matplotlib - specifying colors with an rgba array using pcolormesh

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I have data defined on a (n_y,n_x) grid that I have converted to colors in an (n_y,n_x,4) np.ndarray. I'd like to show these colors using pcolormesh.

I've tried passing the facecolors argument to pcolormesh, which doesn't do anything, and using a ListedColormap to map each (y,x) cell to a color, which doesn't work either.

The code below reproduces the issues I'm having.

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

'''
Define some arbitrary data
'''

dx = 0.01

x_range = [5,8]
y_range = [0,2]

x = np.arange(x_range[0],x_range[1],dx)
y = np.arange(y_range[0],y_range[1],dx)
X,Y = np.meshgrid(x,y)

data = X+Y**2

'''
Define colors based on the data
'''

def data_to_colors(data):
    colors = np.moveaxis(np.zeros_like([data]*4),0,-1) # shape (n_y,n_x,4)
    
    # make the data correspond to the blue channel, scaled between 0 and 1
    colors[...,2] = (data - data.min()) / (data.max()-data.min())

    # make red and green colors 0.5
    colors[...,0] = 0.5
    colors[...,1] = 0.5
    
    # make the alpha values all 1
    colors[...,-1] = 1
    
    return colors

'''
Show with imshow and pcolormesh
'''

fig,axs = plt.subplots(1,3,sharex=True,sharey=True,figsize=(12,4))

# show with imshow
extent = [x_range[0]-dx/2, x_range[-1]+dx/2, y_range[0]-dx/2, y_range[-1]+dx/2]
axs[0].imshow(data_to_colors(data),extent=extent,origin='lower')
axs[0].set_title('imshow (correct)')

# show with pcolormesh and facecolors
axs[1].pcolormesh(X,Y,np.ones_like(X),facecolors=data_to_colors(data.flatten()))
axs[1].set_title('pcolormesh, specifying facecolors')

# show using a ListedColorMap mapping each individual (row,column) to a color
ixs = np.arange(len(x)*len(y))
colors = data_to_colors(data.flatten())
axs[2].pcolormesh(X,Y,ixs.reshape(len(y),len(x)),cmap=ListedColormap(colors))
axs[2].set_title('pcolormesh, using a ListedColormap')

for ax in axs:
    ax.set_aspect('equal')
    ax.set_xlabel('x')
    ax.set_ylabel('y')
fig.tight_layout()

Here is an image of the figure made by the above code.

Is there a way I can get the same result I get from imshow using pcolormesh (or any method that will work when the rows/columns of data don't necessarily correspond to constant values of y/x)?

matplotlib python
1 Answers

For ListedColormap you need a list of (maximum) 256 different colors. You can create them by providing a list of 256 values to the data_to_colors() function. The easiest way to create these input values is with np.linspace(0, 1, 256).

For the facecolors approach, it seems matplotlib needs a call to fig.canvas.draw() for the array of facecolors to be created. Thereafter, they can be set via .set_facecolors. Also important is that a pcolormesh draws faces between the mesh vertices (contrary to imshow which gives a color to each vertex). Therefore, there is one row and one column less than there are vertices in the mesh. Either you need to add an extra row and extra column to the mesh, or leave out one row and one column from the facecolors.

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

dx = 0.01
x_range = [5, 8]
y_range = [0, 2]
x = np.arange(x_range[0], x_range[1], dx)
y = np.arange(y_range[0], y_range[1], dx)
X, Y = np.meshgrid(x, y)
data = X + Y ** 2

# Define colors based on the data
def data_to_colors(data):
    colors = np.moveaxis(np.zeros_like([data] * 4), 0, -1)  # shape (n_y,n_x,4)
    # make the data correspond to the blue channel, scaled between 0 and 1
    colors[..., 2] = (data - data.min()) / (data.max() - data.min())
    # make red and green colors 0.5
    colors[..., 0] = 0.5
    colors[..., 1] = 0.5
    # make the alpha values all 1
    colors[..., -1] = 1
    return colors

fig, axs = plt.subplots(1, 3, sharex=True, sharey=True, figsize=(12, 4))

# show with imshow
extent = [x_range[0] - dx / 2, x_range[-1] + dx / 2, y_range[0] - dx / 2, y_range[-1] + dx / 2]
axs[0].imshow(data_to_colors(data), extent=extent, origin='lower')
axs[0].set_title('imshow (correct)')

# show by updating the facecolors with set_facecolors
pcmesh = axs[1].pcolormesh(X, Y, data)
fig.canvas.draw()
pcmesh.set_facecolors(data_to_colors(data[:-1, :-1]).reshape(-1, 4))
axs[1].set_title('pcolormesh, using facecolors')

# show using a ListedColorMap mapping each individual (row,column) to a color
colors = data_to_colors(np.linspace(0, 1, 256))
axs[2].pcolormesh(X, Y, data, cmap=ListedColormap(colors))
axs[2].set_title('pcolormesh, using a ListedColormap')

for ax in axs:
    ax.set_aspect('equal')
fig.tight_layout()
plt.show()

resulting comparison

user_120464090
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