You can use this package to parse SigmaBAM files in openBIS. There are two resources used for automating the parsing of such files:

1. Using the Python script in tutorials/parser_tutorial.py. This script uses the parser class defined in src/sigmabam2openbis/parser.py and the bam-masterdata parser infrastructure. We recommend using this tutorial. To run it, after setting up your environment (see below), run in the terminal python tutorials/parser_script.py.
2. Using the Jupyter notebook from tutorials/chemical_data_inventory_openbis_mapper_1_0.ipynb. This tutorial uses PyBIS.

Development

If you want to develop locally this package, clone the project and enter in the workspace folder:

git clone https://github.com/BAMresearch/sigmabam2openbis.git
cd sigmabam2openbis

Create a virtual environment (you can use Python>=3.10) in your workspace:

• Using venv:

  python3 -m venv .venv
  source .venv/bin/activate  # in Windows (cmd): source .venv\Scripts\activate

• Using conda:

  conda create --name .venv pip
  conda activate .venv

Install the package in editable mode (with the flag -e):

pip install --upgrade pip
pip install -e '.[dev]'

Note: In order to install faster the package, you can use uv for pip installing Python packages:

pip install --upgrade pip
pip install uv
uv pip install -e '.[dev]'

Run the tests

You can locally run the tests by doing:

python -m pytest -sv tests

where the -s and -v options toggle the output verbosity.

You can also generate a local coverage report:

python -m pytest --cov=src tests

Run auto-formatting and linting

We use Ruff for formatting and linting the code following the rules specified in the pyproject.toml. You can run locally:

ruff check .

This will produce an output with the specific issues found. In order to auto-fix them, run:

ruff format .

If some issues are not possible to fix automatically, you will need to visit the file and fix them by hand.