malariagen_data 9.0.0

A package for accessing and analysing MalariaGEN data.

malariagen_data - analyse MalariaGEN data from Python

This Python package provides methods for accessing and analysing data from MalariaGEN.

Installation

The malariagen_data Python package is available from the Python package index (PyPI) and can be installed via pip, e.g.:

pip install malariagen-data

Documentation

Documentation of classes and methods in the public API are available from the following locations:

• Ag3 API docs

• Amin1 API docs

• Pf7 API docs

• Pv4 API docs

Release notes (change log)

See GitHub releases for release notes.

Developer setup

To get setup for development, see this video and the instructions below.

Fork and clone this repo:

git clone [e-mail]:[username]/malariagen-data-python.git

Install Python, e.g.:

sudo add-apt-repository ppa:deadsnakes/ppa
sudo apt install python3.9 python3.9-venv

Install pipx, e.g.:

python3.9 -m pip install --user pipx
python3.9 -m pipx ensurepath

Install poetry, e.g.:

pipx install poetry==1.8.2 --python=/usr/bin/python3.9

Create development environment:

cd malariagen-data-python
poetry use 3.9
poetry install

Activate development environment:

poetry shell

Install pre-commit and pre-commit hooks:

pipx install pre-commit --python=/usr/bin/python3.9
pre-commit install

Run pre-commit checks (isort, black, blackdoc, flake8, ...) manually:

pre-commit run --all-files

Run fast unit tests using simulated data:

poetry run pytest -v tests/anoph

To run legacy tests which read data from GCS, you'll need to install the Google Cloud CLI. E.g., if on Linux:

./install_gcloud.sh

You'll then need to obtain application-default credentials, e.g.:

./google-cloud-sdk/bin/gcloud auth application-default login

Once this is done, you can run legacy tests:

poetry run pytest --ignore=tests/anoph -v tests

Tests will run slowly the first time, as data required for testing will be read from GCS. Subsequent runs will be faster as data will be cached locally in the "gcs_cache" folder.

Release process

Create a new GitHub release. That's it. This will automatically trigger publishing of a new release to PyPI and a new version of the documentation via GitHub Actions.

The version switcher for the documentation can then be updated by modifying the docs/source/_static/switcher.json file accordingly.