diff --git a/DG_Approximation.py b/DG_Approximation.py
index 661a6776cd76d0c725a403a1e93af247c5cbcaab..4eab4c88271c2ad521a603c1230bc5030d016eaf 100644
--- a/DG_Approximation.py
+++ b/DG_Approximation.py
@@ -2,15 +2,22 @@
"""
@author: Laura C. Kühle
+Discussion:
+TODO: Ask whether cell averages/reconstructions should be contained in basis
+TODO: Contemplate whether basis variables should be public
+TODO: Contemplate a Mesh class (mesh, cell_len, num_grid_cells, bounds, etc.)
+
Urgent:
TODO: Extract do_initial_projection() from DGScheme -> Done
TODO: Move inverse mass matrix to basis -> Done
-TODO: Extract calculate_cell_average() from TCD
+TODO: Extract calculate_cell_average() from TCD -> Done
+TODO: Improve calculate_cell_average()
TODO: Extract calculate_[...]_solution() from Plotting
TODO: Extract plotting from TCD completely
(maybe give indicator which plots are required instead?)
TODO: Contain all plotting in Plotting
TODO: Remove use of DGScheme from ANN_Data_Generator
+TODO: Clean up docstrings
TODO: Adapt TCD from Soraya
(Dropbox->...->TEST_troubled-cell-detector->Troubled_Cell_Detector)
TODO: Add verbose output
@@ -61,6 +68,7 @@ import Limiter
import Quadrature
import Update_Scheme
from Basis_Function import OrthonormalLegendre
+from projection_utils import calculate_cell_average
matplotlib.use('Agg')
x = Symbol('x')
@@ -324,9 +332,10 @@ class DGScheme:
left_bound=self._left_bound, right_bound=self._right_bound,
polynomial_degree=self._polynomial_degree, adjustment=adjustment)
- return self._detector.calculate_cell_average(projection[:, 1:-1],
- stencil_length,
- add_reconstructions)
+ return calculate_cell_average(
+ projection=projection[:, 1:-1], stencil_length=stencil_length,
+ polynomial_degree=self._polynomial_degree, basis=self._basis,
+ add_reconstructions=add_reconstructions)
def do_initial_projection(initial_condition, basis, quadrature,
diff --git a/Troubled_Cell_Detector.py b/Troubled_Cell_Detector.py
index 1a2dd0bd2a6ee47af1dc139c1696debf35ed4094..325d84d741a7150566df66ec1a5a3f8042c09f1c 100644
--- a/Troubled_Cell_Detector.py
+++ b/Troubled_Cell_Detector.py
@@ -19,6 +19,7 @@ import ANN_Model
from Plotting import plot_solution_and_approx, plot_semilog_error, \
plot_error, plot_shock_tube, plot_details, \
calculate_approximate_solution, calculate_exact_solution
+from projection_utils import calculate_cell_average
matplotlib.use('Agg')
x = Symbol('x')
@@ -43,8 +44,6 @@ class TroubledCellDetector:
Returns string of class name.
get_cells(projection)
Calculates troubled cells in a given projection.
- calculate_cell_average_and_reconstructions(projection, stencil_length)
- Calculates cell averages and reconstructions for a given projection.
plot_results(projection, troubled_cell_history, time_history)
Plots results and troubled cells of a projection.
@@ -134,50 +133,6 @@ class TroubledCellDetector:
"""
pass
- def calculate_cell_average(self, projection, stencil_length,
- add_reconstructions=True):
- """Calculates cell averages for a given projection.
-
- Calculate the cell averages of all cells in a projection.
- If desired, reconstructions are calculated for the middle cell
- and added left and right to it, respectively.
-
- Parameters
- ----------
- projection : ndarray
- Matrix of projection for each polynomial degree.
- stencil_length : int
- Size of data array.
- add_reconstructions: bool, optional
- Flag whether reconstructions of the middle cell are included.
- Default: True.
-
- Returns
- -------
- ndarray
- Matrix containing cell averages (and reconstructions) for initial
- projection.
-
- """
- cell_averages = calculate_approximate_solution(
- projection, [0], 0, self._basis.get_basis_vector())
-
- if add_reconstructions:
- left_reconstructions = calculate_approximate_solution(
- projection, [-1], self._polynomial_degree,
- self._basis.get_basis_vector())
- right_reconstructions = calculate_approximate_solution(
- projection, [1], self._polynomial_degree,
- self._basis.get_basis_vector())
- middle_idx = stencil_length//2
- return np.array(
- list(map(np.float64, zip(cell_averages[:, :middle_idx],
- left_reconstructions[:, middle_idx],
- cell_averages[:, middle_idx],
- right_reconstructions[:, middle_idx],
- cell_averages[:, middle_idx+1:]))))
- return np.array(list(map(np.float64, cell_averages)))
-
def plot_results(self, projection, troubled_cell_history, time_history):
"""Plots results and troubled cells of a projection.
@@ -333,10 +288,12 @@ class ArtificialNeuralNetwork(TroubledCellDetector):
projection[:, :num_ghost_cells]), axis=1)
# Calculate input data depending on stencil length
- input_data = torch.from_numpy(
- np.vstack([self.calculate_cell_average(
- projection[:, cell-num_ghost_cells:cell+num_ghost_cells+1],
- self._stencil_len, self._add_reconstructions)
+ input_data = torch.from_numpy(np.vstack([calculate_cell_average(
+ projection=projection[
+ :, cell-num_ghost_cells:cell+num_ghost_cells+1],
+ stencil_length=self._stencil_len, basis=self._basis,
+ polynomial_degree=self._polynomial_degree,
+ add_reconstructions=self._add_reconstructions)
for cell in range(num_ghost_cells,
len(projection[0])-num_ghost_cells)]))
diff --git a/projection_utils.py b/projection_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..c4bdec2ac57c6357f28f1914089c932c0461bd8f
--- /dev/null
+++ b/projection_utils.py
@@ -0,0 +1,57 @@
+# -*- coding: utf-8 -*-
+"""
+@author: Laura C. Kühle
+
+"""
+import numpy as np
+
+from Plotting import calculate_approximate_solution
+
+
+def calculate_cell_average(projection, basis, stencil_length,
+ polynomial_degree, add_reconstructions=True):
+ """Calculates cell averages for a given projection.
+
+ Calculate the cell averages of all cells in a projection.
+ If desired, reconstructions are calculated for the middle cell
+ and added left and right to it, respectively.
+
+ Parameters
+ ----------
+ projection : ndarray
+ Matrix of projection for each polynomial degree.
+ basis : Basis object
+ Basis for calculation.
+ stencil_length : int
+ Size of data array.
+ polynomial_degree : int
+ Polynomial degree.
+ add_reconstructions: bool, optional
+ Flag whether reconstructions of the middle cell are included.
+ Default: True.
+
+ Returns
+ -------
+ ndarray
+ Matrix containing cell averages (and reconstructions) for initial
+ projection.
+
+ """
+ cell_averages = calculate_approximate_solution(
+ projection, [0], 0, basis.get_basis_vector())
+
+ if add_reconstructions:
+ left_reconstructions = calculate_approximate_solution(
+ projection, [-1], polynomial_degree,
+ basis.get_basis_vector())
+ right_reconstructions = calculate_approximate_solution(
+ projection, [1], polynomial_degree,
+ basis.get_basis_vector())
+ middle_idx = stencil_length // 2
+ return np.array(
+ list(map(np.float64, zip(cell_averages[:, :middle_idx],
+ left_reconstructions[:, middle_idx],
+ cell_averages[:, middle_idx],
+ right_reconstructions[:, middle_idx],
+ cell_averages[:, middle_idx+1:]))))
+ return np.array(list(map(np.float64, cell_averages)))