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CONTRIBUTING.md

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Contributing

The datapackage-pipelines project accepts contributions via GitHub pull requests. This document outlines the process to help get your contribution accepted.

The project follows the Open Knowledge International coding standards.

We welcome adding new processors to the standard library, the following guidelines will improve the chances of your processor being accepted:

  • The processor has practical and common use-cases.
  • Minimal new dependencies - preferably, no new dependencies.

Getting Started

Recommended way to get started is to create and activate a project virtual environment.

You should ensure you are using a supported Python version, you can check the .travis.yml to see which versions we use for CI.

  • Pythonz can be used to install a specific Python version.
  • Virtualenvwrapper can help setting up and managing virtualenvs

To install package and development dependencies into active environment:

$ make install

Lint & Test

Before pushing code you should ensure lint and tests pass otherwise build will fail and your Pull request won't be merged :(

You can use the following snippet to ensure everything works:

make install && make lint && make test

Linting

To lint the project codebase:

$ make lint

Under the hood pylama configured in pylama.ini is used. On this stage it's already installed into your environment and could be used separately with more fine-grained control as described in documentation - https://www.pylint.org/.

For example to check only errors:

$ pylanma

Testing

To run tests with coverage:

$ make test

Under the hood tox powered by py.test and coverage configured in tox.ini is used. It's already installed into your environment and could be used separately with more fine-grained control as described in documentation - https://testrun.org/tox/latest/.

For example to check subset of tests against Python 3 environment with increased verbosity. All positional arguments and options after -- will be passed to py.test:

tox -e py35 -- -v tests/<path>

Testing with other databases

By default the tests run with sqlite in-memory database which doesn't require any setup. However, most projects will want to use a real DB, like PostgreSQL.

To run the tests with a different DB, you need to supply the connection string via environment variable. For example, to run with local postgresql databsae:

OVERRIDE_TEST_DB=postgresql://postgres:123456@localhost:5432/postgres py.test