Added more examples

Author: joferkington

examples/specialty_plots/advanced_hillshading.py

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+"""
+Demonstrates a few common tricks with shaded plots.
+"""
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.colors import LightSource, Normalize
+
+def display_colorbar():
+    """Display a correct numeric colorbar for a shaded plot."""
+    y, x = np.mgrid[-4:2:200j, -4:2:200j]
+    z = 10 * np.cos(x**2 + y**2)
+
+    cmap = plt.cm.copper
+    ls = LightSource(315, 45)
+    rgb = ls.shade(z, cmap)
+
+    fig, ax = plt.subplots()
+    ax.imshow(rgb)
+
+    # Use a proxy artist for the colorbar...
+    im = ax.imshow(z, cmap=cmap)
+    im.remove()
+    fig.colorbar(im)
+
+    ax.set_title('Using a colorbar with a shaded plot', size='x-large')
+
+def avoid_outliers():
+    """Use a custom norm to control the displayed z-range of a shaded plot."""
+    y, x = np.mgrid[-4:2:200j, -4:2:200j]
+    z = 10 * np.cos(x**2 + y**2)
+
+    # Add some outliers...
+    z[100, 105] = 2000
+    z[120, 110] = -9000
+
+    ls = LightSource(315, 45)
+    fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(8, 4.5))
+
+    rgb = ls.shade(z, plt.cm.copper)
+    ax1.imshow(rgb)
+    ax1.set_title('Full range of data')
+
+    rgb = ls.shade(z, plt.cm.copper, norm=Normalize(-10, 10)) # Note the norm!!
+    ax2.imshow(rgb)
+    ax2.set_title('Manually set range')
+
+    fig.suptitle('Avoiding Outliers in Shaded Plots', size='x-large')
+
+def shade_other_data():
+    """Demonstrates displaying different variables through shade and color."""
+    y, x = np.mgrid[-4:2:200j, -4:2:200j]
+    z1 = np.sin(x**2) # Data to hillshade
+    z2 = np.cos(x**2 + y**2) # Data to color
+
+    norm=Normalize(z2.min(), z2.max())
+    cmap = plt.cm.jet
+
+    ls = LightSource(315, 45)
+    rgb = ls.shade_rgb(cmap(norm(z2)), z1)
+
+    fig, ax = plt.subplots()
+    ax.imshow(rgb)
+    ax.set_title('Shade by one variable, color by another', size='x-large')
+
+display_colorbar()
+avoid_outliers()
+shade_other_data()
+plt.show()

examples/specialty_plots/topographic_hillshading.py

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+"""
+Demonstrates the visual effect of varying blend mode and vertical exaggeration
+on "hillshaded" plots.
+
+Note that the "overlay" and "soft" blend modes work well for complex surfaces
+such as this example, while the default "hsv" blend mode works best for smooth
+surfaces such as many mathematical functions.
+
+In most cases, hillshading is used purely for visual purposes, and *dx*/*dy*
+can be safely ignored. In that case, you can tweak *vert_exag* (vertical
+exaggeration) by trial and error to give the desired visual effect. However,
+this example demonstrates how to use the *dx* and *dy* kwargs to ensure that
+the *vert_exag* parameter is the true vertical exaggeration.
+"""
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.cbook import get_sample_data
+from matplotlib.colors import LightSource
+
+dem = np.load(get_sample_data('jacksboro_fault_dem.npz'))
+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 realitive 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('{}'.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()