#!/usr/bin/env python import numpy as np import matplotlib.pyplot as plt from matplotlib.colors import LinearSegmentedColormap """ 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. """ 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)) } # 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 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) plt.figure(figsize=(10,4)) plt.subplots_adjust(wspace=0.3) plt.subplot(1,3,1) plt.imshow(Z, interpolation='nearest', cmap=blue_red1) plt.colorbar() plt.subplot(1,3,2) cmap = plt.get_cmap('BlueRed2') plt.imshow(Z, interpolation='nearest', cmap=cmap) plt.colorbar() # 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' # Also see below for an alternative, particularly for # interactive use. plt.subplot(1,3,3) plt.imshow(Z, interpolation='nearest') plt.colorbar() # Or as yet another variation, we could replace the rcParams # specification *before* the imshow with the following *after* # imshow: # # plt.set_cmap('BlueRed3') # # This sets the new default *and* sets the colormap of the last # image-like item plotted via pyplot, if any. plt.suptitle('Custom Blue-Red colormaps') plt.show()

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