-
Notifications
You must be signed in to change notification settings - Fork 0
/
gravsphere-sfdm.py
1877 lines (1638 loc) · 69 KB
/
gravsphere-sfdm.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
###########################################################
#GravSphere
###########################################################
#Python programme to Jeans model discrete data assuming
#a "Scalar Field Dark Matter" spherical dark matter halo and some
#fixed radial profile for the "baryons" with varying
#mass to light ratio. The code and its various improvements
#is described in the following papers:
#https://ui.adsabs.harvard.edu/abs/2017MNRAS.471.4541R/abstract
#https://ui.adsabs.harvard.edu/abs/2018MNRAS.481..860R/abstract
#https://ui.adsabs.harvard.edu/abs/2019MNRAS.484.1401R/abstract
#https://ui.adsabs.harvard.edu/abs/2020MNRAS.498..144G/abstract
#https://ui.adsabs.harvard.edu/abs/2020JCAP...09..004A/abstract
#https://ui.adsabs.harvard.edu/abs/2020arXiv201003572A/abstract
#To run the code, you should first "prepare" your data in
#the format GravSphere needs using the "binulator.py" code.
#You will find examples for how to do this on real and mock
#data there.
###########################################################
#Functions for emcee Jeans fitting:
def lnprior_set_single(theta,n_betpars,bet0min,bet0max,\
betinfmin,betinfmax,\
betr0min,betr0max,betnmin,betnmax,\
nu_components,nupars_min,nupars_max,\
n_mpars,logM200low,logM200high,\
clow,chigh,logrTFlow,logrTFhigh,\
logMcenlow,logMcenhigh,\
acenlow,acenhigh,\
Arotlow,Arothigh,\
drangelow,drangehigh,\
Mstar_min,Mstar_max):
ndims = len(theta)
minarr = np.zeros(ndims)
maxarr = np.zeros(ndims)
minarr[0] = bet0min
maxarr[0] = bet0max
minarr[1] = betinfmin
maxarr[1] = betinfmax
minarr[2] = betr0min
maxarr[2] = betr0max
minarr[3] = betnmin
maxarr[3] = betnmax
minarr[n_betpars:n_betpars+nu_components*2] = nupars_min
maxarr[n_betpars:n_betpars+nu_components*2] = nupars_max
minarr[n_betpars+nu_components*2] = logM200low
maxarr[n_betpars+nu_components*2] = logM200high
minarr[n_betpars+nu_components*2+1] = clow
maxarr[n_betpars+nu_components*2+1] = chigh
minarr[n_betpars+nu_components*2+2] = logrTFlow
maxarr[n_betpars+nu_components*2+2] = logrTFhigh
minarr[n_betpars+nu_components*2+3] = logMcenlow
maxarr[n_betpars+nu_components*2+3] = logMcenhigh
minarr[n_betpars+nu_components*2+4] = acenlow
maxarr[n_betpars+nu_components*2+4] = acenhigh
minarr[n_betpars+nu_components*2+5] = Arotlow
maxarr[n_betpars+nu_components*2+5] = Arothigh
minarr[n_betpars+nu_components*2+6] = drangelow
maxarr[n_betpars+nu_components*2+6] = drangehigh
minarr[ndims-1] = Mstar_min
maxarr[ndims-1] = Mstar_max
if all(minarr < theta < maxarr for minarr,theta,maxarr in \
zip(minarr,theta,maxarr)):
return 0.0
return -np.inf
def lnprob_single(theta,x1,x2,y1,y1err,y2,y2err):
lp = lnprior(theta)
if not np.isfinite(lp):
return -np.inf
return lp + lnlike(theta,x1,x2,y1,y1err,y2,y2err)
def lnprob_single_vs(theta,x1,x2,y1,y1err,\
y2,y2err,\
vsp1val,vsp1pdf,vsp2val,vsp2pdf):
lp = lnprior(theta)
if not np.isfinite(lp):
return -np.inf
return lp + lnlike(theta,x1,x2,y1,y1err,\
y2,y2err,\
vsp1val,vsp1pdf,vsp2val,vsp2pdf)
def lnprob_single_prop(theta,x1,x2,x3,y1,y1err,\
y2,y2err,y3,y3err,y4,y4err):
lp = lnprior(theta)
if not np.isfinite(lp):
return -np.inf
return lp + lnlike(theta,x1,x2,x3,y1,y1err,\
y2,y2err,y3,y3err,y4,y4err)
def lnprob_single_prop_vs(theta,x1,x2,x3,y1,y1err,\
y2,y2err,y3,y3err,y4,y4err,\
vsp1val,vsp1pdf,vsp2val,vsp2pdf):
lp = lnprior(theta)
if not np.isfinite(lp):
return -np.inf
return lp + lnlike(theta,x1,x2,x3,y1,y1err,\
y2,y2err,y3,y3err,y4,y4err,\
vsp1val,vsp1pdf,vsp2val,vsp2pdf)
def lnlike_single(theta,x1,x2,y1,y1err,y2,y2err):
betpars = theta[0:n_betpars]
nupars = theta[n_betpars:n_betpars+nu_components*2]
Mpars = theta[n_betpars+nu_components*2:\
n_betpars+nu_components*2+n_mpars]
Arot = theta[n_betpars+nu_components*2+n_mpars]
drange = theta[n_betpars+nu_components*2+n_mpars+1]
Mstar = theta[ndim-1]
nuparsu = np.array(nupars)
Mparsu = np.array(Mpars)
Mparsu[0] = 10.**Mpars[0]
Mparsu[2] = 10.**Mpars[2]
Mparsu[3] = 10.**Mpars[3]
#Add dummy data points for low and high x1
#to ensure +ve definite surface density
#if using negative Plummer components:
if (nuparsu[0] < 0 or nuparsu[1] < 0 or nuparsu[2] < 0):
x1, y1, y1err = Sig_addpnts(x1,y1,y1err)
sigr2, Sig, sigLOS2 = \
sigp_fit(x1*drange,x2*drange,\
nuparsu,Mparsu,betpars,Mstar,Arot)
#And now, shrink the error wherever the
#total density is negative to disfavour
#those models (see binulator_surffuncs.py
#for details):
if (theta[0] < 0 or theta[1] < 0 or theta[2] < 0):
if (np.min(Sig) < 0):
y1err[np.where(Sig < 0)] = \
np.min(y1err)/np.float(len(x1))/1.0e3
#Handle distance uncertainty. Convert distances to
#angle units before doing model-data comparison:
duse = dgal_kpc * drange
x1u = x1 / dgal_kpc
x2u = x2 / dgal_kpc
#And convert model to angel units, but using
#new rather than default distance:
model1 = Sig
model2 = np.sqrt(sigLOS2)/1000.
inv_sigma2_1 = 1.0/y1err**2
inv_sigma2_2 = 1.0/y2err**2
lnlike_out = -0.5*(np.sum((y1-model1)**2*inv_sigma2_1)+\
np.sum((y2-model2)**2*inv_sigma2_2))
if (lnlike_out != lnlike_out):
lnlike_out = -np.inf
return lnlike_out
def lnlike_single_vs(theta,x1,x2,y1,y1err,y2,y2err,\
vsp1val,vsp1pdf,vsp2val,vsp2pdf):
betpars = theta[0:n_betpars]
nupars = theta[n_betpars:n_betpars+nu_components*2]
Mpars = theta[n_betpars+nu_components*2:\
n_betpars+nu_components*2+n_mpars]
Arot = theta[n_betpars+nu_components*2+n_mpars]
drange = theta[n_betpars+nu_components*2+n_mpars+1]
Mstar = theta[ndim-1]
nuparsu = np.array(nupars)
Mparsu = np.array(Mpars)
Mparsu[0] = 10.**Mpars[0]
Mparsu[2] = 10.**Mpars[2]
Mparsu[3] = 10.**Mpars[3]
#Add dummy data points for low and high x1
#to ensure +ve definite surface density
#if using negative Plummer components:
if (nuparsu[0] < 0 or nuparsu[1] < 0 or nuparsu[2] < 0):
x1, y1, y1err = Sig_addpnts(x1,y1,y1err)
sigr2, Sig, sigLOS2, vs1, vs2 = \
sigp_fit_vs(x1*drange,x2*drange,\
nuparsu,Mparsu,betpars,Mstar,Arot)
#And now, shrink the error wherever the
#total density is negative to disfavour
#those models (see binulator_surffuncs.py
#for details):
if (theta[0] < 0 or theta[1] < 0 or theta[2] < 0):
if (np.min(Sig) < 0):
y1err[np.where(Sig < 0)] = \
np.min(y1err)/np.float(len(x1))/1.0e3
#Handle distance uncertainty. Convert distances to
#angle units before doing model-data comparison:
duse = dgal_kpc * drange
x1u = x1 / dgal_kpc
x2u = x2 / dgal_kpc
#And convert model to angle units, but using
#new rather than default distance:
model1 = Sig
model2 = np.sqrt(sigLOS2)/1000.
model3 = vs1/1.0e12
model4 = vs2/1.0e12
inv_sigma2_1 = 1.0/y1err**2
inv_sigma2_2 = 1.0/y2err**2
lnlike_out = -0.5*(np.sum((y1-model1)**2*inv_sigma2_1)+\
np.sum((y2-model2)**2*inv_sigma2_2))+\
np.log(vsp_pdf(model3,vsp1val,vsp1pdf))+\
np.log(vsp_pdf(model4,vsp2val,vsp2pdf))
if (lnlike_out != lnlike_out):
lnlike_out = -np.inf
return lnlike_out
def lnlike_single_prop(theta,x1,x2,x3,y1,y1err,y2,y2err,\
y3,y3err,y4,y4err):
betpars = theta[0:n_betpars]
nupars = theta[n_betpars:n_betpars+nu_components*2]
Mpars = theta[n_betpars+nu_components*2:\
n_betpars+nu_components*2+n_mpars]
Arot = theta[n_betpars+nu_components*2+n_mpars]
drange = theta[n_betpars+nu_components*2+n_mpars+1]
Mstar = theta[ndim-1]
nuparsu = np.array(nupars)
Mparsu = np.array(Mpars)
Mparsu[0] = 10.**Mpars[0]
Mparsu[2] = 10.**Mpars[2]
Mparsu[3] = 10.**Mpars[3]
#Add dummy data points for low and high x1
#to ensure +ve definite surface density
#if using negative Plummer components:
if (nuparsu[0] < 0 or nuparsu[1] < 0 or nuparsu[2] < 0):
x1, y1, y1err = Sig_addpnts(x1,y1,y1err)
sigr2, Sig, sigLOS2, sigpmr2, sigpmt2 = \
sigp_fit_prop(x1*drange,x2*drange,x3*drange,\
nuparsu,Mparsu,betpars,Mstar,Arot)
#And now, shrink the error wherever the
#total density is negative to disfavour
#those models (see binulator_surffuncs.py
#for details):
if (theta[0] < 0 or theta[1] < 0 or theta[2] < 0):
if (np.min(Sig) < 0):
y1err[np.where(Sig < 0)] = \
np.min(y1err)/np.float(len(x1))/1.0e3
#Handle distance uncertainty. Convert distances to
#angle units before doing model-data comparison:
duse = dgal_kpc * drange
x1u = x1 / dgal_kpc
x2u = x2 / dgal_kpc
x3u = x3 / dgal_kpc
y3u = y3 / dgal_kpc
y3erru = y3err / dgal_kpc
y4u = y4 / dgal_kpc
y4erru = y4err / dgal_kpc
#And convert model to angle units, but using
#new rather than default distance:
model1 = Sig
model2 = np.sqrt(sigLOS2)/1000.
model3 = np.sqrt(sigpmr2)/1000. / duse
model4 = np.sqrt(sigpmt2)/1000. / duse
inv_sigma2_1 = 1.0/y1err**2
inv_sigma2_2 = 1.0/y2err**2
inv_sigma2_3 = 1.0/y3erru**2
inv_sigma2_4 = 1.0/y4erru**2
lnlike_out = -0.5*(np.sum((y1-model1)**2*inv_sigma2_1)+\
np.sum((y2-model2)**2*inv_sigma2_2)+\
np.sum((y3u-model3)**2*inv_sigma2_3)+\
np.sum((y4u-model4)**2*inv_sigma2_4))
if (lnlike_out != lnlike_out):
lnlike_out = -np.inf
return lnlike_out
def lnlike_single_prop_vs(theta,x1,x2,x3,y1,y1err,y2,y2err,\
y3,y3err,y4,y4err,\
vsp1val,vsp1pdf,vsp2val,vsp2pdf):
betpars = theta[0:n_betpars]
nupars = theta[n_betpars:n_betpars+nu_components*2]
Mpars = theta[n_betpars+nu_components*2:\
n_betpars+nu_components*2+n_mpars]
Arot = theta[n_betpars+nu_components*2+n_mpars]
drange = theta[n_betpars+nu_components*2+n_mpars+1]
Mstar = theta[ndim-1]
nuparsu = np.array(nupars)
Mparsu = np.array(Mpars)
Mparsu[0] = 10.**Mpars[0]
Mparsu[2] = 10.**Mpars[2]
Mparsu[3] = 10.**Mpars[3]
#Add dummy data points for low and high x1
#to ensure +ve definite surface density
#if using negative Plummer components:
if (nuparsu[0] < 0 or nuparsu[1] < 0 or nuparsu[2] < 0):
x1, y1, y1err = Sig_addpnts(x1,y1,y1err)
sigr2, Sig, sigLOS2, sigpmr2, sigpmt2, vs1, vs2 = \
sigp_fit_prop_vs(x1*drange,x2*drange,x3*drange,\
nuparsu,Mparsu,betpars,Mstar,Arot)
#And now, shrink the error wherever the
#total density is negative to disfavour
#those models (see binulator_surffuncs.py
#for details):
if (theta[0] < 0 or theta[1] < 0 or theta[2] < 0):
if (np.min(Sig) < 0):
y1err[np.where(Sig < 0)] = \
np.min(y1err)/np.float(len(x1))/1.0e3
#Handle distance uncertainty. Convert distances to
#angle units before doing model-data comparison:
duse = dgal_kpc * drange
x1u = x1 / dgal_kpc
x2u = x2 / dgal_kpc
x3u = x3 / dgal_kpc
y3u = y3 / dgal_kpc
y3erru = y3err / dgal_kpc
y4u = y4 / dgal_kpc
y4erru = y4err / dgal_kpc
#And convert model to angle units, but using
#new rather than default distance:
model1 = Sig
model2 = np.sqrt(sigLOS2)/1000.
model3 = np.sqrt(sigpmr2)/1000. / duse
model4 = np.sqrt(sigpmt2)/1000. / duse
model5 = vs1/1.0e12
model6 = vs2/1.0e12
inv_sigma2_1 = 1.0/y1err**2
inv_sigma2_2 = 1.0/y2err**2
inv_sigma2_3 = 1.0/y3erru**2
inv_sigma2_4 = 1.0/y4erru**2
lnlike_out = -0.5*(np.sum((y1-model1)**2*inv_sigma2_1)+\
np.sum((y2-model2)**2*inv_sigma2_2)+\
np.sum((y3u-model3)**2*inv_sigma2_3)+\
np.sum((y4u-model4)**2*inv_sigma2_4))+\
np.log(vsp_pdf(model5,vsp1val,vsp1pdf))+\
np.log(vsp_pdf(model6,vsp2val,vsp2pdf))
if (lnlike_out != lnlike_out):
lnlike_out = -np.inf
return lnlike_out
###########################################################
#Main code:
#Imports & dependencies:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import rcParams
import emcee
from scipy.integrate import simps as integrator
from functions_sfdm import *
from constants import *
from binulator_surffuncs import *
from binulator_velfuncs import *
from figures import *
import multiprocessing
import sys
#Suppress warning output:
import warnings
warnings.simplefilter("ignore")
#Forbid plots to screen so GravSphere can run
#remotely:
import matplotlib as mpl
mpl.use('Agg')
#Welcome blurb:
print('###### GRAVSPHERE VERSION 1.5 ######\n')
###########################################################
#Code parameters:
datadir = './Data/'
nwalkers = 50
nmodels = 100
#Codemode [run or plot]:
codemode = 'plot'
###########################################################
#Input data selection here:
#MW satellites:
#from gravsphere_initialise_Draco import *
#from gravsphere_initialise_UMi import *
#from gravsphere_initialise_Carina import *
#from gravsphere_initialise_LeoI import *
#from gravsphere_initialise_LeoII import *
#from gravsphere_initialise_Sextans import *
#from gravsphere_initialise_Sculptor import *
#from gravsphere_initialise_Fornax import *
#from gravsphere_initialise_CVnI import *
#from gravsphere_initialise_SegI import *
#from gravsphere_initialise_SMC import *
#Mocks:
#from gravsphere_initialise_PlumCoreOm import *
#from gravsphere_initialise_PlumCuspOm import *
#from gravsphere_initialise_SMCmock import *
#M31 satellites:
#from gravsphere_initialise_And21 import *
#MUSE
from gravsphere_initialise_AntliaB import *
#Output some key choices:
print('DM model: SFDM')
print('Doing galaxy:',whichgal)
print('Model parameters:')
print('M200low, M200high [1e9 Msun]:', \
10.0**logM200low/1.0e9, 10.0**logM200high/1.0e9)
print('clow, chigh:', clow, chigh)
print('rTFlow, rTFhigh:', 10.0**logrTFlow, 10.0**logrTFhigh)
if (logMcenlow > 0.0):
print('Including central dark mass in the range [1e6 Msun]:', \
10.0**logMcenlow/1.0e6, 10.0**logMcenhigh/1.0e6)
if (Arothigh > 1.0e-3):
print('Including rotation')
if (drangelow < 0.999):
print('Allowing distance to vary in the fit over the range [kpc]:', \
dgal_kpc*drangelow, dgal_kpc*drangehigh)
#Set up output data folder structure:
outdir = outdirbase
if (propermotion == 'yes'):
outdir = outdir + 'Propermotion/'
if (virialshape == 'yes'):
outdir = outdir + 'VirialShape/'
#Set tracer model and vel. ani. functions:
#Set tracer model and vel. ani. functions:
n_betpars = 4
nu = multiplumden
nu_components = 3
Sigfunc = multiplumsurf
n_nupars = nu_components * 2
if (propermotion == 'no'):
if (virialshape == 'no'):
lnlike = lnlike_single
lnprob = lnprob_single
lnprior_set = lnprior_set_single
else:
lnlike = lnlike_single_vs
lnprob = lnprob_single_vs
lnprior_set = lnprior_set_single
elif (propermotion == 'yes'):
if (virialshape == 'no'):
lnlike = lnlike_single_prop
lnprob = lnprob_single_prop
lnprior_set = lnprior_set_single
else:
lnlike = lnlike_single_prop_vs
lnprob = lnprob_single_prop_vs
lnprior_set = lnprior_set_single
###########################################################
#Read in the the data. We assume here that the errors
#on the velocity dispersion are Gaussian symmetric.
#If this is not a good approximation (c.f. output from the
#binulator), then this can be improved. The errors on the
#VSPs are rarely Gaussian symmetric and so we use the
#correct likelihood function from the binulator in this case.
data = np.genfromtxt(infile+'_p0best.txt',dtype='f8')
pfits = data
data = np.genfromtxt(infile+'_Rhalf.txt',dtype='f8')
Rhalf = data[0]
data = np.genfromtxt(infile+'_surfden.txt',dtype='f8')
rbin_phot = data[:,0]
surfden = data[:,1]
surfdenerr = data[:,2]
data = np.genfromtxt(infile+'_vel.txt',dtype='f8')
rbin_kin = data[:,0]
sigpmean = data[:,4]
sigperr = (data[:,6]-data[:,5])/2.0
data = np.genfromtxt(infile+'_vsps.txt',dtype='f8')
vs1bin = data[0,0]
vs1err = (data[0,2]-data[0,1])/2.0
vs1lo = data[0,1]
vs1hi = data[0,2]
vs2bin = data[1,0]
vs2err = (data[1,2]-data[1,1])/2.0
vs2lo = data[1,1]
vs2hi = data[1,2]
data = np.genfromtxt(infile+'_vsp1full.txt',dtype='f8')
vsp1val, vsp1pdf = vsppdf_calc(data)
data = np.genfromtxt(infile+'_vsp2full.txt',dtype='f8')
vsp2val, vsp2pdf = vsppdf_calc(data)
if (propermotion == 'yes'):
data = np.genfromtxt(infile+'_velproptan.txt',dtype='f8')
rbin_kinp = data[:,0]
sigpmt = data[:,4]
sigpmterr = (data[:,6]-data[:,5])/2.0
data = np.genfromtxt(infile+'_velpropR.txt',dtype='f8')
rbin_kinp2 = data[:,0]
sigpmr = data[:,4]
sigpmrerr = (data[:,6]-data[:,5])/2.0
#Check data:
if (np.sum(rbin_kinp-rbin_kinp2) != 0):
print('Need same radial binning for tangential and radial propermotions. Oops! Bye bye.')
sys.exit(0)
print('Inner/outer radial bin (phot):', \
np.min(rbin_phot),np.max(rbin_phot))
print('Inner/outer radial bin (kin):', \
np.min(rbin_kin),np.max(rbin_kin))
#Set up the baryonic mass profile. If this is
#not assumed to have the same radial profile
#as the tracer stars, then it must be set, above,
#in the galaxy initialisation script. This
#should be normalised to peak at 1.0 so that
#when multiplied by Mstar, it yields the total
#stellar mass.
if (baryonmass_follows_tracer == 'yes'):
if (barrad_min == 0):
barrad_min = 1.0e-3
Mstar_rad = np.logspace(np.log10(barrad_min),\
np.log10(barrad_max),np.int(bar_pnts))
norm = pfits[0] + pfits[1] + pfits[2]
Mstar_prof = \
threeplummass(Mstar_rad,pfits[0]/norm,\
pfits[1]/norm,pfits[2]/norm,\
pfits[3],pfits[4],pfits[5])
#Set beta scale radius based on Rhalf:
betr0min = np.log10(0.5*Rhalf)
betr0max = np.log10(2.0*Rhalf)
#Set Jeans radial-grid based also on Rhalf:
if (rmax < 0):
rmin = Rhalf / 100.0
rmax = Rhalf * 50.0
print('Inner/outer radial Jeans grid:', rmin, rmax)
#Set up the mass model functions:
M = lambda r, Mpars: \
sfdm_mass(r,Mpars[0],Mpars[1],Mpars[2])
rho = lambda r, Mpars: \
sfdm(r,Mpars[0],Mpars[1],Mpars[2])
dlnrhodlnr = lambda r, Mpars: \
sfdm_dlnrhodlnr(r,Mpars[0],Mpars[1],Mpars[2])
#Central dark mass:
Mcentral = lambda r, Mpars: \
plummass(r,Mpars[3:5])
n_mpars = 5
#Set min/max priors on the stellar mass:
Mstar_min = Mstar - Mstar_err
Mstar_max = Mstar + Mstar_err
#Set up the Jeans functions to use for the fit:
#N.B. +6 on the ndims here corresponds to:
#varying stellar mass to light ratio (+1)
#varying distance (+1)
#varying rotation parameter (+1)
ndim = n_betpars + n_nupars + n_mpars + 3
if (propermotion == 'no'):
if (virialshape == 'no'):
sigp_fit = lambda r1,r2,nupars,Mpars,betpars,Mstar,Arot: \
sigp(r1,r2,nu,Sigfunc,M,Mcentral,beta,betaf,nupars,Mpars,betpars,\
Mstar_rad,Mstar_prof,Mstar,Arot,Rhalf,Guse,rmin,rmax)
else:
sigp_fit_vs = lambda r1,r2,nupars,Mpars,betpars,Mstar,Arot: \
sigp_vs(r1,r2,nu,Sigfunc,M,Mcentral,beta,betaf,nupars,Mpars,betpars,\
Mstar_rad,Mstar_prof,Mstar,Arot,Rhalf,Guse,rmin,rmax)
elif (propermotion == 'yes'):
if (virialshape == 'no'):
sigp_fit_prop = lambda r1,r2,r3,nupars,Mpars,betpars,Mstar,Arot: \
sigp_prop(r1,r2,r3,nu,Sigfunc,M,Mcentral,beta,betaf,nupars,Mpars,betpars,\
Mstar_rad,Mstar_prof,Mstar,Arot,Rhalf,Guse,rmin,rmax)
else:
sigp_fit_prop_vs = lambda r1,r2,r3,nupars,Mpars,betpars,Mstar,Arot: \
sigp_prop_vs(r1,r2,r3,nu,Sigfunc,M,Mcentral,beta,betaf,nupars,Mpars,betpars,\
Mstar_rad,Mstar_prof,Mstar,Arot,Rhalf,Guse,rmin,rmax)
#Set the priors and starting blob for the tracer density profile.
#Code is a bit more involved here just to cope with potentially
#negative Plummer masses, used to fit some steeply falling tracer
#density profiles:
nupars_min = np.zeros(len(pfits))
nupars_max = np.zeros(len(pfits))
nupars_minstart = np.zeros(len(pfits))
nupars_maxstart = np.zeros(len(pfits))
for i in range(len(pfits)):
if (pfits[i] > 0):
nupars_min[i] = pfits[i]*(1.0-tracertol)
nupars_max[i] = pfits[i]*(1.0+tracertol)
else:
nupars_min[i] = pfits[i]*(1.0+tracertol)
nupars_max[i] = pfits[i]*(1.0-tracertol)
if (tracertol < 0.01):
nupars_minstart[i] = nupars_min[i]
nupars_maxstart[i] = nupars_max[i]
else:
if (pfits[i] > 0):
nupars_minstart[i] = pfits[i]*0.99
nupars_maxstart[i] = pfits[i]*1.01
else:
nupars_minstart[i] = pfits[i]*1.01
nupars_maxstart[i] = pfits[i]*0.99
###########################################################
#Emcee fitting code:
#Set up walkers:
if (codemode == 'run'):
print('Running in fitting mode ... ')
print('Will write output to:', outdir)
#Initialise the walkers:
pos = np.zeros((nwalkers, ndim), dtype='float')
pos[:,0] = np.random.uniform(bet0min,bet0max,nwalkers)
pos[:,1] = np.random.uniform(betinfmin,betinfmax,nwalkers)
pos[:,2] = np.random.uniform(betr0min,betr0max,nwalkers)
pos[:,3] = np.random.uniform(betnmin,betnmax,nwalkers)
for i in range(len(pfits)):
pos[:,n_betpars+i] = \
np.random.uniform(nupars_minstart[i],\
nupars_maxstart[i],nwalkers)
pos[:,n_betpars+nu_components*2] = \
np.random.uniform(logM200low,logM200high,nwalkers)
pos[:,n_betpars+nu_components*2+1] = \
np.random.uniform(clow,chigh,nwalkers)
pos[:,n_betpars+nu_components*2+2] = \
np.random.uniform(logrTFlow,logrTFhigh,nwalkers)
pos[:,n_betpars+nu_components*2+3] = \
np.random.uniform(logMcenlow,logMcenhigh,nwalkers)
pos[:,n_betpars+nu_components*2+4] = \
np.random.uniform(acenlow,acenhigh,nwalkers)
pos[:,n_betpars+nu_components*2+5] = \
np.random.uniform(Arotlow,Arothigh,nwalkers)
pos[:,n_betpars+nu_components*2+6] = \
np.random.uniform(drangelow,drangehigh,nwalkers)
pos[:,ndim-1] = \
np.random.uniform(Mstar_min,Mstar_max,nwalkers)
#Set up fitting function and priors:
if (propermotion == 'no'):
x1 = rbin_phot
x2 = rbin_kin
y1 = surfden
y1err = surfdenerr
y2 = sigpmean
y2err = sigperr
elif (propermotion == 'yes'):
x1 = rbin_phot
x2 = rbin_kin
x3 = rbin_kinp
y1 = surfden
y1err = surfdenerr
y2 = sigpmean
y2err = sigperr
y3 = sigpmr
y3err = sigpmrerr
y4 = sigpmt
y4err = sigpmterr
lnprior = lambda theta: \
lnprior_set(theta,n_betpars,bet0min,bet0max,\
betinfmin,betinfmax,\
betr0min,betr0max,betnmin,betnmax,\
nu_components,nupars_min,nupars_max,\
n_mpars,logM200low,logM200high,\
clow,chigh,logrTFlow,logrTFhigh,\
logMcenlow,logMcenhigh,\
acenlow,acenhigh,\
Arotlow,Arothigh,\
drangelow,drangehigh,\
Mstar_min,Mstar_max)
print('Running chains ... ')
with Pool(processes = nprocs) as pool:
if (propermotion == 'no'):
if (virialshape == 'no'):
sampler = \
emcee.EnsembleSampler(nwalkers,ndim,lnprob,\
args=(x1,x2,y1,y1err,y2,y2err),pool=pool)
else:
sampler = \
emcee.EnsembleSampler(nwalkers,ndim,lnprob,\
args=(x1,x2,y1,y1err,y2,y2err,\
vsp1val,vsp1pdf,vsp2val,vsp2pdf),pool=pool)
elif (propermotion == 'yes'):
if (virialshape == 'no'):
sampler = emcee.EnsembleSampler(nwalkers,ndim,lnprob,\
args=(x1,x2,x3,y1,y1err,y2,y2err,\
y3,y3err,y4,y4err),pool=pool)
else:
sampler = emcee.EnsembleSampler(nwalkers,ndim,lnprob,\
args=(x1,x2,x3,y1,y1err,y2,y2err,\
y3,y3err,y4,y4err,\
vsp1val,vsp1pdf,vsp2val,vsp2pdf),pool=pool)
sampler.run_mcmc(pos, nmodels, progress = True)
#Store the output (including the data):
print('Writing data to file ... ')
f = open(outdir+'output_sigp_sfdm.txt','w')
for i in range(len(rbin_kin)):
f.write('%f %f %f\n' % \
(rbin_kin[i], sigpmean[i], sigperr[i]))
f.close()
f = open(outdir+'output_surfden_sfdm.txt','w')
for i in range(len(rbin_phot)):
f.write('%f %f %f\n' % \
(rbin_phot[i], surfden[i], surfdenerr[i]))
f.close()
if (propermotion == 'yes'):
f = open(outdir+'output_prop_sfdm.txt','w')
for i in range(len(rbin_kinp)):
f.write('%f %f %f %f %f\n' % \
(rbin_kinp[i],\
sigpmr[i],sigpmrerr[i],sigpmt[i],\
sigpmterr[i]))
f.close()
burn = np.int(0.75*nmodels)
chisq = -2.0 * \
sampler.get_log_prob(discard=burn, flat=True)
par_test = sampler.get_chain(discard=burn, flat=True)
f = open(outdir+'Boutput_chain_sfdm.txt','w')
for i in range(len(chisq)):
outstr = str(chisq[i]) + ' '
for j in range(ndim):
outstr = outstr + str(par_test[i,j]) + ' '
outstr = outstr + '\n'
f.write(outstr)
f.close()
###########################################################
#Plotting code:
elif (codemode == 'plot'):
print('Running in plotting mode ... ')
print('Loading data from:', outdir)
#Read in the data:
data_in = \
np.genfromtxt(outdir+'output_surfden_sfdm.txt',dtype='f8')
rbin_phot = data_in[:,0]
surfden = data_in[:,1]
surfdenerr = data_in[:,2]
data_in = \
np.genfromtxt(outdir+'output_sigp_sfdm.txt',dtype='f8')
rbin_kin = data_in[:,0]
sigpmean = data_in[:,1]
sigperr = data_in[:,2]
if (propermotion == 'yes'):
data_in = \
np.genfromtxt(outdir+'output_prop_sfdm.txt',dtype='f8')
rbin_kinp = data_in[:,0]
sigpmr = data_in[:,1]
sigpmrerr = data_in[:,2]
sigpmt = data_in[:,3]
sigpmterr = data_in[:,4]
#Set radius array to use for plotting mass profiles
#etc:
if (rplot_inner > 0):
rleft = rplot_inner
else:
rleft = np.min(rbin_phot)
if (rplot_outer > 0):
rright = rplot_outer
else:
rright = np.max(rbin_phot)
if (rplot_pnts < 0):
rplot_pnts = len(rbin_phot)
print('Setting plot range:', rleft, rright)
rbin = np.logspace(np.log10(rleft),np.log10(rright),\
np.int(rplot_pnts))
#Load in the emcee data:
data_in = \
np.genfromtxt(outdir+'Boutput_chain_sfdm.txt',dtype='f8')
chisq = data_in[:,0]
par_test = np.zeros((len(chisq),ndim), dtype='float')
for i in range(1,ndim+1):
par_test[:,i-1] = data_in[:,i]
#Make sure no *really* bad models remain in the chains.
#In practice, this cut makes no difference to the end result.
if (np.min(chisq) == np.inf):
print('No viable models. Uh oh... bye bye. Minimum chisq:', np.min(chisq))
sys.exit(0)
index = np.where(chisq < np.min(chisq)*500.0)[0]
print('Min/max chisq:', np.min(chisq[index]), np.max(chisq[index]))
#Cut the confidence intervals from the chains:
nsamples = 1000
sample_choose = index[np.random.randint(len(index), \
size=nsamples)]
#Set up arrays to store confidence intervals:
M_int = np.zeros((7,len(rbin)))
rho_int = np.zeros((7,len(rbin)))
dlnrhodlnr_int = np.zeros((7,len(rbin)))
Mstar_int = np.zeros((7,len(Mstar_rad)))
Mdynrat_int = np.zeros((7,len(Mstar_rad)))
nu_int = np.zeros((7,len(Mstar_rad)))
Mcen_int = np.zeros((7,len(rbin)))
if (calc_Jfac == 'yes'):
J_int = np.zeros(7)
if (calc_Dfac == 'yes'):
D_int = np.zeros(7)
Mstore = np.zeros((len(rbin),nsamples))
rhostore = np.zeros((len(rbin),nsamples))
dlnrhodlnrstore = np.zeros((len(rbin),nsamples))
Mstarstore = np.zeros((len(Mstar_rad),nsamples))
Mdynratstore = np.zeros((len(Mstar_rad),nsamples))
nustore = np.zeros((len(Mstar_rad),nsamples))
M200store = np.zeros(nsamples)
vmaxstore = np.zeros(nsamples)
cstore = np.zeros(nsamples)
rTFstore = np.zeros(nsamples)
Mcenstore = np.zeros((len(rbin),nsamples))
Arotstore = np.zeros(nsamples)
dstore = np.zeros(nsamples)
McenMstore = np.zeros(nsamples)
Mcenastore = np.zeros(nsamples)
if (calc_Jfac == 'yes'):
Jstore = np.zeros(nsamples)
if (calc_Dfac == 'yes'):
Dstore = np.zeros(nsamples)
bet_int = np.zeros((7,len(rbin)))
betstar_int = np.zeros((7,len(rbin)))
Sig_int = np.zeros((7,len(rbin)))
sigp_int = np.zeros((7,len(rbin)))
vphirot_int = np.zeros((7,len(rbin)))
if (virialshape == 'yes'):
vs1_int = np.zeros((7,1))
vs2_int = np.zeros((7,1))
if (propermotion == 'yes'):
sigpmr_int = np.zeros((7,len(rbin)))
sigpmt_int = np.zeros((7,len(rbin)))
betstore = np.zeros((len(rbin),nsamples))
betstarstore = np.zeros((len(rbin),nsamples))
Sigstore = np.zeros((len(rbin),nsamples))
sigpstore = np.zeros((len(rbin),nsamples))
vphirotstore = np.zeros((len(rbin),nsamples))
if (virialshape == 'yes'):
vs1store = np.zeros(nsamples)
vs2store = np.zeros(nsamples)
if (propermotion == 'yes'):
sigpmrstore = np.zeros((len(rbin),nsamples))
sigpmtstore = np.zeros((len(rbin),nsamples))
for i in range(nsamples):
theta = par_test[sample_choose[i],:]
betpars = theta[0:n_betpars]
nupars = theta[n_betpars:n_betpars+nu_components*2]
Mpars = theta[n_betpars+nu_components*2:\
n_betpars+nu_components*2+n_mpars]
Arot = theta[n_betpars+nu_components*2+n_mpars]
drange = theta[n_betpars+nu_components*2+n_mpars+1]
Mstar = theta[ndim-1]
nuparsu = np.array(nupars)
Mparsu = np.array(Mpars)
Mparsu[0] = 10.**Mpars[0]
Mparsu[2] = 10.**Mpars[2]
Mparsu[3] = 10.**Mpars[3]
#Calculate all profiles we want to plot:
if (propermotion == 'no'):
if (virialshape == 'no'):
sigr2,Sig,sigLOS2 = \
sigp_fit(rbin,rbin,nuparsu,\
Mparsu,betpars,Mstar,Arot)
else:
sigr2,Sig,sigLOS2,vs1,vs2 = \
sigp_fit_vs(rbin,rbin,nuparsu,\
Mparsu,betpars,Mstar,Arot)
elif (propermotion == 'yes'):
if (virialshape == 'no'):
sigr2,Sig,sigLOS2,sigpmr2,sigpmt2 = \
sigp_fit_prop(rbin,rbin,rbin,nuparsu,Mparsu,betpars,\
Mstar,Arot)
else:
sigr2,Sig,sigLOS2,sigpmr2,sigpmt2,vs1,vs2 = \
sigp_fit_prop_vs(rbin,rbin,rbin,nuparsu,Mparsu,betpars,\
Mstar,Arot)
Mr = M(rbin,Mparsu)
betar = beta(rbin,betpars)
rhor = rho(rbin,Mparsu)
dlnrhodlnrr = dlnrhodlnr(rbin,Mparsu)
Mstarr = Mstar_prof*Mstar
nu_mass_r = multiplummass(Mstar_rad,nuparsu)
Mcenr = Mcentral(rbin,Mparsu)
Mstore[:,i] = Mr
betstore[:,i] = betar
betstarstore[:,i] = betar/(2.0-betar)
sigpstore[:,i] = np.sqrt(sigLOS2)/1000.
Sigstore[:,i] = Sig
vphirotstore[:,i] = np.sqrt(2.0*sigLOS2*Arot*rbin/Rhalf)/1000.
rhostore[:,i] = rhor
dlnrhodlnrstore[:,i] = dlnrhodlnrr
Mstarstore[:,i] = Mstarr
Mdynratstore[:,i] = M(Mstar_rad,Mparsu)/Mstarr
nustore[:,i] = nu_mass_r
Mcenstore[:,i] = Mcenr
vmaxstore[i] = vmax_func(Mparsu[0],Mparsu[1],h)
M200store[i] = Mparsu[0]
cstore[i] = Mparsu[1]
rTFstore[i] = Mparsu[2]
Arotstore[i] = Arot
dstore[i] = drange*dgal_kpc
McenMstore[i] = Mparsu[3]
Mcenastore[i] = Mparsu[4]
if (calc_Jfac == 'yes'):
alpha_rmax = dgal_kpc*alpha_Jfac_deg/deg
Jstore[i] = get_J(rho,Mparsu,dgal_kpc,alpha_rmax)
if (calc_Dfac == 'yes'):
alpha_rmax = dgal_kpc*alpha_Dfac_deg/deg
Dstore[i] = get_D(rho,Mparsu,dgal_kpc,alpha_rmax)
if (virialshape == 'yes'):
vs1store[i] = vs1/1.0e12
vs2store[i] = vs2/1.0e12
if (propermotion == 'yes'):
sigpmrstore[:,i] = np.sqrt(sigpmr2)/1000.
sigpmtstore[:,i] = np.sqrt(sigpmt2)/1000.
#Solve for confidence intervals for each of these:
for j in range(len(rbin)):
M_int[0,j], M_int[1,j], M_int[2,j], M_int[3,j], \
M_int[4,j], M_int[5,j], M_int[6,j] = \
calcmedquartnine(Mstore[j,:])
rho_int[0,j], rho_int[1,j], rho_int[2,j], rho_int[3,j], \
rho_int[4,j], rho_int[5,j], rho_int[6,j] = \
calcmedquartnine(rhostore[j,:])
dlnrhodlnr_int[0,j], dlnrhodlnr_int[1,j],\
dlnrhodlnr_int[2,j], \
dlnrhodlnr_int[3,j], \
dlnrhodlnr_int[4,j], \
dlnrhodlnr_int[5,j], \
dlnrhodlnr_int[6,j] = \
calcmedquartnine(dlnrhodlnrstore[j,:])
for j in range(len(Mstar_rad)):
Mstar_int[0,j], Mstar_int[1,j], Mstar_int[2,j], \
Mstar_int[3,j], \
Mstar_int[4,j], \
Mstar_int[5,j], \
Mstar_int[6,j] = \
calcmedquartnine(Mstarstore[j,:])
for j in range(len(Mstar_rad)):
Mdynrat_int[0,j], Mdynrat_int[1,j], \
Mdynrat_int[2,j], \
Mdynrat_int[3,j], \
Mdynrat_int[4,j], \
Mdynrat_int[5,j], \
Mdynrat_int[6,j] = \
calcmedquartnine(Mdynratstore[j,:])
for j in range(len(Mstar_rad)):
nu_int[0,j], nu_int[1,j], nu_int[2,j], \
nu_int[3,j], \
nu_int[4,j], \
nu_int[5,j], \
nu_int[6,j] = \
calcmedquartnine(nustore[j,:])
for j in range(len(rbin)):
Mcen_int[0,j], Mcen_int[1,j], Mcen_int[2,j], \