Replaced 'length' with 'len'.

ANN_Data_Generator.py

@@ -54,7 +54,7 @@ class TrainingDataGenerator:
 
     def build_training_data(self, initial_conditions, num_samples, balance=0.5,
                             directory='test_data', add_reconstructions=True,
-                            stencil_length=3):
+                            stencil_len=3):
         """Builds random training data.
 
         Creates training data consisting of random ANN input and saves it.
@@ -73,7 +73,7 @@ class TrainingDataGenerator:
         add_reconstructions : bool, optional
             Flag whether reconstructions of the middle cell are included.
             Default: True.
-        stencil_length : int, optional
+        stencil_len : int, optional
             Size of training data array. Default: 3.
 
         Returns
@@ -86,15 +86,15 @@ class TrainingDataGenerator:
         tic = time.perf_counter()
 
         # Set stencil length
-        if stencil_length % 2 == 0:
+        if stencil_len % 2 == 0:
             raise ValueError('Invalid stencil length (even value): "%d"'
-                             % stencil_length)
+                             % stencil_len)
 
         print('Calculating training data...\n')
         data_dict = self._calculate_data_set(initial_conditions,
                                              num_samples, balance,
                                              add_reconstructions,
-                                             stencil_length)
+                                             stencil_len)
         print('Finished calculating training data!')
 
         self._save_data(directory=directory, data=data_dict)
@@ -103,7 +103,7 @@ class TrainingDataGenerator:
         return data_dict
 
     def _calculate_data_set(self, initial_conditions, num_samples, balance,
-                            add_reconstructions, stencil_length):
+                            add_reconstructions, stencil_len):
         """Calculates random training data of given stencil length.
 
         Creates training data with a given ratio between smooth and
@@ -119,7 +119,7 @@ class TrainingDataGenerator:
             Ratio between smooth and discontinuous training data.
         add_reconstructions : bool
             Flag whether reconstructions of the middle cell are included.
-        stencil_length : int
+        stencil_len : int
             Size of training data array.
 
         Returns
@@ -142,12 +142,12 @@ class TrainingDataGenerator:
         num_smooth_samples = round(num_samples * balance)
         smooth_input, smooth_output = self._generate_cell_data(
             num_smooth_samples, smooth_functions, add_reconstructions,
-            stencil_length, True)
+            stencil_len, True)
 
         num_troubled_samples = num_samples - num_smooth_samples
         troubled_input, troubled_output = self._generate_cell_data(
             num_troubled_samples, troubled_functions, add_reconstructions,
-            stencil_length, False)
+            stencil_len, False)
 
         # Merge Data
         input_matrix = np.concatenate((smooth_input, troubled_input), axis=0)
@@ -167,7 +167,7 @@ class TrainingDataGenerator:
                 'input_data.normalized': norm_input_matrix}
 
     def _generate_cell_data(self, num_samples, initial_conditions,
-                            add_reconstructions, stencil_length, is_smooth):
+                            add_reconstructions, stencil_len, is_smooth):
         """Generates random training input and output.
 
         Generates random training input and output for either smooth or
@@ -182,7 +182,7 @@ 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
+        stencil_len : int
             Size of training data array.
         is_smooth : bool
             Flag whether initial conditions are smooth.
@@ -201,7 +201,7 @@ class TrainingDataGenerator:
         print('Samples to complete:', num_samples)
         tic = time.perf_counter()
 
-        num_datapoints = stencil_length
+        num_datapoints = stencil_len
         if add_reconstructions:
             num_datapoints += 2
         input_data = np.zeros((num_samples, num_datapoints))
@@ -216,11 +216,11 @@ class TrainingDataGenerator:
 
             # Build mesh for random stencil of given length
             mesh = self._mesh_list[int(np.random.randint(
-                3, high=12, size=1))-3].random_stencil(stencil_length)
+                3, high=12, size=1))-3].random_stencil(stencil_len)
 
             # Induce shift to capture troubled cells
             shift = 0 if initial_condition.is_smooth() \
-                else mesh.non_ghost_cells[stencil_length//2]
+                else mesh.non_ghost_cells[stencil_len//2]
             if initial_condition.discontinuity_position == 'left':
                 shift -= mesh.cell_len/2
             elif initial_condition.discontinuity_position == 'right':
@@ -236,7 +236,7 @@ class TrainingDataGenerator:
             input_data[i] = self._basis_list[
                 polynomial_degree].calculate_cell_average(
                 projection=projection[:, 1:-1],
-                stencil_length=stencil_length,
+                stencil_len=stencil_len,
                 add_reconstructions=add_reconstructions)
 
             count += 1

Basis_Function.py

@@ -143,7 +143,7 @@ class Basis(ABC):
         """Return wavelet projection."""
         pass
 
-    def calculate_cell_average(self, projection: ndarray, stencil_length: int,
+    def calculate_cell_average(self, projection: ndarray, stencil_len: int,
                                add_reconstructions: bool = True) -> ndarray:
         """Calculate cell averages for a given projection.
 
@@ -155,7 +155,7 @@ class Basis(ABC):
         ----------
         projection : ndarray
             Matrix of projection for each polynomial degree.
-        stencil_length : int
+        stencil_len : int
             Size of data array.
         add_reconstructions : bool, optional
             Flag whether reconstructions of the middle cell are included.
@@ -172,7 +172,7 @@ class Basis(ABC):
             projection, np.array([0]), 0, self.basis)
 
         if add_reconstructions:
-            middle_idx = stencil_length // 2
+            middle_idx = stencil_len // 2
             left_reconstructions, right_reconstructions = \
                 self._calculate_reconstructions(
                     projection[:, middle_idx:middle_idx+1])
@@ -474,7 +474,7 @@ class OrthonormalLegendre(Legendre):
             matrix.append(row)
         return np.array(matrix)
 
-    def calculate_cell_average(self, projection: ndarray, stencil_length: int,
+    def calculate_cell_average(self, projection: ndarray, stencil_len: int,
                                add_reconstructions: bool = True) -> ndarray:
         """Calculate cell averages for a given projection.
 
@@ -491,7 +491,7 @@ class OrthonormalLegendre(Legendre):
         ----------
         projection : ndarray
             Matrix of projection for each polynomial degree.
-        stencil_length : int
+        stencil_len : int
             Size of data array.
         add_reconstructions : bool, optional
             Flag whether reconstructions of the middle cell are included.
@@ -507,7 +507,7 @@ class OrthonormalLegendre(Legendre):
         cell_averages = np.array([projection[0] / np.sqrt(2)])
 
         if add_reconstructions:
-            middle_idx = stencil_length // 2
+            middle_idx = stencil_len // 2
             left_reconstructions, right_reconstructions = \
                 self._calculate_reconstructions(
                     projection[:, middle_idx:middle_idx+1])

Troubled_Cell_Detector.py

@@ -180,7 +180,7 @@ class ArtificialNeuralNetwork(TroubledCellDetector):
             self._basis.calculate_cell_average(
                 projection=projection[
                            :, cell-num_ghost_cells:cell+num_ghost_cells+1],
-                stencil_length=self._stencil_len,
+                stencil_len=self._stencil_len,
                 add_reconstructions=self._add_reconstructions)
             for cell in range(num_ghost_cells,
                               len(projection[0])-num_ghost_cells)]))

projection_utils.py

@@ -119,7 +119,7 @@ class Mesh:
                 'right_bound': self._right_bound,
                 'num_ghost_cells': self._num_ghost_cells}
 
-    def random_stencil(self, stencil_length: int) -> Mesh:
+    def random_stencil(self, stencil_len: int) -> Mesh:
         """Return random stencil.
 
         Build mesh with given number of cell centers around a random point
@@ -136,15 +136,15 @@ class Mesh:
 
         # Adjust grid spacing to be within interval if necessary
         grid_spacing = self.cell_len
-        max_spacing = 2/stencil_length*min(point-self._left_bound,
+        max_spacing = 2/stencil_len*min(point-self._left_bound,
                                            self._right_bound-point)
         while grid_spacing > max_spacing:
             grid_spacing /= 2
 
         # Return new mesh instance
-        return Mesh(left_bound=point - stencil_length/2 * grid_spacing,
-                    right_bound=point + stencil_length/2 * grid_spacing,
-                    num_grid_cells=stencil_length, num_ghost_cells=2,
+        return Mesh(left_bound=point - stencil_len/2 * grid_spacing,
+                    right_bound=point + stencil_len/2 * grid_spacing,
+                    num_grid_cells=stencil_len, num_ghost_cells=2,
                     training_data_mode=True)
 
 

workflows/ANN_data.smk

@@ -20,7 +20,7 @@ rule generate_data:
         left_bound = config['left_boundary'],
         right_bound = config['right_boundary'],
         balance = config['smooth_troubled_balance'],
-        stencil_length = config['stencil_length'],
+        stencil_len = config['stencil_length'],
         sample_number = config['sample_number'],
         reconstruction_flag = config['add_reconstructions'],
         functions = expand('{FUNCTION}', FUNCTION=config['functions'])
@@ -43,4 +43,4 @@ rule generate_data:
                 initial_conditions=initial_conditions, directory=DIR,
                 num_samples=params.sample_number,
                 add_reconstructions=params.reconstruction_flag,
-                stencil_length=params.stencil_length)
+                stencil_len=params.stencil_len)
\ No newline at end of file