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1##################### generated by xml-casa (v2) from tclean.xml ####################
2##################### e4216b74f32e985c533b81c5ecdbefef ##############################
3from __future__ import absolute_import
4import numpy
5from casatools.typecheck import CasaValidator as _val_ctor
6_pc = _val_ctor( )
7from casatools.coercetype import coerce as _coerce
8from casatools.errors import create_error_string
9from .private.task_tclean import tclean as _tclean_t
10from casatasks.private.task_logging import start_log as _start_log
11from casatasks.private.task_logging import end_log as _end_log
12from casatasks.private.task_logging import except_log as _except_log
14class _tclean:
15 """
16 tclean ---- Radio Interferometric Image Reconstruction
18 Form images from visibilities and reconstruct a sky model.
19 This task handles continuum images and spectral line cubes,
20 supports outlier fields, contains standard clean based algorithms
21 along with algorithms for multi-scale and wideband image
22 reconstruction, widefield imaging correcting for the w-term,
23 full primary-beam imaging and joint mosaic imaging (with
24 heterogeneous array support for ALMA).
26 --------- parameter descriptions ---------------------------------------------
28 vis Name(s) of input visibility file(s)
29 default: none;
30 example: vis='ngc5921.ms'
31 vis=['ngc5921a.ms','ngc5921b.ms']; multiple MSes
32 selectdata Enable data selection parameters.
33 field to image or mosaic. Use field id(s) or name(s).
34 ['go listobs' to obtain the list id's or names]
35 default: ''= all fields
36 If field string is a non-negative integer, it is assumed to
37 be a field index otherwise, it is assumed to be a
38 field name
39 field='0~2'; field ids 0,1,2
40 field='0,4,5~7'; field ids 0,4,5,6,7
41 field='3C286,3C295'; field named 3C286 and 3C295
42 field = '3,4C\*'; field id 3, all names starting with 4C
43 For multiple MS input, a list of field strings can be used:
44 field = ['0~2','0~4']; field ids 0-2 for the first MS and 0-4
45 for the second
46 field = '0~2'; field ids 0-2 for all input MSes
47 spw l window/channels
48 NOTE: channels de-selected here will contain all zeros if
49 selected by the parameter mode subparameters.
50 default: ''=all spectral windows and channels
51 spw='0~2,4'; spectral windows 0,1,2,4 (all channels)
52 spw='0:5~61'; spw 0, channels 5 to 61
53 spw='<2'; spectral windows less than 2 (i.e. 0,1)
54 spw='0,10,3:3~45'; spw 0,10 all channels, spw 3,
55 channels 3 to 45.
56 spw='0~2:2~6'; spw 0,1,2 with channels 2 through 6 in each.
57 For multiple MS input, a list of spw strings can be used:
58 spw=['0','0~3']; spw ids 0 for the first MS and 0-3 for the second
59 spw='0~3' spw ids 0-3 for all input MS
60 spw='3:10~20;50~60' for multiple channel ranges within spw id 3
61 spw='3:10~20;50~60,4:0~30' for different channel ranges for spw ids 3 and 4
62 spw='0:0~10,1:20~30,2:1;2;3'; spw 0, channels 0-10,
63 spw 1, channels 20-30, and spw 2, channels, 1,2 and 3
64 spw='1~4;6:15~48' for channels 15 through 48 for spw ids 1,2,3,4 and 6
65 timerange Range of time to select from data
67 default: '' (all); examples,
68 timerange = 'YYYY/MM/DD/hh:mm:ss~YYYY/MM/DD/hh:mm:ss'
69 Note: if YYYY/MM/DD is missing date defaults to first
70 day in data set
71 timerange='09:14:0~09:54:0' picks 40 min on first day
72 timerange='25:00:00~27:30:00' picks 1 hr to 3 hr
73 30min on NEXT day
74 timerange='09:44:00' pick data within one integration
75 of time
76 timerange='> 10:24:00' data after this time
77 For multiple MS input, a list of timerange strings can be
78 used:
79 timerange=['09:14:0~09:54:0','> 10:24:00']
80 timerange='09:14:0~09:54:0''; apply the same timerange for
81 all input MSes
82 uvrange Select data within uvrange (default unit is meters)
83 default: '' (all); example:
84 uvrange='0~1000klambda'; uvrange from 0-1000 kilo-lambda
85 uvrange='> 4klambda';uvranges greater than 4 kilo lambda
86 For multiple MS input, a list of uvrange strings can be
87 used:
88 uvrange=['0~1000klambda','100~1000klamda']
89 uvrange='0~1000klambda'; apply 0-1000 kilo-lambda for all
90 input MSes
91 antenna Select data based on antenna/baseline
93 default: '' (all)
94 If antenna string is a non-negative integer, it is
95 assumed to be an antenna index, otherwise, it is
96 considered an antenna name.
97 antenna='5\&6'; baseline between antenna index 5 and
98 index 6.
99 antenna='VA05\&VA06'; baseline between VLA antenna 5
100 and 6.
101 antenna='5\&6;7\&8'; baselines 5-6 and 7-8
102 antenna='5'; all baselines with antenna index 5
103 antenna='05'; all baselines with antenna number 05
104 (VLA old name)
105 antenna='5,6,9'; all baselines with antennas 5,6,9
106 index number
107 For multiple MS input, a list of antenna strings can be
108 used:
109 antenna=['5','5\&6'];
110 antenna='5'; antenna index 5 for all input MSes
111 antenna='!DV14'; use all antennas except DV14
112 scan Scan number range
114 default: '' (all)
115 example: scan='1~5'
116 For multiple MS input, a list of scan strings can be used:
117 scan=['0~100','10~200']
118 scan='0~100; scan ids 0-100 for all input MSes
119 observation Observation ID range
120 default: '' (all)
121 example: observation='1~5'
122 intent Scan Intent(s)
124 default: '' (all)
125 example: intent='TARGET_SOURCE'
126 example: intent='TARGET_SOURCE1,TARGET_SOURCE2'
127 example: intent='TARGET_POINTING\*'
128 datacolumn Data column to image (data or observed, corrected)
129 default:'corrected'
130 ( If 'corrected' does not exist, it will use 'data' instead )
131 imagename Pre-name of output images
133 example : imagename='try'
135 Output images will be (a subset of) :
137 try.psf - Point spread function
138 try.residual - Residual image
139 try.image - Restored image
140 try.model - Model image (contains only flux components)
141 try.sumwt - Single pixel image containing sum-of-weights.
142 (for natural weighting, sensitivity=1/sqrt(sumwt))
143 try.pb - Primary beam model (values depend on the gridder used)
145 Widefield projection algorithms (gridder=mosaic,awproject) will
146 compute the following images too.
147 try.weight - FT of gridded weights or the
148 un-normalized sum of PB-square (for all pointings)
149 Here, PB = sqrt(weight) normalized to a maximum of 1.0
151 For multi-term wideband imaging, all relevant images above will
152 have additional .tt0,.tt1, etc suffixes to indicate Taylor terms,
153 plus the following extra output images.
154 try.alpha - spectral index
155 try.alpha.error - estimate of error on spectral index
156 try.beta - spectral curvature (if nterms \> 2)
158 Tip : Include a directory name in 'imagename' for all
159 output images to be sent there instead of the
160 current working directory : imagename='mydir/try'
162 Tip : Restarting an imaging run without changing 'imagename'
163 implies continuation from the existing model image on disk.
164 - If 'startmodel' was initially specified it needs to be set to ""
165 for the restart run (or tclean will exit with an error message).
166 - By default, the residual image and psf will be recomputed
167 but if no changes were made to relevant parameters between
168 the runs, set calcres=False, calcpsf=False to resume directly from
169 the minor cycle without the (unnecessary) first major cycle.
170 To automatically change 'imagename' with a numerical
171 increment, set restart=False (see tclean docs for 'restart').
173 Note : All imaging runs will by default produce restored images.
174 For a niter=0 run, this will be redundant and can optionally
175 be turned off via the 'restoration=T/F' parameter.
176 imsize Number of pixels
177 example:
179 imsize = [350,250]
180 imsize = 500 is equivalent to [500,500]
182 To take proper advantage of internal optimized FFT routines, the
183 number of pixels must be even and factorizable by 2,3,5 only.
184 To find the nearest optimal imsize to that desired by the user, please use the following tool method:
186 from casatools import synthesisutils
187 su = synthesisutils()
188 su.getOptimumSize(345)
189 Output : 360
190 cell Cell size
191 example: cell=['0.5arcsec,'0.5arcsec'] or
192 cell=['1arcmin', '1arcmin']
193 cell = '1arcsec' is equivalent to ['1arcsec','1arcsec']
194 phasecenter Phase center of the image (string or field id); if the phasecenter is the name known major solar system object ('MERCURY', 'VENUS', 'MARS', 'JUPITER', 'SATURN', 'URANUS', 'NEPTUNE', 'PLUTO', 'SUN', 'MOON') or is an ephemerides table then that source is tracked and the background sources get smeared. There is a special case, when phasecenter='TRACKFIELD', which will use the ephemerides or polynomial phasecenter in the FIELD table of the MS's as the source center to track.
196 Note : If unspecified, tclean will use the phase-center from the first data field of the MS (or list of MSs) selected for imaging.
198 example: phasecenter=6
199 phasecenter='J2000 19h30m00 -40d00m00'
200 phasecenter='J2000 292.5deg -40.0deg'
201 phasecenter='J2000 5.105rad -0.698rad'
202 phasecenter='ICRS 13:05:27.2780 -049.28.04.458'
203 phasecenter='myComet_ephem.tab'
204 phasecenter='MOON'
205 phasecenter='TRACKFIELD'
206 stokes Stokes Planes to make
207 default='I'; example: stokes='IQUV';
208 Options: 'I','Q','U','V','IV','QU','IQ','UV','IQUV','RR','LL','XX','YY','RRLL','XXYY','pseudoI'
210 Note : Due to current internal code constraints, if any correlation pair
211 is flagged, by default, no data for that row in the MS will be used.
212 So, in an MS with XX,YY, if only YY is flagged, neither a
213 Stokes I image nor an XX image can be made from those data points.
214 In such a situation, please split out only the unflagged correlation into
215 a separate MS.
217 Note : The 'pseudoI' option is a partial solution, allowing Stokes I imaging
218 when either of the parallel-hand correlations are unflagged.
220 The remaining constraints shall be removed (where logical) in a future release.
221 projection Coordinate projection
222 Examples : SIN, NCP
223 A list of supported (but untested) projections can be found here :
224 http://casa.nrao.edu/active/docs/doxygen/html/classcasa_1_1Projection.html#a3d5f9ec787e4eabdce57ab5edaf7c0cd
225 startmodel Name of starting model image
227 The contents of the supplied starting model image will be
228 copied to the imagename.model before the run begins.
230 example : startmodel = 'singledish.im'
232 For deconvolver='mtmfs', one image per Taylor term must be provided.
233 example : startmodel = ['try.model.tt0', 'try.model.tt1']
234 startmodel = ['try.model.tt0'] will use a starting model only
235 for the zeroth order term.
236 startmodel = ['','try.model.tt1'] will use a starting model only
237 for the first order term.
239 This starting model can be of a different image shape and size from
240 what is currently being imaged. If so, an image regrid is first triggered
241 to resample the input image onto the target coordinate system.
243 A common usage is to set this parameter equal to a single dish image
245 Negative components in the model image will be included as is.
247 [ Note : If an error occurs during image resampling/regridding,
248 please try using task imregrid to resample the starting model
249 image onto a CASA image with the target shape and
250 coordinate system before supplying it via startmodel ]
251 specmode Spectral definition mode (mfs,cube,cubedata, cubesource, mvc)
253 specmode='mfs' : Continuum imaging with only one output image channel.
254 (mode='cont' can also be used here)
256 specmode='cube' : Spectral line imaging with one or more channels
257 Parameters start, width,and nchan define the spectral
258 coordinate system and can be specified either in terms
259 of channel numbers, frequency or velocity in whatever
260 spectral frame is specified in 'outframe'.
261 All internal and output images are made with outframe as the
262 base spectral frame. However imaging code internally uses the fixed
263 spectral frame, LSRK for automatic internal software
264 Doppler tracking so that a spectral line observed over an
265 extended time range will line up appropriately.
266 Therefore the output images have additional spectral frame conversion
267 layer in LSRK on the top the base frame.
270 (Note : Even if the input parameters are specified in a frame
271 other than LSRK, the viewer still displays spectral
272 axis in LSRK by default because of the conversion frame
273 layer mentioned above. The viewer can be used to relabel
274 the spectral axis in any desired frame - via the spectral
275 reference option under axis label properties in the
276 data display options window.)
282 specmode='cubedata' : Spectral line imaging with one or more channels
283 There is no internal software Doppler tracking so
284 a spectral line observed over an extended time range
285 may be smeared out in frequency. There is strictly
286 no valid spectral frame with which to associate with the
287 output images, thus the image spectral frame will
288 be labelled "Undefined".
291 specmode='cubesource': Spectral line imaging while
292 tracking moving source (near field or solar system
293 objects). The velocity of the source is accounted
294 and the frequency reported is in the source frame.
295 As there is no "SOURCE" frame defined in CASA,
296 the frame in the image will be labelled "REST" (but do note the
297 velocity of a given line reported may be different from the rest frame
298 velocity if the emission region is moving w.r.t the systemic
299 velocity frame of the source)
301 specmode='mvc' : Multiterm continuum imaging with cube major cycles.
302 This mode requires deconvolver='mtmfs' with nterms>1
303 and user-set choices of 'reffreq' and 'nchan'.
305 The output images and minor cycle are similar to specmode='mfs'
306 with deconvolver='mtmfs', but the major cycles are done in
307 cube mode (and require a setting of 'reffreq' and 'nchan').
308 By default, frequency-dependent primary beam correction is
309 applied to each channel, before being combined across frequency
310 to make the inputs to the 'mtmfs' deconvolver. This results in
311 implicit wideband pb-correction, with the deconvolver seeing only
312 the sky spectral structure.
314 Note : There is currently no option to turn off wideband pb correction
315 as part of the flat-sky normalization between the major and minor cycles.
316 Therefore, 'mvc' with the 'standard' and 'wproject' gridders will also apply
317 pblimits per channel, masking all regions outside of pblimit.
318 An option to retain sources outside the pblimit will be added in a future release.
320 Note : Below is some guidance for choosing 'nchan' and 'reffreq' :
322 The cube produced by the major cycle is used in a linear least square fits for Taylor
323 polynomials per pixel. Therefore, one only needs as many channels in the cube, as
324 required for an accurate polynomial fit for sources that have the strongest
325 spectral structure.
327 In general, 'nchan' needs to be greater than or equal to 'nterms', and the
328 frequency range selected by the data will be evenly split into nchan channels.
329 For a low-order polynomial fit, only a small number (around 10)
330 channels are typically needed (for VLA/ALMA bandwidth ratios).
331 'nchan=-1' applies a heuristic that results in a default of 10 cube channels
332 for a 2:1 bandwidth ratio.
334 nchan = MAX( bandwidth/(0.1*startfreq) , nterms+1 )
336 Note: When running in parallel, the nchan selected may limit the speedup if it
337 is smaller than the number of processes used.
339 The 'reffreq' is the reference frequency used for the Taylor polynomial expansion.
340 By default, in specmode='mvc', reffreq is set to the middle of the selected
341 frequency range.
342 reffreq Reference frequency of the output image coordinate system
344 Example : reffreq='1.5GHz' as a string with units.
346 By default, it is calculated as the middle of the selected frequency range.
348 For deconvolver='mtmfs' the Taylor expansion is also done about
349 this specified reference frequency.
350 nchan Number of channels in the output image
351 For default (=-1), the number of channels will be automatically determined
352 based on data selected by 'spw' with 'start' and 'width'.
353 It is often easiest to leave nchan at the default value.
354 example: nchan=100
355 start First channel (e.g. start=3,start=\'1.1GHz\',start=\'15343km/s\')
356 of output cube images specified by data channel number (integer),
357 velocity (string with a unit), or frequency (string with a unit).
358 Default:''; The first channel is automatically determined based on
359 the 'spw' channel selection and 'width'.
360 channels in 'spw'.
361 Since the integer number in 'start' represents the data channel number,
362 when the channel number is used along with the spectral window id selection
363 in 'spw', 'start' specified as an integer should be carefully set otherwise
364 it may result in the blank image channels if the 'start' channel (i.e. absolute
365 channel number) is outside of the channel range specified in 'spw'.
366 In such a case, 'start' can be left as a default (='') to ensure
367 matching with the data spectral channel selection.
368 For specmode='cube', when velocity or frequency is used it is
369 interpreted with the frame defined in outframe. [The parameters of
370 the desired output cube can be estimated by using the 'transform'
371 functionality of 'plotms']
372 examples: start='5.0km/s'; 1st channel, 5.0km/s in outframe
373 start='22.3GHz'; 1st channel, 22.3GHz in outframe
374 width Channel width (e.g. width=2,width=\'0.1MHz\',width=\'10km/s\') of output cube images
375 specified by data channel number (integer), velocity (string with a unit), or
376 or frequency (string with a unit).
377 Default:''; data channel width
378 The sign of width defines the direction of the channels to be incremented.
379 For width specified in velocity or frequency with '-' in front gives image channels in
380 decreasing velocity or frequency, respectively.
381 For specmode='cube', when velocity or frequency is used it is interpreted with
382 the reference frame defined in outframe.
383 examples: width='2.0km/s'; results in channels with increasing velocity
384 width='-2.0km/s'; results in channels with decreasing velocity
385 width='40kHz'; results in channels with increasing frequency
386 width=-2; results in channels averaged of 2 data channels incremented from
387 high to low channel numbers
388 outframe Spectral reference frame in which to interpret \'start\' and \'width\'
389 Options: '','LSRK','LSRD','BARY','GEO','TOPO','GALACTO','LGROUP','CMB'
390 example: outframe='bary' for Barycentric frame
392 REST -- Rest frequency
393 LSRD -- Local Standard of Rest (J2000)
394 -- as the dynamical definition (IAU, [9,12,7] km/s in galactic coordinates)
395 LSRK -- LSR as a kinematical (radio) definition
396 -- 20.0 km/s in direction ra,dec = [270,+30] deg (B1900.0)
397 BARY -- Barycentric (J2000)
398 GEO --- Geocentric
399 TOPO -- Topocentric
400 GALACTO -- Galacto centric (with rotation of 220 km/s in direction l,b = [90,0] deg.
401 LGROUP -- Local group velocity -- 308km/s towards l,b = [105,-7] deg (F. Ghigo)
402 CMB -- CMB velocity -- 369.5km/s towards l,b = [264.4, 48.4] deg (F. Ghigo)
403 DEFAULT = LSRK
404 veltype Velocity type (radio, z, ratio, beta, gamma, optical)
405 For start and/or width specified in velocity, specifies the velocity definition
406 Options: 'radio','optical','z','beta','gamma','optical'
407 NOTE: the viewer always defaults to displaying the 'radio' frame,
408 but that can be changed in the position tracking pull down.
410 The different types (with F = f/f0, the frequency ratio), are:
412 Z = (-1 + 1/F)
413 RATIO = (F) \*
414 RADIO = (1 - F)
415 OPTICAL == Z
416 BETA = ((1 - F2)/(1 + F2))
417 GAMMA = ((1 + F2)/2F) \*
418 RELATIVISTIC == BETA (== v/c)
419 DEFAULT == RADIO
420 Note that the ones with an '\*' have no real interpretation
421 (although the calculation will proceed) if given as a velocity.
422 restfreq List of rest frequencies or a rest frequency in a string.
423 Specify rest frequency to use for output image.
424 \*Currently it uses the first rest frequency in the list for translation of
425 velocities. The list will be stored in the output images.
426 Default: []; look for the rest frequency stored in the MS, if not available,
427 use center frequency of the selected channels
428 examples: restfreq=['1.42GHz']
429 restfreq='1.42GHz'
430 interpolation Spectral interpolation (nearest,linear,cubic)
432 Interpolation rules to use when binning data channels onto image channels
433 and evaluating visibility values at the centers of image channels.
435 Note : 'linear' and 'cubic' interpolation requires data points on both sides of
436 each image frequency. Errors are therefore possible at edge channels, or near
437 flagged data channels. When image channel width is much larger than the data
438 channel width there is nothing much to be gained using linear or cubic thus
439 not worth the extra computation involved.
440 perchanweightdensity When calculating weight density for Briggs
441 style weighting in a cube, this parameter
442 determines whether to calculate the weight
443 density for each channel independently
444 (the default, True)
445 or a common weight density for all of the selected
446 data. This parameter has no
447 meaning for continuum (specmode='mfs') imaging
448 or for natural and radial weighting schemes.
449 For cube imaging
450 perchanweightdensity=True is a recommended
451 option that provides more uniform
452 sensitivity per channel for cubes, but with
453 generally larger psfs than the
454 perchanweightdensity=False (prior behavior)
455 option. When using Briggs style weight with
456 perchanweightdensity=True, the imaging weight
457 density calculations use only the weights of
458 data that contribute specifically to that
459 channel. On the other hand, when
460 perchanweightdensity=False, the imaging
461 weight density calculations sum all of the
462 weights from all of the data channels
463 selected whose (u,v) falls in a given uv cell
464 on the weight density grid. Since the
465 aggregated weights, in any given uv cell,
466 will change depending on the number of
467 channels included when imaging, the psf
468 calculated for a given frequency channel will
469 also necessarily change, resulting in
470 variability in the psf for a given frequency
471 channel when perchanweightdensity=False. In
472 general, perchanweightdensity=False results
473 in smaller psfs for the same value of
474 robustness compared to
475 perchanweightdensity=True, but the rms noise
476 as a function of channel varies and increases
477 toward the edge channels;
478 perchanweightdensity=True provides more
479 uniform sensitivity per channel for
480 cubes. This may make it harder to find
481 estimates of continuum when
482 perchanweightdensity=False. If you intend to
483 image a large cube in many smaller subcubes
484 and subsequently concatenate, it is advisable
485 to use perchanweightdensity=True to avoid
486 surprisingly varying sensitivity and psfs
487 across the concatenated cube.
488 gridder Gridding options (standard, wproject, widefield, mosaic, awproject)
490 The following options choose different gridding convolution
491 functions for the process of convolutional resampling of the measured
492 visibilities onto a regular uv-grid prior to an inverse FFT.
493 Model prediction (degridding) also uses these same functions.
494 Several wide-field effects can be accounted for via careful choices of
495 convolution functions. Gridding (degridding) runtime will rise in
496 proportion to the support size of these convolution functions (in uv-pixels).
498 standard : Prolate Spheroid with 7x7 uv pixel support size
500 [ This mode can also be invoked using 'ft' or 'gridft' ]
502 wproject : W-Projection algorithm to correct for the widefield
503 non-coplanar baseline effect. [Cornwell et.al 2008]
505 wprojplanes is the number of distinct w-values at
506 which to compute and use different gridding convolution
507 functions (see help for wprojplanes).
508 Convolution function support size can range
509 from 5x5 to few 100 x few 100.
511 [ This mode can also be invoked using 'wprojectft' ]
513 widefield : Facetted imaging with or without W-Projection per facet.
515 A set of facets x facets subregions of the specified image
516 are gridded separately using their respective phase centers
517 (to minimize max W). Deconvolution is done on the joint
518 full size image, using a PSF from the first subregion.
520 wprojplanes=1 : standard prolate spheroid gridder per facet.
521 wprojplanes > 1 : W-Projection gridder per facet.
522 nfacets=1, wprojplanes > 1 : Pure W-Projection and no facetting
523 nfacets=1, wprojplanes=1 : Same as standard,ft,gridft
525 A combination of facetting and W-Projection is relevant only for
526 very large fields of view. (In our current version of tclean, this
527 combination runs only with parallel=False.
529 mosaic : A-Projection with azimuthally symmetric beams without
530 sidelobes, beam rotation or squint correction.
531 Gridding convolution functions per visibility are computed
532 from FTs of PB models per antenna.
533 This gridder can be run on single fields as well as mosaics.
535 VLA : PB polynomial fit model (Napier and Rots, 1982)
536 EVLA : PB polynomial fit model (Perley, 2015)
537 ALMA : Airy disks for a 10.7m dish (for 12m dishes) and
538 6.25m dish (for 7m dishes) each with 0.75m
539 blockages (Hunter/Brogan 2011). Joint mosaic
540 imaging supports heterogeneous arrays for ALMA.
542 Typical gridding convolution function support sizes are
543 between 7 and 50 depending on the desired
544 accuracy (given by the uv cell size or image field of view).
546 [ This mode can also be invoked using 'mosaicft' or 'ftmosaic' ]
548 awproject : A-Projection with azimuthally asymmetric beams and
549 including beam rotation, squint correction,
550 conjugate frequency beams and W-projection.
551 [Bhatnagar et.al, 2008]
553 Gridding convolution functions are computed from
554 aperture illumination models per antenna and optionally
555 combined with W-Projection kernels and a prolate spheroid.
556 This gridder can be run on single fields as well as mosaics.
558 VLA : Uses ray traced model (VLA and EVLA) including feed
559 leg and subreflector shadows, off-axis feed location
560 (for beam squint and other polarization effects), and
561 a Gaussian fit for the feed beams (Ref: Brisken 2009)
562 ALMA : Similar ray-traced model as above (but the correctness
563 of its polarization properties remains un-verified).
565 Typical gridding convolution function support sizes are
566 between 7 and 50 depending on the desired
567 accuracy (given by the uv cell size or image field of view).
568 When combined with W-Projection they can be significantly larger.
570 [ This mode can also be invoked using 'awprojectft' ]
572 imagemosaic : (untested implementation)
573 Grid and iFT each pointing separately and combine the
574 images as a linear mosaic (weighted by a PB model) in
575 the image domain before a joint minor cycle.
577 VLA/ALMA PB models are same as for gridder='mosaicft'
579 ------ Notes on PB models :
581 (1) Several different sources of PB models are used in the modes
582 listed above. This is partly for reasons of algorithmic flexibility
583 and partly due to the current lack of a common beam model
584 repository or consensus on what beam models are most appropriate.
586 (2) For ALMA and gridder='mosaic', ray-traced (TICRA) beams
587 are also available via the vpmanager tool.
588 For example, call the following before the tclean run.
589 vp.setpbimage(telescope="ALMA",
590 compleximage='/home/casa/data/trunk/alma/responses/ALMA_0_DV__0_0_360_0_45_90_348.5_373_373_GHz_ticra2007_VP.im',
591 antnames=['DV'+'%02d'%k for k in range(25)])
592 vp.saveastable('mypb.tab')
593 Then, supply vptable='mypb.tab' to tclean.
594 ( Currently this will work only for non-parallel runs )
597 ------ Note on PB masks :
599 In tclean, A-Projection gridders (mosaic and awproject) produce a
600 .pb image and use the 'pblimit' subparameter to decide normalization
601 cutoffs and construct an internal T/F mask in the .pb and .image images.
602 However, this T/F mask cannot directly be used during deconvolution
603 (which needs a 1/0 mask). There are two options for making a pb based
604 deconvolution mask.
605 -- Run tclean with niter=0 to produce the .pb, construct a 1/0 image
606 with the desired threshold (using ia.open('newmask.im');
607 ia.calc('iif("xxx.pb">0.3,1.0,0.0)');ia.close() for example),
608 and supply it via the 'mask' parameter in a subsequent run
609 (with calcres=F and calcpsf=F to restart directly from the minor cycle).
610 -- Run tclean with usemask='pb' for it to automatically construct
611 a 1/0 mask from the internal T/F mask from .pb at a fixed 0.2 threshold.
613 ----- Making PBs for gridders other than mosaic,awproject
615 After the PSF generation, a PB is constructed using the same
616 models used in gridder='mosaic' but just evaluated in the image
617 domain without consideration to weights.
618 facets Number of facets on a side
620 A set of (facets x facets) subregions of the specified image
621 are gridded separately using their respective phase centers
622 (to minimize max W). Deconvolution is done on the joint
623 full size image, using a PSF from the first subregion/facet.
625 In our current version of tclean, facets>1 may be used only
626 with parallel=False.
627 psfphasecenter For mosaic use psf centered on this
628 optional direction. You may need to use
629 this if for example the mosaic does not
630 have any pointing in the center of the
631 image. Another reason; as the psf is
632 approximate for a mosaic, this may help
633 to deconvolve a non central bright source
634 well and quickly.
636 example:
638 psfphasecenter=6 #center psf on field 6
639 psfphasecenter='J2000 19h30m00 -40d00m00'
640 psfphasecenter='J2000 292.5deg -40.0deg'
641 psfphasecenter='J2000 5.105rad -0.698rad'
642 psfphasecenter='ICRS 13:05:27.2780 -049.28.04.458'
643 wprojplanes Number of distinct w-values at which to compute and use different
644 gridding convolution functions for W-Projection
646 An appropriate value of wprojplanes depends on the presence/absence
647 of a bright source far from the phase center, the desired dynamic
648 range of an image in the presence of a bright far out source,
649 the maximum w-value in the measurements, and the desired trade off
650 between accuracy and computing cost.
652 As a (rough) guide, VLA L-Band D-config may require a
653 value of 128 for a source 30arcmin away from the phase
654 center. A-config may require 1024 or more. To converge to an
655 appropriate value, try starting with 128 and then increasing
656 it if artifacts persist. W-term artifacts (for the VLA) typically look
657 like arc-shaped smears in a synthesis image or a shift in source
658 position between images made at different times. These artifacts
659 are more pronounced the further the source is from the phase center.
661 There is no harm in simply always choosing a large value (say, 1024)
662 but there will be a significant performance cost to doing so, especially
663 for gridder='awproject' where it is combined with A-Projection.
665 wprojplanes=-1 is an option for gridder='widefield' or 'wproject'
666 in which the number of planes is automatically computed.
667 vptable vpmanager
669 vptable="" : Choose default beams for different telescopes
670 ALMA : Airy disks
671 EVLA : old VLA models.
673 Other primary beam models can be chosen via the vpmanager tool.
675 Step 1 : Set up the vpmanager tool and save its state in a table
677 vp.setpbpoly(telescope='EVLA', coeff=[1.0, -1.529e-3, 8.69e-7, -1.88e-10])
678 vp.saveastable('myvp.tab')
680 Step 2 : Supply the name of that table in tclean.
682 tclean(....., vptable='myvp.tab',....)
684 Please see the documentation for the vpmanager for more details on how to
685 choose different beam models. Work is in progress to update the defaults
686 for EVLA and ALMA.
688 Note : AWProjection currently does not use this mechanism to choose
689 beam models. It instead uses ray-traced beams computed from
690 parameterized aperture illumination functions, which are not
691 available via the vpmanager. So, gridder='awproject' does not allow
692 the user to set this parameter.
693 mosweight When doing Brigg's style weighting (including uniform) to perform the weight density calculation for each field indepedently if True. If False the weight density is calculated from the average uv distribution of all the fields.
694 aterm Use aperture illumination functions during gridding
696 This parameter turns on the A-term of the AW-Projection gridder.
697 Gridding convolution functions are constructed from aperture illumination
698 function models of each antenna.
699 psterm Include the Prolate Spheroidal (PS) funtion as the anti-aliasing
700 operator in the gridding convolution functions used for gridding.
702 Setting this parameter to true is necessary when aterm is set to
703 false. It can be set to false when aterm is set to true, though
704 with this setting effects of aliasing may be there in the image,
705 particularly near the edges.
707 When set to true, the .pb images will contain the fourier transform
708 of the of the PS funtion. The table below enumarates the functional
709 effects of the psterm, aterm and wprojplanes settings. PB referes to
710 the Primary Beam and FT() refers to the Fourier transform operation.
712 Operation aterm psterm wprojplanes Contents of the .pb image
713 ----------------------------------------------------------------------
714 AW-Projection True True >1 FT(PS) x PB
715 False PB
717 A-Projection True True 1 FT(PS) x PB
718 False PB
720 W-Projection False True >1 FT(PS)
722 Standard False True 1 FT(PS)
723 wbawp Use frequency dependent A-terms
724 Scale aperture illumination functions appropriately with frequency
725 when gridding and combining data from multiple channels.
726 conjbeams Use conjugate frequency for wideband A-terms
728 While gridding data from one frequency channel, choose a convolution
729 function from a 'conjugate' frequency such that the resulting baseline
730 primary beam is approximately constant across frequency. For a system in
731 which the primary beam scales with frequency, this step will eliminate
732 instrumental spectral structure from the measured data and leave only the
733 sky spectrum for the minor cycle to model and reconstruct [Bhatnagar et al., ApJ, 2013].
735 As a rough guideline for when this is relevant, a source at the half power
736 point of the PB at the center frequency will see an artificial spectral
737 index of -1.4 due to the frequency dependence of the PB [Sault and Wieringa, 1994].
738 If left uncorrected during gridding, this spectral structure must be modeled
739 in the minor cycle (using the mtmfs algorithm) to avoid dynamic range limits
740 (of a few hundred for a 2:1 bandwidth).
741 This works for specmode='mfs' and its value is ignored for cubes
742 cfcache Convolution function cache directory name
744 Name of a directory in which to store gridding convolution functions.
745 This cache is filled at the beginning of an imaging run. This step can be time
746 consuming but the cache can be reused across multiple imaging runs that
747 use the same image parameters (cell size, image size , spectral data
748 selections, wprojplanes, wbawp, psterm, aterm). The effect of the wbawp,
749 psterm and aterm settings is frozen-in in the cfcache. Using an existing cfcache
750 made with a different setting of these parameters will not reflect the current
751 settings.
753 In a parallel execution, the construction of the cfcache is also parallelized
754 and the time to compute scales close to linearly with the number of compute
755 cores used. With the re-computation of Convolution Functions (CF) due to PA
756 rotation turned-off (the computepastep parameter), the total number of in the
757 cfcache can be computed as [No. of wprojplanes x No. of selected spectral windows x 4]
759 By default, cfcache = imagename + '.cf'
760 usepointing The usepointing flag informs the gridder that it should utilize the pointing table
761 to use the correct direction in which the antenna is pointing with respect to the pointing phasecenter.
762 computepastep Parallactic angle interval after the AIFs are recomputed (deg)
764 This parameter controls the accuracy of the aperture illumination function
765 used with AProjection for alt-az mount dishes where the AIF rotates on the
766 sky as the synthesis image is built up. Once the PA in the data changes by
767 the given interval, AIFs are re-computed at the new PA.
769 A value of 360.0 deg (the default) implies no re-computation due to PA rotation.
770 AIFs are computed for the PA value of the first valid data received and used for
771 all of the data.
772 rotatepastep Parallactic angle interval after which the nearest AIF is rotated (deg)
774 Instead of recomputing the AIF for every timestep's parallactic angle,
775 the nearest existing AIF is used and rotated
776 after the PA changed by rotatepastep value.
778 A value of 360.0 deg (the default) disables rotation of the AIF.
780 For example, computepastep=360.0 and rotatepastep=5.0 will compute
781 the AIFs at only the starting parallactic angle and all other timesteps will
782 use a rotated version of that AIF at the nearest 5.0 degree point.
783 pointingoffsetsigdev Corrections for heterogenous and time-dependent pointing
784 offsets via AWProjection are controlled by this parameter.
785 It is a vector of 2 ints or doubles each of which is interpreted
786 in units of arcsec. Based on the first threshold, a clustering
787 algorithm is applied to entries from the POINTING subtable
788 of the MS to determine how distinct antenna groups for which
789 the pointing offset must be computed separately. The second
790 number controls how much a pointing change across time can
791 be ignored and after which an antenna rebinning is required.
794 Note : The default value of this parameter is [], due a programmatic constraint.
795 If run with this value, it will internally pick [600,600] and exercise the
796 option of using large tolerances (10arcmin) on both axes. Please choose
797 a setting explicitly for runs that need to use this parameter.
799 Note : This option is available only for gridder='awproject' and usepointing=True and
800 and has been validated primarily with VLASS on-the-fly mosaic data
801 where POINTING subtables have been modified after the data are recorded.
804 Examples of parameter usage :
806 [100.0,100.0] : Pointing offsets of 100 arcsec or less are considered
807 small enough to be ignored. Using large values for both
808 indicates a homogeneous array.
811 [10.0, 100.0] : Based on entries in the POINTING subtable, antennas
812 are grouped into clusters based on a 10arcsec bin size.
813 All antennas in a bin are given a pointing offset calculated
814 as the average of the offsets of all antennas in the bin.
815 On the time axis, offset changes upto 100 arcsec will be ignored.
817 [10.0,10.0] : Calculate separate pointing offsets for each antenna group
818 (with a 10 arcsec bin size). As a function of time, recalculate
819 the antenna binning if the POINTING table entries change by
820 more than 10 arcsec w.r.to the previously computed binning.
822 [1.0, 1.0] : Tight tolerances will imply a fully heterogenous situation where
823 each antenna gets its own pointing offset. Also, time-dependent
824 offset changes greater than 1 arcsec will trigger recomputes of
825 the phase gradients. This is the most general situation and is also
826 the most expensive option as it constructs and uses separate
827 phase gradients for all baselines and timesteps.
829 For VLASS 1.1 data with two kinds of pointing offsets, the recommended
830 setting is [ 30.0, 30.0 ].
832 For VLASS 1.2 data with only the time-dependent pointing offsets, the
833 recommended setting is [ 300.0, 30.0 ] to turn off the antenna grouping
834 but to retain the time dependent corrections required from one timestep
835 to the next.
836 pblimit PB gain level at which to cut off normalizations
838 Divisions by .pb during normalizations have a cut off at a .pb gain
839 level given by pblimit. Outside this limit, image values are set to zero.
840 Additionally, by default, an internal T/F mask is applied to the .pb, .image and
841 .residual images to mask out (T) all invalid pixels outside the pblimit area.
843 Note : This internal T/F mask cannot be used as a deconvolution mask.
844 To do so, please follow the steps listed above in the Notes for the
845 'gridder' parameter.
847 Note : To prevent the internal T/F mask from appearing in anything other
848 than the .pb and .image.pbcor images, 'pblimit' can be set to a
849 negative number.
850 The absolute value will still be used as a valid 'pblimit' for normalization
851 purposes. So, for example, pick pblimit=-0.1 (and not pblimit=-1).
852 A tclean restart using existing output images on disk that already
853 have this T/F mask in the .residual and .image but only pblimit set
854 to a negative value, will remove this mask after the next major cycle.
856 Note : An existing internal T/F mask may be removed from an image as
857 follows (without needing to re-run tclean itself).
858 ia.open('test.image');
859 ia.maskhandler(op='set', name='');
860 ia.done()
861 normtype Normalization type (flatnoise, flatsky, pbsquare)
863 Gridded (and FT'd) images represent the PB-weighted sky image.
864 Qualitatively it can be approximated as two instances of the PB
865 applied to the sky image (one naturally present in the data
866 and one introduced during gridding via the convolution functions).
868 xxx.weight : Weight image approximately equal to sum ( square ( pb ) )
869 xxx.pb : Primary beam calculated as sqrt ( xxx.weight )
871 normtype='flatnoise' : Divide the raw image by sqrt(.weight) so that
872 the input to the minor cycle represents the
873 product of the sky and PB. The noise is 'flat'
874 across the region covered by each PB.
876 normtype='flatsky' : Divide the raw image by .weight so that the input
877 to the minor cycle represents only the sky.
878 The noise is higher in the outer regions of the
879 primary beam where the sensitivity is low.
881 normtype='pbsquare' : No normalization after gridding and FFT.
882 The minor cycle sees the sky times pb square
883 deconvolver Name of minor cycle algorithm (hogbom,clark,multiscale,mtmfs,mem,clarkstokes,asp)
885 Each of the following algorithms operate on residual images and psfs
886 from the gridder and produce output model and restored images.
887 Minor cycles stop and a major cycle is triggered when cyclethreshold
888 or cycleniter are reached. For all methods, components are picked from
889 the entire extent of the image or (if specified) within a mask.
891 hogbom : An adapted version of Hogbom Clean [Hogbom, 1974]
892 - Find the location of the peak residual
893 - Add this delta function component to the model image
894 - Subtract a scaled and shifted PSF of the same size as the image
895 from regions of the residual image where the two overlap.
896 - Repeat
898 clark : An adapted version of Clark Clean [Clark, 1980]
899 - Find the location of max(I^2+Q^2+U^2+V^2)
900 - Add delta functions to each stokes plane of the model image
901 - Subtract a scaled and shifted PSF within a small patch size
902 from regions of the residual image where the two overlap.
903 - After several iterations trigger a Clark major cycle to subtract
904 components from the visibility domain, but without de-gridding.
905 - Repeat
907 ( Note : 'clark' maps to imagermode='' in the old clean task.
908 'clark_exp' is another implementation that maps to
909 imagermode='mosaic' or 'csclean' in the old clean task
910 but the behavior is not identical. For now, please
911 use deconvolver='hogbom' if you encounter problems. )
913 clarkstokes : Clark Clean operating separately per Stokes plane
915 (Note : 'clarkstokes_exp' is an alternate version. See above.)
917 multiscale : MultiScale Clean [Cornwell, 2008]
918 - Smooth the residual image to multiple scale sizes
919 - Find the location and scale at which the peak occurs
920 - Add this multiscale component to the model image
921 - Subtract a scaled,smoothed,shifted PSF (within a small
922 patch size per scale) from all residual images
923 - Repeat from step 2
925 mtmfs : Multi-term (Multi Scale) Multi-Frequency Synthesis [Rau and Cornwell, 2011]
926 - Smooth each Taylor residual image to multiple scale sizes
927 - Solve a NTxNT system of equations per scale size to compute
928 Taylor coefficients for components at all locations
929 - Compute gradient chi-square and pick the Taylor coefficients
930 and scale size at the location with maximum reduction in
931 chi-square
932 - Add multi-scale components to each Taylor-coefficient
933 model image
934 - Subtract scaled,smoothed,shifted PSF (within a small patch size
935 per scale) from all smoothed Taylor residual images
936 - Repeat from step 2
939 mem : Maximum Entropy Method [Cornwell and Evans, 1985]
940 - Iteratively solve for values at all individual pixels via the
941 MEM method. It minimizes an objective function of
942 chi-square plus entropy (here, a measure of difference
943 between the current model and a flat prior model).
945 (Note : This MEM implementation is not very robust.
946 Improvements will be made in the future.)
948 asp : Adaptive Scale Pixel algorithm [Bhatnagar and Cornwell, 2004]
949 - Define a set of initial scales defined as 0, W, 2W 4W and 8W
950 where W is a 2D Gaussian fitting width to the PSF
951 - Smooth the residual image by a Gaussian beam at initial scales
952 - Search for the global peak (F) among these smoothed residual images
953 - form an active Aspen set: amplitude(F), amplitude location(x,y)
954 - Optimize the Aspen set by minimizing the objective function RI-Aspen*PSF,
955 where RI is the residual image and * is the convulition operation.
956 - Compute the model image and update the residual image
957 - Repeat from step 2
959 (Note : This is an experimental version of the ASP algorithm.)
960 scales List of scale sizes (in pixels) for multi-scale and mtmfs algorithms.
961 --> scales=[0,6,20]
962 This set of scale sizes should represent the sizes
963 (diameters in units of number of pixels)
964 of dominant features in the image being reconstructed.
966 The smallest scale size is recommended to be 0 (point source),
967 the second the size of the synthesized beam and the third 3-5
968 times the synthesized beam, etc. For example, if the synthesized
969 beam is 10" FWHM and cell=2",try scales = [0,5,15].
971 For numerical stability, the largest scale must be
972 smaller than the image (or mask) size and smaller than or
973 comparable to the scale corresponding to the lowest measured
974 spatial frequency (as a scale size much larger than what the
975 instrument is sensitive to is unconstrained by the data making
976 it harder to recovery from errors during the minor cycle).
977 nterms Number of Taylor coefficients in the spectral model
979 - nterms=1 : Assume flat spectrum source
980 - nterms=2 : Spectrum is a straight line with a slope
981 - nterms=N : A polynomial of order N-1
983 From a Taylor expansion of the expression of a power law, the
984 spectral index is derived as alpha = taylorcoeff_1 / taylorcoeff_0
986 Spectral curvature is similarly derived when possible.
988 The optimal number of Taylor terms depends on the available
989 signal to noise ratio, bandwidth ratio, and spectral shape of the
990 source as seen by the telescope (sky spectrum x PB spectrum).
992 nterms=2 is a good starting point for wideband EVLA imaging
993 and the lower frequency bands of ALMA (when fractional bandwidth
994 is greater than 10%) and if there is at least one bright source for
995 which a dynamic range of greater than few 100 is desired.
997 Spectral artifacts for the VLA often look like spokes radiating out from
998 a bright source (i.e. in the image made with standard mfs imaging).
999 If increasing the number of terms does not eliminate these artifacts,
1000 check the data for inadequate bandpass calibration. If the source is away
1001 from the pointing center, consider including wide-field corrections too.
1003 (Note : In addition to output Taylor coefficient images .tt0,.tt1,etc
1004 images of spectral index (.alpha), an estimate of error on
1005 spectral index (.alpha.error) and spectral curvature (.beta,
1006 if nterms is greater than 2) are produced.
1007 - These alpha, alpha.error and beta images contain
1008 internal T/F masks based on a threshold computed
1009 as peakresidual/10. Additional masking based on
1010 .alpha/.alpha.error may be desirable.
1011 - .alpha.error is a purely empirical estimate derived
1012 from the propagation of error during the division of
1013 two noisy numbers (alpha = xx.tt1/xx.tt0) where the
1014 'error' on tt1 and tt0 are simply the values picked from
1015 the corresponding residual images. The absolute value
1016 of the error is not always accurate and it is best to interpret
1017 the errors across the image only in a relative sense.)
1018 smallscalebias A numerical control to bias the scales when using multi-scale or mtmfs algorithms.
1019 The peak from each scale's smoothed residual is
1020 multiplied by ( 1 - smallscalebias \* scale/maxscale )
1021 to increase or decrease the amplitude relative to other scales,
1022 before the scale with the largest peak is chosen.
1023 Smallscalebias can be varied between -1.0 and 1.0.
1024 A score of 0.0 gives all scales equal weight (default).
1025 A score larger than 0.0 will bias the solution towards smaller scales.
1026 A score smaller than 0.0 will bias the solution towards larger scales.
1027 The effect of smallscalebias is more pronounced when using multi-scale relative to mtmfs.
1028 fusedthreshold ring Hogbom Clean (number in units of Jy)
1030 fusedthreshold = 0.0001 : 0.1 mJy
1032 This is a subparameter of the Asp Clean deconvolver. When peak residual
1033 is lower than the threshold, Asp Clean is "switched to Hogbom Clean" (i.e. only use the 0 scale for cleaning) for
1034 the following number of iterations until it switches back to Asp Clean.
1036 NumberIterationsInHogbom = 50 + 2 * (exp(0.05 * NthHogbom) - 1)
1038 , where NthHogbom is the number of times Hogbom Clean has been triggered.
1040 When the Asp Clean detects it is approaching convergence, it uses only the 0 scale for the following number of iterations for better computational efficiency.
1042 NumberIterationsInHogbom = 500 + 2 * (exp(0.05 * NthHogbom) - 1)
1044 Set 'fusedthreshold = -1' to make the Asp Clean deconvolver never "switch" to Hogbom Clean.
1045 largestscale xels) allowed for the initial guess for the Asp Clean deconvolver.
1047 largestscale = 100
1049 The default initial scale sizes used by Asp Clean is [0, w, 2w, 4w, 8w],
1050 where `w` is the PSF width. The default `largestscale` is -1 which indicates
1051 users accept these initial scales. If `largestscale` is set, the initial scales
1052 would be [0, w, ... up to the `largestscale`]. This is only an initial guess,
1053 and actual fitted scale sizes may evolve from these initial values.
1055 It is recommended not to set `largestscale` unless Asp Clean picks a large
1056 scale that has no constraints from the data (the UV hole issue).
1057 restoration e.
1059 Construct a restored image : imagename.image by convolving the model
1060 image with a clean beam and adding the residual image to the result.
1061 If a restoringbeam is specified, the residual image is also
1062 smoothed to that target resolution before adding it in.
1064 If a .model does not exist, it will make an empty one and create
1065 the restored image from the residuals ( with additional smoothing if needed ).
1066 With algorithm='mtmfs', this will construct Taylor coefficient maps from
1067 the residuals and compute .alpha and .alpha.error.
1068 restoringbeam ize to use.
1070 - restoringbeam='' or ['']
1071 A Gaussian fitted to the PSF main lobe (separately per image plane).
1073 - restoringbeam='10.0arcsec'
1074 Use a circular Gaussian of this width for all planes
1076 - restoringbeam=['8.0arcsec','10.0arcsec','45deg']
1077 Use this elliptical Gaussian for all planes
1079 - restoringbeam='common'
1080 Automatically estimate a common beam shape/size appropriate for
1081 all planes.
1083 Note : For any restoring beam different from the native resolution
1084 the model image is convolved with the beam and added to
1085 residuals that have been convolved to the same target resolution.
1086 pbcor the output restored image
1088 A new image with extension .image.pbcor will be created from
1089 the evaluation of .image / .pb for all pixels above the specified pblimit.
1091 Note : Stand-alone PB-correction can be triggered by re-running
1092 tclean with the appropriate imagename and with
1093 niter=0, calcpsf=False, calcres=False, pbcor=True, vptable='vp.tab'
1094 ( where vp.tab is the name of the vpmanager file.
1095 See the inline help for the 'vptable' parameter )
1097 Note : For deconvolver='mtmfs', pbcor will divide each Taylor term image by the .tt0 average PB.
1098 For all gridders, this calculation is accurate for small fractional bandwidths.
1100 For large fractional bandwidths, please use one of the following options.
1102 (a) For single pointings, run the tclean task with specmode='mfs', deconvolver='mtmfs',
1103 and gridder='standard' with pbcor=True or False.
1104 If a PB-corrected spectral index is required,
1105 please use the widebandpbcor task to apply multi-tern PB-correction.
1107 (b) For mosaics, run tclean task with specmode='mfs', deconvolver='mtmfs',
1108 and gridder='awproject' , wbawp=True, conjbeams=True, with pbcor=True.
1109 This option applies wideband PB correction as part of the gridding step and
1110 pbcor=True will be accurate because the spectral index map will already
1111 be PB-corrected.
1113 (c) For mosaics, run tclean with specmode='mvc', deconvolver='mtmfs',
1114 and gridder='standard' or 'mosaic' with pbcor=True.
1115 This option applies wideband PB-correction to channelized residual images
1116 prior to the minor cycle and pbcor=True will be accurate because the spectral
1117 index map will already be PB-corrected.
1119 Note : Frequency-dependent PB corrections are typically required for full-band imaging with the VLA.
1120 Wideband PB corrections are required when the amplitude of the
1121 brightest source is known accurately enough to be sensitive
1122 to the difference in the PB gain between the upper and lower
1123 end of the band at its location. As a guideline, the artificial spectral
1124 index due to the PB is -1.4 at the 0.5 gain level and less than -0.2
1125 at the 0.9 gain level at the middle frequency )
1126 outlierfile Name of outlier-field image definitions
1128 A text file containing sets of parameter=value pairs,
1129 one set per outlier field.
1131 Example : outlierfile='outs.txt'
1133 Contents of outs.txt :
1135 imagename=tst1
1136 nchan=1
1137 imsize=[80,80]
1138 cell=[8.0arcsec,8.0arcsec]
1139 phasecenter=J2000 19:58:40.895 +40.55.58.543
1140 mask=circle[[40pix,40pix],10pix]
1142 imagename=tst2
1143 nchan=1
1144 imsize=[100,100]
1145 cell=[8.0arcsec,8.0arcsec]
1146 phasecenter=J2000 19:58:40.895 +40.56.00.000
1147 mask=circle[[60pix,60pix],20pix]
1149 The following parameters are currently allowed to be different between
1150 the main field and the outlier fields (i.e. they will be recognized if found
1151 in the outlier text file). If a parameter is not listed, the value is picked from
1152 what is defined in the main task input.
1154 imagename, imsize, cell, phasecenter, startmodel, mask
1155 specmode, nchan, start, width, nterms, reffreq,
1156 gridder, deconvolver, wprojplanes
1158 Note : 'specmode' is an option, so combinations of mfs and cube
1159 for different image fields, for example, are supported.
1160 'deconvolver' and 'gridder' are also options that allow different
1161 imaging or deconvolution algorithm per image field.
1163 For example, multiscale with wprojection and 16 w-term planes
1164 on the main field and mtmfs with nterms=3 and wprojection
1165 with 64 planes on a bright outlier source for which the frequency
1166 dependence of the primary beam produces a strong effect that
1167 must be modeled. The traditional alternative to this approach is
1168 to first image the outlier, subtract it out of the data (uvsub) and
1169 then image the main field.
1171 Note : If you encounter a use-case where some other parameter needs
1172 to be allowed in the outlier file (and it is logical to do so), please
1173 send us feedback. The above is an initial list.
1174 weighting Weighting scheme (natural,uniform,briggs,superuniform,radial, briggsabs, briggsbwtaper)
1176 During gridding of the dirty or residual image, each visibility value is
1177 multiplied by a weight before it is accumulated on the uv-grid.
1178 The PSF's uv-grid is generated by gridding only the weights (weightgrid).
1180 weighting='natural' : Gridding weights are identical to the data weights
1181 from the MS. For visibilities with similar data weights,
1182 the weightgrid will follow the sample density
1183 pattern on the uv-plane. This weighting scheme
1184 provides the maximum imaging sensitivity at the
1185 expense of a possibly fat PSF with high sidelobes.
1186 It is most appropriate for detection experiments
1187 where sensitivity is most important.
1189 weighting='uniform' : Gridding weights per visibility data point are the
1190 original data weights divided by the total weight of
1191 all data points that map to the same uv grid cell :
1192 ' data_weight / total_wt_per_cell '.
1194 The weightgrid is as close to flat as possible resulting
1195 in a PSF with a narrow main lobe and suppressed
1196 sidelobes. However, since heavily sampled areas of
1197 the uv-plane get down-weighted, the imaging
1198 sensitivity is not as high as with natural weighting.
1199 It is most appropriate for imaging experiments where
1200 a well behaved PSF can help the reconstruction.
1202 weighting='briggs' : Gridding weights per visibility data point are given by
1203 'data_weight / ( A \* total_wt_per_cell + B ) ' where
1204 A and B vary according to the 'robust' parameter.
1206 robust = -2.0 maps to A=1,B=0 or uniform weighting.
1207 robust = +2.0 maps to natural weighting.
1208 (robust=0.5 is equivalent to robust=0.0 in AIPS IMAGR.)
1210 Robust/Briggs weighting generates a PSF that can
1211 vary smoothly between 'natural' and 'uniform' and
1212 allow customized trade-offs between PSF shape and
1213 imaging sensitivity.
1214 weighting='briggsabs' : Experimental option.
1215 Same as Briggs except the formula is different A=
1216 robust\*robust and B is dependent on the
1217 noise per visibility estimated. Giving noise='0Jy'
1218 is a not a reasonable option.
1219 In this mode (or formula) robust values
1220 from -2.0 to 0.0 only make sense (2.0 and
1221 -2.0 will get the same weighting)
1223 weighting='superuniform' : This is similar to uniform weighting except that
1224 the total_wt_per_cell is replaced by the
1225 total_wt_within_NxN_cells around the uv cell of
1226 interest. N=7 is the default (when the
1227 parameter 'npixels' is set to 0 with 'superuniform')
1229 This method tends to give a PSF with inner
1230 sidelobes that are suppressed as in uniform
1231 weighting but with far-out sidelobes closer to
1232 natural weighting. The peak sensitivity is also
1233 closer to natural weighting.
1235 weighting='radial' : Gridding weights are given by ' data_weight \* uvdistance '
1236 This method approximately minimizes rms sidelobes
1237 for an east-west synthesis array.
1239 weighting='briggsbwtaper' : A modified version of Briggs weighting for cubes where an inverse uv taper,
1240 which is proportional to the fractional bandwidth of the entire cube,
1241 is applied per channel. The objective is to modify cube (perchanweightdensity = True)
1242 imaging weights to have a similar density to that of the continuum imaging weights.
1243 This is currently an experimental weighting scheme being developed for ALMA.
1245 For more details on weighting please see Chapter3
1246 of Dan Briggs' thesis (http://www.aoc.nrao.edu/dissertations/dbriggs)
1247 robust Robustness parameter for Briggs weighting.
1249 robust = -2.0 maps to uniform weighting.
1250 robust = +2.0 maps to natural weighting.
1251 (robust=0.5 is equivalent to robust=0.0 in AIPS IMAGR.)
1252 noise noise parameter for briggs abs mode weighting
1253 npixels Number of pixels to determine uv-cell size for super-uniform weighting
1254 (0 defaults to -/+ 3 pixels)
1256 npixels -- uv-box used for weight calculation
1257 a box going from -npixel/2 to +npixel/2 on each side
1258 around a point is used to calculate weight density.
1260 npixels=2 goes from -1 to +1 and covers 3 pixels on a side.
1262 npixels=0 implies a single pixel, which does not make sense for
1263 superuniform weighting. Therefore, for 'superuniform'
1264 weighting, if npixels=0 it will be forced to 6 (or a box
1265 of -3pixels to +3pixels) to cover 7 pixels on a side.
1266 uvtaper uv-taper on outer baselines in uv-plane
1268 Apply a Gaussian taper in addition to the weighting scheme specified
1269 via the 'weighting' parameter. Higher spatial frequencies are weighted
1270 down relative to lower spatial frequencies to suppress artifacts
1271 arising from poorly sampled areas of the uv-plane. It is equivalent to
1272 smoothing the PSF obtained by other weighting schemes and can be
1273 specified either as the HWHM of a Gaussian in uv-space (eg. units of lambda)
1274 or as the FWHM of a Gaussian in the image domain (eg. angular units like arcsec).
1276 uvtaper = [bmaj, bmin, bpa]
1278 Note : FWHM_uv_lambda = (4 log2) / ( pi * FWHM_lm_radians )
1280 A FWHM_lm of 100.000 arcsec maps to a HWHM_uv of 910.18 lambda
1281 A FWHM_lm of 1 arcsec maps to a HWHM_uv of 91 klambda
1283 default: uvtaper=[]; no Gaussian taper applied
1284 example: uvtaper=['5klambda'] circular taper of HWHM=5 kilo-lambda
1285 uvtaper=['5klambda','3klambda','45.0deg'] uv-domain HWHM
1286 uvtaper=['50arcsec','30arcsec','30.0deg'] : image domain FWHM
1287 uvtaper=['10arcsec'] : image domain FWHM
1288 uvtaper=['300.0'] default units are lambda in aperture plane
1289 niter Maximum number of iterations
1291 A stopping criterion based on total iteration count.
1292 Currently the parameter type is defined as an integer therefore the integer value
1293 larger than 2147483647 will not be set properly as it causes an overflow.
1295 Iterations are typically defined as the selecting one flux component
1296 and partially subtracting it out from the residual image.
1298 niter=0 : Do only the initial major cycle (make dirty image, psf, pb, etc)
1300 niter larger than zero : Run major and minor cycles.
1302 Note : Global stopping criteria vs major-cycle triggers
1304 In addition to global stopping criteria, the following rules are
1305 used to determine when to terminate a set of minor cycle iterations
1306 and trigger major cycles [derived from Cotton-Schwab Clean, 1984]
1308 'cycleniter' : controls the maximum number of iterations per image
1309 plane before triggering a major cycle.
1310 'cyclethreshold' : Automatically computed threshold related to the
1311 max sidelobe level of the PSF and peak residual.
1312 Divergence, detected as an increase of 10% in peak residual from the
1313 minimum so far (during minor cycle iterations)
1315 The first criterion to be satisfied takes precedence.
1317 Note : Iteration counts for cubes or multi-field images :
1318 For images with multiple planes (or image fields) on which the
1319 deconvolver operates in sequence, iterations are counted across
1320 all planes (or image fields). The iteration count is compared with
1321 'niter' only after all channels/planes/fields have completed their
1322 minor cycles and exited either due to 'cycleniter' or 'cyclethreshold'.
1323 Therefore, the actual number of iterations reported in the logger
1324 can sometimes be larger than the user specified value in 'niter'.
1325 For example, with niter=100, cycleniter=20,nchan=10,threshold=0,
1326 a total of 200 iterations will be done in the first set of minor cycles
1327 before the total is compared with niter=100 and it exits.
1329 Note : Additional global stopping criteria include
1330 - no change in peak residual across two major cycles
1331 - a 50% or more increase in peak residual across one major cycle
1332 gain Loop gain
1334 Fraction of the source flux to subtract out of the residual image
1335 for the CLEAN algorithm and its variants.
1337 A low value (0.2 or less) is recommended when the sky brightness
1338 distribution is not well represented by the basis functions used by
1339 the chosen deconvolution algorithm. A higher value can be tried when
1340 there is a good match between the true sky brightness structure and
1341 the basis function shapes. For example, for extended emission,
1342 multiscale clean with an appropriate set of scale sizes will tolerate
1343 a higher loop gain than Clark clean (for example).
1344 threshold Stopping threshold (number in units of Jy, or string)
1346 A global stopping threshold that the peak residual (within clean mask)
1347 across all image planes is compared to.
1349 threshold = 0.005 : 5mJy
1350 threshold = '5.0mJy'
1352 Note : A 'cyclethreshold' is internally computed and used as a major cycle
1353 trigger. It is related what fraction of the PSF can be reliably
1354 used during minor cycle updates of the residual image. By default
1355 the minor cycle iterations terminate once the peak residual reaches
1356 the first sidelobe level of the brightest source.
1358 'cyclethreshold' is computed as follows using the settings in
1359 parameters 'cyclefactor','minpsffraction','maxpsffraction','threshold' :
1361 psf_fraction = max_psf_sidelobe_level \* 'cyclefactor'
1362 psf_fraction = max(psf_fraction, 'minpsffraction');
1363 psf_fraction = min(psf_fraction, 'maxpsffraction');
1364 cyclethreshold = peak_residual \* psf_fraction
1365 cyclethreshold = max( cyclethreshold, 'threshold' )
1367 If nsigma is set (>0.0), the N-sigma threshold is calculated (see
1368 the description under nsigma), then cyclethreshold is further modified as,
1370 cyclethreshold = max( cyclethreshold, nsgima_threshold )
1373 'cyclethreshold' is made visible and editable only in the
1374 interactive GUI when tclean is run with interactive=True.
1375 nsigma Multiplicative factor for rms-based threshold stopping
1377 N-sigma threshold is calculated as nsigma \* rms value per image plane determined
1378 from a robust statistics. For nsigma > 0.0, in a minor cycle, a maximum of the two values,
1379 the N-sigma threshold and cyclethreshold, is used to trigger a major cycle
1380 (see also the descreption under 'threshold').
1381 Set nsigma=0.0 to preserve the previous tclean behavior without this feature.
1382 The top level parameter, fastnoise is relevant for the rms noise calculation which is used
1383 to determine the threshold.
1385 The parameter 'nsigma' may be an int, float, or a double.
1386 cycleniter Maximum number of minor-cycle iterations (per plane) before triggering
1387 a major cycle
1389 For example, for a single plane image, if niter=100 and cycleniter=20,
1390 there will be 5 major cycles after the initial one (assuming there is no
1391 threshold based stopping criterion). At each major cycle boundary, if
1392 the number of iterations left over (to reach niter) is less than cycleniter,
1393 it is set to the difference.
1395 Note : cycleniter applies per image plane, even if cycleniter x nplanes
1396 gives a total number of iterations greater than 'niter'. This is to
1397 preserve consistency across image planes within one set of minor
1398 cycle iterations.
1399 cyclefactor Scaling on PSF sidelobe level to compute the minor-cycle stopping threshold.
1401 Please refer to the Note under the documentation for 'threshold' that
1402 discussed the calculation of 'cyclethreshold'
1404 cyclefactor=1.0 results in a cyclethreshold at the first sidelobe level of
1405 the brightest source in the residual image before the minor cycle starts.
1407 cyclefactor=0.5 allows the minor cycle to go deeper.
1408 cyclefactor=2.0 triggers a major cycle sooner.
1409 minpsffraction PSF fraction that marks the max depth of cleaning in the minor cycle
1411 Please refer to the Note under the documentation for 'threshold' that
1412 discussed the calculation of 'cyclethreshold'
1414 For example, minpsffraction=0.5 will stop cleaning at half the height of
1415 the peak residual and trigger a major cycle earlier.
1416 maxpsffraction PSF fraction that marks the minimum depth of cleaning in the minor cycle
1418 Please refer to the Note under the documentation for 'threshold' that
1419 discussed the calculation of 'cyclethreshold'
1421 For example, maxpsffraction=0.8 will ensure that at least the top 20
1422 percent of the source will be subtracted out in the minor cycle even if
1423 the first PSF sidelobe is at the 0.9 level (an extreme example), or if the
1424 cyclefactor is set too high for anything to get cleaned.
1425 interactive Modify masks and parameters at runtime
1427 interactive=True will trigger an interactive GUI at every major cycle
1428 boundary (after the major cycle and before the minor cycle).
1430 Options for runtime parameter modification are :
1432 Interactive clean mask : Draw a 1/0 mask (appears as a contour) by hand.
1433 If a mask is supplied at the task interface or if
1434 automasking is invoked, the current mask is
1435 displayed in the GUI and is available for manual
1436 editing.
1438 Note : If a mask contour is not visible, please
1439 check the cursor display at the bottom of
1440 GUI to see which parts of the mask image
1441 have ones and zeros. If the entire mask=1
1442 no contours will be visible.
1445 Operation buttons : -- Stop execution now (restore current model and exit)
1446 -- Continue on until global stopping criteria are reached
1447 without stopping for any more interaction
1448 -- Continue with minor cycles and return for interaction
1449 after the next major cycle.
1451 Iteration control : -- max cycleniter : Trigger for the next major cycle
1453 The display begins with
1454 [ min( cycleniter, niter - itercount ) ]
1455 and can be edited by hand.
1457 -- iterations left : The display begins with [niter-itercount ]
1458 and can be edited to increase or
1459 decrease the total allowed niter.
1461 -- threshold : Edit global stopping threshold
1463 -- cyclethreshold : The display begins with the
1464 automatically computed value
1465 (see Note in help for 'threshold'),
1466 and can be edited by hand.
1468 All edits will be reflected in the log messages that appear
1469 once minor cycles begin.
1470 nmajor The nmajor parameter limits the number of minor and major cycle sets
1471 that tclean executes. It is defined as the number of major cycles after the
1472 initial set of minor cycle iterations. In other words, the count of nmajor does
1473 not include the initial residual calculation that occurs when calcres=True.
1475 A setting of nmajor=-1 implies no limit (default -1).
1476 A setting of nmajor=0 implies nothing other than the initial residual calculation
1477 A setting of nmajor>0 imples that nmajor sets of minor and major cycles will
1478 be done in addition to the initial residual calculation.
1480 If the major cycle limit is reached, stopcode 9 will be returned. Other stopping
1481 criteria (such as threshold) could cause tclean to stop in fewer than this
1482 number of major cycles. If tclean reaches another stopping criteria, first
1483 or at the same time as nmajor, then that stopcode will be returned instead.
1485 Note however that major cycle ids in the log messages as well as in the return
1486 dictionary do begin with 1 for the initial residual calculation, when it exists.
1488 Example 1 : A tclean run with 'nmajor=5' and 'calcres=True' will iterate for
1489 5 major cycles (not counting the initial residual calculation). But, the return
1490 dictionary will show 'nmajordone:6'. If 'calcres=False', then the return
1491 dictionary will show 'nmajordone:5'.
1493 Example 2 : For both the following cases, there will be a printout in the logs
1494 "Running Major Cycle 1" and the return value will include "nmajordone: 1",
1495 however there is a difference in the purpose of the major cycle and the
1496 number of minor cycles executed:
1497 Case 1; nmajor=0, calcres=True: The major cycle done is for the creation
1498 of the residual, and no minor cycles are executed.
1499 Case 2; nmajor=1, calcres=False: The major cycle is done as part of the
1500 major/minor cycle loop, and 1 minor cycle will be executed.
1501 fullsummary Return dictionary with complete convergence history
1503 fullsummary=True: A full version of the summary dictionary is returned.
1504 Keys include 'iterDone','peakRes','modelFlux','cycleThresh' that record the
1505 convergence state at the end of each set of minor cycle iterations
1506 separately for each image plane (i.e. channel/stokes) being
1507 deconvolved. Additional keys report the convergence state at the
1508 start of minor cycle iterations, stopping criteria that triggered major
1509 cycles, and a processor ID per channel, for parallel cube runs.
1511 fullsummary=False (default): A shorten version of the summary dictionary is returned
1512 with only 'iterDone','peakRes','modelFlux', and 'cycleThresh'.
1515 Detailed information about the return dictionary fields may be found
1516 at CASA Docs > Synthesis Imaging > Iteration Control > Returned Dictionary.
1518 Note : With some parallel cube imaging runs that have a large number of channels
1519 and iterations per cube partition in a parallel run, an MPI message passing limit may
1520 be reached due to the size of the return dictionaries being passed around, causing
1521 CASA to crash (with fullsummary=True). The limit has been estimated to be reached
1522 only when nchan_per_chunk x iterdone_per_minorcycleset > 8e+6. The option to set
1523 fullsummary=False should be used to guard against this.
1524 usemask Type of mask(s) to be used for deconvolution
1526 user: (default) mask image(s) or user specified region file(s) or string CRTF expression(s)
1527 subparameters: mask, pbmask
1528 pb: primary beam mask
1529 subparameter: pbmask
1531 Example: usemask="pb", pbmask=0.2
1532 Construct a mask at the 0.2 pb gain level.
1533 (Currently, this option will work only with
1534 gridders that produce .pb (i.e. mosaic and awproject)
1535 or if an externally produced .pb image exists on disk)
1537 auto-multithresh : auto-masking by multiple thresholds for deconvolution
1538 subparameters : sidelobethreshold, noisethreshold, lownoisethreshold, negativethrehsold, smoothfactor,
1539 minbeamfrac, cutthreshold, pbmask, growiterations, dogrowprune, minpercentchange, verbose
1540 Additional top level parameter relevant to auto-multithresh: fastnoise
1542 if pbmask is >0.0, the region outside the specified pb gain level is excluded from
1543 image statistics in determination of the threshold.
1548 Note: By default the intermediate mask generated by automask at each deconvolution cycle
1549 is over-written in the next cycle but one can save them by setting
1550 the environment variable, SAVE_ALL_AUTOMASKS="true".
1551 (e.g. in the CASA prompt, os.environ['SAVE_ALL_AUTOMASKS']="true" )
1552 The saved CASA mask image name will be imagename.mask.autothresh#, where
1553 # is the iteration cycle number.
1554 mask Mask (a list of image name(s) or region file(s) or region string(s)
1557 The name of a CASA image or region file or region string that specifies
1558 a 1/0 mask to be used for deconvolution. Only locations with value 1 will
1559 be considered for the centers of flux components in the minor cycle.
1560 If regions specified fall completely outside of the image, tclean will throw an error.
1562 Manual mask options/examples :
1564 mask='xxx.mask' : Use this CASA image named xxx.mask and containing
1565 ones and zeros as the mask.
1566 If the mask is only different in spatial coordinates from what is being made
1567 it will be resampled to the target coordinate system before being used.
1568 The mask has to have the same shape in velocity and Stokes planes
1569 as the output image. Exceptions are single velocity and/or single
1570 Stokes plane masks. They will be expanded to cover all velocity and/or
1571 Stokes planes of the output cube.
1573 [ Note : If an error occurs during image resampling or
1574 if the expected mask does not appear, please try
1575 using tasks 'imregrid' or 'makemask' to resample
1576 the mask image onto a CASA image with the target
1577 shape and coordinates and supply it via the 'mask'
1578 parameter. ]
1581 mask='xxx.crtf' : A text file with region strings and the following on the first line
1582 ( #CRTFv0 CASA Region Text Format version 0 )
1583 This is the format of a file created via the viewer's region
1584 tool when saved in CASA region file format.
1586 mask='circle[[40pix,40pix],10pix]' : A CASA region string.
1588 mask=['xxx.mask','xxx.crtf', 'circle[[40pix,40pix],10pix]'] : a list of masks
1594 Note : Mask images for deconvolution must contain 1 or 0 in each pixel.
1595 Such a mask is different from an internal T/F mask that can be
1596 held within each CASA image. These two types of masks are not
1597 automatically interchangeable, so please use the makemask task
1598 to copy between them if you need to construct a 1/0 based mask
1599 from a T/F one.
1601 Note : Work is in progress to generate more flexible masking options and
1602 enable more controls.
1603 pbmask Sub-parameter for usemask: primary beam mask
1605 Examples : pbmask=0.0 (default, no pb mask)
1606 pbmask=0.2 (construct a mask at the 0.2 pb gain level)
1607 sidelobethreshold Sub-parameter for "auto-multithresh": mask threshold based on sidelobe levels: sidelobethreshold \* max_sidelobe_level \* peak residual
1608 noisethreshold Sub-parameter for "auto-multithresh": mask threshold based on the noise level: noisethreshold \* rms + location (=median)
1610 The rms is calculated from MAD with rms = 1.4826\*MAD.
1611 lownoisethreshold Sub-parameter for "auto-multithresh": mask threshold to grow previously masked regions via binary dilation: lownoisethreshold \* rms in residual image + location (=median)
1613 The rms is calculated from MAD with rms = 1.4826\*MAD.
1614 negativethreshold Sub-parameter for "auto-multithresh": mask threshold for negative features: -1.0* negativethreshold \* rms + location(=median)
1616 The rms is calculated from MAD with rms = 1.4826\*MAD.
1617 smoothfactor Sub-parameter for "auto-multithresh": smoothing factor in a unit of the beam
1618 minbeamfrac Sub-parameter for "auto-multithresh": minimum beam fraction in size to prune masks smaller than mimbeamfrac \* beam
1619 <=0.0 : No pruning
1620 cutthreshold Sub-parameter for "auto-multithresh": threshold to cut the smoothed mask to create a final mask: cutthreshold \* peak of the smoothed mask
1621 growiterations Sub-parameter for "auto-multithresh": Maximum number of iterations to perform using binary dilation for growing the mask
1622 dogrowprune Experimental sub-parameter for "auto-multithresh": Do pruning on the grow mask
1623 minpercentchange If the change in the mask size in a particular channel is less than minpercentchange, stop masking that channel in subsequent cycles. This check is only applied when noise based threshold is used and when the previous clean major cycle had a cyclethreshold value equal to the clean threshold. Values equal to -1.0 (or any value less than 0.0) will turn off this check (the default). Automask will still stop masking if the current channel mask is an empty mask and the noise threshold was used to determine the mask.
1624 verbose he summary of automasking at the end of each automasking process
1625 is printed in the logger. Following information per channel will be listed in the summary.
1627 chan: channel number
1628 masking?: F - stop updating automask for the subsequent iteration cycles
1629 RMS: robust rms noise
1630 peak: peak in residual image
1631 thresh_type: type of threshold used (noise or sidelobe)
1632 thresh_value: the value of threshold used
1633 N_reg: number of the automask regions
1634 N_pruned: number of the automask regions removed by pruning
1635 N_grow: number of the grow mask regions
1636 N_grow_pruned: number of the grow mask regions removed by pruning
1637 N_neg_pix: number of pixels for negative mask regions
1639 Note that for a large cube, extra logging may slow down the process.
1640 fastnoise Only relevant when automask (user='multi-autothresh') and/or n-sigma stopping threshold (nsigma>0.0) are/is used. If it is set to True, a simpler but faster noise calucation is used.
1641 In this case, the threshold values are determined based on classic statistics (using all
1642 unmasked pixels for the calculations).
1644 If it is set to False, the new noise calculation
1645 method is used based on pre-existing mask.
1647 Case 1: no exiting mask
1648 Calculate image statistics using Chauvenet algorithm
1650 Case 2: there is an existing mask
1651 Calculate image statistics by classical method on the region
1652 outside the mask and inside the primary beam mask.
1654 In all cases above RMS noise is calculated from MAD.
1655 restart images (and start from an existing model image)
1656 or automatically increment the image name and make a new image set.
1658 True : Re-use existing images. If imagename.model exists the subsequent
1659 run will start from this model (i.e. predicting it using current gridder
1660 settings and starting from the residual image). Care must be taken
1661 when combining this option with startmodel. Currently, only one or
1662 the other can be used.
1664 startmodel='', imagename.model exists :
1665 - Start from imagename.model
1666 startmodel='xxx', imagename.model does not exist :
1667 - Start from startmodel
1668 startmodel='xxx', imagename.model exists :
1669 - Exit with an error message requesting the user to pick
1670 only one model. This situation can arise when doing one
1671 run with startmodel='xxx' to produce an output
1672 imagename.model that includes the content of startmodel,
1673 and wanting to restart a second run to continue deconvolution.
1674 Startmodel should be set to '' before continuing.
1676 If any change in the shape or coordinate system of the image is
1677 desired during the restart, please change the image name and
1678 use the startmodel (and mask) parameter(s) so that the old model
1679 (and mask) can be regridded to the new coordinate system before starting.
1681 False : A convenience feature to increment imagename with '_1', '_2',
1682 etc as suffixes so that all runs of tclean are fresh starts (without
1683 having to change the imagename parameter or delete images).
1685 This mode will search the current directory for all existing
1686 imagename extensions, pick the maximum, and adds 1.
1687 For imagename='try' it will make try.psf, try_2.psf, try_3.psf, etc.
1689 This also works if you specify a directory name in the path :
1690 imagename='outdir/try'. If './outdir' does not exist, it will create it.
1691 Then it will search for existing filenames inside that directory.
1693 If outlier fields are specified, the incrementing happens for each
1694 of them (since each has its own 'imagename'). The counters are
1695 synchronized across imagefields, to make it easier to match up sets
1696 of output images. It adds 1 to the 'max id' from all outlier names
1697 on disk. So, if you do two runs with only the main field
1698 (imagename='try'), and in the third run you add an outlier with
1699 imagename='outtry', you will get the following image names
1700 for the third run : 'try_3' and 'outtry_3' even though
1701 'outry' and 'outtry_2' have not been used.
1702 savemodel Options to save model visibilities (none, virtual, modelcolumn)
1704 Often, model visibilities must be created and saved in the MS
1705 to be later used for self-calibration (or to just plot and view them).
1707 none : Do not save any model visibilities in the MS. The MS is opened
1708 in readonly mode.
1710 Model visibilities can be predicted in a separate step by
1711 restarting tclean with niter=0,savemodel=virtual or modelcolumn
1712 and not changing any image names so that it finds the .model on
1713 disk (or by changing imagename and setting startmodel to the
1714 original imagename).
1716 virtual : In the last major cycle, save the image model and state of the
1717 gridder used during imaging within the SOURCE subtable of the
1718 MS. Images required for de-gridding will also be stored internally.
1719 All future references to model visibilities will activate the
1720 (de)gridder to compute them on-the-fly. This mode is useful
1721 when the dataset is large enough that an additional model data
1722 column on disk may be too much extra disk I/O, when the
1723 gridder is simple enough that on-the-fly recomputing of the
1724 model visibilities is quicker than disk I/O.
1725 For e.g. that gridder='awproject' does not support virtual model.
1727 modelcolumn : In the last major cycle, save predicted model visibilities
1728 in the MODEL_DATA column of the MS. This mode is useful when
1729 the de-gridding cost to produce the model visibilities is higher
1730 than the I/O required to read the model visibilities from disk.
1731 This mode is currently required for gridder='awproject'.
1732 This mode is also required for the ability to later pull out
1733 model visibilities from the MS into a python array for custom
1734 processing.
1736 Note 1 : The imagename.model image on disk will always be constructed
1737 if the minor cycle runs. This savemodel parameter applies only to
1738 model visibilities created by de-gridding the model image.
1740 Note 2 : It is possible for an MS to have both a virtual model
1741 as well as a model_data column, but under normal operation,
1742 the last used mode will get triggered. Use the delmod task to
1743 clear out existing models from an MS if confusion arises.
1744 Note 3: when parallel=True, use savemodel='none'; Other options are not yet ready
1745 for use in parallel. If model visibilities need to be saved (virtual or modelcolumn):
1746 please run tclean in serial mode with niter=0; after the parallel run
1747 calcres Calculate initial residual image
1749 This parameter controls what the first major cycle does.
1751 calcres=False with niter greater than 0 will assume that
1752 a .residual image already exists and that the minor cycle can
1753 begin without recomputing it.
1755 calcres=False with niter=0 implies that only the PSF will be made
1756 and no data will be gridded.
1758 calcres=True requires that calcpsf=True or that the .psf and .sumwt
1759 images already exist on disk (for normalization purposes).
1761 Usage example : For large runs (or a pipeline scripts) it may be
1762 useful to first run tclean with niter=0 to create
1763 an initial .residual to look at and perhaps make
1764 a custom mask for. Imaging can be resumed
1765 without recomputing it.
1766 calcpsf Calculate PSF
1768 This parameter controls what the first major cycle does.
1770 calcpsf=False will assume that a .psf image already exists
1771 and that the minor cycle can begin without recomputing it.
1772 psfcutoff When the .psf image is created a 2 dimensional Gaussian is fit to the main lobe of the PSF.
1773 Which pixels in the PSF are fitted is determined by psfcutoff.
1774 The default value of psfcutoff is 0.35 and can varied from 0.01 to 0.99.
1775 Fitting algorithm:
1776 - A region of 41 x 41 pixels around the peak of the PSF is compared against the psfcutoff.
1777 Sidelobes are ignored by radially searching from the PSF peak.
1778 - Calculate the bottom left corner (blc) and top right corner (trc) from the points. Expand blc and trc with a number of pixels (5).
1779 - Create a new sub-matrix from blc and trc.
1780 - Interpolate matrix to a target number of points (3001) using CUBIC spline.
1781 - All the non-sidelobe points, in the interpolated matrix, that are above the psfcutoff are used to fit a Gaussian.
1782 A Levenberg-Marquardt algorithm is used.
1783 - If the fitting fails the algorithm is repeated with the psfcutoff decreased (psfcutoff=psfcutoff/1.5).
1784 A message in the log will apear if the fitting fails along with the new value of psfcutoff.
1785 This will be done up to 50 times if fitting fails.
1786 This Gaussian beam is defined by a major axis, minor axis, and position angle.
1787 During the restoration process, this Gaussian beam is used as the Clean beam.
1788 Varying psfcutoff might be useful for producing a better fit for highly non-Gaussian PSFs, however, the resulting fits should be carefully checked.
1789 This parameter should rarely be changed.
1791 (This is not the support size for clark clean.)
1792 parallel Run major cycles in parallel (this feature is experimental)
1794 Parallel tclean will run only if casa has already been started using mpirun.
1795 Please refer to HPC documentation for details on how to start this on your system.
1797 Example : mpirun -n 3 -xterm 0 `which casa`
1799 Continuum Imaging :
1800 - Data are partitioned (in time) into NProc pieces
1801 - Gridding/iFT is done separately per partition
1802 - Images (and weights) are gathered and then normalized
1803 - One non-parallel minor cycle is run
1804 - Model image is scattered to all processes
1805 - Major cycle is done in parallel per partition
1807 Cube Imaging :
1808 - Data and Image coordinates are partitioned (in freq) into NProc pieces
1809 - Each partition is processed independently (major and minor cycles)
1810 - All processes are synchronized at major cycle boundaries for convergence checks
1811 - At the end, cubes from all partitions are concatenated along the spectral axis
1813 Note 1 : Iteration control for cube imaging is independent per partition.
1814 - There is currently no communication between them to synchronize
1815 information such as peak residual and cyclethreshold. Therefore,
1816 different chunks may trigger major cycles at different levels.
1817 (Proper synchronization of iteration control is work in progress.)
1818 [1;42mRETURNS[1;m void
1820 --------- examples -----------------------------------------------------------
1824 Please refer to the CASAdocs pages for the task tclean for examples.
1829 """
1831 _info_group_ = """imaging"""
1832 _info_desc_ = """Radio Interferometric Image Reconstruction"""
1834 def __call__( self, vis='', selectdata=True, field='', spw='', timerange='', uvrange='', antenna='', scan='', observation='', intent='', datacolumn='corrected', imagename='', imsize=[ int(100) ], cell=[ ], phasecenter='', stokes='I', projection='SIN', startmodel='', specmode='mfs', reffreq='', nchan=int(-1), start='', width='', outframe='LSRK', veltype='radio', restfreq=[ ], interpolation='linear', perchanweightdensity=True, gridder='standard', facets=int(1), psfphasecenter='', wprojplanes=int(1), vptable='', mosweight=True, aterm=True, psterm=False, wbawp=True, conjbeams=False, cfcache='', usepointing=False, computepastep=float(360.0), rotatepastep=float(360.0), pointingoffsetsigdev=[ ], pblimit=float(0.2), normtype='flatnoise', deconvolver='hogbom', scales=[ ], nterms=int(2), smallscalebias=float(0.0), fusedthreshold=float(0.0), largestscale=int(-1), restoration=True, restoringbeam=[ ], pbcor=False, outlierfile='', weighting='natural', robust=float(0.5), noise='1.0Jy', npixels=int(0), uvtaper=[ '' ], niter=int(0), gain=float(0.1), threshold=float(0.0), nsigma=float(0.0), cycleniter=int(-1), cyclefactor=float(1.0), minpsffraction=float(0.05), maxpsffraction=float(0.8), interactive=False, nmajor=int(-1), fullsummary=False, usemask='user', mask='', pbmask=float(0.0), sidelobethreshold=float(3.0), noisethreshold=float(5.0), lownoisethreshold=float(1.5), negativethreshold=float(0.0), smoothfactor=float(1.0), minbeamfrac=float(0.3), cutthreshold=float(0.01), growiterations=int(75), dogrowprune=True, minpercentchange=float(-1.0), verbose=False, fastnoise=True, restart=True, savemodel='none', calcres=True, calcpsf=True, psfcutoff=float(0.35), parallel=False ):
1835 schema = {'vis': {'anyof': [{'type': 'cReqPath', 'coerce': _coerce.expand_path}, {'type': 'cReqPathVec', 'coerce': [_coerce.to_list,_coerce.expand_pathvec]}]}, 'selectdata': {'type': 'cBool'}, 'field': {'anyof': [{'type': 'cStr', 'coerce': _coerce.to_str}, {'type': 'cStrVec', 'coerce': [_coerce.to_list,_coerce.to_strvec]}]}, 'spw': {'anyof': [{'type': 'cStr', 'coerce': _coerce.to_str}, {'type': 'cStrVec', 'coerce': [_coerce.to_list,_coerce.to_strvec]}]}, 'timerange': {'anyof': [{'type': 'cStr', 'coerce': _coerce.to_str}, {'type': 'cStrVec', 'coerce': [_coerce.to_list,_coerce.to_strvec]}]}, 'uvrange': {'anyof': [{'type': 'cStr', 'coerce': _coerce.to_str}, {'type': 'cStrVec', 'coerce': [_coerce.to_list,_coerce.to_strvec]}]}, 'antenna': {'anyof': [{'type': 'cStr', 'coerce': _coerce.to_str}, {'type': 'cStrVec', 'coerce': [_coerce.to_list,_coerce.to_strvec]}]}, 'scan': {'anyof': [{'type': 'cStr', 'coerce': _coerce.to_str}, {'type': 'cStrVec', 'coerce': [_coerce.to_list,_coerce.to_strvec]}]}, 'observation': {'anyof': [{'type': 'cStr', 'coerce': _coerce.to_str}, {'type': 'cInt'}]}, 'intent': {'anyof': [{'type': 'cStr', 'coerce': _coerce.to_str}, {'type': 'cStrVec', 'coerce': [_coerce.to_list,_coerce.to_strvec]}]}, 'datacolumn': {'type': 'cStr', 'coerce': _coerce.to_str}, 'imagename': {'anyof': [{'type': 'cInt'}, {'type': 'cStr', 'coerce': _coerce.to_str}, {'type': 'cStrVec', 'coerce': [_coerce.to_list,_coerce.to_strvec]}]}, 'imsize': {'anyof': [{'type': 'cInt'}, {'type': 'cIntVec', 'coerce': [_coerce.to_list,_coerce.to_intvec]}]}, 'cell': {'anyof': [{'type': 'cIntVec', 'coerce': [_coerce.to_list,_coerce.to_intvec]}, {'type': 'cStr', 'coerce': _coerce.to_str}, {'type': 'cFloat', 'coerce': _coerce.to_float}, {'type': 'cStrVec', 'coerce': [_coerce.to_list,_coerce.to_strvec]}, {'type': 'cInt'}, {'type': 'cFloatVec', 'coerce': [_coerce.to_list,_coerce.to_floatvec]}]}, 'phasecenter': {'anyof': [{'type': 'cInt'}, {'type': 'cStr', 'coerce': _coerce.to_str}]}, 'stokes': {'type': 'cStr', 'coerce': _coerce.to_str, 'allowed': [ 'I', 'IQUV', 'UV', 'RRLL', 'IQ', 'V', 'pseudoI', 'QU', 'YY', 'RR', 'Q', 'U', 'IV', 'XX', 'XXYY', 'LL' ]}, 'projection': {'type': 'cStr', 'coerce': _coerce.to_str}, 'startmodel': {'type': 'cVariant', 'coerce': [_coerce.to_variant]}, 'specmode': {'type': 'cStr', 'coerce': _coerce.to_str, 'allowed': [ 'cont', 'cubedata', 'cube', 'cubesource', 'mfs', 'mvc' ]}, 'reffreq': {'type': 'cVariant', 'coerce': [_coerce.to_variant]}, 'nchan': {'type': 'cInt'}, 'start': {'type': 'cVariant', 'coerce': [_coerce.to_variant]}, 'width': {'type': 'cVariant', 'coerce': [_coerce.to_variant]}, 'outframe': {'type': 'cStr', 'coerce': _coerce.to_str}, 'veltype': {'type': 'cStr', 'coerce': _coerce.to_str}, 'restfreq': {'type': 'cVariant', 'coerce': [_coerce.to_variant]}, 'interpolation': {'type': 'cStr', 'coerce': _coerce.to_str, 'allowed': [ 'nearest', 'linear', 'cubic' ]}, 'perchanweightdensity': {'type': 'cBool'}, 'gridder': {'type': 'cStr', 'coerce': _coerce.to_str, 'allowed': [ 'widefield', 'wproject', 'imagemosaic', 'standard', 'awproject', 'wprojectft', 'mosaicft', 'ft', 'ftmosaic', 'mosaic', 'awprojectft', 'gridft' ]}, 'facets': {'type': 'cInt'}, 'psfphasecenter': {'anyof': [{'type': 'cInt'}, {'type': 'cStr', 'coerce': _coerce.to_str}]}, 'wprojplanes': {'type': 'cInt'}, 'vptable': {'type': 'cStr', 'coerce': _coerce.to_str}, 'mosweight': {'type': 'cBool'}, 'aterm': {'type': 'cBool'}, 'psterm': {'type': 'cBool'}, 'wbawp': {'type': 'cBool'}, 'conjbeams': {'type': 'cBool'}, 'cfcache': {'type': 'cStr', 'coerce': _coerce.to_str}, 'usepointing': {'type': 'cBool'}, 'computepastep': {'type': 'cFloat', 'coerce': _coerce.to_float}, 'rotatepastep': {'type': 'cFloat', 'coerce': _coerce.to_float}, 'pointingoffsetsigdev': {'anyof': [{'type': 'cIntVec', 'coerce': [_coerce.to_list,_coerce.to_intvec]}, {'type': 'cFloatVec', 'coerce': [_coerce.to_list,_coerce.to_floatvec]}]}, 'pblimit': {'type': 'cFloat', 'coerce': _coerce.to_float}, 'normtype': {'type': 'cStr', 'coerce': _coerce.to_str}, 'deconvolver': {'type': 'cStr', 'coerce': _coerce.to_str, 'allowed': [ 'clarkstokes_exp', 'mtmfs', 'mem', 'clarkstokes', 'hogbom', 'clark_exp', 'clark', 'asp', 'multiscale' ]}, 'scales': {'anyof': [{'type': 'cIntVec', 'coerce': [_coerce.to_list,_coerce.to_intvec]}, {'type': 'cFloatVec', 'coerce': [_coerce.to_list,_coerce.to_floatvec]}]}, 'nterms': {'type': 'cInt'}, 'smallscalebias': {'type': 'cFloat', 'coerce': _coerce.to_float}, 'fusedthreshold': {'type': 'cFloat', 'coerce': _coerce.to_float}, 'largestscale': {'type': 'cInt'}, 'restoration': {'type': 'cBool'}, 'restoringbeam': {'anyof': [{'type': 'cStr', 'coerce': _coerce.to_str}, {'type': 'cStrVec', 'coerce': [_coerce.to_list,_coerce.to_strvec]}]}, 'pbcor': {'type': 'cBool'}, 'outlierfile': {'type': 'cStr', 'coerce': _coerce.to_str}, 'weighting': {'type': 'cStr', 'coerce': _coerce.to_str, 'allowed': [ 'briggsabs', 'briggs', 'briggsbwtaper', 'natural', 'radial', 'superuniform', 'uniform' ]}, 'robust': {'type': 'cFloat', 'coerce': _coerce.to_float, 'min': -2.0, 'max': 2.0}, 'noise': {'type': 'cVariant', 'coerce': [_coerce.to_variant]}, 'npixels': {'type': 'cInt'}, 'uvtaper': {'type': 'cStrVec', 'coerce': [_coerce.to_list,_coerce.to_strvec]}, 'niter': {'type': 'cInt'}, 'gain': {'type': 'cFloat', 'coerce': _coerce.to_float}, 'threshold': {'type': 'cVariant', 'coerce': [_coerce.to_variant]}, 'nsigma': {'type': 'cFloat', 'coerce': _coerce.to_float}, 'cycleniter': {'type': 'cInt'}, 'cyclefactor': {'type': 'cFloat', 'coerce': _coerce.to_float}, 'minpsffraction': {'type': 'cFloat', 'coerce': _coerce.to_float}, 'maxpsffraction': {'type': 'cFloat', 'coerce': _coerce.to_float}, 'interactive': {'type': 'cBool'}, 'nmajor': {'type': 'cInt'}, 'fullsummary': {'type': 'cBool'}, 'usemask': {'type': 'cStr', 'coerce': _coerce.to_str, 'allowed': [ 'user', 'pb', 'auto-multithresh' ]}, 'mask': {'anyof': [{'type': 'cStr', 'coerce': _coerce.to_str}, {'type': 'cStrVec', 'coerce': [_coerce.to_list,_coerce.to_strvec]}]}, 'pbmask': {'type': 'cFloat', 'coerce': _coerce.to_float}, 'sidelobethreshold': {'type': 'cFloat', 'coerce': _coerce.to_float}, 'noisethreshold': {'type': 'cFloat', 'coerce': _coerce.to_float}, 'lownoisethreshold': {'type': 'cFloat', 'coerce': _coerce.to_float}, 'negativethreshold': {'type': 'cFloat', 'coerce': _coerce.to_float}, 'smoothfactor': {'type': 'cFloat', 'coerce': _coerce.to_float}, 'minbeamfrac': {'type': 'cFloat', 'coerce': _coerce.to_float}, 'cutthreshold': {'type': 'cFloat', 'coerce': _coerce.to_float}, 'growiterations': {'type': 'cInt'}, 'dogrowprune': {'type': 'cBool'}, 'minpercentchange': {'type': 'cFloat', 'coerce': _coerce.to_float}, 'verbose': {'type': 'cBool'}, 'fastnoise': {'type': 'cBool'}, 'restart': {'type': 'cBool'}, 'savemodel': {'type': 'cStr', 'coerce': _coerce.to_str, 'allowed': [ 'none', 'virtual', 'modelcolumn' ]}, 'calcres': {'type': 'cBool'}, 'calcpsf': {'type': 'cBool'}, 'psfcutoff': {'type': 'cFloat', 'coerce': _coerce.to_float}, 'parallel': {'type': 'cBool'}}
1836 doc = {'vis': vis, 'selectdata': selectdata, 'field': field, 'spw': spw, 'timerange': timerange, 'uvrange': uvrange, 'antenna': antenna, 'scan': scan, 'observation': observation, 'intent': intent, 'datacolumn': datacolumn, 'imagename': imagename, 'imsize': imsize, 'cell': cell, 'phasecenter': phasecenter, 'stokes': stokes, 'projection': projection, 'startmodel': startmodel, 'specmode': specmode, 'reffreq': reffreq, 'nchan': nchan, 'start': start, 'width': width, 'outframe': outframe, 'veltype': veltype, 'restfreq': restfreq, 'interpolation': interpolation, 'perchanweightdensity': perchanweightdensity, 'gridder': gridder, 'facets': facets, 'psfphasecenter': psfphasecenter, 'wprojplanes': wprojplanes, 'vptable': vptable, 'mosweight': mosweight, 'aterm': aterm, 'psterm': psterm, 'wbawp': wbawp, 'conjbeams': conjbeams, 'cfcache': cfcache, 'usepointing': usepointing, 'computepastep': computepastep, 'rotatepastep': rotatepastep, 'pointingoffsetsigdev': pointingoffsetsigdev, 'pblimit': pblimit, 'normtype': normtype, 'deconvolver': deconvolver, 'scales': scales, 'nterms': nterms, 'smallscalebias': smallscalebias, 'fusedthreshold': fusedthreshold, 'largestscale': largestscale, 'restoration': restoration, 'restoringbeam': restoringbeam, 'pbcor': pbcor, 'outlierfile': outlierfile, 'weighting': weighting, 'robust': robust, 'noise': noise, 'npixels': npixels, 'uvtaper': uvtaper, 'niter': niter, 'gain': gain, 'threshold': threshold, 'nsigma': nsigma, 'cycleniter': cycleniter, 'cyclefactor': cyclefactor, 'minpsffraction': minpsffraction, 'maxpsffraction': maxpsffraction, 'interactive': interactive, 'nmajor': nmajor, 'fullsummary': fullsummary, 'usemask': usemask, 'mask': mask, 'pbmask': pbmask, 'sidelobethreshold': sidelobethreshold, 'noisethreshold': noisethreshold, 'lownoisethreshold': lownoisethreshold, 'negativethreshold': negativethreshold, 'smoothfactor': smoothfactor, 'minbeamfrac': minbeamfrac, 'cutthreshold': cutthreshold, 'growiterations': growiterations, 'dogrowprune': dogrowprune, 'minpercentchange': minpercentchange, 'verbose': verbose, 'fastnoise': fastnoise, 'restart': restart, 'savemodel': savemodel, 'calcres': calcres, 'calcpsf': calcpsf, 'psfcutoff': psfcutoff, 'parallel': parallel}
1837 assert _pc.validate(doc,schema), create_error_string(_pc.errors)
1838 _logging_state_ = _start_log( 'tclean', [ 'vis=' + repr(_pc.document['vis']), 'selectdata=' + repr(_pc.document['selectdata']), 'field=' + repr(_pc.document['field']), 'spw=' + repr(_pc.document['spw']), 'timerange=' + repr(_pc.document['timerange']), 'uvrange=' + repr(_pc.document['uvrange']), 'antenna=' + repr(_pc.document['antenna']), 'scan=' + repr(_pc.document['scan']), 'observation=' + repr(_pc.document['observation']), 'intent=' + repr(_pc.document['intent']), 'datacolumn=' + repr(_pc.document['datacolumn']), 'imagename=' + repr(_pc.document['imagename']), 'imsize=' + repr(_pc.document['imsize']), 'cell=' + repr(_pc.document['cell']), 'phasecenter=' + repr(_pc.document['phasecenter']), 'stokes=' + repr(_pc.document['stokes']), 'projection=' + repr(_pc.document['projection']), 'startmodel=' + repr(_pc.document['startmodel']), 'specmode=' + repr(_pc.document['specmode']), 'reffreq=' + repr(_pc.document['reffreq']), 'nchan=' + repr(_pc.document['nchan']), 'start=' + repr(_pc.document['start']), 'width=' + repr(_pc.document['width']), 'outframe=' + repr(_pc.document['outframe']), 'veltype=' + repr(_pc.document['veltype']), 'restfreq=' + repr(_pc.document['restfreq']), 'interpolation=' + repr(_pc.document['interpolation']), 'perchanweightdensity=' + repr(_pc.document['perchanweightdensity']), 'gridder=' + repr(_pc.document['gridder']), 'facets=' + repr(_pc.document['facets']), 'psfphasecenter=' + repr(_pc.document['psfphasecenter']), 'wprojplanes=' + repr(_pc.document['wprojplanes']), 'vptable=' + repr(_pc.document['vptable']), 'mosweight=' + repr(_pc.document['mosweight']), 'aterm=' + repr(_pc.document['aterm']), 'psterm=' + repr(_pc.document['psterm']), 'wbawp=' + repr(_pc.document['wbawp']), 'conjbeams=' + repr(_pc.document['conjbeams']), 'cfcache=' + repr(_pc.document['cfcache']), 'usepointing=' + repr(_pc.document['usepointing']), 'computepastep=' + repr(_pc.document['computepastep']), 'rotatepastep=' + repr(_pc.document['rotatepastep']), 'pointingoffsetsigdev=' + repr(_pc.document['pointingoffsetsigdev']), 'pblimit=' + repr(_pc.document['pblimit']), 'normtype=' + repr(_pc.document['normtype']), 'deconvolver=' + repr(_pc.document['deconvolver']), 'scales=' + repr(_pc.document['scales']), 'nterms=' + repr(_pc.document['nterms']), 'smallscalebias=' + repr(_pc.document['smallscalebias']), 'fusedthreshold=' + repr(_pc.document['fusedthreshold']), 'largestscale=' + repr(_pc.document['largestscale']), 'restoration=' + repr(_pc.document['restoration']), 'restoringbeam=' + repr(_pc.document['restoringbeam']), 'pbcor=' + repr(_pc.document['pbcor']), 'outlierfile=' + repr(_pc.document['outlierfile']), 'weighting=' + repr(_pc.document['weighting']), 'robust=' + repr(_pc.document['robust']), 'noise=' + repr(_pc.document['noise']), 'npixels=' + repr(_pc.document['npixels']), 'uvtaper=' + repr(_pc.document['uvtaper']), 'niter=' + repr(_pc.document['niter']), 'gain=' + repr(_pc.document['gain']), 'threshold=' + repr(_pc.document['threshold']), 'nsigma=' + repr(_pc.document['nsigma']), 'cycleniter=' + repr(_pc.document['cycleniter']), 'cyclefactor=' + repr(_pc.document['cyclefactor']), 'minpsffraction=' + repr(_pc.document['minpsffraction']), 'maxpsffraction=' + repr(_pc.document['maxpsffraction']), 'interactive=' + repr(_pc.document['interactive']), 'nmajor=' + repr(_pc.document['nmajor']), 'fullsummary=' + repr(_pc.document['fullsummary']), 'usemask=' + repr(_pc.document['usemask']), 'mask=' + repr(_pc.document['mask']), 'pbmask=' + repr(_pc.document['pbmask']), 'sidelobethreshold=' + repr(_pc.document['sidelobethreshold']), 'noisethreshold=' + repr(_pc.document['noisethreshold']), 'lownoisethreshold=' + repr(_pc.document['lownoisethreshold']), 'negativethreshold=' + repr(_pc.document['negativethreshold']), 'smoothfactor=' + repr(_pc.document['smoothfactor']), 'minbeamfrac=' + repr(_pc.document['minbeamfrac']), 'cutthreshold=' + repr(_pc.document['cutthreshold']), 'growiterations=' + repr(_pc.document['growiterations']), 'dogrowprune=' + repr(_pc.document['dogrowprune']), 'minpercentchange=' + repr(_pc.document['minpercentchange']), 'verbose=' + repr(_pc.document['verbose']), 'fastnoise=' + repr(_pc.document['fastnoise']), 'restart=' + repr(_pc.document['restart']), 'savemodel=' + repr(_pc.document['savemodel']), 'calcres=' + repr(_pc.document['calcres']), 'calcpsf=' + repr(_pc.document['calcpsf']), 'psfcutoff=' + repr(_pc.document['psfcutoff']), 'parallel=' + repr(_pc.document['parallel']) ] )
1839 task_result = None
1840 try:
1841 task_result = _tclean_t( _pc.document['vis'], _pc.document['selectdata'], _pc.document['field'], _pc.document['spw'], _pc.document['timerange'], _pc.document['uvrange'], _pc.document['antenna'], _pc.document['scan'], _pc.document['observation'], _pc.document['intent'], _pc.document['datacolumn'], _pc.document['imagename'], _pc.document['imsize'], _pc.document['cell'], _pc.document['phasecenter'], _pc.document['stokes'], _pc.document['projection'], _pc.document['startmodel'], _pc.document['specmode'], _pc.document['reffreq'], _pc.document['nchan'], _pc.document['start'], _pc.document['width'], _pc.document['outframe'], _pc.document['veltype'], _pc.document['restfreq'], _pc.document['interpolation'], _pc.document['perchanweightdensity'], _pc.document['gridder'], _pc.document['facets'], _pc.document['psfphasecenter'], _pc.document['wprojplanes'], _pc.document['vptable'], _pc.document['mosweight'], _pc.document['aterm'], _pc.document['psterm'], _pc.document['wbawp'], _pc.document['conjbeams'], _pc.document['cfcache'], _pc.document['usepointing'], _pc.document['computepastep'], _pc.document['rotatepastep'], _pc.document['pointingoffsetsigdev'], _pc.document['pblimit'], _pc.document['normtype'], _pc.document['deconvolver'], _pc.document['scales'], _pc.document['nterms'], _pc.document['smallscalebias'], _pc.document['fusedthreshold'], _pc.document['largestscale'], _pc.document['restoration'], _pc.document['restoringbeam'], _pc.document['pbcor'], _pc.document['outlierfile'], _pc.document['weighting'], _pc.document['robust'], _pc.document['noise'], _pc.document['npixels'], _pc.document['uvtaper'], _pc.document['niter'], _pc.document['gain'], _pc.document['threshold'], _pc.document['nsigma'], _pc.document['cycleniter'], _pc.document['cyclefactor'], _pc.document['minpsffraction'], _pc.document['maxpsffraction'], _pc.document['interactive'], _pc.document['nmajor'], _pc.document['fullsummary'], _pc.document['usemask'], _pc.document['mask'], _pc.document['pbmask'], _pc.document['sidelobethreshold'], _pc.document['noisethreshold'], _pc.document['lownoisethreshold'], _pc.document['negativethreshold'], _pc.document['smoothfactor'], _pc.document['minbeamfrac'], _pc.document['cutthreshold'], _pc.document['growiterations'], _pc.document['dogrowprune'], _pc.document['minpercentchange'], _pc.document['verbose'], _pc.document['fastnoise'], _pc.document['restart'], _pc.document['savemodel'], _pc.document['calcres'], _pc.document['calcpsf'], _pc.document['psfcutoff'], _pc.document['parallel'] )
1842 except Exception as exc:
1843 _except_log('tclean', exc)
1844 raise
1845 finally:
1846 task_result = _end_log( _logging_state_, 'tclean', task_result )
1847 return task_result
1849tclean = _tclean( )