-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathMakeMask.py
executable file
·722 lines (570 loc) · 26 KB
/
MakeMask.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
#!/usr/bin/env python
import sys,os
if "PYTHONPATH_FIRST" in os.environ.keys() and int(os.environ["PYTHONPATH_FIRST"]):
sys.path = os.environ["PYTHONPATH"].split(":") + sys.path
import os
import sys
#sys.path=os.environ["PYTHONPATH"].split(":")+sys.path
from pyrap.tables import table
from pyrap.images import image
from Sky import ClassSM
import optparse
import numpy as np
import glob
import os
from Other import reformat
SaveFile="last_MyCasapy2BBS.obj"
import pickle
import scipy.ndimage
from Tools import ModFFTW
from SkyModel.PSourceExtract import ClassIslands
from SkyModel.Other.ClassCasaImage import PutDataInNewImage
import scipy.special
from DDFacet.Other import MyLogger
log=MyLogger.getLogger("MakeMask")
from SkyModel.Other.progressbar import ProgressBar
import collections
from SkyModel.Other.MyHist import MyCumulHist
from SkyModel.PSourceExtract import Gaussian
from SkyModel.Sky import ModRegFile
from DDFacet.ToolsDir import ModFFTW
import DDFacet.Imager.SSD.ClassIslandDistanceMachine
from DDFacet.ToolsDir.rad2hmsdms import rad2hmsdms
from DDFacet.Other import MyPickle
def read_options():
desc=""" [email protected]"""
opt = optparse.OptionParser(usage='Task to build a boolean mask file from a restored fits image, Usage: %prog <options>',version='%prog version 1.0',description=desc)
group = optparse.OptionGroup(opt, "* Data-related options")
group.add_option('--RestoredIm',type="str",help="Name of the restored image",default=None)
group.add_option('--Th',type="float",default=10,help="Threshold in sigma above which to draw an island, default is %default")
group.add_option("--Box",type="str",default="30,2",help="Box size to compute the noise (using the very classy min() statistics). Default is %default")
group.add_option("--OutName",type="str",help="Output name (optional). If not specified, will just add .mask.fits at the end if RestoredIm",default="mask")
group.add_option("--OutNameNoiseMap",type="str",help="If you want to save the noise image image. Default is %default",default="")
group.add_option("--ExternalMask",type="str",help="Use an external mask in addition. Default is %default",default="")
group.add_option("--ds9Mask",type="str",help="You can use a ds9 reg file too. Green circle to include, Red circles to explude. This goes in combination to the --Th threshold. Default is %default",default="")
group.add_option('--UseIslands',type="int",help="Deprecated - look at the code",default=0)
group.add_option('--RevertInput',type="int",help="look at the code",default=0)
group.add_option('--ConvNoise',type="int",help="look at the code",default=0)
group.add_option('--OutMaskExtended',type="str",help="look at the code",default=0)
#group.add_option("--MedFilter",type="str",default="50,10")
opt.add_option_group(group)
options, arguments = opt.parse_args()
f = open(SaveFile,"wb")
pickle.dump(options,f)
#####################"
class ClassMakeMask():
def __init__(self,FitsFile=None,
Th=5.,
Box=(50,10),
UseIslands=False,
OutName="mask",
ds9Mask="",
OutNameNoiseMap="",
options=None,
RevertInput=False):
self.ds9Mask=ds9Mask
self.FitsFile=FitsFile
self.Th=Th
self.Box,self.IncrPix=Box
self.Boost=self.IncrPix
self.box=self.Box,self.Box
self.CasaIm=image(self.FitsFile)
self.Restored=self.CasaIm.getdata()
if options.RevertInput:
print>>log, "Reverting the image..."
self.Restored*=-1
self.UseIslands=UseIslands
self.OutName=OutName
self.OutNameNoiseMap=OutNameNoiseMap
self.options=options
im=self.CasaIm
c=im.coordinates()
incr=np.abs(c.dict()["direction0"]["cdelt"][0])
self.incr_rad=incr
if self.UseIslands:
PMaj=(im.imageinfo()["restoringbeam"]["major"]["value"])
PMin=(im.imageinfo()["restoringbeam"]["minor"]["value"])
PPA=(im.imageinfo()["restoringbeam"]["positionangle"]["value"])
ToSig=(1./3600.)*(np.pi/180.)/(2.*np.sqrt(2.*np.log(2)))
SigMaj_rad=PMaj*ToSig
SigMin_rad=PMin*ToSig
SixMaj_pix=SigMaj_rad/incr
SixMin_pix=SigMin_rad/incr
PPA_rad=PPA*np.pi/180
x,y=np.mgrid[-10:11:1,-10:11:1]
self.RefGauss=Gaussian.GaussianXY(x,y,1.,sig=(SixMin_pix,SixMaj_pix),pa=PPA_rad)
self.RefGauss_xy=x,y
self.BeamMin_pix=SixMin_pix*(2.*np.sqrt(2.*np.log(2)))
self.BeamMaj_pix=SixMaj_pix*(2.*np.sqrt(2.*np.log(2)))
self.RBeam_pix=SixMaj_pix
print>>log, "Restoring Beam size of (%3.3f, %3.3f) pixels"%(self.BeamMin_pix, self.BeamMaj_pix)
# #################"
# _,_,nx,ny=self.Restored.shape
# xc,yc=nx/2,nx/2
# sup=200
# x,y=np.mgrid[-sup:sup:1,-sup:sup:1]
# G=Gaussian.GaussianXY(x,y,1.,sig=(7,18),pa=0.)
# self.Restored[0,0,xc:xc+2*sup,yc:yc+2*sup]+=G[:,:]
# xc,yc=nx/2+10,nx/2+10
# G=Gaussian.GaussianXY(x,y,1.,sig=(3,3),pa=0.)
# self.Restored[0,0,xc:xc+2*sup,yc:yc+2*sup]+=G[:,:]
# #################"
#self.Restored=np.load("testim.npy")
self.A=self.Restored[0,0]
def GiveVal(self,A,xin,yin):
x,y=round(xin),round(yin)
s=A.shape[0]-1
cond=(x<0)|(x>s)|(y<0)|(y>s)
if cond:
value="out"
else:
value="%8.2f mJy"%(A.T[x,y]*1000.)
return "x=%4i, y=%4i, value=%10s"%(x,y,value)
def ComputeNoiseMap(self):
print>>log, "Compute noise map..."
Boost=self.Boost
Acopy=self.Restored[0,0,0::Boost,0::Boost].copy()
SBox=(self.box[0]/Boost,self.box[1]/Boost)
# MeanAbs=scipy.ndimage.filters.mean_filter(np.abs(Acopy),SBox)
# Acopy[Acopy>0]=MeanAbs[Acopy>0]
# Noise=np.sqrt(scipy.ndimage.filters.median_filter(np.abs(Acopy)**2,SBox))
x=np.linspace(-10,10,1000)
f=0.5*(1.+scipy.special.erf(x/np.sqrt(2.)))
n=SBox[0]*SBox[1]
F=1.-(1.-f)**n
ratio=np.abs(np.interp(0.5,F,x))
Noise=-scipy.ndimage.filters.minimum_filter(Acopy,SBox)/ratio
Noise[Noise<0]=1e-10
# indxy=(Acopy>5.*Noise)
# Acopy[indxy]=5*Noise[indxy]
# Noise=np.sqrt(scipy.ndimage.filters.median_filter(np.abs(Acopy)**2,SBox))
# indxy=(Acopy>5.*Noise)
# Acopy[indxy]=5*Noise[indxy]
# Noise=np.sqrt(scipy.ndimage.filters.median_filter(np.abs(Acopy)**2,SBox))
#NoiseMed=np.median(Noise)
#Noise[Noise<NoiseMed]=NoiseMed
self.Noise=np.zeros_like(self.Restored[0,0])
for i in range(Boost):
for j in range(Boost):
s00,s01=Noise.shape
s10,s11=self.Noise[i::Boost,j::Boost].shape
s0,s1=min(s00,s10),min(s10,s11)
self.Noise[i::Boost,j::Boost][0:s0,0:s1]=Noise[:,:][0:s0,0:s1]
ind=np.where(self.Noise==0.)
self.Noise[ind]=1e-10
if self.options.OutMaskExtended:
GD=None
MaskExtended=(self.Noise<0.1*np.median(self.Noise))
OutMaskExtended=self.options.OutMaskExtended
nx=MaskExtended.shape[-1]
CurrentNegMask=np.logical_not(MaskExtended).reshape((1,1,nx,nx))
PSFServer=None
IdSharedMem=None
DicoDirty=None
IslandDistanceMachine=DDFacet.Imager.SSD.ClassIslandDistanceMachine.ClassIslandDistanceMachine(GD,
CurrentNegMask,
PSFServer,
DicoDirty,
IdSharedMem=IdSharedMem)
ListIslands=IslandDistanceMachine.SearchIslands(None,Image=self.Restored)
ListIslands=IslandDistanceMachine.ConvexifyIsland(ListIslands)#,PolygonFile="%s.pickle"%OutMaskExtended)
ListPolygons=IslandDistanceMachine.ListPolygons
#ff,pol,_,_dec,ra=self.CasaIm.toworld((0,0,0,0))
ListPolygonsRADEC=[]
for Polygon in ListPolygons:
xx,yy=Polygon.T
ThisPolygon=[]
for iP in range(xx.shape[0]):
xcc,ycc =xx[iP],yy[iP]
ff,pol,dec,ra=self.CasaIm.toworld((0,0,xcc,ycc))
ThisPolygon.append((ra,dec))
ListPolygonsRADEC.append(np.array(ThisPolygon))
FName="%s.pickle"%OutMaskExtended
print>>log,"Saving %s"%FName
MyPickle.Save(ListPolygonsRADEC,FName)
REGName="%s.reg"%OutMaskExtended
RM=ModRegFile.PolygonNpToReg(ListPolygonsRADEC,REGName)
RM.makeRegPolyREG()
# TestArray=np.zeros_like(CurrentNegMask)
# nx=TestArray.shape[-1]
# xx,yy=20,100
# TestArray[0,0,xx,yy]=1
# PutDataInNewImage(self.FitsFile,"TestCoord.fits",np.float32(TestArray))
# ff,pol,dec,ra=self.CasaIm.toworld((0,0,xx,yy))
# pp=[[xx,yy],[yy,xx],
# [nx/2-xx,yy],[nx/2+xx,yy],
# [nx/2-yy,xx],[nx/2+yy,xx]]
# for isol in range(len(pp)):
# xx,yy=pp[isol]
# ff,pol,dec,ra=self.CasaIm.toworld((0,0,xx,yy))
# sRA =rad2hmsdms(ra,Type="ra").replace(" ",":")
# sDEC =rad2hmsdms(dec,Type="dec").replace(" ",":")
# stop
MaskOut=np.zeros_like(CurrentNegMask)
N=0
for Island in ListIslands:
x,y=np.array(Island).T
if x.size<=10: continue
MaskOut[0,0,x,y]=1
N+=1
print>>log,"Number of large enough islands %i"%N
MaskExtended=MaskOut
os.system("rm -rf %s"%OutMaskExtended)
os.system("rm -rf %s.fits"%OutMaskExtended)
PutDataInNewImage(self.FitsFile,OutMaskExtended,np.float32(MaskExtended))
NoiseMed=np.median(self.Noise)
self.Noise[self.Noise<NoiseMed]=NoiseMed
nx=self.Noise.shape[-1]
if self.options.ConvNoise:
print>>log,"Convolve..."
NoiseMap,G=ModFFTW.ConvolveGaussianWrapper(self.Noise.reshape((1,1,nx,nx)),Sig=4*SBox[0])
NoiseMap/=np.sum(G)
self.Noise=NoiseMap[0,0]
if self.OutNameNoiseMap!="":
#print>>log, "Save noise map as %s"%self.OutNameNoiseMap
#self.CasaIm.saveas(self.OutNameNoiseMap)
#CasaNoise=image(self.OutNameNoiseMap)
#CasaNoise.putdata(self.Noise)
#CasaNoise.tofits(self.OutNameNoiseMap+".fits")
#del(CasaNoise)
os.system("rm -rf %s"%self.OutNameNoiseMap)
#os.system("rm -rf %s"%self.OutNameNoiseMap+".fits")
PutDataInNewImage(self.FitsFile,self.OutNameNoiseMap,np.float32(self.Noise))
os.system("rm -rf %s.mean"%self.OutNameNoiseMap)
PutDataInNewImage(self.FitsFile,self.OutNameNoiseMap+".mean",np.float32(np.zeros_like(self.Noise)))
# def ComputeNoiseMap(self):
# print "Compute noise map..."
# Boost=self.Boost
# Acopy=self.Restored[0,0,0::Boost,0::Boost].copy()
# SBox=(self.box[0]/Boost,self.box[1]/Boost)
# Noise=np.sqrt(scipy.ndimage.filters.median_filter(np.abs(Acopy)**2,SBox))
# self.Noise=np.zeros_like(self.Restored[0,0])
# for i in range(Boost):
# for j in range(Boost):
# s00,s01=Noise.shape
# s10,s11=self.Noise[i::Boost,j::Boost].shape
# s0,s1=min(s00,s10),min(s10,s11)
# self.Noise[i::Boost,j::Boost][0:s0,0:s1]=Noise[:,:][0:s0,0:s1]
# print " ... done"
# ind=np.where(self.Noise==0.)
# self.Noise[ind]=1e-10
def MakeMask(self):
self.ImMask=(self.Restored[0,0,:,:]>self.Th*self.Noise)
self.ImMask[:,-1]=0
self.ImMask[:,0]=0
self.ImMask[0,:]=0
self.ImMask[-1,:]=0
#self.ImIsland=scipy.ndimage.filters.median_filter(self.ImIsland,size=(3,3))
def MaskSelectedDS9(self):
ds9Mask=self.ds9Mask
print>>log,"Reading ds9 region file: %s"%ds9Mask
R=ModRegFile.RegToNp(ds9Mask)
R.Read()
IncludeCat=R.CatSel
ExcludeCat=R.CatExclude
print>>log," Excluding pixels"
for iRegExclude in range(R.CatExclude.shape[0]):
rac,decc,Radius=R.CatExclude.ra[iRegExclude],R.CatExclude.dec[iRegExclude],R.CatExclude.Radius[iRegExclude]
RadiusPix=(1.1*Radius/self.incr_rad)
freq,pol,_,_=self.CasaIm.toworld((0,0,0,0))
_,_,yc,xc=self.CasaIm.topixel((freq,pol,decc,rac))
xGrid,yGrid=np.mgrid[int(xc-RadiusPix):int(xc+RadiusPix)+1,int(yc-RadiusPix):int(yc+RadiusPix)+1]
xGrid=xGrid.ravel()
yGrid=yGrid.ravel()
for iPix in range(xGrid.size):
# if iPix%10000==0:
# print iPix,"/",xGrid.size
ipix,jpix=xGrid[iPix],yGrid[iPix]
_,_,dec,ra=self.CasaIm.toworld((0,0,jpix,ipix))
#d=np.sqrt((ra-rac)**2+(dec-decc)**2)
d=self.GiveAngDist(ra,dec,rac,decc)
if d<Radius:
self.ImMask[jpix,ipix]=0
#self.ImMask.fill(0)
print>>log," Including pixels"
for iRegInclude in range(IncludeCat.shape[0]):
rac,decc,Radius=IncludeCat.ra[iRegInclude],IncludeCat.dec[iRegInclude],IncludeCat.Radius[iRegInclude]
RadiusPix=(1.1*Radius/self.incr_rad)
freq,pol,_,_=self.CasaIm.toworld((0,0,0,0))
_,_,yc,xc=self.CasaIm.topixel((freq,pol,decc,rac))
xGrid,yGrid=np.mgrid[int(xc-RadiusPix):int(xc+RadiusPix)+1,int(yc-RadiusPix):int(yc+RadiusPix)+1]
xGrid=xGrid.flatten().tolist()
yGrid=yGrid.flatten().tolist()
for ipix,jpix in zip(xGrid,yGrid):
_,_,dec,ra=self.CasaIm.toworld((0,0,jpix,ipix))
#d=np.sqrt((ra-rac)**2+(dec-decc)**2)
d=self.GiveAngDist(ra,dec,rac,decc)
#print ipix,jpix
if d<Radius:
#print "ones",ipix,jpix
self.ImMask[jpix,ipix]=1
def GiveAngDist(self,ra1,dec1,ra2,dec2):
sin=np.sin
cos=np.cos
#cosA = sin(dec1)*sin(dec2) + cos(dec1)*cos(dec1)*cos(ra1 - ra2)
#A=np.arccos(cosA)
A=np.sqrt(((ra1-ra2)*cos((dec1+dec2)/2.))**2+(dec1-dec2)**2)
return A
def BuildIslandList(self):
import scipy.ndimage
print>>log," Labeling islands"
self.ImIsland,NIslands=scipy.ndimage.label(self.ImMask)
ImIsland=self.ImIsland
NIslands+=1
nx,_=ImIsland.shape
print>>log," Found %i islands"%NIslands
NMaxPix=100000
Island=np.zeros((NIslands,NMaxPix,2),np.int32)
NIslandNonZero=np.zeros((NIslands,),np.int32)
print>>log," Extracting pixels in islands"
pBAR= ProgressBar('white', width=50, block='=', empty=' ',Title=" Extracting ", HeaderSize=10,TitleSize=13)
comment=''
for ipix in range(nx):
pBAR.render(int(100*ipix / (nx-1)), comment)
for jpix in range(nx):
iIsland=self.ImIsland[ipix,jpix]
if iIsland:
NThis=NIslandNonZero[iIsland]
Island[iIsland,NThis,0]=ipix
Island[iIsland,NThis,1]=jpix
NIslandNonZero[iIsland]+=1
print>>log," Listing pixels in islands"
NMinPixIsland=5
DicoIslands=collections.OrderedDict()
for iIsland in range(1,NIslands):
ind=np.where(Island[iIsland,:,0]!=0)[0]
if ind.size < NMinPixIsland: continue
Npix=ind.size
Comps=np.zeros((Npix,3),np.float32)
for ipix in range(Npix):
x,y=Island[iIsland,ipix,0],Island[iIsland,ipix,1]
s=self.Restored[0,0,x,y]
Comps[ipix,0]=x
Comps[ipix,1]=y
Comps[ipix,2]=s
DicoIslands[iIsland]=Comps
print>>log," Final number of islands: %i"%len(DicoIslands)
self.DicoIslands=DicoIslands
def FilterIslands(self):
DicoIslands=self.DicoIslands
NIslands=len(self.DicoIslands)
print>>log, " Filter each individual islands"
#pBAR= ProgressBar('white', width=50, block='=', empty=' ',Title=" Filter ", HeaderSize=10,TitleSize=13)
#comment=''
NormHist=True
for iIsland in DicoIslands.keys():
#pBAR.render(int(100*iIsland / float(len(DicoIslands.keys())-1)), comment)
x,y,s=DicoIslands[iIsland].T
#Im=self.GiveIm(x,y,s)
#pylab.subplot(1,2,1)
#pylab.imshow(Im,interpolation="nearest")
# pylab.subplot(1,2,2)
sr=self.RefGauss.copy()*np.max(s)
xm,ym=int(np.mean(x)),int(np.mean(y))
Th=self.Th*self.Noise[xm,ym]
xg,yg=self.RefGauss_xy
MaskSel=(sr>Th)
xg_sel=xg[MaskSel].ravel()
yg_sel=yg[MaskSel].ravel()
sr_sel=sr[MaskSel].ravel()
if sr_sel.size<7: continue
###############
logs=s*s.size#np.log10(s*s.size)
X,Y=MyCumulHist(logs,Norm=NormHist)
logsr=sr_sel*sr_sel.size#np.log10(sr_sel*sr_sel.size)
Xr,Yr=MyCumulHist(logsr,Norm=NormHist)
Cut=0.9
ThisTh=np.interp(Cut,Yr,Xr)
#ThisTh=(ThisTh)/sr_sel.size
#Im=self.GiveIm(xg_sel,yg_sel,sr_sel)
#pylab.subplot(1,2,2)
#pylab.imshow(Im,interpolation="nearest")
ind=np.where(s*s.size>ThisTh)[0]
#print ThisTh,ind.size/float(s.size )
DicoIslands[iIsland]=DicoIslands[iIsland][ind].copy()
# pylab.clf()
# pylab.plot(X,Y)
# pylab.plot([ThisTh,ThisTh],[0,1],color="black")
# pylab.plot(Xr,Yr,color="black",lw=2,ls="--")
# pylab.draw()
# pylab.show(False)
# pylab.pause(0.1)
# import time
# time.sleep(1)
# stop
# def FilterIslands2(self):
# DicoIslands=self.DicoIslands
# NIslands=len(self.DicoIslands)
# print>>log, " Filter each individual islands"
# #pBAR= ProgressBar('white', width=50, block='=', empty=' ',Title=" Filter ", HeaderSize=10,TitleSize=13)
# #comment=''
# NormHist=False
# gamma=1.
# d0=1.
# for iIsland in [DicoIslands.keys()[0],DicoIslands.keys()[2]]:#DicoIslands.keys():
# #pBAR.render(int(100*iIsland / float(len(DicoIslands.keys())-1)), comment)
# x,y,s=DicoIslands[iIsland].T
# #Im=self.GiveIm(x,y,s)
# #pylab.subplot(1,2,1)
# #pylab.imshow(Im,interpolation="nearest")
# # pylab.subplot(1,2,2)
# #sr=self.RefGauss.copy()*np.max(s)
# xm,ym=int(np.mean(x)),int(np.mean(y))
# Th=self.Th*self.Noise[xm,ym]
# Np=x.size
# DMat=np.sqrt((x.reshape((Np,1))-x.reshape((1,Np)))**2+(y.reshape((Np,1))-y.reshape((1,Np)))**2)#/self.RBeam_pix
# C=s.reshape((Np,1))*(1./(d0+DMat))**gamma
# #C=1./(d0+DMat)**gamma
# #C-=np.diag(np.diag(C))
# #MaxVec=np.mean(C,axis=1)
# #ind=(MaxVec>s).ravel()
# MaxVec=np.sum(C,axis=1)#*s#/Th
# pylab.clf()
# for iPix in range(C.shape[0])[0::10]:
# X,Y=MyCumulHist(C[iPix],Norm=False)
# pylab.plot(X,Y,color="gray")
# ic=np.argmax(s)
# X,Y=MyCumulHist(C[ic],Norm=False)
# pylab.plot(X,Y,color="black",ls="--",lw=2)
# pylab.draw()
# pylab.show(False)
# pylab.pause(0.1)
# # Im0=self.GiveIm(x,y,s)
# # #Im1=self.GiveIm(x,y,MaxVec)
# # MNorm=MaxVec/s
# # Im1=self.GiveIm(x,y,MNorm)
# # ImMask=self.GiveIm(x,y,(MaxVec>1.))
# # pylab.clf()
# # pylab.subplot(1,3,1)
# # pylab.imshow(Im0,interpolation="nearest")
# # pylab.colorbar()
# # #pylab.title("Th = %f"%Th)
# # pylab.subplot(1,3,2)
# # pylab.imshow(Im1,interpolation="nearest",vmin=MNorm.min(),vmax=MNorm.max())
# # pylab.colorbar()
# # pylab.subplot(1,3,3)
# # pylab.imshow(ImMask,interpolation="nearest")
# # pylab.draw()
# # pylab.show(False)
# # pylab.pause(0.1)
# # import time
# # time.sleep(1)
# # # xg,yg=self.RefGauss_xy
# # # MaskSel=(sr>Th)
# # # xg_sel=xg[MaskSel].ravel()
# # # yg_sel=yg[MaskSel].ravel()
# # # sr_sel=sr[MaskSel].ravel()
# # # if sr_sel.size<7: continue
# # #DicoIslands[iIsland]=DicoIslands[iIsland][ind].copy()
# # # pylab.clf()
# # # pylab.plot(X,Y)
# # # pylab.plot([ThisTh,ThisTh],[0,1],color="black")
# # # pylab.plot(Xr,Yr,color="black",lw=2,ls="--")
# # # pylab.draw()
# # # pylab.show(False)
# # # pylab.pause(0.1)
# # # import time
# # # time.sleep(1)
# stop
def IslandsToMask(self):
self.ImMask.fill(0)
DicoIslands=self.DicoIslands
NIslands=len(self.DicoIslands)
print>>log, " Building mask image from filtered islands"
#pBAR= ProgressBar('white', width=50, block='=', empty=' ',Title=" Building ", HeaderSize=10,TitleSize=13)
#comment=''
for iIsland in DicoIslands.keys():
#pBAR.render(int(100*iIsland / float(len(DicoIslands.keys())-1)), comment)
x,y,s=DicoIslands[iIsland].T
self.ImMask[np.int32(x),np.int32(y)]=1
def GiveIm(self,x,y,s):
dx=np.int32(x-x.min())
dy=np.int32(y-y.min())
nx=dx.max()+1
ny=dy.max()+1
print nx,ny
Im=np.zeros((nx,ny),np.float32)
Im[dx,dy]=s
return Im
def CreateMask(self):
self.ComputeNoiseMap()
self.MakeMask()
if self.ds9Mask!="":
self.MaskSelectedDS9()
ExternalAndMask=self.options.ExternalMask
if ExternalAndMask is not None and ExternalAndMask is not "":
from DDFacet.Imager import ClassCasaImage
CleanMaskImageName=ExternalAndMask
print>>log,"Use mask image %s"%CleanMaskImageName
CleanMaskImage = np.bool8(ClassCasaImage.FileToArray(CleanMaskImageName,False))[0,0]
self.ImMask=(self.ImMask & CleanMaskImage)
if self.UseIslands:
# Make island list
self.BuildIslandList()
self.FilterIslands()
self.IslandsToMask()
#self.plot()
nx,ny=self.ImMask.shape
ImWrite=self.ImMask.reshape((1,1,nx,ny))
PutDataInNewImage(self.FitsFile,self.FitsFile+"."+self.OutName,np.float32(ImWrite))
def plot(self):
import pylab
pylab.clf()
ax1=pylab.subplot(2,3,1)
vmin,vmax=-np.max(self.Noise),5*np.max(self.Noise)
MaxRms=np.max(self.Noise)
ax1.imshow(self.A,vmin=vmin,vmax=vmax,interpolation="nearest",cmap="gray",origin="lower")
ax1.format_coord = lambda x,y : self.GiveVal(self.A,x,y)
pylab.title("Image")
ax2=pylab.subplot(2,3,3,sharex=ax1,sharey=ax1)
pylab.imshow(self.Noise,vmin=0.,vmax=np.max(self.Noise),interpolation="nearest",cmap="gray",origin="lower")
ax2.format_coord = lambda x,y : self.GiveVal(self.Noise,x,y)
pylab.title("Noise Image")
pylab.xlim(0,self.A.shape[0]-1)
pylab.ylim(0,self.A.shape[0]-1)
ax3=pylab.subplot(2,3,6,sharex=ax1,sharey=ax1)
ax3.imshow(self.ImMask,vmin=vmin,vmax=vmax,interpolation="nearest",cmap="gray",origin="lower")
ax3.format_coord = lambda x,y : self.GiveVal(self.ImMask,x,y)
pylab.title("Island Image")
pylab.xlim(0,self.A.shape[0]-1)
pylab.ylim(0,self.A.shape[0]-1)
pylab.draw()
pylab.show(False)
def main(options=None):
if options==None:
f = open(SaveFile,'rb')
options = pickle.load(f)
s0,s1=options.Box.split(",")
Box=(int(s0),int(s1))
MaskMachine=ClassMakeMask(options.RestoredIm,
Th=options.Th,
Box=Box,
UseIslands=options.UseIslands,
OutName=options.OutName,
ds9Mask=options.ds9Mask,
OutNameNoiseMap=options.OutNameNoiseMap,
options=options)
MaskMachine.CreateMask()
if __name__=="__main__":
read_options()
f = open(SaveFile,'rb')
options = pickle.load(f)
main(options=options)
def test():
FitsFile="/media/tasse/data/DDFacet/Test/MultiFreqs3.restored.fits"
Conv=ClassMakeMask(FitsFile=FitsFile,Th=5.,Box=(50,10))
Conv.ComputeNoiseMap()
Conv.FindIslands()
nx,ny=Conv.ImIsland.shape
ImWrite=Conv.ImIsland.reshape((1,1,nx,ny))
PutDataInNewImage(FitsFile,FitsFile+".mask",np.float32(ImWrite))
#Conv.plot()
# import pylab
# pylab.clf()
# ax=pylab.subplot(1,2,1)
# pylab.imshow(Conv.Restored[0,0],cmap="gray")
# pylab.subplot(1,2,2,sharex=ax,sharey=ax)
# pylab.imshow(Conv.IslandsMachine.ImIsland,cmap="gray")
# pylab.draw()
# pylab.show(False)
# stop