Included 'check_wavelet()' for all cases in 'build_training_data().

ANN_Training_Data_Generator.py

@@ -133,15 +133,9 @@ class TrainingDataGenerator(object):
                                                   quadrature_config={'num_eval_points': polynomial_degree+1})
 
             if initial_condition.is_smooth():
-                basis_coeffs = dg_scheme.build_training_data(0, initial_condition)
+                input_data[i] = dg_scheme.build_training_data(0, initial_condition)
             else:
-                basis_coeffs = dg_scheme.build_training_data(centers[1], initial_condition)
-
-            data_center = dg_scheme.check_wavelet(basis_coeffs, self._quadrature_center, 0)
-            data_left = dg_scheme.check_wavelet(basis_coeffs, self._quadrature_left, polynomial_degree)
-            data_right = dg_scheme.check_wavelet(basis_coeffs, self._quadrature_right, polynomial_degree)
-            input_data[i] = np.array(list(zip(data_center[:, 0], data_left[:, 1], data_center[:, 1],
-                                              data_right[:, 1], data_center[:, 2])))
+                input_data[i] = dg_scheme.build_training_data(centers[1], initial_condition)
 
             # Update Function ID
             if (i % num_function_samples == num_function_samples - 1) and (function_id != len(initial_conditions)-1):

DG_Approximation.py

@@ -8,6 +8,8 @@ TODO: Replace loops with list comprehension if feasible
 TODO: Write documentation for all methods
 TODO: Contemplate how to make shock tubes comparable
 TODO: Fix bug in approximation -> Done (used num_grid_cells instead of cell_len for cfl_number in last step)
+TODO: Include check_wavelet() for all cases in build_training_data() -> Done
+TODO: Fix data_left/right/center to create data with an x-point stencil
 
 """
 import numpy as np
@@ -188,8 +190,9 @@ class DGScheme(object):
         if initial_condition is None:
             initial_condition = self._init_cond
         projection = self._do_initial_projection(initial_condition, adjustment)
-        return projection[:, 1:-1]
 
-    def check_wavelet(self, projection, quadrature, polynomial_degree):
-        projection = self._detector.calculate_approximate_solution(projection, quadrature, polynomial_degree)
-        return projection
+        data_center = self._detector.calculate_approximate_solution(projection[:, 1:-1], [0], 0)
+        data_left = self._detector.calculate_approximate_solution(projection[:, 1:-1], [-1], self._polynomial_degree)
+        data_right = self._detector.calculate_approximate_solution(projection[:, 1:-1], [1], self._polynomial_degree)
+        return np.array(list(zip(data_center[:, 0], data_left[:, 1], data_center[:, 1], data_right[:, 1],
+                                 data_center[:, 2])))