How to create a heat map in python that ranges from green to red?

Question

I'm trying to plot log ratios from the range -3 to 3 and want negative ratios to be green and positive to be red, with a log ratio of 0 (center) to be white in color. None of the pre-existing color schemes in matplotlib provide this option, and I haven't been able to figure out how to output a nice gradient manually.

Answer 1

Using matplotlib.colors.LinearSegmentedColormap's from_list method seems more intuitive than some of the other answers here.

from  matplotlib.colors import LinearSegmentedColormap
cmap=LinearSegmentedColormap.from_list('rg',["r", "w", "g"], N=256) 

Or for more sophisticated tuning:

from  matplotlib.colors import LinearSegmentedColormap
c = ["darkred","red","lightcoral","white", "palegreen","green","darkgreen"]
v = [0,.15,.4,.5,0.6,.9,1.]
l = list(zip(v,c))
cmap=LinearSegmentedColormap.from_list('rg',l, N=256)

Answer 2

you can create your own using a LinearSegmentedColormap. I like to set the red and green channels to something less than 1.0 at the upper and lower limits so the colours aren't too bright (here I used 0.8). Adjust that to suit your taste.

See the custom_cmap example on the matplotlib website for further details.

Here's an working example:

import matplotlib.pyplot as plt
import matplotlib.colors as colors
import numpy as np

# This dictionary defines the colormap
cdict = {'red':  ((0.0, 0.0, 0.0),   # no red at 0
                  (0.5, 1.0, 1.0),   # all channels set to 1.0 at 0.5 to create white
                  (1.0, 0.8, 0.8)),  # set to 0.8 so its not too bright at 1

        'green': ((0.0, 0.8, 0.8),   # set to 0.8 so its not too bright at 0
                  (0.5, 1.0, 1.0),   # all channels set to 1.0 at 0.5 to create white
                  (1.0, 0.0, 0.0)),  # no green at 1

        'blue':  ((0.0, 0.0, 0.0),   # no blue at 0
                  (0.5, 1.0, 1.0),   # all channels set to 1.0 at 0.5 to create white
                  (1.0, 0.0, 0.0))   # no blue at 1
       }

# Create the colormap using the dictionary
GnRd = colors.LinearSegmentedColormap('GnRd', cdict)

# Make a figure and axes
fig,ax = plt.subplots(1)

# Some fake data in the range -3 to 3
dummydata = np.random.rand(5,5)*6.-3.

# Plot the fake data
p=ax.pcolormesh(dummydata,cmap=GnRd,vmin=-3,vmax=3)

# Make a colorbar
fig.colorbar(p,ax=ax)

plt.show()

Answer 3

How about the following that uses LinearSegmentedColormap:

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.colors import LinearSegmentedColormap


cmapGR = LinearSegmentedColormap(
    'GreenRed',
    {
        'red':  ((0.0, 0.0, 0.0),
                (0.5, 1.0, 1.0),
                (1.0, 1.0, 1.0)),
        'green':((0.0, 1.0, 1.0),
                (0.5, 1.0, 1.0),
                ( 1.0, 0.0, 0.0)),
        'blue': ((0.0, 0.0, 0.0),
                (0.5, 1.0, 1.0),
                (1.0, 0.0, 0.0))
    },)

plt.imshow(np.array([np.arange(200) for i in range(200)]), cmap=cmapGR)
plt.show()

It produces the following

See e.g. http://matplotlib.org/examples/pylab_examples/custom_cmap.html for more uses and other examples.