What is a good converging colourmap for matplotlib

Question

When making scatter plots, there are many options for diverging colour maps, that emphasise data at the extrema, but no options for a converging colour map, that emphasises data near the middle of the range. Can anyone suggest one, or tell me why it's a bad idea to use one?

My use case is a scatter plot where each point represents a fit to a different data set, and colour represents the reduced chi squared of the fit. I want to emphasise values close to one, and de-emphasise bad fits.

Edit

Here's my use case in more detail. I'm measuring the performance of an algorithm designed to distinguish between real and systematic signals in time series data (in this case planet transits in Kepler data versus glitches in the data). For each simulation I have an input value, a number describing the algorithm's decision, and a reduced chi squared measuring the goodness of fit. I want to use the colour scheme to highlight the points that have reduced chi squares closest to one, and not the cases where the fit is bad.

There are many ways I could do this (e.g with point size), but I'd like to do it with colour if I can.

Answer 1

An easy approach is to modify a standard colormap. There's a scipy cookbook page on how to make a colormap transformation, and using the cmap_map function from there, one can do:

inv = cmap_map(lambda x: 1-x, cm.PRGn)  # the "transformation" = 1-x

#  plot the original or modified for comparison
x,y=mgrid[1:2,1:10:0.1]
plt.imshow(y, cmap=cm.PRGn)
plt.title("original")
plt.figure()
plt.imshow(y, cmap=inv)
plt.title("inverted")
plt.show()