# 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](https://matplotlib.org/_images/sphx_glr_topographic_hillshading_001.png)

```python
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.cbook import get_sample_data
from matplotlib.colors import LightSource


with np.load(get_sample_data('jacksboro_fault_dem.npz')) as dem:
    z = dem['elevation']

    #-- Optional dx and dy for accurate vertical exaggeration ----------------
    # If you need topographically accurate vertical exaggeration, or you don't
    # want to guess at what *vert_exag* should be, you'll need to specify the
    # cellsize of the grid (i.e. the *dx* and *dy* parameters).  Otherwise, any
    # *vert_exag* value you specify will be relative to the grid spacing of
    # your input data (in other words, *dx* and *dy* default to 1.0, and
    # *vert_exag* is calculated relative to those parameters).  Similarly, *dx*
    # and *dy* are assumed to be in the same units as your input z-values.
    # Therefore, we'll need to convert the given dx and dy from decimal degrees
    # to meters.
    dx, dy = dem['dx'], dem['dy']
    dy = 111200 * dy
    dx = 111200 * dx * np.cos(np.radians(dem['ymin']))
    #-------------------------------------------------------------------------

# Shade from the northwest, with the sun 45 degrees from horizontal
ls = LightSource(azdeg=315, altdeg=45)
cmap = plt.cm.gist_earth

fig, axes = plt.subplots(nrows=4, ncols=3, figsize=(8, 9))
plt.setp(axes.flat, xticks=[], yticks=[])

# Vary vertical exaggeration and blend mode and plot all combinations
for col, ve in zip(axes.T, [0.1, 1, 10]):
    # Show the hillshade intensity image in the first row
    col[0].imshow(ls.hillshade(z, vert_exag=ve, dx=dx, dy=dy), cmap='gray')

    # Place hillshaded plots with different blend modes in the rest of the rows
    for ax, mode in zip(col[1:], ['hsv', 'overlay', 'soft']):
        rgb = ls.shade(z, cmap=cmap, blend_mode=mode,
                       vert_exag=ve, dx=dx, dy=dy)
        ax.imshow(rgb)

# Label rows and columns
for ax, ve in zip(axes[0], [0.1, 1, 10]):
    ax.set_title('{0}'.format(ve), size=18)
for ax, mode in zip(axes[:, 0], ['Hillshade', 'hsv', 'overlay', 'soft']):
    ax.set_ylabel(mode, size=18)

# Group labels...
axes[0, 1].annotate('Vertical Exaggeration', (0.5, 1), xytext=(0, 30),
                    textcoords='offset points', xycoords='axes fraction',
                    ha='center', va='bottom', size=20)
axes[2, 0].annotate('Blend Mode', (0, 0.5), xytext=(-30, 0),
                    textcoords='offset points', xycoords='axes fraction',
                    ha='right', va='center', size=20, rotation=90)
fig.subplots_adjust(bottom=0.05, right=0.95)

plt.show()
```

## 下载这个示例
            
- [下载python源码: topographic_hillshading.py](https://matplotlib.org/_downloads/topographic_hillshading.py)
- [下载Jupyter notebook: topographic_hillshading.ipynb](https://matplotlib.org/_downloads/topographic_hillshading.ipynb)