pyhf · PyPI

(partial) pure Python HistFactory implementation

These details have been verified by PyPI

Maintainers

kratsg lukasheinrich matthewfeickert scikit-hep

These details have not been verified by PyPI

Project links

Project description

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

The HistFactory p.d.f. template [CERN-OPEN-2012-016] 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]. 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.

Hello World

>>> import pyhf
>>> pdf = pyhf.simplemodels.hepdata_like(signal_data=[12.0, 11.0], bkg_data=[50.0, 52.0], bkg_uncerts=[3.0, 7.0])
>>> CLs_obs, CLs_exp = pyhf.infer.hypotest(1.0, [51, 48] + pdf.config.auxdata, pdf, return_expected=True)
>>> print('Observed: {}, Expected: {}'.format(CLs_obs, CLs_exp))
Observed: [0.05290116], Expected: [0.06445521]

What does it support

Implemented variations:

☑ HistoSys
☑ OverallSys
☑ ShapeSys
☑ NormFactor
☑ Multiple Channels
☑ Import from XML + ROOT via uproot
☑ ShapeFactor
☑ StatError
☑ Lumi Uncertainty

Computational Backends:

☑ NumPy
☑ PyTorch
☑ TensorFlow
☑ JAX

Available Optimizers

NumPy	Tensorflow	PyTorch
SLSQP (scipy.optimize )	Newton’s Method (autodiff)	Newton’s Method (autodiff)
MINUIT (iminuit)	.	.

Todo

☐ StatConfig
☐ Non-asymptotic calculators

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

A one bin example

nobs = 55, b = 50, db = 7, nom_sig = 10.

A two bin example

bin 1: nobs = 100, b = 100, db = 15., nom_sig = 30.
bin 2: nobs = 145, b = 150, db = 20., nom_sig = 45.

Installation

To install pyhf from PyPI with the NumPy backend run

python -m pip install pyhf

and to install pyhf with all additional backends run

python -m pip install pyhf[backends]

or a subset of the options.

To uninstall run

python -m pip uninstall pyhf

Questions

If you have a question about the use of pyhf not covered in the documentation, please ask a question on Stack Overflow with the [pyhf] tag, which the pyhf dev team watches.

If you believe you have found a bug in pyhf, please report it in the GitHub Issues.

Citation

As noted in Use and Citations, the preferred BibTeX entry for citation of pyhf is

@software{pyhf,
  author = "{Heinrich, Lukas and Feickert, Matthew and Stark, Giordon}",
  title = "{pyhf: v0.4.4}",
  version = {0.4.4},
  doi = {10.5281/zenodo.1169739},
  url = {https://github.com/scikit-hep/pyhf},
}

Authors

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

Please check the contribution statistics for a list of contributors.

Acknowledgements

Matthew Feickert has received support to work on pyhf provided by NSF cooperative agreement OAC-1836650 (IRIS-HEP) and grant OAC-1450377 (DIANA/HEP).

Project details

These details have been verified by PyPI

Maintainers

kratsg lukasheinrich matthewfeickert scikit-hep

These details have not been verified by PyPI

Project links

Release history Release notifications | RSS feed

0.7.6

Jan 5, 2024

0.7.5

Oct 26, 2023

0.7.4

Sep 7, 2023

0.7.3

Aug 17, 2023

0.7.2

May 18, 2023

0.7.1

Apr 7, 2023

0.7.0

Sep 24, 2022

0.7.0rc4 pre-release

Sep 10, 2022

0.7.0rc3 pre-release

Sep 3, 2022

0.7.0rc2 pre-release

Aug 29, 2022

0.7.0rc1 pre-release

Jun 29, 2022

0.6.3

Sep 3, 2021

0.6.2

Jun 21, 2021

0.6.1

Mar 9, 2021

0.6.0

Feb 16, 2021

0.5.4

Dec 12, 2020

0.5.3

Oct 20, 2020

0.5.2

Sep 7, 2020

0.5.1

Aug 2, 2020

0.5.0

Jul 25, 2020

This version

0.4.4

Jul 2, 2020

0.4.3

May 31, 2020

0.4.2

May 28, 2020

0.4.1

Mar 4, 2020

0.4.0

Jan 13, 2020

0.3.4

Jan 13, 2020

0.3.3

Jan 12, 2020

0.3.2

Jan 3, 2020

0.3.1

Dec 16, 2019

0.3.0

Dec 9, 2019

0.2.2

Nov 21, 2019

0.2.1

Nov 15, 2019

0.2.0

Nov 14, 2019

0.1.2

Jul 12, 2019

0.1.1

Jun 27, 2019

0.1.0

Apr 13, 2019

0.0.17

Apr 11, 2019

0.0.16

Jan 31, 2019

0.0.15

Sep 2, 2018

0.0.14

Sep 1, 2018

0.0.13

Sep 1, 2018

0.0.12

Aug 23, 2018

0.0.8

Feb 14, 2018

0.0.5

Feb 13, 2018

0.0.4

Feb 9, 2018

0.0.3

Feb 5, 2018

0.0.1

Jan 24, 2018

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