Turbo is an improved rainbow colormap for visualization, created by the Google AI team for computer vision and machine learning. Its purpose is to display depth and disparity data. Please see the Google AI Blog for further details.

BoundaryNorm now has an extend keyword argument, analogous to extend in contourf . When set to 'both', 'min', or 'max', it maps the corresponding out-of-range values to Colormap lookup-table indices near the appropriate ends of their range so that the colors for out-of range values are adjacent to, but distinct from, their in-range neighbors. The colorbar inherits the extend argument from the norm, so with extend='both' , for example, the colorbar will have triangular extensions for out-of-range values with colors that differ from adjacent in-range colors.

The text color of legend labels can now be set by passing a parameter labelcolor to legend . The labelcolor keyword can be:

Previously axes.Axes.pcolor and axes.Axes.pcolormesh handled the situation where x and y have the same (respective) size as C by dropping the last row and column of C, and x and y are regarded as the edges of the remaining rows and columns in C. However, many users want x and y centered on the rows and columns of C.

To accommodate this, shading='nearest' and shading='auto' are new allowed strings for the shading keyword argument. 'nearest' will center the color on x and y if x and y have the same dimensions as C (otherwise an error will be thrown). shading='auto' will choose 'flat' or 'nearest' based on the size of X, Y, C.

If shading='flat' then X, and Y should have dimensions one larger than C. If X and Y have the same dimensions as C, then the previous behavior is used and the last row and column of C are dropped, and a DeprecationWarning is emitted.

Users can also specify this by the new rcParams["pcolor.shading"] (default: 'flat' ) in their .matplotlibrc or via rcParams .

See pcolormesh for examples.