matplotlib · anntzer · Oct 23, 2017 · Oct 22, 2017 · choldgraf · Oct 22, 2017
diff --git a/examples/color/colormap_reference.py b/examples/color/colormap_reference.py
@@ -0,0 +1,94 @@
+"""
+==================
+Colormap reference
+==================
+
+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
+
+
+# Have colormaps separated into categories:
+# http://matplotlib.org/examples/color/colormaps_reference.html
+cmaps = [('Perceptually Uniform Sequential', [
+            'viridis', 'plasma', 'inferno', 'magma']),
+         ('Sequential', [
+            'Greys', 'Purples', 'Blues', 'Greens', 'Oranges', 'Reds',
+            'YlOrBr', 'YlOrRd', 'OrRd', 'PuRd', 'RdPu', 'BuPu',
+            'GnBu', 'PuBu', 'YlGnBu', 'PuBuGn', 'BuGn', 'YlGn']),
+         ('Sequential (2)', [
+            'binary', 'gist_yarg', 'gist_gray', 'gray', 'bone', 'pink',
+            'spring', 'summer', 'autumn', 'winter', 'cool', 'Wistia',
+            'hot', 'afmhot', 'gist_heat', 'copper']),
+         ('Diverging', [
+            'PiYG', 'PRGn', 'BrBG', 'PuOr', 'RdGy', 'RdBu',
+            'RdYlBu', 'RdYlGn', 'Spectral', 'coolwarm', 'bwr', 'seismic']),
+         ('Qualitative', [
+            'Pastel1', 'Pastel2', 'Paired', 'Accent',
+            'Dark2', 'Set1', 'Set2', 'Set3',
+            'tab10', 'tab20', 'tab20b', 'tab20c']),
+         ('Miscellaneous', [
+            'flag', 'prism', 'ocean', 'gist_earth', 'terrain', 'gist_stern',
+            'gnuplot', 'gnuplot2', 'CMRmap', 'cubehelix', 'brg', 'hsv',
+            'gist_rainbow', 'rainbow', 'jet', 'nipy_spectral', 'gist_ncar'])]
+
+
+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, nrows):
+    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, nrows)
+
+plt.show()