mirror of
https://github.com/Xevion/v1.xevion.dev.git
synced 2025-12-07 13:17:03 -06:00
121 lines
3.9 KiB
Python
121 lines
3.9 KiB
Python
import json
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import logging
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import os
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import dateutil.parser
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import matplotlib.pyplot as plt
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import numpy as np
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# Gets all files in tracks folder, returns them in parsed JSON
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def get_files():
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folder = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'tracks')
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files = []
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for file in os.listdir(folder):
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with open(os.path.join(os.path.join(folder, file))) as file:
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files.append(
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json.load(file)
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)
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return files
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# Simple function to combine a bunch of items from different files
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def combine_files(files):
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items = []
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for file in files:
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items.extend(file['items'])
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return items
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# Prints the data in a interesting format
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def print_data(data):
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for i, item in enumerate(data):
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date = dateutil.parser.parse(item['added_at'])
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explicit = '!' if item['track']['explicit'] else ' '
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track_name = item['track']['name']
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artists = ' & '.join(artist['name'] for artist in item['track']['artists'])
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print('[{}] {} "{}" by {}'.format(date, explicit, track_name, artists))
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def process_data(data):
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# Process the data by Month/Year, then by Clean/Explicit
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scores = {}
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for item in data:
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date = dateutil.parser.parse(item['added_at']).strftime('%b %Y')
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if date not in scores.keys():
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scores[date] = [0, 0]
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scores[date][1 if item['track']['explicit'] else 0] += 1
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# Create simplified arrays for each piece of data
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months = list(scores.keys())[::-1]
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clean, explicit = [], []
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for item in list(scores.values())[::-1]:
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clean.append(item[0])
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explicit.append(item[1])
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# Done processing date properly, start plotting work
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logging.info('Processed data, creating plot from data')
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# Weird numpy stuff
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n = len(scores.values())
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ind = np.arange(n)
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width = 0.55
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# Resizer figuresize to be 2.0 wider
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plt.figure(figsize=(10.0, 6.0))
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# Stacked Bars
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p1 = plt.bar(ind, explicit, width)
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p2 = plt.bar(ind, clean, width, bottom=explicit) # bottom= just has the bar sit on top of the explicit
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# Plot labeling
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plt.title('Song Count by Clean/Explicit')
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plt.ylabel('Song Count')
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plt.xlabel('Month')
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plt.xticks(ind, months, rotation=270) # Rotation 90 will have the
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plt.legend((p1[0], p2[0]), ('Explicit', 'Clean'))
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fig = plt.gcf() # Magic to save to image and then show
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# Save the figure, overwriting anything in your way
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logging.info('Saving the figure to the \'export\' folder')
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export_folder = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'export')
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if not os.path.exists(export_folder):
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os.makedirs(export_folder)
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plt.tight_layout()
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fig.savefig(
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os.path.join(
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export_folder,
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'export'
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# datetime.datetime.now().strftime('%Y-%m-%d %H-%M-%S')
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),
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dpi=100,
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quality=95
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)
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# Finally show the figure to
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logging.info('Showing plot to User')
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# plt.show()
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# Copy the figure to your clipboard to paste in Excel
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# logging.info('Copying the plot data to clipboard')
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# copy(months, clean, explicit)
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# Simple method for exporting data to a table like format
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# Will paste into Excel very easily
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def copy(months, clean, explicit):
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from pyperclip import copy
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top = 'Period\tClean\tExplicit\n'
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copy(top + '\n'.join([
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f'{item[0]}\t{item[1]}\t{item[2]}' for item in zip(months, clean, explicit)
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]))
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def main():
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# logging.basicConfig(level=logging.INFO)
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logging.info("Reading track files")
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files = get_files()
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logging.info(f"Read and parse {len(files)} track files")
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logging.info("Combining into single track file for ease of access")
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data = combine_files(files)
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data.sort(key=lambda item: dateutil.parser.parse(item['added_at']).timestamp(), reverse=True)
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logging.info(f'File combined with {len(data)} items')
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logging.info('Processing file...')
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process_data(data)
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