Borda / pyRepoStats

Simple repository contribution statistics

Home Page:https://borda.github.io/pyRepoStats

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Repository Stats

CI testing codecov CodeFactor Language grade: Python pre-commit.ci status

This simple tool aims on open-source projects providing simple repository stats which are a bit out of scope of base Git and need some more information about issues and PRs.

Highlighted features

  • cumulative caching (no need to full download, just incremental/needed update)
  • collection of overall user contributions to issues/PRs
  • visualization of aggregated timeline of past contributions

Installation

Simple install with setuptools/pip as

pip install https://github.com/Borda/pyRepoStats/archive/main.zip

or after cloning the repository

python setup.py install

Sample usage

Let's show how to pull data from Github repository, use of the following calls

  • if you just clone this repo without installation, you need to install dependencies and call script
    pip install -r requirements.txt
    python repostats/cli.py -gh PyTorchLightning/pytorch-lightning-bolts
  • if you have already installed the package with pip or with setup.py you can call executable
    repostat -gh PyTorchLightning/pytorch-lightning-bolts -t <your-personal-token>
    or package with a pythonic way
    python -m repostats.cli -gh PyTorchLightning/pytorch-lightning-bolts
    just note that with this way usage should also consider passing -o argument for output path, otherwise all caches and results will be saved in installation folder, most likely site-packages

To simplify the token passing in each call, you can export the token to environment variables export GH_API_TOKEN=<your-personal-token> for Github.

Github use-case

For GitHub users we recommend using your personal GitHub token which significantly increases request limit per hour.

Extra options

The calls above just pull the data, to get/show some results check available options python -m repostats.cli --help

  • To see following summary table use --users_summary "merged PRs" "commented PRs" "opened issues" "commented issues" where the fist column is used for sorting rows with users:

    user merged PRs commented PRs opened issues commented issues
    williamFalcon 74 21 14 8
    Borda 42 35 4 18
    akihironitta 17 1 5 5
    ananyahjha93 14 2 6 21
    annikabrundyn 12 0 0 2
    djbyrne 11 2 4 4
    nateraw 9 1 6 8
    teddykoker 3 2 0 0
  • With --min_contribution N you can a simple filter what is the minimal number of contribution to show users in Table or Figures.

  • You can also define a time frame with --date_from and --date_to for filtering events - created issues, merged PRs and comments/reviews.

  • We also offer showing some contribution aggregation over time such as Day/Week/Month/Year, to do you use option --user_comments W which draw following double chart: (a) cumulative aggregation over all users and (b) heatmap like image with time in Y and user in X axis. Moreover, you can also specify type such as issue or PR; so with --user_comments W issue pr you can simply get two figures - one with weekly aggregation for issue and another for PRs. The very same way you can specify multiple time sampling --user_comments W M for weekly and monthly aggregations.

    User-comments-aggregation

To deny showing figures set environment variable export SHOW_FIGURES=0.

Contribution

Any help or suggestions are welcome, pls use Issues :]

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About

Simple repository contribution statistics

https://borda.github.io/pyRepoStats

License:MIT License


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Language:Python 100.0%