diff --git a/ANN_Data_Generator.py b/ANN_Data_Generator.py
index e858ad33da207b361604d56d1236e01190c475ed..37f5b44675ddd671fc625273f78ca34561c13060 100644
--- a/ANN_Data_Generator.py
+++ b/ANN_Data_Generator.py
@@ -11,13 +11,13 @@ TODO: Fix bug in initialization of input matrix -> Done
 TODO: Improve function selection (more even distribution) -> Done
 TODO: Add documentation -> Done
 TODO: Improve comments -> Done
+TODO: Remove unnecessary lines -> Done
 
 """
 
 import numpy as np
 import os
 
-import Initial_Condition
 import DG_Approximation
 import timeit
 
@@ -237,10 +237,6 @@ class TrainingDataGenerator(object):
             Length of cell in grid.
 
         """
-        # Calculating Cell centers for a given 1D domain with n elements, and
-        # Calculating Corresponding Legendre Basis Coefficients for given polynomial_degree
-        # Create stencil and basis_coefficients for smooth_function mapped onto stencil
-
         # Select random cell length
         grid_spacing = 2 / (2 ** np.random.randint(3, high=9, size=1))
 
@@ -287,13 +283,3 @@ class TrainingDataGenerator(object):
         for key in data.keys():
             name = self._data_dir + '/' + key + '_data.npy'
             np.save(name, data[key])
-
-
-# Get Training/Validation Datasets
-np.random.seed(1234)
-# generator = TrainingDataGenerator(functions, left_bound=boundary[0], right_bound=boundary[1])
-# generator = TrainingDataGenerator(functions, left_bound=boundary[0], right_bound=boundary[1])
-
-sample_number = 1000
-# data_1 = generator.build_training_data(sample_number, 0)
-# data_2 = generator.build_training_data(sample_number, 1)