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Meandering Data

The random wanderings of a physicist in the world of data science

Quickly fitting data points in python

Posted May 27, 2014

Before I came to data science, I came from the odd intersection of particle physics, nuclear physics, and astrophysics. From the particle physics point of view, the C++ library ROOT reigns supreme.

The ROOT system provides a set of OO frameworks with all the functionality needed to handle and analyze large amounts of data in a very efficient way. … Included are histograming methods in an arbitrary number of dimensions, curve fitting, function evaluation, minimization, graphics and visualization classes to allow the easy setup of an analysis system that can query and process the data…

ROOT is also famous for horrible looking plots. It’s not really ROOT’s fault; ROOT was around when in the computing world, those were the hip new colors on the block. Sure, ROOT can be teased into creating publication worthy plots that are quite nice, but if I was going to spend that much time tweaking and tuning my plots, then I preferred to use matplotlib and make them absolutely stunning.

But what ROOT lacks in its graphical niceties, it made up for in its ability to quickly fit data. Every histogram and graph class had its own Fit method that would gladly take predefined functions (like Gaussian curves), as well string representations of methods (like [0]*x + sin(2*[1]*x)) and user defined C++ functions. This made measuring decay time constants and energy deposition means extremely easy. Often the result of the fit was applied directly to the plot being fit to. Out the door it went to the eager advisor’s inbox!

When I moved to data analysis in Python, making best fits to data was clunky and depended on manual application of scipy’s optimize.leastsq method. To get back a bit of the agility I was used to from ROOT, I wrote the python-fit module, available on PyPi. Like a Unix utility, it’s designed to do one thing and do it well. Quickly and painlessly fit data.

Here it is in action:

$ cat
from matplotlib import pyplot as plt
import fit
from numpy import random, exp, arange

# 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 =, x,y)

print "N:     %.2f +/- %.3f" % (params[0], err[0])
print "mu:    %.2f +/- %.3f" % (params[1], err[1])
print "sigma: %.2f +/- %.3f" % (params[2], err[2])

plt.plot(x,y, 'bo', label='Data')
plt.plot(xf, yf, 'r-', label='Fit')
$ python
N:     9.92 +/- 0.082
mu:    0.01 +/- 0.019
sigma: 2.01 +/- 0.019

The key to this module’s utility is its return values. Most fitting routines focus on returning the parameters that minimize the chi-squared residual. I wanted to focus on what the resulting fit looks like. A set of evenly spaced x points and the corresponding y values are provided so that the data can be immediately plotted. Afterwards, we see the typical output parameters, error on the fit parameters, and the chi-square residual. All of this is powered by scipy’s ODR package.

See the full documentation for extended examples and features. Happy fitting!