Anton Mikhailov's Turbo colormap MATLAB version with spline interpolation

turbo.m

function cmap = turbo(n)
% cmap = turbo(n)
%
% n is the number of levels (spline-interpolated)
%
% Original python code by Anton Mikhailov
% [https://gist.github.com/mikhailov-work/ee72ba4191942acecc03fe6da94fc73f]
%
% (cc) 2019, sgKIM. solleo@gmail.com

turbo_colormap_data = [[0.18995,0.07176,0.23217];[0.19483,0.08339,0.26149];[0.19956,0.09498,0.29024];[0.20415,0.10652,0.31844];[0.20860,0.11802,0.34607];[0.21291,0.12947,0.37314];[0.21708,0.14087,0.39964];[0.22111,0.15223,0.42558];[0.22500,0.16354,0.45096];[0.22875,0.17481,0.47578];[0.23236,0.18603,0.50004];[0.23582,0.19720,0.52373];[0.23915,0.20833,0.54686];[0.24234,0.21941,0.56942];[0.24539,0.23044,0.59142];[0.24830,0.24143,0.61286];[0.25107,0.25237,0.63374];[0.25369,0.26327,0.65406];[0.25618,0.27412,0.67381];[0.25853,0.28492,0.69300];[0.26074,0.29568,0.71162];[0.26280,0.30639,0.72968];[0.26473,0.31706,0.74718];[0.26652,0.32768,0.76412];[0.26816,0.33825,0.78050];[0.26967,0.34878,0.79631];[0.27103,0.35926,0.81156];[0.27226,0.36970,0.82624];[0.27334,0.38008,0.84037];[0.27429,0.39043,0.85393];[0.27509,0.40072,0.86692];[0.27576,0.41097,0.87936];[0.27628,0.42118,0.89123];[0.27667,0.43134,0.90254];[0.27691,0.44145,0.91328];[0.27701,0.45152,0.92347];[0.27698,0.46153,0.93309];[0.27680,0.47151,0.94214];[0.27648,0.48144,0.95064];[0.27603,0.49132,0.95857];[0.27543,0.50115,0.96594];[0.27469,0.51094,0.97275];[0.27381,0.52069,0.97899];[0.27273,0.53040,0.98461];[0.27106,0.54015,0.98930];[0.26878,0.54995,0.99303];[0.26592,0.55979,0.99583];[0.26252,0.56967,0.99773];[0.25862,0.57958,0.99876];[0.25425,0.58950,0.99896];[0.24946,0.59943,0.99835];[0.24427,0.60937,0.99697];[0.23874,0.61931,0.99485];[0.23288,0.62923,0.99202];[0.22676,0.63913,0.98851];[0.22039,0.64901,0.98436];[0.21382,0.65886,0.97959];[0.20708,0.66866,0.97423];[0.20021,0.67842,0.96833];[0.19326,0.68812,0.96190];[0.18625,0.69775,0.95498];[0.17923,0.70732,0.94761];[0.17223,0.71680,0.93981];[0.16529,0.72620,0.93161];[0.15844,0.73551,0.92305];[0.15173,0.74472,0.91416];[0.14519,0.75381,0.90496];[0.13886,0.76279,0.89550];[0.13278,0.77165,0.88580];[0.12698,0.78037,0.87590];[0.12151,0.78896,0.86581];[0.11639,0.79740,0.85559];[0.11167,0.80569,0.84525];[0.10738,0.81381,0.83484];[0.10357,0.82177,0.82437];[0.10026,0.82955,0.81389];[0.09750,0.83714,0.80342];[0.09532,0.84455,0.79299];[0.09377,0.85175,0.78264];[0.09287,0.85875,0.77240];[0.09267,0.86554,0.76230];[0.09320,0.87211,0.75237];[0.09451,0.87844,0.74265];[0.09662,0.88454,0.73316];[0.09958,0.89040,0.72393];[0.10342,0.89600,0.71500];[0.10815,0.90142,0.70599];[0.11374,0.90673,0.69651];[0.12014,0.91193,0.68660];[0.12733,0.91701,0.67627];[0.13526,0.92197,0.66556];[0.14391,0.92680,0.65448];[0.15323,0.93151,0.64308];[0.16319,0.93609,0.63137];[0.17377,0.94053,0.61938];[0.18491,0.94484,0.60713];[0.19659,0.94901,0.59466];[0.20877,0.95304,0.58199];[0.22142,0.95692,0.56914];[0.23449,0.96065,0.55614];[0.24797,0.96423,0.54303];[0.26180,0.96765,0.52981];[0.27597,0.97092,0.51653];[0.29042,0.97403,0.50321];[0.30513,0.97697,0.48987];[0.32006,0.97974,0.47654];[0.33517,0.98234,0.46325];[0.35043,0.98477,0.45002];[0.36581,0.98702,0.43688];[0.38127,0.98909,0.42386];[0.39678,0.99098,0.41098];[0.41229,0.99268,0.39826];[0.42778,0.99419,0.38575];[0.44321,0.99551,0.37345];[0.45854,0.99663,0.36140];[0.47375,0.99755,0.34963];[0.48879,0.99828,0.33816];[0.50362,0.99879,0.32701];[0.51822,0.99910,0.31622];[0.53255,0.99919,0.30581];[0.54658,0.99907,0.29581];[0.56026,0.99873,0.28623];[0.57357,0.99817,0.27712];[0.58646,0.99739,0.26849];[0.59891,0.99638,0.26038];[0.61088,0.99514,0.25280];[0.62233,0.99366,0.24579];[0.63323,0.99195,0.23937];[0.64362,0.98999,0.23356];[0.65394,0.98775,0.22835];[0.66428,0.98524,0.22370];[0.67462,0.98246,0.21960];[0.68494,0.97941,0.21602];[0.69525,0.97610,0.21294];[0.70553,0.97255,0.21032];[0.71577,0.96875,0.20815];[0.72596,0.96470,0.20640];[0.73610,0.96043,0.20504];[0.74617,0.95593,0.20406];[0.75617,0.95121,0.20343];[0.76608,0.94627,0.20311];[0.77591,0.94113,0.20310];[0.78563,0.93579,0.20336];[0.79524,0.93025,0.20386];[0.80473,0.92452,0.20459];[0.81410,0.91861,0.20552];[0.82333,0.91253,0.20663];[0.83241,0.90627,0.20788];[0.84133,0.89986,0.20926];[0.85010,0.89328,0.21074];[0.85868,0.88655,0.21230];[0.86709,0.87968,0.21391];[0.87530,0.87267,0.21555];[0.88331,0.86553,0.21719];[0.89112,0.85826,0.21880];[0.89870,0.85087,0.22038];[0.90605,0.84337,0.22188];[0.91317,0.83576,0.22328];[0.92004,0.82806,0.22456];[0.92666,0.82025,0.22570];[0.93301,0.81236,0.22667];[0.93909,0.80439,0.22744];[0.94489,0.79634,0.22800];[0.95039,0.78823,0.22831];[0.95560,0.78005,0.22836];[0.96049,0.77181,0.22811];[0.96507,0.76352,0.22754];[0.96931,0.75519,0.22663];[0.97323,0.74682,0.22536];[0.97679,0.73842,0.22369];[0.98000,0.73000,0.22161];[0.98289,0.72140,0.21918];[0.98549,0.71250,0.21650];[0.98781,0.70330,0.21358];[0.98986,0.69382,0.21043];[0.99163,0.68408,0.20706];[0.99314,0.67408,0.20348];[0.99438,0.66386,0.19971];[0.99535,0.65341,0.19577];[0.99607,0.64277,0.19165];[0.99654,0.63193,0.18738];[0.99675,0.62093,0.18297];[0.99672,0.60977,0.17842];[0.99644,0.59846,0.17376];[0.99593,0.58703,0.16899];[0.99517,0.57549,0.16412];[0.99419,0.56386,0.15918];[0.99297,0.55214,0.15417];[0.99153,0.54036,0.14910];[0.98987,0.52854,0.14398];[0.98799,0.51667,0.13883];[0.98590,0.50479,0.13367];[0.98360,0.49291,0.12849];[0.98108,0.48104,0.12332];[0.97837,0.46920,0.11817];[0.97545,0.45740,0.11305];[0.97234,0.44565,0.10797];[0.96904,0.43399,0.10294];[0.96555,0.42241,0.09798];[0.96187,0.41093,0.09310];[0.95801,0.39958,0.08831];[0.95398,0.38836,0.08362];[0.94977,0.37729,0.07905];[0.94538,0.36638,0.07461];[0.94084,0.35566,0.07031];[0.93612,0.34513,0.06616];[0.93125,0.33482,0.06218];[0.92623,0.32473,0.05837];[0.92105,0.31489,0.05475];[0.91572,0.30530,0.05134];[0.91024,0.29599,0.04814];[0.90463,0.28696,0.04516];[0.89888,0.27824,0.04243];[0.89298,0.26981,0.03993];[0.88691,0.26152,0.03753];[0.88066,0.25334,0.03521];[0.87422,0.24526,0.03297];[0.86760,0.23730,0.03082];[0.86079,0.22945,0.02875];[0.85380,0.22170,0.02677];[0.84662,0.21407,0.02487];[0.83926,0.20654,0.02305];[0.83172,0.19912,0.02131];[0.82399,0.19182,0.01966];[0.81608,0.18462,0.01809];[0.80799,0.17753,0.01660];[0.79971,0.17055,0.01520];[0.79125,0.16368,0.01387];[0.78260,0.15693,0.01264];[0.77377,0.15028,0.01148];[0.76476,0.14374,0.01041];[0.75556,0.13731,0.00942];[0.74617,0.13098,0.00851];[0.73661,0.12477,0.00769];[0.72686,0.11867,0.00695];[0.71692,0.11268,0.00629];[0.70680,0.10680,0.00571];[0.69650,0.10102,0.00522];[0.68602,0.09536,0.00481];[0.67535,0.08980,0.00449];[0.66449,0.08436,0.00424];[0.65345,0.07902,0.00408];[0.64223,0.07380,0.00401];[0.63082,0.06868,0.00401];[0.61923,0.06367,0.00410];[0.60746,0.05878,0.00427];[0.59550,0.05399,0.00453];[0.58336,0.04931,0.00486];[0.57103,0.04474,0.00529];[0.55852,0.04028,0.00579];[0.54583,0.03593,0.00638];[0.53295,0.03169,0.00705];[0.51989,0.02756,0.00780];[0.50664,0.02354,0.00863];[0.49321,0.01963,0.00955];[0.47960,0.01583,0.01055]];
if ~exist('n','var')
  n = 256;
end
cmap = interp1(linspace(0,1,256), turbo_colormap_data, linspace(0,1,n), 'pchip');
end


%% Test functions:
function testlevels
nlvl = 2.^(1:16);
im=imread('forest.tif');
figure;
for i = 1:numel(nlvl)
  subplot(4,4,i)
  imagesc(im)
  colormap(gca,turbo(nlvl(i)))
end
end

function testothercolormaps
im=imread('forest.tif');
cmaps={gray(16),parula(16),jet(16),turbo(16)};
figure;
for i = 1:numel(cmaps)
  subplot(1,4,i)
  imagesc(im); axis image;
  colorbaro('location','southoutside')
  colormap(gca,cmaps{i})
end
end


%% Original python code:
%{ 
Original python code from: https://gist.github.com/mikhailov-work/ee72ba4191942acecc03fe6da94fc73f

# Copyright 2019 Google LLC.
# SPDX-License-Identifier: Apache-2.0

# Author: Anton Mikhailov

turbo_colormap_data = [[0.18995,0.07176,0.23217],[0.19483,0.08339,0.26149],[0.19956,0.09498,0.29024],[0.20415,0.10652,0.31844],[0.20860,0.11802,0.34607],[0.21291,0.12947,0.37314],[0.21708,0.14087,0.39964],[0.22111,0.15223,0.42558],[0.22500,0.16354,0.45096],[0.22875,0.17481,0.47578],[0.23236,0.18603,0.50004],[0.23582,0.19720,0.52373],[0.23915,0.20833,0.54686],[0.24234,0.21941,0.56942],[0.24539,0.23044,0.59142],[0.24830,0.24143,0.61286],[0.25107,0.25237,0.63374],[0.25369,0.26327,0.65406],[0.25618,0.27412,0.67381],[0.25853,0.28492,0.69300],[0.26074,0.29568,0.71162],[0.26280,0.30639,0.72968],[0.26473,0.31706,0.74718],[0.26652,0.32768,0.76412],[0.26816,0.33825,0.78050],[0.26967,0.34878,0.79631],[0.27103,0.35926,0.81156],[0.27226,0.36970,0.82624],[0.27334,0.38008,0.84037],[0.27429,0.39043,0.85393],[0.27509,0.40072,0.86692],[0.27576,0.41097,0.87936],[0.27628,0.42118,0.89123],[0.27667,0.43134,0.90254],[0.27691,0.44145,0.91328],[0.27701,0.45152,0.92347],[0.27698,0.46153,0.93309],[0.27680,0.47151,0.94214],[0.27648,0.48144,0.95064],[0.27603,0.49132,0.95857],[0.27543,0.50115,0.96594],[0.27469,0.51094,0.97275],[0.27381,0.52069,0.97899],[0.27273,0.53040,0.98461],[0.27106,0.54015,0.98930],[0.26878,0.54995,0.99303],[0.26592,0.55979,0.99583],[0.26252,0.56967,0.99773],[0.25862,0.57958,0.99876],[0.25425,0.58950,0.99896],[0.24946,0.59943,0.99835],[0.24427,0.60937,0.99697],[0.23874,0.61931,0.99485],[0.23288,0.62923,0.99202],[0.22676,0.63913,0.98851],[0.22039,0.64901,0.98436],[0.21382,0.65886,0.97959],[0.20708,0.66866,0.97423],[0.20021,0.67842,0.96833],[0.19326,0.68812,0.96190],[0.18625,0.69775,0.95498],[0.17923,0.70732,0.94761],[0.17223,0.71680,0.93981],[0.16529,0.72620,0.93161],[0.15844,0.73551,0.92305],[0.15173,0.74472,0.91416],[0.14519,0.75381,0.90496],[0.13886,0.76279,0.89550],[0.13278,0.77165,0.88580],[0.12698,0.78037,0.87590],[0.12151,0.78896,0.86581],[0.11639,0.79740,0.85559],[0.11167,0.80569,0.84525],[0.10738,0.81381,0.83484],[0.10357,0.82177,0.82437],[0.10026,0.82955,0.81389],[0.09750,0.83714,0.80342],[0.09532,0.84455,0.79299],[0.09377,0.85175,0.78264],[0.09287,0.85875,0.77240],[0.09267,0.86554,0.76230],[0.09320,0.87211,0.75237],[0.09451,0.87844,0.74265],[0.09662,0.88454,0.73316],[0.09958,0.89040,0.72393],[0.10342,0.89600,0.71500],[0.10815,0.90142,0.70599],[0.11374,0.90673,0.69651],[0.12014,0.91193,0.68660],[0.12733,0.91701,0.67627],[0.13526,0.92197,0.66556],[0.14391,0.92680,0.65448],[0.15323,0.93151,0.64308],[0.16319,0.93609,0.63137],[0.17377,0.94053,0.61938],[0.18491,0.94484,0.60713],[0.19659,0.94901,0.59466],[0.20877,0.95304,0.58199],[0.22142,0.95692,0.56914],[0.23449,0.96065,0.55614],[0.24797,0.96423,0.54303],[0.26180,0.96765,0.52981],[0.27597,0.97092,0.51653],[0.29042,0.97403,0.50321],[0.30513,0.97697,0.48987],[0.32006,0.97974,0.47654],[0.33517,0.98234,0.46325],[0.35043,0.98477,0.45002],[0.36581,0.98702,0.43688],[0.38127,0.98909,0.42386],[0.39678,0.99098,0.41098],[0.41229,0.99268,0.39826],[0.42778,0.99419,0.38575],[0.44321,0.99551,0.37345],[0.45854,0.99663,0.36140],[0.47375,0.99755,0.34963],[0.48879,0.99828,0.33816],[0.50362,0.99879,0.32701],[0.51822,0.99910,0.31622],[0.53255,0.99919,0.30581],[0.54658,0.99907,0.29581],[0.56026,0.99873,0.28623],[0.57357,0.99817,0.27712],[0.58646,0.99739,0.26849],[0.59891,0.99638,0.26038],[0.61088,0.99514,0.25280],[0.62233,0.99366,0.24579],[0.63323,0.99195,0.23937],[0.64362,0.98999,0.23356],[0.65394,0.98775,0.22835],[0.66428,0.98524,0.22370],[0.67462,0.98246,0.21960],[0.68494,0.97941,0.21602],[0.69525,0.97610,0.21294],[0.70553,0.97255,0.21032],[0.71577,0.96875,0.20815],[0.72596,0.96470,0.20640],[0.73610,0.96043,0.20504],[0.74617,0.95593,0.20406],[0.75617,0.95121,0.20343],[0.76608,0.94627,0.20311],[0.77591,0.94113,0.20310],[0.78563,0.93579,0.20336],[0.79524,0.93025,0.20386],[0.80473,0.92452,0.20459],[0.81410,0.91861,0.20552],[0.82333,0.91253,0.20663],[0.83241,0.90627,0.20788],[0.84133,0.89986,0.20926],[0.85010,0.89328,0.21074],[0.85868,0.88655,0.21230],[0.86709,0.87968,0.21391],[0.87530,0.87267,0.21555],[0.88331,0.86553,0.21719],[0.89112,0.85826,0.21880],[0.89870,0.85087,0.22038],[0.90605,0.84337,0.22188],[0.91317,0.83576,0.22328],[0.92004,0.82806,0.22456],[0.92666,0.82025,0.22570],[0.93301,0.81236,0.22667],[0.93909,0.80439,0.22744],[0.94489,0.79634,0.22800],[0.95039,0.78823,0.22831],[0.95560,0.78005,0.22836],[0.96049,0.77181,0.22811],[0.96507,0.76352,0.22754],[0.96931,0.75519,0.22663],[0.97323,0.74682,0.22536],[0.97679,0.73842,0.22369],[0.98000,0.73000,0.22161],[0.98289,0.72140,0.21918],[0.98549,0.71250,0.21650],[0.98781,0.70330,0.21358],[0.98986,0.69382,0.21043],[0.99163,0.68408,0.20706],[0.99314,0.67408,0.20348],[0.99438,0.66386,0.19971],[0.99535,0.65341,0.19577],[0.99607,0.64277,0.19165],[0.99654,0.63193,0.18738],[0.99675,0.62093,0.18297],[0.99672,0.60977,0.17842],[0.99644,0.59846,0.17376],[0.99593,0.58703,0.16899],[0.99517,0.57549,0.16412],[0.99419,0.56386,0.15918],[0.99297,0.55214,0.15417],[0.99153,0.54036,0.14910],[0.98987,0.52854,0.14398],[0.98799,0.51667,0.13883],[0.98590,0.50479,0.13367],[0.98360,0.49291,0.12849],[0.98108,0.48104,0.12332],[0.97837,0.46920,0.11817],[0.97545,0.45740,0.11305],[0.97234,0.44565,0.10797],[0.96904,0.43399,0.10294],[0.96555,0.42241,0.09798],[0.96187,0.41093,0.09310],[0.95801,0.39958,0.08831],[0.95398,0.38836,0.08362],[0.94977,0.37729,0.07905],[0.94538,0.36638,0.07461],[0.94084,0.35566,0.07031],[0.93612,0.34513,0.06616],[0.93125,0.33482,0.06218],[0.92623,0.32473,0.05837],[0.92105,0.31489,0.05475],[0.91572,0.30530,0.05134],[0.91024,0.29599,0.04814],[0.90463,0.28696,0.04516],[0.89888,0.27824,0.04243],[0.89298,0.26981,0.03993],[0.88691,0.26152,0.03753],[0.88066,0.25334,0.03521],[0.87422,0.24526,0.03297],[0.86760,0.23730,0.03082],[0.86079,0.22945,0.02875],[0.85380,0.22170,0.02677],[0.84662,0.21407,0.02487],[0.83926,0.20654,0.02305],[0.83172,0.19912,0.02131],[0.82399,0.19182,0.01966],[0.81608,0.18462,0.01809],[0.80799,0.17753,0.01660],[0.79971,0.17055,0.01520],[0.79125,0.16368,0.01387],[0.78260,0.15693,0.01264],[0.77377,0.15028,0.01148],[0.76476,0.14374,0.01041],[0.75556,0.13731,0.00942],[0.74617,0.13098,0.00851],[0.73661,0.12477,0.00769],[0.72686,0.11867,0.00695],[0.71692,0.11268,0.00629],[0.70680,0.10680,0.00571],[0.69650,0.10102,0.00522],[0.68602,0.09536,0.00481],[0.67535,0.08980,0.00449],[0.66449,0.08436,0.00424],[0.65345,0.07902,0.00408],[0.64223,0.07380,0.00401],[0.63082,0.06868,0.00401],[0.61923,0.06367,0.00410],[0.60746,0.05878,0.00427],[0.59550,0.05399,0.00453],[0.58336,0.04931,0.00486],[0.57103,0.04474,0.00529],[0.55852,0.04028,0.00579],[0.54583,0.03593,0.00638],[0.53295,0.03169,0.00705],[0.51989,0.02756,0.00780],[0.50664,0.02354,0.00863],[0.49321,0.01963,0.00955],[0.47960,0.01583,0.01055]]

# The look-up table contains 256 entries. Each entry is a floating point sRGB triplet.
# To use it with matplotlib, pass cmap=ListedColormap(turbo_colormap_data) as an arg to imshow() (don't forget "from matplotlib.colors import ListedColormap").
# If you have a typical 8-bit greyscale image, you can use the 8-bit value to index into this LUT directly.
# The floating point color values can be converted to 8-bit sRGB via multiplying by 255 and casting/flooring to an integer. Saturation should not be required for IEEE-754 compliant arithmetic.
# If you have a floating point value in the range [0,1], you can use interpolate() to linearly interpolate between the entries.
# If you have 16-bit or 32-bit integer values, convert them to floating point values on the [0,1] range and then use interpolate(). Doing the interpolation in floating point will reduce banding.
# If some of your values may lie outside the [0,1] range, use interpolate_or_clip() to highlight them.

def interpolate(colormap, x):
  x = max(0.0, min(1.0, x))
  a = int(x*255.0)
  b = min(255, a + 1)
  f = x*255.0 - a
  return [colormap[a][0] + (colormap[b][0] - colormap[a][0]) * f,
          colormap[a][1] + (colormap[b][1] - colormap[a][1]) * f,
          colormap[a][2] + (colormap[b][2] - colormap[a][2]) * f]

def interpolate_or_clip(colormap, x):
  if   x < 0.0: return [0.0, 0.0, 0.0]
  elif x > 1.0: return [1.0, 1.0, 1.0]
  else: return interpolate(colormap, x)
%}