diff --git a/DG_Approximation.py b/DG_Approximation.py
index f09d3dfe8dae76a1afa1609aea6c8c9b66efd901..42ce8e44d46deeb5c50f5cf317455a8d8260675c 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 8cbd4b99a134febc0081e464c50797d0099b564e..7ce26f8ba4707ea63d025d5af0e0d70d1828cb95 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: