plot.py

import matplotlib.pyplot as plt
import json
import pandas as pd
import calendar
from datetime import datetime
from style import set_style

set_style(plt)

year = datetime.now().year

with open(f'data/{year}.json', 'r') as file:
    data = json.load(file)

# Skip first date
if year == 2025:
    first_date = sorted(data.keys())[0]
    del data[first_date]

# Per month
df = pd.DataFrame({'date': pd.to_datetime(list(data.keys())),
                   'downloads': list(data.values())
                   })
# Fill missing days with 0
df = df.set_index('date')
full_date_range = pd.date_range(start=pd.to_datetime(f'{year}-01-01'), end=pd.Timestamp.now(), freq='D')
df = df.reindex(full_date_range, fill_value=0)

df['month'] = df.index.to_period('M')
df['month_name'] = df.index.strftime('%b')
monthly_data = df.groupby('month')['downloads'].sum().reset_index()
monthly_data = monthly_data.sort_values(by='month')
monthly_data['month_name'] = monthly_data['month'].apply(lambda x: calendar.month_abbr[x.month])

fig, ax = plt.subplots(figsize=(10, 6))
bars = ax.bar(monthly_data['month_name'], monthly_data['downloads'], color='skyblue')
ax.bar_label(bars, label_type='edge', fmt='{:,.0f}') # Customize format as needed
plt.title(f'Monthly downloads of FAST GitHub releases {year}')
plt.tight_layout()
fig.savefig(f'plots/{year}.svg')
#plt.show()