Metadata-Version: 2.1
Name: hepstats
Version: 0.2.5
Summary: statistics tools and utilities
Home-page: https://github.com/scikit-hep/hepstats
Author: Matthieu Marinangeli
Author-email: matthieu.marinangeli@gmail.com
Maintainer: Scikit-HEP
Maintainer-email: scikit-hep-admins@googlegroups.com
License: BSD 3-Clause License
Description: <img src="https://raw.githubusercontent.com/scikit-hep/hepstats/master/docs/source/logo.png" width="450">
        
        # Scikit-HEP project `hepstats` package: statistics tools and utilities
        
        [![Scikit-HEP][sk-badge]](https://scikit-hep.org/)
        
        [![PyPI](https://img.shields.io/pypi/v/hepstats)](https://pypi.org/project/hepstats/)
        [![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)
        ![CI](https://github.com/scikit-hep/hepstats/workflows/CI/badge.svg)
        [![codecov](https://codecov.io/gh/scikit-hep/hepstats/branch/master/graph/badge.svg)](https://codecov.io/gh/scikit-hep/hepstats)
        [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/scikit-hep/hepstats/master)
        [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
        
        ## Installation
        
        Install `hepstats` like any other Python package:
        
        ```
        pip install hepstats
        ```
        
        or similar (use e.g. `virtualenv` if you wish).
        
        ## Changelog
        See the [changelog](https://github.com/scikit-hep/hepstats/blob/master/CHANGELOG.md) for a history of notable changes.
        
        ## Getting Started
        
        The `hepstats` module includes `modeling`, `hypotests` and `splot` 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/utils/fit/api_check.py)).
        
        We give here a simple example of an upper limit calculation of the yield of a Gaussian signal with known mean and sigma over an exponential background. The fitting backend used is the [zfit](https://github.com/zfit/zfit) package.
        
        ```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, 10)
        >>> 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("Nsig", 1., -20., N)
        >>> Nbkg = zfit.Parameter("Nbkg", N, 0., N*1.1)
        >>> signal = zfit.pdf.Gauss(obs=obs, mu=1.2, sigma=0.1).create_sampler(Nsig)
        >>> background = zfit.pdf.Exponential(obs=obs, lambda_=lambda_).create_sampler(Nbkg)
        >>> total = zfit.pdf.SumPDF([signal, background])
        >>> loss = ExtendedUnbinnedNLL(model=total, data=data)
        
        >>> from hepstats.hypotests.calculators import AsymptoticCalculator
        >>> from hepstats.hypotests import UpperLimit
        >>> from hepstats.hypotests.parameters import POI, POIarray
        
        >>> calculator = AsymptoticCalculator(loss, Minuit(), asimov_bins=100)
        >>> poinull = POIarray(Nsig, np.linspace(0.0, 25, 20))
        >>> poialt = POI(Nsig, 0)
        >>> ul = UpperLimit(calculator, poinull, poialt)
        >>> ul.upperlimit(alpha=0.05, CLs=True)
        
        Observed upper limit: Nsig = 15.725784747406346
        Expected upper limit: Nsig = 11.927442041887158
        Expected upper limit +1 sigma: Nsig = 16.596396280677116
        Expected upper limit -1 sigma: Nsig = 8.592750403611896
        Expected upper limit +2 sigma: Nsig = 22.24864429383046
        Expected upper limit -2 sigma: Nsig = 6.400549971360598
        ```
        
        ![upper limit example](https://raw.githubusercontent.com/scikit-hep/hepstats/master/notebooks/hypotests/asy_ul.png)
        
        ### splots
        
        A full example using the **sPlot** algorithm can be found [here](https://github.com/scikit-hep/hepstats/tree/master/notebooks/splots/splot_example.ipynb). **sWeights** for different components in a data sample, modeled with a sum of extended probability density functions, are derived using the `compute_sweights` function:
        
        ```python
        >>> from hepstats.splot import compute_sweights
        
        # using same model as above for illustration
        >>> sweights = compute_sweights(signal + background, data)
        
        >>> bkg_sweights = sweights[Nbkg]
        >>> sig_sweights = sweights[Nsig]
        ```
        
        The model needs to be fitted to the data for the computation of the **sWeights**, if not an error is raised.
        
        [sk-badge]: https://img.shields.io/badge/Scikit--HEP-Project-blue?logo=data:image/png;base64,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
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Information Technology
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: MacOS
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: C++
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Software Development
Classifier: Topic :: Utilities
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: test
Provides-Extra: dev
