matplotlib
diff --git a/‎examples/color/colormaps_reference_diverging.py
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diff --git a/‎examples/color/colormaps_reference_miscellaneous.py
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diff --git a/‎examples/color/colormaps_reference_qualitative.py
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diff --git a/‎examples/color/colormaps_reference_sequential.py
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diff --git a/‎examples/color/colormaps_reference_sequential2.py
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diff --git a/‎examples/pylab_examples/show_colormaps.py
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+"""
+Reference for "diverging" colormaps.
+
+Diverging colormaps have a median value (usually light in color) and vary
+smoothly to two different color tones at high and low values. Diverging
+colormaps are ideal when your data has a median value that is significant (e.g.
+0, such that positive and negative values are represented by different colors
+of the colormap).
+"""
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+gradient = np.linspace(0, 1, 256)
+gradient = np.vstack((gradient, gradient))
+
+# Note that any of these colormaps can be reversed by appending '_r'.
+cmaps = ['BrBG', 'bwr', 'coolwarm', 'PiYG', 'PRGn', 'PuOr', 'RdBu', 'RdGy',
+         'RdYlBu', 'RdYlGn', 'seismic']
+
+fig, axes = plt.subplots(nrows=len(cmaps))
+fig.subplots_adjust(top=0.99, bottom=0.01, left=0.2, right=0.99)
+
+for ax, m in zip(axes, cmaps):
+    ax.set_axis_off()
+    ax.imshow(gradient, aspect='auto', cmap=plt.get_cmap(m))
+    pos = list(ax.get_position().bounds)
+    x_text = pos[0] - 0.01
+    y_text = pos[1] + pos[3]/2.
+    fig.text(x_text, y_text, m, va='center', ha='right')
+
+plt.show()
@@ -0,0 +1,27 @@
+"""
+Reference for "miscellaneous" colormaps.
+"""
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+gradient = np.linspace(0, 1, 256)
+gradient = np.vstack((gradient, gradient))
+
+# Note that any of these colormaps can be reversed by appending '_r'.
+cmaps = ['flag', 'gist_earth', 'gist_ncar', 'gist_rainbow', 'gist_stern',
+         'jet', 'prism', 'brg', 'CMRmap', 'cubehelix', 'gnuplot', 'gnuplot2',
+         'ocean', 'rainbow', 'terrain']
+
+fig, axes = plt.subplots(nrows=len(cmaps))
+fig.subplots_adjust(top=0.99, bottom=0.01, left=0.2, right=0.99)
+
+for ax, m in zip(axes, cmaps):
+    ax.set_axis_off()
+    ax.imshow(gradient, aspect='auto', cmap=plt.get_cmap(m))
+    pos = list(ax.get_position().bounds)
+    x_text = pos[0] - 0.01
+    y_text = pos[1] + pos[3]/2.
+    fig.text(x_text, y_text, m, va='center', ha='right')
+
+plt.show()
@@ -0,0 +1,34 @@
+"""
+Reference for "qualitative" colormaps.
+
+Qualitative colormaps vary rapidly in color. These colormaps are useful for
+choosing a set of discrete colors. For example::
+
+    color_list = plt.cm.Set3(np.linspace(0, 1, 12))
+
+gives a list of RGB colors that are good for plotting a series of lines on
+a dark background.
+"""
+import numpy as np
+import matplotlib.pyplot as plt
+
 
+gradient = np.linspace(0, 1, 256)
+gradient = np.vstack((gradient, gradient))
+
+# Note that any of these colormaps can be reversed by appending '_r'.
+cmaps = ['Accent', 'Dark2', 'hsv', 'Paired', 'Pastel1', 'Pastel2', 'Set1',
+         'Set2', 'Set3', 'spectral']
+
+fig, axes = plt.subplots(nrows=len(cmaps))
+fig.subplots_adjust(top=0.99, bottom=0.01, left=0.2, right=0.99)
+
+for ax, m in zip(axes, cmaps):
+    ax.set_axis_off()
+    ax.imshow(gradient, aspect='auto', cmap=plt.get_cmap(m))
+    pos = list(ax.get_position().bounds)
+    x_text = pos[0] - 0.01
+    y_text = pos[1] + pos[3]/2.
+    fig.text(x_text, y_text, m, va='center', ha='right')
+
+plt.show()
@@ -0,0 +1,32 @@
+"""
+Reference for "sequential" colormaps.
+
+Sequential colormaps are approximately monochromatic colormaps going from low
+saturation (e.g. white) to high saturation (e.g. a bright blue). Sequential
+colormaps are ideal for representing most scientific data since they show
+a clear progression from low-to-high values.
+"""
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+gradient = np.linspace(0, 1, 256)
+gradient = np.vstack((gradient, gradient))
+
+# Note that any of these colormaps can be reversed by appending '_r'.
+cmaps = ['binary', 'Blues', 'BuGn', 'BuPu', 'gist_yarg', 'GnBu', 'Greens',
+         'Greys', 'Oranges', 'OrRd', 'PuBu', 'PuBuGn', 'PuRd', 'Purples',
+         'RdPu', 'Reds', 'YlGn', 'YlGnBu', 'YlOrBr', 'YlOrRd']
+
+fig, axes = plt.subplots(nrows=len(cmaps))
+fig.subplots_adjust(top=0.99, bottom=0.01, left=0.2, right=0.99)
+
+for ax, m in zip(axes, cmaps):
+    ax.set_axis_off()
+    ax.imshow(gradient, aspect='auto', cmap=plt.get_cmap(m))
+    pos = list(ax.get_position().bounds)
+    x_text = pos[0] - 0.01
+    y_text = pos[1] + pos[3]/2.
+    fig.text(x_text, y_text, m, va='center', ha='right')
+
+plt.show()
@@ -0,0 +1,32 @@
+"""
+Reference for "sequential" colormaps.
+
+These are sequential colormaps, which vary smoothly between two color tones.
+Unlike the sequential colormaps in the first example, these either vary between
+two saturated tones (instead of being nearly monochromatic) or have a reversed
+progression (decreasing in saturation for increasing value). Note, however,
+that any colormap can be reversed by appending "_r" (e.g., "pink_r").
+"""
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+gradient = np.linspace(0, 1, 256)
+gradient = np.vstack((gradient, gradient))
+
+# Note that any of these colormaps can be reversed by appending '_r'.
+cmaps = ['afmhot', 'autumn', 'bone', 'cool', 'copper', 'gist_gray',
+         'gist_heat', 'gray', 'hot', 'pink', 'spring', 'summer', 'winter']
+
+fig, axes = plt.subplots(nrows=len(cmaps))
+fig.subplots_adjust(top=0.99, bottom=0.01, left=0.2, right=0.99)
+
+for ax, m in zip(axes, cmaps):
+    ax.set_axis_off()
+    ax.imshow(gradient, aspect='auto', cmap=plt.get_cmap(m))
+    pos = list(ax.get_positi
96AD
on().bounds)
+    x_text = pos[0] - 0.01
+    y_text = pos[1] + pos[3]/2.
+    fig.text(x_text, y_text, m, va='center', ha='right')
+
+plt.show()
@@ -1,3 +1,7 @@
+# NOTE: This example has been superseded by examples in
+# 'color/colormaps_reference_*, but this example is referenced in the
+# "What's New" page, and the image tutorial.
+
 # This example comes from the Cookbook on www.scipy.org.  According to the
 # history, Andrew Straw did the conversion from an old page, but it is
 # unclear who the original author is.