Removed use of 'DGScheme' from ANN data generation.

ANN_Data_Generator.py

@@ -7,7 +7,14 @@ import os
 import time
 import numpy as np
 
-import DG_Approximation
+from DG_Approximation import do_initial_projection
+from projection_utils import Mesh
+from Quadrature import Gauss
+from Basis_Function import OrthonormalLegendre
+
+
+basis_list = [OrthonormalLegendre(pol_deg) for pol_deg in range(5)]
+quadrature_list = [Gauss({'num_nodes': pol_deg+1}) for pol_deg in range(5)]
 
 
 class TrainingDataGenerator:
@@ -204,14 +211,20 @@ class TrainingDataGenerator:
 
             # Calculate basis coefficients for stencil
             polynomial_degree = np.random.randint(1, high=5)
-            dg_scheme = DG_Approximation.DGScheme(
-                'NoDetection', polynomial_degree=polynomial_degree,
-                num_grid_cells=self._stencil_length, left_bound=left_bound,
-                right_bound=right_bound, quadrature='Gauss',
-                quadrature_config={'num_eval_points': polynomial_degree+1})
-            input_data[i] = dg_scheme.build_training_data(
-                adjustment, self._stencil_length, self._add_reconstructions,
-                initial_condition)
+
+            basis = basis_list[polynomial_degree]
+            mesh = Mesh(num_grid_cells=self._stencil_length, num_ghost_cells=2,
+                        left_bound=left_bound, right_bound=right_bound)
+            projection = do_initial_projection(
+                initial_condition=initial_condition, mesh=mesh,
+                basis=basis,
+                quadrature=quadrature_list[polynomial_degree],
+                adjustment=adjustment)
+
+            input_data[i] = basis.calculate_cell_average(
+                projection=projection[:, 1:-1],
+                stencil_length=self._stencil_length,
+                add_reconstructions=self._add_reconstructions)
 
             count += 1
             if count % 1000 == 0:

DG_Approximation.py

@@ -16,8 +16,10 @@ TODO: Ask whether all quadratures depend on freely chosen num_nodes
 Urgent:
 TODO: Replace getter with property attributes for quadrature -> Done
 TODO: Rename eval_points in Quadrature to nodes -> Done
-TODO: Improve docstring for quadrature
-TODO: Remove use of DGScheme from ANN_Data_Generator
+TODO: Improve docstring for quadrature -> Done
+TODO: Ensure repeatable output through seeds and deterministic models -> Done
+TODO: Remove use of DGScheme from ANN_Data_Generator -> Done
+TODO: Remove unnecessary instance variables from TrainingDataGenerator
 TODO: Find error in centering for ANN training
 TODO: Adapt TCD from Soraya
     (Dropbox->...->TEST_troubled-cell-detector->Troubled_Cell_Detector)
@@ -178,8 +180,8 @@ class DGScheme:
                           left_bound=kwargs.pop('left_bound', -1),
                           right_bound=kwargs.pop('right_bound', 1),
                           num_ghost_cells=2)
-        print(len(self._mesh.cells))
-        print(type(self._mesh.cells))
+        # print(len(self._mesh.cells))
+        # print(type(self._mesh.cells))
 
         # Throw an error if there are extra keyword arguments
         if len(kwargs) > 0:
@@ -284,43 +286,6 @@ class DGScheme:
         # Set additional necessary config parameters
         self._limiter_config['cell_len'] = self._mesh.cell_len
 
-    def build_training_data(self, adjustment, stencil_length,
-                            add_reconstructions, initial_condition=None):
-        """Builds training data set.
-
-        Initializes projection and calculates cell averages and
-        reconstructions for it.
-
-        Parameters
-        ----------
-        adjustment : float
-            Extent of adjustment of each evaluation point in x-direction.
-        stencil_length : int
-            Size of training data array.
-        add_reconstructions: bool
-            Flag whether reconstructions of the middle cell are included.
-        initial_condition : InitialCondition object, optional
-            Initial condition used for calculation.
-            Default: None (i.e. instance variable).
-
-        Returns
-        -------
-        ndarray
-            Matrix containing cell averages and reconstructions for initial
-            projection.
-
-        """
-        if initial_condition is None:
-            initial_condition = self._init_cond
-        projection = do_initial_projection(
-            initial_condition=initial_condition, mesh=self._mesh,
-            basis=self._basis, quadrature=self._quadrature,
-            adjustment=adjustment)
-
-        return self._basis.calculate_cell_average(
-            projection=projection[:, 1:-1], stencil_length=stencil_length,
-            add_reconstructions=add_reconstructions)
-
 
 def do_initial_projection(initial_condition, mesh, basis, quadrature,
                           adjustment=0):

config.yaml

-data_dir: 'model-Mar14'
+data_dir: 'model-Jun03'
 random_seed: 1234
 
 # Parameter for Approximation with Troubled Cell Detection
 Approximation:
-  plot_dir: 'fig-Mar14'
+  plot_dir: 'fig-Jun03'
 
   schemes:
     Separation_Test:
@@ -36,7 +36,7 @@ Approximation:
 
       quadrature: 'Gauss'
       quadrature_config:
-        num_eval_points: 12
+        num_nodes: 12
 
       update_scheme: 'SSPRK3'
     Test_Run: