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ENH: Add Vega categorical palettes.
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‎lib/matplotlib/_cm.py

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@@ -1277,6 +1277,94 @@ def gfunc32(x):
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}
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# Categorical palettes from Vega:
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# https://github.com/vega/vega/wiki/Scales
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# (divided by 255)
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#
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_Vega10_data = (
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(0.12156862745098039, 0.4666666666666667, 0.7058823529411765 ), # 1f77b4
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(1.0, 0.4980392156862745, 0.054901960784313725), # ff7f0e
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(0.17254901960784313, 0.6274509803921569, 0.17254901960784313 ), # 2ca02c
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(0.8392156862745098, 0.15294117647058825, 0.1568627450980392 ), # d62728
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(0.5803921568627451, 0.403921568627451, 0.7411764705882353 ), # 9467bd
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(0.5490196078431373, 0.33725490196078434, 0.29411764705882354 ), # 8c564b
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(0.8901960784313725, 0.4666666666666667, 0.7607843137254902 ), # e377c2
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(0.4980392156862745, 0.4980392156862745, 0.4980392156862745 ), # 7f7f7f
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(0.7372549019607844, 0.7411764705882353, 0.13333333333333333 ), # bcbd22
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(0.09019607843137255, 0.7450980392156863, 0.8117647058823529), # 17becf
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)
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_Vega20_data = (
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(0.12156862745098039, 0.4666666666666667, 0.7058823529411765 ), # 1f77b4
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(0.6823529411764706, 0.7803921568627451, 0.9098039215686274 ), # aec7e8
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(1.0, 0.4980392156862745, 0.054901960784313725), # ff7f0e
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(1.0, 0.7333333333333333, 0.47058823529411764 ), # ffbb78
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(0.17254901960784313, 0.6274509803921569, 0.17254901960784313 ), # 2ca02c
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(0.596078431372549, 0.8745098039215686, 0.5411764705882353 ), # 98df8a
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(0.8392156862745098, 0.15294117647058825, 0.1568627450980392 ), # d62728
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(1.0, 0.596078431372549, 0.5882352941176471 ), # ff9896
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(0.5803921568627451, 0.403921568627451, 0.7411764705882353 ), # 9467bd
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(0.7725490196078432, 0.6901960784313725, 0.8352941176470589 ), # c5b0d5
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(0.5490196078431373, 0.33725490196078434, 0.29411764705882354 ), # 8c564b
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(0.7686274509803922, 0.611764705882353, 0.5803921568627451 ), # c49c94
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(0.8901960784313725, 0.4666666666666667, 0.7607843137254902 ), # e377c2
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(0.9686274509803922, 0.7137254901960784, 0.8235294117647058 ), # f7b6d2
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(0.4980392156862745, 0.4980392156862745, 0.4980392156862745 ), # 7f7f7f
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(0.7803921568627451, 0.7803921568627451, 0.7803921568627451 ), # c7c7c7
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(0.7372549019607844, 0.7411764705882353, 0.13333333333333333 ), # bcbd22
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(0.8588235294117647, 0.8588235294117647, 0.5529411764705883 ), # dbdb8d
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(0.09019607843137255, 0.7450980392156863, 0.8117647058823529 ), # 17becf
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(0.6196078431372549, 0.8549019607843137, 0.8980392156862745), # 9edae5
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)
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_Vega20b_data = (
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(0.2235294117647059, 0.23137254901960785, 0.4745098039215686 ), # 393b79
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(0.3215686274509804, 0.32941176470588235, 0.6392156862745098 ), # 5254a3
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(0.4196078431372549, 0.43137254901960786, 0.8117647058823529 ), # 6b6ecf
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(0.611764705882353, 0.6196078431372549, 0.8705882352941177 ), # 9c9ede
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(0.38823529411764707, 0.4745098039215686, 0.2235294117647059 ), # 637939
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(0.5490196078431373, 0.6352941176470588, 0.3215686274509804 ), # 8ca252
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(0.7098039215686275, 0.8117647058823529, 0.4196078431372549 ), # b5cf6b
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(0.807843137254902, 0.8588235294117647, 0.611764705882353 ), # cedb9c
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(0.5490196078431373, 0.42745098039215684, 0.19215686274509805), # 8c6d31
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(0.7411764705882353, 0.6196078431372549, 0.2235294117647059 ), # bd9e39
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(0.9058823529411765, 0.7294117647058823, 0.3215686274509804 ), # e7ba52
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(0.9058823529411765, 0.796078431372549, 0.5803921568627451 ), # e7cb94
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(0.5176470588235295, 0.23529411764705882, 0.2235294117647059 ), # 843c39
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(0.6784313725490196, 0.28627450980392155, 0.2901960784313726 ), # ad494a
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(0.8392156862745098, 0.3803921568627451, 0.4196078431372549 ), # d6616b
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(0.9058823529411765, 0.5882352941176471, 0.611764705882353 ), # e7969c
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(0.4823529411764706, 0.2549019607843137, 0.45098039215686275), # 7b4173
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(0.6470588235294118, 0.3176470588235294, 0.5803921568627451 ), # a55194
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(0.807843137254902, 0.42745098039215684, 0.7411764705882353 ), # ce6dbd
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(0.8705882352941177, 0.6196078431372549, 0.8392156862745098 ), # de9ed6
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)
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_Vega20c_data = (
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(0.19215686274509805, 0.5098039215686274, 0.7411764705882353 ), # 3182bd
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(0.4196078431372549, 0.6823529411764706, 0.8392156862745098 ), # 6baed6
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(0.6196078431372549, 0.792156862745098, 0.8823529411764706 ), # 9ecae1
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(0.7764705882352941, 0.8588235294117647, 0.9372549019607843 ), # c6dbef
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(0.9019607843137255, 0.3333333333333333, 0.050980392156862744), # e6550d
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(0.9921568627450981, 0.5529411764705883, 0.23529411764705882 ), # fd8d3c
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(0.9921568627450981, 0.6823529411764706, 0.4196078431372549 ), # fdae6b
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(0.9921568627450981, 0.8156862745098039, 0.6352941176470588 ), # fdd0a2
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(0.19215686274509805, 0.6392156862745098, 0.32941176470588235 ), # 31a354
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(0.4549019607843137, 0.7686274509803922, 0.4627450980392157 ), # 74c476
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(0.6313725490196078, 0.8509803921568627, 0.6078431372549019 ), # a1d99b
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(0.7803921568627451, 0.9137254901960784, 0.7529411764705882 ), # c7e9c0
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(0.4588235294117647, 0.4196078431372549, 0.6941176470588235 ), # 756bb1
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(0.6196078431372549, 0.6039215686274509, 0.7843137254901961 ), # 9e9ac8
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(0.7372549019607844, 0.7411764705882353, 0.8627450980392157 ), # bcbddc
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(0.8549019607843137, 0.8549019607843137, 0.9215686274509803 ), # dadaeb
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(0.38823529411764707, 0.38823529411764707, 0.38823529411764707 ), # 636363
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(0.5882352941176471, 0.5882352941176471, 0.5882352941176471 ), # 969696
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(0.7411764705882353, 0.7411764705882353, 0.7411764705882353 ), # bdbdbd
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(0.8509803921568627, 0.8509803921568627, 0.8509803921568627 ), # d9d9d9
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)
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datad = {
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'afmhot': _afmhot_data,
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'autumn': _autumn_data,
@@ -1358,3 +1446,8 @@ def gfunc32(x):
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datad['Set1'] = {'listed': _Set1_data}
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datad['Set2'] = {'listed': _Set2_data}
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datad['Set3'] = {'listed': _Set3_data}
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datad['Vega10'] = {'listed': _Vega10_data}
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datad['Vega20'] = {'listed': _Vega20_data}
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datad['Vega20b'] = {'listed': _Vega20b_data}
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datad['Vega20c'] = {'listed': _Vega20c_data}

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