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DivergingNorm colormap normalizationΒΆ

Sometimes we want to have a different colormap on either side of a conceptual center point, and we want those two colormaps to have different linear scales. An example is a topographic map where the land and ocean have a center at zero, but land typically has a greater elevation range than the water has depth range, and they are often represented by a different colormap.

../../_images/sphx_glr_colormap_normalizations_diverging_001.png
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cbook as cbook
import matplotlib.colors as colors

filename = cbook.get_sample_data('topobathy.npz', asfileobj=False)
with np.load(filename) as dem:
    topo = dem['topo']
    longitude = dem['longitude']
    latitude = dem['latitude']

fig, ax = plt.subplots(constrained_layout=True)
# make a colormap that has land and ocean clearly delineated and of the
# same length (256 + 256)
colors_undersea = plt.cm.terrain(np.linspace(0, 0.17, 256))
colors_land = plt.cm.terrain(np.linspace(0.25, 1, 256))
all_colors = np.vstack((colors_undersea, colors_land))
terrain_map = colors.LinearSegmentedColormap.from_list('terrain_map',
    all_colors)

# make the norm:  Note the center is offset so that the land has more
# dynamic range:
divnorm = colors.DivergingNorm(vmin=-500, vcenter=0, vmax=4000)

pcm = ax.pcolormesh(longitude, latitude, topo, rasterized=True, norm=divnorm,
    cmap=terrain_map,)
ax.set_xlabel('Lon $[^o E]$')
ax.set_ylabel('Lat $[^o N]$')
ax.set_aspect(1 / np.cos(np.deg2rad(49)))
fig.colorbar(pcm, shrink=0.6, extend='both', label='Elevation [m]')
plt.show()

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