CODEWITHSUNDEEP
pythonmatplotlibplotcolor-mapping
silence_of_the_lambdas
silence_of_the_lambdas

Reputation: 1272

Matplotlib image plot - nan values shown as lowest color of colormaps instead of transparent

I am using matplotlib 3.0.3 and want to create an animation of image plots using the FuncAnimation module. For plotting speed, I update the image data using im.set_data for imshow and im.set_array() for plt.pcolormesh (set_data is not available as an attribute). If I update the data partially with NaN values, imshow displays them as blank pixels, while pcolormesh shows them as the lowest color from the colormap (blue for viridis).

Is this intended and if not, why is this the behavior? It seems related to set_array(), since pcolormesh normally does plot NaN as blank pixels.

Minimal example:

import numpy as np
import matplotlib.pyplot as plt

fig1 = plt.figure()
im1 = plt.pcolormesh(np.random.rand(10,10))
im1.set_array((np.zeros((10,10)) * np.nan).ravel())

fig2 = plt.figure()
im2 = plt.imshow(np.random.rand(10,10))
im2.set_data(np.zeros((10,10)) * np.nan)

Upvotes: 0

Views: 2165

Answers (1)

ImportanceOfBeingErnest
ImportanceOfBeingErnest

Reputation: 339052

Matplotlib images do not work well with nans. One should instead use masked arrays. Then both cases are the same (except for the need to flatten the pcolormesh array).

import numpy as np
import matplotlib.pyplot as plt

A = np.random.rand(10,10)
B = np.ma.array(A, mask=np.ones((10,10)))

fig1 = plt.figure()
im1 = plt.pcolormesh(A)
im1.set_array(B.ravel())
plt.colorbar()

fig2 = plt.figure()
im2 = plt.imshow(A)
im2.set_array(B)
plt.colorbar()

plt.show()

Upvotes: 0

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