Contrib

A python package for making stacked area plots of contributions to a git repository over time. Plots can show contributions by authors, or by organizations.

Installation using pip
Usage on your local machine
Docker container usage, locally or via GitHub Actions
Related Projects of interest
License information.

Installation

contrib is on PyPI, so you can just pip install it:

$ pip install contrib

Alternately, you can clone this project, add its directory to your PYTHONPATH, and add the bin directory to your PATH.

Usage

To use contrib, you'll need to create a configuration file telling it where to find your code. Below is an example for Spack; you can find a complete example in the spack-contributions repo.

Here's an example contrib.yaml:

contrib:
  # Path to your git repository. to run git blame on.
  # Consider making this a git submodule.
  repo:   ./spack

  # JSON file mapping authors to organizations (optional)
  orgmap: ./author-to-org.json

  # Separate parts of the repository to process (optional).  For each
  # commit, contrib will look for files that match the patterns in each
  # part.  For a simple repo, you may only need one regular expression
  # per part.  In Spack, the packages have moved around in the repo over
  # time, so we provide multiple patterns.  Contrib will use the first
  # pattern matched by any file in each commit.
  parts:
    packages:
      - ^var/spack/repos/builtin/packages/.*\.py$
      - ^var/spack/packages/.*\.py$
      - ^lib/spack/spack/packages/.*\.py$

The repo needs to be in your local filesystem, preferably in the same directory as contrib.yaml. orgmap is optional (see below for how to generate it). parts is also optional; if you do not specify it, there will be one part called all that matches everything:

    parts:
      all:
        - ^.*$

You can name your parts anything; see the example above for how to model a repository where different logical parts have moved around in subdirectories.

Mapping authors to organizations

The orgmap (author-to-org.json in the example above) is optional. If you choose to provide it, it should be simple json dictionary mapping authors to organizations:

{
  "Author 1": "UIUC",
  "Author 2": "LBL",
  ...
  "Author N": "LLNL"
}

You can run contrib --update-org-map to generate an orgmap to start with. contrib will look at your repository's history and generate the file automatically:

$ contrib --update-org-map
==> Added 503 new authors to 'author-to-org.json'
==> New orgmap file created in 'author-to-org.json'.
==> Add it to './contrib.yaml' like this:

    contrib:
        orgmap: author-to-org.json

If you then add this file to your contrib.yaml, you can update it later as your repository evolves:

$ contrib --update-org-map
==> Added 10 new authors to 'author-to-org.json'

Newly added authors will be labeled as unknown <email from git> in the json file:

  "Author 1": "unknown <foo@bar.com>",
  "Author 2": "unknown <444532+someusername@users.noreply.github.com>",
  "Author 3": "unknown <user@example.com>",

You can replace these with valid organizations, or just leave them and they'll show up as "unknown" in the contrib plots.

Running

Once you've got all of that set up, you can run contrib in the directory where contrib.yaml lives:

$ ls
author-to-org.json  contrib.yaml
$ contrib
==> Indexing 49 commits.

STARTED       0/49 53ab298e88f80454f7f7c20ef200a3dbd0870473
    packages: processed 45/3487 blames (9.04/s)
...

By default, contrib will sample 50 commits from your repository and plot them. If you want it to plot fewer samples, you can run contrib --samples SAMPLES where SAMPLES is a number of your choosing. contrib tries to use the available processors on the machine it is run, and by default it will run parallel git blame jobs. You can control the parallelism with the --jobs JOBS argument.

contrib has to run git blame for each sampled commit and for each file matched by the parts section of your contrib.yaml file (or for all files if parts is not provided), so it can take a long time to run if your repo's history is long. contrib's output shows how many git blame calls remain and how fast blames are currently completing.

Cached data

contrib caches results of git blame in a directory called line-data. For large repositories, this can get to be quite large, so make sure you have a decent amount of space available (gigabytes for large repositories).

Docker

If you don't want to worry about installing dependencies, you can build a local Docker container as follows:

$ docker build -t spack/contrib .

The entrypoint to the container is intended to be run as a GitHub action, however you can provide the same input arguments via environment variables to achieve the same functionality.

Entrypoint

By default, the entrypoint is the entrypoint.sh provided in the repository, so it expects environment variables defined with your inputs and GitHub token:

docker run -it --env GITHUB_TOKEN=$GITHUB_TOKEN \
               --env INPUT_REPO=https://github.com/spack/spack \
               --env INPUT_SAMPLES=2 \
               --env INPUT_FILE=contrib.yaml \
               --env INPUT_FORMAT=pdf \
               --env INPUT_AUTHORS=true \
               --env INPUT_WORKDIR=examples spack/contrib

However you could easily change the entrypoint to interact with contrib directly:

docker run -it --entrypoint /opt/conda/bin/contrib spack/contrib

GitHub Actions

It might be the case that you instead want to run a GitHub action so that the graphic is generated on pull requests, or even as a scheduled task. You can see the .github/workflows/generate-contrib-graphic.yml that is set up to run with this repository and basically:

Defines the input files (authors and yaml) to be in the working directory examples
Clones the spack repository
Runs contrib with all settings set to generate a pdf
Saves a graphic as output, and caches the line-data folder

The GitHub action is defined to exist for this repository, meaning that it's metadata is defined in action.yml and built from the included Dockerfile that we've been using. When you want to generate the action for your repository, you can reference it here. See the examples section below for the full example. A full table of variables that you can use is provided here:

Inputs

| variable name | variable type | variable description | |---------------|----------------------------------------------|------------------------------------------------------------------| | repo | optional | A url to clone, if the repository isn't already in workdir | | samples | optional | Number of commits to sample (default is 2) | | workdir | optional | Change to this working directory before clone or execution | | file | optional | Path to contrib.yaml (default) with configuration and settings | | format | optional | Format of output file (one of png, svg, jpg, gif, pdf) | | authors | optional | If set, update from authors file referenced in config file | | topn | optional | Number of contributions before collapsing into 'other' | | verbose | optional | Print verbose output for generation |

Outputs

| variable name | variable type | variable description | |---------------|-----------------------------------------------------|--------------------------------------------------------------------| | plot | optional to use | the path to the generated output ${{ steps.<step>.outputs.plot }}|

See the example above, or the one provided with the repository in .github/workflows for another example. The action above will generate the plot, and it's up to you to decide what to do with it. You might:

save as an artifact for manual download
open a pull request to another repository
push directly to a branch

Example

The following example will reference just one commit from spack, and generate files called loc-in-packages-by-*.pdf that we upload as an artifact. We will also print verbose output, and using the files in the examples folder, generate a graphic. This run takes about 6 minutes for the graphic generation, and a few more for building the container and uploading the artifact. Here is an example step that uses the master branch.

Contrib

Install / Use

README

Contrib

Installation

Usage

Mapping authors to organizations

Running

Cached data

Docker

Entrypoint

GitHub Actions

Inputs

Outputs

Example