pcolormesh¶

axes.Axes.pcolormesh allows you to generate 2D image-style plots. Note that it is faster than the similar pcolor.

import matplotlib.pyplot as plt
import numpy as np

from matplotlib.colors import BoundaryNorm
from matplotlib.ticker import MaxNLocator

Basic pcolormesh¶

We usually specify a pcolormesh by defining the edge of quadrilaterals and the value of the quadrilateral. Note that here x and y each have one extra element than Z in the respective dimension.

np.random.seed(19680801)
Z = np.random.rand(6, 10)
x = np.arange(-0.5, 10, 1)  # len = 11
y = np.arange(4.5, 11, 1)  # len = 7

fig, ax = plt.subplots()
ax.pcolormesh(x, y, Z)

Non-rectilinear pcolormesh¶

Note that we can also specify matrices for X and Y and have non-rectilinear quadrilaterals.

x = np.arange(-0.5, 10, 1)  # len = 11
y = np.arange(4.5, 11, 1)  # len = 7
X, Y = np.meshgrid(x, y)
X = X + 0.2 * Y  # tilt the coordinates.
Y = Y + 0.3 * X

fig, ax = plt.subplots()
ax.pcolormesh(X, Y, Z)

Centered Coordinates¶

Often a user wants to pass X and Y with the same sizes as Z to axes.Axes.pcolormesh. This is also allowed if shading='auto' is passed (default set by rcParams["pcolor.shading"] (default: 'auto')). Pre Matplotlib 3.3, shading='flat' would drop the last column and row of Z, but now gives an error. If this is really what you want, then simply drop the last row and column of Z manually:

x = np.arange(10)  # len = 10
y = np.arange(6)  # len = 6
X, Y = np.meshgrid(x, y)

fig, axs = plt.subplots(2, 1, sharex=True, sharey=True)
axs[0].pcolormesh(X, Y, Z, vmin=np.min(Z), vmax=np.max(Z), shading='auto')
axs[0].set_title("shading='auto' = 'nearest'")
axs[1].pcolormesh(X, Y, Z[:-1, :-1], vmin=np.min(Z), vmax=np.max(Z),
                  shading='flat')
axs[1].set_title("shading='flat'")

shading='auto' = 'nearest', shading='flat'

Making levels using Norms¶

Shows how to combine Normalization and Colormap instances to draw "levels" in axes.Axes.pcolor, axes.Axes.pcolormesh and axes.Axes.imshow type plots in a similar way to the levels keyword argument to contour/contourf.

# make these smaller to increase the resolution
dx, dy = 0.05, 0.05

# generate 2 2d grids for the x & y bounds
y, x = np.mgrid[slice(1, 5 + dy, dy),
                slice(1, 5 + dx, dx)]

z = np.sin(x)**10 + np.cos(10 + y*x) * np.cos(x)

# x and y are bounds, so z should be the value *inside* those bounds.
# Therefore, remove the last value from the z array.
z = z[:-1, :-1]
levels = MaxNLocator(nbins=15).tick_values(z.min(), z.max())


# pick the desired colormap, sensible levels, and define a normalization
# instance which takes data values and translates those into levels.
cmap = plt.colormaps['PiYG']
norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)

fig, (ax0, ax1) = plt.subplots(nrows=2)

im = ax0.pcolormesh(x, y, z, cmap=cmap, norm=norm)
fig.colorbar(im, ax=ax0)
ax0.set_title('pcolormesh with levels')


# contours are *point* based plots, so convert our bound into point
# centers
cf = ax1.contourf(x[:-1, :-1] + dx/2.,
                  y[:-1, :-1] + dy/2., z, levels=levels,
                  cmap=cmap)
fig.colorbar(cf, ax=ax1)
ax1.set_title('contourf with levels')

# adjust spacing between subplots so `ax1` title and `ax0` tick labels
# don't overlap
fig.tight_layout()

plt.show()

pcolormesh with levels, contourf with levels

References

The use of the following functions, methods, classes and modules is shown in this example:

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