benmaier · August 17, 2018 15:48
diff --git a/cividis.py b/cividis.py
 # Copyright (c) 2017, Battelle Memorial Institute
 # 
 # 1.  Battelle Memorial Institute (hereinafter Battelle) hereby grants
 # permission to any person or entity lawfully obtaining a copy of this software
 # and associated documentation files (hereinafter “the Software”) to
 # redistribute and use the Software in source and binary forms, with or without
 # modification. Such person or entity may use, copy, modify, merge, publish,
 # distribute, sublicense, and/or sell copies of the Software, and may permit
 # others to do so, subject to the following conditions:
 # 
 # + Redistributions of source code must retain the above copyright notice, this
 # list of conditions and the following disclaimers.
 # 
 # + Redistributions in binary form must reproduce the above copyright notice,
 # this list of conditions and the following disclaimer in the documentation
 # and/or other materials provided with the distribution.
 # 
 # + Other than as used herein, neither the name Battelle Memorial Institute or
 # Battelle may be used in any form whatsoever without the express written
 # consent of Battelle.
 # 
 # 2.  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
 # "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
 # THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
 # ARE DISCLAIMED. IN NO EVENT SHALL BATTELLE OR CONTRIBUTORS BE LIABLE FOR ANY
 # DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
 # (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
 # LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
 # ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
 # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
 # SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

 import numpy as np
 import matplotlib.pyplot as pl

 from matplotlib.colors import LinearSegmentedColormap

 def convert_color_array_to_cdict(arr):

    cdict = { 'red' : [],
              'green' : [],
              'blue' : [],
              'alpha': [ (0.0, 1.0, 1.0), (1.0, 1.0, 1.0) ],
            }

    N = arr.shape[0]
    X = np.linspace(0,1,N)

    for x, (r,g,b) in zip(X, arr):
        cdict['red'].append( (x, r, r) )
        cdict['green'].append( (x, g, g) )
        cdict['blue'].append( (x, b, b) )

    for k in cdict.keys():
        cdict[k] = tuple(cdict[k])

    return cdict

 cividis = np.array([
    [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],
 ])

 cividis_cmap = convert_color_array_to_cdict(cividis)
 cividis_linear_segmented = LinearSegmentedColormap('cividis', cividis_cmap)

 pl.register_cmap(cmap=cividis_linear_segmented)

 def get_cividis_colors(N, n_start=0, n_end=255):
    # get a list of colors
    indices = np.linspace(n_start, n_end, N)
    indices = np.array(indices, dtype=int)
    return cividis[indices]

 if __name__=="__main__":
    print(get_cividis_colors(12))

    x = np.arange(0, 2*np.pi, 0.01)
    y = np.arange(0, 2*np.pi, 0.01)
    X, Y = np.meshgrid(x,y)
    Z = np.cos(X) * np.sin(Y) * 20

    pl.imshow(Z,cmap='cividis')
    pl.colorbar()

    pl.show()
	# Copyright (c) 2017, Battelle Memorial Institute
	#
	# 1. Battelle Memorial Institute (hereinafter Battelle) hereby grants
	# permission to any person or entity lawfully obtaining a copy of this software
	# and associated documentation files (hereinafter “the Software”) to
	# redistribute and use the Software in source and binary forms, with or without
	# modification. Such person or entity may use, copy, modify, merge, publish,
	# distribute, sublicense, and/or sell copies of the Software, and may permit
	# others to do so, subject to the following conditions:
	#
	# + Redistributions of source code must retain the above copyright notice, this
	# list of conditions and the following disclaimers.
	#
	# + Redistributions in binary form must reproduce the above copyright notice,
	# this list of conditions and the following disclaimer in the documentation
	# and/or other materials provided with the distribution.
	#
	# + Other than as used herein, neither the name Battelle Memorial Institute or
	# Battelle may be used in any form whatsoever without the express written
	# consent of Battelle.
	#
	# 2. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
	# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
	# THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
	# ARE DISCLAIMED. IN NO EVENT SHALL BATTELLE OR CONTRIBUTORS BE LIABLE FOR ANY
	# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
	# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
	# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
	# ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
	# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
	# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

	import numpy as np
	import matplotlib.pyplot as pl

	from matplotlib.colors import LinearSegmentedColormap

	def convert_color_array_to_cdict(arr):

	cdict = { 'red' : [],
	'green' : [],
	'blue' : [],
	'alpha': [ (0.0, 1.0, 1.0), (1.0, 1.0, 1.0) ],
	}

	N = arr.shape[0]
	X = np.linspace(0,1,N)

	for x, (r,g,b) in zip(X, arr):
	cdict['red'].append( (x, r, r) )
	cdict['green'].append( (x, g, g) )
	cdict['blue'].append( (x, b, b) )

	for k in cdict.keys():
	cdict[k] = tuple(cdict[k])

	return cdict

	cividis = np.array([
	[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],
	])

	cividis_cmap = convert_color_array_to_cdict(cividis)
	cividis_linear_segmented = LinearSegmentedColormap('cividis', cividis_cmap)

	pl.register_cmap(cmap=cividis_linear_segmented)

	def get_cividis_colors(N, n_start=0, n_end=255):
	# get a list of colors
	indices = np.linspace(n_start, n_end, N)
	indices = np.array(indices, dtype=int)
	return cividis[indices]

	if __name__=="__main__":
	print(get_cividis_colors(12))

	x = np.arange(0, 2*np.pi, 0.01)
	y = np.arange(0, 2*np.pi, 0.01)
	X, Y = np.meshgrid(x,y)
	Z = np.cos(X) * np.sin(Y) * 20

	pl.imshow(Z,cmap='cividis')
	pl.colorbar()

	pl.show()