[Python] Why is facecolor argument in plot_surface() of matplotlib not working in python?

I want my python code to generate a plot with alternating colors. For the sake of the problem, the alternating colors are irrelevant so let's say they're red and blue. I'm creating a numpy array of the alternating colors with the same shape as the inputs x, y and z (251, 251)

colortuple = ('red', 'blue')
colors = np.array(
[np.array([colortuple[int((xn + yn) % len(colortuple))] for xn in range(xrange)]) for yn in range(trange)])


The array prints the following to the console:

[['red' 'blue' 'red' ... 'red' 'blue' 'red']
['blue' 'red' 'blue' ... 'blue' 'red' 'blue']
['red' 'blue' 'red' ... 'red' 'blue' 'red']
...
['red' 'blue' 'red' ... 'red' 'blue' 'red']
['blue' 'red' 'blue' ... 'blue' 'red' 'blue']
['red' 'blue' 'red' ... 'red' 'blue' 'red']]


which is what I want.

However, when I pass it to plot_surface() as the facecolors argument

axes.plot_surface(X=x, Y=y, Z=z, facecolors=colors)


the plot displayed only has the color of the first argument in colortuple, in this case red.

All 4 variables {x, y, z, colors} have the same (251, 251) shape and are all numpy arrays. I motivated my code from the following website: https://matplotlib.org/2.0.2/mpl_toolkits/mplot3d/tutorial.html

The problem is similar to facecolors in plot_surface matplotlib however I don't get any errors in the console, just a faulty execution.

What am I doing wrong?

Edit:

When I first posted this issue I was too inexperienced to know of the significance of reproducibility. In case anybody else would like to reproduce this issue, here is a slightly modified version of the original code:

from matplotlib.ticker import LinearLocator
from matplotlib import pyplot as plt
from numpy import pi, sin, array, meshgrid, zeros, linspace

# Constants
xmax = 250
xrange = range(xmax + 1)
c = 20.0
dx = 1 / xmax
dt = 0.05 / xmax
k = 4 * pi
multiplier = (c * c * dt * dt / (dx * dx))

# Position and Time Steps.
xsteps = linspace(0, 1, xmax + 1)
tsteps = linspace(0, xmax * dt, xmax + 1)

# Function Psi
# For explanation look up the manual calculations.
psi = zeros(shape=(len(xrange), len(xrange)))
psi[0] = array([
0 if (xn == 0 or xn == xmax) else
5 * sin(k * xsteps[xn]) for xn in xrange
])
psi[1] = array([
0 if (xn == 0 or xn == xmax) else
5 * sin(k * xsteps[xn]) + multiplier
* (2.5 * sin(k * xsteps[xn - 1]) + 2.5 * sin(k * xsteps[xn + 1]) - 5 * sin(k * xsteps[xn]))
for xn in xrange
])
for tn in range(1, xmax):
psi[tn + 1] = array([
0 if (xn == 0 or xn == xmax)
else 2 * psi[tn, xn] - psi[tn - 1, xn] + multiplier * (psi[tn, xn - 1] + psi[tn, xn + 1] - 2 * psi[tn, xn])
for xn in xrange
])

# Setting the parameters for plotting via matplotlib
axes = plt.figure().add_subplot(projection="3d")
(x, y) = meshgrid(xsteps, tsteps)

# Create an empty array of strings with the same shape as the meshgrid,
# and populate it with two colors in a checkerboard pattern.
colortuple = ("red", "blue")
colors = array([array([colortuple[int((xn + yn) % len(colortuple))] for xn in xrange]) for yn in xrange])

# Plotting function.
axes.plot_surface(x, y, psi, facecolors=colors)

# Customize the z axis.
axes.zaxis.set_major_locator(LinearLocator(6))
axes.set_xlabel(r"$x\,[\mathrm{m}]$")
axes.set_ylabel(r"$t\,[\mathrm{s}]$")
axes.set_zlabel(r"$\psi\,(x,t)$")
plt.tight_layout()
plt.show()

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