Coverage for /home/casatest/venv/lib/python3.12/site-packages/casatasks/deconvolve.py: 56%
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1##################### generated by xml-casa (v2) from deconvolve.xml ################
2##################### 70265d8023e9665dfb4675bd32a692f9 ##############################
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_deconvolve import deconvolve as _deconvolve_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 _deconvolve:
15 """
16 deconvolve ---- Image-domain deconvolution
18 Task deconvolve executes image-domain deconvolution with options for automasking and restoration. It can be viewed as the minor cycle of the iterative image reconstruction offered by the tclean task. The functionality that it introduces is the ability to just run the deconvolution step alone.
21 --------- parameter descriptions ---------------------------------------------
23 imagename Pre-name of input and output images
24 example : imagename='try'
26 Input images (* = required):
28 try.psf* - The point spread function
29 try.residual* - The observed (or residual) image, such as one
30 generated by tclean
31 try.model - An existing model image from a previous run of
32 tclean or deconvolve
33 try.mask - A pre-defined mask to be either used directly or
34 combined with additional masking options
35 try.pb - Primary beam model, if required for masking settings
37 Output images:
39 try.model - A new or updated model image
40 try.residual - An updated residual image
41 try.image - A restored image, created only when restoration=True
42 try.mask - The output mask
43 try.prev.mask - An intermediate by-product of automasking
44 (when usemask='auto-multithresh')
46 For multi-term wideband imaging, all relevant images above will
47 have additional .tt0,.tt1, etc suffixes to indicate Taylor terms,
48 plus the following extra input images.
49 try.alpha - spectral index
50 try.alpha.error - estimate of error on spectral index
51 try.beta - spectral curvature (if nterms > 2)
53 Tip : Include a directory name in 'imagename' for all
54 output images to be sent there instead of the
55 current working directory : imagename='mydir/try'
57 Tip : Restarting a deconvolution run without changing 'imagename'
58 implies continuation from the existing model image on disk.
59 By default, the residual image will be recomputed.
61 Note : All deconvolution runs will by default produce restored images.
62 For a niter=0 run, this will be redundant and can optionally
63 be turned off via the 'restoration=T/F' parameter.
65 Note : All input/output images should be 4D image cubes with axes
66 [ra,dec,pol,chan], as is the default for the task tclean. Any input
67 images without this format must be modified (eg with imregrid,
68 imtrans, image.adddegaxes) in order to be accepted.
69 startmodel Name of starting model image
71 The contents of the supplied starting model image will be
72 copied to the imagename.model before the run begins.
74 example : startmodel = 'singledish.im'
76 For deconvolver='mtmfs', one image per Taylor term must be provided.
77 example : startmodel = ['try.model.tt0', 'try.model.tt1']
78 startmodel = ['try.model.tt0'] will use a starting model only
79 for the zeroth order term.
80 startmodel = ['','try.model.tt1'] will use a starting model only
81 for the first order term.
83 This starting model can be of a different image shape and size from
84 what is currently being imaged. If so, an image regrid is first triggered
85 to resample the input image onto the target coordinate system.
87 If the startmodel is supplied, but a copy of of the destination
88 imagename.model already exists, deconvolve will exit with an error.
90 A common usage is to set this parameter equal to a single dish image
92 Negative components in the model image will be included as is.
93 deconvolver Name of minor cycle algorithm (hogbom,clark,multiscale,mem,clarkstokes,asp)
95 Each of the following algorithms operate on residual images and psfs
96 from the gridder and produce output model and restored images.
97 Minor cycles stop when threshold or niter are reached. For all
98 methods, components are picked from the entire extent of the image or
99 (if specified) within a mask.
101 hogbom : An adapted version of Hogbom Clean [Hogbom, 1974]
102 - Find the location of the peak residual
103 - Add this delta function component to the model image
104 - Subtract a scaled and shifted PSF of the same size as the image
105 from regions of the residual image where the two overlap.
106 - Repeat
108 clark : An adapted version of Clark Clean [Clark, 1980]
109 - Find the location of max(I^2+Q^2+U^2+V^2)
110 - Add delta functions to each stokes plane of the model image
111 - Subtract a scaled and shifted PSF within a small patch size
112 from regions of the residual image where the two overlap.
113 - After several iterations trigger a Clark major cycle to subtract
114 components from the visibility domain, but without de-gridding.
115 - Repeat
117 ( Note : 'clark' maps to imagermode='' in the old clean task.
118 'clark_exp' is another implementation that maps to
119 imagermode='mosaic' or 'csclean' in the old clean task
120 but the behavior is not identical. For now, please
121 use deconvolver='hogbom' if you encounter problems. )
123 clarkstokes : Clark Clean operating separately per Stokes plane
125 (Note : 'clarkstokes_exp' is an alternate version. See above.)
127 multiscale : MultiScale Clean [Cornwell, 2008]
128 - Smooth the residual image to multiple scale sizes
129 - Find the location and scale at which the peak occurs
130 - Add this multiscale component to the model image
131 - Subtract a scaled,smoothed,shifted PSF (within a small
132 patch size per scale) from all residual images
133 - Repeat from step 2
136 mem : Maximum Entropy Method [Cornwell and Evans, 1985]
137 - Iteratively solve for values at all individual pixels via the
138 MEM method. It minimizes an objective function of
139 chi-square plus entropy (here, a measure of difference
140 between the current model and a flat prior model).
142 (Note : This MEM implementation is not very robust.
143 Improvements will be made in the future.)
145 mtmfs : Multi-term (Multi Scale) Multi-Frequency Synthesis [Rau and Cornwell, 2011]
146 - Smooth each Taylor residual image to multiple scale sizes
147 - Solve a NTxNT system of equations per scale size to compute
148 Taylor coefficients for components at all locations
149 - Compute gradient chi-square and pick the Taylor coefficients
150 and scale size at the location with maximum reduction in
151 chi-square
152 - Add multi-scale components to each Taylor-coefficient
153 model image
154 - Subtract scaled,smoothed,shifted PSF (within a small patch size
155 per scale) from all smoothed Taylor residual images
156 - Repeat from step 2
157 scales List of scale sizes (in pixels) for multi-scale and mtmfs algorithms.
158 This set of scale sizes should represent the sizes
159 (diameters in units of number of pixels)
160 of dominant features in the image being reconstructed.
162 The smallest scale size is recommended to be 0 (point source),
163 the second the size of the synthesized beam and the third 3-5
164 times the synthesized beam, etc. For example, if the synthesized
165 beam is 10" FWHM and cell=2",try scales = [0,5,15].
167 For numerical stability, the largest scale must be
168 smaller than the image (or mask) size and smaller than or
169 comparable to the scale corresponding to the lowest measured
170 spatial frequency (as a scale size much larger than what the
171 instrument is sensitive to is unconstrained by the data making
172 it harder to recovery from errors during the minor cycle).
173 nterms Number of Taylor coefficients in the spectral model
175 - nterms=1 : Assume flat spectrum source
176 - nterms=2 : Spectrum is a straight line with a slope
177 - nterms=N : A polynomial of order N-1
179 From a Taylor expansion of the expression of a power law, the
180 spectral index is derived as alpha = taylorcoeff_1 / taylorcoeff_0
182 Spectral curvature is similarly derived when possible.
184 The optimal number of Taylor terms depends on the available
185 signal to noise ratio, bandwidth ratio, and spectral shape of the
186 source as seen by the telescope (sky spectrum x PB spectrum).
188 nterms=2 is a good starting point for wideband EVLA imaging
189 and the lower frequency bands of ALMA (when fractional bandwidth
190 is greater than 10%) and if there is at least one bright source for
191 which a dynamic range of greater than few 100 is desired.
193 Spectral artifacts for the VLA often look like spokes radiating out from
194 a bright source (i.e. in the image made with standard mfs imaging).
195 If increasing the number of terms does not eliminate these artifacts,
196 check the data for inadequate bandpass calibration. If the source is away
197 from the pointing center, consider including wide-field corrections too.
199 (Note : In addition to output Taylor coefficient images .tt0,.tt1,etc
200 images of spectral index (.alpha), an estimate of error on
201 spectral index (.alpha.error) and spectral curvature (.beta,
202 if nterms is greater than 2) are produced.
203 - These alpha, alpha.error and beta images contain
204 internal T/F masks based on a threshold computed
205 as peakresidual/10. Additional masking based on
206 .alpha/.alpha.error may be desirable.
207 - .alpha.error is a purely empirical estimate derived
208 from the propagation of error during the division of
209 two noisy numbers (alpha = xx.tt1/xx.tt0) where the
210 'error' on tt1 and tt0 are simply the values picked from
211 the corresponding residual images. The absolute value
212 of the error is not always accurate and it is best to interpret
213 the errors across the image only in a relative sense.)
214 smallscalebias A numerical control to bias the scales when using multi-scale or mtmfs algorithms.
215 The peak from each scale's smoothed residual is
216 multiplied by ( 1 - smallscalebias * scale/maxscale )
217 to increase or decrease the amplitude relative to other scales,
218 before the scale with the largest peak is chosen.
219 Smallscalebias can be varied between -1.0 and 1.0.
220 A score of 0.0 gives all scales equal weight (default).
221 A score larger than 0.0 will bias the solution towards smaller scales.
222 A score smaller than 0.0 will bias the solution towards larger scales.
223 The effect of smallscalebias is more pronounced when using multi-scale relative to mtmfs.
224 fusedthreshold g Hogbom Clean (number in units of Jy)
226 fusedthreshold = 0.0001 : 0.1 mJy
228 This is a subparameter of the Asp Clean deconvolver. When peak residual
229 is lower than the threshold, Asp Clean is "switched to Hogbom Clean" (i.e. only use the 0 scale for cleaning) for
230 the following number of iterations until it switches back to Asp Clean.
232 NumberIterationsInHogbom = 50 + 2 * (exp(0.05 * NthHogbom) - 1)
234 , where NthHogbom is the number of times Hogbom Clean has been triggered.
236 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.
238 NumberIterationsInHogbom = 500 + 2 * (exp(0.05 * NthHogbom) - 1)
239 largestscale s) allowed for the initial guess for the Asp Clean deconvolver.
241 largestscale = 100
243 The default initial scale sizes used by Asp Clean is [0, w, 2w, 4w, 8w],
244 where `w` is the PSF width. The default `largestscale` is -1 which indicates
245 users accept these initial scales. If `largestscale` is set, the initial scales
246 would be [0, w, ... up to the `largestscale`]. This is only an initial guess,
247 and actual fitted scale sizes may evolve from these initial values.
249 It is recommended not to set `largestscale` unless Asp Clean picks a large
250 scale that has no constraints from the data (the UV hole issue).
251 restoration .
253 Construct a restored image : imagename.image by convolving the model
254 image with a clean beam and adding the residual image to the result.
255 If a restoringbeam is specified, the residual image is also
256 smoothed to that target resolution before adding it in.
258 If a .model does not exist, it will make an empty one and create
259 the restored image from the residuals ( with additional smoothing if needed ).
260 With algorithm='mtmfs', this will construct Taylor coefficient maps from
261 the residuals and compute .alpha and .alpha.error.
262 restoringbeam ze to use.
264 - restoringbeam='' or ['']
265 A Gaussian fitted to the PSF main lobe (separately per image plane).
267 - restoringbeam='10.0arcsec'
268 Use a circular Gaussian of this width for all planes
270 - restoringbeam=['8.0arcsec','10.0arcsec','45deg']
271 Use this elliptical Gaussian for all planes
273 - restoringbeam='common'
274 Automatically estimate a common beam shape/size appropriate for
275 all planes.
277 Note : For any restoring beam different from the native resolution
278 the model image is convolved with the beam and added to
279 residuals that have been convolved to the same target resolution.
280 niter Maximum number of iterations
282 A stopping criterion based on total iteration count.
283 Currently the parameter type is defined as an integer therefore the integer value
284 larger than 2147483647 will not be set properly as it causes an overflow.
286 Iterations are typically defined as the selecting one flux component
287 and partially subtracting it out from the residual image.
289 niter=0 : Skip the deconvolution step (only do restoration)
291 niter larger than zero : The minor cycle.
293 Note : The following additional triggers will also stop minor cycle
294 iterations. Whichever condition is triggered first stops the minor
295 cycle.
297 'threshold' : The parameter value, compared to peak residual.
298 Divergence : As detected by an increase of 10% in peak residual from
299 the minimum so far (during minor cycle iterations).
301 The first criterion to be satisfied takes precedence.
303 Note : Iteration counts for cubes or multi-field images :
304 For images with multiple planes (or image fields) on which the
305 deconvolver operates in sequence, iterations are counted across
306 all planes (or image fields). The iteration count is compared with
307 'niter' only after all channels/planes/fields have completed their
308 minor cycles and exited either due to 'niter' or 'threshold'.
309 Therefore, the actual number of iterations reported in the logger
310 can sometimes be larger than the user specified value in 'niter'.
311 For example, with niter=100, nchan=10,threshold=0,
312 a total of 1000 iterations will be done in the first set of minor cycles
313 before the total is compared with niter=100 and it exits.
314 gain Loop gain
316 Fraction of the source flux to subtract out of the residual image
317 for the CLEAN algorithm and its variants.
319 A low value (0.2 or less) is recommended when the sky brightness
320 distribution is not well represented by the basis functions used by
321 the chosen deconvolution algorithm. A higher value can be tried when
322 there is a good match between the true sky brightness structure and
323 the basis function shapes. For example, for extended emission,
324 multiscale clean with an appropriate set of scale sizes will tolerate
325 a higher loop gain than Clark clean (for example).
326 threshold The minor cycle's stopping threshold (number in units of Jy, or string)
328 threshold = 0.005 : 5mJy
329 threshold = '5.0mJy'
331 Note : If nsigma is set (>0.0), the N-sigma threshold is calculated (see
332 the description under nsigma). Then threshold is modified as:
334 threshold = max( threshold, nsgima_threshold )
335 nsigma Multiplicative factor for rms-based threshold stopping
337 N-sigma threshold is calculated as nsigma * rms value per image plane determined
338 from a robust statistics. For nsigma > 0.0, in a minor cycle, a maximum of the two values,
339 the N-sigma threshold and threshold, is used as a stopping trigger
340 (see also the descreption under 'threshold').
341 Set nsigma=0.0 to preserve the previous deconvolve behavior without this feature.
342 The top level parameter, fastnoise is relevant for the rms noise calculation which is used
343 to determine the threshold.
344 interactive Modify masks and parameters at runtime
346 interactive=True will trigger an interactive GUI after deconvolution finishes.
348 Options for runtime parameter modification are :
350 Interactive clean mask : Draw a 1/0 mask (appears as a contour) by hand.
351 If a mask is supplied at the task interface or if
352 automasking is invoked, the current mask is
353 displayed in the GUI and is available for manual
354 editing.
356 Note : If a mask contour is not visible, please
357 check the cursor display at the bottom of
358 GUI to see which parts of the mask image
359 have ones and zeros. If the entire mask=1
360 no contours will be visible.
363 Operation buttons : -- For the task deconvolve, all options stop the minor cycle.
365 Iteration control : -- max cycleniter : For task deconvolve, this contol has no effect.
366 -- iterations left : Sets the limit on the number of iterations
367 about to be done.
368 -- threshold : Sets the peak residual stopping threshold for
369 for this execution of deconvolve.
370 fullsummary Return dictionary with complete convergence history
372 fullsummary=True: A full version of the summary dictionary is returned.
373 Keys include 'iterDone','peakRes','modelFlux','cycleThresh' that record the
374 convergence state at the end of each set of minor cycle iterations
375 separately for each image plane (i.e. channel/stokes) being
376 deconvolved. Additional keys report the convergence state at the
377 start of minor cycle iterations, stopping criteria that triggered major
378 cycles, and a processor ID per channel, for parallel cube runs.
380 fullsummary=False (default): A shorten version of the summary dictionary is returned
381 with only 'iterDone','peakRes','modelFlux', and 'cycleThresh'.
383 Detailed information about the return dictionary fields may be found
384 at CASA Docs > Synthesis Imaging > Iteration Control > Returned Dictionary.
385 fastnoise mask (user='multi-autothresh') and/or n-sigma stopping threshold
386 (nsigma>0.0) are/is used. If it is set to True, a simpler but faster noise calucation is used.
387 In this case, the threshold values are determined based on classic statistics (using all
388 unmasked pixels for the calculations).
390 If it is set to False, the new noise calculation
391 method is used based on pre-existing mask.
393 Case 1: no exiting mask
394 Calculate image statistics using Chauvenet algorithm
396 Case 2: there is an existing mask
397 Calculate image statistics by classical method on the region
398 outside the mask and inside the primary beam mask.
400 In all cases above RMS noise is calculated from MAD.
401 usemask Type of mask(s) to be used for deconvolution
403 user: (default) mask image(s) or user specified region file(s) or string CRTF expression(s)
404 subparameters: mask, pbmask
405 pb: primary beam mask
406 subparameter: pbmask
408 Example: usemask="pb", pbmask=0.2
409 Construct a mask at the 0.2 pb gain level.
410 (Currently, this option will work only with
411 gridders that produce .pb (i.e. mosaic and awproject)
412 or if an externally produced .pb image exists on disk)
414 auto-multithresh : auto-masking by multiple thresholds for deconvolution
415 subparameters : sidelobethreshold, noisethreshold, lownoisethreshold,
416 negativethrehsold, smoothfactor, minbeamfrac, cutthreshold,
417 pbmask, growiterations, dogrowprune, minpercentchange, verbose
418 Additional top level parameter relevant to auto-multithresh: fastnoise
423 Note: By default the intermediate mask generated by automask at each deconvolution cycle
424 is over-written in the next cycle but one can save them by setting
425 the environment variable, SAVE_ALL_AUTOMASKS="true".
426 (e.g. in the CASA prompt, os.environ['SAVE_ALL_AUTOMASKS']="true" )
427 The saved CASA mask image name will be imagename.mask.autothresh#, where
428 # is the iteration cycle number.
429 mask Mask (a list of image name(s) or region file(s) or region string(s)
432 The name of a CASA image or region file or region string that specifies
433 a 1/0 mask to be used for deconvolution. Only locations with value 1 will
434 be considered for the centers of flux components in the minor cycle.
435 If regions specified fall completely outside of the image, deconvolve will throw an error.
437 Manual mask options/examples :
439 mask='xxx.mask' : Use this CASA image named xxx.mask and containing
440 ones and zeros as the mask.
441 If the mask is only different in spatial coordinates from what is being made
442 it will be resampled to the target coordinate system before being used.
443 The mask has to have the same shape in velocity and Stokes planes
444 as the output image. Exceptions are single velocity and/or single
445 Stokes plane masks. They will be expanded to cover all velocity and/or
446 Stokes planes of the output cube.
448 [ Note : If an error occurs during image resampling or
449 if the expected mask does not appear, please try
450 using tasks 'imregrid' or 'makemask' to resample
451 the mask image onto a CASA image with the target
452 shape and coordinates and supply it via the 'mask'
453 parameter. ]
456 mask='xxx.crtf' : A text file with region strings and the following on the first line
457 ( #CRTFv0 CASA Region Text Format version 0 )
458 This is the format of a file created via the viewer's region
459 tool when saved in CASA region file format.
461 mask='circle[[40pix,40pix],10pix]' : A CASA region string.
463 mask=['xxx.mask','xxx.crtf', 'circle[[40pix,40pix],10pix]'] : a list of masks
469 Note : Mask images for deconvolution must contain 1 or 0 in each pixel.
470 Such a mask is different from an internal T/F mask that can be
471 held within each CASA image. These two types of masks are not
472 automatically interchangeable, so please use the makemask task
473 to copy between them if you need to construct a 1/0 based mask
474 from a T/F one.
476 Note : Work is in progress to generate more flexible masking options and
477 enable more controls.
478 pbmask Sub-parameter for usemask: primary beam mask
480 Examples : pbmask=0.0 (default, no pb mask)
481 pbmask=0.2 (construct a mask at the 0.2 pb gain level)
482 sidelobethreshold Sub-parameter for "auto-multithresh": mask threshold based on sidelobe levels:
483 sidelobethreshold * max_sidelobe_level * peak residual
484 noisethreshold Sub-parameter for "auto-multithresh": mask threshold based on the noise level:
485 noisethreshold * rms + location (=median)
487 The rms is calculated from MAD with rms = 1.4826*MAD.
488 lownoisethreshold Sub-parameter for "auto-multithresh": mask threshold to grow previously masked regions via binary dilation:
489 lownoisethreshold * rms in residual image + location (=median)
491 The rms is calculated from MAD with rms = 1.4826*MAD.
492 negativethreshold Sub-parameter for "auto-multithresh": mask threshold for negative features:
493 -1.0* negativethreshold * rms + location(=median)
495 The rms is calculated from MAD with rms = 1.4826*MAD.
496 smoothfactor Sub-parameter for "auto-multithresh": smoothing factor in a unit of the beam
497 minbeamfrac Sub-parameter for "auto-multithresh": minimum beam fraction in size to prune masks smaller than mimbeamfrac * beam
498 <=0.0 : No pruning
499 cutthreshold Sub-parameter for "auto-multithresh": threshold to cut the smoothed mask to create a final mask:
500 cutthreshold * peak of the smoothed mask
501 growiterations Sub-parameter for "auto-multithresh": Maximum number of iterations to perform using binary dilation for growing the mask
502 dogrowprune Experimental sub-parameter for "auto-multithresh": Do pruning on the grow mask
503 verbose he summary of automasking at the end of each automasking process
504 is printed in the logger. Following information per channel will be listed in the summary.
506 chan: channel number
507 masking?: F - stop updating automask for the subsequent iteration cycles
508 RMS: robust rms noise
509 peak: peak in residual image
510 thresh_type: type of threshold used (noise or sidelobe)
511 thresh_value: the value of threshold used
512 N_reg: number of the automask regions
513 N_pruned: number of the automask regions removed by pruning
514 N_grow: number of the grow mask regions
515 N_grow_pruned: number of the grow mask regions removed by pruning
516 N_neg_pix: number of pixels for negative mask regions
518 Note that for a large cube, extra logging may slow down the process.
519 [1;42mRETURNS[1;m void
521 --------- examples -----------------------------------------------------------
525 Please refer to the CASAdocs pages for the task deconvolve for examples.
530 """
532 _info_group_ = """imaging"""
533 _info_desc_ = """Image-domain deconvolution"""
535 def __call__( self, imagename='', startmodel='', deconvolver='hogbom', scales=[ ], nterms=int(2), smallscalebias=float(0.0), fusedthreshold=float(0.0), largestscale=int(-1), restoration=True, restoringbeam=[ ], niter=int(100), gain=float(0.1), threshold=float(0.0), nsigma=float(0.0), interactive=False, fullsummary=False, fastnoise=True, 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, verbose=False ):
536 schema = {'imagename': {'anyof': [{'type': 'cInt'}, {'type': 'cStr', 'coerce': _coerce.to_str}, {'type': 'cStrVec', 'coerce': [_coerce.to_list,_coerce.to_strvec]}]}, 'startmodel': {'type': 'cVariant', 'coerce': [_coerce.to_variant]}, '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]}]}, 'niter': {'type': 'cInt'}, 'gain': {'type': 'cFloat', 'coerce': _coerce.to_float}, 'threshold': {'type': 'cVariant', 'coerce': [_coerce.to_variant]}, 'nsigma': {'type': 'cFloat', 'coerce': _coerce.to_float}, 'interactive': {'type': 'cBool'}, 'fullsummary': {'type': 'cBool'}, 'fastnoise': {'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'}, 'verbose': {'type': 'cBool'}}
537 doc = {'imagename': imagename, 'startmodel': startmodel, 'deconvolver': deconvolver, 'scales': scales, 'nterms': nterms, 'smallscalebias': smallscalebias, 'fusedthreshold': fusedthreshold, 'largestscale': largestscale, 'restoration': restoration, 'restoringbeam': restoringbeam, 'niter': niter, 'gain': gain, 'threshold': threshold, 'nsigma': nsigma, 'interactive': interactive, 'fullsummary': fullsummary, 'fastnoise': fastnoise, 'usemask': usemask, 'mask': mask, 'pbmask': pbmask, 'sidelobethreshold': sidelobethreshold, 'noisethreshold': noisethreshold, 'lownoisethreshold': lownoisethreshold, 'negativethreshold': negativethreshold, 'smoothfactor': smoothfactor, 'minbeamfrac': minbeamfrac, 'cutthreshold': cutthreshold, 'growiterations': growiterations, 'dogrowprune': dogrowprune, 'verbose': verbose}
538 assert _pc.validate(doc,schema), create_error_string(_pc.errors)
539 _logging_state_ = _start_log( 'deconvolve', [ 'imagename=' + repr(_pc.document['imagename']), 'startmodel=' + repr(_pc.document['startmodel']), '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']), 'niter=' + repr(_pc.document['niter']), 'gain=' + repr(_pc.document['gain']), 'threshold=' + repr(_pc.document['threshold']), 'nsigma=' + repr(_pc.document['nsigma']), 'interactive=' + repr(_pc.document['interactive']), 'fullsummary=' + repr(_pc.document['fullsummary']), 'fastnoise=' + repr(_pc.document['fastnoise']), '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']), 'verbose=' + repr(_pc.document['verbose']) ] )
540 task_result = None
541 try:
542 task_result = _deconvolve_t( _pc.document['imagename'], _pc.document['startmodel'], _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['niter'], _pc.document['gain'], _pc.document['threshold'], _pc.document['nsigma'], _pc.document['interactive'], _pc.document['fullsummary'], _pc.document['fastnoise'], _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['verbose'] )
543 except Exception as exc:
544 _except_log('deconvolve', exc)
545 raise
546 finally:
547 task_result = _end_log( _logging_state_, 'deconvolve', task_result )
548 return task_result
550deconvolve = _deconvolve( )