Combine colormap reference examples

Author: tonysyu

doc/users/image_tutorial.rst

@@ -229,11 +229,9 @@ object:
         imgplot = plt.imshow(lum_img)
         imgplot.set_cmap('spectral')
 
-There are many other colormap schemes available.  See the `list of 
-colormaps 
-<http://matplotlib.org/api/pyplot_summary.html#matplotlib.pyplot.colormaps>`_ 
-and `images of the colormaps
-<http://matplotlib.org/examples/pylab_examples/show_colormaps.html>`_.
+There are many other colormap schemes available.  See the `list and
+images of the colormaps
+<http://matplotlib.org/examples/color/colormaps_reference.html>`_.
 
 .. _`Color Bars`:
 

doc/users/whats_new.rst

@@ -542,7 +542,7 @@ Other improvements
 
 * Pim Schellart added a new colormap called "cubehelix".
   Sameer Grover also added a colormap called "coolwarm". See it and all
-  other colormaps :ref:`here <pylab_examples-show_colormaps>`.
+  other colormaps :ref:`here <color-colormaps_reference>`.
 
 * Many bug fixes and documentation improvements.
 

examples/color/colormaps_reference.py

@@ -0,0 +1,79 @@
+"""
+Reference for colormaps included with Matplotlib.
+
+This reference example shows all colormaps included with Matplotlib. Note that
+any colormap listed here can be reversed by appending "_r" (e.g., "pink_r").
+These colormaps are divided into the following categories:
+
+Sequential:
+    These colormaps are approximately monochromatic colormaps varying smoothly
+    between two color tones---usually 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.
+
+Diverging:
+    These 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).
+
+Qualitative:
+    These colormaps vary rapidly in color. Qualitative 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.
+
+Miscellaneous:
+    Colormaps that don't fit into the categories above.
+
+"""
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+cmaps = [('Sequential',     ['binary', 'Blues', 'BuGn', 'BuPu', 'gist_yarg',
+                             'GnBu', 'Greens', 'Greys', 'Oranges', 'OrRd',
+                             'PuBu', 'PuBuGn', 'PuRd', 'Purples', 'RdPu',
+                             'Reds', 'YlGn', 'YlGnBu', 'YlOrBr', 'YlOrRd']),
+         ('Sequential (2)', ['afmhot', 'autumn', 'bone', 'cool', 'copper',
+                             'gist_gray', 'gist_heat', 'gray', 'hot', 'pink',
+                             'spring', 'summer', 'winter']),
+         ('Diverging',      ['BrBG', 'bwr', 'coolwarm', 'PiYG', 'PRGn', 'PuOr',
+                             'RdBu', 'RdGy', 'RdYlBu', 'RdYlGn', 'seismic']),
+         ('Qualitative',    ['Accent', 'Dark2', 'hsv', 'Paired', 'Pastel1',
+                             'Pastel2', 'Set1', 'Set2', 'Set3', 'spectral']),
+         ('Miscellaneous',  ['gist_earth', 'gist_ncar', 'gist_rainbow',
+                             'gist_stern', 'jet', 'brg', 'CMRmap', 'cubehelix',
+                             'gnuplot', 'gnuplot2', 'ocean', 'rainbow',
+                             'terrain', 'flag', 'prism'])]
+
+
+nrows = max(len(cmap_list) for cmap_category, cmap_list in cmaps)
+gradient = np.linspace(0, 1, 256)
+gradient = np.vstack((gradient, gradient))
+
+def plot_color_gradients(cmap_category, cmap_list):
+    fig, axes = plt.subplots(nrows=nrows)
+    fig.subplots_adjust(top=0.95, bottom=0.01, left=0.2, right=0.99)
+    axes[0].set_title(cmap_category + ' colormaps', fontsize=14)
+
+    for ax, name in zip(axes, cmap_list):
+        ax.imshow(gradient, aspect='auto', cmap=plt.get_cmap(name))
+        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, name, va='center', ha='right', fontsize=10)
+
+    # Turn off *all* ticks & spines, not just the ones with colormaps.
+    for ax in axes:
+        ax.set_axis_off()
+
+for cmap_category, cmap_list in cmaps:
+    plot_color_gradients(cmap_category, cmap_list)
+
+plt.show()