Removed 'stencil_length' as instance variable.

ANN_Data_Generator.py

@@ -33,7 +33,7 @@ class TrainingDataGenerator:
         Builds random training data.
 
     """
-    def __init__(self, left_bound=-1, right_bound=1, stencil_length=3):
+    def __init__(self, left_bound=-1, right_bound=1):
         """Initializes TrainingDataGenerator.
 
         Parameters
@@ -42,8 +42,6 @@ class TrainingDataGenerator:
             Left boundary of interval. Default: -1.
         right_bound : float, optional
             Right boundary of interval. Default: 1.
-        stencil_length : int, optional
-            Size of training data array. Default: 3.
 
         """
         self._basis_list = [OrthonormalLegendre(pol_deg)
@@ -54,14 +52,9 @@ class TrainingDataGenerator:
                                 num_ghost_cells=0, num_grid_cells=2**exp)
                            for exp in range(3, 9)]
 
-        # Set stencil length
-        if stencil_length % 2 == 0:
-            raise ValueError('Invalid stencil length (even value): "%d"'
-                             % stencil_length)
-        self._stencil_length = stencil_length
-
     def build_training_data(self, initial_conditions, num_samples, balance=0.5,
-                            directory='test_data', add_reconstructions=True):
+                            directory='test_data', add_reconstructions=True,
+                            stencil_length=3):
         """Builds random training data.
 
         Creates training data consisting of random ANN input and saves it.
@@ -80,6 +73,8 @@ class TrainingDataGenerator:
         add_reconstructions : bool, optional
             Flag whether reconstructions of the middle cell are included.
             Default: True.
+        stencil_length : int, optional
+            Size of training data array. Default: 3.
 
         Returns
         -------
@@ -89,10 +84,17 @@ class TrainingDataGenerator:
 
         """
         tic = time.perf_counter()
+
+        # Set stencil length
+        if stencil_length % 2 == 0:
+            raise ValueError('Invalid stencil length (even value): "%d"'
+                             % stencil_length)
+
         print('Calculating training data...\n')
         data_dict = self._calculate_data_set(initial_conditions,
                                              num_samples, balance,
-                                             add_reconstructions)
+                                             add_reconstructions,
+                                             stencil_length)
         print('Finished calculating training data!')
 
         self._save_data(directory=directory, data=data_dict)
@@ -101,7 +103,7 @@ class TrainingDataGenerator:
         return data_dict
 
     def _calculate_data_set(self, initial_conditions, num_samples, balance,
-                            add_reconstructions):
+                            add_reconstructions, stencil_length):
         """Calculates random training data of given stencil length.
 
         Creates training data with a given ratio between smooth and
@@ -117,6 +119,8 @@ class TrainingDataGenerator:
             Ratio between smooth and discontinuous training data.
         add_reconstructions : bool
             Flag whether reconstructions of the middle cell are included.
+        stencil_length : int
+            Size of training data array.
 
         Returns
         -------
@@ -137,12 +141,13 @@ class TrainingDataGenerator:
 
         num_smooth_samples = round(num_samples * balance)
         smooth_input, smooth_output = self._generate_cell_data(
-            num_smooth_samples, smooth_functions, add_reconstructions, True)
+            num_smooth_samples, smooth_functions, add_reconstructions,
+            stencil_length, True)
 
         num_troubled_samples = num_samples - num_smooth_samples
         troubled_input, troubled_output = self._generate_cell_data(
             num_troubled_samples, troubled_functions, add_reconstructions,
-            False)
+            stencil_length, False)
 
         # Merge Data
         input_matrix = np.concatenate((smooth_input, troubled_input), axis=0)
@@ -162,7 +167,7 @@ class TrainingDataGenerator:
                 'input_data.normalized': norm_input_matrix}
 
     def _generate_cell_data(self, num_samples, initial_conditions,
-                            add_reconstructions, is_smooth):
+                            add_reconstructions, stencil_length, is_smooth):
         """Generates random training input and output.
 
         Generates random training input and output for either smooth or
@@ -177,6 +182,8 @@ class TrainingDataGenerator:
             List of names of initial conditions for training.
         add_reconstructions : bool
             Flag whether reconstructions of the middle cell are included.
+        stencil_length : int
+            Size of training data array.
         is_smooth : bool
             Flag whether initial conditions are smooth.
 
@@ -194,7 +201,7 @@ class TrainingDataGenerator:
         print('Samples to complete:', num_samples)
         tic = time.perf_counter()
 
-        num_datapoints = self._stencil_length
+        num_datapoints = stencil_length
         if add_reconstructions:
             num_datapoints += 2
         input_data = np.zeros((num_samples, num_datapoints))
@@ -209,11 +216,11 @@ class TrainingDataGenerator:
 
             # Build mesh for random stencil of given length
             mesh = self._mesh_list[int(np.random.randint(
-                3, high=9, size=1))-3].random_stencil(self._stencil_length)
+                3, high=9, size=1))-3].random_stencil(stencil_length)
 
             # Induce adjustment to capture troubled cells
             adjustment = 0 if initial_condition.is_smooth() \
-                else mesh.non_ghost_cells[self._stencil_length//2]
+                else mesh.non_ghost_cells[stencil_length//2]
             initial_condition.induce_adjustment(-mesh.cell_len/3)
 
             # Calculate basis coefficients for stencil
@@ -226,7 +233,7 @@ class TrainingDataGenerator:
             input_data[i] = self._basis_list[
                 polynomial_degree].calculate_cell_average(
                 projection=projection[:, 1:-1],
-                stencil_length=self._stencil_length,
+                stencil_length=stencil_length,
                 add_reconstructions=add_reconstructions)
 
             count += 1

workflows/ANN_data.smk

@@ -38,9 +38,9 @@ rule generate_data:
         with open(str(log), 'w') as logfile:
             sys.stdout = logfile
             generator = ANN_Data_Generator.TrainingDataGenerator(
-                left_bound=params.left_bound, right_bound=params.right_bound,
-                stencil_length=params.stencil_length)
+                left_bound=params.left_bound, right_bound=params.right_bound)
             data = generator.build_training_data(balance=params.balance,
                 initial_conditions=initial_conditions, directory=DIR,
                 num_samples=params.sample_number,
-                add_reconstructions=params.reconstruction_flag)
\ No newline at end of file
+                add_reconstructions=params.reconstruction_flag,
+                stencil_length=params.stencil_length)
\ No newline at end of file