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Pythonic class collection that helps you structure external data from LHC / HEP experiments.

Project description

order logo
Build status Documentation status Package version License Open in binder

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:

Open in 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 and 3.7. To run tests in a docker container, do

# run the tests
./tests/docker.sh python:3.7

# or interactively by adding a flag "1" to the command
./tests/docker.sh python:3.7 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

Project details


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