-
Notifications
You must be signed in to change notification settings - Fork 0
/
ripples_prep_line.py
491 lines (413 loc) · 16.4 KB
/
ripples_prep_line.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
'''
Prepare files for RIPPLES
1) a binary file called u.bin containing the u (in meters)
2) a binary file called v.bin containing the v (in meters)
3) a binary file called vis_chan_0.bin containing the visibilities. For each visibility first the real part and then the imaginary part. So if you have 4 visibilities, for example, this would be length 8 vector of real_1, image_1, real_2, imag_2, real_3 ... etc.
4) a binary file called sigma_squared_inv.bin similar in format to vis_chan_0.bin containing the inverse of errors squared (we'll have to sit together for me to tell you how to compute the errors though).
5) a binary file called ant1.bin containing the names of 1st antennas (they should be indexed, like 0, 1, 2 , etc.) ant1 has the same length as u.bin indicating which antenna was used for that visibility.
6) another file called ant2.bin for the second antenna.
7) a file called frequencies.bin containing the frequencies of the visibilities in Hz
Need updates based on recent changes made to ripples_prep.py
'''
try:
from casa import table as tb
except:
from taskinit import tb
import numpy as np
import math
RAD2DEGREE = 180. / math.pi
DEGREE2RAD = math.pi / 180.
np.set_printoptions(threshold='nan')
def make_line_cube_nobinning(lineVism, lineimagename):
"""
Make a line cube without binning to decide on channel ranges to split
"""
mode = 'velocity'
imagermode = 'csclean'
cell = '0.0300arcsec'
imsize = [800, 800]
restfreq = '151.80526GHz' # from proposal
niter = 0
threshold = '0.0mJy'
interactive = False
mode = 'channel'
start = ''
nchan = -1
weighting = 'briggs'
robust = 0.5
for ext in ['.flux', '.image', '.mask', '.model', '.pbcor', '.psf', '.residual', '.flux.pbcoverage']:
rmtables(lineimagename + ext)
clean(vis=lineVis,
imagename=lineimagename,
spw='',
mode=mode,
start=start,
nchan=nchan,
restfreq=restfreq,
imsize=imsize,
cell=cell,
niter=niter,
threshold=threshold,
interactive=interactive,
imagermode=imagermode,
stokes='I',
weighting=weighting,
robust=robust)
exportfits(lineimagename+'.image', lineimagename+'.fits', overwrite=True)
return lineimagename+'.fits'
def shift_phs_center(vis, pcd, field):
"""
Parameters
----------
vis: str
MS file
pcd: str
in format like: '10h27m51.6s -43d54m18s'
field: str
Returns
-------
outvis: str
filename of the output vis, with the shifted phase center
Example
-------
fixvis(vis=vis,
outpuvis='ngc3256-fixed.ms',
field='NGC3256',
phasecenter='J2000 10h27m51.6s -43d54m18s')
"""
outvis = vis.replace('.ms', 'phsShift.ms')
fixvis(vis=vis, outputvis=outvis, field=field,
phasecenter='J2000 ' + pcd)
return outvis
def ms2ripples(vis, savepath, timebinsec, Nsam=None, timebin=False, overwrite=True, verbose=True, debug=False):
import astropy.constants as c
if timebin:
print("Performing time binning, by {:}s:".format(timebinsec))
prefix = vis.replace('.ms', '_timebin' + '{:}s.ms'.format(timebinsec))
prefix = os.path.basename(prefix)
timebinVis = os.path.join(savepath, prefix)
if overwrite:
rmtables(timebinVis)
default(split2)
split2(vis=vis,
timebin=str(timebinsec)+'s',
outputvis=timebinVis,
datacolumn='data',
keepflags=False
)
if overwrite:
plotms(vis=timebinVis, xaxis='uvdist', yaxis='amp', coloraxis='spw')
if debug and not os.path.exists(timebinVis[:timebinVis.find('.ms')] + '.image'):
clean(vis=timebinVis,
imagename=timebinVis[:timebinVis.find('.ms')],
spw='',
mode='mfs',
nchan=-1,
imsize=800,
cell='0.0300arcsec',
niter=0,
interactive=False,
stokes='I')
oldvis = vis
vis = timebinVis
else:
raw_input("No time binning...proceed with caution. Press Enter to continue.")
# check that the MS has only 1 science target
tb.open(timebinVis + '/FIELD')
src = tb.getcell('NAME')
print("Source in {}: {}\n").format(timebinVis, src)
tb.close()
ms.open(timebinVis)
spwInfo = ms.getspectralwindowinfo()
nchan = spwInfo["0"]["NumChan"]
npol = spwInfo["0"]["NumCorr"]
ms.close()
tb.open(oldvis)
_dataShape = tb.getcol('DATA').shape
tb.close()
tb.open(timebinVis)
# tb.colnames
data = tb.getcol('DATA')
uvw = tb.getcol('UVW')
uvwShape = uvw.shape
nvis = len(uvw[1, :]) # before dropping flagged data
flagRow = tb.getcol('FLAG_ROW')
assert (flagRow == True).any() == False
data_desc_id = tb.getcol("DATA_DESC_ID")
sigma = tb.getcol('SIGMA')
# weight = tb.getcol('WEIGHT')
weight = 1./sigma**2
# del sigma
if Nsam is not None:
from ripples_utils import pick_highSNR
idx = pick_highSNR(weight, Nsam, plothist=verbose)
uvw = uvw[:, idx]
with open(os.path.join(savepath, 'u.bin'), 'wb') as f:
f.write(uvw[0, :])
with open(os.path.join(savepath, 'v.bin'), 'wb') as f:
f.write(uvw[1, :])
if debug:
print uvw[0, :].max()
print uvw[0, :].min()
print uvw[1, :].max()
print uvw[1, :].min()
print uvw.dtype # float64
# del uvw
ant1 = tb.getcol('ANTENNA1')
ant2 = tb.getcol('ANTENNA2')
assert len(ant1) == nvis
assert len(ant2) == nvis
tb.done()
if Nsam is not None:
ant1 = ant1[idx]
ant2 = ant2[idx]
assert len(ant1) == len(uvw[0, :])
with open(os.path.join(savepath, 'ant1.bin'), 'wb') as f:
ant1 = np.asarray(ant1, dtype=np.float_)
# print ant1.dtype
f.write(ant1)
with open(os.path.join(savepath, 'ant2.bin'), 'wb') as f:
ant2 = np.asarray(ant2, dtype=np.float_)
f.write(ant2)
# ant1.tofile(os.path.join(savepath, 'ant1.bin'))
if debug:
print ant1.max()
print ant1.min()
print ant2.max()
print ant2.min()
xx = np.fromfile(os.path.join(savepath, 'ant1.bin'))
if Nsam is None:
assert len(xx) == nvis
else:
assert len(xx) == len(idx)
assert (xx == ant1).all()
xx = np.fromfile(os.path.join(savepath, 'ant2.bin'))
if Nsam is None:
assert len(xx) == nvis
else:
assert len(xx) == len(idx)
assert (xx == ant2).all()
# del ant1, ant2
if verbose:
print("Number of coorelation: ", npol)
print("data shape", data.shape)
print("data shape before time binning", _dataShape)
print("uvw shape", uvwShape)
print("weight shpae", weight.shape) # (npol, nvis)
tb.open(vis + '/SPECTRAL_WINDOW')
SPWFreqs = np.squeeze(tb.getcol("CHAN_FREQ"))
tb.done()
freq_per_vis = np.array([SPWFreqs[fff] for fff in data_desc_id])
# freqs = np.mean(SPWFreqs)
assert len(freq_per_vis) == nvis
if Nsam is not None:
freq_per_vis = freq_per_vis[idx]
with open(os.path.join(savepath, 'frequencies.bin'), 'wb') as f:
f.write(freq_per_vis)
del data_desc_id, SPWFreqs
if Nsam is not None:
data = data[:, :, idx]
weight = weight[:, idx]
if debug:
# randomly sample 1000 to image
from ripples_utils import calc_img_from_vis
_idx = np.random.choice(len(weight[0, :]), size=3000)
if npol == 2:
if data.shape[1] == 1:
# expand the channel axis for weight
__weight = weight[:, np.newaxis, :]
_data = np.average(data, weights=__weight, axis=0)
_weight = np.average(weight, axis=0) # npol, nvis
print _weight.shape
print _data.shape
_real = _data[0, _idx].real # nchan, nvis
_imag = _data[0, _idx].imag
visOut = np.array(zip(_real, _imag)).flatten()
_weight = _weight[_idx]
__weight_factor = len(_idx)/np.sum(_weight * _real**2 + _weight * _imag**2)
print __weight_factor
_weight *= __weight_factor
test_img = calc_img_from_vis(uvw[0, _idx], uvw[1, _idx], _weight, visOut, freq_per_vis[_idx], 800, pixsize=0.01)
import pdb; pdb.set_trace()
if npol == 1:
real = data.real
imag = data.imag
elif npol == 2:
print "Real and Imag shape before averaging two hands (npol, nchan, nvis): ", data.real.shape
if data.shape[1] == 1:
# expand the channel axis for weight
weight = weight[:, np.newaxis, :]
else:
print weight.shape
print("We shouldn't have to enter this condition.")
import pdb; pdb.set_trace()
# average the two hands
data = np.average(data, weights=weight, axis=0)
# print data.shape (nchan, nvis)
weight = np.average(weight, axis=0)
print weight.shape # should be (nchan, nvis)
real = data.real
imag = data.imag
del data
print "Shape after averaging two hands: ", real.shape
elif npol > 2:
raise NotImplementedError("more than 2 hands..")
# rescale weight
# uvmcmcfit way
if Nsam is None:
_factor = nvis/np.sum(weight * real**2 + weight * imag**2)
else:
_factor = len(idx)/np.sum(weight * real**2 + weight * imag**2)
_sigmas = (weight**-0.5) * _factor
if verbose:
print "simple rescale, factor of: ", _factor
print _sigmas.min(), _sigmas.max(), _sigmas.std()
print "New sigma in [mjy/beam]", (_sigmas**-2).sum()**-0.5*1.e3
del _sigmas
# Yashar way
# first grouping the visibilities into bins that probe the same signal
# take differences btw visibilities that null the sky
# then, we simply assume that the variance in the subtracted visibilities is equal to twice the noise variance
plotms(timebinVis, xaxis='V', yaxis='U', coloraxis='baseline')
scaling = []
for a1 in np.unique(ant1):
for a2 in np.unique(ant2):
if a1 < a2:
baselineX = (ant1 == a1) & (ant2 == a2)
if debug:
print a1, a2
print ant1, ant2
print ""
print a1 in ant1
print a2 in ant2
print ""
print np.where(a1 == ant1)
print np.where(a2 == ant2)
print np.where((ant1 == a1) & (ant2 == a2) == True)
if baselineX.any() == True: # important line! if we are picking a subset of points with nsam since we may miss some baselines.
if nchan == 1:
print real.shape
reals = real[0, baselineX]
imags = imag[0, baselineX]
sigs = weight[0, baselineX]**-0.5
else:
raise NotImplementedError("Not implemented for MS files with more than 1 channel per spw...")
# randomly split points into "two sets"
# subtract from "two set"
# subtract from neighboring
diffrs = reals - np.roll(reals, -1)
diffis = imags - np.roll(imags, -1)
std = np.mean([diffrs.std(), diffis.std()])
if debug:
print diffrs.min(), diffis.min()
print diffrs.max(), diffis.max()
print diffrs.std(), diffis.std()
print std / sigs.mean() / np.sqrt(2)
scaling.append(std / sigs.mean() / np.sqrt(2))
del ant1, ant2
sigma = weight**-0.5
scaling = np.asarray(scaling).mean()
sigma *= scaling
print 'Scaling factor: ', scaling
print 'Sigma after scaling [mJy/beam]: ', ((sigma**-2).sum())**-0.5 * 1E+3
# real_1, imag_1, real_2, imag_2, etc
visOut = np.array(zip(real, imag)).flatten()
if Nsam is None:
assert len(visOut) == int(nvis*2)
weight = sigma**-2
weight = np.array(zip(weight, weight)).flatten() # TODO: want to make sure this works for nchan > 1
assert len(weight) == len(visOut)
if Nsam is not None:
assert len(visOut) == Nsam * 2
if Nsam is not None:
assert len(weight) == Nsam * 2
with open(os.path.join(savepath, 'vis_chan_0.bin'), 'wb') as f:
f.write(visOut)
with open(os.path.join(savepath, 'sigma_squared_inv.bin'), 'wb') as f:
f.write(weight)
if Nsam is None:
blah = np.zeros((nvis))
else:
blah = np.zeros((len(idx)))
with open(os.path.join(savepath, 'chan.bin'), 'wb') as f:
f.write(blah)
if debug:
# image the SAVED visibilities
uu = np.fromfile(os.path.join(savepath, 'u.bin'), 'wb')
vv = np.fromfile(os.path.join(savepath, 'v.bin'), 'wb')
weight = np.fromfile(os.path.join(savepath, 'sigma_squared_inv.bin'), 'wb')
visout = np.fromfile(os.path.join(savepath, 'vis_chan_0.bin'), 'wb')
calc_img_from_vis(uu, vv, weight, visOut, 1000, pixsize=0.05)
return None
if __name__ == '__main__':
import sys
sys.path.append("./")
from ripples_prep import get_source_name, get_phs_center, ms2ripples
import config
from ripples_utils import get_nchan, vel2chan
# import subprocess
# For line, do it for different channels, averaged, etc etc
ccc = config.Configurable('ripples_prep.yaml')
setupParam = ccc.config_dict
savepath = setupParam['line']['savepath']
overwrite = setupParam['line']['overwrite']
lineMS = setupParam['line']['data']['lineMS'] # cube, after subtracting continuum
unbinnedIm = setupParam['line']['data']['unbinnedIm']
sliceChan = setupParam['line']['slice']
shiftpcd = setupParam['shiftpcd']['shiftpcd']
timebin = setupParam['line']['timebin']
timebinsec = setupParam['line']['timebinsec']
nsam = setupParam['cont']['nsam']
if not os.path.exists(savepath):
os.mkdir(savepath)
_current = os.getcwd()
if shiftpcd:
field = setupParam['shiftpcd']['field']
pcd = setupParam['shiftpcd']['pcd']
outvis = shift_phs_center(lineMS, pcd, field)
outpcd = get_phs_center(outvis)
print "Phase Center to be shifted to", pcd
print "Phase Center after fixvis: ", outpcd
lineMS = outvis
# if already shifted phase center, then use the MS file, regardless of whether we are setting shiftpcd in .yaml
if not shiftpcd:
outvis = lineMS.replace('.ms', 'phsShift.ms')
if os.path.exists(outvis):
lineMS = outvis
# find ranges of channels to split in unbinned data cube
if not os.path.exists(unbinnedIm + '.fits'):
lineCubeFITS_unbinned = make_line_cube_nobinning(lineMS)
else:
lineCubeFITS_unbinned = unbinnedIm + '.fits'
# split into different channel slices & time bin
chanbin, _ = get_nchan(lineCubeFITS_unbinned)
for ns, chan in sliceChan.iteritems():
# change path
_p = savepath + ns + '/'
if not os.path.exists(_p):
os.mkdir(_p)
os.chdir(_p)
outvis = 'lineSlice.ms'
# split channel
default(split2)
rmtables(outvis)
split2(vis=lineMS, # absolute path
spw=chan,
outputvis=outvis,
datacolumn='data',
keepflags=False,
width=chanbin
)
listobs(outvis)
ms2ripples(contMS, savepath, timebinsec, nsam, timebin, \
overwrite=overwrite, verbose=True, debug=False) # if debug=True, will also image the saved visibilities using python.
os.chdir(_current)
objname = get_source_name(lineMS)
tarballName = objname + '_line_timebin' + str(timebinsec) +'s.tgz'
# change to ripples dir
os.chdir(savepath)
os.system('tar -zcvf ' + tarballName + ' ' + '$(find -name "*.bin"')
os.chdir(_current)
print tarballName
# On Voms: scp [email protected]:/data/dleung/DATA/ALMA/QSO_c5/RXJ0911/calibrated/ripples/RXJ0911_line_timebin60s.tgz .
#