Toolbox for testing if a probability distribution fits a power law
Project description
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. Academics, please cite as:
Jeff Alstott, Ed Bullmore, Dietmar Plenz. (2014). powerlaw: a Python package for analysis of heavy-tailed distributions. PLoS ONE 9(1): e85777
Also available at arXiv:1305.0215 [physics.data-an]
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
Paper illustrating all of powerlaw’s features, with figures
Code examples from manuscript, as an IPython Notebook Note: Some results involving lognormals will now be different from the manuscript, as the lognormal fitting has been improved to allow for greater numerical precision.
Update Notifications, Mailing List, and Contacts
This code was developed and tested for Python 2.x with the Enthought Python Distribution, and later amended to be compatible with 3.x. The full version of Enthought is available for free for academic use.
Acknowledgements
Many thanks to Andreas Klaus, Mika Rubinov and Shan Yu for helpful discussions. Thanks also to Andreas Klaus, Aaron Clauset, Cosma Shalizi, and Adam Ginsburg for making their code available. Their implementations were a critical starting point for making powerlaw.
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