-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathsneratio_api.py
1165 lines (808 loc) · 41 KB
/
sneratio_api.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
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
import pandas as pd
import numpy as np
from scipy import interpolate
from scipy.integrate import trapezoid
from scipy.optimize import least_squares, curve_fit
import matplotlib.pyplot as plt
import datetime
import json
from json import JSONEncoder
import os
import time
# SNeRatio class --------------------------------------------
class SNeRatio:
df_master = None
df_solar = None
df_mass = None
df_Ia = None
df_cc = None
elements = None
default_selections = {
"abund_data": "test_data.txt",
"solar_data": "aspl.txt",
"mass_number_data": "mass_number.txt",
"snIa_data": "Seitenzahl_2013_N40.txt",
"sncc_data": "Nomoto_2013_0.02.txt",
"imf": "salpeter",
"sncc_mass_range": [10.0, 50.0],
"ref_element": "Fe",
"confidence": "1.0",
}
_paths = {
"abund_data": "data",
"solar_data": "data/solar",
"mass_number_data": "data",
"snIa_data": "data/snIa",
"sncc_data": "data/sncc",
}
options = {
"abund_data": ["test_data.txt"],
"solar_data": os.listdir(_paths["solar_data"]),
"mass_number_data": ["mass_number.txt"],
"snIa_data": os.listdir(_paths["snIa_data"]),
"sncc_data": os.listdir(_paths["sncc_data"]),
"ref_element": ["Fe"],
}
selections = default_selections
imf_dict = {
"salpeter": lambda m: m**(-2.35),
"top_heavy": None
}
sigma_to_delta_chisq = {
"1.0": 1.0,
"2.0": 4.0,
"3.0": 9.0,
}
@classmethod
def print_options(cls):
print("\nSNeRatio OPTIONS:\n")
for key,values in cls.options.items():
print(f"-{key}:")
for i,value in enumerate(values):
print(f"\t{i+1}) {value}")
print("")
print("-imf's:")
for i,value in enumerate(cls.imf_dict.keys()):
print(f"\t{i+1}) {value}")
print("")
print("-confidence intervals:")
for i,value in enumerate(cls.sigma_to_delta_chisq.keys()):
print(f"\t{i+1}) {value} sigma")
print("")
@classmethod
def get_file_path(cls, key):
file_path = cls._paths[key]
data_file = cls.selections[key]
return os.path.join(file_path, data_file)
@classmethod
def set_selections(cls, selections):
for key, value in selections.items():
cls.selections[key] = value
@classmethod
def set_a_selection(cls, key, value):
cls.selections[key] = value
@staticmethod
def read_data(filename):
df = pd.read_csv(filename, sep="\s+")
# fixing column name duplications. it happens with Nomoto_2013_0.0.txt
dup_list = [col for col in df.columns if len(col.split("."))>2]
if len(dup_list) > 0:
for dup_name in dup_list:
old_name = dup_name
new_name = dup_name.rsplit(".",1)[0] + "01"
df.rename(columns={old_name: new_name}, inplace=True)
return df
@classmethod
def read_all(cls):
# abund_data is taken as a path from the user, the others are not.
abund_path = cls.selections["abund_data"]
cls.df_master = cls.read_data(abund_path)
solar_path = cls.get_file_path("solar_data")
cls.df_solar = cls.read_data(solar_path)
mass_number_path = cls.get_file_path("mass_number_data")
cls.df_mass = cls.read_data(mass_number_path)
snIa_path = cls.get_file_path("snIa_data")
cls.df_Ia = cls.read_data(snIa_path)
sncc_path = cls.get_file_path("sncc_data")
cls.df_cc = cls.read_data(sncc_path)
cls.elements = cls.df_master.Element.values
@staticmethod
def division_error(x, x_err, y, y_err):
x = float(x)
x_err = float(x_err)
y = float(y)
y_err = float(y_err)
z = x/y
z_err = z * ((x_err / x) ** 2.0 + (y_err / y) ** 2.0) ** 0.5
return z, z_err
@staticmethod
def _calc_snIa_yields(df_Ia, elements):
Ia_yields = []
for e in elements:
y = df_Ia[df_Ia.Element == e][[df_Ia.columns[2]]].sum().values[0]
Ia_yields.append(y)
return Ia_yields
@staticmethod
def _calc_sncc_yields(df_cc, elements, imf, mass_range):
cc_yields = []
mass_min, mass_max = mass_range
x = df_cc.columns[2:].astype(float)
for e in elements:
y = df_cc[df_cc.Element == e][df_cc.columns[2:]].sum().values
m_range = np.linspace(mass_min, mass_max, 1000)
imf_range = imf(m_range)
f = interpolate.interp1d(x, y, fill_value='extrapolate')
f_range = f(m_range)
q = trapezoid(f_range * imf_range, m_range)
w = trapezoid(imf_range, m_range)
integrated_yields = q/w
cc_yields.append(integrated_yields)
return cc_yields
@classmethod
def set_snIa_yields(cls):
elements = cls.elements
Ia_yields = cls._calc_snIa_yields(cls.df_Ia, elements)
cls.df_master["Ia_yields"] = Ia_yields
@classmethod
def set_sncc_yields(cls):
elements = cls.elements
imf = cls.imf_dict[cls.selections["imf"]]
mass_range = cls.selections["sncc_mass_range"]
cc_yields = cls._calc_sncc_yields(cls.df_cc, elements, imf, mass_range)
cls.df_master["cc_yields"] = cc_yields
@classmethod
def set_solar(cls):
cls.df_master["Solar"] = cls.df_solar[cls.df_solar.Element.isin(cls.elements)].Solar.values
@classmethod
def set_mass_number(cls):
cls.df_master["MassNumber"] = cls.df_mass[cls.df_mass.Element.isin(cls.elements)].MassNumber.values
@classmethod
def set_normalisations(cls):
ref_row = cls.df_master.Element == cls.selections["ref_element"]
cls.df_master[ref_row]
norm_abund = cls.df_master.Abund.values / cls.df_master[ref_row].Abund.values
cls.df_master["norm_abund"] = norm_abund
ref_abund = cls.df_master[ref_row].Abund
ref_abund_err = cls.df_master[ref_row].AbundError
_, norm_abund_err = zip(*[cls.division_error(cls.df_master.Abund.values[i], cls.df_master.AbundError.values[i], ref_abund, ref_abund_err) for i in range(len(cls.elements))])
cls.df_master["norm_abund_err"] = norm_abund_err
ref_solar = cls.df_master[ref_row].Solar
norm_solar = cls.df_master.Solar.apply(lambda x: 10**(x-ref_solar))
cls.df_master["norm_solar"] = norm_solar
# SNeStats class --------------------------------------------
class SNeStats:
fit_results = None
fit_results_all = None
fit_results_all_df = None
@staticmethod
def _ratio2percent(r):
r_Ia = r / (1.0+r)
r_cc = 1.0 / (1.0+r)
return r_Ia, r_cc
@staticmethod
def _percent2ratio(r_Ia, r_cc):
return r_Ia/r_cc
@staticmethod
def _calc_contribution(r_Ia, r_cc, Ia_yields, cc_yields, mass_number, norm_solar, elements, ref_element):
x = range(len(elements))
ref_index = elements == ref_element
y_Ia = Ia_yields * r_Ia
y_cc = cc_yields * r_cc
y_Ia_ref = Ia_yields[ref_index] * r_Ia
y_cc_ref = cc_yields[ref_index] * r_cc
mref_mx = mass_number[ref_index] / mass_number[x]
solar = norm_solar
norm = y_Ia_ref + y_cc_ref
cont_Ia = (y_Ia / norm) * (mref_mx / solar)
cont_cc = (y_cc / norm) * (mref_mx / solar)
total_cont = cont_Ia + cont_cc
return total_cont, cont_Ia, cont_cc
@staticmethod
def _modified_fit_func(Ia_yields, cc_yields, mass_number, norm_solar, elements, ref_element):
def _fit_func(x, r):
r_Ia, r_cc = SNeStats._ratio2percent(r)
total_cont, _, _ = SNeStats._calc_contribution(r_Ia, r_cc, Ia_yields, cc_yields, mass_number, norm_solar, elements, ref_element)
return total_cont
return _fit_func
@staticmethod
def _chisq(norm_abund, norm_abund_err, total_cont):
chi = (((norm_abund - total_cont) / norm_abund_err)**2.0).sum()
return chi
@staticmethod
def _modified_fit_func_conf(norm_abund, norm_abund_err, min_chi, delta_chi, Ia_yields, cc_yields, mass_number, norm_solar, elements, ref_element):
def _fit_func_conf(r):
r_Ia, r_cc = SNeStats._ratio2percent(r)
total_cont, _, _ = SNeStats._calc_contribution(r_Ia, r_cc, Ia_yields, cc_yields, mass_number, norm_solar, elements, ref_element)
target_chi = abs(SNeStats._chisq(norm_abund, norm_abund_err, total_cont) - min_chi - delta_chi)
return target_chi
return _fit_func_conf
@classmethod
def calc_contribution(cls, r_Ia, r_cc, SNeRatio):
Ia_yields = SNeRatio.df_master["Ia_yields"].values
cc_yields = SNeRatio.df_master["cc_yields"].values
mass_number = SNeRatio.df_master["MassNumber"].values
norm_solar = SNeRatio.df_master["norm_solar"].values
elements = SNeRatio.df_master["Element"].values
ref_element = SNeRatio.selections["ref_element"]
total_cont, cont_Ia, cont_cc = cls._calc_contribution(r_Ia, r_cc, Ia_yields, cc_yields, mass_number, norm_solar, elements, ref_element)
return total_cont, cont_Ia, cont_cc
@classmethod
def fit(cls, SNeRatio):
# read parameters from SNeRatio class
Ia_table_name = os.path.basename(SNeRatio.selections["snIa_data"]).rsplit(".",1)[0]
cc_table_name = os.path.basename(SNeRatio.selections["sncc_data"]).rsplit(".",1)[0]
Ia_yields, cc_yields, = SNeRatio.df_master["Ia_yields"].values, SNeRatio.df_master["cc_yields"].values
mass_number, norm_solar = SNeRatio.df_master["MassNumber"].values, SNeRatio.df_master["norm_solar"].values
elements, ref_element = SNeRatio.df_master["Element"].values, SNeRatio.selections["ref_element"]
x = range(len(elements))
norm_abund = SNeRatio.df_master.norm_abund
norm_abund_err = SNeRatio.df_master.norm_abund_err
# fit
r_init = 0.5
modified_fit_func = cls._modified_fit_func(Ia_yields, cc_yields, mass_number, norm_solar, elements, ref_element)
r_best, _ = curve_fit(modified_fit_func, x, norm_abund, sigma=norm_abund_err, p0=[r_init], bounds=(0,np.inf))
# best r contributions
r_Ia_best, r_cc_best = SNeStats._ratio2percent(r_best)
total_cont, cont_Ia, cont_cc = cls._calc_contribution(r_Ia_best, r_cc_best, Ia_yields, cc_yields, mass_number, norm_solar, elements, ref_element)
# delta chi for uncertainties
min_chi = cls._chisq(norm_abund, norm_abund_err, total_cont)
delta_chi = SNeRatio.sigma_to_delta_chisq[SNeRatio.selections["confidence"]]
modified_fit_func_conf = cls._modified_fit_func_conf(norm_abund, norm_abund_err, min_chi, delta_chi, Ia_yields, cc_yields, mass_number, norm_solar, elements, ref_element)
# lower uncertainty
r_low_init = r_best/1.5
fit_result_low = least_squares(modified_fit_func_conf, x0=r_low_init)
r_low = fit_result_low.x
# upper uncertainty
r_high_init = min(r_best*1.5, 1.0) # should not exceed 1.0
fit_result_high = least_squares(modified_fit_func_conf, x0=r_high_init)
r_high = fit_result_high.x
# error values
r_Ia_best = r_best/(1.0 + r_best)
r_Ia_low = r_low/(1.0 + r_low)
r_Ia_high = r_high/(1.0 + r_high)
r_Ia_err_n = r_Ia_best - r_Ia_low
r_Ia_err_p = r_Ia_high - r_Ia_best
r_cc_best = 1.0 - r_Ia_best
r_cc_err_n = r_Ia_err_p
r_cc_err_p = r_Ia_err_n
# fit results
dof = len(elements)-1-1
fit_results = {
"Ia_table": Ia_table_name,
"cc_table": cc_table_name,
"elements": elements,
"total_cont": total_cont,
"cont_Ia": cont_Ia,
"cont_cc": cont_cc,
"r_Ia": r_Ia_best[0],
"r_Ia_err_n": r_Ia_err_n[0],
"r_Ia_err_p": r_Ia_err_p[0],
"r_cc": r_cc_best[0],
"r_cc_err_n": r_cc_err_n[0],
"r_cc_err_p": r_cc_err_p[0],
"chisq": min_chi,
"dof": dof,
}
cls.fit_results = fit_results
return fit_results
@classmethod
def fit_all_old(cls, SNeRatio):
snIa_tables = [f for f in SNeRatio.options["snIa_data"] if f.endswith(".txt")]
sncc_tables = [f for f in SNeRatio.options["sncc_data"] if f.endswith(".txt")]
n_Ia = len(snIa_tables)
n_cc = len(sncc_tables)
Ia_yields_list = np.arange(n_Ia, dtype=np.ndarray)
cc_yields_list = np.arange(n_cc, dtype=np.ndarray)
# non-changing values for fitting all combinations
mass_number, norm_solar = SNeRatio.df_master["MassNumber"].values, SNeRatio.df_master["norm_solar"].values
elements, ref_element = SNeRatio.df_master["Element"].values, SNeRatio.selections["ref_element"]
imf = SNeRatio.imf_dict[SNeRatio.selections["imf"]]
mass_range = SNeRatio.selections["sncc_mass_range"]
x = range(len(elements))
norm_abund = SNeRatio.df_master.norm_abund
norm_abund_err = SNeRatio.df_master.norm_abund_err
# yields for all tables
for i,Ia in enumerate(snIa_tables):
Ia_path = SNeRatio._paths["snIa_data"]
temp_df_Ia = SNeRatio.read_data(os.path.join(Ia_path, Ia))
temp_Ia_yields = SNeRatio._calc_snIa_yields(temp_df_Ia, elements)
Ia_yields_list[i] = np.array(temp_Ia_yields)
for i,cc in enumerate(sncc_tables):
cc_path = SNeRatio._paths["sncc_data"]
temp_df_cc = SNeRatio.read_data(os.path.join(cc_path, cc))
temp_cc_yields = SNeRatio._calc_sncc_yields(temp_df_cc, elements, imf, mass_range)
cc_yields_list[i] = np.array(temp_cc_yields)
total_cont_list = np.arange(n_Ia*n_cc, dtype=np.ndarray).reshape(n_Ia,n_cc)
cont_Ia_list = np.arange(n_Ia*n_cc, dtype=np.ndarray).reshape(n_Ia,n_cc)
cont_cc_list = np.arange(n_Ia*n_cc, dtype=np.ndarray).reshape(n_Ia,n_cc)
ratio_Ia_list = np.arange(n_Ia*n_cc*3, dtype=float).reshape(n_Ia,n_cc,3)
ratio_cc_list = np.arange(n_Ia*n_cc*3, dtype=float).reshape(n_Ia,n_cc,3)
chisq_list = np.arange(n_Ia*n_cc, dtype=float).reshape(n_Ia,n_cc)
for i,Ia_yields in enumerate(Ia_yields_list):
for j,cc_yields in enumerate(cc_yields_list):
# fit
r_init = 0.5
modified_fit_func = cls._modified_fit_func(Ia_yields, cc_yields, mass_number, norm_solar, elements, ref_element)
r_best, cov = curve_fit(modified_fit_func, x, norm_abund, sigma=norm_abund_err, p0=[r_init], bounds=(0,np.inf))
# best r contributions
r_Ia_best = r_best / (1.0+r_best)
r_cc_best = 1.0 / (1.0+r_best)
total_cont, cont_Ia, cont_cc = cls._calc_contribution(r_Ia_best, r_cc_best, Ia_yields, cc_yields, mass_number, norm_solar, elements, ref_element)
# delta chi for uncertainties
min_chi = cls._chisq(norm_abund, norm_abund_err, total_cont)
delta_chi = SNeRatio.sigma_to_delta_chisq[SNeRatio.selections["confidence"]]
modified_fit_func_conf = cls._modified_fit_func_conf(norm_abund, norm_abund_err, min_chi, delta_chi, Ia_yields, cc_yields, mass_number, norm_solar, elements, ref_element)
# lower uncertainty
r_low_init = r_best/1.5
fit_result_low = least_squares(modified_fit_func_conf, x0=r_low_init)
r_low = fit_result_low.x
# upper uncertainty
r_high_init = min(r_best*1.5, 1.0) # should not exceed 1.0
fit_result_high = least_squares(modified_fit_func_conf, x0=r_high_init)
r_high = fit_result_high.x
# error values
r_Ia_best = r_best/(1.0 + r_best)
r_Ia_low = r_low/(1.0 + r_low)
r_Ia_high = r_high/(1.0 + r_high)
r_Ia_err_n = r_Ia_best - r_Ia_low
r_Ia_err_p = r_Ia_high - r_Ia_best
r_cc_best = 1.0 - r_Ia_best
r_cc_err_n = r_Ia_err_p
r_cc_err_p = r_Ia_err_n
# fit results
total_cont_list[i][j] = total_cont
cont_Ia_list[i][j] = cont_Ia
cont_cc_list[i][j] = cont_cc
ratio_Ia_list[i][j] = [r_Ia_best[0], r_Ia_err_n[0], r_Ia_err_p[0]]
ratio_cc_list[i][j] = [r_cc_best[0], r_cc_err_n[0], r_cc_err_p[0]]
chisq_list[i][j] = min_chi
fit_results_all = {
"n_Ia": n_Ia,
"n_cc": n_cc,
"Ia_table": snIa_tables,
"cc_table": sncc_tables,
"elements": elements,
"total_cont": total_cont_list,
"cont_Ia": cont_Ia_list,
"cont_cc": cont_cc_list,
"ratio_Ia": ratio_Ia_list,
"ratio_cc": ratio_cc_list,
"chisq": chisq_list,
"dof": len(elements)-1-1,
}
cls.fit_results_all = fit_results_all
return fit_results_all
@classmethod
def fit_all(cls, SNeRatio):
snIa_tables = [f for f in SNeRatio.options["snIa_data"] if f.endswith(".txt")]
sncc_tables = [f for f in SNeRatio.options["sncc_data"] if f.endswith(".txt")]
n_Ia = len(snIa_tables)
n_cc = len(sncc_tables)
Ia_yields_list = np.arange(n_Ia, dtype=np.ndarray)
cc_yields_list = np.arange(n_cc, dtype=np.ndarray)
# non-changing values for fitting all combinations
mass_number, norm_solar = SNeRatio.df_master["MassNumber"].values, SNeRatio.df_master["norm_solar"].values
elements, ref_element = SNeRatio.df_master["Element"].values, SNeRatio.selections["ref_element"]
imf = SNeRatio.imf_dict[SNeRatio.selections["imf"]]
mass_range = SNeRatio.selections["sncc_mass_range"]
x = range(len(elements))
norm_abund = SNeRatio.df_master.norm_abund
norm_abund_err = SNeRatio.df_master.norm_abund_err
# yields for all tables
for i,Ia in enumerate(snIa_tables):
Ia_path = SNeRatio._paths["snIa_data"]
temp_df_Ia = SNeRatio.read_data(os.path.join(Ia_path, Ia))
temp_Ia_yields = SNeRatio._calc_snIa_yields(temp_df_Ia, elements)
Ia_yields_list[i] = np.array(temp_Ia_yields)
for i,cc in enumerate(sncc_tables):
cc_path = SNeRatio._paths["sncc_data"]
temp_df_cc = SNeRatio.read_data(os.path.join(cc_path, cc))
temp_cc_yields = SNeRatio._calc_sncc_yields(temp_df_cc, elements, imf, mass_range)
cc_yields_list[i] = np.array(temp_cc_yields)
col_names_t = ["Ia_table", "cc_table"]
col_names_r = ["r_Ia", "r_Ia_err_n", "r_Ia_err_p", "r_cc", "r_cc_err_n", "r_cc_err_p"]
col_names_s = ["chisq", "dof"]
col_names_tot = [f"Total_cont_{e}" for e in elements]
col_names_Ia = [f"Ia_cont_{e}" for e in elements]
col_names_cc = [f"cc_cont_{e}" for e in elements]
col_names = col_names_t + col_names_r + col_names_s + col_names_tot + col_names_Ia + col_names_cc
r_Ia_array = np.arange(n_Ia*n_cc, dtype=float)
r_Ia_err_n_array = np.arange(n_Ia*n_cc, dtype=float)
r_Ia_err_p_array = np.arange(n_Ia*n_cc, dtype=float)
r_cc_array = np.arange(n_Ia*n_cc, dtype=float)
r_cc_err_n_array = np.arange(n_Ia*n_cc, dtype=float)
r_cc_err_p_array = np.arange(n_Ia*n_cc, dtype=float)
chisq_array = np.arange(n_Ia*n_cc, dtype=float)
dof_array = np.arange(n_Ia*n_cc, dtype=float)
total_cont_array_dict = {
e:np.arange(n_Ia*n_cc, dtype=float) for e in elements
}
Ia_cont_array_dict = total_cont_array_dict.copy()
cc_cont_array_dict = total_cont_array_dict.copy()
for i,Ia_yields in enumerate(Ia_yields_list):
for j,cc_yields in enumerate(cc_yields_list):
# fit
r_init = 0.5
modified_fit_func = cls._modified_fit_func(Ia_yields, cc_yields, mass_number, norm_solar, elements, ref_element)
r_best, cov = curve_fit(modified_fit_func, x, norm_abund, sigma=norm_abund_err, p0=[r_init], bounds=(0,np.inf))
# best r contributions
r_Ia_best = r_best / (1.0+r_best)
r_cc_best = 1.0 / (1.0+r_best)
total_cont, cont_Ia, cont_cc = cls._calc_contribution(r_Ia_best, r_cc_best, Ia_yields, cc_yields, mass_number, norm_solar, elements, ref_element)
# delta chi for uncertainties
min_chi = cls._chisq(norm_abund, norm_abund_err, total_cont)
delta_chi = SNeRatio.sigma_to_delta_chisq[SNeRatio.selections["confidence"]]
modified_fit_func_conf = cls._modified_fit_func_conf(norm_abund, norm_abund_err, min_chi, delta_chi, Ia_yields, cc_yields, mass_number, norm_solar, elements, ref_element)
# lower uncertainty
r_low_init = r_best/1.5
fit_result_low = least_squares(modified_fit_func_conf, x0=r_low_init)
r_low = fit_result_low.x
# upper uncertainty
r_high_init = min(r_best*1.5, 1.0) # should not exceed 1.0
fit_result_high = least_squares(modified_fit_func_conf, x0=r_high_init)
r_high = fit_result_high.x
# error values
r_Ia_best = r_best/(1.0 + r_best)
r_Ia_low = r_low/(1.0 + r_low)
r_Ia_high = r_high/(1.0 + r_high)
r_Ia_err_n = r_Ia_best - r_Ia_low
r_Ia_err_p = r_Ia_high - r_Ia_best
r_cc_best = 1.0 - r_Ia_best
r_cc_err_n = r_Ia_err_p
r_cc_err_p = r_Ia_err_n
# fit results
index = int(i*n_cc + j)
#Ia_table_array[index] = snIa_tables[i]
#cc_table_array[index] = sncc_tables[j]
r_Ia_array[index] = r_Ia_best
r_Ia_err_n_array[index] = r_Ia_err_n
r_Ia_err_p_array[index] = r_Ia_err_p
r_cc_array[index] = r_cc_best
r_cc_err_n_array[index] = r_cc_err_n
r_cc_err_p_array[index] = r_cc_err_p
for k,e in enumerate(elements):
total_cont_array_dict[e][index] = total_cont[k]
Ia_cont_array_dict[e][index] = cont_Ia[k]
cc_cont_array_dict[e][index] = cont_cc[k]
chisq_array[index] = min_chi
dof_array[index] = len(elements)-1-1
Ia_table_array, cc_table_array = np.array(np.meshgrid(snIa_tables, sncc_tables)).reshape(2,-1)
fit_results_all = {
"Ia_table": Ia_table_array,
"cc_table": cc_table_array,
"r_Ia": r_Ia_array,
"r_Ia_err_n": r_Ia_err_n_array,
"r_Ia_err_p": r_Ia_err_p_array,
"r_cc": r_cc_array,
"r_cc_err_n": r_cc_err_n_array,
"r_cc_err_p": r_cc_err_p_array,
"chisq": chisq_array,
"dof": dof_array,
}
for k in range(len(elements)):
fit_results_all[col_names_tot[k]] = total_cont_array_dict[e]
fit_results_all[col_names_Ia[k]] = Ia_cont_array_dict[e]
fit_results_all[col_names_cc[k]] = cc_cont_array_dict[e]
cls.fit_results_all = fit_results_all
cls.fit_results_all_df = pd.DataFrame(fit_results_all)
print(cls.fit_results_all_df.info())
print(cls.fit_results_all_df.iloc[:5])
return cls.fit_results_all_df
@classmethod
def save_fit_results(cls):
now = datetime.datetime.now()
now_str = now.strftime(r'%Y-%m-%d_%H-%M-%S')
filename = "outputs/fit_results_{}.txt".format(now_str)
saveable_fit_results = dict()
try:
if cls.fit_results is not None:
for key,value in cls.fit_results.items():
if isinstance(value, np.ndarray):
saveable_fit_results[key] = value.tolist()
else:
saveable_fit_results[key] = value
with open(filename, "w+") as f:
f.write("Fit Results (Single Fit):\n")
f.write(json.dumps(saveable_fit_results, indent=4))
except:
print("WARNING: Couldn't save the fit_results!")
@classmethod
def save_fit_all_results_old(cls):
now = datetime.datetime.now()
now_str = now.strftime(r'%Y-%m-%d_%H-%M-%S')
filename_all = "outputs/fit_all_results_{}.txt".format(now_str)
saveable_fit_results_all = dict()
try:
if cls.fit_results_all is not None:
for key,value in cls.fit_results_all.items():
if isinstance(value, np.ndarray):
saveable_fit_results_all[key] = value.flatten().tolist()
else:
saveable_fit_results_all[key] = value
with open(filename_all, "w+") as f:
f.write("\n\nFit Results (All Combinations):\n")
f.write(json.dumps(saveable_fit_results_all, indent=4, cls=NumpyArrayEncoder))
except:
print("WARNING: Couldn't save the fit_all_results!")
@classmethod
def save_fit_all_results(cls):
now = datetime.datetime.now()
now_str = now.strftime(r'%Y-%m-%d_%H-%M-%S')
filename_csv = "outputs/fit_all_results_{}.csv".format(now_str)
filename_xlsx = "outputs/fit_all_results_{}.xlsx".format(now_str)
try:
if cls.fit_results_all_df is not None:
with open(filename_csv, "w+") as f:
cls.fit_results_all_df.to_csv(filename_csv, sep=",")
except:
print("WARNING: Couldn't save the fit_all_results!")
try:
import openpyxl
with open(filename_xlsx, "w+") as f:
cls.fit_results_all_df.to_excel(filename_xlsx, encoding="utf-8")
except:
print("WARNING: Couldn't save as .xslx!")
# SNePlots class --------------------------------------------
class SNePlots:
@staticmethod
def plot_contribution(SNeRatio, SNeStats):
norm_abund = SNeRatio.df_master.norm_abund
norm_abund_err = SNeRatio.df_master.norm_abund_err
elements = SNeRatio.df_master["Element"].values
ref_element = SNeRatio.selections["ref_element"]
font = {'size' : 14}
plt.rc('font', **font)
fig, ax = plt.subplots(1,1, figsize=(8,6))
ax.errorbar(x=elements, y=norm_abund, yerr=norm_abund_err, fmt=".", color="black", lw=2.0)
x_values = list(range(len(elements)))
Ia_table = SNeStats.fit_results["Ia_table"]
cc_table = SNeStats.fit_results["cc_table"]
total_cont = SNeStats.fit_results["total_cont"]
cont_Ia = SNeStats.fit_results["cont_Ia"]
cont_cc = SNeStats.fit_results["cont_cc"]
rIa = SNeStats.fit_results["r_Ia"]
rIa_n = SNeStats.fit_results["r_Ia_err_n"]
rIa_p = SNeStats.fit_results["r_Ia_err_p"]
rcc = SNeStats.fit_results["r_cc"]
rcc_n = SNeStats.fit_results["r_cc_err_n"]
rcc_p = SNeStats.fit_results["r_cc_err_p"]
ax.bar(x=x_values, height=cont_Ia, color="red", width=0.4, alpha=0.6, label="SNIa(%): {:.1f}(-{:.1f},+{:.1f}) [{}]".format(rIa*100, rIa_n*100, rIa_p*100, Ia_table))
ax.bar(x=x_values, height=cont_cc, bottom=cont_Ia, color="teal", width=0.4, alpha=0.6, label="SNcc(%): {:.1f}(-{:.1f},+{:.1f}) [{}]".format(rcc*100, rcc_n*100, rcc_p*100, cc_table))
ax.axhline(y=1.0, color="black", ls="--")
# calculating ylim
max_data = max(norm_abund + norm_abund_err)
max_model = max(total_cont)
max_y = max(max_data, max_model)
y_lim = max_y * 1.25
ax.set_ylim([0, y_lim])
ax.set_xlabel("Elements")
ax.set_ylabel("$[X/Fe]_\odot$")
ax.legend(prop={'size':12})
fig.savefig("outputs/plot_fit.jpeg", dpi=200)
@staticmethod
def plot_statistics(SNeRatio, SNeStats):
chisq = SNeStats.fit_results["chisq"]
dof = SNeStats.fit_results["dof"]
norm_abund = SNeRatio.df_master.norm_abund
norm_abund_err = SNeRatio.df_master.norm_abund_err
Ia_yields, cc_yields, = SNeRatio.df_master["Ia_yields"].values, SNeRatio.df_master["cc_yields"].values
mass_number, norm_solar = SNeRatio.df_master["MassNumber"].values, SNeRatio.df_master["norm_solar"].values
elements, ref_element = SNeRatio.df_master["Element"].values, SNeRatio.selections["ref_element"]
x_list = np.linspace(0.0, 1.0, 2000)
chi_list = np.array([((norm_abund - SNeStats._calc_contribution(x, 1.0-x, Ia_yields, cc_yields, mass_number, norm_solar, elements, ref_element)[0])**2.0 / norm_abund_err**2.0).sum() for x in x_list])
chi_min = chi_list.min()
min_index = np.where(chi_min == chi_list)
best_ratio1 = x_list[min_index]
rIa = SNeStats.fit_results["r_Ia"]
rIa_n = SNeStats.fit_results["r_Ia_err_n"]
rIa_p = SNeStats.fit_results["r_Ia_err_p"]
rcc = SNeStats.fit_results["r_cc"]
rcc_n = SNeStats.fit_results["r_cc_err_n"]
rcc_p = SNeStats.fit_results["r_cc_err_p"]
print("R_snIa: {:.3f} (-{:.3f},+{:.3f})".format(rIa, rIa_n, rIa_p))
print("R_sncc: {:.3f} (-{:.3f},+{:.3f})".format(rcc, rcc_n, rcc_p))
font = {'size' : 14}
plt.rc('font', **font)
fig, ax = plt.subplots(1,1, figsize=(8,6))
ax.plot(x_list,chi_list)
ax.axvline(x=best_ratio1, color="red", label="SNIa/(SNIa+SNcc)(%): {:.1f}(-{:.1f},+{:.1f})".format(rIa*100, rIa_n*100, rIa_p*100))
ax.axvline(x=best_ratio1 - rIa_n, color="red", ls="--", label="${}\sigma$ conf. interval".format(SNeRatio.selections["confidence"]))
ax.axvline(x=best_ratio1 + rIa_p, color="red", ls="--")
delta_chisq = SNeRatio.sigma_to_delta_chisq[SNeRatio.selections["confidence"]]
ax.axhline(y=chi_list.min(), color="black", label="$\chi^{2}_{min}$" + "={:.1f}".format(chi_min))
ax.axhline(y=chi_list.min()+delta_chisq, color="black", ls="--", label="$\chi^{2}_{min} + \Delta\chi^{2}$" + "={:.1f}".format(chi_min+delta_chisq))
ax.set_xlim([max(rIa - 2.0*rIa_n, 0), rIa + 2.0*rIa_p])
ax.set_ylim([chi_min-(0.3*delta_chisq), chi_min+(2.5*delta_chisq)])
ax.set_xlabel("Ratio (SNIa/(SNIa+SNcc))")
ax.set_ylabel("$\chi^2$")
ax.legend(prop={'size':10})
fig.savefig("outputs/plot_statistics.jpeg", dpi=200)
@staticmethod
def plot_fit_all_old(SNeRatio, SNeStats):
n_Ia = SNeStats.fit_results_all["n_Ia"]
n_cc = SNeStats.fit_results_all["n_cc"]
n_total = n_Ia * n_cc
cc_tables = SNeStats.fit_results_all["cc_table"]
Ia_tables = SNeStats.fit_results_all["Ia_table"]
chisq = SNeStats.fit_results_all["chisq"]
ratio_Ia, ratio_Ia_err_n, ratio_Ia_err_p = np.dsplit(SNeStats.fit_results_all["ratio_Ia"], 3)
ratio_Ia = ratio_Ia.reshape(n_Ia, n_cc)
ratio_Ia_err_n = ratio_Ia_err_n.reshape(n_Ia, n_cc)
ratio_Ia_err_p = ratio_Ia_err_p.reshape(n_Ia, n_cc)
font = {'size' : 8}
plt.rc('font', **font)
fig = plt.figure(dpi=200, figsize=(10,10), facecolor=(1, 1, 1), edgecolor=(0, 0, 0))
ax = [fig.add_subplot(141),
fig.add_subplot(142),
fig.add_subplot(143)]
fig.subplots_adjust(left=0.15,
bottom=0.175,
right=0.95,
top=0.95,
wspace=0.0,
hspace=0.0)
#---------------------------------------------------------------------------------
xlabel = [t.rsplit(".", 1)[0] for t in cc_tables]
ax[0].set_xticks(range(n_cc))
ax[0].set_xticklabels(xlabel, rotation=80, size=5)
ylabel = [t.rsplit(".", 1)[0] for t in Ia_tables]
ax[0].set_yticks(range(n_Ia))
ax[0].set_yticklabels(ylabel, size=5)
im0 = ax[0].imshow(chisq, cmap=plt.cm.get_cmap('Greens'))
for i in range(len(xlabel)):
for j in range(len(ylabel)):
text = ax[0].text(i, j, "{:.2f}".format(chisq[j, i]), ha="center", va="center",
color="black", fontsize=5)
ax[0].set_title("$\chi^2$")
#---------------------------------------------------------------------------------
ax[1].set_xticks(range(n_cc))
ax[1].set_xticklabels(xlabel, rotation=80, size=5)
ylabel = ["" for _ in Ia_tables]
ax[1].set_yticks(range(n_Ia))
ax[1].set_yticklabels(ylabel, size=5)
im1 = ax[1].imshow(ratio_Ia, vmin=0, vmax=1, cmap=plt.cm.get_cmap('autumn_r'))
for i in range(len(xlabel)):
for j in range(len(ylabel)):
text = ax[1].text(i, j, "{:.2f}".format(ratio_Ia[j, i]), ha="center", va="center",
color="black", fontsize=5)
ax[1].set_title("SNIa Ratio")
#---------------------------------------------------------------------------------
ax[2].set_xticks(range(n_cc))
ax[2].set_xticklabels(xlabel, rotation=80, size=5)
ylabel = ["" for _ in Ia_tables]
ax[2].set_yticks(range(n_Ia))
ax[2].set_yticklabels(ylabel, size=5)
ax[2].imshow(ratio_Ia_err_n, vmin=0, vmax=1, cmap=plt.cm.get_cmap('summer_r'))
for i in range(len(xlabel)):
for j in range(len(ylabel)):
avg_err = (abs(ratio_Ia_err_n[j, i]) + abs(ratio_Ia_err_p[j, i]))/2.0
text = ax[2].text(i, j, "{:.2f}".format(avg_err), ha="center", va="center",
color="black", fontsize=5)
ax[2].set_title("Avg. Error")
#---------------------------------------------------------------------------------
cbar0 = plt.colorbar(im0, ax=ax[0], orientation='vertical', aspect=60)
cbar0.ax.tick_params(axis="both", labelsize=7)
cbar1 = plt.colorbar(im1, ax=ax[1], orientation='vertical', aspect=60)
cbar1.ax.tick_params(axis="both", labelsize=7)
fig.tight_layout()
fig.savefig("outputs/plot_fit_all.jpeg", dpi=200)
@staticmethod
def plot_fit_all(SNeRatio, SNeStats):
Ia_tables = SNeStats.fit_results_all_df["Ia_table"].unique()
cc_tables = SNeStats.fit_results_all_df["cc_table"].unique()