Pythonic class collection that helps you structure external data from LHC / HEP experiments.
Project description
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
analyses,
MC campaigns,
datasets,
physics process and cross sections,
channels,
categories,
variables, and
systematic shifts.
Getting started
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 binder:
You can find the full API documentation on readthedocs.
Installation and dependencies
Install order via pip:
pip install order
The only dependencies are scinum and six, which are installed with the above command.
Contributing and testing
If you like to contribute, I’m happy to receive pull requests. 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, 3.7 and 3.8. To run tests in a docker container, do
# run the tests
./tests/docker.sh python:3.8
# or interactively by adding a flag "1" to the command
./tests/docker.sh python:3.8 1
> pip install -r requirements.txt
> python -m unittest tests
In addition, PEP 8 compatibility should be checked with flake8:
flake8 order tests setup.py
Development
Source hosted at GitHub
Report issues, questions, feature requests on GitHub Issues
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