From 0e8d8f059d428749b5441a0f7bdbcc8919fdf906 Mon Sep 17 00:00:00 2001
From: lakue103 <laura.kuehle@uni-duesseldorf.de>
Date: Wed, 30 Sep 2020 13:47:15 +0200
Subject: [PATCH] Removed completed TODOs.

---
 DG_Approximation.py       | 10 ++--------
 Vectors_of_Polynomials.py |  2 +-
 2 files changed, 3 insertions(+), 9 deletions(-)

diff --git a/DG_Approximation.py b/DG_Approximation.py
index f09d3df..42ce8e4 100644
--- a/DG_Approximation.py
+++ b/DG_Approximation.py
@@ -8,10 +8,6 @@ TODO: Contemplate using Seaborn instead of matplotlib
 TODO: Double-check everything!
 TODO: Replace loops with list comprehension if feasible
 TODO: Combine initial projection and approx solution somehow
-TODO: Contemplate erasing inv_mass-matrix as it is identity
-    -> Done (No, because could be problematic for other functions)
-TODO: Investigate why we need inverse mass matrix for initial projection, \
-    but not A and B
 TODO: Investigate why there are no weights in approx calc
 TODO: Implement argument check for unpacking of all configs
 TODO: Contemplate verbose = show_plot?
@@ -23,14 +19,12 @@ TODO: Write documentation for all methods
 TODO: Add a verbose option
 TODO: Check whether consistency is given/possible for each class instance
 
-TODO: Remove unnecessary code in plot methods -> Done
 TODO: Make sure all instance variables are actually necessary
 TODO: Make sure instance variables are only set in __init__()
 TODO: Contemplate moving plots to pertaining files
-TODO: Discuss details plot!
-TODO: Fix typo in Vectors_of_Polynomials -> Done
-TODO: Replace symbol 'x' with 'z' for Alpert's wavelet -> Done
 TODO: Check time efficiency of details plot
+TODO: Contemplate moving A and B to Vectors_of_Polynomials
+TODO: Combine plot for coarse and fine approximation for wavelet detectors
 
 """
 import numpy as np
diff --git a/Vectors_of_Polynomials.py b/Vectors_of_Polynomials.py
index 8cbd4b9..7ce26f8 100644
--- a/Vectors_of_Polynomials.py
+++ b/Vectors_of_Polynomials.py
@@ -53,7 +53,7 @@ class AlpertsWavelet(Vector):
                     1/3 * np.sqrt(2.5) * (4 - 15*eval_point + 12*(eval_point**2))]
         if degree == 3:
             return [np.sqrt(15/34) * (1 + 4*eval_point - 30*(eval_point**2) + 28*(eval_point**3)),
-                    np.sqrt(1/42) * (-4 + 105 * eval_point - 300*(eval_point**2) + 210*(eval_point**3)),
+                    np.sqrt(1/42) * (-4 + 105*eval_point - 300*(eval_point**2) + 210*(eval_point**3)),
                     1/2 * np.sqrt(35/34) * (-5 + 48*eval_point - 105*(eval_point**2) + 64*(eval_point**3)),
                     1/2 * np.sqrt(5/34) * (-16 + 105*eval_point - 192*(eval_point**2) + 105*(eval_point**3))]
         if degree == 4:
-- 
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