Metadata-Version: 2.1
Name: hepstats
Version: 0.2.0
Summary: statistics tools and utilities
Home-page: https://github.com/scikit-hep/hepstats
Author: Matthieu Marinangeli
Author-email: matthieu.marinangeli@cern.ch
Maintainer: The Scikit-HEP admins
Maintainer-email: scikit-hep-admins@googlegroups.com
License: BSD 3-Clause License
Description: # Scikit-HEP project `hepstats` package: statistics tools and utilities
        
        [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/hepstats)](https://pypi.org/project/hepstats/)
        [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3519200.svg)](https://doi.org/10.5281/zenodo.3519200)
        [![Build Status](https://dev.azure.com/matthieumarinangeli/matthieumarinangeli/_apis/build/status/scikit-hep.hepstats?branchName=master)](https://dev.azure.com/matthieumarinangeli/matthieumarinangeli/_build/latest?definitionId=4&branchName=master)
        ![Azure DevOps coverage](https://img.shields.io/azure-devops/coverage/matthieumarinangeli/matthieumarinangeli/4)
        ![Azure DevOps tests](https://img.shields.io/azure-devops/tests/matthieumarinangeli/matthieumarinangeli/4)
        [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/scikit-hep/hepstats/master)
        
        ## Installation
        
        Install `hepstats` like any other Python package:
        
        ```
        pip install hepstats
        ```
        
        or similar (use e.g. `virtualenv` if you wish).
        
        ## Getting Started
        
        The `hepstats` module includes modeling and hypothesis tests submodules. This a quick user guide to each submodule. The [binder](https://mybinder.org/v2/gh/scikit-hep/hepstats/master) examples are also a good way to get started.
        
        ### modeling
        
        The modeling submodule includes the [Bayesian Block algorithm](https://arxiv.org/pdf/1207.5578.pdf) that can be used to improve the binning of histograms. The visual improvement can be dramatic, and more importantly, this algorithm produces histograms that accurately represent the underlying distribution while being robust to statistical fluctuations. Here is a small example of the algorithm applied on Laplacian sampled data, compared to a histogram of this sample with a fine binning.
        
        ```python
        >>> import numpy as np
        >>> import matplotlib.pyplot as plt
        >>> from hepstats.modeling import bayesian_blocks
        
        >>> data = np.random.laplace(size=10000)
        >>> blocks = bayesian_blocks(data)
        
        >>> plt.hist(data, bins=1000, label='Fine Binning', density=True, alpha=0.6)
        >>> plt.hist(data, bins=blocks, label='Bayesian Blocks', histtype='step', density=True, linewidth=2)
        >>> plt.legend(loc=2)
        ```
        
        ![bayesian blocks example](https://raw.githubusercontent.com/scikit-hep/hepstats/master/notebooks/modeling/bayesian_blocks_example.png)
        
        ### hypotests
        
        This submodule provides tools to do hypothesis tests such as discovery test and computations of upper limits or confidence intervals. hepstats needs a fitting backend to perform computations such as [zfit](https://github.com/zfit/zfit). Any fitting library can be used if their API is compatible  with hepstats (see [api checks](https://github.com/scikit-hep/hepstats/blob/master/hepstats/hypotests/fitutils/api_check.py)).
        
        We give here a simple example of a discovery test, using the [zfit](https://github.com/zfit/zfit)
        fitting package as backend, of a Gaussian signal with known mean and sigma over an exponential background.
        
        ```python
        >>> import zfit
        >>> from zfit.loss import ExtendedUnbinnedNLL
        >>> from zfit.minimize import Minuit
        
        >>> bounds = (0.1, 3.0)
        >>> obs = zfit.Space('x', limits=bounds)
        
        >>> bkg = np.random.exponential(0.5, 300)
        >>> peak = np.random.normal(1.2, 0.1, 25)
        >>> data = np.concatenate((bkg, peak))
        >>> data = data[(data > bounds[0]) & (data < bounds[1])]
        >>> N = data.size
        >>> data = zfit.Data.from_numpy(obs=obs, array=data)
        
        >>> lambda_ = zfit.Parameter("lambda", -2.0, -4.0, -1.0)
        >>> Nsig = zfit.Parameter("Ns", 20., -20., N)
        >>> Nbkg = zfit.Parameter("Nbkg", N, 0., N*1.1)
        >>> signal = Nsig * zfit.pdf.Gauss(obs=obs, mu=1.2, sigma=0.1)
        >>> background = Nbkg * zfit.pdf.Exponential(obs=obs, lambda_=lambda_)
        >>> loss = ExtendedUnbinnedNLL(model=signal + background, data=data)
        
        >>> from hepstats.hypotests.calculators import AsymptoticCalculator
        >>> from hepstats.hypotests import Discovery
        >>> from hepstats.hypotests.parameters import POI
        
        >>> calculator = AsymptoticCalculator(loss, Minuit())
        >>> poinull = POI(Nsig, 0)
        >>> discovery_test = Discovery(calculator, [poinull])
        >>> discovery_test.result()
        
        p_value for the Null hypothesis = 0.0007571045424956679
        Significance (in units of sigma) = 3.1719464825102244
        ```
        
        The discovery test prints out the p-value and the significance of the null hypothesis to be rejected.
        
Keywords: HEP,statistics
Platform: Any
Classifier: Topic :: Scientific/Engineering
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Operating System :: OS Independent
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Development Status :: 4 - Beta
Requires-Python: !=2.*, >=3.6
Description-Content-Type: text/markdown
