{
"info": {
"author": "Lukas Heinrich",
"author_email": "lukas.heinrich@cern.ch",
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"description": "# pure-python fitting/limit-setting/interval estimation HistFactory-style\n\n[](https://doi.org/10.5281/zenodo.1169739)\n\n[](https://travis-ci.org/diana-hep/pyhf)\n[](https://hub.docker.com/r/pyhf/pyhf/)\n[](https://coveralls.io/github/diana-hep/pyhf?branch=master) [](https://landscape.io/github/diana-hep/pyhf/master)\n[](https://www.codefactor.io/repository/github/diana-hep/pyhf)\n[](https://github.com/ambv/black)\n\n[](https://diana-hep.github.io/pyhf)\n[](https://mybinder.org/v2/gh/diana-hep/pyhf/master?filepath=docs%2Fexamples%2Fnotebooks%2Fbinderexample%2FStatisticalAnalysis.ipynb)\n\n[](https://badge.fury.io/py/pyhf)\n[](https://pypi.org/project/pyhf/)\n[](https://hub.docker.com/r/pyhf/pyhf/)\n[](https://hub.docker.com/r/pyhf/pyhf/)\n\nThe 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\nof ROOT, RooFit, RooStats framework.\n\nThis 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.\n\n## Hello World\n\n```python\n>>> import pyhf\n>>> pdf = pyhf.simplemodels.hepdata_like(signal_data=[12.0, 11.0], bkg_data=[50.0, 52.0], bkg_uncerts=[3.0, 7.0])\n>>> CLs_obs, CLs_exp = pyhf.utils.hypotest(1.0, [51, 48] + pdf.config.auxdata, pdf, return_expected=True)\n>>> print('Observed: {}, Expected: {}'.format(CLs_obs, CLs_exp))\nObserved: [0.05290116], Expected: [0.06445521]\n\n```\n\n## What does it support\n\nImplemented variations:\n- [x] HistoSys\n- [x] OverallSys\n- [x] ShapeSys\n- [x] NormFactor\n- [x] Multiple Channels\n- [x] Import from XML + ROOT via [uproot](https://github.com/scikit-hep/uproot)\n- [x] ShapeFactor\n- [x] StatError\n- [x] Lumi Uncertainty\n\nComputational Backends:\n- [x] NumPy\n- [x] PyTorch\n- [x] TensorFlow\n- [x] MXNet\n\nAvailable Optimizers\n\n| NumPy | Tensorflow | PyTorch | MxNet |\n| :----------------------- | :------------------------- | :------------------------- | :---- |\n| SLSQP (`scipy.optimize`) | Newton's Method (autodiff) | Newton's Method (autodiff) | N/A |\n| MINUIT (`iminuit`) | . | . | . |\n\n\n## Todo\n- [ ] StatConfig\n- [ ] Non-asymptotic calculators\n\nresults obtained from this package are validated against output computed from HistFactory workspaces\n\n## A one bin example\n\n```\nnobs = 55, b = 50, db = 7, nom_sig = 10.\n```\n\n![\"manual\"](\"https://raw.githubusercontent.com/diana-hep/pyhf/master/docs/_static/img/manual_1bin_55_50_7.png\")
\n![\"manual\"](\"https://raw.githubusercontent.com/diana-hep/pyhf/master/docs/_static/img/hfh_1bin_55_50_7.png\")
\n\n\n## A two bin example\n\n```\nbin 1: nobs = 100, b = 100, db = 15., nom_sig = 30.\nbin 2: nobs = 145, b = 150, db = 20., nom_sig = 45.\n```\n\n![\"manual\"](\"https://raw.githubusercontent.com/diana-hep/pyhf/master/docs/_static/img/manual_2_bin_100.0_145.0_100.0_150.0_15.0_20.0_30.0_45.0.png\")
\n![\"manual\"](\"https://raw.githubusercontent.com/diana-hep/pyhf/master/docs/_static/img/hfh_2_bin_100.0_145.0_100.0_150.0_15.0_20.0_30.0_45.0.png\")
\n\n## Installation\n\nTo install `pyhf` from PyPI with the NumPy backend run\n```\npip install pyhf\n```\n\nand to install `pyhf` with additional backends run\n```\npip install pyhf[tensorflow,torch,mxnet]\n```\nor a subset of the options.\n\nTo uninstall run\n```bash\npip uninstall pyhf\n```\n\n## Authors\n\nPlease check the [contribution statistics for a list of contributors](https://github.com/diana-hep/pyhf/graphs/contributors)\n\n\n",
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