forked from gbrammer/unicorn
-
Notifications
You must be signed in to change notification settings - Fork 0
/
go_acs.py
876 lines (681 loc) · 31.2 KB
/
go_acs.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
"""
Run the full reduction scripts on the ACS parallels
"""
import threedhst
import threedhst.prep_flt_files
import unicorn
import glob
import os
import shutil
import numpy as np
USE_PLOT_GUI=False
from threedhst.prep_flt_files import process_3dhst_pair as pair
import matplotlib.pyplot as plt
from matplotlib.figure import Figure
from matplotlib.backends.backend_agg import FigureCanvasAgg
import matplotlib.ticker as mticker
def go_all():
import unicorn.go_acs
unicorn.go_acs.test(root='jbhm32')
unicorn.go_acs.test(root='jbhm51')
unicorn.go_acs.test(root='jbhm54')
#### For Danilo
unicorn.go_acs.test(root='jbhm39')
def test(root='jbhm51'):
"""
COSMOS-23 ACS overlaps with COSMOS-25 WFC3
"""
#os.chdir(unicorn.GRISM_HOME+'ACS_PARALLEL/COSMOS/PREP_FLT')
#root='jbhm51'
files=glob.glob('../RAW/%s*fits*' %(root))
for file in files:
os.system('cp %s .' %(file))
os.system('gunzip %s' %(os.path.basename(file)))
print file
#### Destripe + CTE correction
threedhst.prep_flt_files.prep_acs(force=True)
os.system('rm *flt.fits')
asn_direct_file = root+'010_asn.fits'
asn_grism_file = root+'020_asn.fits'
#### Copy corrected FLT files to .
asn = threedhst.utils.ASNFile(asn_direct_file)
for exp in asn.exposures:
print exp
os.system('cp ../FIXED/%s_flt.fits . ' %(exp))
#
asn = threedhst.utils.ASNFile(asn_grism_file)
for exp in asn.exposures:
print exp
os.system('cp ../FIXED/%s_flt.fits . ' %(exp))
#### Flag CRs, subtract background and align WCS
ALIGN = '/3DHST/Ancillary/COSMOS/WIRDS/WIRDS_Ks_100028+021230_T0002.fits'
ALIGN = '/3DHST/Spectra/Work/COSMOS/MOSAIC/COSMOS-F140w_11-05-23_sci.fits'
ALIGN = '/3DHST/Spectra/Work/COSMOS/MOSAIC/COSMOS-F140w_11-09-08_sci.fits'
ALIGN_EXTENSION = 0
#### UDS
ALIGN = '/Users/gbrammer/CANDELS/UDS/PREP_FLT/UDS-F125W_drz.fits'
ALIGN_EXTENSION = 1
threedhst.shifts.run_tweakshifts(asn_direct_file, verbose=True)
threedhst.prep_flt_files.startMultidrizzle(asn_direct_file, use_shiftfile=True,
skysub=True,
final_scale=0.05, pixfrac=1, driz_cr=True,
updatewcs=True, clean=True, median=True)
threedhst.shifts.refine_shifts(ROOT_DIRECT=asn_direct_file.split('_as')[0].upper(),
ALIGN_IMAGE=ALIGN,
ALIGN_EXTENSION = ALIGN_EXTENSION,
fitgeometry='shift', clean=True)
threedhst.prep_flt_files.startMultidrizzle(asn_direct_file, use_shiftfile=True,
skysub=True,
final_scale=0.05, pixfrac=1, driz_cr=False,
updatewcs=False, clean=True, median=False)
### Grism
threedhst.shifts.make_grism_shiftfile(asn_direct_file, asn_grism_file)
threedhst.prep_flt_files.startMultidrizzle(asn_grism_file, use_shiftfile=True,
skysub=True,
final_scale=0.05, pixfrac=1, driz_cr=True,
updatewcs=True, clean=False, median=True)
## Check
threedhst.gmap.makeImageMap(['JBHM54010_drz.fits[1]*4', unicorn.GRISM_HOME+'COSMOS/PREP_FLT/COSMOS-14-F140W_drz.fits', 'JBHM54020_drz.fits[1]', unicorn.GRISM_HOME+'COSMOS/PREP_FLT/COSMOS-14-G141_drz.fits'], aper_list=[16])
###################### Run the grism
os.system('cp *shifts.txt *tweak.fits *asn.fits ../DATA')
os.chdir(unicorn.GRISM_HOME+'ACS_PARALLEL/COSMOS')
unicorn.go_3dhst.set_parameters(direct='F814W', LIMITING_MAGNITUDE=25.5)
threedhst.process_grism.set_ACS_G800L()
#threedhst.options['SKY_BACKGROUND'] = None
threedhst.options['PREFAB_DIRECT_IMAGE'] = '../aXe/'+root.upper()+'010_drz.fits'
#threedhst.options['PREFAB_GRISM_IMAGE'] = '../aXe/'+root.upper()+'020_drz.fits'
## UDS lens: threedhst.options['FORCE_CATALOG'] = 'lens_drz.cat'
threedhst.process_grism.reduction_script(asn_grism_file=root+'020_asn.fits')
### SEDs unicorn.analysis.make_SED_plots(grism_root='jbhm51020')
#os.chdir('../')
grism_root=asn_grism_file.split('_asn')[0]
## read photometric, redshift, SPS catalogs
cat, zout, fout = unicorn.analysis.read_catalogs(root=grism_root, uds=True)
## path where other eazy outputs live
OUTPUT_DIRECTORY = os.path.dirname(zout.filename)
MAIN_OUTPUT_FILE = os.path.basename(zout.filename).split('.zout')[0]
## read grism outputs
grismCat, SPC = unicorn.analysis.read_grism_files(root=grism_root.upper(), GRISM_NAME='G800L')
print 'Matched catalog'
unicorn.analysis.match_grism_to_phot(grism_root=grism_root,
SPC = SPC, cat = cat,
grismCat = grismCat, zout = zout, fout = fout,
OUTPUT = './HTML/SED/'+grism_root+'_match.cat',
OUTPUT_DIRECTORY=OUTPUT_DIRECTORY,
MAIN_OUTPUT_FILE=MAIN_OUTPUT_FILE)
## make figures
for id in grismCat.id:
status = unicorn.analysis.specphot(id=id, grism_root=grism_root, SPC = SPC,
cat = cat,
grismCat = grismCat, zout = zout, fout = fout,
OUT_PATH = './HTML/SED/', OUT_FILE_FORMAT=True, Verbose=False,
MAIN_OUTPUT_FILE = MAIN_OUTPUT_FILE,
OUTPUT_DIRECTORY = OUTPUT_DIRECTORY,
CACHE_FILE = 'Same')
unicorn.go_3dhst.clean_up()
os.system('rsync -avz HTML/ ~/Sites_GLOBAL/P/GRISM_ACS/')
def testing_g800l_background(asn_file='jbhm39020_asn.fits'):
"""
Divide by the aXe sky image, divide by the row to row variation
and subtract the overall average in the ACS grism images.
"""
import threedhst.dq
import numpy as np
import threedhst.prep_flt_files
import pyfits
import matplotlib.pyplot as plt
asn = threedhst.utils.ASNFile(asn_file)
#### ACS has entries in run file for each of two WFC chips
flt = pyfits.open(asn.exposures[0]+'_flt.fits')
inst = flt[0].header.get('INSTRUME').strip()
if inst == 'ACS':
skip=2
else:
skip=1
sky1 = pyfits.open('CONF/ACS.WFC.CHIP1.msky.1.fits')
sky2 = pyfits.open('CONF/ACS.WFC.CHIP2.msky.1.fits')
skies = [sky1, sky2]
extensions = [1,4] ### SCI extensions
run = threedhst.prep_flt_files.MultidrizzleRun((asn_file.split('_asn.fits')[0]).upper())
for i in range(len(asn.exposures)):
exp = run.flt[i*skip]
flt = pyfits.open(exp+'.fits', mode='update')
### Loop through ACS chips
for j in [0,1]:
# run.blot_back(ii=i*skip+j, copy_new=(i == 0) & (j == 0), ACS_CHIP = j+1)
#
# threedhst.prep_flt_files.make_segmap(run.flt[i*skip+j], IS_GRISM=True)
# seg = pyfits.open(exp+'.seg.fits')
#
# ext = extensions[j]
#
# print unicorn.noNewLine+' 1) Mask'
# data = flt[ext].data/skies[j][0].data
# data[seg[0].data > 0] = np.nan
#
# ### Collapse along rows
# print unicorn.noNewLine+' 2) Profile'
# shp = data.shape
# avg = np.zeros(shp[0])
# for k in range(shp[0]):
# cut = data[k,:]
# avg[k] = threedhst.utils.biweight(cut[np.isfinite(cut)], mean=True)
#
# sig = 5
# xkern = np.arange(8*sig)-4*sig
# ykern = np.exp(-1*xkern**2/sig**2/2.)
# ykern /= np.trapz(ykern, xkern)
#
# #### Need to make a larger array for smoothing at the edges
# print unicorn.noNewLine+' 3) Correct'
# avg_grow = np.ones(shp[0]+8*sig)
# avg_grow[0:4*sig] *= avg[0]
# avg_grow[4*sig:-4*sig] = avg
# avg_grow[-4*sig:] *= avg[-1]
# smooth = np.convolve(avg_grow, ykern, mode='same')[4*sig:-4*sig]
# model = np.dot(smooth.reshape((shp[0],1)), np.ones((1,shp[1])))
#
# arr = data[seg[0].data == 0]
# stats = threedhst.utils.biweight(arr, both=True)
#
# flt[ext].data /= skies[j][0].data * model/stats[0]
# #flt[ext].data -= model
#
# data /= skies[j][0].data * model/stats[0]
# arr = data[seg[0].data == 0]
# stats2 = threedhst.utils.biweight(arr, both=True)
ext = extensions[j]
exptime = flt[0].header['EXPTIME']
mask = flt['dq',j+1].data == 0
ratio = flt['sci',j+1].data/exptime/skies[j][0].data
med = np.median(ratio[mask])
mask2 = mask & ((flt['sci',j+1].data-med*exptime)/flt['err',j+1].data < 3)
med2 = np.median(ratio[mask2])
flt['sci', j+1].data -= med2*exptime*skies[j][0].data
flt['sci', j+1].header.update('MDRIZSKY', med2*exptime)
print '%s chip%d: %.3f' %(exp, j+1, med2)
#print unicorn.noNewLine+' 4) Update FLT'
flt.flush()
def testing_f814w_background(asn_file='jbhm39020_asn.fits'):
"""
Divide by the aXe sky image, divide by the row to row variation
and subtract the overall average in the ACS grism images.
"""
import threedhst.dq
import numpy as np
import threedhst.prep_flt_files
import pyfits
import matplotlib.pyplot as plt
asn = threedhst.utils.ASNFile(asn_file)
#### ACS has entries in run file for each of two WFC chips
flt = pyfits.open(asn.exposures[0]+'_flt.fits')
inst = flt[0].header.get('INSTRUME').strip()
if inst == 'ACS':
skip=2
else:
skip=1
extensions = [1,4] ### SCI extensions
run = threedhst.prep_flt_files.MultidrizzleRun((asn_file.split('_asn.fits')[0]).upper())
for i in range(len(asn.exposures)):
exp = run.flt[i*skip]
flt = pyfits.open(exp+'.fits', mode='update')
### Loop through ACS chips
for j in [0,1]:
run.blot_back(ii=i*skip+j, copy_new=(i == 0) & (j == 0), ACS_CHIP = j+1)
threedhst.prep_flt_files.make_segmap(run.flt[i*skip+j], IS_GRISM=True)
seg = pyfits.open(exp+'.seg.fits')
ext = extensions[j]
print unicorn.noNewLine+' 1) Mask'
data = flt[ext].data/np.ones(flt[ext].data.shape)
data[seg[0].data > 0] = np.nan
print unicorn.noNewLine+' 2) Subtract Global Mean'
arr = data[seg[0].data == 0]
stats = threedhst.utils.biweight(arr, both=True)
flt[ext].data -= stats[0]
data -= stats[0]
flt.flush()
def check_lens():
"""
Look at the ACS spectrum of the lens arc in the UDS
"""
os.chdir('/3DHST/Spectra/Work/ACS_PARALLEL/UDS/DATA')
drz = pyfits.open('jbhm19020_drz.fits')
cont = pyfits.open('jbhm19020CONT_drz.fits')
twod = pyfits.open('../HTML/images/jbhm19020_00280_2D.fits.gz')
thumb = pyfits.open('../HTML/images/jbhm19020_00280_thumb.fits.gz')
ds9 = threedhst.dq.myDS9()
ds9.frame(1)
ds9.view(twod[1])
ds9.scale(-10,200)
ds9.frame(2)
ds9.view(twod[1].data-twod[4].data*900, header=twod[1].header)
ds9.scale(-10,200)
ds9.frame(3)
img = twod[1].data*1.
shp = thumb[0].data.shape
img[:,-shp[1]:] = thumb[0].data*100
ds9.view(img, header=twod[1].header)
ds9.scale(-10,200)
### line tracing out the arc in teh 280 thumbnail
trace = np.array([179.6091, 49.73919, 178.48754, 43.850966, 176.43133, 39.738555, 174.93591, 37.68235, 169.98232, 32.167981, 164.74835, 28.803281, 151.56994, 24.036623, 139.32617, 23.849696])
tracex = np.array(trace[0::2]) - (twod[1].data.shape[1] - twod[1].data.shape[0])-1
tracey = np.array(trace[1::2])-1
plt.plot(tracex, tracey, color='yellow', alpha=0.5, linewidth=2, marker='o', ms=10)
from pylab import polyfit as sp_polyfit
from pylab import polyval as sp_polyval
coeff = sp_polyfit(tracey, tracex, 5)
yy = np.arange(1000)/1000.*shp[1]
plt.plot(sp_polyval(coeff, yy), yy, color='green', alpha=0.8)
thumb_shifted = thumb[0].data*1.
#thumb_shifted[:,10] = 0
plt.imshow(thumb_shifted, vmin=-0.1, vmax=0.5, interpolation='nearest')
twod_shifted = twod[1].data*1.
shp = thumb_shifted.shape
for yi in range(22,50):
shift_i = shp[1]/2. - sp_polyval(coeff, yi)
# print yi, shift_i
thumb_shifted[yi,:] = xshift(thumb_shifted[yi,:], shift_i)
twod_shifted[yi,:] = xshift(twod_shifted[yi,:], shift_i)
#twod_shifted[yi-1,:] += xshift(twod_shifted[yi-1,:], shift_i)
#twod_shifted[yi+1,:] += xshift(twod_shifted[yi+1,:], shift_i)
#
plt.imshow(thumb_shifted, vmin=-0.1, vmax=0.5, interpolation='nearest')
ds9.frame(4)
img = twod_shifted*1.
shp = thumb[0].data.shape
img[:,-shp[1]:] = thumb_shifted*100
ds9.view(img, header=twod[1].header)
ds9.scale(-10,200)
### OBJ = 257, 280
def xshift(array, shift, spline=True):
"""
Shift an array by an arbitrary `shift` value, using spline interpolation
and wrapping around array edges.
"""
import scipy.interpolate as interp
shp = array.shape
xpix = np.arange(shp[0])
if spline:
shifted = interp.spline(xpix, array, (xpix-shift) % shp[0])
else:
shifted = np.interp((xpix-shift) % shp[0], xpix, array)
#
return shifted
def process_acs_pair(asn_direct_file='ib3706050_asn.fits',
asn_grism_file='ib3706060_asn.fits',
field = 'COSMOS',
ALIGN_IMAGE='../ACS/h_nz_sect*img.fits',
ALIGN_EXTENSION=0,
SKIP_GRISM=False,
adjust_targname=True,
align_geometry='shift',
PATH_TO_RAW='../RAW',
get_shift=True,
TWEAKSHIFTS_ONLY=False,
FLC=True):
"""
Does the basic processing for ACS F814W and G800L pointings: background subtraction, allignment and drizzlign.
"""
import threedhst
import threedhst.prep_flt_files
from threedhst.prep_flt_files import make_targname_asn
#### Copy corrected FLT files to .
asn = threedhst.utils.ASNFile(asn_direct_file)
for exp in asn.exposures:
print exp
os.system('rm %s_flt.fits' %(exp))
if FLC:
os.system('cp ../RAW/%s_flc.fits %s_flt.fits' %(exp, exp))
else:
os.system('cp ../FIXED/%s_flt.fits . ' %(exp))
#
asn = threedhst.utils.ASNFile(asn_grism_file)
for exp in asn.exposures:
print exp
os.system('rm %s_flt.fits' %(exp))
if FLC:
os.system('cp ../RAW/%s_flc.fits %s_flt.fits' %(exp, exp))
else:
os.system('cp ../FIXED/%s_flt.fits . ' %(exp))
#DIRECT REDUCTION
ROOT_DIRECT = asn_direct_file.split('_asn.fits')[0]
from threedhst.prep_flt_files import make_targname_asn
#this makes new asn.fits files but with ACS the names start with ANY
#must add an optional tag to replace ANY with the field name
if (asn_direct_file is not None) & adjust_targname:
asn_direct_file = make_targname_asn(asn_direct_file,field=field, ext='flc')
if (asn_grism_file is not None) & adjust_targname:
asn_grism_file = make_targname_asn(asn_grism_file,field=field, ext='flc')
#run = threedhst.prep_flt_files.MultidrizzleRun((asn_direct_file.split('_asn.fits')[0]).upper())
threedhst.shifts.run_tweakshifts(asn_direct_file, verbose=True)
threedhst.prep_flt_files.startMultidrizzle(asn_direct_file, use_shiftfile=True,
skysub=True,
final_scale=0.05, pixfrac=1, driz_cr=True,
updatewcs=True, clean=True, median=True)
for i,exp in enumerate(asn.exposures):
asn_mask = asn.exposures[i]+'_flt.fits.mask.reg'
print asn_mask
if os.path.exists(asn_mask):
threedhst.showMessage("Apply ASN mask: %s" %(asn_mask))
threedhst.regions.apply_dq_mask(asn.exposures[i]+'_flt.fits', extension=3,
mask_file = asn_mask)
threedhst.shifts.refine_shifts(ROOT_DIRECT=asn_direct_file.split('_as')[0].upper(),
ALIGN_IMAGE=ALIGN_IMAGE,
ALIGN_EXTENSION = ALIGN_EXTENSION,
fitgeometry=align_geometry, clean=True)
unicorn.go_acs.testing_f814w_background(asn_direct_file)
SCALE = 0.06
PIXFRAC=1.0
threedhst.prep_flt_files.startMultidrizzle(asn_direct_file, use_shiftfile=True,
skysub=True,
final_scale=SCALE, pixfrac=PIXFRAC, driz_cr=False,
updatewcs=True, clean=True, median=False)
#GRISM REDUCTION
threedhst.shifts.make_grism_shiftfile(asn_direct_file, asn_grism_file)
threedhst.prep_flt_files.startMultidrizzle(asn_grism_file, use_shiftfile=True,
skysub=True,
final_scale=SCALE, pixfrac=PIXFRAC, driz_cr=True,
updatewcs=True, clean=False, median=True)
unicorn.go_acs.testing_g800l_background(asn_grism_file)
threedhst.prep_flt_files.startMultidrizzle(asn_grism_file, use_shiftfile=True,
skysub=True,
final_scale=SCALE, pixfrac=PIXFRAC, driz_cr=True,
updatewcs=True, clean=False, median=True)
def make_external_catalog(root='', master_segmentation='', master_catalog='', reference_image=''):
"""
Given a master catalog, a master segmentation map and a reference image, make a catalog and a segmentation
map which contain only the sources within the reference image.
"""
import pyfits
import numpy as np
import pywcs
if (root is None) or (master_segmentation is None) or (reference_image is None):
print 'Missing input: provide root, master segmentation and master catalog.'
return False
if reference_image is None:
reference_image = '../PREP_FLT/'+root+'_drz.fits'
catalog_out = root+'.ext.cat'
segmentation_out = root+'-G800L_seg.fits'
print reference_image
old_cat = threedhst.sex.mySexCat(master_catalog)
ref = pyfits.open(reference_image)
sw_seg = threedhst.sex.SWarp()
sw_seg.swarpMatchImage(reference_image)
sw_seg.options['IMAGEOUT_NAME'] = segmentation_out
sw_seg.options['RESAMPLE'] = 'N'
sw_seg.options['FSCALASTRO_TYPE']='NONE'
if reference_image.find('GOODS-S-08') != -1:
sw_seg.options['CENTER']='03:32:47.08, -27:53:59.57'
if reference_image.find('GOODS-S-27') != -1:
sw_seg.options['CENTER']='03:32:43.089, -27:43:51.20'
if reference_image.find('GOODS-S-34') != -1:
sw_seg.options['CENTER']='03:32:59.051, -27:50:47.03'
if reference_image.find('UDS-06') != -1:
sw_seg.options['CENTER']='02:17:54.043, -05:14:15.15'
if reference_image.find('UDS-16') != -1:
sw_seg.options['CENTER']='02:16:51.057, -05:11:29.29'
sw_seg.swarpImage(master_segmentation)
print sw_seg.options['CENTER']
seg = pyfits.open(segmentation_out)
inter_seg = np.array(seg[0].data)
inter_seg[np.where(ref[1].data==0)]=0
old_cat.change_MAG_AUTO_for_aXe(filter='F806W')
objects_in_seg = np.unique(inter_seg)
pop_id = []
for id in old_cat.id[::-1]:
if id not in objects_in_seg:
#print 'Pop #%05d' %(id)
pop_id.append(id)
old_cat.popItem(np.array(pop_id))
pop_mag = []
for ii in range(old_cat.nrows):
if (old_cat['MAG_F806W'][ii] == 99.0000) | (old_cat['MAG_F806W'][ii] == 0.00):
#print old_cat.MAG_F806W[ii]
pop_mag.append(old_cat.id[ii])
old_cat.popItem(np.array(pop_mag))
tmp1 = np.array(old_cat['A_IMAGE'])*0.06/3600.
old_cat.addColumn(data=tmp1, format='%f', name='A_WORLD', comment='Profile RMS along major axis [deg]', verbose=True)
tmp2 = np.array(old_cat['B_IMAGE'])*0.06/3600.
old_cat.addColumn(data=tmp2, format='%f', name='B_WORLD', comment='Profile RMS along minor axis [deg]', verbose=True)
ref_wcs = pywcs.WCS(ref[1].header)
new_x = np.array(old_cat['X_IMAGE'])
new_y = np.array(old_cat['Y_IMAGE'])
for ii in range(old_cat.nrows):
new_x[ii], new_y[ii] = ref_wcs.wcs_sky2pix([[old_cat['X_WORLD'][ii],old_cat['Y_WORLD'][ii]]],1)[0]
old_cat.renameColumn(original='X_IMAGE', new='X_OLD', verbose=True)
old_cat.renameColumn(original='Y_IMAGE', new='Y_OLD', verbose=True)
old_cat.addColumn(data=new_x, format='%f', name='X_IMAGE', comment = '', verbose=True)
old_cat.addColumn(data=new_y, format='%f', name='Y_IMAGE', comment = '', verbose=True)
old_cat.write(outfile=catalog_out)
def specphot_acs_and_wfc3(id=69, grism_root='ibhm45030',
MAIN_OUTPUT_FILE = 'cosmos-1.v4.6',
OUTPUT_DIRECTORY = '/Users/gbrammer/research/drg/PHOTZ/EAZY/NEWFIRM/v4.6/OUTPUT_KATE/',
CACHE_FILE = 'Same', Verbose=False,
SPC = None, cat=None, grismCat = None,
zout = None, fout = None, OUT_PATH='/tmp/', OUT_FILE_FORMAT=True,
OUT_FILE='junk.png', GET_SPEC_ONLY=False, GET_WFC3=False, WFC3_DIR='/3DHST/Spectra/Release/v2.0/GOODS-S'):
"""
specphot_acs_and_wfc3(id)
Get photometry/SED fit as well as WFC3 spectrum when available and overplot G141 spectrum.
This is different from unicorn.analysis.specphot() which does not get the WFC3 spectrum.
"""
import threedhst.eazyPy as eazy
import threedhst.catIO as catIO
import pyfits
#### Get G141 spectrum
if Verbose:
print 'Read SPC'
if SPC is None:
SPC = threedhst.plotting.SPCFile(grism_root+'_2_opt.SPC.fits',
axe_drizzle_dir='DRIZZLE_G141')
spec = SPC.getSpec(id)
if spec is False:
return False
xmin = 3000
xmax = 2.4e4
lam = spec.field('LAMBDA')
flux = spec.field('FLUX')
ffix = flux-spec.field('CONTAM')
ferr = spec.field('FERROR') #*0.06/0.128254
if Verbose:
print 'Read grism catalog'
#### Read the grism catalog and get coords of desired object
if grismCat is None:
grismCat = threedhst.sex.mySexCat('DATA/'+grism_root+'_drz.cat')
#### Source size
R = np.sqrt(np.cast[float](grismCat.A_IMAGE)*np.cast[float](grismCat.B_IMAGE))
grism_idx = np.where(grismCat.id == id)[0][0]
Rmatch = R[grism_idx]*1.
ra0 = grismCat.ra[grismCat.id == id][0]
de0 = grismCat.dec[grismCat.id == id][0]
#### Read EAZY outputs and get info for desired object
if cat is None:
cat = catIO.ReadASCIICat(OUTPUT_DIRECTORY+'../'+MAIN_OUTPUT_FILE+'.cat')
dr = np.sqrt((cat.ra-ra0)**2*np.cos(de0/360.*2*np.pi)**2+(cat.dec-de0)**2)*3600.
photom_idx = np.where(dr == np.min(dr))[0][0]
drMatch = dr[photom_idx]*1.
if drMatch > 2:
return False
if Verbose:
print 'Read zout'
if zout is None:
zout = catIO.ReadASCIICat(OUTPUT_DIRECTORY+'/'+MAIN_OUTPUT_FILE+'.zout')
if fout is None:
fout = catIO.ReadASCIICat(OUTPUT_DIRECTORY+'/../cosmos-1.m05.v4.6.fout')
if Verbose:
print 'Read binaries'
lambdaz, temp_sed, lci, obs_sed, fobs, efobs = \
eazy.getEazySED(photom_idx, MAIN_OUTPUT_FILE=MAIN_OUTPUT_FILE, \
OUTPUT_DIRECTORY=OUTPUT_DIRECTORY, \
CACHE_FILE = CACHE_FILE)
try:
lambdaz, temp_sed_sm = unicorn.analysis.convolveWithThumb(id, lambdaz, temp_sed, SPC)
except:
temp_sed_sm = temp_sed*1.
wfc3_exist = False
if GET_WFC3:
wfc3_file_path = WFC3_DIR+"/*/1D/FITS/*%05d.1D.fits" %(cat.id[photom_idx])
wfc3_file = glob.glob(wfc3_file_path)
if wfc3_file != []:
wfc3_spec = pyfits.open(wfc3_file[0])
wfc3_exist = True
else:
print 'No WFC3 spectrum.'
if Verbose:
print 'Normalize spectrum'
q = np.where((lam > 0.55e4) & (lam < 1.0e4) & (flux > 0))[0]
if len(q) == 0:
return False
yint = np.interp(lam[q], lambdaz, temp_sed_sm)
anorm = np.sum(yint*ffix[q])/np.sum(ffix[q]**2)
if np.isnan(anorm):
anorm=1.
total_err = np.sqrt((ferr)**2+(1.0*spec.field('CONTAM'))**2)*anorm
if GET_SPEC_ONLY:
if drMatch > 1:
return False
else:
return lam, ffix*anorm, total_err, lci, fobs, efobs, photom_idx
if Verbose:
print 'Start plot'
#### Make the plot
threedhst.plotting.defaultPlotParameters()
xs=5.8
ys = xs/4.8*3.2
if USE_PLOT_GUI:
fig = plt.figure(figsize=[xs,ys],dpi=100)
else:
fig = Figure(figsize=[xs,ys], dpi=100)
fig.subplots_adjust(wspace=0.2,hspace=0.2,left=0.13*4.8/xs, bottom=0.15*4.8/xs,right=1.-0.02*4.8/xs,top=1-0.10*4.8/xs)
ax = fig.add_subplot(111)
ymax = np.max((ffix[q])*anorm)
if Verbose:
print 'Make the plot'
ax.plot(lambdaz, temp_sed_sm, color='red')
ax.plot(lam[q],ffix[q]*anorm, color='blue', alpha=0.2, linewidth=1)
#### Show own extraction
sp1d = threedhst.spec1d.extract1D(id, root=grism_root, path='./HTML', show=False, out2d=False)#, GRISM_NAME='G800L')
lam = sp1d['lam']
flux = sp1d['flux']
ffix = sp1d['flux']-sp1d['contam']
ferr = sp1d['error']
anorm = np.sum(yint*ffix[q])/np.sum(ffix[q]**2)
ax.plot(lam[q],ffix[q]*anorm, color='blue', alpha=0.6, linewidth=1)
#### Show photometry + eazy template
ax.errorbar(lci, fobs, yerr=efobs, color='orange', marker='o', markersize=10, linestyle='None', alpha=0.4)
ax.plot(lambdaz, temp_sed_sm, color='red', alpha=0.4)
if wfc3_exist:
q_wfc3 = np.where((wfc3_spec[1].data.wave > 1.08e4) & (wfc3_spec[1].data.wave < 1.68e4) & (wfc3_spec[1].data.flux > 0))[0]
yint_wfc3 = np.interp(wfc3_spec[1].data.wave[q_wfc3], lambdaz, temp_sed_sm)
spec_wfc3 = (wfc3_spec[1].data.flux-wfc3_spec[1].data.contam)/wfc3_spec[1].data.sensitivity
anorm_wfc3 = np.sum(yint_wfc3*spec_wfc3[q_wfc3])/np.sum(spec_wfc3[q_wfc3]**2)
if np.isnan(anorm_wfc3): anorm_wfc3 = 1.
print 'Scaling factors: ', anorm, anorm_wfc3
ax.plot(wfc3_spec[1].data.wave[q_wfc3], spec_wfc3[q_wfc3]*anorm_wfc3, color='blue',alpha=0.6, linewidth=1)
ax.set_ylabel(r'$f_{\lambda}$')
if plt.rcParams['text.usetex']:
ax.set_xlabel(r'$\lambda$ [\AA]')
ax.set_title('%s: \#%d, z=%4.1f'
%(SPC.filename.split('_2_opt')[0].replace('_','\_'),id,
zout.z_peak[photom_idx]))
else:
ax.set_xlabel(r'$\lambda$ [$\AA$]')
ax.set_title('%s: #%d, z=%4.1f'
%(SPC.filename.split('_2_opt')[0].replace('_','\_'),id,
zout.z_peak[photom_idx]))
#kmag = 25-2.5*np.log10(cat.ktot[photom_idx])
kmag = cat.kmag[photom_idx]
##### Labels
label = 'ID='+r'%s K=%4.1f $\log M$=%4.1f' %(np.int(cat.id[photom_idx]),
kmag, fout.field('lmass')[photom_idx])
ax.text(5e3,1.08*ymax, label, horizontalalignment='left',
verticalalignment='bottom')
label = 'R=%4.1f"' %(drMatch)
if drMatch > 1.1:
label_color = 'red'
else:
label_color = 'black'
ax.text(2.2e4,1.08*ymax, label, horizontalalignment='right',
color=label_color, verticalalignment='bottom')
ax.set_xlim(xmin,xmax)
ax.set_ylim(-0.1*ymax,1.2*ymax)
if Verbose:
print 'Save the plot'
if OUT_FILE_FORMAT:
out_file = '%s_%05d_SED.png' %(grism_root, id)
else:
out_file = OUT_FILE
if USE_PLOT_GUI:
fig.savefig(OUT_PATH+'/'+out_file,dpi=100,transparent=False)
plt.close()
else:
canvas = FigureCanvasAgg(fig)
canvas.print_figure(OUT_PATH+'/'+out_file, dpi=100, transparent=False)
print unicorn.noNewLine+OUT_PATH+'/'+out_file
if Verbose:
print 'Close the plot window'
def reduce_acs(root='',LIMITING_MAGNITUDE=20., match_wfc3 = False, WFC3_DIR='/3DHST/Spectra/Release/v2.0/GOODS-S'):
"""
Set parameters for the aXe reduction, run aXe, read photometric catalog, find matches, make plots of spectra with photometry.
"""
import numpy as np
field = root[0:-3]
if LIMITING_MAGNITUDE is None:
LIMITING_MAGNITUDE=20.
os_command = 'cp PREP_FLT/'+root+'*shifts.txt PREP_FLT/'+root+'*tweak.fits PREP_FLT/'+root+'*asn.fits DATA/'
os.system(os_command)
os.chdir(unicorn.GRISM_HOME+'ACS_PARALLEL/'+field)
unicorn.go_3dhst.set_parameters(direct='F814W', LIMITING_MAGNITUDE=LIMITING_MAGNITUDE)
threedhst.process_grism.set_ACS_G800L()
threedhst.options['PREFAB_DIRECT_IMAGE'] = '../PREP_FLT/'+root+'-F814W_drz.fits'
threedhst.options['PREFAB_GRISM_IMAGE'] = root+'-G800L_drz.fits'
if threedhst.options['PREFAB_GRISM_IMAGE'] != '':
print "Copy PREFAB_GRISM_IMAGE to DATA."
os.system('cp PREP_FLT/'+threedhst.options['PREFAB_GRISM_IMAGE']+' DATA/')
threedhst.options['FORCE_CATALOG']=root+'.ext.cat'
##### Note: segmentation image has to accompany the force_catalog!
threedhst.process_grism.reduction_script(asn_grism_file=root+'-G800L_asn.fits')
### SEDs unicorn.analysis.make_SED_plots(grism_root='jbhm51020')
#os.chdir('../')
## read photometric, redshift, SPS catalogs
cat, zout, fout = unicorn.analysis.read_catalogs(root=field)
cat.star_flag=np.ones(cat.id.size)
## path where other eazy outputs live
OUTPUT_DIRECTORY = os.path.dirname(zout.filename)
MAIN_OUTPUT_FILE = os.path.basename(zout.filename).split('.zout')[0]
## read grism outputs
if unicorn.hostname().startswith('uni'):
BASE_PATH='/Volumes/robot/3DHST/Spectra/Work/ACS_PARALLEL/'+field+'/'
if unicorn.hostname().startswith('hyp'):
BASE_PATH='/3DHST/Spectra/Work/ACS_PARALLEL/'+field+'/'
print 'BASE PATH:', BASE_PATH
grism_root = root+'-G800L'
grismCat, SPC = unicorn.analysis.read_grism_files(root=grism_root.upper(), BASE_PATH=BASE_PATH, GRISM_NAME='G800L')
print 'Matched catalog'
unicorn.analysis.match_grism_to_phot(grism_root=grism_root,
SPC = SPC, cat = cat,
grismCat = grismCat, zout = zout, fout = fout,
OUTPUT = './HTML/SED/'+grism_root+'_match.cat',
OUTPUT_DIRECTORY=OUTPUT_DIRECTORY,
MAIN_OUTPUT_FILE=MAIN_OUTPUT_FILE)
## make figures
if match_wfc3:
for id in grismCat.id:
print id
status = unicorn.go_acs.specphot_acs_and_wfc3(id=id, grism_root=grism_root, SPC = SPC,
cat = cat,
grismCat = grismCat, zout = zout, fout = fout,
OUT_PATH = './HTML/SED/', OUT_FILE_FORMAT=True, Verbose=False,
MAIN_OUTPUT_FILE = MAIN_OUTPUT_FILE,
OUTPUT_DIRECTORY = OUTPUT_DIRECTORY,
CACHE_FILE = 'Same',GET_WFC3 = True, WFC3_DIR=WFC3_DIR)
else:
for id in grismCat.id:
status = unicorn.analysis.specphot(id=id, grism_root=grism_root, SPC = SPC,
cat = cat,
grismCat = grismCat, zout = zout, fout = fout,
OUT_PATH = './HTML/SED/', OUT_FILE_FORMAT=True, Verbose=False,
MAIN_OUTPUT_FILE = MAIN_OUTPUT_FILE,
OUTPUT_DIRECTORY = OUTPUT_DIRECTORY,
CACHE_FILE = 'Same')
unicorn.go_3dhst.clean_up()