/
erics_PuBuGnYl_r.py
282 lines (275 loc) · 13.2 KB
/
erics_PuBuGnYl_r.py
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from matplotlib.colors import LinearSegmentedColormap
from numpy import nan, inf
# Used to reconstruct the colormap in viscm
parameters = {'xp': [22.674387857633945, 11.221508276482126, -14.356589454756971, -47.188177587392218, -34.590010048125208, 0.15039134803535603],
'yp': [-20.102530541012214, -33.082460732984288, -42.24476439790574, -5.5955497382198871, 42.5065445026178, 24.563699825479944],
'min_JK': 18.8671875,
'max_JK': 92.5}
cm_data = [[ 0.26700401, 0.00487433, 0.32941519],
[ 0.2685542 , 0.00957471, 0.33533275],
[ 0.27003443, 0.01455696, 0.34119436],
[ 0.27144484, 0.01982836, 0.34699773],
[ 0.27278686, 0.02539504, 0.35273831],
[ 0.27405872, 0.03126572, 0.35841766],
[ 0.27526033, 0.03744784, 0.36403393],
[ 0.27639307, 0.04380557, 0.36958299],
[ 0.2774554 , 0.04993149, 0.37506557],
[ 0.27844714, 0.05585994, 0.38048004],
[ 0.27936893, 0.06162751, 0.38582368],
[ 0.28022049, 0.0672627 , 0.39109518],
[ 0.28100114, 0.07278804, 0.39629373],
[ 0.28171081, 0.07822095, 0.40141769],
[ 0.28235053, 0.0835745 , 0.40646432],
[ 0.28291925, 0.08886094, 0.4114333 ],
[ 0.2834169 , 0.09408964, 0.41632321],
[ 0.28384358, 0.09926827, 0.42113253],
[ 0.28420026, 0.10440257, 0.42585912],
[ 0.28448608, 0.10949867, 0.43050257],
[ 0.2847012 , 0.11456108, 0.43506157],
[ 0.28484581, 0.11959358, 0.43953491],
[ 0.28492117, 0.12459863, 0.44392086],
[ 0.28492661, 0.12957963, 0.44821903],
[ 0.28486252, 0.1345388 , 0.45242844],
[ 0.28472931, 0.13947799, 0.45654822],
[ 0.28452817, 0.14439827, 0.46057726],
[ 0.28425933, 0.14930113, 0.46451508],
[ 0.28392311, 0.15418778, 0.46836123],
[ 0.28352022, 0.15905901, 0.47211522],
[ 0.28305157, 0.16391535, 0.47577663],
[ 0.28251884, 0.16875671, 0.479345 ],
[ 0.28192206, 0.17358397, 0.48282047],
[ 0.28126222, 0.17839731, 0.486203 ],
[ 0.28054043, 0.18319677, 0.48949266],
[ 0.27975811, 0.18798216, 0.49268964],
[ 0.27891713, 0.19275301, 0.49579421],
[ 0.27801794, 0.19750965, 0.49880695],
[ 0.27706191, 0.20225183, 0.50172842],
[ 0.27605049, 0.20697927, 0.50455929],
[ 0.27498517, 0.21169165, 0.50730037],
[ 0.27386887, 0.21638789, 0.50995262],
[ 0.272702 , 0.22106829, 0.51251706],
[ 0.27148621, 0.22573247, 0.51499482],
[ 0.27022324, 0.23038004, 0.51738712],
[ 0.26891491, 0.23501057, 0.51969528],
[ 0.26756319, 0.2396236 , 0.52192074],
[ 0.26617119, 0.24421813, 0.52406522],
[ 0.26473951, 0.24879442, 0.52613012],
[ 0.26327012, 0.25335212, 0.52811709],
[ 0.26176496, 0.25789086, 0.53002785],
[ 0.26022606, 0.26241031, 0.53186418],
[ 0.25865541, 0.26691017, 0.5336279 ],
[ 0.25705544, 0.27138997, 0.53532102],
[ 0.25542898, 0.27584915, 0.53694571],
[ 0.25377689, 0.28028798, 0.5385036 ],
[ 0.25210122, 0.28470627, 0.53999668],
[ 0.25040396, 0.28910388, 0.54142695],
[ 0.24868713, 0.29348068, 0.54279642],
[ 0.2469527 , 0.29783657, 0.54410709],
[ 0.24520266, 0.3021715 , 0.545361 ],
[ 0.24343892, 0.30648544, 0.54656016],
[ 0.24166391, 0.31077823, 0.54770676],
[ 0.23987982, 0.31504978, 0.54880295],
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[ 0.22726014, 0.34437561, 0.55521608],
[ 0.22545806, 0.34848521, 0.55597407],
[ 0.22366058, 0.35257566, 0.55669764],
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[ 0.22008461, 0.36070037, 0.55804779],
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[ 0.21654188, 0.36875256, 0.55927849],
[ 0.21478568, 0.37275242, 0.55985251],
[ 0.21304088, 0.37673533, 0.5604007 ],
[ 0.21130836, 0.38070172, 0.56092426],
[ 0.20958891, 0.38465203, 0.56142436],
[ 0.20788325, 0.3885867 , 0.56190206],
[ 0.20619204, 0.3925062 , 0.56235839],
[ 0.20451584, 0.39641098, 0.56279428],
[ 0.20285517, 0.40030153, 0.56321061],
[ 0.20121044, 0.40417832, 0.56360819],
[ 0.19958202, 0.40804183, 0.56398778],
[ 0.19797019, 0.41189255, 0.56435004],
[ 0.1963752 , 0.41573098, 0.56469559],
[ 0.19479719, 0.41955759, 0.56502496],
[ 0.19323628, 0.42337288, 0.56533864],
[ 0.19169253, 0.42717733, 0.56563705],
[ 0.19016593, 0.43097144, 0.56592052],
[ 0.18865644, 0.43475567, 0.56618935],
[ 0.18716399, 0.43853052, 0.56644376],
[ 0.18568845, 0.44229644, 0.56668391],
[ 0.1842297 , 0.44605392, 0.5669099 ],
[ 0.18278756, 0.4498034 , 0.56712177],
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[ 0.17581998, 0.4684463 , 0.5679669 ],
[ 0.17447399, 0.47215693, 0.5680918 ],
[ 0.17314359, 0.4758625 , 0.56820133],
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[ 0.17052956, 0.48325995, 0.56837267],
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[ 0.16189991, 0.50904071, 0.56841971],
[ 0.16073977, 0.51271352, 0.5683476 ],
[ 0.15960057, 0.51638451, 0.56825356],
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[ 0.15528438, 0.53105366, 0.56764168],
[ 0.15427564, 0.53471788, 0.56742564],
[ 0.15330014, 0.53838103, 0.56718273],
[ 0.15236029, 0.54204327, 0.56691174],
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[ 0.15060068, 0.54936512, 0.56628168],
[ 0.14978824, 0.55302477, 0.56592069],
[ 0.14902614, 0.55668353, 0.56552775],
[ 0.14831885, 0.56034138, 0.56510188],
[ 0.14767117, 0.56399824, 0.5646421 ],
[ 0.14708824, 0.56765403, 0.56414742],
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[ 0.14578423, 0.57861374, 0.56244438],
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[ 0.14534595, 0.58591236, 0.56111627],
[ 0.14527574, 0.58955873, 0.56039146],
[ 0.14531405, 0.59320283, 0.55962489],
[ 0.14546796, 0.5968444 , 0.55881564],
[ 0.14574467, 0.60048316, 0.5579628 ],
[ 0.14615138, 0.60411879, 0.55706548],
[ 0.14669517, 0.607751 , 0.55612273],
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[ 0.14921896, 0.61862356, 0.55301312],
[ 0.15038025, 0.6222384 , 0.55188028],
[ 0.15171204, 0.62584787, 0.55069804],
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[ 0.19481972, 0.6789925 , 0.52664953],
[ 0.1993043 , 0.68244133, 0.52461034],
[ 0.20398632, 0.68587445, 0.52251639],
[ 0.20886365, 0.68929113, 0.52036688],
[ 0.21393364, 0.69269051, 0.5181632 ],
[ 0.21919356, 0.69607177, 0.51590585],
[ 0.22464052, 0.69943407, 0.51359544],
[ 0.23027153, 0.70277656, 0.51123265],
[ 0.23608352, 0.70609837, 0.50881832],
[ 0.24207335, 0.70939862, 0.50635339],
[ 0.24823786, 0.71267639, 0.50383895],
[ 0.25457385, 0.71593078, 0.50127621],
[ 0.26107813, 0.71916085, 0.49866655],
[ 0.2677475 , 0.72236566, 0.49601153],
[ 0.27457878, 0.72554425, 0.49331285],
[ 0.2815688 , 0.72869565, 0.49057242],
[ 0.2887144 , 0.73181887, 0.48779233],
[ 0.29601244, 0.73491295, 0.48497491],
[ 0.30345981, 0.73797687, 0.48212268],
[ 0.31105336, 0.74100964, 0.47923843],
[ 0.31878998, 0.74401027, 0.47632519],
[ 0.32666653, 0.74697776, 0.47338625],
[ 0.33467983, 0.74991112, 0.47042521],
[ 0.34282668, 0.75280936, 0.46744595],
[ 0.35110384, 0.75567153, 0.46445269],
[ 0.35950797, 0.75849666, 0.46144996],
[ 0.36803567, 0.76128384, 0.45844266],
[ 0.37668344, 0.76403218, 0.45543604],
[ 0.38544765, 0.7667408 , 0.45243575],
[ 0.39432454, 0.7694089 , 0.44944779],
[ 0.40331021, 0.77203572, 0.44647859],
[ 0.41240057, 0.77462055, 0.44353498],
[ 0.42159135, 0.77716276, 0.4406242 ],
[ 0.43087809, 0.77966179, 0.43775387],
[ 0.4402561 , 0.78211718, 0.43493205],
[ 0.44972048, 0.78452855, 0.43216717],
[ 0.45926607, 0.78689563, 0.42946802],
[ 0.46888748, 0.7892183 , 0.42684375],
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[ 0.73373341, 0.83856862, 0.41672307],
[ 0.74293191, 0.84014248, 0.41930934],
[ 0.75205237, 0.84171735, 0.42210392],
[ 0.76109459, 0.84329463, 0.42510159],
[ 0.77005877, 0.84487561, 0.4282969 ],
[ 0.7789455 , 0.84646142, 0.43168424],
[ 0.7877557 , 0.84805303, 0.43525793],
[ 0.79649058, 0.84965132, 0.43901227],
[ 0.80513957, 0.85126153, 0.44293939],
[ 0.81370767, 0.85288321, 0.44703322],
[ 0.82220381, 0.85451429, 0.4512893 ],
[ 0.83063017, 0.85615512, 0.45570229],
[ 0.83898905, 0.85780596, 0.4602671 ],
[ 0.84726348, 0.85947482, 0.46497193],
[ 0.85546684, 0.86115762, 0.46981455],
[ 0.86360871, 0.86285158, 0.47479281],
[ 0.87169181, 0.86455657, 0.47990267],
[ 0.87969472, 0.86628276, 0.48512849],
[ 0.88763554, 0.86802364, 0.49047254],
[ 0.89552455, 0.86977573, 0.49593512],
[ 0.90335389, 0.8715434 , 0.50150708],
[ 0.91111136, 0.87333317, 0.50717547],
[ 0.91882427, 0.87513377, 0.51295184],
[ 0.92648868, 0.87694776, 0.51882966],
[ 0.93408127, 0.87878692, 0.52478777],
[ 0.94163648, 0.88063609, 0.53084585],
[ 0.94914523, 0.88250029, 0.5369936 ],
[ 0.95659136, 0.88438815, 0.54321369],
[ 0.964007 , 0.88628495, 0.54952816],
[ 0.97136951, 0.88820246, 0.55591444],
[ 0.97868693, 0.89013755, 0.56237537],
[ 0.98598024, 0.89208031, 0.56892728]]
test_cm = LinearSegmentedColormap.from_list(__file__, cm_data)
if __name__ == "__main__":
import matplotlib.pyplot as plt
import numpy as np
try:
from viscm import viscm
viscm(test_cm)
except ImportError:
print("viscm not found, falling back on simple display")
plt.imshow(np.linspace(0, 100, 256)[None, :], aspect='auto',
cmap=test_cm)
plt.show()