Package: 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 (givezunzuna look).With python-fit, you get work done.FeaturesCommon fitting curves built-inDefault 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 UsageFit with built in functions:frompylabimport*ion()importfitfromnumpyimportrandom,exprandom.seed(0)# Create some data to fitx=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:GaussiancurveExponentialcurveDoubleexponentialPolynomialsfordegrees0-20Power-LawCrystalBall...andmore!Fit with user defined functions: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.defexample_function(params,x):N,mu,sigma,a,b,c=paramsreturnN*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 thoughexample_functionis 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: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: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:# Create some outliers.y_outlier=y+(random.rand(len(y))**20)*3# I'll just make a cut and say outliers are above 0.55weights=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')