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1##################### generated by xml-casa (v2) from deconvolve.xml ################ 

2##################### 15d515ca00097c91935333421853fdfb ############################## 

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 

13 

14class _deconvolve: 

15 """ 

16 deconvolve ---- Image-domain deconvolution 

17 

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. 

19  

20 

21 --------- parameter descriptions --------------------------------------------- 

22 

23 imagename Pre-name of input and output images 

24 example : imagename='try' 

25  

26 Input images (* = required): 

27  

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 

36  

37 Output images: 

38  

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') 

45  

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) 

52  

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' 

56  

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. 

60  

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. 

64  

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 

70  

71 The contents of the supplied starting model image will be 

72 copied to the imagename.model before the run begins. 

73  

74 example : startmodel = 'singledish.im' 

75  

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. 

82  

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. 

86  

87 If the startmodel is supplied, but a copy of of the destination 

88 imagename.model already exists, deconvolve will exit with an error. 

89  

90 A common usage is to set this parameter equal to a single dish image 

91  

92 Negative components in the model image will be included as is. 

93 deconvolver Name of minor cycle algorithm (hogbom,clark,multiscale,mem,clarkstokes) 

94  

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. 

100  

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 

107  

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 

116  

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. ) 

122  

123 clarkstokes : Clark Clean operating separately per Stokes plane 

124  

125 (Note : 'clarkstokes_exp' is an alternate version. See above.) 

126  

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 

134  

135  

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). 

141  

142 (Note : This MEM implementation is not very robust. 

143 Improvements will be made in the future.) 

144  

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  

158 asp : This deconvolver is not currently considered reliable when used with 

159 task deconvolve and is therefore disabled. Please choose a different 

160 deconvolver. 

161 scales List of scale sizes (in pixels) for multi-scale and mtmfs algorithms. 

162 This set of scale sizes should represent the sizes 

163 (diameters in units of number of pixels) 

164 of dominant features in the image being reconstructed. 

165  

166 The smallest scale size is recommended to be 0 (point source), 

167 the second the size of the synthesized beam and the third 3-5 

168 times the synthesized beam, etc. For example, if the synthesized 

169 beam is 10" FWHM and cell=2",try scales = [0,5,15]. 

170  

171 For numerical stability, the largest scale must be 

172 smaller than the image (or mask) size and smaller than or 

173 comparable to the scale corresponding to the lowest measured 

174 spatial frequency (as a scale size much larger than what the 

175 instrument is sensitive to is unconstrained by the data making 

176 it harder to recovery from errors during the minor cycle). 

177 nterms Number of Taylor coefficients in the spectral model 

178  

179 - nterms=1 : Assume flat spectrum source 

180 - nterms=2 : Spectrum is a straight line with a slope 

181 - nterms=N : A polynomial of order N-1 

182  

183 From a Taylor expansion of the expression of a power law, the 

184 spectral index is derived as alpha = taylorcoeff_1 / taylorcoeff_0 

185  

186 Spectral curvature is similarly derived when possible. 

187  

188 The optimal number of Taylor terms depends on the available 

189 signal to noise ratio, bandwidth ratio, and spectral shape of the 

190 source as seen by the telescope (sky spectrum x PB spectrum). 

191  

192 nterms=2 is a good starting point for wideband EVLA imaging 

193 and the lower frequency bands of ALMA (when fractional bandwidth 

194 is greater than 10%) and if there is at least one bright source for 

195 which a dynamic range of greater than few 100 is desired. 

196  

197 Spectral artifacts for the VLA often look like spokes radiating out from 

198 a bright source (i.e. in the image made with standard mfs imaging). 

199 If increasing the number of terms does not eliminate these artifacts, 

200 check the data for inadequate bandpass calibration. If the source is away 

201 from the pointing center, consider including wide-field corrections too. 

202  

203 (Note : In addition to output Taylor coefficient images .tt0,.tt1,etc 

204 images of spectral index (.alpha), an estimate of error on 

205 spectral index (.alpha.error) and spectral curvature (.beta, 

206 if nterms is greater than 2) are produced. 

207 - These alpha, alpha.error and beta images contain 

208 internal T/F masks based on a threshold computed 

209 as peakresidual/10. Additional masking based on 

210 .alpha/.alpha.error may be desirable. 

211 - .alpha.error is a purely empirical estimate derived 

212 from the propagation of error during the division of 

213 two noisy numbers (alpha = xx.tt1/xx.tt0) where the 

214 'error' on tt1 and tt0 are simply the values picked from 

215 the corresponding residual images. The absolute value 

216 of the error is not always accurate and it is best to interpret 

217 the errors across the image only in a relative sense.) 

218 smallscalebias A numerical control to bias the scales when using multi-scale or mtmfs algorithms. 

219 The peak from each scale's smoothed residual is 

220 multiplied by ( 1 - smallscalebias * scale/maxscale ) 

221 to increase or decrease the amplitude relative to other scales, 

222 before the scale with the largest peak is chosen.  

223 Smallscalebias can be varied between -1.0 and 1.0.  

224 A score of 0.0 gives all scales equal weight (default).  

225 A score larger than 0.0 will bias the solution towards smaller scales.  

226 A score smaller than 0.0 will bias the solution towards larger scales. 

227 The effect of smallscalebias is more pronounced when using multi-scale relative to mtmfs. 

228 restoration . 

229  

230 Construct a restored image : imagename.image by convolving the model 

231 image with a clean beam and adding the residual image to the result. 

232 If a restoringbeam is specified, the residual image is also 

233 smoothed to that target resolution before adding it in. 

234  

235 If a .model does not exist, it will make an empty one and create 

236 the restored image from the residuals ( with additional smoothing if needed ). 

237 With algorithm='mtmfs', this will construct Taylor coefficient maps from 

238 the residuals and compute .alpha and .alpha.error. 

239 restoringbeam ze to use. 

240  

241 - restoringbeam='' or [''] 

242 A Gaussian fitted to the PSF main lobe (separately per image plane). 

243  

244 - restoringbeam='10.0arcsec' 

245 Use a circular Gaussian of this width for all planes 

246  

247 - restoringbeam=['8.0arcsec','10.0arcsec','45deg'] 

248 Use this elliptical Gaussian for all planes 

249  

250 - restoringbeam='common' 

251 Automatically estimate a common beam shape/size appropriate for 

252 all planes. 

253  

254 Note : For any restoring beam different from the native resolution 

255 the model image is convolved with the beam and added to 

256 residuals that have been convolved to the same target resolution. 

257 niter Maximum number of iterations 

258  

259 A stopping criterion based on total iteration count. 

260 Currently the parameter type is defined as an integer therefore the integer value  

261 larger than 2147483647 will not be set properly as it causes an overflow. 

262  

263 Iterations are typically defined as the selecting one flux component 

264 and partially subtracting it out from the residual image. 

265  

266 niter=0 : Skip the deconvolution step (only do restoration) 

267  

268 niter larger than zero : The minor cycle. 

269  

270 Note : The following additional triggers will also stop minor cycle 

271 iterations. Whichever condition is triggered first stops the minor 

272 cycle. 

273  

274 'threshold' : The parameter value, compared to peak residual. 

275 Divergence : As detected by an increase of 10% in peak residual from 

276 the minimum so far (during minor cycle iterations). 

277  

278 The first criterion to be satisfied takes precedence. 

279  

280 Note : Iteration counts for cubes or multi-field images : 

281 For images with multiple planes (or image fields) on which the 

282 deconvolver operates in sequence, iterations are counted across 

283 all planes (or image fields). The iteration count is compared with 

284 'niter' only after all channels/planes/fields have completed their 

285 minor cycles and exited either due to 'niter' or 'threshold'. 

286 Therefore, the actual number of iterations reported in the logger 

287 can sometimes be larger than the user specified value in 'niter'. 

288 For example, with niter=100, nchan=10,threshold=0, 

289 a total of 1000 iterations will be done in the first set of minor cycles 

290 before the total is compared with niter=100 and it exits. 

291 gain Loop gain 

292  

293 Fraction of the source flux to subtract out of the residual image 

294 for the CLEAN algorithm and its variants. 

295  

296 A low value (0.2 or less) is recommended when the sky brightness 

297 distribution is not well represented by the basis functions used by 

298 the chosen deconvolution algorithm. A higher value can be tried when 

299 there is a good match between the true sky brightness structure and 

300 the basis function shapes. For example, for extended emission, 

301 multiscale clean with an appropriate set of scale sizes will tolerate 

302 a higher loop gain than Clark clean (for example). 

303 threshold The minor cycle's stopping threshold (number in units of Jy, or string) 

304  

305 threshold = 0.005 : 5mJy 

306 threshold = '5.0mJy' 

307  

308 Note : If nsigma is set (>0.0), the N-sigma threshold is calculated (see 

309 the description under nsigma). Then threshold is modified as: 

310  

311 threshold = max( threshold, nsgima_threshold ) 

312 nsigma Multiplicative factor for rms-based threshold stopping 

313  

314 N-sigma threshold is calculated as nsigma * rms value per image plane determined 

315 from a robust statistics. For nsigma > 0.0, in a minor cycle, a maximum of the two values, 

316 the N-sigma threshold and threshold, is used as a stopping trigger 

317 (see also the descreption under 'threshold'). 

318 Set nsigma=0.0 to preserve the previous deconvolve behavior without this feature. 

319 The top level parameter, fastnoise is relevant for the rms noise calculation which is used  

320 to determine the threshold. 

321 interactive Modify masks and parameters at runtime 

322  

323 interactive=True will trigger an interactive GUI after deconvolution finishes. 

324  

325 Options for runtime parameter modification are : 

326  

327 Interactive clean mask : Draw a 1/0 mask (appears as a contour) by hand. 

328 If a mask is supplied at the task interface or if 

329 automasking is invoked, the current mask is 

330 displayed in the GUI and is available for manual 

331 editing. 

332  

333 Note : If a mask contour is not visible, please 

334 check the cursor display at the bottom of 

335 GUI to see which parts of the mask image 

336 have ones and zeros. If the entire mask=1 

337 no contours will be visible. 

338  

339  

340 Operation buttons : -- For the task deconvolve, all options stop the minor cycle. 

341  

342 Iteration control : -- max cycleniter : For task deconvolve, this contol has no effect. 

343 -- iterations left : Sets the limit on the number of iterations 

344 about to be done. 

345 -- threshold : Sets the peak residual stopping threshold for 

346 for this execution of deconvolve. 

347 fullsummary Return dictionary with complete convergence history  

348  

349 fullsummary=True: A full version of the summary dictionary is returned. 

350 Keys include 'iterDone','peakRes','modelFlux','cycleThresh' that record the 

351 convergence state at the end of each set of minor cycle iterations 

352 separately for each image plane (i.e. channel/stokes) being 

353 deconvolved. Additional keys report the convergence state at the  

354 start of minor cycle iterations, stopping criteria that triggered major  

355 cycles, and a processor ID per channel, for parallel cube runs. 

356  

357 fullsummary=False (default): A shorten version of the summary dictionary is returned 

358 with only 'iterDone','peakRes','modelFlux', and 'cycleThresh'. 

359  

360 Detailed information about the return dictionary fields may be found  

361 at CASA Docs > Synthesis Imaging > Iteration Control > Returned Dictionary. 

362 fastnoise mask (user='multi-autothresh') and/or n-sigma stopping threshold 

363 (nsigma>0.0) are/is used. If it is set to True, a simpler but faster noise calucation is used.  

364 In this case, the threshold values are determined based on classic statistics (using all 

365 unmasked pixels for the calculations). 

366  

367 If it is set to False, the new noise calculation 

368 method is used based on pre-existing mask.  

369  

370 Case 1: no exiting mask 

371 Calculate image statistics using Chauvenet algorithm  

372  

373 Case 2: there is an existing mask 

374 Calculate image statistics by classical method on the region 

375 outside the mask and inside the primary beam mask. 

376  

377 In all cases above RMS noise is calculated from MAD. 

378 usemask Type of mask(s) to be used for deconvolution 

379  

380 user: (default) mask image(s) or user specified region file(s) or string CRTF expression(s) 

381 subparameters: mask, pbmask 

382 pb: primary beam mask 

383 subparameter: pbmask 

384  

385 Example: usemask="pb", pbmask=0.2 

386 Construct a mask at the 0.2 pb gain level. 

387 (Currently, this option will work only with 

388 gridders that produce .pb (i.e. mosaic and awproject) 

389 or if an externally produced .pb image exists on disk) 

390  

391 auto-multithresh : auto-masking by multiple thresholds for deconvolution 

392 subparameters : sidelobethreshold, noisethreshold, lownoisethreshold, 

393 negativethrehsold, smoothfactor, minbeamfrac, cutthreshold, 

394 pbmask, growiterations, dogrowprune, minpercentchange, verbose 

395 Additional top level parameter relevant to auto-multithresh: fastnoise 

396  

397  

398  

399  

400 Note: By default the intermediate mask generated by automask at each deconvolution cycle 

401 is over-written in the next cycle but one can save them by setting 

402 the environment variable, SAVE_ALL_AUTOMASKS="true". 

403 (e.g. in the CASA prompt, os.environ['SAVE_ALL_AUTOMASKS']="true" ) 

404 The saved CASA mask image name will be imagename.mask.autothresh#, where 

405 # is the iteration cycle number. 

406 mask Mask (a list of image name(s) or region file(s) or region string(s) 

407  

408  

409 The name of a CASA image or region file or region string that specifies 

410 a 1/0 mask to be used for deconvolution. Only locations with value 1 will 

411 be considered for the centers of flux components in the minor cycle. 

412 If regions specified fall completely outside of the image, deconvolve will throw an error. 

413  

414 Manual mask options/examples : 

415  

416 mask='xxx.mask' : Use this CASA image named xxx.mask and containing 

417 ones and zeros as the mask.  

418 If the mask is only different in spatial coordinates from what is being made  

419 it will be resampled to the target coordinate system before being used. 

420 The mask has to have the same shape in velocity and Stokes planes 

421 as the output image. Exceptions are single velocity and/or single 

422 Stokes plane masks. They will be expanded to cover all velocity and/or 

423 Stokes planes of the output cube. 

424  

425 [ Note : If an error occurs during image resampling or 

426 if the expected mask does not appear, please try 

427 using tasks 'imregrid' or 'makemask' to resample 

428 the mask image onto a CASA image with the target 

429 shape and coordinates and supply it via the 'mask' 

430 parameter. ] 

431  

432  

433 mask='xxx.crtf' : A text file with region strings and the following on the first line 

434 ( #CRTFv0 CASA Region Text Format version 0 ) 

435 This is the format of a file created via the viewer's region 

436 tool when saved in CASA region file format. 

437  

438 mask='circle[[40pix,40pix],10pix]' : A CASA region string. 

439  

440 mask=['xxx.mask','xxx.crtf', 'circle[[40pix,40pix],10pix]'] : a list of masks 

441  

442  

443  

444  

445  

446 Note : Mask images for deconvolution must contain 1 or 0 in each pixel. 

447 Such a mask is different from an internal T/F mask that can be 

448 held within each CASA image. These two types of masks are not 

449 automatically interchangeable, so please use the makemask task 

450 to copy between them if you need to construct a 1/0 based mask 

451 from a T/F one. 

452  

453 Note : Work is in progress to generate more flexible masking options and 

454 enable more controls. 

455 pbmask Sub-parameter for usemask: primary beam mask 

456  

457 Examples : pbmask=0.0 (default, no pb mask) 

458 pbmask=0.2 (construct a mask at the 0.2 pb gain level) 

459 sidelobethreshold Sub-parameter for "auto-multithresh": mask threshold based on sidelobe levels: 

460 sidelobethreshold * max_sidelobe_level * peak residual 

461 noisethreshold Sub-parameter for "auto-multithresh": mask threshold based on the noise level: 

462 noisethreshold * rms + location (=median) 

463  

464 The rms is calculated from MAD with rms = 1.4826*MAD. 

465 lownoisethreshold Sub-parameter for "auto-multithresh": mask threshold to grow previously masked regions via binary dilation: 

466 lownoisethreshold * rms in residual image + location (=median) 

467  

468 The rms is calculated from MAD with rms = 1.4826*MAD. 

469 negativethreshold Sub-parameter for "auto-multithresh": mask threshold for negative features: 

470 -1.0* negativethreshold * rms + location(=median) 

471  

472 The rms is calculated from MAD with rms = 1.4826*MAD. 

473 smoothfactor Sub-parameter for "auto-multithresh": smoothing factor in a unit of the beam 

474 minbeamfrac Sub-parameter for "auto-multithresh": minimum beam fraction in size to prune masks smaller than mimbeamfrac * beam 

475 <=0.0 : No pruning 

476 cutthreshold Sub-parameter for "auto-multithresh": threshold to cut the smoothed mask to create a final mask: 

477 cutthreshold * peak of the smoothed mask 

478 growiterations Sub-parameter for "auto-multithresh": Maximum number of iterations to perform using binary dilation for growing the mask 

479 dogrowprune Experimental sub-parameter for "auto-multithresh": Do pruning on the grow mask 

480 verbose he summary of automasking at the end of each automasking process 

481 is printed in the logger. Following information per channel will be listed in the summary. 

482  

483 chan: channel number 

484 masking?: F - stop updating automask for the subsequent iteration cycles 

485 RMS: robust rms noise 

486 peak: peak in residual image 

487 thresh_type: type of threshold used (noise or sidelobe) 

488 thresh_value: the value of threshold used 

489 N_reg: number of the automask regions 

490 N_pruned: number of the automask regions removed by pruning 

491 N_grow: number of the grow mask regions 

492 N_grow_pruned: number of the grow mask regions removed by pruning 

493 N_neg_pix: number of pixels for negative mask regions 

494  

495 Note that for a large cube, extra logging may slow down the process. 

496 RETURNS void 

497 

498 --------- examples ----------------------------------------------------------- 

499 

500  

501  

502 Please refer to the CASAdocs pages for the task deconvolve for examples. 

503  

504  

505 

506 

507 """ 

508 

509 _info_group_ = """imaging""" 

510 _info_desc_ = """Image-domain deconvolution""" 

511 

512 def __call__( self, imagename='', startmodel='', deconvolver='hogbom', scales=[ ], nterms=int(2), smallscalebias=float(0.0), 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 ): 

513 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', '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}, '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'}} 

514 doc = {'imagename': imagename, 'startmodel': startmodel, 'deconvolver': deconvolver, 'scales': scales, 'nterms': nterms, 'smallscalebias': smallscalebias, '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} 

515 assert _pc.validate(doc,schema), create_error_string(_pc.errors) 

516 _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']), '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']) ] ) 

517 task_result = None 

518 try: 

519 task_result = _deconvolve_t( _pc.document['imagename'], _pc.document['startmodel'], _pc.document['deconvolver'], _pc.document['scales'], _pc.document['nterms'], _pc.document['smallscalebias'], _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'] ) 

520 except Exception as exc: 

521 _except_log('deconvolve', exc) 

522 raise 

523 finally: 

524 task_result = _end_log( _logging_state_, 'deconvolve', task_result ) 

525 return task_result 

526 

527deconvolve = _deconvolve( ) 

528