Skip to main content

design and steer profile likelihood fits

Project description

cabinetry logo

CI status Documentation Status Codecov PyPI version Conda version Python version Code style: black

DOI Scikit-HEP

cabinetry is a Python library for building and steering binned template fits. It is written with applications in High Energy Physics in mind. cabinetry interfaces many other powerful libraries to make it easy for an analyzer to run their statistical inference pipeline.

Statistical models in HistFactory format can be built by cabinetry from instructions in a declarative configuration. cabinetry makes heavy use of pyhf for statistical inference, and provides additional utilities to help study and disseminate fit results. This includes commonly used visualizations. Due to its modular approach, analyzers are free to use all of cabinetry's functionality or only some pieces. cabinetry can be used for inference and visualization with any pyhf-compatible model, whether it was built with cabinetry or not.

Installation

cabinetry can be installed with pip:

python -m pip install cabinetry

This will only install the minimum requirements for the core part of cabinetry. The following will install additional optional dependencies needed for ROOT file reading:

python -m pip install cabinetry[contrib]

Hello world

To run the following example, first generate the input files via the script utils/create_ntuples.py.

import cabinetry

config = cabinetry.configuration.load("config_example.yml")

# create template histograms
cabinetry.templates.build(config)

# perform histogram post-processing
cabinetry.templates.postprocess(config)

# build a workspace
ws = cabinetry.workspace.build(config)

# run a fit
model, data = cabinetry.model_utils.model_and_data(ws)
fit_results = cabinetry.fit.fit(model, data)

# visualize the post-fit model prediction and data
prediction_postfit = cabinetry.model_utils.prediction(model, fit_results=fit_results)
cabinetry.visualize.data_mc(prediction_postfit, data, config=config)

The above is an abbreviated version of an example included in example.py, which shows how to use cabinetry. It requires additional dependencies obtained with pip install cabinetry[contrib].

Documentation

Find more information in the documentation and tutorial material in the cabinetry-tutorials repository. cabinetry is also described in a paper submitted to vCHEP 2021: 10.5281/zenodo.4627037.

Acknowledgements

NSF-1836650

This work was supported by the U.S. National Science Foundation (NSF) cooperative agreement OAC-1836650 (IRIS-HEP).

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

cabinetry-0.6.0.tar.gz (112.9 kB view details)

Uploaded Source

Built Distribution

cabinetry-0.6.0-py3-none-any.whl (77.1 kB view details)

Uploaded Python 3

File details

Details for the file cabinetry-0.6.0.tar.gz.

File metadata

  • Download URL: cabinetry-0.6.0.tar.gz
  • Upload date:
  • Size: 112.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.16

File hashes

Hashes for cabinetry-0.6.0.tar.gz
Algorithm Hash digest
SHA256 a111fb3eb0a979555d39fda28ecc358ace6a4a052d31c52df9880d060c9a722c
MD5 62d45fe6c25e6749052c0c8cab9b82b4
BLAKE2b-256 f3a3f2932aed22f1cd94a50ea6d3a7633c2ea4d1e023ce3f81830e8e02e4f4e6

See more details on using hashes here.

File details

Details for the file cabinetry-0.6.0-py3-none-any.whl.

File metadata

  • Download URL: cabinetry-0.6.0-py3-none-any.whl
  • Upload date:
  • Size: 77.1 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.16

File hashes

Hashes for cabinetry-0.6.0-py3-none-any.whl
Algorithm Hash digest
SHA256 d46eed76143e62e801b7245b96bdd6b89f775510491451bfd513a0d3db91e783
MD5 a3473b2ed75034c3ed92bfd4d173b7c2
BLAKE2b-256 27eeb347e8a8a174688296ecd6a400a364134738d14779334bc82429debfdfe9

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page