music.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from music21 import *\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.callbacks import LearningRateScheduler\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "from keras.callbacks import EarlyStopping\n",
    "from keras.layers import Dropout\n",
    "from keras.layers import BatchNormalization\n",
    "from keras.layers import Activation\n",
    "from keras.layers import Flatten\n",
    "from keras.callbacks import History\n",
    "\n",
    "from sklearn import  metrics\n",
    "from help_plot import plot_decision_regions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Zacznijmy od prostego, dwuwymiarowego przypadku, dzięki któremu będziemy mogli przyglądnąć się naszym wynikom:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>liczby_nut</th>\n",
       "      <th>dokladnosci_tonacji</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>214.0</td>\n",
       "      <td>0.907392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>208.0</td>\n",
       "      <td>0.948021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>228.0</td>\n",
       "      <td>0.958867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>247.0</td>\n",
       "      <td>0.935147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>199.0</td>\n",
       "      <td>0.972617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>50.0</td>\n",
       "      <td>0.908990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>45.0</td>\n",
       "      <td>0.791180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>56.0</td>\n",
       "      <td>0.940300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>860.0</td>\n",
       "      <td>0.930243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>34.0</td>\n",
       "      <td>0.801048</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>362 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     liczby_nut  dokladnosci_tonacji\n",
       "0         214.0             0.907392\n",
       "1         208.0             0.948021\n",
       "2         228.0             0.958867\n",
       "3         247.0             0.935147\n",
       "4         199.0             0.972617\n",
       "..          ...                  ...\n",
       "357        50.0             0.908990\n",
       "358        45.0             0.791180\n",
       "359        56.0             0.940300\n",
       "360       860.0             0.930243\n",
       "361        34.0             0.801048\n",
       "\n",
       "[362 rows x 2 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bach = pd.read_csv('bach.csv')\n",
    "bach = bach[[\"liczby_nut\", \"dokladnosci_tonacji\"]]\n",
    "bach= bach.astype(float)\n",
    "\n",
    "bach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>liczby_nut</th>\n",
       "      <th>dokladnosci_tonacji</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>587.0</td>\n",
       "      <td>0.876323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>627.0</td>\n",
       "      <td>0.844736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>513.0</td>\n",
       "      <td>0.865842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1065.0</td>\n",
       "      <td>0.904660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>373.0</td>\n",
       "      <td>0.928705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1313</th>\n",
       "      <td>244.0</td>\n",
       "      <td>0.922416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1314</th>\n",
       "      <td>224.0</td>\n",
       "      <td>0.898357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1315</th>\n",
       "      <td>404.0</td>\n",
       "      <td>0.853625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1316</th>\n",
       "      <td>215.0</td>\n",
       "      <td>0.775142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1317</th>\n",
       "      <td>211.0</td>\n",
       "      <td>0.832412</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1318 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      liczby_nut  dokladnosci_tonacji\n",
       "0          587.0             0.876323\n",
       "1          627.0             0.844736\n",
       "2          513.0             0.865842\n",
       "3         1065.0             0.904660\n",
       "4          373.0             0.928705\n",
       "...          ...                  ...\n",
       "1313       244.0             0.922416\n",
       "1314       224.0             0.898357\n",
       "1315       404.0             0.853625\n",
       "1316       215.0             0.775142\n",
       "1317       211.0             0.832412\n",
       "\n",
       "[1318 rows x 2 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "palestrina = pd.read_csv('palestrina.csv')\n",
    "palestrina = palestrina[[\"liczby_nut\", \"dokladnosci_tonacji\"]]\n",
    "palestrina = palestrina.astype(float)\n",
    "\n",
    "palestrina"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>liczby_nut</th>\n",
       "      <th>dokladnosci_tonacji</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>192.0</td>\n",
       "      <td>0.852309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>115.0</td>\n",
       "      <td>0.806914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>136.0</td>\n",
       "      <td>0.723191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>122.0</td>\n",
       "      <td>0.855020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>131.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1054</th>\n",
       "      <td>67.0</td>\n",
       "      <td>0.778201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1055</th>\n",
       "      <td>109.0</td>\n",
       "      <td>0.885951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1056</th>\n",
       "      <td>115.0</td>\n",
       "      <td>0.785784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1057</th>\n",
       "      <td>78.0</td>\n",
       "      <td>0.910583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1058</th>\n",
       "      <td>122.0</td>\n",
       "      <td>0.869972</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1059 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      liczby_nut  dokladnosci_tonacji\n",
       "0          192.0             0.852309\n",
       "1          115.0             0.806914\n",
       "2          136.0             0.723191\n",
       "3          122.0             0.855020\n",
       "4          131.0             0.818891\n",
       "...          ...                  ...\n",
       "1054        67.0             0.778201\n",
       "1055       109.0             0.885951\n",
       "1056       115.0             0.785784\n",
       "1057        78.0             0.910583\n",
       "1058       122.0             0.869972\n",
       "\n",
       "[1059 rows x 2 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ryans = pd.read_csv('ryans.csv')\n",
    "ryans = ryans[[\"liczby_nut\", \"dokladnosci_tonacji\"]]\n",
    "ryans = ryans.astype(float)\n",
    "\n",
    "ryans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 248,
       "width": 381
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(np.arange(100), bach[:100][\"liczby_nut\"])\n",
    "plt.scatter(np.arange(100), palestrina[:100][\"liczby_nut\"])\n",
    "plt.scatter(np.arange(100), ryans[:100][\"liczby_nut\"])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 465,
       "width": 504
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.figure(figsize=(8,8));\n",
    "#axes = plt.gca()\n",
    "#axes.set_xlim([0,35])\n",
    "#axes.set_ylim([0,1200])\n",
    "#plt.scatter(bach[:100][\"rozstawy_dzwiekow\"], bach[:100][\"liczby_nut\"])\n",
    "#plt.scatter(palestrina[:100][\"rozstawy_dzwiekow\"], palestrina[:100][\"liczby_nut\"])\n",
    "#plt.scatter(ryans[:100][\"rozstawy_dzwiekow\"], ryans[:100][\"liczby_nut\"])\n",
    "\n",
    "#plt.show()\n",
    "\n",
    "\n",
    "plt.figure(figsize=(8,8));\n",
    "axes = plt.gca()\n",
    "axes.set_xlim([0.7,1])\n",
    "axes.set_ylim([0,1300])\n",
    "plt.scatter(bach[:100][\"dokladnosci_tonacji\"], bach[:100][\"liczby_nut\"])\n",
    "plt.scatter(palestrina[:100][\"dokladnosci_tonacji\"], palestrina[:100][\"liczby_nut\"])\n",
    "plt.scatter(ryans[:100][\"dokladnosci_tonacji\"], ryans[:100][\"liczby_nut\"])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Łączę dane w jedną DataFrame i dzielę na train i test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2739, 2)\n",
      "(2739,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[214.        ,   0.9073918 ],\n",
       "       [208.        ,   0.9480214 ],\n",
       "       [228.        ,   0.95886687],\n",
       "       ...,\n",
       "       [115.        ,   0.7857839 ],\n",
       "       [ 78.        ,   0.91058297],\n",
       "       [122.        ,   0.86997151]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = bach.append(palestrina, ignore_index=True)\n",
    "X = X.append(ryans, ignore_index = True)\n",
    "y = [0]*362 + [1]*1318 + [2]*1059\n",
    "X = np.array(X)\n",
    "print(X.shape)\n",
    "y = np.array(y)\n",
    "print(y.shape)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Skalowanie danych:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train)\n",
    "X_train = scaler.transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Softmax Regression:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dominika/miniconda2/envs/mypython3/lib/python3.7/site-packages/sklearn/model_selection/_split.py:296: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'C': 100}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "seed=123\n",
    "kfold = StratifiedKFold(n_splits=5, random_state=seed)\n",
    "\n",
    "param_grid = {'C': [0.001, 0.01, 0.1, 1, 10, 100]}\n",
    "\n",
    "grid = GridSearchCV(LogisticRegression(multi_class=\"multinomial\",solver=\"lbfgs\"), param_grid, cv=kfold, return_train_score=True)\n",
    "\n",
    "grid.fit(X_train, y_train)\n",
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8679848132402878\n",
      "0.8704379562043796\n",
      "0.8657491273772036\n",
      "0.8704379562043796\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 574,
       "width": 594
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "softmax = LogisticRegression(multi_class=\"multinomial\",solver=\"lbfgs\", C=10, random_state=42) \n",
    "softmax.fit(X_train, y_train)\n",
    "\n",
    "model = softmax\n",
    "\n",
    "print(metrics.precision_score(y_test, model.predict(X_test), average = \"weighted\"))\n",
    "print(metrics.recall_score(y_test, model.predict(X_test), average = \"weighted\"))\n",
    "print(metrics.f1_score(y_test, model.predict(X_test), average = \"weighted\"))\n",
    "print(metrics.accuracy_score(y_test, model.predict(X_test)))\n",
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "plot_decision_regions(X_test, y_test, model)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Decision Tree:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                       max_depth=None, max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=4, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
       "                       random_state=42, splitter='best')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "tree = DecisionTreeClassifier(min_samples_leaf=4, random_state=42)\n",
    "tree.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8435770621744597\n",
      "0.8467153284671532\n",
      "0.8448528736230247\n",
      "0.8467153284671532\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 574,
       "width": 594
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = tree\n",
    "print(metrics.precision_score(y_test, model.predict(X_test), average = \"weighted\"))\n",
    "print(metrics.recall_score(y_test, model.predict(X_test), average = \"weighted\"))\n",
    "print(metrics.f1_score(y_test, model.predict(X_test), average = \"weighted\"))\n",
    "print(metrics.accuracy_score(y_test, model.predict(X_test)))\n",
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "plot_decision_regions(X_test, y_test, model)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Random Forest:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8889973460920314\n",
      "0.8905109489051095\n",
      "0.889148793721932\n",
      "0.8905109489051095\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
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0hw8AAAAAcFUikAJW4Ep4JeoDtUSuSht3GwWzRvm+Lk3ffoP681Ezcu/arbJdCZlQ2pjMacHT1Sv/vk4QqtsN5KWS9b/5ww9Hy9YtxeCgdP/9SztG0pET12p05l2SPI1l3ytp4UqAoTU6mPl0tD0hPZTfpx2Zs1HwtG9f8WTj49LJk9qvUY0+9UHp5Mny7QAAAACAqwaBFLBMmSCjN4I3Gq6KihkZJU15aGR9I+s48nqLK+Mls55MYBV2L1x1rprQdZTwfSWziwRkExNRg/MmGBo8peHUCY1575GkBaFUWRg1b9j9uu7b+Pf6E/1s8UTj49JTT2n/tr+Qdu3S/l3Pa/TktqbcBgAAAADA6iOQApZpyp+Sb305cgqhVLVeUpUcOQoUSFZyTLRcnElYGS9UcjZf2G/guWl1zeZ1aUu/es9dkal1St+XdYy8VEK9ZzIaeOL70vT5xW/AwEBjN3QF4ZXjROGTZmY0pg9JKoZSB9Jf0qHMp8rDqNQ3NDL7oByzoXiS0jAqrogaH1/2mAAAaIUTJ+qvFCtJYRj93YjFOQAAnYBACliGbJhVJswoUKBudWtOcwoVSopCKal2P6lQoebsnIyMXOtKJpTbK3WdzWvgu69Kiqqk+l+/pIHnpjW7ZYOym3vVe3524cmMkRIJZbf0KpkLNPDiuWjKX62waXBw6dP1VsgxViPOg1KqrxA+jWXfWxZESfNhVPqLcrIV99vJk9p/5/PSXqbnAQCuTuPj0tGj0p490j33VA+lwlA6fFg6dkwaGqJVIgBg/SOQApYhE2bky5drXCVMQrLSnJ2TlS0EU4sJFUahlWtlA1cD33s9amheEibd9ufP6fQ/267Mdf2alZS6kJWJm5kbI5tMKnvTdnlJKX3F6Lb+d0oH7qj/jQ8dqr99DTjGamT2gBTOaCy8d8H2YeexqDKqMowCAOAqd+JEFEZJUdgkLQylSsMoKdp/YIBKKQDA+lanaBhALYECScVqqIRJqMt0FS7XYio+ClP9kqEGnnltwf4Dz03rjj/6ptKvX1KYcHXxpk2a2dqn2S29mtme1sWbNil0ojDqXc8nNXChDZ/Sg4PSwICca7frwI4vS8nkgs8DO74s59rt0SHPxEsAACAASURBVKvvgQFp61bpzJnoT8pnzrT6FgAAsGyDg1FlVOzYsSh8Cuf/flUZRknR/oODzR0nAADNRoUUsAzxKnlxoORbX3mbX7TBeby9sudU6BlNv+N63fb/LDzmR/7+B9pw7oqe/9BbNf3WAeV7u2Qdo4Tnq/dSTgPJa3XbS4n2DKOkwup8YSgdOvwO6di1C3Y5tOcRjdzzTNlfi/ePj0sn/0K6cxfzFoBlomcN0HqOE1VEScXQqbRSqloYVWtaHwAA6wmBFLAMaSethBLK2qx86xd6SBkZOfOFh/Wm7sUVUq5ceUEgx5Wm33adLt24Rf35hfsPPDetgeemdem6fk2/dUBeKqnkqbMaeC2n/l/61FrdzFUThtLBw+/Q2LGbqm6Pry8LpfbuJYgCVoCeNUD7qBVKlQZREmEUAKCz8OsOWIaUk1Kv0ysjo5xyhTBqsYbmUrTKXrfpVsqk1OP0SNZRMOso39el6dtvqPt9+1+/pFv++kW99S+e0y1/eUz9r19a1dvVqCMnri1MNaglDIv7VYZRw3te0bce+AsN73mlcN3YsZt08PA7Fj0v1saJE2ro//TEieaMBytT2bOmdHpQrFrPGv5/gbUTh1Kl0/dKEUYBADoNFVLAEmWCjKb8KV0OLitQUHUaXj1WVq5cOab4irM3PyMryet2penpxgbi++WXH364sRX0pqclz4u+dndLmzfX3vf8+eK+883QR2f+pUZn3qXh1IloVTxTEb4NDir86P2FEGrHpiuaurihsHl4zyuFSqiRe56RVKyQqlophTVXWUkzMbFwmldpeLF7t/ShD0kvvVR7mhfTwFor7llTbXqQ49CzBmiVOJSqrIySCKMAAJ2HQApYgrP+WU16k8rbvAIFZY3JpShsqlYdFVdPxSvrBQoKU/skyXUDmXyo5OyclK8yZ68aUxF8TUw0FmZ5nmRt9HUx+Xxx3+lpHQmHNBp8VJKnMe890syMRpwHy0Kp0JpCGHV5Lqm/PX6drt90RRt7vLIwSlLNUOqu3ad01+5TDdwJWKnKSppXXpEuXiz/S31peDEzIx05Ir34YvQQrDbNq12mgXVy/yR61gDtKf75WM3hwzwPAQCdhUAKaFAmyGjSm1TO5uQaVyml5MnTnJ2re1zlND4rq9CGKhZSWfkbktp49qIGvndK6upafDCeF61QV62cYWCg/rFxhVQ1Fy5IcyW3p2K/IfOkhp3HNBbeK0nRVzehke1/LMdYhadO62Dm04VwaWO3p5++9XW9fmHDgjAqVhlK7R96gTCqiUoraXI56fhxKZUqhhXvf7/0+OPF7ZcvR/9np09H+1UuTd4uS5fTP4meNUC7qVaZWKqykhEAgPWuYwKpS9mc/vL551s9DLSRwUHptu7bGt5/yp9S3ublGlddJgqNjDULpuhVrqBXrWLKkycbWiVNUnKsErOeBr77Q/XnuxYPlKQoVBoYKKxgtywllU/Fgc1XT9XY15E0Yn9TUqgx3SdJGvPeL2V6dCD9JR0KH9BY9r1Sf3RYHEIdnbhWQ4Onar7AjkOpssqo8XHp5Elp18pW2evkKplGlIYWzz4bhUzZbHT52LHo/pudjaqhurqkdDoKpaRov4EB6c1vji63yzSwyqovaeEbvHYJztZatVCqFGEU0By1fj5WViwSSgEAOknHBFKJl+a07cOTrR4G2sQL99ws/a8zOrPjed194+KhVDbMKhNmFChQSqnC9a7cwlQ8U/IRT82rxcrKly/f+pKx6j49q9u+8q3VuGmLiyuw4iqr0gAsrp5KJov7WBu9Kk5EPy6iUOrzUuhoLPiAJGks+94oiAo9yY0OLa2IaqTiqWy/8XGNPnWbtO1u6akz2q/xZYVSVMk0pjKUkqKwKQ6mUqkoiNqwIdrX96NtqVQUVj3+eHtNA6N/Ujl61gCtVS+MWmx6Lc9PAMB61jGB1DUDCe1/4E2tHgbaxfjX9fO/9G/0E3/zWkO7Z8KMfPlyjStT0rvJMY5c69YNn2opHGOk6/77SQ1891XpuuuWfJ4l27Il+hpXWR04UNx26FDx+ngfzyuEUTHHWI04D0pBqDENL/gWhTDqyfGlj+/kSY2e+aC0bZu0b5/00EMafUpRKFW6/c5dkqSffPjfa2DiqQWn+Z+5d+i5i7+szZJ+OC1Njo/rV9MPyTFW04N36un7v9AxVTKNqBVKlX6NH/r9/dH9NDsbzfB89tn608CaXYFG/6Ry9KwBWmtiov7PnFo/s3bv7szfRwCAztExgRSwEoECSdVX0EuapAIbKFS4YFt8TGlgFZ8j/hpaqys/smm1h7zmHGN1QIeqBlIH+r4g509PaFT7l3Hmu6OwKS5V2rcvqpg6ua3q9oGJp7Rp+oUFZ3m/Pa5nwhv0WBhVcf0P7yfUM3NWn3U+L6nzqmQaUVpJk05H18VhVH9/cb89e6LeUn/0R8WeU+l0MbCq1RC9mRVo9E+K0LMGaL3du6Off/Uqdit/Zg0NEUYBANa/jgmkzk37Gn3wjVYPA21i/PLHdOvXXpC0saH93fl5aNUqoVzjqktdytt81Z5RVrYwlc+VK9e4hXMaGc1oRhdv36ZLN21Rv7+y29VMoTU6pAMLNwShPvRXn9Q9u07K+cS+1flme/cummRcHHjLguv+NzumXGar/jobHfuX+rAUSP/WPtpwlUwn9aAqraQxJgqZ4kDq0qViKHXPPdHtvXgxuhzvE4dStabGNbsCrdP7J9GzBmgfe/dGP//q/T6Jf2ZRGQUA6BQdE0jlb+zVq1+6vdXDQJu4detFDQ5ubLipedpJK6GEsjYbBUymvFIqaZJy5Miznnz5ZY3N4yAqaZKFMKqMNfL7kpq+/Ub1/8PZFd+2hvl+NCXv0KHidfEUvbjRedxDKl5tzxgpkVBojQ6GD2hMHyg/p7XyQ6Nj+bdIA3t1T9jaN7iOsfrV9EOSVAilHgs/oL88/WFdaCCMWo89qGoFbKW3w9poRb18vri9tFLq8GHpZ34mqoyq3J5OR9tLV+eLtaICrVP7J9GzZqFOCpfRnhp5XDlO8x5/PCcAAK3WMYFU9wZPO++YavUw0EZu635nw/umnJTSTlr5IC9PnrrUtWAf10TVT7PhrAIFMjLqNt1y5cox9d7hGVnXyNuw8JxVXbhQDI3qhUmlurqKvaNi9VbZiwMoa3VEezVkj8qZD9kKYVR4rzR/3X09/0ND4RE96b1d/935qHTN1rZ5g1stlCpVrzJqva3UVitgqwyjLl2KekNt2FC+6l7l6ntXrlTf/uyz0fb4stS6aqRO7Z9Ez5py6zFcBlaC5wQAoB10TCCVPeXq+7+1udXDQJvou31GU//sef34LZt0XaKxRuI7EjuUCTPK2ZzyyiupZFmllLVWnrzCFL2EEkqaZANntjKBVfJKfvFdpSgpaCRMalSdVfZG87+kUX1Sw3pUI/otqSyMitxnvi6rDfqP2f9Tn7B/pPd1/Z3+TG+V1D5VF46x+pX0f60aSNUa22Irtb34YhS6fO97xWMqK4Ca8ZflRv/C/cQT0je/GV0uvS1S9TAqCKTLl6XNm4sNzDOZ6GsuF+3b0xNlnQMDUfh06dLC1fn6+1sfRnVi/yR61hStx3AZWIlmPCeovgIANKJjAqnr/Yv6/dNfa/Uw0CZGP3ebXv2t63Vi40Vdd2NjgVTaTWtncqcmvUnlbV5ZZeXaaNU9a60CBXLlqkc9Ck0oz3oKwkChKTY7r6yWstZKxiox42nguz+UlKrynWtIJuuGSZKiaXlhWL1yytricbE41JJ0JBzSqD4pSRrTfZKshnS0PIzSo7Jy9FjuvZJC/Yn26YD7B3phT3tVXYTW6PczH6+6rVaVTL1pTX19xaqbuK9SvaqjtfrL8lL+wv3ss1G4FPd9im/Lrl0Lw6j4YdPVJb3zndG5H39c+u53i1P5HCeqkJqbk/75P4/+HZ+ztGJqYCCavtcOYVSn9U+iZ01ksXCZBQ7Qadb6OUH1FQCgUR0TSKmvj992KNivcf3854a09W9eW9JxWxNb1W26NeVPKRNm5CvqQm6MUZe6lHbS2pHYoZe9l3XenldWWclWNDa3xX5SnjzJOtr03TPqf+W85M73mPL9YmBUKb6+MmTy/eL1lftaW94QqFS1/SUNmSc1rEfnwygVVtP7hP1j/Yk+MR9GRT2Z4iBr2Dymn0r+g063UdVFaI1+L7OvanWUVD+QqBZKffvb0azJUkNDtcMoaW2qLZb6F25jonHfcIP02mvlx915Z7RfEESf4Xzvr56e6Lj4fjh3Tjp9WuruLpvVqSeekH7u56LzxQFdHErNzkovvdTcxwD9k4rarWdNKyz2f97oAgfAerGWzwkqEgEAS9E5gRSwStJuWmk3rWyYVSbMFCqj0k5aKSels/5ZXQ4vK1RYdly8Al+oUL71lbCJqPm5dWQeyUfv5Pv6op3jaqd6KqfnGVNeHSUVK57qbSu9viS0cozViDkkWZWFUsPu1/U7ekDj9t1RGDV/7mH36xrp+ZKe3/aetqm6qBZG3et8Xf92+6Pav+fbDQUSlS/cu7vL+yYt+J5NqrZYzl+43/72hc3G4689PdFtu3w5CpF6eqKH5Pe+V1w57xd/Meor9e1vRw8da6OH7DvfKZ08Wfw+paHU5s3NrzShfxIq1fo/r5zOSRiFTrFWzwkqEgEAS0EgBSxTykkp5ZRPscsEGZ30TmpOc3WPtYr6TfWaXvmzSf1/r+3TB3/WK1bxHToUhVKlU/Ji1abmeZ6USCzcf3o6Cpkqp/eVnqea+esdSSM6KKkklArv1Zjmp+0ZRWHU+/Ia2fhPcsz79bT2R8e2uOqiWhj1vtS4Pjv7eWXMrUuqkqlcqa00cEmny0ObZlVbrOQv3JXHSVEYJUnvfnf0xuDkyWJ/rNLzplLR56ZN0XS9d86vDVDaS0uK7qOenmiKYLObh9M/CdXUeuzHCKPQadbiOUFFIgBgKQikgCWqVRklSS97L2vORmGUkYkqoOpIKimT71nzMa+EY6xG7EFJtjBtr9Rwz99o5J60nCfr39Zm++bc7QvCqF9NPyQnG41zKVUy1VZqi0OpTCYKpVpRbbHcv3BXBmyl4v12745CtmrnjcOramFUu/Rpon8SqmnksQ90krV4TlCRCABoFL8CgAZlgoyOzx3X9+e+rx94P9DL3sv6gfcDfX/u+zo+d1xn/DO6GF4shFCLhVGSdMVekXX8tR76ijnG6oAOVd12oP//assXkz/V84x+oe/PJZWEUab8/yR+0bxnT3S5WpVMvZXaSkOpypZfzXqhXXkbKlUbR7WALXb4cLGHVL3zvu1t0ddqYVS1Y48dK567WXbvXvz+b3UlH5qrkcc+0EnW6jmxnN9NAIDOQ4UU0ICz/tnC6nqBArnGLVRAZW1W+SCvN4I3Ck3OGxUokLrqT+9bqSPaqyH7rbrpc2iNjtp36y79bXRFRb+p0Bod8n59YS8qz9OhzKc0ErY+3d40/cKC6z6jF/T2cFzvnn2qUBlVqV6VTCMrtfX3F3vLp9PRXSQ194X2Uv7CXS9gkxZWM9U6765d0tdKFi6lTxPa3VIf+8B6t9bPCSoSAQCLIZACFpEJMpr0JpWzObnGVUopmTh1ULEfVN7WWMWuDisrucHyBxevxhc3OC9ddU/SaP6XNKpPath7VCOnPh9VCHV1SVu2FPYJrdHB8AGNhfdqv/5I+/XHZecobNcHqg5hLPte6XBeIxuPLqhAaobpwTvrbn+b3lBGt9Y9plqVTCMrtVlbXE0obnIeh1LN7Ju02F+4F2smW2uKXdwAvZqJiajf1JNP0qcJ7W+pj33eLGO9a8ZzotHfTQCAzkUgBSxiyp9S3ublGlddpkuSFNowqm6al1BCnryGpumtqjiMiv9d0qT8iN2rUX1S0nxDci9qUO74ftTo3PMUWumg9+vzYZPVqPbrFr2ou7woZQmt0UGNlIVRw6lv6ED6SzqU+ZTGvPdE5z92k7TpX2jkrX8ZNTpvoqfv/0LZ5RMn6vcNkqIXyRMn6ocji63UVk02G/VX6ulp3hvbam8qcrloHKU9oCpX15Ok66+v3ej82Wej+7LWaoLHjkX3yYc/XH9qHH2a0GqNhMsSoRQ6RzOeE1QkAgAawa8AoI7SBuZJJRXYQLkwp6zNas7OFT6zNrv8MGq1epbE0+nmP4eS39SwHi1sHjPDOqiRqB9EHEZppLB6niQNa0xDOipZO7/9QPl25zGNpL8o14QaSX9Rw85jxfO/9g4dfO5fKGxhb/PxcemRR+r3vYhfJD/ySLR/LfFKbdLCMCo+x/e+F1VExSvPfeQj0h13FM+x1n2Tqr3g37QpCsR6e4tZ5bFj0le+Ur5fKiW9+mpU4SSV9/uwNuqLNTkZraQX3wcPPLCwL9TExOLjpE8TWmmxcLlWz7NGHtvA1WitnxO1Aq9qv0Po3QYAnY1ACqgjE2bky5drXAUKlLM5+fIVVqRIocLlB1LuMl+JeV55J+24Qmr+0/HzGtFvaVhjhV3GdJ8OakSBdXTQfK5s1bxhPRpVUMlKxuio9pZvd7+ukZ7fLUzLc4zViPOghlPfKJ7/tXdo4kz/8m7PCp04UZw+V+tFbuWL5KNHo+Nq2btX+uhHq4dR8TmMiYKrj39cuuuu5jXzrvaC/7rrpIsXo39ns+Wh1OuvR2GVFIVRs7PR2EvvA8eJKql6e6Pj+/qiSqt2a1YOLEW9cDnWyAIHwHqxls+JRqqv+B0CAIgxZQ+oI56WZ63VnOYUKpSRkVOR5RqZBSFV45Y4x627u/jv0lCqsuG4JMfzNGIPSsYUwqUx3Tdf9WQK33rYeUwjzuflePPnSiZ1l57W/uC/ajT4eBRG7fhTOWZz+fmN1Uj6i9KeuzT21BbtH3xC2r5rabdnlQwORi9ya00xqPUieXCw/nkrX4A3+pfl0jGsRTPvauOonJYXh1Jx+HThQlTRlckUm69X3gcvvRQdt3nzwjCqmbcPV4+Gp8lOtO4xsnevNDBQf5xML0UnWavnRLv8jgQAXB0IpIA6XLmSomDKysrMf6wqu8TKqs0lodD0dLFvVDIZvbosNT1dDKWksul38fcd1qMaCQ7KqTLXbr/7kG4JntdQ8KSc01V+XHienNOnNPLsR3SX96O6a+J/6uzEVk2fet+C3k5rbbG+F/X+YrsU8V+Wjx5tbTPvWuOovA9KQ6nNm6MKqtIwqnL87XL7cHUYH48eK9ddJ/38z0uJKj8m4jD42WelW26Jqg5boZHHKNNL0UnW4jnB7xAAwFIQSAF1pJ20HDmF6qfKyqhVEbq1t1WsmrdAXCFlaoRk89VUjqQD9nc05pUEUvPHHEj8jhyTKD9fibs0XtxWaX6aoHP6lO4KXpMkbdIVaeKp+uNeI7VCqcqmqssNo2LtUm1RbRy1Qql8vjidT6p/H7TL7UN7i6fJzsxI3/mOdP689JnPlIdSpWFUJhMFWMZE/dYArE/8DgEANIpACqgj5aTUbbo1a2dX98TzoY8JQv34l8Z039TvSn91ttgE6fz58v19P/parfygdJ/SAKu7u1BNFVqjQ5lPS5mS4Gp+et+h1EGNpL8Y9YYqrbiqVDEdUFK0b1yZNTMTXXfpSu0xNkG1QKbUSsOoWLtUW1Q7f7X7oHSmZyP3QbvcPrSvwcGoMuo734kuT05Kf/AHxVCqMozKZqP+ZS++GIVZPHaA9YvfIQCARtDUHFhE2kkXpunZ+Y9S1a5rVHJmTptePq3rvJelS5eiQGh6OipnKfsmdvGpfRVNzePl0UJrdDDzaY1l31v1sLHse3Uw82mFdpWnIrZQaSBTqVOWmF6r++DEicWbz4Zh/WbxrXI1j70dOU40TW/nzuJ1cSjl+9XDqP7+xnq3oTYexwAAYL3ogLdlwMr0OX1KKFEWSsWr6pWurrec6XzGOOo+n9fryZujd2oDA9U/Y5XXJ5PFZualn/PiMOqrVz5YyLOGncf0rcS7o9X35sOrscx79Juv/2s9kf+pKNjK54ufsfhyaejVpuLKjGo6ZTWftbgPxselRx6pf3z8fR95JNq/XVzNY29niURUEVUZSn32s7XDqE4JhdcCj2MAALCe8JIQWETaSavbdMvIyJUrR04hnIpX3EsooS51NXxOE1otWlR14UKxYiqukIovx59xz6c4IIqn9qkYRj00c79eC67VqXCb7jWPacR5UK4JNWIPatiOSdbKWqvR8BP6eX1Ff6j9dQZeJfzqavx2N0O11fRKdcIS02txH8T9guodX/l9jx5tjyqNq3nsV4NqoVQ2K50+TRi1mngcAwCA9YaXhcAiUk5KaSethBJyjauUifpKdZkudZtupUxKPU6PFiy+54dSEBbDJGulMPq0xkhhqMTsnOa2dBWn7F24UDx+bm5hJVJpdVJpA/LC+cPC9Ue9d+mrl+7VuWCzZK1MEMqEYSG0cozViA7qXufrOqUduqRNuqhN+n39qo4kflrq6tKRxE8rjG9YHEZVVGmFm9+kI7k7Vv1+X860lGpBzJ490gMPRF9j6zmUWqv7YHCw/vG1vm87TM26msd+tYhDqVRq4TbCqNXB4xgAAKw3vDQEGrAjsUNdpkuBDeTLjyqiTJeSJinHOLLWyrd++UGukZz5MMfOfxoVgitjpTBhFCRdJW1eCoJC36cypc3Ea0zNKzMfUt2lI/oV/Z76dVGbdFHXakqP6j4dtAfK+kWZQr2XdI3O6WPun2nIPKnRYJ9+Lfi8DmqkGEpViKuwfu3iiEZzv1j3PlyK5UxLqfVmLH4TfM896z+UWsv7YLHj633fVruax361CEPp8celdHrhtkuXpPe//+q/P1vdu4nHMQAAWG9YZQ9oQNpNa2dypya9SeVtXlll5VpXxhhZaxUomA92oo9Co/PKaXnWSqFkrJV1jebSvZrZsUme6VK3rRJGrdC/MX+sW+wJjWuvHtV9kqQx3Sd50gEd1CEd0KPhPbpWU+rTZX1M/00jwUEdDYY0qo9LshrTsCRpxB4qS7Arm6WPzv0r3eJO6Kf0jRWNuXJairTwTVW1aSmeF12em4tWlKt8M1a58tzcXPR1PS05PTFR/w1ptdX3lnIf1Dq+cmpgO74RXo2xnzhRfxlzKXpsTkysn8dUIypX06uUzZavvnc1Gh+Pfs7Ue3yU/lwaGpL27l39cVzNz0EAAIBKvFQBGrQ1sVW3dt2qa9xrlDIpOSbqJeUYRymT0kZnoxw5xUbnpWHU/JQ51wuV8AO5XhBd7Ridv3Wg+jecP67ulL0G3G2O6HM6qGE9WrhuTPfpDn0rCpuMkYzRx9w/00jy83K6EhpKflPD7tejbZLGNBxVSs1XVlVbuW+46680lHi6oTHVs9xpKXffHc0ivHAhmjZUrSLDcaLrU6lov4GB9RUc7N4dvRGWar8hrayyGBpa2n1QrUqjVDu/EV7J2GkmXV1lGBX3jBoYKJ++V7r63tWm3Xo3Xc3PQQAAgFJX6d8ql+7ctK/RB99o9TDQJsYvf0y3fu0FSRuXdFzaTSvtppUNs8qEGQUK5MqVb3297r+uUFXeqRpJxsgayVo7P1XPkQmsjLW6+CNbNXvjRnW/Mt8zano6Oi4OnGxJsmUX64Re+n1NYVqfI2nEfl7ypDEzXL5fMqnh1Dc0kv5TOWZ7yf5/KmX6NJZ5j2RtVFkVOjpgv6xDmU+Vh1Gpb2jE+V05xipofIRV1aoAkKLra01LmZiI7rpNm6TZ2Wj6ULXKqscfj7Zv2hTtf+LE+gql9u6NwoB6lTzxfbzc6rD4+GpN09v9jfByxr7cqr31FnhWqhVGxT2j3v/+KISanIz2j0Opq61SKg7Jq/08cpzW9G66mp+DAAAAsavoJeHK5G/s1atfur3Vw0CbuHXrRQ0ObtRt3bct6/iUk1LKif78nwkyOu4dV87m5MhRUBrJFAKkqAopTDiyYXSdmw+UmM0r39etMz92gza/cq68Iqpa+GQqejktIaByjNUBHSpMwSt1IP0lOcYu2H8k/UVpZkZjwQckSWPhvRo7/aGy/aIw64tyriwhLFtsrMuYlrKUN43GSD0967fhb6PT75YblsT3ZTWHD7f3G+LljL0dA4l2MDFRO4yK75vPfGZhKPXVr0q/8Avt+xiptNyQfC1v39X8HAQAAIh1TCDV1yfd+e5WjwLtY5OuS1y3Kmea8qeUt3m5xpUkBXY+kJqvjJJUnL5njKwjJXK+NkxfUtCVkHWN8r3dhe2FZuWlq+gtR8V0v9AaHdKB8nPOj+9Q5lNRqFQtlHIelIIwqpCqOOew85hGZh+Uk7VRU3ZJq/VjpdqbwFKN9keSWvemcT2qFr6UqlVB1A6WO3YeW9Xt3i3dcks0PbFaGCUVV9+LQ6m+Pum1166+Plvt1LtpNZ+D9EUDAACt1DGBVNIkVy2AAGKlU/dSSsmXX1yzLghlHamwrN78KnsmlHpPZ9R9KauZa/uVyAZKzubXZoDz4VMoo4M6ULU6SlJh+l3VUKqnSwf0Oxrz7isPzCQd2P7lwjQ/zcxIki5qq6YH71yV4S91Wko7vWlcj+qt5FV6fTuGUksZ+xtvSB/7WPm0ssrHlrXSt77FY+ujH41+LLz4Yu3bHodSX/1qFEYttW9Zu1hqSL4WVvM52C6N2gEAQOfqmEAKWAuZMCNfvlwTrbjnWldGRqFCuX4gWSvrzr/Kt1ETcxNayUT/9jZ0q/fsjAa+88r8PlbK1wmnlloxZUzUgFwjUYXTvGGN6YD+kw45n9OYoil4tUKpcNMWHcp8WsrMh1ED1xa2HdrziEbueSZ6IzPfxfnPtX/V3rUsZ1pKO7xpXA8qKyeqvRF+29uK+9SrIGr1fV3vTXzl2GdmpH/8xyiUqux1FO9rbfRGPpuNKn76+haes5N85COLV9okEtE0vau90qaVvZuW8jgu/VptXPRFAwAA7aDDXjYDqyvuFxVXRTnGkasolLKuI2Mlxw+jzyCUmQ+UrGOU/cbexAAAIABJREFU3bJBydm8tj/zivpfiRruH9FehfP9phb0i5KigEmOjuiu6tsrVA+jHtWIDso1oUacBzWc+kZh21j2vTqY+XTd1fRKjR27SQcPv6PmqmMr0ci0lFornpW+OavUiYHBUlVbUW5iYmEYJUlf+1q0f7WVv44di45rtcqx15ruef31hUI/TU5KX/lK7RX1stno68yMlMtF/+7kx9bu3Yvf9pX0LWsXi4Xka/GzMNbo47iR5+ByVzNd733RAABAc3XoS2dgdbj/P3tvHibHdd7nvqeql+mZQQ9AAMSAi0BAg0UiDdKkuEjkAJZs0xAhw5FlWo5k8jqUAjn3Okwc+4nNEHLIGBSjPJGTSL6xTUnOjSxbkWlbESyCtBQtxICSSAkkAFEmMAAJkQCBGayzYXp6q3P/OHO6qrurepnpAWb5Xj7NRtd2Tm09fX71fb8P4xul8SOK4ipuBCnHmJh7CjzXwXMdtOPgAfmOJLlFbXQMjPD2Lz6PRvEE/5zf4VPs5ON4sURZahwwaYqeYKf7B/yO+hRPOL8ZLUpNClp99FZFRu1gJ85kfx2l2TH+MNuKXzHeUPk8u0Z+hr5TPXinBtl58p+ZKnuTvlHbUt/g+Yf+N9tufL20zZIopesLZI0SNRh66KHag6jK9cOY6UHjXCeqxP26dSZlB3wx6uBB825L3FcOiGdLalaw71FRTI5j0vRWrzafOzvhzTerB+lPPeUfHzDeSclJCzi5tuY30xHJW0Gj13Ej92CUeGX7v1B90QRBEARBuLS4jzzyyOXuw4zz6KOPfmz5yuUr7/3ovZe7K8I8w8XlbPEsOZ0jRgylFI5yTNpeLosXd9BxF+06JnXPUTApTHWeHOZdj+/m2r6jPMsm/iMPAXCY9QyqFfSqvSjPr9jnodjp/Tt26fcBsE//NOs5xHW8Xt2xSaHqOmXm7eOWUmSUY3Ujx4GrrkIt6qR30X4GWcFhby3bu77M+6/Yw07vIVNdz3XBddnW+S123Pw07k9dT2/PKQZH2zk8uNj0eXAxgyNt9F55mBfVLbBq1ZSPab3BUE8PjI7C4KCZNzhoPvf0mN2uN2isXF4oZ8mS6ON73XWwYoVJZ7NiFJjzc/vtfmBfTw+sXAk333xZdiGUVatMutHtt0cPqh0Hbr0Vzp6FbNZMC+6/FaNsdFQqBem0fx3JtTV/ifpeeuCB2t9HraaR67jRe9AuV9n/vXv9zyBilCAIgiAItenre4Lh4VOnHnnkkSeaXVc8pARhGqScFGknTa6YI0+eBInwBbUuG51YHyk8jdKaTexhG7tKpuO7vPeB67KDf4+DN2lKXp1610tfZUt+e5Ns5wnWc4he+iYjoyb7EYvBww8DJlRyhwebj77O5nVreLb/M+x68g7ommzrxtfZcc8iHOdXzfIO7LjnRdPXA0Z82nXiZjZfeQhWNHEAQ2g0LQXKvVLWrTODq7lquj1bqOdFc+RItRgVVpVuNkRGVdJIn2IxuP/+6uvlwAEYHi4Xo3p7YetWubbmO630bmoFjVzHjd6D4rknCIIgCMLlRH5iCEIdMl6GwcIgJwsnGSwMkvEyZfOvil1FQiUo6iI5naPgFcjpHDru4hQ1KlswFfa0RhU8Ok5eoO38RcaXL+L533svp25ZhQJ2sJNt6u9L293FNna6f0ARZ1KM8ivklaKdqGFyrnwvqs2qz0RGRXlTYQYcm9edAsz79t5XTFs3vu4bl1csv+OeF0vpe9t7vs3mFYcaO6g1mGpaSi0xql56ynTo76+/Dc8zy80Voo7X448vjDSesP2PEqNm8toSZget9G6ajYjnnjDTzMe/k4IgCEJrkAgpQYhgpDjCycLJUiU9S4wYaSfNVbGrSLtp0m6a1fHVHMsfI6dzTDBhPKUchXZciPlV9lCaiaWLiF/M4mYLXFyZ5sf/17tY+uIASafADueTkKcsUmoXW/1OKcU252vscD6JowK3b1hlvqiKfHZ6oRA+f5Ltmw6xvnuY3p5TNVOcdtzzIpvXnWLzwLdrbq8ZNm0yaSm1qnbZQdS6debV3z/1yKqpRvPM57LpCz1yIlhNLZv1xSgoF6OCy0Lrri1h9mBF8lr3euU1MFv80xphKtVMBaFR5vPfSUEQBGH6iCAlCCGcKZwpCUxFirhqsnIemozOkCvmGPFGWB1fzfLYcpbHlpNUSd7Iv8FZ72yZyTlgRCANoCgmXDw3hZMvgoLBn76WkVVXsPz4aWMyzk5wHHbx/qp+bdNfZUdxJ06xRmRUAzzLJnr1czVDJO3TzEYqZ21edwoGptaXd375X9F99Lmm1hnouZPvffC/laWlXOpBYyvLpvf31xbf7LaOHr20g9zLWeL+chMcpCeTxuR8ZAQ6OvxlgufNHiut4YUX4Od/3pyry3HehNYzFZF8LtCIUTvM//tdmBla+XdSEARBmJ+IICUIFYwURziWP8aEnsBVLilSqECam0aTJ8+EnuBY/hhJlSxFSnUUOzjjnaneqFKgbHCSNhX4kjHwNBOL2zl380qW/+QUeB4OmoeLf2gMxSt4mMdqp+mFoVRZxb4nih/hieIDbHOeYocX/bRy5+6b2XVgFdt7X2H7pumn4kXRffQ5Fg+80pJtRQ0aw4SDykFjs8KBLZse5RvTaNn02fz0eKFGTtQbpB88CG+8ARcuwE03VR8H+3UhT/3Lma3Ca6O00rtpNlDLG0t80YRW0Kq/k4IgCML8RQQpQajgZOEkOZ3DVS4JVW1SrpQiQYIcOXI6x8nCSdJumjOFM5wsnqyOjipbufS/UlpfrquNTHs7zzrvZnPsObxcgcfcj4OnqtLuHuPh+t5Rfker1n/W6+UJ7yOANsbpu3WVP1RQjAJ4ou9trO8eLvlLzRRD3W9raLl64lXlYDBM8KkcNE5FOKhnZtxI2fTZ/PR4oUZOhO33NdfA8eNQLJrUvUIBBgaMl9T+/WaZLVvgmWeMWJVMmuvu2DE4ccLMX+hP/Wez8LoQmW1G7cL8pBV/JwVBEIT5jQhSghAg42UY8UYoUiRFquayceJkMMufLpzmJ/mfkNPlXk56Mk0PNGVW4laYUlBIxvlKx6+wW23mox1f5nSxjV3u+8EF8nl/Q0qxSxtvqR08hqN0ZUPl2Glal7bTq789Wc3PVOuzopMVpSrFKDCm5r09MytGzRQzLfhE/diuFHGifmTP1qfHCzVyImq/rdhkxafxcbO/1ldq/35zrY2P+zpwe7svRtntLNSn/rNZeF2oTKeaqZwToRmm+3dSEARBmN/I174gBLAG5q5yy9L0wlBK4eJSoMCbhTfJ6RxO8JYq04hUpSRVmu85Dq/qNVAs8ocX/iV/WfygEZDyebbpXTyvb2cbu0qr7WIbO9mBp2v3r5Q3ZFP24nGcRIwd8U+yjb8vzd91YBU7d99M0VOhYlRYhb25Qk+PiW6xVFY/Cw6Cs1kzrVnhIKzCVpBaP7LrVWi7HE+PG4mcmK8V5aIG6bGYeb/pJkinjdjkeeYWymTg/HkTDTUy4otRQRP0hT7QssKrpdZ9aJlNAt58rBA21WqmIkYJU2E6fycFQRCE+Y1ESAlCgCJFAFSleBSBUgpPe4wzTpFi+XomMKoMDWVb1pjYqViqyCm60Wg6GQMN2/h7dvCHOFijc7fkK2UjnHboneWRUn7HjAiVz5v37u7SLAfYceqT0J5mF/eb7R1YVSZEwdTFqP7BLnoivKksl8onZu9ek2oVFAjC0gWGh838DRum9qN4OubfYU+Pn3/eRN0ENdHKH+wzdQwrRZmrrzYRQrUiJ/bvN+lrP/uzre3LpaaWMX5wv4ORUrGYH8hor7Fa520hcrnSdlrhWTWfUw3nq1G7MDtZyEUyBEEQhGhEkBLmLSt/5z/S/r39Ta2T3f4efvLBG2r7QAXQWqPRaK1RKAoUyuZX2ziZSn0K0DZtT4PbbVpcyQBKKbbFn2GH/iROAdAYUcp5HIqen27HL7KZZ9nMnqb2ETDV/NKfgRs3VwlRMHUx6okj7+bJo2u4MdX44K26lmBrsGlCShnhoFKUsj+KrRiVShmT6qmIPNM1/w4O2J97DsbGTH/SadP/MDHKHsP16+GXf7l1AmBQlFm8GN5806Sr1RJnliyB73/faJ9zZTAeRa1BeuV+J5PmNTLiX0NdXf7yIkb5XOq0nVYISQsh1XC+GbULs5eFWiRDEARBqI189Qvzlvbv7Sd56LWmXtf8/Q+IEaOoi+gwX6YAWmuKFEtpeja6qpLqzD+bvmfNzeHa02/wB6lPoRJxtqW/w44Vn62KfHKUiZSy6Xvb+SybVQNilHVgDr7yeZzBUzy8/14YOFX1enjsoaZ/GD47uIEnjr4biE7jChu8fXfi5uYaapBgmpAVpVIVtmBBMSqdNilZzaYJNWL+3UhKm+OYtsfGzOdMxk8BixKjxsbg2WfhC1+I3r5d/sknzSC9ETZtgjvugKGh6H1wHBM5de21/nJ9fXMrbSmKdevqR4woBW1t5j2dNvOCYhTIAKuSS5W2UykkNfpdVHntzvVUQ0GYLbTq76QgCIIw/5CfysK8J7thTUMvgPSpUdJOGheXPPma282Tx8WlXbXj4dWMqqplR+W4mhuvfZnfTP1PPrV4JzvSnwlPw8MXpT7F77Jdfbb+zoNvah58aW2q+Z38jep5+TyPvbil6R+GvVceZts1L5Y+Nzp4uyP5UnMNNUjl4NeKUsPD5rMVo8AXo5odDEft00MPNe+z5Hlw5Ei5aGZFqaeeMvMrPa9sJNWJE1MfdEfx7nfXH4w/80xrjbvngldP5VN+rc05Av/assgAq5pgpFQlrRLwWiUkzUaPN0GYa7Ty76QgCIIw/5CUPUGo4KrYVYx4I0zoCXLkiBMvMzjXWpPVWWN+jouHV+Y9FSVMVafvTU4HTt99HeyEzW3PR3dsUkhygM08W+VPVZd4vPRPL1dgp/sHfjW/inZ2ZX4edueaSttzlGbHDV/lh7yDA0OLgAZ9Yg40uyONU5liZVOrgobTYNL5gj5JjeB5JjLpxAlfcAwrm75/vxGPalWksz/YDx70o21sHzMZP9oDzDJgUsVWr/Yru7W6Qt+l9v2ZC149lcfUilHBcwV+pNR8q0LYCi5F2k4rr12pECYIU6eRIhkwfyu3CoIgCPWRr3tBqCDtplkdX02bakNrTUZnyHpZcjrHuDfOmB4jTx6NpkCBYe2HRdSKkorKANRAtruDg/+0RuqaNSkPVs4LviqXrRGS5WnFTnawy3tfadq21Dd4fsUvsS31jdI0W32vmaeVjtLcc8MbVU89H3/88kUS2NSyyspnllTKTH/mmegns2GRO3/3d7Bvn59Wt3Fjue9QsN2hIRPNdOCA8XIKUvmDXSkjttx9tx8tlcnA179eLkzdeCM8+KCJ7LI0Er1x9GjjUUhRESKNnM9mop1alWI1FRrt5+HD1WJUe7sRn+x5sqmfwSg3eervcynTdqZz7TayrSAiRglCOFGVSyv9CCvv08q/k4IgCML8RX4+CUIIy2PL2ZDYwDJ3GSmVwlEOBV2I9ImqF61U245KoZXiu7/9Hl7bvCZktvIr5VlRKh4vf1UKUDZNT2v/lc+byKj875WM0cGIUTvSn8FVHjvSn2Gb87XSvKmJUrNr8GZTy4JiVCoFK1aUm1BHDYb37DH+S8F5/f1GoOjs9D2otIa//VvfpynYbiplBKnKSlVRwtHWrebV21udwjc8bMSve+4xFd6aGXTv3Vu9L2HHK+g5NZXBeNgxq9XOqVOXx6un0X4+9RR8/vPGcB58MSoYEbV6tW9CX+lXJqLU5UnbaaWQdClSDQVhvmGLZED0/VZ5n/b2iom+IMxm5oK9gjC3kJQ9QYgg7aZJu2kyXobX869zqniqNE9N/gdQLALKfDOHBSaFp+nZWns+mUUpvv+xd9J+bpzulwdqdy5f4W8VpXgFOxSP0+f1squ4rTTPilHWs8pRk9X8Up3s4n7AiFKb151i87pTlVuPZCrlnRcPvNLw9hslOAi2/j7B6nU2miVYfS/Yx6gqW9aj5sABIwoNDppXV5dZ/sory58Md3XB9dfDBz5Q3r96T4+3bvVT9IJpYWvXhle8Czvewcio6VQMa/R8TqUy2d698Cu/Ur7OTKYHNtvPF17wj388DmvWmOqDwf5s2WIEyAMHfFHq2mt9j60DB6oFyYXC5UzbaVWpeakQJghTo1blUou9Txfqd6QgzBXmgr2CMPeQn0+CUIeUk2LIGyql4zk4JTHKKyqKhaC/VPm6tSKjVEVYlVdUDC5dzsu/8NZSJbxSdFMuB8ePm/dg1JN92TS9RMKMgoOvjo5ShNXmq46wfcmTEI+zbdG3Qw3UHaVNpNSNrwOwvfeVpsQoqD94Cz5ZGei5k6Hut5W9di/+MOdXvL1qun0N9NzZ0NMXK/hUVtMLZj6Oj5efp2C6QKU58gsv+Pt1zz0mUimb9X2pbPTSkSPVg+/77qv+w13r6XHwGAbTwjo7q9PuGonemI7RczPnc6rtrF3buhSrRmimn21t5rinUnDrrXD//dXnrTJabdMmc87lqf/lTdtp5tqttw2pECYIU6NW5VKL4yzc70hBmAtcTnsFYX4jEVKCUIcLhQtM6AmAkhBlUa6HqzTByVYfasR03LdAV2SH4iQ6irx603puvSpN10/OhqwQEoJl0/nAjLI/+MHy+Y89ZgSuSbZ3fon1sdfoTb5Qu5rfPS82HRkF4On6gzfwB6Tf++B/K5vfyqcvPT2weLHZfStG2Wp6dv2LF2F01Mzr6CgXDuxAWWvTp0zGT9uKEoBshIylnoAS9vQ4bADc1WWEkWQy2sA8jGD0xlSMnu02Gj2f0zWUbiTaqxWRKM328847zTmyA6uw8xb2lF+e+vvCa637uvJ8tELAixKSstnw+6hy3aNHzfmNumaD08WMWRCmT39/7Ugq8O/NhfydKgiXg2B2ALS+mI6wcBFBShDqcKZ4JiAb+RJSaZoTHhkVJUpVRkbZxXLn43SQIdFZYODW1XSdnKwln8+Xi05BBgbM9Icfbmqfalbzm8RxmIIYpdj98ls4MORPa2bwNpV0r2BaWVlfJpcbGjKiVDLpi1F28HvuHPzwh+ZcjYwYX6B3vav2Po6NmUipYDqeJRh9Fdz3egPURnyloo5hMFUsjMrj2EzFMJjaYHw6lclalWLVCNOtoBY2IKp8yj/fnvpPdcB4qdN2ou6jzk4zfWLC3Lth3zN23f37YckS8x0S3IZUCBOE1iOpQIIwu7nU1ZeFhYNcIoJQhwKFkvjk4aEn/wvSsHdURPE7VSzSzQCdhVG0q8gnXSNEVXpFzWI8rdj58i9x4MTS0rTKwVs98+LppJVVEkwTUio8TejDHzZm1JZTp+CLX6xu7+BB328qlTLiVpDe3moxCpr/Q9yI107l8fn0p83AObh8LaPoRo2eofm+BM/VVA2lW5Fi1QxSQa1xmjWrtwb/lkuVtlMrLfS558y9Cr6vXPDaDa6bzcKhQ+bdbkMqhAlC65FUIEGYG7Sygq0gWOQyEYQ65HVjolCU2NTIfOVBsrOAF3NRRU08WyyvopdINNHjy0Pf6fXsOnFz6fNUBm/1hI5mnr7YNKGxMRMNEUYsBg8+CNddZ7avtameF9aeUmZ7wYioIGHnt1kBpVmvneFhOHYsfMBc6zg24jnVCt+fZiuTXS6vHqmgVp+5NGCMunbXrjX/tgUNoFyU6u8v739bG2zYYARoqRAmCDNHKx9GCYIws8iDPKHVSMqeMO9JHnptyuuOFEe46F1seHmlGoiMqlQuNBSJ4XV2kE9C+9kxuk9MhKfozWI2rzjE9p5v89jRX52WT8x006iCdHcb0amjw0Q5KRWeIrZ2ramals8bAev556vb27gxvI3hYTPwDouQajaFpxmvnXPnTMZmZ6cZOEcJRsF+2Epv1hcnDOs51Qrfn2YqkzWbqtjKHztSQa0+c8k7ota1G7xe02mTqjs8bCpaRglZ1ktKKoQJwswgqUCCMLe4lPYKwvxHBClh3jL+zpumvc7Jwkk8Gg/HiKqqV/KUCqAm/7Pbzycc4uM5ul86Ttebww2196zXS68+WjPU0dOKPq+XzQN9DW1zutx782v03HPjtAZvYT9OgzT6Y7SnB26/vf4g+kc/gqVL/WiJypQ8K0YdPFg+3Vbvs/T2moHtdASURr127rvPeF719zcnGIWZNAcJ9ncqvj/WYwhqt/PCC347YcteKq+eRqKyWtXWXGauDRijrt3K/UinTYTh3r3l6wf734jINN+8wi4lYmQtQGsfRgmCMLPIgzyhlYggJcxbTn3q96e1fsbLMOKNUKDQ0PJRYlRwvhWlrBhV8qJSGu0qOs6Ocf1fvdBQe0+M/VOeKN7LtpHvsMOLNgDdGXuEXc7NbO/8K7Z3fqmhbdPTYwxgjhwx4UON9OfIu2HtWtZtiojhDdDI4K0VT1+aHUT39prPqqJqIpSLUVag6ovQ+KYroDQ6AL733vqDuaBgNJWKYc0Mxq0pbZiAZ9t56qnqioU9PY2lBwb7ZqO9pjNAvZxRWXORyzFgnI5Y0ajg3dZWPl8GvJcOMbIWgrTqYZQgCDOHPMgTWo0IUoIQwYg3Qk7nqgzMw4hK04uqvldZqU8XFV1ns9zxp9+j+6XjddP1np24nSfGPgTk2ZX5edidY8c9L5Z98Xse7Nx9M7uKq6AbnuB3WH/vnY1VzpsUo55gO9CYIMVaWjpSaNXTl0YH0VZEqRSjRkbMgMl6R1lz5L/5G9+HBsz8YFrgTAgoYTQqGNUSo1oRhWQ9hrT2hbrgMQvzaLIVCwHuustEqUwn3bMZwn5QXX21qVpY63hs2QKvvbZwIzUu5YBxJsUKSTe4/LSyqqowf5B7UxBmL/IgT5gJRJAShAiKk/9pdFlEU6VAVcszKlKUQqO1SdVzckVWfOs4P/O5b9H93X5jZDQw4K+QSMAVV5Rtozf5AttS32BX/mcA2HVgFUBJlCqJUZPTAbbd+Dq9Pafg8583Ez7ykdoHYO1aYG3kCG8m0yxa/fSl3iA6KiUvm61OybNtWo+a3t7ydYPCUysFlOnSqEk5TE1Esx5Dzz8ffsygvGKhJZk07dx7r3ldKq+eyuOxeLHxEXvmmWiRbv9+49114sTCjtS4FAPGmRYrJN3g8jOXfMmES4fcm4IwO2mkEjSIKCU0j/vII49c7j7MOI8++ujHlq9cvvLej957ubsizCGGikOc884BfopdVaqdFZs0EBChgoRVX1OT63QMjvDOTzxN7x/sYvHx01AsGsXK1h+3JWY6O6u22Zt8gcGxDg47b4PORRweXMzgaDt39QzwWIgYteOeF3H+x+d5gu3s4xZueelzJkRl1SqqeP11APZxS+j8PXvg6adhdNQMDsL20f7h+ta3zOewZsKI+oP3wAOmvcFBM21wsHb7lShllq30igFYsaI6veyBB4ywMjgIuZwpdrhqlRnsKmX+3d0Nd9xhptm+9fbCzTeXt7lypT/tcrF0qXl/443oiBPb37B9qYddN+qYVVYsvO02My/YztKljVWrtPsyHYLH4+qr4cwZ8zl4XdnIstFRoxEvWQKnT/vrdXe3pi9zDXuP2nsxSDP3ZC2WLKl9v0d9T9x+e/226wnezX63CFOj8vsGyo/9bPMlE2YeuTcFYfZy5Ij/mx6qv5OjvtNXrlyYv5UWGn19TzA8fOrUI4888kSz60qElCA0gEJVfdZooywFIqCifiAFI6WUXcFRpEZyJEYKvBm/jjWdZ4xAlM+bkTyYfwcYvrqLgRu6yafixDN5/uW3/wzOdLKL+wETKRUUoiAgRtkf8SVPqAZT8SqYyciF6Tx9qVcJy/OMf9HERLlnTGWVvLD2nn/eRPJUVuoL7k9U5I5dbjYY907FpLwZoo5ZZeRZMIVv/frLFzlmj8eaNSYyKuqp3pYtfmSUZaFGalwq74iZMlGXdIPZhRhZCxa5NwVhdtOK6suCEIYIUoIQgatcHJyytD2L/bdG+yJUA5Edlfl7F65bwg//zXvoyexjzXfP+DOsEKU15PMMrIzz4w/dxsBPX0uuM4l2FaqoSdz307zr0Cu4P36Zr3zlhqo2q8SoFjCTaRZTTSvLZGpXm7NilDXT7uw0r1opebXaqyU8hTGbjHtnumJYsx5D9dqZaSHPrlNL/HjmmWoxaiEOhmZiwFjr/NprSWvjNdbWNj2xQtINZidiZC3IvSkIc4OZfrApLEzka1wQInAn/wvzjqrykppi6HgxEWPkLYs5/LvvYGDjVSakJB73X0rx6vtu5Ft/9Ku89t4bGF69lGJ7Au26FNsTDK9eyrGfXcvqf/kjrtl8omr7D9/zUst/rNk/NDcGiukdOGB+TNb7UVkP+/Sl1nqV7a9fbwa1lf2wVIpRYDIjP/ABkzZWkQ1Zd3+bfdpTGVFW2T/bx8qIMrtPc5HgAKKSZgYQe/bAk0+GHzOLPXZPPmmWnypR1/Xjjzd+Pff3R/cz2N+5eG4bGTBGfSdE0cj5tWSzJoC0kma+XxoVvCv34+jR+tsWalPv3rCRiNls9TwRHeY/cm8Kwtxh3br638nTebApLDzEQ0oQInBxOVc8R4FCaVqYqfm0UKBdl9ySJOPLO1m/94QfvtPZycCGZez9w3/CyFtXEHMSLMrGSDhtxGNtJJwkbUWXYjLG+ZhL19phzh9eQuZ0e2nzp0dT9Pac8qO4XnqJfUvvBuAW9plpU/CQisoT37u33FdmKk+2rTfT7bdHrxf0ZrrzzmgPEq2rxahUyohK73ynyV7MZuH8eejoiPanmI4X1Ex64cxWWuEx1N9vfMog+rxUHrvp+jqFXddBal3PM+mrNhtotXdEM+f34EETHXX+PMRi5mV54IHGv19m2kdNCKeRe6NQgE9/Gk5NFoFNJPx54hk0/5F7UxAEYW4jHlKCMAOknBRpJ02umCsJUUFxqmUo0AmXn2zq4Ue/fAM/9XdajC+mAAAgAElEQVQvl2b9+EO3cXFZJ/E8tGerf40rFEOn25lo07Qvy7D2/a9y7uVlpfm7DqyCl19mR+ERHKXh7Fnef3Q7AGcmJljOGX9UGCQe54lVj8FHovPGmk2zaHXqVfDpS1Q4f09PuBi1davfj1ak5NXr50x44cxWWuUxdLkqcE2lgtxCKF/fau+IZs/v8LC5f5PJ8u00W3VL0g0uLY3cG1aMOnbMfB4bMwHC9lxLetbCQO5NQRCEhYlESAlCDRIqwQXvAgUKeHil6Chbba8lTFbo067D6Q1XUmiLc2FZgjc3LOXoe69n4op2Fr1xFjU6Zn6pl14XOTncznC2jeI4pJbnuCJxlk99798wfAIOD10JY2McPr+MweEUvcNfQ+VzpDLnaZ84T6owSrEI3kSu6nXeW8Khh75Qt+u1KtcFIxdmOnokKjrjlVfMYCeXCxejwtadiaeuMxlRNptoZYXEy1WBayrRXQslCq7Z6MVa91Ez53d42LzbwgNBplJ161JWc1zo1Ls3KsUogNWr4d/+W/NnbqpVVYW5idybgiAIcxOJkBKEGSLtplkdX82r+VfJ6EzZvJam7gEouLiskx/+xjtou3CRQjJOtiuFU/QoxBTxTKDinoaTdDPMIvPBA3csz+L2C5y5NsmOfQ+DN8Yu732gNbv4RQB28IelTXjxJEPdb6vqxuKBV2DZsqrpYdiBdhg2cuHo0UsTPRIVsdXZaZ6233prtRhVue5MPnWd78a9M2FKe6krcE01umshRcG10hS/kfNbKUZJ1a25Ra17Y8uWcDHqwQdNWqYYWQuCIAjC/Ef+pAtCHZbHlrPCXYGDUxYZ1bIoqeAmXEUxGcOLuxQTrnlPxhi99gqySzogFqOYSnJh6TImlnaQWFLASWq6GCZdGEbHY+RXLsdZuYIdV/0PtqW/U3rcuEtto09tpugmQrsR5LsTN9c1GS4U4AtfqD14370b1qypbXbcytSrKDPtZDJajAquO9MpAK0y+56NzJQpreMYr6+NG8Pn23ZgembhUdfhQw81ZtbdClP0hUjYcbNEiVFTNVG/XMxns/tGiDpfjz4aLUbVWk+MrAVBEARh/iA/iQUhhIyXYbAwyE9yP6E/28+oHsXBIUaMpEqSUAmSKkkbbTitvI2KGjyNO5GnbWgclS+A1hTjLmNXdjJ0TZrha7sorojRuWKC9u4si68bo/PaCfLtcZSnS5FUjtLsSH+Gbc7XANjufJ7Nqn4Zss8V/xn/fuhf1xzc2TSLffv8yldRg/dnnjFPwmeiKl8l9SK2Lvdgdbb3bzpMpUJiIxUL9+yBv/mb6PlWjJpOpb1WVZCrJa5UblPwCRNqs1nj/RYmRgXXme1ixaWsFDmbCTtfbW1+ldNKMSpqvXrfGQtd/BMEQRCEuYak7AlCgJHiCCcLJ7ngXSCnc3iYX7Zl6Xka4iqOq1xQUPAKpeWmi5psRzuKxFgWt6gpxBTK0xQTMYoJFzQ4RQ+HIigPEopsLGXWyYzQ/fJAaXuO0uxwHmdz8dtsdr9HPU/2707czOe8B8BtzoB2/fr6qVlbtlRPb2XqVavMtGeK2d6/VtBqU1priKy1n/bZ1VW+zFNPmfeDB837VNI9G43ugsbM75s1RV/ohAm1yaQRK0ZGjAgxXRP1y8FCMLtvhrB7o7PTiFBhYlTlevW+M/bsqW26D+XHu7fXfGcJgiAIgnD5kJ/GgjDJmcIZDuUOcbp4mozOUKRYqq4XJE+eCT1BTufI6/yU0/Z0SX6yE0x0FGiUp3GzRXLjMTztoF2nlNrn5PI4BQ+n4BErFHGzBTNPQb4jQWZJqqwdR+mGIqMA7ki+xPucp0qfw9Lr/vIvq9Msfv3X60cuvPZa49EjzT7lnm661Uwz2/vXStatqy+6NJoe2dNjUvVGRky0TCbjp3FZ+vp80Qqmlu7Zyuiu+RwFNxPUEmo7O00UTS3sebn33tknLthKgpaZTFeeC0TdG21tJpK21r1R7zujUvwLu9fCxD+JlBIEQRCEy4tESAkCJjLqWP4YGZ0pRTsFPaIqhSkPj6zONidGaX99XVpPoSc/KU+D6+BkC8THc5xkJRfPdbBocQatlC9fOS4U/V/aOmZGzk7BA6X48ftvKIuSKlEogOfhkkOhcfMTxsC8go8XXwLgqwOQT3Zy4MBbAN9I+MQJM1AcG6ufZgHVkQv1okeafcp9112m+lIrzbRbyUyYfS90rJ9UX58RqSxRkTSN0IroroUQBVeL/v7axw/MMTp61By/WveGnZ5Mmug3paKP26Xwf5sKC8nsvh4zfW9Y8S/qe3ShiX+CIAiCMFcQQUoQgJOFk+R0riQ6VRqWB4WpIFOvtKchKEppjdIapSE2nmMweyXDdOEUNbqoUK43uYpCuwpPOebfjknnc3NF2geHyVyZZuCGboav7qLrzYpQEm33bfJde7j5iaqeKTw+XnwUgK9mPwBUp9d1dsKGDfDhDzeXZlEveqSnp/kUl29+0+yajaSYTrrVTNDKdLCFhD3PBw+Wp+ml02aabnGRS5heBblGxBWYv6LUfBOSW8WlrhQ5G7kU94aIf4IgCIIwNxFBSljwZLwMI94IBQqlSKiWGpWHoKBMytJa4SmXeC7PxXNtDGNG4LH2Ig4mPU87yi5s3j2Nmy8Sy+RInbtIfDxHoSNFrj3BwA3d1YKUbQsTbaWVQzFenQ/j5ifQ8ST/Tn+SidRy/hdvr1qm0R/zwcF7I0/ItTbRL9YPqJGn3LfdBosWwd699dOt7DYvpdeMTQerNVif7V44l4NKIc+m1NlrQykjTlm6usy89esv/bFb6FFwU/FK+j//x5zDZNJ8ns9Cbdi+BJnP4silvDdE/BMEQRCEuYcIUsKCx4pRDg5FipFpeFOOhgoGQwVQwXgsBYWcw+hAmvy4W1qm3RnHRU9qUAo3VyR1dhSlTYpf/GIWN++VRCqnaISrfCo+tb4GcJTmt9N/zv/iN6vmNftjvtEn5AcPGkEqSpSqNbBZubJ1ZtqtptVm3wuBMCEP/OsCfFHqxhvN9Msl5C30KLippEvdfvvsFpJbzUI1u7/U98ZCFv8EQRAEYS4if5KFBU+RYt1lqsSoBrUprc2iUelFNhEQIJ+Jk53whaQuhlniXTDzHYXjaeJjWdrPXiR1fpy2oQxuzu/7qO7Ecx0jVGXyoe15KJ6lMedfTyv+y8gDofOaMWdu5Al50PjXCg7WJwjMuo8/PjMpF5eqTHil2XdYu5XpYFNpdz6VPd+0yZhV2wFmWLqnmhR7P/CBy2dq3UpT9LlIVCED+z0Rdf9v3uyf33pC7Ww0LW+GhWp2fznujaAoVYmIUYIgCIIwu5AIKWHB4+LWXyhASVzS/mC45nKlxQPG5OUzQEFbOkesrUBx3CV3LsbK8VMUM7FJrygHZyJP6txYaFtn9DLOOku5ov0iy8+fCTU19zTsZAe72MY/5wm268+GekiZZRWf8H6Pf8hsgq7q+c2kVQSfkE9MmBS7Wk/ItYYXXjCV+5Sq/5S7vx9OnaodaWEFGNsXW+57psqE1zN3tu1ecw3cd1/r2p2PZc+jzK+DWNPrRir8zRQLPQpuqulS0/HtmissdLP7S31v1BP/5utxFgRBEIS5iPxJFhY8aSdNjFipul7N1LxKkSli0cjpoZ+V+YcD8ZRHckmeztUTDF53NaPXLMEpeKiiR/xilvh4bjLsyn+Neh2cZRnJKwpMXExw8oUuFv2wHwYGzKtQwNOqJEYBfJbtkZFSnlY8Xvhdniq+Fzc/wZKBf+TXhv+Ur771X9csYR6FfUI+NgbZbPgydjCycSOMjJiCgIOD9Z9y79kDX/wiPPWUORxhffI8M//P/xyee85M6+szZugzUSZ8zx548snoY2P9doaH4Yc/hD/5k9a0O1/LnkdF2Dz0UHREzuWiEUFsrosrtQiLlAqyENOl5tL1O5NcqnujEfFvPh9nQRAEQZhrLKCfhYIQTspJlUQpW10vUpQKiYjSFT9sa1f/UuhSop4qbbCQc8gOxSlmFHigXI1ud5kYjHPttw/TdewsOu4yvqwT7ZoKe/bV6Y6zePko8UVFxgeT/J8vvYud7Y/hrVgJ3d14V1/Lzq7/xC71S6V2f5G/p5e+qt55WvEYD/M1/T7TW+3xS8W/5T+M/y5Xv7q3ZlpOLbq7IR73jadrrdPWZqr49fXBF74Qvszu3XDokFnGmiKPjFSLUlaM6uuDTMaIYhMTZh82b669L1MpE96IKLRmDaRSpj9g9uMv/mL67a5ZU3t/CgXTzlwqe95suudsGmzOp9TJZpB0KZ+5fP3ORUT8EwRBEIS5h/vII49c7j7MOI8++ujHlq9cvvLej957ubsizFISKsEF70JZpT2gZHAeFKgq0/T05P+UChejwtP6/IlaQ24kTuKNDFeODnAx006eOBQVJ763lOGPFtk68BRnr19JrivF+JWLKLYlKLQnyC5qY7w7TaojTuFcOy/891sYfHkNh9tuZPCmX+CuB9bx2Oi/YtfQZsiMQ7HIz6W+y8fVTlxdoBhvQ7ux0muP+hn+WP8/oIwst9V9mv/7ql20XzzDROdyDm/eTk+PKdc+OGj6PzhoDMWXLo0+vkuWwPh4+Tqjo0YMUcofSBw8CLHJROJUCs6dCz9+g4NG4Fqxwvy7rc1EX2WzRqAaHDQC1ZEjvhhlt/mud8HWreC6hO6L7ddUyoQvWRK9PbufTz8Nx4+bfxcKpk+53NTb3bPHbHNsDLZsMe+V7a9ZA5/+NPzjP5rpicTciFY5cgS+9S3/c2WflQo/h/Wux5nGnpPgua/EXvN2/1aturR9nCnsftnzEaTW8ZiPzNXrdy5ST/yr9V2/UK5HQRAEQZgp+vqeYHj41KlHHnnkiWbXFUFKEICkkyShEox5Y3iT/wFl4lSQ0g/YyVlRv2ftcrV+8HoFhXsyy1X5UzhFj0UTY1wcTcFSF+XCd755G2MvxPjlg1+i2JGg0J5Ex4zvlZsr0HEhw7Xjad59FE69sJLDg4sBODy4mM/t3WA+XzgPExNsYxcf9x4l7mVR2kN5RRyvUHqt1q+Bhpf4aX6RXTwU+8/kFi2jbewsE53LObRpe9UgqrcXbr659vGNGnhFiTCplBGw7HG78UZ44IHq9VesiBaljhyBw4eN6GO32dtrxKh6A8K9e8sH1I2KN43upy13f+WVRhibarv9/Ub4sOtHiVK7d8OZM+ZzLgcbNsCv/MrsFqPAH5S/8Ub0sZjK9TiTVJ6TsEFv5eD5jTdMFOFcFyHqpUs1KwL09xuRt+b3p2fu9dl47Obi9TtXEfFPEARBEC4f0xGkxNRcECZZHltOUiU5WTjJBe8COZ0rCVNAdSqf0qXoqDAqB1Fm7eqRlcoUWZEZmFS1TJjVVd4pBsauJt5ZZNkto+x6/RfZ/OKz/OxLf83wdUsZeMdq8u0J4uM5uk9M0PUbvwXAjnteBGDXgYpwi2yWbXoXO9hpUgJtBFhlviHwMf6MDbxCL314KhW6b1MxoG3U9DhMjAqmuATXP3jQ+E5t3Gj+3dVlvJkqozMqxaig6XitMuHZbLUJe6v286abjHj0zDNTL0/e02OWC7YDZrv28/CwHyEGsHo1fPjDs1+Mssw1s/Coc2LP5VRSMucCtSJUgtMbNfC2Bv1XX20KHMRCfq3YNvfvh/XrTSW+2cZcu37nKtarsFZRh8rv5vlU6VIQBEEQ5ioiSAlCgLSbJu2myXgZRrwRMl6GnM6RcBKkVIqCLvBm4U0yOkORohFNJqvkBQl7oh8mRgE4nYrMkk5iF3IkyZZ+RacLw2hXE1uk2H7F37C58xhwLV1F6Ho+oLgERrKOAw/f81K1IAU8zGM4aPJuEtfLo7SHVuEjpM16DwC1LDamYkBbS/wBU3Xu+PFwMSpq/YMHTbSPrcjX1VUuwKRSkE77YlRYJTq73WCfrJDT2dm8eFNvPytFtrBlGhHBosQvMKLUnj3Vx+LBB8MH97OZuVSJrdY5qRRnYG6kTtajEa8kaFyUsl5sY2Owbx+cP1993QbFqJERc60rZb4LZhtz6fqdy4j4JwiCIAhzjzk2LBGES0PKSZFywqODFjmLOFk4yUBxgKL2wo3OdbkoVdPo3FGMXdnFkonTMI5xSVcKL+6wZGKIX1O7+Cc3HYYPPly3354Hj+3+6dB5j/EwO/hD3GKuZoTUTFMpwtgUO4D77jNpa9/4RnlkkufB0aN+paZ77jHH9IUX4Od/3kRHrF3rRwQFSafNudi92wxUgqbj4A+Wg2XCg1FFu3ebtI716+vvW1jkVaXYpLW/TCvKk4cN+PfvDxejurpMRNZcF0BmO41Gyc0HMQrMvVlLZIs6HlGiQE+PiYzat898PnbMeKBZUapSjMpkzPV9+LC5B0VoWLiI+CcIgiAIc4s5/jNYEGaWjJdhsDDIycJJBguDZLwMaTfNuvgGju96JxfPpdBFhfaqVSkrQlWLUXpSEPJn6ITi/NLlpSnaUeQ7kiRGJ3j33q+YEV8dPA927r45NDoKYBe/yE71cfJOshQZpZWDVg7f4Wco4pY+23n5RKe/fa0iK4I1U1EsKMKMjcGFC76ItHt3tZhnl3/ySSOyBLHL2Wp6lelpUF5978gRk95nOXDArPfUU/5AOWwbjezfnj2mj8EKf5Vik9amP3/+5/Cd77SuPHmwYpdtI0yMana7wtQJq6IWZL6IUeCnS0H9dCl7PGqlSzmOSdNbvdqfZkWpQiFcjOrqmh+pj4IgCIIgCAsJMTUXhBBGiiMcyx/jeOE457xzDHlDXPAucLZ4lhFvlN3/cwVP/+laRk6mWfKWIZLtBdx4saFtq7J3//9F5TIytoi0GiezfBGOB9e++CZv+9LzJm9s06bIbYaJUdtufJ0vPPAdTo+mOPyqC57HYbWB15Pr2cweHF2kGG/js/xz/iO/z4B7FXe6z4MbQ3lm3ujytwKQGD3HJ7zf569ffQdQXhGsmYpi3/wmvPwyvP66iYwaGTHzCwUoFmFgAF55Bdrb4fRpv1LewYNmuTfeMCbmzz/vV+R7/XUzWN23r1qEWbas3Oj89OlyI3StzfbfeMOYolthbOlS0+d43Ax065kxVxpZV/YbyoWifB4OHYKzZ/00pCjj9kZNoJUy1fR2764+Dp/4RHj1PakwNbNYI+W9e6vnPfDA/BCjLKtWmXSp22+P3i97PFaurG/c7Thw663m3hoaMtOGhsz3zYUL4WLUfBH4BEEQBEEQ5hLTMTWXn26CUMGZwhkO5Q5xtniWjM7g4aHReHhkdIbjI+dpv/VFVr3zdV7/7nWM/+BmCm9excRooqHtB72kbOocGpSrmeho48IVS8h3JOg4O8b1X3m57vaixKgd97yI62h23PMi21LfKM37h9x7+IT3e3hasce7i895DwDwNW9raXrZ9rXiE97v8Q8ZI4j19VGKlLJeLxAdeVOZXnPokC8QrV5txKFUygwus1mzjo1q6uszLxtltnGjadNGFWlt1v3Hf/RFmFQK7r7bj9iwkUF2m1Yk+qmf8ge1mYwRw8BP8evt9bdRa//AN7K2hPXbztfabKNQ8Pe30m8nuK1a7QYjtwoFE0FSKUYtWgRf/KLxlGp0u0JrqJeSOd+OvU2prUUz6VKxmEnTC0ZKZTJGUA0To44ebTxSs9U0EyUqCIIgCIIgGESQEoQANjJqQk+glCKlUiRVkoRKkFRJvIsdZMZcFnWPcsv9+9jywE94+9o2nv6vNzIx0tZwO2GilONqFq3IEI8XSL85zB1/+j26Xx6ou62+oytDxaigf8uO9GfY5nyttMzXvK300ctd6jne5zxVNj0oSnla8UcjH+Fr3tbSMsG0mEohplLkiPJ6SSbNeg8+aKrNpdO+KGU5fdpU28tkzLo21c4KSlqbSKpgil2wmt7WrX7fwkSp9vby42jNz5Uy61Vuw+5fWPakFZI2bvT7E9bvri5IJPx17PHYsqXab6deu8EUwULBiE7HjpXvz6JFJhJq3z74sz8LF6UayAYVpkCY0XeQ+SYIzpQgY0WpVIilX1CM2ru3PGU2qv2o9N/pUJmueynbFgRBEARBmMtIyp4gBDiWP8aoN4qjHBIqgarIZ0okoTARI5uFRUuzvP22MTau7iJ7VT+JK4ZRSuPEajmYW8pr7imtUVrTcXqUNXuOcPvnf8C1PzhuZo6N1UzZu27pGAD73lheJUaVtt+3h96LzzDodPOKfhvb1Wf5AH8Lboy71HMMciX92oQt9Ot1nOZK3uV8n8cnI6Mcr4B2Y6y/c3lZWoxNwYlKM7MD8mzW7EZlRIPr+utfuGAGbTalrVj024jHjdBj2wBTke/8eT/lzXV9McpxqvvW1uZHJL373eaVSJjoqqAYBX46VeU2enujU43sui+9ZAQiCO/3HXeYds+dM+06Dlx1lUkTDG6rVruVKYIvvmi2pxTkciai5Pd/3wiBZ86Y5c6eNa9f/mU/fa/W/ghTJ6rq3HRSMmczzaTtfutb5vOqcKu70PWeftp8P1y8WD6vWISPfQxefbX8fgjrR+U5eeMNk2IYvO+mQuW9eCnbFgRBEARBmA1MJ2VPBClBmCTjZTheOE6OHEmVrBKjLKkOjcIh3jGBh0eH005++XGKTo7R0x20dWVLy0aNMcumFz2coqbtdIZ3/fY/cMdffZfOoZw/v44gBXDLqrOs7x7mvtuPhKfM9PWhLo7RG/seVyXP8UHvSzheAccr4HoFNnnPcpor6WcdCk0/6/lz7zd4LXcNjmfUlbs7v8tt/+Id1WJXhCi1d6//ORYzAhJUe70E17eiVKFgonvicd/HKciNN5ry7kqZwd2tt8LP/ZwRe4L9q+zbz/0c3HmnEWE8D378YyPkdHSUDyCDg8pmfG+WLTNi07Fj0f3eutX098wZI5Bt2hS+3VrtLlni79PwsNmW55ljtmEDfOQj8PWvw4kTZtBeKBjxywqDW7YYEUzEqNYTJUbZa76WgDtbRKn+fnON1eqP5xmftHPnZk6QqYywtEKvpVAwfb37biNWRR3TqHNy++3TP+bBe/FSty0IgiAIgjAbmI4gFZuJDgnCXGTEG6FAAVe5kWKUpXORJuu5FChwpniGIgVSSQdvccEUz5tcPSxWSlVMVUB8PM/yvadY8f0T0HjmXxmb152qu4yjNO+K/4A8nVXzfl9/Cs+L8TVvK0p7aOUw7F5BhxpnS/sePnTzIZ4PEbs8z6R9VZZ1DxL0eOnpia7AdeCAiVRKJk1EU2XFPYsd3G/aZAa2Ydus3HawxHxwkNgWcrztPth2mvG92bzZ+GL97d9G9xvg/vvN8ai13WC7/f3+ftp9OnfO977KZEy7t91mxCi7D11d5hiNj5tjeeBA+bGY7QT3Owp7Dc6GfTp6NFqMAv/cgb/cbDone/YY/7NaJuHB++euu8yywX0Bf90oQaZeNbyodN+urvI03WPH4I//GH7rt8rbD/ajlkA4XaLO56VoWxAEQRAEYa4jP4kEYZIiJkdMRcY1lWNFqwLmsb1Gk0hnosOiqBBXNKA12lHEMnne8tdHptLtKXFxybUMdb+t7DWycgP/4qq/pxhvQyuH0043x53reL397az6xG/y/K/916rtBH1R9u71B2aV2AFYlOlx0PxZKV+MGhnxK98FCXq1NGukHDVAfuihcn+l55+Hp56q7YsT5oljxZEwgv1uRuQK86hxHLjvPhMRBWawPjgIf/In8Nxz5ftmvbrApOnNBuGjEeaiN8+6db4ZfpT4UOkTNlvOSTNFCuz9s3dvuZfcxIQRkey6Yffali21vctqiVE33miqRgaNzq0odffd1R5pjz8+84JQlO/bpWhbEARBEARhLiMpe4Iwybg3zgXvAhpNTNUPHizoAkopOlUn43qcAgU0OjINo2q6AqXB8TSrvnGEa//qGMnCOB2xnO98DQ2l7NWlr89sJ5cjkR2lbfwcbWNny16J0XP8l5EHOFpcw6juZJAVaDdeSou59dbygVTlYPP11+EnPzEDyEoa8Zax25mYMO2MjpqBaKFg0s6CkUwDA2YQunFjc2kvjaZTvfqq2Y+TJ02a29q11e2EeeLUM7KeSnpWLY8apeAd7zApe2fO+FEjuZxJGbzllnKvrkbSDptJ15pJD5y57M2zapXpx+23R4sPzaSCXiqmmn52xx3mHnn5ZZMm6nkm/TaYtmuX3bIFnnmmtpfUkSPwzW+Gi1H2er71VnONDA2ZdYaGjKdc0COtkpkUhMJSly9V24IgCIIgCJeT6aTsyU8jQZgk7aSJEaOoi2hd25hca02RIjFiLHeX4+Dg4U3Oi1onfFpyKMPyH9VPt5sWPT1mhNzWxlDXdVXRUedXvJ0/aP/PfNX9AMV4G6l4gc6474V17Bh8+tO+h0vlwNRWvDtxIrz5RqMtxsaMANTe7h8vu87GjeZlI6f27TPRQJ7XePWuRtKpenp8Q/VMxmh5RyqC1yr73dcHhw83FnnVbHW1epUMwQgwwQqFtpJh5b7Vi8KZTRFJjVZwbDYV7FLRbOTebCAq0qdWtJO9xo4eNaKQrZZpK1oGl7ViVPC+Cbtv162D9evDxSh7TG31PRsp1dlpvn9ee61+pGYYragSGEzfa6ZtQRAEQRCEhYpESAnCJHEVZ9QbZUJP4CkPV7mRy+bJA5BSKZJukhFvhDx5tEfNlD0qZzuKeCbPDf/fPhhg5iKkbrjBrP/mm/zl7Z/h0H2PcWjTdg5t2s4/3rWdPxrdzjeH3sFE53ImOpez/q7lfPS32qoiEGyk1NNPV4tRQUGkkYpiYZFReXNYOXvWRPh4nhGHbLW6K680A92zZ/3lzpwxA9Fvf9tMq1W9y0bOvPFGdMTCFVcYUezkSSPAtbUZ0/NaUSIbNxo/p4MHy49B0EcnHq9tZB0VdVSvkuFTT5mBvRUL7QAemovGmm0RSY1WcLRIBEpraKRIAVQf7yVLTIRkLme+sgoFc70kk2abv+5JbG8AACAASURBVPEbvhiVzRpBqZa59/XXG9+zsbHaqY+33mruvYkJk/p4003m2giLUoq6H1pVJdAu00zbgiAIgiAIcx2pslcHEaSERkmoBBe8C+R1Ho3GwSkzONdak9VZIz5hoqSGvWFyOoendZUYFT74KHep8mIuN/zFXrpfO0aqMIKbzfgjsbExs9B0BSnLSy+xb+ndpdFUraiHqLSYPXv8z1FiVCMVxY4e9Qd3YNKWOjuNyAFmYGv9pLJZM8A9ftwIVEr5leNOnzbbjcUaE0jqpVMpZdKPslmTAtTeXl8IWbsWvvOd8GOwZ48ZiK9YYV7BY7FypelrvcFulEhgo7fs8Q+KUZXHu95AeDZWC5uqOCJMj6mkn/X1wY9+ZL66sln/HrWi1IsvGuF4eNhs95pr4EMfqn3O3v72+qmPjmME4ZUrfTGqmZTZVgmxM5GuKwiCIAiCMBeQlD1BaBFpN83q+GraVBtaazI6Q9bLktM5sl625BVVQhmRKpetHmHYQUfY4EMHJCkv7vCD3/4F3ux6G0NtK/2yaMHXDOUh1Uthq0yLASN+ZCez+a65JlqMgugUIFsRLWj+vHWrqTxnTbo7OowYZUWpixeNSJVMmkN03XVGEIvFfH+pRlO26qVTOY7pz223lfc7yqR4w4ZwI+ugSbSNntq40bxbI+uw9L+wtKDKY2lTF4NiVG/v1FMEp5OuVQ+bDlUrLSo4P7j/Yf0KImLUzNBM+pm9zkdGzMtx/HS9TMaIMa+9Zt5tGt6FC7WNzS2Npj5GicX17odWpIY2Wiih2XRdQRAEQRCE+Y5ESAlCBR1OB4ucRRQp4uGhlUah0OiST1SMGCmVIqZiZDJQVAUc1zdMqRShwkQphSo5Uw+vupK/GfoUo7nl3HJ7zJRP27TJf91wQ2t2riJCqpEUNpsWs2ePiXjo7DRRQ2BKrStVe/3KaIveXt/EuTJayZp0nz1rxCcbHRUUvYpFIxRdcYWJjrJ9abUw0WyUSFjkVVjU0YoV5hjcckvzUUe2T3bwHzwu73mPEdHqRabVis6YiYgkmw718ssmSqZWBMo3v+kvZ49psF9791Zv/4EHRIyaCZpJP+vqMufszBkzTWtIp809WiyaVFzPM98fsZiZftNNrYuua7RYQdj9UG9+PSF2Om1LpJQgCIIgCPOB6URI1S8lJggLkLSbJu2myXgZRrwRihQZLAwy5o3hKIeEMh5PRV1EtU3gBh5516qyV2ZsHlzOKdKxagC+3/p9qcRbmsW79jC4eSjGueu93XR3d5UGZ1XLeyblrKvLCETJpD9v924z8LJBXHb9/v7yzzbaYt0638TZ8/xIqSCOY/Q4O8izKWhB8eXAAT9yCmYuSsb2OywNJ6y9sH2xUSZ2GwcP+mmBdh+zWXNca+2HjRw6erRapEul6rd74ED58a+3z8F1gzQbGdXXZ/x9Dh0y/bTbDPpr7d4N+/cbkW1w0Ah5fX3mugpGkYVhr0ERpVpHvfSz4DkE8/0wPu4bmre3m9fwsLl/JybKCyK0txuD81ads0aKFdS6H6LmV+5/2LU/3bYFQRAEQRAWMiJICUINUk6KlJMi42U4oU/g4ZHEV2TyOo+Hh3IATX1DcxWxnILEFSOt7XwFA0s8fvzgbeQ2vgaLT4DSoBUUEly3oZs3nr2et7R3lw22KgemiYQZXFohqFJcABMR09cXPjALilF2u7291fZYlUJQUJRKp8tFv5lM2WqFEFJvsGutwlavjh6ke55vXu66Jkqts9Osl0qZY2KFLtunynZtimAjNCvERWHTofbv98WK4HGwVdesGGXTuaw419PTnDgiotT0qRXxE5xu39eu9QXidNqIiOPj/vm059yKj45j0m9fe611goxN/w373rHUux+mKsS2om1BEARBEISFighSgtAAI94IBQq4yi2ZnHvaq/KTaoiI5TrXnJxeJ2vw6lVFnn97nov6anR7DjzXiFGOBx3DZNQ43DHIC/9wB7etXFMWuVJZTU8pWLzYNzY/cMBUufrwh80g03omhUXCHD1anQYTjISxhAlBXV1+OfmgKDXTYlQrhJCowW4264tK4+NGnKncXlCMsoP7WAzuvNMcyyNHfH+qyj6FRaY1s+9hNBORFNzv/fvNe1CU6u8vFy+sKbsd2Nv2GhVHRJSaHvXSz8KEVYC77jLplDfdBHffDX/8x9Wpfq5rrlvPMyl7rbbF27SpOlKzknr3w1SF2Fa0LQiCIAiCsBCRn+6C0ABFioBfH6+oi0zoCTS61mpN0X7tGbx1xZZtzzKwxOP5t1xgJJ5lPN8OZxKoiQ5Uth010UHu7GImxh26rh6h+xe+z95DAxQK4WKUFROGhuDqq82/x8bghz+ET3/aGI2Hmfja7f31X8Of/EnzBsFg0n/AF6Vs+uNMmATPhElxmEl0MulHOCkVbqhcKUalUsZDyw6Q3/ve1honNyLENbN9u9833WT200ZKDQzAsWPlRteNilH1DNiFqdFo+lnlcV+5Eu6910S8ff3r1emkK1b471dcYb4/ZuJcNWqAHiUI1RNia/V3um0LgiAIgiAsRESQEoQGcHEB0GjyOs+EniiJVC1rI5GnsCXf0m0C/Dh1nDOpFNmLSVS2DdWRLpufTCqYaCd3MU7nsoskb/gxf/mXfkRLpRgFZkD6679uquyNjZlpx47BX/2VGZRWDlg//Wk/LevQIb9KXyMGwRs3+pXpLEFRqtVCRCNRIlMRQqIGu11d5vhagS24vb/4C/jBD6or6W3daiJSnnzSGIa/9a3hlQztNp580qRS1uqbrXA3E9XCwkSpIJVilONMXRxppHKbEE5l5cta6Wf2uNv0s54eE+F34IAvHgfFVnverQfdbBMQWy3ECoIgCIIgCPWRlD1BaIC0kyZGjHE9DlCqttdKlOvhrfcYfiFJV4u2OdzhMbCqE9odmFgG8fB8wa4uGB5JEUsP0X3DAD/QwyxZ0sWFC+FilB2ofvjDJl3v2DHjafTmm2ZQumWLWdYOTgcG/PWDHkGNiFFgUtLSAR2tq8ts16bvtTJlayZMiusNdq0R9Pi4Hyn13HN+Ol9np6lWZsWoo0f91Mi+PvOyIoH1qOnpMdFo1kw8LDWysm/BVMzKfY9K12o2fe/AAXPOgteU9QgLbku8eS4PU00/s/fN8LAf8RZMrVXKXN/B75PZYu7drG+WpIYKgiAIgiC0BvlJJQgNkHJSpB2jiHh4KBROK28frShOJKBdM7Cqo2WbHbjCI5d0YFyV0g2j6EoryMVJtOfovmGAoSHI5aLFKDCeMA8+CLfcYkQTMIM2K0oFjazBfG5rM0JTvWpVNiXQ+iMpZQQHG8FhRQwbbdWqCIbpRImE0WjUkRWltPa9pez05cvhgQeMGGVLyW/c6A/+MxnTxlvfalKn7rrLREYdOuRvI5WCNWui+5bNmuUnJsL3fboRSbYtrY2QGMRG1FSev02bzP7UEgBsv+69t9ocX5gaU0k/6+kxgmZQjLrppvLr3IpSNkJuNgiIMxURKQiCIAiCINRHIqQEoYKMl2HEG6FIEReXtJMm5aRY6i5loGhCfTS6rsDTFJ6DLjqgIJ+YotD15S9XqQP5f3ID+qO30U6OtuGzVavk2zoZW3Jt6XMy4eC1aeIpkzqY9AsKRoozsRjcf391JMGBA34aGphBqOvChQvh3Q9GxKRSZlcOH/aFrqC3kI0i2roVRkdbX069VSbFzZpEB03bg2Le+Lg5HvX26+hRc0x274YTJ/xthJmmV/YtmYQNG8z5aXVEkm0rWE0vSGX1vcpIqXqIN8/lxZ7foSEjSiWTRowKu86tKHXNNUY4vdzMRESkIAiCIAiC0BizRpBSSl0D/AdgC7AUOAX8b+BRrXXEEFYQWsdIcYSThZOlinqWGDHSTpqkShIjRh4j1rQ0ba/oolwPNMRzHiSaWNcKUQMDkC/3oIqfWIbKF1BtcWL58dJ0DWZkOFGxLccjlYqRz8SrmmkkSgXKjdBtJIyNjLJilE03sxE/lrvuMpX6rJgyNmYid4Im3rYvdkBoIxUaFUj6+2sLTf39fiRR1DK2YmC9QWmzg10b8ZRM+hFgVqSynl42jSgsjfHgQT+izE7r7i5PBQxuI6xvthJiq6qFhYlR1jPKCmVBkUrSouYeweu8ra3+da6USe9tRGSdaSQ1VBAEQRAE4fIxKwQppdRbge8CVwJfBQ4BtwH/CtiilLpTa33uMnZRmOecKZzhWP4YOZ0zkVHKRaHQaDI6Q66YQynzOTZ52xQptkSUMu2Am8zDWUX3sy/DovONb2D/fqNkFKtN1rv3vU5iLMvwsk7aTyuUDVlS1dFdGg2xPKOn2xl4ubtq/u7djYlSNjIqKDJYccVG7GQy1aKUFS7efNPv3p13GnGkMoUoGBHTjECyZ0/tgaedb4WSTZuq08CCkUW9vbXTxBod7G7ZYoSwgQETEdbWVn7c7HHcv79cRLJpjFAuRFluvNFs25pNgx+9Vrmc7VsrI5JqiVHBvlmxTUSp2U+loGs/B6/zLVvKxSYr4M5WUadVEZGCIAiCIAhCc8wKQQr47xgx6kGt9WfsRKXUHwG/DTwG/OZl6pswzxkpjnAsf4wJPYGrXFKkUAHBRqPJkyev82g0Li4pJ4WnPfI6T47ctNp3cCg6HsXxBAm1ni8PtMMAbF/77eY25LpGlHLd0qT246Nc+dIJLl6ZJrO0g9S5i74oVUkyQ/ZinGM/7Gb4zWpb9aBIEBZFU+kRlMmYaY5jRIgHHzQV4fr6wkWpWqlt9WhEIOnv99sLEzzsfGvCnkoZgSpoBF6Z5hZlFB6k3mDX84woMz5u0p2CESbBtqxYEzSKDqYxhglSUamBQZo5zs1iI2ey2WoxqrJvQVGqrU3SomYTVnTau7dcXA1+3rLFBGhu3uwLoL29JuoxKODOVlFHUkMFQRAEQRAuPZddkFJKrQHuBn4C/L8Vs/89sB24Tyn1O1rri5e4e8IC4GThJDmdw1UuCZXg4qhDe6cXqA6lSJBAayNMFSnieZrMxRiJzuJk/tvU8fDAUeRHOlj02vXwkZ+FPXt4grUNrf9+fshizK3h4lEM3tYK3valfZy+6RpGVl0BQOr8xbI+azQkM+TJM3QqzctfuR6IrjL1+usm9S7oERMWCVMsmunxuO9f9N73mu1UilI2jccyEyJJT49fhc7uC/jtrFljBBNbETCTMUKSTd+L8oPq6anfdiMeU0pVpzsFhSR7vMAXpez83bvDtx+MarPRa5aJCZMaWOs4B1MTp0IwQmzDBuMxVCuda/9+WLXKLDebImgWMjZq8Jpr4PhxP/Xz3Dn/8/79RrTKZIwxvq3U2NdnUnDffNP/XE/AFQRBEARBEBYOl12QAt4z+f51rXVZ/pPWelQp9RxGsLoD+Oal7pwwvwkamKdIceGMy4WzMRYtLrKsu1AmkiRUgrw2Hk1Do3mGTrZxxdV53M4WdKQQ5+TuO1h2xWSqXDPlwvo6YWwyKspTZRFSACteOsGtn/oWP/id93BxRZqh1cuIX8zhFIoUEklYNET2YpyhU2m+96d3MPByd6QoYiuxpVJ+REswJcxGwihl1o3FoFAw1fqsGBImSiWTfpraTEXsRJkTg5luo5SCRuCZjG8EPp0Iriga8Ziy6XyW4PF66ikzLSw6Krh/9hxZxsbMK5Uy26j08oLmUhNrEYwQi/KnCqZD2eVEtLj8BKMKT5wwVSCtH5n9fPGiKSwwMGDO48CAH+mXSpnlghF9jQi4giAIgiAIwsJgNghS6yff+yPmH8EIUusQQUpoMdbA3FUu42NGjAIYHTKiTlCUUkrhapdcwYNYgbbFGS6OOqQ7YDoF9xQKhpYy9PIaaGHZ+j3eXdylnsNRmtX/8Artp0d55UPv4PRNV5Nb1IZ2HdxskYsDXbz6Qjcvf+X6KjEKqoUcK9Qkk+WREWAGoUuWwPnzZrC6aBF0dFR7A1lR6utfL/dMgpn1DYoSpey/lTLRR93d5X2u5bk0Hep5TAXT+exxt8dLa18ssF5TUVFt/f2+kDAxYcQoCPfysu02m5pYbz+D72EE06FmUoyqZ2oP048Mmy9URhVmMuWi1Pi4mTY+bo5ZoQCJhPlusD5sleml4gkmCIIgCIIgWGaDIGXNaoYj5tvpi+ttSCm1L2LWhmY7JSwMihgjcIWivdNj0eJiSYyqFKW0hsxInEJRU8i5uDGPtkXZKYtRCoWLMU8vONPM+6vgc8V/xue8B3if8xT/zvkkjtKseOkEK146wdhV7Zy6tYdce4KvFH6VLx96H11uFwMDtQeNayczCA8c8NOqJiZgcLC8aprWRpSypd/DTLVtlISNpAhSzzx9utTzVArrc5BWD6yjPKYq0/nSaXj72+H++01Uk40ws1h/nrDKfdYTK502FQsh2ssLpp6aONupZ2oPrYsMmw+E3StBUSroFWf94jzPr64pYpQgCIIgCILw/7P37lFyneWZ7+/bu659qW7drLZkLhJyS9geydj4EmwpEBgQkicTQzzMmYmZhCRMYDIkwzkTYs/KWkMywSHrTE5wzgqTDDDnmCQnxJlhyCBhe8gALdnINjZY2FhuyxbGsty6q6svdd37O398/VXt2rV3VfVV3V3vr1ev6t7Xb9elu76nnvd5W7EcBKl22On+ws7YBQFwMaKTRqOUEZ+ASFHq3FgCz63ilZMce2iY4ZsusuYfvEaJ0pzOnSIFCjztgZp/tz7LiH87X/A/AsDXfaMwWFEKYOCVC/S89kM+43+KL/EBzrgDZLPw3vfCz/xM69Kt22+Hu+4y4oRd1tdXd9xo3T4L6fbbjfvk6NFmMQrg8cfNcaLKyIJjmo+DJSpTyRKXuRRev5CEryMqryqY2XX11XURKYpw5z4wwsE119RFJ4gWpaCxBHC1iAntQu1h4Z1hq4E4USrYRdN1TXmu7xuxKip4f6U/fwRBEARBEISFZzm8RbQOqOa2XoZcaLtYtNY3Rn0DxxZioMLqouAXKOsyGk1VV/F8ryZK9Q96te0mLrmcOJZmYtwh2VOlPJ1kA1fycze9mc2JzSTmqOv6+GitTcmeXriX4u3qUe5wDtR+/7q/n8/4n8LXZoboa8Vn/E/x196HOO/XjYdBt01tjKEJ+uHD5tZOUnftMoLU4GDrSajd/oMfNHkzYffNPfeY28lJ47w6dMi4gPwInc6O6cEHjeNlLthjRHHwoCk9arU+alwLSbtsqR07jLCUzdadT0ePwre+VR/byy/XS/3sPh/+cP34+/cbB5BdXyiYEsonn4w/72IyOtr+fvX9xjyt2WDLzyzPPNP4WM4ntH61E3y9WwYC/7GzWbjiirqTL5i9J2KUIAiCIAiCEMdyeJv4wsxt3GfQttXYHKchgtBI3stzrHSMH5Z+yKvVV6nqKj4+BQoU/SI+XpMoBZAZKFItJEgUBvmlj0/jOJB1siRUoua0mg0VKlSp4igHVYqwCs0RR2nudT4bKUp52uH3+Xd83d9Pn5ok50w0CBqzmaAHnROZTGeT0FZCy7ZtpjMf1B07YVEqysEyW4Ei6rqC/OAHcP/98evDQsZiCCk2WwriRaGf/mn4yEfMdkoZJ8+RI/WxDQ+bcrNcDm68ET72MbOfFfMOH24Wpfr6TKllq/MuBiMjZkytxL75CpFRokrwsVyM0PrVRPD1DqYU1JLL1cv08nnjcLQshYArCIIgCIIgrEyWQ8net2Zu36uUcoKd9pRS/cBtQAE4cjkGJ6wuzlbPcqJygrIu4+HhKhcHx7iV0EYk0lUyKsP6oZmSPaXJDBRJ9VSYGOvnH96wtjZJzTk5EiQoUpzTeHx8XFyq1dQCXmVdlIJ62d7X/f183d+Poj47/HDvV3l+97+slWgFS5naTdDbuYzCk/l2rp9gwHewjGx4GLZvN+f78pdN167gMcIOllblfK2Eh4MHjRiVzzfmYsUFhYMJbT98eHEyieKypYLs2GGus6fHiFHBse3b13gMiC5H27+/Xj5pxSi7/1I5o5aqlK5dqL1FxKhmgq/38fFGR+WZM2a9UvXl1ikV95gKgiAIgiAIwmV/e6i1fgl4BHgz8K9Cqz8N9AIPaK2nlnhowioj7+U5UTlBURdRSpFVWdIqTcbJkFZpnJmXg0ZT0AXGi0Vym8dZ86ZLJNIeE2P9PPXAjXzlD7fVPvHPOln0POPNKrqCTkXUy80Rt1LErRRJVgv8jvdpflZ/DaX92jeA0j53OAf43we+wP79za6R++7rTIzq1EUE7V0/jtPs2HFdePFFc5zPfx6+9726MyOuK12ci6adC2bvXiPs2Al1oWDGsXdvtLvmiSeMg0vr6OuNOudsHV3Dw+0n8Y4D73pXvPPHijZR1751a11kCIpRdvulcLYsdSld1GMZRMSoZoKPQViMUqoeaK51Xcjt6ak7peJeH0JrFruMVRAEQRAE4XKzHBxSAB8HHgPuV0q9G3geuAV4F6ZU799dxrEJq4RT1VOUdRlXuaRUoyMpqZI4OJR0CQ8PrcFJVki6Cq0VSmkmTvcxfSHLt7/bD8BH7znDmHeKaT095zHV3Fn9bSPS2h+rWm5epjT36s/wP/hHDcu1cvjYpv/Bmavf0bbrXCdiVCsXUXDfdq4fK0qByTJKp81xnngCLl40ywsFcwwrFFlauWhGR+vCUdx1HT9uzpHN1sWo6WmTxWSFoeD9lE6bLoOlknEXha93KTOJ4pw/djxR42jVSTC8/2KKM3MZ+3zHtNSh9SuZKDHKlvnaTntWlMrljBAFjd34xCk1e6QjpCAIgiAI3cCyEKS01i8ppd4O/C6wF9gHvA7cD3xaa33hco5PWPkU/AJ5P4+HR5Zs5Da2bE97gAPah/J0AtdV9A5UecPbTtO/8TBPPXAj3z7wJgB2ffLIvBxSGTIUKKBTRdLrxonP9jf81Fd+g6HjjzYs673wqhl/ornsz9eK/6B+hyo9Dcsr6T4+ev0TZqJD5xP0di6jVsJCsDRvdHR2olQ63SwUPfRQZ+JPcGJ3++3RJXa2zK9aNa6sLVvMOfbsaSwLC1/j/v2NIe2LLaS0YjblaFFiVKei4uUe+0KMZbblpt1K8HVVKjWKUW94A/zkJ+Y1Y8Wn3t7669OW7111Fbz2mjneM8+Y11M3dy3sBOkIKQiCIAhCt7AsBCkArfWrwC9d7nEIq5O8n6dKFVe5qGD69gye9kyulK/x/RmFQzs4foo1Aw6gcXsrqDfkufHDTzF9IcuxV6tsKxdxkqBQcxKmtNI42sFzfHrfNEY7QWro+KMMjj3f0bFtN73/4f4jLg5d07Q+ONGB+gR9YPM4Q9eNkcxWqBSS/K8nh/iZmwZwnPY5UHHCQnAS2skn/5aLF03Qdl9fvatXVDZNnPhz/HjjxG7XLvj5n4err44W2fr6jOvp/e83wlTU5M5eo70mW1KzHDKJOnG7tRKjOhUVL9fYZzuGKOEzPJHXuu5yC55bRKnG13s6bTLLLl6E668398/hw+b1PDTUWOpqRajdu40IHHTxiGDSHlvGGvcalI6QgiAIgiCsFpaNICUIi4mHad+maBajwOQ4+b7G9xS+5+C4oBzoH/RQygEUaZWCTBm9aYod+17AK5nAc7QRluZCURdnsqs0TqrS8X6Xht7acr2vFX+U/2W+mb8Z2wAwzgFjc17O+GO853eeY+i6MVI9ZZSr0Z6iPJ3i5deHeHPlWoaHh2rB43ECQVhYCE5CO/3k/8CB+naTk5BMmgnxwECjA6Od+BM3sbv66vq5whO7W24xIeqtxAjHqV/TYggp86Gd220hRMXLNfbZ3IdRwmeUGNXTY55Lg4Nw6ZJZLqKUIdhoICjyWpHPluBu3VoXOa0IFWwsEBRwhfZcjjJWQRAEQRCEy4EIUkJX4M6oMlEuJl/7RrByNI5vetA5jjaiVEi/SqkkXm+BLTeNcelsCuVqYjSujtDoGbEsiV9OwgI027Ni1MOFPbgz3f9aOWAOHYLt//Al3v2bj9N3xRTp3goJnaIwraj6PgNrx6muneb81GnWvnore/Zsbdv9LewisnTyyb8Vo6zjIpttDNwOZ9ME6VRcsedcqIndcsok6qQcbT6i4mKyUKV0UcJn2BlmxSj7PLt0CTZvbiwvA5noh3Pfws+DONEpXOq6VGJUq3JgS6tOnMsF6QgpCIIgCEI3IIKU0BXknBwJEhR0AY1uKNvz8MwyFE4ClPLB1SicmpBlUUrhapfcFdNk1k7MS4xqRDP1yhBcO/8jHSm9jYcL9XTb92VH2LLvmiaRRmszaV+/Y4w9v/44A1fl0eUkicIgCkWPA+MTcPFcDz1rCqTW5rnAEdYVexgeHmo7jqhJaDuBKEqM2r3bZDUFxaNCwYQp2/I9Syfiir1dyIndcskk6qT7IZjxzFVUXCxmM/Z292WU8PnyyyaU23aDC4pRYLZ/73vhr/5KMo/CdHL9Syk6xbHagsCXm/tSEARBEARhoZG3MUJXkHWy5JwcLi4VGkvjwq4p5RpxysXFUc0vEaUUHh4qsYA9zJWmdL51flSnvCPzNHf3fRWAO5wDfDL3xUiRxk4eb/5nzzG4cYLERIlNp19h7evHWPP686x5/XneNP08a6vnKJ9RVPOQyI6Tvvh57vz9t/NTX/mNOY3PTrJ27aove+YZ+Mxn4sWo8D6221c+Xy85hPjW8lHnDLIQYlRwwlgstm95v9Dt2uNyZe65p/m+PngwWowKt5mPEhkWo838bMce9RgHCT/ek5Pw4otQqZh9g2JUqQQ7dxoH1SOPGDFqcNCss86wxbhmYWEJu+LiXnPhIPDl/rgGRakwIkYJgiAIgrDSkbcyQtewKbGJlErVAsz1jGIQzJXSM18KRVIlI4/jax8fHzXztVCYLnsLw4f7vsqnB/+Ye53P4sTkW23fDh/48DhvfvsYPb1Fhs6dIlEt4Aa+E9UCm/2TDOpLFC8k6Okpkr6miEqNNXX7mw1RApHt4gXNc62zoQAAIABJREFUYpTdZ+/eerc9MLelUv0YrQSL2UzswsJMFL4Px441CymDgyYgu6cnXpSyE+MHHzSujvnSSffDdsLOyIgZTyvBZ6HHvVBjj8Luu3mzEaTABHJfvGjKPcEst+LhN75RH8OlS3DrrcY9sxjXLCw81hVniXvNrbQg8Hbuy3avA0EQBEEQhOWMlOwJXUPOzbEluYUTlROUdZkCBVxdL8mzTikHh7RK4yq36Rhaa6pUa9sBtd/nSydd9mbDOzJPx4pRlre/f4zpoTI909MofLxENnK7DVyiR5dxCprSQD9n3vYGNjzeeQh7FOHcpUzGdLmbnGwWo8BMvB56yIgJVpTasQM+9rHmoHaIb5MexcGD8HuTv8mmlw7zwOSdfHPyTtzsiHGXRdyHvlb8x/Ff4S+K/xSvb4C+PrM8mEEUzrp6/HEjfLz//Y1ZRsF27a2ybVpl4wSDyksluPnm2QWVX84284sZsu448Au/ABcuwIkTZlmlYpx16TR4HvT3m25xUO/kuGsXvOtdi3fNwsKzGoPAF7KMVRAEQRAEYTkib2GErmJDYgM7UjtY764nq7I4yjHfOCZDCoeMypBQ0VpthUrNFeXNfC0Imll12Vsw3AqplMaptv6YXQF9ahrH89GOotIT7R6bDVECUV9fvVwqattnnjHiTi4HN95oxKhOXDSdTOz+6ukd/OC19fzVxffjVop8M38zf3rqZ8m9fozBsedr37nXj/Gnp36Wr+XfxWQlVXPZ7NplxI/gOKwoNTFhXDcjI/AXfxHt0mjlxGnnXhoeNp3NxsfNWPr747O07PiCQeWX011iO7nZY7bKAYsaezsSCfjEJ4yIabHOuttvN49NoWC+x8dN+Z4VNlaio6abiftbcN99q0OMmk8ZqyAIgiAIwnJEHFJC15Fzc+TcHAW/QN7P4+FR8kuc9k5T1mU8PBztNASfa62pUKGiK7WyvoVCodA4C9Zlb1Z4SdAKnehsZua7DomqR3J6fuJZK4Eok4GjR43wZCeNYRfN9dd37qLZti3eHRFc/nBhD2l9jnf3P8F/m95Lr5rma+qDFLMbak6pYAfDPn+SPjVNOZttcCSFx2FLxLQ27pwf/ajRiRMlfgSdOEH3knVZRbnHJiaM6yeVgm9+E668Mj5UPuwuutzuknAntyjmGrJunXW5nPk9WO5pnVFRrERHzXJmqbrfrYYg8E7KWKG9K1QQBEEQBGG5I4KU0LVknSxZp26b6Kv2NZXzKaXQWpsQ8xln1EKKUbXj+c6CddmbDc6FIaimqPQm0ap1HpZWUO1NkrlY5Irvv4qmfae9KFpNtuJK76yLplUHrfBEbffu1mJU1MTugN7HuvIEE34Pk6qHjc65WsfCf5P7Ev9X/iM8XNiD1vC6HsJxPG66qTnrKnjcTMa4caamTImYFUR27+7MiWPdS48+asoZrThlzxm8P9NpU44G8Prr0RP7uG5ol7vN/GJ0coty1kGjKJXN1t1TuZwRQ48ebTzOShAxljNL3f0uXA4cZCU8jotZxioIgiAIgrCcWOZvywRh6Ygq51MoHOWQVVlSytiXFjLI3KLKmQXrsvdY8QZ83XqMtmuYMzWAc2GIxHSV4tqelvuU1qRJTFdZ9+x5Bn5ycU5jm0+A9Z49cNddrSeU9hi33mrKsVpN7Kwbo6Ebm+7jcOlm+pRJvT7tr0dr457ae/r/qYlRp/31AGx0zjE6ao4TNQ573IEB6O2NHnM7J47jGFHKm6kOLRTM5P7Agcb7U+u6GDUwYNw/s+0gtpjdCJeaqOfa9debbo7h8j0wz5coTXYlXfNy5HJ0v1vpQeCLXcYqdB+dNupY7l0nBUEQhNWHOKQEIUBUOZ+LS4oUx8rH8Fmkmcx0jFoRw+DY85HLv+D9El/wP8IdzgHudT5LlHYWdiLcvuZaspu/w9SmHnzXI32xRDDHWysjRlV6k/S9NslbvvrirMYaZL6f/Hcy4Tp8GI4cMULDvn3RzqrwfbBvH5wYGeGblZvZnXmC094G+vQ0p/31nPbXs9E5h1I0iFFDjOGrTGyuUNilYcv0CoW6E+fJJ42rKe7+ADNWperXYfOODh2qO3/CYpQ91lzyjla6uwTihc+9e035XvCxsLcvvmj2C1/fSrnm5Yp1+MWVly10PtlqCQJfzDJWobtYaoeiIAiCIMwGEaQEIQJbymdFqbPeWUqUFuVcCgVOZ2WAY9tui133WPEG/uzSx8GFr1HPPjqz7R21baK7hg3xz88e5UcfGWbyyrVMvClHYqqC4/n4rkO1N0liukrfa5P8g/90lPXPnp/ztc619K7TyVZct7jgxC6uc9q/zX2R9059levWX6jlRG10zvFidQsAG53znPbXkff7uTpxAuXD+7IjbNl3Tcs3+EGsEJLPm7FMTRm3zsBAvBhlx3r77c2iFBhxK0qMmuuEu527ZLlP5CFa+LRiVJRACPDSS6a8cu3aRqfUSrnm5cpS5pPNpRx4OT+ui1HGKnQXl7ODqiAIgiB0gghSghAi7+U5VT1F3s9TpQpAVVcXzR2lUGjV2bG/+6HPxa7zfXhj4E3lX3MNz+/6NfPmk9ZOhKsePMmGF57nB7/yDzl/3TqqPUm0o0hUPTIXi6x79jxv+eqL8xKjLIv5yf983BiO0uxxDnNJvZVP5r6I1vDA1AcAyPv95P3+2j6n/fV8VP0Z/yr3d3zN+bWmcbRyaQwMwKlTxtXkOHVBZO/e1pOEw4fh53/e/PzII+Y2KEwNDdWvx17vbIOk48ZdKhknV6uJ/EIEUi8UYeEzLEZBffnnPmfEKDCh8/m8EfmsKLVSxIvlzFLkk0kQuCA0s9QORUEQBEGYLSJICUKAs9WztWBzDw9XuSjUwolRutkJpYolbr3/Ae74xv9J/6HmXca23dZSiLLM14mw8fsnueX3jjC5uY9z163HyyZwC1XWP3uOvtcmZ3OVbVmsT/4X1o3RLissen2rifGBA0YkcRyTCWVLxAoFuP9++MQnIJGIP8Zb3mJEpmy2LkRZzp+Hd76zfj1RZRph0SjswJqYaD5nX5/Zplg0YlrURH45lntY4XPr1mgxyj5Prr4aXnvNiFHB9VAPNxfxYv4sdvc7CQIXhGYudwdVQRAEQWiHCFKCMEPey3OicoKiLuIqlyxZ1IxNQmtNmfKinNdLu1SzPpvHn8eZp+6zEE6EvtcmF1yAWkrmex/4WvFH+V/mkeJuNjrnyfv95JwJNjrnOO2vJ+/3s9E5z4HqPkr59Wzxo4WZ8HnCuG6jKHXiRF2UinPzPPSQEUlyuUZByvOMoGJzkI4fby7TCLqEdu82AlQwEP3AAVOyZjOtdu0yYs3f/m29LHB8vFmUguVb7jE8bAS8uMfj4EH44Q9NmV6w7PHoUfjgB41LShw1C8di5pMtdjmwIKxULncHVUEQBEFohQhSgjDDqeopyrqMq9xaRz3LYnTWqx9c8dy/uI3Uk5o3vtxYEhcXXt6K+TgR5nK+5chc7wMrRtW76a2riVFKma56YJZv5iQPF/bwxkDGUJxLA4z4YcUky8AAnD5tBCWljCh1772NJWPhkjMbYG6zp86fr7t7XnrJlKD963/dXKYxOgrT0+a4IyPmXCdPmvWlmXi0oBhlx22PEyVKbdsWfc3LqdwjTqgIClU2NB7MY7R7N+zYURcrxFGzMCx2PtlyCAKfbZmsICwFi+1QFARBEIS5IoKUIEBDV70s2ab1ms5Cx+eErynn0vzkn1zNG/9g/hlNMHsnwtfXfZh9+ss4Kv46fa04Unob78g8DbQOWF8OzMWNcVjfFhCjTDc9K0YBNVHqtL+e1/UQG/Slpi6AYfEDGh1EYfHjAx+Ap582AhE0hpVff320GAX1UO5166BahXLZTHRfegn+5E+MKAVmv/FxGBszIlYuBz09dTEK4JZboL/f5FSFJybBSUxQlNq/v32Z1HIhSqiIe6y2b68LBeKoWTiWqvvd5QwCl25mwnJmNXRQFQRBEFYfIkgJAtQCzF3l1sr0giilWDRNyvfxki6Xdq5jcnPfgpTLzcaJMDICh85/gq9e/4lVNYmaixtjj3OYX/U+z+979+BQZYgxlA93OAe4x/lD7vN/i6/7+9mkTzLGEKf99bz79sbJbVj8aFUyZsWP2283zigrRhUKxq20b19d9IkSo+yxvv51+OY3G0Wpv/xLuPtuc/6xsfpxobGLXHDyfOWVze6OsCiTyxlH1eHDjffdchWjLFECRJxQFWSxHTXdwGrrfheFdDMTljuroYOqIAiCsPqQfz2CAHh4QHxp3mKW7CnlkJwqU+1Lcu669fM+XidOhIMHzXbhSZRd3up4hw6Z/ZYzs7kPLGPbbuPCxmv4Sd+19CTKbEhewk9leE/uCT6+6e+YuHI7H9/0d7wn9wR+KsOG5CUyKc3ERPN9Njzc7MSBRtHGujR83zigcjnjYAITJJ7JmDFu22YEqzgxynHgjjvgPe+BVKDS9Pnn4Q/+wJTp2ePaMj9LWEQKjjuIFWV27TJiVibTuH65i1GtiLvmIIvlqOkGOul+Z0PkIf7v0HLHdjOzhK9DupkJl5O5/E8UBEEQhKVAHFKCALi4QHxpnouLg7Nw3fZCKM9HOwovO7+X5GydCHv3tm4JfeyYCcq23cbs8YKTqOWWhzJXN8Z3P/Q5RkfhwQeBAbg0s9+WfdfwNefXasfa4sMbQ8dp556Jy7YJjlUpI0ql03XBx55j27bmwPFwB7E77jA/f/vbJlPKuqwymXpuVVCMCl57J0i5hzAXOu1+d/58vYw07jW13P7WBJFuZsJypRscioIgCMLKRf7lCF3N90Z68X3IOTkSJPC0h9aNopTWUJhM1ESrhUZpjZdOoHyNW6jO+ThzcSI89FBdlAouP3gQvvMd+NKXjCPK3iXhSZQ954MPmtK/y8183RhxbqYg4eN0misUduJEjfX66+HTn24e4/Hj8O53tx/XHXfAu95lfrcuK1vqFxajYHafiLcr95BP1oUoOnlNHT4Mr75ad/JFvaaW29+aKOL+xtx3n4hRwuWhWxyKgiAIwspFHFJC1/K3X1jD335pLe/cP8FH7zlDzslR9spUqJDC1D5pDefGEkxcclmzOUGir9JwDIWae+C5BjXzrs9LJ8hOTrP+2XNzvp5OnQjQ7O4JL3/8cbh0qfH4u3dHi1HLKQ9lPveBHXcnnbqOHzdCXitnVCs3RyeThPAYd+2CD36wsxKzwcFGMcpmR4VFqU4/EV+qQGphddLqNWXLhpUy5aVXXWVKVIMsx781cUg3M2E5sRD/EwVBEARhMZG3REJX8r2RXv72S2sB+PaBfv78visYcjeRUik87VHWZXxf18QogErVjww2n1O+lAa3YnKrtFK45SqDR8/PK9B8Pu6e8PJ0uu5WiGK55qEslMOplegzMmJcGg89FH+97dwcnU4Swp9c2+ypVuc8ejRajCoUzGP6qU/N7hPxuMf6nnvkk3Whc+JeU8HsJaXgtddWfvZScJIfRsQoYSlZTNevIAiCICwE4pASupIbbp/infsn+PaBfoCZ22184LdK/Lh6grIuky8V8VyPzKCDm6ySyZWw2pOLi575glk6pbTGrfhordGOi9Ka7NlJ3vg3L877ujpx98R1DQtmBNk8IzBCxsCAETqUWv55KPO5D9qxUJ207CShVYv48CfXrSYJ4XNqDT099W56Voyanq6XaQavodNrgfZOrqjjCEIcqzF7SbqZCcuJxfyfKAiCIAjzRQQpoStxHPjoPWcAQqLUtXzot9J86+kLVLOXSPVUUI4mlQbHUSgUGZXBVS6+9mvd+QCqukqV9hlQSoPvKLTrAJrURIldf/Y4g0cvQN/8r62TN5NRXcPCk6igKDU+bkSpZ55pLkNZjhPEud4H7bBujjgBZjZujoWaJESd86qrjNMEzOM2NGTEKKUaA+2D1xBVpjHXco9s1mRZtSsDXK4B1cLSEvc8Wgl/a8JIeauwHFms/4mCIAiCMF9EkBK6ljhR6tsH3gZA/5V5rth+lmtvnmD4/a9x3juHUgpXmRI+Rzk4M1WvnvY67sCnHWNbURrS4wXe8XsH2PGVp/ArLs7cK/bmRdwkyopS+XxdlAqyEiaIC8lCuzkWYpIQJxodPlx3YO3da5xRrbLD4koXWzm5RkeNoBY8ztAQHDliXFl2eVh4Cj7fdu824pzQ3ayG7CXpZiYIgiAIgjA7RJASuhrHgbs/9Qr9wzD6fIJqMcmZFzYw8XqOiddz3HiD4p/+LJz113DRvxArOlV0BY2u5Um1Ld9Tip6zk9z2fz/GpqNnubjljUyeztEf4ZAa23bbfC+zJZ1MonI5OH3arAuKUt04oVpubo440SjswIoTn9o5sOKcXCMjjefct884o44cqZ/Hdmc8erQuPK2kgGphaQmWDYdZ7n9rpLxVEARBEARh9oggJXQteS/Pqeop8n6eLfur9N2YQPuK8nSSsy9cwbGD2/nV357AcSBHjgQJCrpghCdVDzK3pXtWkGopRtn9FFQ3rufCH/47XtETHB+FI/9x75I7RTqZRGldz00Kd2vr1jyU5ebmiBONojLCorLD2olB4fWjo/A//6cJvw9OrN/1LvMcsWLUyAh4HqxbZ55DV1wR7ehazgHVwtKxkrOXpJuZIAiCIAjC7BFBSuhKzlbPcqJiwss9PIqTSVAaN+WTGyzSs6bAhuGz/NXfXMM//1CSrJMl5+Qoe2UqVEiRqh0rKEYFUTNfYYHK/l6lyqnqKfrd/o7H/VNf+Q2Gjj/atPyx4g3cmv4+jmoWw8a23cZ3P/S5yMyedpOoKAoF08UtLEYs14niYrHc3BxLmRHy+utQKpnvXK7xeWBFzJERmJoyIoPvw7vfDS++aNxSlpVQhiUsDSs9e2mhGxUIgiAIgiB0AyJICV1H3stzonKCoi7i4DJ1up+JS4GXwgVNZqBI/9AE8CMe+NINfPgjHpsSm8j7eYq6SJkySZIo1Sg4BX+2AlVYqLL4+OT9PGknDQGBqxVDxx9lcOz5hmVf8H6JL/gf4Q7nAPc6n40UpeIye1pNouw+R482l+lNTkrpyUp2c8yH0VGTTzUwYHLF8vlmUUprI1b5MxWu5TI8/rgp6bOIGCVYVkv2knQzEwRBEARBmB0iSAldx6nqKcq6jINL/nQPE5fc2rr+QY/1Q1XOjaUoT0PP2mnODrzKn993PR+9B7Ykt9ScVQUKuNrFx29yQcWJUEDNTeXgUKVK0S/SqSBluTT0VsA4o/7s0sfBha/xQYrZDXwy98WaKDU49jy+Vi0ze6ImUVETRPtp/vbtdaFhpUwUF5qV7uaYD8FOg1Gi1A9+YH7Xun7tvb3GVWcRMUqwrLbsJelmJgiCIAiC0DnL8O2cICweBb9A3s/j4ZE/nY0Uo5SC9UNVkiRJZKts2H6Gp57W/Pl9V7DO2cCO1A7Wu+vJqiyOciLFJ1uqF/Vl17uYc3fanS+KW9Pf533ZkdrvDxf28Ef5X8bXZky+VvxR/pfbZvYMDzdO7qJK+fbvN2IU1CeKu3bVt3nmGbPfaiduAn3PPc33x8GDdfFutRB+7K17zopQ+Xw9a6yvz4hRAwP1+DRYvmKCsPR0mr3UjX9rBEEQBEEQVjsyJRC6iryfp0oVv5xoKNMLilHAjCjl4fguqZ4KV2w/y7cP9PP04V5ybo4d6R28OfnmmijlhF5K1jUVJ0hZMQpo2nc2OErzydwXI0UpTzt8xv8UDxfqSemdOlNsKV+rfcITxfnmoYyOthdvfN9sd7noxM3RraJUoWA6MVoxCmDjxmYxClbefbISnpsrlcvxt0YQBEEQBEFYHkjJntBVeHgApNKwZn2Vi+cSTWKURSno6dNozyORqfDzH7nA2/dMASYU/ceVH9dC0V3cBsGpHQqFh0eKFBkn036HFjhKc2vq+2gNjxSN+PRwYQ8PF/bg+kWs9rVzZ+tskzBLmYcyMtI6DBjic7CWEumkVSeqC2MQpWBsrDF/zLLcy66CrJTn5kpGspcEQehmRkfbvz+LakwjCIKwGljmUwFBWFisM0mjWbPBY+NVlUgxqo6mf9Bn751T/PyvXAQaQ9GVUvhTvaRVpqHzXsyhalSooFDknBwpNbv8qDAPTN7Jp8d/A1C8NzMSuc3Oneb2v/5XM8HulHApXxTzzUMZHa0LGnGOorAz6dChy+NGETdHNEFXFIDnmcesWDQlfDt3rsySxpX03FzpLMXfGkEQhOXGyAg8+GDr/4f2/8yDD87uPZwgCMJKQAQpoavIOTkSJPC0h9aa3n4/VozSWuPhkSDB9dema8ttKLqrXKbOZTlzMsW5sUTLLCjtg+8pdNW85KyTavzZrfMqBXqseANfnrwTgEeKuwGFDpm07O9PPmluwxPmYDlS3LkWsxzJhmRbwhP/uDK5cA7WUrFnD9x1V2t3jxWl7rpr9bplfB8OHGh0R2Wz9U56vk/Tc3GllTSutOemIAiCsHKQDz0EQRBEkBK6jKyTJefkcHGpUGm5bYUKLi45J0fWMbPsYCh6eTLNxXOm6nVqCkoV8y4iHHKuNfieudVKY3Wr0mSKL3xmM//fZ98y50/FgqHmWsMDU3dy2l9fEwK0htP+ev7+7+HiRdMRzZbuQeMnc9Vq9Llm+8ncbPN22mUvtcpsulx0u5sjKEZZd1Q2azrpFQqNJYy5HBw9ah5HmH9A9VLmOa3E56YgCIKwMpAPPQRBECRDSuhCNiU2kffzFHWRMmWSJFEBm5TWmgoVPO2RURk2JTbV1tlQdFe5ZPs0/YMeE5dckpkqWmt8T5FwVa3Lnq81aFBKoX0FviKZdKhUYCrvcsX2szx+8I1cOA+DITdJ1KdijxVvYB/P17axoeZaKx6YupO8319bt9E5xyu8kUmvn0qxeZJsP5nTGo4cMb9bceHQoXqmS3gMQ0PxQstc83bispeCb8JAJvzLhRdfbBajdu82nRg//3k4dsx02ANTspfLNWZpBR/r2ZQ0zub59cQT8O53w0//dPzxOsnkkOemIAiCsBjE/X8Bs1w+9BAEoRuQP2lC15Fzc2xJbiGjMmitKegCJb9EWZcp+SUKuoDWmozKsCW5hZybq+1rQ9EVaqYTX5X+QQ/laFCgfUW1YsQtr+LgV40QVZ5K4U1n6U9lyDgZUkmHbK8JSwf44Xeu4Nln238qdmv6+zFX1Rymft5fwwQ5fJxaWaLWdbfK1q3GLTU+DhcumIn5+Hj9XFu3zu6Tuflaz6PcKEFW8xuxldTFzfeNIOXOhOUHxSjHgY99DN7+dhNmbgPN83m4/fa68DOXksbZPL8efdQ4Ag8cMOJY3HV06vzr5uemIAiCsHjEOXHvu0/EKEEQugP5syZ0JRsSG9iR2sF6dz1ZlcVRDgqFoxyyKst6dz07UjvYkNjQsF8wFB2oiVLpjK5pQr4PlbLZxghVmmS2Su9gOZAzZcLSh99arR375MkOSoFUo/Dka8Uf5X+ZR4p72OicI+dMkHMm6GWaC/4gyZmyxGQSenpMyPT4uDn2N75hBKpSyZzT903pYTYLe/fCQw/N7s1Q2Hr++ONGEGglsu3c2SgqBD8tDLNa34ittEBT22mwrw/WrGkUo8Dc3n13/bkwMGBK+a68svE4sy1p7LS04YknYHKyvt2LLy5MJkc3PjcFQRCExUc+9BAEoZuRkj2ha8m5OXJuriEXKpwZ1bSPk8PBoazLKK1QSuHismYNTFRAOT7apSko3XE9KnhUdAWlzcpeevm5O+G1Y2f4zn+7Aui8FGhwzJTtjfi3803vZlyKAPyq+nMO6n1M6D4Gucg4AyRVlVQmidb1jJ902ggb09PmuHa8PT1m2Wc/2zyGvXtblzcdP262AeNQmZysO1r2768LAKWSOX+w858t3bNCQRQHD66+N2Rh1w80X2OUeNKqbHKxsZ0GDx2Cm29u3WkQzLjf8575j7fT0oZ0uh6sbvOrlKqPc66ZHN323BQEQRCWDvs/LvweEOT/iyAIqxsRpISuJ+tkYwWoIHkvX+uw5+NTomREqZkvJ+GjTeVeA2FxyrqrfHx6E1n+t0+9xIXzcORvBprOGRajxrbd1rB+J+f5Z5Pf4MuTd/LezAglNnBm6krO+4P0qwn6mWKcgdo4tIbt2+GVV4wbyjqjlDJOlp6e5vFaMco6pqx4FCSY7fOWt9RdPIWCWf7MM+a44+Nm2fbtZv3Ro+b20CHYuNG4WaLejEG8YLOSsa6fKIFlPuLJYrNnTz1jrF2nQZsbtRB0mue0e7e5tc+v+WZyRD0OQVbjc1MQBEFYOuRDD0EQuhURpAShA85Wz3KicqImRtnQcvsFzS3uLVo3izwA03qapwpPMeAO8L5ffokjf3N10zbhNyDf/dDnmrbpB979Ajw/eg1PPgkXi4ALl8iSyUBaGREomzXf4+N1MUpr8+260NtbD6AOjjdcvhd26IRdPlqbvKDDh815bfC11qZkMJMxgtiZM8bNAsYtNTpaFxCgLhQEhYDVNvFfyYGmnYhMi9FpMOo+C2LvIzDP4/kGkbcqoV3Nz01BEARhaZAPPQRB6Gbkz5ogtCHv5TlROUFRF1FK0aN6SKs0DiZ3CuLFKEvc+nE9zunqaZ5+tC9yfatcIYsNmT56tF6ulM0ax5F1RWWzRhgqFuH06foxXRdSKfMG58wZU66Xz9fHqzXcf39rh04428eWSN1+e33Z5KQRwexxlWoUo+x+wXPYN15RYZ+d3C8rBQk0nT2d5DktRCZHyzy3LnhuCoIgCItL3P+Ze+6R/y+CIHQHMrURhDbYMj1XuaRUCqUUSZUkRco4pSLEpihHVJwoNV2usumWE2zaOda0rt0bkKg3Mrt3m2+l6m4ne+583mQ4WXp7jXDlOFAum+MFRal8Hk6caOy+F57Ex03MX3zRCGGeVy8NrFbN+W33tV274Oqr48WoVsc/fjz6PlmJSKDp7GhX2mBfL/MNIrcB7pZufG4KgiDHqExhAAAgAElEQVQIi4N86CEIgiCClCC0JBh4niQJgKc9in6RMmWqXrPKZMWoTkQprcGrKnrXF3jbP3lu1p+KRU2Y9+8337t2mTH09xsRqFxudD6lUsbFlM2aY9vMIscxQlY+Xy+3syV/e/dGT+KDb5yskPXjHxtxK7yd1kbg2rnT7LNjRz3zJ054Cb8x27378oV6LxbSxa0zOiltsK+XToWrOGyAO3T3c3MhGB3tzO3ZrtuhIAjCakE+9BAEQZAMKUFoSd7PU6WKq1yUUkxOe6hM0SRHaQ0osCKTCohQuv57kzPK7jaz3Ks4JDMV3vz2Ma7sG8cpDLTMFQpOiIMdz8JvZPbtM+c4dAgqlcYhpNNwxRWmG16wpC+RqAtKuZzZ1opR09MmSypOHHEcI1iNjpqyQDDX39trjmHFKHvMIJcrJHs5ERRPikXzGNnnU1Sgqe+37nq4GplNnpN9rgXdd0E6zeSQ5+b8CTY9iLu/g49tVOMEQRCE1Uar93CWcG6ifOghCMJqQwQpQWiBhweAQnHxokanSyR8jeOAUmomQ0o3iFE1AWomzLxJlJrZxeIkfLTn0DtQRq8fg1cHYsOuoya87SbMNrvJjqWvz7yh+d736sJQJgNbthghZGzMLE+n66V1tvQvbgxgJpQPPdQoNmUyM5esTGmg7bIH9SD0/fs7D79ejJDs5UBwMj45ab6z2cb7HaK773XL5L2T0gZoDN+H+n04nyDybn5uzpdw0wOIFleDj0u4cYIgCMJqRT70EASh25EiEEFogYsLQLkEFV3BSfj4nsKrmEBz160LPRDjhmJmGwIOqkA5XzLjkenxUI4Gt25lmk0p0PBw8wTvwAEzsbMBz45jxKjBQTOOm24yv9vxbdtWF5D6+uo/DwxAT4+5tlZjsNZzmzdl9y8Wze34uDlWNmt+LxTM+F58Mfp43YLvwwMPwA9+YO4rK0YF73cw6x94wORwhSfv3VDm1Glpw86ddeGzUDDlqpLJcfkINz0I399xQmOwcYIgCMJqJvweLgr50EMQhNWKCFKC0IKckyNBApWqkOqpopRG+6C1ploFPeOOgpjQchXzM3WRynE1OD7aV+AlG7axE+i77pqdC8a6Eqwbqa8P1q0zYhTUy5je8Q5Ys8aIH48+Chcvmm36Qk3/CgV4wxsaO+eF2bbN7FsoNItRlp0767lVYLr8vfhi94oBvg+f/zw89ZQpk0yljFPNOtMKBSNK+b5Z/73vwe/+bndO3jvNcwq+Ye/rg1tukUyOy0k7EbCV600QBEEQBEFY3chbPkFoQdbJknNyVMsOyvVBgZv0cZI+yvXxZ1SoTjvtNW9kbzW66uJcGGraZLafitlsIdeduYasmch/+tONk0IrSr397cZR4rpm20wmuuXwyZPxTpJjx4wj69KlupiilLGhZ7Pmu78fnnjCLL/9diOE9fV1t0PlW98y9x0Y8am3F3791xvvd+v0mZ4299GZMzAxYdZ12+R9zx4jzra65u3bTRno4CDcdpsEkS80cwknjxOl7rtPxChBEARBEIRuRjKkBKEN489upXzVWdJ9URaoGGcU1MLLO6Wc78WZGpj1+IIEHQd9fSak/Oab6zlN4Zydo0eNAJVK1V1LrXJ5ojJgvvMdI0ZBPa9HqbowtXu3uY8OHzbCymOPGWfWTTfVRbFW2VSrmXe9ywhSx47Vg+UfecSEw0O9BLJQqHeNy2SMuNetk/dOniM//dNw5ZWSybHQzCecPOrvSZBufT4LgiAIgiB0M/LWTxDasPG610n1VGe/4yzEKDRMnbhy9ucIEc7ZueWWuhgFzU6FUgl+/OO6m2u2LYdHR+Hv/77u4snn6wKYPd7739/oFpucNE6p4WFxqDgOfOxjxqVmBbxnnjHh8Hv31kUqMA62nh7YsEEm750gmRwLSzicPMrVGBVOHuWUikKez4IgCIIgCN2HvP0ThBbkvTw/8V4xOU+zoKNyvSC+S+H1dbPcqZlOc3asyJROw/XX18v0ZlvetG2bEaDCmVO7d5vSqr17jbhy9Gg90Dybhfe8x5RWzSUfa7XhOHD33c2i32c/a8r0LNmsyQEDmbwLS89ChJPbbaLo1rJdQRAEQRCEbkZK9gShBT+p/AQPDzAik9bUOufViAgrb0mglM/FxcdHewmmXhmCa+c/5rm0EB4dnVt5U9Dx8MQTRuA6etTcB/v2NU9QrZi1fXt9f3GoRJczaW0cZ1Dvumc5eFBEKWFpaVfC2y6cPEqwChJVDiwIgiAIgiCsbkSQEoQYCn6BcX8cjUZZBUlpo0e1MExpHS9K1fKmZraxYheeQ+n8/PKjgnQi8gTFoNluH14eNVENTzylzKw19n585pm6GGXL9QZCTw2ZvAuXg7m+1lt10wsul+e1IAiCIAhCdyFv+QQhhryfrwtGM2hbUqIC3xFEBZ03LQv+nikw/PGv4q8Zm9tgLzNRWVNBRIxqj520h8UoMA6pT32qdcmUICwFs32ttxKj4jLq5HktCIIgCILQHcj0UBBi8PBqziiNxvf1rILKgwJUbCc+i4L+4Vcp3/ww3pUv1xaH26cvZySweO7YSfsPfhAtRk1P14POZfIuXG5m81oPN1qYbeMEQRAEQRAEYfUiU0RBiMHFrZfqzeQ+aR1wSXWCjhejNM3rdG+eyrVH8NeM1USKBx807daXOxJYPDeCDpJSyYhR2azJAduyBXI5U94Z7L4nk3fhcjKb1/psGy1A93bdFARBEARB6DYkQ0oQYsg5OVIqRVmXjTPKx2Q/BSZTtaDzGKJWqdByPWO8UjMrdHaStfo/89Rvb+AnhT2sAZ59EO76n3/MOzJPx55rbNttfPdDn5vFFS4cElg8N8L3WyYDO3bApUtmcm67FIYzdvburf8uk3dhKZnLa30ujRYEQRAEQRCE1Y8IUoIQouAXavlRzoyJUM84pIJh5fbndqJUkNr+YWeUtsKUhkSZn2we4Miaf4CbLwJwh3OAvRf/Ckd1eKIOadddD8wE9Pjx+EmiBBbPnbhypuPH649LVIj08LBM3oWlZz6v9fk0ThAEQRAEQRBWJyJICcIMeS/Pqeop8n6eKlUAqrpaF49mRClo7qLXiSgVFrMatrcOLKWolFwKPSnW3zDB+Kk1vC87wsdzf0de7Yg99uDY8x1cYSMjI3DoUOvA8eAEdPdu43SIW28JBxaDiFJx2HKm8OMQnJSH78egI0om78JSIa91QRAEQRAEYaERQUoQgLPVs5yonKCsy3h4uCqQH2Vr7GLEKEvb8j3d6JBqFqU0VU9RKTsoV+P2+Lw1+SKfzH1xUZxRhw6Zn+MmjuEJ6KFDpuwmKIJ0GlgcPI91+IiYYpByJmElIK91QRAEQRAEYaGRzy2Frifv5TlROUFRF1FKkVVZ0ipNSqVIKKPZKhXIeZoHtjQvKtBcOaA9B+0rtKdITpf4Uflq/mLq5+Z30gi2bWsOxn7gAagaY1isG2Lr1sauf8HA4s2bTbZRXGDx5s3muK0mqCupq+BCMjzc3kUi5UzC5UTCyQVBEARBEISFRgQpoes5VT1FWZdxlUtKpVAB1cnFreVIESy5m/lycGpOqk5zpFpRLTskMlUKk2lOPn0FSsGXJ+/kseIN8z94gPDEcXISnnoK7r/fiFJRYpQN2A53/duzB7Zvh9deM+ujuukdPgwnTxpBb3Q0umvgSusq2IrR0fZdBbtVfBNWLnv2wF13tS7Ds39b7rqrucRXEARBEARBEIKIICV0NcEA8yTJpvWOcnAJlO/NoGeCpWq3EeLDXNxUSkF5KskrTw3x4o+3oDW8LzvCrenvz/5gbQg6lyYnzbITJ+Dee+EHP6hvF9Xt7dChupgyOgovvGB+fuaZ5rbvVoDK5+H0aRgfb9wfossDV6pYMzJiRLXw/RBkNYlvQUSIW/2Im08QBEEQBEFYKESQEroaG2DuKrfBGRUkqZJNghQYMUqj0aY9Xn25pxo68M0GJ+UxPtbLk3+xg3EG6Hem+M3+L3WcIeX3jlN9wwtU3/ws1Te8gN873vp8DvzCL8CWLfVlhYIRj7SOFqPALN+2zfwcVf4XFGO2boWeHnNce/xs1iyH+PJAe/yVRDibK0qUWk3iW5BuFuIEQRAEQRAEQZg9IkgJXY2HBxApOFlc5ZJW6XrpXpjQrk6iUTyajShVnU4y8ie3cvLpKxhgnAm/lz+e+Ai+rh/kseINDb8DnH7bVTz+O7dSfsfXqV73GJVrnmB6+2OU3/F1yjf+Pf6asdhzJhLwiU8YkchiRak4MSoqzDhOlHrooboIBeZ2erpe3teqc9dKo504t5rEtyDdLMQJgiAIgiAIgjA3pMue0NW4uEC99C6OhErga58Spcj1KtSJL2p9ZMaUXaZM2d8Lj1zN2JNbuSH1JNMlF19leLhgglg+mfsifzH1c3x58k7elx2pdd87+c6r+NFHhpnctBbdOw6VFIVpRdX3cXLjuFdO4685TfK5W3Ff39o0BCsa5XLm96CT6d57YWCgvm27MGNo7LAVFF4GBkw3uelpc3+E17c6/mIzOtq6yx2Y++n48dalSHH3A5jlq0l8C2KFuKhrdpzVK8QJgiAIgiAIgjB3RJASupqckyNBgoIuoNGxZXsAVaqtD9bCCaU1oEFrhfYVvqdQypT6KUejHE25kOTU9zcD8Dcbfp0/e/0OvsYHAXi4sIecmuTB6X213wH+xTv+Oz/8lzuZ2tRDYtqDiUHy44rpaXPeqXM9rN9cINGbp3LtEVSxB+fiUG1cQaFAqWhRCoyY1E48iRJjgsSV/wXXXw5xZmTEuHVanT94P+3e3TqsuRNxDlaPGAXdK8QJgiAIgiAIgjB3ZDogdDVZJ0vOyeHiUqESu13Fr9TK+wCcwJftuGdRgS8wzie/qqiWXbyKi+85aK3wPfPyU0qDhqmzvYw9a8Siz038Er+t/pD3ZU3Qzt19X+VX+r9S+x2MKPXf79hPYX0PqYki6QulBjEKoCerSHo9UEmiM1NUtz5XWxflWrn+evjMZ5rL98bHjZjUSZixFSbC7NtnygNbrb8czqjFKDWLKmMMshoFmbjSzfvuEzFKEARBEARBEIRmZEogdD2bEptIqRSe9ijrsgkpD6C1bijVC2dJWfHJilN2mYODX3bxPQUK3KRPIumRyngkUx6JtIdS4HsOpckUP378KsZfM/VxDxf28Af6t/j3U/+W/+j/Gz4x+RnWnv4Rvzv9f/CPvf+KWykysOkipeuyFDIZ0ucLnPbWN4pRPYFyu1IWEhX8tWP4veOxJVTWwTQw0CxK3X8/VNuYxOxxozh40Ozfan27Dm0LzWJmPrUT51ajINONQpwgCIIgCIIgCHNDSvaErifn5tiS3MKJygnKukyBAq42Xfe01lSpts2YsigUPkbNKBcSVEqKZLYKaJSrcWZecVorvIoRozSKiyf7Ofmta9m5E44ehUq6j6+VPkgxu4FP5r7IJfXW2jk+rv+OYn4DozcNk+j3mZ5K8wLb0TN5WBASo2bGpatJSJTx147x8vcHYsUou9zub8v2TpyAv/xLuPvu9mVtUTzzjHEW2QypqPWwtILFYpaatRPnVqswY+/TqOfBar1mQRAEQRAEQRBmjwhSggBsSGwgrdKcqp4i7+dreVFKKZIkjXMK3bIbXxCtNSpZwfESKEfjuCYnChTVkku17OAkfCqFJBNne3j8/93FD/9+iF/8RRv4/UYA/ppreH7XrzVN5Lf4cGnyWXTqx1R9l2qibmcKi1E1fAeUBrfC8LDJQrLZSXHd9PbuNc6oEyegrw9OnowWU1p1y7PLx8dhbMw4r3I5Ux4YFn0utyj1xBOQTrfOfAIjrLUKN+9EnIPVKdB0qxAnCIIgCIIgCMLsEEFKEGbIuTlybo6CXyDv5/HwcHEp6AInKyepUKk5peKEqZqTSoHjaryyi4uD21fBR6N9DY6PV0kycbqPxPQgF48O8fKjb2TbNti+Ha6+2hwiKNIMDzcKII4DO9+a5JyncNONdW6RYhSA44OXAC8JmGDuoSHYujVajLLCwSc+YZxRJ082jqtdBzW7fu9eI+CMjZl1hYI5r82kauVQWkpRqr8fikXzHb4Pg2JUu3DzTsQ5WJ2iVDcLcYIgCIIgCIIgzA6ZEgjCDAW/wOnqaS76FwFY46xhY2IjWZXFUc3h5VEES/tc5dCbSdLbp0iqJClSuI4pBayWXErPvZWfu/nN/ONfPM+dnzxWE6LCOTy7dze7cXwfvvPfhihPp0j1lo3zaYbx8ZhxJSpQTeFcqHfZGx6Gl1+uCwWlEuzc2SgYJBKmTC+YC/TEE3D8uPn5+PHWZW0vv2zK9GwmVTZrRKmXX46+XjDHs8dfCkZH4fBh49yyIe5BwmIURIebtxPnoq7zcmRnLQZx137PPav3mgVBEARBEARBmDvikBK6nryXbyrVA0iQIOfkWOuuJUGCMuWGjChodEoFl9fW9UxRxnTRs8sSCcitL7H9AydwnB04Dlx940XOf7u+rxUvws4oCE78B+j/qSF6107Ts6bA9IUegFqweYPLJ12AahLnwhDOVKP9x5bvfeMb4HlEYsejtRFiXNc4nuz4guV/YffL8LBxEo2MGGdUodAssoWdUlEi3GKybZsR4mzHPZubZe/DAwfM7dGj9X2iws3biXNxjrCox3kl0YkQB6vbHSYIgiAIgiAIwuwQQUroas5Wz9bCzD08XOWaAHA0BV2g7JXJ+3lSKkVZl2uikw582e3D+KZIr0m0UihIVrjoXaDgF2LH5jitxCjz+7NfvZY333ia3KY8yeQ0+TNZtFY1USo3oI0YlaygpnIkXr428lxDQ8YJ1dtrRBel4sWCdBoyGSPeDA3VBaehISPQRO0TLA98+eVo8aWVCHc5CIpSVqjK5cx9Exdu3k6cg8svvi0G3SrECYIgCIIgCIIwd0SQErqWvJfnROUERV3EwSEx83JQKBIkqExlcHtLFHURHx8Xt3ar0TXBSfvEFr+2KvMrUWKsOkbKSXU03igXykZ3iLWv3oI38DjZ3imSWy5RnEjiew6O61PNVkiQRE3lSD53K87Fochjb9sGt9wS72Cx5z561IhR0OwQaics2PWttosS4Rab4LUFXWW5HOTz9ewry+7drZ097cQ5MALO3r2tBRnfN9utBMGmW4U4QRAEQRAEQRDmjghSQtdyqnqqJjZZcckanfyqolhJ4pxP0b+uTFVX6XV6cbVbc0o5uBSnTZlbqrc600XPECdE2WXWXXXBv8CQEy0ShYl1oYy9BbfUS3Xrc6g1Y5Qp4/maajXBhYs99EwPse78tbFiFLQvq2pVjrWSiRL5du82t0ePGlEK6m6pTmkltIyMNAo37cYVF56+3OhEiFtuLjhBEARBEARBEC4fIkgJXUnBL3DBu0CVaq3kriYW+aCVT6qvjF+tMnkpQ3awSEVX2JLcwnnvPON+nosXNaWiQvuK8pSmf/00JGaX1FzURcq6DLR3SbVyoTgXh0g9NYTfO447OMbR5yucGE3yhuwQt1wb13avkThRKtwx7XKKUaOjrQUPmJ2zKE7kg3rpYlCUGhgwy7dvn5ugMjpaL/+Ly1EKi2TB0sjlTidjvBwuOEEQBEEQBEEQlh8iSAldyVh1jBKl2u9BR5NywK+Yn92kj0eR0rRLsreKj89wcgf/5U97ePnUNIlMhWoxyfB2zfq7HqNEqaNufEGKfpFOBCmId6HUhJqpAZypAd7WB/25xol/nFATFHmiRClLqQQ333z5xKiREfjmN+Gmm2D//ugx+L4JIH/ySXjPe9o7i6JEPjCCkCUoSo2Pm3PPVVDZts2cp11pZFgkC4enC4IgCIIgCIIgrHREkBK6kgv+hVoQeZR4lEhCtaLQPjgJH6+smLiUoLLO488/ewXfPtBf2/ad+ye4++4zfL+UoaRLTUHmYex6+xXuzteOsBgSLgGzwlJYjIoqAYvbd9++RlFkfNw4hPr6Lp8z6hvfgMnJusMoLEpZMerQITPWb3yjM2dRUOSDZkEI6qJUqQQTE+Zcc7kfurU0UhAEQRAEQRAEIYwIUkLXUfALFHWx7XaJpKZaAZTGTXlMTyg+/+838dK3G8Woj95zBseBde468tV8Q/e9MHadM/MF1G7nwnxKwOzP4X2h0SFkxSi7/MorTcnaUqK1yeoCM5awKBUWo8Bsr5ubH0YyPBzvTgoKRZlM/P3cKSuhNFIQBEEQBEEQBGGxkamO0HXk/TwQ7YwKk0jOlPM5Gq3hzAsbauuCYhTAxsRG0ioN0NiFb+bLOqGsGOXhodG4yp3ztdgSMMszzxjxxJ8xXbUqAYva98AB8223D4pRltHR+vGXiquvNs6ubNb8bkWpAweixahs1mx/9dWdHb+VGGUFpFb382yJOmYQEaMEQRAEQRAEQVjtiENK6Do8vIacp3YldsrVaE8xeaafiddzteW/+ttnGgSDrJNlrbOWM96ZmvhkywKhnlOl0VSpAlDVVc56Z/HXTdD7pjGgs457FseB9981zsZbxjh1pkKlkOTHzw5x8OBARyVgQaeO1nXn0cCAEaMANm6EfL6+3IZ9L6Vg4jjGDQV14cmKUkePNgpnVoyKy5mKIraDoQ2Nj3E1zadbXFRppEXEKEEQBEEQBEEQVjsiSAldh4tbu/XwauJRbImdBu0pTn5vc8O6//wHVzQ4pAA2JTaR9/MUdREHx0hQSuFpjyrVhrwoK1CVdRmdrfKmD/0vvFduxX19a0fX4a8Zo7r1Ofy1Y1yXKPOWgun6V55OMfbsEP/lq9cy9mxd4Ipy3VhRxIpRVtQpFIywk8sZ8Wn3brP86FFzO9+ytbkQJ0oFHVxzEaOgdQfD4PmDotTu3fPrFmddWVEcPCiilCAIgiAIgiAIqxsRpISuI+fkSJCgQoUUKcqUayV1QVFKo9EeaAVT53t46VuNrc5ssHlQlMq5OTYnNvNK9RUqumLcV1rh4UWORaHMWLQmvS5Ppe8IqtiDc7G1U8rb9BKVax5HZ6YgUYFKimyPQiV8smvH6V07zcZrTvPd/3QrL39na8sSMMcxpW3WHQWNYlSw+5xSC+cQmgtBUeqRR5rXz0WMssR1MAyff9+++V93VIlgkMsh+AmCIAiCIAiCICwlMtURuo6skyXn5HBx0UqTURkSJJrCxf2KS6WUoDyR5rWnr+LGGxR/eegl3rl/orbNtw/08+f3XYHvQ97Lc6x0jNeqr+HreslenBhlS/dM+Z7CKybRmSmqW59rOX5/zZgRo3rz4DswMYgq9qJKPWR1LxdfGcSrOgxszvNTv3aEoevG2gobO3bUhZ6gGAXN3fds7tF8HULLkeHh9gJQuIPhbInLq7rnnoXNqRIEQRAEQRAEQVjOiCAldCWbEptIqRSe9vDwSKs0WZUlrdKkSFO6lGV6PIX2HCbP9NE7/gY+es8ZXNc4osKi1F9+pcyx8jHOeeco6AIoE14ezJCyOIEvmMmZcqv4lQQkKvhrx/B7x2PHXt36nHFGVZKoUk+Dq2t8HNCK6Qs9lKeS9K2f4ro7n2srbPg+TEzA4GCjGAWNoogVpe66yziKlppggHkUwaDz5chSh6cLgiAIgiAIgiAsV0SQErqSnJtjS3ILGZVBa01BF2oldqWyhlQFR8HEWD+lH13Dhz/iNQRcB0WpDdvPkL7meSYrRZRSZFWWJMmGvKg4amKS0iR6i1BNQqKMv3Yscnu/d9ysS1SglG1YNz4O09P13wsXsyR6KgxdN8aPz43HChtBkSSTaRSjoFkUiXIIddJ5z/fNdnMlrpvexo3x3feWG52Gp4dFqePHl3acgiAIgiAIgiAIi40IUkLXsiGxgR2pHax315NVWRxlQsgzaYVT6uGVI29i+skb+OcfSkYGXFtRase+F1h71STppIuLS0mXKOhCy1K9IFaUclKmdA+lwa1E7mvEqDJUUk3OqKAY1dMDQ0MKyklSPWWGrhuLdNssRPnYyAg8+GBrJ489z4MPmu1nS5wYtXs33HuvuV0JopQNT4f24emruTRSEARBEARBEARBQs2Fribn5si5OQp+gbyfx8PDxSV3ZY61G9dzwwenWgZc3/2pV3jy0ilUtorSSYoUTRh6RKmeRaMjO/op5RshysuAl4ze2a0YwUq3FqMGBszP6ZSDn9Eks0bgCoZlQ/vyseA+UUHbo6P18rm4IO6w6HXokAkPn43IYs8TFqNsgHlU971Dh0xY+44dnZ9nKVjK8HRBEARBEARBEITlighSgoAJOs86jSVwb98z1Xa/SfKkestUtaJMualTXxxN22mM0OR4UE3hXIjpsucljRjlGOtPqRQvRgHg+GSzCTZdkeTZmUW2O5792RJXPhbcLtxZb9s2s1+caBXnwNrW2LCwLY4Drmt+DotRdn1YlHLd5duhrhORab7h6YIgCIIgCIIgCMuZZTpdE4SVgS3L8/Frzif71Q4d+EJhvj0X58IQztRA5D7OhSGopiBZRqNJp6G/36wLi1EaDYkKqprihjcONZWALUT5WLsg7lYOrNkwPAzvf78JXQ+LUcGx7N9v1g8Omu1F0BEEQRAEQRAEQVieiENKEOaBi4uv/Zqw1IkQFYdSGlXoI/HytbHbOFMDOBeG8NLTkC5AqYe+PkgmIZ0ObZwuQDWJc2EItzAQWQK2EOVjcU6q/5+9e42RK83v+/59zjlV1cUmD+fC3uH2rC31LJc70ng10cXrleMZyZcX8gqwEcOyYsAI4iDw5VVeRIm9CGC/yAvDthwgeWE7jq3EcWzAXiG2kdU4iaFoNWNbUixZGe+ORHA507uzy17ONGdIFi9VfW5PXlSfYnWzurpJ9oXN/n4Kg2meOnXqOZxqYOqH////TAdR8Ohh1MOu9Sd/Ej77WcMoSZIkSXqSWSElPYY8yQmELdVR8ODg8r2IMZCufj/JjR3a9TZl771CGC1CpyT27k0qpSbXIRJ796BTEkaLk4Brpxawixd3D4l2a2X8LoQAACAASURBVB+bVSk17XHDqNZ+rFWSJEmSdPQMpKTH0E/6LIQF4H4I9ShhFEBTdIj5zV3PS26cp/Nbv4dwNx/Pkjpzk9i/Mw6n+nfgzE1IGsLdnM47X9g14Nov05VS2+1HGCVJkiRJenr4FVF6TM+nz2+pjHqkQKpKCSHSPHeNZvHWrqena5+m+xt/gPS7LxHuniXU2bg+q84Id8+SfvelyfOHpZ0ZNUs7U0qSJEmSJHCGlPTYXsheYK1eYxRHj1wdBYF6o0OWFTTPXdtxqPm05MZ5ur9xnmbxFs1z1yAtoe7MHYp+UGYNMJ+2ffc9SZIkSdLJZiAlPaZ+0ue55Dk+rD+kpt5zKJWQbKmoinUCWRwHSw8huXv20AOoafN205s+biglSZIkSWr5tVCa4dffXNy1xaxpxucBLGfLLISFPe+yN2v4eUgbiAHqzqMt+gjMC6NmDTp/+23b9yRJkiRJBlLSA37+7z7Lz/7F8/zlP/ciVTX7nKaBv/NXPsFf/wvn+dm/8AJ5mvN8+vyeq6Panfnan2kCaa+Eqkvy8eEMId8PV67sHEbBzqHUlSuHu05JkiRJ0pPFQEqa8utvLvLzP/ccN66n/F8/f5Y/+5MrD4RSbRj1S185w/VrGf/kf3qev/nfZ3xUf0TykL9SYfNBEqk3jmb+0+O4eBFee2388/YwqrU9lHrttfHrJEmSJEknl4GUNOWHft9dPvO7Rty4Ph6v9rV/298SSm0Po27fSjlztiF+8n3ubpRkIeNUOLXr+zQ0ROJUINVQDhbJ3nvlIG/vQLz+OvzUT82fDdWGUj/1U+PzJUmSJEknm4GUNCVJ4C//zat87ncPJ8faUKosZ4dRL/3gR6x8/hpJt6JDh4Zmz7OkJqoOa298geTG8WnXm3bx4u6DypPEyihJkiRJ0piBlLRNlsH/+AurD4RSP3Hxsw+EUUvnS378T77PMy+MSENKQ0MRiz3PkurR43RymuSjF7j59ZcO6pb27PLl3QeON834POlR+TmTJEmSlB31AqQnURtK/dmfXOFr/7YPwO1bKZ2zd1j5sQ/Iny/o9xJeWj7Fj//RG3yrHs+DKmO5tRUPHgin2j8nJJxJz/Bs+ix3isO9v1nefBPeemvnWVCwdVe9116z/U4Pz8+ZJEmSJDCQknaUZfC3v7LKT1z8LGdXrvO7/+Q7/I7/4EN6pwu6vYbeQuTZZwMfNh2aOJ4JVVNPAqnWrPa9NpS619xjMVkEuod1WzNdvjwOCeD+rnnbw4LpkADG558/bxue9s7PmSRJkqSWgZS0g6aBv/fXPsEP/7ErfO5P/H/kL9ylc6pk406Hpkk4/UzJKNYUcWMSRMH9nfNmte21xxMSUlIqKkbNiKMOpC5cGFestCHA9rBge0gA4/MvXDj8ter48nMmSZIkqeUMKWmGdje9r39jyKt/8jd57nfeoioSPnrvLLc/WOT2ep/vXslZoE8SEuK2R7uL3qzjcD+YqmLFveYeMTvanr12F7xXX71/7O23x+HATiHBvF31DouziI6X4/o5kyRJkrT/rJCStmnDqF/6yhk+/+feZvHcPYq7Xaq7CyRJoK7H55Vlw7VrcO58ICGhpt7ze7TnBgJ34h2apRHf8x//Is3wlSPbaa8NC2BrBct0QABPTkjgLKLj6bh9ziRJkiQdDAMpacp0GFVl93jh+9fpnSoZrOWceabm3PmKq99OSPsbdE9VJEnDvRLSzg5lOhFmjJCaenqzciopeeZ3vUfRfEDnnS+QfvdodtybFRZMe1JCAmcRHW/H5XMmSZIk6eD4v/vSlH/3rxb5pa+c4fq1jGc/vU4/L6hGHc6cbTh3vqKm4plP3ebUMxtkvYqQRmKEOCOPinGcR8UHR0lNBAK3r53mw/eeoRglxMUB5Su/SvPstQO7x91MhwXbPSkhQTuLqDXd9gXOIjoOjsPnTJIkSdLB8X/5pSk/8vpdfvfrd7h9KyV/viDrNHS7kXPnKypKhnFIpCFJIwFIkkiSNeMqqOngKW79Z6dQqq4Cw3sJxMDgw1NUww5x4S7VS+8c9K3uqA1zZpkOfY6Ss4iOv+PwOZMkSZJ0cGzZk7b5mb/6ARC4kSb0FiKL+Xji00bc2HJeSMZB06Qjb7o1b0abXowQpo7HZvylu7NQURUp3S5kdR+ymzTPXaNZvEVy9+z+3twuZoU503ZqjzsKziI6vo7T50ySJEnSwfB/9aUZfuavXuOP/hF49tlARcUojog8WOYUwjh7CjMCqFnH2kqpuDlbKoRISCL90yX9/riFj6oDWUHz3OG27e1UWfSlL81vjztKsyqlphlGPXmO4+dMkiRJ0v7za5q0g89/Ac4mOcDMMGpiztDyeaEUQJJGeosVZ5emqq+aBEKEtHzIFT+63drc5rXHHTVnER0fx/lzJkmSJGl/+VVNmuO59LnHvsasUOr+k9A/sy14ShqIAerOY7/3Xl25Mn/m0k5hwZUrh7bEHTmL6Pg4zp8zSZIkSfvLQEqaIxJJSR/7OjuFUiG01Vdx8n5kJVRdko/PP/b77tXFi/Daa+Ofd2pz2x4WvPba+HVHaS+ziAylnhzH9XMmSZIkaf851Fyao6YmDSkxRiqqHc8LhAfb+jbnRMHsXfZiuwMfEJLNxKQ3hKpD8vH5Qx9o/vrrcP48XLiwc5tbGxZcvHj0IcG89q/p4w7I3j+XL8//fMD4v8uVKzt/Po7b50ySJEnSwfDrmTRHWx2VhIQO91vowtQjIZn83IqbQVOMO4dRTZVQVwl1FRjdS8lO34NOSRgtkr33ykHf2kwXL+4e2iTJ0YcEziI6fG++CV/+8vy/x/a/y5e/PD5/J8flcyZJkiTp4BhISXPkSU5GRh1rMjKSzV+ZuUPO28qneP9npoKp6Z35krQhJJG0VxFDQ7ib03nnCyQ3Dq9d7zhyFtHhunwZ3npr/PNO4d72kPCtt8avkyRJkqRZDKSkOfpJnzzJSUmpqLZWQW0+ms3HJKQKEJuEW2unGXywyN2P+tz+cJFqsEg/6dOhQ6zHbYAhQGwCN75zhm/+q5fIfv0PkH73pSO62+PDWUSH68KF+RVnO1WsXbhwuOuUJEmSdHw4Q0raxXK2zMfNx4ziaNdzU1JOh9MMmhFJArc/PDU+nkL3XEVGxc0PFrh7F049d48qjXz36+f4xf/uR1n7zU+x+KcNTfbKWUSHp/17hNmzuea1T0qSJEnSLAZS0l7M6NCbNci8Q4dPZp+k5jvU5zYAGN5YIM3g9q2U27dSCJGFsyOSNBLvLTJ893u5tXaGCxcMTR7WXv6+nEW0P3YKpbbvcGgYJUmSJGkvDKSkXaxVazQ0k6HmNfUkiGqHmbfDzxsaBs2Alc4Kq6ySnis5/fyI0e0OTZ2QpA2dUxXVMCPeW+RHPvUpPveffsRC/xIfffX3HNk9SnsxK5SaZhglSZIkaa/82iDNMWyGDJoBNTW90GMhWaAf+vRCj27o0gs9+qHPQrJAL/SoqRk0A04np3m5+zKf7D9PvrBAXWSEJBLSSHGny41vPcsPvfgin+gskSTwmR++cdS3Ku3JdCi1nWGUJEmSpL3yq4M0x6AZUFGRhpQQxgPNk5DQCR26oUsndEjC+NcohDAZfj5oBuRpzsXOy1z+Z69y+9oZYhPGrX8hcub8bf7d2nf47Y1LDJvhlve8fPnBHcy2axp3MNPRaAeYzzJr9z1JkiRJmsWWPWmOmhpgy+5684QQaGLDjfoGVVPzK78a6b/6AQv5BtlCRXmvQ2wCabcmOTXi24MRi4uR2D8HwJtvwltvzW99mt7R7LXXxsO9pcMwaze9adODzq2UkiRJkjSPgZQ0Rzsbavvw8lnqWFPGkkhkvV7n/TsfcfYHSkIaqTZS4nCBF5YC169l3P4ohY/Hw81JN8ieuc7G6Wv8v//iPLDzF/vtgcBbb413mnNotw7arDCqDU6njxtKSZIkSdoLvy5Ic+RJTkZGHWti3DmUKmPJKI5oaIhEhncTmqQmpA2ESKdX03/+HjUV585XnHmmhhgY3ewzvNVjo4hc/Ml3ePXV+9d8++2tLVA7BQIXLhzQzUub5oVR7UypeZ9dSZIkSdrOQEqao5/0yZOclJSScuY5dawpYkHD5rfvssOtD04Rks0Aq05I0vEOfBtxg4b6figFDG/1IGnofeoaf/inbu34xX5eICAdpCtX5n/2dgqlrlw53HVKkiRJOj5s2ZN2sZwtM2gGjOKIgoIOncmAc2BLGJWQsNDtUC9tEJMIBLIOsDmDKhIpY8lCknLufAXA4GZCiAm9xYJ47hpf/OJZYGsL1PaZPYZROkwXL47nlc2bbza9+14738xWUkmSJEk7MZCSdpGnOSudFVbLVYpYMGRIGtPJAPOKcbAUCPRCjzSknMoLNupImPrSHgg0NNTUNLEhCQnnzld0FxuSXsPGRoS0fOCL/XaGUToKr78+nld24cLOn732s3vxomGUJEmSpPn8SivtwVK2xMvdlzmXnqMf+iQhmey8FwgkJPRDnyxkk2Nhxm9XIBCJ93fvC9A/U0GIxCZA3QG2VptsZxilo3Lx4u6fvSQxjJIkSZK0OyukpD3K05w8zRk2QwbNgJqaQT3gen2dEAJpSCfnpqSTiihgEl7NEmOEpKHZ6JJ8PN5lr50ZNcsbbxhKSZIkSZKONwMp6SH1kz79pA+Mg6ePm4/vDzTflISENKZEdt6Zr9XQQJNw51vnef7u2ZkDzKe1xw2lJEmSJEnHlV9npceQJzkZGXWsx5VOUzqhM2nRm34EAikpMcb7A9HrjOu/8sokjPq1X4P2cq++Cl/60oM7mP3CL8ClS4d4s5IkSZIk7RMDKekx9JM+eZKTklJSbnkuDSnd0CXZ/DWbrpYqY8kwDokx0gkdkpvnuPPN87zxBvzrfw03b8JgAD/wA/crob74xfuhVIzjHc9+7ufgl3/50G5XkiRJkqR9Ycue9JiWs2UGzYBRHFFQ0KFDCOOZUZ3QIcTABhuT6qiMjCQkdOmOK6xCxtVhnw8/hH//7+HOndnv04ZSbRg1HI6P/+Ivwic/uT+DpJvFWzTPXYO0hLpD8vF5krtnH//CkiRJkiRNMZCSHlOe5qx0VlgtVyliwZAhaUwJIRDjeEe9bPPxfPo8i+kiKSl5ktNP+lytrgLwwgvwh/7QuBUP4OzZcUAVws7zok6fhs9/Hi5ceLx7aJ69RvXSO+MwKisgRIgBqvGg9ey9V0hunH+8N5EkSZIkaZOBlLQPlrIleqHHWrXGoBlQUQEQQphUQi1ny+RpPvc6P/ZjcP48XL48DqNg6xDzN94YHz97FhYWxmHU4w43r5ffpfz+XyMu3IWshLI7DqOSBhZvUffu0Tz7AZ13vkD63Zce/Y0kSZIkSdpkICXtkzzNydOcYTNk0AyoqbdUQu3VZz8Ln/nMuDKqDaPefvvBXff2I4xqnr02DqMWB1B24PYzBMLk+Tg6Bb0hcXFA+cqvEkanrJSSJEmSJD02Aylpn/WT/iSAasOpG82Nhwqn2nlR8GAQBePh5o8bRgFUL70zrowqO4SNUw88HwiwcYrIPeLCXaqX3qH7GwZSkiRJkqTHYyAlHYBBPXigfQ8gI9tz+14bSs0KpPYjjJoMMM9KuP3M/JM3+nDmJs1z12gWbznoXJIkSZL0WB7zK610/A2bIR9UH7BWrfFB9QHDZvhY11uv1rlUXOJ6fZ1hHNLQEIk0NAzjkOv1dS4Vl1iv1udep2nGM6NmeeON8fOPYzLAvOxuadObJRCg6kBWjF8nSZIkSdJjsEJKJ9Z+VDFNGzZDrlXXWKvXKGNJRkY/9AlhaiYTkZKSURyxWq7SC72Z12rDqFnVUbB10PkjV0ql5f3d9PaiScbnp+UjvqEkSZIkSWNWSOlE2q8qJhgHW5c2LvG1ja/xfvU+G3GDhoaKio24QR3rybkhBLqhSxpSiliwVq09cL1ZYdSrr8KXvjT+d+vttx+zUqrujMOoEPd2ftKMz687u556+fLu62qa8XmSJEmSpJPHQEonzqAesFquMoojQgj0Q59e6NENXXqhN6lqaquYBvVgx2tNB1v34j0a7qcwbSg1iiOqWG15XYcONTWDZkARi/uv2SGMaiuhvvjF/Qulko/PQ9WFTkFkfigVieNZU1V3/Lo53nwTvvzl+etq7/PLXx6fL0mSJEk6WQykdOKsVWsUsSANKd3Q3dJSB3urYoIHg60O48qhQCDZfMA4mJpVKZWSjgOrZjQ5fuXKzmEU7BxKXbny8H8Pyd2zm6FUB3q7zM3qDaHqkHx8fu5A88uX4a237q9rVii1PXR76y0rpSRJkiTppDGQ0okybIYMmgE19SRA2sl0FdOsQefbg61Zc8HD5iMSKePW2UttEDZdVXXxIrz22vjn7WFUa3so9dpr49c9iuy9VwijReiUxN69ByqlIpHYuwedkjBaJHvvlbnXu3BhfgXXThVgFy482volSZIkSceTQ811orQDzNOQPlAZtV0IgTSOq5gGzYB+0p88Nx1s9Rkf32mnukCgoaGmpokNSRgnTDFGQgiTSqrW66/D+fPjkGangeVtKHXx4qOHUQDJjfN0fuv3UH7/rxEX7sKZm8SqMx5gnjSbbXodwt2czjtfILkxv12vXRfcD52mB7DPa0eUJEmSJJ0cBlI6UWrGbXM7hUfbtaFV+7rWrGArJZ2ET9vfo62SqqlJSIhx/HOXLgvJwgPvu5eQKUkeL4xqpWufJgwXqV56h+a5a5AV40HndQYbp0g+Pk/23iu7hlHT65oVSm3fMdAwSpIkSZJOLgMpnSgpKcCuQ7xbbRVT+7rWdLDVxGbLn/cSdpWUpKTkST5u9ztiyY3zdH/jPM3irXEolZZQ7z4zasfrzQilphlGSZIkSdLJZiClEyVPcjIyhnFIJM5t25uuYsqTfMtzKSlNHO+iVzDepa6tgtpuOqCKMVJQUMeahbDAcrbM7Xh7/27wMSV3zz5SADXzWpuh1KxAyjBKkiRJkk42vxLqROknffIkJyWlpJx77nQV0/T8KIDb9W0KChqaSQg1cyA4cXJOJFJSEmNkISyw0lkhT7cGXU+TdoD5LLN235MkSZIknRxWSOnEWc6WGTQDRnFEQUGHzpZKqRjHwdF0FdO0bxXf4jv1dx76fVNS+mEciC1nyycijJpVHQVbB51bKSVJkiRJJ4+BlE6cPM1Z6aywWq5SxIIhQ9I4Hk7etumlpDOrmAb1gPer9yeDy/eqR48Xsxd5IXvhgWqrp82sMKqdGTV93FBKkiRJkk4uAymdSEvZEr3QY61am+yYB+Nd9dqZUbOqmN6v3p+cm5BMWvHmSUj43s738mLnxflr+r1f4803Pzf58+uvP8qdHa15YdROu++BoZQkSZIknTQGUjqx8jQnT3OGzZBBM5hURs2aGQUwbIbcam5tCaDageXzQqlI5Fp1jbvNXZbSJZ7Nnt3y/IvZi9z89Ds0p2/y6R+9CsAHH8Jvv/si35d9btYln1hXruwcRsHOodTFi+N/JEmSJEkng4GUTrx+0t9TG92gGdDEcave9M55YfOxU7VUJHIr3mJQD1ir11goF/id2e9kuXN/NtUrvVd4Zvnq5M/nPn2Td4DffhfW/83xqZq6eBFeew3eeuvBMKq1PZR67TXDKEmSJEk6aQykpD2qqXd8bi+te+059+I9LpeXGcYhn+5+evL8i9mLW3/+0Xf48KXjVzX1+utw/jxcuLBzG14bSlkZJUmSJEknk4GUtEcpKUlICHFcDTXdrrdbGAVb2/saGr5dfZt+6G+plJo2q2rqytJNfvtXx1VTT3K11F5CpiQxjJIkSZKkk8pAStqjPMnp0KGgAO7PjdpLGNWeFwgkJDSbj/er93cMpGBG1dSFd3juI1j/N49xI5IkSZIkHTH3tZL2qJ/0yZOclHTuzKh5tr9mFEfcqG7s91IlSZIkSXqiGUhJD2E5W2YhLGwZav6wpgOpSGS9Xt+PpUmSJEmSdGwYSEkPIU9zVjornAqnSPbh1ycSqaj2YWWSJEmSJB0fBlLSQ1rKlni5+zKnwql9uV7mKDdJkiRJ0gljICU9gjwdDzhvhc3HI10ryfdrWZIkSZIkHQsGUtIjGDZDNuLGY18nEAhhb0HW1eoqAN/4xmO/rSRJkiRJR8peIekRDJoBTWhIYvLQO+1NS0mpqXc972p1lctrN1l/7xnW/83neP31R35LSZIkSZKOnIGU9AjaEKkNlBqah75GQkIWMlLSuee1YdTqv3zFMEqSJEmS9FSwZU96BG2IFEKgG7okJHueIRUI9OgRCGRke5ohldyxMkqSJEmS9PQwkJIeQZ7kZGTUsSYjYyEs7Gm3vA4dTienIYxDrTzJ6Sf9Q1ixJEmSJElPDgMp6RH0kz55kpOSUlLuuTqqomLYDKljTTd0Wc6WD3ilkiRJkiQ9eQykpEe0nC2TklLEgrvxLiXlrq+JRCoqUlJWOivk6e7tepIkSZIkPW0MpKRHtBE3iOHRdtirqPZ5NZIkSZIkHR8GUtIjGNQDVstVqljtuksejAeZtw8Y79L3bvEug3pw0EuVJEmSJOmJYyAlPYK1ao0iFqQhJQvZlrBplsiDlVQbbLBWrR3kMiVJkiRJeiIZSEkPYdgM+Xb5bT5qPhrPgorpzLBplrj5aDU0DJoBw2Z4UMuVJEmSJOmJtPs+9ZIY1APWqjUGzYCNuEFFRSQyYkSI48qovQZTrUikiAWDZkA/6c8852p1lZv1TT748JnHvgdJkiRJkp4UBlLSLtardVbLVYpYUFMD98Onhuaxrl1Scqe5wwu88MBzV6urXF67yfp7z7D6D36C119/rLeSJEmSJOmJYcueNEc7vHwUR4QQ6Ic+ndCZzIxKSObOjtpNJM4cbN5WRhlGSZIkSZKeRgZS0hzTw8u7oUsIgZSUQJjMhHqcQApgI27MnCP1TPoMd9590TBKkiRJkvTUMZCSdjBshgyaATU1HTqT40lIJqHU5NhD/CqFqUdCQhPGw80lSZIkSTopDKSkHQyawXgnvZASwtYqqLZtL049HqZSqj0/JQWYzKaSJEmSJOkkMJCSdtCGRLOCpraFr62MakOpvUpI6IXe5NptMCVJkiRJ0kngLnvSDtqQaKegqRM6JCSUsaSmfmDHvVlBVlsV1b62oKBLlzzJ9/8GJEmSJEl6QhlISTvIk5yMjGEcjlvswuxKKYAm3g+j2lY+GIdaCQkhhEkYlYRxVVURC1JS8iSnn/QP4Y4kSZIkSXoy2LIn7aCf9MmTnJSUknLmOWUsGcXRpL0vISEjm4RSFRUlJU1siERqauqmpogFdazphi7L2fJh3pYkSZIkSUfOCilpjuVsmUEzYBRHFBR06Ewqpeo4DpbaVr1AYCEskIaUKlYM4xAYt/yVlEx3/iUxoUePlc4KeWq7niRJkiTpZLFCSpojT3NWOisshAVijAzjkI1mgyIWjOJoEkYlJJMwqpXs8uu1vQXw199cpGl2OHlT08Dly492L5IkSZIkPSkMpKRdLGVLvNx9mXPpOfqhTxISYry/q15GxkJYIAvjgsM61mzEDRoawuYDxhVUXbqc4hSd0KGIBavlKoN6wM//3Wf52b94nr/zVz6xYyjVNPDGG/DlL8Obbx7KrUuSJEmSdCBs2ZP2IE9z8jRn2AwZNANu1DdYr9cBWEgWtpxbxnI8BH0qjGrDqyQkpCElJaWgoIgFb3/nQ37+536QMpb8y68sMGgW+MM/c5OPV5+ZXLMNo95+e/znt96C8+fh4sVDuHlJkiRJkvaZgZT0EPpJn37Sp6bm4+bjSdDUamJDTU0kbmnZ2x5MAXToMGTIwic+5gd/+uu8+Q8uQJ3yy//7J3j//R/mBz8/4vu7n3sgjAJ49VW4cOFg71WSJEmSpINiy570CFLGs6K2B1JtGNUGUK32vOnjIYRxpVTc4Ef+xG/x4veU3P3gLHc/OMvXf+EzvPd/7xxGffGLkPjbK0mSJEk6pqyQkmZoW/NqalJS8iSnn/Qnz+dJTkbGMA7HAdS2AeWtSNwSWoUYmM6qQggkJJx9JvLTf+OX+Mf/5e/n8q+8wOnT4xBqOogCwyhJkiRJ0tPBr7XSlEE94NLGJb628TXeLd/lm+U3ebd8l69tfI1LG5cY1ANg3LqXJzkpKSXlA9eJRBqaLWFUJLLBBqNmRB3r8bE4fv53dH4HL3/qGX76b/wSF3/0A+7ceXBt+xVGXb6Mu/lJkiRJko6UgZS0ab1a51Jxiev1dYZxOAmUGhqGccj1+jqXikusV+Nh5svZMt3QpY41RSyIMU5a+WYJBBoaKipGcUTZlNTUZGTci/e4Wd/ko28+w+d/4kNOn37w9fsRRr355niXvjfe2DmUcjc/SZIkSdJBs2VPYlwZtVquMooj0pDSp7+lDS8SKSkZxRGr5Sq90CNPc1Y6K6yWqxSxYMiQQHhgrhSwZcc9gIaGDTZISUlDyje/e4/1957hvf/1J/j615lZIfW3/hb8+T//6KHU5cvj3fngfivg9pDL3fwkSZIkSYfBCikJWKvWKGJBGlK6ofvATKgQAt3QJQ0pRSxYq9YAWMqWeLn7MufSc/RDf9cwavrn9tyzyVk+8QIsnYOb+Te4Ud5i8YUH/1kf3uLvf+WDXdvtdnLhwrjtr/X221srpdzNT5IkSZJ0WI48kAohfCaE8BdCCP9PCOHbIYQihPBBCOGfhxB+/1GvT0+/6QHmHTpzz+3QoaZm0AwYNkMA8jTn5d7LfKbzmZkte23b3/SjlYWMF7MX+b7OK/zbf/ppvvFrn2BhARYW4Mf+2If8rV/9FX7sj304PrZYc/VbHf7+Vz7gl3/54e8zScYVUTuFUu7mJ0mSJEk6LE9Cy95/C/w08FvAG8DHwGeBPwL8kRDCfxFj/B+OcH16yg2aARUVaUh33C2vFUIgjSkVFYNmsGXnvev19ZkDzh+4BoGMbFIhdbMelkOpiAAAIABJREFU8M//2vfw7/7FGTqbb//jP3mbP/OlEUnyIj/z34z4O8mIr/7CGYbZiLXLZ7jXh9dee/iwqA2l4H745G5+kiRJkqTD9iQEUv8n8FdjjL85fTCE8GPAvwT+egjhyzHG7x7J6vTUqxnveDc942meNrRqXwfjGVQf1h9OQqZkW/Fh3Hy079MJncnrV7+R8dVfODM5dxxGfTgJg5IE/syXPgTgX/wf4wqu73wHrlx5tNlOs0KpaYZRkiRJkqSDduRfOWOM/8v2MGrz+C8DXwW6wO897HXp5Gjb7GbNf5olxrjldTCeQVVRAbODrUAgIZkMPS9jObnOpy9W/PH/7GPgwTCq1YZSP/zFq8B4rtPjDBqfDqW2M4ySJEmSJB20J6FCap62/6k60lXoqZYnORkZwzgkEue27cUYqanp0iVPcuD+DKpIJCGZzIjaKZhqaKioCAROcYo8yfnj//kNvvdiwQ/9vrs7hkFJAv/Rf/UOn3pukfXffOmx7rmdGTXLG28YSkmSJEmSDtYTG0iFEL4H+IPAPeDNPb7mN3Z46uX9WpeePv2kT57kFHVBSUmX7o7nFrEAxtVRg2YATM2gIp2EUZE4twWwff5Ucmoyh+pHXr+761qTBD7zA3dZf6CmcO9mDTCf1h43lJIkSZIkHZQnMpAKIfSAfwj0gP86xnjjiJekp9xytsygGTCKIwoKOnS2VEpVTcUGGzQ0BALDOOTd8l2yzV+hIhZb5kQBk3CqnSc16/nb9W0ubVxiOVsmT/MDv895u+lNHzeUkiRJkiQdpH0JpEII3wS+5yFe8g9jjH9qh2ulwD8A/kPgHwM/u9eLxhh/eIdr/gbwQw+xPp0weZqz0llhtVyliAVDhqRxvOteHevJfCgYV0eFMG69uxfvTYInYDIjatr089MSEgoKrtfXGTQDVjorLGVLB3ODzA+jdtp9DwylJEmSJEn7b78qpN4FRg9x/tqsg5th1P8G/BTwT4A/FdvJz9IBW8qW6IUea9XapA2vic1kN7yEhB49smT8a1PHmnIy5uy+hOSBaqjtAoF+6BMIlJSM4ojVcpVe6B1YpdSVKzuHUbBzKHXx4uMNUJckSZIkabt9CaRijH/wca8RQsiAf8Q4jPpHwH8SY6wf97rSw8jTnDzNJ4PKv1N9h6ZpxmFU0ttybhnHYdR0VVQ7G6p9zKqOCgQyMpIwToK6dCkoKGLBWrV2YIHUxYvw2mvw1lsPhlGt7aHUa68ZRkmSJEmS9t8TMUMqhNBlXBH1R4H/FfjTMcbZfU7SIWgHjdexJhLphq2DztvKqXZnPWASSjU0k2OzBAKd0NlyrEOHIeMQbNgMJ++/315/Hc6fhwsXdm7Da0MpK6MkSZIkSQflyCfDbA4w/6eMw6i/h2GUnhCT3fNCumXAOTAJo9qd9LbvqNdsPrYLBHqhRxrSrcdDICWloprs3ndQLl7cfSZUkhhGSZIkSZIOzpNQIfW3gS8C14GrwF/a/uUf+GqM8auHvC6dcO3sqO1h007a86ZnSG2fI9WhQxZm/9q1n/v2fSVJkiRJelo9CYHUyua/zwF/ac55Xz34pUj3pYyrmOYNJ5/WVkw1oy6n+glFLLYMPQ+EydyoGOHenYTFM/erqGKMk0opSZIkSZKeZkceSMUYf/yo1yDNkic5GRnDOByHTVOVeynpZGj59FDzpgp8vLbA4iI8f348g6pt3QuMw6YY4fq1jNs3U549V/HsUk2MkZqaLl3y5GCGmkuSJEmS9KQ48hlS0pOqn/TJk5yUdEulE0ASkkkoNamgagKjOx2aMuX2zZSPrnW3VDuNz08mYRTAjesZd28nlJSkpORJfmADzSVJkiRJelIYSElzLGfLdEOXOtYUsSDG++177U557ayoJAkk9f3d+G7fSrh35/6vWASuX0snYRTAmWcqssURdazphi7L2fLB35QkSZIkSUfsyFv2pCdZnuasdFZYLVcpYsGQIWkc77oXY3xg4Pnp5zdIOhVlHemeKqnLhOpuj1OLkXt3E5IzdzmVZjR1Qn+xpne6BFIWwgIrnRXy1HY9SZIkSdLTz0BK2sVStkQv9Fir1hg0AyoqYLwr3ilOsRAWABjFEUUsWMgL0ioSI4QkMrqbcudGRmwCIUSSNNJfrDl9BjLGbYHL2bJhlCRJkiTpxDCQkvYgT3PyNGfYDBk0A2rqB2Y+XS2v8s3ymwCkWaS422HjTockjZx6bkg1zBjd7tFce4Hf9aOBLHFmlCRJkiTpZDKQkh5CP+nPDJAG9YCr1VVqarKQ0aHD4unA6nd64xM+jiycHbH4/D2eX7nKmfRlK6IkSZIkSSeWQ82lfbBWrVHEgjSkdEMXCFy/NpX3xsDoZp/iXoebdyquVmtHtlZJkiRJko6agZT0mKbb+Dp0iBGuX8u27KbXGt1aoAk1q9+9x91qeASrlSRJkiTp6BlISY+pHXSehpS2Mmo6jDrzTM3KyxuceaaGGCjvZRRVzT/7p9A0R7duSZIkSZKOioGU9JCGzZAPqg9Yq9b4oPqAYRxXOoU4O4w6d74iBDh3viJ/viDJGrqLBTeaj/mf/+YpQylJkiRJ0onjUHNpjwb1gLVqbVIR1YpEqlhRl8mOYVQda8pY0j9XUzeR2MD3/r5vce+jdf71t0/z6qc+4ZBzSZIkSdKJYYWUtAfr1TqXiktcr68zjEMaGiKRhoYyljQ00Cl57pP3gK1hVBlLRnFERUVDQ0giBCDC898zICxd41JxifVq/WhvUpIkSZKkQ2KFlLSLQT1gtVxlFEekIaUXe+MACggEFlhgxIhIJMuHfKITWDyVEGkompKSkkgkECav6aUZWa/HqW5GyTiwWi1X6YWelVKSJEmSpKeegZS0i7VqjSIWBAJNbMYBU4yT5wOBhIS4+Qj9e4xiSrP5aEXi5PxO6LBwpgECXboUFBSxYK1aM5CSJEmSJD31bNmT5hg2w8nMqPYxHTIBNDTU1JPAKRJnntdqg6tpHTrU1AyaAcNmeDA3I0mSJEnSE8JASppj0AwoYrElREpICFOP5CF+jQKBSGQjblDH+v7xEEhJqagYNIN9vw9JkiRJkp4kBlLSHPXmo50B1c6B2m778S5dsqmO2GTz0V4jEiljufUaIUzeU5IkSZKkp5mBlDRHHestA8z3KhDIQjYzxGoDqZqaJk7NmNqcS5WS7sPKJUmSJEl6chlISXu0fe7TvOcikZR0Ej5tf346lIJxGFVTk5GRJw41lyRJkiQ93QykpDnSkD7UjKhWEhKSkExCqd2UlKSk5ElOP+k/ylIlSZIkSTo2DKSkOdLNx3Sl06xqqJ2qpzqhM/+1MVLEgjrWdEOX5Wz5wO5FkiRJkqQnhYGUNEee5HRDd8tuepFIQ7Pl39NVUIEwmQOVhpRu6M58bSRSUhJjZCEssNJZIU9t15MkSZIkPf2y3U+RTq5+0idPcoq6mFQ3tbvuAZOh5SkpFdUknErC/ay3EzokJJSxHA8y3xySnpLSD+PrL2fLhlGSJEmSpBPDQEraxXK2zKAZMIoj0pDSiR2acL8yKokJdahJYjI5VsSCDh1CGFdOtbOoilhQUdGhw4vZi7yQveDMKEmSJEnSiWMgJe0iT3NWOiuslqsUsWCDDdKYEkKgic14IHkcVzs9nz7PR/VHFLFgyHByXruLXkrKqXCKlc4KS9nSUd+aJEmSJElHwkBK2oOlbIle6LFWrTFoBlRUAIQQ6NLd0na3VC/t6TxJkiRJkk4qAylpj/I0J09zhs2QQTOYVDzlSb6l7a4970Z1g/V6nYqKjIyldIlns2eP8A4kSZIkSXoyGEhJD6mf9OfOfRrUgwcqpABuNjfJayukJEmSJEkykJL20Xq1Ppk1VVOThpRAIBIZxiFFXTBoBs6QkiRJkiSdaAZS0j4Z1ANWy9XJbnx9+pNd9gDqWFNQcC/e4xvlN4hEPpF94ghXLEmSJEnS0TCQkvbJWrVGEQvSkNIN3cnxOtaUsaSmJm4+RnHEpeISH9cf28InSZIkSTpxkqNegPQ0mB503qEzOV7FilEcUVHR0Gx5TUXFer3OpeIS69X6YS9ZkiRJkqQjYyAl7YN2gHka0kmbXh1rNuLGJIhKSAgEks1fu3a21CiOWC1XGdSDI1u/JEmSJEmHyZY96SFNV0OlpORJTk0NjEOmVhlLIpGw+ZjW/jkNKQBFLFir1mzdkyRJkiSdCAZS0h4N6gFr1dqkGqqVkdEJHZrY0OZOTWwmM6OSGYWI00FVRsaQccg1bIb0k/5h3ZIkSZIkSUfCQErag/VqndVylSIW48qokE5a7oZxyEbcGFdJRejSnYRR2yujYBxGwbhKKmXc4pfGlIqKQTMwkJIkSZIkPfUMpKRdDOoBq+UqozgiDSl9+pM5UTAOmEpKqjiumtqIG5NWvJ20YVQSNudJtXOnNlv/JEmSJEl6mjnUXNrFWrVGEQvSkNIN3S1hFIzDpG7okm3muxUVVawmlVCtOPUIBDrh/m58MY7PTZkfZEmSJEmS9DQwkJLmmB5g3qEz99xe6D0wwLwNoBqaSUCVkNALvUkVVYyRmpqMjDxxqLkkSZIk6elnICXN0Q4wT0P6QGXUdiGMB5RnZJwJZyYVU21FVEJCRsZCWCAL97tlS8rJbn3Oj5IkSZIknQQGUtIc7UynWcPJZwkhkISEFzov8H3d76NHj4SElJQFFlhIFrZURhWxoI413dBlOVs+sPuQJEmSJOlJ4lBzaY52ptP2eVA7iTGOd80jZSlbApjszrfBBmkzrrRq2/RSUhbCAiudFfLUdj1JkiRJ0slgICXNkSc5GRnDOBy33s1p22tDpi7dySyopWyJXuixVq1N2v9gcxD65nnL2bJhlCRJkiTpRDGQkuboJ33yJKeoC0pKunR3PHenWVB5mpOn+ZYB6c6MkiRJkiSdZAZS0i6Ws2UGzYBRHFFQ0KGzpVIqxkhJSR1rFsLCjrOg+knfAEqSJEmSJBxqLu0qT3NWOisshAVijAzjkI1mYzwXqtkYt/PF6CwoSZIkSZL2yAopaQ+cBSVJkiRJ0v4xkJL2yFlQkiRJkiTtDwMp6SE5C0qSJEmSpMfjDClJkiRJkiQdKgMpSZIkSZIkHSoDKUmSJEmSJB0qAylJkiRJkiQdKgMpSZIkSZIkHSp32ZMewbAZMmgG1NSkpORJ7s57kiRJkiTtkYGU9BAG9YC1ao1BM6CimhzPyMiTnOVsmTzNj3CFkiRJkiQ9+QykpD1ar9ZZLVcpYjGujAopgUAkMoxDirpg0AxY6aywlC0d9XIlSZIkSXpiGUhJezCoB6yWq4ziiDSk9OkTQpg8H4mUlIziiNVylV7oWSklSZIkSdIOHGou7cFatUYRC9KQ0g3dLWEUQAiBbuiShpQiFqxVa0e0UkmSJEmSnnwGUtIupgeYd+jMPbdDh5qaQTNg2AwPaYWSJEmSJB0vBlLSLtoB5mlIH6iM2i6EQEpKRcWgGRzSCiVJkiRJOl4MpKRd1NQABOaHUa02tGpfJ0mSJEmStjKQknaRkgLjweV7EWPc8jpJkiRJkrSVgZS0izzJycioYz0Jm3YSY6SmJiMjT9xlT5IkSZKkWQykpF30kz55kpOSUlLOPbekJCUlT3L6Sf+QVihJkiRJ0vFiICXtwXK2TDd0qWNNEYsHKqVijBSxoI413dBlOVs+opVKkiRJkvTkM5CS9iBPc1Y6KyyEBWKMDOOQjWaDIhZsNBsM45AYIwthgZXOCnlqu54kSZIkSTvJjnoB0nGxlC3RCz3WqjUGzYCKChjvqtelS57kLGfLhlGSJEmSJO3CQEon3rAZMmgG1NS7zn/K05w8zR/qNZIkSZIkaSsDKZ1Yg3rwQLUTMNkhb161Uz/pG0BJkiRJkvSIDKR0Iq1X66yWq+NB5NSkISUQiIznQxV1waAZsNJZYSlbeuz3s6JKkiRJkqT7DKR04gzqAavlKqM4Ig0pffqEECbPRyIlJaM4YrVcpRd6jzwX6nGqsCRJkiRJeloZSOnEWavWKGJBGlK6ofvA8+2Q8oKCIhasVWuPFBoddhWWJEmSJEnHRXLUC5AO03TrXIfO3HM7dKipGTQDhs3wod5nugorhEA/9OmFHt3QpRd69MO4KqutwhrUg8e5LUmSJEmSjhUDKZ0obetcGtItbXqzhBBISamoGDQPFxhtr8La/l4hBLqhSxrSSRWWJEmSJEknhYGUTpSaGoDA/DCq1QZJ7ev24rCqsCRJkiRJOq4MpHSipKTAeHD5XsQYt7xuLw6rCkuSJEmSpOPKQEonSp7kZGTUsZ6ETTuJMVJTT3bE26vDqMKSJEmSJOk4M5DSidJP+uRJTkpKSTn33JKSlJQ8yekn/T2/x2FUYUmSJEmSdJwZSOnEWc6W6YYudawpYvFApVSMkSIW1LGmG7osZ8sPdf3DqMKSJEmSJOk4M5DSiZOnOSudFRbCAjFGhnHIRrNBEQs2mg2GcUiMkYWwwEpnhTx9uKDoMKqwJEmSJEk6zrKjXoB0FJayJXqhx1q1NhlCDuN5Tl265EnOcrb80GFUazlbZtAMGMURBQUdOlsGnMcYKSmpY81CWHjoKixJkiRJko4zAymdWHmak6c5w2bIoBlQU+9btVJbhbVarlLEgiFD0jjeda9t00tJH7kKS5IkSZKk48xASideP+kfSLvcQVdhSZIkSZJ0XBlISQfoIKuwJEmSJEk6rgykpENwUFVYkiRJkiQdR+6yJ0mSJEmSpENlICVJkiRJkqRDZcuedEicIyVJkiRJ0piBlHTABvXggZ32ADIyd9qTJEmSJJ1IBlLSAVqv1lktVyliMa6MCimBQCQyjEOKumDQDFjprLCULR31ciVJkiRJOhQGUtIBGdQDVstVRnFEGlL69AkhTJ6PREpKRnHEarlKL/SslJIkSZIknQgONZcOyFq1RhEL0pDSDd0tYRRACIFu6JKGlCIWrFVrR7RSSZIkSZIOl4GUdACmB5h36Mw9t0OHmppBM2DYDA9phZIkSZIkHR0DKekAtAPM05A+UBm1XQiBlJSKikEzOKQVSpIkSZJ0dAykpANQUwMQmB9GtdrQqn2dJEmSJElPMwMp6QCkpMB4cPlexBi3vE6SJEmSpKeZgZR0APIkJyOjjvUkbNpJjJGamoyMPHGXPUmSJEnS089ASjoA/aRPnuSkpJSUc88tKUlJyZOcftI/pBVKkiRJknR0DKSkA7KcLdMNXepYU8TigUqpGCNFLKhjTTd0Wc6Wj2ilkiRJkiQdLgMp6YDkac6L2YtkIaOKFffiPUbNiCIWbDQbDOOQGCMLYYGVzgp5arueJEmSJOlkyI56AdLTaFAPWKvWGDQDmtgA4wHnFRVJTEhJ6YdxW99ytmwYJUmSJEk6UQykpH22Xq2zWq6O2/GoSUNKRkYTGyoqAAKBF7MXebHz4hGvVpIkSZKkw2cgJe2jQT1gtVxlFEekIaVPnxDC+MkwnhtVUlLHmqvVVc4kZ6yOkiRJkiSdOM6QkvbRWrVGEQvSkNIN3fth1KYQAt3QJQ0pRSxYq9aOaKWSJEmSJB0dK6SkxzBshgyaATU1day52dykpqZPf+7rOnQYMn7tsBnST+afL0mSJEnS08RASnoE00PL27lQdaypqAgEGhpS0h1fH0IgjSkVFYNmYCAlSZIkSTpRDKSkhzRraHkbQsXNxyiO6NEjCzv/irXtfDX1YS1dkiRJkqQngoGU9BDmDS0PBJo4DqUaGjbiBoFAGu5XSjWxoaYmEqliRbr5kCRJkiTpJDGQkh7C9qHl01LGlVKRCEAkUsaSNKTUsaaM5SSMap+PRNbrdfqh7257kiRJkqQTw132pD2aHmDeofPA80lIJqFUG0zV1BRNwSiOqKhoaAC2hFI365tcKi6xXq0f6v1IkiRJknRUDKSkPWoHmKchnbTpbdcJnS1VUg0NBcUkiArcf11CwgILhBAYxRGr5SqDenDwNyJJkiRJ0hEzkJL2qB0+Ph0qbde28v3/7d1/kOT5Xd/317u/PbMzSLQspJWUtYRYKcLnwi6I+GFhG0RQwBhCCgiUK4kJVgwuFVTJ4FAVhx9B2EWMK7FjIPEPMAECSRUmhKQS8ysGGUyIi0QYXAYLgbRSLM8d2jud1HA3s9P97U/+6JnR7t7u3p129tOzM4/H1FbPTHfvvaeq93bmuZ/v5zO56Y/WcZy6+f1JJrlUlzKdTLNd2xlqyGE7zN5y7wFNDwAAAGeHIAXP0fHm4zcHpjvZqq3s1M4dw9Ukk0wzzU7t3HIC31a2MmbMfDXP/mr/dAcHAACAM8am5vAczSazTDPNfttPS7vrZXtJTlZIVSqTTLJV6z2nhgyZ1DM7cFVlaEOWWWa+mmd3svtgvggAAAA4AwQpeI52J7uZTWa5Md7IQTvI9OiPz50i0yKLkyg1relJkLqX48B1fGkgAAAAnFeCFDxH83Gew3aYMWNWWWXZlknWq6GGNmSrtjLJJIssMrYx00zTjt6ei9bWq66OLw0EAACA80qQgufg+vJ6ri2u5bAdJskzTtJbZZVFW9yyR9QfnP7B/Ovlv87T7elUq5MVUHdaUdVay5gx29nObDLr+8UBAABAZ4IUPIv5OM+1xbUctIMMNeRj8jEnAWrM+IxVULPJLJ+w9QlJ1rGqpeVGbqTaOkhV6mRF1VDr1VCLLDJkyGwys38UAAAA554gBc9ib7mXw3aYoYZs13aS9SqnoYas2uokSo1tTKWyXdu50W6crKg6jlU33x4/frttp9X6/Z3ayZXplY19nQAAANCLIAX3sL/az3w1z5gxu3nmyqVJTU42L29p2W/7eXJ8Mh9afegkYk3bNIc5vGUl1fH7BznItK0v8bu6dTWzweV6AAAAnH+CFNzDfDXPMssMNZzsAXU3VetL8Q5zmGr1kRVVlUza5JZL/JJ1lDpeUfXI9iNiFAAAABeGIAX3MGZMst736blaZZUk2cnOyeeGWl/it1wts8zyJEqNGTPJJFu1dYpTAwAAwNk2efaHwMU1ZL3p+M2blt/LccAacuuKqrGNOVgd5EZuZJllxqO3JNlv+3ls+dgpTw4AAABnlyAF9zCbzDLNNGMb09q9o1Rr7WR11OSmP1qLtshBO8gyy5P7T56TljFj9sa9XF9eP/0vAAAAAM4gQQruYXeym9lkliFDFlnc87GLLDI5eju+wm9sYw7b4UmIqtveTp7bFrm2uJb5OH9gXwsAAACcFYIUPIsr0yvZru2TuHT7SqnWWg7bYcY25lIunTy2tZZFW5xsXn57hDo2ySTTTHPYDrO33Ov1ZQEAAMDGCFLwLGbDLFe3rmandtJay37bz43VjRy2w9xY3ch+209rLTu1k9duvzYvnrw4Q4Z1pLrpVL3bHYeqIevT+MaMma/m2V/td/4KAQAAoC+n7MFzcHl6OZfqUvaWe5mv5llmmSSpqmxnO7PJLFemVzIbZrlUlzJfzfN0ezqrrO64MqodvR2fsFdVGdqQZZaZr+bZnexu4ssEAACALgQpeI5mwyyzYZb91X7mq3nGjBkyZDaZ3RKQjldUvevwXbmRG89YIXW8MmqSSS7VpQy1Psnv+FS+49P3AAAA4LwSpOB52p3sPusKpsvTy3lq9VTet3zfM07Wm2SSIUO2auskRiXrvaiq1pfwAQAAwHkmSMED8vLpy/OB8QN5uj2drWydrIAaMmRSt27f1lrLmPHk8j8AAAA4z2xqDg/I7mQ3s8ks00xvuWxvzJhVu3XV1CKLO17+BwAAAOeRFVLwAM0ms1wfr2eRRY6b1PEm50MbMs00q1plbGN2aidXplc2OzAAAAB0IEjBA3J9eT3vX77/jpuat7Ssssoii0zbNDu1k6tbVzMbXK4HAADA+SdIwQMwH+e5triWg3aQoYZsta0ss8yY8SRQ3RyqXjl9ZS5PL29qXAAAAOhKkIIHYG+5l8N2mKGGbNd2UllfntdWJ1GqUid7Sc1X81yJy/UAAAC4GAQpOGX7q/3MV/OMGbObWzcon9Qkk5vOEmhp2W/rx++v9m1oDgAAwIXglD04ZfPVPMssM9SQqrrnY6sqQ4Yss8x8Ne80IQAAAGyWIAWnbMyYZH2a3nNxHK2OnwcAAADnnSAFp2zIkCTPOF3vblprtzwPAAAAzjtBCk7ZbDLLNNOMbTyJTXfTWsuYMdNMM5vMOk0IAAAAmyVIwSnbnexmNpllyJBFFvd87CKLDBkym8xsaA4AAMCFIUjBA3BleiXbtZ2xjTlsh89YKdVay2E7zNjGbNd2rkyvbGhSAAAA6E+QggdgNsxydetqdmonrbXst/3cWN3IYTvMjdWN7Lf9tNayUzu5unU1s8HlegAAAFwc000PAOfV5enlXKpL2VvuZb6aZ5llkvWpetvZzmwyy5XpFTEKAACAC0eQggdoNswyG2bZX+1nvppnzGjPKAAAAC48QQo62J3sClAAAABwxB5SAAAAAHRlhRR04JI9AAAA+AhBCh6g+Th/xqbmSTLN1KbmAAAAXFiCFDwg15fXc21xLYftcL0yqoZUKi0tT7ensz/u54nxibxseFleufVKK6YAAAC4MAQpeADm4zzXFtdy0A4y1JDd7KaqMrYxi7ZIkoxHb+8f35/HV4/nxZMXWzEFAADAhWBTc3gA9pZ7OWyHGWrIdm2nqrJoixy0gyyzzCqrVCpJ0tJy0A7y+Ph43nn4zlxfXt/w9AAAAPBgCVJwym7ewHwrW0mSsY05bIdZZZUkqaO3yW1/BA/aQa4trmU+zrvPDQAAAL0IUnDKjjcwH2pI1XoV1KIt0tJOQtTx6qgkJ+9PapKhhhy2w+wt9zYyOwAAAPQgSMEpGzMm+UhoWrVVxoxpac/63K1sZcyY+Wqe/dX+A50TAAAANkWQglM2ZEiSkwB1HKNuXxl1J1WVIUOWWWa+ctkeAAAA55MgBadsNpllmmnGNqa19qwro45j1XHIOr7M73gP78GpAAAeMElEQVSlFQAAAJw3ghScst3JbmaTWYYMWWRxz1VRN8eoSa3/OLa2DljHgQoAAADOG0EKHoAr0yvZru2MbcyqrVKptNy6Wur440plq9an8bXWMmbMNNPMJrNNjQ8AAAAPlCAFD8BsmOXq1tXs1E6Sj+wndRyhVlklSSaZ5FJdylDr1VCLLDJkyGwyy+5kdzPDAwAAwAM23fQAcF5dnl7OpbqUveVenhyfzEEOblkVNc00W7WVoYa01rLIImMbs1M7uTK9sunxAQAA4IERpOABmg2zzIZZ9lf7ef/y/fnA+IH1ZXxZX8Y3ZsxytcyYMUOG7NROrm5dzWxwuR4AAADnlyAFHexOdvO67dfl5ePLs7fcy3w1zzLLJOtT9bazndlklivTK2IUAAAA554gBfewv9rPfDU/WcF0v3s73bxi6jR/XwAAAHiYCFJwB/Nx/oyVTElOTr+735VMu5NdAQoAAIALS5CC21xfXs+1xbUctsP1CqYaUqm0tOy3/RyOh5mv5rm6dTWXp5c3PS4AAAA8dAQpuMl8nOfa4loO2kGGGrKb3VTVyf0t69PwDtpBri2u5VJdsucTAAAAPE+TTQ8AZ8neci+H7TBDDdmu7VtiVHK0AXltZ6ghh+0we8u9DU0KAAAADy9BCo7cvNH4Vrbu+ditbGXMmPlqnv3VfqcJAQAA4HwQpODI4+PjudFupFJZZplVW931sVWVIUOWWWa+mnecEgAAAB5+9pDiwjs+Ue+J8YksskiSjG1MpTK0IVu1laGGZzzv+HK+MWPXeQEAAOBhJ0hxod18ot5xjDq2yiotLWMbcymXMq1b/7i01k5WSgEAAADPnUv2uLBuPlGvqrKb3Uxu+iNx/P4qq9xoNzK2j6yEaq1lzJhppplNnLIHAAAAz4cgxYV1+4l6w2TIkCGVj5ysV0dvLS2L9pEVVIssMmTIbDLL7mR3E+MDAADAQ0uQ4kK624l6W7V1EqCO344/HjNmXI05bIcZ25jt2s6V6ZUNfhUAAADwcBKkuJDmq3mWWWao4WRz8iQnq6WOL9draVllfdreKqsc5CCttezUTq5uXc1scLkeAAAAPF82NedCOj4Z7+bL845t1VYmmWTRFhkz3rJSaqu28pLJS3JlekWMAgAAgI+SIMWFdHwyXku78/01ZKghq7bKmDGLtsgkk3z89OPzqq1X9RwVAAAAzh2X7HEhzSazTDPN2Ma0ducolSSTmmR61G0v1aW8dHhprxEBAADg3BKkuJB2J7uZTWYZMmSRxT0f60Q9AAAAOF2CFBfWlemVbNd2xrY+Oe/2lVKtNSfqAQAAwAMgSHFhzYZZrm5dzU7tpLWW/bafG6sbOWyHubG6kf2270Q9AAAAeABsas6Fdnl6OZfqUvaWe5mv5llmmSSpqmxnO7PJzIl6AAAAcMoEKS682TDLbJhlf7Wf+WqeMaM9owAAAOABEqTgyO5kV4ACAACADuwhBQAAAEBXghQAAAAAXQlSAAAAAHQlSAEAAADQlSAFAAAAQFeCFAAAAABdCVIAAAAAdCVIAQAAANCVIAUAAABAV9NNDwDc3f5qP/PVPGPGDBmybMtNjwQAAAD3TZCCM2g+zrO33Mt8Nc8yH4lQy7bM+Op35gWvvpzkFZsbEAAAAO6DS/bgjLm+vJ53Hr4zj4+PZ7/tZ5VVWlpWWWWZZdqLHs+r/8zPZ/w33rPpUQEAAOCjYoUUnCHzcZ5ri2s5aAcZashudlNVJ/ePbczYKpdeMs/ihf80dfAxmTxppRQAAAAPFyuk4AzZW+7lsB1mqCHbtX1LjEqSSqWW2xkPttJ2nsryNb+xoUkBAADgo3cmg1RVfX9VtaNf/+am54Eebt7AfCtb93zs8qndZLrI6uMey+oFH+40IQAAAJyOMxekquqLk/wnSX5/07NAT8cbmA81PGNl1DO0SpZbyfQwq497rM+AAAAAcErOVJCqqstJvi/JjyZ5x4bHga7GjEnWl+U9J6tJUi0ZFg9wKgAAADh9ZypIJfneo9uv2+gUsAFDhiRJS3tuT5is1iulxntf3gcAAABnzZkJUlX155J8SZK3tNae2PA40N1sMss004xtTGvPEqWqJdNFstzO5INO2QMAAODhciaCVFW9Osl3JfmR1tr/uul5YBN2J7uZTWYZMmSRe1+GN33BfrLcyuSDr8jkqRd1mhAAAABOx3TTA1TVJMkPZb2J+Vvv8/e6275Tj9zP7wu9XJleyXw1z0E7yGEOs5WtWzY4b2lp08MMldTBLNP3fNIGpwUAAICPzqmskKqq91ZVex6/fuSmp39Dkjcm+ZrW2pOnMQ88rGbDLFe3rmandtJay37bz43VjRy2w9xY3VhvfF4tN56YZes33pDJky7XAwAA4OFzWiuk3p3k4Hk8fi9Jqup1Sb4jyQ+01n7yfodorX3qnT5/tHLq9ff7+0MPl6eXc6kuZW+5l/lqnmWWSZKqyrRNM374pXnfj35WXvpqMQoAAICH06kEqdbamz7Kp35SkktJ3lxVb77LY3776JKlL7W/FBfFbJhlNsyyv9rPfDXPmDFDhjy2fCzz9z2Sp973iuTVm54SAAAAPjqb3kPqvUm+/y73fVGSVyT5sSTzo8fChbI72c3uZPfk48fHxzc4DQAAAJyOjQap1tqvJfnqO91XVf846yD1Ta213+k5FwAAAAAPzqlsag4AAAAAz5UgBQAAAEBXm95D6q5aa5+z6RkAAAAAOH1WSAEAAADQlSAFAAAAQFeCFAAAAABdCVIAAAAAdCVIAQAAANCVIAUAAABAV4IUAAAAAF0JUgAAAAB0JUgBAAAA0JUgBQAAAEBXghQAAAAAXQlSAAAAAHQlSAEAAADQlSAFAAAAQFeCFAAAAABdCVIAAAAAdCVIAQAAANCVIAUAAABAV4IUAAAAAF0JUgAAAAB0JUgBAAAA0JUgBQAAAEBXghQAAAAAXQlSAAAAAHQlSAEAAADQlSAFAAAAQFeCFAAAAABdCVIAAAAAdCVIAQAAANCVIAUAAABAV4IUAAAAAF0JUgAAAAB0JUgBAAAA0JUgBQAAAEBXghQAAAAAXQlSAAAAAHQlSAEAAADQlSAFAAAAQFeCFAAAAABdCVIAAAAAdCVIAQAAANCVIAUAAABAV4IUAAAAAF0JUgAAAAB0JUgBAAAA0JUgBQAAAEBXghQAAAAAXQlSAAAAAHQlSAEAAADQlSAFAAAAQFeCFAAAAABdCVIAAAAAdCVIAQAAANCVIAUAAABAV4IUAAAAAF0JUgAAAAB0JUgBAAAA0JUgBQAAAEBXghQAAAAAXQlSAAAAAHQlSAEAAADQlSAFAAAAQFeCFAAAAABdCVIAAAAAdCVIAQAAANCVIAUAAABAV4IUAAAAAF0JUgAAAAB0JUgBAAAA0JUgBQAAAEBXghQAAAAAXQlSAAAAAHQlSAEAAADQlSAFAAAAQFeCFAAAAABdCVIAAAAAdCVIAQAAANCVIAUAAABAV4IUAAAAAF0JUgAAAAB0JUgBAAAA0JUgBQAAAEBXghQAAAAAXQlSAAAAAHQlSAEAAADQlSAFAAAAQFeCFAAAAABdCVIAAAAAdCVIAQAAANCVIAUAAABAV4IUAAAAAF0JUgAAAAB0JUgBAAAA0JUgBQAAAEBXghQAAAAAXQlSAAAAAHQlSAEAAADQlSAFAAAAQFeCFAAAAABdCVIAAAAAdCVIAQAAANCVIAUAAABAV4IUAAAAAF0JUgAAAAB0JUgBAAAA0JUgBQAAAEBXghQAAAAAXQlSAAAAAHQlSAEAAADQlSAFAAAAQFeCFAAAAABdCVIAAAAAdCVIAQAAANCVIAUAAABAV4IUAAAAAF0JUgAAAAB0JUgBAAAA0JUgBQAAAEBXghQAAAAAXQlSAAAAAHQlSAEAAADQlSAFAAAAQFeCFAAAAABdCVIAAAAAdCVIAQAAANCVIAUAAABAV4IUAAAAAF0JUgAAAAB0JUgBAAAA0JUgBQAAAEBXghQAAAAAXQlSAAAAAHQlSAEAAADQlSAFAAAAQFeCFAAAAABdCVIAAAAAdCVIAQAAANCVIAUAAABAV4IUAAAAAF0JUgAAAAB0JUgBAAAA0JUgBQAAAEBXghQAAAAAXQlSAAAAAHQlSAEAAADQlSAFAAAAQFeCFAAAAABdCVIAAAAAdCVIAQAAANCVIAUAAABAV4IUAAAAAF0JUgAAAAB0JUgBAAAA0JUgBQAAAEBXghQAAAAAXQlSAAAAAHQlSAEAAADQlSAFAAAAQFeCFAAAAABdCVIAAAAAdCVIAQAAANCVIAUAAABAV4IUAAAAAF0JUgAAAAB0JUgBAAAA0JUgBQAAAEBXghQAAAAAXQlSAAAAAHQlSAEAAADQlSAFAAAAQFeCFAAAAABdCVIAAAAAdCVIAQAAANCVIAUAAABAV4IUAAAAAF0JUgAAAAB0JUgBAAAA0JUgBQAAAEBXghQAAAAAXQlSAAAAAHQlSAEAAADQlSAFAAAAQFeCFAAAAABdCVIAAAAAdCVIAQAAANCVIAUAAABAV4IUAAAAAF0JUgAAAAB0JUgBAAAA0JUgBQAAAEBXghQAAAAAXQlSAAAAAHQlSAEAAADQlSAFAAAAQFeCFAAAAABdCVIAAAAAdCVIAQAAANCVIAUAAABAV4IUAAAAAF0JUgAAAAB0JUgBAAAA0JUgBQAAAEBXZyZI1dpXVdU/rqoPVtV+VV2rqn9QVZ+46fkAAAAAOB3TTQ+QJFW1k+THkvy7SX4ryf+U5PeSXEnyWUk+Mcm7NjYgAAAAAKfmTASpJH8j6xj115J8S2ttdfOdVbW1kakAAAAAOHUbD1JV9dokb0ny/yT55tZau/0xrbVF98EAAAAAeCA2HqSS/AdZ72X1Q0lmVfXFSV6V5IkkP99a+51NDgcAAADA6ToLQerTj25flOTdSV5y032tqv5Okre21sZn+42q6h13ueuR+xsRAAAAgNNyFk7Ze9nR7V9J8v8m+aNJPjbJm7IOVF+b5Fs3MxoAAAAAp+1UVkhV1XuTvPp5POV/bK392aP3h6PbR5N8aWtt/+jjn6+qL0/yq0n+UlX9l621w3v9pq21T73LfO9I8vrnMR8AAAAAD8hpXbL37iQHz+Pxeze9/+TR7U/fFKOSJK21X6+qa0lem+QPJ/n1+5oSAAAAgI07lSDVWnvTfTz9t5J8fpIP3eX+42C1ex//DQAAAADOiLOwh9TPHd3+kdvvqKpLSV539OF7ew0EAAAAwINzFoLUTyV5T5I/VVWfd9t935r16Xu/0Fp7rPtkAAAAAJy609pD6qPWWjusqq9K8rNJfqqqfiLJ+5J8epLPTnI9yV/Y4IgAAAAAnKKzsEIqrbVfSvJpSX48yRuTvDXJa5J8b5LXt9betcHxAAAAADhFG18hday19ptJ/sym5wAAAADgwToTK6QAAAAAuDgEKQAAAAC6EqQAAAAA6EqQAgAAAKArQQoAAACArgQpAAAAALoSpAAAAADoarrpAeA82l/tZ76aZ8yYIUNmk1l2J7ubHgsAAADOBEEKTtF8nGdvuZf5ap5lliefn2aa2WSWK9MrmQ2zDU4IAAAAmydIwSm5vryea4trOWyH65VRNaRSaWnZb/s5HA8zX81zdetqLk8vb3pcAAAA2BhBCk7BfJzn2uJaDtpBhhqym91U1cn9LS2LLHLQDnJtcS2X6pKVUgAAAFxYNjWHU7C33MthO8xQQ7Zr+5YYlSRVle3azlBDDtth9pZ7G5oUAAAANk+Qgvt08wbmW9m652O3spUxY+arefZX+50mBAAAgLNFkIL7dLyB+VDDM1ZG3a6qMmTIMsvMV/NOEwIAAMDZIkjBfRozJkkq945Rx46j1fHzAAAA4KIRpOA+DRmSrDcufy5aa7c8DwAAAC4aQQru02wyyzTTjG08iU1301rLmDHTTDObOGUPAACAi0mQgvu0O9nNbDLLkCGLLO752EUWGTJkNplld7LbaUIAAAA4WwQpOAVXpleyXdsZ25jDdviMlVKttRy2w4xtzHZt58r0yoYmBQAAgM0TpOAUzIZZrm5dzU7tpLWW/bafG6sbOWyHubG6kf22n9ZadmonV7euZja4XA8AAICLa7rpAeC8uDy9nEt1KXvLvcxX8yyzTLI+VW8725lNZrkyvSJGAQAAcOEJUnCKZsMss2GW/dV+5qt5xoz2jAIAAIDbCFLwAOxOdgUoAAAAuAt7SAEAAADQlSAFAAAAQFeCFAAAAABdCVIAAAAAdCVIAQAAANCVIAUAAABAV4IUAAAAAF0JUgAAAAB0JUgBAAAA0JUgBQAAAEBXghQAAAAAXQlSAAAAAHQlSAEAAADQlSAFAAAAQFeCFAAAAABdCVIAAAAAdCVIAQAAANCVIAUAAABAV4IUAAAAAF0JUgAAAAB0JUgBAAAA0JUgBQAAAEBXghQAAAAAXQlSAAAAAHQlSAEAAADQlSAFAAAAQFeCFAAAAABdCVIAAAAAdCVIAQAAANCVIAUAAABAV4IUAAAAAF0JUgAAAAB0JUgBAAAA0JUgBQAAAEBXghQAAAAAXQlSAAAAAHQlSAEAAADQlSAFAAAAQFeCFAAAAABdCVIAAAAAdCVIAQAAANCVIAUAAABAV4IUAAAAAF0JUgAAAAB0JUgBAAAA0JUgBQAAAEBXghQAAAAAXQlSAAAAAHQlSAEAAADQlSAFAAAAQFeCFAAAAABdCVIAAAAAdCVIAQAAANCVIAUAAABAV4IUAAAAAF0JUgAAAAB0JUgBAAAA0JUgBQAAAEBXghQAAAAAXQlSAAAAAHQlSAEAAADQlSAFAAAAQFeCFAAAAABdCVIAAAAAdCVIAQAAANCVIAUAAABAV4IUAAAAAF0JUgAAAAB0JUgBAAAA0JUgBQAAAEBXghQAAAAAXQlSAAAAAHQlSAEAAADQlSAFAAAAQFeCFAAAAABdCVIAAAAAdCVIAQAAANCVIAUAAABAV4IUAAAAAF0JUgAAAAB0JUgBAAAA0JUgBQAAAEBXghQAAAAAXQlSAAAAAHQlSAEAAADQlSAFAAAAQFeCFAAAAABdCVIAAAAAdCVIAQAAANCVIAUAAABAV4IUAAAAAF0JUgAAAAB0JUgBAAAA0JUgBQAAAEBXghQAAAAAXQlSAAAAAHQlSAEAAADQlSAFAAAAQFeCFAAAAABdVWtt0zM8cFX1xM7uzsddfeTqpkeB+3KwOsjyqZ089cHdvPCFm54GAACAi+zRR/9lFov9D7bWXvJ8n3tRgtS1JLMk793wKNy/R45u37nRKTgrvB64ndcEN/N64HZeE9zM64HbeU1wO6+JZ/cJSeattee9AuhCBCnOj6p6R5K01j5107OweV4P3M5rgpt5PXA7rwlu5vXA7bwmuJ3XxINlDykAAAAAuhKkAAAAAOhKkAIAAACgK0EKAAAAgK4EKQAAAAC6csoeAAAAAF1ZIQUAAABAV4IUAAAAAF0JUgAAAAB0JUgBAAAA0JUgBQAAAEBXghQAAAAAXQlSAAAAAHQlSPHQqqqtqvqLVfUDVfVrVXVYVa2qvnrTs/FgVdUrq+q/r6q9qrpRVe+tqr9VVS/e9Gz0V1VfXlXfU1X/pKrmR/8f+JFNz0V/VfWSqvrqqvqJqvqdqtqvqg9X1S9V1Z+vKt/3XEBV9der6ueq6l8dvSY+WFX/rKq+rapesun52Lyq+sqjvzt8H3kBHX0f2e7y67FNz8fmVNVnVdWPV9WjRz9zPFpVP1tVX7jp2c6L6aYHgPvwgiR/6+j9303yWJJXbW4ceqiq1yb55SQvS/K/JXlnks9I8heTfEFV/YnW2hMbHJH+viXJJyf5/STvT/LIZsdhg74iyd9J8miStyf5/5K8PMmXJfn7Sf50VX1Fa61tbkQ24BuS/GqS/zPJB7L+/uENSd6W5C9U1Rtaa/9qc+OxSVX1qiTfk/XfIS/c8DhszofzkZ8rbvb7vQfhbKiqb0nyV5M8nuT/yPp7i5cm+beSfE6Sn9zYcOeIIMXD7OkkX5jk11prj1bV25J822ZHooO/nXWMemtr7XuOP1lVfzPrHzq+I8lbNjQbm/ENWYeo30nyxqxDBBfTu5L8e0n+YWttdfzJqvqmJL+S5N/POk79+GbGY0NmrbWD2z9ZVd+R5JuS/OdJvrb7VGxcVVWSH0jyRJL/Jck3bnYiNuhDrbW3bXoIzoaq+oqsY9Q/SvJlrbXfu+3+rY0Mdg5Zus5Dq7V22Fr7qdbao5uehT6q6jVJPj/Je5P8d7fd/W1JnkrylVX1gs6jsUGttbe31n7bqhdaaz/fWvvfb45RR59/LMnfPfrwc7oPxkbdKUYd+QdHt6/rNQtnzluTfG6SN2f9PQRwwR1d3v/Xs1788B/eHqOSpLW26D7YOWWFFPAw+dyj25+9ww+cv1dV/1fWweoNSX6u93DAmXb8zeNyo1Nwlnzx0e0/3+gUbERV/eEk35nku1prv1hVn/tsz+Fcu1RVfzbJx2cdJ/95kl9srY2bHYsN+ONJrib5n5M8WVVflOSPJDlI8iuttf97k8OdN4IU8DD5Q0e377rL/b+ddZD6xAhSwJGqmib5j48+/OlNzsLmVNU3Zr1H0IuSfFqSP5n1D53fucm56O/o/wk/nPU+c9+04XE4G16R9WviZteq6s2ttV/YxEBszKcf3f5u1vsP/tGb76yqX0zy5a21670HO49csgc8TF50dPvhu9x//Pk/0GEW4OHxnVn/6+ZPttZ+ZtPDsDHfmPXl3V+fdYz66SSf74eKC+m/yHpj4j/XWtvf9DBs3A8keVPWUeoFWQeIv5fkE5L8VFV98uZGYwNednT7liS7Sf6dJB+b9fcRP5Pks5P82GZGO38EKTbqWY5ZvdMvR7lzL3V0ay8hIElSVW9N8p9mfSLnV254HDaotfaK1lpl/UPnlyV5TZJ/VlWv3+xk9FRVn5H1qqi/4dIbkqS19u1HexD+bmvt6dbav2itvSXJ38w6SLxtsxPS2XB0W1mvhPq51trvt9Z+I8mXZn2Qzhur6jM3NuE54pI9Nu3dWV+P+1ztPahBeCgcr4B60V3un932OOACq6qvS/JdSX4zyZtaax/c8EicAa21303yE1X1q1lfAv4/ZP0v35xzN12q964k37rhcTj7/m7W/6Dx2ZsehK6ePLp9T2vt12++o7W2X1U/k+TPJ/mMJKL2fRKk2KjW2ps2PQMPld86uv3Eu9x/fFLS3faYAi6Iqvr6JP9Nkn+RdYz6wIZH4oxprb2vqn4zyadU1Utba49veiYeuBfmI99DHFTVnR7zfVX1fVlvdv713SbjLDr+e8PpzRfL8c8bH7rL/cfBarfDLOeeIAU8TN5+dPv5VTW5+aS9qvrYJH8iyX6Sf7qJ4YCzoar+s6z3jfq1JJ8nNHAPV45unaR1MdxI8v13ue/1We8r9UtZ/0Bq5QPHl2S9Z6NT0NsvZn0i7+uqaru1dnjb/ccrat/bdapzSpACHhqttXdX1c9mfZLe1yX5npvu/vas/wXr77XWntrEfMDmVdW3JvkrSd6R9YbVLtO7wKrqkSQfaq09dtvnJ0n+atab1/5ya+3JOz2f8+VoA/OvvtN9VfW2rIPUD7XW/n7PudicqvqkJI/e/ndFVb06yX979KE9bC+Q1trjVfWjSf6jrA9A+Jbj+6rq85L8qay3B3Fq7ykQpHioVdVfTvLI0YefcnT75qr6k0fv/5JvKs6dr03yy0m+u6relORfJvljSf7trC/V++YNzsYGVNWXJPmSow9fcXT7mVX1g0fvP95a+8bug9FdVX1V1jFqTPJPkrz1DpfkvLe19oOdR2NzviDJf3V0TPe7kzyR5OVJ3pj1puaPJfmazY0HbNhXJPnLVfX2JNeS/F6S1yb5oiQ7SX4yyX+9ufHYkL+U9c8X31xVn53kV5K8OutNzcckX9Nau9slfTwPghQPuy/I+pvKm/3xo1/HBKlz5GiV1Kdl/UPnFyT5wiSPJvnuJN9uNcSF9ClJvuq2z73m6FeSvC/r4945/64e3Q5J7rb3yy8k+cEu03AW/KMk35v1Jd2fnOQPJHkq63/A+OEk3+3vDbjQ3p7kD2W9Ou4zs15t/6GsL9384SQ/3FpzevMF01r7QFX9saxXR31pkjdkHSv/YZK/1lqzPcgpKX++AAAAAOhpsukBAAAAALhYBCkAAAAAuhKkAAAAAOhKkAIAAACgK0EKAAAAgK4EKQAAAAC6EqQAAAAA6EqQAgAAAKArQQoAAACArgQpAAAAALoSpAAAAADoSpACAAAAoCtBCgAAAICuBCkAAAAAuhKkAAAAAOhKkAIAAACgK0EKAAAAgK7+f1Ao1b4HFsfEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 574,
       "width": 594
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "forest = RandomForestClassifier(n_estimators=50, max_leaf_nodes=20, n_jobs=-1, random_state=42)\n",
    "forest.fit(X_train, y_train)\n",
    "\n",
    "model = forest\n",
    "\n",
    "print(metrics.precision_score(y_test, model.predict(X_test), average = \"weighted\"))\n",
    "print(metrics.recall_score(y_test, model.predict(X_test), average = \"weighted\"))\n",
    "print(metrics.f1_score(y_test, model.predict(X_test), average = \"weighted\"))\n",
    "print(metrics.accuracy_score(y_test, model.predict(X_test)))\n",
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "plot_decision_regions(X_test, y_test, model)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Porównanie metod dla danych dwuwymiarowych:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "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>Method</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1</th>\n",
       "      <th>accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>softmax</td>\n",
       "      <td>0.867985</td>\n",
       "      <td>0.870438</td>\n",
       "      <td>0.865749</td>\n",
       "      <td>0.870438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tree</td>\n",
       "      <td>0.843577</td>\n",
       "      <td>0.846715</td>\n",
       "      <td>0.844853</td>\n",
       "      <td>0.846715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>forest</td>\n",
       "      <td>0.888997</td>\n",
       "      <td>0.890511</td>\n",
       "      <td>0.889149</td>\n",
       "      <td>0.890511</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Method  precision    recall        f1  accuracy\n",
       "0  softmax   0.867985  0.870438  0.865749  0.870438\n",
       "1     tree   0.843577  0.846715  0.844853  0.846715\n",
       "2   forest   0.888997  0.890511  0.889149  0.890511"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models = [softmax, tree, forest]\n",
    "\n",
    "\n",
    "precision = []\n",
    "recall = []\n",
    "f1 = []\n",
    "accuracy = []\n",
    "\n",
    "\n",
    "for model in models:\n",
    "    precision.append(metrics.precision_score(y_test, model.predict(X_test), average = \"weighted\"))\n",
    "    recall.append(metrics.recall_score(y_test, model.predict(X_test), average = \"weighted\"))\n",
    "    f1.append(metrics.f1_score(y_test, model.predict(X_test), average = \"weighted\"))\n",
    "    accuracy.append(metrics.accuracy_score(y_test, model.predict(X_test)))\n",
    "\n",
    "\n",
    "d = {'precision': precision,\n",
    "     'recall' : recall,\n",
    "     'f1': f1,\n",
    "     'accuracy': accuracy\n",
    "    }\n",
    "\n",
    "df = pd.DataFrame(data=d)\n",
    "df.insert(loc=0, column='Method', value=[\"softmax\", \"tree\", \"forest\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "WYKORZYSTUJEMY WSZYSTKIE DOSTĘPNE PARAMETRY:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Preprocessing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "bach = pd.read_csv('bach.csv')\n",
    "palestrina = pd.read_csv('palestrina.csv')\n",
    "ryans = pd.read_csv('ryans.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1318\n",
       "Name: liczby_uzytych_metrum, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bach[\"liczby_uzytych_metrum\"].value_counts()\n",
    "palestrina[\"liczby_uzytych_metrum\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1318\n",
       "Name: liczby_uzytych_tonacji, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bach[\"liczby_uzytych_tonacji\"].value_counts()\n",
    "palestrina[\"liczby_uzytych_tonacji\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dane z powyższych dwóch kolumn są nieprzydatne, więc ich nie ma sensu przetwarzać i nie będziemy ich brać pod uwagę."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "liczby_nut                0\n",
      "dokladnosci_tonacji       0\n",
      "pauzy                     0\n",
      "rozstawy_dzwiekow         0\n",
      "najwyzsze_dzwieki      1059\n",
      "najnizsze_dzwieki      1059\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "bach = bach[[\"liczby_nut\", \"dokladnosci_tonacji\", \"pauzy\", \"rozstawy_dzwiekow\", \"najwyzsze_dzwieki\", \"najnizsze_dzwieki\"]]\n",
    "bach= bach.astype(float)\n",
    "\n",
    "palestrina = palestrina[[\"liczby_nut\", \"dokladnosci_tonacji\" , \"pauzy\", \"rozstawy_dzwiekow\", \"najwyzsze_dzwieki\", \"najnizsze_dzwieki\"]]\n",
    "palestrina = palestrina.astype(float)\n",
    "\n",
    "ryans = ryans[[\"liczby_nut\", \"dokladnosci_tonacji\", \"pauzy\", \"rozstawy_dzwiekow\", \"najwyzsze_dzwieki\", \"najnizsze_dzwieki\"]]\n",
    "ryans = ryans.astype(float)\n",
    "\n",
    "\n",
    "X = bach.append(palestrina, ignore_index=True)\n",
    "X = X.append(ryans, ignore_index = True)\n",
    "print(X.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mamy missing data, więc dzielimy na train i test i pozbywamy się missing data. Mediana wydaje się w tym przypadku rozsądnym wyborem:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "77.0\n",
      "46.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dominika/miniconda2/envs/mypython3/lib/python3.7/site-packages/pandas/core/indexing.py:671: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n",
      "/home/dominika/miniconda2/envs/mypython3/lib/python3.7/site-packages/ipykernel_launcher.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  if __name__ == '__main__':\n",
      "/home/dominika/miniconda2/envs/mypython3/lib/python3.7/site-packages/ipykernel_launcher.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "/home/dominika/miniconda2/envs/mypython3/lib/python3.7/site-packages/ipykernel_launcher.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  # This is added back by InteractiveShellApp.init_path()\n",
      "/home/dominika/miniconda2/envs/mypython3/lib/python3.7/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  if sys.path[0] == '':\n"
     ]
    }
   ],
   "source": [
    "y = [0]*362 + [1]*1318 + [2]*1059\n",
    "#X = np.array(X)\n",
    "y = np.array(y)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "print(X_train[\"najwyzsze_dzwieki\"].median())\n",
    "print(X_train[\"najnizsze_dzwieki\"].median())\n",
    "\n",
    "X_train.loc[X_train.najwyzsze_dzwieki.isnull(), \"najwyzsze_dzwieki\"] = 77.0\n",
    "X_train.loc[X_train.najnizsze_dzwieki.isnull(), \"najnizsze_dzwieki\"] = 46.0\n",
    "X_test.loc[X_test.najwyzsze_dzwieki.isnull(), \"najwyzsze_dzwieki\"] = 77.0\n",
    "X_test.loc[X_test.najnizsze_dzwieki.isnull(), \"najnizsze_dzwieki\"] = 46.0\n",
    "X.loc[X.najwyzsze_dzwieki.isnull(), \"najwyzsze_dzwieki\"] = 77.0\n",
    "X.loc[X.najnizsze_dzwieki.isnull(), \"najnizsze_dzwieki\"] = 46.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train)\n",
    "X_train = scaler.transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "#X_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Modele dla pełnych danych:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Decision Tree:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 248,
       "width": 455
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9858620184517172\n",
      "0.9854014598540146\n",
      "0.9855070540376792\n",
      "0.9854014598540146\n"
     ]
    }
   ],
   "source": [
    "tree2 = DecisionTreeClassifier(min_samples_leaf=4, random_state=42)\n",
    "tree2.fit(X_train, y_train)\n",
    "\n",
    "feat_importances = pd.Series(tree2.feature_importances_, index = [\"liczby_nut\", \"dokladnosci_tonacji\" , \"pauzy\", \"rozstawy_dzwiekow\", \"najwyzsze_dzwieki\", \"najnizsze_dzwieki\"])\n",
    "feat_importances.nlargest(6).plot(kind='barh')\n",
    "plt.show()\n",
    "\n",
    "model = tree2\n",
    "\n",
    "print(metrics.precision_score(y_test, model.predict(X_test), average = \"weighted\"))\n",
    "print(metrics.recall_score(y_test, model.predict(X_test), average = \"weighted\"))\n",
    "print(metrics.f1_score(y_test, model.predict(X_test), average = \"weighted\"))\n",
    "print(metrics.accuracy_score(y_test, model.predict(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prosta sieć neuronowa:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 100)               700       \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 10)                1010      \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 3)                 33        \n",
      "=================================================================\n",
      "Total params: 1,743\n",
      "Trainable params: 1,743\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "history1 = History()\n",
    "neural = Sequential()\n",
    "neural.add(Dense(100,activation=\"sigmoid\",input_shape=(X_train.shape[1],)))\n",
    "neural.add(Dense(10,activation=\"sigmoid\"))\n",
    "neural.add(Dense(3,activation=\"sigmoid\"))\n",
    "neural.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1643 samples, validate on 548 samples\n",
      "Epoch 1/100\n",
      "1643/1643 [==============================] - 0s 121us/step - loss: 1.0927 - categorical_accuracy: 0.3749 - val_loss: 1.0356 - val_categorical_accuracy: 0.4799\n",
      "Epoch 2/100\n",
      "1643/1643 [==============================] - 0s 41us/step - loss: 1.0117 - categorical_accuracy: 0.6932 - val_loss: 0.9893 - val_categorical_accuracy: 0.7847\n",
      "Epoch 3/100\n",
      "1643/1643 [==============================] - 0s 44us/step - loss: 0.9650 - categorical_accuracy: 0.7912 - val_loss: 0.9381 - val_categorical_accuracy: 0.7591\n",
      "Epoch 4/100\n",
      "1643/1643 [==============================] - 0s 42us/step - loss: 0.9085 - categorical_accuracy: 0.7906 - val_loss: 0.8809 - val_categorical_accuracy: 0.8047\n",
      "Epoch 5/100\n",
      "1643/1643 [==============================] - 0s 44us/step - loss: 0.8440 - categorical_accuracy: 0.8235 - val_loss: 0.8068 - val_categorical_accuracy: 0.8139\n",
      "Epoch 6/100\n",
      "1643/1643 [==============================] - 0s 47us/step - loss: 0.7575 - categorical_accuracy: 0.8411 - val_loss: 0.7148 - val_categorical_accuracy: 0.8193\n",
      "Epoch 7/100\n",
      "1643/1643 [==============================] - 0s 49us/step - loss: 0.6638 - categorical_accuracy: 0.8545 - val_loss: 0.6259 - val_categorical_accuracy: 0.8412\n",
      "Epoch 8/100\n",
      "1643/1643 [==============================] - 0s 43us/step - loss: 0.5729 - categorical_accuracy: 0.8667 - val_loss: 0.5350 - val_categorical_accuracy: 0.8741\n",
      "Epoch 9/100\n",
      "1643/1643 [==============================] - 0s 36us/step - loss: 0.4829 - categorical_accuracy: 0.9227 - val_loss: 0.4565 - val_categorical_accuracy: 0.9361\n",
      "Epoch 10/100\n",
      "1643/1643 [==============================] - 0s 43us/step - loss: 0.4168 - categorical_accuracy: 0.9477 - val_loss: 0.4020 - val_categorical_accuracy: 0.9562\n",
      "Epoch 11/100\n",
      "1643/1643 [==============================] - 0s 43us/step - loss: 0.3681 - categorical_accuracy: 0.9629 - val_loss: 0.3588 - val_categorical_accuracy: 0.9562\n",
      "Epoch 12/100\n",
      "1643/1643 [==============================] - 0s 37us/step - loss: 0.3263 - categorical_accuracy: 0.9732 - val_loss: 0.3188 - val_categorical_accuracy: 0.9562\n",
      "Epoch 13/100\n",
      "1643/1643 [==============================] - 0s 36us/step - loss: 0.2867 - categorical_accuracy: 0.9720 - val_loss: 0.2844 - val_categorical_accuracy: 0.9617\n",
      "Epoch 14/100\n",
      "1643/1643 [==============================] - 0s 36us/step - loss: 0.2542 - categorical_accuracy: 0.9726 - val_loss: 0.2572 - val_categorical_accuracy: 0.9635\n",
      "Epoch 15/100\n",
      "1643/1643 [==============================] - 0s 41us/step - loss: 0.2281 - categorical_accuracy: 0.9744 - val_loss: 0.2357 - val_categorical_accuracy: 0.9617\n",
      "Epoch 16/100\n",
      "1643/1643 [==============================] - 0s 36us/step - loss: 0.2065 - categorical_accuracy: 0.9775 - val_loss: 0.2176 - val_categorical_accuracy: 0.9635\n",
      "Epoch 17/100\n",
      "1643/1643 [==============================] - 0s 34us/step - loss: 0.1884 - categorical_accuracy: 0.9750 - val_loss: 0.2032 - val_categorical_accuracy: 0.9708\n",
      "Epoch 18/100\n",
      "1643/1643 [==============================] - 0s 41us/step - loss: 0.1736 - categorical_accuracy: 0.9799 - val_loss: 0.1901 - val_categorical_accuracy: 0.9672\n",
      "Epoch 19/100\n",
      "1643/1643 [==============================] - 0s 40us/step - loss: 0.1609 - categorical_accuracy: 0.9787 - val_loss: 0.1788 - val_categorical_accuracy: 0.9708\n",
      "Epoch 20/100\n",
      "1643/1643 [==============================] - 0s 44us/step - loss: 0.1498 - categorical_accuracy: 0.9811 - val_loss: 0.1697 - val_categorical_accuracy: 0.9708\n",
      "Epoch 21/100\n",
      "1643/1643 [==============================] - 0s 46us/step - loss: 0.1401 - categorical_accuracy: 0.9817 - val_loss: 0.1624 - val_categorical_accuracy: 0.9708\n",
      "Epoch 22/100\n",
      "1643/1643 [==============================] - 0s 44us/step - loss: 0.1316 - categorical_accuracy: 0.9817 - val_loss: 0.1551 - val_categorical_accuracy: 0.9690\n",
      "Epoch 23/100\n",
      "1643/1643 [==============================] - 0s 42us/step - loss: 0.1236 - categorical_accuracy: 0.9836 - val_loss: 0.1482 - val_categorical_accuracy: 0.9708\n",
      "Epoch 24/100\n",
      "1643/1643 [==============================] - 0s 37us/step - loss: 0.1173 - categorical_accuracy: 0.9836 - val_loss: 0.1426 - val_categorical_accuracy: 0.9708\n",
      "Epoch 25/100\n",
      "1643/1643 [==============================] - 0s 35us/step - loss: 0.1110 - categorical_accuracy: 0.9842 - val_loss: 0.1382 - val_categorical_accuracy: 0.9708\n",
      "Epoch 26/100\n",
      "1643/1643 [==============================] - 0s 34us/step - loss: 0.1053 - categorical_accuracy: 0.9866 - val_loss: 0.1340 - val_categorical_accuracy: 0.9708\n",
      "Epoch 27/100\n",
      "1643/1643 [==============================] - 0s 38us/step - loss: 0.1013 - categorical_accuracy: 0.9842 - val_loss: 0.1295 - val_categorical_accuracy: 0.9708\n",
      "Epoch 28/100\n",
      "1643/1643 [==============================] - 0s 35us/step - loss: 0.0965 - categorical_accuracy: 0.9866 - val_loss: 0.1273 - val_categorical_accuracy: 0.9708\n",
      "Epoch 29/100\n",
      "1643/1643 [==============================] - 0s 40us/step - loss: 0.0924 - categorical_accuracy: 0.9854 - val_loss: 0.1221 - val_categorical_accuracy: 0.9708\n",
      "Epoch 30/100\n",
      "1643/1643 [==============================] - 0s 36us/step - loss: 0.0878 - categorical_accuracy: 0.9878 - val_loss: 0.1209 - val_categorical_accuracy: 0.9726\n",
      "Epoch 31/100\n",
      "1643/1643 [==============================] - 0s 35us/step - loss: 0.0850 - categorical_accuracy: 0.9866 - val_loss: 0.1171 - val_categorical_accuracy: 0.9726\n",
      "Epoch 32/100\n",
      "1643/1643 [==============================] - 0s 40us/step - loss: 0.0813 - categorical_accuracy: 0.9854 - val_loss: 0.1147 - val_categorical_accuracy: 0.9708\n",
      "Epoch 33/100\n",
      "1643/1643 [==============================] - 0s 34us/step - loss: 0.0786 - categorical_accuracy: 0.9872 - val_loss: 0.1122 - val_categorical_accuracy: 0.9726\n",
      "Epoch 34/100\n",
      "1643/1643 [==============================] - 0s 34us/step - loss: 0.0760 - categorical_accuracy: 0.9890 - val_loss: 0.1112 - val_categorical_accuracy: 0.9745\n",
      "Epoch 35/100\n",
      "1643/1643 [==============================] - 0s 41us/step - loss: 0.0734 - categorical_accuracy: 0.9872 - val_loss: 0.1081 - val_categorical_accuracy: 0.9726\n",
      "Epoch 36/100\n",
      "1643/1643 [==============================] - 0s 36us/step - loss: 0.0707 - categorical_accuracy: 0.9878 - val_loss: 0.1063 - val_categorical_accuracy: 0.9726\n",
      "Epoch 37/100\n",
      "1643/1643 [==============================] - 0s 35us/step - loss: 0.0685 - categorical_accuracy: 0.9884 - val_loss: 0.1049 - val_categorical_accuracy: 0.9726\n",
      "Epoch 38/100\n",
      "1643/1643 [==============================] - 0s 39us/step - loss: 0.0667 - categorical_accuracy: 0.9878 - val_loss: 0.1050 - val_categorical_accuracy: 0.9745\n",
      "Epoch 39/100\n",
      "1643/1643 [==============================] - 0s 33us/step - loss: 0.0652 - categorical_accuracy: 0.9884 - val_loss: 0.1026 - val_categorical_accuracy: 0.9726\n",
      "Epoch 40/100\n",
      "1643/1643 [==============================] - 0s 36us/step - loss: 0.0628 - categorical_accuracy: 0.9872 - val_loss: 0.1010 - val_categorical_accuracy: 0.9726\n",
      "Epoch 41/100\n",
      "1643/1643 [==============================] - 0s 34us/step - loss: 0.0622 - categorical_accuracy: 0.9878 - val_loss: 0.0985 - val_categorical_accuracy: 0.9745\n",
      "Epoch 42/100\n",
      "1643/1643 [==============================] - 0s 36us/step - loss: 0.0597 - categorical_accuracy: 0.9897 - val_loss: 0.0979 - val_categorical_accuracy: 0.9726\n",
      "Epoch 43/100\n",
      "1643/1643 [==============================] - 0s 34us/step - loss: 0.0582 - categorical_accuracy: 0.9897 - val_loss: 0.0970 - val_categorical_accuracy: 0.9745\n",
      "Epoch 44/100\n",
      "1643/1643 [==============================] - 0s 36us/step - loss: 0.0567 - categorical_accuracy: 0.9921 - val_loss: 0.0960 - val_categorical_accuracy: 0.9763\n",
      "Epoch 45/100\n",
      "1643/1643 [==============================] - 0s 33us/step - loss: 0.0551 - categorical_accuracy: 0.9921 - val_loss: 0.0955 - val_categorical_accuracy: 0.9763\n",
      "Epoch 46/100\n",
      "1643/1643 [==============================] - 0s 36us/step - loss: 0.0540 - categorical_accuracy: 0.9909 - val_loss: 0.0940 - val_categorical_accuracy: 0.9763\n",
      "Epoch 47/100\n",
      "1643/1643 [==============================] - 0s 34us/step - loss: 0.0539 - categorical_accuracy: 0.9897 - val_loss: 0.0937 - val_categorical_accuracy: 0.9763\n",
      "Epoch 48/100\n",
      "1643/1643 [==============================] - 0s 36us/step - loss: 0.0517 - categorical_accuracy: 0.9927 - val_loss: 0.0938 - val_categorical_accuracy: 0.9763\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 49/100\n",
      "1643/1643 [==============================] - 0s 33us/step - loss: 0.0504 - categorical_accuracy: 0.9921 - val_loss: 0.0923 - val_categorical_accuracy: 0.9763\n",
      "Epoch 50/100\n",
      "1643/1643 [==============================] - 0s 35us/step - loss: 0.0495 - categorical_accuracy: 0.9921 - val_loss: 0.0910 - val_categorical_accuracy: 0.9763\n",
      "Epoch 51/100\n",
      "1643/1643 [==============================] - 0s 32us/step - loss: 0.0490 - categorical_accuracy: 0.9927 - val_loss: 0.0907 - val_categorical_accuracy: 0.9781\n",
      "Epoch 52/100\n",
      "1643/1643 [==============================] - 0s 33us/step - loss: 0.0475 - categorical_accuracy: 0.9933 - val_loss: 0.0910 - val_categorical_accuracy: 0.9763\n",
      "Epoch 53/100\n",
      "1643/1643 [==============================] - 0s 36us/step - loss: 0.0472 - categorical_accuracy: 0.9921 - val_loss: 0.0899 - val_categorical_accuracy: 0.9781\n",
      "Epoch 54/100\n",
      "1643/1643 [==============================] - 0s 36us/step - loss: 0.0459 - categorical_accuracy: 0.9933 - val_loss: 0.0892 - val_categorical_accuracy: 0.9781\n",
      "Epoch 55/100\n",
      "1643/1643 [==============================] - 0s 37us/step - loss: 0.0451 - categorical_accuracy: 0.9915 - val_loss: 0.0894 - val_categorical_accuracy: 0.9781\n",
      "Epoch 56/100\n",
      "1643/1643 [==============================] - 0s 36us/step - loss: 0.0444 - categorical_accuracy: 0.9927 - val_loss: 0.0896 - val_categorical_accuracy: 0.9763\n",
      "Epoch 57/100\n",
      "1643/1643 [==============================] - 0s 35us/step - loss: 0.0443 - categorical_accuracy: 0.9927 - val_loss: 0.0873 - val_categorical_accuracy: 0.9781\n",
      "Epoch 58/100\n",
      "1643/1643 [==============================] - 0s 37us/step - loss: 0.0430 - categorical_accuracy: 0.9933 - val_loss: 0.0879 - val_categorical_accuracy: 0.9781\n",
      "Epoch 59/100\n",
      "1643/1643 [==============================] - 0s 37us/step - loss: 0.0424 - categorical_accuracy: 0.9921 - val_loss: 0.0876 - val_categorical_accuracy: 0.9781\n",
      "Epoch 60/100\n",
      "1643/1643 [==============================] - 0s 34us/step - loss: 0.0420 - categorical_accuracy: 0.9921 - val_loss: 0.0864 - val_categorical_accuracy: 0.9781\n",
      "Epoch 61/100\n",
      "1643/1643 [==============================] - 0s 36us/step - loss: 0.0414 - categorical_accuracy: 0.9933 - val_loss: 0.0862 - val_categorical_accuracy: 0.9781\n",
      "Epoch 62/100\n",
      "1643/1643 [==============================] - 0s 34us/step - loss: 0.0404 - categorical_accuracy: 0.9927 - val_loss: 0.0872 - val_categorical_accuracy: 0.9781\n",
      "Epoch 63/100\n",
      "1643/1643 [==============================] - 0s 36us/step - loss: 0.0402 - categorical_accuracy: 0.9927 - val_loss: 0.0874 - val_categorical_accuracy: 0.9781\n",
      "Epoch 64/100\n",
      "1643/1643 [==============================] - 0s 37us/step - loss: 0.0396 - categorical_accuracy: 0.9939 - val_loss: 0.0859 - val_categorical_accuracy: 0.9781\n",
      "Epoch 65/100\n",
      "1643/1643 [==============================] - 0s 35us/step - loss: 0.0396 - categorical_accuracy: 0.9921 - val_loss: 0.0861 - val_categorical_accuracy: 0.9781\n",
      "Epoch 66/100\n",
      "1643/1643 [==============================] - 0s 38us/step - loss: 0.0386 - categorical_accuracy: 0.9933 - val_loss: 0.0851 - val_categorical_accuracy: 0.9781\n",
      "Epoch 67/100\n",
      "1643/1643 [==============================] - 0s 38us/step - loss: 0.0380 - categorical_accuracy: 0.9921 - val_loss: 0.0855 - val_categorical_accuracy: 0.9799\n",
      "Epoch 68/100\n",
      "1643/1643 [==============================] - 0s 34us/step - loss: 0.0375 - categorical_accuracy: 0.9927 - val_loss: 0.0847 - val_categorical_accuracy: 0.9781\n",
      "Epoch 69/100\n",
      "1643/1643 [==============================] - 0s 38us/step - loss: 0.0369 - categorical_accuracy: 0.9921 - val_loss: 0.0848 - val_categorical_accuracy: 0.9781\n",
      "Epoch 70/100\n",
      "1643/1643 [==============================] - 0s 34us/step - loss: 0.0362 - categorical_accuracy: 0.9933 - val_loss: 0.0843 - val_categorical_accuracy: 0.9781\n",
      "Epoch 71/100\n",
      "1643/1643 [==============================] - 0s 39us/step - loss: 0.0358 - categorical_accuracy: 0.9927 - val_loss: 0.0838 - val_categorical_accuracy: 0.9781\n",
      "Epoch 72/100\n",
      "1643/1643 [==============================] - 0s 36us/step - loss: 0.0356 - categorical_accuracy: 0.9927 - val_loss: 0.0847 - val_categorical_accuracy: 0.9781\n",
      "Epoch 73/100\n",
      "1643/1643 [==============================] - 0s 33us/step - loss: 0.0350 - categorical_accuracy: 0.9921 - val_loss: 0.0829 - val_categorical_accuracy: 0.9781\n",
      "Epoch 74/100\n",
      "1643/1643 [==============================] - 0s 37us/step - loss: 0.0349 - categorical_accuracy: 0.9927 - val_loss: 0.0822 - val_categorical_accuracy: 0.9781\n",
      "Epoch 75/100\n",
      "1643/1643 [==============================] - 0s 33us/step - loss: 0.0348 - categorical_accuracy: 0.9927 - val_loss: 0.0819 - val_categorical_accuracy: 0.9781\n",
      "Epoch 76/100\n",
      "1643/1643 [==============================] - 0s 36us/step - loss: 0.0340 - categorical_accuracy: 0.9933 - val_loss: 0.0824 - val_categorical_accuracy: 0.9781\n",
      "Epoch 77/100\n",
      "1643/1643 [==============================] - 0s 34us/step - loss: 0.0335 - categorical_accuracy: 0.9933 - val_loss: 0.0814 - val_categorical_accuracy: 0.9781\n",
      "Epoch 78/100\n",
      "1643/1643 [==============================] - 0s 38us/step - loss: 0.0332 - categorical_accuracy: 0.9945 - val_loss: 0.0831 - val_categorical_accuracy: 0.9781\n",
      "Epoch 79/100\n",
      "1643/1643 [==============================] - 0s 38us/step - loss: 0.0331 - categorical_accuracy: 0.9945 - val_loss: 0.0826 - val_categorical_accuracy: 0.9781\n",
      "Epoch 80/100\n",
      "1643/1643 [==============================] - 0s 35us/step - loss: 0.0331 - categorical_accuracy: 0.9939 - val_loss: 0.0824 - val_categorical_accuracy: 0.9781\n",
      "Epoch 81/100\n",
      "1643/1643 [==============================] - 0s 35us/step - loss: 0.0322 - categorical_accuracy: 0.9933 - val_loss: 0.0806 - val_categorical_accuracy: 0.9781\n",
      "Epoch 82/100\n",
      "1643/1643 [==============================] - 0s 39us/step - loss: 0.0317 - categorical_accuracy: 0.9933 - val_loss: 0.0824 - val_categorical_accuracy: 0.9781\n",
      "Epoch 83/100\n",
      "1643/1643 [==============================] - 0s 35us/step - loss: 0.0313 - categorical_accuracy: 0.9933 - val_loss: 0.0809 - val_categorical_accuracy: 0.9781\n",
      "Epoch 84/100\n",
      "1643/1643 [==============================] - 0s 39us/step - loss: 0.0315 - categorical_accuracy: 0.9927 - val_loss: 0.0814 - val_categorical_accuracy: 0.9781\n",
      "Epoch 85/100\n",
      "1643/1643 [==============================] - 0s 38us/step - loss: 0.0310 - categorical_accuracy: 0.9933 - val_loss: 0.0811 - val_categorical_accuracy: 0.9781\n",
      "Epoch 86/100\n",
      "1643/1643 [==============================] - 0s 37us/step - loss: 0.0307 - categorical_accuracy: 0.9933 - val_loss: 0.0806 - val_categorical_accuracy: 0.9781\n",
      "Epoch 87/100\n",
      "1643/1643 [==============================] - 0s 35us/step - loss: 0.0303 - categorical_accuracy: 0.9933 - val_loss: 0.0808 - val_categorical_accuracy: 0.9781\n",
      "Epoch 88/100\n",
      "1643/1643 [==============================] - 0s 37us/step - loss: 0.0299 - categorical_accuracy: 0.9933 - val_loss: 0.0808 - val_categorical_accuracy: 0.9781\n",
      "Epoch 89/100\n",
      "1643/1643 [==============================] - 0s 36us/step - loss: 0.0297 - categorical_accuracy: 0.9933 - val_loss: 0.0805 - val_categorical_accuracy: 0.9781\n",
      "Epoch 90/100\n",
      "1643/1643 [==============================] - 0s 34us/step - loss: 0.0296 - categorical_accuracy: 0.9933 - val_loss: 0.0799 - val_categorical_accuracy: 0.9781\n",
      "Epoch 91/100\n",
      "1643/1643 [==============================] - 0s 36us/step - loss: 0.0292 - categorical_accuracy: 0.9933 - val_loss: 0.0803 - val_categorical_accuracy: 0.9781\n",
      "Epoch 92/100\n",
      "1643/1643 [==============================] - 0s 33us/step - loss: 0.0295 - categorical_accuracy: 0.9933 - val_loss: 0.0790 - val_categorical_accuracy: 0.9781\n",
      "Epoch 93/100\n",
      "1643/1643 [==============================] - 0s 37us/step - loss: 0.0287 - categorical_accuracy: 0.9933 - val_loss: 0.0795 - val_categorical_accuracy: 0.9781\n",
      "Epoch 94/100\n",
      "1643/1643 [==============================] - 0s 36us/step - loss: 0.0294 - categorical_accuracy: 0.9927 - val_loss: 0.0802 - val_categorical_accuracy: 0.9799\n",
      "Epoch 95/100\n",
      "1643/1643 [==============================] - 0s 39us/step - loss: 0.0283 - categorical_accuracy: 0.9939 - val_loss: 0.0803 - val_categorical_accuracy: 0.9781\n",
      "Epoch 96/100\n",
      "1643/1643 [==============================] - 0s 37us/step - loss: 0.0281 - categorical_accuracy: 0.9933 - val_loss: 0.0783 - val_categorical_accuracy: 0.9781\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 97/100\n",
      "1643/1643 [==============================] - 0s 38us/step - loss: 0.0289 - categorical_accuracy: 0.9927 - val_loss: 0.0796 - val_categorical_accuracy: 0.9781\n",
      "Epoch 98/100\n",
      "1643/1643 [==============================] - 0s 33us/step - loss: 0.0282 - categorical_accuracy: 0.9939 - val_loss: 0.0779 - val_categorical_accuracy: 0.9799\n",
      "Epoch 99/100\n",
      "1643/1643 [==============================] - 0s 36us/step - loss: 0.0282 - categorical_accuracy: 0.9933 - val_loss: 0.0803 - val_categorical_accuracy: 0.9781\n",
      "Epoch 100/100\n",
      "1643/1643 [==============================] - 0s 34us/step - loss: 0.0277 - categorical_accuracy: 0.9933 - val_loss: 0.0784 - val_categorical_accuracy: 0.9781\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x7f85c709c990>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.utils import to_categorical\n",
    "y_binary = to_categorical(y_train)\n",
    "\n",
    "early_stopping = EarlyStopping(patience=20,monitor=\"val_loss\")\n",
    "neural.compile(loss=\"categorical_crossentropy\",optimizer=\"adam\",metrics=[\"categorical_accuracy\"])\n",
    "neural.fit(X_train, y_binary, validation_split=0.25,epochs=100,callbacks=[early_stopping, history1])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bardziej skomplikowana sieć neuronowa:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_4 (Dense)              (None, 100)               700       \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 100)               400       \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 100)               400       \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 10)                1010      \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 3)                 33        \n",
      "=================================================================\n",
      "Total params: 2,543\n",
      "Trainable params: 2,143\n",
      "Non-trainable params: 400\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "history2 = History()\n",
    "neural2 = Sequential()\n",
    "neural2.add(Dense(100,activation=\"sigmoid\",input_shape=(X_train.shape[1],)))\n",
    "neural2.add(Activation(\"relu\"))\n",
    "neural2.add(BatchNormalization())\n",
    "neural2.add(Dropout(0.2))\n",
    "neural2.add(Activation(\"relu\"))\n",
    "neural2.add(BatchNormalization())\n",
    "neural2.add(Dropout(0.2))\n",
    "neural2.add(Dense(10,activation=\"sigmoid\"))\n",
    "neural2.add(Dense(3,activation=\"sigmoid\"))\n",
    "neural2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1643 samples, validate on 548 samples\n",
      "Epoch 1/100\n",
      "1643/1643 [==============================] - 0s 290us/step - loss: 0.8628 - categorical_accuracy: 0.7212 - val_loss: 0.9574 - val_categorical_accuracy: 0.6515\n",
      "Epoch 2/100\n",
      "1643/1643 [==============================] - 0s 62us/step - loss: 0.6101 - categorical_accuracy: 0.9324 - val_loss: 0.7973 - val_categorical_accuracy: 0.8504\n",
      "Epoch 3/100\n",
      "1643/1643 [==============================] - 0s 53us/step - loss: 0.4875 - categorical_accuracy: 0.9574 - val_loss: 0.6666 - val_categorical_accuracy: 0.8522\n",
      "Epoch 4/100\n",
      "1643/1643 [==============================] - 0s 60us/step - loss: 0.4157 - categorical_accuracy: 0.9629 - val_loss: 0.5816 - val_categorical_accuracy: 0.8467\n",
      "Epoch 5/100\n",
      "1643/1643 [==============================] - 0s 59us/step - loss: 0.3537 - categorical_accuracy: 0.9647 - val_loss: 0.5033 - val_categorical_accuracy: 0.8595\n",
      "Epoch 6/100\n",
      "1643/1643 [==============================] - 0s 59us/step - loss: 0.2993 - categorical_accuracy: 0.9714 - val_loss: 0.4360 - val_categorical_accuracy: 0.8887\n",
      "Epoch 7/100\n",
      "1643/1643 [==============================] - 0s 52us/step - loss: 0.2655 - categorical_accuracy: 0.9750 - val_loss: 0.3766 - val_categorical_accuracy: 0.9015\n",
      "Epoch 8/100\n",
      "1643/1643 [==============================] - 0s 60us/step - loss: 0.2352 - categorical_accuracy: 0.9696 - val_loss: 0.3094 - val_categorical_accuracy: 0.9234\n",
      "Epoch 9/100\n",
      "1643/1643 [==============================] - 0s 60us/step - loss: 0.2072 - categorical_accuracy: 0.9775 - val_loss: 0.2565 - val_categorical_accuracy: 0.9416\n",
      "Epoch 10/100\n",
      "1643/1643 [==============================] - 0s 61us/step - loss: 0.1950 - categorical_accuracy: 0.9763 - val_loss: 0.2086 - val_categorical_accuracy: 0.9617\n",
      "Epoch 11/100\n",
      "1643/1643 [==============================] - 0s 67us/step - loss: 0.1687 - categorical_accuracy: 0.9811 - val_loss: 0.1774 - val_categorical_accuracy: 0.9745\n",
      "Epoch 12/100\n",
      "1643/1643 [==============================] - 0s 61us/step - loss: 0.1527 - categorical_accuracy: 0.9830 - val_loss: 0.1555 - val_categorical_accuracy: 0.9763\n",
      "Epoch 13/100\n",
      "1643/1643 [==============================] - 0s 61us/step - loss: 0.1386 - categorical_accuracy: 0.9848 - val_loss: 0.1435 - val_categorical_accuracy: 0.9799\n",
      "Epoch 14/100\n",
      "1643/1643 [==============================] - 0s 61us/step - loss: 0.1341 - categorical_accuracy: 0.9817 - val_loss: 0.1329 - val_categorical_accuracy: 0.9781\n",
      "Epoch 15/100\n",
      "1643/1643 [==============================] - 0s 60us/step - loss: 0.1221 - categorical_accuracy: 0.9836 - val_loss: 0.1188 - val_categorical_accuracy: 0.9799\n",
      "Epoch 16/100\n",
      "1643/1643 [==============================] - 0s 61us/step - loss: 0.1074 - categorical_accuracy: 0.9890 - val_loss: 0.1167 - val_categorical_accuracy: 0.9799\n",
      "Epoch 17/100\n",
      "1643/1643 [==============================] - 0s 60us/step - loss: 0.1093 - categorical_accuracy: 0.9823 - val_loss: 0.1063 - val_categorical_accuracy: 0.9818\n",
      "Epoch 18/100\n",
      "1643/1643 [==============================] - 0s 62us/step - loss: 0.0965 - categorical_accuracy: 0.9884 - val_loss: 0.1018 - val_categorical_accuracy: 0.9818\n",
      "Epoch 19/100\n",
      "1643/1643 [==============================] - 0s 58us/step - loss: 0.0928 - categorical_accuracy: 0.9866 - val_loss: 0.0963 - val_categorical_accuracy: 0.9836\n",
      "Epoch 20/100\n",
      "1643/1643 [==============================] - 0s 62us/step - loss: 0.0879 - categorical_accuracy: 0.9884 - val_loss: 0.0903 - val_categorical_accuracy: 0.9836\n",
      "Epoch 21/100\n",
      "1643/1643 [==============================] - 0s 62us/step - loss: 0.0802 - categorical_accuracy: 0.9884 - val_loss: 0.0829 - val_categorical_accuracy: 0.9854\n",
      "Epoch 22/100\n",
      "1643/1643 [==============================] - 0s 56us/step - loss: 0.0863 - categorical_accuracy: 0.9872 - val_loss: 0.0813 - val_categorical_accuracy: 0.9836\n",
      "Epoch 23/100\n",
      "1643/1643 [==============================] - 0s 59us/step - loss: 0.0884 - categorical_accuracy: 0.9811 - val_loss: 0.0913 - val_categorical_accuracy: 0.9781\n",
      "Epoch 24/100\n",
      "1643/1643 [==============================] - 0s 55us/step - loss: 0.0726 - categorical_accuracy: 0.9890 - val_loss: 0.0834 - val_categorical_accuracy: 0.9836\n",
      "Epoch 25/100\n",
      "1643/1643 [==============================] - 0s 55us/step - loss: 0.0736 - categorical_accuracy: 0.9866 - val_loss: 0.0782 - val_categorical_accuracy: 0.9836\n",
      "Epoch 26/100\n",
      "1643/1643 [==============================] - 0s 57us/step - loss: 0.0712 - categorical_accuracy: 0.9866 - val_loss: 0.0739 - val_categorical_accuracy: 0.9854\n",
      "Epoch 27/100\n",
      "1643/1643 [==============================] - 0s 56us/step - loss: 0.0700 - categorical_accuracy: 0.9854 - val_loss: 0.0757 - val_categorical_accuracy: 0.9836\n",
      "Epoch 28/100\n",
      "1643/1643 [==============================] - 0s 56us/step - loss: 0.0605 - categorical_accuracy: 0.9903 - val_loss: 0.0718 - val_categorical_accuracy: 0.9836\n",
      "Epoch 29/100\n",
      "1643/1643 [==============================] - 0s 54us/step - loss: 0.0549 - categorical_accuracy: 0.9933 - val_loss: 0.0667 - val_categorical_accuracy: 0.9836\n",
      "Epoch 30/100\n",
      "1643/1643 [==============================] - 0s 57us/step - loss: 0.0589 - categorical_accuracy: 0.9884 - val_loss: 0.0684 - val_categorical_accuracy: 0.9854\n",
      "Epoch 31/100\n",
      "1643/1643 [==============================] - 0s 57us/step - loss: 0.0687 - categorical_accuracy: 0.9854 - val_loss: 0.0739 - val_categorical_accuracy: 0.9854\n",
      "Epoch 32/100\n",
      "1643/1643 [==============================] - 0s 58us/step - loss: 0.0572 - categorical_accuracy: 0.9909 - val_loss: 0.0689 - val_categorical_accuracy: 0.9836\n",
      "Epoch 33/100\n",
      "1643/1643 [==============================] - 0s 57us/step - loss: 0.0524 - categorical_accuracy: 0.9897 - val_loss: 0.0631 - val_categorical_accuracy: 0.9872\n",
      "Epoch 34/100\n",
      "1643/1643 [==============================] - 0s 56us/step - loss: 0.0551 - categorical_accuracy: 0.9890 - val_loss: 0.0660 - val_categorical_accuracy: 0.9854\n",
      "Epoch 35/100\n",
      "1643/1643 [==============================] - 0s 62us/step - loss: 0.0705 - categorical_accuracy: 0.9842 - val_loss: 0.0654 - val_categorical_accuracy: 0.9854\n",
      "Epoch 36/100\n",
      "1643/1643 [==============================] - 0s 57us/step - loss: 0.0505 - categorical_accuracy: 0.9909 - val_loss: 0.0674 - val_categorical_accuracy: 0.9854\n",
      "Epoch 37/100\n",
      "1643/1643 [==============================] - 0s 57us/step - loss: 0.0518 - categorical_accuracy: 0.9890 - val_loss: 0.0611 - val_categorical_accuracy: 0.9872\n",
      "Epoch 38/100\n",
      "1643/1643 [==============================] - 0s 58us/step - loss: 0.0524 - categorical_accuracy: 0.9878 - val_loss: 0.0588 - val_categorical_accuracy: 0.9872\n",
      "Epoch 39/100\n",
      "1643/1643 [==============================] - 0s 58us/step - loss: 0.0503 - categorical_accuracy: 0.9897 - val_loss: 0.0588 - val_categorical_accuracy: 0.9872\n",
      "Epoch 40/100\n",
      "1643/1643 [==============================] - 0s 61us/step - loss: 0.0469 - categorical_accuracy: 0.9890 - val_loss: 0.0625 - val_categorical_accuracy: 0.9854\n",
      "Epoch 41/100\n",
      "1643/1643 [==============================] - 0s 55us/step - loss: 0.0440 - categorical_accuracy: 0.9909 - val_loss: 0.0601 - val_categorical_accuracy: 0.9854\n",
      "Epoch 42/100\n",
      "1643/1643 [==============================] - 0s 56us/step - loss: 0.0489 - categorical_accuracy: 0.9897 - val_loss: 0.0582 - val_categorical_accuracy: 0.9854\n",
      "Epoch 43/100\n",
      "1643/1643 [==============================] - 0s 58us/step - loss: 0.0433 - categorical_accuracy: 0.9897 - val_loss: 0.0618 - val_categorical_accuracy: 0.9872\n",
      "Epoch 44/100\n",
      "1643/1643 [==============================] - 0s 55us/step - loss: 0.0485 - categorical_accuracy: 0.9897 - val_loss: 0.0570 - val_categorical_accuracy: 0.9854\n",
      "Epoch 45/100\n",
      "1643/1643 [==============================] - 0s 55us/step - loss: 0.0440 - categorical_accuracy: 0.9903 - val_loss: 0.0506 - val_categorical_accuracy: 0.9891\n",
      "Epoch 46/100\n",
      "1643/1643 [==============================] - 0s 61us/step - loss: 0.0444 - categorical_accuracy: 0.9897 - val_loss: 0.0578 - val_categorical_accuracy: 0.9854\n",
      "Epoch 47/100\n",
      "1643/1643 [==============================] - 0s 55us/step - loss: 0.0357 - categorical_accuracy: 0.9945 - val_loss: 0.0509 - val_categorical_accuracy: 0.9872\n",
      "Epoch 48/100\n",
      "1643/1643 [==============================] - 0s 53us/step - loss: 0.0396 - categorical_accuracy: 0.9915 - val_loss: 0.0515 - val_categorical_accuracy: 0.9872\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 49/100\n",
      "1643/1643 [==============================] - 0s 58us/step - loss: 0.0457 - categorical_accuracy: 0.9884 - val_loss: 0.0523 - val_categorical_accuracy: 0.9872\n",
      "Epoch 50/100\n",
      "1643/1643 [==============================] - 0s 57us/step - loss: 0.0483 - categorical_accuracy: 0.9872 - val_loss: 0.0507 - val_categorical_accuracy: 0.9872\n",
      "Epoch 51/100\n",
      "1643/1643 [==============================] - 0s 57us/step - loss: 0.0332 - categorical_accuracy: 0.9927 - val_loss: 0.0500 - val_categorical_accuracy: 0.9872\n",
      "Epoch 52/100\n",
      "1643/1643 [==============================] - 0s 60us/step - loss: 0.0381 - categorical_accuracy: 0.9903 - val_loss: 0.0472 - val_categorical_accuracy: 0.9891\n",
      "Epoch 53/100\n",
      "1643/1643 [==============================] - 0s 55us/step - loss: 0.0312 - categorical_accuracy: 0.9933 - val_loss: 0.0494 - val_categorical_accuracy: 0.9891\n",
      "Epoch 54/100\n",
      "1643/1643 [==============================] - 0s 54us/step - loss: 0.0458 - categorical_accuracy: 0.9872 - val_loss: 0.0472 - val_categorical_accuracy: 0.9872\n",
      "Epoch 55/100\n",
      "1643/1643 [==============================] - 0s 55us/step - loss: 0.0404 - categorical_accuracy: 0.9909 - val_loss: 0.0475 - val_categorical_accuracy: 0.9872\n",
      "Epoch 56/100\n",
      "1643/1643 [==============================] - 0s 53us/step - loss: 0.0295 - categorical_accuracy: 0.9945 - val_loss: 0.0490 - val_categorical_accuracy: 0.9872\n",
      "Epoch 57/100\n",
      "1643/1643 [==============================] - 0s 59us/step - loss: 0.0347 - categorical_accuracy: 0.9921 - val_loss: 0.0545 - val_categorical_accuracy: 0.9872\n",
      "Epoch 58/100\n",
      "1643/1643 [==============================] - 0s 54us/step - loss: 0.0326 - categorical_accuracy: 0.9927 - val_loss: 0.0460 - val_categorical_accuracy: 0.9891\n",
      "Epoch 59/100\n",
      "1643/1643 [==============================] - 0s 57us/step - loss: 0.0324 - categorical_accuracy: 0.9921 - val_loss: 0.0499 - val_categorical_accuracy: 0.9872\n",
      "Epoch 60/100\n",
      "1643/1643 [==============================] - 0s 54us/step - loss: 0.0335 - categorical_accuracy: 0.9927 - val_loss: 0.0496 - val_categorical_accuracy: 0.9872\n",
      "Epoch 61/100\n",
      "1643/1643 [==============================] - 0s 57us/step - loss: 0.0440 - categorical_accuracy: 0.9890 - val_loss: 0.0518 - val_categorical_accuracy: 0.9854\n",
      "Epoch 62/100\n",
      "1643/1643 [==============================] - 0s 52us/step - loss: 0.0298 - categorical_accuracy: 0.9927 - val_loss: 0.0479 - val_categorical_accuracy: 0.9872\n",
      "Epoch 63/100\n",
      "1643/1643 [==============================] - 0s 59us/step - loss: 0.0292 - categorical_accuracy: 0.9927 - val_loss: 0.0497 - val_categorical_accuracy: 0.9872\n",
      "Epoch 64/100\n",
      "1643/1643 [==============================] - 0s 57us/step - loss: 0.0327 - categorical_accuracy: 0.9921 - val_loss: 0.0512 - val_categorical_accuracy: 0.9872\n",
      "Epoch 65/100\n",
      "1643/1643 [==============================] - 0s 54us/step - loss: 0.0387 - categorical_accuracy: 0.9903 - val_loss: 0.0580 - val_categorical_accuracy: 0.9836\n",
      "Epoch 66/100\n",
      "1643/1643 [==============================] - 0s 58us/step - loss: 0.0355 - categorical_accuracy: 0.9909 - val_loss: 0.0513 - val_categorical_accuracy: 0.9872\n",
      "Epoch 67/100\n",
      "1643/1643 [==============================] - 0s 56us/step - loss: 0.0326 - categorical_accuracy: 0.9921 - val_loss: 0.0499 - val_categorical_accuracy: 0.9872\n",
      "Epoch 68/100\n",
      "1643/1643 [==============================] - 0s 62us/step - loss: 0.0322 - categorical_accuracy: 0.9921 - val_loss: 0.0412 - val_categorical_accuracy: 0.9891\n",
      "Epoch 69/100\n",
      "1643/1643 [==============================] - 0s 53us/step - loss: 0.0322 - categorical_accuracy: 0.9915 - val_loss: 0.0367 - val_categorical_accuracy: 0.9927\n",
      "Epoch 70/100\n",
      "1643/1643 [==============================] - 0s 59us/step - loss: 0.0382 - categorical_accuracy: 0.9903 - val_loss: 0.0441 - val_categorical_accuracy: 0.9891\n",
      "Epoch 71/100\n",
      "1643/1643 [==============================] - 0s 55us/step - loss: 0.0310 - categorical_accuracy: 0.9903 - val_loss: 0.0411 - val_categorical_accuracy: 0.9909\n",
      "Epoch 72/100\n",
      "1643/1643 [==============================] - 0s 58us/step - loss: 0.0319 - categorical_accuracy: 0.9915 - val_loss: 0.0435 - val_categorical_accuracy: 0.9872\n",
      "Epoch 73/100\n",
      "1643/1643 [==============================] - 0s 54us/step - loss: 0.0351 - categorical_accuracy: 0.9884 - val_loss: 0.0557 - val_categorical_accuracy: 0.9854\n",
      "Epoch 74/100\n",
      "1643/1643 [==============================] - 0s 63us/step - loss: 0.0260 - categorical_accuracy: 0.9945 - val_loss: 0.0514 - val_categorical_accuracy: 0.9872\n",
      "Epoch 75/100\n",
      "1643/1643 [==============================] - 0s 56us/step - loss: 0.0302 - categorical_accuracy: 0.9909 - val_loss: 0.0453 - val_categorical_accuracy: 0.9872\n",
      "Epoch 76/100\n",
      "1643/1643 [==============================] - 0s 61us/step - loss: 0.0441 - categorical_accuracy: 0.9872 - val_loss: 0.0441 - val_categorical_accuracy: 0.9891\n",
      "Epoch 77/100\n",
      "1643/1643 [==============================] - 0s 57us/step - loss: 0.0399 - categorical_accuracy: 0.9878 - val_loss: 0.0465 - val_categorical_accuracy: 0.9891\n",
      "Epoch 78/100\n",
      "1643/1643 [==============================] - 0s 56us/step - loss: 0.0263 - categorical_accuracy: 0.9933 - val_loss: 0.0481 - val_categorical_accuracy: 0.9872\n",
      "Epoch 79/100\n",
      "1643/1643 [==============================] - 0s 53us/step - loss: 0.0345 - categorical_accuracy: 0.9939 - val_loss: 0.0441 - val_categorical_accuracy: 0.9891\n",
      "Epoch 80/100\n",
      "1643/1643 [==============================] - 0s 55us/step - loss: 0.0265 - categorical_accuracy: 0.9921 - val_loss: 0.0446 - val_categorical_accuracy: 0.9909\n",
      "Epoch 81/100\n",
      "1643/1643 [==============================] - 0s 53us/step - loss: 0.0211 - categorical_accuracy: 0.9957 - val_loss: 0.0446 - val_categorical_accuracy: 0.9891\n",
      "Epoch 82/100\n",
      "1643/1643 [==============================] - 0s 58us/step - loss: 0.0331 - categorical_accuracy: 0.9897 - val_loss: 0.0462 - val_categorical_accuracy: 0.9891\n",
      "Epoch 83/100\n",
      "1643/1643 [==============================] - 0s 53us/step - loss: 0.0220 - categorical_accuracy: 0.9957 - val_loss: 0.0464 - val_categorical_accuracy: 0.9872\n",
      "Epoch 84/100\n",
      "1643/1643 [==============================] - 0s 60us/step - loss: 0.0264 - categorical_accuracy: 0.9933 - val_loss: 0.0469 - val_categorical_accuracy: 0.9872\n",
      "Epoch 85/100\n",
      "1643/1643 [==============================] - 0s 54us/step - loss: 0.0310 - categorical_accuracy: 0.9927 - val_loss: 0.0435 - val_categorical_accuracy: 0.9891\n",
      "Epoch 86/100\n",
      "1643/1643 [==============================] - 0s 56us/step - loss: 0.0317 - categorical_accuracy: 0.9915 - val_loss: 0.0459 - val_categorical_accuracy: 0.9891\n",
      "Epoch 87/100\n",
      "1643/1643 [==============================] - 0s 55us/step - loss: 0.0317 - categorical_accuracy: 0.9915 - val_loss: 0.0482 - val_categorical_accuracy: 0.9872\n",
      "Epoch 88/100\n",
      "1643/1643 [==============================] - 0s 55us/step - loss: 0.0266 - categorical_accuracy: 0.9933 - val_loss: 0.0454 - val_categorical_accuracy: 0.9872\n",
      "Epoch 89/100\n",
      "1643/1643 [==============================] - 0s 57us/step - loss: 0.0279 - categorical_accuracy: 0.9921 - val_loss: 0.0463 - val_categorical_accuracy: 0.9891\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x7f85ac63fb50>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neural2.compile(loss=\"categorical_crossentropy\",optimizer=\"adam\",metrics=[\"categorical_accuracy\"])\n",
    "neural2.fit(X_train, y_binary, validation_split=0.25,epochs=100,callbacks=[early_stopping, history2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 248,
       "width": 372
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history1.history['categorical_accuracy'], label = \"Siec neutronowa 1\")\n",
    "plt.plot(history2.history['categorical_accuracy'], label = \"Siec neutronowa 2\")\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Najszybciej nauczyła się sieć nr 2, spróbujmy wykorzystać ją do oszacowania przykładów zawartych w pdfach (0 - Bach, 1 - Palestrina, 2 - Ryans):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = np.array(X)\n",
    "X = scaler.transform(X)\n",
    "neural2.predict_classes(X[[0,363,1681],:])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wszystkie trzy zostały sklasyfikowane poprawnie."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9927007299270073"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid = neural2.predict_classes(X_test)==y_test\n",
    "len(valid[valid == True])/len(valid)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}