.. image:: https://raw.githubusercontent.com/scikit-hep/pyhf/main/docs/_static/img/pyhf-logo.svg :alt: pyhf logo :width: 320 :align: center

pure-python fitting/limit-setting/interval estimation HistFactory-style

The HistFactory p.d.f. template [CERN-OPEN-2012-016 <https://cds.cern.ch/record/1456844>__] is per-se independent of its implementation in ROOT and sometimes, it’s useful to be able to run statistical analysis outside of ROOT, RooFit, RooStats framework.

This repo is a pure-python implementation of that statistical model for multi-bin histogram-based analysis and its interval estimation is based on the asymptotic formulas of “Asymptotic formulae for likelihood-based tests of new physics” [arXiv:1007.1727 <https://arxiv.org/abs/1007.1727>__]. The aim is also to support modern computational graph libraries such as PyTorch and TensorFlow in order to make use of features such as autodifferentiation and GPU acceleration.

.. Comment: JupyterLite segment goes here in docs

User Guide

For an in depth walkthrough of usage of the latest release of pyhf visit the |pyhf tutorial|_.

.. |pyhf tutorial| replace:: pyhf tutorial .. _pyhf tutorial: https://pyhf.github.io/pyhf-tutorial/

Hello World

This is how you use the pyhf Python API to build a statistical model and run basic inference:

.. code:: pycon

import pyhf pyhf.set_backend("numpy") model = pyhf.simplemodels.uncorrelated_background( ... signal=[12.0, 11.0], bkg=[50.0, 52.0], bkg_uncertainty=[3.0, 7.0] ... ) data = [51, 48] + model.config.auxdata test_mu = 1.0 CLs_obs, CLs_exp = pyhf.infer.hypotest( ... test_mu, data, model, test_stat="qtilde", return_expected=True ... ) print(f"Observed: {CLs_obs:.8f}, Expected: {CLs_exp:.8f}") Observed: 0.05251497, Expected: 0.06445321

Alternatively the statistical model and observational data can be read from its serialized JSON representation (see next section).

.. code:: pycon

import pyhf import requests pyhf.set_backend("numpy") url = "https://raw.githubusercontent.com/scikit-hep/pyhf/main/docs/examples/json/2-bin_1-channel.json" wspace = pyhf.Workspace(requests.get(url).json()) model = wspace.model() data = wspace.data(model) test_mu = 1.0 CLs_obs, CLs_exp = pyhf.infer.hypotest( ... test_mu, data, model, test_stat="qtilde", return_expected=True ... ) print(f"Observed: {CLs_obs:.8f}, Expected: {CLs_exp:.8f}") Observed: 0.35998409, Expected: 0.35998409

Finally, you can also use the command line interface that pyhf provides

.. code:: bash

$ cat << EOF | tee likelihood.json | pyhf cls { "channels": [ { "name": "singlechannel", "samples": [ { "name": "signal", "data": [12.0, 11.0], "modifiers": [ { "name": "mu", "type": "normfactor", "data": null} ] }, { "name": "background", "data": [50.0, 52.0], "modifiers": [ {"name": "uncorr_bkguncrt", "type": "shapesys", "data": [3.0, 7.0]} ] } ] } ], "observations": [ { "name": "singlechannel", "data": [51.0, 48.0] } ], "measurements": [ { "name": "Measurement", "config": {"poi": "mu", "parameters": []} } ], "version": "1.0.0" } EOF

which should produce the following JSON output:

.. code:: json

{ "CLs_exp": [ 0.0026062609501074576, 0.01382005356161206, 0.06445320535890459, 0.23525643861460702, 0.573036205919389 ], "CLs_obs": 0.05251497423736956 }

What does it support

Implemented variations:

☑ HistoSys
☑ OverallSys
☑ ShapeSys
☑ NormFactor
☑ Multiple Channels
☑ Import from XML + ROOT via uproot <https://github.com/scikit-hep/uproot4>__
☑ ShapeFactor
☑ StatError
☑ Lumi Uncertainty
☑ Non-asymptotic calculators

Computational Backends:

☑ NumPy
☑ PyTorch
☑ TensorFlow
☑ JAX

Optimizers:

☑ SciPy (scipy.optimize)
☑ MINUIT (iminuit)

All backends can be used in combination with all optimizers. Custom user backends and optimizers can be used as well.

Todo

☐ StatConfig

results obtained from this package are validated against output computed from HistFactory workspaces

A one bin example

.. code:: python

import pyhf import numpy as np import matplotlib.pyplot as plt from pyhf.contrib.viz import brazil

pyhf.set_backend("numpy") model = pyhf.simplemodels.uncorrelated_background( signal=[10.0], bkg=[50.0], bkg_uncertainty=[7.0] ) data = [55.0] + model.config.auxdata

poi_vals = np.linspace(0, 5, 41) results = [ pyhf.infer.hypotest( test_poi, data, model, test_stat="qtilde", return_expected_set=True ) for test_poi in poi_vals ]

fig, ax = plt.subplots() fig.set_size_inches(7, 5) brazil.plot_results(poi_vals, results, ax=ax) fig.show()

pyhf

.. image:: https://raw.githubusercontent.com/scikit-hep/pyhf/main/docs/_static/img/README_1bin_example.png :alt: manual :width: 500 :align: center

ROOT

.. image:: https://raw.githubusercontent.com/scikit-hep/pyhf/main/docs/_static/img/hfh_1bin_55_50_7.png :alt: manual :width: 500 :align: center

A two bin example

.. code:: python

import pyhf import numpy as np import matplotlib.pyplot as plt from pyhf.contrib.viz import brazil

pyhf.set_backend("numpy") model = pyhf.simplemodels.uncorrelated_background( signal=[30.0, 45.0], bkg=[100.0, 150.0], bkg_uncertainty=[15.0, 20.0] ) data = [100.0, 145.0] + model.config.auxdata

poi_vals = np.linspace(0, 5, 41) results = [ pyhf.infer.hypotest( test_poi, data, model, test_stat="qtilde", return_expected_set=True ) for test_poi in poi_vals ]

fig, ax = plt.subplots() fig.set_size_inches(7, 5) brazil.plot_results(poi_vals, results, ax=ax) fig.show()

pyhf

.. image:: https://raw.githubusercontent.com/scikit-hep/pyhf/main/docs/_static/img/README_2bin_example.png :alt: manual :width: 500 :align: center

ROOT

.. image:: https://raw.githubusercontent.com/scikit-hep/pyhf/main/docs/_static/img/hfh_2_bin_100.0_145.0_100.0_150.0_15.0_20.0_30.0_45.0.png :alt: manual :width: 500 :align: center

Installation

To install pyhf from PyPI with the NumPy backend run

.. code:: bash

python -m pip install pyhf

and to install pyhf with all additional backends run

.. code:: bash

python -m pip install pyhf[backends]

or a subset of the options.

To uninstall run

.. code:: bash

python -m pip uninstall pyhf

Documentation

For model specification, API reference, examples, and answers to FAQs visit the |pyhf documentation|_.

.. |pyhf documentation| replace:: pyhf documentation .. _pyhf documentation: https://pyhf.readthedocs.io/

Questions

If you have a question about the use of pyhf not covered in the documentation <https://pyhf.readthedocs.io/>, please ask a question on the GitHub Discussions <https://github.com/scikit-hep/pyhf/discussions>.

If you believe you have found a bug in pyhf, please report it in the GitHub Issues <https://github.com/scikit-hep/pyhf/issues/new?template=Bug-Report.md&labels=bug&title=Bug+Report+:+Title+Here>_. If you're interested in getting updates from the pyhf dev team and release announcements you can join the |pyhf-announcements mailing list|.

.. |pyhf-announcements mailing list| replace:: pyhf-announcements mailing list .. _pyhf-announcements mailing list: https://groups.google.com/group/pyhf-announcements/subscribe

Citation

As noted in Use and Citations <https://scikit-hep.org/pyhf/citations.html>, the preferred BibTeX entry for citation of pyhf includes both the Zenodo <https://zenodo.org/> archive and the JOSS <https://joss.theoj.org/>__ paper:

.. code:: bibtex

@software{pyhf, author = {Lukas Heinrich and Matthew Feickert and Giordon Stark}, title = "{pyhf: v0.7.6}", version = {0.7.6}, doi = {10.5281/zenodo.1169739}, url = {https://doi.org/10.5281/zenodo.1169739}, note = {https://github.com/scikit-hep/pyhf/releases/tag/v0.7.6} }

@article{pyhf_joss, doi = {10.21105/joss.02823}, url = {https://doi.org/10.21105/joss.02823}, year = {2021}, publisher = {The Open Journal}, volume = {6}, number = {58}, pages = {2823}, author = {Lukas Heinrich and Matthew Feickert and Giordon Stark and Kyle Cranmer}, title = {pyhf: pure-Python implementation of HistFactory statistical models}, journal = {Journal of Open Source Software} }

Authors

pyhf is openly developed by Lukas Heinrich, Matthew Feickert, and Giordon Stark.

Please check the contribution statistics for a list of contributors <https://github.com/scikit-hep/pyhf/graphs/contributors>__.

Milestones

2022-09-12: 2000 GitHub issues and pull requests. (See PR #2000 <https://github.com/scikit-hep/pyhf/pull/2000>__)
2021-12-09: 1000 commits to the project. (See PR #1710 <https://github.com/scikit-hep/pyhf/pull/1710>__)
2020-07-28: 1000 GitHub issues and pull requests. (See PR #1000 <https://github.com/scikit-hep/pyhf/pull/1000>__)

Acknowledgements

Matthew Feickert has received support to work on pyhf provided by NSF cooperative agreements OAC-1836650 <https://nsf.gov/awardsearch/showAward?AWD_ID=1836650> and PHY-2323298 <https://nsf.gov/awardsearch/showAward?AWD_ID=2323298>__ (IRIS-HEP) and grant OAC-1450377 <https://www.nsf.gov/awardsearch/showAward?AWD_ID=1450377> (DIANA/HEP).

pyhf is a NumFOCUS Affiliated Project <https://numfocus.org/sponsored-projects/affiliated-projects>__.

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.. |Code Coverage| image:: https://codecov.io/gh/scikit-hep/pyhf/graph/badge.svg?branch=main :target: https://codecov.io/gh/scikit-hep/pyhf?branch=main .. |CodeFactor| image:: https://www.codefactor.io/repository/github/scikit-hep/pyhf/badge :target: https://www.codefactor.io/repository/github/scikit-hep/pyhf .. |pre-commit.ci Status| image:: https://results.pre-commit.ci/badge/github/scikit-hep/pyhf/main.svg :target: https://results.pre-commit.ci/latest/github/scikit-hep/pyhf/main :alt: pre-commit.ci status .. |Code style: black| image:: https://img.shields.io/badge/code%20style-black-000000.svg :target: https://github.com/psf/black

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scikit-hep / pyhf

readme

pure-python fitting/limit-setting/interval estimation HistFactory-style

User Guide

Hello World

What does it support

Todo

A one bin example

A two bin example

Installation

Documentation

Questions

Citation

Authors

Milestones

Acknowledgements