地形山体阴影

在“山体阴影”图上展示不同混合模式和垂直夸大的视觉效果。

请注意，“叠加”和“柔和”混合模式适用于复杂曲面，例如此示例，而默认的“hsv”混合模式最适用于光滑曲面，例如许多数学函数。

在大多数情况下，山体阴影纯粹用于视觉目的，可以安全地忽略dx / dy。 在这种情况下，您可以通过反复试验调整vert_exag（垂直夸大）以获得所需的视觉效果。 但是，此示例演示了如何使用dx和dy kwargs来确保vert_exag参数是真正的垂直夸大。

地形山体阴影示例

  1. import numpy as np
  2. import matplotlib.pyplot as plt
  3. from matplotlib.cbook import get_sample_data
  4. from matplotlib.colors import LightSource
  7. with np.load(get_sample_data('jacksboro_fault_dem.npz')) as dem:
  8.     z = dem['elevation']
  10.     #-- Optional dx and dy for accurate vertical exaggeration ----------------
  11.     # If you need topographically accurate vertical exaggeration, or you don't
  12.     # want to guess at what *vert_exag* should be, you'll need to specify the
  13.     # cellsize of the grid (i.e. the *dx* and *dy* parameters).  Otherwise, any
  14.     # *vert_exag* value you specify will be relative to the grid spacing of
  15.     # your input data (in other words, *dx* and *dy* default to 1.0, and
  16.     # *vert_exag* is calculated relative to those parameters).  Similarly, *dx*
  17.     # and *dy* are assumed to be in the same units as your input z-values.
  18.     # Therefore, we'll need to convert the given dx and dy from decimal degrees
  19.     # to meters.
  20.     dx, dy = dem['dx'], dem['dy']
  21.     dy = 111200 * dy
  22.     dx = 111200 * dx * np.cos(np.radians(dem['ymin']))
  23.     #-------------------------------------------------------------------------
  25. # Shade from the northwest, with the sun 45 degrees from horizontal
  26. ls = LightSource(azdeg=315, altdeg=45)
  27. cmap = plt.cm.gist_earth
  29. fig, axes = plt.subplots(nrows=4, ncols=3, figsize=(8, 9))
  30. plt.setp(axes.flat, xticks=[], yticks=[])
  32. # Vary vertical exaggeration and blend mode and plot all combinations
  33. for col, ve in zip(axes.T, [0.1, 1, 10]):
  34.     # Show the hillshade intensity image in the first row
  35.     col[0].imshow(ls.hillshade(z, vert_exag=ve, dx=dx, dy=dy), cmap='gray')
  37.     # Place hillshaded plots with different blend modes in the rest of the rows
  38.     for ax, mode in zip(col[1:], ['hsv', 'overlay', 'soft']):
  39.         rgb = ls.shade(z, cmap=cmap, blend_mode=mode,
  40.                        vert_exag=ve, dx=dx, dy=dy)
  41.         ax.imshow(rgb)
  43. # Label rows and columns
  44. for ax, ve in zip(axes[0], [0.1, 1, 10]):
  45.     ax.set_title('{0}'.format(ve), size=18)
  46. for ax, mode in zip(axes[:, 0], ['Hillshade', 'hsv', 'overlay', 'soft']):
  47.     ax.set_ylabel(mode, size=18)
  49. # Group labels...
  50. axes[0, 1].annotate('Vertical Exaggeration', (0.5, 1), xytext=(0, 30),
  51.                     textcoords='offset points', xycoords='axes fraction',
  52.                     ha='center', va='bottom', size=20)
  53. axes[2, 0].annotate('Blend Mode', (0, 0.5), xytext=(-30, 0),
  54.                     textcoords='offset points', xycoords='axes fraction',
  55.                     ha='right', va='center', size=20, rotation=90)
  56. fig.subplots_adjust(bottom=0.05, right=0.95)
  58. plt.show()

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