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Implements the Okabe-Ito accessible colormap. (#30821)
Add Okabe_Ito color sequence (https://jfly.uni-koeln.de/color/)
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‎doc/release/next_whats_new/okabe_ito_colormap.rst‎

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Okabe-Ito accessible color sequence
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-----------------------------------
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Matplotlib now includes the `Okabe-Ito color sequence`_. Its colors remain distinguishable for common forms of color-vision deficiency and when printed.
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.. _Okabe-Ito color sequence: https://jfly.uni-koeln.de/color/#pallet
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For example, to set it as the default colormap for your plots and image-like artists, use:
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.. code-block:: python
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import matplotlib.pyplot as plt
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from cycler import cycler
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plt.rcParams['axes.prop_cycle'] = cycler('color', plt.colormaps['okabe_ito'].colors)
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plt.rcParams['image.cmap'] = 'okabe_ito'
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Or, when creating plots, you can pass it explicitly:
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.. plot::
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import matplotlib.pyplot as plt
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colors = plt.colormaps['okabe_ito'].colors
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x = range(5)
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for i, c in enumerate(colors):
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plt.plot(x, [v*(i+1) for v in x], color=c, label=f'line {i}')
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plt.legend()
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plt.show()

‎galleries/examples/color/color_sequences.py‎

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@@ -38,8 +38,8 @@ def plot_color_sequences(names, ax):
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built_in_color_sequences = [
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'tab10', 'tab20', 'tab20b', 'tab20c', 'Pastel1', 'Pastel2', 'Paired',
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'Accent', 'Dark2', 'Set1', 'Set2', 'Set3', 'petroff6', 'petroff8',
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'petroff10']
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'Accent', 'okabe_ito', 'Dark2', 'Set1', 'Set2', 'Set3', 'petroff6',
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'petroff8', 'petroff10']
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fig, ax = plt.subplots(figsize=(6.4, 9.6), layout='constrained')

‎galleries/examples/color/colormap_reference.py‎

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'berlin', 'managua', 'vanimo']),
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('Cyclic', ['twilight', 'twilight_shifted', 'hsv']),
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('Qualitative', [
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'Pastel1', 'Pastel2', 'Paired', 'Accent',
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'Pastel1', 'Pastel2', 'Paired', 'Accent', 'okabe_ito',
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'Dark2', 'Set1', 'Set2', 'Set3',
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'tab10', 'tab20', 'tab20b', 'tab20c']),
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('Miscellaneous', [

‎galleries/users_explain/colors/colormaps.py‎

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# These would not be good options for use as perceptual colormaps.
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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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['Pastel1', 'Pastel2', 'Paired', 'Accent', 'okabe_ito',
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'Dark2', 'Set1', 'Set2', 'Set3', 'tab10', 'tab20',
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'tab20b', 'tab20c'])
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# %%
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# Miscellaneous

‎lib/matplotlib/_cm.py‎

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@@ -852,7 +852,6 @@ def _g36(x): return 2 * x - 1
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(0.50196078431372548, 0.0 , 0.14901960784313725)
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)
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# ColorBrewer's qualitative maps, implemented using ListedColormap
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# for use with mpl.colors.NoNorm
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(0.4, 0.4, 0.4 ),
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)
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# Okabe-Ito accessible and print-friendly color palette.
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# By Masataka Okabe (Jikei Medical School) and Kei Ito (University of Tokyo).
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# Qualitative color palette that is unambiguous regardless of whether
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# the viewer has colorblindness. https://jfly.uni-koeln.de/color/#pallet
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_okabe_ito_data = (
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(0.0, 0.0, 0.0), # black
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(0.9019607843137255, 0.6235294117647059, 0.0), # e69f00
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(0.33725490196078434, 0.7058823529411765, 0.9137254901960784), # 56b4e9
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(0.0, 0.6196078431372549, 0.45098039215686275), # 009e73
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(0.9411764705882353, 0.8941176470588236, 0.25882352941176473), # f0e442
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(0.0, 0.4470588235294118, 0.6980392156862745), # 0072b2
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(0.8352941176470589, 0.3686274509803922, 0.0), # d55e00
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(0.8, 0.4745098039215686, 0.6549019607843137), # cc79a7
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)
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_Paired_data = (
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(0.65098039215686276, 0.80784313725490198, 0.8901960784313725 ),
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(0.12156862745098039, 0.47058823529411764, 0.70588235294117652),
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'winter': _winter_data,
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# Qualitative
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'Accent': {'listed': _Accent_data},
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'okabe_ito': {'listed': _okabe_ito_data},
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'Dark2': {'listed': _Dark2_data},
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'Paired': {'listed': _Paired_data},
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'Pastel1': {'listed': _Pastel1_data},

‎lib/matplotlib/cm.pyi‎

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winter: colors.Colormap
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Accent: colors.Colormap
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Dark2: colors.Colormap
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okabe_ito: colors.Colormap
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Paired: colors.Colormap
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Pastel1: colors.Colormap
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Pastel2: colors.Colormap
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winter_r: colors.Colormap
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Accent_r: colors.Colormap
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Dark2_r: colors.Colormap
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okabe_ito_r: colors.Colormap
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Paired_r: colors.Colormap
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Pastel1_r: colors.Colormap
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Pastel2_r: colors.Colormap

‎lib/matplotlib/colors.py‎

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@@ -128,6 +128,7 @@ class ColorSequenceRegistry(Mapping):
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'Pastel2': _cm._Pastel2_data,
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'Paired': _cm._Paired_data,
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'Accent': _cm._Accent_data,
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'okabe_ito': _cm._okabe_ito_data,
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'Dark2': _cm._Dark2_data,
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'Set1': _cm._Set1_data,
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'Set2': _cm._Set2_data,

‎lib/matplotlib/tests/test_colors.py‎

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assert plt.color_sequences is matplotlib.color_sequences # same registry
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assert list(plt.color_sequences) == [
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'tab10', 'tab20', 'tab20b', 'tab20c', 'Pastel1', 'Pastel2', 'Paired',
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'Accent', 'Dark2', 'Set1', 'Set2', 'Set3', 'petroff6', 'petroff8',
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'petroff10']
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'Accent', 'okabe_ito', 'Dark2', 'Set1', 'Set2', 'Set3', 'petroff6',
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'petroff8', 'petroff10']
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assert len(plt.color_sequences['tab10']) == 10
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assert len(plt.color_sequences['tab20']) == 20
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