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casa
$Rev:20696$
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casa
$Rev:20696$
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00001 # This file was automatically generated by SWIG (http://www.swig.org). 00002 # Version 2.0.5 00003 # 00004 # Do not make changes to this file unless you know what you are doing--modify 00005 # the SWIG interface file instead. 00006 00007 00008 00009 from sys import version_info 00010 if version_info >= (2,6,0): 00011 def swig_import_helper(): 00012 from os.path import dirname 00013 import imp 00014 fp = None 00015 try: 00016 fp, pathname, description = imp.find_module('_deconvolver', [dirname(__file__)]) 00017 except ImportError: 00018 import _deconvolver 00019 return _deconvolver 00020 if fp is not None: 00021 try: 00022 _mod = imp.load_module('_deconvolver', fp, pathname, description) 00023 finally: 00024 fp.close() 00025 return _mod 00026 _deconvolver = swig_import_helper() 00027 del swig_import_helper 00028 else: 00029 import _deconvolver 00030 del version_info 00031 try: 00032 _swig_property = property 00033 except NameError: 00034 pass # Python < 2.2 doesn't have 'property'. 00035 def _swig_setattr_nondynamic(self,class_type,name,value,static=1): 00036 if (name == "thisown"): return self.this.own(value) 00037 if (name == "this"): 00038 if type(value).__name__ == 'SwigPyObject': 00039 self.__dict__[name] = value 00040 return 00041 method = class_type.__swig_setmethods__.get(name,None) 00042 if method: return method(self,value) 00043 if (not static): 00044 self.__dict__[name] = value 00045 else: 00046 raise AttributeError("You cannot add attributes to %s" % self) 00047 00048 def _swig_setattr(self,class_type,name,value): 00049 return _swig_setattr_nondynamic(self,class_type,name,value,0) 00050 00051 def _swig_getattr(self,class_type,name): 00052 if (name == "thisown"): return self.this.own() 00053 method = class_type.__swig_getmethods__.get(name,None) 00054 if method: return method(self) 00055 raise AttributeError(name) 00056 00057 def _swig_repr(self): 00058 try: strthis = "proxy of " + self.this.__repr__() 00059 except: strthis = "" 00060 return "<%s.%s; %s >" % (self.__class__.__module__, self.__class__.__name__, strthis,) 00061 00062 try: 00063 _object = object 00064 _newclass = 1 00065 except AttributeError: 00066 class _object : pass 00067 _newclass = 0 00068 00069 00070 class deconvolver(_object): 00071 """Proxy of C++ casac::deconvolver class""" 00072 __swig_setmethods__ = {} 00073 __setattr__ = lambda self, name, value: _swig_setattr(self, deconvolver, name, value) 00074 __swig_getmethods__ = {} 00075 __getattr__ = lambda self, name: _swig_getattr(self, deconvolver, name) 00076 __repr__ = _swig_repr 00077 def __init__(self): 00078 """__init__(self) -> deconvolver""" 00079 this = _deconvolver.new_deconvolver() 00080 try: self.this.append(this) 00081 except: self.this = this 00082 __swig_destroy__ = _deconvolver.delete_deconvolver 00083 __del__ = lambda self : None; 00084 def open(self, *args, **kwargs): 00085 """ 00086 open(self, dirty=string(""), psf=string(""), warn=True) -> bool 00087 00088 Summary 00089 Open a new dirty image and PSF 00090 00091 Input Parameters: 00092 dirty Dirty image to be processed 00093 psf point spread function to be processed 00094 warn Produce warning messages if psf is not provided true 00095 00096 -------------------------------------------------------------------------------- 00097 00098 """ 00099 return _deconvolver.deconvolver_open(self, *args, **kwargs) 00100 00101 def reopen(self): 00102 """ 00103 reopen(self) -> bool 00104 00105 Summary 00106 Reopen the dirty image and PSF 00107 -------------------------------------------------------------------------------- 00108 00109 """ 00110 return _deconvolver.deconvolver_reopen(self) 00111 00112 def close(self): 00113 """ 00114 close(self) -> bool 00115 00116 Summary 00117 Close the deconvolver tool 00118 -------------------------------------------------------------------------------- 00119 00120 """ 00121 return _deconvolver.deconvolver_close(self) 00122 00123 def done(self): 00124 """ 00125 done(self) -> bool 00126 00127 Summary 00128 Terminate the deconvolver process 00129 -------------------------------------------------------------------------------- 00130 00131 """ 00132 return _deconvolver.deconvolver_done(self) 00133 00134 def summary(self): 00135 """ 00136 summary(self) -> bool 00137 00138 Summary 00139 Summarize the current state 00140 -------------------------------------------------------------------------------- 00141 00142 """ 00143 return _deconvolver.deconvolver_summary(self) 00144 00145 def boxmask(self, *args, **kwargs): 00146 """ 00147 boxmask(self, mask=string(""), blc=initialize_vector(1, (int)-1), trc=initialize_vector(1, (int)-1), 00148 fillvalue=initialize_variant("1.0Jy"), outsidevalue=initialize_variant("0.0Jy")) -> bool 00149 00150 Summary 00151 Construct a mask from blc, trc 00152 00153 Input Parameters: 00154 mask name of mask image 00155 blc Bottom left corner -1 00156 trc Top right corner -1 00157 fillvalue Value to fill in 1.0Jy 00158 outsidevalue outside value 0.0Jy 00159 00160 -------------------------------------------------------------------------------- 00161 00162 """ 00163 return _deconvolver.deconvolver_boxmask(self, *args, **kwargs) 00164 00165 def regionmask(self, *args, **kwargs): 00166 """ 00167 regionmask(self, mask=string(""), region=initialize_record(""), boxes=initialize_variant(""), value=1.0) -> bool 00168 00169 Summary 00170 Construct a mask image from a region 00171 00172 Input Parameters: 00173 mask name of mask image 00174 region Region record usually from regionmanager unset 00175 boxes list of 4 elements lists e.g [[xblc1, yblc1, xtrc1, ytrc1], [[xblc2, yblc2, xtrc2, ytrc2]] 00176 value Value to set the mask to 1.0 00177 00178 -------------------------------------------------------------------------------- 00179 00180 """ 00181 return _deconvolver.deconvolver_regionmask(self, *args, **kwargs) 00182 00183 def clipimage(self, *args, **kwargs): 00184 """ 00185 clipimage(self, clippedimage=string(""), inputimage=string(""), threshold=initialize_variant("0.0Jy")) -> bool 00186 00187 Summary 00188 Zero all pixels where Stokes I is below 00189 a threshold 00190 00191 Input Parameters: 00192 clippedimage name of clipped image 00193 inputimage name of input image 00194 threshold Threshold 0.0Jy 00195 00196 -------------------------------------------------------------------------------- 00197 00198 """ 00199 return _deconvolver.deconvolver_clipimage(self, *args, **kwargs) 00200 00201 def clarkclean(self, *args, **kwargs): 00202 """ 00203 clarkclean(self, niter=1000, gain=0.1, threshold=initialize_variant("0Jy"), displayprogress=False, 00204 model=string(""), mask=string(""), histbins=500, psfpatchsize=initialize_vector(2,(int)51, (int)51), 00205 maxextpsf=0.2, speedup=0.0, maxnumpix=10000, maxnummajcycles=-1, 00206 maxnumminoriter=-1) -> bool 00207 00208 Summary 00209 Make a clean image using the Clark Clean 00210 a threshold 00211 00212 Input Parameters: 00213 niter Number of iterations 1000 00214 gain Loop Gain for CLEANing 0.1 00215 threshold Flux level at which to stop CLEANing 0Jy 00216 displayprogress Display the progress of the cleaning? false 00217 model Name of images 00218 mask Name of mask images used for CLEANing 00219 histbins Number of bins in the pixel-flux histogram 500 00220 psfpatchsize Size of PSF for minor cycle 51 51 00221 maxextpsf maximum external sidelobe, used to set depth of minor cycle clean 0.2 00222 speedup Cleaning speedup exponent 0.0 00223 maxnumpix Maximum number of pixels used in each minor cycle 10000 00224 maxnummajcycles Max number of major cycles; -1 = no restrictions -1 00225 maxnumminoriter Max number of minor iterations; -1 = no restrictions -1 00226 00227 -------------------------------------------------------------------------------- 00228 00229 """ 00230 return _deconvolver.deconvolver_clarkclean(self, *args, **kwargs) 00231 00232 def fullclarkclean(self, *args, **kwargs): 00233 """ 00234 fullclarkclean(self, niter=1000, gain=0.1, threshold=initialize_variant("0Jy"), model=string(""), mask=string(""), 00235 cyclefactor=1.5) -> record * 00236 00237 Summary 00238 Make a clean image using the Clark Clean 00239 a threshold 00240 00241 Input Parameters: 00242 niter Number of iterations 1000 00243 gain Loop Gain for CLEANing 0.1 00244 threshold Flux level at which to stop CLEANing 0Jy 00245 model Name of model image that will contain the clean components 00246 mask Name of mask image used for CLEANing 00247 cyclefactor Factor to determine how deep to go in a Clark minor cycle 1.5 00248 00249 -------------------------------------------------------------------------------- 00250 00251 """ 00252 return _deconvolver.deconvolver_fullclarkclean(self, *args, **kwargs) 00253 00254 def dirtyname(self): 00255 """ 00256 dirtyname(self) -> string 00257 00258 Summary 00259 Return the name of the dirty-image table 00260 -------------------------------------------------------------------------------- 00261 00262 """ 00263 return _deconvolver.deconvolver_dirtyname(self) 00264 00265 def psfname(self): 00266 """ 00267 psfname(self) -> string 00268 00269 Summary 00270 Return the name of the PSF-image table 00271 -------------------------------------------------------------------------------- 00272 00273 """ 00274 return _deconvolver.deconvolver_psfname(self) 00275 00276 def make(self, *args, **kwargs): 00277 """ 00278 make(self, image=string(""), async=False) -> bool 00279 00280 Summary 00281 Make an empty image 00282 00283 Input Parameters: 00284 image Name of the new image on the disk 00285 async Run asynchronously in the background? false 00286 00287 -------------------------------------------------------------------------------- 00288 00289 """ 00290 return _deconvolver.deconvolver_make(self, *args, **kwargs) 00291 00292 def convolve(self, *args, **kwargs): 00293 """ 00294 convolve(self, convolvemodel=string(""), model=string("")) -> bool 00295 00296 Summary 00297 Convolves an image with the PSF 00298 00299 Input Parameters: 00300 convolvemodel Name of the output image on the disk to hold the result of the convolution 00301 model The input image to be convolved with the PSF 00302 00303 -------------------------------------------------------------------------------- 00304 00305 """ 00306 return _deconvolver.deconvolver_convolve(self, *args, **kwargs) 00307 00308 def makegaussian(self, *args, **kwargs): 00309 """ 00310 makegaussian(self, gaussianimage=string(""), bmaj=initialize_variant("0rad"), bmin=initialize_variant("0rad"), 00311 bpa=initialize_variant("0deg"), normalize=True, async=False) -> bool 00312 00313 Summary 00314 Make an image with a single gaussian 00315 component 00316 00317 Input Parameters: 00318 gaussianimage Name of the output image on the disk 00319 bmaj The major axis of the gaussian 0rad 00320 bmin The minor axis of the gaussian 0rad 00321 bpa The Position Angle of the gaussian 0deg 00322 normalize Normalize the area under the gaussian to 1.0? true 00323 async Run asynchronously in the background? false 00324 00325 -------------------------------------------------------------------------------- 00326 00327 """ 00328 return _deconvolver.deconvolver_makegaussian(self, *args, **kwargs) 00329 00330 def state(self): 00331 """ 00332 state(self) -> bool 00333 00334 Summary 00335 Return the ``state'' of the tool 00336 -------------------------------------------------------------------------------- 00337 00338 """ 00339 return _deconvolver.deconvolver_state(self) 00340 00341 def updatestate(self, *args, **kwargs): 00342 """ 00343 updatestate(self, f=string(""), method=string("INIT")) -> bool 00344 00345 Summary 00346 [A GUI builders related function]Update the GUI to reflect the currect state 00347 00348 Input Parameters: 00349 f Glish variable for the GUI to be updated 00350 method The method to be used for updating DONE close INIT 00351 00352 -------------------------------------------------------------------------------- 00353 00354 """ 00355 return _deconvolver.deconvolver_updatestate(self, *args, **kwargs) 00356 00357 def clean(self, *args, **kwargs): 00358 """ 00359 clean(self, algorithm=string("hogbom"), niter=1000, gain=0.1, threshold=initialize_variant("0Jy"), 00360 displayprogress=False, model=string(""), mask=string(""), async=False) -> record * 00361 00362 Summary 00363 Make a clean image with Hogbom or MultiScale Clean 00364 00365 Input Parameters: 00366 algorithm Algorithm to use fullmsclean msclean hogbom 00367 niter Number of Iterations, set to zero for no CLEANing 1000 00368 gain Loop Gain for CLEANing, try 0.7 for msclean or fullmsclean 0.1 00369 threshold Flux level at which to stop CLEANing 0Jy 00370 displayprogress Display progress false 00371 model Name of images 00372 mask Name of mask images used for CLEANing 00373 async Run asynchronously in the background? false 00374 00375 -------------------------------------------------------------------------------- 00376 00377 """ 00378 return _deconvolver.deconvolver_clean(self, *args, **kwargs) 00379 00380 def setscales(self, *args, **kwargs): 00381 """ 00382 setscales(self, scalemethod=string("nscales"), nscales=5, uservector=initialize_vector(3,(double)0.0, (double)3.0, (double)10.0)) -> bool 00383 00384 Summary 00385 Set the scale sizes for MultiScale Clean 00386 00387 Input Parameters: 00388 scalemethod Method by which scales are set uservector nscales 00389 nscales Number of scales 5 00390 uservector Vector of scale sizes to use 0.0 3.0 10.0 00391 00392 -------------------------------------------------------------------------------- 00393 00394 """ 00395 return _deconvolver.deconvolver_setscales(self, *args, **kwargs) 00396 00397 def ft(self, *args, **kwargs): 00398 """ 00399 ft(self, model=string(""), transform=string(""), async=False) -> bool 00400 00401 Summary 00402 Fourier transform the specified model 00403 00404 Input Parameters: 00405 model Name of image 00406 transform Name of transform image 00407 async Run asynchronously in the background? false 00408 00409 -------------------------------------------------------------------------------- 00410 00411 """ 00412 return _deconvolver.deconvolver_ft(self, *args, **kwargs) 00413 00414 def restore(self, *args, **kwargs): 00415 """ 00416 restore(self, model=string(""), image=string(""), bmaj=initialize_variant("0rad"), bmin=initialize_variant("0rad"), 00417 bpa=initialize_variant("0deg"), async=False) -> bool 00418 00419 Summary 00420 Restore the residuals 00421 00422 Input Parameters: 00423 model Name of input model 00424 image Name of output restored image 00425 bmaj Major axis of beam 0rad 00426 bmin Minor axis of beam 0rad 00427 bpa 0deg Position angle of beam 00428 async Run asynchronously in the background false 00429 00430 -------------------------------------------------------------------------------- 00431 00432 """ 00433 return _deconvolver.deconvolver_restore(self, *args, **kwargs) 00434 00435 def residual(self, *args, **kwargs): 00436 """ 00437 residual(self, model=string(""), image=string(""), async=False) -> bool 00438 00439 Summary 00440 Find the residuals 00441 00442 Input Parameters: 00443 model Names of input models 00444 image Names of output residual images 00445 async Run asynchronously in the background false 00446 00447 -------------------------------------------------------------------------------- 00448 00449 """ 00450 return _deconvolver.deconvolver_residual(self, *args, **kwargs) 00451 00452 def smooth(self, *args, **kwargs): 00453 """ 00454 smooth(self, model=string(""), image=string(""), bmaj=initialize_variant("0rad"), bmin=initialize_variant("0rad"), 00455 bpa=initialize_variant("0deg"), normalize=True, async=False) -> bool 00456 00457 Summary 00458 smooth the image 00459 00460 Input Parameters: 00461 model Name of input model 00462 image Name of output smoothed image 00463 bmaj Major axis of beam 0rad 00464 bmin Minor axis of beam 0rad 00465 bpa Position angle of beam 0deg 00466 normalize Normalize volume of psf to unity true 00467 async Run asynchronously in the background false 00468 00469 -------------------------------------------------------------------------------- 00470 00471 """ 00472 return _deconvolver.deconvolver_smooth(self, *args, **kwargs) 00473 00474 def mem(self, *args, **kwargs): 00475 """ 00476 mem(self, entropy=string("entropy"), niter=20, sigma=initialize_variant("0.001Jy"), targetflux=initialize_variant("1.0Jy"), 00477 constrainflux=False, displayprogress=False, 00478 model=string(""), prior=string(""), mask=string(""), imageplane=False, async=False) -> bool 00479 00480 Summary 00481 Make the mem image 00482 00483 Input Parameters: 00484 entropy entropy to use emptiness entropy 00485 niter Number of Iterations, set to zero for no MEMing 20 00486 sigma Noise level to try to achieve 0.001Jy 00487 targetflux Total image flux to try to achieve 1.0Jy 00488 constrainflux Use targetflux as a constraint? (or starting flux) false 00489 displayprogress Display progress false 00490 model Name of input/output model image 00491 prior Name of prior (default) image used for mem 00492 mask Mask image restricting emission (all pixels 0 or 1) 00493 imageplane Is this an image plane problem (like single dish)? false 00494 async Run asynchronously in the background? false 00495 00496 -------------------------------------------------------------------------------- 00497 00498 """ 00499 return _deconvolver.deconvolver_mem(self, *args, **kwargs) 00500 00501 def makeprior(self, *args, **kwargs): 00502 """ 00503 makeprior(self, prior=string(""), templateimage=string(""), lowclipfrom=initialize_variant("0.0Jy"), 00504 lowclipto=initialize_variant("0.0Jy"), highclipfrom=initialize_variant("9e20Jy"), 00505 highclipto=initialize_variant("9e20Jy"), blc=initialize_vector(1, (int)-1), 00506 trc=initialize_vector(1, (int)-1), async=False) -> bool 00507 00508 Summary 00509 Make the mem's prior image, or make a mask 00510 00511 Input Parameters: 00512 prior output prior image 00513 templateimage starting point for prior image 00514 lowclipfrom Clip any pixel below this level 0.0Jy 00515 lowclipto Any clipped pixel will be given this value 0.0Jy 00516 highclipfrom Clip any pixel above this level 9e20Jy 00517 highclipto Any clipped pixel will be given this value 9e20Jy 00518 blc Bottom left hand corner for box; outside box is clipped -1 00519 trc Top right hand corner for box; outside box is clipped -1 00520 async Run asynchronously in the background? false 00521 00522 -------------------------------------------------------------------------------- 00523 00524 """ 00525 return _deconvolver.deconvolver_makeprior(self, *args, **kwargs) 00526 00527 def mtopen(self, *args, **kwargs): 00528 """ 00529 mtopen(self, ntaylor=2, scalevector=initialize_vector(3,(double)0.0, (double)3.0, (double)10.0), 00530 psfs=std::vector< string >(1, ""), async=False) -> bool 00531 00532 Summary 00533 Init : Make a series of images using a Multi-Term Clean algorithm 00534 00535 Input Parameters: 00536 ntaylor Number of terms in the taylor polynomial 2 00537 scalevector Vector of scale sizes to use 0.0 3.0 10.0 00538 psfs Intpu : List of names of 2N-1 psfs. This is valid only for a Taylor-polynomial model. 00539 async Run asynchronously in the background? false 00540 00541 -------------------------------------------------------------------------------- 00542 00543 """ 00544 return _deconvolver.deconvolver_mtopen(self, *args, **kwargs) 00545 00546 def mtclean(self, *args, **kwargs): 00547 """ 00548 mtclean(self, residuals=std::vector< string >(1, ""), models=std::vector< string >(1, ""), niter=1000, 00549 gain=0.1, threshold=initialize_variant("0Jy"), displayprogress=False, 00550 mask=string(""), async=False) -> record * 00551 00552 Summary 00553 Make a series of images using a Multi-Term Clean algorithm 00554 00555 Input Parameters: 00556 residuals Input : List of names of N residual images 00557 models Output : List of names of N model images 00558 niter Number of Iterations, set to zero for no CLEANing 1000 00559 gain Loop Gain for CLEANing, try 0.7 for msclean or fullmsclean 0.1 00560 threshold Flux level at which to stop CLEANing 0Jy 00561 displayprogress Display progress false 00562 mask Name of mask images used for CLEANing 00563 async Run asynchronously in the background? false 00564 00565 -------------------------------------------------------------------------------- 00566 00567 """ 00568 return _deconvolver.deconvolver_mtclean(self, *args, **kwargs) 00569 00570 def mtrestore(self, *args, **kwargs): 00571 """ 00572 mtrestore(self, models=std::vector< string >(1, ""), residuals=std::vector< string >(1, ""), images=std::vector< string >(1, ""), 00573 bmaj=initialize_variant("0rad"), bmin=initialize_variant("0rad"), 00574 bpa=initialize_variant("0deg"), async=False) -> bool 00575 00576 Summary 00577 Restore the Multi-Term residuals 00578 00579 Input Parameters: 00580 models Input : Name of input model 00581 residuals Input : Name of residual image 00582 images Output : Name of output restored image 00583 bmaj Major axis of beam 0rad 00584 bmin Minor axis of beam 0rad 00585 bpa 0deg Position angle of beam 00586 async Run asynchronously in the background false 00587 00588 -------------------------------------------------------------------------------- 00589 00590 """ 00591 return _deconvolver.deconvolver_mtrestore(self, *args, **kwargs) 00592 00593 def mtcalcpowerlaw(self, *args, **kwargs): 00594 """ 00595 mtcalcpowerlaw(self, images=std::vector< string >(1, ""), residuals=std::vector< string >(1, ""), alphaname=string(""), 00596 betaname=string(""), threshold=initialize_variant("0.0Jy"), 00597 calcerror=False, async=False) -> bool 00598 00599 Summary 00600 Interpret Taylor coefficients as a power law, and compute spectral index 00601 00602 Input Parameters: 00603 images Input : Names of input restored images 00604 residuals Input : Names of input residuals images ( for error calcs ) 00605 alphaname Output : Name of output spectral-index image 00606 betaname Output : Name of output spectral-curvature image 00607 threshold Threshold 0.0Jy 00608 calcerror Calculate an error image for spectral index false 00609 async Run asynchronously in the background false 00610 00611 -------------------------------------------------------------------------------- 00612 00613 """ 00614 return _deconvolver.deconvolver_mtcalcpowerlaw(self, *args, **kwargs) 00615 00616 deconvolver_swigregister = _deconvolver.deconvolver_swigregister 00617 deconvolver_swigregister(deconvolver) 00618 00619 # This file is compatible with both classic and new-style classes. 00620 00621
1.8.0
