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aces white point derivation
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aces_wp.py
#!/usr/bin/env python
import numpy as np
from scipy.interpolate import interp1d
def daylightCCT_2_xy(cct):
"""
Calculate CIE xy chromaticity coordinates for CIE daylight from the
Correlated Color Temperature.
Parameters
----------
cct : numeric
Correlated Color Temperature.
Returns
-------
ndarray
CIE xy chromaticity coordinates for CIE daylight.
References
----------
- CIE 15:2004 Section 3.1
"""
# Check input values
if cct < 4000 or cct > 25000:
raise ValueError(
'D-Illuminants for CCT values < 4000 and > 25000 are not defined')
# Apply polynomials
if cct <= 7000:
x = 0.244063 + 0.09911 * ((10**3) / cct) + 2.9678 * (
(10**6) / cct**2) - 4.6070 * ((10**9) / (cct**3))
else:
x = 0.237040 + 0.24748 * (10**3 / cct) + 1.9018 * (
10**6 / cct**2) - 2.0064 * (10**9 / cct**3)
y = -3 * (x**2) + 2.87 * x - 0.275
# Place x and y in an array and return
xy = np.array([x, y])
return xy
def daylight(cct, wl):
"""
Calculate a CIE daylight spectral power distribution.
Parameters
----------
cct : numeric
Correlated Color Temperature.
wl : array_like
Wavelengths to calculate the spectral power distribution for.
Returns
-------
ndarray
CIE daylight spectral power distribution.
References
----------
- CIE 15:2004 Section 3.1
"""
# Check input values
if cct < 4000 or cct > 25000:
raise ValueError(
'D-Illuminants for CCT values < 4000 and > 25000 are not defined')
# Define s_series wl array
wl_s = np.arange(300, 835, 5)
# Define s_series values
s0 = np.array([
0.04, 3.02, 6, 17.8, 29.6, 42.45, 55.3, 56.3, 57.3, 59.55, 61.8, 61.65,
61.5, 65.15, 68.8, 66.1, 63.4, 64.6, 65.8, 80.3, 94.8, 99.8, 104.8,
105.35, 105.9, 101.35, 96.8, 105.35, 113.9, 119.75, 125.6, 125.55,
125.5, 123.4, 121.3, 121.3, 121.3, 117.4, 113.5, 113.3, 113.1, 111.95,
110.8, 108.65, 106.5, 107.65, 108.8, 107.05, 105.3, 104.85, 104.4,
102.2, 100, 98, 96, 95.55, 95.1, 92.1, 89.1, 89.8, 90.5, 90.4, 90.3,
89.35, 88.4, 86.2, 84, 84.55, 85.1, 83.5, 81.9, 82.25, 82.6, 83.75,
84.9, 83.1, 81.3, 76.6, 71.9, 73.1, 74.3, 75.35, 76.4, 69.85, 63.3,
67.5, 71.7, 74.35, 77, 71.1, 65.2, 56.45, 47.7, 58.15, 68.6, 66.8, 65,
65.5, 66, 63.5, 61, 57.15, 53.3, 56.1, 58.9, 60.4, 61.9
])
s1 = np.array([
0.02, 2.26, 4.5, 13.45, 22.4, 32.2, 42, 41.3, 40.6, 41.1, 41.6, 39.8,
38, 40.2, 42.4, 40.45, 38.5, 36.75, 35, 39.2, 43.4, 44.85, 46.3, 45.1,
43.9, 40.5, 37.1, 36.9, 36.7, 36.3, 35.9, 34.25, 32.6, 30.25, 27.9,
26.1, 24.3, 22.2, 20.1, 18.15, 16.2, 14.7, 13.2, 10.9, 8.6, 7.35, 6.1,
5.15, 4.2, 3.05, 1.9, 0.95, 0, -0.8, -1.6, -2.55, -3.5, -3.5, -3.5,
-4.65, -5.8, -6.5, -7.2, -7.9, -8.6, -9.05, -9.5, -10.2, -10.9, -10.8,
-10.7, -11.35, -12, -13, -14, -13.8, -13.6, -12.8, -12, -12.65, -13.3,
-13.1, -12.9, -11.75, -10.6, -11.1, -11.6, -11.9, -12.2, -11.2, -10.2,
-9, -7.8, -9.5, -11.2, -10.8, -10.4, -10.5, -10.6, -10.15, -9.7, -9,
-8.3, -8.8, -9.3, -9.55, -9.8
])
s2 = np.array([
0, 1, 2, 3, 4, 6.25, 8.5, 8.15, 7.8, 7.25, 6.7, 6, 5.3, 5.7, 6.1, 4.55,
3, 2.1, 1.2, 0.05, -1.1, -0.8, -0.5, -0.6, -0.7, -0.95, -1.2, -1.9,
-2.6, -2.75, -2.9, -2.85, -2.8, -2.7, -2.6, -2.6, -2.6, -2.2, -1.8,
-1.65, -1.5, -1.4, -1.3, -1.25, -1.2, -1.1, -1, -0.75, -0.5, -0.4,
-0.3, -0.15, 0, 0.1, 0.2, 0.35, 0.5, 1.3, 2.1, 2.65, 3.2, 3.65, 4.1,
4.4, 4.7, 4.9, 5.1, 5.9, 6.7, 7, 7.3, 7.95, 8.6, 9.2, 9.8, 10, 10.2,
9.25, 8.3, 8.95, 9.6, 9.05, 8.5, 7.75, 7, 7.3, 7.6, 7.8, 8, 7.35, 6.7,
5.95, 5.2, 6.3, 7.4, 7.1, 6.8, 6.9, 7, 6.7, 6.4, 5.95, 5.5, 5.8, 6.1,
6.3, 6.5
])
# Define interpolation function
f_s0 = interp1d(wl_s, s0, kind='linear')
f_s1 = interp1d(wl_s, s1, kind='linear')
f_s2 = interp1d(wl_s, s2, kind='linear')
# Interpolate s_series
s0_i = f_s0(wl)
s1_i = f_s1(wl)
s2_i = f_s2(wl)
# Calculate CIE xy chromaticity coordinates for daylight CCT
xy = daylightCCT_2_xy(cct)
x = xy[0]
y = xy[1]
# Calculate m0, m1, and m2
m0 = 0.0241 + 0.2562 * x - 0.7341 * y
m1 = -1.3515 - 1.7703 * x + 5.9114 * y
m2 = 0.03000 - 31.4424 * x + 30.0717 * y
m1 = np.round(m1 / m0, decimals=3)
m2 = np.round(m2 / m0, decimals=3)
# Calculate the spectral power distribution from interpolated s_series
# and m values
spec = (s0_i + np.tile(m1, (1, s1_i.shape[0])) * s1_i +
np.tile(m2, (1, s2_i.shape[0])) * s2_i)
return spec
def illumXYZ(spec, wl):
"""
Calculate the CIE XYZ tristimulus values of an illuminant spectral power
distribution.
Parameters
----------
spec : array_like
Illuminant spectral power distribution.
wl : array_like
Wavelengths of the spectral power distribution.
Returns
-------
ndarray
CIE XYZ tristimulus values.
References
----------
- CIE 15:2004 Section 7.1
"""
# CIE 1931 standard observer data
cie2 = np.array([
[360, 0.000129900000, 0.000003917000, 0.000606100000],
[361, 0.000145847000, 0.000004393581, 0.000680879200],
[362, 0.000163802100, 0.000004929604, 0.000765145600],
[363, 0.000184003700, 0.000005532136, 0.000860012400],
[364, 0.000206690200, 0.000006208245, 0.000966592800],
[365, 0.000232100000, 0.000006965000, 0.001086000000],
[366, 0.000260728000, 0.000007813219, 0.001220586000],
[367, 0.000293075000, 0.000008767336, 0.001372729000],
[368, 0.000329388000, 0.000009839844, 0.001543579000],
[369, 0.000369914000, 0.000011043230, 0.001734286000],
[370, 0.000414900000, 0.000012390000, 0.001946000000],
[371, 0.000464158700, 0.000013886410, 0.002177777000],
[372, 0.000518986000, 0.000015557280, 0.002435809000],
[373, 0.000581854000, 0.000017442960, 0.002731953000],
[374, 0.000655234700, 0.000019583750, 0.003078064000],
[375, 0.000741600000, 0.000022020000, 0.003486000000],
[376, 0.000845029600, 0.000024839650, 0.003975227000],
[377, 0.000964526800, 0.000028041260, 0.004540880000],
[378, 0.001094949000, 0.000031531040, 0.005158320000],
[379, 0.001231154000, 0.000035215210, 0.005802907000],
[380, 0.001368000000, 0.000039000000, 0.006450001000],
[381, 0.001502050000, 0.000042826400, 0.007083216000],
[382, 0.001642328000, 0.000046914600, 0.007745488000],
[383, 0.001802382000, 0.000051589600, 0.008501152000],
[384, 0.001995757000, 0.000057176400, 0.009414544000],
[385, 0.002236000000, 0.000064000000, 0.010549990000],
[386, 0.002535385000, 0.000072344210, 0.011965800000],
[387, 0.002892603000, 0.000082212240, 0.013655870000],
[388, 0.003300829000, 0.000093508160, 0.015588050000],
[389, 0.003753236000, 0.000106136100, 0.017730150000],
[390, 0.004243000000, 0.000120000000, 0.020050010000],
[391, 0.004762389000, 0.000134984000, 0.022511360000],
[392, 0.005330048000, 0.000151492000, 0.025202880000],
[393, 0.005978712000, 0.000170208000, 0.028279720000],
[394, 0.006741117000, 0.000191816000, 0.031897040000],
[395, 0.007650000000, 0.000217000000, 0.036210000000],
[396, 0.008751373000, 0.000246906700, 0.041437710000],
[397, 0.010028880000, 0.000281240000, 0.047503720000],
[398, 0.011421700000, 0.000318520000, 0.054119880000],
[399, 0.012869010000, 0.000357266700, 0.060998030000],
[400, 0.014310000000, 0.000396000000, 0.067850010000],
[401, 0.015704430000, 0.000433714700, 0.074486320000],
[402, 0.017147440000, 0.000473024000, 0.081361560000],
[403, 0.018781220000, 0.000517876000, 0.089153640000],
[404, 0.020748010000, 0.000572218700, 0.098540480000],
[405, 0.023190000000, 0.000640000000, 0.110200000000],
[406, 0.026207360000, 0.000724560000, 0.124613300000],
[407, 0.029782480000, 0.000825500000, 0.141701700000],
[408, 0.033880920000, 0.000941160000, 0.161303500000],
[409, 0.038468240000, 0.001069880000, 0.183256800000],
[410, 0.043510000000, 0.001210000000, 0.207400000000],
[411, 0.048995600000, 0.001362091000, 0.233692100000],
[412, 0.055022600000, 0.001530752000, 0.262611400000],
[413, 0.061718800000, 0.001720368000, 0.294774600000],
[414, 0.069212000000, 0.001935323000, 0.330798500000],
[415, 0.077630000000, 0.002180000000, 0.371300000000],
[416, 0.086958110000, 0.002454800000, 0.416209100000],
[417, 0.097176720000, 0.002764000000, 0.465464200000],
[418, 0.108406300000, 0.003117800000, 0.519694800000],
[419, 0.120767200000, 0.003526400000, 0.579530300000],
[420, 0.134380000000, 0.004000000000, 0.645600000000],
[421, 0.149358200000, 0.004546240000, 0.718483800000],
[422, 0.165395700000, 0.005159320000, 0.796713300000],
[423, 0.181983100000, 0.005829280000, 0.877845900000],
[424, 0.198611000000, 0.006546160000, 0.959439000000],
[425, 0.214770000000, 0.007300000000, 1.039050100000],
[426, 0.230186800000, 0.008086507000, 1.115367300000],
[427, 0.244879700000, 0.008908720000, 1.188497100000],
[428, 0.258777300000, 0.009767680000, 1.258123300000],
[429, 0.271807900000, 0.010664430000, 1.323929600000],
[430, 0.283900000000, 0.011600000000, 1.385600000000],
[431, 0.294943800000, 0.012573170000, 1.442635200000],
[432, 0.304896500000, 0.013582720000, 1.494803500000],
[433, 0.313787300000, 0.014629680000, 1.542190300000],
[434, 0.321645400000, 0.015715090000, 1.584880700000],
[435, 0.328500000000, 0.016840000000, 1.622960000000],
[436, 0.334351300000, 0.018007360000, 1.656404800000],
[437, 0.339210100000, 0.019214480000, 1.685295900000],
[438, 0.343121300000, 0.020453920000, 1.709874500000],
[439, 0.346129600000, 0.021718240000, 1.730382100000],
[440, 0.348280000000, 0.023000000000, 1.747060000000],
[441, 0.349599900000, 0.024294610000, 1.760044600000],
[442, 0.350147400000, 0.025610240000, 1.769623300000],
[443, 0.350013000000, 0.026958570000, 1.776263700000],
[444, 0.349287000000, 0.028351250000, 1.780433400000],
[445, 0.348060000000, 0.029800000000, 1.782600000000],
[446, 0.346373300000, 0.031310830000, 1.782968200000],
[447, 0.344262400000, 0.032883680000, 1.781699800000],
[448, 0.341808800000, 0.034521120000, 1.779198200000],
[449, 0.339094100000, 0.036225710000, 1.775867100000],
[450, 0.336200000000, 0.038000000000, 1.772110000000],
[451, 0.333197700000, 0.039846670000, 1.768258900000],
[452, 0.330041100000, 0.041768000000, 1.764039000000],
[453, 0.326635700000, 0.043766000000, 1.758943800000],
[454, 0.322886800000, 0.045842670000, 1.752466300000],
[455, 0.318700000000, 0.048000000000, 1.744100000000],
[456, 0.314025100000, 0.050243680000, 1.733559500000],
[457, 0.308884000000, 0.052573040000, 1.720858100000],
[458, 0.303290400000, 0.054980560000, 1.705936900000],
[459, 0.297257900000, 0.057458720000, 1.688737200000],
[460, 0.290800000000, 0.060000000000, 1.669200000000],
[461, 0.283970100000, 0.062601970000, 1.647528700000],
[462, 0.276721400000, 0.065277520000, 1.623412700000],
[463, 0.268917800000, 0.068042080000, 1.596022300000],
[464, 0.260422700000, 0.070911090000, 1.564528000000],
[465, 0.251100000000, 0.073900000000, 1.528100000000],
[466, 0.240847500000, 0.077016000000, 1.486111400000],
[467, 0.229851200000, 0.080266400000, 1.439521500000],
[468, 0.218407200000, 0.083666800000, 1.389879900000],
[469, 0.206811500000, 0.087232800000, 1.338736200000],
[470, 0.195360000000, 0.090980000000, 1.287640000000],
[471, 0.184213600000, 0.094917550000, 1.237422300000],
[472, 0.173327300000, 0.099045840000, 1.187824300000],
[473, 0.162688100000, 0.103367400000, 1.138761100000],
[474, 0.152283300000, 0.107884600000, 1.090148000000],
[475, 0.142100000000, 0.112600000000, 1.041900000000],
[476, 0.132178600000, 0.117532000000, 0.994197600000],
[477, 0.122569600000, 0.122674400000, 0.947347300000],
[478, 0.113275200000, 0.127992800000, 0.901453100000],
[479, 0.104297900000, 0.133452800000, 0.856619300000],
[480, 0.095640000000, 0.139020000000, 0.812950100000],
[481, 0.087299550000, 0.144676400000, 0.770517300000],
[482, 0.079308040000, 0.150469300000, 0.729444800000],
[483, 0.071717760000, 0.156461900000, 0.689913600000],
[484, 0.064580990000, 0.162717700000, 0.652104900000],
[485, 0.057950010000, 0.169300000000, 0.616200000000],
[486, 0.051862110000, 0.176243100000, 0.582328600000],
[487, 0.046281520000, 0.183558100000, 0.550416200000],
[488, 0.041150880000, 0.191273500000, 0.520337600000],
[489, 0.036412830000, 0.199418000000, 0.491967300000],
[490, 0.032010000000, 0.208020000000, 0.465180000000],
[491, 0.027917200000, 0.217119900000, 0.439924600000],
[492, 0.024144400000, 0.226734500000, 0.416183600000],
[493, 0.020687000000, 0.236857100000, 0.393882200000],
[494, 0.017540400000, 0.247481200000, 0.372945900000],
[495, 0.014700000000, 0.258600000000, 0.353300000000],
[496, 0.012161790000, 0.270184900000, 0.334857800000],
[497, 0.009919960000, 0.282293900000, 0.317552100000],
[498, 0.007967240000, 0.295050500000, 0.301337500000],
[499, 0.006296346000, 0.308578000000, 0.286168600000],
[500, 0.004900000000, 0.323000000000, 0.272000000000],
[501, 0.003777173000, 0.338402100000, 0.258817100000],
[502, 0.002945320000, 0.354685800000, 0.246483800000],
[503, 0.002424880000, 0.371698600000, 0.234771800000],
[504, 0.002236293000, 0.389287500000, 0.223453300000],
[505, 0.002400000000, 0.407300000000, 0.212300000000],
[506, 0.002925520000, 0.425629900000, 0.201169200000],
[507, 0.003836560000, 0.444309600000, 0.190119600000],
[508, 0.005174840000, 0.463394400000, 0.179225400000],
[509, 0.006982080000, 0.482939500000, 0.168560800000],
[510, 0.009300000000, 0.503000000000, 0.158200000000],
[511, 0.012149490000, 0.523569300000, 0.148138300000],
[512, 0.015535880000, 0.544512000000, 0.138375800000],
[513, 0.019477520000, 0.565690000000, 0.128994200000],
[514, 0.023992770000, 0.586965300000, 0.120075100000],
[515, 0.029100000000, 0.608200000000, 0.111700000000],
[516, 0.034814850000, 0.629345600000, 0.103904800000],
[517, 0.041120160000, 0.650306800000, 0.096667480000],
[518, 0.047985040000, 0.670875200000, 0.089982720000],
[519, 0.055378610000, 0.690842400000, 0.083845310000],
[520, 0.063270000000, 0.710000000000, 0.078249990000],
[521, 0.071635010000, 0.728185200000, 0.073208990000],
[522, 0.080462240000, 0.745463600000, 0.068678160000],
[523, 0.089739960000, 0.761969400000, 0.064567840000],
[524, 0.099456450000, 0.777836800000, 0.060788350000],
[525, 0.109600000000, 0.793200000000, 0.057250010000],
[526, 0.120167400000, 0.808110400000, 0.053904350000],
[527, 0.131114500000, 0.822496200000, 0.050746640000],
[528, 0.142367900000, 0.836306800000, 0.047752760000],
[529, 0.153854200000, 0.849491600000, 0.044898590000],
[530, 0.165500000000, 0.862000000000, 0.042160000000],
[531, 0.177257100000, 0.873810800000, 0.039507280000],
[532, 0.189140000000, 0.884962400000, 0.036935640000],
[533, 0.201169400000, 0.895493600000, 0.034458360000],
[534, 0.213365800000, 0.905443200000, 0.032088720000],
[535, 0.225749900000, 0.914850100000, 0.029840000000],
[536, 0.238320900000, 0.923734800000, 0.027711810000],
[537, 0.251066800000, 0.932092400000, 0.025694440000],
[538, 0.263992200000, 0.939922600000, 0.023787160000],
[539, 0.277101700000, 0.947225200000, 0.021989250000],
[540, 0.290400000000, 0.954000000000, 0.020300000000],
[541, 0.303891200000, 0.960256100000, 0.018718050000],
[542, 0.317572600000, 0.966007400000, 0.017240360000],
[543, 0.331438400000, 0.971260600000, 0.015863640000],
[544, 0.345482800000, 0.976022500000, 0.014584610000],
[545, 0.359700000000, 0.980300000000, 0.013400000000],
[546, 0.374083900000, 0.984092400000, 0.012307230000],
[547, 0.388639600000, 0.987418200000, 0.011301880000],
[548, 0.403378400000, 0.990312800000, 0.010377920000],
[549, 0.418311500000, 0.992811600000, 0.009529306000],
[550, 0.433449900000, 0.994950100000, 0.008749999000],
[551, 0.448795300000, 0.996710800000, 0.008035200000],
[552, 0.464336000000, 0.998098300000, 0.007381600000],
[553, 0.480064000000, 0.999112000000, 0.006785400000],
[554, 0.495971300000, 0.999748200000, 0.006242800000],
[555, 0.512050100000, 1.000000000000, 0.005749999000],
[556, 0.528295900000, 0.999856700000, 0.005303600000],
[557, 0.544691600000, 0.999304600000, 0.004899800000],
[558, 0.561209400000, 0.998325500000, 0.004534200000],
[559, 0.577821500000, 0.996898700000, 0.004202400000],
[560, 0.594500000000, 0.995000000000, 0.003900000000],
[561, 0.611220900000, 0.992600500000, 0.003623200000],
[562, 0.627975800000, 0.989742600000, 0.003370600000],
[563, 0.644760200000, 0.986444400000, 0.003141400000],
[564, 0.661569700000, 0.982724100000, 0.002934800000],
[565, 0.678400000000, 0.978600000000, 0.002749999000],
[566, 0.695239200000, 0.974083700000, 0.002585200000],
[567, 0.712058600000, 0.969171200000, 0.002438600000],
[568, 0.728828400000, 0.963856800000, 0.002309400000],
[569, 0.745518800000, 0.958134900000, 0.002196800000],
[570, 0.762100000000, 0.952000000000, 0.002100000000],
[571, 0.778543200000, 0.945450400000, 0.002017733000],
[572, 0.794825600000, 0.938499200000, 0.001948200000],
[573, 0.810926400000, 0.931162800000, 0.001889800000],
[574, 0.826824800000, 0.923457600000, 0.001840933000],
[575, 0.842500000000, 0.915400000000, 0.001800000000],
[576, 0.857932500000, 0.907006400000, 0.001766267000],
[577, 0.873081600000, 0.898277200000, 0.001737800000],
[578, 0.887894400000, 0.889204800000, 0.001711200000],
[579, 0.902318100000, 0.879781600000, 0.001683067000],
[580, 0.916300000000, 0.870000000000, 0.001650001000],
[581, 0.929799500000, 0.859861300000, 0.001610133000],
[582, 0.942798400000, 0.849392000000, 0.001564400000],
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[584, 0.967217900000, 0.827581300000, 0.001458533000],
[585, 0.978600000000, 0.816300000000, 0.001400000000],
[586, 0.989385600000, 0.804794700000, 0.001336667000],
[587, 0.999548800000, 0.793082000000, 0.001270000000],
[588, 1.009089200000, 0.781192000000, 0.001205000000],
[589, 1.018006400000, 0.769154700000, 0.001146667000],
[590, 1.026300000000, 0.757000000000, 0.001100000000],
[591, 1.033982700000, 0.744754100000, 0.001068800000],
[592, 1.040986000000, 0.732422400000, 0.001049400000],
[593, 1.047188000000, 0.720003600000, 0.001035600000],
[594, 1.052466700000, 0.707496500000, 0.001021200000],
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[601, 1.060735200000, 0.618155500000, 0.000760960000],
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[649, 0.298001100000, 0.112769100000, 0.000001333333],
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[823, 0.000002043902, 0.000000738090, 0.000000000000],
[824, 0.000001905497, 0.000000688110, 0.000000000000],
[825, 0.000001776509, 0.000000641530, 0.000000000000],
[826, 0.000001656215, 0.000000598090, 0.000000000000],
[827, 0.000001544022, 0.000000557575, 0.000000000000],
[828, 0.000001439440, 0.000000519808, 0.000000000000],
[829, 0.000001341977, 0.000000484612, 0.000000000000],
[830, 0.000001251141, 0.000000451810, 0.000000000000],
])
# Define interpolation function
f_xbar = interp1d(
cie2[:, 0],
cie2[:, 1],
kind='linear',
bounds_error=False,
fill_value=0)
f_ybar = interp1d(
cie2[:, 0],
cie2[:, 2],
kind='linear',
bounds_error=False,
fill_value=0)
f_zbar = interp1d(
cie2[:, 0],
cie2[:, 3],
kind='linear',
bounds_error=False,
fill_value=0)
# Interpolate the standard observer data
xbar = f_xbar(wl)
ybar = f_ybar(wl)
zbar = f_zbar(wl)
# Put the interpolated standard observer data into an array
cmf = np.array([xbar, ybar, zbar])
# Multiply the standard observer data by the spectra
XYZ = cmf.dot(spec.T).T
# Normalize the CIE XYZ values so that Y equals 1
XYZ = XYZ[:] / XYZ[:, 1]
return XYZ
def XYZ_2_xyY(XYZ):
"""
Calculate CIE XYZ tristimulus values to CIE xyY chromaticity coordinates.
Parameters
----------
XYZ : array_like
CIE XYZ tristimulus values.
Returns
-------
ndarray
CIE xyY chromaticity coordinates.
References
----------
- CIE 15:2004 Section 7.3
"""
# Convert CIE XYZ to CIE xyY
x = XYZ[:, 0] / (XYZ[:, 0] + XYZ[:, 1] + XYZ[:, 2])
y = XYZ[:, 1] / (XYZ[:, 0] + XYZ[:, 1] + XYZ[:, 2])
Y = XYZ[:, 1]
xyY = np.array([x, y, Y]).T
return xyY
if __name__ == "__main__":
# Define wavelenghts from 300 to 830 in 1nm increments and CCT of 6000K
wl = np.arange(300, 831, 1)
cct = 6000
# Calculate the CIE daylight spectral power distribution for CCT of 6000K
spec = daylight(cct, wl)
# Determine the XYZ values of the CIE daylight spectral power distribution
XYZ = illumXYZ(spec, wl)
# Calculate the CIE xy chromaticity coordinates from the CIE XYZ values
xyY = XYZ_2_xyY(XYZ)
# Round to 5 decimal places
aces_wp_xyY = np.round(xyY, decimals=5)
# Print the results
print(aces_wp_xyY)
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