diff --git a/Snakefile b/Snakefile
index 94421e233f25667e0d4de4849b3dbde795cc3717..bf8805b9d581022280c9d07d3fab2b13d22ce382 100644
--- a/Snakefile
+++ b/Snakefile
@@ -28,7 +28,7 @@ TODO: Enforce periodic boundary condition for projection with decorator -> Done
TODO: Enforce Boxplot bounds with decorator -> Done
TODO: Enforce Boxplot folds with decorator -> Done
TODO: Enforce boundary for initial condition in exact solution only -> Done
-TODO: Adapt number of ghost cells based on ANN stencil
+TODO: Adapt number of ghost cells based on ANN stencil -> Done
TODO: Ensure exact solution is calculated in Equation class
TODO: Extract objects from UpdateScheme
TODO: Enforce num_ghost_cells to be positive integer for DG (not training)
diff --git a/scripts/approximate_solution.py b/scripts/approximate_solution.py
index cef66cfde1177d0c51bb802ec276b97f5ba0e39a..ec3669045939471c3bc23d1a6d3cb97c2c43af39 100644
--- a/scripts/approximate_solution.py
+++ b/scripts/approximate_solution.py
@@ -27,15 +27,25 @@ def main() -> None:
params = snakemake.params['dg_params']
+ num_ghost_cells = params.pop('num_ghost_cells', 1)
+
if len(snakemake.input) > 0:
params['detector_config']['model_state'] = snakemake.input[0]
+ # Adapt number of ghost cells
+ if 'stencil_len' in params['detector_config']:
+ if num_ghost_cells < params['detector_config']['stencil_len']//2:
+ num_ghost_cells = params['detector_config']['stencil_len']//2
+ print(f'Number of ghost cells was increased to '
+ f'{num_ghost_cells} to accommodate the stencil length.')
+
print(params)
# Initialize mesh with two ghost cells on each side
mesh = Mesh(num_cells=params.pop('num_mesh_cells', 64),
left_bound=params.pop('left_bound', -1),
- right_bound=params.pop('right_bound', 1))
+ right_bound=params.pop('right_bound', 1),
+ num_ghost_cells=num_ghost_cells)
# Initialize basis
basis = OrthonormalLegendre(params.pop('polynomial_degree', 2))
diff --git a/scripts/tcd/ANN_Data_Generator.py b/scripts/tcd/ANN_Data_Generator.py
index cbc8eed1aca622dba9bb5157ded0333e0ea5841a..1aef27ce705284c194bac112fd2e20282b89e9c2 100644
--- a/scripts/tcd/ANN_Data_Generator.py
+++ b/scripts/tcd/ANN_Data_Generator.py
@@ -40,7 +40,7 @@ class TrainingDataGenerator:
self._quadrature_list = [Gauss({'num_nodes': pol_deg+1})
for pol_deg in range(7)]
self._mesh_list = [Mesh(left_bound=-1, right_bound=1,
- num_cells=2**exp, training_data_mode=True)
+ num_cells=2**exp, num_ghost_cells=0)
for exp in range(5, 12)]
def build_training_data(self, init_cond_list, num_samples, balance=0.5,
diff --git a/scripts/tcd/Mesh.py b/scripts/tcd/Mesh.py
index 649fd55799dbc9287b1fb3fa7a5145a16b7761d9..445ad170a3d3ee72e61b5f4d1529f2782fe1590a 100644
--- a/scripts/tcd/Mesh.py
+++ b/scripts/tcd/Mesh.py
@@ -45,7 +45,7 @@ class Mesh:
"""
def __init__(self, num_cells: int, left_bound: float, right_bound: float,
- training_data_mode: bool = False) -> None:
+ num_ghost_cells: int = 1) -> None:
"""Initialize Mesh.
Parameters
@@ -57,9 +57,9 @@ class Mesh:
Left boundary of the mesh interval.
right_bound : float
Right boundary of the mesh interval.
- training_data_mode : bool, optional
- Flag indicating whether the mesh is used for training data
- generation. Default: False.
+ num_ghost_cells : int, optional
+ Number of ghost cells on each side of the mesh, respectively.
+ Default: False.
Raises
------
@@ -68,10 +68,8 @@ class Mesh:
"""
self._num_cells = num_cells
- self._mode = 'training' if training_data_mode else 'evaluation'
- self._num_ghost_cells = 0
- if not training_data_mode:
- self._num_ghost_cells = 1
+ self._num_ghost_cells = num_ghost_cells
+ if self._num_ghost_cells != 0:
if not math.log(self._num_cells, 2).is_integer():
raise ValueError('The number of cells in the mesh has to be '
'an exponential of 2')
@@ -79,11 +77,6 @@ class Mesh:
self._right_bound = right_bound
self.boundary = 'periodic'
- @property
- def mode(self) -> str:
- """Return mode ('training' or 'evaluation')."""
- return self._mode
-
@property
def num_cells(self) -> int:
"""Return number of mesh cells."""
@@ -128,7 +121,8 @@ class Mesh:
"""Return dictionary with data necessary to construct mesh."""
return {'num_cells': self._num_cells,
'left_bound': self._left_bound,
- 'right_bound': self._right_bound}
+ 'right_bound': self._right_bound,
+ 'num_ghost_cells': self._num_ghost_cells}
def random_stencil(self, stencil_len: int) -> Mesh:
"""Return random stencil.
@@ -155,4 +149,4 @@ class Mesh:
# Return new mesh instance
return Mesh(left_bound=point - stencil_len/2 * mesh_spacing,
right_bound=point + stencil_len/2 * mesh_spacing,
- num_cells=stencil_len, training_data_mode=True)
+ num_cells=stencil_len, num_ghost_cells=0)
diff --git a/scripts/tcd/Plotting.py b/scripts/tcd/Plotting.py
index 5fdc6d67dbb756d603fd8cdee2d5e9552a3468ed..07b708ca5dab2504e48855386daefb0d28276561 100644
--- a/scripts/tcd/Plotting.py
+++ b/scripts/tcd/Plotting.py
@@ -430,7 +430,7 @@ def plot_results(projection: ndarray, troubled_cell_history: list,
coarse_mesh = Mesh(num_cells=mesh.num_cells//2,
left_bound=mesh.bounds[0],
right_bound=mesh.bounds[1],
- training_data_mode=True)
+ num_ghost_cells=0)
# Plot exact and approximate solutions for coarse mesh
coarse_grid, coarse_exact = calculate_exact_solution(
diff --git a/scripts/tcd/Troubled_Cell_Detector.py b/scripts/tcd/Troubled_Cell_Detector.py
index 8be6c1695a7ee6504571aa30f8e705255f2ee3cc..9b65ce6f3fc8a31f8b238e31d6bebb85b08b7cff 100644
--- a/scripts/tcd/Troubled_Cell_Detector.py
+++ b/scripts/tcd/Troubled_Cell_Detector.py
@@ -177,14 +177,6 @@ class ArtificialNeuralNetwork(TroubledCellDetector):
List of indices for all detected troubled cells.
"""
- # Reset ghost cells to adjust for stencil length
- num_ghost_cells = self._stencil_len//2
- projection = projection[:, self._mesh.num_ghost_cells:
- -self._mesh.num_ghost_cells]
- projection = np.concatenate((projection[:, -num_ghost_cells:],
- projection,
- projection[:, :num_ghost_cells]), axis=1)
-
# Calculate input data depending on stencil length
projection_window = projection[:, self._window_mask]
input_data = torch.from_numpy(self._basis.calculate_cell_average(