Found error in reading of output files. Need to edit how to output the files

Data_Generation/Dataset_Generation_Functions.py

@@ -71,43 +71,13 @@ def make_boxes(image_size, densities):
 
 
 ########################################################################################################################
-image_size = 9
-densities = [0.2, 0.4, 0.6, 0.8, 1]
-boxes = make_boxes(image_size, densities)
-
-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]
-print(type(box_arrays[0]))
-
-
-class NumpyArrayEncoder(JSONEncoder):
-    def default(self, obj):
-        if isinstance(obj, np.ndarray):
-            return obj.tolist()
-        return JSONEncoder.default(self, obj)
-
-
-box_arrays = [json.dumps(x, cls=NumpyArrayEncoder) for x in box_arrays] # find argument for json dumps for numpy
-
-# 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, back_slash_box_thickness,hot_dog_box_thickness, hamburger_box_thickness)).T).astype(str)
-
-# Rename the columns to the desired outputs
-dataframe = dataframe.rename(columns={0: "Array", 1: "Density", 2:"Basic Box Thickness", 3:"Forward Slash Strut Thickness", 4:"Back Slash Strut Thickness", 5:"Vertical Strut Thickness", 6:"Horizontal Strut Thickness"})
-
-csv = dataframe.to_csv('2D_Lattice.csv')
-
-
+# How to read the files
+'''
 df = pd.read_csv('2D_Lattice.csv')
-# print(df)
-row = 0
+print(np.shape(df))
+row = 1
 box = df.iloc[row, 1]
-print(box)
 array = np.array(json.loads(box))
-plt.imshow(array)
+plt.imshow(array, vmin=0, vmax=1)
 plt.show()
-
-# import matplotlib.pyplot as plt
-# plt.imshow(np.array(box))
-# plt.show()
-# print(df)
+'''

app.py

@@ -1,14 +1,15 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
-
+import json
+from json import JSONEncoder
 import streamlit as st
+
 from Data_Generation.Dataset_Generation_Functions import make_boxes
 from Data_Generation.Piecewise_Box_Functions import basic_box_array, forward_slash_array, combine_arrays, add_thickness
 ########################################################################################################################
 # User Inputs
 image_size = st.slider('Select a value for the image size', min_value=9, max_value=16)
-# st.write(x, 'squared is', x * x)
 
 density_selection = st.slider('Select a value for the number of equally spaced density values (0, 1]', min_value=1, max_value=10)
 ########################################################################################################################
@@ -19,27 +20,19 @@ densities = np.linspace(0, 1, num=density_selection+1)[1:]
 sample_basic_box = basic_box_array(image_size, 1)
 sample_forward_slash_box = forward_slash_array(image_size, 1)
 sample_combined = combine_arrays([sample_forward_slash_box, sample_basic_box])
-# print(sample_combined)
 
 sample_density = np.array([sample_combined * density_value for density_value in densities])
-
-# copy = sample_combined
 sample_thickness = []
-# test = add_thickness(copy, 2)
-# sample_thickness.append(test)
-# print(sample_thickness)
+
 for i in [1, 2, 3, 4]:
     copy = sample_combined
-    # print(i)
     test = add_thickness(copy, i)
-    # print(test)
     sample_thickness.append(test)
-    # print(sample_thickness)
+
 ########################################################################################################################
 # Output Example Shapes
 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
@@ -52,6 +45,7 @@ if st.button('Generate Samples'):  # Generate the samples
             plt.tick_params(left=False, labelleft=False)
         plt.title("Density: " + str(round(densities[i], 4)), fontsize=6)
     plt.figure(1)
+    # These settings can be used to display a colorbar
     # cax = plt.axes([0.85, 0.1, 0.075, 0.8])
     # plt.colorbar(cax=cax, shrink=0.1)
     st.pyplot(plt.figure(1))
@@ -65,6 +59,7 @@ if st.button('Generate Samples'):  # Generate the samples
             plt.tick_params(left=False, labelleft=False)
         plt.title("Thickness: " + str(i+1), fontsize=6)
     plt.figure(2)
+    # These settings can be used to display a colorbar
     # cax = plt.axes([0.85, 0.1, 0.075, 0.8])
     # plt.colorbar(cax=cax, shrink=0.1)
     st.pyplot(plt.figure(2))
@@ -72,19 +67,34 @@ 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 samples
-    boxes = make_boxes(image_size, densities)
+if st.button('Generate Dataset'):  # Generate the dataset
+    boxes = make_boxes(image_size, densities) # Create all of the data points
+    # Unpack all of 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]
 
+    class NumpyArrayEncoder(JSONEncoder):
+        def default(self, obj):
+            if isinstance(obj, np.ndarray):
+                return obj.tolist()
+            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
+
     # 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, back_slash_box_thickness,hot_dog_box_thickness, hamburger_box_thickness)).T).astype(str)
+    dataframe = (pd.DataFrame((box_arrays, box_density, basic_box_thickness, forward_slash_box_thickness,
+                               back_slash_box_thickness, hot_dog_box_thickness, hamburger_box_thickness)).T).astype(str)
 
     # Rename the columns to the desired outputs
-    dataframe = dataframe.rename(columns={0: "Array", 1: "Density", 2:"Basic Box Thickness", 3:"Forward Slash Strut Thickness", 4:"Back Slash Strut Thickness", 5:"Vertical Strut Thickness", 6:"Horizontal Strut Thickness"})
+    dataframe = dataframe.rename(
+        columns={0: "Array", 1: "Density", 2: "Basic Box Thickness", 3: "Forward Slash Strut Thickness",
+                 4: "Back Slash Strut Thickness", 5: "Vertical Strut Thickness", 6: "Horizontal Strut Thickness"})
 
+    # Convert the dataframe to CSV
     csv = dataframe.to_csv()
-    st.write("Here is what the generated data looks like (double click on the 'Array' cells to view the full array):")
-    st.write(dataframe)  # Display the data generated
+
+    st.write("Here is what a portion of the generated data looks like (double click on the 'Array' cells to view the full array):")
+    st.write(dataframe.iloc[:100,:])  # Display the data generated
     st.write("Click 'Download' to download a CSV file of the dataset:")
-    st.download_button("Download Dataset", csv, file_name='2D_Lattice.csv')
+    st.download_button("Download Dataset", csv, file_name='2D_Lattice.csv')  # Provide download for user