fitting.txt

======================================
:mod:`numkit.fitting` --- Fitting data
======================================

The module contains functions to do least square fits of functions of
one variable f(x) to data points (x,y).

Example
=======

For example, to fit a un-normalized Gaussian with :class:`FitGauss` to
data distributed with mean 5.0 and standard deviation 3.0::

   from numkit.fitting import FitGauss
   import numpy, numpy.random

   # generate suitably noisy data
   mu, sigma = 5.0, 3.0
   Y,edges = numpy.histogram(sigma*numpy.random.randn(10000), bins=100, density=True)
   X = 0.5*(edges[1:]+edges[:-1]) + mu

   g = FitGauss(X, Y)

   print(g.parameters)
   # [ 4.98084541  3.00044102  1.00069061]
   print(numpy.array([mu, sigma, 1]) - g.parameters)
   # [ 0.01915459 -0.00044102 -0.00069061]

   import matplotlib.pyplot as plt
   plt.plot(X, Y, 'ko', label="data")
   plt.plot(X, g.fit(X), 'r-', label="fit")

.. figure:: FitGauss.png
   :scale: 60 %
   :alt: Gaussian fit with data points

   A Gaussian (red) was fit to the data points (black circles) with
   the :class:`numkit.fitting.FitGauss` class.

If the initial parameters for the least square optimization do not
lead to a solution then one can provide customized starting values in
the *parameters* keyword argument::

   g = FitGauss(X, Y, parameters=[10, 1, 1])

The *parameters* have different meaning for the different fit
functions; the documentation for each function shows them in the
context of the fit function.


Creating new fit functions
==========================

New fit function classes can be derived from :class:`FitFunc`. The
documentation and the methods :meth:`FitFunc.f_factory` and
:meth:`FitFunc.initial_values` must be overriden. For example, the
class :class:`FitGauss` is implemented as ::

   class FitGauss(FitFunc):
       '''y = f(x) = p[2] * 1/sqrt(2*pi*p[1]**2) * exp(-(x-p[0])**2/(2*p[1]**2))'''
       def f_factory(self):
           def fitfunc(p,x):
               return p[2] * 1.0/(p[1]*numpy.sqrt(2*numpy.pi)) * numpy.exp(-(x-p[0])**2/(2*p[1]**2))
           return fitfunc
       def initial_values(self):
           return [0.0,1.0,0.0]

The function to be fitted is defined in :func:`fitfunc`. The
parameters are accessed as ``p[0]``, ``p[1]``, ... For each parameter,
a suitable initial value must be provided.


Functions and classes
=====================

.. autofunction:: numkit.fitting.Pearson_r
.. autofunction:: numkit.fitting.linfit

.. autoclass:: numkit.fitting.FitFunc
   :members:
.. autoclass:: numkit.fitting.FitLin
.. autoclass:: numkit.fitting.FitExp
.. autoclass:: numkit.fitting.FitExp2
.. autoclass:: numkit.fitting.FitGauss