Extracting bivariate histograms from a color image

Rather than overlaying univariate histograms of intensities in distinct channels, it is also possible to view the joint variation of pixel intensity in two different channels.

For this final exercise, you will use the same color image of the Helix Nebula as seen by the Hubble and the Cerro Tololo Inter-American Observatory. The separate RGB (red-green-blue) channels will be extracted for you as one-dimensional arrays red_pixels, green_pixels, & blue_pixels respectively.

Make a 2-D histogram (not a regular histogram) in the top left subplot showing the joint variation of red_pixels (on the x-axis) and green_pixels (on the y-axis). Use bins=(32,32) to control binning.
Make another 2-D histogram in the top right subplot showing the joint variation of green_pixels (on the x-axis) and blue_pixels (on the y-axis). Use bins=(32,32) to control binning.
Make another 2-D histogram in the bottom left subplot showing the joint variation of blue_pixels (on the x-axis) and red_pixels (on the y-axis). Use bins=(32,32) to control binning.

Exercise

Extracting bivariate histograms from a color image

Instructions