Pythonic class collection that helps you structure external data from LHC / HEP experiments.
If you're designing a high-energy physics analysis (e.g. with data recorded by an LHC experiment at CERN, manual bookkeeping of external data can get complicated quite fast. order provides a pythonic class collection that helps you structuring
- MC campaigns,
- physics process and cross sections,
- variables, and
- systematic shifts.
See the intro.ipynb notebook for an introduction to the most important classes and an example setup of a small analysis. You can also run the notebook interactively on colab or binder:
You can find the full API documentation on readthedocs.
Projects using order
Installation and dependencies
Install order via pip:
pip install order
Contributing and testing
If you like to contribute, feel free to open a pull request 🎉. Just make sure to add new test cases and run them via:
python -m unittest tests
In general, tests should be run for Python 2.7, 3.6 - 3.11. To run tests in a docker container, do
# run the tests ./tests/docker.sh python:3.9 # or interactively by adding a flag "1" to the command ./tests/docker.sh python:3.9 1 > pip install -r requirements.txt > python -m unittest tests
flake8 order tests setup.py
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