Metadata-Version: 2.1
Name: probfit
Version: 1.2.0
Summary: Distribution Fitting/Regression Library
Home-page: https://github.com/scikit-hep/probfit
Author: Piti Ongmongkolkul
Author-email: piti118@gmail.com
Maintainer: Scikit-HEP
Maintainer-email: scikit-hep-admins@googlegroups.com
License: MIT
Platform: UNKNOWN
Classifier: Development Status :: 7 - Inactive
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Physics
Requires-Python: !=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.7
Description-Content-Type: text/x-rst
Requires-Dist: numpy
Requires-Dist: iminuit (<1.4) ; python_version < "3.5"
Requires-Dist: iminuit (<2) ; python_version >= "3.5"
Provides-Extra: dev
Requires-Dist: matplotlib (>=2.0) ; extra == 'dev'
Requires-Dist: pytest (>=4.6) ; extra == 'dev'
Requires-Dist: pytest-cov ; extra == 'dev'
Requires-Dist: pytest-mpl (<0.11) ; (python_version < "3.6") and extra == 'dev'
Requires-Dist: pytest-mpl ; (python_version >= "3.6") and extra == 'dev'
Provides-Extra: docs
Requires-Dist: ipython ; extra == 'docs'
Requires-Dist: sphinx ; extra == 'docs'
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Provides-Extra: test
Requires-Dist: pytest (>=4.6) ; extra == 'test'
Requires-Dist: pytest-cov ; extra == 'test'
Requires-Dist: pytest-mpl (<0.11) ; (python_version < "3.6") and extra == 'test'
Requires-Dist: pytest-mpl ; (python_version >= "3.6") and extra == 'test'

.. -*- 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.1477853.svg
   :target: https://doi.org/10.5281/zenodo.1477853

.. 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).


