May 24, 2025 · May 24, 2025 · May 25, 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 create a colorbar for an image and
 add a histogram of the data values alongside it. This is useful for
 visualizing the distribution of values mapped to colors.

 """
 import numpy as np
 import matplotlib.pyplot as plt
 import matplotlib.colors as mcolors
 from matplotlib.ticker import MaxNLocator

 # === 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)

 # === Histogram from actual Z data ===
 counts, bins = np.histogram(Z, bins=30)

 # === Colormap & Normalization ===
 cmap = plt.get_cmap('RdYlBu_r')
 norm = mcolors.BoundaryNorm(bins, 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 – Positioning adjusted ===
 cax = ax.inset_axes([1.18, 0.02, 0.25, 0.95])  # left, bottom, width, height

 # === Plot Histogram ===
 midpoints = bins[:-1] + np.diff(bins) / 2
 height = np.median(np.diff(bins)) * 0.8
 colors = cmap(norm(midpoints))

 cax.barh(midpoints, counts, height=height, color=colors)

 # === Clean up ===
 cax.spines[:].set_visible(False)
 cax.margins(0)
 cax.tick_params(axis='both', which='both', length=0)

 # === Axis labels ===
 cax.set_xlabel('Count', labelpad=10)
 cax.set_ylabel('Value', labelpad=6)

 # === Ticks ===
 cax.set_yticks(bins)
 cax.yaxis.set_major_locator(MaxNLocator(nbins=8))

 plt.show()
Original file line number	Diff line number	Diff line change
		@@ -0,0 +1,61 @@
		"""
		=====================
		Histogram as colorbar
		=====================

		This example demonstrates how to create a colorbar for an image and
		add a histogram of the data values alongside it. This is useful for
		visualizing the distribution of values mapped to colors.

		"""
		import numpy as np
		import matplotlib.pyplot as plt
		import matplotlib.colors as mcolors
		from matplotlib.ticker import MaxNLocator

		# === 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 = 2.5np.exp(-(X - 1)2 - (Y - 1)*2)
		Z = (Z10.25 - Z20.5)

		# === Histogram from actual Z data ===
		counts, bins = np.histogram(Z, bins=30)

		# === Colormap & Normalization ===
		cmap = plt.get_cmap('RdYlBu_r')
		norm = mcolors.BoundaryNorm(bins, 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 – Positioning adjusted ===
		cax = ax.inset_axes([1.18, 0.02, 0.25, 0.95]) # left, bottom, width, height

		# === Plot Histogram ===
		midpoints = bins[:-1] + np.diff(bins) / 2
		height = np.median(np.diff(bins)) * 0.8
		colors = cmap(norm(midpoints))

		cax.barh(midpoints, counts, height=height, color=colors)

		# === Clean up ===
		cax.spines[:].set_visible(False)
		cax.margins(0)
		cax.tick_params(axis='both', which='both', length=0)

		# === Axis labels ===
		cax.set_xlabel('Count', labelpad=10)
		cax.set_ylabel('Value', labelpad=6)

		# === Ticks ===
		cax.set_yticks(bins)
		cax.yaxis.set_major_locator(MaxNLocator(nbins=8))

		plt.show()