Colormaps for linear visual perception AND grayscale printing

Thanks to @yarchik, I made it work for Mathematica, by simply replacing [ and ] with { and } using a text editor:

viridis=Module[{colorlist},
colorlist={{0.26700401,0.00487433,0.32941519},{0.26851048,0.00960483,0.33542652},{0.26994384,0.01462494,0.34137895},{0.27130489,0.01994186,0.34726862},{0.27259384,0.02556309,0.35309303},{0.27380934,0.03149748,0.35885256},{0.27495242,0.03775181,0.36454323},{0.27602238,0.04416723,0.37016418},{0.2770184,0.05034437,0.37571452},{0.27794143,0.05632444,0.38119074},{0.27879067,0.06214536,0.38659204},{0.2795655,0.06783587,0.39191723},{0.28026658,0.07341724,0.39716349},{0.28089358,0.07890703,0.40232944},{0.28144581,0.0843197,0.40741404},{0.28192358,0.08966622,0.41241521},{0.28232739,0.09495545,0.41733086},{0.28265633,0.10019576,0.42216032},{0.28291049,0.10539345,0.42690202},{0.28309095,0.11055307,0.43155375},{0.28319704,0.11567966,0.43611482},{0.28322882,0.12077701,0.44058404},{0.28318684,0.12584799,0.44496},{0.283072,0.13089477,0.44924127},{0.28288389,0.13592005,0.45342734},{0.28262297,0.14092556,0.45751726},{0.28229037,0.14591233,0.46150995},{0.28188676,0.15088147,0.46540474},{0.28141228,0.15583425,0.46920128},{0.28086773,0.16077132,0.47289909},{0.28025468,0.16569272,0.47649762},{0.27957399,0.17059884,0.47999675},{0.27882618,0.1754902,0.48339654},{0.27801236,0.18036684,0.48669702},{0.27713437,0.18522836,0.48989831},{0.27619376,0.19007447,0.49300074},{0.27519116,0.1949054,0.49600488},{0.27412802,0.19972086,0.49891131},{0.27300596,0.20452049,0.50172076},{0.27182812,0.20930306,0.50443413},{0.27059473,0.21406899,0.50705243},{0.26930756,0.21881782,0.50957678},{0.26796846,0.22354911,0.5120084},{0.26657984,0.2282621,0.5143487},{0.2651445,0.23295593,0.5165993},{0.2636632,0.23763078,0.51876163},{0.26213801,0.24228619,0.52083736},{0.26057103,0.2469217,0.52282822},{0.25896451,0.25153685,0.52473609},{0.25732244,0.2561304,0.52656332},{0.25564519,0.26070284,0.52831152},{0.25393498,0.26525384,0.52998273},{0.25219404,0.26978306,0.53157905},{0.25042462,0.27429024,0.53310261},{0.24862899,0.27877509,0.53455561},{0.2468114,0.28323662,0.53594093},{0.24497208,0.28767547,0.53726018},{0.24311324,0.29209154,0.53851561},{0.24123708,0.29648471,0.53970946},{0.23934575,0.30085494,0.54084398},{0.23744138,0.30520222,0.5419214},{0.23552606,0.30952657,0.54294396},{0.23360277,0.31382773,0.54391424},{0.2316735,0.3181058,0.54483444},{0.22973926,0.32236127,0.54570633},{0.22780192,0.32659432,0.546532},{0.2258633,0.33080515,0.54731353},{0.22392515,0.334994,0.54805291},{0.22198915,0.33916114,0.54875211},{0.22005691,0.34330688,0.54941304},{0.21812995,0.34743154,0.55003755},{0.21620971,0.35153548,0.55062743},{0.21429757,0.35561907,0.5511844},{0.21239477,0.35968273,0.55171011},{0.2105031,0.36372671,0.55220646},{0.20862342,0.36775151,0.55267486},{0.20675628,0.37175775,0.55311653},{0.20490257,0.37574589,0.55353282},{0.20306309,0.37971644,0.55392505},{0.20123854,0.38366989,0.55429441},{0.1994295,0.38760678,0.55464205},{0.1976365,0.39152762,0.55496905},{0.19585993,0.39543297,0.55527637},{0.19410009,0.39932336,0.55556494},{0.19235719,0.40319934,0.55583559},{0.19063135,0.40706148,0.55608907},{0.18892259,0.41091033,0.55632606},{0.18723083,0.41474645,0.55654717},{0.18555593,0.4185704,0.55675292},{0.18389763,0.42238275,0.55694377},{0.18225561,0.42618405,0.5571201},{0.18062949,0.42997486,0.55728221},{0.17901879,0.43375572,0.55743035},{0.17742298,0.4375272,0.55756466},{0.17584148,0.44128981,0.55768526},{0.17427363,0.4450441,0.55779216},{0.17271876,0.4487906,0.55788532},{0.17117615,0.4525298,0.55796464},{0.16964573,0.45626209,0.55803034},{0.16812641,0.45998802,0.55808199},{0.1666171,0.46370813,0.55811913},{0.16511703,0.4674229,0.55814141},{0.16362543,0.47113278,0.55814842},{0.16214155,0.47483821,0.55813967},{0.16066467,0.47853961,0.55811466},{0.15919413,0.4822374,0.5580728},{0.15772933,0.48593197,0.55801347},{0.15626973,0.4896237,0.557936},{0.15481488,0.49331293,0.55783967},{0.15336445,0.49700003,0.55772371},{0.1519182,0.50068529,0.55758733},{0.15047605,0.50436904,0.55742968},{0.14903918,0.50805136,0.5572505},{0.14760731,0.51173263,0.55704861},{0.14618026,0.51541316,0.55682271},{0.14475863,0.51909319,0.55657181},{0.14334327,0.52277292,0.55629491},{0.14193527,0.52645254,0.55599097},{0.14053599,0.53013219,0.55565893},{0.13914708,0.53381201,0.55529773},{0.13777048,0.53749213,0.55490625},{0.1364085,0.54117264,0.55448339},{0.13506561,0.54485335,0.55402906},{0.13374299,0.54853458,0.55354108},{0.13244401,0.55221637,0.55301828},{0.13117249,0.55589872,0.55245948},{0.1299327,0.55958162,0.55186354},{0.12872938,0.56326503,0.55122927},{0.12756771,0.56694891,0.55055551},{0.12645338,0.57063316,0.5498411},{0.12539383,0.57431754,0.54908564},{0.12439474,0.57800205,0.5482874},{0.12346281,0.58168661,0.54744498},{0.12260562,0.58537105,0.54655722},{0.12183122,0.58905521,0.54562298},{0.12114807,0.59273889,0.54464114},{0.12056501,0.59642187,0.54361058},{0.12009154,0.60010387,0.54253043},{0.11973756,0.60378459,0.54139999},{0.11951163,0.60746388,0.54021751},{0.11942341,0.61114146,0.53898192},{0.11948255,0.61481702,0.53769219},{0.11969858,0.61849025,0.53634733},{0.12008079,0.62216081,0.53494633},{0.12063824,0.62582833,0.53348834},{0.12137972,0.62949242,0.53197275},{0.12231244,0.63315277,0.53039808},{0.12344358,0.63680899,0.52876343},{0.12477953,0.64046069,0.52706792},{0.12632581,0.64410744,0.52531069},{0.12808703,0.64774881,0.52349092},{0.13006688,0.65138436,0.52160791},{0.13226797,0.65501363,0.51966086},{0.13469183,0.65863619,0.5176488},{0.13733921,0.66225157,0.51557101},{0.14020991,0.66585927,0.5134268},{0.14330291,0.66945881,0.51121549},{0.1466164,0.67304968,0.50893644},{0.15014782,0.67663139,0.5065889},{0.15389405,0.68020343,0.50417217},{0.15785146,0.68376525,0.50168574},{0.16201598,0.68731632,0.49912906},{0.1663832,0.69085611,0.49650163},{0.1709484,0.69438405,0.49380294},{0.17570671,0.6978996,0.49103252},{0.18065314,0.70140222,0.48818938},{0.18578266,0.70489133,0.48527326},{0.19109018,0.70836635,0.48228395},{0.19657063,0.71182668,0.47922108},{0.20221902,0.71527175,0.47608431},{0.20803045,0.71870095,0.4728733},{0.21400015,0.72211371,0.46958774},{0.22012381,0.72550945,0.46622638},{0.2263969,0.72888753,0.46278934},{0.23281498,0.73224735,0.45927675},{0.2393739,0.73558828,0.45568838},{0.24606968,0.73890972,0.45202405},{0.25289851,0.74221104,0.44828355},{0.25985676,0.74549162,0.44446673},{0.26694127,0.74875084,0.44057284},{0.27414922,0.75198807,0.4366009},{0.28147681,0.75520266,0.43255207},{0.28892102,0.75839399,0.42842626},{0.29647899,0.76156142,0.42422341},{0.30414796,0.76470433,0.41994346},{0.31192534,0.76782207,0.41558638},{0.3198086,0.77091403,0.41115215},{0.3277958,0.77397953,0.40664011},{0.33588539,0.7770179,0.40204917},{0.34407411,0.78002855,0.39738103},{0.35235985,0.78301086,0.39263579},{0.36074053,0.78596419,0.38781353},{0.3692142,0.78888793,0.38291438},{0.37777892,0.79178146,0.3779385},{0.38643282,0.79464415,0.37288606},{0.39517408,0.79747541,0.36775726},{0.40400101,0.80027461,0.36255223},{0.4129135,0.80304099,0.35726893},{0.42190813,0.80577412,0.35191009},{0.43098317,0.80847343,0.34647607},{0.44013691,0.81113836,0.3409673},{0.44936763,0.81376835,0.33538426},{0.45867362,0.81636288,0.32972749},{0.46805314,0.81892143,0.32399761},{0.47750446,0.82144351,0.31819529},{0.4870258,0.82392862,0.31232133},{0.49661536,0.82637633,0.30637661},{0.5062713,0.82878621,0.30036211},{0.51599182,0.83115784,0.29427888},{0.52577622,0.83349064,0.2881265},{0.5356211,0.83578452,0.28190832},{0.5455244,0.83803918,0.27562602},{0.55548397,0.84025437,0.26928147},{0.5654976,0.8424299,0.26287683},{0.57556297,0.84456561,0.25641457},{0.58567772,0.84666139,0.24989748},{0.59583934,0.84871722,0.24332878},{0.60604528,0.8507331,0.23671214},{0.61629283,0.85270912,0.23005179},{0.62657923,0.85464543,0.22335258},{0.63690157,0.85654226,0.21662012},{0.64725685,0.85839991,0.20986086},{0.65764197,0.86021878,0.20308229},{0.66805369,0.86199932,0.19629307},{0.67848868,0.86374211,0.18950326},{0.68894351,0.86544779,0.18272455},{0.69941463,0.86711711,0.17597055},{0.70989842,0.86875092,0.16925712},{0.72039115,0.87035015,0.16260273},{0.73088902,0.87191584,0.15602894},{0.74138803,0.87344918,0.14956101},{0.75188414,0.87495143,0.14322828},{0.76237342,0.87642392,0.13706449},{0.77285183,0.87786808,0.13110864},{0.78331535,0.87928545,0.12540538},{0.79375994,0.88067763,0.12000532},{0.80418159,0.88204632,0.11496505},{0.81457634,0.88339329,0.11034678},{0.82494028,0.88472036,0.10621724},{0.83526959,0.88602943,0.1026459},{0.84556056,0.88732243,0.09970219},{0.8558096,0.88860134,0.09745186},{0.86601325,0.88986815,0.09595277},{0.87616824,0.89112487,0.09525046},{0.88627146,0.89237353,0.09537439},{0.89632002,0.89361614,0.09633538},{0.90631121,0.89485467,0.09812496},{0.91624212,0.89609127,0.1007168},{0.92610579,0.89732977,0.10407067},{0.93590444,0.8985704,0.10813094},{0.94563626,0.899815,0.11283773},{0.95529972,0.90106534,0.11812832},{0.96489353,0.90232311,0.12394051},{0.97441665,0.90358991,0.13021494},{0.98386829,0.90486726,0.13689671},{0.99324789,0.90615657,0.1439362}};
Evaluate[Blend[RGBColor @@@ colorlist, #] &]
];
BarLegend[{viridis, {0, 1}}]

Edit:

Another Example to visualize the grayscale-improvement ("Rainbow" vs. viridis):

mPlots = ContourPlot[Cos[x] Sin[y], {x, -10, 10}, {y, -10, 10}, PlotRange -> All, ColorFunction -> #, PlotPoints -> 75, ImageSize -> Medium] & /@
    {"Rainbow", viridis}
mPlots /. x: _RGBColor | _Hue | _CMYKColor :> ColorConvert[x, "Grayscale"]

we should not need to manually fix the format.. cut-paste from https://github.com/BIDS/colormap/blob/master/option_b.py

pyarray = " [[  1.46159096e-03,   4.66127766e-04,   1.38655200e-02],
   [  2.26726368e-03,   1.26992553e-03,   1.85703520e-02],
   [  3.29899092e-03,   2.24934863e-03,   2.42390508e-02],
   [  4.54690615e-03,   3.39180156e-03,   3.09092475e-02],
   [  6.00552565e-03,   4.69194561e-03,   3.85578980e-02],
   [  7.67578856e-03,   6.13611626e-03,   4.68360336e-02],
   [  9.56051094e-03,   7.71344131e-03,   5.51430756e-02],
   [  1.16634769e-02,   9.41675403e-03,   6.34598080e-02],
   [  1.39950388e-02,   1.12247138e-02,   7.18616890e-02],
   [  1.65605595e-02,   1.31362262e-02,   8.02817951e-02],
   [  1.93732295e-02,   1.51325789e-02,   8.87668094e-02],
   [  2.24468865e-02,   1.71991484e-02,   9.73274383e-02],
   [  2.57927373e-02,   1.93306298e-02,   1.05929835e-01],
   [  2.94324251e-02,   2.15030771e-02,   1.14621328e-01],
   [  3.33852235e-02,   2.37024271e-02,   1.23397286e-01],
   [  3.76684211e-02,   2.59207864e-02,   1.32232108e-01],
   [  4.22525554e-02,   2.81385015e-02,   1.41140519e-01],
   [  4.69146287e-02,   3.03236129e-02,   1.50163867e-01],
   [  5.16437624e-02,   3.24736172e-02,   1.59254277e-01],
   [  5.64491009e-02,   3.45691867e-02,   1.68413539e-01],
   [  6.13397200e-02,   3.65900213e-02,   1.77642172e-01],
   [  6.63312620e-02,   3.85036268e-02,   1.86961588e-01],
   [  7.14289181e-02,   4.02939095e-02,   1.96353558e-01],
   [  7.66367560e-02,   4.19053329e-02,   2.05798788e-01],
   [  8.19620773e-02,   4.33278666e-02,   2.15289113e-01],
   [  8.74113897e-02,   4.45561662e-02,   2.24813479e-01],
   [  9.29901526e-02,   4.55829503e-02,   2.34357604e-01],
   [  9.87024972e-02,   4.64018731e-02,   2.43903700e-01],
   [  1.04550936e-01,   4.70080541e-02,   2.53430300e-01],
   [  1.10536084e-01,   4.73986708e-02,   2.62912235e-01],
   [  1.16656423e-01,   4.75735920e-02,   2.72320803e-01],
   [  1.22908126e-01,   4.75360183e-02,   2.81624170e-01],
   [  1.29284984e-01,   4.72930838e-02,   2.90788012e-01],
   [  1.35778450e-01,   4.68563678e-02,   2.99776404e-01],
   [  1.42377819e-01,   4.62422566e-02,   3.08552910e-01],
   [  1.49072957e-01,   4.54676444e-02,   3.17085139e-01],
   [  1.55849711e-01,   4.45588056e-02,   3.25338414e-01],
   [  1.62688939e-01,   4.35542881e-02,   3.33276678e-01],
   [  1.69575148e-01,   4.24893149e-02,   3.40874188e-01],
   [  1.76493202e-01,   4.14017089e-02,   3.48110606e-01],
   [  1.83428775e-01,   4.03288858e-02,   3.54971391e-01],
   [  1.90367453e-01,   3.93088888e-02,   3.61446945e-01],
   [  1.97297425e-01,   3.84001825e-02,   3.67534629e-01],
   [  2.04209298e-01,   3.76322609e-02,   3.73237557e-01],
   [  2.11095463e-01,   3.70296488e-02,   3.78563264e-01],
   [  2.17948648e-01,   3.66146049e-02,   3.83522415e-01],
   [  2.24762908e-01,   3.64049901e-02,   3.88128944e-01],
   [  2.31538148e-01,   3.64052511e-02,   3.92400150e-01],
   [  2.38272961e-01,   3.66209949e-02,   3.96353388e-01],
   [  2.44966911e-01,   3.70545017e-02,   4.00006615e-01],
   [  2.51620354e-01,   3.77052832e-02,   4.03377897e-01],
   [  2.58234265e-01,   3.85706153e-02,   4.06485031e-01],
   [  2.64809649e-01,   3.96468666e-02,   4.09345373e-01],
   [  2.71346664e-01,   4.09215821e-02,   4.11976086e-01],
   [  2.77849829e-01,   4.23528741e-02,   4.14392106e-01],
   [  2.84321318e-01,   4.39325787e-02,   4.16607861e-01],
   [  2.90763373e-01,   4.56437598e-02,   4.18636756e-01],
   [  2.97178251e-01,   4.74700293e-02,   4.20491164e-01],
   [  3.03568182e-01,   4.93958927e-02,   4.22182449e-01],
   [  3.09935342e-01,   5.14069729e-02,   4.23720999e-01],
   [  3.16281835e-01,   5.34901321e-02,   4.25116277e-01],
   [  3.22609671e-01,   5.56335178e-02,   4.26376869e-01],
   [  3.28920763e-01,   5.78265505e-02,   4.27510546e-01],
   [  3.35216916e-01,   6.00598734e-02,   4.28524320e-01],
   [  3.41499828e-01,   6.23252772e-02,   4.29424503e-01],
   [  3.47771086e-01,   6.46156100e-02,   4.30216765e-01],
   [  3.54032169e-01,   6.69246832e-02,   4.30906186e-01],
   [  3.60284449e-01,   6.92471753e-02,   4.31497309e-01],
   [  3.66529195e-01,   7.15785403e-02,   4.31994185e-01],
   [  3.72767575e-01,   7.39149211e-02,   4.32400419e-01],
   [  3.79000659e-01,   7.62530701e-02,   4.32719214e-01],
   [  3.85228383e-01,   7.85914864e-02,   4.32954973e-01],
   [  3.91452659e-01,   8.09267058e-02,   4.33108763e-01],
   [  3.97674379e-01,   8.32568129e-02,   4.33182647e-01],
   [  4.03894278e-01,   8.55803445e-02,   4.33178526e-01],
   [  4.10113015e-01,   8.78961593e-02,   4.33098056e-01],
   [  4.16331169e-01,   9.02033992e-02,   4.32942678e-01],
   [  4.22549249e-01,   9.25014543e-02,   4.32713635e-01],
   [  4.28767696e-01,   9.47899342e-02,   4.32411996e-01],
   [  4.34986885e-01,   9.70686417e-02,   4.32038673e-01],
   [  4.41207124e-01,   9.93375510e-02,   4.31594438e-01],
   [  4.47428382e-01,   1.01597079e-01,   4.31080497e-01],
   [  4.53650614e-01,   1.03847716e-01,   4.30497898e-01],
   [  4.59874623e-01,   1.06089165e-01,   4.29845789e-01],
   [  4.66100494e-01,   1.08321923e-01,   4.29124507e-01],
   [  4.72328255e-01,   1.10546584e-01,   4.28334320e-01],
   [  4.78557889e-01,   1.12763831e-01,   4.27475431e-01],
   [  4.84789325e-01,   1.14974430e-01,   4.26547991e-01],
   [  4.91022448e-01,   1.17179219e-01,   4.25552106e-01],
   [  4.97257069e-01,   1.19379132e-01,   4.24487908e-01],
   [  5.03492698e-01,   1.21575414e-01,   4.23356110e-01],
   [  5.09729541e-01,   1.23768654e-01,   4.22155676e-01],
   [  5.15967304e-01,   1.25959947e-01,   4.20886594e-01],
   [  5.22205646e-01,   1.28150439e-01,   4.19548848e-01],
   [  5.28444192e-01,   1.30341324e-01,   4.18142411e-01],
   [  5.34682523e-01,   1.32533845e-01,   4.16667258e-01],
   [  5.40920186e-01,   1.34729286e-01,   4.15123366e-01],
   [  5.47156706e-01,   1.36928959e-01,   4.13510662e-01],
   [  5.53391649e-01,   1.39134147e-01,   4.11828882e-01],
   [  5.59624442e-01,   1.41346265e-01,   4.10078028e-01],
   [  5.65854477e-01,   1.43566769e-01,   4.08258132e-01],
   [  5.72081108e-01,   1.45797150e-01,   4.06369246e-01],
   [  5.78303656e-01,   1.48038934e-01,   4.04411444e-01],
   [  5.84521407e-01,   1.50293679e-01,   4.02384829e-01],
   [  5.90733615e-01,   1.52562977e-01,   4.00289528e-01],
   [  5.96939751e-01,   1.54848232e-01,   3.98124897e-01],
   [  6.03138930e-01,   1.57151161e-01,   3.95891308e-01],
   [  6.09330184e-01,   1.59473549e-01,   3.93589349e-01],
   [  6.15512627e-01,   1.61817111e-01,   3.91219295e-01],
   [  6.21685340e-01,   1.64183582e-01,   3.88781456e-01],
   [  6.27847374e-01,   1.66574724e-01,   3.86276180e-01],
   [  6.33997746e-01,   1.68992314e-01,   3.83703854e-01],
   [  6.40135447e-01,   1.71438150e-01,   3.81064906e-01],
   [  6.46259648e-01,   1.73913876e-01,   3.78358969e-01],
   [  6.52369348e-01,   1.76421271e-01,   3.75586209e-01],
   [  6.58463166e-01,   1.78962399e-01,   3.72748214e-01],
   [  6.64539964e-01,   1.81539111e-01,   3.69845599e-01],
   [  6.70598572e-01,   1.84153268e-01,   3.66879025e-01],
   [  6.76637795e-01,   1.86806728e-01,   3.63849195e-01],
   [  6.82656407e-01,   1.89501352e-01,   3.60756856e-01],
   [  6.88653158e-01,   1.92238994e-01,   3.57602797e-01],
   [  6.94626769e-01,   1.95021500e-01,   3.54387853e-01],
   [  7.00575937e-01,   1.97850703e-01,   3.51112900e-01],
   [  7.06499709e-01,   2.00728196e-01,   3.47776863e-01],
   [  7.12396345e-01,   2.03656029e-01,   3.44382594e-01],
   [  7.18264447e-01,   2.06635993e-01,   3.40931208e-01],
   [  7.24102613e-01,   2.09669834e-01,   3.37423766e-01],
   [  7.29909422e-01,   2.12759270e-01,   3.33861367e-01],
   [  7.35683432e-01,   2.15905976e-01,   3.30245147e-01],
   [  7.41423185e-01,   2.19111589e-01,   3.26576275e-01],
   [  7.47127207e-01,   2.22377697e-01,   3.22855952e-01],
   [  7.52794009e-01,   2.25705837e-01,   3.19085410e-01],
   [  7.58422090e-01,   2.29097492e-01,   3.15265910e-01],
   [  7.64009940e-01,   2.32554083e-01,   3.11398734e-01],
   [  7.69556038e-01,   2.36076967e-01,   3.07485188e-01],
   [  7.75058888e-01,   2.39667435e-01,   3.03526312e-01],
   [  7.80517023e-01,   2.43326720e-01,   2.99522665e-01],
   [  7.85928794e-01,   2.47055968e-01,   2.95476756e-01],
   [  7.91292674e-01,   2.50856232e-01,   2.91389943e-01],
   [  7.96607144e-01,   2.54728485e-01,   2.87263585e-01],
   [  8.01870689e-01,   2.58673610e-01,   2.83099033e-01],
   [  8.07081807e-01,   2.62692401e-01,   2.78897629e-01],
   [  8.12239008e-01,   2.66785558e-01,   2.74660698e-01],
   [  8.17340818e-01,   2.70953688e-01,   2.70389545e-01],
   [  8.22385784e-01,   2.75197300e-01,   2.66085445e-01],
   [  8.27372474e-01,   2.79516805e-01,   2.61749643e-01],
   [  8.32299481e-01,   2.83912516e-01,   2.57383341e-01],
   [  8.37165425e-01,   2.88384647e-01,   2.52987700e-01],
   [  8.41968959e-01,   2.92933312e-01,   2.48563825e-01],
   [  8.46708768e-01,   2.97558528e-01,   2.44112767e-01],
   [  8.51383572e-01,   3.02260213e-01,   2.39635512e-01],
   [  8.55992130e-01,   3.07038188e-01,   2.35132978e-01],
   [  8.60533241e-01,   3.11892183e-01,   2.30606009e-01],
   [  8.65005747e-01,   3.16821833e-01,   2.26055368e-01],
   [  8.69408534e-01,   3.21826685e-01,   2.21481734e-01],
   [  8.73740530e-01,   3.26906201e-01,   2.16885699e-01],
   [  8.78000715e-01,   3.32059760e-01,   2.12267762e-01],
   [  8.82188112e-01,   3.37286663e-01,   2.07628326e-01],
   [  8.86301795e-01,   3.42586137e-01,   2.02967696e-01],
   [  8.90340885e-01,   3.47957340e-01,   1.98286080e-01],
   [  8.94304553e-01,   3.53399363e-01,   1.93583583e-01],
   [  8.98192017e-01,   3.58911240e-01,   1.88860212e-01],
   [  9.02002544e-01,   3.64491949e-01,   1.84115876e-01],
   [  9.05735448e-01,   3.70140419e-01,   1.79350388e-01],
   [  9.09390090e-01,   3.75855533e-01,   1.74563472e-01],
   [  9.12965874e-01,   3.81636138e-01,   1.69754764e-01],
   [  9.16462251e-01,   3.87481044e-01,   1.64923826e-01],
   [  9.19878710e-01,   3.93389034e-01,   1.60070152e-01],
   [  9.23214783e-01,   3.99358867e-01,   1.55193185e-01],
   [  9.26470039e-01,   4.05389282e-01,   1.50292329e-01],
   [  9.29644083e-01,   4.11479007e-01,   1.45366973e-01],
   [  9.32736555e-01,   4.17626756e-01,   1.40416519e-01],
   [  9.35747126e-01,   4.23831237e-01,   1.35440416e-01],
   [  9.38675494e-01,   4.30091162e-01,   1.30438175e-01],
   [  9.41521384e-01,   4.36405243e-01,   1.25409440e-01],
   [  9.44284543e-01,   4.42772199e-01,   1.20354038e-01],
   [  9.46964741e-01,   4.49190757e-01,   1.15272059e-01],
   [  9.49561766e-01,   4.55659658e-01,   1.10163947e-01],
   [  9.52075421e-01,   4.62177656e-01,   1.05030614e-01],
   [  9.54505523e-01,   4.68743522e-01,   9.98735931e-02],
   [  9.56851903e-01,   4.75356048e-01,   9.46952268e-02],
   [  9.59114397e-01,   4.82014044e-01,   8.94989073e-02],
   [  9.61292850e-01,   4.88716345e-01,   8.42893891e-02],
   [  9.63387110e-01,   4.95461806e-01,   7.90731907e-02],
   [  9.65397031e-01,   5.02249309e-01,   7.38591143e-02],
   [  9.67322465e-01,   5.09077761e-01,   6.86589199e-02],
   [  9.69163264e-01,   5.15946092e-01,   6.34881971e-02],
   [  9.70919277e-01,   5.22853259e-01,   5.83674890e-02],
   [  9.72590351e-01,   5.29798246e-01,   5.33237243e-02],
   [  9.74176327e-01,   5.36780059e-01,   4.83920090e-02],
   [  9.75677038e-01,   5.43797733e-01,   4.36177922e-02],
   [  9.77092313e-01,   5.50850323e-01,   3.90500131e-02],
   [  9.78421971e-01,   5.57936911e-01,   3.49306227e-02],
   [  9.79665824e-01,   5.65056600e-01,   3.14091591e-02],
   [  9.80823673e-01,   5.72208516e-01,   2.85075931e-02],
   [  9.81895311e-01,   5.79391803e-01,   2.62497353e-02],
   [  9.82880522e-01,   5.86605627e-01,   2.46613416e-02],
   [  9.83779081e-01,   5.93849168e-01,   2.37702263e-02],
   [  9.84590755e-01,   6.01121626e-01,   2.36063833e-02],
   [  9.85315301e-01,   6.08422211e-01,   2.42021174e-02],
   [  9.85952471e-01,   6.15750147e-01,   2.55921853e-02],
   [  9.86502013e-01,   6.23104667e-01,   2.78139496e-02],
   [  9.86963670e-01,   6.30485011e-01,   3.09075459e-02],
   [  9.87337182e-01,   6.37890424e-01,   3.49160639e-02],
   [  9.87622296e-01,   6.45320152e-01,   3.98857472e-02],
   [  9.87818759e-01,   6.52773439e-01,   4.55808037e-02],
   [  9.87926330e-01,   6.60249526e-01,   5.17503867e-02],
   [  9.87944783e-01,   6.67747641e-01,   5.83286889e-02],
   [  9.87873910e-01,   6.75267000e-01,   6.52570167e-02],
   [  9.87713535e-01,   6.82806802e-01,   7.24892330e-02],
   [  9.87463516e-01,   6.90366218e-01,   7.99897176e-02],
   [  9.87123759e-01,   6.97944391e-01,   8.77314215e-02],
   [  9.86694229e-01,   7.05540424e-01,   9.56941797e-02],
   [  9.86174970e-01,   7.13153375e-01,   1.03863324e-01],
   [  9.85565739e-01,   7.20782460e-01,   1.12228756e-01],
   [  9.84865203e-01,   7.28427497e-01,   1.20784651e-01],
   [  9.84075129e-01,   7.36086521e-01,   1.29526579e-01],
   [  9.83195992e-01,   7.43758326e-01,   1.38453063e-01],
   [  9.82228463e-01,   7.51441596e-01,   1.47564573e-01],
   [  9.81173457e-01,   7.59134892e-01,   1.56863224e-01],
   [  9.80032178e-01,   7.66836624e-01,   1.66352544e-01],
   [  9.78806183e-01,   7.74545028e-01,   1.76037298e-01],
   [  9.77497453e-01,   7.82258138e-01,   1.85923357e-01],
   [  9.76108474e-01,   7.89973753e-01,   1.96017589e-01],
   [  9.74637842e-01,   7.97691563e-01,   2.06331925e-01],
   [  9.73087939e-01,   8.05409333e-01,   2.16876839e-01],
   [  9.71467822e-01,   8.13121725e-01,   2.27658046e-01],
   [  9.69783146e-01,   8.20825143e-01,   2.38685942e-01],
   [  9.68040817e-01,   8.28515491e-01,   2.49971582e-01],
   [  9.66242589e-01,   8.36190976e-01,   2.61533898e-01],
   [  9.64393924e-01,   8.43848069e-01,   2.73391112e-01],
   [  9.62516656e-01,   8.51476340e-01,   2.85545675e-01],
   [  9.60625545e-01,   8.59068716e-01,   2.98010219e-01],
   [  9.58720088e-01,   8.66624355e-01,   3.10820466e-01],
   [  9.56834075e-01,   8.74128569e-01,   3.23973947e-01],
   [  9.54997177e-01,   8.81568926e-01,   3.37475479e-01],
   [  9.53215092e-01,   8.88942277e-01,   3.51368713e-01],
   [  9.51546225e-01,   8.96225909e-01,   3.65627005e-01],
   [  9.50018481e-01,   9.03409063e-01,   3.80271225e-01],
   [  9.48683391e-01,   9.10472964e-01,   3.95289169e-01],
   [  9.47594362e-01,   9.17399053e-01,   4.10665194e-01],
   [  9.46809163e-01,   9.24168246e-01,   4.26373236e-01],
   [  9.46391536e-01,   9.30760752e-01,   4.42367495e-01],
   [  9.46402951e-01,   9.37158971e-01,   4.58591507e-01],
   [  9.46902568e-01,   9.43347775e-01,   4.74969778e-01],
   [  9.47936825e-01,   9.49317522e-01,   4.91426053e-01],
   [  9.49544830e-01,   9.55062900e-01,   5.07859649e-01],
   [  9.51740304e-01,   9.60586693e-01,   5.24203026e-01],
   [  9.54529281e-01,   9.65895868e-01,   5.40360752e-01],
   [  9.57896053e-01,   9.71003330e-01,   5.56275090e-01],
   [  9.61812020e-01,   9.75924241e-01,   5.71925382e-01],
   [  9.66248822e-01,   9.80678193e-01,   5.87205773e-01],
   [  9.71161622e-01,   9.85282161e-01,   6.02154330e-01],
   [  9.76510983e-01,   9.89753437e-01,   6.16760413e-01],
   [  9.82257307e-01,   9.94108844e-01,   6.31017009e-01], [ 9.88362068e-01, 9.98364143e-01, 6.44924005e-01]]"

the tough part is converting the e- format:

pyArrayToList[p_String] := 
 ToExpression@
  StringReplace[
   p, {m : NumberString ~~ "e" ~~ exp : NumberString :> 
     m <> " 10^" <> exp, "[" -> "{", "]" -> "}"}]

inferno = With[{x = RGBColor @@@ pyArrayToList@pyarray}, Blend[x, #] &];
BarLegend[{inferno, {0, 1}}]

note per comment by @Kuba this works as well:

inferno = With[{x=RGBColor @@@ ImportString[pyarray, "JSON"]},Blend[x, #] &];

the StringReplace is actually a tad faster, but not so much to make a difference.

Many months ago, I decided to make "reduced" versions of the popular color gradients from Matplotlib, as well as the parula colormap from MATLAB. (I previously blogged about parula here.)

These are "reduced" in the sense that unlike the original colormaps that need to keep an array of 256 RGB triplets around, I was able to pare down the list of colors to a smaller portion, while still preserving the features of the color gradient. Since Blend[] is fine with handling non-equispaced colors, one can use it with these "reduced" color gradients.

I posted these colormaps here and here. (Update: "cividis", a color-blind-friendly variation of "viridis" presented in this paper, is now also implemented.)

To load these gradients into Mathematica, evaluate e.g.

Get["https://pastebin.com/raw/qcyE3vzF"]

To use the color gradients, just evaluate makeBlend[colorName]; e.g.

ContourPlot[Sin[x + Cos[y]] Cos[Sin[x] + y], {x, -3 π, 3 π}, {y, -3 π, 3 π}, 
            ColorFunction -> makeBlend["Parula"], PlotPoints -> 75]

---

Here is how the color gradients look, as well as the result of converting them to grayscale with ColorConvert[], and applying color blindness effects to them with ImageEffect[]:

I will continue this discussion with a specific example, the "viridis" color gradient. We can use Kovesi's test image to see how "viridis" performs after being transformed. First, generate the test image:

kovesi = Block[{nc = 64 + 1/2, pixc = 8, a = 1/20, w, h},
               w = pixc nc + 1; h = Round[(w - 1)/4];
               Image[N[Join[Outer[Times, (Range[h, 1, -1]/(h - 1))^2,
                                  -a Cos[2 π Range[0, w - 1]/pixc]] + 
                            ConstantArray[Subdivide[a, 1 - a, w - 1], h], 
                            ConstantArray[Subdivide[w - 1], Round[h/4]]]]]]

We can use Colorize[] to test "viridis":

img = Colorize[kovesi, ColorFunction -> makeBlend["Viridis"]]

Convert to grayscale:

ColorConvert[img, GrayLevel]

How a protanopic person might see the image:

ImageEffect[img, {"ColorBlindness", "Protanopia"}]

Basically an update on what Jason B. has already but with the addition of some stuff for making pretty Format forms / faking a proper ColorDataFunction:

getPyArray[url_] :=
 ToExpression@StringReplace[
   "[" <>
    Riffle[
     StringCases[
      Import[URL[url], "Text"],
      Whitespace ~~ "[" ~~
       Whitespace ~~ 
       NumberString ~~ (("e" ~~ NumberString) | "") ~~ "," ~~
       
       Whitespace ~~ NumberString ~~ (("e" ~~ NumberString) | "") ~~ "," ~~
  
            Whitespace ~~ NumberString ~~ (("e" ~~ NumberString) | "") ~~ "]"
      ],
     ","
     ] <> "]",
   {
    "e" -> "*^",
    "[" -> "{",
    "]" -> "}"
    }]

Unprotect[ColorDataFunction];
(* hacked this from the ColorDataFunction FormatValues and Subvalues *)

MakeBoxes[
   colf : ColorDataFunction[ scheme_, "Matplotlib",  range_, cf_Function],  
   fmt_
   ] :=
  With[{ thumbBoxes = LinearGradientImage[cf, {100, 15}], 
    rangeBoxes = MakeBoxes[ range, fmt]},
   BoxForm`ArrangeSummaryBox[
    ColorDataFunction,
    colf,
    None,
    {
     BoxForm`MakeSummaryItem[{"Name:", scheme}, fmt],
      BoxForm`MakeSummaryItem[{"Gradient:", thumbBoxes}, fmt]
     },
    {
      BoxForm`MakeSummaryItem[{"Domain:", range}, fmt],
     BoxForm`MakeSummaryItem[{"Class:", "Matplotlib"}, fmt]
     },
    fmt
    ]
   ];
ColorDataFunction[ scheme_, "Matplotlib", range_, f_][ x_] := Quiet[ f[ x]]
Protect[ColorDataFunction];
$MPLColorMaps =
  MapThread[
    # ->
      With[{d = RGBColor @@@ getPyArray[#2]},
       ColorDataFunction[#, "Matplotlib", {0, 1}, Blend[d, #] &]
       ] &,
    {
     {"Magma", "Inferno", "Plasma", "Viridis", "EricsRdBuGnYl", 
      "EricsRdBuGnYl2", "EricsPuBuGnYl", "FakeParula", "JoesBluGrnPnk2"},
     {"https://raw.githubusercontent.com/BIDS/colormap/master/option_a.py", 
      "https://raw.githubusercontent.com/BIDS/colormap/master/option_b.py", 
      "https://raw.githubusercontent.com/BIDS/colormap/master/option_c.py", 
      "https://raw.githubusercontent.com/BIDS/colormap/master/option_d.py", 
      "https://github.com/BIDS/colormap/raw/master/erics-RdBuGnYl_r.py", 
      "https://github.com/BIDS/colormap/raw/master/erics-RdBuGnYl_r_v2.py", 
      "https://github.com/BIDS/colormap/raw/master/erics_PuBuGnYl_r.py", 
      "https://github.com/BIDS/colormap/raw/master/fake_parula.py", 
      "https://github.com/BIDS/colormap/raw/master/joes-blu_grn_pnk2.py"}
     }
    ] // Association;

Here's what they look like