Distribution Fitting/Regression Library

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  • License: MIT License (MIT)
  • Author: Piti Ongmongkolkul
  • Maintainer: Scikit-HEP
  • Requires: Python !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, >=2.7
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Project description

https://img.shields.io/pypi/v/probfit.svg https://zenodo.org/badge/DOI/10.5281/zenodo.1477853.svg https://github.com/scikit-hep/probfit/actions/workflows/main.yml/badge.svg

probfit is a set of functions that helps you construct a complex fit. It’s intended to be used with iminuit. The tool includes Binned/Unbinned Likelihood estimators, 𝝌² regression, Binned 𝝌² estimator and Simultaneous fit estimator. Various functors for manipulating PDFs such as Normalization and Convolution (with caching) and various built-in functions normally used in B physics are also provided.

Strict dependencies

Optional dependencies

Getting started

import numpy as np
from iminuit import Minuit
from probfit import UnbinnedLH, gaussian
data = np.random.randn(10000)
unbinned_likelihood = UnbinnedLH(gaussian, data)
minuit = Minuit(unbinned_likelihood, mean=0.1, sigma=1.1)
minuit.migrad()
unbinned_likelihood.draw(minuit)

Documentation and Tutorial

  • Documentation

  • The tutorial is an IPython notebook that you can view online here. To run it locally: cd tutorial; ipython notebook –pylab=inline tutorial.ipynb.

  • Developing probfit: see the development page

License

The package is licensed under the MIT license (open source).

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  • Author: Piti Ongmongkolkul
  • Maintainer: Scikit-HEP
  • Requires: Python !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, >=2.7
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