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Cleaning up 'grayscale.py' script
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doc/users/plotting/colormaps/grayscale.py

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@@ -1,39 +1,37 @@
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'''
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Show what matplotlib colormaps look like in grayscale.
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==================================
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Grayscale version of the colormaps
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==================================
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Show what Matplotlib colormaps look like in grayscale.
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Uses lightness L* as a proxy for grayscale value.
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'''
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from colormaps import cmaps
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#from skimage import color
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# we are using a local copy of colorconv from scikit-image to reduce dependencies.
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# You should probably use the one from scikit-image in most cases.
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import numpy as np
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import matplotlib as mpl
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import matplotlib.pyplot as plt
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from matplotlib import cm
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import matplotlib as mpl
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from colorspacious import cspace_converter
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from colormaps import cmaps # the colormaps, grouped by category
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mpl.rcParams.update({'font.size': 14})
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# indices to step through colormap
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# Indices to step through colormap.
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x = np.linspace(0.0, 1.0, 100)
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# nrows = max(len(cmap_list) for cmap_category, cmap_list in cmaps)
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gradient = np.linspace(0, 1, 256)
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gradient = np.vstack((gradient, gradient))
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def plot_color_gradients(cmap_category, cmap_list):
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nrows = len(cmap_list)
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fig, axes = plt.subplots(nrows=nrows, ncols=2)
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fig, axes = plt.subplots(nrows=len(cmap_list), ncols=2)
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fig.subplots_adjust(top=0.95, bottom=0.01, left=0.2, right=0.99,
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wspace=0.05)
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fig.suptitle(cmap_category + ' colormaps', fontsize=14, y=1.0, x=0.6)
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for ax, name in zip(axes, cmap_list):
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# Get rgb values for colormap
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# Get RGB values for colormap.
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rgb = cm.get_cmap(plt.get_cmap(name))(x)[np.newaxis,:,:3]
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# Get colormap in CAM02-UCS colorspace. We want the lightness.
@@ -49,9 +47,9 @@ def plot_color_gradients(cmap_category, cmap_list):
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fig.text(x_text, y_text, name, va='center', ha='right', fontsize=10)
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# Turn off *all* ticks & spines, not just the ones with colormaps.
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for ax in axes:
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ax[0].set_axis_off()
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ax[1].set_axis_off()
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for ax in axes.flat:
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ax.set_axis_off()
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plt.show()
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