From 978b159457a8f031995406fb0b038024529426ba Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?K=C3=BChle=2C=20Laura=20Christine=20=28lakue103=29?=
<laura.kuehle@uni-duesseldorf.de>
Date: Tue, 15 Mar 2022 14:18:13 +0100
Subject: [PATCH] Improved 'calculate_cell_average()'.
---
DG_Approximation.py | 6 +++---
Troubled_Cell_Detector.py | 4 ++--
projection_utils.py | 35 ++++++++++++++++-------------------
3 files changed, 21 insertions(+), 24 deletions(-)
diff --git a/DG_Approximation.py b/DG_Approximation.py
index 4eab4c8..21b67e1 100644
--- a/DG_Approximation.py
+++ b/DG_Approximation.py
@@ -11,7 +11,7 @@ Urgent:
TODO: Extract do_initial_projection() from DGScheme -> Done
TODO: Move inverse mass matrix to basis -> Done
TODO: Extract calculate_cell_average() from TCD -> Done
-TODO: Improve calculate_cell_average()
+TODO: Improve calculate_cell_average() -> Done
TODO: Extract calculate_[...]_solution() from Plotting
TODO: Extract plotting from TCD completely
(maybe give indicator which plots are required instead?)
@@ -334,8 +334,8 @@ class DGScheme:
return calculate_cell_average(
projection=projection[:, 1:-1], stencil_length=stencil_length,
- polynomial_degree=self._polynomial_degree, basis=self._basis,
- add_reconstructions=add_reconstructions)
+ polynomial_degree=self._polynomial_degree if add_reconstructions
+ else -1, basis=self._basis)
def do_initial_projection(initial_condition, basis, quadrature,
diff --git a/Troubled_Cell_Detector.py b/Troubled_Cell_Detector.py
index 325d84d..2d8aaeb 100644
--- a/Troubled_Cell_Detector.py
+++ b/Troubled_Cell_Detector.py
@@ -292,8 +292,8 @@ class ArtificialNeuralNetwork(TroubledCellDetector):
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)
+ polynomial_degree=self._polynomial_degree if
+ self._add_reconstructions else -1)
for cell in range(num_ghost_cells,
len(projection[0])-num_ghost_cells)]))
diff --git a/projection_utils.py b/projection_utils.py
index c4bdec2..e860b10 100644
--- a/projection_utils.py
+++ b/projection_utils.py
@@ -9,8 +9,8 @@ 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.
+ polynomial_degree=-1):
+ """Calculate 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
@@ -24,11 +24,9 @@ def calculate_cell_average(projection, basis, stencil_length,
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.
+ polynomial_degree : int, optional
+ Polynomial degree for reconstructions of the middle cell. If -1 no
+ reconstructions will be included. Default: -1.
Returns
-------
@@ -37,21 +35,20 @@ def calculate_cell_average(projection, basis, stencil_length,
projection.
"""
+ basis_vector = basis.get_basis_vector()
cell_averages = calculate_approximate_solution(
- projection, [0], 0, basis.get_basis_vector())
+ projection, np.array([0]), 0, basis_vector)
- if add_reconstructions:
+ if polynomial_degree != -1:
left_reconstructions = calculate_approximate_solution(
- projection, [-1], polynomial_degree,
- basis.get_basis_vector())
+ projection, np.array([-1]), polynomial_degree, basis_vector)
right_reconstructions = calculate_approximate_solution(
- projection, [1], polynomial_degree,
- basis.get_basis_vector())
+ projection, np.array([1]), polynomial_degree, 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, 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)))
--
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