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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)

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Filename, size & hash SHA256 hash help File type Python version Upload date
probfit-1.0.5.tar.gz (960.7 kB) Copy SHA256 hash SHA256 Source None Sep 14, 2013

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