-
Notifications
You must be signed in to change notification settings - Fork 17
/
Copy pathciexyz.py
823 lines (772 loc) · 44.9 KB
/
ciexyz.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
'''
ciexyz.py - Spectral response curves for 1931 CIE XYZ 2 degree field of view matching functions.
Description:
This module provides the CIE standard XYZ color matching functions.
The 1931 tabulation, for a 2 degree field of view, is used in preference to the 10 degree 1964 set,
as is conventional in computer graphics.
The matching functions are stored internally at 1 nm increments, and linear interpolation is
used for any wavelength in between.
ColorPy attempts to scale the matching functions so that:
A spectrum, constant with wavelength, over the range 360 nm to 830 nm, with a total intensity
equal to the (assumed) physical intensity of the monitor, will sample with Y = 1.0.
This scaling corresponds with that in colormodels.py, which assumes Y = 1.0 at full white.
NOTE - I suspect that the scaling is not quite correct. I think it is at least close.
Ideally, we would like the spectrum of the actual monitor display, at full white, which is not
independent of wavelength, to sample to Y = 1.0.
Constants and Functions:
start_wl_nm, end_wl_nm - Default starting and ending range of wavelengths, in nm, as integers.
delta_wl_nm - Default wavelength spacing, in nm, as a float.
DEFAULT_DISPLAY_INTENSITY - Default assumed intensity of monitor display, in W/m^2
def init (monitor_intensity = DEFAULT_DISPLAY_INTENSITY) -
Initialization of color matching curves. Called at module startup with default arguments.
This can be called again to change the assumed display intensity.
def empty_spectrum () -
Get a black (no intensity) ColorPy spectrum.
This is a 2D numpy array, with one row for each wavelength in the visible range,
360 nm to 830 nm, with a spacing of delta_wl_nm (1.0 nm), and two columns.
The first column is filled with the wavelength [nm].
The second column is filled with 0.0. It should later be filled with the intensity.
The result can be passed to xyz_from_spectrum() to convert to an xyz color.
def xyz_from_wavelength (wl_nm) -
Given a wavelength (nm), return the corresponding xyz color, for unit intensity.
def xyz_from_spectrum (spectrum) -
Determine the xyz color of the spectrum.
The spectrum is assumed to be a 2D numpy array, with a row for each wavelength,
and two columns. The first column should hold the wavelength (nm), and the
second should hold the light intensity. The set of wavelengths can be arbitrary,
it does not have to be the set that empty_spectrum() returns.
def get_normalized_spectral_line_colors (
brightness = 1.0,
num_purples = 0,
dwl_angstroms = 10):
Get an array of xyz colors covering the visible spectrum.
Optionally add a number of 'purples', which are colors interpolated between the color
of the lowest wavelength (violet) and the highest (red).
brightness - Desired maximum rgb component of each color. Default 1.0. (Maxiumum displayable brightness)
num_purples - Number of colors to interpolate in the 'purple' range. Default 0. (No purples)
dwl_angstroms - Wavelength separation, in angstroms (0.1 nm). Default 10 A. (1 nm spacing)
References:
Wyszecki and Stiles, Color Science: Concepts and Methods, Quantitative Data and Formulae,
2nd edition, John Wiley, 1982. Wiley Classics Library Edition 2000. ISBN 0-471-39918-3.
CVRL Color and Vision Database - http://cvrl.ioo.ucl.ac.uk/index.htm - (accessed 17 Sep 2008)
Color and Vision Research Laboratories.
Provides a set of data sets related to color vision.
ColorPy uses the tables from this site for the 1931 CIE XYZ matching functions,
and for Illuminant D65, both at 1 nm wavelength increments.
CIE Standards - http://cvrl.ioo.ucl.ac.uk/cie.htm - (accessed 17 Sep 2008)
CIE standards as maintained by CVRL.
The 1931 CIE XYZ and D65 tables that ColorPy uses were obtained from the following files, linked here:
http://cvrl.ioo.ucl.ac.uk/database/data/cmfs/ciexyz31_1.txt
http://cvrl.ioo.ucl.ac.uk/database/data/cie/Illuminantd65.txt
CIE International Commission on Illumination - http://www.cie.co.at/ - (accessed 17 Sep 2008)
Official website of the CIE.
There are tables of the standard functions (matching functions, illuminants) here:
http://www.cie.co.at/main/freepubs.html
http://www.cie.co.at/publ/abst/datatables15_2004/x2.txt
http://www.cie.co.at/publ/abst/datatables15_2004/y2.txt
http://www.cie.co.at/publ/abst/datatables15_2004/z2.txt
http://www.cie.co.at/publ/abst/datatables15_2004/sid65.txt
ColorPy does not use these specific files.
Charles Poynton - Frequently asked questions about Gamma and Color,
posted to comp.graphics.algorithms, 25 Jan 1995.
License:
Copyright (C) 2008 Mark Kness
Author - Mark Kness - [email protected]
This file is part of ColorPy.
ColorPy is free software: you can redistribute it and/or modify
it under the terms of the GNU Lesser General Public License as
published by the Free Software Foundation, either version 3 of
the License, or (at your option) any later version.
ColorPy is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License
along with ColorPy. If not, see <http://www.gnu.org/licenses/>.
'''
import math, numpy
import colormodels
# Assumed physical brightness of the monitor [W/m^2]
# 80 cd/m^2 * 20.3 mW/cd (assuming light at 556 nm)
DEFAULT_DISPLAY_INTENSITY = 1.624
# For reference, the physical luminance of several interesting objects ...
# All values in cd/m^2, where 1 cd = 1 candle = 20.3 milliwatts of light at 5560 A
# Typical monitor at full blast = 80 cd/m^2 [Poynton, Color FAQ, p.4]
# Candle = 5000
# 40W Frosted Light Bulb = 25000
# Clear sky = 4000
# Moon = 2500
# Sun = 1.6 x 10^9
#
# an advertised LCD display (2008) = 300 cd/m^2
# table of 1931 CIE XYZ matching functions.
# data from: http://cvrl.ioo.ucl.ac.uk/database/data/cmfs/ciexyz31_1.txt
# massaged into this format.
_CIEXYZ_1931_table = [
[ 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 ],
[ 583, 0.955277600000, 0.838622000000, 0.001513600000 ],
[ 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 ],
[ 595, 1.056700000000, 0.694900000000, 0.001000000000 ],
[ 596, 1.059794400000, 0.682219200000, 0.000968640000 ],
[ 597, 1.061799200000, 0.669471600000, 0.000929920000 ],
[ 598, 1.062806800000, 0.656674400000, 0.000886880000 ],
[ 599, 1.062909600000, 0.643844800000, 0.000842560000 ],
[ 600, 1.062200000000, 0.631000000000, 0.000800000000 ],
[ 601, 1.060735200000, 0.618155500000, 0.000760960000 ],
[ 602, 1.058443600000, 0.605314400000, 0.000723680000 ],
[ 603, 1.055224400000, 0.592475600000, 0.000685920000 ],
[ 604, 1.050976800000, 0.579637900000, 0.000645440000 ],
[ 605, 1.045600000000, 0.566800000000, 0.000600000000 ],
[ 606, 1.039036900000, 0.553961100000, 0.000547866700 ],
[ 607, 1.031360800000, 0.541137200000, 0.000491600000 ],
[ 608, 1.022666200000, 0.528352800000, 0.000435400000 ],
[ 609, 1.013047700000, 0.515632300000, 0.000383466700 ],
[ 610, 1.002600000000, 0.503000000000, 0.000340000000 ],
[ 611, 0.991367500000, 0.490468800000, 0.000307253300 ],
[ 612, 0.979331400000, 0.478030400000, 0.000283160000 ],
[ 613, 0.966491600000, 0.465677600000, 0.000265440000 ],
[ 614, 0.952847900000, 0.453403200000, 0.000251813300 ],
[ 615, 0.938400000000, 0.441200000000, 0.000240000000 ],
[ 616, 0.923194000000, 0.429080000000, 0.000229546700 ],
[ 617, 0.907244000000, 0.417036000000, 0.000220640000 ],
[ 618, 0.890502000000, 0.405032000000, 0.000211960000 ],
[ 619, 0.872920000000, 0.393032000000, 0.000202186700 ],
[ 620, 0.854449900000, 0.381000000000, 0.000190000000 ],
[ 621, 0.835084000000, 0.368918400000, 0.000174213300 ],
[ 622, 0.814946000000, 0.356827200000, 0.000155640000 ],
[ 623, 0.794186000000, 0.344776800000, 0.000135960000 ],
[ 624, 0.772954000000, 0.332817600000, 0.000116853300 ],
[ 625, 0.751400000000, 0.321000000000, 0.000100000000 ],
[ 626, 0.729583600000, 0.309338100000, 0.000086133330 ],
[ 627, 0.707588800000, 0.297850400000, 0.000074600000 ],
[ 628, 0.685602200000, 0.286593600000, 0.000065000000 ],
[ 629, 0.663810400000, 0.275624500000, 0.000056933330 ],
[ 630, 0.642400000000, 0.265000000000, 0.000049999990 ],
[ 631, 0.621514900000, 0.254763200000, 0.000044160000 ],
[ 632, 0.601113800000, 0.244889600000, 0.000039480000 ],
[ 633, 0.581105200000, 0.235334400000, 0.000035720000 ],
[ 634, 0.561397700000, 0.226052800000, 0.000032640000 ],
[ 635, 0.541900000000, 0.217000000000, 0.000030000000 ],
[ 636, 0.522599500000, 0.208161600000, 0.000027653330 ],
[ 637, 0.503546400000, 0.199548800000, 0.000025560000 ],
[ 638, 0.484743600000, 0.191155200000, 0.000023640000 ],
[ 639, 0.466193900000, 0.182974400000, 0.000021813330 ],
[ 640, 0.447900000000, 0.175000000000, 0.000020000000 ],
[ 641, 0.429861300000, 0.167223500000, 0.000018133330 ],
[ 642, 0.412098000000, 0.159646400000, 0.000016200000 ],
[ 643, 0.394644000000, 0.152277600000, 0.000014200000 ],
[ 644, 0.377533300000, 0.145125900000, 0.000012133330 ],
[ 645, 0.360800000000, 0.138200000000, 0.000010000000 ],
[ 646, 0.344456300000, 0.131500300000, 0.000007733333 ],
[ 647, 0.328516800000, 0.125024800000, 0.000005400000 ],
[ 648, 0.313019200000, 0.118779200000, 0.000003200000 ],
[ 649, 0.298001100000, 0.112769100000, 0.000001333333 ],
[ 650, 0.283500000000, 0.107000000000, 0.000000000000 ],
[ 651, 0.269544800000, 0.101476200000, 0.000000000000 ],
[ 652, 0.256118400000, 0.096188640000, 0.000000000000 ],
[ 653, 0.243189600000, 0.091122960000, 0.000000000000 ],
[ 654, 0.230727200000, 0.086264850000, 0.000000000000 ],
[ 655, 0.218700000000, 0.081600000000, 0.000000000000 ],
[ 656, 0.207097100000, 0.077120640000, 0.000000000000 ],
[ 657, 0.195923200000, 0.072825520000, 0.000000000000 ],
[ 658, 0.185170800000, 0.068710080000, 0.000000000000 ],
[ 659, 0.174832300000, 0.064769760000, 0.000000000000 ],
[ 660, 0.164900000000, 0.061000000000, 0.000000000000 ],
[ 661, 0.155366700000, 0.057396210000, 0.000000000000 ],
[ 662, 0.146230000000, 0.053955040000, 0.000000000000 ],
[ 663, 0.137490000000, 0.050673760000, 0.000000000000 ],
[ 664, 0.129146700000, 0.047549650000, 0.000000000000 ],
[ 665, 0.121200000000, 0.044580000000, 0.000000000000 ],
[ 666, 0.113639700000, 0.041758720000, 0.000000000000 ],
[ 667, 0.106465000000, 0.039084960000, 0.000000000000 ],
[ 668, 0.099690440000, 0.036563840000, 0.000000000000 ],
[ 669, 0.093330610000, 0.034200480000, 0.000000000000 ],
[ 670, 0.087400000000, 0.032000000000, 0.000000000000 ],
[ 671, 0.081900960000, 0.029962610000, 0.000000000000 ],
[ 672, 0.076804280000, 0.028076640000, 0.000000000000 ],
[ 673, 0.072077120000, 0.026329360000, 0.000000000000 ],
[ 674, 0.067686640000, 0.024708050000, 0.000000000000 ],
[ 675, 0.063600000000, 0.023200000000, 0.000000000000 ],
[ 676, 0.059806850000, 0.021800770000, 0.000000000000 ],
[ 677, 0.056282160000, 0.020501120000, 0.000000000000 ],
[ 678, 0.052971040000, 0.019281080000, 0.000000000000 ],
[ 679, 0.049818610000, 0.018120690000, 0.000000000000 ],
[ 680, 0.046770000000, 0.017000000000, 0.000000000000 ],
[ 681, 0.043784050000, 0.015903790000, 0.000000000000 ],
[ 682, 0.040875360000, 0.014837180000, 0.000000000000 ],
[ 683, 0.038072640000, 0.013810680000, 0.000000000000 ],
[ 684, 0.035404610000, 0.012834780000, 0.000000000000 ],
[ 685, 0.032900000000, 0.011920000000, 0.000000000000 ],
[ 686, 0.030564190000, 0.011068310000, 0.000000000000 ],
[ 687, 0.028380560000, 0.010273390000, 0.000000000000 ],
[ 688, 0.026344840000, 0.009533311000, 0.000000000000 ],
[ 689, 0.024452750000, 0.008846157000, 0.000000000000 ],
[ 690, 0.022700000000, 0.008210000000, 0.000000000000 ],
[ 691, 0.021084290000, 0.007623781000, 0.000000000000 ],
[ 692, 0.019599880000, 0.007085424000, 0.000000000000 ],
[ 693, 0.018237320000, 0.006591476000, 0.000000000000 ],
[ 694, 0.016987170000, 0.006138485000, 0.000000000000 ],
[ 695, 0.015840000000, 0.005723000000, 0.000000000000 ],
[ 696, 0.014790640000, 0.005343059000, 0.000000000000 ],
[ 697, 0.013831320000, 0.004995796000, 0.000000000000 ],
[ 698, 0.012948680000, 0.004676404000, 0.000000000000 ],
[ 699, 0.012129200000, 0.004380075000, 0.000000000000 ],
[ 700, 0.011359160000, 0.004102000000, 0.000000000000 ],
[ 701, 0.010629350000, 0.003838453000, 0.000000000000 ],
[ 702, 0.009938846000, 0.003589099000, 0.000000000000 ],
[ 703, 0.009288422000, 0.003354219000, 0.000000000000 ],
[ 704, 0.008678854000, 0.003134093000, 0.000000000000 ],
[ 705, 0.008110916000, 0.002929000000, 0.000000000000 ],
[ 706, 0.007582388000, 0.002738139000, 0.000000000000 ],
[ 707, 0.007088746000, 0.002559876000, 0.000000000000 ],
[ 708, 0.006627313000, 0.002393244000, 0.000000000000 ],
[ 709, 0.006195408000, 0.002237275000, 0.000000000000 ],
[ 710, 0.005790346000, 0.002091000000, 0.000000000000 ],
[ 711, 0.005409826000, 0.001953587000, 0.000000000000 ],
[ 712, 0.005052583000, 0.001824580000, 0.000000000000 ],
[ 713, 0.004717512000, 0.001703580000, 0.000000000000 ],
[ 714, 0.004403507000, 0.001590187000, 0.000000000000 ],
[ 715, 0.004109457000, 0.001484000000, 0.000000000000 ],
[ 716, 0.003833913000, 0.001384496000, 0.000000000000 ],
[ 717, 0.003575748000, 0.001291268000, 0.000000000000 ],
[ 718, 0.003334342000, 0.001204092000, 0.000000000000 ],
[ 719, 0.003109075000, 0.001122744000, 0.000000000000 ],
[ 720, 0.002899327000, 0.001047000000, 0.000000000000 ],
[ 721, 0.002704348000, 0.000976589600, 0.000000000000 ],
[ 722, 0.002523020000, 0.000911108800, 0.000000000000 ],
[ 723, 0.002354168000, 0.000850133200, 0.000000000000 ],
[ 724, 0.002196616000, 0.000793238400, 0.000000000000 ],
[ 725, 0.002049190000, 0.000740000000, 0.000000000000 ],
[ 726, 0.001910960000, 0.000690082700, 0.000000000000 ],
[ 727, 0.001781438000, 0.000643310000, 0.000000000000 ],
[ 728, 0.001660110000, 0.000599496000, 0.000000000000 ],
[ 729, 0.001546459000, 0.000558454700, 0.000000000000 ],
[ 730, 0.001439971000, 0.000520000000, 0.000000000000 ],
[ 731, 0.001340042000, 0.000483913600, 0.000000000000 ],
[ 732, 0.001246275000, 0.000450052800, 0.000000000000 ],
[ 733, 0.001158471000, 0.000418345200, 0.000000000000 ],
[ 734, 0.001076430000, 0.000388718400, 0.000000000000 ],
[ 735, 0.000999949300, 0.000361100000, 0.000000000000 ],
[ 736, 0.000928735800, 0.000335383500, 0.000000000000 ],
[ 737, 0.000862433200, 0.000311440400, 0.000000000000 ],
[ 738, 0.000800750300, 0.000289165600, 0.000000000000 ],
[ 739, 0.000743396000, 0.000268453900, 0.000000000000 ],
[ 740, 0.000690078600, 0.000249200000, 0.000000000000 ],
[ 741, 0.000640515600, 0.000231301900, 0.000000000000 ],
[ 742, 0.000594502100, 0.000214685600, 0.000000000000 ],
[ 743, 0.000551864600, 0.000199288400, 0.000000000000 ],
[ 744, 0.000512429000, 0.000185047500, 0.000000000000 ],
[ 745, 0.000476021300, 0.000171900000, 0.000000000000 ],
[ 746, 0.000442453600, 0.000159778100, 0.000000000000 ],
[ 747, 0.000411511700, 0.000148604400, 0.000000000000 ],
[ 748, 0.000382981400, 0.000138301600, 0.000000000000 ],
[ 749, 0.000356649100, 0.000128792500, 0.000000000000 ],
[ 750, 0.000332301100, 0.000120000000, 0.000000000000 ],
[ 751, 0.000309758600, 0.000111859500, 0.000000000000 ],
[ 752, 0.000288887100, 0.000104322400, 0.000000000000 ],
[ 753, 0.000269539400, 0.000097335600, 0.000000000000 ],
[ 754, 0.000251568200, 0.000090845870, 0.000000000000 ],
[ 755, 0.000234826100, 0.000084800000, 0.000000000000 ],
[ 756, 0.000219171000, 0.000079146670, 0.000000000000 ],
[ 757, 0.000204525800, 0.000073858000, 0.000000000000 ],
[ 758, 0.000190840500, 0.000068916000, 0.000000000000 ],
[ 759, 0.000178065400, 0.000064302670, 0.000000000000 ],
[ 760, 0.000166150500, 0.000060000000, 0.000000000000 ],
[ 761, 0.000155023600, 0.000055981870, 0.000000000000 ],
[ 762, 0.000144621900, 0.000052225600, 0.000000000000 ],
[ 763, 0.000134909800, 0.000048718400, 0.000000000000 ],
[ 764, 0.000125852000, 0.000045447470, 0.000000000000 ],
[ 765, 0.000117413000, 0.000042400000, 0.000000000000 ],
[ 766, 0.000109551500, 0.000039561040, 0.000000000000 ],
[ 767, 0.000102224500, 0.000036915120, 0.000000000000 ],
[ 768, 0.000095394450, 0.000034448680, 0.000000000000 ],
[ 769, 0.000089023900, 0.000032148160, 0.000000000000 ],
[ 770, 0.000083075270, 0.000030000000, 0.000000000000 ],
[ 771, 0.000077512690, 0.000027991250, 0.000000000000 ],
[ 772, 0.000072313040, 0.000026113560, 0.000000000000 ],
[ 773, 0.000067457780, 0.000024360240, 0.000000000000 ],
[ 774, 0.000062928440, 0.000022724610, 0.000000000000 ],
[ 775, 0.000058706520, 0.000021200000, 0.000000000000 ],
[ 776, 0.000054770280, 0.000019778550, 0.000000000000 ],
[ 777, 0.000051099180, 0.000018452850, 0.000000000000 ],
[ 778, 0.000047676540, 0.000017216870, 0.000000000000 ],
[ 779, 0.000044485670, 0.000016064590, 0.000000000000 ],
[ 780, 0.000041509940, 0.000014990000, 0.000000000000 ],
[ 781, 0.000038733240, 0.000013987280, 0.000000000000 ],
[ 782, 0.000036142030, 0.000013051550, 0.000000000000 ],
[ 783, 0.000033723520, 0.000012178180, 0.000000000000 ],
[ 784, 0.000031464870, 0.000011362540, 0.000000000000 ],
[ 785, 0.000029353260, 0.000010600000, 0.000000000000 ],
[ 786, 0.000027375730, 0.000009885877, 0.000000000000 ],
[ 787, 0.000025524330, 0.000009217304, 0.000000000000 ],
[ 788, 0.000023793760, 0.000008592362, 0.000000000000 ],
[ 789, 0.000022178700, 0.000008009133, 0.000000000000 ],
[ 790, 0.000020673830, 0.000007465700, 0.000000000000 ],
[ 791, 0.000019272260, 0.000006959567, 0.000000000000 ],
[ 792, 0.000017966400, 0.000006487995, 0.000000000000 ],
[ 793, 0.000016749910, 0.000006048699, 0.000000000000 ],
[ 794, 0.000015616480, 0.000005639396, 0.000000000000 ],
[ 795, 0.000014559770, 0.000005257800, 0.000000000000 ],
[ 796, 0.000013573870, 0.000004901771, 0.000000000000 ],
[ 797, 0.000012654360, 0.000004569720, 0.000000000000 ],
[ 798, 0.000011797230, 0.000004260194, 0.000000000000 ],
[ 799, 0.000010998440, 0.000003971739, 0.000000000000 ],
[ 800, 0.000010253980, 0.000003702900, 0.000000000000 ],
[ 801, 0.000009559646, 0.000003452163, 0.000000000000 ],
[ 802, 0.000008912044, 0.000003218302, 0.000000000000 ],
[ 803, 0.000008308358, 0.000003000300, 0.000000000000 ],
[ 804, 0.000007745769, 0.000002797139, 0.000000000000 ],
[ 805, 0.000007221456, 0.000002607800, 0.000000000000 ],
[ 806, 0.000006732475, 0.000002431220, 0.000000000000 ],
[ 807, 0.000006276423, 0.000002266531, 0.000000000000 ],
[ 808, 0.000005851304, 0.000002113013, 0.000000000000 ],
[ 809, 0.000005455118, 0.000001969943, 0.000000000000 ],
[ 810, 0.000005085868, 0.000001836600, 0.000000000000 ],
[ 811, 0.000004741466, 0.000001712230, 0.000000000000 ],
[ 812, 0.000004420236, 0.000001596228, 0.000000000000 ],
[ 813, 0.000004120783, 0.000001488090, 0.000000000000 ],
[ 814, 0.000003841716, 0.000001387314, 0.000000000000 ],
[ 815, 0.000003581652, 0.000001293400, 0.000000000000 ],
[ 816, 0.000003339127, 0.000001205820, 0.000000000000 ],
[ 817, 0.000003112949, 0.000001124143, 0.000000000000 ],
[ 818, 0.000002902121, 0.000001048009, 0.000000000000 ],
[ 819, 0.000002705645, 0.000000977058, 0.000000000000 ],
[ 820, 0.000002522525, 0.000000910930, 0.000000000000 ],
[ 821, 0.000002351726, 0.000000849251, 0.000000000000 ],
[ 822, 0.000002192415, 0.000000791721, 0.000000000000 ],
[ 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 ]
]
# Public - default range of wavelengths in spectra (nm).
# start_wl_nm and end_wl_nm are integers, delta_wl_nm is a float.
start_wl_nm = None
end_wl_nm = None
delta_wl_nm = None
# Private - tables of spectral curves
_wavelengths = None
_xyz_colors = None
_xyz_deltas = None
def init (display_intensity = DEFAULT_DISPLAY_INTENSITY):
'''Initialize the spectral sampling curves.'''
# Expect that the table ranges from 360 to 830
global start_wl_nm, end_wl_nm, delta_wl_nm
table_size = len (_CIEXYZ_1931_table)
start_wl_nm = 360
end_wl_nm = 830
delta_wl_nm = 1.0
first = _CIEXYZ_1931_table [0][0]
last = _CIEXYZ_1931_table [-1][0]
assert (first == start_wl_nm), 'Expecting first wavelength as %d but instead is %d' % (start_wl_nm, first)
assert (last == end_wl_nm), 'Expecting last wavelength as %d but instead is %d' % (end_wl_nm, last)
assert (table_size == 471), 'Expecting 471 wavelength, each 1 nm from 360 to 830 nm, instead table size is %d' % (table_size)
# Assume that the color for the wl just before and after the table (359 and 831) are zero.
# Also assume linear interpolation of the values for in-between nanometer wavelengths.
# Construct arrays, with elements for each wavelength, as the xyz color,
# and the change in color to the next largest nanometer.
# We will add an (empty) entry for 359 nm and 831 nm.
global _wavelengths, _xyz_colors, _xyz_deltas
create_table_size = table_size + 2
_wavelengths = numpy.empty ((create_table_size), int)
_xyz_colors = numpy.empty ((create_table_size, 3))
_xyz_deltas = numpy.empty ((create_table_size, 3))
# fill in first row as 359 nm with zero color
_wavelengths [0] = start_wl_nm - 1
_xyz_colors [0] = colormodels.xyz_color (0.0, 0.0, 0.0)
# fill in last row as 831 nm with zero color
_wavelengths [create_table_size-1] = end_wl_nm + 1
_xyz_colors [create_table_size-1] = colormodels.xyz_color (0.0, 0.0, 0.0)
# fill in the middle rows from the source data
for i in range (0, len (_CIEXYZ_1931_table)):
(wl,x,y,z) = _CIEXYZ_1931_table [i]
_wavelengths [i+1] = wl
_xyz_colors [i+1] = colormodels.xyz_color (x,y,z)
# get the integrals of each curve
integral = numpy.zeros (3)
for i in range (0, create_table_size-1):
d_integral = 0.5 * (_xyz_colors [i] + _xyz_colors [i+1]) * delta_wl_nm
integral += d_integral
# scale the sampling curves so that:
# A spectrum, constant with wavelength, with total intensity equal to the
# physical intensity of the monitor, will sample with Y = 1.0.
# This scaling corresponds with that in colormodels, which assumes Y = 1.0 at full white.
# Ideally, we would like the spectrum of the actual monitor display, at full white,
# to sample to Y = 1.0, not the constant with wavelength spectrum that is assumed here.
num_wl = table_size
scaling = num_wl / (integral [1] * display_intensity)
_xyz_colors *= scaling
# now calculate all the deltas
for i in range (0, create_table_size-1):
_xyz_deltas [i] = _xyz_colors [i+1] - _xyz_colors [i]
_xyz_deltas [create_table_size-1] = colormodels.xyz_color (0.0, 0.0, 0.0)
#
def empty_spectrum ():
'''Get a black (no intensity) ColorPy spectrum.
This is a 2D numpy array, with one row for each wavelength in the visible range,
360 nm to 830 nm, with a spacing of delta_wl_nm (1.0 nm), and two columns.
The first column is filled with the wavelength [nm].
The second column is filled with 0.0. It should later be filled with the intensity.
The result can be passed to xyz_from_spectrum() to convert to an xyz color.
'''
wl_nm_range = range (start_wl_nm, end_wl_nm + 1)
num_wl = len (wl_nm_range)
spectrum = numpy.zeros ((num_wl, 2))
for i in range (0, num_wl):
spectrum [i][0] = float (wl_nm_range [i])
return spectrum
def xyz_from_wavelength (wl_nm):
'''Given a wavelength (nm), return the corresponding xyz color, for unit intensity.'''
# separate wl_nm into integer and fraction
int_wl_nm = math.floor (wl_nm)
frac_wl_nm = wl_nm - float (int_wl_nm)
# skip out of range (invisible) wavelengths
if (int_wl_nm < start_wl_nm - 1) or (int_wl_nm > end_wl_nm + 1):
return colormodels.xyz_color (0.0, 0.0, 0.0)
# get index into main table
index = int(round(int_wl_nm - start_wl_nm + 1))
# apply linear interpolation to get the color
return _xyz_colors [index] + frac_wl_nm * _xyz_deltas [index]
def xyz_from_spectrum (spectrum):
'''Determine the xyz color of the spectrum.
The spectrum is assumed to be a 2D numpy array, with a row for each wavelength,
and two columns. The first column should hold the wavelength (nm), and the
second should hold the light intensity. The set of wavelengths can be arbitrary,
it does not have to be the set that empty_spectrum() returns.'''
shape = numpy.shape (spectrum)
(num_wl, num_col) = shape
assert num_col == 2, 'Expecting 2D array with each row: wavelength [nm], specific intensity [W/unit solid angle]'
# integrate
rtn = colormodels.xyz_color (0.0, 0.0, 0.0)
for i in range (0, num_wl):
wl_nm_i = spectrum [i][0]
specific_intensity_i = spectrum [i][1]
xyz = xyz_from_wavelength (wl_nm_i)
rtn += specific_intensity_i * xyz
return rtn
def get_normalized_spectral_line_colors (
brightness = 1.0,
num_purples = 0,
dwl_angstroms = 10):
'''Get an array of xyz colors covering the visible spectrum.
Optionally add a number of 'purples', which are colors interpolated between the color
of the lowest wavelength (violet) and the highest (red).
brightness - Desired maximum rgb component of each color. Default 1.0. (Maxiumum displayable brightness)
num_purples - Number of colors to interpolate in the 'purple' range. Default 0. (No purples)
dwl_angstroms - Wavelength separation, in angstroms (0.1 nm). Default 10 A. (1 nm spacing)
'''
# get range of wavelengths, in angstroms, so that we can have finer resolution than 1 nm
wl_angstrom_range = range (10*start_wl_nm, 10*(end_wl_nm + 1), dwl_angstroms)
# get total point count
num_spectral = len (wl_angstrom_range)
num_points = num_spectral + num_purples
xyzs = numpy.empty ((num_points, 3))
# build list of normalized color x,y values proceeding along each wavelength
i = 0
for wl_A in wl_angstrom_range:
wl_nm = wl_A * 0.1
xyz = xyz_from_wavelength (wl_nm)
colormodels.xyz_normalize (xyz)
xyzs [i] = xyz
i += 1
# interpolate from end point to start point (filling in the purples)
first_xyz = xyzs [0]
last_xyz = xyzs [num_spectral - 1]
for ipurple in range (0, num_purples):
t = float (ipurple) / float (num_purples - 1)
omt = 1.0 - t
xyz = t * first_xyz + omt * last_xyz
colormodels.xyz_normalize (xyz)
xyzs [i] = xyz
i += 1
# scale each color to have the max rgb component equal to the desired brightness
for i in range (0, num_points):
rgb = colormodels.brightest_rgb_from_xyz (xyzs [i], brightness)
xyzs [i] = colormodels.xyz_from_rgb (rgb)
# done
return xyzs
def get_normalized_spectral_line_colors_annotated (
brightness = 1.0,
num_purples = 0,
dwl_angstroms = 10):
'''Get an array of xyz colors covering the visible spectrum.
Optionally add a number of 'purples', which are colors interpolated between the color
of the lowest wavelength (violet) and the highest (red).
A text string describing the color is supplied for each color.
brightness - Desired maximum rgb component of each color. Default 1.0. (Maxiumum displayable brightness)
num_purples - Number of colors to interpolate in the 'purple' range. Default 0. (No purples)
dwl_angstroms - Wavelength separation, in angstroms (0.1 nm). Default 10 A. (1 nm spacing)
'''
# get range of wavelengths, in angstroms, so that we can have finer resolution than 1 nm
wl_angstrom_range = range (10*start_wl_nm, 10*(end_wl_nm + 1), dwl_angstroms)
# get total point count
num_spectral = len (wl_angstrom_range)
num_points = num_spectral + num_purples
xyzs = numpy.empty ((num_points, 3))
names = []
# build list of normalized color x,y values proceeding along each wavelength
i = 0
for wl_A in wl_angstrom_range:
wl_nm = wl_A * 0.1
xyz = xyz_from_wavelength (wl_nm)
colormodels.xyz_normalize (xyz)
xyzs [i] = xyz
name = '%.1f nm' % wl_nm
names.append (name)
i += 1
# interpolate from end point to start point (filling in the purples)
first_xyz = xyzs [0]
last_xyz = xyzs [num_spectral - 1]
for ipurple in range (0, num_purples):
t = float (ipurple) / float (num_purples - 1)
omt = 1.0 - t
xyz = t * first_xyz + omt * last_xyz
colormodels.xyz_normalize (xyz)
xyzs [i] = xyz
name = '%03d purple' % math.floor (1000.0 * t + 0.5)
names.append (name)
i += 1
# scale each color to have the max rgb component equal to the desired brightness
for i in range (0, num_points):
rgb = colormodels.brightest_rgb_from_xyz (xyzs [i], brightness)
xyzs [i] = colormodels.xyz_from_rgb (rgb)
# done
return (xyzs, names)
# Initialize at module startup
init()