get truth solutions

rbnics/problems/base/parametrized_reduced_differential_problem.py

@@ -511,6 +511,26 @@ def _compute_error(self, **kwargs):
         #
         return error
 
+    def compute_reduced_truth_solutions(self, **kwargs):
+        """
+        returns the reduced and truth solutions similar to 'compute_error' function
+
+        """
+
+        self.truth_problem.solve(**kwargs)
+        (components, inner_product) = self._preprocess_compute_error_and_relative_error_kwargs(**kwargs)
+
+        N = self._solution.N
+        reduced_solution = self.basis_functions[:N] * self._solution
+        truth_solution = self.truth_problem._solution
+
+        return [reduced_solution, truth_solution]
+
+
+
+
+
+
     def compute_relative_error(self, **kwargs):
         """
         It returns the function _compute_relative_error() evaluated for the desired parameter.

rbnics/reduction_methods/base/rb_reduction.py

@@ -401,6 +401,45 @@ def N_generator_max():
             error_analysis_table.save(
                 self.folder["error_analysis"], "error_analysis" if filename is None else filename)
 
+        def save_data(self, N_generator=None, filename=None, **kwargs):
+            """
+            It computes the error of the reduced order approximation with respect to the full order one
+            over the testing set.
+
+            :param N_generator: generator of dimension of reduced problem.
+            """
+            self._init_error_analysis(**kwargs)
+            reduced_solution_temp, truth_solution_temp = self._save_data(filename, **kwargs)
+            # self._finalize_error_analysis(**kwargs)
+            return [reduced_solution_temp, truth_solution_temp]
+
+        def _save_data(self, filename=None, **kwargs):
+
+            print(TextBox(self.truth_problem.name() + " " + self.label + " data gen begins", fill="="))
+            print("")
+
+
+            # for (mu_index, mu) in enumerate(self.testing_set):
+            for (mu_index, mu) in enumerate(self.training_set):
+                print(TextLine(str(mu_index), fill="#"))
+
+                self.reduced_problem.set_mu(mu)
+                self.reduced_problem.solve(**kwargs)
+
+                reduced_solution_temp, truth_solution_temp = self.reduced_problem.compute_reduced_truth_solutions(**kwargs)
+
+
+            # Print
+            print("")
+
+            print("")
+            print(TextBox(self.truth_problem.name() + " " + self.label + " data generation ends", fill="="))
+            print("")
+
+            return [reduced_solution_temp, truth_solution_temp]
+
+
+
         def speedup_analysis(self, N_generator=None, filename=None, **kwargs):
             """
             It computes the speedup of the reduced order approximation with respect to the full order one

tutorials/01_thermal_block/data/generate_mesh.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -37,7 +37,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -76,7 +76,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -98,12 +98,15 @@
   },
   "language_info": {
    "codemirror_mode": {
-    "name": "ipython"
+    "name": "ipython",
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
-   "nbconvert_exporter": "python"
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
   }
  },
  "nbformat": 4,