Skip to main content

Distribution Fitting/Regression Library

## Project description

probfit
-------

*probfit* is a set of functions that helps you construct a complex fit. It's
intended to be used with iminuit <http://iminuit.github.io/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)
minuit.migrad()
unbinned_likelihood.draw(minuit)

* MIT <http://opensource.org/licenses/MIT>_ license (open source)
* Documentation <http://iminuit.github.io/probfit/>_
* The tutorial is an IPython notebook that you can view online
here <http://nbviewer.ipython.org/urls/raw.github.com/iminuit/probfit/master/tutorial/tutorial.ipynb>_.
To run it locally: cd tutorial; ipython notebook --pylab=inline tutorial.ipynb.
* Dependencies:
- iminuit <http://iminuit.github.io/iminuit/>_
- numpy <http://www.numpy.org/>_
- matplotlib <http://matplotlib.org/>_ (optional, for plotting)

1.0.5

This version

1.0.4

1.0.3

1.0.2

1.0.1

1.0.0

## Download files

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

Filename, size & hash SHA256 hash help File type Python version Upload date
probfit-1.0.4.tar.gz (960.5 kB) Copy SHA256 hash SHA256 Source None Jun 4, 2013