get_your_commits.py

import requests
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from datetime import datetime, timedelta

username = "lemire" # use your GH username

GITHUB_TOKEN = ''  # put your GH token
HEADERS = {
    'Authorization': f'token {GITHUB_TOKEN}',
    'Accept': 'application/vnd.github.v3+json'
}

# Fetch contribution calendar via GraphQL (weekly-ish granularity via days)
all_weeks = []
for year in range(2016, 2027):
    from_date = f"{year}-01-01T00:00:00Z"
    to_date = f"{year}-12-31T23:59:59Z"
    query = """
query {
  user(login: "%s") {
    contributionsCollection(from: "%s", to: "%s") {
      contributionCalendar {
        weeks {
          contributionDays {
            contributionCount
            date
          }
        }
      }
    }
  }
}
""" % (username, from_date, to_date)
    
    response = requests.post(
        "https://api.github.com/graphql",
        headers=HEADERS,
        json={"query": query}
    ).json()
    
    if "errors" in response:
        print(f"Erreurs GraphQL pour {year}:", response["errors"])
        continue
    
    if "data" not in response or response["data"] is None:
        print(f"Erreur dans la réponse GraphQL pour {year}:", response)
        continue
    
    weeks = response["data"]["user"]["contributionsCollection"]["contributionCalendar"]["weeks"]
    all_weeks.extend(weeks)

# Parse the data
weeks = all_weeks
data = []
for week in weeks:
    for day in week["contributionDays"]:
        if day["contributionCount"] > 0:
            data.append({
                "date": datetime.fromisoformat(day["date"]),
                "count": day["contributionCount"]
            })

df = pd.DataFrame(data)
df.set_index("date", inplace=True)

# Aggregate to weekly (resample to week start)
weekly = df.resample("W-MON")["count"].sum().fillna(0)

# Plot
plt.rcParams.update({'font.size': 24})
fig, ax = plt.subplots(figsize=(14, 6))
ax.bar(weekly.index, weekly.values, width=5, color="steelblue", alpha=0.6, label='Weekly commits')

# Median before 2022 and after 2023
cutoff_before = pd.Timestamp('2022-01-01')
cutoff_after = pd.Timestamp('2023-01-01')
median_before = weekly[weekly.index < cutoff_before].median()
median_after = weekly[weekly.index >= cutoff_after].median()

ax.hlines(median_before, weekly.index[0], cutoff_before, colors='red', linewidth=2,
          label=f'Median before 2022: {median_before:.0f}')
ax.hlines(median_after, cutoff_after, weekly.index[-1], colors='orange', linewidth=2,
          label=f'Median from 2023: {median_after:.0f}')
ax.axvline(cutoff_before, color='gray', linewidth=1, linestyle='--')
ax.axvline(cutoff_after, color='gray', linewidth=1, linestyle='--')

ax.legend(fontsize=24)
ax.xaxis.set_major_locator(mdates.YearLocator())
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y'))
ax.set_xlim(weekly.index[0], weekly.index[-1])
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

ax.set_title(f"Weekly commits – @{username}", fontsize=24)
ax.set_xlabel("Year", fontsize=24)
ax.set_ylabel("Number of commits", fontsize=24)
ax.tick_params(labelsize=24)
plt.tight_layout()
plt.savefig('contributions.png', dpi=150)
print("Graph saved to contributions.png")