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Move colormaps to their respective tutorial sections.
It is strange to have them all at the end "in" the Miscellaneous section. And when sphinx-gallery sees multiple figures together, it makes those into a "list" at half size, which makes them much too small in the new theme.
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‎tutorials/colors/colormaps.py‎

Lines changed: 58 additions & 53 deletions
Original file line numberDiff line numberDiff line change
@@ -47,6 +47,8 @@
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is from [IBM]_.
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.. _color-colormaps_reference:
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Classes of colormaps
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====================
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@@ -82,8 +84,39 @@
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from colorspacious import cspace_converter
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###############################################################################
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#
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# First, we'll show the range of each colormap. Note that some seem
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# to change more "quickly" than others.
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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(category, cmap_list):
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# Create figure and adjust figure height to number of colormaps
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nrows = len(cmap_list)
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figh = 0.35 + 0.15 + (nrows + (nrows - 1) * 0.1) * 0.22
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fig, axs = plt.subplots(nrows=nrows + 1, figsize=(6.4, figh))
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fig.subplots_adjust(top=1 - 0.35 / figh, bottom=0.15 / figh,
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left=0.2, right=0.99)
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axs[0].set_title(f'{category} colormaps', fontsize=14)
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for ax, name in zip(axs, cmap_list):
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ax.imshow(gradient, aspect='auto', cmap=plt.get_cmap(name))
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ax.text(-0.01, 0.5, name, va='center', ha='right', fontsize=10,
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transform=ax.transAxes)
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# Turn off *all* ticks & spines, not just the ones with colormaps.
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for ax in axs:
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ax.set_axis_off()
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# Save colormap list for later.
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cmaps[category] = cmap_list
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###############################################################################
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# Sequential
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# ----------
@@ -96,13 +129,15 @@
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# amongst the colormaps: some are approximately linear in :math:`L^*` and others
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# are more curved.
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cmaps['Perceptually Uniform Sequential'] = [
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'viridis', 'plasma', 'inferno', 'magma', 'cividis']
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plot_color_gradients('Perceptually Uniform Sequential',
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['viridis', 'plasma', 'inferno', 'magma', 'cividis'])
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###############################################################################
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cmaps['Sequential'] = [
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'Greys', 'Purples', 'Blues', 'Greens', 'Oranges', 'Reds',
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'YlOrBr', 'YlOrRd', 'OrRd', 'PuRd', 'RdPu', 'BuPu',
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'GnBu', 'PuBu', 'YlGnBu', 'PuBuGn', 'BuGn', 'YlGn']
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plot_color_gradients('Sequential',
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['Greys', 'Purples', 'Blues', 'Greens', 'Oranges', 'Reds',
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'YlOrBr', 'YlOrRd', 'OrRd', 'PuRd', 'RdPu', 'BuPu',
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'GnBu', 'PuBu', 'YlGnBu', 'PuBuGn', 'BuGn', 'YlGn'])
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###############################################################################
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# Sequential2
@@ -116,10 +151,10 @@
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# banding of the data in those values in the colormap (see [mycarta-banding]_ for
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# an excellent example of this).
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cmaps['Sequential (2)'] = [
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'binary', 'gist_yarg', 'gist_gray', 'gray', 'bone', 'pink',
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'spring', 'summer', 'autumn', 'winter', 'cool', 'Wistia',
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'hot', 'afmhot', 'gist_heat', 'copper']
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plot_color_gradients('Sequential (2)',
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['binary', 'gist_yarg', 'gist_gray', 'gray', 'bone',
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'pink', 'spring', 'summer', 'autumn', 'winter', 'cool',
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'Wistia', 'hot', 'afmhot', 'gist_heat', 'copper'])
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###############################################################################
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# Diverging
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# measures, BrBG and RdBu are good options. coolwarm is a good option, but it
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# doesn't span a wide range of :math:`L^*` values (see grayscale section below).
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cmaps['Diverging'] = [
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'PiYG', 'PRGn', 'BrBG', 'PuOr', 'RdGy', 'RdBu',
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'RdYlBu', 'RdYlGn', 'Spectral', 'coolwarm', 'bwr', 'seismic']
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plot_color_gradients('Diverging',
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['PiYG', 'PRGn', 'BrBG', 'PuOr', 'RdGy', 'RdBu', 'RdYlBu',
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'RdYlGn', 'Spectral', 'coolwarm', 'bwr', 'seismic'])
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###############################################################################
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# Cyclic
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# for viewers to see perceptually. See an extension on this idea at
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# [mycarta-jet]_.
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cmaps['Cyclic'] = ['twilight', 'twilight_shifted', 'hsv']
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plot_color_gradients('Cyclic', ['twilight', 'twilight_shifted', 'hsv'])
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###############################################################################
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# Qualitative
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# the place throughout the colormap, and are clearly not monotonically increasing.
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# These would not be good options for use as perceptual colormaps.
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cmaps['Qualitative'] = ['Pastel1', 'Pastel2', 'Paired', 'Accent',
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'Dark2', 'Set1', 'Set2', 'Set3',
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'tab10', 'tab20', 'tab20b', 'tab20c']
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plot_color_gradients('Qualitative',
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['Pastel1', 'Pastel2', 'Paired', 'Accent', 'Dark2',
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'Set1', 'Set2', 'Set3', 'tab10', 'tab20', 'tab20b',
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'tab20c'])
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###############################################################################
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# Miscellaneous
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# poor choice for representing data for viewers to see perceptually. See an
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# extension on this idea at [mycarta-jet]_ and [turbo]_.
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cmaps['Miscellaneous'] = [
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'flag', 'prism', 'ocean', 'gist_earth', 'terrain', 'gist_stern',
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'gnuplot', 'gnuplot2', 'CMRmap', 'cubehelix', 'brg',
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'gist_rainbow', 'rainbow', 'jet', 'turbo', 'nipy_spectral',
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'gist_ncar']
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###############################################################################
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# .. _color-colormaps_reference:
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#
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# First, we'll show the range of each colormap. Note that some seem
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# to change more "quickly" than others.
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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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# Create figure and adjust figure height to number of colormaps
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nrows = len(cmap_list)
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figh = 0.35 + 0.15 + (nrows + (nrows - 1) * 0.1) * 0.22
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fig, axs = plt.subplots(nrows=nrows + 1, figsize=(6.4, figh))
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fig.subplots_adjust(top=1 - 0.35 / figh, bottom=0.15 / figh,
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left=0.2, right=0.99)
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axs[0].set_title(cmap_category + ' colormaps', fontsize=14)
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for ax, name in zip(axs, cmap_list):
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ax.imshow(gradient, aspect='auto', cmap=plt.get_cmap(name))
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ax.text(-0.01, 0.5, name, va='center', ha='right', fontsize=10,
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transform=ax.transAxes)
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# Turn off *all* ticks & spines, not just the ones with colormaps.
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for ax in axs:
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ax.set_axis_off()
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for cmap_category, cmap_list in cmaps.items():
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plot_color_gradients(cmap_category, cmap_list)
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plot_color_gradients('Miscellaneous',
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['flag', 'prism', 'ocean', 'gist_earth', 'terrain',
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'gist_stern', 'gnuplot', 'gnuplot2', 'CMRmap',
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'cubehelix', 'brg', 'gist_rainbow', 'rainbow', 'jet',
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'turbo', 'nipy_spectral', 'gist_ncar'])
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plt.show()
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