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Merged
merged 9 commits into from
Feb 11, 2018
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Merged

Cividis colormap added with short description in whats_new #9871

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93a2805
Cividis colormap added with short description in what's new
jamienunez Nov 27, 2017
559d76c
Switched whats_new to correct file and added to colormap_reference.py
jamienunez Nov 28, 2017
07332a1
Traded out unicode character
jamienunez Nov 28, 2017
7fcb2f9
Added cividis colormap to what's new as separate file
jamienunez Dec 5, 2017
c2601a6
Added plot to cividis info
jamienunez Dec 5, 2017
b77137c
Minor edit to cividis's what's new
jamienunez Dec 5, 2017
997d9b9
Fixed directive notation
jamienunez Dec 5, 2017
1ab19dc
Edited formatting for the example plot
jamienunez Dec 6, 2017
877f3af
Update to cividis description
jamienunez Dec 7, 2017
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13 changes: 13 additions & 0 deletions doc/users/next_whats_new/2017-12-05_cividis_colormap.rst
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Original file line number Diff line number Diff line change
@@ -0,0 +1,13 @@
Cividis colormap
----------------------------

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."

.. plot::

import matplotlib.pyplot as plt
import numpy as np

fig, ax = plt.subplots()
pcm = ax.pcolormesh(np.random.rand(32,32), cmap='cividis')
fig.colorbar(pcm)
2 changes: 1 addition & 1 deletion examples/color/colormap_reference.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,7 +17,7 @@


cmaps = [('Perceptually Uniform Sequential', [
'viridis', 'plasma', 'inferno', 'magma']),
'viridis', 'plasma', 'inferno', 'magma', 'cividis']),
('Sequential', [
'Greys', 'Purples', 'Blues', 'Greens', 'Oranges', 'Reds',
'YlOrBr', 'YlOrRd', 'OrRd', 'PuRd', 'RdPu', 'BuPu',
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259 changes: 258 additions & 1 deletion lib/matplotlib/_cm_listed.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1028,12 +1028,269 @@
[0.983868, 0.904867, 0.136897],
[0.993248, 0.906157, 0.143936]]

_cividis_data = [[0.000000, 0.135112, 0.304751],
[0.000000, 0.138068, 0.311105],
[0.000000, 0.141013, 0.317579],
[0.000000, 0.143951, 0.323982],
[0.000000, 0.146877, 0.330479],
[0.000000, 0.149791, 0.337065],
[0.000000, 0.152673, 0.343704],
[0.000000, 0.155377, 0.350500],
[0.000000, 0.157932, 0.357521],
[0.000000, 0.160495, 0.364534],
[0.000000, 0.163058, 0.371608],
[0.000000, 0.165621, 0.378769],
[0.000000, 0.168204, 0.385902],
[0.000000, 0.170800, 0.393100],
[0.000000, 0.173420, 0.400353],
[0.000000, 0.176082, 0.407577],
[0.000000, 0.178802, 0.414764],
[0.000000, 0.181610, 0.421859],
[0.000000, 0.184550, 0.428802],
[0.000000, 0.186915, 0.435532],
[0.000000, 0.188769, 0.439563],
[0.000000, 0.190950, 0.441085],
[0.000000, 0.193366, 0.441561],
[0.003602, 0.195911, 0.441564],
[0.017852, 0.198528, 0.441248],
[0.032110, 0.201199, 0.440785],
[0.046205, 0.203903, 0.440196],
[0.058378, 0.206629, 0.439531],
[0.068968, 0.209372, 0.438863],
[0.078624, 0.212122, 0.438105],
[0.087465, 0.214879, 0.437342],
[0.095645, 0.217643, 0.436593],
[0.103401, 0.220406, 0.435790],
[0.110658, 0.223170, 0.435067],
[0.117612, 0.225935, 0.434308],
[0.124291, 0.228697, 0.433547],
[0.130669, 0.231458, 0.432840],
[0.136830, 0.234216, 0.432148],
[0.142852, 0.236972, 0.431404],
[0.148638, 0.239724, 0.430752],
[0.154261, 0.242475, 0.430120],
[0.159733, 0.245221, 0.429528],
[0.165113, 0.247965, 0.428908],
[0.170362, 0.250707, 0.428325],
[0.175490, 0.253444, 0.427790],
[0.180503, 0.256180, 0.427299],
[0.185453, 0.258914, 0.426788],
[0.190303, 0.261644, 0.426329],
[0.195057, 0.264372, 0.425924],
[0.199764, 0.267099, 0.425497],
[0.204385, 0.269823, 0.425126],
[0.208926, 0.272546, 0.424809],
[0.213431, 0.275266, 0.424480],
[0.217863, 0.277985, 0.424206],
[0.222264, 0.280702, 0.423914],
[0.226598, 0.283419, 0.423678],
[0.230871, 0.286134, 0.423498],
[0.235120, 0.288848, 0.423304],
[0.239312, 0.291562, 0.423167],
[0.243485, 0.294274, 0.423014],
[0.247605, 0.296986, 0.422917],
[0.251675, 0.299698, 0.422873],
[0.255731, 0.302409, 0.422814],
[0.259740, 0.305120, 0.422810],
[0.263738, 0.307831, 0.422789],
[0.267693, 0.310542, 0.422821],
[0.271639, 0.313253, 0.422837],
[0.275513, 0.315965, 0.422979],
[0.279411, 0.318677, 0.423031],
[0.283240, 0.321390, 0.423211],
[0.287065, 0.324103, 0.423373],
[0.290884, 0.326816, 0.423517],
[0.294669, 0.329531, 0.423716],
[0.298421, 0.332247, 0.423973],
[0.302169, 0.334963, 0.424213],
[0.305886, 0.337681, 0.424512],
[0.309601, 0.340399, 0.424790],
[0.313287, 0.343120, 0.425120],
[0.316941, 0.345842, 0.425512],
[0.320595, 0.348565, 0.425889],
[0.324250, 0.351289, 0.426250],
[0.327875, 0.354016, 0.426670],
[0.331474, 0.356744, 0.427144],
[0.335073, 0.359474, 0.427605],
[0.338673, 0.362206, 0.428053],
[0.342246, 0.364939, 0.428559],
[0.345793, 0.367676, 0.429127],
[0.349341, 0.370414, 0.429685],
[0.352892, 0.373153, 0.430226],
[0.356418, 0.375896, 0.430823],
[0.359916, 0.378641, 0.431501],
[0.363446, 0.381388, 0.432075],
[0.366923, 0.384139, 0.432796],
[0.370430, 0.386890, 0.433428],
[0.373884, 0.389646, 0.434209],
[0.377371, 0.392404, 0.434890],
[0.380830, 0.395164, 0.435653],
[0.384268, 0.397928, 0.436475],
[0.387705, 0.400694, 0.437305],
[0.391151, 0.403464, 0.438096],
[0.394568, 0.406236, 0.438986],
[0.397991, 0.409011, 0.439848],
[0.401418, 0.411790, 0.440708],
[0.404820, 0.414572, 0.441642],
[0.408226, 0.417357, 0.442570],
[0.411607, 0.420145, 0.443577],
[0.414992, 0.422937, 0.444578],
[0.418383, 0.425733, 0.445560],
[0.421748, 0.428531, 0.446640],
[0.425120, 0.431334, 0.447692],
[0.428462, 0.434140, 0.448864],
[0.431817, 0.436950, 0.449982],
[0.435168, 0.439763, 0.451134],
[0.438504, 0.442580, 0.452341],
[0.441810, 0.445402, 0.453659],
[0.445148, 0.448226, 0.454885],
[0.448447, 0.451053, 0.456264],
[0.451759, 0.453887, 0.457582],
[0.455072, 0.456718, 0.458976],
[0.458366, 0.459552, 0.460457],
[0.461616, 0.462405, 0.461969],
[0.464947, 0.465241, 0.463395],
[0.468254, 0.468083, 0.464908],
[0.471501, 0.470960, 0.466357],
[0.474812, 0.473832, 0.467681],
[0.478186, 0.476699, 0.468845],
[0.481622, 0.479573, 0.469767],
[0.485141, 0.482451, 0.470384],
[0.488697, 0.485318, 0.471008],
[0.492278, 0.488198, 0.471453],
[0.495913, 0.491076, 0.471751],
[0.499552, 0.493960, 0.472032],
[0.503185, 0.496851, 0.472305],
[0.506866, 0.499743, 0.472432],
[0.510540, 0.502643, 0.472550],
[0.514226, 0.505546, 0.472640],
[0.517920, 0.508454, 0.472707],
[0.521643, 0.511367, 0.472639],
[0.525348, 0.514285, 0.472660],
[0.529086, 0.517207, 0.472543],
[0.532829, 0.520135, 0.472401],
[0.536553, 0.523067, 0.472352],
[0.540307, 0.526005, 0.472163],
[0.544069, 0.528948, 0.471947],
[0.547840, 0.531895, 0.471704],
[0.551612, 0.534849, 0.471439],
[0.555393, 0.537807, 0.471147],
[0.559181, 0.540771, 0.470829],
[0.562972, 0.543741, 0.470488],
[0.566802, 0.546715, 0.469988],
[0.570607, 0.549695, 0.469593],
[0.574417, 0.552682, 0.469172],
[0.578236, 0.555673, 0.468724],
[0.582087, 0.558670, 0.468118],
[0.585916, 0.561674, 0.467618],
[0.589753, 0.564682, 0.467090],
[0.593622, 0.567697, 0.466401],
[0.597469, 0.570718, 0.465821],
[0.601354, 0.573743, 0.465074],
[0.605211, 0.576777, 0.464441],
[0.609105, 0.579816, 0.463638],
[0.612977, 0.582861, 0.462950],
[0.616852, 0.585913, 0.462237],
[0.620765, 0.588970, 0.461351],
[0.624654, 0.592034, 0.460583],
[0.628576, 0.595104, 0.459641],
[0.632506, 0.598180, 0.458668],
[0.636412, 0.601264, 0.457818],
[0.640352, 0.604354, 0.456791],
[0.644270, 0.607450, 0.455886],
[0.648222, 0.610553, 0.454801],
[0.652178, 0.613664, 0.453689],
[0.656114, 0.616780, 0.452702],
[0.660082, 0.619904, 0.451534],
[0.664055, 0.623034, 0.450338],
[0.668008, 0.626171, 0.449270],
[0.671991, 0.629316, 0.448018],
[0.675981, 0.632468, 0.446736],
[0.679979, 0.635626, 0.445424],
[0.683950, 0.638793, 0.444251],
[0.687957, 0.641966, 0.442886],
[0.691971, 0.645145, 0.441491],
[0.695985, 0.648334, 0.440072],
[0.700008, 0.651529, 0.438624],
[0.704037, 0.654731, 0.437147],
[0.708067, 0.657942, 0.435647],
[0.712105, 0.661160, 0.434117],
[0.716177, 0.664384, 0.432386],
[0.720222, 0.667618, 0.430805],
[0.724274, 0.670859, 0.429194],
[0.728334, 0.674107, 0.427554],
[0.732422, 0.677364, 0.425717],
[0.736488, 0.680629, 0.424028],
[0.740589, 0.683900, 0.422131],
[0.744664, 0.687181, 0.420393],
[0.748772, 0.690470, 0.418448],
[0.752886, 0.693766, 0.416472],
[0.756975, 0.697071, 0.414659],
[0.761096, 0.700384, 0.412638],
[0.765223, 0.703705, 0.410587],
[0.769353, 0.707035, 0.408516],
[0.773486, 0.710373, 0.406422],
[0.777651, 0.713719, 0.404112],
[0.781795, 0.717074, 0.401966],
[0.785965, 0.720438, 0.399613],
[0.790116, 0.723810, 0.397423],
[0.794298, 0.727190, 0.395016],
[0.798480, 0.730580, 0.392597],
[0.802667, 0.733978, 0.390153],
[0.806859, 0.737385, 0.387684],
[0.811054, 0.740801, 0.385198],
[0.815274, 0.744226, 0.382504],
[0.819499, 0.747659, 0.379785],
[0.823729, 0.751101, 0.377043],
[0.827959, 0.754553, 0.374292],
[0.832192, 0.758014, 0.371529],
[0.836429, 0.761483, 0.368747],
[0.840693, 0.764962, 0.365746],
[0.844957, 0.768450, 0.362741],
[0.849223, 0.771947, 0.359729],
[0.853515, 0.775454, 0.356500],
[0.857809, 0.778969, 0.353259],
[0.862105, 0.782494, 0.350011],
[0.866421, 0.786028, 0.346571],
[0.870717, 0.789572, 0.343333],
[0.875057, 0.793125, 0.339685],
[0.879378, 0.796687, 0.336241],
[0.883720, 0.800258, 0.332599],
[0.888081, 0.803839, 0.328770],
[0.892440, 0.807430, 0.324968],
[0.896818, 0.811030, 0.320982],
[0.901195, 0.814639, 0.317021],
[0.905589, 0.818257, 0.312889],
[0.910000, 0.821885, 0.308594],
[0.914407, 0.825522, 0.304348],
[0.918828, 0.829168, 0.299960],
[0.923279, 0.832822, 0.295244],
[0.927724, 0.836486, 0.290611],
[0.932180, 0.840159, 0.285880],
[0.936660, 0.843841, 0.280876],
[0.941147, 0.847530, 0.275815],
[0.945654, 0.851228, 0.270532],
[0.950178, 0.854933, 0.265085],
[0.954725, 0.858646, 0.259365],
[0.959284, 0.862365, 0.253563],
[0.963872, 0.866089, 0.247445],
[0.968469, 0.869819, 0.241310],
[0.973114, 0.873550, 0.234677],
[0.977780, 0.877281, 0.227954],
[0.982497, 0.881008, 0.220878],
[0.987293, 0.884718, 0.213336],
[0.992218, 0.888385, 0.205468],
[0.994847, 0.892954, 0.203445],
[0.995249, 0.898384, 0.207561],
[0.995503, 0.903866, 0.212370],
[0.995737, 0.909344, 0.217772]]

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

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