Adding type hints to the functions in these scripts

app.py

@@ -10,7 +10,8 @@ from Data_Generation.Piecewise_Box_Functions import basic_box_array, forward_sla
 from Data_Plotting.Plot_TSNE import TSNE_reduction, plot_dimensionality_reduction
 ########################################################################################################################
 # User Inputs
-image_size = st.slider('Select a value for the image size', min_value=9, max_value=16)
+image_size = st.slider('Select a value for the image size', min_value=9, max_value=16)  # Max value is limited due to
+# computational limitations of streamlit
 
 density_selection = st.slider('Select a value for the number of equally spaced density values (0, 1]', min_value=1, max_value=10)
 ########################################################################################################################
@@ -35,11 +36,10 @@ for i in [1, 2, 3, 4]:
 st.write("Click 'Generate Samples' to show some density values that would exist in your dataset:")
 
 # Show samples of various density values
-
 if st.button('Generate Samples'):  # Generate the samples
     plt.figure(1)
     st.header("Sample Density Figures:")
-    max_figures = min(density_selection, 5)
+    max_figures = min(density_selection, 5)  # Determine the number of figures to display
     for i in range(max_figures):
         plt.subplot(1, max_figures+1, i+1), plt.imshow(sample_density[i], cmap='gray', vmin=0, vmax=1)
         if i != 0:  # Show y-label for only first figure
@@ -69,22 +69,21 @@ if st.button('Generate Samples'):  # Generate the samples
 # Output Entire Dataset
 st.write("Click 'Generate Dataset' to generate the dataset based on the conditions set previously:")
 if st.button('Generate Dataset'):  # Generate the dataset
-    boxes = make_boxes(image_size, densities) # Create all the data points
-    # Unpack all of the data
+    boxes = make_boxes(image_size, densities)  # Create all the data points
+
+    # Unpack all the data
     box_arrays, box_density, basic_box_thickness, forward_slash_box_thickness, back_slash_box_thickness,hot_dog_box_thickness, hamburger_box_thickness\
         = list(zip(*boxes))[0], list(zip(*boxes))[1], list(zip(*boxes))[2], list(zip(*boxes))[3], list(zip(*boxes))[4], list(zip(*boxes))[5], list(zip(*boxes))[6]
 
     # Plot TSNE of the data
-    # define a function to flatten a 2D array
-    def flatten_array(array):
+    # Determine the labels of the TSNE Plot
+    def flatten_array(array):  # define a function to flatten a 2D array
         return array.flatten()
-
     # apply the flatten_array function to each array in the list and create a list of flattened arrays
     flattened_arrays = np.array([flatten_array(a) for a in box_arrays])
     # calculate the average density for each array
     avg_density = np.sum(flattened_arrays, axis=1)/(np.shape(box_arrays[0])[0]*np.shape(box_arrays[0])[1])
 
-
     # Perform the TSNE Reduction
     x, y, title, embedding = TSNE_reduction(flattened_arrays)
     plt.figure(3)
@@ -96,7 +95,7 @@ if st.button('Generate Dataset'):  # Generate the dataset
     plt.figure(3)
     st.pyplot(plt.figure(3))
 
-
+    # Create a class to read the information from the generated CSV file
     class NumpyArrayEncoder(JSONEncoder):
         def default(self, obj):
             if isinstance(obj, np.ndarray):
@@ -104,7 +103,7 @@ if st.button('Generate Dataset'):  # Generate the dataset
             return JSONEncoder.default(self, obj)
 
     # Save the arrays in a JSON format so they can be read
-    box_arrays = [json.dumps(x, cls=NumpyArrayEncoder) for x in box_arrays]  # find argument for json dumps for numpy
+    box_arrays = [json.dumps(x, cls=NumpyArrayEncoder) for x in box_arrays]
 
     # Create a dataframe to convert the data to a csv file
     dataframe = (pd.DataFrame((box_arrays, box_density, basic_box_thickness, forward_slash_box_thickness,