Note
Go to the endto download the full example code.
For more detail on creating and manipulating colormaps seeCreating Colormaps in Matplotlib.
Creating a colormap from a list of colorscan be done with the LinearSegmentedColormap.from_list method. You mustpass a list of RGB tuples that define the mixture of colors from 0 to 1.
Creating custom colormaps#
It is also possible to create a custom mapping for a colormap. This isaccomplished by creating dictionary that specifies how the RGB channelschange from one end of the cmap to the other.
Example: suppose you want red to increase from 0 to 1 over the bottomhalf, green to do the same over the middle half, and blue over the tophalf. Then you would use:
cdict = { 'red': ( (0.0, 0.0, 0.0), (0.5, 1.0, 1.0), (1.0, 1.0, 1.0), ), 'green': ( (0.0, 0.0, 0.0), (0.25, 0.0, 0.0), (0.75, 1.0, 1.0), (1.0, 1.0, 1.0), ), 'blue': ( (0.0, 0.0, 0.0), (0.5, 0.0, 0.0), (1.0, 1.0, 1.0), )}
If, as in this example, there are no discontinuities in the r, g, and bcomponents, then it is quite simple: the second and third element ofeach tuple, above, is the same -- call it "y". The first element ("x")defines interpolation intervals over the full range of 0 to 1, and itmust span that whole range. In other words, the values of x divide the0-to-1 range into a set of segments, and y gives the end-point colorvalues for each segment.
Now consider the green, cdict['green'] is saying that for:
0 <=
x<= 0.25,yis zero; no green.0.25 <
x<= 0.75,yvaries linearly from 0 to 1.0.75 <
x<= 1,yremains at 1, full green.
If there are discontinuities, then it is a little more complicated. Label the 3elements in each row in the cdict entry for a given color as (x, y0,y1). Then for values of x between x[i] and x[i+1] the color valueis interpolated between y1[i] and y0[i+1].
Going back to a cookbook example:
cdict = { 'red': ( (0.0, 0.0, 0.0), (0.5, 1.0, 0.7), (1.0, 1.0, 1.0), ), 'green': ( (0.0, 0.0, 0.0), (0.5, 1.0, 0.0), (1.0, 1.0, 1.0), ), 'blue': ( (0.0, 0.0, 0.0), (0.5, 0.0, 0.0), (1.0, 1.0, 1.0), )}
and look at cdict['red'][1]; because y0 != y1, it is saying that forx from 0 to 0.5, red increases from 0 to 1, but then it jumps down, so thatfor x from 0.5 to 1, red increases from 0.7 to 1. Green ramps from 0 to 1as x goes from 0 to 0.5, then jumps back to 0, and ramps back to 1 as xgoes from 0.5 to 1.
Above is an attempt to show that for x in the range x[i] to x[i+1],the interpolation is between y1[i] and y0[i+1]. So, y0[0] andy1[-1] are never used.
Colormaps from a list#
colors = [(1, 0, 0), (0, 1, 0), (0, 0, 1)] # R -> G -> Bn_bins = [3, 6, 10, 100] # Discretizes the interpolation into binscmap_name = 'my_list'fig, axs = plt.subplots(2, 2, figsize=(6, 9))fig.subplots_adjust(left=0.02, bottom=0.06, right=0.95, top=0.94, wspace=0.05)for n_bin, ax in zip(n_bins, axs.flat): # Create the colormap cmap = LinearSegmentedColormap.from_list(cmap_name, colors, N=n_bin) # Fewer bins will result in "coarser" colomap interpolation im = ax.imshow(Z, origin='lower', cmap=cmap) ax.set_title("N bins: %s" % n_bin) fig.colorbar(im, ax=ax)
Custom colormaps#
cdict1 = { 'red': ( (0.0, 0.0, 0.0), (0.5, 0.0, 0.1), (1.0, 1.0, 1.0), ), 'green': ( (0.0, 0.0, 0.0), (1.0, 0.0, 0.0), ), 'blue': ( (0.0, 0.0, 1.0), (0.5, 0.1, 0.0), (1.0, 0.0, 0.0), )}cdict2 = { 'red': ( (0.0, 0.0, 0.0), (0.5, 0.0, 1.0), (1.0, 0.1, 1.0), ), 'green': ( (0.0, 0.0, 0.0), (1.0, 0.0, 0.0), ), 'blue': ( (0.0, 0.0, 0.1), (0.5, 1.0, 0.0), (1.0, 0.0, 0.0), )}cdict3 = { 'red': ( (0.0, 0.0, 0.0), (0.25, 0.0, 0.0), (0.5, 0.8, 1.0), (0.75, 1.0, 1.0), (1.0, 0.4, 1.0), ), 'green': ( (0.0, 0.0, 0.0), (0.25, 0.0, 0.0), (0.5, 0.9, 0.9), (0.75, 0.0, 0.0), (1.0, 0.0, 0.0), ), 'blue': ( (0.0, 0.0, 0.4), (0.25, 1.0, 1.0), (0.5, 1.0, 0.8), (0.75, 0.0, 0.0), (1.0, 0.0, 0.0), )}# Make a modified version of cdict3 with some transparency# in the middle of the range.cdict4 = { **cdict3, 'alpha': ( (0.0, 1.0, 1.0), # (0.25, 1.0, 1.0), (0.5, 0.3, 0.3), # (0.75, 1.0, 1.0), (1.0, 1.0, 1.0), ),}
Now we will use this example to illustrate 2 ways ofhandling custom colormaps.First, the most direct and explicit:
blue_red1 = LinearSegmentedColormap('BlueRed1', cdict1)
Second, create the map explicitly and register it.Like the first method, this method works with any kindof Colormap, not justa LinearSegmentedColormap:
Make the figure, with 4 subplots:
fig, axs = plt.subplots(2, 2, figsize=(6, 9))fig.subplots_adjust(left=0.02, bottom=0.06, right=0.95, top=0.94, wspace=0.05)im1 = axs[0, 0].imshow(Z, cmap=blue_red1)fig.colorbar(im1, ax=axs[0, 0])im2 = axs[1, 0].imshow(Z, cmap='BlueRed2')fig.colorbar(im2, ax=axs[1, 0])# Now we will set the third cmap as the default. One would# not normally do this in the middle of a script like this;# it is done here just to illustrate the method.plt.rcParams['image.cmap'] = 'BlueRed3'im3 = axs[0, 1].imshow(Z)fig.colorbar(im3, ax=axs[0, 1])axs[0, 1].set_title("Alpha = 1")# Or as yet another variation, we can replace the rcParams# specification *before* the imshow with the following *after*# imshow.# This sets the new default *and* sets the colormap of the last# image-like item plotted via pyplot, if any.## Draw a line with low zorder so it will be behind the image.axs[1, 1].plot([0, 10 * np.pi], [0, 20 * np.pi], color='c', lw=20, zorder=-1)im4 = axs[1, 1].imshow(Z)fig.colorbar(im4, ax=axs[1, 1])# Here it is: changing the colormap for the current image and its# colorbar after they have been plotted.im4.set_cmap('BlueRedAlpha')axs[1, 1].set_title("Varying alpha")fig.suptitle('Custom Blue-Red colormaps', fontsize=16)fig.subplots_adjust(top=0.9)plt.show()
References
The use of the following functions, methods, classes and modules is shownin this example:
matplotlib.axes.Axes.imshow / matplotlib.pyplot.imshow
matplotlib.figure.Figure.colorbar / matplotlib.pyplot.colorbar
matplotlib.colors
matplotlib.colors.LinearSegmentedColormap
matplotlib.colors.LinearSegmentedColormap.from_list
matplotlib.cm
matplotlib.cm.ScalarMappable.set_cmap
matplotlib.cm.ColormapRegistry.register
Total running time of the script: (0 minutes 1.999 seconds)
Download Jupyter notebook: custom_cmap.ipynb
Download Python source code: custom_cmap.py
