### Related Topics

Demonstrates a few common tricks with shaded plots.

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LightSource, Normalize

def display_colorbar():
"""Display a correct numeric colorbar for a shaded plot."""
y, x = np.mgrid[-4:2:200j, -4:2:200j]
z = 10 * np.cos(x**2 + y**2)

cmap = plt.cm.copper
ls = LightSource(315, 45)

fig, ax = plt.subplots()
ax.imshow(rgb, interpolation='bilinear')

# Use a proxy artist for the colorbar...
im = ax.imshow(z, cmap=cmap)
im.remove()
fig.colorbar(im)

ax.set_title('Using a colorbar with a shaded plot', size='x-large')

def avoid_outliers():
"""Use a custom norm to control the displayed z-range of a shaded plot."""
y, x = np.mgrid[-4:2:200j, -4:2:200j]
z = 10 * np.cos(x**2 + y**2)

z[100, 105] = 2000
z[120, 110] = -9000

ls = LightSource(315, 45)
fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(8, 4.5))

ax1.imshow(rgb, interpolation='bilinear')
ax1.set_title('Full range of data')

rgb = ls.shade(z, plt.cm.copper, vmin=-10, vmax=10)
ax2.imshow(rgb, interpolation='bilinear')
ax2.set_title('Manually set range')

fig.suptitle('Avoiding Outliers in Shaded Plots', size='x-large')

"""Demonstrates displaying different variables through shade and color."""
y, x = np.mgrid[-4:2:200j, -4:2:200j]
z1 = np.sin(x**2)  # Data to hillshade
z2 = np.cos(x**2 + y**2)  # Data to color

norm = Normalize(z2.min(), z2.max())
cmap = plt.cm.RdBu

ls = LightSource(315, 45)

fig, ax = plt.subplots()
ax.imshow(rgb, interpolation='bilinear')
ax.set_title('Shade by one variable, color by another', size='x-large')

display_colorbar()
avoid_outliers()
plt.show()


Keywords: matplotlib code example, codex, python plot, pyplot Gallery generated by Sphinx-Gallery