forked from pvlib/pvlib-python
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathtest_irradiance.py
950 lines (785 loc) · 36.5 KB
/
test_irradiance.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
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
import datetime
from collections import OrderedDict
import warnings
import numpy as np
from numpy import array, nan
import pandas as pd
import pytest
from numpy.testing import assert_almost_equal, assert_allclose
from pvlib import irradiance
from conftest import (
assert_frame_equal,
assert_series_equal,
requires_ephem,
requires_numba
)
# fixtures create realistic test input data
# test input data generated at Location(32.2, -111, 'US/Arizona', 700)
# test input data is hard coded to avoid dependencies on other parts of pvlib
@pytest.fixture
def times():
# must include night values
return pd.date_range(start='20140624', freq='6H', periods=4,
tz='US/Arizona')
@pytest.fixture
def irrad_data(times):
return pd.DataFrame(np.array(
[[ 0. , 0. , 0. ],
[ 79.73860422, 316.1949056 , 40.46149818],
[1042.48031487, 939.95469881, 118.45831879],
[ 257.20751138, 646.22886049, 62.03376265]]),
columns=['ghi', 'dni', 'dhi'], index=times)
@pytest.fixture
def ephem_data(times):
return pd.DataFrame(np.array(
[[124.0390863 , 124.0390863 , -34.0390863 , -34.0390863 ,
352.69550699, -2.36677158],
[ 82.85457044, 82.97705621, 7.14542956, 7.02294379,
66.71410338, -2.42072165],
[ 10.56413562, 10.56725766, 79.43586438, 79.43274234,
144.76567754, -2.47457321],
[ 72.41687122, 72.46903556, 17.58312878, 17.53096444,
287.04104128, -2.52831909]]),
columns=['apparent_zenith', 'zenith', 'apparent_elevation',
'elevation', 'azimuth', 'equation_of_time'],
index=times)
@pytest.fixture
def dni_et(times):
return np.array(
[1321.1655834833093, 1321.1655834833093, 1321.1655834833093,
1321.1655834833093])
@pytest.fixture
def relative_airmass(times):
return pd.Series([np.nan, 7.58831596, 1.01688136, 3.27930443], times)
# setup for et rad test. put it here for readability
timestamp = pd.Timestamp('20161026')
dt_index = pd.DatetimeIndex([timestamp])
doy = timestamp.dayofyear
dt_date = timestamp.date()
dt_datetime = datetime.datetime.combine(dt_date, datetime.time(0))
dt_np64 = np.datetime64(dt_datetime)
value = 1383.636203
@pytest.mark.parametrize('testval, expected', [
(doy, value),
(np.float64(doy), value),
(dt_date, value),
(dt_datetime, value),
(dt_np64, value),
(np.array([doy]), np.array([value])),
(pd.Series([doy]), np.array([value])),
(dt_index, pd.Series([value], index=dt_index)),
(timestamp, value)
])
@pytest.mark.parametrize('method', [
'asce', 'spencer', 'nrel', pytest.param('pyephem', marks=requires_ephem)])
def test_get_extra_radiation(testval, expected, method):
out = irradiance.get_extra_radiation(testval, method=method)
assert_allclose(out, expected, atol=10)
def test_get_extra_radiation_epoch_year():
out = irradiance.get_extra_radiation(doy, method='nrel', epoch_year=2012)
assert_allclose(out, 1382.4926804890767, atol=0.1)
@requires_numba
def test_get_extra_radiation_nrel_numba(times):
with warnings.catch_warnings():
# don't warn on method reload or num threads
warnings.simplefilter("ignore")
result = irradiance.get_extra_radiation(
times, method='nrel', how='numba', numthreads=4)
# and reset to no-numba state
irradiance.get_extra_radiation(times, method='nrel')
assert_allclose(result,
[1322.332316, 1322.296282, 1322.261205, 1322.227091])
def test_get_extra_radiation_invalid():
with pytest.raises(ValueError):
irradiance.get_extra_radiation(300, method='invalid')
def test_grounddiffuse_simple_float():
result = irradiance.get_ground_diffuse(40, 900)
assert_allclose(result, 26.32000014911496)
def test_grounddiffuse_simple_series(irrad_data):
ground_irrad = irradiance.get_ground_diffuse(40, irrad_data['ghi'])
assert ground_irrad.name == 'diffuse_ground'
def test_grounddiffuse_albedo_0(irrad_data):
ground_irrad = irradiance.get_ground_diffuse(
40, irrad_data['ghi'], albedo=0)
assert 0 == ground_irrad.all()
def test_grounddiffuse_albedo_invalid_surface(irrad_data):
with pytest.raises(KeyError):
irradiance.get_ground_diffuse(
40, irrad_data['ghi'], surface_type='invalid')
def test_grounddiffuse_albedo_surface(irrad_data):
result = irradiance.get_ground_diffuse(40, irrad_data['ghi'],
surface_type='sand')
assert_allclose(result, [0, 3.731058, 48.778813, 12.035025], atol=1e-4)
def test_isotropic_float():
result = irradiance.isotropic(40, 100)
assert_allclose(result, 88.30222215594891)
def test_isotropic_series(irrad_data):
result = irradiance.isotropic(40, irrad_data['dhi'])
assert_allclose(result, [0, 35.728402, 104.601328, 54.777191], atol=1e-4)
def test_klucher_series_float():
# klucher inputs
surface_tilt, surface_azimuth = 40.0, 180.0
dhi, ghi = 100.0, 900.0
solar_zenith, solar_azimuth = 20.0, 180.0
# expect same result for floats and pd.Series
expected = irradiance.klucher(
surface_tilt, surface_azimuth,
pd.Series(dhi), pd.Series(ghi),
pd.Series(solar_zenith), pd.Series(solar_azimuth)
) # 94.99429931664851
result = irradiance.klucher(
surface_tilt, surface_azimuth, dhi, ghi, solar_zenith, solar_azimuth
)
assert_allclose(result, expected[0])
def test_klucher_series(irrad_data, ephem_data):
result = irradiance.klucher(40, 180, irrad_data['dhi'], irrad_data['ghi'],
ephem_data['apparent_zenith'],
ephem_data['azimuth'])
# pvlib matlab 1.4 does not contain the max(cos_tt, 0) correction
# so, these values are different
assert_allclose(result, [0., 36.789794, 109.209347, 56.965916], atol=1e-4)
# expect same result for np.array and pd.Series
expected = irradiance.klucher(
40, 180, irrad_data['dhi'].values, irrad_data['ghi'].values,
ephem_data['apparent_zenith'].values, ephem_data['azimuth'].values
)
assert_allclose(result, expected, atol=1e-4)
def test_haydavies(irrad_data, ephem_data, dni_et):
result = irradiance.haydavies(
40, 180, irrad_data['dhi'], irrad_data['dni'], dni_et,
ephem_data['apparent_zenith'], ephem_data['azimuth'])
# values from matlab 1.4 code
assert_allclose(result, [0, 27.1775, 102.9949, 33.1909], atol=1e-4)
def test_reindl(irrad_data, ephem_data, dni_et):
result = irradiance.reindl(
40, 180, irrad_data['dhi'], irrad_data['dni'], irrad_data['ghi'],
dni_et, ephem_data['apparent_zenith'], ephem_data['azimuth'])
# values from matlab 1.4 code
assert_allclose(result, [0., 27.9412, 104.1317, 34.1663], atol=1e-4)
def test_king(irrad_data, ephem_data):
result = irradiance.king(40, irrad_data['dhi'], irrad_data['ghi'],
ephem_data['apparent_zenith'])
assert_allclose(result, [0, 44.629352, 115.182626, 79.719855], atol=1e-4)
def test_perez(irrad_data, ephem_data, dni_et, relative_airmass):
dni = irrad_data['dni'].copy()
dni.iloc[2] = np.nan
out = irradiance.perez(40, 180, irrad_data['dhi'], dni,
dni_et, ephem_data['apparent_zenith'],
ephem_data['azimuth'], relative_airmass)
expected = pd.Series(np.array(
[ 0. , 31.46046871, np.nan, 45.45539877]),
index=irrad_data.index)
assert_series_equal(out, expected, check_less_precise=2)
def test_perez_components(irrad_data, ephem_data, dni_et, relative_airmass):
dni = irrad_data['dni'].copy()
dni.iloc[2] = np.nan
out = irradiance.perez(40, 180, irrad_data['dhi'], dni,
dni_et, ephem_data['apparent_zenith'],
ephem_data['azimuth'], relative_airmass,
return_components=True)
expected = pd.DataFrame(np.array(
[[ 0. , 31.46046871, np.nan, 45.45539877],
[ 0. , 26.84138589, np.nan, 31.72696071],
[ 0. , 0. , np.nan, 4.47966439],
[ 0. , 4.62212181, np.nan, 9.25316454]]).T,
columns=['sky_diffuse', 'isotropic', 'circumsolar', 'horizon'],
index=irrad_data.index
)
expected_for_sum = expected['sky_diffuse'].copy()
expected_for_sum.iloc[2] = 0
sum_components = out.iloc[:, 1:].sum(axis=1)
sum_components.name = 'sky_diffuse'
assert_frame_equal(out, expected, check_less_precise=2)
assert_series_equal(sum_components, expected_for_sum, check_less_precise=2)
def test_perez_arrays(irrad_data, ephem_data, dni_et, relative_airmass):
dni = irrad_data['dni'].copy()
dni.iloc[2] = np.nan
out = irradiance.perez(40, 180, irrad_data['dhi'].values, dni.values,
dni_et, ephem_data['apparent_zenith'].values,
ephem_data['azimuth'].values,
relative_airmass.values)
expected = np.array(
[ 0. , 31.46046871, np.nan, 45.45539877])
assert_allclose(out, expected, atol=1e-2)
assert isinstance(out, np.ndarray)
def test_perez_scalar():
# copied values from fixtures
out = irradiance.perez(40, 180, 118.45831879, 939.95469881,
1321.1655834833093, 10.56413562, 144.76567754,
1.01688136)
# this will fail. out is ndarry with ndim == 0. fix in future version.
# assert np.isscalar(out)
assert_allclose(out, 109.084332)
@pytest.mark.parametrize('model', ['isotropic', 'klucher', 'haydavies',
'reindl', 'king', 'perez'])
def test_sky_diffuse_zenith_close_to_90(model):
# GH 432
sky_diffuse = irradiance.get_sky_diffuse(
30, 180, 89.999, 230,
dni=10, ghi=51, dhi=50, dni_extra=1360, airmass=12, model=model)
assert sky_diffuse < 100
def test_get_sky_diffuse_invalid():
with pytest.raises(ValueError):
irradiance.get_sky_diffuse(
30, 180, 0, 180, 1000, 1100, 100, dni_extra=1360, airmass=1,
model='invalid')
def test_campbell_norman():
expected = pd.DataFrame(np.array(
[[863.859736967, 653.123094076, 220.65905025]]),
columns=['ghi', 'dni', 'dhi'],
index=[0])
out = irradiance.campbell_norman(
pd.Series([10]), pd.Series([0.5]), pd.Series([109764.21013135818]),
dni_extra=1400)
assert_frame_equal(out, expected)
def test_get_total_irradiance(irrad_data, ephem_data, dni_et, relative_airmass):
models = ['isotropic', 'klucher',
'haydavies', 'reindl', 'king', 'perez']
for model in models:
total = irradiance.get_total_irradiance(
32, 180,
ephem_data['apparent_zenith'], ephem_data['azimuth'],
dni=irrad_data['dni'], ghi=irrad_data['ghi'],
dhi=irrad_data['dhi'],
dni_extra=dni_et, airmass=relative_airmass,
model=model,
surface_type='urban')
assert total.columns.tolist() == ['poa_global', 'poa_direct',
'poa_diffuse', 'poa_sky_diffuse',
'poa_ground_diffuse']
@pytest.mark.parametrize('model', ['isotropic', 'klucher',
'haydavies', 'reindl', 'king', 'perez'])
def test_get_total_irradiance_scalars(model):
total = irradiance.get_total_irradiance(
32, 180,
10, 180,
dni=1000, ghi=1100,
dhi=100,
dni_extra=1400, airmass=1,
model=model,
surface_type='urban')
assert list(total.keys()) == ['poa_global', 'poa_direct',
'poa_diffuse', 'poa_sky_diffuse',
'poa_ground_diffuse']
# test that none of the values are nan
assert np.isnan(np.array(list(total.values()))).sum() == 0
def test_poa_components(irrad_data, ephem_data, dni_et, relative_airmass):
aoi = irradiance.aoi(40, 180, ephem_data['apparent_zenith'],
ephem_data['azimuth'])
gr_sand = irradiance.get_ground_diffuse(40, irrad_data['ghi'],
surface_type='sand')
diff_perez = irradiance.perez(
40, 180, irrad_data['dhi'], irrad_data['dni'], dni_et,
ephem_data['apparent_zenith'], ephem_data['azimuth'], relative_airmass)
out = irradiance.poa_components(
aoi, irrad_data['dni'], diff_perez, gr_sand)
expected = pd.DataFrame(np.array(
[[ 0. , -0. , 0. , 0. ,
0. ],
[ 35.19456561, 0. , 35.19456561, 31.4635077 ,
3.73105791],
[956.18253696, 798.31939281, 157.86314414, 109.08433162,
48.77881252],
[ 90.99624896, 33.50143401, 57.49481495, 45.45978964,
12.03502531]]),
columns=['poa_global', 'poa_direct', 'poa_diffuse', 'poa_sky_diffuse',
'poa_ground_diffuse'],
index=irrad_data.index)
assert_frame_equal(out, expected)
@pytest.mark.parametrize('pressure,expected', [
(93193, [[830.46567, 0.79742, 0.93505],
[676.09497, 0.63776, 3.02102]]),
(None, [[868.72425, 0.79742, 1.01664],
[680.66679, 0.63776, 3.28463]]),
(101325, [[868.72425, 0.79742, 1.01664],
[680.66679, 0.63776, 3.28463]])
])
def test_disc_value(pressure, expected):
# see GH 449 for pressure=None vs. 101325.
columns = ['dni', 'kt', 'airmass']
times = pd.DatetimeIndex(['2014-06-24T1200', '2014-06-24T1800'],
tz='America/Phoenix')
ghi = pd.Series([1038.62, 254.53], index=times)
zenith = pd.Series([10.567, 72.469], index=times)
out = irradiance.disc(ghi, zenith, times, pressure=pressure)
expected_values = np.array(expected)
expected = pd.DataFrame(expected_values, columns=columns, index=times)
# check the pandas dataframe. check_less_precise is weird
assert_frame_equal(out, expected, check_less_precise=True)
# use np.assert_allclose to check values more clearly
assert_allclose(out.values, expected_values, atol=1e-5)
def test_disc_overirradiance():
columns = ['dni', 'kt', 'airmass']
ghi = np.array([3000])
solar_zenith = np.full_like(ghi, 0)
times = pd.date_range(start='2016-07-19 12:00:00', freq='1s',
periods=len(ghi), tz='America/Phoenix')
out = irradiance.disc(ghi=ghi, solar_zenith=solar_zenith,
datetime_or_doy=times)
expected = pd.DataFrame(np.array(
[[8.72544336e+02, 1.00000000e+00, 9.99493933e-01]]),
columns=columns, index=times)
assert_frame_equal(out, expected)
def test_disc_min_cos_zenith_max_zenith():
# map out behavior under difficult conditions with various
# limiting kwargs settings
columns = ['dni', 'kt', 'airmass']
times = pd.DatetimeIndex(['2016-07-19 06:11:00'], tz='America/Phoenix')
out = irradiance.disc(ghi=1.0, solar_zenith=89.99, datetime_or_doy=times)
expected = pd.DataFrame(np.array(
[[0.00000000e+00, 1.16046346e-02, 12.0]]),
columns=columns, index=times)
assert_frame_equal(out, expected)
# max_zenith and/or max_airmass keep these results reasonable
out = irradiance.disc(ghi=1.0, solar_zenith=89.99, datetime_or_doy=times,
min_cos_zenith=0)
expected = pd.DataFrame(np.array(
[[0.00000000e+00, 1.0, 12.0]]),
columns=columns, index=times)
assert_frame_equal(out, expected)
# still get reasonable values because of max_airmass=12 limit
out = irradiance.disc(ghi=1.0, solar_zenith=89.99, datetime_or_doy=times,
max_zenith=100)
expected = pd.DataFrame(np.array(
[[0., 1.16046346e-02, 12.0]]),
columns=columns, index=times)
assert_frame_equal(out, expected)
# still get reasonable values because of max_airmass=12 limit
out = irradiance.disc(ghi=1.0, solar_zenith=89.99, datetime_or_doy=times,
min_cos_zenith=0, max_zenith=100)
expected = pd.DataFrame(np.array(
[[277.50185968, 1.0, 12.0]]),
columns=columns, index=times)
assert_frame_equal(out, expected)
# max_zenith keeps this result reasonable
out = irradiance.disc(ghi=1.0, solar_zenith=89.99, datetime_or_doy=times,
min_cos_zenith=0, max_airmass=100)
expected = pd.DataFrame(np.array(
[[0.00000000e+00, 1.0, 36.39544757]]),
columns=columns, index=times)
assert_frame_equal(out, expected)
# allow zenith to be close to 90 and airmass to be infinite
# and we get crazy values
out = irradiance.disc(ghi=1.0, solar_zenith=89.99, datetime_or_doy=times,
max_zenith=100, max_airmass=100)
expected = pd.DataFrame(np.array(
[[6.68577449e+03, 1.16046346e-02, 3.63954476e+01]]),
columns=columns, index=times)
assert_frame_equal(out, expected)
# allow min cos zenith to be 0, zenith to be close to 90,
# and airmass to be very big and we get even higher DNI values
out = irradiance.disc(ghi=1.0, solar_zenith=89.99, datetime_or_doy=times,
min_cos_zenith=0, max_zenith=100, max_airmass=100)
expected = pd.DataFrame(np.array(
[[7.21238390e+03, 1., 3.63954476e+01]]),
columns=columns, index=times)
assert_frame_equal(out, expected)
def test_dirint_value():
times = pd.DatetimeIndex(['2014-06-24T12-0700', '2014-06-24T18-0700'])
ghi = pd.Series([1038.62, 254.53], index=times)
zenith = pd.Series([10.567, 72.469], index=times)
pressure = 93193.
dirint_data = irradiance.dirint(ghi, zenith, times, pressure=pressure)
assert_almost_equal(dirint_data.values,
np.array([868.8, 699.7]), 1)
def test_dirint_nans():
times = pd.date_range(start='2014-06-24T12-0700', periods=5, freq='6H')
ghi = pd.Series([np.nan, 1038.62, 1038.62, 1038.62, 1038.62], index=times)
zenith = pd.Series([10.567, np.nan, 10.567, 10.567, 10.567], index=times)
pressure = pd.Series([93193., 93193., np.nan, 93193., 93193.], index=times)
temp_dew = pd.Series([10, 10, 10, np.nan, 10], index=times)
dirint_data = irradiance.dirint(ghi, zenith, times, pressure=pressure,
temp_dew=temp_dew)
assert_almost_equal(dirint_data.values,
np.array([np.nan, np.nan, np.nan, np.nan, 893.1]), 1)
def test_dirint_tdew():
times = pd.DatetimeIndex(['2014-06-24T12-0700', '2014-06-24T18-0700'])
ghi = pd.Series([1038.62, 254.53], index=times)
zenith = pd.Series([10.567, 72.469], index=times)
pressure = 93193.
dirint_data = irradiance.dirint(ghi, zenith, times, pressure=pressure,
temp_dew=10)
assert_almost_equal(dirint_data.values,
np.array([882.1, 672.6]), 1)
def test_dirint_no_delta_kt():
times = pd.DatetimeIndex(['2014-06-24T12-0700', '2014-06-24T18-0700'])
ghi = pd.Series([1038.62, 254.53], index=times)
zenith = pd.Series([10.567, 72.469], index=times)
pressure = 93193.
dirint_data = irradiance.dirint(ghi, zenith, times, pressure=pressure,
use_delta_kt_prime=False)
assert_almost_equal(dirint_data.values,
np.array([861.9, 670.4]), 1)
def test_dirint_coeffs():
coeffs = irradiance._get_dirint_coeffs()
assert coeffs[0, 0, 0, 0] == 0.385230
assert coeffs[0, 1, 2, 1] == 0.229970
assert coeffs[3, 2, 6, 3] == 1.032260
def test_dirint_min_cos_zenith_max_zenith():
# map out behavior under difficult conditions with various
# limiting kwargs settings
# times don't have any physical relevance
times = pd.DatetimeIndex(['2014-06-24T12-0700', '2014-06-24T18-0700'])
ghi = pd.Series([0, 1], index=times)
solar_zenith = pd.Series([90, 89.99], index=times)
out = irradiance.dirint(ghi, solar_zenith, times)
expected = pd.Series([0.0, 0.0], index=times, name='dni')
assert_series_equal(out, expected)
out = irradiance.dirint(ghi, solar_zenith, times, min_cos_zenith=0)
expected = pd.Series([0.0, 0.0], index=times, name='dni')
assert_series_equal(out, expected)
out = irradiance.dirint(ghi, solar_zenith, times, max_zenith=90)
expected = pd.Series([0.0, 0.0], index=times, name='dni')
assert_series_equal(out, expected, check_less_precise=True)
out = irradiance.dirint(ghi, solar_zenith, times, min_cos_zenith=0,
max_zenith=90)
expected = pd.Series([0.0, 144.264507], index=times, name='dni')
assert_series_equal(out, expected, check_less_precise=True)
out = irradiance.dirint(ghi, solar_zenith, times, min_cos_zenith=0,
max_zenith=100)
expected = pd.Series([0.0, 144.264507], index=times, name='dni')
assert_series_equal(out, expected, check_less_precise=True)
def test_gti_dirint():
times = pd.DatetimeIndex(
['2014-06-24T06-0700', '2014-06-24T09-0700', '2014-06-24T12-0700'])
poa_global = np.array([20, 300, 1000])
aoi = np.array([100, 70, 10])
zenith = np.array([80, 45, 20])
azimuth = np.array([90, 135, 180])
surface_tilt = 30
surface_azimuth = 180
# test defaults
output = irradiance.gti_dirint(
poa_global, aoi, zenith, azimuth, times, surface_tilt, surface_azimuth)
expected_col_order = ['ghi', 'dni', 'dhi']
expected = pd.DataFrame(array(
[[ 21.05796198, 0. , 21.05796198],
[ 288.22574368, 60.59964218, 245.37532576],
[ 931.04078010, 695.94965324, 277.06172442]]),
columns=expected_col_order, index=times)
assert_frame_equal(output, expected)
# test ignore calculate_gt_90
output = irradiance.gti_dirint(
poa_global, aoi, zenith, azimuth, times, surface_tilt, surface_azimuth,
calculate_gt_90=False)
expected_no_90 = expected.copy()
expected_no_90.iloc[0, :] = np.nan
assert_frame_equal(output, expected_no_90)
# test pressure input
pressure = 93193.
output = irradiance.gti_dirint(
poa_global, aoi, zenith, azimuth, times, surface_tilt, surface_azimuth,
pressure=pressure)
expected = pd.DataFrame(array(
[[ 21.05796198, 0. , 21.05796198],
[ 289.81109139, 60.52460392, 247.01373353],
[ 932.46756378, 648.05001357, 323.49974813]]),
columns=expected_col_order, index=times)
assert_frame_equal(output, expected)
# test albedo input
albedo = 0.05
output = irradiance.gti_dirint(
poa_global, aoi, zenith, azimuth, times, surface_tilt, surface_azimuth,
albedo=albedo)
expected = pd.DataFrame(array(
[[ 21.3592591, 0. , 21.3592591 ],
[ 292.5162373, 64.42628826, 246.95997198],
[ 941.6753031, 727.16311901, 258.36548605]]),
columns=expected_col_order, index=times)
assert_frame_equal(output, expected)
# test temp_dew input
temp_dew = np.array([70, 80, 20])
output = irradiance.gti_dirint(
poa_global, aoi, zenith, azimuth, times, surface_tilt, surface_azimuth,
temp_dew=temp_dew)
expected = pd.DataFrame(array(
[[ 21.05796198, 0. , 21.05796198],
[ 292.40468994, 36.79559287, 266.3862767 ],
[ 931.79627208, 689.81549269, 283.5817439]]),
columns=expected_col_order, index=times)
assert_frame_equal(output, expected)
def test_erbs():
index = pd.DatetimeIndex(['20190101']*3 + ['20190620'])
ghi = pd.Series([0, 50, 1000, 1000], index=index)
zenith = pd.Series([120, 85, 10, 10], index=index)
expected = pd.DataFrame(np.array(
[[0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
[9.67192672e+01, 4.15703604e+01, 4.05723511e-01],
[7.94205651e+02, 2.17860117e+02, 7.18132729e-01],
[8.42001578e+02, 1.70790318e+02, 7.68214312e-01]]),
columns=['dni', 'dhi', 'kt'], index=index)
out = irradiance.erbs(ghi, zenith, index)
assert_frame_equal(np.round(out, 0), np.round(expected, 0))
def test_boland():
index = pd.DatetimeIndex(['20190101']*3 + ['20190620'])
ghi = pd.Series([0, 50, 1000, 1000], index=index)
zenith = pd.Series([120, 85, 10, 10], index=index)
expected = pd.DataFrame(np.array(
[[0.0, 0.0, 0.0],
[103.735879, 40.958822, 0.405724],
[776.006568, 235.782716, 0.718133],
[845.794317, 167.055199, 0.768214]]),
columns=['dni', 'dhi', 'kt'], index=index)
out = irradiance.boland(ghi, zenith, index)
assert_frame_equal(np.round(out, 0), np.round(expected, 0))
def test_erbs_min_cos_zenith_max_zenith():
# map out behavior under difficult conditions with various
# limiting kwargs settings
columns = ['dni', 'dhi', 'kt']
times = pd.DatetimeIndex(['2016-07-19 06:11:00'], tz='America/Phoenix')
# max_zenith keeps these results reasonable
out = irradiance.erbs(ghi=1.0, zenith=89.99999,
datetime_or_doy=times, min_cos_zenith=0)
expected = pd.DataFrame(np.array(
[[0., 1., 1.]]),
columns=columns, index=times)
assert_frame_equal(out, expected)
# 4-5 9s will produce bad behavior without max_zenith limit
out = irradiance.erbs(ghi=1.0, zenith=89.99999,
datetime_or_doy=times, max_zenith=100)
expected = pd.DataFrame(np.array(
[[6.00115286e+03, 9.98952601e-01, 1.16377640e-02]]),
columns=columns, index=times)
assert_frame_equal(out, expected)
# 1-2 9s will produce bad behavior without either limit
out = irradiance.erbs(ghi=1.0, zenith=89.99, datetime_or_doy=times,
min_cos_zenith=0, max_zenith=100)
expected = pd.DataFrame(np.array(
[[4.78419761e+03, 1.65000000e-01, 1.00000000e+00]]),
columns=columns, index=times)
assert_frame_equal(out, expected)
# check default behavior under hardest condition
out = irradiance.erbs(ghi=1.0, zenith=90, datetime_or_doy=times)
expected = pd.DataFrame(np.array(
[[0., 1., 0.01163776]]),
columns=columns, index=times)
assert_frame_equal(out, expected)
def test_erbs_all_scalar():
ghi = 1000
zenith = 10
doy = 180
expected = OrderedDict()
expected['dni'] = 8.42358014e+02
expected['dhi'] = 1.70439297e+02
expected['kt'] = 7.68919470e-01
out = irradiance.erbs(ghi, zenith, doy)
for k, v in out.items():
assert_allclose(v, expected[k], 5)
def test_dirindex(times):
ghi = pd.Series([0, 0, 1038.62, 254.53], index=times)
ghi_clearsky = pd.Series(
np.array([0., 79.73860422, 1042.48031487, 257.20751138]),
index=times
)
dni_clearsky = pd.Series(
np.array([0., 316.1949056, 939.95469881, 646.22886049]),
index=times
)
zenith = pd.Series(
np.array([124.0390863, 82.85457044, 10.56413562, 72.41687122]),
index=times
)
pressure = 93193.
tdew = 10.
out = irradiance.dirindex(ghi, ghi_clearsky, dni_clearsky,
zenith, times, pressure=pressure,
temp_dew=tdew)
dirint_close_values = irradiance.dirint(ghi, zenith, times,
pressure=pressure,
use_delta_kt_prime=True,
temp_dew=tdew).values
expected_out = np.array([np.nan, 0., 748.31562753, 630.72592644])
tolerance = 1e-8
assert np.allclose(out, expected_out, rtol=tolerance, atol=0,
equal_nan=True)
tol_dirint = 0.2
assert np.allclose(out.values, dirint_close_values, rtol=tol_dirint, atol=0,
equal_nan=True)
def test_dirindex_min_cos_zenith_max_zenith():
# map out behavior under difficult conditions with various
# limiting kwargs settings
# times don't have any physical relevance
times = pd.DatetimeIndex(['2014-06-24T12-0700', '2014-06-24T18-0700'])
ghi = pd.Series([0, 1], index=times)
ghi_clearsky = pd.Series([0, 1], index=times)
dni_clearsky = pd.Series([0, 5], index=times)
solar_zenith = pd.Series([90, 89.99], index=times)
out = irradiance.dirindex(ghi, ghi_clearsky, dni_clearsky, solar_zenith,
times)
expected = pd.Series([nan, nan], index=times)
assert_series_equal(out, expected)
out = irradiance.dirindex(ghi, ghi_clearsky, dni_clearsky, solar_zenith,
times, min_cos_zenith=0)
expected = pd.Series([nan, nan], index=times)
assert_series_equal(out, expected)
out = irradiance.dirindex(ghi, ghi_clearsky, dni_clearsky, solar_zenith,
times, max_zenith=90)
expected = pd.Series([nan, nan], index=times)
assert_series_equal(out, expected)
out = irradiance.dirindex(ghi, ghi_clearsky, dni_clearsky, solar_zenith,
times, min_cos_zenith=0, max_zenith=100)
expected = pd.Series([nan, 5.], index=times)
assert_series_equal(out, expected)
def test_dni():
ghi = pd.Series([90, 100, 100, 100, 100])
dhi = pd.Series([100, 90, 50, 50, 50])
zenith = pd.Series([80, 100, 85, 70, 85])
clearsky_dni = pd.Series([50, 50, 200, 50, 300])
dni = irradiance.dni(ghi, dhi, zenith,
clearsky_dni=clearsky_dni, clearsky_tolerance=2)
assert_series_equal(dni,
pd.Series([float('nan'), float('nan'), 400,
146.190220008, 573.685662283]))
dni = irradiance.dni(ghi, dhi, zenith)
assert_series_equal(dni,
pd.Series([float('nan'), float('nan'), 573.685662283,
146.190220008, 573.685662283]))
@pytest.mark.parametrize(
'surface_tilt,surface_azimuth,solar_zenith,' +
'solar_azimuth,aoi_expected,aoi_proj_expected',
[(0, 0, 0, 0, 0, 1),
(30, 180, 30, 180, 0, 1),
(30, 180, 150, 0, 180, -1),
(90, 0, 30, 60, 75.5224878, 0.25),
(90, 0, 30, 170, 119.4987042, -0.4924038)])
def test_aoi_and_aoi_projection(surface_tilt, surface_azimuth, solar_zenith,
solar_azimuth, aoi_expected,
aoi_proj_expected):
aoi = irradiance.aoi(surface_tilt, surface_azimuth, solar_zenith,
solar_azimuth)
assert_allclose(aoi, aoi_expected, atol=1e-6)
aoi_projection = irradiance.aoi_projection(
surface_tilt, surface_azimuth, solar_zenith, solar_azimuth)
assert_allclose(aoi_projection, aoi_proj_expected, atol=1e-6)
@pytest.fixture
def airmass_kt():
# disc algorithm stopped at am=12. test am > 12 for out of range behavior
return np.array([1, 5, 12, 20])
def test_kt_kt_prime_factor(airmass_kt):
out = irradiance._kt_kt_prime_factor(airmass_kt)
expected = np.array([ 0.999971, 0.723088, 0.548811, 0.471068])
assert_allclose(out, expected, atol=1e-5)
def test_clearsky_index():
ghi = np.array([-1., 0., 1., 500., 1000., np.nan])
ghi_measured, ghi_modeled = np.meshgrid(ghi, ghi)
# default max_clearsky_index
with np.errstate(invalid='ignore', divide='ignore'):
out = irradiance.clearsky_index(ghi_measured, ghi_modeled)
expected = np.array(
[[1. , 0. , 0. , 0. , 0. , np.nan],
[0. , 0. , 0. , 0. , 0. , np.nan],
[0. , 0. , 1. , 2. , 2. , np.nan],
[0. , 0. , 0.002 , 1. , 2. , np.nan],
[0. , 0. , 0.001 , 0.5 , 1. , np.nan],
[np.nan, np.nan, np.nan, np.nan, np.nan, np.nan]])
assert_allclose(out, expected, atol=0.001)
# specify max_clearsky_index
with np.errstate(invalid='ignore', divide='ignore'):
out = irradiance.clearsky_index(ghi_measured, ghi_modeled,
max_clearsky_index=1.5)
expected = np.array(
[[1. , 0. , 0. , 0. , 0. , np.nan],
[0. , 0. , 0. , 0. , 0. , np.nan],
[0. , 0. , 1. , 1.5 , 1.5 , np.nan],
[0. , 0. , 0.002 , 1. , 1.5 , np.nan],
[0. , 0. , 0.001 , 0.5 , 1. , np.nan],
[np.nan, np.nan, np.nan, np.nan, np.nan, np.nan]])
assert_allclose(out, expected, atol=0.001)
# scalars
out = irradiance.clearsky_index(10, 1000)
expected = 0.01
assert_allclose(out, expected, atol=0.001)
# series
times = pd.date_range(start='20180601', periods=2, freq='12H')
ghi_measured = pd.Series([100, 500], index=times)
ghi_modeled = pd.Series([500, 1000], index=times)
out = irradiance.clearsky_index(ghi_measured, ghi_modeled)
expected = pd.Series([0.2, 0.5], index=times)
assert_series_equal(out, expected)
def test_clearness_index():
ghi = np.array([-1, 0, 1, 1000])
solar_zenith = np.array([180, 90, 89.999, 0])
ghi, solar_zenith = np.meshgrid(ghi, solar_zenith)
# default min_cos_zenith
out = irradiance.clearness_index(ghi, solar_zenith, 1370)
# np.set_printoptions(precision=3, floatmode='maxprec', suppress=True)
expected = np.array(
[[0. , 0. , 0.011, 2. ],
[0. , 0. , 0.011, 2. ],
[0. , 0. , 0.011, 2. ],
[0. , 0. , 0.001, 0.73 ]])
assert_allclose(out, expected, atol=0.001)
# specify min_cos_zenith
with np.errstate(invalid='ignore', divide='ignore'):
out = irradiance.clearness_index(ghi, solar_zenith, 1400,
min_cos_zenith=0)
expected = np.array(
[[0. , nan, 2. , 2. ],
[0. , 0. , 2. , 2. ],
[0. , 0. , 2. , 2. ],
[0. , 0. , 0.001, 0.714]])
assert_allclose(out, expected, atol=0.001)
# specify max_clearness_index
out = irradiance.clearness_index(ghi, solar_zenith, 1370,
max_clearness_index=0.82)
expected = np.array(
[[ 0. , 0. , 0.011, 0.82 ],
[ 0. , 0. , 0.011, 0.82 ],
[ 0. , 0. , 0.011, 0.82 ],
[ 0. , 0. , 0.001, 0.73 ]])
assert_allclose(out, expected, atol=0.001)
# specify min_cos_zenith and max_clearness_index
with np.errstate(invalid='ignore', divide='ignore'):
out = irradiance.clearness_index(ghi, solar_zenith, 1400,
min_cos_zenith=0,
max_clearness_index=0.82)
expected = np.array(
[[ 0. , nan, 0.82 , 0.82 ],
[ 0. , 0. , 0.82 , 0.82 ],
[ 0. , 0. , 0.82 , 0.82 ],
[ 0. , 0. , 0.001, 0.714]])
assert_allclose(out, expected, atol=0.001)
# scalars
out = irradiance.clearness_index(1000, 10, 1400)
expected = 0.725
assert_allclose(out, expected, atol=0.001)
# series
times = pd.date_range(start='20180601', periods=2, freq='12H')
ghi = pd.Series([0, 1000], index=times)
solar_zenith = pd.Series([90, 0], index=times)
extra_radiation = pd.Series([1360, 1400], index=times)
out = irradiance.clearness_index(ghi, solar_zenith, extra_radiation)
expected = pd.Series([0, 0.714285714286], index=times)
assert_series_equal(out, expected)
def test_clearness_index_zenith_independent(airmass_kt):
clearness_index = np.array([-1, 0, .1, 1])
clearness_index, airmass_kt = np.meshgrid(clearness_index, airmass_kt)
out = irradiance.clearness_index_zenith_independent(clearness_index,
airmass_kt)
expected = np.array(
[[0. , 0. , 0.1 , 1. ],
[0. , 0. , 0.138, 1.383],
[0. , 0. , 0.182, 1.822],
[0. , 0. , 0.212, 2. ]])
assert_allclose(out, expected, atol=0.001)
# test max_clearness_index
out = irradiance.clearness_index_zenith_independent(
clearness_index, airmass_kt, max_clearness_index=0.82)
expected = np.array(
[[ 0. , 0. , 0.1 , 0.82 ],
[ 0. , 0. , 0.138, 0.82 ],
[ 0. , 0. , 0.182, 0.82 ],
[ 0. , 0. , 0.212, 0.82 ]])
assert_allclose(out, expected, atol=0.001)
# scalars
out = irradiance.clearness_index_zenith_independent(.4, 2)
expected = 0.443
assert_allclose(out, expected, atol=0.001)
# series
times = pd.date_range(start='20180601', periods=2, freq='12H')
clearness_index = pd.Series([0, .5], index=times)
airmass = pd.Series([np.nan, 2], index=times)
out = irradiance.clearness_index_zenith_independent(clearness_index,
airmass)
expected = pd.Series([np.nan, 0.553744437562], index=times)
assert_series_equal(out, expected)