scikit-hep / Probfit
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.. -- mode: rst --
probfit
.. image:: https://img.shields.io/pypi/v/probfit.svg :target: https://pypi.python.org/pypi/probfit
.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.1477852.svg :target: https://doi.org/10.5281/zenodo.1477852
.. image:: https://github.com/scikit-hep/probfit/actions/workflows/main.yml/badge.svg :target: https://github.com/scikit-hep/probfit/actions/workflows/main.yml
probfit is a set of functions that helps you construct a complex fit. It's
intended to be used with iminuit <http://iminuit.readthedocs.org/>
_. 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
-
Python <http://docs.python-guide.org/en/latest/starting/installation/>
__ (2.7+, 3.5+) -
NumPy <https://scipy.org/install.html>
__ -
iminuit <http://iminuit.readthedocs.org/>
_ (<2)
Optional dependencies
-
matplotlib <http://matplotlib.org/>
_ for the plotting functions
Getting started
.. code-block:: python
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 <http://probfit.readthedocs.org/>
_ - The tutorial is an IPython notebook that you can view online
here <http://nbviewer.ipython.org/urls/raw.github.com/scikit-hep/probfit/master/tutorial/tutorial.ipynb>
_. To run it locally:cd tutorial; ipython notebook --pylab=inline tutorial.ipynb
. - Developing probfit: see the
development page <http://probfit.readthedocs.io/en/latest/development.html>
_
License
The package is licensed under the MIT <http://opensource.org/licenses/MIT>
_ license (open source).