Skip to main content
This is a pre-production deployment of Warehouse. Changes made here affect the production instance of PyPI (
Help us improve Python packaging - Donate today!

Distribution Fitting/Regression Library

Project Description

*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 estimator, :math:`\chi^2` regression,
Binned :math:`\chi^2` estimator and Simultaneous fit estimator.
Various functors for manipulating PDF such as Normalization and
Convolution(with caching) and various builtin functions
normally used in B physics is also provided.


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)

* `MIT <>`_ license (open source)
* `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`.
* Dependencies:
- `iminuit <>`_
- `numpy <>`_
- `matplotlib <>`_ (optional, for plotting)
Release History

Release History

This version
History Node


History Node


History Node


History Node


History Node


History Node


Download Files

Download Files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
probfit-1.0.5.tar.gz (960.7 kB) Copy SHA256 Checksum SHA256 Source Sep 14, 2013

Supported By

WebFaction WebFaction Technical Writing Elastic Elastic Search Pingdom Pingdom Monitoring Dyn Dyn DNS Sentry Sentry Error Logging CloudAMQP CloudAMQP RabbitMQ Heroku Heroku PaaS Kabu Creative Kabu Creative UX & Design Fastly Fastly CDN DigiCert DigiCert EV Certificate Rackspace Rackspace Cloud Servers DreamHost DreamHost Log Hosting