PyCharm grand repo wide reformat

app/spotify_explicit/process.py

@@ -1,13 +1,11 @@
-import os
-import sys
 import json
 import logging
-import datetime
-import collections
-import numpy as np
+import os
+
 import dateutil.parser
-import PIL.Image as Image
 import matplotlib.pyplot as plt
+import numpy as np
+
 
 # Gets all files in tracks folder, returns them in parsed JSON
 def get_files():
@@ -20,6 +18,7 @@ def get_files():
             )
     return files
 
+
 # Simple function to combine a bunch of items from different files
 def combine_files(files):
     items = []
@@ -27,6 +26,7 @@ def combine_files(files):
         items.extend(file['items'])
     return items
 
+
 # Prints the data in a interesting format
 def print_data(data):
     for i, item in enumerate(data):
@@ -36,6 +36,7 @@ def print_data(data):
         artists = ' & '.join(artist['name'] for artist in item['track']['artists'])
         print('[{}] {} "{}" by {}'.format(date, explicit, track_name, artists))
 
+
 def process_data(data):
     # Process the data by Month/Year, then by Clean/Explicit
     scores = {}
@@ -44,7 +45,7 @@ def process_data(data):
         if date not in scores.keys():
             scores[date] = [0, 0]
         scores[date][1 if item['track']['explicit'] else 0] += 1
-    
+
     # Create simplified arrays for each piece of data
     months = list(scores.keys())[::-1]
     clean, explicit = [], []
@@ -59,17 +60,17 @@ def process_data(data):
     ind = np.arange(n)
     width = 0.55
     # Resizer figuresize to be 2.0 wider
-    plt.figure(figsize=(10.0, 6.0))    
+    plt.figure(figsize=(10.0, 6.0))
     # Stacked Bars
     p1 = plt.bar(ind, explicit, width)
-    p2 = plt.bar(ind, clean, width, bottom=explicit) # bottom= just has the bar sit on top of the explicit
+    p2 = plt.bar(ind, clean, width, bottom=explicit)  # bottom= just has the bar sit on top of the explicit
     # Plot labeling
     plt.title('Song Count by Clean/Explicit')
     plt.ylabel('Song Count')
     plt.xlabel('Month')
-    plt.xticks(ind, months, rotation=270) # Rotation 90 will have the 
+    plt.xticks(ind, months, rotation=270)  # Rotation 90 will have the
     plt.legend((p1[0], p2[0]), ('Explicit', 'Clean'))
-    fig = plt.gcf() # Magic to save to image and then show
+    fig = plt.gcf()  # Magic to save to image and then show
 
     # Save the figure, overwriting anything in your way
     logging.info('Saving the figure to the \'export\' folder')
@@ -86,7 +87,7 @@ def process_data(data):
         dpi=100,
         quality=95
     )
-    
+
     # Finally show the figure to 
     logging.info('Showing plot to User')
     # plt.show()
@@ -95,6 +96,7 @@ def process_data(data):
     # logging.info('Copying the plot data to clipboard')
     # copy(months, clean, explicit)
 
+
 # Simple method for exporting data to a table like format
 # Will paste into Excel very easily
 def copy(months, clean, explicit):
@@ -104,6 +106,7 @@ def copy(months, clean, explicit):
         f'{item[0]}\t{item[1]}\t{item[2]}' for item in zip(months, clean, explicit)
     ]))
 
+
 def main():
     # logging.basicConfig(level=logging.INFO)
     logging.info("Reading track files")
@@ -111,7 +114,7 @@ def main():
     logging.info(f"Read and parse {len(files)} track files")
     logging.info("Combining into single track file for ease of access")
     data = combine_files(files)
-    data.sort(key=lambda item : dateutil.parser.parse(item['added_at']).timestamp(), reverse=True)
+    data.sort(key=lambda item: dateutil.parser.parse(item['added_at']).timestamp(), reverse=True)
     logging.info(f'File combined with {len(data)} items')
     logging.info('Processing file...')
-    process_data(data)
+    process_data(data)