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1##################### generated by xml-casa (v2) from tclean.xml ####################
2##################### 214e4c0dbbbeb35a4a041ccbbea77d9a ##############################
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 MSs
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 names 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 MSs.
47 spw l window/channels.
48 NOTE: channels not selected here will contain all zeros if
49 selected by other 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, and 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 MSs.
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 MSs.
91 uvrange='0~1000'; apply 0-1000 meter for all input MSs.
92 antenna Select data based on antenna/baseline
94 default: '' (all)
95 If antenna string is a non-negative integer, it is
96 assumed to be an antenna index, otherwise, it is
97 considered an antenna name.
98 antenna='5\&6'; baseline between antenna index 5 and
99 index 6.
100 antenna='VA05\&VA06'; baseline between VLA antenna 5
101 and 6.
102 antenna='5\&6;7\&8'; baselines 5-6 and 7-8.
103 antenna='5'; all baselines with antenna index 5.
104 antenna='05'; all baselines with antenna number 05
105 (VLA old name).
106 antenna='5,6,9'; all baselines with antennas 5,6,9
107 index number.
108 For multiple MS input, a list of antenna strings can be
109 used:
110 antenna=['5','5\&6'];
111 antenna='5'; antenna index 5 for all input MSs.
112 antenna='!DV14'; use all antennas except DV14.
113 scan Scan number range
115 default: '' (all).
116 example: scan='1~5'.
117 For multiple MS input, a list of scan strings can be used:
118 scan=['0~100','10~200'].
119 scan='0~100; scan ids 0-100 for all input MSs.
120 observation Observation ID range
121 default: '' (all).
122 example: observation='1~5'.
123 intent Scan Intent(s)
125 default: '' (all).
126 example: intent='TARGET_SOURCE'.
127 example: intent='TARGET_SOURCE1,TARGET_SOURCE2'.
128 example: intent='TARGET_POINTING\*'.
129 datacolumn Data column to image (data or observed, corrected)
130 default:'corrected'
131 ( If 'corrected' does not exist, it will use 'data' instead )
132 imagename Pre-name of output images
134 example : imagename='try'
136 Output images will be (a subset of) :
138 try.psf - Point Spread Function (PSF).
139 try.residual - Residual image.
140 try.image - Restored image.
141 try.model - Model image (contains only flux components).
142 try.sumwt - Single pixel image containing sum-of-weights.
143 (for natural weighting, sensitivity=1/sqrt(sumwt)).
144 try.pb - Primary Beam (PB) model (values depend on the gridder used).
146 A-projection algorithms (gridder=mosaic,awproject, awp2) will
147 compute the following images too.
149 try.weight - FT of gridded weights or the
150 un-normalized sum of PB-square (for all pointings).
151 Here, PB = sqrt(weight) normalized to a maximum of 1.0.
153 For multi-term wideband imaging, all relevant images above will
154 have additional .tt0,.tt1, etc suffixes to indicate Taylor terms,
155 plus the following extra output images.
156 try.alpha - spectral index.
157 try.alpha.error - estimate of error on spectral index.
158 try.beta - spectral curvature (if nterms \> 2).
160 Tip : Include a directory name in 'imagename' for all
161 output images to be sent there instead of the
162 current working directory : imagename='mydir/try'.
164 Tip : Restarting an imaging run without changing 'imagename'
165 implies continuation from the existing model image on disk.
166 - If 'startmodel' was initially specified it needs to be set to ""
167 for the restart run (or tclean will exit with an error message).
168 - By default, the residual image and psf will be recomputed
169 but if no changes were made to relevant parameters between
170 the runs, set calcres=False, calcpsf=False to resume directly from
171 the minor cycle without the (unnecessary) first major cycle.
172 To automatically change 'imagename' with a numerical
173 increment, set restart=False (see tclean docs for 'restart').
175 Note : All imaging runs will by default produce restored images.
176 For a niter=0 run, this will be redundant and can optionally
177 be turned off via the 'restoration=T/F' parameter.
178 imsize Number of pixels
179 example:
181 imsize = [350,250].
182 imsize = 500 is equivalent to [500,500].
184 To take proper advantage of internal optimized FFT routines, the
185 number of pixels must be even and factorizable by 2,3,5 only.
186 To find the nearest optimal imsize to that desired by the user, please use the following tool method:
188 from casatools import synthesisutils
189 su = synthesisutils()
190 su.getOptimumSize(345)
191 Output : 360
192 cell Cell size
193 example: cell=['0.5arcsec,'0.5arcsec'] or
194 cell=['1arcmin', '1arcmin'].
195 cell = '1arcsec' is equivalent to ['1arcsec','1arcsec'].
196 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.
198 Note : If unspecified, tclean will use the phase-center from the first data field of the MS (or list of MSs) selected for imaging.
200 example: phasecenter='6'.
201 phasecenter='J2000 19h30m00 -40d00m00'.
202 phasecenter='J2000 292.5deg -40.0deg'.
203 phasecenter='J2000 5.105rad -0.698rad'.
204 phasecenter='ICRS 13:05:27.2780 -049.28.04.458'.
205 phasecenter='myComet_ephem.tab'.
206 phasecenter='MOON'.
207 phasecenter='TRACKFIELD'.
208 stokes Stokes Planes to make
209 default='I'; example: stokes='IQUV';
210 Options: 'I','Q','U','V','IV','QU','IQ','UV','IQUV','RR','LL','XX','YY','RRLL','XXYY','pseudoI'
212 Note : Due to current internal code constraints, if any correlation pair
213 is flagged, by default, no data for that row in the MS will be used.
214 So, in an MS with XX,YY, if only YY is flagged, neither a
215 Stokes I image nor an XX image can be made from those data points.
216 In such a situation, please split out only the unflagged correlation into
217 a separate MS, or use the option 'pseudoI'.
219 Note : The 'pseudoI' option is a partial solution, allowing Stokes I imaging
220 when either of the parallel-hand correlations are unflagged.
222 The remaining constraints shall be removed (where logical) in a future release.
223 projection Coordinate projection
224 Examples : SIN, NCP.
225 A list of supported (but untested) projections can be found here :
226 http://casa.nrao.edu/active/docs/doxygen/html/classcasa_1_1Projection.html#a3d5f9ec787e4eabdce57ab5edaf7c0cd
227 startmodel Name of starting model image
229 The contents of the supplied starting model image will be
230 copied to the imagename.model before the run begins.
232 example : startmodel = 'singledish.im'.
234 For deconvolver='mtmfs', one image per Taylor term must be provided.
235 example : startmodel = ['try.model.tt0', 'try.model.tt1'].
236 startmodel = ['try.model.tt0'] will use a starting model only
237 for the zeroth order term.
238 startmodel = ['','try.model.tt1'] will use a starting model only
239 for the first order term.
241 This starting model can be of a different image shape and size from
242 what is currently being imaged. If so, an image regrid is first triggered
243 to resample the input image onto the target coordinate system.
245 A common usage is to set this parameter equal to a single dish image.
247 Negative components in the model image will be included as is.
249 Note : If an error occurs during image resampling/regridding,
250 please try using task imregrid to resample the starting model
251 image onto a CASA image with the target shape and
252 coordinate system before supplying it via startmodel.
253 specmode Spectral definition mode (mfs,cube,cubedata, cubesource, mvc)
255 specmode='mfs' : Continuum imaging with only one output image channel.
256 (mode='cont' can also be used here)
258 specmode='cube' : Spectral line imaging with one or more channels.
259 Parameters start, width,and nchan define the spectral
260 coordinate system and can be specified either in terms
261 of channel numbers, frequency or velocity in whatever
262 spectral frame is specified in 'outframe'.
263 All internal and output images are made with outframe as the
264 base spectral frame. However imaging code internally uses the fixed
265 spectral frame, LSRK for automatic internal software
266 Doppler correction, so that a spectral line observed over an
267 extended time range will line up appropriately.
268 Therefore the output images have additional spectral frame conversion
269 layer in LSRK on the top the base frame.
274 specmode='cubedata' : Spectral line imaging with one or more channels.
275 There is no internal software Doppler correction, so
276 a spectral line observed over an extended time range
277 may be smeared out in frequency. There is strictly
278 no valid spectral frame with which to associate with the
279 output images, thus the image spectral frame will
280 be labelled "Undefined".
283 specmode='cubesource': Spectral line imaging while
284 tracking moving source (near field or solar system
285 objects). The velocity of the source is accounted
286 and the frequency reported is in the source frame.
287 As there is no "SOURCE" frame defined in CASA,
288 the frame in the image will be labelled "REST" (but do note the
289 velocity of a given line reported may be different from the rest frame
290 velocity if the emission region is moving w.r.t the systemic
291 velocity frame of the source).
293 specmode='mvc' : Multiterm continuum imaging with cube major cycles.
294 This mode requires deconvolver='mtmfs' with nterms>1
295 and user-set choices of 'reffreq' and 'nchan'.
297 The output images and minor cycle are similar to specmode='mfs'
298 with deconvolver='mtmfs', but the major cycles are done in
299 cube mode (and require a setting of 'reffreq' and 'nchan').
300 By default, frequency-dependent primary beam correction is
301 applied to each channel, before being combined across frequency
302 to make the inputs to the 'mtmfs' deconvolver. This results in
303 implicit wideband pb-correction, with the deconvolver seeing only
304 the sky spectral structure.
306 Note : There is currently no option to turn off wideband pb correction
307 as part of the flat-sky normalization between the major and minor cycles.
308 Therefore, 'mvc' with the 'standard' and 'wproject' gridders will also apply
309 pblimits per channel, masking all regions outside of pblimit.
310 An option to retain sources outside the pblimit will be added in a future release.
312 Note : Below is some guidance for choosing 'nchan' and 'reffreq' :
314 The cube produced by the major cycle is used in a linear least square fits for Taylor
315 polynomials per pixel. Therefore, one only needs as many channels in the cube, as
316 required for an accurate polynomial fit for sources that have the strongest
317 spectral structure.
319 In general, 'nchan' needs to be greater than or equal to 'nterms', and the
320 frequency range selected by the data will be evenly split into nchan channels.
321 For a low-order polynomial fit, only a small number (around 10)
322 channels are typically needed (for VLA/ALMA bandwidth ratios).
323 'nchan=-1' applies a heuristic that results in a default of 10 cube channels
324 for a 2:1 bandwidth ratio.
326 nchan = MAX( bandwidth/(0.1*startfreq) , nterms+1 )
328 Note: When running in parallel, the nchan selected may limit the speedup if it
329 is smaller than the number of processes used.
331 The 'reffreq' is the reference frequency used for the Taylor polynomial expansion.
332 By default, in specmode='mvc', reffreq is set to the middle of the selected
333 frequency range.
334 reffreq Reference frequency of the output image coordinate system.
336 Example : reffreq='1.5GHz' as a string with units.
338 By default, it is calculated as the middle of the selected frequency range.
340 For deconvolver='mtmfs' the Taylor expansion is also done about
341 this specified reference frequency.
342 nchan Number of channels in the output image.
343 For default (=-1), the number of channels will be automatically determined
344 based on data selected by 'spw' with 'start' and 'width'.
345 It is often easiest to leave nchan at the default value.
346 example: nchan=100
347 start First channel (e.g. start=3,start=\'1.1GHz\',start=\'15343km/s\')
348 of output cube images specified by data channel number (integer),
349 velocity (string with a unit), or frequency (string with a unit).
350 Default:''; The first channel is automatically determined based on
351 the 'spw' channel selection and 'width'.
352 channels in 'spw'.
353 Since the integer number in 'start' represents the data channel number,
354 when the channel number is used along with the spectral window id selection
355 in 'spw', 'start' specified as an integer should be carefully set otherwise
356 it may result in the blank image channels if the 'start' channel (i.e. absolute
357 channel number) is outside of the channel range specified in 'spw'.
358 In such a case, 'start' can be left as a default (='') to ensure
359 matching with the data spectral channel selection.
360 For specmode='cube', when velocity or frequency is used it is
361 interpreted with the frame defined in outframe. [The parameters of
362 the desired output cube can be estimated by using the 'transform'
363 functionality of 'plotms'].
364 examples: start='5.0km/s'; 1st channel, 5.0km/s in outframe.
365 start='22.3GHz'; 1st channel, 22.3GHz in outframe.
366 width Channel width (e.g. width=2,width=\'0.1MHz\',width=\'10km/s\') of output cube images
367 specified by data channel number (integer), velocity (string with a unit), or
368 or frequency (string with a unit).
369 Default:''; data channel width.
370 The sign of width defines the direction of the channels to be incremented.
371 For width specified in velocity or frequency with '-' in front gives image channels in
372 decreasing velocity or frequency, respectively.
373 For specmode='cube', when velocity or frequency is used it is interpreted with
374 the reference frame defined in outframe.
375 examples: width='2.0km/s'; results in channels with increasing velocity.
376 width='-2.0km/s'; results in channels with decreasing velocity.
377 width='40kHz'; results in channels with increasing frequency.
378 width=-2; results in channels averaged of 2 data channels incremented from
379 high to low channel numbers.
380 outframe Spectral reference frame in which to interpret \'start\' and \'width\'
381 Options: '','LSRK','LSRD','BARY','GEO','TOPO','GALACTO','LGROUP','CMB'
382 example: outframe='bary' for Barycentric frame.
384 REST -- Rest frequency.
385 LSRD -- Local Standard of Rest (J2000).
386 -- as the dynamical definition (IAU, [9,12,7] km/s in galactic coordinates).
387 LSRK -- LSR as a kinematical (radio) definition.
388 -- 20.0 km/s in direction ra,dec = [270,+30] deg (B1900.0).
389 BARY -- Barycentric (J2000).
390 GEO --- Geocentric.
391 TOPO -- Topocentric.
392 GALACTO -- Galacto centric (with rotation of 220 km/s in direction l,b = [90,0] deg.
393 LGROUP -- Local group velocity -- 308km/s towards l,b = [105,-7] deg (F. Ghigo).
394 CMB -- CMB velocity -- 369.5km/s towards l,b = [264.4, 48.4] deg (F. Ghigo).
395 DEFAULT = LSRK.
396 veltype Velocity type (radio, z, ratio, beta, gamma, optical)
397 For 'start' and/or 'width' specified in velocity, specifies the velocity definition
398 Options: 'radio','optical','z','beta','gamma','optical'
399 NOTE: the viewer always defaults to displaying the 'radio' frame,
400 but that can be changed in the position tracking pull down.
402 The different types (with F = f/f0, the frequency ratio), are:
404 Z = (-1 + 1/F).
405 RATIO = (F) \*.
406 RADIO = (1 - F).
407 OPTICAL == Z.
408 BETA = ((1 - F^2)/(1 + F^2)).
409 GAMMA = ((1 + F^2)/2F) \*.
410 RELATIVISTIC == BETA (== v/c).
411 DEFAULT == RADIO.
412 Note that the ones with an '\*' have no real interpretation
413 (although the calculation will proceed) if given as a velocity.
414 restfreq List of rest frequencies or a rest frequency in a string.
415 Specify rest frequency to use for output image.
417 Currently it uses the first rest frequency in the list for translation of
418 velocities. The list will be stored in the output images.
419 Default: []; look for the rest frequency stored in the MS, if not available,
420 use center frequency of the selected channels.
421 examples: restfreq=['1.42GHz'].
422 restfreq='1.42GHz'.
423 interpolation Spectral interpolation (nearest,linear,cubic)
425 Interpolation rules to use when binning data channels onto image channels
426 and evaluating visibility values at the centers of image channels.
428 Note : 'linear' and 'cubic' interpolation requires data points on both sides of
429 each image frequency. Errors are therefore possible at edge channels, or near
430 flagged data channels. When image channel width is much larger than the data
431 channel width there is nothing much to be gained using linear or cubic thus
432 not worth the extra computation involved.
433 perchanweightdensity When calculating weight density for Briggs
434 style weighting in a cube, this parameter
435 determines whether to calculate the weight
436 density for each channel independently
437 (the default, True)
438 or a common weight density for all of the selected
439 data. This parameter has no
440 meaning for continuum (specmode='mfs') imaging
441 or for natural and radial weighting schemes.
442 For cube imaging
443 perchanweightdensity=True is a recommended
444 option that provides more uniform
445 sensitivity per channel for cubes, but with
446 generally larger psfs than the
447 perchanweightdensity=False (prior behavior)
448 option. When using Briggs style weight with
449 perchanweightdensity=True, the imaging weight
450 density calculations use only the weights of
451 data that contribute specifically to that
452 channel. On the other hand, when
453 perchanweightdensity=False, the imaging
454 weight density calculations sum all of the
455 weights from all of the data channels
456 selected whose (u,v) falls in a given uv cell
457 on the weight density grid. Since the
458 aggregated weights, in any given uv cell,
459 will change depending on the number of
460 channels included when imaging, the psf
461 calculated for a given frequency channel will
462 also necessarily change, resulting in
463 variability in the psf for a given frequency
464 channel when perchanweightdensity=False. In
465 general, perchanweightdensity=False results
466 in smaller psfs for the same value of
467 robustness compared to
468 perchanweightdensity=True, but the rms noise
469 as a function of channel varies and increases
470 toward the edge channels;
471 perchanweightdensity=True provides more
472 uniform sensitivity per channel for
473 cubes. This may make it harder to find
474 estimates of continuum when
475 perchanweightdensity=False. If you intend to
476 image a large cube in many smaller subcubes
477 and subsequently concatenate, it is advisable
478 to use perchanweightdensity=True to avoid
479 surprisingly varying sensitivity and psfs
480 across the concatenated cube.
481 gridder Gridding options (standard, wproject, widefield, mosaic, awproject, awp2, awphpg)
484 The following options choose different gridding convolution
485 functions for the process of convolutional resampling of the measured
486 visibilities onto a regular uv-grid prior to an inverse FFT.
487 Model prediction (degridding) also uses these same functions.
488 Several wide-field effects can be accounted for via careful choices of
489 convolution functions. Gridding (degridding) runtime will rise in
490 proportion to the support size of these convolution functions (in uv-pixels).
492 standard : Prolate Spheroid with 7x7 uv pixel support size.
494 [ This mode can also be invoked using 'ft' or 'gridft' ]
496 wproject : W-Projection algorithm to correct for the widefield
497 non-coplanar baseline effect. [Cornwell et.al 2008]
499 wprojplanes is the number of distinct w-values at
500 which to compute and use different gridding convolution
501 functions (see help for wprojplanes).
502 Convolution function support size can range
503 from 5x5 to few 100 x few 100.
505 [ This mode can also be invoked using 'wprojectft' ]
507 widefield : Facetted imaging with or without W-Projection per facet.
509 A set of facets x facets subregions of the specified image
510 are gridded separately using their respective phase centers
511 (to minimize max W). Deconvolution is done on the joint
512 full size image, using a PSF from the first subregion.
514 wprojplanes=1 : standard prolate spheroid gridder per facet.
515 wprojplanes > 1 : W-Projection gridder per facet.
516 nfacets=1, wprojplanes > 1 : Pure W-Projection and no facetting.
517 nfacets=1, wprojplanes=1 : Same as standard,ft,gridft.
519 A combination of facetting and W-Projection is relevant only for
520 very large fields of view. (In our current version of tclean, this
521 combination runs only with parallel=False.
523 mosaic : A-Projection with azimuthally symmetric beams without
524 sidelobes, beam rotation or squint correction.
525 Gridding convolution functions per visibility are computed
526 from FTs of PB models per antenna.
527 This gridder can be run on single fields as well as mosaics.
529 VLA : PB polynomial fit model (Napier and Rots, 1982).
530 EVLA : PB polynomial fit model (Perley, 2015).
531 ALMA : Airy disks for a 10.7m dish (for 12m dishes) and
532 6.25m dish (for 7m dishes) each with 0.75m
533 blockages (Hunter/Brogan 2011). Joint mosaic
534 imaging supports heterogeneous arrays for ALMA.
536 Typical gridding convolution function support sizes are
537 between 7 and 50 depending on the desired
538 accuracy (given by the uv cell size or image field of view).
540 [ This mode can also be invoked using 'mosaicft' or 'ftmosaic' ]
542 awproject : A-Projection with azimuthally asymmetric beams and
543 including beam rotation, squint correction,
544 conjugate frequency beams and W-projection.
545 [Bhatnagar et.al, 2008]
547 Gridding convolution functions are computed from
548 aperture illumination models per antenna and optionally
549 combined with W-Projection kernels and a prolate spheroid.
550 This gridder can be run on single fields as well as mosaics.
552 VLA : Uses ray traced model (VLA and EVLA) including feed
553 leg and subreflector shadows, off-axis feed location
554 (for beam squint and other polarization effects), and
555 a Gaussian fit for the feed beams (Brisken 2009)
556 ALMA : Similar ray-traced model as above (but the correctness
557 of its polarization properties remains un-verified).
559 Typical gridding convolution function support sizes are
560 between 7 and 50 depending on the desired
561 accuracy (given by the uv cell size or image field of view).
562 When combined with W-Projection they can be significantly larger.
564 [ This mode can also be invoked using 'awprojectft' ]
567 awp2 : A-Projection with azimuthally asymmetric beams and
568 including beam rotation, squint correction and W-projection.
569 [Bhatnagar et.al, 2008]
571 Gridding convolution functions are computed from
572 aperture illumination models (assuming similar antennas) and optionally
573 combined with W-Projection kernels.
574 This gridder can be run on single fields as well as mosaics.
575 The other sub-parameters that are of significance when using this gridder
576 are wprojplanes, computepastep, mosweight, usepointing, pblimit and normtype.
578 Only supports VLA : Uses ray traced model (VLA and EVLA) including feed
579 leg and subreflector shadows, off-axis feed location
580 (for beam squint and other polarization effects), and
581 a Gaussian fit for the feed beams (Ref: Brisken 2009)
583 For squint correction the value passed in computepastep has to be smaller than 180.
584 Anything larger awp2 will use an average of LL and RR beams. If computepastep=5, for
585 e.g., PB every 5 degrees, over the range of parallactic angle covered by the data, will be calculated
586 and the nearest beam to every integration will be used to correct for the squint between the L and R beams.
588 NOTE : For mtmfs with nterms >1 and using awp2 gridder, for accurate results always use specmode="mvc"
589 as awp2 with specmode="mfs" does not use conjugate beams to remove the spectral
590 index of the primary beam.
592 awphpg : Implementation of the high performance gridder (HPG; Pokorny, ngVLA Computing Memo #5).
593 For CASA 6.7.0 this mode is only available on the internal VLASS release of CASA.
594 It will be made available for general use in a future CASA release.
597 imagemosaic : (untested implementation).
599 Grid and iFT each pointing separately and combine the
600 images as a linear mosaic (weighted by a PB model) in
601 the image domain before a joint minor cycle.
603 VLA/ALMA PB models are same as for gridder='mosaicft'.
605 ------ Notes on PB models :
607 (1) Several different sources of PB models are used in the modes
608 listed above. This is partly for reasons of algorithmic flexibility
609 and partly due to the current lack of a common beam model
610 repository or consensus on what beam models are most appropriate.
612 (2) For ALMA and gridder='mosaic', ray-traced (TICRA) beams
613 are also available via the vpmanager tool.
614 For example, call the following before the tclean run.
615 vp.setpbimage(telescope="ALMA",
616 compleximage='/home/casa/data/trunk/alma/responses/ALMA_0_DV__0_0_360_0_45_90_348.5_373_373_GHz_ticra2007_VP.im',
617 antnames=['DV'+'%02d'%k for k in range(25)])
618 vp.saveastable('mypb.tab')
619 Then, supply vptable='mypb.tab' to tclean.
620 ( Currently this will work only for non-parallel runs )
623 ------ Note on PB masks :
625 In tclean, A-Projection gridders (mosaic, awproject, and awp2) produce a
626 .pb image and use the 'pblimit' subparameter to decide normalization
627 cutoffs and construct an internal T/F mask in the .pb and .image images.
628 However, this T/F mask cannot directly be used during deconvolution
629 (which needs a 1/0 mask). There are two options for making a pb based
630 deconvolution mask.
631 -- Run tclean with niter=0 to produce the .pb, construct a 1/0 image
632 with the desired threshold (using ia.open('newmask.im');
633 ia.calc('iif("xxx.pb">0.3,1.0,0.0)');ia.close() for example),
634 and supply it via the 'mask' parameter in a subsequent run
635 (with calcres=F and calcpsf=F to restart directly from the minor cycle).
636 -- Run tclean with usemask='pb' for it to automatically construct
637 a 1/0 mask from the internal T/F mask from .pb at a fixed 0.2 threshold.
640 ----- Making PBs for gridders other than mosaic, awproject, awp2
642 After the PSF generation, a PB is constructed using the same
643 models used in gridder='mosaic' but just evaluated in the image
644 domain without consideration to weights.
645 facets Number of facets on a side
647 A set of (facets x facets) subregions of the specified image
648 are gridded separately using their respective phase centers
649 (to minimize max W). Deconvolution is done on the joint
650 full size image, using a PSF from the first subregion/facet.
652 In our current version of tclean, facets>1 may be used only
653 with parallel=False.
654 psfphasecenter For mosaic use psf centered on this
655 optional direction. You may need to use
656 this if for example the mosaic does not
657 have any pointing in the center of the
658 image. Another reason; as the psf is
659 approximate for a mosaic, this may help
660 to deconvolve a non central bright source
661 well and quickly.
663 example:
665 psfphasecenter='6' #center psf on field 6.
666 psfphasecenter='J2000 19h30m00 -40d00m00'.
667 psfphasecenter='J2000 292.5deg -40.0deg'.
668 psfphasecenter='J2000 5.105rad -0.698rad'.
669 psfphasecenter='ICRS 13:05:27.2780 -049.28.04.458'.
670 wprojplanes Number of distinct w-values at which to compute and use different
671 gridding convolution functions for W-Projection
673 An appropriate value of wprojplanes depends on the presence/absence
674 of a bright source far from the phase center, the desired dynamic
675 range of an image in the presence of a bright far out source,
676 the maximum w-value in the measurements, and the desired trade off
677 between accuracy and computing cost.
679 As a (rough) guide, VLA L-Band D-config may require a
680 value of 128 for a source 30arcmin away from the phase
681 center. A-config may require 1024 or more. To converge to an
682 appropriate value, try starting with 128 and then increasing
683 it if artifacts persist. W-term artifacts (for the VLA) typically look
684 like arc-shaped smears in a synthesis image or a shift in source
685 position between images made at different times. These artifacts
686 are more pronounced the further the source is from the phase center.
688 There is no harm in simply always choosing a large value (say, 1024)
689 but there will be a significant performance cost to doing so, especially
690 for gridder='awproject' where it is combined with A-Projection.
692 wprojplanes=-1 is an option for gridder='widefield' or 'wproject'
693 in which the number of planes is automatically computed.
694 vptable vpmanager
696 vptable="" : Choose default beams for different telescopes.
697 ALMA : Airy disks.
698 EVLA : old VLA models.
700 Other primary beam models can be chosen via the vpmanager tool.
702 Step 1 : Set up the vpmanager tool and save its state in a table.
704 vp.setpbpoly(telescope='EVLA', coeff=[1.0, -1.529e-3, 8.69e-7, -1.88e-10])
705 vp.saveastable('myvp.tab')
707 Step 2 : Supply the name of that table in tclean.
709 tclean(....., vptable='myvp.tab',....)
711 Please see the documentation for the vpmanager for more details on how to
712 choose different beam models. Work is in progress to update the defaults
713 for EVLA and ALMA.
715 Note : AWProjection currently does not use this mechanism to choose
716 beam models. It instead uses ray-traced beams computed from
717 parameterized aperture illumination functions, which are not
718 available via the vpmanager. So, gridder='awproject' does not allow
719 the user to set this parameter.
720 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.
721 aterm Use aperture illumination functions during gridding.
723 This parameter turns on the A-term of the AW-Projection gridder.
724 Gridding convolution functions are constructed from aperture illumination
725 function models of each antenna.
726 psterm Include the Prolate Spheroidal (PS) funtion as the anti-aliasing
727 operator in the gridding convolution functions used for gridding.
729 Setting this parameter to true is necessary when aterm is set to
730 false. It can be set to false when aterm is set to true, though
731 with this setting effects of aliasing may be there in the image,
732 particularly near the edges.
734 When set to true, the .pb images will contain the fourier transform
735 of the of the PS funtion.
737 For more information on the functional
738 effects of the psterm, aterm and wprojplanes settings, see the
739 'Wide-field Imaging' pages in CASA Docs (https://casadocs.readthedocs.io).
740 wbawp Use frequency dependent A-terms.
741 Scale aperture illumination functions appropriately with frequency
742 when gridding and combining data from multiple channels.
743 conjbeams Use conjugate frequency for wideband A-terms.
745 While gridding data from one frequency channel, choose a convolution
746 function from a 'conjugate' frequency such that the resulting baseline
747 primary beam is approximately constant across frequency. For a system in
748 which the primary beam scales with frequency, this step will eliminate
749 instrumental spectral structure from the measured data and leave only the
750 sky spectrum for the minor cycle to model and reconstruct [Bhatnagar et al., ApJ, 2013].
752 As a rough guideline for when this is relevant, a source at the half power
753 point of the PB at the center frequency will see an artificial spectral
754 index of -1.4 due to the frequency dependence of the PB [Sault and Wieringa, 1994].
755 If left uncorrected during gridding, this spectral structure must be modeled
756 in the minor cycle (using the mtmfs algorithm) to avoid dynamic range limits
757 (of a few hundred for a 2:1 bandwidth).
758 This works for specmode='mfs' and its value is ignored for cubes.
759 cfcache Convolution function cache directory name.
761 Name of a directory in which to store gridding convolution functions.
762 This cache is filled at the beginning of an imaging run. This step can be time
763 consuming but the cache can be reused across multiple imaging runs that
764 use the same image parameters (cell size, image size , spectral data
765 selections, wprojplanes, wbawp, psterm, aterm). The effect of the wbawp,
766 psterm and aterm settings is frozen-in in the cfcache. Using an existing cfcache
767 made with a different setting of these parameters will not reflect the current
768 settings.
770 In a parallel execution, the construction of the cfcache is also parallelized
771 and the time to compute scales close to linearly with the number of compute
772 cores used. With the re-computation of Convolution Functions (CF) due to PA
773 rotation turned-off (the computepastep parameter), the total number of in the
774 cfcache can be computed as [No. of wprojplanes x No. of selected spectral windows x 4]
776 By default, cfcache = imagename + '.cf'
777 usepointing The usepointing flag informs the gridder that it should utilize the pointing table
778 to use the correct direction in which the antenna is pointing with respect to the pointing phasecenter.
779 computepastep Parallactic angle interval after the AIFs are recomputed (deg).
781 This parameter controls the accuracy of the aperture illumination function
782 used with AProjection for alt-az mount dishes where the AIF rotates on the
783 sky as the synthesis image is built up. Once the PA in the data changes by
784 the given interval, AIFs are re-computed at the new PA.
786 A value of 360.0 deg (the default) implies no re-computation due to PA rotation.
787 AIFs are computed for the PA value of the first valid data received and used for
788 all of the data.
790 For gridder=awp2 a value of 180.0 deg or larger implies no squint correction will be
791 attempted i.e an average beam of the left hand and right hand polarization will be calculated
792 rotatepastep Parallactic angle interval after which the nearest AIF is rotated (deg)
794 Instead of recomputing the AIF for every timestep's parallactic angle,
795 the nearest existing AIF is used and rotated
796 after the PA changed by rotatepastep value.
798 A value of 360.0 deg (the default) disables rotation of the AIF.
800 For example, computepastep=360.0 and rotatepastep=5.0 will compute
801 the AIFs at only the starting parallactic angle and all other timesteps will
802 use a rotated version of that AIF at the nearest 5.0 degree point.
803 pointingoffsetsigdev Corrections for heterogenous and time-dependent pointing
804 offsets via AWProjection are controlled by this parameter.
805 It is a vector of 2 ints or doubles each of which is interpreted
806 in units of arcsec. Based on the first threshold, a clustering
807 algorithm is applied to entries from the POINTING subtable
808 of the MS to determine how distinct antenna groups for which
809 the pointing offset must be computed separately. The second
810 number controls how much a pointing change across time can
811 be ignored and after which an antenna rebinning is required.
814 Note : The default value of this parameter is [], due a programmatic constraint.
815 If run with this value, it will internally pick [600,600] and exercise the
816 option of using large tolerances (10arcmin) on both axes. Please choose
817 a setting explicitly for runs that need to use this parameter.
819 Note : This option is available only for gridder='awproject' and usepointing=True and
820 and has been validated primarily with VLASS on-the-fly mosaic data
821 where POINTING subtables have been modified after the data are recorded.
824 Examples of parameter usage :
826 [100.0,100.0] : Pointing offsets of 100 arcsec or less are considered
827 small enough to be ignored. Using large values for both
828 indicates a homogeneous array.
831 [10.0, 100.0] : Based on entries in the POINTING subtable, antennas
832 are grouped into clusters based on a 10arcsec bin size.
833 All antennas in a bin are given a pointing offset calculated
834 as the average of the offsets of all antennas in the bin.
835 On the time axis, offset changes upto 100 arcsec will be ignored.
837 [10.0,10.0] : Calculate separate pointing offsets for each antenna group
838 (with a 10 arcsec bin size). As a function of time, recalculate
839 the antenna binning if the POINTING table entries change by
840 more than 10 arcsec w.r.to the previously computed binning.
842 [1.0, 1.0] : Tight tolerances will imply a fully heterogenous situation where
843 each antenna gets its own pointing offset. Also, time-dependent
844 offset changes greater than 1 arcsec will trigger recomputes of
845 the phase gradients. This is the most general situation and is also
846 the most expensive option as it constructs and uses separate
847 phase gradients for all baselines and timesteps.
849 For VLASS 1.1 data with two kinds of pointing offsets, the recommended
850 setting is [ 30.0, 30.0 ].
852 For VLASS 1.2 data with only the time-dependent pointing offsets, the
853 recommended setting is [ 300.0, 30.0 ] to turn off the antenna grouping
854 but to retain the time dependent corrections required from one timestep
855 to the next.
856 pblimit PB gain level at which to cut off normalizations.
858 Divisions by .pb during normalizations have a cut off at a .pb gain
859 level given by pblimit. Outside this limit, image values are set to zero.
860 Additionally, by default, an internal T/F mask is applied to the .pb, .image and
861 .residual images to mask out (T) all invalid pixels outside the pblimit area.
863 Note : This internal T/F mask cannot be used as a deconvolution mask.
864 To do so, please follow the steps listed above in the Notes for the
865 'gridder' parameter.
867 Note : To prevent the internal T/F mask from appearing in anything other
868 than the .pb and .image.pbcor images, 'pblimit' can be set to a
869 negative number.
870 The absolute value will still be used as a valid 'pblimit' for normalization
871 purposes. So, for example, pick pblimit=-0.1 (and not pblimit=-1).
872 A tclean restart using existing output images on disk that already
873 have this T/F mask in the .residual and .image but only pblimit set
874 to a negative value, will remove this mask after the next major cycle.
876 Note : An existing internal T/F mask may be removed from an image as
877 follows (without needing to re-run tclean itself).
878 ia.open('test.image');
879 ia.maskhandler(op='set', name='');
880 ia.done()
881 normtype Normalization type (flatnoise, flatsky, pbsquare).
883 Gridded (and FT'd) images represent the PB-weighted sky image.
884 Qualitatively it can be approximated as two instances of the PB
885 applied to the sky image (one naturally present in the data
886 and one introduced during gridding via the convolution functions).
888 xxx.weight : Weight image approximately equal to sum ( square ( pb ) )
889 xxx.pb : Primary beam calculated as sqrt ( xxx.weight )
891 normtype='flatnoise' : Divide the raw image by sqrt(.weight) so that
892 the input to the minor cycle represents the
893 product of the sky and PB. The noise is 'flat'
894 across the region covered by each PB.
896 normtype='flatsky' : Divide the raw image by .weight so that the input
897 to the minor cycle represents only the sky.
898 The noise is higher in the outer regions of the
899 primary beam where the sensitivity is low.
901 normtype='pbsquare' : No normalization after gridding and FFT.
902 The minor cycle sees the sky times pb square
903 deconvolver Name of minor cycle algorithm (hogbom,clark,multiscale,mtmfs,mem,clarkstokes,asp)
905 Each of the following algorithms operate on residual images and PSFs
906 from the gridder and produce output model and restored images.
907 Minor cycles stop and a major cycle is triggered when cyclethreshold
908 or cycleniter are reached. For all methods, components are picked from
909 the entire extent of the image or (if specified) within a mask.
911 hogbom : An adapted version of Hogbom Clean [Hogbom, 1974].
912 - Find the location of the peak residual.
913 - Add this delta function component to the model image.
914 - Subtract a scaled and shifted PSF of the same size as the image
915 from regions of the residual image where the two overlap.
916 - Repeat.
918 clark : An adapted version of Clark Clean [Clark, 1980].
919 - Find the location of max(I^2+Q^2+U^2+V^2).
920 - Add delta functions to each stokes plane of the model image.
921 - Subtract a scaled and shifted PSF within a small patch size
922 from regions of the residual image where the two overlap.
923 - After several iterations trigger a Clark major cycle to subtract
924 components from the visibility domain, but without de-gridding.
925 - Repeat.
927 ( Note : 'clark' maps to imagermode='' in the old clean task.
928 'clark_exp' is another implementation that maps to
929 imagermode='mosaic' or 'csclean' in the old clean task
930 but the behavior is not identical. For now, please
931 use deconvolver='hogbom' if you encounter problems. )
933 clarkstokes : Clark Clean operating separately per Stokes plane.
935 (Note : 'clarkstokes_exp' is an alternate version. See above.)
937 multiscale : MultiScale Clean [Cornwell, 2008].
938 - Smooth the residual image to multiple scale sizes.
939 - Find the location and scale at which the peak occurs.
940 - Add this multiscale component to the model image.
941 - Subtract a scaled,smoothed,shifted PSF (within a small
942 patch size per scale) from all residual images.
943 - Repeat from step 2.
945 mtmfs : Multi-term (Multi Scale) Multi-Frequency Synthesis [Rau and Cornwell, 2011].
946 - Smooth each Taylor residual image to multiple scale sizes.
947 - Solve a NTxNT system of equations per scale size to compute
948 Taylor coefficients for components at all locations.
949 - Compute gradient chi-square and pick the Taylor coefficients
950 and scale size at the location with maximum reduction in
951 chi-square.
952 - Add multi-scale components to each Taylor-coefficient
953 model image.
954 - Subtract scaled,smoothed,shifted PSF (within a small patch size
955 per scale) from all smoothed Taylor residual images.
956 - Repeat from step 2.
959 mem : Maximum Entropy Method [Cornwell and Evans, 1985].
960 - Iteratively solve for values at all individual pixels via the
961 MEM method. It minimizes an objective function of
962 chi-square plus entropy (here, a measure of difference
963 between the current model and a flat prior model).
965 (Note : This MEM implementation is not very robust.
966 Improvements will be made in the future.)
968 asp : Adaptive Scale Pixel algorithm [Bhatnagar and Cornwell, 2004].
969 - Define a set of initial scales defined as 0, W, 2W 4W and 8W.
970 where W is a 2D Gaussian fitting width to the PSF.
971 - Smooth the residual image by a Gaussian beam at initial scales.
972 - Search for the global peak (F) among these smoothed residual images.
973 - form an active Aspen set: amplitude(F), amplitude location(x,y).
974 - Optimize the Aspen set by minimizing the objective function RI-Aspen*PSF,
975 where RI is the residual image and * is the convulition operation.
976 - Compute the model image and update the residual image
977 - Repeat from step 2
978 scales List of scale sizes (in pixels) for multi-scale and mtmfs algorithms.
979 --> scales=[0,6,20]
980 This set of scale sizes should represent the sizes
981 (diameters in units of number of pixels)
982 of dominant features in the image being reconstructed.
984 The smallest scale size is recommended to be 0 (point source),
985 the second being the size of the synthesized beam and the third being 3-5
986 times the synthesized beam, etc. For example, if the synthesized
987 beam is 10" FWHM and cell=2",try scales = [0,5,15].
989 For numerical stability, the largest scale must be
990 smaller than the image (or mask) size and smaller than or
991 comparable to the scale corresponding to the lowest measured
992 spatial frequency (as a scale size much larger than what the
993 instrument is sensitive to is unconstrained by the data making
994 it harder to recover from errors during the minor cycle).
995 nterms Number of Taylor coefficients in the spectral model.
997 - nterms=1 : Assume flat spectrum source.
998 - nterms=2 : Spectrum is a straight line with a slope.
999 - nterms=N : A polynomial of order N-1.
1001 From a Taylor expansion of the expression of a power law, the
1002 spectral index is derived as alpha = taylorcoeff_1 / taylorcoeff_0.
1004 Spectral curvature is similarly derived when possible.
1006 The optimal number of Taylor terms depends on the available
1007 signal to noise ratio, bandwidth ratio, and spectral shape of the
1008 source as seen by the telescope (sky spectrum x PB spectrum).
1010 nterms=2 is a good starting point for wideband EVLA imaging
1011 and the lower frequency bands of ALMA (when fractional bandwidth
1012 is greater than 10%) and if there is at least one bright source for
1013 which a dynamic range of greater than few 100 is desired.
1015 Spectral artifacts for the VLA often look like spokes radiating out from
1016 a bright source (i.e. in the image made with standard mfs imaging).
1017 If increasing the number of terms does not eliminate these artifacts,
1018 check the data for inadequate bandpass calibration. If the source is away
1019 from the pointing center, consider including wide-field corrections too.
1021 (Note : In addition to output Taylor coefficient images .tt0,.tt1,etc
1022 images of spectral index (.alpha), an estimate of error on
1023 spectral index (.alpha.error) and spectral curvature (.beta,
1024 if nterms is greater than 2) are produced.
1025 - These alpha, alpha.error and beta images contain
1026 internal T/F masks based on a threshold computed
1027 as peakresidual/10. Additional masking based on
1028 .alpha/.alpha.error may be desirable.
1029 - .alpha.error is a purely empirical estimate derived
1030 from the propagation of error during the division of
1031 two noisy numbers (alpha = xx.tt1/xx.tt0) where the
1032 'error' on tt1 and tt0 are simply the values picked from
1033 the corresponding residual images. The absolute value
1034 of the error is not always accurate and it is best to interpret
1035 the errors across the image only in a relative sense.
1036 smallscalebias A numerical control to bias the scales when using multi-scale or mtmfs algorithms.
1037 The peak from each scale's smoothed residual is
1038 multiplied by ( 1 - smallscalebias \* scale/maxscale )
1039 to increase or decrease the amplitude relative to other scales,
1040 before the scale with the largest peak is chosen.
1041 Smallscalebias can be varied between -1.0 and 1.0.
1042 A score of 0.0 gives all scales equal weight (default).
1043 A score larger than 0.0 will bias the solution towards smaller scales.
1044 A score smaller than 0.0 will bias the solution towards larger scales.
1045 The effect of smallscalebias is more pronounced when using multi-scale relative to mtmfs.
1046 fusedthreshold ring Hogbom Clean (number in units of Jy).
1048 fusedthreshold = 0.0001 : 0.1 mJy.
1050 This is a subparameter of the Asp Clean deconvolver. When peak residual
1051 is lower than the threshold, Asp Clean is "switched to Hogbom Clean" (i.e. only use the 0 scale for cleaning) for
1052 the following number of iterations until it switches back to Asp Clean.
1054 NumberIterationsInHogbom = 50 + 2 * (exp(0.05 * NthHogbom) - 1)
1056 , where NthHogbom is the number of times Hogbom Clean has been triggered.
1058 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.
1060 NumberIterationsInHogbom = 500 + 2 * (exp(0.05 * NthHogbom) - 1)
1062 Set 'fusedthreshold = -1' to make the Asp Clean deconvolver never "switch" to Hogbom Clean.
1063 largestscale xels) allowed for the initial guess for the Asp Clean deconvolver.
1065 largestscale = 100
1067 The default initial scale sizes used by Asp Clean is [0, w, 2w, 4w, 8w],
1068 where `w` is the PSF width. The default `largestscale` is -1 which indicates
1069 users accept these initial scales. If `largestscale` is set, the initial scales
1070 would be [0, w, ... up to the `largestscale`]. This is only an initial guess,
1071 and actual fitted scale sizes may evolve from these initial values.
1073 It is recommended not to set `largestscale` unless Asp Clean picks a large
1074 scale that has no constraints from the data (the UV hole issue).
1075 restoration e.
1077 Construct a restored image : imagename.image by convolving the model
1078 image with a clean beam and adding the residual image to the result.
1079 If a restoringbeam is specified, the residual image is also
1080 smoothed to that target resolution before adding it in.
1082 If a .model does not exist, it will make an empty one and create
1083 the restored image from the residuals ( with additional smoothing if needed ).
1084 With algorithm='mtmfs', this will construct Taylor coefficient maps from
1085 the residuals and compute .alpha and .alpha.error.
1086 restoringbeam ize to use.
1088 - restoringbeam='' or [''].
1089 A Gaussian fitted to the PSF main lobe (separately per image plane).
1091 - restoringbeam='10.0arcsec'.
1092 Use a circular Gaussian of this width for all planes.
1094 - restoringbeam=['8.0arcsec','10.0arcsec','45deg'].
1095 Use this elliptical Gaussian for all planes.
1097 - restoringbeam='common'.
1098 Automatically estimate a common beam shape/size appropriate for
1099 all planes. This option can be used when the beam shape is different as a function of frequency, and will smooth all planes to a single beam, defined by the largest beam in the cube.
1101 Note : For any restoring beam different from the native resolution
1102 the model image is convolved with the beam and added to
1103 residuals that have been convolved to the same target resolution.
1104 pbcor the output restored image.
1106 A new image with extension .image.pbcor will be created from
1107 the evaluation of .image / .pb for all pixels above the specified pblimit.
1109 Note : Stand-alone PB-correction can be triggered by re-running
1110 tclean with the appropriate imagename and with
1111 niter=0, calcpsf=False, calcres=False, pbcor=True, vptable='vp.tab'
1112 ( where vp.tab is the name of the vpmanager file;
1113 see the inline help for the 'vptable' parameter ). Alternatively, task impbcor can be used for primary beam correction using the .image and .pb files.
1115 Note : For deconvolver='mtmfs', pbcor will divide each Taylor term image by the .tt0 average PB.
1116 For all gridders, this calculation is accurate for small fractional bandwidths.
1118 For large fractional bandwidths, please use one of the following options.
1120 (a) For single pointings, run the tclean task with specmode='mfs', deconvolver='mtmfs',
1121 and gridder='standard' with pbcor=True or False.
1122 If a PB-corrected spectral index is required,
1123 please use the widebandpbcor task to apply multi-tern PB-correction.
1125 (b) For mosaics, run tclean task with specmode='mfs', deconvolver='mtmfs',
1126 and gridder='awproject' , wbawp=True, conjbeams=True, with pbcor=True.
1127 This option applies wideband PB correction as part of the gridding step and
1128 pbcor=True will be accurate because the spectral index map will already
1129 be PB-corrected.
1131 (c) For mosaics, run tclean with specmode='mvc', deconvolver='mtmfs',
1132 and gridder='mosaic' or 'awp2' with pbcor=True.
1133 This option applies wideband PB-correction to channelized residual images
1134 prior to the minor cycle and pbcor=True will be accurate because the spectral
1135 index map will already be PB-corrected.
1137 Note : Frequency-dependent PB corrections are typically required for full-band imaging with the VLA.
1138 Wideband PB corrections are required when the amplitude of the
1139 brightest source is known accurately enough to be sensitive
1140 to the difference in the PB gain between the upper and lower
1141 end of the band at its location. As a guideline, the artificial spectral
1142 index due to the PB is -1.4 at the 0.5 gain level and less than -0.2
1143 at the 0.9 gain level at the middle frequency )
1144 outlierfile Name of outlier-field image definitions.
1146 A text file containing sets of parameter=value pairs,
1147 one set per outlier field.
1149 Example : outlierfile='outs.txt'
1151 Contents of outs.txt :
1153 imagename=tst1
1154 nchan=1
1155 imsize=[80,80]
1156 cell=[8.0arcsec,8.0arcsec]
1157 phasecenter=J2000 19:58:40.895 +40.55.58.543
1158 mask=circle[[40pix,40pix],10pix]
1160 imagename=tst2
1161 nchan=1
1162 imsize=[100,100]
1163 cell=[8.0arcsec,8.0arcsec]
1164 phasecenter=J2000 19:58:40.895 +40.56.00.000
1165 mask=circle[[60pix,60pix],20pix]
1167 The following parameters are currently allowed to be different between
1168 the main field and the outlier fields (i.e. they will be recognized if found
1169 in the outlier text file). If a parameter is not listed, the value is picked from
1170 what is defined in the main task input.
1172 imagename, imsize, cell, phasecenter, startmodel, mask
1173 specmode, nchan, start, width, nterms, reffreq,
1174 gridder, deconvolver, wprojplanes.
1176 Note : 'specmode' is an option, so combinations of mfs and cube
1177 for different image fields, for example, are supported.
1178 'deconvolver' and 'gridder' are also options that allow different
1179 imaging or deconvolution algorithm per image field.
1181 For example, multiscale with wprojection and 16 w-term planes
1182 on the main field and mtmfs with nterms=3 and wprojection
1183 with 64 planes on a bright outlier source for which the frequency
1184 dependence of the primary beam produces a strong effect that
1185 must be modeled. The traditional alternative to this approach is
1186 to first image the outlier, subtract it out of the data (uvsub) and
1187 then image the main field.
1188 weighting Weighting scheme (natural,uniform,briggs,superuniform,radial, briggsabs, briggsbwtaper).
1190 During gridding of the dirty or residual image, each visibility value is
1191 multiplied by a weight before it is accumulated on the uv-grid.
1192 The PSF's uv-grid is generated by gridding only the weights (weightgrid).
1194 weighting='natural' : Gridding weights are identical to the data weights
1195 from the MS. For visibilities with similar data weights,
1196 the weightgrid will follow the sample density
1197 pattern on the uv-plane. This weighting scheme
1198 provides the maximum imaging sensitivity at the
1199 expense of a PSF with possibly wider main lobes and high sidelobes.
1200 It is most appropriate for detection experiments
1201 where sensitivity is most important.
1203 weighting='uniform' : Gridding weights per visibility data point are the
1204 original data weights divided by the total weight of
1205 all data points that map to the same uv grid cell :
1206 ' data_weight / total_wt_per_cell '.
1208 The weightgrid is as close to flat as possible resulting
1209 in a PSF with a narrow main lobe and suppressed
1210 sidelobes. However, since heavily sampled areas of
1211 the uv-plane get down-weighted, the imaging
1212 sensitivity is not as high as with natural weighting.
1213 It is most appropriate for imaging experiments where
1214 a well behaved PSF can help the reconstruction.
1216 weighting='briggs' : Gridding weights per visibility data point are given by
1217 'data_weight / ( A \* total_wt_per_cell + B ) ' where
1218 A and B vary according to the 'robust' parameter.
1220 robust = -2.0 maps to A=1,B=0 or uniform weighting.
1221 robust = +2.0 maps to natural weighting.
1222 (robust=0.5 is equivalent to robust=0.0 in AIPS IMAGR.)
1224 Robust/Briggs weighting generates a PSF that can
1225 vary smoothly between 'natural' and 'uniform' and
1226 allow customized trade-offs between PSF shape and
1227 imaging sensitivity.
1228 weighting='briggsabs' : Experimental option.
1229 Same as Briggs except the formula is different A=
1230 robust\*robust and B is dependent on the
1231 noise per visibility estimated. Giving noise='0Jy'
1232 is a not a reasonable option.
1233 In this mode (or formula) robust values
1234 from -2.0 to 0.0 only make sense (2.0 and
1235 -2.0 will get the same weighting)
1237 weighting='superuniform' : This is similar to uniform weighting except that
1238 the total_wt_per_cell is replaced by the
1239 total_wt_within_NxN_cells around the uv cell of
1240 interest. N=7 is the default (when the
1241 parameter 'npixels' is set to 0 with 'superuniform')
1243 This method tends to give a PSF with inner
1244 sidelobes that are suppressed as in uniform
1245 weighting but with far-out sidelobes closer to
1246 natural weighting. The peak sensitivity is also
1247 closer to natural weighting.
1249 weighting='radial' : Gridding weights are given by ' data_weight \* uvdistance '
1250 This method approximately minimizes rms sidelobes
1251 for an east-west synthesis array.
1253 weighting='briggsbwtaper' : A modified version of Briggs weighting for cubes where an inverse uv taper,
1254 which is proportional to the fractional bandwidth of the entire cube,
1255 is applied per channel. The objective is to modify cube (perchanweightdensity = True)
1256 imaging weights to have a similar density to that of the continuum imaging weights.
1257 This is currently an experimental weighting scheme being developed for ALMA.
1259 For more details on weighting please see Chapter3
1260 of Dan Briggs' thesis (http://www.aoc.nrao.edu/dissertations/dbriggs)
1261 robust Robustness parameter for Briggs weighting.
1263 robust = -2.0 maps to uniform weighting.
1264 robust = +2.0 maps to natural weighting.
1265 (robust=0.5 is equivalent to robust=0.0 in AIPS IMAGR.)
1266 noise noise parameter for briggs abs mode weighting
1267 npixels Number of pixels to determine uv-cell size for super-uniform weighting
1268 (0 defaults to -/+ 3 pixels).
1270 npixels -- uv-box used for weight calculation
1271 a box going from -npixel/2 to +npixel/2 on each side
1272 around a point is used to calculate weight density.
1274 npixels=2 goes from -1 to +1 and covers 3 pixels on a side.
1276 npixels=0 implies a single pixel, which does not make sense for
1277 superuniform weighting. Therefore, for 'superuniform'
1278 weighting, if npixels=0 it will be forced to 6 (or a box
1279 of -3pixels to +3pixels) to cover 7 pixels on a side.
1280 uvtaper uv-taper on outer baselines in uv-plane.
1282 Apply a Gaussian taper in addition to the weighting scheme specified
1283 via the 'weighting' parameter. Higher spatial frequencies are weighted
1284 down relative to lower spatial frequencies to suppress artifacts
1285 arising from poorly sampled areas of the uv-plane. It is equivalent to
1286 smoothing the PSF obtained by other weighting schemes and can be
1287 specified either as the HWHM of a Gaussian in uv-space (eg. units of lambda)
1288 or as the FWHM of a Gaussian in the image domain (eg. angular units like arcsec).
1290 uvtaper = [bmaj, bmin, bpa].
1292 Note : FWHM_uv_lambda = (4 log2) / ( pi * FWHM_lm_radians ).
1294 A FWHM_lm of 100.000 arcsec maps to a HWHM_uv of 910.18 lambda.
1295 A FWHM_lm of 1 arcsec maps to a HWHM_uv of 91 klambda.
1297 default: uvtaper=[]; no Gaussian taper applied.
1298 example: uvtaper=['5klambda'] circular taper of HWHM=5 kilo-lambda.
1299 uvtaper=['5klambda','3klambda','45.0deg'] uv-domain HWHM.
1300 uvtaper=['50arcsec','30arcsec','30.0deg'] : image domain FWHM.
1301 uvtaper=['10arcsec'] : image domain FWHM.
1302 uvtaper=['300.0'] default units are lambda in aperture plane.
1303 niter Maximum number of iterations.
1305 A stopping criterion based on total iteration count.
1306 Currently the parameter type is defined as an integer therefore the integer value
1307 larger than 2147483647 will not be set properly as it causes an overflow.
1309 Iterations are typically defined as the selecting one flux component
1310 and partially subtracting it out from the residual image.
1312 niter=0 : Do only the initial major cycle (make dirty image, psf, pb, etc).
1314 niter larger than zero : Run major and minor cycles.
1316 Note : Global stopping criteria vs major-cycle triggers.
1318 In addition to global stopping criteria, the following rules are
1319 used to determine when to terminate a set of minor cycle iterations
1320 and trigger major cycles [derived from Cotton-Schwab Clean, 1984].
1322 'cycleniter' : controls the maximum number of iterations per image
1323 plane before triggering a major cycle.
1324 'cyclethreshold' : Automatically computed threshold related to the
1325 max sidelobe level of the PSF and peak residual.
1326 Divergence, detected as an increase of 10% in peak residual from the
1327 minimum so far (during minor cycle iterations).
1329 The first criterion to be satisfied takes precedence.
1331 Note : Iteration counts for cubes or multi-field images :
1332 For images with multiple planes (or image fields) on which the
1333 deconvolver operates in sequence, iterations are counted across
1334 all planes (or image fields). The iteration count is compared with
1335 'niter' only after all channels/planes/fields have completed their
1336 minor cycles and exited either due to 'cycleniter' or 'cyclethreshold'.
1337 Therefore, the actual number of iterations reported in the logger
1338 can sometimes be larger than the user specified value in 'niter'.
1339 For example, with niter=100, cycleniter=20,nchan=10,threshold=0,
1340 a total of 200 iterations will be done in the first set of minor cycles
1341 before the total is compared with niter=100 and it exits.
1343 Note : Additional global stopping criteria include:
1344 - no change in peak residual across two major cycles.
1345 - a 50% or more increase in peak residual across one major cycle.
1346 gain Loop gain.
1348 Fraction of the source flux to subtract out of the residual image
1349 for the CLEAN algorithm and its variants.
1351 A low value (0.2 or less) is recommended when the sky brightness
1352 distribution is not well represented by the basis functions used by
1353 the chosen deconvolution algorithm. A higher value can be tried when
1354 there is a good match between the true sky brightness structure and
1355 the basis function shapes. For example, for extended emission,
1356 multiscale clean with an appropriate set of scale sizes will tolerate
1357 a higher loop gain than Clark clean.
1358 threshold Stopping threshold (number in units of Jy, or string).
1360 A global stopping threshold that the peak residual (within clean mask)
1361 across all image planes is compared to.
1363 threshold = 0.005 : 5mJy
1364 threshold = '5.0mJy'
1366 Note : A 'cyclethreshold' is internally computed and used as a major cycle
1367 trigger. It is related to what fraction of the PSF can be reliably
1368 used during minor cycle updates of the residual image. By default
1369 the minor cycle iterations terminate once the peak residual reaches
1370 the first sidelobe level of the brightest source.
1372 'cyclethreshold' is computed as follows using the settings in
1373 parameters 'cyclefactor','minpsffraction','maxpsffraction','threshold' :
1375 psf_fraction = max_psf_sidelobe_level \* 'cyclefactor'
1376 psf_fraction = max(psf_fraction, 'minpsffraction');
1377 psf_fraction = min(psf_fraction, 'maxpsffraction');
1378 cyclethreshold = peak_residual \* psf_fraction
1379 cyclethreshold = max( cyclethreshold, 'threshold' )
1381 If nsigma is set (>0.0), the N-sigma threshold is calculated (see
1382 the description under nsigma), then cyclethreshold is further modified as,
1384 cyclethreshold = max( cyclethreshold, nsgima_threshold ).
1387 'cyclethreshold' is made visible and editable only in the
1388 interactive GUI when tclean is run with interactive=True.
1389 nsigma Multiplicative factor for rms-based threshold stopping.
1391 N-sigma threshold is calculated as nsigma \* rms value per image plane determined
1392 from a robust statistics. For nsigma > 0.0, in a minor cycle, a maximum of the two values,
1393 the N-sigma threshold and cyclethreshold, is used to trigger a major cycle
1394 (see also the descreption under 'threshold').
1395 Set nsigma=0.0 to preserve the previous tclean behavior without this feature.
1396 The top level parameter, fastnoise is relevant for the rms noise calculation which is used
1397 to determine the threshold.
1399 The parameter 'nsigma' may be an int, float, or a double.
1400 cycleniter Maximum number of minor-cycle iterations (per plane) before triggering
1401 a major cycle.
1403 For example, for a single plane image, if niter=100 and cycleniter=20,
1404 there will be 5 major cycles after the initial one (assuming there is no
1405 threshold based stopping criterion). At each major cycle boundary, if
1406 the number of iterations left over (to reach niter) is less than cycleniter,
1407 it is set to the difference.
1409 Note : cycleniter applies per image plane, even if cycleniter x nplanes
1410 gives a total number of iterations greater than 'niter'. This is to
1411 preserve consistency across image planes within one set of minor
1412 cycle iterations.
1413 cyclefactor Scaling on PSF sidelobe level to compute the minor-cycle stopping threshold.
1415 Please refer to the Note under the documentation for 'threshold' that
1416 discussed the calculation of 'cyclethreshold'.
1418 cyclefactor=1.0 results in a cyclethreshold at the first sidelobe level of
1419 the brightest source in the residual image before the minor cycle starts.
1421 cyclefactor=0.5 allows the minor cycle to go deeper.
1422 cyclefactor=2.0 triggers a major cycle sooner.
1423 minpsffraction PSF fraction that marks the max depth of cleaning in the minor cycle.
1425 Please refer to the Note under the documentation for 'threshold' that
1426 discussed the calculation of 'cyclethreshold'.
1428 For example, minpsffraction=0.5 will stop cleaning at half the height of
1429 the peak residual and trigger a major cycle earlier.
1430 maxpsffraction PSF fraction that marks the minimum depth of cleaning in the minor cycle.
1432 Please refer to the Note under the documentation for 'threshold' that
1433 discussed the calculation of 'cyclethreshold'.
1435 For example, maxpsffraction=0.8 will ensure that at least the top 20
1436 percent of the source will be subtracted out in the minor cycle even if
1437 the first PSF sidelobe is at the 0.9 level (an extreme example), or if the
1438 cyclefactor is set too high for anything to get cleaned.
1439 interactive Modify masks and parameters at runtime.
1441 interactive=True will trigger an interactive GUI at every major cycle
1442 boundary (after the major cycle and before the minor cycle).
1444 Options for runtime parameter modification are :
1446 Interactive clean mask : Draw a 1/0 mask (appears as a contour) by hand.
1447 If a mask is supplied at the task interface or if
1448 automasking is invoked, the current mask is
1449 displayed in the GUI and is available for manual
1450 editing.
1452 Note : If a mask contour is not visible, please
1453 check the cursor display at the bottom of
1454 GUI to see which parts of the mask image
1455 have ones and zeros. If the entire mask=1
1456 no contours will be visible.
1459 Operation buttons : -- Stop execution now (restore current model and exit).
1460 -- Continue on until global stopping criteria are reached
1461 without stopping for any more interaction.
1462 -- Continue with minor cycles and return for interaction
1463 after the next major cycle.
1465 Iteration control : -- max cycleniter : Trigger for the next major cycle.
1467 The display begins with
1468 [ min( cycleniter, niter - itercount ) ]
1469 and can be edited by hand.
1471 -- iterations left : The display begins with [niter-itercount ]
1472 and can be edited to increase or
1473 decrease the total allowed niter.
1475 -- threshold : Edit global stopping threshold.
1477 -- cyclethreshold : The display begins with the
1478 automatically computed value
1479 (see Note in help for 'threshold'),
1480 and can be edited by hand.
1482 All edits will be reflected in the log messages that appear
1483 once minor cycles begin.
1484 nmajor The nmajor parameter limits the number of minor and major cycle sets
1485 that tclean executes. It is defined as the number of major cycles after the
1486 initial set of minor cycle iterations. In other words, the count of nmajor does
1487 not include the initial residual calculation that occurs when calcres=True.
1489 A setting of nmajor=-1 implies no limit (default -1).
1490 A setting of nmajor=0 implies nothing other than the initial residual calculation
1491 A setting of nmajor>0 imples that nmajor sets of minor and major cycles will
1492 be done in addition to the initial residual calculation.
1494 If the major cycle limit is reached, stopcode 9 will be returned. Other stopping
1495 criteria (such as threshold) could cause tclean to stop in fewer than this
1496 number of major cycles. If tclean reaches another stopping criteria, first
1497 or at the same time as nmajor, then that stopcode will be returned instead.
1499 Note however that major cycle ids in the log messages as well as in the return
1500 dictionary do begin with 1 for the initial residual calculation, when it exists.
1502 Example 1 : A tclean run with 'nmajor=5' and 'calcres=True' will iterate for
1503 5 major cycles (not counting the initial residual calculation). But, the return
1504 dictionary will show 'nmajordone:6'. If 'calcres=False', then the return
1505 dictionary will show 'nmajordone:5'.
1507 Example 2 : For both the following cases, there will be a printout in the logs
1508 "Running Major Cycle 1" and the return value will include "nmajordone: 1",
1509 however there is a difference in the purpose of the major cycle and the
1510 number of minor cycles executed:
1511 Case 1; nmajor=0, calcres=True: The major cycle done is for the creation
1512 of the residual, and no minor cycles are executed.
1513 Case 2; nmajor=1, calcres=False: The major cycle is done as part of the
1514 major/minor cycle loop, and 1 minor cycle will be executed.
1515 fullsummary Return dictionary with complete convergence history.
1517 fullsummary=True: A full version of the summary dictionary is returned.
1518 Keys include 'iterDone','peakRes','modelFlux','cycleThresh' that record the
1519 convergence state at the end of each set of minor cycle iterations
1520 separately for each image plane (i.e. channel/stokes) being
1521 deconvolved. Additional keys report the convergence state at the
1522 start of minor cycle iterations, stopping criteria that triggered major
1523 cycles, and a processor ID per channel, for parallel cube runs.
1525 fullsummary=False (default): A shorten version of the summary dictionary is returned
1526 with only 'iterDone','peakRes','modelFlux', and 'cycleThresh'.
1529 Detailed information about the return dictionary fields may be found
1530 at CASA Docs > Synthesis Imaging > Iteration Control > Returned Dictionary.
1532 Note : With some parallel cube imaging runs that have a large number of channels
1533 and iterations per cube partition in a parallel run, an MPI message passing limit may
1534 be reached due to the size of the return dictionaries being passed around, causing
1535 CASA to crash (with fullsummary=True). The limit has been estimated to be reached
1536 only when nchan_per_chunk x iterdone_per_minorcycleset > 8e+6. The option to set
1537 fullsummary=False should be used to guard against this.
1538 usemask Type of mask(s) to be used for deconvolution.
1540 user: (default) mask image(s) or user specified region file(s) or string CRTF expression(s).
1541 subparameters: mask, pbmask.
1542 pb: primary beam mask.
1543 subparameter: pbmask.
1545 Example: usemask="pb", pbmask=0.2.
1546 Construct a mask at the 0.2 pb gain level.
1547 (Currently, this option will work only with
1549 gridders that produce .pb (i.e. mosaic, awp2 and awproject)
1550 or if an externally produced .pb image exists on disk)
1553 auto-multithresh : auto-masking by multiple thresholds for deconvolution.
1554 subparameters : sidelobethreshold, noisethreshold, lownoisethreshold, negativethrehsold, smoothfactor,
1555 minbeamfrac, cutthreshold, pbmask, growiterations, dogrowprune, minpercentchange, verbose.
1556 Additional top level parameter relevant to auto-multithresh: fastnoise.
1558 if pbmask is >0.0, the region outside the specified pb gain level is excluded from
1559 image statistics in determination of the threshold.
1564 Note: By default the intermediate mask generated by automask at each deconvolution cycle
1565 is over-written in the next cycle but one can save them by setting
1566 the environment variable, SAVE_ALL_AUTOMASKS="true".
1567 (e.g. in the CASA prompt, os.environ['SAVE_ALL_AUTOMASKS']="true" )
1568 The saved CASA mask image name will be imagename.mask.autothresh#, where
1569 # is the iteration cycle number.
1570 mask Mask (a list of image name(s) or region file(s) or region string(s).
1573 The name of a CASA image or region file or region string that specifies
1574 a 1/0 mask to be used for deconvolution. Only locations with value 1 will
1575 be considered for the centers of flux components in the minor cycle.
1576 If regions specified fall completely outside of the image, tclean will throw an error.
1578 Manual mask options/examples :
1580 mask='xxx.mask' : Use this CASA image named xxx.mask and containing
1581 ones and zeros as the mask.
1582 If the mask is only different in spatial coordinates from what is being made
1583 it will be resampled to the target coordinate system before being used.
1584 The mask has to have the same shape in velocity and Stokes planes
1585 as the output image. Exceptions are single velocity and/or single
1586 Stokes plane masks. They will be expanded to cover all velocity and/or
1587 Stokes planes of the output cube.
1589 [ Note : If an error occurs during image resampling or
1590 if the expected mask does not appear, please try
1591 using tasks 'imregrid' or 'makemask' to resample
1592 the mask image onto a CASA image with the target
1593 shape and coordinates and supply it via the 'mask'
1594 parameter. ]
1597 mask='xxx.crtf' : A text file with region strings and the following on the first line
1598 ( #CRTFv0 CASA Region Text Format version 0 )
1599 This is the format of a file created via the viewer's region
1600 tool when saved in CASA region file format.
1602 mask='circle[[40pix,40pix],10pix]' : A CASA region string.
1604 mask=['xxx.mask','xxx.crtf', 'circle[[40pix,40pix],10pix]'] : a list of masks.
1610 Note : Mask images for deconvolution must contain 1 or 0 in each pixel.
1611 Such a mask is different from an internal T/F mask that can be
1612 held within each CASA image. These two types of masks are not
1613 automatically interchangeable, so please use the makemask task
1614 to copy between them if you need to construct a 1/0 based mask
1615 from a T/F one.
1617 Note : Work is in progress to generate more flexible masking options and
1618 enable more controls.
1619 pbmask Sub-parameter for usemask: primary beam mask.
1621 Examples : pbmask=0.0 (default, no pb mask).
1622 pbmask=0.2 (construct a mask at the 0.2 pb gain level).
1623 sidelobethreshold Sub-parameter for "auto-multithresh": mask threshold based on sidelobe levels: sidelobethreshold \* max_sidelobe_level \* peak residual.
1624 noisethreshold Sub-parameter for "auto-multithresh": mask threshold based on the noise level: noisethreshold \* rms + location (=median).
1626 The rms is calculated from the median absolute deviation (MAD), with rms = 1.4826\*MAD.
1627 lownoisethreshold Sub-parameter for "auto-multithresh": mask threshold to grow previously masked regions via binary dilation: lownoisethreshold \* rms in residual image + location (=median).
1629 The rms is calculated from the median absolute deviation (MAD), with rms = 1.4826\*MAD.
1630 negativethreshold Sub-parameter for "auto-multithresh": mask threshold for negative features: -1.0* negativethreshold \* rms + location(=median).
1632 The rms is calculated from the median absolute deviation (MAD), with rms = 1.4826\*MAD.
1633 smoothfactor Sub-parameter for "auto-multithresh": smoothing factor in a unit of the beam.
1634 minbeamfrac Sub-parameter for "auto-multithresh": minimum beam fraction in size to prune masks smaller than mimbeamfrac \* beam
1635 <=0.0 : No pruning
1636 cutthreshold Sub-parameter for "auto-multithresh": threshold to cut the smoothed mask to create a final mask: cutthreshold \* peak of the smoothed mask.
1637 growiterations Sub-parameter for "auto-multithresh": Maximum number of iterations to perform using binary dilation for growing the mask.
1638 dogrowprune Experimental sub-parameter for "auto-multithresh": Do pruning on the grow mask.
1639 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.
1640 verbose he summary of automasking at the end of each automasking process
1641 is printed in the logger. Following information per channel will be listed in the summary.
1643 chan: channel number.
1644 masking?: F - stop updating automask for the subsequent iteration cycles.
1645 RMS: robust rms noise.
1646 peak: peak in residual image.
1647 thresh_type: type of threshold used (noise or sidelobe).
1648 thresh_value: the value of threshold used.
1649 N_reg: number of the automask regions.
1650 N_pruned: number of the automask regions removed by pruning.
1651 N_grow: number of the grow mask regions.
1652 N_grow_pruned: number of the grow mask regions removed by pruning.
1653 N_neg_pix: number of pixels for negative mask regions.
1655 Note that for a large cube, extra logging may slow down the process.
1656 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.
1657 In this case, the threshold values are determined based on classic statistics (using all
1658 unmasked pixels for the calculations).
1660 If it is set to False, the new noise calculation
1661 method is used based on pre-existing mask.
1663 Case 1: no exiting mask.
1664 Calculate image statistics using Chauvenet algorithm.
1666 Case 2: there is an existing mask.
1667 Calculate image statistics by classical method on the region
1668 outside the mask and inside the primary beam mask.
1670 In all cases above RMS noise is calculated from the median absolute deviation (MAD).
1671 restart images (and start from an existing model image)
1672 or automatically increment the image name and make a new image set.
1674 True : Re-use existing images. If imagename.model exists the subsequent
1675 run will start from this model (i.e. predicting it using current gridder
1676 settings and starting from the residual image). Care must be taken
1677 when combining this option with startmodel. Currently, only one or
1678 the other can be used.
1680 startmodel='', imagename.model exists :
1681 - Start from imagename.model.
1682 startmodel='xxx', imagename.model does not exist :
1683 - Start from startmodel.
1684 startmodel='xxx', imagename.model exists :
1685 - Exit with an error message requesting the user to pick
1686 only one model. This situation can arise when doing one
1687 run with startmodel='xxx' to produce an output
1688 imagename.model that includes the content of startmodel,
1689 and wanting to restart a second run to continue deconvolution.
1690 Startmodel should be set to '' before continuing.
1692 If any change in the shape or coordinate system of the image is
1693 desired during the restart, please change the image name and
1694 use the startmodel (and mask) parameter(s) so that the old model
1695 (and mask) can be regridded to the new coordinate system before starting.
1697 False : A convenience feature to increment imagename with '_1', '_2',
1698 etc as suffixes so that all runs of tclean are fresh starts (without
1699 having to change the imagename parameter or delete images).
1701 This mode will search the current directory for all existing
1702 imagename extensions, pick the maximum, and adds 1.
1703 For imagename='try' it will make try.psf, try_2.psf, try_3.psf, etc.
1705 This also works if you specify a directory name in the path :
1706 imagename='outdir/try'. If './outdir' does not exist, it will create it.
1707 Then it will search for existing filenames inside that directory.
1709 If outlier fields are specified, the incrementing happens for each
1710 of them (since each has its own 'imagename'). The counters are
1711 synchronized across imagefields, to make it easier to match up sets
1712 of output images. It adds 1 to the 'max id' from all outlier names
1713 on disk. So, if you do two runs with only the main field
1714 (imagename='try'), and in the third run you add an outlier with
1715 imagename='outtry', you will get the following image names
1716 for the third run : 'try_3' and 'outtry_3' even though
1717 'outry' and 'outtry_2' have not been used.
1718 savemodel Options to save model visibilities (none, virtual, modelcolumn).
1720 Often, model visibilities must be created and saved in the MS
1721 to be later used for self-calibration (or to just plot and view them).
1723 none : Do not save any model visibilities in the MS. The MS is opened
1724 in readonly mode.
1726 Model visibilities can be predicted in a separate step by
1727 restarting tclean with niter=0,savemodel=virtual or modelcolumn
1728 and not changing any image names so that it finds the .model on
1729 disk (or by changing imagename and setting startmodel to the
1730 original imagename).
1732 virtual : In the last major cycle, save the image model and state of the
1733 gridder used during imaging within the SOURCE subtable of the
1734 MS. Images required for de-gridding will also be stored internally.
1735 All future references to model visibilities will activate the
1736 (de)gridder to compute them on-the-fly. This mode is useful
1737 when the dataset is large enough that an additional model data
1738 column on disk may be too much extra disk I/O, when the
1739 gridder is simple enough that on-the-fly recomputing of the
1740 model visibilities is quicker than disk I/O.
1741 For e.g. that gridder='awproject' and 'awp2' does not support virtual model.
1743 modelcolumn : In the last major cycle, save predicted model visibilities
1744 in the MODEL_DATA column of the MS. This mode is useful when
1745 the de-gridding cost to produce the model visibilities is higher
1746 than the I/O required to read the model visibilities from disk.
1747 This mode is currently required for gridder='awproject' and 'awp2'.
1748 This mode is also required for the ability to later pull out
1749 model visibilities from the MS into a python array for custom
1750 processing.
1752 Note 1 : The imagename.model image on disk will always be constructed
1753 if the minor cycle runs. This savemodel parameter applies only to
1754 model visibilities created by de-gridding the model image.
1756 Note 2 : It is possible for an MS to have both a virtual model
1757 as well as a model_data column, but under normal operation,
1758 the last used mode will get triggered. Use the delmod task to
1759 clear out existing models from an MS if confusion arises.
1760 Note 3: when parallel=True, use savemodel='none'; Other options are not yet ready
1761 for use in parallel. If model visibilities need to be saved (virtual or modelcolumn):
1762 please run tclean in serial mode with niter=0; after the parallel run
1763 calcres Calculate initial residual image.
1765 This parameter controls what the first major cycle does.
1767 calcres=False with niter greater than 0 will assume that
1768 a .residual image already exists and that the minor cycle can
1769 begin without recomputing it.
1771 calcres=False with niter=0 implies that only the PSF will be made
1772 and no data will be gridded.
1774 calcres=True requires that calcpsf=True or that the .psf and .sumwt
1775 images already exist on disk (for normalization purposes).
1777 Usage example : For large runs (or a pipeline scripts) it may be
1778 useful to first run tclean with niter=0 to create
1779 an initial .residual to look at and perhaps make
1780 a custom mask for. Imaging can be resumed
1781 without recomputing it.
1782 calcpsf Calculate PSF
1784 This parameter controls what the first major cycle does.
1786 calcpsf=False will assume that a .psf image already exists
1787 and that the minor cycle can begin without recomputing it.
1788 psfcutoff When the .psf image is created a 2 dimensional Gaussian is fit to the main lobe of the PSF.
1789 Which pixels in the PSF are fitted is determined by psfcutoff.
1790 The default value of psfcutoff is 0.35 and can varied from 0.01 to 0.99.
1791 Fitting algorithm:
1792 - A region of 41 x 41 pixels around the peak of the PSF is compared against the psfcutoff.
1793 Sidelobes are ignored by radially searching from the PSF peak.
1794 - Calculate the bottom left corner (blc) and top right corner (trc) from the points. Expand blc and trc with a number of pixels (5).
1795 - Create a new sub-matrix from blc and trc.
1796 - Interpolate matrix to a target number of points (3001) using CUBIC spline.
1797 - All the non-sidelobe points, in the interpolated matrix, that are above the psfcutoff are used to fit a Gaussian.
1798 A Levenberg-Marquardt algorithm is used.
1799 - If the fitting fails the algorithm is repeated with the psfcutoff decreased (psfcutoff=psfcutoff/1.5).
1800 A message in the log will apear if the fitting fails along with the new value of psfcutoff.
1801 This will be done up to 50 times if fitting fails.
1802 This Gaussian beam is defined by a major axis, minor axis, and position angle.
1803 During the restoration process, this Gaussian beam is used as the Clean beam.
1804 Varying psfcutoff might be useful for producing a better fit for highly non-Gaussian PSFs, however, the resulting fits should be carefully checked.
1805 This parameter should rarely be changed.
1807 (This is not the support size for clark clean.)
1808 parallel Run major cycles in parallel.
1810 Parallel tclean will run only if casa has already been started using mpirun.
1811 Please refer to external resources on high performance computing for details on how to start this on your system.
1813 Example : mpirun -n 3 -xterm 0 `which casa`
1815 Continuum Imaging :
1816 - Data are partitioned (in time) into NProc pieces.
1817 - Gridding/iFT is done separately per partition.
1818 - Images (and weights) are gathered and then normalized.
1819 - One non-parallel minor cycle is run.
1820 - Model image is scattered to all processes.
1821 - Major cycle is done in parallel per partition.
1823 Cube Imaging :
1824 - Data and Image coordinates are partitioned (in freq) into NProc pieces.
1825 - Each partition is processed independently (major and minor cycles).
1826 - All processes are synchronized at major cycle boundaries for convergence checks.
1827 - At the end, cubes from all partitions are concatenated along the spectral axis.
1829 Note 1 : Iteration control for cube imaging is independent per partition.
1830 - There is currently no communication between them to synchronize
1831 information such as peak residual and cyclethreshold. Therefore,
1832 different chunks may trigger major cycles at different levels.
1833 (Proper synchronization of iteration control is work in progress.)
1834 [1;42mRETURNS[1;m void
1836 --------- examples -----------------------------------------------------------
1840 For more information, see the task pages of tclean in CASA Docs:
1842 https://casadocs.readthedocs.io
1848 """
1850 _info_group_ = """imaging"""
1851 _info_desc_ = """Radio Interferometric Image Reconstruction"""
1853 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 ):
1854 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', 'awphpg', 'imagemosaic', 'standard', 'awproject', 'wprojectft', 'mosaicft', 'ft', 'ftmosaic', 'mosaic', 'awprojectft', 'gridft', 'awp2' ]}, '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'}}
1855 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}
1856 assert _pc.validate(doc,schema), create_error_string(_pc.errors)
1857 _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']) ] )
1858 task_result = None
1859 try:
1860 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'] )
1861 except Exception as exc:
1862 _except_log('tclean', exc)
1863 raise
1864 finally:
1865 task_result = _end_log( _logging_state_, 'tclean', task_result )
1866 return task_result
1868tclean = _tclean( )