Welcome to plotutils’s documentation!

plotutils contains utilities that are designed to make working with samples from 1- and 2-D probability density functions easier with matplotlib. In particular, from a set of samples drawn from some posterior distribution you can

• Plot 1- and 2-D histogram estimates of PDFs using plotutils.plotutils.plot_histogram_posterior() and plotutils.plotutils.plot_histogram_posterior_2d(). The bin sizes will be set automatically using a recipe that attempts to minimize the expected squared error between the histogram PDF and the true PDF.
• Plot 1- and 2-D kernel density estimates of PDFs using plotutils.plotutils.plot_kde_posterior() and plotutils.plotutils.plot_kde_posterior_2d().
• Plot 1- and 2-D probability intervals containing a specified fraction of the total posterior. In 2-D, the interval will be accumulated greedily—starting with the most likely sample points, and working down in posterior probability—using either the histogram or kernel density estimate of the PDF. See plotutils.plotutils.plot_interval(), plotutils.plotutils.plot_greedy_histogram_interval_2d(), and plotutils.plotutils.plot_greedy_kde_interval_2d().
• Estimate autocorrelation functions and autocorrelation lengths of one-dimensional series. See plotutils.autocorr.autocorrelation_function() and plotutils.autocorr.autocorrelation_length_estimate().
• Easily run emcee simulations with the plotutils.runner.EnsembleSamplerRunner class.
• Use several common, efficient parameterizations for objects that often appear in posterior functions with the plotutils.parameterizations module.

To install plotutils, simply execute python setup.py install in the root directory.

You can find the source for the plotutils package at http://github.com/farr/plotutils.

Contents:

• plotutils Package
  • plotutils Module
  • autocorr Module
  • runner Module
  • parameterizations Module
  • bounded_kde Module
  • log_kde Module
  • contour Module

Indices and tables

• Index
• Module Index
• Search Page