diff --git a/ANN_Data_Generator.py b/ANN_Data_Generator.py
index 4168fe9a75a8cf1cb072b30294f454ff95aa462f..f9d2e67259fc5da81db468877b4aeb62809150e7 100644
--- a/ANN_Data_Generator.py
+++ b/ANN_Data_Generator.py
@@ -13,15 +13,20 @@ 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:
"""Class for training data generator.
Generates random training data for given initial conditions.
+ Attributes
+ ----------
+ basis_list : list
+ List of basis instances for degree 1 to 4.
+ quadrature_list : list
+ List of Gauss quadrature instances for degree 2 to 5.
+ mesh_list : list
+ List of Mesh instances for 2**(3 to 8) cells.
+
Methods
-------
build_training_data(num_samples)
@@ -41,8 +46,13 @@ class TrainingDataGenerator:
Size of training data array. Default: 3.
"""
- self._left_bound = left_bound
- self._right_bound = right_bound
+ self._basis_list = [OrthonormalLegendre(pol_deg)
+ for pol_deg in range(5)]
+ self._quadrature_list = [Gauss({'num_nodes': pol_deg+1})
+ for pol_deg in range(5)]
+ self._mesh_list = [Mesh(left_bound=left_bound, right_bound=right_bound,
+ num_ghost_cells=0, num_grid_cells=2**exp)
+ for exp in range(3, 9)]
# Set stencil length
if stencil_length % 2 == 0:
@@ -147,6 +157,8 @@ class TrainingDataGenerator:
# Create normalized input data
norm_input_matrix = self._normalize_data(input_matrix)
+ # print(input_matrix)
+ # print(norm_input_matrix)
return {'input_data.raw': input_matrix, 'output_data': output_matrix,
'input_data.normalized': norm_input_matrix}
@@ -197,28 +209,28 @@ class TrainingDataGenerator:
initial_condition.randomize(
initial_conditions[function_id]['config'])
- # Build random stencil of given length
- interval, centers, spacing = self._build_stencil()
- left_bound, right_bound = interval
- centers = [center[0] for center in centers]
+ # 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)
# Induce adjustment to capture troubled cells
adjustment = 0 if initial_condition.is_smooth() \
- else centers[self._stencil_length//2]
- initial_condition.induce_adjustment(-spacing[0]/3)
+ else mesh.non_ghost_cells[self._stencil_length//2]
+ initial_condition.induce_adjustment(-mesh.cell_len/3)
+ # print(initial_condition.is_smooth())
+ # print(mesh.interval_len, mesh.non_ghost_cells, mesh.cell_len)
+ # print(adjustment, -mesh.cell_len/3)
+ # print()
# Calculate basis coefficients for stencil
polynomial_degree = np.random.randint(1, high=5)
-
- 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_list[polynomial_degree],
- quadrature=quadrature_list[polynomial_degree],
+ basis=self._basis_list[polynomial_degree],
+ quadrature=self._quadrature_list[polynomial_degree],
adjustment=adjustment)
-
- input_data[i] = basis_list[
+ # print(projection)
+ input_data[i] = self._basis_list[
polynomial_degree].calculate_cell_average(
projection=projection[:, 1:-1],
stencil_length=self._stencil_length,
@@ -239,42 +251,6 @@ class TrainingDataGenerator:
return input_data, output_data
- def _build_stencil(self):
- """Builds random stencil.
-
- Calculates fixed number of cell centers around a random point in a
- given 1D domain.
-
- Returns
- -------
- interval : ndarray
- List containing left and right bound of interval.
- stencil : ndarray
- List of cell centers in stencil.
- grid_spacing : float
- Length of cell in grid.
-
- """
- # Select random cell length
- grid_spacing = 2 / (2 ** np.random.randint(3, high=9, size=1))
-
- # Pick random point between left and right bound
- point = np.random.uniform(self._left_bound, self._right_bound)
-
- # Adjust grid spacing if necessary for stencil creation
- while point - self._stencil_length/2 * grid_spacing < self._left_bound\
- or point + self._stencil_length/2 * \
- grid_spacing > self._right_bound:
- grid_spacing /= 2
-
- # Build x-point stencil
- interval = np.array([point - self._stencil_length/2 * grid_spacing,
- point + self._stencil_length/2 * grid_spacing])
- stencil = np.array([point + factor * grid_spacing
- for factor in range(-(self._stencil_length//2),
- self._stencil_length//2 + 1)])
- return interval, stencil, grid_spacing
-
@staticmethod
def _normalize_data(input_data):
"""Normalizes data.
diff --git a/DG_Approximation.py b/DG_Approximation.py
index c44669b7de9cfea7f551e41feb13f8e7068ff52f..d0acc65730f0cd9efe79f01f759f833addb7ab92 100644
--- a/DG_Approximation.py
+++ b/DG_Approximation.py
@@ -12,10 +12,16 @@ TODO: Contemplate containing coarse mesh generation in Mesh
TODO: Contemplate extracting boundary condition from InitialCondition
TODO: Contemplate containing boundary condition in Mesh
TODO: Ask whether all quadratures depend on freely chosen num_nodes
+TODO: Contemplate saving each IC separately
+TODO: Contemplate removing TrainingDataGenerator class
Urgent:
-TODO: Remove unnecessary instance variables from TrainingDataGenerator
-TODO: Find error in centering for ANN training
+TODO: Remove unnecessary instance variables from TrainingDataGenerator -> Done
+TODO: Fix bug during adjustment setting -> Done
+TODO: Move stencil building to Mesh -> Done
+TODO: Investigate self-referencing in classes
+TODO: Remove stencil_length as instance variable
+TODO: Find errors in centering for ANN training
TODO: Adapt TCD from Soraya
(Dropbox->...->TEST_troubled-cell-detector->Troubled_Cell_Detector)
TODO: Add TC condition to only flag cell if left-adjacent one is flagged as
diff --git a/projection_utils.py b/projection_utils.py
index 9e3a9d4bcc8166dc62c0fdaedffef18df6b5c468..39fe4c7e465f95eca3cf76f5df634b9375e0e4a8 100644
--- a/projection_utils.py
+++ b/projection_utils.py
@@ -93,13 +93,40 @@ class Mesh:
"""Return the cell centers of the mesh (excluding ghost cells)."""
return self.cells[self._num_ghost_cells:-self._num_ghost_cells]
- def create_data_dict(self):
+ def create_data_dict(self) -> dict:
"""Return dictionary with data necessary to construct mesh."""
return {'num_grid_cells': self._num_grid_cells,
'left_bound': self._left_bound,
'right_bound': self._right_bound,
'num_ghost_cells': self._num_ghost_cells}
+ def random_stencil(self, stencil_length: int) -> 'Mesh':
+ """Return random stencil.
+
+ Build mesh with given number of cell centers around a random point
+ in the underlying interval.
+
+ Returns
+ -------
+ Mesh object
+ Mesh of given size around random point.
+
+ """
+ # Pick random point between left and right bound
+ point = np.random.uniform(self._left_bound, self._right_bound)
+
+ # Adjust grid spacing to be within interval if necessary
+ # creation
+ grid_spacing = self.cell_len
+ while point - stencil_length/2 * grid_spacing < self._left_bound \
+ or point + stencil_length/2 * grid_spacing > self._right_bound:
+ 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)
+
def calculate_approximate_solution(
projection: ndarray, points: ndarray, polynomial_degree: int,