# Matplotlib reverse colormap

## Problem

In order to use plot surface, I’d like to know how to simply invert the color order of a given colormap.

## 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.)

## 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')
``````

## 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])
``````

## 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.

## 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 goes from 0 to 1
row i:   x  y0  y1 -> t t t
/
/
row i+1: x  y0  y1 -> t[n] t t

so the inverse should do the same:
row i+1: x  y1  y0 -> 1-t t t
/
/
row i:   x  y1  y0 -> 1-t[n] t t
"""
reverse = []
k = []

for key in cmap._segmentdata:
k.append(key)
channel = cmap._segmentdata[key]
data = []

for t in channel:
data.append((1-t,t,t))
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')
``````