Jun 8, 2025 · May 24, 2025 · May 24, 2025 · May 25, 2025 · May 25, 2025 · May 25, 2025
diff --git a/galleries/examples/color/colorbar_histogram.py b/galleries/examples/color/colorbar_histogram.py
 """
 =====================
 Histogram as colorbar
 =====================

 This example demonstrates how to use a colored histogram instead of a colorbar
 to not only show the color-to-value mapping, but also visualize the
 distribution of values.
 """

 import matplotlib.pyplot as plt
 import numpy as np

 import matplotlib.colors as mcolors

 # surface data
 delta = 0.025
 x = y = np.arange(-2.0, 2.0, delta)
 X, Y = np.meshgrid(x, y)
 Z1 = np.exp(-(((X + 1) * 1.3) ** 2) - ((Y + 1) * 1.3) ** 2)
 Z2 = 2.5 * np.exp(-((X - 1) ** 2) - (Y - 1) ** 2)
 Z = Z1**0.25 - Z2**0.5

 # colormap & normalization
 bins = 30
 cmap = plt.get_cmap("RdYlBu_r")
 bin_edges = np.linspace(Z.min(), Z.max(), bins + 1)
 norm = mcolors.BoundaryNorm(bin_edges, cmap.N)

 # main plot
 fig, ax = plt.subplots(layout="constrained")
 im = ax.imshow(Z, cmap=cmap, origin="lower", extent=[-3, 3, -3, 3], norm=norm)

 # inset histogram
 cax = ax.inset_axes([1.18, 0.02, 0.25, 0.95])  # left, bottom, width, height

 # plot histogram
 counts, _ = np.histogram(Z, bins=bin_edges)
 midpoints = (bin_edges[:-1] + bin_edges[1:]) / 2
 distance = midpoints[1] - midpoints[0]
 cax.barh(midpoints, counts, height=0.8 * distance, color=cmap(norm(midpoints)))

 # styling
 cax.spines[:].set_visible(False)
 cax.set_yticks(bin_edges)
 cax.tick_params(axis="both", which="both", length=0)

 plt.show()
Original file line number	Diff line number	Diff line change
		@@ -0,0 +1,48 @@
		"""
		=====================
		Histogram as colorbar
		=====================

		This example demonstrates how to use a colored histogram instead of a colorbar
		to not only show the color-to-value mapping, but also visualize the
		distribution of values.
		"""

		import matplotlib.pyplot as plt
		import numpy as np

		import matplotlib.colors as mcolors

		# surface data
		delta = 0.025
		x = y = np.arange(-2.0, 2.0, delta)
		X, Y = np.meshgrid(x, y)
		Z1 = np.exp(-(((X + 1) * 1.3) ** 2) - ((Y + 1) * 1.3) ** 2)
		Z2 = 2.5 * np.exp(-((X - 1) 2) - (Y - 1) 2)
		Z = Z10.25 - Z20.5

		# colormap & normalization
		bins = 30
		cmap = plt.get_cmap("RdYlBu_r")
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		bin_edges = np.linspace(Z.min(), Z.max(), bins + 1)
		norm = mcolors.BoundaryNorm(bin_edges, cmap.N)

		# main plot
		fig, ax = plt.subplots(layout="constrained")
		im = ax.imshow(Z, cmap=cmap, origin="lower", extent=[-3, 3, -3, 3], norm=norm)

		# inset histogram
		cax = ax.inset_axes([1.18, 0.02, 0.25, 0.95]) # left, bottom, width, height

		# plot histogram
		counts, _ = np.histogram(Z, bins=bin_edges)
		midpoints = (bin_edges[:-1] + bin_edges[1:]) / 2
		distance = midpoints[1] - midpoints[0]
		cax.barh(midpoints, counts, height=0.8 * distance, color=cmap(norm(midpoints)))

		# styling
		cax.spines[:].set_visible(False)
		cax.set_yticks(bin_edges)
		cax.tick_params(axis="both", which="both", length=0)

		plt.show()