NHP-pRF/Toolboxes/matplotlib/cividis.m

function map = cividis(N)
% Perceptually uniform sequential colormap from MatPlotLib. Designed for colorblind.
%
% Copyright (c) 2017-2019 Stephen Cobeldick
%
%%% Syntax:
%  map = cividis
%  map = cividis(N)
%
% Colormap designed by Jamie R. Nuñez, Christopher R. Anderton, Ryan S. Renslow.
%
% Developed with consideration for color vision deficiency to enable accurate
% interpretation of scientific data. Full paper available here:
% <https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0199239>
% The RGB data is from here: <https://doi.org/10.1371/journal.pone.0199239.s002>
%
% Note VIRIDIS replaces the awful JET as the MatPlotLib default colormap.
%
%% Examples %%
%
%%% Plot the scheme's RGB values:
% rgbplot(cividis(256))
%
%%% New colors for the COLORMAP example:
% load spine
% image(X)
% colormap(cividis)
%
%%% New colors for the SURF example:
% [X,Y,Z] = peaks(30);
% surfc(X,Y,Z)
% colormap(cividis)
% axis([-3,3,-3,3,-10,5])
%
%% Input and Output Arguments %%
%
%%% Inputs (*=default):
% N = NumericScalar, N>=0, an integer to define the colormap length.
%   = *[], use the length of the current figure's colormap (see COLORMAP).
%
%%% Outputs:
% map = NumericMatrix, size Nx3, a colormap of RGB values between 0 and 1.
%
% See also INFERNO MAGMA PLASMA VIRIDIS TWILIGHT TAB10 SET LINES COLORMAP PARULA

if nargin<1 || isempty(N)
	N = size(get(gcf,'colormap'),1);
else
	assert(isscalar(N)&&isreal(N),'First argument must be a real numeric scalar.')
end
%
raw = [0.0000,0.1262,0.3015;0.0000,0.1292,0.3077;0.0000,0.1321,0.3142;0.0000,0.1350,0.3205;0.0000,0.1379,0.3269;0.0000,0.1408,0.3334;0.0000,0.1437,0.3400;0.0000,0.1465,0.3467;0.0000,0.1492,0.3537;0.0000,0.1519,0.3606;0.0000,0.1546,0.3676;0.0000,0.1574,0.3746;0.0000,0.1601,0.3817;0.0000,0.1629,0.3888;0.0000,0.1657,0.3960;0.0000,0.1685,0.4031;0.0000,0.1714,0.4102;0.0000,0.1743,0.4172;0.0000,0.1773,0.4241;0.0000,0.1798,0.4307;0.0000,0.1817,0.4347;0.0000,0.1834,0.4363;0.0000,0.1852,0.4368;0.0000,0.1872,0.4368;0.0000,0.1901,0.4365;0.0000,0.1930,0.4361;0.0000,0.1958,0.4356;0.0000,0.1987,0.4349;0.0000,0.2015,0.4343;0.0000,0.2044,0.4336;0.0000,0.2073,0.4329;0.0055,0.2101,0.4322;0.0236,0.2130,0.4314;0.0416,0.2158,0.4308;0.0576,0.2187,0.4301;0.0710,0.2215,0.4293;0.0827,0.2244,0.4287;0.0932,0.2272,0.4280;0.1030,0.2300,0.4274;0.1120,0.2329,0.4268;0.1204,0.2357,0.4262;0.1283,0.2385,0.4256;0.1359,0.2414,0.4251;0.1431,0.2442,0.4245;0.1500,0.2470,0.4241;0.1566,0.2498,0.4236;0.1630,0.2526,0.4232;0.1692,0.2555,0.4228;0.1752,0.2583,0.4224;0.1811,0.2611,0.4220;0.1868,0.2639,0.4217;0.1923,0.2667,0.4214;0.1977,0.2695,0.4212;0.2030,0.2723,0.4209;0.2082,0.2751,0.4207;0.2133,0.2780,0.4205;0.2183,0.2808,0.4204;0.2232,0.2836,0.4203;0.2281,0.2864,0.4202;0.2328,0.2892,0.4201;0.2375,0.2920,0.4200;0.2421,0.2948,0.4200;0.2466,0.2976,0.4200;0.2511,0.3004,0.4201;0.2556,0.3032,0.4201;0.2599,0.3060,0.4202;0.2643,0.3088,0.4203;0.2686,0.3116,0.4205;0.2728,0.3144,0.4206;0.2770,0.3172,0.4208;0.2811,0.3200,0.4210;0.2853,0.3228,0.4212;0.2894,0.3256,0.4215;0.2934,0.3284,0.4218;0.2974,0.3312,0.4221;0.3014,0.3340,0.4224;0.3054,0.3368,0.4227;0.3093,0.3396,0.4231;0.3132,0.3424,0.4236;0.3170,0.3453,0.4240;0.3209,0.3481,0.4244;0.3247,0.3509,0.4249;0.3285,0.3537,0.4254;0.3323,0.3565,0.4259;0.3361,0.3593,0.4264;0.3398,0.3622,0.4270;0.3435,0.3650,0.4276;0.3472,0.3678,0.4282;0.3509,0.3706,0.4288;0.3546,0.3734,0.4294;0.3582,0.3763,0.4302;0.3619,0.3791,0.4308;0.3655,0.3819,0.4316;0.3691,0.3848,0.4322;0.3727,0.3876,0.4331;0.3763,0.3904,0.4338;0.3798,0.3933,0.4346;0.3834,0.3961,0.4355;0.3869,0.3990,0.4364;0.3905,0.4018,0.4372;0.3940,0.4047,0.4381;0.3975,0.4075,0.4390;0.4010,0.4104,0.4400;0.4045,0.4132,0.4409;0.4080,0.4161,0.4419;0.4114,0.4189,0.4430;0.4149,0.4218,0.4440;0.4183,0.4247,0.4450;0.4218,0.4275,0.4462;0.4252,0.4304,0.4473;0.4286,0.4333,0.4485;0.4320,0.4362,0.4496;0.4354,0.4390,0.4508;0.4388,0.4419,0.4521;0.4422,0.4448,0.4534;0.4456,0.4477,0.4547;0.4489,0.4506,0.4561;0.4523,0.4535,0.4575;0.4556,0.4564,0.4589;0.4589,0.4593,0.4604;0.4622,0.4622,0.4620;0.4656,0.4651,0.4635;0.4689,0.4680,0.4650;0.4722,0.4709,0.4665;0.4756,0.4738,0.4679;0.4790,0.4767,0.4691;0.4825,0.4797,0.4701;0.4861,0.4826,0.4707;0.4897,0.4856,0.4714;0.4934,0.4886,0.4719;0.4971,0.4915,0.4723;0.5008,0.4945,0.4727;0.5045,0.4975,0.4730;0.5083,0.5005,0.4732;0.5121,0.5035,0.4734;0.5158,0.5065,0.4736;0.5196,0.5095,0.4737;0.5234,0.5125,0.4738;0.5272,0.5155,0.4739;0.5310,0.5186,0.4739;0.5349,0.5216,0.4738;0.5387,0.5246,0.4739;0.5425,0.5277,0.4738;0.5464,0.5307,0.4736;0.5502,0.5338,0.4735;0.5541,0.5368,0.4733;0.5579,0.5399,0.4732;0.5618,0.5430,0.4729;0.5657,0.5461,0.4727;0.5696,0.5491,0.4723;0.5735,0.5522,0.4720;0.5774,0.5553,0.4717;0.5813,0.5584,0.4714;0.5852,0.5615,0.4709;0.5892,0.5646,0.4705;0.5931,0.5678,0.4701;0.5970,0.5709,0.4696;0.6010,0.5740,0.4691;0.6050,0.5772,0.4685;0.6089,0.5803,0.4680;0.6129,0.5835,0.4673;0.6168,0.5866,0.4668;0.6208,0.5898,0.4662;0.6248,0.5929,0.4655;0.6288,0.5961,0.4649;0.6328,0.5993,0.4641;0.6368,0.6025,0.4632;0.6408,0.6057,0.4625;0.6449,0.6089,0.4617;0.6489,0.6121,0.4609;0.6529,0.6153,0.4600;0.6570,0.6185,0.4591;0.6610,0.6217,0.4583;0.6651,0.6250,0.4573;0.6691,0.6282,0.4562;0.6732,0.6315,0.4553;0.6773,0.6347,0.4543;0.6813,0.6380,0.4532;0.6854,0.6412,0.4521;0.6895,0.6445,0.4511;0.6936,0.6478,0.4499;0.6977,0.6511,0.4487;0.7018,0.6544,0.4475;0.7060,0.6577,0.4463;0.7101,0.6610,0.4450;0.7142,0.6643,0.4437;0.7184,0.6676,0.4424;0.7225,0.6710,0.4409;0.7267,0.6743,0.4396;0.7308,0.6776,0.4382;0.7350,0.6810,0.4368;0.7392,0.6844,0.4352;0.7434,0.6877,0.4338;0.7476,0.6911,0.4322;0.7518,0.6945,0.4307;0.7560,0.6979,0.4290;0.7602,0.7013,0.4273;0.7644,0.7047,0.4258;0.7686,0.7081,0.4241;0.7729,0.7115,0.4223;0.7771,0.7150,0.4205;0.7814,0.7184,0.4188;0.7856,0.7218,0.4168;0.7899,0.7253,0.4150;0.7942,0.7288,0.4129;0.7985,0.7322,0.4111;0.8027,0.7357,0.4090;0.8070,0.7392,0.4070;0.8114,0.7427,0.4049;0.8157,0.7462,0.4028;0.8200,0.7497,0.4007;0.8243,0.7532,0.3984;0.8287,0.7568,0.3961;0.8330,0.7603,0.3938;0.8374,0.7639,0.3915;0.8417,0.7674,0.3892;0.8461,0.7710,0.3869;0.8505,0.7745,0.3843;0.8548,0.7781,0.3818;0.8592,0.7817,0.3793;0.8636,0.7853,0.3766;0.8681,0.7889,0.3739;0.8725,0.7926,0.3712;0.8769,0.7962,0.3684;0.8813,0.7998,0.3657;0.8858,0.8035,0.3627;0.8902,0.8071,0.3599;0.8947,0.8108,0.3569;0.8992,0.8145,0.3538;0.9037,0.8182,0.3507;0.9082,0.8219,0.3474;0.9127,0.8256,0.3442;0.9172,0.8293,0.3409;0.9217,0.8330,0.3374;0.9262,0.8367,0.3340;0.9308,0.8405,0.3306;0.9353,0.8442,0.3268;0.9399,0.8480,0.3232;0.9444,0.8518,0.3195;0.9490,0.8556,0.3155;0.9536,0.8593,0.3116;0.9582,0.8632,0.3076;0.9628,0.8670,0.3034;0.9674,0.8708,0.2990;0.9721,0.8746,0.2947;0.9767,0.8785,0.2901;0.9814,0.8823,0.2856;0.9860,0.8862,0.2807;0.9907,0.8901,0.2759;0.9954,0.8940,0.2708;1.0000,0.8979,0.2655;1.0000,0.9018,0.2600;1.0000,0.9057,0.2593;1.0000,0.9094,0.2634;1.0000,0.9131,0.2680;1.0000,0.9169,0.2731];
%
num = size(raw,1);
% With small extrapolation when N>num:
vec = linspace(0,num+1,N+2);
map = interp1(1:num,raw,vec(2:N+1),'linear','extrap');
% Interpolation only for all values of N:
%map = interp1(1:num,raw,linspace(1,num,N),'spline')
% Range limits:
map = max(0,min(1,map));
%
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%cividis