powerlaw is a toolbox using the statistical methods developed in Clauset et al. 2007 and Klaus et al. 2011 to determine if a probability distribution fits a power law. This package is in “open beta”, which means everything pretty much works but it’s being tweaked and expanded on. Academics, please cite as:
Jeff Alstott. (2012). powerlaw Python package. Web address: pypi.python.org/pypi/powerlaw.
Basic Usage
For the simplest, typical use cases, this tells you everything you need to know.:
import powerlaw
data = array([1.7, 3.2 ...]) #data can be list or Numpy array
results = powerlaw.Fit(data)
print results.power_law.alpha
print results.power_law.xmin
R, p = results.distribution_compare('power_law', 'lognormal')
For more explanation, understanding, and figures, see the working paper, which illustrates all of powerlaw’s features. For details of the math, see Clauset et al. 2007, which developed these methods.
Quick Links
Working paper illustrating all of powerlaw’s features, with figures
Update Notifications, Mailing List, and Contacts
Note! This code works on Python 2.x, not 3.x. This code was developed and tested with the Enthought Python Distribution, and will update to 3.x whenever Enthought updates to 3.x. The full version of Enthought is available for free for academic use.
Acknowledgements
Many thanks to Mika Rubinov and Shan Yu for helpful discussions and to Adam Ginsburg for posting his code, which inspired the xmin-selection function of this toolbox.
