gimie


Namegimie JSON
Version 0.7.0 PyPI version JSON
download
home_pagehttps://github.com/sdsc-ordes/gimie
SummaryExtract structured metadata from git repositories.
upload_time2024-12-03 10:20:31
maintainerNone
docs_urlNone
authorSwiss Data Science Center
requires_python<4.0,>=3.9
licenseApache-2.0
keywords metadata git extraction linked-data
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls.
            [![gimie](docs/logo.svg)](https://github.com/sdsc-ordes/gimie)

[![PyPI version](https://badge.fury.io/py/gimie.svg)](https://badge.fury.io/py/gimie) [![Python Poetry Test](https://github.com/sdsc-ordes/gimie/actions/workflows/poetry-pytest.yml/badge.svg)](https://github.com/sdsc-ordes/gimie/actions/workflows/poetry-pytest.yml) [![docs](https://github.com/sdsc-ordes/gimie/actions/workflows/sphinx-docs.yml/badge.svg)](https://sdsc-ordes.github.io/gimie) [![Coverage Status](https://coveralls.io/repos/github/sdsc-ordes/gimie/badge.svg?branch=main)](https://coveralls.io/github/sdsc-ordes/gimie?branch=main)

Gimie (GIt Meta Information Extractor) is a python library and command line tool to extract structured metadata from git repositories.


## Context
Scientific code repositories contain valuable metadata which can be used to enrich existing catalogues, platforms or databases. This tool aims to easily extract structured metadata from a generic git repositories. It can extract extract metadata from the Git provider (GitHub or GitLab) or from the git index itself.

----------------------------------------------------------------------

Using Gimie: easy peasy, it's a 3 step process.

## 1: Installation

To install the stable version on PyPI:

```shell
pip install gimie
```

To install the dev version from github:

```shell
pip install git+https://github.com/sdsc-ordes/gimie.git@main#egg=gimie
```

Gimie is also available as a docker container hosted on the [Github container registry](https://github.com/sdsc-ordes/gimie/pkgs/container/gimie):

```shell
docker pull ghcr.io/sdsc-ordes/gimie:latest

# The access token can be provided as an environment variable
docker run -e GITHUB_TOKEN=$GITHUB_TOKEN ghcr.io/sdsc-ordes/gimie:latest gimie data <repo>
```

## 2 : Set your credentials

In order to access the github api, you need to provide a github token with the `read:org` scope.

### A. Create access tokens

New to access tokens? Or don't know how to get your Github / Gitlab token ?

Have no fear, see
[here for Github tokens](https://docs.github.com/en/enterprise-server@3.4/authentication/keeping-your-account-and-data-secure/creating-a-personal-access-token) and [here for Gitlab tokens](https://docs.gitlab.com/ee/user/profile/personal_access_tokens.html).
(Note: tokens are as precious as passwords! Treat them as such.)

### B. Set your access tokens via the Terminal

Gimie will use your access tokens to gather information for you. If you want info about a Github repo, Gimie needs your Github token; if you want info about a Gitlab Project then Gimie needs your Gitlab token.

Add your tokens one by one in your terminal:
your Github token:
```bash
export GITHUB_TOKEN=
```
and/or your Gitlab token:
```bash
export GITLAB_TOKEN=
```

## 3: GIMIE info ! Run Gimie

### As a command line tool

```shell
gimie data https://github.com/numpy/numpy
```
(want a Gitlab project instead? Just replace the URL in the command line)

### As a python library

```python
from gimie.project import Project
proj = Project("https://github.com/numpy/numpy")

# To retrieve the rdflib.Graph object
g = proj.extract()

# To retrieve the serialized graph
g_in_ttl = g.serialize(format='ttl')
print(g_in_ttl)
```
For more advanced use see [the documentation](https://sdsc-ordes.github.io/gimie/intro/usage_python.html).
## Outputs

The default output is [Turtle](https://www.w3.org/TR/turtle/), a textual syntax for [RDF](https://en.wikipedia.org/wiki/Resource_Description_Framework) data model. We follow the schema recommended by [codemeta](https://codemeta.github.io/).
Supported formats are turtle, json-ld and n-triples (by specifying the `--format` argument in your call i.e. `gimie data https://github.com/numpy/numpy --format 'ttl'`).

With no specifications, Gimie will print results in the terminal. Want to save Gimie output to a file? Add your file path to the end : `gimie data https://github.com/numpy/numpy > path_to_output/gimie_output.ttl`

----------------------------------------------------------------------

## Contributing

All contributions are welcome. New functions and classes should have associated tests and docstrings following the [numpy style guide](https://numpydoc.readthedocs.io/en/latest/format.html).

The code formatting standard we use is [black](https://github.com/psf/black), with `--line-length=79` to follow [PEP8](https://peps.python.org/pep-0008/) recommendations. We use [pytest](https://docs.pytest.org/en/7.2.x/) as our testing framework. This project uses [pyproject.toml](https://pip.pypa.io/en/stable/reference/build-system/pyproject-toml/) to define package information, requirements and tooling configuration.

### For development:

activate a conda or virtual environment with Python 3.8 or higher

```shell
git clone https://github.com/sdsc-ordes/gimie && cd gimie
make install
```

run tests:

```shell
make test
```

run checks:

```shell
make check
```
for an easier use Github/Gitlab APIs, place your access tokens in the `.env` file: (and don't worry, the `.gitignore` will ignore them when you push to GitHub)

```
cp .env.dist .env
```

build documentation:

```shell
make doc
```

## Releases and Publishing on Pypi

Releases are done via github release

- a release will trigger a github workflow to publish the package on Pypi
- Make sure to update to a new version in `pyproject.toml` before making the release
- It is possible to test the publishing on Pypi.test by running a manual workflow: go to github actions and run the Workflow: 'Publish on Pypi Test'

            

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    "description": "[![gimie](docs/logo.svg)](https://github.com/sdsc-ordes/gimie)\n\n[![PyPI version](https://badge.fury.io/py/gimie.svg)](https://badge.fury.io/py/gimie) [![Python Poetry Test](https://github.com/sdsc-ordes/gimie/actions/workflows/poetry-pytest.yml/badge.svg)](https://github.com/sdsc-ordes/gimie/actions/workflows/poetry-pytest.yml) [![docs](https://github.com/sdsc-ordes/gimie/actions/workflows/sphinx-docs.yml/badge.svg)](https://sdsc-ordes.github.io/gimie) [![Coverage Status](https://coveralls.io/repos/github/sdsc-ordes/gimie/badge.svg?branch=main)](https://coveralls.io/github/sdsc-ordes/gimie?branch=main)\n\nGimie (GIt Meta Information Extractor) is a python library and command line tool to extract structured metadata from git repositories.\n\n\n## Context\nScientific code repositories contain valuable metadata which can be used to enrich existing catalogues, platforms or databases. This tool aims to easily extract structured metadata from a generic git repositories. It can extract extract metadata from the Git provider (GitHub or GitLab) or from the git index itself.\n\n----------------------------------------------------------------------\n\nUsing Gimie: easy peasy, it's a 3 step process.\n\n## 1: Installation\n\nTo install the stable version on PyPI:\n\n```shell\npip install gimie\n```\n\nTo install the dev version from github:\n\n```shell\npip install git+https://github.com/sdsc-ordes/gimie.git@main#egg=gimie\n```\n\nGimie is also available as a docker container hosted on the [Github container registry](https://github.com/sdsc-ordes/gimie/pkgs/container/gimie):\n\n```shell\ndocker pull ghcr.io/sdsc-ordes/gimie:latest\n\n# The access token can be provided as an environment variable\ndocker run -e GITHUB_TOKEN=$GITHUB_TOKEN ghcr.io/sdsc-ordes/gimie:latest gimie data <repo>\n```\n\n## 2 : Set your credentials\n\nIn order to access the github api, you need to provide a github token with the `read:org` scope.\n\n### A. Create access tokens\n\nNew to access tokens? Or don't know how to get your Github / Gitlab token ?\n\nHave no fear, see\n[here for Github tokens](https://docs.github.com/en/enterprise-server@3.4/authentication/keeping-your-account-and-data-secure/creating-a-personal-access-token) and [here for Gitlab tokens](https://docs.gitlab.com/ee/user/profile/personal_access_tokens.html).\n(Note: tokens are as precious as passwords! Treat them as such.)\n\n### B. Set your access tokens via the Terminal\n\nGimie will use your access tokens to gather information for you. If you want info about a Github repo, Gimie needs your Github token; if you want info about a Gitlab Project then Gimie needs your Gitlab token.\n\nAdd your tokens one by one in your terminal:\nyour Github token:\n```bash\nexport GITHUB_TOKEN=\n```\nand/or your Gitlab token:\n```bash\nexport GITLAB_TOKEN=\n```\n\n## 3: GIMIE info ! Run Gimie\n\n### As a command line tool\n\n```shell\ngimie data https://github.com/numpy/numpy\n```\n(want a Gitlab project instead? Just replace the URL in the command line)\n\n### As a python library\n\n```python\nfrom gimie.project import Project\nproj = Project(\"https://github.com/numpy/numpy\")\n\n# To retrieve the rdflib.Graph object\ng = proj.extract()\n\n# To retrieve the serialized graph\ng_in_ttl = g.serialize(format='ttl')\nprint(g_in_ttl)\n```\nFor more advanced use see [the documentation](https://sdsc-ordes.github.io/gimie/intro/usage_python.html).\n## Outputs\n\nThe default output is [Turtle](https://www.w3.org/TR/turtle/), a textual syntax for [RDF](https://en.wikipedia.org/wiki/Resource_Description_Framework) data model. 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