Metadata-Version: 1.1
Name: python-fit
Version: 1.0.0
Summary: A python module using scipy's orthogonal distance regression that makes fitting data easy.
Home-page: https://github.com/vgm64/python-fit
Author: Michael Woods
Author-email: physicsmichael@gmail.com
License: MIT
Description: python-fit
        ==========
        
        The python-fit module is designed for people who need to fit data
        frequently and quickly. The module is not designed for huge amounts of
        control over the minimization process but rather tries to make fitting
        data simple and painless. If you want to fit data several times a day,
        every day, and you really just want to see if the fit you've made looks
        good against your data, check out this software. If you want one very
        statistically aware and neurotically controlled fit, you might consider
        looking elsewhere.
        
        With python-fit, you get work done.
        
        Features
        --------
        
        -  Common fitting curves built-in
        -  Default parameters for built-in functions intelligently calculated
           using your data.
        -  Fit with user defined functions, too.
        -  A ready-to-plot-fit always conviently returned.
        -  Get fit parameters and associated errors.
        -  Chi-squared residual.
        
        Example Usage
        -------------
        
        Fit with built in functions:
        
        .. code-block:: python
        
            from pylab import *
            ion()
            import fit
            from numpy import random, exp
            random.seed(0)
        
            # Create some data to fit
            x = arange(-10, 10, .2)
            # A gaussian of height 10, width 2, centered at zero. With noise.
            y = 10*exp(-x**2/8) + (random.rand(100) - 0.5)
        
        
            # No need to provide first guess at parameters for fit.gaus
            (xf, yf), params, err, chi = fit.fit(fit.gaus, x,y)
        
            print "N:    %.2f +/- %.3f" % (params[0], err[0])
            print "N:    %.2f +/- %.3f" % (params[1], err[1])
            print "N:    %.2f +/- %.3f" % (params[2], err[2])
        
            plot(x,y, 'bo', label='Data')
            plot(xf, yf, 'r-', label='Fit')
            legend() 
        
        Functions available:
        
        .. code-block:: python
        
            Gaussian curve
            Exponential curve
            Double exponential
            Polynomials for degrees 0-20
            Power-Law
            Crystal Ball
            ... and more!
        
        Fit with user defined functions:
        
        .. code-block:: python
        
            x = (4.2105303, 5.2631601, 6.2405997, 7.5187997, 8.7218, 9.7744402, 10.676691, 11.65414, 12.63158, 13.83459, 14.887219, 16.015039, 17.06767, 
            18.270679, 19.24812, 20.300751, 21.50376, 23.157888, 25.789471, 28.345871, 30.601501, 33.458643, 39.022559, 46.015039, 48.270679)
            y = (0.18942001, 0.2099, 0.23891001, 0.27816002, 0.31911, 0.35836001, 0.39932001, 0.43686003, 0.46416002, 0.49829001, 0.51536004, 0.52556, 0.51876995, 
            0.5, 0.47271, 0.44026, 0.39249001, 0.33106002, 0.24060, 0.17746, 0.13311001, 0.11262, 0.095566, 0.095566, 0.095566)
        
            x = array(x)
            y = array(y)
        
            # Guassian with quadratic background.
            def example_function(params, x):
                N,mu,sigma,a,b,c = params
                return N*exp(-0.5 * ((x-mu)/sigma)**2 ) + a*x**2 + b*x + c
              
            # It will still try to guess parameters, but they are dumb!
            (xf,yf),p,e,chi = fit.fit(example_function, x,y)
            plot(x,y, 'bo', label='Data')
            plot(xf,yf, 'r-', label='Fit')
            legend()
        
        Even though ``example_function`` is defined by the user, python-fit will
        guess parameters (the median value of the xdata for all parameters; it
        works if x and y are on similar scales). If the fit fails, then provide
        some decent parameters as a first guess:
        
        .. code-block:: python
        
            results = fit.fit(example_function, x, y, default_pars = [1, 12, 10, 1, 1, 1])
            plot(results[0][0], results[0][1], 'r--')
        
        Fit a sub-range:
        
        .. code-block:: python
        
            clf()
            results = fit.fit(fit.gaus, x, y, data_range=[0, 23])
            plot(results[0][0], results[0][1], 'r-.')
        
        Define your own weights to prevent outliers from wreaking havoc on your
        fit:
        
        .. code-block:: python
        
            # Create some outliers.
            y_outlier = y + (random.rand(len(y))**20)*3
            # I'll just make a cut and say outliers are above 0.55
            weights = 1. * (y_outlier < .55)
            results = fit.fit(example_function, x, y_outlier, we=weights)
            clf()
            plot(x,y_outlier, 'bo', label='Data w/ Outliers')
            plot(results[0][0], results[0][1], 'r-.', label='Fit around outliers')
        
Keywords: fitting curve
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Utilities
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python
Classifier: License :: OSI Approved :: MIT License
