[python] view plain copy
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', 'inferno', 'plasma', 'magma']),  
         ('Sequential',     ['Blues', 'BuGn', 'BuPu',  
                             'GnBu', 'Greens', 'Greys', 'Oranges', 'OrRd',  
                             'PuBu', 'PuBuGn', 'PuRd', 'Purples', 'RdPu',  
                             'Reds', 'YlGn', 'YlGnBu', 'YlOrBr', 'YlOrRd']),  
         ('Sequential (2)', ['afmhot', 'autumn', 'bone', 'cool',  
                             'copper', 'gist_heat', 'gray', 'hot',  
                             'pink', 'spring', 'summer', 'winter']),  
         ('Diverging',      ['BrBG', 'bwr', 'coolwarm', 'PiYG', 'PRGn', 'PuOr',  
                             'RdBu', 'RdGy', 'RdYlBu', 'RdYlGn', 'Spectral',  
                             'seismic']),  
         ('Qualitative',    ['Accent', 'Dark2', 'Paired', 'Pastel1',  
                             'Pastel2', 'Set1', 'Set2', 'Set3']),  
         ('Miscellaneous',  ['gist_earth', 'terrain', 'ocean', 'gist_stern',  
                             'brg', 'CMRmap', 'cubehelix',  
                             'gnuplot', 'gnuplot2', 'gist_ncar',  
                             'nipy_spectral', 'jet', 'rainbow',  
                             'gist_rainbow', 'hsv', '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()