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
Description: .. -*- 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).
        
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
Provides-Extra: dev
Provides-Extra: docs
Provides-Extra: test
