Commit frequency plotter

analyse_commit_info.py

# load commit_data.csv
from typing import List, Optional

import pandas as pd

df = pd.read_csv("commit_data.csv")

# filter where username is Palash Tyagi or Spalash

df = df[(df["username"] == "Palash Tyagi") | (df["username"] == "Spalash")]

# get the most active hours 

# time_of_day is a string HH:MM, just use the hour part

df["hour"] = df["time_of_day"].str.split(":").str[0]

# group by hour and sum the additions and deletions

df = df.groupby("hour").sum(numeric_only=True)

# plot the results

# drop total column
df = df.drop(columns="total")

import matplotlib.pyplot as plt

df.plot(kind="bar")
plt.show()

get_commit_info.py

import pygit2
import pandas as pd
from datetime import datetime

# Path to your local git repository
repo_path = "C:/Users/PalashTyagi/Code/repo/repo"

# Initialize the repository object
repo = pygit2.Repository(repo_path)

# Prepare a list to hold commit data
commits_data = []

# Iterate through the repository's commits
for commit in repo.walk(
    repo.head.target, pygit2.GIT_SORT_TOPOLOGICAL | pygit2.GIT_SORT_REVERSE
):
    additions = 0
    deletions = 0

    # For each commit, calculate additions and deletions by diffing with its parent
    if commit.parents:
        parent_commit = commit.parents[0]
        diff = repo.diff(parent_commit, commit)

        for patch in diff:
            for hunk in patch.hunks:
                for line in hunk.lines:
                    if line.origin == "+":
                        additions += 1
                    elif line.origin == "-":
                        deletions += 1
    # Note: This simplistic approach may not account for all edge cases in diff analysis

    # Append commit data to the list
    commits_data.append(
        {
            "sha": commit.hex,
            "time": datetime.fromtimestamp(commit.commit_time),
            # time of day - hour and minute
            "time_of_day": datetime.fromtimestamp(commit.commit_time).strftime("%H:%M"),
            "additions": additions,
            "deletions": deletions,
            "total": additions + deletions,
            "username": commit.author.name,
        }
    )

# Convert the list to a DataFrame
df = pd.DataFrame(commits_data)
# save the dataframe to a csv file

# save to ./commit_data.csv
df.to_csv("commit_data.csv", index=False)

output.md

Productivity by hours