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Contributor Guidelines

This repository is designed to map information from different acquisition machines to aind-data-schema models, and is therefore shared amongst multiple teams.

This document will go through best practices for contributing to this project.

Issues and Feature Requests

Questions, feature requests and bug reports are all welcome as discussions or issues. Please use the provided templates to ensure we have enough information to work with.

NOTE: If your package requires upgrading aind-data-schema, create a separate ticket and a dedicated engineer will handle the upgrade.

Installation and Development

To develop the software, clone the repository and create a new branch for your changes. Please do not fork this repository unless you are an external developer.

git clone [email protected]:AllenNeuralDynamics/aind-metadata-mapper.git
git checkout -b my-new-feature-branch

Then in bash, run

pip install -e .[dev]

to set up your environment. Once these steps are complete, you can get developing🚀.

Project Organization

This codebase is organized by acquisition machine, so if you're adding a new machine, create a new directory. Otherwise, put your code in its corresponding directory. Your package should abide to the following structure:

acquisition_machine/
|
├──__init__.py
├──{desired_model_type}.py (ex: session.py)
├──models.py

Please define a JobSettings class in the models module which will be used by your ETL to produce the desired model. The JobSettings in the bergamo dir can be used as reference.

Unit Testing

Testing is required to open a PR in this repository to ensure robustness and reliability of our codebase.

  • 1:1 Correspondence: Structure unit tests in a manner that mirrors the module structure.
    • For every package in the src directory, there should be a corresponding test package.
    • For every module in a package, there should be a corresponding unit test module.
    • For every method in a module, there should be a corresponding unit test.
  • Mocking Writes: Your unit tests should not write anything out. You can use the unittest.mock library to intercept file operations and test your method without actually creating a file.
  • Test Coverage: Aim for comprehensive test coverage to validate all critical paths and edge cases within the module. To open a PR, you will need at least 80% coverage.
    • Please test your changes using the coverage library, which will run the tests and log a coverage report: 
      coverage run -m unittest discover && coverage report
      To open the coverage report in a browser, you can run 
      coverage html
      and find the report in the htmlcov/index.html.

There are several libraries used to run linters and check documentation. We've included these in the development package. You can run them as described here.

Integration Testing

To ensure that an ETL runs as expected against data on the VAST, you can run an integration test locally by pointing to the input directory on VAST. For example, to test the 'bergamo' package:

    python tests/integration/bergamo/session.py --input_source "/allen/aind/scratch/svc_aind_upload/test_data_sets/bergamo" IntegrationTestBergamo

Linters

There are several libraries used to run linters, check documentation, and run tests.

  • Please test your changes using the coverage library, which will run the tests and log a coverage report:
coverage run -m unittest discover && coverage report
  • Use interrogate to check that modules, methods, etc. have been documented thoroughly:
interrogate .
  • Use flake8 to check that code is up to standards (no unused imports, etc.):
flake8 .
  • Use black to automatically format the code into PEP standards:
black .
  • Use isort to automatically sort import statements:
isort .

Pull requests

For internal members, please create a branch. For external members, please fork the repository and open a pull request from the fork. We'll primarily use Angular style for commit messages. Roughly, they should follow the pattern:

<type>(<scope>): <short summary>

where scope (optional) describes the packages affected by the code changes and type (mandatory) is one of:

  • build: Changes that affect build tools or external dependencies (example scopes: pyproject.toml, setup.py)
  • ci: Changes to our CI configuration files and scripts (examples: .github/workflows/ci.yml)
  • docs: Documentation only changes
  • feat: A new feature
  • fix: A bugfix
  • perf: A code change that improves performance
  • refactor: A code change that neither fixes a bug nor adds a feature
  • test: Adding missing tests or correcting existing tests

Semantic Release

The table below, from semantic release, shows which commit message gets you which release type when semantic-release runs (using the default configuration):

Commit message Release type
fix(pencil): stop graphite breaking when too much pressure applied Patch Fix Release, Default release
feat(pencil): add 'graphiteWidth' option Minor Feature Release
perf(pencil): remove graphiteWidth option

BREAKING CHANGE: The graphiteWidth option has been removed.
The default graphite width of 10mm is always used for performance reasons.		 Major Breaking Release
(Note that the BREAKING CHANGE: token must be in the footer of the commit)