8000 Merge pull request #9871 from jamienunez/cividis_temp · matplotlib/matplotlib@fd4b541 · GitHub
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Merge pull request #9871 from jamienunez/cividis_temp
ENH: Cividis colormap added with short description in whats_new
2 parents 4900681 + 877f3af commit fd4b541

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‎doc/users/next_whats_new/2017-12-05_cividis_colormap.rst

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Cividis colormap
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----------------------------
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A new dark blue/yellow colormap named 'cividis' was added. Like viridis, cividis is perceptually uniform and colorblind friendly. However, cividis also goes a step further: not only is it usable by colorblind users, it should actually look effectively identical to colorblind and non-colorblind users. For more details, see Nunez J, Anderton C, and Renslow R. (submitted). Optimizing colormaps with consideration for color vision deficiency to enable accurate interpretation of scientific data."
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.. plot::
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import matplotlib.pyplot as plt
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import numpy as np
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fig, ax = plt.subplots()
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pcm = ax.pcolormesh(np.random.rand(32,32), cmap='cividis')
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fig.colorbar(pcm)

‎examples/color/colormap_reference.py

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cmaps = [('Perceptually Uniform Sequential', [
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'viridis', 'plasma', 'inferno', 'magma']),
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'viridis', 'plasma', 'inferno', 'magma', 'cividis']),
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('Sequential', [
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'Greys', 'Purples', 'Blues', 'Greens', 'Oranges', 'Reds',
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'YlOrBr', 'YlOrRd', 'OrRd', 'PuRd', 'RdPu', 'BuPu',

‎lib/matplotlib/_cm_listed.py

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@@ -1028,12 +1028,269 @@
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[0.983868, 0.904867, 0.136897],
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[0.993248, 0.906157, 0.143936]]
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_cividis_data = [[0.000000, 0.135112, 0.304751],
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[0.000000, 0.138068, 0.311105],
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[0.000000, 0.141013, 0.317579],
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[0.000000, 0.143951, 0.323982],
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[0.000000, 0.146877, 0.330479],
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[0.000000, 0.149791, 0.337065],
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[0.000000, 0.152673, 0.343704],
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[0.000000, 0.155377, 0.350500],
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[0.000000, 0.157932, 0.357521],
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[0.000000, 0.160495, 0.364534],
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[0.000000, 0.163058, 0.371608],
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[0.000000, 0.165621, 0.378769],
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[0.000000, 0.168204, 0.385902],
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[0.000000, 0.170800, 0.393100],
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[0.000000, 0.173420, 0.400353],
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[0.000000, 0.176082, 0.407577],
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[0.000000, 0.178802, 0.414764],
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[0.000000, 0.181610, 0.421859],
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[0.000000, 0.184550, 0.428802],
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[0.000000, 0.186915, 0.435532],
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[0.000000, 0.188769, 0.439563],
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[0.000000, 0.190950, 0.441085],
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[0.000000, 0.193366, 0.441561],
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[0.003602, 0.195911, 0.441564],
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[0.017852, 0.198528, 0.441248],
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[0.032110, 0.201199, 0.440785],
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[0.046205, 0.203903, 0.440196],
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[0.058378, 0.206629, 0.439531],
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[0.068968, 0.209372, 0.438863],
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[0.078624, 0.212122, 0.438105],
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[0.087465, 0.214879, 0.437342],
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[0.095645, 0.217643, 0.436593],
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[0.103401, 0.220406, 0.435790],
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[0.110658, 0.223170, 0.435067],
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[0.117612, 0.225935, 0.434308],
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[0.136830, 0.234216, 0.432148],
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[0.142852, 0.236972, 0.431404],
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[0.148638, 0.239724, 0.430752],
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[0.154261, 0.242475, 0.430120],
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[0.159733, 0.245221, 0.429528],
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[0.165113, 0.247965, 0.428908],
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[0.170362, 0.250707, 0.428325],
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[0.175490, 0.253444, 0.427790],
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[0.190303, 0.261644, 0.426329],
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[0.195057, 0.264372, 0.425924],
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[0.199764, 0.267099, 0.425497],
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[0.213431, 0.275266, 0.424480],
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[0.217863, 0.277985, 0.424206],
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[0.222264, 0.280702, 0.423914],
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[0.247605, 0.296986, 0.422917],
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[0.251675, 0.299698, 0.422873],
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[0.255731, 0.302409, 0.422814],
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[0.283240, 0.321390, 0.423211],
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[0.287065, 0.324103, 0.423373],
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[0.294669, 0.329531, 0.423716],
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[0.298421, 0.332247, 0.423973],
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[0.302169, 0.334963, 0.424213],
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[0.305886, 0.337681, 0.424512],
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[0.309601, 0.340399, 0.424790],
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[0.313287, 0.343120, 0.425120],
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[0.316941, 0.345842, 0.425512],
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[0.320595, 0.348565, 0.425889],
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[0.324250, 0.351289, 0.426250],
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[0.335073, 0.359474, 0.427605],
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[0.338673, 0.362206, 0.428053],
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[0.345793, 0.367676, 0.429127],
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[0.349341, 0.370414, 0.429685],
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[0.352892, 0.373153, 0.430226],
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[0.356418, 0.375896, 0.430823],
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[0.359916, 0.378641, 0.431501],
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[0.363446, 0.381388, 0.432075],
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[0.366923, 0.384139, 0.432796],
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[0.370430, 0.386890, 0.433428],
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[0.418383, 0 10000 .425733, 0.445560],
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[0.441810, 0.445402, 0.453659],
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[0.570607, 0.549695, 0.469593],
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[0.675981, 0.632468, 0.446736],
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[0.679979, 0.635626, 0.445424],
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[0.683950, 0.638793, 0.444251],
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[0.687957, 0.641966, 0.442886],
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[0.691971, 0.645145, 0.441491],
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[0.695985, 0.648334, 0.440072],
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[0.700008, 0.651529, 0.438624],
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[0.704037, 0.654731, 0.437147],
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[0.708067, 0.657942, 0.435647],
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[0.712105, 0.661160, 0.434117],
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[0.716177, 0.664384, 0.432386],
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[0.724274, 0.670859, 0.429194],
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[0.740589, 0.683900, 0.422131],
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[0.744664, 0.687181, 0.420393],
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[0.765223, 0.703705, 0.410587],
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[0.769353, 0.707035, 0.408516],
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[0.773486, 0.710373, 0.406422],
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[0.777651, 0.713719, 0.404112],
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[0.781795, 0.717074, 0.401966],
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[0.785965, 0.720438, 0.399613],
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[0.790116, 0.723810, 0.397423],
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[0.794298, 0.727190, 0.395016],
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[0.802667, 0.733978, 0.390153],
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[0.806859, 0.737385, 0.387684],
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[0.823729, 0.751101, 0.377043],
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[0.827959, 0.754553, 0.374292],
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[0.832192, 0.758014, 0.371529],
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[0.836429, 0.761483, 0.368747],
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[0.840693, 0.764962, 0.365746],
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[0.844957, 0.768450, 0.362741],
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[0.849223, 0.771947, 0.359729],
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[0.853515, 0.775454, 0.356500],
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[0.857809, 0.778969, 0.353259],
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[0.862105, 0.782494, 0.350011],
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[0.866421, 0.786028, 0.346571],
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[0.870717, 0.789572, 0.343333],
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[0.875057, 0.793125, 0.339685],
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[0.879378, 0.796687, 0.336241],
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[0.883720, 0.800258, 0.332599],
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[0.888081, 0.803839, 0.328770],
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[0.892440, 0.807430, 0.324968],
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[0.896818, 0.811030, 0.320982],
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[0.936660, 0.843841, 0.280876],
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[0.941147, 0.847530, 0.275815],
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[0.945654, 0.851228, 0.270532],
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[0.950178, 0.854933, 0.265085],
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[0.954725, 0.858646, 0.259365],
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[0.959284, 0.862365, 0.253563],
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[0.963872, 0.866089, 0.247445],
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[0.968469, 0.869819, 0.241310],
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[0.973114, 0.873550, 0.234677],
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[0.977780, 0.877281, 0.227954],
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[0.982497, 0.881008, 0.220878],
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[0.987293, 0.884718, 0.213336],
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[0.992218, 0.888385, 0.205468],
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[0.994847, 0.892954, 0.203445],
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[0.995249, 0.898384, 0.207561],
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[0.995503, 0.903866, 0.212370],
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[0.995737, 0.909344, 0.217772]]
10311287

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cmaps = {}
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for (name, data) in (('magma', _magma_data),
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('inferno', _inferno_data),
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('plasma', _plasma_data),
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('viridis', _viridis_data)):
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('viridis', _viridis_data),
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('cividis', _cividis_data)):
10371294

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cmaps[name] = ListedColormap(data, name=name)
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# generate reversed colormap

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