Creating Colormaps in Matplotlib
Matplotlib has a number of built-in colormaps accessible via matplotlib.cm.get_cmap. There are also external libraries like palettable that have many extra colormaps.
However, we often want to create or manipulate colormaps in Matplotlib. This can be done using the class ListedColormap and a Nx4 numpy array of values between 0 and 1 to represent the RGBA values of the colormap. There is also a LinearSegmentedColormap class that allows colormaps to be specified with a few anchor points defining segments, and linearly interpolating between the anchor points.
Getting colormaps and accessing their values
First, getting a named colormap, most of which are listed in Choosing Colormaps in Matplotlib requires the use of matplotlib.cm.get_cmap, which returns a matplotlib.colors.ListedColormap object. The second argument gives the size of the list of colors used to define the colormap, and below we use a modest value of 12 so there are not a lot of values to look at.
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.colors import ListedColormap, LinearSegmentedColormap
viridis = cm.get_cmap('viridis', 12)
print(viridis)Out:
<matplotlib.colors.ListedColormap object at 0x7f7f3c724390>The object viridis is a callable, that when passed a float between 0 and 1 returns an RGBA value from the colormap:
print(viridis(0.56))Out:
(0.119512, 0.607464, 0.540218, 1.0)The list of colors that comprise the colormap can be directly accessed using the colors property, or it can be accessed indirectly by calling viridis with an array of values matching the length of the colormap. Note that the returned list is in the form of an RGBA Nx4 array, where N is the length of the colormap.
print('viridis.colors', viridis.colors)
print('viridis(range(12))', viridis(range(12)))
print('viridis(np.linspace(0, 1, 12))', viridis(np.linspace(0, 1, 12)))Out:
viridis.colors [[0.267004 0.004874 0.329415 1. ]
[0.283072 0.130895 0.449241 1. ]
[0.262138 0.242286 0.520837 1. ]
[0.220057 0.343307 0.549413 1. ]
[0.177423 0.437527 0.557565 1. ]
[0.143343 0.522773 0.556295 1. ]
[0.119512 0.607464 0.540218 1. ]
[0.166383 0.690856 0.496502 1. ]
[0.319809 0.770914 0.411152 1. ]
[0.525776 0.833491 0.288127 1. ]
[0.762373 0.876424 0.137064 1. ]
[0.993248 0.906157 0.143936 1. ]]
viridis(range(12)) [[0.267004 0.004874 0.329415 1. ]
[0.283072 0.130895 0.449241 1. ]
[0.262138 0.242286 0.520837 1. ]
[0.220057 0.343307 0.549413 1. ]
[0.177423 0.437527 0.557565 1. ]
[0.143343 0.522773 0.556295 1. ]
[0.119512 0.607464 0.540218 1. ]
[0.166383 0.690856 0.496502 1. ]
[0.319809 0.770914 0.411152 1. ]
[0.525776 0.833491 0.288127 1. ]
[0.762373 0.876424 0.137064 1. ]
[0.993248 0.906157 0.143936 1. ]]
viridis(np.linspace(0, 1, 12)) [[0.267004 0.004874 0.329415 1. ]
[0.283072 0.130895 0.449241 1. ]
[0.262138 0.242286 0.520837 1. ]
[0.220057 0.343307 0.549413 1. ]
[0.177423 0.437527 0.557565 1. ]
[0.143343 0.522773 0.556295 1. ]
[0.119512 0.607464 0.540218 1. ]
[0.166383 0.690856 0.496502 1. ]
[0.319809 0.770914 0.411152 1. ]
[0.525776 0.833491 0.288127 1. ]
[0.762373 0.876424 0.137064 1. ]
[0.993248 0.906157 0.143936 1. ]]The colormap is a lookup table, so "oversampling" the colormap returns nearest-neighbor interpolation (note the repeated colors in the list below)
print('viridis(np.linspace(0, 1, 15))', viridis(np.linspace(0, 1, 15)))Out:
viridis(np.linspace(0, 1, 15)) [[0.267004 0.004874 0.329415 1. ]
[0.267004 0.004874 0.329415 1. ]
[0.283072 0.130895 0.449241 1. ]
[0.262138 0.242286 0.520837 1. ]
[0.220057 0.343307 0.549413 1. ]
[0.177423 0.437527 0.557565 1. ]
[0.143343 0.522773 0.556295 1. ]
[0.119512 0.607464 0.540218 1. ]
[0.119512 0.607464 0.540218 1. ]
[0.166383 0.690856 0.496502 1. ]
[0.319809 0.770914 0.411152 1. ]
[0.525776 0.833491 0.288127 1. ]
[0.762373 0.876424 0.137064 1. ]
[0.993248 0.906157 0.143936 1. ]
[0.993248 0.906157 0.143936 1. ]]Creating listed colormaps
This is essential the inverse operation of the above where we supply a Nx4 numpy array with all values between 0 and 1, to ListedColormap to make a new colormap. This means that any numpy operations that we can do on a Nx4 array make carpentry of new colormaps from existing colormaps quite straight forward.
Suppose we want to make the first 25 entries of a 256-length "viridis" colormap pink for some reason:
viridis = cm.get_cmap('viridis', 256)
newcolors = viridis(np.linspace(0, 1, 256))
pink = np.array([248/256, 24/256, 148/256, 1])
newcolors[:25, :] = pink
newcmp = ListedColormap(newcolors)
def plot_examples(cms):
"""
helper function to plot two colormaps
"""
np.random.seed(19680801)
data = np.random.randn(30, 30)
fig, axs = plt.subplots(1, 2, figsize=(6, 3), constrained_layout=True)
for [ax, cmap] in zip(axs, cms):
psm = ax.pcolormesh(data, cmap=cmap, rasterized=True, vmin=-4, vmax=4)
fig.colorbar(psm, ax=ax)
plt.show()
plot_examples([viridis, newcmp])
We can easily reduce the dynamic range of a colormap; here we choose the middle 0.5 of the colormap. However, we need to interpolate from a larger colormap, otherwise the new colormap will have repeated values.
viridisBig = cm.get_cmap('viridis', 512)
newcmp = ListedColormap(viridisBig(np.linspace(0.25, 0.75, 256)))
plot_examples([viridis, newcmp])
and we can easily concatenate two colormaps:
top = cm.get_cmap('Oranges_r', 128)
bottom = cm.get_cmap('Blues', 128)
newcolors = np.vstack((top(np.linspace(0, 1, 128)),
bottom(np.linspace(0, 1, 128))))
newcmp = ListedColormap(newcolors, name='OrangeBlue')
plot_examples([viridis, newcmp])
Of course we need not start from a named colormap, we just need to create the Nx4 array to pass to ListedColormap. Here we create a brown colormap that goes to white....
N = 256
vals = np.ones((N, 4))
vals[:, 0] = np.linspace(90/256, 1, N)
vals[:, 1] = np.linspace(39/256, 1, N)
vals[:, 2] = np.linspace(41/256, 1, N)
newcmp = ListedColormap(vals)
plot_examples([viridis, newcmp])
Creating linear segmented colormaps
LinearSegmentedColormap class specifies colormaps using anchor points between which RGB(A) values are interpolated.
The format to specify these colormaps allows discontinuities at the anchor points. Each anchor point is specified as a row in a matrix of the form [x[i] yleft[i] yright[i]], where x[i] is the anchor, and yleft[i] and yright[i] are the values of the color on either side of the anchor point.
If there are no discontinuities, then yleft[i]=yright[i]:
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]]}
def plot_linearmap(cdict):
newcmp = LinearSegmentedColormap('testCmap', segmentdata=cdict, N=256)
rgba = newcmp(np.linspace(0, 1, 256))
fig, ax = plt.subplots(figsize=(4, 3), constrained_layout=True)
col = ['r', 'g', 'b']
for xx in [0.25, 0.5, 0.75]:
ax.axvline(xx, color='0.7', linestyle='--')
for i in range(3):
ax.plot(np.arange(256)/256, rgba[:, i], color=col[i])
ax.set_xlabel('index')
ax.set_ylabel('RGB')
plt.show()
plot_linearmap(cdict)
In order to make a discontinuity at an anchor point, the third column is different than the second. The matrix for each of "red", "green", "blue", and optionally "alpha" is set up as:
cdict['red'] = [...
[x[i] yleft[i] yright[i]],
[x[i+1] yleft[i+1] yright[i+1]],
...]and for values passed to the colormap between x[i] and x[i+1], the interpolation is between yright[i] and yleft[i+1].
In the example below there is a discontinuity in red at 0.5. The interpolation between 0 and 0.5 goes from 0.3 to 1, and between 0.5 and 1 it goes from 0.9 to 1. Note that red[0, 1], and red[2, 2] are both superfluous to the interpolation because red[0, 1] is the value to the left of 0, and red[2, 2] is the value to the right of 1.0.
cdict['red'] = [[0.0, 0.0, 0.3],
[0.5, 1.0, 0.9],
[1.0, 1.0, 1.0]]
plot_linearmap(cdict)
References
The use of the following functions, methods, classes and modules is shown in this example:
import matplotlib
matplotlib.axes.Axes.pcolormesh
matplotlib.figure.Figure.colorbar
matplotlib.colors
matplotlib.colors.LinearSegmentedColormap
matplotlib.colors.ListedColormap
matplotlib.cm
matplotlib.cm.get_cmapTotal running time of the script: ( 0 minutes 2.220 seconds)