.. _pylab_examples-custom_cmap: pylab_examples example code: custom_cmap.py =========================================== .. plot:: /home/tcaswell/source/p/matplotlib/doc/mpl_examples/pylab_examples/custom_cmap.py :: #!/usr/bin/env python import numpy as np import matplotlib.pyplot as plt from matplotlib.colors import LinearSegmentedColormap """ Creating a colormap from a list of colors ----------------------------------------- Creating a colormap from a list of colors can be done with the `from_list` method of `LinearSegmentedColormap`. You must pass 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 is accomplished by creating dictionary that specifies how the RGB channels change from one end of the cmap to the other. Example: suppose you want red to increase from 0 to 1 over the bottom half, green to do the same over the middle half, and blue over the top half. 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 b components, then it is quite simple: the second and third element of each tuple, above, is the same--call it "y". The first element ("x") defines interpolation intervals over the full range of 0 to 1, and it must span that whole range. In other words, the values of x divide the 0-to-1 range into a set of segments, and y gives the end-point color values for each segment. Now consider the green. cdict['green'] is saying that for 0 <= x <= 0.25, y is zero; no green. 0.25 < x <= 0.75, y varies linearly from 0 to 1. x > 0.75, y remains at 1, full green. If there are discontinuities, then it is a little more complicated. Label the 3 elements 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 value is interpolated between y1[i] and y0[i+1]. Going back to the cookbook example, look at cdict['red']; because y0 != y1, it is saying that for x from 0 to 0.5, red increases from 0 to 1, but then it jumps down, so that for x from 0.5 to 1, red increases from 0.7 to 1. Green ramps from 0 to 1 as x goes from 0 to 0.5, then jumps back to 0, and ramps back to 1 as x goes from 0.5 to 1. row i: x y0 y1 / / row i+1: x y0 y1 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] and y1[-1] are never used. """ # Make some illustrative fake data: x = np.arange(0, np.pi, 0.1) y = np.arange(0, 2*np.pi, 0.1) X, Y = np.meshgrid(x, y) Z = np.cos(X) * np.sin(Y) * 10 # --- Colormaps from a list --- colors = [(1, 0, 0), (0, 1, 0), (0, 0, 1)] # R -> G -> B n_bins = [3, 6, 10, 100] # Discretizes the interpolation into bins cmap_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.ravel()): # Create the colormap cm = LinearSegmentedColormap.from_list( cmap_name, colors, N=n_bin) # Fewer bins will result in "coarser" colomap interpolation im = ax.imshow(Z, interpolation='nearest', origin='lower', cmap=cm) 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.copy() cdict4['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 3 ways of # handling 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 kind # of Colormap, not just # a LinearSegmentedColormap: blue_red2 = LinearSegmentedColormap('BlueRed2', cdict2) plt.register_cmap(cmap=blue_red2) # Third, for LinearSegmentedColormap only, # leave everything to register_cmap: plt.register_cmap(name='BlueRed3', data=cdict3) # optional lut kwarg plt.register_cmap(name='BlueRedAlpha', data=cdict4) # Make the figure: 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) # Make 4 subplots: im1 = axs[0, 0].imshow(Z, interpolation='nearest', cmap=blue_red1) fig.colorbar(im1, ax=axs[0, 0]) cmap = plt.get_cmap('BlueRed2') im2 = axs[1, 0].imshow(Z, interpolation='nearest', cmap=cmap) 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, interpolation='nearest') 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, interpolation='nearest') 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) plt.show() Keywords: python, matplotlib, pylab, example, codex (see :ref:`how-to-search-examples`)