|
| 1 | +""" |
| 2 | +===================== |
| 3 | +Histogram as colorbar |
| 4 | +===================== |
| 5 | +
|
| 6 | +This example demonstrates how to use a colored histogram instead of a colorbar |
| 7 | +to not only show the color-to-value mapping, but also visualize the |
| 8 | +distribution of values. |
| 9 | +""" |
| 10 | + |
| 11 | +import matplotlib.pyplot as plt |
| 12 | +import numpy as np |
| 13 | + |
| 14 | +import matplotlib.colors as mcolors |
| 15 | + |
| 16 | +# surface data |
| 17 | +delta = 0.025 |
| 18 | +x = y = np.arange(-2.0, 2.0, delta) |
| 19 | +X, Y = np.meshgrid(x, y) |
| 20 | +Z1 = np.exp(-(((X + 1) * 1.3) ** 2) - ((Y + 1) * 1.3) ** 2) |
| 21 | +Z2 = 2.5 * np.exp(-((X - 1) ** 2) - (Y - 1) ** 2) |
| 22 | +Z = Z1**0.25 - Z2**0.5 |
| 23 | + |
| 24 | +# colormap & normalization |
| 25 | +bins = 30 |
| 26 | +cmap = plt.get_cmap("RdYlBu_r") |
| 27 | +bin_edges = np.linspace(Z.min(), Z.max(), bins + 1) |
| 28 | +norm = mcolors.BoundaryNorm(bin_edges, cmap.N) |
| 29 | + |
| 30 | +# main plot |
| 31 | +fig, ax = plt.subplots(layout="constrained") |
| 32 | +im = ax.imshow(Z, cmap=cmap, origin="lower", extent=[-3, 3, -3, 3], norm=norm) |
| 33 | + |
| 34 | +# inset histogram |
| 35 | +cax = ax.inset_axes([1.18, 0.02, 0.25, 0.95]) # left, bottom, width, height |
| 36 | + |
| 37 | +# plot histogram |
| 38 | +counts, _ = np.histogram(Z, bins=bin_edges) |
| 39 | +midpoints = (bin_edges[:-1] + bin_edges[1:]) / 2 |
| 40 | +distance = midpoints[1] - midpoints[0] |
| 41 | +cax.barh(midpoints, counts, height=0.8 * distance, color=cmap(norm(midpoints))) |
| 42 | + |
| 43 | +# styling |
| 44 | +cax.spines[:].set_visible(False) |
| 45 | +cax.set_yticks(bin_edges) |
| 46 | +cax.tick_params(axis="both", which="both", length=0) |
| 47 | + |
| 48 | +plt.show() |
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