densitycontour -- draw density contours from sample points.

densitycontour is a Python module that helps with creating contour plots from a sample of points. It is useful for visualizing the output of Markov Chain Monte Carlo (MCMC) sampling.

Typical usage is like follows:

import pylab	# Import matplotlib environment.
import densitycontour

# Create scatter-data and rasterized image objects.
# x_array and y_array are "raw" inputs.
sample_data = densitycontour.ScatterData(x_array, y_array)

# Create a raster array for plotting, using default binning.
raster = sample_data.rasterize()

# Use the ZoomedContourVisualizer post-processor on the raster array.
contours = densitycontour.ZoomedContourVisualizer(raster, mode="nearest")

# Plot the contours for confidence levels 50% and 90% respectively,
# using default settings.
contours.plot((0.9, 0.5))

# Show the figure.
pylab.show()

The resulting figure should look like the image showed in one of the following panels:

You can run the module as a Python script to see the test diagrams.

densitycontour requires the numpy, scipy, and matplotlib packages.

Copyright © 2014 Cong Ma. License BSD: See the COPYING file.

This is free software: you are free to change and redistribute it.
There is NO WARRANTY, to the extent permitted by law.

Available from https://github.com/congma/densitycontour.