From 44c1b800e0ba2af0cf25f9e6cd76ec99ccb24580 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: Sun, 13 Nov 2022 23:57:57 +0100
Subject: [PATCH] Removed unnecessary dimension during cell average
 calculation.

---
 Snakefile                     | 1 +
 scripts/tcd/Basis_Function.py | 8 ++++----
 2 files changed, 5 insertions(+), 4 deletions(-)

diff --git a/Snakefile b/Snakefile
index 1312c34..e4fbf82 100644
--- a/Snakefile
+++ b/Snakefile
@@ -25,6 +25,7 @@ TODO: Discuss how wavelet details should be plotted
 
 Urgent:
 TODO: Vectorize 'plot_details()' -> Done
+TODO: Remove unnecessary dimension during cell average calculation -> Done
 TODO: Replace loops/list comprehension with vectorization if feasible
 TODO: Replace loops with list comprehension if feasible
 TODO: Rework ICs to allow vector input
diff --git a/scripts/tcd/Basis_Function.py b/scripts/tcd/Basis_Function.py
index 01eaf27..2b3c809 100644
--- a/scripts/tcd/Basis_Function.py
+++ b/scripts/tcd/Basis_Function.py
@@ -500,7 +500,7 @@ class OrthonormalLegendre(Legendre):
             projection.
 
         """
-        cell_averages = np.array([projection[0] / np.sqrt(2)])
+        cell_averages = projection[0] / np.sqrt(2)
 
         if add_reconstructions:
             middle_idx = stencil_len // 2
@@ -508,11 +508,11 @@ class OrthonormalLegendre(Legendre):
                 self._calculate_reconstructions(
                     projection[:, middle_idx:middle_idx+1])
             return np.array(list(map(
-                np.float64, zip(cell_averages[:, :middle_idx],
+                np.float64, zip(cell_averages[:middle_idx],
                                 left_reconstructions,
-                                cell_averages[:, middle_idx],
+                                cell_averages[middle_idx:middle_idx+1],
                                 right_reconstructions,
-                                cell_averages[:, middle_idx+1:]))))
+                                cell_averages[middle_idx+1:]))))
         return np.array(list(map(np.float64, cell_averages)))
 
     def _calculate_reconstructions(self, projection: ndarray) \
-- 
GitLab