[13]=e[1]*v+e[5]*g+e[9]*y+e[13],t[14]=e[2]*v+e[6]*g+e[10]*y+e[14],t[15]=e[3]*v+e[7]*g+e[11]*y+e[15]):(n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],u=e[6],c=e[7],f=e[8],h=e[9],p=e[10],d=e[11],t[0]=n,t[1]=i,t[2]=a,t[3]=o,t[4]=s,t[5]=l,t[6]=u,t[7]=c,t[8]=f,t[9]=h,t[10]=p,t[11]=d,t[12]=n*v+s*g+f*y+e[12],t[13]=i*v+l*g+h*y+e[13],t[14]=a*v+u*g+p*y+e[14],t[15]=o*v+c*g+d*y+e[15]),t}},88654:function(t){t.exports=function(t,e){if(t===e){var r=e[1],n=e[2],i=e[3],a=e[6],o=e[7],s=e[11];t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=r,t[6]=e[9],t[7]=e[13],t[8]=n,t[9]=a,t[11]=e[14],t[12]=i,t[13]=o,t[14]=s}else t[0]=e[0],t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=e[1],t[5]=e[5],t[6]=e[9],t[7]=e[13],t[8]=e[2],t[9]=e[6],t[10]=e[10],t[11]=e[14],t[12]=e[3],t[13]=e[7],t[14]=e[11],t[15]=e[15];return t}},42505:function(t,e,r){\"use strict\";var n=r(72791),i=r(71299),a=r(98580),o=r(12018),s=r(83522),l=r(25075),u=r(68016),c=r(58404),f=r(18863),h=r(10973),p=r(25677),d=r(75686),v=r(53545),g=r(56131),y=r(32879),m=r(30120),x=r(13547).nextPow2,b=new s,_=!1;if(document.body){var w=document.body.appendChild(document.createElement(\"div\"));w.style.font=\"italic small-caps bold condensed 16px/2 cursive\",getComputedStyle(w).fontStretch&&(_=!0),document.body.removeChild(w)}var T=function(t){!function(t){return\"function\"==typeof t&&t._gl&&t.prop&&t.texture&&t.buffer}(t)?this.gl=o(t):(t={regl:t},this.gl=t.regl._gl),this.shader=b.get(this.gl),this.shader?this.regl=this.shader.regl:this.regl=t.regl||a({gl:this.gl}),this.charBuffer=this.regl.buffer({type:\"uint8\",usage:\"stream\"}),this.sizeBuffer=this.regl.buffer({type:\"float\",usage:\"stream\"}),this.shader||(this.shader=this.createShader(),b.set(this.gl,this.shader)),this.batch=[],this.fontSize=[],this.font=[],this.fontAtlas=[],this.draw=this.shader.draw.bind(this),this.render=function(){this.regl._refresh(),this.draw(this.batch)},this.canvas=this.gl.canvas,this.update(h(t)?t:{})};T.prototype.createShader=function(){var t=this.regl,e=t({blend:{enable:!0,color:[0,0,0,1],func:{srcRGB:\"src alpha\",dstRGB:\"one minus src alpha\",srcAlpha:\"one minus dst 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float fontSize, charStep, em, align, baseline;\\n\\t\\t\\tuniform vec4 viewport;\\n\\t\\t\\tuniform vec4 color;\\n\\t\\t\\tuniform vec2 atlasSize, atlasDim, scale, translate, positionOffset;\\n\\t\\t\\tvarying vec2 charCoord, charId;\\n\\t\\t\\tvarying float charWidth;\\n\\t\\t\\tvarying vec4 fontColor;\\n\\t\\t\\tvoid main () {\\n\\t\\t\\t\\tvec2 offset = floor(em * (vec2(align + charOffset, baseline)\\n\\t\\t\\t\\t\\t+ vec2(positionOffset.x, -positionOffset.y)))\\n\\t\\t\\t\\t\\t/ (viewport.zw * scale.xy);\\n\\n\\t\\t\\t\\tvec2 position = (position + translate) * scale;\\n\\t\\t\\t\\tposition += offset * scale;\\n\\n\\t\\t\\t\\tcharCoord = position * viewport.zw + viewport.xy;\\n\\n\\t\\t\\t\\tgl_Position = vec4(position * 2. - 1., 0, 1);\\n\\n\\t\\t\\t\\tgl_PointSize = charStep;\\n\\n\\t\\t\\t\\tcharId.x = mod(char, atlasDim.x);\\n\\t\\t\\t\\tcharId.y = floor(char / atlasDim.x);\\n\\n\\t\\t\\t\\tcharWidth = width * em;\\n\\n\\t\\t\\t\\tfontColor = color / 255.;\\n\\t\\t\\t}\",frag:\"\\n\\t\\t\\tprecision highp float;\\n\\t\\t\\tuniform float fontSize, charStep, opacity;\\n\\t\\t\\tuniform vec2 atlasSize;\\n\\t\\t\\tuniform vec4 viewport;\\n\\t\\t\\tuniform sampler2D atlas;\\n\\t\\t\\tvarying vec4 fontColor;\\n\\t\\t\\tvarying vec2 charCoord, charId;\\n\\t\\t\\tvarying float charWidth;\\n\\n\\t\\t\\tfloat lightness(vec4 color) {\\n\\t\\t\\t\\treturn color.r * 0.299 + color.g * 0.587 + color.b * 0.114;\\n\\t\\t\\t}\\n\\n\\t\\t\\tvoid main () {\\n\\t\\t\\t\\tvec2 uv = gl_FragCoord.xy - charCoord + charStep * .5;\\n\\t\\t\\t\\tfloat halfCharStep = floor(charStep * .5 + .5);\\n\\n\\t\\t\\t\\t// invert y and shift by 1px (FF expecially needs that)\\n\\t\\t\\t\\tuv.y = charStep - uv.y;\\n\\n\\t\\t\\t\\t// ignore points outside of character bounding box\\n\\t\\t\\t\\tfloat halfCharWidth = ceil(charWidth * .5);\\n\\t\\t\\t\\tif (floor(uv.x) > halfCharStep + halfCharWidth ||\\n\\t\\t\\t\\t\\tfloor(uv.x) < halfCharStep - halfCharWidth) return;\\n\\n\\t\\t\\t\\tuv += charId * charStep;\\n\\t\\t\\t\\tuv = uv / atlasSize;\\n\\n\\t\\t\\t\\tvec4 color = fontColor;\\n\\t\\t\\t\\tvec4 mask = texture2D(atlas, uv);\\n\\n\\t\\t\\t\\tfloat maskY = lightness(mask);\\n\\t\\t\\t\\t// float colorY = lightness(color);\\n\\t\\t\\t\\tcolor.a *= maskY;\\n\\t\\t\\t\\tcolor.a *= opacity;\\n\\n\\t\\t\\t\\t// color.a += .1;\\n\\n\\t\\t\\t\\t// antialiasing, see yiq color space y-channel formula\\n\\t\\t\\t\\t// color.rgb += (1. - color.rgb) * (1. - mask.rgb);\\n\\n\\t\\t\\t\\tgl_FragColor = color;\\n\\t\\t\\t}\"});return{regl:t,draw:e,atlas:{}}},T.prototype.update=function(t){var e=this;if(\"string\"==typeof t)t={text:t};else if(!t)return;null!=(t=i(t,{position:\"position positions coord coords coordinates\",font:\"font fontFace fontface typeface cssFont css-font family fontFamily\",fontSize:\"fontSize fontsize size font-size\",text:\"text texts chars characters value values symbols\",align:\"align alignment textAlign textbaseline\",baseline:\"baseline textBaseline textbaseline\",direction:\"dir direction textDirection\",color:\"color colour fill fill-color fillColor textColor textcolor\",kerning:\"kerning kern\",range:\"range dataBox\",viewport:\"vp viewport viewBox viewbox viewPort\",opacity:\"opacity alpha transparency visible visibility opaque\",offset:\"offset positionOffset padding shift indent indentation\"},!0)).opacity&&(Array.isArray(t.opacity)?this.opacity=t.opacity.map((function(t){return parseFloat(t)})):this.opacity=parseFloat(t.opacity)),null!=t.viewport&&(this.viewport=f(t.viewport),this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),null==this.viewport&&(this.viewport={x:0,y:0,width:this.gl.drawingBufferWidth,height:this.gl.drawingBufferHeight},this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),null!=t.kerning&&(this.kerning=t.kerning),null!=t.offset&&(\"number\"==typeof t.offset&&(t.offset=[t.offset,0]),this.positionOffset=m(t.offset)),t.direction&&(this.direction=t.direction),t.range&&(this.range=t.range,this.scale=[1/(t.range[2]-t.range[0]),1/(t.range[3]-t.range[1])],this.translate=[-t.range[0],-t.range[1]]),t.scale&&(this.scale=t.scale),t.translate&&(this.translate=t.translate),this.scale||(this.scale=[1/this.viewport.width,1/this.viewport.height]),this.translate||(this.translate=[0,0]),this.font.length||t.font||(t.font=T.baseFontSize+\"px sans-serif\");var r,a=!1,o=!1;if(t.font&&(Array.isArray(t.font)?t.font:[t.font]).forEach((function(t,r){if(\"string\"==typeof t)try{t=n.parse(t)}catch(e){t=n.parse(T.baseFontSize+\"px \"+t)}else t=n.parse(n.stringify(t));var i=n.stringify({size:T.baseFontSize,family:t.family,stretch:_?t.stretch:void 0,variant:t.variant,weight:t.weight,style:t.style}),s=p(t.size),l=Math.round(s[0]*d(s[1]));if(l!==e.fontSize[r]&&(o=!0,e.fontSize[r]=l),!(e.font[r]&&i==e.font[r].baseString||(a=!0,e.font[r]=T.fonts[i],e.font[r]))){var u=t.family.join(\", \"),c=[t.style];t.style!=t.variant&&c.push(t.variant),t.variant!=t.weight&&c.push(t.weight),_&&t.weight!=t.stretch&&c.push(t.stretch),e.font[r]={baseString:i,family:u,weight:t.weight,stretch:t.stretch,style:t.style,variant:t.variant,width:{},kerning:{},metrics:y(u,{origin:\"top\",fontSize:T.baseFontSize,fontStyle:c.join(\" \")})},T.fonts[i]=e.font[r]}})),(a||o)&&this.font.forEach((function(r,i){var a=n.stringify({size:e.fontSize[i],family:r.family,stretch:_?r.stretch:void 0,variant:r.variant,weight:r.weight,style:r.style});if(e.fontAtlas[i]=e.shader.atlas[a],!e.fontAtlas[i]){var o=r.metrics;e.shader.atlas[a]=e.fontAtlas[i]={fontString:a,step:2*Math.ceil(e.fontSize[i]*o.bottom*.5),em:e.fontSize[i],cols:0,rows:0,height:0,width:0,chars:[],ids:{},texture:e.regl.texture()}}null==t.text&&(t.text=e.text)})),\"string\"==typeof t.text&&t.position&&t.position.length>2){for(var 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Math.atan2(this.y-t.y,this.x-t.x)},angleWith:function(t){return this.angleWithSep(t.x,t.y)},angleWithSep:function(t,e){return Math.atan2(this.x*e-this.y*t,this.x*t+this.y*e)},_matMult:function(t){var e=t[0]*this.x+t[1]*this.y,r=t[2]*this.x+t[3]*this.y;return this.x=e,this.y=r,this},_add:function(t){return this.x+=t.x,this.y+=t.y,this},_sub:function(t){return this.x-=t.x,this.y-=t.y,this},_mult:function(t){return this.x*=t,this.y*=t,this},_div:function(t){return this.x/=t,this.y/=t,this},_multByPoint:function(t){return this.x*=t.x,this.y*=t.y,this},_divByPoint:function(t){return this.x/=t.x,this.y/=t.y,this},_unit:function(){return this._div(this.mag()),this},_perp:function(){var t=this.y;return this.y=this.x,this.x=-t,this},_rotate:function(t){var e=Math.cos(t),r=Math.sin(t),n=e*this.x-r*this.y,i=r*this.x+e*this.y;return this.x=n,this.y=i,this},_rotateAround:function(t,e){var r=Math.cos(t),n=Math.sin(t),i=e.x+r*(this.x-e.x)-n*(this.y-e.y),a=e.y+n*(this.x-e.x)+r*(this.y-e.y);return 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r.width=t.width,r.height=t.height,n.drawImage(t,0,0,t.width,t.height),n.getImageData(-e,-e,t.width+2*e,t.height+2*e)},resolveURL:function(t){return C||(C=self.document.createElement(\"a\")),C.href=t,C.href},hardwareConcurrency:self.navigator.hardwareConcurrency||4,get devicePixelRatio(){return self.devicePixelRatio},get prefersReducedMotion(){return!!self.matchMedia&&(null==P&&(P=self.matchMedia(\"(prefers-reduced-motion: reduce)\")),P.matches)}},B={API_URL:\"https://api.mapbox.com\",get EVENTS_URL(){return this.API_URL?0===this.API_URL.indexOf(\"https://api.mapbox.cn\")?\"https://events.mapbox.cn/events/v2\":0===this.API_URL.indexOf(\"https://api.mapbox.com\")?\"https://events.mapbox.com/events/v2\":null:null},FEEDBACK_URL:\"https://apps.mapbox.com/feedback\",REQUIRE_ACCESS_TOKEN:!0,ACCESS_TOKEN:null,MAX_PARALLEL_IMAGE_REQUESTS:16},N={supported:!1,testSupport:function(t){!j&&I&&(U?V(t):O=t)}},j=!1,U=!1;function V(t){var 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Date.now()>this._skuTokenExpiresAt},q.prototype.transformRequest=function(t,e){return this._transformRequestFn&&this._transformRequestFn(t,e)||{url:t}},q.prototype.normalizeStyleURL=function(t,e){if(!G(t))return t;var r=X(t);return r.path=\"/styles/v1\"+r.path,this._makeAPIURL(r,this._customAccessToken||e)},q.prototype.normalizeGlyphsURL=function(t,e){if(!G(t))return t;var r=X(t);return r.path=\"/fonts/v1\"+r.path,this._makeAPIURL(r,this._customAccessToken||e)},q.prototype.normalizeSourceURL=function(t,e){if(!G(t))return t;var r=X(t);return r.path=\"/v4/\"+r.authority+\".json\",r.params.push(\"secure\"),this._makeAPIURL(r,this._customAccessToken||e)},q.prototype.normalizeSpriteURL=function(t,e,r,n){var i=X(t);return G(t)?(i.path=\"/styles/v1\"+i.path+\"/sprite\"+e+r,this._makeAPIURL(i,this._customAccessToken||n)):(i.path+=\"\"+e+r,J(i))},q.prototype.normalizeTileURL=function(t,e){if(this._isSkuTokenExpired()&&this._createSkuToken(),t&&!G(t))return t;var r=X(t),n=F.devicePixelRatio>=2||512===e?\"@2x\":\"\",i=N.supported?\".webp\":\"$1\";r.path=r.path.replace(/(\\.(png|jpg)\\d*)(?=$)/,\"\"+n+i),r.path=r.path.replace(/^.+\\/v4\\//,\"/\"),r.path=\"/v4\"+r.path;var a=this._customAccessToken||function(t){for(var e=0,r=t;e<r.length;e+=1){var n=r[e].match(/^access_token=(.*)$/);if(n)return n[1]}return null}(r.params)||B.ACCESS_TOKEN;return B.REQUIRE_ACCESS_TOKEN&&a&&this._skuToken&&r.params.push(\"sku=\"+this._skuToken),this._makeAPIURL(r,a)},q.prototype.canonicalizeTileURL=function(t,e){var r=X(t);if(!r.path.match(/(^\\/v4\\/)/)||!r.path.match(/\\.[\\w]+$/))return t;var n=\"mapbox://tiles/\";n+=r.path.replace(\"/v4/\",\"\");var i=r.params;return e&&(i=i.filter((function(t){return!t.match(/^access_token=/)}))),i.length&&(n+=\"?\"+i.join(\"&\")),n},q.prototype.canonicalizeTileset=function(t,e){for(var r=!!e&&G(e),n=[],i=0,a=t.tiles||[];i<a.length;i+=1){var o=a[i];Y(o)?n.push(this.canonicalizeTileURL(o,r)):n.push(o)}return n},q.prototype._makeAPIURL=function(t,e){var r=\"See https://www.mapbox.com/api-documentation/#access-tokens-and-token-scopes\",n=X(B.API_URL);if(t.protocol=n.protocol,t.authority=n.authority,\"/\"!==n.path&&(t.path=\"\"+n.path+t.path),!B.REQUIRE_ACCESS_TOKEN)return J(t);if(!(e=e||B.ACCESS_TOKEN))throw new Error(\"An API access token is required to use Mapbox GL. \"+r);if(\"s\"===e[0])throw new Error(\"Use a public access token (pk.*) with Mapbox GL, not a secret access token (sk.*). \"+r);return t.params=t.params.filter((function(t){return-1===t.indexOf(\"access_token\")})),t.params.push(\"access_token=\"+e),J(t)};var Z=/^((https?:)?\\/\\/)?([^\\/]+\\.)?mapbox\\.c(n|om)(\\/|\\?|$)/i;function Y(t){return Z.test(t)}var W=/^(\\w+):\\/\\/([^/?]*)(\\/[^?]+)?\\??(.+)?/;function X(t){var e=t.match(W);if(!e)throw new Error(\"Unable to parse URL object\");return{protocol:e[1],authority:e[2],path:e[3]||\"/\",params:e[4]?e[4].split(\"&\"):[]}}function J(t){var e=t.params.length?\"?\"+t.params.join(\"&\"):\"\";return t.protocol+\"://\"+t.authority+t.path+e}var K=\"mapbox.eventData\";function $(t){if(!t)return null;var e,r=t.split(\".\");if(!r||3!==r.length)return null;try{return JSON.parse((e=r[1],decodeURIComponent(self.atob(e).split(\"\").map((function(t){return\"%\"+(\"00\"+t.charCodeAt(0).toString(16)).slice(-2)})).join(\"\"))))}catch(t){return null}}var Q=function(t){this.type=t,this.anonId=null,this.eventData={},this.queue=[],this.pendingRequest=null};Q.prototype.getStorageKey=function(t){var e,r,n=$(B.ACCESS_TOKEN);return e=n&&n.u?(r=n.u,self.btoa(encodeURIComponent(r).replace(/%([0-9A-F]{2})/g,(function(t,e){return String.fromCharCode(Number(\"0x\"+e))})))):B.ACCESS_TOKEN||\"\",t?K+\".\"+t+\":\"+e:K+\":\"+e},Q.prototype.fetchEventData=function(){var t=L(\"localStorage\"),e=this.getStorageKey(),r=this.getStorageKey(\"uuid\");if(t)try{var n=self.localStorage.getItem(e);n&&(this.eventData=JSON.parse(n));var 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r=this.queue.shift(),n=r.id,i=r.timestamp;n&&this.success[n]||(this.anonId||this.fetchEventData(),v(this.anonId)||(this.anonId=d()),this.postEvent(i,{skuToken:this.skuToken},(function(t){t||n&&(e.success[n]=!0)}),t))}},e}(Q),nt=function(t){function e(e){t.call(this,\"appUserTurnstile\"),this._customAccessToken=e}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.postTurnstileEvent=function(t,e){B.EVENTS_URL&&B.ACCESS_TOKEN&&Array.isArray(t)&&t.some((function(t){return G(t)||Y(t)}))&&this.queueRequest(Date.now(),e)},e.prototype.processRequests=function(t){var e=this;if(!this.pendingRequest&&0!==this.queue.length){this.anonId&&this.eventData.lastSuccess&&this.eventData.tokenU||this.fetchEventData();var r=$(B.ACCESS_TOKEN),n=r?r.u:B.ACCESS_TOKEN,i=n!==this.eventData.tokenU;v(this.anonId)||(this.anonId=d(),i=!0);var a=this.queue.shift();if(this.eventData.lastSuccess){var o=new Date(this.eventData.lastSuccess),s=new 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n(t){return(t=Math.round(t))<0?0:t>255?255:t}function i(t){return t<0?0:t>1?1:t}function a(t){return\"%\"===t[t.length-1]?n(parseFloat(t)/100*255):n(parseInt(t))}function o(t){return\"%\"===t[t.length-1]?i(parseFloat(t)/100):i(parseFloat(t))}function s(t,e,r){return r<0?r+=1:r>1&&(r-=1),6*r<1?t+(e-t)*r*6:2*r<1?e:3*r<2?t+(e-t)*(2/3-r)*6:t}try{e.parseCSSColor=function(t){var e,i=t.replace(/ /g,\"\").toLowerCase();if(i in r)return r[i].slice();if(\"#\"===i[0])return 4===i.length?(e=parseInt(i.substr(1),16))>=0&&e<=4095?[(3840&e)>>4|(3840&e)>>8,240&e|(240&e)>>4,15&e|(15&e)<<4,1]:null:7===i.length&&(e=parseInt(i.substr(1),16))>=0&&e<=16777215?[(16711680&e)>>16,(65280&e)>>8,255&e,1]:null;var l=i.indexOf(\"(\"),u=i.indexOf(\")\");if(-1!==l&&u+1===i.length){var c=i.substr(0,l),f=i.substr(l+1,u-(l+1)).split(\",\"),h=1;switch(c){case\"rgba\":if(4!==f.length)return null;h=o(f.pop());case\"rgb\":return 3!==f.length?null:[a(f[0]),a(f[1]),a(f[2]),h];case\"hsla\":if(4!==f.length)return 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se=function(t,e,r){this.sensitivity=t?e?\"variant\":\"case\":e?\"accent\":\"base\",this.locale=r,this.collator=new Intl.Collator(this.locale?this.locale:[],{sensitivity:this.sensitivity,usage:\"search\"})};se.prototype.compare=function(t,e){return this.collator.compare(t,e)},se.prototype.resolvedLocale=function(){return new Intl.Collator(this.locale?this.locale:[]).resolvedOptions().locale};var le=function(t,e,r,n,i){this.text=t,this.image=e,this.scale=r,this.fontStack=n,this.textColor=i},ue=function(t){this.sections=t};ue.fromString=function(t){return new ue([new le(t,null,null,null,null)])},ue.prototype.isEmpty=function(){return 0===this.sections.length||!this.sections.some((function(t){return 0!==t.text.length||t.image&&0!==t.image.name.length}))},ue.factory=function(t){return t instanceof ue?t:ue.fromString(t)},ue.prototype.toString=function(){return 0===this.sections.length?\"\":this.sections.map((function(t){return t.text})).join(\"\")},ue.prototype.serialize=function(){for(var t=[\"format\"],e=0,r=this.sections;e<r.length;e+=1){var n=r[e];if(n.image)t.push([\"image\",n.image.name]);else{t.push(n.text);var i={};n.fontStack&&(i[\"text-font\"]=[\"literal\",n.fontStack.split(\",\")]),n.scale&&(i[\"font-scale\"]=n.scale),n.textColor&&(i[\"text-color\"]=[\"rgba\"].concat(n.textColor.toArray())),t.push(i)}}return t};var ce=function(t){this.name=t.name,this.available=t.available};function fe(t,e,r,n){return\"number\"==typeof t&&t>=0&&t<=255&&\"number\"==typeof e&&e>=0&&e<=255&&\"number\"==typeof r&&r>=0&&r<=255?void 0===n||\"number\"==typeof n&&n>=0&&n<=1?null:\"Invalid rgba value [\"+[t,e,r,n].join(\", \")+\"]: 'a' must be between 0 and 1.\":\"Invalid rgba value [\"+(\"number\"==typeof n?[t,e,r,n]:[t,e,r]).join(\", \")+\"]: 'r', 'g', and 'b' must be between 0 and 255.\"}function he(t){if(null===t)return!0;if(\"string\"==typeof t)return!0;if(\"boolean\"==typeof t)return!0;if(\"number\"==typeof t)return!0;if(t instanceof oe)return!0;if(t instanceof se)return!0;if(t 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ve=function(t,e){this.type=t,this.value=e};ve.parse=function(t,e){if(2!==t.length)return e.error(\"'literal' expression requires exactly one argument, but found \"+(t.length-1)+\" instead.\");if(!he(t[1]))return e.error(\"invalid value\");var r=t[1],n=pe(r),i=e.expectedType;return\"array\"!==n.kind||0!==n.N||!i||\"array\"!==i.kind||\"number\"==typeof i.N&&0!==i.N||(n=i),new ve(n,r)},ve.prototype.evaluate=function(){return this.value},ve.prototype.eachChild=function(){},ve.prototype.outputDefined=function(){return!0},ve.prototype.serialize=function(){return\"array\"===this.type.kind||\"object\"===this.type.kind?[\"literal\",this.value]:this.value instanceof oe?[\"rgba\"].concat(this.value.toArray()):this.value instanceof ue?this.value.serialize():this.value};var ge=function(t){this.name=\"ExpressionEvaluationError\",this.message=t};ge.prototype.toJSON=function(){return this.message};var ye={string:qt,number:Ht,boolean:Gt,object:Yt},me=function(t,e){this.type=t,this.args=e};me.parse=function(t,e){if(t.length<2)return e.error(\"Expected at least one argument.\");var r,n=1,i=t[0];if(\"array\"===i){var a,o;if(t.length>2){var s=t[1];if(\"string\"!=typeof s||!(s in ye)||\"object\"===s)return e.error('The item type argument of \"array\" must be one of string, number, boolean',1);a=ye[s],n++}else a=Wt;if(t.length>3){if(null!==t[2]&&(\"number\"!=typeof t[2]||t[2]<0||t[2]!==Math.floor(t[2])))return e.error('The length argument to \"array\" must be a positive integer literal',2);o=t[2],n++}r=$t(a,o)}else r=ye[i];for(var l=[];n<t.length;n++){var u=e.parse(t[n],n,Wt);if(!u)return null;l.push(u)}return new me(r,l)},me.prototype.evaluate=function(t){for(var e=0;e<this.args.length;e++){var r=this.args[e].evaluate(t);if(!ee(this.type,pe(r)))return r;if(e===this.args.length-1)throw new ge(\"Expected value to be of type \"+Qt(this.type)+\", but found \"+Qt(pe(r))+\" instead.\")}return null},me.prototype.eachChild=function(t){this.args.forEach(t)},me.prototype.outputDefined=function(){return this.args.every((function(t){return t.outputDefined()}))},me.prototype.serialize=function(){var t=this.type,e=[t.kind];if(\"array\"===t.kind){var r=t.itemType;if(\"string\"===r.kind||\"number\"===r.kind||\"boolean\"===r.kind){e.push(r.kind);var n=t.N;(\"number\"==typeof n||this.args.length>1)&&e.push(n)}}return e.concat(this.args.map((function(t){return t.serialize()})))};var xe=function(t){this.type=Jt,this.sections=t};xe.parse=function(t,e){if(t.length<2)return e.error(\"Expected at least one argument.\");var r=t[1];if(!Array.isArray(r)&&\"object\"==typeof r)return e.error(\"First argument must be an image or text section.\");for(var n=[],i=!1,a=1;a<=t.length-1;++a){var o=t[a];if(i&&\"object\"==typeof o&&!Array.isArray(o)){i=!1;var s=null;if(o[\"font-scale\"]&&!(s=e.parse(o[\"font-scale\"],1,Ht)))return null;var l=null;if(o[\"text-font\"]&&!(l=e.parse(o[\"text-font\"],1,$t(qt))))return null;var u=null;if(o[\"text-color\"]&&!(u=e.parse(o[\"text-color\"],1,Zt)))return null;var c=n[n.length-1];c.scale=s,c.font=l,c.textColor=u}else{var f=e.parse(t[a],1,Wt);if(!f)return null;var h=f.type.kind;if(\"string\"!==h&&\"value\"!==h&&\"null\"!==h&&\"resolvedImage\"!==h)return e.error(\"Formatted text type must be 'string', 'value', 'image' or 'null'.\");i=!0,n.push({content:f,scale:null,font:null,textColor:null})}}return new xe(n)},xe.prototype.evaluate=function(t){return new ue(this.sections.map((function(e){var r=e.content.evaluate(t);return pe(r)===Kt?new le(\"\",r,null,null,null):new le(de(r),null,e.scale?e.scale.evaluate(t):null,e.font?e.font.evaluate(t).join(\",\"):null,e.textColor?e.textColor.evaluate(t):null)})))},xe.prototype.eachChild=function(t){for(var e=0,r=this.sections;e<r.length;e+=1){var n=r[e];t(n.content),n.scale&&t(n.scale),n.font&&t(n.font),n.textColor&&t(n.textColor)}},xe.prototype.outputDefined=function(){return!1},xe.prototype.serialize=function(){for(var t=[\"format\"],e=0,r=this.sections;e<r.length;e+=1){var n=r[e];t.push(n.content.serialize());var i={};n.scale&&(i[\"font-scale\"]=n.scale.serialize()),n.font&&(i[\"text-font\"]=n.font.serialize()),n.textColor&&(i[\"text-color\"]=n.textColor.serialize()),t.push(i)}return t};var be=function(t){this.type=Kt,this.input=t};be.parse=function(t,e){if(2!==t.length)return e.error(\"Expected two arguments.\");var r=e.parse(t[1],1,qt);return r?new be(r):e.error(\"No image name provided.\")},be.prototype.evaluate=function(t){var e=this.input.evaluate(t),r=ce.fromString(e);return r&&t.availableImages&&(r.available=t.availableImages.indexOf(e)>-1),r},be.prototype.eachChild=function(t){t(this.input)},be.prototype.outputDefined=function(){return!1},be.prototype.serialize=function(){return[\"image\",this.input.serialize()]};var 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a=Je(e,n),o=e[a],s=e[a+1],l=xr.interpolationFactor(this.interpolation,n,o,s),u=r[a].evaluate(t),c=r[a+1].evaluate(t);return\"interpolate\"===this.operator?Qe[this.type.kind.toLowerCase()](u,c,l):\"interpolate-hcl\"===this.operator?yr.reverse(yr.interpolate(yr.forward(u),yr.forward(c),l)):gr.reverse(gr.interpolate(gr.forward(u),gr.forward(c),l))},xr.prototype.eachChild=function(t){t(this.input);for(var e=0,r=this.outputs;e<r.length;e+=1)t(r[e])},xr.prototype.outputDefined=function(){return this.outputs.every((function(t){return t.outputDefined()}))},xr.prototype.serialize=function(){var t;t=\"linear\"===this.interpolation.name?[\"linear\"]:\"exponential\"===this.interpolation.name?1===this.interpolation.base?[\"linear\"]:[\"exponential\",this.interpolation.base]:[\"cubic-bezier\"].concat(this.interpolation.controlPoints);for(var e=[this.operator,t,this.input.serialize()],r=0;r<this.labels.length;r++)e.push(this.labels[r],this.outputs[r].serialize());return e};var _r=function(t,e){this.type=t,this.args=e};_r.parse=function(t,e){if(t.length<2)return e.error(\"Expectected at least one argument.\");var r=null,n=e.expectedType;n&&\"value\"!==n.kind&&(r=n);for(var i=[],a=0,o=t.slice(1);a<o.length;a+=1){var s=o[a],l=e.parse(s,1+i.length,r,void 0,{typeAnnotation:\"omit\"});if(!l)return null;r=r||l.type,i.push(l)}var u=n&&i.some((function(t){return ee(n,t.type)}));return new _r(u?Wt:r,i)},_r.prototype.evaluate=function(t){for(var e,r=null,n=0,i=0,a=this.args;i<a.length&&(n++,(r=a[i].evaluate(t))&&r instanceof ce&&!r.available&&(e||(e=r.name),r=null,n===this.args.length&&(r=e)),null===r);i+=1);return r},_r.prototype.eachChild=function(t){this.args.forEach(t)},_r.prototype.outputDefined=function(){return this.args.every((function(t){return t.outputDefined()}))},_r.prototype.serialize=function(){var t=[\"coalesce\"];return this.eachChild((function(e){t.push(e.serialize())})),t};var wr=function(t,e){this.type=e.type,this.bindings=[].concat(t),this.result=e};wr.prototype.evaluate=function(t){return this.result.evaluate(t)},wr.prototype.eachChild=function(t){for(var e=0,r=this.bindings;e<r.length;e+=1)t(r[e][1]);t(this.result)},wr.parse=function(t,e){if(t.length<4)return e.error(\"Expected at least 3 arguments, but found \"+(t.length-1)+\" instead.\");for(var r=[],n=1;n<t.length-1;n+=2){var i=t[n];if(\"string\"!=typeof i)return e.error(\"Expected string, but found \"+typeof i+\" instead.\",n);if(/[^a-zA-Z0-9_]/.test(i))return e.error(\"Variable names must contain only alphanumeric characters or '_'.\",n);var a=e.parse(t[n+1],n+1);if(!a)return null;r.push([i,a])}var o=e.parse(t[t.length-1],t.length-1,e.expectedType,r);return o?new wr(r,o):null},wr.prototype.outputDefined=function(){return this.result.outputDefined()},wr.prototype.serialize=function(){for(var t=[\"let\"],e=0,r=this.bindings;e<r.length;e+=1){var n=r[e],i=n[0],a=n[1];t.push(i,a.serialize())}return 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kr=function(t,e){this.type=Gt,this.needle=t,this.haystack=e};kr.parse=function(t,e){if(3!==t.length)return e.error(\"Expected 2 arguments, but found \"+(t.length-1)+\" instead.\");var r=e.parse(t[1],1,Wt),n=e.parse(t[2],2,Wt);return r&&n?re(r.type,[Gt,qt,Ht,Vt,Wt])?new kr(r,n):e.error(\"Expected first argument to be of type boolean, string, number or null, but found \"+Qt(r.type)+\" instead\"):null},kr.prototype.evaluate=function(t){var e=this.needle.evaluate(t),r=this.haystack.evaluate(t);if(!r)return!1;if(!ne(e,[\"boolean\",\"string\",\"number\",\"null\"]))throw new ge(\"Expected first argument to be of type boolean, string, number or null, but found \"+Qt(pe(e))+\" instead.\");if(!ne(r,[\"string\",\"array\"]))throw new ge(\"Expected second argument to be of type array or string, but found \"+Qt(pe(r))+\" instead.\");return r.indexOf(e)>=0},kr.prototype.eachChild=function(t){t(this.needle),t(this.haystack)},kr.prototype.outputDefined=function(){return!0},kr.prototype.serialize=function(){return[\"in\",this.needle.serialize(),this.haystack.serialize()]};var Ar=function(t,e,r){this.type=Ht,this.needle=t,this.haystack=e,this.fromIndex=r};Ar.parse=function(t,e){if(t.length<=2||t.length>=5)return e.error(\"Expected 3 or 4 arguments, but found \"+(t.length-1)+\" instead.\");var r=e.parse(t[1],1,Wt),n=e.parse(t[2],2,Wt);if(!r||!n)return null;if(!re(r.type,[Gt,qt,Ht,Vt,Wt]))return e.error(\"Expected first argument to be of type boolean, string, number or null, but found \"+Qt(r.type)+\" instead\");if(4===t.length){var i=e.parse(t[3],3,Ht);return i?new Ar(r,n,i):null}return new Ar(r,n)},Ar.prototype.evaluate=function(t){var e=this.needle.evaluate(t),r=this.haystack.evaluate(t);if(!ne(e,[\"boolean\",\"string\",\"number\",\"null\"]))throw new ge(\"Expected first argument to be of type boolean, string, number or 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Er(r.type,r,n)},Er.prototype.evaluate=function(t){var e=this.input.evaluate(t),r=this.beginIndex.evaluate(t);if(!ne(e,[\"string\",\"array\"]))throw new ge(\"Expected first argument to be of type array or string, but found \"+Qt(pe(e))+\" instead.\");if(this.endIndex){var n=this.endIndex.evaluate(t);return e.slice(r,n)}return e.slice(r)},Er.prototype.eachChild=function(t){t(this.input),t(this.beginIndex),this.endIndex&&t(this.endIndex)},Er.prototype.outputDefined=function(){return!1},Er.prototype.serialize=function(){if(null!=this.endIndex&&void 0!==this.endIndex){var t=this.endIndex.serialize();return[\"slice\",this.input.serialize(),this.beginIndex.serialize(),t]}return[\"slice\",this.input.serialize(),this.beginIndex.serialize()]};var Or=Pr(\"==\",(function(t,e,r){return e===r}),Cr),Ir=Pr(\"!=\",(function(t,e,r){return e!==r}),(function(t,e,r,n){return!Cr(0,e,r,n)})),Dr=Pr(\"<\",(function(t,e,r){return e<r}),(function(t,e,r,n){return 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e=[],a=t.value,s=t.key;if(\"array\"!==Jr(a))return[new zt(s,a,\"array expected, \"+Jr(a)+\" found\")];if(2!==a.length)return[new zt(s,a,\"array length 2 expected, length \"+a.length+\" found\")];if(u){if(\"object\"!==Jr(a[0]))return[new zt(s,a,\"object expected, \"+Jr(a[0])+\" found\")];if(void 0===a[0].zoom)return[new zt(s,a,\"object stop key must have zoom\")];if(void 0===a[0].value)return[new zt(s,a,\"object stop key must have value\")];if(n&&n>Bt(a[0].zoom))return[new zt(s,a[0].zoom,\"stop zoom values must appear in ascending order\")];Bt(a[0].zoom)!==n&&(n=Bt(a[0].zoom),r=void 0,o={}),e=e.concat(dn({key:s+\"[0]\",value:a[0],valueSpec:{zoom:{}},style:t.style,styleSpec:t.styleSpec,objectElementValidators:{zoom:gn,value:h}}))}else e=e.concat(h({key:s+\"[0]\",value:a[0],valueSpec:{},style:t.style,styleSpec:t.styleSpec},a));return sn(Nt(a[1]))?e.concat([new zt(s+\"[1]\",a[1],\"expressions are not allowed in function 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t.length<=1?\"any\"!==r:\"==\"===r?Mn(t[1],t[2],\"==\"):\"!=\"===r?Ln(Mn(t[1],t[2],\"==\")):\"<\"===r||\">\"===r||\"<=\"===r||\">=\"===r?Mn(t[1],t[2],r):\"any\"===r?(e=t.slice(1),[\"any\"].concat(e.map(An))):\"all\"===r?[\"all\"].concat(t.slice(1).map(An)):\"none\"===r?[\"all\"].concat(t.slice(1).map(An).map(Ln)):\"in\"===r?Sn(t[1],t.slice(2)):\"!in\"===r?Ln(Sn(t[1],t.slice(2))):\"has\"===r?En(t[1]):\"!has\"===r?Ln(En(t[1])):\"within\"!==r||t}function Mn(t,e,r){switch(t){case\"$type\":return[\"filter-type-\"+r,e];case\"$id\":return[\"filter-id-\"+r,e];default:return[\"filter-\"+r,t,e]}}function Sn(t,e){if(0===e.length)return!1;switch(t){case\"$type\":return[\"filter-type-in\",[\"literal\",e]];case\"$id\":return[\"filter-id-in\",[\"literal\",e]];default:return e.length>200&&!e.some((function(t){return typeof t!=typeof e[0]}))?[\"filter-in-large\",t,[\"literal\",e.sort(Tn)]]:[\"filter-in-small\",t,[\"literal\",e]]}}function 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r=t.key,n=t.style,i=t.styleSpec,a=t.value,o=t.objectKey,s=i[e+\"_\"+t.layerType];if(!s)return[];var l=o.match(/^(.*)-transition$/);if(\"paint\"===e&&l&&s[l[1]]&&s[l[1]].transition)return Un({key:r,value:a,valueSpec:i.transition,style:n,styleSpec:i});var u,c=t.valueSpec||s[o];if(!c)return[new zt(r,a,'unknown property \"'+o+'\"')];if(\"string\"===Jr(a)&&Yr(c)&&!c.tokens&&(u=/^{([^}]+)}$/.exec(a)))return[new zt(r,a,'\"'+o+'\" does not support interpolation syntax\\nUse an identity property function instead: `{ \"type\": \"identity\", \"property\": '+JSON.stringify(u[1])+\" }`.\")];var f=[];return\"symbol\"===t.layerType&&(\"text-field\"===o&&n&&!n.glyphs&&f.push(new zt(r,a,'use of \"text-field\" requires a style \"glyphs\" property')),\"text-font\"===o&&Kr(Nt(a))&&\"identity\"===Bt(a.type)&&f.push(new zt(r,a,'\"text-font\" does not support identity 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h=i.sources&&i.sources[r.source],p=h&&Bt(h.type);h?\"vector\"===p&&\"raster\"===s?e.push(new zt(n,r.source,'layer \"'+r.id+'\" requires a raster source')):\"raster\"===p&&\"raster\"!==s?e.push(new zt(n,r.source,'layer \"'+r.id+'\" requires a vector source')):\"vector\"!==p||r[\"source-layer\"]?\"raster-dem\"===p&&\"hillshade\"!==s?e.push(new zt(n,r.source,\"raster-dem source can only be used with layer type 'hillshade'.\")):\"line\"!==s||!r.paint||!r.paint[\"line-gradient\"]||\"geojson\"===p&&h.lineMetrics||e.push(new zt(n,r,'layer \"'+r.id+'\" specifies a line-gradient, which requires a GeoJSON source with `lineMetrics` enabled.')):e.push(new zt(n,r,'layer \"'+r.id+'\" must specify a \"source-layer\"')):e.push(new zt(n,r.source,'source \"'+r.source+'\" not found'))}else e.push(new zt(n,r,'missing required property \"source\"'));return e=e.concat(dn({key:n,value:r,valueSpec:a.layer,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{\"*\":function(){return[]},type:function(){return Un({key:n+\".type\",value:r.type,valueSpec:a.layer.type,style:t.style,styleSpec:t.styleSpec,object:r,objectKey:\"type\"})},filter:Cn,layout:function(t){return dn({layer:r,key:t.key,value:t.value,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{\"*\":function(t){return Dn(Ft({layerType:s},t))}}})},paint:function(t){return dn({layer:r,key:t.key,value:t.value,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{\"*\":function(t){return In(Ft({layerType:s},t))}}})}}})),e}function Rn(t){var e=t.value,r=t.key,n=Jr(e);return\"string\"!==n?[new zt(r,e,\"string expected, \"+n+\" found\")]:[]}var Fn={promoteId:function(t){var e=t.key,r=t.value;if(\"string\"===Jr(r))return Rn({key:e,value:r});var n=[];for(var i in r)n.push.apply(n,Rn({key:e+\".\"+i,value:r[i]}));return n}};function Bn(t){var e=t.value,r=t.key,n=t.styleSpec,i=t.style;if(!e.type)return[new zt(r,e,'\"type\" is required')];var a,o=Bt(e.type);switch(o){case\"vector\":case\"raster\":case\"raster-dem\":return dn({key:r,value:e,valueSpec:n[\"source_\"+o.replace(\"-\",\"_\")],style:t.style,styleSpec:n,objectElementValidators:Fn});case\"geojson\":if(a=dn({key:r,value:e,valueSpec:n.source_geojson,style:i,styleSpec:n,objectElementValidators:Fn}),e.cluster)for(var s in e.clusterProperties){var l=e.clusterProperties[s],u=l[0],c=l[1],f=\"string\"==typeof u?[u,[\"accumulated\"],[\"get\",s]]:u;a.push.apply(a,mn({key:r+\".\"+s+\".map\",value:c,expressionContext:\"cluster-map\"})),a.push.apply(a,mn({key:r+\".\"+s+\".reduce\",value:f,expressionContext:\"cluster-reduce\"}))}return a;case\"video\":return dn({key:r,value:e,valueSpec:n.source_video,style:i,styleSpec:n});case\"image\":return dn({key:r,value:e,valueSpec:n.source_image,style:i,styleSpec:n});case\"canvas\":return[new zt(r,null,\"Please use runtime APIs to add canvas sources, rather than including them in stylesheets.\",\"source.canvas\")];default:return xn({key:r+\".type\",value:e.type,valueSpec:{values:[\"vector\",\"raster\",\"raster-dem\",\"geojson\",\"video\",\"image\"]},style:i,styleSpec:n})}}function Nn(t){var e=t.value,r=t.styleSpec,n=r.light,i=t.style,a=[],o=Jr(e);if(void 0===e)return a;if(\"object\"!==o)return a.concat([new zt(\"light\",e,\"object expected, \"+o+\" found\")]);for(var s in e){var l=s.match(/^(.*)-transition$/);a=l&&n[l[1]]&&n[l[1]].transition?a.concat(Un({key:s,value:e[s],valueSpec:r.transition,style:i,styleSpec:r})):n[s]?a.concat(Un({key:s,value:e[s],valueSpec:n[s],style:i,styleSpec:r})):a.concat([new zt(s,e[s],'unknown property \"'+s+'\"')])}return a}var jn={\"*\":function(){return[]},array:vn,boolean:function(t){var e=t.value,r=t.key,n=Jr(e);return\"boolean\"!==n?[new zt(r,e,\"boolean expected, \"+n+\" found\")]:[]},number:gn,color:function(t){var e=t.key,r=t.value,n=Jr(r);return\"string\"!==n?[new zt(e,r,\"color 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o=i[$n+a],s=i[$n+a+1];n.push(o===s?null:i.subarray(o,s))}var l=i[$n+n.length],u=i[$n+n.length+1];this.keys=i.subarray(l,u),this.bboxes=i.subarray(u),this.insert=this._insertReadonly}else{this.d=e+2*r;for(var c=0;c<this.d*this.d;c++)n.push([]);this.keys=[],this.bboxes=[]}this.n=e,this.extent=t,this.padding=r,this.scale=e/t,this.uid=0;var f=r/e*t;this.min=-f,this.max=t+f}Qn.prototype.insert=function(t,e,r,n,i){this._forEachCell(e,r,n,i,this._insertCell,this.uid++),this.keys.push(t),this.bboxes.push(e),this.bboxes.push(r),this.bboxes.push(n),this.bboxes.push(i)},Qn.prototype._insertReadonly=function(){throw\"Cannot insert into a GridIndex created from an ArrayBuffer.\"},Qn.prototype._insertCell=function(t,e,r,n,i,a){this.cells[i].push(a)},Qn.prototype.query=function(t,e,r,n,i){var a=this.min,o=this.max;if(t<=a&&e<=a&&o<=r&&o<=n&&!i)return Array.prototype.slice.call(this.keys);var s=[];return this._forEachCell(t,e,r,n,this._queryCell,s,{},i),s},Qn.prototype._queryCell=function(t,e,r,n,i,a,o,s){var l=this.cells[i];if(null!==l)for(var u=this.keys,c=this.bboxes,f=0;f<l.length;f++){var h=l[f];if(void 0===o[h]){var p=4*h;(s?s(c[p+0],c[p+1],c[p+2],c[p+3]):t<=c[p+2]&&e<=c[p+3]&&r>=c[p+0]&&n>=c[p+1])?(o[h]=!0,a.push(u[h])):o[h]=!1}}},Qn.prototype._forEachCell=function(t,e,r,n,i,a,o,s){for(var l=this._convertToCellCoord(t),u=this._convertToCellCoord(e),c=this._convertToCellCoord(r),f=this._convertToCellCoord(n),h=l;h<=c;h++)for(var p=u;p<=f;p++){var d=this.d*p+h;if((!s||s(this._convertFromCellCoord(h),this._convertFromCellCoord(p),this._convertFromCellCoord(h+1),this._convertFromCellCoord(p+1)))&&i.call(this,t,e,r,n,d,a,o,s))return}},Qn.prototype._convertFromCellCoord=function(t){return(t-this.padding)/this.scale},Qn.prototype._convertToCellCoord=function(t){return Math.max(0,Math.min(this.d-1,Math.floor(t*this.scale)+this.padding))},Qn.prototype.toArrayBuffer=function(){if(this.arrayBuffer)return this.arrayBuffer;for(var t=this.cells,e=$n+this.cells.length+1+1,r=0,n=0;n<this.cells.length;n++)r+=this.cells[n].length;var i=new Int32Array(e+r+this.keys.length+this.bboxes.length);i[0]=this.extent,i[1]=this.n,i[2]=this.padding;for(var a=e,o=0;o<t.length;o++){var s=t[o];i[$n+o]=a,i.set(s,a),a+=s.length}return i[$n+t.length]=a,i.set(this.keys,a),a+=this.keys.length,i[$n+t.length+1]=a,i.set(this.bboxes,a),a+=this.bboxes.length,i.buffer};var ti=self.ImageData,ei=self.ImageBitmap,ri={};function ni(t,e,r){void 0===r&&(r={}),Object.defineProperty(e,\"_classRegistryKey\",{value:t,writeable:!1}),ri[t]={klass:e,omit:r.omit||[],shallow:r.shallow||[]}}for(var ii in ni(\"Object\",Object),Kn.serialize=function(t,e){var r=t.toArrayBuffer();return e&&e.push(r),{buffer:r}},Kn.deserialize=function(t){return new Kn(t.buffer)},ni(\"Grid\",Kn),ni(\"Color\",oe),ni(\"Error\",Error),ni(\"ResolvedImage\",ce),ni(\"StylePropertyFunction\",hn),ni(\"StyleExpression\",on,{omit:[\"_evaluator\"]}),ni(\"ZoomDependentExpression\",cn),ni(\"ZoomConstantExpression\",un),ni(\"CompoundExpression\",Ae,{omit:[\"_evaluate\"]}),jr)jr[ii]._classRegistryKey||ni(\"Expression_\"+ii,jr[ii]);function ai(t){return t&&\"undefined\"!=typeof ArrayBuffer&&(t instanceof ArrayBuffer||t.constructor&&\"ArrayBuffer\"===t.constructor.name)}function oi(t){return ei&&t instanceof ei}function si(t,e){if(null==t||\"boolean\"==typeof t||\"number\"==typeof t||\"string\"==typeof t||t instanceof Boolean||t instanceof Number||t instanceof String||t instanceof Date||t instanceof RegExp)return t;if(ai(t)||oi(t))return e&&e.push(t),t;if(ArrayBuffer.isView(t)){var r=t;return e&&e.push(r.buffer),r}if(t instanceof ti)return e&&e.push(t.data.buffer),t;if(Array.isArray(t)){for(var n=[],i=0,a=t;i<a.length;i+=1){var o=a[i];n.push(si(o,e))}return n}if(\"object\"==typeof t){var s=t.constructor,l=s._classRegistryKey;if(!l)throw new Error(\"can't serialize object of unregistered class\");var u=s.serialize?s.serialize(t,e):{};if(!s.serialize){for(var c in t)if(t.hasOwnProperty(c)&&!(ri[l].omit.indexOf(c)>=0)){var f=t[c];u[c]=ri[l].shallow.indexOf(c)>=0?f:si(f,e)}t instanceof Error&&(u.message=t.message)}if(u.$name)throw new Error(\"$name property is reserved for worker serialization logic.\");return\"Object\"!==l&&(u.$name=l),u}throw new Error(\"can't serialize object of type \"+typeof t)}function li(t){if(null==t||\"boolean\"==typeof t||\"number\"==typeof t||\"string\"==typeof t||t instanceof Boolean||t instanceof Number||t instanceof String||t instanceof Date||t instanceof RegExp||ai(t)||oi(t)||ArrayBuffer.isView(t)||t instanceof ti)return t;if(Array.isArray(t))return t.map(li);if(\"object\"==typeof t){var e=t.$name||\"Object\",r=ri[e].klass;if(!r)throw new Error(\"can't deserialize unregistered class 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Aboriginal Syllabics\":function(t){return t>=5120&&t<=5759},Khmer:function(t){return t>=6016&&t<=6143},\"Unified Canadian Aboriginal Syllabics Extended\":function(t){return t>=6320&&t<=6399},\"General Punctuation\":function(t){return t>=8192&&t<=8303},\"Letterlike Symbols\":function(t){return t>=8448&&t<=8527},\"Number Forms\":function(t){return t>=8528&&t<=8591},\"Miscellaneous Technical\":function(t){return t>=8960&&t<=9215},\"Control Pictures\":function(t){return t>=9216&&t<=9279},\"Optical Character Recognition\":function(t){return t>=9280&&t<=9311},\"Enclosed Alphanumerics\":function(t){return t>=9312&&t<=9471},\"Geometric Shapes\":function(t){return t>=9632&&t<=9727},\"Miscellaneous Symbols\":function(t){return t>=9728&&t<=9983},\"Miscellaneous Symbols and Arrows\":function(t){return t>=11008&&t<=11263},\"CJK Radicals Supplement\":function(t){return t>=11904&&t<=12031},\"Kangxi Radicals\":function(t){return t>=12032&&t<=12255},\"Ideographic Description 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t&&t.indexOf(\"NetworkError\")>-1&&(Ti=_i),wi&&wi(t)};function Mi(){Si.fire(new Pt(\"pluginStateChange\",{pluginStatus:Ti,pluginURL:ki}))}var Si=new It,Ei=function(){return Ti},Li=function(){if(Ti!==mi||!ki)throw new Error(\"rtl-text-plugin cannot be downloaded unless a pluginURL is specified\");Ti=xi,Mi(),ki&&kt({url:ki},(function(t){t?Ai(t):(Ti=bi,Mi())}))},Ci={applyArabicShaping:null,processBidirectionalText:null,processStyledBidirectionalText:null,isLoaded:function(){return Ti===bi||null!=Ci.applyArabicShaping},isLoading:function(){return Ti===xi},setState:function(t){Ti=t.pluginStatus,ki=t.pluginURL},isParsed:function(){return null!=Ci.applyArabicShaping&&null!=Ci.processBidirectionalText&&null!=Ci.processStyledBidirectionalText},getPluginURL:function(){return ki}},Pi=function(t,e){this.zoom=t,e?(this.now=e.now,this.fadeDuration=e.fadeDuration,this.zoomHistory=e.zoomHistory,this.transition=e.transition):(this.now=0,this.fadeDuration=0,this.zoomHistory=new 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b(this._values[t].transition)},Di.prototype.setTransition=function(t,e){this._values.hasOwnProperty(t)||(this._values[t]=new Ii(this._values[t].property)),this._values[t].transition=b(e)||void 0},Di.prototype.serialize=function(){for(var t={},e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e],i=this.getValue(n);void 0!==i&&(t[n]=i);var a=this.getTransition(n);void 0!==a&&(t[n+\"-transition\"]=a)}return t},Di.prototype.transitioned=function(t,e){for(var r=new Ri(this._properties),n=0,i=Object.keys(this._values);n<i.length;n+=1){var a=i[n];r._values[a]=this._values[a].transitioned(t,e._values[a])}return r},Di.prototype.untransitioned=function(){for(var t=new Ri(this._properties),e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e];t._values[n]=this._values[n].untransitioned()}return t};var zi=function(t,e,r,n,i){this.property=t,this.value=e,this.begin=i+n.delay||0,this.end=this.begin+n.duration||0,t.specification.transition&&(n.delay||n.duration)&&(this.prior=r)};zi.prototype.possiblyEvaluate=function(t,e,r){var n=t.now||0,i=this.value.possiblyEvaluate(t,e,r),a=this.prior;if(a){if(n>this.end)return this.prior=null,i;if(this.value.isDataDriven())return this.prior=null,i;if(n<this.begin)return a.possiblyEvaluate(t,e,r);var o=(n-this.begin)/(this.end-this.begin);return this.property.interpolate(a.possiblyEvaluate(t,e,r),i,function(t){if(t<=0)return 0;if(t>=1)return 1;var e=t*t,r=e*t;return 4*(t<.5?r:3*(t-e)+r-.75)}(o))}return i};var Ri=function(t){this._properties=t,this._values=Object.create(t.defaultTransitioningPropertyValues)};Ri.prototype.possiblyEvaluate=function(t,e,r){for(var n=new Ni(this._properties),i=0,a=Object.keys(this._values);i<a.length;i+=1){var o=a[i];n._values[o]=this._values[o].possiblyEvaluate(t,e,r)}return n},Ri.prototype.hasTransition=function(){for(var t=0,e=Object.keys(this._values);t<e.length;t+=1){var r=e[t];if(this._values[r].prior)return!0}return!1};var Fi=function(t){this._properties=t,this._values=Object.create(t.defaultPropertyValues)};Fi.prototype.getValue=function(t){return b(this._values[t].value)},Fi.prototype.setValue=function(t,e){this._values[t]=new Oi(this._values[t].property,null===e?void 0:b(e))},Fi.prototype.serialize=function(){for(var t={},e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e],i=this.getValue(n);void 0!==i&&(t[n]=i)}return t},Fi.prototype.possiblyEvaluate=function(t,e,r){for(var n=new Ni(this._properties),i=0,a=Object.keys(this._values);i<a.length;i+=1){var o=a[i];n._values[o]=this._values[o].possiblyEvaluate(t,e,r)}return n};var Bi=function(t,e,r){this.property=t,this.value=e,this.parameters=r};Bi.prototype.isConstant=function(){return\"constant\"===this.value.kind},Bi.prototype.constantOr=function(t){return\"constant\"===this.value.kind?this.value.value:t},Bi.prototype.evaluate=function(t,e,r,n){return this.property.evaluate(this.value,this.parameters,t,e,r,n)};var Ni=function(t){this._properties=t,this._values=Object.create(t.defaultPossiblyEvaluatedValues)};Ni.prototype.get=function(t){return this._values[t]};var ji=function(t){this.specification=t};ji.prototype.possiblyEvaluate=function(t,e){return t.expression.evaluate(e)},ji.prototype.interpolate=function(t,e,r){var n=Qe[this.specification.type];return n?n(t,e,r):t};var Ui=function(t,e){this.specification=t,this.overrides=e};Ui.prototype.possiblyEvaluate=function(t,e,r,n){return\"constant\"===t.expression.kind||\"camera\"===t.expression.kind?new Bi(this,{kind:\"constant\",value:t.expression.evaluate(e,null,{},r,n)},e):new Bi(this,t.expression,e)},Ui.prototype.interpolate=function(t,e,r){if(\"constant\"!==t.value.kind||\"constant\"!==e.value.kind)return t;if(void 0===t.value.value||void 0===e.value.value)return new Bi(this,{kind:\"constant\",value:void 0},t.parameters);var n=Qe[this.specification.type];return n?new Bi(this,{kind:\"constant\",value:n(t.value.value,e.value.value,r)},t.parameters):t},Ui.prototype.evaluate=function(t,e,r,n,i,a){return\"constant\"===t.kind?t.value:t.evaluate(e,r,n,i,a)};var Vi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.possiblyEvaluate=function(t,e,r,n){if(void 0===t.value)return new Bi(this,{kind:\"constant\",value:void 0},e);if(\"constant\"===t.expression.kind){var i=t.expression.evaluate(e,null,{},r,n),a=\"resolvedImage\"===t.property.specification.type&&\"string\"!=typeof i?i.name:i,o=this._calculate(a,a,a,e);return new Bi(this,{kind:\"constant\",value:o},e)}if(\"camera\"===t.expression.kind){var s=this._calculate(t.expression.evaluate({zoom:e.zoom-1}),t.expression.evaluate({zoom:e.zoom}),t.expression.evaluate({zoom:e.zoom+1}),e);return new Bi(this,{kind:\"constant\",value:s},e)}return new Bi(this,t.expression,e)},e.prototype.evaluate=function(t,e,r,n,i,a){if(\"source\"===t.kind){var o=t.evaluate(e,r,n,i,a);return this._calculate(o,o,o,e)}return\"composite\"===t.kind?this._calculate(t.evaluate({zoom:Math.floor(e.zoom)-1},r,n),t.evaluate({zoom:Math.floor(e.zoom)},r,n),t.evaluate({zoom:Math.floor(e.zoom)+1},r,n),e):t.value},e.prototype._calculate=function(t,e,r,n){return n.zoom>n.zoomHistory.lastIntegerZoom?{from:t,to:e}:{from:r,to:e}},e.prototype.interpolate=function(t){return t},e}(Ui),Hi=function(t){this.specification=t};Hi.prototype.possiblyEvaluate=function(t,e,r,n){if(void 0!==t.value){if(\"constant\"===t.expression.kind){var i=t.expression.evaluate(e,null,{},r,n);return this._calculate(i,i,i,e)}return this._calculate(t.expression.evaluate(new Pi(Math.floor(e.zoom-1),e)),t.expression.evaluate(new Pi(Math.floor(e.zoom),e)),t.expression.evaluate(new Pi(Math.floor(e.zoom+1),e)),e)}},Hi.prototype._calculate=function(t,e,r,n){return n.zoom>n.zoomHistory.lastIntegerZoom?{from:t,to:e}:{from:r,to:e}},Hi.prototype.interpolate=function(t){return t};var qi=function(t){this.specification=t};qi.prototype.possiblyEvaluate=function(t,e,r,n){return!!t.expression.evaluate(e,null,{},r,n)},qi.prototype.interpolate=function(){return!1};var Gi=function(t){for(var e in this.properties=t,this.defaultPropertyValues={},this.defaultTransitionablePropertyValues={},this.defaultTransitioningPropertyValues={},this.defaultPossiblyEvaluatedValues={},this.overridableProperties=[],t){var r=t[e];r.specification.overridable&&this.overridableProperties.push(e);var n=this.defaultPropertyValues[e]=new Oi(r,void 0),i=this.defaultTransitionablePropertyValues[e]=new Ii(r);this.defaultTransitioningPropertyValues[e]=i.untransitioned(),this.defaultPossiblyEvaluatedValues[e]=n.possiblyEvaluate({})}};ni(\"DataDrivenProperty\",Ui),ni(\"DataConstantProperty\",ji),ni(\"CrossFadedDataDrivenProperty\",Vi),ni(\"CrossFadedProperty\",Hi),ni(\"ColorRampProperty\",qi);var Zi=\"-transition\",Yi=function(t){function e(e,r){if(t.call(this),this.id=e.id,this.type=e.type,this._featureFilter={filter:function(){return!0},needGeometry:!1},\"custom\"!==e.type&&(this.metadata=e.metadata,this.minzoom=e.minzoom,this.maxzoom=e.maxzoom,\"background\"!==e.type&&(this.source=e.source,this.sourceLayer=e[\"source-layer\"],this.filter=e.filter),r.layout&&(this._unevaluatedLayout=new Fi(r.layout)),r.paint)){for(var n in this._transitionablePaint=new Di(r.paint),e.paint)this.setPaintProperty(n,e.paint[n],{validate:!1});for(var i in e.layout)this.setLayoutProperty(i,e.layout[i],{validate:!1});this._transitioningPaint=this._transitionablePaint.untransitioned(),this.paint=new Ni(r.paint)}}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getCrossfadeParameters=function(){return this._crossfadeParameters},e.prototype.getLayoutProperty=function(t){return\"visibility\"===t?this.visibility:this._unevaluatedLayout.getValue(t)},e.prototype.setLayoutProperty=function(t,e,r){if(void 0===r&&(r={}),null!=e){var n=\"layers.\"+this.id+\".layout.\"+t;if(this._validate(Xn,n,t,e,r))return}\"visibility\"!==t?this._unevaluatedLayout.setValue(t,e):this.visibility=e},e.prototype.getPaintProperty=function(t){return y(t,Zi)?this._transitionablePaint.getTransition(t.slice(0,-11)):this._transitionablePaint.getValue(t)},e.prototype.setPaintProperty=function(t,e,r){if(void 0===r&&(r={}),null!=e){var n=\"layers.\"+this.id+\".paint.\"+t;if(this._validate(Wn,n,t,e,r))return!1}if(y(t,Zi))return this._transitionablePaint.setTransition(t.slice(0,-11),e||void 0),!1;var i=this._transitionablePaint._values[t],a=\"cross-faded-data-driven\"===i.property.specification[\"property-type\"],o=i.value.isDataDriven(),s=i.value;this._transitionablePaint.setValue(t,e),this._handleSpecialPaintPropertyUpdate(t);var l=this._transitionablePaint._values[t].value;return l.isDataDriven()||o||a||this._handleOverridablePaintPropertyUpdate(t,s,l)},e.prototype._handleSpecialPaintPropertyUpdate=function(t){},e.prototype._handleOverridablePaintPropertyUpdate=function(t,e,r){return!1},e.prototype.isHidden=function(t){return!!(this.minzoom&&t<this.minzoom)||!!(this.maxzoom&&t>=this.maxzoom)||\"none\"===this.visibility},e.prototype.updateTransitions=function(t){this._transitioningPaint=this._transitionablePaint.transitioned(t,this._transitioningPaint)},e.prototype.hasTransition=function(){return this._transitioningPaint.hasTransition()},e.prototype.recalculate=function(t,e){t.getCrossfadeParameters&&(this._crossfadeParameters=t.getCrossfadeParameters()),this._unevaluatedLayout&&(this.layout=this._unevaluatedLayout.possiblyEvaluate(t,void 0,e)),this.paint=this._transitioningPaint.possiblyEvaluate(t,void 0,e)},e.prototype.serialize=function(){var t={id:this.id,type:this.type,source:this.source,\"source-layer\":this.sourceLayer,metadata:this.metadata,minzoom:this.minzoom,maxzoom:this.maxzoom,filter:this.filter,layout:this._unevaluatedLayout&&this._unevaluatedLayout.serialize(),paint:this._transitionablePaint&&this._transitionablePaint.serialize()};return this.visibility&&(t.layout=t.layout||{},t.layout.visibility=this.visibility),x(t,(function(t,e){return!(void 0===t||\"layout\"===e&&!Object.keys(t).length||\"paint\"===e&&!Object.keys(t).length)}))},e.prototype._validate=function(t,e,r,n,i){return void 0===i&&(i={}),(!i||!1!==i.validate)&&Jn(this,t.call(Zn,{key:e,layerType:this.type,objectKey:r,value:n,styleSpec:Dt,style:{glyphs:!0,sprite:!0}}))},e.prototype.is3D=function(){return!1},e.prototype.isTileClipped=function(){return!1},e.prototype.hasOffscreenPass=function(){return!1},e.prototype.resize=function(){},e.prototype.isStateDependent=function(){for(var t in this.paint._values){var e=this.paint.get(t);if(e instanceof Bi&&Yr(e.property.specification)&&(\"source\"===e.value.kind||\"composite\"===e.value.kind)&&e.value.isStateDependent)return!0}return!1},e}(It),Wi={Int8:Int8Array,Uint8:Uint8Array,Int16:Int16Array,Uint16:Uint16Array,Int32:Int32Array,Uint32:Uint32Array,Float32:Float32Array},Xi=function(t,e){this._structArray=t,this._pos1=e*this.size,this._pos2=this._pos1/2,this._pos4=this._pos1/4,this._pos8=this._pos1/8},Ji=function(){this.isTransferred=!1,this.capacity=-1,this.resize(0)};function Ki(t,e){void 0===e&&(e=1);var r=0,n=0;return{members:t.map((function(t){var i,a=(i=t.type,Wi[i].BYTES_PER_ELEMENT),o=r=$i(r,Math.max(e,a)),s=t.components||1;return n=Math.max(n,a),r+=a*s,{name:t.name,type:t.type,components:s,offset:o}})),size:$i(r,Math.max(n,e)),alignment:e}}function $i(t,e){return Math.ceil(t/e)*e}Ji.serialize=function(t,e){return t._trim(),e&&(t.isTransferred=!0,e.push(t.arrayBuffer)),{length:t.length,arrayBuffer:t.arrayBuffer}},Ji.deserialize=function(t){var e=Object.create(this.prototype);return e.arrayBuffer=t.arrayBuffer,e.length=t.length,e.capacity=t.arrayBuffer.byteLength/e.bytesPerElement,e._refreshViews(),e},Ji.prototype._trim=function(){this.length!==this.capacity&&(this.capacity=this.length,this.arrayBuffer=this.arrayBuffer.slice(0,this.length*this.bytesPerElement),this._refreshViews())},Ji.prototype.clear=function(){this.length=0},Ji.prototype.resize=function(t){this.reserve(t),this.length=t},Ji.prototype.reserve=function(t){if(t>this.capacity){this.capacity=Math.max(t,Math.floor(5*this.capacity),128),this.arrayBuffer=new ArrayBuffer(this.capacity*this.bytesPerElement);var e=this.uint8;this._refreshViews(),e&&this.uint8.set(e)}},Ji.prototype._refreshViews=function(){throw new Error(\"_refreshViews() must be implemented by each concrete StructArray layout\")};var Qi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.int16[n+0]=e,this.int16[n+1]=r,t},e}(Ji);Qi.prototype.bytesPerElement=4,ni(\"StructArrayLayout2i4\",Qi);var ta=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new 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this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t},e}(Ji);ea.prototype.bytesPerElement=12,ni(\"StructArrayLayout2i4i12\",ea);var ra=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=4*t,l=8*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.uint8[l+4]=n,this.uint8[l+5]=i,this.uint8[l+6]=a,this.uint8[l+7]=o,t},e}(Ji);ra.prototype.bytesPerElement=8,ni(\"StructArrayLayout2i4ub8\",ra);var na=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,u){var c=this.length;return this.resize(c+1),this.emplace(c,t,e,r,n,i,a,o,s,l,u)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,u,c){var f=9*t,h=18*t;return this.uint16[f+0]=e,this.uint16[f+1]=r,this.uint16[f+2]=n,this.uint16[f+3]=i,this.uint16[f+4]=a,this.uint16[f+5]=o,this.uint16[f+6]=s,this.uint16[f+7]=l,this.uint8[h+16]=u,this.uint8[h+17]=c,t},e}(Ji);na.prototype.bytesPerElement=18,ni(\"StructArrayLayout8ui2ub18\",na);var ia=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,u,c,f){var h=this.length;return this.resize(h+1),this.emplace(h,t,e,r,n,i,a,o,s,l,u,c,f)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,u,c,f,h){var p=12*t;return this.int16[p+0]=e,this.int16[p+1]=r,this.int16[p+2]=n,this.int16[p+3]=i,this.uint16[p+4]=a,this.uint16[p+5]=o,this.uint16[p+6]=s,this.uint16[p+7]=l,this.int16[p+8]=u,this.int16[p+9]=c,this.int16[p+10]=f,this.int16[p+11]=h,t},e}(Ji);ia.prototype.bytesPerElement=24,ni(\"StructArrayLayout4i4ui4i24\",ia);var aa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.float32[i+0]=e,this.float32[i+1]=r,this.float32[i+2]=n,t},e}(Ji);aa.prototype.bytesPerElement=12,ni(\"StructArrayLayout3f12\",aa);var oa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.uint32[r+0]=e,t},e}(Ji);oa.prototype.bytesPerElement=4,ni(\"StructArrayLayout1ul4\",oa);var sa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l){var u=this.length;return this.resize(u+1),this.emplace(u,t,e,r,n,i,a,o,s,l)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,u){var c=10*t,f=5*t;return this.int16[c+0]=e,this.int16[c+1]=r,this.int16[c+2]=n,this.int16[c+3]=i,this.int16[c+4]=a,this.int16[c+5]=o,this.uint32[f+3]=s,this.uint16[c+8]=l,this.uint16[c+9]=u,t},e}(Ji);sa.prototype.bytesPerElement=20,ni(\"StructArrayLayout6i1ul2ui20\",sa);var la=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=6*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t},e}(Ji);la.prototype.bytesPerElement=12,ni(\"StructArrayLayout2i2i2i12\",la);var ua=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i){var a=this.length;return this.resize(a+1),this.emplace(a,t,e,r,n,i)},e.prototype.emplace=function(t,e,r,n,i,a){var o=4*t,s=8*t;return this.float32[o+0]=e,this.float32[o+1]=r,this.float32[o+2]=n,this.int16[s+6]=i,this.int16[s+7]=a,t},e}(Ji);ua.prototype.bytesPerElement=16,ni(\"StructArrayLayout2f1f2i16\",ua);var ca=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=12*t,o=3*t;return this.uint8[a+0]=e,this.uint8[a+1]=r,this.float32[o+1]=n,this.float32[o+2]=i,t},e}(Ji);ca.prototype.bytesPerElement=12,ni(\"StructArrayLayout2ub2f12\",ca);var fa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return 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this.int16[m+0]=e,this.int16[m+1]=r,this.uint16[m+2]=n,this.uint16[m+3]=i,this.uint32[x+2]=a,this.uint32[x+3]=o,this.uint32[x+4]=s,this.uint16[m+10]=l,this.uint16[m+11]=u,this.uint16[m+12]=c,this.float32[x+7]=f,this.float32[x+8]=h,this.uint8[b+36]=p,this.uint8[b+37]=d,this.uint8[b+38]=v,this.uint32[x+10]=g,this.int16[m+22]=y,t},e}(Ji);ha.prototype.bytesPerElement=48,ni(\"StructArrayLayout2i2ui3ul3ui2f3ub1ul1i48\",ha);var pa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,u,c,f,h,p,d,v,g,y,m,x,b,_,w,T,k,A,M,S){var E=this.length;return this.resize(E+1),this.emplace(E,t,e,r,n,i,a,o,s,l,u,c,f,h,p,d,v,g,y,m,x,b,_,w,T,k,A,M,S)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,u,c,f,h,p,d,v,g,y,m,x,b,_,w,T,k,A,M,S,E){var L=34*t,C=17*t;return this.int16[L+0]=e,this.int16[L+1]=r,this.int16[L+2]=n,this.int16[L+3]=i,this.int16[L+4]=a,this.int16[L+5]=o,this.int16[L+6]=s,this.int16[L+7]=l,this.uint16[L+8]=u,this.uint16[L+9]=c,this.uint16[L+10]=f,this.uint16[L+11]=h,this.uint16[L+12]=p,this.uint16[L+13]=d,this.uint16[L+14]=v,this.uint16[L+15]=g,this.uint16[L+16]=y,this.uint16[L+17]=m,this.uint16[L+18]=x,this.uint16[L+19]=b,this.uint16[L+20]=_,this.uint16[L+21]=w,this.uint16[L+22]=T,this.uint32[C+12]=k,this.float32[C+13]=A,this.float32[C+14]=M,this.float32[C+15]=S,this.float32[C+16]=E,t},e}(Ji);pa.prototype.bytesPerElement=68,ni(\"StructArrayLayout8i15ui1ul4f68\",pa);var da=function(t){function e(){t.apply(this,arguments)}return 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this.float32[a+0]=e,this.float32[a+1]=r,this.float32[a+2]=n,this.float32[a+3]=i,t},e}(Ji);ba.prototype.bytesPerElement=16,ni(\"StructArrayLayout4f16\",ba);var _a=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorPointX:{configurable:!0},anchorPointY:{configurable:!0},x1:{configurable:!0},y1:{configurable:!0},x2:{configurable:!0},y2:{configurable:!0},featureIndex:{configurable:!0},sourceLayerIndex:{configurable:!0},bucketIndex:{configurable:!0},anchorPoint:{configurable:!0}};return r.anchorPointX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorPointY.get=function(){return this._structArray.int16[this._pos2+1]},r.x1.get=function(){return this._structArray.int16[this._pos2+2]},r.y1.get=function(){return this._structArray.int16[this._pos2+3]},r.x2.get=function(){return this._structArray.int16[this._pos2+4]},r.y2.get=function(){return 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this._structArray.uint32[this._pos4+10]},r.crossTileID.set=function(t){this._structArray.uint32[this._pos4+10]=t},r.associatedIconIndex.get=function(){return this._structArray.int16[this._pos2+22]},Object.defineProperties(e.prototype,r),e}(Xi);Ta.prototype.size=48;var ka=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new Ta(this,t)},e}(ha);ni(\"PlacedSymbolArray\",ka);var Aa=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorX:{configurable:!0},anchorY:{configurable:!0},rightJustifiedTextSymbolIndex:{configurable:!0},centerJustifiedTextSymbolIndex:{configurable:!0},leftJustifiedTextSymbolIndex:{configurable:!0},verticalPlacedTextSymbolIndex:{configurable:!0},placedIconSymbolIndex:{configurable:!0},verticalPlacedIconSymbolIndex:{configurable:!0},key:{configurable:!0},textBoxStartIndex:{configurable:!0},textBoxEndIndex:{configurable:!0},verticalTextBoxStartIndex:{configurable:!0},verticalTextBoxEndIndex:{configurable:!0},iconBoxStartIndex:{configurable:!0},iconBoxEndIndex:{configurable:!0},verticalIconBoxStartIndex:{configurable:!0},verticalIconBoxEndIndex:{configurable:!0},featureIndex:{configurable:!0},numHorizontalGlyphVertices:{configurable:!0},numVerticalGlyphVertices:{configurable:!0},numIconVertices:{configurable:!0},numVerticalIconVertices:{configurable:!0},useRuntimeCollisionCircles:{configurable:!0},crossTileID:{configurable:!0},textBoxScale:{configurable:!0},textOffset0:{configurable:!0},textOffset1:{configurable:!0},collisionCircleDiameter:{configurable:!0}};return r.anchorX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorY.get=function(){return this._structArray.int16[this._pos2+1]},r.rightJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+2]},r.centerJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+3]},r.leftJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+4]},r.verticalPlacedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+5]},r.placedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+6]},r.verticalPlacedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+7]},r.key.get=function(){return this._structArray.uint16[this._pos2+8]},r.textBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+9]},r.textBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+10]},r.verticalTextBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+11]},r.verticalTextBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+12]},r.iconBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+13]},r.iconBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+14]},r.verticalIconBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+15]},r.verticalIconBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+16]},r.featureIndex.get=function(){return this._structArray.uint16[this._pos2+17]},r.numHorizontalGlyphVertices.get=function(){return this._structArray.uint16[this._pos2+18]},r.numVerticalGlyphVertices.get=function(){return this._structArray.uint16[this._pos2+19]},r.numIconVertices.get=function(){return this._structArray.uint16[this._pos2+20]},r.numVerticalIconVertices.get=function(){return this._structArray.uint16[this._pos2+21]},r.useRuntimeCollisionCircles.get=function(){return 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t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.set=function(t){this.current!==t&&(this.current=t,this.gl.uniform1i(this.location,t))},e}(Ga),Ya=function(t){function e(e,r){t.call(this,e,r),this.current=0}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.set=function(t){this.current!==t&&(this.current=t,this.gl.uniform1f(this.location,t))},e}(Ga),Wa=function(t){function e(e,r){t.call(this,e,r),this.current=[0,0]}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.set=function(t){t[0]===this.current[0]&&t[1]===this.current[1]||(this.current=t,this.gl.uniform2f(this.location,t[0],t[1]))},e}(Ga),Xa=function(t){function e(e,r){t.call(this,e,r),this.current=[0,0,0]}return 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Float32Array(16),Qa=function(t){function e(e,r){t.call(this,e,r),this.current=$a}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.set=function(t){if(t[12]!==this.current[12]||t[0]!==this.current[0])return this.current=t,void this.gl.uniformMatrix4fv(this.location,!1,t);for(var e=1;e<16;e++)if(t[e]!==this.current[e]){this.current=t,this.gl.uniformMatrix4fv(this.location,!1,t);break}},e}(Ga);function to(t){return[Ia(255*t.r,255*t.g),Ia(255*t.b,255*t.a)]}var eo=function(t,e,r){this.value=t,this.uniformNames=e.map((function(t){return\"u_\"+t})),this.type=r};eo.prototype.setUniform=function(t,e,r){t.set(r.constantOr(this.value))},eo.prototype.getBinding=function(t,e,r){return\"color\"===this.type?new Ka(t,e):new Ya(t,e)};var ro=function(t,e){this.uniformNames=e.map((function(t){return\"u_\"+t})),this.patternFrom=null,this.patternTo=null,this.pixelRatioFrom=1,this.pixelRatioTo=1};ro.prototype.setConstantPatternPositions=function(t,e){this.pixelRatioFrom=e.pixelRatio,this.pixelRatioTo=t.pixelRatio,this.patternFrom=e.tlbr,this.patternTo=t.tlbr},ro.prototype.setUniform=function(t,e,r,n){var i=\"u_pattern_to\"===n?this.patternTo:\"u_pattern_from\"===n?this.patternFrom:\"u_pixel_ratio_to\"===n?this.pixelRatioTo:\"u_pixel_ratio_from\"===n?this.pixelRatioFrom:null;i&&t.set(i)},ro.prototype.getBinding=function(t,e,r){return\"u_pattern\"===r.substr(0,9)?new Ja(t,e):new Ya(t,e)};var no=function(t,e,r,n){this.expression=t,this.type=r,this.maxValue=0,this.paintVertexAttributes=e.map((function(t){return{name:\"a_\"+t,type:\"Float32\",components:\"color\"===r?2:1,offset:0}})),this.paintVertexArray=new n};no.prototype.populatePaintArray=function(t,e,r,n,i){var a=this.paintVertexArray.length,o=this.expression.evaluate(new Pi(0),e,{},n,[],i);this.paintVertexArray.resize(t),this._setPaintValue(a,t,o)},no.prototype.updatePaintArray=function(t,e,r,n){var i=this.expression.evaluate({zoom:0},r,n);this._setPaintValue(t,e,i)},no.prototype._setPaintValue=function(t,e,r){if(\"color\"===this.type)for(var n=to(r),i=t;i<e;i++)this.paintVertexArray.emplace(i,n[0],n[1]);else{for(var a=t;a<e;a++)this.paintVertexArray.emplace(a,r);this.maxValue=Math.max(this.maxValue,Math.abs(r))}},no.prototype.upload=function(t){this.paintVertexArray&&this.paintVertexArray.arrayBuffer&&(this.paintVertexBuffer&&this.paintVertexBuffer.buffer?this.paintVertexBuffer.updateData(this.paintVertexArray):this.paintVertexBuffer=t.createVertexBuffer(this.paintVertexArray,this.paintVertexAttributes,this.expression.isStateDependent))},no.prototype.destroy=function(){this.paintVertexBuffer&&this.paintVertexBuffer.destroy()};var io=function(t,e,r,n,i,a){this.expression=t,this.uniformNames=e.map((function(t){return\"u_\"+t+\"_t\"})),this.type=r,this.useIntegerZoom=n,this.zoom=i,this.maxValue=0,this.paintVertexAttributes=e.map((function(t){return{name:\"a_\"+t,type:\"Float32\",components:\"color\"===r?4:2,offset:0}})),this.paintVertexArray=new a};io.prototype.populatePaintArray=function(t,e,r,n,i){var a=this.expression.evaluate(new Pi(this.zoom),e,{},n,[],i),o=this.expression.evaluate(new Pi(this.zoom+1),e,{},n,[],i),s=this.paintVertexArray.length;this.paintVertexArray.resize(t),this._setPaintValue(s,t,a,o)},io.prototype.updatePaintArray=function(t,e,r,n){var i=this.expression.evaluate({zoom:this.zoom},r,n),a=this.expression.evaluate({zoom:this.zoom+1},r,n);this._setPaintValue(t,e,i,a)},io.prototype._setPaintValue=function(t,e,r,n){if(\"color\"===this.type)for(var i=to(r),a=to(n),o=t;o<e;o++)this.paintVertexArray.emplace(o,i[0],i[1],a[0],a[1]);else{for(var s=t;s<e;s++)this.paintVertexArray.emplace(s,r,n);this.maxValue=Math.max(this.maxValue,Math.abs(r),Math.abs(n))}},io.prototype.upload=function(t){this.paintVertexArray&&this.paintVertexArray.arrayBuffer&&(this.paintVertexBuffer&&this.paintVertexBuffer.buffer?this.paintVertexBuffer.updateData(this.paintVertexArray):this.paintVertexBuffer=t.createVertexBuffer(this.paintVertexArray,this.paintVertexAttributes,this.expression.isStateDependent))},io.prototype.destroy=function(){this.paintVertexBuffer&&this.paintVertexBuffer.destroy()},io.prototype.setUniform=function(t,e){var r=this.useIntegerZoom?Math.floor(e.zoom):e.zoom,n=u(this.expression.interpolationFactor(r,this.zoom,this.zoom+1),0,1);t.set(n)},io.prototype.getBinding=function(t,e,r){return new Ya(t,e)};var ao=function(t,e,r,n,i,a){this.expression=t,this.type=e,this.useIntegerZoom=r,this.zoom=n,this.layerId=a,this.zoomInPaintVertexArray=new i,this.zoomOutPaintVertexArray=new i};ao.prototype.populatePaintArray=function(t,e,r){var n=this.zoomInPaintVertexArray.length;this.zoomInPaintVertexArray.resize(t),this.zoomOutPaintVertexArray.resize(t),this._setPaintValues(n,t,e.patterns&&e.patterns[this.layerId],r)},ao.prototype.updatePaintArray=function(t,e,r,n,i){this._setPaintValues(t,e,r.patterns&&r.patterns[this.layerId],i)},ao.prototype._setPaintValues=function(t,e,r,n){if(n&&r){var i=r.min,a=r.mid,o=r.max,s=n[i],l=n[a],u=n[o];if(s&&l&&u)for(var c=t;c<e;c++)this.zoomInPaintVertexArray.emplace(c,l.tl[0],l.tl[1],l.br[0],l.br[1],s.tl[0],s.tl[1],s.br[0],s.br[1],l.pixelRatio,s.pixelRatio),this.zoomOutPaintVertexArray.emplace(c,l.tl[0],l.tl[1],l.br[0],l.br[1],u.tl[0],u.tl[1],u.br[0],u.br[1],l.pixelRatio,u.pixelRatio)}},ao.prototype.upload=function(t){this.zoomInPaintVertexArray&&this.zoomInPaintVertexArray.arrayBuffer&&this.zoomOutPaintVertexArray&&this.zoomOutPaintVertexArray.arrayBuffer&&(this.zoomInPaintVertexBuffer=t.createVertexBuffer(this.zoomInPaintVertexArray,Da.members,this.expression.isStateDependent),this.zoomOutPaintVertexBuffer=t.createVertexBuffer(this.zoomOutPaintVertexArray,Da.members,this.expression.isStateDependent))},ao.prototype.destroy=function(){this.zoomOutPaintVertexBuffer&&this.zoomOutPaintVertexBuffer.destroy(),this.zoomInPaintVertexBuffer&&this.zoomInPaintVertexBuffer.destroy()};var oo=function(t,e,r,n){this.binders={},this.layoutAttributes=n,this._buffers=[];var i=[];for(var a in t.paint._values)if(r(a)){var o=t.paint.get(a);if(o instanceof Bi&&Yr(o.property.specification)){var s=lo(a,t.type),l=o.value,u=o.property.specification.type,c=o.property.useIntegerZoom,f=o.property.specification[\"property-type\"],h=\"cross-faded\"===f||\"cross-faded-data-driven\"===f;if(\"constant\"===l.kind)this.binders[a]=h?new ro(l.value,s):new eo(l.value,s,u),i.push(\"/u_\"+a);else if(\"source\"===l.kind||h){var p=uo(a,u,\"source\");this.binders[a]=h?new ao(l,u,c,e,p,t.id):new no(l,s,u,p),i.push(\"/a_\"+a)}else{var d=uo(a,u,\"composite\");this.binders[a]=new io(l,s,u,c,e,d),i.push(\"/z_\"+a)}}}this.cacheKey=i.sort().join(\"\")};oo.prototype.getMaxValue=function(t){var e=this.binders[t];return e instanceof no||e instanceof io?e.maxValue:0},oo.prototype.populatePaintArrays=function(t,e,r,n,i){for(var a in this.binders){var o=this.binders[a];(o instanceof no||o instanceof io||o instanceof ao)&&o.populatePaintArray(t,e,r,n,i)}},oo.prototype.setConstantPatternPositions=function(t,e){for(var r in this.binders){var n=this.binders[r];n instanceof ro&&n.setConstantPatternPositions(t,e)}},oo.prototype.updatePaintArrays=function(t,e,r,n,i){var a=!1;for(var o in t)for(var s=0,l=e.getPositions(o);s<l.length;s+=1){var u=l[s],c=r.feature(u.index);for(var f in this.binders){var h=this.binders[f];if((h instanceof no||h instanceof io||h instanceof ao)&&!0===h.expression.isStateDependent){var p=n.paint.get(f);h.expression=p.value,h.updatePaintArray(u.start,u.end,c,t[o],i),a=!0}}}return a},oo.prototype.defines=function(){var t=[];for(var e in this.binders){var r=this.binders[e];(r instanceof eo||r instanceof ro)&&t.push.apply(t,r.uniformNames.map((function(t){return\"#define HAS_UNIFORM_\"+t})))}return t},oo.prototype.getPaintVertexBuffers=function(){return this._buffers},oo.prototype.getUniforms=function(t,e){var r=[];for(var n in this.binders){var i=this.binders[n];if(i instanceof eo||i instanceof ro||i instanceof io)for(var a=0,o=i.uniformNames;a<o.length;a+=1){var s=o[a];if(e[s]){var l=i.getBinding(t,e[s],s);r.push({name:s,property:n,binding:l})}}}return r},oo.prototype.setUniforms=function(t,e,r,n){for(var i=0,a=e;i<a.length;i+=1){var o=a[i],s=o.name,l=o.property,u=o.binding;this.binders[l].setUniform(u,n,r.get(l),s)}},oo.prototype.updatePaintBuffers=function(t){for(var e in this._buffers=[],this.binders){var r=this.binders[e];if(t&&r instanceof ao){var n=2===t.fromScale?r.zoomInPaintVertexBuffer:r.zoomOutPaintVertexBuffer;n&&this._buffers.push(n)}else(r instanceof no||r instanceof io)&&r.paintVertexBuffer&&this._buffers.push(r.paintVertexBuffer)}},oo.prototype.upload=function(t){for(var e in this.binders){var r=this.binders[e];(r instanceof no||r instanceof io||r instanceof ao)&&r.upload(t)}this.updatePaintBuffers()},oo.prototype.destroy=function(){for(var t in this.binders){var e=this.binders[t];(e instanceof no||e instanceof io||e instanceof ao)&&e.destroy()}};var so=function(t,e,r,n){void 0===n&&(n=function(){return!0}),this.programConfigurations={};for(var i=0,a=e;i<a.length;i+=1){var o=a[i];this.programConfigurations[o.id]=new oo(o,r,n,t)}this.needsUpload=!1,this._featureMap=new ja,this._bufferOffset=0};function lo(t,e){return{\"text-opacity\":[\"opacity\"],\"icon-opacity\":[\"opacity\"],\"text-color\":[\"fill_color\"],\"icon-color\":[\"fill_color\"],\"text-halo-color\":[\"halo_color\"],\"icon-halo-color\":[\"halo_color\"],\"text-halo-blur\":[\"halo_blur\"],\"icon-halo-blur\":[\"halo_blur\"],\"text-halo-width\":[\"halo_width\"],\"icon-halo-width\":[\"halo_width\"],\"line-gap-width\":[\"gapwidth\"],\"line-pattern\":[\"pattern_to\",\"pattern_from\",\"pixel_ratio_to\",\"pixel_ratio_from\"],\"fill-pattern\":[\"pattern_to\",\"pattern_from\",\"pixel_ratio_to\",\"pixel_ratio_from\"],\"fill-extrusion-pattern\":[\"pattern_to\",\"pattern_from\",\"pixel_ratio_to\",\"pixel_ratio_from\"]}[t]||[t.replace(e+\"-\",\"\").replace(/-/g,\"_\")]}function uo(t,e,r){var n={color:{source:xa,composite:ba},number:{source:da,composite:xa}},i=function(t){return{\"line-pattern\":{source:na,composite:na},\"fill-pattern\":{source:na,composite:na},\"fill-extrusion-pattern\":{source:na,composite:na}}[t]}(t);return i&&i[r]||n[e][r]}so.prototype.populatePaintArrays=function(t,e,r,n,i,a){for(var o in this.programConfigurations)this.programConfigurations[o].populatePaintArrays(t,e,n,i,a);void 0!==e.id&&this._featureMap.add(e.id,r,this._bufferOffset,t),this._bufferOffset=t,this.needsUpload=!0},so.prototype.updatePaintArrays=function(t,e,r,n){for(var i=0,a=r;i<a.length;i+=1){var o=a[i];this.needsUpload=this.programConfigurations[o.id].updatePaintArrays(t,this._featureMap,e,o,n)||this.needsUpload}},so.prototype.get=function(t){return this.programConfigurations[t]},so.prototype.upload=function(t){if(this.needsUpload){for(var e in this.programConfigurations)this.programConfigurations[e].upload(t);this.needsUpload=!1}},so.prototype.destroy=function(){for(var t in this.programConfigurations)this.programConfigurations[t].destroy()},ni(\"ConstantBinder\",eo),ni(\"CrossFadedConstantBinder\",ro),ni(\"SourceExpressionBinder\",no),ni(\"CrossFadedCompositeBinder\",ao),ni(\"CompositeExpressionBinder\",io),ni(\"ProgramConfiguration\",oo,{omit:[\"_buffers\"]}),ni(\"ProgramConfigurationSet\",so);var co=8192;var fo,ho=(fo=15,{min:-1*Math.pow(2,fo-1),max:Math.pow(2,fo-1)-1});function po(t){for(var e=co/t.extent,r=t.loadGeometry(),n=0;n<r.length;n++)for(var i=r[n],a=0;a<i.length;a++){var o=i[a];o.x=Math.round(o.x*e),o.y=Math.round(o.y*e),(o.x<ho.min||o.x>ho.max||o.y<ho.min||o.y>ho.max)&&(w(\"Geometry exceeds allowed extent, reduce your vector tile buffer size\"),o.x=u(o.x,ho.min,ho.max),o.y=u(o.y,ho.min,ho.max))}return r}function vo(t,e,r,n,i){t.emplaceBack(2*e+(n+1)/2,2*r+(i+1)/2)}var go=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.layoutVertexArray=new Qi,this.indexArray=new fa,this.segments=new Oa,this.programConfigurations=new so(Pa,t.layers,t.zoom),this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};function yo(t,e){for(var r=0;r<t.length;r++)if(Mo(e,t[r]))return!0;for(var n=0;n<e.length;n++)if(Mo(t,e[n]))return!0;return!!_o(t,e)}function mo(t,e,r){return!!Mo(t,e)||!!To(e,t,r)}function xo(t,e){if(1===t.length)return Ao(e,t[0]);for(var r=0;r<e.length;r++)for(var n=e[r],i=0;i<n.length;i++)if(Mo(t,n[i]))return!0;for(var a=0;a<t.length;a++)if(Ao(e,t[a]))return!0;for(var o=0;o<e.length;o++)if(_o(t,e[o]))return!0;return!1}function bo(t,e,r){if(t.length>1){if(_o(t,e))return!0;for(var n=0;n<e.length;n++)if(To(e[n],t,r))return!0}for(var i=0;i<t.length;i++)if(To(t[i],e,r))return!0;return!1}function _o(t,e){if(0===t.length||0===e.length)return!1;for(var r=0;r<t.length-1;r++)for(var 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n=t.readVarint()>>3;e=1===n?t.readString():2===n?t.readFloat():3===n?t.readDouble():4===n?t.readVarint64():5===n?t.readVarint():6===n?t.readSVarint():7===n?t.readBoolean():null}return e}(r))}function Xs(t,e,r){if(3===t){var n=new Zs(r,r.readVarint()+r.pos);n.length&&(e[n.name]=n)}}Ys.prototype.feature=function(t){if(t<0||t>=this._features.length)throw new Error(\"feature index out of bounds\");this._pbf.pos=this._features[t];var e=this._pbf.readVarint()+this._pbf.pos;return new Vs(this._pbf,e,this.extent,this._keys,this._values)};var Js={VectorTile:function(t,e){this.layers=t.readFields(Xs,{},e)},VectorTileFeature:Vs,VectorTileLayer:Zs},Ks=Js.VectorTileFeature.types,$s=Math.pow(2,13);function Qs(t,e,r,n,i,a,o,s){t.emplaceBack(e,r,2*Math.floor(n*$s)+o,i*$s*2,a*$s*2,Math.round(s))}var tl=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.layoutVertexArray=new ea,this.indexArray=new fa,this.programConfigurations=new so(Us,t.layers,t.zoom),this.segments=new Oa,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};function el(t,e){return t.x===e.x&&(t.x<0||t.x>co)||t.y===e.y&&(t.y<0||t.y>co)}tl.prototype.populate=function(t,e,r){this.features=[],this.hasPattern=zs(\"fill-extrusion\",this.layers,e);for(var n=0,i=t;n<i.length;n+=1){var a=i[n],o=a.feature,s=a.id,l=a.index,u=a.sourceLayerIndex,c=this.layers[0]._featureFilter.needGeometry,f={type:o.type,id:s,properties:o.properties,geometry:c?po(o):[]};if(this.layers[0]._featureFilter.filter(new Pi(this.zoom),f,r)){var h={id:s,sourceLayerIndex:u,index:l,geometry:c?f.geometry:po(o),properties:o.properties,type:o.type,patterns:{}};void 0!==o.id&&(h.id=o.id),this.hasPattern?this.features.push(Rs(\"fill-extrusion\",this.layers,h,this.zoom,e)):this.addFeature(h,h.geometry,l,r,{}),e.featureIndex.insert(o,h.geometry,l,u,this.index,!0)}}},tl.prototype.addFeatures=function(t,e,r){for(var n=0,i=this.features;n<i.length;n+=1){var a=i[n],o=a.geometry;this.addFeature(a,o,a.index,e,r)}},tl.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},tl.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},tl.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},tl.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,Us),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},tl.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},tl.prototype.addFeature=function(t,e,r,n,i){for(var a=0,o=Is(e,500);a<o.length;a+=1){for(var s=o[a],l=0,u=0,c=s;u<c.length;u+=1)l+=c[u].length;for(var f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray),h=0,p=s;h<p.length;h+=1){var d=p[h];if(0!==d.length&&!((O=d).every((function(t){return t.x<0}))||O.every((function(t){return t.x>co}))||O.every((function(t){return t.y<0}))||O.every((function(t){return t.y>co}))))for(var v=0,g=0;g<d.length;g++){var y=d[g];if(g>=1){var m=d[g-1];if(!el(y,m)){f.vertexLength+4>Oa.MAX_VERTEX_ARRAY_LENGTH&&(f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray));var x=y.sub(m)._perp()._unit(),b=m.dist(y);v+b>32768&&(v=0),Qs(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,0,v),Qs(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,1,v),v+=b,Qs(this.layoutVertexArray,m.x,m.y,x.x,x.y,0,0,v),Qs(this.layoutVertexArray,m.x,m.y,x.x,x.y,0,1,v);var _=f.vertexLength;this.indexArray.emplaceBack(_,_+2,_+1),this.indexArray.emplaceBack(_+1,_+2,_+3),f.vertexLength+=4,f.primitiveLength+=2}}}}if(f.vertexLength+l>Oa.MAX_VERTEX_ARRAY_LENGTH&&(f=this.segments.prepareSegment(l,this.layoutVertexArray,this.indexArray)),\"Polygon\"===Ks[t.type]){for(var w=[],T=[],k=f.vertexLength,A=0,M=s;A<M.length;A+=1){var S=M[A];if(0!==S.length){S!==s[0]&&T.push(w.length/2);for(var E=0;E<S.length;E++){var L=S[E];Qs(this.layoutVertexArray,L.x,L.y,0,0,1,1,0),w.push(L.x),w.push(L.y)}}}for(var C=es(w,T),P=0;P<C.length;P+=3)this.indexArray.emplaceBack(k+C[P],k+C[P+2],k+C[P+1]);f.primitiveLength+=C.length/3,f.vertexLength+=l}}var O;this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,i,n)},ni(\"FillExtrusionBucket\",tl,{omit:[\"layers\",\"features\"]});var rl={paint:new Gi({\"fill-extrusion-opacity\":new ji(Dt[\"paint_fill-extrusion\"][\"fill-extrusion-opacity\"]),\"fill-extrusion-color\":new Ui(Dt[\"paint_fill-extrusion\"][\"fill-extrusion-color\"]),\"fill-extrusion-translate\":new ji(Dt[\"paint_fill-extrusion\"][\"fill-extrusion-translate\"]),\"fill-extrusion-translate-anchor\":new ji(Dt[\"paint_fill-extrusion\"][\"fill-extrusion-translate-anchor\"]),\"fill-extrusion-pattern\":new Vi(Dt[\"paint_fill-extrusion\"][\"fill-extrusion-pattern\"]),\"fill-extrusion-height\":new Ui(Dt[\"paint_fill-extrusion\"][\"fill-extrusion-height\"]),\"fill-extrusion-base\":new Ui(Dt[\"paint_fill-extrusion\"][\"fill-extrusion-base\"]),\"fill-extrusion-vertical-gradient\":new ji(Dt[\"paint_fill-extrusion\"][\"fill-extrusion-vertical-gradient\"])})},nl=function(t){function e(e){t.call(this,e,rl)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.createBucket=function(t){return new tl(t)},e.prototype.queryRadius=function(){return Lo(this.paint.get(\"fill-extrusion-translate\"))},e.prototype.is3D=function(){return!0},e.prototype.queryIntersectsFeature=function(t,e,r,n,i,o,s,l){var u=Co(t,this.paint.get(\"fill-extrusion-translate\"),this.paint.get(\"fill-extrusion-translate-anchor\"),o.angle,s),c=this.paint.get(\"fill-extrusion-height\").evaluate(e,r),f=this.paint.get(\"fill-extrusion-base\").evaluate(e,r),h=function(t,e,r,n){for(var i=[],o=0,s=t;o<s.length;o+=1){var l=s[o],u=[l.x,l.y,n,1];No(u,u,e),i.push(new a(u[0]/u[3],u[1]/u[3]))}return i}(u,l,0,0),p=function(t,e,r,n){for(var i=[],o=[],s=n[8]*e,l=n[9]*e,u=n[10]*e,c=n[11]*e,f=n[8]*r,h=n[9]*r,p=n[10]*r,d=n[11]*r,v=0,g=t;v<g.length;v+=1){for(var y=[],m=[],x=0,b=g[v];x<b.length;x+=1){var _=b[x],w=_.x,T=_.y,k=n[0]*w+n[4]*T+n[12],A=n[1]*w+n[5]*T+n[13],M=n[2]*w+n[6]*T+n[14],S=n[3]*w+n[7]*T+n[15],E=M+u,L=S+c,C=k+f,P=A+h,O=M+p,I=S+d,D=new a((k+s)/L,(A+l)/L);D.z=E/L,y.push(D);var z=new a(C/I,P/I);z.z=O/I,m.push(z)}i.push(y),o.push(m)}return[i,o]}(n,f,c,l);return function(t,e,r){var n=1/0;xo(r,e)&&(n=al(r,e[0]));for(var i=0;i<e.length;i++)for(var a=e[i],o=t[i],s=0;s<a.length-1;s++){var l=a[s],u=a[s+1],c=o[s],f=[l,u,o[s+1],c,l];yo(r,f)&&(n=Math.min(n,al(r,f)))}return n!==1/0&&n}(p[0],p[1],h)},e}(Yi);function il(t,e){return t.x*e.x+t.y*e.y}function al(t,e){if(1===t.length){for(var r,n=0,i=e[n++];!r||i.equals(r);)if(!(r=e[n++]))return 1/0;for(;n<e.length;n++){var a=e[n],o=t[0],s=r.sub(i),l=a.sub(i),u=o.sub(i),c=il(s,s),f=il(s,l),h=il(l,l),p=il(u,s),d=il(u,l),v=c*h-f*f,g=(h*p-f*d)/v,y=(c*d-f*p)/v,m=1-g-y,x=i.z*m+r.z*g+a.z*y;if(isFinite(x))return x}return 1/0}for(var b=1/0,_=0,w=e;_<w.length;_+=1){var T=w[_];b=Math.min(b,T.z)}return b}var ol=Ki([{name:\"a_pos_normal\",components:2,type:\"Int16\"},{name:\"a_data\",components:4,type:\"Uint8\"}],4).members,sl=Js.VectorTileFeature.types,ll=Math.cos(Math.PI/180*37.5),ul=Math.pow(2,14)/.5,cl=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.patternFeatures=[],this.layoutVertexArray=new ra,this.indexArray=new fa,this.programConfigurations=new so(ol,t.layers,t.zoom),this.segments=new Oa,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};cl.prototype.populate=function(t,e,r){this.hasPattern=zs(\"line\",this.layers,e);for(var n=this.layers[0].layout.get(\"line-sort-key\"),i=[],a=0,o=t;a<o.length;a+=1){var s=o[a],l=s.feature,u=s.id,c=s.index,f=s.sourceLayerIndex,h=this.layers[0]._featureFilter.needGeometry,p={type:l.type,id:u,properties:l.properties,geometry:h?po(l):[]};if(this.layers[0]._featureFilter.filter(new Pi(this.zoom),p,r)){h||(p.geometry=po(l));var d=n?n.evaluate(p,{},r):void 0,v={id:u,properties:l.properties,type:l.type,sourceLayerIndex:f,index:c,geometry:p.geometry,patterns:{},sortKey:d};i.push(v)}}n&&i.sort((function(t,e){return t.sortKey-e.sortKey}));for(var g=0,y=i;g<y.length;g+=1){var m=y[g],x=m,b=x.geometry,_=x.index,w=x.sourceLayerIndex;if(this.hasPattern){var T=Rs(\"line\",this.layers,m,this.zoom,e);this.patternFeatures.push(T)}else this.addFeature(m,b,_,r,{});var k=t[_].feature;e.featureIndex.insert(k,b,_,w,this.index)}},cl.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},cl.prototype.addFeatures=function(t,e,r){for(var n=0,i=this.patternFeatures;n<i.length;n+=1){var a=i[n];this.addFeature(a,a.geometry,a.index,e,r)}},cl.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},cl.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},cl.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,ol),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},cl.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},cl.prototype.addFeature=function(t,e,r,n,i){for(var a=this.layers[0].layout,o=a.get(\"line-join\").evaluate(t,{}),s=a.get(\"line-cap\"),l=a.get(\"line-miter-limit\"),u=a.get(\"line-round-limit\"),c=0,f=e;c<f.length;c+=1){var h=f[c];this.addLine(h,t,o,s,l,u)}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,i,n)},cl.prototype.addLine=function(t,e,r,n,i,a){if(this.distance=0,this.scaledDistance=0,this.totalDistance=0,e.properties&&e.properties.hasOwnProperty(\"mapbox_clip_start\")&&e.properties.hasOwnProperty(\"mapbox_clip_end\")){this.clipStart=+e.properties.mapbox_clip_start,this.clipEnd=+e.properties.mapbox_clip_end;for(var o=0;o<t.length-1;o++)this.totalDistance+=t[o].dist(t[o+1]);this.updateScaledDistance()}for(var s=\"Polygon\"===sl[e.type],l=t.length;l>=2&&t[l-1].equals(t[l-2]);)l--;for(var u=0;u<l-1&&t[u].equals(t[u+1]);)u++;if(!(l<(s?3:2))){\"bevel\"===r&&(i=1.05);var c,f=this.overscaling<=16?15*co/(512*this.overscaling):0,h=this.segments.prepareSegment(10*l,this.layoutVertexArray,this.indexArray),p=void 0,d=void 0,v=void 0,g=void 0;this.e1=this.e2=-1,s&&(c=t[l-2],g=t[u].sub(c)._unit()._perp());for(var y=u;y<l;y++)if(!(d=y===l-1?s?t[u+1]:void 0:t[y+1])||!t[y].equals(d)){g&&(v=g),c&&(p=c),c=t[y],g=d?d.sub(c)._unit()._perp():v;var m=(v=v||g).add(g);0===m.x&&0===m.y||m._unit();var x=v.x*g.x+v.y*g.y,b=m.x*g.x+m.y*g.y,_=0!==b?1/b:1/0,w=2*Math.sqrt(2-2*b),T=b<ll&&p&&d,k=v.x*g.y-v.y*g.x>0;if(T&&y>u){var A=c.dist(p);if(A>2*f){var M=c.sub(c.sub(p)._mult(f/A)._round());this.updateDistance(p,M),this.addCurrentVertex(M,v,0,0,h),p=M}}var S=p&&d,E=S?r:s?\"butt\":n;if(S&&\"round\"===E&&(_<a?E=\"miter\":_<=2&&(E=\"fakeround\")),\"miter\"===E&&_>i&&(E=\"bevel\"),\"bevel\"===E&&(_>2&&(E=\"flipbevel\"),_<i&&(E=\"miter\")),p&&this.updateDistance(p,c),\"miter\"===E)m._mult(_),this.addCurrentVertex(c,m,0,0,h);else if(\"flipbevel\"===E){if(_>100)m=g.mult(-1);else{var L=_*v.add(g).mag()/v.sub(g).mag();m._perp()._mult(L*(k?-1:1))}this.addCurrentVertex(c,m,0,0,h),this.addCurrentVertex(c,m.mult(-1),0,0,h)}else if(\"bevel\"===E||\"fakeround\"===E){var C=-Math.sqrt(_*_-1),P=k?C:0,O=k?0:C;if(p&&this.addCurrentVertex(c,v,P,O,h),\"fakeround\"===E)for(var I=Math.round(180*w/Math.PI/20),D=1;D<I;D++){var z=D/I;if(.5!==z){var R=z-.5;z+=z*R*(z-1)*((1.0904+x*(x*(3.55645-1.43519*x)-3.2452))*R*R+(.848013+x*(.215638*x-1.06021)))}var F=g.sub(v)._mult(z)._add(v)._unit()._mult(k?-1:1);this.addHalfVertex(c,F.x,F.y,!1,k,0,h)}d&&this.addCurrentVertex(c,g,-P,-O,h)}else if(\"butt\"===E)this.addCurrentVertex(c,m,0,0,h);else if(\"square\"===E){var B=p?1:-1;this.addCurrentVertex(c,m,B,B,h)}else\"round\"===E&&(p&&(this.addCurrentVertex(c,v,0,0,h),this.addCurrentVertex(c,v,1,1,h,!0)),d&&(this.addCurrentVertex(c,g,-1,-1,h,!0),this.addCurrentVertex(c,g,0,0,h)));if(T&&y<l-1){var N=c.dist(d);if(N>2*f){var j=c.add(d.sub(c)._mult(f/N)._round());this.updateDistance(c,j),this.addCurrentVertex(j,g,0,0,h),c=j}}}}},cl.prototype.addCurrentVertex=function(t,e,r,n,i,a){void 0===a&&(a=!1);var o=e.x+e.y*r,s=e.y-e.x*r,l=-e.x+e.y*n,u=-e.y-e.x*n;this.addHalfVertex(t,o,s,a,!1,r,i),this.addHalfVertex(t,l,u,a,!0,-n,i),this.distance>ul/2&&0===this.totalDistance&&(this.distance=0,this.addCurrentVertex(t,e,r,n,i,a))},cl.prototype.addHalfVertex=function(t,e,r,n,i,a,o){var s=t.x,l=t.y,u=.5*this.scaledDistance;this.layoutVertexArray.emplaceBack((s<<1)+(n?1:0),(l<<1)+(i?1:0),Math.round(63*e)+128,Math.round(63*r)+128,1+(0===a?0:a<0?-1:1)|(63&u)<<2,u>>6);var c=o.vertexLength++;this.e1>=0&&this.e2>=0&&(this.indexArray.emplaceBack(this.e1,this.e2,c),o.primitiveLength++),i?this.e2=c:this.e1=c},cl.prototype.updateScaledDistance=function(){this.scaledDistance=this.totalDistance>0?(this.clipStart+(this.clipEnd-this.clipStart)*this.distance/this.totalDistance)*(ul-1):this.distance},cl.prototype.updateDistance=function(t,e){this.distance+=t.dist(e),this.updateScaledDistance()},ni(\"LineBucket\",cl,{omit:[\"layers\",\"patternFeatures\"]});var fl=new Gi({\"line-cap\":new ji(Dt.layout_line[\"line-cap\"]),\"line-join\":new Ui(Dt.layout_line[\"line-join\"]),\"line-miter-limit\":new ji(Dt.layout_line[\"line-miter-limit\"]),\"line-round-limit\":new ji(Dt.layout_line[\"line-round-limit\"]),\"line-sort-key\":new Ui(Dt.layout_line[\"line-sort-key\"])}),hl={paint:new Gi({\"line-opacity\":new Ui(Dt.paint_line[\"line-opacity\"]),\"line-color\":new Ui(Dt.paint_line[\"line-color\"]),\"line-translate\":new ji(Dt.paint_line[\"line-translate\"]),\"line-translate-anchor\":new ji(Dt.paint_line[\"line-translate-anchor\"]),\"line-width\":new Ui(Dt.paint_line[\"line-width\"]),\"line-gap-width\":new Ui(Dt.paint_line[\"line-gap-width\"]),\"line-offset\":new Ui(Dt.paint_line[\"line-offset\"]),\"line-blur\":new Ui(Dt.paint_line[\"line-blur\"]),\"line-dasharray\":new Hi(Dt.paint_line[\"line-dasharray\"]),\"line-pattern\":new Vi(Dt.paint_line[\"line-pattern\"]),\"line-gradient\":new qi(Dt.paint_line[\"line-gradient\"])}),layout:fl},pl=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.possiblyEvaluate=function(e,r){return r=new Pi(Math.floor(r.zoom),{now:r.now,fadeDuration:r.fadeDuration,zoomHistory:r.zoomHistory,transition:r.transition}),t.prototype.possiblyEvaluate.call(this,e,r)},e.prototype.evaluate=function(e,r,n,i){return r=f({},r,{zoom:Math.floor(r.zoom)}),t.prototype.evaluate.call(this,e,r,n,i)},e}(Ui),dl=new pl(hl.paint.properties[\"line-width\"].specification);dl.useIntegerZoom=!0;var vl=function(t){function e(e){t.call(this,e,hl)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._handleSpecialPaintPropertyUpdate=function(t){\"line-gradient\"===t&&this._updateGradient()},e.prototype._updateGradient=function(){var t=this._transitionablePaint._values[\"line-gradient\"].value.expression;this.gradient=Jo(t,\"lineProgress\"),this.gradientTexture=null},e.prototype.recalculate=function(e,r){t.prototype.recalculate.call(this,e,r),this.paint._values[\"line-floorwidth\"]=dl.possiblyEvaluate(this._transitioningPaint._values[\"line-width\"].value,e)},e.prototype.createBucket=function(t){return new cl(t)},e.prototype.queryRadius=function(t){var e=t,r=gl(Eo(\"line-width\",this,e),Eo(\"line-gap-width\",this,e)),n=Eo(\"line-offset\",this,e);return r/2+Math.abs(n)+Lo(this.paint.get(\"line-translate\"))},e.prototype.queryIntersectsFeature=function(t,e,r,n,i,o,s){var l=Co(t,this.paint.get(\"line-translate\"),this.paint.get(\"line-translate-anchor\"),o.angle,s),u=s/2*gl(this.paint.get(\"line-width\").evaluate(e,r),this.paint.get(\"line-gap-width\").evaluate(e,r)),c=this.paint.get(\"line-offset\").evaluate(e,r);return c&&(n=function(t,e){for(var r=[],n=new a(0,0),i=0;i<t.length;i++){for(var o=t[i],s=[],l=0;l<o.length;l++){var u=o[l-1],c=o[l],f=o[l+1],h=0===l?n:c.sub(u)._unit()._perp(),p=l===o.length-1?n:f.sub(c)._unit()._perp(),d=h._add(p)._unit(),v=d.x*p.x+d.y*p.y;d._mult(1/v),s.push(d._mult(e)._add(c))}r.push(s)}return r}(n,c*s)),function(t,e,r){for(var n=0;n<e.length;n++){var i=e[n];if(t.length>=3)for(var a=0;a<i.length;a++)if(Mo(t,i[a]))return!0;if(bo(t,i,r))return!0}return!1}(l,n,u)},e.prototype.isTileClipped=function(){return!0},e}(Yi);function gl(t,e){return e>0?e+2*t:t}var 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Yl=3;function Wl(t,e,r){1===t&&r.readMessage(Xl,e)}function Xl(t,e,r){if(3===t){var n=r.readMessage(Jl,{}),i=n.id,a=n.bitmap,o=n.width,s=n.height,l=n.left,u=n.top,c=n.advance;e.push({id:i,bitmap:new Yo({width:o+2*Yl,height:s+2*Yl},a),metrics:{width:o,height:s,left:l,top:u,advance:c}})}}function Jl(t,e,r){1===t?e.id=r.readVarint():2===t?e.bitmap=r.readBytes():3===t?e.width=r.readVarint():4===t?e.height=r.readVarint():5===t?e.left=r.readSVarint():6===t?e.top=r.readSVarint():7===t&&(e.advance=r.readVarint())}var Kl=Yl;function $l(t){for(var e=0,r=0,n=0,i=t;n<i.length;n+=1){var a=i[n];e+=a.w*a.h,r=Math.max(r,a.w)}t.sort((function(t,e){return e.h-t.h}));for(var o=[{x:0,y:0,w:Math.max(Math.ceil(Math.sqrt(e/.95)),r),h:1/0}],s=0,l=0,u=0,c=t;u<c.length;u+=1)for(var f=c[u],h=o.length-1;h>=0;h--){var p=o[h];if(!(f.w>p.w||f.h>p.h)){if(f.x=p.x,f.y=p.y,l=Math.max(l,f.y+f.h),s=Math.max(s,f.x+f.w),f.w===p.w&&f.h===p.h){var d=o.pop();h<o.length&&(o[h]=d)}else 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t.dispatchRenderCallbacks(this.haveRenderCallbacks),t.updatedImages)this.patchUpdatedImage(this.iconPositions[r],t.getImage(r),e),this.patchUpdatedImage(this.patternPositions[r],t.getImage(r),e)},ru.prototype.patchUpdatedImage=function(t,e,r){if(t&&e&&t.version!==e.version){t.version=e.version;var n=t.tl,i=n[0],a=n[1];r.update(e.data,void 0,{x:i,y:a})}},ni(\"ImagePosition\",tu),ni(\"ImageAtlas\",ru);var nu={horizontal:1,vertical:2,horizontalOnly:3},iu=-17;var au=function(){this.scale=1,this.fontStack=\"\",this.imageName=null};au.forText=function(t,e){var r=new au;return r.scale=t||1,r.fontStack=e,r},au.forImage=function(t){var e=new au;return e.imageName=t,e};var ou=function(){this.text=\"\",this.sectionIndex=[],this.sections=[],this.imageSectionID=null};function su(t,e,r,n,i,a,o,s,l,u,c,f,h,p,d,v){var g,y=ou.fromFeature(t,i);f===nu.vertical&&y.verticalizePunctuation();var m=Ci.processBidirectionalText,x=Ci.processStyledBidirectionalText;if(m&&1===y.sections.length){g=[];for(var b=0,_=m(y.toString(),vu(y,u,a,e,n,p,d));b<_.length;b+=1){var w=_[b],T=new ou;T.text=w,T.sections=y.sections;for(var k=0;k<w.length;k++)T.sectionIndex.push(0);g.push(T)}}else if(x){g=[];for(var A=0,M=x(y.text,y.sectionIndex,vu(y,u,a,e,n,p,d));A<M.length;A+=1){var S=M[A],E=new ou;E.text=S[0],E.sectionIndex=S[1],E.sections=y.sections,g.push(E)}}else g=function(t,e){for(var r=[],n=t.text,i=0,a=0,o=e;a<o.length;a+=1){var s=o[a];r.push(t.substring(i,s)),i=s}return i<n.length&&r.push(t.substring(i,n.length)),r}(y,vu(y,u,a,e,n,p,d));var L=[],C={positionedLines:L,text:y.toString(),top:c[1],bottom:c[1],left:c[0],right:c[0],writingMode:f,iconsInText:!1,verticalizable:!1};return function(t,e,r,n,i,a,o,s,l,u,c,f){for(var h=0,p=iu,d=0,v=0,g=\"right\"===s?1:\"left\"===s?0:.5,y=0,m=0,x=i;m<x.length;m+=1){var b=x[m];b.trim();var _=b.getMaxScale(),w=(_-1)*kl,T={positionedGlyphs:[],lineOffset:0};t.positionedLines[y]=T;var k=T.positionedGlyphs,A=0;if(b.length()){for(var M=0;M<b.length();M++){var S=b.getSection(M),E=b.getSectionIndex(M),L=b.getCharCode(M),C=0,P=null,O=null,I=null,D=kl,z=!(l===nu.horizontal||!c&&!hi(L)||c&&(lu[L]||di(L)));if(S.imageName){var R=n[S.imageName];if(!R)continue;I=S.imageName,t.iconsInText=t.iconsInText||!0,O=R.paddedRect;var F=R.displaySize;S.scale=S.scale*kl/f,P={width:F[0],height:F[1],left:Ql,top:-Kl,advance:z?F[1]:F[0]},C=w+(kl-F[1]*S.scale),D=P.advance;var B=z?F[0]*S.scale-kl*_:F[1]*S.scale-kl*_;B>0&&B>A&&(A=B)}else{var N=r[S.fontStack],j=N&&N[L];if(j&&j.rect)O=j.rect,P=j.metrics;else{var U=e[S.fontStack],V=U&&U[L];if(!V)continue;P=V.metrics}C=(_-S.scale)*kl}z?(t.verticalizable=!0,k.push({glyph:L,imageName:I,x:h,y:p+C,vertical:z,scale:S.scale,fontStack:S.fontStack,sectionIndex:E,metrics:P,rect:O}),h+=D*S.scale+u):(k.push({glyph:L,imageName:I,x:h,y:p+C,vertical:z,scale:S.scale,fontStack:S.fontStack,sectionIndex:E,metrics:P,rect:O}),h+=P.advance*S.scale+u)}if(0!==k.length){var H=h-u;d=Math.max(H,d),yu(k,0,k.length-1,g,A)}h=0;var q=a*_+A;T.lineOffset=Math.max(A,w),p+=q,v=Math.max(q,v),++y}else p+=a,++y}var G=p-iu,Z=gu(o),Y=Z.horizontalAlign,W=Z.verticalAlign;(function(t,e,r,n,i,a,o,s,l){var u=(e-r)*i,c=0;c=a!==o?-s*n-iu:(-n*l+.5)*o;for(var f=0,h=t;f<h.length;f+=1)for(var p=0,d=h[f].positionedGlyphs;p<d.length;p+=1){var v=d[p];v.x+=u,v.y+=c}})(t.positionedLines,g,Y,W,d,v,a,G,i.length),t.top+=-W*G,t.bottom=t.top+G,t.left+=-Y*d,t.right=t.left+d}(C,e,r,n,g,o,s,l,f,u,h,v),!function(t){for(var e=0,r=t;e<r.length;e+=1)if(0!==r[e].positionedGlyphs.length)return!1;return!0}(L)&&C}ou.fromFeature=function(t,e){for(var r=new ou,n=0;n<t.sections.length;n++){var i=t.sections[n];i.image?r.addImageSection(i):r.addTextSection(i,e)}return r},ou.prototype.length=function(){return this.text.length},ou.prototype.getSection=function(t){return this.sections[this.sectionIndex[t]]},ou.prototype.getSectionIndex=function(t){return this.sectionIndex[t]},ou.prototype.getCharCode=function(t){return this.text.charCodeAt(t)},ou.prototype.verticalizePunctuation=function(){this.text=function(t){for(var e=\"\",r=0;r<t.length;r++){var n=t.charCodeAt(r+1)||null,i=t.charCodeAt(r-1)||null;n&&pi(n)&&!Tl[t[r+1]]||i&&pi(i)&&!Tl[t[r-1]]||!Tl[t[r]]?e+=t[r]:e+=Tl[t[r]]}return e}(this.text)},ou.prototype.trim=function(){for(var t=0,e=0;e<this.text.length&&lu[this.text.charCodeAt(e)];e++)t++;for(var r=this.text.length,n=this.text.length-1;n>=0&&n>=t&&lu[this.text.charCodeAt(n)];n--)r--;this.text=this.text.substring(t,r),this.sectionIndex=this.sectionIndex.slice(t,r)},ou.prototype.substring=function(t,e){var r=new ou;return r.text=this.text.substring(t,e),r.sectionIndex=this.sectionIndex.slice(t,e),r.sections=this.sections,r},ou.prototype.toString=function(){return this.text},ou.prototype.getMaxScale=function(){var t=this;return this.sectionIndex.reduce((function(e,r){return Math.max(e,t.sections[r].scale)}),0)},ou.prototype.addTextSection=function(t,e){this.text+=t.text,this.sections.push(au.forText(t.scale,t.fontStack||e));for(var r=this.sections.length-1,n=0;n<t.text.length;++n)this.sectionIndex.push(r)},ou.prototype.addImageSection=function(t){var e=t.image?t.image.name:\"\";if(0!==e.length){var r=this.getNextImageSectionCharCode();r?(this.text+=String.fromCharCode(r),this.sections.push(au.forImage(e)),this.sectionIndex.push(this.sections.length-1)):w(\"Reached maximum number of images 6401\")}else w(\"Can't add FormattedSection with an empty image.\")},ou.prototype.getNextImageSectionCharCode=function(){return this.imageSectionID?this.imageSectionID>=63743?null:++this.imageSectionID:(this.imageSectionID=57344,this.imageSectionID)};var lu={9:!0,10:!0,11:!0,12:!0,13:!0,32:!0},uu={};function cu(t,e,r,n,i,a){if(e.imageName){var o=n[e.imageName];return o?o.displaySize[0]*e.scale*kl/a+i:0}var s=r[e.fontStack],l=s&&s[t];return l?l.metrics.advance*e.scale+i:0}function fu(t,e,r,n){var i=Math.pow(t-e,2);return n?t<e?i/2:2*i:i+Math.abs(r)*r}function hu(t,e,r){var n=0;return 10===t&&(n-=1e4),r&&(n+=150),40!==t&&65288!==t||(n+=50),41!==e&&65289!==e||(n+=50),n}function pu(t,e,r,n,i,a){for(var o=null,s=fu(e,r,i,a),l=0,u=n;l<u.length;l+=1){var c=u[l],f=fu(e-c.x,r,i,a)+c.badness;f<=s&&(o=c,s=f)}return{index:t,x:e,priorBreak:o,badness:s}}function du(t){return t?du(t.priorBreak).concat(t.index):[]}function vu(t,e,r,n,i,a,o){if(\"point\"!==a)return[];if(!t)return[];for(var s=[],l=function(t,e,r,n,i,a){for(var o=0,s=0;s<t.length();s++){var l=t.getSection(s);o+=cu(t.getCharCode(s),l,n,i,e,a)}return o/Math.max(1,Math.ceil(o/r))}(t,e,r,n,i,o),u=t.text.indexOf(\"​\")>=0,c=0,f=0;f<t.length();f++){var h=t.getSection(f),p=t.getCharCode(f);if(lu[p]||(c+=cu(p,h,n,i,e,o)),f<t.length()-1){var d=!((v=p)<11904||!(ci[\"Bopomofo Extended\"](v)||ci.Bopomofo(v)||ci[\"CJK Compatibility Forms\"](v)||ci[\"CJK Compatibility 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yu(t,e,r,n,i){if(n||i)for(var a=t[r],o=a.metrics.advance*a.scale,s=(t[r].x+o)*n,l=e;l<=r;l++)t[l].x-=s,t[l].y+=i}function mu(t,e,r,n,i,a){var o,s=t.image;if(s.content){var l=s.content,u=s.pixelRatio||1;o=[l[0]/u,l[1]/u,s.displaySize[0]-l[2]/u,s.displaySize[1]-l[3]/u]}var c,f,h,p,d=e.left*a,v=e.right*a;\"width\"===r||\"both\"===r?(p=i[0]+d-n[3],f=i[0]+v+n[1]):f=(p=i[0]+(d+v-s.displaySize[0])/2)+s.displaySize[0];var g=e.top*a,y=e.bottom*a;return\"height\"===r||\"both\"===r?(c=i[1]+g-n[0],h=i[1]+y+n[2]):h=(c=i[1]+(g+y-s.displaySize[1])/2)+s.displaySize[1],{image:s,top:c,right:f,bottom:h,left:p,collisionPadding:o}}uu[10]=!0,uu[32]=!0,uu[38]=!0,uu[40]=!0,uu[41]=!0,uu[43]=!0,uu[45]=!0,uu[47]=!0,uu[173]=!0,uu[183]=!0,uu[8203]=!0,uu[8208]=!0,uu[8211]=!0,uu[8231]=!0;var xu=function(t){function e(e,r,n,i){t.call(this,e,r),this.angle=n,void 0!==i&&(this.segment=i)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.clone=function(){return new 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Oa,this.collisionVertexArray=new ca};oc.prototype.upload=function(t){this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,this.layoutAttributes),this.indexBuffer=t.createIndexBuffer(this.indexArray),this.collisionVertexBuffer=t.createVertexBuffer(this.collisionVertexArray,xl.members,!0)},oc.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.segments.destroy(),this.collisionVertexBuffer.destroy())},ni(\"CollisionBuffers\",oc);var sc=function(t){this.collisionBoxArray=t.collisionBoxArray,this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.pixelRatio=t.pixelRatio,this.sourceLayerIndex=t.sourceLayerIndex,this.hasPattern=!1,this.hasRTLText=!1,this.sortKeyRanges=[],this.collisionCircleArray=[],this.placementInvProjMatrix=Do([]),this.placementViewportMatrix=Do([]);var e=this.layers[0]._unevaluatedLayout._values;this.textSizeData=_u(this.zoom,e[\"text-size\"]),this.iconSizeData=_u(this.zoom,e[\"icon-size\"]);var r=this.layers[0].layout,n=r.get(\"symbol-sort-key\"),i=r.get(\"symbol-z-order\");this.sortFeaturesByKey=\"viewport-y\"!==i&&void 0!==n.constantOr(1);var a=\"viewport-y\"===i||\"auto\"===i&&!this.sortFeaturesByKey;this.sortFeaturesByY=a&&(r.get(\"text-allow-overlap\")||r.get(\"icon-allow-overlap\")||r.get(\"text-ignore-placement\")||r.get(\"icon-ignore-placement\")),\"point\"===r.get(\"symbol-placement\")&&(this.writingModes=r.get(\"text-writing-mode\").map((function(t){return nu[t]}))),this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id})),this.sourceID=t.sourceID};sc.prototype.createArrays=function(){this.text=new ac(new so(yl.members,this.layers,this.zoom,(function(t){return/^text/.test(t)}))),this.icon=new ac(new so(yl.members,this.layers,this.zoom,(function(t){return/^icon/.test(t)}))),this.glyphOffsetArray=new Sa,this.lineVertexArray=new Ea,this.symbolInstances=new Ma},sc.prototype.calculateGlyphDependencies=function(t,e,r,n,i){for(var a=0;a<t.length;a++)if(e[t.charCodeAt(a)]=!0,(r||n)&&i){var o=Tl[t.charAt(a)];o&&(e[o.charCodeAt(0)]=!0)}},sc.prototype.populate=function(t,e,r){var n=this.layers[0],i=n.layout,a=i.get(\"text-font\"),o=i.get(\"text-field\"),s=i.get(\"icon-image\"),l=(\"constant\"!==o.value.kind||o.value.value instanceof ue&&!o.value.value.isEmpty()||o.value.value.toString().length>0)&&(\"constant\"!==a.value.kind||a.value.value.length>0),u=\"constant\"!==s.value.kind||!!s.value.value||Object.keys(s.parameters).length>0,c=i.get(\"symbol-sort-key\");if(this.features=[],l||u){for(var f=e.iconDependencies,h=e.glyphDependencies,p=e.availableImages,d=new Pi(this.zoom),v=0,g=t;v<g.length;v+=1){var y=g[v],m=y.feature,x=y.id,b=y.index,_=y.sourceLayerIndex,w=n._featureFilter.needGeometry,T={type:m.type,id:x,properties:m.properties,geometry:w?po(m):[]};if(n._featureFilter.filter(d,T,r)){w||(T.geometry=po(m));var k=void 0;if(l){var A=n.getValueAndResolveTokens(\"text-field\",T,r,p),M=ue.factory(A);ic(M)&&(this.hasRTLText=!0),(!this.hasRTLText||\"unavailable\"===Ei()||this.hasRTLText&&Ci.isParsed())&&(k=wl(M,n,T))}var S=void 0;if(u){var E=n.getValueAndResolveTokens(\"icon-image\",T,r,p);S=E instanceof ce?E:ce.fromString(E)}if(k||S){var L=this.sortFeaturesByKey?c.evaluate(T,{},r):void 0,C={id:x,text:k,icon:S,index:b,sourceLayerIndex:_,geometry:po(m),properties:m.properties,type:tc[m.type],sortKey:L};if(this.features.push(C),S&&(f[S.name]=!0),k){var P=a.evaluate(T,{},r).join(\",\"),O=\"map\"===i.get(\"text-rotation-alignment\")&&\"point\"!==i.get(\"symbol-placement\");this.allowVerticalPlacement=this.writingModes&&this.writingModes.indexOf(nu.vertical)>=0;for(var I=0,D=k.sections;I<D.length;I+=1){var z=D[I];if(z.image)f[z.image.name]=!0;else{var R=fi(k.toString()),F=z.fontStack||P,B=h[F]=h[F]||{};this.calculateGlyphDependencies(z.text,B,O,this.allowVerticalPlacement,R)}}}}}}\"line\"===i.get(\"symbol-placement\")&&(this.features=function(t){var e={},r={},n=[],i=0;function a(e){n.push(t[e]),i++}function o(t,e,i){var a=r[t];return delete r[t],r[e]=a,n[a].geometry[0].pop(),n[a].geometry[0]=n[a].geometry[0].concat(i[0]),a}function s(t,r,i){var a=e[r];return delete e[r],e[t]=a,n[a].geometry[0].shift(),n[a].geometry[0]=i[0].concat(n[a].geometry[0]),a}function l(t,e,r){var n=r?e[0][e[0].length-1]:e[0][0];return t+\":\"+n.x+\":\"+n.y}for(var u=0;u<t.length;u++){var c=t[u],f=c.geometry,h=c.text?c.text.toString():null;if(h){var p=l(h,f),d=l(h,f,!0);if(p in r&&d in e&&r[p]!==e[d]){var v=s(p,d,f),g=o(p,d,n[v].geometry);delete e[p],delete r[d],r[l(h,n[g].geometry,!0)]=g,n[v].geometry=null}else p in r?o(p,d,f):d in e?s(p,d,f):(a(u),e[p]=i-1,r[d]=i-1)}else a(u)}return n.filter((function(t){return t.geometry}))}(this.features)),this.sortFeaturesByKey&&this.features.sort((function(t,e){return t.sortKey-e.sortKey}))}},sc.prototype.update=function(t,e,r){this.stateDependentLayers.length&&(this.text.programConfigurations.updatePaintArrays(t,e,this.layers,r),this.icon.programConfigurations.updatePaintArrays(t,e,this.layers,r))},sc.prototype.isEmpty=function(){return 0===this.symbolInstances.length&&!this.hasRTLText},sc.prototype.uploadPending=function(){return!this.uploaded||this.text.programConfigurations.needsUpload||this.icon.programConfigurations.needsUpload},sc.prototype.upload=function(t){!this.uploaded&&this.hasDebugData()&&(this.textCollisionBox.upload(t),this.iconCollisionBox.upload(t)),this.text.upload(t,this.sortFeaturesByY,!this.uploaded,this.text.programConfigurations.needsUpload),this.icon.upload(t,this.sortFeaturesByY,!this.uploaded,this.icon.programConfigurations.needsUpload),this.uploaded=!0},sc.prototype.destroyDebugData=function(){this.textCollisionBox.destroy(),this.iconCollisionBox.destroy()},sc.prototype.destroy=function(){this.text.destroy(),this.icon.destroy(),this.hasDebugData()&&this.destroyDebugData()},sc.prototype.addToLineVertexArray=function(t,e){var r=this.lineVertexArray.length;if(void 0!==t.segment){for(var n=t.dist(e[t.segment+1]),i=t.dist(e[t.segment]),a={},o=t.segment+1;o<e.length;o++)a[o]={x:e[o].x,y:e[o].y,tileUnitDistanceFromAnchor:n},o<e.length-1&&(n+=e[o+1].dist(e[o]));for(var s=t.segment||0;s>=0;s--)a[s]={x:e[s].x,y:e[s].y,tileUnitDistanceFromAnchor:i},s>0&&(i+=e[s-1].dist(e[s]));for(var l=0;l<e.length;l++){var u=a[l];this.lineVertexArray.emplaceBack(u.x,u.y,u.tileUnitDistanceFromAnchor)}}return{lineStartIndex:r,lineLength:this.lineVertexArray.length-r}},sc.prototype.addSymbols=function(t,e,r,n,i,a,o,s,l,u,c,f){for(var h=t.indexArray,p=t.layoutVertexArray,d=t.segments.prepareSegment(4*e.length,p,h,a.sortKey),v=this.glyphOffsetArray.length,g=d.vertexLength,y=this.allowVerticalPlacement&&o===nu.vertical?Math.PI/2:0,m=a.text&&a.text.sections,x=0;x<e.length;x++){var b=e[x],_=b.tl,w=b.tr,T=b.bl,k=b.br,A=b.tex,M=b.pixelOffsetTL,S=b.pixelOffsetBR,E=b.minFontScaleX,L=b.minFontScaleY,C=b.glyphOffset,P=b.isSDF,O=b.sectionIndex,I=d.vertexLength,D=C[1];rc(p,s.x,s.y,_.x,D+_.y,A.x,A.y,r,P,M.x,M.y,E,L),rc(p,s.x,s.y,w.x,D+w.y,A.x+A.w,A.y,r,P,S.x,M.y,E,L),rc(p,s.x,s.y,T.x,D+T.y,A.x,A.y+A.h,r,P,M.x,S.y,E,L),rc(p,s.x,s.y,k.x,D+k.y,A.x+A.w,A.y+A.h,r,P,S.x,S.y,E,L),nc(t.dynamicLayoutVertexArray,s,y),h.emplaceBack(I,I+1,I+2),h.emplaceBack(I+1,I+2,I+3),d.vertexLength+=4,d.primitiveLength+=2,this.glyphOffsetArray.emplaceBack(C[0]),x!==e.length-1&&O===e[x+1].sectionIndex||t.programConfigurations.populatePaintArrays(p.length,a,a.index,{},f,m&&m[O])}t.placedSymbolArray.emplaceBack(s.x,s.y,v,this.glyphOffsetArray.length-v,g,l,u,s.segment,r?r[0]:0,r?r[1]:0,n[0],n[1],o,0,!1,0,c)},sc.prototype._addCollisionDebugVertex=function(t,e,r,n,i,a){return e.emplaceBack(0,0),t.emplaceBack(r.x,r.y,n,i,Math.round(a.x),Math.round(a.y))},sc.prototype.addCollisionDebugVertices=function(t,e,r,n,i,o,s){var l=i.segments.prepareSegment(4,i.layoutVertexArray,i.indexArray),u=l.vertexLength,c=i.layoutVertexArray,f=i.collisionVertexArray,h=s.anchorX,p=s.anchorY;this._addCollisionDebugVertex(c,f,o,h,p,new a(t,e)),this._addCollisionDebugVertex(c,f,o,h,p,new a(r,e)),this._addCollisionDebugVertex(c,f,o,h,p,new a(r,n)),this._addCollisionDebugVertex(c,f,o,h,p,new a(t,n)),l.vertexLength+=4;var d=i.indexArray;d.emplaceBack(u,u+1),d.emplaceBack(u+1,u+2),d.emplaceBack(u+2,u+3),d.emplaceBack(u+3,u),l.primitiveLength+=4},sc.prototype.addDebugCollisionBoxes=function(t,e,r,n){for(var i=t;i<e;i++){var a=this.collisionBoxArray.get(i),o=a.x1,s=a.y1,l=a.x2,u=a.y2;this.addCollisionDebugVertices(o,s,l,u,n?this.textCollisionBox:this.iconCollisionBox,a.anchorPoint,r)}},sc.prototype.generateCollisionDebugBuffers=function(){this.hasDebugData()&&this.destroyDebugData(),this.textCollisionBox=new oc(la,bl.members,ya),this.iconCollisionBox=new oc(la,bl.members,ya);for(var t=0;t<this.symbolInstances.length;t++){var e=this.symbolInstances.get(t);this.addDebugCollisionBoxes(e.textBoxStartIndex,e.textBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.verticalTextBoxStartIndex,e.verticalTextBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.iconBoxStartIndex,e.iconBoxEndIndex,e,!1),this.addDebugCollisionBoxes(e.verticalIconBoxStartIndex,e.verticalIconBoxEndIndex,e,!1)}},sc.prototype._deserializeCollisionBoxesForSymbol=function(t,e,r,n,i,a,o,s,l){for(var u={},c=e;c<r;c++){var f=t.get(c);u.textBox={x1:f.x1,y1:f.y1,x2:f.x2,y2:f.y2,anchorPointX:f.anchorPointX,anchorPointY:f.anchorPointY},u.textFeatureIndex=f.featureIndex;break}for(var h=n;h<i;h++){var p=t.get(h);u.verticalTextBox={x1:p.x1,y1:p.y1,x2:p.x2,y2:p.y2,anchorPointX:p.anchorPointX,anchorPointY:p.anchorPointY},u.verticalTextFeatureIndex=p.featureIndex;break}for(var d=a;d<o;d++){var v=t.get(d);u.iconBox={x1:v.x1,y1:v.y1,x2:v.x2,y2:v.y2,anchorPointX:v.anchorPointX,anchorPointY:v.anchorPointY},u.iconFeatureIndex=v.featureIndex;break}for(var g=s;g<l;g++){var y=t.get(g);u.verticalIconBox={x1:y.x1,y1:y.y1,x2:y.x2,y2:y.y2,anchorPointX:y.anchorPointX,anchorPointY:y.anchorPointY},u.verticalIconFeatureIndex=y.featureIndex;break}return u},sc.prototype.deserializeCollisionBoxes=function(t){this.collisionArrays=[];for(var e=0;e<this.symbolInstances.length;e++){var r=this.symbolInstances.get(e);this.collisionArrays.push(this._deserializeCollisionBoxesForSymbol(t,r.textBoxStartIndex,r.textBoxEndIndex,r.verticalTextBoxStartIndex,r.verticalTextBoxEndIndex,r.iconBoxStartIndex,r.iconBoxEndIndex,r.verticalIconBoxStartIndex,r.verticalIconBoxEndIndex))}},sc.prototype.hasTextData=function(){return this.text.segments.get().length>0},sc.prototype.hasIconData=function(){return this.icon.segments.get().length>0},sc.prototype.hasDebugData=function(){return this.textCollisionBox&&this.iconCollisionBox},sc.prototype.hasTextCollisionBoxData=function(){return this.hasDebugData()&&this.textCollisionBox.segments.get().length>0},sc.prototype.hasIconCollisionBoxData=function(){return this.hasDebugData()&&this.iconCollisionBox.segments.get().length>0},sc.prototype.addIndicesForPlacedSymbol=function(t,e){for(var r=t.placedSymbolArray.get(e),n=r.vertexStartIndex+4*r.numGlyphs,i=r.vertexStartIndex;i<n;i+=4)t.indexArray.emplaceBack(i,i+1,i+2),t.indexArray.emplaceBack(i+1,i+2,i+3)},sc.prototype.getSortedSymbolIndexes=function(t){if(this.sortedAngle===t&&void 0!==this.symbolInstanceIndexes)return this.symbolInstanceIndexes;for(var e=Math.sin(t),r=Math.cos(t),n=[],i=[],a=[],o=0;o<this.symbolInstances.length;++o){a.push(o);var s=this.symbolInstances.get(o);n.push(0|Math.round(e*s.anchorX+r*s.anchorY)),i.push(s.featureIndex)}return a.sort((function(t,e){return n[t]-n[e]||i[e]-i[t]})),a},sc.prototype.addToSortKeyRanges=function(t,e){var r=this.sortKeyRanges[this.sortKeyRanges.length-1];r&&r.sortKey===e?r.symbolInstanceEnd=t+1:this.sortKeyRanges.push({sortKey:e,symbolInstanceStart:t,symbolInstanceEnd:t+1})},sc.prototype.sortFeatures=function(t){var e=this;if(this.sortFeaturesByY&&this.sortedAngle!==t&&!(this.text.segments.get().length>1||this.icon.segments.get().length>1)){this.symbolInstanceIndexes=this.getSortedSymbolIndexes(t),this.sortedAngle=t,this.text.indexArray.clear(),this.icon.indexArray.clear(),this.featureSortOrder=[];for(var r=0,n=this.symbolInstanceIndexes;r<n.length;r+=1){var i=n[r],a=this.symbolInstances.get(i);this.featureSortOrder.push(a.featureIndex),[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t,r,n){t>=0&&n.indexOf(t)===r&&e.addIndicesForPlacedSymbol(e.text,t)})),a.verticalPlacedTextSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.text,a.verticalPlacedTextSymbolIndex),a.placedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,a.placedIconSymbolIndex),a.verticalPlacedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,a.verticalPlacedIconSymbolIndex)}this.text.indexBuffer&&this.text.indexBuffer.updateData(this.text.indexArray),this.icon.indexBuffer&&this.icon.indexBuffer.updateData(this.icon.indexArray)}},ni(\"SymbolBucket\",sc,{omit:[\"layers\",\"collisionBoxArray\",\"features\",\"compareText\"]}),sc.MAX_GLYPHS=65535,sc.addDynamicAttributes=nc;var lc=new Gi({\"symbol-placement\":new ji(Dt.layout_symbol[\"symbol-placement\"]),\"symbol-spacing\":new ji(Dt.layout_symbol[\"symbol-spacing\"]),\"symbol-avoid-edges\":new ji(Dt.layout_symbol[\"symbol-avoid-edges\"]),\"symbol-sort-key\":new Ui(Dt.layout_symbol[\"symbol-sort-key\"]),\"symbol-z-order\":new ji(Dt.layout_symbol[\"symbol-z-order\"]),\"icon-allow-overlap\":new ji(Dt.layout_symbol[\"icon-allow-overlap\"]),\"icon-ignore-placement\":new ji(Dt.layout_symbol[\"icon-ignore-placement\"]),\"icon-optional\":new ji(Dt.layout_symbol[\"icon-optional\"]),\"icon-rotation-alignment\":new ji(Dt.layout_symbol[\"icon-rotation-alignment\"]),\"icon-size\":new Ui(Dt.layout_symbol[\"icon-size\"]),\"icon-text-fit\":new ji(Dt.layout_symbol[\"icon-text-fit\"]),\"icon-text-fit-padding\":new ji(Dt.layout_symbol[\"icon-text-fit-padding\"]),\"icon-image\":new Ui(Dt.layout_symbol[\"icon-image\"]),\"icon-rotate\":new Ui(Dt.layout_symbol[\"icon-rotate\"]),\"icon-padding\":new ji(Dt.layout_symbol[\"icon-padding\"]),\"icon-keep-upright\":new ji(Dt.layout_symbol[\"icon-keep-upright\"]),\"icon-offset\":new Ui(Dt.layout_symbol[\"icon-offset\"]),\"icon-anchor\":new Ui(Dt.layout_symbol[\"icon-anchor\"]),\"icon-pitch-alignment\":new ji(Dt.layout_symbol[\"icon-pitch-alignment\"]),\"text-pitch-alignment\":new ji(Dt.layout_symbol[\"text-pitch-alignment\"]),\"text-rotation-alignment\":new ji(Dt.layout_symbol[\"text-rotation-alignment\"]),\"text-field\":new Ui(Dt.layout_symbol[\"text-field\"]),\"text-font\":new Ui(Dt.layout_symbol[\"text-font\"]),\"text-size\":new Ui(Dt.layout_symbol[\"text-size\"]),\"text-max-width\":new Ui(Dt.layout_symbol[\"text-max-width\"]),\"text-line-height\":new ji(Dt.layout_symbol[\"text-line-height\"]),\"text-letter-spacing\":new Ui(Dt.layout_symbol[\"text-letter-spacing\"]),\"text-justify\":new Ui(Dt.layout_symbol[\"text-justify\"]),\"text-radial-offset\":new Ui(Dt.layout_symbol[\"text-radial-offset\"]),\"text-variable-anchor\":new ji(Dt.layout_symbol[\"text-variable-anchor\"]),\"text-anchor\":new Ui(Dt.layout_symbol[\"text-anchor\"]),\"text-max-angle\":new ji(Dt.layout_symbol[\"text-max-angle\"]),\"text-writing-mode\":new ji(Dt.layout_symbol[\"text-writing-mode\"]),\"text-rotate\":new Ui(Dt.layout_symbol[\"text-rotate\"]),\"text-padding\":new ji(Dt.layout_symbol[\"text-padding\"]),\"text-keep-upright\":new ji(Dt.layout_symbol[\"text-keep-upright\"]),\"text-transform\":new Ui(Dt.layout_symbol[\"text-transform\"]),\"text-offset\":new Ui(Dt.layout_symbol[\"text-offset\"]),\"text-allow-overlap\":new ji(Dt.layout_symbol[\"text-allow-overlap\"]),\"text-ignore-placement\":new ji(Dt.layout_symbol[\"text-ignore-placement\"]),\"text-optional\":new ji(Dt.layout_symbol[\"text-optional\"])}),uc={paint:new Gi({\"icon-opacity\":new Ui(Dt.paint_symbol[\"icon-opacity\"]),\"icon-color\":new Ui(Dt.paint_symbol[\"icon-color\"]),\"icon-halo-color\":new Ui(Dt.paint_symbol[\"icon-halo-color\"]),\"icon-halo-width\":new Ui(Dt.paint_symbol[\"icon-halo-width\"]),\"icon-halo-blur\":new Ui(Dt.paint_symbol[\"icon-halo-blur\"]),\"icon-translate\":new ji(Dt.paint_symbol[\"icon-translate\"]),\"icon-translate-anchor\":new ji(Dt.paint_symbol[\"icon-translate-anchor\"]),\"text-opacity\":new Ui(Dt.paint_symbol[\"text-opacity\"]),\"text-color\":new Ui(Dt.paint_symbol[\"text-color\"],{runtimeType:Zt,getOverride:function(t){return t.textColor},hasOverride:function(t){return!!t.textColor}}),\"text-halo-color\":new Ui(Dt.paint_symbol[\"text-halo-color\"]),\"text-halo-width\":new Ui(Dt.paint_symbol[\"text-halo-width\"]),\"text-halo-blur\":new Ui(Dt.paint_symbol[\"text-halo-blur\"]),\"text-translate\":new ji(Dt.paint_symbol[\"text-translate\"]),\"text-translate-anchor\":new ji(Dt.paint_symbol[\"text-translate-anchor\"])}),layout:lc},cc=function(t){this.type=t.property.overrides?t.property.overrides.runtimeType:Vt,this.defaultValue=t};cc.prototype.evaluate=function(t){if(t.formattedSection){var e=this.defaultValue.property.overrides;if(e&&e.hasOverride(t.formattedSection))return e.getOverride(t.formattedSection)}return t.feature&&t.featureState?this.defaultValue.evaluate(t.feature,t.featureState):this.defaultValue.property.specification.default},cc.prototype.eachChild=function(t){this.defaultValue.isConstant()||t(this.defaultValue.value._styleExpression.expression)},cc.prototype.outputDefined=function(){return!1},cc.prototype.serialize=function(){return null},ni(\"FormatSectionOverride\",cc,{omit:[\"defaultValue\"]});var fc=function(t){function e(e){t.call(this,e,uc)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.recalculate=function(e,r){if(t.prototype.recalculate.call(this,e,r),\"auto\"===this.layout.get(\"icon-rotation-alignment\")&&(\"point\"!==this.layout.get(\"symbol-placement\")?this.layout._values[\"icon-rotation-alignment\"]=\"map\":this.layout._values[\"icon-rotation-alignment\"]=\"viewport\"),\"auto\"===this.layout.get(\"text-rotation-alignment\")&&(\"point\"!==this.layout.get(\"symbol-placement\")?this.layout._values[\"text-rotation-alignment\"]=\"map\":this.layout._values[\"text-rotation-alignment\"]=\"viewport\"),\"auto\"===this.layout.get(\"text-pitch-alignment\")&&(this.layout._values[\"text-pitch-alignment\"]=this.layout.get(\"text-rotation-alignment\")),\"auto\"===this.layout.get(\"icon-pitch-alignment\")&&(this.layout._values[\"icon-pitch-alignment\"]=this.layout.get(\"icon-rotation-alignment\")),\"point\"===this.layout.get(\"symbol-placement\")){var n=this.layout.get(\"text-writing-mode\");if(n){for(var i=[],a=0,o=n;a<o.length;a+=1){var s=o[a];i.indexOf(s)<0&&i.push(s)}this.layout._values[\"text-writing-mode\"]=i}else this.layout._values[\"text-writing-mode\"]=[\"horizontal\"]}this._setPaintOverrides()},e.prototype.getValueAndResolveTokens=function(t,e,r,n){var i=this.layout.get(t).evaluate(e,{},r,n),a=this._unevaluatedLayout._values[t];return a.isDataDriven()||sn(a.value)||!i?i:function(t,e){return e.replace(/{([^{}]+)}/g,(function(e,r){return r in t?String(t[r]):\"\"}))}(e.properties,i)},e.prototype.createBucket=function(t){return new sc(t)},e.prototype.queryRadius=function(){return 0},e.prototype.queryIntersectsFeature=function(){return!1},e.prototype._setPaintOverrides=function(){for(var t=0,r=uc.paint.overridableProperties;t<r.length;t+=1){var n=r[t];if(e.hasPaintOverride(this.layout,n)){var i,a=this.paint.get(n),o=new cc(a),s=new on(o,a.property.specification);i=\"constant\"===a.value.kind||\"source\"===a.value.kind?new un(\"source\",s):new cn(\"composite\",s,a.value.zoomStops,a.value._interpolationType),this.paint._values[n]=new Bi(a.property,i,a.parameters)}}},e.prototype._handleOverridablePaintPropertyUpdate=function(t,r,n){return!(!this.layout||r.isDataDriven()||n.isDataDriven())&&e.hasPaintOverride(this.layout,t)},e.hasPaintOverride=function(t,e){var r=t.get(\"text-field\"),n=uc.paint.properties[e],i=!1,a=function(t){for(var e=0,r=t;e<r.length;e+=1){var a=r[e];if(n.overrides&&n.overrides.hasOverride(a))return void(i=!0)}};if(\"constant\"===r.value.kind&&r.value.value instanceof ue)a(r.value.value.sections);else if(\"source\"===r.value.kind){var o=function(t){if(!i)if(t instanceof ve&&pe(t.value)===Jt){var e=t.value;a(e.sections)}else t instanceof xe?a(t.sections):t.eachChild(o)},s=r.value;s._styleExpression&&o(s._styleExpression.expression)}return i},e}(Yi),hc={paint:new Gi({\"background-color\":new ji(Dt.paint_background[\"background-color\"]),\"background-pattern\":new Hi(Dt.paint_background[\"background-pattern\"]),\"background-opacity\":new ji(Dt.paint_background[\"background-opacity\"])})},pc=function(t){function e(e){t.call(this,e,hc)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(Yi),dc={paint:new Gi({\"raster-opacity\":new ji(Dt.paint_raster[\"raster-opacity\"]),\"raster-hue-rotate\":new ji(Dt.paint_raster[\"raster-hue-rotate\"]),\"raster-brightness-min\":new ji(Dt.paint_raster[\"raster-brightness-min\"]),\"raster-brightness-max\":new ji(Dt.paint_raster[\"raster-brightness-max\"]),\"raster-saturation\":new ji(Dt.paint_raster[\"raster-saturation\"]),\"raster-contrast\":new ji(Dt.paint_raster[\"raster-contrast\"]),\"raster-resampling\":new ji(Dt.paint_raster[\"raster-resampling\"]),\"raster-fade-duration\":new ji(Dt.paint_raster[\"raster-fade-duration\"])})},vc=function(t){function e(e){t.call(this,e,dc)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(Yi);var gc=function(t){function e(e){t.call(this,e,{}),this.implementation=e}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.is3D=function(){return\"3d\"===this.implementation.renderingMode},e.prototype.hasOffscreenPass=function(){return void 0!==this.implementation.prerender},e.prototype.recalculate=function(){},e.prototype.updateTransitions=function(){},e.prototype.hasTransition=function(){},e.prototype.serialize=function(){},e.prototype.onAdd=function(t){this.implementation.onAdd&&this.implementation.onAdd(t,t.painter.context.gl)},e.prototype.onRemove=function(t){this.implementation.onRemove&&this.implementation.onRemove(t,t.painter.context.gl)},e}(Yi),yc={circle:Uo,heatmap:Ko,hillshade:Qo,fill:js,\"fill-extrusion\":nl,line:vl,symbol:fc,background:pc,raster:vc};var mc=self.HTMLImageElement,xc=self.HTMLCanvasElement,bc=self.HTMLVideoElement,_c=self.ImageData,wc=self.ImageBitmap,Tc=function(t,e,r,n){this.context=t,this.format=r,this.texture=t.gl.createTexture(),this.update(e,n)};Tc.prototype.update=function(t,e,r){var n=t.width,i=t.height,a=!(this.size&&this.size[0]===n&&this.size[1]===i||r),o=this.context,s=o.gl;if(this.useMipmap=Boolean(e&&e.useMipmap),s.bindTexture(s.TEXTURE_2D,this.texture),o.pixelStoreUnpackFlipY.set(!1),o.pixelStoreUnpack.set(1),o.pixelStoreUnpackPremultiplyAlpha.set(this.format===s.RGBA&&(!e||!1!==e.premultiply)),a)this.size=[n,i],t instanceof mc||t instanceof xc||t instanceof bc||t instanceof _c||wc&&t instanceof wc?s.texImage2D(s.TEXTURE_2D,0,this.format,this.format,s.UNSIGNED_BYTE,t):s.texImage2D(s.TEXTURE_2D,0,this.format,n,i,0,this.format,s.UNSIGNED_BYTE,t.data);else{var l=r||{x:0,y:0},u=l.x,c=l.y;t instanceof mc||t instanceof xc||t instanceof bc||t instanceof _c||wc&&t instanceof wc?s.texSubImage2D(s.TEXTURE_2D,0,u,c,s.RGBA,s.UNSIGNED_BYTE,t):s.texSubImage2D(s.TEXTURE_2D,0,u,c,n,i,s.RGBA,s.UNSIGNED_BYTE,t.data)}this.useMipmap&&this.isSizePowerOfTwo()&&s.generateMipmap(s.TEXTURE_2D)},Tc.prototype.bind=function(t,e,r){var n=this.context.gl;n.bindTexture(n.TEXTURE_2D,this.texture),r!==n.LINEAR_MIPMAP_NEAREST||this.isSizePowerOfTwo()||(r=n.LINEAR),t!==this.filter&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,t),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,r||t),this.filter=t),e!==this.wrap&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,e),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,e),this.wrap=e)},Tc.prototype.isSizePowerOfTwo=function(){return this.size[0]===this.size[1]&&Math.log(this.size[0])/Math.LN2%1==0},Tc.prototype.destroy=function(){this.context.gl.deleteTexture(this.texture),this.texture=null};var kc=function(t){var e=this;this._callback=t,this._triggered=!1,\"undefined\"!=typeof MessageChannel&&(this._channel=new MessageChannel,this._channel.port2.onmessage=function(){e._triggered=!1,e._callback()})};kc.prototype.trigger=function(){var t=this;this._triggered||(this._triggered=!0,this._channel?this._channel.port1.postMessage(!0):setTimeout((function(){t._triggered=!1,t._callback()}),0))},kc.prototype.remove=function(){delete this._channel,this._callback=function(){}};var Ac=function(t,e,r){this.target=t,this.parent=e,this.mapId=r,this.callbacks={},this.tasks={},this.taskQueue=[],this.cancelCallbacks={},g([\"receive\",\"process\"],this),this.invoker=new kc(this.process),this.target.addEventListener(\"message\",this.receive,!1),this.globalScope=A()?t:self};function Mc(t,e,r){var n=2*Math.PI*6378137/256/Math.pow(2,r);return[t*n-2*Math.PI*6378137/2,e*n-2*Math.PI*6378137/2]}Ac.prototype.send=function(t,e,r,n,i){var a=this;void 0===i&&(i=!1);var o=Math.round(1e18*Math.random()).toString(36).substring(0,10);r&&(this.callbacks[o]=r);var s=E(this.globalScope)?void 0:[];return this.target.postMessage({id:o,type:t,hasCallback:!!r,targetMapId:n,mustQueue:i,sourceMapId:this.mapId,data:si(e,s)},s),{cancel:function(){r&&delete a.callbacks[o],a.target.postMessage({id:o,type:\"<cancel>\",targetMapId:n,sourceMapId:a.mapId})}}},Ac.prototype.receive=function(t){var e=t.data,r=e.id;if(r&&(!e.targetMapId||this.mapId===e.targetMapId))if(\"<cancel>\"===e.type){delete this.tasks[r];var n=this.cancelCallbacks[r];delete this.cancelCallbacks[r],n&&n()}else A()||e.mustQueue?(this.tasks[r]=e,this.taskQueue.push(r),this.invoker.trigger()):this.processTask(r,e)},Ac.prototype.process=function(){if(this.taskQueue.length){var t=this.taskQueue.shift(),e=this.tasks[t];delete this.tasks[t],this.taskQueue.length&&this.invoker.trigger(),e&&this.processTask(t,e)}},Ac.prototype.processTask=function(t,e){var r=this;if(\"<response>\"===e.type){var n=this.callbacks[t];delete this.callbacks[t],n&&(e.error?n(li(e.error)):n(null,li(e.data)))}else{var i=!1,a=E(this.globalScope)?void 0:[],o=e.hasCallback?function(e,n){i=!0,delete r.cancelCallbacks[t],r.target.postMessage({id:t,type:\"<response>\",sourceMapId:r.mapId,error:e?si(e):null,data:si(n,a)},a)}:function(t){i=!0},s=null,l=li(e.data);if(this.parent[e.type])s=this.parent[e.type](e.sourceMapId,l,o);else if(this.parent.getWorkerSource){var u=e.type.split(\".\");s=this.parent.getWorkerSource(e.sourceMapId,u[0],l.source)[u[1]](l,o)}else o(new Error(\"Could not find function \"+e.type));!i&&s&&s.cancel&&(this.cancelCallbacks[t]=s.cancel)}},Ac.prototype.remove=function(){this.invoker.remove(),this.target.removeEventListener(\"message\",this.receive,!1)};var Sc=function(t,e){t&&(e?this.setSouthWest(t).setNorthEast(e):4===t.length?this.setSouthWest([t[0],t[1]]).setNorthEast([t[2],t[3]]):this.setSouthWest(t[0]).setNorthEast(t[1]))};Sc.prototype.setNorthEast=function(t){return this._ne=t instanceof Lc?new Lc(t.lng,t.lat):Lc.convert(t),this},Sc.prototype.setSouthWest=function(t){return this._sw=t instanceof Lc?new Lc(t.lng,t.lat):Lc.convert(t),this},Sc.prototype.extend=function(t){var e,r,n=this._sw,i=this._ne;if(t instanceof Lc)e=t,r=t;else{if(!(t instanceof Sc)){if(Array.isArray(t)){if(4===t.length||t.every(Array.isArray)){var a=t;return this.extend(Sc.convert(a))}var o=t;return this.extend(Lc.convert(o))}return this}if(e=t._sw,r=t._ne,!e||!r)return this}return n||i?(n.lng=Math.min(e.lng,n.lng),n.lat=Math.min(e.lat,n.lat),i.lng=Math.max(r.lng,i.lng),i.lat=Math.max(r.lat,i.lat)):(this._sw=new Lc(e.lng,e.lat),this._ne=new Lc(r.lng,r.lat)),this},Sc.prototype.getCenter=function(){return new Lc((this._sw.lng+this._ne.lng)/2,(this._sw.lat+this._ne.lat)/2)},Sc.prototype.getSouthWest=function(){return this._sw},Sc.prototype.getNorthEast=function(){return this._ne},Sc.prototype.getNorthWest=function(){return new Lc(this.getWest(),this.getNorth())},Sc.prototype.getSouthEast=function(){return new Lc(this.getEast(),this.getSouth())},Sc.prototype.getWest=function(){return this._sw.lng},Sc.prototype.getSouth=function(){return this._sw.lat},Sc.prototype.getEast=function(){return this._ne.lng},Sc.prototype.getNorth=function(){return this._ne.lat},Sc.prototype.toArray=function(){return[this._sw.toArray(),this._ne.toArray()]},Sc.prototype.toString=function(){return\"LngLatBounds(\"+this._sw.toString()+\", \"+this._ne.toString()+\")\"},Sc.prototype.isEmpty=function(){return!(this._sw&&this._ne)},Sc.prototype.contains=function(t){var e=Lc.convert(t),r=e.lng,n=e.lat,i=this._sw.lat<=n&&n<=this._ne.lat,a=this._sw.lng<=r&&r<=this._ne.lng;return this._sw.lng>this._ne.lng&&(a=this._sw.lng>=r&&r>=this._ne.lng),i&&a},Sc.convert=function(t){return!t||t instanceof Sc?t:new Sc(t)};var Ec=6371008.8,Lc=function(t,e){if(isNaN(t)||isNaN(e))throw new Error(\"Invalid LngLat object: (\"+t+\", \"+e+\")\");if(this.lng=+t,this.lat=+e,this.lat>90||this.lat<-90)throw new Error(\"Invalid LngLat latitude value: must be between -90 and 90\")};Lc.prototype.wrap=function(){return new Lc(c(this.lng,-180,180),this.lat)},Lc.prototype.toArray=function(){return[this.lng,this.lat]},Lc.prototype.toString=function(){return\"LngLat(\"+this.lng+\", \"+this.lat+\")\"},Lc.prototype.distanceTo=function(t){var e=Math.PI/180,r=this.lat*e,n=t.lat*e,i=Math.sin(r)*Math.sin(n)+Math.cos(r)*Math.cos(n)*Math.cos((t.lng-this.lng)*e);return Ec*Math.acos(Math.min(i,1))},Lc.prototype.toBounds=function(t){void 0===t&&(t=0);var e=360*t/40075017,r=e/Math.cos(Math.PI/180*this.lat);return new Sc(new Lc(this.lng-r,this.lat-e),new Lc(this.lng+r,this.lat+e))},Lc.convert=function(t){if(t instanceof Lc)return t;if(Array.isArray(t)&&(2===t.length||3===t.length))return new Lc(Number(t[0]),Number(t[1]));if(!Array.isArray(t)&&\"object\"==typeof t&&null!==t)return new Lc(Number(\"lng\"in t?t.lng:t.lon),Number(t.lat));throw new Error(\"`LngLatLike` argument must be specified as a LngLat instance, an object {lng: <lng>, lat: <lat>}, an object {lon: <lng>, lat: <lat>}, or an array of [<lng>, <lat>]\")};var Cc=2*Math.PI*Ec;function Pc(t){return Cc*Math.cos(t*Math.PI/180)}function Oc(t){return(180+t)/360}function Ic(t){return(180-180/Math.PI*Math.log(Math.tan(Math.PI/4+t*Math.PI/360)))/360}function Dc(t,e){return t/Pc(e)}function zc(t){var e=180-360*t;return 360/Math.PI*Math.atan(Math.exp(e*Math.PI/180))-90}var Rc=function(t,e,r){void 0===r&&(r=0),this.x=+t,this.y=+e,this.z=+r};Rc.fromLngLat=function(t,e){void 0===e&&(e=0);var r=Lc.convert(t);return new Rc(Oc(r.lng),Ic(r.lat),Dc(e,r.lat))},Rc.prototype.toLngLat=function(){return new Lc(360*this.x-180,zc(this.y))},Rc.prototype.toAltitude=function(){return t=this.z,e=this.y,t*Pc(zc(e));var t,e},Rc.prototype.meterInMercatorCoordinateUnits=function(){return 1/Cc*(t=zc(this.y),1/Math.cos(t*Math.PI/180));var t};var Fc=function(t,e,r){this.z=t,this.x=e,this.y=r,this.key=jc(0,t,t,e,r)};Fc.prototype.equals=function(t){return this.z===t.z&&this.x===t.x&&this.y===t.y},Fc.prototype.url=function(t,e){var r,n,i,a,o,s=(r=this.x,n=this.y,i=this.z,a=Mc(256*r,256*(n=Math.pow(2,i)-n-1),i),o=Mc(256*(r+1),256*(n+1),i),a[0]+\",\"+a[1]+\",\"+o[0]+\",\"+o[1]),l=function(t,e,r){for(var n,i=\"\",a=t;a>0;a--)i+=(e&(n=1<<a-1)?1:0)+(r&n?2:0);return i}(this.z,this.x,this.y);return t[(this.x+this.y)%t.length].replace(\"{prefix}\",(this.x%16).toString(16)+(this.y%16).toString(16)).replace(\"{z}\",String(this.z)).replace(\"{x}\",String(this.x)).replace(\"{y}\",String(\"tms\"===e?Math.pow(2,this.z)-this.y-1:this.y)).replace(\"{quadkey}\",l).replace(\"{bbox-epsg-3857}\",s)},Fc.prototype.getTilePoint=function(t){var e=Math.pow(2,this.z);return new a((t.x*e-this.x)*co,(t.y*e-this.y)*co)},Fc.prototype.toString=function(){return this.z+\"/\"+this.x+\"/\"+this.y};var Bc=function(t,e){this.wrap=t,this.canonical=e,this.key=jc(t,e.z,e.z,e.x,e.y)},Nc=function(t,e,r,n,i){this.overscaledZ=t,this.wrap=e,this.canonical=new Fc(r,+n,+i),this.key=jc(e,t,r,n,i)};function jc(t,e,r,n,i){(t*=2)<0&&(t=-1*t-1);var a=1<<r;return(a*a*t+a*i+n).toString(36)+r.toString(36)+e.toString(36)}Nc.prototype.equals=function(t){return this.overscaledZ===t.overscaledZ&&this.wrap===t.wrap&&this.canonical.equals(t.canonical)},Nc.prototype.scaledTo=function(t){var e=this.canonical.z-t;return t>this.canonical.z?new Nc(t,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y):new Nc(t,this.wrap,t,this.canonical.x>>e,this.canonical.y>>e)},Nc.prototype.calculateScaledKey=function(t,e){var r=this.canonical.z-t;return t>this.canonical.z?jc(this.wrap*+e,t,this.canonical.z,this.canonical.x,this.canonical.y):jc(this.wrap*+e,t,t,this.canonical.x>>r,this.canonical.y>>r)},Nc.prototype.isChildOf=function(t){if(t.wrap!==this.wrap)return!1;var e=this.canonical.z-t.canonical.z;return 0===t.overscaledZ||t.overscaledZ<this.overscaledZ&&t.canonical.x===this.canonical.x>>e&&t.canonical.y===this.canonical.y>>e},Nc.prototype.children=function(t){if(this.overscaledZ>=t)return[new Nc(this.overscaledZ+1,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y)];var e=this.canonical.z+1,r=2*this.canonical.x,n=2*this.canonical.y;return[new Nc(e,this.wrap,e,r,n),new Nc(e,this.wrap,e,r+1,n),new Nc(e,this.wrap,e,r,n+1),new Nc(e,this.wrap,e,r+1,n+1)]},Nc.prototype.isLessThan=function(t){return this.wrap<t.wrap||!(this.wrap>t.wrap)&&(this.overscaledZ<t.overscaledZ||!(this.overscaledZ>t.overscaledZ)&&(this.canonical.x<t.canonical.x||!(this.canonical.x>t.canonical.x)&&this.canonical.y<t.canonical.y))},Nc.prototype.wrapped=function(){return new Nc(this.overscaledZ,0,this.canonical.z,this.canonical.x,this.canonical.y)},Nc.prototype.unwrapTo=function(t){return new Nc(this.overscaledZ,t,this.canonical.z,this.canonical.x,this.canonical.y)},Nc.prototype.overscaleFactor=function(){return Math.pow(2,this.overscaledZ-this.canonical.z)},Nc.prototype.toUnwrapped=function(){return new Bc(this.wrap,this.canonical)},Nc.prototype.toString=function(){return this.overscaledZ+\"/\"+this.canonical.x+\"/\"+this.canonical.y},Nc.prototype.getTilePoint=function(t){return this.canonical.getTilePoint(new Rc(t.x-this.wrap,t.y))},ni(\"CanonicalTileID\",Fc),ni(\"OverscaledTileID\",Nc,{omit:[\"posMatrix\"]});var Uc=function(t,e,r){if(this.uid=t,e.height!==e.width)throw new RangeError(\"DEM tiles must be square\");if(r&&\"mapbox\"!==r&&\"terrarium\"!==r)return w('\"'+r+'\" is not a valid encoding type. Valid types include \"mapbox\" and \"terrarium\".');this.stride=e.height;var n=this.dim=e.height-2;this.data=new Uint32Array(e.data.buffer),this.encoding=r||\"mapbox\";for(var i=0;i<n;i++)this.data[this._idx(-1,i)]=this.data[this._idx(0,i)],this.data[this._idx(n,i)]=this.data[this._idx(n-1,i)],this.data[this._idx(i,-1)]=this.data[this._idx(i,0)],this.data[this._idx(i,n)]=this.data[this._idx(i,n-1)];this.data[this._idx(-1,-1)]=this.data[this._idx(0,0)],this.data[this._idx(n,-1)]=this.data[this._idx(n-1,0)],this.data[this._idx(-1,n)]=this.data[this._idx(0,n-1)],this.data[this._idx(n,n)]=this.data[this._idx(n-1,n-1)]};Uc.prototype.get=function(t,e){var r=new Uint8Array(this.data.buffer),n=4*this._idx(t,e);return(\"terrarium\"===this.encoding?this._unpackTerrarium:this._unpackMapbox)(r[n],r[n+1],r[n+2])},Uc.prototype.getUnpackVector=function(){return\"terrarium\"===this.encoding?[256,1,1/256,32768]:[6553.6,25.6,.1,1e4]},Uc.prototype._idx=function(t,e){if(t<-1||t>=this.dim+1||e<-1||e>=this.dim+1)throw new RangeError(\"out of range source coordinates for DEM data\");return(e+1)*this.stride+(t+1)},Uc.prototype._unpackMapbox=function(t,e,r){return(256*t*256+256*e+r)/10-1e4},Uc.prototype._unpackTerrarium=function(t,e,r){return 256*t+e+r/256-32768},Uc.prototype.getPixels=function(){return new Wo({width:this.stride,height:this.stride},new Uint8Array(this.data.buffer))},Uc.prototype.backfillBorder=function(t,e,r){if(this.dim!==t.dim)throw new Error(\"dem dimension mismatch\");var n=e*this.dim,i=e*this.dim+this.dim,a=r*this.dim,o=r*this.dim+this.dim;switch(e){case-1:n=i-1;break;case 1:i=n+1}switch(r){case-1:a=o-1;break;case 1:o=a+1}for(var s=-e*this.dim,l=-r*this.dim,u=a;u<o;u++)for(var c=n;c<i;c++)this.data[this._idx(c,u)]=t.data[this._idx(c+s,u+l)]},ni(\"DEMData\",Uc);var Vc=function(t){this._stringToNumber={},this._numberToString=[];for(var e=0;e<t.length;e++){var r=t[e];this._stringToNumber[r]=e,this._numberToString[e]=r}};Vc.prototype.encode=function(t){return this._stringToNumber[t]},Vc.prototype.decode=function(t){return this._numberToString[t]};var Hc=function(t,e,r,n,i){this.type=\"Feature\",this._vectorTileFeature=t,t._z=e,t._x=r,t._y=n,this.properties=t.properties,this.id=i},qc={geometry:{configurable:!0}};qc.geometry.get=function(){return void 0===this._geometry&&(this._geometry=this._vectorTileFeature.toGeoJSON(this._vectorTileFeature._x,this._vectorTileFeature._y,this._vectorTileFeature._z).geometry),this._geometry},qc.geometry.set=function(t){this._geometry=t},Hc.prototype.toJSON=function(){var t={geometry:this.geometry};for(var e in this)\"_geometry\"!==e&&\"_vectorTileFeature\"!==e&&(t[e]=this[e]);return t},Object.defineProperties(Hc.prototype,qc);var Gc=function(){this.state={},this.stateChanges={},this.deletedStates={}};Gc.prototype.updateState=function(t,e,r){var n=String(e);if(this.stateChanges[t]=this.stateChanges[t]||{},this.stateChanges[t][n]=this.stateChanges[t][n]||{},f(this.stateChanges[t][n],r),null===this.deletedStates[t])for(var i in this.deletedStates[t]={},this.state[t])i!==n&&(this.deletedStates[t][i]=null);else if(this.deletedStates[t]&&null===this.deletedStates[t][n])for(var a in this.deletedStates[t][n]={},this.state[t][n])r[a]||(this.deletedStates[t][n][a]=null);else for(var o in r)this.deletedStates[t]&&this.deletedStates[t][n]&&null===this.deletedStates[t][n][o]&&delete this.deletedStates[t][n][o]},Gc.prototype.removeFeatureState=function(t,e,r){if(null!==this.deletedStates[t]){var n=String(e);if(this.deletedStates[t]=this.deletedStates[t]||{},r&&void 0!==e)null!==this.deletedStates[t][n]&&(this.deletedStates[t][n]=this.deletedStates[t][n]||{},this.deletedStates[t][n][r]=null);else if(void 0!==e)if(this.stateChanges[t]&&this.stateChanges[t][n])for(r in this.deletedStates[t][n]={},this.stateChanges[t][n])this.deletedStates[t][n][r]=null;else this.deletedStates[t][n]=null;else this.deletedStates[t]=null}},Gc.prototype.getState=function(t,e){var r=String(e),n=this.state[t]||{},i=this.stateChanges[t]||{},a=f({},n[r],i[r]);if(null===this.deletedStates[t])return{};if(this.deletedStates[t]){var o=this.deletedStates[t][e];if(null===o)return{};for(var s in o)delete a[s]}return a},Gc.prototype.initializeTileState=function(t,e){t.setFeatureState(this.state,e)},Gc.prototype.coalesceChanges=function(t,e){var r={};for(var n in this.stateChanges){this.state[n]=this.state[n]||{};var i={};for(var a in this.stateChanges[n])this.state[n][a]||(this.state[n][a]={}),f(this.state[n][a],this.stateChanges[n][a]),i[a]=this.state[n][a];r[n]=i}for(var o in this.deletedStates){this.state[o]=this.state[o]||{};var s={};if(null===this.deletedStates[o])for(var l in this.state[o])s[l]={},this.state[o][l]={};else for(var u in this.deletedStates[o]){if(null===this.deletedStates[o][u])this.state[o][u]={};else for(var c=0,h=Object.keys(this.deletedStates[o][u]);c<h.length;c+=1){var p=h[c];delete this.state[o][u][p]}s[u]=this.state[o][u]}r[o]=r[o]||{},f(r[o],s)}if(this.stateChanges={},this.deletedStates={},0!==Object.keys(r).length)for(var d in t)t[d].setFeatureState(r,e)};var Zc=function(t,e){this.tileID=t,this.x=t.canonical.x,this.y=t.canonical.y,this.z=t.canonical.z,this.grid=new Kn(co,16,0),this.grid3D=new Kn(co,16,0),this.featureIndexArray=new Ca,this.promoteId=e};function Yc(t,e,r,n,i){return m(t,(function(t,a){var o=e instanceof Ni?e.get(a):null;return o&&o.evaluate?o.evaluate(r,n,i):o}))}function Wc(t){for(var e=1/0,r=1/0,n=-1/0,i=-1/0,a=0,o=t;a<o.length;a+=1){var s=o[a];e=Math.min(e,s.x),r=Math.min(r,s.y),n=Math.max(n,s.x),i=Math.max(i,s.y)}return{minX:e,minY:r,maxX:n,maxY:i}}function Xc(t,e){return e-t}Zc.prototype.insert=function(t,e,r,n,i,a){var o=this.featureIndexArray.length;this.featureIndexArray.emplaceBack(r,n,i);for(var s=a?this.grid3D:this.grid,l=0;l<e.length;l++){for(var u=e[l],c=[1/0,1/0,-1/0,-1/0],f=0;f<u.length;f++){var h=u[f];c[0]=Math.min(c[0],h.x),c[1]=Math.min(c[1],h.y),c[2]=Math.max(c[2],h.x),c[3]=Math.max(c[3],h.y)}c[0]<co&&c[1]<co&&c[2]>=0&&c[3]>=0&&s.insert(o,c[0],c[1],c[2],c[3])}},Zc.prototype.loadVTLayers=function(){return this.vtLayers||(this.vtLayers=new Js.VectorTile(new Sl(this.rawTileData)).layers,this.sourceLayerCoder=new Vc(this.vtLayers?Object.keys(this.vtLayers).sort():[\"_geojsonTileLayer\"])),this.vtLayers},Zc.prototype.query=function(t,e,r,n){var i=this;this.loadVTLayers();for(var o=t.params||{},s=co/t.tileSize/t.scale,l=wn(o.filter),u=t.queryGeometry,c=t.queryPadding*s,f=Wc(u),h=this.grid.query(f.minX-c,f.minY-c,f.maxX+c,f.maxY+c),p=Wc(t.cameraQueryGeometry),d=0,v=this.grid3D.query(p.minX-c,p.minY-c,p.maxX+c,p.maxY+c,(function(e,r,n,i){return function(t,e,r,n,i){for(var o=0,s=t;o<s.length;o+=1){var l=s[o];if(e<=l.x&&r<=l.y&&n>=l.x&&i>=l.y)return!0}var u=[new a(e,r),new a(e,i),new a(n,i),new a(n,r)];if(t.length>2)for(var c=0,f=u;c<f.length;c+=1)if(Mo(t,f[c]))return!0;for(var h=0;h<t.length-1;h++)if(So(t[h],t[h+1],u))return!0;return!1}(t.cameraQueryGeometry,e-c,r-c,n+c,i+c)}));d<v.length;d+=1){var g=v[d];h.push(g)}h.sort(Xc);for(var y,m={},x=function(a){var c=h[a];if(c!==y){y=c;var f=i.featureIndexArray.get(c),p=null;i.loadMatchingFeature(m,f.bucketIndex,f.sourceLayerIndex,f.featureIndex,l,o.layers,o.availableImages,e,r,n,(function(e,r,n){return p||(p=po(e)),r.queryIntersectsFeature(u,e,n,p,i.z,t.transform,s,t.pixelPosMatrix)}))}},b=0;b<h.length;b++)x(b);return m},Zc.prototype.loadMatchingFeature=function(t,e,r,n,i,a,o,s,l,u,c){var f=this.bucketLayerIDs[e];if(!a||function(t,e){for(var r=0;r<t.length;r++)if(e.indexOf(t[r])>=0)return!0;return!1}(a,f)){var h=this.sourceLayerCoder.decode(r),p=this.vtLayers[h].feature(n);if(i.filter(new Pi(this.tileID.overscaledZ),p))for(var d=this.getId(p,h),v=0;v<f.length;v++){var g=f[v];if(!(a&&a.indexOf(g)<0)){var y=s[g];if(y){var m={};void 0!==d&&u&&(m=u.getState(y.sourceLayer||\"_geojsonTileLayer\",d));var x=l[g];x.paint=Yc(x.paint,y.paint,p,m,o),x.layout=Yc(x.layout,y.layout,p,m,o);var b=!c||c(p,y,m);if(b){var _=new Hc(p,this.z,this.x,this.y,d);_.layer=x;var w=t[g];void 0===w&&(w=t[g]=[]),w.push({featureIndex:n,feature:_,intersectionZ:b})}}}}}},Zc.prototype.lookupSymbolFeatures=function(t,e,r,n,i,a,o,s){var l={};this.loadVTLayers();for(var u=wn(i),c=0,f=t;c<f.length;c+=1){var h=f[c];this.loadMatchingFeature(l,r,n,h,u,a,o,s,e)}return l},Zc.prototype.hasLayer=function(t){for(var e=0,r=this.bucketLayerIDs;e<r.length;e+=1)for(var n=0,i=r[e];n<i.length;n+=1)if(t===i[n])return!0;return!1},Zc.prototype.getId=function(t,e){var r=t.id;if(this.promoteId){var n=\"string\"==typeof this.promoteId?this.promoteId:this.promoteId[e];\"boolean\"==typeof(r=t.properties[n])&&(r=Number(r))}return r},ni(\"FeatureIndex\",Zc,{omit:[\"rawTileData\",\"sourceLayerCoder\"]});var Jc=function(t,e){this.tileID=t,this.uid=p(),this.uses=0,this.tileSize=e,this.buckets={},this.expirationTime=null,this.queryPadding=0,this.hasSymbolBuckets=!1,this.hasRTLText=!1,this.dependencies={},this.expiredRequestCount=0,this.state=\"loading\"};Jc.prototype.registerFadeDuration=function(t){var e=t+this.timeAdded;e<F.now()||this.fadeEndTime&&e<this.fadeEndTime||(this.fadeEndTime=e)},Jc.prototype.wasRequested=function(){return\"errored\"===this.state||\"loaded\"===this.state||\"reloading\"===this.state},Jc.prototype.loadVectorData=function(t,e,r){if(this.hasData()&&this.unloadVectorData(),this.state=\"loaded\",t){for(var n in t.featureIndex&&(this.latestFeatureIndex=t.featureIndex,t.rawTileData?(this.latestRawTileData=t.rawTileData,this.latestFeatureIndex.rawTileData=t.rawTileData):this.latestRawTileData&&(this.latestFeatureIndex.rawTileData=this.latestRawTileData)),this.collisionBoxArray=t.collisionBoxArray,this.buckets=function(t,e){var r={};if(!e)return r;for(var n=function(){var t=a[i],n=t.layerIds.map((function(t){return e.getLayer(t)})).filter(Boolean);if(0!==n.length){t.layers=n,t.stateDependentLayerIds&&(t.stateDependentLayers=t.stateDependentLayerIds.map((function(t){return n.filter((function(e){return e.id===t}))[0]})));for(var o=0,s=n;o<s.length;o+=1){var l=s[o];r[l.id]=t}}},i=0,a=t;i<a.length;i+=1)n();return r}(t.buckets,e.style),this.hasSymbolBuckets=!1,this.buckets){var i=this.buckets[n];if(i instanceof sc){if(this.hasSymbolBuckets=!0,!r)break;i.justReloaded=!0}}if(this.hasRTLText=!1,this.hasSymbolBuckets)for(var a in this.buckets){var o=this.buckets[a];if(o instanceof sc&&o.hasRTLText){this.hasRTLText=!0,Ci.isLoading()||Ci.isLoaded()||\"deferred\"!==Ei()||Li();break}}for(var s in this.queryPadding=0,this.buckets){var l=this.buckets[s];this.queryPadding=Math.max(this.queryPadding,e.style.getLayer(s).queryRadius(l))}t.imageAtlas&&(this.imageAtlas=t.imageAtlas),t.glyphAtlasImage&&(this.glyphAtlasImage=t.glyphAtlasImage)}else this.collisionBoxArray=new wa},Jc.prototype.unloadVectorData=function(){for(var t in this.buckets)this.buckets[t].destroy();this.buckets={},this.imageAtlasTexture&&this.imageAtlasTexture.destroy(),this.imageAtlas&&(this.imageAtlas=null),this.glyphAtlasTexture&&this.glyphAtlasTexture.destroy(),this.latestFeatureIndex=null,this.state=\"unloaded\"},Jc.prototype.getBucket=function(t){return this.buckets[t.id]},Jc.prototype.upload=function(t){for(var e in this.buckets){var r=this.buckets[e];r.uploadPending()&&r.upload(t)}var n=t.gl;this.imageAtlas&&!this.imageAtlas.uploaded&&(this.imageAtlasTexture=new Tc(t,this.imageAtlas.image,n.RGBA),this.imageAtlas.uploaded=!0),this.glyphAtlasImage&&(this.glyphAtlasTexture=new Tc(t,this.glyphAtlasImage,n.ALPHA),this.glyphAtlasImage=null)},Jc.prototype.prepare=function(t){this.imageAtlas&&this.imageAtlas.patchUpdatedImages(t,this.imageAtlasTexture)},Jc.prototype.queryRenderedFeatures=function(t,e,r,n,i,a,o,s,l,u){return this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData?this.latestFeatureIndex.query({queryGeometry:n,cameraQueryGeometry:i,scale:a,tileSize:this.tileSize,pixelPosMatrix:u,transform:s,params:o,queryPadding:this.queryPadding*l},t,e,r):{}},Jc.prototype.querySourceFeatures=function(t,e){var r=this.latestFeatureIndex;if(r&&r.rawTileData){var n=r.loadVTLayers(),i=e?e.sourceLayer:\"\",a=n._geojsonTileLayer||n[i];if(a)for(var o=wn(e&&e.filter),s=this.tileID.canonical,l=s.z,u=s.x,c=s.y,f={z:l,x:u,y:c},h=0;h<a.length;h++){var p=a.feature(h);if(o.filter(new Pi(this.tileID.overscaledZ),p)){var d=r.getId(p,i),v=new Hc(p,l,u,c,d);v.tile=f,t.push(v)}}}},Jc.prototype.hasData=function(){return\"loaded\"===this.state||\"reloading\"===this.state||\"expired\"===this.state},Jc.prototype.patternsLoaded=function(){return this.imageAtlas&&!!Object.keys(this.imageAtlas.patternPositions).length},Jc.prototype.setExpiryData=function(t){var e=this.expirationTime;if(t.cacheControl){var r=M(t.cacheControl);r[\"max-age\"]&&(this.expirationTime=Date.now()+1e3*r[\"max-age\"])}else t.expires&&(this.expirationTime=new Date(t.expires).getTime());if(this.expirationTime){var n=Date.now(),i=!1;if(this.expirationTime>n)i=!1;else if(e)if(this.expirationTime<e)i=!0;else{var a=this.expirationTime-e;a?this.expirationTime=n+Math.max(a,3e4):i=!0}else i=!0;i?(this.expiredRequestCount++,this.state=\"expired\"):this.expiredRequestCount=0}},Jc.prototype.getExpiryTimeout=function(){if(this.expirationTime)return this.expiredRequestCount?1e3*(1<<Math.min(this.expiredRequestCount-1,31)):Math.min(this.expirationTime-(new Date).getTime(),Math.pow(2,31)-1)},Jc.prototype.setFeatureState=function(t,e){if(this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData&&0!==Object.keys(t).length){var r=this.latestFeatureIndex.loadVTLayers();for(var n in this.buckets)if(e.style.hasLayer(n)){var i=this.buckets[n],a=i.layers[0].sourceLayer||\"_geojsonTileLayer\",o=r[a],s=t[a];if(o&&s&&0!==Object.keys(s).length){i.update(s,o,this.imageAtlas&&this.imageAtlas.patternPositions||{});var l=e&&e.style&&e.style.getLayer(n);l&&(this.queryPadding=Math.max(this.queryPadding,l.queryRadius(i)))}}}},Jc.prototype.holdingForFade=function(){return void 0!==this.symbolFadeHoldUntil},Jc.prototype.symbolFadeFinished=function(){return!this.symbolFadeHoldUntil||this.symbolFadeHoldUntil<F.now()},Jc.prototype.clearFadeHold=function(){this.symbolFadeHoldUntil=void 0},Jc.prototype.setHoldDuration=function(t){this.symbolFadeHoldUntil=F.now()+t},Jc.prototype.setDependencies=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1)r[i[n]]=!0;this.dependencies[t]=r},Jc.prototype.hasDependency=function(t,e){for(var r=0,n=t;r<n.length;r+=1){var i=n[r],a=this.dependencies[i];if(a)for(var o=0,s=e;o<s.length;o+=1)if(a[s[o]])return!0}return!1};var Kc=self.performance,$c=function(t){this._marks={start:[t.url,\"start\"].join(\"#\"),end:[t.url,\"end\"].join(\"#\"),measure:t.url.toString()},Kc.mark(this._marks.start)};$c.prototype.finish=function(){Kc.mark(this._marks.end);var t=Kc.getEntriesByName(this._marks.measure);return 0===t.length&&(Kc.measure(this._marks.measure,this._marks.start,this._marks.end),t=Kc.getEntriesByName(this._marks.measure),Kc.clearMarks(this._marks.start),Kc.clearMarks(this._marks.end),Kc.clearMeasures(this._marks.measure)),t},t.Actor=Ac,t.AlphaImage=Yo,t.CanonicalTileID=Fc,t.CollisionBoxArray=wa,t.Color=oe,t.DEMData=Uc,t.DataConstantProperty=ji,t.DictionaryCoder=Vc,t.EXTENT=co,t.ErrorEvent=Ot,t.EvaluationParameters=Pi,t.Event=Pt,t.Evented=It,t.FeatureIndex=Zc,t.FillBucket=Fs,t.FillExtrusionBucket=tl,t.ImageAtlas=ru,t.ImagePosition=tu,t.LineBucket=cl,t.LngLat=Lc,t.LngLatBounds=Sc,t.MercatorCoordinate=Rc,t.ONE_EM=kl,t.OverscaledTileID=Nc,t.Point=a,t.Point$1=a,t.Properties=Gi,t.Protobuf=Sl,t.RGBAImage=Wo,t.RequestManager=q,t.RequestPerformance=$c,t.ResourceType=xt,t.SegmentVector=Oa,t.SourceFeatureState=Gc,t.StructArrayLayout1ui2=ma,t.StructArrayLayout2f1f2i16=ua,t.StructArrayLayout2i4=Qi,t.StructArrayLayout3ui6=fa,t.StructArrayLayout4i8=ta,t.SymbolBucket=sc,t.Texture=Tc,t.Tile=Jc,t.Transitionable=Di,t.Uniform1f=Ya,t.Uniform1i=Za,t.Uniform2f=Wa,t.Uniform3f=Xa,t.Uniform4f=Ja,t.UniformColor=Ka,t.UniformMatrix4f=Qa,t.UnwrappedTileID=Bc,t.ValidationError=zt,t.WritingMode=nu,t.ZoomHistory=ui,t.add=function(t,e,r){return t[0]=e[0]+r[0],t[1]=e[1]+r[1],t[2]=e[2]+r[2],t},t.addDynamicAttributes=nc,t.asyncAll=function(t,e,r){if(!t.length)return r(null,[]);var n=t.length,i=new Array(t.length),a=null;t.forEach((function(t,o){e(t,(function(t,e){t&&(a=t),i[o]=e,0==--n&&r(a,i)}))}))},t.bezier=s,t.bindAll=g,t.browser=F,t.cacheEntryPossiblyAdded=function(t){++yt>ct&&(t.getActor().send(\"enforceCacheSizeLimit\",ut),yt=0)},t.clamp=u,t.clearTileCache=function(t){var e=self.caches.delete(lt);t&&e.catch(t).then((function(){return t()}))},t.clipLine=Ou,t.clone=function(t){var e=new Io(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e},t.clone$1=b,t.clone$2=function(t){var e=new Io(3);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e},t.collisionCircleLayout=_l,t.config=B,t.create=function(){var t=new Io(16);return Io!=Float32Array&&(t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[11]=0,t[12]=0,t[13]=0,t[14]=0),t[0]=1,t[5]=1,t[10]=1,t[15]=1,t},t.create$1=function(){var t=new Io(9);return Io!=Float32Array&&(t[1]=0,t[2]=0,t[3]=0,t[5]=0,t[6]=0,t[7]=0),t[0]=1,t[4]=1,t[8]=1,t},t.create$2=function(){var t=new Io(4);return Io!=Float32Array&&(t[1]=0,t[2]=0),t[0]=1,t[3]=1,t},t.createCommonjsModule=e,t.createExpression=ln,t.createLayout=Ki,t.createStyleLayer=function(t){return\"custom\"===t.type?new gc(t):new yc[t.type](t)},t.cross=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=r[0],s=r[1],l=r[2];return t[0]=i*l-a*s,t[1]=a*o-n*l,t[2]=n*s-i*o,t},t.deepEqual=function t(e,r){if(Array.isArray(e)){if(!Array.isArray(r)||e.length!==r.length)return!1;for(var n=0;n<e.length;n++)if(!t(e[n],r[n]))return!1;return!0}if(\"object\"==typeof e&&null!==e&&null!==r){if(\"object\"!=typeof r)return!1;if(Object.keys(e).length!==Object.keys(r).length)return!1;for(var i in e)if(!t(e[i],r[i]))return!1;return!0}return e===r},t.dot=function(t,e){return t[0]*e[0]+t[1]*e[1]+t[2]*e[2]},t.dot$1=function(t,e){return t[0]*e[0]+t[1]*e[1]+t[2]*e[2]+t[3]*e[3]},t.ease=l,t.emitValidationErrors=Jn,t.endsWith=y,t.enforceCacheSizeLimit=function(t){ht(),tt&&tt.then((function(e){e.keys().then((function(r){for(var n=0;n<r.length-t;n++)e.delete(r[n])}))}))},t.evaluateSizeForFeature=wu,t.evaluateSizeForZoom=Tu,t.evaluateVariableOffset=Yu,t.evented=Si,t.extend=f,t.featureFilter=wn,t.filterObject=x,t.fromRotation=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=n,t[1]=r,t[2]=0,t[3]=-r,t[4]=n,t[5]=0,t[6]=0,t[7]=0,t[8]=1,t},t.getAnchorAlignment=gu,t.getAnchorJustification=Wu,t.getArrayBuffer=kt,t.getImage=Et,t.getJSON=function(t,e){return Tt(f(t,{type:\"json\"}),e)},t.getRTLTextPluginStatus=Ei,t.getReferrer=_t,t.getVideo=function(t,e){var r,n,i=self.document.createElement(\"video\");i.muted=!0,i.onloadstart=function(){e(null,i)};for(var a=0;a<t.length;a++){var 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c={},e.tags)c[f]=e.tags[f];c.mapbox_clip_start=i.start/i.size,c.mapbox_clip_end=i.end/i.size}var h={geometry:o,type:\"Polygon\"===a||\"MultiPolygon\"===a?3:\"LineString\"===a||\"MultiLineString\"===a?2:1,tags:c};null!==e.id&&(h.id=e.id),t.features.push(h)}}function wt(t,e,r,n,i,a){var o=n*n;if(n>0&&e.size<(i?o:n))r.numPoints+=e.length/3;else{for(var s=[],l=0;l<e.length;l+=3)(0===n||e[l+2]>o)&&(r.numSimplified++,s.push(e[l]),s.push(e[l+1])),r.numPoints++;i&&function(t,e){for(var r=0,n=0,i=t.length,a=i-2;n<i;a=n,n+=2)r+=(t[n]-t[a])*(t[n+1]+t[a+1]);if(r>0===e)for(n=0,i=t.length;n<i/2;n+=2){var o=t[n],s=t[n+1];t[n]=t[i-2-n],t[n+1]=t[i-1-n],t[i-2-n]=o,t[i-1-n]=s}}(s,a),t.push(s)}}function Tt(t,e){var r=(e=this.options=function(t,e){for(var r in e)t[r]=e[r];return t}(Object.create(this.options),e)).debug;if(r&&console.time(\"preprocess data\"),e.maxZoom<0||e.maxZoom>24)throw new Error(\"maxZoom should be in the 0-24 range\");if(e.promoteId&&e.generateId)throw new Error(\"promoteId and generateId cannot be used together.\");var n=function(t,e){var r=[];if(\"FeatureCollection\"===t.type)for(var n=0;n<t.features.length;n++)rt(r,t.features[n],e,n);else\"Feature\"===t.type?rt(r,t,e):rt(r,{geometry:t},e);return r}(t,e);this.tiles={},this.tileCoords=[],r&&(console.timeEnd(\"preprocess data\"),console.log(\"index: maxZoom: %d, maxPoints: %d\",e.indexMaxZoom,e.indexMaxPoints),console.time(\"generate tiles\"),this.stats={},this.total=0),(n=function(t,e){var r=e.buffer/e.extent,n=t,i=lt(t,1,-1-r,r,0,-1,2,e),a=lt(t,1,1-r,2+r,0,-1,2,e);return(i||a)&&(n=lt(t,1,-r,1+r,0,-1,2,e)||[],i&&(n=gt(i,1).concat(n)),a&&(n=n.concat(gt(a,-1)))),n}(n,e)).length&&this.splitTile(n,0,0,0),r&&(n.length&&console.log(\"features: %d, points: %d\",this.tiles[0].numFeatures,this.tiles[0].numPoints),console.timeEnd(\"generate tiles\"),console.log(\"tiles generated:\",this.total,JSON.stringify(this.stats)))}function kt(t,e,r){return 32*((1<<t)*r+e)+t}function At(t,e){var r=t.tileID.canonical;if(!this._geoJSONIndex)return e(null,null);var n=this._geoJSONIndex.getTile(r.z,r.x,r.y);if(!n)return e(null,null);var i=new g(n.features),a=_(i);0===a.byteOffset&&a.byteLength===a.buffer.byteLength||(a=new Uint8Array(a)),e(null,{vectorTile:i,rawData:a.buffer})}V.prototype.load=function(t){var e=this.options,r=e.log,n=e.minZoom,i=e.maxZoom,a=e.nodeSize;r&&console.time(\"total time\");var o=\"prepare \"+t.length+\" points\";r&&console.time(o),this.points=t;for(var s=[],l=0;l<t.length;l++)t[l].geometry&&s.push(q(t[l],l));this.trees[i+1]=new j(s,J,K,a,Float32Array),r&&console.timeEnd(o);for(var u=i;u>=n;u--){var c=+Date.now();s=this._cluster(s,u),this.trees[u]=new j(s,J,K,a,Float32Array),r&&console.log(\"z%d: %d clusters in %dms\",u,s.length,+Date.now()-c)}return r&&console.timeEnd(\"total time\"),this},V.prototype.getClusters=function(t,e){var r=((t[0]+180)%360+360)%360-180,n=Math.max(-90,Math.min(90,t[1])),i=180===t[2]?180:((t[2]+180)%360+360)%360-180,a=Math.max(-90,Math.min(90,t[3]));if(t[2]-t[0]>=360)r=-180,i=180;else if(r>i){var o=this.getClusters([r,n,180,a],e),s=this.getClusters([-180,n,i,a],e);return o.concat(s)}for(var l=this.trees[this._limitZoom(e)],u=[],c=0,f=l.range(Y(r),W(a),Y(i),W(n));c<f.length;c+=1){var h=f[c],p=l.points[h];u.push(p.numPoints?G(p):this.points[p.index])}return u},V.prototype.getChildren=function(t){var e=this._getOriginId(t),r=this._getOriginZoom(t),n=\"No cluster with the specified id.\",i=this.trees[r];if(!i)throw new Error(n);var a=i.points[e];if(!a)throw new Error(n);for(var o=this.options.radius/(this.options.extent*Math.pow(2,r-1)),s=[],l=0,u=i.within(a.x,a.y,o);l<u.length;l+=1){var c=u[l],f=i.points[c];f.parentId===t&&s.push(f.numPoints?G(f):this.points[f.index])}if(0===s.length)throw new Error(n);return s},V.prototype.getLeaves=function(t,e,r){e=e||10,r=r||0;var n=[];return this._appendLeaves(n,t,e,r,0),n},V.prototype.getTile=function(t,e,r){var n=this.trees[this._limitZoom(t)],i=Math.pow(2,t),a=this.options,o=a.extent,s=a.radius/o,l=(r-s)/i,u=(r+1+s)/i,c={features:[]};return this._addTileFeatures(n.range((e-s)/i,l,(e+1+s)/i,u),n.points,e,r,i,c),0===e&&this._addTileFeatures(n.range(1-s/i,l,1,u),n.points,i,r,i,c),e===i-1&&this._addTileFeatures(n.range(0,l,s/i,u),n.points,-1,r,i,c),c.features.length?c:null},V.prototype.getClusterExpansionZoom=function(t){for(var e=this._getOriginZoom(t)-1;e<=this.options.maxZoom;){var r=this.getChildren(t);if(e++,1!==r.length)break;t=r[0].properties.cluster_id}return e},V.prototype._appendLeaves=function(t,e,r,n,i){for(var a=0,o=this.getChildren(e);a<o.length;a+=1){var s=o[a],l=s.properties;if(l&&l.cluster?i+l.point_count<=n?i+=l.point_count:i=this._appendLeaves(t,l.cluster_id,r,n,i):i<n?i++:t.push(s),t.length===r)break}return i},V.prototype._addTileFeatures=function(t,e,r,n,i,a){for(var o=0,s=t;o<s.length;o+=1){var l=e[s[o]],u=l.numPoints,c={type:1,geometry:[[Math.round(this.options.extent*(l.x*i-r)),Math.round(this.options.extent*(l.y*i-n))]],tags:u?Z(l):this.points[l.index].properties},f=void 0;u?f=l.id:this.options.generateId?f=l.index:this.points[l.index].id&&(f=this.points[l.index].id),void 0!==f&&(c.id=f),a.features.push(c)}},V.prototype._limitZoom=function(t){return Math.max(this.options.minZoom,Math.min(t,this.options.maxZoom+1))},V.prototype._cluster=function(t,e){for(var r=[],n=this.options,i=n.radius,a=n.extent,o=n.reduce,s=i/(a*Math.pow(2,e)),l=0;l<t.length;l++){var u=t[l];if(!(u.zoom<=e)){u.zoom=e;for(var c=this.trees[e+1],f=c.within(u.x,u.y,s),h=u.numPoints||1,p=u.x*h,d=u.y*h,v=o&&h>1?this._map(u,!0):null,g=(l<<5)+(e+1)+this.points.length,y=0,m=f;y<m.length;y+=1){var x=m[y],b=c.points[x];if(!(b.zoom<=e)){b.zoom=e;var _=b.numPoints||1;p+=b.x*_,d+=b.y*_,h+=_,b.parentId=g,o&&(v||(v=this._map(u,!0)),o(v,this._map(b)))}}1===h?r.push(u):(u.parentId=g,r.push(H(p/h,d/h,g,h,v)))}}return r},V.prototype._getOriginId=function(t){return t-this.points.length>>5},V.prototype._getOriginZoom=function(t){return(t-this.points.length)%32},V.prototype._map=function(t,e){if(t.numPoints)return e?X({},t.properties):t.properties;var r=this.points[t.index].properties,n=this.options.map(r);return e&&n===r?X({},n):n},Tt.prototype.options={maxZoom:14,indexMaxZoom:5,indexMaxPoints:1e5,tolerance:3,extent:4096,buffer:64,lineMetrics:!1,promoteId:null,generateId:!1,debug:0},Tt.prototype.splitTile=function(t,e,r,n,i,a,o){for(var s=[t,e,r,n],l=this.options,u=l.debug;s.length;){n=s.pop(),r=s.pop(),e=s.pop(),t=s.pop();var c=1<<e,f=kt(e,r,n),h=this.tiles[f];if(!h&&(u>1&&console.time(\"creation\"),h=this.tiles[f]=bt(t,e,r,n,l),this.tileCoords.push({z:e,x:r,y:n}),u)){u>1&&(console.log(\"tile z%d-%d-%d (features: %d, points: %d, simplified: %d)\",e,r,n,h.numFeatures,h.numPoints,h.numSimplified),console.timeEnd(\"creation\"));var p=\"z\"+e;this.stats[p]=(this.stats[p]||0)+1,this.total++}if(h.source=t,i){if(e===l.maxZoom||e===i)continue;var d=1<<i-e;if(r!==Math.floor(a/d)||n!==Math.floor(o/d))continue}else if(e===l.indexMaxZoom||h.numPoints<=l.indexMaxPoints)continue;if(h.source=null,0!==t.length){u>1&&console.time(\"clipping\");var v,g,y,m,x,b,_=.5*l.buffer/l.extent,w=.5-_,T=.5+_,k=1+_;v=g=y=m=null,x=lt(t,c,r-_,r+T,0,h.minX,h.maxX,l),b=lt(t,c,r+w,r+k,0,h.minX,h.maxX,l),t=null,x&&(v=lt(x,c,n-_,n+T,1,h.minY,h.maxY,l),g=lt(x,c,n+w,n+k,1,h.minY,h.maxY,l),x=null),b&&(y=lt(b,c,n-_,n+T,1,h.minY,h.maxY,l),m=lt(b,c,n+w,n+k,1,h.minY,h.maxY,l),b=null),u>1&&console.timeEnd(\"clipping\"),s.push(v||[],e+1,2*r,2*n),s.push(g||[],e+1,2*r,2*n+1),s.push(y||[],e+1,2*r+1,2*n),s.push(m||[],e+1,2*r+1,2*n+1)}}},Tt.prototype.getTile=function(t,e,r){var n=this.options,i=n.extent,a=n.debug;if(t<0||t>24)return null;var o=1<<t,s=kt(t,e=(e%o+o)%o,r);if(this.tiles[s])return mt(this.tiles[s],i);a>1&&console.log(\"drilling down to z%d-%d-%d\",t,e,r);for(var l,u=t,c=e,f=r;!l&&u>0;)u--,c=Math.floor(c/2),f=Math.floor(f/2),l=this.tiles[kt(u,c,f)];return l&&l.source?(a>1&&console.log(\"found parent tile z%d-%d-%d\",u,c,f),a>1&&console.time(\"drilling down\"),this.splitTile(l.source,u,c,f,t,e,r),a>1&&console.timeEnd(\"drilling down\"),this.tiles[s]?mt(this.tiles[s],i):null):null};var Mt=function(e){function r(t,r,n,i){e.call(this,t,r,n,At),i&&(this.loadGeoJSON=i)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadData=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),this._pendingCallback=e,this._pendingLoadDataParams=t,this._state&&\"Idle\"!==this._state?this._state=\"NeedsLoadData\":(this._state=\"Coalescing\",this._loadData())},r.prototype._loadData=function(){var e=this;if(this._pendingCallback&&this._pendingLoadDataParams){var r=this._pendingCallback,n=this._pendingLoadDataParams;delete this._pendingCallback,delete this._pendingLoadDataParams;var i=!!(n&&n.request&&n.request.collectResourceTiming)&&new t.RequestPerformance(n.request);this.loadGeoJSON(n,(function(a,o){if(a||!o)return r(a);if(\"object\"!=typeof o)return r(new Error(\"Input data given to '\"+n.source+\"' is not a valid GeoJSON object.\"));f(o,!0);try{e._geoJSONIndex=n.cluster?new V(function(e){var r=e.superclusterOptions,n=e.clusterProperties;if(!n||!r)return r;for(var i={},a={},o={accumulated:null,zoom:0},s={properties:null},l=Object.keys(n),u=0,c=l;u<c.length;u+=1){var f=c[u],h=n[f],p=h[0],d=h[1],v=t.createExpression(d),g=t.createExpression(\"string\"==typeof p?[p,[\"accumulated\"],[\"get\",f]]:p);i[f]=v.value,a[f]=g.value}return r.map=function(t){s.properties=t;for(var e={},r=0,n=l;r<n.length;r+=1){var a=n[r];e[a]=i[a].evaluate(o,s)}return e},r.reduce=function(t,e){s.properties=e;for(var r=0,n=l;r<n.length;r+=1){var i=n[r];o.accumulated=t[i],t[i]=a[i].evaluate(o,s)}},r}(n)).load(o.features):function(t,e){return new Tt(t,e)}(o,n.geojsonVtOptions)}catch(a){return r(a)}e.loaded={};var s={};if(i){var l=i.finish();l&&(s.resourceTiming={},s.resourceTiming[n.source]=JSON.parse(JSON.stringify(l)))}r(null,s)}))}},r.prototype.coalesce=function(){\"Coalescing\"===this._state?this._state=\"Idle\":\"NeedsLoadData\"===this._state&&(this._state=\"Coalescing\",this._loadData())},r.prototype.reloadTile=function(t,r){var n=this.loaded,i=t.uid;return n&&n[i]?e.prototype.reloadTile.call(this,t,r):this.loadTile(t,r)},r.prototype.loadGeoJSON=function(e,r){if(e.request)t.getJSON(e.request,r);else{if(\"string\"!=typeof e.data)return r(new Error(\"Input data given to '\"+e.source+\"' is not a valid GeoJSON object.\"));try{return r(null,JSON.parse(e.data))}catch(t){return r(new Error(\"Input data given to '\"+e.source+\"' is not a valid GeoJSON object.\"))}}},r.prototype.removeSource=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),e()},r.prototype.getClusterExpansionZoom=function(t,e){try{e(null,this._geoJSONIndex.getClusterExpansionZoom(t.clusterId))}catch(t){e(t)}},r.prototype.getClusterChildren=function(t,e){try{e(null,this._geoJSONIndex.getChildren(t.clusterId))}catch(t){e(t)}},r.prototype.getClusterLeaves=function(t,e){try{e(null,this._geoJSONIndex.getLeaves(t.clusterId,t.limit,t.offset))}catch(t){e(t)}},r}(l);var St=function(e){var r=this;this.self=e,this.actor=new t.Actor(e,this),this.layerIndexes={},this.availableImages={},this.workerSourceTypes={vector:l,geojson:Mt},this.workerSources={},this.demWorkerSources={},this.self.registerWorkerSource=function(t,e){if(r.workerSourceTypes[t])throw new Error('Worker source with name \"'+t+'\" already registered.');r.workerSourceTypes[t]=e},this.self.registerRTLTextPlugin=function(e){if(t.plugin.isParsed())throw new Error(\"RTL text plugin already registered.\");t.plugin.applyArabicShaping=e.applyArabicShaping,t.plugin.processBidirectionalText=e.processBidirectionalText,t.plugin.processStyledBidirectionalText=e.processStyledBidirectionalText}};return St.prototype.setReferrer=function(t,e){this.referrer=e},St.prototype.setImages=function(t,e,r){for(var n in this.availableImages[t]=e,this.workerSources[t]){var i=this.workerSources[t][n];for(var a in i)i[a].availableImages=e}r()},St.prototype.setLayers=function(t,e,r){this.getLayerIndex(t).replace(e),r()},St.prototype.updateLayers=function(t,e,r){this.getLayerIndex(t).update(e.layers,e.removedIds),r()},St.prototype.loadTile=function(t,e,r){this.getWorkerSource(t,e.type,e.source).loadTile(e,r)},St.prototype.loadDEMTile=function(t,e,r){this.getDEMWorkerSource(t,e.source).loadTile(e,r)},St.prototype.reloadTile=function(t,e,r){this.getWorkerSource(t,e.type,e.source).reloadTile(e,r)},St.prototype.abortTile=function(t,e,r){this.getWorkerSource(t,e.type,e.source).abortTile(e,r)},St.prototype.removeTile=function(t,e,r){this.getWorkerSource(t,e.type,e.source).removeTile(e,r)},St.prototype.removeDEMTile=function(t,e){this.getDEMWorkerSource(t,e.source).removeTile(e)},St.prototype.removeSource=function(t,e,r){if(this.workerSources[t]&&this.workerSources[t][e.type]&&this.workerSources[t][e.type][e.source]){var n=this.workerSources[t][e.type][e.source];delete this.workerSources[t][e.type][e.source],void 0!==n.removeSource?n.removeSource(e,r):r()}},St.prototype.loadWorkerSource=function(t,e,r){try{this.self.importScripts(e.url),r()}catch(t){r(t.toString())}},St.prototype.syncRTLPluginState=function(e,r,n){try{t.plugin.setState(r);var i=t.plugin.getPluginURL();if(t.plugin.isLoaded()&&!t.plugin.isParsed()&&null!=i){this.self.importScripts(i);var a=t.plugin.isParsed();n(a?void 0:new Error(\"RTL Text Plugin failed to import scripts from \"+i),a)}}catch(t){n(t.toString())}},St.prototype.getAvailableImages=function(t){var e=this.availableImages[t];return e||(e=[]),e},St.prototype.getLayerIndex=function(t){var e=this.layerIndexes[t];return e||(e=this.layerIndexes[t]=new n),e},St.prototype.getWorkerSource=function(t,e,r){var n=this;if(this.workerSources[t]||(this.workerSources[t]={}),this.workerSources[t][e]||(this.workerSources[t][e]={}),!this.workerSources[t][e][r]){var i={send:function(e,r,i){n.actor.send(e,r,i,t)}};this.workerSources[t][e][r]=new this.workerSourceTypes[e](i,this.getLayerIndex(t),this.getAvailableImages(t))}return this.workerSources[t][e][r]},St.prototype.getDEMWorkerSource=function(t,e){return this.demWorkerSources[t]||(this.demWorkerSources[t]={}),this.demWorkerSources[t][e]||(this.demWorkerSources[t][e]=new c),this.demWorkerSources[t][e]},St.prototype.enforceCacheSizeLimit=function(e,r){t.enforceCacheSizeLimit(r)},\"undefined\"!=typeof WorkerGlobalScope&&void 0!==t.window&&t.window instanceof WorkerGlobalScope&&(t.window.worker=new St(t.window)),St})),n(0,(function(t){var e=t.createCommonjsModule((function(t){function e(t){return!r(t)}function r(t){return\"undefined\"!=typeof window&&\"undefined\"!=typeof 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t=document.createElement(\"canvas\");t.width=t.height=1;var e=t.getContext(\"2d\");if(!e)return!1;var r=e.getImageData(0,0,1,1);return r&&r.width===t.width}()?(r=t&&t.failIfMajorPerformanceCaveat,void 0===n[r]&&(n[r]=function(t){var r=function(t){var r=document.createElement(\"canvas\"),n=Object.create(e.webGLContextAttributes);return n.failIfMajorPerformanceCaveat=t,r.probablySupportsContext?r.probablySupportsContext(\"webgl\",n)||r.probablySupportsContext(\"experimental-webgl\",n):r.supportsContext?r.supportsContext(\"webgl\",n)||r.supportsContext(\"experimental-webgl\",n):r.getContext(\"webgl\",n)||r.getContext(\"experimental-webgl\",n)}(t);if(!r)return!1;var n=r.createShader(r.VERTEX_SHADER);return!(!n||r.isContextLost())&&(r.shaderSource(n,\"void main() {}\"),r.compileShader(n),!0===r.getShaderParameter(n,r.COMPILE_STATUS))}(r)),n[r]?void 0:\"insufficient WebGL support\"):\"insufficient Canvas/getImageData support\":\"insufficient ArrayBuffer support\":\"insufficient 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s=i([\"transform\",\"WebkitTransform\"]);r.setTransform=function(t,e){t.style[s]=e};var l=!1;try{var u=Object.defineProperty({},\"passive\",{get:function(){l=!0}});t.window.addEventListener(\"test\",u,u),t.window.removeEventListener(\"test\",u,u)}catch(t){l=!1}r.addEventListener=function(t,e,r,n){void 0===n&&(n={}),\"passive\"in n&&l?t.addEventListener(e,r,n):t.addEventListener(e,r,n.capture)},r.removeEventListener=function(t,e,r,n){void 0===n&&(n={}),\"passive\"in n&&l?t.removeEventListener(e,r,n):t.removeEventListener(e,r,n.capture)};var c=function(e){e.preventDefault(),e.stopPropagation(),t.window.removeEventListener(\"click\",c,!0)};function f(t){var e=t.userImage;return!!(e&&e.render&&e.render())&&(t.data.replace(new Uint8Array(e.data.buffer)),!0)}r.suppressClick=function(){t.window.addEventListener(\"click\",c,!0),t.window.setTimeout((function(){t.window.removeEventListener(\"click\",c,!0)}),0)},r.mousePos=function(e,r){var n=e.getBoundingClientRect();return new 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n=r[e],i=n.ids,a=n.callback;this._notify(i,a)}this.requestors=[]}},r.prototype.getImage=function(t){return this.images[t]},r.prototype.addImage=function(t,e){this._validate(t,e)&&(this.images[t]=e)},r.prototype._validate=function(e,r){var n=!0;return this._validateStretch(r.stretchX,r.data&&r.data.width)||(this.fire(new t.ErrorEvent(new Error('Image \"'+e+'\" has invalid \"stretchX\" value'))),n=!1),this._validateStretch(r.stretchY,r.data&&r.data.height)||(this.fire(new t.ErrorEvent(new Error('Image \"'+e+'\" has invalid \"stretchY\" value'))),n=!1),this._validateContent(r.content,r)||(this.fire(new t.ErrorEvent(new Error('Image \"'+e+'\" has invalid \"content\" value'))),n=!1),n},r.prototype._validateStretch=function(t,e){if(!t)return!0;for(var r=0,n=0,i=t;n<i.length;n+=1){var 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s=this.images[o];s?n[o]={data:s.data.clone(),pixelRatio:s.pixelRatio,sdf:s.sdf,version:s.version,stretchX:s.stretchX,stretchY:s.stretchY,content:s.content,hasRenderCallback:Boolean(s.userImage&&s.userImage.render)}:t.warnOnce('Image \"'+o+'\" could not be loaded. Please make sure you have added the image with map.addImage() or a \"sprite\" property in your style. You can provide missing images by listening for the \"styleimagemissing\" map event.')}r(null,n)},r.prototype.getPixelSize=function(){var t=this.atlasImage;return{width:t.width,height:t.height}},r.prototype.getPattern=function(e){var r=this.patterns[e],n=this.getImage(e);if(!n)return null;if(r&&r.position.version===n.version)return r.position;if(r)r.position.version=n.version;else{var i={w:n.data.width+2,h:n.data.height+2,x:0,y:0},a=new t.ImagePosition(i,n);this.patterns[e]={bin:i,position:a}}return this._updatePatternAtlas(),this.patterns[e].position},r.prototype.bind=function(e){var r=e.gl;this.atlasTexture?this.dirty&&(this.atlasTexture.update(this.atlasImage),this.dirty=!1):this.atlasTexture=new t.Texture(e,this.atlasImage,r.RGBA),this.atlasTexture.bind(r.LINEAR,r.CLAMP_TO_EDGE)},r.prototype._updatePatternAtlas=function(){var e=[];for(var r in this.patterns)e.push(this.patterns[r].bin);var n=t.potpack(e),i=n.w,a=n.h,o=this.atlasImage;for(var s in o.resize({width:i||1,height:a||1}),this.patterns){var l=this.patterns[s].bin,u=l.x+1,c=l.y+1,f=this.images[s].data,h=f.width,p=f.height;t.RGBAImage.copy(f,o,{x:0,y:0},{x:u,y:c},{width:h,height:p}),t.RGBAImage.copy(f,o,{x:0,y:p-1},{x:u,y:c-1},{width:h,height:1}),t.RGBAImage.copy(f,o,{x:0,y:0},{x:u,y:c+p},{width:h,height:1}),t.RGBAImage.copy(f,o,{x:h-1,y:0},{x:u-1,y:c},{width:1,height:p}),t.RGBAImage.copy(f,o,{x:0,y:0},{x:u+h,y:c},{width:1,height:p})}this.dirty=!0},r.prototype.beginFrame=function(){this.callbackDispatchedThisFrame={}},r.prototype.dispatchRenderCallbacks=function(t){for(var e=0,r=t;e<r.length;e+=1){var n=r[e];if(!this.callbackDispatchedThisFrame[n]){this.callbackDispatchedThisFrame[n]=!0;var i=this.images[n];f(i)&&this.updateImage(n,i)}}},r}(t.Evented);var p=g,d=g,v=1e20;function g(t,e,r,n,i,a){this.fontSize=t||24,this.buffer=void 0===e?3:e,this.cutoff=n||.25,this.fontFamily=i||\"sans-serif\",this.fontWeight=a||\"normal\",this.radius=r||8;var o=this.size=this.fontSize+2*this.buffer;this.canvas=document.createElement(\"canvas\"),this.canvas.width=this.canvas.height=o,this.ctx=this.canvas.getContext(\"2d\"),this.ctx.font=this.fontWeight+\" \"+this.fontSize+\"px \"+this.fontFamily,this.ctx.textBaseline=\"middle\",this.ctx.fillStyle=\"black\",this.gridOuter=new Float64Array(o*o),this.gridInner=new Float64Array(o*o),this.f=new Float64Array(o),this.d=new Float64Array(o),this.z=new Float64Array(o+1),this.v=new Int16Array(o),this.middle=Math.round(o/2*(navigator.userAgent.indexOf(\"Gecko/\")>=0?1.2:1))}function y(t,e,r,n,i,a,o){for(var s=0;s<e;s++){for(var l=0;l<r;l++)n[l]=t[l*e+s];for(m(n,i,a,o,r),l=0;l<r;l++)t[l*e+s]=i[l]}for(l=0;l<r;l++){for(s=0;s<e;s++)n[s]=t[l*e+s];for(m(n,i,a,o,e),s=0;s<e;s++)t[l*e+s]=Math.sqrt(i[s])}}function m(t,e,r,n,i){r[0]=0,n[0]=-v,n[1]=+v;for(var a=1,o=0;a<i;a++){for(var s=(t[a]+a*a-(t[r[o]]+r[o]*r[o]))/(2*a-2*r[o]);s<=n[o];)o--,s=(t[a]+a*a-(t[r[o]]+r[o]*r[o]))/(2*a-2*r[o]);r[++o]=a,n[o]=s,n[o+1]=+v}for(a=0,o=0;a<i;a++){for(;n[o+1]<a;)o++;e[a]=(a-r[o])*(a-r[o])+t[r[o]]}}g.prototype.draw=function(t){this.ctx.clearRect(0,0,this.size,this.size),this.ctx.fillText(t,this.buffer,this.middle);for(var e=this.ctx.getImageData(0,0,this.size,this.size),r=new Uint8ClampedArray(this.size*this.size),n=0;n<this.size*this.size;n++){var i=e.data[4*n+3]/255;this.gridOuter[n]=1===i?0:0===i?v:Math.pow(Math.max(0,.5-i),2),this.gridInner[n]=1===i?v:0===i?0:Math.pow(Math.max(0,i-.5),2)}for(y(this.gridOuter,this.size,this.size,this.f,this.d,this.v,this.z),y(this.gridInner,this.size,this.size,this.f,this.d,this.v,this.z),n=0;n<this.size*this.size;n++){var a=this.gridOuter[n]-this.gridInner[n];r[n]=Math.max(0,Math.min(255,Math.round(255-255*(a/this.radius+this.cutoff))))}return r},p.default=d;var x=function(t,e){this.requestManager=t,this.localIdeographFontFamily=e,this.entries={}};x.prototype.setURL=function(t){this.url=t},x.prototype.getGlyphs=function(e,r){var n=this,i=[];for(var a in e)for(var o=0,s=e[a];o<s.length;o+=1){var l=s[o];i.push({stack:a,id:l})}t.asyncAll(i,(function(t,e){var r=t.stack,i=t.id,a=n.entries[r];a||(a=n.entries[r]={glyphs:{},requests:{},ranges:{}});var o=a.glyphs[i];if(void 0===o){if(o=n._tinySDF(a,r,i))return a.glyphs[i]=o,void e(null,{stack:r,id:i,glyph:o});var s=Math.floor(i/256);if(256*s>65535)e(new Error(\"glyphs > 65535 not supported\"));else if(a.ranges[s])e(null,{stack:r,id:i,glyph:o});else{var l=a.requests[s];l||(l=a.requests[s]=[],x.loadGlyphRange(r,s,n.url,n.requestManager,(function(t,e){if(e){for(var r in e)n._doesCharSupportLocalGlyph(+r)||(a.glyphs[+r]=e[+r]);a.ranges[s]=!0}for(var i=0,o=l;i<o.length;i+=1)(0,o[i])(t,e);delete a.requests[s]}))),l.push((function(t,n){t?e(t):n&&e(null,{stack:r,id:i,glyph:n[i]||null})}))}}else e(null,{stack:r,id:i,glyph:o})}),(function(t,e){if(t)r(t);else if(e){for(var n={},i=0,a=e;i<a.length;i+=1){var o=a[i],s=o.stack,l=o.id,u=o.glyph;(n[s]||(n[s]={}))[l]=u&&{id:u.id,bitmap:u.bitmap.clone(),metrics:u.metrics}}r(null,n)}}))},x.prototype._doesCharSupportLocalGlyph=function(e){return!!this.localIdeographFontFamily&&(t.isChar[\"CJK Unified Ideographs\"](e)||t.isChar[\"Hangul Syllables\"](e)||t.isChar.Hiragana(e)||t.isChar.Katakana(e))},x.prototype._tinySDF=function(e,r,n){var i=this.localIdeographFontFamily;if(i&&this._doesCharSupportLocalGlyph(n)){var a=e.tinySDF;if(!a){var o=\"400\";/bold/i.test(r)?o=\"900\":/medium/i.test(r)?o=\"500\":/light/i.test(r)&&(o=\"200\"),a=e.tinySDF=new x.TinySDF(24,3,8,.25,i,o)}return{id:n,bitmap:new t.AlphaImage({width:30,height:30},a.draw(String.fromCharCode(n))),metrics:{width:24,height:24,left:0,top:-8,advance:24}}}},x.loadGlyphRange=function(e,r,n,i,a){var o=256*r,s=o+255,l=i.transformRequest(i.normalizeGlyphsURL(n).replace(\"{fontstack}\",e).replace(\"{range}\",o+\"-\"+s),t.ResourceType.Glyphs);t.getArrayBuffer(l,(function(e,r){if(e)a(e);else if(r){for(var n={},i=0,o=t.parseGlyphPBF(r);i<o.length;i+=1){var s=o[i];n[s.id]=s}a(null,n)}}))},x.TinySDF=p;var b=function(){this.specification=t.styleSpec.light.position};b.prototype.possiblyEvaluate=function(e,r){return t.sphericalToCartesian(e.expression.evaluate(r))},b.prototype.interpolate=function(e,r,n){return{x:t.number(e.x,r.x,n),y:t.number(e.y,r.y,n),z:t.number(e.z,r.z,n)}};var _=new t.Properties({anchor:new t.DataConstantProperty(t.styleSpec.light.anchor),position:new b,color:new t.DataConstantProperty(t.styleSpec.light.color),intensity:new t.DataConstantProperty(t.styleSpec.light.intensity)}),w=\"-transition\",T=function(e){function r(r){e.call(this),this._transitionable=new t.Transitionable(_),this.setLight(r),this._transitioning=this._transitionable.untransitioned()}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getLight=function(){return this._transitionable.serialize()},r.prototype.setLight=function(e,r){if(void 0===r&&(r={}),!this._validate(t.validateLight,e,r))for(var n in e){var i=e[n];t.endsWith(n,w)?this._transitionable.setTransition(n.slice(0,-11),i):this._transitionable.setValue(n,i)}},r.prototype.updateTransitions=function(t){this._transitioning=this._transitionable.transitioned(t,this._transitioning)},r.prototype.hasTransition=function(){return this._transitioning.hasTransition()},r.prototype.recalculate=function(t){this.properties=this._transitioning.possiblyEvaluate(t)},r.prototype._validate=function(e,r,n){return(!n||!1!==n.validate)&&t.emitValidationErrors(this,e.call(t.validateStyle,t.extend({value:r,style:{glyphs:!0,sprite:!0},styleSpec:t.styleSpec})))},r}(t.Evented),k=function(t,e){this.width=t,this.height=e,this.nextRow=0,this.data=new Uint8Array(this.width*this.height),this.dashEntry={}};k.prototype.getDash=function(t,e){var r=t.join(\",\")+String(e);return this.dashEntry[r]||(this.dashEntry[r]=this.addDash(t,e)),this.dashEntry[r]},k.prototype.getDashRanges=function(t,e,r){var n=[],i=t.length%2==1?-t[t.length-1]*r:0,a=t[0]*r,o=!0;n.push({left:i,right:a,isDash:o,zeroLength:0===t[0]});for(var s=t[0],l=1;l<t.length;l++){o=!o;var u=t[l];i=s*r,a=(s+=u)*r,n.push({left:i,right:a,isDash:o,zeroLength:0===u})}return n},k.prototype.addRoundDash=function(t,e,r){for(var n=e/2,i=-r;i<=r;i++)for(var a=this.nextRow+r+i,o=this.width*a,s=0,l=t[s],u=0;u<this.width;u++){u/l.right>1&&(l=t[++s]);var c=Math.abs(u-l.left),f=Math.abs(u-l.right),h=Math.min(c,f),p=void 0,d=i/r*(n+1);if(l.isDash){var v=n-Math.abs(d);p=Math.sqrt(h*h+v*v)}else p=n-Math.sqrt(h*h+d*d);this.data[o+u]=Math.max(0,Math.min(255,p+128))}},k.prototype.addRegularDash=function(t){for(var e=t.length-1;e>=0;--e){var r=t[e],n=t[e+1];r.zeroLength?t.splice(e,1):n&&n.isDash===r.isDash&&(n.left=r.left,t.splice(e,1))}var i=t[0],a=t[t.length-1];i.isDash===a.isDash&&(i.left=a.left-this.width,a.right=i.right+this.width);for(var o=this.width*this.nextRow,s=0,l=t[s],u=0;u<this.width;u++){u/l.right>1&&(l=t[++s]);var c=Math.abs(u-l.left),f=Math.abs(u-l.right),h=Math.min(c,f),p=l.isDash?h:-h;this.data[o+u]=Math.max(0,Math.min(255,p+128))}},k.prototype.addDash=function(e,r){var n=r?7:0,i=2*n+1;if(this.nextRow+i>this.height)return t.warnOnce(\"LineAtlas out of space\"),null;for(var a=0,o=0;o<e.length;o++)a+=e[o];if(0!==a){var s=this.width/a,l=this.getDashRanges(e,this.width,s);r?this.addRoundDash(l,s,n):this.addRegularDash(l)}var u={y:(this.nextRow+n+.5)/this.height,height:2*n/this.height,width:a};return this.nextRow+=i,this.dirty=!0,u},k.prototype.bind=function(t){var e=t.gl;this.texture?(e.bindTexture(e.TEXTURE_2D,this.texture),this.dirty&&(this.dirty=!1,e.texSubImage2D(e.TEXTURE_2D,0,0,0,this.width,this.height,e.ALPHA,e.UNSIGNED_BYTE,this.data))):(this.texture=e.createTexture(),e.bindTexture(e.TEXTURE_2D,this.texture),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_S,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_T,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,e.LINEAR),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,e.LINEAR),e.texImage2D(e.TEXTURE_2D,0,e.ALPHA,this.width,this.height,0,e.ALPHA,e.UNSIGNED_BYTE,this.data))};var A=function e(r,n){this.workerPool=r,this.actors=[],this.currentActor=0,this.id=t.uniqueId();for(var i=this.workerPool.acquire(this.id),a=0;a<i.length;a++){var o=i[a],s=new e.Actor(o,n,this.id);s.name=\"Worker \"+a,this.actors.push(s)}};function M(e,r,n){var i=function(i,a){if(i)return n(i);if(a){var o=t.pick(t.extend(a,e),[\"tiles\",\"minzoom\",\"maxzoom\",\"attribution\",\"mapbox_logo\",\"bounds\",\"scheme\",\"tileSize\",\"encoding\"]);a.vector_layers&&(o.vectorLayers=a.vector_layers,o.vectorLayerIds=o.vectorLayers.map((function(t){return t.id}))),o.tiles=r.canonicalizeTileset(o,e.url),n(null,o)}};return e.url?t.getJSON(r.transformRequest(r.normalizeSourceURL(e.url),t.ResourceType.Source),i):t.browser.frame((function(){return i(null,e)}))}A.prototype.broadcast=function(e,r,n){n=n||function(){},t.asyncAll(this.actors,(function(t,n){t.send(e,r,n)}),n)},A.prototype.getActor=function(){return this.currentActor=(this.currentActor+1)%this.actors.length,this.actors[this.currentActor]},A.prototype.remove=function(){this.actors.forEach((function(t){t.remove()})),this.actors=[],this.workerPool.release(this.id)},A.Actor=t.Actor;var S=function(e,r,n){this.bounds=t.LngLatBounds.convert(this.validateBounds(e)),this.minzoom=r||0,this.maxzoom=n||24};S.prototype.validateBounds=function(t){return Array.isArray(t)&&4===t.length?[Math.max(-180,t[0]),Math.max(-90,t[1]),Math.min(180,t[2]),Math.min(90,t[3])]:[-180,-90,180,90]},S.prototype.contains=function(e){var r=Math.pow(2,e.z),n=Math.floor(t.mercatorXfromLng(this.bounds.getWest())*r),i=Math.floor(t.mercatorYfromLat(this.bounds.getNorth())*r),a=Math.ceil(t.mercatorXfromLng(this.bounds.getEast())*r),o=Math.ceil(t.mercatorYfromLat(this.bounds.getSouth())*r);return e.x>=n&&e.x<a&&e.y>=i&&e.y<o};var E=function(e){function r(r,n,i,a){if(e.call(this),this.id=r,this.dispatcher=i,this.type=\"vector\",this.minzoom=0,this.maxzoom=22,this.scheme=\"xyz\",this.tileSize=512,this.reparseOverscaled=!0,this.isTileClipped=!0,this._loaded=!1,t.extend(this,t.pick(n,[\"url\",\"scheme\",\"tileSize\",\"promoteId\"])),this._options=t.extend({type:\"vector\"},n),this._collectResourceTiming=n.collectResourceTiming,512!==this.tileSize)throw new Error(\"vector tile sources must have a tileSize of 512\");this.setEventedParent(a)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._tileJSONRequest=M(this._options,this.map._requestManager,(function(r,n){e._tileJSONRequest=null,e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new S(n.bounds,e.minzoom,e.maxzoom)),t.postTurnstileEvent(n.tiles,e.map._requestManager._customAccessToken),t.postMapLoadEvent(n.tiles,e.map._getMapId(),e.map._requestManager._skuToken,e.map._requestManager._customAccessToken),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.loadTile=function(e,r){var n=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme)),i={request:this.map._requestManager.transformRequest(n,t.ResourceType.Tile),uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,tileSize:this.tileSize*e.tileID.overscaleFactor(),type:this.type,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};function a(n,i){return delete e.request,e.aborted?r(null):n&&404!==n.status?r(n):(i&&i.resourceTiming&&(e.resourceTiming=i.resourceTiming),this.map._refreshExpiredTiles&&i&&e.setExpiryData(i),e.loadVectorData(i,this.map.painter),t.cacheEntryPossiblyAdded(this.dispatcher),r(null),void(e.reloadCallback&&(this.loadTile(e,e.reloadCallback),e.reloadCallback=null)))}i.request.collectResourceTiming=this._collectResourceTiming,e.actor&&\"expired\"!==e.state?\"loading\"===e.state?e.reloadCallback=r:e.request=e.actor.send(\"reloadTile\",i,a.bind(this)):(e.actor=this.dispatcher.getActor(),e.request=e.actor.send(\"loadTile\",i,a.bind(this)))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.actor&&t.actor.send(\"abortTile\",{uid:t.uid,type:this.type,source:this.id},void 0)},r.prototype.unloadTile=function(t){t.unloadVectorData(),t.actor&&t.actor.send(\"removeTile\",{uid:t.uid,type:this.type,source:this.id},void 0)},r.prototype.hasTransition=function(){return!1},r}(t.Evented),L=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.dispatcher=i,this.setEventedParent(a),this.type=\"raster\",this.minzoom=0,this.maxzoom=22,this.roundZoom=!0,this.scheme=\"xyz\",this.tileSize=512,this._loaded=!1,this._options=t.extend({type:\"raster\"},n),t.extend(this,t.pick(n,[\"url\",\"scheme\",\"tileSize\"]))}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._tileJSONRequest=M(this._options,this.map._requestManager,(function(r,n){e._tileJSONRequest=null,e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new S(n.bounds,e.minzoom,e.maxzoom)),t.postTurnstileEvent(n.tiles),t.postMapLoadEvent(n.tiles,e.map._getMapId(),e.map._requestManager._skuToken),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.loadTile=function(e,r){var n=this,i=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);e.request=t.getImage(this.map._requestManager.transformRequest(i,t.ResourceType.Tile),(function(i,a){if(delete e.request,e.aborted)e.state=\"unloaded\",r(null);else if(i)e.state=\"errored\",r(i);else if(a){n.map._refreshExpiredTiles&&e.setExpiryData(a),delete a.cacheControl,delete a.expires;var o=n.map.painter.context,s=o.gl;e.texture=n.map.painter.getTileTexture(a.width),e.texture?e.texture.update(a,{useMipmap:!0}):(e.texture=new t.Texture(o,a,s.RGBA,{useMipmap:!0}),e.texture.bind(s.LINEAR,s.CLAMP_TO_EDGE,s.LINEAR_MIPMAP_NEAREST),o.extTextureFilterAnisotropic&&s.texParameterf(s.TEXTURE_2D,o.extTextureFilterAnisotropic.TEXTURE_MAX_ANISOTROPY_EXT,o.extTextureFilterAnisotropicMax)),e.state=\"loaded\",t.cacheEntryPossiblyAdded(n.dispatcher),r(null)}}))},r.prototype.abortTile=function(t,e){t.request&&(t.request.cancel(),delete t.request),e()},r.prototype.unloadTile=function(t,e){t.texture&&this.map.painter.saveTileTexture(t.texture),e()},r.prototype.hasTransition=function(){return!1},r}(t.Evented),C=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),this.type=\"raster-dem\",this.maxzoom=22,this._options=t.extend({type:\"raster-dem\"},n),this.encoding=n.encoding||\"mapbox\"}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.serialize=function(){return{type:\"raster-dem\",url:this.url,tileSize:this.tileSize,tiles:this.tiles,bounds:this.bounds,encoding:this.encoding}},r.prototype.loadTile=function(e,r){var n=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);function i(t,n){t&&(e.state=\"errored\",r(t)),n&&(e.dem=n,e.needsHillshadePrepare=!0,e.state=\"loaded\",r(null))}e.request=t.getImage(this.map._requestManager.transformRequest(n,t.ResourceType.Tile),function(n,a){if(delete e.request,e.aborted)e.state=\"unloaded\",r(null);else if(n)e.state=\"errored\",r(n);else if(a){this.map._refreshExpiredTiles&&e.setExpiryData(a),delete a.cacheControl,delete a.expires;var o=t.window.ImageBitmap&&a instanceof t.window.ImageBitmap&&t.offscreenCanvasSupported()?a:t.browser.getImageData(a,1),s={uid:e.uid,coord:e.tileID,source:this.id,rawImageData:o,encoding:this.encoding};e.actor&&\"expired\"!==e.state||(e.actor=this.dispatcher.getActor(),e.actor.send(\"loadDEMTile\",s,i.bind(this)))}}.bind(this)),e.neighboringTiles=this._getNeighboringTiles(e.tileID)},r.prototype._getNeighboringTiles=function(e){var r=e.canonical,n=Math.pow(2,r.z),i=(r.x-1+n)%n,a=0===r.x?e.wrap-1:e.wrap,o=(r.x+1+n)%n,s=r.x+1===n?e.wrap+1:e.wrap,l={};return l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y).key]={backfilled:!1},r.y>0&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y-1).key]={backfilled:!1}),r.y+1<n&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y+1).key]={backfilled:!1}),l},r.prototype.unloadTile=function(t){t.demTexture&&this.map.painter.saveTileTexture(t.demTexture),t.fbo&&(t.fbo.destroy(),delete t.fbo),t.dem&&delete t.dem,delete t.neighboringTiles,t.state=\"unloaded\",t.actor&&t.actor.send(\"removeDEMTile\",{uid:t.uid,source:this.id})},r}(L),P=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.type=\"geojson\",this.minzoom=0,this.maxzoom=18,this.tileSize=512,this.isTileClipped=!0,this.reparseOverscaled=!0,this._removed=!1,this._loaded=!1,this.actor=i.getActor(),this.setEventedParent(a),this._data=n.data,this._options=t.extend({},n),this._collectResourceTiming=n.collectResourceTiming,this._resourceTiming=[],void 0!==n.maxzoom&&(this.maxzoom=n.maxzoom),n.type&&(this.type=n.type),n.attribution&&(this.attribution=n.attribution),this.promoteId=n.promoteId;var o=t.EXTENT/this.tileSize;this.workerOptions=t.extend({source:this.id,cluster:n.cluster||!1,geojsonVtOptions:{buffer:(void 0!==n.buffer?n.buffer:128)*o,tolerance:(void 0!==n.tolerance?n.tolerance:.375)*o,extent:t.EXTENT,maxZoom:this.maxzoom,lineMetrics:n.lineMetrics||!1,generateId:n.generateId||!1},superclusterOptions:{maxZoom:void 0!==n.clusterMaxZoom?Math.min(n.clusterMaxZoom,this.maxzoom-1):this.maxzoom-1,extent:t.EXTENT,radius:(n.clusterRadius||50)*o,log:!1,generateId:n.generateId||!1},clusterProperties:n.clusterProperties},n.workerOptions)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._updateWorkerData((function(r){if(r)e.fire(new t.ErrorEvent(r));else{var n={dataType:\"source\",sourceDataType:\"metadata\"};e._collectResourceTiming&&e._resourceTiming&&e._resourceTiming.length>0&&(n.resourceTiming=e._resourceTiming,e._resourceTiming=[]),e.fire(new t.Event(\"data\",n))}}))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setData=function(e){var r=this;return this._data=e,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._updateWorkerData((function(e){if(e)r.fire(new t.ErrorEvent(e));else{var n={dataType:\"source\",sourceDataType:\"content\"};r._collectResourceTiming&&r._resourceTiming&&r._resourceTiming.length>0&&(n.resourceTiming=r._resourceTiming,r._resourceTiming=[]),r.fire(new t.Event(\"data\",n))}})),this},r.prototype.getClusterExpansionZoom=function(t,e){return this.actor.send(\"geojson.getClusterExpansionZoom\",{clusterId:t,source:this.id},e),this},r.prototype.getClusterChildren=function(t,e){return this.actor.send(\"geojson.getClusterChildren\",{clusterId:t,source:this.id},e),this},r.prototype.getClusterLeaves=function(t,e,r,n){return this.actor.send(\"geojson.getClusterLeaves\",{source:this.id,clusterId:t,limit:e,offset:r},n),this},r.prototype._updateWorkerData=function(e){var r=this;this._loaded=!1;var n=t.extend({},this.workerOptions),i=this._data;\"string\"==typeof i?(n.request=this.map._requestManager.transformRequest(t.browser.resolveURL(i),t.ResourceType.Source),n.request.collectResourceTiming=this._collectResourceTiming):n.data=JSON.stringify(i),this.actor.send(this.type+\".loadData\",n,(function(t,i){r._removed||i&&i.abandoned||(r._loaded=!0,i&&i.resourceTiming&&i.resourceTiming[r.id]&&(r._resourceTiming=i.resourceTiming[r.id].slice(0)),r.actor.send(r.type+\".coalesce\",{source:n.source},null),e(t))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.loadTile=function(e,r){var n=this,i=e.actor?\"reloadTile\":\"loadTile\";e.actor=this.actor;var a={type:this.type,uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,maxZoom:this.maxzoom,tileSize:this.tileSize,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};e.request=this.actor.send(i,a,(function(t,a){return delete e.request,e.unloadVectorData(),e.aborted?r(null):t?r(t):(e.loadVectorData(a,n.map.painter,\"reloadTile\"===i),r(null))}))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.aborted=!0},r.prototype.unloadTile=function(t){t.unloadVectorData(),this.actor.send(\"removeTile\",{uid:t.uid,type:this.type,source:this.id})},r.prototype.onRemove=function(){this._removed=!0,this.actor.send(\"removeSource\",{type:this.type,source:this.id})},r.prototype.serialize=function(){return t.extend({},this._options,{type:this.type,data:this._data})},r.prototype.hasTransition=function(){return!1},r}(t.Evented),O=t.createLayout([{name:\"a_pos\",type:\"Int16\",components:2},{name:\"a_texture_pos\",type:\"Int16\",components:2}]),I=function(e){function r(t,r,n,i){e.call(this),this.id=t,this.dispatcher=n,this.coordinates=r.coordinates,this.type=\"image\",this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.tiles={},this._loaded=!1,this.setEventedParent(i),this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(e,r){var n=this;this._loaded=!1,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this.url=this.options.url,t.getImage(this.map._requestManager.transformRequest(this.url,t.ResourceType.Image),(function(i,a){n._loaded=!0,i?n.fire(new t.ErrorEvent(i)):a&&(n.image=a,e&&(n.coordinates=e),r&&r(),n._finishLoading())}))},r.prototype.loaded=function(){return this._loaded},r.prototype.updateImage=function(t){var e=this;return this.image&&t.url?(this.options.url=t.url,this.load(t.coordinates,(function(){e.texture=null})),this):this},r.prototype._finishLoading=function(){this.map&&(this.setCoordinates(this.coordinates),this.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setCoordinates=function(e){var r=this;this.coordinates=e;var n=e.map(t.MercatorCoordinate.fromLngLat);this.tileID=function(e){for(var r=1/0,n=1/0,i=-1/0,a=-1/0,o=0,s=e;o<s.length;o+=1){var l=s[o];r=Math.min(r,l.x),n=Math.min(n,l.y),i=Math.max(i,l.x),a=Math.max(a,l.y)}var u=i-r,c=a-n,f=Math.max(u,c),h=Math.max(0,Math.floor(-Math.log(f)/Math.LN2)),p=Math.pow(2,h);return new t.CanonicalTileID(h,Math.floor((r+i)/2*p),Math.floor((n+a)/2*p))}(n),this.minzoom=this.maxzoom=this.tileID.z;var i=n.map((function(t){return r.tileID.getTilePoint(t)._round()}));return this._boundsArray=new t.StructArrayLayout4i8,this._boundsArray.emplaceBack(i[0].x,i[0].y,0,0),this._boundsArray.emplaceBack(i[1].x,i[1].y,t.EXTENT,0),this._boundsArray.emplaceBack(i[3].x,i[3].y,0,t.EXTENT),this._boundsArray.emplaceBack(i[2].x,i[2].y,t.EXTENT,t.EXTENT),this.boundsBuffer&&(this.boundsBuffer.destroy(),delete this.boundsBuffer),this.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})),this},r.prototype.prepare=function(){if(0!==Object.keys(this.tiles).length&&this.image){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,O.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture||(this.texture=new t.Texture(e,this.image,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];\"loaded\"!==i.state&&(i.state=\"loaded\",i.texture=this.texture)}}},r.prototype.loadTile=function(t,e){this.tileID&&this.tileID.equals(t.tileID.canonical)?(this.tiles[String(t.tileID.wrap)]=t,t.buckets={},e(null)):(t.state=\"errored\",e(null))},r.prototype.serialize=function(){return{type:\"image\",url:this.options.url,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return!1},r}(t.Evented);var D=function(e){function r(t,r,n,i){e.call(this,t,r,n,i),this.roundZoom=!0,this.type=\"video\",this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1;var r=this.options;this.urls=[];for(var n=0,i=r.urls;n<i.length;n+=1){var a=i[n];this.urls.push(this.map._requestManager.transformRequest(a,t.ResourceType.Source).url)}t.getVideo(this.urls,(function(r,n){e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(e.video=n,e.video.loop=!0,e.video.addEventListener(\"playing\",(function(){e.map.triggerRepaint()})),e.map&&e.video.play(),e._finishLoading())}))},r.prototype.pause=function(){this.video&&this.video.pause()},r.prototype.play=function(){this.video&&this.video.play()},r.prototype.seek=function(e){if(this.video){var r=this.video.seekable;e<r.start(0)||e>r.end(0)?this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+this.id,null,\"Playback for this video can be set only between the \"+r.start(0)+\" and \"+r.end(0)+\"-second mark.\"))):this.video.currentTime=e}},r.prototype.getVideo=function(){return this.video},r.prototype.onAdd=function(t){this.map||(this.map=t,this.load(),this.video&&(this.video.play(),this.setCoordinates(this.coordinates)))},r.prototype.prepare=function(){if(!(0===Object.keys(this.tiles).length||this.video.readyState<2)){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,O.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?this.video.paused||(this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE),r.texSubImage2D(r.TEXTURE_2D,0,0,0,r.RGBA,r.UNSIGNED_BYTE,this.video)):(this.texture=new t.Texture(e,this.video,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];\"loaded\"!==i.state&&(i.state=\"loaded\",i.texture=this.texture)}}},r.prototype.serialize=function(){return{type:\"video\",urls:this.urls,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this.video&&!this.video.paused},r}(I),z=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),n.coordinates?Array.isArray(n.coordinates)&&4===n.coordinates.length&&!n.coordinates.some((function(t){return!Array.isArray(t)||2!==t.length||t.some((function(t){return\"number\"!=typeof t}))}))||this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'\"coordinates\" property must be an array of 4 longitude/latitude array pairs'))):this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'missing required property \"coordinates\"'))),n.animate&&\"boolean\"!=typeof n.animate&&this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'optional \"animate\" property must be a boolean value'))),n.canvas?\"string\"==typeof n.canvas||n.canvas instanceof t.window.HTMLCanvasElement||this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'\"canvas\" must be either a string representing the ID of the canvas element from which to read, or an HTMLCanvasElement instance'))):this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'missing required property \"canvas\"'))),this.options=n,this.animate=void 0===n.animate||n.animate}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){this._loaded=!0,this.canvas||(this.canvas=this.options.canvas instanceof t.window.HTMLCanvasElement?this.options.canvas:t.window.document.getElementById(this.options.canvas)),this.width=this.canvas.width,this.height=this.canvas.height,this._hasInvalidDimensions()?this.fire(new t.ErrorEvent(new Error(\"Canvas dimensions cannot be less than or equal to zero.\"))):(this.play=function(){this._playing=!0,this.map.triggerRepaint()},this.pause=function(){this._playing&&(this.prepare(),this._playing=!1)},this._finishLoading())},r.prototype.getCanvas=function(){return this.canvas},r.prototype.onAdd=function(t){this.map=t,this.load(),this.canvas&&this.animate&&this.play()},r.prototype.onRemove=function(){this.pause()},r.prototype.prepare=function(){var e=!1;if(this.canvas.width!==this.width&&(this.width=this.canvas.width,e=!0),this.canvas.height!==this.height&&(this.height=this.canvas.height,e=!0),!this._hasInvalidDimensions()&&0!==Object.keys(this.tiles).length){var r=this.map.painter.context,n=r.gl;for(var i in this.boundsBuffer||(this.boundsBuffer=r.createVertexBuffer(this._boundsArray,O.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?(e||this._playing)&&this.texture.update(this.canvas,{premultiply:!0}):this.texture=new t.Texture(r,this.canvas,n.RGBA,{premultiply:!0}),this.tiles){var a=this.tiles[i];\"loaded\"!==a.state&&(a.state=\"loaded\",a.texture=this.texture)}}},r.prototype.serialize=function(){return{type:\"canvas\",coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this._playing},r.prototype._hasInvalidDimensions=function(){for(var t=0,e=[this.canvas.width,this.canvas.height];t<e.length;t+=1){var r=e[t];if(isNaN(r)||r<=0)return!0}return!1},r}(I),R={vector:E,raster:L,\"raster-dem\":C,geojson:P,video:D,image:I,canvas:z};function F(e,r){var n=t.identity([]);return t.translate(n,n,[1,1,0]),t.scale(n,n,[.5*e.width,.5*e.height,1]),t.multiply(n,n,e.calculatePosMatrix(r.toUnwrapped()))}function B(t,e,r,n,i,a){var o=function(t,e,r){if(t)for(var n=0,i=t;n<i.length;n+=1){var a=e[i[n]];if(a&&a.source===r&&\"fill-extrusion\"===a.type)return!0}else for(var o in e){var s=e[o];if(s.source===r&&\"fill-extrusion\"===s.type)return!0}return!1}(i&&i.layers,e,t.id),s=a.maxPitchScaleFactor(),l=t.tilesIn(n,s,o);l.sort(N);for(var u=[],c=0,f=l;c<f.length;c+=1){var h=f[c];u.push({wrappedTileID:h.tileID.wrapped().key,queryResults:h.tile.queryRenderedFeatures(e,r,t._state,h.queryGeometry,h.cameraQueryGeometry,h.scale,i,a,s,F(t.transform,h.tileID))})}var p=function(t){for(var e={},r={},n=0,i=t;n<i.length;n+=1){var a=i[n],o=a.queryResults,s=a.wrappedTileID,l=r[s]=r[s]||{};for(var u in o)for(var c=o[u],f=l[u]=l[u]||{},h=e[u]=e[u]||[],p=0,d=c;p<d.length;p+=1){var v=d[p];f[v.featureIndex]||(f[v.featureIndex]=!0,h.push(v))}}return e}(u);for(var d in p)p[d].forEach((function(e){var r=e.feature,n=t.getFeatureState(r.layer[\"source-layer\"],r.id);r.source=r.layer.source,r.layer[\"source-layer\"]&&(r.sourceLayer=r.layer[\"source-layer\"]),r.state=n}));return p}function N(t,e){var r=t.tileID,n=e.tileID;return r.overscaledZ-n.overscaledZ||r.canonical.y-n.canonical.y||r.wrap-n.wrap||r.canonical.x-n.canonical.x}var j=function(t,e){this.max=t,this.onRemove=e,this.reset()};j.prototype.reset=function(){for(var t in this.data)for(var e=0,r=this.data[t];e<r.length;e+=1){var n=r[e];n.timeout&&clearTimeout(n.timeout),this.onRemove(n.value)}return this.data={},this.order=[],this},j.prototype.add=function(t,e,r){var n=this,i=t.wrapped().key;void 0===this.data[i]&&(this.data[i]=[]);var a={value:e,timeout:void 0};if(void 0!==r&&(a.timeout=setTimeout((function(){n.remove(t,a)}),r)),this.data[i].push(a),this.order.push(i),this.order.length>this.max){var o=this._getAndRemoveByKey(this.order[0]);o&&this.onRemove(o)}return this},j.prototype.has=function(t){return t.wrapped().key in this.data},j.prototype.getAndRemove=function(t){return this.has(t)?this._getAndRemoveByKey(t.wrapped().key):null},j.prototype._getAndRemoveByKey=function(t){var e=this.data[t].shift();return e.timeout&&clearTimeout(e.timeout),0===this.data[t].length&&delete this.data[t],this.order.splice(this.order.indexOf(t),1),e.value},j.prototype.getByKey=function(t){var e=this.data[t];return e?e[0].value:null},j.prototype.get=function(t){return this.has(t)?this.data[t.wrapped().key][0].value:null},j.prototype.remove=function(t,e){if(!this.has(t))return this;var r=t.wrapped().key,n=void 0===e?0:this.data[r].indexOf(e),i=this.data[r][n];return this.data[r].splice(n,1),i.timeout&&clearTimeout(i.timeout),0===this.data[r].length&&delete this.data[r],this.onRemove(i.value),this.order.splice(this.order.indexOf(r),1),this},j.prototype.setMaxSize=function(t){for(this.max=t;this.order.length>this.max;){var e=this._getAndRemoveByKey(this.order[0]);e&&this.onRemove(e)}return this},j.prototype.filter=function(t){var e=[];for(var r in this.data)for(var n=0,i=this.data[r];n<i.length;n+=1){var a=i[n];t(a.value)||e.push(a)}for(var o=0,s=e;o<s.length;o+=1){var l=s[o];this.remove(l.value.tileID,l)}};var U=function(t,e,r){this.context=t;var n=t.gl;this.buffer=n.createBuffer(),this.dynamicDraw=Boolean(r),this.context.unbindVAO(),t.bindElementBuffer.set(this.buffer),n.bufferData(n.ELEMENT_ARRAY_BUFFER,e.arrayBuffer,this.dynamicDraw?n.DYNAMIC_DRAW:n.STATIC_DRAW),this.dynamicDraw||delete e.arrayBuffer};U.prototype.bind=function(){this.context.bindElementBuffer.set(this.buffer)},U.prototype.updateData=function(t){var e=this.context.gl;this.context.unbindVAO(),this.bind(),e.bufferSubData(e.ELEMENT_ARRAY_BUFFER,0,t.arrayBuffer)},U.prototype.destroy=function(){var t=this.context.gl;this.buffer&&(t.deleteBuffer(this.buffer),delete this.buffer)};var V={Int8:\"BYTE\",Uint8:\"UNSIGNED_BYTE\",Int16:\"SHORT\",Uint16:\"UNSIGNED_SHORT\",Int32:\"INT\",Uint32:\"UNSIGNED_INT\",Float32:\"FLOAT\"},H=function(t,e,r,n){this.length=e.length,this.attributes=r,this.itemSize=e.bytesPerElement,this.dynamicDraw=n,this.context=t;var i=t.gl;this.buffer=i.createBuffer(),t.bindVertexBuffer.set(this.buffer),i.bufferData(i.ARRAY_BUFFER,e.arrayBuffer,this.dynamicDraw?i.DYNAMIC_DRAW:i.STATIC_DRAW),this.dynamicDraw||delete e.arrayBuffer};H.prototype.bind=function(){this.context.bindVertexBuffer.set(this.buffer)},H.prototype.updateData=function(t){var e=this.context.gl;this.bind(),e.bufferSubData(e.ARRAY_BUFFER,0,t.arrayBuffer)},H.prototype.enableAttributes=function(t,e){for(var r=0;r<this.attributes.length;r++){var n=this.attributes[r],i=e.attributes[n.name];void 0!==i&&t.enableVertexAttribArray(i)}},H.prototype.setVertexAttribPointers=function(t,e,r){for(var n=0;n<this.attributes.length;n++){var i=this.attributes[n],a=e.attributes[i.name];void 0!==a&&t.vertexAttribPointer(a,i.components,t[V[i.type]],!1,this.itemSize,i.offset+this.itemSize*(r||0))}},H.prototype.destroy=function(){var t=this.context.gl;this.buffer&&(t.deleteBuffer(this.buffer),delete this.buffer)};var q=function(t){this.gl=t.gl,this.default=this.getDefault(),this.current=this.default,this.dirty=!1};q.prototype.get=function(){return this.current},q.prototype.set=function(t){},q.prototype.getDefault=function(){return this.default},q.prototype.setDefault=function(){this.set(this.default)};var G=function(e){function r(){e.apply(this,arguments)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getDefault=function(){return t.Color.transparent},r.prototype.set=function(t){var e=this.current;(t.r!==e.r||t.g!==e.g||t.b!==e.b||t.a!==e.a||this.dirty)&&(this.gl.clearColor(t.r,t.g,t.b,t.a),this.current=t,this.dirty=!1)},r}(q),Z=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return 1},e.prototype.set=function(t){(t!==this.current||this.dirty)&&(this.gl.clearDepth(t),this.current=t,this.dirty=!1)},e}(q),Y=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return 0},e.prototype.set=function(t){(t!==this.current||this.dirty)&&(this.gl.clearStencil(t),this.current=t,this.dirty=!1)},e}(q),W=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return[!0,!0,!0,!0]},e.prototype.set=function(t){var e=this.current;(t[0]!==e[0]||t[1]!==e[1]||t[2]!==e[2]||t[3]!==e[3]||this.dirty)&&(this.gl.colorMask(t[0],t[1],t[2],t[3]),this.current=t,this.dirty=!1)},e}(q),X=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return!0},e.prototype.set=function(t){(t!==this.current||this.dirty)&&(this.gl.depthMask(t),this.current=t,this.dirty=!1)},e}(q),J=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return 255},e.prototype.set=function(t){(t!==this.current||this.dirty)&&(this.gl.stencilMask(t),this.current=t,this.dirty=!1)},e}(q),K=function(t){function e(){t.apply(this,arguments)}return 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t=this.gl;return[t.ONE,t.ZERO]},e.prototype.set=function(t){var e=this.current;(t[0]!==e[0]||t[1]!==e[1]||this.dirty)&&(this.gl.blendFunc(t[0],t[1]),this.current=t,this.dirty=!1)},e}(q),at=function(e){function r(){e.apply(this,arguments)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getDefault=function(){return t.Color.transparent},r.prototype.set=function(t){var e=this.current;(t.r!==e.r||t.g!==e.g||t.b!==e.b||t.a!==e.a||this.dirty)&&(this.gl.blendColor(t.r,t.g,t.b,t.a),this.current=t,this.dirty=!1)},r}(q),ot=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return this.gl.FUNC_ADD},e.prototype.set=function(t){(t!==this.current||this.dirty)&&(this.gl.blendEquation(t),this.current=t,this.dirty=!1)},e}(q),st=function(t){function e(){t.apply(this,arguments)}return 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e=this.current;(t[0]!==e[0]||t[1]!==e[1]||t[2]!==e[2]||t[3]!==e[3]||this.dirty)&&(this.gl.viewport(t[0],t[1],t[2],t[3]),this.current=t,this.dirty=!1)},e}(q),pt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return null},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.bindFramebuffer(e.FRAMEBUFFER,t),this.current=t,this.dirty=!1}},e}(q),dt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return null},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.bindRenderbuffer(e.RENDERBUFFER,t),this.current=t,this.dirty=!1}},e}(q),vt=function(t){function e(){t.apply(this,arguments)}return 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e(e){t.call(this,e),this.vao=e.extVertexArrayObject}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return null},e.prototype.set=function(t){this.vao&&(t!==this.current||this.dirty)&&(this.vao.bindVertexArrayOES(t),this.current=t,this.dirty=!1)},e}(q),xt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return 4},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.pixelStorei(e.UNPACK_ALIGNMENT,t),this.current=t,this.dirty=!1}},e}(q),bt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return!1},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.pixelStorei(e.UNPACK_PREMULTIPLY_ALPHA_WEBGL,t),this.current=t,this.dirty=!1}},e}(q),_t=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return!1},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.pixelStorei(e.UNPACK_FLIP_Y_WEBGL,t),this.current=t,this.dirty=!1}},e}(q),wt=function(t){function e(e,r){t.call(this,e),this.context=e,this.parent=r}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return null},e}(q),Tt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.setDirty=function(){this.dirty=!0},e.prototype.set=function(t){if(t!==this.current||this.dirty){this.context.bindFramebuffer.set(this.parent);var e=this.gl;e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,t,0),this.current=t,this.dirty=!1}},e}(wt),kt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.set=function(t){if(t!==this.current||this.dirty){this.context.bindFramebuffer.set(this.parent);var e=this.gl;e.framebufferRenderbuffer(e.FRAMEBUFFER,e.DEPTH_ATTACHMENT,e.RENDERBUFFER,t),this.current=t,this.dirty=!1}},e}(wt),At=function(t,e,r,n){this.context=t,this.width=e,this.height=r;var i=t.gl,a=this.framebuffer=i.createFramebuffer();this.colorAttachment=new Tt(t,a),n&&(this.depthAttachment=new kt(t,a))};At.prototype.destroy=function(){var t=this.context.gl,e=this.colorAttachment.get();if(e&&t.deleteTexture(e),this.depthAttachment){var r=this.depthAttachment.get();r&&t.deleteRenderbuffer(r)}t.deleteFramebuffer(this.framebuffer)};var Mt=function(t,e,r){this.func=t,this.mask=e,this.range=r};Mt.ReadOnly=!1,Mt.ReadWrite=!0,Mt.disabled=new Mt(519,Mt.ReadOnly,[0,1]);var St=7680,Et=function(t,e,r,n,i,a){this.test=t,this.ref=e,this.mask=r,this.fail=n,this.depthFail=i,this.pass=a};Et.disabled=new Et({func:519,mask:0},0,0,St,St,St);var Lt=function(t,e,r){this.blendFunction=t,this.blendColor=e,this.mask=r};Lt.Replace=[1,0],Lt.disabled=new Lt(Lt.Replace,t.Color.transparent,[!1,!1,!1,!1]),Lt.unblended=new Lt(Lt.Replace,t.Color.transparent,[!0,!0,!0,!0]),Lt.alphaBlended=new Lt([1,771],t.Color.transparent,[!0,!0,!0,!0]);var Ct=function(t,e,r){this.enable=t,this.mode=e,this.frontFace=r};Ct.disabled=new Ct(!1,1029,2305),Ct.backCCW=new Ct(!0,1029,2305);var Pt=function(t){this.gl=t,this.extVertexArrayObject=this.gl.getExtension(\"OES_vertex_array_object\"),this.clearColor=new G(this),this.clearDepth=new Z(this),this.clearStencil=new Y(this),this.colorMask=new W(this),this.depthMask=new X(this),this.stencilMask=new J(this),this.stencilFunc=new K(this),this.stencilOp=new $(this),this.stencilTest=new Q(this),this.depthRange=new tt(this),this.depthTest=new et(this),this.depthFunc=new rt(this),this.blend=new nt(this),this.blendFunc=new it(this),this.blendColor=new at(this),this.blendEquation=new ot(this),this.cullFace=new st(this),this.cullFaceSide=new lt(this),this.frontFace=new ut(this),this.program=new ct(this),this.activeTexture=new ft(this),this.viewport=new ht(this),this.bindFramebuffer=new pt(this),this.bindRenderbuffer=new dt(this),this.bindTexture=new vt(this),this.bindVertexBuffer=new gt(this),this.bindElementBuffer=new yt(this),this.bindVertexArrayOES=this.extVertexArrayObject&&new mt(this),this.pixelStoreUnpack=new xt(this),this.pixelStoreUnpackPremultiplyAlpha=new bt(this),this.pixelStoreUnpackFlipY=new _t(this),this.extTextureFilterAnisotropic=t.getExtension(\"EXT_texture_filter_anisotropic\")||t.getExtension(\"MOZ_EXT_texture_filter_anisotropic\")||t.getExtension(\"WEBKIT_EXT_texture_filter_anisotropic\"),this.extTextureFilterAnisotropic&&(this.extTextureFilterAnisotropicMax=t.getParameter(this.extTextureFilterAnisotropic.MAX_TEXTURE_MAX_ANISOTROPY_EXT)),this.extTextureHalfFloat=t.getExtension(\"OES_texture_half_float\"),this.extTextureHalfFloat&&(t.getExtension(\"OES_texture_half_float_linear\"),this.extRenderToTextureHalfFloat=t.getExtension(\"EXT_color_buffer_half_float\")),this.extTimerQuery=t.getExtension(\"EXT_disjoint_timer_query\")};Pt.prototype.setDefault=function(){this.unbindVAO(),this.clearColor.setDefault(),this.clearDepth.setDefault(),this.clearStencil.setDefault(),this.colorMask.setDefault(),this.depthMask.setDefault(),this.stencilMask.setDefault(),this.stencilFunc.setDefault(),this.stencilOp.setDefault(),this.stencilTest.setDefault(),this.depthRange.setDefault(),this.depthTest.setDefault(),this.depthFunc.setDefault(),this.blend.setDefault(),this.blendFunc.setDefault(),this.blendColor.setDefault(),this.blendEquation.setDefault(),this.cullFace.setDefault(),this.cullFaceSide.setDefault(),this.frontFace.setDefault(),this.program.setDefault(),this.activeTexture.setDefault(),this.bindFramebuffer.setDefault(),this.pixelStoreUnpack.setDefault(),this.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.pixelStoreUnpackFlipY.setDefault()},Pt.prototype.setDirty=function(){this.clearColor.dirty=!0,this.clearDepth.dirty=!0,this.clearStencil.dirty=!0,this.colorMask.dirty=!0,this.depthMask.dirty=!0,this.stencilMask.dirty=!0,this.stencilFunc.dirty=!0,this.stencilOp.dirty=!0,this.stencilTest.dirty=!0,this.depthRange.dirty=!0,this.depthTest.dirty=!0,this.depthFunc.dirty=!0,this.blend.dirty=!0,this.blendFunc.dirty=!0,this.blendColor.dirty=!0,this.blendEquation.dirty=!0,this.cullFace.dirty=!0,this.cullFaceSide.dirty=!0,this.frontFace.dirty=!0,this.program.dirty=!0,this.activeTexture.dirty=!0,this.viewport.dirty=!0,this.bindFramebuffer.dirty=!0,this.bindRenderbuffer.dirty=!0,this.bindTexture.dirty=!0,this.bindVertexBuffer.dirty=!0,this.bindElementBuffer.dirty=!0,this.extVertexArrayObject&&(this.bindVertexArrayOES.dirty=!0),this.pixelStoreUnpack.dirty=!0,this.pixelStoreUnpackPremultiplyAlpha.dirty=!0,this.pixelStoreUnpackFlipY.dirty=!0},Pt.prototype.createIndexBuffer=function(t,e){return new U(this,t,e)},Pt.prototype.createVertexBuffer=function(t,e,r){return new H(this,t,e,r)},Pt.prototype.createRenderbuffer=function(t,e,r){var n=this.gl,i=n.createRenderbuffer();return this.bindRenderbuffer.set(i),n.renderbufferStorage(n.RENDERBUFFER,t,e,r),this.bindRenderbuffer.set(null),i},Pt.prototype.createFramebuffer=function(t,e,r){return new At(this,t,e,r)},Pt.prototype.clear=function(t){var e=t.color,r=t.depth,n=this.gl,i=0;e&&(i|=n.COLOR_BUFFER_BIT,this.clearColor.set(e),this.colorMask.set([!0,!0,!0,!0])),void 0!==r&&(i|=n.DEPTH_BUFFER_BIT,this.depthRange.set([0,1]),this.clearDepth.set(r),this.depthMask.set(!0)),n.clear(i)},Pt.prototype.setCullFace=function(t){!1===t.enable?this.cullFace.set(!1):(this.cullFace.set(!0),this.cullFaceSide.set(t.mode),this.frontFace.set(t.frontFace))},Pt.prototype.setDepthMode=function(t){t.func!==this.gl.ALWAYS||t.mask?(this.depthTest.set(!0),this.depthFunc.set(t.func),this.depthMask.set(t.mask),this.depthRange.set(t.range)):this.depthTest.set(!1)},Pt.prototype.setStencilMode=function(t){t.test.func!==this.gl.ALWAYS||t.mask?(this.stencilTest.set(!0),this.stencilMask.set(t.mask),this.stencilOp.set([t.fail,t.depthFail,t.pass]),this.stencilFunc.set({func:t.test.func,ref:t.ref,mask:t.test.mask})):this.stencilTest.set(!1)},Pt.prototype.setColorMode=function(e){t.deepEqual(e.blendFunction,Lt.Replace)?this.blend.set(!1):(this.blend.set(!0),this.blendFunc.set(e.blendFunction),this.blendColor.set(e.blendColor)),this.colorMask.set(e.mask)},Pt.prototype.unbindVAO=function(){this.extVertexArrayObject&&this.bindVertexArrayOES.set(null)};var Ot=function(e){function r(r,n,i){var a=this;e.call(this),this.id=r,this.dispatcher=i,this.on(\"data\",(function(t){\"source\"===t.dataType&&\"metadata\"===t.sourceDataType&&(a._sourceLoaded=!0),a._sourceLoaded&&!a._paused&&\"source\"===t.dataType&&\"content\"===t.sourceDataType&&(a.reload(),a.transform&&a.update(a.transform))})),this.on(\"error\",(function(){a._sourceErrored=!0})),this._source=function(e,r,n,i){var a=new R[r.type](e,r,n,i);if(a.id!==e)throw new Error(\"Expected Source id to be \"+e+\" instead of \"+a.id);return t.bindAll([\"load\",\"abort\",\"unload\",\"serialize\",\"prepare\"],a),a}(r,n,i,this),this._tiles={},this._cache=new j(0,this._unloadTile.bind(this)),this._timers={},this._cacheTimers={},this._maxTileCacheSize=null,this._loadedParentTiles={},this._coveredTiles={},this._state=new t.SourceFeatureState}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.onAdd=function(t){this.map=t,this._maxTileCacheSize=t?t._maxTileCacheSize:null,this._source&&this._source.onAdd&&this._source.onAdd(t)},r.prototype.onRemove=function(t){this._source&&this._source.onRemove&&this._source.onRemove(t)},r.prototype.loaded=function(){if(this._sourceErrored)return!0;if(!this._sourceLoaded)return!1;if(!this._source.loaded())return!1;for(var t in this._tiles){var e=this._tiles[t];if(\"loaded\"!==e.state&&\"errored\"!==e.state)return!1}return!0},r.prototype.getSource=function(){return this._source},r.prototype.pause=function(){this._paused=!0},r.prototype.resume=function(){if(this._paused){var t=this._shouldReloadOnResume;this._paused=!1,this._shouldReloadOnResume=!1,t&&this.reload(),this.transform&&this.update(this.transform)}},r.prototype._loadTile=function(t,e){return this._source.loadTile(t,e)},r.prototype._unloadTile=function(t){if(this._source.unloadTile)return this._source.unloadTile(t,(function(){}))},r.prototype._abortTile=function(t){if(this._source.abortTile)return this._source.abortTile(t,(function(){}))},r.prototype.serialize=function(){return this._source.serialize()},r.prototype.prepare=function(t){for(var e in this._source.prepare&&this._source.prepare(),this._state.coalesceChanges(this._tiles,this.map?this.map.painter:null),this._tiles){var r=this._tiles[e];r.upload(t),r.prepare(this.map.style.imageManager)}},r.prototype.getIds=function(){return t.values(this._tiles).map((function(t){return t.tileID})).sort(It).map((function(t){return t.key}))},r.prototype.getRenderableIds=function(e){var r=this,n=[];for(var i in this._tiles)this._isIdRenderable(i,e)&&n.push(this._tiles[i]);return e?n.sort((function(e,n){var i=e.tileID,a=n.tileID,o=new t.Point(i.canonical.x,i.canonical.y)._rotate(r.transform.angle),s=new t.Point(a.canonical.x,a.canonical.y)._rotate(r.transform.angle);return i.overscaledZ-a.overscaledZ||s.y-o.y||s.x-o.x})).map((function(t){return t.tileID.key})):n.map((function(t){return t.tileID})).sort(It).map((function(t){return t.key}))},r.prototype.hasRenderableParent=function(t){var e=this.findLoadedParent(t,0);return!!e&&this._isIdRenderable(e.tileID.key)},r.prototype._isIdRenderable=function(t,e){return this._tiles[t]&&this._tiles[t].hasData()&&!this._coveredTiles[t]&&(e||!this._tiles[t].holdingForFade())},r.prototype.reload=function(){if(this._paused)this._shouldReloadOnResume=!0;else for(var t in this._cache.reset(),this._tiles)\"errored\"!==this._tiles[t].state&&this._reloadTile(t,\"reloading\")},r.prototype._reloadTile=function(t,e){var r=this._tiles[t];r&&(\"loading\"!==r.state&&(r.state=e),this._loadTile(r,this._tileLoaded.bind(this,r,t,e)))},r.prototype._tileLoaded=function(e,r,n,i){if(i)return e.state=\"errored\",void(404!==i.status?this._source.fire(new t.ErrorEvent(i,{tile:e})):this.update(this.transform));e.timeAdded=t.browser.now(),\"expired\"===n&&(e.refreshedUponExpiration=!0),this._setTileReloadTimer(r,e),\"raster-dem\"===this.getSource().type&&e.dem&&this._backfillDEM(e),this._state.initializeTileState(e,this.map?this.map.painter:null),this._source.fire(new t.Event(\"data\",{dataType:\"source\",tile:e,coord:e.tileID}))},r.prototype._backfillDEM=function(t){for(var e=this.getRenderableIds(),r=0;r<e.length;r++){var n=e[r];if(t.neighboringTiles&&t.neighboringTiles[n]){var i=this.getTileByID(n);a(t,i),a(i,t)}}function a(t,e){t.needsHillshadePrepare=!0;var r=e.tileID.canonical.x-t.tileID.canonical.x,n=e.tileID.canonical.y-t.tileID.canonical.y,i=Math.pow(2,t.tileID.canonical.z),a=e.tileID.key;0===r&&0===n||Math.abs(n)>1||(Math.abs(r)>1&&(1===Math.abs(r+i)?r+=i:1===Math.abs(r-i)&&(r-=i)),e.dem&&t.dem&&(t.dem.backfillBorder(e.dem,r,n),t.neighboringTiles&&t.neighboringTiles[a]&&(t.neighboringTiles[a].backfilled=!0)))}},r.prototype.getTile=function(t){return this.getTileByID(t.key)},r.prototype.getTileByID=function(t){return this._tiles[t]},r.prototype._retainLoadedChildren=function(t,e,r,n){for(var i in this._tiles){var a=this._tiles[i];if(!(n[i]||!a.hasData()||a.tileID.overscaledZ<=e||a.tileID.overscaledZ>r)){for(var o=a.tileID;a&&a.tileID.overscaledZ>e+1;){var s=a.tileID.scaledTo(a.tileID.overscaledZ-1);(a=this._tiles[s.key])&&a.hasData()&&(o=s)}for(var l=o;l.overscaledZ>e;)if(t[(l=l.scaledTo(l.overscaledZ-1)).key]){n[o.key]=o;break}}}},r.prototype.findLoadedParent=function(t,e){if(t.key in this._loadedParentTiles){var r=this._loadedParentTiles[t.key];return r&&r.tileID.overscaledZ>=e?r:null}for(var n=t.overscaledZ-1;n>=e;n--){var i=t.scaledTo(n),a=this._getLoadedTile(i);if(a)return a}},r.prototype._getLoadedTile=function(t){var e=this._tiles[t.key];return e&&e.hasData()?e:this._cache.getByKey(t.wrapped().key)},r.prototype.updateCacheSize=function(t){var e=(Math.ceil(t.width/this._source.tileSize)+1)*(Math.ceil(t.height/this._source.tileSize)+1),r=Math.floor(5*e),n=\"number\"==typeof this._maxTileCacheSize?Math.min(this._maxTileCacheSize,r):r;this._cache.setMaxSize(n)},r.prototype.handleWrapJump=function(t){var e=(t-(void 0===this._prevLng?t:this._prevLng))/360,r=Math.round(e);if(this._prevLng=t,r){var n={};for(var i in this._tiles){var a=this._tiles[i];a.tileID=a.tileID.unwrapTo(a.tileID.wrap+r),n[a.tileID.key]=a}for(var o in this._tiles=n,this._timers)clearTimeout(this._timers[o]),delete this._timers[o];for(var s in this._tiles){var l=this._tiles[s];this._setTileReloadTimer(s,l)}}},r.prototype.update=function(e){var n=this;if(this.transform=e,this._sourceLoaded&&!this._paused){var i;this.updateCacheSize(e),this.handleWrapJump(this.transform.center.lng),this._coveredTiles={},this.used?this._source.tileID?i=e.getVisibleUnwrappedCoordinates(this._source.tileID).map((function(e){return new t.OverscaledTileID(e.canonical.z,e.wrap,e.canonical.z,e.canonical.x,e.canonical.y)})):(i=e.coveringTiles({tileSize:this._source.tileSize,minzoom:this._source.minzoom,maxzoom:this._source.maxzoom,roundZoom:this._source.roundZoom,reparseOverscaled:this._source.reparseOverscaled}),this._source.hasTile&&(i=i.filter((function(t){return n._source.hasTile(t)})))):i=[];var a=e.coveringZoomLevel(this._source),o=Math.max(a-r.maxOverzooming,this._source.minzoom),s=Math.max(a+r.maxUnderzooming,this._source.minzoom),l=this._updateRetainedTiles(i,a);if(Dt(this._source.type)){for(var u={},c={},f=0,h=Object.keys(l);f<h.length;f+=1){var p=h[f],d=l[p],v=this._tiles[p];if(v&&!(v.fadeEndTime&&v.fadeEndTime<=t.browser.now())){var g=this.findLoadedParent(d,o);g&&(this._addTile(g.tileID),u[g.tileID.key]=g.tileID),c[p]=d}}for(var y in this._retainLoadedChildren(c,a,s,l),u)l[y]||(this._coveredTiles[y]=!0,l[y]=u[y])}for(var m in l)this._tiles[m].clearFadeHold();for(var x=0,b=t.keysDifference(this._tiles,l);x<b.length;x+=1){var _=b[x],w=this._tiles[_];w.hasSymbolBuckets&&!w.holdingForFade()?w.setHoldDuration(this.map._fadeDuration):w.hasSymbolBuckets&&!w.symbolFadeFinished()||this._removeTile(_)}this._updateLoadedParentTileCache()}},r.prototype.releaseSymbolFadeTiles=function(){for(var t in this._tiles)this._tiles[t].holdingForFade()&&this._removeTile(t)},r.prototype._updateRetainedTiles=function(t,e){for(var n={},i={},a=Math.max(e-r.maxOverzooming,this._source.minzoom),o=Math.max(e+r.maxUnderzooming,this._source.minzoom),s={},l=0,u=t;l<u.length;l+=1){var c=u[l],f=this._addTile(c);n[c.key]=c,f.hasData()||e<this._source.maxzoom&&(s[c.key]=c)}this._retainLoadedChildren(s,e,o,n);for(var h=0,p=t;h<p.length;h+=1){var d=p[h],v=this._tiles[d.key];if(!v.hasData()){if(e+1>this._source.maxzoom){var g=d.children(this._source.maxzoom)[0],y=this.getTile(g);if(y&&y.hasData()){n[g.key]=g;continue}}else{var m=d.children(this._source.maxzoom);if(n[m[0].key]&&n[m[1].key]&&n[m[2].key]&&n[m[3].key])continue}for(var x=v.wasRequested(),b=d.overscaledZ-1;b>=a;--b){var _=d.scaledTo(b);if(i[_.key])break;if(i[_.key]=!0,!(v=this.getTile(_))&&x&&(v=this._addTile(_)),v&&(n[_.key]=_,x=v.wasRequested(),v.hasData()))break}}}return n},r.prototype._updateLoadedParentTileCache=function(){for(var t in this._loadedParentTiles={},this._tiles){for(var e=[],r=void 0,n=this._tiles[t].tileID;n.overscaledZ>0;){if(n.key in this._loadedParentTiles){r=this._loadedParentTiles[n.key];break}e.push(n.key);var i=n.scaledTo(n.overscaledZ-1);if(r=this._getLoadedTile(i))break;n=i}for(var a=0,o=e;a<o.length;a+=1){var s=o[a];this._loadedParentTiles[s]=r}}},r.prototype._addTile=function(e){var r=this._tiles[e.key];if(r)return r;(r=this._cache.getAndRemove(e))&&(this._setTileReloadTimer(e.key,r),r.tileID=e,this._state.initializeTileState(r,this.map?this.map.painter:null),this._cacheTimers[e.key]&&(clearTimeout(this._cacheTimers[e.key]),delete this._cacheTimers[e.key],this._setTileReloadTimer(e.key,r)));var n=Boolean(r);return n||(r=new t.Tile(e,this._source.tileSize*e.overscaleFactor()),this._loadTile(r,this._tileLoaded.bind(this,r,e.key,r.state))),r?(r.uses++,this._tiles[e.key]=r,n||this._source.fire(new t.Event(\"dataloading\",{tile:r,coord:r.tileID,dataType:\"source\"})),r):null},r.prototype._setTileReloadTimer=function(t,e){var r=this;t in this._timers&&(clearTimeout(this._timers[t]),delete this._timers[t]);var n=e.getExpiryTimeout();n&&(this._timers[t]=setTimeout((function(){r._reloadTile(t,\"expired\"),delete r._timers[t]}),n))},r.prototype._removeTile=function(t){var e=this._tiles[t];e&&(e.uses--,delete this._tiles[t],this._timers[t]&&(clearTimeout(this._timers[t]),delete this._timers[t]),e.uses>0||(e.hasData()&&\"reloading\"!==e.state?this._cache.add(e.tileID,e,e.getExpiryTimeout()):(e.aborted=!0,this._abortTile(e),this._unloadTile(e))))},r.prototype.clearTiles=function(){for(var t in this._shouldReloadOnResume=!1,this._paused=!1,this._tiles)this._removeTile(t);this._cache.reset()},r.prototype.tilesIn=function(e,r,n){var i=this,a=[],o=this.transform;if(!o)return a;for(var s=n?o.getCameraQueryGeometry(e):e,l=e.map((function(t){return o.pointCoordinate(t)})),u=s.map((function(t){return o.pointCoordinate(t)})),c=this.getIds(),f=1/0,h=1/0,p=-1/0,d=-1/0,v=0,g=u;v<g.length;v+=1){var y=g[v];f=Math.min(f,y.x),h=Math.min(h,y.y),p=Math.max(p,y.x),d=Math.max(d,y.y)}for(var m=function(e){var n=i._tiles[c[e]];if(!n.holdingForFade()){var s=n.tileID,v=Math.pow(2,o.zoom-n.tileID.overscaledZ),g=r*n.queryPadding*t.EXTENT/n.tileSize/v,y=[s.getTilePoint(new t.MercatorCoordinate(f,h)),s.getTilePoint(new t.MercatorCoordinate(p,d))];if(y[0].x-g<t.EXTENT&&y[0].y-g<t.EXTENT&&y[1].x+g>=0&&y[1].y+g>=0){var m=l.map((function(t){return s.getTilePoint(t)})),x=u.map((function(t){return s.getTilePoint(t)}));a.push({tile:n,tileID:s,queryGeometry:m,cameraQueryGeometry:x,scale:v})}}},x=0;x<c.length;x++)m(x);return a},r.prototype.getVisibleCoordinates=function(t){for(var e=this,r=this.getRenderableIds(t).map((function(t){return e._tiles[t].tileID})),n=0,i=r;n<i.length;n+=1){var a=i[n];a.posMatrix=this.transform.calculatePosMatrix(a.toUnwrapped())}return r},r.prototype.hasTransition=function(){if(this._source.hasTransition())return!0;if(Dt(this._source.type))for(var e in this._tiles){var r=this._tiles[e];if(void 0!==r.fadeEndTime&&r.fadeEndTime>=t.browser.now())return!0}return!1},r.prototype.setFeatureState=function(t,e,r){t=t||\"_geojsonTileLayer\",this._state.updateState(t,e,r)},r.prototype.removeFeatureState=function(t,e,r){t=t||\"_geojsonTileLayer\",this._state.removeFeatureState(t,e,r)},r.prototype.getFeatureState=function(t,e){return t=t||\"_geojsonTileLayer\",this._state.getState(t,e)},r.prototype.setDependencies=function(t,e,r){var n=this._tiles[t];n&&n.setDependencies(e,r)},r.prototype.reloadTilesForDependencies=function(t,e){for(var r in this._tiles)this._tiles[r].hasDependency(t,e)&&this._reloadTile(r,\"reloading\");this._cache.filter((function(r){return!r.hasDependency(t,e)}))},r}(t.Evented);function It(t,e){var r=Math.abs(2*t.wrap)-+(t.wrap<0),n=Math.abs(2*e.wrap)-+(e.wrap<0);return t.overscaledZ-e.overscaledZ||n-r||e.canonical.y-t.canonical.y||e.canonical.x-t.canonical.x}function Dt(t){return\"raster\"===t||\"image\"===t||\"video\"===t}function zt(){return new t.window.Worker(na.workerUrl)}Ot.maxOverzooming=10,Ot.maxUnderzooming=3;var Rt=\"mapboxgl_preloaded_worker_pool\",Ft=function(){this.active={}};Ft.prototype.acquire=function(t){if(!this.workers)for(this.workers=[];this.workers.length<Ft.workerCount;)this.workers.push(new zt);return this.active[t]=!0,this.workers.slice()},Ft.prototype.release=function(t){delete this.active[t],0===this.numActive()&&(this.workers.forEach((function(t){t.terminate()})),this.workers=null)},Ft.prototype.isPreloaded=function(){return!!this.active[Rt]},Ft.prototype.numActive=function(){return Object.keys(this.active).length};var Bt,Nt=Math.floor(t.browser.hardwareConcurrency/2);function jt(){return Bt||(Bt=new Ft),Bt}function Ut(e,r){var n={};for(var i in e)\"ref\"!==i&&(n[i]=e[i]);return t.refProperties.forEach((function(t){t in r&&(n[t]=r[t])})),n}function Vt(t){t=t.slice();for(var e=Object.create(null),r=0;r<t.length;r++)e[t[r].id]=t[r];for(var n=0;n<t.length;n++)\"ref\"in t[n]&&(t[n]=Ut(t[n],e[t[n].ref]));return t}Ft.workerCount=Math.max(Math.min(Nt,6),1);var Ht={setStyle:\"setStyle\",addLayer:\"addLayer\",removeLayer:\"removeLayer\",setPaintProperty:\"setPaintProperty\",setLayoutProperty:\"setLayoutProperty\",setFilter:\"setFilter\",addSource:\"addSource\",removeSource:\"removeSource\",setGeoJSONSourceData:\"setGeoJSONSourceData\",setLayerZoomRange:\"setLayerZoomRange\",setLayerProperty:\"setLayerProperty\",setCenter:\"setCenter\",setZoom:\"setZoom\",setBearing:\"setBearing\",setPitch:\"setPitch\",setSprite:\"setSprite\",setGlyphs:\"setGlyphs\",setTransition:\"setTransition\",setLight:\"setLight\"};function qt(t,e,r){r.push({command:Ht.addSource,args:[t,e[t]]})}function Gt(t,e,r){e.push({command:Ht.removeSource,args:[t]}),r[t]=!0}function Zt(t,e,r,n){Gt(t,r,n),qt(t,e,r)}function Yt(e,r,n){var i;for(i in e[n])if(e[n].hasOwnProperty(i)&&\"data\"!==i&&!t.deepEqual(e[n][i],r[n][i]))return!1;for(i in r[n])if(r[n].hasOwnProperty(i)&&\"data\"!==i&&!t.deepEqual(e[n][i],r[n][i]))return!1;return!0}function Wt(e,r,n,i,a,o){var s;for(s in r=r||{},e=e||{})e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}));for(s in r)r.hasOwnProperty(s)&&!e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}))}function Xt(t){return t.id}function Jt(t,e){return t[e.id]=e,t}function Kt(e,r){if(!e)return[{command:Ht.setStyle,args:[r]}];var n=[];try{if(!t.deepEqual(e.version,r.version))return[{command:Ht.setStyle,args:[r]}];t.deepEqual(e.center,r.center)||n.push({command:Ht.setCenter,args:[r.center]}),t.deepEqual(e.zoom,r.zoom)||n.push({command:Ht.setZoom,args:[r.zoom]}),t.deepEqual(e.bearing,r.bearing)||n.push({command:Ht.setBearing,args:[r.bearing]}),t.deepEqual(e.pitch,r.pitch)||n.push({command:Ht.setPitch,args:[r.pitch]}),t.deepEqual(e.sprite,r.sprite)||n.push({command:Ht.setSprite,args:[r.sprite]}),t.deepEqual(e.glyphs,r.glyphs)||n.push({command:Ht.setGlyphs,args:[r.glyphs]}),t.deepEqual(e.transition,r.transition)||n.push({command:Ht.setTransition,args:[r.transition]}),t.deepEqual(e.light,r.light)||n.push({command:Ht.setLight,args:[r.light]});var i={},a=[];!function(e,r,n,i){var a;for(a in r=r||{},e=e||{})e.hasOwnProperty(a)&&(r.hasOwnProperty(a)||Gt(a,n,i));for(a in r)r.hasOwnProperty(a)&&(e.hasOwnProperty(a)?t.deepEqual(e[a],r[a])||(\"geojson\"===e[a].type&&\"geojson\"===r[a].type&&Yt(e,r,a)?n.push({command:Ht.setGeoJSONSourceData,args:[a,r[a].data]}):Zt(a,r,n,i)):qt(a,r,n))}(e.sources,r.sources,a,i);var o=[];e.layers&&e.layers.forEach((function(t){i[t.source]?n.push({command:Ht.removeLayer,args:[t.id]}):o.push(t)})),n=n.concat(a),function(e,r,n){r=r||[];var i,a,o,s,l,u,c,f=(e=e||[]).map(Xt),h=r.map(Xt),p=e.reduce(Jt,{}),d=r.reduce(Jt,{}),v=f.slice(),g=Object.create(null);for(i=0,a=0;i<f.length;i++)o=f[i],d.hasOwnProperty(o)?a++:(n.push({command:Ht.removeLayer,args:[o]}),v.splice(v.indexOf(o,a),1));for(i=0,a=0;i<h.length;i++)o=h[h.length-1-i],v[v.length-1-i]!==o&&(p.hasOwnProperty(o)?(n.push({command:Ht.removeLayer,args:[o]}),v.splice(v.lastIndexOf(o,v.length-a),1)):a++,u=v[v.length-i],n.push({command:Ht.addLayer,args:[d[o],u]}),v.splice(v.length-i,0,o),g[o]=!0);for(i=0;i<h.length;i++)if(s=p[o=h[i]],l=d[o],!g[o]&&!t.deepEqual(s,l))if(t.deepEqual(s.source,l.source)&&t.deepEqual(s[\"source-layer\"],l[\"source-layer\"])&&t.deepEqual(s.type,l.type)){for(c in Wt(s.layout,l.layout,n,o,null,Ht.setLayoutProperty),Wt(s.paint,l.paint,n,o,null,Ht.setPaintProperty),t.deepEqual(s.filter,l.filter)||n.push({command:Ht.setFilter,args:[o,l.filter]}),t.deepEqual(s.minzoom,l.minzoom)&&t.deepEqual(s.maxzoom,l.maxzoom)||n.push({command:Ht.setLayerZoomRange,args:[o,l.minzoom,l.maxzoom]}),s)s.hasOwnProperty(c)&&\"layout\"!==c&&\"paint\"!==c&&\"filter\"!==c&&\"metadata\"!==c&&\"minzoom\"!==c&&\"maxzoom\"!==c&&(0===c.indexOf(\"paint.\")?Wt(s[c],l[c],n,o,c.slice(6),Ht.setPaintProperty):t.deepEqual(s[c],l[c])||n.push({command:Ht.setLayerProperty,args:[o,c,l[c]]}));for(c in l)l.hasOwnProperty(c)&&!s.hasOwnProperty(c)&&\"layout\"!==c&&\"paint\"!==c&&\"filter\"!==c&&\"metadata\"!==c&&\"minzoom\"!==c&&\"maxzoom\"!==c&&(0===c.indexOf(\"paint.\")?Wt(s[c],l[c],n,o,c.slice(6),Ht.setPaintProperty):t.deepEqual(s[c],l[c])||n.push({command:Ht.setLayerProperty,args:[o,c,l[c]]}))}else n.push({command:Ht.removeLayer,args:[o]}),u=v[v.lastIndexOf(o)+1],n.push({command:Ht.addLayer,args:[l,u]})}(o,r.layers,n)}catch(t){console.warn(\"Unable to compute style diff:\",t),n=[{command:Ht.setStyle,args:[r]}]}return n}var $t=function(t,e){this.reset(t,e)};$t.prototype.reset=function(t,e){this.points=t||[],this._distances=[0];for(var r=1;r<this.points.length;r++)this._distances[r]=this._distances[r-1]+this.points[r].dist(this.points[r-1]);this.length=this._distances[this._distances.length-1],this.padding=Math.min(e||0,.5*this.length),this.paddedLength=this.length-2*this.padding},$t.prototype.lerp=function(e){if(1===this.points.length)return this.points[0];e=t.clamp(e,0,1);for(var r=1,n=this._distances[r],i=e*this.paddedLength+this.padding;n<i&&r<this._distances.length;)n=this._distances[++r];var a=r-1,o=this._distances[a],s=n-o,l=s>0?(i-o)/s:0;return this.points[a].mult(1-l).add(this.points[r].mult(l))};var Qt=function(t,e,r){var n=this.boxCells=[],i=this.circleCells=[];this.xCellCount=Math.ceil(t/r),this.yCellCount=Math.ceil(e/r);for(var a=0;a<this.xCellCount*this.yCellCount;a++)n.push([]),i.push([]);this.circleKeys=[],this.boxKeys=[],this.bboxes=[],this.circles=[],this.width=t,this.height=e,this.xScale=this.xCellCount/t,this.yScale=this.yCellCount/e,this.boxUid=0,this.circleUid=0};function te(e,r,n,i,a){var o=t.create();return r?(t.scale(o,o,[1/a,1/a,1]),n||t.rotateZ(o,o,i.angle)):t.multiply(o,i.labelPlaneMatrix,e),o}function ee(e,r,n,i,a){if(r){var o=t.clone(e);return t.scale(o,o,[a,a,1]),n||t.rotateZ(o,o,-i.angle),o}return i.glCoordMatrix}function re(e,r){var n=[e.x,e.y,0,1];pe(n,n,r);var i=n[3];return{point:new t.Point(n[0]/i,n[1]/i),signedDistanceFromCamera:i}}function ne(t,e){return.5+t/e*.5}function ie(t,e){var r=t[0]/t[3],n=t[1]/t[3];return r>=-e[0]&&r<=e[0]&&n>=-e[1]&&n<=e[1]}function ae(e,r,n,i,a,o,s,l){var u=i?e.textSizeData:e.iconSizeData,c=t.evaluateSizeForZoom(u,n.transform.zoom),f=[256/n.width*2+1,256/n.height*2+1],h=i?e.text.dynamicLayoutVertexArray:e.icon.dynamicLayoutVertexArray;h.clear();for(var p=e.lineVertexArray,d=i?e.text.placedSymbolArray:e.icon.placedSymbolArray,v=n.transform.width/n.transform.height,g=!1,y=0;y<d.length;y++){var m=d.get(y);if(m.hidden||m.writingMode===t.WritingMode.vertical&&!g)he(m.numGlyphs,h);else{g=!1;var x=[m.anchorX,m.anchorY,0,1];if(t.transformMat4(x,x,r),ie(x,f)){var b=x[3],_=ne(n.transform.cameraToCenterDistance,b),w=t.evaluateSizeForFeature(u,c,m),T=s?w/_:w*_,k=new t.Point(m.anchorX,m.anchorY),A=re(k,a).point,M={},S=le(m,T,!1,l,r,a,o,e.glyphOffsetArray,p,h,A,k,M,v);g=S.useVertical,(S.notEnoughRoom||g||S.needsFlipping&&le(m,T,!0,l,r,a,o,e.glyphOffsetArray,p,h,A,k,M,v).notEnoughRoom)&&he(m.numGlyphs,h)}else he(m.numGlyphs,h)}}i?e.text.dynamicLayoutVertexBuffer.updateData(h):e.icon.dynamicLayoutVertexBuffer.updateData(h)}function oe(t,e,r,n,i,a,o,s,l,u,c){var f=s.glyphStartIndex+s.numGlyphs,h=s.lineStartIndex,p=s.lineStartIndex+s.lineLength,d=e.getoffsetX(s.glyphStartIndex),v=e.getoffsetX(f-1),g=ce(t*d,r,n,i,a,o,s.segment,h,p,l,u,c);if(!g)return null;var y=ce(t*v,r,n,i,a,o,s.segment,h,p,l,u,c);return y?{first:g,last:y}:null}function se(e,r,n,i){return e===t.WritingMode.horizontal&&Math.abs(n.y-r.y)>Math.abs(n.x-r.x)*i?{useVertical:!0}:(e===t.WritingMode.vertical?r.y<n.y:r.x>n.x)?{needsFlipping:!0}:null}function le(e,r,n,i,a,o,s,l,u,c,f,h,p,d){var v,g=r/24,y=e.lineOffsetX*g,m=e.lineOffsetY*g;if(e.numGlyphs>1){var x=e.glyphStartIndex+e.numGlyphs,b=e.lineStartIndex,_=e.lineStartIndex+e.lineLength,w=oe(g,l,y,m,n,f,h,e,u,o,p);if(!w)return{notEnoughRoom:!0};var T=re(w.first.point,s).point,k=re(w.last.point,s).point;if(i&&!n){var A=se(e.writingMode,T,k,d);if(A)return A}v=[w.first];for(var M=e.glyphStartIndex+1;M<x-1;M++)v.push(ce(g*l.getoffsetX(M),y,m,n,f,h,e.segment,b,_,u,o,p));v.push(w.last)}else{if(i&&!n){var S=re(h,a).point,E=e.lineStartIndex+e.segment+1,L=new t.Point(u.getx(E),u.gety(E)),C=re(L,a),P=C.signedDistanceFromCamera>0?C.point:ue(h,L,S,1,a),O=se(e.writingMode,S,P,d);if(O)return O}var I=ce(g*l.getoffsetX(e.glyphStartIndex),y,m,n,f,h,e.segment,e.lineStartIndex,e.lineStartIndex+e.lineLength,u,o,p);if(!I)return{notEnoughRoom:!0};v=[I]}for(var D=0,z=v;D<z.length;D+=1){var R=z[D];t.addDynamicAttributes(c,R.point,R.angle)}return{}}function ue(t,e,r,n,i){var a=re(t.add(t.sub(e)._unit()),i).point,o=r.sub(a);return r.add(o._mult(n/o.mag()))}function ce(e,r,n,i,a,o,s,l,u,c,f,h){var p=i?e-r:e+r,d=p>0?1:-1,v=0;i&&(d*=-1,v=Math.PI),d<0&&(v+=Math.PI);for(var g=d>0?l+s:l+s+1,y=a,m=a,x=0,b=0,_=Math.abs(p),w=[];x+b<=_;){if((g+=d)<l||g>=u)return null;if(m=y,w.push(y),void 0===(y=h[g])){var T=new t.Point(c.getx(g),c.gety(g)),k=re(T,f);if(k.signedDistanceFromCamera>0)y=h[g]=k.point;else{var A=g-d;y=ue(0===x?o:new t.Point(c.getx(A),c.gety(A)),T,m,_-x+1,f)}}x+=b,b=m.dist(y)}var M=(_-x)/b,S=y.sub(m),E=S.mult(M)._add(m);E._add(S._unit()._perp()._mult(n*d));var L=v+Math.atan2(y.y-m.y,y.x-m.x);return w.push(E),{point:E,angle:L,path:w}}Qt.prototype.keysLength=function(){return this.boxKeys.length+this.circleKeys.length},Qt.prototype.insert=function(t,e,r,n,i){this._forEachCell(e,r,n,i,this._insertBoxCell,this.boxUid++),this.boxKeys.push(t),this.bboxes.push(e),this.bboxes.push(r),this.bboxes.push(n),this.bboxes.push(i)},Qt.prototype.insertCircle=function(t,e,r,n){this._forEachCell(e-n,r-n,e+n,r+n,this._insertCircleCell,this.circleUid++),this.circleKeys.push(t),this.circles.push(e),this.circles.push(r),this.circles.push(n)},Qt.prototype._insertBoxCell=function(t,e,r,n,i,a){this.boxCells[i].push(a)},Qt.prototype._insertCircleCell=function(t,e,r,n,i,a){this.circleCells[i].push(a)},Qt.prototype._query=function(t,e,r,n,i,a){if(r<0||t>this.width||n<0||e>this.height)return!i&&[];var o=[];if(t<=0&&e<=0&&this.width<=r&&this.height<=n){if(i)return!0;for(var s=0;s<this.boxKeys.length;s++)o.push({key:this.boxKeys[s],x1:this.bboxes[4*s],y1:this.bboxes[4*s+1],x2:this.bboxes[4*s+2],y2:this.bboxes[4*s+3]});for(var l=0;l<this.circleKeys.length;l++){var u=this.circles[3*l],c=this.circles[3*l+1],f=this.circles[3*l+2];o.push({key:this.circleKeys[l],x1:u-f,y1:c-f,x2:u+f,y2:c+f})}return a?o.filter(a):o}var h={hitTest:i,seenUids:{box:{},circle:{}}};return this._forEachCell(t,e,r,n,this._queryCell,o,h,a),i?o.length>0:o},Qt.prototype._queryCircle=function(t,e,r,n,i){var a=t-r,o=t+r,s=e-r,l=e+r;if(o<0||a>this.width||l<0||s>this.height)return!n&&[];var u=[],c={hitTest:n,circle:{x:t,y:e,radius:r},seenUids:{box:{},circle:{}}};return this._forEachCell(a,s,o,l,this._queryCellCircle,u,c,i),n?u.length>0:u},Qt.prototype.query=function(t,e,r,n,i){return this._query(t,e,r,n,!1,i)},Qt.prototype.hitTest=function(t,e,r,n,i){return this._query(t,e,r,n,!0,i)},Qt.prototype.hitTestCircle=function(t,e,r,n){return this._queryCircle(t,e,r,!0,n)},Qt.prototype._queryCell=function(t,e,r,n,i,a,o,s){var l=o.seenUids,u=this.boxCells[i];if(null!==u)for(var c=this.bboxes,f=0,h=u;f<h.length;f+=1){var p=h[f];if(!l.box[p]){l.box[p]=!0;var d=4*p;if(t<=c[d+2]&&e<=c[d+3]&&r>=c[d+0]&&n>=c[d+1]&&(!s||s(this.boxKeys[p]))){if(o.hitTest)return a.push(!0),!0;a.push({key:this.boxKeys[p],x1:c[d],y1:c[d+1],x2:c[d+2],y2:c[d+3]})}}}var v=this.circleCells[i];if(null!==v)for(var g=this.circles,y=0,m=v;y<m.length;y+=1){var x=m[y];if(!l.circle[x]){l.circle[x]=!0;var b=3*x;if(this._circleAndRectCollide(g[b],g[b+1],g[b+2],t,e,r,n)&&(!s||s(this.circleKeys[x]))){if(o.hitTest)return a.push(!0),!0;var _=g[b],w=g[b+1],T=g[b+2];a.push({key:this.circleKeys[x],x1:_-T,y1:w-T,x2:_+T,y2:w+T})}}}},Qt.prototype._queryCellCircle=function(t,e,r,n,i,a,o,s){var l=o.circle,u=o.seenUids,c=this.boxCells[i];if(null!==c)for(var f=this.bboxes,h=0,p=c;h<p.length;h+=1){var d=p[h];if(!u.box[d]){u.box[d]=!0;var v=4*d;if(this._circleAndRectCollide(l.x,l.y,l.radius,f[v+0],f[v+1],f[v+2],f[v+3])&&(!s||s(this.boxKeys[d])))return a.push(!0),!0}}var g=this.circleCells[i];if(null!==g)for(var y=this.circles,m=0,x=g;m<x.length;m+=1){var b=x[m];if(!u.circle[b]){u.circle[b]=!0;var _=3*b;if(this._circlesCollide(y[_],y[_+1],y[_+2],l.x,l.y,l.radius)&&(!s||s(this.circleKeys[b])))return a.push(!0),!0}}},Qt.prototype._forEachCell=function(t,e,r,n,i,a,o,s){for(var l=this._convertToXCellCoord(t),u=this._convertToYCellCoord(e),c=this._convertToXCellCoord(r),f=this._convertToYCellCoord(n),h=l;h<=c;h++)for(var p=u;p<=f;p++){var d=this.xCellCount*p+h;if(i.call(this,t,e,r,n,d,a,o,s))return}},Qt.prototype._convertToXCellCoord=function(t){return Math.max(0,Math.min(this.xCellCount-1,Math.floor(t*this.xScale)))},Qt.prototype._convertToYCellCoord=function(t){return Math.max(0,Math.min(this.yCellCount-1,Math.floor(t*this.yScale)))},Qt.prototype._circlesCollide=function(t,e,r,n,i,a){var o=n-t,s=i-e,l=r+a;return l*l>o*o+s*s},Qt.prototype._circleAndRectCollide=function(t,e,r,n,i,a,o){var s=(a-n)/2,l=Math.abs(t-(n+s));if(l>s+r)return!1;var u=(o-i)/2,c=Math.abs(e-(i+u));if(c>u+r)return!1;if(l<=s||c<=u)return!0;var f=l-s,h=c-u;return f*f+h*h<=r*r};var fe=new Float32Array([-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0]);function he(t,e){for(var r=0;r<t;r++){var n=e.length;e.resize(n+4),e.float32.set(fe,3*n)}}function pe(t,e,r){var n=e[0],i=e[1];return t[0]=r[0]*n+r[4]*i+r[12],t[1]=r[1]*n+r[5]*i+r[13],t[3]=r[3]*n+r[7]*i+r[15],t}var de=100,ve=function(t,e,r){void 0===e&&(e=new Qt(t.width+200,t.height+200,25)),void 0===r&&(r=new Qt(t.width+200,t.height+200,25)),this.transform=t,this.grid=e,this.ignoredGrid=r,this.pitchfactor=Math.cos(t._pitch)*t.cameraToCenterDistance,this.screenRightBoundary=t.width+de,this.screenBottomBoundary=t.height+de,this.gridRightBoundary=t.width+200,this.gridBottomBoundary=t.height+200};function ge(e,r,n){return r*(t.EXTENT/(e.tileSize*Math.pow(2,n-e.tileID.overscaledZ)))}ve.prototype.placeCollisionBox=function(t,e,r,n,i){var a=this.projectAndGetPerspectiveRatio(n,t.anchorPointX,t.anchorPointY),o=r*a.perspectiveRatio,s=t.x1*o+a.point.x,l=t.y1*o+a.point.y,u=t.x2*o+a.point.x,c=t.y2*o+a.point.y;return!this.isInsideGrid(s,l,u,c)||!e&&this.grid.hitTest(s,l,u,c,i)?{box:[],offscreen:!1}:{box:[s,l,u,c],offscreen:this.isOffscreen(s,l,u,c)}},ve.prototype.placeCollisionCircles=function(e,r,n,i,a,o,s,l,u,c,f,h,p){var d=[],v=new t.Point(r.anchorX,r.anchorY),g=re(v,o),y=ne(this.transform.cameraToCenterDistance,g.signedDistanceFromCamera),m=(c?a/y:a*y)/t.ONE_EM,x=re(v,s).point,b=oe(m,i,r.lineOffsetX*m,r.lineOffsetY*m,!1,x,v,r,n,s,{}),_=!1,w=!1,T=!0;if(b){for(var k=.5*h*y+p,A=new t.Point(-100,-100),M=new t.Point(this.screenRightBoundary,this.screenBottomBoundary),S=new $t,E=b.first,L=b.last,C=[],P=E.path.length-1;P>=1;P--)C.push(E.path[P]);for(var O=1;O<L.path.length;O++)C.push(L.path[O]);var I=2.5*k;if(l){var D=C.map((function(t){return re(t,l)}));C=D.some((function(t){return t.signedDistanceFromCamera<=0}))?[]:D.map((function(t){return t.point}))}var z=[];if(C.length>0){for(var R=C[0].clone(),F=C[0].clone(),B=1;B<C.length;B++)R.x=Math.min(R.x,C[B].x),R.y=Math.min(R.y,C[B].y),F.x=Math.max(F.x,C[B].x),F.y=Math.max(F.y,C[B].y);z=R.x>=A.x&&F.x<=M.x&&R.y>=A.y&&F.y<=M.y?[C]:F.x<A.x||R.x>M.x||F.y<A.y||R.y>M.y?[]:t.clipLine([C],A.x,A.y,M.x,M.y)}for(var N=0,j=z;N<j.length;N+=1){var U=j[N];S.reset(U,.25*k);var V;V=S.length<=.5*k?1:Math.ceil(S.paddedLength/I)+1;for(var H=0;H<V;H++){var q=H/Math.max(V-1,1),G=S.lerp(q),Z=G.x+de,Y=G.y+de;d.push(Z,Y,k,0);var W=Z-k,X=Y-k,J=Z+k,K=Y+k;if(T=T&&this.isOffscreen(W,X,J,K),w=w||this.isInsideGrid(W,X,J,K),!e&&this.grid.hitTestCircle(Z,Y,k,f)&&(_=!0,!u))return{circles:[],offscreen:!1,collisionDetected:_}}}}return{circles:!u&&_||!w?[]:d,offscreen:T,collisionDetected:_}},ve.prototype.queryRenderedSymbols=function(e){if(0===e.length||0===this.grid.keysLength()&&0===this.ignoredGrid.keysLength())return{};for(var r=[],n=1/0,i=1/0,a=-1/0,o=-1/0,s=0,l=e;s<l.length;s+=1){var u=l[s],c=new t.Point(u.x+de,u.y+de);n=Math.min(n,c.x),i=Math.min(i,c.y),a=Math.max(a,c.x),o=Math.max(o,c.y),r.push(c)}for(var f={},h={},p=0,d=this.grid.query(n,i,a,o).concat(this.ignoredGrid.query(n,i,a,o));p<d.length;p+=1){var v=d[p],g=v.key;if(void 0===f[g.bucketInstanceId]&&(f[g.bucketInstanceId]={}),!f[g.bucketInstanceId][g.featureIndex]){var y=[new t.Point(v.x1,v.y1),new t.Point(v.x2,v.y1),new t.Point(v.x2,v.y2),new t.Point(v.x1,v.y2)];t.polygonIntersectsPolygon(r,y)&&(f[g.bucketInstanceId][g.featureIndex]=!0,void 0===h[g.bucketInstanceId]&&(h[g.bucketInstanceId]=[]),h[g.bucketInstanceId].push(g.featureIndex))}}return h},ve.prototype.insertCollisionBox=function(t,e,r,n,i){var a={bucketInstanceId:r,featureIndex:n,collisionGroupID:i};(e?this.ignoredGrid:this.grid).insert(a,t[0],t[1],t[2],t[3])},ve.prototype.insertCollisionCircles=function(t,e,r,n,i){for(var a=e?this.ignoredGrid:this.grid,o={bucketInstanceId:r,featureIndex:n,collisionGroupID:i},s=0;s<t.length;s+=4)a.insertCircle(o,t[s],t[s+1],t[s+2])},ve.prototype.projectAndGetPerspectiveRatio=function(e,r,n){var i=[r,n,0,1];return pe(i,i,e),{point:new t.Point((i[0]/i[3]+1)/2*this.transform.width+de,(-i[1]/i[3]+1)/2*this.transform.height+de),perspectiveRatio:.5+this.transform.cameraToCenterDistance/i[3]*.5}},ve.prototype.isOffscreen=function(t,e,r,n){return r<de||t>=this.screenRightBoundary||n<de||e>this.screenBottomBoundary},ve.prototype.isInsideGrid=function(t,e,r,n){return r>=0&&t<this.gridRightBoundary&&n>=0&&e<this.gridBottomBoundary},ve.prototype.getViewportMatrix=function(){var e=t.identity([]);return t.translate(e,e,[-100,-100,0]),e};var ye=function(t,e,r,n){this.opacity=t?Math.max(0,Math.min(1,t.opacity+(t.placed?e:-e))):n&&r?1:0,this.placed=r};ye.prototype.isHidden=function(){return 0===this.opacity&&!this.placed};var me=function(t,e,r,n,i){this.text=new ye(t?t.text:null,e,r,i),this.icon=new ye(t?t.icon:null,e,n,i)};me.prototype.isHidden=function(){return this.text.isHidden()&&this.icon.isHidden()};var xe=function(t,e,r){this.text=t,this.icon=e,this.skipFade=r},be=function(){this.invProjMatrix=t.create(),this.viewportMatrix=t.create(),this.circles=[]},_e=function(t,e,r,n,i){this.bucketInstanceId=t,this.featureIndex=e,this.sourceLayerIndex=r,this.bucketIndex=n,this.tileID=i},we=function(t){this.crossSourceCollisions=t,this.maxGroupID=0,this.collisionGroups={}};function Te(e,r,n,i,a){var o=t.getAnchorAlignment(e),s=-(o.horizontalAlign-.5)*r,l=-(o.verticalAlign-.5)*n,u=t.evaluateVariableOffset(e,i);return new t.Point(s+u[0]*a,l+u[1]*a)}function ke(e,r,n,i,a,o){var s=e.x1,l=e.x2,u=e.y1,c=e.y2,f=e.anchorPointX,h=e.anchorPointY,p=new t.Point(r,n);return i&&p._rotate(a?o:-o),{x1:s+p.x,y1:u+p.y,x2:l+p.x,y2:c+p.y,anchorPointX:f,anchorPointY:h}}we.prototype.get=function(t){if(this.crossSourceCollisions)return{ID:0,predicate:null};if(!this.collisionGroups[t]){var e=++this.maxGroupID;this.collisionGroups[t]={ID:e,predicate:function(t){return t.collisionGroupID===e}}}return this.collisionGroups[t]};var Ae=function(t,e,r,n){this.transform=t.clone(),this.collisionIndex=new ve(this.transform),this.placements={},this.opacities={},this.variableOffsets={},this.stale=!1,this.commitTime=0,this.fadeDuration=e,this.retainedQueryData={},this.collisionGroups=new we(r),this.collisionCircleArrays={},this.prevPlacement=n,n&&(n.prevPlacement=void 0),this.placedOrientations={}};function Me(t,e,r,n,i){t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0)}Ae.prototype.getBucketParts=function(e,r,n,i){var a=n.getBucket(r),o=n.latestFeatureIndex;if(a&&o&&r.id===a.layerIds[0]){var s=n.collisionBoxArray,l=a.layers[0].layout,u=Math.pow(2,this.transform.zoom-n.tileID.overscaledZ),c=n.tileSize/t.EXTENT,f=this.transform.calculatePosMatrix(n.tileID.toUnwrapped()),h=\"map\"===l.get(\"text-pitch-alignment\"),p=\"map\"===l.get(\"text-rotation-alignment\"),d=ge(n,1,this.transform.zoom),v=te(f,h,p,this.transform,d),g=null;if(h){var y=ee(f,h,p,this.transform,d);g=t.multiply([],this.transform.labelPlaneMatrix,y)}this.retainedQueryData[a.bucketInstanceId]=new _e(a.bucketInstanceId,o,a.sourceLayerIndex,a.index,n.tileID);var m={bucket:a,layout:l,posMatrix:f,textLabelPlaneMatrix:v,labelToScreenMatrix:g,scale:u,textPixelRatio:c,holdingForFade:n.holdingForFade(),collisionBoxArray:s,partiallyEvaluatedTextSize:t.evaluateSizeForZoom(a.textSizeData,this.transform.zoom),collisionGroup:this.collisionGroups.get(a.sourceID)};if(i)for(var x=0,b=a.sortKeyRanges;x<b.length;x+=1){var _=b[x],w=_.sortKey,T=_.symbolInstanceStart,k=_.symbolInstanceEnd;e.push({sortKey:w,symbolInstanceStart:T,symbolInstanceEnd:k,parameters:m})}else e.push({symbolInstanceStart:0,symbolInstanceEnd:a.symbolInstances.length,parameters:m})}},Ae.prototype.attemptAnchorPlacement=function(t,e,r,n,i,a,o,s,l,u,c,f,h,p,d){var v,g=[f.textOffset0,f.textOffset1],y=Te(t,r,n,g,i),m=this.collisionIndex.placeCollisionBox(ke(e,y.x,y.y,a,o,this.transform.angle),c,s,l,u.predicate);if(!d||0!==this.collisionIndex.placeCollisionBox(ke(d,y.x,y.y,a,o,this.transform.angle),c,s,l,u.predicate).box.length)return m.box.length>0?(this.prevPlacement&&this.prevPlacement.variableOffsets[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID].text&&(v=this.prevPlacement.variableOffsets[f.crossTileID].anchor),this.variableOffsets[f.crossTileID]={textOffset:g,width:r,height:n,anchor:t,textBoxScale:i,prevAnchor:v},this.markUsedJustification(h,t,f,p),h.allowVerticalPlacement&&(this.markUsedOrientation(h,p,f),this.placedOrientations[f.crossTileID]=p),{shift:y,placedGlyphBoxes:m}):void 0},Ae.prototype.placeLayerBucketPart=function(e,r,n){var i=this,a=e.parameters,o=a.bucket,s=a.layout,l=a.posMatrix,u=a.textLabelPlaneMatrix,c=a.labelToScreenMatrix,f=a.textPixelRatio,h=a.holdingForFade,p=a.collisionBoxArray,d=a.partiallyEvaluatedTextSize,v=a.collisionGroup,g=s.get(\"text-optional\"),y=s.get(\"icon-optional\"),m=s.get(\"text-allow-overlap\"),x=s.get(\"icon-allow-overlap\"),b=\"map\"===s.get(\"text-rotation-alignment\"),_=\"map\"===s.get(\"text-pitch-alignment\"),w=\"none\"!==s.get(\"icon-text-fit\"),T=\"viewport-y\"===s.get(\"symbol-z-order\"),k=m&&(x||!o.hasIconData()||y),A=x&&(m||!o.hasTextData()||g);!o.collisionArrays&&p&&o.deserializeCollisionBoxes(p);var M=function(e,a){if(!r[e.crossTileID])if(h)i.placements[e.crossTileID]=new xe(!1,!1,!1);else{var p,T=!1,M=!1,S=!0,E=null,L={box:null,offscreen:null},C={box:null,offscreen:null},P=null,O=null,I=0,D=0,z=0;a.textFeatureIndex?I=a.textFeatureIndex:e.useRuntimeCollisionCircles&&(I=e.featureIndex),a.verticalTextFeatureIndex&&(D=a.verticalTextFeatureIndex);var R=a.textBox;if(R){var F=function(r){var n=t.WritingMode.horizontal;if(o.allowVerticalPlacement&&!r&&i.prevPlacement){var a=i.prevPlacement.placedOrientations[e.crossTileID];a&&(i.placedOrientations[e.crossTileID]=a,n=a,i.markUsedOrientation(o,n,e))}return n},B=function(r,n){if(o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&a.verticalTextBox)for(var i=0,s=o.writingModes;i<s.length&&(s[i]===t.WritingMode.vertical?(L=n(),C=L):L=r(),!(L&&L.box&&L.box.length));i+=1);else L=r()};if(s.get(\"text-variable-anchor\")){var N=s.get(\"text-variable-anchor\");if(i.prevPlacement&&i.prevPlacement.variableOffsets[e.crossTileID]){var j=i.prevPlacement.variableOffsets[e.crossTileID];N.indexOf(j.anchor)>0&&(N=N.filter((function(t){return t!==j.anchor}))).unshift(j.anchor)}var U=function(t,r,n){for(var a=t.x2-t.x1,s=t.y2-t.y1,u=e.textBoxScale,c=w&&!x?r:null,h={box:[],offscreen:!1},p=m?2*N.length:N.length,d=0;d<p;++d){var g=N[d%N.length],y=d>=N.length,k=i.attemptAnchorPlacement(g,t,a,s,u,b,_,f,l,v,y,e,o,n,c);if(k&&(h=k.placedGlyphBoxes)&&h.box&&h.box.length){T=!0,E=k.shift;break}}return h};B((function(){return U(R,a.iconBox,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox,n=L&&L.box&&L.box.length;return o.allowVerticalPlacement&&!n&&e.numVerticalGlyphVertices>0&&r?U(r,a.verticalIconBox,t.WritingMode.vertical):{box:null,offscreen:null}})),L&&(T=L.box,S=L.offscreen);var V=F(L&&L.box);if(!T&&i.prevPlacement){var H=i.prevPlacement.variableOffsets[e.crossTileID];H&&(i.variableOffsets[e.crossTileID]=H,i.markUsedJustification(o,H.anchor,e,V))}}else{var q=function(t,r){var n=i.collisionIndex.placeCollisionBox(t,m,f,l,v.predicate);return n&&n.box&&n.box.length&&(i.markUsedOrientation(o,r,e),i.placedOrientations[e.crossTileID]=r),n};B((function(){return q(R,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox;return o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&r?q(r,t.WritingMode.vertical):{box:null,offscreen:null}})),F(L&&L.box&&L.box.length)}}if(T=(p=L)&&p.box&&p.box.length>0,S=p&&p.offscreen,e.useRuntimeCollisionCircles){var G=o.text.placedSymbolArray.get(e.centerJustifiedTextSymbolIndex),Z=t.evaluateSizeForFeature(o.textSizeData,d,G),Y=s.get(\"text-padding\"),W=e.collisionCircleDiameter;P=i.collisionIndex.placeCollisionCircles(m,G,o.lineVertexArray,o.glyphOffsetArray,Z,l,u,c,n,_,v.predicate,W,Y),T=m||P.circles.length>0&&!P.collisionDetected,S=S&&P.offscreen}if(a.iconFeatureIndex&&(z=a.iconFeatureIndex),a.iconBox){var X=function(t){var e=w&&E?ke(t,E.x,E.y,b,_,i.transform.angle):t;return i.collisionIndex.placeCollisionBox(e,x,f,l,v.predicate)};M=C&&C.box&&C.box.length&&a.verticalIconBox?(O=X(a.verticalIconBox)).box.length>0:(O=X(a.iconBox)).box.length>0,S=S&&O.offscreen}var J=g||0===e.numHorizontalGlyphVertices&&0===e.numVerticalGlyphVertices,K=y||0===e.numIconVertices;if(J||K?K?J||(M=M&&T):T=M&&T:M=T=M&&T,T&&p&&p.box&&(C&&C.box&&D?i.collisionIndex.insertCollisionBox(p.box,s.get(\"text-ignore-placement\"),o.bucketInstanceId,D,v.ID):i.collisionIndex.insertCollisionBox(p.box,s.get(\"text-ignore-placement\"),o.bucketInstanceId,I,v.ID)),M&&O&&i.collisionIndex.insertCollisionBox(O.box,s.get(\"icon-ignore-placement\"),o.bucketInstanceId,z,v.ID),P&&(T&&i.collisionIndex.insertCollisionCircles(P.circles,s.get(\"text-ignore-placement\"),o.bucketInstanceId,I,v.ID),n)){var $=o.bucketInstanceId,Q=i.collisionCircleArrays[$];void 0===Q&&(Q=i.collisionCircleArrays[$]=new be);for(var tt=0;tt<P.circles.length;tt+=4)Q.circles.push(P.circles[tt+0]),Q.circles.push(P.circles[tt+1]),Q.circles.push(P.circles[tt+2]),Q.circles.push(P.collisionDetected?1:0)}i.placements[e.crossTileID]=new xe(T||k,M||A,S||o.justReloaded),r[e.crossTileID]=!0}};if(T)for(var S=o.getSortedSymbolIndexes(this.transform.angle),E=S.length-1;E>=0;--E){var L=S[E];M(o.symbolInstances.get(L),o.collisionArrays[L])}else for(var C=e.symbolInstanceStart;C<e.symbolInstanceEnd;C++)M(o.symbolInstances.get(C),o.collisionArrays[C]);if(n&&o.bucketInstanceId in this.collisionCircleArrays){var P=this.collisionCircleArrays[o.bucketInstanceId];t.invert(P.invProjMatrix,l),P.viewportMatrix=this.collisionIndex.getViewportMatrix()}o.justReloaded=!1},Ae.prototype.markUsedJustification=function(e,r,n,i){var a,o={left:n.leftJustifiedTextSymbolIndex,center:n.centerJustifiedTextSymbolIndex,right:n.rightJustifiedTextSymbolIndex};a=i===t.WritingMode.vertical?n.verticalPlacedTextSymbolIndex:o[t.getAnchorJustification(r)];for(var s=0,l=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex,n.verticalPlacedTextSymbolIndex];s<l.length;s+=1){var u=l[s];u>=0&&(e.text.placedSymbolArray.get(u).crossTileID=a>=0&&u!==a?0:n.crossTileID)}},Ae.prototype.markUsedOrientation=function(e,r,n){for(var i=r===t.WritingMode.horizontal||r===t.WritingMode.horizontalOnly?r:0,a=r===t.WritingMode.vertical?r:0,o=0,s=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex];o<s.length;o+=1){var l=s[o];e.text.placedSymbolArray.get(l).placedOrientation=i}n.verticalPlacedTextSymbolIndex&&(e.text.placedSymbolArray.get(n.verticalPlacedTextSymbolIndex).placedOrientation=a)},Ae.prototype.commit=function(t){this.commitTime=t,this.zoomAtLastRecencyCheck=this.transform.zoom;var e=this.prevPlacement,r=!1;this.prevZoomAdjustment=e?e.zoomAdjustment(this.transform.zoom):0;var n=e?e.symbolFadeChange(t):1,i=e?e.opacities:{},a=e?e.variableOffsets:{},o=e?e.placedOrientations:{};for(var s in this.placements){var l=this.placements[s],u=i[s];u?(this.opacities[s]=new me(u,n,l.text,l.icon),r=r||l.text!==u.text.placed||l.icon!==u.icon.placed):(this.opacities[s]=new me(null,n,l.text,l.icon,l.skipFade),r=r||l.text||l.icon)}for(var c in i){var f=i[c];if(!this.opacities[c]){var h=new me(f,n,!1,!1);h.isHidden()||(this.opacities[c]=h,r=r||f.text.placed||f.icon.placed)}}for(var p in a)this.variableOffsets[p]||!this.opacities[p]||this.opacities[p].isHidden()||(this.variableOffsets[p]=a[p]);for(var d in o)this.placedOrientations[d]||!this.opacities[d]||this.opacities[d].isHidden()||(this.placedOrientations[d]=o[d]);r?this.lastPlacementChangeTime=t:\"number\"!=typeof this.lastPlacementChangeTime&&(this.lastPlacementChangeTime=e?e.lastPlacementChangeTime:t)},Ae.prototype.updateLayerOpacities=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1){var a=i[n],o=a.getBucket(t);o&&a.latestFeatureIndex&&t.id===o.layerIds[0]&&this.updateBucketOpacities(o,r,a.collisionBoxArray)}},Ae.prototype.updateBucketOpacities=function(e,r,n){var i=this;e.hasTextData()&&e.text.opacityVertexArray.clear(),e.hasIconData()&&e.icon.opacityVertexArray.clear(),e.hasIconCollisionBoxData()&&e.iconCollisionBox.collisionVertexArray.clear(),e.hasTextCollisionBoxData()&&e.textCollisionBox.collisionVertexArray.clear();var a=e.layers[0].layout,o=new me(null,0,!1,!1,!0),s=a.get(\"text-allow-overlap\"),l=a.get(\"icon-allow-overlap\"),u=a.get(\"text-variable-anchor\"),c=\"map\"===a.get(\"text-rotation-alignment\"),f=\"map\"===a.get(\"text-pitch-alignment\"),h=\"none\"!==a.get(\"icon-text-fit\"),p=new me(null,0,s&&(l||!e.hasIconData()||a.get(\"icon-optional\")),l&&(s||!e.hasTextData()||a.get(\"text-optional\")),!0);!e.collisionArrays&&n&&(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData())&&e.deserializeCollisionBoxes(n);for(var d=function(t,e,r){for(var n=0;n<e/4;n++)t.opacityVertexArray.emplaceBack(r)},v=function(n){var a=e.symbolInstances.get(n),s=a.numHorizontalGlyphVertices,l=a.numVerticalGlyphVertices,v=a.crossTileID,g=r[v],y=i.opacities[v];g?y=o:y||(y=p,i.opacities[v]=y),r[v]=!0;var m=s>0||l>0,x=a.numIconVertices>0,b=i.placedOrientations[a.crossTileID],_=b===t.WritingMode.vertical,w=b===t.WritingMode.horizontal||b===t.WritingMode.horizontalOnly;if(m){var T=De(y.text),k=_?ze:T;d(e.text,s,k);var A=w?ze:T;d(e.text,l,A);var M=y.text.isHidden();[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t){t>=0&&(e.text.placedSymbolArray.get(t).hidden=M||_?1:0)})),a.verticalPlacedTextSymbolIndex>=0&&(e.text.placedSymbolArray.get(a.verticalPlacedTextSymbolIndex).hidden=M||w?1:0);var S=i.variableOffsets[a.crossTileID];S&&i.markUsedJustification(e,S.anchor,a,b);var E=i.placedOrientations[a.crossTileID];E&&(i.markUsedJustification(e,\"left\",a,E),i.markUsedOrientation(e,E,a))}if(x){var L=De(y.icon),C=!(h&&a.verticalPlacedIconSymbolIndex&&_);if(a.placedIconSymbolIndex>=0){var P=C?L:ze;d(e.icon,a.numIconVertices,P),e.icon.placedSymbolArray.get(a.placedIconSymbolIndex).hidden=y.icon.isHidden()}if(a.verticalPlacedIconSymbolIndex>=0){var O=C?ze:L;d(e.icon,a.numVerticalIconVertices,O),e.icon.placedSymbolArray.get(a.verticalPlacedIconSymbolIndex).hidden=y.icon.isHidden()}}if(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData()){var I=e.collisionArrays[n];if(I){var D=new t.Point(0,0);if(I.textBox||I.verticalTextBox){var z=!0;if(u){var R=i.variableOffsets[v];R?(D=Te(R.anchor,R.width,R.height,R.textOffset,R.textBoxScale),c&&D._rotate(f?i.transform.angle:-i.transform.angle)):z=!1}I.textBox&&Me(e.textCollisionBox.collisionVertexArray,y.text.placed,!z||_,D.x,D.y),I.verticalTextBox&&Me(e.textCollisionBox.collisionVertexArray,y.text.placed,!z||w,D.x,D.y)}var F=Boolean(!w&&I.verticalIconBox);I.iconBox&&Me(e.iconCollisionBox.collisionVertexArray,y.icon.placed,F,h?D.x:0,h?D.y:0),I.verticalIconBox&&Me(e.iconCollisionBox.collisionVertexArray,y.icon.placed,!F,h?D.x:0,h?D.y:0)}}},g=0;g<e.symbolInstances.length;g++)v(g);if(e.sortFeatures(this.transform.angle),this.retainedQueryData[e.bucketInstanceId]&&(this.retainedQueryData[e.bucketInstanceId].featureSortOrder=e.featureSortOrder),e.hasTextData()&&e.text.opacityVertexBuffer&&e.text.opacityVertexBuffer.updateData(e.text.opacityVertexArray),e.hasIconData()&&e.icon.opacityVertexBuffer&&e.icon.opacityVertexBuffer.updateData(e.icon.opacityVertexArray),e.hasIconCollisionBoxData()&&e.iconCollisionBox.collisionVertexBuffer&&e.iconCollisionBox.collisionVertexBuffer.updateData(e.iconCollisionBox.collisionVertexArray),e.hasTextCollisionBoxData()&&e.textCollisionBox.collisionVertexBuffer&&e.textCollisionBox.collisionVertexBuffer.updateData(e.textCollisionBox.collisionVertexArray),e.bucketInstanceId in this.collisionCircleArrays){var y=this.collisionCircleArrays[e.bucketInstanceId];e.placementInvProjMatrix=y.invProjMatrix,e.placementViewportMatrix=y.viewportMatrix,e.collisionCircleArray=y.circles,delete this.collisionCircleArrays[e.bucketInstanceId]}},Ae.prototype.symbolFadeChange=function(t){return 0===this.fadeDuration?1:(t-this.commitTime)/this.fadeDuration+this.prevZoomAdjustment},Ae.prototype.zoomAdjustment=function(t){return Math.max(0,(this.transform.zoom-t)/1.5)},Ae.prototype.hasTransitions=function(t){return this.stale||t-this.lastPlacementChangeTime<this.fadeDuration},Ae.prototype.stillRecent=function(t,e){var r=this.zoomAtLastRecencyCheck===e?1-this.zoomAdjustment(e):1;return this.zoomAtLastRecencyCheck=e,this.commitTime+this.fadeDuration*r>t},Ae.prototype.setStale=function(){this.stale=!0};var Se=Math.pow(2,25),Ee=Math.pow(2,24),Le=Math.pow(2,17),Ce=Math.pow(2,16),Pe=Math.pow(2,9),Oe=Math.pow(2,8),Ie=Math.pow(2,1);function De(t){if(0===t.opacity&&!t.placed)return 0;if(1===t.opacity&&t.placed)return 4294967295;var e=t.placed?1:0,r=Math.floor(127*t.opacity);return r*Se+e*Ee+r*Le+e*Ce+r*Pe+e*Oe+r*Ie+e}var ze=0,Re=function(t){this._sortAcrossTiles=\"viewport-y\"!==t.layout.get(\"symbol-z-order\")&&void 0!==t.layout.get(\"symbol-sort-key\").constantOr(1),this._currentTileIndex=0,this._currentPartIndex=0,this._seenCrossTileIDs={},this._bucketParts=[]};Re.prototype.continuePlacement=function(t,e,r,n,i){for(var a=this._bucketParts;this._currentTileIndex<t.length;){var o=t[this._currentTileIndex];if(e.getBucketParts(a,n,o,this._sortAcrossTiles),this._currentTileIndex++,i())return!0}for(this._sortAcrossTiles&&(this._sortAcrossTiles=!1,a.sort((function(t,e){return t.sortKey-e.sortKey})));this._currentPartIndex<a.length;){var s=a[this._currentPartIndex];if(e.placeLayerBucketPart(s,this._seenCrossTileIDs,r),this._currentPartIndex++,i())return!0}return!1};var Fe=function(t,e,r,n,i,a,o){this.placement=new Ae(t,i,a,o),this._currentPlacementIndex=e.length-1,this._forceFullPlacement=r,this._showCollisionBoxes=n,this._done=!1};Fe.prototype.isDone=function(){return this._done},Fe.prototype.continuePlacement=function(e,r,n){for(var i=this,a=t.browser.now(),o=function(){var e=t.browser.now()-a;return!i._forceFullPlacement&&e>2};this._currentPlacementIndex>=0;){var s=r[e[this._currentPlacementIndex]],l=this.placement.collisionIndex.transform.zoom;if(\"symbol\"===s.type&&(!s.minzoom||s.minzoom<=l)&&(!s.maxzoom||s.maxzoom>l)){if(this._inProgressLayer||(this._inProgressLayer=new Re(s)),this._inProgressLayer.continuePlacement(n[s.source],this.placement,this._showCollisionBoxes,s,o))return;delete this._inProgressLayer}this._currentPlacementIndex--}this._done=!0},Fe.prototype.commit=function(t){return this.placement.commit(t),this.placement};var Be=512/t.EXTENT/2,Ne=function(t,e,r){this.tileID=t,this.indexedSymbolInstances={},this.bucketInstanceId=r;for(var n=0;n<e.length;n++){var i=e.get(n),a=i.key;this.indexedSymbolInstances[a]||(this.indexedSymbolInstances[a]=[]),this.indexedSymbolInstances[a].push({crossTileID:i.crossTileID,coord:this.getScaledCoordinates(i,t)})}};Ne.prototype.getScaledCoordinates=function(e,r){var n=r.canonical.z-this.tileID.canonical.z,i=Be/Math.pow(2,n);return{x:Math.floor((r.canonical.x*t.EXTENT+e.anchorX)*i),y:Math.floor((r.canonical.y*t.EXTENT+e.anchorY)*i)}},Ne.prototype.findMatches=function(t,e,r){for(var n=this.tileID.canonical.z<e.canonical.z?1:Math.pow(2,this.tileID.canonical.z-e.canonical.z),i=0;i<t.length;i++){var a=t.get(i);if(!a.crossTileID){var o=this.indexedSymbolInstances[a.key];if(o)for(var s=this.getScaledCoordinates(a,e),l=0,u=o;l<u.length;l+=1){var c=u[l];if(Math.abs(c.coord.x-s.x)<=n&&Math.abs(c.coord.y-s.y)<=n&&!r[c.crossTileID]){r[c.crossTileID]=!0,a.crossTileID=c.crossTileID;break}}}}};var je=function(){this.maxCrossTileID=0};je.prototype.generate=function(){return++this.maxCrossTileID};var Ue=function(){this.indexes={},this.usedCrossTileIDs={},this.lng=0};Ue.prototype.handleWrapJump=function(t){var e=Math.round((t-this.lng)/360);if(0!==e)for(var r in this.indexes){var n=this.indexes[r],i={};for(var a in n){var o=n[a];o.tileID=o.tileID.unwrapTo(o.tileID.wrap+e),i[o.tileID.key]=o}this.indexes[r]=i}this.lng=t},Ue.prototype.addBucket=function(t,e,r){if(this.indexes[t.overscaledZ]&&this.indexes[t.overscaledZ][t.key]){if(this.indexes[t.overscaledZ][t.key].bucketInstanceId===e.bucketInstanceId)return!1;this.removeBucketCrossTileIDs(t.overscaledZ,this.indexes[t.overscaledZ][t.key])}for(var n=0;n<e.symbolInstances.length;n++)e.symbolInstances.get(n).crossTileID=0;this.usedCrossTileIDs[t.overscaledZ]||(this.usedCrossTileIDs[t.overscaledZ]={});var i=this.usedCrossTileIDs[t.overscaledZ];for(var a in this.indexes){var o=this.indexes[a];if(Number(a)>t.overscaledZ)for(var s in o){var l=o[s];l.tileID.isChildOf(t)&&l.findMatches(e.symbolInstances,t,i)}else{var u=o[t.scaledTo(Number(a)).key];u&&u.findMatches(e.symbolInstances,t,i)}}for(var c=0;c<e.symbolInstances.length;c++){var f=e.symbolInstances.get(c);f.crossTileID||(f.crossTileID=r.generate(),i[f.crossTileID]=!0)}return void 0===this.indexes[t.overscaledZ]&&(this.indexes[t.overscaledZ]={}),this.indexes[t.overscaledZ][t.key]=new Ne(t,e.symbolInstances,e.bucketInstanceId),!0},Ue.prototype.removeBucketCrossTileIDs=function(t,e){for(var r in e.indexedSymbolInstances)for(var n=0,i=e.indexedSymbolInstances[r];n<i.length;n+=1){var a=i[n];delete this.usedCrossTileIDs[t][a.crossTileID]}},Ue.prototype.removeStaleBuckets=function(t){var e=!1;for(var r in this.indexes){var n=this.indexes[r];for(var i in n)t[n[i].bucketInstanceId]||(this.removeBucketCrossTileIDs(r,n[i]),delete n[i],e=!0)}return e};var Ve=function(){this.layerIndexes={},this.crossTileIDs=new je,this.maxBucketInstanceId=0,this.bucketsInCurrentPlacement={}};Ve.prototype.addLayer=function(t,e,r){var n=this.layerIndexes[t.id];void 0===n&&(n=this.layerIndexes[t.id]=new Ue);var i=!1,a={};n.handleWrapJump(r);for(var o=0,s=e;o<s.length;o+=1){var l=s[o],u=l.getBucket(t);u&&t.id===u.layerIds[0]&&(u.bucketInstanceId||(u.bucketInstanceId=++this.maxBucketInstanceId),n.addBucket(l.tileID,u,this.crossTileIDs)&&(i=!0),a[u.bucketInstanceId]=!0)}return n.removeStaleBuckets(a)&&(i=!0),i},Ve.prototype.pruneUnusedLayers=function(t){var e={};for(var r in t.forEach((function(t){e[t]=!0})),this.layerIndexes)e[r]||delete this.layerIndexes[r]};var He=function(e,r){return t.emitValidationErrors(e,r&&r.filter((function(t){return\"source.canvas\"!==t.identifier})))},qe=t.pick(Ht,[\"addLayer\",\"removeLayer\",\"setPaintProperty\",\"setLayoutProperty\",\"setFilter\",\"addSource\",\"removeSource\",\"setLayerZoomRange\",\"setLight\",\"setTransition\",\"setGeoJSONSourceData\"]),Ge=t.pick(Ht,[\"setCenter\",\"setZoom\",\"setBearing\",\"setPitch\"]),Ze=function(){var e={},r=t.styleSpec.$version;for(var n in t.styleSpec.$root){var i=t.styleSpec.$root[n];if(i.required){var a;null!=(a=\"version\"===n?r:\"array\"===i.type?[]:{})&&(e[n]=a)}}return e}(),Ye=function(e){function r(n,i){var a=this;void 0===i&&(i={}),e.call(this),this.map=n,this.dispatcher=new A(jt(),this),this.imageManager=new h,this.imageManager.setEventedParent(this),this.glyphManager=new x(n._requestManager,i.localIdeographFontFamily),this.lineAtlas=new k(256,512),this.crossTileSymbolIndex=new Ve,this._layers={},this._serializedLayers={},this._order=[],this.sourceCaches={},this.zoomHistory=new t.ZoomHistory,this._loaded=!1,this._availableImages=[],this._resetUpdates(),this.dispatcher.broadcast(\"setReferrer\",t.getReferrer());var o=this;this._rtlTextPluginCallback=r.registerForPluginStateChange((function(e){var r={pluginStatus:e.pluginStatus,pluginURL:e.pluginURL};o.dispatcher.broadcast(\"syncRTLPluginState\",r,(function(e,r){if(t.triggerPluginCompletionEvent(e),r&&r.every((function(t){return t})))for(var n in o.sourceCaches)o.sourceCaches[n].reload()}))})),this.on(\"data\",(function(t){if(\"source\"===t.dataType&&\"metadata\"===t.sourceDataType){var e=a.sourceCaches[t.sourceId];if(e){var r=e.getSource();if(r&&r.vectorLayerIds)for(var n in a._layers){var i=a._layers[n];i.source===r.id&&a._validateLayer(i)}}}}))}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadURL=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event(\"dataloading\",{dataType:\"style\"}));var i=\"boolean\"==typeof r.validate?r.validate:!t.isMapboxURL(e);e=this.map._requestManager.normalizeStyleURL(e,r.accessToken);var a=this.map._requestManager.transformRequest(e,t.ResourceType.Style);this._request=t.getJSON(a,(function(e,r){n._request=null,e?n.fire(new t.ErrorEvent(e)):r&&n._load(r,i)}))},r.prototype.loadJSON=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event(\"dataloading\",{dataType:\"style\"})),this._request=t.browser.frame((function(){n._request=null,n._load(e,!1!==r.validate)}))},r.prototype.loadEmpty=function(){this.fire(new t.Event(\"dataloading\",{dataType:\"style\"})),this._load(Ze,!1)},r.prototype._load=function(e,r){if(!r||!He(this,t.validateStyle(e))){for(var n in this._loaded=!0,this.stylesheet=e,e.sources)this.addSource(n,e.sources[n],{validate:!1});e.sprite?this._loadSprite(e.sprite):this.imageManager.setLoaded(!0),this.glyphManager.setURL(e.glyphs);var i=Vt(this.stylesheet.layers);this._order=i.map((function(t){return t.id})),this._layers={},this._serializedLayers={};for(var a=0,o=i;a<o.length;a+=1){var s=o[a];(s=t.createStyleLayer(s)).setEventedParent(this,{layer:{id:s.id}}),this._layers[s.id]=s,this._serializedLayers[s.id]=s.serialize()}this.dispatcher.broadcast(\"setLayers\",this._serializeLayers(this._order)),this.light=new T(this.stylesheet.light),this.fire(new t.Event(\"data\",{dataType:\"style\"})),this.fire(new t.Event(\"style.load\"))}},r.prototype._loadSprite=function(e){var r=this;this._spriteRequest=function(e,r,n){var i,a,o,s=t.browser.devicePixelRatio>1?\"@2x\":\"\",l=t.getJSON(r.transformRequest(r.normalizeSpriteURL(e,s,\".json\"),t.ResourceType.SpriteJSON),(function(t,e){l=null,o||(o=t,i=e,c())})),u=t.getImage(r.transformRequest(r.normalizeSpriteURL(e,s,\".png\"),t.ResourceType.SpriteImage),(function(t,e){u=null,o||(o=t,a=e,c())}));function c(){if(o)n(o);else if(i&&a){var e=t.browser.getImageData(a),r={};for(var s in i){var l=i[s],u=l.width,c=l.height,f=l.x,h=l.y,p=l.sdf,d=l.pixelRatio,v=l.stretchX,g=l.stretchY,y=l.content,m=new t.RGBAImage({width:u,height:c});t.RGBAImage.copy(e,m,{x:f,y:h},{x:0,y:0},{width:u,height:c}),r[s]={data:m,pixelRatio:d,sdf:p,stretchX:v,stretchY:g,content:y}}n(null,r)}}return{cancel:function(){l&&(l.cancel(),l=null),u&&(u.cancel(),u=null)}}}(e,this.map._requestManager,(function(e,n){if(r._spriteRequest=null,e)r.fire(new t.ErrorEvent(e));else if(n)for(var i in n)r.imageManager.addImage(i,n[i]);r.imageManager.setLoaded(!0),r._availableImages=r.imageManager.listImages(),r.dispatcher.broadcast(\"setImages\",r._availableImages),r.fire(new t.Event(\"data\",{dataType:\"style\"}))}))},r.prototype._validateLayer=function(e){var r=this.sourceCaches[e.source];if(r){var n=e.sourceLayer;if(n){var i=r.getSource();(\"geojson\"===i.type||i.vectorLayerIds&&-1===i.vectorLayerIds.indexOf(n))&&this.fire(new t.ErrorEvent(new Error('Source layer \"'+n+'\" does not exist on source \"'+i.id+'\" as specified by style layer \"'+e.id+'\"')))}}},r.prototype.loaded=function(){if(!this._loaded)return!1;if(Object.keys(this._updatedSources).length)return!1;for(var t in this.sourceCaches)if(!this.sourceCaches[t].loaded())return!1;return!!this.imageManager.isLoaded()},r.prototype._serializeLayers=function(t){for(var e=[],r=0,n=t;r<n.length;r+=1){var i=n[r],a=this._layers[i];\"custom\"!==a.type&&e.push(a.serialize())}return e},r.prototype.hasTransitions=function(){if(this.light&&this.light.hasTransition())return!0;for(var t in this.sourceCaches)if(this.sourceCaches[t].hasTransition())return!0;for(var e in this._layers)if(this._layers[e].hasTransition())return!0;return!1},r.prototype._checkLoaded=function(){if(!this._loaded)throw new Error(\"Style is not done loading\")},r.prototype.update=function(e){if(this._loaded){var r=this._changed;if(this._changed){var n=Object.keys(this._updatedLayers),i=Object.keys(this._removedLayers);for(var a in(n.length||i.length)&&this._updateWorkerLayers(n,i),this._updatedSources){var o=this._updatedSources[a];\"reload\"===o?this._reloadSource(a):\"clear\"===o&&this._clearSource(a)}for(var s in this._updateTilesForChangedImages(),this._updatedPaintProps)this._layers[s].updateTransitions(e);this.light.updateTransitions(e),this._resetUpdates()}for(var l in this.sourceCaches)this.sourceCaches[l].used=!1;for(var u=0,c=this._order;u<c.length;u+=1){var f=c[u],h=this._layers[f];h.recalculate(e,this._availableImages),!h.isHidden(e.zoom)&&h.source&&(this.sourceCaches[h.source].used=!0)}this.light.recalculate(e),this.z=e.zoom,r&&this.fire(new t.Event(\"data\",{dataType:\"style\"}))}},r.prototype._updateTilesForChangedImages=function(){var t=Object.keys(this._changedImages);if(t.length){for(var e in this.sourceCaches)this.sourceCaches[e].reloadTilesForDependencies([\"icons\",\"patterns\"],t);this._changedImages={}}},r.prototype._updateWorkerLayers=function(t,e){this.dispatcher.broadcast(\"updateLayers\",{layers:this._serializeLayers(t),removedIds:e})},r.prototype._resetUpdates=function(){this._changed=!1,this._updatedLayers={},this._removedLayers={},this._updatedSources={},this._updatedPaintProps={},this._changedImages={}},r.prototype.setState=function(e){var r=this;if(this._checkLoaded(),He(this,t.validateStyle(e)))return!1;(e=t.clone$1(e)).layers=Vt(e.layers);var n=Kt(this.serialize(),e).filter((function(t){return!(t.command in Ge)}));if(0===n.length)return!1;var i=n.filter((function(t){return!(t.command in qe)}));if(i.length>0)throw new Error(\"Unimplemented: \"+i.map((function(t){return t.command})).join(\", \")+\".\");return n.forEach((function(t){\"setTransition\"!==t.command&&r[t.command].apply(r,t.args)})),this.stylesheet=e,!0},r.prototype.addImage=function(e,r){if(this.getImage(e))return this.fire(new t.ErrorEvent(new Error(\"An image with this name already exists.\")));this.imageManager.addImage(e,r),this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.fire(new t.Event(\"data\",{dataType:\"style\"}))},r.prototype.updateImage=function(t,e){this.imageManager.updateImage(t,e)},r.prototype.getImage=function(t){return this.imageManager.getImage(t)},r.prototype.removeImage=function(e){if(!this.getImage(e))return this.fire(new t.ErrorEvent(new Error(\"No image with this name exists.\")));this.imageManager.removeImage(e),this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.fire(new t.Event(\"data\",{dataType:\"style\"}))},r.prototype.listImages=function(){return this._checkLoaded(),this.imageManager.listImages()},r.prototype.addSource=function(e,r,n){var i=this;if(void 0===n&&(n={}),this._checkLoaded(),void 0!==this.sourceCaches[e])throw new Error(\"There is already a source with this ID\");if(!r.type)throw new Error(\"The type property must be defined, but the only the following properties were given: \"+Object.keys(r).join(\", \")+\".\");if(!([\"vector\",\"raster\",\"geojson\",\"video\",\"image\"].indexOf(r.type)>=0&&this._validate(t.validateStyle.source,\"sources.\"+e,r,null,n))){this.map&&this.map._collectResourceTiming&&(r.collectResourceTiming=!0);var a=this.sourceCaches[e]=new Ot(e,r,this.dispatcher);a.style=this,a.setEventedParent(this,(function(){return{isSourceLoaded:i.loaded(),source:a.serialize(),sourceId:e}})),a.onAdd(this.map),this._changed=!0}},r.prototype.removeSource=function(e){if(this._checkLoaded(),void 0===this.sourceCaches[e])throw new Error(\"There is no source with this ID\");for(var r in this._layers)if(this._layers[r].source===e)return this.fire(new t.ErrorEvent(new Error('Source \"'+e+'\" cannot be removed while layer \"'+r+'\" is using it.')));var n=this.sourceCaches[e];delete this.sourceCaches[e],delete this._updatedSources[e],n.fire(new t.Event(\"data\",{sourceDataType:\"metadata\",dataType:\"source\",sourceId:e})),n.setEventedParent(null),n.clearTiles(),n.onRemove&&n.onRemove(this.map),this._changed=!0},r.prototype.setGeoJSONSourceData=function(t,e){this._checkLoaded(),this.sourceCaches[t].getSource().setData(e),this._changed=!0},r.prototype.getSource=function(t){return this.sourceCaches[t]&&this.sourceCaches[t].getSource()},r.prototype.addLayer=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=e.id;if(this.getLayer(i))this.fire(new t.ErrorEvent(new Error('Layer with id \"'+i+'\" already exists on this map')));else{var a;if(\"custom\"===e.type){if(He(this,t.validateCustomStyleLayer(e)))return;a=t.createStyleLayer(e)}else{if(\"object\"==typeof e.source&&(this.addSource(i,e.source),e=t.clone$1(e),e=t.extend(e,{source:i})),this._validate(t.validateStyle.layer,\"layers.\"+i,e,{arrayIndex:-1},n))return;a=t.createStyleLayer(e),this._validateLayer(a),a.setEventedParent(this,{layer:{id:i}}),this._serializedLayers[a.id]=a.serialize()}var o=r?this._order.indexOf(r):this._order.length;if(r&&-1===o)this.fire(new t.ErrorEvent(new Error('Layer with id \"'+r+'\" does not exist on this map.')));else{if(this._order.splice(o,0,i),this._layerOrderChanged=!0,this._layers[i]=a,this._removedLayers[i]&&a.source&&\"custom\"!==a.type){var s=this._removedLayers[i];delete this._removedLayers[i],s.type!==a.type?this._updatedSources[a.source]=\"clear\":(this._updatedSources[a.source]=\"reload\",this.sourceCaches[a.source].pause())}this._updateLayer(a),a.onAdd&&a.onAdd(this.map)}}},r.prototype.moveLayer=function(e,r){if(this._checkLoaded(),this._changed=!0,this._layers[e]){if(e!==r){var n=this._order.indexOf(e);this._order.splice(n,1);var i=r?this._order.indexOf(r):this._order.length;r&&-1===i?this.fire(new t.ErrorEvent(new Error('Layer with id \"'+r+'\" does not exist on this map.'))):(this._order.splice(i,0,e),this._layerOrderChanged=!0)}}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be moved.\")))},r.prototype.removeLayer=function(e){this._checkLoaded();var r=this._layers[e];if(r){r.setEventedParent(null);var n=this._order.indexOf(e);this._order.splice(n,1),this._layerOrderChanged=!0,this._changed=!0,this._removedLayers[e]=r,delete this._layers[e],delete this._serializedLayers[e],delete this._updatedLayers[e],delete this._updatedPaintProps[e],r.onRemove&&r.onRemove(this.map)}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be removed.\")))},r.prototype.getLayer=function(t){return this._layers[t]},r.prototype.hasLayer=function(t){return t in this._layers},r.prototype.setLayerZoomRange=function(e,r,n){this._checkLoaded();var i=this.getLayer(e);i?i.minzoom===r&&i.maxzoom===n||(null!=r&&(i.minzoom=r),null!=n&&(i.maxzoom=n),this._updateLayer(i)):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot have zoom extent.\")))},r.prototype.setFilter=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=this.getLayer(e);if(i){if(!t.deepEqual(i.filter,r))return null==r?(i.filter=void 0,void this._updateLayer(i)):void(this._validate(t.validateStyle.filter,\"layers.\"+i.id+\".filter\",r,null,n)||(i.filter=t.clone$1(r),this._updateLayer(i)))}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be filtered.\")))},r.prototype.getFilter=function(e){return t.clone$1(this.getLayer(e).filter)},r.prototype.setLayoutProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getLayoutProperty(r),n)||(a.setLayoutProperty(r,n,i),this._updateLayer(a)):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be styled.\")))},r.prototype.getLayoutProperty=function(e,r){var n=this.getLayer(e);if(n)return n.getLayoutProperty(r);this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style.\")))},r.prototype.setPaintProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getPaintProperty(r),n)||(a.setPaintProperty(r,n,i)&&this._updateLayer(a),this._changed=!0,this._updatedPaintProps[e]=!0):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be styled.\")))},r.prototype.getPaintProperty=function(t,e){return this.getLayer(t).getPaintProperty(e)},r.prototype.setFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=e.sourceLayer,a=this.sourceCaches[n];if(void 0!==a){var o=a.getSource().type;\"geojson\"===o&&i?this.fire(new t.ErrorEvent(new Error(\"GeoJSON sources cannot have a sourceLayer parameter.\"))):\"vector\"!==o||i?(void 0===e.id&&this.fire(new t.ErrorEvent(new Error(\"The feature id parameter must be provided.\"))),a.setFeatureState(i,e.id,r)):this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+n+\"' does not exist in the map's style.\")))},r.prototype.removeFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=this.sourceCaches[n];if(void 0!==i){var a=i.getSource().type,o=\"vector\"===a?e.sourceLayer:void 0;\"vector\"!==a||o?r&&\"string\"!=typeof e.id&&\"number\"!=typeof e.id?this.fire(new t.ErrorEvent(new Error(\"A feature id is requred to remove its specific state property.\"))):i.removeFeatureState(o,e.id,r):this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+n+\"' does not exist in the map's style.\")))},r.prototype.getFeatureState=function(e){this._checkLoaded();var r=e.source,n=e.sourceLayer,i=this.sourceCaches[r];if(void 0!==i){if(\"vector\"!==i.getSource().type||n)return void 0===e.id&&this.fire(new t.ErrorEvent(new Error(\"The feature id parameter must be provided.\"))),i.getFeatureState(n,e.id);this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+r+\"' does not exist in the map's style.\")))},r.prototype.getTransition=function(){return t.extend({duration:300,delay:0},this.stylesheet&&this.stylesheet.transition)},r.prototype.serialize=function(){return t.filterObject({version:this.stylesheet.version,name:this.stylesheet.name,metadata:this.stylesheet.metadata,light:this.stylesheet.light,center:this.stylesheet.center,zoom:this.stylesheet.zoom,bearing:this.stylesheet.bearing,pitch:this.stylesheet.pitch,sprite:this.stylesheet.sprite,glyphs:this.stylesheet.glyphs,transition:this.stylesheet.transition,sources:t.mapObject(this.sourceCaches,(function(t){return t.serialize()})),layers:this._serializeLayers(this._order)},(function(t){return void 0!==t}))},r.prototype._updateLayer=function(t){this._updatedLayers[t.id]=!0,t.source&&!this._updatedSources[t.source]&&\"raster\"!==this.sourceCaches[t.source].getSource().type&&(this._updatedSources[t.source]=\"reload\",this.sourceCaches[t.source].pause()),this._changed=!0},r.prototype._flattenAndSortRenderedFeatures=function(t){for(var e=this,r=function(t){return\"fill-extrusion\"===e._layers[t].type},n={},i=[],a=this._order.length-1;a>=0;a--){var o=this._order[a];if(r(o)){n[o]=a;for(var s=0,l=t;s<l.length;s+=1){var u=l[s][o];if(u)for(var c=0,f=u;c<f.length;c+=1){var h=f[c];i.push(h)}}}}i.sort((function(t,e){return e.intersectionZ-t.intersectionZ}));for(var p=[],d=this._order.length-1;d>=0;d--){var v=this._order[d];if(r(v))for(var g=i.length-1;g>=0;g--){var y=i[g].feature;if(n[y.layer.id]<d)break;p.push(y),i.pop()}else for(var m=0,x=t;m<x.length;m+=1){var b=x[m][v];if(b)for(var _=0,w=b;_<w.length;_+=1){var T=w[_];p.push(T.feature)}}}return p},r.prototype.queryRenderedFeatures=function(e,r,n){r&&r.filter&&this._validate(t.validateStyle.filter,\"queryRenderedFeatures.filter\",r.filter,null,r);var i={};if(r&&r.layers){if(!Array.isArray(r.layers))return this.fire(new t.ErrorEvent(new Error(\"parameters.layers must be an Array.\"))),[];for(var a=0,o=r.layers;a<o.length;a+=1){var s=o[a],l=this._layers[s];if(!l)return this.fire(new t.ErrorEvent(new Error(\"The layer '\"+s+\"' does not exist in the map's style and cannot be queried for features.\"))),[];i[l.source]=!0}}var u=[];for(var c in r.availableImages=this._availableImages,this.sourceCaches)r.layers&&!i[c]||u.push(B(this.sourceCaches[c],this._layers,this._serializedLayers,e,r,n));return this.placement&&u.push(function(t,e,r,n,i,a,o){for(var s={},l=a.queryRenderedSymbols(n),u=[],c=0,f=Object.keys(l).map(Number);c<f.length;c+=1){var h=f[c];u.push(o[h])}u.sort(N);for(var p=function(){var r=v[d],n=r.featureIndex.lookupSymbolFeatures(l[r.bucketInstanceId],e,r.bucketIndex,r.sourceLayerIndex,i.filter,i.layers,i.availableImages,t);for(var a in n){var o=s[a]=s[a]||[],u=n[a];u.sort((function(t,e){var n=r.featureSortOrder;if(n){var i=n.indexOf(t.featureIndex);return n.indexOf(e.featureIndex)-i}return e.featureIndex-t.featureIndex}));for(var c=0,f=u;c<f.length;c+=1){var h=f[c];o.push(h)}}},d=0,v=u;d<v.length;d+=1)p();var g=function(e){s[e].forEach((function(n){var i=n.feature,a=t[e],o=r[a.source].getFeatureState(i.layer[\"source-layer\"],i.id);i.source=i.layer.source,i.layer[\"source-layer\"]&&(i.sourceLayer=i.layer[\"source-layer\"]),i.state=o}))};for(var y in s)g(y);return s}(this._layers,this._serializedLayers,this.sourceCaches,e,r,this.placement.collisionIndex,this.placement.retainedQueryData)),this._flattenAndSortRenderedFeatures(u)},r.prototype.querySourceFeatures=function(e,r){r&&r.filter&&this._validate(t.validateStyle.filter,\"querySourceFeatures.filter\",r.filter,null,r);var n=this.sourceCaches[e];return n?function(t,e){for(var r=t.getRenderableIds().map((function(e){return t.getTileByID(e)})),n=[],i={},a=0;a<r.length;a++){var o=r[a],s=o.tileID.canonical.key;i[s]||(i[s]=!0,o.querySourceFeatures(n,e))}return n}(n,r):[]},r.prototype.addSourceType=function(t,e,n){return r.getSourceType(t)?n(new Error('A source type called \"'+t+'\" already exists.')):(r.setSourceType(t,e),e.workerSourceURL?void this.dispatcher.broadcast(\"loadWorkerSource\",{name:t,url:e.workerSourceURL},n):n(null,null))},r.prototype.getLight=function(){return this.light.getLight()},r.prototype.setLight=function(e,r){void 0===r&&(r={}),this._checkLoaded();var n=this.light.getLight(),i=!1;for(var a in e)if(!t.deepEqual(e[a],n[a])){i=!0;break}if(i){var o={now:t.browser.now(),transition:t.extend({duration:300,delay:0},this.stylesheet.transition)};this.light.setLight(e,r),this.light.updateTransitions(o)}},r.prototype._validate=function(e,r,n,i,a){return void 0===a&&(a={}),(!a||!1!==a.validate)&&He(this,e.call(t.validateStyle,t.extend({key:r,style:this.serialize(),value:n,styleSpec:t.styleSpec},i)))},r.prototype._remove=function(){for(var e in this._request&&(this._request.cancel(),this._request=null),this._spriteRequest&&(this._spriteRequest.cancel(),this._spriteRequest=null),t.evented.off(\"pluginStateChange\",this._rtlTextPluginCallback),this._layers)this._layers[e].setEventedParent(null);for(var r in this.sourceCaches)this.sourceCaches[r].clearTiles(),this.sourceCaches[r].setEventedParent(null);this.imageManager.setEventedParent(null),this.setEventedParent(null),this.dispatcher.remove()},r.prototype._clearSource=function(t){this.sourceCaches[t].clearTiles()},r.prototype._reloadSource=function(t){this.sourceCaches[t].resume(),this.sourceCaches[t].reload()},r.prototype._updateSources=function(t){for(var e in this.sourceCaches)this.sourceCaches[e].update(t)},r.prototype._generateCollisionBoxes=function(){for(var t in this.sourceCaches)this._reloadSource(t)},r.prototype._updatePlacement=function(e,r,n,i,a){void 0===a&&(a=!1);for(var o=!1,s=!1,l={},u=0,c=this._order;u<c.length;u+=1){var f=c[u],h=this._layers[f];if(\"symbol\"===h.type){if(!l[h.source]){var p=this.sourceCaches[h.source];l[h.source]=p.getRenderableIds(!0).map((function(t){return p.getTileByID(t)})).sort((function(t,e){return e.tileID.overscaledZ-t.tileID.overscaledZ||(t.tileID.isLessThan(e.tileID)?-1:1)}))}var d=this.crossTileSymbolIndex.addLayer(h,l[h.source],e.center.lng);o=o||d}}if(this.crossTileSymbolIndex.pruneUnusedLayers(this._order),((a=a||this._layerOrderChanged||0===n)||!this.pauseablePlacement||this.pauseablePlacement.isDone()&&!this.placement.stillRecent(t.browser.now(),e.zoom))&&(this.pauseablePlacement=new Fe(e,this._order,a,r,n,i,this.placement),this._layerOrderChanged=!1),this.pauseablePlacement.isDone()?this.placement.setStale():(this.pauseablePlacement.continuePlacement(this._order,this._layers,l),this.pauseablePlacement.isDone()&&(this.placement=this.pauseablePlacement.commit(t.browser.now()),s=!0),o&&this.pauseablePlacement.placement.setStale()),s||o)for(var v=0,g=this._order;v<g.length;v+=1){var y=g[v],m=this._layers[y];\"symbol\"===m.type&&this.placement.updateLayerOpacities(m,l[m.source])}return!this.pauseablePlacement.isDone()||this.placement.hasTransitions(t.browser.now())},r.prototype._releaseSymbolFadeTiles=function(){for(var t in this.sourceCaches)this.sourceCaches[t].releaseSymbolFadeTiles()},r.prototype.getImages=function(t,e,r){this.imageManager.getImages(e.icons,r),this._updateTilesForChangedImages();var n=this.sourceCaches[e.source];n&&n.setDependencies(e.tileID.key,e.type,e.icons)},r.prototype.getGlyphs=function(t,e,r){this.glyphManager.getGlyphs(e.stacks,r)},r.prototype.getResource=function(e,r,n){return t.makeRequest(r,n)},r}(t.Evented);Ye.getSourceType=function(t){return R[t]},Ye.setSourceType=function(t,e){R[t]=e},Ye.registerForPluginStateChange=t.registerForPluginStateChange;var We=t.createLayout([{name:\"a_pos\",type:\"Int16\",components:2}]),Xe=_r(\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#if !defined(lowp)\\n#define lowp\\n#endif\\n#if !defined(mediump)\\n#define mediump\\n#endif\\n#if !defined(highp)\\n#define highp\\n#endif\\n#endif\",\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#if !defined(lowp)\\n#define lowp\\n#endif\\n#if !defined(mediump)\\n#define mediump\\n#endif\\n#if !defined(highp)\\n#define highp\\n#endif\\n#endif\\nvec2 unpack_float(const float packedValue) {int packedIntValue=int(packedValue);int v0=packedIntValue/256;return vec2(v0,packedIntValue-v0*256);}vec2 unpack_opacity(const float packedOpacity) {int intOpacity=int(packedOpacity)/2;return vec2(float(intOpacity)/127.0,mod(packedOpacity,2.0));}vec4 decode_color(const vec2 encodedColor) {return vec4(unpack_float(encodedColor[0])/255.0,unpack_float(encodedColor[1])/255.0\\n);}float unpack_mix_vec2(const vec2 packedValue,const float t) {return mix(packedValue[0],packedValue[1],t);}vec4 unpack_mix_color(const vec4 packedColors,const float t) {vec4 minColor=decode_color(vec2(packedColors[0],packedColors[1]));vec4 maxColor=decode_color(vec2(packedColors[2],packedColors[3]));return mix(minColor,maxColor,t);}vec2 get_pattern_pos(const vec2 pixel_coord_upper,const vec2 pixel_coord_lower,const vec2 pattern_size,const float tile_units_to_pixels,const vec2 pos) {vec2 offset=mod(mod(mod(pixel_coord_upper,pattern_size)*256.0,pattern_size)*256.0+pixel_coord_lower,pattern_size);return (tile_units_to_pixels*pos+offset)/pattern_size;}\"),Je=_r(\"uniform vec4 u_color;uniform float u_opacity;void main() {gl_FragColor=u_color*u_opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}\"),Ke=_r(\"uniform vec2 u_pattern_tl_a;uniform vec2 u_pattern_br_a;uniform vec2 u_pattern_tl_b;uniform vec2 u_pattern_br_b;uniform vec2 u_texsize;uniform float u_mix;uniform float u_opacity;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(u_pattern_tl_a/u_texsize,u_pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(u_pattern_tl_b/u_texsize,u_pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_mix)*u_opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pattern_size_a;uniform vec2 u_pattern_size_b;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_scale_a;uniform float u_scale_b;uniform float u_tile_units_to_pixels;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_a*u_pattern_size_a,u_tile_units_to_pixels,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_b*u_pattern_size_b,u_tile_units_to_pixels,a_pos);}\"),$e=_r(\"varying vec3 v_data;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define mediump float radius\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define highp vec4 stroke_color\\n#pragma mapbox: define mediump float stroke_width\\n#pragma mapbox: define lowp float stroke_opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize mediump float radius\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize highp vec4 stroke_color\\n#pragma mapbox: initialize mediump float stroke_width\\n#pragma mapbox: initialize lowp float stroke_opacity\\nvec2 extrude=v_data.xy;float extrude_length=length(extrude);lowp float antialiasblur=v_data.z;float antialiased_blur=-max(blur,antialiasblur);float opacity_t=smoothstep(0.0,antialiased_blur,extrude_length-1.0);float color_t=stroke_width < 0.01 ? 0.0 : smoothstep(antialiased_blur,0.0,extrude_length-radius/(radius+stroke_width));gl_FragColor=opacity_t*mix(color*opacity,stroke_color*stroke_opacity,color_t);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform bool u_scale_with_map;uniform bool u_pitch_with_map;uniform vec2 u_extrude_scale;uniform lowp float u_device_pixel_ratio;uniform highp float u_camera_to_center_distance;attribute vec2 a_pos;varying vec3 v_data;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define mediump float radius\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define highp vec4 stroke_color\\n#pragma mapbox: define mediump float stroke_width\\n#pragma mapbox: define lowp float stroke_opacity\\nvoid main(void) {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize mediump float radius\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize highp vec4 stroke_color\\n#pragma mapbox: initialize mediump float stroke_width\\n#pragma mapbox: initialize lowp float stroke_opacity\\nvec2 extrude=vec2(mod(a_pos,2.0)*2.0-1.0);vec2 circle_center=floor(a_pos*0.5);if (u_pitch_with_map) {vec2 corner_position=circle_center;if (u_scale_with_map) {corner_position+=extrude*(radius+stroke_width)*u_extrude_scale;} else {vec4 projected_center=u_matrix*vec4(circle_center,0,1);corner_position+=extrude*(radius+stroke_width)*u_extrude_scale*(projected_center.w/u_camera_to_center_distance);}gl_Position=u_matrix*vec4(corner_position,0,1);} else {gl_Position=u_matrix*vec4(circle_center,0,1);if (u_scale_with_map) {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*u_camera_to_center_distance;} else {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*gl_Position.w;}}lowp float antialiasblur=1.0/u_device_pixel_ratio/(radius+stroke_width);v_data=vec3(extrude.x,extrude.y,antialiasblur);}\"),Qe=_r(\"void main() {gl_FragColor=vec4(1.0);}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}\"),tr=_r(\"uniform highp float u_intensity;varying vec2 v_extrude;\\n#pragma mapbox: define highp float weight\\n#define GAUSS_COEF 0.3989422804014327\\nvoid main() {\\n#pragma mapbox: initialize highp float weight\\nfloat d=-0.5*3.0*3.0*dot(v_extrude,v_extrude);float val=weight*u_intensity*GAUSS_COEF*exp(d);gl_FragColor=vec4(val,1.0,1.0,1.0);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform float u_extrude_scale;uniform float u_opacity;uniform float u_intensity;attribute vec2 a_pos;varying vec2 v_extrude;\\n#pragma mapbox: define highp float weight\\n#pragma mapbox: define mediump float radius\\nconst highp float ZERO=1.0/255.0/16.0;\\n#define GAUSS_COEF 0.3989422804014327\\nvoid main(void) {\\n#pragma mapbox: initialize highp float weight\\n#pragma mapbox: initialize mediump float radius\\nvec2 unscaled_extrude=vec2(mod(a_pos,2.0)*2.0-1.0);float S=sqrt(-2.0*log(ZERO/weight/u_intensity/GAUSS_COEF))/3.0;v_extrude=S*unscaled_extrude;vec2 extrude=v_extrude*radius*u_extrude_scale;vec4 pos=vec4(floor(a_pos*0.5)+extrude,0,1);gl_Position=u_matrix*pos;}\"),er=_r(\"uniform sampler2D u_image;uniform sampler2D u_color_ramp;uniform float u_opacity;varying vec2 v_pos;void main() {float t=texture2D(u_image,v_pos).r;vec4 color=texture2D(u_color_ramp,vec2(t,0.5));gl_FragColor=color*u_opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(0.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_world;attribute vec2 a_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos*u_world,0,1);v_pos.x=a_pos.x;v_pos.y=1.0-a_pos.y;}\"),rr=_r(\"varying float v_placed;varying float v_notUsed;void main() {float alpha=0.5;gl_FragColor=vec4(1.0,0.0,0.0,1.0)*alpha;if (v_placed > 0.5) {gl_FragColor=vec4(0.0,0.0,1.0,0.5)*alpha;}if (v_notUsed > 0.5) {gl_FragColor*=.1;}}\",\"attribute vec2 a_pos;attribute vec2 a_anchor_pos;attribute vec2 a_extrude;attribute vec2 a_placed;attribute vec2 a_shift;uniform mat4 u_matrix;uniform vec2 u_extrude_scale;uniform float u_camera_to_center_distance;varying float v_placed;varying float v_notUsed;void main() {vec4 projectedPoint=u_matrix*vec4(a_anchor_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);gl_Position=u_matrix*vec4(a_pos,0.0,1.0);gl_Position.xy+=(a_extrude+a_shift)*u_extrude_scale*gl_Position.w*collision_perspective_ratio;v_placed=a_placed.x;v_notUsed=a_placed.y;}\"),nr=_r(\"varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;void main() {float alpha=0.5*min(v_perspective_ratio,1.0);float stroke_radius=0.9*max(v_perspective_ratio,1.0);float distance_to_center=length(v_extrude);float distance_to_edge=abs(distance_to_center-v_radius);float opacity_t=smoothstep(-stroke_radius,0.0,-distance_to_edge);vec4 color=mix(vec4(0.0,0.0,1.0,0.5),vec4(1.0,0.0,0.0,1.0),v_collision);gl_FragColor=color*alpha*opacity_t;}\",\"attribute vec2 a_pos;attribute float a_radius;attribute vec2 a_flags;uniform mat4 u_matrix;uniform mat4 u_inv_matrix;uniform vec2 u_viewport_size;uniform float u_camera_to_center_distance;varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;vec3 toTilePosition(vec2 screenPos) {vec4 rayStart=u_inv_matrix*vec4(screenPos,-1.0,1.0);vec4 rayEnd  =u_inv_matrix*vec4(screenPos, 1.0,1.0);rayStart.xyz/=rayStart.w;rayEnd.xyz  /=rayEnd.w;highp float t=(0.0-rayStart.z)/(rayEnd.z-rayStart.z);return mix(rayStart.xyz,rayEnd.xyz,t);}void main() {vec2 quadCenterPos=a_pos;float radius=a_radius;float collision=a_flags.x;float vertexIdx=a_flags.y;vec2 quadVertexOffset=vec2(mix(-1.0,1.0,float(vertexIdx >=2.0)),mix(-1.0,1.0,float(vertexIdx >=1.0 && vertexIdx <=2.0)));vec2 quadVertexExtent=quadVertexOffset*radius;vec3 tilePos=toTilePosition(quadCenterPos);vec4 clipPos=u_matrix*vec4(tilePos,1.0);highp float camera_to_anchor_distance=clipPos.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);float padding_factor=1.2;v_radius=radius;v_extrude=quadVertexExtent*padding_factor;v_perspective_ratio=collision_perspective_ratio;v_collision=collision;gl_Position=vec4(clipPos.xyz/clipPos.w,1.0)+vec4(quadVertexExtent*padding_factor/u_viewport_size*2.0,0.0,0.0);}\"),ir=_r(\"uniform highp vec4 u_color;uniform sampler2D u_overlay;varying vec2 v_uv;void main() {vec4 overlay_color=texture2D(u_overlay,v_uv);gl_FragColor=mix(u_color,overlay_color,overlay_color.a);}\",\"attribute vec2 a_pos;varying vec2 v_uv;uniform mat4 u_matrix;uniform float u_overlay_scale;void main() {v_uv=a_pos/8192.0;gl_Position=u_matrix*vec4(a_pos*u_overlay_scale,0,1);}\"),ar=_r(\"#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float opacity\\ngl_FragColor=color*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float opacity\\ngl_Position=u_matrix*vec4(a_pos,0,1);}\"),or=_r(\"varying vec2 v_pos;\\n#pragma mapbox: define highp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 outline_color\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=outline_color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;uniform vec2 u_world;varying vec2 v_pos;\\n#pragma mapbox: define highp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 outline_color\\n#pragma mapbox: initialize lowp float opacity\\ngl_Position=u_matrix*vec4(a_pos,0,1);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}\"),sr=_r(\"uniform vec2 u_texsize;uniform sampler2D u_image;uniform float u_fade;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);float dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=mix(color1,color2,u_fade)*alpha*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_world;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;gl_Position=u_matrix*vec4(a_pos,0,1);vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,a_pos);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}\"),lr=_r(\"uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_fade)*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileZoomRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileZoomRatio,a_pos);}\"),ur=_r(\"varying vec4 v_color;void main() {gl_FragColor=v_color;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;uniform float u_vertical_gradient;uniform lowp float u_opacity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec4 v_color;\\n#pragma mapbox: define highp float base\\n#pragma mapbox: define highp float height\\n#pragma mapbox: define highp vec4 color\\nvoid main() {\\n#pragma mapbox: initialize highp float base\\n#pragma mapbox: initialize highp float height\\n#pragma mapbox: initialize highp vec4 color\\nvec3 normal=a_normal_ed.xyz;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);gl_Position=u_matrix*vec4(a_pos,t > 0.0 ? height : base,1);float colorvalue=color.r*0.2126+color.g*0.7152+color.b*0.0722;v_color=vec4(0.0,0.0,0.0,1.0);vec4 ambientlight=vec4(0.03,0.03,0.03,1.0);color+=ambientlight;float directional=clamp(dot(normal/16384.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((1.0-colorvalue+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_color.r+=clamp(color.r*directional*u_lightcolor.r,mix(0.0,0.3,1.0-u_lightcolor.r),1.0);v_color.g+=clamp(color.g*directional*u_lightcolor.g,mix(0.0,0.3,1.0-u_lightcolor.g),1.0);v_color.b+=clamp(color.b*directional*u_lightcolor.b,mix(0.0,0.3,1.0-u_lightcolor.b),1.0);v_color*=u_opacity;}\"),cr=_r(\"uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\\n#pragma mapbox: define lowp float base\\n#pragma mapbox: define lowp float height\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float base\\n#pragma mapbox: initialize lowp float height\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);vec4 mixedColor=mix(color1,color2,u_fade);gl_FragColor=mixedColor*v_lighting;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_height_factor;uniform vec3 u_scale;uniform float u_vertical_gradient;uniform lowp float u_opacity;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\\n#pragma mapbox: define lowp float base\\n#pragma mapbox: define lowp float height\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float base\\n#pragma mapbox: initialize lowp float height\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec3 normal=a_normal_ed.xyz;float edgedistance=a_normal_ed.w;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);float z=t > 0.0 ? height : base;gl_Position=u_matrix*vec4(a_pos,z,1);vec2 pos=normal.x==1.0 && normal.y==0.0 && normal.z==16384.0\\n? a_pos\\n: vec2(edgedistance,z*u_height_factor);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,pos);v_lighting=vec4(0.0,0.0,0.0,1.0);float directional=clamp(dot(normal/16383.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((0.5+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_lighting.rgb+=clamp(directional*u_lightcolor,mix(vec3(0.0),vec3(0.3),1.0-u_lightcolor),vec3(1.0));v_lighting*=u_opacity;}\"),fr=_r(\"#ifdef GL_ES\\nprecision highp float;\\n#endif\\nuniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_dimension;uniform float u_zoom;uniform float u_maxzoom;uniform vec4 u_unpack;float getElevation(vec2 coord,float bias) {vec4 data=texture2D(u_image,coord)*255.0;data.a=-1.0;return dot(data,u_unpack)/4.0;}void main() {vec2 epsilon=1.0/u_dimension;float a=getElevation(v_pos+vec2(-epsilon.x,-epsilon.y),0.0);float b=getElevation(v_pos+vec2(0,-epsilon.y),0.0);float c=getElevation(v_pos+vec2(epsilon.x,-epsilon.y),0.0);float d=getElevation(v_pos+vec2(-epsilon.x,0),0.0);float e=getElevation(v_pos,0.0);float f=getElevation(v_pos+vec2(epsilon.x,0),0.0);float g=getElevation(v_pos+vec2(-epsilon.x,epsilon.y),0.0);float h=getElevation(v_pos+vec2(0,epsilon.y),0.0);float i=getElevation(v_pos+vec2(epsilon.x,epsilon.y),0.0);float exaggeration=u_zoom < 2.0 ? 0.4 : u_zoom < 4.5 ? 0.35 : 0.3;vec2 deriv=vec2((c+f+f+i)-(a+d+d+g),(g+h+h+i)-(a+b+b+c))/ pow(2.0,(u_zoom-u_maxzoom)*exaggeration+19.2562-u_zoom);gl_FragColor=clamp(vec4(deriv.x/2.0+0.5,deriv.y/2.0+0.5,1.0,1.0),0.0,1.0);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_dimension;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);highp vec2 epsilon=1.0/u_dimension;float scale=(u_dimension.x-2.0)/u_dimension.x;v_pos=(a_texture_pos/8192.0)*scale+epsilon;}\"),hr=_r(\"uniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_latrange;uniform vec2 u_light;uniform vec4 u_shadow;uniform vec4 u_highlight;uniform vec4 u_accent;\\n#define PI 3.141592653589793\\nvoid main() {vec4 pixel=texture2D(u_image,v_pos);vec2 deriv=((pixel.rg*2.0)-1.0);float scaleFactor=cos(radians((u_latrange[0]-u_latrange[1])*(1.0-v_pos.y)+u_latrange[1]));float slope=atan(1.25*length(deriv)/scaleFactor);float aspect=deriv.x !=0.0 ? atan(deriv.y,-deriv.x) : PI/2.0*(deriv.y > 0.0 ? 1.0 :-1.0);float intensity=u_light.x;float azimuth=u_light.y+PI;float base=1.875-intensity*1.75;float maxValue=0.5*PI;float scaledSlope=intensity !=0.5 ? ((pow(base,slope)-1.0)/(pow(base,maxValue)-1.0))*maxValue : slope;float accent=cos(scaledSlope);vec4 accent_color=(1.0-accent)*u_accent*clamp(intensity*2.0,0.0,1.0);float shade=abs(mod((aspect+azimuth)/PI+0.5,2.0)-1.0);vec4 shade_color=mix(u_shadow,u_highlight,shade)*sin(scaledSlope)*clamp(intensity*2.0,0.0,1.0);gl_FragColor=accent_color*(1.0-shade_color.a)+shade_color;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos=a_texture_pos/8192.0;}\"),pr=_r(\"uniform lowp float u_device_pixel_ratio;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform vec2 u_units_to_pixels;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_linesofar;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}\"),dr=_r(\"uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;varying highp float v_lineprogress;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);vec4 color=texture2D(u_image,vec2(v_lineprogress,0.5));gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define MAX_LINE_DISTANCE 32767.0\\n#define scale 0.015873016\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_lineprogress;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_lineprogress=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0/MAX_LINE_DISTANCE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}\"),vr=_r(\"uniform lowp float u_device_pixel_ratio;uniform vec2 u_texsize;uniform float u_fade;uniform mediump vec3 u_scale;uniform sampler2D u_image;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;vec2 pattern_size_a=vec2(display_size_a.x*fromScale/tileZoomRatio,display_size_a.y);vec2 pattern_size_b=vec2(display_size_b.x*toScale/tileZoomRatio,display_size_b.y);float aspect_a=display_size_a.y/v_width;float aspect_b=display_size_b.y/v_width;float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float x_a=mod(v_linesofar/pattern_size_a.x*aspect_a,1.0);float x_b=mod(v_linesofar/pattern_size_b.x*aspect_b,1.0);float y=0.5*v_normal.y+0.5;vec2 texel_size=1.0/u_texsize;vec2 pos_a=mix(pattern_tl_a*texel_size-texel_size,pattern_br_a*texel_size+texel_size,vec2(x_a,y));vec2 pos_b=mix(pattern_tl_b*texel_size-texel_size,pattern_br_b*texel_size+texel_size,vec2(x_b,y));vec4 color=mix(texture2D(u_image,pos_a),texture2D(u_image,pos_b),u_fade);gl_FragColor=color*alpha*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\n#define LINE_DISTANCE_SCALE 2.0\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform vec2 u_units_to_pixels;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_linesofar=a_linesofar;v_width2=vec2(outset,inset);v_width=floorwidth;}\"),gr=_r(\"uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;uniform float u_sdfgamma;uniform float u_mix;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float sdfdist_a=texture2D(u_image,v_tex_a).a;float sdfdist_b=texture2D(u_image,v_tex_b).a;float sdfdist=mix(sdfdist_a,sdfdist_b,u_mix);alpha*=smoothstep(0.5-u_sdfgamma/floorwidth,0.5+u_sdfgamma/floorwidth,sdfdist);gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\n#define LINE_DISTANCE_SCALE 2.0\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_patternscale_a;uniform float u_tex_y_a;uniform vec2 u_patternscale_b;uniform float u_tex_y_b;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_tex_a=vec2(a_linesofar*u_patternscale_a.x/floorwidth,normal.y*u_patternscale_a.y+u_tex_y_a);v_tex_b=vec2(a_linesofar*u_patternscale_b.x/floorwidth,normal.y*u_patternscale_b.y+u_tex_y_b);v_width2=vec2(outset,inset);}\"),yr=_r(\"uniform float u_fade_t;uniform float u_opacity;uniform sampler2D u_image0;uniform sampler2D u_image1;varying vec2 v_pos0;varying vec2 v_pos1;uniform float u_brightness_low;uniform float u_brightness_high;uniform float u_saturation_factor;uniform float u_contrast_factor;uniform vec3 u_spin_weights;void main() {vec4 color0=texture2D(u_image0,v_pos0);vec4 color1=texture2D(u_image1,v_pos1);if (color0.a > 0.0) {color0.rgb=color0.rgb/color0.a;}if (color1.a > 0.0) {color1.rgb=color1.rgb/color1.a;}vec4 color=mix(color0,color1,u_fade_t);color.a*=u_opacity;vec3 rgb=color.rgb;rgb=vec3(dot(rgb,u_spin_weights.xyz),dot(rgb,u_spin_weights.zxy),dot(rgb,u_spin_weights.yzx));float average=(color.r+color.g+color.b)/3.0;rgb+=(average-rgb)*u_saturation_factor;rgb=(rgb-0.5)*u_contrast_factor+0.5;vec3 u_high_vec=vec3(u_brightness_low,u_brightness_low,u_brightness_low);vec3 u_low_vec=vec3(u_brightness_high,u_brightness_high,u_brightness_high);gl_FragColor=vec4(mix(u_high_vec,u_low_vec,rgb)*color.a,color.a);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_tl_parent;uniform float u_scale_parent;uniform float u_buffer_scale;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos0;varying vec2 v_pos1;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos0=(((a_texture_pos/8192.0)-0.5)/u_buffer_scale )+0.5;v_pos1=(v_pos0*u_scale_parent)+u_tl_parent;}\"),mr=_r(\"uniform sampler2D u_texture;varying vec2 v_tex;varying float v_fade_opacity;\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\nlowp float alpha=opacity*v_fade_opacity;gl_FragColor=texture2D(u_texture,v_tex)*alpha;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform highp float u_camera_to_center_distance;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform float u_fade_change;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform vec2 u_texsize;varying vec2 v_tex;varying float v_fade_opacity;\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;vec2 a_minFontScale=a_pixeloffset.zw/256.0;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*max(a_minFontScale,fontScale)+a_pxoffset/16.0),0.0,1.0);v_tex=a_tex/u_texsize;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;v_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));}\"),xr=_r(\"#define SDF_PX 8.0\\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;uniform bool u_is_text;varying vec2 v_data0;varying vec3 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nfloat EDGE_GAMMA=0.105/u_device_pixel_ratio;vec2 tex=v_data0.xy;float gamma_scale=v_data1.x;float size=v_data1.y;float fade_opacity=v_data1[2];float fontScale=u_is_text ? size/24.0 : size;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;varying vec2 v_data0;varying vec3 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale+a_pxoffset),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0=a_tex/u_texsize;v_data1=vec3(gamma_scale,size,interpolated_fade_opacity);}\"),br=_r(\"#define SDF_PX 8.0\\n#define SDF 1.0\\n#define ICON 0.0\\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform sampler2D u_texture_icon;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;varying vec4 v_data0;varying vec4 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nfloat fade_opacity=v_data1[2];if (v_data1.w==ICON) {vec2 tex_icon=v_data0.zw;lowp float alpha=opacity*fade_opacity;gl_FragColor=texture2D(u_texture_icon,tex_icon)*alpha;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\nreturn;}vec2 tex=v_data0.xy;float EDGE_GAMMA=0.105/u_device_pixel_ratio;float gamma_scale=v_data1.x;float size=v_data1.y;float fontScale=size/24.0;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;uniform vec2 u_texsize_icon;varying vec4 v_data0;varying vec4 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);float is_sdf=a_size[0]-2.0*a_size_min;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=size/24.0;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0.xy=a_tex/u_texsize;v_data0.zw=a_tex/u_texsize_icon;v_data1=vec4(gamma_scale,size,interpolated_fade_opacity,is_sdf);}\");function _r(t,e){var r=/#pragma mapbox: ([\\w]+) ([\\w]+) ([\\w]+) ([\\w]+)/g,n={};return{fragmentSource:t=t.replace(r,(function(t,e,r,i,a){return n[a]=!0,\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\nvarying \"+r+\" \"+i+\" \"+a+\";\\n#else\\nuniform \"+r+\" \"+i+\" u_\"+a+\";\\n#endif\\n\":\"\\n#ifdef HAS_UNIFORM_u_\"+a+\"\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\"})),vertexSource:e=e.replace(r,(function(t,e,r,i,a){var o=\"float\"===i?\"vec2\":\"vec4\",s=a.match(/color/)?\"color\":o;return n[a]?\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\nuniform lowp float u_\"+a+\"_t;\\nattribute \"+r+\" \"+o+\" a_\"+a+\";\\nvarying \"+r+\" \"+i+\" \"+a+\";\\n#else\\nuniform \"+r+\" \"+i+\" u_\"+a+\";\\n#endif\\n\":\"vec4\"===s?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+a+\" = a_\"+a+\";\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\":\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+a+\" = unpack_mix_\"+s+\"(a_\"+a+\", u_\"+a+\"_t);\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\":\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\nuniform lowp float u_\"+a+\"_t;\\nattribute \"+r+\" \"+o+\" a_\"+a+\";\\n#else\\nuniform \"+r+\" \"+i+\" u_\"+a+\";\\n#endif\\n\":\"vec4\"===s?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+r+\" \"+i+\" \"+a+\" = a_\"+a+\";\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\":\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+r+\" \"+i+\" \"+a+\" = unpack_mix_\"+s+\"(a_\"+a+\", u_\"+a+\"_t);\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\"}))}}var wr=Object.freeze({__proto__:null,prelude:Xe,background:Je,backgroundPattern:Ke,circle:$e,clippingMask:Qe,heatmap:tr,heatmapTexture:er,collisionBox:rr,collisionCircle:nr,debug:ir,fill:ar,fillOutline:or,fillOutlinePattern:sr,fillPattern:lr,fillExtrusion:ur,fillExtrusionPattern:cr,hillshadePrepare:fr,hillshade:hr,line:pr,lineGradient:dr,linePattern:vr,lineSDF:gr,raster:yr,symbolIcon:mr,symbolSDF:xr,symbolTextAndIcon:br}),Tr=function(){this.boundProgram=null,this.boundLayoutVertexBuffer=null,this.boundPaintVertexBuffers=[],this.boundIndexBuffer=null,this.boundVertexOffset=null,this.boundDynamicVertexBuffer=null,this.vao=null};Tr.prototype.bind=function(t,e,r,n,i,a,o,s){this.context=t;for(var l=this.boundPaintVertexBuffers.length!==n.length,u=0;!l&&u<n.length;u++)this.boundPaintVertexBuffers[u]!==n[u]&&(l=!0);var c=!this.vao||this.boundProgram!==e||this.boundLayoutVertexBuffer!==r||l||this.boundIndexBuffer!==i||this.boundVertexOffset!==a||this.boundDynamicVertexBuffer!==o||this.boundDynamicVertexBuffer2!==s;!t.extVertexArrayObject||c?this.freshBind(e,r,n,i,a,o,s):(t.bindVertexArrayOES.set(this.vao),o&&o.bind(),i&&i.dynamicDraw&&i.bind(),s&&s.bind())},Tr.prototype.freshBind=function(t,e,r,n,i,a,o){var s,l=t.numAttributes,u=this.context,c=u.gl;if(u.extVertexArrayObject)this.vao&&this.destroy(),this.vao=u.extVertexArrayObject.createVertexArrayOES(),u.bindVertexArrayOES.set(this.vao),s=0,this.boundProgram=t,this.boundLayoutVertexBuffer=e,this.boundPaintVertexBuffers=r,this.boundIndexBuffer=n,this.boundVertexOffset=i,this.boundDynamicVertexBuffer=a,this.boundDynamicVertexBuffer2=o;else{s=u.currentNumAttributes||0;for(var f=l;f<s;f++)c.disableVertexAttribArray(f)}e.enableAttributes(c,t);for(var h=0,p=r;h<p.length;h+=1)p[h].enableAttributes(c,t);a&&a.enableAttributes(c,t),o&&o.enableAttributes(c,t),e.bind(),e.setVertexAttribPointers(c,t,i);for(var d=0,v=r;d<v.length;d+=1){var g=v[d];g.bind(),g.setVertexAttribPointers(c,t,i)}a&&(a.bind(),a.setVertexAttribPointers(c,t,i)),n&&n.bind(),o&&(o.bind(),o.setVertexAttribPointers(c,t,i)),u.currentNumAttributes=l},Tr.prototype.destroy=function(){this.vao&&(this.context.extVertexArrayObject.deleteVertexArrayOES(this.vao),this.vao=null)};var kr=function(t,e,r,n,i){var a=t.gl;this.program=a.createProgram();var o=r?r.defines():[];i&&o.push(\"#define OVERDRAW_INSPECTOR;\");var s=o.concat(Xe.fragmentSource,e.fragmentSource).join(\"\\n\"),l=o.concat(Xe.vertexSource,e.vertexSource).join(\"\\n\"),u=a.createShader(a.FRAGMENT_SHADER);if(a.isContextLost())this.failedToCreate=!0;else{a.shaderSource(u,s),a.compileShader(u),a.attachShader(this.program,u);var c=a.createShader(a.VERTEX_SHADER);if(a.isContextLost())this.failedToCreate=!0;else{a.shaderSource(c,l),a.compileShader(c),a.attachShader(this.program,c);for(var f=r?r.layoutAttributes:[],h=0;h<f.length;h++)a.bindAttribLocation(this.program,h,f[h].name);a.linkProgram(this.program),a.deleteShader(c),a.deleteShader(u),this.numAttributes=a.getProgramParameter(this.program,a.ACTIVE_ATTRIBUTES),this.attributes={};for(var p={},d=0;d<this.numAttributes;d++){var v=a.getActiveAttrib(this.program,d);v&&(this.attributes[v.name]=a.getAttribLocation(this.program,v.name))}for(var g=a.getProgramParameter(this.program,a.ACTIVE_UNIFORMS),y=0;y<g;y++){var m=a.getActiveUniform(this.program,y);m&&(p[m.name]=a.getUniformLocation(this.program,m.name))}this.fixedUniforms=n(t,p),this.binderUniforms=r?r.getUniforms(t,p):[]}}};function Ar(t,e,r){var n=1/ge(r,1,e.transform.tileZoom),i=Math.pow(2,r.tileID.overscaledZ),a=r.tileSize*Math.pow(2,e.transform.tileZoom)/i,o=a*(r.tileID.canonical.x+r.tileID.wrap*i),s=a*r.tileID.canonical.y;return{u_image:0,u_texsize:r.imageAtlasTexture.size,u_scale:[n,t.fromScale,t.toScale],u_fade:t.t,u_pixel_coord_upper:[o>>16,s>>16],u_pixel_coord_lower:[65535&o,65535&s]}}kr.prototype.draw=function(t,e,r,n,i,a,o,s,l,u,c,f,h,p,d,v){var g,y=t.gl;if(!this.failedToCreate){for(var m in t.program.set(this.program),t.setDepthMode(r),t.setStencilMode(n),t.setColorMode(i),t.setCullFace(a),this.fixedUniforms)this.fixedUniforms[m].set(o[m]);p&&p.setUniforms(t,this.binderUniforms,f,{zoom:h});for(var x=(g={},g[y.LINES]=2,g[y.TRIANGLES]=3,g[y.LINE_STRIP]=1,g)[e],b=0,_=c.get();b<_.length;b+=1){var w=_[b],T=w.vaos||(w.vaos={});(T[s]||(T[s]=new Tr)).bind(t,this,l,p?p.getPaintVertexBuffers():[],u,w.vertexOffset,d,v),y.drawElements(e,w.primitiveLength*x,y.UNSIGNED_SHORT,w.primitiveOffset*x*2)}}};var Mr=function(e,r,n,i){var a=r.style.light,o=a.properties.get(\"position\"),s=[o.x,o.y,o.z],l=t.create$1();\"viewport\"===a.properties.get(\"anchor\")&&t.fromRotation(l,-r.transform.angle),t.transformMat3(s,s,l);var u=a.properties.get(\"color\");return{u_matrix:e,u_lightpos:s,u_lightintensity:a.properties.get(\"intensity\"),u_lightcolor:[u.r,u.g,u.b],u_vertical_gradient:+n,u_opacity:i}},Sr=function(e,r,n,i,a,o,s){return t.extend(Mr(e,r,n,i),Ar(o,r,s),{u_height_factor:-Math.pow(2,a.overscaledZ)/s.tileSize/8})},Er=function(t){return{u_matrix:t}},Lr=function(e,r,n,i){return t.extend(Er(e),Ar(n,r,i))},Cr=function(t,e){return{u_matrix:t,u_world:e}},Pr=function(e,r,n,i,a){return t.extend(Lr(e,r,n,i),{u_world:a})},Or=function(e,r,n,i){var a,o,s=e.transform;if(\"map\"===i.paint.get(\"circle-pitch-alignment\")){var l=ge(n,1,s.zoom);a=!0,o=[l,l]}else a=!1,o=s.pixelsToGLUnits;return{u_camera_to_center_distance:s.cameraToCenterDistance,u_scale_with_map:+(\"map\"===i.paint.get(\"circle-pitch-scale\")),u_matrix:e.translatePosMatrix(r.posMatrix,n,i.paint.get(\"circle-translate\"),i.paint.get(\"circle-translate-anchor\")),u_pitch_with_map:+a,u_device_pixel_ratio:t.browser.devicePixelRatio,u_extrude_scale:o}},Ir=function(t,e,r){var n=ge(r,1,e.zoom),i=Math.pow(2,e.zoom-r.tileID.overscaledZ),a=r.tileID.overscaleFactor();return{u_matrix:t,u_camera_to_center_distance:e.cameraToCenterDistance,u_pixels_to_tile_units:n,u_extrude_scale:[e.pixelsToGLUnits[0]/(n*i),e.pixelsToGLUnits[1]/(n*i)],u_overscale_factor:a}},Dr=function(t,e,r){return{u_matrix:t,u_inv_matrix:e,u_camera_to_center_distance:r.cameraToCenterDistance,u_viewport_size:[r.width,r.height]}},zr=function(t,e,r){return void 0===r&&(r=1),{u_matrix:t,u_color:e,u_overlay:0,u_overlay_scale:r}},Rr=function(t){return{u_matrix:t}},Fr=function(t,e,r,n){return{u_matrix:t,u_extrude_scale:ge(e,1,r),u_intensity:n}},Br=function(e,r,n,i){var a=t.create();t.ortho(a,0,e.width,e.height,0,0,1);var o=e.context.gl;return{u_matrix:a,u_world:[o.drawingBufferWidth,o.drawingBufferHeight],u_image:n,u_color_ramp:i,u_opacity:r.paint.get(\"heatmap-opacity\")}},Nr=function(e,r,n){var i=n.paint.get(\"hillshade-shadow-color\"),a=n.paint.get(\"hillshade-highlight-color\"),o=n.paint.get(\"hillshade-accent-color\"),s=n.paint.get(\"hillshade-illumination-direction\")*(Math.PI/180);\"viewport\"===n.paint.get(\"hillshade-illumination-anchor\")&&(s-=e.transform.angle);var l,u,c,f=!e.options.moving;return{u_matrix:e.transform.calculatePosMatrix(r.tileID.toUnwrapped(),f),u_image:0,u_latrange:(l=r.tileID,u=Math.pow(2,l.canonical.z),c=l.canonical.y,[new t.MercatorCoordinate(0,c/u).toLngLat().lat,new t.MercatorCoordinate(0,(c+1)/u).toLngLat().lat]),u_light:[n.paint.get(\"hillshade-exaggeration\"),s],u_shadow:i,u_highlight:a,u_accent:o}},jr=function(e,r,n){var i=r.stride,a=t.create();return t.ortho(a,0,t.EXTENT,-t.EXTENT,0,0,1),t.translate(a,a,[0,-t.EXTENT,0]),{u_matrix:a,u_image:1,u_dimension:[i,i],u_zoom:e.overscaledZ,u_maxzoom:n,u_unpack:r.getUnpackVector()}};var Ur=function(e,r,n){var i=e.transform;return{u_matrix:Zr(e,r,n),u_ratio:1/ge(r,1,i.zoom),u_device_pixel_ratio:t.browser.devicePixelRatio,u_units_to_pixels:[1/i.pixelsToGLUnits[0],1/i.pixelsToGLUnits[1]]}},Vr=function(e,r,n){return t.extend(Ur(e,r,n),{u_image:0})},Hr=function(e,r,n,i){var a=e.transform,o=Gr(r,a);return{u_matrix:Zr(e,r,n),u_texsize:r.imageAtlasTexture.size,u_ratio:1/ge(r,1,a.zoom),u_device_pixel_ratio:t.browser.devicePixelRatio,u_image:0,u_scale:[o,i.fromScale,i.toScale],u_fade:i.t,u_units_to_pixels:[1/a.pixelsToGLUnits[0],1/a.pixelsToGLUnits[1]]}},qr=function(e,r,n,i,a){var o=e.transform,s=e.lineAtlas,l=Gr(r,o),u=\"round\"===n.layout.get(\"line-cap\"),c=s.getDash(i.from,u),f=s.getDash(i.to,u),h=c.width*a.fromScale,p=f.width*a.toScale;return t.extend(Ur(e,r,n),{u_patternscale_a:[l/h,-c.height/2],u_patternscale_b:[l/p,-f.height/2],u_sdfgamma:s.width/(256*Math.min(h,p)*t.browser.devicePixelRatio)/2,u_image:0,u_tex_y_a:c.y,u_tex_y_b:f.y,u_mix:a.t})};function Gr(t,e){return 1/ge(t,1,e.tileZoom)}function Zr(t,e,r){return t.translatePosMatrix(e.tileID.posMatrix,e,r.paint.get(\"line-translate\"),r.paint.get(\"line-translate-anchor\"))}var Yr=function(t,e,r,n,i){return{u_matrix:t,u_tl_parent:e,u_scale_parent:r,u_buffer_scale:1,u_fade_t:n.mix,u_opacity:n.opacity*i.paint.get(\"raster-opacity\"),u_image0:0,u_image1:1,u_brightness_low:i.paint.get(\"raster-brightness-min\"),u_brightness_high:i.paint.get(\"raster-brightness-max\"),u_saturation_factor:(o=i.paint.get(\"raster-saturation\"),o>0?1-1/(1.001-o):-o),u_contrast_factor:(a=i.paint.get(\"raster-contrast\"),a>0?1/(1-a):1+a),u_spin_weights:Wr(i.paint.get(\"raster-hue-rotate\"))};var a,o};function Wr(t){t*=Math.PI/180;var e=Math.sin(t),r=Math.cos(t);return[(2*r+1)/3,(-Math.sqrt(3)*e-r+1)/3,(Math.sqrt(3)*e-r+1)/3]}var Xr,Jr=function(t,e,r,n,i,a,o,s,l,u){var c=i.transform;return{u_is_size_zoom_constant:+(\"constant\"===t||\"source\"===t),u_is_size_feature_constant:+(\"constant\"===t||\"camera\"===t),u_size_t:e?e.uSizeT:0,u_size:e?e.uSize:0,u_camera_to_center_distance:c.cameraToCenterDistance,u_pitch:c.pitch/360*2*Math.PI,u_rotate_symbol:+r,u_aspect_ratio:c.width/c.height,u_fade_change:i.options.fadeDuration?i.symbolFadeChange:1,u_matrix:a,u_label_plane_matrix:o,u_coord_matrix:s,u_is_text:+l,u_pitch_with_map:+n,u_texsize:u,u_texture:0}},Kr=function(e,r,n,i,a,o,s,l,u,c,f){var h=a.transform;return t.extend(Jr(e,r,n,i,a,o,s,l,u,c),{u_gamma_scale:i?Math.cos(h._pitch)*h.cameraToCenterDistance:1,u_device_pixel_ratio:t.browser.devicePixelRatio,u_is_halo:+f})},$r=function(e,r,n,i,a,o,s,l,u,c){return t.extend(Kr(e,r,n,i,a,o,s,l,!0,u,!0),{u_texsize_icon:c,u_texture_icon:1})},Qr=function(t,e,r){return{u_matrix:t,u_opacity:e,u_color:r}},tn=function(e,r,n,i,a,o){return t.extend(function(t,e,r,n){var i=r.imageManager.getPattern(t.from.toString()),a=r.imageManager.getPattern(t.to.toString()),o=r.imageManager.getPixelSize(),s=o.width,l=o.height,u=Math.pow(2,n.tileID.overscaledZ),c=n.tileSize*Math.pow(2,r.transform.tileZoom)/u,f=c*(n.tileID.canonical.x+n.tileID.wrap*u),h=c*n.tileID.canonical.y;return{u_image:0,u_pattern_tl_a:i.tl,u_pattern_br_a:i.br,u_pattern_tl_b:a.tl,u_pattern_br_b:a.br,u_texsize:[s,l],u_mix:e.t,u_pattern_size_a:i.displaySize,u_pattern_size_b:a.displaySize,u_scale_a:e.fromScale,u_scale_b:e.toScale,u_tile_units_to_pixels:1/ge(n,1,r.transform.tileZoom),u_pixel_coord_upper:[f>>16,h>>16],u_pixel_coord_lower:[65535&f,65535&h]}}(i,o,n,a),{u_matrix:e,u_opacity:r})},en={fillExtrusion:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fillExtrusionPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_height_factor:new t.Uniform1f(e,r.u_height_factor),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fill:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},fillPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},fillOutline:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world)}},fillOutlinePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},circle:function(e,r){return{u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_scale_with_map:new t.Uniform1i(e,r.u_scale_with_map),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},collisionBox:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pixels_to_tile_units:new t.Uniform1f(e,r.u_pixels_to_tile_units),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_overscale_factor:new t.Uniform1f(e,r.u_overscale_factor)}},collisionCircle:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_inv_matrix:new t.UniformMatrix4f(e,r.u_inv_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_viewport_size:new t.Uniform2f(e,r.u_viewport_size)}},debug:function(e,r){return{u_color:new t.UniformColor(e,r.u_color),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_overlay:new t.Uniform1i(e,r.u_overlay),u_overlay_scale:new t.Uniform1f(e,r.u_overlay_scale)}},clippingMask:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmap:function(e,r){return{u_extrude_scale:new t.Uniform1f(e,r.u_extrude_scale),u_intensity:new t.Uniform1f(e,r.u_intensity),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmapTexture:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_color_ramp:new t.Uniform1i(e,r.u_color_ramp),u_opacity:new t.Uniform1f(e,r.u_opacity)}},hillshade:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_latrange:new t.Uniform2f(e,r.u_latrange),u_light:new t.Uniform2f(e,r.u_light),u_shadow:new t.UniformColor(e,r.u_shadow),u_highlight:new t.UniformColor(e,r.u_highlight),u_accent:new t.UniformColor(e,r.u_accent)}},hillshadePrepare:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_dimension:new t.Uniform2f(e,r.u_dimension),u_zoom:new t.Uniform1f(e,r.u_zoom),u_maxzoom:new t.Uniform1f(e,r.u_maxzoom),u_unpack:new t.Uniform4f(e,r.u_unpack)}},line:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels)}},lineGradient:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_image:new t.Uniform1i(e,r.u_image)}},linePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_texsize:new t.Uniform2f(e,r.u_texsize),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_image:new t.Uniform1i(e,r.u_image),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},lineSDF:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_patternscale_a:new t.Uniform2f(e,r.u_patternscale_a),u_patternscale_b:new t.Uniform2f(e,r.u_patternscale_b),u_sdfgamma:new t.Uniform1f(e,r.u_sdfgamma),u_image:new t.Uniform1i(e,r.u_image),u_tex_y_a:new t.Uniform1f(e,r.u_tex_y_a),u_tex_y_b:new t.Uniform1f(e,r.u_tex_y_b),u_mix:new t.Uniform1f(e,r.u_mix)}},raster:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_tl_parent:new t.Uniform2f(e,r.u_tl_parent),u_scale_parent:new t.Uniform1f(e,r.u_scale_parent),u_buffer_scale:new t.Uniform1f(e,r.u_buffer_scale),u_fade_t:new t.Uniform1f(e,r.u_fade_t),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image0:new t.Uniform1i(e,r.u_image0),u_image1:new t.Uniform1i(e,r.u_image1),u_brightness_low:new t.Uniform1f(e,r.u_brightness_low),u_brightness_high:new t.Uniform1f(e,r.u_brightness_high),u_saturation_factor:new t.Uniform1f(e,r.u_saturation_factor),u_contrast_factor:new t.Uniform1f(e,r.u_contrast_factor),u_spin_weights:new t.Uniform3f(e,r.u_spin_weights)}},symbolIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture)}},symbolSDF:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},symbolTextAndIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texsize_icon:new t.Uniform2f(e,r.u_texsize_icon),u_texture:new t.Uniform1i(e,r.u_texture),u_texture_icon:new t.Uniform1i(e,r.u_texture_icon),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},background:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_color:new t.UniformColor(e,r.u_color)}},backgroundPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image:new t.Uniform1i(e,r.u_image),u_pattern_tl_a:new t.Uniform2f(e,r.u_pattern_tl_a),u_pattern_br_a:new t.Uniform2f(e,r.u_pattern_br_a),u_pattern_tl_b:new t.Uniform2f(e,r.u_pattern_tl_b),u_pattern_br_b:new t.Uniform2f(e,r.u_pattern_br_b),u_texsize:new t.Uniform2f(e,r.u_texsize),u_mix:new t.Uniform1f(e,r.u_mix),u_pattern_size_a:new t.Uniform2f(e,r.u_pattern_size_a),u_pattern_size_b:new t.Uniform2f(e,r.u_pattern_size_b),u_scale_a:new t.Uniform1f(e,r.u_scale_a),u_scale_b:new t.Uniform1f(e,r.u_scale_b),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_tile_units_to_pixels:new t.Uniform1f(e,r.u_tile_units_to_pixels)}}};function rn(e,r,n,i,a,o,s){for(var l=e.context,u=l.gl,c=e.useProgram(\"collisionBox\"),f=[],h=0,p=0,d=0;d<i.length;d++){var v=i[d],g=r.getTile(v),y=g.getBucket(n);if(y){var m=v.posMatrix;0===a[0]&&0===a[1]||(m=e.translatePosMatrix(v.posMatrix,g,a,o));var x=s?y.textCollisionBox:y.iconCollisionBox,b=y.collisionCircleArray;if(b.length>0){var _=t.create(),w=m;t.mul(_,y.placementInvProjMatrix,e.transform.glCoordMatrix),t.mul(_,_,y.placementViewportMatrix),f.push({circleArray:b,circleOffset:p,transform:w,invTransform:_}),p=h+=b.length/4}x&&c.draw(l,u.LINES,Mt.disabled,Et.disabled,e.colorModeForRenderPass(),Ct.disabled,Ir(m,e.transform,g),n.id,x.layoutVertexBuffer,x.indexBuffer,x.segments,null,e.transform.zoom,null,null,x.collisionVertexBuffer)}}if(s&&f.length){var T=e.useProgram(\"collisionCircle\"),k=new t.StructArrayLayout2f1f2i16;k.resize(4*h),k._trim();for(var A=0,M=0,S=f;M<S.length;M+=1)for(var E=S[M],L=0;L<E.circleArray.length/4;L++){var C=4*L,P=E.circleArray[C+0],O=E.circleArray[C+1],I=E.circleArray[C+2],D=E.circleArray[C+3];k.emplace(A++,P,O,I,D,0),k.emplace(A++,P,O,I,D,1),k.emplace(A++,P,O,I,D,2),k.emplace(A++,P,O,I,D,3)}(!Xr||Xr.length<2*h)&&(Xr=function(e){var r=2*e,n=new t.StructArrayLayout3ui6;n.resize(r),n._trim();for(var i=0;i<r;i++){var a=6*i;n.uint16[a+0]=4*i+0,n.uint16[a+1]=4*i+1,n.uint16[a+2]=4*i+2,n.uint16[a+3]=4*i+2,n.uint16[a+4]=4*i+3,n.uint16[a+5]=4*i+0}return n}(h));for(var z=l.createIndexBuffer(Xr,!0),R=l.createVertexBuffer(k,t.collisionCircleLayout.members,!0),F=0,B=f;F<B.length;F+=1){var N=B[F],j=Dr(N.transform,N.invTransform,e.transform);T.draw(l,u.TRIANGLES,Mt.disabled,Et.disabled,e.colorModeForRenderPass(),Ct.disabled,j,n.id,R,z,t.SegmentVector.simpleSegment(0,2*N.circleOffset,N.circleArray.length,N.circleArray.length/2),null,e.transform.zoom,null,null,null)}R.destroy(),z.destroy()}}var nn=t.identity(new Float32Array(16));function an(e,r,n,i,a,o){var s=t.getAnchorAlignment(e),l=-(s.horizontalAlign-.5)*r,u=-(s.verticalAlign-.5)*n,c=t.evaluateVariableOffset(e,i);return new t.Point((l/a+c[0])*o,(u/a+c[1])*o)}function on(e,r,n,i,a,o,s,l,u,c,f){var h=e.text.placedSymbolArray,p=e.text.dynamicLayoutVertexArray,d=e.icon.dynamicLayoutVertexArray,v={};p.clear();for(var g=0;g<h.length;g++){var y=h.get(g),m=e.allowVerticalPlacement&&!y.placedOrientation,x=y.hidden||!y.crossTileID||m?null:i[y.crossTileID];if(x){var b=new t.Point(y.anchorX,y.anchorY),_=re(b,n?l:s),w=ne(o.cameraToCenterDistance,_.signedDistanceFromCamera),T=a.evaluateSizeForFeature(e.textSizeData,c,y)*w/t.ONE_EM;n&&(T*=e.tilePixelRatio/u);for(var k=x.width,A=x.height,M=an(x.anchor,k,A,x.textOffset,x.textBoxScale,T),S=n?re(b.add(M),s).point:_.point.add(r?M.rotate(-o.angle):M),E=e.allowVerticalPlacement&&y.placedOrientation===t.WritingMode.vertical?Math.PI/2:0,L=0;L<y.numGlyphs;L++)t.addDynamicAttributes(p,S,E);f&&y.associatedIconIndex>=0&&(v[y.associatedIconIndex]={shiftedAnchor:S,angle:E})}else he(y.numGlyphs,p)}if(f){d.clear();for(var C=e.icon.placedSymbolArray,P=0;P<C.length;P++){var O=C.get(P);if(O.hidden)he(O.numGlyphs,d);else{var I=v[P];if(I)for(var D=0;D<O.numGlyphs;D++)t.addDynamicAttributes(d,I.shiftedAnchor,I.angle);else he(O.numGlyphs,d)}}e.icon.dynamicLayoutVertexBuffer.updateData(d)}e.text.dynamicLayoutVertexBuffer.updateData(p)}function sn(t,e,r){return r.iconsInText&&e?\"symbolTextAndIcon\":t?\"symbolSDF\":\"symbolIcon\"}function ln(e,r,n,i,a,o,s,l,u,c,f,h){for(var p=e.context,d=p.gl,v=e.transform,g=\"map\"===l,y=\"map\"===u,m=g&&\"point\"!==n.layout.get(\"symbol-placement\"),x=g&&!y&&!m,b=void 0!==n.layout.get(\"symbol-sort-key\").constantOr(1),_=e.depthModeForSublayer(0,Mt.ReadOnly),w=n.layout.get(\"text-variable-anchor\"),T=[],k=0,A=i;k<A.length;k+=1){var M=A[k],S=r.getTile(M),E=S.getBucket(n);if(E){var L=a?E.text:E.icon;if(L&&L.segments.get().length){var C=L.programConfigurations.get(n.id),P=a||E.sdfIcons,O=a?E.textSizeData:E.iconSizeData,I=y||0!==v.pitch,D=e.useProgram(sn(P,a,E),C),z=t.evaluateSizeForZoom(O,v.zoom),R=void 0,F=[0,0],B=void 0,N=void 0,j=null,U=void 0;if(a){if(B=S.glyphAtlasTexture,N=d.LINEAR,R=S.glyphAtlasTexture.size,E.iconsInText){F=S.imageAtlasTexture.size,j=S.imageAtlasTexture;var V=\"composite\"===O.kind||\"camera\"===O.kind;U=I||e.options.rotating||e.options.zooming||V?d.LINEAR:d.NEAREST}}else{var H=1!==n.layout.get(\"icon-size\").constantOr(0)||E.iconsNeedLinear;B=S.imageAtlasTexture,N=P||e.options.rotating||e.options.zooming||H||I?d.LINEAR:d.NEAREST,R=S.imageAtlasTexture.size}var q=ge(S,1,e.transform.zoom),G=te(M.posMatrix,y,g,e.transform,q),Z=ee(M.posMatrix,y,g,e.transform,q),Y=w&&E.hasTextData(),W=\"none\"!==n.layout.get(\"icon-text-fit\")&&Y&&E.hasIconData();m&&ae(E,M.posMatrix,e,a,G,Z,y,c);var X=e.translatePosMatrix(M.posMatrix,S,o,s),J=m||a&&w||W?nn:G,K=e.translatePosMatrix(Z,S,o,s,!0),$=P&&0!==n.paint.get(a?\"text-halo-width\":\"icon-halo-width\").constantOr(1),Q={program:D,buffers:L,uniformValues:P?E.iconsInText?$r(O.kind,z,x,y,e,X,J,K,R,F):Kr(O.kind,z,x,y,e,X,J,K,a,R,!0):Jr(O.kind,z,x,y,e,X,J,K,a,R),atlasTexture:B,atlasTextureIcon:j,atlasInterpolation:N,atlasInterpolationIcon:U,isSDF:P,hasHalo:$};if(b)for(var tt=0,et=L.segments.get();tt<et.length;tt+=1){var rt=et[tt];T.push({segments:new t.SegmentVector([rt]),sortKey:rt.sortKey,state:Q})}else T.push({segments:L.segments,sortKey:0,state:Q})}}}b&&T.sort((function(t,e){return t.sortKey-e.sortKey}));for(var nt=0,it=T;nt<it.length;nt+=1){var at=it[nt],ot=at.state;if(p.activeTexture.set(d.TEXTURE0),ot.atlasTexture.bind(ot.atlasInterpolation,d.CLAMP_TO_EDGE),ot.atlasTextureIcon&&(p.activeTexture.set(d.TEXTURE1),ot.atlasTextureIcon&&ot.atlasTextureIcon.bind(ot.atlasInterpolationIcon,d.CLAMP_TO_EDGE)),ot.isSDF){var st=ot.uniformValues;ot.hasHalo&&(st.u_is_halo=1,un(ot.buffers,at.segments,n,e,ot.program,_,f,h,st)),st.u_is_halo=0}un(ot.buffers,at.segments,n,e,ot.program,_,f,h,ot.uniformValues)}}function un(t,e,r,n,i,a,o,s,l){var u=n.context,c=u.gl;i.draw(u,c.TRIANGLES,a,o,s,Ct.disabled,l,r.id,t.layoutVertexBuffer,t.indexBuffer,e,r.paint,n.transform.zoom,t.programConfigurations.get(r.id),t.dynamicLayoutVertexBuffer,t.opacityVertexBuffer)}function cn(t,e,r,n,i,a,o){var s,l,u,c,f,h=t.context.gl,p=r.paint.get(\"fill-pattern\"),d=p&&p.constantOr(1),v=r.getCrossfadeParameters();o?(l=d&&!r.getPaintProperty(\"fill-outline-color\")?\"fillOutlinePattern\":\"fillOutline\",s=h.LINES):(l=d?\"fillPattern\":\"fill\",s=h.TRIANGLES);for(var g=0,y=n;g<y.length;g+=1){var m=y[g],x=e.getTile(m);if(!d||x.patternsLoaded()){var b=x.getBucket(r);if(b){var _=b.programConfigurations.get(r.id),w=t.useProgram(l,_);d&&(t.context.activeTexture.set(h.TEXTURE0),x.imageAtlasTexture.bind(h.LINEAR,h.CLAMP_TO_EDGE),_.updatePaintBuffers(v));var T=p.constantOr(null);if(T&&x.imageAtlas){var k=x.imageAtlas,A=k.patternPositions[T.to.toString()],M=k.patternPositions[T.from.toString()];A&&M&&_.setConstantPatternPositions(A,M)}var S=t.translatePosMatrix(m.posMatrix,x,r.paint.get(\"fill-translate\"),r.paint.get(\"fill-translate-anchor\"));if(o){c=b.indexBuffer2,f=b.segments2;var E=[h.drawingBufferWidth,h.drawingBufferHeight];u=\"fillOutlinePattern\"===l&&d?Pr(S,t,v,x,E):Cr(S,E)}else c=b.indexBuffer,f=b.segments,u=d?Lr(S,t,v,x):Er(S);w.draw(t.context,s,i,t.stencilModeForClipping(m),a,Ct.disabled,u,r.id,b.layoutVertexBuffer,c,f,r.paint,t.transform.zoom,_)}}}}function fn(t,e,r,n,i,a,o){for(var s=t.context,l=s.gl,u=r.paint.get(\"fill-extrusion-pattern\"),c=u.constantOr(1),f=r.getCrossfadeParameters(),h=r.paint.get(\"fill-extrusion-opacity\"),p=0,d=n;p<d.length;p+=1){var v=d[p],g=e.getTile(v),y=g.getBucket(r);if(y){var m=y.programConfigurations.get(r.id),x=t.useProgram(c?\"fillExtrusionPattern\":\"fillExtrusion\",m);c&&(t.context.activeTexture.set(l.TEXTURE0),g.imageAtlasTexture.bind(l.LINEAR,l.CLAMP_TO_EDGE),m.updatePaintBuffers(f));var b=u.constantOr(null);if(b&&g.imageAtlas){var _=g.imageAtlas,w=_.patternPositions[b.to.toString()],T=_.patternPositions[b.from.toString()];w&&T&&m.setConstantPatternPositions(w,T)}var k=t.translatePosMatrix(v.posMatrix,g,r.paint.get(\"fill-extrusion-translate\"),r.paint.get(\"fill-extrusion-translate-anchor\")),A=r.paint.get(\"fill-extrusion-vertical-gradient\"),M=c?Sr(k,t,A,h,v,f,g):Mr(k,t,A,h);x.draw(s,s.gl.TRIANGLES,i,a,o,Ct.backCCW,M,r.id,y.layoutVertexBuffer,y.indexBuffer,y.segments,r.paint,t.transform.zoom,m)}}}function hn(t,e,r,n,i,a){var o=t.context,s=o.gl,l=e.fbo;if(l){var u=t.useProgram(\"hillshade\");o.activeTexture.set(s.TEXTURE0),s.bindTexture(s.TEXTURE_2D,l.colorAttachment.get());var c=Nr(t,e,r);u.draw(o,s.TRIANGLES,n,i,a,Ct.disabled,c,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}}function pn(e,r,n,i,a,o,s){var l=e.context,u=l.gl,c=r.dem;if(c&&c.data){var f=c.dim,h=c.stride,p=c.getPixels();if(l.activeTexture.set(u.TEXTURE1),l.pixelStoreUnpackPremultiplyAlpha.set(!1),r.demTexture=r.demTexture||e.getTileTexture(h),r.demTexture){var d=r.demTexture;d.update(p,{premultiply:!1}),d.bind(u.NEAREST,u.CLAMP_TO_EDGE)}else r.demTexture=new t.Texture(l,p,u.RGBA,{premultiply:!1}),r.demTexture.bind(u.NEAREST,u.CLAMP_TO_EDGE);l.activeTexture.set(u.TEXTURE0);var v=r.fbo;if(!v){var g=new t.Texture(l,{width:f,height:f,data:null},u.RGBA);g.bind(u.LINEAR,u.CLAMP_TO_EDGE),(v=r.fbo=l.createFramebuffer(f,f,!0)).colorAttachment.set(g.texture)}l.bindFramebuffer.set(v.framebuffer),l.viewport.set([0,0,f,f]),e.useProgram(\"hillshadePrepare\").draw(l,u.TRIANGLES,a,o,s,Ct.disabled,jr(r.tileID,c,i),n.id,e.rasterBoundsBuffer,e.quadTriangleIndexBuffer,e.rasterBoundsSegments),r.needsHillshadePrepare=!1}}function dn(e,r,n,i,a){var o=i.paint.get(\"raster-fade-duration\");if(o>0){var s=t.browser.now(),l=(s-e.timeAdded)/o,u=r?(s-r.timeAdded)/o:-1,c=n.getSource(),f=a.coveringZoomLevel({tileSize:c.tileSize,roundZoom:c.roundZoom}),h=!r||Math.abs(r.tileID.overscaledZ-f)>Math.abs(e.tileID.overscaledZ-f),p=h&&e.refreshedUponExpiration?1:t.clamp(h?l:1-u,0,1);return e.refreshedUponExpiration&&l>=1&&(e.refreshedUponExpiration=!1),r?{opacity:1,mix:1-p}:{opacity:p,mix:0}}return{opacity:1,mix:0}}var vn=new t.Color(1,0,0,1),gn=new t.Color(0,1,0,1),yn=new t.Color(0,0,1,1),mn=new t.Color(1,0,1,1),xn=new t.Color(0,1,1,1);function bn(t){var e=t.transform.padding;_n(t,t.transform.height-(e.top||0),3,vn),_n(t,e.bottom||0,3,gn),wn(t,e.left||0,3,yn),wn(t,t.transform.width-(e.right||0),3,mn);var r=t.transform.centerPoint;!function(t,e,r,n){var i=20,a=2;Tn(t,e-a/2,r-i/2,a,i,n),Tn(t,e-i/2,r-a/2,i,a,n)}(t,r.x,t.transform.height-r.y,xn)}function _n(t,e,r,n){Tn(t,0,e+r/2,t.transform.width,r,n)}function wn(t,e,r,n){Tn(t,e-r/2,0,r,t.transform.height,n)}function Tn(e,r,n,i,a,o){var s=e.context,l=s.gl;l.enable(l.SCISSOR_TEST),l.scissor(r*t.browser.devicePixelRatio,n*t.browser.devicePixelRatio,i*t.browser.devicePixelRatio,a*t.browser.devicePixelRatio),s.clear({color:o}),l.disable(l.SCISSOR_TEST)}function kn(e,r,n){var i=e.context,a=i.gl,o=n.posMatrix,s=e.useProgram(\"debug\"),l=Mt.disabled,u=Et.disabled,c=e.colorModeForRenderPass(),f=\"$debug\";i.activeTexture.set(a.TEXTURE0),e.emptyTexture.bind(a.LINEAR,a.CLAMP_TO_EDGE),s.draw(i,a.LINE_STRIP,l,u,c,Ct.disabled,zr(o,t.Color.red),f,e.debugBuffer,e.tileBorderIndexBuffer,e.debugSegments);var h=r.getTileByID(n.key).latestRawTileData,p=h&&h.byteLength||0,d=Math.floor(p/1024),v=r.getTile(n).tileSize,g=512/Math.min(v,512)*(n.overscaledZ/e.transform.zoom)*.5,y=n.canonical.toString();n.overscaledZ!==n.canonical.z&&(y+=\" => \"+n.overscaledZ),function(t,e){t.initDebugOverlayCanvas();var r=t.debugOverlayCanvas,n=t.context.gl,i=t.debugOverlayCanvas.getContext(\"2d\");i.clearRect(0,0,r.width,r.height),i.shadowColor=\"white\",i.shadowBlur=2,i.lineWidth=1.5,i.strokeStyle=\"white\",i.textBaseline=\"top\",i.font=\"bold 36px Open Sans, sans-serif\",i.fillText(e,5,5),i.strokeText(e,5,5),t.debugOverlayTexture.update(r),t.debugOverlayTexture.bind(n.LINEAR,n.CLAMP_TO_EDGE)}(e,y+\" \"+d+\"kb\"),s.draw(i,a.TRIANGLES,l,u,Lt.alphaBlended,Ct.disabled,zr(o,t.Color.transparent,g),f,e.debugBuffer,e.quadTriangleIndexBuffer,e.debugSegments)}var An={symbol:function(e,r,n,i,a){if(\"translucent\"===e.renderPass){var o=Et.disabled,s=e.colorModeForRenderPass();n.layout.get(\"text-variable-anchor\")&&function(e,r,n,i,a,o,s){for(var l=r.transform,u=\"map\"===a,c=\"map\"===o,f=0,h=e;f<h.length;f+=1){var p=h[f],d=i.getTile(p),v=d.getBucket(n);if(v&&v.text&&v.text.segments.get().length){var g=v.textSizeData,y=t.evaluateSizeForZoom(g,l.zoom),m=ge(d,1,r.transform.zoom),x=te(p.posMatrix,c,u,r.transform,m),b=\"none\"!==n.layout.get(\"icon-text-fit\")&&v.hasIconData();if(y){var _=Math.pow(2,l.zoom-d.tileID.overscaledZ);on(v,u,c,s,t.symbolSize,l,x,p.posMatrix,_,y,b)}}}}(i,e,n,r,n.layout.get(\"text-rotation-alignment\"),n.layout.get(\"text-pitch-alignment\"),a),0!==n.paint.get(\"icon-opacity\").constantOr(1)&&ln(e,r,n,i,!1,n.paint.get(\"icon-translate\"),n.paint.get(\"icon-translate-anchor\"),n.layout.get(\"icon-rotation-alignment\"),n.layout.get(\"icon-pitch-alignment\"),n.layout.get(\"icon-keep-upright\"),o,s),0!==n.paint.get(\"text-opacity\").constantOr(1)&&ln(e,r,n,i,!0,n.paint.get(\"text-translate\"),n.paint.get(\"text-translate-anchor\"),n.layout.get(\"text-rotation-alignment\"),n.layout.get(\"text-pitch-alignment\"),n.layout.get(\"text-keep-upright\"),o,s),r.map.showCollisionBoxes&&(rn(e,r,n,i,n.paint.get(\"text-translate\"),n.paint.get(\"text-translate-anchor\"),!0),rn(e,r,n,i,n.paint.get(\"icon-translate\"),n.paint.get(\"icon-translate-anchor\"),!1))}},circle:function(e,r,n,i){if(\"translucent\"===e.renderPass){var a=n.paint.get(\"circle-opacity\"),o=n.paint.get(\"circle-stroke-width\"),s=n.paint.get(\"circle-stroke-opacity\"),l=void 0!==n.layout.get(\"circle-sort-key\").constantOr(1);if(0!==a.constantOr(1)||0!==o.constantOr(1)&&0!==s.constantOr(1)){for(var u=e.context,c=u.gl,f=e.depthModeForSublayer(0,Mt.ReadOnly),h=Et.disabled,p=e.colorModeForRenderPass(),d=[],v=0;v<i.length;v++){var g=i[v],y=r.getTile(g),m=y.getBucket(n);if(m){var x=m.programConfigurations.get(n.id),b={programConfiguration:x,program:e.useProgram(\"circle\",x),layoutVertexBuffer:m.layoutVertexBuffer,indexBuffer:m.indexBuffer,uniformValues:Or(e,g,y,n)};if(l)for(var _=0,w=m.segments.get();_<w.length;_+=1){var T=w[_];d.push({segments:new t.SegmentVector([T]),sortKey:T.sortKey,state:b})}else d.push({segments:m.segments,sortKey:0,state:b})}}l&&d.sort((function(t,e){return t.sortKey-e.sortKey}));for(var k=0,A=d;k<A.length;k+=1){var M=A[k],S=M.state,E=S.programConfiguration,L=S.program,C=S.layoutVertexBuffer,P=S.indexBuffer,O=S.uniformValues,I=M.segments;L.draw(u,c.TRIANGLES,f,h,p,Ct.disabled,O,n.id,C,P,I,n.paint,e.transform.zoom,E)}}}},heatmap:function(e,r,n,i){if(0!==n.paint.get(\"heatmap-opacity\"))if(\"offscreen\"===e.renderPass){var a=e.context,o=a.gl,s=Et.disabled,l=new Lt([o.ONE,o.ONE],t.Color.transparent,[!0,!0,!0,!0]);(function(t,e,r){var n=t.gl;t.activeTexture.set(n.TEXTURE1),t.viewport.set([0,0,e.width/4,e.height/4]);var i=r.heatmapFbo;if(i)n.bindTexture(n.TEXTURE_2D,i.colorAttachment.get()),t.bindFramebuffer.set(i.framebuffer);else{var a=n.createTexture();n.bindTexture(n.TEXTURE_2D,a),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,n.LINEAR),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,n.LINEAR),i=r.heatmapFbo=t.createFramebuffer(e.width/4,e.height/4,!1),function(t,e,r,n){var i=t.gl,a=t.extRenderToTextureHalfFloat?t.extTextureHalfFloat.HALF_FLOAT_OES:i.UNSIGNED_BYTE;i.texImage2D(i.TEXTURE_2D,0,i.RGBA,e.width/4,e.height/4,0,i.RGBA,a,null),n.colorAttachment.set(r)}(t,e,a,i)}})(a,e,n),a.clear({color:t.Color.transparent});for(var u=0;u<i.length;u++){var c=i[u];if(!r.hasRenderableParent(c)){var f=r.getTile(c),h=f.getBucket(n);if(h){var p=h.programConfigurations.get(n.id),d=e.useProgram(\"heatmap\",p),v=e.transform.zoom;d.draw(a,o.TRIANGLES,Mt.disabled,s,l,Ct.disabled,Fr(c.posMatrix,f,v,n.paint.get(\"heatmap-intensity\")),n.id,h.layoutVertexBuffer,h.indexBuffer,h.segments,n.paint,e.transform.zoom,p)}}}a.viewport.set([0,0,e.width,e.height])}else\"translucent\"===e.renderPass&&(e.context.setColorMode(e.colorModeForRenderPass()),function(e,r){var n=e.context,i=n.gl,a=r.heatmapFbo;if(a){n.activeTexture.set(i.TEXTURE0),i.bindTexture(i.TEXTURE_2D,a.colorAttachment.get()),n.activeTexture.set(i.TEXTURE1);var o=r.colorRampTexture;o||(o=r.colorRampTexture=new t.Texture(n,r.colorRamp,i.RGBA)),o.bind(i.LINEAR,i.CLAMP_TO_EDGE),e.useProgram(\"heatmapTexture\").draw(n,i.TRIANGLES,Mt.disabled,Et.disabled,e.colorModeForRenderPass(),Ct.disabled,Br(e,r,0,1),r.id,e.viewportBuffer,e.quadTriangleIndexBuffer,e.viewportSegments,r.paint,e.transform.zoom)}}(e,n))},line:function(e,r,n,i){if(\"translucent\"===e.renderPass){var a=n.paint.get(\"line-opacity\"),o=n.paint.get(\"line-width\");if(0!==a.constantOr(1)&&0!==o.constantOr(1)){var s=e.depthModeForSublayer(0,Mt.ReadOnly),l=e.colorModeForRenderPass(),u=n.paint.get(\"line-dasharray\"),c=n.paint.get(\"line-pattern\"),f=c.constantOr(1),h=n.paint.get(\"line-gradient\"),p=n.getCrossfadeParameters(),d=f?\"linePattern\":u?\"lineSDF\":h?\"lineGradient\":\"line\",v=e.context,g=v.gl,y=!0;if(h){v.activeTexture.set(g.TEXTURE0);var m=n.gradientTexture;if(!n.gradient)return;m||(m=n.gradientTexture=new t.Texture(v,n.gradient,g.RGBA)),m.bind(g.LINEAR,g.CLAMP_TO_EDGE)}for(var x=0,b=i;x<b.length;x+=1){var _=b[x],w=r.getTile(_);if(!f||w.patternsLoaded()){var T=w.getBucket(n);if(T){var k=T.programConfigurations.get(n.id),A=e.context.program.get(),M=e.useProgram(d,k),S=y||M.program!==A,E=c.constantOr(null);if(E&&w.imageAtlas){var L=w.imageAtlas,C=L.patternPositions[E.to.toString()],P=L.patternPositions[E.from.toString()];C&&P&&k.setConstantPatternPositions(C,P)}var O=f?Hr(e,w,n,p):u?qr(e,w,n,u,p):h?Vr(e,w,n):Ur(e,w,n);f?(v.activeTexture.set(g.TEXTURE0),w.imageAtlasTexture.bind(g.LINEAR,g.CLAMP_TO_EDGE),k.updatePaintBuffers(p)):u&&(S||e.lineAtlas.dirty)&&(v.activeTexture.set(g.TEXTURE0),e.lineAtlas.bind(v)),M.draw(v,g.TRIANGLES,s,e.stencilModeForClipping(_),l,Ct.disabled,O,n.id,T.layoutVertexBuffer,T.indexBuffer,T.segments,n.paint,e.transform.zoom,k),y=!1}}}}}},fill:function(e,r,n,i){var a=n.paint.get(\"fill-color\"),o=n.paint.get(\"fill-opacity\");if(0!==o.constantOr(1)){var s=e.colorModeForRenderPass(),l=n.paint.get(\"fill-pattern\"),u=e.opaquePassEnabledForLayer()&&!l.constantOr(1)&&1===a.constantOr(t.Color.transparent).a&&1===o.constantOr(0)?\"opaque\":\"translucent\";if(e.renderPass===u){var c=e.depthModeForSublayer(1,\"opaque\"===e.renderPass?Mt.ReadWrite:Mt.ReadOnly);cn(e,r,n,i,c,s,!1)}if(\"translucent\"===e.renderPass&&n.paint.get(\"fill-antialias\")){var f=e.depthModeForSublayer(n.getPaintProperty(\"fill-outline-color\")?2:0,Mt.ReadOnly);cn(e,r,n,i,f,s,!0)}}},\"fill-extrusion\":function(t,e,r,n){var i=r.paint.get(\"fill-extrusion-opacity\");if(0!==i&&\"translucent\"===t.renderPass){var a=new Mt(t.context.gl.LEQUAL,Mt.ReadWrite,t.depthRangeFor3D);if(1!==i||r.paint.get(\"fill-extrusion-pattern\").constantOr(1))fn(t,e,r,n,a,Et.disabled,Lt.disabled),fn(t,e,r,n,a,t.stencilModeFor3D(),t.colorModeForRenderPass());else{var o=t.colorModeForRenderPass();fn(t,e,r,n,a,Et.disabled,o)}}},hillshade:function(t,e,r,n){if(\"offscreen\"===t.renderPass||\"translucent\"===t.renderPass){for(var i=t.context,a=e.getSource().maxzoom,o=t.depthModeForSublayer(0,Mt.ReadOnly),s=t.colorModeForRenderPass(),l=\"translucent\"===t.renderPass?t.stencilConfigForOverlap(n):[{},n],u=l[0],c=0,f=l[1];c<f.length;c+=1){var h=f[c],p=e.getTile(h);p.needsHillshadePrepare&&\"offscreen\"===t.renderPass?pn(t,p,r,a,o,Et.disabled,s):\"translucent\"===t.renderPass&&hn(t,p,r,o,u[h.overscaledZ],s)}i.viewport.set([0,0,t.width,t.height])}},raster:function(t,e,r,n){if(\"translucent\"===t.renderPass&&0!==r.paint.get(\"raster-opacity\")&&n.length)for(var i=t.context,a=i.gl,o=e.getSource(),s=t.useProgram(\"raster\"),l=t.colorModeForRenderPass(),u=o instanceof I?[{},n]:t.stencilConfigForOverlap(n),c=u[0],f=u[1],h=f[f.length-1].overscaledZ,p=!t.options.moving,d=0,v=f;d<v.length;d+=1){var g=v[d],y=t.depthModeForSublayer(g.overscaledZ-h,1===r.paint.get(\"raster-opacity\")?Mt.ReadWrite:Mt.ReadOnly,a.LESS),m=e.getTile(g),x=t.transform.calculatePosMatrix(g.toUnwrapped(),p);m.registerFadeDuration(r.paint.get(\"raster-fade-duration\"));var b=e.findLoadedParent(g,0),_=dn(m,b,e,r,t.transform),w=void 0,T=void 0,k=\"nearest\"===r.paint.get(\"raster-resampling\")?a.NEAREST:a.LINEAR;i.activeTexture.set(a.TEXTURE0),m.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),i.activeTexture.set(a.TEXTURE1),b?(b.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),w=Math.pow(2,b.tileID.overscaledZ-m.tileID.overscaledZ),T=[m.tileID.canonical.x*w%1,m.tileID.canonical.y*w%1]):m.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST);var A=Yr(x,T||[0,0],w||1,_,r);o instanceof I?s.draw(i,a.TRIANGLES,y,Et.disabled,l,Ct.disabled,A,r.id,o.boundsBuffer,t.quadTriangleIndexBuffer,o.boundsSegments):s.draw(i,a.TRIANGLES,y,c[g.overscaledZ],l,Ct.disabled,A,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}},background:function(t,e,r){var n=r.paint.get(\"background-color\"),i=r.paint.get(\"background-opacity\");if(0!==i){var a=t.context,o=a.gl,s=t.transform,l=s.tileSize,u=r.paint.get(\"background-pattern\");if(!t.isPatternMissing(u)){var c=!u&&1===n.a&&1===i&&t.opaquePassEnabledForLayer()?\"opaque\":\"translucent\";if(t.renderPass===c){var f=Et.disabled,h=t.depthModeForSublayer(0,\"opaque\"===c?Mt.ReadWrite:Mt.ReadOnly),p=t.colorModeForRenderPass(),d=t.useProgram(u?\"backgroundPattern\":\"background\"),v=s.coveringTiles({tileSize:l});u&&(a.activeTexture.set(o.TEXTURE0),t.imageManager.bind(t.context));for(var g=r.getCrossfadeParameters(),y=0,m=v;y<m.length;y+=1){var x=m[y],b=t.transform.calculatePosMatrix(x.toUnwrapped()),_=u?tn(b,i,t,u,{tileID:x,tileSize:l},g):Qr(b,i,n);d.draw(a,o.TRIANGLES,h,f,p,Ct.disabled,_,r.id,t.tileExtentBuffer,t.quadTriangleIndexBuffer,t.tileExtentSegments)}}}}},debug:function(t,e,r){for(var n=0;n<r.length;n++)kn(t,e,r[n])},custom:function(t,e,r){var n=t.context,i=r.implementation;if(\"offscreen\"===t.renderPass){var a=i.prerender;a&&(t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),a.call(i,n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState())}else if(\"translucent\"===t.renderPass){t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),n.setStencilMode(Et.disabled);var o=\"3d\"===i.renderingMode?new Mt(t.context.gl.LEQUAL,Mt.ReadWrite,t.depthRangeFor3D):t.depthModeForSublayer(0,Mt.ReadOnly);n.setDepthMode(o),i.render(n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState(),n.bindFramebuffer.set(null)}}},Mn=function(t,e){this.context=new Pt(t),this.transform=e,this._tileTextures={},this.setup(),this.numSublayers=Ot.maxUnderzooming+Ot.maxOverzooming+1,this.depthEpsilon=1/Math.pow(2,16),this.crossTileSymbolIndex=new Ve,this.gpuTimers={}};Mn.prototype.resize=function(e,r){if(this.width=e*t.browser.devicePixelRatio,this.height=r*t.browser.devicePixelRatio,this.context.viewport.set([0,0,this.width,this.height]),this.style)for(var n=0,i=this.style._order;n<i.length;n+=1){var a=i[n];this.style._layers[a].resize()}},Mn.prototype.setup=function(){var e=this.context,r=new t.StructArrayLayout2i4;r.emplaceBack(0,0),r.emplaceBack(t.EXTENT,0),r.emplaceBack(0,t.EXTENT),r.emplaceBack(t.EXTENT,t.EXTENT),this.tileExtentBuffer=e.createVertexBuffer(r,We.members),this.tileExtentSegments=t.SegmentVector.simpleSegment(0,0,4,2);var n=new t.StructArrayLayout2i4;n.emplaceBack(0,0),n.emplaceBack(t.EXTENT,0),n.emplaceBack(0,t.EXTENT),n.emplaceBack(t.EXTENT,t.EXTENT),this.debugBuffer=e.createVertexBuffer(n,We.members),this.debugSegments=t.SegmentVector.simpleSegment(0,0,4,5);var i=new t.StructArrayLayout4i8;i.emplaceBack(0,0,0,0),i.emplaceBack(t.EXTENT,0,t.EXTENT,0),i.emplaceBack(0,t.EXTENT,0,t.EXTENT),i.emplaceBack(t.EXTENT,t.EXTENT,t.EXTENT,t.EXTENT),this.rasterBoundsBuffer=e.createVertexBuffer(i,O.members),this.rasterBoundsSegments=t.SegmentVector.simpleSegment(0,0,4,2);var a=new t.StructArrayLayout2i4;a.emplaceBack(0,0),a.emplaceBack(1,0),a.emplaceBack(0,1),a.emplaceBack(1,1),this.viewportBuffer=e.createVertexBuffer(a,We.members),this.viewportSegments=t.SegmentVector.simpleSegment(0,0,4,2);var o=new t.StructArrayLayout1ui2;o.emplaceBack(0),o.emplaceBack(1),o.emplaceBack(3),o.emplaceBack(2),o.emplaceBack(0),this.tileBorderIndexBuffer=e.createIndexBuffer(o);var s=new t.StructArrayLayout3ui6;s.emplaceBack(0,1,2),s.emplaceBack(2,1,3),this.quadTriangleIndexBuffer=e.createIndexBuffer(s),this.emptyTexture=new t.Texture(e,{width:1,height:1,data:new Uint8Array([0,0,0,0])},e.gl.RGBA);var l=this.context.gl;this.stencilClearMode=new Et({func:l.ALWAYS,mask:0},0,255,l.ZERO,l.ZERO,l.ZERO)},Mn.prototype.clearStencil=function(){var e=this.context,r=e.gl;this.nextStencilID=1,this.currentStencilSource=void 0;var n=t.create();t.ortho(n,0,this.width,this.height,0,0,1),t.scale(n,n,[r.drawingBufferWidth,r.drawingBufferHeight,0]),this.useProgram(\"clippingMask\").draw(e,r.TRIANGLES,Mt.disabled,this.stencilClearMode,Lt.disabled,Ct.disabled,Rr(n),\"$clipping\",this.viewportBuffer,this.quadTriangleIndexBuffer,this.viewportSegments)},Mn.prototype._renderTileClippingMasks=function(t,e){if(this.currentStencilSource!==t.source&&t.isTileClipped()&&e&&e.length){this.currentStencilSource=t.source;var r=this.context,n=r.gl;this.nextStencilID+e.length>256&&this.clearStencil(),r.setColorMode(Lt.disabled),r.setDepthMode(Mt.disabled);var i=this.useProgram(\"clippingMask\");this._tileClippingMaskIDs={};for(var a=0,o=e;a<o.length;a+=1){var s=o[a],l=this._tileClippingMaskIDs[s.key]=this.nextStencilID++;i.draw(r,n.TRIANGLES,Mt.disabled,new Et({func:n.ALWAYS,mask:0},l,255,n.KEEP,n.KEEP,n.REPLACE),Lt.disabled,Ct.disabled,Rr(s.posMatrix),\"$clipping\",this.tileExtentBuffer,this.quadTriangleIndexBuffer,this.tileExtentSegments)}}},Mn.prototype.stencilModeFor3D=function(){this.currentStencilSource=void 0,this.nextStencilID+1>256&&this.clearStencil();var t=this.nextStencilID++,e=this.context.gl;return new Et({func:e.NOTEQUAL,mask:255},t,255,e.KEEP,e.KEEP,e.REPLACE)},Mn.prototype.stencilModeForClipping=function(t){var e=this.context.gl;return new Et({func:e.EQUAL,mask:255},this._tileClippingMaskIDs[t.key],0,e.KEEP,e.KEEP,e.REPLACE)},Mn.prototype.stencilConfigForOverlap=function(t){var e,r=this.context.gl,n=t.sort((function(t,e){return e.overscaledZ-t.overscaledZ})),i=n[n.length-1].overscaledZ,a=n[0].overscaledZ-i+1;if(a>1){this.currentStencilSource=void 0,this.nextStencilID+a>256&&this.clearStencil();for(var o={},s=0;s<a;s++)o[s+i]=new Et({func:r.GEQUAL,mask:255},s+this.nextStencilID,255,r.KEEP,r.KEEP,r.REPLACE);return this.nextStencilID+=a,[o,n]}return[(e={},e[i]=Et.disabled,e),n]},Mn.prototype.colorModeForRenderPass=function(){var e=this.context.gl;if(this._showOverdrawInspector){var r=1/8;return new Lt([e.CONSTANT_COLOR,e.ONE],new t.Color(r,r,r,0),[!0,!0,!0,!0])}return\"opaque\"===this.renderPass?Lt.unblended:Lt.alphaBlended},Mn.prototype.depthModeForSublayer=function(t,e,r){if(!this.opaquePassEnabledForLayer())return Mt.disabled;var n=1-((1+this.currentLayer)*this.numSublayers+t)*this.depthEpsilon;return new Mt(r||this.context.gl.LEQUAL,e,[n,n])},Mn.prototype.opaquePassEnabledForLayer=function(){return this.currentLayer<this.opaquePassCutoff},Mn.prototype.render=function(e,r){var n=this;this.style=e,this.options=r,this.lineAtlas=e.lineAtlas,this.imageManager=e.imageManager,this.glyphManager=e.glyphManager,this.symbolFadeChange=e.placement.symbolFadeChange(t.browser.now()),this.imageManager.beginFrame();var i=this.style._order,a=this.style.sourceCaches;for(var o in a){var s=a[o];s.used&&s.prepare(this.context)}var l,u,c={},f={},h={};for(var p in a){var d=a[p];c[p]=d.getVisibleCoordinates(),f[p]=c[p].slice().reverse(),h[p]=d.getVisibleCoordinates(!0).reverse()}this.opaquePassCutoff=1/0;for(var v=0;v<i.length;v++){var g=i[v];if(this.style._layers[g].is3D()){this.opaquePassCutoff=v;break}}this.renderPass=\"offscreen\";for(var y=0,m=i;y<m.length;y+=1){var x=m[y],b=this.style._layers[x];if(b.hasOffscreenPass()&&!b.isHidden(this.transform.zoom)){var _=f[b.source];(\"custom\"===b.type||_.length)&&this.renderLayer(this,a[b.source],b,_)}}for(this.context.bindFramebuffer.set(null),this.context.clear({color:r.showOverdrawInspector?t.Color.black:t.Color.transparent,depth:1}),this.clearStencil(),this._showOverdrawInspector=r.showOverdrawInspector,this.depthRangeFor3D=[0,1-(e._order.length+2)*this.numSublayers*this.depthEpsilon],this.renderPass=\"opaque\",this.currentLayer=i.length-1;this.currentLayer>=0;this.currentLayer--){var w=this.style._layers[i[this.currentLayer]],T=a[w.source],k=c[w.source];this._renderTileClippingMasks(w,k),this.renderLayer(this,T,w,k)}for(this.renderPass=\"translucent\",this.currentLayer=0;this.currentLayer<i.length;this.currentLayer++){var A=this.style._layers[i[this.currentLayer]],M=a[A.source],S=(\"symbol\"===A.type?h:f)[A.source];this._renderTileClippingMasks(A,c[A.source]),this.renderLayer(this,M,A,S)}this.options.showTileBoundaries&&(t.values(this.style._layers).forEach((function(t){t.source&&!t.isHidden(n.transform.zoom)&&(t.source!==(u&&u.id)&&(u=n.style.sourceCaches[t.source]),(!l||l.getSource().maxzoom<u.getSource().maxzoom)&&(l=u))})),l&&An.debug(this,l,l.getVisibleCoordinates())),this.options.showPadding&&bn(this),this.context.setDefault()},Mn.prototype.renderLayer=function(t,e,r,n){r.isHidden(this.transform.zoom)||(\"background\"===r.type||\"custom\"===r.type||n.length)&&(this.id=r.id,this.gpuTimingStart(r),An[r.type](t,e,r,n,this.style.placement.variableOffsets),this.gpuTimingEnd())},Mn.prototype.gpuTimingStart=function(t){if(this.options.gpuTiming){var e=this.context.extTimerQuery,r=this.gpuTimers[t.id];r||(r=this.gpuTimers[t.id]={calls:0,cpuTime:0,query:e.createQueryEXT()}),r.calls++,e.beginQueryEXT(e.TIME_ELAPSED_EXT,r.query)}},Mn.prototype.gpuTimingEnd=function(){if(this.options.gpuTiming){var t=this.context.extTimerQuery;t.endQueryEXT(t.TIME_ELAPSED_EXT)}},Mn.prototype.collectGpuTimers=function(){var t=this.gpuTimers;return this.gpuTimers={},t},Mn.prototype.queryGpuTimers=function(t){var e={};for(var r in t){var n=t[r],i=this.context.extTimerQuery,a=i.getQueryObjectEXT(n.query,i.QUERY_RESULT_EXT)/1e6;i.deleteQueryEXT(n.query),e[r]=a}return e},Mn.prototype.translatePosMatrix=function(e,r,n,i,a){if(!n[0]&&!n[1])return e;var o=a?\"map\"===i?this.transform.angle:0:\"viewport\"===i?-this.transform.angle:0;if(o){var s=Math.sin(o),l=Math.cos(o);n=[n[0]*l-n[1]*s,n[0]*s+n[1]*l]}var u=[a?n[0]:ge(r,n[0],this.transform.zoom),a?n[1]:ge(r,n[1],this.transform.zoom),0],c=new Float32Array(16);return t.translate(c,e,u),c},Mn.prototype.saveTileTexture=function(t){var e=this._tileTextures[t.size[0]];e?e.push(t):this._tileTextures[t.size[0]]=[t]},Mn.prototype.getTileTexture=function(t){var e=this._tileTextures[t];return e&&e.length>0?e.pop():null},Mn.prototype.isPatternMissing=function(t){if(!t)return!1;if(!t.from||!t.to)return!0;var e=this.imageManager.getPattern(t.from.toString()),r=this.imageManager.getPattern(t.to.toString());return!e||!r},Mn.prototype.useProgram=function(t,e){this.cache=this.cache||{};var r=\"\"+t+(e?e.cacheKey:\"\")+(this._showOverdrawInspector?\"/overdraw\":\"\");return this.cache[r]||(this.cache[r]=new kr(this.context,wr[t],e,en[t],this._showOverdrawInspector)),this.cache[r]},Mn.prototype.setCustomLayerDefaults=function(){this.context.unbindVAO(),this.context.cullFace.setDefault(),this.context.activeTexture.setDefault(),this.context.pixelStoreUnpack.setDefault(),this.context.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.context.pixelStoreUnpackFlipY.setDefault()},Mn.prototype.setBaseState=function(){var t=this.context.gl;this.context.cullFace.set(!1),this.context.viewport.set([0,0,this.width,this.height]),this.context.blendEquation.set(t.FUNC_ADD)},Mn.prototype.initDebugOverlayCanvas=function(){if(null==this.debugOverlayCanvas){this.debugOverlayCanvas=t.window.document.createElement(\"canvas\"),this.debugOverlayCanvas.width=512,this.debugOverlayCanvas.height=512;var e=this.context.gl;this.debugOverlayTexture=new t.Texture(this.context,this.debugOverlayCanvas,e.RGBA)}},Mn.prototype.destroy=function(){this.emptyTexture.destroy(),this.debugOverlayTexture&&this.debugOverlayTexture.destroy()};var Sn=function(t,e){this.points=t,this.planes=e};Sn.fromInvProjectionMatrix=function(e,r,n){var i=Math.pow(2,n),a=[[-1,1,-1,1],[1,1,-1,1],[1,-1,-1,1],[-1,-1,-1,1],[-1,1,1,1],[1,1,1,1],[1,-1,1,1],[-1,-1,1,1]].map((function(r){return t.transformMat4([],r,e)})).map((function(e){return t.scale$1([],e,1/e[3]/r*i)})),o=[[0,1,2],[6,5,4],[0,3,7],[2,1,5],[3,2,6],[0,4,5]].map((function(e){var r=t.sub([],a[e[0]],a[e[1]]),n=t.sub([],a[e[2]],a[e[1]]),i=t.normalize([],t.cross([],r,n)),o=-t.dot(i,a[e[1]]);return i.concat(o)}));return new Sn(a,o)};var En=function(e,r){this.min=e,this.max=r,this.center=t.scale$2([],t.add([],this.min,this.max),.5)};En.prototype.quadrant=function(e){for(var r=[e%2==0,e<2],n=t.clone$2(this.min),i=t.clone$2(this.max),a=0;a<r.length;a++)n[a]=r[a]?this.min[a]:this.center[a],i[a]=r[a]?this.center[a]:this.max[a];return i[2]=this.max[2],new En(n,i)},En.prototype.distanceX=function(t){return Math.max(Math.min(this.max[0],t[0]),this.min[0])-t[0]},En.prototype.distanceY=function(t){return Math.max(Math.min(this.max[1],t[1]),this.min[1])-t[1]},En.prototype.intersects=function(e){for(var r=[[this.min[0],this.min[1],0,1],[this.max[0],this.min[1],0,1],[this.max[0],this.max[1],0,1],[this.min[0],this.max[1],0,1]],n=!0,i=0;i<e.planes.length;i++){for(var a=e.planes[i],o=0,s=0;s<r.length;s++)o+=t.dot$1(a,r[s])>=0;if(0===o)return 0;o!==r.length&&(n=!1)}if(n)return 2;for(var l=0;l<3;l++){for(var u=Number.MAX_VALUE,c=-Number.MAX_VALUE,f=0;f<e.points.length;f++){var h=e.points[f][l]-this.min[l];u=Math.min(u,h),c=Math.max(c,h)}if(c<0||u>this.max[l]-this.min[l])return 0}return 1};var Ln=function(t,e,r,n){if(void 0===t&&(t=0),void 0===e&&(e=0),void 0===r&&(r=0),void 0===n&&(n=0),isNaN(t)||t<0||isNaN(e)||e<0||isNaN(r)||r<0||isNaN(n)||n<0)throw new Error(\"Invalid value for edge-insets, top, bottom, left and right must all be numbers\");this.top=t,this.bottom=e,this.left=r,this.right=n};Ln.prototype.interpolate=function(e,r,n){return null!=r.top&&null!=e.top&&(this.top=t.number(e.top,r.top,n)),null!=r.bottom&&null!=e.bottom&&(this.bottom=t.number(e.bottom,r.bottom,n)),null!=r.left&&null!=e.left&&(this.left=t.number(e.left,r.left,n)),null!=r.right&&null!=e.right&&(this.right=t.number(e.right,r.right,n)),this},Ln.prototype.getCenter=function(e,r){var n=t.clamp((this.left+e-this.right)/2,0,e),i=t.clamp((this.top+r-this.bottom)/2,0,r);return new t.Point(n,i)},Ln.prototype.equals=function(t){return this.top===t.top&&this.bottom===t.bottom&&this.left===t.left&&this.right===t.right},Ln.prototype.clone=function(){return new Ln(this.top,this.bottom,this.left,this.right)},Ln.prototype.toJSON=function(){return{top:this.top,bottom:this.bottom,left:this.left,right:this.right}};var Cn=function(e,r,n,i,a){this.tileSize=512,this.maxValidLatitude=85.051129,this._renderWorldCopies=void 0===a||a,this._minZoom=e||0,this._maxZoom=r||22,this._minPitch=null==n?0:n,this._maxPitch=null==i?60:i,this.setMaxBounds(),this.width=0,this.height=0,this._center=new t.LngLat(0,0),this.zoom=0,this.angle=0,this._fov=.6435011087932844,this._pitch=0,this._unmodified=!0,this._edgeInsets=new Ln,this._posMatrixCache={},this._alignedPosMatrixCache={}},Pn={minZoom:{configurable:!0},maxZoom:{configurable:!0},minPitch:{configurable:!0},maxPitch:{configurable:!0},renderWorldCopies:{configurable:!0},worldSize:{configurable:!0},centerOffset:{configurable:!0},size:{configurable:!0},bearing:{configurable:!0},pitch:{configurable:!0},fov:{configurable:!0},zoom:{configurable:!0},center:{configurable:!0},padding:{configurable:!0},centerPoint:{configurable:!0},unmodified:{configurable:!0},point:{configurable:!0}};Cn.prototype.clone=function(){var t=new Cn(this._minZoom,this._maxZoom,this._minPitch,this.maxPitch,this._renderWorldCopies);return t.tileSize=this.tileSize,t.latRange=this.latRange,t.width=this.width,t.height=this.height,t._center=this._center,t.zoom=this.zoom,t.angle=this.angle,t._fov=this._fov,t._pitch=this._pitch,t._unmodified=this._unmodified,t._edgeInsets=this._edgeInsets.clone(),t._calcMatrices(),t},Pn.minZoom.get=function(){return this._minZoom},Pn.minZoom.set=function(t){this._minZoom!==t&&(this._minZoom=t,this.zoom=Math.max(this.zoom,t))},Pn.maxZoom.get=function(){return this._maxZoom},Pn.maxZoom.set=function(t){this._maxZoom!==t&&(this._maxZoom=t,this.zoom=Math.min(this.zoom,t))},Pn.minPitch.get=function(){return this._minPitch},Pn.minPitch.set=function(t){this._minPitch!==t&&(this._minPitch=t,this.pitch=Math.max(this.pitch,t))},Pn.maxPitch.get=function(){return this._maxPitch},Pn.maxPitch.set=function(t){this._maxPitch!==t&&(this._maxPitch=t,this.pitch=Math.min(this.pitch,t))},Pn.renderWorldCopies.get=function(){return this._renderWorldCopies},Pn.renderWorldCopies.set=function(t){void 0===t?t=!0:null===t&&(t=!1),this._renderWorldCopies=t},Pn.worldSize.get=function(){return this.tileSize*this.scale},Pn.centerOffset.get=function(){return this.centerPoint._sub(this.size._div(2))},Pn.size.get=function(){return new t.Point(this.width,this.height)},Pn.bearing.get=function(){return-this.angle/Math.PI*180},Pn.bearing.set=function(e){var r=-t.wrap(e,-180,180)*Math.PI/180;this.angle!==r&&(this._unmodified=!1,this.angle=r,this._calcMatrices(),this.rotationMatrix=t.create$2(),t.rotate(this.rotationMatrix,this.rotationMatrix,this.angle))},Pn.pitch.get=function(){return this._pitch/Math.PI*180},Pn.pitch.set=function(e){var r=t.clamp(e,this.minPitch,this.maxPitch)/180*Math.PI;this._pitch!==r&&(this._unmodified=!1,this._pitch=r,this._calcMatrices())},Pn.fov.get=function(){return this._fov/Math.PI*180},Pn.fov.set=function(t){t=Math.max(.01,Math.min(60,t)),this._fov!==t&&(this._unmodified=!1,this._fov=t/180*Math.PI,this._calcMatrices())},Pn.zoom.get=function(){return this._zoom},Pn.zoom.set=function(t){var e=Math.min(Math.max(t,this.minZoom),this.maxZoom);this._zoom!==e&&(this._unmodified=!1,this._zoom=e,this.scale=this.zoomScale(e),this.tileZoom=Math.floor(e),this.zoomFraction=e-this.tileZoom,this._constrain(),this._calcMatrices())},Pn.center.get=function(){return this._center},Pn.center.set=function(t){t.lat===this._center.lat&&t.lng===this._center.lng||(this._unmodified=!1,this._center=t,this._constrain(),this._calcMatrices())},Pn.padding.get=function(){return this._edgeInsets.toJSON()},Pn.padding.set=function(t){this._edgeInsets.equals(t)||(this._unmodified=!1,this._edgeInsets.interpolate(this._edgeInsets,t,1),this._calcMatrices())},Pn.centerPoint.get=function(){return this._edgeInsets.getCenter(this.width,this.height)},Cn.prototype.isPaddingEqual=function(t){return this._edgeInsets.equals(t)},Cn.prototype.interpolatePadding=function(t,e,r){this._unmodified=!1,this._edgeInsets.interpolate(t,e,r),this._constrain(),this._calcMatrices()},Cn.prototype.coveringZoomLevel=function(t){var e=(t.roundZoom?Math.round:Math.floor)(this.zoom+this.scaleZoom(this.tileSize/t.tileSize));return Math.max(0,e)},Cn.prototype.getVisibleUnwrappedCoordinates=function(e){var r=[new t.UnwrappedTileID(0,e)];if(this._renderWorldCopies)for(var n=this.pointCoordinate(new t.Point(0,0)),i=this.pointCoordinate(new t.Point(this.width,0)),a=this.pointCoordinate(new t.Point(this.width,this.height)),o=this.pointCoordinate(new t.Point(0,this.height)),s=Math.floor(Math.min(n.x,i.x,a.x,o.x)),l=Math.floor(Math.max(n.x,i.x,a.x,o.x)),u=s-1;u<=l+1;u++)0!==u&&r.push(new t.UnwrappedTileID(u,e));return r},Cn.prototype.coveringTiles=function(e){var r=this.coveringZoomLevel(e),n=r;if(void 0!==e.minzoom&&r<e.minzoom)return[];void 0!==e.maxzoom&&r>e.maxzoom&&(r=e.maxzoom);var i=t.MercatorCoordinate.fromLngLat(this.center),a=Math.pow(2,r),o=[a*i.x,a*i.y,0],s=Sn.fromInvProjectionMatrix(this.invProjMatrix,this.worldSize,r),l=e.minzoom||0;this.pitch<=60&&this._edgeInsets.top<.1&&(l=r);var u=function(t){return{aabb:new En([t*a,0,0],[(t+1)*a,a,0]),zoom:0,x:0,y:0,wrap:t,fullyVisible:!1}},c=[],f=[],h=r,p=e.reparseOverscaled?n:r;if(this._renderWorldCopies)for(var d=1;d<=3;d++)c.push(u(-d)),c.push(u(d));for(c.push(u(0));c.length>0;){var v=c.pop(),g=v.x,y=v.y,m=v.fullyVisible;if(!m){var x=v.aabb.intersects(s);if(0===x)continue;m=2===x}var b=v.aabb.distanceX(o),_=v.aabb.distanceY(o),w=Math.max(Math.abs(b),Math.abs(_)),T=3+(1<<h-v.zoom)-2;if(v.zoom===h||w>T&&v.zoom>=l)f.push({tileID:new t.OverscaledTileID(v.zoom===h?p:v.zoom,v.wrap,v.zoom,g,y),distanceSq:t.sqrLen([o[0]-.5-g,o[1]-.5-y])});else for(var k=0;k<4;k++){var A=(g<<1)+k%2,M=(y<<1)+(k>>1);c.push({aabb:v.aabb.quadrant(k),zoom:v.zoom+1,x:A,y:M,wrap:v.wrap,fullyVisible:m})}}return f.sort((function(t,e){return t.distanceSq-e.distanceSq})).map((function(t){return t.tileID}))},Cn.prototype.resize=function(t,e){this.width=t,this.height=e,this.pixelsToGLUnits=[2/t,-2/e],this._constrain(),this._calcMatrices()},Pn.unmodified.get=function(){return this._unmodified},Cn.prototype.zoomScale=function(t){return Math.pow(2,t)},Cn.prototype.scaleZoom=function(t){return Math.log(t)/Math.LN2},Cn.prototype.project=function(e){var r=t.clamp(e.lat,-this.maxValidLatitude,this.maxValidLatitude);return new t.Point(t.mercatorXfromLng(e.lng)*this.worldSize,t.mercatorYfromLat(r)*this.worldSize)},Cn.prototype.unproject=function(e){return new t.MercatorCoordinate(e.x/this.worldSize,e.y/this.worldSize).toLngLat()},Pn.point.get=function(){return this.project(this.center)},Cn.prototype.setLocationAtPoint=function(e,r){var n=this.pointCoordinate(r),i=this.pointCoordinate(this.centerPoint),a=this.locationCoordinate(e),o=new t.MercatorCoordinate(a.x-(n.x-i.x),a.y-(n.y-i.y));this.center=this.coordinateLocation(o),this._renderWorldCopies&&(this.center=this.center.wrap())},Cn.prototype.locationPoint=function(t){return this.coordinatePoint(this.locationCoordinate(t))},Cn.prototype.pointLocation=function(t){return this.coordinateLocation(this.pointCoordinate(t))},Cn.prototype.locationCoordinate=function(e){return t.MercatorCoordinate.fromLngLat(e)},Cn.prototype.coordinateLocation=function(t){return t.toLngLat()},Cn.prototype.pointCoordinate=function(e){var r=[e.x,e.y,0,1],n=[e.x,e.y,1,1];t.transformMat4(r,r,this.pixelMatrixInverse),t.transformMat4(n,n,this.pixelMatrixInverse);var i=r[3],a=n[3],o=r[0]/i,s=n[0]/a,l=r[1]/i,u=n[1]/a,c=r[2]/i,f=n[2]/a,h=c===f?0:(0-c)/(f-c);return new t.MercatorCoordinate(t.number(o,s,h)/this.worldSize,t.number(l,u,h)/this.worldSize)},Cn.prototype.coordinatePoint=function(e){var r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix),new t.Point(r[0]/r[3],r[1]/r[3])},Cn.prototype.getBounds=function(){return(new t.LngLatBounds).extend(this.pointLocation(new t.Point(0,0))).extend(this.pointLocation(new t.Point(this.width,0))).extend(this.pointLocation(new t.Point(this.width,this.height))).extend(this.pointLocation(new t.Point(0,this.height)))},Cn.prototype.getMaxBounds=function(){return this.latRange&&2===this.latRange.length&&this.lngRange&&2===this.lngRange.length?new t.LngLatBounds([this.lngRange[0],this.latRange[0]],[this.lngRange[1],this.latRange[1]]):null},Cn.prototype.setMaxBounds=function(t){t?(this.lngRange=[t.getWest(),t.getEast()],this.latRange=[t.getSouth(),t.getNorth()],this._constrain()):(this.lngRange=null,this.latRange=[-this.maxValidLatitude,this.maxValidLatitude])},Cn.prototype.calculatePosMatrix=function(e,r){void 0===r&&(r=!1);var n=e.key,i=r?this._alignedPosMatrixCache:this._posMatrixCache;if(i[n])return i[n];var a=e.canonical,o=this.worldSize/this.zoomScale(a.z),s=a.x+Math.pow(2,a.z)*e.wrap,l=t.identity(new Float64Array(16));return t.translate(l,l,[s*o,a.y*o,0]),t.scale(l,l,[o/t.EXTENT,o/t.EXTENT,1]),t.multiply(l,r?this.alignedProjMatrix:this.projMatrix,l),i[n]=new Float32Array(l),i[n]},Cn.prototype.customLayerMatrix=function(){return this.mercatorMatrix.slice()},Cn.prototype._constrain=function(){if(this.center&&this.width&&this.height&&!this._constraining){this._constraining=!0;var e,r,n,i,a=-90,o=90,s=-180,l=180,u=this.size,c=this._unmodified;if(this.latRange){var f=this.latRange;a=t.mercatorYfromLat(f[1])*this.worldSize,e=(o=t.mercatorYfromLat(f[0])*this.worldSize)-a<u.y?u.y/(o-a):0}if(this.lngRange){var h=this.lngRange;s=t.mercatorXfromLng(h[0])*this.worldSize,r=(l=t.mercatorXfromLng(h[1])*this.worldSize)-s<u.x?u.x/(l-s):0}var p=this.point,d=Math.max(r||0,e||0);if(d)return this.center=this.unproject(new t.Point(r?(l+s)/2:p.x,e?(o+a)/2:p.y)),this.zoom+=this.scaleZoom(d),this._unmodified=c,void(this._constraining=!1);if(this.latRange){var v=p.y,g=u.y/2;v-g<a&&(i=a+g),v+g>o&&(i=o-g)}if(this.lngRange){var y=p.x,m=u.x/2;y-m<s&&(n=s+m),y+m>l&&(n=l-m)}void 0===n&&void 0===i||(this.center=this.unproject(new t.Point(void 0!==n?n:p.x,void 0!==i?i:p.y))),this._unmodified=c,this._constraining=!1}},Cn.prototype._calcMatrices=function(){if(this.height){var e=this._fov/2,r=this.centerOffset;this.cameraToCenterDistance=.5/Math.tan(e)*this.height;var n=Math.PI/2+this._pitch,i=this._fov*(.5+r.y/this.height),a=Math.sin(i)*this.cameraToCenterDistance/Math.sin(t.clamp(Math.PI-n-i,.01,Math.PI-.01)),o=this.point,s=o.x,l=o.y,u=1.01*(Math.cos(Math.PI/2-this._pitch)*a+this.cameraToCenterDistance),c=this.height/50,f=new Float64Array(16);t.perspective(f,this._fov,this.width/this.height,c,u),f[8]=2*-r.x/this.width,f[9]=2*r.y/this.height,t.scale(f,f,[1,-1,1]),t.translate(f,f,[0,0,-this.cameraToCenterDistance]),t.rotateX(f,f,this._pitch),t.rotateZ(f,f,this.angle),t.translate(f,f,[-s,-l,0]),this.mercatorMatrix=t.scale([],f,[this.worldSize,this.worldSize,this.worldSize]),t.scale(f,f,[1,1,t.mercatorZfromAltitude(1,this.center.lat)*this.worldSize,1]),this.projMatrix=f,this.invProjMatrix=t.invert([],this.projMatrix);var h=this.width%2/2,p=this.height%2/2,d=Math.cos(this.angle),v=Math.sin(this.angle),g=s-Math.round(s)+d*h+v*p,y=l-Math.round(l)+d*p+v*h,m=new Float64Array(f);if(t.translate(m,m,[g>.5?g-1:g,y>.5?y-1:y,0]),this.alignedProjMatrix=m,f=t.create(),t.scale(f,f,[this.width/2,-this.height/2,1]),t.translate(f,f,[1,-1,0]),this.labelPlaneMatrix=f,f=t.create(),t.scale(f,f,[1,-1,1]),t.translate(f,f,[-1,-1,0]),t.scale(f,f,[2/this.width,2/this.height,1]),this.glCoordMatrix=f,this.pixelMatrix=t.multiply(new Float64Array(16),this.labelPlaneMatrix,this.projMatrix),!(f=t.invert(new Float64Array(16),this.pixelMatrix)))throw new Error(\"failed to invert matrix\");this.pixelMatrixInverse=f,this._posMatrixCache={},this._alignedPosMatrixCache={}}},Cn.prototype.maxPitchScaleFactor=function(){if(!this.pixelMatrixInverse)return 1;var e=this.pointCoordinate(new t.Point(0,0)),r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix)[3]/this.cameraToCenterDistance},Cn.prototype.getCameraPoint=function(){var e=this._pitch,r=Math.tan(e)*(this.cameraToCenterDistance||1);return this.centerPoint.add(new t.Point(0,r))},Cn.prototype.getCameraQueryGeometry=function(e){var r=this.getCameraPoint();if(1===e.length)return[e[0],r];for(var n=r.x,i=r.y,a=r.x,o=r.y,s=0,l=e;s<l.length;s+=1){var u=l[s];n=Math.min(n,u.x),i=Math.min(i,u.y),a=Math.max(a,u.x),o=Math.max(o,u.y)}return[new t.Point(n,i),new t.Point(a,i),new t.Point(a,o),new t.Point(n,o),new t.Point(n,i)]},Object.defineProperties(Cn.prototype,Pn);var On=function(e){var r,n,i,a,o;this._hashName=e&&encodeURIComponent(e),t.bindAll([\"_getCurrentHash\",\"_onHashChange\",\"_updateHash\"],this),this._updateHash=(r=this._updateHashUnthrottled.bind(this),n=300,i=!1,a=null,o=function(){a=null,i&&(r(),a=setTimeout(o,n),i=!1)},function(){return i=!0,a||o(),a})};On.prototype.addTo=function(e){return this._map=e,t.window.addEventListener(\"hashchange\",this._onHashChange,!1),this._map.on(\"moveend\",this._updateHash),this},On.prototype.remove=function(){return t.window.removeEventListener(\"hashchange\",this._onHashChange,!1),this._map.off(\"moveend\",this._updateHash),clearTimeout(this._updateHash()),delete this._map,this},On.prototype.getHashString=function(e){var r=this._map.getCenter(),n=Math.round(100*this._map.getZoom())/100,i=Math.ceil((n*Math.LN2+Math.log(512/360/.5))/Math.LN10),a=Math.pow(10,i),o=Math.round(r.lng*a)/a,s=Math.round(r.lat*a)/a,l=this._map.getBearing(),u=this._map.getPitch(),c=\"\";if(c+=e?\"/\"+o+\"/\"+s+\"/\"+n:n+\"/\"+s+\"/\"+o,(l||u)&&(c+=\"/\"+Math.round(10*l)/10),u&&(c+=\"/\"+Math.round(u)),this._hashName){var f=this._hashName,h=!1,p=t.window.location.hash.slice(1).split(\"&\").map((function(t){var e=t.split(\"=\")[0];return e===f?(h=!0,e+\"=\"+c):t})).filter((function(t){return t}));return h||p.push(f+\"=\"+c),\"#\"+p.join(\"&\")}return\"#\"+c},On.prototype._getCurrentHash=function(){var e,r=this,n=t.window.location.hash.replace(\"#\",\"\");return this._hashName?(n.split(\"&\").map((function(t){return t.split(\"=\")})).forEach((function(t){t[0]===r._hashName&&(e=t)})),(e&&e[1]||\"\").split(\"/\")):n.split(\"/\")},On.prototype._onHashChange=function(){var t=this._getCurrentHash();if(t.length>=3&&!t.some((function(t){return isNaN(t)}))){var e=this._map.dragRotate.isEnabled()&&this._map.touchZoomRotate.isEnabled()?+(t[3]||0):this._map.getBearing();return this._map.jumpTo({center:[+t[2],+t[1]],zoom:+t[0],bearing:e,pitch:+(t[4]||0)}),!0}return!1},On.prototype._updateHashUnthrottled=function(){var e=this.getHashString();try{t.window.history.replaceState(t.window.history.state,\"\",e)}catch(t){}};var In={linearity:.3,easing:t.bezier(0,0,.3,1)},Dn=t.extend({deceleration:2500,maxSpeed:1400},In),zn=t.extend({deceleration:20,maxSpeed:1400},In),Rn=t.extend({deceleration:1e3,maxSpeed:360},In),Fn=t.extend({deceleration:1e3,maxSpeed:90},In),Bn=function(t){this._map=t,this.clear()};function Nn(t,e){(!t.duration||t.duration<e.duration)&&(t.duration=e.duration,t.easing=e.easing)}function jn(e,r,n){var i=n.maxSpeed,a=n.linearity,o=n.deceleration,s=t.clamp(e*a/(r/1e3),-i,i),l=Math.abs(s)/(o*a);return{easing:n.easing,duration:1e3*l,amount:s*(l/2)}}Bn.prototype.clear=function(){this._inertiaBuffer=[]},Bn.prototype.record=function(e){this._drainInertiaBuffer(),this._inertiaBuffer.push({time:t.browser.now(),settings:e})},Bn.prototype._drainInertiaBuffer=function(){for(var e=this._inertiaBuffer,r=t.browser.now();e.length>0&&r-e[0].time>160;)e.shift()},Bn.prototype._onMoveEnd=function(e){if(this._drainInertiaBuffer(),!(this._inertiaBuffer.length<2)){for(var r={zoom:0,bearing:0,pitch:0,pan:new t.Point(0,0),pinchAround:void 0,around:void 0},n=0,i=this._inertiaBuffer;n<i.length;n+=1){var a=i[n].settings;r.zoom+=a.zoomDelta||0,r.bearing+=a.bearingDelta||0,r.pitch+=a.pitchDelta||0,a.panDelta&&r.pan._add(a.panDelta),a.around&&(r.around=a.around),a.pinchAround&&(r.pinchAround=a.pinchAround)}var o=this._inertiaBuffer[this._inertiaBuffer.length-1].time-this._inertiaBuffer[0].time,s={};if(r.pan.mag()){var l=jn(r.pan.mag(),o,t.extend({},Dn,e||{}));s.offset=r.pan.mult(l.amount/r.pan.mag()),s.center=this._map.transform.center,Nn(s,l)}if(r.zoom){var u=jn(r.zoom,o,zn);s.zoom=this._map.transform.zoom+u.amount,Nn(s,u)}if(r.bearing){var c=jn(r.bearing,o,Rn);s.bearing=this._map.transform.bearing+t.clamp(c.amount,-179,179),Nn(s,c)}if(r.pitch){var f=jn(r.pitch,o,Fn);s.pitch=this._map.transform.pitch+f.amount,Nn(s,f)}if(s.zoom||s.bearing){var h=void 0===r.pinchAround?r.around:r.pinchAround;s.around=h?this._map.unproject(h):this._map.getCenter()}return this.clear(),t.extend(s,{noMoveStart:!0})}};var Un=function(e){function n(n,i,a,o){void 0===o&&(o={});var s=r.mousePos(i.getCanvasContainer(),a),l=i.unproject(s);e.call(this,n,t.extend({point:s,lngLat:l,originalEvent:a},o)),this._defaultPrevented=!1,this.target=i}e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n;var i={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},i.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,i),n}(t.Event),Vn=function(e){function n(n,i,a){var o=\"touchend\"===n?a.changedTouches:a.touches,s=r.touchPos(i.getCanvasContainer(),o),l=s.map((function(t){return i.unproject(t)})),u=s.reduce((function(t,e,r,n){return t.add(e.div(n.length))}),new t.Point(0,0)),c=i.unproject(u);e.call(this,n,{points:s,point:u,lngLats:l,lngLat:c,originalEvent:a}),this._defaultPrevented=!1}e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n;var i={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},i.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,i),n}(t.Event),Hn=function(t){function e(e,r,n){t.call(this,e,{originalEvent:n}),this._defaultPrevented=!1}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={defaultPrevented:{configurable:!0}};return e.prototype.preventDefault=function(){this._defaultPrevented=!0},r.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(e.prototype,r),e}(t.Event),qn=function(t,e){this._map=t,this._clickTolerance=e.clickTolerance};qn.prototype.reset=function(){delete this._mousedownPos},qn.prototype.wheel=function(t){return this._firePreventable(new Hn(t.type,this._map,t))},qn.prototype.mousedown=function(t,e){return this._mousedownPos=e,this._firePreventable(new Un(t.type,this._map,t))},qn.prototype.mouseup=function(t){this._map.fire(new Un(t.type,this._map,t))},qn.prototype.click=function(t,e){this._mousedownPos&&this._mousedownPos.dist(e)>=this._clickTolerance||this._map.fire(new Un(t.type,this._map,t))},qn.prototype.dblclick=function(t){return this._firePreventable(new Un(t.type,this._map,t))},qn.prototype.mouseover=function(t){this._map.fire(new Un(t.type,this._map,t))},qn.prototype.mouseout=function(t){this._map.fire(new Un(t.type,this._map,t))},qn.prototype.touchstart=function(t){return this._firePreventable(new Vn(t.type,this._map,t))},qn.prototype.touchmove=function(t){this._map.fire(new Vn(t.type,this._map,t))},qn.prototype.touchend=function(t){this._map.fire(new Vn(t.type,this._map,t))},qn.prototype.touchcancel=function(t){this._map.fire(new Vn(t.type,this._map,t))},qn.prototype._firePreventable=function(t){if(this._map.fire(t),t.defaultPrevented)return{}},qn.prototype.isEnabled=function(){return!0},qn.prototype.isActive=function(){return!1},qn.prototype.enable=function(){},qn.prototype.disable=function(){};var Gn=function(t){this._map=t};Gn.prototype.reset=function(){this._delayContextMenu=!1,delete this._contextMenuEvent},Gn.prototype.mousemove=function(t){this._map.fire(new Un(t.type,this._map,t))},Gn.prototype.mousedown=function(){this._delayContextMenu=!0},Gn.prototype.mouseup=function(){this._delayContextMenu=!1,this._contextMenuEvent&&(this._map.fire(new Un(\"contextmenu\",this._map,this._contextMenuEvent)),delete this._contextMenuEvent)},Gn.prototype.contextmenu=function(t){this._delayContextMenu?this._contextMenuEvent=t:this._map.fire(new Un(t.type,this._map,t)),this._map.listens(\"contextmenu\")&&t.preventDefault()},Gn.prototype.isEnabled=function(){return!0},Gn.prototype.isActive=function(){return!1},Gn.prototype.enable=function(){},Gn.prototype.disable=function(){};var Zn=function(t,e){this._map=t,this._el=t.getCanvasContainer(),this._container=t.getContainer(),this._clickTolerance=e.clickTolerance||1};function Yn(t,e){for(var r={},n=0;n<t.length;n++)r[t[n].identifier]=e[n];return r}Zn.prototype.isEnabled=function(){return!!this._enabled},Zn.prototype.isActive=function(){return!!this._active},Zn.prototype.enable=function(){this.isEnabled()||(this._enabled=!0)},Zn.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},Zn.prototype.mousedown=function(t,e){this.isEnabled()&&t.shiftKey&&0===t.button&&(r.disableDrag(),this._startPos=this._lastPos=e,this._active=!0)},Zn.prototype.mousemoveWindow=function(t,e){if(this._active){var n=e;if(!(this._lastPos.equals(n)||!this._box&&n.dist(this._startPos)<this._clickTolerance)){var i=this._startPos;this._lastPos=n,this._box||(this._box=r.create(\"div\",\"mapboxgl-boxzoom\",this._container),this._container.classList.add(\"mapboxgl-crosshair\"),this._fireEvent(\"boxzoomstart\",t));var a=Math.min(i.x,n.x),o=Math.max(i.x,n.x),s=Math.min(i.y,n.y),l=Math.max(i.y,n.y);r.setTransform(this._box,\"translate(\"+a+\"px,\"+s+\"px)\"),this._box.style.width=o-a+\"px\",this._box.style.height=l-s+\"px\"}}},Zn.prototype.mouseupWindow=function(e,n){var i=this;if(this._active&&0===e.button){var a=this._startPos,o=n;if(this.reset(),r.suppressClick(),a.x!==o.x||a.y!==o.y)return this._map.fire(new t.Event(\"boxzoomend\",{originalEvent:e})),{cameraAnimation:function(t){return t.fitScreenCoordinates(a,o,i._map.getBearing(),{linear:!0})}};this._fireEvent(\"boxzoomcancel\",e)}},Zn.prototype.keydown=function(t){this._active&&27===t.keyCode&&(this.reset(),this._fireEvent(\"boxzoomcancel\",t))},Zn.prototype.reset=function(){this._active=!1,this._container.classList.remove(\"mapboxgl-crosshair\"),this._box&&(r.remove(this._box),this._box=null),r.enableDrag(),delete this._startPos,delete this._lastPos},Zn.prototype._fireEvent=function(e,r){return this._map.fire(new t.Event(e,{originalEvent:r}))};var Wn=function(t){this.reset(),this.numTouches=t.numTouches};Wn.prototype.reset=function(){delete this.centroid,delete this.startTime,delete this.touches,this.aborted=!1},Wn.prototype.touchstart=function(e,r,n){(this.centroid||n.length>this.numTouches)&&(this.aborted=!0),this.aborted||(void 0===this.startTime&&(this.startTime=e.timeStamp),n.length===this.numTouches&&(this.centroid=function(e){for(var r=new t.Point(0,0),n=0,i=e;n<i.length;n+=1){var a=i[n];r._add(a)}return r.div(e.length)}(r),this.touches=Yn(n,r)))},Wn.prototype.touchmove=function(t,e,r){if(!this.aborted&&this.centroid){var n=Yn(r,e);for(var i in this.touches){var a=this.touches[i],o=n[i];(!o||o.dist(a)>30)&&(this.aborted=!0)}}},Wn.prototype.touchend=function(t,e,r){if((!this.centroid||t.timeStamp-this.startTime>500)&&(this.aborted=!0),0===r.length){var n=!this.aborted&&this.centroid;if(this.reset(),n)return n}};var Xn=function(t){this.singleTap=new Wn(t),this.numTaps=t.numTaps,this.reset()};Xn.prototype.reset=function(){this.lastTime=1/0,delete this.lastTap,this.count=0,this.singleTap.reset()},Xn.prototype.touchstart=function(t,e,r){this.singleTap.touchstart(t,e,r)},Xn.prototype.touchmove=function(t,e,r){this.singleTap.touchmove(t,e,r)},Xn.prototype.touchend=function(t,e,r){var n=this.singleTap.touchend(t,e,r);if(n){var i=t.timeStamp-this.lastTime<500,a=!this.lastTap||this.lastTap.dist(n)<30;if(i&&a||this.reset(),this.count++,this.lastTime=t.timeStamp,this.lastTap=n,this.count===this.numTaps)return this.reset(),n}};var Jn=function(){this._zoomIn=new Xn({numTouches:1,numTaps:2}),this._zoomOut=new Xn({numTouches:2,numTaps:1}),this.reset()};Jn.prototype.reset=function(){this._active=!1,this._zoomIn.reset(),this._zoomOut.reset()},Jn.prototype.touchstart=function(t,e,r){this._zoomIn.touchstart(t,e,r),this._zoomOut.touchstart(t,e,r)},Jn.prototype.touchmove=function(t,e,r){this._zoomIn.touchmove(t,e,r),this._zoomOut.touchmove(t,e,r)},Jn.prototype.touchend=function(t,e,r){var n=this,i=this._zoomIn.touchend(t,e,r),a=this._zoomOut.touchend(t,e,r);return i?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()+1,around:e.unproject(i)},{originalEvent:t})}}):a?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()-1,around:e.unproject(a)},{originalEvent:t})}}):void 0},Jn.prototype.touchcancel=function(){this.reset()},Jn.prototype.enable=function(){this._enabled=!0},Jn.prototype.disable=function(){this._enabled=!1,this.reset()},Jn.prototype.isEnabled=function(){return this._enabled},Jn.prototype.isActive=function(){return this._active};var Kn=function(t){this.reset(),this._clickTolerance=t.clickTolerance||1};Kn.prototype.reset=function(){this._active=!1,this._moved=!1,delete this._lastPoint,delete this._eventButton},Kn.prototype._correctButton=function(t,e){return!1},Kn.prototype._move=function(t,e){return{}},Kn.prototype.mousedown=function(t,e){if(!this._lastPoint){var n=r.mouseButton(t);this._correctButton(t,n)&&(this._lastPoint=e,this._eventButton=n)}},Kn.prototype.mousemoveWindow=function(t,e){var r=this._lastPoint;if(r&&(t.preventDefault(),this._moved||!(e.dist(r)<this._clickTolerance)))return this._moved=!0,this._lastPoint=e,this._move(r,e)},Kn.prototype.mouseupWindow=function(t){r.mouseButton(t)===this._eventButton&&(this._moved&&r.suppressClick(),this.reset())},Kn.prototype.enable=function(){this._enabled=!0},Kn.prototype.disable=function(){this._enabled=!1,this.reset()},Kn.prototype.isEnabled=function(){return this._enabled},Kn.prototype.isActive=function(){return this._active};var $n=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.mousedown=function(e,r){t.prototype.mousedown.call(this,e,r),this._lastPoint&&(this._active=!0)},e.prototype._correctButton=function(t,e){return 0===e&&!t.ctrlKey},e.prototype._move=function(t,e){return{around:e,panDelta:e.sub(t)}},e}(Kn),Qn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._correctButton=function(t,e){return 0===e&&t.ctrlKey||2===e},e.prototype._move=function(t,e){var r=.8*(e.x-t.x);if(r)return this._active=!0,{bearingDelta:r}},e.prototype.contextmenu=function(t){t.preventDefault()},e}(Kn),ti=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._correctButton=function(t,e){return 0===e&&t.ctrlKey||2===e},e.prototype._move=function(t,e){var r=-.5*(e.y-t.y);if(r)return this._active=!0,{pitchDelta:r}},e.prototype.contextmenu=function(t){t.preventDefault()},e}(Kn),ei=function(t){this._minTouches=1,this._clickTolerance=t.clickTolerance||1,this.reset()};ei.prototype.reset=function(){this._active=!1,this._touches={},this._sum=new t.Point(0,0)},ei.prototype.touchstart=function(t,e,r){return this._calculateTransform(t,e,r)},ei.prototype.touchmove=function(t,e,r){if(this._active)return t.preventDefault(),this._calculateTransform(t,e,r)},ei.prototype.touchend=function(t,e,r){this._calculateTransform(t,e,r),this._active&&r.length<this._minTouches&&this.reset()},ei.prototype.touchcancel=function(){this.reset()},ei.prototype._calculateTransform=function(e,r,n){n.length>0&&(this._active=!0);var i=Yn(n,r),a=new t.Point(0,0),o=new t.Point(0,0),s=0;for(var l in i){var u=i[l],c=this._touches[l];c&&(a._add(u),o._add(u.sub(c)),s++,i[l]=u)}if(this._touches=i,!(s<this._minTouches)&&o.mag()){var f=o.div(s);if(this._sum._add(f),!(this._sum.mag()<this._clickTolerance))return{around:a.div(s),panDelta:f}}},ei.prototype.enable=function(){this._enabled=!0},ei.prototype.disable=function(){this._enabled=!1,this.reset()},ei.prototype.isEnabled=function(){return this._enabled},ei.prototype.isActive=function(){return this._active};var ri=function(){this.reset()};function ni(t,e,r){for(var n=0;n<t.length;n++)if(t[n].identifier===r)return e[n]}ri.prototype.reset=function(){this._active=!1,delete this._firstTwoTouches},ri.prototype._start=function(t){},ri.prototype._move=function(t,e,r){return{}},ri.prototype.touchstart=function(t,e,r){this._firstTwoTouches||r.length<2||(this._firstTwoTouches=[r[0].identifier,r[1].identifier],this._start([e[0],e[1]]))},ri.prototype.touchmove=function(t,e,r){if(this._firstTwoTouches){t.preventDefault();var n=this._firstTwoTouches,i=n[0],a=n[1],o=ni(r,e,i),s=ni(r,e,a);if(o&&s){var l=this._aroundCenter?null:o.add(s).div(2);return this._move([o,s],l,t)}}},ri.prototype.touchend=function(t,e,n){if(this._firstTwoTouches){var i=this._firstTwoTouches,a=i[0],o=i[1],s=ni(n,e,a),l=ni(n,e,o);s&&l||(this._active&&r.suppressClick(),this.reset())}},ri.prototype.touchcancel=function(){this.reset()},ri.prototype.enable=function(t){this._enabled=!0,this._aroundCenter=!!t&&\"center\"===t.around},ri.prototype.disable=function(){this._enabled=!1,this.reset()},ri.prototype.isEnabled=function(){return this._enabled},ri.prototype.isActive=function(){return this._active};function ii(t,e){return Math.log(t/e)/Math.LN2}var ai=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),delete this._distance,delete this._startDistance},e.prototype._start=function(t){this._startDistance=this._distance=t[0].dist(t[1])},e.prototype._move=function(t,e){var r=this._distance;if(this._distance=t[0].dist(t[1]),this._active||!(Math.abs(ii(this._distance,this._startDistance))<.1))return this._active=!0,{zoomDelta:ii(this._distance,r),pinchAround:e}},e}(ri);function oi(t,e){return 180*t.angleWith(e)/Math.PI}var si=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),delete this._minDiameter,delete this._startVector,delete this._vector},e.prototype._start=function(t){this._startVector=this._vector=t[0].sub(t[1]),this._minDiameter=t[0].dist(t[1])},e.prototype._move=function(t,e){var r=this._vector;if(this._vector=t[0].sub(t[1]),this._active||!this._isBelowThreshold(this._vector))return this._active=!0,{bearingDelta:oi(this._vector,r),pinchAround:e}},e.prototype._isBelowThreshold=function(t){this._minDiameter=Math.min(this._minDiameter,t.mag());var e=25/(Math.PI*this._minDiameter)*360,r=oi(t,this._startVector);return Math.abs(r)<e},e}(ri);function li(t){return Math.abs(t.y)>Math.abs(t.x)}var ui=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),this._valid=void 0,delete this._firstMove,delete this._lastPoints},e.prototype._start=function(t){this._lastPoints=t,li(t[0].sub(t[1]))&&(this._valid=!1)},e.prototype._move=function(t,e,r){var n=t[0].sub(this._lastPoints[0]),i=t[1].sub(this._lastPoints[1]);if(this._valid=this.gestureBeginsVertically(n,i,r.timeStamp),this._valid)return this._lastPoints=t,this._active=!0,{pitchDelta:(n.y+i.y)/2*-.5}},e.prototype.gestureBeginsVertically=function(t,e,r){if(void 0!==this._valid)return this._valid;var n=t.mag()>=2,i=e.mag()>=2;if(n||i){if(!n||!i)return void 0===this._firstMove&&(this._firstMove=r),r-this._firstMove<100&&void 0;var a=t.y>0==e.y>0;return li(t)&&li(e)&&a}},e}(ri),ci={panStep:100,bearingStep:15,pitchStep:10},fi=function(){var t=ci;this._panStep=t.panStep,this._bearingStep=t.bearingStep,this._pitchStep=t.pitchStep};function hi(t){return t*(2-t)}fi.prototype.reset=function(){this._active=!1},fi.prototype.keydown=function(t){var e=this;if(!(t.altKey||t.ctrlKey||t.metaKey)){var r=0,n=0,i=0,a=0,o=0;switch(t.keyCode){case 61:case 107:case 171:case 187:r=1;break;case 189:case 109:case 173:r=-1;break;case 37:t.shiftKey?n=-1:(t.preventDefault(),a=-1);break;case 39:t.shiftKey?n=1:(t.preventDefault(),a=1);break;case 38:t.shiftKey?i=1:(t.preventDefault(),o=-1);break;case 40:t.shiftKey?i=-1:(t.preventDefault(),o=1);break;default:return}return{cameraAnimation:function(s){var l=s.getZoom();s.easeTo({duration:300,easeId:\"keyboardHandler\",easing:hi,zoom:r?Math.round(l)+r*(t.shiftKey?2:1):l,bearing:s.getBearing()+n*e._bearingStep,pitch:s.getPitch()+i*e._pitchStep,offset:[-a*e._panStep,-o*e._panStep],center:s.getCenter()},{originalEvent:t})}}}},fi.prototype.enable=function(){this._enabled=!0},fi.prototype.disable=function(){this._enabled=!1,this.reset()},fi.prototype.isEnabled=function(){return this._enabled},fi.prototype.isActive=function(){return this._active};var pi=4.000244140625,di=function(e,r){this._map=e,this._el=e.getCanvasContainer(),this._handler=r,this._delta=0,this._defaultZoomRate=.01,this._wheelZoomRate=.0022222222222222222,t.bindAll([\"_onWheel\",\"_onTimeout\",\"_onScrollFrame\",\"_onScrollFinished\"],this)};di.prototype.setZoomRate=function(t){this._defaultZoomRate=t},di.prototype.setWheelZoomRate=function(t){this._wheelZoomRate=t},di.prototype.isEnabled=function(){return!!this._enabled},di.prototype.isActive=function(){return!!this._active||void 0!==this._finishTimeout},di.prototype.isZooming=function(){return!!this._zooming},di.prototype.enable=function(t){this.isEnabled()||(this._enabled=!0,this._aroundCenter=t&&\"center\"===t.around)},di.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},di.prototype.wheel=function(e){if(this.isEnabled()){var r=e.deltaMode===t.window.WheelEvent.DOM_DELTA_LINE?40*e.deltaY:e.deltaY,n=t.browser.now(),i=n-(this._lastWheelEventTime||0);this._lastWheelEventTime=n,0!==r&&r%pi==0?this._type=\"wheel\":0!==r&&Math.abs(r)<4?this._type=\"trackpad\":i>400?(this._type=null,this._lastValue=r,this._timeout=setTimeout(this._onTimeout,40,e)):this._type||(this._type=Math.abs(i*r)<200?\"trackpad\":\"wheel\",this._timeout&&(clearTimeout(this._timeout),this._timeout=null,r+=this._lastValue)),e.shiftKey&&r&&(r/=4),this._type&&(this._lastWheelEvent=e,this._delta-=r,this._active||this._start(e)),e.preventDefault()}},di.prototype._onTimeout=function(t){this._type=\"wheel\",this._delta-=this._lastValue,this._active||this._start(t)},di.prototype._start=function(e){if(this._delta){this._frameId&&(this._frameId=null),this._active=!0,this.isZooming()||(this._zooming=!0),this._finishTimeout&&(clearTimeout(this._finishTimeout),delete this._finishTimeout);var n=r.mousePos(this._el,e);this._around=t.LngLat.convert(this._aroundCenter?this._map.getCenter():this._map.unproject(n)),this._aroundPoint=this._map.transform.locationPoint(this._around),this._frameId||(this._frameId=!0,this._handler._triggerRenderFrame())}},di.prototype.renderFrame=function(){return this._onScrollFrame()},di.prototype._onScrollFrame=function(){var e=this;if(this._frameId&&(this._frameId=null,this.isActive())){var r=this._map.transform;if(0!==this._delta){var n=\"wheel\"===this._type&&Math.abs(this._delta)>pi?this._wheelZoomRate:this._defaultZoomRate,i=2/(1+Math.exp(-Math.abs(this._delta*n)));this._delta<0&&0!==i&&(i=1/i);var a=\"number\"==typeof this._targetZoom?r.zoomScale(this._targetZoom):r.scale;this._targetZoom=Math.min(r.maxZoom,Math.max(r.minZoom,r.scaleZoom(a*i))),\"wheel\"===this._type&&(this._startZoom=r.zoom,this._easing=this._smoothOutEasing(200)),this._delta=0}var o,s=\"number\"==typeof this._targetZoom?this._targetZoom:r.zoom,l=this._startZoom,u=this._easing,c=!1;if(\"wheel\"===this._type&&l&&u){var f=Math.min((t.browser.now()-this._lastWheelEventTime)/200,1),h=u(f);o=t.number(l,s,h),f<1?this._frameId||(this._frameId=!0):c=!0}else o=s,c=!0;return this._active=!0,c&&(this._active=!1,this._finishTimeout=setTimeout((function(){e._zooming=!1,e._handler._triggerRenderFrame(),delete e._targetZoom,delete e._finishTimeout}),200)),{noInertia:!0,needsRenderFrame:!c,zoomDelta:o-r.zoom,around:this._aroundPoint,originalEvent:this._lastWheelEvent}}},di.prototype._smoothOutEasing=function(e){var r=t.ease;if(this._prevEase){var n=this._prevEase,i=(t.browser.now()-n.start)/n.duration,a=n.easing(i+.01)-n.easing(i),o=.27/Math.sqrt(a*a+1e-4)*.01,s=Math.sqrt(.0729-o*o);r=t.bezier(o,s,.25,1)}return this._prevEase={start:t.browser.now(),duration:e,easing:r},r},di.prototype.reset=function(){this._active=!1};var vi=function(t,e){this._clickZoom=t,this._tapZoom=e};vi.prototype.enable=function(){this._clickZoom.enable(),this._tapZoom.enable()},vi.prototype.disable=function(){this._clickZoom.disable(),this._tapZoom.disable()},vi.prototype.isEnabled=function(){return this._clickZoom.isEnabled()&&this._tapZoom.isEnabled()},vi.prototype.isActive=function(){return this._clickZoom.isActive()||this._tapZoom.isActive()};var gi=function(){this.reset()};gi.prototype.reset=function(){this._active=!1},gi.prototype.dblclick=function(t,e){return t.preventDefault(),{cameraAnimation:function(r){r.easeTo({duration:300,zoom:r.getZoom()+(t.shiftKey?-1:1),around:r.unproject(e)},{originalEvent:t})}}},gi.prototype.enable=function(){this._enabled=!0},gi.prototype.disable=function(){this._enabled=!1,this.reset()},gi.prototype.isEnabled=function(){return this._enabled},gi.prototype.isActive=function(){return this._active};var yi=function(){this._tap=new Xn({numTouches:1,numTaps:1}),this.reset()};yi.prototype.reset=function(){this._active=!1,delete this._swipePoint,delete this._swipeTouch,delete this._tapTime,this._tap.reset()},yi.prototype.touchstart=function(t,e,r){this._swipePoint||(this._tapTime&&t.timeStamp-this._tapTime>500&&this.reset(),this._tapTime?r.length>0&&(this._swipePoint=e[0],this._swipeTouch=r[0].identifier):this._tap.touchstart(t,e,r))},yi.prototype.touchmove=function(t,e,r){if(this._tapTime){if(this._swipePoint){if(r[0].identifier!==this._swipeTouch)return;var n=e[0],i=n.y-this._swipePoint.y;return this._swipePoint=n,t.preventDefault(),this._active=!0,{zoomDelta:i/128}}}else this._tap.touchmove(t,e,r)},yi.prototype.touchend=function(t,e,r){this._tapTime?this._swipePoint&&0===r.length&&this.reset():this._tap.touchend(t,e,r)&&(this._tapTime=t.timeStamp)},yi.prototype.touchcancel=function(){this.reset()},yi.prototype.enable=function(){this._enabled=!0},yi.prototype.disable=function(){this._enabled=!1,this.reset()},yi.prototype.isEnabled=function(){return this._enabled},yi.prototype.isActive=function(){return this._active};var mi=function(t,e,r){this._el=t,this._mousePan=e,this._touchPan=r};mi.prototype.enable=function(t){this._inertiaOptions=t||{},this._mousePan.enable(),this._touchPan.enable(),this._el.classList.add(\"mapboxgl-touch-drag-pan\")},mi.prototype.disable=function(){this._mousePan.disable(),this._touchPan.disable(),this._el.classList.remove(\"mapboxgl-touch-drag-pan\")},mi.prototype.isEnabled=function(){return this._mousePan.isEnabled()&&this._touchPan.isEnabled()},mi.prototype.isActive=function(){return this._mousePan.isActive()||this._touchPan.isActive()};var xi=function(t,e,r){this._pitchWithRotate=t.pitchWithRotate,this._mouseRotate=e,this._mousePitch=r};xi.prototype.enable=function(){this._mouseRotate.enable(),this._pitchWithRotate&&this._mousePitch.enable()},xi.prototype.disable=function(){this._mouseRotate.disable(),this._mousePitch.disable()},xi.prototype.isEnabled=function(){return this._mouseRotate.isEnabled()&&(!this._pitchWithRotate||this._mousePitch.isEnabled())},xi.prototype.isActive=function(){return this._mouseRotate.isActive()||this._mousePitch.isActive()};var bi=function(t,e,r,n){this._el=t,this._touchZoom=e,this._touchRotate=r,this._tapDragZoom=n,this._rotationDisabled=!1,this._enabled=!0};bi.prototype.enable=function(t){this._touchZoom.enable(t),this._rotationDisabled||this._touchRotate.enable(t),this._tapDragZoom.enable(),this._el.classList.add(\"mapboxgl-touch-zoom-rotate\")},bi.prototype.disable=function(){this._touchZoom.disable(),this._touchRotate.disable(),this._tapDragZoom.disable(),this._el.classList.remove(\"mapboxgl-touch-zoom-rotate\")},bi.prototype.isEnabled=function(){return this._touchZoom.isEnabled()&&(this._rotationDisabled||this._touchRotate.isEnabled())&&this._tapDragZoom.isEnabled()},bi.prototype.isActive=function(){return this._touchZoom.isActive()||this._touchRotate.isActive()||this._tapDragZoom.isActive()},bi.prototype.disableRotation=function(){this._rotationDisabled=!0,this._touchRotate.disable()},bi.prototype.enableRotation=function(){this._rotationDisabled=!1,this._touchZoom.isEnabled()&&this._touchRotate.enable()};var _i=function(t){return t.zoom||t.drag||t.pitch||t.rotate},wi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(t.Event);function Ti(t){return t.panDelta&&t.panDelta.mag()||t.zoomDelta||t.bearingDelta||t.pitchDelta}var ki=function(e,n){this._map=e,this._el=this._map.getCanvasContainer(),this._handlers=[],this._handlersById={},this._changes=[],this._inertia=new Bn(e),this._bearingSnap=n.bearingSnap,this._previousActiveHandlers={},this._eventsInProgress={},this._addDefaultHandlers(n),t.bindAll([\"handleEvent\",\"handleWindowEvent\"],this);var i=this._el;this._listeners=[[i,\"touchstart\",{passive:!1}],[i,\"touchmove\",{passive:!1}],[i,\"touchend\",void 0],[i,\"touchcancel\",void 0],[i,\"mousedown\",void 0],[i,\"mousemove\",void 0],[i,\"mouseup\",void 0],[t.window.document,\"mousemove\",{capture:!0}],[t.window.document,\"mouseup\",void 0],[i,\"mouseover\",void 0],[i,\"mouseout\",void 0],[i,\"dblclick\",void 0],[i,\"click\",void 0],[i,\"keydown\",{capture:!1}],[i,\"keyup\",void 0],[i,\"wheel\",{passive:!1}],[i,\"contextmenu\",void 0],[t.window,\"blur\",void 0]];for(var a=0,o=this._listeners;a<o.length;a+=1){var s=o[a],l=s[0],u=s[1],c=s[2];r.addEventListener(l,u,l===t.window.document?this.handleWindowEvent:this.handleEvent,c)}};ki.prototype.destroy=function(){for(var e=0,n=this._listeners;e<n.length;e+=1){var i=n[e],a=i[0],o=i[1],s=i[2];r.removeEventListener(a,o,a===t.window.document?this.handleWindowEvent:this.handleEvent,s)}},ki.prototype._addDefaultHandlers=function(t){var e=this._map,r=e.getCanvasContainer();this._add(\"mapEvent\",new qn(e,t));var n=e.boxZoom=new Zn(e,t);this._add(\"boxZoom\",n);var i=new Jn,a=new gi;e.doubleClickZoom=new vi(a,i),this._add(\"tapZoom\",i),this._add(\"clickZoom\",a);var o=new yi;this._add(\"tapDragZoom\",o);var s=e.touchPitch=new ui;this._add(\"touchPitch\",s);var l=new Qn(t),u=new ti(t);e.dragRotate=new xi(t,l,u),this._add(\"mouseRotate\",l,[\"mousePitch\"]),this._add(\"mousePitch\",u,[\"mouseRotate\"]);var c=new $n(t),f=new ei(t);e.dragPan=new mi(r,c,f),this._add(\"mousePan\",c),this._add(\"touchPan\",f,[\"touchZoom\",\"touchRotate\"]);var h=new si,p=new ai;e.touchZoomRotate=new bi(r,p,h,o),this._add(\"touchRotate\",h,[\"touchPan\",\"touchZoom\"]),this._add(\"touchZoom\",p,[\"touchPan\",\"touchRotate\"]);var d=e.scrollZoom=new di(e,this);this._add(\"scrollZoom\",d,[\"mousePan\"]);var v=e.keyboard=new fi;this._add(\"keyboard\",v),this._add(\"blockableMapEvent\",new Gn(e));for(var g=0,y=[\"boxZoom\",\"doubleClickZoom\",\"tapDragZoom\",\"touchPitch\",\"dragRotate\",\"dragPan\",\"touchZoomRotate\",\"scrollZoom\",\"keyboard\"];g<y.length;g+=1){var m=y[g];t.interactive&&t[m]&&e[m].enable(t[m])}},ki.prototype._add=function(t,e,r){this._handlers.push({handlerName:t,handler:e,allowed:r}),this._handlersById[t]=e},ki.prototype.stop=function(){if(!this._updatingCamera){for(var t=0,e=this._handlers;t<e.length;t+=1)e[t].handler.reset();this._inertia.clear(),this._fireEvents({},{}),this._changes=[]}},ki.prototype.isActive=function(){for(var t=0,e=this._handlers;t<e.length;t+=1)if(e[t].handler.isActive())return!0;return!1},ki.prototype.isZooming=function(){return!!this._eventsInProgress.zoom||this._map.scrollZoom.isZooming()},ki.prototype.isRotating=function(){return!!this._eventsInProgress.rotate},ki.prototype.isMoving=function(){return Boolean(_i(this._eventsInProgress))||this.isZooming()},ki.prototype._blockedByActive=function(t,e,r){for(var n in t)if(n!==r&&(!e||e.indexOf(n)<0))return!0;return!1},ki.prototype.handleWindowEvent=function(t){this.handleEvent(t,t.type+\"Window\")},ki.prototype._getMapTouches=function(t){for(var e=[],r=0,n=t;r<n.length;r+=1){var i=n[r],a=i.target;this._el.contains(a)&&e.push(i)}return e},ki.prototype.handleEvent=function(t,e){if(\"blur\"!==t.type){this._updatingCamera=!0;for(var n=\"renderFrame\"===t.type?void 0:t,i={needsRenderFrame:!1},a={},o={},s=t.touches?this._getMapTouches(t.touches):void 0,l=s?r.touchPos(this._el,s):r.mousePos(this._el,t),u=0,c=this._handlers;u<c.length;u+=1){var f=c[u],h=f.handlerName,p=f.handler,d=f.allowed;if(p.isEnabled()){var v=void 0;this._blockedByActive(o,d,h)?p.reset():p[e||t.type]&&(v=p[e||t.type](t,l,s),this.mergeHandlerResult(i,a,v,h,n),v&&v.needsRenderFrame&&this._triggerRenderFrame()),(v||p.isActive())&&(o[h]=p)}}var g={};for(var y in this._previousActiveHandlers)o[y]||(g[y]=n);this._previousActiveHandlers=o,(Object.keys(g).length||Ti(i))&&(this._changes.push([i,a,g]),this._triggerRenderFrame()),(Object.keys(o).length||Ti(i))&&this._map._stop(!0),this._updatingCamera=!1;var m=i.cameraAnimation;m&&(this._inertia.clear(),this._fireEvents({},{}),this._changes=[],m(this._map))}else this.stop()},ki.prototype.mergeHandlerResult=function(e,r,n,i,a){if(n){t.extend(e,n);var o={handlerName:i,originalEvent:n.originalEvent||a};void 0!==n.zoomDelta&&(r.zoom=o),void 0!==n.panDelta&&(r.drag=o),void 0!==n.pitchDelta&&(r.pitch=o),void 0!==n.bearingDelta&&(r.rotate=o)}},ki.prototype._applyChanges=function(){for(var e={},r={},n={},i=0,a=this._changes;i<a.length;i+=1){var o=a[i],s=o[0],l=o[1],u=o[2];s.panDelta&&(e.panDelta=(e.panDelta||new t.Point(0,0))._add(s.panDelta)),s.zoomDelta&&(e.zoomDelta=(e.zoomDelta||0)+s.zoomDelta),s.bearingDelta&&(e.bearingDelta=(e.bearingDelta||0)+s.bearingDelta),s.pitchDelta&&(e.pitchDelta=(e.pitchDelta||0)+s.pitchDelta),void 0!==s.around&&(e.around=s.around),void 0!==s.pinchAround&&(e.pinchAround=s.pinchAround),s.noInertia&&(e.noInertia=s.noInertia),t.extend(r,l),t.extend(n,u)}this._updateMapTransform(e,r,n),this._changes=[]},ki.prototype._updateMapTransform=function(t,e,r){var n=this._map,i=n.transform;if(!Ti(t))return this._fireEvents(e,r);var a=t.panDelta,o=t.zoomDelta,s=t.bearingDelta,l=t.pitchDelta,u=t.around,c=t.pinchAround;void 0!==c&&(u=c),n._stop(!0),u=u||n.transform.centerPoint;var f=i.pointLocation(a?u.sub(a):u);s&&(i.bearing+=s),l&&(i.pitch+=l),o&&(i.zoom+=o),i.setLocationAtPoint(f,u),this._map._update(),t.noInertia||this._inertia.record(t),this._fireEvents(e,r)},ki.prototype._fireEvents=function(e,r){var n=this,i=_i(this._eventsInProgress),a=_i(e),o={};for(var s in e){var l=e[s].originalEvent;this._eventsInProgress[s]||(o[s+\"start\"]=l),this._eventsInProgress[s]=e[s]}for(var u in!i&&a&&this._fireEvent(\"movestart\",a.originalEvent),o)this._fireEvent(u,o[u]);for(var c in e.rotate&&(this._bearingChanged=!0),a&&this._fireEvent(\"move\",a.originalEvent),e){var f=e[c].originalEvent;this._fireEvent(c,f)}var h,p={};for(var d in this._eventsInProgress){var v=this._eventsInProgress[d],g=v.handlerName,y=v.originalEvent;this._handlersById[g].isActive()||(delete this._eventsInProgress[d],h=r[g]||y,p[d+\"end\"]=h)}for(var m in p)this._fireEvent(m,p[m]);var x=_i(this._eventsInProgress);if((i||a)&&!x){this._updatingCamera=!0;var b=this._inertia._onMoveEnd(this._map.dragPan._inertiaOptions),_=function(t){return 0!==t&&-n._bearingSnap<t&&t<n._bearingSnap};b?(_(b.bearing||this._map.getBearing())&&(b.bearing=0),this._map.easeTo(b,{originalEvent:h})):(this._map.fire(new t.Event(\"moveend\",{originalEvent:h})),_(this._map.getBearing())&&this._map.resetNorth()),this._bearingChanged=!1,this._updatingCamera=!1}},ki.prototype._fireEvent=function(e,r){this._map.fire(new t.Event(e,r?{originalEvent:r}:{}))},ki.prototype._triggerRenderFrame=function(){var t=this;void 0===this._frameId&&(this._frameId=this._map._requestRenderFrame((function(e){delete t._frameId,t.handleEvent(new wi(\"renderFrame\",{timeStamp:e})),t._applyChanges()})))};var Ai=function(e){function r(r,n){e.call(this),this._moving=!1,this._zooming=!1,this.transform=r,this._bearingSnap=n.bearingSnap,t.bindAll([\"_renderFrameCallback\"],this)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getCenter=function(){return new t.LngLat(this.transform.center.lng,this.transform.center.lat)},r.prototype.setCenter=function(t,e){return this.jumpTo({center:t},e)},r.prototype.panBy=function(e,r,n){return e=t.Point.convert(e).mult(-1),this.panTo(this.transform.center,t.extend({offset:e},r),n)},r.prototype.panTo=function(e,r,n){return this.easeTo(t.extend({center:e},r),n)},r.prototype.getZoom=function(){return this.transform.zoom},r.prototype.setZoom=function(t,e){return this.jumpTo({zoom:t},e),this},r.prototype.zoomTo=function(e,r,n){return this.easeTo(t.extend({zoom:e},r),n)},r.prototype.zoomIn=function(t,e){return this.zoomTo(this.getZoom()+1,t,e),this},r.prototype.zoomOut=function(t,e){return this.zoomTo(this.getZoom()-1,t,e),this},r.prototype.getBearing=function(){return this.transform.bearing},r.prototype.setBearing=function(t,e){return this.jumpTo({bearing:t},e),this},r.prototype.getPadding=function(){return this.transform.padding},r.prototype.setPadding=function(t,e){return this.jumpTo({padding:t},e),this},r.prototype.rotateTo=function(e,r,n){return this.easeTo(t.extend({bearing:e},r),n)},r.prototype.resetNorth=function(e,r){return this.rotateTo(0,t.extend({duration:1e3},e),r),this},r.prototype.resetNorthPitch=function(e,r){return this.easeTo(t.extend({bearing:0,pitch:0,duration:1e3},e),r),this},r.prototype.snapToNorth=function(t,e){return Math.abs(this.getBearing())<this._bearingSnap?this.resetNorth(t,e):this},r.prototype.getPitch=function(){return this.transform.pitch},r.prototype.setPitch=function(t,e){return this.jumpTo({pitch:t},e),this},r.prototype.cameraForBounds=function(e,r){return e=t.LngLatBounds.convert(e),this._cameraForBoxAndBearing(e.getNorthWest(),e.getSouthEast(),0,r)},r.prototype._cameraForBoxAndBearing=function(e,r,n,i){var a={top:0,bottom:0,right:0,left:0};if(\"number\"==typeof(i=t.extend({padding:a,offset:[0,0],maxZoom:this.transform.maxZoom},i)).padding){var o=i.padding;i.padding={top:o,bottom:o,right:o,left:o}}i.padding=t.extend(a,i.padding);var s=this.transform,l=s.padding,u=s.project(t.LngLat.convert(e)),c=s.project(t.LngLat.convert(r)),f=u.rotate(-n*Math.PI/180),h=c.rotate(-n*Math.PI/180),p=new t.Point(Math.max(f.x,h.x),Math.max(f.y,h.y)),d=new t.Point(Math.min(f.x,h.x),Math.min(f.y,h.y)),v=p.sub(d),g=(s.width-(l.left+l.right+i.padding.left+i.padding.right))/v.x,y=(s.height-(l.top+l.bottom+i.padding.top+i.padding.bottom))/v.y;if(!(y<0||g<0)){var m=Math.min(s.scaleZoom(s.scale*Math.min(g,y)),i.maxZoom),x=t.Point.convert(i.offset),b=(i.padding.left-i.padding.right)/2,_=(i.padding.top-i.padding.bottom)/2,w=new t.Point(x.x+b,x.y+_).mult(s.scale/s.zoomScale(m));return{center:s.unproject(u.add(c).div(2).sub(w)),zoom:m,bearing:n}}t.warnOnce(\"Map cannot fit within canvas with the given bounds, padding, and/or offset.\")},r.prototype.fitBounds=function(t,e,r){return this._fitInternal(this.cameraForBounds(t,e),e,r)},r.prototype.fitScreenCoordinates=function(e,r,n,i,a){return this._fitInternal(this._cameraForBoxAndBearing(this.transform.pointLocation(t.Point.convert(e)),this.transform.pointLocation(t.Point.convert(r)),n,i),i,a)},r.prototype._fitInternal=function(e,r,n){return e?(delete(r=t.extend(e,r)).padding,r.linear?this.easeTo(r,n):this.flyTo(r,n)):this},r.prototype.jumpTo=function(e,r){this.stop();var n=this.transform,i=!1,a=!1,o=!1;return\"zoom\"in e&&n.zoom!==+e.zoom&&(i=!0,n.zoom=+e.zoom),void 0!==e.center&&(n.center=t.LngLat.convert(e.center)),\"bearing\"in e&&n.bearing!==+e.bearing&&(a=!0,n.bearing=+e.bearing),\"pitch\"in e&&n.pitch!==+e.pitch&&(o=!0,n.pitch=+e.pitch),null==e.padding||n.isPaddingEqual(e.padding)||(n.padding=e.padding),this.fire(new t.Event(\"movestart\",r)).fire(new t.Event(\"move\",r)),i&&this.fire(new t.Event(\"zoomstart\",r)).fire(new t.Event(\"zoom\",r)).fire(new t.Event(\"zoomend\",r)),a&&this.fire(new t.Event(\"rotatestart\",r)).fire(new t.Event(\"rotate\",r)).fire(new t.Event(\"rotateend\",r)),o&&this.fire(new t.Event(\"pitchstart\",r)).fire(new t.Event(\"pitch\",r)).fire(new t.Event(\"pitchend\",r)),this.fire(new t.Event(\"moveend\",r))},r.prototype.easeTo=function(e,r){var n=this;this._stop(!1,e.easeId),(!1===(e=t.extend({offset:[0,0],duration:500,easing:t.ease},e)).animate||!e.essential&&t.browser.prefersReducedMotion)&&(e.duration=0);var i=this.transform,a=this.getZoom(),o=this.getBearing(),s=this.getPitch(),l=this.getPadding(),u=\"zoom\"in e?+e.zoom:a,c=\"bearing\"in e?this._normalizeBearing(e.bearing,o):o,f=\"pitch\"in e?+e.pitch:s,h=\"padding\"in e?e.padding:i.padding,p=t.Point.convert(e.offset),d=i.centerPoint.add(p),v=i.pointLocation(d),g=t.LngLat.convert(e.center||v);this._normalizeCenter(g);var y,m,x=i.project(v),b=i.project(g).sub(x),_=i.zoomScale(u-a);e.around&&(y=t.LngLat.convert(e.around),m=i.locationPoint(y));var w={moving:this._moving,zooming:this._zooming,rotating:this._rotating,pitching:this._pitching};return this._zooming=this._zooming||u!==a,this._rotating=this._rotating||o!==c,this._pitching=this._pitching||f!==s,this._padding=!i.isPaddingEqual(h),this._easeId=e.easeId,this._prepareEase(r,e.noMoveStart,w),clearTimeout(this._easeEndTimeoutID),this._ease((function(e){if(n._zooming&&(i.zoom=t.number(a,u,e)),n._rotating&&(i.bearing=t.number(o,c,e)),n._pitching&&(i.pitch=t.number(s,f,e)),n._padding&&(i.interpolatePadding(l,h,e),d=i.centerPoint.add(p)),y)i.setLocationAtPoint(y,m);else{var v=i.zoomScale(i.zoom-a),g=u>a?Math.min(2,_):Math.max(.5,_),w=Math.pow(g,1-e),T=i.unproject(x.add(b.mult(e*w)).mult(v));i.setLocationAtPoint(i.renderWorldCopies?T.wrap():T,d)}n._fireMoveEvents(r)}),(function(t){n._afterEase(r,t)}),e),this},r.prototype._prepareEase=function(e,r,n){void 0===n&&(n={}),this._moving=!0,r||n.moving||this.fire(new t.Event(\"movestart\",e)),this._zooming&&!n.zooming&&this.fire(new t.Event(\"zoomstart\",e)),this._rotating&&!n.rotating&&this.fire(new t.Event(\"rotatestart\",e)),this._pitching&&!n.pitching&&this.fire(new t.Event(\"pitchstart\",e))},r.prototype._fireMoveEvents=function(e){this.fire(new t.Event(\"move\",e)),this._zooming&&this.fire(new t.Event(\"zoom\",e)),this._rotating&&this.fire(new t.Event(\"rotate\",e)),this._pitching&&this.fire(new t.Event(\"pitch\",e))},r.prototype._afterEase=function(e,r){if(!this._easeId||!r||this._easeId!==r){delete this._easeId;var n=this._zooming,i=this._rotating,a=this._pitching;this._moving=!1,this._zooming=!1,this._rotating=!1,this._pitching=!1,this._padding=!1,n&&this.fire(new t.Event(\"zoomend\",e)),i&&this.fire(new t.Event(\"rotateend\",e)),a&&this.fire(new t.Event(\"pitchend\",e)),this.fire(new t.Event(\"moveend\",e))}},r.prototype.flyTo=function(e,r){var n=this;if(!e.essential&&t.browser.prefersReducedMotion){var i=t.pick(e,[\"center\",\"zoom\",\"bearing\",\"pitch\",\"around\"]);return this.jumpTo(i,r)}this.stop(),e=t.extend({offset:[0,0],speed:1.2,curve:1.42,easing:t.ease},e);var a=this.transform,o=this.getZoom(),s=this.getBearing(),l=this.getPitch(),u=this.getPadding(),c=\"zoom\"in e?t.clamp(+e.zoom,a.minZoom,a.maxZoom):o,f=\"bearing\"in e?this._normalizeBearing(e.bearing,s):s,h=\"pitch\"in e?+e.pitch:l,p=\"padding\"in e?e.padding:a.padding,d=a.zoomScale(c-o),v=t.Point.convert(e.offset),g=a.centerPoint.add(v),y=a.pointLocation(g),m=t.LngLat.convert(e.center||y);this._normalizeCenter(m);var x=a.project(y),b=a.project(m).sub(x),_=e.curve,w=Math.max(a.width,a.height),T=w/d,k=b.mag();if(\"minZoom\"in e){var A=t.clamp(Math.min(e.minZoom,o,c),a.minZoom,a.maxZoom),M=w/a.zoomScale(A-o);_=Math.sqrt(M/k*2)}var S=_*_;function E(t){var e=(T*T-w*w+(t?-1:1)*S*S*k*k)/(2*(t?T:w)*S*k);return Math.log(Math.sqrt(e*e+1)-e)}function L(t){return(Math.exp(t)-Math.exp(-t))/2}function C(t){return(Math.exp(t)+Math.exp(-t))/2}var P=E(0),O=function(t){return C(P)/C(P+_*t)},I=function(t){return w*((C(P)*(L(e=P+_*t)/C(e))-L(P))/S)/k;var e},D=(E(1)-P)/_;if(Math.abs(k)<1e-6||!isFinite(D)){if(Math.abs(w-T)<1e-6)return this.easeTo(e,r);var z=T<w?-1:1;D=Math.abs(Math.log(T/w))/_,I=function(){return 0},O=function(t){return Math.exp(z*_*t)}}if(\"duration\"in e)e.duration=+e.duration;else{var R=\"screenSpeed\"in e?+e.screenSpeed/_:+e.speed;e.duration=1e3*D/R}return e.maxDuration&&e.duration>e.maxDuration&&(e.duration=0),this._zooming=!0,this._rotating=s!==f,this._pitching=h!==l,this._padding=!a.isPaddingEqual(p),this._prepareEase(r,!1),this._ease((function(e){var i=e*D,d=1/O(i);a.zoom=1===e?c:o+a.scaleZoom(d),n._rotating&&(a.bearing=t.number(s,f,e)),n._pitching&&(a.pitch=t.number(l,h,e)),n._padding&&(a.interpolatePadding(u,p,e),g=a.centerPoint.add(v));var y=1===e?m:a.unproject(x.add(b.mult(I(i))).mult(d));a.setLocationAtPoint(a.renderWorldCopies?y.wrap():y,g),n._fireMoveEvents(r)}),(function(){return n._afterEase(r)}),e),this},r.prototype.isEasing=function(){return!!this._easeFrameId},r.prototype.stop=function(){return this._stop()},r.prototype._stop=function(t,e){if(this._easeFrameId&&(this._cancelRenderFrame(this._easeFrameId),delete this._easeFrameId,delete this._onEaseFrame),this._onEaseEnd){var r=this._onEaseEnd;delete this._onEaseEnd,r.call(this,e)}if(!t){var n=this.handlers;n&&n.stop()}return this},r.prototype._ease=function(e,r,n){!1===n.animate||0===n.duration?(e(1),r()):(this._easeStart=t.browser.now(),this._easeOptions=n,this._onEaseFrame=e,this._onEaseEnd=r,this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback))},r.prototype._renderFrameCallback=function(){var e=Math.min((t.browser.now()-this._easeStart)/this._easeOptions.duration,1);this._onEaseFrame(this._easeOptions.easing(e)),e<1?this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback):this.stop()},r.prototype._normalizeBearing=function(e,r){e=t.wrap(e,-180,180);var n=Math.abs(e-r);return Math.abs(e-360-r)<n&&(e-=360),Math.abs(e+360-r)<n&&(e+=360),e},r.prototype._normalizeCenter=function(t){var e=this.transform;if(e.renderWorldCopies&&!e.lngRange){var r=t.lng-e.center.lng;t.lng+=r>180?-360:r<-180?360:0}},r}(t.Evented),Mi=function(e){void 0===e&&(e={}),this.options=e,t.bindAll([\"_updateEditLink\",\"_updateData\",\"_updateCompact\"],this)};Mi.prototype.getDefaultPosition=function(){return\"bottom-right\"},Mi.prototype.onAdd=function(t){var e=this.options&&this.options.compact;return this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-attrib\"),this._innerContainer=r.create(\"div\",\"mapboxgl-ctrl-attrib-inner\",this._container),e&&this._container.classList.add(\"mapboxgl-compact\"),this._updateAttributions(),this._updateEditLink(),this._map.on(\"styledata\",this._updateData),this._map.on(\"sourcedata\",this._updateData),this._map.on(\"moveend\",this._updateEditLink),void 0===e&&(this._map.on(\"resize\",this._updateCompact),this._updateCompact()),this._container},Mi.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"styledata\",this._updateData),this._map.off(\"sourcedata\",this._updateData),this._map.off(\"moveend\",this._updateEditLink),this._map.off(\"resize\",this._updateCompact),this._map=void 0,this._attribHTML=void 0},Mi.prototype._updateEditLink=function(){var e=this._editLink;e||(e=this._editLink=this._container.querySelector(\".mapbox-improve-map\"));var r=[{key:\"owner\",value:this.styleOwner},{key:\"id\",value:this.styleId},{key:\"access_token\",value:this._map._requestManager._customAccessToken||t.config.ACCESS_TOKEN}];if(e){var n=r.reduce((function(t,e,n){return e.value&&(t+=e.key+\"=\"+e.value+(n<r.length-1?\"&\":\"\")),t}),\"?\");e.href=t.config.FEEDBACK_URL+\"/\"+n+(this._map._hash?this._map._hash.getHashString(!0):\"\"),e.rel=\"noopener nofollow\"}},Mi.prototype._updateData=function(t){!t||\"metadata\"!==t.sourceDataType&&\"style\"!==t.dataType||(this._updateAttributions(),this._updateEditLink())},Mi.prototype._updateAttributions=function(){if(this._map.style){var t=[];if(this.options.customAttribution&&(Array.isArray(this.options.customAttribution)?t=t.concat(this.options.customAttribution.map((function(t){return\"string\"!=typeof t?\"\":t}))):\"string\"==typeof this.options.customAttribution&&t.push(this.options.customAttribution)),this._map.style.stylesheet){var e=this._map.style.stylesheet;this.styleOwner=e.owner,this.styleId=e.id}var r=this._map.style.sourceCaches;for(var n in r){var i=r[n];if(i.used){var a=i.getSource();a.attribution&&t.indexOf(a.attribution)<0&&t.push(a.attribution)}}t.sort((function(t,e){return t.length-e.length}));var o=(t=t.filter((function(e,r){for(var n=r+1;n<t.length;n++)if(t[n].indexOf(e)>=0)return!1;return!0}))).join(\" | \");o!==this._attribHTML&&(this._attribHTML=o,t.length?(this._innerContainer.innerHTML=o,this._container.classList.remove(\"mapboxgl-attrib-empty\")):this._container.classList.add(\"mapboxgl-attrib-empty\"),this._editLink=null)}},Mi.prototype._updateCompact=function(){this._map.getCanvasContainer().offsetWidth<=640?this._container.classList.add(\"mapboxgl-compact\"):this._container.classList.remove(\"mapboxgl-compact\")};var Si=function(){t.bindAll([\"_updateLogo\"],this),t.bindAll([\"_updateCompact\"],this)};Si.prototype.onAdd=function(t){this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl\");var e=r.create(\"a\",\"mapboxgl-ctrl-logo\");return e.target=\"_blank\",e.rel=\"noopener nofollow\",e.href=\"https://www.mapbox.com/\",e.setAttribute(\"aria-label\",this._map._getUIString(\"LogoControl.Title\")),e.setAttribute(\"rel\",\"noopener nofollow\"),this._container.appendChild(e),this._container.style.display=\"none\",this._map.on(\"sourcedata\",this._updateLogo),this._updateLogo(),this._map.on(\"resize\",this._updateCompact),this._updateCompact(),this._container},Si.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"sourcedata\",this._updateLogo),this._map.off(\"resize\",this._updateCompact)},Si.prototype.getDefaultPosition=function(){return\"bottom-left\"},Si.prototype._updateLogo=function(t){t&&\"metadata\"!==t.sourceDataType||(this._container.style.display=this._logoRequired()?\"block\":\"none\")},Si.prototype._logoRequired=function(){if(this._map.style){var t=this._map.style.sourceCaches;for(var e in t)if(t[e].getSource().mapbox_logo)return!0;return!1}},Si.prototype._updateCompact=function(){var t=this._container.children;if(t.length){var e=t[0];this._map.getCanvasContainer().offsetWidth<250?e.classList.add(\"mapboxgl-compact\"):e.classList.remove(\"mapboxgl-compact\")}};var Ei=function(){this._queue=[],this._id=0,this._cleared=!1,this._currentlyRunning=!1};Ei.prototype.add=function(t){var e=++this._id;return this._queue.push({callback:t,id:e,cancelled:!1}),e},Ei.prototype.remove=function(t){for(var e=this._currentlyRunning,r=0,n=e?this._queue.concat(e):this._queue;r<n.length;r+=1){var i=n[r];if(i.id===t)return void(i.cancelled=!0)}},Ei.prototype.run=function(t){void 0===t&&(t=0);var e=this._currentlyRunning=this._queue;this._queue=[];for(var r=0,n=e;r<n.length;r+=1){var i=n[r];if(!i.cancelled&&(i.callback(t),this._cleared))break}this._cleared=!1,this._currentlyRunning=!1},Ei.prototype.clear=function(){this._currentlyRunning&&(this._cleared=!0),this._queue=[]};var Li={\"FullscreenControl.Enter\":\"Enter fullscreen\",\"FullscreenControl.Exit\":\"Exit fullscreen\",\"GeolocateControl.FindMyLocation\":\"Find my location\",\"GeolocateControl.LocationNotAvailable\":\"Location not available\",\"LogoControl.Title\":\"Mapbox logo\",\"NavigationControl.ResetBearing\":\"Reset bearing to north\",\"NavigationControl.ZoomIn\":\"Zoom in\",\"NavigationControl.ZoomOut\":\"Zoom out\",\"ScaleControl.Feet\":\"ft\",\"ScaleControl.Meters\":\"m\",\"ScaleControl.Kilometers\":\"km\",\"ScaleControl.Miles\":\"mi\",\"ScaleControl.NauticalMiles\":\"nm\"},Ci=t.window.HTMLImageElement,Pi=t.window.HTMLElement,Oi=t.window.ImageBitmap,Ii=60,Di={center:[0,0],zoom:0,bearing:0,pitch:0,minZoom:-2,maxZoom:22,minPitch:0,maxPitch:Ii,interactive:!0,scrollZoom:!0,boxZoom:!0,dragRotate:!0,dragPan:!0,keyboard:!0,doubleClickZoom:!0,touchZoomRotate:!0,touchPitch:!0,bearingSnap:7,clickTolerance:3,pitchWithRotate:!0,hash:!1,attributionControl:!0,failIfMajorPerformanceCaveat:!1,preserveDrawingBuffer:!1,trackResize:!0,renderWorldCopies:!0,refreshExpiredTiles:!0,maxTileCacheSize:null,localIdeographFontFamily:\"sans-serif\",transformRequest:null,accessToken:null,fadeDuration:300,crossSourceCollisions:!0},zi=function(n){function i(e){var r=this;if(null!=(e=t.extend({},Di,e)).minZoom&&null!=e.maxZoom&&e.minZoom>e.maxZoom)throw new Error(\"maxZoom must be greater than or equal to minZoom\");if(null!=e.minPitch&&null!=e.maxPitch&&e.minPitch>e.maxPitch)throw new Error(\"maxPitch must be greater than or equal to minPitch\");if(null!=e.minPitch&&e.minPitch<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(null!=e.maxPitch&&e.maxPitch>Ii)throw new Error(\"maxPitch must be less than or equal to 60\");var i=new Cn(e.minZoom,e.maxZoom,e.minPitch,e.maxPitch,e.renderWorldCopies);if(n.call(this,i,e),this._interactive=e.interactive,this._maxTileCacheSize=e.maxTileCacheSize,this._failIfMajorPerformanceCaveat=e.failIfMajorPerformanceCaveat,this._preserveDrawingBuffer=e.preserveDrawingBuffer,this._antialias=e.antialias,this._trackResize=e.trackResize,this._bearingSnap=e.bearingSnap,this._refreshExpiredTiles=e.refreshExpiredTiles,this._fadeDuration=e.fadeDuration,this._crossSourceCollisions=e.crossSourceCollisions,this._crossFadingFactor=1,this._collectResourceTiming=e.collectResourceTiming,this._renderTaskQueue=new Ei,this._controls=[],this._mapId=t.uniqueId(),this._locale=t.extend({},Li,e.locale),this._requestManager=new t.RequestManager(e.transformRequest,e.accessToken),\"string\"==typeof e.container){if(this._container=t.window.document.getElementById(e.container),!this._container)throw new Error(\"Container '\"+e.container+\"' not found.\")}else{if(!(e.container instanceof Pi))throw new Error(\"Invalid type: 'container' must be a String or HTMLElement.\");this._container=e.container}if(e.maxBounds&&this.setMaxBounds(e.maxBounds),t.bindAll([\"_onWindowOnline\",\"_onWindowResize\",\"_contextLost\",\"_contextRestored\"],this),this._setupContainer(),this._setupPainter(),void 0===this.painter)throw new Error(\"Failed to initialize WebGL.\");this.on(\"move\",(function(){return r._update(!1)})),this.on(\"moveend\",(function(){return r._update(!1)})),this.on(\"zoom\",(function(){return r._update(!0)})),void 0!==t.window&&(t.window.addEventListener(\"online\",this._onWindowOnline,!1),t.window.addEventListener(\"resize\",this._onWindowResize,!1)),this.handlers=new ki(this,e);var a=\"string\"==typeof e.hash&&e.hash||void 0;this._hash=e.hash&&new On(a).addTo(this),this._hash&&this._hash._onHashChange()||(this.jumpTo({center:e.center,zoom:e.zoom,bearing:e.bearing,pitch:e.pitch}),e.bounds&&(this.resize(),this.fitBounds(e.bounds,t.extend({},e.fitBoundsOptions,{duration:0})))),this.resize(),this._localIdeographFontFamily=e.localIdeographFontFamily,e.style&&this.setStyle(e.style,{localIdeographFontFamily:e.localIdeographFontFamily}),e.attributionControl&&this.addControl(new Mi({customAttribution:e.customAttribution})),this.addControl(new Si,e.logoPosition),this.on(\"style.load\",(function(){r.transform.unmodified&&r.jumpTo(r.style.stylesheet)})),this.on(\"data\",(function(e){r._update(\"style\"===e.dataType),r.fire(new t.Event(e.dataType+\"data\",e))})),this.on(\"dataloading\",(function(e){r.fire(new t.Event(e.dataType+\"dataloading\",e))}))}n&&(i.__proto__=n),i.prototype=Object.create(n&&n.prototype),i.prototype.constructor=i;var a={showTileBoundaries:{configurable:!0},showPadding:{configurable:!0},showCollisionBoxes:{configurable:!0},showOverdrawInspector:{configurable:!0},repaint:{configurable:!0},vertices:{configurable:!0},version:{configurable:!0}};return i.prototype._getMapId=function(){return this._mapId},i.prototype.addControl=function(e,r){if(void 0===r&&e.getDefaultPosition&&(r=e.getDefaultPosition()),void 0===r&&(r=\"top-right\"),!e||!e.onAdd)return this.fire(new t.ErrorEvent(new Error(\"Invalid argument to map.addControl(). Argument must be a control with onAdd and onRemove methods.\")));var n=e.onAdd(this);this._controls.push(e);var i=this._controlPositions[r];return-1!==r.indexOf(\"bottom\")?i.insertBefore(n,i.firstChild):i.appendChild(n),this},i.prototype.removeControl=function(e){if(!e||!e.onRemove)return this.fire(new t.ErrorEvent(new Error(\"Invalid argument to map.removeControl(). Argument must be a control with onAdd and onRemove methods.\")));var r=this._controls.indexOf(e);return r>-1&&this._controls.splice(r,1),e.onRemove(this),this},i.prototype.resize=function(e){var r=this._containerDimensions(),n=r[0],i=r[1];this._resizeCanvas(n,i),this.transform.resize(n,i),this.painter.resize(n,i);var a=!this._moving;return a&&(this.stop(),this.fire(new t.Event(\"movestart\",e)).fire(new t.Event(\"move\",e))),this.fire(new t.Event(\"resize\",e)),a&&this.fire(new t.Event(\"moveend\",e)),this},i.prototype.getBounds=function(){return this.transform.getBounds()},i.prototype.getMaxBounds=function(){return this.transform.getMaxBounds()},i.prototype.setMaxBounds=function(e){return this.transform.setMaxBounds(t.LngLatBounds.convert(e)),this._update()},i.prototype.setMinZoom=function(t){if((t=null==t?-2:t)>=-2&&t<=this.transform.maxZoom)return this.transform.minZoom=t,this._update(),this.getZoom()<t&&this.setZoom(t),this;throw new Error(\"minZoom must be between -2 and the current maxZoom, inclusive\")},i.prototype.getMinZoom=function(){return this.transform.minZoom},i.prototype.setMaxZoom=function(t){if((t=null==t?22:t)>=this.transform.minZoom)return this.transform.maxZoom=t,this._update(),this.getZoom()>t&&this.setZoom(t),this;throw new Error(\"maxZoom must be greater than the current minZoom\")},i.prototype.getMaxZoom=function(){return this.transform.maxZoom},i.prototype.setMinPitch=function(t){if((t=null==t?0:t)<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(t>=0&&t<=this.transform.maxPitch)return this.transform.minPitch=t,this._update(),this.getPitch()<t&&this.setPitch(t),this;throw new Error(\"minPitch must be between 0 and the current maxPitch, inclusive\")},i.prototype.getMinPitch=function(){return this.transform.minPitch},i.prototype.setMaxPitch=function(t){if((t=null==t?Ii:t)>Ii)throw new Error(\"maxPitch must be less than or equal to 60\");if(t>=this.transform.minPitch)return this.transform.maxPitch=t,this._update(),this.getPitch()>t&&this.setPitch(t),this;throw new Error(\"maxPitch must be greater than the current minPitch\")},i.prototype.getMaxPitch=function(){return this.transform.maxPitch},i.prototype.getRenderWorldCopies=function(){return this.transform.renderWorldCopies},i.prototype.setRenderWorldCopies=function(t){return this.transform.renderWorldCopies=t,this._update()},i.prototype.project=function(e){return this.transform.locationPoint(t.LngLat.convert(e))},i.prototype.unproject=function(e){return this.transform.pointLocation(t.Point.convert(e))},i.prototype.isMoving=function(){return this._moving||this.handlers.isMoving()},i.prototype.isZooming=function(){return this._zooming||this.handlers.isZooming()},i.prototype.isRotating=function(){return this._rotating||this.handlers.isRotating()},i.prototype._createDelegatedListener=function(t,e,r){var n,i=this;if(\"mouseenter\"===t||\"mouseover\"===t){var a=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){var o=i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[];o.length?a||(a=!0,r.call(i,new Un(t,i,n.originalEvent,{features:o}))):a=!1},mouseout:function(){a=!1}}}}if(\"mouseleave\"===t||\"mouseout\"===t){var o=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){(i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[]).length?o=!0:o&&(o=!1,r.call(i,new Un(t,i,n.originalEvent)))},mouseout:function(e){o&&(o=!1,r.call(i,new Un(t,i,e.originalEvent)))}}}}return{layer:e,listener:r,delegates:(n={},n[t]=function(t){var n=i.getLayer(e)?i.queryRenderedFeatures(t.point,{layers:[e]}):[];n.length&&(t.features=n,r.call(i,t),delete t.features)},n)}},i.prototype.on=function(t,e,r){if(void 0===r)return n.prototype.on.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in this._delegatedListeners=this._delegatedListeners||{},this._delegatedListeners[t]=this._delegatedListeners[t]||[],this._delegatedListeners[t].push(i),i.delegates)this.on(a,i.delegates[a]);return this},i.prototype.once=function(t,e,r){if(void 0===r)return n.prototype.once.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in i.delegates)this.once(a,i.delegates[a]);return this},i.prototype.off=function(t,e,r){var i=this;if(void 0===r)return n.prototype.off.call(this,t,e);return this._delegatedListeners&&this._delegatedListeners[t]&&function(n){for(var a=n[t],o=0;o<a.length;o++){var s=a[o];if(s.layer===e&&s.listener===r){for(var l in s.delegates)i.off(l,s.delegates[l]);return a.splice(o,1),i}}}(this._delegatedListeners),this},i.prototype.queryRenderedFeatures=function(e,r){if(!this.style)return[];var n;if(void 0!==r||void 0===e||e instanceof t.Point||Array.isArray(e)||(r=e,e=void 0),r=r||{},(e=e||[[0,0],[this.transform.width,this.transform.height]])instanceof t.Point||\"number\"==typeof e[0])n=[t.Point.convert(e)];else{var i=t.Point.convert(e[0]),a=t.Point.convert(e[1]);n=[i,new t.Point(a.x,i.y),a,new t.Point(i.x,a.y),i]}return this.style.queryRenderedFeatures(n,r,this.transform)},i.prototype.querySourceFeatures=function(t,e){return this.style.querySourceFeatures(t,e)},i.prototype.setStyle=function(e,r){return!1!==(r=t.extend({},{localIdeographFontFamily:this._localIdeographFontFamily},r)).diff&&r.localIdeographFontFamily===this._localIdeographFontFamily&&this.style&&e?(this._diffStyle(e,r),this):(this._localIdeographFontFamily=r.localIdeographFontFamily,this._updateStyle(e,r))},i.prototype._getUIString=function(t){var e=this._locale[t];if(null==e)throw new Error(\"Missing UI string '\"+t+\"'\");return e},i.prototype._updateStyle=function(t,e){return this.style&&(this.style.setEventedParent(null),this.style._remove()),t?(this.style=new Ye(this,e||{}),this.style.setEventedParent(this,{style:this.style}),\"string\"==typeof t?this.style.loadURL(t):this.style.loadJSON(t),this):(delete this.style,this)},i.prototype._lazyInitEmptyStyle=function(){this.style||(this.style=new Ye(this,{}),this.style.setEventedParent(this,{style:this.style}),this.style.loadEmpty())},i.prototype._diffStyle=function(e,r){var n=this;if(\"string\"==typeof e){var i=this._requestManager.normalizeStyleURL(e),a=this._requestManager.transformRequest(i,t.ResourceType.Style);t.getJSON(a,(function(e,i){e?n.fire(new t.ErrorEvent(e)):i&&n._updateDiff(i,r)}))}else\"object\"==typeof e&&this._updateDiff(e,r)},i.prototype._updateDiff=function(e,r){try{this.style.setState(e)&&this._update(!0)}catch(n){t.warnOnce(\"Unable to perform style diff: \"+(n.message||n.error||n)+\".  Rebuilding the style from scratch.\"),this._updateStyle(e,r)}},i.prototype.getStyle=function(){if(this.style)return this.style.serialize()},i.prototype.isStyleLoaded=function(){return this.style?this.style.loaded():t.warnOnce(\"There is no style added to the map.\")},i.prototype.addSource=function(t,e){return this._lazyInitEmptyStyle(),this.style.addSource(t,e),this._update(!0)},i.prototype.isSourceLoaded=function(e){var r=this.style&&this.style.sourceCaches[e];if(void 0!==r)return r.loaded();this.fire(new t.ErrorEvent(new Error(\"There is no source with ID '\"+e+\"'\")))},i.prototype.areTilesLoaded=function(){var t=this.style&&this.style.sourceCaches;for(var e in t){var r=t[e]._tiles;for(var n in r){var i=r[n];if(\"loaded\"!==i.state&&\"errored\"!==i.state)return!1}}return!0},i.prototype.addSourceType=function(t,e,r){return this._lazyInitEmptyStyle(),this.style.addSourceType(t,e,r)},i.prototype.removeSource=function(t){return this.style.removeSource(t),this._update(!0)},i.prototype.getSource=function(t){return this.style.getSource(t)},i.prototype.addImage=function(e,r,n){void 0===n&&(n={});var i=n.pixelRatio;void 0===i&&(i=1);var a=n.sdf;void 0===a&&(a=!1);var o=n.stretchX,s=n.stretchY,l=n.content;this._lazyInitEmptyStyle();if(r instanceof Ci||Oi&&r instanceof Oi){var u=t.browser.getImageData(r),c=u.width,f=u.height,h=u.data;this.style.addImage(e,{data:new t.RGBAImage({width:c,height:f},h),pixelRatio:i,stretchX:o,stretchY:s,content:l,sdf:a,version:0})}else{if(void 0===r.width||void 0===r.height)return this.fire(new t.ErrorEvent(new Error(\"Invalid arguments to map.addImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`\")));var p=r.width,d=r.height,v=r.data,g=r;this.style.addImage(e,{data:new t.RGBAImage({width:p,height:d},new Uint8Array(v)),pixelRatio:i,stretchX:o,stretchY:s,content:l,sdf:a,version:0,userImage:g}),g.onAdd&&g.onAdd(this,e)}},i.prototype.updateImage=function(e,r){var n=this.style.getImage(e);if(!n)return this.fire(new t.ErrorEvent(new Error(\"The map has no image with that id. If you are adding a new image use `map.addImage(...)` instead.\")));var i=r instanceof Ci||Oi&&r instanceof Oi?t.browser.getImageData(r):r,a=i.width,o=i.height,s=i.data;if(void 0===a||void 0===o)return this.fire(new t.ErrorEvent(new Error(\"Invalid arguments to map.updateImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`\")));if(a!==n.data.width||o!==n.data.height)return this.fire(new t.ErrorEvent(new Error(\"The width and height of the updated image must be that same as the previous version of the image\")));var l=!(r instanceof Ci||Oi&&r instanceof Oi);n.data.replace(s,l),this.style.updateImage(e,n)},i.prototype.hasImage=function(e){return e?!!this.style.getImage(e):(this.fire(new t.ErrorEvent(new Error(\"Missing required image id\"))),!1)},i.prototype.removeImage=function(t){this.style.removeImage(t)},i.prototype.loadImage=function(e,r){t.getImage(this._requestManager.transformRequest(e,t.ResourceType.Image),r)},i.prototype.listImages=function(){return this.style.listImages()},i.prototype.addLayer=function(t,e){return this._lazyInitEmptyStyle(),this.style.addLayer(t,e),this._update(!0)},i.prototype.moveLayer=function(t,e){return this.style.moveLayer(t,e),this._update(!0)},i.prototype.removeLayer=function(t){return this.style.removeLayer(t),this._update(!0)},i.prototype.getLayer=function(t){return this.style.getLayer(t)},i.prototype.setLayerZoomRange=function(t,e,r){return this.style.setLayerZoomRange(t,e,r),this._update(!0)},i.prototype.setFilter=function(t,e,r){return void 0===r&&(r={}),this.style.setFilter(t,e,r),this._update(!0)},i.prototype.getFilter=function(t){return this.style.getFilter(t)},i.prototype.setPaintProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setPaintProperty(t,e,r,n),this._update(!0)},i.prototype.getPaintProperty=function(t,e){return this.style.getPaintProperty(t,e)},i.prototype.setLayoutProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setLayoutProperty(t,e,r,n),this._update(!0)},i.prototype.getLayoutProperty=function(t,e){return this.style.getLayoutProperty(t,e)},i.prototype.setLight=function(t,e){return void 0===e&&(e={}),this._lazyInitEmptyStyle(),this.style.setLight(t,e),this._update(!0)},i.prototype.getLight=function(){return this.style.getLight()},i.prototype.setFeatureState=function(t,e){return this.style.setFeatureState(t,e),this._update()},i.prototype.removeFeatureState=function(t,e){return this.style.removeFeatureState(t,e),this._update()},i.prototype.getFeatureState=function(t){return this.style.getFeatureState(t)},i.prototype.getContainer=function(){return this._container},i.prototype.getCanvasContainer=function(){return this._canvasContainer},i.prototype.getCanvas=function(){return this._canvas},i.prototype._containerDimensions=function(){var t=0,e=0;return this._container&&(t=this._container.clientWidth||400,e=this._container.clientHeight||300),[t,e]},i.prototype._detectMissingCSS=function(){\"rgb(250, 128, 114)\"!==t.window.getComputedStyle(this._missingCSSCanary).getPropertyValue(\"background-color\")&&t.warnOnce(\"This page appears to be missing CSS declarations for Mapbox GL JS, which may cause the map to display incorrectly. Please ensure your page includes mapbox-gl.css, as described in https://www.mapbox.com/mapbox-gl-js/api/.\")},i.prototype._setupContainer=function(){var t=this._container;t.classList.add(\"mapboxgl-map\"),(this._missingCSSCanary=r.create(\"div\",\"mapboxgl-canary\",t)).style.visibility=\"hidden\",this._detectMissingCSS();var e=this._canvasContainer=r.create(\"div\",\"mapboxgl-canvas-container\",t);this._interactive&&e.classList.add(\"mapboxgl-interactive\"),this._canvas=r.create(\"canvas\",\"mapboxgl-canvas\",e),this._canvas.addEventListener(\"webglcontextlost\",this._contextLost,!1),this._canvas.addEventListener(\"webglcontextrestored\",this._contextRestored,!1),this._canvas.setAttribute(\"tabindex\",\"0\"),this._canvas.setAttribute(\"aria-label\",\"Map\");var n=this._containerDimensions();this._resizeCanvas(n[0],n[1]);var i=this._controlContainer=r.create(\"div\",\"mapboxgl-control-container\",t),a=this._controlPositions={};[\"top-left\",\"top-right\",\"bottom-left\",\"bottom-right\"].forEach((function(t){a[t]=r.create(\"div\",\"mapboxgl-ctrl-\"+t,i)}))},i.prototype._resizeCanvas=function(e,r){var n=t.browser.devicePixelRatio||1;this._canvas.width=n*e,this._canvas.height=n*r,this._canvas.style.width=e+\"px\",this._canvas.style.height=r+\"px\"},i.prototype._setupPainter=function(){var r=t.extend({},e.webGLContextAttributes,{failIfMajorPerformanceCaveat:this._failIfMajorPerformanceCaveat,preserveDrawingBuffer:this._preserveDrawingBuffer,antialias:this._antialias||!1}),n=this._canvas.getContext(\"webgl\",r)||this._canvas.getContext(\"experimental-webgl\",r);n?(this.painter=new Mn(n,this.transform),t.webpSupported.testSupport(n)):this.fire(new t.ErrorEvent(new Error(\"Failed to initialize WebGL\")))},i.prototype._contextLost=function(e){e.preventDefault(),this._frame&&(this._frame.cancel(),this._frame=null),this.fire(new t.Event(\"webglcontextlost\",{originalEvent:e}))},i.prototype._contextRestored=function(e){this._setupPainter(),this.resize(),this._update(),this.fire(new t.Event(\"webglcontextrestored\",{originalEvent:e}))},i.prototype.loaded=function(){return!this._styleDirty&&!this._sourcesDirty&&!!this.style&&this.style.loaded()},i.prototype._update=function(t){return this.style?(this._styleDirty=this._styleDirty||t,this._sourcesDirty=!0,this.triggerRepaint(),this):this},i.prototype._requestRenderFrame=function(t){return this._update(),this._renderTaskQueue.add(t)},i.prototype._cancelRenderFrame=function(t){this._renderTaskQueue.remove(t)},i.prototype._render=function(e){var r,n=this,i=0,a=this.painter.context.extTimerQuery;if(this.listens(\"gpu-timing-frame\")&&(r=a.createQueryEXT(),a.beginQueryEXT(a.TIME_ELAPSED_EXT,r),i=t.browser.now()),this.painter.context.setDirty(),this.painter.setBaseState(),this._renderTaskQueue.run(e),!this._removed){var o=!1;if(this.style&&this._styleDirty){this._styleDirty=!1;var s=this.transform.zoom,l=t.browser.now();this.style.zoomHistory.update(s,l);var u=new t.EvaluationParameters(s,{now:l,fadeDuration:this._fadeDuration,zoomHistory:this.style.zoomHistory,transition:this.style.getTransition()}),c=u.crossFadingFactor();1===c&&c===this._crossFadingFactor||(o=!0,this._crossFadingFactor=c),this.style.update(u)}if(this.style&&this._sourcesDirty&&(this._sourcesDirty=!1,this.style._updateSources(this.transform)),this._placementDirty=this.style&&this.style._updatePlacement(this.painter.transform,this.showCollisionBoxes,this._fadeDuration,this._crossSourceCollisions),this.painter.render(this.style,{showTileBoundaries:this.showTileBoundaries,showOverdrawInspector:this._showOverdrawInspector,rotating:this.isRotating(),zooming:this.isZooming(),moving:this.isMoving(),fadeDuration:this._fadeDuration,showPadding:this.showPadding,gpuTiming:!!this.listens(\"gpu-timing-layer\")}),this.fire(new t.Event(\"render\")),this.loaded()&&!this._loaded&&(this._loaded=!0,this.fire(new t.Event(\"load\"))),this.style&&(this.style.hasTransitions()||o)&&(this._styleDirty=!0),this.style&&!this._placementDirty&&this.style._releaseSymbolFadeTiles(),this.listens(\"gpu-timing-frame\")){var f=t.browser.now()-i;a.endQueryEXT(a.TIME_ELAPSED_EXT,r),setTimeout((function(){var e=a.getQueryObjectEXT(r,a.QUERY_RESULT_EXT)/1e6;a.deleteQueryEXT(r),n.fire(new t.Event(\"gpu-timing-frame\",{cpuTime:f,gpuTime:e}))}),50)}if(this.listens(\"gpu-timing-layer\")){var h=this.painter.collectGpuTimers();setTimeout((function(){var e=n.painter.queryGpuTimers(h);n.fire(new t.Event(\"gpu-timing-layer\",{layerTimes:e}))}),50)}return this._sourcesDirty||this._styleDirty||this._placementDirty||this._repaint?this.triggerRepaint():!this.isMoving()&&this.loaded()&&(this._fullyLoaded||(this._fullyLoaded=!0),this.fire(new t.Event(\"idle\"))),this}},i.prototype.remove=function(){this._hash&&this._hash.remove();for(var e=0,r=this._controls;e<r.length;e+=1)r[e].onRemove(this);this._controls=[],this._frame&&(this._frame.cancel(),this._frame=null),this._renderTaskQueue.clear(),this.painter.destroy(),this.handlers.destroy(),delete this.handlers,this.setStyle(null),void 0!==t.window&&(t.window.removeEventListener(\"resize\",this._onWindowResize,!1),t.window.removeEventListener(\"online\",this._onWindowOnline,!1));var n=this.painter.context.gl.getExtension(\"WEBGL_lose_context\");n&&n.loseContext(),Ri(this._canvasContainer),Ri(this._controlContainer),Ri(this._missingCSSCanary),this._container.classList.remove(\"mapboxgl-map\"),this._removed=!0,this.fire(new t.Event(\"remove\"))},i.prototype.triggerRepaint=function(){var e=this;this.style&&!this._frame&&(this._frame=t.browser.frame((function(t){e._frame=null,e._render(t)})))},i.prototype._onWindowOnline=function(){this._update()},i.prototype._onWindowResize=function(t){this._trackResize&&this.resize({originalEvent:t})._update()},a.showTileBoundaries.get=function(){return!!this._showTileBoundaries},a.showTileBoundaries.set=function(t){this._showTileBoundaries!==t&&(this._showTileBoundaries=t,this._update())},a.showPadding.get=function(){return!!this._showPadding},a.showPadding.set=function(t){this._showPadding!==t&&(this._showPadding=t,this._update())},a.showCollisionBoxes.get=function(){return!!this._showCollisionBoxes},a.showCollisionBoxes.set=function(t){this._showCollisionBoxes!==t&&(this._showCollisionBoxes=t,t?this.style._generateCollisionBoxes():this._update())},a.showOverdrawInspector.get=function(){return!!this._showOverdrawInspector},a.showOverdrawInspector.set=function(t){this._showOverdrawInspector!==t&&(this._showOverdrawInspector=t,this._update())},a.repaint.get=function(){return!!this._repaint},a.repaint.set=function(t){this._repaint!==t&&(this._repaint=t,this.triggerRepaint())},a.vertices.get=function(){return!!this._vertices},a.vertices.set=function(t){this._vertices=t,this._update()},i.prototype._setCacheLimits=function(e,r){t.setCacheLimits(e,r)},a.version.get=function(){return t.version},Object.defineProperties(i.prototype,a),i}(Ai);function Ri(t){t.parentNode&&t.parentNode.removeChild(t)}var Fi={showCompass:!0,showZoom:!0,visualizePitch:!1},Bi=function(e){var n=this;this.options=t.extend({},Fi,e),this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),this._container.addEventListener(\"contextmenu\",(function(t){return t.preventDefault()})),this.options.showZoom&&(t.bindAll([\"_setButtonTitle\",\"_updateZoomButtons\"],this),this._zoomInButton=this._createButton(\"mapboxgl-ctrl-zoom-in\",(function(t){return n._map.zoomIn({},{originalEvent:t})})),r.create(\"span\",\"mapboxgl-ctrl-icon\",this._zoomInButton).setAttribute(\"aria-hidden\",!0),this._zoomOutButton=this._createButton(\"mapboxgl-ctrl-zoom-out\",(function(t){return n._map.zoomOut({},{originalEvent:t})})),r.create(\"span\",\"mapboxgl-ctrl-icon\",this._zoomOutButton).setAttribute(\"aria-hidden\",!0)),this.options.showCompass&&(t.bindAll([\"_rotateCompassArrow\"],this),this._compass=this._createButton(\"mapboxgl-ctrl-compass\",(function(t){n.options.visualizePitch?n._map.resetNorthPitch({},{originalEvent:t}):n._map.resetNorth({},{originalEvent:t})})),this._compassIcon=r.create(\"span\",\"mapboxgl-ctrl-icon\",this._compass),this._compassIcon.setAttribute(\"aria-hidden\",!0))};Bi.prototype._updateZoomButtons=function(){var t=this._map.getZoom();this._zoomInButton.disabled=t===this._map.getMaxZoom(),this._zoomOutButton.disabled=t===this._map.getMinZoom()},Bi.prototype._rotateCompassArrow=function(){var t=this.options.visualizePitch?\"scale(\"+1/Math.pow(Math.cos(this._map.transform.pitch*(Math.PI/180)),.5)+\") rotateX(\"+this._map.transform.pitch+\"deg) rotateZ(\"+this._map.transform.angle*(180/Math.PI)+\"deg)\":\"rotate(\"+this._map.transform.angle*(180/Math.PI)+\"deg)\";this._compassIcon.style.transform=t},Bi.prototype.onAdd=function(t){return this._map=t,this.options.showZoom&&(this._setButtonTitle(this._zoomInButton,\"ZoomIn\"),this._setButtonTitle(this._zoomOutButton,\"ZoomOut\"),this._map.on(\"zoom\",this._updateZoomButtons),this._updateZoomButtons()),this.options.showCompass&&(this._setButtonTitle(this._compass,\"ResetBearing\"),this.options.visualizePitch&&this._map.on(\"pitch\",this._rotateCompassArrow),this._map.on(\"rotate\",this._rotateCompassArrow),this._rotateCompassArrow(),this._handler=new Ni(this._map,this._compass,this.options.visualizePitch)),this._container},Bi.prototype.onRemove=function(){r.remove(this._container),this.options.showZoom&&this._map.off(\"zoom\",this._updateZoomButtons),this.options.showCompass&&(this.options.visualizePitch&&this._map.off(\"pitch\",this._rotateCompassArrow),this._map.off(\"rotate\",this._rotateCompassArrow),this._handler.off(),delete this._handler),delete this._map},Bi.prototype._createButton=function(t,e){var n=r.create(\"button\",t,this._container);return n.type=\"button\",n.addEventListener(\"click\",e),n},Bi.prototype._setButtonTitle=function(t,e){var r=this._map._getUIString(\"NavigationControl.\"+e);t.title=r,t.setAttribute(\"aria-label\",r)};var Ni=function(e,n,i){void 0===i&&(i=!1),this._clickTolerance=10,this.element=n,this.mouseRotate=new Qn({clickTolerance:e.dragRotate._mouseRotate._clickTolerance}),this.map=e,i&&(this.mousePitch=new ti({clickTolerance:e.dragRotate._mousePitch._clickTolerance})),t.bindAll([\"mousedown\",\"mousemove\",\"mouseup\",\"touchstart\",\"touchmove\",\"touchend\",\"reset\"],this),r.addEventListener(n,\"mousedown\",this.mousedown),r.addEventListener(n,\"touchstart\",this.touchstart,{passive:!1}),r.addEventListener(n,\"touchmove\",this.touchmove),r.addEventListener(n,\"touchend\",this.touchend),r.addEventListener(n,\"touchcancel\",this.reset)};function ji(e,r,n){if(e=new t.LngLat(e.lng,e.lat),r){var i=new t.LngLat(e.lng-360,e.lat),a=new t.LngLat(e.lng+360,e.lat),o=n.locationPoint(e).distSqr(r);n.locationPoint(i).distSqr(r)<o?e=i:n.locationPoint(a).distSqr(r)<o&&(e=a)}for(;Math.abs(e.lng-n.center.lng)>180;){var s=n.locationPoint(e);if(s.x>=0&&s.y>=0&&s.x<=n.width&&s.y<=n.height)break;e.lng>n.center.lng?e.lng-=360:e.lng+=360}return e}Ni.prototype.down=function(t,e){this.mouseRotate.mousedown(t,e),this.mousePitch&&this.mousePitch.mousedown(t,e),r.disableDrag()},Ni.prototype.move=function(t,e){var r=this.map,n=this.mouseRotate.mousemoveWindow(t,e);if(n&&n.bearingDelta&&r.setBearing(r.getBearing()+n.bearingDelta),this.mousePitch){var i=this.mousePitch.mousemoveWindow(t,e);i&&i.pitchDelta&&r.setPitch(r.getPitch()+i.pitchDelta)}},Ni.prototype.off=function(){var t=this.element;r.removeEventListener(t,\"mousedown\",this.mousedown),r.removeEventListener(t,\"touchstart\",this.touchstart,{passive:!1}),r.removeEventListener(t,\"touchmove\",this.touchmove),r.removeEventListener(t,\"touchend\",this.touchend),r.removeEventListener(t,\"touchcancel\",this.reset),this.offTemp()},Ni.prototype.offTemp=function(){r.enableDrag(),r.removeEventListener(t.window,\"mousemove\",this.mousemove),r.removeEventListener(t.window,\"mouseup\",this.mouseup)},Ni.prototype.mousedown=function(e){this.down(t.extend({},e,{ctrlKey:!0,preventDefault:function(){return e.preventDefault()}}),r.mousePos(this.element,e)),r.addEventListener(t.window,\"mousemove\",this.mousemove),r.addEventListener(t.window,\"mouseup\",this.mouseup)},Ni.prototype.mousemove=function(t){this.move(t,r.mousePos(this.element,t))},Ni.prototype.mouseup=function(t){this.mouseRotate.mouseupWindow(t),this.mousePitch&&this.mousePitch.mouseupWindow(t),this.offTemp()},Ni.prototype.touchstart=function(t){1!==t.targetTouches.length?this.reset():(this._startPos=this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.down({type:\"mousedown\",button:0,ctrlKey:!0,preventDefault:function(){return t.preventDefault()}},this._startPos))},Ni.prototype.touchmove=function(t){1!==t.targetTouches.length?this.reset():(this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.move({preventDefault:function(){return t.preventDefault()}},this._lastPos))},Ni.prototype.touchend=function(t){0===t.targetTouches.length&&this._startPos&&this._lastPos&&this._startPos.dist(this._lastPos)<this._clickTolerance&&this.element.click(),this.reset()},Ni.prototype.reset=function(){this.mouseRotate.reset(),this.mousePitch&&this.mousePitch.reset(),delete this._startPos,delete this._lastPos,this.offTemp()};var Ui={center:\"translate(-50%,-50%)\",top:\"translate(-50%,0)\",\"top-left\":\"translate(0,0)\",\"top-right\":\"translate(-100%,0)\",bottom:\"translate(-50%,-100%)\",\"bottom-left\":\"translate(0,-100%)\",\"bottom-right\":\"translate(-100%,-100%)\",left:\"translate(0,-50%)\",right:\"translate(-100%,-50%)\"};function Vi(t,e,r){var n=t.classList;for(var i in Ui)n.remove(\"mapboxgl-\"+r+\"-anchor-\"+i);n.add(\"mapboxgl-\"+r+\"-anchor-\"+e)}var Hi,qi=function(e){function n(n,i){var a=this;if(e.call(this),(n instanceof t.window.HTMLElement||i)&&(n=t.extend({element:n},i)),t.bindAll([\"_update\",\"_onMove\",\"_onUp\",\"_addDragHandler\",\"_onMapClick\",\"_onKeyPress\"],this),this._anchor=n&&n.anchor||\"center\",this._color=n&&n.color||\"#3FB1CE\",this._draggable=n&&n.draggable||!1,this._state=\"inactive\",this._rotation=n&&n.rotation||0,this._rotationAlignment=n&&n.rotationAlignment||\"auto\",this._pitchAlignment=n&&n.pitchAlignment&&\"auto\"!==n.pitchAlignment?n.pitchAlignment:this._rotationAlignment,n&&n.element)this._element=n.element,this._offset=t.Point.convert(n&&n.offset||[0,0]);else{this._defaultMarker=!0,this._element=r.create(\"div\"),this._element.setAttribute(\"aria-label\",\"Map marker\");var o=r.createNS(\"http://www.w3.org/2000/svg\",\"svg\");o.setAttributeNS(null,\"display\",\"block\"),o.setAttributeNS(null,\"height\",\"41px\"),o.setAttributeNS(null,\"width\",\"27px\"),o.setAttributeNS(null,\"viewBox\",\"0 0 27 41\");var s=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");s.setAttributeNS(null,\"stroke\",\"none\"),s.setAttributeNS(null,\"stroke-width\",\"1\"),s.setAttributeNS(null,\"fill\",\"none\"),s.setAttributeNS(null,\"fill-rule\",\"evenodd\");var l=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");l.setAttributeNS(null,\"fill-rule\",\"nonzero\");var u=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");u.setAttributeNS(null,\"transform\",\"translate(3.0, 29.0)\"),u.setAttributeNS(null,\"fill\",\"#000000\");for(var c=0,f=[{rx:\"10.5\",ry:\"5.25002273\"},{rx:\"10.5\",ry:\"5.25002273\"},{rx:\"9.5\",ry:\"4.77275007\"},{rx:\"8.5\",ry:\"4.29549936\"},{rx:\"7.5\",ry:\"3.81822308\"},{rx:\"6.5\",ry:\"3.34094679\"},{rx:\"5.5\",ry:\"2.86367051\"},{rx:\"4.5\",ry:\"2.38636864\"}];c<f.length;c+=1){var h=f[c],p=r.createNS(\"http://www.w3.org/2000/svg\",\"ellipse\");p.setAttributeNS(null,\"opacity\",\"0.04\"),p.setAttributeNS(null,\"cx\",\"10.5\"),p.setAttributeNS(null,\"cy\",\"5.80029008\"),p.setAttributeNS(null,\"rx\",h.rx),p.setAttributeNS(null,\"ry\",h.ry),u.appendChild(p)}var d=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");d.setAttributeNS(null,\"fill\",this._color);var v=r.createNS(\"http://www.w3.org/2000/svg\",\"path\");v.setAttributeNS(null,\"d\",\"M27,13.5 C27,19.074644 20.250001,27.000002 14.75,34.500002 C14.016665,35.500004 12.983335,35.500004 12.25,34.500002 C6.7499993,27.000002 0,19.222562 0,13.5 C0,6.0441559 6.0441559,0 13.5,0 C20.955844,0 27,6.0441559 27,13.5 Z\"),d.appendChild(v);var g=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");g.setAttributeNS(null,\"opacity\",\"0.25\"),g.setAttributeNS(null,\"fill\",\"#000000\");var y=r.createNS(\"http://www.w3.org/2000/svg\",\"path\");y.setAttributeNS(null,\"d\",\"M13.5,0 C6.0441559,0 0,6.0441559 0,13.5 C0,19.222562 6.7499993,27 12.25,34.5 C13,35.522727 14.016664,35.500004 14.75,34.5 C20.250001,27 27,19.074644 27,13.5 C27,6.0441559 20.955844,0 13.5,0 Z M13.5,1 C20.415404,1 26,6.584596 26,13.5 C26,15.898657 24.495584,19.181431 22.220703,22.738281 C19.945823,26.295132 16.705119,30.142167 13.943359,33.908203 C13.743445,34.180814 13.612715,34.322738 13.5,34.441406 C13.387285,34.322738 13.256555,34.180814 13.056641,33.908203 C10.284481,30.127985 7.4148684,26.314159 5.015625,22.773438 C2.6163816,19.232715 1,15.953538 1,13.5 C1,6.584596 6.584596,1 13.5,1 Z\"),g.appendChild(y);var m=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");m.setAttributeNS(null,\"transform\",\"translate(6.0, 7.0)\"),m.setAttributeNS(null,\"fill\",\"#FFFFFF\");var x=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");x.setAttributeNS(null,\"transform\",\"translate(8.0, 8.0)\");var b=r.createNS(\"http://www.w3.org/2000/svg\",\"circle\");b.setAttributeNS(null,\"fill\",\"#000000\"),b.setAttributeNS(null,\"opacity\",\"0.25\"),b.setAttributeNS(null,\"cx\",\"5.5\"),b.setAttributeNS(null,\"cy\",\"5.5\"),b.setAttributeNS(null,\"r\",\"5.4999962\");var _=r.createNS(\"http://www.w3.org/2000/svg\",\"circle\");_.setAttributeNS(null,\"fill\",\"#FFFFFF\"),_.setAttributeNS(null,\"cx\",\"5.5\"),_.setAttributeNS(null,\"cy\",\"5.5\"),_.setAttributeNS(null,\"r\",\"5.4999962\"),x.appendChild(b),x.appendChild(_),l.appendChild(u),l.appendChild(d),l.appendChild(g),l.appendChild(m),l.appendChild(x),o.appendChild(l),this._element.appendChild(o),this._offset=t.Point.convert(n&&n.offset||[0,-14])}this._element.classList.add(\"mapboxgl-marker\"),this._element.addEventListener(\"dragstart\",(function(t){t.preventDefault()})),this._element.addEventListener(\"mousedown\",(function(t){t.preventDefault()})),this._element.addEventListener(\"focus\",(function(){var t=a._map.getContainer();t.scrollTop=0,t.scrollLeft=0})),Vi(this._element,this._anchor,\"marker\"),this._popup=null}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.addTo=function(t){return this.remove(),this._map=t,t.getCanvasContainer().appendChild(this._element),t.on(\"move\",this._update),t.on(\"moveend\",this._update),this.setDraggable(this._draggable),this._update(),this._map.on(\"click\",this._onMapClick),this},n.prototype.remove=function(){return this._map&&(this._map.off(\"click\",this._onMapClick),this._map.off(\"move\",this._update),this._map.off(\"moveend\",this._update),this._map.off(\"mousedown\",this._addDragHandler),this._map.off(\"touchstart\",this._addDragHandler),this._map.off(\"mouseup\",this._onUp),this._map.off(\"touchend\",this._onUp),this._map.off(\"mousemove\",this._onMove),this._map.off(\"touchmove\",this._onMove),delete this._map),r.remove(this._element),this._popup&&this._popup.remove(),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._popup&&this._popup.setLngLat(this._lngLat),this._update(),this},n.prototype.getElement=function(){return this._element},n.prototype.setPopup=function(t){if(this._popup&&(this._popup.remove(),this._popup=null,this._element.removeEventListener(\"keypress\",this._onKeyPress),this._originalTabIndex||this._element.removeAttribute(\"tabindex\")),t){if(!(\"offset\"in t.options)){var e=13.5,r=Math.sqrt(Math.pow(e,2)/2);t.options.offset=this._defaultMarker?{top:[0,0],\"top-left\":[0,0],\"top-right\":[0,0],bottom:[0,-38.1],\"bottom-left\":[r,-1*(24.6+r)],\"bottom-right\":[-r,-1*(24.6+r)],left:[e,-24.6],right:[-13.5,-24.6]}:this._offset}this._popup=t,this._lngLat&&this._popup.setLngLat(this._lngLat),this._originalTabIndex=this._element.getAttribute(\"tabindex\"),this._originalTabIndex||this._element.setAttribute(\"tabindex\",\"0\"),this._element.addEventListener(\"keypress\",this._onKeyPress)}return this},n.prototype._onKeyPress=function(t){var e=t.code,r=t.charCode||t.keyCode;\"Space\"!==e&&\"Enter\"!==e&&32!==r&&13!==r||this.togglePopup()},n.prototype._onMapClick=function(t){var e=t.originalEvent.target,r=this._element;this._popup&&(e===r||r.contains(e))&&this.togglePopup()},n.prototype.getPopup=function(){return this._popup},n.prototype.togglePopup=function(){var t=this._popup;return t?(t.isOpen()?t.remove():t.addTo(this._map),this):this},n.prototype._update=function(t){if(this._map){this._map.transform.renderWorldCopies&&(this._lngLat=ji(this._lngLat,this._pos,this._map.transform)),this._pos=this._map.project(this._lngLat)._add(this._offset);var e=\"\";\"viewport\"===this._rotationAlignment||\"auto\"===this._rotationAlignment?e=\"rotateZ(\"+this._rotation+\"deg)\":\"map\"===this._rotationAlignment&&(e=\"rotateZ(\"+(this._rotation-this._map.getBearing())+\"deg)\");var n=\"\";\"viewport\"===this._pitchAlignment||\"auto\"===this._pitchAlignment?n=\"rotateX(0deg)\":\"map\"===this._pitchAlignment&&(n=\"rotateX(\"+this._map.getPitch()+\"deg)\"),t&&\"moveend\"!==t.type||(this._pos=this._pos.round()),r.setTransform(this._element,Ui[this._anchor]+\" translate(\"+this._pos.x+\"px, \"+this._pos.y+\"px) \"+n+\" \"+e)}},n.prototype.getOffset=function(){return this._offset},n.prototype.setOffset=function(e){return this._offset=t.Point.convert(e),this._update(),this},n.prototype._onMove=function(e){this._pos=e.point.sub(this._positionDelta),this._lngLat=this._map.unproject(this._pos),this.setLngLat(this._lngLat),this._element.style.pointerEvents=\"none\",\"pending\"===this._state&&(this._state=\"active\",this.fire(new t.Event(\"dragstart\"))),this.fire(new t.Event(\"drag\"))},n.prototype._onUp=function(){this._element.style.pointerEvents=\"auto\",this._positionDelta=null,this._map.off(\"mousemove\",this._onMove),this._map.off(\"touchmove\",this._onMove),\"active\"===this._state&&this.fire(new t.Event(\"dragend\")),this._state=\"inactive\"},n.prototype._addDragHandler=function(t){this._element.contains(t.originalEvent.target)&&(t.preventDefault(),this._positionDelta=t.point.sub(this._pos).add(this._offset),this._state=\"pending\",this._map.on(\"mousemove\",this._onMove),this._map.on(\"touchmove\",this._onMove),this._map.once(\"mouseup\",this._onUp),this._map.once(\"touchend\",this._onUp))},n.prototype.setDraggable=function(t){return this._draggable=!!t,this._map&&(t?(this._map.on(\"mousedown\",this._addDragHandler),this._map.on(\"touchstart\",this._addDragHandler)):(this._map.off(\"mousedown\",this._addDragHandler),this._map.off(\"touchstart\",this._addDragHandler))),this},n.prototype.isDraggable=function(){return this._draggable},n.prototype.setRotation=function(t){return this._rotation=t||0,this._update(),this},n.prototype.getRotation=function(){return this._rotation},n.prototype.setRotationAlignment=function(t){return this._rotationAlignment=t||\"auto\",this._update(),this},n.prototype.getRotationAlignment=function(){return this._rotationAlignment},n.prototype.setPitchAlignment=function(t){return this._pitchAlignment=t&&\"auto\"!==t?t:this._rotationAlignment,this._update(),this},n.prototype.getPitchAlignment=function(){return this._pitchAlignment},n}(t.Evented),Gi={positionOptions:{enableHighAccuracy:!1,maximumAge:0,timeout:6e3},fitBoundsOptions:{maxZoom:15},trackUserLocation:!1,showAccuracyCircle:!0,showUserLocation:!0};var Zi=0,Yi=!1,Wi=function(e){function n(r){e.call(this),this.options=t.extend({},Gi,r),t.bindAll([\"_onSuccess\",\"_onError\",\"_onZoom\",\"_finish\",\"_setupUI\",\"_updateCamera\",\"_updateMarker\"],this)}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.onAdd=function(e){return this._map=e,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),n=this._setupUI,void 0!==Hi?n(Hi):void 0!==t.window.navigator.permissions?t.window.navigator.permissions.query({name:\"geolocation\"}).then((function(t){Hi=\"denied\"!==t.state,n(Hi)})):(Hi=!!t.window.navigator.geolocation,n(Hi)),this._container;var n},n.prototype.onRemove=function(){void 0!==this._geolocationWatchID&&(t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0),this.options.showUserLocation&&this._userLocationDotMarker&&this._userLocationDotMarker.remove(),this.options.showAccuracyCircle&&this._accuracyCircleMarker&&this._accuracyCircleMarker.remove(),r.remove(this._container),this._map.off(\"zoom\",this._onZoom),this._map=void 0,Zi=0,Yi=!1},n.prototype._isOutOfMapMaxBounds=function(t){var e=this._map.getMaxBounds(),r=t.coords;return e&&(r.longitude<e.getWest()||r.longitude>e.getEast()||r.latitude<e.getSouth()||r.latitude>e.getNorth())},n.prototype._setErrorState=function(){switch(this._watchState){case\"WAITING_ACTIVE\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\");break;case\"ACTIVE_LOCK\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\");break;case\"BACKGROUND\":this._watchState=\"BACKGROUND_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\")}},n.prototype._onSuccess=function(e){if(this._map){if(this._isOutOfMapMaxBounds(e))return this._setErrorState(),this.fire(new t.Event(\"outofmaxbounds\",e)),this._updateMarker(),void this._finish();if(this.options.trackUserLocation)switch(this._lastKnownPosition=e,this._watchState){case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"BACKGROUND\":case\"BACKGROUND_ERROR\":this._watchState=\"BACKGROUND\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\")}this.options.showUserLocation&&\"OFF\"!==this._watchState&&this._updateMarker(e),this.options.trackUserLocation&&\"ACTIVE_LOCK\"!==this._watchState||this._updateCamera(e),this.options.showUserLocation&&this._dotElement.classList.remove(\"mapboxgl-user-location-dot-stale\"),this.fire(new t.Event(\"geolocate\",e)),this._finish()}},n.prototype._updateCamera=function(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude),n=e.coords.accuracy,i=this._map.getBearing(),a=t.extend({bearing:i},this.options.fitBoundsOptions);this._map.fitBounds(r.toBounds(n),a,{geolocateSource:!0})},n.prototype._updateMarker=function(e){if(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude);this._accuracyCircleMarker.setLngLat(r).addTo(this._map),this._userLocationDotMarker.setLngLat(r).addTo(this._map),this._accuracy=e.coords.accuracy,this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()}else this._userLocationDotMarker.remove(),this._accuracyCircleMarker.remove()},n.prototype._updateCircleRadius=function(){var t=this._map._container.clientHeight/2,e=this._map.unproject([0,t]),r=this._map.unproject([1,t]),n=e.distanceTo(r),i=Math.ceil(2*this._accuracy/n);this._circleElement.style.width=i+\"px\",this._circleElement.style.height=i+\"px\"},n.prototype._onZoom=function(){this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()},n.prototype._onError=function(e){if(this._map){if(this.options.trackUserLocation)if(1===e.code){this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.disabled=!0;var r=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.title=r,this._geolocateButton.setAttribute(\"aria-label\",r),void 0!==this._geolocationWatchID&&this._clearWatch()}else{if(3===e.code&&Yi)return;this._setErrorState()}\"OFF\"!==this._watchState&&this.options.showUserLocation&&this._dotElement.classList.add(\"mapboxgl-user-location-dot-stale\"),this.fire(new t.Event(\"error\",e)),this._finish()}},n.prototype._finish=function(){this._timeoutId&&clearTimeout(this._timeoutId),this._timeoutId=void 0},n.prototype._setupUI=function(e){var n=this;if(this._container.addEventListener(\"contextmenu\",(function(t){return t.preventDefault()})),this._geolocateButton=r.create(\"button\",\"mapboxgl-ctrl-geolocate\",this._container),r.create(\"span\",\"mapboxgl-ctrl-icon\",this._geolocateButton).setAttribute(\"aria-hidden\",!0),this._geolocateButton.type=\"button\",!1===e){t.warnOnce(\"Geolocation support is not available so the GeolocateControl will be disabled.\");var i=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.disabled=!0,this._geolocateButton.title=i,this._geolocateButton.setAttribute(\"aria-label\",i)}else{var a=this._map._getUIString(\"GeolocateControl.FindMyLocation\");this._geolocateButton.title=a,this._geolocateButton.setAttribute(\"aria-label\",a)}this.options.trackUserLocation&&(this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this._watchState=\"OFF\"),this.options.showUserLocation&&(this._dotElement=r.create(\"div\",\"mapboxgl-user-location-dot\"),this._userLocationDotMarker=new qi(this._dotElement),this._circleElement=r.create(\"div\",\"mapboxgl-user-location-accuracy-circle\"),this._accuracyCircleMarker=new qi({element:this._circleElement,pitchAlignment:\"map\"}),this.options.trackUserLocation&&(this._watchState=\"OFF\"),this._map.on(\"zoom\",this._onZoom)),this._geolocateButton.addEventListener(\"click\",this.trigger.bind(this)),this._setup=!0,this.options.trackUserLocation&&this._map.on(\"movestart\",(function(e){var r=e.originalEvent&&\"resize\"===e.originalEvent.type;e.geolocateSource||\"ACTIVE_LOCK\"!==n._watchState||r||(n._watchState=\"BACKGROUND\",n._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\"),n._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),n.fire(new t.Event(\"trackuserlocationend\")))}))},n.prototype.trigger=function(){if(!this._setup)return t.warnOnce(\"Geolocate control triggered before added to a map\"),!1;if(this.options.trackUserLocation){switch(this._watchState){case\"OFF\":this._watchState=\"WAITING_ACTIVE\",this.fire(new t.Event(\"trackuserlocationstart\"));break;case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":case\"BACKGROUND_ERROR\":Zi--,Yi=!1,this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this.fire(new t.Event(\"trackuserlocationend\"));break;case\"BACKGROUND\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._lastKnownPosition&&this._updateCamera(this._lastKnownPosition),this.fire(new t.Event(\"trackuserlocationstart\"))}switch(this._watchState){case\"WAITING_ACTIVE\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"ACTIVE_LOCK\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"ACTIVE_ERROR\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\");break;case\"BACKGROUND\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\");break;case\"BACKGROUND_ERROR\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background-error\")}if(\"OFF\"===this._watchState&&void 0!==this._geolocationWatchID)this._clearWatch();else if(void 0===this._geolocationWatchID){var e;this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"true\"),++Zi>1?(e={maximumAge:6e5,timeout:0},Yi=!0):(e=this.options.positionOptions,Yi=!1),this._geolocationWatchID=t.window.navigator.geolocation.watchPosition(this._onSuccess,this._onError,e)}}else t.window.navigator.geolocation.getCurrentPosition(this._onSuccess,this._onError,this.options.positionOptions),this._timeoutId=setTimeout(this._finish,1e4);return!0},n.prototype._clearWatch=function(){t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0,this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this.options.showUserLocation&&this._updateMarker(null)},n}(t.Evented),Xi={maxWidth:100,unit:\"metric\"},Ji=function(e){this.options=t.extend({},Xi,e),t.bindAll([\"_onMove\",\"setUnit\"],this)};function Ki(t,e,r){var n=r&&r.maxWidth||100,i=t._container.clientHeight/2,a=t.unproject([0,i]),o=t.unproject([n,i]),s=a.distanceTo(o);if(r&&\"imperial\"===r.unit){var l=3.2808*s;l>5280?$i(e,n,l/5280,t._getUIString(\"ScaleControl.Miles\")):$i(e,n,l,t._getUIString(\"ScaleControl.Feet\"))}else r&&\"nautical\"===r.unit?$i(e,n,s/1852,t._getUIString(\"ScaleControl.NauticalMiles\")):s>=1e3?$i(e,n,s/1e3,t._getUIString(\"ScaleControl.Kilometers\")):$i(e,n,s,t._getUIString(\"ScaleControl.Meters\"))}function $i(t,e,r,n){var i,a,o,s=(i=r,(a=Math.pow(10,(\"\"+Math.floor(i)).length-1))*((o=i/a)>=10?10:o>=5?5:o>=3?3:o>=2?2:o>=1?1:function(t){var e=Math.pow(10,Math.ceil(-Math.log(t)/Math.LN10));return Math.round(t*e)/e}(o))),l=s/r;t.style.width=e*l+\"px\",t.innerHTML=s+\"&nbsp;\"+n}Ji.prototype.getDefaultPosition=function(){return\"bottom-left\"},Ji.prototype._onMove=function(){Ki(this._map,this._container,this.options)},Ji.prototype.onAdd=function(t){return this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-scale\",t.getContainer()),this._map.on(\"move\",this._onMove),this._onMove(),this._container},Ji.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"move\",this._onMove),this._map=void 0},Ji.prototype.setUnit=function(t){this.options.unit=t,Ki(this._map,this._container,this.options)};var Qi=function(e){this._fullscreen=!1,e&&e.container&&(e.container instanceof t.window.HTMLElement?this._container=e.container:t.warnOnce(\"Full screen control 'container' must be a DOM element.\")),t.bindAll([\"_onClickFullscreen\",\"_changeIcon\"],this),\"onfullscreenchange\"in t.window.document?this._fullscreenchange=\"fullscreenchange\":\"onmozfullscreenchange\"in t.window.document?this._fullscreenchange=\"mozfullscreenchange\":\"onwebkitfullscreenchange\"in t.window.document?this._fullscreenchange=\"webkitfullscreenchange\":\"onmsfullscreenchange\"in t.window.document&&(this._fullscreenchange=\"MSFullscreenChange\")};Qi.prototype.onAdd=function(e){return this._map=e,this._container||(this._container=this._map.getContainer()),this._controlContainer=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),this._checkFullscreenSupport()?this._setupUI():(this._controlContainer.style.display=\"none\",t.warnOnce(\"This device does not support fullscreen mode.\")),this._controlContainer},Qi.prototype.onRemove=function(){r.remove(this._controlContainer),this._map=null,t.window.document.removeEventListener(this._fullscreenchange,this._changeIcon)},Qi.prototype._checkFullscreenSupport=function(){return!!(t.window.document.fullscreenEnabled||t.window.document.mozFullScreenEnabled||t.window.document.msFullscreenEnabled||t.window.document.webkitFullscreenEnabled)},Qi.prototype._setupUI=function(){var e=this._fullscreenButton=r.create(\"button\",\"mapboxgl-ctrl-fullscreen\",this._controlContainer);r.create(\"span\",\"mapboxgl-ctrl-icon\",e).setAttribute(\"aria-hidden\",!0),e.type=\"button\",this._updateTitle(),this._fullscreenButton.addEventListener(\"click\",this._onClickFullscreen),t.window.document.addEventListener(this._fullscreenchange,this._changeIcon)},Qi.prototype._updateTitle=function(){var t=this._getTitle();this._fullscreenButton.setAttribute(\"aria-label\",t),this._fullscreenButton.title=t},Qi.prototype._getTitle=function(){return this._map._getUIString(this._isFullscreen()?\"FullscreenControl.Exit\":\"FullscreenControl.Enter\")},Qi.prototype._isFullscreen=function(){return this._fullscreen},Qi.prototype._changeIcon=function(){(t.window.document.fullscreenElement||t.window.document.mozFullScreenElement||t.window.document.webkitFullscreenElement||t.window.document.msFullscreenElement)===this._container!==this._fullscreen&&(this._fullscreen=!this._fullscreen,this._fullscreenButton.classList.toggle(\"mapboxgl-ctrl-shrink\"),this._fullscreenButton.classList.toggle(\"mapboxgl-ctrl-fullscreen\"),this._updateTitle())},Qi.prototype._onClickFullscreen=function(){this._isFullscreen()?t.window.document.exitFullscreen?t.window.document.exitFullscreen():t.window.document.mozCancelFullScreen?t.window.document.mozCancelFullScreen():t.window.document.msExitFullscreen?t.window.document.msExitFullscreen():t.window.document.webkitCancelFullScreen&&t.window.document.webkitCancelFullScreen():this._container.requestFullscreen?this._container.requestFullscreen():this._container.mozRequestFullScreen?this._container.mozRequestFullScreen():this._container.msRequestFullscreen?this._container.msRequestFullscreen():this._container.webkitRequestFullscreen&&this._container.webkitRequestFullscreen()};var ta={closeButton:!0,closeOnClick:!0,className:\"\",maxWidth:\"240px\"},ea=function(e){function n(r){e.call(this),this.options=t.extend(Object.create(ta),r),t.bindAll([\"_update\",\"_onClose\",\"remove\",\"_onMouseMove\",\"_onMouseUp\",\"_onDrag\"],this)}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.addTo=function(e){return this._map&&this.remove(),this._map=e,this.options.closeOnClick&&this._map.on(\"click\",this._onClose),this.options.closeOnMove&&this._map.on(\"move\",this._onClose),this._map.on(\"remove\",this.remove),this._update(),this._trackPointer?(this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"mouseup\",this._onMouseUp),this._container&&this._container.classList.add(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"mapboxgl-track-pointer\")):this._map.on(\"move\",this._update),this.fire(new t.Event(\"open\")),this},n.prototype.isOpen=function(){return!!this._map},n.prototype.remove=function(){return this._content&&r.remove(this._content),this._container&&(r.remove(this._container),delete this._container),this._map&&(this._map.off(\"move\",this._update),this._map.off(\"move\",this._onClose),this._map.off(\"click\",this._onClose),this._map.off(\"remove\",this.remove),this._map.off(\"mousemove\",this._onMouseMove),this._map.off(\"mouseup\",this._onMouseUp),this._map.off(\"drag\",this._onDrag),delete this._map),this.fire(new t.Event(\"close\")),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._trackPointer=!1,this._update(),this._map&&(this._map.on(\"move\",this._update),this._map.off(\"mousemove\",this._onMouseMove),this._container&&this._container.classList.remove(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.remove(\"mapboxgl-track-pointer\")),this},n.prototype.trackPointer=function(){return this._trackPointer=!0,this._pos=null,this._update(),this._map&&(this._map.off(\"move\",this._update),this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"drag\",this._onDrag),this._container&&this._container.classList.add(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"mapboxgl-track-pointer\")),this},n.prototype.getElement=function(){return this._container},n.prototype.setText=function(e){return this.setDOMContent(t.window.document.createTextNode(e))},n.prototype.setHTML=function(e){var r,n=t.window.document.createDocumentFragment(),i=t.window.document.createElement(\"body\");for(i.innerHTML=e;r=i.firstChild;)n.appendChild(r);return this.setDOMContent(n)},n.prototype.getMaxWidth=function(){return this._container&&this._container.style.maxWidth},n.prototype.setMaxWidth=function(t){return this.options.maxWidth=t,this._update(),this},n.prototype.setDOMContent=function(t){return this._createContent(),this._content.appendChild(t),this._update(),this},n.prototype.addClassName=function(t){this._container&&this._container.classList.add(t)},n.prototype.removeClassName=function(t){this._container&&this._container.classList.remove(t)},n.prototype.toggleClassName=function(t){if(this._container)return this._container.classList.toggle(t)},n.prototype._createContent=function(){this._content&&r.remove(this._content),this._content=r.create(\"div\",\"mapboxgl-popup-content\",this._container),this.options.closeButton&&(this._closeButton=r.create(\"button\",\"mapboxgl-popup-close-button\",this._content),this._closeButton.type=\"button\",this._closeButton.setAttribute(\"aria-label\",\"Close popup\"),this._closeButton.innerHTML=\"&#215;\",this._closeButton.addEventListener(\"click\",this._onClose))},n.prototype._onMouseUp=function(t){this._update(t.point)},n.prototype._onMouseMove=function(t){this._update(t.point)},n.prototype._onDrag=function(t){this._update(t.point)},n.prototype._update=function(t){var e=this,n=this._lngLat||this._trackPointer;if(this._map&&n&&this._content&&(this._container||(this._container=r.create(\"div\",\"mapboxgl-popup\",this._map.getContainer()),this._tip=r.create(\"div\",\"mapboxgl-popup-tip\",this._container),this._container.appendChild(this._content),this.options.className&&this.options.className.split(\" \").forEach((function(t){return e._container.classList.add(t)})),this._trackPointer&&this._container.classList.add(\"mapboxgl-popup-track-pointer\")),this.options.maxWidth&&this._container.style.maxWidth!==this.options.maxWidth&&(this._container.style.maxWidth=this.options.maxWidth),this._map.transform.renderWorldCopies&&!this._trackPointer&&(this._lngLat=ji(this._lngLat,this._pos,this._map.transform)),!this._trackPointer||t)){var i=this._pos=this._trackPointer&&t?t:this._map.project(this._lngLat),a=this.options.anchor,o=ra(this.options.offset);if(!a){var s,l=this._container.offsetWidth,u=this._container.offsetHeight;s=i.y+o.bottom.y<u?[\"top\"]:i.y>this._map.transform.height-u?[\"bottom\"]:[],i.x<l/2?s.push(\"left\"):i.x>this._map.transform.width-l/2&&s.push(\"right\"),a=0===s.length?\"bottom\":s.join(\"-\")}var c=i.add(o[a]).round();r.setTransform(this._container,Ui[a]+\" translate(\"+c.x+\"px,\"+c.y+\"px)\"),Vi(this._container,a,\"popup\")}},n.prototype._onClose=function(){this.remove()},n}(t.Evented);function ra(e){if(e){if(\"number\"==typeof e){var r=Math.round(Math.sqrt(.5*Math.pow(e,2)));return{center:new t.Point(0,0),top:new t.Point(0,e),\"top-left\":new t.Point(r,r),\"top-right\":new t.Point(-r,r),bottom:new t.Point(0,-e),\"bottom-left\":new t.Point(r,-r),\"bottom-right\":new t.Point(-r,-r),left:new t.Point(e,0),right:new t.Point(-e,0)}}if(e instanceof t.Point||Array.isArray(e)){var n=t.Point.convert(e);return{center:n,top:n,\"top-left\":n,\"top-right\":n,bottom:n,\"bottom-left\":n,\"bottom-right\":n,left:n,right:n}}return{center:t.Point.convert(e.center||[0,0]),top:t.Point.convert(e.top||[0,0]),\"top-left\":t.Point.convert(e[\"top-left\"]||[0,0]),\"top-right\":t.Point.convert(e[\"top-right\"]||[0,0]),bottom:t.Point.convert(e.bottom||[0,0]),\"bottom-left\":t.Point.convert(e[\"bottom-left\"]||[0,0]),\"bottom-right\":t.Point.convert(e[\"bottom-right\"]||[0,0]),left:t.Point.convert(e.left||[0,0]),right:t.Point.convert(e.right||[0,0])}}return ra(new t.Point(0,0))}var na={version:t.version,supported:e,setRTLTextPlugin:t.setRTLTextPlugin,getRTLTextPluginStatus:t.getRTLTextPluginStatus,Map:zi,NavigationControl:Bi,GeolocateControl:Wi,AttributionControl:Mi,ScaleControl:Ji,FullscreenControl:Qi,Popup:ea,Marker:qi,Style:Ye,LngLat:t.LngLat,LngLatBounds:t.LngLatBounds,Point:t.Point,MercatorCoordinate:t.MercatorCoordinate,Evented:t.Evented,config:t.config,prewarm:function(){jt().acquire(Rt)},clearPrewarmedResources:function(){var t=Bt;t&&(t.isPreloaded()&&1===t.numActive()?(t.release(Rt),Bt=null):console.warn(\"Could not clear WebWorkers since there are active Map instances that still reference it. 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e=[u(t.region,!1)];c.push(e);for(var r=0;r<t.children.length;r++)e.push(h(t.children[r]))}function h(t){for(var e=0;e<t.children.length;e++)f(t.children[e]);return u(t.region,!0)}for(s=0;s<a.children.length;s++)f(a.children[s]);return c.length<=0?{type:\"Polygon\",coordinates:[]}:1==c.length?{type:\"Polygon\",coordinates:c[0]}:{type:\"MultiPolygon\",coordinates:c}}};t.exports=e},87263:function(t,e,r){var n=r(26859);t.exports=function(t,e,r){function i(t,e,n){return{id:r?r.segmentId():-1,start:t,end:e,myFill:{above:n.myFill.above,below:n.myFill.below},otherFill:null}}var a=n.create();function o(t,r){a.insertBefore(t,(function(n){return i=t.isStart,a=t.pt,o=r,s=n.isStart,l=n.pt,u=n.other.pt,(0!==(c=e.pointsCompare(a,l))?c:e.pointsSame(o,u)?0:i!==s?i?1:-1:e.pointAboveOrOnLine(o,s?l:u,s?u:l)?1:-1)<0;var i,a,o,s,l,u,c}))}function s(t,e){var r=function(t,e){var r=n.node({isStart:!0,pt:t.start,seg:t,primary:e,other:null,status:null});return o(r,t.end),r}(t,e);return function(t,e,r){var i=n.node({isStart:!1,pt:e.end,seg:e,primary:r,other:t,status:null});t.other=i,o(i,t.pt)}(r,t,e),r}function l(t,e){var n=i(e,t.seg.end,t.seg);return function(t,e){r&&r.segmentChop(t.seg,e),t.other.remove(),t.seg.end=e,t.other.pt=e,o(t.other,t.pt)}(t,e),s(n,t.primary)}function u(i,o){var s=n.create();function u(t){return s.findTransition((function(r){var n,i,a,o,s,l;return n=t,i=r.ev,a=n.seg.start,o=n.seg.end,s=i.seg.start,l=i.seg.end,(e.pointsCollinear(a,s,l)?e.pointsCollinear(o,s,l)||e.pointAboveOrOnLine(o,s,l)?1:-1:e.pointAboveOrOnLine(a,s,l)?1:-1)>0}))}function c(t,n){var i=t.seg,a=n.seg,o=i.start,s=i.end,u=a.start,c=a.end;r&&r.checkIntersection(i,a);var f=e.linesIntersect(o,s,u,c);if(!1===f){if(!e.pointsCollinear(o,s,u))return!1;if(e.pointsSame(o,c)||e.pointsSame(s,u))return!1;var h=e.pointsSame(o,u),p=e.pointsSame(s,c);if(h&&p)return n;var d=!h&&e.pointBetween(o,u,c),v=!p&&e.pointBetween(s,u,c);if(h)return v?l(n,s):l(t,c),n;d&&(p||(v?l(n,s):l(t,c)),l(n,o))}else 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a=n[t],o=n[i],s=a[a.length-1],l=a[a.length-2],u=o[0],c=o[1];e.pointsCollinear(l,s,u)&&(r&&r.chainRemoveTail(t,s),a.pop(),s=l),e.pointsCollinear(s,u,c)&&(r&&r.chainRemoveHead(i,u),o.shift()),r&&r.chainJoin(t,i),n[t]=a.concat(o),n.splice(i,1)}})),i}},55968:function(t){function e(t,e,r){var n=[];return t.forEach((function(t){var i=(t.myFill.above?8:0)+(t.myFill.below?4:0)+(t.otherFill&&t.otherFill.above?2:0)+(t.otherFill&&t.otherFill.below?1:0);0!==e[i]&&n.push({id:r?r.segmentId():-1,start:t.start,end:t.end,myFill:{above:1===e[i],below:2===e[i]},otherFill:null})})),r&&r.selected(n),n}var r={union:function(t,r){return e(t,[0,2,1,0,2,2,0,0,1,0,1,0,0,0,0,0],r)},intersect:function(t,r){return e(t,[0,0,0,0,0,2,0,2,0,0,1,1,0,2,1,0],r)},difference:function(t,r){return e(t,[0,0,0,0,2,0,2,0,1,1,0,0,0,1,2,0],r)},differenceRev:function(t,r){return e(t,[0,2,1,0,0,0,1,1,0,2,0,2,0,0,0,0],r)},xor:function(t,r){return e(t,[0,2,1,0,2,0,0,1,1,0,0,2,0,1,2,0],r)}};t.exports=r},14847:function(t,e,r){\"use 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t&&(t=x[t]),null!=t&&t&&t.count&&t.color&&t.opacity&&t.positions&&t.positions.length>1&&(t.scaleRatio=[t.scale[0]*t.viewport.width,t.scale[1]*t.viewport.height],r(t),t.after&&t.after(t))}function T(t){if(t){null!=t.length?\"number\"==typeof t[0]&&(t=[{positions:t}]):Array.isArray(t)||(t=[t]);var e=0,r=0;if(b.groups=x=t.map((function(t,u){var c=x[u];return t?(\"function\"==typeof t?t={after:t}:\"number\"==typeof t[0]&&(t={positions:t}),t=o(t,{color:\"color colors fill\",capSize:\"capSize cap capsize cap-size\",lineWidth:\"lineWidth line-width width line thickness\",opacity:\"opacity alpha\",range:\"range dataBox\",viewport:\"viewport viewBox\",errors:\"errors error\",positions:\"positions position data points\"}),c||(x[u]=c={id:u,scale:null,translate:null,scaleFract:null,translateFract:null,draw:!0},t=s({},m,t)),a(c,t,[{lineWidth:function(t){return.5*+t},capSize:function(t){return.5*+t},opacity:parseFloat,errors:function(t){return t=l(t),r+=t.length,t},positions:function(t,r){return t=l(t,\"float64\"),r.count=Math.floor(t.length/2),r.bounds=n(t,2),r.offset=e,e+=r.count,t}},{color:function(t,e){var r=e.count;if(t||(t=\"transparent\"),!Array.isArray(t)||\"number\"==typeof t[0]){var n=t;t=Array(r);for(var a=0;a<r;a++)t[a]=n}if(t.length<r)throw Error(\"Not enough colors\");for(var o=new Uint8Array(4*r),s=0;s<r;s++){var l=i(t[s],\"uint8\");o.set(l,4*s)}return o},range:function(t,e,r){var n=e.bounds;return t||(t=n),e.scale=[1/(t[2]-t[0]),1/(t[3]-t[1])],e.translate=[-t[0],-t[1]],e.scaleFract=f(e.scale),e.translateFract=f(e.translate),t},viewport:function(t){var e;return Array.isArray(t)?e={x:t[0],y:t[1],width:t[2]-t[0],height:t[3]-t[1]}:t?(e={x:t.x||t.left||0,y:t.y||t.top||0},t.right?e.width=t.right-e.x:e.width=t.w||t.width||0,t.bottom?e.height=t.bottom-e.y:e.height=t.h||t.height||0):e={x:0,y:0,width:y.drawingBufferWidth,height:y.drawingBufferHeight},e}}]),c):c})),e||r){var h=x.reduce((function(t,e,r){return t+(e?e.count:0)}),0),g=new Float64Array(2*h),_=new Uint8Array(4*h),w=new Float32Array(4*h);x.forEach((function(t,e){if(t){var r=t.positions,n=t.count,i=t.offset,a=t.color,o=t.errors;n&&(_.set(a,4*i),w.set(o,4*i),g.set(r,2*i))}}));var T=c(g);u(T);var k=f(g,T);p(k),d(_),v(w)}}}function k(){u.destroy(),p.destroy(),d.destroy(),v.destroy(),g.destroy()}};var h=[[1,0,0,1,0,0],[1,0,0,-1,0,0],[-1,0,0,-1,0,0],[-1,0,0,-1,0,0],[-1,0,0,1,0,0],[1,0,0,1,0,0],[1,0,-1,0,0,1],[1,0,-1,0,0,-1],[1,0,1,0,0,-1],[1,0,1,0,0,-1],[1,0,1,0,0,1],[1,0,-1,0,0,1],[-1,0,-1,0,0,1],[-1,0,-1,0,0,-1],[-1,0,1,0,0,-1],[-1,0,1,0,0,-1],[-1,0,1,0,0,1],[-1,0,-1,0,0,1],[0,1,1,0,0,0],[0,1,-1,0,0,0],[0,-1,-1,0,0,0],[0,-1,-1,0,0,0],[0,1,1,0,0,0],[0,-1,1,0,0,0],[0,1,0,-1,1,0],[0,1,0,-1,-1,0],[0,1,0,1,-1,0],[0,1,0,1,1,0],[0,1,0,-1,1,0],[0,1,0,1,-1,0],[0,-1,0,-1,1,0],[0,-1,0,-1,-1,0],[0,-1,0,1,-1,0],[0,-1,0,1,1,0],[0,-1,0,-1,1,0],[0,-1,0,1,-1,0]]},46075:function(t,e,r){\"use strict\";var n=r(25075),i=r(21527),a=r(56131),o=r(56068),s=r(71299),l=r(30120),u=r(11474),c=r(54),f=r(57060),h=f.float32,p=f.fract32,d=r(83522),v=r(18863),g=r(6851);function y(t,e){if(!(this instanceof y))return new y(t,e);if(\"function\"==typeof t?(e||(e={}),e.regl=t):e=t,e.length&&(e.positions=e),!(t=e.regl).hasExtension(\"ANGLE_instanced_arrays\"))throw Error(\"regl-error2d: `ANGLE_instanced_arrays` extension should be enabled\");this.gl=t._gl,this.regl=t,this.passes=[],this.shaders=y.shaders.has(t)?y.shaders.get(t):y.shaders.set(t,y.createShaders(t)).get(t),this.update(e)}t.exports=y,y.dashMult=2,y.maxPatternLength=256,y.precisionThreshold=3e6,y.maxPoints=1e4,y.maxLines=2048,y.shaders=new d,y.createShaders=function(t){var e,r=t.buffer({usage:\"static\",type:\"float\",data:[0,1,0,0,1,1,1,0]}),n={primitive:\"triangle strip\",instances:t.prop(\"count\"),count:4,offset:0,uniforms:{miterMode:function(t,e){return\"round\"===e.join?2:1},miterLimit:t.prop(\"miterLimit\"),scale:t.prop(\"scale\"),scaleFract:t.prop(\"scaleFract\"),translateFract:t.prop(\"translateFract\"),translate:t.prop(\"translate\"),thickness:t.prop(\"thickness\"),dashTexture:t.prop(\"dashTexture\"),opacity:t.prop(\"opacity\"),pixelRatio:t.context(\"pixelRatio\"),id:t.prop(\"id\"),dashLength:t.prop(\"dashLength\"),viewport:function(t,e){return[e.viewport.x,e.viewport.y,t.viewportWidth,t.viewportHeight]},depth:t.prop(\"depth\")},blend:{enable:!0,color:[0,0,0,0],equation:{rgb:\"add\",alpha:\"add\"},func:{srcRGB:\"src alpha\",dstRGB:\"one minus src alpha\",srcAlpha:\"one minus dst alpha\",dstAlpha:\"one\"}},depth:{enable:function(t,e){return!e.overlay}},stencil:{enable:!1},scissor:{enable:!0,box:t.prop(\"viewport\")},viewport:t.prop(\"viewport\")},i=t(a({vert:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 aCoord, bCoord, aCoordFract, bCoordFract;\\nattribute vec4 color;\\nattribute float lineEnd, lineTop;\\n\\nuniform vec2 scale, scaleFract, translate, translateFract;\\nuniform float thickness, pixelRatio, id, depth;\\nuniform vec4 viewport;\\n\\nvarying vec4 fragColor;\\nvarying vec2 tangent;\\n\\nvec2 project(vec2 position, vec2 positionFract, vec2 scale, vec2 scaleFract, vec2 translate, vec2 translateFract) {\\n\\t// the order is important\\n\\treturn position * scale + translate\\n       + positionFract * scale + translateFract\\n       + position * scaleFract\\n       + positionFract * scaleFract;\\n}\\n\\nvoid main() {\\n\\tfloat lineStart = 1. - lineEnd;\\n\\tfloat lineOffset = lineTop * 2. - 1.;\\n\\n\\tvec2 diff = (bCoord + bCoordFract - aCoord - aCoordFract);\\n\\ttangent = normalize(diff * scale * viewport.zw);\\n\\tvec2 normal = vec2(-tangent.y, tangent.x);\\n\\n\\tvec2 position = project(aCoord, aCoordFract, scale, scaleFract, translate, translateFract) * lineStart\\n\\t\\t+ project(bCoord, bCoordFract, scale, scaleFract, translate, translateFract) * lineEnd\\n\\n\\t\\t+ thickness * normal * .5 * lineOffset / viewport.zw;\\n\\n\\tgl_Position = vec4(position * 2.0 - 1.0, depth, 1);\\n\\n\\tfragColor = color / 255.;\\n}\\n\"]),frag:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform float dashLength, pixelRatio, thickness, opacity, id;\\nuniform sampler2D dashTexture;\\n\\nvarying vec4 fragColor;\\nvarying vec2 tangent;\\n\\nvoid main() {\\n\\tfloat alpha = 1.;\\n\\n\\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashLength) * .5 + .25;\\n\\tfloat dash = texture2D(dashTexture, vec2(t, .5)).r;\\n\\n\\tgl_FragColor = fragColor;\\n\\tgl_FragColor.a *= alpha * opacity * dash;\\n}\\n\"]),attributes:{lineEnd:{buffer:r,divisor:0,stride:8,offset:0},lineTop:{buffer:r,divisor:0,stride:8,offset:4},aCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:8,divisor:1},bCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:16,divisor:1},aCoordFract:{buffer:t.prop(\"positionFractBuffer\"),stride:8,offset:8,divisor:1},bCoordFract:{buffer:t.prop(\"positionFractBuffer\"),stride:8,offset:16,divisor:1},color:{buffer:t.prop(\"colorBuffer\"),stride:4,offset:0,divisor:1}}},n));try{e=t(a({cull:{enable:!0,face:\"back\"},vert:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 aCoord, bCoord, nextCoord, prevCoord;\\nattribute vec4 aColor, bColor;\\nattribute float lineEnd, lineTop;\\n\\nuniform vec2 scale, translate;\\nuniform float thickness, pixelRatio, id, depth;\\nuniform vec4 viewport;\\nuniform float miterLimit, miterMode;\\n\\nvarying vec4 fragColor;\\nvarying vec4 startCutoff, endCutoff;\\nvarying vec2 tangent;\\nvarying vec2 startCoord, endCoord;\\nvarying float enableStartMiter, enableEndMiter;\\n\\nconst float REVERSE_THRESHOLD = -.875;\\nconst float MIN_DIFF = 1e-6;\\n\\n// TODO: possible optimizations: avoid overcalculating all for vertices and calc just one instead\\n// TODO: precalculate dot products, normalize things beforehead etc.\\n// TODO: refactor to rectangular algorithm\\n\\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\\n\\tvec2 diff = b - a;\\n\\tvec2 perp = normalize(vec2(-diff.y, diff.x));\\n\\treturn dot(p - a, perp);\\n}\\n\\nbool isNaN( float val ){\\n  return ( val < 0.0 || 0.0 < val || val == 0.0 ) ? false : true;\\n}\\n\\nvoid main() {\\n\\tvec2 aCoord = aCoord, bCoord = bCoord, prevCoord = prevCoord, nextCoord = nextCoord;\\n\\n  vec2 adjustedScale;\\n  adjustedScale.x = (abs(scale.x) < MIN_DIFF) ? MIN_DIFF : scale.x;\\n  adjustedScale.y = (abs(scale.y) < MIN_DIFF) ? MIN_DIFF : scale.y;\\n\\n  vec2 scaleRatio = adjustedScale * viewport.zw;\\n\\tvec2 normalWidth = thickness / scaleRatio;\\n\\n\\tfloat lineStart = 1. - lineEnd;\\n\\tfloat lineBot = 1. - lineTop;\\n\\n\\tfragColor = (lineStart * aColor + lineEnd * bColor) / 255.;\\n\\n\\tif (isNaN(aCoord.x) || isNaN(aCoord.y) || isNaN(bCoord.x) || isNaN(bCoord.y)) return;\\n\\n\\tif (aCoord == prevCoord) prevCoord = aCoord + normalize(bCoord - aCoord);\\n\\tif (bCoord == nextCoord) nextCoord = bCoord - normalize(bCoord - aCoord);\\n\\n\\tvec2 prevDiff = aCoord - prevCoord;\\n\\tvec2 currDiff = bCoord - aCoord;\\n\\tvec2 nextDiff = nextCoord - bCoord;\\n\\n\\tvec2 prevTangent = normalize(prevDiff * scaleRatio);\\n\\tvec2 currTangent = normalize(currDiff * scaleRatio);\\n\\tvec2 nextTangent = normalize(nextDiff * scaleRatio);\\n\\n\\tvec2 prevNormal = vec2(-prevTangent.y, prevTangent.x);\\n\\tvec2 currNormal = vec2(-currTangent.y, currTangent.x);\\n\\tvec2 nextNormal = vec2(-nextTangent.y, nextTangent.x);\\n\\n\\tvec2 startJoinDirection = normalize(prevTangent - currTangent);\\n\\tvec2 endJoinDirection = normalize(currTangent - nextTangent);\\n\\n\\t// collapsed/unidirectional segment cases\\n\\t// FIXME: there should be more elegant solution\\n\\tvec2 prevTanDiff = abs(prevTangent - currTangent);\\n\\tvec2 nextTanDiff = abs(nextTangent - currTangent);\\n\\tif (max(prevTanDiff.x, prevTanDiff.y) < MIN_DIFF) {\\n\\t\\tstartJoinDirection = currNormal;\\n\\t}\\n\\tif (max(nextTanDiff.x, nextTanDiff.y) < MIN_DIFF) {\\n\\t\\tendJoinDirection = currNormal;\\n\\t}\\n\\tif (aCoord == bCoord) {\\n\\t\\tendJoinDirection = startJoinDirection;\\n\\t\\tcurrNormal = prevNormal;\\n\\t\\tcurrTangent = prevTangent;\\n\\t}\\n\\n\\ttangent = currTangent;\\n\\n\\t//calculate join shifts relative to normals\\n\\tfloat startJoinShift = dot(currNormal, startJoinDirection);\\n\\tfloat endJoinShift = dot(currNormal, endJoinDirection);\\n\\n\\tfloat startMiterRatio = abs(1. / startJoinShift);\\n\\tfloat endMiterRatio = abs(1. / endJoinShift);\\n\\n\\tvec2 startJoin = startJoinDirection * startMiterRatio;\\n\\tvec2 endJoin = endJoinDirection * endMiterRatio;\\n\\n\\tvec2 startTopJoin, startBotJoin, endTopJoin, endBotJoin;\\n\\tstartTopJoin = sign(startJoinShift) * startJoin * .5;\\n\\tstartBotJoin = -startTopJoin;\\n\\n\\tendTopJoin = sign(endJoinShift) * endJoin * .5;\\n\\tendBotJoin = -endTopJoin;\\n\\n\\tvec2 aTopCoord = aCoord + normalWidth * startTopJoin;\\n\\tvec2 bTopCoord = bCoord + normalWidth * endTopJoin;\\n\\tvec2 aBotCoord = aCoord + normalWidth * startBotJoin;\\n\\tvec2 bBotCoord = bCoord + normalWidth * endBotJoin;\\n\\n\\t//miter anti-clipping\\n\\tfloat baClipping = distToLine(bCoord, aCoord, aBotCoord) / dot(normalize(normalWidth * endBotJoin), normalize(normalWidth.yx * vec2(-startBotJoin.y, startBotJoin.x)));\\n\\tfloat abClipping = distToLine(aCoord, bCoord, bTopCoord) / dot(normalize(normalWidth * startBotJoin), normalize(normalWidth.yx * vec2(-endBotJoin.y, endBotJoin.x)));\\n\\n\\t//prevent close to reverse direction switch\\n\\tbool prevReverse = dot(currTangent, prevTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, prevNormal)) * min(length(prevDiff), length(currDiff)) <  length(normalWidth * currNormal);\\n\\tbool nextReverse = dot(currTangent, nextTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, nextNormal)) * min(length(nextDiff), length(currDiff)) <  length(normalWidth * currNormal);\\n\\n\\tif (prevReverse) {\\n\\t\\t//make join rectangular\\n\\t\\tvec2 miterShift = normalWidth * startJoinDirection * miterLimit * .5;\\n\\t\\tfloat normalAdjust = 1. - min(miterLimit / startMiterRatio, 1.);\\n\\t\\taBotCoord = aCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\\n\\t\\taTopCoord = aCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\\n\\t}\\n\\telse if (!nextReverse && baClipping > 0. && baClipping < length(normalWidth * endBotJoin)) {\\n\\t\\t//handle miter clipping\\n\\t\\tbTopCoord -= normalWidth * endTopJoin;\\n\\t\\tbTopCoord += normalize(endTopJoin * normalWidth) * baClipping;\\n\\t}\\n\\n\\tif (nextReverse) {\\n\\t\\t//make join rectangular\\n\\t\\tvec2 miterShift = normalWidth * endJoinDirection * miterLimit * .5;\\n\\t\\tfloat normalAdjust = 1. - min(miterLimit / endMiterRatio, 1.);\\n\\t\\tbBotCoord = bCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\\n\\t\\tbTopCoord = bCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\\n\\t}\\n\\telse if (!prevReverse && abClipping > 0. && abClipping < length(normalWidth * startBotJoin)) {\\n\\t\\t//handle miter clipping\\n\\t\\taBotCoord -= normalWidth * startBotJoin;\\n\\t\\taBotCoord += normalize(startBotJoin * normalWidth) * abClipping;\\n\\t}\\n\\n\\tvec2 aTopPosition = (aTopCoord) * adjustedScale + translate;\\n\\tvec2 aBotPosition = (aBotCoord) * adjustedScale + translate;\\n\\n\\tvec2 bTopPosition = (bTopCoord) * adjustedScale + translate;\\n\\tvec2 bBotPosition = (bBotCoord) * adjustedScale + translate;\\n\\n\\t//position is normalized 0..1 coord on the screen\\n\\tvec2 position = (aTopPosition * lineTop + aBotPosition * lineBot) * lineStart + (bTopPosition * lineTop + bBotPosition * lineBot) * lineEnd;\\n\\n\\tstartCoord = aCoord * scaleRatio + translate * viewport.zw + viewport.xy;\\n\\tendCoord = bCoord * scaleRatio + translate * viewport.zw + viewport.xy;\\n\\n\\tgl_Position = vec4(position  * 2.0 - 1.0, depth, 1);\\n\\n\\tenableStartMiter = step(dot(currTangent, prevTangent), .5);\\n\\tenableEndMiter = step(dot(currTangent, nextTangent), .5);\\n\\n\\t//bevel miter cutoffs\\n\\tif (miterMode == 1.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * miterLimit * .5;\\n\\t\\t\\tstartCutoff = vec4(aCoord, aCoord);\\n\\t\\t\\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\\n\\t\\t\\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tstartCutoff += viewport.xyxy;\\n\\t\\t\\tstartCutoff += startMiterWidth.xyxy;\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * miterLimit * .5;\\n\\t\\t\\tendCutoff = vec4(bCoord, bCoord);\\n\\t\\t\\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\\n\\t\\t\\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tendCutoff += viewport.xyxy;\\n\\t\\t\\tendCutoff += endMiterWidth.xyxy;\\n\\t\\t}\\n\\t}\\n\\n\\t//round miter cutoffs\\n\\telse if (miterMode == 2.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * abs(dot(startJoinDirection, currNormal)) * .5;\\n\\t\\t\\tstartCutoff = vec4(aCoord, aCoord);\\n\\t\\t\\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\\n\\t\\t\\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tstartCutoff += viewport.xyxy;\\n\\t\\t\\tstartCutoff += startMiterWidth.xyxy;\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * abs(dot(endJoinDirection, currNormal)) * .5;\\n\\t\\t\\tendCutoff = vec4(bCoord, bCoord);\\n\\t\\t\\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\\n\\t\\t\\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tendCutoff += viewport.xyxy;\\n\\t\\t\\tendCutoff += endMiterWidth.xyxy;\\n\\t\\t}\\n\\t}\\n}\\n\"]),frag:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform float dashLength, pixelRatio, thickness, opacity, id, miterMode;\\nuniform sampler2D dashTexture;\\n\\nvarying vec4 fragColor;\\nvarying vec2 tangent;\\nvarying vec4 startCutoff, endCutoff;\\nvarying vec2 startCoord, endCoord;\\nvarying float enableStartMiter, enableEndMiter;\\n\\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\\n\\tvec2 diff = b - a;\\n\\tvec2 perp = normalize(vec2(-diff.y, diff.x));\\n\\treturn dot(p - a, perp);\\n}\\n\\nvoid main() {\\n\\tfloat alpha = 1., distToStart, distToEnd;\\n\\tfloat cutoff = thickness * .5;\\n\\n\\t//bevel miter\\n\\tif (miterMode == 1.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\\n\\t\\t\\tif (distToStart < -1.) {\\n\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\treturn;\\n\\t\\t\\t}\\n\\t\\t\\talpha *= min(max(distToStart + 1., 0.), 1.);\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\\n\\t\\t\\tif (distToEnd < -1.) {\\n\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\treturn;\\n\\t\\t\\t}\\n\\t\\t\\talpha *= min(max(distToEnd + 1., 0.), 1.);\\n\\t\\t}\\n\\t}\\n\\n\\t// round miter\\n\\telse if (miterMode == 2.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\\n\\t\\t\\tif (distToStart < 0.) {\\n\\t\\t\\t\\tfloat radius = length(gl_FragCoord.xy - startCoord);\\n\\n\\t\\t\\t\\tif(radius > cutoff + .5) {\\n\\t\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\t\\treturn;\\n\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\\n\\t\\t\\t}\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\\n\\t\\t\\tif (distToEnd < 0.) {\\n\\t\\t\\t\\tfloat radius = length(gl_FragCoord.xy - endCoord);\\n\\n\\t\\t\\t\\tif(radius > cutoff + .5) {\\n\\t\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\t\\treturn;\\n\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\\n\\t\\t\\t}\\n\\t\\t}\\n\\t}\\n\\n\\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashLength) * .5 + .25;\\n\\tfloat dash = texture2D(dashTexture, vec2(t, .5)).r;\\n\\n\\tgl_FragColor = fragColor;\\n\\tgl_FragColor.a *= alpha * opacity * 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main() {\\n\\tfloat depth = (MAX_LINES - 4. - id) / (MAX_LINES);\\n\\n\\tvec2 position = position * scale + translate\\n       + positionFract * scale + translateFract\\n       + position * scaleFract\\n       + positionFract * scaleFract;\\n\\n\\tgl_Position = vec4(position * 2.0 - 1.0, depth, 1);\\n\\n\\tfragColor = color / 255.;\\n\\tfragColor.a *= opacity;\\n}\\n\"]),frag:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n\\tgl_FragColor = 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t=this,e=[],r=arguments.length;r--;)e[r]=arguments[r];return(e.length?e:this.passes).forEach((function(e,r){var n;if(e&&Array.isArray(e))return(n=t).draw.apply(n,e);\"number\"==typeof e&&(e=t.passes[e]),e&&e.count>1&&e.opacity&&(t.regl._refresh(),e.fill&&e.triangles&&e.triangles.length>2&&t.shaders.fill(e),e.thickness&&(e.scale[0]*e.viewport.width>y.precisionThreshold||e.scale[1]*e.viewport.height>y.precisionThreshold||\"rect\"===e.join||!e.join&&(e.thickness<=2||e.count>=y.maxPoints)?t.shaders.rect(e):t.shaders.miter(e)))})),this},y.prototype.update=function(t){var e=this;if(t){null!=t.length?\"number\"==typeof t[0]&&(t=[{positions:t}]):Array.isArray(t)||(t=[t]);var r=this.regl,o=this.gl;if(t.forEach((function(t,f){var d=e.passes[f];if(void 0!==t)if(null!==t){if(\"number\"==typeof t[0]&&(t={positions:t}),t=s(t,{positions:\"positions points data coords\",thickness:\"thickness lineWidth lineWidths line-width linewidth width stroke-width strokewidth strokeWidth\",join:\"lineJoin linejoin 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Uint8Array})},t=a({},y.defaults,t)),null!=t.thickness&&(d.thickness=parseFloat(t.thickness)),null!=t.opacity&&(d.opacity=parseFloat(t.opacity)),null!=t.miterLimit&&(d.miterLimit=parseFloat(t.miterLimit)),null!=t.overlay&&(d.overlay=!!t.overlay,f<y.maxLines&&(d.depth=2*(y.maxLines-1-f%y.maxLines)/y.maxLines-1)),null!=t.join&&(d.join=t.join),null!=t.hole&&(d.hole=t.hole),null!=t.fill&&(d.fill=t.fill?n(t.fill,\"uint8\"):null),null!=t.viewport&&(d.viewport=v(t.viewport)),d.viewport||(d.viewport=v([o.drawingBufferWidth,o.drawingBufferHeight])),null!=t.close&&(d.close=t.close),null===t.positions&&(t.positions=[]),t.positions){var m,x;if(t.positions.x&&t.positions.y){var b=t.positions.x,_=t.positions.y;x=d.count=Math.max(b.length,_.length),m=new Float64Array(2*x);for(var w=0;w<x;w++)m[2*w]=b[w],m[2*w+1]=_[w]}else m=l(t.positions,\"float64\"),x=d.count=Math.floor(m.length/2);var T=d.bounds=i(m,2);if(d.fill){for(var k=[],A={},M=0,S=0,E=0,L=d.count;S<L;S++){var C=m[2*S],P=m[2*S+1];isNaN(C)||isNaN(P)||null==C||null==P?(C=m[2*M],P=m[2*M+1],A[S]=M):M=S,k[E++]=C,k[E++]=P}if(t.splitNull){d.count-1 in A||(A[d.count]=d.count-1);var O=Object.keys(A).map(Number).sort((function(t,e){return t-e})),I=[],D=0,z=null!=d.hole?d.hole[0]:null;if(null!=z){var R=g(O,(function(t){return t>=z}));(O=O.slice(0,R)).push(z)}for(var F=function(t){var e=k.slice(2*D,2*O[t]).concat(z?k.slice(2*z):[]),r=(d.hole||[]).map((function(e){return e-z+(O[t]-D)})),n=u(e,r);n=n.map((function(e){return e+D+(e+D<O[t]?0:z-O[t])})),I.push.apply(I,n),D=O[t]+1},B=0;B<O.length;B++)F(B);for(var N=0,j=I.length;N<j;N++)null!=A[I[N]]&&(I[N]=A[I[N]]);d.triangles=I}else{for(var U=u(k,d.hole||[]),V=0,H=U.length;V<H;V++)null!=A[U[V]]&&(U[V]=A[U[V]]);d.triangles=U}}var q=new Float64Array(m);c(q,2,T);var G=new Float64Array(2*x+6);d.close?m[0]===m[2*x-2]&&m[1]===m[2*x-1]?(G[0]=q[2*x-4],G[1]=q[2*x-3]):(G[0]=q[2*x-2],G[1]=q[2*x-1]):(G[0]=q[0],G[1]=q[1]),G.set(q,2),d.close?m[0]===m[2*x-2]&&m[1]===m[2*x-1]?(G[2*x+2]=q[2],G[2*x+3]=q[3],d.count-=1):(G[2*x+2]=q[0],G[2*x+3]=q[1],G[2*x+4]=q[2],G[2*x+5]=q[3]):(G[2*x+2]=q[2*x-2],G[2*x+3]=q[2*x-1],G[2*x+4]=q[2*x-2],G[2*x+5]=q[2*x-1]);var Z=h(G);d.positionBuffer(Z);var Y=p(G,Z);d.positionFractBuffer(Y)}if(t.range?d.range=t.range:d.range||(d.range=d.bounds),(t.range||t.positions)&&d.count){var W=d.bounds,X=W[2]-W[0],J=W[3]-W[1],K=d.range[2]-d.range[0],$=d.range[3]-d.range[1];d.scale=[X/K,J/$],d.translate=[-d.range[0]/K+W[0]/K||0,-d.range[1]/$+W[1]/$||0],d.scaleFract=p(d.scale),d.translateFract=p(d.translate)}if(t.dashes){var Q,tt=0;if(!t.dashes||t.dashes.length<2)tt=1,Q=new Uint8Array([255,255,255,255,255,255,255,255]);else{tt=0;for(var et=0;et<t.dashes.length;++et)tt+=t.dashes[et];Q=new Uint8Array(tt*y.dashMult);for(var rt=0,nt=255,it=0;it<2;it++)for(var at=0;at<t.dashes.length;++at){for(var ot=0,st=t.dashes[at]*y.dashMult*.5;ot<st;++ot)Q[rt++]=nt;nt^=255}}d.dashLength=tt,d.dashTexture({channels:1,data:Q,width:Q.length,height:1,mag:\"linear\",min:\"linear\"},0,0)}if(t.color){var lt=d.count,ut=t.color;ut||(ut=\"transparent\");var ct=new Uint8Array(4*lt+4);if(Array.isArray(ut)&&\"number\"!=typeof ut[0]){for(var ft=0;ft<lt;ft++){var ht=n(ut[ft],\"uint8\");ct.set(ht,4*ft)}ct.set(n(ut[0],\"uint8\"),4*lt)}else for(var pt=n(ut,\"uint8\"),dt=0;dt<lt+1;dt++)ct.set(pt,4*dt);d.colorBuffer({usage:\"dynamic\",type:\"uint8\",data:ct})}}else e.passes[f]=null})),t.length<this.passes.length){for(var f=t.length;f<this.passes.length;f++){var d=this.passes[f];d&&(d.colorBuffer.destroy(),d.positionBuffer.destroy(),d.dashTexture.destroy())}this.passes.length=t.length}for(var m=[],x=0;x<this.passes.length;x++)null!==this.passes[x]&&m.push(this.passes[x]);return this.passes=m,this}},y.prototype.destroy=function(){return this.passes.forEach((function(t){t.colorBuffer.destroy(),t.positionBuffer.destroy(),t.dashTexture.destroy()})),this.passes.length=0,this}},11870:function(t,e,r){\"use strict\";function n(t,e){return function(t){if(Array.isArray(t))return t}(t)||function(t,e){var r=null==t?null:\"undefined\"!=typeof Symbol&&t[Symbol.iterator]||t[\"@@iterator\"];if(null!=r){var n,i,a,o,s=[],l=!0,u=!1;try{if(a=(r=r.call(t)).next,0===e){if(Object(r)!==r)return;l=!1}else for(;!(l=(n=a.call(r)).done)&&(s.push(n.value),s.length!==e);l=!0);}catch(t){u=!0,i=t}finally{try{if(!l&&null!=r.return&&(o=r.return(),Object(o)!==o))return}finally{if(u)throw i}}return s}}(t,e)||i(t,e)||function(){throw new TypeError(\"Invalid attempt to destructure non-iterable instance.\\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.\")}()}function i(t,e){if(t){if(\"string\"==typeof t)return a(t,e);var r=Object.prototype.toString.call(t).slice(8,-1);return\"Object\"===r&&t.constructor&&(r=t.constructor.name),\"Map\"===r||\"Set\"===r?Array.from(t):\"Arguments\"===r||/^(?:Ui|I)nt(?:8|16|32)(?:Clamped)?Array$/.test(r)?a(t,e):void 0}}function a(t,e){(null==e||e>t.length)&&(e=t.length);for(var r=0,n=new Array(e);r<e;r++)n[r]=t[r];return n}var o=r(25075),s=r(21527),l=r(6475),u=r(88294),c=r(56131),f=r(56068),h=r(71299),p=r(93447),d=r(30120),v=r(62683),g=r(57060),y=r(18863),m=x;function x(t,e){var r=this;if(!(this instanceof x))return new x(t,e);\"function\"==typeof t?(e||(e={}),e.regl=t):(e=t,t=null),e&&e.length&&(e.positions=e);var n,i=(t=e.regl)._gl,a=[];this.tooManyColors=v,n=t.texture({data:new Uint8Array(1020),width:255,height:1,type:\"uint8\",format:\"rgba\",wrapS:\"clamp\",wrapT:\"clamp\",mag:\"nearest\",min:\"nearest\"}),c(this,{regl:t,gl:i,groups:[],markerCache:[null],markerTextures:[null],palette:a,paletteIds:{},paletteTexture:n,maxColors:255,maxSize:100,canvas:i.canvas}),this.update(e);var o={uniforms:{constPointSize:!!e.constPointSize,opacity:t.prop(\"opacity\"),paletteSize:function(t,e){return[r.tooManyColors?0:255,n.height]},pixelRatio:t.context(\"pixelRatio\"),scale:t.prop(\"scale\"),scaleFract:t.prop(\"scaleFract\"),translate:t.prop(\"translate\"),translateFract:t.prop(\"translateFract\"),markerTexture:t.prop(\"markerTexture\"),paletteTexture:n},attributes:{x:function(t,e){return e.xAttr||{buffer:e.positionBuffer,stride:8,offset:0}},y:function(t,e){return e.yAttr||{buffer:e.positionBuffer,stride:8,offset:4}},xFract:function(t,e){return e.xAttr?{constant:[0,0]}:{buffer:e.positionFractBuffer,stride:8,offset:0}},yFract:function(t,e){return e.yAttr?{constant:[0,0]}:{buffer:e.positionFractBuffer,stride:8,offset:4}},size:function(t,e){return e.size.length?{buffer:e.sizeBuffer,stride:2,offset:0}:{constant:[Math.round(255*e.size/r.maxSize)]}},borderSize:function(t,e){return e.borderSize.length?{buffer:e.sizeBuffer,stride:2,offset:1}:{constant:[Math.round(255*e.borderSize/r.maxSize)]}},colorId:function(t,e){return e.color.length?{buffer:e.colorBuffer,stride:r.tooManyColors?8:4,offset:0}:{constant:r.tooManyColors?a.slice(4*e.color,4*e.color+4):[e.color]}},borderColorId:function(t,e){return e.borderColor.length?{buffer:e.colorBuffer,stride:r.tooManyColors?8:4,offset:r.tooManyColors?4:2}:{constant:r.tooManyColors?a.slice(4*e.borderColor,4*e.borderColor+4):[e.borderColor]}},isActive:function(t,e){return!0===e.activation?{constant:[1]}:e.activation?e.activation:{constant:[0]}}},blend:{enable:!0,color:[0,0,0,1],func:{srcRGB:\"src alpha\",dstRGB:\"one minus src alpha\",srcAlpha:\"one minus dst alpha\",dstAlpha:\"one\"}},scissor:{enable:!0,box:t.prop(\"viewport\")},viewport:t.prop(\"viewport\"),stencil:{enable:!1},depth:{enable:!1},elements:t.prop(\"elements\"),count:t.prop(\"count\"),offset:t.prop(\"offset\"),primitive:\"points\"},s=c({},o);s.frag=f([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform float opacity;\\nuniform sampler2D markerTexture;\\n\\nvarying vec4 fragColor, fragBorderColor;\\nvarying float fragWidth, fragBorderColorLevel, fragColorLevel;\\n\\nfloat smoothStep(float x, float y) {\\n  return 1.0 / (1.0 + exp(50.0*(x - y)));\\n}\\n\\nvoid main() {\\n  float dist = texture2D(markerTexture, gl_PointCoord).r, delta = fragWidth;\\n\\n  // max-distance alpha\\n  if (dist < 0.003) discard;\\n\\n  // null-border case\\n  if (fragBorderColorLevel == fragColorLevel || fragBorderColor.a == 0.) {\\n    float colorAmt = smoothstep(.5 - delta, .5 + delta, dist);\\n    gl_FragColor = vec4(fragColor.rgb, colorAmt * fragColor.a * opacity);\\n  }\\n  else {\\n    float borderColorAmt = smoothstep(fragBorderColorLevel - delta, fragBorderColorLevel + delta, dist);\\n    float colorAmt = smoothstep(fragColorLevel - delta, fragColorLevel + delta, dist);\\n\\n    vec4 color = fragBorderColor;\\n    color.a *= borderColorAmt;\\n    color = mix(color, fragColor, colorAmt);\\n    color.a *= opacity;\\n\\n    gl_FragColor = color;\\n  }\\n\\n}\\n\"]),s.vert=f([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute float x, y, xFract, yFract;\\nattribute float size, borderSize;\\nattribute vec4 colorId, borderColorId;\\nattribute float isActive;\\n\\nuniform bool constPointSize;\\nuniform float pixelRatio;\\nuniform vec2 scale, scaleFract, translate, translateFract, paletteSize;\\nuniform sampler2D paletteTexture;\\n\\nconst float maxSize = 100.;\\nconst float borderLevel = .5;\\n\\nvarying vec4 fragColor, fragBorderColor;\\nvarying float fragPointSize, fragBorderRadius, fragWidth, fragBorderColorLevel, fragColorLevel;\\n\\nfloat pointSizeScale = (constPointSize) ? 2. : pixelRatio;\\n\\nbool isDirect = (paletteSize.x < 1.);\\n\\nvec4 getColor(vec4 id) {\\n  return isDirect ? id / 255. : texture2D(paletteTexture,\\n    vec2(\\n      (id.x + .5) / paletteSize.x,\\n      (id.y + .5) / paletteSize.y\\n    )\\n  );\\n}\\n\\nvoid main() {\\n  // ignore inactive points\\n  if (isActive == 0.) return;\\n\\n  vec2 position = vec2(x, y);\\n  vec2 positionFract = vec2(xFract, yFract);\\n\\n  vec4 color = getColor(colorId);\\n  vec4 borderColor = getColor(borderColorId);\\n\\n  float size = size * maxSize / 255.;\\n  float borderSize = borderSize * maxSize / 255.;\\n\\n  gl_PointSize = 2. * size * pointSizeScale;\\n  fragPointSize = size * pixelRatio;\\n\\n  vec2 pos = (position + translate) * scale\\n      + (positionFract + translateFract) * scale\\n      + (position + translate) * scaleFract\\n      + (positionFract + translateFract) * scaleFract;\\n\\n  gl_Position = vec4(pos * 2. - 1., 0., 1.);\\n\\n  fragColor = color;\\n  fragBorderColor = borderColor;\\n  fragWidth = 1. / gl_PointSize;\\n\\n  fragBorderColorLevel = clamp(borderLevel - borderLevel * borderSize / size, 0., 1.);\\n  fragColorLevel = clamp(borderLevel + (1. - borderLevel) * borderSize / size, 0., 1.);\\n}\"]),this.drawMarker=t(s);var l=c({},o);l.frag=f([\"precision highp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor, fragBorderColor;\\nvarying float fragBorderRadius, fragWidth;\\n\\nuniform float opacity;\\n\\nfloat smoothStep(float edge0, float edge1, float x) {\\n\\tfloat t;\\n\\tt = clamp((x - edge0) / (edge1 - edge0), 0.0, 1.0);\\n\\treturn t * t * (3.0 - 2.0 * t);\\n}\\n\\nvoid main() {\\n\\tfloat radius, alpha = 1.0, delta = fragWidth;\\n\\n\\tradius = length(2.0 * gl_PointCoord.xy - 1.0);\\n\\n\\tif (radius > 1.0 + delta) {\\n\\t\\tdiscard;\\n\\t}\\n\\n\\talpha -= smoothstep(1.0 - delta, 1.0 + delta, radius);\\n\\n\\tfloat borderRadius = fragBorderRadius;\\n\\tfloat ratio = smoothstep(borderRadius - delta, borderRadius + delta, radius);\\n\\tvec4 color = mix(fragColor, fragBorderColor, ratio);\\n\\tcolor.a *= alpha * opacity;\\n\\tgl_FragColor = color;\\n}\\n\"]),l.vert=f([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute float x, y, xFract, yFract;\\nattribute float size, borderSize;\\nattribute vec4 colorId, borderColorId;\\nattribute float isActive;\\n\\nuniform bool constPointSize;\\nuniform float pixelRatio;\\nuniform vec2 paletteSize, scale, scaleFract, translate, translateFract;\\nuniform sampler2D paletteTexture;\\n\\nconst float maxSize = 100.;\\n\\nvarying vec4 fragColor, fragBorderColor;\\nvarying float fragBorderRadius, fragWidth;\\n\\nfloat pointSizeScale = (constPointSize) ? 2. : pixelRatio;\\n\\nbool isDirect = (paletteSize.x < 1.);\\n\\nvec4 getColor(vec4 id) {\\n  return isDirect ? id / 255. : texture2D(paletteTexture,\\n    vec2(\\n      (id.x + .5) / paletteSize.x,\\n      (id.y + .5) / paletteSize.y\\n    )\\n  );\\n}\\n\\nvoid main() {\\n  // ignore inactive points\\n  if (isActive == 0.) return;\\n\\n  vec2 position = vec2(x, y);\\n  vec2 positionFract = vec2(xFract, yFract);\\n\\n  vec4 color = getColor(colorId);\\n  vec4 borderColor = getColor(borderColorId);\\n\\n  float size = size * maxSize / 255.;\\n  float borderSize = borderSize * maxSize / 255.;\\n\\n  gl_PointSize = (size + borderSize) * pointSizeScale;\\n\\n  vec2 pos = (position + translate) * scale\\n      + (positionFract + translateFract) * scale\\n      + (position + translate) * scaleFract\\n      + (positionFract + translateFract) * scaleFract;\\n\\n  gl_Position = vec4(pos * 2. - 1., 0., 1.);\\n\\n  fragBorderRadius = 1. - 2. * borderSize / (size + borderSize);\\n  fragColor = color;\\n  fragBorderColor = borderColor.a == 0. || borderSize == 0. ? vec4(color.rgb, 0.) : borderColor;\\n  fragWidth = 1. / gl_PointSize;\\n}\\n\"]),v&&(l.frag=l.frag.replace(\"smoothstep\",\"smoothStep\"),s.frag=s.frag.replace(\"smoothstep\",\"smoothStep\")),this.drawCircle=t(l)}x.defaults={color:\"black\",borderColor:\"transparent\",borderSize:0,size:12,opacity:1,marker:void 0,viewport:null,range:null,pixelSize:null,count:0,offset:0,bounds:null,positions:[],snap:1e4},x.prototype.render=function(){return arguments.length&&this.update.apply(this,arguments),this.draw(),this},x.prototype.draw=function(){for(var t=this,e=arguments.length,r=new Array(e),n=0;n<e;n++)r[n]=arguments[n];var i=this.groups;if(1===r.length&&Array.isArray(r[0])&&(null===r[0][0]||Array.isArray(r[0][0]))&&(r=r[0]),this.regl._refresh(),r.length)for(var a=0;a<r.length;a++)this.drawItem(a,r[a]);else i.forEach((function(e,r){t.drawItem(r)}));return this},x.prototype.drawItem=function(t,e){var r,n=this.groups,o=n[t];if(\"number\"==typeof e&&(t=e,o=n[e],e=null),o&&o.count&&o.opacity){o.activation[0]&&this.drawCircle(this.getMarkerDrawOptions(0,o,e));for(var s=[],l=1;l<o.activation.length;l++)o.activation[l]&&(!0===o.activation[l]||o.activation[l].data.length)&&s.push.apply(s,function(t){if(Array.isArray(t))return a(t)}(r=this.getMarkerDrawOptions(l,o,e))||function(t){if(\"undefined\"!=typeof Symbol&&null!=t[Symbol.iterator]||null!=t[\"@@iterator\"])return Array.from(t)}(r)||i(r)||function(){throw new TypeError(\"Invalid attempt to spread non-iterable instance.\\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.\")}());s.length&&this.drawMarker(s)}},x.prototype.getMarkerDrawOptions=function(t,e,r){var i=e.range,a=e.tree,o=e.viewport,s=e.activation,l=e.selectionBuffer,u=e.count;if(this.regl,!a)return r?[c({},e,{markerTexture:this.markerTextures[t],activation:s[t],count:r.length,elements:r,offset:0})]:[c({},e,{markerTexture:this.markerTextures[t],activation:s[t],offset:0})];var f=[],h=a.range(i,{lod:!0,px:[(i[2]-i[0])/o.width,(i[3]-i[1])/o.height]});if(r){for(var p=s[t].data,d=new Uint8Array(u),v=0;v<r.length;v++){var g=r[v];d[g]=p?p[g]:1}l.subdata(d)}for(var y=h.length;y--;){var m=n(h[y],2),x=m[0],b=m[1];f.push(c({},e,{markerTexture:this.markerTextures[t],activation:r?l:s[t],offset:x,count:b-x}))}return f},x.prototype.update=function(){for(var t=this,e=arguments.length,r=new Array(e),n=0;n<e;n++)r[n]=arguments[n];if(r.length){1===r.length&&Array.isArray(r[0])&&(r=r[0]);var i=this.groups,a=this.gl,o=this.regl,l=this.maxSize,f=this.maxColors,v=this.palette;this.groups=i=r.map((function(e,r){var n=i[r];if(void 0===e)return n;null===e?e={positions:null}:\"function\"==typeof e?e={ondraw:e}:\"number\"==typeof e[0]&&(e={positions:e}),null===(e=h(e,{positions:\"positions data points\",snap:\"snap cluster lod tree\",size:\"sizes size radius\",borderSize:\"borderSizes borderSize border-size bordersize borderWidth borderWidths border-width borderwidth stroke-width strokeWidth strokewidth outline\",color:\"colors color fill fill-color fillColor\",borderColor:\"borderColors borderColor stroke stroke-color strokeColor\",marker:\"markers marker shape\",range:\"range dataBox databox\",viewport:\"viewport viewPort viewBox viewbox\",opacity:\"opacity alpha transparency\",bounds:\"bound bounds boundaries limits\",tooManyColors:\"tooManyColors palette paletteMode optimizePalette enablePalette\"})).positions&&(e.positions=[]),null!=e.tooManyColors&&(t.tooManyColors=e.tooManyColors),n||(i[r]=n={id:r,scale:null,translate:null,scaleFract:null,translateFract:null,activation:[],selectionBuffer:o.buffer({data:new Uint8Array(0),usage:\"stream\",type:\"uint8\"}),sizeBuffer:o.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"uint8\"}),colorBuffer:o.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"uint8\"}),positionBuffer:o.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"float\"}),positionFractBuffer:o.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"float\"})},e=c({},x.defaults,e)),e.positions&&!(\"marker\"in e)&&(e.marker=n.marker,delete n.marker),e.marker&&!(\"positions\"in e)&&(e.positions=n.positions,delete n.positions);var m=0,b=0;if(p(n,e,[{snap:!0,size:function(t,e){return null==t&&(t=x.defaults.size),m+=t&&t.length?1:0,t},borderSize:function(t,e){return null==t&&(t=x.defaults.borderSize),m+=t&&t.length?1:0,t},opacity:parseFloat,color:function(e,r){return null==e&&(e=x.defaults.color),e=t.updateColor(e),b++,e},borderColor:function(e,r){return null==e&&(e=x.defaults.borderColor),e=t.updateColor(e),b++,e},bounds:function(t,e,r){return\"range\"in r||(r.range=null),t},positions:function(t,e,r){var n=e.snap,i=e.positionBuffer,a=e.positionFractBuffer,l=e.selectionBuffer;if(t.x||t.y)return t.x.length?e.xAttr={buffer:o.buffer(t.x),offset:0,stride:4,count:t.x.length}:e.xAttr={buffer:t.x.buffer,offset:4*t.x.offset||0,stride:4*(t.x.stride||1),count:t.x.count},t.y.length?e.yAttr={buffer:o.buffer(t.y),offset:0,stride:4,count:t.y.length}:e.yAttr={buffer:t.y.buffer,offset:4*t.y.offset||0,stride:4*(t.y.stride||1),count:t.y.count},e.count=Math.max(e.xAttr.count,e.yAttr.count),t;t=d(t,\"float64\");var c=e.count=Math.floor(t.length/2),f=e.bounds=c?s(t,2):null;if(r.range||e.range||(delete e.range,r.range=f),r.marker||e.marker||(delete e.marker,r.marker=null),n&&(!0===n||c>n)?e.tree=u(t,{bounds:f}):n&&n.length&&(e.tree=n),e.tree){var h={primitive:\"points\",usage:\"static\",data:e.tree,type:\"uint32\"};e.elements?e.elements(h):e.elements=o.elements(h)}var p=g.float32(t);return i({data:p,usage:\"dynamic\"}),a({data:g.fract32(t,p),usage:\"dynamic\"}),l({data:new Uint8Array(c),type:\"uint8\",usage:\"stream\"}),t}},{marker:function(e,r,n){var i=r.activation;if(i.forEach((function(t){return t&&t.destroy&&t.destroy()})),i.length=0,e&&\"number\"!=typeof e[0]){for(var a=[],s=0,l=Math.min(e.length,r.count);s<l;s++){var u=t.addMarker(e[s]);a[u]||(a[u]=new Uint8Array(r.count)),a[u][s]=1}for(var c=0;c<a.length;c++)if(a[c]){var f={data:a[c],type:\"uint8\",usage:\"static\"};i[c]?i[c](f):i[c]=o.buffer(f),i[c].data=a[c]}}else i[t.addMarker(e)]=!0;return e},range:function(t,e,r){var n=e.bounds;if(n)return t||(t=n),e.scale=[1/(t[2]-t[0]),1/(t[3]-t[1])],e.translate=[-t[0],-t[1]],e.scaleFract=g.fract(e.scale),e.translateFract=g.fract(e.translate),t},viewport:function(t){return y(t||[a.drawingBufferWidth,a.drawingBufferHeight])}}]),m){var _=n,w=_.count,T=_.size,k=_.borderSize,A=_.sizeBuffer,M=new Uint8Array(2*w);if(T.length||k.length)for(var S=0;S<w;S++)M[2*S]=Math.round(255*(null==T[S]?T:T[S])/l),M[2*S+1]=Math.round(255*(null==k[S]?k:k[S])/l);A({data:M,usage:\"dynamic\"})}if(b){var E,L=n,C=L.count,P=L.color,O=L.borderColor,I=L.colorBuffer;if(t.tooManyColors){if(P.length||O.length){E=new Uint8Array(8*C);for(var D=0;D<C;D++){var z=P[D];E[8*D]=v[4*z],E[8*D+1]=v[4*z+1],E[8*D+2]=v[4*z+2],E[8*D+3]=v[4*z+3];var R=O[D];E[8*D+4]=v[4*R],E[8*D+5]=v[4*R+1],E[8*D+6]=v[4*R+2],E[8*D+7]=v[4*R+3]}}}else if(P.length||O.length){E=new Uint8Array(4*C+2);for(var F=0;F<C;F++)null!=P[F]&&(E[4*F]=P[F]%f,E[4*F+1]=Math.floor(P[F]/f)),null!=O[F]&&(E[4*F+2]=O[F]%f,E[4*F+3]=Math.floor(O[F]/f))}I({data:E||new Uint8Array(0),type:\"uint8\",usage:\"dynamic\"})}return n}))}},x.prototype.addMarker=function(t){var e,r=this.markerTextures,n=this.regl,i=this.markerCache,a=null==t?0:i.indexOf(t);if(a>=0)return a;if(t instanceof Uint8Array||t instanceof Uint8ClampedArray)e=t;else{e=new Uint8Array(t.length);for(var o=0,s=t.length;o<s;o++)e[o]=255*t[o]}var l=Math.floor(Math.sqrt(e.length));return a=r.length,i.push(t),r.push(n.texture({channels:1,data:e,radius:l,mag:\"linear\",min:\"linear\"})),a},x.prototype.updateColor=function(t){var e=this.paletteIds,r=this.palette,n=this.maxColors;Array.isArray(t)||(t=[t]);var i=[];if(\"number\"==typeof t[0]){var a=[];if(Array.isArray(t))for(var s=0;s<t.length;s+=4)a.push(t.slice(s,s+4));else for(var u=0;u<t.length;u+=4)a.push(t.subarray(u,u+4));t=a}for(var c=0;c<t.length;c++){var f=t[c];f=o(f,\"uint8\");var h=l(f,!1);if(null==e[h]){var p=r.length;e[h]=Math.floor(p/4),r[p]=f[0],r[p+1]=f[1],r[p+2]=f[2],r[p+3]=f[3]}i[c]=e[h]}return!this.tooManyColors&&r.length>4*n&&(this.tooManyColors=!0),this.updatePalette(r),1===i.length?i[0]:i},x.prototype.updatePalette=function(t){if(!this.tooManyColors){var e=this.maxColors,r=this.paletteTexture,n=Math.ceil(.25*t.length/e);if(n>1)for(var i=.25*(t=t.slice()).length%e;i<n*e;i++)t.push(0,0,0,0);r.height<n&&r.resize(e,n),r.subimage({width:Math.min(.25*t.length,e),height:n,data:t},0,0)}},x.prototype.destroy=function(){return this.groups.forEach((function(t){t.sizeBuffer.destroy(),t.positionBuffer.destroy(),t.positionFractBuffer.destroy(),t.colorBuffer.destroy(),t.activation.forEach((function(t){return t&&t.destroy&&t.destroy()})),t.selectionBuffer.destroy(),t.elements&&t.elements.destroy()})),this.groups.length=0,this.paletteTexture.destroy(),this.markerTextures.forEach((function(t){return t&&t.destroy&&t.destroy()})),this};var b=r(56131);t.exports=function(t,e){var r=new m(t,e),n=r.render.bind(r);return b(n,{render:n,update:r.update.bind(r),draw:r.draw.bind(r),destroy:r.destroy.bind(r),regl:r.regl,gl:r.gl,canvas:r.gl.canvas,groups:r.groups,markers:r.markerCache,palette:r.palette}),n}},60487:function(t,e,r){\"use strict\";var n=r(11870),i=r(71299),a=r(21527),o=r(5877),s=r(57471),l=r(18863),u=r(30120);function c(t,e){if(!(this instanceof c))return new c(t,e);this.traces=[],this.passes={},this.regl=t,this.scatter=n(t),this.canvas=this.scatter.canvas}function f(t,e,r){return(null!=t.id?t.id:t)<<16|(255&e)<<8|255&r}function h(t,e,r){var n,i,a,o,s=t[e],l=t[r];return s.length>2?(s[0],s[2],n=s[1],i=s[3]):s.length?(n=s[0],i=s[1]):(s.x,n=s.y,s.x,s.width,i=s.y+s.height),l.length>2?(a=l[0],o=l[2],l[1],l[3]):l.length?(a=l[0],o=l[1]):(a=l.x,l.y,o=l.x+l.width,l.y,l.height),[a,n,o,i]}function p(t){if(\"number\"==typeof t)return[t,t,t,t];if(2===t.length)return[t[0],t[1],t[0],t[1]];var e=l(t);return[e.x,e.y,e.x+e.width,e.y+e.height]}t.exports=c,c.prototype.render=function(){for(var t,e=this,r=[],n=arguments.length;n--;)r[n]=arguments[n];return r.length&&(t=this).update.apply(t,r),this.regl.attributes.preserveDrawingBuffer?this.draw():(this.dirty?null==this.planned&&(this.planned=o((function(){e.draw(),e.dirty=!0,e.planned=null}))):(this.draw(),this.dirty=!0,o((function(){e.dirty=!1}))),this)},c.prototype.update=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];if(e.length){for(var n=0;n<e.length;n++)this.updateItem(n,e[n]);this.traces=this.traces.filter(Boolean);for(var i=[],a=0,o=0;o<this.traces.length;o++){for(var s=this.traces[o],l=this.traces[o].passes,u=0;u<l.length;u++)i.push(this.passes[l[u]]);s.passOffset=a,a+=s.passes.length}return(t=this.scatter).update.apply(t,i),this}},c.prototype.updateItem=function(t,e){var r=this.regl;if(null===e)return this.traces[t]=null,this;if(!e)return this;var n,o=i(e,{data:\"data items columns rows values dimensions samples x\",snap:\"snap cluster\",size:\"sizes size radius\",color:\"colors color fill fill-color fillColor\",opacity:\"opacity alpha transparency 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Uint8Array}),color:\"black\",marker:null,size:12,borderColor:\"transparent\",borderSize:1,viewport:l([r._gl.drawingBufferWidth,r._gl.drawingBufferHeight]),padding:[0,0,0,0],opacity:1,diagonal:!0,upper:!0,lower:!0});if(null!=o.color&&(s.color=o.color),null!=o.size&&(s.size=o.size),null!=o.marker&&(s.marker=o.marker),null!=o.borderColor&&(s.borderColor=o.borderColor),null!=o.borderSize&&(s.borderSize=o.borderSize),null!=o.opacity&&(s.opacity=o.opacity),o.viewport&&(s.viewport=l(o.viewport)),null!=o.diagonal&&(s.diagonal=o.diagonal),null!=o.upper&&(s.upper=o.upper),null!=o.lower&&(s.lower=o.lower),o.data){s.buffer(u(o.data)),s.columns=o.data.length,s.count=o.data[0].length,s.bounds=[];for(var c=0;c<s.columns;c++)s.bounds[c]=a(o.data[c],1)}o.range&&(s.range=o.range,n=s.range&&\"number\"!=typeof s.range[0]),o.domain&&(s.domain=o.domain);var d=!1;null!=o.padding&&(Array.isArray(o.padding)&&o.padding.length===s.columns&&\"number\"==typeof o.padding[o.padding.length-1]?(s.padding=o.padding.map(p),d=!0):s.padding=p(o.padding));var v=s.columns,g=s.count,y=s.viewport.width,m=s.viewport.height,x=s.viewport.x,b=s.viewport.y,_=y/v,w=m/v;s.passes=[];for(var T=0;T<v;T++)for(var k=0;k<v;k++)if((s.diagonal||k!==T)&&(s.upper||!(T>k))&&(s.lower||!(T<k))){var A=f(s.id,T,k),M=this.passes[A]||(this.passes[A]={});if(o.data&&(o.transpose?M.positions={x:{buffer:s.buffer,offset:k,count:g,stride:v},y:{buffer:s.buffer,offset:T,count:g,stride:v}}:M.positions={x:{buffer:s.buffer,offset:k*g,count:g},y:{buffer:s.buffer,offset:T*g,count:g}},M.bounds=h(s.bounds,T,k)),o.domain||o.viewport||o.data){var S=d?h(s.padding,T,k):s.padding;if(s.domain){var E=h(s.domain,T,k),L=E[0],C=E[1],P=E[2],O=E[3];M.viewport=[x+L*y+S[0],b+C*m+S[1],x+P*y-S[2],b+O*m-S[3]]}else M.viewport=[x+k*_+_*S[0],b+T*w+w*S[1],x+(k+1)*_-_*S[2],b+(T+1)*w-w*S[3]]}o.color&&(M.color=s.color),o.size&&(M.size=s.size),o.marker&&(M.marker=s.marker),o.borderSize&&(M.borderSize=s.borderSize),o.borderColor&&(M.borderColor=s.borderColor),o.opacity&&(M.opacity=s.opacity),o.range&&(M.range=n?h(s.range,T,k):s.range||M.bounds),s.passes.push(A)}return this},c.prototype.draw=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];if(e.length){for(var n=[],i=0;i<e.length;i++)if(\"number\"==typeof e[i]){var a=this.traces[e[i]],o=a.passes,l=a.passOffset;n.push.apply(n,s(l,l+o.length))}else if(e[i].length){var u=e[i],c=this.traces[i],f=c.passes,h=c.passOffset;f=f.map((function(t,e){n[h+e]=u}))}(t=this.scatter).draw.apply(t,n)}else this.scatter.draw();return this},c.prototype.destroy=function(){return this.traces.forEach((function(t){t.buffer&&t.buffer.destroy&&t.buffer.destroy()})),this.traces=null,this.passes=null,this.scatter.destroy(),this}},98580:function(t){t.exports=function(){function t(t,e){this.id=Z++,this.type=t,this.data=e}function e(t){if(0===t.length)return[];var r=t.charAt(0),n=t.charAt(t.length-1);if(1<t.length&&r===n&&('\"'===r||\"'\"===r))return['\"'+t.substr(1,t.length-2).replace(/\\\\/g,\"\\\\\\\\\").replace(/\"/g,'\\\\\"')+'\"'];if(r=/\\[(false|true|null|\\d+|'[^']*'|\"[^\"]*\")\\]/.exec(t))return e(t.substr(0,r.index)).concat(e(r[1])).concat(e(t.substr(r.index+r[0].length)));if(1===(r=t.split(\".\")).length)return['\"'+t.replace(/\\\\/g,\"\\\\\\\\\").replace(/\"/g,'\\\\\"')+'\"'];for(t=[],n=0;n<r.length;++n)t=t.concat(e(r[n]));return t}function r(t){return\"[\"+e(t).join(\"][\")+\"]\"}function n(t){return\"string\"==typeof t?t.split():t}function i(t){return\"string\"==typeof t?document.querySelector(t):t}function a(t){var e,r,a,o,s=t||{};t={};var 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e=Math.floor(t/400)+1;t-=400*(e-1),t+=t>15?16:0;var r=Math.floor(t/32)+1,n=t-32*(r-1)+1;return this.newDate(e<=0?e-1:e,r,n)}});var o={20:\"Fruitbat\",21:\"Anchovy\"};n.calendars.discworld=a},37715:function(t,e,r){var n=r(63489),i=r(56131);function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new 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n=r(56131);function i(){this.regionalOptions=[],this.regionalOptions[\"\"]={invalidCalendar:\"Calendar {0} not found\",invalidDate:\"Invalid {0} date\",invalidMonth:\"Invalid {0} month\",invalidYear:\"Invalid {0} year\",differentCalendars:\"Cannot mix {0} and {1} dates\"},this.local=this.regionalOptions[\"\"],this.calendars={},this._localCals={}}function a(t,e,r,n){if(this._calendar=t,this._year=e,this._month=r,this._day=n,0===this._calendar._validateLevel&&!this._calendar.isValid(this._year,this._month,this._day))throw(u.local.invalidDate||u.regionalOptions[\"\"].invalidDate).replace(/\\{0\\}/,this._calendar.local.name)}function o(t,e){return\"000000\".substring(0,e-(t=\"\"+t).length)+t}function s(){this.shortYearCutoff=\"+10\"}function l(t){this.local=this.regionalOptions[t]||this.regionalOptions[\"\"]}n(i.prototype,{instance:function(t,e){t=(t||\"gregorian\").toLowerCase(),e=e||\"\";var r=this._localCals[t+\"-\"+e];if(!r&&this.calendars[t]&&(r=new this.calendars[t](e),this._localCals[t+\"-\"+e]=r),!r)throw(this.local.invalidCalendar||this.regionalOptions[\"\"].invalidCalendar).replace(/\\{0\\}/,t);return r},newDate:function(t,e,r,n,i){return(n=(null!=t&&t.year?t.calendar():\"string\"==typeof n?this.instance(n,i):n)||this.instance()).newDate(t,e,r)},substituteDigits:function(t){return function(e){return(e+\"\").replace(/[0-9]/g,(function(e){return t[e]}))}},substituteChineseDigits:function(t,e){return function(r){for(var n=\"\",i=0;r>0;){var a=r%10;n=(0===a?\"\":t[a]+e[i])+n,i++,r=Math.floor(r/10)}return 0===n.indexOf(t[1]+e[1])&&(n=n.substr(1)),n||t[0]}}}),n(a.prototype,{newDate:function(t,e,r){return this._calendar.newDate(null==t?this:t,e,r)},year:function(t){return 0===arguments.length?this._year:this.set(t,\"y\")},month:function(t){return 0===arguments.length?this._month:this.set(t,\"m\")},day:function(t){return 0===arguments.length?this._day:this.set(t,\"d\")},date:function(t,e,r){if(!this._calendar.isValid(t,e,r))throw(u.local.invalidDate||u.regionalOptions[\"\"].invalidDate).replace(/\\{0\\}/,this._calendar.local.name);return this._year=t,this._month=e,this._day=r,this},leapYear:function(){return this._calendar.leapYear(this)},epoch:function(){return this._calendar.epoch(this)},formatYear:function(){return this._calendar.formatYear(this)},monthOfYear:function(){return this._calendar.monthOfYear(this)},weekOfYear:function(){return this._calendar.weekOfYear(this)},daysInYear:function(){return this._calendar.daysInYear(this)},dayOfYear:function(){return this._calendar.dayOfYear(this)},daysInMonth:function(){return this._calendar.daysInMonth(this)},dayOfWeek:function(){return this._calendar.dayOfWeek(this)},weekDay:function(){return this._calendar.weekDay(this)},extraInfo:function(){return this._calendar.extraInfo(this)},add:function(t,e){return this._calendar.add(this,t,e)},set:function(t,e){return this._calendar.set(this,t,e)},compareTo:function(t){if(this._calendar.name!==t._calendar.name)throw(u.local.differentCalendars||u.regionalOptions[\"\"].differentCalendars).replace(/\\{0\\}/,this._calendar.local.name).replace(/\\{1\\}/,t._calendar.local.name);var e=this._year!==t._year?this._year-t._year:this._month!==t._month?this.monthOfYear()-t.monthOfYear():this._day-t._day;return 0===e?0:e<0?-1:1},calendar:function(){return this._calendar},toJD:function(){return this._calendar.toJD(this)},fromJD:function(t){return this._calendar.fromJD(t)},toJSDate:function(){return this._calendar.toJSDate(this)},fromJSDate:function(t){return this._calendar.fromJSDate(t)},toString:function(){return(this.year()<0?\"-\":\"\")+o(Math.abs(this.year()),4)+\"-\"+o(this.month(),2)+\"-\"+o(this.day(),2)}}),n(s.prototype,{_validateLevel:0,newDate:function(t,e,r){return null==t?this.today():(t.year&&(this._validate(t,e,r,u.local.invalidDate||u.regionalOptions[\"\"].invalidDate),r=t.day(),e=t.month(),t=t.year()),new a(this,t,e,r))},today:function(){return this.fromJSDate(new Date)},epoch:function(t){return this._validate(t,this.minMonth,this.minDay,u.local.invalidYear||u.regionalOptions[\"\"].invalidYear).year()<0?this.local.epochs[0]:this.local.epochs[1]},formatYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,u.local.invalidYear||u.regionalOptions[\"\"].invalidYear);return(e.year()<0?\"-\":\"\")+o(Math.abs(e.year()),4)},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,u.local.invalidYear||u.regionalOptions[\"\"].invalidYear),12},monthOfYear:function(t,e){var r=this._validate(t,e,this.minDay,u.local.invalidMonth||u.regionalOptions[\"\"].invalidMonth);return(r.month()+this.monthsInYear(r)-this.firstMonth)%this.monthsInYear(r)+this.minMonth},fromMonthOfYear:function(t,e){var r=(e+this.firstMonth-2*this.minMonth)%this.monthsInYear(t)+this.minMonth;return this._validate(t,r,this.minDay,u.local.invalidMonth||u.regionalOptions[\"\"].invalidMonth),r},daysInYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,u.local.invalidYear||u.regionalOptions[\"\"].invalidYear);return this.leapYear(e)?366:365},dayOfYear:function(t,e,r){var n=this._validate(t,e,r,u.local.invalidDate||u.regionalOptions[\"\"].invalidDate);return n.toJD()-this.newDate(n.year(),this.fromMonthOfYear(n.year(),this.minMonth),this.minDay).toJD()+1},daysInWeek:function(){return 7},dayOfWeek:function(t,e,r){var n=this._validate(t,e,r,u.local.invalidDate||u.regionalOptions[\"\"].invalidDate);return(Math.floor(this.toJD(n))+2)%this.daysInWeek()},extraInfo:function(t,e,r){return this._validate(t,e,r,u.local.invalidDate||u.regionalOptions[\"\"].invalidDate),{}},add:function(t,e,r){return this._validate(t,this.minMonth,this.minDay,u.local.invalidDate||u.regionalOptions[\"\"].invalidDate),this._correctAdd(t,this._add(t,e,r),e,r)},_add:function(t,e,r){if(this._validateLevel++,\"d\"===r||\"w\"===r){var n=t.toJD()+e*(\"w\"===r?this.daysInWeek():1),i=t.calendar().fromJD(n);return this._validateLevel--,[i.year(),i.month(),i.day()]}try{var a=t.year()+(\"y\"===r?e:0),o=t.monthOfYear()+(\"m\"===r?e:0);i=t.day(),\"y\"===r?(t.month()!==this.fromMonthOfYear(a,o)&&(o=this.newDate(a,t.month(),this.minDay).monthOfYear()),o=Math.min(o,this.monthsInYear(a)),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o)))):\"m\"===r&&(function(t){for(;o<t.minMonth;)a--,o+=t.monthsInYear(a);for(var e=t.monthsInYear(a);o>e-1+t.minMonth;)a++,o-=e,e=t.monthsInYear(a)}(this),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o))));var s=[a,this.fromMonthOfYear(a,o),i];return this._validateLevel--,s}catch(t){throw 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n=this.newDate(t,e,r);return n.add(4-(n.dayOfWeek()||7),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,u.local.invalidMonth||u.regionalOptions[\"\"].invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var n=this._validate(t,e,r,u.local.invalidDate||u.regionalOptions[\"\"].invalidDate);t=n.year(),e=n.month(),r=n.day(),t<0&&t++,e<3&&(e+=12,t--);var i=Math.floor(t/100),a=2-i+Math.floor(i/4);return Math.floor(365.25*(t+4716))+Math.floor(30.6001*(e+1))+r+a-1524.5},fromJD:function(t){var e=Math.floor(t+.5),r=Math.floor((e-1867216.25)/36524.25),n=1524+(r=e+1+r-Math.floor(r/4)),i=Math.floor((n-122.1)/365.25),a=Math.floor(365.25*i),o=Math.floor((n-a)/30.6001),s=n-a-Math.floor(30.6001*o),l=o-(o>13.5?13:1),u=i-(l>2.5?4716:4715);return u<=0&&u--,this.newDate(u,l,s)},toJSDate:function(t,e,r){var n=this._validate(t,e,r,u.local.invalidDate||u.regionalOptions[\"\"].invalidDate),i=new Date(n.year(),n.month()-1,n.day());return i.setHours(0),i.setMinutes(0),i.setSeconds(0),i.setMilliseconds(0),i.setHours(i.getHours()>12?i.getHours()+2:0),i},fromJSDate:function(t){return this.newDate(t.getFullYear(),t.getMonth()+1,t.getDate())}});var u=t.exports=new i;u.cdate=a,u.baseCalendar=s,u.calendars.gregorian=l},94338:function(t,e,r){var n=r(56131),i=r(63489);n(i.regionalOptions[\"\"],{invalidArguments:\"Invalid arguments\",invalidFormat:\"Cannot format a date from another calendar\",missingNumberAt:\"Missing number at position {0}\",unknownNameAt:\"Unknown name at position {0}\",unexpectedLiteralAt:\"Unexpected literal at position {0}\",unexpectedText:\"Additional text found at end\"}),i.local=i.regionalOptions[\"\"],n(i.cdate.prototype,{formatDate:function(t,e){return\"string\"!=typeof 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Tokens' Impact on the Model\",\"x\":0.5},\"margin\":{\"t\":80,\"b\":20},\"plot_bgcolor\":\"rgba(0,0,0,0)\",\"height\":900,\"xaxis\":{\"gridcolor\":\"LightGrey\",\"linewidth\":1,\"linecolor\":\"Grey\",\"showline\":true,\"mirror\":false,\"zeroline\":true,\"zerolinewidth\":1,\"zerolinecolor\":\"DarkGrey\",\"categoryorder\":\"total ascending\",\"side\":\"top\",\"visible\":true,\"showticklabels\":true},\"yaxis\":{\"gridcolor\":\"rgba(0,0,0,0)\",\"linewidth\":1,\"linecolor\":\"Grey\",\"showline\":true,\"mirror\":false,\"zeroline\":true,\"zerolinewidth\":1,\"zerolinecolor\":\"DarkGrey\",\"categoryorder\":\"total ascending\",\"side\":\"left\",\"visible\":true,\"showticklabels\":true}},                        {\"responsive\": true}                    ).then(function(){\n",
        "                            \n",
-       "var gd = document.getElementById('ca90429a-4393-4737-87da-564fa3705c93');\n",
+       "var gd = document.getElementById('d88b4deb-c1fc-4aff-a0e6-3b53050faf71');\n",
        "var x = new MutationObserver(function (mutations, observer) {{\n",
        "        var display = window.getComputedStyle(gd).display;\n",
        "        if (!display || display === 'none') {{\n",
@@ -2714,7 +7826,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "018ab143",
+   "id": "4a95f0ce",
    "metadata": {},
    "source": [
     "<a id='MLX-local'></a>\n",
@@ -2723,15 +7835,15 @@
   },
   {
    "cell_type": "markdown",
-   "id": "091dc72f",
+   "id": "4c96ef2c",
    "metadata": {},
    "source": [
-    "For text classification tasks, since we first extract tokens (features), the Oracle AutoMLx solution offers a single way to compute a notion of token importance: Local Token Importance. The notion of Local Token Importance intuitively measures how much a token impacts an instance's predictions (relative to the provided train labels). This notion of token importance considers each token independently from all other tokens."
+    "For text classification tasks, since we first extract tokens (features), the Oracle AutoMLx solution offers a single way to compute a notion of token importance: Local Token Importance. The notion of Local Token Importance intuitively measures how much a token impacts an instance's predictions."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "d91325ed",
+   "id": "4697c2c9",
    "metadata": {},
    "source": [
     "#### Compute the importance"
@@ -2739,35 +7851,36 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a08a3012",
+   "id": "d85e9ca2",
    "metadata": {},
    "source": [
     "By default we use a surrogate method to successively measure the importance of each token in a given instance. Such a method therefore runs in linear time with respect to the number of features in the dataset.\n",
     "\n",
-    "The method `explain_prediction()` allows to compute such feature importances. It also provides 95% confidence intervals for each feature importance attribution."
+    "The method `explain_prediction()` allows to compute such feature importances."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "id": "4bc2a878",
+   "execution_count": 21,
+   "id": "8d0d5fcf",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-03-22T16:28:26.074293Z",
-     "iopub.status.busy": "2023-03-22T16:28:26.073799Z",
-     "iopub.status.idle": "2023-03-22T16:28:40.343332Z",
-     "shell.execute_reply": "2023-03-22T16:28:40.342649Z"
+     "iopub.execute_input": "2024-05-23T19:05:29.913850Z",
+     "iopub.status.busy": "2024-05-23T19:05:29.913135Z",
+     "iopub.status.idle": "2024-05-23T19:06:35.391095Z",
+     "shell.execute_reply": "2024-05-23T19:06:35.390303Z"
     }
    },
    "outputs": [],
    "source": [
     "index = 0\n",
-    "result_explain_prediction_default = explainer.explain_prediction(X_train.iloc[index:index + 1, :])"
+    "X_train.reset_index(drop=True, inplace=True)\n",
+    "result_explain_prediction_default = explainer.explain_prediction(X_train.iloc[index:index + 1])"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "77e984b5",
+   "id": "45c0bb14",
    "metadata": {},
    "source": [
     "There are two options to show the explanation's results:\n",
@@ -2779,7 +7892,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f2df1846",
+   "id": "7646f8c6",
    "metadata": {},
    "source": [
     "#### Visualization"
@@ -2787,14 +7900,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "id": "a7868d70",
+   "execution_count": 22,
+   "id": "77c66f1e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-03-22T16:28:40.346815Z",
-     "iopub.status.busy": "2023-03-22T16:28:40.346402Z",
-     "iopub.status.idle": "2023-03-22T16:28:40.407747Z",
-     "shell.execute_reply": "2023-03-22T16:28:40.407208Z"
+     "iopub.execute_input": "2024-05-23T19:06:35.395432Z",
+     "iopub.status.busy": "2024-05-23T19:06:35.394769Z",
+     "iopub.status.idle": "2024-05-23T19:06:37.723910Z",
+     "shell.execute_reply": "2024-05-23T19:06:37.723233Z"
     }
    },
    "outputs": [
@@ -2827,33 +7940,33 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>1.347056e-01</td>\n",
-       "      <td>space</td>\n",
-       "      <td>14</td>\n",
+       "      <td>9.653031e-01</td>\n",
+       "      <td>Turkish</td>\n",
+       "      <td>talk.politics.mideast</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>1.291750e-01</td>\n",
-       "      <td>location</td>\n",
-       "      <td>14</td>\n",
+       "      <td>1.108578e-02</td>\n",
+       "      <td>Argic</td>\n",
+       "      <td>talk.politics.mideast</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>1.062831e-01</td>\n",
-       "      <td>rattling</td>\n",
-       "      <td>14</td>\n",
+       "      <td>-5.332916e-03</td>\n",
+       "      <td>Turks</td>\n",
+       "      <td>talk.politics.mideast</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>7.680890e-02</td>\n",
-       "      <td>launch</td>\n",
-       "      <td>14</td>\n",
+       "      <td>-1.512145e-03</td>\n",
+       "      <td>000</td>\n",
+       "      <td>talk.politics.mideast</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>6.210265e-02</td>\n",
-       "      <td>wrench</td>\n",
-       "      <td>14</td>\n",
+       "      <td>-1.293691e-03</td>\n",
+       "      <td>killed</td>\n",
+       "      <td>talk.politics.mideast</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -2862,58 +7975,58 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>83</th>\n",
-       "      <td>2.211368e-09</td>\n",
-       "      <td>com</td>\n",
-       "      <td>14</td>\n",
+       "      <th>109</th>\n",
+       "      <td>8.963141e-15</td>\n",
+       "      <td>Sarikamish</td>\n",
+       "      <td>talk.politics.mideast</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>84</th>\n",
-       "      <td>-1.906403e-09</td>\n",
-       "      <td>Thiokol</td>\n",
-       "      <td>14</td>\n",
+       "      <th>110</th>\n",
+       "      <td>-7.590106e-15</td>\n",
+       "      <td>eastern</td>\n",
+       "      <td>talk.politics.mideast</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>85</th>\n",
-       "      <td>-5.362070e-10</td>\n",
-       "      <td>had</td>\n",
-       "      <td>14</td>\n",
+       "      <th>111</th>\n",
+       "      <td>-1.061330e-16</td>\n",
+       "      <td>600</td>\n",
+       "      <td>talk.politics.mideast</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>86</th>\n",
-       "      <td>9.392158e-12</td>\n",
-       "      <td>I</td>\n",
-       "      <td>14</td>\n",
+       "      <th>112</th>\n",
+       "      <td>-3.483082e-17</td>\n",
+       "      <td>26</td>\n",
+       "      <td>talk.politics.mideast</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>87</th>\n",
-       "      <td>4.174225e-12</td>\n",
-       "      <td>some</td>\n",
-       "      <td>14</td>\n",
+       "      <th>113</th>\n",
+       "      <td>-1.813207e-17</td>\n",
+       "      <td>thousand</td>\n",
+       "      <td>talk.politics.mideast</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>88 rows × 3 columns</p>\n",
+       "<p>114 rows × 3 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "     Attribution     Token  Target\n",
-       "0   1.347056e-01     space      14\n",
-       "1   1.291750e-01  location      14\n",
-       "2   1.062831e-01  rattling      14\n",
-       "3   7.680890e-02    launch      14\n",
-       "4   6.210265e-02    wrench      14\n",
-       "..           ...       ...     ...\n",
-       "83  2.211368e-09       com      14\n",
-       "84 -1.906403e-09   Thiokol      14\n",
-       "85 -5.362070e-10       had      14\n",
-       "86  9.392158e-12         I      14\n",
-       "87  4.174225e-12      some      14\n",
+       "      Attribution       Token                 Target\n",
+       "0    9.653031e-01     Turkish  talk.politics.mideast\n",
+       "1    1.108578e-02       Argic  talk.politics.mideast\n",
+       "2   -5.332916e-03       Turks  talk.politics.mideast\n",
+       "3   -1.512145e-03         000  talk.politics.mideast\n",
+       "4   -1.293691e-03      killed  talk.politics.mideast\n",
+       "..            ...         ...                    ...\n",
+       "109  8.963141e-15  Sarikamish  talk.politics.mideast\n",
+       "110 -7.590106e-15     eastern  talk.politics.mideast\n",
+       "111 -1.061330e-16         600  talk.politics.mideast\n",
+       "112 -3.483082e-17          26  talk.politics.mideast\n",
+       "113 -1.813207e-17    thousand  talk.politics.mideast\n",
        "\n",
-       "[88 rows x 3 columns]"
+       "[114 rows x 3 columns]"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2924,14 +8037,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "id": "6174fe02",
+   "execution_count": 23,
+   "id": "01f0d681",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-03-22T16:28:40.410510Z",
-     "iopub.status.busy": "2023-03-22T16:28:40.410125Z",
-     "iopub.status.idle": "2023-03-22T16:28:41.498752Z",
-     "shell.execute_reply": "2023-03-22T16:28:41.497971Z"
+     "iopub.execute_input": "2024-05-23T19:06:37.726713Z",
+     "iopub.status.busy": "2024-05-23T19:06:37.726321Z",
+     "iopub.status.idle": "2024-05-23T19:06:39.305850Z",
+     "shell.execute_reply": "2024-05-23T19:06:39.305173Z"
     }
    },
    "outputs": [
@@ -2948,7 +8061,7 @@
          "marker": {
           "color": "#626567"
          },
-         "name": "61779=negative",
+         "name": "83491=negative",
          "orientation": "h",
          "showlegend": false,
          "text": "",
@@ -2956,110 +8069,194 @@
          "type": "bar",
          "width": 0.3,
          "x": [
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\",\"thousand\",\"26\",\"600\",\"eastern\",\"Sarikamish\",\"sera\",\"We\",\"Kurds\",\"world\",\"Turkey\",\"volunteers\",\"Hassan\",\"Source\",\"more\",\"When\",\"today\",\"1914\",\"Russian\",\"for\",\"blood\",\"In\",\"Kurdish\",\"certain\",\"roads\",\"thirsty\",\"London\",\"between\",\"Arfa\",\"Soviet\",\"single\",\"armies\",\"people\",\"the\",\"these\",\"by\",\"Distribution\",\"disaster\",\"Serdar\",\"serve\",\"mountain\",\"ways\",\"zuma\",\"Armenian\",\"in\",\"being\",\"might\",\"Andranik\",\"was\",\"commanded\",\"Sahak\",\"battalions\",\"adventurer\",\"then\",\"of\",\"and\",\"vilayets\",\"1968\",\"closed\",\"passes\",\"escape\",\"as\",\"committed\",\"25\",\"hundred\",\"from\",\"excesses\",\"slaughter\",\"kinds\",\"proceeded\",\"after\",\"columns\",\"21\",\"Lines\",\"UUCP\",\"pp\",\"1920\",\"Armenia\",\"Caucasus\",\"These\",\"Subject\",\"a\",\"The\",\"there\",\"invaded\",\"One\",\"From\",\"Appressian\",\"both\",\"that\",\"extermination\",\"their\",\"Reply\",\"1915\",\"exists\",\"preceded\",\"1916\",\"work\",\"1919\",\"six\",\"than\",\"To\",\"all\",\"irregular\",\"were\",\"no\",\"Ohanus\",\"soul\",\"Melkonian\",\"longer\",\"killed\",\"000\",\"Turks\",\"Argic\",\"Turkish\"],\"side\":\"top\",\"visible\":false,\"showticklabels\":false,\"type\":\"category\"}},                        {\"responsive\": true}                    ).then(function(){\n",
        "                            \n",
-       "var gd = document.getElementById('17dd4429-afa0-4cd1-a7d2-f793c84db671');\n",
+       "var gd = document.getElementById('398bd9e2-a665-4d74-b47a-94f484b50cfa');\n",
        "var x = new MutationObserver(function (mutations, observer) {{\n",
        "        var display = window.getComputedStyle(gd).display;\n",
        "        if (!display || display === 'none') {{\n",
@@ -5164,12 +10640,11 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9aedf3b7",
+   "id": "4eaece57",
    "metadata": {},
    "source": [
     "<a id='ref'></a>\n",
     "## References\n",
-    "* More examples and details: http://automl.oraclecorp.com/\n",
     "* Oracle AutoML http://www.vldb.org/pvldb/vol13/p3166-yakovlev.pdf\n",
     "* scikit-learn https://scikit-learn.org/stable/\n",
     "* Interpretable Machine Learning https://christophm.github.io/interpretable-ml-book/\n",
diff --git a/demos/OracleAutoMLx_ExecutionEngineSetup.html b/demos/OracleAutoMLx_ExecutionEngineSetup.html
index 394b923..319ca22 100644
--- a/demos/OracleAutoMLx_ExecutionEngineSetup.html
+++ b/demos/OracleAutoMLx_ExecutionEngineSetup.html
@@ -4981,7 +4981,7 @@
   .bp3-running-text table th, table.bp3-html-table th{
     color:#182026;
     font-weight:600; }
-
+  
   .bp3-running-text table td,
   table.bp3-html-table td{
     color:#182026; }
@@ -14595,7 +14595,7 @@ <h1><font color=red>Using AutoMLx Execution engine</font></h1>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
 </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
 <p>Execution Engine Setup Notebook.</p>
-<p>Copyright © 2024, Oracle and/or its affiliates.</p>
+<p>Copyright © 2025, Oracle and/or its affiliates.</p>
 <p>Licensed under the Universal Permissive License v 1.0 as shown at <a href="https://oss.oracle.com/licenses/upl/">https://oss.oracle.com/licenses/upl/</a></p>
 
 </div>
diff --git a/demos/OracleAutoMLx_ExecutionEngineSetup.ipynb b/demos/OracleAutoMLx_ExecutionEngineSetup.ipynb
index 13e0acf..6b873b8 100644
--- a/demos/OracleAutoMLx_ExecutionEngineSetup.ipynb
+++ b/demos/OracleAutoMLx_ExecutionEngineSetup.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "93cb42a9",
+   "id": "b8a2894f",
    "metadata": {
     "lines_to_next_cell": 0
    },
@@ -16,21 +16,21 @@
   },
   {
    "cell_type": "markdown",
-   "id": "79b7e8be",
+   "id": "6e2563fb",
    "metadata": {
     "lines_to_next_cell": 0
    },
    "source": [
     " Execution Engine Setup Notebook.\n",
     "\n",
-    " Copyright © 2024, Oracle and/or its affiliates.\n",
+    " Copyright © 2025, Oracle and/or its affiliates.\n",
     "\n",
     " Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "ce38d50b",
+   "id": "8a17e91d",
    "metadata": {},
    "source": [
     " ## Overview of this Notebook\n",
@@ -45,7 +45,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "420f2021",
+   "id": "a4edb840",
    "metadata": {
     "lines_to_next_cell": 0
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@@ -73,7 +73,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ae1419da",
+   "id": "b64bea8f",
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     "lines_to_next_cell": 0
    },
@@ -85,7 +85,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e6e6aabc",
+   "id": "fde0a68b",
    "metadata": {
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    },
@@ -98,7 +98,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "902d0b6a",
+   "id": "5a50d1e6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -121,7 +121,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "985b2386",
+   "id": "12c79fa4",
    "metadata": {},
    "source": [
     " <a id='default-setup'></a>\n",
@@ -144,7 +144,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c32cc567",
+   "id": "292c0af7",
    "metadata": {
     "lines_to_next_cell": 0
    },
@@ -173,7 +173,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "31168c31",
+   "id": "97310204",
    "metadata": {},
    "source": [
     " <a id='custom-setup'></a>\n",
@@ -191,7 +191,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "dcb8d479",
+   "id": "3466a001",
    "metadata": {
     "lines_to_next_cell": 0
    },
@@ -202,7 +202,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9835d701",
+   "id": "9ef44c69",
    "metadata": {},
    "source": [
     " <a id='ray'></a>\n",
@@ -212,7 +212,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "16409de2",
+   "id": "ff8580a8",
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@@ -231,7 +231,7 @@
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-   "id": "3a600858",
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@@ -242,7 +242,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7887e2d4",
+   "id": "5a410fad",
    "metadata": {},
    "source": [
     " <a id='mult-ray'></a>\n",
@@ -272,7 +272,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c96b1786",
+   "id": "dfea82d9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -282,7 +282,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "012f9b1d",
+   "id": "e654be32",
    "metadata": {},
    "source": [
     "The following action will be to run this method on each of the worker nodes:"
@@ -291,7 +291,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b80fbdf2",
+   "id": "12a6d643",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -300,7 +300,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f0312314",
+   "id": "9dc59a15",
    "metadata": {},
    "source": [
     "This will initialize the worker node by connecting to the IP address and default port (6379, can be changed) to the head node.\n",
@@ -313,7 +313,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "bf4505bb",
+   "id": "cb50f25e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -329,7 +329,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "334924d0",
+   "id": "1e4a49df",
    "metadata": {},
    "source": [
     "TLSConfig or TLS connection requires several certificates and key files. The certificate issued by the certification authority will need to be in each of the nodes.\n",
@@ -343,7 +343,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "923de00a",
+   "id": "81527bea",
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@@ -352,7 +352,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "222be822",
+   "id": "f54e458b",
    "metadata": {},
    "source": [
     "In the worker nodes:"
@@ -361,7 +361,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c7ac10d1",
+   "id": "e74d2edf",
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    "outputs": [],
    "source": [
@@ -370,7 +370,7 @@
   },
   {
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@@ -388,7 +388,7 @@
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@@ -408,7 +408,7 @@
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@@ -427,7 +427,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "936d7077",
+   "id": "707e83df",
    "metadata": {},
    "source": [
     " <a id='automlx-ray'></a>\n",
@@ -440,7 +440,7 @@
   {
    "cell_type": "code",
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-   "id": "d027c7b0",
+   "id": "ce5fac0b",
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   },
   {
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@@ -463,7 +463,7 @@
   },
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@@ -499,7 +499,7 @@
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   },
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    "cell_type": "markdown",
-   "id": "2eda675f",
+   "id": "72a2101c",
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    "source": [
     " <a id='multiproc'></a>\n",
@@ -520,7 +520,7 @@
   {
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-   "id": "b23cbc38",
+   "id": "b326b364",
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@@ -531,7 +531,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "caf9fb0a",
+   "id": "522eae91",
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    "source": [
     " <a id='threading'></a>\n",
@@ -542,7 +542,7 @@
   {
    "cell_type": "code",
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-   "id": "f4ed8577",
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diff --git a/demos/OracleAutoMLx_Fairness.html b/demos/OracleAutoMLx_Fairness.html
index 1417524..c099a08 100644
--- a/demos/OracleAutoMLx_Fairness.html
+++ b/demos/OracleAutoMLx_Fairness.html
@@ -14622,7 +14622,7 @@ <h1><font color=red>Fairness with AutoMLx</font></h1>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
 </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
 <p>Fairness Demo notebook.</p>
-<p>Copyright © 2024, Oracle and/or its affiliates.</p>
+<p>Copyright © 2025, Oracle and/or its affiliates.</p>
 <p>Licensed under the Universal Permissive License v 1.0 as shown at <a href="https://oss.oracle.com/licenses/upl/">https://oss.oracle.com/licenses/upl/</a></p>
 
 </div>
@@ -14741,19 +14741,19 @@ <h2 id="Preliminaries">Preliminaries<a class="anchor-link" href="#Preliminaries"
 <div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[2]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">plotly.express</span> <span class="k">as</span> <span class="nn">px</span>
-<span class="kn">import</span> <span class="nn">sklearn</span>
-<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">roc_auc_score</span>
-<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">fetch_openml</span>
-<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pd</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">plotly.express</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">px</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">sklearn</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.metrics</span><span class="w"> </span><span class="kn">import</span> <span class="n">roc_auc_score</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.datasets</span><span class="w"> </span><span class="kn">import</span> <span class="n">fetch_openml</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <span class="n">train_test_split</span>
 
 <span class="c1"># Settings for plots</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">&#39;figure.figsize&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">7</span><span class="p">]</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">&#39;font.size&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">15</span>
 
-<span class="kn">import</span> <span class="nn">automlx</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">automlx</span>
 </pre></div>
 
      </div>
@@ -14878,36 +14878,36 @@ <h3 id="Train-and-evaluate-an-AutoML-model">Train and evaluate an AutoML model<a
 
 
 <div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
-<pre>[2024-12-26 05:55:08,347] [automlx.backend] Overwriting ray session directory to /tmp/ey_znjyn/ray, which will be deleted at engine shutdown. If you wish to retain ray logs, provide _temp_dir in ray_setup dict of engine_opts when initializing the AutoMLx engine.
-[2024-12-26 05:55:12,455] [automlx.interface] Dataset shape: (29304,14)
-[2024-12-26 05:55:16,591] [sanerec.autotuning.parameter] Hyperparameter epsilon autotune range is set to its validation range. This could lead to long training times
-[2024-12-26 05:55:17,123] [sanerec.autotuning.parameter] Hyperparameter repeat_quality_threshold autotune range is set to its validation range. This could lead to long training times
-[2024-12-26 05:55:17,132] [sanerec.autotuning.parameter] Hyperparameter scope autotune range is set to its validation range. This could lead to long training times
-[2024-12-26 05:55:17,206] [automlx.data_transform] Running preprocessing. Number of features: 15
-[2024-12-26 05:55:17,893] [automlx.data_transform] Preprocessing completed. Took 0.687 secs
-[2024-12-26 05:55:17,941] [automlx.process] Running Model Generation
-[2024-12-26 05:55:17,995] [automlx.process] KNeighborsClassifier is disabled. The KNeighborsClassifier model is only recommended for datasets with less than 10000 samples and 1000 features.
-[2024-12-26 05:55:17,996] [automlx.process] SVC is disabled. The SVC model is only recommended for datasets with less than 10000 samples and 1000 features.
-[2024-12-26 05:55:17,997] [automlx.process] Model Generation completed.
-[2024-12-26 05:55:18,071] [automlx.model_selection] Running Model Selection
-<span class="ansi-cyan-fg">(run pid=1608)</span> [LightGBM] [Info] Number of positive: 2000, number of negative: 2000
-<span class="ansi-cyan-fg">(run pid=1608)</span> [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000325 seconds.
-<span class="ansi-cyan-fg">(run pid=1608)</span> You can set `force_row_wise=true` to remove the overhead.
-<span class="ansi-cyan-fg">(run pid=1608)</span> And if memory is not enough, you can set `force_col_wise=true`.
-<span class="ansi-cyan-fg">(run pid=1608)</span> [LightGBM] [Info] Total Bins 391
-<span class="ansi-cyan-fg">(run pid=1608)</span> [LightGBM] [Info] Number of data points in the train set: 4000, number of used features: 15
-<span class="ansi-cyan-fg">(run pid=1608)</span> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -&gt; initscore=0.000000
-[2024-12-26 05:55:37,133] [automlx.model_selection] Model Selection completed - Took 19.062 sec - Selected models: [[&#39;XGBClassifier&#39;]]
-[2024-12-26 05:55:37,161] [automlx.adaptive_sampling] Running Adaptive Sampling. Dataset shape: (29304,16).
-[2024-12-26 05:55:39,389] [automlx.trials] Adaptive Sampling completed - Took 2.2280 sec.
-[2024-12-26 05:55:39,486] [automlx.feature_selection] Starting feature ranking for XGBClassifier
-[2024-12-26 05:55:47,233] [automlx.feature_selection] Feature Selection completed. Took 7.767 secs.
-[2024-12-26 05:55:47,292] [automlx.trials] Running Model Tuning for [&#39;XGBClassifier&#39;]
-[2024-12-26 05:56:33,713] [automlx.trials] Best parameters for XGBClassifier: {&#39;learning_rate&#39;: 0.10242113515453982, &#39;min_child_weight&#39;: 12, &#39;max_depth&#39;: 4, &#39;reg_alpha&#39;: 0, &#39;booster&#39;: &#39;gbtree&#39;, &#39;reg_lambda&#39;: 0.01878279410038923, &#39;n_estimators&#39;: 143, &#39;use_label_encoder&#39;: False}
-[2024-12-26 05:56:33,715] [automlx.trials] Model Tuning completed. Took: 46.423 secs
-[2024-12-26 05:56:40,193] [automlx.interface] Re-fitting pipeline
-[2024-12-26 05:56:40,208] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_b733852b-b
-[2024-12-26 05:56:42,465] [automlx.interface] AutoMLx completed.
+<pre>[2025-04-25 03:08:03,813] [automlx.backend] Overwriting ray session directory to /tmp/ct1h5q1c/ray, which will be deleted at engine shutdown. If you wish to retain ray logs, provide _temp_dir in ray_setup dict of engine_opts when initializing the AutoMLx engine.
+[2025-04-25 03:08:07,973] [automlx.interface] Dataset shape: (29304,14)
+[2025-04-25 03:08:12,868] [sanerec.autotuning.parameter] Hyperparameter epsilon autotune range is set to its validation range. This could lead to long training times
+[2025-04-25 03:08:13,548] [sanerec.autotuning.parameter] Hyperparameter repeat_quality_threshold autotune range is set to its validation range. This could lead to long training times
+[2025-04-25 03:08:13,561] [sanerec.autotuning.parameter] Hyperparameter scope autotune range is set to its validation range. This could lead to long training times
+[2025-04-25 03:08:13,642] [automlx.data_transform] Running preprocessing. Number of features: 15
+[2025-04-25 03:08:14,283] [automlx.data_transform] Preprocessing completed. Took 0.641 secs
+[2025-04-25 03:08:14,310] [automlx.process] Running Model Generation
+[2025-04-25 03:08:14,361] [automlx.process] KNeighborsClassifier is disabled. The KNeighborsClassifier model is only recommended for datasets with less than 10000 samples and 1000 features.
+[2025-04-25 03:08:14,361] [automlx.process] SVC is disabled. The SVC model is only recommended for datasets with less than 10000 samples and 1000 features.
+[2025-04-25 03:08:14,363] [automlx.process] Model Generation completed.
+[2025-04-25 03:08:14,432] [automlx.model_selection] Running Model Selection
+<span class="ansi-cyan-fg">(run pid=2668571)</span> [LightGBM] [Info] Number of positive: 2000, number of negative: 2000
+<span class="ansi-cyan-fg">(run pid=2668571)</span> [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000439 seconds.
+<span class="ansi-cyan-fg">(run pid=2668571)</span> You can set `force_row_wise=true` to remove the overhead.
+<span class="ansi-cyan-fg">(run pid=2668571)</span> And if memory is not enough, you can set `force_col_wise=true`.
+<span class="ansi-cyan-fg">(run pid=2668571)</span> [LightGBM] [Info] Total Bins 391
+<span class="ansi-cyan-fg">(run pid=2668571)</span> [LightGBM] [Info] Number of data points in the train set: 4000, number of used features: 15
+<span class="ansi-cyan-fg">(run pid=2668571)</span> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -&gt; initscore=0.000000
+[2025-04-25 03:08:33,792] [automlx.model_selection] Model Selection completed - Took 19.360 sec - Selected models: [[&#39;XGBClassifier&#39;]]
+[2025-04-25 03:08:33,820] [automlx.adaptive_sampling] Running Adaptive Sampling. Dataset shape: (29304,16).
+[2025-04-25 03:08:35,976] [automlx.trials] Adaptive Sampling completed - Took 2.1557 sec.
+[2025-04-25 03:08:36,067] [automlx.feature_selection] Starting feature ranking for XGBClassifier
+[2025-04-25 03:08:43,777] [automlx.feature_selection] Feature Selection completed. Took 7.726 secs.
+[2025-04-25 03:08:43,832] [automlx.trials] Running Model Tuning for [&#39;XGBClassifier&#39;]
+[2025-04-25 03:09:28,601] [automlx.trials] Best parameters for XGBClassifier: {&#39;learning_rate&#39;: 0.10242113515453982, &#39;min_child_weight&#39;: 12, &#39;max_depth&#39;: 4, &#39;reg_alpha&#39;: 0, &#39;booster&#39;: &#39;gbtree&#39;, &#39;reg_lambda&#39;: 0.01878279410038923, &#39;n_estimators&#39;: 143, &#39;use_label_encoder&#39;: False}
+[2025-04-25 03:09:28,602] [automlx.trials] Model Tuning completed. Took: 44.770 secs
+[2025-04-25 03:09:35,259] [automlx.interface] Re-fitting pipeline
+[2025-04-25 03:09:35,275] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_4c481463-9
+[2025-04-25 03:09:37,613] [automlx.interface] AutoMLx completed.
 </pre>
 </div>
 </div>
@@ -14921,7 +14921,7 @@ <h3 id="Train-and-evaluate-an-AutoML-model">Train and evaluate an AutoML model<a
 
 
 <div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
-<pre>&lt;automlx._interface.classifier.AutoClassifier at 0x1542f1b3ee80&gt;</pre>
+<pre>&lt;automlx._interface.classifier.AutoClassifier at 0x150a49d219a0&gt;</pre>
 </div>
 
 </div>
@@ -14993,7 +14993,7 @@ <h3 id="Evaluate-Fairness-of-the-Model-by-Computing-Statistical-Parity">Evaluate
 <div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[6]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">automlx.fairness.metrics</span> <span class="kn">import</span> <span class="n">ModelStatisticalParityScorer</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">automlx.fairness.metrics</span><span class="w"> </span><span class="kn">import</span> <span class="n">ModelStatisticalParityScorer</span>
 
 <span class="n">fairness_score</span> <span class="o">=</span> <span class="n">ModelStatisticalParityScorer</span><span class="p">(</span><span class="n">protected_attributes</span><span class="o">=</span><span class="s1">&#39;sex&#39;</span><span class="p">)</span>
 <span class="n">parity_test_model</span> <span class="o">=</span> <span class="n">fairness_score</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">X_test</span><span class="p">)</span>
@@ -15127,9 +15127,9 @@ <h3 id="Compute-Fairness-Feature-Importance">Compute Fairness Feature Importance
 
 
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@@ -15181,7 +15181,7 @@ <h3 id="Mitigating-a-Model's-Unintended-Bias">Mitigating a Model's Unintended Bi
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@@ -16224,8 +16224,8 @@ <h5 id="Train-a-Model-Using-Scikit-learn">Train a Model Using Scikit-learn<a cla
 <div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[20]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">RandomForestClassifier</span>
-<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">OneHotEncoder</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.ensemble</span><span class="w"> </span><span class="kn">import</span> <span class="n">RandomForestClassifier</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.preprocessing</span><span class="w"> </span><span class="kn">import</span> <span class="n">OneHotEncoder</span>
 
 <span class="n">sklearn_model</span> <span class="o">=</span> <span class="n">sklearn</span><span class="o">.</span><span class="n">pipeline</span><span class="o">.</span><span class="n">Pipeline</span><span class="p">(</span>
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@@ -16415,25 +16415,25 @@ <h5 id="Train-a-Model-Using-AutoML">Train a Model Using AutoML<a class="anchor-l
 
 
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-<pre>[2024-12-26 05:58:48,435] [automlx.interface] Dataset shape: (29304,14)
-[2024-12-26 05:58:48,543] [automlx.data_transform] Running preprocessing. Number of features: 15
-[2024-12-26 05:58:49,006] [automlx.data_transform] Preprocessing completed. Took 0.462 secs
-[2024-12-26 05:58:49,046] [automlx.process] Running Model Generation
-[2024-12-26 05:58:49,098] [automlx.process] KNeighborsClassifier is disabled. The KNeighborsClassifier model is only recommended for datasets with less than 10000 samples and 1000 features.
-[2024-12-26 05:58:49,099] [automlx.process] SVC is disabled. The SVC model is only recommended for datasets with less than 10000 samples and 1000 features.
-[2024-12-26 05:58:49,101] [automlx.process] Model Generation completed.
-[2024-12-26 05:58:49,184] [automlx.model_selection] Running Model Selection
-[2024-12-26 05:59:04,249] [automlx.model_selection] Model Selection completed - Took 15.065 sec - Selected models: [[&#39;XGBClassifier&#39;]]
-[2024-12-26 05:59:04,279] [automlx.adaptive_sampling] Running Adaptive Sampling. Dataset shape: (29304,16).
-[2024-12-26 05:59:06,388] [automlx.trials] Adaptive Sampling completed - Took 2.1095 sec.
-[2024-12-26 05:59:06,488] [automlx.feature_selection] Starting feature ranking for XGBClassifier
-[2024-12-26 05:59:14,300] [automlx.feature_selection] Feature Selection completed. Took 7.832 secs.
-[2024-12-26 05:59:14,343] [automlx.trials] Running Model Tuning for [&#39;XGBClassifier&#39;]
-[2024-12-26 06:00:01,661] [automlx.trials] Best parameters for XGBClassifier: {&#39;learning_rate&#39;: 0.10242113515453982, &#39;min_child_weight&#39;: 12, &#39;max_depth&#39;: 4, &#39;reg_alpha&#39;: 0, &#39;booster&#39;: &#39;gbtree&#39;, &#39;reg_lambda&#39;: 0.01878279410038923, &#39;n_estimators&#39;: 143, &#39;use_label_encoder&#39;: False}
-[2024-12-26 06:00:01,663] [automlx.trials] Model Tuning completed. Took: 47.320 secs
-[2024-12-26 06:00:08,621] [automlx.interface] Re-fitting pipeline
-[2024-12-26 06:00:08,636] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_0253a656-3
-[2024-12-26 06:00:10,353] [automlx.interface] AutoMLx completed.
+<pre>[2025-04-25 03:11:35,769] [automlx.interface] Dataset shape: (29304,14)
+[2025-04-25 03:11:35,874] [automlx.data_transform] Running preprocessing. Number of features: 15
+[2025-04-25 03:11:36,280] [automlx.data_transform] Preprocessing completed. Took 0.406 secs
+[2025-04-25 03:11:36,306] [automlx.process] Running Model Generation
+[2025-04-25 03:11:36,357] [automlx.process] KNeighborsClassifier is disabled. The KNeighborsClassifier model is only recommended for datasets with less than 10000 samples and 1000 features.
+[2025-04-25 03:11:36,357] [automlx.process] SVC is disabled. The SVC model is only recommended for datasets with less than 10000 samples and 1000 features.
+[2025-04-25 03:11:36,358] [automlx.process] Model Generation completed.
+[2025-04-25 03:11:36,430] [automlx.model_selection] Running Model Selection
+[2025-04-25 03:11:53,842] [automlx.model_selection] Model Selection completed - Took 17.412 sec - Selected models: [[&#39;XGBClassifier&#39;]]
+[2025-04-25 03:11:53,871] [automlx.adaptive_sampling] Running Adaptive Sampling. Dataset shape: (29304,16).
+[2025-04-25 03:11:55,656] [automlx.trials] Adaptive Sampling completed - Took 1.7846 sec.
+[2025-04-25 03:11:55,771] [automlx.feature_selection] Starting feature ranking for XGBClassifier
+[2025-04-25 03:12:03,593] [automlx.feature_selection] Feature Selection completed. Took 7.839 secs.
+[2025-04-25 03:12:03,647] [automlx.trials] Running Model Tuning for [&#39;XGBClassifier&#39;]
+[2025-04-25 03:12:49,388] [automlx.trials] Best parameters for XGBClassifier: {&#39;learning_rate&#39;: 0.10242113515453982, &#39;min_child_weight&#39;: 12, &#39;max_depth&#39;: 4, &#39;reg_alpha&#39;: 0, &#39;booster&#39;: &#39;gbtree&#39;, &#39;reg_lambda&#39;: 0.01878279410038923, &#39;n_estimators&#39;: 143, &#39;use_label_encoder&#39;: False}
+[2025-04-25 03:12:49,389] [automlx.trials] Model Tuning completed. Took: 45.742 secs
+[2025-04-25 03:12:56,342] [automlx.interface] Re-fitting pipeline
+[2025-04-25 03:12:56,357] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_2070da66-2
+[2025-04-25 03:12:58,079] [automlx.interface] AutoMLx completed.
 </pre>
 </div>
 </div>
@@ -16447,7 +16447,7 @@ <h5 id="Train-a-Model-Using-AutoML">Train a Model Using AutoML<a class="anchor-l
 
 
 <div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
-<pre>&lt;automlx._interface.classifier.AutoClassifier at 0x153aa9421760&gt;</pre>
+<pre>&lt;automlx._interface.classifier.AutoClassifier at 0x1501d42f5f40&gt;</pre>
 </div>
 
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@@ -16680,7 +16680,7 @@ <h5 id="Train-a-Model-Using-AutoML">Train a Model Using AutoML<a class="anchor-l
 <div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[27]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">automlx.fairness.metrics</span> <span class="kn">import</span> <span class="n">model_statistical_parity</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">automlx.fairness.metrics</span><span class="w"> </span><span class="kn">import</span> <span class="n">model_statistical_parity</span>
 
 <span class="n">y_pred</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
 <span class="n">subgroups</span> <span class="o">=</span> <span class="n">X_test</span><span class="p">[[</span><span class="s1">&#39;sex&#39;</span><span class="p">]]</span>
@@ -16739,7 +16739,7 @@ <h4 id="Measure-the-Compliance-of-the-True-Labels-of-a-Dataset-with-a-Fairness-M
 <div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[28]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">automlx.fairness.metrics</span> <span class="kn">import</span> <span class="n">DatasetStatisticalParityScorer</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">automlx.fairness.metrics</span><span class="w"> </span><span class="kn">import</span> <span class="n">DatasetStatisticalParityScorer</span>
 
 <span class="n">DSPS</span> <span class="o">=</span> <span class="n">DatasetStatisticalParityScorer</span><span class="p">(</span><span class="n">protected_attributes</span><span class="o">=</span><span class="s1">&#39;sex&#39;</span><span class="p">)</span>
 
@@ -16771,7 +16771,7 @@ <h4 id="Measure-the-Compliance-of-the-True-Labels-of-a-Dataset-with-a-Fairness-M
 <div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[29]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">automlx.fairness.metrics</span> <span class="kn">import</span> <span class="n">dataset_statistical_parity</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">automlx.fairness.metrics</span><span class="w"> </span><span class="kn">import</span> <span class="n">dataset_statistical_parity</span>
 
 <span class="n">subgroups</span> <span class="o">=</span> <span class="n">X_test</span><span class="p">[[</span><span class="s1">&#39;sex&#39;</span><span class="p">]]</span>
 
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@@ -16913,7 +16913,7 @@ <h4 id="Other-Fairness-Metrics">Other Fairness Metrics<a class="anchor-link" hre
 <div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[31]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">automlx.fairness.metrics</span> <span class="kn">import</span> <span class="n">EqualizedOddsScorer</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">automlx.fairness.metrics</span><span class="w"> </span><span class="kn">import</span> <span class="n">EqualizedOddsScorer</span>
 
 <span class="n">fairness_score</span> <span class="o">=</span> <span class="n">EqualizedOddsScorer</span><span class="p">(</span><span class="n">protected_attributes</span><span class="o">=</span><span class="s1">&#39;sex&#39;</span><span class="p">,</span> <span class="n">distance_measure</span><span class="o">=</span><span class="s1">&#39;diff&#39;</span><span class="p">)</span>
 <span class="n">EO_original</span> <span class="o">=</span> <span class="n">fairness_score</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
@@ -17021,7 +17021,7 @@ <h4 id="Other-Fairness-Metrics">Other Fairness Metrics<a class="anchor-link" hre
 <div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[33]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">automlx.fairness.metrics</span> <span class="kn">import</span> <span class="n">smoothed_edf</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">automlx.fairness.metrics</span><span class="w"> </span><span class="kn">import</span> <span class="n">smoothed_edf</span>
 
 <span class="n">subgroups</span> <span class="o">=</span> <span class="n">X_train</span><span class="p">[[</span><span class="s1">&#39;race&#39;</span><span class="p">,</span> <span class="s1">&#39;sex&#39;</span><span class="p">]]</span>
 <span class="n">smoothed_edf_score</span> <span class="o">=</span> <span class="n">smoothed_edf</span><span class="p">(</span><span class="n">y_train</span><span class="p">,</span> <span class="n">subgroups</span><span class="p">)</span>
@@ -17179,9 +17179,9 @@ <h3 id="Model-Explanations-(Global-Feature-Importance)">Model Explanations (Glob
 
 
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@@ -17258,9 +17258,9 @@ <h3 id="Model-Fairness-Explanations-(Fairness-Feature-Importance)">Model Fairnes
 
 
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@@ -17326,7 +17326,7 @@ <h3 id="Global-vs-Fairness-Feature-Importance">Global vs Fairness Feature Import
 <div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[38]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">compare</span><span class="p">(</span><span class="n">global_exp</span><span class="p">,</span> <span class="n">fairness_exp</span><span class="p">):</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span><span class="w"> </span><span class="nf">compare</span><span class="p">(</span><span class="n">global_exp</span><span class="p">,</span> <span class="n">fairness_exp</span><span class="p">):</span>
     <span class="n">dfg</span> <span class="o">=</span> <span class="n">global_exp</span><span class="o">.</span><span class="n">to_dataframe</span><span class="p">()</span>
     <span class="n">dff</span> <span class="o">=</span> <span class="n">fairness_exp</span><span class="o">.</span><span class="n">to_dataframe</span><span class="p">()</span>
 
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@@ -17469,7 +17469,7 @@ <h1 id="Model-Bias-Mitigation">Model Bias Mitigation<a class="anchor-link" href=
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 <span class="n">bias_mitigated_model</span> <span class="o">=</span> <span class="n">ModelBiasMitigator</span><span class="p">(</span>
     <span class="n">model</span><span class="p">,</span>
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@@ -17592,7 +17592,7 @@ <h1 id="Model-Bias-Mitigation">Model Bias Mitigation<a class="anchor-link" href=
 
 
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+<pre>&lt;automlx.fairness.bias_mitigation._sklearn.ModelBiasMitigator at 0x1501cf1e32b0&gt;</pre>
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diff --git a/demos/OracleAutoMLx_Fairness.ipynb b/demos/OracleAutoMLx_Fairness.ipynb
index da231e5..e0d5dbf 100644
--- a/demos/OracleAutoMLx_Fairness.ipynb
+++ b/demos/OracleAutoMLx_Fairness.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "b7117866",
+   "id": "dd500224",
    "metadata": {},
    "source": [
     "***\n",
@@ -14,19 +14,19 @@
   },
   {
    "cell_type": "markdown",
-   "id": "000106f8",
+   "id": "a3b16238",
    "metadata": {},
    "source": [
     "Fairness Demo notebook.\n",
     "\n",
-    "Copyright © 2024, Oracle and/or its affiliates.\n",
+    "Copyright © 2025, Oracle and/or its affiliates.\n",
     "\n",
     "Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "8eba3bb1",
+   "id": "e5af43bf",
    "metadata": {},
    "source": [
     "## Overview of this Notebook\n",
@@ -39,7 +39,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7e526dbc",
+   "id": "5b5cb8bd",
    "metadata": {},
    "source": [
     "## Table of Contents\n",
@@ -71,7 +71,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b3668617",
+   "id": "dc8a98f4",
    "metadata": {},
    "source": [
     "<a id='pre'></a>\n",
@@ -83,13 +83,13 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "9615a60d",
+   "id": "1cd3ca15",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:55:03.143684Z",
-     "iopub.status.busy": "2024-12-26T13:55:03.143352Z",
-     "iopub.status.idle": "2024-12-26T13:55:04.067033Z",
-     "shell.execute_reply": "2024-12-26T13:55:04.066292Z"
+     "iopub.execute_input": "2025-04-25T10:07:58.558358Z",
+     "iopub.status.busy": "2025-04-25T10:07:58.558054Z",
+     "iopub.status.idle": "2025-04-25T10:07:59.533770Z",
+     "shell.execute_reply": "2025-04-25T10:07:59.533221Z"
     }
    },
    "outputs": [],
@@ -102,7 +102,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9ccd2314",
+   "id": "e51713c2",
    "metadata": {},
    "source": [
     "Load the required modules."
@@ -111,13 +111,13 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "c5054c0b",
+   "id": "f9684cc6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:55:04.069359Z",
-     "iopub.status.busy": "2024-12-26T13:55:04.069046Z",
-     "iopub.status.idle": "2024-12-26T13:55:07.199554Z",
-     "shell.execute_reply": "2024-12-26T13:55:07.198874Z"
+     "iopub.execute_input": "2025-04-25T10:07:59.535956Z",
+     "iopub.status.busy": "2025-04-25T10:07:59.535670Z",
+     "iopub.status.idle": "2025-04-25T10:08:02.653664Z",
+     "shell.execute_reply": "2025-04-25T10:08:02.653083Z"
     }
    },
    "outputs": [],
@@ -139,7 +139,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "29cce97b",
+   "id": "ab459d45",
    "metadata": {},
    "source": [
     "<a id='quick'></a>\n",
@@ -153,13 +153,13 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "a9e0ce8d",
+   "id": "8b7d328e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:55:07.202223Z",
-     "iopub.status.busy": "2024-12-26T13:55:07.201441Z",
-     "iopub.status.idle": "2024-12-26T13:55:08.000592Z",
-     "shell.execute_reply": "2024-12-26T13:55:07.999927Z"
+     "iopub.execute_input": "2025-04-25T10:08:02.656232Z",
+     "iopub.status.busy": "2025-04-25T10:08:02.655620Z",
+     "iopub.status.idle": "2025-04-25T10:08:03.461950Z",
+     "shell.execute_reply": "2025-04-25T10:08:03.461444Z"
     }
    },
    "outputs": [
@@ -198,7 +198,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "08e0f74c",
+   "id": "05550545",
    "metadata": {},
    "source": [
     "<a id='quick-train'></a>\n",
@@ -209,13 +209,13 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "9f36f6ff",
+   "id": "88f4f620",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:55:08.002830Z",
-     "iopub.status.busy": "2024-12-26T13:55:08.002210Z",
-     "iopub.status.idle": "2024-12-26T13:56:43.997276Z",
-     "shell.execute_reply": "2024-12-26T13:56:43.996486Z"
+     "iopub.execute_input": "2025-04-25T10:08:03.464094Z",
+     "iopub.status.busy": "2025-04-25T10:08:03.463628Z",
+     "iopub.status.idle": "2025-04-25T10:09:39.431745Z",
+     "shell.execute_reply": "2025-04-25T10:09:39.431019Z"
     }
    },
    "outputs": [
@@ -223,180 +223,180 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:55:08,347] [automlx.backend] Overwriting ray session directory to /tmp/ey_znjyn/ray, which will be deleted at engine shutdown. If you wish to retain ray logs, provide _temp_dir in ray_setup dict of engine_opts when initializing the AutoMLx engine.\n"
+      "[2025-04-25 03:08:03,813] [automlx.backend] Overwriting ray session directory to /tmp/ct1h5q1c/ray, which will be deleted at engine shutdown. If you wish to retain ray logs, provide _temp_dir in ray_setup dict of engine_opts when initializing the AutoMLx engine.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:55:12,455] [automlx.interface] Dataset shape: (29304,14)\n"
+      "[2025-04-25 03:08:07,973] [automlx.interface] Dataset shape: (29304,14)\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:55:16,591] [sanerec.autotuning.parameter] Hyperparameter epsilon autotune range is set to its validation range. This could lead to long training times\n"
+      "[2025-04-25 03:08:12,868] [sanerec.autotuning.parameter] Hyperparameter epsilon autotune range is set to its validation range. This could lead to long training times\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:55:17,123] [sanerec.autotuning.parameter] Hyperparameter repeat_quality_threshold autotune range is set to its validation range. This could lead to long training times\n"
+      "[2025-04-25 03:08:13,548] [sanerec.autotuning.parameter] Hyperparameter repeat_quality_threshold autotune range is set to its validation range. This could lead to long training times\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:55:17,132] [sanerec.autotuning.parameter] Hyperparameter scope autotune range is set to its validation range. This could lead to long training times\n"
+      "[2025-04-25 03:08:13,561] [sanerec.autotuning.parameter] Hyperparameter scope autotune range is set to its validation range. This could lead to long training times\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:55:17,206] [automlx.data_transform] Running preprocessing. Number of features: 15\n"
+      "[2025-04-25 03:08:13,642] [automlx.data_transform] Running preprocessing. Number of features: 15\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:55:17,893] [automlx.data_transform] Preprocessing completed. Took 0.687 secs\n"
+      "[2025-04-25 03:08:14,283] [automlx.data_transform] Preprocessing completed. Took 0.641 secs\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:55:17,941] [automlx.process] Running Model Generation\n"
+      "[2025-04-25 03:08:14,310] [automlx.process] Running Model Generation\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:55:17,995] [automlx.process] KNeighborsClassifier is disabled. The KNeighborsClassifier model is only recommended for datasets with less than 10000 samples and 1000 features.\n"
+      "[2025-04-25 03:08:14,361] [automlx.process] KNeighborsClassifier is disabled. The KNeighborsClassifier model is only recommended for datasets with less than 10000 samples and 1000 features.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:55:17,996] [automlx.process] SVC is disabled. The SVC model is only recommended for datasets with less than 10000 samples and 1000 features.\n"
+      "[2025-04-25 03:08:14,361] [automlx.process] SVC is disabled. The SVC model is only recommended for datasets with less than 10000 samples and 1000 features.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:55:17,997] [automlx.process] Model Generation completed.\n"
+      "[2025-04-25 03:08:14,363] [automlx.process] Model Generation completed.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:55:18,071] [automlx.model_selection] Running Model Selection\n"
+      "[2025-04-25 03:08:14,432] [automlx.model_selection] Running Model Selection\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[36m(run pid=1608)\u001b[0m [LightGBM] [Info] Number of positive: 2000, number of negative: 2000\n",
-      "\u001b[36m(run pid=1608)\u001b[0m [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000325 seconds.\n",
-      "\u001b[36m(run pid=1608)\u001b[0m You can set `force_row_wise=true` to remove the overhead.\n",
-      "\u001b[36m(run pid=1608)\u001b[0m And if memory is not enough, you can set `force_col_wise=true`.\n",
-      "\u001b[36m(run pid=1608)\u001b[0m [LightGBM] [Info] Total Bins 391\n",
-      "\u001b[36m(run pid=1608)\u001b[0m [LightGBM] [Info] Number of data points in the train set: 4000, number of used features: 15\n",
-      "\u001b[36m(run pid=1608)\u001b[0m [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n"
+      "\u001b[36m(run pid=2668571)\u001b[0m [LightGBM] [Info] Number of positive: 2000, number of negative: 2000\n",
+      "\u001b[36m(run pid=2668571)\u001b[0m [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000439 seconds.\n",
+      "\u001b[36m(run pid=2668571)\u001b[0m You can set `force_row_wise=true` to remove the overhead.\n",
+      "\u001b[36m(run pid=2668571)\u001b[0m And if memory is not enough, you can set `force_col_wise=true`.\n",
+      "\u001b[36m(run pid=2668571)\u001b[0m [LightGBM] [Info] Total Bins 391\n",
+      "\u001b[36m(run pid=2668571)\u001b[0m [LightGBM] [Info] Number of data points in the train set: 4000, number of used features: 15\n",
+      "\u001b[36m(run pid=2668571)\u001b[0m [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:55:37,133] [automlx.model_selection] Model Selection completed - Took 19.062 sec - Selected models: [['XGBClassifier']]\n"
+      "[2025-04-25 03:08:33,792] [automlx.model_selection] Model Selection completed - Took 19.360 sec - Selected models: [['XGBClassifier']]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:55:37,161] [automlx.adaptive_sampling] Running Adaptive Sampling. Dataset shape: (29304,16).\n"
+      "[2025-04-25 03:08:33,820] [automlx.adaptive_sampling] Running Adaptive Sampling. Dataset shape: (29304,16).\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:55:39,389] [automlx.trials] Adaptive Sampling completed - Took 2.2280 sec.\n"
+      "[2025-04-25 03:08:35,976] [automlx.trials] Adaptive Sampling completed - Took 2.1557 sec.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:55:39,486] [automlx.feature_selection] Starting feature ranking for XGBClassifier\n"
+      "[2025-04-25 03:08:36,067] [automlx.feature_selection] Starting feature ranking for XGBClassifier\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:55:47,233] [automlx.feature_selection] Feature Selection completed. Took 7.767 secs.\n"
+      "[2025-04-25 03:08:43,777] [automlx.feature_selection] Feature Selection completed. Took 7.726 secs.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:55:47,292] [automlx.trials] Running Model Tuning for ['XGBClassifier']\n"
+      "[2025-04-25 03:08:43,832] [automlx.trials] Running Model Tuning for ['XGBClassifier']\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:56:33,713] [automlx.trials] Best parameters for XGBClassifier: {'learning_rate': 0.10242113515453982, 'min_child_weight': 12, 'max_depth': 4, 'reg_alpha': 0, 'booster': 'gbtree', 'reg_lambda': 0.01878279410038923, 'n_estimators': 143, 'use_label_encoder': False}\n"
+      "[2025-04-25 03:09:28,601] [automlx.trials] Best parameters for XGBClassifier: {'learning_rate': 0.10242113515453982, 'min_child_weight': 12, 'max_depth': 4, 'reg_alpha': 0, 'booster': 'gbtree', 'reg_lambda': 0.01878279410038923, 'n_estimators': 143, 'use_label_encoder': False}\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:56:33,715] [automlx.trials] Model Tuning completed. Took: 46.423 secs\n"
+      "[2025-04-25 03:09:28,602] [automlx.trials] Model Tuning completed. Took: 44.770 secs\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:56:40,193] [automlx.interface] Re-fitting pipeline\n"
+      "[2025-04-25 03:09:35,259] [automlx.interface] Re-fitting pipeline\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:56:40,208] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_b733852b-b\n"
+      "[2025-04-25 03:09:35,275] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_4c481463-9\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:56:42,465] [automlx.interface] AutoMLx completed.\n"
+      "[2025-04-25 03:09:37,613] [automlx.interface] AutoMLx completed.\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<automlx._interface.classifier.AutoClassifier at 0x1542f1b3ee80>"
+       "<automlx._interface.classifier.AutoClassifier at 0x150a49d219a0>"
       ]
      },
      "execution_count": 4,
@@ -412,13 +412,13 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "9ffd26ec",
+   "id": "e7bea093",
    "metadata": {
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-     "iopub.execute_input": "2024-12-26T13:56:44.000063Z",
-     "iopub.status.busy": "2024-12-26T13:56:43.999408Z",
-     "iopub.status.idle": "2024-12-26T13:56:45.227443Z",
-     "shell.execute_reply": "2024-12-26T13:56:45.226883Z"
+     "iopub.execute_input": "2025-04-25T10:09:39.434213Z",
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+     "iopub.status.idle": "2025-04-25T10:09:41.521223Z",
+     "shell.execute_reply": "2025-04-25T10:09:41.520693Z"
     }
    },
    "outputs": [
@@ -439,7 +439,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "91adfe8f",
+   "id": "effd4f15",
    "metadata": {},
    "source": [
     "<a id='quick-fairness'></a>\n",
@@ -450,13 +450,13 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "29dd334a",
+   "id": "39b70f68",
    "metadata": {
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-     "iopub.execute_input": "2024-12-26T13:56:45.229599Z",
-     "iopub.status.busy": "2024-12-26T13:56:45.229149Z",
-     "iopub.status.idle": "2024-12-26T13:56:46.604955Z",
-     "shell.execute_reply": "2024-12-26T13:56:46.604249Z"
+     "iopub.execute_input": "2025-04-25T10:09:41.523172Z",
+     "iopub.status.busy": "2025-04-25T10:09:41.522807Z",
+     "iopub.status.idle": "2025-04-25T10:09:43.373430Z",
+     "shell.execute_reply": "2025-04-25T10:09:43.372877Z"
     }
    },
    "outputs": [
@@ -478,7 +478,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "23783a3f",
+   "id": "e31379ee",
    "metadata": {},
    "source": [
     "<a id='quick-importance'></a>\n",
@@ -489,13 +489,13 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "5fba4e38",
+   "id": "cdee2eba",
    "metadata": {
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-     "iopub.execute_input": "2024-12-26T13:56:46.606958Z",
-     "iopub.status.busy": "2024-12-26T13:56:46.606583Z",
-     "iopub.status.idle": "2024-12-26T13:56:46.676414Z",
-     "shell.execute_reply": "2024-12-26T13:56:46.675758Z"
+     "iopub.execute_input": "2025-04-25T10:09:43.375397Z",
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+     "iopub.status.idle": "2025-04-25T10:09:44.118367Z",
+     "shell.execute_reply": "2025-04-25T10:09:44.117801Z"
     }
    },
    "outputs": [],
@@ -510,13 +510,13 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "8140aa9a",
+   "id": "dd511c90",
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-     "iopub.execute_input": "2024-12-26T13:56:46.678316Z",
-     "iopub.status.busy": "2024-12-26T13:56:46.677917Z",
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-     "shell.execute_reply": "2024-12-26T13:57:05.398745Z"
+     "iopub.execute_input": "2025-04-25T10:09:44.120584Z",
+     "iopub.status.busy": "2025-04-25T10:09:44.120184Z",
+     "iopub.status.idle": "2025-04-25T10:10:02.706390Z",
+     "shell.execute_reply": "2025-04-25T10:10:02.705839Z"
     }
    },
    "outputs": [
@@ -559,8 +559,7 @@
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-          "education",
+          "education-num",
+          "occupation",
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+          "capitalloss",
+          "hoursperweek",
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@@ -1494,9 +1494,9 @@
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@@ -1533,7 +1533,7 @@
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    "cell_type": "markdown",
-   "id": "ccb73da8",
+   "id": "91f9d183",
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@@ -1544,13 +1544,13 @@
   {
    "cell_type": "code",
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-   "id": "e2071b3e",
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-     "shell.execute_reply": "2024-12-26T13:57:10.042024Z"
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@@ -1568,7 +1568,7 @@
   },
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    "cell_type": "markdown",
-   "id": "011ae630",
+   "id": "4f55e9b0",
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     "The `ModelBiasMitigator` can be called with the usual `scikit-learn` interface, notably being trained with a single call to `fit`."
@@ -1577,20 +1577,20 @@
   {
    "cell_type": "code",
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-   "id": "459a2efe",
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-     "shell.execute_reply": "2024-12-26T13:57:23.667797Z"
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@@ -1618,7 +1618,7 @@
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@@ -1627,13 +1627,13 @@
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-   "id": "3c262be9",
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@@ -1654,7 +1654,7 @@
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   {
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-   "id": "d8d77b58",
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@@ -1663,13 +1663,13 @@
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        "                            \n",
-       "var gd = document.getElementById('558f541c-c97d-4c4a-a973-84ace8c5beeb');\n",
+       "var gd = document.getElementById('b4540829-5dc8-4b9e-b06e-e0bc47ab1d38');\n",
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@@ -2677,7 +2677,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d7aa4e56",
+   "id": "9a156af5",
    "metadata": {},
    "source": [
     "A summary of these models can be accessed as below."
@@ -2686,13 +2686,13 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "6083f176",
+   "id": "3a1d88e4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:57:24.207868Z",
-     "iopub.status.busy": "2024-12-26T13:57:24.207451Z",
-     "iopub.status.idle": "2024-12-26T13:57:24.270043Z",
-     "shell.execute_reply": "2024-12-26T13:57:24.269405Z"
+     "iopub.execute_input": "2025-04-25T10:10:22.803906Z",
+     "iopub.status.busy": "2025-04-25T10:10:22.803574Z",
+     "iopub.status.idle": "2025-04-25T10:10:22.863050Z",
+     "shell.execute_reply": "2025-04-25T10:10:22.862563Z"
     }
    },
    "outputs": [
@@ -2862,7 +2862,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e435639c",
+   "id": "73e9efbc",
    "metadata": {},
    "source": [
     "Each of these models can be selected as the final bias-mitigated model and inference can be performed like before. For example, the model with `index=1` can be selected as below."
@@ -2871,13 +2871,13 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "b08353fe",
+   "id": "773a9c7a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:57:24.272073Z",
-     "iopub.status.busy": "2024-12-26T13:57:24.271653Z",
-     "iopub.status.idle": "2024-12-26T13:57:24.324521Z",
-     "shell.execute_reply": "2024-12-26T13:57:24.323906Z"
+     "iopub.execute_input": "2025-04-25T10:10:22.864808Z",
+     "iopub.status.busy": "2025-04-25T10:10:22.864448Z",
+     "iopub.status.idle": "2025-04-25T10:10:22.916511Z",
+     "shell.execute_reply": "2025-04-25T10:10:22.915985Z"
     }
    },
    "outputs": [],
@@ -2887,7 +2887,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6e4db098",
+   "id": "01e0d742",
    "metadata": {},
    "source": [
     "<a id='load-data'></a>\n",
@@ -2898,13 +2898,13 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "6300398c",
+   "id": "ff0f5c3a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:57:24.326573Z",
-     "iopub.status.busy": "2024-12-26T13:57:24.326183Z",
-     "iopub.status.idle": "2024-12-26T13:57:25.046656Z",
-     "shell.execute_reply": "2024-12-26T13:57:25.045874Z"
+     "iopub.execute_input": "2025-04-25T10:10:22.918347Z",
+     "iopub.status.busy": "2025-04-25T10:10:22.918021Z",
+     "iopub.status.idle": "2025-04-25T10:10:23.627581Z",
+     "shell.execute_reply": "2025-04-25T10:10:23.626953Z"
     }
    },
    "outputs": [],
@@ -2915,7 +2915,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4e524c82",
+   "id": "01f0a5d8",
    "metadata": {},
    "source": [
     "Lets look at a few of the values in the data"
@@ -2924,13 +2924,13 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "8127ee05",
+   "id": "01be9ffa",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:57:25.048887Z",
-     "iopub.status.busy": "2024-12-26T13:57:25.048447Z",
-     "iopub.status.idle": "2024-12-26T13:57:25.113158Z",
-     "shell.execute_reply": "2024-12-26T13:57:25.112475Z"
+     "iopub.execute_input": "2025-04-25T10:10:23.629519Z",
+     "iopub.status.busy": "2025-04-25T10:10:23.629224Z",
+     "iopub.status.idle": "2025-04-25T10:10:23.696481Z",
+     "shell.execute_reply": "2025-04-25T10:10:23.695957Z"
     }
    },
    "outputs": [
@@ -3095,7 +3095,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "68f67602",
+   "id": "166805e2",
    "metadata": {},
    "source": [
     "The Adult dataset contains a mix of numerical and string data, making it a challenging problem to train machine learning models on."
@@ -3103,7 +3103,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b1de8cc1",
+   "id": "d1f3aeaf",
    "metadata": {},
    "source": [
     "We visualize the distribution of the target variable in the training data."
@@ -3112,13 +3112,13 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "c7dcd0b4",
+   "id": "3b00b1be",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:57:25.115189Z",
-     "iopub.status.busy": "2024-12-26T13:57:25.114799Z",
-     "iopub.status.idle": "2024-12-26T13:57:25.966338Z",
-     "shell.execute_reply": "2024-12-26T13:57:25.965768Z"
+     "iopub.execute_input": "2025-04-25T10:10:23.698422Z",
+     "iopub.status.busy": "2025-04-25T10:10:23.697957Z",
+     "iopub.status.idle": "2025-04-25T10:10:24.727732Z",
+     "shell.execute_reply": "2025-04-25T10:10:24.727166Z"
     }
    },
    "outputs": [
@@ -52840,9 +52840,9 @@
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        "                            \n",
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@@ -52881,7 +52881,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a649cfb5",
+   "id": "7bcc2f52",
    "metadata": {},
    "source": [
     "We now visualize the distribution of the target variable conditioned on values of `sex`. We can already see some biases in this dataset."
@@ -52890,13 +52890,13 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "d1f77e6d",
+   "id": "119ae459",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:57:25.979143Z",
-     "iopub.status.busy": "2024-12-26T13:57:25.978601Z",
-     "iopub.status.idle": "2024-12-26T13:57:26.124349Z",
-     "shell.execute_reply": "2024-12-26T13:57:26.123715Z"
+     "iopub.execute_input": "2025-04-25T10:10:24.740668Z",
+     "iopub.status.busy": "2025-04-25T10:10:24.740394Z",
+     "iopub.status.idle": "2025-04-25T10:10:24.872329Z",
+     "shell.execute_reply": "2025-04-25T10:10:24.871758Z"
     }
    },
    "outputs": [
@@ -53812,9 +53812,9 @@
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        "                            \n",
-       "var gd = document.getElementById('d83dc3df-fcd6-457c-86e6-149be54075ab');\n",
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@@ -53856,7 +53856,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2856b4ad",
+   "id": "1da1cdc5",
    "metadata": {},
    "source": [
     "We now separate the predictions (`y`) from the training data (`X`) for both the training (70%) and test (30%) datasets. The training set will be used to create a Machine Learning model using AutoML, and the test set will be used to evaluate the model's performance on unseen data."
@@ -53865,13 +53865,13 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "56766592",
+   "id": "32ab049a",
    "metadata": {
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-     "iopub.status.idle": "2024-12-26T13:57:26.196896Z",
-     "shell.execute_reply": "2024-12-26T13:57:26.196293Z"
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+     "shell.execute_reply": "2025-04-25T10:10:24.944154Z"
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    "outputs": [
@@ -53907,7 +53907,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9d5bb209",
+   "id": "8e3212c6",
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    "source": [
     "<a id='Fairness'></a>\n",
@@ -53917,7 +53917,7 @@
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   {
    "cell_type": "markdown",
-   "id": "7c8b7b4e",
+   "id": "38e3ac38",
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     "<a id='Metrics-overview'></a>\n",
@@ -53943,7 +53943,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2a65bd0e",
+   "id": "ca9058e8",
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    "source": [
     "<a id='Fairness-measure'></a>\n",
@@ -53954,7 +53954,7 @@
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   {
    "cell_type": "markdown",
-   "id": "f7299d5c",
+   "id": "7dcbe270",
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    "source": [
     "<a id='Fairness-measure_model'></a>\n",
@@ -53966,7 +53966,7 @@
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   {
    "cell_type": "markdown",
-   "id": "047a0c70",
+   "id": "fd5813cd",
    "metadata": {},
    "source": [
     "<a id='Train-model-sklearn'></a>\n",
@@ -53977,13 +53977,13 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "c5fd1a3c",
+   "id": "6fe8e54f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T13:57:26.198989Z",
-     "iopub.status.busy": "2024-12-26T13:57:26.198658Z",
-     "iopub.status.idle": "2024-12-26T13:58:44.178587Z",
-     "shell.execute_reply": "2024-12-26T13:58:44.177989Z"
+     "iopub.execute_input": "2025-04-25T10:10:24.946775Z",
+     "iopub.status.busy": "2025-04-25T10:10:24.946327Z",
+     "iopub.status.idle": "2025-04-25T10:11:31.500283Z",
+     "shell.execute_reply": "2025-04-25T10:11:31.499749Z"
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    },
    "outputs": [
@@ -54016,7 +54016,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "11d684f4",
+   "id": "bc984f41",
    "metadata": {},
    "source": [
     "We use the `roc_auc_score` scoring metric to evaluate the performance of this model on unseen data (`X_test`)."
@@ -54025,13 +54025,13 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "62ec894b",
+   "id": "d331fc2a",
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-     "iopub.status.idle": "2024-12-26T13:58:47.821404Z",
-     "shell.execute_reply": "2024-12-26T13:58:47.820815Z"
+     "iopub.execute_input": "2025-04-25T10:11:31.502144Z",
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+     "shell.execute_reply": "2025-04-25T10:11:35.179102Z"
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    "outputs": [
@@ -54052,7 +54052,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3443e24a",
+   "id": "e808ecc7",
    "metadata": {},
    "source": [
     "Now, we can also compute the statistical parity of the model on test data."
@@ -54061,13 +54061,13 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "25b07505",
+   "id": "cc3af095",
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@@ -54087,7 +54087,7 @@
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-   "id": "6f301878",
+   "id": "fcc481b6",
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@@ -54098,13 +54098,13 @@
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-   "id": "7bc5cebd",
+   "id": "759ec331",
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-     "shell.execute_reply": "2024-12-26T14:00:10.717135Z"
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+     "shell.execute_reply": "2025-04-25T10:12:58.440200Z"
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    "outputs": [
@@ -54112,139 +54112,139 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:58:48,435] [automlx.interface] Dataset shape: (29304,14)\n"
+      "[2025-04-25 03:11:35,769] [automlx.interface] Dataset shape: (29304,14)\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:58:48,543] [automlx.data_transform] Running preprocessing. Number of features: 15\n"
+      "[2025-04-25 03:11:35,874] [automlx.data_transform] Running preprocessing. Number of features: 15\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:58:49,006] [automlx.data_transform] Preprocessing completed. Took 0.462 secs\n"
+      "[2025-04-25 03:11:36,280] [automlx.data_transform] Preprocessing completed. Took 0.406 secs\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:58:49,046] [automlx.process] Running Model Generation\n"
+      "[2025-04-25 03:11:36,306] [automlx.process] Running Model Generation\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:58:49,098] [automlx.process] KNeighborsClassifier is disabled. The KNeighborsClassifier model is only recommended for datasets with less than 10000 samples and 1000 features.\n"
+      "[2025-04-25 03:11:36,357] [automlx.process] KNeighborsClassifier is disabled. The KNeighborsClassifier model is only recommended for datasets with less than 10000 samples and 1000 features.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:58:49,099] [automlx.process] SVC is disabled. The SVC model is only recommended for datasets with less than 10000 samples and 1000 features.\n"
+      "[2025-04-25 03:11:36,357] [automlx.process] SVC is disabled. The SVC model is only recommended for datasets with less than 10000 samples and 1000 features.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:58:49,101] [automlx.process] Model Generation completed.\n"
+      "[2025-04-25 03:11:36,358] [automlx.process] Model Generation completed.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:58:49,184] [automlx.model_selection] Running Model Selection\n"
+      "[2025-04-25 03:11:36,430] [automlx.model_selection] Running Model Selection\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:59:04,249] [automlx.model_selection] Model Selection completed - Took 15.065 sec - Selected models: [['XGBClassifier']]\n"
+      "[2025-04-25 03:11:53,842] [automlx.model_selection] Model Selection completed - Took 17.412 sec - Selected models: [['XGBClassifier']]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:59:04,279] [automlx.adaptive_sampling] Running Adaptive Sampling. Dataset shape: (29304,16).\n"
+      "[2025-04-25 03:11:53,871] [automlx.adaptive_sampling] Running Adaptive Sampling. Dataset shape: (29304,16).\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:59:06,388] [automlx.trials] Adaptive Sampling completed - Took 2.1095 sec.\n"
+      "[2025-04-25 03:11:55,656] [automlx.trials] Adaptive Sampling completed - Took 1.7846 sec.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:59:06,488] [automlx.feature_selection] Starting feature ranking for XGBClassifier\n"
+      "[2025-04-25 03:11:55,771] [automlx.feature_selection] Starting feature ranking for XGBClassifier\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:59:14,300] [automlx.feature_selection] Feature Selection completed. Took 7.832 secs.\n"
+      "[2025-04-25 03:12:03,593] [automlx.feature_selection] Feature Selection completed. Took 7.839 secs.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 05:59:14,343] [automlx.trials] Running Model Tuning for ['XGBClassifier']\n"
+      "[2025-04-25 03:12:03,647] [automlx.trials] Running Model Tuning for ['XGBClassifier']\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:00:01,661] [automlx.trials] Best parameters for XGBClassifier: {'learning_rate': 0.10242113515453982, 'min_child_weight': 12, 'max_depth': 4, 'reg_alpha': 0, 'booster': 'gbtree', 'reg_lambda': 0.01878279410038923, 'n_estimators': 143, 'use_label_encoder': False}\n"
+      "[2025-04-25 03:12:49,388] [automlx.trials] Best parameters for XGBClassifier: {'learning_rate': 0.10242113515453982, 'min_child_weight': 12, 'max_depth': 4, 'reg_alpha': 0, 'booster': 'gbtree', 'reg_lambda': 0.01878279410038923, 'n_estimators': 143, 'use_label_encoder': False}\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:00:01,663] [automlx.trials] Model Tuning completed. Took: 47.320 secs\n"
+      "[2025-04-25 03:12:49,389] [automlx.trials] Model Tuning completed. Took: 45.742 secs\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:00:08,621] [automlx.interface] Re-fitting pipeline\n"
+      "[2025-04-25 03:12:56,342] [automlx.interface] Re-fitting pipeline\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:00:08,636] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_0253a656-3\n"
+      "[2025-04-25 03:12:56,357] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_2070da66-2\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:00:10,353] [automlx.interface] AutoMLx completed.\n"
+      "[2025-04-25 03:12:58,079] [automlx.interface] AutoMLx completed.\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<automlx._interface.classifier.AutoClassifier at 0x153aa9421760>"
+       "<automlx._interface.classifier.AutoClassifier at 0x1501d42f5f40>"
       ]
      },
      "execution_count": 23,
@@ -54259,7 +54259,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "454dff18",
+   "id": "ec84c60d",
    "metadata": {},
    "source": [
     "Again, we use the `roc_auc_score` scoring metric to evaluate the performance of this model on unseen data (`X_test`)."
@@ -54268,13 +54268,13 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "abc9d3d8",
+   "id": "e7353023",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:00:10.720248Z",
-     "iopub.status.busy": "2024-12-26T14:00:10.719723Z",
-     "iopub.status.idle": "2024-12-26T14:00:13.493715Z",
-     "shell.execute_reply": "2024-12-26T14:00:13.493012Z"
+     "iopub.execute_input": "2025-04-25T10:12:58.442844Z",
+     "iopub.status.busy": "2025-04-25T10:12:58.442520Z",
+     "iopub.status.idle": "2025-04-25T10:13:01.601095Z",
+     "shell.execute_reply": "2025-04-25T10:13:01.600465Z"
     }
    },
    "outputs": [
@@ -54295,7 +54295,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "da9e8fe1",
+   "id": "c430df22",
    "metadata": {},
    "source": [
     "We can now go on with this model to show-case the rest of fairness features and metrics, but everything could work for a scikit-learn model as well."
@@ -54304,13 +54304,13 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "b0527301",
+   "id": "d7c6c360",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:00:13.496079Z",
-     "iopub.status.busy": "2024-12-26T14:00:13.495462Z",
-     "iopub.status.idle": "2024-12-26T14:00:13.630134Z",
-     "shell.execute_reply": "2024-12-26T14:00:13.629452Z"
+     "iopub.execute_input": "2025-04-25T10:13:01.603092Z",
+     "iopub.status.busy": "2025-04-25T10:13:01.602699Z",
+     "iopub.status.idle": "2025-04-25T10:13:01.731885Z",
+     "shell.execute_reply": "2025-04-25T10:13:01.731314Z"
     }
    },
    "outputs": [
@@ -54330,7 +54330,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cc7caf0e",
+   "id": "a489dce6",
    "metadata": {},
    "source": [
     "Below is another way to visually see statistical parity. The difference of the bars amounts to statistical disparity."
@@ -54339,13 +54339,13 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "id": "c217d9bf",
+   "id": "5960c08a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:00:13.632160Z",
-     "iopub.status.busy": "2024-12-26T14:00:13.631716Z",
-     "iopub.status.idle": "2024-12-26T14:00:13.818284Z",
-     "shell.execute_reply": "2024-12-26T14:00:13.817629Z"
+     "iopub.execute_input": "2025-04-25T10:13:01.733960Z",
+     "iopub.status.busy": "2025-04-25T10:13:01.733522Z",
+     "iopub.status.idle": "2025-04-25T10:13:01.909171Z",
+     "shell.execute_reply": "2025-04-25T10:13:01.908663Z"
     }
    },
    "outputs": [
@@ -55231,9 +55231,9 @@
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@@ -55277,7 +55277,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "970b8372",
+   "id": "4d7ec16c",
    "metadata": {},
    "source": [
     "We can see here that our tuned model has a statistical disparity with respect to sex of `0.33`, meaning that among the two values of `sex` in the dataset, the model predicts favorable outcomes for one sex 33% more than the other."
@@ -55285,7 +55285,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ab43699b",
+   "id": "7845eda6",
    "metadata": {},
    "source": [
     "Model fairness metrics are also available as functions taking as inputs `y_true`, `y_pred` and `subgroups` - though note that statistical parity, by definition, does not require the true labels."
@@ -55294,13 +55294,13 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "id": "4846c718",
+   "id": "8e9ee0b1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:00:13.820130Z",
-     "iopub.status.busy": "2024-12-26T14:00:13.819918Z",
-     "iopub.status.idle": "2024-12-26T14:00:13.955916Z",
-     "shell.execute_reply": "2024-12-26T14:00:13.955246Z"
+     "iopub.execute_input": "2025-04-25T10:13:01.911293Z",
+     "iopub.status.busy": "2025-04-25T10:13:01.910860Z",
+     "iopub.status.idle": "2025-04-25T10:13:02.038667Z",
+     "shell.execute_reply": "2025-04-25T10:13:02.038122Z"
     }
    },
    "outputs": [
@@ -55324,7 +55324,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "aacd8020",
+   "id": "c38a38cb",
    "metadata": {},
    "source": [
     "<a id='Fairness-measure_data'></a>\n",
@@ -55336,13 +55336,13 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "id": "7efba427",
+   "id": "42774c1b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:00:13.958063Z",
-     "iopub.status.busy": "2024-12-26T14:00:13.957484Z",
-     "iopub.status.idle": "2024-12-26T14:00:14.035324Z",
-     "shell.execute_reply": "2024-12-26T14:00:14.034524Z"
+     "iopub.execute_input": "2025-04-25T10:13:02.040613Z",
+     "iopub.status.busy": "2025-04-25T10:13:02.040273Z",
+     "iopub.status.idle": "2025-04-25T10:13:02.115203Z",
+     "shell.execute_reply": "2025-04-25T10:13:02.114677Z"
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    },
    "outputs": [],
@@ -55356,7 +55356,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a2bb2cff",
+   "id": "82149bea",
    "metadata": {},
    "source": [
     "Dataset fairness metrics are also available as functions taking as inputs `y_true` and `subgroups`."
@@ -55365,13 +55365,13 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "id": "598d9495",
+   "id": "65ff1a3b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:00:14.037136Z",
-     "iopub.status.busy": "2024-12-26T14:00:14.036925Z",
-     "iopub.status.idle": "2024-12-26T14:00:14.112438Z",
-     "shell.execute_reply": "2024-12-26T14:00:14.111768Z"
+     "iopub.execute_input": "2025-04-25T10:13:02.117402Z",
+     "iopub.status.busy": "2025-04-25T10:13:02.117023Z",
+     "iopub.status.idle": "2025-04-25T10:13:02.189762Z",
+     "shell.execute_reply": "2025-04-25T10:13:02.189254Z"
     }
    },
    "outputs": [
@@ -55394,7 +55394,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "74272ab0",
+   "id": "1344efd7",
    "metadata": {},
    "source": [
     "We can see here that the test set of the Adult Census Income Dataset has a statistical parity with respect to sex of `0.20`, meaning that men have 20% more `>50k` labels than women.\n",
@@ -55404,13 +55404,13 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "id": "3f146fd8",
+   "id": "ef6c3b25",
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     "execution": {
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-     "shell.execute_reply": "2024-12-26T14:00:14.202939Z"
+     "iopub.execute_input": "2025-04-25T10:13:02.191654Z",
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+     "iopub.status.idle": "2025-04-25T10:13:02.277046Z",
+     "shell.execute_reply": "2025-04-25T10:13:02.276555Z"
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    "outputs": [
@@ -56296,9 +56296,9 @@
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@@ -56341,7 +56341,7 @@
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@@ -56352,13 +56352,13 @@
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-       "var gd = document.getElementById('32fa2667-b84a-4394-b3b2-9743db037a6d');\n",
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   },
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-   "id": "72997553",
+   "id": "321d09db",
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    "source": [
     "Here, we see that `marital-status` is considered to be the feature that contributes most to the model's unfairness.\n",
@@ -58541,7 +58541,7 @@
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   {
    "cell_type": "markdown",
-   "id": "6ec8a1b8",
+   "id": "5a3c8399",
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@@ -58552,13 +58552,13 @@
   {
    "cell_type": "code",
    "execution_count": 38,
-   "id": "4f1dfb52",
+   "id": "84610f0b",
    "metadata": {
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-     "iopub.status.busy": "2024-12-26T14:00:39.634737Z",
-     "iopub.status.idle": "2024-12-26T14:00:39.699943Z",
-     "shell.execute_reply": "2024-12-26T14:00:39.699428Z"
+     "iopub.execute_input": "2025-04-25T10:13:28.437680Z",
+     "iopub.status.busy": "2025-04-25T10:13:28.437091Z",
+     "iopub.status.idle": "2025-04-25T10:13:28.709408Z",
+     "shell.execute_reply": "2025-04-25T10:13:28.708804Z"
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    },
    "outputs": [],
@@ -58595,13 +58595,13 @@
   {
    "cell_type": "code",
    "execution_count": 39,
-   "id": "bb13eb21",
+   "id": "1cca107d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:00:39.701987Z",
-     "iopub.status.busy": "2024-12-26T14:00:39.701634Z",
-     "iopub.status.idle": "2024-12-26T14:00:39.832823Z",
-     "shell.execute_reply": "2024-12-26T14:00:39.832232Z"
+     "iopub.execute_input": "2025-04-25T10:13:28.711646Z",
+     "iopub.status.busy": "2025-04-25T10:13:28.711094Z",
+     "iopub.status.idle": "2025-04-25T10:13:28.839803Z",
+     "shell.execute_reply": "2025-04-25T10:13:28.839212Z"
     }
    },
    "outputs": [
@@ -58625,25 +58625,24 @@
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-          "hoursperweek",
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-          "relationship",
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-          "native-country",
-          "education",
-          "fnlwgt",
+          "education-num",
+          "occupation",
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+          "education",
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@@ -59527,9 +59527,9 @@
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@@ -59564,7 +59564,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8e299649",
+   "id": "b72e98a4",
    "metadata": {},
    "source": [
     "AutoMLx provides other explainers that can sometimes also reveal unintended biases that the model has learned. For more explainers, please refer to the `OracleAutoMLx_Classification` notebook."
@@ -59572,7 +59572,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "96e9ad92",
+   "id": "f82bc0ae",
    "metadata": {},
    "source": [
     "<a id='MLX-bias-mitigation'></a>\n",
@@ -59582,7 +59582,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8fc9c43d",
+   "id": "aa73d55c",
    "metadata": {},
    "source": [
     "First, we need to initialize a `ModelBiasMitigator`. It requires a base estimator, the name of the protected attributes to use, a fairness metric, and an accuracy metric. There are many more options that can be configured, for example, let's say you'd like the bias mitigated model to be constrained to not exceeding an `absolute` value for the fairness metric. This is how that can be configured."
@@ -59591,13 +59591,13 @@
   {
    "cell_type": "code",
    "execution_count": 40,
-   "id": "905026a9",
+   "id": "606bc2c6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:00:39.835076Z",
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-     "shell.execute_reply": "2024-12-26T14:00:39.892905Z"
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@@ -59617,7 +59617,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4eab399a",
+   "id": "4973d734",
    "metadata": {},
    "source": [
     "Similarly, let's say you'd like the bias mitigated model to be constrained to not decreasing accuracy more than 5% `relative` to the most accurate (but potentially unfair) model. The following is how this would be configured - these are also the default options. Other common options such as time limit or random seed can also be specified."
@@ -59626,13 +59626,13 @@
   {
    "cell_type": "code",
    "execution_count": 41,
-   "id": "5a890606",
+   "id": "1a23abfd",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:00:39.895560Z",
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-     "iopub.status.idle": "2024-12-26T14:00:39.952769Z",
-     "shell.execute_reply": "2024-12-26T14:00:39.952252Z"
+     "iopub.execute_input": "2025-04-25T10:13:28.900864Z",
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@@ -59653,7 +59653,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "af75b903",
+   "id": "0f8b846b",
    "metadata": {},
    "source": [
     "The `ModelBiasMitigator` can be called with the usual `scikit-learn` interface, notably being trained with a single call to `fit`."
@@ -59662,20 +59662,20 @@
   {
    "cell_type": "code",
    "execution_count": 42,
-   "id": "840e9f2e",
+   "id": "ee71f344",
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     {
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       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b8f6757a893745568dfa8e075b5dcf83",
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        "version_major": 2,
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@@ -59689,7 +59689,7 @@
     {
      "data": {
       "text/plain": [
-       "<automlx.fairness.bias_mitigation._sklearn.ModelBiasMitigator at 0x153aa2e5a940>"
+       "<automlx.fairness.bias_mitigation._sklearn.ModelBiasMitigator at 0x1501cf1e32b0>"
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      "execution_count": 42,
@@ -59703,7 +59703,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0bb3c04e",
+   "id": "1cb36c78",
    "metadata": {},
    "source": [
     "The fitted model can then be used to collect probabilities and labels like any usual model."
@@ -59712,13 +59712,13 @@
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    "execution_count": 43,
-   "id": "86bfec72",
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-     "shell.execute_reply": "2024-12-26T14:00:44.213213Z"
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@@ -59746,13 +59746,13 @@
   {
    "cell_type": "code",
    "execution_count": 44,
-   "id": "ad774033",
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+     "iopub.execute_input": "2025-04-25T10:13:33.163611Z",
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+     "shell.execute_reply": "2025-04-25T10:13:33.286742Z"
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    "outputs": [
@@ -59773,7 +59773,7 @@
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   {
    "cell_type": "markdown",
-   "id": "96c8c8f5",
+   "id": "c268adbc",
    "metadata": {},
    "source": [
     "We can see a summary of the best models found within the `tradeoff_summary_` dataframe."
@@ -59782,13 +59782,13 @@
   {
    "cell_type": "code",
    "execution_count": 45,
-   "id": "99d5e382",
+   "id": "ab4754b2",
    "metadata": {
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-     "shell.execute_reply": "2024-12-26T14:00:44.411246Z"
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+     "shell.execute_reply": "2025-04-25T10:13:33.350174Z"
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@@ -59922,7 +59922,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dbc4de51",
+   "id": "dc86af0e",
    "metadata": {},
    "source": [
     "We can also visualize all of the best models that were found by our approach using a single `show_tradeoff` call."
@@ -59931,13 +59931,13 @@
   {
    "cell_type": "code",
    "execution_count": 46,
-   "id": "44fec238",
+   "id": "fbd68f21",
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    "outputs": [
@@ -60896,9 +60896,9 @@
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diff --git a/demos/OracleAutoMLx_Forecasting.html b/demos/OracleAutoMLx_Forecasting.html
index faabc32..3cbc226 100644
--- a/demos/OracleAutoMLx_Forecasting.html
+++ b/demos/OracleAutoMLx_Forecasting.html
@@ -14622,7 +14622,7 @@ <h1><font color=red>Building a Forecaster using AutoMLx</font></h1>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
 </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
 <p>Forecasting Demo notebook.</p>
-<p>Copyright © 2024, Oracle and/or its affiliates.</p>
+<p>Copyright © 2025, Oracle and/or its affiliates.</p>
 <p>Licensed under the Universal Permissive License v 1.0 as shown at <a href="https://oss.oracle.com/licenses/upl/">https://oss.oracle.com/licenses/upl/</a></p>
 
 </div>
@@ -14739,27 +14739,27 @@ <h2 id="Setup">Setup<a class="anchor-link" href="#Setup">&#182;</a></h2><p>Basic
 <div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[2]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">datetime</span>
-<span class="kn">import</span> <span class="nn">time</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">datetime</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">time</span>
 
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pd</span>
 
 <span class="k">try</span><span class="p">:</span>
-    <span class="kn">from</span> <span class="nn">sktime.forecasting.model_selection</span> <span class="kn">import</span> <span class="n">temporal_train_test_split</span>
+    <span class="kn">from</span><span class="w"> </span><span class="nn">sktime.forecasting.model_selection</span><span class="w"> </span><span class="kn">import</span> <span class="n">temporal_train_test_split</span>
 <span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
     <span class="k">try</span><span class="p">:</span>
-        <span class="kn">from</span> <span class="nn">sktime.split</span> <span class="kn">import</span> <span class="n">TemporalTrainTestSplitter</span> <span class="k">as</span> <span class="n">temporal_train_test_split</span>
+        <span class="kn">from</span><span class="w"> </span><span class="nn">sktime.split</span><span class="w"> </span><span class="kn">import</span> <span class="n">TemporalTrainTestSplitter</span> <span class="k">as</span> <span class="n">temporal_train_test_split</span>
     <span class="k">except</span> <span class="ne">ImportError</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
         <span class="k">raise</span> <span class="ne">ImportError</span><span class="p">(</span><span class="s2">&quot;Failed to import Splitters from sktime. &quot;</span>
-                          <span class="s2">&quot;Please ensure you have the correct version of sktime installed.&quot;</span><span class="p">)</span> <span class="kn">from</span> <span class="nn">e</span>
+                          <span class="s2">&quot;Please ensure you have the correct version of sktime installed.&quot;</span><span class="p">)</span> <span class="kn">from</span><span class="w"> </span><span class="nn">e</span>
 
 <span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s2">&quot;figure.figsize&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="mi">15</span><span class="p">,</span> <span class="mi">5</span><span class="p">]</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s2">&quot;font.size&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">15</span>
 
-<span class="kn">import</span> <span class="nn">automlx</span>
-<span class="kn">from</span> <span class="nn">automlx</span> <span class="kn">import</span> <span class="n">init</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">automlx</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">automlx</span><span class="w"> </span><span class="kn">import</span> <span class="n">init</span>
 </pre></div>
 
      </div>
@@ -15183,7 +15183,7 @@ <h3 id="Setting-the-execution-engine">Setting the execution engine<a class="anch
 
 
 <div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
-<pre>[2024-12-26 06:01:01,052] [automlx.backend] Overwriting ray session directory to /tmp/rnkq7h6h/ray, which will be deleted at engine shutdown. If you wish to retain ray logs, provide _temp_dir in ray_setup dict of engine_opts when initializing the AutoMLx engine.
+<pre>[2025-04-25 03:13:51,723] [automlx.backend] Overwriting ray session directory to /tmp/427vi97k/ray, which will be deleted at engine shutdown. If you wish to retain ray logs, provide _temp_dir in ray_setup dict of engine_opts when initializing the AutoMLx engine.
 </pre>
 </div>
 </div>
@@ -15283,32 +15283,32 @@ <h3 id="Train-a-forecasting-model-using-Oracle-AutoMLx">Train a forecasting mode
 
 
 <div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
-<pre>[2024-12-26 06:01:15,735] [automlx.data_transform] Number of simple differencing orders required: d = 1
-[2024-12-26 06:01:15,738] [automlx.data_transform] Seasonal Periodicities; from decomposed/adjusted: [52, 35, 1]
-[2024-12-26 06:01:15,805] [automlx.interface] Dataset shape: (174,2)
-[2024-12-26 06:01:19,506] [sanerec.autotuning.parameter] Hyperparameter epsilon autotune range is set to its validation range. This could lead to long training times
-[2024-12-26 06:01:20,048] [sanerec.autotuning.parameter] Hyperparameter repeat_quality_threshold autotune range is set to its validation range. This could lead to long training times
-[2024-12-26 06:01:20,058] [sanerec.autotuning.parameter] Hyperparameter scope autotune range is set to its validation range. This could lead to long training times
-[2024-12-26 06:01:20,133] [automlx.data_transform] Running preprocessing. Number of features: 3
-[2024-12-26 06:01:20,221] [automlx.data_transform] Preprocessing completed. Took 0.089 secs
-[2024-12-26 06:01:20,232] [automlx.process] Running Model Generation
-[2024-12-26 06:01:20,282] [automlx] Provided model (VARMAXForecaster) is not supported.Supported models are: [&#39;LGBMForecaster&#39;, &#39;ExtraTreesForecaster&#39;, &#39;ExpSmoothForecaster&#39;, &#39;NaiveForecaster&#39;, &#39;SARIMAXForecaster&#39;, &#39;STLwARIMAForecaster&#39;, &#39;ThetaForecaster&#39;, &#39;ProphetForecaster&#39;, &#39;ETSForecaster&#39;, &#39;STLwESForecaster&#39;, &#39;XGBForecaster&#39;]
-[2024-12-26 06:01:20,283] [automlx] Provided model (DynFactorForecaster) is not supported.Supported models are: [&#39;LGBMForecaster&#39;, &#39;ExtraTreesForecaster&#39;, &#39;ExpSmoothForecaster&#39;, &#39;NaiveForecaster&#39;, &#39;SARIMAXForecaster&#39;, &#39;STLwARIMAForecaster&#39;, &#39;ThetaForecaster&#39;, &#39;ProphetForecaster&#39;, &#39;ETSForecaster&#39;, &#39;STLwESForecaster&#39;, &#39;XGBForecaster&#39;]
-[2024-12-26 06:01:20,284] [automlx.process] Model Generation completed.
-[2024-12-26 06:01:20,351] [automlx.model_selection] Running Model Selection
-[2024-12-26 06:01:24,247] [automlx.dataset] Forecast horizon set to 13 for validation sets.
-[2024-12-26 06:01:38,911] [automlx.model_selection] Model Selection completed - Took 18.560 sec - Selected models: [[&#39;ETSForecaster&#39;, &#39;STLwARIMAForecaster&#39;]]
-[2024-12-26 06:01:39,075] [automlx.trials] Running Model Tuning for [&#39;ETSForecaster&#39;]
-[2024-12-26 06:01:39,148] [automlx.dataset] Forecast horizon set to 13 for validation sets.
-[2024-12-26 06:01:47,060] [automlx.trials] Best parameters for ETSForecaster: {&#39;error&#39;: &#39;add&#39;, &#39;trend&#39;: &#39;add&#39;, &#39;damped_trend&#39;: True, &#39;seasonal&#39;: &#39;add&#39;, &#39;seasonal_periods&#39;: 52, &#39;initialization_method&#39;: &#39;estimated&#39;, &#39;initial_level&#39;: None, &#39;initial_trend&#39;: None, &#39;initial_seasonal&#39;: None, &#39;bounds&#39;: None, &#39;dates&#39;: None, &#39;freq&#39;: None, &#39;missing&#39;: &#39;none&#39;, &#39;start_params&#39;: None, &#39;maxiter&#39;: 1500, &#39;disp&#39;: -1, &#39;return_params&#39;: False}
-[2024-12-26 06:01:47,062] [automlx.trials] Model Tuning completed. Took: 7.986 secs
-[2024-12-26 06:01:47,245] [automlx.trials] Running Model Tuning for [&#39;STLwARIMAForecaster&#39;]
-[2024-12-26 06:01:47,330] [automlx.dataset] Forecast horizon set to 13 for validation sets.
-[2024-12-26 06:01:50,145] [automlx.trials] Best parameters for STLwARIMAForecaster: {&#39;seasonal_deg&#39;: 1, &#39;trend_deg&#39;: 1, &#39;low_pass_deg&#39;: 1, &#39;period&#39;: 52, &#39;arima_p&#39;: 2, &#39;arima_d&#39;: 1, &#39;arima_q&#39;: 2, &#39;arima_trend&#39;: &#39;n&#39;, &#39;concentrate_scale&#39;: True}
-[2024-12-26 06:01:50,146] [automlx.trials] Model Tuning completed. Took: 2.901 secs
-[2024-12-26 06:01:52,195] [automlx.interface] Re-fitting pipeline
-[2024-12-26 06:01:52,219] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_ffdf6aab-0
-[2024-12-26 06:01:53,994] [automlx.interface] AutoMLx completed.
+<pre>[2025-04-25 03:14:06,599] [automlx.data_transform] Number of simple differencing orders required: d = 1
+[2025-04-25 03:14:06,602] [automlx.data_transform] Seasonal Periodicities; from decomposed/adjusted: [52, 35, 1]
+[2025-04-25 03:14:06,666] [automlx.interface] Dataset shape: (174,2)
+[2025-04-25 03:14:10,990] [sanerec.autotuning.parameter] Hyperparameter epsilon autotune range is set to its validation range. This could lead to long training times
+[2025-04-25 03:14:11,547] [sanerec.autotuning.parameter] Hyperparameter repeat_quality_threshold autotune range is set to its validation range. This could lead to long training times
+[2025-04-25 03:14:11,557] [sanerec.autotuning.parameter] Hyperparameter scope autotune range is set to its validation range. This could lead to long training times
+[2025-04-25 03:14:11,627] [automlx.data_transform] Running preprocessing. Number of features: 3
+[2025-04-25 03:14:11,712] [automlx.data_transform] Preprocessing completed. Took 0.085 secs
+[2025-04-25 03:14:11,736] [automlx.process] Running Model Generation
+[2025-04-25 03:14:11,786] [automlx] Provided model (VARMAXForecaster) is not supported.Supported models are: [&#39;LGBMForecaster&#39;, &#39;ExtraTreesForecaster&#39;, &#39;ExpSmoothForecaster&#39;, &#39;NaiveForecaster&#39;, &#39;SARIMAXForecaster&#39;, &#39;STLwARIMAForecaster&#39;, &#39;ThetaForecaster&#39;, &#39;ProphetForecaster&#39;, &#39;ETSForecaster&#39;, &#39;STLwESForecaster&#39;, &#39;XGBForecaster&#39;]
+[2025-04-25 03:14:11,787] [automlx] Provided model (DynFactorForecaster) is not supported.Supported models are: [&#39;LGBMForecaster&#39;, &#39;ExtraTreesForecaster&#39;, &#39;ExpSmoothForecaster&#39;, &#39;NaiveForecaster&#39;, &#39;SARIMAXForecaster&#39;, &#39;STLwARIMAForecaster&#39;, &#39;ThetaForecaster&#39;, &#39;ProphetForecaster&#39;, &#39;ETSForecaster&#39;, &#39;STLwESForecaster&#39;, &#39;XGBForecaster&#39;]
+[2025-04-25 03:14:11,788] [automlx.process] Model Generation completed.
+[2025-04-25 03:14:11,852] [automlx.model_selection] Running Model Selection
+[2025-04-25 03:14:15,674] [automlx.dataset] Forecast horizon set to 13 for validation sets.
+[2025-04-25 03:14:27,185] [automlx.model_selection] Model Selection completed - Took 15.332 sec - Selected models: [[&#39;ETSForecaster&#39;, &#39;STLwARIMAForecaster&#39;]]
+[2025-04-25 03:14:27,240] [automlx.trials] Running Model Tuning for [&#39;ETSForecaster&#39;]
+[2025-04-25 03:14:27,314] [automlx.dataset] Forecast horizon set to 13 for validation sets.
+[2025-04-25 03:14:34,808] [automlx.trials] Best parameters for ETSForecaster: {&#39;error&#39;: &#39;add&#39;, &#39;trend&#39;: &#39;add&#39;, &#39;damped_trend&#39;: True, &#39;seasonal&#39;: &#39;add&#39;, &#39;seasonal_periods&#39;: 52, &#39;initialization_method&#39;: &#39;estimated&#39;, &#39;initial_level&#39;: None, &#39;initial_trend&#39;: None, &#39;initial_seasonal&#39;: None, &#39;bounds&#39;: None, &#39;dates&#39;: None, &#39;freq&#39;: None, &#39;missing&#39;: &#39;none&#39;, &#39;start_params&#39;: None, &#39;maxiter&#39;: 1500, &#39;disp&#39;: -1, &#39;return_params&#39;: False}
+[2025-04-25 03:14:34,809] [automlx.trials] Model Tuning completed. Took: 7.569 secs
+[2025-04-25 03:14:34,939] [automlx.trials] Running Model Tuning for [&#39;STLwARIMAForecaster&#39;]
+[2025-04-25 03:14:35,020] [automlx.dataset] Forecast horizon set to 13 for validation sets.
+[2025-04-25 03:14:37,741] [automlx.trials] Best parameters for STLwARIMAForecaster: {&#39;seasonal_deg&#39;: 1, &#39;trend_deg&#39;: 1, &#39;low_pass_deg&#39;: 1, &#39;period&#39;: 52, &#39;arima_p&#39;: 2, &#39;arima_d&#39;: 1, &#39;arima_q&#39;: 2, &#39;arima_trend&#39;: &#39;n&#39;, &#39;concentrate_scale&#39;: True}
+[2025-04-25 03:14:37,742] [automlx.trials] Model Tuning completed. Took: 2.803 secs
+[2025-04-25 03:14:39,547] [automlx.interface] Re-fitting pipeline
+[2025-04-25 03:14:39,570] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_615a3221-8
+[2025-04-25 03:14:41,143] [automlx.interface] AutoMLx completed.
 Selected model: ETSForecaster
 Selected model params: {&#39;error&#39;: &#39;add&#39;, &#39;trend&#39;: &#39;add&#39;, &#39;damped_trend&#39;: True, &#39;seasonal&#39;: &#39;add&#39;, &#39;seasonal_periods&#39;: 52, &#39;initialization_method&#39;: &#39;estimated&#39;, &#39;initial_level&#39;: None, &#39;initial_trend&#39;: None, &#39;initial_seasonal&#39;: None, &#39;bounds&#39;: None, &#39;dates&#39;: None, &#39;freq&#39;: None, &#39;missing&#39;: &#39;none&#39;, &#39;start_params&#39;: None, &#39;maxiter&#39;: 1500, &#39;disp&#39;: -1, &#39;return_params&#39;: False}
 </pre>
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@@ -15759,7 +15724,7 @@ <h3 id="Analyze-the-AutoML-optimization-process">Analyze the AutoML optimization
       <td>{'error': 'add', 'trend': 'add', 'damped_trend': True, 'seasonal': 'add', 'seasonal_periods': 52, 'initialization_method': 'estimated', 'initial_level': None, 'initial_trend': None, 'initial_seasonal': None, 'bounds': None, 'dates': None, 'freq': None, 'missing': 'none', 'start_params': None, 'maxiter': 1500, 'disp': -1, 'return_params': False}</td>
     </tr>
     <tr>
-      <td>25.1.0</td>
+      <td>25.2.1</td>
     </tr>
     <tr>
       <td>3.9.21 (main, Dec 11 2024, 16:24:11) \n[GCC 11.2.0]</td>
@@ -15815,69 +15780,69 @@ <h3 id="Analyze-the-AutoML-optimization-process">Analyze the AutoML optimization
       <td>{'error': 'add', 'trend': 'add', 'damped_trend': False, 'seasonal': 'add', 'seasonal_periods': 52, 'initialization_method': 'estimated', 'initial_level': None, 'initial_trend': None, 'initial_seasonal': None, 'bounds': None, 'dates': None, 'freq': None, 'missing': 'none', 'start_params': None, 'maxiter': 1500, 'disp': -1, 'return_params': False}</td>
       <td>-0.0361</td>
       <td>{'neg_sym_mean_abs_percent_error': -0.03605508323686911}</td>
-      <td>6.3818</td>
-      <td>0.2981</td>
-      <td>Thu Dec 26 06:01:31 2024</td>
+      <td>5.5388</td>
+      <td>0.2991</td>
+      <td>Fri Apr 25 03:14:22 2025</td>
     </tr>
     <tr>
       <td>Model Selection</td>
-      <td>{1: 109, 2: 122, 3: 135, 4: 148, 5: 161}</td>
+      <td>{2: 122, 1: 109, 3: 135, 4: 148, 5: 161}</td>
       <td>2</td>
       <td>STLwARIMAForecaster</td>
       <td>{'seasonal_deg': 1, 'trend_deg': 1, 'low_pass_deg': 0, 'period': 52, 'arima_p': 2, 'arima_d': 1, 'arima_q': 2, 'arima_trend': 'n', 'concentrate_scale': True}</td>
       <td>-0.0855</td>
       <td>{'neg_sym_mean_abs_percent_error': -0.08549289627140871}</td>
-      <td>1.7426</td>
-      <td>0.3161</td>
-      <td>Thu Dec 26 06:01:30 2024</td>
+      <td>1.6676</td>
+      <td>0.3153</td>
+      <td>Fri Apr 25 03:14:22 2025</td>
     </tr>
     <tr>
       <td>Model Selection</td>
-      <td>{1: 109, 2: 122, 3: 135, 5: 161, 4: 148}</td>
+      <td>{1: 109, 2: 122, 3: 135, 4: 148, 5: 161}</td>
       <td>2</td>
       <td>XGBForecaster</td>
       <td>{'differencing_order': 1, 'acf_local_maxima': '[52, 35, 17, 26, 22, 4, 48, 30, 13, 56, 39, 61, 44, 9, 65, 1]', 'use_X': False, 'n_estimators': 50, 'max_depth': 5}</td>
       <td>-0.0951</td>
-      <td>{'neg_sym_mean_abs_percent_error': -0.09511310353650582}</td>
-      <td>22.3198</td>
-      <td>0.7327</td>
-      <td>Thu Dec 26 06:01:38 2024</td>
+      <td>{'neg_sym_mean_abs_percent_error': -0.09511310353650583}</td>
+      <td>4.3645</td>
+      <td>0.3241</td>
+      <td>Fri Apr 25 03:14:24 2025</td>
     </tr>
     <tr>
       <td>Model Selection</td>
-      <td>{3: 135, 1: 109, 2: 122, 5: 161, 4: 148}</td>
+      <td>{2: 122, 5: 161, 1: 109, 3: 135, 4: 148}</td>
       <td>2</td>
       <td>ExtraTreesForecaster</td>
       <td>{'differencing_order': 1, 'acf_local_maxima': '[52, 35, 17, 26, 22, 4, 48, 30, 13, 56, 39, 61, 44, 9, 65, 1]', 'use_X': False, 'n_estimators': 40, 'min_samples_leaf': 0.030458}</td>
       <td>-0.1252</td>
       <td>{'neg_sym_mean_abs_percent_error': -0.12522681848759207}</td>
-      <td>23.5931</td>
-      <td>0.7242</td>
-      <td>Thu Dec 26 06:01:35 2024</td>
+      <td>4.9313</td>
+      <td>0.3217</td>
+      <td>Fri Apr 25 03:14:23 2025</td>
     </tr>
     <tr>
       <td>Model Selection</td>
-      <td>{2: 122, 1: 109, 3: 135, 5: 161, 4: 148}</td>
+      <td>{2: 122, 3: 135, 1: 109, 4: 148, 5: 161}</td>
       <td>2</td>
       <td>LGBMForecaster</td>
       <td>{'differencing_order': 1, 'acf_local_maxima': '[52, 35, 17, 26, 22, 4, 48, 30, 13, 56, 39, 61, 44, 9, 65, 1]', 'use_X': False, 'max_depth': 4, 'n_estimators': 37}</td>
       <td>-0.13</td>
       <td>{'neg_sym_mean_abs_percent_error': -0.13003654391153155}</td>
-      <td>10.1184</td>
-      <td>0.7436</td>
-      <td>Thu Dec 26 06:01:38 2024</td>
+      <td>5.0983</td>
+      <td>0.3536</td>
+      <td>Fri Apr 25 03:14:25 2025</td>
     </tr>
     <tr>
       <td>Model Selection</td>
-      <td>{1: 109, 4: 148, 2: 122, 3: 135, 5: 161}</td>
+      <td>{2: 122, 3: 135, 1: 109, 5: 161, 4: 148}</td>
       <td>2</td>
       <td>ThetaForecaster</td>
       <td>{'sp': 52, 'deseasonalize': False, 'initial_level': None}</td>
       <td>-0.2091</td>
       <td>{'neg_sym_mean_abs_percent_error': -0.2090874120377979}</td>
-      <td>11.9759</td>
-      <td>0.3004</td>
-      <td>Thu Dec 26 06:01:30 2024</td>
+      <td>13.0709</td>
+      <td>0.3049</td>
+      <td>Fri Apr 25 03:14:21 2025</td>
     </tr>
     <tr>
       <td>Model Selection</td>
@@ -15887,45 +15852,45 @@ <h3 id="Analyze-the-AutoML-optimization-process">Analyze the AutoML optimization
       <td>{'trend': 'add', 'damped_trend': True, 'seasonal': None, 'sp': 52, 'use_boxcox': False}</td>
       <td>-0.2475</td>
       <td>{'neg_sym_mean_abs_percent_error': -0.2474953233635102}</td>
-      <td>7.9147</td>
-      <td>0.2986</td>
-      <td>Thu Dec 26 06:01:29 2024</td>
+      <td>10.3261</td>
+      <td>0.2994</td>
+      <td>Fri Apr 25 03:14:21 2025</td>
     </tr>
     <tr>
       <td>Model Selection</td>
-      <td>{3: 135, 4: 148, 1: 109, 2: 122, 5: 161}</td>
+      <td>{4: 148, 3: 135, 1: 109, 5: 161, 2: 122}</td>
       <td>2</td>
       <td>NaiveForecaster</td>
       <td>{'strategy': 'last', 'sp': 52, 'window_length': None}</td>
       <td>-0.4494</td>
       <td>{'neg_sym_mean_abs_percent_error': -0.4493967043524097}</td>
-      <td>9.6946</td>
-      <td>0.2843</td>
-      <td>Thu Dec 26 06:01:29 2024</td>
+      <td>10.5111</td>
+      <td>0.2871</td>
+      <td>Fri Apr 25 03:14:21 2025</td>
     </tr>
     <tr>
       <td>Model Selection</td>
-      <td>{1: 109, 2: 122, 3: 135, 4: 148, 5: 161}</td>
+      <td>{1: 109, 2: 122, 4: 148, 3: 135, 5: 161}</td>
       <td>2</td>
       <td>STLwESForecaster</td>
       <td>{'seasonal_deg': 1, 'trend_deg': 1, 'low_pass_deg': 1, 'period': 52, 'es_trend': 'add', 'es_damped_trend': True, 'concentrate_scale': True}</td>
       <td>-inf</td>
       <td>{'neg_sym_mean_abs_percent_error': -inf}</td>
-      <td>1.6243</td>
-      <td>0.3093</td>
-      <td>Thu Dec 26 06:01:30 2024</td>
+      <td>1.9590</td>
+      <td>0.3116</td>
+      <td>Fri Apr 25 03:14:22 2025</td>
     </tr>
     <tr>
       <td>Model Selection</td>
-      <td>{1: 109, 2: 122, 4: 148, 3: 135, 5: 161}</td>
+      <td>{1: 109, 3: 135, 2: 122, 4: 148, 5: 161}</td>
       <td>2</td>
       <td>SARIMAXForecaster</td>
       <td>{'sp': 52, 'p': 2, 'd': 1, 'q': 2, 'P': 1, 'D': 0, 'Q': 1, 'trend': 'n', 'use_X': False, 'enforce_stationarity': True, 'enforce_invertibility': True, 'method': 'lbfgs', 'disp': -1, 'concentrate_scale': False}</td>
       <td>-inf</td>
       <td>{'neg_sym_mean_abs_percent_error': -inf}</td>
-      <td>25.7013</td>
-      <td>0.3546</td>
-      <td>Thu Dec 26 06:01:36 2024</td>
+      <td>19.8842</td>
+      <td>0.3231</td>
+      <td>Fri Apr 25 03:14:26 2025</td>
     </tr>
     <tr>
       <td>...</td>
@@ -15939,6 +15904,18 @@ <h3 id="Analyze-the-AutoML-optimization-process">Analyze the AutoML optimization
       <td>...</td>
       <td>...</td>
     </tr>
+    <tr>
+      <td>Model Tuning</td>
+      <td>{2: 122, 1: 109, 3: 135, 5: 161, 4: 148}</td>
+      <td>2</td>
+      <td>ETSForecaster</td>
+      <td>{'error': 'add', 'trend': 'add', 'damped_trend': False, 'seasonal': None, 'seasonal_periods': 1, 'initialization_method': 'estimated', 'initial_level': None, 'initial_trend': None, 'initial_seasonal': None, 'bounds': None, 'dates': None, 'freq': None, 'missing': 'none', 'start_params': None, 'maxiter': 1500, 'disp': -1, 'return_params': False}</td>
+      <td>-0.2919</td>
+      <td>{'neg_sym_mean_abs_percent_error': -0.29186918028720626}</td>
+      <td>0.5513</td>
+      <td>0.3295</td>
+      <td>Fri Apr 25 03:14:31 2025</td>
+    </tr>
     <tr>
       <td>Model Tuning</td>
       <td>None</td>
@@ -15947,8 +15924,8 @@ <h3 id="Analyze-the-AutoML-optimization-process">Analyze the AutoML optimization
       <td>None</td>
       <td>-inf</td>
       <td>None</td>
-      <td>0.9222</td>
-      <td>0.7255</td>
+      <td>0.8307</td>
+      <td>0.3423</td>
       <td>-1</td>
     </tr>
     <tr>
@@ -15959,8 +15936,8 @@ <h3 id="Analyze-the-AutoML-optimization-process">Analyze the AutoML optimization
       <td>None</td>
       <td>-inf</td>
       <td>None</td>
-      <td>0.2932</td>
-      <td>0.7519</td>
+      <td>0.3418</td>
+      <td>0.3551</td>
       <td>-1</td>
     </tr>
     <tr>
@@ -15971,45 +15948,33 @@ <h3 id="Analyze-the-AutoML-optimization-process">Analyze the AutoML optimization
       <td>{'error': 'add', 'trend': 'add', 'damped_trend': False, 'seasonal': 'add', 'seasonal_periods': 35, 'initialization_method': 'estimated', 'initial_level': None, 'initial_trend': None, 'initial_seasonal': None, 'bounds': None, 'dates': None, 'freq': None, 'missing': 'none', 'start_params': None, 'maxiter': 1500, 'disp': -1, 'return_params': False}</td>
       <td>-inf</td>
       <td>{'neg_sym_mean_abs_percent_error': -inf}</td>
-      <td>6.9497</td>
-      <td>0.7446</td>
-      <td>Thu Dec 26 06:01:43 2024</td>
+      <td>7.1072</td>
+      <td>0.3547</td>
+      <td>Fri Apr 25 03:14:32 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
-      <td>{1: 109, 2: 122, 3: 135, 4: 148, 5: 161}</td>
+      <td>{2: 122, 1: 109, 4: 148, 5: 161, 3: 135}</td>
       <td>2</td>
       <td>ETSForecaster</td>
       <td>{'error': 'add', 'trend': 'mul', 'damped_trend': False, 'seasonal': 'add', 'seasonal_periods': 52, 'initialization_method': 'estimated', 'initial_level': None, 'initial_trend': None, 'initial_seasonal': None, 'bounds': None, 'dates': None, 'freq': None, 'missing': 'none', 'start_params': None, 'maxiter': 1500, 'disp': -1, 'return_params': False}</td>
       <td>-inf</td>
       <td>{'neg_sym_mean_abs_percent_error': -inf}</td>
-      <td>6.8606</td>
-      <td>0.7366</td>
-      <td>Thu Dec 26 06:01:45 2024</td>
-    </tr>
-    <tr>
-      <td>Model Tuning</td>
-      <td>{1: 109, 2: 122, 3: 135, 4: 148, 5: 161}</td>
-      <td>2</td>
-      <td>ETSForecaster</td>
-      <td>{'error': 'add', 'trend': None, 'damped_trend': False, 'seasonal': 'add', 'seasonal_periods': 52, 'initialization_method': 'estimated', 'initial_level': None, 'initial_trend': None, 'initial_seasonal': None, 'bounds': None, 'dates': None, 'freq': None, 'missing': 'none', 'start_params': None, 'maxiter': 1500, 'disp': -1, 'return_params': False}</td>
-      <td>-inf</td>
-      <td>{'neg_sym_mean_abs_percent_error': -inf}</td>
-      <td>5.9248</td>
-      <td>0.7447</td>
-      <td>Thu Dec 26 06:01:45 2024</td>
+      <td>6.7682</td>
+      <td>0.3423</td>
+      <td>Fri Apr 25 03:14:33 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
-      <td>{1: 109, 3: 135, 2: 122, 4: 148, 5: 161}</td>
+      <td>{2: 122, 1: 109, 3: 135, 4: 148, 5: 161}</td>
       <td>2</td>
       <td>ETSForecaster</td>
       <td>{'error': 'mul', 'trend': 'add', 'damped_trend': False, 'seasonal': 'add', 'seasonal_periods': 52, 'initialization_method': 'estimated', 'initial_level': None, 'initial_trend': None, 'initial_seasonal': None, 'bounds': None, 'dates': None, 'freq': None, 'missing': 'none', 'start_params': None, 'maxiter': 1500, 'disp': -1, 'return_params': False}</td>
       <td>-inf</td>
       <td>{'neg_sym_mean_abs_percent_error': -inf}</td>
-      <td>11.7031</td>
-      <td>0.7446</td>
-      <td>Thu Dec 26 06:01:42 2024</td>
+      <td>9.8725</td>
+      <td>0.3547</td>
+      <td>Fri Apr 25 03:14:30 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
@@ -16019,9 +15984,9 @@ <h3 id="Analyze-the-AutoML-optimization-process">Analyze the AutoML optimization
       <td>{'seasonal_deg': 0, 'trend_deg': 0, 'low_pass_deg': 0, 'period': 52, 'arima_p': 0, 'arima_d': 1, 'arima_q': 0, 'arima_trend': 'n', 'concentrate_scale': True}</td>
       <td>-inf</td>
       <td>{'neg_sym_mean_abs_percent_error': -inf}</td>
-      <td>0.4488</td>
-      <td>0.7451</td>
-      <td>Thu Dec 26 06:01:49 2024</td>
+      <td>0.5099</td>
+      <td>0.3397</td>
+      <td>Fri Apr 25 03:14:37 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
@@ -16031,9 +15996,9 @@ <h3 id="Analyze-the-AutoML-optimization-process">Analyze the AutoML optimization
       <td>{'seasonal_deg': 0, 'trend_deg': 1, 'low_pass_deg': 0, 'period': 52, 'arima_p': 2, 'arima_d': 1, 'arima_q': 2, 'arima_trend': 'n', 'concentrate_scale': True}</td>
       <td>-inf</td>
       <td>{'neg_sym_mean_abs_percent_error': -inf}</td>
-      <td>1.5059</td>
-      <td>0.7451</td>
-      <td>Thu Dec 26 06:01:49 2024</td>
+      <td>1.1175</td>
+      <td>0.3551</td>
+      <td>Fri Apr 25 03:14:36 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
@@ -16043,21 +16008,21 @@ <h3 id="Analyze-the-AutoML-optimization-process">Analyze the AutoML optimization
       <td>{'seasonal_deg': 1, 'trend_deg': 1, 'low_pass_deg': 0, 'period': 1, 'arima_p': 2, 'arima_d': 1, 'arima_q': 2, 'arima_trend': 'n', 'concentrate_scale': True}</td>
       <td>-inf</td>
       <td>{'neg_sym_mean_abs_percent_error': -inf}</td>
-      <td>0.3548</td>
-      <td>0.7401</td>
-      <td>Thu Dec 26 06:01:49 2024</td>
+      <td>0.4096</td>
+      <td>0.3551</td>
+      <td>Fri Apr 25 03:14:36 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
-      <td>{1: 109, 2: 122, 4: 148, 3: 135, 5: 161}</td>
+      <td>{1: 109, 3: 135, 2: 122, 4: 148, 5: 161}</td>
       <td>2</td>
       <td>STLwARIMAForecaster</td>
-      <td>{'seasonal_deg': 1, 'trend_deg': 1, 'low_pass_deg': 0, 'period': 52, 'arima_p': 3, 'arima_d': 1, 'arima_q': 2, 'arima_trend': 'n', 'concentrate_scale': True}</td>
+      <td>{'seasonal_deg': 1, 'trend_deg': 1, 'low_pass_deg': 0, 'period': 52, 'arima_p': 5, 'arima_d': 1, 'arima_q': 2, 'arima_trend': 'n', 'concentrate_scale': True}</td>
       <td>-inf</td>
       <td>{'neg_sym_mean_abs_percent_error': -inf}</td>
-      <td>1.4462</td>
-      <td>0.7400</td>
-      <td>Thu Dec 26 06:01:48 2024</td>
+      <td>2.0701</td>
+      <td>0.3547</td>
+      <td>Fri Apr 25 03:14:36 2025</td>
     </tr>
   </tbody>
 </table>
@@ -16314,7 +16279,6 @@ <h4 id="Hyperparameter-Tuning">Hyperparameter Tuning<a class="anchor-link" href=
 <pre>### Plotting is enabled for total models tuned = 2.
 ### Model_name		 Val_score 
 ETSForecaster  	 -0.0361 	
-[2024-12-26 06:01:56,207] [automlx.model] Forecast called on ETSForecaster that&#39;s not been fitted.
 </pre>
 </div>
 </div>
@@ -16327,9 +16291,9 @@ <h4 id="Hyperparameter-Tuning">Hyperparameter Tuning<a class="anchor-link" href=
 
 
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@@ -16591,23 +16555,23 @@ <h2 id="Single-target-Forecasting-with-Exogenous-Variables">Single-target Foreca
 
 
 <div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
-<pre>[2024-12-26 06:01:56,556] [automlx.data_transform] Number of simple differencing orders required: d = 0
-[2024-12-26 06:01:56,558] [automlx.data_transform] Seasonal Periodicities; from decomposed/adjusted: [1]
-[2024-12-26 06:01:56,632] [automlx.interface] Dataset shape: (90,3)
-[2024-12-26 06:01:56,737] [automlx.data_transform] Running preprocessing. Number of features: 4
-[2024-12-26 06:01:56,846] [automlx.data_transform] Preprocessing completed. Took 0.109 secs
-[2024-12-26 06:01:56,892] [automlx.process] Running Model Generation
-[2024-12-26 06:01:56,938] [automlx] Provided model (SARIMAXForecaster) is not supported.Supported models are: [&#39;LGBMForecaster&#39;, &#39;ExtraTreesForecaster&#39;, &#39;ExpSmoothForecaster&#39;, &#39;NaiveForecaster&#39;, &#39;ThetaForecaster&#39;, &#39;ETSForecaster&#39;, &#39;XGBForecaster&#39;]
-[2024-12-26 06:01:56,938] [automlx] Provided model (ProphetForecaster) is not supported.Supported models are: [&#39;LGBMForecaster&#39;, &#39;ExtraTreesForecaster&#39;, &#39;ExpSmoothForecaster&#39;, &#39;NaiveForecaster&#39;, &#39;ThetaForecaster&#39;, &#39;ETSForecaster&#39;, &#39;XGBForecaster&#39;]
-[2024-12-26 06:01:56,940] [automlx.process] Model Generation completed.
-[2024-12-26 06:01:56,990] [automlx.model_selection] Running Model Selection
-[2024-12-26 06:02:03,120] [automlx.model_selection] Model Selection completed - Took 6.130 sec - Selected models: [[&#39;XGBForecaster&#39;]]
-[2024-12-26 06:02:03,204] [automlx.trials] Running Model Tuning for [&#39;XGBForecaster&#39;]
-[2024-12-26 06:02:18,214] [automlx.trials] Best parameters for XGBForecaster: {&#39;differencing_order&#39;: 1, &#39;acf_local_maxima&#39;: &#39;[4, 9, 18, 22, 14, 31, 26, 12, 1]&#39;, &#39;use_X&#39;: True, &#39;n_estimators&#39;: 16, &#39;max_depth&#39;: 2}
-[2024-12-26 06:02:18,215] [automlx.trials] Model Tuning completed. Took: 15.011 secs
-[2024-12-26 06:02:18,672] [automlx.interface] Re-fitting pipeline
-[2024-12-26 06:02:18,683] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_85b858b0-1
-[2024-12-26 06:02:22,110] [automlx.interface] AutoMLx completed.
+<pre>[2025-04-25 03:14:43,695] [automlx.data_transform] Number of simple differencing orders required: d = 0
+[2025-04-25 03:14:43,697] [automlx.data_transform] Seasonal Periodicities; from decomposed/adjusted: [1]
+[2025-04-25 03:14:43,743] [automlx.interface] Dataset shape: (90,3)
+[2025-04-25 03:14:43,827] [automlx.data_transform] Running preprocessing. Number of features: 4
+[2025-04-25 03:14:43,934] [automlx.data_transform] Preprocessing completed. Took 0.107 secs
+[2025-04-25 03:14:43,960] [automlx.process] Running Model Generation
+[2025-04-25 03:14:44,005] [automlx] Provided model (SARIMAXForecaster) is not supported.Supported models are: [&#39;LGBMForecaster&#39;, &#39;ExtraTreesForecaster&#39;, &#39;ExpSmoothForecaster&#39;, &#39;NaiveForecaster&#39;, &#39;ThetaForecaster&#39;, &#39;ETSForecaster&#39;, &#39;XGBForecaster&#39;]
+[2025-04-25 03:14:44,005] [automlx] Provided model (ProphetForecaster) is not supported.Supported models are: [&#39;LGBMForecaster&#39;, &#39;ExtraTreesForecaster&#39;, &#39;ExpSmoothForecaster&#39;, &#39;NaiveForecaster&#39;, &#39;ThetaForecaster&#39;, &#39;ETSForecaster&#39;, &#39;XGBForecaster&#39;]
+[2025-04-25 03:14:44,007] [automlx.process] Model Generation completed.
+[2025-04-25 03:14:44,043] [automlx.model_selection] Running Model Selection
+[2025-04-25 03:14:45,991] [automlx.model_selection] Model Selection completed - Took 1.948 sec - Selected models: [[&#39;XGBForecaster&#39;]]
+[2025-04-25 03:14:46,045] [automlx.trials] Running Model Tuning for [&#39;XGBForecaster&#39;]
+[2025-04-25 03:14:55,529] [automlx.trials] Best parameters for XGBForecaster: {&#39;differencing_order&#39;: 1, &#39;acf_local_maxima&#39;: &#39;[4, 9, 18, 22, 14, 31, 26, 12, 1]&#39;, &#39;use_X&#39;: True, &#39;n_estimators&#39;: 16, &#39;max_depth&#39;: 2}
+[2025-04-25 03:14:55,530] [automlx.trials] Model Tuning completed. Took: 9.485 secs
+[2025-04-25 03:14:55,869] [automlx.interface] Re-fitting pipeline
+[2025-04-25 03:14:55,879] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_ea8a88bb-f
+[2025-04-25 03:14:56,184] [automlx.interface] AutoMLx completed.
 Selected model: XGBForecaster
 Selected model params: {&#39;differencing_order&#39;: 1, &#39;acf_local_maxima&#39;: &#39;[4, 9, 18, 22, 14, 31, 26, 12, 1]&#39;, &#39;use_X&#39;: True, &#39;n_estimators&#39;: 16, &#39;max_depth&#39;: 2}
 </pre>
@@ -16765,19 +16729,19 @@ <h3 id="Specify-the-number-of-cross-validation-(CV)-folds">Specify the number of
 
 
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-<pre>[2024-12-26 06:02:23,859] [automlx.data_transform] Number of simple differencing orders required: d = 0
-[2024-12-26 06:02:23,861] [automlx.data_transform] Seasonal Periodicities; from decomposed/adjusted: [1]
-[2024-12-26 06:02:23,951] [automlx.interface] Dataset shape: (90,5)
-[2024-12-26 06:02:23,994] [automlx.interface] Model Tune disabled.
-[2024-12-26 06:02:24,068] [automlx.data_transform] Running preprocessing. Number of features: 6
-[2024-12-26 06:02:24,177] [automlx.data_transform] Preprocessing completed. Took 0.109 secs
-[2024-12-26 06:02:24,213] [automlx.process] Running Model Generation
-[2024-12-26 06:02:24,251] [automlx.process] Model Generation completed.
-[2024-12-26 06:02:24,297] [automlx.model_selection] Running Model Selection
-[2024-12-26 06:02:24,297] [automlx.trials] Fewer models (2) than top_k (2) provided, skipping...
-[2024-12-26 06:02:24,363] [automlx.interface] Re-fitting pipeline
-[2024-12-26 06:02:24,381] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_f5a58512-a
-[2024-12-26 06:02:26,317] [automlx.interface] AutoMLx completed.
+<pre>[2025-04-25 03:14:56,679] [automlx.data_transform] Number of simple differencing orders required: d = 0
+[2025-04-25 03:14:56,681] [automlx.data_transform] Seasonal Periodicities; from decomposed/adjusted: [1]
+[2025-04-25 03:14:56,731] [automlx.interface] Dataset shape: (90,5)
+[2025-04-25 03:14:56,773] [automlx.interface] Model Tune disabled.
+[2025-04-25 03:14:56,812] [automlx.data_transform] Running preprocessing. Number of features: 6
+[2025-04-25 03:14:56,927] [automlx.data_transform] Preprocessing completed. Took 0.115 secs
+[2025-04-25 03:14:56,955] [automlx.process] Running Model Generation
+[2025-04-25 03:14:56,995] [automlx.process] Model Generation completed.
+[2025-04-25 03:14:57,028] [automlx.model_selection] Running Model Selection
+[2025-04-25 03:14:57,029] [automlx.trials] Fewer models (2) than top_k (2) provided, skipping...
+[2025-04-25 03:14:57,063] [automlx.interface] Re-fitting pipeline
+[2025-04-25 03:14:57,080] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_d557d931-8
+[2025-04-25 03:14:58,713] [automlx.interface] AutoMLx completed.
 </pre>
 </div>
 </div>
@@ -16791,7 +16755,7 @@ <h3 id="Specify-the-number-of-cross-validation-(CV)-folds">Specify the number of
 
 
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-<pre>&lt;automlx._interface.forecaster.AutoForecaster at 0x1536f1ce2dc0&gt;</pre>
+<pre>&lt;automlx._interface.forecaster.AutoForecaster at 0x145fb2ad68b0&gt;</pre>
 </div>
 
 </div>
@@ -16847,7 +16811,7 @@ <h3 id="Specify-the-number-of-cross-validation-(CV)-folds">Specify the number of
 
 
 <div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
-<pre>[2024-12-26 06:02:26,803] [automlx.model] Predict called on DynFactorForecaster that&#39;s not been fitted.
+<pre>[2025-04-25 03:14:58,824] [automlx.model] Predict called on DynFactorForecaster that&#39;s not been fitted.
 Ranked models: [&#39;DynFactorForecaster&#39;, &#39;VARMAXForecaster&#39;]
 Selected model: DynFactorForecaster
 Selected model params: {&#39;k_factors&#39;: 1, &#39;error_order&#39;: 2, &#39;factor_order&#39;: 2, &#39;error_cov_type&#39;: &#39;diagonal&#39;, &#39;use_X&#39;: True, &#39;error_var&#39;: False, &#39;enforce_stationarity&#39;: True}
@@ -16922,8 +16886,8 @@ <h3 id="Make-predictions">Make predictions<a class="anchor-link" href="#Make-pre
 
 
 <div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
-<pre>[2024-12-26 06:02:27,002] [automlx.model] Predict called on DynFactorForecaster that&#39;s not been fitted.
-[2024-12-26 06:02:27,024] [automlx.model] Forecast called on DynFactorForecaster that&#39;s not been fitted.
+<pre>[2025-04-25 03:14:58,902] [automlx.model] Predict called on DynFactorForecaster that&#39;s not been fitted.
+[2025-04-25 03:14:58,923] [automlx.model] Forecast called on DynFactorForecaster that&#39;s not been fitted.
 </pre>
 </div>
 </div>
@@ -17125,7 +17089,7 @@ <h3 id="Make-predictions">Make predictions<a class="anchor-link" href="#Make-pre
 
 
 <div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
-<pre>[2024-12-26 06:02:27,225] [automlx.model] Predict called on DynFactorForecaster that&#39;s not been fitted.
+<pre>[2025-04-25 03:14:59,059] [automlx.model] Predict called on DynFactorForecaster that&#39;s not been fitted.
 Tuned model testing score (negative sMAPE):  -0.45821993166134833
 </pre>
 </div>
@@ -17190,9 +17154,9 @@ <h3 id="Visualization">Visualization<a class="anchor-link" href="#Visualization"
 
 
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diff --git a/demos/OracleAutoMLx_Forecasting.ipynb b/demos/OracleAutoMLx_Forecasting.ipynb
index 9907749..edaa378 100644
--- a/demos/OracleAutoMLx_Forecasting.ipynb
+++ b/demos/OracleAutoMLx_Forecasting.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "66d43fc7",
+   "id": "e2dba652",
    "metadata": {},
    "source": [
     "***\n",
@@ -14,19 +14,19 @@
   },
   {
    "cell_type": "markdown",
-   "id": "af464207",
+   "id": "72e85a5a",
    "metadata": {},
    "source": [
     "Forecasting Demo notebook.\n",
     "\n",
-    "Copyright © 2024, Oracle and/or its affiliates.\n",
+    "Copyright © 2025, Oracle and/or its affiliates.\n",
     "\n",
     "Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "5b4d0868",
+   "id": "10766224",
    "metadata": {},
    "source": [
     "## Overview of this Notebook\n",
@@ -78,7 +78,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "47c2b101",
+   "id": "4f8cd973",
    "metadata": {},
    "source": [
     "<a id='setup'></a>\n",
@@ -90,13 +90,13 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "1294c375",
+   "id": "0ee2deb5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:00:52.631951Z",
-     "iopub.status.busy": "2024-12-26T14:00:52.631604Z",
-     "iopub.status.idle": "2024-12-26T14:00:53.166461Z",
-     "shell.execute_reply": "2024-12-26T14:00:53.165780Z"
+     "iopub.execute_input": "2025-04-25T10:13:42.269625Z",
+     "iopub.status.busy": "2025-04-25T10:13:42.269212Z",
+     "iopub.status.idle": "2025-04-25T10:13:42.802212Z",
+     "shell.execute_reply": "2025-04-25T10:13:42.801679Z"
     }
    },
    "outputs": [],
@@ -108,7 +108,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "539b4d5d",
+   "id": "bf19498b",
    "metadata": {
     "lines_to_next_cell": 0
    },
@@ -119,13 +119,13 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "b861f174",
+   "id": "7f98170e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:00:53.169131Z",
-     "iopub.status.busy": "2024-12-26T14:00:53.168872Z",
-     "iopub.status.idle": "2024-12-26T14:00:57.867074Z",
-     "shell.execute_reply": "2024-12-26T14:00:57.866480Z"
+     "iopub.execute_input": "2025-04-25T10:13:42.804699Z",
+     "iopub.status.busy": "2025-04-25T10:13:42.804220Z",
+     "iopub.status.idle": "2025-04-25T10:13:48.150979Z",
+     "shell.execute_reply": "2025-04-25T10:13:48.150414Z"
     }
    },
    "outputs": [],
@@ -155,7 +155,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f5f4dc50",
+   "id": "7909aaa6",
    "metadata": {},
    "source": [
     "<a id='load-m4'></a>\n",
@@ -168,13 +168,13 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "4cfc8842",
+   "id": "e6b68f77",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:00:57.870076Z",
-     "iopub.status.busy": "2024-12-26T14:00:57.869280Z",
-     "iopub.status.idle": "2024-12-26T14:01:00.086625Z",
-     "shell.execute_reply": "2024-12-26T14:01:00.085787Z"
+     "iopub.execute_input": "2025-04-25T10:13:48.153400Z",
+     "iopub.status.busy": "2025-04-25T10:13:48.152987Z",
+     "iopub.status.idle": "2025-04-25T10:13:50.716740Z",
+     "shell.execute_reply": "2025-04-25T10:13:50.716027Z"
     }
    },
    "outputs": [],
@@ -186,13 +186,13 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "a6262d45",
+   "id": "90c3c3a0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:01:00.089875Z",
-     "iopub.status.busy": "2024-12-26T14:01:00.089168Z",
-     "iopub.status.idle": "2024-12-26T14:01:00.299637Z",
-     "shell.execute_reply": "2024-12-26T14:01:00.298950Z"
+     "iopub.execute_input": "2025-04-25T10:13:50.719199Z",
+     "iopub.status.busy": "2025-04-25T10:13:50.718731Z",
+     "iopub.status.idle": "2025-04-25T10:13:50.926656Z",
+     "shell.execute_reply": "2025-04-25T10:13:50.926090Z"
     }
    },
    "outputs": [],
@@ -204,7 +204,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3277a952",
+   "id": "2465a691",
    "metadata": {},
    "source": [
     "<a id='univariate'></a>\n",
@@ -214,7 +214,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2529c7b6",
+   "id": "150ada09",
    "metadata": {},
    "source": [
     "We select one time series from the finance sector with weekly collection frequency. M4 dataset requires additional preprocessing to reconstruct the time series."
@@ -223,13 +223,13 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "5ebf3bcf",
+   "id": "c2ec1d8b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:01:00.301746Z",
-     "iopub.status.busy": "2024-12-26T14:01:00.301272Z",
-     "iopub.status.idle": "2024-12-26T14:01:00.347078Z",
-     "shell.execute_reply": "2024-12-26T14:01:00.346481Z"
+     "iopub.execute_input": "2025-04-25T10:13:50.929099Z",
+     "iopub.status.busy": "2025-04-25T10:13:50.928731Z",
+     "iopub.status.idle": "2025-04-25T10:13:50.974262Z",
+     "shell.execute_reply": "2025-04-25T10:13:50.973751Z"
     }
    },
    "outputs": [],
@@ -253,7 +253,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e17f99d4",
+   "id": "247177a2",
    "metadata": {},
    "source": [
     "We can now visualize the last 200 weeks of data we have on hand."
@@ -262,13 +262,13 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "26fd2fb3",
+   "id": "487fd050",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:01:00.349362Z",
-     "iopub.status.busy": "2024-12-26T14:01:00.348805Z",
-     "iopub.status.idle": "2024-12-26T14:01:00.603729Z",
-     "shell.execute_reply": "2024-12-26T14:01:00.603093Z"
+     "iopub.execute_input": "2025-04-25T10:13:50.976301Z",
+     "iopub.status.busy": "2025-04-25T10:13:50.975886Z",
+     "iopub.status.idle": "2025-04-25T10:13:51.265606Z",
+     "shell.execute_reply": "2025-04-25T10:13:51.265073Z"
     }
    },
    "outputs": [
@@ -300,7 +300,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a1f2ccce",
+   "id": "1ea52a15",
    "metadata": {},
    "source": [
     "One must ensure that the data points are in a Pandas DataFrame, sorted in chronological order."
@@ -309,13 +309,13 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "cd90f1e3",
+   "id": "abf33df9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:01:00.605826Z",
-     "iopub.status.busy": "2024-12-26T14:01:00.605285Z",
-     "iopub.status.idle": "2024-12-26T14:01:00.631855Z",
-     "shell.execute_reply": "2024-12-26T14:01:00.631287Z"
+     "iopub.execute_input": "2025-04-25T10:13:51.267650Z",
+     "iopub.status.busy": "2025-04-25T10:13:51.267225Z",
+     "iopub.status.idle": "2025-04-25T10:13:51.293566Z",
+     "shell.execute_reply": "2025-04-25T10:13:51.293077Z"
     }
    },
    "outputs": [
@@ -348,7 +348,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a71d06cb",
+   "id": "49654a93",
    "metadata": {},
    "source": [
     "<a id='task'></a>\n",
@@ -359,13 +359,13 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "711b1778",
+   "id": "2b84708b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:01:00.633612Z",
-     "iopub.status.busy": "2024-12-26T14:01:00.633268Z",
-     "iopub.status.idle": "2024-12-26T14:01:00.657980Z",
-     "shell.execute_reply": "2024-12-26T14:01:00.657374Z"
+     "iopub.execute_input": "2025-04-25T10:13:51.295363Z",
+     "iopub.status.busy": "2025-04-25T10:13:51.294960Z",
+     "iopub.status.idle": "2025-04-25T10:13:51.319238Z",
+     "shell.execute_reply": "2025-04-25T10:13:51.318736Z"
     }
    },
    "outputs": [
@@ -385,13 +385,13 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "bd10f162",
+   "id": "0c1c51b0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:01:00.659972Z",
-     "iopub.status.busy": "2024-12-26T14:01:00.659442Z",
-     "iopub.status.idle": "2024-12-26T14:01:00.690573Z",
-     "shell.execute_reply": "2024-12-26T14:01:00.689991Z"
+     "iopub.execute_input": "2025-04-25T10:13:51.320902Z",
+     "iopub.status.busy": "2025-04-25T10:13:51.320598Z",
+     "iopub.status.idle": "2025-04-25T10:13:51.354938Z",
+     "shell.execute_reply": "2025-04-25T10:13:51.354471Z"
     }
    },
    "outputs": [
@@ -451,7 +451,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a9af8822",
+   "id": "c078d9bb",
    "metadata": {},
    "source": [
     "<a id='Engine'></a>\n",
@@ -462,13 +462,13 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "9a3a79c3",
+   "id": "b0eb6ea8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:01:00.692459Z",
-     "iopub.status.busy": "2024-12-26T14:01:00.692046Z",
-     "iopub.status.idle": "2024-12-26T14:01:06.091795Z",
-     "shell.execute_reply": "2024-12-26T14:01:06.090822Z"
+     "iopub.execute_input": "2025-04-25T10:13:51.356611Z",
+     "iopub.status.busy": "2025-04-25T10:13:51.356329Z",
+     "iopub.status.idle": "2025-04-25T10:13:55.789689Z",
+     "shell.execute_reply": "2025-04-25T10:13:55.788679Z"
     }
    },
    "outputs": [
@@ -476,7 +476,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:01:01,052] [automlx.backend] Overwriting ray session directory to /tmp/rnkq7h6h/ray, which will be deleted at engine shutdown. If you wish to retain ray logs, provide _temp_dir in ray_setup dict of engine_opts when initializing the AutoMLx engine.\n"
+      "[2025-04-25 03:13:51,723] [automlx.backend] Overwriting ray session directory to /tmp/427vi97k/ray, which will be deleted at engine shutdown. If you wish to retain ray logs, provide _temp_dir in ray_setup dict of engine_opts when initializing the AutoMLx engine.\n"
      ]
     }
    ],
@@ -486,7 +486,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "501fd8b9",
+   "id": "fc1c98d9",
    "metadata": {},
    "source": [
     "<a id='provider'></a>\n",
@@ -521,7 +521,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "562b904c",
+   "id": "b675e1a6",
    "metadata": {},
    "source": [
     "<a id='default'></a>\n",
@@ -535,13 +535,13 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "11bf7a7b",
+   "id": "b7292c53",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:01:06.095615Z",
-     "iopub.status.busy": "2024-12-26T14:01:06.095007Z",
-     "iopub.status.idle": "2024-12-26T14:01:54.718085Z",
-     "shell.execute_reply": "2024-12-26T14:01:54.717503Z"
+     "iopub.execute_input": "2025-04-25T10:13:55.793504Z",
+     "iopub.status.busy": "2025-04-25T10:13:55.792580Z",
+     "iopub.status.idle": "2025-04-25T10:14:41.527344Z",
+     "shell.execute_reply": "2025-04-25T10:14:41.526655Z"
     }
    },
    "outputs": [
@@ -549,182 +549,182 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:01:15,735] [automlx.data_transform] Number of simple differencing orders required: d = 1\n"
+      "[2025-04-25 03:14:06,599] [automlx.data_transform] Number of simple differencing orders required: d = 1\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:01:15,738] [automlx.data_transform] Seasonal Periodicities; from decomposed/adjusted: [52, 35, 1]\n"
+      "[2025-04-25 03:14:06,602] [automlx.data_transform] Seasonal Periodicities; from decomposed/adjusted: [52, 35, 1]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:01:15,805] [automlx.interface] Dataset shape: (174,2)\n"
+      "[2025-04-25 03:14:06,666] [automlx.interface] Dataset shape: (174,2)\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:01:19,506] [sanerec.autotuning.parameter] Hyperparameter epsilon autotune range is set to its validation range. This could lead to long training times\n"
+      "[2025-04-25 03:14:10,990] [sanerec.autotuning.parameter] Hyperparameter epsilon autotune range is set to its validation range. This could lead to long training times\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:01:20,048] [sanerec.autotuning.parameter] Hyperparameter repeat_quality_threshold autotune range is set to its validation range. This could lead to long training times\n"
+      "[2025-04-25 03:14:11,547] [sanerec.autotuning.parameter] Hyperparameter repeat_quality_threshold autotune range is set to its validation range. This could lead to long training times\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:01:20,058] [sanerec.autotuning.parameter] Hyperparameter scope autotune range is set to its validation range. This could lead to long training times\n"
+      "[2025-04-25 03:14:11,557] [sanerec.autotuning.parameter] Hyperparameter scope autotune range is set to its validation range. This could lead to long training times\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:01:20,133] [automlx.data_transform] Running preprocessing. Number of features: 3\n"
+      "[2025-04-25 03:14:11,627] [automlx.data_transform] Running preprocessing. Number of features: 3\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:01:20,221] [automlx.data_transform] Preprocessing completed. Took 0.089 secs\n"
+      "[2025-04-25 03:14:11,712] [automlx.data_transform] Preprocessing completed. Took 0.085 secs\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:01:20,232] [automlx.process] Running Model Generation\n"
+      "[2025-04-25 03:14:11,736] [automlx.process] Running Model Generation\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:01:20,282] [automlx] Provided model (VARMAXForecaster) is not supported.Supported models are: ['LGBMForecaster', 'ExtraTreesForecaster', 'ExpSmoothForecaster', 'NaiveForecaster', 'SARIMAXForecaster', 'STLwARIMAForecaster', 'ThetaForecaster', 'ProphetForecaster', 'ETSForecaster', 'STLwESForecaster', 'XGBForecaster']\n"
+      "[2025-04-25 03:14:11,786] [automlx] Provided model (VARMAXForecaster) is not supported.Supported models are: ['LGBMForecaster', 'ExtraTreesForecaster', 'ExpSmoothForecaster', 'NaiveForecaster', 'SARIMAXForecaster', 'STLwARIMAForecaster', 'ThetaForecaster', 'ProphetForecaster', 'ETSForecaster', 'STLwESForecaster', 'XGBForecaster']\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:01:20,283] [automlx] Provided model (DynFactorForecaster) is not supported.Supported models are: ['LGBMForecaster', 'ExtraTreesForecaster', 'ExpSmoothForecaster', 'NaiveForecaster', 'SARIMAXForecaster', 'STLwARIMAForecaster', 'ThetaForecaster', 'ProphetForecaster', 'ETSForecaster', 'STLwESForecaster', 'XGBForecaster']\n"
+      "[2025-04-25 03:14:11,787] [automlx] Provided model (DynFactorForecaster) is not supported.Supported models are: ['LGBMForecaster', 'ExtraTreesForecaster', 'ExpSmoothForecaster', 'NaiveForecaster', 'SARIMAXForecaster', 'STLwARIMAForecaster', 'ThetaForecaster', 'ProphetForecaster', 'ETSForecaster', 'STLwESForecaster', 'XGBForecaster']\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:01:20,284] [automlx.process] Model Generation completed.\n"
+      "[2025-04-25 03:14:11,788] [automlx.process] Model Generation completed.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:01:20,351] [automlx.model_selection] Running Model Selection\n"
+      "[2025-04-25 03:14:11,852] [automlx.model_selection] Running Model Selection\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:01:24,247] [automlx.dataset] Forecast horizon set to 13 for validation sets.\n"
+      "[2025-04-25 03:14:15,674] [automlx.dataset] Forecast horizon set to 13 for validation sets.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:01:38,911] [automlx.model_selection] Model Selection completed - Took 18.560 sec - Selected models: [['ETSForecaster', 'STLwARIMAForecaster']]\n"
+      "[2025-04-25 03:14:27,185] [automlx.model_selection] Model Selection completed - Took 15.332 sec - Selected models: [['ETSForecaster', 'STLwARIMAForecaster']]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:01:39,075] [automlx.trials] Running Model Tuning for ['ETSForecaster']\n"
+      "[2025-04-25 03:14:27,240] [automlx.trials] Running Model Tuning for ['ETSForecaster']\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:01:39,148] [automlx.dataset] Forecast horizon set to 13 for validation sets.\n"
+      "[2025-04-25 03:14:27,314] [automlx.dataset] Forecast horizon set to 13 for validation sets.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:01:47,060] [automlx.trials] Best parameters for ETSForecaster: {'error': 'add', 'trend': 'add', 'damped_trend': True, 'seasonal': 'add', 'seasonal_periods': 52, 'initialization_method': 'estimated', 'initial_level': None, 'initial_trend': None, 'initial_seasonal': None, 'bounds': None, 'dates': None, 'freq': None, 'missing': 'none', 'start_params': None, 'maxiter': 1500, 'disp': -1, 'return_params': False}\n"
+      "[2025-04-25 03:14:34,808] [automlx.trials] Best parameters for ETSForecaster: {'error': 'add', 'trend': 'add', 'damped_trend': True, 'seasonal': 'add', 'seasonal_periods': 52, 'initialization_method': 'estimated', 'initial_level': None, 'initial_trend': None, 'initial_seasonal': None, 'bounds': None, 'dates': None, 'freq': None, 'missing': 'none', 'start_params': None, 'maxiter': 1500, 'disp': -1, 'return_params': False}\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:01:47,062] [automlx.trials] Model Tuning completed. Took: 7.986 secs\n"
+      "[2025-04-25 03:14:34,809] [automlx.trials] Model Tuning completed. Took: 7.569 secs\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:01:47,245] [automlx.trials] Running Model Tuning for ['STLwARIMAForecaster']\n"
+      "[2025-04-25 03:14:34,939] [automlx.trials] Running Model Tuning for ['STLwARIMAForecaster']\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:01:47,330] [automlx.dataset] Forecast horizon set to 13 for validation sets.\n"
+      "[2025-04-25 03:14:35,020] [automlx.dataset] Forecast horizon set to 13 for validation sets.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:01:50,145] [automlx.trials] Best parameters for STLwARIMAForecaster: {'seasonal_deg': 1, 'trend_deg': 1, 'low_pass_deg': 1, 'period': 52, 'arima_p': 2, 'arima_d': 1, 'arima_q': 2, 'arima_trend': 'n', 'concentrate_scale': True}\n"
+      "[2025-04-25 03:14:37,741] [automlx.trials] Best parameters for STLwARIMAForecaster: {'seasonal_deg': 1, 'trend_deg': 1, 'low_pass_deg': 1, 'period': 52, 'arima_p': 2, 'arima_d': 1, 'arima_q': 2, 'arima_trend': 'n', 'concentrate_scale': True}\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:01:50,146] [automlx.trials] Model Tuning completed. Took: 2.901 secs\n"
+      "[2025-04-25 03:14:37,742] [automlx.trials] Model Tuning completed. Took: 2.803 secs\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:01:52,195] [automlx.interface] Re-fitting pipeline\n"
+      "[2025-04-25 03:14:39,547] [automlx.interface] Re-fitting pipeline\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:01:52,219] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_ffdf6aab-0\n"
+      "[2025-04-25 03:14:39,570] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_615a3221-8\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:01:53,994] [automlx.interface] AutoMLx completed.\n"
+      "[2025-04-25 03:14:41,143] [automlx.interface] AutoMLx completed.\n"
      ]
     },
     {
@@ -746,7 +746,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f9ffd253",
+   "id": "6da55d4f",
    "metadata": {},
    "source": [
     "<a id='forecast'></a>\n",
@@ -758,31 +758,23 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "28fc3ff1",
+   "id": "99e493b7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:01:54.720675Z",
-     "iopub.status.busy": "2024-12-26T14:01:54.719759Z",
-     "iopub.status.idle": "2024-12-26T14:01:55.028037Z",
-     "shell.execute_reply": "2024-12-26T14:01:55.027419Z"
+     "iopub.execute_input": "2025-04-25T10:14:41.529838Z",
+     "iopub.status.busy": "2025-04-25T10:14:41.529179Z",
+     "iopub.status.idle": "2025-04-25T10:14:41.908579Z",
+     "shell.execute_reply": "2025-04-25T10:14:41.907974Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[2024-12-26 06:01:55,016] [automlx.model] Forecast called on ETSForecaster that's not been fitted.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "predictions = est1.forecast(periods=len(y_test), alpha=0.05)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "a81e89e0",
+   "id": "33b94c7e",
    "metadata": {},
    "source": [
     "The `predict(X)` interface supports absolute index-based forecasts, but does not support confidence intervals (CIs). It also downcasts the index to int64.\n",
@@ -793,13 +785,13 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "bb50e805",
+   "id": "3400d47a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:01:55.030328Z",
-     "iopub.status.busy": "2024-12-26T14:01:55.029815Z",
-     "iopub.status.idle": "2024-12-26T14:01:55.083321Z",
-     "shell.execute_reply": "2024-12-26T14:01:55.082701Z"
+     "iopub.execute_input": "2025-04-25T10:14:41.911054Z",
+     "iopub.status.busy": "2025-04-25T10:14:41.910545Z",
+     "iopub.status.idle": "2025-04-25T10:14:41.963369Z",
+     "shell.execute_reply": "2025-04-25T10:14:41.962860Z"
     }
    },
    "outputs": [
@@ -824,23 +816,16 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "c2bd77c4",
+   "id": "a6801dc2",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:01:55.085319Z",
-     "iopub.status.busy": "2024-12-26T14:01:55.084903Z",
-     "iopub.status.idle": "2024-12-26T14:01:55.156107Z",
-     "shell.execute_reply": "2024-12-26T14:01:55.155521Z"
+     "iopub.execute_input": "2025-04-25T10:14:41.965231Z",
+     "iopub.status.busy": "2025-04-25T10:14:41.964850Z",
+     "iopub.status.idle": "2025-04-25T10:14:42.037263Z",
+     "shell.execute_reply": "2025-04-25T10:14:42.036810Z"
     }
    },
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[2024-12-26 06:01:55,144] [automlx.model] Predict called on ETSForecaster that's not been fitted.\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
@@ -935,7 +920,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "78053c92",
+   "id": "637c6f21",
    "metadata": {},
    "source": [
     "AutoML provides a simple one-line tool to visualize forecasts and confidence intervals."
@@ -944,13 +929,13 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "3d5af2b0",
+   "id": "5ccd034c",
    "metadata": {
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-     "iopub.execute_input": "2024-12-26T14:01:55.158102Z",
-     "iopub.status.busy": "2024-12-26T14:01:55.157623Z",
-     "iopub.status.idle": "2024-12-26T14:01:55.555134Z",
-     "shell.execute_reply": "2024-12-26T14:01:55.554472Z"
+     "iopub.execute_input": "2025-04-25T10:14:42.039032Z",
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+     "shell.execute_reply": "2025-04-25T10:14:42.460954Z"
     }
    },
    "outputs": [
@@ -3304,9 +3289,9 @@
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Model: ETSForecaster\\u003cbr\\u003eModel params: {'error': 'add', 'trend': 'add', 'damped_trend': True, 'seasonal': 'add', 'seasonal_periods': 52, 'initialization_method': 'estimated', 'initial_level': None, 'initial_trend': None, 'initial_seasonal': None, 'bounds': None, 'dates': None, 'freq': None, 'missing': 'none', 'start_params': None, 'maxiter': 1500, 'disp': -1, 'return_params': False}\",\"x\":0.5},\"margin\":{\"t\":75,\"b\":50},\"plot_bgcolor\":\"rgba(0,0,0,0)\",\"height\":350,\"xaxis\":{\"title\":{\"text\":\"Date\"},\"gridcolor\":\"LightGrey\",\"linewidth\":1,\"linecolor\":\"Grey\",\"showline\":true,\"mirror\":true,\"zeroline\":true,\"zerolinewidth\":1,\"zerolinecolor\":\"DarkGrey\",\"categoryorder\":\"category 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        "                            \n",
-       "var gd = document.getElementById('26cc1fe9-5736-41b0-b921-c1a4b5fcb7e0');\n",
+       "var gd = document.getElementById('03df2137-8b65-4cbc-be2e-8d8d290c05bb');\n",
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@@ -3341,7 +3326,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dc33f7a0",
+   "id": "0fb1ae28",
    "metadata": {},
    "source": [
     "<a id='analysis'></a>\n",
@@ -3356,7 +3341,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "77a6a48c",
+   "id": "cb97c537",
    "metadata": {},
    "source": [
     "AutoML provides a `print_summary()` API to output all the different trials performed."
@@ -3365,13 +3350,13 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "421fc70e",
+   "id": "dda7b8e0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:01:55.582915Z",
-     "iopub.status.busy": "2024-12-26T14:01:55.582426Z",
-     "iopub.status.idle": "2024-12-26T14:01:55.651871Z",
-     "shell.execute_reply": "2024-12-26T14:01:55.651363Z"
+     "iopub.execute_input": "2025-04-25T10:14:42.490707Z",
+     "iopub.status.busy": "2025-04-25T10:14:42.490081Z",
+     "iopub.status.idle": "2025-04-25T10:14:42.557691Z",
+     "shell.execute_reply": "2025-04-25T10:14:42.557184Z"
     }
    },
    "outputs": [
@@ -3416,7 +3401,7 @@
        "      <td>{'error': 'add', 'trend': 'add', 'damped_trend': True, 'seasonal': 'add', 'seasonal_periods': 52, 'initialization_method': 'estimated', 'initial_level': None, 'initial_trend': None, 'initial_seasonal': None, 'bounds': None, 'dates': None, 'freq': None, 'missing': 'none', 'start_params': None, 'maxiter': 1500, 'disp': -1, 'return_params': False}</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>25.1.0</td>\n",
+       "      <td>25.2.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>3.9.21 (main, Dec 11 2024, 16:24:11) \\n[GCC 11.2.0]</td>\n",
@@ -3470,69 +3455,69 @@
        "      <td>{'error': 'add', 'trend': 'add', 'damped_trend': False, 'seasonal': 'add', 'seasonal_periods': 52, 'initialization_method': 'estimated', 'initial_level': None, 'initial_trend': None, 'initial_seasonal': None, 'bounds': None, 'dates': None, 'freq': None, 'missing': 'none', 'start_params': None, 'maxiter': 1500, 'disp': -1, 'return_params': False}</td>\n",
        "      <td>-0.0361</td>\n",
        "      <td>{'neg_sym_mean_abs_percent_error': -0.03605508323686911}</td>\n",
-       "      <td>6.3818</td>\n",
-       "      <td>0.2981</td>\n",
-       "      <td>Thu Dec 26 06:01:31 2024</td>\n",
+       "      <td>5.5388</td>\n",
+       "      <td>0.2991</td>\n",
+       "      <td>Fri Apr 25 03:14:22 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Selection</td>\n",
-       "      <td>{1: 109, 2: 122, 3: 135, 4: 148, 5: 161}</td>\n",
+       "      <td>{2: 122, 1: 109, 3: 135, 4: 148, 5: 161}</td>\n",
        "      <td>2</td>\n",
        "      <td>STLwARIMAForecaster</td>\n",
        "      <td>{'seasonal_deg': 1, 'trend_deg': 1, 'low_pass_deg': 0, 'period': 52, 'arima_p': 2, 'arima_d': 1, 'arima_q': 2, 'arima_trend': 'n', 'concentrate_scale': True}</td>\n",
        "      <td>-0.0855</td>\n",
        "      <td>{'neg_sym_mean_abs_percent_error': -0.08549289627140871}</td>\n",
-       "      <td>1.7426</td>\n",
-       "      <td>0.3161</td>\n",
-       "      <td>Thu Dec 26 06:01:30 2024</td>\n",
+       "      <td>1.6676</td>\n",
+       "      <td>0.3153</td>\n",
+       "      <td>Fri Apr 25 03:14:22 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Selection</td>\n",
-       "      <td>{1: 109, 2: 122, 3: 135, 5: 161, 4: 148}</td>\n",
+       "      <td>{1: 109, 2: 122, 3: 135, 4: 148, 5: 161}</td>\n",
        "      <td>2</td>\n",
        "      <td>XGBForecaster</td>\n",
        "      <td>{'differencing_order': 1, 'acf_local_maxima': '[52, 35, 17, 26, 22, 4, 48, 30, 13, 56, 39, 61, 44, 9, 65, 1]', 'use_X': False, 'n_estimators': 50, 'max_depth': 5}</td>\n",
        "      <td>-0.0951</td>\n",
-       "      <td>{'neg_sym_mean_abs_percent_error': -0.09511310353650582}</td>\n",
-       "      <td>22.3198</td>\n",
-       "      <td>0.7327</td>\n",
-       "      <td>Thu Dec 26 06:01:38 2024</td>\n",
+       "      <td>{'neg_sym_mean_abs_percent_error': -0.09511310353650583}</td>\n",
+       "      <td>4.3645</td>\n",
+       "      <td>0.3241</td>\n",
+       "      <td>Fri Apr 25 03:14:24 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Selection</td>\n",
-       "      <td>{3: 135, 1: 109, 2: 122, 5: 161, 4: 148}</td>\n",
+       "      <td>{2: 122, 5: 161, 1: 109, 3: 135, 4: 148}</td>\n",
        "      <td>2</td>\n",
        "      <td>ExtraTreesForecaster</td>\n",
        "      <td>{'differencing_order': 1, 'acf_local_maxima': '[52, 35, 17, 26, 22, 4, 48, 30, 13, 56, 39, 61, 44, 9, 65, 1]', 'use_X': False, 'n_estimators': 40, 'min_samples_leaf': 0.030458}</td>\n",
        "      <td>-0.1252</td>\n",
        "      <td>{'neg_sym_mean_abs_percent_error': -0.12522681848759207}</td>\n",
-       "      <td>23.5931</td>\n",
-       "      <td>0.7242</td>\n",
-       "      <td>Thu Dec 26 06:01:35 2024</td>\n",
+       "      <td>4.9313</td>\n",
+       "      <td>0.3217</td>\n",
+       "      <td>Fri Apr 25 03:14:23 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Selection</td>\n",
-       "      <td>{2: 122, 1: 109, 3: 135, 5: 161, 4: 148}</td>\n",
+       "      <td>{2: 122, 3: 135, 1: 109, 4: 148, 5: 161}</td>\n",
        "      <td>2</td>\n",
        "      <td>LGBMForecaster</td>\n",
        "      <td>{'differencing_order': 1, 'acf_local_maxima': '[52, 35, 17, 26, 22, 4, 48, 30, 13, 56, 39, 61, 44, 9, 65, 1]', 'use_X': False, 'max_depth': 4, 'n_estimators': 37}</td>\n",
        "      <td>-0.13</td>\n",
        "      <td>{'neg_sym_mean_abs_percent_error': -0.13003654391153155}</td>\n",
-       "      <td>10.1184</td>\n",
-       "      <td>0.7436</td>\n",
-       "      <td>Thu Dec 26 06:01:38 2024</td>\n",
+       "      <td>5.0983</td>\n",
+       "      <td>0.3536</td>\n",
+       "      <td>Fri Apr 25 03:14:25 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Selection</td>\n",
-       "      <td>{1: 109, 4: 148, 2: 122, 3: 135, 5: 161}</td>\n",
+       "      <td>{2: 122, 3: 135, 1: 109, 5: 161, 4: 148}</td>\n",
        "      <td>2</td>\n",
        "      <td>ThetaForecaster</td>\n",
        "      <td>{'sp': 52, 'deseasonalize': False, 'initial_level': None}</td>\n",
        "      <td>-0.2091</td>\n",
        "      <td>{'neg_sym_mean_abs_percent_error': -0.2090874120377979}</td>\n",
-       "      <td>11.9759</td>\n",
-       "      <td>0.3004</td>\n",
-       "      <td>Thu Dec 26 06:01:30 2024</td>\n",
+       "      <td>13.0709</td>\n",
+       "      <td>0.3049</td>\n",
+       "      <td>Fri Apr 25 03:14:21 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Selection</td>\n",
@@ -3542,45 +3527,45 @@
        "      <td>{'trend': 'add', 'damped_trend': True, 'seasonal': None, 'sp': 52, 'use_boxcox': False}</td>\n",
        "      <td>-0.2475</td>\n",
        "      <td>{'neg_sym_mean_abs_percent_error': -0.2474953233635102}</td>\n",
-       "      <td>7.9147</td>\n",
-       "      <td>0.2986</td>\n",
-       "      <td>Thu Dec 26 06:01:29 2024</td>\n",
+       "      <td>10.3261</td>\n",
+       "      <td>0.2994</td>\n",
+       "      <td>Fri Apr 25 03:14:21 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Selection</td>\n",
-       "      <td>{3: 135, 4: 148, 1: 109, 2: 122, 5: 161}</td>\n",
+       "      <td>{4: 148, 3: 135, 1: 109, 5: 161, 2: 122}</td>\n",
        "      <td>2</td>\n",
        "      <td>NaiveForecaster</td>\n",
        "      <td>{'strategy': 'last', 'sp': 52, 'window_length': None}</td>\n",
        "      <td>-0.4494</td>\n",
        "      <td>{'neg_sym_mean_abs_percent_error': -0.4493967043524097}</td>\n",
-       "      <td>9.6946</td>\n",
-       "      <td>0.2843</td>\n",
-       "      <td>Thu Dec 26 06:01:29 2024</td>\n",
+       "      <td>10.5111</td>\n",
+       "      <td>0.2871</td>\n",
+       "      <td>Fri Apr 25 03:14:21 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Selection</td>\n",
-       "      <td>{1: 109, 2: 122, 3: 135, 4: 148, 5: 161}</td>\n",
+       "      <td>{1: 109, 2: 122, 4: 148, 3: 135, 5: 161}</td>\n",
        "      <td>2</td>\n",
        "      <td>STLwESForecaster</td>\n",
        "      <td>{'seasonal_deg': 1, 'trend_deg': 1, 'low_pass_deg': 1, 'period': 52, 'es_trend': 'add', 'es_damped_trend': True, 'concentrate_scale': True}</td>\n",
        "      <td>-inf</td>\n",
        "      <td>{'neg_sym_mean_abs_percent_error': -inf}</td>\n",
-       "      <td>1.6243</td>\n",
-       "      <td>0.3093</td>\n",
-       "      <td>Thu Dec 26 06:01:30 2024</td>\n",
+       "      <td>1.9590</td>\n",
+       "      <td>0.3116</td>\n",
+       "      <td>Fri Apr 25 03:14:22 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Selection</td>\n",
-       "      <td>{1: 109, 2: 122, 4: 148, 3: 135, 5: 161}</td>\n",
+       "      <td>{1: 109, 3: 135, 2: 122, 4: 148, 5: 161}</td>\n",
        "      <td>2</td>\n",
        "      <td>SARIMAXForecaster</td>\n",
        "      <td>{'sp': 52, 'p': 2, 'd': 1, 'q': 2, 'P': 1, 'D': 0, 'Q': 1, 'trend': 'n', 'use_X': False, 'enforce_stationarity': True, 'enforce_invertibility': True, 'method': 'lbfgs', 'disp': -1, 'concentrate_scale': False}</td>\n",
        "      <td>-inf</td>\n",
        "      <td>{'neg_sym_mean_abs_percent_error': -inf}</td>\n",
-       "      <td>25.7013</td>\n",
-       "      <td>0.3546</td>\n",
-       "      <td>Thu Dec 26 06:01:36 2024</td>\n",
+       "      <td>19.8842</td>\n",
+       "      <td>0.3231</td>\n",
+       "      <td>Fri Apr 25 03:14:26 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>...</td>\n",
@@ -3596,14 +3581,26 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
+       "      <td>{2: 122, 1: 109, 3: 135, 5: 161, 4: 148}</td>\n",
+       "      <td>2</td>\n",
+       "      <td>ETSForecaster</td>\n",
+       "      <td>{'error': 'add', 'trend': 'add', 'damped_trend': False, 'seasonal': None, 'seasonal_periods': 1, 'initialization_method': 'estimated', 'initial_level': None, 'initial_trend': None, 'initial_seasonal': None, 'bounds': None, 'dates': None, 'freq': None, 'missing': 'none', 'start_params': None, 'maxiter': 1500, 'disp': -1, 'return_params': False}</td>\n",
+       "      <td>-0.2919</td>\n",
+       "      <td>{'neg_sym_mean_abs_percent_error': -0.29186918028720626}</td>\n",
+       "      <td>0.5513</td>\n",
+       "      <td>0.3295</td>\n",
+       "      <td>Fri Apr 25 03:14:31 2025</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>Model Tuning</td>\n",
        "      <td>None</td>\n",
        "      <td>0</td>\n",
        "      <td>ETSForecaster</td>\n",
        "      <td>None</td>\n",
        "      <td>-inf</td>\n",
        "      <td>None</td>\n",
-       "      <td>0.9222</td>\n",
-       "      <td>0.7255</td>\n",
+       "      <td>0.8307</td>\n",
+       "      <td>0.3423</td>\n",
        "      <td>-1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -3614,8 +3611,8 @@
        "      <td>None</td>\n",
        "      <td>-inf</td>\n",
        "      <td>None</td>\n",
-       "      <td>0.2932</td>\n",
-       "      <td>0.7519</td>\n",
+       "      <td>0.3418</td>\n",
+       "      <td>0.3551</td>\n",
        "      <td>-1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -3626,45 +3623,33 @@
        "      <td>{'error': 'add', 'trend': 'add', 'damped_trend': False, 'seasonal': 'add', 'seasonal_periods': 35, 'initialization_method': 'estimated', 'initial_level': None, 'initial_trend': None, 'initial_seasonal': None, 'bounds': None, 'dates': None, 'freq': None, 'missing': 'none', 'start_params': None, 'maxiter': 1500, 'disp': -1, 'return_params': False}</td>\n",
        "      <td>-inf</td>\n",
        "      <td>{'neg_sym_mean_abs_percent_error': -inf}</td>\n",
-       "      <td>6.9497</td>\n",
-       "      <td>0.7446</td>\n",
-       "      <td>Thu Dec 26 06:01:43 2024</td>\n",
+       "      <td>7.1072</td>\n",
+       "      <td>0.3547</td>\n",
+       "      <td>Fri Apr 25 03:14:32 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
-       "      <td>{1: 109, 2: 122, 3: 135, 4: 148, 5: 161}</td>\n",
+       "      <td>{2: 122, 1: 109, 4: 148, 5: 161, 3: 135}</td>\n",
        "      <td>2</td>\n",
        "      <td>ETSForecaster</td>\n",
        "      <td>{'error': 'add', 'trend': 'mul', 'damped_trend': False, 'seasonal': 'add', 'seasonal_periods': 52, 'initialization_method': 'estimated', 'initial_level': None, 'initial_trend': None, 'initial_seasonal': None, 'bounds': None, 'dates': None, 'freq': None, 'missing': 'none', 'start_params': None, 'maxiter': 1500, 'disp': -1, 'return_params': False}</td>\n",
        "      <td>-inf</td>\n",
        "      <td>{'neg_sym_mean_abs_percent_error': -inf}</td>\n",
-       "      <td>6.8606</td>\n",
-       "      <td>0.7366</td>\n",
-       "      <td>Thu Dec 26 06:01:45 2024</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>Model Tuning</td>\n",
-       "      <td>{1: 109, 2: 122, 3: 135, 4: 148, 5: 161}</td>\n",
-       "      <td>2</td>\n",
-       "      <td>ETSForecaster</td>\n",
-       "      <td>{'error': 'add', 'trend': None, 'damped_trend': False, 'seasonal': 'add', 'seasonal_periods': 52, 'initialization_method': 'estimated', 'initial_level': None, 'initial_trend': None, 'initial_seasonal': None, 'bounds': None, 'dates': None, 'freq': None, 'missing': 'none', 'start_params': None, 'maxiter': 1500, 'disp': -1, 'return_params': False}</td>\n",
-       "      <td>-inf</td>\n",
-       "      <td>{'neg_sym_mean_abs_percent_error': -inf}</td>\n",
-       "      <td>5.9248</td>\n",
-       "      <td>0.7447</td>\n",
-       "      <td>Thu Dec 26 06:01:45 2024</td>\n",
+       "      <td>6.7682</td>\n",
+       "      <td>0.3423</td>\n",
+       "      <td>Fri Apr 25 03:14:33 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
-       "      <td>{1: 109, 3: 135, 2: 122, 4: 148, 5: 161}</td>\n",
+       "      <td>{2: 122, 1: 109, 3: 135, 4: 148, 5: 161}</td>\n",
        "      <td>2</td>\n",
        "      <td>ETSForecaster</td>\n",
        "      <td>{'error': 'mul', 'trend': 'add', 'damped_trend': False, 'seasonal': 'add', 'seasonal_periods': 52, 'initialization_method': 'estimated', 'initial_level': None, 'initial_trend': None, 'initial_seasonal': None, 'bounds': None, 'dates': None, 'freq': None, 'missing': 'none', 'start_params': None, 'maxiter': 1500, 'disp': -1, 'return_params': False}</td>\n",
        "      <td>-inf</td>\n",
        "      <td>{'neg_sym_mean_abs_percent_error': -inf}</td>\n",
-       "      <td>11.7031</td>\n",
-       "      <td>0.7446</td>\n",
-       "      <td>Thu Dec 26 06:01:42 2024</td>\n",
+       "      <td>9.8725</td>\n",
+       "      <td>0.3547</td>\n",
+       "      <td>Fri Apr 25 03:14:30 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
@@ -3674,9 +3659,9 @@
        "      <td>{'seasonal_deg': 0, 'trend_deg': 0, 'low_pass_deg': 0, 'period': 52, 'arima_p': 0, 'arima_d': 1, 'arima_q': 0, 'arima_trend': 'n', 'concentrate_scale': True}</td>\n",
        "      <td>-inf</td>\n",
        "      <td>{'neg_sym_mean_abs_percent_error': -inf}</td>\n",
-       "      <td>0.4488</td>\n",
-       "      <td>0.7451</td>\n",
-       "      <td>Thu Dec 26 06:01:49 2024</td>\n",
+       "      <td>0.5099</td>\n",
+       "      <td>0.3397</td>\n",
+       "      <td>Fri Apr 25 03:14:37 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
@@ -3686,9 +3671,9 @@
        "      <td>{'seasonal_deg': 0, 'trend_deg': 1, 'low_pass_deg': 0, 'period': 52, 'arima_p': 2, 'arima_d': 1, 'arima_q': 2, 'arima_trend': 'n', 'concentrate_scale': True}</td>\n",
        "      <td>-inf</td>\n",
        "      <td>{'neg_sym_mean_abs_percent_error': -inf}</td>\n",
-       "      <td>1.5059</td>\n",
-       "      <td>0.7451</td>\n",
-       "      <td>Thu Dec 26 06:01:49 2024</td>\n",
+       "      <td>1.1175</td>\n",
+       "      <td>0.3551</td>\n",
+       "      <td>Fri Apr 25 03:14:36 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
@@ -3698,21 +3683,21 @@
        "      <td>{'seasonal_deg': 1, 'trend_deg': 1, 'low_pass_deg': 0, 'period': 1, 'arima_p': 2, 'arima_d': 1, 'arima_q': 2, 'arima_trend': 'n', 'concentrate_scale': True}</td>\n",
        "      <td>-inf</td>\n",
        "      <td>{'neg_sym_mean_abs_percent_error': -inf}</td>\n",
-       "      <td>0.3548</td>\n",
-       "      <td>0.7401</td>\n",
-       "      <td>Thu Dec 26 06:01:49 2024</td>\n",
+       "      <td>0.4096</td>\n",
+       "      <td>0.3551</td>\n",
+       "      <td>Fri Apr 25 03:14:36 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
-       "      <td>{1: 109, 2: 122, 4: 148, 3: 135, 5: 161}</td>\n",
+       "      <td>{1: 109, 3: 135, 2: 122, 4: 148, 5: 161}</td>\n",
        "      <td>2</td>\n",
        "      <td>STLwARIMAForecaster</td>\n",
-       "      <td>{'seasonal_deg': 1, 'trend_deg': 1, 'low_pass_deg': 0, 'period': 52, 'arima_p': 3, 'arima_d': 1, 'arima_q': 2, 'arima_trend': 'n', 'concentrate_scale': True}</td>\n",
+       "      <td>{'seasonal_deg': 1, 'trend_deg': 1, 'low_pass_deg': 0, 'period': 52, 'arima_p': 5, 'arima_d': 1, 'arima_q': 2, 'arima_trend': 'n', 'concentrate_scale': True}</td>\n",
        "      <td>-inf</td>\n",
        "      <td>{'neg_sym_mean_abs_percent_error': -inf}</td>\n",
-       "      <td>1.4462</td>\n",
-       "      <td>0.7400</td>\n",
-       "      <td>Thu Dec 26 06:01:48 2024</td>\n",
+       "      <td>2.0701</td>\n",
+       "      <td>0.3547</td>\n",
+       "      <td>Fri Apr 25 03:14:36 2025</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>"
@@ -3731,7 +3716,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f39aef04",
+   "id": "e714dea0",
    "metadata": {},
    "source": [
     "We also provide the capability to visualize the results of each stage of the AutoML pipeline."
@@ -3739,7 +3724,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ae4ab72f",
+   "id": "a101b8ae",
    "metadata": {},
    "source": [
     "<a id='algorithm-selection'></a>\n",
@@ -3751,13 +3736,13 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "6cf970d5",
+   "id": "574d7643",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:01:55.653927Z",
-     "iopub.status.busy": "2024-12-26T14:01:55.653424Z",
-     "iopub.status.idle": "2024-12-26T14:01:55.899122Z",
-     "shell.execute_reply": "2024-12-26T14:01:55.898595Z"
+     "iopub.execute_input": "2025-04-25T10:14:42.559683Z",
+     "iopub.status.busy": "2025-04-25T10:14:42.559217Z",
+     "iopub.status.idle": "2025-04-25T10:14:42.801090Z",
+     "shell.execute_reply": "2025-04-25T10:14:42.800580Z"
     }
    },
    "outputs": [
@@ -3809,7 +3794,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c3c38141",
+   "id": "7df43722",
    "metadata": {},
    "source": [
     "<a id='hyperparameter-tuning'></a>\n",
@@ -3821,13 +3806,13 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "608c7988",
+   "id": "4e5c12e1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:01:55.901618Z",
-     "iopub.status.busy": "2024-12-26T14:01:55.901100Z",
-     "iopub.status.idle": "2024-12-26T14:01:56.149479Z",
-     "shell.execute_reply": "2024-12-26T14:01:56.148946Z"
+     "iopub.execute_input": "2025-04-25T10:14:42.803159Z",
+     "iopub.status.busy": "2025-04-25T10:14:42.802685Z",
+     "iopub.status.idle": "2025-04-25T10:14:43.306912Z",
+     "shell.execute_reply": "2025-04-25T10:14:43.306386Z"
     }
    },
    "outputs": [
@@ -3877,7 +3862,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6f0efeb7",
+   "id": "44b7b752",
    "metadata": {
     "lines_to_next_cell": 0
    },
@@ -3888,13 +3873,13 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "b727bf4a",
+   "id": "644f92fd",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:01:56.151580Z",
-     "iopub.status.busy": "2024-12-26T14:01:56.151028Z",
-     "iopub.status.idle": "2024-12-26T14:01:56.314184Z",
-     "shell.execute_reply": "2024-12-26T14:01:56.313670Z"
+     "iopub.execute_input": "2025-04-25T10:14:43.309043Z",
+     "iopub.status.busy": "2025-04-25T10:14:43.308562Z",
+     "iopub.status.idle": "2025-04-25T10:14:43.468359Z",
+     "shell.execute_reply": "2025-04-25T10:14:43.467831Z"
     }
    },
    "outputs": [
@@ -3904,8 +3889,7 @@
      "text": [
       "### Plotting is enabled for total models tuned = 2.\n",
       "### Model_name\t\t Val_score \n",
-      "ETSForecaster  \t -0.0361 \t\n",
-      "[2024-12-26 06:01:56,207] [automlx.model] Forecast called on ETSForecaster that's not been fitted.\n"
+      "ETSForecaster  \t -0.0361 \t\n"
      ]
     },
     {
@@ -6189,7 +6173,7 @@
          }
         },
         "title": {
-         "text": "Selected Model: ETSForecaster<br>Model params: {'sample_weight': None, 'failed_fit': True, 'error': 'add', 'trend': 'add', 'damped_trend': True, 'seasonal': 'add', 'seasonal_periods': 52, 'initialization_method': 'estimated', 'initial_level': None, 'initial_trend': None, 'initial_seasonal': None, 'bounds': None, 'dates': None, 'freq': None, 'missing': 'none', 'start_params': None, 'maxiter': 1500, 'disp': -1, 'return_params': False, 'inference_mode': 'predict', 'seed': 7, 'reproducible': False}",
+         "text": "Selected Model: ETSForecaster<br>Model params: {'sample_weight': None, 'failed_fit': False, 'error': 'add', 'trend': 'add', 'damped_trend': True, 'seasonal': 'add', 'seasonal_periods': 52, 'initialization_method': 'estimated', 'initial_level': None, 'initial_trend': None, 'initial_seasonal': None, 'bounds': None, 'dates': None, 'freq': None, 'missing': 'none', 'start_params': None, 'maxiter': 1500, 'disp': -1, 'return_params': False, 'inference_mode': 'predict', 'seed': 7, 'reproducible': False}",
          "x": 0.5
         },
         "xaxis": {
@@ -6229,9 +6213,9 @@
        }
       },
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Model: STLwARIMAForecaster\\u003cbr\\u003eModel params: {'sample_weight': None, 'failed_fit': False, 'seasonal_deg': 1, 'trend_deg': 1, 'low_pass_deg': 1, 'period': 52, 'arima_p': 2, 'arima_d': 1, 'arima_q': 2, 'arima_trend': 'n', 'concentrate_scale': True, 'inference_mode': 'predict', 'seed': 7, 'reproducible': False}\",\"x\":0.5},\"margin\":{\"t\":75,\"b\":50},\"plot_bgcolor\":\"rgba(0,0,0,0)\",\"height\":350,\"xaxis\":{\"title\":{\"text\":\"Date\"},\"gridcolor\":\"LightGrey\",\"linewidth\":1,\"linecolor\":\"Grey\",\"showline\":true,\"mirror\":true,\"zeroline\":true,\"zerolinewidth\":1,\"zerolinecolor\":\"DarkGrey\",\"categoryorder\":\"category ascending\",\"side\":\"bottom\",\"visible\":true,\"showticklabels\":true},\"yaxis\":{\"title\":{\"text\":\"Finance_W142\"},\"gridcolor\":\"LightGrey\",\"linewidth\":1,\"linecolor\":\"Grey\",\"showline\":true,\"mirror\":true,\"zeroline\":true,\"zerolinewidth\":1,\"zerolinecolor\":\"DarkGrey\",\"categoryorder\":\"category 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        "                            \n",
-       "var gd = document.getElementById('0c48ed4b-3780-48da-b33f-74e55c69f925');\n",
+       "var gd = document.getElementById('97373836-2e44-4f4a-8496-12cdc069de4c');\n",
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@@ -8638,7 +8622,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "55b2ac64",
+   "id": "46381018",
    "metadata": {},
    "source": [
     "<a id='multi'></a>\n",
@@ -8647,7 +8631,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5994feec",
+   "id": "6ae836b5",
    "metadata": {},
    "source": [
     "We now display the use of the Oracle AutoMLx solution for multivariate time series.\n",
@@ -8664,7 +8648,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "41416b0b",
+   "id": "ea784926",
    "metadata": {},
    "source": [
     "<a id='single-target'></a>\n",
@@ -8673,7 +8657,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "19cdb6e2",
+   "id": "b0b5efae",
    "metadata": {},
    "source": [
     "We will now take two time series from the M4 dataset: one target variable, and one additional variable used solely as explanatory variables.\n",
@@ -8684,13 +8668,13 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "445968a8",
+   "id": "f7670918",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:01:56.316215Z",
-     "iopub.status.busy": "2024-12-26T14:01:56.315761Z",
-     "iopub.status.idle": "2024-12-26T14:01:56.373259Z",
-     "shell.execute_reply": "2024-12-26T14:01:56.372759Z"
+     "iopub.execute_input": "2025-04-25T10:14:43.470739Z",
+     "iopub.status.busy": "2025-04-25T10:14:43.470148Z",
+     "iopub.status.idle": "2025-04-25T10:14:43.528282Z",
+     "shell.execute_reply": "2025-04-25T10:14:43.527748Z"
     }
    },
    "outputs": [],
@@ -8716,7 +8700,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b292fbd1",
+   "id": "45edfc0f",
    "metadata": {},
    "source": [
     "We then split it using a temporal train-test split as done previously."
@@ -8725,13 +8709,13 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "6766cea9",
+   "id": "2676caa2",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:01:56.375241Z",
-     "iopub.status.busy": "2024-12-26T14:01:56.374742Z",
-     "iopub.status.idle": "2024-12-26T14:01:56.430162Z",
-     "shell.execute_reply": "2024-12-26T14:01:56.429674Z"
+     "iopub.execute_input": "2025-04-25T10:14:43.530359Z",
+     "iopub.status.busy": "2025-04-25T10:14:43.529948Z",
+     "iopub.status.idle": "2025-04-25T10:14:43.583691Z",
+     "shell.execute_reply": "2025-04-25T10:14:43.583181Z"
     }
    },
    "outputs": [],
@@ -8742,7 +8726,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6177da49",
+   "id": "3d9f2c69",
    "metadata": {},
    "source": [
     "We will now fit the pipeline on this data. For the single-target forecasting task with additional variables, the pipeline considers all available models by default. However, the models that actually support additional variables are the following:\n",
@@ -8757,13 +8741,13 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "ad96e8d9",
+   "id": "dc89c770",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:01:56.432183Z",
-     "iopub.status.busy": "2024-12-26T14:01:56.431662Z",
-     "iopub.status.idle": "2024-12-26T14:02:22.189715Z",
-     "shell.execute_reply": "2024-12-26T14:02:22.189177Z"
+     "iopub.execute_input": "2025-04-25T10:14:43.585670Z",
+     "iopub.status.busy": "2025-04-25T10:14:43.585299Z",
+     "iopub.status.idle": "2025-04-25T10:14:56.268715Z",
+     "shell.execute_reply": "2025-04-25T10:14:56.268058Z"
     }
    },
    "outputs": [
@@ -8771,119 +8755,119 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:01:56,556] [automlx.data_transform] Number of simple differencing orders required: d = 0\n"
+      "[2025-04-25 03:14:43,695] [automlx.data_transform] Number of simple differencing orders required: d = 0\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:01:56,558] [automlx.data_transform] Seasonal Periodicities; from decomposed/adjusted: [1]\n"
+      "[2025-04-25 03:14:43,697] [automlx.data_transform] Seasonal Periodicities; from decomposed/adjusted: [1]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:01:56,632] [automlx.interface] Dataset shape: (90,3)\n"
+      "[2025-04-25 03:14:43,743] [automlx.interface] Dataset shape: (90,3)\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:01:56,737] [automlx.data_transform] Running preprocessing. Number of features: 4\n"
+      "[2025-04-25 03:14:43,827] [automlx.data_transform] Running preprocessing. Number of features: 4\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:01:56,846] [automlx.data_transform] Preprocessing completed. Took 0.109 secs\n"
+      "[2025-04-25 03:14:43,934] [automlx.data_transform] Preprocessing completed. Took 0.107 secs\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:01:56,892] [automlx.process] Running Model Generation\n"
+      "[2025-04-25 03:14:43,960] [automlx.process] Running Model Generation\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:01:56,938] [automlx] Provided model (SARIMAXForecaster) is not supported.Supported models are: ['LGBMForecaster', 'ExtraTreesForecaster', 'ExpSmoothForecaster', 'NaiveForecaster', 'ThetaForecaster', 'ETSForecaster', 'XGBForecaster']\n"
+      "[2025-04-25 03:14:44,005] [automlx] Provided model (SARIMAXForecaster) is not supported.Supported models are: ['LGBMForecaster', 'ExtraTreesForecaster', 'ExpSmoothForecaster', 'NaiveForecaster', 'ThetaForecaster', 'ETSForecaster', 'XGBForecaster']\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:01:56,938] [automlx] Provided model (ProphetForecaster) is not supported.Supported models are: ['LGBMForecaster', 'ExtraTreesForecaster', 'ExpSmoothForecaster', 'NaiveForecaster', 'ThetaForecaster', 'ETSForecaster', 'XGBForecaster']\n"
+      "[2025-04-25 03:14:44,005] [automlx] Provided model (ProphetForecaster) is not supported.Supported models are: ['LGBMForecaster', 'ExtraTreesForecaster', 'ExpSmoothForecaster', 'NaiveForecaster', 'ThetaForecaster', 'ETSForecaster', 'XGBForecaster']\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:01:56,940] [automlx.process] Model Generation completed.\n"
+      "[2025-04-25 03:14:44,007] [automlx.process] Model Generation completed.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:01:56,990] [automlx.model_selection] Running Model Selection\n"
+      "[2025-04-25 03:14:44,043] [automlx.model_selection] Running Model Selection\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:02:03,120] [automlx.model_selection] Model Selection completed - Took 6.130 sec - Selected models: [['XGBForecaster']]\n"
+      "[2025-04-25 03:14:45,991] [automlx.model_selection] Model Selection completed - Took 1.948 sec - Selected models: [['XGBForecaster']]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:02:03,204] [automlx.trials] Running Model Tuning for ['XGBForecaster']\n"
+      "[2025-04-25 03:14:46,045] [automlx.trials] Running Model Tuning for ['XGBForecaster']\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:02:18,214] [automlx.trials] Best parameters for XGBForecaster: {'differencing_order': 1, 'acf_local_maxima': '[4, 9, 18, 22, 14, 31, 26, 12, 1]', 'use_X': True, 'n_estimators': 16, 'max_depth': 2}\n"
+      "[2025-04-25 03:14:55,529] [automlx.trials] Best parameters for XGBForecaster: {'differencing_order': 1, 'acf_local_maxima': '[4, 9, 18, 22, 14, 31, 26, 12, 1]', 'use_X': True, 'n_estimators': 16, 'max_depth': 2}\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:02:18,215] [automlx.trials] Model Tuning completed. Took: 15.011 secs\n"
+      "[2025-04-25 03:14:55,530] [automlx.trials] Model Tuning completed. Took: 9.485 secs\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:02:18,672] [automlx.interface] Re-fitting pipeline\n"
+      "[2025-04-25 03:14:55,869] [automlx.interface] Re-fitting pipeline\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:02:18,683] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_85b858b0-1\n"
+      "[2025-04-25 03:14:55,879] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_ea8a88bb-f\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:02:22,110] [automlx.interface] AutoMLx completed.\n"
+      "[2025-04-25 03:14:56,184] [automlx.interface] AutoMLx completed.\n"
      ]
     },
     {
@@ -8914,7 +8898,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b8d85fa5",
+   "id": "27056cd8",
    "metadata": {},
    "source": [
     "<a id='multi-target'></a>\n",
@@ -8923,7 +8907,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "02099fb3",
+   "id": "636c1265",
    "metadata": {},
    "source": [
     "For this use case, we now take 3 variables from the M4 dataset: two target variables (`W142`, `W143`) and one additional variables (`W141`). We will add a constant variable as an additional variable, so we have a total of two additional variables. We then split the data using a temporal train-test split and drop the datetime index, as done previously,"
@@ -8932,13 +8916,13 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "fd00e9e9",
+   "id": "14588df6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:02:22.191981Z",
-     "iopub.status.busy": "2024-12-26T14:02:22.191436Z",
-     "iopub.status.idle": "2024-12-26T14:02:23.642278Z",
-     "shell.execute_reply": "2024-12-26T14:02:23.641720Z"
+     "iopub.execute_input": "2025-04-25T10:14:56.271066Z",
+     "iopub.status.busy": "2025-04-25T10:14:56.270374Z",
+     "iopub.status.idle": "2025-04-25T10:14:56.486358Z",
+     "shell.execute_reply": "2025-04-25T10:14:56.485765Z"
     }
    },
    "outputs": [],
@@ -8969,7 +8953,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e1b0e7a6",
+   "id": "30717529",
    "metadata": {},
    "source": [
     "<a id='advanced'></a>\n",
@@ -8982,13 +8966,13 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "8c35640e",
+   "id": "1c7c17c9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:02:23.644693Z",
-     "iopub.status.busy": "2024-12-26T14:02:23.644084Z",
-     "iopub.status.idle": "2024-12-26T14:02:23.700247Z",
-     "shell.execute_reply": "2024-12-26T14:02:23.699736Z"
+     "iopub.execute_input": "2025-04-25T10:14:56.488434Z",
+     "iopub.status.busy": "2025-04-25T10:14:56.487971Z",
+     "iopub.status.idle": "2025-04-25T10:14:56.541643Z",
+     "shell.execute_reply": "2025-04-25T10:14:56.541053Z"
     }
    },
    "outputs": [],
@@ -9010,7 +8994,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "91c6e0ae",
+   "id": "21dedf2f",
    "metadata": {},
    "source": [
     "<a id='WFCV'></a>\n",
@@ -9022,13 +9006,13 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "3696af96",
+   "id": "8905fe4f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:02:23.702538Z",
-     "iopub.status.busy": "2024-12-26T14:02:23.701981Z",
-     "iopub.status.idle": "2024-12-26T14:02:26.369456Z",
-     "shell.execute_reply": "2024-12-26T14:02:26.368920Z"
+     "iopub.execute_input": "2025-04-25T10:14:56.543674Z",
+     "iopub.status.busy": "2025-04-25T10:14:56.543209Z",
+     "iopub.status.idle": "2025-04-25T10:14:58.752419Z",
+     "shell.execute_reply": "2025-04-25T10:14:58.751819Z"
     }
    },
    "outputs": [
@@ -9036,97 +9020,97 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:02:23,859] [automlx.data_transform] Number of simple differencing orders required: d = 0\n"
+      "[2025-04-25 03:14:56,679] [automlx.data_transform] Number of simple differencing orders required: d = 0\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:02:23,861] [automlx.data_transform] Seasonal Periodicities; from decomposed/adjusted: [1]\n"
+      "[2025-04-25 03:14:56,681] [automlx.data_transform] Seasonal Periodicities; from decomposed/adjusted: [1]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:02:23,951] [automlx.interface] Dataset shape: (90,5)\n"
+      "[2025-04-25 03:14:56,731] [automlx.interface] Dataset shape: (90,5)\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:02:23,994] [automlx.interface] Model Tune disabled.\n"
+      "[2025-04-25 03:14:56,773] [automlx.interface] Model Tune disabled.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:02:24,068] [automlx.data_transform] Running preprocessing. Number of features: 6\n"
+      "[2025-04-25 03:14:56,812] [automlx.data_transform] Running preprocessing. Number of features: 6\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:02:24,177] [automlx.data_transform] Preprocessing completed. Took 0.109 secs\n"
+      "[2025-04-25 03:14:56,927] [automlx.data_transform] Preprocessing completed. Took 0.115 secs\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:02:24,213] [automlx.process] Running Model Generation\n"
+      "[2025-04-25 03:14:56,955] [automlx.process] Running Model Generation\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:02:24,251] [automlx.process] Model Generation completed.\n"
+      "[2025-04-25 03:14:56,995] [automlx.process] Model Generation completed.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:02:24,297] [automlx.model_selection] Running Model Selection\n"
+      "[2025-04-25 03:14:57,028] [automlx.model_selection] Running Model Selection\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:02:24,297] [automlx.trials] Fewer models (2) than top_k (2) provided, skipping...\n"
+      "[2025-04-25 03:14:57,029] [automlx.trials] Fewer models (2) than top_k (2) provided, skipping...\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:02:24,363] [automlx.interface] Re-fitting pipeline\n"
+      "[2025-04-25 03:14:57,063] [automlx.interface] Re-fitting pipeline\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:02:24,381] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_f5a58512-a\n"
+      "[2025-04-25 03:14:57,080] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_d557d931-8\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:02:26,317] [automlx.interface] AutoMLx completed.\n"
+      "[2025-04-25 03:14:58,713] [automlx.interface] AutoMLx completed.\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<automlx._interface.forecaster.AutoForecaster at 0x1536f1ce2dc0>"
+       "<automlx._interface.forecaster.AutoForecaster at 0x145fb2ad68b0>"
       ]
      },
      "execution_count": 25,
@@ -9140,7 +9124,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "28e6aab6",
+   "id": "23427ab6",
    "metadata": {},
    "source": [
     "The AutoML pipeline provides attributes to get the selected features, the chosen model, hyperparameters as well as the score on the test set."
@@ -9149,13 +9133,13 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "id": "27346685",
+   "id": "dc8e6524",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:02:26.371584Z",
-     "iopub.status.busy": "2024-12-26T14:02:26.371087Z",
-     "iopub.status.idle": "2024-12-26T14:02:26.819508Z",
-     "shell.execute_reply": "2024-12-26T14:02:26.818855Z"
+     "iopub.execute_input": "2025-04-25T10:14:58.754536Z",
+     "iopub.status.busy": "2025-04-25T10:14:58.754048Z",
+     "iopub.status.idle": "2025-04-25T10:14:58.839473Z",
+     "shell.execute_reply": "2025-04-25T10:14:58.838768Z"
     }
    },
    "outputs": [
@@ -9163,7 +9147,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:02:26,803] [automlx.model] Predict called on DynFactorForecaster that's not been fitted.\n"
+      "[2025-04-25 03:14:58,824] [automlx.model] Predict called on DynFactorForecaster that's not been fitted.\n"
      ]
     },
     {
@@ -9188,7 +9172,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c3915c6f",
+   "id": "e45a1035",
    "metadata": {},
    "source": [
     "<a id='multi-making'></a>\n",
@@ -9197,7 +9181,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "24c89455",
+   "id": "8316f4ff",
    "metadata": {},
    "source": [
     "As mention in the univariate data case, there are two ways of making a prediction :\n",
@@ -9210,13 +9194,13 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "id": "606d6353",
+   "id": "de31dbf3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:02:26.821598Z",
-     "iopub.status.busy": "2024-12-26T14:02:26.821068Z",
-     "iopub.status.idle": "2024-12-26T14:02:27.037677Z",
-     "shell.execute_reply": "2024-12-26T14:02:27.037047Z"
+     "iopub.execute_input": "2025-04-25T10:14:58.841523Z",
+     "iopub.status.busy": "2025-04-25T10:14:58.841033Z",
+     "iopub.status.idle": "2025-04-25T10:14:58.935561Z",
+     "shell.execute_reply": "2025-04-25T10:14:58.934975Z"
     }
    },
    "outputs": [
@@ -9224,14 +9208,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:02:27,002] [automlx.model] Predict called on DynFactorForecaster that's not been fitted.\n"
+      "[2025-04-25 03:14:58,902] [automlx.model] Predict called on DynFactorForecaster that's not been fitted.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:02:27,024] [automlx.model] Forecast called on DynFactorForecaster that's not been fitted.\n"
+      "[2025-04-25 03:14:58,923] [automlx.model] Forecast called on DynFactorForecaster that's not been fitted.\n"
      ]
     }
    ],
@@ -9244,7 +9228,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f0ceb4c6",
+   "id": "e3497ce7",
    "metadata": {},
    "source": [
     "The obtained forecast contains predictions for the two target variables, as well as lower and upper confidence intervals, for each timestamp in the test set."
@@ -9253,13 +9237,13 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "id": "912ef195",
+   "id": "d58c188b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:02:27.040021Z",
-     "iopub.status.busy": "2024-12-26T14:02:27.039513Z",
-     "iopub.status.idle": "2024-12-26T14:02:27.129881Z",
-     "shell.execute_reply": "2024-12-26T14:02:27.129262Z"
+     "iopub.execute_input": "2025-04-25T10:14:58.937459Z",
+     "iopub.status.busy": "2025-04-25T10:14:58.937034Z",
+     "iopub.status.idle": "2025-04-25T10:14:58.993215Z",
+     "shell.execute_reply": "2025-04-25T10:14:58.992651Z"
     }
    },
    "outputs": [
@@ -9390,7 +9374,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "87cbf131",
+   "id": "29346327",
    "metadata": {},
    "source": [
     "One can also directly compute the score of the tuned model on the test set, without needing to run `forecast()` or `predict()`."
@@ -9399,13 +9383,13 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "id": "677a0d3b",
+   "id": "807008fc",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:02:27.131958Z",
-     "iopub.status.busy": "2024-12-26T14:02:27.131400Z",
-     "iopub.status.idle": "2024-12-26T14:02:27.238954Z",
-     "shell.execute_reply": "2024-12-26T14:02:27.238332Z"
+     "iopub.execute_input": "2025-04-25T10:14:58.994809Z",
+     "iopub.status.busy": "2025-04-25T10:14:58.994625Z",
+     "iopub.status.idle": "2025-04-25T10:14:59.072449Z",
+     "shell.execute_reply": "2025-04-25T10:14:59.071866Z"
     }
    },
    "outputs": [
@@ -9413,7 +9397,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:02:27,225] [automlx.model] Predict called on DynFactorForecaster that's not been fitted.\n"
+      "[2025-04-25 03:14:59,059] [automlx.model] Predict called on DynFactorForecaster that's not been fitted.\n"
      ]
     },
     {
@@ -9432,7 +9416,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b5e5f008",
+   "id": "694b768b",
    "metadata": {},
    "source": [
     "<a id='multi-visualization'></a>\n",
@@ -9441,7 +9425,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5f8993b3",
+   "id": "d2c3c4ca",
    "metadata": {},
    "source": [
     "Finally, when given as input the forecasted variables, the `plot_forecast()` method displays an interactive plot of the predictions (for each target variable) and confidence intervals."
@@ -9450,13 +9434,13 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "id": "d85e1e87",
+   "id": "8a295bf1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:02:27.240987Z",
-     "iopub.status.busy": "2024-12-26T14:02:27.240451Z",
-     "iopub.status.idle": "2024-12-26T14:02:27.539512Z",
-     "shell.execute_reply": "2024-12-26T14:02:27.538836Z"
+     "iopub.execute_input": "2025-04-25T10:14:59.074440Z",
+     "iopub.status.busy": "2025-04-25T10:14:59.073912Z",
+     "iopub.status.idle": "2025-04-25T10:14:59.338725Z",
+     "shell.execute_reply": "2025-04-25T10:14:59.338130Z"
     }
    },
    "outputs": [
@@ -11968,9 +11952,9 @@
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        "                            \n",
-       "var gd = document.getElementById('a46074b8-51c8-4c24-b6a4-7ea07829cdca');\n",
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@@ -12005,7 +11989,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "abf48e42",
+   "id": "0592efb2",
    "metadata": {},
    "source": [
     "<a id='MLX'></a>\n",
@@ -12014,7 +11998,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fc398c67",
+   "id": "1151ab23",
    "metadata": {},
    "source": [
     "For a variety of decision-making tasks, getting only a prediction as model output is not sufficient. A user may wish to know why the model outputs that prediction, or which data features are relevant for that prediction. For that purpose the Oracle AutoMLx solution defines the MLExplainer object, which allows to compute multiple types of model explanations."
@@ -12022,7 +12006,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "44fb807f",
+   "id": "5434138a",
    "metadata": {},
    "source": [
     "<a id='MLX-initializing'></a>\n",
@@ -12031,7 +12015,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4db87fe1",
+   "id": "43a9a2e8",
    "metadata": {},
    "source": [
     "The `MLExplainer` object takes as arguments the trained model, the training data and labels, as well as the task. Let's assume that we want to learn more about the decision-making process of the model `est1` that we trained based on the M4 dataset (Finance_W142) previously. Thus, we pass `est1` along with its training series (`y_train`) to the `MLExplainer` to produce the appropriate explainer."
@@ -12040,24 +12024,16 @@
   {
    "cell_type": "code",
    "execution_count": 31,
-   "id": "1b336f72",
+   "id": "d534dbf2",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:02:27.541866Z",
-     "iopub.status.busy": "2024-12-26T14:02:27.541343Z",
-     "iopub.status.idle": "2024-12-26T14:02:27.835528Z",
-     "shell.execute_reply": "2024-12-26T14:02:27.834981Z"
+     "iopub.execute_input": "2025-04-25T10:14:59.340708Z",
+     "iopub.status.busy": "2025-04-25T10:14:59.340519Z",
+     "iopub.status.idle": "2025-04-25T10:14:59.511540Z",
+     "shell.execute_reply": "2025-04-25T10:14:59.510951Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[2024-12-26 06:02:27,736] [automlx.model] Predict called on ETSForecaster that's not been fitted.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "explainer = automlx.MLExplainer(est1,\n",
     "                                None,\n",
@@ -12067,7 +12043,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "17b013b3",
+   "id": "cc5c1691",
    "metadata": {},
    "source": [
     "<a id='MLX-local'></a>\n",
@@ -12076,7 +12052,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fe4b2540",
+   "id": "ba874fbb",
    "metadata": {},
    "source": [
     "For any data point within the forecasting horizon, explanations indicate which factors increased or decreased the model's prediction compared to a previous reference point. This comparison provides additional context, making the information more interpretable for the user.\n",
@@ -12087,34 +12063,20 @@
   {
    "cell_type": "code",
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-   "id": "8e47c3d6",
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-      "[2024-12-26 06:02:27,936] [automlx.model] Forecast called on ETSForecaster that's not been fitted.\n"
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     "#### Visualization"
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-      "[2024-12-26 06:02:32,773] [automlx.mlx] Displaying only non-zero attributions.\n"
+      "[2025-04-25 03:15:07,387] [automlx.mlx] Displaying only non-zero attributions.\n"
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+<span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">plotly.express</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">px</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.metrics</span><span class="w"> </span><span class="kn">import</span> <span class="n">balanced_accuracy_score</span><span class="p">,</span> <span class="n">roc_auc_score</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">datasets</span><span class="w"> </span><span class="kn">import</span> <span class="n">load_dataset</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <span class="n">train_test_split</span>
 
 <span class="c1"># Settings for plots</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">&#39;figure.figsize&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">&#39;font.size&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">15</span>
 
-<span class="kn">import</span> <span class="nn">automlx</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">automlx</span>
 </pre></div>
 
      </div>
@@ -14952,9 +14952,9 @@ <h2 id="Load-the-PneumoniaMNIST-dataset">Load the PneumoniaMNIST dataset<a class
 
 
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@@ -15107,30 +15107,30 @@ <h3 id="Train-a-model-using-AutoML">Train a model using AutoML<a class="anchor-l
 
 
 <div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
-<pre>[2024-12-26 06:02:47,732] [automlx.backend] Overwriting ray session directory to /tmp/ytqjboor/ray, which will be deleted at engine shutdown. If you wish to retain ray logs, provide _temp_dir in ray_setup dict of engine_opts when initializing the AutoMLx engine.
-[2024-12-26 06:02:54,014] [automlx.interface] Dataset shape: (1000,1)
-[2024-12-26 06:02:57,160] [sanerec.autotuning.parameter] Hyperparameter epsilon autotune range is set to its validation range. This could lead to long training times
-[2024-12-26 06:02:57,519] [sanerec.autotuning.parameter] Hyperparameter repeat_quality_threshold autotune range is set to its validation range. This could lead to long training times
-[2024-12-26 06:02:57,526] [sanerec.autotuning.parameter] Hyperparameter scope autotune range is set to its validation range. This could lead to long training times
-[2024-12-26 06:02:57,596] [automlx.data_transform] Running preprocessing. Number of features: 2
-[2024-12-26 06:02:58,488] [automlx.data_transform] Preprocessing completed. Took 0.892 secs
-[2024-12-26 06:02:58,531] [automlx.process] Running Model Generation
-[2024-12-26 06:02:58,571] [automlx.process] Model Generation completed.
-[2024-12-26 06:02:58,637] [automlx.model_selection] Running Model Selection
-[2024-12-26 06:05:35,557] [automlx.model_selection] Model Selection completed - Took 156.920 sec - Selected models: [[&#39;ResNet&#39;]]
-[2024-12-26 06:05:35,587] [automlx.adaptive_sampling] Running Adaptive Sampling. Dataset shape: (1000,2).
-[2024-12-26 06:05:35,638] [automlx.adaptive_sampling] Adaptive Sampling: top_limit: 583 &lt; bottom_limit: 1000,
+<pre>[2025-04-25 03:15:24,710] [automlx.backend] Overwriting ray session directory to /tmp/j9903zbf/ray, which will be deleted at engine shutdown. If you wish to retain ray logs, provide _temp_dir in ray_setup dict of engine_opts when initializing the AutoMLx engine.
+[2025-04-25 03:15:29,829] [automlx.interface] Dataset shape: (1000,1)
+[2025-04-25 03:15:33,331] [sanerec.autotuning.parameter] Hyperparameter epsilon autotune range is set to its validation range. This could lead to long training times
+[2025-04-25 03:15:33,859] [sanerec.autotuning.parameter] Hyperparameter repeat_quality_threshold autotune range is set to its validation range. This could lead to long training times
+[2025-04-25 03:15:33,868] [sanerec.autotuning.parameter] Hyperparameter scope autotune range is set to its validation range. This could lead to long training times
+[2025-04-25 03:15:33,941] [automlx.data_transform] Running preprocessing. Number of features: 2
+[2025-04-25 03:15:34,824] [automlx.data_transform] Preprocessing completed. Took 0.883 secs
+[2025-04-25 03:15:34,870] [automlx.process] Running Model Generation
+[2025-04-25 03:15:34,911] [automlx.process] Model Generation completed.
+[2025-04-25 03:15:34,981] [automlx.model_selection] Running Model Selection
+[2025-04-25 03:18:12,401] [automlx.model_selection] Model Selection completed - Took 157.420 sec - Selected models: [[&#39;ResNet&#39;]]
+[2025-04-25 03:18:12,429] [automlx.adaptive_sampling] Running Adaptive Sampling. Dataset shape: (1000,2).
+[2025-04-25 03:18:12,479] [automlx.adaptive_sampling] Adaptive Sampling: top_limit: 583 &lt; bottom_limit: 1000,
 sampling process will be skipped
-[2024-12-26 06:05:35,648] [automlx.adaptive_sampling] Adaptive Sampling: top_limit: 146 &lt; bottom_limit: 1000,
+[2025-04-25 03:18:12,489] [automlx.adaptive_sampling] Adaptive Sampling: top_limit: 146 &lt; bottom_limit: 1000,
 sampling process will be skipped
-[2024-12-26 06:05:35,742] [automlx.feature_selection] Starting feature ranking for ResNet
-[2024-12-26 06:05:35,743] [automlx.feature_selection] min_features is larger than model input features for ResNet, skipping feature selection for it
-[2024-12-26 06:05:35,779] [automlx.trials] Running Model Tuning for [&#39;ResNet&#39;]
-[2024-12-26 06:15:29,101] [automlx.trials] Best parameters for ResNet: {&#39;optimizer_class&#39;: &#39;Adam&#39;, &#39;shuffle_dataset_each_epoch&#39;: True, &#39;optimizer_params&#39;: {}, &#39;criterion_class&#39;: None, &#39;criterion_params&#39;: {}, &#39;scheduler_class&#39;: None, &#39;scheduler_params&#39;: {}, &#39;batch_size&#39;: 128, &#39;lr&#39;: 0.0031630600334930504, &#39;epochs&#39;: 18, &#39;input_transform&#39;: &#39;auto&#39;, &#39;tensorboard_dir&#39;: None, &#39;use_tqdm&#39;: None, &#39;prediction_batch_size&#39;: 128, &#39;prediction_input_transform&#39;: &#39;auto&#39;, &#39;shuffling_buffer_size&#39;: None, &#39;freeze.encoder&#39;: False, &#39;load.encoder&#39;: None, &#39;size&#39;: &#39;18&#39;}
-[2024-12-26 06:15:29,103] [automlx.trials] Model Tuning completed. Took: 593.323 secs
-[2024-12-26 06:15:29,820] [automlx.interface] Re-fitting pipeline
-[2024-12-26 06:15:29,837] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_1235d2a6-6
-[2024-12-26 06:17:35,770] [automlx.interface] AutoMLx completed.
+[2025-04-25 03:18:12,584] [automlx.feature_selection] Starting feature ranking for ResNet
+[2025-04-25 03:18:12,585] [automlx.feature_selection] min_features is larger than model input features for ResNet, skipping feature selection for it
+[2025-04-25 03:18:12,620] [automlx.trials] Running Model Tuning for [&#39;ResNet&#39;]
+[2025-04-25 03:31:47,337] [automlx.trials] Best parameters for ResNet: {&#39;optimizer_class&#39;: &#39;Adam&#39;, &#39;shuffle_dataset_each_epoch&#39;: True, &#39;optimizer_params&#39;: {}, &#39;criterion_class&#39;: None, &#39;criterion_params&#39;: {}, &#39;scheduler_class&#39;: None, &#39;scheduler_params&#39;: {}, &#39;batch_size&#39;: 128, &#39;lr&#39;: 0.0031630600334930504, &#39;epochs&#39;: 18, &#39;input_transform&#39;: &#39;auto&#39;, &#39;tensorboard_dir&#39;: None, &#39;use_tqdm&#39;: None, &#39;prediction_batch_size&#39;: 128, &#39;prediction_input_transform&#39;: &#39;auto&#39;, &#39;shuffling_buffer_size&#39;: None, &#39;freeze.encoder&#39;: False, &#39;load.encoder&#39;: None, &#39;size&#39;: &#39;18&#39;}
+[2025-04-25 03:31:47,338] [automlx.trials] Model Tuning completed. Took: 814.718 secs
+[2025-04-25 03:31:48,319] [automlx.interface] Re-fitting pipeline
+[2025-04-25 03:31:48,337] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_d9a60282-f
+[2025-04-25 03:33:54,686] [automlx.interface] AutoMLx completed.
 </pre>
 </div>
 </div>
@@ -15144,7 +15144,7 @@ <h3 id="Train-a-model-using-AutoML">Train a model using AutoML<a class="anchor-l
 
 
 <div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
-<pre>&lt;automlx._interface.classifier.AutoClassifier at 0x14b73b6c7dc0&gt;</pre>
+<pre>&lt;automlx._interface.classifier.AutoClassifier at 0x145660035130&gt;</pre>
 </div>
 
 </div>
@@ -15310,7 +15310,7 @@ <h3 id="Analyze-the-AutoML-optimization-process">Analyze the AutoML optimization
       <td>{'optimizer_class': 'Adam', 'shuffle_dataset_each_epoch': True, 'optimizer_params': {}, 'criterion_class': None, 'criterion_params': {}, 'scheduler_class': None, 'scheduler_params': {}, 'batch_size': 128, 'lr': 0.0031630600334930504, 'epochs': 18, 'input_transform': 'auto', 'tensorboard_dir': None, 'use_tqdm': None, 'prediction_batch_size': 128, 'prediction_input_transform': 'auto', 'shuffling_buffer_size': None, 'freeze.encoder': False, 'load.encoder': None, 'size': '18'}</td>
     </tr>
     <tr>
-      <td>25.1.0</td>
+      <td>25.2.1</td>
     </tr>
     <tr>
       <td>3.9.21 (main, Dec 11 2024, 16:24:11) \n[GCC 11.2.0]</td>
@@ -15360,51 +15360,51 @@ <h3 id="Analyze-the-AutoML-optimization-process">Analyze the AutoML optimization
   <tbody>
     <tr>
       <td>Model Selection</td>
-      <td>{3: 800, 1: 800, 5: 800, 2: 800, 4: 800}</td>
+      <td>{5: 800, 2: 800, 3: 800, 1: 800, 4: 800}</td>
       <td>1</td>
       <td>ResNet</td>
       <td>{'optimizer_class': 'Adam', 'shuffle_dataset_each_epoch': True, 'optimizer_params': {}, 'criterion_class': None, 'criterion_params': {}, 'scheduler_class': None, 'scheduler_params': {}, 'batch_size': 128, 'lr': 0.001, 'epochs': 18, 'input_transform': 'auto', 'tensorboard_dir': None, 'use_tqdm': None, 'prediction_batch_size': 128, 'prediction_input_transform': 'auto', 'shuffling_buffer_size': None, 'freeze.encoder': False, 'load.encoder': None, 'size': '18'}</td>
       <td>0.9429</td>
       <td>{'balanced_accuracy': 0.9429372134190274}</td>
-      <td>546.2951</td>
-      <td>0.8530</td>
-      <td>Thu Dec 26 06:04:56 2024</td>
+      <td>566.4878</td>
+      <td>0.8595</td>
+      <td>Fri Apr 25 03:17:38 2025</td>
     </tr>
     <tr>
       <td>Model Selection</td>
-      <td>{2: 800, 3: 800, 4: 800, 1: 800, 5: 800}</td>
+      <td>{1: 800, 3: 800, 5: 800, 2: 800, 4: 800}</td>
       <td>1</td>
       <td>EfficientNet</td>
       <td>{'optimizer_class': 'Adam', 'shuffle_dataset_each_epoch': True, 'optimizer_params': {}, 'criterion_class': None, 'criterion_params': {}, 'scheduler_class': None, 'scheduler_params': {}, 'batch_size': 128, 'lr': 0.001, 'epochs': 18, 'input_transform': 'auto', 'tensorboard_dir': None, 'use_tqdm': None, 'prediction_batch_size': 128, 'prediction_input_transform': 'auto', 'shuffling_buffer_size': None, 'freeze.encoder': False, 'load.encoder': None, 'size': 'b0'}</td>
       <td>0.8153</td>
       <td>{'balanced_accuracy': 0.8153082390588295}</td>
-      <td>728.0001</td>
-      <td>0.8687</td>
-      <td>Thu Dec 26 06:05:35 2024</td>
+      <td>734.1541</td>
+      <td>0.8685</td>
+      <td>Fri Apr 25 03:18:12 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
-      <td>{4: 800, 3: 800, 1: 800, 2: 800, 5: 800}</td>
+      <td>{4: 800, 3: 800, 2: 800, 1: 800, 5: 800}</td>
       <td>1</td>
       <td>ResNet</td>
       <td>{'optimizer_class': 'Adam', 'shuffle_dataset_each_epoch': True, 'optimizer_params': {}, 'criterion_class': None, 'criterion_params': {}, 'scheduler_class': None, 'scheduler_params': {}, 'batch_size': 128, 'lr': 0.0031630600334930504, 'epochs': 18, 'input_transform': 'auto', 'tensorboard_dir': None, 'use_tqdm': None, 'prediction_batch_size': 128, 'prediction_input_transform': 'auto', 'shuffling_buffer_size': None, 'freeze.encoder': False, 'load.encoder': None, 'size': '18'}</td>
       <td>0.9421</td>
-      <td>{'balanced_accuracy': 0.9420862061013218}</td>
-      <td>1311.0154</td>
-      <td>0.9147</td>
-      <td>Thu Dec 26 06:12:54 2024</td>
+      <td>{'balanced_accuracy': 0.9420862061013219}</td>
+      <td>1679.7299</td>
+      <td>0.9625</td>
+      <td>Fri Apr 25 03:27:58 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
-      <td>{1: 800, 2: 800, 3: 800, 5: 800, 4: 800}</td>
+      <td>{4: 800, 3: 800, 1: 800, 5: 800, 2: 800}</td>
       <td>1</td>
       <td>ResNet</td>
       <td>{'optimizer_class': 'Adam', 'shuffle_dataset_each_epoch': True, 'optimizer_params': {}, 'criterion_class': None, 'criterion_params': {}, 'scheduler_class': None, 'scheduler_params': {}, 'batch_size': 128, 'lr': 0.001, 'epochs': 18, 'input_transform': 'auto', 'tensorboard_dir': None, 'use_tqdm': None, 'prediction_batch_size': 128, 'prediction_input_transform': 'auto', 'shuffling_buffer_size': None, 'freeze.encoder': False, 'load.encoder': None, 'size': '18'}</td>
       <td>0.9409</td>
       <td>{'balanced_accuracy': 0.9409403911576797}</td>
-      <td>1295.1165</td>
-      <td>0.8465</td>
-      <td>Thu Dec 26 06:10:09 2024</td>
+      <td>1654.4550</td>
+      <td>0.8447</td>
+      <td>Fri Apr 25 03:24:15 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
@@ -15414,9 +15414,9 @@ <h3 id="Analyze-the-AutoML-optimization-process">Analyze the AutoML optimization
       <td>{'optimizer_class': 'Adam', 'shuffle_dataset_each_epoch': True, 'optimizer_params': {}, 'criterion_class': None, 'criterion_params': {}, 'scheduler_class': None, 'scheduler_params': {}, 'batch_size': 128, 'lr': 0.001000099, 'epochs': 18, 'input_transform': 'auto', 'tensorboard_dir': None, 'use_tqdm': None, 'prediction_batch_size': 128, 'prediction_input_transform': 'auto', 'shuffling_buffer_size': None, 'freeze.encoder': False, 'load.encoder': None, 'size': '18'}</td>
       <td>0.94</td>
       <td>{'balanced_accuracy': 0.9399735188445627}</td>
-      <td>1384.2000</td>
-      <td>0.8510</td>
-      <td>Thu Dec 26 06:10:26 2024</td>
+      <td>1676.6548</td>
+      <td>0.8474</td>
+      <td>Fri Apr 25 03:24:22 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
@@ -15426,21 +15426,21 @@ <h3 id="Analyze-the-AutoML-optimization-process">Analyze the AutoML optimization
       <td>{'optimizer_class': 'Adam', 'shuffle_dataset_each_epoch': True, 'optimizer_params': {}, 'criterion_class': None, 'criterion_params': {}, 'scheduler_class': None, 'scheduler_params': {}, 'batch_size': 128, 'lr': 0.0031631590334930504, 'epochs': 18, 'input_transform': 'auto', 'tensorboard_dir': None, 'use_tqdm': None, 'prediction_batch_size': 128, 'prediction_input_transform': 'auto', 'shuffling_buffer_size': None, 'freeze.encoder': False, 'load.encoder': None, 'size': '18'}</td>
       <td>0.932</td>
       <td>{'balanced_accuracy': 0.9320400827675271}</td>
-      <td>1144.2416</td>
-      <td>0.9041</td>
-      <td>Thu Dec 26 06:12:59 2024</td>
+      <td>1606.3738</td>
+      <td>0.9188</td>
+      <td>Fri Apr 25 03:28:28 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
-      <td>{2: 800, 1: 800, 3: 800, 5: 800, 4: 800}</td>
+      <td>{1: 800, 2: 800, 3: 800, 4: 800, 5: 800}</td>
       <td>1</td>
       <td>ResNet</td>
       <td>{'optimizer_class': 'Adam', 'shuffle_dataset_each_epoch': True, 'optimizer_params': {}, 'criterion_class': None, 'criterion_params': {}, 'scheduler_class': None, 'scheduler_params': {}, 'batch_size': 128, 'lr': 0.001, 'epochs': 18, 'input_transform': 'auto', 'tensorboard_dir': None, 'use_tqdm': None, 'prediction_batch_size': 128, 'prediction_input_transform': 'auto', 'shuffling_buffer_size': None, 'freeze.encoder': False, 'load.encoder': None, 'size': '34'}</td>
-      <td>0.9263</td>
-      <td>{'balanced_accuracy': 0.9263416880185892}</td>
-      <td>1208.9238</td>
-      <td>1.0352</td>
-      <td>Thu Dec 26 06:15:28 2024</td>
+      <td>0.9319</td>
+      <td>{'balanced_accuracy': 0.9318589293978995}</td>
+      <td>1835.8036</td>
+      <td>1.0745</td>
+      <td>Fri Apr 25 03:31:47 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
@@ -15448,59 +15448,59 @@ <h3 id="Analyze-the-AutoML-optimization-process">Analyze the AutoML optimization
       <td>1</td>
       <td>ResNet</td>
       <td>{'optimizer_class': 'Adagrad', 'shuffle_dataset_each_epoch': True, 'optimizer_params': {}, 'criterion_class': None, 'criterion_params': {}, 'scheduler_class': None, 'scheduler_params': {}, 'batch_size': 128, 'lr': 0.001, 'epochs': 18, 'input_transform': 'auto', 'tensorboard_dir': None, 'use_tqdm': None, 'prediction_batch_size': 128, 'prediction_input_transform': 'auto', 'shuffling_buffer_size': None, 'freeze.encoder': False, 'load.encoder': None, 'size': '18'}</td>
-      <td>0.925</td>
-      <td>{'balanced_accuracy': 0.9250455746251683}</td>
-      <td>1142.6463</td>
-      <td>0.9129</td>
-      <td>Thu Dec 26 06:13:39 2024</td>
+      <td>0.9304</td>
+      <td>{'balanced_accuracy': 0.9303550701472287}</td>
+      <td>1599.9909</td>
+      <td>0.8875</td>
+      <td>Fri Apr 25 03:28:47 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
-      <td>{1: 800, 3: 800, 4: 800, 5: 800, 2: 800}</td>
+      <td>{1: 800, 3: 800, 2: 800, 5: 800, 4: 800}</td>
       <td>1</td>
       <td>ResNet</td>
       <td>{'optimizer_class': 'Adam', 'shuffle_dataset_each_epoch': True, 'optimizer_params': {}, 'criterion_class': None, 'criterion_params': {}, 'scheduler_class': None, 'scheduler_params': {}, 'batch_size': 128, 'lr': 0.000100198, 'epochs': 18, 'input_transform': 'auto', 'tensorboard_dir': None, 'use_tqdm': None, 'prediction_batch_size': 128, 'prediction_input_transform': 'auto', 'shuffling_buffer_size': None, 'freeze.encoder': False, 'load.encoder': None, 'size': '18'}</td>
       <td>0.9215</td>
       <td>{'balanced_accuracy': 0.9215387221291802}</td>
-      <td>1076.8221</td>
-      <td>0.8557</td>
-      <td>Thu Dec 26 06:09:13 2024</td>
+      <td>1473.4144</td>
+      <td>0.8585</td>
+      <td>Fri Apr 25 03:23:27 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
-      <td>{1: 800, 2: 800, 4: 800, 3: 800, 5: 800}</td>
+      <td>{1: 800, 2: 800, 3: 800, 4: 800, 5: 800}</td>
       <td>1</td>
       <td>ResNet</td>
       <td>{'optimizer_class': 'Adam', 'shuffle_dataset_each_epoch': True, 'optimizer_params': {}, 'criterion_class': None, 'criterion_params': {}, 'scheduler_class': None, 'scheduler_params': {}, 'batch_size': 128, 'lr': 0.000100099, 'epochs': 18, 'input_transform': 'auto', 'tensorboard_dir': None, 'use_tqdm': None, 'prediction_batch_size': 128, 'prediction_input_transform': 'auto', 'shuffling_buffer_size': None, 'freeze.encoder': False, 'load.encoder': None, 'size': '18'}</td>
       <td>0.921</td>
       <td>{'balanced_accuracy': 0.9210114051871254}</td>
-      <td>1103.0360</td>
-      <td>0.9537</td>
-      <td>Thu Dec 26 06:09:23 2024</td>
+      <td>1514.9727</td>
+      <td>0.9292</td>
+      <td>Fri Apr 25 03:23:26 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
-      <td>{1: 800, 2: 800, 3: 800, 4: 800, 5: 800}</td>
+      <td>{1: 800, 3: 800, 2: 800, 4: 800, 5: 800}</td>
       <td>1</td>
       <td>ResNet</td>
       <td>{'optimizer_class': 'RMSprop', 'shuffle_dataset_each_epoch': True, 'optimizer_params': {}, 'criterion_class': None, 'criterion_params': {}, 'scheduler_class': None, 'scheduler_params': {}, 'batch_size': 128, 'lr': 0.001, 'epochs': 18, 'input_transform': 'auto', 'tensorboard_dir': None, 'use_tqdm': None, 'prediction_batch_size': 128, 'prediction_input_transform': 'auto', 'shuffling_buffer_size': None, 'freeze.encoder': False, 'load.encoder': None, 'size': '18'}</td>
-      <td>0.8968</td>
-      <td>{'balanced_accuracy': 0.896807581403708}</td>
-      <td>1130.6056</td>
-      <td>0.8116</td>
-      <td>Thu Dec 26 06:13:47 2024</td>
+      <td>0.8965</td>
+      <td>{'balanced_accuracy': 0.8965285352342509}</td>
+      <td>1535.3766</td>
+      <td>0.8253</td>
+      <td>Fri Apr 25 03:28:50 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
-      <td>{1: 800, 2: 800, 4: 800, 3: 800, 5: 800}</td>
+      <td>{1: 800, 2: 800, 3: 800, 4: 800, 5: 800}</td>
       <td>1</td>
       <td>ResNet</td>
       <td>{'optimizer_class': 'SGD', 'shuffle_dataset_each_epoch': True, 'optimizer_params': {}, 'criterion_class': None, 'criterion_params': {}, 'scheduler_class': None, 'scheduler_params': {}, 'batch_size': 128, 'lr': 0.001, 'epochs': 18, 'input_transform': 'auto', 'tensorboard_dir': None, 'use_tqdm': None, 'prediction_batch_size': 128, 'prediction_input_transform': 'auto', 'shuffling_buffer_size': None, 'freeze.encoder': False, 'load.encoder': None, 'size': '18'}</td>
       <td>0.8746</td>
-      <td>{'balanced_accuracy': 0.8746495024246558}</td>
-      <td>1050.4174</td>
-      <td>0.7554</td>
-      <td>Thu Dec 26 06:13:42 2024</td>
+      <td>{'balanced_accuracy': 0.874649502424656}</td>
+      <td>1438.3949</td>
+      <td>0.7970</td>
+      <td>Fri Apr 25 03:29:03 2025</td>
     </tr>
   </tbody>
 </table>
@@ -15675,7 +15675,7 @@ <h4 id="Model-Tuning">Model Tuning<a class="anchor-link" href="#Model-Tuning">&#
 
 
 <div class="jp-RenderedImage jp-OutputArea-output ">
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"
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"
 class="
 "
 >
@@ -15824,82 +15824,165 @@ <h3 id="Advanced-AutoML-Configuration">Advanced AutoML Configuration<a class="an
 
 
 <div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
-<pre>[2024-12-26 06:17:40,761] [automlx.interface] Dataset shape: (1000,1)
-[2024-12-26 06:17:40,811] [automlx.interface] Adaptive Sampling disabled.
-[2024-12-26 06:17:40,848] [automlx.data_transform] Running preprocessing. Number of features: 2
-[2024-12-26 06:17:41,735] [automlx.data_transform] Preprocessing completed. Took 0.887 secs
-[2024-12-26 06:17:41,755] [automlx.process] Running Model Generation
-[2024-12-26 06:17:41,803] [automlx.process] Model Generation completed.
-[2024-12-26 06:17:41,842] [automlx.model_selection] Running Model Selection
-[2024-12-26 06:17:41,842] [automlx.trials] Fewer models (1) than top_k (2) provided, skipping...
-[2024-12-26 06:17:41,936] [automlx.feature_selection] Starting feature ranking for EfficientNet
-[2024-12-26 06:17:41,936] [automlx.feature_selection] min_features is larger than model input features for EfficientNet, skipping feature selection for it
-[2024-12-26 06:17:41,969] [automlx.trials] Running Model Tuning for [&#39;EfficientNet&#39;]
-[2024-12-26 06:17:56,688] [automlx.backend] <span class="ansi-red-intense-fg">Job 2b5ff2de-c394-11ef-ba13-02001701e468 &lt;&gt; EfficientNet - Trial: 2 - Split: 4 of 5
+<pre>[2025-04-25 03:34:00,140] [automlx.interface] Dataset shape: (1000,1)
+[2025-04-25 03:34:00,188] [automlx.interface] Adaptive Sampling disabled.
+[2025-04-25 03:34:00,225] [automlx.data_transform] Running preprocessing. Number of features: 2
+[2025-04-25 03:34:01,091] [automlx.data_transform] Preprocessing completed. Took 0.866 secs
+[2025-04-25 03:34:01,123] [automlx.process] Running Model Generation
+[2025-04-25 03:34:01,170] [automlx.process] Model Generation completed.
+[2025-04-25 03:34:01,196] [automlx.model_selection] Running Model Selection
+[2025-04-25 03:34:01,197] [automlx.trials] Fewer models (1) than top_k (2) provided, skipping...
+[2025-04-25 03:34:01,305] [automlx.feature_selection] Starting feature ranking for EfficientNet
+[2025-04-25 03:34:01,305] [automlx.feature_selection] min_features is larger than model input features for EfficientNet, skipping feature selection for it
+[2025-04-25 03:34:01,340] [automlx.trials] Running Model Tuning for [&#39;EfficientNet&#39;]
+[2025-04-25 03:34:16,157] [automlx.backend] <span class="ansi-red-intense-fg">Job cda0b026-21c0-11f0-b4ee-00001701c949 &lt;&gt; EfficientNet - Trial: 2 - Split: 3 of 5
                             failed
 Stack trace:
 Traceback (most recent call last):
-  File &#34;/scratch_user/jenkins/workspace/v2_long_regress@4/src/automlx/_backend/utils.py&#34;, line 65, in _handle_job_exception
+  File &#34;/scratch_user/jenkins/workspace/v2_long_regress/src/automlx/_backend/utils.py&#34;, line 65, in _handle_job_exception
     complete_results = profile.function(
-  File &#34;/scratch_user/jenkins/workspace/v2_long_regress@4/src/automlx/_utils/profile.py&#34;, line 133, in profile_wrapper
+  File &#34;/scratch_user/jenkins/workspace/v2_long_regress/src/automlx/_utils/profile.py&#34;, line 133, in profile_wrapper
     ) = _profile_resources(
-  File &#34;/scratch_user/jenkins/workspace/v2_long_regress@4/src/automlx/_utils/profile.py&#34;, line 655, in _profile_resources
+  File &#34;/scratch_user/jenkins/workspace/v2_long_regress/src/automlx/_utils/profile.py&#34;, line 655, in _profile_resources
     function_output = func(*args, **kwargs, **pf)
-  File &#34;/scratch_user/jenkins/workspace/v2_long_regress@4/src/automlx/_trials/strategies/base.py&#34;, line 112, in run
+  File &#34;/scratch_user/jenkins/workspace/v2_long_regress/src/automlx/_trials/strategies/base.py&#34;, line 112, in run
     return self._run(reporter, resources, pipeline, train_dataset, callback_manager, pf=pf)
-  File &#34;/scratch_user/jenkins/workspace/v2_long_regress@4/src/automlx/_trials/strategies/standard.py&#34;, line 45, in _run
+  File &#34;/scratch_user/jenkins/workspace/v2_long_regress/src/automlx/_trials/strategies/standard.py&#34;, line 45, in _run
     pipeline.fit(train_dataset, {model_idx: callback_manager}, pf=pf)
-  File &#34;/scratch_user/jenkins/workspace/v2_long_regress@4/src/automlx/_pipeline/pipeline.py&#34;, line 282, in fit
+  File &#34;/scratch_user/jenkins/workspace/v2_long_regress/src/automlx/_pipeline/pipeline.py&#34;, line 282, in fit
     self.stages[i] = stage.fit(dataset, callback=callback)
-  File &#34;/scratch_user/jenkins/workspace/v2_long_regress@4/src/automlx/_model/distributed/distributed_model.py&#34;, line 69, in fit
+  File &#34;/scratch_user/jenkins/workspace/v2_long_regress/src/automlx/_model/distributed/distributed_model.py&#34;, line 69, in fit
     self._fit(dataset, callback)
-  File &#34;/scratch_user/jenkins/workspace/v2_long_regress@4/src/automlx/_model/pytorch/base.py&#34;, line 161, in _fit
+  File &#34;/scratch_user/jenkins/workspace/v2_long_regress/src/automlx/_model/pytorch/base.py&#34;, line 161, in _fit
     output = self._wrapped_model(batch)
-  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-forewardportAMLX2203todev/lib/python3.9/site-packages/torch/nn/modules/module.py&#34;, line 1501, in _call_impl
+  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/module.py&#34;, line 1501, in _call_impl
     return forward_call(*args, **kwargs)
-  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-forewardportAMLX2203todev/lib/python3.9/site-packages/torch/nn/modules/container.py&#34;, line 217, in forward
+  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/container.py&#34;, line 217, in forward
     input = module(input)
-  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-forewardportAMLX2203todev/lib/python3.9/site-packages/torch/nn/modules/module.py&#34;, line 1501, in _call_impl
+  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/module.py&#34;, line 1501, in _call_impl
     return forward_call(*args, **kwargs)
-  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-forewardportAMLX2203todev/lib/python3.9/site-packages/torchvision/models/efficientnet.py&#34;, line 343, in forward
+  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torchvision/models/efficientnet.py&#34;, line 343, in forward
     return self._forward_impl(x)
-  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-forewardportAMLX2203todev/lib/python3.9/site-packages/torchvision/models/efficientnet.py&#34;, line 333, in _forward_impl
+  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torchvision/models/efficientnet.py&#34;, line 333, in _forward_impl
     x = self.features(x)
-  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-forewardportAMLX2203todev/lib/python3.9/site-packages/torch/nn/modules/module.py&#34;, line 1501, in _call_impl
+  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/module.py&#34;, line 1501, in _call_impl
     return forward_call(*args, **kwargs)
-  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-forewardportAMLX2203todev/lib/python3.9/site-packages/torch/nn/modules/container.py&#34;, line 217, in forward
+  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/container.py&#34;, line 217, in forward
     input = module(input)
-  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-forewardportAMLX2203todev/lib/python3.9/site-packages/torch/nn/modules/module.py&#34;, line 1501, in _call_impl
+  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/module.py&#34;, line 1501, in _call_impl
     return forward_call(*args, **kwargs)
-  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-forewardportAMLX2203todev/lib/python3.9/site-packages/torch/nn/modules/container.py&#34;, line 217, in forward
+  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/container.py&#34;, line 217, in forward
     input = module(input)
-  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-forewardportAMLX2203todev/lib/python3.9/site-packages/torch/nn/modules/module.py&#34;, line 1501, in _call_impl
+  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/module.py&#34;, line 1501, in _call_impl
     return forward_call(*args, **kwargs)
-  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-forewardportAMLX2203todev/lib/python3.9/site-packages/torchvision/models/efficientnet.py&#34;, line 164, in forward
+  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torchvision/models/efficientnet.py&#34;, line 164, in forward
     result = self.block(input)
-  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-forewardportAMLX2203todev/lib/python3.9/site-packages/torch/nn/modules/module.py&#34;, line 1501, in _call_impl
+  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/module.py&#34;, line 1501, in _call_impl
     return forward_call(*args, **kwargs)
-  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-forewardportAMLX2203todev/lib/python3.9/site-packages/torch/nn/modules/container.py&#34;, line 217, in forward
+  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/container.py&#34;, line 217, in forward
     input = module(input)
-  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-forewardportAMLX2203todev/lib/python3.9/site-packages/torch/nn/modules/module.py&#34;, line 1501, in _call_impl
+  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/module.py&#34;, line 1501, in _call_impl
     return forward_call(*args, **kwargs)
-  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-forewardportAMLX2203todev/lib/python3.9/site-packages/torch/nn/modules/container.py&#34;, line 217, in forward
+  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/container.py&#34;, line 217, in forward
     input = module(input)
-  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-forewardportAMLX2203todev/lib/python3.9/site-packages/torch/nn/modules/module.py&#34;, line 1501, in _call_impl
+  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/module.py&#34;, line 1501, in _call_impl
     return forward_call(*args, **kwargs)
-  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-forewardportAMLX2203todev/lib/python3.9/site-packages/torch/nn/modules/batchnorm.py&#34;, line 171, in forward
+  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/batchnorm.py&#34;, line 171, in forward
     return F.batch_norm(
-  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-forewardportAMLX2203todev/lib/python3.9/site-packages/torch/nn/functional.py&#34;, line 2448, in batch_norm
+  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/functional.py&#34;, line 2448, in batch_norm
     _verify_batch_size(input.size())
-  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-forewardportAMLX2203todev/lib/python3.9/site-packages/torch/nn/functional.py&#34;, line 2416, in _verify_batch_size
+  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/functional.py&#34;, line 2416, in _verify_batch_size
     raise ValueError(&#34;Expected more than 1 value per channel when training, got input size {}&#34;.format(size))
 ValueError: Expected more than 1 value per channel when training, got input size torch.Size([1, 720, 1, 1])
 </span>
-[2024-12-26 06:18:01,203] [automlx.trials] Best parameters for EfficientNet: {&#39;optimizer_class&#39;: &#39;Adam&#39;, &#39;shuffle_dataset_each_epoch&#39;: True, &#39;optimizer_params&#39;: {}, &#39;criterion_class&#39;: None, &#39;criterion_params&#39;: {}, &#39;scheduler_class&#39;: None, &#39;scheduler_params&#39;: {}, &#39;batch_size&#39;: 16, &#39;lr&#39;: 0.001, &#39;epochs&#39;: 1, &#39;input_transform&#39;: &#39;auto&#39;, &#39;tensorboard_dir&#39;: None, &#39;use_tqdm&#39;: None, &#39;prediction_batch_size&#39;: 128, &#39;prediction_input_transform&#39;: &#39;auto&#39;, &#39;shuffling_buffer_size&#39;: None, &#39;freeze.encoder&#39;: False, &#39;load.encoder&#39;: None, &#39;size&#39;: &#39;b2&#39;}
-[2024-12-26 06:18:01,204] [automlx.trials] Model Tuning completed. Took: 19.236 secs
-[2024-12-26 06:18:01,395] [automlx.interface] Re-fitting pipeline
-[2024-12-26 06:18:01,412] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_c5b54410-5
-[2024-12-26 06:18:22,071] [automlx.interface] AutoMLx completed.
+[2025-04-25 03:34:16,206] [automlx.backend] <span class="ansi-red-intense-fg">Job cda0b026-21c0-11f0-b4ee-00001701c949 &lt;&gt; EfficientNet - Trial: 2 - Split: 2 of 5
+                            failed
+Stack trace:
+Traceback (most recent call last):
+  File &#34;/scratch_user/jenkins/workspace/v2_long_regress/src/automlx/_backend/utils.py&#34;, line 65, in _handle_job_exception
+    complete_results = profile.function(
+  File &#34;/scratch_user/jenkins/workspace/v2_long_regress/src/automlx/_utils/profile.py&#34;, line 133, in profile_wrapper
+    ) = _profile_resources(
+  File &#34;/scratch_user/jenkins/workspace/v2_long_regress/src/automlx/_utils/profile.py&#34;, line 655, in _profile_resources
+    function_output = func(*args, **kwargs, **pf)
+  File &#34;/scratch_user/jenkins/workspace/v2_long_regress/src/automlx/_trials/strategies/base.py&#34;, line 112, in run
+    return self._run(reporter, resources, pipeline, train_dataset, callback_manager, pf=pf)
+  File &#34;/scratch_user/jenkins/workspace/v2_long_regress/src/automlx/_trials/strategies/standard.py&#34;, line 45, in _run
+    pipeline.fit(train_dataset, {model_idx: callback_manager}, pf=pf)
+  File &#34;/scratch_user/jenkins/workspace/v2_long_regress/src/automlx/_pipeline/pipeline.py&#34;, line 282, in fit
+    self.stages[i] = stage.fit(dataset, callback=callback)
+  File &#34;/scratch_user/jenkins/workspace/v2_long_regress/src/automlx/_model/distributed/distributed_model.py&#34;, line 69, in fit
+    self._fit(dataset, callback)
+  File &#34;/scratch_user/jenkins/workspace/v2_long_regress/src/automlx/_model/pytorch/base.py&#34;, line 161, in _fit
+    output = self._wrapped_model(batch)
+  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/module.py&#34;, line 1501, in _call_impl
+    return forward_call(*args, **kwargs)
+  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/container.py&#34;, line 217, in forward
+    input = module(input)
+  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/module.py&#34;, line 1501, in _call_impl
+    return forward_call(*args, **kwargs)
+  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torchvision/models/efficientnet.py&#34;, line 343, in forward
+    return self._forward_impl(x)
+  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torchvision/models/efficientnet.py&#34;, line 333, in _forward_impl
+    x = self.features(x)
+  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/module.py&#34;, line 1501, in _call_impl
+    return forward_call(*args, **kwargs)
+  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/container.py&#34;, line 217, in forward
+    input = module(input)
+  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/module.py&#34;, line 1501, in _call_impl
+    return forward_call(*args, **kwargs)
+  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/container.py&#34;, line 217, in forward
+    input = module(input)
+  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/module.py&#34;, line 1501, in _call_impl
+    return forward_call(*args, **kwargs)
+  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torchvision/models/efficientnet.py&#34;, line 164, in forward
+    result = self.block(input)
+  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/module.py&#34;, line 1501, in _call_impl
+    return forward_call(*args, **kwargs)
+  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/container.py&#34;, line 217, in forward
+    input = module(input)
+  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/module.py&#34;, line 1501, in _call_impl
+    return forward_call(*args, **kwargs)
+  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/container.py&#34;, line 217, in forward
+    input = module(input)
+  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/module.py&#34;, line 1501, in _call_impl
+    return forward_call(*args, **kwargs)
+  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/batchnorm.py&#34;, line 171, in forward
+    return F.batch_norm(
+  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/functional.py&#34;, line 2448, in batch_norm
+    _verify_batch_size(input.size())
+  File &#34;/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/functional.py&#34;, line 2416, in _verify_batch_size
+    raise ValueError(&#34;Expected more than 1 value per channel when training, got input size {}&#34;.format(size))
+ValueError: Expected more than 1 value per channel when training, got input size torch.Size([1, 720, 1, 1])
+</span>
+[2025-04-25 03:34:17,092] [automlx.trials] Best parameters for EfficientNet: {&#39;optimizer_class&#39;: &#39;Adam&#39;, &#39;shuffle_dataset_each_epoch&#39;: True, &#39;optimizer_params&#39;: {}, &#39;criterion_class&#39;: None, &#39;criterion_params&#39;: {}, &#39;scheduler_class&#39;: None, &#39;scheduler_params&#39;: {}, &#39;batch_size&#39;: 16, &#39;lr&#39;: 0.001, &#39;epochs&#39;: 1, &#39;input_transform&#39;: &#39;auto&#39;, &#39;tensorboard_dir&#39;: None, &#39;use_tqdm&#39;: None, &#39;prediction_batch_size&#39;: 128, &#39;prediction_input_transform&#39;: &#39;auto&#39;, &#39;shuffling_buffer_size&#39;: None, &#39;freeze.encoder&#39;: False, &#39;load.encoder&#39;: None, &#39;size&#39;: &#39;b2&#39;}
+[2025-04-25 03:34:17,094] [automlx.trials] Model Tuning completed. Took: 15.754 secs
+[2025-04-25 03:34:17,268] [automlx.interface] Re-fitting pipeline
+[2025-04-25 03:34:17,284] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_8072097a-a
+</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>2025-04-25 03:34:21,520	ERROR worker.py:409 -- Unhandled error (suppress with &#39;RAY_IGNORE_UNHANDLED_ERRORS=1&#39;): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
+</pre>
+</div>
+</div>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>[2025-04-25 03:34:37,909] [automlx.interface] AutoMLx completed.
 Score on test data : 0.5000
 </pre>
 </div>
diff --git a/demos/OracleAutoMLx_ImageClassification.ipynb b/demos/OracleAutoMLx_ImageClassification.ipynb
index 46519c2..9f53466 100644
--- a/demos/OracleAutoMLx_ImageClassification.ipynb
+++ b/demos/OracleAutoMLx_ImageClassification.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "b279068d",
+   "id": "322dd88a",
    "metadata": {},
    "source": [
     "***\n",
@@ -14,19 +14,19 @@
   },
   {
    "cell_type": "markdown",
-   "id": "04019b0c",
+   "id": "6d8cbf4b",
    "metadata": {},
    "source": [
     "Image Classification Demo Notebook.\n",
     "\n",
-    "Copyright © 2024, Oracle and/or its affiliates.\n",
+    "Copyright © 2025, Oracle and/or its affiliates.\n",
     "\n",
     "Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "cc63219a",
+   "id": "faf204cc",
    "metadata": {},
    "source": [
     "## Overview of this Notebook\n",
@@ -68,7 +68,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "30c6905c",
+   "id": "25354007",
    "metadata": {},
    "source": [
     "<a id='setup'></a>\n",
@@ -80,13 +80,13 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "632e9643",
+   "id": "24950533",
    "metadata": {
     "execution": {
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-     "iopub.status.idle": "2024-12-26T14:02:43.075596Z",
-     "shell.execute_reply": "2024-12-26T14:02:43.074900Z"
+     "iopub.execute_input": "2025-04-25T10:15:17.928127Z",
+     "iopub.status.busy": "2025-04-25T10:15:17.927799Z",
+     "iopub.status.idle": "2025-04-25T10:15:18.665789Z",
+     "shell.execute_reply": "2025-04-25T10:15:18.665103Z"
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    "outputs": [],
@@ -99,7 +99,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "63465614",
+   "id": "7c105ab0",
    "metadata": {},
    "source": [
     "Load the required modules."
@@ -108,13 +108,13 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "a0517f7d",
+   "id": "7a3b26ba",
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-     "iopub.status.idle": "2024-12-26T14:02:45.498689Z",
-     "shell.execute_reply": "2024-12-26T14:02:45.498015Z"
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+     "iopub.status.idle": "2025-04-25T10:15:21.742265Z",
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@@ -137,7 +137,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fcbbfe35",
+   "id": "168a2cd3",
    "metadata": {},
    "source": [
     "<a id='load-data'></a>\n",
@@ -148,13 +148,13 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "860917b8",
+   "id": "b4da04c9",
    "metadata": {
     "execution": {
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-     "iopub.status.idle": "2024-12-26T14:02:46.294277Z",
-     "shell.execute_reply": "2024-12-26T14:02:46.293645Z"
+     "iopub.execute_input": "2025-04-25T10:15:21.744596Z",
+     "iopub.status.busy": "2025-04-25T10:15:21.744172Z",
+     "iopub.status.idle": "2025-04-25T10:15:23.306705Z",
+     "shell.execute_reply": "2025-04-25T10:15:23.306165Z"
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    "outputs": [],
@@ -164,7 +164,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "179e67ee",
+   "id": "fedcf8c9",
    "metadata": {},
    "source": [
     "Lets look at a few of the values in the data"
@@ -173,13 +173,13 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "bed3fe03",
+   "id": "1bababf0",
    "metadata": {
     "execution": {
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-     "iopub.status.idle": "2024-12-26T14:02:46.342531Z",
-     "shell.execute_reply": "2024-12-26T14:02:46.341947Z"
+     "iopub.execute_input": "2025-04-25T10:15:23.308953Z",
+     "iopub.status.busy": "2025-04-25T10:15:23.308481Z",
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+     "shell.execute_reply": "2025-04-25T10:15:23.359486Z"
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    "outputs": [
@@ -205,7 +205,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6f017ef7",
+   "id": "fe8c24e5",
    "metadata": {},
    "source": [
     "Plot one of the images as an example"
@@ -214,13 +214,13 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "87aab7e3",
+   "id": "16fcf459",
    "metadata": {
     "execution": {
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-     "iopub.status.idle": "2024-12-26T14:02:46.370833Z",
-     "shell.execute_reply": "2024-12-26T14:02:46.370249Z"
+     "iopub.execute_input": "2025-04-25T10:15:23.361844Z",
+     "iopub.status.busy": "2025-04-25T10:15:23.361436Z",
+     "iopub.status.idle": "2025-04-25T10:15:23.387475Z",
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@@ -251,7 +251,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "950a5f6d",
+   "id": "92c5c6cf",
    "metadata": {},
    "source": [
     "We visualize the distribution of the target variable in the training data."
@@ -260,13 +260,13 @@
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-     "shell.execute_reply": "2024-12-26T14:02:47.160005Z"
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@@ -5884,9 +5884,9 @@
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@@ -5925,7 +5925,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e4f7c17c",
+   "id": "49de4f9e",
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@@ -5936,13 +5936,13 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "010b3340",
+   "id": "c811f583",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:02:47.195175Z",
-     "iopub.status.busy": "2024-12-26T14:02:47.194420Z",
-     "iopub.status.idle": "2024-12-26T14:02:47.512614Z",
-     "shell.execute_reply": "2024-12-26T14:02:47.511944Z"
+     "iopub.execute_input": "2025-04-25T10:15:24.176941Z",
+     "iopub.status.busy": "2025-04-25T10:15:24.176415Z",
+     "iopub.status.idle": "2025-04-25T10:15:24.505743Z",
+     "shell.execute_reply": "2025-04-25T10:15:24.505155Z"
     }
    },
    "outputs": [],
@@ -5958,7 +5958,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e70a3611",
+   "id": "2f98a92b",
    "metadata": {},
    "source": [
     "<a id='AutoML'></a>\n",
@@ -5967,7 +5967,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "29035c1d",
+   "id": "310a3cb4",
    "metadata": {},
    "source": [
     "<a id='provider'></a>\n",
@@ -5985,7 +5985,7 @@
   },
   {
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-   "id": "f4c208e2",
+   "id": "a1e146e1",
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@@ -5994,7 +5994,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "265d2a75",
+   "id": "8d2ed040",
    "metadata": {},
    "source": [
     "The AutoMLx API is quite simple to work with. We create an instance of the pipeline. Next, the training data is passed to the `fit()` function which executes the previously mentioned steps.\n",
@@ -6004,13 +6004,13 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "ea69e760",
+   "id": "6ff83718",
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-     "iopub.status.idle": "2024-12-26T14:17:37.670442Z",
-     "shell.execute_reply": "2024-12-26T14:17:37.669823Z"
+     "iopub.execute_input": "2025-04-25T10:15:24.508237Z",
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+     "iopub.status.idle": "2025-04-25T10:33:56.789064Z",
+     "shell.execute_reply": "2025-04-25T10:33:56.788491Z"
     }
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    "outputs": [
@@ -6018,91 +6018,91 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:02:47,732] [automlx.backend] Overwriting ray session directory to /tmp/ytqjboor/ray, which will be deleted at engine shutdown. If you wish to retain ray logs, provide _temp_dir in ray_setup dict of engine_opts when initializing the AutoMLx engine.\n"
+      "[2025-04-25 03:15:24,710] [automlx.backend] Overwriting ray session directory to /tmp/j9903zbf/ray, which will be deleted at engine shutdown. If you wish to retain ray logs, provide _temp_dir in ray_setup dict of engine_opts when initializing the AutoMLx engine.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:02:54,014] [automlx.interface] Dataset shape: (1000,1)\n"
+      "[2025-04-25 03:15:29,829] [automlx.interface] Dataset shape: (1000,1)\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:02:57,160] [sanerec.autotuning.parameter] Hyperparameter epsilon autotune range is set to its validation range. This could lead to long training times\n"
+      "[2025-04-25 03:15:33,331] [sanerec.autotuning.parameter] Hyperparameter epsilon autotune range is set to its validation range. This could lead to long training times\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:02:57,519] [sanerec.autotuning.parameter] Hyperparameter repeat_quality_threshold autotune range is set to its validation range. This could lead to long training times\n"
+      "[2025-04-25 03:15:33,859] [sanerec.autotuning.parameter] Hyperparameter repeat_quality_threshold autotune range is set to its validation range. This could lead to long training times\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:02:57,526] [sanerec.autotuning.parameter] Hyperparameter scope autotune range is set to its validation range. This could lead to long training times\n"
+      "[2025-04-25 03:15:33,868] [sanerec.autotuning.parameter] Hyperparameter scope autotune range is set to its validation range. This could lead to long training times\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:02:57,596] [automlx.data_transform] Running preprocessing. Number of features: 2\n"
+      "[2025-04-25 03:15:33,941] [automlx.data_transform] Running preprocessing. Number of features: 2\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:02:58,488] [automlx.data_transform] Preprocessing completed. Took 0.892 secs\n"
+      "[2025-04-25 03:15:34,824] [automlx.data_transform] Preprocessing completed. Took 0.883 secs\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:02:58,531] [automlx.process] Running Model Generation\n"
+      "[2025-04-25 03:15:34,870] [automlx.process] Running Model Generation\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:02:58,571] [automlx.process] Model Generation completed.\n"
+      "[2025-04-25 03:15:34,911] [automlx.process] Model Generation completed.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:02:58,637] [automlx.model_selection] Running Model Selection\n"
+      "[2025-04-25 03:15:34,981] [automlx.model_selection] Running Model Selection\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:05:35,557] [automlx.model_selection] Model Selection completed - Took 156.920 sec - Selected models: [['ResNet']]\n"
+      "[2025-04-25 03:18:12,401] [automlx.model_selection] Model Selection completed - Took 157.420 sec - Selected models: [['ResNet']]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:05:35,587] [automlx.adaptive_sampling] Running Adaptive Sampling. Dataset shape: (1000,2).\n"
+      "[2025-04-25 03:18:12,429] [automlx.adaptive_sampling] Running Adaptive Sampling. Dataset shape: (1000,2).\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:05:35,638] [automlx.adaptive_sampling] Adaptive Sampling: top_limit: 583 < bottom_limit: 1000,\n",
+      "[2025-04-25 03:18:12,479] [automlx.adaptive_sampling] Adaptive Sampling: top_limit: 583 < bottom_limit: 1000,\n",
       "sampling process will be skipped\n"
      ]
     },
@@ -6110,7 +6110,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:05:35,648] [automlx.adaptive_sampling] Adaptive Sampling: top_limit: 146 < bottom_limit: 1000,\n",
+      "[2025-04-25 03:18:12,489] [automlx.adaptive_sampling] Adaptive Sampling: top_limit: 146 < bottom_limit: 1000,\n",
       "sampling process will be skipped\n"
      ]
     },
@@ -6118,62 +6118,62 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:05:35,742] [automlx.feature_selection] Starting feature ranking for ResNet\n"
+      "[2025-04-25 03:18:12,584] [automlx.feature_selection] Starting feature ranking for ResNet\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:05:35,743] [automlx.feature_selection] min_features is larger than model input features for ResNet, skipping feature selection for it\n"
+      "[2025-04-25 03:18:12,585] [automlx.feature_selection] min_features is larger than model input features for ResNet, skipping feature selection for it\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:05:35,779] [automlx.trials] Running Model Tuning for ['ResNet']\n"
+      "[2025-04-25 03:18:12,620] [automlx.trials] Running Model Tuning for ['ResNet']\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:15:29,101] [automlx.trials] Best parameters for ResNet: {'optimizer_class': 'Adam', 'shuffle_dataset_each_epoch': True, 'optimizer_params': {}, 'criterion_class': None, 'criterion_params': {}, 'scheduler_class': None, 'scheduler_params': {}, 'batch_size': 128, 'lr': 0.0031630600334930504, 'epochs': 18, 'input_transform': 'auto', 'tensorboard_dir': None, 'use_tqdm': None, 'prediction_batch_size': 128, 'prediction_input_transform': 'auto', 'shuffling_buffer_size': None, 'freeze.encoder': False, 'load.encoder': None, 'size': '18'}\n"
+      "[2025-04-25 03:31:47,337] [automlx.trials] Best parameters for ResNet: {'optimizer_class': 'Adam', 'shuffle_dataset_each_epoch': True, 'optimizer_params': {}, 'criterion_class': None, 'criterion_params': {}, 'scheduler_class': None, 'scheduler_params': {}, 'batch_size': 128, 'lr': 0.0031630600334930504, 'epochs': 18, 'input_transform': 'auto', 'tensorboard_dir': None, 'use_tqdm': None, 'prediction_batch_size': 128, 'prediction_input_transform': 'auto', 'shuffling_buffer_size': None, 'freeze.encoder': False, 'load.encoder': None, 'size': '18'}\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:15:29,103] [automlx.trials] Model Tuning completed. Took: 593.323 secs\n"
+      "[2025-04-25 03:31:47,338] [automlx.trials] Model Tuning completed. Took: 814.718 secs\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:15:29,820] [automlx.interface] Re-fitting pipeline\n"
+      "[2025-04-25 03:31:48,319] [automlx.interface] Re-fitting pipeline\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:15:29,837] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_1235d2a6-6\n"
+      "[2025-04-25 03:31:48,337] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_d9a60282-f\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:17:35,770] [automlx.interface] AutoMLx completed.\n"
+      "[2025-04-25 03:33:54,686] [automlx.interface] AutoMLx completed.\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<automlx._interface.classifier.AutoClassifier at 0x14b73b6c7dc0>"
+       "<automlx._interface.classifier.AutoClassifier at 0x145660035130>"
       ]
      },
      "execution_count": 8,
@@ -6188,7 +6188,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0545cd0f",
+   "id": "892f72ed",
    "metadata": {},
    "source": [
     "A model is then generated (`est1`) and can be used for prediction tasks. We use the `balanced accuracy` scoring metric to evaluate the performance of this model on unseen data (`X_test`)."
@@ -6197,13 +6197,13 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "1f894cdc",
+   "id": "bd059b93",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:17:37.672593Z",
-     "iopub.status.busy": "2024-12-26T14:17:37.672327Z",
-     "iopub.status.idle": "2024-12-26T14:17:39.979035Z",
-     "shell.execute_reply": "2024-12-26T14:17:39.978523Z"
+     "iopub.execute_input": "2025-04-25T10:33:56.791707Z",
+     "iopub.status.busy": "2025-04-25T10:33:56.791117Z",
+     "iopub.status.idle": "2025-04-25T10:33:59.365352Z",
+     "shell.execute_reply": "2025-04-25T10:33:59.364801Z"
     }
    },
    "outputs": [
@@ -6224,7 +6224,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3cca5b5f",
+   "id": "73ccec9b",
    "metadata": {},
    "source": [
     "<a id='analyze'></a>\n",
@@ -6239,7 +6239,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b6c12e9c",
+   "id": "82f5595b",
    "metadata": {},
    "source": [
     "AutoMLx provides a `print_summary` API to output all the different trials performed."
@@ -6248,13 +6248,13 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "42f3c1c9",
+   "id": "ce80e60f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:17:39.980885Z",
-     "iopub.status.busy": "2024-12-26T14:17:39.980647Z",
-     "iopub.status.idle": "2024-12-26T14:17:40.057259Z",
-     "shell.execute_reply": "2024-12-26T14:17:40.056747Z"
+     "iopub.execute_input": "2025-04-25T10:33:59.367479Z",
+     "iopub.status.busy": "2025-04-25T10:33:59.366979Z",
+     "iopub.status.idle": "2025-04-25T10:33:59.444125Z",
+     "shell.execute_reply": "2025-04-25T10:33:59.443618Z"
     }
    },
    "outputs": [
@@ -6299,7 +6299,7 @@
        "      <td>{'optimizer_class': 'Adam', 'shuffle_dataset_each_epoch': True, 'optimizer_params': {}, 'criterion_class': None, 'criterion_params': {}, 'scheduler_class': None, 'scheduler_params': {}, 'batch_size': 128, 'lr': 0.0031630600334930504, 'epochs': 18, 'input_transform': 'auto', 'tensorboard_dir': None, 'use_tqdm': None, 'prediction_batch_size': 128, 'prediction_input_transform': 'auto', 'shuffling_buffer_size': None, 'freeze.encoder': False, 'load.encoder': None, 'size': '18'}</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>25.1.0</td>\n",
+       "      <td>25.2.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>3.9.21 (main, Dec 11 2024, 16:24:11) \\n[GCC 11.2.0]</td>\n",
@@ -6347,51 +6347,51 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <td>Model Selection</td>\n",
-       "      <td>{3: 800, 1: 800, 5: 800, 2: 800, 4: 800}</td>\n",
+       "      <td>{5: 800, 2: 800, 3: 800, 1: 800, 4: 800}</td>\n",
        "      <td>1</td>\n",
        "      <td>ResNet</td>\n",
        "      <td>{'optimizer_class': 'Adam', 'shuffle_dataset_each_epoch': True, 'optimizer_params': {}, 'criterion_class': None, 'criterion_params': {}, 'scheduler_class': None, 'scheduler_params': {}, 'batch_size': 128, 'lr': 0.001, 'epochs': 18, 'input_transform': 'auto', 'tensorboard_dir': None, 'use_tqdm': None, 'prediction_batch_size': 128, 'prediction_input_transform': 'auto', 'shuffling_buffer_size': None, 'freeze.encoder': False, 'load.encoder': None, 'size': '18'}</td>\n",
        "      <td>0.9429</td>\n",
        "      <td>{'balanced_accuracy': 0.9429372134190274}</td>\n",
-       "      <td>546.2951</td>\n",
-       "      <td>0.8530</td>\n",
-       "      <td>Thu Dec 26 06:04:56 2024</td>\n",
+       "      <td>566.4878</td>\n",
+       "      <td>0.8595</td>\n",
+       "      <td>Fri Apr 25 03:17:38 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Selection</td>\n",
-       "      <td>{2: 800, 3: 800, 4: 800, 1: 800, 5: 800}</td>\n",
+       "      <td>{1: 800, 3: 800, 5: 800, 2: 800, 4: 800}</td>\n",
        "      <td>1</td>\n",
        "      <td>EfficientNet</td>\n",
        "      <td>{'optimizer_class': 'Adam', 'shuffle_dataset_each_epoch': True, 'optimizer_params': {}, 'criterion_class': None, 'criterion_params': {}, 'scheduler_class': None, 'scheduler_params': {}, 'batch_size': 128, 'lr': 0.001, 'epochs': 18, 'input_transform': 'auto', 'tensorboard_dir': None, 'use_tqdm': None, 'prediction_batch_size': 128, 'prediction_input_transform': 'auto', 'shuffling_buffer_size': None, 'freeze.encoder': False, 'load.encoder': None, 'size': 'b0'}</td>\n",
        "      <td>0.8153</td>\n",
        "      <td>{'balanced_accuracy': 0.8153082390588295}</td>\n",
-       "      <td>728.0001</td>\n",
-       "      <td>0.8687</td>\n",
-       "      <td>Thu Dec 26 06:05:35 2024</td>\n",
+       "      <td>734.1541</td>\n",
+       "      <td>0.8685</td>\n",
+       "      <td>Fri Apr 25 03:18:12 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
-       "      <td>{4: 800, 3: 800, 1: 800, 2: 800, 5: 800}</td>\n",
+       "      <td>{4: 800, 3: 800, 2: 800, 1: 800, 5: 800}</td>\n",
        "      <td>1</td>\n",
        "      <td>ResNet</td>\n",
        "      <td>{'optimizer_class': 'Adam', 'shuffle_dataset_each_epoch': True, 'optimizer_params': {}, 'criterion_class': None, 'criterion_params': {}, 'scheduler_class': None, 'scheduler_params': {}, 'batch_size': 128, 'lr': 0.0031630600334930504, 'epochs': 18, 'input_transform': 'auto', 'tensorboard_dir': None, 'use_tqdm': None, 'prediction_batch_size': 128, 'prediction_input_transform': 'auto', 'shuffling_buffer_size': None, 'freeze.encoder': False, 'load.encoder': None, 'size': '18'}</td>\n",
        "      <td>0.9421</td>\n",
-       "      <td>{'balanced_accuracy': 0.9420862061013218}</td>\n",
-       "      <td>1311.0154</td>\n",
-       "      <td>0.9147</td>\n",
-       "      <td>Thu Dec 26 06:12:54 2024</td>\n",
+       "      <td>{'balanced_accuracy': 0.9420862061013219}</td>\n",
+       "      <td>1679.7299</td>\n",
+       "      <td>0.9625</td>\n",
+       "      <td>Fri Apr 25 03:27:58 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
-       "      <td>{1: 800, 2: 800, 3: 800, 5: 800, 4: 800}</td>\n",
+       "      <td>{4: 800, 3: 800, 1: 800, 5: 800, 2: 800}</td>\n",
        "      <td>1</td>\n",
        "      <td>ResNet</td>\n",
        "      <td>{'optimizer_class': 'Adam', 'shuffle_dataset_each_epoch': True, 'optimizer_params': {}, 'criterion_class': None, 'criterion_params': {}, 'scheduler_class': None, 'scheduler_params': {}, 'batch_size': 128, 'lr': 0.001, 'epochs': 18, 'input_transform': 'auto', 'tensorboard_dir': None, 'use_tqdm': None, 'prediction_batch_size': 128, 'prediction_input_transform': 'auto', 'shuffling_buffer_size': None, 'freeze.encoder': False, 'load.encoder': None, 'size': '18'}</td>\n",
        "      <td>0.9409</td>\n",
        "      <td>{'balanced_accuracy': 0.9409403911576797}</td>\n",
-       "      <td>1295.1165</td>\n",
-       "      <td>0.8465</td>\n",
-       "      <td>Thu Dec 26 06:10:09 2024</td>\n",
+       "      <td>1654.4550</td>\n",
+       "      <td>0.8447</td>\n",
+       "      <td>Fri Apr 25 03:24:15 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
@@ -6401,9 +6401,9 @@
        "      <td>{'optimizer_class': 'Adam', 'shuffle_dataset_each_epoch': True, 'optimizer_params': {}, 'criterion_class': None, 'criterion_params': {}, 'scheduler_class': None, 'scheduler_params': {}, 'batch_size': 128, 'lr': 0.001000099, 'epochs': 18, 'input_transform': 'auto', 'tensorboard_dir': None, 'use_tqdm': None, 'prediction_batch_size': 128, 'prediction_input_transform': 'auto', 'shuffling_buffer_size': None, 'freeze.encoder': False, 'load.encoder': None, 'size': '18'}</td>\n",
        "      <td>0.94</td>\n",
        "      <td>{'balanced_accuracy': 0.9399735188445627}</td>\n",
-       "      <td>1384.2000</td>\n",
-       "      <td>0.8510</td>\n",
-       "      <td>Thu Dec 26 06:10:26 2024</td>\n",
+       "      <td>1676.6548</td>\n",
+       "      <td>0.8474</td>\n",
+       "      <td>Fri Apr 25 03:24:22 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
@@ -6413,21 +6413,21 @@
        "      <td>{'optimizer_class': 'Adam', 'shuffle_dataset_each_epoch': True, 'optimizer_params': {}, 'criterion_class': None, 'criterion_params': {}, 'scheduler_class': None, 'scheduler_params': {}, 'batch_size': 128, 'lr': 0.0031631590334930504, 'epochs': 18, 'input_transform': 'auto', 'tensorboard_dir': None, 'use_tqdm': None, 'prediction_batch_size': 128, 'prediction_input_transform': 'auto', 'shuffling_buffer_size': None, 'freeze.encoder': False, 'load.encoder': None, 'size': '18'}</td>\n",
        "      <td>0.932</td>\n",
        "      <td>{'balanced_accuracy': 0.9320400827675271}</td>\n",
-       "      <td>1144.2416</td>\n",
-       "      <td>0.9041</td>\n",
-       "      <td>Thu Dec 26 06:12:59 2024</td>\n",
+       "      <td>1606.3738</td>\n",
+       "      <td>0.9188</td>\n",
+       "      <td>Fri Apr 25 03:28:28 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
-       "      <td>{2: 800, 1: 800, 3: 800, 5: 800, 4: 800}</td>\n",
+       "      <td>{1: 800, 2: 800, 3: 800, 4: 800, 5: 800}</td>\n",
        "      <td>1</td>\n",
        "      <td>ResNet</td>\n",
        "      <td>{'optimizer_class': 'Adam', 'shuffle_dataset_each_epoch': True, 'optimizer_params': {}, 'criterion_class': None, 'criterion_params': {}, 'scheduler_class': None, 'scheduler_params': {}, 'batch_size': 128, 'lr': 0.001, 'epochs': 18, 'input_transform': 'auto', 'tensorboard_dir': None, 'use_tqdm': None, 'prediction_batch_size': 128, 'prediction_input_transform': 'auto', 'shuffling_buffer_size': None, 'freeze.encoder': False, 'load.encoder': None, 'size': '34'}</td>\n",
-       "      <td>0.9263</td>\n",
-       "      <td>{'balanced_accuracy': 0.9263416880185892}</td>\n",
-       "      <td>1208.9238</td>\n",
-       "      <td>1.0352</td>\n",
-       "      <td>Thu Dec 26 06:15:28 2024</td>\n",
+       "      <td>0.9319</td>\n",
+       "      <td>{'balanced_accuracy': 0.9318589293978995}</td>\n",
+       "      <td>1835.8036</td>\n",
+       "      <td>1.0745</td>\n",
+       "      <td>Fri Apr 25 03:31:47 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
@@ -6435,59 +6435,59 @@
        "      <td>1</td>\n",
        "      <td>ResNet</td>\n",
        "      <td>{'optimizer_class': 'Adagrad', 'shuffle_dataset_each_epoch': True, 'optimizer_params': {}, 'criterion_class': None, 'criterion_params': {}, 'scheduler_class': None, 'scheduler_params': {}, 'batch_size': 128, 'lr': 0.001, 'epochs': 18, 'input_transform': 'auto', 'tensorboard_dir': None, 'use_tqdm': None, 'prediction_batch_size': 128, 'prediction_input_transform': 'auto', 'shuffling_buffer_size': None, 'freeze.encoder': False, 'load.encoder': None, 'size': '18'}</td>\n",
-       "      <td>0.925</td>\n",
-       "      <td>{'balanced_accuracy': 0.9250455746251683}</td>\n",
-       "      <td>1142.6463</td>\n",
-       "      <td>0.9129</td>\n",
-       "      <td>Thu Dec 26 06:13:39 2024</td>\n",
+       "      <td>0.9304</td>\n",
+       "      <td>{'balanced_accuracy': 0.9303550701472287}</td>\n",
+       "      <td>1599.9909</td>\n",
+       "      <td>0.8875</td>\n",
+       "      <td>Fri Apr 25 03:28:47 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
-       "      <td>{1: 800, 3: 800, 4: 800, 5: 800, 2: 800}</td>\n",
+       "      <td>{1: 800, 3: 800, 2: 800, 5: 800, 4: 800}</td>\n",
        "      <td>1</td>\n",
        "      <td>ResNet</td>\n",
        "      <td>{'optimizer_class': 'Adam', 'shuffle_dataset_each_epoch': True, 'optimizer_params': {}, 'criterion_class': None, 'criterion_params': {}, 'scheduler_class': None, 'scheduler_params': {}, 'batch_size': 128, 'lr': 0.000100198, 'epochs': 18, 'input_transform': 'auto', 'tensorboard_dir': None, 'use_tqdm': None, 'prediction_batch_size': 128, 'prediction_input_transform': 'auto', 'shuffling_buffer_size': None, 'freeze.encoder': False, 'load.encoder': None, 'size': '18'}</td>\n",
        "      <td>0.9215</td>\n",
        "      <td>{'balanced_accuracy': 0.9215387221291802}</td>\n",
-       "      <td>1076.8221</td>\n",
-       "      <td>0.8557</td>\n",
-       "      <td>Thu Dec 26 06:09:13 2024</td>\n",
+       "      <td>1473.4144</td>\n",
+       "      <td>0.8585</td>\n",
+       "      <td>Fri Apr 25 03:23:27 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
-       "      <td>{1: 800, 2: 800, 4: 800, 3: 800, 5: 800}</td>\n",
+       "      <td>{1: 800, 2: 800, 3: 800, 4: 800, 5: 800}</td>\n",
        "      <td>1</td>\n",
        "      <td>ResNet</td>\n",
        "      <td>{'optimizer_class': 'Adam', 'shuffle_dataset_each_epoch': True, 'optimizer_params': {}, 'criterion_class': None, 'criterion_params': {}, 'scheduler_class': None, 'scheduler_params': {}, 'batch_size': 128, 'lr': 0.000100099, 'epochs': 18, 'input_transform': 'auto', 'tensorboard_dir': None, 'use_tqdm': None, 'prediction_batch_size': 128, 'prediction_input_transform': 'auto', 'shuffling_buffer_size': None, 'freeze.encoder': False, 'load.encoder': None, 'size': '18'}</td>\n",
        "      <td>0.921</td>\n",
        "      <td>{'balanced_accuracy': 0.9210114051871254}</td>\n",
-       "      <td>1103.0360</td>\n",
-       "      <td>0.9537</td>\n",
-       "      <td>Thu Dec 26 06:09:23 2024</td>\n",
+       "      <td>1514.9727</td>\n",
+       "      <td>0.9292</td>\n",
+       "      <td>Fri Apr 25 03:23:26 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
-       "      <td>{1: 800, 2: 800, 3: 800, 4: 800, 5: 800}</td>\n",
+       "      <td>{1: 800, 3: 800, 2: 800, 4: 800, 5: 800}</td>\n",
        "      <td>1</td>\n",
        "      <td>ResNet</td>\n",
        "      <td>{'optimizer_class': 'RMSprop', 'shuffle_dataset_each_epoch': True, 'optimizer_params': {}, 'criterion_class': None, 'criterion_params': {}, 'scheduler_class': None, 'scheduler_params': {}, 'batch_size': 128, 'lr': 0.001, 'epochs': 18, 'input_transform': 'auto', 'tensorboard_dir': None, 'use_tqdm': None, 'prediction_batch_size': 128, 'prediction_input_transform': 'auto', 'shuffling_buffer_size': None, 'freeze.encoder': False, 'load.encoder': None, 'size': '18'}</td>\n",
-       "      <td>0.8968</td>\n",
-       "      <td>{'balanced_accuracy': 0.896807581403708}</td>\n",
-       "      <td>1130.6056</td>\n",
-       "      <td>0.8116</td>\n",
-       "      <td>Thu Dec 26 06:13:47 2024</td>\n",
+       "      <td>0.8965</td>\n",
+       "      <td>{'balanced_accuracy': 0.8965285352342509}</td>\n",
+       "      <td>1535.3766</td>\n",
+       "      <td>0.8253</td>\n",
+       "      <td>Fri Apr 25 03:28:50 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
-       "      <td>{1: 800, 2: 800, 4: 800, 3: 800, 5: 800}</td>\n",
+       "      <td>{1: 800, 2: 800, 3: 800, 4: 800, 5: 800}</td>\n",
        "      <td>1</td>\n",
        "      <td>ResNet</td>\n",
        "      <td>{'optimizer_class': 'SGD', 'shuffle_dataset_each_epoch': True, 'optimizer_params': {}, 'criterion_class': None, 'criterion_params': {}, 'scheduler_class': None, 'scheduler_params': {}, 'batch_size': 128, 'lr': 0.001, 'epochs': 18, 'input_transform': 'auto', 'tensorboard_dir': None, 'use_tqdm': None, 'prediction_batch_size': 128, 'prediction_input_transform': 'auto', 'shuffling_buffer_size': None, 'freeze.encoder': False, 'load.encoder': None, 'size': '18'}</td>\n",
        "      <td>0.8746</td>\n",
-       "      <td>{'balanced_accuracy': 0.8746495024246558}</td>\n",
-       "      <td>1050.4174</td>\n",
-       "      <td>0.7554</td>\n",
-       "      <td>Thu Dec 26 06:13:42 2024</td>\n",
+       "      <td>{'balanced_accuracy': 0.874649502424656}</td>\n",
+       "      <td>1438.3949</td>\n",
+       "      <td>0.7970</td>\n",
+       "      <td>Fri Apr 25 03:29:03 2025</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>"
@@ -6506,7 +6506,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e176a096",
+   "id": "4a92c0c5",
    "metadata": {},
    "source": [
     "We also provide the capability to visualize the results of each stage of the AutoML."
@@ -6514,7 +6514,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "86d87bff",
+   "id": "47ddd5d7",
    "metadata": {},
    "source": [
     "<a id='algorithm-selection'></a>\n",
@@ -6526,13 +6526,13 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "09df46f2",
+   "id": "8a8ef05a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:17:40.058992Z",
-     "iopub.status.busy": "2024-12-26T14:17:40.058736Z",
-     "iopub.status.idle": "2024-12-26T14:17:40.234778Z",
-     "shell.execute_reply": "2024-12-26T14:17:40.234176Z"
+     "iopub.execute_input": "2025-04-25T10:33:59.446108Z",
+     "iopub.status.busy": "2025-04-25T10:33:59.445592Z",
+     "iopub.status.idle": "2025-04-25T10:33:59.625186Z",
+     "shell.execute_reply": "2025-04-25T10:33:59.624597Z"
     },
     "lines_to_next_cell": 0
    },
@@ -6580,7 +6580,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a50d72b0",
+   "id": "1c950833",
    "metadata": {},
    "source": [
     "<a id='model-tuning'></a>\n",
@@ -6592,19 +6592,19 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "ba44b43c",
+   "id": "7236cdb7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:17:40.237009Z",
-     "iopub.status.busy": "2024-12-26T14:17:40.236614Z",
-     "iopub.status.idle": "2024-12-26T14:17:40.457145Z",
-     "shell.execute_reply": "2024-12-26T14:17:40.456524Z"
+     "iopub.execute_input": "2025-04-25T10:33:59.627391Z",
+     "iopub.status.busy": "2025-04-25T10:33:59.626981Z",
+     "iopub.status.idle": "2025-04-25T10:33:59.833480Z",
+     "shell.execute_reply": "2025-04-25T10:33:59.832905Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 400x300 with 1 Axes>"
       ]
@@ -6642,7 +6642,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "81c44416",
+   "id": "1199dc89",
    "metadata": {},
    "source": [
     "<a id='advanced'></a>\n",
@@ -6654,13 +6654,13 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "2ff966e8",
+   "id": "69f4fed8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:17:40.458929Z",
-     "iopub.status.busy": "2024-12-26T14:17:40.458598Z",
-     "iopub.status.idle": "2024-12-26T14:17:40.520430Z",
-     "shell.execute_reply": "2024-12-26T14:17:40.519929Z"
+     "iopub.execute_input": "2025-04-25T10:33:59.835207Z",
+     "iopub.status.busy": "2025-04-25T10:33:59.834876Z",
+     "iopub.status.idle": "2025-04-25T10:33:59.896817Z",
+     "shell.execute_reply": "2025-04-25T10:33:59.896321Z"
     }
    },
    "outputs": [],
@@ -6685,7 +6685,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3d907495",
+   "id": "021f5459",
    "metadata": {},
    "source": [
     "You can also configure the pipeline with suitable parameters according to your needs."
@@ -6694,13 +6694,13 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "2f43f675",
+   "id": "bbc9bc67",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:17:40.522364Z",
-     "iopub.status.busy": "2024-12-26T14:17:40.521951Z",
-     "iopub.status.idle": "2024-12-26T14:17:40.582079Z",
-     "shell.execute_reply": "2024-12-26T14:17:40.581516Z"
+     "iopub.execute_input": "2025-04-25T10:33:59.898782Z",
+     "iopub.status.busy": "2025-04-25T10:33:59.898355Z",
+     "iopub.status.idle": "2025-04-25T10:33:59.959143Z",
+     "shell.execute_reply": "2025-04-25T10:33:59.958659Z"
     }
    },
    "outputs": [],
@@ -6720,7 +6720,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "25a28b18",
+   "id": "070f7f6c",
    "metadata": {},
    "source": [
     "A few of the advanced settings can be passed directly to the pipeline's fit method, instead of its constructor."
@@ -6729,13 +6729,13 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "1b57f63b",
+   "id": "cc152bb0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:17:40.584065Z",
-     "iopub.status.busy": "2024-12-26T14:17:40.583711Z",
-     "iopub.status.idle": "2024-12-26T14:18:25.776841Z",
-     "shell.execute_reply": "2024-12-26T14:18:25.776278Z"
+     "iopub.execute_input": "2025-04-25T10:33:59.960968Z",
+     "iopub.status.busy": "2025-04-25T10:33:59.960565Z",
+     "iopub.status.idle": "2025-04-25T10:34:41.395271Z",
+     "shell.execute_reply": "2025-04-25T10:34:41.394722Z"
     }
    },
    "outputs": [
@@ -6743,140 +6743,140 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:17:40,761] [automlx.interface] Dataset shape: (1000,1)\n"
+      "[2025-04-25 03:34:00,140] [automlx.interface] Dataset shape: (1000,1)\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:17:40,811] [automlx.interface] Adaptive Sampling disabled.\n"
+      "[2025-04-25 03:34:00,188] [automlx.interface] Adaptive Sampling disabled.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:17:40,848] [automlx.data_transform] Running preprocessing. Number of features: 2\n"
+      "[2025-04-25 03:34:00,225] [automlx.data_transform] Running preprocessing. Number of features: 2\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:17:41,735] [automlx.data_transform] Preprocessing completed. Took 0.887 secs\n"
+      "[2025-04-25 03:34:01,091] [automlx.data_transform] Preprocessing completed. Took 0.866 secs\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:17:41,755] [automlx.process] Running Model Generation\n"
+      "[2025-04-25 03:34:01,123] [automlx.process] Running Model Generation\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:17:41,803] [automlx.process] Model Generation completed.\n"
+      "[2025-04-25 03:34:01,170] [automlx.process] Model Generation completed.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:17:41,842] [automlx.model_selection] Running Model Selection\n"
+      "[2025-04-25 03:34:01,196] [automlx.model_selection] Running Model Selection\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:17:41,842] [automlx.trials] Fewer models (1) than top_k (2) provided, skipping...\n"
+      "[2025-04-25 03:34:01,197] [automlx.trials] Fewer models (1) than top_k (2) provided, skipping...\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:17:41,936] [automlx.feature_selection] Starting feature ranking for EfficientNet\n"
+      "[2025-04-25 03:34:01,305] [automlx.feature_selection] Starting feature ranking for EfficientNet\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:17:41,936] [automlx.feature_selection] min_features is larger than model input features for EfficientNet, skipping feature selection for it\n"
+      "[2025-04-25 03:34:01,305] [automlx.feature_selection] min_features is larger than model input features for EfficientNet, skipping feature selection for it\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:17:41,969] [automlx.trials] Running Model Tuning for ['EfficientNet']\n"
+      "[2025-04-25 03:34:01,340] [automlx.trials] Running Model Tuning for ['EfficientNet']\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:17:56,688] [automlx.backend] \u001b[91mJob 2b5ff2de-c394-11ef-ba13-02001701e468 <> EfficientNet - Trial: 2 - Split: 4 of 5\n",
+      "[2025-04-25 03:34:16,157] [automlx.backend] \u001b[91mJob cda0b026-21c0-11f0-b4ee-00001701c949 <> EfficientNet - Trial: 2 - Split: 3 of 5\n",
       "                            failed\n",
       "Stack trace:\n",
       "Traceback (most recent call last):\n",
-      "  File \"/scratch_user/jenkins/workspace/v2_long_regress@4/src/automlx/_backend/utils.py\", line 65, in _handle_job_exception\n",
+      "  File \"/scratch_user/jenkins/workspace/v2_long_regress/src/automlx/_backend/utils.py\", line 65, in _handle_job_exception\n",
       "    complete_results = profile.function(\n",
-      "  File \"/scratch_user/jenkins/workspace/v2_long_regress@4/src/automlx/_utils/profile.py\", line 133, in profile_wrapper\n",
+      "  File \"/scratch_user/jenkins/workspace/v2_long_regress/src/automlx/_utils/profile.py\", line 133, in profile_wrapper\n",
       "    ) = _profile_resources(\n",
-      "  File \"/scratch_user/jenkins/workspace/v2_long_regress@4/src/automlx/_utils/profile.py\", line 655, in _profile_resources\n",
+      "  File \"/scratch_user/jenkins/workspace/v2_long_regress/src/automlx/_utils/profile.py\", line 655, in _profile_resources\n",
       "    function_output = func(*args, **kwargs, **pf)\n",
-      "  File \"/scratch_user/jenkins/workspace/v2_long_regress@4/src/automlx/_trials/strategies/base.py\", line 112, in run\n",
+      "  File \"/scratch_user/jenkins/workspace/v2_long_regress/src/automlx/_trials/strategies/base.py\", line 112, in run\n",
       "    return self._run(reporter, resources, pipeline, train_dataset, callback_manager, pf=pf)\n",
-      "  File \"/scratch_user/jenkins/workspace/v2_long_regress@4/src/automlx/_trials/strategies/standard.py\", line 45, in _run\n",
+      "  File \"/scratch_user/jenkins/workspace/v2_long_regress/src/automlx/_trials/strategies/standard.py\", line 45, in _run\n",
       "    pipeline.fit(train_dataset, {model_idx: callback_manager}, pf=pf)\n",
-      "  File \"/scratch_user/jenkins/workspace/v2_long_regress@4/src/automlx/_pipeline/pipeline.py\", line 282, in fit\n",
+      "  File \"/scratch_user/jenkins/workspace/v2_long_regress/src/automlx/_pipeline/pipeline.py\", line 282, in fit\n",
       "    self.stages[i] = stage.fit(dataset, callback=callback)\n",
-      "  File \"/scratch_user/jenkins/workspace/v2_long_regress@4/src/automlx/_model/distributed/distributed_model.py\", line 69, in fit\n",
+      "  File \"/scratch_user/jenkins/workspace/v2_long_regress/src/automlx/_model/distributed/distributed_model.py\", line 69, in fit\n",
       "    self._fit(dataset, callback)\n",
-      "  File \"/scratch_user/jenkins/workspace/v2_long_regress@4/src/automlx/_model/pytorch/base.py\", line 161, in _fit\n",
+      "  File \"/scratch_user/jenkins/workspace/v2_long_regress/src/automlx/_model/pytorch/base.py\", line 161, in _fit\n",
       "    output = self._wrapped_model(batch)\n",
-      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-forewardportAMLX2203todev/lib/python3.9/site-packages/torch/nn/modules/module.py\", line 1501, in _call_impl\n",
+      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/module.py\", line 1501, in _call_impl\n",
       "    return forward_call(*args, **kwargs)\n",
-      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-forewardportAMLX2203todev/lib/python3.9/site-packages/torch/nn/modules/container.py\", line 217, in forward\n",
+      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/container.py\", line 217, in forward\n",
       "    input = module(input)\n",
-      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-forewardportAMLX2203todev/lib/python3.9/site-packages/torch/nn/modules/module.py\", line 1501, in _call_impl\n",
+      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/module.py\", line 1501, in _call_impl\n",
       "    return forward_call(*args, **kwargs)\n",
-      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-forewardportAMLX2203todev/lib/python3.9/site-packages/torchvision/models/efficientnet.py\", line 343, in forward\n",
+      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torchvision/models/efficientnet.py\", line 343, in forward\n",
       "    return self._forward_impl(x)\n",
-      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-forewardportAMLX2203todev/lib/python3.9/site-packages/torchvision/models/efficientnet.py\", line 333, in _forward_impl\n",
+      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torchvision/models/efficientnet.py\", line 333, in _forward_impl\n",
       "    x = self.features(x)\n",
-      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-forewardportAMLX2203todev/lib/python3.9/site-packages/torch/nn/modules/module.py\", line 1501, in _call_impl\n",
+      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/module.py\", line 1501, in _call_impl\n",
       "    return forward_call(*args, **kwargs)\n",
-      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-forewardportAMLX2203todev/lib/python3.9/site-packages/torch/nn/modules/container.py\", line 217, in forward\n",
+      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/container.py\", line 217, in forward\n",
       "    input = module(input)\n",
-      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-forewardportAMLX2203todev/lib/python3.9/site-packages/torch/nn/modules/module.py\", line 1501, in _call_impl\n",
+      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/module.py\", line 1501, in _call_impl\n",
       "    return forward_call(*args, **kwargs)\n",
-      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-forewardportAMLX2203todev/lib/python3.9/site-packages/torch/nn/modules/container.py\", line 217, in forward\n",
+      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/container.py\", line 217, in forward\n",
       "    input = module(input)\n",
-      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-forewardportAMLX2203todev/lib/python3.9/site-packages/torch/nn/modules/module.py\", line 1501, in _call_impl\n",
+      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/module.py\", line 1501, in _call_impl\n",
       "    return forward_call(*args, **kwargs)\n",
-      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-forewardportAMLX2203todev/lib/python3.9/site-packages/torchvision/models/efficientnet.py\", line 164, in forward\n",
+      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torchvision/models/efficientnet.py\", line 164, in forward\n",
       "    result = self.block(input)\n",
-      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-forewardportAMLX2203todev/lib/python3.9/site-packages/torch/nn/modules/module.py\", line 1501, in _call_impl\n",
+      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/module.py\", line 1501, in _call_impl\n",
       "    return forward_call(*args, **kwargs)\n",
-      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-forewardportAMLX2203todev/lib/python3.9/site-packages/torch/nn/modules/container.py\", line 217, in forward\n",
+      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/container.py\", line 217, in forward\n",
       "    input = module(input)\n",
-      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-forewardportAMLX2203todev/lib/python3.9/site-packages/torch/nn/modules/module.py\", line 1501, in _call_impl\n",
+      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/module.py\", line 1501, in _call_impl\n",
       "    return forward_call(*args, **kwargs)\n",
-      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-forewardportAMLX2203todev/lib/python3.9/site-packages/torch/nn/modules/container.py\", line 217, in forward\n",
+      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/container.py\", line 217, in forward\n",
       "    input = module(input)\n",
-      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-forewardportAMLX2203todev/lib/python3.9/site-packages/torch/nn/modules/module.py\", line 1501, in _call_impl\n",
+      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/module.py\", line 1501, in _call_impl\n",
       "    return forward_call(*args, **kwargs)\n",
-      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-forewardportAMLX2203todev/lib/python3.9/site-packages/torch/nn/modules/batchnorm.py\", line 171, in forward\n",
+      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/batchnorm.py\", line 171, in forward\n",
       "    return F.batch_norm(\n",
-      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-forewardportAMLX2203todev/lib/python3.9/site-packages/torch/nn/functional.py\", line 2448, in batch_norm\n",
+      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/functional.py\", line 2448, in batch_norm\n",
       "    _verify_batch_size(input.size())\n",
-      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-forewardportAMLX2203todev/lib/python3.9/site-packages/torch/nn/functional.py\", line 2416, in _verify_batch_size\n",
+      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/functional.py\", line 2416, in _verify_batch_size\n",
       "    raise ValueError(\"Expected more than 1 value per channel when training, got input size {}\".format(size))\n",
       "ValueError: Expected more than 1 value per channel when training, got input size torch.Size([1, 720, 1, 1])\n",
       "\u001b[0m\n"
@@ -6886,35 +6886,108 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:18:01,203] [automlx.trials] Best parameters for EfficientNet: {'optimizer_class': 'Adam', 'shuffle_dataset_each_epoch': True, 'optimizer_params': {}, 'criterion_class': None, 'criterion_params': {}, 'scheduler_class': None, 'scheduler_params': {}, 'batch_size': 16, 'lr': 0.001, 'epochs': 1, 'input_transform': 'auto', 'tensorboard_dir': None, 'use_tqdm': None, 'prediction_batch_size': 128, 'prediction_input_transform': 'auto', 'shuffling_buffer_size': None, 'freeze.encoder': False, 'load.encoder': None, 'size': 'b2'}\n"
+      "[2025-04-25 03:34:16,206] [automlx.backend] \u001b[91mJob cda0b026-21c0-11f0-b4ee-00001701c949 <> EfficientNet - Trial: 2 - Split: 2 of 5\n",
+      "                            failed\n",
+      "Stack trace:\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/scratch_user/jenkins/workspace/v2_long_regress/src/automlx/_backend/utils.py\", line 65, in _handle_job_exception\n",
+      "    complete_results = profile.function(\n",
+      "  File \"/scratch_user/jenkins/workspace/v2_long_regress/src/automlx/_utils/profile.py\", line 133, in profile_wrapper\n",
+      "    ) = _profile_resources(\n",
+      "  File \"/scratch_user/jenkins/workspace/v2_long_regress/src/automlx/_utils/profile.py\", line 655, in _profile_resources\n",
+      "    function_output = func(*args, **kwargs, **pf)\n",
+      "  File \"/scratch_user/jenkins/workspace/v2_long_regress/src/automlx/_trials/strategies/base.py\", line 112, in run\n",
+      "    return self._run(reporter, resources, pipeline, train_dataset, callback_manager, pf=pf)\n",
+      "  File \"/scratch_user/jenkins/workspace/v2_long_regress/src/automlx/_trials/strategies/standard.py\", line 45, in _run\n",
+      "    pipeline.fit(train_dataset, {model_idx: callback_manager}, pf=pf)\n",
+      "  File \"/scratch_user/jenkins/workspace/v2_long_regress/src/automlx/_pipeline/pipeline.py\", line 282, in fit\n",
+      "    self.stages[i] = stage.fit(dataset, callback=callback)\n",
+      "  File \"/scratch_user/jenkins/workspace/v2_long_regress/src/automlx/_model/distributed/distributed_model.py\", line 69, in fit\n",
+      "    self._fit(dataset, callback)\n",
+      "  File \"/scratch_user/jenkins/workspace/v2_long_regress/src/automlx/_model/pytorch/base.py\", line 161, in _fit\n",
+      "    output = self._wrapped_model(batch)\n",
+      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/module.py\", line 1501, in _call_impl\n",
+      "    return forward_call(*args, **kwargs)\n",
+      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/container.py\", line 217, in forward\n",
+      "    input = module(input)\n",
+      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/module.py\", line 1501, in _call_impl\n",
+      "    return forward_call(*args, **kwargs)\n",
+      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torchvision/models/efficientnet.py\", line 343, in forward\n",
+      "    return self._forward_impl(x)\n",
+      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torchvision/models/efficientnet.py\", line 333, in _forward_impl\n",
+      "    x = self.features(x)\n",
+      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/module.py\", line 1501, in _call_impl\n",
+      "    return forward_call(*args, **kwargs)\n",
+      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/container.py\", line 217, in forward\n",
+      "    input = module(input)\n",
+      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/module.py\", line 1501, in _call_impl\n",
+      "    return forward_call(*args, **kwargs)\n",
+      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/container.py\", line 217, in forward\n",
+      "    input = module(input)\n",
+      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/module.py\", line 1501, in _call_impl\n",
+      "    return forward_call(*args, **kwargs)\n",
+      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torchvision/models/efficientnet.py\", line 164, in forward\n",
+      "    result = self.block(input)\n",
+      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/module.py\", line 1501, in _call_impl\n",
+      "    return forward_call(*args, **kwargs)\n",
+      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/container.py\", line 217, in forward\n",
+      "    input = module(input)\n",
+      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/module.py\", line 1501, in _call_impl\n",
+      "    return forward_call(*args, **kwargs)\n",
+      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/container.py\", line 217, in forward\n",
+      "    input = module(input)\n",
+      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/module.py\", line 1501, in _call_impl\n",
+      "    return forward_call(*args, **kwargs)\n",
+      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/modules/batchnorm.py\", line 171, in forward\n",
+      "    return F.batch_norm(\n",
+      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/functional.py\", line 2448, in batch_norm\n",
+      "    _verify_batch_size(input.size())\n",
+      "  File \"/scratch_user/olautoml/.conda/envs/pipeline-run-3.9.19-releasev252/lib/python3.9/site-packages/torch/nn/functional.py\", line 2416, in _verify_batch_size\n",
+      "    raise ValueError(\"Expected more than 1 value per channel when training, got input size {}\".format(size))\n",
+      "ValueError: Expected more than 1 value per channel when training, got input size torch.Size([1, 720, 1, 1])\n",
+      "\u001b[0m\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:18:01,204] [automlx.trials] Model Tuning completed. Took: 19.236 secs\n"
+      "[2025-04-25 03:34:17,092] [automlx.trials] Best parameters for EfficientNet: {'optimizer_class': 'Adam', 'shuffle_dataset_each_epoch': True, 'optimizer_params': {}, 'criterion_class': None, 'criterion_params': {}, 'scheduler_class': None, 'scheduler_params': {}, 'batch_size': 16, 'lr': 0.001, 'epochs': 1, 'input_transform': 'auto', 'tensorboard_dir': None, 'use_tqdm': None, 'prediction_batch_size': 128, 'prediction_input_transform': 'auto', 'shuffling_buffer_size': None, 'freeze.encoder': False, 'load.encoder': None, 'size': 'b2'}\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:18:01,395] [automlx.interface] Re-fitting pipeline\n"
+      "[2025-04-25 03:34:17,094] [automlx.trials] Model Tuning completed. Took: 15.754 secs\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:18:01,412] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_c5b54410-5\n"
+      "[2025-04-25 03:34:17,268] [automlx.interface] Re-fitting pipeline\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2025-04-25 03:34:17,284] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_8072097a-a\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-04-25 03:34:21,520\tERROR worker.py:409 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:18:22,071] [automlx.interface] AutoMLx completed.\n"
+      "[2025-04-25 03:34:37,909] [automlx.interface] AutoMLx completed.\n"
      ]
     },
     {
@@ -6944,7 +7017,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "25cd301f",
+   "id": "29eaed82",
    "metadata": {},
    "source": [
     "<a id='ref'></a>\n",
diff --git a/demos/OracleAutoMLx_Recommendation.html b/demos/OracleAutoMLx_Recommendation.html
index 6fcc18e..ff795f6 100644
--- a/demos/OracleAutoMLx_Recommendation.html
+++ b/demos/OracleAutoMLx_Recommendation.html
@@ -14621,7 +14621,7 @@
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
 </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
 <p>Recommendation Demo Notebook.</p>
-<p>Copyright © 2024, Oracle and/or its affiliates.</p>
+<p>Copyright © 2025, Oracle and/or its affiliates.</p>
 <p>Licensed under the Universal Permissive License v 1.0 as shown at <a href="https://oss.oracle.com/licenses/upl/">https://oss.oracle.com/licenses/upl/</a></p>
 
 </div>
@@ -14685,17 +14685,17 @@ <h2 id="Setup">Setup<a class="anchor-link" href="#Setup">&#182;</a></h2><p>Basic
 <div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[1]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">datetime</span>
-<span class="kn">import</span> <span class="nn">logging</span>
-<span class="kn">import</span> <span class="nn">os</span>
-<span class="kn">import</span> <span class="nn">time</span>
-<span class="kn">import</span> <span class="nn">urllib</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">datetime</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">logging</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">os</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">time</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">urllib</span>
 
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pd</span>
 
-<span class="kn">from</span> <span class="nn">automlx</span> <span class="kn">import</span> <span class="n">AutoRecommender</span><span class="p">,</span> <span class="n">init</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">automlx</span><span class="w"> </span><span class="kn">import</span> <span class="n">AutoRecommender</span><span class="p">,</span> <span class="n">init</span>
 
 <span class="c1"># Settings for plots</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s2">&quot;figure.figsize&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">7</span><span class="p">]</span>
@@ -14727,7 +14727,7 @@ <h2 id="Setup">Setup<a class="anchor-link" href="#Setup">&#182;</a></h2><p>Basic
 
 
 <div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
-<pre>[2024-12-26 06:18:36,255] [automlx.backend] Overwriting ray session directory to /tmp/gbbsq6z5/ray, which will be deleted at engine shutdown. If you wish to retain ray logs, provide _temp_dir in ray_setup dict of engine_opts when initializing the AutoMLx engine.
+<pre>[2025-04-25 03:34:51,937] [automlx.backend] Overwriting ray session directory to /tmp/1frrc9a7/ray, which will be deleted at engine shutdown. If you wish to retain ray logs, provide _temp_dir in ray_setup dict of engine_opts when initializing the AutoMLx engine.
 </pre>
 </div>
 </div>
@@ -15132,17 +15132,17 @@ <h2 id="Train-a-model-using-AutoMLx">Train a model using AutoMLx<a class="anchor
 
 
 <div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
-<pre>[2024-12-26 06:18:49,097] [automlx.interface] Dataset shape: (49055,3)
-[2024-12-26 06:18:49,166] [automlx.process] Running Model Generation
-[2024-12-26 06:18:49,207] [automlx.process] Model Generation completed.
-[2024-12-26 06:18:49,250] [automlx.model_selection] Running Model Selection
-[2024-12-26 06:19:39,708] [automlx.model_selection] Model Selection completed - Took 50.458 sec - Selected models: [[&#39;ItemKNNRecommender&#39;]]
-[2024-12-26 06:19:39,753] [automlx.trials] Running Model Tuning for [&#39;ItemKNNRecommender&#39;]
-[2024-12-26 06:19:59,182] [automlx.trials] Best parameters for ItemKNNRecommender: {&#39;n_recommendations&#39;: 10, &#39;num_of_neighbors&#39;: 506, &#39;bias&#39;: 0.0001, &#39;hist_len&#39;: 10, &#39;reciprocal_ranking&#39;: False, &#39;normalize_scores&#39;: False, &#39;cache_users_states&#39;: True}
-[2024-12-26 06:19:59,184] [automlx.trials] Model Tuning completed. Took: 19.431 secs
-[2024-12-26 06:19:59,639] [automlx.interface] Re-fitting pipeline
-[2024-12-26 06:19:59,646] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_ea4e7717-4
-[2024-12-26 06:20:00,886] [automlx.interface] AutoMLx completed.
+<pre>[2025-04-25 03:35:07,292] [automlx.interface] Dataset shape: (49055,3)
+[2025-04-25 03:35:07,362] [automlx.process] Running Model Generation
+[2025-04-25 03:35:07,402] [automlx.process] Model Generation completed.
+[2025-04-25 03:35:07,446] [automlx.model_selection] Running Model Selection
+[2025-04-25 03:35:56,451] [automlx.model_selection] Model Selection completed - Took 49.005 sec - Selected models: [[&#39;ItemKNNRecommender&#39;]]
+[2025-04-25 03:35:56,513] [automlx.trials] Running Model Tuning for [&#39;ItemKNNRecommender&#39;]
+[2025-04-25 03:36:12,913] [automlx.trials] Best parameters for ItemKNNRecommender: {&#39;n_recommendations&#39;: 10, &#39;num_of_neighbors&#39;: 506, &#39;bias&#39;: 0.0001, &#39;hist_len&#39;: 10, &#39;reciprocal_ranking&#39;: False, &#39;normalize_scores&#39;: False, &#39;cache_users_states&#39;: True}
+[2025-04-25 03:36:12,915] [automlx.trials] Model Tuning completed. Took: 16.401 secs
+[2025-04-25 03:36:13,337] [automlx.interface] Re-fitting pipeline
+[2025-04-25 03:36:13,344] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_19cf1d36-6
+[2025-04-25 03:36:14,240] [automlx.interface] AutoMLx completed.
 </pre>
 </div>
 </div>
@@ -15222,33 +15222,33 @@ <h2 id="Generate-recommendations">Generate recommendations<a class="anchor-link"
   <tbody>
     <tr>
       <th>0</th>
-      <td>731</td>
-      <td>190</td>
-      <td>21.294812</td>
+      <td>628</td>
+      <td>330</td>
+      <td>15.370814</td>
     </tr>
     <tr>
       <th>1</th>
-      <td>731</td>
-      <td>96</td>
-      <td>20.401263</td>
+      <td>628</td>
+      <td>286</td>
+      <td>15.029380</td>
     </tr>
     <tr>
       <th>2</th>
-      <td>731</td>
-      <td>97</td>
-      <td>19.808079</td>
+      <td>628</td>
+      <td>258</td>
+      <td>14.119169</td>
     </tr>
     <tr>
       <th>3</th>
-      <td>731</td>
-      <td>181</td>
-      <td>19.745601</td>
+      <td>628</td>
+      <td>272</td>
+      <td>13.703196</td>
     </tr>
     <tr>
       <th>4</th>
-      <td>731</td>
-      <td>174</td>
-      <td>19.685528</td>
+      <td>628</td>
+      <td>313</td>
+      <td>13.564656</td>
     </tr>
   </tbody>
 </table>
@@ -15353,7 +15353,7 @@ <h2 id="Analyze-the-AutoMLx-optimization-process">Analyze the AutoMLx optimizati
       <td>{'n_recommendations': 10, 'num_of_neighbors': 506, 'bias': 0.0001, 'hist_len': 10, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}</td>
     </tr>
     <tr>
-      <td>25.1.0</td>
+      <td>25.2.1</td>
     </tr>
     <tr>
       <td>3.9.21 (main, Dec 11 2024, 16:24:11) \n[GCC 11.2.0]</td>
@@ -15409,9 +15409,9 @@ <h2 id="Analyze-the-AutoMLx-optimization-process">Analyze the AutoMLx optimizati
       <td>{'n_recommendations': 10, 'num_of_neighbors': 100, 'bias': 25, 'hist_len': 20, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}</td>
       <td>0.0882</td>
       <td>{'hr': 0.08820403825717323}</td>
-      <td>1.0596</td>
-      <td>0.7370</td>
-      <td>Thu Dec 26 06:19:02 2024</td>
+      <td>1.0319</td>
+      <td>0.7063</td>
+      <td>Fri Apr 25 03:35:20 2025</td>
     </tr>
     <tr>
       <td>Model Selection</td>
@@ -15419,11 +15419,11 @@ <h2 id="Analyze-the-AutoMLx-optimization-process">Analyze the AutoMLx optimizati
       <td>2</td>
       <td>AlsRecommender</td>
       <td>{'n_recommendations': 10, 'iterations': 10, 'factors': 16, 'regularization': 0.01, 'cache_users_states': True}</td>
-      <td>0.0765</td>
-      <td>{'hr': 0.0765143464399575}</td>
-      <td>4.5466</td>
-      <td>0.6639</td>
-      <td>Thu Dec 26 06:19:01 2024</td>
+      <td>0.0691</td>
+      <td>{'hr': 0.06907545164718384}</td>
+      <td>4.5845</td>
+      <td>0.7055</td>
+      <td>Fri Apr 25 03:35:19 2025</td>
     </tr>
     <tr>
       <td>Model Selection</td>
@@ -15431,11 +15431,11 @@ <h2 id="Analyze-the-AutoMLx-optimization-process">Analyze the AutoMLx optimizati
       <td>2</td>
       <td>TRexxRecommender</td>
       <td>{'n_recommendations': 10, 'embedding_dim': 32, 'sequence_length': 5, 'num_sampled': 100, 'dropout_rate': 0.2, 'num_blocks': 2, 'num_head': 4, 'l2_reg_embedding': 1e-06, 'dnn_activation': 'tanh', 'optimizer_name': 'lazyadam', 'optimizer_learning_rate': 0.01, 'future_blinding': False, 'embeddings_on_cpu': False, 'cache_users_states': False, 'negative_sampling_method': CandidateSamplingMethod.UNIFORM_CANDIDATE_SAMPLING, 'epochs': 10, 'batch_size': 512, 'verbose': 1, 'augment_data': True, 'early_stopping_patience': -1}</td>
-      <td>0.0733</td>
-      <td>{'hr': 0.07332624867162593}</td>
-      <td>36.8648</td>
-      <td>1.2107</td>
-      <td>Thu Dec 26 06:19:39 2024</td>
+      <td>0.0531</td>
+      <td>{'hr': 0.053134962805526036}</td>
+      <td>35.1017</td>
+      <td>1.1860</td>
+      <td>Fri Apr 25 03:35:56 2025</td>
     </tr>
     <tr>
       <td>Model Selection</td>
@@ -15443,11 +15443,11 @@ <h2 id="Analyze-the-AutoMLx-optimization-process">Analyze the AutoMLx optimizati
       <td>2</td>
       <td>BprRecommender</td>
       <td>{'n_recommendations': 10, 'iterations': 10, 'factors': 16, 'regularization': 0.01, 'cache_users_states': True}</td>
-      <td>0.0446</td>
-      <td>{'hr': 0.044633368756641874}</td>
-      <td>0.2655</td>
-      <td>0.7351</td>
-      <td>Thu Dec 26 06:19:02 2024</td>
+      <td>0.0372</td>
+      <td>{'hr': 0.03719447396386823}</td>
+      <td>0.4148</td>
+      <td>0.7046</td>
+      <td>Fri Apr 25 03:35:21 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
@@ -15457,9 +15457,9 @@ <h2 id="Analyze-the-AutoMLx-optimization-process">Analyze the AutoMLx optimizati
       <td>{'n_recommendations': 10, 'num_of_neighbors': 505, 'bias': 0.0001, 'hist_len': 10, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}</td>
       <td>0.0999</td>
       <td>{'hr': 0.09989373007438895}</td>
-      <td>1.4546</td>
-      <td>0.6808</td>
-      <td>Thu Dec 26 06:19:55 2024</td>
+      <td>1.3955</td>
+      <td>0.6848</td>
+      <td>Fri Apr 25 03:36:10 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
@@ -15469,9 +15469,9 @@ <h2 id="Analyze-the-AutoMLx-optimization-process">Analyze the AutoMLx optimizati
       <td>{'n_recommendations': 10, 'num_of_neighbors': 506, 'bias': 0.0001, 'hist_len': 10, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}</td>
       <td>0.0999</td>
       <td>{'hr': 0.09989373007438895}</td>
-      <td>1.1579</td>
-      <td>1.2015</td>
-      <td>Thu Dec 26 06:19:58 2024</td>
+      <td>1.0910</td>
+      <td>0.6842</td>
+      <td>Fri Apr 25 03:36:12 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
@@ -15481,9 +15481,9 @@ <h2 id="Analyze-the-AutoMLx-optimization-process">Analyze the AutoMLx optimizati
       <td>{'n_recommendations': 10, 'num_of_neighbors': 506, 'bias': 0.0001, 'hist_len': 10, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}</td>
       <td>0.0999</td>
       <td>{'hr': 0.09989373007438895}</td>
-      <td>1.4794</td>
-      <td>1.2011</td>
-      <td>Thu Dec 26 06:19:55 2024</td>
+      <td>1.3582</td>
+      <td>0.6869</td>
+      <td>Fri Apr 25 03:36:09 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
@@ -15493,9 +15493,9 @@ <h2 id="Analyze-the-AutoMLx-optimization-process">Analyze the AutoMLx optimizati
       <td>{'n_recommendations': 10, 'num_of_neighbors': 10, 'bias': 28.25660795027468, 'hist_len': 10, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}</td>
       <td>0.0956</td>
       <td>{'hr': 0.09564293304994687}</td>
-      <td>1.3933</td>
-      <td>1.1959</td>
-      <td>Thu Dec 26 06:19:52 2024</td>
+      <td>1.4462</td>
+      <td>0.6762</td>
+      <td>Fri Apr 25 03:36:08 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
@@ -15505,9 +15505,9 @@ <h2 id="Analyze-the-AutoMLx-optimization-process">Analyze the AutoMLx optimizati
       <td>{'n_recommendations': 10, 'num_of_neighbors': 10, 'bias': 28.26160794927468, 'hist_len': 10, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}</td>
       <td>0.0956</td>
       <td>{'hr': 0.09564293304994687}</td>
-      <td>1.1074</td>
-      <td>0.6722</td>
-      <td>Thu Dec 26 06:19:55 2024</td>
+      <td>1.1324</td>
+      <td>0.6785</td>
+      <td>Fri Apr 25 03:36:10 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
@@ -15517,9 +15517,9 @@ <h2 id="Analyze-the-AutoMLx-optimization-process">Analyze the AutoMLx optimizati
       <td>{'n_recommendations': 10, 'num_of_neighbors': 10, 'bias': 28.26160794927468, 'hist_len': 10, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}</td>
       <td>0.0956</td>
       <td>{'hr': 0.09564293304994687}</td>
-      <td>1.2934</td>
-      <td>0.6724</td>
-      <td>Thu Dec 26 06:19:54 2024</td>
+      <td>1.3547</td>
+      <td>0.6758</td>
+      <td>Fri Apr 25 03:36:08 2025</td>
     </tr>
     <tr>
       <td>...</td>
@@ -15541,9 +15541,9 @@ <h2 id="Analyze-the-AutoMLx-optimization-process">Analyze the AutoMLx optimizati
       <td>{'n_recommendations': 10, 'num_of_neighbors': 10, 'bias': 0.0001, 'hist_len': 20, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}</td>
       <td>0.084</td>
       <td>{'hr': 0.08395324123273114}</td>
-      <td>1.5070</td>
-      <td>0.6691</td>
-      <td>Thu Dec 26 06:19:55 2024</td>
+      <td>1.3191</td>
+      <td>0.6742</td>
+      <td>Fri Apr 25 03:36:08 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
@@ -15553,9 +15553,9 @@ <h2 id="Analyze-the-AutoMLx-optimization-process">Analyze the AutoMLx optimizati
       <td>{'n_recommendations': 10, 'num_of_neighbors': 10, 'bias': 0.0001, 'hist_len': 21, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}</td>
       <td>0.084</td>
       <td>{'hr': 0.08395324123273114}</td>
-      <td>1.2909</td>
-      <td>0.6714</td>
-      <td>Thu Dec 26 06:19:54 2024</td>
+      <td>1.3325</td>
+      <td>0.6755</td>
+      <td>Fri Apr 25 03:36:08 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
@@ -15565,9 +15565,9 @@ <h2 id="Analyze-the-AutoMLx-optimization-process">Analyze the AutoMLx optimizati
       <td>{'n_recommendations': 10, 'num_of_neighbors': 505, 'bias': 25, 'hist_len': 20, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}</td>
       <td>0.0829</td>
       <td>{'hr': 0.08289054197662062}</td>
-      <td>1.3678</td>
-      <td>1.2010</td>
-      <td>Thu Dec 26 06:19:42 2024</td>
+      <td>1.1355</td>
+      <td>1.1758</td>
+      <td>Fri Apr 25 03:36:01 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
@@ -15577,9 +15577,9 @@ <h2 id="Analyze-the-AutoMLx-optimization-process">Analyze the AutoMLx optimizati
       <td>{'n_recommendations': 10, 'num_of_neighbors': 506, 'bias': 25, 'hist_len': 20, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}</td>
       <td>0.0829</td>
       <td>{'hr': 0.08289054197662062}</td>
-      <td>1.3354</td>
-      <td>1.2011</td>
-      <td>Thu Dec 26 06:19:46 2024</td>
+      <td>1.3934</td>
+      <td>1.1758</td>
+      <td>Fri Apr 25 03:36:02 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
@@ -15589,9 +15589,9 @@ <h2 id="Analyze-the-AutoMLx-optimization-process">Analyze the AutoMLx optimizati
       <td>{'n_recommendations': 10, 'num_of_neighbors': 752, 'bias': 25, 'hist_len': 20, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}</td>
       <td>0.0818</td>
       <td>{'hr': 0.0818278427205101}</td>
-      <td>1.2453</td>
-      <td>1.2065</td>
-      <td>Thu Dec 26 06:19:49 2024</td>
+      <td>1.4294</td>
+      <td>1.1812</td>
+      <td>Fri Apr 25 03:36:05 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
@@ -15601,9 +15601,9 @@ <h2 id="Analyze-the-AutoMLx-optimization-process">Analyze the AutoMLx optimizati
       <td>{'n_recommendations': 10, 'num_of_neighbors': 753, 'bias': 25, 'hist_len': 20, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}</td>
       <td>0.0818</td>
       <td>{'hr': 0.0818278427205101}</td>
-      <td>1.2532</td>
-      <td>1.2065</td>
-      <td>Thu Dec 26 06:19:47 2024</td>
+      <td>1.2525</td>
+      <td>1.1812</td>
+      <td>Fri Apr 25 03:36:04 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
@@ -15613,9 +15613,9 @@ <h2 id="Analyze-the-AutoMLx-optimization-process">Analyze the AutoMLx optimizati
       <td>{'n_recommendations': 10, 'num_of_neighbors': 10, 'bias': 0.0001, 'hist_len': 132, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}</td>
       <td>0.0797</td>
       <td>{'hr': 0.07970244420828905}</td>
-      <td>1.5203</td>
-      <td>0.6742</td>
-      <td>Thu Dec 26 06:19:55 2024</td>
+      <td>1.4449</td>
+      <td>0.6758</td>
+      <td>Fri Apr 25 03:36:09 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
@@ -15625,9 +15625,9 @@ <h2 id="Analyze-the-AutoMLx-optimization-process">Analyze the AutoMLx optimizati
       <td>{'n_recommendations': 10, 'num_of_neighbors': 10, 'bias': 0.0001, 'hist_len': 133, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}</td>
       <td>0.0797</td>
       <td>{'hr': 0.07970244420828905}</td>
-      <td>1.5426</td>
-      <td>0.6726</td>
-      <td>Thu Dec 26 06:19:55 2024</td>
+      <td>1.3044</td>
+      <td>0.6773</td>
+      <td>Fri Apr 25 03:36:08 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
@@ -15637,9 +15637,9 @@ <h2 id="Analyze-the-AutoMLx-optimization-process">Analyze the AutoMLx optimizati
       <td>{'n_recommendations': 10, 'num_of_neighbors': 10, 'bias': 0.0001, 'hist_len': 255, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}</td>
       <td>0.0797</td>
       <td>{'hr': 0.07970244420828905}</td>
-      <td>1.4628</td>
-      <td>0.6716</td>
-      <td>Thu Dec 26 06:19:55 2024</td>
+      <td>1.4471</td>
+      <td>0.6760</td>
+      <td>Fri Apr 25 03:36:09 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
@@ -15649,9 +15649,9 @@ <h2 id="Analyze-the-AutoMLx-optimization-process">Analyze the AutoMLx optimizati
       <td>{'n_recommendations': 10, 'num_of_neighbors': 10, 'bias': 0.0001, 'hist_len': 256, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}</td>
       <td>0.0797</td>
       <td>{'hr': 0.07970244420828905}</td>
-      <td>1.2602</td>
-      <td>0.6713</td>
-      <td>Thu Dec 26 06:19:54 2024</td>
+      <td>1.4831</td>
+      <td>0.6730</td>
+      <td>Fri Apr 25 03:36:09 2025</td>
     </tr>
   </tbody>
 </table>
@@ -15685,7 +15685,7 @@ <h3 id="Algorithm-Selection">Algorithm Selection<a class="anchor-link" href="#Al
 <div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[11]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">plot_model_selection_scores</span><span class="p">(</span><span class="n">_pipeline</span><span class="p">):</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span><span class="w"> </span><span class="nf">plot_model_selection_scores</span><span class="p">(</span><span class="n">_pipeline</span><span class="p">):</span>
     <span class="c1"># Each trial is a row in a dataframe that contains</span>
     <span class="c1"># Algorithm, Number of Samples, Number of Features, Hyperparameters, Score, Runtime, Memory Usage, Step as features</span>
     <span class="n">trials</span> <span class="o">=</span> <span class="n">_pipeline</span><span class="o">.</span><span class="n">completed_trials_summary_</span><span class="p">[</span>
@@ -15741,7 +15741,7 @@ <h3 id="Algorithm-Selection">Algorithm Selection<a class="anchor-link" href="#Al
 
 
 <div class="jp-RenderedImage jp-OutputArea-output ">
-<img 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"
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"
 class="
 "
 >
@@ -15774,7 +15774,7 @@ <h3 id="Hyperparameter-Tuning">Hyperparameter Tuning<a class="anchor-link" href=
 <div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[12]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">plot_hp_tuning_scores</span><span class="p">(</span><span class="n">_pipeline</span><span class="p">):</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span><span class="w"> </span><span class="nf">plot_hp_tuning_scores</span><span class="p">(</span><span class="n">_pipeline</span><span class="p">):</span>
     <span class="c1"># Each trial is a row in a dataframe that contains</span>
     <span class="c1"># Algorithm, Number of Samples, Number of Features, Hyperparameters, Score, Runtime, Memory Usage, Step as features</span>
     <span class="n">trials</span> <span class="o">=</span> <span class="n">_pipeline</span><span class="o">.</span><span class="n">completed_trials_summary_</span><span class="p">[</span>
@@ -15830,7 +15830,7 @@ <h3 id="Hyperparameter-Tuning">Hyperparameter Tuning<a class="anchor-link" href=
 
 
 <div class="jp-RenderedImage jp-OutputArea-output ">
-<img 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"
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"
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@@ -15950,20 +15950,20 @@ <h2 id="Use-a-custom-validation-set">Use a custom validation set<a class="anchor
 
 
 <div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
-<pre>[2024-12-26 06:20:01,738] [automlx.interface] Dataset shape: (49055,3)
-[2024-12-26 06:20:01,808] [automlx.process] Running Model Generation
-[2024-12-26 06:20:01,853] [automlx.process] Model Generation completed.
-[2024-12-26 06:20:01,884] [automlx.model_selection] Running Model Selection
-[2024-12-26 06:20:03,779] [automlx.model_selection] Model Selection completed - Took 1.895 sec - Selected models: [[&#39;ItemKNNRecommender&#39;, &#39;AlsRecommender&#39;]]
-[2024-12-26 06:20:03,892] [automlx.trials] Running Model Tuning for [&#39;ItemKNNRecommender&#39;]
-[2024-12-26 06:20:07,284] [automlx.trials] Best parameters for ItemKNNRecommender: {&#39;n_recommendations&#39;: 10, &#39;num_of_neighbors&#39;: 10, &#39;bias&#39;: 0.010099998000000002, &#39;hist_len&#39;: 10, &#39;reciprocal_ranking&#39;: False, &#39;normalize_scores&#39;: False, &#39;cache_users_states&#39;: True}
-[2024-12-26 06:20:07,286] [automlx.trials] Model Tuning completed. Took: 3.394 secs
-[2024-12-26 06:20:07,439] [automlx.trials] Running Model Tuning for [&#39;AlsRecommender&#39;]
-[2024-12-26 06:20:09,915] [automlx.trials] Best parameters for AlsRecommender: {&#39;n_recommendations&#39;: 10, &#39;iterations&#39;: 10, &#39;factors&#39;: 16, &#39;regularization&#39;: 0.01, &#39;cache_users_states&#39;: True}
-[2024-12-26 06:20:09,916] [automlx.trials] Model Tuning completed. Took: 2.478 secs
-[2024-12-26 06:20:10,336] [automlx.interface] Re-fitting pipeline
-[2024-12-26 06:20:10,346] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_b09b807d-a
-[2024-12-26 06:20:11,503] [automlx.interface] AutoMLx completed.
+<pre>[2025-04-25 03:36:15,105] [automlx.interface] Dataset shape: (49055,3)
+[2025-04-25 03:36:15,170] [automlx.process] Running Model Generation
+[2025-04-25 03:36:15,215] [automlx.process] Model Generation completed.
+[2025-04-25 03:36:15,246] [automlx.model_selection] Running Model Selection
+[2025-04-25 03:36:16,875] [automlx.model_selection] Model Selection completed - Took 1.629 sec - Selected models: [[&#39;ItemKNNRecommender&#39;, &#39;AlsRecommender&#39;]]
+[2025-04-25 03:36:16,953] [automlx.trials] Running Model Tuning for [&#39;ItemKNNRecommender&#39;]
+[2025-04-25 03:36:20,323] [automlx.trials] Best parameters for ItemKNNRecommender: {&#39;n_recommendations&#39;: 10, &#39;num_of_neighbors&#39;: 10, &#39;bias&#39;: 0.010099998000000002, &#39;hist_len&#39;: 10, &#39;reciprocal_ranking&#39;: False, &#39;normalize_scores&#39;: False, &#39;cache_users_states&#39;: True}
+[2025-04-25 03:36:20,324] [automlx.trials] Model Tuning completed. Took: 3.371 secs
+[2025-04-25 03:36:20,456] [automlx.trials] Running Model Tuning for [&#39;AlsRecommender&#39;]
+[2025-04-25 03:36:23,155] [automlx.trials] Best parameters for AlsRecommender: {&#39;n_recommendations&#39;: 10, &#39;iterations&#39;: 10, &#39;factors&#39;: 16, &#39;regularization&#39;: 0.00044721247746457157, &#39;cache_users_states&#39;: True}
+[2025-04-25 03:36:23,156] [automlx.trials] Model Tuning completed. Took: 2.700 secs
+[2025-04-25 03:36:23,552] [automlx.interface] Re-fitting pipeline
+[2025-04-25 03:36:23,562] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_22bd3002-a
+[2025-04-25 03:36:25,050] [automlx.interface] AutoMLx completed.
 </pre>
 </div>
 </div>
@@ -16042,33 +16042,33 @@ <h2 id="Use-a-custom-validation-set">Use a custom validation set<a class="anchor
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diff --git a/demos/OracleAutoMLx_Recommendation.ipynb b/demos/OracleAutoMLx_Recommendation.ipynb
index 0c5bde7..fc8af1c 100644
--- a/demos/OracleAutoMLx_Recommendation.ipynb
+++ b/demos/OracleAutoMLx_Recommendation.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "b33f1c32",
+   "id": "81880ec8",
    "metadata": {},
    "source": [
     "\n",
@@ -14,19 +14,19 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d4420aae",
+   "id": "ed4786de",
    "metadata": {},
    "source": [
     "Recommendation Demo Notebook.\n",
     "\n",
-    "Copyright © 2024, Oracle and/or its affiliates.\n",
+    "Copyright © 2025, Oracle and/or its affiliates.\n",
     "\n",
     "Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "3e132352",
+   "id": "96fd4d93",
    "metadata": {},
    "source": [
     "# Overview of this Notebook\n",
@@ -76,13 +76,13 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "615868b8",
+   "id": "f21246b4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:18:33.871934Z",
-     "iopub.status.busy": "2024-12-26T14:18:33.871540Z",
-     "iopub.status.idle": "2024-12-26T14:18:40.377293Z",
-     "shell.execute_reply": "2024-12-26T14:18:40.376404Z"
+     "iopub.execute_input": "2025-04-25T10:34:49.524994Z",
+     "iopub.status.busy": "2025-04-25T10:34:49.524508Z",
+     "iopub.status.idle": "2025-04-25T10:34:56.014433Z",
+     "shell.execute_reply": "2025-04-25T10:34:56.013264Z"
     }
    },
    "outputs": [
@@ -90,7 +90,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:18:36,255] [automlx.backend] Overwriting ray session directory to /tmp/gbbsq6z5/ray, which will be deleted at engine shutdown. If you wish to retain ray logs, provide _temp_dir in ray_setup dict of engine_opts when initializing the AutoMLx engine.\n"
+      "[2025-04-25 03:34:51,937] [automlx.backend] Overwriting ray session directory to /tmp/1frrc9a7/ray, which will be deleted at engine shutdown. If you wish to retain ray logs, provide _temp_dir in ray_setup dict of engine_opts when initializing the AutoMLx engine.\n"
      ]
     }
    ],
@@ -122,7 +122,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6eec4c8a",
+   "id": "60de0bc8",
    "metadata": {},
    "source": [
     "<a id='load-data'></a>\n",
@@ -137,13 +137,13 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "fae5912e",
+   "id": "10d71715",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:18:40.380010Z",
-     "iopub.status.busy": "2024-12-26T14:18:40.379607Z",
-     "iopub.status.idle": "2024-12-26T14:18:43.293176Z",
-     "shell.execute_reply": "2024-12-26T14:18:43.292310Z"
+     "iopub.execute_input": "2025-04-25T10:34:56.019370Z",
+     "iopub.status.busy": "2025-04-25T10:34:56.018522Z",
+     "iopub.status.idle": "2025-04-25T10:35:01.532354Z",
+     "shell.execute_reply": "2025-04-25T10:35:01.531578Z"
     },
     "lines_to_next_cell": 2
    },
@@ -157,13 +157,13 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "c83d4b6f",
+   "id": "d5f67d34",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:18:43.295997Z",
-     "iopub.status.busy": "2024-12-26T14:18:43.295319Z",
-     "iopub.status.idle": "2024-12-26T14:18:43.341015Z",
-     "shell.execute_reply": "2024-12-26T14:18:43.340404Z"
+     "iopub.execute_input": "2025-04-25T10:35:01.534915Z",
+     "iopub.status.busy": "2025-04-25T10:35:01.534278Z",
+     "iopub.status.idle": "2025-04-25T10:35:01.580798Z",
+     "shell.execute_reply": "2025-04-25T10:35:01.580270Z"
     }
    },
    "outputs": [
@@ -262,7 +262,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2b3dbea2",
+   "id": "ff0c9b09",
    "metadata": {},
    "source": [
     "In order to be used for the recommendation task, the data must have a timestamp column that is used to infer the temporal order of the samples. We also require to set the timestamp column as index of the dataframes used in our AutoML pipelines.\n",
@@ -273,13 +273,13 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "ba70ef5d",
+   "id": "812d547d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:18:43.343403Z",
-     "iopub.status.busy": "2024-12-26T14:18:43.342866Z",
-     "iopub.status.idle": "2024-12-26T14:18:43.350233Z",
-     "shell.execute_reply": "2024-12-26T14:18:43.349772Z"
+     "iopub.execute_input": "2025-04-25T10:35:01.582716Z",
+     "iopub.status.busy": "2025-04-25T10:35:01.582278Z",
+     "iopub.status.idle": "2025-04-25T10:35:01.588223Z",
+     "shell.execute_reply": "2025-04-25T10:35:01.587772Z"
     }
    },
    "outputs": [
@@ -374,7 +374,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bb766ce0",
+   "id": "b963f4dc",
    "metadata": {},
    "source": [
     "<a id='coltypes'></a>\n",
@@ -392,13 +392,13 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "20d51b70",
+   "id": "cfc2901b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:18:43.352070Z",
-     "iopub.status.busy": "2024-12-26T14:18:43.351674Z",
-     "iopub.status.idle": "2024-12-26T14:18:43.354241Z",
-     "shell.execute_reply": "2024-12-26T14:18:43.353797Z"
+     "iopub.execute_input": "2025-04-25T10:35:01.590086Z",
+     "iopub.status.busy": "2025-04-25T10:35:01.589606Z",
+     "iopub.status.idle": "2025-04-25T10:35:01.592388Z",
+     "shell.execute_reply": "2025-04-25T10:35:01.591922Z"
     }
    },
    "outputs": [],
@@ -410,7 +410,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8118c470",
+   "id": "b46f96e5",
    "metadata": {},
    "source": [
     "<a id='dataset-split'></a>\n",
@@ -425,13 +425,13 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "4f801f67",
+   "id": "278bbc69",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:18:43.355950Z",
-     "iopub.status.busy": "2024-12-26T14:18:43.355575Z",
-     "iopub.status.idle": "2024-12-26T14:18:48.754372Z",
-     "shell.execute_reply": "2024-12-26T14:18:48.753623Z"
+     "iopub.execute_input": "2025-04-25T10:35:01.594188Z",
+     "iopub.status.busy": "2025-04-25T10:35:01.593720Z",
+     "iopub.status.idle": "2025-04-25T10:35:06.946927Z",
+     "shell.execute_reply": "2025-04-25T10:35:06.946304Z"
     }
    },
    "outputs": [],
@@ -443,7 +443,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "de17c7cb",
+   "id": "16104052",
    "metadata": {},
    "source": [
     "<a id='automl'></a>\n",
@@ -460,13 +460,13 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "dd7c6313",
+   "id": "c041b433",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:18:48.757538Z",
-     "iopub.status.busy": "2024-12-26T14:18:48.756569Z",
-     "iopub.status.idle": "2024-12-26T14:18:48.760522Z",
-     "shell.execute_reply": "2024-12-26T14:18:48.759949Z"
+     "iopub.execute_input": "2025-04-25T10:35:06.949707Z",
+     "iopub.status.busy": "2025-04-25T10:35:06.948792Z",
+     "iopub.status.idle": "2025-04-25T10:35:06.952588Z",
+     "shell.execute_reply": "2025-04-25T10:35:06.952107Z"
     }
    },
    "outputs": [],
@@ -478,7 +478,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2550f528",
+   "id": "0974f27a",
    "metadata": {},
    "source": [
     "<a id='default'></a>\n",
@@ -490,13 +490,13 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "611e8210",
+   "id": "8b379a85",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:18:48.762605Z",
-     "iopub.status.busy": "2024-12-26T14:18:48.762077Z",
-     "iopub.status.idle": "2024-12-26T14:20:00.950534Z",
-     "shell.execute_reply": "2024-12-26T14:20:00.949856Z"
+     "iopub.execute_input": "2025-04-25T10:35:06.954439Z",
+     "iopub.status.busy": "2025-04-25T10:35:06.953936Z",
+     "iopub.status.idle": "2025-04-25T10:36:14.308745Z",
+     "shell.execute_reply": "2025-04-25T10:36:14.308200Z"
     }
    },
    "outputs": [
@@ -504,77 +504,77 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:18:49,097] [automlx.interface] Dataset shape: (49055,3)\n"
+      "[2025-04-25 03:35:07,292] [automlx.interface] Dataset shape: (49055,3)\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:18:49,166] [automlx.process] Running Model Generation\n"
+      "[2025-04-25 03:35:07,362] [automlx.process] Running Model Generation\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:18:49,207] [automlx.process] Model Generation completed.\n"
+      "[2025-04-25 03:35:07,402] [automlx.process] Model Generation completed.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:18:49,250] [automlx.model_selection] Running Model Selection\n"
+      "[2025-04-25 03:35:07,446] [automlx.model_selection] Running Model Selection\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:19:39,708] [automlx.model_selection] Model Selection completed - Took 50.458 sec - Selected models: [['ItemKNNRecommender']]\n"
+      "[2025-04-25 03:35:56,451] [automlx.model_selection] Model Selection completed - Took 49.005 sec - Selected models: [['ItemKNNRecommender']]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:19:39,753] [automlx.trials] Running Model Tuning for ['ItemKNNRecommender']\n"
+      "[2025-04-25 03:35:56,513] [automlx.trials] Running Model Tuning for ['ItemKNNRecommender']\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:19:59,182] [automlx.trials] Best parameters for ItemKNNRecommender: {'n_recommendations': 10, 'num_of_neighbors': 506, 'bias': 0.0001, 'hist_len': 10, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}\n"
+      "[2025-04-25 03:36:12,913] [automlx.trials] Best parameters for ItemKNNRecommender: {'n_recommendations': 10, 'num_of_neighbors': 506, 'bias': 0.0001, 'hist_len': 10, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:19:59,184] [automlx.trials] Model Tuning completed. Took: 19.431 secs\n"
+      "[2025-04-25 03:36:12,915] [automlx.trials] Model Tuning completed. Took: 16.401 secs\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:19:59,639] [automlx.interface] Re-fitting pipeline\n"
+      "[2025-04-25 03:36:13,337] [automlx.interface] Re-fitting pipeline\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:19:59,646] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_ea4e7717-4\n"
+      "[2025-04-25 03:36:13,344] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_19cf1d36-6\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:20:00,886] [automlx.interface] AutoMLx completed.\n"
+      "[2025-04-25 03:36:14,240] [automlx.interface] AutoMLx completed.\n"
      ]
     }
    ],
@@ -586,7 +586,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "34708471",
+   "id": "4a4d8214",
    "metadata": {},
    "source": [
     "<a id='recommending'></a>\n",
@@ -598,13 +598,13 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "b6513047",
+   "id": "e31a99f9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:20:00.952936Z",
-     "iopub.status.busy": "2024-12-26T14:20:00.952431Z",
-     "iopub.status.idle": "2024-12-26T14:20:01.011187Z",
-     "shell.execute_reply": "2024-12-26T14:20:01.010693Z"
+     "iopub.execute_input": "2025-04-25T10:36:14.310959Z",
+     "iopub.status.busy": "2025-04-25T10:36:14.310411Z",
+     "iopub.status.idle": "2025-04-25T10:36:14.371178Z",
+     "shell.execute_reply": "2025-04-25T10:36:14.370685Z"
     }
    },
    "outputs": [
@@ -637,33 +637,33 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>731</td>\n",
-       "      <td>190</td>\n",
-       "      <td>21.294812</td>\n",
+       "      <td>628</td>\n",
+       "      <td>330</td>\n",
+       "      <td>15.370814</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>731</td>\n",
-       "      <td>96</td>\n",
-       "      <td>20.401263</td>\n",
+       "      <td>628</td>\n",
+       "      <td>286</td>\n",
+       "      <td>15.029380</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>731</td>\n",
-       "      <td>97</td>\n",
-       "      <td>19.808079</td>\n",
+       "      <td>628</td>\n",
+       "      <td>258</td>\n",
+       "      <td>14.119169</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>731</td>\n",
-       "      <td>181</td>\n",
-       "      <td>19.745601</td>\n",
+       "      <td>628</td>\n",
+       "      <td>272</td>\n",
+       "      <td>13.703196</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>731</td>\n",
-       "      <td>174</td>\n",
-       "      <td>19.685528</td>\n",
+       "      <td>628</td>\n",
+       "      <td>313</td>\n",
+       "      <td>13.564656</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -671,11 +671,11 @@
       ],
       "text/plain": [
        "   user_id  movie_id      score\n",
-       "0      731       190  21.294812\n",
-       "1      731        96  20.401263\n",
-       "2      731        97  19.808079\n",
-       "3      731       181  19.745601\n",
-       "4      731       174  19.685528"
+       "0      628       330  15.370814\n",
+       "1      628       286  15.029380\n",
+       "2      628       258  14.119169\n",
+       "3      628       272  13.703196\n",
+       "4      628       313  13.564656"
       ]
      },
      "execution_count": 9,
@@ -692,7 +692,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fd477e94",
+   "id": "801fc125",
    "metadata": {},
    "source": [
     "<a id='analyze'></a>\n",
@@ -710,13 +710,13 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "7291b01d",
+   "id": "d7fa1f8f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:20:01.013145Z",
-     "iopub.status.busy": "2024-12-26T14:20:01.012716Z",
-     "iopub.status.idle": "2024-12-26T14:20:01.026771Z",
-     "shell.execute_reply": "2024-12-26T14:20:01.026308Z"
+     "iopub.execute_input": "2025-04-25T10:36:14.373161Z",
+     "iopub.status.busy": "2025-04-25T10:36:14.372643Z",
+     "iopub.status.idle": "2025-04-25T10:36:14.387085Z",
+     "shell.execute_reply": "2025-04-25T10:36:14.386548Z"
     }
    },
    "outputs": [
@@ -761,7 +761,7 @@
        "      <td>{'n_recommendations': 10, 'num_of_neighbors': 506, 'bias': 0.0001, 'hist_len': 10, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>25.1.0</td>\n",
+       "      <td>25.2.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>3.9.21 (main, Dec 11 2024, 16:24:11) \\n[GCC 11.2.0]</td>\n",
@@ -815,9 +815,9 @@
        "      <td>{'n_recommendations': 10, 'num_of_neighbors': 100, 'bias': 25, 'hist_len': 20, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}</td>\n",
        "      <td>0.0882</td>\n",
        "      <td>{'hr': 0.08820403825717323}</td>\n",
-       "      <td>1.0596</td>\n",
-       "      <td>0.7370</td>\n",
-       "      <td>Thu Dec 26 06:19:02 2024</td>\n",
+       "      <td>1.0319</td>\n",
+       "      <td>0.7063</td>\n",
+       "      <td>Fri Apr 25 03:35:20 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Selection</td>\n",
@@ -825,11 +825,11 @@
        "      <td>2</td>\n",
        "      <td>AlsRecommender</td>\n",
        "      <td>{'n_recommendations': 10, 'iterations': 10, 'factors': 16, 'regularization': 0.01, 'cache_users_states': True}</td>\n",
-       "      <td>0.0765</td>\n",
-       "      <td>{'hr': 0.0765143464399575}</td>\n",
-       "      <td>4.5466</td>\n",
-       "      <td>0.6639</td>\n",
-       "      <td>Thu Dec 26 06:19:01 2024</td>\n",
+       "      <td>0.0691</td>\n",
+       "      <td>{'hr': 0.06907545164718384}</td>\n",
+       "      <td>4.5845</td>\n",
+       "      <td>0.7055</td>\n",
+       "      <td>Fri Apr 25 03:35:19 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Selection</td>\n",
@@ -837,11 +837,11 @@
        "      <td>2</td>\n",
        "      <td>TRexxRecommender</td>\n",
        "      <td>{'n_recommendations': 10, 'embedding_dim': 32, 'sequence_length': 5, 'num_sampled': 100, 'dropout_rate': 0.2, 'num_blocks': 2, 'num_head': 4, 'l2_reg_embedding': 1e-06, 'dnn_activation': 'tanh', 'optimizer_name': 'lazyadam', 'optimizer_learning_rate': 0.01, 'future_blinding': False, 'embeddings_on_cpu': False, 'cache_users_states': False, 'negative_sampling_method': CandidateSamplingMethod.UNIFORM_CANDIDATE_SAMPLING, 'epochs': 10, 'batch_size': 512, 'verbose': 1, 'augment_data': True, 'early_stopping_patience': -1}</td>\n",
-       "      <td>0.0733</td>\n",
-       "      <td>{'hr': 0.07332624867162593}</td>\n",
-       "      <td>36.8648</td>\n",
-       "      <td>1.2107</td>\n",
-       "      <td>Thu Dec 26 06:19:39 2024</td>\n",
+       "      <td>0.0531</td>\n",
+       "      <td>{'hr': 0.053134962805526036}</td>\n",
+       "      <td>35.1017</td>\n",
+       "      <td>1.1860</td>\n",
+       "      <td>Fri Apr 25 03:35:56 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Selection</td>\n",
@@ -849,11 +849,11 @@
        "      <td>2</td>\n",
        "      <td>BprRecommender</td>\n",
        "      <td>{'n_recommendations': 10, 'iterations': 10, 'factors': 16, 'regularization': 0.01, 'cache_users_states': True}</td>\n",
-       "      <td>0.0446</td>\n",
-       "      <td>{'hr': 0.044633368756641874}</td>\n",
-       "      <td>0.2655</td>\n",
-       "      <td>0.7351</td>\n",
-       "      <td>Thu Dec 26 06:19:02 2024</td>\n",
+       "      <td>0.0372</td>\n",
+       "      <td>{'hr': 0.03719447396386823}</td>\n",
+       "      <td>0.4148</td>\n",
+       "      <td>0.7046</td>\n",
+       "      <td>Fri Apr 25 03:35:21 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
@@ -863,9 +863,9 @@
        "      <td>{'n_recommendations': 10, 'num_of_neighbors': 505, 'bias': 0.0001, 'hist_len': 10, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}</td>\n",
        "      <td>0.0999</td>\n",
        "      <td>{'hr': 0.09989373007438895}</td>\n",
-       "      <td>1.4546</td>\n",
-       "      <td>0.6808</td>\n",
-       "      <td>Thu Dec 26 06:19:55 2024</td>\n",
+       "      <td>1.3955</td>\n",
+       "      <td>0.6848</td>\n",
+       "      <td>Fri Apr 25 03:36:10 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
@@ -875,9 +875,9 @@
        "      <td>{'n_recommendations': 10, 'num_of_neighbors': 506, 'bias': 0.0001, 'hist_len': 10, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}</td>\n",
        "      <td>0.0999</td>\n",
        "      <td>{'hr': 0.09989373007438895}</td>\n",
-       "      <td>1.1579</td>\n",
-       "      <td>1.2015</td>\n",
-       "      <td>Thu Dec 26 06:19:58 2024</td>\n",
+       "      <td>1.0910</td>\n",
+       "      <td>0.6842</td>\n",
+       "      <td>Fri Apr 25 03:36:12 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
@@ -887,9 +887,9 @@
        "      <td>{'n_recommendations': 10, 'num_of_neighbors': 506, 'bias': 0.0001, 'hist_len': 10, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}</td>\n",
        "      <td>0.0999</td>\n",
        "      <td>{'hr': 0.09989373007438895}</td>\n",
-       "      <td>1.4794</td>\n",
-       "      <td>1.2011</td>\n",
-       "      <td>Thu Dec 26 06:19:55 2024</td>\n",
+       "      <td>1.3582</td>\n",
+       "      <td>0.6869</td>\n",
+       "      <td>Fri Apr 25 03:36:09 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
@@ -899,9 +899,9 @@
        "      <td>{'n_recommendations': 10, 'num_of_neighbors': 10, 'bias': 28.25660795027468, 'hist_len': 10, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}</td>\n",
        "      <td>0.0956</td>\n",
        "      <td>{'hr': 0.09564293304994687}</td>\n",
-       "      <td>1.3933</td>\n",
-       "      <td>1.1959</td>\n",
-       "      <td>Thu Dec 26 06:19:52 2024</td>\n",
+       "      <td>1.4462</td>\n",
+       "      <td>0.6762</td>\n",
+       "      <td>Fri Apr 25 03:36:08 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
@@ -911,9 +911,9 @@
        "      <td>{'n_recommendations': 10, 'num_of_neighbors': 10, 'bias': 28.26160794927468, 'hist_len': 10, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}</td>\n",
        "      <td>0.0956</td>\n",
        "      <td>{'hr': 0.09564293304994687}</td>\n",
-       "      <td>1.1074</td>\n",
-       "      <td>0.6722</td>\n",
-       "      <td>Thu Dec 26 06:19:55 2024</td>\n",
+       "      <td>1.1324</td>\n",
+       "      <td>0.6785</td>\n",
+       "      <td>Fri Apr 25 03:36:10 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
@@ -923,9 +923,9 @@
        "      <td>{'n_recommendations': 10, 'num_of_neighbors': 10, 'bias': 28.26160794927468, 'hist_len': 10, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}</td>\n",
        "      <td>0.0956</td>\n",
        "      <td>{'hr': 0.09564293304994687}</td>\n",
-       "      <td>1.2934</td>\n",
-       "      <td>0.6724</td>\n",
-       "      <td>Thu Dec 26 06:19:54 2024</td>\n",
+       "      <td>1.3547</td>\n",
+       "      <td>0.6758</td>\n",
+       "      <td>Fri Apr 25 03:36:08 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>...</td>\n",
@@ -947,9 +947,9 @@
        "      <td>{'n_recommendations': 10, 'num_of_neighbors': 10, 'bias': 0.0001, 'hist_len': 20, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}</td>\n",
        "      <td>0.084</td>\n",
        "      <td>{'hr': 0.08395324123273114}</td>\n",
-       "      <td>1.5070</td>\n",
-       "      <td>0.6691</td>\n",
-       "      <td>Thu Dec 26 06:19:55 2024</td>\n",
+       "      <td>1.3191</td>\n",
+       "      <td>0.6742</td>\n",
+       "      <td>Fri Apr 25 03:36:08 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
@@ -959,9 +959,9 @@
        "      <td>{'n_recommendations': 10, 'num_of_neighbors': 10, 'bias': 0.0001, 'hist_len': 21, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}</td>\n",
        "      <td>0.084</td>\n",
        "      <td>{'hr': 0.08395324123273114}</td>\n",
-       "      <td>1.2909</td>\n",
-       "      <td>0.6714</td>\n",
-       "      <td>Thu Dec 26 06:19:54 2024</td>\n",
+       "      <td>1.3325</td>\n",
+       "      <td>0.6755</td>\n",
+       "      <td>Fri Apr 25 03:36:08 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
@@ -971,9 +971,9 @@
        "      <td>{'n_recommendations': 10, 'num_of_neighbors': 505, 'bias': 25, 'hist_len': 20, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}</td>\n",
        "      <td>0.0829</td>\n",
        "      <td>{'hr': 0.08289054197662062}</td>\n",
-       "      <td>1.3678</td>\n",
-       "      <td>1.2010</td>\n",
-       "      <td>Thu Dec 26 06:19:42 2024</td>\n",
+       "      <td>1.1355</td>\n",
+       "      <td>1.1758</td>\n",
+       "      <td>Fri Apr 25 03:36:01 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
@@ -983,9 +983,9 @@
        "      <td>{'n_recommendations': 10, 'num_of_neighbors': 506, 'bias': 25, 'hist_len': 20, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}</td>\n",
        "      <td>0.0829</td>\n",
        "      <td>{'hr': 0.08289054197662062}</td>\n",
-       "      <td>1.3354</td>\n",
-       "      <td>1.2011</td>\n",
-       "      <td>Thu Dec 26 06:19:46 2024</td>\n",
+       "      <td>1.3934</td>\n",
+       "      <td>1.1758</td>\n",
+       "      <td>Fri Apr 25 03:36:02 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
@@ -995,9 +995,9 @@
        "      <td>{'n_recommendations': 10, 'num_of_neighbors': 752, 'bias': 25, 'hist_len': 20, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}</td>\n",
        "      <td>0.0818</td>\n",
        "      <td>{'hr': 0.0818278427205101}</td>\n",
-       "      <td>1.2453</td>\n",
-       "      <td>1.2065</td>\n",
-       "      <td>Thu Dec 26 06:19:49 2024</td>\n",
+       "      <td>1.4294</td>\n",
+       "      <td>1.1812</td>\n",
+       "      <td>Fri Apr 25 03:36:05 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
@@ -1007,9 +1007,9 @@
        "      <td>{'n_recommendations': 10, 'num_of_neighbors': 753, 'bias': 25, 'hist_len': 20, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}</td>\n",
        "      <td>0.0818</td>\n",
        "      <td>{'hr': 0.0818278427205101}</td>\n",
-       "      <td>1.2532</td>\n",
-       "      <td>1.2065</td>\n",
-       "      <td>Thu Dec 26 06:19:47 2024</td>\n",
+       "      <td>1.2525</td>\n",
+       "      <td>1.1812</td>\n",
+       "      <td>Fri Apr 25 03:36:04 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
@@ -1019,9 +1019,9 @@
        "      <td>{'n_recommendations': 10, 'num_of_neighbors': 10, 'bias': 0.0001, 'hist_len': 132, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}</td>\n",
        "      <td>0.0797</td>\n",
        "      <td>{'hr': 0.07970244420828905}</td>\n",
-       "      <td>1.5203</td>\n",
-       "      <td>0.6742</td>\n",
-       "      <td>Thu Dec 26 06:19:55 2024</td>\n",
+       "      <td>1.4449</td>\n",
+       "      <td>0.6758</td>\n",
+       "      <td>Fri Apr 25 03:36:09 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
@@ -1031,9 +1031,9 @@
        "      <td>{'n_recommendations': 10, 'num_of_neighbors': 10, 'bias': 0.0001, 'hist_len': 133, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}</td>\n",
        "      <td>0.0797</td>\n",
        "      <td>{'hr': 0.07970244420828905}</td>\n",
-       "      <td>1.5426</td>\n",
-       "      <td>0.6726</td>\n",
-       "      <td>Thu Dec 26 06:19:55 2024</td>\n",
+       "      <td>1.3044</td>\n",
+       "      <td>0.6773</td>\n",
+       "      <td>Fri Apr 25 03:36:08 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
@@ -1043,9 +1043,9 @@
        "      <td>{'n_recommendations': 10, 'num_of_neighbors': 10, 'bias': 0.0001, 'hist_len': 255, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}</td>\n",
        "      <td>0.0797</td>\n",
        "      <td>{'hr': 0.07970244420828905}</td>\n",
-       "      <td>1.4628</td>\n",
-       "      <td>0.6716</td>\n",
-       "      <td>Thu Dec 26 06:19:55 2024</td>\n",
+       "      <td>1.4471</td>\n",
+       "      <td>0.6760</td>\n",
+       "      <td>Fri Apr 25 03:36:09 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
@@ -1055,9 +1055,9 @@
        "      <td>{'n_recommendations': 10, 'num_of_neighbors': 10, 'bias': 0.0001, 'hist_len': 256, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}</td>\n",
        "      <td>0.0797</td>\n",
        "      <td>{'hr': 0.07970244420828905}</td>\n",
-       "      <td>1.2602</td>\n",
-       "      <td>0.6713</td>\n",
-       "      <td>Thu Dec 26 06:19:54 2024</td>\n",
+       "      <td>1.4831</td>\n",
+       "      <td>0.6730</td>\n",
+       "      <td>Fri Apr 25 03:36:09 2025</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>"
@@ -1078,7 +1078,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b0f42742",
+   "id": "30691ab3",
    "metadata": {},
    "source": [
     "We also provide the capability to visualize the results of each stage of the AutoMLx pipeline.\n",
@@ -1092,19 +1092,19 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "2c2c5e99",
+   "id": "faccd1b4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:20:01.028571Z",
-     "iopub.status.busy": "2024-12-26T14:20:01.028156Z",
-     "iopub.status.idle": "2024-12-26T14:20:01.212411Z",
-     "shell.execute_reply": "2024-12-26T14:20:01.211915Z"
+     "iopub.execute_input": "2025-04-25T10:36:14.388999Z",
+     "iopub.status.busy": "2025-04-25T10:36:14.388508Z",
+     "iopub.status.idle": "2025-04-25T10:36:14.578365Z",
+     "shell.execute_reply": "2025-04-25T10:36:14.577825Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x700 with 1 Axes>"
       ]
@@ -1153,7 +1153,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9ff55095",
+   "id": "b1bd701b",
    "metadata": {},
    "source": [
     "<a id='hyperparameter-tuning'></a>\n",
@@ -1165,19 +1165,19 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "2ece9b6b",
+   "id": "efc97132",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:20:01.214316Z",
-     "iopub.status.busy": "2024-12-26T14:20:01.213859Z",
-     "iopub.status.idle": "2024-12-26T14:20:01.402519Z",
-     "shell.execute_reply": "2024-12-26T14:20:01.401986Z"
+     "iopub.execute_input": "2025-04-25T10:36:14.580268Z",
+     "iopub.status.busy": "2025-04-25T10:36:14.579872Z",
+     "iopub.status.idle": "2025-04-25T10:36:14.768618Z",
+     "shell.execute_reply": "2025-04-25T10:36:14.768077Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x700 with 1 Axes>"
       ]
@@ -1226,7 +1226,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6f699aea",
+   "id": "e419e8b2",
    "metadata": {},
    "source": [
     "<a id='modellist'></a>\n",
@@ -1238,13 +1238,13 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "17ecd010",
+   "id": "cce288b6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:20:01.404418Z",
-     "iopub.status.busy": "2024-12-26T14:20:01.403988Z",
-     "iopub.status.idle": "2024-12-26T14:20:01.407480Z",
-     "shell.execute_reply": "2024-12-26T14:20:01.407057Z"
+     "iopub.execute_input": "2025-04-25T10:36:14.770632Z",
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+     "shell.execute_reply": "2025-04-25T10:36:14.773370Z"
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@@ -1268,7 +1268,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a8fa275e",
+   "id": "f68bee6c",
    "metadata": {},
    "source": [
     "<a id='custom-validation'></a>\n",
@@ -1280,13 +1280,13 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "9fa672c6",
+   "id": "a8f1a67e",
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-     "iopub.execute_input": "2024-12-26T14:20:01.409283Z",
-     "iopub.status.busy": "2024-12-26T14:20:01.408820Z",
-     "iopub.status.idle": "2024-12-26T14:20:01.576448Z",
-     "shell.execute_reply": "2024-12-26T14:20:01.575921Z"
+     "iopub.execute_input": "2025-04-25T10:36:14.775669Z",
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    "outputs": [],
@@ -1302,13 +1302,13 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "122eaa38",
+   "id": "0787d525",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:20:01.578550Z",
-     "iopub.status.busy": "2024-12-26T14:20:01.578116Z",
-     "iopub.status.idle": "2024-12-26T14:20:11.647411Z",
-     "shell.execute_reply": "2024-12-26T14:20:11.646791Z"
+     "iopub.execute_input": "2025-04-25T10:36:14.949398Z",
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+     "iopub.status.idle": "2025-04-25T10:36:25.228649Z",
+     "shell.execute_reply": "2025-04-25T10:36:25.228075Z"
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    "outputs": [
@@ -1316,98 +1316,98 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:20:01,738] [automlx.interface] Dataset shape: (49055,3)\n"
+      "[2025-04-25 03:36:15,105] [automlx.interface] Dataset shape: (49055,3)\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:20:01,808] [automlx.process] Running Model Generation\n"
+      "[2025-04-25 03:36:15,170] [automlx.process] Running Model Generation\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:20:01,853] [automlx.process] Model Generation completed.\n"
+      "[2025-04-25 03:36:15,215] [automlx.process] Model Generation completed.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:20:01,884] [automlx.model_selection] Running Model Selection\n"
+      "[2025-04-25 03:36:15,246] [automlx.model_selection] Running Model Selection\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:20:03,779] [automlx.model_selection] Model Selection completed - Took 1.895 sec - Selected models: [['ItemKNNRecommender', 'AlsRecommender']]\n"
+      "[2025-04-25 03:36:16,875] [automlx.model_selection] Model Selection completed - Took 1.629 sec - Selected models: [['ItemKNNRecommender', 'AlsRecommender']]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:20:03,892] [automlx.trials] Running Model Tuning for ['ItemKNNRecommender']\n"
+      "[2025-04-25 03:36:16,953] [automlx.trials] Running Model Tuning for ['ItemKNNRecommender']\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:20:07,284] [automlx.trials] Best parameters for ItemKNNRecommender: {'n_recommendations': 10, 'num_of_neighbors': 10, 'bias': 0.010099998000000002, 'hist_len': 10, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}\n"
+      "[2025-04-25 03:36:20,323] [automlx.trials] Best parameters for ItemKNNRecommender: {'n_recommendations': 10, 'num_of_neighbors': 10, 'bias': 0.010099998000000002, 'hist_len': 10, 'reciprocal_ranking': False, 'normalize_scores': False, 'cache_users_states': True}\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:20:07,286] [automlx.trials] Model Tuning completed. Took: 3.394 secs\n"
+      "[2025-04-25 03:36:20,324] [automlx.trials] Model Tuning completed. Took: 3.371 secs\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:20:07,439] [automlx.trials] Running Model Tuning for ['AlsRecommender']\n"
+      "[2025-04-25 03:36:20,456] [automlx.trials] Running Model Tuning for ['AlsRecommender']\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:20:09,915] [automlx.trials] Best parameters for AlsRecommender: {'n_recommendations': 10, 'iterations': 10, 'factors': 16, 'regularization': 0.01, 'cache_users_states': True}\n"
+      "[2025-04-25 03:36:23,155] [automlx.trials] Best parameters for AlsRecommender: {'n_recommendations': 10, 'iterations': 10, 'factors': 16, 'regularization': 0.00044721247746457157, 'cache_users_states': True}\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:20:09,916] [automlx.trials] Model Tuning completed. Took: 2.478 secs\n"
+      "[2025-04-25 03:36:23,156] [automlx.trials] Model Tuning completed. Took: 2.700 secs\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:20:10,336] [automlx.interface] Re-fitting pipeline\n"
+      "[2025-04-25 03:36:23,552] [automlx.interface] Re-fitting pipeline\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:20:10,346] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_b09b807d-a\n"
+      "[2025-04-25 03:36:23,562] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_22bd3002-a\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:20:11,503] [automlx.interface] AutoMLx completed.\n"
+      "[2025-04-25 03:36:25,050] [automlx.interface] AutoMLx completed.\n"
      ]
     }
    ],
@@ -1424,7 +1424,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a4a116f9",
+   "id": "f90f31e9",
    "metadata": {},
    "source": [
     "Now that the custom AutoML pipeline is completed, we can generate recommendations.\n",
@@ -1434,13 +1434,13 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "d0570c10",
+   "id": "03976c22",
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-     "iopub.status.idle": "2024-12-26T14:20:11.709528Z",
-     "shell.execute_reply": "2024-12-26T14:20:11.709017Z"
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+     "shell.execute_reply": "2025-04-25T10:36:25.290013Z"
     }
    },
    "outputs": [
@@ -1473,33 +1473,33 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>731</td>\n",
-       "      <td>97</td>\n",
-       "      <td>19.207128</td>\n",
+       "      <td>628</td>\n",
+       "      <td>286</td>\n",
+       "      <td>13.964525</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>731</td>\n",
-       "      <td>132</td>\n",
-       "      <td>18.304316</td>\n",
+       "      <td>628</td>\n",
+       "      <td>330</td>\n",
+       "      <td>13.761287</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>731</td>\n",
-       "      <td>176</td>\n",
-       "      <td>18.152165</td>\n",
+       "      <td>628</td>\n",
+       "      <td>272</td>\n",
+       "      <td>12.332191</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>731</td>\n",
-       "      <td>181</td>\n",
-       "      <td>18.135894</td>\n",
+       "      <td>628</td>\n",
+       "      <td>331</td>\n",
+       "      <td>12.210435</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>731</td>\n",
-       "      <td>96</td>\n",
-       "      <td>17.946448</td>\n",
+       "      <td>628</td>\n",
+       "      <td>313</td>\n",
+       "      <td>12.163628</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1507,11 +1507,11 @@
       ],
       "text/plain": [
        "   user_id  movie_id      score\n",
-       "0      731        97  19.207128\n",
-       "1      731       132  18.304316\n",
-       "2      731       176  18.152165\n",
-       "3      731       181  18.135894\n",
-       "4      731        96  17.946448"
+       "0      628       286  13.964525\n",
+       "1      628       330  13.761287\n",
+       "2      628       272  12.332191\n",
+       "3      628       331  12.210435\n",
+       "4      628       313  12.163628"
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      },
      "execution_count": 16,
@@ -1527,7 +1527,7 @@
   },
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-   "id": "8e9d90cb",
+   "id": "b3867203",
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@@ -1541,13 +1541,13 @@
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-   "id": "35cfa1b0",
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diff --git a/demos/OracleAutoMLx_Regression.html b/demos/OracleAutoMLx_Regression.html
index 89fd975..e590175 100644
--- a/demos/OracleAutoMLx_Regression.html
+++ b/demos/OracleAutoMLx_Regression.html
@@ -14622,7 +14622,7 @@ <h1><font color=red>Building and Explaining a Regressor using AutoMLx</font></h1
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
 </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
 <p>Regression Demo Notebook.</p>
-<p>Copyright © 2024, Oracle and/or its affiliates.</p>
+<p>Copyright © 2025, Oracle and/or its affiliates.</p>
 <p>Licensed under the Universal Permissive License v 1.0 as shown at <a href="https://oss.oracle.com/licenses/upl/">https://oss.oracle.com/licenses/upl/</a></p>
 
 </div>
@@ -14745,24 +14745,24 @@ <h2 id="Setup">Setup<a class="anchor-link" href="#Setup">&#182;</a></h2><p>Basic
 <div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[2]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">time</span>
-<span class="kn">import</span> <span class="nn">datetime</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">plotly.figure_factory</span> <span class="k">as</span> <span class="nn">ff</span>
-<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">fetch_california_housing</span>
-<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LinearRegression</span>
-<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">mean_squared_error</span>
-<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
-<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LinearRegression</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">time</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">datetime</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pd</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">plotly.figure_factory</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">ff</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.datasets</span><span class="w"> </span><span class="kn">import</span> <span class="n">fetch_california_housing</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.linear_model</span><span class="w"> </span><span class="kn">import</span> <span class="n">LinearRegression</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.metrics</span><span class="w"> </span><span class="kn">import</span> <span class="n">mean_squared_error</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <span class="n">train_test_split</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.linear_model</span><span class="w"> </span><span class="kn">import</span> <span class="n">LinearRegression</span>
 
 <span class="c1"># Settings for plots</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">&#39;figure.figsize&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">7</span><span class="p">]</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">&#39;font.size&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">15</span>
 
-<span class="kn">import</span> <span class="nn">automlx</span>
-<span class="kn">from</span> <span class="nn">automlx</span> <span class="kn">import</span> <span class="n">init</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">automlx</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">automlx</span><span class="w"> </span><span class="kn">import</span> <span class="n">init</span>
 </pre></div>
 
      </div>
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@@ -15195,7 +15195,7 @@ <h3 id="Setting-the-execution-engine">Setting the execution engine<a class="anch
 
 
 <div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
-<pre>[2024-12-26 06:20:24,731] [automlx.backend] Overwriting ray session directory to /tmp/nt_eo1ir/ray, which will be deleted at engine shutdown. If you wish to retain ray logs, provide _temp_dir in ray_setup dict of engine_opts when initializing the AutoMLx engine.
+<pre>[2025-04-25 03:36:38,599] [automlx.backend] Overwriting ray session directory to /tmp/lp_0i5w4/ray, which will be deleted at engine shutdown. If you wish to retain ray logs, provide _temp_dir in ray_setup dict of engine_opts when initializing the AutoMLx engine.
 </pre>
 </div>
 </div>
@@ -15283,28 +15283,28 @@ <h3 id="Train-a-model-using-Oracle-AutoMLx">Train a model using Oracle AutoMLx<a
 
 
 <div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
-<pre>[2024-12-26 06:20:28,865] [automlx.interface] Dataset shape: (14448,8)
-[2024-12-26 06:20:31,490] [sanerec.autotuning.parameter] Hyperparameter epsilon autotune range is set to its validation range. This could lead to long training times
-[2024-12-26 06:20:31,790] [sanerec.autotuning.parameter] Hyperparameter repeat_quality_threshold autotune range is set to its validation range. This could lead to long training times
-[2024-12-26 06:20:31,796] [sanerec.autotuning.parameter] Hyperparameter scope autotune range is set to its validation range. This could lead to long training times
-[2024-12-26 06:20:31,865] [automlx.data_transform] Running preprocessing. Number of features: 9
-[2024-12-26 06:20:32,030] [automlx.data_transform] Preprocessing completed. Took 0.166 secs
-[2024-12-26 06:20:32,060] [automlx.process] Running Model Generation
-[2024-12-26 06:20:32,107] [automlx.process] KNeighborsRegressor is disabled. The KNeighborsRegressor model is only recommended for datasets with less than 10000 samples and 1000 features.
-[2024-12-26 06:20:32,108] [automlx.process] SVR is disabled. The SVR model is only recommended for datasets with less than 10000 samples and 1000 features.
-[2024-12-26 06:20:32,109] [automlx.process] Model Generation completed.
-[2024-12-26 06:20:32,173] [automlx.model_selection] Running Model Selection
-[2024-12-26 06:20:54,278] [automlx.model_selection] Model Selection completed - Took 22.105 sec - Selected models: [[&#39;LGBMRegressor&#39;]]
-[2024-12-26 06:20:54,307] [automlx.adaptive_sampling] Running Adaptive Sampling. Dataset shape: (14448,9).
-[2024-12-26 06:20:57,507] [automlx.trials] Adaptive Sampling completed - Took 3.2005 sec.
-[2024-12-26 06:20:57,590] [automlx.feature_selection] Starting feature ranking for LGBMRegressor
-[2024-12-26 06:21:05,841] [automlx.feature_selection] Feature Selection completed. Took 8.269 secs.
-[2024-12-26 06:21:05,898] [automlx.trials] Running Model Tuning for [&#39;LGBMRegressor&#39;]
-[2024-12-26 06:21:55,284] [automlx.trials] Best parameters for LGBMRegressor: {&#39;num_leaves&#39;: 31, &#39;boosting_type&#39;: &#39;gbdt&#39;, &#39;subsample&#39;: 1, &#39;colsample_bytree&#39;: 0.7952797110155084, &#39;max_depth&#39;: 63, &#39;reg_alpha&#39;: 0, &#39;reg_lambda&#39;: 0, &#39;n_estimators&#39;: 376, &#39;learning_rate&#39;: 0.1, &#39;min_child_weight&#39;: 0.001}
-[2024-12-26 06:21:55,284] [automlx.trials] Model Tuning completed. Took: 49.387 secs
-[2024-12-26 06:22:07,814] [automlx.interface] Re-fitting pipeline
-[2024-12-26 06:22:07,827] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_168f9288-9
-[2024-12-26 06:22:09,947] [automlx.interface] AutoMLx completed.
+<pre>[2025-04-25 03:36:43,786] [automlx.interface] Dataset shape: (14448,8)
+[2025-04-25 03:36:47,477] [sanerec.autotuning.parameter] Hyperparameter epsilon autotune range is set to its validation range. This could lead to long training times
+[2025-04-25 03:36:47,980] [sanerec.autotuning.parameter] Hyperparameter repeat_quality_threshold autotune range is set to its validation range. This could lead to long training times
+[2025-04-25 03:36:47,990] [sanerec.autotuning.parameter] Hyperparameter scope autotune range is set to its validation range. This could lead to long training times
+[2025-04-25 03:36:48,062] [automlx.data_transform] Running preprocessing. Number of features: 9
+[2025-04-25 03:36:48,219] [automlx.data_transform] Preprocessing completed. Took 0.157 secs
+[2025-04-25 03:36:48,243] [automlx.process] Running Model Generation
+[2025-04-25 03:36:48,290] [automlx.process] KNeighborsRegressor is disabled. The KNeighborsRegressor model is only recommended for datasets with less than 10000 samples and 1000 features.
+[2025-04-25 03:36:48,291] [automlx.process] SVR is disabled. The SVR model is only recommended for datasets with less than 10000 samples and 1000 features.
+[2025-04-25 03:36:48,292] [automlx.process] Model Generation completed.
+[2025-04-25 03:36:48,354] [automlx.model_selection] Running Model Selection
+[2025-04-25 03:37:11,017] [automlx.model_selection] Model Selection completed - Took 22.663 sec - Selected models: [[&#39;LGBMRegressor&#39;]]
+[2025-04-25 03:37:11,044] [automlx.adaptive_sampling] Running Adaptive Sampling. Dataset shape: (14448,9).
+[2025-04-25 03:37:13,777] [automlx.trials] Adaptive Sampling completed - Took 2.7323 sec.
+[2025-04-25 03:37:13,886] [automlx.feature_selection] Starting feature ranking for LGBMRegressor
+[2025-04-25 03:37:21,712] [automlx.feature_selection] Feature Selection completed. Took 7.846 secs.
+[2025-04-25 03:37:21,752] [automlx.trials] Running Model Tuning for [&#39;LGBMRegressor&#39;]
+[2025-04-25 03:38:10,028] [automlx.trials] Best parameters for LGBMRegressor: {&#39;num_leaves&#39;: 31, &#39;boosting_type&#39;: &#39;gbdt&#39;, &#39;subsample&#39;: 1, &#39;colsample_bytree&#39;: 0.7952797110155084, &#39;max_depth&#39;: 63, &#39;reg_alpha&#39;: 0, &#39;reg_lambda&#39;: 0, &#39;n_estimators&#39;: 376, &#39;learning_rate&#39;: 0.1, &#39;min_child_weight&#39;: 0.001}
+[2025-04-25 03:38:10,029] [automlx.trials] Model Tuning completed. Took: 48.277 secs
+[2025-04-25 03:38:22,207] [automlx.interface] Re-fitting pipeline
+[2025-04-25 03:38:22,221] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_dff48243-0
+[2025-04-25 03:38:24,366] [automlx.interface] AutoMLx completed.
 </pre>
 </div>
 </div>
@@ -15318,7 +15318,7 @@ <h3 id="Train-a-model-using-Oracle-AutoMLx">Train a model using Oracle AutoMLx<a
 
 
 <div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
-<pre>&lt;automlx._interface.regressor.AutoRegressor at 0x14d008128580&gt;</pre>
+<pre>&lt;automlx._interface.regressor.AutoRegressor at 0x14b5d3077610&gt;</pre>
 </div>
 
 </div>
@@ -15485,7 +15485,7 @@ <h3 id="Analyze-the-AutoMLx-optimization-process">Analyze the AutoMLx optimizati
       <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'subsample': 1, 'colsample_bytree': 0.7952797110155084, 'max_depth': 63, 'reg_alpha': 0, 'reg_lambda': 0, 'n_estimators': 376, 'learning_rate': 0.1, 'min_child_weight': 0.001}</td>
     </tr>
     <tr>
-      <td>25.1.0</td>
+      <td>25.2.1</td>
     </tr>
     <tr>
       <td>3.9.21 (main, Dec 11 2024, 16:24:11) \n[GCC 11.2.0]</td>
@@ -15535,27 +15535,27 @@ <h3 id="Analyze-the-AutoMLx-optimization-process">Analyze the AutoMLx optimizati
   <tbody>
     <tr>
       <td>Model Selection</td>
-      <td>{2: 4000, 1: 4000, 3: 4000, 4: 4000, 5: 4000, 7: 4000, 6: 4000, 8: 4000, 9: 4000, 10: 4000}</td>
+      <td>{1: 4000, 2: 4000, 3: 4000, 4: 4000, 6: 4000, 5: 4000, 7: 4000, 8: 4000, 10: 4000, 9: 4000}</td>
       <td>8</td>
       <td>LGBMRegressor</td>
       <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'subsample': 1, 'colsample_bytree': 1, 'max_depth': 63, 'reg_alpha': 0, 'reg_lambda': 0, 'n_estimators': 100, 'learning_rate': 0.1, 'min_child_weight': 0.001}</td>
       <td>-0.249</td>
       <td>{'neg_mean_squared_error': -0.2490324005700733}</td>
-      <td>4.8061</td>
-      <td>0.3090</td>
-      <td>Thu Dec 26 06:20:41 2024</td>
+      <td>4.8204</td>
+      <td>0.3142</td>
+      <td>Fri Apr 25 03:36:57 2025</td>
     </tr>
     <tr>
       <td>Model Selection</td>
-      <td>{1: 4000, 2: 4000, 3: 4000, 4: 4000, 6: 4000, 5: 4000, 7: 4000, 8: 4000, 9: 4000, 10: 4000}</td>
+      <td>{1: 4000, 2: 4000, 3: 4000, 4: 4000, 6: 4000, 7: 4000, 5: 4000, 8: 4000, 10: 4000, 9: 4000}</td>
       <td>8</td>
       <td>XGBRegressor</td>
       <td>{'n_estimators': 100, 'min_child_weight': 1, 'reg_alpha': 0, 'booster': 'gbtree', 'max_depth': 6, 'learning_rate': 0.1, 'reg_lambda': 1}</td>
       <td>-0.2605</td>
       <td>{'neg_mean_squared_error': -0.2604646831437227}</td>
-      <td>5.7123</td>
-      <td>0.3274</td>
-      <td>Thu Dec 26 06:20:45 2024</td>
+      <td>6.7072</td>
+      <td>0.3330</td>
+      <td>Fri Apr 25 03:37:02 2025</td>
     </tr>
     <tr>
       <td>Model Selection</td>
@@ -15564,58 +15564,58 @@ <h3 id="Analyze-the-AutoMLx-optimization-process">Analyze the AutoMLx optimizati
       <td>RandomForestRegressor</td>
       <td>{'n_estimators': 100, 'min_samples_split': 0.0003, 'min_samples_leaf': 0.00015, 'max_features': 0.777777778}</td>
       <td>-0.3021</td>
-      <td>{'neg_mean_squared_error': -0.30213400269297647}</td>
-      <td>13.5107</td>
-      <td>0.3457</td>
-      <td>Thu Dec 26 06:20:43 2024</td>
+      <td>{'neg_mean_squared_error': -0.3021340026929764}</td>
+      <td>12.1182</td>
+      <td>0.3436</td>
+      <td>Fri Apr 25 03:36:59 2025</td>
     </tr>
     <tr>
       <td>Model Selection</td>
-      <td>{1: 4000, 2: 4000, 3: 4000, 4: 4000, 5: 4000, 6: 4000, 7: 4000, 8: 4000, 9: 4000, 10: 4000}</td>
+      <td>{1: 4000, 2: 4000, 4: 4000, 3: 4000, 6: 4000, 5: 4000, 7: 4000, 8: 4000, 9: 4000, 10: 4000}</td>
       <td>8</td>
       <td>ExtraTreesRegressor</td>
       <td>{'n_estimators': 100, 'min_samples_split': 0.00125, 'min_samples_leaf': 0.000625, 'max_features': 0.777777778}</td>
       <td>-0.3025</td>
-      <td>{'neg_mean_squared_error': -0.30251850889218895}</td>
-      <td>4.2041</td>
-      <td>0.2884</td>
-      <td>Thu Dec 26 06:20:40 2024</td>
+      <td>{'neg_mean_squared_error': -0.3025185088921889}</td>
+      <td>4.0168</td>
+      <td>0.2891</td>
+      <td>Fri Apr 25 03:36:57 2025</td>
     </tr>
     <tr>
       <td>Model Selection</td>
-      <td>{2: 4000, 1: 4000, 3: 4000, 4: 4000, 5: 4000, 6: 4000, 7: 4000, 8: 4000, 9: 4000, 10: 4000}</td>
+      <td>{1: 4000, 2: 4000, 3: 4000, 5: 4000, 4: 4000, 6: 4000, 7: 4000, 8: 4000, 9: 4000, 10: 4000}</td>
       <td>8</td>
       <td>DecisionTreeRegressor</td>
       <td>{'min_samples_split': 0.004, 'min_samples_leaf': 0.002, 'max_features': 1.0}</td>
       <td>-0.4589</td>
       <td>{'neg_mean_squared_error': -0.4589419999457891}</td>
-      <td>1.7458</td>
-      <td>0.2793</td>
-      <td>Thu Dec 26 06:20:40 2024</td>
+      <td>2.5777</td>
+      <td>0.2825</td>
+      <td>Fri Apr 25 03:36:56 2025</td>
     </tr>
     <tr>
       <td>Model Selection</td>
-      <td>{1: 4000, 2: 4000, 6: 4000, 3: 4000, 5: 4000, 4: 4000, 9: 4000, 8: 4000, 7: 4000, 10: 4000}</td>
+      <td>{3: 4000, 2: 4000, 6: 4000, 1: 4000, 4: 4000, 5: 4000, 8: 4000, 7: 4000, 9: 4000, 10: 4000}</td>
       <td>8</td>
       <td>AdaBoostRegressor</td>
       <td>{'learning_rate': 0.667, 'n_estimators': 50}</td>
       <td>-0.6038</td>
       <td>{'neg_mean_squared_error': -0.6038106242123971}</td>
-      <td>6.3336</td>
-      <td>0.2758</td>
-      <td>Thu Dec 26 06:20:40 2024</td>
+      <td>6.0882</td>
+      <td>0.2775</td>
+      <td>Fri Apr 25 03:36:56 2025</td>
     </tr>
     <tr>
       <td>Model Selection</td>
-      <td>{2: 4000, 1: 4000, 5: 4000, 3: 4000, 6: 4000, 4: 4000, 8: 4000, 7: 4000, 9: 4000, 10: 4000}</td>
+      <td>{2: 4000, 1: 4000, 6: 4000, 4: 4000, 3: 4000, 5: 4000, 7: 4000, 9: 4000, 10: 4000, 8: 4000}</td>
       <td>8</td>
       <td>TorchMLPRegressor</td>
       <td>{'optimizer_class': 'Adam', 'shuffle_dataset_each_epoch': True, 'optimizer_params': {}, 'criterion_class': None, 'criterion_params': {}, 'scheduler_class': None, 'scheduler_params': {}, 'batch_size': 128, 'lr': 0.001, 'epochs': 18, 'input_transform': 'auto', 'tensorboard_dir': None, 'use_tqdm': None, 'prediction_batch_size': 128, 'prediction_input_transform': 'auto', 'shuffling_buffer_size': None, 'depth': 4, 'num_logits': 1000, 'div_factor': 2, 'activation': 'ReLU', 'dropout': 0.1}</td>
-      <td>-2.6877</td>
-      <td>{'neg_mean_squared_error': -2.687676060398235}</td>
-      <td>109.4393</td>
-      <td>0.6019</td>
-      <td>Thu Dec 26 06:20:54 2024</td>
+      <td>-2.4536</td>
+      <td>{'neg_mean_squared_error': -2.4536325523119644}</td>
+      <td>114.0048</td>
+      <td>0.6130</td>
+      <td>Fri Apr 25 03:37:10 2025</td>
     </tr>
     <tr>
       <td>Model Selection</td>
@@ -15625,21 +15625,21 @@ <h3 id="Analyze-the-AutoMLx-optimization-process">Analyze the AutoMLx optimizati
       <td>{'C': 1.0}</td>
       <td>-3.3478</td>
       <td>{'neg_mean_squared_error': -3.347771732858513}</td>
-      <td>1.7487</td>
-      <td>0.3126</td>
-      <td>Thu Dec 26 06:20:41 2024</td>
+      <td>1.4416</td>
+      <td>0.3151</td>
+      <td>Fri Apr 25 03:36:58 2025</td>
     </tr>
     <tr>
       <td>Model Selection</td>
-      <td>{1: 4000, 2: 4000, 4: 4000, 3: 4000, 5: 4000, 6: 4000, 7: 4000, 8: 4000, 9: 4000, 10: 4000}</td>
+      <td>{1: 4000, 2: 4000, 3: 4000, 4: 4000, 5: 4000, 6: 4000, 7: 4000, 8: 4000, 9: 4000, 10: 4000}</td>
       <td>8</td>
       <td>LinearRegression</td>
       <td>{}</td>
       <td>-3.5573</td>
       <td>{'neg_mean_squared_error': -3.5573203151697577}</td>
-      <td>0.7166</td>
-      <td>0.3029</td>
-      <td>Thu Dec 26 06:20:41 2024</td>
+      <td>0.6169</td>
+      <td>0.3101</td>
+      <td>Fri Apr 25 03:36:57 2025</td>
     </tr>
     <tr>
       <td>Adaptive Sampling</td>
@@ -15649,9 +15649,9 @@ <h3 id="Analyze-the-AutoMLx-optimization-process">Analyze the AutoMLx optimizati
       <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'subsample': 1, 'colsample_bytree': 1, 'max_depth': 63, 'reg_alpha': 0, 'reg_lambda': 0, 'n_estimators': 100, 'learning_rate': 0.1, 'min_child_weight': 0.001}</td>
       <td>-0.2183</td>
       <td>{'neg_mean_squared_error': -0.21828611974711234}</td>
-      <td>2.6583</td>
-      <td>0.5968</td>
-      <td>Thu Dec 26 06:20:57 2024</td>
+      <td>2.7049</td>
+      <td>0.6072</td>
+      <td>Fri Apr 25 03:37:13 2025</td>
     </tr>
     <tr>
       <td>...</td>
@@ -15667,27 +15667,27 @@ <h3 id="Analyze-the-AutoMLx-optimization-process">Analyze the AutoMLx optimizati
     </tr>
     <tr>
       <td>Model Tuning</td>
-      <td>{1: 11558, 2: 11558, 4: 11559, 3: 11558, 5: 11559, 7: 11558, 6: 11558, 8: 11558, 9: 11559, 10: 11559}</td>
+      <td>{1: 11558, 2: 11558, 3: 11558, 4: 11559, 5: 11559, 6: 11558, 7: 11558, 8: 11558, 9: 11559, 10: 11559}</td>
       <td>6</td>
       <td>LGBMRegressor</td>
       <td>{'num_leaves': 7, 'boosting_type': 'gbdt', 'subsample': 0.4, 'colsample_bytree': 0.4, 'max_depth': 2, 'reg_alpha': 9.999999990000003e-07, 'reg_lambda': 1e-10, 'n_estimators': 5, 'learning_rate': 0.1, 'min_child_weight': 0.001}</td>
       <td>-1.1075</td>
       <td>{'neg_mean_squared_error': -1.1074975253749682}</td>
-      <td>0.9261</td>
-      <td>0.6105</td>
-      <td>Thu Dec 26 06:21:24 2024</td>
+      <td>0.9045</td>
+      <td>0.6196</td>
+      <td>Fri Apr 25 03:37:39 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
-      <td>{1: 11558, 2: 11558, 3: 11558, 4: 11559, 5: 11559, 6: 11558, 7: 11558, 8: 11558, 9: 11559, 10: 11559}</td>
+      <td>{1: 11558, 2: 11558, 3: 11558, 4: 11559, 5: 11559, 6: 11558, 7: 11558, 9: 11559, 8: 11558, 10: 11559}</td>
       <td>6</td>
       <td>LGBMRegressor</td>
       <td>{'num_leaves': 7, 'boosting_type': 'gbdt', 'subsample': 0.4, 'colsample_bytree': 0.4, 'max_depth': 2, 'reg_alpha': 1e-10, 'reg_lambda': 1.7784127779939314e-05, 'n_estimators': 5, 'learning_rate': 0.1, 'min_child_weight': 0.001}</td>
       <td>-1.1075</td>
       <td>{'neg_mean_squared_error': -1.1074975274208296}</td>
-      <td>0.9028</td>
-      <td>0.6108</td>
-      <td>Thu Dec 26 06:21:25 2024</td>
+      <td>0.8745</td>
+      <td>0.6196</td>
+      <td>Fri Apr 25 03:37:41 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
@@ -15697,45 +15697,45 @@ <h3 id="Analyze-the-AutoMLx-optimization-process">Analyze the AutoMLx optimizati
       <td>{'num_leaves': 7, 'boosting_type': 'gbdt', 'subsample': 0.4, 'colsample_bytree': 0.4, 'max_depth': 2, 'reg_alpha': 1e-10, 'reg_lambda': 1.8784127778939314e-05, 'n_estimators': 5, 'learning_rate': 0.1, 'min_child_weight': 0.001}</td>
       <td>-1.1075</td>
       <td>{'neg_mean_squared_error': -1.1074975275461456}</td>
-      <td>0.8935</td>
-      <td>0.6108</td>
-      <td>Thu Dec 26 06:21:26 2024</td>
+      <td>0.7852</td>
+      <td>0.6196</td>
+      <td>Fri Apr 25 03:37:41 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
-      <td>{1: 11558, 2: 11558, 3: 11558, 4: 11559, 6: 11558, 5: 11559, 7: 11558, 8: 11558, 9: 11559, 10: 11559}</td>
+      <td>{1: 11558, 2: 11558, 3: 11558, 5: 11559, 4: 11559, 6: 11558, 7: 11558, 9: 11559, 8: 11558, 10: 11559}</td>
       <td>6</td>
       <td>LGBMRegressor</td>
       <td>{'num_leaves': 7, 'boosting_type': 'gbdt', 'subsample': 0.4, 'colsample_bytree': 0.4, 'max_depth': 2, 'reg_alpha': 1.7784127779939314e-05, 'reg_lambda': 1e-10, 'n_estimators': 5, 'learning_rate': 0.1, 'min_child_weight': 0.001}</td>
       <td>-1.1075</td>
       <td>{'neg_mean_squared_error': -1.1074975277105636}</td>
-      <td>0.9613</td>
-      <td>0.6105</td>
-      <td>Thu Dec 26 06:21:24 2024</td>
+      <td>0.8380</td>
+      <td>0.6196</td>
+      <td>Fri Apr 25 03:37:40 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
-      <td>{1: 11558, 2: 11558, 3: 11558, 4: 11559, 5: 11559, 6: 11558, 8: 11558, 7: 11558, 9: 11559, 10: 11559}</td>
+      <td>{1: 11558, 2: 11558, 3: 11558, 4: 11559, 5: 11559, 6: 11558, 7: 11558, 9: 11559, 8: 11558, 10: 11559}</td>
       <td>6</td>
       <td>LGBMRegressor</td>
       <td>{'num_leaves': 7, 'boosting_type': 'gbdt', 'subsample': 0.4, 'colsample_bytree': 0.4, 'max_depth': 2, 'reg_alpha': 1.8784127778939314e-05, 'reg_lambda': 1e-10, 'n_estimators': 5, 'learning_rate': 0.1, 'min_child_weight': 0.001}</td>
       <td>-1.1075</td>
       <td>{'neg_mean_squared_error': -1.1074975278443453}</td>
-      <td>0.8826</td>
-      <td>0.6105</td>
-      <td>Thu Dec 26 06:21:24 2024</td>
+      <td>0.9636</td>
+      <td>0.6196</td>
+      <td>Fri Apr 25 03:37:40 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
-      <td>{1: 11558, 2: 11558, 3: 11558, 4: 11559, 5: 11559, 7: 11558, 6: 11558, 8: 11558, 9: 11559, 10: 11559}</td>
+      <td>{1: 11558, 2: 11558, 3: 11558, 4: 11559, 5: 11559, 6: 11558, 7: 11558, 8: 11558, 9: 11559, 10: 11559}</td>
       <td>6</td>
       <td>LGBMRegressor</td>
       <td>{'num_leaves': 7, 'boosting_type': 'gbdt', 'subsample': 0.4, 'colsample_bytree': 0.4, 'max_depth': 2, 'reg_alpha': 1e-10, 'reg_lambda': 0.005623553830557401, 'n_estimators': 5, 'learning_rate': 0.1, 'min_child_weight': 0.001}</td>
       <td>-1.1075</td>
       <td>{'neg_mean_squared_error': -1.107498217910651}</td>
-      <td>0.9198</td>
-      <td>0.6108</td>
-      <td>Thu Dec 26 06:21:26 2024</td>
+      <td>0.7935</td>
+      <td>0.6196</td>
+      <td>Fri Apr 25 03:37:41 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
@@ -15745,33 +15745,33 @@ <h3 id="Analyze-the-AutoMLx-optimization-process">Analyze the AutoMLx optimizati
       <td>{'num_leaves': 7, 'boosting_type': 'gbdt', 'subsample': 0.4, 'colsample_bytree': 0.4, 'max_depth': 2, 'reg_alpha': 1e-10, 'reg_lambda': 0.0056245538305564015, 'n_estimators': 5, 'learning_rate': 0.1, 'min_child_weight': 0.001}</td>
       <td>-1.1075</td>
       <td>{'neg_mean_squared_error': -1.1074982180406503}</td>
-      <td>0.8972</td>
-      <td>0.6108</td>
-      <td>Thu Dec 26 06:21:26 2024</td>
+      <td>0.8447</td>
+      <td>0.6196</td>
+      <td>Fri Apr 25 03:37:42 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
-      <td>{1: 11558, 2: 11558, 3: 11558, 4: 11559, 5: 11559, 6: 11558, 7: 11558, 8: 11558, 9: 11559, 10: 11559}</td>
+      <td>{1: 11558, 2: 11558, 3: 11558, 4: 11559, 5: 11559, 7: 11558, 6: 11558, 9: 11559, 8: 11558, 10: 11559}</td>
       <td>6</td>
       <td>LGBMRegressor</td>
       <td>{'num_leaves': 7, 'boosting_type': 'gbdt', 'subsample': 0.4, 'colsample_bytree': 0.4, 'max_depth': 2, 'reg_alpha': 0.005623553830557401, 'reg_lambda': 1e-10, 'n_estimators': 5, 'learning_rate': 0.1, 'min_child_weight': 0.001}</td>
       <td>-1.1075</td>
-      <td>{'neg_mean_squared_error': -1.1074982822779322}</td>
-      <td>0.8164</td>
-      <td>0.6108</td>
-      <td>Thu Dec 26 06:21:25 2024</td>
+      <td>{'neg_mean_squared_error': -1.1074982822779325}</td>
+      <td>0.8752</td>
+      <td>0.6196</td>
+      <td>Fri Apr 25 03:37:40 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
-      <td>{1: 11558, 2: 11558, 3: 11558, 5: 11559, 4: 11559, 6: 11558, 8: 11558, 7: 11558, 9: 11559, 10: 11559}</td>
+      <td>{2: 11558, 1: 11558, 3: 11558, 4: 11559, 5: 11559, 6: 11558, 7: 11558, 8: 11558, 9: 11559, 10: 11559}</td>
       <td>6</td>
       <td>LGBMRegressor</td>
       <td>{'num_leaves': 7, 'boosting_type': 'gbdt', 'subsample': 0.4, 'colsample_bytree': 0.4, 'max_depth': 2, 'reg_alpha': 0.0056245538305564015, 'reg_lambda': 1e-10, 'n_estimators': 5, 'learning_rate': 0.1, 'min_child_weight': 0.001}</td>
       <td>-1.1075</td>
       <td>{'neg_mean_squared_error': -1.1074982824185997}</td>
-      <td>0.9685</td>
-      <td>0.6108</td>
-      <td>Thu Dec 26 06:21:25 2024</td>
+      <td>0.9160</td>
+      <td>0.6196</td>
+      <td>Fri Apr 25 03:37:40 2025</td>
     </tr>
     <tr>
       <td>Model Tuning</td>
@@ -15781,8 +15781,8 @@ <h3 id="Analyze-the-AutoMLx-optimization-process">Analyze the AutoMLx optimizati
       <td>None</td>
       <td>-inf</td>
       <td>None</td>
-      <td>3.7785</td>
-      <td>0.6110</td>
+      <td>2.0835</td>
+      <td>0.6214</td>
       <td>-1</td>
     </tr>
   </tbody>
@@ -15879,7 +15879,7 @@ <h4 id="Algorithm-Selection">Algorithm Selection<a class="anchor-link" href="#Al
 
 
 <div class="jp-RenderedImage jp-OutputArea-output ">
-<img 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"
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"
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@@ -16126,7 +16126,7 @@ <h4 id="Hyperparameter-Tuning">Hyperparameter Tuning<a class="anchor-link" href=
 
 
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"
+<img 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"
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@@ -16275,23 +16275,23 @@ <h3 id="Use-a-custom-validation-set">Use a custom validation set<a class="anchor
 
 
 <div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
-<pre>[2024-12-26 06:22:13,641] [automlx.interface] Dataset shape: (14448,8)
-[2024-12-26 06:22:13,693] [automlx.interface] Adaptive Sampling disabled.
-[2024-12-26 06:22:13,733] [automlx.data_transform] Running preprocessing. Number of features: 9
-[2024-12-26 06:22:13,881] [automlx.data_transform] Preprocessing completed. Took 0.148 secs
-[2024-12-26 06:22:13,913] [automlx.process] Running Model Generation
-[2024-12-26 06:22:13,963] [automlx.process] Model Generation completed.
-[2024-12-26 06:22:13,994] [automlx.model_selection] Running Model Selection
-[2024-12-26 06:22:15,353] [automlx.model_selection] Model Selection completed - Took 1.359 sec - Selected models: [[&#39;XGBRegressor&#39;, &#39;AdaBoostRegressor&#39;]]
-[2024-12-26 06:22:15,431] [automlx.trials] Running Model Tuning for [&#39;XGBRegressor&#39;]
-[2024-12-26 06:22:17,804] [automlx.trials] Best parameters for XGBRegressor: {&#39;n_estimators&#39;: 100, &#39;min_child_weight&#39;: 1, &#39;reg_alpha&#39;: 0, &#39;booster&#39;: &#39;dart&#39;, &#39;max_depth&#39;: 6, &#39;learning_rate&#39;: 0.1, &#39;reg_lambda&#39;: 1}
-[2024-12-26 06:22:17,805] [automlx.trials] Model Tuning completed. Took: 2.374 secs
-[2024-12-26 06:22:17,908] [automlx.trials] Running Model Tuning for [&#39;AdaBoostRegressor&#39;]
-[2024-12-26 06:22:19,021] [automlx.trials] Best parameters for AdaBoostRegressor: {&#39;learning_rate&#39;: 0.000996552734375, &#39;n_estimators&#39;: 10}
-[2024-12-26 06:22:19,022] [automlx.trials] Model Tuning completed. Took: 1.114 secs
-[2024-12-26 06:22:19,211] [automlx.interface] Re-fitting pipeline
-[2024-12-26 06:22:19,231] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_ea8cde56-6
-[2024-12-26 06:22:21,408] [automlx.interface] AutoMLx completed.
+<pre>[2025-04-25 03:38:29,002] [automlx.interface] Dataset shape: (14448,8)
+[2025-04-25 03:38:29,056] [automlx.interface] Adaptive Sampling disabled.
+[2025-04-25 03:38:29,095] [automlx.data_transform] Running preprocessing. Number of features: 9
+[2025-04-25 03:38:29,243] [automlx.data_transform] Preprocessing completed. Took 0.148 secs
+[2025-04-25 03:38:29,269] [automlx.process] Running Model Generation
+[2025-04-25 03:38:29,318] [automlx.process] Model Generation completed.
+[2025-04-25 03:38:29,353] [automlx.model_selection] Running Model Selection
+[2025-04-25 03:38:30,704] [automlx.model_selection] Model Selection completed - Took 1.352 sec - Selected models: [[&#39;XGBRegressor&#39;, &#39;AdaBoostRegressor&#39;]]
+[2025-04-25 03:38:30,781] [automlx.trials] Running Model Tuning for [&#39;XGBRegressor&#39;]
+[2025-04-25 03:38:33,261] [automlx.trials] Best parameters for XGBRegressor: {&#39;n_estimators&#39;: 100, &#39;min_child_weight&#39;: 1, &#39;reg_alpha&#39;: 0, &#39;booster&#39;: &#39;dart&#39;, &#39;max_depth&#39;: 6, &#39;learning_rate&#39;: 0.1, &#39;reg_lambda&#39;: 1}
+[2025-04-25 03:38:33,262] [automlx.trials] Model Tuning completed. Took: 2.482 secs
+[2025-04-25 03:38:33,308] [automlx.trials] Running Model Tuning for [&#39;AdaBoostRegressor&#39;]
+[2025-04-25 03:38:34,419] [automlx.trials] Best parameters for AdaBoostRegressor: {&#39;learning_rate&#39;: 0.000996552734375, &#39;n_estimators&#39;: 10}
+[2025-04-25 03:38:34,420] [automlx.trials] Model Tuning completed. Took: 1.112 secs
+[2025-04-25 03:38:34,588] [automlx.interface] Re-fitting pipeline
+[2025-04-25 03:38:34,606] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_f9b0e6c7-1
+[2025-04-25 03:38:36,476] [automlx.interface] AutoMLx completed.
 Prediction error (MSE) on test data : 0.23716484748805952
 </pre>
 </div>
@@ -16503,44 +16503,44 @@ <h4 id="Visualization">Visualization<a class="anchor-link" href="#Visualization"
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       <th>0</th>
       <td>Latitude</td>
-      <td>1.255858</td>
-      <td>1.215503</td>
-      <td>1.296213</td>
+      <td>1.254504</td>
+      <td>1.231705</td>
+      <td>1.277303</td>
     </tr>
     <tr>
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-      <td>1.068550</td>
-      <td>1.054161</td>
-      <td>1.082938</td>
+      <td>1.074243</td>
+      <td>1.027264</td>
+      <td>1.121221</td>
     </tr>
     <tr>
       <th>2</th>
       <td>MedInc</td>
-      <td>0.377138</td>
-      <td>0.353964</td>
-      <td>0.400313</td>
+      <td>0.370338</td>
+      <td>0.353111</td>
+      <td>0.387564</td>
     </tr>
     <tr>
       <th>3</th>
       <td>AveOccup</td>
-      <td>0.151267</td>
-      <td>0.145928</td>
-      <td>0.156607</td>
+      <td>0.151174</td>
+      <td>0.142189</td>
+      <td>0.160159</td>
     </tr>
     <tr>
       <th>4</th>
       <td>AveRooms</td>
-      <td>0.116828</td>
-      <td>0.112639</td>
-      <td>0.121016</td>
+      <td>0.116548</td>
+      <td>0.114087</td>
+      <td>0.119010</td>
     </tr>
     <tr>
       <th>5</th>
       <td>HouseAge</td>
-      <td>0.063427</td>
-      <td>0.058393</td>
-      <td>0.068462</td>
+      <td>0.064133</td>
+      <td>0.060462</td>
+      <td>0.067804</td>
     </tr>
   </tbody>
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@@ -17558,10 +17558,10 @@ <h5 id="Explain-predictions-with-observational-evaluator_type">Explain predictio
 
 
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-<pre>[2024-12-26 06:22:42,770] [automlx.mlx] AutoMLx got an unexpected keyword argument &#39;evaluator_type&#39;, which is not a configurable attribute of any of [&#39;TabularLocalSurrogateExplainer&#39;, &#39;RandomSampleGeneration&#39;, &#39;DistanceWeighting&#39;, &#39;SurrogateHandler&#39;].
+<pre>[2025-04-25 03:39:01,261] [automlx.mlx] AutoMLx got an unexpected keyword argument &#39;evaluator_type&#39;, which is not a configurable attribute of any of [&#39;TabularLocalSurrogateExplainer&#39;, &#39;RandomSampleGeneration&#39;, &#39;DistanceWeighting&#39;, &#39;SurrogateHandler&#39;].
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 {&#39;TabularLocalSurrogateExplainer&#39;: [&#39;method&#39;, &#39;exp_sorting&#39;, &#39;num_features&#39;, &#39;scale_weight&#39;], &#39;RandomSampleGeneration&#39;: [&#39;discretizer&#39;, &#39;num_samples&#39;, &#39;sample_around_instance&#39;], &#39;DistanceWeighting&#39;: [&#39;kernel_width&#39;, &#39;distance_metric&#39;, &#39;dataset_type&#39;], &#39;SurrogateHandler&#39;: [&#39;model&#39;, &#39;feature_selection&#39;, &#39;force_fit_sample&#39;]}
-[2024-12-26 06:22:42,771] [automlx.mlx] AutoMLx got an unexpected keyword argument &#39;tabulator_type&#39;, which is not a configurable attribute of any of [&#39;TabularLocalSurrogateExplainer&#39;, &#39;RandomSampleGeneration&#39;, &#39;DistanceWeighting&#39;, &#39;SurrogateHandler&#39;].
+[2025-04-25 03:39:01,261] [automlx.mlx] AutoMLx got an unexpected keyword argument &#39;tabulator_type&#39;, which is not a configurable attribute of any of [&#39;TabularLocalSurrogateExplainer&#39;, &#39;RandomSampleGeneration&#39;, &#39;DistanceWeighting&#39;, &#39;SurrogateHandler&#39;].
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class=\"col_heading level0 col0\" >MedInc</th>\n      <th id=\"T_54395_level0_col1\" class=\"col_heading level0 col1\" >HouseAge</th>\n      <th id=\"T_54395_level0_col2\" class=\"col_heading level0 col2\" >AveRooms</th>\n      <th id=\"T_54395_level0_col3\" class=\"col_heading level0 col3\" >AveBedrms</th>\n      <th id=\"T_54395_level0_col4\" class=\"col_heading level0 col4\" >Population</th>\n      <th id=\"T_54395_level0_col5\" class=\"col_heading level0 col5\" >AveOccup</th>\n      <th id=\"T_54395_level0_col6\" class=\"col_heading level0 col6\" >Latitude</th>\n      <th id=\"T_54395_level0_col7\" class=\"col_heading level0 col7\" >Longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_54395_level0_row0\" class=\"row_heading level0 row0\" >Original Sample</th>\n      <td id=\"T_54395_row0_col0\" class=\"data row0 col0\" >2.0278</td>\n      <td id=\"T_54395_row0_col1\" class=\"data row0 col1\" >31.0</td>\n      <td id=\"T_54395_row0_col2\" class=\"data row0 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style=\"text-align: right;\">\n      <th></th>\n      <th>MedInc</th>\n      <th>HouseAge</th>\n      <th>AveRooms</th>\n      <th>AveBedrms</th>\n      <th>Population</th>\n      <th>AveOccup</th>\n      <th>Latitude</th>\n      <th>Longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>3.8214</td>\n      <td>36.0</td>\n      <td>5.600823</td>\n      <td>1.059671</td>\n      <td>1390.0</td>\n      <td>2.860082</td>\n      <td>33.95</td>\n      <td>-118.04</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2.0938</td>\n      <td>16.0</td>\n      <td>6.444444</td>\n      <td>1.833333</td>\n      <td>123.0</td>\n      <td>2.277778</td>\n      <td>32.75</td>\n      <td>-115.72</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2.9798</td>\n      <td>17.0</td>\n      <td>5.530488</td>\n      <td>0.969512</td>\n      <td>1672.0</td>\n      <td>3.398374</td>\n      <td>35.02</td>\n      <td>-120.48</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>5.5385</td>\n      <td>43.0</td>\n      <td>6.972093</td>\n      <td>1.093023</td>\n      <td>970.0</td>\n      <td>2.255814</td>\n      <td>37.93</td>\n      <td>-122.54</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4.9444</td>\n      <td>16.0</td>\n      <td>5.533981</td>\n      <td>0.878641</td>\n      <td>627.0</td>\n      <td>3.043689</td>\n      <td>34.11</td>\n      <td>-117.39</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>10.5793</td>\n      <td>16.0</td>\n      <td>7.750000</td>\n      <td>1.062500</td>\n      <td>567.0</td>\n      <td>2.725962</td>\n      <td>37.27</td>\n      <td>-122.06</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>8.0095</td>\n      <td>52.0</td>\n      <td>6.252577</td>\n      <td>1.000000</td>\n      <td>503.0</td>\n      <td>2.592784</td>\n      <td>34.06</td>\n      <td>-118.39</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>3.4167</td>\n      <td>36.0</td>\n      <td>5.196347</td>\n      <td>1.041096</td>\n      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diff --git a/demos/OracleAutoMLx_Regression.ipynb b/demos/OracleAutoMLx_Regression.ipynb
index 2a7a76b..1528a6a 100644
--- a/demos/OracleAutoMLx_Regression.ipynb
+++ b/demos/OracleAutoMLx_Regression.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "451491f5",
+   "id": "9e256324",
    "metadata": {},
    "source": [
     "***\n",
@@ -14,19 +14,19 @@
   },
   {
    "cell_type": "markdown",
-   "id": "72f279bf",
+   "id": "8219b040",
    "metadata": {},
    "source": [
     "Regression Demo Notebook.\n",
     "\n",
-    "Copyright © 2024, Oracle and/or its affiliates.\n",
+    "Copyright © 2025, Oracle and/or its affiliates.\n",
     "\n",
     "Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "96dee9b9",
+   "id": "7429c73d",
    "metadata": {},
    "source": [
     "## Overview of this Notebook\n",
@@ -82,7 +82,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "56f06f03",
+   "id": "8e6581e4",
    "metadata": {},
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@@ -94,13 +94,13 @@
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    "cell_type": "code",
    "execution_count": 1,
-   "id": "abd97bdd",
+   "id": "657a4697",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:20:20.343955Z",
-     "iopub.status.busy": "2024-12-26T14:20:20.343507Z",
-     "iopub.status.idle": "2024-12-26T14:20:20.924286Z",
-     "shell.execute_reply": "2024-12-26T14:20:20.923614Z"
+     "iopub.execute_input": "2025-04-25T10:36:33.861880Z",
+     "iopub.status.busy": "2025-04-25T10:36:33.861443Z",
+     "iopub.status.idle": "2025-04-25T10:36:34.477398Z",
+     "shell.execute_reply": "2025-04-25T10:36:34.476766Z"
     }
    },
    "outputs": [],
@@ -113,7 +113,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4da04600",
+   "id": "ec534f07",
    "metadata": {},
    "source": [
     "Load the required modules."
@@ -122,13 +122,13 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "bd505a78",
+   "id": "2012d12d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:20:20.927297Z",
-     "iopub.status.busy": "2024-12-26T14:20:20.926648Z",
-     "iopub.status.idle": "2024-12-26T14:20:23.772741Z",
-     "shell.execute_reply": "2024-12-26T14:20:23.772066Z"
+     "iopub.execute_input": "2025-04-25T10:36:34.479458Z",
+     "iopub.status.busy": "2025-04-25T10:36:34.479204Z",
+     "iopub.status.idle": "2025-04-25T10:36:37.568973Z",
+     "shell.execute_reply": "2025-04-25T10:36:37.568338Z"
     }
    },
    "outputs": [],
@@ -155,7 +155,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7a07e074",
+   "id": "c6587d8e",
    "metadata": {},
    "source": [
     "<a id='load-data'></a>\n",
@@ -167,13 +167,13 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "ba49dc49",
+   "id": "61f405a3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:20:23.775201Z",
-     "iopub.status.busy": "2024-12-26T14:20:23.774842Z",
-     "iopub.status.idle": "2024-12-26T14:20:23.856944Z",
-     "shell.execute_reply": "2024-12-26T14:20:23.856367Z"
+     "iopub.execute_input": "2025-04-25T10:36:37.571130Z",
+     "iopub.status.busy": "2025-04-25T10:36:37.570795Z",
+     "iopub.status.idle": "2025-04-25T10:36:37.643865Z",
+     "shell.execute_reply": "2025-04-25T10:36:37.643328Z"
     }
    },
    "outputs": [
@@ -201,13 +201,13 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "12e17c58",
+   "id": "536b326c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:20:23.858627Z",
-     "iopub.status.busy": "2024-12-26T14:20:23.858353Z",
-     "iopub.status.idle": "2024-12-26T14:20:23.893204Z",
-     "shell.execute_reply": "2024-12-26T14:20:23.892654Z"
+     "iopub.execute_input": "2025-04-25T10:36:37.645702Z",
+     "iopub.status.busy": "2025-04-25T10:36:37.645227Z",
+     "iopub.status.idle": "2025-04-25T10:36:37.679582Z",
+     "shell.execute_reply": "2025-04-25T10:36:37.679046Z"
     }
    },
    "outputs": [
@@ -335,7 +335,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0d9527b5",
+   "id": "0a26be41",
    "metadata": {},
    "source": [
     "We first display the density of the target median price."
@@ -344,13 +344,13 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "636ee189",
+   "id": "87b6a751",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:20:23.895199Z",
-     "iopub.status.busy": "2024-12-26T14:20:23.894715Z",
-     "iopub.status.idle": "2024-12-26T14:20:24.378036Z",
-     "shell.execute_reply": "2024-12-26T14:20:24.377375Z"
+     "iopub.execute_input": "2025-04-25T10:36:37.681497Z",
+     "iopub.status.busy": "2025-04-25T10:36:37.681026Z",
+     "iopub.status.idle": "2025-04-25T10:36:38.222119Z",
+     "shell.execute_reply": "2025-04-25T10:36:38.221520Z"
     },
     "lines_to_next_cell": 2
    },
@@ -2256,9 +2256,9 @@
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       },
       "text/html": [
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        "                            \n",
-       "var gd = document.getElementById('81e87764-9e5f-4ef0-9639-00c32522ac4e');\n",
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@@ -2304,7 +2304,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "28ed9078",
+   "id": "13e6e81a",
    "metadata": {},
    "source": [
     "We now separate the targets (`y`) from the training features (`X`) and split the dataset into training (70%) and test (30%) datasets. The training set will be used to create a Machine Learning model using Oracle AutoMLx, and the test set will be used to evaluate the model's performance on unseen data."
@@ -2313,13 +2313,13 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "acd2548c",
+   "id": "1830d9bc",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:20:24.416653Z",
-     "iopub.status.busy": "2024-12-26T14:20:24.415981Z",
-     "iopub.status.idle": "2024-12-26T14:20:24.447632Z",
-     "shell.execute_reply": "2024-12-26T14:20:24.447081Z"
+     "iopub.execute_input": "2025-04-25T10:36:38.261724Z",
+     "iopub.status.busy": "2025-04-25T10:36:38.261082Z",
+     "iopub.status.idle": "2025-04-25T10:36:38.307527Z",
+     "shell.execute_reply": "2025-04-25T10:36:38.307006Z"
     }
    },
    "outputs": [
@@ -2345,7 +2345,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "153f6efb",
+   "id": "b2542f6e",
    "metadata": {},
    "source": [
     "<a id='AutoML'></a>\n",
@@ -2354,7 +2354,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "472cb178",
+   "id": "2a9cca1e",
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    "source": [
     "<a id='Engine'></a>\n",
@@ -2365,13 +2365,13 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "9a49a87c",
+   "id": "7d089dd1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:20:24.449710Z",
-     "iopub.status.busy": "2024-12-26T14:20:24.449148Z",
-     "iopub.status.idle": "2024-12-26T14:20:28.761803Z",
-     "shell.execute_reply": "2024-12-26T14:20:28.760774Z"
+     "iopub.execute_input": "2025-04-25T10:36:38.309474Z",
+     "iopub.status.busy": "2025-04-25T10:36:38.308941Z",
+     "iopub.status.idle": "2025-04-25T10:36:43.680294Z",
+     "shell.execute_reply": "2025-04-25T10:36:43.679595Z"
     }
    },
    "outputs": [
@@ -2379,7 +2379,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:20:24,731] [automlx.backend] Overwriting ray session directory to /tmp/nt_eo1ir/ray, which will be deleted at engine shutdown. If you wish to retain ray logs, provide _temp_dir in ray_setup dict of engine_opts when initializing the AutoMLx engine.\n"
+      "[2025-04-25 03:36:38,599] [automlx.backend] Overwriting ray session directory to /tmp/lp_0i5w4/ray, which will be deleted at engine shutdown. If you wish to retain ray logs, provide _temp_dir in ray_setup dict of engine_opts when initializing the AutoMLx engine.\n"
      ]
     }
    ],
@@ -2389,7 +2389,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dce6e7e8",
+   "id": "5bc95110",
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    "source": [
     "<a id='provider'></a>\n",
@@ -2421,7 +2421,7 @@
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   {
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-   "id": "d17327e2",
+   "id": "29f937eb",
    "metadata": {},
    "source": [
     "<a id='default'></a>\n",
@@ -2433,13 +2433,13 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "9daa83dd",
+   "id": "68215d8c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:20:28.764954Z",
-     "iopub.status.busy": "2024-12-26T14:20:28.764274Z",
-     "iopub.status.idle": "2024-12-26T14:22:11.181565Z",
-     "shell.execute_reply": "2024-12-26T14:22:11.180869Z"
+     "iopub.execute_input": "2025-04-25T10:36:43.683393Z",
+     "iopub.status.busy": "2025-04-25T10:36:43.682870Z",
+     "iopub.status.idle": "2025-04-25T10:38:25.792696Z",
+     "shell.execute_reply": "2025-04-25T10:38:25.792007Z"
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    "outputs": [
@@ -2447,160 +2447,160 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:20:28,865] [automlx.interface] Dataset shape: (14448,8)\n"
+      "[2025-04-25 03:36:43,786] [automlx.interface] Dataset shape: (14448,8)\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:20:31,490] [sanerec.autotuning.parameter] Hyperparameter epsilon autotune range is set to its validation range. This could lead to long training times\n"
+      "[2025-04-25 03:36:47,477] [sanerec.autotuning.parameter] Hyperparameter epsilon autotune range is set to its validation range. This could lead to long training times\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:20:31,790] [sanerec.autotuning.parameter] Hyperparameter repeat_quality_threshold autotune range is set to its validation range. This could lead to long training times\n"
+      "[2025-04-25 03:36:47,980] [sanerec.autotuning.parameter] Hyperparameter repeat_quality_threshold autotune range is set to its validation range. This could lead to long training times\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:20:31,796] [sanerec.autotuning.parameter] Hyperparameter scope autotune range is set to its validation range. This could lead to long training times\n"
+      "[2025-04-25 03:36:47,990] [sanerec.autotuning.parameter] Hyperparameter scope autotune range is set to its validation range. This could lead to long training times\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:20:31,865] [automlx.data_transform] Running preprocessing. Number of features: 9\n"
+      "[2025-04-25 03:36:48,062] [automlx.data_transform] Running preprocessing. Number of features: 9\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:20:32,030] [automlx.data_transform] Preprocessing completed. Took 0.166 secs\n"
+      "[2025-04-25 03:36:48,219] [automlx.data_transform] Preprocessing completed. Took 0.157 secs\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:20:32,060] [automlx.process] Running Model Generation\n"
+      "[2025-04-25 03:36:48,243] [automlx.process] Running Model Generation\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:20:32,107] [automlx.process] KNeighborsRegressor is disabled. The KNeighborsRegressor model is only recommended for datasets with less than 10000 samples and 1000 features.\n"
+      "[2025-04-25 03:36:48,290] [automlx.process] KNeighborsRegressor is disabled. The KNeighborsRegressor model is only recommended for datasets with less than 10000 samples and 1000 features.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:20:32,108] [automlx.process] SVR is disabled. The SVR model is only recommended for datasets with less than 10000 samples and 1000 features.\n"
+      "[2025-04-25 03:36:48,291] [automlx.process] SVR is disabled. The SVR model is only recommended for datasets with less than 10000 samples and 1000 features.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:20:32,109] [automlx.process] Model Generation completed.\n"
+      "[2025-04-25 03:36:48,292] [automlx.process] Model Generation completed.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:20:32,173] [automlx.model_selection] Running Model Selection\n"
+      "[2025-04-25 03:36:48,354] [automlx.model_selection] Running Model Selection\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:20:54,278] [automlx.model_selection] Model Selection completed - Took 22.105 sec - Selected models: [['LGBMRegressor']]\n"
+      "[2025-04-25 03:37:11,017] [automlx.model_selection] Model Selection completed - Took 22.663 sec - Selected models: [['LGBMRegressor']]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:20:54,307] [automlx.adaptive_sampling] Running Adaptive Sampling. Dataset shape: (14448,9).\n"
+      "[2025-04-25 03:37:11,044] [automlx.adaptive_sampling] Running Adaptive Sampling. Dataset shape: (14448,9).\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:20:57,507] [automlx.trials] Adaptive Sampling completed - Took 3.2005 sec.\n"
+      "[2025-04-25 03:37:13,777] [automlx.trials] Adaptive Sampling completed - Took 2.7323 sec.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:20:57,590] [automlx.feature_selection] Starting feature ranking for LGBMRegressor\n"
+      "[2025-04-25 03:37:13,886] [automlx.feature_selection] Starting feature ranking for LGBMRegressor\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:21:05,841] [automlx.feature_selection] Feature Selection completed. Took 8.269 secs.\n"
+      "[2025-04-25 03:37:21,712] [automlx.feature_selection] Feature Selection completed. Took 7.846 secs.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:21:05,898] [automlx.trials] Running Model Tuning for ['LGBMRegressor']\n"
+      "[2025-04-25 03:37:21,752] [automlx.trials] Running Model Tuning for ['LGBMRegressor']\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:21:55,284] [automlx.trials] Best parameters for LGBMRegressor: {'num_leaves': 31, 'boosting_type': 'gbdt', 'subsample': 1, 'colsample_bytree': 0.7952797110155084, 'max_depth': 63, 'reg_alpha': 0, 'reg_lambda': 0, 'n_estimators': 376, 'learning_rate': 0.1, 'min_child_weight': 0.001}\n"
+      "[2025-04-25 03:38:10,028] [automlx.trials] Best parameters for LGBMRegressor: {'num_leaves': 31, 'boosting_type': 'gbdt', 'subsample': 1, 'colsample_bytree': 0.7952797110155084, 'max_depth': 63, 'reg_alpha': 0, 'reg_lambda': 0, 'n_estimators': 376, 'learning_rate': 0.1, 'min_child_weight': 0.001}\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:21:55,284] [automlx.trials] Model Tuning completed. Took: 49.387 secs\n"
+      "[2025-04-25 03:38:10,029] [automlx.trials] Model Tuning completed. Took: 48.277 secs\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:22:07,814] [automlx.interface] Re-fitting pipeline\n"
+      "[2025-04-25 03:38:22,207] [automlx.interface] Re-fitting pipeline\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:22:07,827] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_168f9288-9\n"
+      "[2025-04-25 03:38:22,221] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_dff48243-0\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:22:09,947] [automlx.interface] AutoMLx completed.\n"
+      "[2025-04-25 03:38:24,366] [automlx.interface] AutoMLx completed.\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<automlx._interface.regressor.AutoRegressor at 0x14d008128580>"
+       "<automlx._interface.regressor.AutoRegressor at 0x14b5d3077610>"
       ]
      },
      "execution_count": 8,
@@ -2615,7 +2615,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bd3558a4",
+   "id": "15a6bd05",
    "metadata": {},
    "source": [
     "A model is then generated (`est1`) and can be used for prediction tasks. We use the `mean_squared_error` scoring metric to evaluate the performance of this model on unseen data (`X_test`)."
@@ -2624,13 +2624,13 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "6fe82816",
+   "id": "9dbeef9e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:22:11.183815Z",
-     "iopub.status.busy": "2024-12-26T14:22:11.183487Z",
-     "iopub.status.idle": "2024-12-26T14:22:12.471952Z",
-     "shell.execute_reply": "2024-12-26T14:22:12.471251Z"
+     "iopub.execute_input": "2025-04-25T10:38:25.795163Z",
+     "iopub.status.busy": "2025-04-25T10:38:25.794594Z",
+     "iopub.status.idle": "2025-04-25T10:38:27.832407Z",
+     "shell.execute_reply": "2025-04-25T10:38:27.831686Z"
     }
    },
    "outputs": [
@@ -2651,7 +2651,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "70b060ea",
+   "id": "ebfbb529",
    "metadata": {},
    "source": [
     "<a id='analyze'></a>\n",
@@ -2667,7 +2667,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5ce24b80",
+   "id": "5f6b14fc",
    "metadata": {},
    "source": [
     "AutoMLx provides a `print_summary` API to output all the different trials performed."
@@ -2676,13 +2676,13 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "2b6bac4f",
+   "id": "8fecb5f2",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:22:12.474342Z",
-     "iopub.status.busy": "2024-12-26T14:22:12.473966Z",
-     "iopub.status.idle": "2024-12-26T14:22:12.537289Z",
-     "shell.execute_reply": "2024-12-26T14:22:12.536690Z"
+     "iopub.execute_input": "2025-04-25T10:38:27.834684Z",
+     "iopub.status.busy": "2025-04-25T10:38:27.834170Z",
+     "iopub.status.idle": "2025-04-25T10:38:27.899003Z",
+     "shell.execute_reply": "2025-04-25T10:38:27.898460Z"
     }
    },
    "outputs": [
@@ -2727,7 +2727,7 @@
        "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'subsample': 1, 'colsample_bytree': 0.7952797110155084, 'max_depth': 63, 'reg_alpha': 0, 'reg_lambda': 0, 'n_estimators': 376, 'learning_rate': 0.1, 'min_child_weight': 0.001}</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>25.1.0</td>\n",
+       "      <td>25.2.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>3.9.21 (main, Dec 11 2024, 16:24:11) \\n[GCC 11.2.0]</td>\n",
@@ -2775,27 +2775,27 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <td>Model Selection</td>\n",
-       "      <td>{2: 4000, 1: 4000, 3: 4000, 4: 4000, 5: 4000, 7: 4000, 6: 4000, 8: 4000, 9: 4000, 10: 4000}</td>\n",
+       "      <td>{1: 4000, 2: 4000, 3: 4000, 4: 4000, 6: 4000, 5: 4000, 7: 4000, 8: 4000, 10: 4000, 9: 4000}</td>\n",
        "      <td>8</td>\n",
        "      <td>LGBMRegressor</td>\n",
        "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'subsample': 1, 'colsample_bytree': 1, 'max_depth': 63, 'reg_alpha': 0, 'reg_lambda': 0, 'n_estimators': 100, 'learning_rate': 0.1, 'min_child_weight': 0.001}</td>\n",
        "      <td>-0.249</td>\n",
        "      <td>{'neg_mean_squared_error': -0.2490324005700733}</td>\n",
-       "      <td>4.8061</td>\n",
-       "      <td>0.3090</td>\n",
-       "      <td>Thu Dec 26 06:20:41 2024</td>\n",
+       "      <td>4.8204</td>\n",
+       "      <td>0.3142</td>\n",
+       "      <td>Fri Apr 25 03:36:57 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Selection</td>\n",
-       "      <td>{1: 4000, 2: 4000, 3: 4000, 4: 4000, 6: 4000, 5: 4000, 7: 4000, 8: 4000, 9: 4000, 10: 4000}</td>\n",
+       "      <td>{1: 4000, 2: 4000, 3: 4000, 4: 4000, 6: 4000, 7: 4000, 5: 4000, 8: 4000, 10: 4000, 9: 4000}</td>\n",
        "      <td>8</td>\n",
        "      <td>XGBRegressor</td>\n",
        "      <td>{'n_estimators': 100, 'min_child_weight': 1, 'reg_alpha': 0, 'booster': 'gbtree', 'max_depth': 6, 'learning_rate': 0.1, 'reg_lambda': 1}</td>\n",
        "      <td>-0.2605</td>\n",
        "      <td>{'neg_mean_squared_error': -0.2604646831437227}</td>\n",
-       "      <td>5.7123</td>\n",
-       "      <td>0.3274</td>\n",
-       "      <td>Thu Dec 26 06:20:45 2024</td>\n",
+       "      <td>6.7072</td>\n",
+       "      <td>0.3330</td>\n",
+       "      <td>Fri Apr 25 03:37:02 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Selection</td>\n",
@@ -2804,58 +2804,58 @@
        "      <td>RandomForestRegressor</td>\n",
        "      <td>{'n_estimators': 100, 'min_samples_split': 0.0003, 'min_samples_leaf': 0.00015, 'max_features': 0.777777778}</td>\n",
        "      <td>-0.3021</td>\n",
-       "      <td>{'neg_mean_squared_error': -0.30213400269297647}</td>\n",
-       "      <td>13.5107</td>\n",
-       "      <td>0.3457</td>\n",
-       "      <td>Thu Dec 26 06:20:43 2024</td>\n",
+       "      <td>{'neg_mean_squared_error': -0.3021340026929764}</td>\n",
+       "      <td>12.1182</td>\n",
+       "      <td>0.3436</td>\n",
+       "      <td>Fri Apr 25 03:36:59 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Selection</td>\n",
-       "      <td>{1: 4000, 2: 4000, 3: 4000, 4: 4000, 5: 4000, 6: 4000, 7: 4000, 8: 4000, 9: 4000, 10: 4000}</td>\n",
+       "      <td>{1: 4000, 2: 4000, 4: 4000, 3: 4000, 6: 4000, 5: 4000, 7: 4000, 8: 4000, 9: 4000, 10: 4000}</td>\n",
        "      <td>8</td>\n",
        "      <td>ExtraTreesRegressor</td>\n",
        "      <td>{'n_estimators': 100, 'min_samples_split': 0.00125, 'min_samples_leaf': 0.000625, 'max_features': 0.777777778}</td>\n",
        "      <td>-0.3025</td>\n",
-       "      <td>{'neg_mean_squared_error': -0.30251850889218895}</td>\n",
-       "      <td>4.2041</td>\n",
-       "      <td>0.2884</td>\n",
-       "      <td>Thu Dec 26 06:20:40 2024</td>\n",
+       "      <td>{'neg_mean_squared_error': -0.3025185088921889}</td>\n",
+       "      <td>4.0168</td>\n",
+       "      <td>0.2891</td>\n",
+       "      <td>Fri Apr 25 03:36:57 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Selection</td>\n",
-       "      <td>{2: 4000, 1: 4000, 3: 4000, 4: 4000, 5: 4000, 6: 4000, 7: 4000, 8: 4000, 9: 4000, 10: 4000}</td>\n",
+       "      <td>{1: 4000, 2: 4000, 3: 4000, 5: 4000, 4: 4000, 6: 4000, 7: 4000, 8: 4000, 9: 4000, 10: 4000}</td>\n",
        "      <td>8</td>\n",
        "      <td>DecisionTreeRegressor</td>\n",
        "      <td>{'min_samples_split': 0.004, 'min_samples_leaf': 0.002, 'max_features': 1.0}</td>\n",
        "      <td>-0.4589</td>\n",
        "      <td>{'neg_mean_squared_error': -0.4589419999457891}</td>\n",
-       "      <td>1.7458</td>\n",
-       "      <td>0.2793</td>\n",
-       "      <td>Thu Dec 26 06:20:40 2024</td>\n",
+       "      <td>2.5777</td>\n",
+       "      <td>0.2825</td>\n",
+       "      <td>Fri Apr 25 03:36:56 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Selection</td>\n",
-       "      <td>{1: 4000, 2: 4000, 6: 4000, 3: 4000, 5: 4000, 4: 4000, 9: 4000, 8: 4000, 7: 4000, 10: 4000}</td>\n",
+       "      <td>{3: 4000, 2: 4000, 6: 4000, 1: 4000, 4: 4000, 5: 4000, 8: 4000, 7: 4000, 9: 4000, 10: 4000}</td>\n",
        "      <td>8</td>\n",
        "      <td>AdaBoostRegressor</td>\n",
        "      <td>{'learning_rate': 0.667, 'n_estimators': 50}</td>\n",
        "      <td>-0.6038</td>\n",
        "      <td>{'neg_mean_squared_error': -0.6038106242123971}</td>\n",
-       "      <td>6.3336</td>\n",
-       "      <td>0.2758</td>\n",
-       "      <td>Thu Dec 26 06:20:40 2024</td>\n",
+       "      <td>6.0882</td>\n",
+       "      <td>0.2775</td>\n",
+       "      <td>Fri Apr 25 03:36:56 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Selection</td>\n",
-       "      <td>{2: 4000, 1: 4000, 5: 4000, 3: 4000, 6: 4000, 4: 4000, 8: 4000, 7: 4000, 9: 4000, 10: 4000}</td>\n",
+       "      <td>{2: 4000, 1: 4000, 6: 4000, 4: 4000, 3: 4000, 5: 4000, 7: 4000, 9: 4000, 10: 4000, 8: 4000}</td>\n",
        "      <td>8</td>\n",
        "      <td>TorchMLPRegressor</td>\n",
        "      <td>{'optimizer_class': 'Adam', 'shuffle_dataset_each_epoch': True, 'optimizer_params': {}, 'criterion_class': None, 'criterion_params': {}, 'scheduler_class': None, 'scheduler_params': {}, 'batch_size': 128, 'lr': 0.001, 'epochs': 18, 'input_transform': 'auto', 'tensorboard_dir': None, 'use_tqdm': None, 'prediction_batch_size': 128, 'prediction_input_transform': 'auto', 'shuffling_buffer_size': None, 'depth': 4, 'num_logits': 1000, 'div_factor': 2, 'activation': 'ReLU', 'dropout': 0.1}</td>\n",
-       "      <td>-2.6877</td>\n",
-       "      <td>{'neg_mean_squared_error': -2.687676060398235}</td>\n",
-       "      <td>109.4393</td>\n",
-       "      <td>0.6019</td>\n",
-       "      <td>Thu Dec 26 06:20:54 2024</td>\n",
+       "      <td>-2.4536</td>\n",
+       "      <td>{'neg_mean_squared_error': -2.4536325523119644}</td>\n",
+       "      <td>114.0048</td>\n",
+       "      <td>0.6130</td>\n",
+       "      <td>Fri Apr 25 03:37:10 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Selection</td>\n",
@@ -2865,21 +2865,21 @@
        "      <td>{'C': 1.0}</td>\n",
        "      <td>-3.3478</td>\n",
        "      <td>{'neg_mean_squared_error': -3.347771732858513}</td>\n",
-       "      <td>1.7487</td>\n",
-       "      <td>0.3126</td>\n",
-       "      <td>Thu Dec 26 06:20:41 2024</td>\n",
+       "      <td>1.4416</td>\n",
+       "      <td>0.3151</td>\n",
+       "      <td>Fri Apr 25 03:36:58 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Selection</td>\n",
-       "      <td>{1: 4000, 2: 4000, 4: 4000, 3: 4000, 5: 4000, 6: 4000, 7: 4000, 8: 4000, 9: 4000, 10: 4000}</td>\n",
+       "      <td>{1: 4000, 2: 4000, 3: 4000, 4: 4000, 5: 4000, 6: 4000, 7: 4000, 8: 4000, 9: 4000, 10: 4000}</td>\n",
        "      <td>8</td>\n",
        "      <td>LinearRegression</td>\n",
        "      <td>{}</td>\n",
        "      <td>-3.5573</td>\n",
        "      <td>{'neg_mean_squared_error': -3.5573203151697577}</td>\n",
-       "      <td>0.7166</td>\n",
-       "      <td>0.3029</td>\n",
-       "      <td>Thu Dec 26 06:20:41 2024</td>\n",
+       "      <td>0.6169</td>\n",
+       "      <td>0.3101</td>\n",
+       "      <td>Fri Apr 25 03:36:57 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Adaptive Sampling</td>\n",
@@ -2889,9 +2889,9 @@
        "      <td>{'num_leaves': 31, 'boosting_type': 'gbdt', 'subsample': 1, 'colsample_bytree': 1, 'max_depth': 63, 'reg_alpha': 0, 'reg_lambda': 0, 'n_estimators': 100, 'learning_rate': 0.1, 'min_child_weight': 0.001}</td>\n",
        "      <td>-0.2183</td>\n",
        "      <td>{'neg_mean_squared_error': -0.21828611974711234}</td>\n",
-       "      <td>2.6583</td>\n",
-       "      <td>0.5968</td>\n",
-       "      <td>Thu Dec 26 06:20:57 2024</td>\n",
+       "      <td>2.7049</td>\n",
+       "      <td>0.6072</td>\n",
+       "      <td>Fri Apr 25 03:37:13 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>...</td>\n",
@@ -2907,27 +2907,27 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
-       "      <td>{1: 11558, 2: 11558, 4: 11559, 3: 11558, 5: 11559, 7: 11558, 6: 11558, 8: 11558, 9: 11559, 10: 11559}</td>\n",
+       "      <td>{1: 11558, 2: 11558, 3: 11558, 4: 11559, 5: 11559, 6: 11558, 7: 11558, 8: 11558, 9: 11559, 10: 11559}</td>\n",
        "      <td>6</td>\n",
        "      <td>LGBMRegressor</td>\n",
        "      <td>{'num_leaves': 7, 'boosting_type': 'gbdt', 'subsample': 0.4, 'colsample_bytree': 0.4, 'max_depth': 2, 'reg_alpha': 9.999999990000003e-07, 'reg_lambda': 1e-10, 'n_estimators': 5, 'learning_rate': 0.1, 'min_child_weight': 0.001}</td>\n",
        "      <td>-1.1075</td>\n",
        "      <td>{'neg_mean_squared_error': -1.1074975253749682}</td>\n",
-       "      <td>0.9261</td>\n",
-       "      <td>0.6105</td>\n",
-       "      <td>Thu Dec 26 06:21:24 2024</td>\n",
+       "      <td>0.9045</td>\n",
+       "      <td>0.6196</td>\n",
+       "      <td>Fri Apr 25 03:37:39 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
-       "      <td>{1: 11558, 2: 11558, 3: 11558, 4: 11559, 5: 11559, 6: 11558, 7: 11558, 8: 11558, 9: 11559, 10: 11559}</td>\n",
+       "      <td>{1: 11558, 2: 11558, 3: 11558, 4: 11559, 5: 11559, 6: 11558, 7: 11558, 9: 11559, 8: 11558, 10: 11559}</td>\n",
        "      <td>6</td>\n",
        "      <td>LGBMRegressor</td>\n",
        "      <td>{'num_leaves': 7, 'boosting_type': 'gbdt', 'subsample': 0.4, 'colsample_bytree': 0.4, 'max_depth': 2, 'reg_alpha': 1e-10, 'reg_lambda': 1.7784127779939314e-05, 'n_estimators': 5, 'learning_rate': 0.1, 'min_child_weight': 0.001}</td>\n",
        "      <td>-1.1075</td>\n",
        "      <td>{'neg_mean_squared_error': -1.1074975274208296}</td>\n",
-       "      <td>0.9028</td>\n",
-       "      <td>0.6108</td>\n",
-       "      <td>Thu Dec 26 06:21:25 2024</td>\n",
+       "      <td>0.8745</td>\n",
+       "      <td>0.6196</td>\n",
+       "      <td>Fri Apr 25 03:37:41 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
@@ -2937,45 +2937,45 @@
        "      <td>{'num_leaves': 7, 'boosting_type': 'gbdt', 'subsample': 0.4, 'colsample_bytree': 0.4, 'max_depth': 2, 'reg_alpha': 1e-10, 'reg_lambda': 1.8784127778939314e-05, 'n_estimators': 5, 'learning_rate': 0.1, 'min_child_weight': 0.001}</td>\n",
        "      <td>-1.1075</td>\n",
        "      <td>{'neg_mean_squared_error': -1.1074975275461456}</td>\n",
-       "      <td>0.8935</td>\n",
-       "      <td>0.6108</td>\n",
-       "      <td>Thu Dec 26 06:21:26 2024</td>\n",
+       "      <td>0.7852</td>\n",
+       "      <td>0.6196</td>\n",
+       "      <td>Fri Apr 25 03:37:41 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
-       "      <td>{1: 11558, 2: 11558, 3: 11558, 4: 11559, 6: 11558, 5: 11559, 7: 11558, 8: 11558, 9: 11559, 10: 11559}</td>\n",
+       "      <td>{1: 11558, 2: 11558, 3: 11558, 5: 11559, 4: 11559, 6: 11558, 7: 11558, 9: 11559, 8: 11558, 10: 11559}</td>\n",
        "      <td>6</td>\n",
        "      <td>LGBMRegressor</td>\n",
        "      <td>{'num_leaves': 7, 'boosting_type': 'gbdt', 'subsample': 0.4, 'colsample_bytree': 0.4, 'max_depth': 2, 'reg_alpha': 1.7784127779939314e-05, 'reg_lambda': 1e-10, 'n_estimators': 5, 'learning_rate': 0.1, 'min_child_weight': 0.001}</td>\n",
        "      <td>-1.1075</td>\n",
        "      <td>{'neg_mean_squared_error': -1.1074975277105636}</td>\n",
-       "      <td>0.9613</td>\n",
-       "      <td>0.6105</td>\n",
-       "      <td>Thu Dec 26 06:21:24 2024</td>\n",
+       "      <td>0.8380</td>\n",
+       "      <td>0.6196</td>\n",
+       "      <td>Fri Apr 25 03:37:40 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
-       "      <td>{1: 11558, 2: 11558, 3: 11558, 4: 11559, 5: 11559, 6: 11558, 8: 11558, 7: 11558, 9: 11559, 10: 11559}</td>\n",
+       "      <td>{1: 11558, 2: 11558, 3: 11558, 4: 11559, 5: 11559, 6: 11558, 7: 11558, 9: 11559, 8: 11558, 10: 11559}</td>\n",
        "      <td>6</td>\n",
        "      <td>LGBMRegressor</td>\n",
        "      <td>{'num_leaves': 7, 'boosting_type': 'gbdt', 'subsample': 0.4, 'colsample_bytree': 0.4, 'max_depth': 2, 'reg_alpha': 1.8784127778939314e-05, 'reg_lambda': 1e-10, 'n_estimators': 5, 'learning_rate': 0.1, 'min_child_weight': 0.001}</td>\n",
        "      <td>-1.1075</td>\n",
        "      <td>{'neg_mean_squared_error': -1.1074975278443453}</td>\n",
-       "      <td>0.8826</td>\n",
-       "      <td>0.6105</td>\n",
-       "      <td>Thu Dec 26 06:21:24 2024</td>\n",
+       "      <td>0.9636</td>\n",
+       "      <td>0.6196</td>\n",
+       "      <td>Fri Apr 25 03:37:40 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
-       "      <td>{1: 11558, 2: 11558, 3: 11558, 4: 11559, 5: 11559, 7: 11558, 6: 11558, 8: 11558, 9: 11559, 10: 11559}</td>\n",
+       "      <td>{1: 11558, 2: 11558, 3: 11558, 4: 11559, 5: 11559, 6: 11558, 7: 11558, 8: 11558, 9: 11559, 10: 11559}</td>\n",
        "      <td>6</td>\n",
        "      <td>LGBMRegressor</td>\n",
        "      <td>{'num_leaves': 7, 'boosting_type': 'gbdt', 'subsample': 0.4, 'colsample_bytree': 0.4, 'max_depth': 2, 'reg_alpha': 1e-10, 'reg_lambda': 0.005623553830557401, 'n_estimators': 5, 'learning_rate': 0.1, 'min_child_weight': 0.001}</td>\n",
        "      <td>-1.1075</td>\n",
        "      <td>{'neg_mean_squared_error': -1.107498217910651}</td>\n",
-       "      <td>0.9198</td>\n",
-       "      <td>0.6108</td>\n",
-       "      <td>Thu Dec 26 06:21:26 2024</td>\n",
+       "      <td>0.7935</td>\n",
+       "      <td>0.6196</td>\n",
+       "      <td>Fri Apr 25 03:37:41 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
@@ -2985,33 +2985,33 @@
        "      <td>{'num_leaves': 7, 'boosting_type': 'gbdt', 'subsample': 0.4, 'colsample_bytree': 0.4, 'max_depth': 2, 'reg_alpha': 1e-10, 'reg_lambda': 0.0056245538305564015, 'n_estimators': 5, 'learning_rate': 0.1, 'min_child_weight': 0.001}</td>\n",
        "      <td>-1.1075</td>\n",
        "      <td>{'neg_mean_squared_error': -1.1074982180406503}</td>\n",
-       "      <td>0.8972</td>\n",
-       "      <td>0.6108</td>\n",
-       "      <td>Thu Dec 26 06:21:26 2024</td>\n",
+       "      <td>0.8447</td>\n",
+       "      <td>0.6196</td>\n",
+       "      <td>Fri Apr 25 03:37:42 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
-       "      <td>{1: 11558, 2: 11558, 3: 11558, 4: 11559, 5: 11559, 6: 11558, 7: 11558, 8: 11558, 9: 11559, 10: 11559}</td>\n",
+       "      <td>{1: 11558, 2: 11558, 3: 11558, 4: 11559, 5: 11559, 7: 11558, 6: 11558, 9: 11559, 8: 11558, 10: 11559}</td>\n",
        "      <td>6</td>\n",
        "      <td>LGBMRegressor</td>\n",
        "      <td>{'num_leaves': 7, 'boosting_type': 'gbdt', 'subsample': 0.4, 'colsample_bytree': 0.4, 'max_depth': 2, 'reg_alpha': 0.005623553830557401, 'reg_lambda': 1e-10, 'n_estimators': 5, 'learning_rate': 0.1, 'min_child_weight': 0.001}</td>\n",
        "      <td>-1.1075</td>\n",
-       "      <td>{'neg_mean_squared_error': -1.1074982822779322}</td>\n",
-       "      <td>0.8164</td>\n",
-       "      <td>0.6108</td>\n",
-       "      <td>Thu Dec 26 06:21:25 2024</td>\n",
+       "      <td>{'neg_mean_squared_error': -1.1074982822779325}</td>\n",
+       "      <td>0.8752</td>\n",
+       "      <td>0.6196</td>\n",
+       "      <td>Fri Apr 25 03:37:40 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
-       "      <td>{1: 11558, 2: 11558, 3: 11558, 5: 11559, 4: 11559, 6: 11558, 8: 11558, 7: 11558, 9: 11559, 10: 11559}</td>\n",
+       "      <td>{2: 11558, 1: 11558, 3: 11558, 4: 11559, 5: 11559, 6: 11558, 7: 11558, 8: 11558, 9: 11559, 10: 11559}</td>\n",
        "      <td>6</td>\n",
        "      <td>LGBMRegressor</td>\n",
        "      <td>{'num_leaves': 7, 'boosting_type': 'gbdt', 'subsample': 0.4, 'colsample_bytree': 0.4, 'max_depth': 2, 'reg_alpha': 0.0056245538305564015, 'reg_lambda': 1e-10, 'n_estimators': 5, 'learning_rate': 0.1, 'min_child_weight': 0.001}</td>\n",
        "      <td>-1.1075</td>\n",
        "      <td>{'neg_mean_squared_error': -1.1074982824185997}</td>\n",
-       "      <td>0.9685</td>\n",
-       "      <td>0.6108</td>\n",
-       "      <td>Thu Dec 26 06:21:25 2024</td>\n",
+       "      <td>0.9160</td>\n",
+       "      <td>0.6196</td>\n",
+       "      <td>Fri Apr 25 03:37:40 2025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Model Tuning</td>\n",
@@ -3021,8 +3021,8 @@
        "      <td>None</td>\n",
        "      <td>-inf</td>\n",
        "      <td>None</td>\n",
-       "      <td>3.7785</td>\n",
-       "      <td>0.6110</td>\n",
+       "      <td>2.0835</td>\n",
+       "      <td>0.6214</td>\n",
        "      <td>-1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -3042,7 +3042,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6d1ac8be",
+   "id": "86f3295e",
    "metadata": {},
    "source": [
     "We also provide the capability to visualize the results of each stage of the AutoMLx pipeline."
@@ -3050,7 +3050,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "03b15551",
+   "id": "b6038067",
    "metadata": {},
    "source": [
     "<a id='algorithm-selection'></a>\n",
@@ -3062,19 +3062,19 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "a63104aa",
+   "id": "b276efc5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:22:12.539186Z",
-     "iopub.status.busy": "2024-12-26T14:22:12.538818Z",
-     "iopub.status.idle": "2024-12-26T14:22:12.809549Z",
-     "shell.execute_reply": "2024-12-26T14:22:12.809016Z"
+     "iopub.execute_input": "2025-04-25T10:38:27.900926Z",
+     "iopub.status.busy": "2025-04-25T10:38:27.900503Z",
+     "iopub.status.idle": "2025-04-25T10:38:28.181222Z",
+     "shell.execute_reply": "2025-04-25T10:38:28.180636Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x700 with 1 Axes>"
       ]
@@ -3116,7 +3116,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f5b78bd4",
+   "id": "cd3b0edc",
    "metadata": {},
    "source": [
     "<a id='adaptive-sampling'></a>\n",
@@ -3128,13 +3128,13 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "51983ffd",
+   "id": "36d33290",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:22:12.811384Z",
-     "iopub.status.busy": "2024-12-26T14:22:12.811041Z",
-     "iopub.status.idle": "2024-12-26T14:22:12.996923Z",
-     "shell.execute_reply": "2024-12-26T14:22:12.996293Z"
+     "iopub.execute_input": "2025-04-25T10:38:28.183336Z",
+     "iopub.status.busy": "2025-04-25T10:38:28.182745Z",
+     "iopub.status.idle": "2025-04-25T10:38:28.363795Z",
+     "shell.execute_reply": "2025-04-25T10:38:28.363194Z"
     }
    },
    "outputs": [
@@ -3171,7 +3171,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f853cad1",
+   "id": "abd78e62",
    "metadata": {},
    "source": [
     "<a id='feature-selection'></a>\n",
@@ -3183,13 +3183,13 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "b761d081",
+   "id": "23974d4e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:22:12.998830Z",
-     "iopub.status.busy": "2024-12-26T14:22:12.998440Z",
-     "iopub.status.idle": "2024-12-26T14:22:13.197510Z",
-     "shell.execute_reply": "2024-12-26T14:22:13.196910Z"
+     "iopub.execute_input": "2025-04-25T10:38:28.365783Z",
+     "iopub.status.busy": "2025-04-25T10:38:28.365275Z",
+     "iopub.status.idle": "2025-04-25T10:38:28.559586Z",
+     "shell.execute_reply": "2025-04-25T10:38:28.558999Z"
     }
    },
    "outputs": [
@@ -3240,7 +3240,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "063c24d0",
+   "id": "95fd3fff",
    "metadata": {},
    "source": [
     "<a id='hyperparameter-tuning'></a>\n",
@@ -3252,19 +3252,19 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "1f7f4c53",
+   "id": "d1a905d1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:22:13.199423Z",
-     "iopub.status.busy": "2024-12-26T14:22:13.199062Z",
-     "iopub.status.idle": "2024-12-26T14:22:13.439638Z",
-     "shell.execute_reply": "2024-12-26T14:22:13.439013Z"
+     "iopub.execute_input": "2025-04-25T10:38:28.561609Z",
+     "iopub.status.busy": "2025-04-25T10:38:28.561051Z",
+     "iopub.status.idle": "2025-04-25T10:38:28.804767Z",
+     "shell.execute_reply": "2025-04-25T10:38:28.804181Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 1000x700 with 1 Axes>"
       ]
@@ -3304,7 +3304,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1fdafc5e",
+   "id": "d7afa416",
    "metadata": {},
    "source": [
     "<a id='modellist'></a>\n",
@@ -3316,13 +3316,13 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "00737b0e",
+   "id": "d1d7f6c9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:22:13.441810Z",
-     "iopub.status.busy": "2024-12-26T14:22:13.441441Z",
-     "iopub.status.idle": "2024-12-26T14:22:13.493612Z",
-     "shell.execute_reply": "2024-12-26T14:22:13.493001Z"
+     "iopub.execute_input": "2025-04-25T10:38:28.806911Z",
+     "iopub.status.busy": "2025-04-25T10:38:28.806349Z",
+     "iopub.status.idle": "2025-04-25T10:38:28.857856Z",
+     "shell.execute_reply": "2025-04-25T10:38:28.857307Z"
     }
    },
    "outputs": [],
@@ -3354,7 +3354,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "add99067",
+   "id": "d4681aa4",
    "metadata": {},
    "source": [
     "<a id='custom-validation'></a>\n",
@@ -3365,13 +3365,13 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "544d5f02",
+   "id": "bc5e4df4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:22:13.495644Z",
-     "iopub.status.busy": "2024-12-26T14:22:13.495275Z",
-     "iopub.status.idle": "2024-12-26T14:22:13.545345Z",
-     "shell.execute_reply": "2024-12-26T14:22:13.544735Z"
+     "iopub.execute_input": "2025-04-25T10:38:28.859917Z",
+     "iopub.status.busy": "2025-04-25T10:38:28.859468Z",
+     "iopub.status.idle": "2025-04-25T10:38:28.908722Z",
+     "shell.execute_reply": "2025-04-25T10:38:28.908169Z"
     }
    },
    "outputs": [],
@@ -3381,7 +3381,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "719bdaae",
+   "id": "a76698df",
    "metadata": {},
    "source": [
     "A few of the advanced settings can be passed directly to the pipeline's fit method, instead of its constructor."
@@ -3390,13 +3390,13 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "025e4538",
+   "id": "684828ce",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:22:13.547174Z",
-     "iopub.status.busy": "2024-12-26T14:22:13.546816Z",
-     "iopub.status.idle": "2024-12-26T14:22:21.767533Z",
-     "shell.execute_reply": "2024-12-26T14:22:21.766750Z"
+     "iopub.execute_input": "2025-04-25T10:38:28.910709Z",
+     "iopub.status.busy": "2025-04-25T10:38:28.910269Z",
+     "iopub.status.idle": "2025-04-25T10:38:36.826319Z",
+     "shell.execute_reply": "2025-04-25T10:38:36.825627Z"
     }
    },
    "outputs": [
@@ -3404,119 +3404,119 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:22:13,641] [automlx.interface] Dataset shape: (14448,8)\n"
+      "[2025-04-25 03:38:29,002] [automlx.interface] Dataset shape: (14448,8)\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:22:13,693] [automlx.interface] Adaptive Sampling disabled.\n"
+      "[2025-04-25 03:38:29,056] [automlx.interface] Adaptive Sampling disabled.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:22:13,733] [automlx.data_transform] Running preprocessing. Number of features: 9\n"
+      "[2025-04-25 03:38:29,095] [automlx.data_transform] Running preprocessing. Number of features: 9\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:22:13,881] [automlx.data_transform] Preprocessing completed. Took 0.148 secs\n"
+      "[2025-04-25 03:38:29,243] [automlx.data_transform] Preprocessing completed. Took 0.148 secs\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:22:13,913] [automlx.process] Running Model Generation\n"
+      "[2025-04-25 03:38:29,269] [automlx.process] Running Model Generation\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:22:13,963] [automlx.process] Model Generation completed.\n"
+      "[2025-04-25 03:38:29,318] [automlx.process] Model Generation completed.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:22:13,994] [automlx.model_selection] Running Model Selection\n"
+      "[2025-04-25 03:38:29,353] [automlx.model_selection] Running Model Selection\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:22:15,353] [automlx.model_selection] Model Selection completed - Took 1.359 sec - Selected models: [['XGBRegressor', 'AdaBoostRegressor']]\n"
+      "[2025-04-25 03:38:30,704] [automlx.model_selection] Model Selection completed - Took 1.352 sec - Selected models: [['XGBRegressor', 'AdaBoostRegressor']]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:22:15,431] [automlx.trials] Running Model Tuning for ['XGBRegressor']\n"
+      "[2025-04-25 03:38:30,781] [automlx.trials] Running Model Tuning for ['XGBRegressor']\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:22:17,804] [automlx.trials] Best parameters for XGBRegressor: {'n_estimators': 100, 'min_child_weight': 1, 'reg_alpha': 0, 'booster': 'dart', 'max_depth': 6, 'learning_rate': 0.1, 'reg_lambda': 1}\n"
+      "[2025-04-25 03:38:33,261] [automlx.trials] Best parameters for XGBRegressor: {'n_estimators': 100, 'min_child_weight': 1, 'reg_alpha': 0, 'booster': 'dart', 'max_depth': 6, 'learning_rate': 0.1, 'reg_lambda': 1}\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:22:17,805] [automlx.trials] Model Tuning completed. Took: 2.374 secs\n"
+      "[2025-04-25 03:38:33,262] [automlx.trials] Model Tuning completed. Took: 2.482 secs\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:22:17,908] [automlx.trials] Running Model Tuning for ['AdaBoostRegressor']\n"
+      "[2025-04-25 03:38:33,308] [automlx.trials] Running Model Tuning for ['AdaBoostRegressor']\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:22:19,021] [automlx.trials] Best parameters for AdaBoostRegressor: {'learning_rate': 0.000996552734375, 'n_estimators': 10}\n"
+      "[2025-04-25 03:38:34,419] [automlx.trials] Best parameters for AdaBoostRegressor: {'learning_rate': 0.000996552734375, 'n_estimators': 10}\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:22:19,022] [automlx.trials] Model Tuning completed. Took: 1.114 secs\n"
+      "[2025-04-25 03:38:34,420] [automlx.trials] Model Tuning completed. Took: 1.112 secs\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:22:19,211] [automlx.interface] Re-fitting pipeline\n"
+      "[2025-04-25 03:38:34,588] [automlx.interface] Re-fitting pipeline\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:22:19,231] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_ea8cde56-6\n"
+      "[2025-04-25 03:38:34,606] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_f9b0e6c7-1\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2024-12-26 06:22:21,408] [automlx.interface] AutoMLx completed.\n"
+      "[2025-04-25 03:38:36,476] [automlx.interface] AutoMLx completed.\n"
      ]
     },
     {
@@ -3548,7 +3548,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a9d9e981",
+   "id": "9b4937da",
    "metadata": {},
    "source": [
     "<a id='MLX'></a>\n",
@@ -3557,7 +3557,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c9e4bd0f",
+   "id": "2c4d5e6e",
    "metadata": {},
    "source": [
     "For a variety of decision-making tasks, getting only a prediction as model output is not sufficient. A user may wish to know why the model outputs that prediction, or which data features are relevant for that prediction. For that purpose the Oracle AutoMLx solution defines the `MLExplainer` object, which allows to compute multiple types of model explanations.\n",
@@ -3571,13 +3571,13 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "da72e7a2",
+   "id": "54a92527",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:22:21.770236Z",
-     "iopub.status.busy": "2024-12-26T14:22:21.769744Z",
-     "iopub.status.idle": "2024-12-26T14:22:22.045536Z",
-     "shell.execute_reply": "2024-12-26T14:22:22.044542Z"
+     "iopub.execute_input": "2025-04-25T10:38:36.828608Z",
+     "iopub.status.busy": "2025-04-25T10:38:36.828142Z",
+     "iopub.status.idle": "2025-04-25T10:38:37.099040Z",
+     "shell.execute_reply": "2025-04-25T10:38:37.098403Z"
     }
    },
    "outputs": [],
@@ -3590,7 +3590,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "211bb526",
+   "id": "81788980",
    "metadata": {},
    "source": [
     "<a id='global'></a>\n",
@@ -3599,7 +3599,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "563afc38",
+   "id": "d4383e76",
    "metadata": {},
    "source": [
     "The notion of Global Feature Importance intuitively measures how much the model's performance (relative to the provided train labels) would change if a given feature were dropped from the dataset, <b><i>without</i></b> allowing the model to be retrained. This notion of feature importance considers each feature independently from all other features."
@@ -3607,7 +3607,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "79da670e",
+   "id": "aa07f57c",
    "metadata": {},
    "source": [
     "#### Compute the importance"
@@ -3615,7 +3615,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ffaf09bf",
+   "id": "47956229",
    "metadata": {},
    "source": [
     "By default we use a permutation method to successively measure the importance of each feature. Such a method therefore runs in linear time with respect to the\n",
@@ -3627,13 +3627,13 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "e0555dcb",
+   "id": "eef5ffdb",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:22:22.049641Z",
-     "iopub.status.busy": "2024-12-26T14:22:22.047980Z",
-     "iopub.status.idle": "2024-12-26T14:22:23.050901Z",
-     "shell.execute_reply": "2024-12-26T14:22:23.050274Z"
+     "iopub.execute_input": "2025-04-25T10:38:37.102276Z",
+     "iopub.status.busy": "2025-04-25T10:38:37.101106Z",
+     "iopub.status.idle": "2025-04-25T10:38:38.102890Z",
+     "shell.execute_reply": "2025-04-25T10:38:38.102297Z"
     }
    },
    "outputs": [],
@@ -3648,7 +3648,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "007c6a50",
+   "id": "a7b63010",
    "metadata": {},
    "source": [
     "#### Visualization"
@@ -3656,7 +3656,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b2f6d3e6",
+   "id": "25c011d1",
    "metadata": {},
    "source": [
     "There are two options to show the explanation's results:\n",
@@ -3669,13 +3669,13 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "0ce05f60",
+   "id": "93adbe6d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:22:23.053534Z",
-     "iopub.status.busy": "2024-12-26T14:22:23.053051Z",
-     "iopub.status.idle": "2024-12-26T14:22:23.113434Z",
-     "shell.execute_reply": "2024-12-26T14:22:23.112949Z"
+     "iopub.execute_input": "2025-04-25T10:38:38.105341Z",
+     "iopub.status.busy": "2025-04-25T10:38:38.104893Z",
+     "iopub.status.idle": "2025-04-25T10:38:38.163721Z",
+     "shell.execute_reply": "2025-04-25T10:38:38.163234Z"
     }
    },
    "outputs": [
@@ -3710,44 +3710,44 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>Latitude</td>\n",
-       "      <td>1.255858</td>\n",
-       "      <td>1.215503</td>\n",
-       "      <td>1.296213</td>\n",
+       "      <td>1.254504</td>\n",
+       "      <td>1.231705</td>\n",
+       "      <td>1.277303</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>Longitude</td>\n",
-       "      <td>1.068550</td>\n",
-       "      <td>1.054161</td>\n",
-       "      <td>1.082938</td>\n",
+       "      <td>1.074243</td>\n",
+       "      <td>1.027264</td>\n",
+       "      <td>1.121221</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>MedInc</td>\n",
-       "      <td>0.377138</td>\n",
-       "      <td>0.353964</td>\n",
-       "      <td>0.400313</td>\n",
+       "      <td>0.370338</td>\n",
+       "      <td>0.353111</td>\n",
+       "      <td>0.387564</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>AveOccup</td>\n",
-       "      <td>0.151267</td>\n",
-       "      <td>0.145928</td>\n",
-       "      <td>0.156607</td>\n",
+       "      <td>0.151174</td>\n",
+       "      <td>0.142189</td>\n",
+       "      <td>0.160159</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>AveRooms</td>\n",
-       "      <td>0.116828</td>\n",
-       "      <td>0.112639</td>\n",
-       "      <td>0.121016</td>\n",
+       "      <td>0.116548</td>\n",
+       "      <td>0.114087</td>\n",
+       "      <td>0.119010</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>HouseAge</td>\n",
-       "      <td>0.063427</td>\n",
-       "      <td>0.058393</td>\n",
-       "      <td>0.068462</td>\n",
+       "      <td>0.064133</td>\n",
+       "      <td>0.060462</td>\n",
+       "      <td>0.067804</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -3755,12 +3755,12 @@
       ],
       "text/plain": [
        "     Feature  Attribution  Lower Bound  Upper Bound\n",
-       "0   Latitude     1.255858     1.215503     1.296213\n",
-       "1  Longitude     1.068550     1.054161     1.082938\n",
-       "2     MedInc     0.377138     0.353964     0.400313\n",
-       "3   AveOccup     0.151267     0.145928     0.156607\n",
-       "4   AveRooms     0.116828     0.112639     0.121016\n",
-       "5   HouseAge     0.063427     0.058393     0.068462"
+       "0   Latitude     1.254504     1.231705     1.277303\n",
+       "1  Longitude     1.074243     1.027264     1.121221\n",
+       "2     MedInc     0.370338     0.353111     0.387564\n",
+       "3   AveOccup     0.151174     0.142189     0.160159\n",
+       "4   AveRooms     0.116548     0.114087     0.119010\n",
+       "5   HouseAge     0.064133     0.060462     0.067804"
       ]
      },
      "execution_count": 20,
@@ -3775,13 +3775,13 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "3673fb4a",
+   "id": "4de55053",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:22:23.115381Z",
-     "iopub.status.busy": "2024-12-26T14:22:23.114858Z",
-     "iopub.status.idle": "2024-12-26T14:22:23.254087Z",
-     "shell.execute_reply": "2024-12-26T14:22:23.253598Z"
+     "iopub.execute_input": "2025-04-25T10:38:38.165656Z",
+     "iopub.status.busy": "2025-04-25T10:38:38.165212Z",
+     "iopub.status.idle": "2025-04-25T10:38:38.303814Z",
+     "shell.execute_reply": "2025-04-25T10:38:38.303323Z"
     }
    },
    "outputs": [
@@ -3795,20 +3795,20 @@
         {
          "error_x": {
           "array": [
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-           0.014388707496279629,
-           0.02317456551798991,
-           0.005339556194352113,
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-           0.005034440112905728
+           0.02279923901816372,
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           "arrayminus": [
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+           0.02279923901816372,
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+           0.017226423228137944,
+           0.008984958379256669,
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           "type": "data"
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@@ -3823,12 +3823,12 @@
          "type": "bar",
          "width": 0.3,
          "x": [
-          1.2558578115854446,
-          1.068549722517505,
-          0.3771382702637779,
-          0.15126718058130353,
-          0.11682775420042171,
-          0.06342737330286492
+          1.2545042273040707,
+          1.0742425873288592,
+          0.3703378500230231,
+          0.15117437788995763,
+          0.11654810373051364,
+          0.06413275511008436
          ],
          "y": [
           "Latitude",
@@ -4698,9 +4698,9 @@
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       "text/html": [
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        "                            \n",
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@@ -6064,7 +6064,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "eeda2f33",
+   "id": "b4086447",
    "metadata": {},
    "source": [
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@@ -6075,13 +6075,13 @@
   {
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-     "shell.execute_reply": "2024-12-26T14:22:26.612272Z"
+     "iopub.execute_input": "2025-04-25T10:38:41.977386Z",
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+     "iopub.status.idle": "2025-04-25T10:38:42.194715Z",
+     "shell.execute_reply": "2025-04-25T10:38:42.194187Z"
     }
    },
    "outputs": [
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        "                            \n",
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@@ -10285,7 +10285,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "63b03a84",
+   "id": "920f74bd",
    "metadata": {},
    "source": [
     "We can also plot the PDP for multiple features. The plot below is the PDP for the `Latitude` and `Longitude` features. The X-axis still shows the values of `Latitude`, while there is a different curve and confidence interval for each value of the feature `Longitude`.\n",
@@ -10296,13 +10296,13 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "6f7a53f6",
+   "id": "9cb48b7b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:22:26.615350Z",
-     "iopub.status.busy": "2024-12-26T14:22:26.614796Z",
-     "iopub.status.idle": "2024-12-26T14:22:31.609641Z",
-     "shell.execute_reply": "2024-12-26T14:22:31.608744Z"
+     "iopub.execute_input": "2025-04-25T10:38:42.196483Z",
+     "iopub.status.busy": "2025-04-25T10:38:42.196208Z",
+     "iopub.status.idle": "2025-04-25T10:38:50.721687Z",
+     "shell.execute_reply": "2025-04-25T10:38:50.721153Z"
     }
    },
    "outputs": [
@@ -13109,9 +13109,9 @@
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@@ -28971,7 +28971,7 @@
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-      "[2024-12-26 06:22:42,771] [automlx.mlx] AutoMLx got an unexpected keyword argument 'tabulator_type', which is not a configurable attribute of any of ['TabularLocalSurrogateExplainer', 'RandomSampleGeneration', 'DistanceWeighting', 'SurrogateHandler'].\n",
+      "[2025-04-25 03:39:01,261] [automlx.mlx] AutoMLx got an unexpected keyword argument 'tabulator_type', which is not a configurable attribute of any of ['TabularLocalSurrogateExplainer', 'RandomSampleGeneration', 'DistanceWeighting', 'SurrogateHandler'].\n",
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+     "shell.execute_reply": "2025-04-25T10:39:01.502485Z"
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@@ -31285,7 +31285,7 @@
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        "                            \n",
-       "var gd = document.getElementById('ad4f584f-909a-4d02-8a3a-13885d0f0ed2');\n",
+       "var gd = document.getElementById('b2a76ac7-0eee-474d-9791-bcbfa35e153a');\n",
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@@ -32382,7 +32382,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3f4360f0",
+   "id": "412bea11",
    "metadata": {},
    "source": [
     "<a id='MLX-advanced-ale'></a>\n",
@@ -32395,7 +32395,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "81ecc9c7",
+   "id": "02c20e68",
    "metadata": {},
    "source": [
     "We can  compute ALEs for two features (At least one is numerical). The plot below is the ALE plot for the `Latitude` and `Longitude` features. The X-axis still shows the values of `Latitude`, while there is multiple lines, one for each value of the feature `Longitude`.\n",
@@ -32406,13 +32406,13 @@
   {
    "cell_type": "code",
    "execution_count": 36,
-   "id": "518ab806",
+   "id": "0fdd7e12",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:22:43.005095Z",
-     "iopub.status.busy": "2024-12-26T14:22:43.004522Z",
-     "iopub.status.idle": "2024-12-26T14:22:43.651037Z",
-     "shell.execute_reply": "2024-12-26T14:22:43.650450Z"
+     "iopub.execute_input": "2025-04-25T10:39:01.505209Z",
+     "iopub.status.busy": "2025-04-25T10:39:01.504795Z",
+     "iopub.status.idle": "2025-04-25T10:39:02.126599Z",
+     "shell.execute_reply": "2025-04-25T10:39:02.126040Z"
     }
    },
    "outputs": [
@@ -35219,9 +35219,9 @@
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       },
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@@ -37095,14 +36823,38 @@
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+       "description": "Show all features",
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@@ -37246,7 +36993,25 @@
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-       "value": "<style type=\"text/css\">\n#T_54395 thead th {\n  border-bottom: 1px solid #DDD;\n  background-color: #F5F5F5;\n  min-width: 20px;\n  padding: 0 10px;\n  text-align: right;\n}\n#T_54395 tbody th {\n  border-bottom: 1px solid #DDD;\n  background-color: #F5F5F5;\n  min-width: 115px;\n  padding: 0 10px;\n  text-align: right;\n}\n#T_54395 td {\n  text-align: right;\n  padding: 0 10px;\n}\n#T_54395 tr:nth-child(even) {\n  background-color: #F9F9F9;\n}\n#T_54395  {\n  border-collapse: collapse;\n  border: 1px solid #DDD;\n}\n</style>\n<table id=\"T_54395\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_54395_level0_col0\" class=\"col_heading level0 col0\" >MedInc</th>\n      <th id=\"T_54395_level0_col1\" class=\"col_heading level0 col1\" >HouseAge</th>\n      <th id=\"T_54395_level0_col2\" class=\"col_heading level0 col2\" >AveRooms</th>\n      <th id=\"T_54395_level0_col3\" class=\"col_heading level0 col3\" >AveBedrms</th>\n      <th id=\"T_54395_level0_col4\" class=\"col_heading level0 col4\" >Population</th>\n      <th id=\"T_54395_level0_col5\" class=\"col_heading level0 col5\" >AveOccup</th>\n      <th id=\"T_54395_level0_col6\" class=\"col_heading level0 col6\" >Latitude</th>\n      <th id=\"T_54395_level0_col7\" class=\"col_heading level0 col7\" >Longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_54395_level0_row0\" class=\"row_heading level0 row0\" >Original Sample</th>\n      <td id=\"T_54395_row0_col0\" class=\"data row0 col0\" >2.0278</td>\n      <td id=\"T_54395_row0_col1\" class=\"data row0 col1\" >31.0</td>\n      <td id=\"T_54395_row0_col2\" class=\"data row0 col2\" >2.8469284994964754</td>\n      <td id=\"T_54395_row0_col3\" class=\"data row0 col3\" >1.0916414904330312</td>\n      <td id=\"T_54395_row0_col4\" class=\"data row0 col4\" >3107.0</td>\n      <td id=\"T_54395_row0_col5\" class=\"data row0 col5\" >3.1289023162134946</td>\n      <td id=\"T_54395_row0_col6\" class=\"data row0 col6\" >34.06</td>\n      <td id=\"T_54395_row0_col7\" class=\"data row0 col7\" >-118.31</td>\n    </tr>\n    <tr>\n      <th id=\"T_54395_level0_row1\" class=\"row_heading level0 row1\" >Modified Sample</th>\n      <td id=\"T_54395_row1_col0\" class=\"data row1 col0\" >2.0278</td>\n      <td id=\"T_54395_row1_col1\" class=\"data row1 col1\" >31.0</td>\n      <td id=\"T_54395_row1_col2\" class=\"data row1 col2\" >2.8469284994964754</td>\n      <td id=\"T_54395_row1_col3\" class=\"data row1 col3\" >1.0916414904330312</td>\n      <td id=\"T_54395_row1_col4\" class=\"data row1 col4\" >3107.0</td>\n      <td id=\"T_54395_row1_col5\" class=\"data row1 col5\" >3.1289023162134946</td>\n      <td id=\"T_54395_row1_col6\" class=\"data row1 col6\" >34.06</td>\n      <td id=\"T_54395_row1_col7\" class=\"data row1 col7\" >-118.31</td>\n    </tr>\n  </tbody>\n</table>\n"
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          "showlegend": false,
          "type": "scatter",
-         "uid": "2f4c748f-ef30-4673-b45a-77d5f9444b3f",
+         "uid": "f7e842b5-5ce3-4fa5-9509-65f09a996afc",
          "x": {
           "dtype": "float64",
           "shape": [
@@ -40626,305 +41219,30 @@
        "tooltip": null
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      },
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+     "c65653ff036042b4a186e911610e2455": {
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diff --git a/demos/OracleAutoMLx_train_model.html b/demos/OracleAutoMLx_train_model.html
index 7ca0c78..007f56a 100644
--- a/demos/OracleAutoMLx_train_model.html
+++ b/demos/OracleAutoMLx_train_model.html
@@ -14595,7 +14595,7 @@ <h1><font color=red>Building and Evaluating a Machine Learning Model using AutoM
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
 </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
 <p>AutoMLx Demo Notebook.</p>
-<p>Copyright © 2024, Oracle and/or its affiliates.</p>
+<p>Copyright © 2025, Oracle and/or its affiliates.</p>
 <p>Licensed under the Universal Permissive License v 1.0 as shown at <a href="https://oss.oracle.com/licenses/upl/">https://oss.oracle.com/licenses/upl/</a></p>
 
 </div>
@@ -14664,7 +14664,7 @@ <h2 id="Train-a-Model-using-AutoMLx">Train a Model using AutoMLx<a class="anchor
 <div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[1]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">automlx</span> <span class="kn">import</span> <span class="n">train_model</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">automlx</span><span class="w"> </span><span class="kn">import</span> <span class="n">train_model</span>
 
 <span class="n">model</span> <span class="o">=</span> <span class="n">train_model</span><span class="p">(</span>
     <span class="n">data</span> <span class="o">=</span> <span class="s2">&quot;classification_train.csv&quot;</span><span class="p">,</span>  <span class="c1"># path to dataset CSV file or a pandas DataFrame</span>
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 <div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
-<pre><span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Info] Number of positive: 116, number of negative: 396
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000045 seconds.
-<span class="ansi-cyan-fg">(run pid=31671)</span> You can set `force_col_wise=true` to remove the overhead.
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Info] Total Bins 55
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Info] Number of data points in the train set: 512, number of used features: 4
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.773437 -&gt; initscore=1.227824
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Info] Start training from score 1.227824
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
-<span class="ansi-cyan-fg">(run pid=31671)</span> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
+<pre><span class="ansi-cyan-fg">(run pid=2697274)</span> [LightGBM] [Info] Number of positive: 116, number of negative: 396
+<span class="ansi-cyan-fg">(run pid=2697274)</span> [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000050 seconds.
+<span class="ansi-cyan-fg">(run pid=2697274)</span> You can set `force_col_wise=true` to remove the overhead.
+<span class="ansi-cyan-fg">(run pid=2697274)</span> [LightGBM] [Info] Total Bins 55
+<span class="ansi-cyan-fg">(run pid=2697274)</span> [LightGBM] [Info] Number of data points in the train set: 512, number of used features: 4
+<span class="ansi-cyan-fg">(run pid=2697274)</span> [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.773437 -&gt; initscore=1.227824
+<span class="ansi-cyan-fg">(run pid=2697274)</span> [LightGBM] [Info] Start training from score 1.227824
 </pre>
 </div>
 </div>
@@ -14892,7 +14792,7 @@ <h2 id="Inspect-the-Model's-Quality">Inspect the Model's Quality<a class="anchor
       <th>neg_log_loss</th>
     </tr>
     <tr>
-      <th>Evaluated on 2024-12-26</th>
+      <th>Evaluated on 2025-04-25</th>
       <th></th>
     </tr>
   </thead>
@@ -15148,7 +15048,7 @@ <h2 id="Evaluate-the-Quality-of-a-Model-on-a-New-Dataset">Evaluate the Quality o
 <div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[5]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">automlx</span> <span class="kn">import</span> <span class="n">evaluate_model_quality</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">automlx</span><span class="w"> </span><span class="kn">import</span> <span class="n">evaluate_model_quality</span>
 
 <span class="n">score</span> <span class="o">=</span> <span class="n">evaluate_model_quality</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="s2">&quot;classification_test.csv&quot;</span><span class="p">)</span>
 
@@ -15277,7 +15177,7 @@ <h2 id="Load-a-model">Load a model<a class="anchor-link" href="#Load-a-model">&#
 <div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[7]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">automlx</span> <span class="kn">import</span> <span class="n">load_model</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">automlx</span><span class="w"> </span><span class="kn">import</span> <span class="n">load_model</span>
 
 <span class="n">loaded_model</span> <span class="o">=</span> <span class="n">load_model</span><span class="p">(</span><span class="s1">&#39;model.amlx&#39;</span><span class="p">)</span>
 
@@ -15325,7 +15225,7 @@ <h2 id="Load-a-model">Load a model<a class="anchor-link" href="#Load-a-model">&#
       <th>neg_log_loss</th>
     </tr>
     <tr>
-      <th>Evaluated on 2024-12-26</th>
+      <th>Evaluated on 2025-04-25</th>
       <th></th>
     </tr>
   </thead>
@@ -15382,7 +15282,7 @@ <h2 id="Train-a-Regression-Model-using-AutoMLx">Train a Regression Model using A
 <div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[8]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">automlx</span> <span class="kn">import</span> <span class="n">Task</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">automlx</span><span class="w"> </span><span class="kn">import</span> <span class="n">Task</span>
 
 <span class="n">model_regression</span> <span class="o">=</span> <span class="n">train_model</span><span class="p">(</span>
     <span class="n">data</span> <span class="o">=</span> <span class="s2">&quot;regression_train.csv&quot;</span><span class="p">,</span>
@@ -15451,7 +15351,7 @@ <h2 id="Train-a-Regression-Model-using-AutoMLx">Train a Regression Model using A
       <th>neg_mean_squared_error</th>
     </tr>
     <tr>
-      <th>Evaluated on 2024-12-26</th>
+      <th>Evaluated on 2025-04-25</th>
       <th></th>
     </tr>
   </thead>
diff --git a/demos/OracleAutoMLx_train_model.ipynb b/demos/OracleAutoMLx_train_model.ipynb
index a028ec0..4c1ee13 100644
--- a/demos/OracleAutoMLx_train_model.ipynb
+++ b/demos/OracleAutoMLx_train_model.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "88c154e0",
+   "id": "4531659d",
    "metadata": {},
    "source": [
     "***\n",
@@ -14,19 +14,19 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cccb4f86",
+   "id": "a533d5ef",
    "metadata": {},
    "source": [
     "AutoMLx Demo Notebook.\n",
     "\n",
-    "Copyright © 2024, Oracle and/or its affiliates.\n",
+    "Copyright © 2025, Oracle and/or its affiliates.\n",
     "\n",
     "Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "b60f51c4",
+   "id": "d5a80c14",
    "metadata": {},
    "source": [
     "## Overview of this Notebook\n",
@@ -64,7 +64,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "073b3a38",
+   "id": "332cdfe2",
    "metadata": {},
    "source": [
     "<a id='train'></a>\n",
@@ -84,14 +84,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "104d6600",
+   "execution_count": null,
+   "id": "da1ed94b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:22:51.656164Z",
-     "iopub.status.busy": "2024-12-26T14:22:51.655988Z",
-     "iopub.status.idle": "2024-12-26T14:23:35.323486Z",
-     "shell.execute_reply": "2024-12-26T14:23:35.322909Z"
+     "iopub.execute_input": "2025-04-25T10:39:10.190935Z",
+     "iopub.status.busy": "2025-04-25T10:39:10.190600Z",
+     "iopub.status.idle": "2025-04-25T10:39:53.778220Z",
+     "shell.execute_reply": "2025-04-25T10:39:53.777643Z"
     }
    },
    "outputs": [
@@ -99,113 +99,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Info] Number of positive: 116, number of negative: 396\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000045 seconds.\n",
-      "\u001b[36m(run pid=31671)\u001b[0m You can set `force_col_wise=true` to remove the overhead.\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Info] Total Bins 55\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Info] Number of data points in the train set: 512, number of used features: 4\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.773437 -> initscore=1.227824\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Info] Start training from score 1.227824\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
-      "\u001b[36m(run pid=31671)\u001b[0m [LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n"
+      "\u001b[36m(run pid=2697274)\u001b[0m [LightGBM] [Info] Number of positive: 116, number of negative: 396\n",
+      "\u001b[36m(run pid=2697274)\u001b[0m [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000050 seconds.\n",
+      "\u001b[36m(run pid=2697274)\u001b[0m You can set `force_col_wise=true` to remove the overhead.\n",
+      "\u001b[36m(run pid=2697274)\u001b[0m [LightGBM] [Info] Total Bins 55\n",
+      "\u001b[36m(run pid=2697274)\u001b[0m [LightGBM] [Info] Number of data points in the train set: 512, number of used features: 4\n",
+      "\u001b[36m(run pid=2697274)\u001b[0m [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.773437 -> initscore=1.227824\n",
+      "\u001b[36m(run pid=2697274)\u001b[0m [LightGBM] [Info] Start training from score 1.227824\n"
      ]
     }
    ],
@@ -221,7 +121,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c109147d",
+   "id": "dab02e32",
    "metadata": {},
    "source": [
     "That's it! The model is fully trained and ready to be used to make predictions or to be deployed."
@@ -229,7 +129,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8469a7b6",
+   "id": "f7cfc1a8",
    "metadata": {},
    "source": [
     "<a id='quality'></a>\n",
@@ -249,13 +149,13 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "f8c3e9ff",
+   "id": "3e3e604b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:23:35.326524Z",
-     "iopub.status.busy": "2024-12-26T14:23:35.325613Z",
-     "iopub.status.idle": "2024-12-26T14:23:35.334264Z",
-     "shell.execute_reply": "2024-12-26T14:23:35.333812Z"
+     "iopub.execute_input": "2025-04-25T10:39:53.780979Z",
+     "iopub.status.busy": "2025-04-25T10:39:53.780185Z",
+     "iopub.status.idle": "2025-04-25T10:39:53.788682Z",
+     "shell.execute_reply": "2025-04-25T10:39:53.788188Z"
     }
    },
    "outputs": [
@@ -283,7 +183,7 @@
        "      <th>neg_log_loss</th>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Evaluated on 2024-12-26</th>\n",
+       "      <th>Evaluated on 2025-04-25</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
@@ -302,7 +202,7 @@
       ],
       "text/plain": [
        "                                   neg_log_loss\n",
-       "Evaluated on 2024-12-26                        \n",
+       "Evaluated on 2025-04-25                        \n",
        "Measured quality on training data     -0.109292\n",
        "Estimate of future quality            -0.131209"
       ]
@@ -318,7 +218,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8c0659fd",
+   "id": "fbdd36f9",
    "metadata": {},
    "source": [
     "The more similar the two scores are the better. If there is a large gap between them, it may mean that the model learned to rely on spurious correlations. However, if the model quality is still good on the stress test data, then the gap may not be a cause for concern."
@@ -326,7 +226,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "32e16d06",
+   "id": "e1c4b051",
    "metadata": {},
    "source": [
     "<a id='prediction'></a>\n",
@@ -339,13 +239,13 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "00434d42",
+   "id": "f5534176",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:23:35.336129Z",
-     "iopub.status.busy": "2024-12-26T14:23:35.335687Z",
-     "iopub.status.idle": "2024-12-26T14:23:35.359746Z",
-     "shell.execute_reply": "2024-12-26T14:23:35.359287Z"
+     "iopub.execute_input": "2025-04-25T10:39:53.790627Z",
+     "iopub.status.busy": "2025-04-25T10:39:53.790290Z",
+     "iopub.status.idle": "2025-04-25T10:39:53.813806Z",
+     "shell.execute_reply": "2025-04-25T10:39:53.813336Z"
     }
    },
    "outputs": [
@@ -416,7 +316,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9e2d1c54",
+   "id": "f626fc6d",
    "metadata": {},
    "source": [
     "We can also save the dataset with the predictions by passing desired path to a new CSV file the `output` parameter."
@@ -425,13 +325,13 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "fe0815c1",
+   "id": "18f16aa4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:23:35.361684Z",
-     "iopub.status.busy": "2024-12-26T14:23:35.361158Z",
-     "iopub.status.idle": "2024-12-26T14:23:35.414873Z",
-     "shell.execute_reply": "2024-12-26T14:23:35.414401Z"
+     "iopub.execute_input": "2025-04-25T10:39:53.815708Z",
+     "iopub.status.busy": "2025-04-25T10:39:53.815218Z",
+     "iopub.status.idle": "2025-04-25T10:39:53.877102Z",
+     "shell.execute_reply": "2025-04-25T10:39:53.876619Z"
     }
    },
    "outputs": [
@@ -502,7 +402,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "741db142",
+   "id": "572f8854",
    "metadata": {},
    "source": [
     "<a id='evaluate'></a>\n",
@@ -517,13 +417,13 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "e37fca1b",
+   "id": "c2298471",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:23:35.416628Z",
-     "iopub.status.busy": "2024-12-26T14:23:35.416322Z",
-     "iopub.status.idle": "2024-12-26T14:23:35.443846Z",
-     "shell.execute_reply": "2024-12-26T14:23:35.443373Z"
+     "iopub.execute_input": "2025-04-25T10:39:53.878752Z",
+     "iopub.status.busy": "2025-04-25T10:39:53.878578Z",
+     "iopub.status.idle": "2025-04-25T10:39:53.904520Z",
+     "shell.execute_reply": "2025-04-25T10:39:53.904023Z"
     }
    },
    "outputs": [
@@ -580,7 +480,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "909befe4",
+   "id": "99a916a5",
    "metadata": {},
    "source": [
     "You can always compare the new results to `model.quality` to see if the model is still performing as well as it\n",
@@ -590,7 +490,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "127a6d75",
+   "id": "7becf842",
    "metadata": {},
    "source": [
     "<a id='save'></a>\n",
@@ -603,13 +503,13 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "549287f8",
+   "id": "045be6ca",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:23:35.445745Z",
-     "iopub.status.busy": "2024-12-26T14:23:35.445251Z",
-     "iopub.status.idle": "2024-12-26T14:23:35.817393Z",
-     "shell.execute_reply": "2024-12-26T14:23:35.816774Z"
+     "iopub.execute_input": "2025-04-25T10:39:53.906138Z",
+     "iopub.status.busy": "2025-04-25T10:39:53.905965Z",
+     "iopub.status.idle": "2025-04-25T10:39:54.499194Z",
+     "shell.execute_reply": "2025-04-25T10:39:54.498556Z"
     }
    },
    "outputs": [],
@@ -619,7 +519,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e58e7368",
+   "id": "276946fa",
    "metadata": {},
    "source": [
     "<a id='load'></a>\n",
@@ -631,13 +531,13 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "866735bb",
+   "id": "3650bcca",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:23:35.820171Z",
-     "iopub.status.busy": "2024-12-26T14:23:35.819618Z",
-     "iopub.status.idle": "2024-12-26T14:23:36.306420Z",
-     "shell.execute_reply": "2024-12-26T14:23:36.305857Z"
+     "iopub.execute_input": "2025-04-25T10:39:54.501661Z",
+     "iopub.status.busy": "2025-04-25T10:39:54.501044Z",
+     "iopub.status.idle": "2025-04-25T10:39:54.729626Z",
+     "shell.execute_reply": "2025-04-25T10:39:54.729031Z"
     }
    },
    "outputs": [
@@ -665,7 +565,7 @@
        "      <th>neg_log_loss</th>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Evaluated on 2024-12-26</th>\n",
+       "      <th>Evaluated on 2025-04-25</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
@@ -684,7 +584,7 @@
       ],
       "text/plain": [
        "                                   neg_log_loss\n",
-       "Evaluated on 2024-12-26                        \n",
+       "Evaluated on 2025-04-25                        \n",
        "Measured quality on training data     -0.109292\n",
        "Estimate of future quality            -0.131209"
       ]
@@ -704,7 +604,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "369ef6cc",
+   "id": "d8500e44",
    "metadata": {},
    "source": [
     "<a id='load'></a>\n",
@@ -726,13 +626,13 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "4cee7548",
+   "id": "79e81718",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:23:36.308596Z",
-     "iopub.status.busy": "2024-12-26T14:23:36.308065Z",
-     "iopub.status.idle": "2024-12-26T14:23:57.995953Z",
-     "shell.execute_reply": "2024-12-26T14:23:57.995245Z"
+     "iopub.execute_input": "2025-04-25T10:39:54.731710Z",
+     "iopub.status.busy": "2025-04-25T10:39:54.731204Z",
+     "iopub.status.idle": "2025-04-25T10:40:15.989396Z",
+     "shell.execute_reply": "2025-04-25T10:40:15.988723Z"
     }
    },
    "outputs": [],
@@ -752,13 +652,13 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "a8c27319",
+   "id": "7de52324",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-12-26T14:23:57.998741Z",
-     "iopub.status.busy": "2024-12-26T14:23:57.998400Z",
-     "iopub.status.idle": "2024-12-26T14:23:58.004098Z",
-     "shell.execute_reply": "2024-12-26T14:23:58.003542Z"
+     "iopub.execute_input": "2025-04-25T10:40:15.991902Z",
+     "iopub.status.busy": "2025-04-25T10:40:15.991477Z",
+     "iopub.status.idle": "2025-04-25T10:40:15.997596Z",
+     "shell.execute_reply": "2025-04-25T10:40:15.996986Z"
     }
    },
    "outputs": [
@@ -786,7 +686,7 @@
        "      <th>neg_mean_squared_error</th>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Evaluated on 2024-12-26</th>\n",
+       "      <th>Evaluated on 2025-04-25</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
@@ -805,7 +705,7 @@
       ],
       "text/plain": [
        "                                   neg_mean_squared_error\n",
-       "Evaluated on 2024-12-26                                  \n",
+       "Evaluated on 2025-04-25                                  \n",
        "Measured quality on training data           -3.781031e+07\n",
        "Estimate of future quality                  -3.829070e+07"
       ]
@@ -821,7 +721,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "14624174",
+   "id": "9830f361",
    "metadata": {},
    "source": [
     "<a id='ref'></a>\n",