Matplotlib imshow: Color of NaN changes when change the center value of colorbar

Question

I plotted a data with np.NaN. And I also want to change the center value of the colorbar due to the distribution of original data. But when I change the Vmin, Vmax and vcenter value of the colorbar, the color of np.NaN value changes to other colors other than white. So how can I fix that? Here follows the codes:

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as colors_tmp
class MidpointNormalize(colors_tmp.Normalize):
    def __init__(self, vmin=None, vmax=None, vcenter=None, clip=False):
        self.vcenter = vcenter
        colors_tmp.Normalize.__init__(self, vmin, vmax, clip)

    def __call__(self, value, clip=None):
        # I'm ignoring masked values and all kinds of edge cases to make a
        # simple example...
        x, y = [self.vmin, self.vcenter, self.vmax], [0, 0.5, 1]
        return np.ma.masked_array(np.interp(value, x, y))

img = np.linspace(1,1000,1000).reshape((20,50))
img[(img>700)*(img<800)] = np.nan

fig, ax = plt.subplots(1,1)
sc = ax.imshow(img)

axpos = ax.get_position()
cbar_ax = fig.add_axes(
    [axpos.x1, axpos.y0, 0.01, axpos.height])  # l, b, w, h
cbar = fig.colorbar(sc, cax=cbar_ax)

Then I change the Vmin, Vmax and vcenter of the colorbar like this:

fig, ax = plt.subplots(1,1)
sc = ax.imshow(img)

axpos = ax.get_position()
cbar_ax = fig.add_axes(
    [axpos.x1, axpos.y0, 0.01, axpos.height])  # l, b, w, h
cbar = fig.colorbar(sc, cax=cbar_ax)
midnorm = MidpointNormalize(vmin=0, vcenter=200, vmax=500)
cbar.mappable.set_norm(midnorm)
cbar.mappable.set_cmap('BrBG')

The results are like below, we can see that the color of np.NaN is still white.

But when I change it to vmin=0, vcenter=800, vmax=1000, things get weird:

fig, ax = plt.subplots(1,1)
sc = ax.imshow(img)

axpos = ax.get_position()
cbar_ax = fig.add_axes(
    [axpos.x1, axpos.y0, 0.01, axpos.height])  # l, b, w, h
cbar = fig.colorbar(sc, cax=cbar_ax)
midnorm = MidpointNormalize(vmin=0, vcenter=800, vmax=1000)
cbar.mappable.set_norm(midnorm)
cbar.mappable.set_cmap('BrBG')

So why is that? and I want to keep the np.NaN value as white, I tried the ax.set_patch and also the set_bad(color="white"), they didn't work...so is there anyone who could help me? Thanks a lot!

Answer 1

Using the latest matplotlib version (3.4.2), the code seems to work as expected.

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

class MidpointNormalize(Normalize):
    def __init__(self, vmin=None, vmax=None, vcenter=None, clip=False):
        self.vcenter = vcenter
        colors_tmp.Normalize.__init__(self, vmin, vmax, clip)

    def __call__(self, value, clip=None):
        # I'm ignoring masked values and all kinds of edge cases to make a
        # simple example...
        x, y = [self.vmin, self.vcenter, self.vmax], [0, 0.5, 1]
        return np.ma.masked_array(np.interp(value, x, y))

img = np.linspace(1, 1000, 1000).reshape((20, 50))
img[(img > 700) * (img < 800)] = np.nan

fig, ax = plt.subplots(1, 1)
sc = ax.imshow(img)

axpos = ax.get_position()
cbar_ax = fig.add_axes(
    [axpos.x1 + 0.01, axpos.y0, 0.01, axpos.height])  # l, b, w, h
cbar = fig.colorbar(sc, cax=cbar_ax)
midnorm = MidpointNormalize(vmin=0, vcenter=800, vmax=1000)
cbar.mappable.set_norm(midnorm)
cbar.mappable.set_cmap('BrBG')
plt.show()

Additional, you could try:

• to set the norm and the cmap directly when calling imshow
• to use TwoSlopeNorm instead of a custom norm
• to explicitly set the "bad" color (to either 'none' for transparent, showing the background, or 'white' to fix the color undependent of the background)

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

img = np.linspace(1, 1000, 1000).reshape((20, 50))
img[(img > 700) & (img < 800)] = np.nan

fig, ax = plt.subplots(1, 1)
cmap = plt.get_cmap('BrBG')
cmap.set_bad('white')
midnorm = mcolors.TwoSlopeNorm(vmin=0, vcenter=800, vmax=1000)
sc = ax.imshow(img, norm=midnorm, cmap=cmap)

axpos = ax.get_position()
cbar_ax = fig.add_axes(
    [axpos.x1 + 0.01, axpos.y0, 0.01, axpos.height])  # l, b, w, h
cbar = fig.colorbar(sc, cax=cbar_ax)
plt.show()