diff --git a/ANN_Data_Generator.py b/ANN_Data_Generator.py
index f56e026d965a9444408a2425925f3376696a9dc4..992f4abf15be8cb52dcac549ace723a8b1cf0b17 100644
--- a/ANN_Data_Generator.py
+++ b/ANN_Data_Generator.py
@@ -10,7 +10,7 @@ import numpy as np
 import DG_Approximation
 
 
-class TrainingDataGenerator(object):
+class TrainingDataGenerator:
     """Class for training data generator.
 
     Generates random training data for given initial conditions.
diff --git a/ANN_Training.py b/ANN_Training.py
index f937c444c8dc030ec413ab127a62a907bd5c8ed3..687e5981b41fc9ca03a46063d135594ebcde42ab 100644
--- a/ANN_Training.py
+++ b/ANN_Training.py
@@ -6,7 +6,7 @@ Code-Style: E226, W503
 Docstring-Style: D200, D400
 
 TODO: Add README for ANN training
-TODO: Fix random seed
+TODO: Fix random seed -> Done
 TODO: Improve file structure and naming (e.g. use '.' instead of '__') -> Done
 TODO: Write-protect all data and models -> Done
 TODO: Put legend outside plot (bbox_to_anchor) -> Done
@@ -34,7 +34,7 @@ from Plotting import plot_classification_barplot, plot_classification_boxplot
 matplotlib.use('Agg')
 
 
-class ModelTrainer(object):
+class ModelTrainer:
     """Class for ANN model training.
 
     Trains and tests a model with set loss function and optimizer.
diff --git a/Basis_Function.py b/Basis_Function.py
index 96d0b4ebff7304d4b5199357b2caebb266428f2b..af43bdfbbc32f2b01fccbb35067bc20ae51e9c7d 100644
--- a/Basis_Function.py
+++ b/Basis_Function.py
@@ -13,7 +13,7 @@ x = Symbol('x')
 z = Symbol('z')
 
 
-class Vector(object):
+class Vector:
     """Class for basis vector.
 
     Attributes
diff --git a/DG_Approximation.py b/DG_Approximation.py
index a4d675efab14ba6f3df5231564729b9f29bc049b..cab56cfed73ef70578df114f97a9f152309b291f 100644
--- a/DG_Approximation.py
+++ b/DG_Approximation.py
@@ -2,31 +2,36 @@
 """
 @author: Laura C. Kühle
 
-Plotter:
-TODO: Double-check everything! (also with pylint, pytype, pydoc, pycodestyle)
-TODO: Replace loops with list comprehension if feasible
-TODO: Write documentation for all methods (important)
-TODO: Discuss adding kwargs to attributes in documentation
-TODO: Check whether documentation style is correct
-TODO: Check whether all types in doc are correct
-TODO: Check whether 'projection' is always a np.array()
-TODO: Check whether all instance variables sensible
-TODO: Use cfl_number for updating, not just time
-TODO: Adjust code to allow classes for all equations
-    (Burger, linear advection, 1D Euler)
-TODO: Add documentation to ANN files
-TODO: Limit line to 80 characters
-TODO: Remove object inheritance from classes
-TODO: Rename files according to standard
-TODO: Add verbose output
+Urgent:
 TODO: Adapt TCD from Soraya
     (Dropbox->...->TEST_troubled-cell-detector->Troubled_Cell_Detector)
-TODO: Improve file naming (e.g. use '.' instead of '__')
 TODO: Add way of saving data (np.savez('data/' + name,
     coefficients=projection, troubled_cells=troubled_cells) )
+TODO: Add verbose output
+TODO: Improve file naming (e.g. use '.' instead of '__')
+TODO: Move plotting into separate function
+
+Critical, but not urgent:
+TODO: Use cfl_number for updating, not just time
+TODO: Adjust code to allow classes for all equations
+    (Burger, linear advection, 1D Euler)
+
+Currently not critical:
+TODO: Remove object inheritance from classes -> Done
+TODO: Replace loops with list comprehension if feasible
+TODO: Check whether 'projection' is always a np.array()
+TODO: Check whether all instance variables are sensible
+TODO: Rename files according to standard
 TODO: Outsource scripts into separate directory
 TODO: Allow comparison between ANN training datasets
 TODO: Add a default model state
+TODO: Add an environment file for Snakemake
+
+Not feasible yet or doc-related:
+TODO: Double-check everything! (also with pylint, pytype, pydoc, pycodestyle)
+TODO: Check whether documentation style is correct
+TODO: Check whether all types in doc are correct
+TODO: Discuss adding kwargs to attributes in documentation
 TODO: Add type annotations to function heads
 
 """
@@ -48,7 +53,7 @@ matplotlib.use('Agg')
 x = Symbol('x')
 
 
-class DGScheme(object):
+class DGScheme:
     """Class for Discontinuous Galerkin Method.
 
     Approximates linear advection equation using Discontinuous Galerkin Method
diff --git a/Initial_Condition.py b/Initial_Condition.py
index 74b8efe304b59e715046fdbabfa5937e8b0244f8..cd06375818e94b6e27c270c2de50a232d4e38e02 100644
--- a/Initial_Condition.py
+++ b/Initial_Condition.py
@@ -6,7 +6,7 @@
 import numpy as np
 
 
-class InitialCondition(object):
+class InitialCondition:
     """Class for initial condition function.
 
     Attributes
diff --git a/Limiter.py b/Limiter.py
index e5c1f9381f83caa04653470c799945893473178d..fbcc6aa9087c9a67ded90606258047c09a2f4711 100644
--- a/Limiter.py
+++ b/Limiter.py
@@ -5,7 +5,7 @@
 """
 
 
-class Limiter(object):
+class Limiter:
     """Class for limiting function.
 
     Methods
diff --git a/Quadrature.py b/Quadrature.py
index 23fba30932c47f3ac1c7bce9c94af16a31de2f8d..c790f9dce579d58116b423227a6e86f48c7e9418 100644
--- a/Quadrature.py
+++ b/Quadrature.py
@@ -6,7 +6,7 @@
 import numpy.polynomial.legendre as leg
 
 
-class Quadrature(object):
+class Quadrature:
     """Class for quadrature.
 
     A quadrature is used to determine the approximation of a definite integral
diff --git a/Troubled_Cell_Detector.py b/Troubled_Cell_Detector.py
index ca66c5cdb445ade49c2751e473a3b95d3f9be913..18f6cb73ec46098553c5b029cc21a20caca352f5 100644
--- a/Troubled_Cell_Detector.py
+++ b/Troubled_Cell_Detector.py
@@ -25,7 +25,7 @@ x = Symbol('x')
 z = Symbol('z')
 
 
-class TroubledCellDetector(object):
+class TroubledCellDetector:
     """Class for troubled-cell detection.
 
     Detects troubled cells, i.e., cells in the mesh containing instabilities.
diff --git a/Update_Scheme.py b/Update_Scheme.py
index aaa080b7c7d7921f281263daba33813ce2cd27fb..c44aaf55a4f5da1bdfbfa81eaeef28d45d0f1514 100644
--- a/Update_Scheme.py
+++ b/Update_Scheme.py
@@ -11,7 +11,7 @@ import numpy as np
 import time
 
 
-class UpdateScheme(object):
+class UpdateScheme:
     """Class for updating projections at a time step.
 
     Attributes