Problem
In order to use plot surface, I’d like to know how to simply invert the color order of a given colormap.
Asked by Mermoz
Solution #1
All of the basic colormaps have reversed variants as well. They have the same names, but with the suffix _r added at the end. (Documentation can be found here.)
Answered by ptomato
Solution #2
The solution is relatively simple. Let’s say you wish to employ the colormap scheme “fall.” The standard version is as follows:
cmap = matplotlib.cm.autumn
Use the get cmap() function and append ‘_r’ to the colormap title to reverse the colormap color spectrum:
cmap_reversed = matplotlib.cm.get_cmap('autumn_r')
Answered by Jm M
Solution #3
A color map is not a list in matplotlib, but it does contain a list of its colors as colormap.colors. ListedColormap() is a function in the matplotlib.colors module that generates a color map from a list. As a result, you can reverse any color map by doing the following.
colormap_r = ListedColormap(colormap.colors[::-1])
Answered by Gilles
Solution #4
As of Matplotlib 2.0, there is a reversed() method for ListedColormap and LinearSegmentedColorMap objects, so you can just do
cmap_reversed = cmap.reversed()
The documentation can be found here.
Answered by David Stansby
Solution #5
Because LinearSegmentedColormaps is built on a red, green, and blue dictionary, each item must be reversed:
import matplotlib.pyplot as plt
import matplotlib as mpl
def reverse_colourmap(cmap, name = 'my_cmap_r'):
"""
In:
cmap, name
Out:
my_cmap_r
Explanation:
t[0] goes from 0 to 1
row i: x y0 y1 -> t[0] t[1] t[2]
/
/
row i+1: x y0 y1 -> t[n] t[1] t[2]
so the inverse should do the same:
row i+1: x y1 y0 -> 1-t[0] t[2] t[1]
/
/
row i: x y1 y0 -> 1-t[n] t[2] t[1]
"""
reverse = []
k = []
for key in cmap._segmentdata:
k.append(key)
channel = cmap._segmentdata[key]
data = []
for t in channel:
data.append((1-t[0],t[2],t[1]))
reverse.append(sorted(data))
LinearL = dict(zip(k,reverse))
my_cmap_r = mpl.colors.LinearSegmentedColormap(name, LinearL)
return my_cmap_r
Check to see if it works:
my_cmap
<matplotlib.colors.LinearSegmentedColormap at 0xd5a0518>
my_cmap_r = reverse_colourmap(my_cmap)
fig = plt.figure(figsize=(8, 2))
ax1 = fig.add_axes([0.05, 0.80, 0.9, 0.15])
ax2 = fig.add_axes([0.05, 0.475, 0.9, 0.15])
norm = mpl.colors.Normalize(vmin=0, vmax=1)
cb1 = mpl.colorbar.ColorbarBase(ax1, cmap = my_cmap, norm=norm,orientation='horizontal')
cb2 = mpl.colorbar.ColorbarBase(ax2, cmap = my_cmap_r, norm=norm, orientation='horizontal')
EDIT
I don’t understand user3445587’s comment. On the rainbow colormap, it works perfectly:
cmap = mpl.cm.jet
cmap_r = reverse_colourmap(cmap)
fig = plt.figure(figsize=(8, 2))
ax1 = fig.add_axes([0.05, 0.80, 0.9, 0.15])
ax2 = fig.add_axes([0.05, 0.475, 0.9, 0.15])
norm = mpl.colors.Normalize(vmin=0, vmax=1)
cb1 = mpl.colorbar.ColorbarBase(ax1, cmap = cmap, norm=norm,orientation='horizontal')
cb2 = mpl.colorbar.ColorbarBase(ax2, cmap = cmap_r, norm=norm, orientation='horizontal')
It’s especially useful for custom declared colormaps, because there’s no default _r for custom declared colormaps. From http://matplotlib.org/examples/pylab examples/custom cmap.html, here’s an example:
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))
}
blue_red1 = mpl.colors.LinearSegmentedColormap('BlueRed1', cdict1)
blue_red1_r = reverse_colourmap(blue_red1)
fig = plt.figure(figsize=(8, 2))
ax1 = fig.add_axes([0.05, 0.80, 0.9, 0.15])
ax2 = fig.add_axes([0.05, 0.475, 0.9, 0.15])
norm = mpl.colors.Normalize(vmin=0, vmax=1)
cb1 = mpl.colorbar.ColorbarBase(ax1, cmap = blue_red1, norm=norm,orientation='horizontal')
cb2 = mpl.colorbar.ColorbarBase(ax2, cmap = blue_red1_r, norm=norm, orientation='horizontal')
Answered by Mattijn
Post is based on https://stackoverflow.com/questions/3279560/reverse-colormap-in-matplotlib